From 9f595199590e7a1848a0669cbbd2b18081c321d8 Mon Sep 17 00:00:00 2001
From: William Lin <SolitaryThinker@users.noreply.github.com>
Date: Wed, 14 Oct 2026 23:16:32 -0700
Subject: [PATCH] [misc] Remove DreamX World, HunyuanGameCraft, GLM-Image,
 LongCat, TurboDiffusion, and Z-Image (#1926)

---
 .agents/skills/add-model-02-parity/SKILL.md   |    2 +-
 .../templates/component_parity_test.py        |    2 +-
 .agents/skills/add-model-03-port-dit/SKILL.md |    4 +-
 .agents/skills/add-model-04-port-vae/SKILL.md |    2 +-
 .../skills/add-model-07-conversion/SKILL.md   |   11 +-
 .agents/skills/add-model-09-pipeline/SKILL.md |    2 +-
 .agents/skills/add-model/SKILL.md             |    2 +-
 .agents/skills/seed-ssim-references/SKILL.md  |    4 +-
 .github/scripts/plan_merge_ci.py              |   70 +-
 .github/workflows/ci-trigger-full-suite.yml   |    7 +
 .gitignore                                    |    1 -
 .../infinite_livestream/backend.py            |    4 +-
 docs/assets/cookbook-recipes.json             |  128 +-
 docs/assets/logos/SOURCES.md                  |   11 +-
 docs/assets/logos/meituan-longcat.webp        |  Bin 5192 -> 0 bytes
 docs/assets/logos/tongyi.webp                 |  Bin 6150 -> 0 bytes
 docs/assets/logos/zai.webp                    |  Bin 2506 -> 0 bytes
 docs/contributing/env_vars.md                 |   42 +-
 docs/cookbook/cosmos.md                       |    2 +-
 docs/cookbook/flux.md                         |    2 +-
 docs/cookbook/glm-image.md                    |  140 --
 docs/cookbook/hunyuan.md                      |    2 +-
 docs/cookbook/index.md                        |  115 --
 docs/cookbook/kandinsky5.md                   |    2 +-
 docs/cookbook/kandinsky6.md                   |    2 +-
 docs/cookbook/longcat.md                      |  140 --
 docs/cookbook/ltx.md                          |    2 +-
 docs/cookbook/matrix-game.md                  |    2 +-
 docs/cookbook/minimax-h3.md                   |    2 +-
 docs/cookbook/mmaudio.md                      |    2 +-
 docs/cookbook/stable-audio.md                 |    2 +-
 docs/cookbook/stable-diffusion.md             |    2 +-
 docs/cookbook/turbodiffusion.md               |  140 --
 docs/cookbook/wan.md                          |    2 +-
 docs/cookbook/z-image.md                      |  140 --
 .../inference_schema_parity_inventory.yaml    |   53 -
 docs/generate_examples.py                     |    4 -
 docs/inference/architecture.md                |    4 +-
 docs/inference/offloading.md                  |    4 +-
 docs/inference/support_matrix.md              |   64 +-
 .../inference/basic/basic_dreamx_world.py     |   61 -
 examples/inference/basic/basic_gamecraft.py   |  118 --
 examples/inference/basic/basic_glm_image.py   |  107 --
 examples/inference/basic/basic_longcat_i2v.py |  204 ---
 examples/inference/basic/basic_longcat_t2v.py |  192 ---
 examples/inference/basic/basic_longcat_vc.py  |  218 ---
 .../inference/basic/basic_turbodiffusion.py   |   51 -
 .../basic/basic_turbodiffusion_14b.py         |   45 -
 .../basic/basic_turbodiffusion_i2v.py         |   38 -
 examples/inference/basic/basic_zimage.py      |   94 --
 examples/inference/basic/edit_glm_image.py    |  120 --
 .../configs/fine_tuning/longcat/t2v.yaml      |   71 -
 .../fine_tuning/wan/turbo_t2v_lora_sla.yaml   |   89 --
 fastvideo/api/sampling_param.py               |   46 +-
 fastvideo/api/schema.py                       |    4 -
 fastvideo/attention/utils/flash_attn_cute.py  |    2 +-
 fastvideo/configs/models/dits/__init__.py     |   18 +-
 fastvideo/configs/models/dits/dreamx_world.py |   71 -
 fastvideo/configs/models/dits/glm_image.py    |   61 -
 .../configs/models/dits/hunyuangamecraft.py   |  143 --
 fastvideo/configs/models/dits/longcat.py      |  133 --
 fastvideo/configs/models/dits/zimage.py       |   60 -
 fastvideo/configs/models/encoders/base.py     |    2 +-
 fastvideo/configs/models/vaes/__init__.py     |    4 -
 fastvideo/configs/models/vaes/gamecraftvae.py |   50 -
 fastvideo/configs/models/vaes/glm_image.py    |   95 --
 fastvideo/configs/pipelines/__init__.py       |    9 +-
 fastvideo/configs/pipelines/dreamx_world.py   |  127 --
 fastvideo/configs/pipelines/glm_image.py      |   46 -
 .../configs/pipelines/hunyuangamecraft.py     |   69 -
 fastvideo/configs/pipelines/longcat.py        |  362 -----
 fastvideo/configs/pipelines/turbodiffusion.py |  121 --
 fastvideo/configs/pipelines/zimage.py         |   52 -
 fastvideo/envs.py                             |   48 +-
 fastvideo/fastvideo_args.py                   |   35 +-
 fastvideo/layers/rotary_embedding.py          |    6 +-
 fastvideo/models/AGENTS.md                    |    1 -
 fastvideo/models/camera/__init__.py           |    6 -
 fastvideo/models/camera/trajectory.py         |  387 ------
 fastvideo/models/dits/dreamx_world.py         |  452 ------
 fastvideo/models/dits/dreamx_world_ar.py      |  982 -------------
 fastvideo/models/dits/glm_image.py            |  702 ----------
 fastvideo/models/dits/hunyuangamecraft.py     |  348 -----
 fastvideo/models/dits/longcat.py              | 1216 -----------------
 fastvideo/models/dits/zimage.py               |  563 --------
 .../models/encoders/glm_image_ar_loader.py    |   63 -
 fastvideo/models/loader/component_loader.py   |   18 -
 fastvideo/models/registry.py                  |   21 -
 .../scheduling_flow_match_euler_discrete.py   |    4 +-
 fastvideo/models/schedulers/scheduling_rcm.py |  323 -----
 fastvideo/models/vaes/gamecraftvae.py         |  450 ------
 fastvideo/models/vaes/gamecraftvae_blocks.py  |  420 ------
 fastvideo/models/wan/AGENTS.md                |    4 +-
 fastvideo/models/wan/definition.py            |  270 ++--
 fastvideo/pipelines/AGENTS.md                 |   12 +-
 .../pipelines/basic/dreamx_world/__init__.py  |   28 -
 .../basic/dreamx_world/ar_denoising.py        |  222 ---
 .../basic/dreamx_world/camera_conditioning.py |  228 ----
 .../pipelines/basic/dreamx_world/config.py    |   20 -
 .../dreamx_world/dreamx_world_ar_pipeline.py  |   67 -
 .../dreamx_world/dreamx_world_pipeline.py     |   81 --
 .../pipelines/basic/dreamx_world/presets.py   |   58 -
 .../pipelines/basic/dreamx_world/stages.py    |  156 ---
 .../basic/flux_2/flux_2_latent_preparation.py |    3 +-
 .../pipelines/basic/gamecraft/__init__.py     |    0
 .../basic/gamecraft/gamecraft_pipeline.py     |  100 --
 .../pipelines/basic/gamecraft/presets.py      |   33 -
 .../pipelines/basic/glm_image/__init__.py     |    7 -
 .../basic/glm_image/glm_image_pipeline.py     |   82 --
 .../basic/glm_image/stages/__init__.py        |   14 -
 .../glm_image/stages/before_denoising.py      |  269 ----
 .../glm_image/stages/condition_encoding.py    |   67 -
 .../basic/glm_image/stages/decoding.py        |   35 -
 .../basic/glm_image/stages/denoising.py       |  198 ---
 fastvideo/pipelines/basic/longcat/__init__.py |    8 -
 .../basic/longcat/longcat_i2v_pipeline.py     |  136 --
 .../basic/longcat/longcat_pipeline.py         |  123 --
 .../basic/longcat/longcat_vc_pipeline.py      |  157 ---
 fastvideo/pipelines/basic/longcat/presets.py  |   65 -
 .../pipelines/basic/magi_human/JOURNAL.md     |    2 +-
 .../basic/turbodiffusion/__init__.py          |    6 -
 .../pipelines/basic/turbodiffusion/presets.py |   73 -
 .../turbodiffusion_i2v_pipeline.py            |   74 -
 .../turbodiffusion/turbodiffusion_pipeline.py |   64 -
 fastvideo/pipelines/basic/zimage/__init__.py  |    6 -
 fastvideo/pipelines/basic/zimage/presets.py   |   40 -
 fastvideo/pipelines/basic/zimage/stages.py    |  340 -----
 .../pipelines/basic/zimage/zimage_pipeline.py |   63 -
 fastvideo/pipelines/composed_pipeline_base.py |    4 +-
 fastvideo/pipelines/pipeline_batch_info.py    |   14 +-
 fastvideo/pipelines/stages/__init__.py        |   13 -
 fastvideo/pipelines/stages/decoding.py        |   10 -
 fastvideo/pipelines/stages/denoising.py       |   24 -
 .../pipelines/stages/gamecraft_denoising.py   |  318 -----
 .../stages/gamecraft_image_encoding.py        |  173 ---
 .../pipelines/stages/latent_preparation.py    |    6 +-
 .../pipelines/stages/longcat_denoising.py     |  164 ---
 .../pipelines/stages/longcat_i2v_denoising.py |  154 ---
 .../stages/longcat_i2v_latent_preparation.py  |   95 --
 .../stages/longcat_image_vae_encoding.py      |  149 --
 .../pipelines/stages/longcat_kv_cache_init.py |  112 --
 .../pipelines/stages/longcat_refine_init.py   |  273 ----
 .../stages/longcat_refine_timestep.py         |   96 --
 .../pipelines/stages/longcat_vc_denoising.py  |  194 ---
 .../stages/longcat_video_vae_encoding.py      |  163 ---
 fastvideo/registry.py                         |  174 +--
 fastvideo/tests/api/test_ltx2_continuation.py |    2 +-
 fastvideo/tests/api/test_parser.py            |    4 -
 fastvideo/tests/api/test_presets.py           |   56 +-
 fastvideo/tests/api/test_wan_definitions.py   |    2 -
 fastvideo/tests/contract/test_env_policy.py   |    2 +-
 .../tests/contract/test_merge_ci_plan.py      |   22 +
 .../distributed/test_sp_longcat_layout.py     |   90 --
 fastvideo/tests/golden_gate/test_dreamx.py    |  127 --
 fastvideo/tests/golden_gate/test_gamecraft.py |   87 --
 fastvideo/tests/golden_gate/test_glm_image.py |   86 --
 fastvideo/tests/golden_gate/test_longcat.py   |   80 --
 fastvideo/tests/golden_gate/test_wan_t2v.py   |    2 +-
 fastvideo/tests/golden_gate/test_zimage.py    |   89 --
 .../lora/test_lora_inference_similarity.py    |    7 +-
 .../tests/loader/test_wan_family_imports.py   |    5 -
 .../ssim/test_dreamx_world_similarity.py      |  186 ---
 .../tests/ssim/test_gamecraft_similarity.py   |  382 ------
 .../tests/ssim/test_glm_image_similarity.py   |  144 --
 .../tests/ssim/test_longcat_similarity.py     |  470 -------
 .../ssim/test_turbodiffusion_similarity.py    |  172 ---
 .../tests/ssim/test_zimage_similarity.py      |  117 --
 .../tests/stages/test_wan_pipeline_wiring.py  |    3 -
 .../tests/train/fixtures/longcat_t2v_min.yaml |   23 -
 .../methods/test_matrixgame2_finetune.py      |    3 +-
 .../tests/train/models/test_load_longcat.py   |   83 --
 .../models/test_wan_override_contract.py      |    5 +-
 .../test_finetune_config_construction.py      |    2 +-
 .../transformers/test_hunyuangamecraft.py     |  144 --
 .../block_sparse_attention/__init__.py        |    0
 .../block_sparse_attention/bsa_interface.py   |  737 ----------
 .../block_sparse_attention/common.py          |  130 --
 .../block_sparse_attention/communicate.py     |   33 -
 .../flash_attn_bsa_varlen_mask.py             | 1130 ---------------
 fastvideo/train/AGENTS.md                     |    2 +-
 fastvideo/train/entrypoint/train.py           |   11 +-
 fastvideo/train/models/longcat/__init__.py    |    5 -
 fastvideo/train/models/longcat/longcat.py     |  140 --
 mkdocs.yml                                    |    4 -
 pyproject.toml                                |    7 +-
 pyproject_other.toml                          |    3 +-
 scripts/checkpoint_conversion/AGENTS.md       |    8 -
 .../convert_gamecraft_full.py                 |  239 ----
 .../convert_gamecraft_vae.py                  |   93 --
 .../convert_gamecraft_weights.py              |  242 ----
 ...convert_turbodiffusion_i2v_to_diffusers.py |  197 ---
 .../convert_turbodiffusion_to_diffusers.py    |  204 ---
 .../dreamx_world_ar_to_diffusers.py           |  124 --
 .../dreamx_world_to_diffusers.py              |  226 ---
 .../extract_llava_text_encoder.py             |  232 ----
 .../longcat_to_fastvideo.py                   |  706 ----------
 .../validate_longcat_weights.py               |  237 ----
 scripts/inference/inference_longcat.yaml      |   43 -
 .../inference/inference_longcat_distill.yaml  |   44 -
 scripts/inference/inference_longcat_i2v.yaml  |   42 -
 .../inference_longcat_refine_fromvideo.yaml   |   51 -
 scripts/inference/inference_longcat_vc.yaml   |   43 -
 tests/local_tests/README.md                   |    1 -
 tests/local_tests/dreamx_world/PORT_STATUS.md |  189 ---
 tests/local_tests/dreamx_world/README.md      |  254 ----
 tests/local_tests/dreamx_world/__init__.py    |    1 -
 .../test_dreamx_world_ar_conversion.py        |   58 -
 ...test_dreamx_world_ar_transformer_parity.py |  189 ---
 ...dreamx_world_camera_conditioning_parity.py |   95 --
 .../test_dreamx_world_conversion.py           |   79 --
 .../test_dreamx_world_pipeline_config.py      |  324 -----
 .../test_dreamx_world_scheduler_parity.py     |   52 -
 .../test_dreamx_world_text_encoder_parity.py  |  162 ---
 .../test_dreamx_world_transformer_parity.py   |  330 -----
 .../test_dreamx_world_vae_parity.py           |  221 ---
 tests/local_tests/gamecraft/README.md         |   67 -
 tests/local_tests/gamecraft/__init__.py       |    0
 .../test_gamecraft_encoders_parity.py         |  244 ----
 .../gamecraft/test_gamecraft_parity.py        |  753 ----------
 .../test_gamecraft_pipeline_parity.py         |  508 -------
 .../gamecraft/test_gamecraft_vae_parity.py    |  274 ----
 tests/local_tests/glm_image/README.md         |   74 -
 tests/local_tests/glm_image/test_ar_parity.py |  107 --
 .../glm_image/test_edit_pipeline_parity.py    |  124 --
 .../glm_image/test_pipeline_parity.py         |  308 -----
 tests/local_tests/glm_image/test_t5_parity.py |  161 ---
 .../glm_image/test_transformer_parity.py      |  193 ---
 .../glm_image/test_transformer_strict_load.py |   93 --
 .../local_tests/glm_image/test_vae_parity.py  |  104 --
 .../test_dreamx_world_pipeline_parity.py      |  105 --
 .../test_dreamx_world_pipeline_smoke.py       |  155 ---
 .../pipelines/test_zimage_pipeline_parity.py  |  326 -----
 .../pipelines/test_zimage_pipeline_smoke.py   |  155 ---
 tests/local_tests/zimage/PORT_STATUS.md       |  162 ---
 tests/local_tests/zimage/README.md            |  200 ---
 tests/local_tests/zimage/__init__.py          |    1 -
 .../zimage/test_zimage_encoder_parity.py      |  716 ----------
 .../zimage/test_zimage_scheduler_parity.py    |  224 ---
 .../zimage/test_zimage_tokenizer_parity.py    |  127 --
 .../zimage/test_zimage_transformer_parity.py  |  343 -----
 .../zimage/test_zimage_vae_parity.py          |  195 ---
 241 files changed, 356 insertions(+), 29941 deletions(-)
 delete mode 100644 docs/assets/logos/meituan-longcat.webp
 delete mode 100644 docs/assets/logos/tongyi.webp
 delete mode 100644 docs/assets/logos/zai.webp
 delete mode 100644 docs/cookbook/glm-image.md
 delete mode 100644 docs/cookbook/longcat.md
 delete mode 100644 docs/cookbook/turbodiffusion.md
 delete mode 100644 docs/cookbook/z-image.md
 delete mode 100644 examples/inference/basic/basic_dreamx_world.py
 delete mode 100644 examples/inference/basic/basic_gamecraft.py
 delete mode 100644 examples/inference/basic/basic_glm_image.py
 delete mode 100644 examples/inference/basic/basic_longcat_i2v.py
 delete mode 100644 examples/inference/basic/basic_longcat_t2v.py
 delete mode 100644 examples/inference/basic/basic_longcat_vc.py
 delete mode 100644 examples/inference/basic/basic_turbodiffusion.py
 delete mode 100644 examples/inference/basic/basic_turbodiffusion_14b.py
 delete mode 100644 examples/inference/basic/basic_turbodiffusion_i2v.py
 delete mode 100644 examples/inference/basic/basic_zimage.py
 delete mode 100644 examples/inference/basic/edit_glm_image.py
 delete mode 100644 examples/train/configs/fine_tuning/longcat/t2v.yaml
 delete mode 100644 examples/train/configs/fine_tuning/wan/turbo_t2v_lora_sla.yaml
 delete mode 100644 fastvideo/configs/models/dits/dreamx_world.py
 delete mode 100644 fastvideo/configs/models/dits/glm_image.py
 delete mode 100644 fastvideo/configs/models/dits/hunyuangamecraft.py
 delete mode 100644 fastvideo/configs/models/dits/longcat.py
 delete mode 100644 fastvideo/configs/models/dits/zimage.py
 delete mode 100644 fastvideo/configs/models/vaes/gamecraftvae.py
 delete mode 100644 fastvideo/configs/models/vaes/glm_image.py
 delete mode 100644 fastvideo/configs/pipelines/dreamx_world.py
 delete mode 100644 fastvideo/configs/pipelines/glm_image.py
 delete mode 100644 fastvideo/configs/pipelines/hunyuangamecraft.py
 delete mode 100644 fastvideo/configs/pipelines/longcat.py
 delete mode 100644 fastvideo/configs/pipelines/turbodiffusion.py
 delete mode 100644 fastvideo/configs/pipelines/zimage.py
 delete mode 100644 fastvideo/models/camera/__init__.py
 delete mode 100644 fastvideo/models/camera/trajectory.py
 delete mode 100644 fastvideo/models/dits/dreamx_world.py
 delete mode 100644 fastvideo/models/dits/dreamx_world_ar.py
 delete mode 100644 fastvideo/models/dits/glm_image.py
 delete mode 100644 fastvideo/models/dits/hunyuangamecraft.py
 delete mode 100644 fastvideo/models/dits/longcat.py
 delete mode 100644 fastvideo/models/dits/zimage.py
 delete mode 100644 fastvideo/models/encoders/glm_image_ar_loader.py
 delete mode 100644 fastvideo/models/schedulers/scheduling_rcm.py
 delete mode 100644 fastvideo/models/vaes/gamecraftvae.py
 delete mode 100644 fastvideo/models/vaes/gamecraftvae_blocks.py
 delete mode 100644 fastvideo/pipelines/basic/dreamx_world/__init__.py
 delete mode 100644 fastvideo/pipelines/basic/dreamx_world/ar_denoising.py
 delete mode 100644 fastvideo/pipelines/basic/dreamx_world/camera_conditioning.py
 delete mode 100644 fastvideo/pipelines/basic/dreamx_world/config.py
 delete mode 100644 fastvideo/pipelines/basic/dreamx_world/dreamx_world_ar_pipeline.py
 delete mode 100644 fastvideo/pipelines/basic/dreamx_world/dreamx_world_pipeline.py
 delete mode 100644 fastvideo/pipelines/basic/dreamx_world/presets.py
 delete mode 100644 fastvideo/pipelines/basic/dreamx_world/stages.py
 delete mode 100644 fastvideo/pipelines/basic/gamecraft/__init__.py
 delete mode 100644 fastvideo/pipelines/basic/gamecraft/gamecraft_pipeline.py
 delete mode 100644 fastvideo/pipelines/basic/gamecraft/presets.py
 delete mode 100644 fastvideo/pipelines/basic/glm_image/__init__.py
 delete mode 100644 fastvideo/pipelines/basic/glm_image/glm_image_pipeline.py
 delete mode 100644 fastvideo/pipelines/basic/glm_image/stages/__init__.py
 delete mode 100644 fastvideo/pipelines/basic/glm_image/stages/before_denoising.py
 delete mode 100644 fastvideo/pipelines/basic/glm_image/stages/condition_encoding.py
 delete mode 100644 fastvideo/pipelines/basic/glm_image/stages/decoding.py
 delete mode 100644 fastvideo/pipelines/basic/glm_image/stages/denoising.py
 delete mode 100644 fastvideo/pipelines/basic/longcat/__init__.py
 delete mode 100644 fastvideo/pipelines/basic/longcat/longcat_i2v_pipeline.py
 delete mode 100644 fastvideo/pipelines/basic/longcat/longcat_pipeline.py
 delete mode 100644 fastvideo/pipelines/basic/longcat/longcat_vc_pipeline.py
 delete mode 100644 fastvideo/pipelines/basic/longcat/presets.py
 delete mode 100644 fastvideo/pipelines/basic/turbodiffusion/__init__.py
 delete mode 100644 fastvideo/pipelines/basic/turbodiffusion/presets.py
 delete mode 100644 fastvideo/pipelines/basic/turbodiffusion/turbodiffusion_i2v_pipeline.py
 delete mode 100644 fastvideo/pipelines/basic/turbodiffusion/turbodiffusion_pipeline.py
 delete mode 100644 fastvideo/pipelines/basic/zimage/__init__.py
 delete mode 100644 fastvideo/pipelines/basic/zimage/presets.py
 delete mode 100644 fastvideo/pipelines/basic/zimage/stages.py
 delete mode 100644 fastvideo/pipelines/basic/zimage/zimage_pipeline.py
 delete mode 100644 fastvideo/pipelines/stages/gamecraft_denoising.py
 delete mode 100644 fastvideo/pipelines/stages/gamecraft_image_encoding.py
 delete mode 100644 fastvideo/pipelines/stages/longcat_denoising.py
 delete mode 100644 fastvideo/pipelines/stages/longcat_i2v_denoising.py
 delete mode 100644 fastvideo/pipelines/stages/longcat_i2v_latent_preparation.py
 delete mode 100644 fastvideo/pipelines/stages/longcat_image_vae_encoding.py
 delete mode 100644 fastvideo/pipelines/stages/longcat_kv_cache_init.py
 delete mode 100644 fastvideo/pipelines/stages/longcat_refine_init.py
 delete mode 100644 fastvideo/pipelines/stages/longcat_refine_timestep.py
 delete mode 100644 fastvideo/pipelines/stages/longcat_vc_denoising.py
 delete mode 100644 fastvideo/pipelines/stages/longcat_video_vae_encoding.py
 delete mode 100644 fastvideo/tests/distributed/test_sp_longcat_layout.py
 delete mode 100644 fastvideo/tests/golden_gate/test_dreamx.py
 delete mode 100644 fastvideo/tests/golden_gate/test_gamecraft.py
 delete mode 100644 fastvideo/tests/golden_gate/test_glm_image.py
 delete mode 100644 fastvideo/tests/golden_gate/test_longcat.py
 delete mode 100644 fastvideo/tests/golden_gate/test_zimage.py
 delete mode 100644 fastvideo/tests/ssim/test_dreamx_world_similarity.py
 delete mode 100644 fastvideo/tests/ssim/test_gamecraft_similarity.py
 delete mode 100644 fastvideo/tests/ssim/test_glm_image_similarity.py
 delete mode 100644 fastvideo/tests/ssim/test_longcat_similarity.py
 delete mode 100644 fastvideo/tests/ssim/test_turbodiffusion_similarity.py
 delete mode 100644 fastvideo/tests/ssim/test_zimage_similarity.py
 delete mode 100644 fastvideo/tests/train/fixtures/longcat_t2v_min.yaml
 delete mode 100644 fastvideo/tests/train/models/test_load_longcat.py
 delete mode 100644 fastvideo/tests/transformers/test_hunyuangamecraft.py
 delete mode 100644 fastvideo/third_party/longcat_video/block_sparse_attention/__init__.py
 delete mode 100644 fastvideo/third_party/longcat_video/block_sparse_attention/bsa_interface.py
 delete mode 100644 fastvideo/third_party/longcat_video/block_sparse_attention/common.py
 delete mode 100644 fastvideo/third_party/longcat_video/block_sparse_attention/communicate.py
 delete mode 100644 fastvideo/third_party/longcat_video/block_sparse_attention/flash_attn_bsa_varlen_mask.py
 delete mode 100644 fastvideo/train/models/longcat/__init__.py
 delete mode 100644 fastvideo/train/models/longcat/longcat.py
 delete mode 100644 scripts/checkpoint_conversion/convert_gamecraft_full.py
 delete mode 100644 scripts/checkpoint_conversion/convert_gamecraft_vae.py
 delete mode 100644 scripts/checkpoint_conversion/convert_gamecraft_weights.py
 delete mode 100644 scripts/checkpoint_conversion/convert_turbodiffusion_i2v_to_diffusers.py
 delete mode 100644 scripts/checkpoint_conversion/convert_turbodiffusion_to_diffusers.py
 delete mode 100755 scripts/checkpoint_conversion/dreamx_world_ar_to_diffusers.py
 delete mode 100644 scripts/checkpoint_conversion/dreamx_world_to_diffusers.py
 delete mode 100644 scripts/checkpoint_conversion/extract_llava_text_encoder.py
 delete mode 100644 scripts/checkpoint_conversion/longcat_to_fastvideo.py
 delete mode 100644 scripts/checkpoint_conversion/validate_longcat_weights.py
 delete mode 100644 scripts/inference/inference_longcat.yaml
 delete mode 100644 scripts/inference/inference_longcat_distill.yaml
 delete mode 100644 scripts/inference/inference_longcat_i2v.yaml
 delete mode 100644 scripts/inference/inference_longcat_refine_fromvideo.yaml
 delete mode 100644 scripts/inference/inference_longcat_vc.yaml
 delete mode 100644 tests/local_tests/dreamx_world/PORT_STATUS.md
 delete mode 100644 tests/local_tests/dreamx_world/README.md
 delete mode 100644 tests/local_tests/dreamx_world/__init__.py
 delete mode 100644 tests/local_tests/dreamx_world/test_dreamx_world_ar_conversion.py
 delete mode 100644 tests/local_tests/dreamx_world/test_dreamx_world_ar_transformer_parity.py
 delete mode 100644 tests/local_tests/dreamx_world/test_dreamx_world_camera_conditioning_parity.py
 delete mode 100644 tests/local_tests/dreamx_world/test_dreamx_world_conversion.py
 delete mode 100644 tests/local_tests/dreamx_world/test_dreamx_world_pipeline_config.py
 delete mode 100644 tests/local_tests/dreamx_world/test_dreamx_world_scheduler_parity.py
 delete mode 100644 tests/local_tests/dreamx_world/test_dreamx_world_text_encoder_parity.py
 delete mode 100644 tests/local_tests/dreamx_world/test_dreamx_world_transformer_parity.py
 delete mode 100644 tests/local_tests/dreamx_world/test_dreamx_world_vae_parity.py
 delete mode 100644 tests/local_tests/gamecraft/README.md
 delete mode 100644 tests/local_tests/gamecraft/__init__.py
 delete mode 100644 tests/local_tests/gamecraft/test_gamecraft_encoders_parity.py
 delete mode 100644 tests/local_tests/gamecraft/test_gamecraft_parity.py
 delete mode 100644 tests/local_tests/gamecraft/test_gamecraft_pipeline_parity.py
 delete mode 100644 tests/local_tests/gamecraft/test_gamecraft_vae_parity.py
 delete mode 100644 tests/local_tests/glm_image/README.md
 delete mode 100644 tests/local_tests/glm_image/test_ar_parity.py
 delete mode 100644 tests/local_tests/glm_image/test_edit_pipeline_parity.py
 delete mode 100644 tests/local_tests/glm_image/test_pipeline_parity.py
 delete mode 100644 tests/local_tests/glm_image/test_t5_parity.py
 delete mode 100644 tests/local_tests/glm_image/test_transformer_parity.py
 delete mode 100644 tests/local_tests/glm_image/test_transformer_strict_load.py
 delete mode 100644 tests/local_tests/glm_image/test_vae_parity.py
 delete mode 100644 tests/local_tests/pipelines/test_dreamx_world_pipeline_parity.py
 delete mode 100644 tests/local_tests/pipelines/test_dreamx_world_pipeline_smoke.py
 delete mode 100644 tests/local_tests/pipelines/test_zimage_pipeline_parity.py
 delete mode 100644 tests/local_tests/pipelines/test_zimage_pipeline_smoke.py
 delete mode 100644 tests/local_tests/zimage/PORT_STATUS.md
 delete mode 100644 tests/local_tests/zimage/README.md
 delete mode 100644 tests/local_tests/zimage/__init__.py
 delete mode 100644 tests/local_tests/zimage/test_zimage_encoder_parity.py
 delete mode 100644 tests/local_tests/zimage/test_zimage_scheduler_parity.py
 delete mode 100644 tests/local_tests/zimage/test_zimage_tokenizer_parity.py
 delete mode 100644 tests/local_tests/zimage/test_zimage_transformer_parity.py
 delete mode 100644 tests/local_tests/zimage/test_zimage_vae_parity.py

diff --git a/.agents/skills/add-model-02-parity/SKILL.md b/.agents/skills/add-model-02-parity/SKILL.md
index 5a96f34a5..a11341759 100644
--- a/.agents/skills/add-model-02-parity/SKILL.md
+++ b/.agents/skills/add-model-02-parity/SKILL.md
@@ -68,7 +68,7 @@ Copy `templates/component_parity_test.py` and fill every `TODO` marker. The
 template is distilled from:
 
 - `tests/local_tests/transformers/test_ltx2.py`
-- `tests/local_tests/transformers/test_gamecraft_parity.py`
+- `tests/local_tests/gen3c/test_gen3c.py`
 - `tests/local_tests/encoders/test_ltx2_gemma_parity.py`
 - `tests/local_tests/vaes/test_oobleck_vae_parity.py`
 - `tests/local_tests/sd35/test_sd35_component_parity.py`
diff --git a/.agents/skills/add-model-02-parity/templates/component_parity_test.py b/.agents/skills/add-model-02-parity/templates/component_parity_test.py
index 8d24e292a..c1137f298 100644
--- a/.agents/skills/add-model-02-parity/templates/component_parity_test.py
+++ b/.agents/skills/add-model-02-parity/templates/component_parity_test.py
@@ -87,7 +87,7 @@ def _load_official_model(device: torch.device, dtype: torch.dtype) -> torch.nn.M
     # TODO: import official class/factory and load real weights strictly.
     # Examples in-tree:
     # - LTX2: SingleGPUModelBuilder(...).build(device=device, dtype=dtype)
-    # - GameCraft: torch.load(...)["module"] -> official_model.load_state_dict(...)
+    # - GEN3C: torch.load(...)["state_dict"] -> official_model.load_state_dict(...)
     # - Oobleck: create_model_from_config(config) + ckpt state_dict
     OfficialClass = _import_or_skip(OFFICIAL_MODULE, OFFICIAL_CLASS)
     model = OfficialClass()  # TODO: pass official config kwargs.
diff --git a/.agents/skills/add-model-03-port-dit/SKILL.md b/.agents/skills/add-model-03-port-dit/SKILL.md
index 028f6b346..4d185bda6 100644
--- a/.agents/skills/add-model-03-port-dit/SKILL.md
+++ b/.agents/skills/add-model-03-port-dit/SKILL.md
@@ -52,8 +52,8 @@ scaling constants, dtype casts, state-dict names, and every output head.
 - Loader path: `TransformerLoader` reads `transformer/config.json`, calls
   `dit_config.update_model_arch(config)`, resolves `_class_name` through
   `ModelRegistry`, and constructs the class with `config` and `hf_config`.
-- Reference examples: `stable_audio.py`, `wanvideo.py`, `sd3.py`, `longcat.py`,
-  and `ltx2.py`.
+- Reference examples: `stable_audio.py`, `wanvideo.py`, `sd3.py`, and
+  `ltx2.py`.
 - Layer guidance: `fastvideo/layers/AGENTS.md`.
 
 ## Implementation Rules
diff --git a/.agents/skills/add-model-04-port-vae/SKILL.md b/.agents/skills/add-model-04-port-vae/SKILL.md
index 16c50bc3f..14e8047f9 100644
--- a/.agents/skills/add-model-04-port-vae/SKILL.md
+++ b/.agents/skills/add-model-04-port-vae/SKILL.md
@@ -51,7 +51,7 @@ posterior behavior, encode/decode output objects, tiling flags, and cropping.
 - Loader path: VAE loaders resolve `_class_name` through `ModelRegistry` and
   load converted component weights from the VAE subdir.
 - Reference examples: `oobleck.py`, `autoencoder_kl.py`, `wanvae.py`,
-  `ltx2vae.py`, and `gamecraftvae.py`.
+  `ltx2vae.py`, and `hunyuanvae.py`.
 - Layer guidance: `fastvideo/layers/AGENTS.md`.
 
 ## Implementation Rules
diff --git a/.agents/skills/add-model-07-conversion/SKILL.md b/.agents/skills/add-model-07-conversion/SKILL.md
index 727b80f06..73d8f201b 100644
--- a/.agents/skills/add-model-07-conversion/SKILL.md
+++ b/.agents/skills/add-model-07-conversion/SKILL.md
@@ -43,11 +43,12 @@ from `../add-model/contracts/conversion_request.md`.
 - `scripts/checkpoint_conversion/stable_audio_to_diffusers.py`: monolithic
   `model.safetensors` split into transformer/VAE/conditioner, plus copied
   passthrough subfolders. Use this shape for single-checkpoint official repos.
-- `scripts/checkpoint_conversion/convert_gamecraft_full.py`: separate official
-  sources for transformer, VAE, text encoders, tokenizers, scheduler, and root
-  `model_index.json`.
-- `scripts/checkpoint_conversion/longcat_to_fastvideo.py`: fused QKV/KV split,
-  renamed native transformer weights, and copied existing Diffusers components.
+- `scripts/checkpoint_conversion/convert_mmaudio_to_diffusers.py`: separate
+  official sources for transformer, VAE, encoders, and vocoder, assembled under
+  a root `model_index.json`.
+- `scripts/checkpoint_conversion/convert_flux2_klein.py`: fused QKV split,
+  renamed native transformer weights, and copied passthrough text encoder,
+  tokenizer, and scheduler components.
 - `scripts/checkpoint_conversion/pt_to_safetensors.py`: simple `.pt` extraction
   helper for nested checkpoint dictionaries.
 
diff --git a/.agents/skills/add-model-09-pipeline/SKILL.md b/.agents/skills/add-model-09-pipeline/SKILL.md
index e91257b0a..1f3e55557 100644
--- a/.agents/skills/add-model-09-pipeline/SKILL.md
+++ b/.agents/skills/add-model-09-pipeline/SKILL.md
@@ -247,7 +247,7 @@ setup gap, not a pass.
 - `fastvideo/configs/pipelines/stable_audio.py` and
   `fastvideo/pipelines/basic/stable_audio/presets.py` for config/preset shape.
 - `fastvideo/registry.py` for `register_configs(...)` and preset registration.
-- `tests/local_tests/pipelines/test_gamecraft_pipeline_parity.py` for latent
+- `tests/local_tests/pipelines/test_lingbot_video_pipeline_parity.py` for latent
   parity structure.
 - `tests/local_tests/pipelines/test_stable_audio_pipeline_parity.py` for audio
   parity structure.
diff --git a/.agents/skills/add-model/SKILL.md b/.agents/skills/add-model/SKILL.md
index d7f83de23..674967140 100644
--- a/.agents/skills/add-model/SKILL.md
+++ b/.agents/skills/add-model/SKILL.md
@@ -413,7 +413,7 @@ matching `*secret*`.
 - `fastvideo/pipelines/basic/wan/` for standard T2V/I2V/DMD/Causal variants.
 - `fastvideo/pipelines/basic/ltx2/` for non-standard stages and audio/video
   patterns.
-- `tests/local_tests/pipelines/test_gamecraft_pipeline_parity.py` for pipeline
+- `tests/local_tests/pipelines/test_lingbot_video_pipeline_parity.py` for pipeline
   parity shape.
 - `tests/local_tests/transformers/test_ltx2.py`,
   `tests/local_tests/vaes/test_ltx2_vae.py`, and
diff --git a/.agents/skills/seed-ssim-references/SKILL.md b/.agents/skills/seed-ssim-references/SKILL.md
index ca2266ae1..851494605 100644
--- a/.agents/skills/seed-ssim-references/SKILL.md
+++ b/.agents/skills/seed-ssim-references/SKILL.md
@@ -105,8 +105,8 @@ Detect artefact type by inspecting the file's imports / helper call:
 - **pixel** (`.mp4`) — file imports
   `run_text_to_video_similarity_test` / `run_image_to_video_similarity_test`
   from `fastvideo.tests.ssim.inference_similarity_utils`, OR uses the
-  legacy custom-inline helper pattern (see `test_gamecraft`,
-  `test_longcat`, etc.). Default to pixel when both heuristics fail.
+  legacy custom-inline helper pattern (see `test_gen3c`). Default to pixel
+  when both heuristics fail.
 
 Record `ARTEFACT_TYPE ∈ {pixel, latent}` for use in step 4. Steps 2, 3, 5,
 and 6 are artefact-type-agnostic — `_iter_reference_files`,
diff --git a/.github/scripts/plan_merge_ci.py b/.github/scripts/plan_merge_ci.py
index eb6dfad58..0d4a01d06 100644
--- a/.github/scripts/plan_merge_ci.py
+++ b/.github/scripts/plan_merge_ci.py
@@ -12,6 +12,7 @@ from __future__ import annotations
 import argparse
 import fnmatch
 import re
+from collections.abc import Iterable
 from dataclasses import dataclass, field
 from pathlib import Path
 from typing import TextIO
@@ -130,11 +131,6 @@ class FamilyCoverage:
 
 
 FAMILY_COVERAGE = (
-    FamilyCoverage(
-        re.compile(r"(^|[/_.-])dreamx(_world)?([/_.-]|$)"),
-        ("test_dreamx.py", ),
-        ("test_dreamx_world_similarity.py", ),
-    ),
     FamilyCoverage(
         re.compile(r"(^|[/_.-])flux[_-]?2([/_.-]|$)"),
         ("test_flux2_klein.py", ),
@@ -145,21 +141,11 @@ FAMILY_COVERAGE = (
         ("test_flux.py", ),
         ("test_flux_t2i_similarity.py", ),
     ),
-    FamilyCoverage(
-        re.compile(r"(^|[/_.-])(hunyuan)?gamecraft([/_.-]|$)"),
-        ("test_gamecraft.py", ),
-        ("test_gamecraft_similarity.py", ),
-    ),
     FamilyCoverage(
         re.compile(r"(^|[/_.-])gen3c([/_.-]|$)"),
         ("test_gen3c.py", ),
         ("test_gen3c_similarity.py", ),
     ),
-    FamilyCoverage(
-        re.compile(r"(^|[/_.-])glm[_-]?image([/_.-]|$)"),
-        ("test_glm_image.py", ),
-        ("test_glm_image_similarity.py", ),
-    ),
     FamilyCoverage(
         re.compile(r"(^|[/_.-])hunyuan(video)?15([a-z0-9_-]*)([/_.-]|$)"),
         (),
@@ -175,11 +161,6 @@ FAMILY_COVERAGE = (
         ("test_lingbot.py", ),
         ("test_lingbot_similarity.py", ),
     ),
-    FamilyCoverage(
-        re.compile(r"(^|[/_.-])longcat([/_.-]|$)"),
-        ("test_longcat.py", ),
-        ("test_longcat_similarity.py", ),
-    ),
     FamilyCoverage(
         re.compile(r"(^|[/_.-])ltx[_-]?2([/_.-]|$)"),
         ("test_ltx2.py", ),
@@ -210,11 +191,6 @@ FAMILY_COVERAGE = (
         ("test_stable_audio.py", ),
         ("test_stable_audio_similarity.py", ),
     ),
-    FamilyCoverage(
-        re.compile(r"(^|[/_.-])turbo(diffusion)?([/_.-]|$)"),
-        (),
-        ("test_turbodiffusion_similarity.py", ),
-    ),
     FamilyCoverage(
         re.compile(r"(^|[/_.-])wan(video|vae)?([/_.-]|$)"),
         ("test_wan_t2v.py", "test_wan_vae.py", "test_wan_causal.py", "test_wan_denoising.py"),
@@ -225,11 +201,6 @@ FAMILY_COVERAGE = (
             "test_wan_ti2v_similarity.py",
         ),
     ),
-    FamilyCoverage(
-        re.compile(r"(^|[/_.-])z[_-]?image([/_.-]|$)"),
-        ("test_zimage.py", ),
-        ("test_zimage_similarity.py", ),
-    ),
 )
 
 
@@ -325,16 +296,22 @@ def _select_output_coverage(plan: MergePlan, path: str) -> None:
         plan.add_ssim(SSIM_SMOKE_TESTS, reason=f"shared output SSIM smoke coverage: {path}")
 
 
-def classify_paths(paths: list[str]) -> MergePlan:
+def _normalize_path(raw_path: str) -> str:
+    path = raw_path.strip()
+    while path.startswith("./"):
+        path = path[2:]
+    return path
+
+
+def classify_paths(paths: list[str], removed_paths: Iterable[str] = ()) -> MergePlan:
+    """Plan merge lanes for ``paths``.
+
+    ``removed_paths`` lists changed paths that no longer exist at the PR head
+    (deleted files and rename sources); removed golden/SSIM tests are not run.
+    """
     plan = MergePlan()
-    normalized_paths: list[str] = []
-    for raw_path in paths:
-        path = raw_path.strip()
-        while path.startswith("./"):
-            path = path[2:]
-        if path:
-            normalized_paths.append(path)
-    normalized_paths = sorted(set(normalized_paths))
+    normalized_paths = sorted({path for path in map(_normalize_path, paths) if path})
+    removed = {path for path in map(_normalize_path, removed_paths) if path}
     if not normalized_paths:
         plan.require_all("changed-file list was empty; failing closed")
         return plan
@@ -374,7 +351,10 @@ def classify_paths(paths: list[str]) -> MergePlan:
         if path.startswith("fastvideo/tests/golden_gate/"):
             name = Path(path).name
             if name.startswith("test_") and name.endswith(".py"):
-                plan.add_golden((name, ), reason=f"changed golden test: {path}")
+                if path in removed:
+                    plan.reasons.append(f"removed golden test has nothing to run: {path}")
+                else:
+                    plan.add_golden((name, ), reason=f"changed golden test: {path}")
             elif name in {"AGENTS.md", "README.md"}:
                 plan.reasons.append(f"golden documentation only: {path}")
             else:
@@ -385,7 +365,10 @@ def classify_paths(paths: list[str]) -> MergePlan:
         if path.startswith("fastvideo/tests/ssim/"):
             name = Path(path).name
             if name.startswith("test_") and name.endswith(".py"):
-                plan.add_ssim((name, ), reason=f"changed SSIM test: {path}")
+                if path in removed:
+                    plan.reasons.append(f"removed SSIM test has nothing to run: {path}")
+                else:
+                    plan.add_ssim((name, ), reason=f"changed SSIM test: {path}")
             elif path.endswith((".py", ".json", ".pt", ".png", ".mp4")):
                 plan.ssim_all = True
                 plan.add_lanes("ssim", reason=f"shared SSIM harness/reference: {path}")
@@ -565,6 +548,7 @@ def _write_summary(output: TextIO, plan: MergePlan) -> None:
 def parse_args() -> argparse.Namespace:
     parser = argparse.ArgumentParser(description=__doc__)
     parser.add_argument("--paths-file", type=Path, required=True)
+    parser.add_argument("--removed-paths-file", type=Path)
     parser.add_argument("--github-output", type=Path)
     parser.add_argument("--summary-file", type=Path)
     return parser.parse_args()
@@ -573,7 +557,9 @@ def parse_args() -> argparse.Namespace:
 def main() -> int:
     args = parse_args()
     paths = args.paths_file.read_text(encoding="utf-8").splitlines()
-    plan = classify_paths(paths)
+    removed_paths = (args.removed_paths_file.read_text(encoding="utf-8").splitlines()
+                     if args.removed_paths_file else [])
+    plan = classify_paths(paths, removed_paths)
     print(f"MERGE_TEST_PLAN={plan.encoded_lanes()}")
     print(f"MERGE_GOLDEN_TESTS={plan.encoded_golden_tests()}")
     print(f"MERGE_SSIM_TESTS={plan.encoded_ssim_tests()}")
diff --git a/.github/workflows/ci-trigger-full-suite.yml b/.github/workflows/ci-trigger-full-suite.yml
index 31102f683..bc8bfcc48 100644
--- a/.github/workflows/ci-trigger-full-suite.yml
+++ b/.github/workflows/ci-trigger-full-suite.yml
@@ -180,6 +180,8 @@ jobs:
           set -euo pipefail
           changed_json="$RUNNER_TEMP/merge-changed-files.json"
           changed_paths="$RUNNER_TEMP/merge-changed-paths.txt"
+          removed_paths="$RUNNER_TEMP/merge-removed-paths.txt"
+          : > "$removed_paths"
           if gh api --paginate --slurp \
               "repos/${GITHUB_REPOSITORY}/pulls/${PR_NUMBER}/files?per_page=100" \
               > "$changed_json"; then
@@ -187,6 +189,10 @@ jobs:
             if [ "$observed" = "$EXPECTED_CHANGED_FILES" ]; then
               jq -r '.[][] | .filename, (.previous_filename // empty)' "$changed_json" \
                 | sort -u > "$changed_paths"
+              # Paths absent from the PR head, so the planner never selects a deleted test.
+              jq -r '.[][] | if .status == "removed" then .filename
+                  elif .status == "renamed" then (.previous_filename // empty) else empty end' \
+                "$changed_json" | sort -u > "$removed_paths"
             else
               echo "::warning::Changed-file API returned $observed of $EXPECTED_CHANGED_FILES paths; selecting all merge lanes."
               echo '__FASTVIDEO_CI_PLAN_ALL__' > "$changed_paths"
@@ -202,6 +208,7 @@ jobs:
         run: |
           python3 .github/scripts/plan_merge_ci.py \
             --paths-file "$RUNNER_TEMP/merge-changed-paths.txt" \
+            --removed-paths-file "$RUNNER_TEMP/merge-removed-paths.txt" \
             --github-output "$GITHUB_OUTPUT" \
             --summary-file "$GITHUB_STEP_SUMMARY"
 
diff --git a/.gitignore b/.gitignore
index dc591b8cf..e4d9d47b0 100644
--- a/.gitignore
+++ b/.gitignore
@@ -36,7 +36,6 @@ env
 *.log
 weights/
 logs/
-/Z-Image/
 official_weights/
 converted_weights/
 
diff --git a/apps/infinite_livestream/infinite_livestream/backend.py b/apps/infinite_livestream/infinite_livestream/backend.py
index 9a44bfe47..cd4f2b501 100644
--- a/apps/infinite_livestream/infinite_livestream/backend.py
+++ b/apps/infinite_livestream/infinite_livestream/backend.py
@@ -160,8 +160,8 @@ class FastH3Backend:
         Each clip length is a torch.compile shape, and the fullgraph regional
         route treats exceeding dynamo's recompile limit as a crash rather than
         a fallback. FastVideo's own imports lower it (`layers/lora/linear.py`
-        to 16, longcat's `bsa_interface.py` to 32), so this raises it again
-        and turns overflow back into a recompile.
+        to 16), so this raises it again and turns overflow back into a
+        recompile.
 
         One pinned clip length keeps every process far under even the lowered
         limit; a varied `warmup_lengths` is where this becomes the seatbelt it
diff --git a/docs/assets/cookbook-recipes.json b/docs/assets/cookbook-recipes.json
index 8e8cc4576..1497272e0 100644
--- a/docs/assets/cookbook-recipes.json
+++ b/docs/assets/cookbook-recipes.json
@@ -1,5 +1,5 @@
 {
-  "version": 15,
+  "version": 16,
   "recipes": [
     {
       "id": "fastwan21-t2v",
@@ -161,53 +161,6 @@
         "Native MLX FastMetal T2V on 36 GB+ unified memory. Same --fast, --fast-spatial, and --refine flags as the 1.3B script."
       ]
     },
-    {
-      "id": "turbodiffusion-wan21-1-3b-t2v",
-      "family": "turbodiffusion",
-      "stage": "inference",
-      "task": "Text to video",
-      "label": "TurboWan2.1 1.3B",
-      "summary": "A TurboDiffusion-accelerated Wan2.1 1.3B text-to-video run from its maintained single-GPU example.",
-      "model": "loayrashid/TurboWan2.1-T2V-1.3B-Diffusers",
-      "source": "examples/inference/basic/basic_turbodiffusion.py",
-      "command": "python examples/inference/basic/basic_turbodiffusion.py",
-      "gpu_types": ["NVIDIA"],
-      "hardware": {"gpu_count": 1, "evidence": "source-configured"},
-      "evidence": "Source-backed",
-      "expected_artifact": "MP4 videos under video_samples_turbodiffusion/",
-      "related": ["turbodiffusion-wan21-14b-t2v", "turbowan22-i2v"]
-    },
-    {
-      "id": "turbodiffusion-wan21-14b-t2v",
-      "family": "turbodiffusion",
-      "stage": "inference",
-      "task": "Text to video",
-      "label": "TurboWan2.1 14B",
-      "summary": "TurboDiffusion acceleration applied to the 14B Wan2.1 text-to-video checkpoint; the checked-in source is configured for two GPUs.",
-      "model": "loayrashid/TurboWan2.1-T2V-14B-Diffusers",
-      "source": "examples/inference/basic/basic_turbodiffusion_14b.py",
-      "command": "python examples/inference/basic/basic_turbodiffusion_14b.py",
-      "gpu_types": ["NVIDIA"],
-      "hardware": {"gpu_count": 2, "evidence": "source-configured"},
-      "evidence": "Source-backed",
-      "expected_artifact": "MP4 videos under video_samples_turbodiffusion_14B/",
-      "related": ["turbodiffusion-wan21-1-3b-t2v", "turbowan22-i2v"]
-    },
-    {
-      "id": "turbowan22-i2v",
-      "family": "turbodiffusion",
-      "stage": "inference",
-      "task": "Image to video",
-      "label": "TurboWan2.2 A14B",
-      "summary": "A one-to-four-step image-to-video path using TurboDiffusion and the SLA attention backend from its maintained example.",
-      "model": "loayrashid/TurboWan2.2-I2V-A14B-Diffusers",
-      "source": "examples/inference/basic/basic_turbodiffusion_i2v.py",
-      "command": "python examples/inference/basic/basic_turbodiffusion_i2v.py",
-      "gpu_types": ["NVIDIA"],
-      "hardware": {"gpu_count": 2, "evidence": "source-configured"},
-      "evidence": "Source-backed",
-      "related": ["turbodiffusion-wan21-14b-t2v"]
-    },
     {
       "id": "ltx2-distilled-t2v",
       "family": "ltx2",
@@ -476,53 +429,6 @@
       "expected_artifact": "PNG images under outputs/flux_dev/samples/",
       "limitations": ["FLUX.1 is loadable by ID but registers no model_family in fastvideo/registry.py; it is grouped under FLUX for documentation only."]
     },
-    {
-      "id": "glm-image-t2i",
-      "family": "glm_image",
-      "stage": "inference",
-      "task": "Text to image",
-      "label": "GLM-Image",
-      "summary": "GLM-Image text-to-image generation from its maintained example.",
-      "model": "zai-org/GLM-Image",
-      "source": "examples/inference/basic/basic_glm_image.py",
-      "command": "python examples/inference/basic/basic_glm_image.py",
-      "gpu_types": ["NVIDIA"],
-      "hardware": {"gpu_count": 1, "evidence": "source-configured"},
-      "evidence": "Source-backed",
-      "expected_artifact": "PNG image at image_output/landscape.png",
-      "related": ["glm-image-edit"]
-    },
-    {
-      "id": "glm-image-edit",
-      "family": "glm_image",
-      "stage": "inference",
-      "task": "Image editing",
-      "label": "GLM-Image editing",
-      "summary": "Edit an input image with an instruction prompt using GLM-Image, from its maintained editing example.",
-      "model": "zai-org/GLM-Image",
-      "source": "examples/inference/basic/edit_glm_image.py",
-      "command": "python examples/inference/basic/edit_glm_image.py",
-      "gpu_types": ["NVIDIA"],
-      "hardware": {"gpu_count": 1, "evidence": "source-configured"},
-      "evidence": "Source-backed",
-      "expected_artifact": "PNG image at image_output/edited.png (input: assets/images/couple.jpg)",
-      "related": ["glm-image-t2i"]
-    },
-    {
-      "id": "zimage-turbo-t2i",
-      "family": "zimage",
-      "stage": "inference",
-      "task": "Text to image",
-      "label": "Z-Image Turbo",
-      "summary": "Z-Image Turbo text-to-image on a single GPU from its maintained example.",
-      "model": "Tongyi-MAI/Z-Image-Turbo",
-      "source": "examples/inference/basic/basic_zimage.py",
-      "command": "python examples/inference/basic/basic_zimage.py",
-      "gpu_types": ["NVIDIA"],
-      "hardware": {"gpu_count": 1, "evidence": "source-configured"},
-      "evidence": "Source-backed",
-      "expected_artifact": "PNG image at outputs/zimage/zimage_turbo.png"
-    },
     {
       "id": "sd35-medium-t2i",
       "family": "sd35",
@@ -914,38 +820,6 @@
       ],
       "limitations": ["Supply a compatible FastH3 adapter. The script infers dense or VSA attention from the adapter payload unless you override it."]
     },
-    {
-      "id": "longcat-t2v",
-      "family": "longcat",
-      "stage": "inference",
-      "task": "Text to video",
-      "label": "LongCat Video T2V",
-      "summary": "LongCat Video text-to-video at 480p (50 steps), with distilled and 720p refinement passes included in the same maintained script.",
-      "model": "FastVideo/LongCat-Video-T2V-Diffusers",
-      "source": "examples/inference/basic/basic_longcat_t2v.py",
-      "command": "python examples/inference/basic/basic_longcat_t2v.py",
-      "gpu_types": ["NVIDIA"],
-      "hardware": {"gpu_count": 1, "evidence": "source-configured"},
-      "evidence": "Source-backed",
-      "expected_artifact": "MP4 videos under outputs_video/longcat_t2v_basic/, longcat_t2v_distill/, and longcat_t2v_refine_720p/",
-      "related": ["longcat-i2v"]
-    },
-    {
-      "id": "longcat-i2v",
-      "family": "longcat",
-      "stage": "inference",
-      "task": "Image to video",
-      "label": "LongCat Video I2V",
-      "summary": "LongCat Video image-to-video with optional distilled and refinement passes, from its maintained example.",
-      "model": "FastVideo/LongCat-Video-I2V-Diffusers",
-      "source": "examples/inference/basic/basic_longcat_i2v.py",
-      "command": "python examples/inference/basic/basic_longcat_i2v.py",
-      "gpu_types": ["NVIDIA"],
-      "hardware": {"gpu_count": 1, "evidence": "source-configured"},
-      "evidence": "Source-backed",
-      "expected_artifact": "MP4 videos under outputs_video/longcat_i2v_basic/ and longcat_i2v_distill/",
-      "related": ["longcat-t2v"]
-    },
     {
       "id": "stable-audio-open-t2a",
       "family": "stable_audio",
diff --git a/docs/assets/logos/SOURCES.md b/docs/assets/logos/SOURCES.md
index 21247b395..17b3907f4 100644
--- a/docs/assets/logos/SOURCES.md
+++ b/docs/assets/logos/SOURCES.md
@@ -9,18 +9,15 @@ plain typographic tile instead — that tile is a UI placeholder, not a logo.
 | --- | --- | --- |
 | `wan-ai.webp` | [Official Wan-AI Hugging Face organization avatar](https://huggingface.co/Wan-AI) | Wan family card and page header |
 | `ltx.webp` | [Official Lightricks Hugging Face organization avatar](https://huggingface.co/Lightricks) | LTX family card and page header |
-| `tencent-hunyuan.webp` | [Official Tencent Hunyuan Hugging Face organization avatar](https://huggingface.co/Tencent-Hunyuan) | Hunyuan and GameCraft cards, Hunyuan page header |
+| `tencent-hunyuan.webp` | [Official Tencent Hunyuan Hugging Face organization avatar](https://huggingface.co/Tencent-Hunyuan) | Hunyuan family card and page header |
 | `nvidia.webp` | [Official NVIDIA Hugging Face organization avatar](https://huggingface.co/nvidia) | Cosmos and GEN3C cards, Cosmos page header |
 | `kandinsky.webp` | [Official Kandinsky Lab Hugging Face organization avatar](https://huggingface.co/kandinskylab) | Kandinsky 5 and Kandinsky 6 cards and page headers |
 | `black-forest-labs.webp` | [Official Black Forest Labs Hugging Face organization avatar](https://huggingface.co/black-forest-labs) | FLUX family card and page header |
 | `minimax.webp` | [Official MiniMax Hugging Face organization avatar](https://huggingface.co/MiniMaxAI) | MiniMax H3 family card and page header |
-| `tongyi.webp` | [Official Tongyi MAI Hugging Face organization avatar](https://huggingface.co/Tongyi-MAI) | Z-Image family card and page header |
-| `zai.webp` | [Official Z.ai Hugging Face organization avatar](https://huggingface.co/zai-org) | GLM-Image family card and page header |
 | `stabilityai.webp` | [Official Stability AI Hugging Face organization avatar](https://huggingface.co/stabilityai) | Stable Diffusion and Stable Audio cards and page headers |
-| `meituan-longcat.webp` | [Official Meituan LongCat Hugging Face organization avatar](https://huggingface.co/meituan-longcat) | LongCat family card and page header |
-| `fastvideo.webp` | [Official FastVideo Hugging Face organization avatar](https://huggingface.co/FastVideo) | Matrix Game and MMAudio cards (converted weights published by this org), DreamX card |
+| `fastvideo.webp` | [Official FastVideo Hugging Face organization avatar](https://huggingface.co/FastVideo) | Matrix Game and MMAudio cards (converted weights published by this org) |
 
-Typographic tiles (no vendored image): TurboDiffusion ("Turbo"), HY-World
-("HY"), LingBot ("LB"), MMAudio page header ("MMA"). These publishers have no
+Typographic tiles (no vendored image): HY-World ("HY"), LingBot ("LB"),
+MMAudio page header ("MMA"). These publishers have no
 single official mark appropriate for reuse in the catalog; add a licensed
 asset here if one becomes available.
diff --git a/docs/assets/logos/meituan-longcat.webp b/docs/assets/logos/meituan-longcat.webp
deleted file mode 100644
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UolxYM1L@wey8x=7zyJUM07;JVxBvhE

diff --git a/docs/contributing/env_vars.md b/docs/contributing/env_vars.md
index 62b6c56d6..3bdf74996 100644
--- a/docs/contributing/env_vars.md
+++ b/docs/contributing/env_vars.md
@@ -304,36 +304,44 @@ longer exists also fails the test, so the fixing pull request deletes its entry.
 | `FASTVIDEO_TEST_WAN22_5B_ALLOW_LOW_MEMORY`                 | bool                           | `0`                                                   | test        | Run the MLX Wan2.2 5B real-weights parity test on hosts with little memory. Deprecated names: `FASTVIDEO_WAN22_5B_ALLOW_LOW_MEMORY`.                                                                                                                                         |
 | `FASTVIDEO_TEST_WAN22_5B_ROOT`                             | str                            | unset                                                 | test        | Local Wan2.2 5B checkpoint for the MLX real-weights parity test. Deprecated names: `FASTVIDEO_WAN22_5B_ROOT`.                                                                                                                                                                |
 | `FASTVIDEO_TEST_GRADNORM_UPDATE`                           | bool                           | `0`                                                   | test        | Gradient-norm regression tests update their references. Deprecated names: `FASTVIDEO_GRADNORM_UPDATE`.                                                                                                                                                                       |
-| `FASTVIDEO_TEST_DREAMX_WORLD_SSIM_MODEL_PATH`              | str                            | `FastVideo/DreamX-World-5B-Cam-Diffusers`             | test        | Model for the DreamX-World camera SSIM test. Deprecated names: `DREAMX_WORLD_SSIM_MODEL_PATH`.                                                                                                                                                                               |
-| `FASTVIDEO_TEST_DREAMX_WORLD_AR_SSIM_MODEL_PATH`           | str                            | `FastVideo/DreamX-World-5B-Diffusers`                 | test        | Model for the DreamX-World autoregressive SSIM test. Deprecated names: `DREAMX_WORLD_AR_SSIM_MODEL_PATH`.                                                                                                                                                                    |
 | `FASTVIDEO_TEST_FLUX_T2I_MODEL_DIR`                        | str                            | `black-forest-labs/FLUX.1-dev`                        | test        | Model for the Flux text-to-image SSIM test. Deprecated names: `FLUX_T2I_MODEL_DIR`.                                                                                                                                                                                          |
 | `FASTVIDEO_TEST_FLUX_TRANSFORMER_PATH`                     | str                            | unset                                                 | test        | Local Flux transformer for the Flux transformer test. Deprecated names: `FLUX_TRANSFORMER_PATH`.                                                                                                                                                                             |
-| `FASTVIDEO_TEST_GAMECRAFT_MODEL_PATH`                      | str                            | `FastVideo/HunyuanGameCraft-Diffusers`                | test        | Model for the HunyuanGameCraft SSIM test. Deprecated names: `GAMECRAFT_MODEL_PATH`.                                                                                                                                                                                          |
 | `FASTVIDEO_TEST_GEN3C_MODEL_PATH`                          | str                            | `FastVideo/GEN3C-Cosmos-7B-Diffusers`                 | test        | Model for the GEN3C SSIM test. Deprecated names: `GEN3C_MODEL_PATH`.                                                                                                                                                                                                         |
 | `FASTVIDEO_TEST_GEN3C_IMAGE_PATH`                          | str                            | unset                                                 | test        | Input image for the GEN3C SSIM test. Deprecated names: `GEN3C_TEST_IMAGE_PATH`.                                                                                                                                                                                              |
-| `FASTVIDEO_TEST_GLM_IMAGE_LOCAL_WEIGHTS_DIR`               | str                            | unset                                                 | test        | Local official GLM-Image weights for the GLM-Image SSIM test. Deprecated names: `GLM_IMAGE_LOCAL_WEIGHTS_DIR`.                                                                                                                                                               |
-| `FASTVIDEO_TEST_GLM_IMAGE_MODEL_DIR`                       | str                            | unset                                                 | test        | Model for the GLM-Image SSIM test. Deprecated names: `GLM_IMAGE_MODEL_DIR`.                                                                                                                                                                                                  |
 | `FASTVIDEO_TEST_KANDINSKY5_E2E_NUM_GPUS`                   | int                            | `1`                                                   | test        | GPUs for the Kandinsky5 nightly end-to-end overfit test. Deprecated names: `KANDINSKY5_E2E_NUM_GPUS`.                                                                                                                                                                        |
 | `FASTVIDEO_TEST_KANDINSKY5_E2E_WRITE_REFERENCE`            | bool                           | `0`                                                   | test        | The Kandinsky5 nightly end-to-end test writes a missing reference video. Deprecated names: `KANDINSKY5_E2E_WRITE_REFERENCE`.                                                                                                                                                 |
-| `FASTVIDEO_TEST_LONGCAT_MODEL_ROOT`                        | str                            | unset                                                 | test        | Local LongCat-Video checkpoint for the golden-gate test. Deprecated names: `LONGCAT_MODEL_ROOT`.                                                                                                                                                                             |
 | `FASTVIDEO_TEST_MINIMAX_H3_GATE_GOLDEN_DIR`                | str                            | unset                                                 | test        | Local directory of MiniMax-H3 golden-gate tensors. Deprecated names: `MINIMAX_H3_GATE_GOLDEN_DIR`.                                                                                                                                                                           |
 | `FASTVIDEO_TEST_MINIMAX_H3_GATE_LAYER`                     | int                            | `0`                                                   | test        | Transformer layer that the MiniMax-H3 golden-gate test checks. Deprecated names: `MINIMAX_H3_GATE_LAYER`.                                                                                                                                                                    |
 | `FASTVIDEO_TEST_MINIMAX_H3_MODEL_ROOT`                     | str                            | unset                                                 | test        | Local MiniMax-H3 checkpoint for the golden-gate test. Deprecated names: `MINIMAX_H3_MODEL_ROOT`.                                                                                                                                                                             |
 | `FASTVIDEO_TEST_SD35_MODEL_DIR`                            | str                            | `stabilityai/stable-diffusion-3.5-medium`             | test        | Model for the Stable Diffusion 3.5 SSIM test. Deprecated names: `SD35_MODEL_DIR`.                                                                                                                                                                                            |
 | `FASTVIDEO_TEST_TAEH3_REFERENCE_DIR`                       | str                            | unset                                                 | test        | Upstream taehv checkout for the MLX TAEH3 parity test. Deprecated names: `TAEH3_REFERENCE_DIR`.                                                                                                                                                                              |
 | `FASTVIDEO_TEST_WAN_ANIMATE_MODEL_DIR`                     | str                            | unset                                                 | test        | Local Wan2.2-Animate-14B checkpoint for the Wan-Animate weight tests. Deprecated names: `WAN_ANIMATE_MODEL_PATH`.                                                                                                                                                            |
-| `FASTVIDEO_TEST_ZIMAGE_MODEL_DIR`                          | str                            | `Tongyi-MAI/Z-Image-Turbo`                            | test        | Model for the Z-Image SSIM test. Deprecated names: `ZIMAGE_MODEL_DIR`.                                                                                                                                                                                                       |
-| `FASTVIDEO_TEST_ZIMAGE_MODEL_REVISION`                     | str                            | `f332072aa78be7aecdf3ee76d5c247082da564a6`            | test        | Hugging Face revision of the Z-Image model for its SSIM test. Deprecated names: `ZIMAGE_MODEL_REVISION`.                                                                                                                                                                     |
 
 Variables that FastVideo no longer reads; setting one logs a warning:
 
-| Deprecated variable                       | Reason           |
-| ----------------------------------------- | ---------------- |
-| `FASTVIDEO_TARGET_DEVICE`                 | no code reads it |
-| `FASTVIDEO_USE_PRECOMPILED`               | no code reads it |
-| `FASTVIDEO_RINGBUFFER_WARNING_INTERVAL`   | no code reads it |
-| `FASTVIDEO_ENGINE_ITERATION_TIMEOUT_S`    | no code reads it |
-| `FASTVIDEO_SERVER_DEV_MODE`               | no code reads it |
-| `FASTVIDEO_TEST_DYNAMO_FULLGRAPH_CAPTURE` | no code reads it |
-| `FASTVIDEO_TRACE_FUNCTION`                | no code reads it |
+| Deprecated variable                              | Reason                       |
+| ------------------------------------------------ | ---------------------------- |
+| `FASTVIDEO_TARGET_DEVICE`                        | no code reads it             |
+| `FASTVIDEO_USE_PRECOMPILED`                      | no code reads it             |
+| `FASTVIDEO_RINGBUFFER_WARNING_INTERVAL`          | no code reads it             |
+| `FASTVIDEO_ENGINE_ITERATION_TIMEOUT_S`           | no code reads it             |
+| `FASTVIDEO_SERVER_DEV_MODE`                      | no code reads it             |
+| `FASTVIDEO_TEST_DYNAMO_FULLGRAPH_CAPTURE`        | no code reads it             |
+| `FASTVIDEO_TRACE_FUNCTION`                       | no code reads it             |
+| `FASTVIDEO_TEST_DREAMX_WORLD_SSIM_MODEL_PATH`    | DreamX World was removed     |
+| `DREAMX_WORLD_SSIM_MODEL_PATH`                   | DreamX World was removed     |
+| `FASTVIDEO_TEST_DREAMX_WORLD_AR_SSIM_MODEL_PATH` | DreamX World was removed     |
+| `DREAMX_WORLD_AR_SSIM_MODEL_PATH`                | DreamX World was removed     |
+| `FASTVIDEO_TEST_GAMECRAFT_MODEL_PATH`            | HunyuanGameCraft was removed |
+| `GAMECRAFT_MODEL_PATH`                           | HunyuanGameCraft was removed |
+| `FASTVIDEO_TEST_GLM_IMAGE_LOCAL_WEIGHTS_DIR`     | GLM-Image was removed        |
+| `GLM_IMAGE_LOCAL_WEIGHTS_DIR`                    | GLM-Image was removed        |
+| `FASTVIDEO_TEST_GLM_IMAGE_MODEL_DIR`             | GLM-Image was removed        |
+| `GLM_IMAGE_MODEL_DIR`                            | GLM-Image was removed        |
+| `FASTVIDEO_TEST_LONGCAT_MODEL_ROOT`              | LongCat was removed          |
+| `LONGCAT_MODEL_ROOT`                             | LongCat was removed          |
+| `FASTVIDEO_TEST_ZIMAGE_MODEL_DIR`                | Z-Image was removed          |
+| `ZIMAGE_MODEL_DIR`                               | Z-Image was removed          |
+| `FASTVIDEO_TEST_ZIMAGE_MODEL_REVISION`           | Z-Image was removed          |
+| `ZIMAGE_MODEL_REVISION`                          | Z-Image was removed          |
 <!-- END GENERATED ENV TABLE -->
diff --git a/docs/cookbook/cosmos.md b/docs/cookbook/cosmos.md
index 89ad17313..4059bd659 100644
--- a/docs/cookbook/cosmos.md
+++ b/docs/cookbook/cosmos.md
@@ -5,7 +5,7 @@ hide:
 
 # Cosmos recipes
 
-<div class="cookbook-shell cookbook-family-page" data-cookbook data-family="cosmos" data-recipes="../../assets/cookbook-recipes.json?v=15">
+<div class="cookbook-shell cookbook-family-page" data-cookbook data-family="cosmos" data-recipes="../../assets/cookbook-recipes.json?v=16">
   <header class="cookbook-family-header">
     <a class="cookbook-back-link" href="../"><span aria-hidden="true">←</span> All model families</a>
     <div class="cookbook-family-header__body">
diff --git a/docs/cookbook/flux.md b/docs/cookbook/flux.md
index bcb59a2fe..aa5b7f016 100644
--- a/docs/cookbook/flux.md
+++ b/docs/cookbook/flux.md
@@ -5,7 +5,7 @@ hide:
 
 # FLUX recipes
 
-<div class="cookbook-shell cookbook-family-page" data-cookbook data-family="flux" data-recipes="../../assets/cookbook-recipes.json?v=15">
+<div class="cookbook-shell cookbook-family-page" data-cookbook data-family="flux" data-recipes="../../assets/cookbook-recipes.json?v=16">
   <header class="cookbook-family-header">
     <a class="cookbook-back-link" href="../"><span aria-hidden="true">←</span> All model families</a>
     <div class="cookbook-family-header__body">
diff --git a/docs/cookbook/glm-image.md b/docs/cookbook/glm-image.md
deleted file mode 100644
index 64bcdae21..000000000
--- a/docs/cookbook/glm-image.md
+++ /dev/null
@@ -1,140 +0,0 @@
----
-hide:
-- toc
----
-
-# GLM-Image recipes
-
-<div class="cookbook-shell cookbook-family-page" data-cookbook data-family="glm_image" data-recipes="../../assets/cookbook-recipes.json?v=15">
-  <header class="cookbook-family-header">
-    <a class="cookbook-back-link" href="../"><span aria-hidden="true">←</span> All model families</a>
-    <div class="cookbook-family-header__body">
-      <span class="cookbook-family-header__logo">
-        <img class="off-glb" src="../../assets/logos/zai.webp" alt="Z.ai" width="112" height="112">
-      </span>
-      <div>
-        <p class="cookbook-eyebrow">Maintained family · Inference</p>
-        <h2>GLM-Image inference recipes</h2>
-        <p>GLM-Image from Z.ai supports both text-to-image generation and instruction-based image editing, each with a maintained example.</p>
-      </div>
-    </div>
-    <div class="cookbook-lifecycle" aria-label="Lifecycle stages">
-      <span class="cookbook-lifecycle__stage cookbook-lifecycle__stage--active">Inference <small>live</small></span>
-      <span class="cookbook-lifecycle__stage">Distillation <small>planned</small></span>
-      <span class="cookbook-lifecycle__stage">Fine-tuning <small>planned</small></span>
-      <span class="cookbook-lifecycle__stage">Training <small>planned</small></span>
-      <span class="cookbook-lifecycle__stage">Evaluation <small>planned</small></span>
-      <span class="cookbook-lifecycle__stage">Optimization <small>planned</small></span>
-      <span class="cookbook-lifecycle__stage">Deployment <small>planned</small></span>
-    </div>
-  </header>
-  <nav class="cookbook-jumpnav" aria-label="Recipe page sections">
-    <a href="#recipe-builder">Builder</a>
-    <a href="#cookbook-setup">Setup</a>
-    <a href="#cookbook-troubleshooting">Troubleshooting</a>
-    <a href="#cookbook-evidence">Evidence</a>
-  </nav>
-
-  <section class="cookbook-builder" id="recipe-builder" aria-labelledby="builder-heading">
-    <div class="cookbook-builder__intro">
-      <h2 id="builder-heading">Pick a recipe and runtime</h2>
-      <p>Start with the result you want, then choose one of the runtimes FastVideo actually maintains for it.</p>
-    </div>
-
-    <div class="cookbook-builder__layout">
-      <div class="cookbook-controls">
-        <div class="cookbook-selection-row">
-          <div class="cookbook-selection-row__label">
-            <strong>Recipe</strong>
-            <span>Task and checkpoint</span>
-          </div>
-          <div class="cookbook-option-grid cookbook-option-grid--models" data-cookbook-model-options role="group" aria-label="Recipe">
-            <button type="button" disabled>Loading GLM-Image recipes...</button>
-          </div>
-        </div>
-
-        <div class="cookbook-selection-row">
-          <div class="cookbook-selection-row__label">
-            <strong>Runtime</strong>
-            <span>Maintained paths only</span>
-          </div>
-          <div class="cookbook-option-grid cookbook-option-grid--hardware" data-cookbook-hardware-options role="group" aria-label="Runtime">
-            <button type="button" disabled>Loading runtimes...</button>
-          </div>
-        </div>
-
-        <p class="cookbook-selection-description" data-cookbook-description>Loading recipe details...</p>
-        <p class="cookbook-hardware-note">Exact device and memory details appear only when a recorded run supports them.</p>
-
-        <div class="cookbook-hardware-state" data-cookbook-hardware-state role="status" aria-live="polite">
-          Reading recipe evidence...
-        </div>
-      </div>
-
-      <article class="cookbook-result">
-        <div class="cookbook-result__header">
-          <h3 data-cookbook-label>Loading...</h3>
-          <div class="cookbook-result__badges">
-            <span class="cookbook-badge">Maintained</span>
-            <span class="cookbook-badge" data-cookbook-evidence>Source-backed</span>
-            <span class="cookbook-badge cookbook-badge--neutral" data-cookbook-hardware-badge>Source config</span>
-          </div>
-        </div>
-
-        <dl class="cookbook-result__facts">
-          <div><dt>Model</dt><dd data-cookbook-model>Loading...</dd></div>
-          <div><dt>Workload</dt><dd data-cookbook-task>Loading...</dd></div>
-          <div><dt>Source configuration</dt><dd data-cookbook-gpus>Loading...</dd></div>
-          <div><dt>Expected output</dt><dd data-cookbook-artifact>Loading...</dd></div>
-        </dl>
-
-        <div class="cookbook-command">
-          <div class="cookbook-command__bar">
-            <span>Terminal</span>
-          </div>
-          <pre><code class="language-bash" data-cookbook-command>Loading...</code></pre>
-        </div>
-
-        <div class="cookbook-result__footer">
-          <a data-cookbook-source href="../../inference/examples/basic/">Open example source</a>
-          <a data-cookbook-model-link href="https://huggingface.co/zai-org">View model card</a>
-        </div>
-        <p class="cookbook-picker__status" role="status" aria-live="polite" data-cookbook-status></p>
-      </article>
-    </div>
-
-    <noscript>
-      <div class="cookbook-noscript">
-        JavaScript is needed for the guided selector. You can still browse the
-        <a href="../../inference/examples/examples_inference_index/">maintained inference examples</a>.
-      </div>
-    </noscript>
-  </section>
-</div>
-
-<details class="cookbook-collapsible" id="cookbook-setup">
-  <summary>Setup</summary>
-  <div class="cookbook-collapsible__body">
-      <p>The generated commands expect a local clone:</p>
-      <pre><code>git clone https://github.com/hao-ai-lab/FastVideo.git
-cd FastVideo</code></pre>
-      <p>Use <a href="../../inference/configuration/">Configuration</a> for supported Python and CLI settings, <a href="../../inference/optimizations/">Optimizations</a> for attention and memory tradeoffs, and the <a href="../../inference/support_matrix/">support matrix</a> for the supported model and optimization surface.</p>
-  </div>
-</details>
-
-<details class="cookbook-collapsible" id="cookbook-troubleshooting">
-  <summary>Troubleshooting</summary>
-  <div class="cookbook-collapsible__body">
-      <ul>
-        <li>The editing example reads <code>assets/images/couple.jpg</code> from the repository root, so run it from a repo checkout rather than an arbitrary working directory.</li>
-        <li>Output paths default under <code>image_output/</code>; pass <code>--output</code> to change them.</li>
-      </ul>
-  </div>
-</details>
-
-<details class="cookbook-collapsible" id="cookbook-evidence">
-  <summary>Evidence status</summary>
-  <div class="cookbook-collapsible__body">
-      <p>All recipes on this page are <strong>Source-backed</strong>: their commands, model IDs, and flags were validated against the checked-in FastVideo sources listed above (static validation). No runtime GPU validation is recorded for these recipes, so GPU model fit, memory use, throughput, and runtime duration are <strong>Unknown</strong> and deliberately not claimed. Runtime buttons show only the GPU counts configured in checked-in sources.</p>
-  </div>
-</details>
diff --git a/docs/cookbook/hunyuan.md b/docs/cookbook/hunyuan.md
index 52c11cd56..7d0f89932 100644
--- a/docs/cookbook/hunyuan.md
+++ b/docs/cookbook/hunyuan.md
@@ -5,7 +5,7 @@ hide:
 
 # Hunyuan recipes
 
-<div class="cookbook-shell cookbook-family-page" data-cookbook data-family="hunyuan" data-recipes="../../assets/cookbook-recipes.json?v=15">
+<div class="cookbook-shell cookbook-family-page" data-cookbook data-family="hunyuan" data-recipes="../../assets/cookbook-recipes.json?v=16">
   <header class="cookbook-family-header">
     <a class="cookbook-back-link" href="../"><span aria-hidden="true">←</span> All model families</a>
     <div class="cookbook-family-header__body">
diff --git a/docs/cookbook/index.md b/docs/cookbook/index.md
index 6b4b43497..22e735066 100644
--- a/docs/cookbook/index.md
+++ b/docs/cookbook/index.md
@@ -163,50 +163,6 @@ hide:
           </ul>
         </span>
       </a>
-
-      <a class="cookbook-family-tile cookbook-family-tile--ready" href="./longcat/" aria-label="Open LongCat recipes">
-        <span class="cookbook-family-tile__visual" data-evervault>
-          <span class="cookbook-evervault" aria-hidden="true">
-            <span class="cookbook-evervault__gradient"></span>
-            <span class="cookbook-evervault__noise" data-cookbook-pattern></span>
-          </span>
-          <span class="cookbook-family-tile__logo-wrap">
-            <img class="off-glb" src="../assets/logos/meituan-longcat.webp" alt="" width="132" height="132" loading="lazy">
-          </span>
-        </span>
-        <span class="cookbook-family-tile__footer">
-          <span class="cookbook-family-tile__footer-top">
-            <span><strong>LongCat</strong><small>T2V, I2V, optional refine</small></span>
-            <span class="cookbook-count">2 recipes</span>
-          </span>
-          <ul class="cookbook-mode-row">
-            <li>T2V</li>
-            <li>I2V</li>
-          </ul>
-        </span>
-      </a>
-
-      <a class="cookbook-family-tile cookbook-family-tile--ready" href="./turbodiffusion/" aria-label="Open TurboDiffusion recipes">
-        <span class="cookbook-family-tile__visual" data-evervault>
-          <span class="cookbook-evervault" aria-hidden="true">
-            <span class="cookbook-evervault__gradient"></span>
-            <span class="cookbook-evervault__noise" data-cookbook-pattern></span>
-          </span>
-          <span class="cookbook-family-tile__logo-wrap">
-            <span class="cookbook-family-tile__monogram" aria-hidden="true">Turbo</span>
-          </span>
-        </span>
-        <span class="cookbook-family-tile__footer">
-          <span class="cookbook-family-tile__footer-top">
-            <span><strong>TurboDiffusion</strong><small>Accelerated Wan profiles</small></span>
-            <span class="cookbook-count">3 recipes</span>
-          </span>
-          <ul class="cookbook-mode-row">
-            <li>T2V</li>
-            <li>I2V</li>
-          </ul>
-        </span>
-      </a>
     </div>
   </section>
 
@@ -236,49 +192,6 @@ hide:
         </span>
       </a>
 
-      <a class="cookbook-family-tile cookbook-family-tile--ready" href="./glm-image/" aria-label="Open GLM-Image recipes">
-        <span class="cookbook-family-tile__visual" data-evervault>
-          <span class="cookbook-evervault" aria-hidden="true">
-            <span class="cookbook-evervault__gradient"></span>
-            <span class="cookbook-evervault__noise" data-cookbook-pattern></span>
-          </span>
-          <span class="cookbook-family-tile__logo-wrap">
-            <img class="off-glb" src="../assets/logos/zai.webp" alt="" width="132" height="132" loading="lazy">
-          </span>
-        </span>
-        <span class="cookbook-family-tile__footer">
-          <span class="cookbook-family-tile__footer-top">
-            <span><strong>GLM-Image</strong><small>Generate and edit</small></span>
-            <span class="cookbook-count">2 recipes</span>
-          </span>
-          <ul class="cookbook-mode-row">
-            <li>T2I</li>
-            <li>Edit</li>
-          </ul>
-        </span>
-      </a>
-
-      <a class="cookbook-family-tile cookbook-family-tile--ready" href="./z-image/" aria-label="Open Z-Image recipes">
-        <span class="cookbook-family-tile__visual" data-evervault>
-          <span class="cookbook-evervault" aria-hidden="true">
-            <span class="cookbook-evervault__gradient"></span>
-            <span class="cookbook-evervault__noise" data-cookbook-pattern></span>
-          </span>
-          <span class="cookbook-family-tile__logo-wrap">
-            <img class="off-glb" src="../assets/logos/tongyi.webp" alt="" width="132" height="132" loading="lazy">
-          </span>
-        </span>
-        <span class="cookbook-family-tile__footer">
-          <span class="cookbook-family-tile__footer-top">
-            <span><strong>Z-Image</strong><small>Turbo text to image</small></span>
-            <span class="cookbook-count">1 recipe</span>
-          </span>
-          <ul class="cookbook-mode-row">
-            <li>T2I</li>
-          </ul>
-        </span>
-      </a>
-
       <a class="cookbook-family-tile cookbook-family-tile--ready" href="./stable-diffusion/" aria-label="Open Stable Diffusion recipes">
         <span class="cookbook-family-tile__visual" data-evervault>
           <span class="cookbook-evervault" aria-hidden="true">
@@ -407,20 +320,6 @@ hide:
       <p>These families already have runnable examples. The cookbook page is not ready, so the cards are not links.</p>
     </div>
     <div class="cookbook-family-grid">
-      <article class="cookbook-family-tile cookbook-family-tile--coming" aria-label="GameCraft cookbook page planned; runnable examples exist">
-        <span class="cookbook-family-tile__visual">
-          <span class="cookbook-family-tile__logo-wrap">
-            <img class="off-glb" src="../assets/logos/tencent-hunyuan.webp" alt="" width="132" height="132" loading="lazy">
-          </span>
-        </span>
-        <span class="cookbook-family-tile__footer">
-          <span class="cookbook-family-tile__footer-top">
-            <span><strong>GameCraft</strong><small>Game world generation</small></span>
-            <span class="cookbook-count">Page planned</span>
-          </span>
-        </span>
-      </article>
-
       <article class="cookbook-family-tile cookbook-family-tile--coming" aria-label="GEN3C cookbook page planned; runnable examples exist">
         <span class="cookbook-family-tile__visual">
           <span class="cookbook-family-tile__logo-wrap">
@@ -449,20 +348,6 @@ hide:
         </span>
       </article>
 
-      <article class="cookbook-family-tile cookbook-family-tile--coming" aria-label="DreamX cookbook page planned; runnable examples exist">
-        <span class="cookbook-family-tile__visual">
-          <span class="cookbook-family-tile__logo-wrap">
-            <img class="off-glb" src="../assets/logos/fastvideo.webp" alt="" width="132" height="132" loading="lazy">
-          </span>
-        </span>
-        <span class="cookbook-family-tile__footer">
-          <span class="cookbook-family-tile__footer-top">
-            <span><strong>DreamX</strong><small>World generation</small></span>
-            <span class="cookbook-count">Page planned</span>
-          </span>
-        </span>
-      </article>
-
       <article class="cookbook-family-tile cookbook-family-tile--coming" aria-label="LingBot cookbook page planned; runnable examples exist">
         <span class="cookbook-family-tile__visual">
           <span class="cookbook-family-tile__logo-wrap">
diff --git a/docs/cookbook/kandinsky5.md b/docs/cookbook/kandinsky5.md
index 2389f1bd6..4aa2697a3 100644
--- a/docs/cookbook/kandinsky5.md
+++ b/docs/cookbook/kandinsky5.md
@@ -5,7 +5,7 @@ hide:
 
 # Kandinsky 5 recipes
 
-<div class="cookbook-shell cookbook-family-page" data-cookbook data-family="kandinsky5" data-recipes="../../assets/cookbook-recipes.json?v=15">
+<div class="cookbook-shell cookbook-family-page" data-cookbook data-family="kandinsky5" data-recipes="../../assets/cookbook-recipes.json?v=16">
   <header class="cookbook-family-header">
     <a class="cookbook-back-link" href="../"><span aria-hidden="true">←</span> All model families</a>
     <div class="cookbook-family-header__body">
diff --git a/docs/cookbook/kandinsky6.md b/docs/cookbook/kandinsky6.md
index 9fd289c66..cd0f06f14 100644
--- a/docs/cookbook/kandinsky6.md
+++ b/docs/cookbook/kandinsky6.md
@@ -5,7 +5,7 @@ hide:
 
 # Kandinsky 6 recipes
 
-<div class="cookbook-shell cookbook-family-page" data-cookbook data-family="kandinsky6" data-recipes="../../assets/cookbook-recipes.json?v=15">
+<div class="cookbook-shell cookbook-family-page" data-cookbook data-family="kandinsky6" data-recipes="../../assets/cookbook-recipes.json?v=16">
   <header class="cookbook-family-header">
     <a class="cookbook-back-link" href="../"><span aria-hidden="true">←</span> All model families</a>
     <div class="cookbook-family-header__body">
diff --git a/docs/cookbook/longcat.md b/docs/cookbook/longcat.md
deleted file mode 100644
index 33addd0f4..000000000
--- a/docs/cookbook/longcat.md
+++ /dev/null
@@ -1,140 +0,0 @@
----
-hide:
-- toc
----
-
-# LongCat recipes
-
-<div class="cookbook-shell cookbook-family-page" data-cookbook data-family="longcat" data-recipes="../../assets/cookbook-recipes.json?v=15">
-  <header class="cookbook-family-header">
-    <a class="cookbook-back-link" href="../"><span aria-hidden="true">←</span> All model families</a>
-    <div class="cookbook-family-header__body">
-      <span class="cookbook-family-header__logo">
-        <img class="off-glb" src="../../assets/logos/meituan-longcat.webp" alt="Meituan LongCat" width="112" height="112">
-      </span>
-      <div>
-        <p class="cookbook-eyebrow">Maintained family · Inference</p>
-        <h2>LongCat inference recipes</h2>
-        <p>LongCat Video from Meituan covers text-to-video and image-to-video, and its maintained examples chain optional distilled and 720p refinement passes.</p>
-      </div>
-    </div>
-    <div class="cookbook-lifecycle" aria-label="Lifecycle stages">
-      <span class="cookbook-lifecycle__stage cookbook-lifecycle__stage--active">Inference <small>live</small></span>
-      <span class="cookbook-lifecycle__stage">Distillation <small>planned</small></span>
-      <span class="cookbook-lifecycle__stage">Fine-tuning <small>planned</small></span>
-      <span class="cookbook-lifecycle__stage">Training <small>planned</small></span>
-      <span class="cookbook-lifecycle__stage">Evaluation <small>planned</small></span>
-      <span class="cookbook-lifecycle__stage">Optimization <small>planned</small></span>
-      <span class="cookbook-lifecycle__stage">Deployment <small>planned</small></span>
-    </div>
-  </header>
-  <nav class="cookbook-jumpnav" aria-label="Recipe page sections">
-    <a href="#recipe-builder">Builder</a>
-    <a href="#cookbook-setup">Setup</a>
-    <a href="#cookbook-troubleshooting">Troubleshooting</a>
-    <a href="#cookbook-evidence">Evidence</a>
-  </nav>
-
-  <section class="cookbook-builder" id="recipe-builder" aria-labelledby="builder-heading">
-    <div class="cookbook-builder__intro">
-      <h2 id="builder-heading">Pick a recipe and runtime</h2>
-      <p>Start with the result you want, then choose one of the runtimes FastVideo actually maintains for it.</p>
-    </div>
-
-    <div class="cookbook-builder__layout">
-      <div class="cookbook-controls">
-        <div class="cookbook-selection-row">
-          <div class="cookbook-selection-row__label">
-            <strong>Recipe</strong>
-            <span>Task and checkpoint</span>
-          </div>
-          <div class="cookbook-option-grid cookbook-option-grid--models" data-cookbook-model-options role="group" aria-label="Recipe">
-            <button type="button" disabled>Loading LongCat recipes...</button>
-          </div>
-        </div>
-
-        <div class="cookbook-selection-row">
-          <div class="cookbook-selection-row__label">
-            <strong>Runtime</strong>
-            <span>Maintained paths only</span>
-          </div>
-          <div class="cookbook-option-grid cookbook-option-grid--hardware" data-cookbook-hardware-options role="group" aria-label="Runtime">
-            <button type="button" disabled>Loading runtimes...</button>
-          </div>
-        </div>
-
-        <p class="cookbook-selection-description" data-cookbook-description>Loading recipe details...</p>
-        <p class="cookbook-hardware-note">Exact device and memory details appear only when a recorded run supports them.</p>
-
-        <div class="cookbook-hardware-state" data-cookbook-hardware-state role="status" aria-live="polite">
-          Reading recipe evidence...
-        </div>
-      </div>
-
-      <article class="cookbook-result">
-        <div class="cookbook-result__header">
-          <h3 data-cookbook-label>Loading...</h3>
-          <div class="cookbook-result__badges">
-            <span class="cookbook-badge">Maintained</span>
-            <span class="cookbook-badge" data-cookbook-evidence>Source-backed</span>
-            <span class="cookbook-badge cookbook-badge--neutral" data-cookbook-hardware-badge>Source config</span>
-          </div>
-        </div>
-
-        <dl class="cookbook-result__facts">
-          <div><dt>Model</dt><dd data-cookbook-model>Loading...</dd></div>
-          <div><dt>Workload</dt><dd data-cookbook-task>Loading...</dd></div>
-          <div><dt>Source configuration</dt><dd data-cookbook-gpus>Loading...</dd></div>
-          <div><dt>Expected output</dt><dd data-cookbook-artifact>Loading...</dd></div>
-        </dl>
-
-        <div class="cookbook-command">
-          <div class="cookbook-command__bar">
-            <span>Terminal</span>
-          </div>
-          <pre><code class="language-bash" data-cookbook-command>Loading...</code></pre>
-        </div>
-
-        <div class="cookbook-result__footer">
-          <a data-cookbook-source href="../../inference/examples/basic/">Open example source</a>
-          <a data-cookbook-model-link href="https://huggingface.co/meituan-longcat">View model card</a>
-        </div>
-        <p class="cookbook-picker__status" role="status" aria-live="polite" data-cookbook-status></p>
-      </article>
-    </div>
-
-    <noscript>
-      <div class="cookbook-noscript">
-        JavaScript is needed for the guided selector. You can still browse the
-        <a href="../../inference/examples/examples_inference_index/">maintained inference examples</a>.
-      </div>
-    </noscript>
-  </section>
-</div>
-
-<details class="cookbook-collapsible" id="cookbook-setup">
-  <summary>Setup</summary>
-  <div class="cookbook-collapsible__body">
-      <p>The generated commands expect a local clone:</p>
-      <pre><code>git clone https://github.com/hao-ai-lab/FastVideo.git
-cd FastVideo</code></pre>
-      <p>Use <a href="../../inference/configuration/">Configuration</a> for supported Python and CLI settings, <a href="../../inference/optimizations/">Optimizations</a> for attention and memory tradeoffs, and the <a href="../../inference/support_matrix/">support matrix</a> for the supported model and optimization surface.</p>
-  </div>
-</details>
-
-<details class="cookbook-collapsible" id="cookbook-troubleshooting">
-  <summary>Troubleshooting</summary>
-  <div class="cookbook-collapsible__body">
-      <ul>
-        <li>Each LongCat script runs multiple passes (basic, distilled, refine); total runtime scales accordingly, and every pass prints its own output directory.</li>
-        <li>Out of memory: the sources already enable VAE and text-encoder CPU offload; further options are covered in <a href="../../inference/offloading/">Offloading</a>.</li>
-      </ul>
-  </div>
-</details>
-
-<details class="cookbook-collapsible" id="cookbook-evidence">
-  <summary>Evidence status</summary>
-  <div class="cookbook-collapsible__body">
-      <p>All recipes on this page are <strong>Source-backed</strong>: their commands, model IDs, and flags were validated against the checked-in FastVideo sources listed above (static validation). No runtime GPU validation is recorded for these recipes, so GPU model fit, memory use, throughput, and runtime duration are <strong>Unknown</strong> and deliberately not claimed. Runtime buttons show only the GPU counts configured in checked-in sources.</p>
-  </div>
-</details>
diff --git a/docs/cookbook/ltx.md b/docs/cookbook/ltx.md
index d8878be7b..4e7cbeef5 100644
--- a/docs/cookbook/ltx.md
+++ b/docs/cookbook/ltx.md
@@ -5,7 +5,7 @@ hide:
 
 # LTX recipes
 
-<div class="cookbook-shell cookbook-family-page" data-cookbook data-family="ltx2" data-recipes="../../assets/cookbook-recipes.json?v=15">
+<div class="cookbook-shell cookbook-family-page" data-cookbook data-family="ltx2" data-recipes="../../assets/cookbook-recipes.json?v=16">
   <header class="cookbook-family-header">
     <a class="cookbook-back-link" href="../"><span aria-hidden="true">←</span> All model families</a>
     <div class="cookbook-family-header__body">
diff --git a/docs/cookbook/matrix-game.md b/docs/cookbook/matrix-game.md
index 10f0eb4ca..684f8b2cf 100644
--- a/docs/cookbook/matrix-game.md
+++ b/docs/cookbook/matrix-game.md
@@ -5,7 +5,7 @@ hide:
 
 # Matrix Game recipes
 
-<div class="cookbook-shell cookbook-family-page" data-cookbook data-family="matrixgame" data-recipes="../../assets/cookbook-recipes.json?v=15">
+<div class="cookbook-shell cookbook-family-page" data-cookbook data-family="matrixgame" data-recipes="../../assets/cookbook-recipes.json?v=16">
   <header class="cookbook-family-header">
     <a class="cookbook-back-link" href="../"><span aria-hidden="true">←</span> All model families</a>
     <div class="cookbook-family-header__body">
diff --git a/docs/cookbook/minimax-h3.md b/docs/cookbook/minimax-h3.md
index 265a508d6..debc4ff24 100644
--- a/docs/cookbook/minimax-h3.md
+++ b/docs/cookbook/minimax-h3.md
@@ -18,7 +18,7 @@ eight-forward T2VA command](../getting_started/installation/mlx.md#pruned-eight-
 It reads `fastvideo_inference.json` for the trained schedule. The command
 uses native 832x480 resolution and all requested frames.
 
-<div class="cookbook-shell cookbook-family-page" data-cookbook data-family="minimax_h3" data-default-recipe="fasth3-preview-cuda" data-recipes="../../assets/cookbook-recipes.json?v=15">
+<div class="cookbook-shell cookbook-family-page" data-cookbook data-family="minimax_h3" data-default-recipe="fasth3-preview-cuda" data-recipes="../../assets/cookbook-recipes.json?v=16">
   <header class="cookbook-family-header">
     <a class="cookbook-back-link" href="../"><span aria-hidden="true">←</span> All model families</a>
     <div class="cookbook-family-header__body">
diff --git a/docs/cookbook/mmaudio.md b/docs/cookbook/mmaudio.md
index 8cd584cfa..e2d022c4c 100644
--- a/docs/cookbook/mmaudio.md
+++ b/docs/cookbook/mmaudio.md
@@ -5,7 +5,7 @@ hide:
 
 # MMAudio recipes
 
-<div class="cookbook-shell cookbook-family-page" data-cookbook data-family="mmaudio" data-recipes="../../assets/cookbook-recipes.json?v=15">
+<div class="cookbook-shell cookbook-family-page" data-cookbook data-family="mmaudio" data-recipes="../../assets/cookbook-recipes.json?v=16">
   <header class="cookbook-family-header">
     <a class="cookbook-back-link" href="../"><span aria-hidden="true">←</span> All model families</a>
     <div class="cookbook-family-header__body">
diff --git a/docs/cookbook/stable-audio.md b/docs/cookbook/stable-audio.md
index 64f52ce32..6a873e1b7 100644
--- a/docs/cookbook/stable-audio.md
+++ b/docs/cookbook/stable-audio.md
@@ -5,7 +5,7 @@ hide:
 
 # Stable Audio recipes
 
-<div class="cookbook-shell cookbook-family-page" data-cookbook data-family="stable_audio" data-recipes="../../assets/cookbook-recipes.json?v=15">
+<div class="cookbook-shell cookbook-family-page" data-cookbook data-family="stable_audio" data-recipes="../../assets/cookbook-recipes.json?v=16">
   <header class="cookbook-family-header">
     <a class="cookbook-back-link" href="../"><span aria-hidden="true">←</span> All model families</a>
     <div class="cookbook-family-header__body">
diff --git a/docs/cookbook/stable-diffusion.md b/docs/cookbook/stable-diffusion.md
index 9bd005ea0..8d688c309 100644
--- a/docs/cookbook/stable-diffusion.md
+++ b/docs/cookbook/stable-diffusion.md
@@ -5,7 +5,7 @@ hide:
 
 # Stable Diffusion recipes
 
-<div class="cookbook-shell cookbook-family-page" data-cookbook data-family="sd35" data-recipes="../../assets/cookbook-recipes.json?v=15">
+<div class="cookbook-shell cookbook-family-page" data-cookbook data-family="sd35" data-recipes="../../assets/cookbook-recipes.json?v=16">
   <header class="cookbook-family-header">
     <a class="cookbook-back-link" href="../"><span aria-hidden="true">←</span> All model families</a>
     <div class="cookbook-family-header__body">
diff --git a/docs/cookbook/turbodiffusion.md b/docs/cookbook/turbodiffusion.md
deleted file mode 100644
index 0c72c4fea..000000000
--- a/docs/cookbook/turbodiffusion.md
+++ /dev/null
@@ -1,140 +0,0 @@
----
-hide:
-- toc
----
-
-# TurboDiffusion recipes
-
-<div class="cookbook-shell cookbook-family-page" data-cookbook data-family="turbodiffusion" data-recipes="../../assets/cookbook-recipes.json?v=15">
-  <header class="cookbook-family-header">
-    <a class="cookbook-back-link" href="../"><span aria-hidden="true">←</span> All model families</a>
-    <div class="cookbook-family-header__body">
-      <span class="cookbook-family-header__logo">
-        <span class="cookbook-family-tile__monogram" aria-hidden="true">Turbo</span>
-      </span>
-      <div>
-        <p class="cookbook-eyebrow">Maintained family · Inference</p>
-        <h2>TurboDiffusion inference recipes</h2>
-        <p>TurboDiffusion profiles accelerate Wan checkpoints with step-distilled sampling and the SLA attention backend. These recipes follow the registry's `turbodiffusion` model family.</p>
-      </div>
-    </div>
-    <div class="cookbook-lifecycle" aria-label="Lifecycle stages">
-      <span class="cookbook-lifecycle__stage cookbook-lifecycle__stage--active">Inference <small>live</small></span>
-      <span class="cookbook-lifecycle__stage">Distillation <small>planned</small></span>
-      <span class="cookbook-lifecycle__stage">Fine-tuning <small>planned</small></span>
-      <span class="cookbook-lifecycle__stage">Training <small>planned</small></span>
-      <span class="cookbook-lifecycle__stage">Evaluation <small>planned</small></span>
-      <span class="cookbook-lifecycle__stage">Optimization <small>planned</small></span>
-      <span class="cookbook-lifecycle__stage">Deployment <small>planned</small></span>
-    </div>
-  </header>
-  <nav class="cookbook-jumpnav" aria-label="Recipe page sections">
-    <a href="#recipe-builder">Builder</a>
-    <a href="#cookbook-setup">Setup</a>
-    <a href="#cookbook-troubleshooting">Troubleshooting</a>
-    <a href="#cookbook-evidence">Evidence</a>
-  </nav>
-
-  <section class="cookbook-builder" id="recipe-builder" aria-labelledby="builder-heading">
-    <div class="cookbook-builder__intro">
-      <h2 id="builder-heading">Pick a recipe and runtime</h2>
-      <p>Start with the result you want, then choose one of the runtimes FastVideo actually maintains for it.</p>
-    </div>
-
-    <div class="cookbook-builder__layout">
-      <div class="cookbook-controls">
-        <div class="cookbook-selection-row">
-          <div class="cookbook-selection-row__label">
-            <strong>Recipe</strong>
-            <span>Task and checkpoint</span>
-          </div>
-          <div class="cookbook-option-grid cookbook-option-grid--models" data-cookbook-model-options role="group" aria-label="Recipe">
-            <button type="button" disabled>Loading TurboDiffusion recipes...</button>
-          </div>
-        </div>
-
-        <div class="cookbook-selection-row">
-          <div class="cookbook-selection-row__label">
-            <strong>Runtime</strong>
-            <span>Maintained paths only</span>
-          </div>
-          <div class="cookbook-option-grid cookbook-option-grid--hardware" data-cookbook-hardware-options role="group" aria-label="Runtime">
-            <button type="button" disabled>Loading runtimes...</button>
-          </div>
-        </div>
-
-        <p class="cookbook-selection-description" data-cookbook-description>Loading recipe details...</p>
-        <p class="cookbook-hardware-note">Exact device and memory details appear only when a recorded run supports them.</p>
-
-        <div class="cookbook-hardware-state" data-cookbook-hardware-state role="status" aria-live="polite">
-          Reading recipe evidence...
-        </div>
-      </div>
-
-      <article class="cookbook-result">
-        <div class="cookbook-result__header">
-          <h3 data-cookbook-label>Loading...</h3>
-          <div class="cookbook-result__badges">
-            <span class="cookbook-badge">Maintained</span>
-            <span class="cookbook-badge" data-cookbook-evidence>Source-backed</span>
-            <span class="cookbook-badge cookbook-badge--neutral" data-cookbook-hardware-badge>Source config</span>
-          </div>
-        </div>
-
-        <dl class="cookbook-result__facts">
-          <div><dt>Model</dt><dd data-cookbook-model>Loading...</dd></div>
-          <div><dt>Workload</dt><dd data-cookbook-task>Loading...</dd></div>
-          <div><dt>Source configuration</dt><dd data-cookbook-gpus>Loading...</dd></div>
-          <div><dt>Expected output</dt><dd data-cookbook-artifact>Loading...</dd></div>
-        </dl>
-
-        <div class="cookbook-command">
-          <div class="cookbook-command__bar">
-            <span>Terminal</span>
-          </div>
-          <pre><code class="language-bash" data-cookbook-command>Loading...</code></pre>
-        </div>
-
-        <div class="cookbook-result__footer">
-          <a data-cookbook-source href="../../inference/examples/basic/">Open example source</a>
-          <a data-cookbook-model-link href="https://huggingface.co/loayrashid">View model card</a>
-        </div>
-        <p class="cookbook-picker__status" role="status" aria-live="polite" data-cookbook-status></p>
-      </article>
-    </div>
-
-    <noscript>
-      <div class="cookbook-noscript">
-        JavaScript is needed for the guided selector. You can still browse the
-        <a href="../../inference/examples/examples_inference_index/">maintained inference examples</a>.
-      </div>
-    </noscript>
-  </section>
-</div>
-
-<details class="cookbook-collapsible" id="cookbook-setup">
-  <summary>Setup</summary>
-  <div class="cookbook-collapsible__body">
-      <p>The generated commands expect a local clone:</p>
-      <pre><code>git clone https://github.com/hao-ai-lab/FastVideo.git
-cd FastVideo</code></pre>
-      <p>Use <a href="../../inference/configuration/">Configuration</a> for supported Python and CLI settings, <a href="../../inference/optimizations/">Optimizations</a> for attention and memory tradeoffs, and the <a href="../../inference/support_matrix/">support matrix</a> for the supported model and optimization surface.</p>
-  </div>
-</details>
-
-<details class="cookbook-collapsible" id="cookbook-troubleshooting">
-  <summary>Troubleshooting</summary>
-  <div class="cookbook-collapsible__body">
-      <ul>
-        <li>TurboDiffusion paths load community-published <code>loayrashid/TurboWan*</code> checkpoints; availability is governed by those repos.</li>
-        <li>The SLA attention backend used by the I2V recipe is selected inside the example source; do not combine it with another <code>FASTVIDEO_ATTENTION_BACKEND</code> override in the same shell.</li>
-      </ul>
-  </div>
-</details>
-
-<details class="cookbook-collapsible" id="cookbook-evidence">
-  <summary>Evidence status</summary>
-  <div class="cookbook-collapsible__body">
-      <p>All recipes on this page are <strong>Source-backed</strong>: their commands, model IDs, and flags were validated against the checked-in FastVideo sources listed above (static validation). No runtime GPU validation is recorded for these recipes, so GPU model fit, memory use, throughput, and runtime duration are <strong>Unknown</strong> and deliberately not claimed. Runtime buttons show only the GPU counts configured in checked-in sources.</p>
-  </div>
-</details>
diff --git a/docs/cookbook/wan.md b/docs/cookbook/wan.md
index d23766ab3..9f6721baa 100644
--- a/docs/cookbook/wan.md
+++ b/docs/cookbook/wan.md
@@ -5,7 +5,7 @@ hide:
 
 # Wan recipes
 
-<div class="cookbook-shell cookbook-family-page" data-cookbook data-family="wan" data-recipes="../../assets/cookbook-recipes.json?v=15">
+<div class="cookbook-shell cookbook-family-page" data-cookbook data-family="wan" data-recipes="../../assets/cookbook-recipes.json?v=16">
   <header class="cookbook-family-header">
     <a class="cookbook-back-link" href="../"><span aria-hidden="true">←</span> All model families</a>
     <div class="cookbook-family-header__body">
diff --git a/docs/cookbook/z-image.md b/docs/cookbook/z-image.md
deleted file mode 100644
index e912e932a..000000000
--- a/docs/cookbook/z-image.md
+++ /dev/null
@@ -1,140 +0,0 @@
----
-hide:
-- toc
----
-
-# Z-Image recipes
-
-<div class="cookbook-shell cookbook-family-page" data-cookbook data-family="zimage" data-recipes="../../assets/cookbook-recipes.json?v=15">
-  <header class="cookbook-family-header">
-    <a class="cookbook-back-link" href="../"><span aria-hidden="true">←</span> All model families</a>
-    <div class="cookbook-family-header__body">
-      <span class="cookbook-family-header__logo">
-        <img class="off-glb" src="../../assets/logos/tongyi.webp" alt="Tongyi MAI" width="112" height="112">
-      </span>
-      <div>
-        <p class="cookbook-eyebrow">Maintained family · Inference</p>
-        <h2>Z-Image inference recipes</h2>
-        <p>Z-Image Turbo from Tongyi MAI is a fast text-to-image model. The maintained example runs it on a single GPU.</p>
-      </div>
-    </div>
-    <div class="cookbook-lifecycle" aria-label="Lifecycle stages">
-      <span class="cookbook-lifecycle__stage cookbook-lifecycle__stage--active">Inference <small>live</small></span>
-      <span class="cookbook-lifecycle__stage">Distillation <small>planned</small></span>
-      <span class="cookbook-lifecycle__stage">Fine-tuning <small>planned</small></span>
-      <span class="cookbook-lifecycle__stage">Training <small>planned</small></span>
-      <span class="cookbook-lifecycle__stage">Evaluation <small>planned</small></span>
-      <span class="cookbook-lifecycle__stage">Optimization <small>planned</small></span>
-      <span class="cookbook-lifecycle__stage">Deployment <small>planned</small></span>
-    </div>
-  </header>
-  <nav class="cookbook-jumpnav" aria-label="Recipe page sections">
-    <a href="#recipe-builder">Builder</a>
-    <a href="#cookbook-setup">Setup</a>
-    <a href="#cookbook-troubleshooting">Troubleshooting</a>
-    <a href="#cookbook-evidence">Evidence</a>
-  </nav>
-
-  <section class="cookbook-builder" id="recipe-builder" aria-labelledby="builder-heading">
-    <div class="cookbook-builder__intro">
-      <h2 id="builder-heading">Pick a recipe and runtime</h2>
-      <p>Start with the result you want, then choose one of the runtimes FastVideo actually maintains for it.</p>
-    </div>
-
-    <div class="cookbook-builder__layout">
-      <div class="cookbook-controls">
-        <div class="cookbook-selection-row">
-          <div class="cookbook-selection-row__label">
-            <strong>Recipe</strong>
-            <span>Task and checkpoint</span>
-          </div>
-          <div class="cookbook-option-grid cookbook-option-grid--models" data-cookbook-model-options role="group" aria-label="Recipe">
-            <button type="button" disabled>Loading Z-Image recipes...</button>
-          </div>
-        </div>
-
-        <div class="cookbook-selection-row">
-          <div class="cookbook-selection-row__label">
-            <strong>Runtime</strong>
-            <span>Maintained paths only</span>
-          </div>
-          <div class="cookbook-option-grid cookbook-option-grid--hardware" data-cookbook-hardware-options role="group" aria-label="Runtime">
-            <button type="button" disabled>Loading runtimes...</button>
-          </div>
-        </div>
-
-        <p class="cookbook-selection-description" data-cookbook-description>Loading recipe details...</p>
-        <p class="cookbook-hardware-note">Exact device and memory details appear only when a recorded run supports them.</p>
-
-        <div class="cookbook-hardware-state" data-cookbook-hardware-state role="status" aria-live="polite">
-          Reading recipe evidence...
-        </div>
-      </div>
-
-      <article class="cookbook-result">
-        <div class="cookbook-result__header">
-          <h3 data-cookbook-label>Loading...</h3>
-          <div class="cookbook-result__badges">
-            <span class="cookbook-badge">Maintained</span>
-            <span class="cookbook-badge" data-cookbook-evidence>Source-backed</span>
-            <span class="cookbook-badge cookbook-badge--neutral" data-cookbook-hardware-badge>Source config</span>
-          </div>
-        </div>
-
-        <dl class="cookbook-result__facts">
-          <div><dt>Model</dt><dd data-cookbook-model>Loading...</dd></div>
-          <div><dt>Workload</dt><dd data-cookbook-task>Loading...</dd></div>
-          <div><dt>Source configuration</dt><dd data-cookbook-gpus>Loading...</dd></div>
-          <div><dt>Expected output</dt><dd data-cookbook-artifact>Loading...</dd></div>
-        </dl>
-
-        <div class="cookbook-command">
-          <div class="cookbook-command__bar">
-            <span>Terminal</span>
-          </div>
-          <pre><code class="language-bash" data-cookbook-command>Loading...</code></pre>
-        </div>
-
-        <div class="cookbook-result__footer">
-          <a data-cookbook-source href="../../inference/examples/basic/">Open example source</a>
-          <a data-cookbook-model-link href="https://huggingface.co/Tongyi-MAI">View model card</a>
-        </div>
-        <p class="cookbook-picker__status" role="status" aria-live="polite" data-cookbook-status></p>
-      </article>
-    </div>
-
-    <noscript>
-      <div class="cookbook-noscript">
-        JavaScript is needed for the guided selector. You can still browse the
-        <a href="../../inference/examples/examples_inference_index/">maintained inference examples</a>.
-      </div>
-    </noscript>
-  </section>
-</div>
-
-<details class="cookbook-collapsible" id="cookbook-setup">
-  <summary>Setup</summary>
-  <div class="cookbook-collapsible__body">
-      <p>The generated commands expect a local clone:</p>
-      <pre><code>git clone https://github.com/hao-ai-lab/FastVideo.git
-cd FastVideo</code></pre>
-      <p>Use <a href="../../inference/configuration/">Configuration</a> for supported Python and CLI settings, <a href="../../inference/optimizations/">Optimizations</a> for attention and memory tradeoffs, and the <a href="../../inference/support_matrix/">support matrix</a> for the supported model and optimization surface.</p>
-  </div>
-</details>
-
-<details class="cookbook-collapsible" id="cookbook-troubleshooting">
-  <summary>Troubleshooting</summary>
-  <div class="cookbook-collapsible__body">
-      <ul>
-        <li>Output defaults to <code>outputs/zimage/zimage_turbo.png</code>; pass <code>--output</code> to redirect.</li>
-        <li>Gated or missing checkpoints: run <code>huggingface-cli login</code> and confirm you accepted the model's license on Hugging Face.</li>
-      </ul>
-  </div>
-</details>
-
-<details class="cookbook-collapsible" id="cookbook-evidence">
-  <summary>Evidence status</summary>
-  <div class="cookbook-collapsible__body">
-      <p>All recipes on this page are <strong>Source-backed</strong>: their commands, model IDs, and flags were validated against the checked-in FastVideo sources listed above (static validation). No runtime GPU validation is recorded for these recipes, so GPU model fit, memory use, throughput, and runtime duration are <strong>Unknown</strong> and deliberately not claimed. Runtime buttons show only the GPU counts configured in checked-in sources.</p>
-  </div>
-</details>
diff --git a/docs/design/inference_schema_parity_inventory.yaml b/docs/design/inference_schema_parity_inventory.yaml
index 10329f347..258cbaad1 100644
--- a/docs/design/inference_schema_parity_inventory.yaml
+++ b/docs/design/inference_schema_parity_inventory.yaml
@@ -219,9 +219,6 @@ surfaces:
         sources:
           - fastvideo.configs.pipelines.gen3c.Gen3CConfig
           - fastvideo.configs.pipelines.gen3c.Gen3CInferenceConfig
-      color_correction_strength:
-        sources:
-          - fastvideo.configs.pipelines.dreamx_world.DreamXWorld5BARPipelineConfig
       default_camera_rotation:
         sources:
           - fastvideo.configs.pipelines.gen3c.Gen3CConfig
@@ -296,11 +293,6 @@ surfaces:
         sources:
           - fastvideo.configs.pipelines.lingbotworld.LingBotWorldI2V480PConfig
           - fastvideo.configs.pipelines.lingbotworld.Wan2_2_I2V_A14B_Config
-          - fastvideo.configs.pipelines.turbodiffusion.TurboDiffusionI2VConfig
-          - fastvideo.configs.pipelines.turbodiffusion.TurboDiffusionI2V_A14B_Config
-          - fastvideo.configs.pipelines.turbodiffusion.TurboDiffusionT2VConfig
-          - fastvideo.configs.pipelines.turbodiffusion.TurboDiffusionT2V_14B_Config
-          - fastvideo.configs.pipelines.turbodiffusion.TurboDiffusionT2V_1_3B_Config
           - fastvideo.configs.pipelines.wan.FastWan2_1_T2V_480P_Config
           - fastvideo.configs.pipelines.wan.FastWan2_2_TI2V_5B_Config
           - fastvideo.configs.pipelines.matrixgame2.MatrixGame2BaseI2V480PConfig
@@ -319,11 +311,6 @@ surfaces:
         sources:
           - fastvideo.configs.pipelines.lingbotworld.LingBotWorldI2V480PConfig
           - fastvideo.configs.pipelines.lingbotworld.Wan2_2_I2V_A14B_Config
-          - fastvideo.configs.pipelines.turbodiffusion.TurboDiffusionI2VConfig
-          - fastvideo.configs.pipelines.turbodiffusion.TurboDiffusionI2V_A14B_Config
-          - fastvideo.configs.pipelines.turbodiffusion.TurboDiffusionT2VConfig
-          - fastvideo.configs.pipelines.turbodiffusion.TurboDiffusionT2V_14B_Config
-          - fastvideo.configs.pipelines.turbodiffusion.TurboDiffusionT2V_1_3B_Config
           - fastvideo.configs.pipelines.wan.FastWan2_1_T2V_480P_Config
           - fastvideo.configs.pipelines.wan.FastWan2_2_TI2V_5B_Config
           - fastvideo.configs.pipelines.matrixgame2.MatrixGame2BaseI2V480PConfig
@@ -338,48 +325,21 @@ surfaces:
           - fastvideo.configs.pipelines.wan.WanI2V720PConfig
           - fastvideo.configs.pipelines.wan.WanT2V480PConfig
           - fastvideo.configs.pipelines.wan.WanT2V720PConfig
-      bsa_cdf_threshold:
-        sources: [fastvideo.configs.pipelines.longcat.LongCatT2V480PConfig, fastvideo.configs.pipelines.longcat.LongCatT2V704PConfig]
-      bsa_chunk_k:
-        sources: [fastvideo.configs.pipelines.longcat.LongCatT2V480PConfig, fastvideo.configs.pipelines.longcat.LongCatT2V704PConfig]
-      bsa_chunk_q:
-        sources: [fastvideo.configs.pipelines.longcat.LongCatT2V480PConfig, fastvideo.configs.pipelines.longcat.LongCatT2V704PConfig]
-      bsa_params:
-        sources: [fastvideo.configs.pipelines.longcat.LongCatT2V480PConfig, fastvideo.configs.pipelines.longcat.LongCatT2V704PConfig]
-      bsa_sparsity:
-        sources: [fastvideo.configs.pipelines.longcat.LongCatT2V480PConfig, fastvideo.configs.pipelines.longcat.LongCatT2V704PConfig]
-      enable_bsa:
-        sources: [fastvideo.configs.pipelines.longcat.LongCatT2V480PConfig, fastvideo.configs.pipelines.longcat.LongCatT2V704PConfig]
-      enable_kv_cache:
-        sources: [fastvideo.configs.pipelines.longcat.LongCatT2V480PConfig, fastvideo.configs.pipelines.longcat.LongCatT2V704PConfig]
-      enhance_hf:
-        sources: [fastvideo.configs.pipelines.longcat.LongCatT2V480PConfig, fastvideo.configs.pipelines.longcat.LongCatT2V704PConfig]
-      offload_kv_cache:
-        sources: [fastvideo.configs.pipelines.longcat.LongCatT2V480PConfig, fastvideo.configs.pipelines.longcat.LongCatT2V704PConfig]
-      t_thresh:
-        sources: [fastvideo.configs.pipelines.longcat.LongCatT2V480PConfig, fastvideo.configs.pipelines.longcat.LongCatT2V704PConfig]
-      use_distill:
-        sources: [fastvideo.configs.pipelines.longcat.LongCatT2V480PConfig, fastvideo.configs.pipelines.longcat.LongCatT2V704PConfig]
       scheduler_arch:
         sources:
           - fastvideo.configs.pipelines.sd35.SD35Config
-          - fastvideo.configs.pipelines.zimage.ZImagePipelineConfig
       text_encoder_archs:
         sources:
           - fastvideo.configs.pipelines.sd35.SD35Config
-          - fastvideo.configs.pipelines.zimage.ZImagePipelineConfig
       tokenizer_archs:
         sources:
           - fastvideo.configs.pipelines.sd35.SD35Config
-          - fastvideo.configs.pipelines.zimage.ZImagePipelineConfig
       transformer_arch:
         sources:
           - fastvideo.configs.pipelines.sd35.SD35Config
-          - fastvideo.configs.pipelines.zimage.ZImagePipelineConfig
       vae_arch:
         sources:
           - fastvideo.configs.pipelines.sd35.SD35Config
-          - fastvideo.configs.pipelines.zimage.ZImagePipelineConfig
       expand_timesteps:
         sources:
           - fastvideo.configs.pipelines.wan.FastWan2_2_TI2V_5B_Config
@@ -494,8 +454,6 @@ surfaces:
       image_conditioning: "MagiHuman preset variant marker for reference-image conditioning."
       pdd_step_indices: "MiniMax-H3 PDD fine-grid partition; resolve_checkpoint_settings sets it from fastvideo_inference.json."
       ref_audio_offset: "MagiHuman internal data-proxy audio alignment offset."
-      scheduler_sigma_min: "Z-Image scheduler parity invariant; not part of the public typed inference API."
-      scheduler_use_reference_discrete_timesteps: "Z-Image scheduler parity invariant; not part of the public typed inference API."
       sr_local_attn_layers: "MagiHuman SR internal sparse-attention layer selection."
       text_offset: "MagiHuman internal data-proxy text alignment offset."
       vae_stride: "MagiHuman internal VAE/data-proxy stride setting."
@@ -527,8 +485,6 @@ surfaces:
       pose: request.inputs.pose
       c2ws_plucker_emb: request.inputs.c2ws_plucker_emb
       action_path: request.inputs.action_path
-      refine_from: request.inputs.refine_from
-      stage1_video: request.inputs.stage1_video
       prompt: request.prompt
       negative_prompt: request.negative_prompt
       prompt_path: request.inputs.prompt_path
@@ -548,8 +504,6 @@ surfaces:
       guidance_scale: request.sampling.guidance_scale
       batch_cfg: request.sampling.batch_cfg
       guidance_scale_2: request.sampling.guidance_scale_2
-      cfg_normalization: request.sampling.cfg_normalization
-      cfg_truncation: request.sampling.cfg_truncation
       guidance_rescale: request.sampling.guidance_rescale
       use_embedded_guidance: request.sampling.use_embedded_guidance
       true_cfg_scale: request.sampling.true_cfg_scale
@@ -573,19 +527,12 @@ surfaces:
       return_continuation_state: request.output.return_state
     preset_owned:
       t_thresh: request.stage_overrides.refine.t_thresh
-      spatial_refine_only: request.stage_overrides.refine.spatial_refine_only
       num_cond_frames: request.stage_overrides.refine.num_cond_frames
       trajectory_type: request.extensions.gen3c.trajectory_type
       movement_distance: request.extensions.gen3c.movement_distance
       camera_rotation: request.extensions.gen3c.camera_rotation
       prompt_attention_mask: request.extensions.hyworld.prompt_attention_mask
       negative_attention_mask: request.extensions.hyworld.negative_attention_mask
-      camera_states: request.extensions.hunyuangamecraft.camera_states
-      camera_trajectory: request.extensions.hunyuangamecraft.camera_trajectory
-      action_list: request.extensions.hunyuangamecraft.action_list
-      action_speed_list: request.extensions.hunyuangamecraft.action_speed_list
-      gt_latents: request.extensions.hunyuangamecraft.gt_latents
-      conditioning_mask: request.extensions.hunyuangamecraft.conditioning_mask
       ltx2_cfg_scale_video: request.extensions.ltx2.cfg_scale_video
       ltx2_cfg_scale_audio: request.extensions.ltx2.cfg_scale_audio
       ltx2_modality_scale_video: request.extensions.ltx2.modality_scale_video
diff --git a/docs/generate_examples.py b/docs/generate_examples.py
index bc2d9dd63..21c49f372 100644
--- a/docs/generate_examples.py
+++ b/docs/generate_examples.py
@@ -38,18 +38,14 @@ COOKBOOK_SOURCE_ROOTS = (
 # (e.g. black-forest-labs/FLUX.1-dev) and are grouped for documentation only.
 COOKBOOK_FAMILIES = {
     "wan",
-    "turbodiffusion",
     "ltx2",
     "hunyuan",
     "cosmos",
     "kandinsky5",
     "kandinsky6",
     "flux",
-    "glm_image",
-    "zimage",
     "sd35",
     "minimax_h3",
-    "longcat",
     "stable_audio",
     "mmaudio",
     "matrixgame",
diff --git a/docs/inference/architecture.md b/docs/inference/architecture.md
index f0500e1ab..937d3bfb8 100644
--- a/docs/inference/architecture.md
+++ b/docs/inference/architecture.md
@@ -232,8 +232,7 @@ Dataclass carrying all pipeline state between stages. Key field groups:
 - **Scheduler**: `timesteps`, `num_inference_steps`, `guidance_scale`,
   `sigmas`.
 - **Task-specific**: `mouse_cond`/`keyboard_cond` (Matrix-Game 2.0), `pose`
-  (HYWorld), `camera_states` (GameCraft), `c2ws_plucker_emb`
-  (LingBotWorld).
+  (HYWorld), `c2ws_plucker_emb` (LingBotWorld).
 - **Output**: `output: Tensor | None`.
 - **Logging**: `logging_info: PipelineLoggingInfo`.
 
@@ -256,7 +255,6 @@ Standard stages (typical execution order):
 | `DecodingStage` | `stages/decoding.py` | Decodes latents to video via VAE |
 
 Specialized variants: `CausalDenoisingStage`, `LTX2DenoisingStage`,
-`LongCatDenoisingStage`, `GameCraftDenoisingStage`,
 `HYWorldDenoisingStage`, `MatrixGame2CausalDenoisingStage`,
 `SRDenoisingStage`, `LTX2AudioDecodingStage`, `SD35ConditioningStage`,
 `LTX2TextEncodingStage`, `LTX2LatentPreparationStage`.
diff --git a/docs/inference/offloading.md b/docs/inference/offloading.md
index 904581271..ca27b88d2 100644
--- a/docs/inference/offloading.md
+++ b/docs/inference/offloading.md
@@ -168,8 +168,8 @@ and logs a warning if the flag is set.
 
 Deferral is opt-in per pipeline. Releasing a component and loading it again is
 only safe when nothing outside the loader has changed it, and two common habits
-break that without raising: mutating a component after load, as LongCat does
-when it enables block-sparse attention, and reading a component's attributes
+break that without raising: mutating a component after load, such as enabling
+a sparse-attention mode in `initialize_pipeline`, and reading a component's attributes
 while stages are built, as the shared denoising stage does to pick an attention
 backend. A pipeline therefore lists the components it has checked in
 `_lazy_module_names`, which is empty in the base class. MiniMax-H3 opts in. On
diff --git a/docs/inference/support_matrix.md b/docs/inference/support_matrix.md
index cccd441e6..cdbdedd48 100644
--- a/docs/inference/support_matrix.md
+++ b/docs/inference/support_matrix.md
@@ -31,14 +31,10 @@ column links a runnable script in `examples/inference/basic/` where one exists.
 | cosmos | `nvidia/Cosmos-Predict2-2B-Video2World` | T2V | — |
 | cosmos25 | `KyleShao/Cosmos-Predict2.5-2B-Diffusers` | T2V | [basic_cosmos2_5_t2w.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_cosmos2_5_t2w.py) |
 | cosmos25 | `nvidia/Cosmos-Predict2.5-14B` | T2V | [basic_cosmos2_5_t2w.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_cosmos2_5_t2w.py) |
-| dreamx_world | `FastVideo/DreamX-World-5B-Cam-Diffusers` | I2V | [basic_dreamx_world.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_dreamx_world.py) |
-| dreamx_world | `FastVideo/DreamX-World-5B-Diffusers` | I2V | [basic_dreamx_world.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_dreamx_world.py) |
 | flux | `black-forest-labs/FLUX.1-dev` | T2I | [basic_flux_dev.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_flux_dev.py) |
 | flux2 | `black-forest-labs/FLUX.2-klein-4B`<br>`black-forest-labs/FLUX.2-klein-9B` | T2I | [basic_flux2_klein.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_flux2_klein.py) |
 | flux2 | `black-forest-labs/FLUX.2-dev` | T2I | [basic_flux2.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_flux2.py) |
-| gamecraft | `FastVideo/HunyuanGameCraft-Diffusers` | I2V | [basic_gamecraft.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_gamecraft.py) |
 | gen3c | `FastVideo/GEN3C-Cosmos-7B-Diffusers` | T2V | [basic_gen3c.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_gen3c.py) |
-| glm_image | `zai-org/GLM-Image` | T2I | [basic_glm_image.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_glm_image.py) |
 | hunyuan | `hunyuanvideo-community/HunyuanVideo` | T2V | — |
 | hunyuan | `FastVideo/FastHunyuan-diffusers` | T2V | — |
 | hunyuan15 | `hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v` | T2V | [basic_hy15.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_hy15.py) |
@@ -62,9 +58,6 @@ column links a runnable script in `examples/inference/basic/` where one exists.
 | lingbotworld | `FastVideo/LingBot-World-Base-Cam-Diffusers` | I2V | [basic_lingbotworld_base_cam.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_lingbotworld_base_cam.py) |
 | lingbotworld2 | `robbyant/lingbot-world-v2-14b-causal-fast` | I2V | [basic_lingbotworld2_causal_fast.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_lingbotworld2_causal_fast.py) |
 | lingbotworld_fast | `FastVideo/LingBot-World-Fast-Diffusers` | I2V | [basic_lingbotworld_fast.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_lingbotworld_fast.py) |
-| longcat | `FastVideo/LongCat-Video-T2V-Diffusers` | T2V | [basic_longcat_t2v.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_longcat_t2v.py) |
-| longcat | `FastVideo/LongCat-Video-I2V-Diffusers` | I2V | [basic_longcat_i2v.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_longcat_i2v.py) |
-| longcat | `FastVideo/LongCat-Video-VC-Diffusers` | — | [basic_longcat_vc.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_longcat_vc.py) |
 | ltx2 | `FastVideo/LTX2-Distilled-Diffusers`<br>`FastVideo/LTX2.3-Distilled-Diffusers`<br>`FastVideo/LTX-2.3-Distilled-Diffusers` | T2V | [basic_ltx2_distilled.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_ltx2_distilled.py) |
 | ltx2 | `FastVideo/LTX-2.5-Distilled-Diffusers` | T2V, I2V | [T2AV](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_ltx2_5_t2av.py)<br>[I2AV](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_ltx2_5_i2av.py) |
 | ltx2 | `FastVideo/LTX-2.5-Dev-Diffusers` | T2V, I2V | [T2AV](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_ltx2_5_t2av.py)<br>[I2AV](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_ltx2_5_i2av.py) |
@@ -77,9 +70,6 @@ column links a runnable script in `examples/inference/basic/` where one exists.
 | sd35 | `stabilityai/stable-diffusion-3.5-medium` | T2I | [basic_sd35_t2i.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_sd35_t2i.py) |
 | stable_audio | `FastVideo/stable-audio-open-1.0-Diffusers` | T2V | [basic_stable_audio.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_stable_audio.py) |
 | stable_audio | `FastVideo/stable-audio-open-small-Diffusers` | T2V | [basic_stable_audio_small.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_stable_audio_small.py) |
-| turbodiffusion | `loayrashid/TurboWan2.1-T2V-1.3B-Diffusers` | T2V | [basic_turbodiffusion.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_turbodiffusion.py) |
-| turbodiffusion | `loayrashid/TurboWan2.1-T2V-14B-Diffusers` | T2V | [basic_turbodiffusion_14b.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_turbodiffusion_14b.py) |
-| turbodiffusion | `loayrashid/TurboWan2.2-I2V-A14B-Diffusers` | I2V | [basic_turbodiffusion_i2v.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_turbodiffusion_i2v.py) |
 | wan | `Wan-AI/Wan2.1-T2V-1.3B-Diffusers` | T2V | [basic.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic.py) |
 | wan | `Wan-AI/Wan2.1-T2V-14B-Diffusers`<br>`FastVideo/Wan2.1-VSA-T2V-14B-720P-Diffusers` | T2V | — |
 | wan | `Wan-AI/Wan2.1-I2V-14B-480P-Diffusers` | I2V | — |
@@ -97,7 +87,6 @@ column links a runnable script in `examples/inference/basic/` where one exists.
 | wan | `wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers` | T2V | [basic_self_forcing_causal.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_self_forcing_causal.py) |
 | wan | `rand0nmr/SFWan2.2-T2V-A14B-Diffusers` | T2V | [basic_self_forcing_causal_wan2_2_t2v.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_self_forcing_causal_wan2_2_t2v.py) |
 | wan | `FastVideo/SFWan2.2-I2V-A14B-Preview-Diffusers` | I2V | [basic_self_forcing_causal_wan2_2_i2v.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_self_forcing_causal_wan2_2_i2v.py) |
-| zimage | `Tongyi-MAI/Z-Image-Turbo` | T2I | [basic_zimage.py](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_zimage.py) |
 
 **Note (stable_audio)**: the Stable Audio Open pipelines generate audio
 (`StableAudioT2AConfig` / `StableAudioOpenSmallConfig`); they are registered
@@ -184,33 +173,27 @@ optimizations: absence means **untested**, not incompatible.
   }
 </style>
 
-| Model Name | HuggingFace Model ID | Resolutions | TeaCache | Sliding Tile Attn (Legacy Branch) | Sage Attn | VSA | BSA |
-|------------|---------------------|-------------|----------|-------------------|-----------|-----|-----|
-| FastWan2.1 T2V 1.3B | `FastVideo/FastWan2.1-T2V-1.3B-Diffusers` | 480P | ⭕ | ⭕ | ⭕ | ✅ | ⭕ |
-| FastWan2.2 TI2V 5B Full Attn | `FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers` | 720P | ⭕ | ⭕ | ⭕ | ✅ | ⭕ |
-| Wan2.2 TI2V 5B | `Wan-AI/Wan2.2-TI2V-5B-Diffusers` | 720P | ⭕ | ⭕ | ✅ | ⭕ | ⭕ |
-| DreamX-World 5B Cam | `FastVideo/DreamX-World-5B-Cam-Diffusers` | 480P | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ |
-| DreamX-World 5B AR | `FastVideo/DreamX-World-5B-Diffusers` | 704px1280p | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ |
-| Lucy Edit Dev 5B*** | `decart-ai/Lucy-Edit-Dev` | 480P | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ |
-| Wan2.2 S2V 14B**** | `FastVideo/Wan2.2-S2V-14B-Diffusers` | 480P | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ |
-| Wan2.2 T2V A14B | `Wan-AI/Wan2.2-T2V-A14B-Diffusers` | 480P<br>720P | ❌ | ❌ | ✅ | ⭕ | ⭕ |
-| Wan2.2 I2V A14B | `Wan-AI/Wan2.2-I2V-A14B-Diffusers` | 480P<br>720P | ❌ | ❌ | ✅ | ⭕ | ⭕ |
-| Wan2.2 Animate 14B | `Wan-AI/Wan2.2-Animate-14B-Diffusers` | 720P | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ |
-| HunyuanVideo | `hunyuanvideo-community/HunyuanVideo` | 720px1280p<br>544px960p | ❌ | ✅ | ✅ | ⭕ | ⭕ |
-| FastHunyuan | `FastVideo/FastHunyuan-diffusers` | 720px1280p<br>544px960p | ❌ | ✅ | ✅ | ⭕ | ⭕ |
-| Wan2.1 T2V 1.3B | `Wan-AI/Wan2.1-T2V-1.3B-Diffusers` | 480P | ✅ | ✅ | ✅ | ⭕ | ⭕ |
-| Wan2.1 T2V 14B | `Wan-AI/Wan2.1-T2V-14B-Diffusers` | 480P, 720P | ✅ | ✅ | ✅ | ⭕ | ⭕ |
-| Wan2.1 I2V 480P | `Wan-AI/Wan2.1-I2V-14B-480P-Diffusers` | 480P | ✅ | ✅ | ✅ | ⭕ | ⭕ |
-| Wan2.1 I2V 720P | `Wan-AI/Wan2.1-I2V-14B-720P-Diffusers` | 720P | ✅ | ✅ | ✅ | ⭕ | ⭕ |
-| TurboWan2.1 T2V 1.3B | `loayrashid/TurboWan2.1-T2V-1.3B-Diffusers` | 480P | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ |
-| TurboWan2.1 T2V 14B | `loayrashid/TurboWan2.1-T2V-14B-Diffusers` | 480P, 720P | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ |
-| TurboWan2.2 I2V A14B | `loayrashid/TurboWan2.2-I2V-A14B-Diffusers` | 480P<br>720P | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ |
-| LongCat T2V 13.6B | `FastVideo/LongCat-Video-T2V-Diffusers` | 480P<br>720P | ❌ | ❌ | ❌ | ⭕ | ✅ |
-| Matrix Game 2.0 Base Distilled | `FastVideo/Matrix-Game-2.0-Base-Distilled-Diffusers` | 352x640 | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ |
-| Matrix Game 2.0 GTA Distilled | `FastVideo/Matrix-Game-2.0-GTA-Distilled-Diffusers` | 352x640 | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ |
-| Matrix Game 2.0 TempleRun Distilled | `FastVideo/Matrix-Game-2.0-TempleRun-Distilled-Diffusers` | 352x640 | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ |
-| Matrix Game 3.0 Base Distilled | `FastVideo/Matrix-Game-3.0-Base-Distilled-Diffusers` | 720x1280 | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ |
-| GEN3C Cosmos 7B | `FastVideo/GEN3C-Cosmos-7B-Diffusers` | 704px1280p | ❌ | ❌ | ❌ | ⭕ | ⭕ |
+| Model Name | HuggingFace Model ID | Resolutions | TeaCache | Sliding Tile Attn (Legacy Branch) | Sage Attn | VSA |
+|------------|---------------------|-------------|----------|-------------------|-----------|-----|
+| FastWan2.1 T2V 1.3B | `FastVideo/FastWan2.1-T2V-1.3B-Diffusers` | 480P | ⭕ | ⭕ | ⭕ | ✅ |
+| FastWan2.2 TI2V 5B Full Attn | `FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers` | 720P | ⭕ | ⭕ | ⭕ | ✅ |
+| Wan2.2 TI2V 5B | `Wan-AI/Wan2.2-TI2V-5B-Diffusers` | 720P | ⭕ | ⭕ | ✅ | ⭕ |
+| Lucy Edit Dev 5B*** | `decart-ai/Lucy-Edit-Dev` | 480P | ⭕ | ⭕ | ⭕ | ⭕ |
+| Wan2.2 S2V 14B**** | `FastVideo/Wan2.2-S2V-14B-Diffusers` | 480P | ⭕ | ⭕ | ⭕ | ⭕ |
+| Wan2.2 T2V A14B | `Wan-AI/Wan2.2-T2V-A14B-Diffusers` | 480P<br>720P | ❌ | ❌ | ✅ | ⭕ |
+| Wan2.2 I2V A14B | `Wan-AI/Wan2.2-I2V-A14B-Diffusers` | 480P<br>720P | ❌ | ❌ | ✅ | ⭕ |
+| Wan2.2 Animate 14B | `Wan-AI/Wan2.2-Animate-14B-Diffusers` | 720P | ⭕ | ⭕ | ⭕ | ⭕ |
+| HunyuanVideo | `hunyuanvideo-community/HunyuanVideo` | 720px1280p<br>544px960p | ❌ | ✅ | ✅ | ⭕ |
+| FastHunyuan | `FastVideo/FastHunyuan-diffusers` | 720px1280p<br>544px960p | ❌ | ✅ | ✅ | ⭕ |
+| Wan2.1 T2V 1.3B | `Wan-AI/Wan2.1-T2V-1.3B-Diffusers` | 480P | ✅ | ✅ | ✅ | ⭕ |
+| Wan2.1 T2V 14B | `Wan-AI/Wan2.1-T2V-14B-Diffusers` | 480P, 720P | ✅ | ✅ | ✅ | ⭕ |
+| Wan2.1 I2V 480P | `Wan-AI/Wan2.1-I2V-14B-480P-Diffusers` | 480P | ✅ | ✅ | ✅ | ⭕ |
+| Wan2.1 I2V 720P | `Wan-AI/Wan2.1-I2V-14B-720P-Diffusers` | 720P | ✅ | ✅ | ✅ | ⭕ |
+| Matrix Game 2.0 Base Distilled | `FastVideo/Matrix-Game-2.0-Base-Distilled-Diffusers` | 352x640 | ⭕ | ⭕ | ⭕ | ⭕ |
+| Matrix Game 2.0 GTA Distilled | `FastVideo/Matrix-Game-2.0-GTA-Distilled-Diffusers` | 352x640 | ⭕ | ⭕ | ⭕ | ⭕ |
+| Matrix Game 2.0 TempleRun Distilled | `FastVideo/Matrix-Game-2.0-TempleRun-Distilled-Diffusers` | 352x640 | ⭕ | ⭕ | ⭕ | ⭕ |
+| Matrix Game 3.0 Base Distilled | `FastVideo/Matrix-Game-3.0-Base-Distilled-Diffusers` | 720x1280 | ⭕ | ⭕ | ⭕ | ⭕ |
+| GEN3C Cosmos 7B | `FastVideo/GEN3C-Cosmos-7B-Diffusers` | 704px1280p | ❌ | ❌ | ❌ | ⭕ |
 
 ## Apple Silicon native runtime
 
@@ -284,11 +267,6 @@ listed under [Special requirements](#special-requirements).
   https://github.com/hao-ai-lab/FastVideo/tree/sta_do_not_delete
 - STA currently requires Hopper GPUs (H100s).
 
-### TurboWan2.1 (TurboDiffusion)
-- Uses TurboDiffusionPipeline with RCM scheduler for 1-4 step generation
-- Requires SLA attention backend: `export FASTVIDEO_ATTENTION_BACKEND=SLA_ATTN`
-- Uses `guidance_scale=1.0` (no classifier-free guidance)
-
 ### Matrix Game 2.0
 - Image-to-video game world models with keyboard/mouse control input
 - Three variants available: Base (universal), GTA, and TempleRun
diff --git a/examples/inference/basic/basic_dreamx_world.py b/examples/inference/basic/basic_dreamx_world.py
deleted file mode 100644
index 9af4a02ed..000000000
--- a/examples/inference/basic/basic_dreamx_world.py
+++ /dev/null
@@ -1,61 +0,0 @@
-import os
-
-from fastvideo import VideoGenerator
-
-OUTPUT_PATH = os.getenv("DREAMX_WORLD_OUTPUT_PATH", "video_samples_dreamx_world")
-
-
-def _env_int(name: str, default: int) -> int:
-    return int(os.getenv(name, str(default)))
-
-
-def _env_float(name: str, default: float) -> float:
-    return float(os.getenv(name, str(default)))
-
-
-def main():
-    model_name = os.getenv("DREAMX_WORLD_MODEL_DIR", "FastVideo/DreamX-World-5B-Cam-Diffusers")
-    generator = VideoGenerator.from_pretrained(
-        model_name,
-        num_gpus=1,
-        use_fsdp_inference=False,
-        dit_cpu_offload=False,
-        vae_cpu_offload=True,
-        text_encoder_cpu_offload=True,
-        pin_cpu_memory=False,
-        override_pipeline_cls_name="DreamXWorldPipeline",
-    )
-
-    prompt = os.getenv(
-        "DREAMX_WORLD_PROMPT",
-        "A cinematic first-person drive through a futuristic coastal city at "
-        "sunrise, reflective glass towers, clean streets, soft volumetric light.",
-    )
-    image_path = os.getenv(
-        "DREAMX_WORLD_IMAGE_PATH",
-        "https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/wan_i2v_input.JPG",
-    )
-
-    kwargs = {
-        "output_path": OUTPUT_PATH,
-        "save_video": os.getenv("DREAMX_WORLD_SAVE_VIDEO", "1") != "0",
-        "height": _env_int("DREAMX_WORLD_HEIGHT", 480),
-        "width": _env_int("DREAMX_WORLD_WIDTH", 832),
-        "num_frames": _env_int("DREAMX_WORLD_NUM_FRAMES", 161),
-        "num_inference_steps": _env_int("DREAMX_WORLD_STEPS", 30),
-        "guidance_scale": _env_float("DREAMX_WORLD_GUIDANCE", 5.0),
-        "action_list": os.getenv("DREAMX_WORLD_ACTIONS", "w,d,w").split(","),
-        "action_speed_list":
-        [float(value) for value in os.getenv("DREAMX_WORLD_ACTION_SPEEDS", "4.0,2.0,4.0").split(",")],
-    }
-    if image_path:
-        kwargs["image_path"] = image_path
-
-    try:
-        generator.generate_video(prompt, **kwargs)
-    finally:
-        generator.shutdown()
-
-
-if __name__ == "__main__":
-    main()
diff --git a/examples/inference/basic/basic_gamecraft.py b/examples/inference/basic/basic_gamecraft.py
deleted file mode 100644
index c086d86e4..000000000
--- a/examples/inference/basic/basic_gamecraft.py
+++ /dev/null
@@ -1,118 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-Basic inference script for HunyuanGameCraft video generation.
-
-HunyuanGameCraft generates game-like videos with camera/action control.
-It takes an optional image input and generates video with camera motion
-based on simple action commands (forward, left, right, backward, rotations).
-
-Available actions:
-    - forward (w): Move camera forward
-    - backward (s): Move camera backward  
-    - left (a): Move camera left (strafe)
-    - right (d): Move camera right (strafe)
-    - left_rot: Rotate camera left (pan)
-    - right_rot: Rotate camera right (pan)
-    - up_rot: Rotate camera up (tilt)
-    - down_rot: Rotate camera down (tilt)
-
-T2V vs I2V:
-    - Default: I2V (uses a default reference image). Set GAMECRAFT_I2V_IMAGE to a
-      URL or path to use a different image.
-    - T2V only (no reference image): run with GAMECRAFT_I2V_IMAGE= (empty).
-"""
-import os
-
-import torch
-
-from fastvideo import VideoGenerator
-from fastvideo.models.camera import create_camera_trajectory
-
-# Model configuration (use GAMECRAFT_MODEL_PATH for local weights)
-MODEL_PATH = os.environ.get("GAMECRAFT_MODEL_PATH", "FastVideo/HunyuanGameCraft-Diffusers")
-
-# Default prompts for demo
-DEFAULT_PROMPTS = {
-    "village":
-    "A charming medieval village with cobblestone streets, thatched-roof houses, and vibrant flower gardens under a bright blue sky.",
-    "temple":
-    "A majestic ancient temple stands under a clear blue sky, its grandeur highlighted by towering Doric columns and intricate architectural details.",
-    "forest":
-    "A lush green forest with tall trees, dappled sunlight filtering through the leaves, and a winding dirt path.",
-    "beach": "A tropical beach with crystal clear turquoise water, white sand, and palm trees swaying in the breeze.",
-}
-
-# I2V: default reference image (URL). Can override with a local path.
-DEFAULT_I2V_IMAGE_URL = ("https://huggingface.co/datasets/huggingface/documentation-images/"
-                         "resolve/main/diffusers/astronaut.jpg")
-DEFAULT_I2V_PROMPT = ("An astronaut hatching from an egg, on the surface of the moon, "
-                      "the darkness and depth of space realised in the background.")
-
-OUTPUT_PATH = "video_samples_gamecraft"
-
-
-def main():
-    # Initialize generator
-    # FastVideo will automatically download weights from HuggingFace
-    generator = VideoGenerator.from_pretrained(
-        MODEL_PATH,
-        num_gpus=1,
-        use_fsdp_inference=True,
-        dit_cpu_offload=True,
-        vae_cpu_offload=True,
-        text_encoder_cpu_offload=True,
-        pin_cpu_memory=True,
-    )
-
-    # Video parameters
-    height = 704
-    width = 1280
-    num_frames = 33
-    action = "forward"
-    action_speed = 0.2
-
-    # Create camera trajectory (Plücker coordinates)
-    camera_states = create_camera_trajectory(
-        action=action,
-        height=height,
-        width=width,
-        num_frames=num_frames,
-        action_speed=action_speed,
-        dtype=torch.bfloat16,
-    )
-    print(f"Camera states shape: {camera_states.shape}")
-
-    # I2V vs T2V: unset GAMECRAFT_I2V_IMAGE -> I2V (default image). Set to "" -> T2V.
-    env_image = os.environ.get("GAMECRAFT_I2V_IMAGE")
-    if env_image is None:
-        image_path = DEFAULT_I2V_IMAGE_URL  # default: I2V
-    elif env_image.strip() == "":
-        image_path = None  # T2V
-    else:
-        image_path = env_image.strip()  # I2V with given URL/path
-
-    is_i2v = image_path is not None
-    prompt = DEFAULT_I2V_PROMPT if is_i2v else DEFAULT_PROMPTS["temple"]
-    print(f"Mode: {'I2V' if is_i2v else 'T2V'}, prompt: {prompt[:60]}...")
-
-    gen_kw = dict(
-        prompt=prompt,
-        negative_prompt="",
-        camera_states=camera_states,
-        height=height,
-        width=width,
-        num_frames=num_frames,
-        num_inference_steps=50,
-        guidance_scale=6.0,
-        seed=42,
-        fps=24,
-        output_path=OUTPUT_PATH,
-        save_video=True,
-    )
-    if is_i2v:
-        gen_kw["image_path"] = image_path
-    generator.generate_video(**gen_kw)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/examples/inference/basic/basic_glm_image.py b/examples/inference/basic/basic_glm_image.py
deleted file mode 100644
index 534218909..000000000
--- a/examples/inference/basic/basic_glm_image.py
+++ /dev/null
@@ -1,107 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""Run GLM-Image text-to-image generation through FastVideo.
-
-User story:
-    "I have the HF `zai-org/GLM-Image` checkpoint and want a minimal
-    text-to-image generation command, saved as a PNG."
-"""
-import argparse
-from pathlib import Path
-
-from PIL import Image
-
-from fastvideo import VideoGenerator
-from fastvideo.api import (
-    EngineConfig,
-    GenerationRequest,
-    GeneratorConfig,
-    OutputConfig,
-    ParallelismConfig,
-    PipelineSelection,
-    SamplingConfig,
-)
-
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser(description="Run GLM-Image text-to-image generation.")
-    parser.add_argument(
-        "--model-path",
-        default="zai-org/GLM-Image",
-        help="HF id or local diffusers-format GLM-Image weights directory.",
-    )
-    parser.add_argument(
-        "--output",
-        default="image_output/landscape.png",
-        help="Output PNG path.",
-    )
-    parser.add_argument(
-        "--prompt",
-        default=("A beautiful landscape photography with rolling hills, "
-                 "a winding river, and a vibrant sunset in the background. "
-                 "Warm golden light, photorealistic style."),
-        help="Text prompt.",
-    )
-    parser.add_argument("--height", type=int, default=1024)
-    parser.add_argument("--width", type=int, default=1024)
-    parser.add_argument("--steps", type=int, default=50)
-    parser.add_argument("--guidance-scale", type=float, default=1.5)
-    parser.add_argument("--seed", type=int, default=1024)
-    parser.add_argument("--num-gpus", type=int, default=1)
-    parser.add_argument("--tp-size", type=int, default=None)
-    parser.add_argument("--sp-size", type=int, default=None)
-    return parser.parse_args()
-
-
-def main() -> None:
-    args = parse_args()
-
-    output = Path(args.output)
-    output.parent.mkdir(parents=True, exist_ok=True)
-    tp_size = args.tp_size if args.tp_size is not None else (args.num_gpus if args.num_gpus > 1 else 1)
-    sp_size = args.sp_size if args.sp_size is not None else (1 if args.num_gpus > 1 else args.num_gpus)
-
-    # GLM-Image needs trust_remote_code for its AR encoder; offload and the
-    # pipeline class come from the model's registered defaults — don't override.
-    generator_config = GeneratorConfig(
-        model_path=args.model_path,
-        trust_remote_code=True,
-        engine=EngineConfig(
-            num_gpus=args.num_gpus,
-            parallelism=ParallelismConfig(tp_size=tp_size, sp_size=sp_size),
-        ),
-        pipeline=PipelineSelection(workload_type="t2i"),
-    )
-
-    generator = VideoGenerator.from_config(generator_config)
-    try:
-        request = GenerationRequest(
-            prompt=args.prompt,
-            sampling=SamplingConfig(
-                height=args.height,
-                width=args.width,
-                num_frames=1,
-                fps=1,
-                num_inference_steps=args.steps,
-                guidance_scale=args.guidance_scale,
-                seed=args.seed,
-            ),
-            output=OutputConfig(
-                output_path=str(output.parent),
-                save_video=False,
-                return_frames=True,
-            ),
-        )
-        result = generator.generate(request)
-        if isinstance(result, list):
-            result = result[0]
-
-        frames = result.frames
-        if frames is not None and len(frames):
-            Image.fromarray(frames[0]).save(output)
-            print(f"Saved image to {output}")
-    finally:
-        generator.shutdown()
-
-
-if __name__ == "__main__":
-    main()
diff --git a/examples/inference/basic/basic_longcat_i2v.py b/examples/inference/basic/basic_longcat_i2v.py
deleted file mode 100644
index 001885247..000000000
--- a/examples/inference/basic/basic_longcat_i2v.py
+++ /dev/null
@@ -1,204 +0,0 @@
-"""
-LongCat Image-to-Video (I2V) Example Script
-
-This script demonstrates LongCat I2V inference using the FastVideo Python API.
-LongCat I2V takes an input image and generates a video from it.
-
-It runs both basic generation (50 steps) and distill+refine generation
-(16 steps distill + 50 steps refinement to 720p with BSA).
-
-Usage:
-    python examples/inference/basic/basic_longcat_i2v.py
-
-Note:
-    Refinement uses 768x768 dimensions where latent (48x48) is divisible by 8,
-    compatible with BSA chunks [4, 4, 8].
-"""
-
-import glob
-import os
-
-from fastvideo import VideoGenerator
-
-# Common prompts and settings matching the shell script examples
-PROMPT = ("A woman sits at a wooden table by the window in a cozy café. She reaches out "
-          "with her right hand, picks up the white coffee cup from the saucer, and gently "
-          "brings it to her lips to take a sip. After drinking, she places the cup back on "
-          "the table and looks out the window, enjoying the peaceful atmosphere.")
-
-NEGATIVE_PROMPT = ("Bright tones, overexposed, static, blurred details, subtitles, style, works, "
-                   "paintings, images, static, overall gray, worst quality, low quality, JPEG compression "
-                   "residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, "
-                   "deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, "
-                   "three legs, many people in the background, walking backwards")
-
-# Input image path
-IMAGE_PATH = "assets/girl.png"
-
-SEED = 42
-
-
-def basic_generation():
-    """
-    Run basic LongCat I2V generation (50 steps at 480p).
-    
-    This uses the full 50-step denoising process for highest quality.
-    """
-    print("=" * 60)
-    print("LongCat I2V: Basic Generation (50 steps, 480p)")
-    print("=" * 60)
-
-    generator = VideoGenerator.from_pretrained(
-        "FastVideo/LongCat-Video-I2V-Diffusers",
-        num_gpus=1,
-        use_fsdp_inference=False,  # set to True if GPU is out of memory
-        dit_cpu_offload=False,
-        vae_cpu_offload=True,
-        text_encoder_cpu_offload=True,
-        pin_cpu_memory=False,
-        enable_bsa=False,
-    )
-
-    output_path = "outputs_video/longcat_i2v_basic"
-
-    generator.generate_video(
-        prompt=PROMPT,
-        negative_prompt=NEGATIVE_PROMPT,
-        image_path=IMAGE_PATH,
-        output_path=output_path,
-        save_video=True,
-        height=480,
-        width=480,  # Square
-        num_frames=93,
-        num_inference_steps=50,
-        fps=15,
-        guidance_scale=4.0,
-        seed=SEED,
-    )
-
-    print(f"\nBasic generation complete! Video saved to: {output_path}")
-    generator.shutdown()
-
-
-def distill_refine_generation():
-    """
-    Run LongCat I2V with distill+refine pipeline (16 steps + refinement to 768p).
-    
-    This uses the distilled LoRA for fast 480p generation (16 steps),
-    then refines to 768p using the refinement LoRA with BSA enabled.
-    """
-    print("\n" + "=" * 60)
-    print("LongCat I2V: Distill + Refine Pipeline")
-    print("=" * 60)
-
-    # Stage 1: Distilled generation (16 steps at 480p)
-    print("\n[Stage 1] Distilled generation (16 steps, 480p)")
-    print("-" * 40)
-
-    generator = VideoGenerator.from_pretrained(
-        "FastVideo/LongCat-Video-I2V-Diffusers",
-        num_gpus=1,
-        use_fsdp_inference=True,
-        dit_cpu_offload=False,
-        vae_cpu_offload=True,
-        text_encoder_cpu_offload=True,
-        pin_cpu_memory=False,
-        enable_bsa=False,
-        lora_path="FastVideo/LongCat-Video-T2V-Distilled-LoRA",
-        lora_nickname="distilled",
-    )
-
-    distill_output_path = "outputs_video/longcat_i2v_distill"
-
-    generator.generate_video(
-        prompt=PROMPT,
-        negative_prompt=NEGATIVE_PROMPT,
-        image_path=IMAGE_PATH,
-        output_path=distill_output_path,
-        save_video=True,
-        height=480,
-        width=480,  # Square
-        num_frames=93,
-        num_inference_steps=16,
-        fps=15,
-        guidance_scale=1.0,
-        seed=SEED,
-    )
-
-    print(f"Distilled generation complete! Video saved to: {distill_output_path}")
-    generator.shutdown()
-
-    # Stage 2: Refinement (480p -> 768p)
-    print("\n[Stage 2] Refinement (480p -> 768p with BSA)")
-    print("-" * 40)
-
-    # Find the actual saved video file from stage 1
-    video_files = glob.glob(os.path.join(distill_output_path, "*.mp4"))
-    if not video_files:
-        raise FileNotFoundError(f"No video file found in {distill_output_path}")
-    # Use the most recently created video file
-    distill_video_path = max(video_files, key=os.path.getmtime)
-    print(f"Using stage 1 video: {distill_video_path}")
-
-    # Create a new generator with refinement LoRA and BSA enabled
-    # Note: Refinement uses the T2V model (not I2V) since it's upscaling the generated video
-    # For BSA [4, 4, 8]: latent must be divisible by 8
-    # 768x768: latent 48x48, 48%8=0 ✓
-    refine_generator = VideoGenerator.from_pretrained(
-        "FastVideo/LongCat-Video-T2V-Diffusers",
-        num_gpus=1,
-        use_fsdp_inference=True,
-        dit_cpu_offload=True,
-        vae_cpu_offload=True,
-        text_encoder_cpu_offload=True,
-        pin_cpu_memory=False,
-        enable_bsa=True,
-        bsa_sparsity=0.875,
-        bsa_chunk_q=[4, 4, 4],
-        bsa_chunk_k=[4, 4, 4],
-        lora_path="FastVideo/LongCat-Video-T2V-Refinement-LoRA",
-        lora_nickname="refinement",
-    )
-
-    refine_output_path = "outputs_video/longcat_i2v_refine_720p"
-
-    refine_generator.generate_video(
-        prompt=PROMPT,
-        negative_prompt=NEGATIVE_PROMPT,
-        output_path=refine_output_path,
-        save_video=True,
-        refine_from=distill_video_path,
-        t_thresh=0.5,
-        spatial_refine_only=False,
-        num_cond_frames=0,
-        height=720,
-        width=720,
-        num_inference_steps=50,
-        fps=30,
-        guidance_scale=1.0,
-        seed=SEED,
-    )
-
-    print(f"Refinement complete! Video saved to: {refine_output_path}")
-    refine_generator.shutdown()
-
-
-def main():
-    """Run both basic and distill+refine generation pipelines."""
-    print("\n" + "=" * 60)
-    print("LongCat Image-to-Video Example")
-    print("=" * 60 + "\n")
-
-    # Run basic generation
-    basic_generation()
-
-    # Run distill+refine pipeline
-    distill_refine_generation()
-
-    print("\n" + "=" * 60)
-    print("All generations complete!")
-    print("=" * 60)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/examples/inference/basic/basic_longcat_t2v.py b/examples/inference/basic/basic_longcat_t2v.py
deleted file mode 100644
index 58c87dc3a..000000000
--- a/examples/inference/basic/basic_longcat_t2v.py
+++ /dev/null
@@ -1,192 +0,0 @@
-"""
-LongCat Text-to-Video (T2V) Example Script
-
-This script demonstrates LongCat T2V inference using the FastVideo Python API.
-It runs both basic generation (50 steps) and distill+refine generation
-(16 steps distill + 50 steps refinement to 720p).
-
-Usage:
-    python examples/inference/basic/basic_longcat_t2v.py
-"""
-
-import glob
-import os
-
-from fastvideo import VideoGenerator
-
-# Common prompts and settings matching the shell script examples
-PROMPT = ("In a realistic photography style, a white boy around seven or eight years old "
-          "sits on a park bench, wearing a light blue T-shirt, denim shorts, and white sneakers. "
-          "He holds an ice cream cone with vanilla and chocolate flavors, and beside him is a "
-          "medium-sized golden Labrador. Smiling, the boy offers the ice cream to the dog, "
-          "who eagerly licks it with its tongue. The sun is shining brightly, and the background "
-          "features a green lawn and several tall trees, creating a warm and loving scene.")
-
-NEGATIVE_PROMPT = ("Bright tones, overexposed, static, blurred details, subtitles, style, works, "
-                   "paintings, images, static, overall gray, worst quality, low quality, JPEG compression "
-                   "residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, "
-                   "deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, "
-                   "three legs, many people in the background, walking backwards")
-
-SEED = 42
-
-
-def basic_generation():
-    """
-    Run basic LongCat T2V generation (50 steps at 480p).
-    
-    This uses the full 50-step denoising process for highest quality.
-    """
-    print("=" * 60)
-    print("LongCat T2V: Basic Generation (50 steps, 480p)")
-    print("=" * 60)
-
-    generator = VideoGenerator.from_pretrained(
-        "FastVideo/LongCat-Video-T2V-Diffusers",
-        num_gpus=1,
-        use_fsdp_inference=False,  # set to True if GPU is out of memory
-        dit_cpu_offload=False,
-        vae_cpu_offload=True,
-        text_encoder_cpu_offload=True,
-        pin_cpu_memory=False,
-        enable_bsa=False,
-    )
-
-    output_path = "outputs_video/longcat_t2v_basic"
-
-    generator.generate_video(
-        prompt=PROMPT,
-        negative_prompt=NEGATIVE_PROMPT,
-        output_path=output_path,
-        save_video=True,
-        height=480,
-        width=832,
-        num_frames=93,
-        num_inference_steps=50,
-        fps=15,
-        guidance_scale=4.0,
-        seed=SEED,
-    )
-
-    print(f"\nBasic generation complete! Video saved to: {output_path}")
-    generator.shutdown()
-
-
-def distill_refine_generation():
-    """
-    Run LongCat T2V with distill+refine pipeline (16 steps + refinement to 720p).
-    
-    This uses the distilled LoRA for fast 480p generation (16 steps),
-    then refines to 720p using the refinement LoRA with BSA enabled.
-    """
-    print("\n" + "=" * 60)
-    print("LongCat T2V: Distill + Refine Pipeline")
-    print("=" * 60)
-
-    # Stage 1: Distilled generation (16 steps at 480p)
-    print("\n[Stage 1] Distilled generation (16 steps, 480p)")
-    print("-" * 40)
-
-    generator = VideoGenerator.from_pretrained(
-        "FastVideo/LongCat-Video-T2V-Diffusers",
-        num_gpus=1,
-        use_fsdp_inference=True,
-        dit_cpu_offload=False,
-        vae_cpu_offload=True,
-        text_encoder_cpu_offload=True,
-        pin_cpu_memory=False,
-        enable_bsa=False,
-        lora_path="FastVideo/LongCat-Video-T2V-Distilled-LoRA",
-        lora_nickname="distilled",
-    )
-
-    distill_output_path = "outputs_video/longcat_t2v_distill"
-
-    generator.generate_video(
-        prompt=PROMPT,
-        negative_prompt=NEGATIVE_PROMPT,
-        output_path=distill_output_path,
-        save_video=True,
-        height=480,
-        width=832,
-        num_frames=93,
-        num_inference_steps=16,
-        fps=15,
-        guidance_scale=1.0,
-        seed=SEED,
-    )
-
-    print(f"Distilled generation complete! Video saved to: {distill_output_path}")
-    generator.shutdown()
-
-    # Stage 2: Refinement (480p -> 720p)
-    print("\n[Stage 2] Refinement (480p -> 720p with BSA)")
-    print("-" * 40)
-
-    # Find the actual saved video file from stage 1
-    video_files = glob.glob(os.path.join(distill_output_path, "*.mp4"))
-    if not video_files:
-        raise FileNotFoundError(f"No video file found in {distill_output_path}")
-    # Use the most recently created video file
-    distill_video_path = max(video_files, key=os.path.getmtime)
-    print(f"Using stage 1 video: {distill_video_path}")
-
-    # Create a new generator with refinement LoRA and BSA enabled
-    refine_generator = VideoGenerator.from_pretrained(
-        "FastVideo/LongCat-Video-T2V-Diffusers",
-        num_gpus=1,
-        use_fsdp_inference=True,
-        dit_cpu_offload=True,
-        vae_cpu_offload=True,
-        text_encoder_cpu_offload=True,
-        pin_cpu_memory=False,
-        enable_bsa=True,
-        bsa_sparsity=0.875,
-        bsa_chunk_q=[4, 4, 8],
-        bsa_chunk_k=[4, 4, 8],
-        lora_path="FastVideo/LongCat-Video-T2V-Refinement-LoRA",
-        lora_nickname="refinement",
-    )
-
-    refine_output_path = "outputs_video/longcat_t2v_refine_720p"
-
-    refine_generator.generate_video(
-        prompt=PROMPT,
-        negative_prompt=NEGATIVE_PROMPT,
-        output_path=refine_output_path,
-        save_video=True,
-        refine_from=distill_video_path,
-        t_thresh=0.5,
-        spatial_refine_only=False,
-        num_cond_frames=0,
-        height=720,
-        width=1280,
-        num_inference_steps=50,
-        fps=30,
-        guidance_scale=1.0,
-        seed=SEED,
-    )
-
-    print(f"Refinement complete! Video saved to: {refine_output_path}")
-    refine_generator.shutdown()
-
-
-def main():
-    """Run both basic and distill+refine generation pipelines."""
-    print("\n" + "=" * 60)
-    print("LongCat Text-to-Video Example")
-    print("=" * 60 + "\n")
-
-    # Run basic generation
-    basic_generation()
-
-    # Run distill+refine pipeline
-    distill_refine_generation()
-
-    print("\n" + "=" * 60)
-    print("All generations complete!")
-    print("=" * 60)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/examples/inference/basic/basic_longcat_vc.py b/examples/inference/basic/basic_longcat_vc.py
deleted file mode 100644
index 36d5f0935..000000000
--- a/examples/inference/basic/basic_longcat_vc.py
+++ /dev/null
@@ -1,218 +0,0 @@
-"""
-LongCat Video Continuation (VC) Example Script
-
-This script demonstrates LongCat VC inference using the FastVideo Python API.
-LongCat VC takes an input video and generates a continuation of it.
-
-It runs both basic generation (50 steps) and distill+refine generation
-(16 steps distill + 50 steps refinement to 720p).
-
-Usage:
-    python examples/inference/basic/basic_longcat_vc.py
-
-Prerequisites:
-    - Ensure the input video exists at assets/motorcycle.mp4
-      (or provide your own video)
-"""
-
-import glob
-import os
-
-from fastvideo import VideoGenerator
-
-# Common prompts and settings matching the shell script examples
-PROMPT = ("A person rides a motorcycle along a long, straight road that stretches between "
-          "a body of water and a forested hillside. The rider steadily accelerates, keeping "
-          "the motorcycle centered between the guardrails, while the scenery passes by on "
-          "both sides. The video captures the journey from the rider's perspective, emphasizing "
-          "the sense of motion and adventure.")
-
-NEGATIVE_PROMPT = ("Bright tones, overexposed, static, blurred details, subtitles, style, works, "
-                   "paintings, images, static, overall gray, worst quality, low quality, JPEG compression "
-                   "residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, "
-                   "deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, "
-                   "three legs, many people in the background, walking backwards")
-
-# Input video path
-VIDEO_PATH = "assets/motorcycle.mp4"
-
-# Number of conditioning frames from the input video
-NUM_COND_FRAMES = 13
-
-SEED = 42
-
-
-def basic_generation():
-    """
-    Run basic LongCat VC generation (50 steps at 480p).
-    
-    This uses the full 50-step denoising process for highest quality.
-    """
-    print("=" * 60)
-    print("LongCat VC: Basic Generation (50 steps, 480p)")
-    print("=" * 60)
-
-    # Check if video exists
-    if not os.path.exists(VIDEO_PATH):
-        raise FileNotFoundError(f"Video not found at {VIDEO_PATH}. "
-                                "Please provide a valid video path.")
-
-    generator = VideoGenerator.from_pretrained(
-        "FastVideo/LongCat-Video-VC-Diffusers",
-        num_gpus=1,
-        use_fsdp_inference=False,  # set to True if GPU is out of memory
-        dit_cpu_offload=False,
-        vae_cpu_offload=True,
-        text_encoder_cpu_offload=True,
-        pin_cpu_memory=False,
-        enable_bsa=False,
-    )
-
-    output_path = "outputs_video/longcat_vc_basic"
-
-    generator.generate_video(
-        prompt=PROMPT,
-        negative_prompt=NEGATIVE_PROMPT,
-        video_path=VIDEO_PATH,
-        num_cond_frames=NUM_COND_FRAMES,
-        output_path=output_path,
-        save_video=True,
-        height=480,
-        width=832,
-        num_frames=93,
-        num_inference_steps=50,
-        fps=15,
-        guidance_scale=4.0,
-        seed=SEED,
-    )
-
-    print(f"\nBasic generation complete! Video saved to: {output_path}")
-    generator.shutdown()
-
-
-def distill_refine_generation():
-    """
-    Run LongCat VC with distill+refine pipeline (16 steps + refinement to 720p).
-    
-    This uses the distilled LoRA for fast 480p generation (16 steps),
-    then refines to 720p using the refinement LoRA with BSA enabled.
-    """
-    print("\n" + "=" * 60)
-    print("LongCat VC: Distill + Refine Pipeline")
-    print("=" * 60)
-
-    # Check if video exists
-    if not os.path.exists(VIDEO_PATH):
-        raise FileNotFoundError(f"Video not found at {VIDEO_PATH}. "
-                                "Please provide a valid video path.")
-
-    # Stage 1: Distilled generation (16 steps at 480p)
-    print("\n[Stage 1] Distilled generation (16 steps, 480p)")
-    print("-" * 40)
-
-    generator = VideoGenerator.from_pretrained(
-        "FastVideo/LongCat-Video-VC-Diffusers",
-        num_gpus=1,
-        use_fsdp_inference=True,
-        dit_cpu_offload=False,
-        vae_cpu_offload=True,
-        text_encoder_cpu_offload=True,
-        pin_cpu_memory=False,
-        enable_bsa=False,
-        lora_path="FastVideo/LongCat-Video-T2V-Distilled-LoRA",
-        lora_nickname="distilled",
-    )
-
-    distill_output_path = "outputs_video/longcat_vc_distill"
-
-    generator.generate_video(
-        prompt=PROMPT,
-        negative_prompt=NEGATIVE_PROMPT,
-        video_path=VIDEO_PATH,
-        num_cond_frames=NUM_COND_FRAMES,
-        output_path=distill_output_path,
-        save_video=True,
-        height=480,
-        width=832,
-        num_frames=93,
-        num_inference_steps=16,
-        fps=15,
-        guidance_scale=1.0,
-        seed=SEED,
-    )
-
-    print(f"Distilled generation complete! Video saved to: {distill_output_path}")
-    generator.shutdown()
-
-    # Stage 2: Refinement (480p -> 720p)
-    print("\n[Stage 2] Refinement (480p -> 720p with BSA)")
-    print("-" * 40)
-
-    # Find the actual saved video file from stage 1
-    video_files = glob.glob(os.path.join(distill_output_path, "*.mp4"))
-    if not video_files:
-        raise FileNotFoundError(f"No video file found in {distill_output_path}")
-    # Use the most recently created video file
-    distill_video_path = max(video_files, key=os.path.getmtime)
-    print(f"Using stage 1 video: {distill_video_path}")
-
-    # Create a new generator with refinement LoRA and BSA enabled
-    # Note: Refinement uses the T2V model (not VC) since it's upscaling the generated video
-    refine_generator = VideoGenerator.from_pretrained(
-        "FastVideo/LongCat-Video-T2V-Diffusers",
-        num_gpus=1,
-        use_fsdp_inference=True,
-        dit_cpu_offload=True,
-        vae_cpu_offload=True,
-        text_encoder_cpu_offload=True,
-        pin_cpu_memory=False,
-        enable_bsa=True,
-        bsa_sparsity=0.875,
-        bsa_chunk_q=[4, 4, 8],
-        bsa_chunk_k=[4, 4, 8],
-        lora_path="FastVideo/LongCat-Video-T2V-Refinement-LoRA",
-        lora_nickname="refinement",
-    )
-
-    refine_output_path = "outputs_video/longcat_vc_refine_720p"
-
-    refine_generator.generate_video(
-        prompt=PROMPT,
-        negative_prompt=NEGATIVE_PROMPT,
-        output_path=refine_output_path,
-        save_video=True,
-        refine_from=distill_video_path,
-        t_thresh=0.5,
-        spatial_refine_only=False,
-        num_cond_frames=0,  # For refinement, no conditioning frames
-        height=720,
-        width=1280,
-        num_inference_steps=50,
-        fps=30,
-        guidance_scale=1.0,
-        seed=SEED,
-    )
-
-    print(f"Refinement complete! Video saved to: {refine_output_path}")
-    refine_generator.shutdown()
-
-
-def main():
-    """Run both basic and distill+refine generation pipelines."""
-    print("\n" + "=" * 60)
-    print("LongCat Video Continuation Example")
-    print("=" * 60 + "\n")
-
-    # Run basic generation
-    basic_generation()
-
-    # Run distill+refine pipeline
-    distill_refine_generation()
-
-    print("\n" + "=" * 60)
-    print("All generations complete!")
-    print("=" * 60)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/examples/inference/basic/basic_turbodiffusion.py b/examples/inference/basic/basic_turbodiffusion.py
deleted file mode 100644
index 6e85a734e..000000000
--- a/examples/inference/basic/basic_turbodiffusion.py
+++ /dev/null
@@ -1,51 +0,0 @@
-import os
-
-# Set SLA attention backend BEFORE fastvideo imports
-os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "SLA_ATTN"
-
-from fastvideo import VideoGenerator
-
-OUTPUT_PATH = "video_samples_turbodiffusion"
-
-
-def main() -> None:
-    # TurboDiffusion: 1-4 step video generation using RCM scheduler + SLA attention
-    # FastVideo will automatically use TurboDiffusionPipeline when specified
-    generator = VideoGenerator.from_pretrained(
-        "loayrashid/TurboWan2.1-T2V-1.3B-Diffusers",
-        # FastVideo will automatically handle distributed setup
-        num_gpus=1,
-        use_fsdp_inference=False,  # set to True if GPU is out of memory
-
-        # set to false if using RTX 4090
-        # pin_cpu_memory=False,
-    )
-
-    # Generate videos with the same simple API, regardless of GPU count
-    # TurboDiffusion defaults: guidance_scale=1.0 and num_inference_steps=4 (from config)
-    prompt = ("A curious raccoon peers through a vibrant field of yellow sunflowers, its eyes "
-              "wide with interest. The playful yet serene atmosphere is complemented by soft "
-              "natural light filtering through the petals. Mid-shot, warm and cheerful tones.")
-    video = generator.generate_video(
-        prompt,
-        output_path=OUTPUT_PATH,
-        save_video=True,
-        seed=42,
-    )
-
-    # Generate another video with a different prompt, without reloading the model!
-    prompt2 = ("A majestic lion strides across the golden savanna, its powerful frame "
-               "glistening under the warm afternoon sun. The tall grass ripples gently in "
-               "the breeze, enhancing the lion's commanding presence. The tone is vibrant, "
-               "embodying the raw energy of the wild. Low angle, steady tracking shot, "
-               "cinematic.")
-    video2 = generator.generate_video(
-        prompt2,
-        output_path=OUTPUT_PATH,
-        save_video=True,
-        seed=42,
-    )
-
-
-if __name__ == "__main__":
-    main()
diff --git a/examples/inference/basic/basic_turbodiffusion_14b.py b/examples/inference/basic/basic_turbodiffusion_14b.py
deleted file mode 100644
index ebaa39c3b..000000000
--- a/examples/inference/basic/basic_turbodiffusion_14b.py
+++ /dev/null
@@ -1,45 +0,0 @@
-import os
-
-# Set SLA attention backend BEFORE fastvideo imports
-os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "SLA_ATTN"
-
-from fastvideo import VideoGenerator
-
-OUTPUT_PATH = "video_samples_turbodiffusion_14B"
-
-
-def main() -> None:
-    # TurboDiffusion 14B: 1-4 step video generation using RCM scheduler + SLA attention
-    # FastVideo will automatically use TurboDiffusionPipeline when specified
-    generator = VideoGenerator.from_pretrained(
-        "loayrashid/TurboWan2.1-T2V-14B-Diffusers",
-        # 14B model needs more GPUs
-        num_gpus=2,
-    )
-
-    prompt = ("A curious raccoon peers through a vibrant field of yellow sunflowers, its eyes "
-              "wide with interest. The playful yet serene atmosphere is complemented by soft "
-              "natural light filtering through the petals. Mid-shot, warm and cheerful tones.")
-    video = generator.generate_video(
-        prompt,
-        output_path=OUTPUT_PATH,
-        save_video=True,
-        seed=42,
-    )
-
-    # Generate another video with a different prompt, without reloading the model!
-    prompt2 = ("A majestic lion strides across the golden savanna, its powerful frame "
-               "glistening under the warm afternoon sun. The tall grass ripples gently in "
-               "the breeze, enhancing the lion's commanding presence. The tone is vibrant, "
-               "embodying the raw energy of the wild. Low angle, steady tracking shot, "
-               "cinematic.")
-    video2 = generator.generate_video(
-        prompt2,
-        output_path=OUTPUT_PATH,
-        save_video=True,
-        seed=42,
-    )
-
-
-if __name__ == "__main__":
-    main()
diff --git a/examples/inference/basic/basic_turbodiffusion_i2v.py b/examples/inference/basic/basic_turbodiffusion_i2v.py
deleted file mode 100644
index b28092f21..000000000
--- a/examples/inference/basic/basic_turbodiffusion_i2v.py
+++ /dev/null
@@ -1,38 +0,0 @@
-import os
-
-# Set SLA attention backend BEFORE fastvideo imports
-os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "SLA_ATTN"
-
-from fastvideo import VideoGenerator
-
-# Use local model path
-MODEL_PATH = "loayrashid/TurboWan2.2-I2V-A14B-Diffusers"
-OUTPUT_PATH = "video_samples_turbodiffusion_i2v"
-
-
-def main() -> None:
-    # TurboDiffusion I2V: 1-4 step image-to-video generation
-    generator = VideoGenerator.from_pretrained(
-        MODEL_PATH,
-        num_gpus=2,
-    )
-
-    # Example prompt and image for I2V
-    prompt = (
-        "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside."
-    )
-
-    # Use an example image path
-    image_path = "https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/wan_i2v_input.JPG"
-
-    video = generator.generate_video(
-        prompt,
-        image_path=image_path,
-        output_path=OUTPUT_PATH,
-        save_video=True,
-        seed=42,
-    )
-
-
-if __name__ == "__main__":
-    main()
diff --git a/examples/inference/basic/basic_zimage.py b/examples/inference/basic/basic_zimage.py
deleted file mode 100644
index 732894163..000000000
--- a/examples/inference/basic/basic_zimage.py
+++ /dev/null
@@ -1,94 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""Run Z-Image-Turbo text-to-image generation through FastVideo.
-
-User story:
-    "I want the official Z-Image-Turbo defaults and a deterministic PNG from
-    a local or Hugging Face checkpoint."
-"""
-
-import argparse
-from pathlib import Path
-
-from fastvideo import VideoGenerator
-from fastvideo.api import (
-    EngineConfig,
-    GenerationRequest,
-    GeneratorConfig,
-    OutputConfig,
-    ParallelismConfig,
-    PipelineSelection,
-    SamplingConfig,
-)
-
-DEFAULT_PROMPT = (
-    "Young Chinese woman in red Hanfu, intricate embroidery. Impeccable makeup, red floral forehead pattern. "
-    "Elaborate high bun, golden phoenix headdress, red flowers, beads. Holds round folding fan with lady, trees, bird. "
-    "Neon lightning-bolt lamp (⚡️), bright yellow glow, above extended left palm. Soft-lit outdoor night background, "
-    "silhouetted tiered pagoda (西安大雁塔), blurred colorful distant lights.")
-DEFAULT_REVISION = "f332072aa78be7aecdf3ee76d5c247082da564a6"
-
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser(description="Run Z-Image-Turbo text-to-image generation.")
-    parser.add_argument("--model-path", default="Tongyi-MAI/Z-Image-Turbo")
-    parser.add_argument("--revision", default=DEFAULT_REVISION)
-    parser.add_argument("--output", default="outputs/zimage/zimage_turbo.png")
-    parser.add_argument("--prompt", default=DEFAULT_PROMPT)
-    parser.add_argument("--negative-prompt", default="")
-    parser.add_argument("--height", type=int, default=1024)
-    parser.add_argument("--width", type=int, default=1024)
-    parser.add_argument("--steps", type=int, default=8)
-    parser.add_argument("--guidance-scale", type=float, default=0.0)
-    parser.add_argument("--max-sequence-length", type=int, default=512)
-    parser.add_argument("--cfg-normalization", action=argparse.BooleanOptionalAction, default=False)
-    parser.add_argument("--cfg-truncation", type=float, default=1.0)
-    parser.add_argument("--seed", type=int, default=42)
-    return parser.parse_args()
-
-
-def main() -> None:
-    args = parse_args()
-    output = Path(args.output)
-    output.parent.mkdir(parents=True, exist_ok=True)
-
-    generator = VideoGenerator.from_config(
-        GeneratorConfig(
-            model_path=args.model_path,
-            revision=args.revision,
-            engine=EngineConfig(
-                num_gpus=1,
-                parallelism=ParallelismConfig(tp_size=1, sp_size=1),
-                use_fsdp_inference=False,
-            ),
-            # The model registry selects the native zimage_turbo preset.
-            pipeline=PipelineSelection(workload_type="t2i"),
-        ))
-    try:
-        generator.generate(
-            GenerationRequest(
-                prompt=args.prompt,
-                negative_prompt=args.negative_prompt,
-                sampling=SamplingConfig(
-                    height=args.height,
-                    width=args.width,
-                    num_frames=1,
-                    fps=1,
-                    num_inference_steps=args.steps,
-                    guidance_scale=args.guidance_scale,
-                    max_sequence_length=args.max_sequence_length,
-                    cfg_normalization=args.cfg_normalization,
-                    cfg_truncation=args.cfg_truncation,
-                    seed=args.seed,
-                ),
-                output=OutputConfig(
-                    output_path=str(output),
-                    save_video=True,
-                    return_frames=False,
-                ),
-            ))
-    finally:
-        generator.shutdown()
-
-
-if __name__ == "__main__":
-    main()
diff --git a/examples/inference/basic/edit_glm_image.py b/examples/inference/basic/edit_glm_image.py
deleted file mode 100644
index 9fb95c75b..000000000
--- a/examples/inference/basic/edit_glm_image.py
+++ /dev/null
@@ -1,120 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""Run GLM-Image image-to-image (edit) generation through FastVideo.
-
-User story:
-    "I have the HF `zai-org/GLM-Image` checkpoint and a condition image, and
-    want a minimal edit command (text + image -> edited image), saved as a PNG."
-
-GLM-Image is a single unified pipeline: passing a condition image switches it
-from text-to-image to the edit path (the condition enters the DiT via a KV-cache
-write pass), so the generator config is identical to `basic_glm_image.py` — the
-`inputs.pil_image` on the request is what selects the edit mode.
-"""
-import argparse
-from pathlib import Path
-
-from PIL import Image
-
-from fastvideo import VideoGenerator
-from fastvideo.api import (
-    EngineConfig,
-    GenerationRequest,
-    GeneratorConfig,
-    InputConfig,
-    OutputConfig,
-    ParallelismConfig,
-    PipelineSelection,
-    SamplingConfig,
-)
-
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser(description="Run GLM-Image image-to-image (edit) generation.")
-    parser.add_argument(
-        "--model-path",
-        default="zai-org/GLM-Image",
-        help="HF id or local diffusers-format GLM-Image weights directory.",
-    )
-    parser.add_argument(
-        "--image",
-        default="assets/images/couple.jpg",
-        help="Condition image to edit.",
-    )
-    parser.add_argument(
-        "--output",
-        default="image_output/edited.png",
-        help="Output PNG path.",
-    )
-    parser.add_argument(
-        "--prompt",
-        default="Change the background to a snowy mountain landscape at golden hour.",
-        help="Edit instruction.",
-    )
-    parser.add_argument("--height", type=int, default=1024)
-    parser.add_argument("--width", type=int, default=1024)
-    parser.add_argument("--steps", type=int, default=50)
-    parser.add_argument("--guidance-scale", type=float, default=1.5)
-    parser.add_argument("--seed", type=int, default=1024)
-    parser.add_argument("--num-gpus", type=int, default=1)
-    parser.add_argument("--tp-size", type=int, default=None)
-    parser.add_argument("--sp-size", type=int, default=None)
-    return parser.parse_args()
-
-
-def main() -> None:
-    args = parse_args()
-
-    output = Path(args.output)
-    output.parent.mkdir(parents=True, exist_ok=True)
-    condition = Image.open(args.image).convert("RGB")
-    tp_size = args.tp_size if args.tp_size is not None else (args.num_gpus if args.num_gpus > 1 else 1)
-    sp_size = args.sp_size if args.sp_size is not None else (1 if args.num_gpus > 1 else args.num_gpus)
-
-    # GLM-Image needs trust_remote_code for its AR encoder; offload and the
-    # pipeline class come from the model's registered defaults — don't override.
-    # The pipeline is registered as t2i; passing inputs.pil_image below switches
-    # it to the edit path.
-    generator_config = GeneratorConfig(
-        model_path=args.model_path,
-        trust_remote_code=True,
-        engine=EngineConfig(
-            num_gpus=args.num_gpus,
-            parallelism=ParallelismConfig(tp_size=tp_size, sp_size=sp_size),
-        ),
-        pipeline=PipelineSelection(workload_type="t2i"),
-    )
-
-    generator = VideoGenerator.from_config(generator_config)
-    try:
-        request = GenerationRequest(
-            prompt=args.prompt,
-            inputs=InputConfig(pil_image=condition),
-            sampling=SamplingConfig(
-                height=args.height,
-                width=args.width,
-                num_frames=1,
-                fps=1,
-                num_inference_steps=args.steps,
-                guidance_scale=args.guidance_scale,
-                seed=args.seed,
-            ),
-            output=OutputConfig(
-                output_path=str(output.parent),
-                save_video=False,
-                return_frames=True,
-            ),
-        )
-        result = generator.generate(request)
-        if isinstance(result, list):
-            result = result[0]
-
-        frames = result.frames
-        if frames is not None and len(frames):
-            Image.fromarray(frames[0]).save(output)
-            print(f"Saved image to {output}")
-    finally:
-        generator.shutdown()
-
-
-if __name__ == "__main__":
-    main()
diff --git a/examples/train/configs/fine_tuning/longcat/t2v.yaml b/examples/train/configs/fine_tuning/longcat/t2v.yaml
deleted file mode 100644
index 58aba7ae4..000000000
--- a/examples/train/configs/fine_tuning/longcat/t2v.yaml
+++ /dev/null
@@ -1,71 +0,0 @@
-# LongCat-Video T2V 13.6B bidirectional finetune.
-
-models:
-  student:
-    _target_: fastvideo.train.models.longcat.LongCatModel
-    init_from: FastVideo/LongCat-Video-T2V-Diffusers
-    trainable: true
-
-method:
-  _target_: fastvideo.train.methods.fine_tuning.finetune.FineTuneMethod
-
-training:
-  model_path: FastVideo/LongCat-Video-T2V-Diffusers
-
-  distributed:
-    num_gpus: 8
-    sp_size: 1
-    tp_size: 1
-    hsdp_replicate_dim: 1
-    hsdp_shard_dim: 8
-
-  data:
-    data_path: data/LongCat-Syn
-    dataloader_num_workers: 4
-    train_batch_size: 1
-    training_cfg_rate: 0.0
-    seed: 1000
-    num_latent_t: 20
-    num_height: 480
-    num_width: 848
-    num_frames: 77
-
-  optimizer:
-    learning_rate: 1.0e-6
-    betas: [0.9, 0.999]
-    weight_decay: 0.01
-    lr_scheduler: constant
-    lr_warmup_steps: 0
-
-  loop:
-    max_train_steps: 4000
-    gradient_accumulation_steps: 1
-
-  checkpoint:
-    output_dir: outputs/longcat_finetune
-    training_state_checkpointing_steps: 1000
-    checkpoints_total_limit: 3
-
-  tracker:
-    project_name: fastvideo
-    run_name: longcat_finetune
-
-  model:
-    enable_gradient_checkpointing_type: full
-
-callbacks:
-  grad_clip:
-    _target_: fastvideo.train.callbacks.grad_clip.GradNormClipCallback
-    max_grad_norm: 1.0
-  validation:
-    _target_: fastvideo.train.callbacks.validation.ValidationCallback
-    pipeline_target: fastvideo.pipelines.basic.longcat.longcat_pipeline.LongCatPipeline
-    dataset_file: data/validation_prompts.json
-    every_steps: 100
-    sampling_steps: [50]
-    guidance_scale: 5.0
-
-pipeline:
-  # Match the released LongCat scheduler config. flow_shift=0.0 collapses
-  # FlowMatch training timesteps to zero in FastVideo's scheduler.
-  flow_shift: 12.0
diff --git a/examples/train/configs/fine_tuning/wan/turbo_t2v_lora_sla.yaml b/examples/train/configs/fine_tuning/wan/turbo_t2v_lora_sla.yaml
deleted file mode 100644
index 3f9daf02c..000000000
--- a/examples/train/configs/fine_tuning/wan/turbo_t2v_lora_sla.yaml
+++ /dev/null
@@ -1,89 +0,0 @@
-# TurboWan 2.1 T2V 1.3B LoRA finetune with SLA attention.
-#
-# Notes:
-# - TurboDiffusion checkpoints expect the SLA attention backend. The training
-#   entrypoint now auto-selects `SLA_ATTN` for model IDs containing
-#   `turbodiffusion` or `turbowan`, unless you override the environment
-#   variable manually.
-# - This debug config reuses the existing crush-smol T2V parquet recipe. If you
-#   preprocess data with the official TurboWan recipe, update the temporal and
-#   spatial fields to match it.
-
-models:
-  student:
-    _target_: fastvideo.train.models.wan.WanModel
-    init_from: loayrashid/TurboWan2.1-T2V-1.3B-Diffusers
-    trainable: true
-    lora:
-      enable: true
-      rank: 16
-      alpha: 32
-      target_modules:
-        - to_q
-        - to_k
-        - to_v
-        - to_out
-
-method:
-  _target_: fastvideo.train.methods.fine_tuning.finetune.FineTuneMethod
-
-training:
-  dit_precision: bf16
-
-  distributed:
-    num_gpus: 1
-    sp_size: 1
-    tp_size: 1
-    hsdp_replicate_dim: 1
-    hsdp_shard_dim: 1
-
-  data:
-    data_path: data/crush-smol_processed_t2v/combined_parquet_dataset
-    dataloader_num_workers: 2
-    train_batch_size: 1
-    training_cfg_rate: 0.0
-    seed: 42
-    num_latent_t: 20
-    num_height: 480
-    num_width: 832
-    num_frames: 77
-
-  optimizer:
-    learning_rate: 1.0e-5
-    betas: [0.9, 0.999]
-    weight_decay: 0.01
-    lr_scheduler: constant
-    lr_warmup_steps: 0
-
-  loop:
-    max_train_steps: 10
-    gradient_accumulation_steps: 5
-
-  checkpoint:
-    output_dir: outputs/turbo_wan_t2v_lora_sla
-    training_state_checkpointing_steps: 5
-    checkpoints_total_limit: 2
-    resume_from_checkpoint: null
-
-  tracker:
-    project_name: fastvideo
-    run_name: turbo_wan_t2v_lora_sla
-
-  model:
-    enable_gradient_checkpointing_type: full
-
-callbacks:
-  grad_clip:
-    _target_: fastvideo.train.callbacks.grad_clip.GradNormClipCallback
-    max_grad_norm: 1.0
-
-  validation:
-    _target_: fastvideo.train.callbacks.validation.ValidationCallback
-    pipeline_target: fastvideo.pipelines.basic.turbodiffusion.turbodiffusion_pipeline.TurboDiffusionPipeline
-    dataset_file: examples/training/finetune/wan_t2v_1.3B/crush_smol/validation.json
-    every_steps: 5
-    sampling_steps: [4]
-    guidance_scale: 1.0
-
-pipeline:
-  flow_shift: 3
diff --git a/fastvideo/api/sampling_param.py b/fastvideo/api/sampling_param.py
index 69a7d3894..1e3c82684 100644
--- a/fastvideo/api/sampling_param.py
+++ b/fastvideo/api/sampling_param.py
@@ -62,28 +62,13 @@ class SamplingParam:
     prompt_attention_mask: list = field(default_factory=list)
     negative_attention_mask: list = field(default_factory=list)
 
-    # Camera/action control inputs (GameCraft)
-    camera_states: Any | None = None  # Plücker coordinates [B, T_video, 6, H, W]
-    camera_trajectory: str | None = None
-    action_list: list[str] | None = None
-    action_speed_list: list[float] | None = None
-    gt_latents: Any | None = None  # Ground truth latents [B, 16, T, H, W]
-    conditioning_mask: Any | None = None  # Mask [B, 1, T, H, W]
-
     # Camera control inputs (LingBotWorld and LingBotWorld2)
     c2ws_plucker_emb: Any | None = None  # Plucker embedding: [B, C, F_lat, H_lat, W_lat]
     action_path: str | None = None  # Directory containing poses.npy and intrinsics.npy
 
-    # Refine inputs (LongCat 480p->720p upscaling)
-    # Path-based refine (load stage1 video from disk, e.g. MP4)
-    refine_from: str | None = None  # Path to stage1 video (480p output from distill)
+    # Refine / conditioning-frame inputs
     t_thresh: float = 0.5  # Threshold for timestep scheduling in refinement
-    spatial_refine_only: bool = False  # If True, only spatial (no temporal doubling)
     num_cond_frames: int = 0  # Number of conditioning frames
-    # In-memory refine input (for two-stage pipeline where stage1 frames are already in memory)
-    # This mirrors LongCat's demo where a list of frames (e.g. np.ndarray or PIL.Image)
-    # is passed directly to the refinement pipeline instead of reloading from disk.
-    stage1_video: Any | None = None
 
     # Text inputs
     prompt: str | list[str] | None = None
@@ -111,11 +96,6 @@ class SamplingParam:
     guidance_scale: float = 1.0
     batch_cfg: bool = False
     guidance_scale_2: float | None = None
-    # Z-Image CFG controls. ``cfg_normalization=True`` caps the guided
-    # prediction norm at the positive-prediction norm; ``cfg_truncation``
-    # disables CFG above the normalized-noise threshold.
-    cfg_normalization: bool = False
-    cfg_truncation: float | None = 1.0
     # Embedded guidance (FLUX): do not treat ``guidance_scale > 1`` as classic CFG.
     use_embedded_guidance: bool = False
     # Diffusers-style true CFG for FLUX when > 1 (requires negative prompt encoding).
@@ -365,18 +345,6 @@ class SamplingParam:
             default=SamplingParam.guidance_scale,
             help="Classifier-free guidance scale",
         )
-        parser.add_argument(
-            "--cfg-normalization",
-            action=StoreBoolean,
-            default=SamplingParam.cfg_normalization,
-            help="Cap Z-Image CFG prediction norm to the positive-prediction norm",
-        )
-        parser.add_argument(
-            "--cfg-truncation",
-            type=float,
-            default=SamplingParam.cfg_truncation,
-            help="Disable Z-Image CFG above this normalized-noise threshold",
-        )
         parser.add_argument(
             "--batch-cfg",
             action=StoreBoolean,
@@ -493,24 +461,12 @@ class SamplingParam:
             default=SamplingParam.audio_path,
             help="Path to input audio for speech-driven generation (Wan S2V)",
         )
-        parser.add_argument(
-            "--refine-from",
-            type=str,
-            default=SamplingParam.refine_from,
-            help="Path to stage1 video for refinement (LongCat 480p->720p)",
-        )
         parser.add_argument(
             "--t-thresh",
             type=float,
             default=SamplingParam.t_thresh,
             help="Threshold for timestep scheduling in refinement (default: 0.5)",
         )
-        parser.add_argument(
-            "--spatial-refine-only",
-            action=StoreBoolean,
-            default=SamplingParam.spatial_refine_only,
-            help="Only perform spatial super-resolution (no temporal doubling)",
-        )
         parser.add_argument(
             "--num-cond-frames",
             type=int,
diff --git a/fastvideo/api/schema.py b/fastvideo/api/schema.py
index 4663d7c46..7bdcf40ac 100644
--- a/fastvideo/api/schema.py
+++ b/fastvideo/api/schema.py
@@ -158,8 +158,6 @@ class InputConfig:
     grid_sizes: Any | None = None
     c2ws_plucker_emb: Any | None = None
     action_path: str | None = None
-    refine_from: str | None = None
-    stage1_video: Any | None = None
 
 
 @dataclass
@@ -178,8 +176,6 @@ class SamplingConfig:
     guidance_scale: float = 1.0
     batch_cfg: bool = False
     guidance_scale_2: float | None = None
-    cfg_normalization: bool = False
-    cfg_truncation: float | None = 1.0
     guidance_rescale: float = 0.0
     true_cfg_scale: float | None = None
     use_embedded_guidance: bool | None = None
diff --git a/fastvideo/attention/utils/flash_attn_cute.py b/fastvideo/attention/utils/flash_attn_cute.py
index 320884351..00dafb06d 100644
--- a/fastvideo/attention/utils/flash_attn_cute.py
+++ b/fastvideo/attention/utils/flash_attn_cute.py
@@ -67,7 +67,7 @@ def _use_fa2(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor) -> bool:
     # capability gate, not a runtime fallback):
     #   * the backward asserts sm90+ (L40S/sm_89 dies on its arch check);
     #   * GQA fails CuTeDSL JIT in pack_gqa ("ValueError: Operation creation
-    #     failed", observed on sm_89 with HunyuanGameCraft/LTX2).
+    #     failed", observed on sm_89 with LTX2).
     if q.shape[-2] != k.shape[-2]:
         return True
     return torch.is_grad_enabled() and any(t.requires_grad for t in (q, k, v))
diff --git a/fastvideo/configs/models/dits/__init__.py b/fastvideo/configs/models/dits/__init__.py
index 7c9586241..bba086442 100644
--- a/fastvideo/configs/models/dits/__init__.py
+++ b/fastvideo/configs/models/dits/__init__.py
@@ -1,14 +1,10 @@
 from fastvideo.configs.models.dits.cosmos import CosmosVideoConfig
 from fastvideo.configs.models.dits.cosmos2_5 import Cosmos25VideoConfig
-from fastvideo.configs.models.dits.dreamx_world import DreamXWorldARConfig, DreamXWorldConfig
 from fastvideo.configs.models.dits.flux import FluxDiTConfig
 from fastvideo.configs.models.dits.flux_2 import Flux2Config
-from fastvideo.configs.models.dits.glm_image import GlmImageDiTConfig
 from fastvideo.configs.models.dits.helios import HeliosConfig
-from fastvideo.configs.models.dits.hunyuangamecraft import HunyuanGameCraftConfig
 from fastvideo.configs.models.dits.hunyuanvideo import HunyuanVideoConfig
 from fastvideo.configs.models.dits.hunyuanvideo15 import HunyuanVideo15Config
-from fastvideo.configs.models.dits.longcat import LongCatVideoConfig
 from fastvideo.configs.models.dits.ltx2 import LTX2VideoConfig
 from fastvideo.configs.models.dits.magi_human import MagiHumanVideoConfig
 from fastvideo.configs.models.dits.minimax_h3 import MiniMaxH3Config
@@ -22,7 +18,6 @@ from fastvideo.configs.models.dits.mmaudio import (
 )
 from fastvideo.configs.models.dits.stable_audio import StableAudioConfig
 from fastvideo.models.wan.config import WanVideoConfig
-from fastvideo.configs.models.dits.zimage import ZImageDiTConfig
 from fastvideo.configs.models.dits.hyworld import HYWorldConfig
 from fastvideo.configs.models.dits.kandinsky5 import Kandinsky5VideoConfig
 from fastvideo.configs.models.dits.kandinsky6 import Kandinsky6ArchConfig, Kandinsky6VideoAudioConfig
@@ -32,12 +27,11 @@ from fastvideo.configs.models.dits.lingbotworld_fast import LingBotWorldFastVide
 from fastvideo.configs.models.dits.lingbot_video import LingBotVideoConfig
 
 __all__ = [
-    "HunyuanVideoConfig", "HunyuanVideo15Config", "HunyuanGameCraftConfig", "WanVideoConfig", "DreamXWorldConfig",
-    "DreamXWorldARConfig", "CosmosVideoConfig", "Cosmos25VideoConfig", "FluxDiTConfig", "Flux2Config",
-    "LongCatVideoConfig", "LTX2VideoConfig", "HYWorldConfig", "Kandinsky5VideoConfig", "Kandinsky6ArchConfig",
+    "HunyuanVideoConfig", "HunyuanVideo15Config", "WanVideoConfig", "CosmosVideoConfig", "Cosmos25VideoConfig",
+    "FluxDiTConfig", "Flux2Config", "LTX2VideoConfig", "HYWorldConfig", "Kandinsky5VideoConfig", "Kandinsky6ArchConfig",
     "Kandinsky6VideoAudioConfig", "Kandinsky6SRArchConfig", "Kandinsky6SRConfig", "MagiHumanVideoConfig",
-    "StableAudioConfig", "GlmImageDiTConfig", "HeliosConfig", "LingBotWorld2CausalFastVideoConfig",
-    "LingBotWorldFastVideoConfig", "LingBotVideoConfig", "MiniMaxH3Config", "ZImageDiTConfig", "MMAudioArchConfig",
-    "MMAudioTransformerConfig", "MMAUDIO_VARIANT_ARCHITECTURES", "MMAUDIO_TRAINING_VARIANTS",
-    "MMAUDIO_44K_TRAINING_VARIANTS", "get_mmaudio_transformer_config"
+    "StableAudioConfig", "HeliosConfig", "LingBotWorld2CausalFastVideoConfig", "LingBotWorldFastVideoConfig",
+    "LingBotVideoConfig", "MiniMaxH3Config", "MMAudioArchConfig", "MMAudioTransformerConfig",
+    "MMAUDIO_VARIANT_ARCHITECTURES", "MMAUDIO_TRAINING_VARIANTS", "MMAUDIO_44K_TRAINING_VARIANTS",
+    "get_mmaudio_transformer_config"
 ]
diff --git a/fastvideo/configs/models/dits/dreamx_world.py b/fastvideo/configs/models/dits/dreamx_world.py
deleted file mode 100644
index bbd5177bb..000000000
--- a/fastvideo/configs/models/dits/dreamx_world.py
+++ /dev/null
@@ -1,71 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-from dataclasses import dataclass, field
-
-from fastvideo.configs.models.dits.base import DiTArchConfig, DiTConfig
-from fastvideo.models.wan.config import WanVideoArchConfig
-
-
-@dataclass
-class DreamXWorldArchConfig(WanVideoArchConfig):
-    """DreamX-World DiT config with camera PRoPE control fields."""
-
-    add_control_adapter: bool = True
-    cam_method: str | None = "prope"
-    attn_compress: int = 1
-    cam_self_attn_layers: tuple[int, ...] | None = None
-
-
-@dataclass
-class DreamXWorldConfig(DiTConfig):
-    arch_config: DiTArchConfig = field(default_factory=DreamXWorldArchConfig)
-
-    prefix: str = "Wan"
-
-
-@dataclass
-class DreamXWorldARArchConfig(DreamXWorldArchConfig):
-    """DreamX-World-5B autoregressive causal DiT config."""
-
-    model_type: str = "ti2v"
-    patch_size: tuple[int, int, int] = (1, 2, 2)
-    text_len: int = 512
-    text_dim: int = 4096
-    freq_dim: int = 256
-    attn_compress: int = 4
-    cam_self_attn_layers: tuple[int, ...] | None = tuple(range(30))
-    local_attn_size: int = 12
-    sink_size: int = 3
-    num_frames_per_block: int = 3
-    rope_cache_policy: str = "block_relativistic"
-    # The official AR checkpoint (AMAP-ML/DreamX-World ``model.safetensors``)
-    # already uses FastVideo's native key names and the converter copies the
-    # tensors verbatim, so every rule is an identity. The rules enumerate the
-    # full state-dict surface of ``DreamXWorldARTransformer3DModel`` (norm1 /
-    # norm2 / head.norm are affine-free and have no parameters).
-    param_names_mapping: dict = field(
-        default_factory=lambda: {
-            r"^patch_embedding\.(.*)$": r"patch_embedding.\1",
-            r"^text_embedding\.([02])\.(.*)$": r"text_embedding.\1.\2",
-            r"^time_embedding\.([02])\.(.*)$": r"time_embedding.\1.\2",
-            r"^time_projection\.1\.(.*)$": r"time_projection.1.\1",
-            r"^blocks\.(\d+)\.self_attn\.(q|k|v|o)\.(.*)$": r"blocks.\1.self_attn.\2.\3",
-            r"^blocks\.(\d+)\.self_attn\.norm_(q|k)\.weight$": r"blocks.\1.self_attn.norm_\2.weight",
-            r"^blocks\.(\d+)\.cross_attn\.(q|k|v|o)\.(.*)$": r"blocks.\1.cross_attn.\2.\3",
-            r"^blocks\.(\d+)\.cross_attn\.norm_(q|k)\.weight$": r"blocks.\1.cross_attn.norm_\2.weight",
-            r"^blocks\.(\d+)\.cam_self_attn\.(q_proj|k_proj|v_proj|out_proj)\.(.*)$": r"blocks.\1.cam_self_attn.\2.\3",
-            r"^blocks\.(\d+)\.cam_self_attn\.norm_(q|k)\.weight$": r"blocks.\1.cam_self_attn.norm_\2.weight",
-            r"^blocks\.(\d+)\.norm3\.(.*)$": r"blocks.\1.norm3.\2",
-            r"^blocks\.(\d+)\.ffn\.([02])\.(.*)$": r"blocks.\1.ffn.\2.\3",
-            r"^blocks\.(\d+)\.modulation$": r"blocks.\1.modulation",
-            r"^head\.head\.(.*)$": r"head.head.\1",
-            r"^head\.modulation$": r"head.modulation",
-        })
-    reverse_param_names_mapping: dict = field(default_factory=dict)
-    lora_param_names_mapping: dict = field(default_factory=dict)
-
-
-@dataclass
-class DreamXWorldARConfig(DreamXWorldConfig):
-    arch_config: DiTArchConfig = field(default_factory=DreamXWorldARArchConfig)
-
-    prefix: str = "Wan"
diff --git a/fastvideo/configs/models/dits/glm_image.py b/fastvideo/configs/models/dits/glm_image.py
deleted file mode 100644
index 8e83ab439..000000000
--- a/fastvideo/configs/models/dits/glm_image.py
+++ /dev/null
@@ -1,61 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-from dataclasses import dataclass, field
-
-from fastvideo.configs.models.dits.base import DiTArchConfig, DiTConfig
-
-
-def is_blocks(n: str, m) -> bool:
-    return "transformer_blocks" in n and str.isdigit(n.split(".")[-1])
-
-
-@dataclass
-class GlmImageDiTArchConfig(DiTArchConfig):
-
-    _fsdp_shard_conditions: list = field(default_factory=lambda: [is_blocks])
-
-    hidden_size: int = 4096
-    num_attention_heads: int = 32
-    attention_head_dim: int = 128
-    in_channels: int = 16
-    out_channels: int = 16
-    num_layers: int = 30
-
-    text_embed_dim: int = 1472
-    time_embed_dim: int = 512
-    condition_dim: int = 256
-
-    prior_vq_quantizer_codebook_size: int = 16384
-
-    patch_size: int = 2
-
-    max_height: int = 2048
-    max_width: int = 2048
-
-    qk_norm: str = "layer_norm"
-    eps: float = 1e-5
-
-    exclude_lora_layers: list[str] = field(
-        default_factory=lambda: ["image_projector", "glyph_projector", "prior_token_embedding"])
-
-    param_names_mapping: dict = field(
-        default_factory=lambda: {
-            r"^glyph_projector\.net\.0\.proj\.(.*)$": r"glyph_projector.fc_in.\1",
-            r"^glyph_projector\.net\.2\.(.*)$": r"glyph_projector.fc_out.\1",
-            r"^prior_projector\.net\.0\.proj\.(.*)$": r"prior_projector.fc_in.\1",
-            r"^prior_projector\.net\.2\.(.*)$": r"prior_projector.fc_out.\1",
-            r"^transformer_blocks\.(\d+)\.ff\.net\.0\.proj\.(.*)$": r"transformer_blocks.\1.ff.fc_in.\2",
-            r"^transformer_blocks\.(\d+)\.ff\.net\.2\.(.*)$": r"transformer_blocks.\1.ff.fc_out.\2",
-        })
-
-    reverse_param_names_mapping: dict = field(default_factory=dict)
-    lora_param_names_mapping: dict = field(default_factory=dict)
-
-    def __post_init__(self):
-        super().__post_init__()
-        self.num_channels_latents = self.out_channels
-
-
-@dataclass
-class GlmImageDiTConfig(DiTConfig):
-    arch_config: DiTArchConfig = field(default_factory=GlmImageDiTArchConfig)
-    prefix: str = "GlmImage"
diff --git a/fastvideo/configs/models/dits/hunyuangamecraft.py b/fastvideo/configs/models/dits/hunyuangamecraft.py
deleted file mode 100644
index 2cbeae22f..000000000
--- a/fastvideo/configs/models/dits/hunyuangamecraft.py
+++ /dev/null
@@ -1,143 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-Configuration for HunyuanGameCraft transformer model.
-
-HunyuanGameCraft extends HunyuanVideo with:
-1. CameraNet for camera/action conditioning
-2. 33 input channels (16 latent + 16 gt_latent + 1 mask)
-3. Mask-based conditioning for autoregressive generation
-"""
-from dataclasses import dataclass, field
-
-import torch
-
-from fastvideo.configs.models.dits.base import DiTArchConfig, DiTConfig
-
-
-def is_double_block(n: str, m) -> bool:
-    return "double" in n and str.isdigit(n.split(".")[-1])
-
-
-def is_single_block(n: str, m) -> bool:
-    return "single" in n and str.isdigit(n.split(".")[-1])
-
-
-def is_refiner_block(n: str, m) -> bool:
-    return "refiner" in n and str.isdigit(n.split(".")[-1])
-
-
-def is_txt_in(n: str, m) -> bool:
-    return n.split(".")[-1] == "txt_in"
-
-
-def is_camera_net(n: str, m) -> bool:
-    return "camera_net" in n
-
-
-@dataclass
-class HunyuanGameCraftArchConfig(DiTArchConfig):
-    """Architecture config for HunyuanGameCraft transformer."""
-
-    # Version field for compatibility with saved config.json
-    _fastvideo_version: str = "0.1.0"
-
-    # Camera net flag (for config.json compatibility)
-    camera_net: bool = True
-
-    _fsdp_shard_conditions: list = field(
-        default_factory=lambda: [is_double_block, is_single_block, is_refiner_block, is_camera_net])
-
-    _compile_conditions: list = field(default_factory=lambda: [is_double_block, is_single_block, is_txt_in])
-
-    # Parameter names mapping from official checkpoint to FastVideo naming
-    # GameCraft weights are already close to FastVideo format with minor adjustments
-    param_names_mapping: dict = field(
-        default_factory=lambda: {
-            # MLP naming: fc1 -> fc_in, fc2 -> fc_out
-            r"^(.*)\.img_mlp\.fc1\.(.*)$": r"\1.img_mlp.fc_in.\2",
-            r"^(.*)\.img_mlp\.fc2\.(.*)$": r"\1.img_mlp.fc_out.\2",
-            r"^(.*)\.txt_mlp\.fc1\.(.*)$": r"\1.txt_mlp.fc_in.\2",
-            r"^(.*)\.txt_mlp\.fc2\.(.*)$": r"\1.txt_mlp.fc_out.\2",
-
-            # Single block MLP naming
-            r"^single_blocks\.(\d+)\.mlp\.fc1\.(.*)$": r"single_blocks.\1.mlp.fc_in.\2",
-            r"^single_blocks\.(\d+)\.mlp\.fc2\.(.*)$": r"single_blocks.\1.mlp.fc_out.\2",
-
-            # Token refiner naming
-            r"^txt_in\.individual_token_refiner\.blocks\.(\d+)\.(.*)$": r"txt_in.refiner_blocks.\1.\2",
-
-            # Vector in naming
-            r"^vector_in\.in_layer\.(.*)$": r"vector_in.fc_in.\1",
-            r"^vector_in\.out_layer\.(.*)$": r"vector_in.fc_out.\1",
-
-            # Time embedder naming
-            r"^time_in\.mlp\.0\.(.*)$": r"time_in.mlp.fc_in.\1",
-            r"^time_in\.mlp\.2\.(.*)$": r"time_in.mlp.fc_out.\1",
-
-            # Guidance embedder naming (if present)
-            r"^guidance_in\.mlp\.0\.(.*)$": r"guidance_in.mlp.fc_in.\1",
-            r"^guidance_in\.mlp\.2\.(.*)$": r"guidance_in.mlp.fc_out.\1",
-
-            # Final layer adaLN modulation
-            r"^final_layer\.adaLN_modulation\.1\.(.*)$": r"final_layer.adaLN_modulation.linear.\1",
-
-            # Refiner block MLP naming
-            r"^txt_in\.refiner_blocks\.(\d+)\.mlp\.fc1\.(.*)$": r"txt_in.refiner_blocks.\1.mlp.fc_in.\2",
-            r"^txt_in\.refiner_blocks\.(\d+)\.mlp\.fc2\.(.*)$": r"txt_in.refiner_blocks.\1.mlp.fc_out.\2",
-
-            # Camera net weights are already correctly named
-        })
-
-    # Reverse mapping for saving checkpoints
-    reverse_param_names_mapping: dict = field(default_factory=lambda: {})
-
-    # Model architecture parameters
-    # patch_size can be int or tuple - if tuple, it's [T, H, W]
-    patch_size: int | tuple[int, int, int] = 2
-    patch_size_t: int = 1
-    in_channels: int = 33  # 16 latent + 16 gt_latent + 1 mask
-    out_channels: int = 16
-    num_attention_heads: int = 24
-    attention_head_dim: int = 128
-    mlp_ratio: float = 4.0
-    num_layers: int = 20  # Double stream blocks
-    num_single_layers: int = 40  # Single stream blocks
-    num_refiner_layers: int = 2
-    rope_axes_dim: tuple[int, int, int] = (16, 56, 56)
-    guidance_embeds: bool = False  # GameCraft doesn't use guidance
-    dtype: torch.dtype | None = None
-    text_embed_dim: int = 4096  # LLaMA-3 hidden size
-    pooled_projection_dim: int = 768  # CLIP pooled output dim
-    rope_theta: int = 256
-    qk_norm: str = "rms_norm"
-
-    # Camera net parameters
-    camera_in_channels: int = 6  # Plücker coordinates
-    camera_downscale_coef: int = 8
-    camera_out_channels: int = 16
-
-    # Layers to exclude from LoRA
-    exclude_lora_layers: list[str] = field(
-        default_factory=lambda: ["img_in", "txt_in", "time_in", "vector_in", "camera_net"])
-
-    def __post_init__(self):
-        super().__post_init__()
-        self.hidden_size: int = self.attention_head_dim * self.num_attention_heads
-        self.num_channels_latents: int = 16  # Output is 16 channels
-
-        # Convert patch_size list to tuple if needed (from JSON deserialization)
-        if isinstance(self.patch_size, list):
-            self.patch_size = tuple(self.patch_size)
-
-        # Convert rope_axes_dim list to tuple if needed
-        if isinstance(self.rope_axes_dim, list):
-            self.rope_axes_dim = tuple(self.rope_axes_dim)
-
-
-@dataclass
-class HunyuanGameCraftConfig(DiTConfig):
-    """Full config for HunyuanGameCraft model."""
-
-    arch_config: DiTArchConfig = field(default_factory=HunyuanGameCraftArchConfig)
-
-    prefix: str = "HunyuanGameCraft"
diff --git a/fastvideo/configs/models/dits/longcat.py b/fastvideo/configs/models/dits/longcat.py
deleted file mode 100644
index 735b84eea..000000000
--- a/fastvideo/configs/models/dits/longcat.py
+++ /dev/null
@@ -1,133 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-LongCat Video DiT configuration for native FastVideo implementation.
-"""
-
-from dataclasses import dataclass, field
-
-from fastvideo.configs.models.dits.base import DiTArchConfig, DiTConfig
-from fastvideo.platforms import AttentionBackendEnum
-
-
-def is_longcat_blocks(n: str, m) -> bool:
-    """FSDP shard condition for LongCat transformer blocks."""
-    return "blocks" in n and str.isdigit(n.split(".")[-1])
-
-
-@dataclass
-class LongCatVideoArchConfig(DiTArchConfig):
-    """Architecture configuration for native LongCat Video DiT."""
-
-    _fsdp_shard_conditions: list = field(default_factory=lambda: [is_longcat_blocks])
-
-    # Enable torch.compile for transformer blocks (major speedup!)
-    _compile_conditions: list = field(default_factory=lambda: [is_longcat_blocks])
-
-    # Parameter name mapping for weight conversion
-    param_names_mapping: dict = field(
-        default_factory=lambda: {
-            # Embedders
-            r"^x_embedder\.(.*)$": r"patch_embed.\1",
-            r"^t_embedder\.mlp\.0\.(.*)$": r"time_embedder.linear_1.\1",
-            r"^t_embedder\.mlp\.2\.(.*)$": r"time_embedder.linear_2.\1",
-            r"^y_embedder\.y_proj\.0\.(.*)$": r"caption_embedder.linear_1.\1",
-            r"^y_embedder\.y_proj\.2\.(.*)$": r"caption_embedder.linear_2.\1",
-
-            # Transformer blocks - AdaLN modulation
-            r"^blocks\.(\d+)\.adaLN_modulation\.1\.(.*)$": r"blocks.\1.adaln_linear_1.\2",
-
-            # Transformer blocks - Normalization
-            r"^blocks\.(\d+)\.mod_norm_attn\.(.*)$": r"blocks.\1.norm_attn.\2",
-            r"^blocks\.(\d+)\.mod_norm_ffn\.(.*)$": r"blocks.\1.norm_ffn.\2",
-            r"^blocks\.(\d+)\.pre_crs_attn_norm\.(.*)$": r"blocks.\1.norm_cross.\2",
-
-            # Self-attention: QKV fused -> separate (will need splitting in converter)
-            # Original has attn.qkv.weight -> need to split into to_q, to_k, to_v
-            r"^blocks\.(\d+)\.attn\.qkv\.(.*)$": r"blocks.\1.self_attn.qkv_fused.\2",  # Marker for splitting
-            r"^blocks\.(\d+)\.attn\.proj\.(.*)$": r"blocks.\1.self_attn.to_out.\2",
-            r"^blocks\.(\d+)\.attn\.q_norm\.(.*)$": r"blocks.\1.self_attn.q_norm.\2",
-            r"^blocks\.(\d+)\.attn\.k_norm\.(.*)$": r"blocks.\1.self_attn.k_norm.\2",
-
-            # Cross-attention
-            r"^blocks\.(\d+)\.cross_attn\.q_linear\.(.*)$": r"blocks.\1.cross_attn.to_q.\2",
-            r"^blocks\.(\d+)\.cross_attn\.kv_linear\.(.*)$":
-            r"blocks.\1.cross_attn.kv_fused.\2",  # Marker for splitting
-            r"^blocks\.(\d+)\.cross_attn\.proj\.(.*)$": r"blocks.\1.cross_attn.to_out.\2",
-            r"^blocks\.(\d+)\.cross_attn\.q_norm\.(.*)$": r"blocks.\1.cross_attn.q_norm.\2",
-            r"^blocks\.(\d+)\.cross_attn\.k_norm\.(.*)$": r"blocks.\1.cross_attn.k_norm.\2",
-
-            # FFN (SwiGLU)
-            r"^blocks\.(\d+)\.ffn\.w1\.(.*)$": r"blocks.\1.ffn.w1.\2",  # gate
-            r"^blocks\.(\d+)\.ffn\.w2\.(.*)$": r"blocks.\1.ffn.w2.\2",  # down
-            r"^blocks\.(\d+)\.ffn\.w3\.(.*)$": r"blocks.\1.ffn.w3.\2",  # up
-
-            # Final layer
-            r"^final_layer\.adaLN_modulation\.1\.(.*)$": r"final_layer.adaln_linear.\1",
-            r"^final_layer\.norm_final\.(.*)$": r"final_layer.norm.\1",
-            r"^final_layer\.linear\.(.*)$": r"final_layer.proj.\1",
-        })
-
-    # Reverse mapping for saving checkpoints: custom -> hf
-    reverse_param_names_mapping: dict = field(default_factory=lambda: {})
-
-    # LoRA parameter name mapping
-    lora_param_names_mapping: dict = field(default_factory=lambda: {})
-
-    # Model architecture parameters
-    hidden_size: int = 4096
-    depth: int = 48  # Number of transformer blocks
-    num_attention_heads: int = 32
-    attention_head_dim: int = 128  # hidden_size / num_attention_heads
-
-    in_channels: int = 16  # Latent space channels
-    out_channels: int = 16
-    num_channels_latents: int = 16
-
-    # Patch embedding
-    patch_size: tuple[int, int, int] = (1, 2, 2)  # [T, H, W] - no temporal compression
-
-    # Text/caption embedding
-    caption_channels: int = 4096  # UMT5 d_model
-
-    # Timestep embedding
-    adaln_tembed_dim: int = 512
-    frequency_embedding_size: int = 256
-
-    # FFN
-    mlp_ratio: int = 4
-
-    # Attention backend support
-    _supported_attention_backends: tuple = field(default_factory=lambda: (
-        AttentionBackendEnum.FLASH_ATTN,
-        AttentionBackendEnum.TORCH_SDPA,
-    ))
-
-    # Text padding behavior
-    text_tokens_zero_pad: bool = True
-
-    # Block Sparse Attention (BSA)
-    enable_bsa: bool = False
-    bsa_params: dict | None = field(default_factory=lambda: {
-        "sparsity": 0.9375,
-        "cdf_threshold": None,
-        "chunk_3d_shape_q": [4, 4, 4],
-        "chunk_3d_shape_k": [4, 4, 4],
-    })
-
-    # LoRA exclusions
-    exclude_lora_layers: list[str] = field(default_factory=lambda: [])
-
-    def __post_init__(self):
-        super().__post_init__()
-        self.out_channels = self.out_channels or self.in_channels
-        # Ensure attention_head_dim matches
-        self.attention_head_dim = self.hidden_size // self.num_attention_heads
-
-
-@dataclass
-class LongCatVideoConfig(DiTConfig):
-    """Main configuration for LongCat Video DiT."""
-
-    arch_config: DiTArchConfig = field(default_factory=LongCatVideoArchConfig)
-
-    prefix: str = "longcat"
diff --git a/fastvideo/configs/models/dits/zimage.py b/fastvideo/configs/models/dits/zimage.py
deleted file mode 100644
index ce9bf7848..000000000
--- a/fastvideo/configs/models/dits/zimage.py
+++ /dev/null
@@ -1,60 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-from dataclasses import dataclass, field
-
-from fastvideo.configs.models.dits.base import DiTArchConfig, DiTConfig
-from fastvideo.platforms import AttentionBackendEnum
-
-
-def is_zimage_block(name: str, module) -> bool:
-    parts = name.split(".")
-    return len(parts) >= 2 and parts[-2] in {"noise_refiner", "context_refiner", "layers"} and parts[-1].isdigit()
-
-
-@dataclass
-class ZImageDiTArchConfig(DiTArchConfig):
-    _fsdp_shard_conditions: list = field(default_factory=lambda: [is_zimage_block])
-    _supported_attention_backends: tuple[AttentionBackendEnum, ...] = (AttentionBackendEnum.TORCH_SDPA, )
-
-    all_patch_size: tuple[int, ...] = (2, )
-    all_f_patch_size: tuple[int, ...] = (1, )
-    in_channels: int = 16
-    dim: int = 3840
-    n_layers: int = 30
-    n_refiner_layers: int = 2
-    n_heads: int = 30
-    n_kv_heads: int = 30
-    norm_eps: float = 1e-5
-    qk_norm: bool = True
-    cap_feat_dim: int = 2560
-    rope_theta: float = 256.0
-    t_scale: float = 1000.0
-    axes_dims: tuple[int, ...] = (32, 48, 48)
-    axes_lens: tuple[int, ...] = (1536, 512, 512)
-
-    adaln_embed_dim: int = 256
-    frequency_embedding_size: int = 256
-    timestep_mid_size: int = 1024
-    max_period: int = 10000
-    seq_multi_of: int = 32
-
-    def __post_init__(self) -> None:
-        super().__post_init__()
-        if len(self.all_patch_size) != len(self.all_f_patch_size):
-            raise ValueError("all_patch_size and all_f_patch_size must have equal length")
-        if self.dim % self.n_heads:
-            raise ValueError("dim must be divisible by n_heads")
-        if self.dim // self.n_heads != sum(self.axes_dims):
-            raise ValueError("attention head dimension must equal sum(axes_dims)")
-        if len(self.axes_dims) != len(self.axes_lens) or any(dim % 2 for dim in self.axes_dims):
-            raise ValueError("RoPE axes require matching lengths and even dimensions")
-
-        self.hidden_size = self.dim
-        self.num_attention_heads = self.n_heads
-        self.num_channels_latents = self.in_channels
-        self.out_channels = self.in_channels
-
-
-@dataclass
-class ZImageDiTConfig(DiTConfig):
-    arch_config: DiTArchConfig = field(default_factory=ZImageDiTArchConfig)
-    prefix: str = "ZImage"
diff --git a/fastvideo/configs/models/encoders/base.py b/fastvideo/configs/models/encoders/base.py
index 09ee57067..5dd4a531f 100644
--- a/fastvideo/configs/models/encoders/base.py
+++ b/fastvideo/configs/models/encoders/base.py
@@ -44,7 +44,7 @@ class TextEncoderArchConfig(EncoderArchConfig):
 
     def __post_init__(self) -> None:
         # update_model_arch re-runs __post_init__ after pipeline configs may
-        # have customized tokenizer_kwargs (e.g. kandinsky5/gen3c/longcat set
+        # have customized tokenizer_kwargs (e.g. kandinsky5/gen3c set
         # "padding"); rebuilding the dict here would silently wipe those
         # customizations, so only fill in defaults for keys not already set.
         defaults = {
diff --git a/fastvideo/configs/models/vaes/__init__.py b/fastvideo/configs/models/vaes/__init__.py
index 13210e3a2..acc5ca9c0 100644
--- a/fastvideo/configs/models/vaes/__init__.py
+++ b/fastvideo/configs/models/vaes/__init__.py
@@ -1,8 +1,6 @@
 from fastvideo.configs.models.vaes.cosmosvae import CosmosVAEConfig
 from fastvideo.configs.models.vaes.cosmos2_5vae import Cosmos25VAEConfig
-from fastvideo.configs.models.vaes.gamecraftvae import GameCraftVAEConfig
 from fastvideo.configs.models.vaes.gen3cvae import Gen3CVAEConfig
-from fastvideo.configs.models.vaes.glm_image import GlmImageVAEConfig
 from fastvideo.configs.models.vaes.hunyuanvae import HunyuanVAEConfig
 from fastvideo.configs.models.vaes.hunyuan15vae import Hunyuan15VAEConfig
 from fastvideo.configs.models.vaes.kandinsky6_sr import Kandinsky6SRVAEArchConfig, Kandinsky6SRVAEConfig
@@ -17,7 +15,6 @@ from fastvideo.configs.models.vaes.flux2vae import Flux2VAEConfig
 from fastvideo.models.wan.vae_config import WanVAEConfig
 
 __all__ = [
-    "GameCraftVAEConfig",
     "HunyuanVAEConfig",
     "WanVAEConfig",
     "CosmosVAEConfig",
@@ -34,5 +31,4 @@ __all__ = [
     "OobleckVAEArchConfig",
     "OobleckVAEConfig",
     "Flux2VAEConfig",
-    "GlmImageVAEConfig",
 ]
diff --git a/fastvideo/configs/models/vaes/gamecraftvae.py b/fastvideo/configs/models/vaes/gamecraftvae.py
deleted file mode 100644
index 2780583ce..000000000
--- a/fastvideo/configs/models/vaes/gamecraftvae.py
+++ /dev/null
@@ -1,50 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-GameCraft VAE config - matches official config.json from Hunyuan-GameCraft-1.0.
-"""
-from dataclasses import dataclass, field
-
-from fastvideo.configs.models.vaes.base import VAEArchConfig, VAEConfig
-
-
-@dataclass
-class GameCraftVAEArchConfig(VAEArchConfig):
-    """Architecture config matching official AutoencoderKLCausal3D config.json."""
-
-    in_channels: int = 3
-    out_channels: int = 3
-    latent_channels: int = 16
-    down_block_types: tuple[str, ...] = (
-        "DownEncoderBlockCausal3D",
-        "DownEncoderBlockCausal3D",
-        "DownEncoderBlockCausal3D",
-        "DownEncoderBlockCausal3D",
-    )
-    up_block_types: tuple[str, ...] = (
-        "UpDecoderBlockCausal3D",
-        "UpDecoderBlockCausal3D",
-        "UpDecoderBlockCausal3D",
-        "UpDecoderBlockCausal3D",
-    )
-    block_out_channels: tuple[int, ...] = (128, 256, 512, 512)
-    layers_per_block: int = 2
-    act_fn: str = "silu"
-    norm_num_groups: int = 32
-    scaling_factor: float = 0.476986
-    spatial_compression_ratio: int = 8
-    temporal_compression_ratio: int = 4
-    time_compression_ratio: int = 4  # alias for DecoderCausal3D
-    mid_block_add_attention: bool = True
-    mid_block_causal_attn: bool = True
-    sample_size: int = 256  # from config.json
-    sample_tsize: int = 64  # from config.json
-
-    def __post_init__(self):
-        self.spatial_compression_ratio = 2**(len(self.block_out_channels) - 1)
-
-
-@dataclass
-class GameCraftVAEConfig(VAEConfig):
-    """Full config for GameCraft VAE."""
-
-    arch_config: VAEArchConfig = field(default_factory=GameCraftVAEArchConfig)
diff --git a/fastvideo/configs/models/vaes/glm_image.py b/fastvideo/configs/models/vaes/glm_image.py
deleted file mode 100644
index a7bd1becd..000000000
--- a/fastvideo/configs/models/vaes/glm_image.py
+++ /dev/null
@@ -1,95 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-
-from fastvideo.configs.models.vaes.autoencoder_kl import (AutoencoderKLArchConfig, AutoencoderKLVAEConfig)
-
-_GLM_IMAGE_LATENTS_MEAN: tuple[float, ...] = (
-    -0.2080078125,
-    1.875,
-    -0.470703125,
-    -1.265625,
-    -1.421875,
-    0.77734375,
-    -0.3671875,
-    -0.9453125,
-    0.318359375,
-    0.7734375,
-    -0.1884765625,
-    -0.022216796875,
-    -0.220703125,
-    -1.59375,
-    -0.81640625,
-    -0.255859375,
-)
-_GLM_IMAGE_LATENTS_STD: tuple[float, ...] = (
-    3.0625,
-    2.203125,
-    2.265625,
-    4.84375,
-    2.5,
-    3.9375,
-    2.203125,
-    3.03125,
-    2.1875,
-    2.046875,
-    2.71875,
-    2.390625,
-    2.390625,
-    2.453125,
-    2.25,
-    2.15625,
-)
-
-
-@dataclass
-class GlmImageVAEArchConfig(AutoencoderKLArchConfig):
-    act_fn: str = "silu"
-    block_out_channels: tuple[int, ...] = (128, 512, 1024, 1024)
-    down_block_types: tuple[str, ...] = (
-        "DownEncoderBlock2D",
-        "DownEncoderBlock2D",
-        "DownEncoderBlock2D",
-        "DownEncoderBlock2D",
-    )
-    up_block_types: tuple[str, ...] = (
-        "UpDecoderBlock2D",
-        "UpDecoderBlock2D",
-        "UpDecoderBlock2D",
-        "UpDecoderBlock2D",
-    )
-    force_upcast: bool = True
-    in_channels: int = 3
-    out_channels: int = 3
-    latent_channels: int = 16
-    latents_mean: tuple[float, ...] = _GLM_IMAGE_LATENTS_MEAN
-    latents_std: tuple[float, ...] = _GLM_IMAGE_LATENTS_STD
-    layers_per_block: int = 3
-    mid_block_add_attention: bool = False
-    norm_num_groups: int = 32
-    sample_size: int = 1024
-    scaling_factor: float = 0.18215
-    shift_factor: float | None = None
-    use_quant_conv: bool = False
-    use_post_quant_conv: bool = False
-
-    temporal_compression_ratio: int = 1
-    spatial_compression_ratio: int = 8
-
-
-@dataclass
-class GlmImageVAEConfig(AutoencoderKLVAEConfig):
-    arch_config: GlmImageVAEArchConfig = field(default_factory=GlmImageVAEArchConfig)
-
-    use_tiling: bool = True
-    use_temporal_tiling: bool = False
-    use_parallel_tiling: bool = False
-
-    tile_sample_min_height: int = 512
-    tile_sample_min_width: int = 512
-    tile_sample_stride_height: int = 384
-    tile_sample_stride_width: int = 384
-
-    load_encoder: bool = True
-    load_decoder: bool = True
diff --git a/fastvideo/configs/pipelines/__init__.py b/fastvideo/configs/pipelines/__init__.py
index 456d33231..73636280c 100644
--- a/fastvideo/configs/pipelines/__init__.py
+++ b/fastvideo/configs/pipelines/__init__.py
@@ -1,10 +1,8 @@
 from fastvideo.configs.pipelines.base import PipelineConfig
 from fastvideo.configs.pipelines.cosmos import CosmosConfig
 from fastvideo.configs.pipelines.cosmos2_5 import Cosmos25Config
-from fastvideo.configs.pipelines.dreamx_world import DreamXWorld5BARPipelineConfig, DreamXWorld5BCamPipelineConfig
 from fastvideo.configs.pipelines.hunyuan import FastHunyuanConfig, HunyuanConfig
 from fastvideo.configs.pipelines.hunyuan15 import Hunyuan15T2V480PConfig, Hunyuan15T2V720PConfig
-from fastvideo.configs.pipelines.hunyuangamecraft import HunyuanGameCraftPipelineConfig
 from fastvideo.configs.pipelines.helios import HeliosPipelineConfig
 from fastvideo.configs.pipelines.hyworld import HYWorldConfig
 from fastvideo.configs.pipelines.kandinsky5 import Kandinsky5DMDConfig, Kandinsky5I2VConfig, Kandinsky5T2VConfig
@@ -22,10 +20,9 @@ from fastvideo.models.wan.pipeline_config import (LucyEditDevConfig, SelfForcing
                                                   WanI2V720PConfig, WanT2V480PConfig, WanT2V720PConfig)
 
 __all__ = [
-    "HunyuanConfig", "FastHunyuanConfig", "HunyuanGameCraftPipelineConfig", "PipelineConfig", "Hunyuan15T2V480PConfig",
-    "Hunyuan15T2V720PConfig", "WanT2V480PConfig", "WanI2V480PConfig", "WanT2V720PConfig", "WanI2V720PConfig",
-    "SelfForcingWanT2V480PConfig", "LucyEditDevConfig", "CosmosConfig", "Cosmos25Config", "LTX2T2VConfig",
-    "DreamXWorld5BCamPipelineConfig", "DreamXWorld5BARPipelineConfig", "HeliosPipelineConfig", "HYWorldConfig",
+    "HunyuanConfig", "FastHunyuanConfig", "PipelineConfig", "Hunyuan15T2V480PConfig", "Hunyuan15T2V720PConfig",
+    "WanT2V480PConfig", "WanI2V480PConfig", "WanT2V720PConfig", "WanI2V720PConfig", "SelfForcingWanT2V480PConfig",
+    "LucyEditDevConfig", "CosmosConfig", "Cosmos25Config", "LTX2T2VConfig", "HeliosPipelineConfig", "HYWorldConfig",
     "Kandinsky5T2VConfig", "Kandinsky5I2VConfig", "Kandinsky5DMDConfig", "Kandinsky6TI2VAConfig",
     "Kandinsky6SRPipelineConfig", "LingBotWorld2CausalFastI2V480PConfig", "LingBotWorldFastI2V480PConfig",
     "LingBotVideoT2VConfig", "MatrixGame2I2V480PConfig", "MatrixGame3I2V720PConfig", "MMAudioV2AConfig",
diff --git a/fastvideo/configs/pipelines/dreamx_world.py b/fastvideo/configs/pipelines/dreamx_world.py
deleted file mode 100644
index 91158255f..000000000
--- a/fastvideo/configs/pipelines/dreamx_world.py
+++ /dev/null
@@ -1,127 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""DreamX-World-5B-Cam FastVideo model configuration helpers."""
-
-from collections.abc import Callable
-from dataclasses import dataclass, field
-
-import torch
-
-from fastvideo.configs.models import DiTConfig, EncoderConfig, VAEConfig
-from fastvideo.configs.models.dits.dreamx_world import (DreamXWorldARArchConfig, DreamXWorldARConfig,
-                                                        DreamXWorldArchConfig, DreamXWorldConfig)
-from fastvideo.configs.models.encoders import BaseEncoderOutput, T5Config
-from fastvideo.configs.models.encoders.t5 import T5ArchConfig
-from fastvideo.configs.models.vaes import WanVAEConfig
-from fastvideo.configs.pipelines.base import PipelineConfig
-from fastvideo.models.wan.pipeline_config import LucyEditDevConfig, t5_postprocess_text
-
-
-def make_dreamx_world_5b_cam_dit_config() -> DreamXWorldConfig:
-    """Return the DreamX-World DiT config matching DreamX-World-5B-Cam."""
-    return DreamXWorldConfig(arch_config=DreamXWorldArchConfig(
-        num_attention_heads=24,
-        attention_head_dim=128,
-        in_channels=48,
-        out_channels=48,
-        ffn_dim=14336,
-        num_layers=30,
-        cross_attn_norm=True,
-        qk_norm="rms_norm_across_heads",
-        add_control_adapter=True,
-        cam_method="prope",
-        attn_compress=1,
-        cam_self_attn_layers=None,
-    ))
-
-
-def make_dreamx_world_5b_ar_dit_config() -> DreamXWorldARConfig:
-    """Return the DreamX-World-5B autoregressive causal DiT config."""
-    return DreamXWorldARConfig(arch_config=DreamXWorldARArchConfig(
-        model_type="ti2v",
-        num_attention_heads=24,
-        attention_head_dim=128,
-        in_channels=48,
-        out_channels=48,
-        ffn_dim=14336,
-        num_layers=30,
-        cross_attn_norm=True,
-        qk_norm=True,
-        add_control_adapter=True,
-        cam_method="prope",
-        attn_compress=4,
-        cam_self_attn_layers=tuple(range(30)),
-        local_attn_size=12,
-        sink_size=3,
-        num_frames_per_block=3,
-    ))
-
-
-def make_dreamx_world_5b_cam_vae_config() -> WanVAEConfig:
-    """Return the Wan2.2 48-channel VAE config used by DreamX-World-5B-Cam."""
-    return LucyEditDevConfig().vae_config
-
-
-def make_dreamx_world_5b_cam_text_encoder_config() -> T5Config:
-    """Return the UMT5-XXL text encoder config used by DreamX-World-5B-Cam."""
-    return T5Config(
-        arch_config=T5ArchConfig(
-            vocab_size=256384,
-            d_model=4096,
-            d_kv=64,
-            d_ff=10240,
-            num_layers=24,
-            num_decoder_layers=None,
-            num_heads=64,
-            relative_attention_num_buckets=32,
-            dropout_rate=0.0,
-            text_len=512,
-            feed_forward_proj="gelu",
-            is_encoder_decoder=False,
-        ),
-        prefix="umt5",
-    )
-
-
-@dataclass
-class DreamXWorld5BCamPipelineConfig(PipelineConfig):
-    """Pipeline config for the first-scope DreamX-World-5B-Cam mode."""
-
-    dit_config: DiTConfig = field(default_factory=make_dreamx_world_5b_cam_dit_config)
-    vae_config: VAEConfig = field(default_factory=make_dreamx_world_5b_cam_vae_config)
-    text_encoder_configs: tuple[EncoderConfig,
-                                ...] = field(default_factory=lambda: (make_dreamx_world_5b_cam_text_encoder_config(), ))
-    postprocess_text_funcs: tuple[Callable[[BaseEncoderOutput], torch.Tensor],
-                                  ...] = field(default_factory=lambda: (t5_postprocess_text, ))
-    text_encoder_precisions: tuple[str, ...] = field(default_factory=lambda: ("bf16", ))
-    flow_shift: float | None = 3.0
-    ti2v_task: bool = True
-    expand_timesteps: bool = True
-    vae_tiling: bool = False
-    vae_sp: bool = False
-    vae_precision: str = "fp32"
-    vae_decode_precision: str | None = "bf16"
-    dit_precision: str = "bf16"
-
-    def __post_init__(self) -> None:
-        self.vae_config.load_encoder = True
-        self.vae_config.load_decoder = True
-        self.dit_config.expand_timesteps = self.expand_timesteps
-
-
-@dataclass
-class DreamXWorld5BARPipelineConfig(DreamXWorld5BCamPipelineConfig):
-    """Pipeline config for DreamX-World-5B autoregressive forcing."""
-
-    dit_config: DiTConfig = field(default_factory=make_dreamx_world_5b_ar_dit_config)
-    flow_shift: float | None = 5.0
-    ti2v_task: bool = True
-    is_causal: bool = True
-    dmd_denoising_steps: tuple[int, ...] = (1000, 750, 500, 250)
-    warp_denoising_step: bool = True
-    context_noise: float = 0.1
-    num_frames_per_block: int = 3
-    color_correction_strength: float = 1.0
-
-    def __post_init__(self) -> None:
-        super().__post_init__()
-        self.dit_config.expand_timesteps = True
diff --git a/fastvideo/configs/pipelines/glm_image.py b/fastvideo/configs/pipelines/glm_image.py
deleted file mode 100644
index 7b2f46279..000000000
--- a/fastvideo/configs/pipelines/glm_image.py
+++ /dev/null
@@ -1,46 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-from collections.abc import Callable
-from dataclasses import dataclass, field
-
-import torch
-
-from fastvideo.configs.models import DiTConfig, EncoderConfig, VAEConfig
-from fastvideo.configs.models.dits.glm_image import GlmImageDiTConfig
-from fastvideo.configs.models.encoders import BaseEncoderOutput, T5Config
-from fastvideo.configs.models.vaes.glm_image import GlmImageVAEConfig
-from fastvideo.configs.pipelines.base import PipelineConfig
-
-
-def glm_image_t5_postprocess(outputs: BaseEncoderOutput) -> torch.Tensor:
-    mask: torch.Tensor = outputs.attention_mask
-    hidden_state: torch.Tensor = outputs.last_hidden_state
-    seq_lens = mask.gt(0).sum(dim=1).long()
-
-    assert torch.isnan(hidden_state).sum() == 0, "T5 hidden states contain NaN"
-
-    max_len = 512
-    prompt_embeds = [u[:min(v, max_len)] for u, v in zip(hidden_state, seq_lens, strict=True)]
-    prompt_embeds_tensor: torch.Tensor = torch.stack(
-        [torch.cat([u, u.new_zeros(max_len - u.size(0), u.size(1))]) for u in prompt_embeds], dim=0)
-
-    return prompt_embeds_tensor
-
-
-@dataclass
-class GlmImageConfig(PipelineConfig):
-
-    dit_config: DiTConfig = field(default_factory=GlmImageDiTConfig)
-    dit_precision: str = "bf16"
-
-    vae_config: VAEConfig = field(default_factory=GlmImageVAEConfig)
-    vae_precision: str = "fp32"
-    vae_tiling: bool = True
-    vae_sp: bool = False
-
-    text_encoder_configs: tuple[EncoderConfig, ...] = field(default_factory=lambda: (T5Config(), ))
-    text_encoder_precisions: tuple[str, ...] = field(default_factory=lambda: ("fp32", ))
-    postprocess_text_funcs: tuple[Callable[[BaseEncoderOutput], torch.Tensor],
-                                  ...] = field(default_factory=lambda: (glm_image_t5_postprocess, ))
-
-    flow_shift: float | None = 1.0
-    embedded_cfg_scale: float = 7.5
diff --git a/fastvideo/configs/pipelines/hunyuangamecraft.py b/fastvideo/configs/pipelines/hunyuangamecraft.py
deleted file mode 100644
index d67846fe3..000000000
--- a/fastvideo/configs/pipelines/hunyuangamecraft.py
+++ /dev/null
@@ -1,69 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-Pipeline configuration for HunyuanGameCraft.
-
-HunyuanGameCraft extends HunyuanVideo with:
-1. CameraNet for camera/action conditioning (Plücker coordinates)
-2. Mask-based conditioning for autoregressive generation
-3. 33 input channels (16 latent + 16 gt_latent + 1 mask)
-
-Text encoders are the same as HunyuanVideo:
-- LLaVA-LLaMA-3-8B for primary text encoding (4096 dim)
-- CLIP ViT-L/14 for secondary pooled embeddings (768 dim)
-"""
-from collections.abc import Callable
-from dataclasses import dataclass, field
-
-import torch
-
-from fastvideo.configs.models import DiTConfig, EncoderConfig, VAEConfig
-from fastvideo.configs.models.dits import HunyuanGameCraftConfig
-from fastvideo.configs.models.encoders import (BaseEncoderOutput, CLIPTextConfig, LlamaConfig)
-from fastvideo.configs.models.vaes import GameCraftVAEConfig
-from fastvideo.configs.pipelines.base import PipelineConfig
-from fastvideo.configs.pipelines.hunyuan import (clip_postprocess_text, clip_preprocess_text, llama_postprocess_text,
-                                                 llama_preprocess_text)
-
-
-@dataclass
-class HunyuanGameCraftPipelineConfig(PipelineConfig):
-    """Configuration for HunyuanGameCraft pipeline.
-    
-    Inherits text encoding from HunyuanVideo but uses:
-    - GameCraft DiT with CameraNet
-    - Same VAE (HunyuanVAE)
-    - Same text encoders (LLaMA + CLIP)
-    """
-
-    # DiT config - uses GameCraft config (33 input channels)
-    dit_config: DiTConfig = field(default_factory=HunyuanGameCraftConfig)
-
-    # VAE config - GameCraft VAE (mid_block_causal_attn=True, etc.)
-    vae_config: VAEConfig = field(default_factory=GameCraftVAEConfig)
-
-    # Denoising parameters
-    # Official GameCraft does NOT use embedded guidance (passes guidance=None)
-    # It uses standard CFG with guidance_scale=6.0 instead
-    embedded_cfg_scale = None
-    flow_shift: int = 5  # Official GameCraft uses flow_shift=5.0
-
-    # Text encoding stage - same as HunyuanVideo
-    # Uses LLaMA-3-8B (via LLaVA) + CLIP
-    text_encoder_configs: tuple[EncoderConfig, ...] = field(default_factory=lambda: (LlamaConfig(), CLIPTextConfig()))
-    preprocess_text_funcs: tuple[Callable[[str], str],
-                                 ...] = field(default_factory=lambda: (llama_preprocess_text, clip_preprocess_text))
-    postprocess_text_funcs: tuple[Callable[[BaseEncoderOutput], torch.Tensor],
-                                  ...] = field(default_factory=lambda: (llama_postprocess_text, clip_postprocess_text))
-
-    # Precision for each component
-    dit_precision: str = "bf16"
-    vae_precision: str = "fp16"
-    text_encoder_precisions: tuple[str, ...] = field(default_factory=lambda: ("fp16", "fp16"))
-
-    def __post_init__(self):
-        # VAE only needs decoder for inference
-        self.vae_config.load_encoder = False
-        self.vae_config.load_decoder = True
-        if self.text_encoder_configs:
-            # Hunyuan postprocess consumes intermediate LLaMA hidden states.
-            self.text_encoder_configs[0].arch_config.output_hidden_states = True
diff --git a/fastvideo/configs/pipelines/longcat.py b/fastvideo/configs/pipelines/longcat.py
deleted file mode 100644
index a8d778b98..000000000
--- a/fastvideo/configs/pipelines/longcat.py
+++ /dev/null
@@ -1,362 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-
-from collections.abc import Callable
-from dataclasses import dataclass, field
-import html
-
-import ftfy
-import regex as re
-import torch
-
-from fastvideo.configs.models import DiTConfig, VAEConfig
-from fastvideo.configs.models.dits.base import DiTArchConfig
-from fastvideo.configs.models.encoders import BaseEncoderOutput, T5Config
-from fastvideo.configs.models.encoders.base import TextEncoderArchConfig
-from fastvideo.configs.models.encoders.t5 import T5ArchConfig
-from fastvideo.configs.models.vaes import WanVAEConfig
-from fastvideo.configs.pipelines.base import PipelineConfig
-
-
-@dataclass
-class LongCatT5ArchConfig(T5ArchConfig):
-    """T5 arch that pads tokenizer output to ``max_length``.
-
-    LongCat's denoising stage concatenates positive and negative
-    attention masks along the batch dimension for CFG, which requires
-    uniform seq length. The shared :class:`T5ArchConfig` dropped the
-    ``"padding": "max_length"`` tokenizer kwarg so other DiTs could run
-    with variable-length masks; LongCat still needs the uniform
-    contract.
-    """
-
-    def __post_init__(self) -> None:
-        super().__post_init__()
-        self.tokenizer_kwargs["padding"] = "max_length"
-
-
-@dataclass
-class LongCatT5Config(T5Config):
-    arch_config: TextEncoderArchConfig = field(default_factory=LongCatT5ArchConfig)
-
-
-@dataclass
-class LongCatDiTArchConfig(DiTArchConfig):
-    """Extended DiTArchConfig with LongCat-specific fields."""
-    # LongCat-specific architecture parameters
-    adaln_tembed_dim: int = 512
-    caption_channels: int = 4096
-    depth: int = 48
-    enable_bsa: bool = False
-    enable_flashattn3: bool = False
-    enable_flashattn2: bool = True
-    enable_xformers: bool = False
-    frequency_embedding_size: int = 256
-    in_channels: int = 16
-    mlp_ratio: int = 4
-    num_heads: int = 32
-    out_channels: int = 16
-    text_tokens_zero_pad: bool = True
-    patch_size: list[int] = field(default_factory=lambda: [1, 2, 2])
-    cp_split_hw: list[int] | None = None
-    bsa_params: dict | None = None
-
-
-def longcat_preprocess_text(prompt: str) -> str:
-    """Clean and preprocess text like original LongCat implementation.
-    
-    This function applies the same text cleaning pipeline as the original
-    LongCat-Video implementation to ensure identical tokenization results.
-    
-    Steps:
-    1. basic_clean: Fix unicode issues and unescape HTML entities
-    2. whitespace_clean: Normalize whitespace to single spaces
-    
-    Args:
-        prompt: Raw input text prompt
-        
-    Returns:
-        Cleaned and normalized text prompt
-    """
-    # basic_clean: fix unicode and HTML entities
-    text = ftfy.fix_text(prompt)
-    text = html.unescape(html.unescape(text))
-    text = text.strip()
-
-    # whitespace_clean: normalize whitespace
-    text = re.sub(r"\s+", " ", text)
-    text = text.strip()
-
-    return text
-
-
-def umt5_postprocess_text(outputs: BaseEncoderOutput) -> torch.Tensor:
-    """
-    Postprocess UMT5/T5 encoder outputs to fixed length 512 embeddings.
-    """
-    mask: torch.Tensor = outputs.attention_mask
-    hidden_state: torch.Tensor = outputs.last_hidden_state
-    seq_lens = mask.gt(0).sum(dim=1).long()
-    assert torch.isnan(hidden_state).sum() == 0
-    prompt_embeds = [u[:v] for u, v in zip(hidden_state, seq_lens, strict=True)]
-    prompt_embeds_tensor: torch.Tensor = torch.stack(
-        [torch.cat([u, u.new_zeros(512 - u.size(0), u.size(1))]) for u in prompt_embeds], dim=0)
-    return prompt_embeds_tensor
-
-
-@dataclass
-class LongCatT2V480PConfig(PipelineConfig):
-    """Configuration for LongCat pipeline (480p).
-
-    Components expected by loaders:
-      - tokenizer: AutoTokenizer
-      - text_encoder: UMT5EncoderModel
-      - transformer: LongCatTransformer3DModel
-      - vae: AutoencoderKLWan (Wan VAE, 4x8 compression)
-      - scheduler: FlowMatchEulerDiscreteScheduler
-    """
-
-    dit_config: DiTConfig = field(default_factory=lambda: DiTConfig(arch_config=LongCatDiTArchConfig()))
-
-    # VAE config: Wan VAE with encoder+decoder enabled
-    vae_config: VAEConfig = field(default_factory=WanVAEConfig)
-    vae_tiling: bool = False
-    vae_sp: bool = False
-
-    # Precision defaults
-    dit_precision: str = "bf16"
-    vae_precision: str = "bf16"
-    text_encoder_precisions: tuple[str, ...] = field(default_factory=lambda: ("bf16", ))
-
-    # UMT5 uses T5-like config; postprocess pads to 512. LongCatT5Config
-    # restores ``padding="max_length"`` for the CFG concat contract.
-    text_encoder_configs: tuple[T5Config, ...] = field(default_factory=lambda: (LongCatT5Config(), ))
-    preprocess_text_funcs: tuple[Callable[[str], str], ...] = field(default_factory=lambda: (longcat_preprocess_text, ))
-    postprocess_text_funcs: tuple[Callable[[BaseEncoderOutput], torch.Tensor],
-                                  ...] = field(default_factory=lambda: (umt5_postprocess_text, ))
-
-    # LongCat-specific runtime toggles (consumed by pipeline/stages)
-    enable_kv_cache: bool = True
-    offload_kv_cache: bool = False
-    enable_bsa: bool = False
-    use_distill: bool = False
-    enhance_hf: bool = False
-    # Optional BSA parameter dict (kept for backward/phase-1 compatibility).
-    # `LongCatPipeline.initialize_pipeline()` uses this as a base and then applies
-    # CLI overrides (bsa_sparsity / bsa_chunk_{q,k} / bsa_cdf_threshold).
-    bsa_params: dict | None = None
-    # BSA runtime overrides (preferred over bsa_params if provided via CLI)
-    bsa_sparsity: float | None = None
-    bsa_cdf_threshold: float | None = None
-    bsa_chunk_q: list[int] | None = None
-    bsa_chunk_k: list[int] | None = None
-    t_thresh: float | None = None  # refine stage default controlled by sampling args
-
-    # LongCat does not need flow_shift
-    flow_shift: float | None = None
-    dmd_denoising_steps: list[int] | None = None
-
-    def __post_init__(self):
-        # LongCat inference requires vae encoder and decoder
-        self.vae_config.load_encoder = True
-        self.vae_config.load_decoder = True
-
-
-@dataclass
-class LongCatT2V704PConfig(LongCatT2V480PConfig):
-    """Configuration for LongCat pipeline (704p) with BSA enabled by default.
-    
-    Uses the same resolution and BSA parameters as original LongCat refinement stage.
-    BSA parameters configured in transformer config.json with chunk_3d_shape=[4,4,4]:
-    - Input: 704×1280×96
-    - VAE (8x): 88×160×96  
-    - Patch [1,2,2]: 44×80×96
-    - chunk [4,4,4]: 96%4=0, 44%4=0, 80%4=0 ✅
-    
-    This configuration matches the original LongCat refinement stage parameters.
-    """
-
-    # Enable BSA by default for 704p
-    enable_bsa: bool = True
-
-
-ASPECT_RATIO_627 = {
-    '0.26': ([320, 1216], 1),
-    '0.31': ([352, 1120], 1),
-    '0.38': ([384, 1024], 1),
-    '0.43': ([416, 960], 1),
-    '0.52': ([448, 864], 1),
-    '0.58': ([480, 832], 1),
-    '0.67': ([512, 768], 1),
-    '0.74': ([544, 736], 1),
-    '0.86': ([576, 672], 1),
-    '0.95': ([608, 640], 1),
-    '1.05': ([640, 608], 1),
-    '1.17': ([672, 576], 1),
-    '1.29': ([704, 544], 1),
-    '1.35': ([736, 544], 1),
-    '1.50': ([768, 512], 1),
-    '1.67': ([800, 480], 1),
-    '1.73': ([832, 480], 1),
-    '2.00': ([896, 448], 1),
-    '2.31': ([960, 416], 1),
-    '2.58': ([992, 384], 1),
-    '2.75': ([1056, 384], 1),
-    '3.09': ([1088, 352], 1),
-    '3.70': ([1184, 320], 1),
-    '3.80': ([1216, 320], 1),
-    '3.90': ([1248, 320], 1),
-    '4.00': ([1280, 320], 1)
-}
-
-ASPECT_RATIO_627_F64 = {
-    '0.26': ([320, 1216], 1),
-    '0.38': ([384, 1024], 1),
-    '0.50': ([448, 896], 1),
-    '0.67': ([512, 768], 1),
-    '0.82': ([576, 704], 1),
-    '1.00': ([640, 640], 1),
-    '1.22': ([704, 576], 1),
-    '1.50': ([768, 512], 1),
-    '1.86': ([832, 448], 1),
-    '2.00': ([896, 448], 1),
-    '2.50': ([960, 384], 1),
-    '2.83': ([1088, 384], 1),
-    '3.60': ([1152, 320], 1),
-    '3.80': ([1216, 320], 1),
-    '4.00': ([1280, 320], 1)
-}
-
-ASPECT_RATIO_627_F128 = {
-    '0.25': ([256, 1024], 1),
-    '0.38': ([384, 1024], 1),
-    '0.43': ([384, 896], 1),
-    '0.57': ([512, 896], 1),
-    '0.67': ([512, 768], 1),
-    '1.00': ([640, 640], 1),
-    '1.50': ([768, 512], 1),
-    '1.75': ([896, 512], 1),
-    '2.33': ([896, 384], 1),
-    '2.67': ([1024, 384], 1),
-    '4.00': ([1024, 256], 1),
-}
-
-ASPECT_RATIO_627_F256 = {
-    '0.25': ([256, 1024], 1),
-    '0.33': ([256, 768], 1),
-    '0.50': ([256, 512], 1),
-    '0.67': ([512, 768], 1),
-    '1.00': ([512, 512], 1),
-    '1.50': ([768, 512], 1),
-    '2.00': ([512, 256], 1),
-    '3.00': ([768, 256], 1),
-    '4.00': ([1024, 256], 1),
-}
-
-ASPECT_RATIO_960 = {
-    '0.25': ([480, 1920], 1),
-    '0.29': ([512, 1792], 1),
-    '0.32': ([544, 1696], 1),
-    '0.36': ([576, 1600], 1),
-    '0.40': ([608, 1504], 1),
-    '0.49': ([672, 1376], 1),
-    '0.54': ([704, 1312], 1),
-    '0.59': ([736, 1248], 1),
-    '0.69': ([800, 1152], 1),
-    '0.74': ([832, 1120], 1),
-    '0.82': ([864, 1056], 1),
-    '0.88': ([896, 1024], 1),
-    '0.94': ([928, 992], 1),
-    '1.00': ([960, 960], 1),
-    '1.07': ([992, 928], 1),
-    '1.14': ([1024, 896], 1),
-    '1.22': ([1056, 864], 1),
-    '1.31': ([1088, 832], 1),
-    '1.35': ([1120, 832], 1),
-    '1.44': ([1152, 800], 1),
-    '1.70': ([1248, 736], 1),
-    '2.00': ([1344, 672], 1),
-    '2.05': ([1376, 672], 1),
-    '2.47': ([1504, 608], 1),
-    '2.53': ([1536, 608], 1),
-    '2.83': ([1632, 576], 1),
-    '3.06': ([1664, 544], 1),
-    '3.12': ([1696, 544], 1),
-    '3.62': ([1856, 512], 1),
-    '3.93': ([1888, 480], 1),
-    '4.00': ([1920, 480], 1)
-}
-
-ASPECT_RATIO_960_F64 = {
-    '0.22': ([448, 2048], 1),
-    '0.29': ([512, 1792], 1),
-    '0.36': ([576, 1600], 1),
-    '0.45': ([640, 1408], 1),
-    '0.55': ([704, 1280], 1),
-    '0.63': ([768, 1216], 1),
-    '0.76': ([832, 1088], 1),
-    '0.88': ([896, 1024], 1),
-    '1.00': ([960, 960], 1),
-    '1.14': ([1024, 896], 1),
-    '1.31': ([1088, 832], 1),
-    '1.50': ([1152, 768], 1),
-    '1.58': ([1216, 768], 1),
-    '1.82': ([1280, 704], 1),
-    '1.91': ([1344, 704], 1),
-    '2.20': ([1408, 640], 1),
-    '2.30': ([1472, 640], 1),
-    '2.67': ([1536, 576], 1),
-    '2.89': ([1664, 576], 1),
-    '3.62': ([1856, 512], 1),
-    '3.75': ([1920, 512], 1)
-}
-
-ASPECT_RATIO_960_F128 = {
-    '0.20': ([384, 1920], 1),
-    '0.27': ([512, 1920], 1),
-    '0.33': ([512, 1536], 1),
-    '0.42': ([640, 1536], 1),
-    '0.50': ([640, 1280], 1),
-    '0.60': ([768, 1280], 1),
-    '0.67': ([768, 1152], 1),
-    '0.78': ([896, 1152], 1),
-    '1.00': ([1024, 1024], 1),
-    '1.29': ([1152, 896], 1),
-    '1.50': ([1152, 768], 1),
-    '1.67': ([1280, 768], 1),
-    '2.00': ([1280, 640], 1),
-    '2.40': ([1536, 640], 1),
-    '3.00': ([1536, 512], 1),
-    '3.75': ([1920, 512], 1),
-    '5.00': ([1920, 384], 1),
-}
-
-ASPECT_RATIO_960_F256 = {
-    '0.33': ([512, 1536], 1),
-    '0.60': ([768, 1280], 1),
-    '1.00': ([1024, 1024], 1),
-    '1.67': ([1280, 768], 1),
-    '3.00': ([1536, 512], 1),
-}
-
-
-def get_bucket_config(resolution, scale_factor_spatial):
-    if resolution == '480p':
-        if scale_factor_spatial == 16 or scale_factor_spatial == 32:
-            return ASPECT_RATIO_627
-        elif scale_factor_spatial == 64:
-            return ASPECT_RATIO_627_F64
-        elif scale_factor_spatial == 128:
-            return ASPECT_RATIO_627_F128
-        elif scale_factor_spatial == 256:
-            return ASPECT_RATIO_627_F256
-    elif resolution == '720p':
-        if scale_factor_spatial == 16 or scale_factor_spatial == 32:
-            return ASPECT_RATIO_960
-        elif scale_factor_spatial == 64:
-            return ASPECT_RATIO_960_F64
-        elif scale_factor_spatial == 128:
-            return ASPECT_RATIO_960_F128
-        elif scale_factor_spatial == 256:
-            return ASPECT_RATIO_960_F256
-
-    raise ValueError(f"Unsupported resolution '{resolution}' or scale_factor_spatial '{scale_factor_spatial}'")
diff --git a/fastvideo/configs/pipelines/turbodiffusion.py b/fastvideo/configs/pipelines/turbodiffusion.py
deleted file mode 100644
index ae073f41b..000000000
--- a/fastvideo/configs/pipelines/turbodiffusion.py
+++ /dev/null
@@ -1,121 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-TurboDiffusion pipeline configurations.
-
-TurboDiffusion uses RCM (recurrent Consistency Model) scheduler with
-SLA (Sparse-Linear Attention) for fast 1-4 step video generation.
-"""
-from dataclasses import dataclass, field
-
-from fastvideo.configs.models import DiTConfig, EncoderConfig, VAEConfig
-from fastvideo.configs.models.dits import WanVideoConfig
-from fastvideo.configs.models.encoders import CLIPVisionConfig
-from fastvideo.configs.models.vaes import WanVAEConfig
-from fastvideo.configs.pipelines.base import PipelineConfig
-from fastvideo.models.wan.pipeline_config import t5_postprocess_text, T5Config, BaseEncoderOutput
-
-import torch
-from collections.abc import Callable
-
-
-@dataclass
-class TurboDiffusionT2VConfig(PipelineConfig):
-    """Base configuration for TurboDiffusion T2V pipeline.
-    
-    Uses RCM scheduler with sigma_max=80 for 1-4 step generation.
-    No boundary_ratio (single model, no switching).
-    """
-    # DiT
-    dit_config: DiTConfig = field(default_factory=WanVideoConfig)
-    # VAE
-    vae_config: VAEConfig = field(default_factory=WanVAEConfig)
-    vae_tiling: bool = False
-    vae_sp: bool = False
-
-    # Denoising stage
-    flow_shift: float | None = 3.0
-
-    # No boundary_ratio for T2V (single model)
-    boundary_ratio: float | None = None
-
-    # Text encoding stage
-    text_encoder_configs: tuple[EncoderConfig, ...] = field(default_factory=lambda: (T5Config(), ))
-    postprocess_text_funcs: tuple[Callable[[BaseEncoderOutput], torch.Tensor],
-                                  ...] = field(default_factory=lambda: (t5_postprocess_text, ))
-
-    # Precision for each component
-    precision: str = "bf16"
-    vae_precision: str = "fp32"
-    text_encoder_precisions: tuple[str, ...] = field(default_factory=lambda: ("fp32", ))
-
-    # self-forcing params
-    warp_denoising_step: bool = True
-
-    def __post_init__(self):
-        self.vae_config.load_encoder = False
-        self.vae_config.load_decoder = True
-        # Ensure no boundary_ratio is set in dit_config
-        self.dit_config.boundary_ratio = None
-
-
-@dataclass
-class TurboDiffusionT2V_1_3B_Config(TurboDiffusionT2VConfig):
-    """Configuration for TurboDiffusion T2V 1.3B model."""
-    pass
-
-
-@dataclass
-class TurboDiffusionT2V_14B_Config(TurboDiffusionT2VConfig):
-    """Configuration for TurboDiffusion T2V 14B model.
-    
-    Uses same config as 1.3B but with higher flow_shift for 14B model.
-    """
-    flow_shift: float | None = 5.0
-
-
-@dataclass
-class TurboDiffusionI2VConfig(PipelineConfig):
-    """Base configuration for TurboDiffusion I2V pipeline.
-    
-    Uses RCM scheduler with sigma_max=200 for 1-4 step generation.
-    Uses boundary_ratio=0.9 for high-noise to low-noise model switching.
-    """
-    # DiT
-    dit_config: DiTConfig = field(default_factory=WanVideoConfig)
-    # VAE
-    vae_config: VAEConfig = field(default_factory=WanVAEConfig)
-    vae_tiling: bool = False
-    vae_sp: bool = False
-
-    # Denoising stage
-    flow_shift: float | None = 5.0
-
-    boundary_ratio: float | None = 0.9
-
-    # Text encoding stage
-    text_encoder_configs: tuple[EncoderConfig, ...] = field(default_factory=lambda: (T5Config(), ))
-    postprocess_text_funcs: tuple[Callable[[BaseEncoderOutput], torch.Tensor],
-                                  ...] = field(default_factory=lambda: (t5_postprocess_text, ))
-
-    # Image encoder for I2V
-    image_encoder_config: EncoderConfig = field(default_factory=CLIPVisionConfig)
-    image_encoder_precision: str = "fp32"
-
-    # Precision for each component
-    precision: str = "bf16"
-    vae_precision: str = "fp32"
-    text_encoder_precisions: tuple[str, ...] = field(default_factory=lambda: ("fp32", ))
-
-    # self-forcing params
-    warp_denoising_step: bool = True
-
-    def __post_init__(self):
-        self.vae_config.load_encoder = True
-        self.vae_config.load_decoder = True
-        self.dit_config.boundary_ratio = self.boundary_ratio
-
-
-@dataclass
-class TurboDiffusionI2V_A14B_Config(TurboDiffusionI2VConfig):
-    """Configuration for TurboDiffusion I2V A14B model."""
-    pass
diff --git a/fastvideo/configs/pipelines/zimage.py b/fastvideo/configs/pipelines/zimage.py
deleted file mode 100644
index c4395fac8..000000000
--- a/fastvideo/configs/pipelines/zimage.py
+++ /dev/null
@@ -1,52 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-
-from __future__ import annotations
-
-from collections.abc import Callable
-from dataclasses import dataclass, field
-
-import torch
-
-from fastvideo.configs.models import EncoderConfig
-from fastvideo.configs.models.dits.zimage import ZImageDiTConfig
-from fastvideo.configs.models.encoders import BaseEncoderOutput
-from fastvideo.configs.models.encoders.qwen3 import Qwen3TextConfig
-from fastvideo.configs.models.vaes.autoencoder_kl import AutoencoderKLVAEConfig
-from fastvideo.configs.pipelines.base import PipelineConfig, preprocess_text
-
-
-def _zimage_text_postprocess(outputs: BaseEncoderOutput) -> torch.Tensor:
-    if outputs.hidden_states is None:
-        raise RuntimeError("Z-Image requires Qwen3 hidden states")
-    return outputs.hidden_states[-2]
-
-
-@dataclass
-class ZImagePipelineConfig(PipelineConfig):
-    """Configuration for the native Z-Image text-to-image pipeline."""
-
-    scheduler_arch: str = "FlowMatchEulerDiscreteScheduler"
-    transformer_arch: str = "ZImageTransformer2DModel"
-    vae_arch: str = "AutoencoderKL"
-    text_encoder_archs: tuple[str, ...] = ("Qwen3Model", )
-    tokenizer_archs: tuple[str, ...] = ("Qwen2Tokenizer", )
-
-    dit_config: ZImageDiTConfig = field(default_factory=ZImageDiTConfig)
-    vae_config: AutoencoderKLVAEConfig = field(default_factory=AutoencoderKLVAEConfig)
-    text_encoder_configs: tuple[EncoderConfig, ...] = field(
-        default_factory=lambda: (Qwen3TextConfig(chat_template_enable_thinking=True), ))
-    preprocess_text_funcs: tuple[Callable[[str], str], ...] = field(default_factory=lambda: (preprocess_text, ))
-    postprocess_text_funcs: tuple[Callable[[BaseEncoderOutput], torch.Tensor],
-                                  ...] = field(default_factory=lambda: (_zimage_text_postprocess, ))
-
-    dit_precision: str = "bf16"
-    vae_precision: str = "fp32"
-    text_encoder_precisions: tuple[str, ...] = field(default_factory=lambda: ("bf16", ))
-    vae_tiling: bool = False
-    vae_sp: bool = False
-
-    embedded_cfg_scale: float = 0.0
-    flow_shift: float | None = 3.0
-    scheduler_step_in_fp32: bool = True
-    scheduler_sigma_min: float = 0.0
-    scheduler_use_reference_discrete_timesteps: bool = True
diff --git a/fastvideo/envs.py b/fastvideo/envs.py
index 696a32b6d..4f84433a5 100644
--- a/fastvideo/envs.py
+++ b/fastvideo/envs.py
@@ -801,14 +801,6 @@ FASTVIDEO_TEST_GRADNORM_UPDATE = EnvBool(False,
                                          category="test",
                                          doc="Gradient-norm regression tests update their references.",
                                          deprecated_names=("FASTVIDEO_GRADNORM_UPDATE", ))
-FASTVIDEO_TEST_DREAMX_WORLD_SSIM_MODEL_PATH = EnvStr("FastVideo/DreamX-World-5B-Cam-Diffusers",
-                                                     category="test",
-                                                     doc="Model for the DreamX-World camera SSIM test.",
-                                                     deprecated_names=("DREAMX_WORLD_SSIM_MODEL_PATH", ))
-FASTVIDEO_TEST_DREAMX_WORLD_AR_SSIM_MODEL_PATH = EnvStr("FastVideo/DreamX-World-5B-Diffusers",
-                                                        category="test",
-                                                        doc="Model for the DreamX-World autoregressive SSIM test.",
-                                                        deprecated_names=("DREAMX_WORLD_AR_SSIM_MODEL_PATH", ))
 FASTVIDEO_TEST_FLUX_T2I_MODEL_DIR = EnvStr("black-forest-labs/FLUX.1-dev",
                                            category="test",
                                            doc="Model for the Flux text-to-image SSIM test.",
@@ -817,10 +809,6 @@ FASTVIDEO_TEST_FLUX_TRANSFORMER_PATH = EnvStr(None,
                                               category="test",
                                               doc="Local Flux transformer for the Flux transformer test.",
                                               deprecated_names=("FLUX_TRANSFORMER_PATH", ))
-FASTVIDEO_TEST_GAMECRAFT_MODEL_PATH = EnvStr("FastVideo/HunyuanGameCraft-Diffusers",
-                                             category="test",
-                                             doc="Model for the HunyuanGameCraft SSIM test.",
-                                             deprecated_names=("GAMECRAFT_MODEL_PATH", ))
 FASTVIDEO_TEST_GEN3C_MODEL_PATH = EnvStr("FastVideo/GEN3C-Cosmos-7B-Diffusers",
                                          category="test",
                                          doc="Model for the GEN3C SSIM test.",
@@ -829,14 +817,6 @@ FASTVIDEO_TEST_GEN3C_IMAGE_PATH = EnvStr(None,
                                          category="test",
                                          doc="Input image for the GEN3C SSIM test.",
                                          deprecated_names=("GEN3C_TEST_IMAGE_PATH", ))
-FASTVIDEO_TEST_GLM_IMAGE_LOCAL_WEIGHTS_DIR = EnvStr(None,
-                                                    category="test",
-                                                    doc="Local official GLM-Image weights for the GLM-Image SSIM test.",
-                                                    deprecated_names=("GLM_IMAGE_LOCAL_WEIGHTS_DIR", ))
-FASTVIDEO_TEST_GLM_IMAGE_MODEL_DIR = EnvStr(None,
-                                            category="test",
-                                            doc="Model for the GLM-Image SSIM test.",
-                                            deprecated_names=("GLM_IMAGE_MODEL_DIR", ))
 FASTVIDEO_TEST_KANDINSKY5_E2E_NUM_GPUS = EnvInt(1,
                                                 category="test",
                                                 doc="GPUs for the Kandinsky5 nightly end-to-end overfit test.",
@@ -846,10 +826,6 @@ FASTVIDEO_TEST_KANDINSKY5_E2E_WRITE_REFERENCE = EnvBool(
     category="test",
     doc="The Kandinsky5 nightly end-to-end test writes a missing reference video.",
     deprecated_names=("KANDINSKY5_E2E_WRITE_REFERENCE", ))
-FASTVIDEO_TEST_LONGCAT_MODEL_ROOT = EnvStr(None,
-                                           category="test",
-                                           doc="Local LongCat-Video checkpoint for the golden-gate test.",
-                                           deprecated_names=("LONGCAT_MODEL_ROOT", ))
 FASTVIDEO_TEST_MINIMAX_H3_GATE_GOLDEN_DIR = EnvStr(None,
                                                    category="test",
                                                    doc="Local directory of MiniMax-H3 golden-gate tensors.",
@@ -875,14 +851,6 @@ FASTVIDEO_TEST_WAN_ANIMATE_MODEL_DIR = EnvStr(
     category="test",
     doc="Local Wan2.2-Animate-14B checkpoint for the Wan-Animate weight tests.",
     deprecated_names=("WAN_ANIMATE_MODEL_PATH", ))
-FASTVIDEO_TEST_ZIMAGE_MODEL_DIR = EnvStr("Tongyi-MAI/Z-Image-Turbo",
-                                         category="test",
-                                         doc="Model for the Z-Image SSIM test.",
-                                         deprecated_names=("ZIMAGE_MODEL_DIR", ))
-FASTVIDEO_TEST_ZIMAGE_MODEL_REVISION = EnvStr("f332072aa78be7aecdf3ee76d5c247082da564a6",
-                                              category="test",
-                                              doc="Hugging Face revision of the Z-Image model for its SSIM test.",
-                                              deprecated_names=("ZIMAGE_MODEL_REVISION", ))
 
 # Variables that FastVideo no longer reads. Setting one logs a warning; delete
 # the entries in the next minor release.
@@ -894,6 +862,22 @@ DEPRECATED_VARIABLES = {
     "FASTVIDEO_SERVER_DEV_MODE": "no code reads it",
     "FASTVIDEO_TEST_DYNAMO_FULLGRAPH_CAPTURE": "no code reads it",
     "FASTVIDEO_TRACE_FUNCTION": "no code reads it",
+    "FASTVIDEO_TEST_DREAMX_WORLD_SSIM_MODEL_PATH": "DreamX World was removed",
+    "DREAMX_WORLD_SSIM_MODEL_PATH": "DreamX World was removed",
+    "FASTVIDEO_TEST_DREAMX_WORLD_AR_SSIM_MODEL_PATH": "DreamX World was removed",
+    "DREAMX_WORLD_AR_SSIM_MODEL_PATH": "DreamX World was removed",
+    "FASTVIDEO_TEST_GAMECRAFT_MODEL_PATH": "HunyuanGameCraft was removed",
+    "GAMECRAFT_MODEL_PATH": "HunyuanGameCraft was removed",
+    "FASTVIDEO_TEST_GLM_IMAGE_LOCAL_WEIGHTS_DIR": "GLM-Image was removed",
+    "GLM_IMAGE_LOCAL_WEIGHTS_DIR": "GLM-Image was removed",
+    "FASTVIDEO_TEST_GLM_IMAGE_MODEL_DIR": "GLM-Image was removed",
+    "GLM_IMAGE_MODEL_DIR": "GLM-Image was removed",
+    "FASTVIDEO_TEST_LONGCAT_MODEL_ROOT": "LongCat was removed",
+    "LONGCAT_MODEL_ROOT": "LongCat was removed",
+    "FASTVIDEO_TEST_ZIMAGE_MODEL_DIR": "Z-Image was removed",
+    "ZIMAGE_MODEL_DIR": "Z-Image was removed",
+    "FASTVIDEO_TEST_ZIMAGE_MODEL_REVISION": "Z-Image was removed",
+    "ZIMAGE_MODEL_REVISION": "Z-Image was removed",
 }
 
 
diff --git a/fastvideo/fastvideo_args.py b/fastvideo/fastvideo_args.py
index 1c9e6632a..f6f574021 100644
--- a/fastvideo/fastvideo_args.py
+++ b/fastvideo/fastvideo_args.py
@@ -751,37 +751,6 @@ class FastVideoArgs:
             help="Optional list of module name substrings to restrict LoRA injection (e.g. q_proj k_proj v_proj).",
         )
 
-        # BSA runtime control (LongCat)
-        parser.add_argument(
-            "--enable-bsa",
-            action=StoreBoolean,
-            help="Enable Block Sparse Attention (BSA) at runtime (overrides config).",
-        )
-        parser.add_argument(
-            "--bsa-sparsity",
-            type=float,
-            help="BSA sparsity (e.g., 0.9375).",
-        )
-        parser.add_argument(
-            "--bsa-cdf-threshold",
-            type=float,
-            help="BSA CDF threshold (optional).",
-        )
-        parser.add_argument(
-            "--bsa-chunk-q",
-            nargs=3,
-            type=int,
-            metavar=("T", "H", "W"),
-            help="BSA chunk_3d_shape_q as three ints, e.g., 4 4 4.",
-        )
-        parser.add_argument(
-            "--bsa-chunk-k",
-            nargs=3,
-            type=int,
-            metavar=("T", "H", "W"),
-            help="BSA chunk_3d_shape_k as three ints, e.g., 4 4 4.",
-        )
-
         parser.add_argument(
             "--enable-torch-compile",
             action=StoreBoolean,
@@ -1048,8 +1017,8 @@ class FastVideoArgs:
         kwargs['pipeline_config'] = PipelineConfig.from_kwargs(kwargs)
         kwargs['preprocess_config'] = PreprocessConfig.from_kwargs(kwargs)
         # Filter to only FastVideoArgs dataclass fields — pipeline-specific CLI
-        # args (e.g. enable_bsa, bsa_sparsity) live in PipelineConfig and must
-        # not be forwarded to the FastVideoArgs constructor.
+        # args live in PipelineConfig and must not be forwarded to the
+        # FastVideoArgs constructor.
         valid_fields = {f.name for f in dataclasses.fields(cls)}
         return cls(**{k: v for k, v in kwargs.items() if k in valid_fields})
 
diff --git a/fastvideo/layers/rotary_embedding.py b/fastvideo/layers/rotary_embedding.py
index 85effdce7..cd8cf59e5 100644
--- a/fastvideo/layers/rotary_embedding.py
+++ b/fastvideo/layers/rotary_embedding.py
@@ -117,7 +117,7 @@ def _apply_rotary_emb(
             positional embeddings.
     
     The function auto-detects whether cos/sin are full or half head_size:
-    - If cos/sin have head_size: use rotate_half style (for HunyuanVideo/GameCraft)
+    - If cos/sin have head_size: use rotate_half style (for HunyuanVideo)
     - If cos/sin have head_size // 2: use Neox/GPT-J style
     """
     head_size = x.shape[-1]
@@ -125,7 +125,7 @@ def _apply_rotary_emb(
 
     # Check if cos/sin are full head_dim (rotate_half style) or half (traditional style)
     if rope_dim == head_size:
-        # Full head_dim - use rotate_half style (HunyuanVideo, GameCraft)
+        # Full head_dim - use rotate_half style (HunyuanVideo)
         # x * cos + rotate_half(x) * sin
         cos = cos.unsqueeze(-2)  # [num_tokens, 1, head_size]
         sin = sin.unsqueeze(-2)  # [num_tokens, 1, head_size]
@@ -335,7 +335,7 @@ def get_1d_rotary_pos_embed(
     freqs_sin = freqs.sin()  # [S, D/2]
 
     if use_real:
-        # For rotate_half style RoPE (used by HunyuanVideo, GameCraft),
+        # For rotate_half style RoPE (used by HunyuanVideo),
         # we need to expand cos/sin to full head_dim using repeat_interleave.
         # The rotate_half operation works on consecutive PAIRS: (x0,x1), (x2,x3)...
         # so cos/sin must be interleaved: [c0,c0,c1,c1,...] to match the pairing.
diff --git a/fastvideo/models/AGENTS.md b/fastvideo/models/AGENTS.md
index 635f41eee..ab7483cd0 100644
--- a/fastvideo/models/AGENTS.md
+++ b/fastvideo/models/AGENTS.md
@@ -21,7 +21,6 @@ models/
 ├── schedulers/                 # FlowMatch / EulerDiscrete / DPM custom schedulers
 ├── upsamplers/                 # Hunyuan15 super-resolution
 ├── audio/                      # Audio-VAE/decoder modules (LTX-2 audio, Stable Audio)
-├── camera/                     # Camera-conditioning modules (Gen3C)
 └── loader/                     # component_loader.py, fsdp_load.py, weight_utils.py
 ```
 
diff --git a/fastvideo/models/camera/__init__.py b/fastvideo/models/camera/__init__.py
deleted file mode 100644
index cb032e447..000000000
--- a/fastvideo/models/camera/__init__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""Camera trajectory utilities for video generation models."""
-
-from fastvideo.models.camera.trajectory import create_camera_trajectory
-
-__all__ = ["create_camera_trajectory"]
diff --git a/fastvideo/models/camera/trajectory.py b/fastvideo/models/camera/trajectory.py
deleted file mode 100644
index 796d363b8..000000000
--- a/fastvideo/models/camera/trajectory.py
+++ /dev/null
@@ -1,387 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-Camera trajectory generation for HunyuanGameCraft.
-
-Generates Plücker coordinate embeddings from simple action commands
-(forward, backward, left, right, rotations) for camera conditioning.
-
-This is a self-contained implementation that does not depend on the
-official Hunyuan-GameCraft-1.0 repository.
-"""
-import math
-
-import numpy as np
-import torch
-from packaging import version as pver
-
-# Action name -> motion type mapping
-ACTION_DICT = {
-    "w": "forward",
-    "a": "left",
-    "d": "right",
-    "s": "backward",
-    "forward": "forward",
-    "backward": "backward",
-    "left": "left",
-    "right": "right",
-    "left_rot": "left_rot",
-    "right_rot": "right_rot",
-    "up_rot": "up_rot",
-    "down_rot": "down_rot",
-}
-
-
-def _custom_meshgrid(*args):
-    """Torch meshgrid with consistent indexing."""
-    if pver.parse(torch.__version__) < pver.parse("1.10"):
-        return torch.meshgrid(*args)
-    else:
-        return torch.meshgrid(*args, indexing="ij")
-
-
-def _generate_motion_segment(
-    current_pose: dict,
-    motion_type: str,
-    value: float,
-    duration: int = 30,
-) -> tuple[list, list, dict]:
-    """
-    Generate camera motion segment.
-
-    Args:
-        current_pose: Dict with 'position' (xyz) and 'rotation' (pitch, yaw, roll).
-        motion_type: One of 'forward', 'backward', 'left', 'right',
-                     'left_rot', 'right_rot', 'up_rot', 'down_rot'.
-        value: Translation (meters) or rotation (degrees).
-        duration: Number of frames.
-
-    Returns:
-        positions: List of position arrays.
-        rotations: List of rotation arrays.
-        current_pose: Updated pose dict.
-    """
-    positions = []
-    rotations = []
-
-    if motion_type in ["forward", "backward"]:
-        yaw_rad = np.radians(current_pose["rotation"][1])
-        pitch_rad = np.radians(current_pose["rotation"][0])
-
-        forward_vec = np.array([
-            -math.sin(yaw_rad) * math.cos(pitch_rad),
-            math.sin(pitch_rad),
-            -math.cos(yaw_rad) * math.cos(pitch_rad),
-        ])
-
-        direction = 1 if motion_type == "forward" else -1
-        total_move = forward_vec * value * direction
-        step = total_move / duration
-
-        for i in range(1, duration + 1):
-            new_pos = current_pose["position"] + step * i
-            positions.append(new_pos.copy())
-            rotations.append(current_pose["rotation"].copy())
-
-        current_pose["position"] = positions[-1]
-
-    elif motion_type in ["left", "right"]:
-        yaw_rad = np.radians(current_pose["rotation"][1])
-        right_vec = np.array([math.cos(yaw_rad), 0, -math.sin(yaw_rad)])
-
-        direction = -1 if motion_type == "right" else 1
-        total_move = right_vec * value * direction
-        step = total_move / duration
-
-        for i in range(1, duration + 1):
-            new_pos = current_pose["position"] + step * i
-            positions.append(new_pos.copy())
-            rotations.append(current_pose["rotation"].copy())
-
-        current_pose["position"] = positions[-1]
-
-    elif motion_type.endswith("rot"):
-        axis = motion_type.split("_")[0]
-        total_rotation = np.zeros(3)
-
-        if axis == "left":
-            total_rotation[0] = value
-        elif axis == "right":
-            total_rotation[0] = -value
-        elif axis == "up":
-            total_rotation[2] = -value
-        elif axis == "down":
-            total_rotation[2] = value
-
-        step = total_rotation / duration
-
-        for i in range(1, duration + 1):
-            positions.append(current_pose["position"].copy())
-            new_rot = current_pose["rotation"] + step * i
-            rotations.append(new_rot.copy())
-
-        current_pose["rotation"] = rotations[-1]
-
-    return positions, rotations, current_pose
-
-
-def _euler_to_quaternion(angles: np.ndarray) -> list[float]:
-    """Convert Euler angles (pitch, yaw, roll in degrees) to quaternion."""
-    pitch, yaw, roll = np.radians(angles)
-
-    cy = math.cos(yaw * 0.5)
-    sy = math.sin(yaw * 0.5)
-    cp = math.cos(pitch * 0.5)
-    sp = math.sin(pitch * 0.5)
-    cr = math.cos(roll * 0.5)
-    sr = math.sin(roll * 0.5)
-
-    qw = cy * cp * cr + sy * sp * sr
-    qx = cy * cp * sr - sy * sp * cr
-    qy = sy * cp * sr + cy * sp * cr
-    qz = sy * cp * cr - cy * sp * sr
-
-    return [qw, qx, qy, qz]
-
-
-def _quaternion_to_rotation_matrix(q: list[float]) -> np.ndarray:
-    """Convert quaternion to 3x3 rotation matrix."""
-    qw, qx, qy, qz = q
-    return np.array([
-        [1 - 2 * (qy**2 + qz**2), 2 * (qx * qy - qw * qz), 2 * (qx * qz + qw * qy)],
-        [2 * (qx * qy + qw * qz), 1 - 2 * (qx**2 + qz**2), 2 * (qy * qz - qw * qx)],
-        [2 * (qx * qz - qw * qy), 2 * (qy * qz + qw * qx), 1 - 2 * (qx**2 + qy**2)],
-    ])
-
-
-def _action_to_pose_list(action_id: str, value: float = 0.2, duration: int = 33) -> list[str]:
-    """
-    Convert an action ID to a list of pose strings.
-
-    Args:
-        action_id: Action identifier (e.g., 'w', 'forward', 'left_rot').
-        value: Motion magnitude (translation in meters, rotation in degrees).
-        duration: Number of frames.
-
-    Returns:
-        List of pose strings in the official GameCraft format.
-    """
-    all_positions = []
-    all_rotations = []
-    current_pose = {
-        "position": np.array([0.0, 0.0, 0.0]),
-        "rotation": np.array([0.0, 0.0, 0.0]),
-    }
-    intrinsic = [0.50505, 0.8979, 0.5, 0.5]
-
-    motion_type = ACTION_DICT.get(action_id, action_id)
-    positions, rotations, current_pose = _generate_motion_segment(current_pose, motion_type, value, duration)
-    all_positions.extend(positions)
-    all_rotations.extend(rotations)
-
-    pose_list = []
-
-    # First frame: identity pose
-    row = [0] + intrinsic + [0, 0] + [1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0]
-    first_row = " ".join(map(str, row))
-    pose_list.append(first_row)
-
-    for i, (pos, rot) in enumerate(zip(all_positions, all_rotations)):
-        quat = _euler_to_quaternion(rot)
-        R = _quaternion_to_rotation_matrix(quat)
-        extrinsic = np.hstack([R, pos.reshape(3, 1)])
-
-        row = [i] + intrinsic + [0, 0] + extrinsic.flatten().tolist()
-        pose_list.append(" ".join(map(str, row)))
-
-    return pose_list
-
-
-class _Camera:
-    """Camera parameters from a pose string."""
-
-    def __init__(self, entry: list[float]):
-        fx, fy, cx, cy = entry[1:5]
-        self.fx = fx
-        self.fy = fy
-        self.cx = cx
-        self.cy = cy
-        w2c_mat = np.array(entry[7:]).reshape(3, 4)
-        w2c_mat_4x4 = np.eye(4)
-        w2c_mat_4x4[:3, :] = w2c_mat
-        self.w2c_mat = w2c_mat_4x4
-        self.c2w_mat = np.linalg.inv(w2c_mat_4x4)
-
-
-def _get_relative_pose(cam_params: list[_Camera]) -> np.ndarray:
-    """Convert camera parameters to relative poses."""
-    abs_w2cs = [cam_param.w2c_mat for cam_param in cam_params]
-    abs_c2ws = [cam_param.c2w_mat for cam_param in cam_params]
-    target_cam_c2w = np.array([
-        [1, 0, 0, 0],
-        [0, 1, 0, 0],
-        [0, 0, 1, 0],
-        [0, 0, 0, 1],
-    ])
-    abs2rel = target_cam_c2w @ abs_w2cs[0]
-    ret_poses = [target_cam_c2w] + [abs2rel @ abs_c2w for abs_c2w in abs_c2ws[1:]]
-    for pose in ret_poses:
-        pose[:3, -1:] *= 10
-    ret_poses = np.array(ret_poses, dtype=np.float32)
-    return ret_poses
-
-
-def _get_c2w(w2cs: list[np.ndarray], transform_matrix: np.ndarray) -> np.ndarray:
-    """Convert w2c matrices to c2w with relative transform."""
-    target_cam_c2w = np.array([
-        [1, 0, 0, 0],
-        [0, 1, 0, 0],
-        [0, 0, 1, 0],
-        [0, 0, 0, 1],
-    ])
-    abs2rel = target_cam_c2w @ w2cs[0]
-    ret_poses = [target_cam_c2w] + [abs2rel @ np.linalg.inv(w2c) for w2c in w2cs[1:]]
-    for pose in ret_poses:
-        pose[:3, -1:] *= 2
-    ret_poses = [transform_matrix @ x for x in ret_poses]
-    return np.array(ret_poses, dtype=np.float32)
-
-
-def _ray_condition(
-    K: torch.Tensor,
-    c2w: torch.Tensor,
-    H: int,
-    W: int,
-    device: str | torch.device = "cpu",
-    flip_flag: torch.Tensor | None = None,
-) -> torch.Tensor:
-    """
-    Compute Plücker coordinates from camera intrinsics and extrinsics.
-
-    Args:
-        K: Intrinsics [B, V, 4] (fx, fy, cx, cy).
-        c2w: Camera-to-world matrices [B, V, 4, 4].
-        H: Image height.
-        W: Image width.
-        device: Torch device.
-        flip_flag: Optional flip flags [V].
-
-    Returns:
-        Plücker coordinates [B, V, H, W, 6].
-    """
-    B, V = K.shape[:2]
-
-    j, i = _custom_meshgrid(
-        torch.linspace(0, H - 1, H, device=device, dtype=c2w.dtype),
-        torch.linspace(0, W - 1, W, device=device, dtype=c2w.dtype),
-    )
-    i = i.reshape([1, 1, H * W]).expand([B, V, H * W]) + 0.5
-    j = j.reshape([1, 1, H * W]).expand([B, V, H * W]) + 0.5
-
-    n_flip = torch.sum(flip_flag).item() if flip_flag is not None else 0
-    if n_flip > 0:
-        j_flip, i_flip = _custom_meshgrid(
-            torch.linspace(0, H - 1, H, device=device, dtype=c2w.dtype),
-            torch.linspace(W - 1, 0, W, device=device, dtype=c2w.dtype),
-        )
-        i_flip = i_flip.reshape([1, 1, H * W]).expand(B, 1, H * W) + 0.5
-        j_flip = j_flip.reshape([1, 1, H * W]).expand(B, 1, H * W) + 0.5
-        i[:, flip_flag, ...] = i_flip
-        j[:, flip_flag, ...] = j_flip
-
-    fx, fy, cx, cy = K.chunk(4, dim=-1)
-
-    zs = torch.ones_like(i)
-    xs = (i - cx) / fx * zs
-    ys = (j - cy) / fy * zs
-    zs = zs.expand_as(ys)
-
-    directions = torch.stack((xs, ys, zs), dim=-1)
-    directions = directions / directions.norm(dim=-1, keepdim=True)
-
-    rays_d = directions @ c2w[..., :3, :3].transpose(-1, -2)
-    rays_o = c2w[..., :3, 3]
-    rays_o = rays_o[:, :, None].expand_as(rays_d)
-    rays_dxo = torch.linalg.cross(rays_o, rays_d)
-    plucker = torch.cat([rays_dxo, rays_d], dim=-1)
-    plucker = plucker.reshape(B, c2w.shape[1], H, W, 6)
-    return plucker
-
-
-def create_camera_trajectory(
-    action: str,
-    height: int,
-    width: int,
-    num_frames: int,
-    action_speed: float = 0.2,
-    device: torch.device | str = "cpu",
-    dtype: torch.dtype = torch.bfloat16,
-) -> torch.Tensor:
-    """
-    Create Plücker coordinate embeddings from an action command.
-
-    Args:
-        action: One of 'forward', 'backward', 'left', 'right',
-                'left_rot', 'right_rot', 'up_rot', 'down_rot'
-                (or shorthand 'w', 'a', 's', 'd').
-        height: Video height in pixels.
-        width: Video width in pixels.
-        num_frames: Number of video frames.
-        action_speed: Speed of motion (default 0.2).
-        device: Torch device for output tensor.
-        dtype: Torch dtype for output tensor.
-
-    Returns:
-        camera_states: [1, num_frames, 6, height, width] Plücker embeddings.
-    """
-    # Generate pose list from action
-    poses = _action_to_pose_list(action, value=action_speed, duration=num_frames)
-
-    # Parse poses
-    poses_parsed = [pose.split(" ") for pose in poses]
-    start_idx = 0
-    sample_id = [start_idx + i for i in range(num_frames)]
-    poses_parsed = [poses_parsed[i] for i in sample_id]
-
-    # Convert to w2c matrices
-    w2cs = [np.asarray([float(p) for p in pose[7:]]).reshape(3, 4) for pose in poses_parsed]
-    transform_matrix = np.asarray([[1, 0, 0, 0], [0, 0, 1, 0], [0, -1, 0, 0], [0, 0, 0, 1]]).reshape(4, 4)
-    last_row = np.zeros((1, 4))
-    last_row[0, -1] = 1.0
-    w2cs = [np.concatenate((w2c, last_row), axis=0) for w2c in w2cs]
-    c2ws = _get_c2w(w2cs, transform_matrix)
-
-    # Parse camera parameters
-    cam_params = [[float(x) for x in pose] for pose in poses_parsed]
-    assert len(cam_params) == num_frames
-    cam_params = [_Camera(cam_param) for cam_param in cam_params]
-
-    # Compute scaled intrinsics
-    monst3r_w = cam_params[0].cx * 2
-    monst3r_h = cam_params[0].cy * 2
-    ratio_w, ratio_h = width / monst3r_w, height / monst3r_h
-    intrinsics = np.asarray(
-        [[
-            cam_param.fx * ratio_w,
-            cam_param.fy * ratio_h,
-            cam_param.cx * ratio_w,
-            cam_param.cy * ratio_h,
-        ] for cam_param in cam_params],
-        dtype=np.float32,
-    )
-    intrinsics = torch.as_tensor(intrinsics)[None]  # [1, n_frame, 4]
-
-    # Get relative poses
-    c2w_poses = _get_relative_pose(cam_params)
-    c2w = torch.as_tensor(c2w_poses)[None]  # [1, n_frame, 4, 4]
-
-    # Compute Plücker embeddings
-    flip_flag = torch.zeros(num_frames, dtype=torch.bool, device="cpu")
-    plucker_embedding = _ray_condition(intrinsics, c2w, height, width, device="cpu", flip_flag=flip_flag)
-    # [1, n_frame, H, W, 6] -> [1, n_frame, 6, H, W]
-    plucker_embedding = plucker_embedding[0].permute(0, 3, 1, 2).contiguous()
-
-    # Add batch dim and convert to target dtype/device
-    # Shape: [n_frame, 6, H, W] -> [1, n_frame, 6, H, W]
-    camera_states = plucker_embedding.unsqueeze(0).to(device=device, dtype=dtype)
-
-    return camera_states
diff --git a/fastvideo/models/dits/dreamx_world.py b/fastvideo/models/dits/dreamx_world.py
deleted file mode 100644
index ba1d3ca8d..000000000
--- a/fastvideo/models/dits/dreamx_world.py
+++ /dev/null
@@ -1,452 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-
-import math
-from typing import Any
-
-import torch
-import torch.nn as nn
-
-from fastvideo.configs.models.dits.dreamx_world import DreamXWorldConfig
-from fastvideo.distributed.communication_op import (sequence_model_parallel_all_gather_with_unpad,
-                                                    sequence_model_parallel_shard)
-from fastvideo.distributed.parallel_state import get_sp_world_size
-from fastvideo.layers.layernorm import RMSNorm
-from fastvideo.layers.linear import ReplicatedLinear
-from fastvideo.layers.rotary_embedding import get_rotary_pos_embed
-from fastvideo.models.wan.transformer import (LayerNormScaleShift, PatchEmbed, WanTimeTextImageEmbedding,
-                                            WanTransformer3DModel, WanTransformerBlock)
-from fastvideo.platforms import AttentionBackendEnum, current_platform
-from fastvideo.attention import LocalAttention
-from fastvideo.distributed.parallel_state import get_sp_world_size
-from fastvideo.layers.quantization import QuantizationConfig
-from fastvideo.models.dits.base import BaseDiT
-
-
-def _dreamx_invert_se3(transforms: torch.Tensor) -> torch.Tensor:
-    assert transforms.shape[-2:] == (4, 4)
-    rot_inv = transforms[..., :3, :3].transpose(-1, -2)
-    out = torch.zeros_like(transforms)
-    out[..., :3, :3] = rot_inv
-    out[..., :3, 3] = -torch.einsum("...ij,...j->...i", rot_inv, transforms[..., :3, 3])
-    out[..., 3, 3] = 1.0
-    return out.to(dtype=transforms.dtype)
-
-
-def _dreamx_lift_k(intrinsics: torch.Tensor) -> torch.Tensor:
-    assert intrinsics.shape[-2:] == (3, 3)
-    out = torch.zeros(intrinsics.shape[:-2] + (4, 4), device=intrinsics.device, dtype=intrinsics.dtype)
-    out[..., :3, :3] = intrinsics
-    out[..., 3, 3] = 1.0
-    return out
-
-
-def _dreamx_invert_k(intrinsics: torch.Tensor) -> torch.Tensor:
-    assert intrinsics.shape[-2:] == (3, 3)
-    out = torch.zeros_like(intrinsics)
-    out[..., 0, 0] = 1.0 / intrinsics[..., 0, 0]
-    out[..., 1, 1] = 1.0 / intrinsics[..., 1, 1]
-    out[..., 0, 2] = -intrinsics[..., 0, 2] / intrinsics[..., 0, 0]
-    out[..., 1, 2] = -intrinsics[..., 1, 2] / intrinsics[..., 1, 1]
-    out[..., 2, 2] = 1.0
-    return out.to(dtype=intrinsics.dtype)
-
-
-def _dreamx_apply_tiled_projmat(feats: torch.Tensor, matrix: torch.Tensor) -> torch.Tensor:
-    batch, num_heads, seq_len, feat_dim = feats.shape
-    proj_dim = matrix.shape[-1]
-    assert feat_dim % proj_dim == 0
-
-    if matrix.shape[1] == seq_len:
-        feats = feats.view(batch, num_heads, seq_len, feat_dim // proj_dim, proj_dim)
-        out = torch.einsum("btij,bntpj->bntpi", matrix, feats)
-        return out.reshape(batch, num_heads, seq_len, feat_dim)
-
-    cameras = matrix.shape[1]
-    assert seq_len > cameras and seq_len % cameras == 0
-    feats = feats.reshape(batch, num_heads, cameras, -1, feat_dim // proj_dim, proj_dim)
-    out = torch.einsum("bcij,bncpkj->bncpki", matrix, feats)
-    return out.reshape(batch, num_heads, seq_len, feat_dim)
-
-
-def _dreamx_prope_qkv(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, viewmats: torch.Tensor,
-                      intrinsics: torch.Tensor):
-    batch, num_heads, seq_len, head_dim = q.shape
-    cameras = viewmats.shape[1]
-    assert q.shape == k.shape == v.shape
-    assert viewmats.shape == (batch, cameras, 4, 4)
-    assert intrinsics.shape == (batch, cameras, 3, 3)
-    assert head_dim % 4 == 0
-
-    intrinsics_norm = torch.zeros_like(intrinsics)
-    intrinsics_norm[..., 0, 0] = intrinsics[..., 0, 0]
-    intrinsics_norm[..., 1, 1] = intrinsics[..., 1, 1]
-    intrinsics_norm[..., 2, 2] = 1.0
-
-    proj = torch.einsum("...ij,...jk->...ik", _dreamx_lift_k(intrinsics_norm), viewmats)
-    proj_t = proj.transpose(-1, -2).to(dtype=viewmats.dtype)
-    proj_inv = torch.einsum(
-        "...ij,...jk->...ik",
-        _dreamx_invert_se3(viewmats),
-        _dreamx_lift_k(_dreamx_invert_k(intrinsics_norm)),
-    ).to(dtype=viewmats.dtype)
-
-    q = _dreamx_apply_tiled_projmat(q, proj_t)
-    k = _dreamx_apply_tiled_projmat(k, proj_inv)
-    v = _dreamx_apply_tiled_projmat(v, proj_inv)
-    return q, k, v, proj
-
-
-class DreamXPropeSelfAttention(nn.Module):
-    """DreamX-World parallel PRoPE camera self-attention branch."""
-
-    def __init__(self,
-                 dim: int,
-                 attn_dim: int,
-                 num_heads: int,
-                 qk_norm: str | bool = True,
-                 eps: float = 1e-6,
-                 quant_config: QuantizationConfig | None = None,
-                 prefix: str = ""):
-        super().__init__()
-        assert attn_dim % num_heads == 0
-        self.attn_dim = attn_dim
-        self.num_heads = num_heads
-        self.head_dim = attn_dim // num_heads
-        self.qk_norm = qk_norm
-
-        self.q_proj = ReplicatedLinear(dim, attn_dim, quant_config=quant_config, prefix=f"{prefix}.q_proj")
-        self.k_proj = ReplicatedLinear(dim, attn_dim, quant_config=quant_config, prefix=f"{prefix}.k_proj")
-        self.v_proj = ReplicatedLinear(dim, attn_dim, quant_config=quant_config, prefix=f"{prefix}.v_proj")
-        self.out_proj = ReplicatedLinear(attn_dim, dim, quant_config=quant_config, prefix=f"{prefix}.out_proj")
-
-        if qk_norm == "rms_norm":
-            self.norm_q = RMSNorm(self.head_dim, eps=eps)
-            self.norm_k = RMSNorm(self.head_dim, eps=eps)
-        elif qk_norm in (True, "rms_norm_across_heads"):
-            self.norm_q = RMSNorm(attn_dim, eps=eps)
-            self.norm_k = RMSNorm(attn_dim, eps=eps)
-        elif qk_norm is False:
-            self.norm_q = nn.Identity()
-            self.norm_k = nn.Identity()
-        else:
-            raise ValueError(f"Unsupported qk_norm for DreamX PRoPE: {qk_norm}")
-
-        nn.init.zeros_(self.out_proj.weight)
-        if self.out_proj.bias is not None:
-            nn.init.zeros_(self.out_proj.bias)
-
-        self.attn = LocalAttention(num_heads=num_heads,
-                                   head_size=self.head_dim,
-                                   dropout_rate=0,
-                                   softmax_scale=None,
-                                   causal=False,
-                                   supported_attention_backends=(AttentionBackendEnum.FLASH_ATTN,
-                                                                 AttentionBackendEnum.TORCH_SDPA))
-
-    def forward(self, hidden_states: torch.Tensor, y_camera: dict[str, torch.Tensor]) -> torch.Tensor:
-        if get_sp_world_size() > 1:
-            # The transformer shards the sequence before the block loop and
-            # this branch uses LocalAttention (no all-to-all): under
-            # sequence parallelism each rank would attend only within its
-            # own shard — silently wrong output. Fail loudly until this
-            # path is ported to DistributedAttention and validated.
-            raise NotImplementedError("DreamXPropeSelfAttention does not support sequence "
-                                      "parallelism yet (LocalAttention on a sharded sequence "
-                                      "corrupts output). Run with sp_size=1.")
-        batch_size, seq_len, _ = hidden_states.shape
-
-        query, _ = self.q_proj(hidden_states)
-        key, _ = self.k_proj(hidden_states)
-        value, _ = self.v_proj(hidden_states)
-
-        if self.qk_norm == "rms_norm":
-            query = query.view(batch_size, seq_len, self.num_heads, self.head_dim)
-            key = key.view(batch_size, seq_len, self.num_heads, self.head_dim)
-            query = self.norm_q(query)
-            key = self.norm_k(key)
-        else:
-            query = self.norm_q(query).view(batch_size, seq_len, self.num_heads, self.head_dim)
-            key = self.norm_k(key).view(batch_size, seq_len, self.num_heads, self.head_dim)
-
-        value = value.view(batch_size, seq_len, self.num_heads, self.head_dim)
-        query = query.transpose(1, 2)
-        key = key.transpose(1, 2)
-        value = value.transpose(1, 2)
-
-        query, key, value, output_projection = _dreamx_prope_qkv(
-            query,
-            key,
-            value,
-            viewmats=y_camera["viewmats"],
-            intrinsics=y_camera["K"],
-        )
-
-        out = self.attn(query.transpose(1, 2), key.transpose(1, 2), value.transpose(1, 2))
-        out = _dreamx_apply_tiled_projmat(out.transpose(1, 2), output_projection).transpose(1, 2)
-        out = out.flatten(2)
-        out, _ = self.out_proj(out)
-        return out
-
-
-class DreamXWorldTransformerBlock(WanTransformerBlock):
-
-    def __init__(self,
-                 dim: int,
-                 ffn_dim: int,
-                 num_heads: int,
-                 qk_norm: str = "rms_norm_across_heads",
-                 cross_attn_norm: bool = False,
-                 eps: float = 1e-6,
-                 added_kv_proj_dim: int | None = None,
-                 supported_attention_backends: tuple[AttentionBackendEnum, ...]
-                 | None = None,
-                 quant_config: QuantizationConfig | None = None,
-                 prefix: str = "",
-                 add_control_adapter: bool = True,
-                 cam_method: str | None = "prope",
-                 attn_compress: int = 1,
-                 cam_self_attn_layers: tuple[int, ...] | None = None,
-                 layer_idx: int | None = None):
-        super().__init__(dim, ffn_dim, num_heads, qk_norm, cross_attn_norm, eps, added_kv_proj_dim,
-                         supported_attention_backends, quant_config, prefix)
-        self.cam_self_attn = None
-        add_cam_attn = add_control_adapter and cam_method == "prope"
-        if add_cam_attn and cam_self_attn_layers is not None:
-            add_cam_attn = layer_idx in cam_self_attn_layers
-        if add_cam_attn:
-            if num_heads % attn_compress != 0 or dim % attn_compress != 0:
-                raise ValueError("DreamX attn_compress must divide dim and num_heads")
-            self.cam_self_attn = DreamXPropeSelfAttention(dim,
-                                                          dim // attn_compress,
-                                                          num_heads // attn_compress,
-                                                          qk_norm=qk_norm,
-                                                          eps=eps,
-                                                          quant_config=quant_config,
-                                                          prefix=f"{prefix}.cam_self_attn")
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        encoder_hidden_states: torch.Tensor,
-        temb: torch.Tensor,
-        freqs_cis: tuple[torch.Tensor, torch.Tensor],
-        original_seq_len: int,
-        y_camera: dict[str, torch.Tensor] | None = None,
-    ) -> torch.Tensor:
-        if hidden_states.dim() == 4:
-            hidden_states = hidden_states.squeeze(1)
-        orig_dtype = hidden_states.dtype
-
-        if temb.dim() == 4:
-            shift_msa, scale_msa, gate_msa, c_shift_msa, c_scale_msa, c_gate_msa = (
-                self.scale_shift_table.unsqueeze(0) + temb.float()).chunk(6, dim=2)
-            shift_msa = shift_msa.squeeze(2)
-            scale_msa = scale_msa.squeeze(2)
-            gate_msa = gate_msa.squeeze(2)
-            c_shift_msa = c_shift_msa.squeeze(2)
-            c_scale_msa = c_scale_msa.squeeze(2)
-            c_gate_msa = c_gate_msa.squeeze(2)
-        else:
-            e = self.scale_shift_table + temb.float()
-            shift_msa, scale_msa, gate_msa, c_shift_msa, c_scale_msa, c_gate_msa = e.chunk(6, dim=1)
-        assert shift_msa.dtype == torch.float32
-
-        norm_hidden_states = (self.norm1(hidden_states.float()) * (1 + scale_msa) + shift_msa).to(orig_dtype)
-        query, _ = self.to_q(norm_hidden_states)
-        key, _ = self.to_k(norm_hidden_states)
-        value, _ = self.to_v(norm_hidden_states)
-
-        if self.norm_q is not None:
-            query = self.norm_q(query)
-        if self.norm_k is not None:
-            key = self.norm_k(key)
-
-        query = query.squeeze(1).unflatten(2, (self.num_attention_heads, -1))
-        key = key.squeeze(1).unflatten(2, (self.num_attention_heads, -1))
-        value = value.squeeze(1).unflatten(2, (self.num_attention_heads, -1))
-
-        attn_output, _ = self.attn1(
-            query,
-            key,
-            value,
-            original_seq_len,
-            freqs_cis=freqs_cis,
-        )
-        attn_output = attn_output.flatten(2)
-        attn_output, _ = self.to_out(attn_output)
-        attn_output = attn_output.squeeze(1)
-        if self.cam_self_attn is not None and y_camera is not None:
-            attn_output = attn_output + self.cam_self_attn(norm_hidden_states, y_camera)
-
-        null_shift = null_scale = torch.tensor([0], device=hidden_states.device)
-        norm_hidden_states, hidden_states = self.self_attn_residual_norm(hidden_states, attn_output, gate_msa,
-                                                                         null_shift, null_scale)
-        norm_hidden_states, hidden_states = norm_hidden_states.to(orig_dtype), hidden_states.to(orig_dtype)
-
-        attn_output = self.attn2(norm_hidden_states, context=encoder_hidden_states, context_lens=None)
-        norm_hidden_states, hidden_states = self.cross_attn_residual_norm(hidden_states, attn_output, 1, c_shift_msa,
-                                                                          c_scale_msa)
-        norm_hidden_states, hidden_states = norm_hidden_states.to(orig_dtype), hidden_states.to(orig_dtype)
-
-        ff_output = self.ffn(norm_hidden_states)
-        hidden_states = self.mlp_residual(hidden_states, ff_output, c_gate_msa)
-        hidden_states = hidden_states.to(orig_dtype)
-
-        return hidden_states
-
-
-class DreamXWorldTransformer3DModel(WanTransformer3DModel):
-    _fsdp_shard_conditions = DreamXWorldConfig()._fsdp_shard_conditions
-    _compile_conditions = DreamXWorldConfig()._compile_conditions
-    _supported_attention_backends = DreamXWorldConfig()._supported_attention_backends
-    param_names_mapping = DreamXWorldConfig().param_names_mapping
-    reverse_param_names_mapping = DreamXWorldConfig().reverse_param_names_mapping
-    lora_param_names_mapping = DreamXWorldConfig().lora_param_names_mapping
-
-    def __init__(self, config: DreamXWorldConfig, hf_config: dict[str, Any]) -> None:
-        BaseDiT.__init__(self, config=config, hf_config=hf_config)
-        self.quant_config = config.quant_config
-
-        inner_dim = config.num_attention_heads * config.attention_head_dim
-        self.hidden_size = config.hidden_size
-        self.num_attention_heads = config.num_attention_heads
-        self.in_channels = config.in_channels
-        self.out_channels = config.out_channels
-        self.num_channels_latents = config.num_channels_latents
-        self.patch_size = config.patch_size
-        self.text_len = config.text_len
-
-        assert config.num_attention_heads % get_sp_world_size(
-        ) == 0, f"The number of attention heads ({config.num_attention_heads}) must be divisible by the sequence parallel size ({get_sp_world_size()})"
-
-        self.patch_embedding = PatchEmbed(in_chans=config.in_channels,
-                                          embed_dim=inner_dim,
-                                          patch_size=config.patch_size,
-                                          flatten=False)
-        self.condition_embedder = WanTimeTextImageEmbedding(
-            dim=inner_dim,
-            time_freq_dim=config.freq_dim,
-            text_embed_dim=config.text_dim,
-            image_embed_dim=config.image_dim,
-        )
-        self.blocks = nn.ModuleList([
-            DreamXWorldTransformerBlock(inner_dim,
-                                        config.ffn_dim,
-                                        config.num_attention_heads,
-                                        config.qk_norm,
-                                        config.cross_attn_norm,
-                                        config.eps,
-                                        config.added_kv_proj_dim,
-                                        self._supported_attention_backends,
-                                        quant_config=config.quant_config,
-                                        prefix=f"{config.prefix}.blocks.{i}",
-                                        add_control_adapter=config.add_control_adapter,
-                                        cam_method=config.cam_method,
-                                        attn_compress=config.attn_compress,
-                                        cam_self_attn_layers=config.cam_self_attn_layers,
-                                        layer_idx=i) for i in range(config.num_layers)
-        ])
-        self.norm_out = LayerNormScaleShift(inner_dim,
-                                            norm_type="layer",
-                                            eps=config.eps,
-                                            elementwise_affine=False,
-                                            dtype=torch.float32,
-                                            compute_dtype=torch.float32)
-        self.proj_out = nn.Linear(inner_dim, config.out_channels * math.prod(config.patch_size))
-        self.scale_shift_table = nn.Parameter(torch.randn(1, 2, inner_dim) / inner_dim**0.5)
-        self.gradient_checkpointing = False
-        self.__post_init__()
-
-    def forward(self,
-                hidden_states: torch.Tensor,
-                encoder_hidden_states: torch.Tensor | list[torch.Tensor],
-                timestep: torch.LongTensor,
-                encoder_hidden_states_image: torch.Tensor | list[torch.Tensor]
-                | None = None,
-                guidance=None,
-                y_camera: dict[str, torch.Tensor] | None = None,
-                **kwargs) -> torch.Tensor:
-        orig_dtype = hidden_states.dtype
-        if encoder_hidden_states is not None and not isinstance(encoder_hidden_states, torch.Tensor):
-            encoder_hidden_states = encoder_hidden_states[0]
-        if isinstance(encoder_hidden_states_image, list):
-            encoder_hidden_states_image = (encoder_hidden_states_image[0]
-                                           if len(encoder_hidden_states_image) > 0 else None)
-
-        batch_size, _, num_frames, height, width = hidden_states.shape
-        p_t, p_h, p_w = self.patch_size
-        post_patch_num_frames = num_frames // p_t
-        post_patch_height = height // p_h
-        post_patch_width = width // p_w
-
-        d = self.hidden_size // self.num_attention_heads
-        rope_dim_list = [d - 4 * (d // 6), 2 * (d // 6), 2 * (d // 6)]
-        freqs_cos, freqs_sin = get_rotary_pos_embed((post_patch_num_frames, post_patch_height, post_patch_width),
-                                                    self.hidden_size,
-                                                    self.num_attention_heads,
-                                                    rope_dim_list,
-                                                    dtype=torch.float32 if current_platform.is_mps() else torch.float64,
-                                                    rope_theta=10000)
-        freqs_cis = (freqs_cos.to(hidden_states.device).float(), freqs_sin.to(hidden_states.device).float())
-
-        hidden_states = self.patch_embedding(hidden_states)
-        hidden_states = hidden_states.flatten(2).transpose(1, 2)
-        hidden_states, original_seq_len = sequence_model_parallel_shard(hidden_states, dim=1)
-
-        if timestep.dim() == 2:
-            ts_seq_len = timestep.shape[1]
-            timestep = timestep.flatten()
-        else:
-            ts_seq_len = None
-
-        temb, timestep_proj, encoder_hidden_states, encoder_hidden_states_image = self.condition_embedder(
-            timestep, encoder_hidden_states, encoder_hidden_states_image, timestep_seq_len=ts_seq_len)
-        if ts_seq_len is not None:
-            timestep_proj = timestep_proj.unflatten(2, (6, -1))
-        else:
-            timestep_proj = timestep_proj.unflatten(1, (6, -1))
-
-        if encoder_hidden_states_image is not None:
-            if encoder_hidden_states is not None:
-                encoder_hidden_states = torch.concat([encoder_hidden_states_image, encoder_hidden_states], dim=1)
-            else:
-                encoder_hidden_states = encoder_hidden_states_image
-
-        if current_platform.is_mps() or current_platform.is_npu():
-            encoder_hidden_states = encoder_hidden_states.to(orig_dtype)
-
-        assert encoder_hidden_states.dtype == orig_dtype
-
-        if torch.is_grad_enabled() and self.gradient_checkpointing:
-            for block in self.blocks:
-                hidden_states = self._gradient_checkpointing_func(block, hidden_states, encoder_hidden_states,
-                                                                  timestep_proj, freqs_cis, original_seq_len, y_camera)
-        else:
-            for block in self.blocks:
-                hidden_states = block(hidden_states,
-                                      encoder_hidden_states,
-                                      timestep_proj,
-                                      freqs_cis,
-                                      original_seq_len,
-                                      y_camera=y_camera)
-
-        if temb.dim() == 3:
-            shift, scale = (self.scale_shift_table.unsqueeze(0) + temb.unsqueeze(2)).chunk(2, dim=2)
-            shift = shift.squeeze(2)
-            scale = scale.squeeze(2)
-        else:
-            shift, scale = (self.scale_shift_table + temb.unsqueeze(1)).chunk(2, dim=1)
-
-        hidden_states = self.norm_out(hidden_states, shift, scale)
-        hidden_states = sequence_model_parallel_all_gather_with_unpad(hidden_states, original_seq_len, dim=1)
-        hidden_states = self.proj_out(hidden_states)
-
-        hidden_states = hidden_states.reshape(batch_size, post_patch_num_frames, post_patch_height, post_patch_width,
-                                              p_t, p_h, p_w, -1)
-        hidden_states = hidden_states.permute(0, 7, 1, 4, 2, 5, 3, 6)
-        output = hidden_states.flatten(6, 7).flatten(4, 5).flatten(2, 3)
-
-        return output
-
-
-EntryClass = DreamXWorldTransformer3DModel
diff --git a/fastvideo/models/dits/dreamx_world_ar.py b/fastvideo/models/dits/dreamx_world_ar.py
deleted file mode 100644
index cc001ff40..000000000
--- a/fastvideo/models/dits/dreamx_world_ar.py
+++ /dev/null
@@ -1,982 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""DreamX-World autoregressive causal DiT.
-
-Adapted from DreamX-World's Apache-2.0
-``wan/modules/causal_camera_model_2_2_prope_infinity.py``. The implementation is
-kept native to FastVideo: no production import from DreamX, Diffusers, or
-Transformers is required.
-"""
-
-from typing import Any
-
-import math
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from fastvideo.configs.models.dits.dreamx_world import DreamXWorldARConfig
-from fastvideo.layers.linear import ReplicatedLinear
-from fastvideo.models.dits.base import BaseDiT
-from fastvideo.models.dits.dreamx_world import (_dreamx_apply_tiled_projmat, _dreamx_prope_qkv)
-
-
-def attention(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor) -> torch.Tensor:
-    # Deliberately raw SDPA rather than fastvideo.attention.LocalAttention:
-    # (1) LocalAttention dispatches through the attention-backend registry, so
-    #     FLASH_ATTN could be selected and its kernel is not bit-identical to
-    #     torch SDPA — the AR KV-cache rollout must stay numerically frozen;
-    # (2) LocalAttention requires an active ForwardContext, which direct
-    #     transformer invocations (parity tests) do not set;
-    # (3) the sibling causal model keeps raw SDPA in the same KV-cache window
-    #     path (matrixgame2/causal_model.py).
-    # Sequence-parallel gap: this model never shards the sequence; run with
-    # sp_size=1 (see fastvideo/layers/AGENTS.md on documenting raw SDPA).
-    q_bhld = q.transpose(1, 2)
-    k_bhld = k.transpose(1, 2)
-    v_bhld = v.transpose(1, 2)
-    out = F.scaled_dot_product_attention(q_bhld, k_bhld, v_bhld, dropout_p=0.0)
-    return out.transpose(1, 2)
-
-
-def prope_qkv(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, viewmats: torch.Tensor, Ks: torch.Tensor):
-    q, k, v, output_projection = _dreamx_prope_qkv(q, k, v, viewmats, Ks)
-
-    def apply_fn_o(x: torch.Tensor) -> torch.Tensor:
-        return _dreamx_apply_tiled_projmat(x, output_projection)
-
-    return q, k, v, apply_fn_o
-
-
-def sinusoidal_embedding_1d(dim, position):
-    assert dim % 2 == 0
-    half = dim // 2
-    position = position.type(torch.float64)
-    sinusoid = torch.outer(position, torch.pow(10000, -torch.arange(half).to(position).div(half)))
-    return torch.cat([torch.cos(sinusoid), torch.sin(sinusoid)], dim=1)
-
-
-def rope_params(max_seq_len, dim, theta=10000):
-    assert dim % 2 == 0
-    freqs = torch.outer(torch.arange(max_seq_len), 1.0 / torch.pow(theta,
-                                                                   torch.arange(0, dim, 2).to(torch.float64).div(dim)))
-    return torch.polar(torch.ones_like(freqs), freqs)
-
-
-class WanRMSNorm(nn.Module):
-    """Kept private instead of fastvideo.layers.layernorm.RMSNorm.
-
-    The official DreamX-World ``model_2_2.py`` computes the RMS statistics in
-    the *input* dtype — the upstream code has the fp32 upcast explicitly
-    commented out (``# return self._norm(x.float())...``). FastVideo's RMSNorm
-    always normalizes in fp32, which is not bit-identical under bf16, so the
-    verbatim implementation stays.
-    """
-
-    def __init__(self, dim, eps=1e-5):
-        super().__init__()
-        self.dim = dim
-        self.eps = eps
-        self.weight = nn.Parameter(torch.ones(dim))
-
-    def forward(self, x):
-        return self._norm(x).type_as(x) * self.weight
-
-    def _norm(self, x):
-        return x * torch.rsqrt(x.pow(2).mean(dim=-1, keepdim=True) + self.eps)
-
-
-class WanLayerNorm(nn.LayerNorm):
-    """Kept private instead of fastvideo.layers.layernorm.FP32LayerNorm.
-
-    The official DreamX-World ``model_2_2.py`` normalizes in the *input* dtype
-    (no ``x.float()`` upcast, unlike Wan2.1). FP32LayerNorm casts input and
-    affine params to fp32, which is not bit-identical under bf16, so the
-    verbatim implementation stays.
-    """
-
-    def __init__(self, dim, eps=1e-6, elementwise_affine=False):
-        super().__init__(dim, elementwise_affine=elementwise_affine, eps=eps)
-
-    def forward(self, x):
-        return super().forward(x).type_as(x)
-
-
-class WanCrossAttention(nn.Module):
-
-    def __init__(self, dim, num_heads, window_size=(-1, -1), qk_norm=True, eps=1e-6):
-        super().__init__()
-        self.num_heads = num_heads
-        self.head_dim = dim // num_heads
-        self.q = ReplicatedLinear(dim, dim)
-        self.k = ReplicatedLinear(dim, dim)
-        self.v = ReplicatedLinear(dim, dim)
-        self.o = ReplicatedLinear(dim, dim)
-        self.norm_q = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity()
-        self.norm_k = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity()
-
-    def _kv(self, context, b, n, d):
-        k, _ = self.k(context)
-        k = self.norm_k(k).view(b, -1, n, d)
-        v, _ = self.v(context)
-        v = v.view(b, -1, n, d)
-        return k, v
-
-    def forward(self, x, context, context_lens, crossattn_cache=None):
-        b, n, d = x.size(0), self.num_heads, self.head_dim
-        q, _ = self.q(x)
-        q = self.norm_q(q).view(b, -1, n, d)
-
-        if crossattn_cache is not None:
-            if not crossattn_cache["is_init"]:
-                crossattn_cache["is_init"] = True
-                k, v = self._kv(context, b, n, d)
-                crossattn_cache["k"] = k
-                crossattn_cache["v"] = v
-            else:
-                k = crossattn_cache["k"]
-                v = crossattn_cache["v"]
-        else:
-            k, v = self._kv(context, b, n, d)
-
-        x = attention(q, k, v)
-        x = x.flatten(2)
-        out, _ = self.o(x)
-        return out
-
-
-def block_relativistic_rope(x, grid_sizes, freqs, start_frame=0, relative_frame_indices=None):
-    """
-    Apply Block-Relativistic RoPE to input tensor.
-    Adapted from Infinity-RoPE (https://arxiv.org/abs/2511.20649).
-
-    Args:
-        x: Input tensor [B, L, num_heads, head_dim]
-        grid_sizes: Tensor [B, 3] containing (F, H, W)
-        freqs: RoPE frequencies
-        start_frame: Starting frame index for sequential RoPE
-        relative_frame_indices: Optional tensor [F] specifying explicit frame indices
-                               for Block-Relativistic RoPE. Overrides start_frame if provided.
-    """
-    n, c = x.size(2), x.size(3) // 2
-    freqs = freqs.split([c - 2 * (c // 3), c // 3, c // 3], dim=1)
-
-    output = []
-    for i, (f, h, w) in enumerate(grid_sizes.tolist()):
-        seq_len = f * h * w
-        x_i = torch.view_as_complex(x[i, :seq_len].to(torch.float64).reshape(seq_len, n, -1, 2))
-
-        if relative_frame_indices is not None:
-            frame_indices = relative_frame_indices.long()
-            freqs_temporal = freqs[0][frame_indices].view(f, 1, 1, -1).expand(f, h, w, -1)
-        else:
-            freqs_temporal = freqs[0][start_frame:start_frame + f].view(f, 1, 1, -1).expand(f, h, w, -1)
-
-        freqs_i = torch.cat([
-            freqs_temporal, freqs[1][:h].view(1, h, 1, -1).expand(f, h, w, -1), freqs[2][:w].view(1, 1, w, -1).expand(
-                f, h, w, -1)
-        ],
-                            dim=-1).reshape(seq_len, 1, -1)
-
-        x_i = torch.view_as_real(x_i * freqs_i).flatten(2)
-        x_i = torch.cat([x_i, x[i, seq_len:]])
-        output.append(x_i)
-
-    return torch.stack(output).type_as(x)
-
-
-class CausalWanSelfAttention(nn.Module):
-    """Self-attention with KV cache and Block-Relativistic RoPE for causal inference."""
-
-    def __init__(self, dim, num_heads, local_attn_size=6, sink_size=1, qk_norm=True, eps=1e-6):
-        assert dim % num_heads == 0
-        super().__init__()
-        self.dim = dim
-        self.num_heads = num_heads
-        self.head_dim = dim // num_heads
-        self.local_attn_size = local_attn_size
-        self.sink_size = sink_size
-        self.qk_norm = qk_norm
-        self.eps = eps
-        self.max_attention_size = 39600 if local_attn_size == -1 else local_attn_size * 880
-
-        self.q = ReplicatedLinear(dim, dim)
-        self.k = ReplicatedLinear(dim, dim)
-        self.v = ReplicatedLinear(dim, dim)
-        self.o = ReplicatedLinear(dim, dim)
-        self.norm_q = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity()
-        self.norm_k = WanRMSNorm(dim, eps=eps) if qk_norm else nn.Identity()
-
-    def forward(self,
-                x,
-                seq_lens,
-                grid_sizes,
-                freqs,
-                kv_cache,
-                current_start=0,
-                cache_start=None,
-                sink_recache_after_switch=False):
-        """
-        Args:
-            x: Shape [B, L, C]
-            seq_lens: Shape [B]
-            grid_sizes: Shape [B, 3] containing (F, H, W)
-            freqs: RoPE frequencies [1024, head_dim / 2]
-            kv_cache: Dict with 'k', 'v', 'global_end_index', 'local_end_index'
-            current_start: Current position in the global token sequence
-        """
-        b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim
-        if cache_start is None:
-            cache_start = current_start
-
-        q, _ = self.q(x)
-        q = self.norm_q(q).view(b, s, n, d)
-        k, _ = self.k(x)
-        k = self.norm_k(k).view(b, s, n, d)
-        v, _ = self.v(x)
-        v = v.view(b, s, n, d)
-
-        frame_seqlen = math.prod(grid_sizes[0][1:]).item()
-        num_new_frames = grid_sizes[0][0].item()
-        current_end = current_start + q.shape[1]
-        sink_tokens = self.sink_size * frame_seqlen
-        kv_cache_size = kv_cache["k"].shape[1]
-        num_new_tokens = q.shape[1]
-
-        cache_update_info = None
-        is_recompute = current_end <= kv_cache["global_end_index"].item() and current_start > 0
-
-        if self.local_attn_size != -1 and (current_end > kv_cache["global_end_index"].item()) and (
-                num_new_tokens + kv_cache["local_end_index"].item() > kv_cache_size):
-            # === ROLLING MODE: cache full, evict oldest non-sink tokens ===
-            num_evicted_tokens = num_new_tokens + kv_cache["local_end_index"].item() - kv_cache_size
-            num_rolled_tokens = kv_cache["local_end_index"].item() - num_evicted_tokens - sink_tokens
-            local_end_index = kv_cache["local_end_index"].item() + current_end - \
-                kv_cache["global_end_index"].item() - num_evicted_tokens
-            local_start_index = local_end_index - num_new_tokens
-
-            temp_k = kv_cache["k"].detach().clone()
-            temp_v = kv_cache["v"].detach().clone()
-            temp_k[:, sink_tokens:sink_tokens + num_rolled_tokens] = \
-                temp_k[:, sink_tokens + num_evicted_tokens:sink_tokens + num_evicted_tokens + num_rolled_tokens].clone()
-            temp_v[:, sink_tokens:sink_tokens + num_rolled_tokens] = \
-                temp_v[:, sink_tokens + num_evicted_tokens:sink_tokens + num_evicted_tokens + num_rolled_tokens].clone()
-
-            write_start_index = max(local_start_index, sink_tokens) if is_recompute else local_start_index
-            roped_offset = max(0, write_start_index - local_start_index)
-            write_len = max(0, local_end_index - write_start_index)
-            if write_len > 0:
-                temp_k[:, write_start_index:local_end_index] = k[:, roped_offset:roped_offset + write_len]
-                temp_v[:, write_start_index:local_end_index] = v[:, roped_offset:roped_offset + write_len]
-
-            # Block-Relativistic RoPE: query uses window-relative indices
-            query_relative_indices = torch.arange(self.local_attn_size - num_new_frames,
-                                                  self.local_attn_size,
-                                                  device=q.device)
-            roped_query = block_relativistic_rope(q, grid_sizes, freqs,
-                                                  relative_frame_indices=query_relative_indices).type_as(v)
-
-            # Block-Relativistic RoPE: cached K uses position-in-window indices
-            num_cache_frames = local_end_index // frame_seqlen
-            cache_relative_indices = torch.arange(0, num_cache_frames, device=k.device)
-            cache_grid_sizes = grid_sizes.clone()
-            cache_grid_sizes[0, 0] = num_cache_frames
-            roped_temp_k = block_relativistic_rope(temp_k[:, :local_end_index].view(b, num_cache_frames, frame_seqlen,
-                                                                                    n, d).flatten(1, 2),
-                                                   cache_grid_sizes,
-                                                   freqs,
-                                                   relative_frame_indices=cache_relative_indices).type_as(v)
-
-            cache_update_info = {
-                "action": "roll_and_insert",
-                "sink_tokens": sink_tokens,
-                "num_rolled_tokens": num_rolled_tokens,
-                "num_evicted_tokens": num_evicted_tokens,
-                "local_start_index": local_start_index,
-                "local_end_index": local_end_index,
-                "write_start_index": write_start_index,
-                "write_end_index": local_end_index,
-                "new_k": k[:, roped_offset:roped_offset + write_len],
-                "new_v": v[:, roped_offset:roped_offset + write_len],
-                "current_end": current_end,
-                "is_recompute": is_recompute
-            }
-        else:
-            # === DIRECT INSERT MODE: cache not yet full ===
-            local_end_index = kv_cache["local_end_index"].item() + current_end - kv_cache["global_end_index"].item()
-            local_start_index = local_end_index - num_new_tokens
-
-            temp_k = kv_cache["k"].detach().clone()
-            temp_v = kv_cache["v"].detach().clone()
-
-            write_start_index = max(local_start_index, sink_tokens) if is_recompute else local_start_index
-            if sink_recache_after_switch:
-                write_start_index = local_start_index
-            roped_offset = max(0, write_start_index - local_start_index)
-            write_len = max(0, local_end_index - write_start_index)
-            if write_len > 0:
-                temp_k[:, write_start_index:local_end_index] = k[:, roped_offset:roped_offset + write_len]
-                temp_v[:, write_start_index:local_end_index] = v[:, roped_offset:roped_offset + write_len]
-
-            # RoPE with relative indices (growing sequentially before cache fills)
-            current_frame_in_window = local_start_index // frame_seqlen
-            query_relative_indices = torch.arange(current_frame_in_window,
-                                                  current_frame_in_window + num_new_frames,
-                                                  device=q.device)
-            roped_query = block_relativistic_rope(q, grid_sizes, freqs,
-                                                  relative_frame_indices=query_relative_indices).type_as(v)
-
-            num_cache_frames = local_end_index // frame_seqlen
-            cache_relative_indices = torch.arange(0, num_cache_frames, device=k.device)
-            cache_grid_sizes = grid_sizes.clone()
-            cache_grid_sizes[0, 0] = num_cache_frames
-            roped_temp_k = block_relativistic_rope(temp_k[:, :local_end_index].view(b, num_cache_frames, frame_seqlen,
-                                                                                    n, d).flatten(1, 2),
-                                                   cache_grid_sizes,
-                                                   freqs,
-                                                   relative_frame_indices=cache_relative_indices).type_as(v)
-
-            cache_update_info = {
-                "action": "direct_insert",
-                "local_start_index": local_start_index,
-                "local_end_index": local_end_index,
-                "write_start_index": write_start_index,
-                "write_end_index": local_end_index,
-                "new_k": k[:, roped_offset:roped_offset + write_len],
-                "new_v": v[:, roped_offset:roped_offset + write_len],
-                "current_end": current_end,
-                "is_recompute": is_recompute
-            }
-
-        # Attention: sink tokens + local window
-        if sink_tokens > 0:
-            local_budget = self.max_attention_size - sink_tokens
-            k_sink = roped_temp_k[:, :sink_tokens]
-            v_sink = temp_v[:, :sink_tokens]
-            if local_budget > 0:
-                local_start_for_window = max(sink_tokens, local_end_index - local_budget)
-                k_local = roped_temp_k[:, local_start_for_window:local_end_index]
-                v_local = temp_v[:, local_start_for_window:local_end_index]
-                k_cat = torch.cat([k_sink, k_local], dim=1)
-                v_cat = torch.cat([v_sink, v_local], dim=1)
-            else:
-                k_cat = k_sink
-                v_cat = v_sink
-            x = attention(roped_query, k_cat, v_cat)
-        else:
-            window_start = max(0, local_end_index - self.max_attention_size)
-            x = attention(roped_query, roped_temp_k[:, window_start:local_end_index],
-                          temp_v[:, window_start:local_end_index])
-
-        x = x.flatten(2)
-        x, _ = self.o(x)
-        return x, (current_end, local_end_index, cache_update_info)
-
-
-class CausalPropeSelfAttention(nn.Module):
-    """PRoPE self-attention with optional KV cache for camera-controlled inference."""
-
-    def __init__(self,
-                 dim,
-                 attn_dim,
-                 num_heads,
-                 window_size=(-1, -1),
-                 local_attn_size=-1,
-                 sink_size=0,
-                 qk_norm=True,
-                 eps=1e-6):
-        assert dim % num_heads == 0
-        assert attn_dim % num_heads == 0
-        super().__init__()
-        self.dim = dim
-        self.attn_dim = attn_dim
-        self.num_heads = num_heads
-        self.head_dim = attn_dim // num_heads
-        self.local_attn_size = local_attn_size
-        self.sink_size = sink_size
-        self.qk_norm = qk_norm
-        self.eps = eps
-        self.window_size = window_size
-        self.max_attention_size = 39600 if local_attn_size == -1 else local_attn_size * 880
-
-        self.q_proj = ReplicatedLinear(dim, attn_dim)
-        self.k_proj = ReplicatedLinear(dim, attn_dim)
-        self.v_proj = ReplicatedLinear(dim, attn_dim)
-        self.out_proj = ReplicatedLinear(attn_dim, dim)
-
-        self.norm_q = WanRMSNorm(attn_dim, eps=eps) if qk_norm else nn.Identity()
-        self.norm_k = WanRMSNorm(attn_dim, eps=eps) if qk_norm else nn.Identity()
-
-        nn.init.zeros_(self.out_proj.weight)
-        nn.init.zeros_(self.out_proj.bias)
-
-    def forward(self,
-                x,
-                cam_viewmats,
-                cam_K,
-                seq_lens,
-                grid_sizes,
-                freqs,
-                kv_cache=None,
-                current_start=0,
-                cache_start=None,
-                sink_recache_after_switch=False,
-                cache_update_policy="commit_detached"):
-        """
-        Args:
-            x: Shape [B, L, C]
-            cam_viewmats: Camera view matrices
-            cam_K: Camera intrinsics
-            kv_cache: Optional KV cache dict. When None, runs full attention over current chunk.
-        """
-        b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim
-        if cache_start is None:
-            cache_start = current_start
-
-        q, _ = self.q_proj(x)
-        q = self.norm_q(q).view(b, s, n, d)
-        k, _ = self.k_proj(x)
-        k = self.norm_k(k).view(b, s, n, d)
-        v, _ = self.v_proj(x)
-        v = v.view(b, s, n, d)
-
-        # Apply PRoPE (Positional Rotary Position Embedding from camera parameters)
-        q_t, k_t, v_t, apply_fn_o = prope_qkv(q.transpose(1, 2),
-                                              k.transpose(1, 2),
-                                              v.transpose(1, 2),
-                                              viewmats=cam_viewmats,
-                                              Ks=cam_K)
-        proped_q = q_t.transpose(1, 2)
-        proped_k = k_t.transpose(1, 2)
-        proped_v = v_t.transpose(1, 2)
-
-        if kv_cache is None:
-            # No cache: full attention over current chunk
-            x_out = attention(proped_q, proped_k, proped_v)
-        else:
-            # KV cache mode with rolling cache support
-            frame_seqlen = math.prod(grid_sizes[0][1:]).item()
-            num_new_tokens = s
-            current_end = current_start + num_new_tokens
-            sink_tokens = self.sink_size * frame_seqlen
-            kv_cache_size = kv_cache["k"].shape[1]
-            is_recompute = (current_end <= kv_cache["global_end_index"].item()) and (current_start > 0)
-
-            if self.local_attn_size != -1 and (current_end > kv_cache["global_end_index"].item()) and (
-                    num_new_tokens + kv_cache["local_end_index"].item() > kv_cache_size):
-                # === ROLLING MODE ===
-                num_evicted_tokens = num_new_tokens + kv_cache["local_end_index"].item() - kv_cache_size
-                num_rolled_tokens = kv_cache["local_end_index"].item() - num_evicted_tokens - sink_tokens
-                local_end_index = kv_cache["local_end_index"].item() + current_end - \
-                    kv_cache["global_end_index"].item() - num_evicted_tokens
-                local_start_index = local_end_index - num_new_tokens
-
-                if cache_update_policy != "none":
-                    with torch.no_grad():
-                        kv_cache["k"][:, sink_tokens:sink_tokens + num_rolled_tokens] = \
-                            kv_cache["k"][:, sink_tokens + num_evicted_tokens:sink_tokens + num_evicted_tokens + num_rolled_tokens].clone()
-                        kv_cache["v"][:, sink_tokens:sink_tokens + num_rolled_tokens] = \
-                            kv_cache["v"][:, sink_tokens + num_evicted_tokens:sink_tokens + num_evicted_tokens + num_rolled_tokens].clone()
-
-                    write_start_index = max(local_start_index, sink_tokens) if is_recompute else local_start_index
-                    roped_offset = max(0, write_start_index - local_start_index)
-                    write_len = max(0, local_end_index - write_start_index)
-                    if write_len > 0:
-                        with torch.no_grad():
-                            kv_cache["k"][:,
-                                          write_start_index:local_end_index] = proped_k[:, roped_offset:roped_offset +
-                                                                                        write_len].detach()
-                            kv_cache["v"][:,
-                                          write_start_index:local_end_index] = proped_v[:, roped_offset:roped_offset +
-                                                                                        write_len].detach()
-            else:
-                # === DIRECT INSERT MODE ===
-                local_end_index = kv_cache["local_end_index"].item() + current_end - kv_cache["global_end_index"].item()
-                local_start_index = local_end_index - num_new_tokens
-
-                if cache_update_policy != "none":
-                    write_start_index = max(local_start_index, sink_tokens) if is_recompute else local_start_index
-                    if sink_recache_after_switch:
-                        write_start_index = local_start_index
-                    roped_offset = max(0, write_start_index - local_start_index)
-                    write_len = max(0, local_end_index - write_start_index)
-                    if write_len > 0:
-                        with torch.no_grad():
-                            kv_cache["k"][:,
-                                          write_start_index:local_end_index] = proped_k[:, roped_offset:roped_offset +
-                                                                                        write_len].detach()
-                            kv_cache["v"][:,
-                                          write_start_index:local_end_index] = proped_v[:, roped_offset:roped_offset +
-                                                                                        write_len].detach()
-
-            # Attention: sink tokens + local window
-            if sink_tokens > 0:
-                local_budget = self.max_attention_size - sink_tokens
-                k_sink = kv_cache["k"][:, :sink_tokens].detach()
-                v_sink = kv_cache["v"][:, :sink_tokens].detach()
-                if local_budget > 0:
-                    local_start_for_window = max(sink_tokens, local_end_index - local_budget)
-                    k_local = kv_cache["k"][:, local_start_for_window:local_end_index].detach()
-                    v_local = kv_cache["v"][:, local_start_for_window:local_end_index].detach()
-                    k_cat = torch.cat([k_sink, k_local], dim=1)
-                    v_cat = torch.cat([v_sink, v_local], dim=1)
-                else:
-                    k_cat = k_sink
-                    v_cat = v_sink
-                x_out = attention(proped_q, k_cat, v_cat)
-            else:
-                window_start = max(0, local_end_index - self.max_attention_size)
-                x_out = attention(proped_q, kv_cache["k"][:, window_start:local_end_index].detach(),
-                                  kv_cache["v"][:, window_start:local_end_index].detach())
-
-            if not is_recompute and cache_update_policy != "none":
-                kv_cache["global_end_index"].fill_(current_end)
-                kv_cache["local_end_index"].fill_(local_end_index)
-
-        # Apply inverse PRoPE
-        x = apply_fn_o(x_out.transpose(1, 2)).transpose(1, 2)
-        x = x.flatten(2)
-        x, _ = self.out_proj(x)
-        return x
-
-
-class CausalWanAttentionBlock(nn.Module):
-
-    def __init__(self,
-                 dim,
-                 ffn_dim,
-                 num_heads,
-                 local_attn_size=-1,
-                 sink_size=0,
-                 qk_norm=True,
-                 cross_attn_norm=False,
-                 eps=1e-6,
-                 **kwargs):
-        super().__init__()
-        self.dim = dim
-        self.ffn_dim = ffn_dim
-        self.num_heads = num_heads
-        self.local_attn_size = local_attn_size
-        self.qk_norm = qk_norm
-        self.cross_attn_norm = cross_attn_norm
-        self.eps = eps
-
-        self.add_control_adapter = kwargs.get('add_control_adapter', False)
-        self.cam_method = kwargs.get('cam_method')
-        self.attn_compress = kwargs.get('attn_compress', 1)
-        self.layer_idx = kwargs.get('layer_idx')
-        cam_self_attn_layers = kwargs.get('cam_self_attn_layers')
-
-        # layers
-        self.norm1 = WanLayerNorm(dim, eps)
-        self.self_attn = CausalWanSelfAttention(dim, num_heads, local_attn_size, sink_size, qk_norm, eps)
-        self.norm3 = WanLayerNorm(dim, eps, elementwise_affine=True) if cross_attn_norm else nn.Identity()
-        self.cross_attn = WanCrossAttention(dim, num_heads, (-1, -1), qk_norm, eps)
-        self.norm2 = WanLayerNorm(dim, eps)
-        # nn.Linear (not ReplicatedLinear) on purpose: the official checkpoint
-        # stores these as positional Sequential keys (ffn.0 / ffn.2) that the
-        # copy-only converter and the strict-load tests require verbatim, and
-        # ReplicatedLinear's (out, bias) tuple return cannot compose inside
-        # nn.Sequential without renaming the state-dict surface.
-        self.ffn = nn.Sequential(nn.Linear(dim, ffn_dim), nn.GELU(approximate='tanh'), nn.Linear(ffn_dim, dim))
-
-        # PRoPE self-attention branch for camera control
-        add_cam_attn = self.add_control_adapter and self.cam_method == 'prope'
-        if add_cam_attn and cam_self_attn_layers is not None:
-            add_cam_attn = self.layer_idx in cam_self_attn_layers
-        if add_cam_attn:
-            self.cam_self_attn = CausalPropeSelfAttention(dim,
-                                                          dim // self.attn_compress,
-                                                          num_heads,
-                                                          local_attn_size=local_attn_size,
-                                                          sink_size=sink_size,
-                                                          qk_norm=qk_norm,
-                                                          eps=eps)
-
-        # modulation
-        self.modulation = nn.Parameter(torch.randn(1, 6, dim) / dim**0.5)
-
-    def forward(self,
-                x,
-                e,
-                seq_lens,
-                grid_sizes,
-                freqs,
-                context,
-                context_lens,
-                kv_cache,
-                crossattn_cache=None,
-                current_start=0,
-                cache_start=None,
-                cam_viewmats=None,
-                cam_K=None,
-                sink_recache_after_switch=False,
-                cache_update_policy="commit_detached"):
-        num_frames, frame_seqlen = e.shape[1], x.shape[1] // e.shape[1]
-        e = (self.modulation.unsqueeze(1) + e).chunk(6, dim=2)
-
-        # self-attention
-        attn_input = (self.norm1(x).unflatten(dim=1, sizes=(num_frames, frame_seqlen)) * (1 + e[1]) + e[0]).flatten(
-            1, 2)
-        y, cache_update_info = self.self_attn(attn_input, seq_lens, grid_sizes, freqs, kv_cache, current_start,
-                                              cache_start, sink_recache_after_switch)
-
-        # PRoPE camera attention (parallel branch)
-        if hasattr(self, 'cam_self_attn') and cam_viewmats is not None and cam_K is not None:
-            prope_kv_cache = None
-            if kv_cache is not None and "prope_k" in kv_cache:
-                prope_kv_cache = {
-                    "k": kv_cache["prope_k"],
-                    "v": kv_cache["prope_v"],
-                    "global_end_index": kv_cache["prope_global_end_index"],
-                    "local_end_index": kv_cache["prope_local_end_index"],
-                }
-            y = y + self.cam_self_attn(attn_input,
-                                       cam_viewmats,
-                                       cam_K,
-                                       seq_lens,
-                                       grid_sizes,
-                                       freqs,
-                                       kv_cache=prope_kv_cache,
-                                       current_start=current_start,
-                                       cache_start=cache_start,
-                                       cache_update_policy=cache_update_policy)
-
-        x = x + (y.unflatten(dim=1, sizes=(num_frames, frame_seqlen)) * e[2]).flatten(1, 2)
-
-        # cross-attention & FFN
-        x = x + self.cross_attn(self.norm3(x), context, context_lens, crossattn_cache=crossattn_cache)
-        y = self.ffn(
-            (self.norm2(x).unflatten(dim=1, sizes=(num_frames, frame_seqlen)) * (1 + e[4]) + e[3]).flatten(1, 2))
-        x = x + (y.unflatten(dim=1, sizes=(num_frames, frame_seqlen)) * e[5]).flatten(1, 2)
-
-        return x, cache_update_info
-
-
-class CausalHead(nn.Module):
-
-    def __init__(self, dim, out_dim, patch_size, eps=1e-6):
-        super().__init__()
-        self.dim = dim
-        self.out_dim = out_dim
-        self.patch_size = patch_size
-        self.eps = eps
-
-        out_dim = math.prod(patch_size) * out_dim
-        self.norm = WanLayerNorm(dim, eps)
-        self.head = ReplicatedLinear(dim, out_dim)
-        self.modulation = nn.Parameter(torch.randn(1, 2, dim) / dim**0.5)
-
-    def forward(self, x, e):
-        num_frames, frame_seqlen = e.shape[1], x.shape[1] // e.shape[1]
-        e = (self.modulation.unsqueeze(1) + e).chunk(2, dim=2)
-        x, _ = self.head(self.norm(x).unflatten(dim=1, sizes=(num_frames, frame_seqlen)) * (1 + e[1]) + e[0])
-        return x
-
-
-class DreamXWorldARTransformer3DModel(BaseDiT):
-    """DreamX-World-5B autoregressive causal transformer."""
-
-    _fsdp_shard_conditions = DreamXWorldARConfig()._fsdp_shard_conditions
-    _compile_conditions = DreamXWorldARConfig()._compile_conditions
-    _supported_attention_backends = DreamXWorldARConfig()._supported_attention_backends
-    param_names_mapping = DreamXWorldARConfig().param_names_mapping
-    reverse_param_names_mapping = DreamXWorldARConfig().reverse_param_names_mapping
-    lora_param_names_mapping = DreamXWorldARConfig().lora_param_names_mapping
-    _no_split_modules = ["CausalWanAttentionBlock"]
-
-    def __init__(self, config: DreamXWorldARConfig, hf_config: dict[str, Any]) -> None:
-        super().__init__(config=config, hf_config=hf_config)
-
-        model_type = config.model_type
-        patch_size = config.patch_size
-        text_len = config.text_len
-        in_dim = config.in_channels
-        dim = config.hidden_size
-        ffn_dim = config.ffn_dim
-        freq_dim = config.freq_dim
-        text_dim = config.text_dim
-        out_dim = config.out_channels
-        num_heads = config.num_attention_heads
-        num_layers = config.num_layers
-        local_attn_size = config.local_attn_size
-        sink_size = config.sink_size
-        qk_norm = bool(config.qk_norm)
-        cross_attn_norm = config.cross_attn_norm
-        eps = config.eps
-        add_control_adapter = config.add_control_adapter
-        cam_method = config.cam_method
-        attn_compress = config.attn_compress
-        cam_self_attn_layers = config.cam_self_attn_layers
-
-        assert model_type in ['t2v', 'i2v', 'ti2v']
-        self.model_type = model_type
-        self.patch_size = patch_size
-        self.text_len = text_len
-        self.in_dim = in_dim
-        self.dim = dim
-        self.ffn_dim = ffn_dim
-        self.freq_dim = freq_dim
-        self.text_dim = text_dim
-        self.out_dim = out_dim
-        self.num_heads = num_heads
-        self.num_layers = num_layers
-        self.local_attn_size = local_attn_size
-        self.qk_norm = qk_norm
-        self.cross_attn_norm = cross_attn_norm
-        self.eps = eps
-
-        # embeddings — nn.Linear inside nn.Sequential on purpose: the official
-        # checkpoint keys are positional (text_embedding.0/.2, time_embedding.0/.2,
-        # time_projection.1) and must load verbatim (see ffn comment above).
-        self.patch_embedding = nn.Conv3d(in_dim, dim, kernel_size=patch_size, stride=patch_size)
-        self.text_embedding = nn.Sequential(nn.Linear(text_dim, dim), nn.GELU(approximate='tanh'), nn.Linear(dim, dim))
-        self.time_embedding = nn.Sequential(nn.Linear(freq_dim, dim), nn.SiLU(), nn.Linear(dim, dim))
-        self.time_projection = nn.Sequential(nn.SiLU(), nn.Linear(dim, dim * 6))
-
-        # transformer blocks
-        self.blocks = nn.ModuleList([
-            CausalWanAttentionBlock(dim,
-                                    ffn_dim,
-                                    num_heads,
-                                    local_attn_size,
-                                    sink_size,
-                                    qk_norm,
-                                    cross_attn_norm,
-                                    eps,
-                                    add_control_adapter=add_control_adapter,
-                                    cam_method=cam_method,
-                                    attn_compress=attn_compress,
-                                    layer_idx=layer_idx,
-                                    cam_self_attn_layers=cam_self_attn_layers) for layer_idx in range(num_layers)
-        ])
-        for layer_idx, block in enumerate(self.blocks):
-            block.self_attn.layer_idx = layer_idx
-            block.self_attn.num_layers = self.num_layers
-
-        # head
-        self.head = CausalHead(dim, out_dim, patch_size, eps)
-
-        # RoPE frequencies
-        assert (dim % num_heads) == 0 and (dim // num_heads) % 2 == 0
-        d = dim // num_heads
-        self.freqs = torch.cat(
-            [rope_params(1024, d - 4 * (d // 6)),
-             rope_params(1024, 2 * (d // 6)),
-             rope_params(1024, 2 * (d // 6))],
-            dim=1)
-
-        self.num_attention_heads = num_heads
-        self.attention_head_dim = dim // num_heads
-        self.hidden_size = dim
-        self.in_channels = in_dim
-        self.out_channels = out_dim
-        self.num_channels_latents = out_dim
-        self.init_weights()
-        self.num_frame_per_block = config.arch_config.num_frames_per_block
-        self.__post_init__()
-
-    def forward(self,
-                x=None,
-                t=None,
-                context=None,
-                seq_len=None,
-                y=None,
-                y_camera=None,
-                kv_cache=None,
-                crossattn_cache=None,
-                current_start=0,
-                cache_start=0,
-                cache_update_policy="commit_detached",
-                hidden_states=None,
-                encoder_hidden_states=None,
-                timestep=None,
-                **kwargs):
-        """
-        Causal inference with KV caching.
-        See Algorithm 2 of CausVid (https://arxiv.org/abs/2412.07772).
-
-        Args:
-            x: List of input video tensors [C_in, F, H, W]
-            t: Timestep tensor [B, L]
-            context: List of text embeddings [L, C]
-            seq_len: Maximum sequence length for positional encoding
-            y: Optional conditional video inputs (I2V mode)
-            y_camera: Camera parameters dict {'viewmats': ..., 'K': ...}
-            kv_cache: List of KV cache dicts per transformer block
-            crossattn_cache: List of cross-attention cache dicts
-            current_start: Current position in global token sequence
-            cache_start: Cache start position
-            cache_update_policy: Cache update strategy ('commit_detached' or 'none')
-
-        Returns:
-            Stacked output tensors [B, C_out, F, H/8, W/8]
-        """
-        if x is None and hidden_states is not None:
-            x = [sample for sample in hidden_states]
-        if t is None and timestep is not None:
-            t = timestep
-        if context is None and encoder_hidden_states is not None:
-            if isinstance(encoder_hidden_states, torch.Tensor):
-                context = [sample for sample in encoder_hidden_states]
-            else:
-                context = encoder_hidden_states
-        if seq_len is None:
-            if torch.is_tensor(t):
-                seq_len = int(t.shape[1]) if t.dim() > 1 else int(t.numel())
-            elif x is not None:
-                sample = x[0]
-                seq_len = (sample.shape[1] // self.patch_size[0]) * (sample.shape[2] // self.patch_size[1]) * (
-                    sample.shape[3] // self.patch_size[2])
-        if x is None or t is None or context is None or seq_len is None:
-            raise ValueError("DreamXWorldARTransformer3DModel requires x/t/context/seq_len or FastVideo aliases")
-
-        device = self.patch_embedding.weight.device
-        if self.freqs.is_meta or self.freqs.device != device:
-            d = self.dim // self.num_heads
-            self.freqs = torch.cat([
-                rope_params(1024, d - 4 * (d // 6)),
-                rope_params(1024, 2 * (d // 6)),
-                rope_params(1024, 2 * (d // 6)),
-            ],
-                                   dim=1).to(device)
-
-        if y is not None:
-            x = [torch.cat([u, v], dim=0) for u, v in zip(x, y, strict=True)]
-
-        # patch embedding
-        x = [self.patch_embedding(u.unsqueeze(0)) for u in x]
-        grid_sizes = torch.stack([torch.tensor(u.shape[2:], dtype=torch.long) for u in x])
-        x = [u.flatten(2).transpose(1, 2) for u in x]
-        seq_lens = torch.tensor([u.size(1) for u in x], dtype=torch.long)
-        assert seq_lens.max() <= seq_len
-        x = torch.cat(x)
-
-        # time embedding
-        e = self.time_embedding(sinusoidal_embedding_1d(self.freq_dim, t.flatten()).type_as(x))
-        e0 = self.time_projection(e).unflatten(1, (6, self.dim)).unflatten(dim=0, sizes=t.shape)
-
-        # text embedding
-        context_lens = None
-        context = self.text_embedding(
-            torch.stack([torch.cat([u, u.new_zeros(self.text_len - u.size(0), u.size(1))]) for u in context]))
-
-        # camera parameters
-        if y_camera is not None and isinstance(y_camera, dict):
-            cam_viewmats = y_camera['viewmats']
-            cam_K = y_camera['K']
-        else:
-            cam_viewmats = None
-            cam_K = None
-
-        block_kwargs = dict(
-            e=e0,
-            seq_lens=seq_lens,
-            grid_sizes=grid_sizes,
-            freqs=self.freqs,
-            context=context,
-            context_lens=context_lens,
-            cam_viewmats=cam_viewmats,
-            cam_K=cam_K,
-            cache_update_policy=cache_update_policy,
-        )
-
-        cache_update_infos = []
-        for block_index, block in enumerate(self.blocks):
-            block_kwargs.update({
-                "kv_cache": kv_cache[block_index] if kv_cache is not None else None,
-                "crossattn_cache": crossattn_cache[block_index] if crossattn_cache is not None else None,
-                "current_start": current_start,
-                "cache_start": cache_start,
-            })
-            x, block_cache_update_info = block(x, **block_kwargs)
-            if kv_cache is not None:
-                cache_update_infos.append((block_index, block_cache_update_info))
-
-        # Apply deferred cache updates
-        if kv_cache is not None and cache_update_infos and cache_update_policy != "none":
-            self._apply_cache_updates(kv_cache, cache_update_infos)
-
-        # head & unpatchify
-        x = self.head(x, e.unflatten(dim=0, sizes=t.shape).unsqueeze(2))
-        x = self.unpatchify(x, grid_sizes)
-        return torch.stack(x)
-
-    def _apply_cache_updates(self, kv_cache, cache_update_infos):
-        """Apply deferred cache updates collected from all transformer blocks.
-
-        For Block-Relativistic RoPE, this stores un-roped K values in the cache.
-        RoPE is applied dynamically during attention based on each token's current
-        relative position in the sliding window.
-        """
-        with torch.no_grad():
-            for block_index, (current_end, local_end_index, update_info) in cache_update_infos:
-                if update_info is not None:
-                    cache = kv_cache[block_index]
-
-                    if update_info["action"] == "roll_and_insert":
-                        sink_tokens = update_info["sink_tokens"]
-                        num_rolled_tokens = update_info["num_rolled_tokens"]
-                        num_evicted_tokens = update_info["num_evicted_tokens"]
-                        write_start_index = update_info.get("write_start_index", update_info["local_start_index"])
-                        write_end_index = update_info.get("write_end_index", update_info["local_end_index"])
-                        new_k = update_info["new_k"].detach()
-                        new_v = update_info["new_v"].detach()
-
-                        cache["k"][:, sink_tokens:sink_tokens + num_rolled_tokens] = \
-                            cache["k"][:, sink_tokens + num_evicted_tokens:sink_tokens + num_evicted_tokens + num_rolled_tokens].clone()
-                        cache["v"][:, sink_tokens:sink_tokens + num_rolled_tokens] = \
-                            cache["v"][:, sink_tokens + num_evicted_tokens:sink_tokens + num_evicted_tokens + num_rolled_tokens].clone()
-
-                        if write_end_index > write_start_index and new_k.shape[1] == (write_end_index -
-                                                                                      write_start_index):
-                            cache["k"][:, write_start_index:write_end_index] = new_k
-                            cache["v"][:, write_start_index:write_end_index] = new_v
-
-                    elif update_info["action"] == "direct_insert":
-                        write_start_index = update_info.get("write_start_index", update_info["local_start_index"])
-                        write_end_index = update_info.get("write_end_index", update_info["local_end_index"])
-                        new_k = update_info["new_k"].detach()
-                        new_v = update_info["new_v"].detach()
-
-                        if write_end_index > write_start_index and new_k.shape[1] == (write_end_index -
-                                                                                      write_start_index):
-                            cache["k"][:, write_start_index:write_end_index] = new_k
-                            cache["v"][:, write_start_index:write_end_index] = new_v
-
-                is_recompute = False if update_info is None else update_info.get("is_recompute", False)
-                if not is_recompute:
-                    kv_cache[block_index]["global_end_index"].fill_(current_end)
-                    kv_cache[block_index]["local_end_index"].fill_(local_end_index)
-
-    def unpatchify(self, x, grid_sizes):
-        """Reconstruct video tensors from patch embeddings."""
-        c = self.out_dim
-        out = []
-        for u, v in zip(x, grid_sizes.tolist(), strict=True):
-            u = u[:math.prod(v)].view(*v, *self.patch_size, c)
-            u = torch.einsum('fhwpqrc->cfphqwr', u)
-            u = u.reshape(c, *[i * j for i, j in zip(v, self.patch_size, strict=True)])
-            out.append(u)
-        return out
-
-    def init_weights(self):
-        """Initialize model parameters using Xavier initialization."""
-        for m in self.modules():
-            if isinstance(m, (nn.Linear, ReplicatedLinear)):
-                nn.init.xavier_uniform_(m.weight)
-                if m.bias is not None:
-                    nn.init.zeros_(m.bias)
-
-        nn.init.xavier_uniform_(self.patch_embedding.weight.flatten(1))
-        for m in self.text_embedding.modules():
-            if isinstance(m, nn.Linear):
-                nn.init.normal_(m.weight, std=.02)
-        for m in self.time_embedding.modules():
-            if isinstance(m, nn.Linear):
-                nn.init.normal_(m.weight, std=.02)
-
-        nn.init.zeros_(self.head.head.weight)
-
-
-EntryClass = DreamXWorldARTransformer3DModel
diff --git a/fastvideo/models/dits/glm_image.py b/fastvideo/models/dits/glm_image.py
deleted file mode 100644
index 6471713d2..000000000
--- a/fastvideo/models/dits/glm_image.py
+++ /dev/null
@@ -1,702 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-from typing import Any, Dict, List, Optional, Tuple, Union
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from fastvideo.layers.mlp import MLP
-
-from fastvideo.attention import LocalAttention
-from fastvideo.configs.models.dits.glm_image import GlmImageDiTConfig
-from fastvideo.layers.layernorm import ScaleResidualLayerNormScaleShift
-from fastvideo.layers.linear import ReplicatedLinear
-from fastvideo.layers.rotary_embedding import _apply_rotary_emb
-from fastvideo.layers.visual_embedding import Timesteps
-from fastvideo.models.dits.base import BaseDiT
-from fastvideo.platforms import AttentionBackendEnum
-
-
-class GlmImageLayerKVCache:
-
-    def __init__(self):
-        self.k_cache = None
-        self.v_cache = None
-        self.mode: Optional[str] = None
-
-    def store(self, k: torch.Tensor, v: torch.Tensor):
-        # Append along seq (dim=1).
-        if self.k_cache is None:
-            self.k_cache = k
-            self.v_cache = v
-        else:
-            self.k_cache = torch.cat([self.k_cache, k], dim=1)
-            self.v_cache = torch.cat([self.v_cache, v], dim=1)
-
-    def get(self):
-        return self.k_cache, self.v_cache
-
-    def clear(self):
-        self.k_cache = None
-        self.v_cache = None
-        self.mode = None
-
-
-class GlmImageKVCache:
-
-    def __init__(self, num_layers: int):
-        self.num_layers = num_layers
-        self.caches = [GlmImageLayerKVCache() for _ in range(num_layers)]
-
-    def __getitem__(self, layer_idx: int) -> GlmImageLayerKVCache:
-        return self.caches[layer_idx]
-
-    def set_mode(self, mode: Optional[str]):
-        if mode is not None and mode not in ["write", "read", "skip"]:
-            raise ValueError(f"Invalid mode: {mode}, must be one of 'write', 'read', 'skip'")
-        for cache in self.caches:
-            cache.mode = mode
-
-    def clear(self):
-        for cache in self.caches:
-            cache.clear()
-
-
-# =============================================================================
-# Timestep and Text Projection
-# =============================================================================
-class GlmImageTimestepEmbedding(nn.Module):
-
-    def __init__(
-        self,
-        in_channels: int,
-        time_embed_dim: int,
-        act_fn: str = "silu",
-        out_dim: int = None,
-    ):
-        super().__init__()
-        if out_dim is None:
-            out_dim = time_embed_dim
-        self.linear_1 = ReplicatedLinear(in_channels, time_embed_dim, bias=True)
-        if act_fn == "silu":
-            self.act = nn.SiLU()
-        elif act_fn == "gelu":
-            self.act = nn.GELU(approximate="tanh")
-        else:
-            self.act = nn.SiLU()
-        self.linear_2 = ReplicatedLinear(time_embed_dim, out_dim, bias=True)
-
-    def forward(self, sample: torch.Tensor) -> torch.Tensor:
-        sample, _ = self.linear_1(sample)
-        sample = self.act(sample)
-        sample, _ = self.linear_2(sample)
-        return sample
-
-
-class GlmImageTextProjection(nn.Module):
-
-    def __init__(
-        self,
-        in_features: int,
-        hidden_size: int,
-        out_features: int = None,
-        act_fn: str = "silu",
-    ):
-        super().__init__()
-        if out_features is None:
-            out_features = hidden_size
-        self.linear_1 = ReplicatedLinear(in_features, hidden_size, bias=True)
-        if act_fn == "silu":
-            self.act_1 = nn.SiLU()
-        elif act_fn == "gelu_tanh":
-            self.act_1 = nn.GELU(approximate="tanh")
-        else:
-            self.act_1 = nn.SiLU()
-        self.linear_2 = ReplicatedLinear(hidden_size, out_features, bias=True)
-
-    def forward(self, caption: torch.Tensor) -> torch.Tensor:
-        hidden_states, _ = self.linear_1(caption)
-        hidden_states = self.act_1(hidden_states)
-        hidden_states, _ = self.linear_2(hidden_states)
-        return hidden_states
-
-
-class GlmImageCombinedTimestepSizeEmbeddings(nn.Module):
-
-    def __init__(
-        self,
-        embedding_dim: int,
-        condition_dim: int,
-        pooled_projection_dim: int,
-        timesteps_dim: int = 256,
-    ):
-        super().__init__()
-
-        self.time_proj = Timesteps(num_channels=timesteps_dim, flip_sin_to_cos=True, downscale_freq_shift=0)
-        self.condition_proj = Timesteps(num_channels=condition_dim, flip_sin_to_cos=True, downscale_freq_shift=0)
-        self.timestep_embedder = GlmImageTimestepEmbedding(in_channels=timesteps_dim, time_embed_dim=embedding_dim)
-        self.condition_embedder = GlmImageTextProjection(pooled_projection_dim, embedding_dim, act_fn="silu")
-
-    def forward(
-        self,
-        timestep: torch.Tensor,
-        target_size: torch.Tensor,
-        crop_coords: torch.Tensor,
-        hidden_dtype: torch.dtype,
-    ) -> torch.Tensor:
-        timesteps_proj = self.time_proj(timestep)
-
-        crop_coords_proj = self.condition_proj(crop_coords.flatten()).view(crop_coords.size(0), -1)
-        target_size_proj = self.condition_proj(target_size.flatten()).view(target_size.size(0), -1)
-
-        condition_proj = torch.cat([crop_coords_proj, target_size_proj], dim=1)
-
-        timesteps_emb = self.timestep_embedder(timesteps_proj.to(dtype=hidden_dtype))
-        condition_emb = self.condition_embedder(condition_proj.to(dtype=hidden_dtype))
-
-        conditioning = timesteps_emb + condition_emb
-        return conditioning
-
-
-# =============================================================================
-# Image Projector
-# =============================================================================
-class GlmImageImageProjector(nn.Module):
-
-    def __init__(
-        self,
-        in_channels: int = 16,
-        hidden_size: int = 2560,
-        patch_size: int = 2,
-    ):
-        super().__init__()
-        self.patch_size = patch_size
-        self.proj = nn.Linear(in_channels * patch_size**2, hidden_size)
-
-    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
-        batch_size, channel, height, width = hidden_states.shape
-        post_patch_height = height // self.patch_size
-        post_patch_width = width // self.patch_size
-
-        hidden_states = hidden_states.reshape(
-            batch_size,
-            channel,
-            post_patch_height,
-            self.patch_size,
-            post_patch_width,
-            self.patch_size,
-        )
-        hidden_states = (hidden_states.permute(0, 2, 4, 1, 3, 5).flatten(3, 5).flatten(1, 2))
-        hidden_states = self.proj(hidden_states)
-
-        return hidden_states
-
-
-# =============================================================================
-# AdaLayerNorm
-# =============================================================================
-class GlmImageAdaLayerNormZero(nn.Module):
-
-    def __init__(self, embedding_dim: int, dim: int) -> None:
-        super().__init__()
-
-        self.norm = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-5)
-        self.norm_context = nn.LayerNorm(dim, elementwise_affine=False, eps=1e-5)
-        self.linear = ReplicatedLinear(embedding_dim, 12 * dim, bias=True)
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        encoder_hidden_states: torch.Tensor,
-        temb: torch.Tensor,
-    ) -> Tuple[torch.Tensor, ...]:
-        dtype = hidden_states.dtype
-        norm_hidden_states = self.norm(hidden_states).to(dtype=dtype)
-        norm_encoder_hidden_states = self.norm_context(encoder_hidden_states).to(dtype=dtype)
-
-        emb, _ = self.linear(temb)
-        (
-            shift_msa,
-            c_shift_msa,
-            scale_msa,
-            c_scale_msa,
-            gate_msa,
-            c_gate_msa,
-            shift_mlp,
-            c_shift_mlp,
-            scale_mlp,
-            c_scale_mlp,
-            gate_mlp,
-            c_gate_mlp,
-        ) = emb.chunk(12, dim=1)
-
-        hidden_states = norm_hidden_states * (1 + scale_msa.unsqueeze(1)) + shift_msa.unsqueeze(1)
-        encoder_hidden_states = norm_encoder_hidden_states * (1 + c_scale_msa.unsqueeze(1)) + c_shift_msa.unsqueeze(1)
-
-        return (
-            hidden_states,
-            gate_msa,
-            shift_mlp,
-            scale_mlp,
-            gate_mlp,
-            encoder_hidden_states,
-            c_gate_msa,
-            c_shift_mlp,
-            c_scale_mlp,
-            c_gate_mlp,
-        )
-
-
-# =============================================================================
-# Attention
-# =============================================================================
-class GlmImageAttention(nn.Module):
-
-    def __init__(
-        self,
-        query_dim: int,
-        heads: int,
-        dim_head: int,
-        out_dim: int,
-        bias: bool = True,
-        qk_norm: str = "layer_norm",
-        elementwise_affine: bool = False,
-        eps: float = 1e-5,
-        supported_attention_backends: set[AttentionBackendEnum] | None = None,
-        prefix: str = "",
-    ):
-        super().__init__()
-
-        self.heads = out_dim // dim_head if out_dim is not None else heads
-        self.num_kv_heads = self.heads
-        self.dim_head = dim_head
-        self.inner_dim = out_dim if out_dim is not None else dim_head * heads
-        self.inner_kv_dim = self.inner_dim
-        self.out_dim = out_dim if out_dim is not None else query_dim
-
-        self.to_q = ReplicatedLinear(query_dim, self.inner_dim, bias=bias)
-        self.to_k = ReplicatedLinear(query_dim, self.inner_kv_dim, bias=bias)
-        self.to_v = ReplicatedLinear(query_dim, self.inner_kv_dim, bias=bias)
-
-        self.to_out = nn.ModuleList([ReplicatedLinear(self.inner_dim, self.out_dim, bias=True)])
-
-        if qk_norm is None:
-            self.norm_q = None
-            self.norm_k = None
-        elif qk_norm == "layer_norm":
-            self.norm_q = nn.LayerNorm(dim_head, eps=eps, elementwise_affine=elementwise_affine)
-            self.norm_k = nn.LayerNorm(dim_head, eps=eps, elementwise_affine=elementwise_affine)
-        else:
-            raise ValueError(f"unknown qk_norm: {qk_norm}")
-
-        self.attn = LocalAttention(
-            num_heads=self.heads,
-            head_size=dim_head,
-            num_kv_heads=self.heads,
-            softmax_scale=None,
-            causal=False,
-            supported_attention_backends=supported_attention_backends,
-        )
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        encoder_hidden_states: torch.Tensor,
-        image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
-        kv_cache: Optional[GlmImageLayerKVCache] = None,
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        dtype = encoder_hidden_states.dtype
-
-        batch_size, text_seq_length, embed_dim = encoder_hidden_states.shape
-        batch_size, image_seq_length, embed_dim = hidden_states.shape
-        hidden_states = torch.cat([encoder_hidden_states, hidden_states], dim=1)
-
-        # 1. QKV projections
-        query, _ = self.to_q(hidden_states)
-        key, _ = self.to_k(hidden_states)
-        value, _ = self.to_v(hidden_states)
-
-        query = query.unflatten(2, (self.heads, -1))
-        key = key.unflatten(2, (self.heads, -1))
-        value = value.unflatten(2, (self.heads, -1))
-
-        # 2. QK normalization
-        if self.norm_q is not None:
-            query = self.norm_q(query).to(dtype=dtype)
-        if self.norm_k is not None:
-            key = self.norm_k(key).to(dtype=dtype)
-
-        # 3. Rotational positional embeddings applied to latent stream
-        if image_rotary_emb is not None:
-            cos, sin = image_rotary_emb
-
-            query[:, text_seq_length:, :, :] = _apply_rotary_emb(query[:, text_seq_length:, :, :],
-                                                                 cos,
-                                                                 sin,
-                                                                 is_neox_style=True)
-            key[:, text_seq_length:, :, :] = _apply_rotary_emb(key[:, text_seq_length:, :, :],
-                                                               cos,
-                                                               sin,
-                                                               is_neox_style=True)
-
-        # 4. KV Cache handling
-        if kv_cache is not None:
-            if kv_cache.mode == "write":
-                kv_cache.store(key, value)
-            elif kv_cache.mode == "read":
-                # Prepend cached condition k/v along seq (dim=1).
-                k_cache, v_cache = kv_cache.get()
-                key = torch.cat([k_cache, key], dim=1) if k_cache is not None else key
-                value = (torch.cat([v_cache, value], dim=1) if v_cache is not None else value)
-            elif kv_cache.mode == "skip":
-                pass
-
-        hidden_states = self.attn(query, key, value)
-        hidden_states = hidden_states.flatten(2, 3)
-        hidden_states = hidden_states.to(query.dtype)
-
-        # 6. Output projection
-        hidden_states, _ = self.to_out[0](hidden_states)
-
-        encoder_hidden_states, hidden_states = hidden_states.split(
-            [text_seq_length, hidden_states.size(1) - text_seq_length], dim=1)
-        return hidden_states, encoder_hidden_states
-
-
-# =============================================================================
-# Transformer Block
-# =============================================================================
-class GlmImageTransformerBlock(nn.Module):
-
-    def __init__(
-        self,
-        dim: int = 2560,
-        num_attention_heads: int = 64,
-        attention_head_dim: int = 40,
-        time_embed_dim: int = 512,
-        supported_attention_backends: set[AttentionBackendEnum] | None = None,
-        prefix: str = "",
-    ) -> None:
-        super().__init__()
-
-        # 1. Attention
-        self.norm1 = GlmImageAdaLayerNormZero(time_embed_dim, dim)
-
-        self.attn1 = GlmImageAttention(
-            query_dim=dim,
-            heads=num_attention_heads,
-            dim_head=attention_head_dim,
-            out_dim=dim,
-            bias=True,
-            qk_norm="layer_norm",
-            elementwise_affine=False,
-            eps=1e-5,
-            supported_attention_backends=supported_attention_backends,
-            prefix=f"{prefix}.attn1",
-        )
-
-        # 2. Feedforward with fused ScaleResidualLayerNorm
-        self.norm2 = ScaleResidualLayerNormScaleShift(dim, norm_type="layer", eps=1e-5, elementwise_affine=False)
-        self.norm2_context = ScaleResidualLayerNormScaleShift(dim,
-                                                              norm_type="layer",
-                                                              eps=1e-5,
-                                                              elementwise_affine=False)
-        self.ff = MLP(input_dim=dim, mlp_hidden_dim=dim * 4, output_dim=dim, act_type="gelu_pytorch_tanh")
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        encoder_hidden_states: torch.Tensor,
-        temb: Optional[torch.Tensor] = None,
-        image_rotary_emb: Optional[Union[
-            Tuple[torch.Tensor, torch.Tensor],
-            List[Tuple[torch.Tensor, torch.Tensor]],
-        ]] = None,
-        attention_kwargs: Optional[Dict[str, Any]] = None,
-        kv_cache: Optional[GlmImageLayerKVCache] = None,
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        # 1. Timestep conditioning
-        (
-            norm_hidden_states,
-            gate_msa,
-            shift_mlp,
-            scale_mlp,
-            gate_mlp,
-            norm_encoder_hidden_states,
-            c_gate_msa,
-            c_shift_mlp,
-            c_scale_mlp,
-            c_gate_mlp,
-        ) = self.norm1(hidden_states, encoder_hidden_states, temb)
-
-        # 2. Attention
-        if attention_kwargs is None:
-            attention_kwargs = {}
-
-        attn_hidden_states, attn_encoder_hidden_states = self.attn1(
-            hidden_states=norm_hidden_states,
-            encoder_hidden_states=norm_encoder_hidden_states,
-            image_rotary_emb=image_rotary_emb,
-            kv_cache=kv_cache,
-            **attention_kwargs,
-        )
-
-        # 3. Feedforward (fused residual + norm + scale/shift)
-        norm_hidden_states, hidden_states = self.norm2(
-            hidden_states,
-            attn_hidden_states,
-            gate_msa.unsqueeze(1),
-            shift_mlp.unsqueeze(1),
-            scale_mlp.unsqueeze(1),
-        )
-        norm_encoder_hidden_states, encoder_hidden_states = self.norm2_context(
-            encoder_hidden_states,
-            attn_encoder_hidden_states,
-            c_gate_msa.unsqueeze(1),
-            c_shift_mlp.unsqueeze(1),
-            c_scale_mlp.unsqueeze(1),
-        )
-
-        ff_output = self.ff(norm_hidden_states)
-        ff_output_context = self.ff(norm_encoder_hidden_states)
-        hidden_states = hidden_states + ff_output * gate_mlp.unsqueeze(1)
-        encoder_hidden_states = (encoder_hidden_states + ff_output_context * c_gate_mlp.unsqueeze(1))
-
-        return hidden_states, encoder_hidden_states
-
-
-# =============================================================================
-# Rotary Positional Embedding
-# =============================================================================
-class GlmImageRotaryPosEmbed(nn.Module):
-
-    def __init__(self, dim: int, patch_size: int, theta: float = 10000.0) -> None:
-        super().__init__()
-        self.dim = dim
-        self.patch_size = patch_size
-        self.theta = theta
-        self._cache_key: tuple | None = None
-        self._cache_value: tuple[torch.Tensor, torch.Tensor] | None = None
-
-    def forward(self, hidden_states: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
-        _, _, raw_h, raw_w = hidden_states.shape
-        height = raw_h // self.patch_size
-        width = raw_w // self.patch_size
-        device = hidden_states.device
-
-        cache_key = (height, width, device.type, device.index if device.index is not None else -1)
-        if self._cache_key == cache_key and self._cache_value is not None:
-            return self._cache_value
-
-        dim_h, dim_w = self.dim // 2, self.dim // 2
-        h_inv_freq = 1.0 / (self.theta
-                            **(torch.arange(0, dim_h, 2, dtype=torch.float32, device=device)[:(dim_h // 2)] / dim_h))
-        w_inv_freq = 1.0 / (self.theta
-                            **(torch.arange(0, dim_w, 2, dtype=torch.float32, device=device)[:(dim_w // 2)] / dim_w))
-        h_seq = torch.arange(height, device=device)
-        w_seq = torch.arange(width, device=device)
-        freqs_h = torch.outer(h_seq, h_inv_freq).unsqueeze(1).expand(height, width, -1)
-        freqs_w = torch.outer(w_seq, w_inv_freq).unsqueeze(0).expand(height, width, -1)
-
-        freqs = torch.cat([freqs_h, freqs_w], dim=-1).reshape(height * width, -1)
-        result = (freqs.cos(), freqs.sin())
-        self._cache_key = cache_key
-        self._cache_value = result
-        return result
-
-
-# =============================================================================
-# Final AdaLayerNorm
-# =============================================================================
-class GlmImageAdaLayerNormContinuous(nn.Module):
-
-    def __init__(
-        self,
-        embedding_dim: int,
-        conditioning_embedding_dim: int,
-        elementwise_affine: bool = True,
-        eps: float = 1e-5,
-        bias: bool = True,
-        norm_type: str = "layer_norm",
-    ):
-        super().__init__()
-        self.linear = nn.Linear(conditioning_embedding_dim, embedding_dim * 2, bias=bias)
-        if norm_type == "layer_norm":
-            self.norm = nn.LayerNorm(embedding_dim, eps, elementwise_affine, bias)
-        elif norm_type == "rms_norm":
-            self.norm = nn.RMSNorm(embedding_dim, eps, elementwise_affine)
-        else:
-            raise ValueError(f"unknown norm_type {norm_type}")
-
-    def forward(self, x: torch.Tensor, conditioning_embedding: torch.Tensor) -> torch.Tensor:
-        emb = self.linear(conditioning_embedding.to(x.dtype))
-        scale, shift = torch.chunk(emb, 2, dim=1)
-        x = self.norm(x) * (1 + scale)[:, None, :] + shift[:, None, :]
-        return x
-
-
-# =============================================================================
-# Main Model
-# =============================================================================
-class GlmImageTransformer2DModel(BaseDiT):
-
-    _fsdp_shard_conditions = GlmImageDiTConfig().arch_config._fsdp_shard_conditions
-    _compile_conditions = GlmImageDiTConfig().arch_config._compile_conditions
-    _supported_attention_backends = (
-        AttentionBackendEnum.FLASH_ATTN,
-        AttentionBackendEnum.TORCH_SDPA,
-    )
-
-    param_names_mapping = GlmImageDiTConfig().arch_config.param_names_mapping
-    reverse_param_names_mapping = {}
-    lora_param_names_mapping = {}
-
-    def __init__(
-        self,
-        config: GlmImageDiTConfig,
-        hf_config: dict[str, Any],
-    ):
-        super().__init__(config=config, hf_config=hf_config)
-
-        arch_config = config.arch_config
-
-        self.in_channels = arch_config.in_channels
-        self.out_channels = arch_config.out_channels
-        self.patch_size = arch_config.patch_size
-        self.num_layers = arch_config.num_layers
-        self.attention_head_dim = arch_config.attention_head_dim
-        self.num_attention_heads = arch_config.num_attention_heads
-        self.text_embed_dim = arch_config.text_embed_dim
-        self.time_embed_dim = arch_config.time_embed_dim
-
-        # GlmImage uses 2 additional SDXL-like conditions - target_size, crop_coords
-        # Each of these are sincos embeddings of shape 2 * condition_dim
-        pooled_projection_dim = 2 * 2 * arch_config.condition_dim
-        inner_dim = arch_config.num_attention_heads * arch_config.attention_head_dim
-
-        self.hidden_size = inner_dim
-        self.num_channels_latents = arch_config.out_channels
-
-        # 1. RoPE
-        self.rotary_emb = GlmImageRotaryPosEmbed(arch_config.attention_head_dim, arch_config.patch_size, theta=10000.0)
-
-        # 2. Patch & Text-timestep embedding
-        self.image_projector = GlmImageImageProjector(arch_config.in_channels, inner_dim, arch_config.patch_size)
-        self.glyph_projector = MLP(
-            input_dim=arch_config.text_embed_dim,
-            mlp_hidden_dim=inner_dim,
-            output_dim=inner_dim,
-            act_type="gelu",
-        )
-        self.prior_token_embedding = nn.Embedding(arch_config.prior_vq_quantizer_codebook_size, inner_dim)
-        self.prior_projector = MLP(
-            input_dim=inner_dim,
-            mlp_hidden_dim=inner_dim,
-            output_dim=inner_dim,
-            act_type="silu",
-        )
-
-        self.time_condition_embed = GlmImageCombinedTimestepSizeEmbeddings(
-            embedding_dim=arch_config.time_embed_dim,
-            condition_dim=arch_config.condition_dim,
-            pooled_projection_dim=pooled_projection_dim,
-            timesteps_dim=arch_config.time_embed_dim,
-        )
-
-        # 3. Transformer blocks
-        self.transformer_blocks = nn.ModuleList([
-            GlmImageTransformerBlock(
-                inner_dim,
-                arch_config.num_attention_heads,
-                arch_config.attention_head_dim,
-                arch_config.time_embed_dim,
-                supported_attention_backends=self._supported_attention_backends,
-                prefix=f"transformer_blocks.{i}",
-            ) for i in range(arch_config.num_layers)
-        ])
-
-        # 4. Output projection
-        self.norm_out = GlmImageAdaLayerNormContinuous(inner_dim, arch_config.time_embed_dim, elementwise_affine=False)
-        self.proj_out = nn.Linear(
-            inner_dim,
-            arch_config.patch_size * arch_config.patch_size * arch_config.out_channels,
-            bias=True,
-        )
-
-        self.gradient_checkpointing = False
-
-        self.__post_init__()
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        encoder_hidden_states: torch.Tensor,
-        prior_token_id: torch.Tensor,
-        prior_token_drop: torch.Tensor,
-        timestep: torch.LongTensor,
-        target_size: torch.Tensor,
-        crop_coords: torch.Tensor,
-        attention_kwargs: Optional[Dict[str, Any]] = None,
-        kv_caches: Optional[GlmImageKVCache] = None,
-        kv_caches_mode: Optional[str] = None,
-        freqs_cis: Optional[Union[
-            Tuple[torch.Tensor, torch.Tensor],
-            List[Tuple[torch.Tensor, torch.Tensor]],
-        ]] = None,
-        guidance: torch.Tensor = None,
-        **kwargs,
-    ) -> torch.Tensor:
-        if kv_caches is not None:
-            kv_caches.set_mode(kv_caches_mode)
-
-        batch_size, num_channels, height, width = hidden_states.shape
-
-        if isinstance(encoder_hidden_states, list):
-            encoder_hidden_states = encoder_hidden_states[0]
-
-        # 1. RoPE
-        image_rotary_emb = freqs_cis
-        if image_rotary_emb is None:
-            image_rotary_emb = self.rotary_emb(hidden_states)
-
-        # 2. Patch & Timestep embeddings
-        p = self.patch_size
-        post_patch_height = height // p
-        post_patch_width = width // p
-
-        hidden_states = self.image_projector(hidden_states)
-        encoder_hidden_states = self.glyph_projector(encoder_hidden_states)
-        prior_embedding = self.prior_token_embedding(prior_token_id)
-        # Zero dropped priors by multiply: boolean indexing + .any() syncs each step.
-        keep = (~prior_token_drop).to(device=prior_embedding.device, dtype=prior_embedding.dtype)
-        while keep.dim() < prior_embedding.dim():
-            keep = keep.unsqueeze(-1)
-        prior_embedding = prior_embedding * keep
-        prior_hidden_states = self.prior_projector(prior_embedding)
-        hidden_states = hidden_states + prior_hidden_states
-
-        temb = self.time_condition_embed(timestep, target_size, crop_coords, hidden_states.dtype)
-        temb = F.silu(temb)
-
-        # 3. Transformer blocks
-        for idx, block in enumerate(self.transformer_blocks):
-            hidden_states, encoder_hidden_states = block(
-                hidden_states,
-                encoder_hidden_states,
-                temb,
-                image_rotary_emb,
-                attention_kwargs,
-                kv_cache=kv_caches[idx] if kv_caches is not None else None,
-            )
-
-        # 4. Output norm & projection
-        hidden_states = self.norm_out(hidden_states, temb)
-        hidden_states = self.proj_out(hidden_states)
-
-        # 5. Unpatchify
-        hidden_states = hidden_states.reshape(batch_size, post_patch_height, post_patch_width, -1, p, p)
-        output = hidden_states.permute(0, 3, 1, 4, 2, 5).flatten(4, 5).flatten(2, 3)
-
-        return output.float()
-
-
-EntryClass = GlmImageTransformer2DModel
diff --git a/fastvideo/models/dits/hunyuangamecraft.py b/fastvideo/models/dits/hunyuangamecraft.py
deleted file mode 100644
index ec2def561..000000000
--- a/fastvideo/models/dits/hunyuangamecraft.py
+++ /dev/null
@@ -1,348 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-HunyuanGameCraft Transformer model for FastVideo.
-
-Ported from official Hunyuan-GameCraft-1.0 implementation.
-"""
-from typing import Any, List, Optional
-
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from einops import rearrange
-
-from fastvideo.configs.models.dits.hunyuangamecraft import (
-    HunyuanGameCraftArchConfig,
-    HunyuanGameCraftConfig,
-)
-from fastvideo.layers.linear import ReplicatedLinear
-from fastvideo.layers.mlp import MLP
-from fastvideo.layers.rotary_embedding import get_rotary_pos_embed
-from fastvideo.layers.visual_embedding import ModulateProjection, PatchEmbed, TimestepEmbedder, unpatchify
-from fastvideo.models.dits.base import BaseDiT
-from fastvideo.models.dits.hunyuanvideo import (
-    MMDoubleStreamBlock,
-    MMSingleStreamBlock,
-    SingleTokenRefiner,
-)
-
-
-class GameCraftFinalLayer(nn.Module):
-    """
-    GameCraft-specific FinalLayer with correct shift/scale order.
-    
-    The official GameCraft implementation uses shift, scale order (not scale, shift).
-    This differs from the HunyuanVideo FinalLayer.
-    """
-
-    def __init__(self, hidden_size, patch_size, out_channels, dtype=None, prefix: str = "") -> None:
-        super().__init__()
-
-        self.norm_final = nn.LayerNorm(hidden_size, eps=1e-6, elementwise_affine=False, dtype=dtype)
-
-        output_dim = patch_size[0] * patch_size[1] * patch_size[2] * out_channels
-
-        self.linear = ReplicatedLinear(hidden_size,
-                                       output_dim,
-                                       bias=True,
-                                       params_dtype=dtype,
-                                       prefix=f"{prefix}.linear")
-
-        self.adaLN_modulation = ModulateProjection(hidden_size,
-                                                   factor=2,
-                                                   act_layer="silu",
-                                                   dtype=dtype,
-                                                   prefix=f"{prefix}.adaLN_modulation")
-
-    def forward(self, x, c):
-        # GameCraft uses shift, scale order (verified against official implementation)
-        shift, scale = self.adaLN_modulation(c).chunk(2, dim=-1)
-        x = self.norm_final(x) * (1.0 + scale.unsqueeze(1)) + shift.unsqueeze(1)
-        x, _ = self.linear(x)
-        return x
-
-
-class CameraNet(nn.Module):
-    """
-    Camera state encoding network - ported from official GameCraft.
-    
-    Processes camera parameters (Plücker coordinates) into feature embeddings.
-    """
-
-    def __init__(
-        self,
-        in_channels: int = 6,
-        downscale_coef: int = 8,
-        out_channels: int = 16,
-        patch_size: List[int] = [1, 2, 2],
-        hidden_size: int = 3072,
-        dtype: Optional[torch.dtype] = None,
-        prefix: str = "",
-    ):
-        super().__init__()
-        _ = prefix  # Unused
-
-        start_channels = in_channels * (downscale_coef**2)
-        input_channels = [start_channels, start_channels // 2, start_channels // 4]
-        self.input_channels = input_channels
-
-        self.unshuffle = nn.PixelUnshuffle(downscale_coef)
-
-        self.encode_first = nn.Sequential(
-            nn.Conv2d(input_channels[0], input_channels[1], kernel_size=1, stride=1, padding=0),
-            nn.GroupNorm(2, input_channels[1]),
-            nn.ReLU(),
-        )
-        self._initialize_weights(self.encode_first)
-
-        self.encode_second = nn.Sequential(
-            nn.Conv2d(input_channels[1], input_channels[2], kernel_size=1, stride=1, padding=0),
-            nn.GroupNorm(2, input_channels[2]),
-            nn.ReLU(),
-        )
-        self._initialize_weights(self.encode_second)
-
-        self.final_proj = nn.Conv2d(input_channels[2], out_channels, kernel_size=1)
-        self._zeros_init_linear(self.final_proj)
-
-        self.scale = nn.Parameter(torch.ones(1))
-
-        self.camera_in = PatchEmbed(
-            patch_size=patch_size,
-            in_chans=out_channels,
-            embed_dim=hidden_size,
-        )
-
-    def _zeros_init_linear(self, linear):
-        if hasattr(linear, "weight"):
-            nn.init.zeros_(linear.weight)
-        if hasattr(linear, "bias") and linear.bias is not None:
-            nn.init.zeros_(linear.bias)
-
-    def _initialize_weights(self, block):
-        for m in block:
-            if isinstance(m, nn.Conv2d):
-                n = m.kernel_size[0] * m.kernel_size[1] * m.in_channels
-                nn.init.normal_(m.weight, mean=0.0, std=np.sqrt(2.0 / n))
-                if m.bias is not None:
-                    nn.init.zeros_(m.bias)
-
-    def compress_time(self, x: torch.Tensor, num_frames: int) -> torch.Tensor:
-        x = rearrange(x, '(b f) c h w -> b f c h w', f=num_frames)
-        batch_size, frames, channels, height, width = x.shape
-        x = rearrange(x, 'b f c h w -> (b h w) c f')
-
-        if x.shape[-1] == 66 or x.shape[-1] == 34:
-            x_len = x.shape[-1]
-            x_clip1 = x[..., :x_len // 2]
-            x_clip1_first = x_clip1[..., 0].unsqueeze(-1)
-            x_clip1_rest = F.avg_pool1d(x_clip1[..., 1:], kernel_size=2, stride=2)
-            x_clip2 = x[..., x_len // 2:]
-            x_clip2_first = x_clip2[..., 0].unsqueeze(-1)
-            x_clip2_rest = F.avg_pool1d(x_clip2[..., 1:], kernel_size=2, stride=2)
-            x = torch.cat([x_clip1_first, x_clip1_rest, x_clip2_first, x_clip2_rest], dim=-1)
-        elif x.shape[-1] % 2 == 1:
-            x_first = x[..., 0]
-            x_rest = x[..., 1:]
-            if x_rest.shape[-1] > 0:
-                x_rest = F.avg_pool1d(x_rest, kernel_size=2, stride=2)
-            x = torch.cat([x_first[..., None], x_rest], dim=-1)
-        else:
-            x = F.avg_pool1d(x, kernel_size=2, stride=2)
-
-        x = rearrange(x, '(b h w) c f -> (b f) c h w', b=batch_size, h=height, w=width)
-        return x
-
-    def forward(self, camera_states: torch.Tensor) -> torch.Tensor:
-        batch_size, num_frames, channels, height, width = camera_states.shape
-        camera_states = rearrange(camera_states, 'b f c h w -> (b f) c h w')
-        camera_states = self.unshuffle(camera_states)
-        camera_states = self.encode_first(camera_states)
-        camera_states = self.compress_time(camera_states, num_frames=num_frames)
-        num_frames = camera_states.shape[0] // batch_size
-        camera_states = self.encode_second(camera_states)
-        camera_states = self.compress_time(camera_states, num_frames=num_frames)
-        camera_states = self.final_proj(camera_states)
-        camera_states = rearrange(camera_states, "(b f) c h w -> b c f h w", b=batch_size)
-        camera_states = self.camera_in(camera_states)
-        return camera_states * self.scale
-
-
-class HunyuanGameCraftTransformer3DModel(BaseDiT):
-    """
-    HunyuanGameCraft Transformer - ported from official implementation.
-    """
-
-    _fsdp_shard_conditions = HunyuanGameCraftArchConfig()._fsdp_shard_conditions
-    _compile_conditions = HunyuanGameCraftArchConfig()._compile_conditions
-    _supported_attention_backends = HunyuanGameCraftArchConfig()._supported_attention_backends
-    param_names_mapping = HunyuanGameCraftConfig().param_names_mapping
-    reverse_param_names_mapping = HunyuanGameCraftConfig().reverse_param_names_mapping
-
-    def __init__(self, config: HunyuanGameCraftConfig, hf_config: dict[str, Any]):
-        super().__init__(config=config, hf_config=hf_config)
-
-        arch = config.arch_config
-
-        if isinstance(arch.patch_size, (list, tuple)):
-            self.patch_size = list(arch.patch_size)
-        else:
-            self.patch_size = [arch.patch_size_t, arch.patch_size, arch.patch_size]
-
-        self.in_channels = arch.in_channels
-        self.out_channels = arch.out_channels
-        self.unpatchify_channels = self.out_channels
-        self.num_channels_latents = self.out_channels  # Alias for latent_preparation stage
-        self.hidden_size = arch.hidden_size
-        self.num_heads = arch.num_attention_heads
-        self.num_attention_heads = arch.num_attention_heads  # Alias for compatibility
-        self.guidance_embeds = arch.guidance_embeds
-        self.rope_dim_list = list(arch.rope_axes_dim)
-        self.rope_theta = arch.rope_theta
-        self.text_states_dim = arch.text_embed_dim
-        self.text_states_dim_2 = arch.pooled_projection_dim
-        self.dtype = arch.dtype
-
-        pe_dim = self.hidden_size // self.num_heads
-        if sum(self.rope_dim_list) != pe_dim:
-            raise ValueError(f"rope_axes_dim sum {sum(self.rope_dim_list)} != {pe_dim}")
-
-        factory_kwargs = {'dtype': self.dtype}
-
-        self.img_in = PatchEmbed(
-            patch_size=self.patch_size,
-            in_chans=self.in_channels,
-            embed_dim=self.hidden_size,
-            **factory_kwargs,
-        )
-
-        self.txt_in = SingleTokenRefiner(
-            self.text_states_dim,
-            self.hidden_size,
-            self.num_heads,
-            depth=arch.num_refiner_layers,
-            **factory_kwargs,
-        )
-
-        self.time_in = TimestepEmbedder(self.hidden_size, **factory_kwargs)
-
-        self.vector_in = MLP(
-            self.text_states_dim_2,
-            self.hidden_size,
-            self.hidden_size,
-            act_type="silu",
-            **factory_kwargs,
-        )
-
-        self.guidance_in = (TimestepEmbedder(self.hidden_size, **factory_kwargs) if self.guidance_embeds else None)
-
-        self.double_blocks = nn.ModuleList([
-            MMDoubleStreamBlock(
-                hidden_size=self.hidden_size,
-                num_attention_heads=self.num_heads,
-                mlp_ratio=arch.mlp_ratio,
-                supported_attention_backends=self._supported_attention_backends,
-                **factory_kwargs,
-            ) for _ in range(arch.num_layers)
-        ])
-
-        self.single_blocks = nn.ModuleList([
-            MMSingleStreamBlock(
-                hidden_size=self.hidden_size,
-                num_attention_heads=self.num_heads,
-                mlp_ratio=arch.mlp_ratio,
-                supported_attention_backends=self._supported_attention_backends,
-                **factory_kwargs,
-            ) for _ in range(arch.num_single_layers)
-        ])
-
-        self.final_layer = GameCraftFinalLayer(
-            self.hidden_size,
-            self.patch_size,
-            self.out_channels,
-            **factory_kwargs,
-        )
-
-        self.camera_net = CameraNet(
-            in_channels=arch.camera_in_channels,
-            out_channels=16,
-            downscale_coef=arch.camera_downscale_coef,
-            patch_size=self.patch_size,
-            hidden_size=self.hidden_size,
-        )
-
-    def forward(
-        self,
-        x: torch.Tensor,
-        encoder_hidden_states: List[torch.Tensor],
-        timestep: torch.Tensor,
-        camera_states: Optional[torch.Tensor] = None,
-        encoder_attention_mask: Optional[List[torch.Tensor]] = None,
-        guidance: Optional[torch.Tensor] = None,
-        return_dict: bool = False,
-    ) -> torch.Tensor:
-        img = x
-        _, _, ot, oh, ow = x.shape
-        tt = ot // self.patch_size[0]
-        th = oh // self.patch_size[1]
-        tw = ow // self.patch_size[2]
-
-        text_states = encoder_hidden_states[0]
-        text_states_2 = encoder_hidden_states[1] if len(encoder_hidden_states) > 1 else None
-        text_mask = encoder_attention_mask[0] if encoder_attention_mask else None
-
-        vec = self.time_in(timestep)
-
-        if text_states_2 is not None:
-            vec = vec + self.vector_in(text_states_2)
-
-        if self.guidance_in is not None and guidance is not None:
-            vec = vec + self.guidance_in(guidance)
-
-        img = self.img_in(img)
-
-        if camera_states is not None:
-            latent_len = ot
-            if latent_len == 18:
-                camera_latents = torch.cat(
-                    [self.camera_net(torch.zeros_like(camera_states)),
-                     self.camera_net(camera_states)], dim=1)
-            elif latent_len == 9:
-                camera_latents = self.camera_net(camera_states)
-            elif latent_len == 10:
-                camera_latents = torch.cat(
-                    [self.camera_net(torch.zeros_like(camera_states[:, 0:4, :, :, :])),
-                     self.camera_net(camera_states)],
-                    dim=1)
-            else:
-                camera_latents = self.camera_net(camera_states)
-            img = img + camera_latents
-
-        txt = self.txt_in(text_states, timestep)
-        txt_seq_len = txt.shape[1]
-
-        freqs_cos, freqs_sin = get_rotary_pos_embed(
-            (tt, th, tw),
-            self.hidden_size,
-            self.num_heads,
-            self.rope_dim_list,
-            self.rope_theta,
-        )
-        freqs_cos = freqs_cos.to(device=img.device, dtype=img.dtype)
-        freqs_sin = freqs_sin.to(device=img.device, dtype=img.dtype)
-        freqs_cis = (freqs_cos, freqs_sin)
-
-        for block in self.double_blocks:
-            img, txt = block(img, txt, vec, freqs_cis)
-
-        x = torch.cat([img, txt], dim=1)
-
-        for block in self.single_blocks:
-            x = block(x, vec, txt_seq_len, freqs_cis)
-
-        img = x[:, :-txt_seq_len, ...]
-        img = self.final_layer(img, vec)
-        img = unpatchify(img, tt, th, tw, self.patch_size, self.out_channels)
-
-        return img
diff --git a/fastvideo/models/dits/longcat.py b/fastvideo/models/dits/longcat.py
deleted file mode 100644
index 1943d87ae..000000000
--- a/fastvideo/models/dits/longcat.py
+++ /dev/null
@@ -1,1216 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-Native LongCat Video DiT implementation using FastVideo conventions.
-"""
-
-from typing import Any
-import math
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from einops import rearrange
-
-from fastvideo.configs.models.dits import LongCatVideoConfig
-from fastvideo.layers.linear import ReplicatedLinear
-from fastvideo.layers.layernorm import RMSNorm, FP32LayerNorm
-from fastvideo.layers.activation import get_act_fn
-from fastvideo.layers.rotary_embedding_3d import RotaryPositionalEmbedding3D
-from fastvideo.attention.layer import DistributedAttention, LocalAttention
-from fastvideo.models.dits.base import BaseDiT
-from fastvideo.distributed.communication_op import (sequence_model_parallel_all_gather,
-                                                    sequence_model_parallel_all_to_all_4D)
-from fastvideo.distributed.parallel_state import get_sp_parallel_rank, get_sp_world_size
-from fastvideo.platforms import AttentionBackendEnum
-from fastvideo.third_party.longcat_video.block_sparse_attention.bsa_interface import flash_attn_bsa_3d
-
-
-class LongCatSPLayout:
-    """Convert between global THW and rank-major spatial partitions.
-
-    All tensors use [batch, sequence, ...]; trailing channel/head axes are opaque.
-    Local spatial dimensions need not be divisible by the BSA chunk dimensions.
-    """
-
-    def __init__(self, grid: tuple[int, int, int], size: int):
-        if size < 1 or len(grid) != 3 or min(grid) < 1:
-            raise ValueError("LongCat SP requires positive grid dimensions and group size")
-        self.grid = tuple(grid)
-        self.size = size
-        self.split_h = max(factor for factor in range(1, math.isqrt(size) + 1) if size % factor == 0)
-        self.split_w = size // self.split_h
-        self.t, self.h, self.w = self.grid
-        if self.h % self.split_h or self.w % self.split_w:
-            raise ValueError(f"LongCat token grid {grid} cannot be split across SP={size}")
-        self.local_h = self.h // self.split_h
-        self.local_w = self.w // self.split_w
-
-    def shard(self, x: torch.Tensor, rank: int) -> torch.Tensor:
-        if not 0 <= rank < self.size:
-            raise ValueError(f"Invalid SP rank {rank} for size {self.size}")
-        b, n, *tail = x.shape
-        if n != self.t * self.h * self.w:
-            raise ValueError("LongCat SP input does not match the global token grid")
-        row, col = divmod(rank, self.split_w)
-        spatial = x.reshape(b, self.t, self.h, self.w, *tail)
-        local = spatial[:, :, row * self.local_h:(row + 1) * self.local_h,
-                        col * self.local_w:(col + 1) * self.local_w]
-        return local.reshape(b, n // self.size, *tail).contiguous()
-
-    def rank_to_global(self, x: torch.Tensor) -> torch.Tensor:
-        b, n, *tail = x.shape
-        if n != self.t * self.h * self.w:
-            raise ValueError("Gathered LongCat SP sequence does not match the global token grid")
-        ranked = x.reshape(b, self.split_h, self.split_w, self.t, self.local_h, self.local_w, *tail)
-        return ranked.permute(0, 3, 1, 4, 2, 5, *range(6, ranked.ndim)).reshape(b, n, *tail).contiguous()
-
-    def global_to_rank(self, x: torch.Tensor) -> torch.Tensor:
-        b, n, *tail = x.shape
-        if n != self.t * self.h * self.w:
-            raise ValueError("Global LongCat SP sequence does not match the token grid")
-        spatial = x.reshape(b, self.t, self.split_h, self.local_h, self.split_w, self.local_w, *tail)
-        return spatial.permute(0, 2, 4, 1, 3, 5, *range(6, spatial.ndim)).reshape(b, n, *tail).contiguous()
-
-
-# ============================================================================
-# Embeddings
-# ============================================================================
-
-
-class PatchEmbed3D(nn.Module):
-    """
-    3D patch embedding using Conv3d.
-    """
-
-    def __init__(
-            self,
-            patch_size: tuple[int, int, int] = (1, 2, 2),
-            in_channels: int = 16,
-            embed_dim: int = 4096,
-    ):
-        super().__init__()
-        self.patch_size = patch_size
-        self.in_channels = in_channels
-        self.embed_dim = embed_dim
-
-        self.proj = nn.Conv3d(
-            in_channels,
-            embed_dim,
-            kernel_size=patch_size,
-            stride=patch_size,
-            bias=True,
-        )
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        """
-        Args:
-            x: [B, C, T, H, W]
-        Returns:
-            [B, N, C] where N = (T/pt) * (H/ph) * (W/pw)
-        """
-        # Padding if needed
-        _, _, T, H, W = x.shape
-        if W % self.patch_size[2] != 0:
-            x = F.pad(x, (0, self.patch_size[2] - W % self.patch_size[2]))
-        if H % self.patch_size[1] != 0:
-            x = F.pad(x, (0, 0, 0, self.patch_size[1] - H % self.patch_size[1]))
-        if T % self.patch_size[0] != 0:
-            x = F.pad(x, (0, 0, 0, 0, 0, self.patch_size[0] - T % self.patch_size[0]))
-
-        x = self.proj(x)  # [B, C, T', H', W']
-        x = x.flatten(2).transpose(1, 2)  # [B, N, C]
-        return x
-
-
-class TimestepEmbedder(nn.Module):
-    """
-    Sinusoidal timestep embedding + MLP projection.
-    """
-
-    def __init__(
-        self,
-        frequency_embedding_size: int = 256,
-        adaln_tembed_dim: int = 512,
-        dtype: torch.dtype | None = None,
-    ):
-        super().__init__()
-        self.frequency_embedding_size = frequency_embedding_size
-
-        # Use FastVideo's ReplicatedLinear
-        self.linear_1 = ReplicatedLinear(
-            frequency_embedding_size,
-            adaln_tembed_dim,
-            bias=True,
-            params_dtype=dtype,
-        )
-        self.act = nn.SiLU()
-        self.linear_2 = ReplicatedLinear(
-            adaln_tembed_dim,
-            adaln_tembed_dim,
-            bias=True,
-            params_dtype=dtype,
-        )
-
-    @staticmethod
-    def timestep_embedding(t: torch.Tensor, dim: int, max_period: int = 10000) -> torch.Tensor:
-        """
-        Create sinusoidal timestep embeddings.
-        """
-        half = dim // 2
-        freqs = torch.exp(-math.log(max_period) *
-                          torch.arange(start=0, end=half, dtype=torch.float32, device=t.device) / half)
-        args = t[:, None].float() * freqs[None]
-        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
-        if dim % 2:
-            embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
-        return embedding
-
-    def forward(self, t: torch.Tensor, latent_shape: tuple | None = None) -> torch.Tensor:
-        """
-        Args:
-            t: [B] or [B, T] timesteps
-            latent_shape: (T, H, W) for temporal expansion
-        Returns:
-            [B, T, C]
-        """
-        # Sinusoidal embedding in FP32
-        t_freq = self.timestep_embedding(t.flatten(), self.frequency_embedding_size)
-
-        # Cast to model dtype before MLP (matching original LongCat)
-        # Handle LoRA wrapper if present
-        linear_layer = self.linear_1.base_layer if hasattr(self.linear_1, 'base_layer') else self.linear_1
-        target_dtype = linear_layer.weight.dtype
-        if t_freq.dtype != target_dtype:
-            t_freq = t_freq.to(target_dtype)
-
-        # MLP projection
-        t_emb, _ = self.linear_1(t_freq)
-        t_emb = self.act(t_emb)
-        t_emb, _ = self.linear_2(t_emb)
-
-        # Reshape if needed
-        if latent_shape is not None and len(t.shape) > 1:
-            B = t.shape[0]
-            T = latent_shape[0]
-            t_emb = t_emb.reshape(B, T, -1)
-
-        return t_emb
-
-
-class CaptionEmbedder(nn.Module):
-    """
-    Caption embedding with MLP projection and optional text compaction.
-    """
-
-    def __init__(
-        self,
-        caption_channels: int = 4096,
-        hidden_size: int = 4096,
-        text_tokens_zero_pad: bool = True,
-        dtype: torch.dtype | None = None,
-    ):
-        super().__init__()
-        self.text_tokens_zero_pad = text_tokens_zero_pad
-
-        # Two-layer MLP using ReplicatedLinear
-        # CRITICAL: Original LongCat uses GELU(approximate="tanh"), NOT SiLU!
-        self.linear_1 = ReplicatedLinear(
-            caption_channels,
-            hidden_size,
-            bias=True,
-            params_dtype=dtype,
-        )
-        self.act = nn.GELU(approximate="tanh")  # Match original LongCat
-        self.linear_2 = ReplicatedLinear(
-            hidden_size,
-            hidden_size,
-            bias=True,
-            params_dtype=dtype,
-        )
-
-    def forward(
-        self,
-        encoder_hidden_states: torch.Tensor,
-        encoder_attention_mask: torch.Tensor | None = None,
-    ) -> torch.Tensor:
-        """
-        Args:
-            encoder_hidden_states: [B, N_text, C_text] or [B, 1, N_text, C_text]
-            encoder_attention_mask: [B, N_text] or [B, 1, 1, N_text]
-        Returns:
-            y: [B, N_text, C] - standard padded representation (like other models)
-        """
-        # Handle extra dimension from wrapper
-        if len(encoder_hidden_states.shape) == 4:
-            encoder_hidden_states = encoder_hidden_states.squeeze(1)
-
-        # Project
-        y, _ = self.linear_1(encoder_hidden_states)
-        y = self.act(y)
-        y, _ = self.linear_2(y)  # [B, N_text, C]
-
-        # Handle attention masking - just zero out padded tokens if requested
-        if encoder_attention_mask is not None:
-            # Remove extra dimensions
-            if len(encoder_attention_mask.shape) == 4:
-                encoder_attention_mask = encoder_attention_mask.squeeze(1).squeeze(1)
-            elif len(encoder_attention_mask.shape) == 3:
-                encoder_attention_mask = encoder_attention_mask.squeeze(1)
-
-            seq_len = int(y.shape[1])
-            mask_len = int(encoder_attention_mask.shape[1])
-            if mask_len < seq_len:
-                encoder_attention_mask = F.pad(
-                    encoder_attention_mask,
-                    (0, seq_len - mask_len),
-                    value=0,
-                )
-            elif mask_len > seq_len:
-                encoder_attention_mask = encoder_attention_mask[:, :seq_len]
-
-            # Zero out padded tokens if requested
-            if self.text_tokens_zero_pad:
-                y = y * encoder_attention_mask.unsqueeze(-1)
-
-        # Return standard format [B, N_text, C] - no compaction!
-        return y
-
-
-# ============================================================================
-# Attention Modules (Placeholders for now)
-# ============================================================================
-
-
-class LongCatSelfAttention(nn.Module):
-    """
-    Self-attention with 3D RoPE support and optional BSA.
-    """
-
-    def __init__(
-        self,
-        dim: int,
-        num_heads: int,
-        config: LongCatVideoConfig,
-        dtype: torch.dtype | None = None,
-    ):
-        super().__init__()
-        self.dim = dim
-        self.num_heads = num_heads
-        self.head_dim = dim // num_heads
-
-        # Separate Q/K/V projections (not fused like original)
-        self.to_q = ReplicatedLinear(dim, dim, bias=True, params_dtype=dtype)
-        self.to_k = ReplicatedLinear(dim, dim, bias=True, params_dtype=dtype)
-        self.to_v = ReplicatedLinear(dim, dim, bias=True, params_dtype=dtype)
-
-        # Per-head RMS normalization
-        self.q_norm = RMSNorm(self.head_dim, eps=1e-6, dtype=dtype or torch.float32)
-        self.k_norm = RMSNorm(self.head_dim, eps=1e-6, dtype=dtype or torch.float32)
-
-        # Output projection
-        self.to_out = ReplicatedLinear(dim, dim, bias=True, params_dtype=dtype)
-
-        # 3D RoPE
-        self.rope_3d = RotaryPositionalEmbedding3D(head_dim=self.head_dim)
-
-        # BSA configuration
-        self.enable_bsa = getattr(config, 'enable_bsa', False)
-        self.bsa_params = getattr(config, 'bsa_params', None)
-
-        # FastVideo attention backend (used when BSA is disabled)
-        self.attn = DistributedAttention(
-            num_heads=num_heads,
-            head_size=self.head_dim,
-            supported_attention_backends=config._supported_attention_backends,
-        )
-
-    def forward(
-            self,
-            x: torch.Tensor,  # [B, N, C]
-            latent_shape: tuple,  # (T, H, W)
-            num_cond_latents: int = 0,  # Number of conditioning latent frames (for I2V)
-            return_kv: bool = False,  # Return K/V for caching
-            sp_layout: LongCatSPLayout | None = None,
-            **kwargs) -> torch.Tensor | tuple:
-        """
-        Forward pass with 3D RoPE and optional BSA.
-        
-        For I2V mode (num_cond_latents > 0):
-        - Conditioned tokens only attend to themselves
-        - Noise tokens attend to ALL tokens (cond + noise)
-        
-        Args:
-            return_kv: If True, return (output, (k_cache, v_cache)) for KV caching
-        """
-        B, N, C = x.shape
-        T, H, W = latent_shape
-
-        if sp_layout is not None:
-            if self.num_heads % sp_layout.size:
-                raise ValueError("LongCat attention heads must be divisible by SP size")
-            if N * sp_layout.size != T * H * W:
-                raise ValueError("LongCat SP attention requires already-sharded input tokens")
-
-        # Project to Q/K/V
-        q, _ = self.to_q(x)
-        k, _ = self.to_k(x)
-        v, _ = self.to_v(x)
-
-        # Reshape to heads: [B, N, num_heads, head_dim]
-        q = q.view(B, N, self.num_heads, self.head_dim)
-        k = k.view(B, N, self.num_heads, self.head_dim)
-        v = v.view(B, N, self.num_heads, self.head_dim)
-
-        # Per-head RMS normalization
-        q = self.q_norm(q)
-        k = self.k_norm(k)
-
-        if sp_layout is not None:
-            # Local sequence / all heads -> global sequence / local heads.
-            # Restore THW before RoPE and BSA so position and sparse blocks
-            # match the single-GPU computation exactly in layout.
-            qkv = sequence_model_parallel_all_to_all_4D(torch.cat([q, k, v], dim=0),
-                                                       scatter_dim=2, gather_dim=1)
-            q, k, v = sp_layout.rank_to_global(qkv).chunk(3, dim=0)
-            del qkv
-
-        # Save pre-RoPE K/V for cache if requested (before RoPE is applied)
-        if return_kv:
-            # [B, N, num_heads, head_dim] -> [B, num_heads, N, head_dim]
-            k_cache = k.transpose(1, 2).clone()
-            v_cache = v.transpose(1, 2).clone()
-
-        # For RoPE: need [B, num_heads, N, head_dim]
-        q_rope = q.transpose(1, 2)
-        k_rope = k.transpose(1, 2)
-
-        # Apply 3D RoPE
-        q_rope, k_rope = self.rope_3d(q_rope, k_rope, grid_size=latent_shape)
-
-        # Transpose back: [B, N, num_heads, head_dim] or [B, H, N, D] for BSA
-        q = q_rope.transpose(1, 2)
-        k = k_rope.transpose(1, 2)
-
-        # === I2V Split Attention ===
-        # For I2V, conditioned tokens and noise tokens are processed separately
-        if num_cond_latents > 0:
-            # Calculate number of conditioned tokens (cond_latents * spatial_tokens_per_frame)
-            num_cond_tokens = num_cond_latents * (N // T)
-
-            # Conditioned tokens: only attend to themselves (same seq length, use self.attn)
-            q_cond = q[:, :num_cond_tokens].contiguous()
-            k_cond = k[:, :num_cond_tokens].contiguous()
-            v_cond = v[:, :num_cond_tokens].contiguous()
-            out_cond, _ = self.attn(q_cond, k_cond, v_cond)
-
-            # Noise tokens: attend to ALL tokens (different seq lengths!)
-            # Need to use flash attention directly since q has different length than k/v
-            q_noise = q[:, num_cond_tokens:].contiguous()  # [B, N_noise, num_heads, head_dim]
-            # k, v are full: [B, N, num_heads, head_dim]
-
-            # Transpose for flash attention: [B, num_heads, seq, head_dim]
-            q_noise_t = q_noise.transpose(1, 2)
-            k_t = k.transpose(1, 2)
-            v_t = v.transpose(1, 2)
-
-            # Use scaled dot product attention (handles different q/kv lengths)
-            out_noise_t = torch.nn.functional.scaled_dot_product_attention(
-                q_noise_t, k_t, v_t, attn_mask=None, dropout_p=0.0,
-                is_causal=False)  # [B, num_heads, N_noise, head_dim]
-
-            # Transpose back: [B, N_noise, num_heads, head_dim]
-            out_noise = out_noise_t.transpose(1, 2)
-
-            # Merge conditioned and noise outputs
-            out = torch.cat([out_cond, out_noise], dim=1)
-
-            # Reshape and project out
-            out = out.reshape(B, N, C)
-            out, _ = self.to_out(out)
-
-            if return_kv:
-                return out, (k_cache, v_cache)
-            return out
-
-        # === Attention: BSA or standard ===
-        if self.enable_bsa and T > 1:  # Only use BSA for multi-frame videos
-            # BSA expects [B, H, S, D] format
-            q_bsa = q.transpose(1, 2).contiguous()  # [B, num_heads, N, head_dim]
-            k_bsa = k.transpose(1, 2).contiguous()
-            v_bsa = v.transpose(1, 2).contiguous()
-
-            # Each rank now owns complete spatial sequences for its heads.
-            # Keep the global grid even when a local shard cuts a BSA block.
-            out = flash_attn_bsa_3d(q_bsa,
-                                    k_bsa,
-                                    v_bsa,
-                                    latent_shape_q=latent_shape,
-                                    latent_shape_k=latent_shape,
-                                    **self.bsa_params)  # [B, num_heads, N, head_dim]
-
-            # Transpose back: [B, N, num_heads, head_dim]
-            out = out.transpose(1, 2)
-        else:
-            # Standard attention: [B, N, num_heads, head_dim]
-            out, _ = self.attn(q, k, v)
-
-        if sp_layout is not None:
-            out = sequence_model_parallel_all_to_all_4D(sp_layout.global_to_rank(out),
-                                                        scatter_dim=1, gather_dim=2)
-
-        # Reshape and project out
-        out = out.reshape(B, N, C)
-        out, _ = self.to_out(out)
-
-        if return_kv:
-            return out, (k_cache, v_cache)
-        return out
-
-    def forward_with_kv_cache(
-            self,
-            x: torch.Tensor,  # [B, N_noise, C] - only noise tokens
-            latent_shape: tuple,  # (T_noise, H, W) - shape for noise only
-            num_cond_latents: int,  # Number of conditioning latent frames
-            kv_cache: tuple,  # (k_cond, v_cond) - [B, heads, N_cond, head_dim]
-    ) -> torch.Tensor:
-        """
-        Forward using cached K/V from conditioning frames.
-        
-        x contains only NOISE tokens.
-        kv_cache contains pre-computed K/V for CONDITIONING tokens.
-        
-        CRITICAL: RoPE positions for noise tokens must start AFTER conditioning.
-        We achieve this by padding Q with dummy tokens for conditioning positions,
-        applying RoPE to the full sequence, then extracting only noise token Q.
-        """
-        B, N, C = x.shape
-        T, H, W = latent_shape
-
-        k_cache, v_cache = kv_cache
-
-        # Handle batch size mismatch (cache might be smaller for CFG)
-        # When using CFG, latent_model_input is doubled [neg, pos], but cache is for original batch
-        if k_cache.shape[0] != B:
-            # Expand cache to match input batch size
-            # For CFG: repeat the cache for both negative and positive branches
-            repeat_factor = B // k_cache.shape[0]
-            k_cache = k_cache.repeat(repeat_factor, 1, 1, 1)
-            v_cache = v_cache.repeat(repeat_factor, 1, 1, 1)
-
-        # Project to Q/K/V for noise tokens
-        q, _ = self.to_q(x)
-        k, _ = self.to_k(x)
-        v, _ = self.to_v(x)
-
-        # Reshape to heads: [B, N, num_heads, head_dim]
-        q = q.view(B, N, self.num_heads, self.head_dim)
-        k = k.view(B, N, self.num_heads, self.head_dim)
-        v = v.view(B, N, self.num_heads, self.head_dim)
-
-        # Per-head RMS normalization
-        q = self.q_norm(q)
-        k = self.k_norm(k)
-
-        # Transpose for RoPE: [B, heads, N, head_dim]
-        q_rope = q.transpose(1, 2)
-        k_rope = k.transpose(1, 2)
-        v = v.transpose(1, 2)
-
-        # CRITICAL: Apply RoPE with correct positional offset
-        # Noise frame queries need positions starting from num_cond_latents
-        # Following the original LongCat approach:
-        # 1. Pad Q with dummy tokens matching k_cache shape
-        # 2. Apply RoPE to full sequence (T_cond + T_noise)
-        # 3. Extract only the noise portion of Q
-
-        # Create dummy Q padding to fill conditioning positions
-        # k_cache shape: [B, heads, N_cond, head_dim]
-        q_padding = torch.cat([torch.empty_like(k_cache), q_rope], dim=2).contiguous()
-
-        # Concatenate cached K with noise K for RoPE
-        k_full = torch.cat([k_cache, k_rope], dim=2)
-        v_full = torch.cat([v_cache, v], dim=2)
-
-        # Apply RoPE to full sequence (includes both cond and noise positions)
-        # Grid size: (T_cond + T_noise, H, W)
-        full_T = num_cond_latents + T
-        q_padding, k_full = self.rope_3d(q_padding, k_full, grid_size=(full_T, H, W))
-
-        # Extract only the noise portion of Q (last N tokens)
-        q_rope = q_padding[:, :, -N:].contiguous()
-
-        # Run attention: Q_noise attends to full K/V (cond + noise)
-        out = torch.nn.functional.scaled_dot_product_attention(q_rope,
-                                                               k_full,
-                                                               v_full,
-                                                               attn_mask=None,
-                                                               dropout_p=0.0,
-                                                               is_causal=False)  # [B, heads, N_noise, head_dim]
-
-        # Transpose back: [B, N_noise, heads, head_dim]
-        out = out.transpose(1, 2)
-        out = out.reshape(B, N, C)
-        out, _ = self.to_out(out)
-
-        return out
-
-
-class LongCatCrossAttention(nn.Module):
-    """
-    Cross-attention for text conditioning (standard implementation like other models).
-    """
-
-    def __init__(
-        self,
-        dim: int,
-        num_heads: int,
-        config: LongCatVideoConfig,
-        dtype: torch.dtype | None = None,
-    ):
-        super().__init__()
-        self.dim = dim
-        self.num_heads = num_heads
-        self.head_dim = dim // num_heads
-
-        # Separate Q/K/V projections
-        self.to_q = ReplicatedLinear(dim, dim, bias=True, params_dtype=dtype)
-        self.to_k = ReplicatedLinear(dim, dim, bias=True, params_dtype=dtype)
-        self.to_v = ReplicatedLinear(dim, dim, bias=True, params_dtype=dtype)
-
-        # Per-head RMS normalization
-        self.q_norm = RMSNorm(self.head_dim, eps=1e-6, dtype=dtype or torch.float32)
-        self.k_norm = RMSNorm(self.head_dim, eps=1e-6, dtype=dtype or torch.float32)
-
-        # Output projection
-        self.to_out = ReplicatedLinear(dim, dim, bias=True, params_dtype=dtype)
-
-        # Cross-attention uses LocalAttention (FastVideo standard)
-        self.attn = LocalAttention(
-            num_heads=num_heads,
-            head_size=self.head_dim,
-            dropout_rate=0,
-            softmax_scale=None,
-            causal=False,
-            supported_attention_backends=config.arch_config._supported_attention_backends,
-        )
-
-    def forward(
-            self,
-            x: torch.Tensor,  # [B, N_img, C]
-            context: torch.Tensor,  # [B, N_text, C]
-            latent_shape: tuple = None,  # (T, H, W) - needed for I2V
-            num_cond_latents: int = 0,  # Number of conditioning latent frames (for I2V)
-            **kwargs) -> torch.Tensor:
-        """
-        Forward pass for cross-attention (standard implementation).
-        
-        Args:
-            x: Image tokens [B, N_img, C]
-            context: Text tokens [B, N_text, C] (standard padded format)
-            latent_shape: (T, H, W) - needed for calculating num_cond_tokens
-            num_cond_latents: Number of conditioning latent frames (for I2V)
-            
-        For I2V mode (num_cond_latents > 0):
-        - Conditioned tokens get ZERO cross-attention output
-        - Only noise tokens get cross-attention with text
-        """
-        B, N_img, C = x.shape
-
-        # === I2V: Only noise tokens get cross-attention ===
-        if num_cond_latents > 0 and latent_shape is not None:
-            T, H, W = latent_shape
-            num_cond_tokens = num_cond_latents * (N_img // T)
-
-            # Only process noise tokens
-            x_noise = x[:, num_cond_tokens:]  # [B, N_noise, C]
-
-            # Project Q, K, V for noise tokens only
-            q, _ = self.to_q(x_noise)
-            k, _ = self.to_k(context)
-            v, _ = self.to_v(context)
-
-            N_text = context.shape[1]
-            N_noise = x_noise.shape[1]
-
-            # Reshape to heads
-            q = q.view(B, N_noise, self.num_heads, self.head_dim)
-            k = k.view(B, N_text, self.num_heads, self.head_dim)
-            v = v.view(B, N_text, self.num_heads, self.head_dim)
-
-            # Per-head RMS normalization
-            q = self.q_norm(q)
-            k = self.k_norm(k)
-
-            # Run cross-attention
-            out_noise = self.attn(q, k, v)  # [B, N_noise, num_heads, head_dim]
-            out_noise = out_noise.reshape(B, N_noise, C)
-            out_noise, _ = self.to_out(out_noise)
-
-            # Conditioned tokens get zero output
-            out_cond = torch.zeros((B, num_cond_tokens, C), dtype=out_noise.dtype, device=out_noise.device)
-
-            # Merge
-            out = torch.cat([out_cond, out_noise], dim=1)
-            return out
-
-        # === Standard cross-attention ===
-        # Project Q, K, V (standard cross-attention like WanVideo/Cosmos)
-        q, _ = self.to_q(x)
-        k, _ = self.to_k(context)
-        v, _ = self.to_v(context)
-
-        N_text = context.shape[1]
-
-        # Reshape to heads
-        q = q.view(B, N_img, self.num_heads, self.head_dim)
-        k = k.view(B, N_text, self.num_heads, self.head_dim)
-        v = v.view(B, N_text, self.num_heads, self.head_dim)
-
-        # Per-head RMS normalization
-        q = self.q_norm(q)
-        k = self.k_norm(k)
-
-        # Run cross-attention using FastVideo's LocalAttention
-        # LocalAttention handles different q and k/v sequence lengths automatically
-        out = self.attn(q, k, v)  # [B, N_img, num_heads, head_dim]
-
-        # Reshape and project out
-        out = out.reshape(B, N_img, C)
-        out, _ = self.to_out(out)
-
-        return out
-
-
-# ============================================================================
-# Feed-Forward Network
-# ============================================================================
-
-
-class LongCatSwiGLUFFN(nn.Module):
-    """
-    SwiGLU feed-forward network using FastVideo's ReplicatedLinear.
-    
-    FFN(x) = down(gate(x) * SiLU(up(x)))
-    """
-
-    def __init__(
-        self,
-        dim: int,
-        hidden_dim: int,
-        dtype: torch.dtype | None = None,
-    ):
-        super().__init__()
-
-        # Three projections for SwiGLU (no bias as per original)
-        self.w1 = ReplicatedLinear(dim, hidden_dim, bias=False, params_dtype=dtype)  # gate
-        self.w3 = ReplicatedLinear(dim, hidden_dim, bias=False, params_dtype=dtype)  # up
-        self.w2 = ReplicatedLinear(hidden_dim, dim, bias=False, params_dtype=dtype)  # down
-
-        self.act = nn.SiLU()
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        """
-        Forward pass: SiLU(w1(x)) * w3(x) -> w2 (matching original LongCat)
-        """
-        w1_out, _ = self.w1(x)
-        w3_out, _ = self.w3(x)
-        combined = self.act(w1_out) * w3_out
-        out, _ = self.w2(combined)
-        return out
-
-
-# ============================================================================
-# Modulation Utilities
-# ============================================================================
-
-
-def modulate_fp32(norm: nn.Module, x: torch.Tensor, shift: torch.Tensor, scale: torch.Tensor) -> torch.Tensor:
-    """
-    Apply modulation in FP32 for numerical stability.
-    
-    Converts inputs to FP32 for the modulation operation, then casts back.
-    """
-    orig_dtype = x.dtype
-
-    # Convert to FP32 for numerical stability
-    shift_fp32 = shift.float()
-    scale_fp32 = scale.float()
-
-    # Normalize and modulate in FP32
-    x_norm = norm(x.to(torch.float32))
-    x_mod = x_norm * (scale_fp32 + 1) + shift_fp32
-
-    return x_mod.to(orig_dtype)
-
-
-# ============================================================================
-# Transformer Block
-# ============================================================================
-
-
-class LongCatTransformerBlock(nn.Module):
-    """
-    Single-stream transformer block with:
-    - AdaLN modulation (FP32)
-    - Self-attention
-    - Cross-attention
-    - SwiGLU FFN
-    """
-
-    def __init__(
-        self,
-        hidden_size: int,
-        num_heads: int,
-        mlp_ratio: int,
-        adaln_tembed_dim: int,
-        config: LongCatVideoConfig,
-        dtype: torch.dtype | None = None,
-    ):
-        super().__init__()
-
-        self.hidden_size = hidden_size
-        self.num_heads = num_heads
-
-        # AdaLN modulation (6 parameters: scale/shift for attn & ffn, gate for residual)
-        self.adaln_linear_1 = ReplicatedLinear(
-            adaln_tembed_dim,
-            6 * hidden_size,
-            bias=True,
-            params_dtype=dtype,
-        )
-        self.adaln_act = nn.SiLU()
-
-        # Normalization layers (CRITICAL: Use LayerNorm not RMSNorm like original!)
-        # Original LongCat uses LayerNorm_FP32 with elementwise_affine=False
-        self.norm_attn = FP32LayerNorm(hidden_size, eps=1e-6, elementwise_affine=False)
-        self.norm_ffn = FP32LayerNorm(hidden_size, eps=1e-6, elementwise_affine=False)
-        # Cross-attention norm has elementwise_affine=True (has weight and bias)
-        self.norm_cross = FP32LayerNorm(hidden_size, eps=1e-6, elementwise_affine=True)
-
-        # Self-attention
-        self.self_attn = LongCatSelfAttention(
-            dim=hidden_size,
-            num_heads=num_heads,
-            config=config,
-            dtype=dtype,
-        )
-
-        # Cross-attention
-        self.cross_attn = LongCatCrossAttention(
-            dim=hidden_size,
-            num_heads=num_heads,
-            config=config,
-            dtype=dtype,
-        )
-
-        # SwiGLU FFN
-        ffn_hidden_dim = int(hidden_size * mlp_ratio * 2 / 3)
-        # Round up to nearest multiple of 256
-        ffn_hidden_dim = 256 * ((ffn_hidden_dim + 255) // 256)
-
-        self.ffn = LongCatSwiGLUFFN(
-            dim=hidden_size,
-            hidden_dim=ffn_hidden_dim,
-            dtype=dtype,
-        )
-
-    def forward(
-            self,
-            x: torch.Tensor,  # [B, N, C]
-            context: torch.Tensor,  # [B, N_text, C]
-            t: torch.Tensor,  # [B, T, C_t]
-            latent_shape: tuple,  # (T, H, W)
-            num_cond_latents: int = 0,  # Number of conditioning latent frames (for I2V)
-            return_kv: bool = False,  # Return K/V for caching
-            kv_cache: tuple | None = None,  # Pre-computed K/V cache
-            skip_crs_attn: bool = False,  # Skip cross-attention (for cache init)
-            sp_layout: LongCatSPLayout | None = None,
-            **kwargs) -> torch.Tensor | tuple:
-        """
-        Forward pass with AdaLN modulation.
-        
-        Args:
-            num_cond_latents: For I2V, number of conditioning latent frames.
-                              These frames use split attention behavior.
-            return_kv: If True, return (x, (k_cache, v_cache))
-            kv_cache: Pre-computed K/V from conditioning frames
-            skip_crs_attn: If True, skip cross-attention (used during cache init)
-        """
-        B, N, C = x.shape
-        T, H, W = latent_shape
-        x_orig_dtype = x.dtype  # Save for later casting
-
-        # === AdaLN Modulation (CRITICAL: FP32 for stability like original) ===
-        # Use autocast to compute modulation params in FP32
-        with torch.amp.autocast(device_type='cuda', dtype=torch.float32):
-            t_mod = self.adaln_act(t)
-            mod_params, _ = self.adaln_linear_1(t_mod)
-            # Ensure FP32 output (needed when LoRA is applied)
-            if mod_params.dtype != torch.float32:
-                mod_params = mod_params.float()
-            shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = \
-                mod_params.unsqueeze(2).chunk(6, dim=-1)  # [B, T, 1, C]
-
-        # === Self-Attention ===
-        x_norm = modulate_fp32(self.norm_attn, x.view(B, T, -1, C), shift_msa, scale_msa)
-        x_norm = x_norm.view(B, N, C)
-
-        # Handle KV cache
-        if kv_cache is not None:
-            # Move cache to device if offloaded
-            kv_cache = (kv_cache[0].to(x.device), kv_cache[1].to(x.device))
-            attn_out = self.self_attn.forward_with_kv_cache(
-                x_norm,
-                latent_shape=latent_shape,
-                num_cond_latents=num_cond_latents,
-                kv_cache=kv_cache,
-            )
-            kv_cache_new = None  # Don't return cache when using cache
-        else:
-            attn_result = self.self_attn(
-                x_norm,
-                latent_shape=latent_shape,
-                num_cond_latents=num_cond_latents,
-                return_kv=return_kv,
-                sp_layout=sp_layout,
-            )
-            if return_kv:
-                attn_out, kv_cache_new = attn_result
-            else:
-                attn_out = attn_result
-                kv_cache_new = None
-
-        # Residual with gating (CRITICAL: FP32 like original, then cast back)
-        with torch.amp.autocast(device_type='cuda', dtype=torch.float32):
-            x = x + (gate_msa * attn_out.view(B, T, -1, C)).view(B, N, C)
-        x = x.to(x_orig_dtype)
-
-        # === Cross-Attention (skip if requested) ===
-        if not skip_crs_attn:
-            x_norm_cross = self.norm_cross(x)
-            # When using KV cache, no need for num_cond_latents in cross-attn
-            cross_num_cond = 0 if kv_cache is not None else num_cond_latents
-            cross_out = self.cross_attn(x_norm_cross,
-                                        context,
-                                        latent_shape=latent_shape,
-                                        num_cond_latents=cross_num_cond)
-            x = x + cross_out
-
-        # === FFN ===
-        x_norm_ffn = modulate_fp32(self.norm_ffn, x.view(B, T, -1, C), shift_mlp, scale_mlp)
-        x_norm_ffn = x_norm_ffn.view(B, N, C)
-
-        ffn_out = self.ffn(x_norm_ffn)
-
-        # Residual with gating (CRITICAL: FP32 like original, then cast back)
-        with torch.amp.autocast(device_type='cuda', dtype=torch.float32):
-            x = x + (gate_mlp * ffn_out.view(B, T, -1, C)).view(B, N, C)
-        x = x.to(x_orig_dtype)
-
-        if return_kv:
-            return x, kv_cache_new
-        return x
-
-
-# ============================================================================
-# Final Layer
-# ============================================================================
-
-
-class FinalLayer(nn.Module):
-    """
-    Final output projection with AdaLN modulation.
-    """
-
-    def __init__(
-        self,
-        hidden_size: int,
-        out_channels: int,
-        adaln_tembed_dim: int,
-        patch_size: tuple[int, int, int],
-        dtype: torch.dtype | None = None,
-    ):
-        super().__init__()
-
-        # AdaLN for final layer (2 parameters: scale and shift)
-        self.adaln_linear = ReplicatedLinear(
-            adaln_tembed_dim,
-            2 * hidden_size,
-            bias=True,
-            params_dtype=dtype,
-        )
-        self.adaln_act = nn.SiLU()
-
-        # CRITICAL: Use LayerNorm not RMSNorm! (matches original)
-        self.norm = FP32LayerNorm(hidden_size, eps=1e-6, elementwise_affine=False)
-
-        # Output projection
-        num_patch = patch_size[0] * patch_size[1] * patch_size[2]
-        self.proj = ReplicatedLinear(
-            hidden_size,
-            num_patch * out_channels,
-            bias=True,
-            params_dtype=dtype,
-        )
-
-    def forward(
-        self,
-        x: torch.Tensor,  # [B, N, C]
-        t: torch.Tensor,  # [B, T, C_t]
-        latent_shape: tuple,
-    ) -> torch.Tensor:
-        """
-        Returns: [B, N, out_channels * patch_size^3]
-        """
-        B, N, C = x.shape
-        T, _, _ = latent_shape
-
-        # AdaLN modulation
-        t_mod = self.adaln_act(t)
-        mod_params, _ = self.adaln_linear(t_mod)
-        shift, scale = mod_params.unsqueeze(2).chunk(2, dim=-1)
-
-        # Modulate (converts to FP32 internally for stability)
-        x = modulate_fp32(self.norm, x.view(B, T, -1, C), shift, scale)
-        x = x.reshape(B, N, C)
-
-        # Project
-        x, _ = self.proj(x)
-
-        return x
-
-
-# ============================================================================
-# Main Model
-# ============================================================================
-
-
-class LongCatTransformer3DModel(BaseDiT):
-    """
-    Native LongCat Video Transformer using FastVideo layers.
-    """
-
-    # FSDP sharding: shard at each transformer block
-    _fsdp_shard_conditions = [
-        lambda n, m: "blocks" in n and n.split(".")[-1].isdigit(),
-    ]
-    # torch.compile optimization: compile each transformer block for speedup
-    _compile_conditions = [
-        lambda n, m: "blocks" in n and n.split(".")[-1].isdigit(),
-    ]
-
-    # Parameter name mapping (for weight conversion)
-    param_names_mapping = {}  # Will be defined in config
-    reverse_param_names_mapping = {}
-    lora_param_names_mapping = {}
-
-    # Supported attention backends
-    _supported_attention_backends = (
-        AttentionBackendEnum.FLASH_ATTN,
-        AttentionBackendEnum.TORCH_SDPA,
-    )
-
-    def __init__(self, config: LongCatVideoConfig, hf_config: dict[str, Any]):
-        super().__init__(config=config, hf_config=hf_config)
-
-        # Extract architecture parameters
-        self.hidden_size = config.hidden_size  # 4096
-        self.num_attention_heads = config.num_attention_heads  # 32
-        self.depth = config.depth  # 48
-        self.mlp_ratio = config.mlp_ratio  # 4
-        self.in_channels = config.in_channels  # 16
-        self.out_channels = config.out_channels  # 16
-        self.num_channels_latents = config.in_channels
-        self.patch_size = config.patch_size  # [1, 2, 2]
-
-        # Embeddings
-        self.patch_embed = PatchEmbed3D(
-            patch_size=self.patch_size,
-            in_channels=self.in_channels,
-            embed_dim=self.hidden_size,
-        )
-
-        self.time_embedder = TimestepEmbedder(
-            frequency_embedding_size=config.frequency_embedding_size,
-            adaln_tembed_dim=config.adaln_tembed_dim,
-        )
-
-        self.caption_embedder = CaptionEmbedder(
-            caption_channels=config.caption_channels,
-            hidden_size=self.hidden_size,
-            text_tokens_zero_pad=getattr(config, 'text_tokens_zero_pad', True),
-        )
-
-        # Transformer blocks (48 blocks)
-        self.blocks = nn.ModuleList([
-            LongCatTransformerBlock(
-                hidden_size=self.hidden_size,
-                num_heads=self.num_attention_heads,
-                mlp_ratio=self.mlp_ratio,
-                adaln_tembed_dim=config.adaln_tembed_dim,
-                config=config,
-            ) for _ in range(self.depth)
-        ])
-
-        # Output projection
-        self.final_layer = FinalLayer(
-            hidden_size=self.hidden_size,
-            out_channels=self.out_channels,
-            adaln_tembed_dim=config.adaln_tembed_dim,
-            patch_size=self.patch_size,
-        )
-
-    def enable_bsa(self):
-        """Enable BSA for all self-attention layers."""
-        for block in self.blocks:
-            block.self_attn.enable_bsa = True
-
-    def disable_bsa(self):
-        """Disable BSA for all self-attention layers."""
-        for block in self.blocks:
-            block.self_attn.enable_bsa = False
-
-    def forward(
-            self,
-            hidden_states: torch.Tensor,  # [B, C, T, H, W]
-            encoder_hidden_states: torch.Tensor | list[torch.Tensor],  # [B, N_text, C_text]
-            timestep: torch.LongTensor,  # [B] or [B, T]
-            encoder_attention_mask: torch.Tensor | None = None,  # [B, N_text]
-            encoder_hidden_states_image: torch.Tensor | list[torch.Tensor] | None = None,
-            guidance: float | None = None,  # Unused, for API compatibility
-            num_cond_latents: int = 0,  # For I2V: number of conditioning latent frames
-            # === KV Cache Parameters ===
-        return_kv: bool = False,  # If True, return (output, kv_cache_dict)
-            kv_cache_dict: dict | None = None,  # Pre-computed {block_idx: (k, v)}
-            skip_crs_attn: bool = False,  # Skip cross-attention (for cache init)
-            offload_kv_cache: bool = False,  # Move cache to CPU after compute
-            **kwargs) -> torch.Tensor | tuple[torch.Tensor, dict]:
-        """
-        Forward pass with FastVideo parameter ordering.
-        
-        NOTE: This follows FastVideo convention:
-              (hidden_states, encoder_hidden_states, timestep)
-              
-        Args:
-            num_cond_latents: For I2V, number of conditioning latent frames.
-                              These frames are treated as "clean" (timestep=0)
-                              and use split attention behavior.
-            return_kv: If True, return (output, kv_cache_dict)
-            kv_cache_dict: Pre-computed K/V cache {block_idx: (k, v)}
-            skip_crs_attn: If True, skip cross-attention (for cache init)
-            offload_kv_cache: If True, move cache to CPU after compute
-        """
-        B, _, T, H, W = hidden_states.shape
-
-        N_t = T // self.patch_size[0]
-        N_h = H // self.patch_size[1]
-        N_w = W // self.patch_size[2]
-
-        sp_size = get_sp_world_size()
-        sp_layout = None
-        # Apply the new spatial sharding only to BSA refinement inference.
-        # Other modes retain their existing full-sequence execution paths.
-        # NOTE: `not torch.is_grad_enabled()` infers inference from ambient grad mode
-        # (ComposedPipelineBase.forward is @torch.no_grad()). A caller driving this
-        # forward with grad enabled silently takes the full-sequence path (correct
-        # output, no SP speedup) instead of erroring.
-        if (sp_size > 1 and not torch.is_grad_enabled() and not num_cond_latents
-                and not return_kv and not kv_cache_dict and N_t > 1
-                and all(block.self_attn.enable_bsa for block in self.blocks)):
-            if self.num_attention_heads % sp_size:
-                raise ValueError("LongCat attention heads must be divisible by SP size")
-            if any(dim % patch for dim, patch in zip((T, H, W), self.patch_size)):
-                raise ValueError("LongCat SP requires patch-aligned latent dimensions")
-            sp_layout = LongCatSPLayout((N_t, N_h, N_w), sp_size)
-
-        # Handle list of encoder outputs (take first one)
-        if isinstance(encoder_hidden_states, list):
-            encoder_hidden_states = encoder_hidden_states[0]
-
-        # 1. Patch embedding
-        x = self.patch_embed(hidden_states)  # [B, N, C]
-        if sp_layout is not None:
-            x = sp_layout.shard(x, get_sp_parallel_rank())
-
-        # 2. Timestep embedding
-        # Expand timestep from [B] to [B, T] if needed
-        if timestep.ndim == 1:
-            timestep = timestep.unsqueeze(1).expand(-1, N_t)  # [B, T]
-
-        t = self.time_embedder(timestep.flatten(), latent_shape=(N_t, N_h, N_w))
-        if t.ndim == 2:
-            t = t.reshape(B, N_t, -1)  # [B, T, C_t]
-
-        # 3. Caption embedding (standard format, no compaction)
-        context = self.caption_embedder(encoder_hidden_states,
-                                        encoder_attention_mask=encoder_attention_mask)  # [B, N_text, C]
-
-        # 4. Transformer blocks with optional KV cache
-        kv_cache_dict_ret = {} if return_kv else None
-
-        for i, block in enumerate(self.blocks):
-            # Get cache for this block if available
-            block_kv_cache = kv_cache_dict.get(i, None) if kv_cache_dict else None
-
-            block_out = block(
-                x,
-                context,
-                t,
-                latent_shape=(N_t, N_h, N_w),
-                num_cond_latents=num_cond_latents,
-                return_kv=return_kv,
-                kv_cache=block_kv_cache,
-                skip_crs_attn=skip_crs_attn,
-                sp_layout=sp_layout,
-            )
-
-            if return_kv:
-                x, kv_cache = block_out
-                # Store cache
-                if offload_kv_cache:
-                    kv_cache_dict_ret[i] = (kv_cache[0].cpu(), kv_cache[1].cpu())
-                else:
-                    kv_cache_dict_ret[i] = (kv_cache[0].contiguous(), kv_cache[1].contiguous())
-            else:
-                x = block_out
-
-        # 5. Output projection
-        output = self.final_layer(x, t, latent_shape=(N_t, N_h, N_w))
-
-        if sp_layout is not None:
-            output = sp_layout.rank_to_global(sequence_model_parallel_all_gather(output.contiguous(), dim=1))
-
-        # Reshape to [B, C_out, T, H, W]
-        output = self.unpatchify(output, N_t, N_h, N_w)
-
-        # Cast to float32 for better accuracy (as per original)
-        output = output.to(torch.float32)
-
-        if return_kv:
-            return output, kv_cache_dict_ret
-        return output
-
-    def unpatchify(self, x: torch.Tensor, N_t: int, N_h: int, N_w: int) -> torch.Tensor:
-        """
-        Args:
-            x: [B, N, C] where C = T_p * H_p * W_p * C_out
-        Returns:
-            [B, C_out, T, H, W]
-        """
-        T_p, H_p, W_p = self.patch_size
-        x = rearrange(
-            x,
-            "B (N_t N_h N_w) (T_p H_p W_p C_out) -> B C_out (N_t T_p) (N_h H_p) (N_w W_p)",
-            N_t=N_t,
-            N_h=N_h,
-            N_w=N_w,
-            T_p=T_p,
-            H_p=H_p,
-            W_p=W_p,
-            C_out=self.out_channels,
-        )
-        return x
-
-
-# Entry point for model registry
-EntryClass = LongCatTransformer3DModel
diff --git a/fastvideo/models/dits/zimage.py b/fastvideo/models/dits/zimage.py
deleted file mode 100644
index 219327790..000000000
--- a/fastvideo/models/dits/zimage.py
+++ /dev/null
@@ -1,563 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""FastVideo-native Z-Image transformer.
-
-Z-Image attends over padded variable-length image/text streams and requires a
-key-padding mask. FastVideo's distributed attention wrappers do not expose that
-mask contract yet, so this implementation uses torch SDPA and is SP=1 only.
-"""
-
-from __future__ import annotations
-
-import math
-from typing import Any
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch.nn.utils.rnn import pad_sequence
-
-from fastvideo.configs.models.dits.zimage import ZImageDiTConfig
-from fastvideo.distributed.parallel_state import get_sp_world_size, model_parallel_is_initialized
-from fastvideo.layers.linear import ReplicatedLinear
-from fastvideo.models.dits.base import BaseDiT
-from fastvideo.platforms import AttentionBackendEnum
-
-
-def _linear(layer: ReplicatedLinear, x: torch.Tensor) -> torch.Tensor:
-    return layer(x)[0]
-
-
-def _prepare_attention_mask(attention_mask: torch.Tensor | None, dtype: torch.dtype) -> torch.Tensor | None:
-    if attention_mask is None:
-        return None
-    if attention_mask.ndim == 2:
-        attention_mask = attention_mask[:, None, None, :]
-    if attention_mask.dtype == torch.bool:
-        additive_mask = torch.zeros_like(attention_mask, dtype=dtype)
-        additive_mask.masked_fill_(~attention_mask, float("-inf"))
-        return additive_mask
-    return attention_mask
-
-
-class TimestepEmbedder(nn.Module):
-
-    def __init__(
-        self,
-        out_size: int,
-        mid_size: int,
-        frequency_embedding_size: int,
-        max_period: int,
-    ) -> None:
-        super().__init__()
-        self.mlp = nn.ModuleList([
-            ReplicatedLinear(frequency_embedding_size, mid_size, bias=True),
-            nn.SiLU(),
-            ReplicatedLinear(mid_size, out_size, bias=True),
-        ])
-        self.frequency_embedding_size = frequency_embedding_size
-        self.max_period = max_period
-
-    @staticmethod
-    def timestep_embedding(t: torch.Tensor, dim: int, max_period: int) -> torch.Tensor:
-        with torch.amp.autocast("cuda", enabled=False):
-            half = dim // 2
-            freqs = torch.exp(-math.log(max_period) * torch.arange(half, dtype=torch.float32, device=t.device) / half)
-            args = t[:, None].float() * freqs[None]
-            embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
-            if dim % 2:
-                embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
-            return embedding
-
-    def forward(self, t: torch.Tensor) -> torch.Tensor:
-        t_freq = self.timestep_embedding(t, self.frequency_embedding_size, self.max_period)
-        weight_dtype = self.mlp[0].weight.dtype
-        if weight_dtype.is_floating_point:
-            t_freq = t_freq.to(weight_dtype)
-        return _linear(self.mlp[2], self.mlp[1](_linear(self.mlp[0], t_freq)))
-
-
-class RMSNorm(nn.Module):
-
-    def __init__(self, dim: int, eps: float = 1e-5) -> None:
-        super().__init__()
-        self.eps = eps
-        self.weight = nn.Parameter(torch.ones(dim))
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        output = x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
-        return output * self.weight
-
-
-class FeedForward(nn.Module):
-
-    def __init__(self, dim: int, hidden_dim: int) -> None:
-        super().__init__()
-        self.w1 = ReplicatedLinear(dim, hidden_dim, bias=False)
-        self.w2 = ReplicatedLinear(hidden_dim, dim, bias=False)
-        self.w3 = ReplicatedLinear(dim, hidden_dim, bias=False)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        return _linear(self.w2, F.silu(_linear(self.w1, x)) * _linear(self.w3, x))
-
-
-def apply_rotary_emb(x_in: torch.Tensor, freqs_cis: torch.Tensor) -> torch.Tensor:
-    with torch.amp.autocast("cuda", enabled=False):
-        x = torch.view_as_complex(x_in.float().reshape(*x_in.shape[:-1], -1, 2))
-        x_out = torch.view_as_real(x * freqs_cis.unsqueeze(2)).flatten(3)
-        return x_out.type_as(x_in)
-
-
-class ZImageAttention(nn.Module):
-
-    def __init__(self, dim: int, n_heads: int, n_kv_heads: int, qk_norm: bool = True, eps: float = 1e-5) -> None:
-        super().__init__()
-        self.n_heads = n_heads
-        self.n_kv_heads = n_kv_heads
-        self.head_dim = dim // n_heads
-
-        self.to_q = ReplicatedLinear(dim, n_heads * self.head_dim, bias=False)
-        self.to_k = ReplicatedLinear(dim, n_kv_heads * self.head_dim, bias=False)
-        self.to_v = ReplicatedLinear(dim, n_kv_heads * self.head_dim, bias=False)
-        self.to_out = nn.ModuleList([ReplicatedLinear(n_heads * self.head_dim, dim, bias=False)])
-
-        self.norm_q = RMSNorm(self.head_dim, eps=eps) if qk_norm else None
-        self.norm_k = RMSNorm(self.head_dim, eps=eps) if qk_norm else None
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: torch.Tensor | None = None,
-        freqs_cis: torch.Tensor | None = None,
-    ) -> torch.Tensor:
-        query = _linear(self.to_q, hidden_states).unflatten(-1, (self.n_heads, -1))
-        key = _linear(self.to_k, hidden_states).unflatten(-1, (self.n_kv_heads, -1))
-        value = _linear(self.to_v, hidden_states).unflatten(-1, (self.n_kv_heads, -1))
-
-        if self.norm_q is not None:
-            query = self.norm_q(query)
-        if self.norm_k is not None:
-            key = self.norm_k(key)
-        if freqs_cis is not None:
-            query = apply_rotary_emb(query, freqs_cis)
-            key = apply_rotary_emb(key, freqs_cis)
-
-        mask = _prepare_attention_mask(attention_mask, query.dtype)
-        hidden_states = F.scaled_dot_product_attention(
-            query.transpose(1, 2),
-            key.transpose(1, 2),
-            value.transpose(1, 2),
-            attn_mask=mask,
-            dropout_p=0.0,
-            is_causal=False,
-        ).transpose(1, 2).contiguous()
-        return _linear(self.to_out[0], hidden_states.flatten(2, 3).to(query.dtype))
-
-
-class ZImageTransformerBlock(nn.Module):
-
-    def __init__(
-        self,
-        layer_id: int,
-        dim: int,
-        n_heads: int,
-        n_kv_heads: int,
-        norm_eps: float,
-        qk_norm: bool,
-        adaln_embed_dim: int,
-        modulation: bool = True,
-    ) -> None:
-        super().__init__()
-        self.dim = dim
-        self.head_dim = dim // n_heads
-        self.layer_id = layer_id
-        self.modulation = modulation
-
-        self.attention = ZImageAttention(dim, n_heads, n_kv_heads, qk_norm, norm_eps)
-        self.feed_forward = FeedForward(dim=dim, hidden_dim=int(dim / 3 * 8))
-        self.attention_norm1 = RMSNorm(dim, eps=norm_eps)
-        self.ffn_norm1 = RMSNorm(dim, eps=norm_eps)
-        self.attention_norm2 = RMSNorm(dim, eps=norm_eps)
-        self.ffn_norm2 = RMSNorm(dim, eps=norm_eps)
-
-        if modulation:
-            self.adaLN_modulation = nn.ModuleList([ReplicatedLinear(min(dim, adaln_embed_dim), 4 * dim, bias=True)])
-
-    def forward(
-        self,
-        x: torch.Tensor,
-        attn_mask: torch.Tensor,
-        freqs_cis: torch.Tensor,
-        adaln_input: torch.Tensor | None = None,
-    ) -> torch.Tensor:
-        if self.modulation:
-            assert adaln_input is not None
-            scale_msa, gate_msa, scale_mlp, gate_mlp = _linear(self.adaLN_modulation[0],
-                                                               adaln_input).unsqueeze(1).chunk(4, dim=2)
-            gate_msa, gate_mlp = gate_msa.tanh(), gate_mlp.tanh()
-            scale_msa, scale_mlp = 1.0 + scale_msa, 1.0 + scale_mlp
-
-            attn_out = self.attention(
-                self.attention_norm1(x) * scale_msa,
-                attention_mask=attn_mask,
-                freqs_cis=freqs_cis,
-            )
-            x = x + gate_msa * self.attention_norm2(attn_out)
-            x = x + gate_mlp * self.ffn_norm2(self.feed_forward(self.ffn_norm1(x) * scale_mlp))
-        else:
-            attn_out = self.attention(
-                self.attention_norm1(x),
-                attention_mask=attn_mask,
-                freqs_cis=freqs_cis,
-            )
-            x = x + self.attention_norm2(attn_out)
-            x = x + self.ffn_norm2(self.feed_forward(self.ffn_norm1(x)))
-        return x
-
-
-class FinalLayer(nn.Module):
-
-    def __init__(self, hidden_size: int, out_channels: int, adaln_embed_dim: int) -> None:
-        super().__init__()
-        self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
-        self.linear = ReplicatedLinear(hidden_size, out_channels, bias=True)
-        self.adaLN_modulation = nn.ModuleList([
-            nn.SiLU(),
-            ReplicatedLinear(min(hidden_size, adaln_embed_dim), hidden_size, bias=True),
-        ])
-
-    def forward(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor:
-        scale = 1.0 + _linear(self.adaLN_modulation[1], self.adaLN_modulation[0](c))
-        return _linear(self.linear, self.norm_final(x) * scale.unsqueeze(1))
-
-
-class RopeEmbedder:
-
-    def __init__(self, theta: float, axes_dims: tuple[int, ...], axes_lens: tuple[int, ...]) -> None:
-        if len(axes_dims) != len(axes_lens):
-            raise ValueError("RoPE axes require matching dimensions and lengths")
-        self.theta = theta
-        self.axes_dims = axes_dims
-        self.axes_lens = axes_lens
-        self.freqs_cis: list[torch.Tensor] | None = None
-
-    @staticmethod
-    def precompute_freqs_cis(dim: tuple[int, ...], end: tuple[int, ...], theta: float) -> list[torch.Tensor]:
-        with torch.device("cpu"):
-            freqs_cis = []
-            for axis_dim, axis_end in zip(dim, end):
-                freqs = 1.0 / (theta**(torch.arange(0, axis_dim, 2, dtype=torch.float64) / axis_dim))
-                timestep = torch.arange(axis_end, dtype=torch.float64)
-                angles = torch.outer(timestep, freqs).float()
-                freqs_cis.append(torch.polar(torch.ones_like(angles), angles).to(torch.complex64))
-            return freqs_cis
-
-    def __call__(self, ids: torch.Tensor) -> torch.Tensor:
-        if ids.ndim != 2 or ids.shape[-1] != len(self.axes_dims):
-            raise ValueError("RoPE ids must have shape [sequence, number_of_axes]")
-        if self.freqs_cis is None:
-            self.freqs_cis = [
-                freqs.to(ids.device) for freqs in self.precompute_freqs_cis(self.axes_dims, self.axes_lens, self.theta)
-            ]
-        elif self.freqs_cis[0].device != ids.device:
-            self.freqs_cis = [freqs.to(ids.device) for freqs in self.freqs_cis]
-        return torch.cat([self.freqs_cis[i][ids[:, i]] for i in range(len(self.axes_dims))], dim=-1)
-
-
-class ZImageTransformer2DModel(BaseDiT):
-    _default_config = ZImageDiTConfig()
-    _fsdp_shard_conditions = _default_config.arch_config._fsdp_shard_conditions
-    _compile_conditions = _default_config.arch_config._compile_conditions
-    _supported_attention_backends = (AttentionBackendEnum.TORCH_SDPA, )
-    param_names_mapping = _default_config.arch_config.param_names_mapping
-    reverse_param_names_mapping = _default_config.arch_config.reverse_param_names_mapping
-
-    def __init__(self, config: ZImageDiTConfig, hf_config: dict[str, Any]) -> None:
-        super().__init__(config=config, hf_config=hf_config)
-        arch = config.arch_config
-
-        self.in_channels = arch.in_channels
-        self.out_channels = arch.in_channels
-        self.all_patch_size = tuple(arch.all_patch_size)
-        self.all_f_patch_size = tuple(arch.all_f_patch_size)
-        self.dim = arch.dim
-        self.n_heads = arch.n_heads
-        self.rope_theta = arch.rope_theta
-        self.t_scale = arch.t_scale
-        self.seq_multi_of = arch.seq_multi_of
-
-        self.hidden_size = arch.dim
-        self.num_attention_heads = arch.n_heads
-        self.num_channels_latents = arch.in_channels
-
-        self.all_x_embedder = nn.ModuleDict({
-            f"{patch_size}-{f_patch_size}":
-            ReplicatedLinear(f_patch_size * patch_size * patch_size * arch.in_channels, arch.dim, bias=True)
-            for patch_size, f_patch_size in zip(self.all_patch_size, self.all_f_patch_size)
-        })
-        self.all_final_layer = nn.ModuleDict({
-            f"{patch_size}-{f_patch_size}":
-            FinalLayer(
-                arch.dim,
-                patch_size * patch_size * f_patch_size * self.out_channels,
-                arch.adaln_embed_dim,
-            )
-            for patch_size, f_patch_size in zip(self.all_patch_size, self.all_f_patch_size)
-        })
-
-        block_kwargs = {
-            "dim": arch.dim,
-            "n_heads": arch.n_heads,
-            "n_kv_heads": arch.n_kv_heads,
-            "norm_eps": arch.norm_eps,
-            "qk_norm": arch.qk_norm,
-            "adaln_embed_dim": arch.adaln_embed_dim,
-        }
-        self.noise_refiner = nn.ModuleList([
-            ZImageTransformerBlock(1000 + layer_id, modulation=True, **block_kwargs)
-            for layer_id in range(arch.n_refiner_layers)
-        ])
-        self.context_refiner = nn.ModuleList([
-            ZImageTransformerBlock(layer_id, modulation=False, **block_kwargs)
-            for layer_id in range(arch.n_refiner_layers)
-        ])
-        self.t_embedder = TimestepEmbedder(
-            min(arch.dim, arch.adaln_embed_dim),
-            mid_size=arch.timestep_mid_size,
-            frequency_embedding_size=arch.frequency_embedding_size,
-            max_period=arch.max_period,
-        )
-        self.cap_embedder = nn.ModuleList([
-            RMSNorm(arch.cap_feat_dim, eps=arch.norm_eps),
-            ReplicatedLinear(arch.cap_feat_dim, arch.dim, bias=True),
-        ])
-        self.x_pad_token = nn.Parameter(torch.empty((1, arch.dim)))
-        self.cap_pad_token = nn.Parameter(torch.empty((1, arch.dim)))
-        self.layers = nn.ModuleList(
-            [ZImageTransformerBlock(layer_id, modulation=True, **block_kwargs) for layer_id in range(arch.n_layers)])
-        self.axes_dims = tuple(arch.axes_dims)
-        self.axes_lens = tuple(arch.axes_lens)
-        self.rope_embedder = RopeEmbedder(arch.rope_theta, self.axes_dims, self.axes_lens)
-        self.__post_init__()
-
-    def unpatchify(
-        self,
-        x: list[torch.Tensor],
-        size: list[tuple[int, int, int]],
-        patch_size: int,
-        f_patch_size: int,
-    ) -> list[torch.Tensor]:
-        patch_height = patch_width = patch_size
-        patch_frames = f_patch_size
-        if len(x) != len(size):
-            raise ValueError("output batch and original sizes must have equal length")
-        for i, (frames, height, width) in enumerate(size):
-            original_length = (frames // patch_frames) * (height // patch_height) * (width // patch_width)
-            x[i] = (x[i][:original_length].view(
-                frames // patch_frames,
-                height // patch_height,
-                width // patch_width,
-                patch_frames,
-                patch_height,
-                patch_width,
-                self.out_channels,
-            ).permute(6, 0, 3, 1, 4, 2, 5).reshape(self.out_channels, frames, height, width))
-        return x
-
-    @staticmethod
-    def create_coordinate_grid(
-        size: tuple[int, int, int],
-        start: tuple[int, int, int] | None = None,
-        device: torch.device | None = None,
-    ) -> torch.Tensor:
-        start = start or (0, ) * len(size)
-        axes = [
-            torch.arange(axis_start, axis_start + span, dtype=torch.int32, device=device)
-            for axis_start, span in zip(start, size)
-        ]
-        return torch.stack(torch.meshgrid(axes, indexing="ij"), dim=-1)
-
-    def patchify_and_embed(
-        self,
-        all_image: list[torch.Tensor],
-        all_cap_feats: list[torch.Tensor],
-        patch_size: int,
-        f_patch_size: int,
-    ) -> tuple[
-            list[torch.Tensor],
-            list[torch.Tensor],
-            list[tuple[int, int, int]],
-            list[torch.Tensor],
-            list[torch.Tensor],
-            list[torch.Tensor],
-            list[torch.Tensor],
-    ]:
-        patch_height = patch_width = patch_size
-        patch_frames = f_patch_size
-        device = all_image[0].device
-
-        image_out = []
-        image_sizes = []
-        image_pos_ids = []
-        image_pad_masks = []
-        cap_pos_ids = []
-        cap_pad_masks = []
-        cap_feats_out = []
-
-        for image, cap_feat in zip(all_image, all_cap_feats):
-            cap_length = len(cap_feat)
-            cap_padding = (-cap_length) % self.seq_multi_of
-            cap_pos_ids.append(
-                self.create_coordinate_grid(
-                    (cap_length + cap_padding, 1, 1),
-                    start=(1, 0, 0),
-                    device=device,
-                ).flatten(0, 2))
-            cap_pad_masks.append(
-                torch.cat([
-                    torch.zeros(cap_length, dtype=torch.bool, device=device),
-                    torch.ones(cap_padding, dtype=torch.bool, device=device),
-                ]) if cap_padding else torch.zeros(cap_length, dtype=torch.bool, device=device))
-            cap_feats_out.append(
-                torch.cat([cap_feat, cap_feat[-1:].repeat(cap_padding, 1)]) if cap_padding else cap_feat)
-
-            channels, frames, height, width = image.size()
-            image_sizes.append((frames, height, width))
-            frame_tokens, height_tokens, width_tokens = (
-                frames // patch_frames,
-                height // patch_height,
-                width // patch_width,
-            )
-            image = image.view(
-                channels,
-                frame_tokens,
-                patch_frames,
-                height_tokens,
-                patch_height,
-                width_tokens,
-                patch_width,
-            )
-            image = image.permute(1, 3, 5, 2, 4, 6, 0).reshape(
-                frame_tokens * height_tokens * width_tokens,
-                patch_frames * patch_height * patch_width * channels,
-            )
-
-            image_length = len(image)
-            image_padding = (-image_length) % self.seq_multi_of
-            original_pos_ids = self.create_coordinate_grid(
-                (frame_tokens, height_tokens, width_tokens),
-                start=(cap_length + cap_padding + 1, 0, 0),
-                device=device,
-            ).flatten(0, 2)
-            if image_padding:
-                padding_pos_ids = self.create_coordinate_grid((1, 1, 1),
-                                                              device=device).flatten(0, 2).repeat(image_padding, 1)
-                image_pos_ids.append(torch.cat([original_pos_ids, padding_pos_ids]))
-            else:
-                image_pos_ids.append(original_pos_ids)
-            image_pad_masks.append(
-                torch.cat([
-                    torch.zeros(image_length, dtype=torch.bool, device=device),
-                    torch.ones(image_padding, dtype=torch.bool, device=device),
-                ]) if image_padding else torch.zeros(image_length, dtype=torch.bool, device=device))
-            image_out.append(torch.cat([image, image[-1:].repeat(image_padding, 1)]) if image_padding else image)
-
-        return (
-            image_out,
-            cap_feats_out,
-            image_sizes,
-            image_pos_ids,
-            cap_pos_ids,
-            image_pad_masks,
-            cap_pad_masks,
-        )
-
-    @staticmethod
-    def _attention_mask(lengths: list[int], device: torch.device) -> torch.Tensor:
-        mask = torch.zeros((len(lengths), max(lengths)), dtype=torch.bool, device=device)
-        for i, length in enumerate(lengths):
-            mask[i, :length] = True
-        return mask
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor | list[torch.Tensor],
-        encoder_hidden_states: torch.Tensor | list[torch.Tensor],
-        timestep: torch.LongTensor,
-        encoder_hidden_states_image: torch.Tensor | list[torch.Tensor] | None = None,
-        guidance=None,
-        patch_size: int = 2,
-        f_patch_size: int = 1,
-        **kwargs,
-    ) -> tuple[list[torch.Tensor], dict]:
-        del encoder_hidden_states_image, guidance, kwargs
-        if model_parallel_is_initialized() and get_sp_world_size() != 1:
-            raise NotImplementedError("Z-Image masked SDPA does not support sequence parallelism; run with sp_size=1")
-        if patch_size not in self.all_patch_size or f_patch_size not in self.all_f_patch_size:
-            raise ValueError(f"unsupported patch sizes: spatial={patch_size}, temporal={f_patch_size}")
-        if isinstance(hidden_states, torch.Tensor):
-            hidden_states = list(hidden_states.unbind(0))
-        if isinstance(encoder_hidden_states, torch.Tensor):
-            encoder_hidden_states = list(encoder_hidden_states.unbind(0))
-
-        device = hidden_states[0].device
-        timestep_embedding = self.t_embedder(timestep * self.t_scale)
-        (
-            hidden_states,
-            encoder_hidden_states,
-            image_sizes,
-            image_pos_ids,
-            cap_pos_ids,
-            image_inner_pad_masks,
-            cap_inner_pad_masks,
-        ) = self.patchify_and_embed(hidden_states, encoder_hidden_states, patch_size, f_patch_size)
-
-        image_lengths = [len(item) for item in hidden_states]
-        if not all(length % self.seq_multi_of == 0 for length in image_lengths):
-            raise ValueError("padded image sequence lengths must be aligned")
-        hidden_states = torch.cat(hidden_states)
-        hidden_states = _linear(self.all_x_embedder[f"{patch_size}-{f_patch_size}"], hidden_states)
-        adaln_input = timestep_embedding.type_as(hidden_states)
-        hidden_states[torch.cat(image_inner_pad_masks)] = self.x_pad_token
-        hidden_states = list(hidden_states.split(image_lengths))
-        image_freqs_cis = list(
-            self.rope_embedder(torch.cat(image_pos_ids)).split([len(item) for item in image_pos_ids]))
-        hidden_states = pad_sequence(hidden_states, batch_first=True, padding_value=0.0)
-        image_freqs_cis = pad_sequence(image_freqs_cis, batch_first=True, padding_value=0.0)
-        image_freqs_cis = image_freqs_cis[:, :hidden_states.shape[1]]
-        image_attn_mask = self._attention_mask(image_lengths, device)
-        for layer in self.noise_refiner:
-            hidden_states = layer(hidden_states, image_attn_mask, image_freqs_cis, adaln_input)
-
-        cap_lengths = [len(item) for item in encoder_hidden_states]
-        if not all(length % self.seq_multi_of == 0 for length in cap_lengths):
-            raise ValueError("padded caption sequence lengths must be aligned")
-        encoder_hidden_states = torch.cat(encoder_hidden_states)
-        encoder_hidden_states = _linear(self.cap_embedder[1], self.cap_embedder[0](encoder_hidden_states))
-        encoder_hidden_states[torch.cat(cap_inner_pad_masks)] = self.cap_pad_token
-        encoder_hidden_states = list(encoder_hidden_states.split(cap_lengths))
-        cap_freqs_cis = list(self.rope_embedder(torch.cat(cap_pos_ids)).split([len(item) for item in cap_pos_ids]))
-        encoder_hidden_states = pad_sequence(encoder_hidden_states, batch_first=True, padding_value=0.0)
-        cap_freqs_cis = pad_sequence(cap_freqs_cis, batch_first=True, padding_value=0.0)
-        cap_freqs_cis = cap_freqs_cis[:, :encoder_hidden_states.shape[1]]
-        cap_attn_mask = self._attention_mask(cap_lengths, device)
-        for layer in self.context_refiner:
-            encoder_hidden_states = layer(encoder_hidden_states, cap_attn_mask, cap_freqs_cis)
-
-        unified = []
-        unified_freqs_cis = []
-        for i, (image_length, cap_length) in enumerate(zip(image_lengths, cap_lengths)):
-            unified.append(torch.cat([hidden_states[i][:image_length], encoder_hidden_states[i][:cap_length]]))
-            unified_freqs_cis.append(torch.cat([image_freqs_cis[i][:image_length], cap_freqs_cis[i][:cap_length]]))
-        unified_lengths = [image_length + cap_length for image_length, cap_length in zip(image_lengths, cap_lengths)]
-        unified = pad_sequence(unified, batch_first=True, padding_value=0.0)
-        unified_freqs_cis = pad_sequence(unified_freqs_cis, batch_first=True, padding_value=0.0)
-        unified_attn_mask = self._attention_mask(unified_lengths, device)
-        for layer in self.layers:
-            unified = layer(unified, unified_attn_mask, unified_freqs_cis, adaln_input)
-
-        unified = self.all_final_layer[f"{patch_size}-{f_patch_size}"](unified, adaln_input)
-        outputs = self.unpatchify(list(unified.unbind(0)), image_sizes, patch_size, f_patch_size)
-        return outputs, {}
-
-
-EntryClass = ZImageTransformer2DModel
diff --git a/fastvideo/models/encoders/glm_image_ar_loader.py b/fastvideo/models/encoders/glm_image_ar_loader.py
deleted file mode 100644
index 7d6be0f49..000000000
--- a/fastvideo/models/encoders/glm_image_ar_loader.py
+++ /dev/null
@@ -1,63 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""Sole HF-import boundary for GLM-Image's AR encoder (lazy-wrapper exception E001)."""
-from __future__ import annotations
-
-from typing import Any
-
-import torch
-import torch.nn as nn
-
-from fastvideo.logger import init_logger
-
-logger = init_logger(__name__)
-
-
-class GlmImageARLoader(nn.Module):
-
-    def __init__(self,
-                 model_path: str,
-                 processor_path: str | None = None,
-                 *,
-                 torch_dtype: torch.dtype = torch.bfloat16,
-                 trust_remote_code: bool = True) -> None:
-        super().__init__()
-        from transformers import (AutoProcessor, GlmImageForConditionalGeneration)
-        logger.info("Loading GLM-Image AR encoder from %s", model_path)
-        self._model = GlmImageForConditionalGeneration.from_pretrained(
-            model_path,
-            torch_dtype=torch_dtype,
-            trust_remote_code=trust_remote_code,
-        )
-        if processor_path is not None:
-            logger.info("Loading GLM-Image processor from %s", processor_path)
-            self.processor = AutoProcessor.from_pretrained(processor_path, trust_remote_code=trust_remote_code)
-        else:
-            self.processor = None
-
-    @torch.no_grad()
-    def generate(self, *args: Any, **kwargs: Any) -> torch.Tensor:
-        return self._model.generate(*args, **kwargs)
-
-    @torch.no_grad()
-    def get_image_features(self, pixel_values: torch.Tensor, image_grid_thw: torch.Tensor) -> Any:
-        return self._model.get_image_features(pixel_values, image_grid_thw)
-
-    @torch.no_grad()
-    def get_image_tokens(self, image_embeds: torch.Tensor, image_grid_thw: torch.Tensor) -> torch.Tensor:
-        return self._model.get_image_tokens(image_embeds, image_grid_thw)
-
-    @property
-    def config(self):  # type: ignore[no-untyped-def]
-        return self._model.config
-
-    @property
-    def generation_config(self):  # type: ignore[no-untyped-def]
-        return self._model.generation_config
-
-    def to(self, *args, **kwargs):  # type: ignore[override]
-        self._model = self._model.to(*args, **kwargs)
-        return super().to(*args, **kwargs)
-
-    def eval(self):  # type: ignore[override]
-        self._model = self._model.eval()
-        return super().eval()
diff --git a/fastvideo/models/loader/component_loader.py b/fastvideo/models/loader/component_loader.py
index 521f49fe1..6d9f3d7d8 100644
--- a/fastvideo/models/loader/component_loader.py
+++ b/fastvideo/models/loader/component_loader.py
@@ -113,7 +113,6 @@ class ComponentLoader(ABC):
             # Wan-S2V speech conditioning: wav2vec2 encoder + its feature extractor.
             "audio_encoder": (AudioEncoderLoader, "transformers"),
             "audio_processor": (AudioProcessorLoader, "transformers"),
-            "vision_language_encoder": (VisionLanguageEncoderLoader, "transformers"),
             "processor": (ProcessorLoader, "transformers"),
             "upsampler": (UpsamplerLoader, "diffusers"),
             "upsampler_2": (UpsamplerLoader, "diffusers"),
@@ -623,23 +622,6 @@ class ImageEncoderLoader(TextEncoderLoader):
         )
 
 
-class VisionLanguageEncoderLoader(ComponentLoader):
-    """Loader for vision-language autoregressive encoders."""
-
-    def load(self, model_path: str, fastvideo_args: FastVideoArgs):
-        from fastvideo.distributed.parallel_state import get_local_torch_device
-        from fastvideo.models.encoders.glm_image_ar_loader import (GlmImageARLoader)
-
-        logger.info("Loading vision-language encoder from %s", model_path)
-        target_device = get_local_torch_device()
-        loader = GlmImageARLoader(
-            model_path,
-            torch_dtype=torch.bfloat16,
-            trust_remote_code=fastvideo_args.trust_remote_code,
-        ).to(target_device).eval()
-        return loader
-
-
 class ProcessorLoader(ComponentLoader):
     """Loader for HF processors that pair with vision-language encoders."""
 
diff --git a/fastvideo/models/registry.py b/fastvideo/models/registry.py
index 72d6d8344..0ba35fb97 100644
--- a/fastvideo/models/registry.py
+++ b/fastvideo/models/registry.py
@@ -25,19 +25,13 @@ logger = init_logger(__name__)
 _TEXT_TO_VIDEO_DIT_MODELS = {
     "MMAudioTransformer": ("dits", "mmaudio", "MMAudioTransformer"),
     "HunyuanVideoTransformer3DModel": ("dits", "hunyuanvideo", "HunyuanVideoTransformer3DModel"),
-    "HunyuanGameCraftTransformer3DModel": ("dits", "hunyuangamecraft", "HunyuanGameCraftTransformer3DModel"),
     "HunyuanVideo15Transformer3DModel": ("dits", "hunyuanvideo15", "HunyuanVideo15Transformer3DModel"),
     "HYWorldTransformer3DModel": ("dits", "hyworld", "HYWorldTransformer3DModel"),
     "WanTransformer3DModel": ("wan", "transformer", "WanTransformer3DModel"),
     "WanModel_S2V": ("dits", "wan_s2v", "WanS2VTransformer3DModel"),
-    "DreamXWorldTransformer3DModel": ("dits", "dreamx_world", "DreamXWorldTransformer3DModel"),
-    "DreamXWorldARTransformer3DModel": ("dits", "dreamx_world_ar", "DreamXWorldARTransformer3DModel"),
     "CausalWanTransformer3DModel": ("wan", "causal_transformer", "CausalWanTransformer3DModel"),
     "CosmosTransformer3DModel": ("dits", "cosmos", "CosmosTransformer3DModel"),
     "Cosmos25Transformer3DModel": ("dits", "cosmos2_5", "Cosmos25Transformer3DModel"),
-    "LongCatVideoTransformer3DModel":
-    ("dits", "longcat_video_dit", "LongCatVideoTransformer3DModel"),  # Wrapper (Phase 1)
-    "LongCatTransformer3DModel": ("dits", "longcat", "LongCatTransformer3DModel"),  # Native (Phase 2)
     "LTX2Transformer3DModel": ("dits", "ltx2", "LTX2Transformer3DModel"),
     "SD3Transformer2DModel": ("dits", "sd3", "SD3Transformer2DModel"),
     "LingBotWorldTransformer3DModel": ("dits", "lingbotworld", "LingBotWorldTransformer3DModel"),
@@ -56,8 +50,6 @@ _IMAGE_TO_VIDEO_DIT_MODELS = {
     # "HunyuanVideoTransformer3DModel": ("dits", "hunyuanvideo", "HunyuanVideoDiT"),
     "WanTransformer3DModel": ("wan", "transformer", "WanTransformer3DModel"),
     "WanModel_S2V": ("dits", "wan_s2v", "WanS2VTransformer3DModel"),
-    "DreamXWorldTransformer3DModel": ("dits", "dreamx_world", "DreamXWorldTransformer3DModel"),
-    "DreamXWorldARTransformer3DModel": ("dits", "dreamx_world_ar", "DreamXWorldARTransformer3DModel"),
     "CausalWanTransformer3DModel": ("wan", "causal_transformer", "CausalWanTransformer3DModel"),
     "LingBotWorld2CausalFastTransformer3DModel": (
         "dits",
@@ -79,12 +71,6 @@ _IMAGE_TO_VIDEO_DIT_MODELS = {
     "MatrixGame3WanModel": ("dits", "matrixgame3", "MatrixGame3WanModel"),
 }
 
-# Text-to-image DiT models (2D image generation)
-_TEXT_TO_IMAGE_DIT_MODELS = {
-    "GlmImageTransformer2DModel": ("dits", "glm_image", "GlmImageTransformer2DModel"),
-    "ZImageTransformer2DModel": ("dits", "zimage", "ZImageTransformer2DModel"),
-}
-
 _TEXT_ENCODER_MODELS = {
     "MMAudioDFNCLIPTextEncoder": ("encoders", "mmaudio_clip", "MMAudioDFNCLIPTextEncoder"),
     "CLIPTextModel": ("encoders", "clip", "CLIPTextModel"),
@@ -97,9 +83,6 @@ _TEXT_ENCODER_MODELS = {
     "Qwen2_5_VLTextModel": ("encoders", "qwen2_5", "Qwen2_5_VLTextModel"),
     "Reason1TextEncoder": ("encoders", "reason1", "Reason1TextEncoder"),
     "Qwen2_5_VLForConditionalGeneration": ("encoders", "reason1", "Reason1TextEncoder"),
-    # Z-Image-Turbo's text_encoder/config.json declares architecture
-    # "Qwen3Model"; route it to the shared Qwen3 encoder (added for Flux2 Klein).
-    "Qwen3Model": ("encoders", "qwen3", "Qwen3ForCausalLM"),
     "LTX2GemmaTextEncoderModel": ("encoders", "gemma", "LTX2GemmaTextEncoderModel"),
     "Qwen3ForCausalLM": ("encoders", "qwen3", "Qwen3ForCausalLM"),
     "Mistral3ForConditionalGeneration": ("encoders", "mistral3", "Mistral3ForConditionalGeneration"),
@@ -120,14 +103,12 @@ _IMAGE_ENCODER_MODELS: dict[str, tuple] = {
 
 _VAE_MODELS = {
     "AutoencoderKLHunyuanVideo": ("vaes", "hunyuanvae", "AutoencoderKLHunyuanVideo"),
-    "AutoencoderKLCausal3D": ("vaes", "gamecraftvae", "GameCraftVAE"),
     "AutoencoderKLHYWorld": ("vaes", "hyworldvae", "AutoencoderKLHYWorld"),
     "AutoencoderKLHunyuanVideo15": ("vaes", "hunyuan15vae", "AutoencoderKLHunyuanVideo15"),
     "AutoencoderKLWan": ("wan", "vae", "AutoencoderKLWan"),
     "LingBotWorld2WanVAE": ("vaes", "lingbotworld2_wanvae", "LingBotWorld2WanVAE"),
     "AutoencoderKL": ("vaes", "autoencoder_kl", "AutoencoderKL"),
     "AutoencoderKLGen3CTokenizer": ("vaes", "gen3c_tokenizer_vae", "AutoencoderKLGen3CTokenizer"),
-    "AutoencoderKLStepvideo": ("vaes", "stepvideovae", "AutoencoderKLStepvideo"),
     "CausalVideoAutoencoder": ("vaes", "ltx2vae", "LTX2CausalVideoAutoencoder"),
     "AutoencoderKLFlux2": ("vaes", "flux2vae", "AutoencoderKLFlux2"),
     # `stable-audio-open-1.0/vae/config.json` ships `_class_name="AutoencoderOobleck"`
@@ -151,7 +132,6 @@ _SCHEDULERS = {
     "FlowUniPCMultistepScheduler": ("schedulers", "scheduling_flow_unipc_multistep", "FlowUniPCMultistepScheduler"),
     "SelfForcingFlowMatchScheduler":
     ("schedulers", "scheduling_self_forcing_flow_match", "SelfForcingFlowMatchScheduler"),
-    "RCMScheduler": ("schedulers", "scheduling_rcm", "RCMScheduler"),
     "HeliosDMDScheduler": ("schedulers", "scheduling_helios_dmd", "HeliosDMDScheduler"),
     "Cosmos25DistilledScheduler": (
         "schedulers",
@@ -174,7 +154,6 @@ _UPSAMPLERS = {
 _LEGACY_FAST_VIDEO_MODELS = {
     **_TEXT_TO_VIDEO_DIT_MODELS,
     **_IMAGE_TO_VIDEO_DIT_MODELS,
-    **_TEXT_TO_IMAGE_DIT_MODELS,
     **_TEXT_ENCODER_MODELS,
     **_IMAGE_ENCODER_MODELS,
     **_VAE_MODELS,
diff --git a/fastvideo/models/schedulers/scheduling_flow_match_euler_discrete.py b/fastvideo/models/schedulers/scheduling_flow_match_euler_discrete.py
index 5daffcdb1..7fad5b97f 100644
--- a/fastvideo/models/schedulers/scheduling_flow_match_euler_discrete.py
+++ b/fastvideo/models/schedulers/scheduling_flow_match_euler_discrete.py
@@ -96,7 +96,7 @@ class FlowMatchEulerDiscreteScheduler(SchedulerMixin, ConfigMixin, BaseScheduler
         sigma_data (`float`, *optional*):
             The sigma data value for scaling.
         use_reference_discrete_timesteps (`bool`, defaults to False):
-            Some reference schedulers (e.g. Z-Image) construct the timestep
+            Some reference schedulers (e.g. MMAudio) construct the timestep
             schedule by linspacing `num_inference_steps + 1` points from
             `t_max` to `t_min` and dropping the terminal point. Default
             (`False`) preserves the original `np.linspace(t_max, t_min,
@@ -340,7 +340,7 @@ class FlowMatchEulerDiscreteScheduler(SchedulerMixin, ConfigMixin, BaseScheduler
                 t_max = self._sigma_to_t(self.sigma_max)
                 t_min = self._sigma_to_t(self.sigma_min)
                 if self.config.use_reference_discrete_timesteps:
-                    # Some reference schedulers (for example Z-Image) build a
+                    # Some reference schedulers (for example MMAudio) build a
                     # float64 num_steps+1 linspace and drop the terminal point.
                     timesteps_array = np.linspace(
                         t_max,
diff --git a/fastvideo/models/schedulers/scheduling_rcm.py b/fastvideo/models/schedulers/scheduling_rcm.py
deleted file mode 100644
index e48066af7..000000000
--- a/fastvideo/models/schedulers/scheduling_rcm.py
+++ /dev/null
@@ -1,323 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# rCM (recurrent Consistency Model) Scheduler for TurboDiffusion support
-#
-# This scheduler implements the rCM sampling method from TurboDiffusion,
-# enabling 1-4 step video generation with distilled checkpoints.
-#
-# Reference:
-#   TurboDiffusion: Accelerating Video Diffusion Models by 100-200 Times
-#   https://arxiv.org/pdf/2512.16093
-
-import math
-from dataclasses import dataclass
-from typing import Any
-
-import torch
-from diffusers.configuration_utils import ConfigMixin, register_to_config
-from diffusers.schedulers.scheduling_utils import SchedulerMixin
-from diffusers.utils import BaseOutput
-
-from fastvideo.logger import init_logger
-from fastvideo.models.schedulers.base import BaseScheduler
-
-logger = init_logger(__name__)
-
-
-@dataclass
-class RCMSchedulerOutput(BaseOutput):
-    """
-    Output class for the RCM scheduler's `step` function output.
-
-    Args:
-        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, ...)`):
-            Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used
-            as next model input in the denoising loop.
-    """
-
-    prev_sample: torch.FloatTensor
-
-
-class RCMScheduler(SchedulerMixin, ConfigMixin, BaseScheduler):
-    """
-    rCM (recurrent Consistency Model) scheduler for TurboDiffusion.
-
-    This scheduler implements the rCM sampling method which enables 1-4 step
-    video generation using distilled checkpoints. It uses:
-    1. TrigFlow → RectifiedFlow timestep conversion
-    2. SDE sampling formula: x = (1 - t_next) * (x - t_cur * v_pred) + t_next * noise
-
-    Args:
-        num_train_timesteps (`int`, defaults to 1000):
-            The number of diffusion steps used to train the model.
-        sigma_max (`float`, defaults to 80.0):
-            The initial sigma value for rCM sampling. Controls the noise level
-            at the start of sampling.
-        mid_timesteps (`list[float]`, *optional*):
-            Custom intermediate timesteps. If None, uses optimized defaults
-            [1.5, 1.4, 1.0] for better visual quality.
-    """
-
-    _compatibles: list[Any] = []
-    order = 1
-
-    @register_to_config
-    def __init__(
-        self,
-        num_train_timesteps: int = 1000,
-        sigma_max: float = 80.0,
-        mid_timesteps: list[float] | None = None,
-    ):
-        # Default mid timesteps optimized for visual quality
-        if mid_timesteps is None:
-            mid_timesteps = [1.5, 1.4, 1.0]
-        self._mid_timesteps = mid_timesteps
-
-        self.num_train_timesteps = num_train_timesteps
-        self.sigma_max = sigma_max
-
-        # Initialize with default timesteps (will be set properly via set_timesteps)
-        self.timesteps = torch.tensor([1.0, 0.0], dtype=torch.float64)
-        self.sigmas = self.timesteps.clone()
-
-        self._step_index: int | None = None
-        self._begin_index: int | None = None
-
-        BaseScheduler.__init__(self)
-
-    @property
-    def step_index(self) -> int | None:
-        """
-        The index counter for current timestep. Increases by 1 after each scheduler step.
-        """
-        return self._step_index
-
-    @property
-    def begin_index(self) -> int | None:
-        """
-        The index for the first timestep.
-        """
-        return self._begin_index
-
-    @property
-    def init_noise_sigma(self) -> float:
-        """
-        Initial noise sigma for scaling latents.
-        
-        In rCM, initial noise is scaled by the first sigma value:
-            x_0 = noise * sigmas[0]
-        
-        This property is used by LatentPreparationStage to scale initial latents.
-        """
-        return float(self.sigmas[0])
-
-    def set_begin_index(self, begin_index: int = 0) -> None:
-        """
-        Sets the begin index for the scheduler.
-
-        Args:
-            begin_index (`int`):
-                The begin index for the scheduler.
-        """
-        self._begin_index = begin_index
-
-    def set_shift(self, shift: float) -> None:
-        """
-        rCM doesn't use shift parameter, but required by BaseScheduler.
-        """
-        pass
-
-    def set_timesteps(
-        self,
-        num_inference_steps: int,
-        device: str | torch.device | None = None,
-        sigma_max: float | None = None,
-    ) -> None:
-        """
-        Sets the discrete timesteps used for the rCM sampling process.
-
-        The timesteps are computed using TrigFlow → RectifiedFlow conversion:
-        1. Start with atan(sigma_max) and intermediate values
-        2. Convert via: t = sin(t) / (cos(t) + sin(t))
-
-        Args:
-            num_inference_steps (`int`):
-                The number of diffusion steps (1-4 for rCM).
-            device (`str` or `torch.device`, *optional*):
-                The device to move timesteps to.
-            sigma_max (`float`, *optional*):
-                Override the initial sigma value.
-        """
-        if num_inference_steps < 1 or num_inference_steps > 4:
-            logger.warning("rCM is optimized for 1-4 steps, got %d steps. "
-                           "Performance may be suboptimal.", num_inference_steps)
-
-        self.num_inference_steps = num_inference_steps
-
-        if sigma_max is not None:
-            self.sigma_max = sigma_max
-
-        # Build timestep schedule
-        mid_t = self._mid_timesteps[:num_inference_steps - 1]
-
-        # TrigFlow timesteps: [atan(sigma_max), mid_t..., 0]
-        t_steps = torch.tensor(
-            [math.atan(self.sigma_max), *mid_t, 0],
-            dtype=torch.float64,
-            device=device,
-        )
-
-        # Convert TrigFlow → RectifiedFlow: t = sin(t) / (cos(t) + sin(t))
-        t_steps = torch.sin(t_steps) / (torch.cos(t_steps) + torch.sin(t_steps))
-
-        # Store raw sigmas for use in step() formula
-        self.sigmas = t_steps.clone()
-
-        # Scale timesteps by 1000 for model input (as per TurboDiffusion)
-        self.timesteps = t_steps * 1000
-
-        self._step_index = None
-        self._begin_index = None
-
-        logger.debug("rCM timesteps (scaled): %s", self.timesteps.tolist())
-        logger.debug("rCM sigmas (raw): %s", self.sigmas.tolist())
-
-    def _init_step_index(self, timestep: torch.FloatTensor | None = None) -> None:
-        """Initialize step index at the beginning of sampling."""
-        if self._begin_index is None:
-            self._step_index = 0
-        else:
-            self._step_index = self._begin_index
-
-    def scale_model_input(
-        self,
-        sample: torch.Tensor,
-        timestep: int | None = None,
-    ) -> torch.Tensor:
-        """
-        rCM doesn't scale model input, returns sample as-is.
-        """
-        return sample
-
-    def scale_noise(
-        self,
-        sample: torch.FloatTensor,
-        timestep: torch.FloatTensor | None = None,
-        noise: torch.FloatTensor | None = None,
-    ) -> torch.FloatTensor:
-        """
-        Scale initial noise for rCM sampling.
-        
-        In rCM, initial noise is scaled by the first timestep (raw sigma):
-            x_0 = noise * t_steps[0]
-
-        Args:
-            sample: Not used (for API compatibility)
-            timestep: Not used (for API compatibility)
-            noise: The noise tensor to scale
-
-        Returns:
-            Scaled noise tensor ready for sampling
-        """
-        if noise is None:
-            raise ValueError("noise must be provided for rCM scale_noise")
-
-        # Use raw sigma (not scaled timestep) for initial noise scaling
-        t_initial = self.sigmas[0]
-        return noise.to(torch.float64) * t_initial
-
-    def step(
-        self,
-        model_output: torch.FloatTensor,
-        timestep: int | torch.Tensor,
-        sample: torch.FloatTensor,
-        generator: torch.Generator | None = None,
-        return_dict: bool = True,
-    ) -> RCMSchedulerOutput | tuple[torch.FloatTensor, ...]:
-        """
-        Predict the sample from the previous timestep using rCM update rule.
-
-        The rCM update formula is:
-            x_{t+1} = (1 - t_next) * (x_t - t_cur * v_pred) + t_next * noise
-
-        Args:
-            model_output (`torch.FloatTensor`):
-                The velocity prediction from the model (v_pred).
-            timestep (`int` or `torch.Tensor`):
-                The current timestep index (not the actual timestep value).
-                For rCM, this should be the index into self.timesteps.
-            sample (`torch.FloatTensor`):
-                Current sample x_t.
-            generator (`torch.Generator`, *optional*):
-                Random number generator for noise.
-            return_dict (`bool`):
-                Whether to return RCMSchedulerOutput or tuple.
-
-        Returns:
-            `RCMSchedulerOutput` or `tuple`:
-                The denoised sample for the next step.
-        """
-        if self._step_index is None:
-            self._init_step_index()
-
-        assert self._step_index is not None
-
-        # Get current and next sigma values (raw, unscaled) for rCM formula
-        # Note: self.timesteps is scaled by 1000 for model input,
-        # but we need raw values for the step formula
-        t_cur = self.sigmas[self._step_index]
-        # On the final step, t_next should be 0 (fully denoised)
-        if self._step_index + 1 < len(self.sigmas):
-            t_next = self.sigmas[self._step_index + 1]
-        else:
-            t_next = torch.tensor(0.0, device=sample.device, dtype=torch.float64)
-
-        # Ensure we're working in float64 for precision
-        sample = sample.to(torch.float64)
-        model_output = model_output.to(torch.float64)
-
-        # rCM update: x = (1 - t_next) * (x - t_cur * v_pred) + t_next * noise
-        x_denoised = sample - t_cur * model_output
-
-        # Generate noise for SDE sampling
-        if isinstance(generator, list):
-            generator = generator[0]
-        noise = torch.randn(
-            sample.shape,
-            dtype=torch.float32,
-            device="cpu",
-            generator=generator,
-        ).to(sample.device).to(torch.float64)
-
-        prev_sample = (1 - t_next) * x_denoised + t_next * noise
-
-        # Increment step counter
-        self._step_index += 1
-
-        # Cast back to model output dtype
-        prev_sample = prev_sample.to(model_output.dtype)
-
-        if not return_dict:
-            return (prev_sample, )
-
-        return RCMSchedulerOutput(prev_sample=prev_sample)
-
-    def add_noise(
-        self,
-        original_samples: torch.Tensor,
-        noise: torch.Tensor,
-        timesteps: torch.IntTensor,
-    ) -> torch.Tensor:
-        """
-        Add noise to samples (forward diffusion process).
-        
-        Not typically used for rCM inference, but provided for API compatibility.
-        """
-        raise NotImplementedError("add_noise is not implemented for RCMScheduler. "
-                                  "Use scale_noise for initializing the sampling process.")
-
-    def __len__(self) -> int:
-        return self.config.num_train_timesteps
-
-
-# Entry point for model registry
-EntryClass = RCMScheduler
diff --git a/fastvideo/models/vaes/gamecraftvae.py b/fastvideo/models/vaes/gamecraftvae.py
deleted file mode 100644
index e687c346c..000000000
--- a/fastvideo/models/vaes/gamecraftvae.py
+++ /dev/null
@@ -1,450 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-GameCraft VAE - ported from official Hunyuan-GameCraft-1.0/hymm_sp/vae/.
-
-Matches the official AutoencoderKLCausal3D structure exactly for weight loading.
-"""
-from dataclasses import dataclass
-from typing import Optional, Tuple
-
-import numpy as np
-import torch
-import torch.nn as nn
-
-from fastvideo.configs.models.vaes.gamecraftvae import GameCraftVAEConfig
-from fastvideo.models.vaes.common import DiagonalGaussianDistribution
-from fastvideo.models.vaes.gamecraftvae_blocks import (
-    CausalConv3d,
-    DownEncoderBlockCausal3D,
-    UpDecoderBlockCausal3D,
-    UNetMidBlockCausal3D,
-)
-
-
-@dataclass
-class AutoencoderKLOutput:
-    """Matches official AutoencoderKLOutput interface."""
-
-    latent_dist: DiagonalGaussianDistribution
-
-
-@dataclass
-class DecoderOutput:
-    """Matches official DecoderOutput interface."""
-
-    sample: torch.Tensor
-
-
-class EncoderCausal3D(nn.Module):
-    """Encoder - ported from official vae.py. Structure matches for weight loading."""
-
-    def __init__(
-        self,
-        in_channels: int = 3,
-        out_channels: int = 16,
-        down_block_types: Tuple[str, ...] = ("DownEncoderBlockCausal3D", ),
-        block_out_channels: Tuple[int, ...] = (128, 256, 512, 512),
-        layers_per_block: int = 2,
-        norm_num_groups: int = 32,
-        act_fn: str = "silu",
-        double_z: bool = True,
-        mid_block_add_attention: bool = True,
-        time_compression_ratio: int = 4,
-        spatial_compression_ratio: int = 8,
-        disable_causal: bool = False,
-        mid_block_causal_attn: bool = False,
-    ):
-        super().__init__()
-        self.layers_per_block = layers_per_block
-
-        self.conv_in = CausalConv3d(in_channels,
-                                    block_out_channels[0],
-                                    kernel_size=3,
-                                    stride=1,
-                                    disable_causal=disable_causal)
-        self.down_blocks = nn.ModuleList([])
-
-        output_channel = block_out_channels[0]
-        for i, _ in enumerate(down_block_types):
-            input_channel = output_channel
-            output_channel = block_out_channels[i]
-            is_final_block = i == len(block_out_channels) - 1
-            num_spatial = int(np.log2(spatial_compression_ratio))
-            num_time = int(np.log2(time_compression_ratio))
-
-            if time_compression_ratio == 4:
-                add_spatial = bool(i < num_spatial)
-                add_time = bool(i >= (len(block_out_channels) - 1 - num_time) and not is_final_block)
-            elif time_compression_ratio == 8:
-                add_spatial = bool(i < num_spatial)
-                add_time = bool(i < num_time)
-            else:
-                raise ValueError(f"Unsupported time_compression_ratio: {time_compression_ratio}")
-
-            downsample_stride_HW = (2, 2) if add_spatial else (1, 1)
-            downsample_stride_T = (2, ) if add_time else (1, )
-            downsample_stride = tuple(downsample_stride_T + downsample_stride_HW)
-
-            down_block = DownEncoderBlockCausal3D(
-                in_channels=input_channel,
-                out_channels=output_channel,
-                num_layers=layers_per_block,
-                resnet_eps=1e-6,
-                resnet_act_fn=act_fn,
-                resnet_groups=norm_num_groups,
-                add_downsample=bool(add_spatial or add_time),
-                downsample_stride=downsample_stride,
-                downsample_padding=0,
-                disable_causal=disable_causal,
-            )
-            self.down_blocks.append(down_block)
-
-        self.mid_block = UNetMidBlockCausal3D(
-            in_channels=block_out_channels[-1],
-            temb_channels=None,
-            num_layers=1,
-            resnet_eps=1e-6,
-            resnet_act_fn=act_fn,
-            resnet_groups=norm_num_groups,
-            add_attention=mid_block_add_attention,
-            attention_head_dim=block_out_channels[-1],
-            disable_causal=disable_causal,
-            causal_attention=mid_block_causal_attn,
-        )
-
-        self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[-1], num_groups=norm_num_groups, eps=1e-6)
-        self.conv_act = nn.SiLU()
-        conv_out_channels = 2 * out_channels if double_z else out_channels
-        self.conv_out = CausalConv3d(
-            block_out_channels[-1],
-            conv_out_channels,
-            kernel_size=3,
-            disable_causal=disable_causal,
-        )
-
-    def forward(self, sample: torch.Tensor) -> torch.Tensor:
-        sample = self.conv_in(sample)
-        for down_block in self.down_blocks:
-            sample = down_block(sample, scale=1.0)
-        sample = self.mid_block(sample, temb=None)
-        sample = self.conv_norm_out(sample)
-        sample = self.conv_act(sample)
-        sample = self.conv_out(sample)
-        return sample
-
-
-class DecoderCausal3D(nn.Module):
-    """Decoder - ported from official vae.py. Structure matches for weight loading."""
-
-    def __init__(
-        self,
-        in_channels: int = 16,
-        out_channels: int = 3,
-        up_block_types: Tuple[str, ...] = ("UpDecoderBlockCausal3D", ),
-        block_out_channels: Tuple[int, ...] = (128, 256, 512, 512),
-        layers_per_block: int = 2,
-        norm_num_groups: int = 32,
-        act_fn: str = "silu",
-        mid_block_add_attention: bool = True,
-        time_compression_ratio: int = 4,
-        spatial_compression_ratio: int = 8,
-        disable_causal: bool = False,
-        mid_block_causal_attn: bool = False,
-    ):
-        super().__init__()
-        self.layers_per_block = layers_per_block
-
-        self.conv_in = CausalConv3d(
-            in_channels,
-            block_out_channels[-1],
-            kernel_size=3,
-            stride=1,
-            disable_causal=disable_causal,
-        )
-
-        self.mid_block = UNetMidBlockCausal3D(
-            in_channels=block_out_channels[-1],
-            temb_channels=None,
-            num_layers=1,
-            resnet_eps=1e-6,
-            resnet_act_fn=act_fn,
-            resnet_groups=norm_num_groups,
-            add_attention=mid_block_add_attention,
-            attention_head_dim=block_out_channels[-1],
-            disable_causal=disable_causal,
-            causal_attention=mid_block_causal_attn,
-        )
-
-        self.up_blocks = nn.ModuleList([])
-        reversed_channels = list(reversed(block_out_channels))
-        output_channel = reversed_channels[0]
-        for i, _ in enumerate(up_block_types):
-            prev_output_channel = output_channel
-            output_channel = reversed_channels[i]
-            is_final_block = i == len(block_out_channels) - 1
-            num_spatial = int(np.log2(spatial_compression_ratio))
-            num_time = int(np.log2(time_compression_ratio))
-
-            if time_compression_ratio == 4:
-                add_spatial = bool(i < num_spatial)
-                add_time = bool(i >= len(block_out_channels) - 1 - num_time and not is_final_block)
-            elif time_compression_ratio == 8:
-                add_spatial = bool(i >= len(block_out_channels) - num_spatial)
-                add_time = bool(i >= len(block_out_channels) - num_time)
-            else:
-                raise ValueError(f"Unsupported time_compression_ratio: {time_compression_ratio}")
-
-            upsample_HW = (2, 2) if add_spatial else (1, 1)
-            upsample_T = (2, ) if add_time else (1, )
-            upsample_scale_factor = tuple(upsample_T + upsample_HW)
-
-            up_block = UpDecoderBlockCausal3D(
-                in_channels=prev_output_channel,
-                out_channels=output_channel,
-                num_layers=layers_per_block + 1,
-                resnet_eps=1e-6,
-                resnet_act_fn=act_fn,
-                resnet_groups=norm_num_groups,
-                add_upsample=bool(add_spatial or add_time),
-                upsample_scale_factor=upsample_scale_factor,
-                temb_channels=None,
-                disable_causal=disable_causal,
-            )
-            self.up_blocks.append(up_block)
-            output_channel = output_channel
-
-        self.conv_norm_out = nn.GroupNorm(num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=1e-6)
-        self.conv_act = nn.SiLU()
-        self.conv_out = CausalConv3d(block_out_channels[0], out_channels, kernel_size=3, disable_causal=disable_causal)
-
-    def forward(
-        self,
-        sample: torch.Tensor,
-        latent_embeds: Optional[torch.Tensor] = None,
-    ) -> torch.Tensor:
-        sample = self.conv_in(sample)
-        sample = self.mid_block(sample, temb=latent_embeds)
-        for up_block in self.up_blocks:
-            sample = up_block(sample, temb=latent_embeds, scale=1.0)
-        sample = self.conv_norm_out(sample)
-        sample = self.conv_act(sample)
-        sample = self.conv_out(sample)
-        return sample
-
-
-class GameCraftVAE(nn.Module):
-    """
-    GameCraft VAE - ported from official AutoencoderKLCausal3D.
-    Structure matches exactly for loading official weights.
-    """
-
-    def __init__(self, config: GameCraftVAEConfig):
-        super().__init__()
-        self.config = config
-        arch = config.arch_config
-
-        time_ratio = getattr(arch, "time_compression_ratio", arch.temporal_compression_ratio)
-        self.encoder = EncoderCausal3D(
-            in_channels=arch.in_channels,
-            out_channels=arch.latent_channels,
-            down_block_types=tuple(arch.down_block_types),
-            block_out_channels=tuple(arch.block_out_channels),
-            layers_per_block=arch.layers_per_block,
-            norm_num_groups=arch.norm_num_groups,
-            act_fn=arch.act_fn,
-            double_z=True,
-            time_compression_ratio=time_ratio,
-            spatial_compression_ratio=arch.spatial_compression_ratio,
-            disable_causal=getattr(arch, "disable_causal_conv", False),
-            mid_block_add_attention=arch.mid_block_add_attention,
-            mid_block_causal_attn=getattr(arch, "mid_block_causal_attn", False),
-        )
-
-        self.decoder = DecoderCausal3D(
-            in_channels=arch.latent_channels,
-            out_channels=arch.out_channels,
-            up_block_types=tuple(arch.up_block_types),
-            block_out_channels=tuple(arch.block_out_channels),
-            layers_per_block=arch.layers_per_block,
-            norm_num_groups=arch.norm_num_groups,
-            act_fn=arch.act_fn,
-            time_compression_ratio=time_ratio,
-            spatial_compression_ratio=arch.spatial_compression_ratio,
-            disable_causal=getattr(arch, "disable_causal_conv", False),
-            mid_block_add_attention=arch.mid_block_add_attention,
-            mid_block_causal_attn=getattr(arch, "mid_block_causal_attn", False),
-        )
-
-        self.quant_conv = nn.Conv3d(2 * arch.latent_channels, 2 * arch.latent_channels, kernel_size=1)
-        self.post_quant_conv = nn.Conv3d(arch.latent_channels, arch.latent_channels, kernel_size=1)
-
-        # Scaling factor for latent normalization (required for decoding stage)
-        self.scaling_factor = arch.scaling_factor
-
-        # Tiling support - matches official GameCraft VAE settings
-        self._tiling_enabled = False
-        self.tile_overlap_factor = 0.25
-
-        # Temporal tiling params (for >64 output frames)
-        self.tile_sample_min_tsize = 64  # Minimum sample temporal size (video frames)
-        self.tile_latent_min_tsize = 16  # = 64 // 4 (time_compression_ratio)
-
-        # Spatial tiling params - use small tiles to reduce memory
-        self.tile_sample_min_size = 256  # Minimum spatial tile size in pixel space
-        self.tile_latent_min_size = 32  # = 256 // 8 (spatial_compression_ratio)
-
-    def encode(self, x: torch.Tensor) -> AutoencoderKLOutput:
-        """Encode to latent distribution."""
-        h = self.encoder(x)
-        moments = self.quant_conv(h)
-        posterior = DiagonalGaussianDistribution(moments)
-        return AutoencoderKLOutput(latent_dist=posterior)
-
-    def decode(self, z: torch.Tensor) -> torch.Tensor:
-        """Decode from latents.
-        
-        Args:
-            z: Latent tensor [B, C, T, H, W]
-            
-        Returns:
-            Decoded tensor [B, C, T_out, H_out, W_out]
-        """
-        # Use tiled decode for memory efficiency when enabled
-        if self._tiling_enabled:
-            # Check if temporal tiling needed (>64 output frames)
-            if z.shape[2] > self.tile_latent_min_tsize:
-                return self._temporal_tiled_decode(z)
-            # Check if spatial tiling needed (large H or W)
-            if z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size:
-                return self._spatial_tiled_decode(z)
-
-        z = self.post_quant_conv(z)
-        dec = self.decoder(z, latent_embeds=None)
-        return dec
-
-    def _temporal_tiled_decode(self, z: torch.Tensor) -> torch.Tensor:
-        """Decode latents in temporal tiles with overlapping and blending.
-        
-        Based on official GameCraft temporal_tiled_decode implementation.
-        Only used when T > tile_latent_min_tsize (16).
-        """
-        B, C, T, H, W = z.shape
-
-        # Use the pre-configured tiling parameters
-        overlap_size = int(self.tile_latent_min_tsize * (1 - self.tile_overlap_factor))
-        blend_extent = int(self.tile_sample_min_tsize * self.tile_overlap_factor)
-        t_limit = self.tile_sample_min_tsize - blend_extent
-
-        row = []
-        for i in range(0, T, overlap_size):
-            tile = z[:, :, i:i + self.tile_latent_min_tsize + 1, :, :]
-            tile = self.post_quant_conv(tile)
-            decoded = self.decoder(tile, latent_embeds=None)
-            if i > 0:
-                decoded = decoded[:, :, 1:, :, :]  # Skip first frame for non-first tiles
-            row.append(decoded)
-
-        # Blend overlapping regions
-        result_row = []
-        for i, tile in enumerate(row):
-            if i > 0:
-                tile = self._blend_t(row[i - 1], tile, blend_extent)
-                result_row.append(tile[:, :, :t_limit, :, :])
-            else:
-                result_row.append(tile[:, :, :t_limit + 1, :, :])
-
-        return torch.cat(result_row, dim=2)
-
-    def _spatial_tiled_decode(self, z: torch.Tensor) -> torch.Tensor:
-        """Decode latents in spatial tiles with overlapping and blending.
-        
-        Based on official GameCraft spatial_tiled_decode implementation.
-        """
-        overlap_size = int(self.tile_latent_min_size * (1 - self.tile_overlap_factor))
-        blend_extent = int(self.tile_sample_min_size * self.tile_overlap_factor)
-        row_limit = self.tile_sample_min_size - blend_extent
-
-        # Split z into overlapping tiles and decode them separately
-        rows = []
-        for i in range(0, z.shape[-2], overlap_size):
-            row = []
-            for j in range(0, z.shape[-1], overlap_size):
-                tile = z[:, :, :, i:i + self.tile_latent_min_size, j:j + self.tile_latent_min_size]
-                tile = self.post_quant_conv(tile)
-                decoded = self.decoder(tile, latent_embeds=None)
-                row.append(decoded)
-            rows.append(row)
-
-        # Blend overlapping regions
-        result_rows = []
-        for i, row in enumerate(rows):
-            result_row = []
-            for j, tile in enumerate(row):
-                # Blend with above tile and left tile
-                if i > 0:
-                    tile = self._blend_v(rows[i - 1][j], tile, blend_extent)
-                if j > 0:
-                    tile = self._blend_h(row[j - 1], tile, blend_extent)
-                result_row.append(tile[:, :, :, :row_limit, :row_limit])
-            result_rows.append(torch.cat(result_row, dim=-1))
-
-        return torch.cat(result_rows, dim=-2)
-
-    def _blend_t(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.Tensor:
-        """Blend two tensors along temporal dimension."""
-        blend_extent = min(a.shape[-3], b.shape[-3], blend_extent)
-        if blend_extent == 0:
-            return b
-
-        a_region = a[..., -blend_extent:, :, :]
-        b_region = b[..., :blend_extent, :, :]
-
-        weights = torch.arange(blend_extent, device=a.device, dtype=a.dtype) / blend_extent
-        weights = weights.view(1, 1, blend_extent, 1, 1)
-
-        blended = a_region * (1 - weights) + b_region * weights
-        b[..., :blend_extent, :, :] = blended
-        return b
-
-    def _blend_v(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.Tensor:
-        """Blend two tensors along vertical (height) dimension."""
-        blend_extent = min(a.shape[-2], b.shape[-2], blend_extent)
-        if blend_extent == 0:
-            return b
-
-        a_region = a[..., -blend_extent:, :]
-        b_region = b[..., :blend_extent, :]
-
-        weights = torch.arange(blend_extent, device=a.device, dtype=a.dtype) / blend_extent
-        weights = weights.view(1, 1, 1, blend_extent, 1)
-
-        blended = a_region * (1 - weights) + b_region * weights
-        b[..., :blend_extent, :] = blended
-        return b
-
-    def _blend_h(self, a: torch.Tensor, b: torch.Tensor, blend_extent: int) -> torch.Tensor:
-        """Blend two tensors along horizontal (width) dimension."""
-        blend_extent = min(a.shape[-1], b.shape[-1], blend_extent)
-        if blend_extent == 0:
-            return b
-
-        a_region = a[..., -blend_extent:]
-        b_region = b[..., :blend_extent]
-
-        weights = torch.arange(blend_extent, device=a.device, dtype=a.dtype) / blend_extent
-        weights = weights.view(1, 1, 1, 1, blend_extent)
-
-        blended = a_region * (1 - weights) + b_region * weights
-        b[..., :blend_extent] = blended
-        return b
-
-    def enable_tiling(self) -> None:
-        """Enable tiling for large inputs."""
-        self._tiling_enabled = True
-
-    def disable_tiling(self) -> None:
-        """Disable tiling."""
-        self._tiling_enabled = False
-
-
-EntryClass = GameCraftVAE
diff --git a/fastvideo/models/vaes/gamecraftvae_blocks.py b/fastvideo/models/vaes/gamecraftvae_blocks.py
deleted file mode 100644
index 7f6bedd7a..000000000
--- a/fastvideo/models/vaes/gamecraftvae_blocks.py
+++ /dev/null
@@ -1,420 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-GameCraft VAE building blocks - ported from official Hunyuan-GameCraft-1.0/hymm_sp/vae/unet_causal_3d_blocks.py.
-
-Matches the official structure exactly for weight loading.
-"""
-from typing import Optional, Tuple, Union
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from einops import rearrange
-
-
-def prepare_causal_attention_mask(n_frame: int, n_hw: int, dtype, device, batch_size: Optional[int] = None):
-    seq_len = n_frame * n_hw
-    mask = torch.full((seq_len, seq_len), float("-inf"), dtype=dtype, device=device)
-    for i in range(seq_len):
-        i_frame = i // n_hw
-        mask[i, :(i_frame + 1) * n_hw] = 0
-    if batch_size is not None:
-        mask = mask.unsqueeze(0).expand(batch_size, -1, -1)
-    return mask
-
-
-class CausalConv3d(nn.Module):
-    """Causal 3D convolution - matches official structure (has .conv)."""
-
-    def __init__(
-        self,
-        chan_in: int,
-        chan_out: int,
-        kernel_size: Union[int, Tuple[int, int, int]] = 3,
-        stride: Union[int, Tuple[int, int, int]] = 1,
-        dilation: Union[int, Tuple[int, int, int]] = 1,
-        pad_mode: str = "replicate",
-        disable_causal: bool = False,
-        **kwargs,
-    ):
-        super().__init__()
-        self.pad_mode = pad_mode
-        k = kernel_size if isinstance(kernel_size, int) else kernel_size[0]
-        if disable_causal:
-            padding = (k // 2, k // 2, k // 2, k // 2, k // 2, k // 2)
-        else:
-            padding = (k // 2, k // 2, k // 2, k // 2, k - 1, 0)
-        self.time_causal_padding = padding
-        self.conv = nn.Conv3d(chan_in, chan_out, kernel_size, stride=stride, dilation=dilation, **kwargs)
-
-    def forward(self, x: torch.Tensor) -> torch.Tensor:
-        x = F.pad(x, self.time_causal_padding, mode=self.pad_mode)
-        return self.conv(x)
-
-
-class DownsampleCausal3D(nn.Module):
-    """Causal 3D downsampling - matches official (has .conv)."""
-
-    def __init__(
-        self,
-        channels: int,
-        out_channels: Optional[int] = None,
-        padding: int = 1,
-        stride: Union[int, Tuple[int, int, int]] = 2,
-        kernel_size: int = 3,
-        bias: bool = True,
-        disable_causal: bool = False,
-    ):
-        super().__init__()
-        self.out_channels = out_channels or channels
-        self.conv = CausalConv3d(
-            channels,
-            self.out_channels,
-            kernel_size=kernel_size,
-            stride=stride,
-            padding=0,
-            disable_causal=disable_causal,
-            bias=bias,
-        )
-
-    def forward(self, hidden_states: torch.Tensor, scale: float = 1.0) -> torch.Tensor:
-        return self.conv(hidden_states)
-
-
-class UpsampleCausal3D(nn.Module):
-    """Causal 3D upsampling - matches official (has .conv when use_conv=True)."""
-
-    def __init__(
-            self,
-            channels: int,
-            out_channels: Optional[int] = None,
-            kernel_size: int = 3,
-            upsample_factor: Tuple[int, int, int] = (2, 2, 2),
-            disable_causal: bool = False,
-            bias: bool = True,
-    ):
-        super().__init__()
-        self.out_channels = out_channels or channels
-        self.upsample_factor = upsample_factor
-        self.disable_causal = disable_causal
-        self.conv = CausalConv3d(
-            channels,
-            self.out_channels,
-            kernel_size=kernel_size,
-            stride=1,
-            disable_causal=disable_causal,
-            bias=bias,
-        )
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        output_size: Optional[int] = None,
-        scale: float = 1.0,
-    ) -> torch.Tensor:
-        B, C, T, H, W = hidden_states.shape
-        dtype = hidden_states.dtype
-        if dtype == torch.bfloat16:
-            hidden_states = hidden_states.to(torch.float32)
-
-        if not self.disable_causal and T > 1:
-            first_h, other_h = hidden_states.split((1, T - 1), dim=2)
-            other_h = F.interpolate(other_h, scale_factor=self.upsample_factor, mode="nearest")
-            first_h = F.interpolate(first_h.squeeze(2), scale_factor=self.upsample_factor[1:],
-                                    mode="nearest").unsqueeze(2)
-            hidden_states = torch.cat((first_h, other_h), dim=2)
-        else:
-            hidden_states = F.interpolate(hidden_states, scale_factor=self.upsample_factor, mode="nearest")
-
-        if dtype == torch.bfloat16:
-            hidden_states = hidden_states.to(dtype)
-        return self.conv(hidden_states)
-
-
-class GameCraftVAEAttention(nn.Module):
-    """Attention block matching official diffusers Attention structure (group_norm, to_q, to_k, to_v, to_out)."""
-
-    def __init__(
-        self,
-        in_channels: int,
-        heads: int,
-        dim_head: int,
-        eps: float = 1e-6,
-        norm_num_groups: Optional[int] = 32,
-        bias: bool = True,
-    ):
-        super().__init__()
-        self.heads = heads
-        self.dim_head = dim_head
-        inner_dim = heads * dim_head
-        self.group_norm = nn.GroupNorm(norm_num_groups or in_channels, in_channels, eps=eps)
-        self.to_q = nn.Linear(in_channels, inner_dim, bias=bias)
-        self.to_k = nn.Linear(in_channels, inner_dim, bias=bias)
-        self.to_v = nn.Linear(in_channels, inner_dim, bias=bias)
-        self.to_out = nn.Sequential(nn.Linear(inner_dim, in_channels, bias=bias))
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        attention_mask: Optional[torch.Tensor] = None,
-    ) -> torch.Tensor:
-        residual = hidden_states
-        batch_size, seq_len, _ = hidden_states.shape
-
-        hidden_states = self.group_norm(hidden_states.permute(0, 2, 1)).permute(0, 2, 1)
-
-        q = self.to_q(hidden_states)
-        k = self.to_k(hidden_states)
-        v = self.to_v(hidden_states)
-
-        q = q.view(batch_size, seq_len, self.heads, self.dim_head).transpose(1, 2)
-        k = k.view(batch_size, seq_len, self.heads, self.dim_head).transpose(1, 2)
-        v = v.view(batch_size, seq_len, self.heads, self.dim_head).transpose(1, 2)
-
-        scale = self.dim_head**-0.5
-        attn = torch.matmul(q, k.transpose(-2, -1)) * scale
-        if attention_mask is not None:
-            attn = attn + attention_mask
-        # Official uses upcast_softmax=True: compute softmax in fp32 for numerical stability
-        attn = F.softmax(attn.float(), dim=-1).to(q.dtype)
-        hidden_states = torch.matmul(attn, v)
-        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, seq_len, -1)
-        hidden_states = self.to_out(hidden_states) + residual
-        return hidden_states
-
-
-class ResnetBlockCausal3D(nn.Module):
-    """ResNet block - matches official structure (conv1.conv, conv2.conv, norm1, norm2)."""
-
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: Optional[int] = None,
-        temb_channels: Optional[int] = None,
-        eps: float = 1e-6,
-        groups: int = 32,
-        dropout: float = 0.0,
-        non_linearity: str = "swish",
-        disable_causal: bool = False,
-    ):
-        super().__init__()
-        out_channels = out_channels or in_channels
-        self.norm1 = nn.GroupNorm(groups, in_channels, eps=eps)
-        self.conv1 = CausalConv3d(in_channels, out_channels, 3, 1, disable_causal=disable_causal)
-        self.norm2 = nn.GroupNorm(groups, out_channels, eps=eps)
-        self.conv2 = CausalConv3d(out_channels, out_channels, 3, 1, disable_causal=disable_causal)
-        self.dropout = nn.Dropout(dropout)
-        self.conv_shortcut = (CausalConv3d(in_channels, out_channels, 1, 1, disable_causal=disable_causal)
-                              if in_channels != out_channels else None)
-        self.nonlinearity = getattr(F, non_linearity, F.silu)
-
-    def forward(
-        self,
-        x: torch.Tensor,
-        temb: Optional[torch.Tensor] = None,
-        scale: float = 1.0,
-    ) -> torch.Tensor:
-        h = self.norm1(x)
-        h = self.nonlinearity(h)
-        h = self.conv1(h)
-        h = self.norm2(h)
-        h = self.nonlinearity(h)
-        h = self.dropout(h)
-        h = self.conv2(h)
-        if self.conv_shortcut is not None:
-            x = self.conv_shortcut(x)
-        return (x + h) / 1.0
-
-
-class UNetMidBlockCausal3D(nn.Module):
-    """Mid block with resnets and optional attention - matches official structure."""
-
-    def __init__(
-        self,
-        in_channels: int,
-        temb_channels: Optional[int] = None,
-        num_layers: int = 1,
-        resnet_eps: float = 1e-6,
-        resnet_act_fn: str = "swish",
-        resnet_groups: int = 32,
-        add_attention: bool = True,
-        attention_head_dim: int = 1,
-        disable_causal: bool = False,
-        causal_attention: bool = False,
-    ):
-        super().__init__()
-        self.add_attention = add_attention
-        self.causal_attention = causal_attention
-
-        self.resnets = nn.ModuleList()
-        self.attentions = nn.ModuleList()
-
-        self.resnets.append(
-            ResnetBlockCausal3D(
-                in_channels=in_channels,
-                out_channels=in_channels,
-                temb_channels=temb_channels,
-                eps=resnet_eps,
-                groups=resnet_groups,
-                dropout=0.0,
-                non_linearity=resnet_act_fn,
-                disable_causal=disable_causal,
-            ))
-
-        for _ in range(num_layers):
-            if add_attention:
-                self.attentions.append(
-                    GameCraftVAEAttention(
-                        in_channels=in_channels,
-                        heads=in_channels // attention_head_dim,
-                        dim_head=attention_head_dim,
-                        eps=resnet_eps,
-                        norm_num_groups=resnet_groups,
-                        bias=True,
-                    ))
-            else:
-                self.attentions.append(None)
-            self.resnets.append(
-                ResnetBlockCausal3D(
-                    in_channels=in_channels,
-                    out_channels=in_channels,
-                    temb_channels=temb_channels,
-                    eps=resnet_eps,
-                    groups=resnet_groups,
-                    dropout=0.0,
-                    non_linearity=resnet_act_fn,
-                    disable_causal=disable_causal,
-                ))
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        temb: Optional[torch.Tensor] = None,
-    ) -> torch.Tensor:
-        hidden_states = self.resnets[0](hidden_states, temb)
-        for attn, resnet in zip(self.attentions, self.resnets[1:]):
-            if attn is not None:
-                B, C, T, H, W = hidden_states.shape
-                hidden_states = rearrange(hidden_states, "b c f h w -> b (f h w) c")
-                if self.causal_attention:
-                    mask = prepare_causal_attention_mask(T,
-                                                         H * W,
-                                                         hidden_states.dtype,
-                                                         hidden_states.device,
-                                                         batch_size=B)
-                else:
-                    mask = None
-                hidden_states = attn(hidden_states, attention_mask=mask)
-                hidden_states = rearrange(hidden_states, "b (f h w) c -> b c f h w", f=T, h=H, w=W)
-            hidden_states = resnet(hidden_states, temb)
-        return hidden_states
-
-
-class DownEncoderBlockCausal3D(nn.Module):
-    """Encoder down block - matches official structure."""
-
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int,
-        num_layers: int = 2,
-        resnet_eps: float = 1e-6,
-        resnet_act_fn: str = "swish",
-        resnet_groups: int = 32,
-        add_downsample: bool = True,
-        downsample_stride: Union[int, Tuple[int, int, int]] = 2,
-        downsample_padding: int = 0,
-        disable_causal: bool = False,
-    ):
-        super().__init__()
-        self.resnets = nn.ModuleList()
-        for i in range(num_layers):
-            inc = in_channels if i == 0 else out_channels
-            self.resnets.append(
-                ResnetBlockCausal3D(
-                    in_channels=inc,
-                    out_channels=out_channels,
-                    temb_channels=None,
-                    eps=resnet_eps,
-                    groups=resnet_groups,
-                    dropout=0.0,
-                    non_linearity=resnet_act_fn,
-                    disable_causal=disable_causal,
-                ))
-
-        self.downsamplers = None
-        if add_downsample:
-            self.downsamplers = nn.ModuleList([
-                DownsampleCausal3D(
-                    out_channels,
-                    out_channels=out_channels,
-                    padding=downsample_padding,
-                    stride=downsample_stride,
-                    disable_causal=disable_causal,
-                )
-            ])
-
-    def forward(self, hidden_states: torch.Tensor, scale: float = 1.0) -> torch.Tensor:
-        for resnet in self.resnets:
-            hidden_states = resnet(hidden_states, temb=None, scale=scale)
-        if self.downsamplers is not None:
-            for ds in self.downsamplers:
-                hidden_states = ds(hidden_states, scale)
-        return hidden_states
-
-
-class UpDecoderBlockCausal3D(nn.Module):
-    """Decoder up block - matches official structure."""
-
-    def __init__(
-        self,
-        in_channels: int,
-        out_channels: int,
-        num_layers: int = 3,
-        resnet_eps: float = 1e-6,
-        resnet_act_fn: str = "swish",
-        resnet_groups: int = 32,
-        add_upsample: bool = True,
-        upsample_scale_factor: Tuple[int, int, int] = (2, 2, 2),
-        temb_channels: Optional[int] = None,
-        disable_causal: bool = False,
-    ):
-        super().__init__()
-        self.resnets = nn.ModuleList()
-        for i in range(num_layers):
-            inc = in_channels if i == 0 else out_channels
-            self.resnets.append(
-                ResnetBlockCausal3D(
-                    in_channels=inc,
-                    out_channels=out_channels,
-                    temb_channels=temb_channels,
-                    eps=resnet_eps,
-                    groups=resnet_groups,
-                    dropout=0.0,
-                    non_linearity=resnet_act_fn,
-                    disable_causal=disable_causal,
-                ))
-
-        self.upsamplers = None
-        if add_upsample:
-            self.upsamplers = nn.ModuleList([
-                UpsampleCausal3D(
-                    out_channels,
-                    out_channels=out_channels,
-                    upsample_factor=upsample_scale_factor,
-                    disable_causal=disable_causal,
-                )
-            ])
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        temb: Optional[torch.Tensor] = None,
-        scale: float = 1.0,
-    ) -> torch.Tensor:
-        for resnet in self.resnets:
-            hidden_states = resnet(hidden_states, temb=temb, scale=scale)
-        if self.upsamplers is not None:
-            for us in self.upsamplers:
-                hidden_states = us(hidden_states)
-        return hidden_states
diff --git a/fastvideo/models/wan/AGENTS.md b/fastvideo/models/wan/AGENTS.md
index 46f478bba..e0f0aa32f 100644
--- a/fastvideo/models/wan/AGENTS.md
+++ b/fastvideo/models/wan/AGENTS.md
@@ -15,8 +15,8 @@ explicit compatibility exports.
 
 - Keep `__init__.py` lightweight: no eager model or pipeline imports.
 - Keep `definition.py` data-only. Config and preset names reference their
-  existing owners; do not copy defaults into the catalog. Its registration
-  groups preserve first-match detector ordering around DreamX. Checkpoint
+  existing owners; do not copy defaults into the catalog. Declaration order
+  is first-match detector order. Checkpoint
   manifests and explicit pipeline overrides still choose the executable
   pipeline; a definition must not silently pin that choice.
 - Keep `configs.pipelines.wan` as explicit compatibility aliases, including
diff --git a/fastvideo/models/wan/definition.py b/fastvideo/models/wan/definition.py
index d26b024e2..2fd68b8a9 100644
--- a/fastvideo/models/wan/definition.py
+++ b/fastvideo/models/wan/definition.py
@@ -34,146 +34,136 @@ class WanModelDefinition:
                 and not any(token in value for token in self.exclude))
 
 
-# The two groups preserve the registry's existing first-match ordering around
-# DreamX. Keeping this boundary avoids changing local/custom-checkpoint
-# resolution when both a Wan and DreamX detector match. Within each group,
-# declaration order is registration order.
-WAN_MODEL_DEFINITION_GROUPS = (
-    (
-        WanModelDefinition(
-            pipeline_config="WanT2V480PConfig",
-            preset="wan_t2v_1_3b",
-            sampling="unipc",
-            hf_model_paths=("Wan-AI/Wan2.1-T2V-1.3B-Diffusers", ),
-            workload_types=("t2v", ),
-            match_any=("wanpipeline", ),
-        ),
-        WanModelDefinition(
-            pipeline_config="WanT2V720PConfig",
-            preset="wan_t2v_14b",
-            sampling="unipc",
-            hf_model_paths=("Wan-AI/Wan2.1-T2V-14B-Diffusers", "FastVideo/Wan2.1-VSA-T2V-14B-720P-Diffusers"),
-            workload_types=("t2v", ),
-        ),
-        WanModelDefinition(
-            pipeline_config="WanI2V480PConfig",
-            preset="wan_i2v_14b_480p",
-            sampling="unipc",
-            hf_model_paths=("Wan-AI/Wan2.1-I2V-14B-480P-Diffusers", ),
-            workload_types=("i2v", ),
-            match_any=("wanimagetovideo", ),
-        ),
-        WanModelDefinition(
-            pipeline_config="WanI2V720PConfig",
-            preset="wan_i2v_14b_720p",
-            sampling="unipc",
-            hf_model_paths=("Wan-AI/Wan2.1-I2V-14B-720P-Diffusers", ),
-            workload_types=("i2v", ),
-        ),
-        WanModelDefinition(
-            pipeline_config="WanAnimate14BConfig",
-            preset="wan_animate_14b",
-            sampling="unipc",
-            hf_model_paths=("Wan-AI/Wan2.2-Animate-14B-Diffusers", ),
-            workload_types=("i2v", ),
-            match_any=("animate", ),
-        ),
-        WanModelDefinition(
-            pipeline_config="WanI2V480PConfig",
-            preset="wan_fun_1_3b_inp",
-            sampling="unipc",
-            hf_model_paths=("weizhou03/Wan2.1-Fun-1.3B-InP-Diffusers", ),
-            workload_types=("i2v", ),
-        ),
-        WanModelDefinition(
-            pipeline_config="WANV2VConfig",
-            preset="wan_fun_1_3b_control",
-            sampling="unipc",
-            hf_model_paths=("IRMChen/Wan2.1-Fun-1.3B-Control-Diffusers", ),
-            workload_types=(),
-        ),
-        WanModelDefinition(
-            pipeline_config="FastWan2_1_T2V_480P_Config",
-            preset="fast_wan_t2v_480p",
-            sampling="dmd",
-            hf_model_paths=("FastVideo/FastWan2.1-T2V-1.3B-Diffusers", "FastVideo/FastWan2.1-T2V-14B-480P-Diffusers"),
-            workload_types=("t2v", ),
-            match_any=("wandmdpipeline", ),
-        ),
-        WanModelDefinition(
-            pipeline_config="Wan2_2_TI2V_5B_Config",
-            preset="wan_2_2_ti2v_5b",
-            sampling="unipc",
-            hf_model_paths=("Wan-AI/Wan2.2-TI2V-5B-Diffusers", ),
-            workload_types=("t2v", "i2v"),
-        ),
+# Declaration order is registration order; model detectors are first-match.
+WAN_MODEL_DEFINITIONS = (
+    WanModelDefinition(
+        pipeline_config="WanT2V480PConfig",
+        preset="wan_t2v_1_3b",
+        sampling="unipc",
+        hf_model_paths=("Wan-AI/Wan2.1-T2V-1.3B-Diffusers", ),
+        workload_types=("t2v", ),
+        match_any=("wanpipeline", ),
     ),
-    (
-        WanModelDefinition(
-            pipeline_config="FastWan2_2_TI2V_5B_Config",
-            preset="fast_wan_2_2_ti2v_5b",
-            sampling="dmd",
-            hf_model_paths=("FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers",
-                            "FastVideo/FastWan2.2-TI2V-5B-Diffusers"),
-            workload_types=("t2v", "i2v"),
-        ),
-        WanModelDefinition(
-            pipeline_config="LucyEditDevConfig",
-            preset="lucy_edit_dev",
-            sampling="unipc",
-            hf_model_paths=("decart-ai/Lucy-Edit-Dev", "decart-ai/Lucy-Edit-1.1-Dev"),
-            workload_types=(),
-            match_any=("lucy-edit", ),
-        ),
-        WanModelDefinition(
-            pipeline_config="Wan2_2_T2V_A14B_Config",
-            preset="wan_2_2_t2v_a14b",
-            sampling="unipc",
-            hf_model_paths=("Wan-AI/Wan2.2-T2V-A14B-Diffusers", ),
-            workload_types=("t2v", ),
-        ),
-        WanModelDefinition(
-            pipeline_config="Wan2_2_I2V_A14B_Config",
-            preset="wan_2_2_i2v_a14b",
-            sampling="unipc",
-            hf_model_paths=("Wan-AI/Wan2.2-I2V-A14B-Diffusers", ),
-            workload_types=("i2v", ),
-        ),
-        WanModelDefinition(
-            pipeline_config="SelfForcingWanT2V480PConfig",
-            preset="sf_wan_t2v_1_3b",
-            sampling="causal_dmd",
-            hf_model_paths=("wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers", ),
-            workload_types=("t2v", ),
-            match_any=("wancausaldmdpipeline", ),
-        ),
-        WanModelDefinition(
-            pipeline_config="SelfForcingWan2_2_T2V480PConfig",
-            preset="sf_wan_2_2_t2v_a14b",
-            sampling="causal_dmd",
-            hf_model_paths=("rand0nmr/SFWan2.2-T2V-A14B-Diffusers", ),
-            workload_types=("t2v", ),
-            match_any=("sfwan2.2", "sfwan2_2"),
-            exclude=("i2v", ),
-        ),
-        WanModelDefinition(
-            pipeline_config="SelfForcingWan2_2_T2V480PConfig",
-            preset="sf_wan_2_2_i2v_a14b",
-            sampling="causal_dmd",
-            hf_model_paths=("FastVideo/SFWan2.2-I2V-A14B-Preview-Diffusers", ),
-            workload_types=("i2v", ),
-            match_any=("sfwan2.2", "sfwan2_2"),
-            match_all=("i2v", ),
-        ),
-        WanModelDefinition(
-            pipeline_config="WanS2V14BConfig",
-            preset="wan_s2v_14b",
-            sampling="unipc",
-            hf_model_paths=("FastVideo/Wan2.2-S2V-14B-Diffusers", ),
-            workload_types=(),
-            match_any=("s2v", ),
-        ),
+    WanModelDefinition(
+        pipeline_config="WanT2V720PConfig",
+        preset="wan_t2v_14b",
+        sampling="unipc",
+        hf_model_paths=("Wan-AI/Wan2.1-T2V-14B-Diffusers", "FastVideo/Wan2.1-VSA-T2V-14B-720P-Diffusers"),
+        workload_types=("t2v", ),
+    ),
+    WanModelDefinition(
+        pipeline_config="WanI2V480PConfig",
+        preset="wan_i2v_14b_480p",
+        sampling="unipc",
+        hf_model_paths=("Wan-AI/Wan2.1-I2V-14B-480P-Diffusers", ),
+        workload_types=("i2v", ),
+        match_any=("wanimagetovideo", ),
+    ),
+    WanModelDefinition(
+        pipeline_config="WanI2V720PConfig",
+        preset="wan_i2v_14b_720p",
+        sampling="unipc",
+        hf_model_paths=("Wan-AI/Wan2.1-I2V-14B-720P-Diffusers", ),
+        workload_types=("i2v", ),
+    ),
+    WanModelDefinition(
+        pipeline_config="WanAnimate14BConfig",
+        preset="wan_animate_14b",
+        sampling="unipc",
+        hf_model_paths=("Wan-AI/Wan2.2-Animate-14B-Diffusers", ),
+        workload_types=("i2v", ),
+        match_any=("animate", ),
+    ),
+    WanModelDefinition(
+        pipeline_config="WanI2V480PConfig",
+        preset="wan_fun_1_3b_inp",
+        sampling="unipc",
+        hf_model_paths=("weizhou03/Wan2.1-Fun-1.3B-InP-Diffusers", ),
+        workload_types=("i2v", ),
+    ),
+    WanModelDefinition(
+        pipeline_config="WANV2VConfig",
+        preset="wan_fun_1_3b_control",
+        sampling="unipc",
+        hf_model_paths=("IRMChen/Wan2.1-Fun-1.3B-Control-Diffusers", ),
+        workload_types=(),
+    ),
+    WanModelDefinition(
+        pipeline_config="FastWan2_1_T2V_480P_Config",
+        preset="fast_wan_t2v_480p",
+        sampling="dmd",
+        hf_model_paths=("FastVideo/FastWan2.1-T2V-1.3B-Diffusers", "FastVideo/FastWan2.1-T2V-14B-480P-Diffusers"),
+        workload_types=("t2v", ),
+        match_any=("wandmdpipeline", ),
+    ),
+    WanModelDefinition(
+        pipeline_config="Wan2_2_TI2V_5B_Config",
+        preset="wan_2_2_ti2v_5b",
+        sampling="unipc",
+        hf_model_paths=("Wan-AI/Wan2.2-TI2V-5B-Diffusers", ),
+        workload_types=("t2v", "i2v"),
+    ),
+    WanModelDefinition(
+        pipeline_config="FastWan2_2_TI2V_5B_Config",
+        preset="fast_wan_2_2_ti2v_5b",
+        sampling="dmd",
+        hf_model_paths=("FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers", "FastVideo/FastWan2.2-TI2V-5B-Diffusers"),
+        workload_types=("t2v", "i2v"),
+    ),
+    WanModelDefinition(
+        pipeline_config="LucyEditDevConfig",
+        preset="lucy_edit_dev",
+        sampling="unipc",
+        hf_model_paths=("decart-ai/Lucy-Edit-Dev", "decart-ai/Lucy-Edit-1.1-Dev"),
+        workload_types=(),
+        match_any=("lucy-edit", ),
+    ),
+    WanModelDefinition(
+        pipeline_config="Wan2_2_T2V_A14B_Config",
+        preset="wan_2_2_t2v_a14b",
+        sampling="unipc",
+        hf_model_paths=("Wan-AI/Wan2.2-T2V-A14B-Diffusers", ),
+        workload_types=("t2v", ),
+    ),
+    WanModelDefinition(
+        pipeline_config="Wan2_2_I2V_A14B_Config",
+        preset="wan_2_2_i2v_a14b",
+        sampling="unipc",
+        hf_model_paths=("Wan-AI/Wan2.2-I2V-A14B-Diffusers", ),
+        workload_types=("i2v", ),
+    ),
+    WanModelDefinition(
+        pipeline_config="SelfForcingWanT2V480PConfig",
+        preset="sf_wan_t2v_1_3b",
+        sampling="causal_dmd",
+        hf_model_paths=("wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers", ),
+        workload_types=("t2v", ),
+        match_any=("wancausaldmdpipeline", ),
+    ),
+    WanModelDefinition(
+        pipeline_config="SelfForcingWan2_2_T2V480PConfig",
+        preset="sf_wan_2_2_t2v_a14b",
+        sampling="causal_dmd",
+        hf_model_paths=("rand0nmr/SFWan2.2-T2V-A14B-Diffusers", ),
+        workload_types=("t2v", ),
+        match_any=("sfwan2.2", "sfwan2_2"),
+        exclude=("i2v", ),
+    ),
+    WanModelDefinition(
+        pipeline_config="SelfForcingWan2_2_T2V480PConfig",
+        preset="sf_wan_2_2_i2v_a14b",
+        sampling="causal_dmd",
+        hf_model_paths=("FastVideo/SFWan2.2-I2V-A14B-Preview-Diffusers", ),
+        workload_types=("i2v", ),
+        match_any=("sfwan2.2", "sfwan2_2"),
+        match_all=("i2v", ),
+    ),
+    WanModelDefinition(
+        pipeline_config="WanS2V14BConfig",
+        preset="wan_s2v_14b",
+        sampling="unipc",
+        hf_model_paths=("FastVideo/Wan2.2-S2V-14B-Diffusers", ),
+        workload_types=(),
+        match_any=("s2v", ),
     ),
 )
-
-WAN_MODEL_DEFINITIONS = tuple(definition for group in WAN_MODEL_DEFINITION_GROUPS for definition in group)
diff --git a/fastvideo/pipelines/AGENTS.md b/fastvideo/pipelines/AGENTS.md
index 991b47867..018452338 100644
--- a/fastvideo/pipelines/AGENTS.md
+++ b/fastvideo/pipelines/AGENTS.md
@@ -20,15 +20,13 @@ pipelines/
 │   ├── conditioning.py         #   CFG / negative prompt fan-out
 │   ├── denoising.py            #   Standard diffusion loop
 │   ├── sd35_conditioning.py    #   Per-model overrides (named by family)
-│   ├── longcat_*.py            #   LongCat I2V/V2V/refine variants
 │   ├── gen3c_stages.py         #   Gen3C-specific stages
-│   ├── gamecraft_denoising.py  #   GameCraft-specific
 │   └── matrixgame2_denoising.py #  Matrix-Game 2.0-specific
 ├── basic/                      # Per-model end-to-end pipelines
-│   ├── cosmos/, dreamx_world/, flux/, flux_2/, gamecraft/, gen3c/, glm_image/
-│   ├── hunyuan/, hunyuan15/, hyworld/, kandinsky5/, lingbot_video/, lingbotworld/
-│   ├── lingbotworld2/, longcat/, ltx2/, magi_human/, matrixgame2/, matrixgame3/
-│   ├── minimax_h3/, mmaudio/, sd35/, stable_audio/, turbodiffusion/, wan/, zimage/
+│   ├── cosmos/, flux/, flux_2/, gen3c/, hunyuan/, hunyuan15/, hyworld/
+│   ├── kandinsky5/, kandinsky6/, kandinsky6_sr/, lingbot_video/, lingbotworld/
+│   ├── lingbotworld2/, ltx2/, magi_human/, matrixgame2/, matrixgame3/
+│   ├── minimax_h3/, mmaudio/, sd35/, stable_audio/, wan/
 │   └── <model>/{<model>_pipeline.py, presets.py, __init__.py}
 ├── preprocess/                 # Data preprocessing pipelines (ltx2, wan, matrixgame2)
 └── training/                   # Training-time pipeline glue
@@ -63,7 +61,7 @@ Reuse `stages/text_encoding.py` if your model takes text → embeddings via a st
 - The model needs a **different ForwardBatch shape** (extra inputs, different output keys).
 - The denoising loop has structural differences (causal, refine-then-denoise, multi-stream).
 
-When forking, keep the file name model-prefixed (`longcat_*`, `gamecraft_*`) so the registry stays grep-able.
+When forking, keep the file name model-prefixed (`gen3c_*`, `matrixgame2_*`) so the registry stays grep-able.
 
 Wan's family-specific stages live in `basic/wan/stages/`; see
 `basic/wan/AGENTS.md`. Shared dense scheduling/CFG stays in `stages/denoising.py`.
diff --git a/fastvideo/pipelines/basic/dreamx_world/__init__.py b/fastvideo/pipelines/basic/dreamx_world/__init__.py
deleted file mode 100644
index 38c7f6aa2..000000000
--- a/fastvideo/pipelines/basic/dreamx_world/__init__.py
+++ /dev/null
@@ -1,28 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-from fastvideo.configs.pipelines.dreamx_world import (
-    DreamXWorld5BARPipelineConfig,
-    DreamXWorld5BCamPipelineConfig,
-    make_dreamx_world_5b_ar_dit_config,
-    make_dreamx_world_5b_cam_dit_config,
-    make_dreamx_world_5b_cam_text_encoder_config,
-    make_dreamx_world_5b_cam_vae_config,
-)
-from fastvideo.pipelines.basic.dreamx_world.dreamx_world_ar_pipeline import DreamXWorldARPipeline
-from fastvideo.pipelines.basic.dreamx_world.dreamx_world_pipeline import DreamXWorldPipeline
-from fastvideo.pipelines.basic.dreamx_world.stages import (
-    DREAMX_Y_CAMERA_KEY,
-    DreamXWorldCameraConditioningStage,
-)
-
-__all__ = [
-    "DREAMX_Y_CAMERA_KEY",
-    "DreamXWorld5BARPipelineConfig",
-    "DreamXWorld5BCamPipelineConfig",
-    "DreamXWorldCameraConditioningStage",
-    "DreamXWorldARPipeline",
-    "DreamXWorldPipeline",
-    "make_dreamx_world_5b_ar_dit_config",
-    "make_dreamx_world_5b_cam_dit_config",
-    "make_dreamx_world_5b_cam_text_encoder_config",
-    "make_dreamx_world_5b_cam_vae_config",
-]
diff --git a/fastvideo/pipelines/basic/dreamx_world/ar_denoising.py b/fastvideo/pipelines/basic/dreamx_world/ar_denoising.py
deleted file mode 100644
index 4282500ab..000000000
--- a/fastvideo/pipelines/basic/dreamx_world/ar_denoising.py
+++ /dev/null
@@ -1,222 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""DreamX-World autoregressive causal denoising stage."""
-
-from __future__ import annotations
-
-from typing import Any
-
-import torch
-from tqdm.auto import tqdm
-
-from fastvideo.fastvideo_args import FastVideoArgs
-from fastvideo.pipelines.basic.dreamx_world.stages import DREAMX_Y_CAMERA_KEY
-from fastvideo.pipelines.pipeline_batch_info import ForwardBatch
-from fastvideo.pipelines.stages.denoising import DenoisingStage
-
-
-class DreamXWorldARCausalDenoisingStage(DenoisingStage):
-    """Official DreamX AR-forcing denoising loop with KV cache."""
-
-    _AR_NOISE_SEED_OFFSET = 1_000_003
-
-    def __init__(self, transformer, scheduler, pipeline=None, vae=None) -> None:
-        super().__init__(transformer=transformer, scheduler=scheduler, pipeline=pipeline, vae=vae)
-        self.num_transformer_blocks = len(self.transformer.blocks)
-        self.num_frame_per_block = int(getattr(self.transformer, "num_frame_per_block", 3))
-        self.local_attn_size = int(getattr(self.transformer, "local_attn_size", 12))
-
-    def forward(self, batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> ForwardBatch:
-        assert batch.latents is not None, "latents must be prepared before DreamX AR denoising"
-        assert batch.prompt_embeds, "prompt embeds must be prepared before DreamX AR denoising"
-        latents = batch.latents
-        device = latents.device
-        target_dtype = torch.bfloat16
-        autocast_enabled = device.type == "cuda" and not fastvideo_args.disable_autocast
-
-        frame_seq_length = (latents.shape[-2] // self.transformer.patch_size[1]) * (latents.shape[-1] //
-                                                                                    self.transformer.patch_size[2])
-        timesteps = torch.tensor(
-            tuple(getattr(fastvideo_args.pipeline_config, "dmd_denoising_steps", (1000, 750, 500, 250))),
-            dtype=torch.long,
-        ).cpu()
-        if getattr(fastvideo_args.pipeline_config, "warp_denoising_step", True):
-            self.scheduler.set_timesteps(1000)
-            scheduler_timesteps = torch.cat((self.scheduler.timesteps.cpu(), torch.tensor([0], dtype=torch.float32)))
-            timesteps = scheduler_timesteps[1000 - timesteps]
-        timesteps = timesteps.to(device)
-
-        if latents.shape[2] % self.num_frame_per_block != 0:
-            raise ValueError("DreamX AR latent frames must be divisible by num_frame_per_block")
-
-        y_camera = batch.extra.get(DREAMX_Y_CAMERA_KEY, batch.extra.get("y_camera"))
-        if isinstance(y_camera, dict):
-            y_camera = {
-                k: v.to(device=device, dtype=target_dtype) if torch.is_tensor(v) else v
-                for k, v in y_camera.items()
-            }
-
-        if batch.image_latent is not None and batch.image_latent.shape[1] == latents.shape[1]:
-            latents[:, :, :batch.image_latent.shape[2]] = batch.image_latent.to(device=device, dtype=latents.dtype)
-
-        kv_cache = self._initialize_kv_cache(latents.shape[0], target_dtype, device, frame_seq_length)
-        crossattn_cache = self._initialize_crossattn_cache(latents.shape[0], target_dtype, device)
-        prompt = batch.prompt_embeds[0]
-        if torch.is_tensor(prompt):
-            prompt = prompt.to(device=device, dtype=target_dtype)
-            context = [sample for sample in prompt]
-        else:
-            context = prompt
-
-        num_blocks = latents.shape[2] // self.num_frame_per_block
-        start = 0
-        first_frame_mask = torch.ones_like(latents)
-        first_frame_mask[:, :, 0] = 0
-        base_generator = batch.generator[0] if isinstance(batch.generator, list) else batch.generator
-        noise_generator = self._make_noise_generator(base_generator, device)
-
-        with tqdm(total=num_blocks * len(timesteps), desc="DreamX AR denoising", leave=False) as progress:
-            for _ in range(num_blocks):
-                current_num_frames = self.num_frame_per_block
-                block_latents = latents[:, :, start:start + current_num_frames]
-                noisy_input = block_latents.clone()
-                mask_block = first_frame_mask[:, :, start:start + current_num_frames]
-                camera_block = self._slice_camera(y_camera, start, current_num_frames)
-
-                for idx, current_timestep in enumerate(timesteps):
-                    # Stop if interrupted
-                    if getattr(self, "interrupt", False):
-                        break
-                    timestep = torch.full(
-                        (latents.shape[0], current_num_frames * frame_seq_length),
-                        int(current_timestep.item()),
-                        device=device,
-                        dtype=torch.long,
-                    )
-                    if start == 0:
-                        timestep[:, :frame_seq_length] = 0
-                    with torch.autocast(device_type="cuda", dtype=target_dtype, enabled=autocast_enabled):
-                        denoised = self.transformer(
-                            hidden_states=block_latents.to(target_dtype),
-                            encoder_hidden_states=torch.stack(context).to(target_dtype),
-                            timestep=timestep,
-                            y_camera=camera_block,
-                            kv_cache=kv_cache,
-                            crossattn_cache=crossattn_cache,
-                            current_start=start * frame_seq_length,
-                        )
-                    denoised = denoised.to(latents.dtype)
-                    if idx < len(timesteps) - 1:
-                        next_timestep = torch.full((latents.shape[0], current_num_frames),
-                                                   int(timesteps[idx + 1].item()),
-                                                   device=device,
-                                                   dtype=torch.long)
-                        noise_kwargs = {"device": device, "dtype": denoised.dtype}
-                        if noise_generator is not None:
-                            noise_kwargs["generator"] = noise_generator
-                        noise = torch.randn(denoised.permute(0, 2, 1, 3, 4).shape, **noise_kwargs)
-                        block_btchw = self.scheduler.add_noise(
-                            denoised.permute(0, 2, 1, 3, 4).flatten(0, 1),
-                            noise.flatten(0, 1),
-                            next_timestep.flatten(),
-                        ).unflatten(0, (latents.shape[0], current_num_frames))
-                        block_latents = block_btchw.permute(0, 2, 1, 3, 4)
-                        block_latents = block_latents * mask_block + noisy_input * (1 - mask_block)
-                    else:
-                        block_latents = denoised * mask_block + noisy_input * (1 - mask_block)
-                    progress.update()
-
-                latents[:, :, start:start + current_num_frames] = block_latents
-                self._update_context_cache(block_latents, context, camera_block, kv_cache, crossattn_cache, start,
-                                           frame_seq_length, target_dtype, autocast_enabled,
-                                           float(getattr(fastvideo_args.pipeline_config, "context_noise", 0.1)))
-                start += current_num_frames
-
-        batch.latents = latents
-        return batch
-
-    def _make_noise_generator(self, generator: torch.Generator | None, device: torch.device) -> torch.Generator | None:
-        if generator is None:
-            return None
-        if getattr(generator, "device", None) == device:
-            return generator
-        seed = int(generator.initial_seed()) + self._AR_NOISE_SEED_OFFSET
-        return torch.Generator(device=device).manual_seed(seed)
-
-    @staticmethod
-    def _context_noise_timestep(context_noise: float) -> int:
-        if 0.0 < context_noise <= 1.0:
-            return int(context_noise * 1000)
-        return int(context_noise)
-
-    def _slice_camera(self, y_camera: Any, start: int, num_frames: int):
-        if not isinstance(y_camera, dict):
-            return y_camera
-        return {
-            "viewmats": y_camera["viewmats"][:, start:start + num_frames],
-            "K": y_camera["K"][:, start:start + num_frames],
-        }
-
-    def _initialize_kv_cache(self, batch_size: int, dtype: torch.dtype, device: torch.device,
-                             frame_seq_length: int) -> list[dict[str, Any]]:
-        size = self.local_attn_size * frame_seq_length if self.local_attn_size != -1 else 18480
-        heads = self.transformer.num_attention_heads
-        head_dim = self.transformer.attention_head_dim
-        cam_self_attn = next(
-            (getattr(block, "cam_self_attn", None)
-             for block in self.transformer.blocks if getattr(block, "cam_self_attn", None) is not None),
-            None,
-        )
-
-        caches = []
-        for _ in range(self.num_transformer_blocks):
-            cache = {
-                "k": torch.zeros(batch_size, size, heads, head_dim, dtype=dtype, device=device),
-                "v": torch.zeros(batch_size, size, heads, head_dim, dtype=dtype, device=device),
-                "global_end_index": torch.tensor([0], dtype=torch.long, device=device),
-                "local_end_index": torch.tensor([0], dtype=torch.long, device=device),
-            }
-            if cam_self_attn is not None:
-                cam_heads = int(cam_self_attn.num_heads)
-                cam_head_dim = int(cam_self_attn.head_dim)
-                cache.update({
-                    "prope_k":
-                    torch.zeros(batch_size, size, cam_heads, cam_head_dim, dtype=dtype, device=device),
-                    "prope_v":
-                    torch.zeros(batch_size, size, cam_heads, cam_head_dim, dtype=dtype, device=device),
-                    "prope_global_end_index":
-                    torch.tensor([0], dtype=torch.long, device=device),
-                    "prope_local_end_index":
-                    torch.tensor([0], dtype=torch.long, device=device),
-                })
-            caches.append(cache)
-        return caches
-
-    def _initialize_crossattn_cache(self, batch_size: int, dtype: torch.dtype, device: torch.device):
-        heads = self.transformer.num_attention_heads
-        head_dim = self.transformer.attention_head_dim
-        return [{
-            "k": torch.zeros(batch_size, 512, heads, head_dim, dtype=dtype, device=device),
-            "v": torch.zeros(batch_size, 512, heads, head_dim, dtype=dtype, device=device),
-            "is_init": False,
-        } for _ in range(self.num_transformer_blocks)]
-
-    def _update_context_cache(self, block_latents: torch.Tensor, context: Any, camera_block: Any,
-                              kv_cache: list[dict[str, Any]], crossattn_cache: list[dict[str, Any]], start: int,
-                              frame_seq_length: int, target_dtype: torch.dtype, autocast_enabled: bool,
-                              context_noise: float) -> None:
-        timestep = torch.full(
-            (block_latents.shape[0], block_latents.shape[2] * frame_seq_length),
-            self._context_noise_timestep(context_noise),
-            device=block_latents.device,
-            dtype=torch.long,
-        )
-        with torch.autocast(device_type="cuda", dtype=target_dtype, enabled=autocast_enabled):
-            self.transformer(
-                hidden_states=block_latents.to(target_dtype),
-                encoder_hidden_states=torch.stack(context).to(target_dtype),
-                timestep=timestep,
-                y_camera=camera_block,
-                kv_cache=kv_cache,
-                crossattn_cache=crossattn_cache,
-                current_start=start * frame_seq_length,
-            )
diff --git a/fastvideo/pipelines/basic/dreamx_world/camera_conditioning.py b/fastvideo/pipelines/basic/dreamx_world/camera_conditioning.py
deleted file mode 100644
index 13b87f154..000000000
--- a/fastvideo/pipelines/basic/dreamx_world/camera_conditioning.py
+++ /dev/null
@@ -1,228 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-from __future__ import annotations
-
-import math
-from dataclasses import dataclass
-
-import numpy as np
-import torch
-from scipy.interpolate import interp1d
-from scipy.spatial.transform import Rotation, Slerp
-
-_ACTION_TO_MOTION = {
-    "w": "forward",
-    "a": "left",
-    "d": "right",
-    "s": "backward",
-    "j": "left_rot",
-    "l": "right_rot",
-    "i": "up_rot",
-    "k": "down_rot",
-}
-_TRANSLATION_BASE_UNIT = 1.0
-_ROTATION_BASE_UNIT = 10.0
-_INTRINSIC_ROW = [0.8, 0.5, 0.5, 0.5]
-
-
-@dataclass
-class DreamXCamera:
-    fx: float
-    fy: float
-    cx: float
-    cy: float
-    w2c_mat: np.ndarray
-
-    @property
-    def c2w_mat(self) -> np.ndarray:
-        return np.linalg.inv(self.w2c_mat)
-
-    @classmethod
-    def from_pose_row(cls, row: list[float]) -> DreamXCamera:
-        w2c_mat = np.eye(4, dtype=np.float64)
-        w2c_mat[:3, :] = np.asarray(row[7:], dtype=np.float64).reshape(3, 4)
-        return cls(
-            fx=float(row[1]),
-            fy=float(row[2]),
-            cx=float(row[3]),
-            cy=float(row[4]),
-            w2c_mat=w2c_mat,
-        )
-
-
-def _translation_step(motion_type: str, current_pose: dict[str, np.ndarray], value: float, duration: int) -> np.ndarray:
-    if motion_type in ("forward", "backward"):
-        yaw = np.radians(current_pose["rotation"][1])
-        pitch = np.radians(current_pose["rotation"][0])
-        forward = np.array([-math.sin(yaw) * math.cos(pitch), math.sin(pitch), math.cos(yaw) * math.cos(pitch)])
-        direction = 1 if motion_type == "forward" else -1
-        return forward * value * direction / duration
-    if motion_type in ("left", "right"):
-        yaw = np.radians(current_pose["rotation"][1])
-        right = np.array([math.cos(yaw), 0.0, math.sin(yaw)])
-        direction = -1 if motion_type == "left" else 1
-        return right * value * direction / duration
-    return np.zeros(3)
-
-
-def _rotation_step(motion_type: str, value: float, duration: int) -> np.ndarray:
-    if not motion_type.endswith("rot"):
-        return np.zeros(3)
-    axis = motion_type.split("_")[0]
-    rotation = np.zeros(3)
-    if axis == "left":
-        rotation[1] = value
-    elif axis == "right":
-        rotation[1] = -value
-    elif axis == "up":
-        rotation[0] = -value
-    elif axis == "down":
-        rotation[0] = value
-    return rotation / duration
-
-
-def _euler_to_quaternion(angles: np.ndarray) -> list[float]:
-    pitch, yaw, roll = np.radians(angles)
-    cy = math.cos(yaw * 0.5)
-    sy = math.sin(yaw * 0.5)
-    cp = math.cos(pitch * 0.5)
-    sp = math.sin(pitch * 0.5)
-    cr = math.cos(roll * 0.5)
-    sr = math.sin(roll * 0.5)
-    return [
-        cy * cp * cr + sy * sp * sr,
-        cy * sp * cr + sy * cp * sr,
-        sy * cp * cr - cy * sp * sr,
-        cy * cp * sr - sy * sp * cr,
-    ]
-
-
-def _quaternion_to_rotation_matrix(quaternion: list[float]) -> np.ndarray:
-    qw, qx, qy, qz = quaternion
-    return np.array([
-        [1 - 2 * (qy**2 + qz**2), 2 * (qx * qy - qw * qz), 2 * (qx * qz + qw * qy)],
-        [2 * (qx * qy + qw * qz), 1 - 2 * (qx**2 + qz**2), 2 * (qy * qz - qw * qx)],
-        [2 * (qx * qz - qw * qy), 2 * (qy * qz + qw * qx), 1 - 2 * (qx**2 + qy**2)],
-    ])
-
-
-def _pose_rows_from_actions(action_seq: list[str], action_speed_list: list[float], duration: int) -> list[list[float]]:
-    if len(action_seq) != len(action_speed_list):
-        raise ValueError("action_seq and action_speed_list must have the same length")
-
-    positions: list[np.ndarray] = []
-    rotations: list[np.ndarray] = []
-    current_pose = {
-        "position": np.array([0.0, 0.0, 0.0]),
-        "rotation": np.array([0.0, 0.0, 0.0]),
-    }
-
-    for action_id, speed in zip(action_seq, action_speed_list, strict=True):
-        motion_types = [_ACTION_TO_MOTION[key] for key in list(action_id)]
-        translation_step = np.zeros(3)
-        rotation_step = np.zeros(3)
-        for motion_type in motion_types:
-            translation_step += _translation_step(motion_type, current_pose,
-                                                  float(speed) * _TRANSLATION_BASE_UNIT, duration)
-            rotation_step += _rotation_step(motion_type, float(speed) * _ROTATION_BASE_UNIT, duration)
-
-        segment_positions = []
-        segment_rotations = []
-        for index in range(1, duration + 1):
-            segment_positions.append(current_pose["position"] + translation_step * index)
-            segment_rotations.append(current_pose["rotation"] + rotation_step * index)
-        current_pose["position"] = segment_positions[-1].copy()
-        current_pose["rotation"] = segment_rotations[-1].copy()
-        positions.extend(segment_positions)
-        rotations.extend(segment_rotations)
-
-    rows: list[list[float]] = [[0.0] + _INTRINSIC_ROW + [0.0, 0.0] +
-                               [1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0]]
-    for index, (position, rotation) in enumerate(zip(positions, rotations, strict=False)):
-        rotation_matrix = _quaternion_to_rotation_matrix(_euler_to_quaternion(rotation))
-        translation = -rotation_matrix @ position
-        extrinsic = np.hstack([rotation_matrix, translation.reshape(3, 1)])
-        rows.append([float(index)] + _INTRINSIC_ROW + [0.0, 0.0] + extrinsic.flatten().tolist())
-    return rows
-
-
-def _interpolate_camera_poses(
-    cameras: list[DreamXCamera],
-    src_indices: np.ndarray,
-    tgt_indices: np.ndarray,
-) -> list[DreamXCamera]:
-    if len(cameras) <= 1:
-        return [cameras[0]] * len(tgt_indices) if cameras else []
-    src_rot_mat = np.array([camera.w2c_mat[:3, :3] for camera in cameras])
-    src_trans_vec = np.array([camera.w2c_mat[:3, 3] for camera in cameras])
-
-    dets = np.linalg.det(src_rot_mat)
-    flip_handedness = dets.size > 0 and np.median(dets) < 0.0
-    if flip_handedness:
-        flip_mat = np.diag([1.0, 1.0, -1.0]).astype(src_rot_mat.dtype)
-        src_rot_mat = src_rot_mat @ flip_mat
-
-    trans = interp1d(src_indices, src_trans_vec, axis=0, kind="linear", bounds_error=False,
-                     fill_value="extrapolate")(tgt_indices)
-    quats = Rotation.from_matrix(src_rot_mat).as_quat().copy()
-    for index in range(1, len(quats)):
-        if np.dot(quats[index], quats[index - 1]) < 0:
-            quats[index] = -quats[index]
-    rot = Slerp(src_indices, Rotation.from_quat(quats))(tgt_indices).as_matrix()
-    if flip_handedness:
-        rot = rot @ flip_mat
-
-    ref = cameras[0]
-    result = []
-    for index in range(len(tgt_indices)):
-        w2c_mat = np.eye(4, dtype=np.float64)
-        w2c_mat[:3, :] = np.hstack([rot[index], trans[index].reshape(3, 1)])
-        result.append(DreamXCamera(ref.fx, ref.fy, ref.cx, ref.cy, w2c_mat))
-    return result
-
-
-def _relative_c2w_poses(cameras: list[DreamXCamera]) -> np.ndarray:
-    abs_w2cs = [camera.w2c_mat for camera in cameras]
-    abs_c2ws = [camera.c2w_mat for camera in cameras]
-    target_cam_c2w = np.eye(4, dtype=np.float64)
-    abs2rel = target_cam_c2w @ abs_w2cs[0]
-    poses = [target_cam_c2w] + [abs2rel @ c2w for c2w in abs_c2ws[1:]]
-    return np.asarray(poses, dtype=np.float32)
-
-
-def _invert_se3(transforms: torch.Tensor) -> torch.Tensor:
-    rotation_inv = transforms[..., :3, :3].transpose(-1, -2)
-    output = torch.zeros_like(transforms)
-    output[..., :3, :3] = rotation_inv
-    output[..., :3, 3] = -torch.einsum("...ij,...j->...i", rotation_inv, transforms[..., :3, 3])
-    output[..., 3, 3] = 1.0
-    return output
-
-
-def build_dreamx_camera_condition(
-    action_seq: list[str],
-    action_speed_list: list[float],
-    *,
-    num_frames: int,
-    height: int,
-    width: int,
-    dtype: torch.dtype = torch.float32,
-    device: torch.device | str = "cpu",
-) -> dict[str, torch.Tensor]:
-    del height, width  # DreamX-World-5B-Cam uses fixed normalized intrinsics.
-    duration = math.ceil(num_frames / len(action_seq))
-    rows = _pose_rows_from_actions(action_seq, action_speed_list, duration)[:num_frames]
-    cameras = [DreamXCamera.from_pose_row(row) for row in rows]
-
-    latent_frame_count = 1 + (len(cameras) - 1) // 4
-    src_indices = np.arange(len(cameras), dtype=np.float64)
-    tgt_indices = np.linspace(0, len(cameras) - 1, latent_frame_count)
-    cameras = _interpolate_camera_poses(cameras, src_indices, tgt_indices)
-
-    c2ws = torch.as_tensor(_relative_c2w_poses(cameras), dtype=dtype, device=device)
-    viewmats = _invert_se3(c2ws)
-
-    intrinsics = torch.zeros((latent_frame_count, 3, 3), dtype=dtype, device=device)
-    intrinsics[:, 0, 0] = 969.6969696969696 / (960.0 * 2)
-    intrinsics[:, 1, 1] = 969.6969696969696 / (540.0 * 2)
-    intrinsics[:, 2, 2] = 1.0
-    return {"viewmats": viewmats, "K": intrinsics}
diff --git a/fastvideo/pipelines/basic/dreamx_world/config.py b/fastvideo/pipelines/basic/dreamx_world/config.py
deleted file mode 100644
index 760f5ed60..000000000
--- a/fastvideo/pipelines/basic/dreamx_world/config.py
+++ /dev/null
@@ -1,20 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""Compatibility exports for DreamX-World pipeline configs."""
-
-from fastvideo.configs.pipelines.dreamx_world import (
-    DreamXWorld5BARPipelineConfig,
-    DreamXWorld5BCamPipelineConfig,
-    make_dreamx_world_5b_ar_dit_config,
-    make_dreamx_world_5b_cam_dit_config,
-    make_dreamx_world_5b_cam_text_encoder_config,
-    make_dreamx_world_5b_cam_vae_config,
-)
-
-__all__ = [
-    "DreamXWorld5BARPipelineConfig",
-    "DreamXWorld5BCamPipelineConfig",
-    "make_dreamx_world_5b_ar_dit_config",
-    "make_dreamx_world_5b_cam_dit_config",
-    "make_dreamx_world_5b_cam_text_encoder_config",
-    "make_dreamx_world_5b_cam_vae_config",
-]
diff --git a/fastvideo/pipelines/basic/dreamx_world/dreamx_world_ar_pipeline.py b/fastvideo/pipelines/basic/dreamx_world/dreamx_world_ar_pipeline.py
deleted file mode 100644
index 8cee19f2e..000000000
--- a/fastvideo/pipelines/basic/dreamx_world/dreamx_world_ar_pipeline.py
+++ /dev/null
@@ -1,67 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""DreamX-World-5B autoregressive pipeline entrypoint."""
-
-from fastvideo.api.sampling_param import SamplingParam
-from fastvideo.configs.pipelines.dreamx_world import DreamXWorld5BARPipelineConfig
-from fastvideo.fastvideo_args import FastVideoArgs
-from fastvideo.logger import init_logger
-from fastvideo.models.schedulers.scheduling_flow_match_euler_discrete import FlowMatchEulerDiscreteScheduler
-from fastvideo.pipelines import ComposedPipelineBase, LoRAPipeline
-from fastvideo.pipelines.basic.dreamx_world.ar_denoising import DreamXWorldARCausalDenoisingStage
-from fastvideo.pipelines.basic.dreamx_world.stages import (
-    DreamXWorldCameraConditioningStage,
-    DreamXWorldImageVAEEncodingStage,
-)
-from fastvideo.pipelines.stages import (
-    ConditioningStage,
-    DecodingStage,
-    InputValidationStage,
-    LatentPreparationStage,
-    TextEncodingStage,
-    TimestepPreparationStage,
-)
-
-logger = init_logger(__name__)
-
-
-class DreamXWorldARPipeline(LoRAPipeline, ComposedPipelineBase):
-    """DreamX-World-5B autoregressive causal camera pipeline."""
-
-    _required_config_modules = ["text_encoder", "tokenizer", "vae", "transformer", "scheduler"]
-    pipeline_config_cls = DreamXWorld5BARPipelineConfig
-    sampling_params_cls = SamplingParam
-
-    def initialize_pipeline(self, fastvideo_args: FastVideoArgs):
-        self.modules["scheduler"] = FlowMatchEulerDiscreteScheduler(shift=fastvideo_args.pipeline_config.flow_shift)
-        self.modules["scheduler"].set_timesteps(1000)
-
-    def create_pipeline_stages(self, fastvideo_args: FastVideoArgs) -> None:
-        self.add_stage(stage_name="input_validation_stage", stage=InputValidationStage())
-        self.add_stage(stage_name="prompt_encoding_stage",
-                       stage=TextEncodingStage(
-                           text_encoders=[self.get_module("text_encoder")],
-                           tokenizers=[self.get_module("tokenizer")],
-                       ))
-        self.add_stage(stage_name="conditioning_stage", stage=ConditioningStage())
-        self.add_stage(stage_name="timestep_preparation_stage",
-                       stage=TimestepPreparationStage(scheduler=self.get_module("scheduler")))
-        self.add_stage(stage_name="latent_preparation_stage",
-                       stage=LatentPreparationStage(
-                           scheduler=self.get_module("scheduler"),
-                           transformer=self.get_module("transformer", None),
-                       ))
-        self.add_stage(stage_name="image_latent_preparation_stage",
-                       stage=DreamXWorldImageVAEEncodingStage(vae=self.get_module("vae")))
-        self.add_stage(stage_name="dreamx_camera_conditioning_stage", stage=DreamXWorldCameraConditioningStage())
-        self.add_stage(stage_name="denoising_stage",
-                       stage=DreamXWorldARCausalDenoisingStage(
-                           transformer=self.get_module("transformer"),
-                           scheduler=self.get_module("scheduler"),
-                           vae=self.get_module("vae"),
-                           pipeline=self,
-                       ))
-        self.add_stage(stage_name="decoding_stage", stage=DecodingStage(vae=self.get_module("vae"), pipeline=self))
-        logger.info("DreamXWorldARPipeline initialized with autoregressive causal denoising")
-
-
-EntryClass = DreamXWorldARPipeline
diff --git a/fastvideo/pipelines/basic/dreamx_world/dreamx_world_pipeline.py b/fastvideo/pipelines/basic/dreamx_world/dreamx_world_pipeline.py
deleted file mode 100644
index e4bf62e84..000000000
--- a/fastvideo/pipelines/basic/dreamx_world/dreamx_world_pipeline.py
+++ /dev/null
@@ -1,81 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""DreamX-World video pipeline entrypoint."""
-
-from fastvideo.api.sampling_param import SamplingParam
-from fastvideo.pipelines.basic.wan.stages.conditioning import WanFirstFrameEncodingStage
-from fastvideo.pipelines.basic.wan.stages.denoising import WanDenoisingStage
-from fastvideo.fastvideo_args import FastVideoArgs
-from fastvideo.logger import init_logger
-from fastvideo.models.schedulers.scheduling_flow_match_euler_discrete import FlowMatchEulerDiscreteScheduler
-from fastvideo.pipelines import ComposedPipelineBase, LoRAPipeline
-from fastvideo.configs.pipelines.dreamx_world import DreamXWorld5BCamPipelineConfig
-from fastvideo.pipelines.basic.dreamx_world.stages import DreamXWorldCameraConditioningStage
-from fastvideo.pipelines.stages import (
-    ConditioningStage,
-    DecodingStage,
-    InputValidationStage,
-    LatentPreparationStage,
-    TextEncodingStage,
-    TimestepPreparationStage,
-)
-
-logger = init_logger(__name__)
-
-
-class DreamXWorldPipeline(LoRAPipeline, ComposedPipelineBase):
-    """DreamX-World-5B-Cam pipeline with native FastVideo camera conditioning."""
-
-    _required_config_modules = ["text_encoder", "tokenizer", "vae", "transformer", "scheduler"]
-    pipeline_config_cls = DreamXWorld5BCamPipelineConfig
-    sampling_params_cls = SamplingParam
-
-    def initialize_pipeline(self, fastvideo_args: FastVideoArgs):
-        self.modules["scheduler"] = FlowMatchEulerDiscreteScheduler(shift=fastvideo_args.pipeline_config.flow_shift)
-
-    def create_pipeline_stages(self, fastvideo_args: FastVideoArgs) -> None:
-        self.add_stage(stage_name="input_validation_stage", stage=InputValidationStage())
-
-        self.add_stage(
-            stage_name="prompt_encoding_stage",
-            stage=TextEncodingStage(
-                text_encoders=[self.get_module("text_encoder")],
-                tokenizers=[self.get_module("tokenizer")],
-            ),
-        )
-
-        self.add_stage(stage_name="conditioning_stage", stage=ConditioningStage())
-
-        self.add_stage(
-            stage_name="timestep_preparation_stage",
-            stage=TimestepPreparationStage(scheduler=self.get_module("scheduler")),
-        )
-
-        self.add_stage(
-            stage_name="latent_preparation_stage",
-            stage=LatentPreparationStage(
-                scheduler=self.get_module("scheduler"),
-                transformer=self.get_module("transformer", None),
-            ),
-        )
-
-        self.add_stage(stage_name="dreamx_camera_conditioning_stage", stage=DreamXWorldCameraConditioningStage())
-
-        self.add_stage(stage_name="first_frame_encoding_stage",
-                       stage=WanFirstFrameEncodingStage(vae=self.get_module("vae")))
-
-        self.add_stage(
-            stage_name="denoising_stage",
-            stage=WanDenoisingStage(
-                transformer=self.get_module("transformer"),
-                transformer_2=self.get_module("transformer_2", None),
-                scheduler=self.get_module("scheduler"),
-                pipeline=self,
-            ),
-        )
-
-        self.add_stage(stage_name="decoding_stage", stage=DecodingStage(vae=self.get_module("vae"), pipeline=self))
-
-        logger.info("DreamXWorldPipeline initialized with native camera conditioning")
-
-
-EntryClass = DreamXWorldPipeline
diff --git a/fastvideo/pipelines/basic/dreamx_world/presets.py b/fastvideo/pipelines/basic/dreamx_world/presets.py
deleted file mode 100644
index 2d9d6e407..000000000
--- a/fastvideo/pipelines/basic/dreamx_world/presets.py
+++ /dev/null
@@ -1,58 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""DreamX-World model family pipeline presets."""
-
-from fastvideo.api.presets import InferencePreset, PresetStageSpec
-
-_NEGATIVE_PROMPT_CN = ("色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，"
-                       "静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，"
-                       "多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，"
-                       "形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，"
-                       "背景人很多，倒着走")
-
-_DENOISE_STAGE = PresetStageSpec(
-    name="denoise",
-    kind="denoising",
-    description="DreamX-World camera-conditioned denoising pass",
-    allowed_overrides=frozenset({
-        "num_inference_steps",
-        "guidance_scale",
-    }),
-)
-
-DREAMX_WORLD_5B_CAM = InferencePreset(
-    name="dreamx_world_5b_cam",
-    version=1,
-    model_family="dreamx_world",
-    description="DreamX-World 5B camera-control video generation",
-    workload_type="i2v",
-    stage_schemas=(_DENOISE_STAGE, ),
-    defaults={
-        "height": 480,
-        "width": 832,
-        "num_frames": 161,
-        "fps": 16,
-        "guidance_scale": 5.0,
-        "num_inference_steps": 30,
-        "negative_prompt": _NEGATIVE_PROMPT_CN,
-    },
-)
-
-DREAMX_WORLD_5B_AR = InferencePreset(
-    name="dreamx_world_5b_ar",
-    version=1,
-    model_family="dreamx_world",
-    description="DreamX-World 5B autoregressive camera-control generation",
-    workload_type="i2v",
-    stage_schemas=(_DENOISE_STAGE, ),
-    defaults={
-        "height": 704,
-        "width": 1280,
-        "num_frames": 1005,
-        "fps": 16,
-        "guidance_scale": 1.0,
-        "num_inference_steps": 4,
-        "negative_prompt": _NEGATIVE_PROMPT_CN,
-    },
-)
-
-ALL_PRESETS = (DREAMX_WORLD_5B_CAM, DREAMX_WORLD_5B_AR)
diff --git a/fastvideo/pipelines/basic/dreamx_world/stages.py b/fastvideo/pipelines/basic/dreamx_world/stages.py
deleted file mode 100644
index 10e0b6741..000000000
--- a/fastvideo/pipelines/basic/dreamx_world/stages.py
+++ /dev/null
@@ -1,156 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""DreamX-World pipeline stages."""
-
-from __future__ import annotations
-
-import torch
-
-from fastvideo.fastvideo_args import FastVideoArgs
-from fastvideo.logger import init_logger
-from fastvideo.pipelines.pipeline_batch_info import ForwardBatch
-from fastvideo.pipelines.stages.base import PipelineStage
-from fastvideo.pipelines.stages.validators import VerificationResult
-
-from fastvideo.pipelines.basic.dreamx_world.camera_conditioning import (
-    build_dreamx_camera_condition, )
-
-DREAMX_Y_CAMERA_KEY = "dreamx_y_camera"
-
-logger = init_logger(__name__)
-
-
-class DreamXWorldCameraConditioningStage(PipelineStage):
-    """Build PRoPE camera conditioning for DreamX-World-5B-Cam."""
-
-    def forward(
-        self,
-        batch: ForwardBatch,
-        fastvideo_args: FastVideoArgs,
-    ) -> ForwardBatch:
-        del fastvideo_args
-        if DREAMX_Y_CAMERA_KEY in batch.extra:
-            return batch
-
-        action_seq = batch.extra.get("dreamx_action_seq", batch.action_list)
-        action_speed_list = batch.extra.get("dreamx_action_speed_list", batch.action_speed_list)
-        if action_seq is None:
-            action_seq = ["w"]
-        if action_speed_list is None:
-            action_speed_list = [4]
-
-        if isinstance(action_seq, str):
-            action_seq = [action_seq]
-        if isinstance(action_speed_list, int | float):
-            action_speed_list = [action_speed_list]
-        if len(action_speed_list) == 1 and len(action_seq) > 1:
-            action_speed_list = list(action_speed_list) * len(action_seq)
-        action_speed_list = [float(speed) for speed in action_speed_list]
-
-        height = int(batch.height) if batch.height is not None else 704
-        width = int(batch.width) if batch.width is not None else 1280
-        num_frames = int(batch.num_frames)
-        dtype = batch.latents.dtype if torch.is_tensor(batch.latents) else torch.float32
-        device = batch.latents.device if torch.is_tensor(batch.latents) else "cpu"
-
-        y_camera = build_dreamx_camera_condition(
-            list(action_seq),
-            action_speed_list,
-            num_frames=num_frames,
-            height=height,
-            width=width,
-            dtype=dtype,
-            device=device,
-        )
-        batch.extra[DREAMX_Y_CAMERA_KEY] = {key: value.unsqueeze(0) for key, value in y_camera.items()}
-        return batch
-
-    def verify_output(
-        self,
-        batch: ForwardBatch,
-        fastvideo_args: FastVideoArgs,
-    ) -> VerificationResult:
-        del fastvideo_args
-        result = VerificationResult()
-        y_camera = batch.extra.get(DREAMX_Y_CAMERA_KEY)
-        result.add_check("dreamx_y_camera", y_camera, lambda value: isinstance(value, dict))
-        if isinstance(y_camera, dict):
-            result.add_check("dreamx_y_camera.viewmats", y_camera.get("viewmats"), torch.is_tensor)
-            result.add_check("dreamx_y_camera.K", y_camera.get("K"), torch.is_tensor)
-        return result
-
-
-class DreamXWorldImageVAEEncodingStage(PipelineStage):
-    """Encode the conditioning image into the first-frame latent.
-
-    Official AR-forcing flow (AMAP-ML/DreamX-World inference_ar_forcing.py):
-    the input image is resized, normalized to [-1, 1], VAE-encoded
-    deterministically, and written into frame 0 of the noise — the causal
-    denoiser then treats frame 0 as clean context. This stage produces
-    ``batch.image_latent`` ([B, C, 1, H_lat, W_lat]); the injection into
-    the latents happens in DreamXWorldARCausalDenoisingStage.
-    """
-
-    def __init__(self, vae) -> None:
-        super().__init__()
-        self.vae = vae
-
-    def forward(
-        self,
-        batch: ForwardBatch,
-        fastvideo_args: FastVideoArgs,
-    ) -> ForwardBatch:
-        if batch.pil_image is None:
-            # No conditioning image: the causal denoiser falls back to
-            # running from pure noise (frame 0 uninitialized). Warn loudly —
-            # this pipeline is registered I2V and the official flow always
-            # forces from a frame.
-            logger.warning("DreamXWorldARPipeline called without an input image; "
-                           "first-frame context will be noise (T2V-style). Pass an "
-                           "image for the official AR-forcing behavior.")
-            return batch
-
-        from fastvideo.platforms import get_local_torch_device
-        from fastvideo.utils import PRECISION_TO_TYPE
-
-        device = get_local_torch_device()
-        image = batch.pil_image
-        if not isinstance(image, torch.Tensor):
-            import numpy as np
-            import PIL.Image
-            assert isinstance(image, PIL.Image.Image)
-            width = batch.width if isinstance(batch.width, int) else batch.width[0]
-            height = batch.height if isinstance(batch.height, int) else batch.height[0]
-            image = image.convert("RGB").resize((width, height), PIL.Image.Resampling.LANCZOS)
-            arr = torch.from_numpy(np.asarray(image)).float().permute(2, 0, 1) / 255.0
-            image = (arr - 0.5) / 0.5  # official Normalize([0.5], [0.5])
-            image = image.unsqueeze(0)  # [1, C, H, W]
-        if image.dim() == 4:
-            image = image.unsqueeze(2)  # [B, C, 1, H, W]
-        elif image.dim() == 5:
-            image = image[:, :, :1]
-        image = image.to(device=device, dtype=torch.float32)
-
-        vae_dtype = PRECISION_TO_TYPE[fastvideo_args.pipeline_config.vae_precision]
-        vae_autocast_enabled = (vae_dtype != torch.float32) and not fastvideo_args.disable_autocast
-        self.vae = self.vae.to(device)
-        with torch.autocast(device_type="cuda", dtype=vae_dtype, enabled=vae_autocast_enabled):
-            if not vae_autocast_enabled:
-                image = image.to(vae_dtype)
-            encoder_output = self.vae.encode(image)
-
-        # Official encode_to_latent is deterministic ((mean - mu) / sigma per
-        # channel); the posterior mean + shift/scale is the FastVideo
-        # equivalent of that normalization.
-        latent = encoder_output.mean
-        if getattr(self.vae, "shift_factor", None) is not None:
-            shift = self.vae.shift_factor
-            latent = latent - (shift.to(latent.device, latent.dtype) if isinstance(shift, torch.Tensor) else shift)
-        scale = self.vae.scaling_factor
-        latent = latent * (scale.to(latent.device, latent.dtype) if isinstance(scale, torch.Tensor) else scale)
-
-        batch.image_latent = latent
-        return batch
-
-    def verify_input(self, batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> VerificationResult:
-        result = VerificationResult()
-        return result
diff --git a/fastvideo/pipelines/basic/flux_2/flux_2_latent_preparation.py b/fastvideo/pipelines/basic/flux_2/flux_2_latent_preparation.py
index c30dd561c..7aa57de5a 100644
--- a/fastvideo/pipelines/basic/flux_2/flux_2_latent_preparation.py
+++ b/fastvideo/pipelines/basic/flux_2/flux_2_latent_preparation.py
@@ -120,8 +120,7 @@ class Flux2LatentPreparationStage(LatentPreparationStage):
                 latents = latents * self.scheduler.init_noise_sigma
         else:
             latents = latents.to(device)
-            is_longcat_refine = (batch.refine_from is not None or batch.stage1_video is not None)
-            if (not is_longcat_refine) and hasattr(self.scheduler, "init_noise_sigma"):
+            if hasattr(self.scheduler, "init_noise_sigma"):
                 latents = latents * self.scheduler.init_noise_sigma
 
         batch.latents = latents
diff --git a/fastvideo/pipelines/basic/gamecraft/__init__.py b/fastvideo/pipelines/basic/gamecraft/__init__.py
deleted file mode 100644
index e69de29bb..000000000
diff --git a/fastvideo/pipelines/basic/gamecraft/gamecraft_pipeline.py b/fastvideo/pipelines/basic/gamecraft/gamecraft_pipeline.py
deleted file mode 100644
index 00a5a8192..000000000
--- a/fastvideo/pipelines/basic/gamecraft/gamecraft_pipeline.py
+++ /dev/null
@@ -1,100 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-HunyuanGameCraft video diffusion pipeline implementation.
-
-This module implements the HunyuanGameCraft pipeline for camera/action-conditioned
-video generation with the modular pipeline architecture.
-"""
-
-from fastvideo.fastvideo_args import FastVideoArgs
-from fastvideo.logger import init_logger
-from fastvideo.pipelines.composed_pipeline_base import ComposedPipelineBase
-from fastvideo.pipelines.stages import (
-    ConditioningStage,
-    DecodingStage,
-    InputValidationStage,
-    LatentPreparationStage,
-    TextEncodingStage,
-    TimestepPreparationStage,
-)
-from fastvideo.pipelines.stages.gamecraft_denoising import GameCraftDenoisingStage
-
-logger = init_logger(__name__)
-
-
-class HunyuanGameCraftPipeline(ComposedPipelineBase):
-    """
-    Pipeline for HunyuanGameCraft video generation.
-    
-    This pipeline supports:
-    - Text-to-video generation with camera/action conditioning
-    - Autoregressive generation with history frames
-    - 33-channel input (16 latent + 16 gt_latent + 1 mask)
-    - CameraNet for encoding Plücker coordinates
-    """
-
-    _required_config_modules = [
-        "text_encoder",
-        "text_encoder_2",
-        "tokenizer",
-        "tokenizer_2",
-        "vae",
-        "transformer",
-        "scheduler",
-    ]
-
-    def create_pipeline_stages(self, fastvideo_args: FastVideoArgs):
-        """Set up pipeline stages with proper dependency injection."""
-
-        self.add_stage(
-            stage_name="input_validation_stage",
-            stage=InputValidationStage(),
-        )
-
-        self.add_stage(
-            stage_name="prompt_encoding_stage_primary",
-            stage=TextEncodingStage(
-                text_encoders=[
-                    self.get_module("text_encoder"),
-                    self.get_module("text_encoder_2"),
-                ],
-                tokenizers=[
-                    self.get_module("tokenizer"),
-                    self.get_module("tokenizer_2"),
-                ],
-            ),
-        )
-
-        self.add_stage(
-            stage_name="conditioning_stage",
-            stage=ConditioningStage(),
-        )
-
-        self.add_stage(
-            stage_name="timestep_preparation_stage",
-            stage=TimestepPreparationStage(scheduler=self.get_module("scheduler")),
-        )
-
-        self.add_stage(
-            stage_name="latent_preparation_stage",
-            stage=LatentPreparationStage(
-                scheduler=self.get_module("scheduler"),
-                transformer=self.get_module("transformer"),
-            ),
-        )
-
-        self.add_stage(
-            stage_name="denoising_stage",
-            stage=GameCraftDenoisingStage(
-                transformer=self.get_module("transformer"),
-                scheduler=self.get_module("scheduler"),
-            ),
-        )
-
-        self.add_stage(
-            stage_name="decoding_stage",
-            stage=DecodingStage(vae=self.get_module("vae")),
-        )
-
-
-EntryClass = HunyuanGameCraftPipeline
diff --git a/fastvideo/pipelines/basic/gamecraft/presets.py b/fastvideo/pipelines/basic/gamecraft/presets.py
deleted file mode 100644
index 1d2261ac0..000000000
--- a/fastvideo/pipelines/basic/gamecraft/presets.py
+++ /dev/null
@@ -1,33 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""HunyuanGameCraft model family pipeline presets."""
-from fastvideo.api.presets import InferencePreset, PresetStageSpec
-
-_DENOISE_STAGE = PresetStageSpec(
-    name="denoise",
-    kind="denoising",
-    description="Action-controlled denoising pass",
-    allowed_overrides=frozenset({
-        "num_inference_steps",
-        "guidance_scale",
-    }),
-)
-
-GAMECRAFT_I2V = InferencePreset(
-    name="gamecraft_i2v",
-    version=1,
-    model_family="gamecraft",
-    description="HunyuanGameCraft I2V at 704x1280",
-    workload_type="i2v",
-    stage_schemas=(_DENOISE_STAGE, ),
-    defaults={
-        "height": 704,
-        "width": 1280,
-        "num_frames": 33,
-        "fps": 24,
-        "guidance_scale": 6.0,
-        "num_inference_steps": 50,
-        "negative_prompt": "",
-    },
-)
-
-ALL_PRESETS = (GAMECRAFT_I2V, )
diff --git a/fastvideo/pipelines/basic/glm_image/__init__.py b/fastvideo/pipelines/basic/glm_image/__init__.py
deleted file mode 100644
index f85bca3cc..000000000
--- a/fastvideo/pipelines/basic/glm_image/__init__.py
+++ /dev/null
@@ -1,7 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""GLM-Image pipeline package."""
-
-from fastvideo.pipelines.basic.glm_image.glm_image_pipeline import (
-    GlmImagePipeline, )
-
-__all__ = ["GlmImagePipeline"]
diff --git a/fastvideo/pipelines/basic/glm_image/glm_image_pipeline.py b/fastvideo/pipelines/basic/glm_image/glm_image_pipeline.py
deleted file mode 100644
index d649414fe..000000000
--- a/fastvideo/pipelines/basic/glm_image/glm_image_pipeline.py
+++ /dev/null
@@ -1,82 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-from fastvideo.fastvideo_args import FastVideoArgs
-from fastvideo.logger import init_logger
-from fastvideo.models.schedulers.scheduling_flow_match_euler_discrete import (
-    FlowMatchEulerDiscreteScheduler, )
-from fastvideo.pipelines import ComposedPipelineBase, LoRAPipeline
-from fastvideo.pipelines.basic.glm_image.stages import (
-    GlmImageBeforeDenoisingStage,
-    GlmImageConditionEncodingStage,
-    GlmImageDecodingStage,
-    GlmImageDenoisingStage,
-)
-from fastvideo.pipelines.stages import InputValidationStage
-
-logger = init_logger(__name__)
-
-
-class GlmImagePipeline(LoRAPipeline, ComposedPipelineBase):
-    pipeline_name = "GlmImagePipeline"
-
-    _required_config_modules = [
-        "text_encoder",
-        "tokenizer",
-        "vae",
-        "transformer",
-        "scheduler",
-        "vision_language_encoder",
-        "processor",
-    ]
-
-    _optional_config_modules: list[str] = []
-
-    def initialize_pipeline(self, fastvideo_args: FastVideoArgs) -> None:
-        self.modules["scheduler"] = FlowMatchEulerDiscreteScheduler(shift=1.0)
-
-    def create_pipeline_stages(self, fastvideo_args: FastVideoArgs) -> None:
-        self.add_stage(
-            stage_name="input_validation_stage",
-            stage=InputValidationStage(),
-        )
-
-        self.add_stage(
-            stage_name="glm_image_before_denoising_stage",
-            stage=GlmImageBeforeDenoisingStage(
-                vae=self.get_module("vae"),
-                text_encoder=self.get_module("text_encoder"),
-                tokenizer=self.get_module("tokenizer"),
-                processor=self.get_module("processor"),
-                transformer=self.get_module("transformer"),
-                scheduler=self.get_module("scheduler"),
-                vision_language_encoder=self.get_module("vision_language_encoder"),
-            ),
-        )
-
-        self.add_stage(
-            stage_name="glm_image_condition_encoding_stage",
-            stage=GlmImageConditionEncodingStage(
-                vae=self.get_module("vae"),
-                transformer=self.get_module("transformer"),
-            ),
-        )
-
-        self.add_stage(
-            stage_name="denoising_stage",
-            stage=GlmImageDenoisingStage(
-                transformer=self.get_module("transformer"),
-                scheduler=self.get_module("scheduler"),
-                vae=self.get_module("vae"),
-                pipeline=self,
-            ),
-        )
-
-        self.add_stage(
-            stage_name="decoding_stage",
-            stage=GlmImageDecodingStage(
-                vae=self.get_module("vae"),
-                pipeline=self,
-            ),
-        )
-
-
-EntryClass = GlmImagePipeline
diff --git a/fastvideo/pipelines/basic/glm_image/stages/__init__.py b/fastvideo/pipelines/basic/glm_image/stages/__init__.py
deleted file mode 100644
index c9f13124d..000000000
--- a/fastvideo/pipelines/basic/glm_image/stages/__init__.py
+++ /dev/null
@@ -1,14 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""GLM-Image pipeline stages."""
-
-from fastvideo.pipelines.basic.glm_image.stages.before_denoising import (GlmImageBeforeDenoisingStage)
-from fastvideo.pipelines.basic.glm_image.stages.condition_encoding import (GlmImageConditionEncodingStage)
-from fastvideo.pipelines.basic.glm_image.stages.decoding import (GlmImageDecodingStage)
-from fastvideo.pipelines.basic.glm_image.stages.denoising import (GlmImageDenoisingStage)
-
-__all__ = [
-    "GlmImageBeforeDenoisingStage",
-    "GlmImageConditionEncodingStage",
-    "GlmImageDecodingStage",
-    "GlmImageDenoisingStage",
-]
diff --git a/fastvideo/pipelines/basic/glm_image/stages/before_denoising.py b/fastvideo/pipelines/basic/glm_image/stages/before_denoising.py
deleted file mode 100644
index f3a0331d2..000000000
--- a/fastvideo/pipelines/basic/glm_image/stages/before_denoising.py
+++ /dev/null
@@ -1,269 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-from __future__ import annotations
-
-import re
-from math import sqrt
-
-import numpy as np
-import torch
-
-from fastvideo.distributed import get_local_torch_device
-from fastvideo.fastvideo_args import FastVideoArgs
-from fastvideo.logger import init_logger
-from fastvideo.pipelines.pipeline_batch_info import ForwardBatch
-from fastvideo.pipelines.stages.base import PipelineStage
-from fastvideo.pipelines.stages.validators import StageValidators as V
-from fastvideo.pipelines.stages.validators import VerificationResult
-
-logger = init_logger(__name__)
-
-
-def calculate_shift(
-    image_seq_len: int,
-    base_seq_len: int = 256,
-    base_shift: float = 0.25,
-    max_shift: float = 0.75,
-) -> float:
-    return (image_seq_len / base_seq_len)**0.5 * max_shift + base_shift
-
-
-def get_glyph_texts(prompt: str | list[str]) -> list[str] | list[list[str]]:
-    if isinstance(prompt, str):
-        prompts: list[str] = [prompt]
-        is_batch = False
-    else:
-        prompts = prompt
-        is_batch = True
-    out: list[list[str]] = []
-    for p in prompts:
-        out.append(
-            re.findall(r"'([^']*)'", p) + re.findall(r"“([^“”]*)”", p) + re.findall(r'"([^"]*)"', p) +
-            re.findall(r"「([^「」]*)」", p))
-    return out if is_batch else out[0]
-
-
-def compute_glyph_embeds(
-    prompts: list[str],
-    tokenizer,
-    text_encoder,
-    device: torch.device,
-    dtype: torch.dtype,
-    max_sequence_length: int = 2048,
-) -> torch.Tensor:
-    all_glyph_texts = get_glyph_texts(prompts)
-    all_glyph_embeds = []
-    for glyph_texts in all_glyph_texts:
-        if len(glyph_texts) == 0:
-            glyph_texts = [""]
-        input_ids = tokenizer(
-            glyph_texts,
-            max_length=max_sequence_length,
-            truncation=True,
-        ).input_ids
-        input_ids = [[tokenizer.pad_token_id] * ((len(input_ids) + 1) % 2) + ids for ids in input_ids]
-        max_length = max(len(ids) for ids in input_ids)
-        attention_mask = torch.tensor(
-            [[1] * len(ids) + [0] * (max_length - len(ids)) for ids in input_ids],
-            device=device,
-        )
-        input_ids_t = torch.tensor(
-            [ids + [tokenizer.pad_token_id] * (max_length - len(ids)) for ids in input_ids],
-            device=device,
-        )
-        outputs = text_encoder(input_ids_t, attention_mask=attention_mask)
-        glyph_embeds = outputs.last_hidden_state[attention_mask.bool()].unsqueeze(0)
-        all_glyph_embeds.append(glyph_embeds)
-
-    max_seq_len = max(emb.size(1) for emb in all_glyph_embeds)
-    padded = []
-    for emb in all_glyph_embeds:
-        if emb.size(1) < max_seq_len:
-            pad = torch.zeros(emb.size(0), max_seq_len - emb.size(1), emb.size(2), device=device, dtype=emb.dtype)
-            emb = torch.cat([pad, emb], dim=1)
-        padded.append(emb)
-    return torch.cat(padded, dim=0).to(device=device, dtype=dtype)
-
-
-def _grid_dims(height: int, width: int) -> tuple[int, int, int, int]:
-    th, tw = height // 32, width // 32
-    ratio = th / tw
-    pth = int(sqrt(ratio) * 16)
-    ptw = int(sqrt(1 / ratio) * 16)
-    return th, tw, pth, ptw
-
-
-def _upsample_d32_to_d16(tokens: torch.Tensor, th: int, tw: int) -> torch.Tensor:
-    tokens = tokens.view(1, 1, th, tw).float()
-    tokens = torch.nn.functional.interpolate(tokens, scale_factor=2, mode="nearest").long()
-    return tokens.view(1, -1)
-
-
-class GlmImageBeforeDenoisingStage(PipelineStage):
-
-    def __init__(self,
-                 vae,
-                 text_encoder,
-                 tokenizer,
-                 processor,
-                 transformer,
-                 scheduler,
-                 vision_language_encoder=None) -> None:
-        super().__init__()
-        self.vae = vae
-        self.text_encoder = text_encoder
-        self.tokenizer = tokenizer
-        self.processor = processor
-        self.transformer = transformer
-        self.scheduler = scheduler
-        if isinstance(vision_language_encoder, tuple):
-            self.vision_language_encoder, self.vl_processor = (vision_language_encoder[0], vision_language_encoder[1]
-                                                               or processor)
-        else:
-            self.vision_language_encoder = vision_language_encoder
-            self.vl_processor = processor
-
-    @torch.no_grad()
-    def forward(self, batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> ForwardBatch:
-        device = get_local_torch_device()
-        dtype = torch.bfloat16
-        th, tw, pth, ptw = _grid_dims(batch.height, batch.width)
-
-        if batch.seed is not None:
-            torch.manual_seed(batch.seed)
-            if torch.cuda.is_available():
-                torch.cuda.manual_seed(batch.seed)
-
-        # 1-3. AR token generation. I2I prepends the condition image and uses a
-        # single-scale target grid; T2I is multi-scale.
-        is_t2i = batch.pil_image is None
-        if self.vision_language_encoder is not None:
-            content = [{"type": "text", "text": batch.prompt}]
-            if not is_t2i:
-                content.insert(0, {"type": "image", "image": batch.pil_image})
-            messages = [{"role": "user", "content": content}]
-            inputs = self.vl_processor.apply_chat_template(messages,
-                                                           tokenize=True,
-                                                           target_h=batch.height,
-                                                           target_w=batch.width,
-                                                           return_dict=True,
-                                                           return_tensors="pt").to(device)
-
-            if is_t2i:
-                up_h, up_w = th, tw
-                large_start, large_count = pth * ptw, th * tw
-                max_new = large_count + (pth * ptw) + 1
-            else:
-                # Condition grid(s) first, target grid last.
-                _, t_h, t_w = inputs["image_grid_thw"][-1].tolist()
-                up_h, up_w = int(t_h), int(t_w)
-                large_start, large_count = 0, up_h * up_w
-                max_new = large_count + 1
-
-            outputs = self.vision_language_encoder.generate(**inputs, max_new_tokens=max_new, do_sample=True)
-            gen_tokens = outputs[0][inputs.input_ids.shape[-1]:]
-            if gen_tokens.shape[0] >= large_start + large_count:
-                large_tokens = gen_tokens[large_start:large_start + large_count]
-            else:
-                available = gen_tokens[large_start:]
-                large_tokens = torch.zeros(large_count, dtype=gen_tokens.dtype, device=gen_tokens.device)
-                if available.shape[0] > 0:
-                    large_tokens[:min(available.shape[0], large_count)] = available[:large_count]
-                logger.warning("AR generated %d tokens, expected %d. Padding with zeros.", gen_tokens.shape[0],
-                               large_start + large_count)
-            batch.prior_token_id = _upsample_d32_to_d16(large_tokens, up_h, up_w)
-            batch.prior_token_drop = torch.zeros(batch.prior_token_id.shape, dtype=torch.bool, device=device)
-
-            if not is_t2i:
-                self._compute_source_prior_tokens(batch, inputs)
-        else:
-            num_prior_tokens = 4 * th * tw
-            logger.warning("No vision_language_encoder provided; using random dropped priors.")
-            batch.prior_token_id = torch.randint(0, 16384, (1, num_prior_tokens), device=device)
-            batch.prior_token_drop = torch.ones(batch.prior_token_id.shape, dtype=torch.bool, device=device)
-
-        # 4. Glyph T5 encoding.
-        prompts = [batch.prompt] if isinstance(batch.prompt, str) else list(batch.prompt)
-        prompt_embeds = compute_glyph_embeds(prompts, self.tokenizer, self.text_encoder, device, dtype)
-
-        # 5. CFG-side negative encoding.
-        if batch.do_classifier_free_guidance:
-            neg_prompts = [batch.negative_prompt or ""] * len(prompts)
-            neg_embeds = compute_glyph_embeds(neg_prompts, self.tokenizer, self.text_encoder, device, dtype)
-            L_pos, L_neg = prompt_embeds.shape[1], neg_embeds.shape[1]
-            max_L = max(L_pos, L_neg)
-            if L_pos < max_L:
-                pad = torch.zeros(prompt_embeds.shape[0],
-                                  max_L - L_pos,
-                                  prompt_embeds.shape[2],
-                                  device=device,
-                                  dtype=dtype)
-                prompt_embeds = torch.cat([pad, prompt_embeds], dim=1)
-            if L_neg < max_L:
-                pad = torch.zeros(neg_embeds.shape[0], max_L - L_neg, neg_embeds.shape[2], device=device, dtype=dtype)
-                neg_embeds = torch.cat([pad, neg_embeds], dim=1)
-            # Row 0 conditional (positive), row 1 unconditional (negative).
-            prompt_embeds = torch.cat([prompt_embeds, neg_embeds], dim=0)
-            att_pos = torch.ones((1, max_L), device=device)
-            att_neg = torch.ones((1, max_L), device=device)
-            if L_pos < max_L:
-                att_pos[:, :max_L - L_pos] = 0
-            if L_neg < max_L:
-                att_neg[:, :max_L - L_neg] = 0
-            attention_mask = torch.cat([att_pos, att_neg], dim=0)
-        else:
-            attention_mask = torch.ones((1, prompt_embeds.shape[1]), device=device)
-
-        batch.prompt_embeds = [prompt_embeds]
-        batch.attention_mask = attention_mask
-
-        # 6. Latents + dynamic flow shift.
-        if batch.seed is not None:
-            torch.manual_seed(batch.seed)
-            if torch.cuda.is_available():
-                torch.cuda.manual_seed(batch.seed)
-        batch.latents = torch.randn((1, 16, 1, batch.height // 8, batch.width // 8), device=device, dtype=dtype)
-
-        # Integer-cast linspace timesteps with resolution-dependent shift applied to
-        # sigmas only; the DiT is conditioned on the unshifted integer timesteps.
-        ntt = self.scheduler.config.num_train_timesteps
-        patch_size = self.transformer.patch_size
-        image_seq_len = ((batch.height // 8) * (batch.width // 8)) // (patch_size**2)
-        sched_timesteps = np.linspace(ntt, 1.0, batch.num_inference_steps + 1)[:-1].astype(np.int64).astype(np.float32)
-        sched_sigmas = sched_timesteps / ntt
-        self.scheduler.set_shift(calculate_shift(image_seq_len))
-        self.scheduler.set_timesteps(batch.num_inference_steps,
-                                     device=device,
-                                     sigmas=sched_sigmas.tolist(),
-                                     timesteps=sched_timesteps.tolist())
-        batch.timesteps = self.scheduler.timesteps
-        return batch
-
-    @torch.no_grad()
-    def _compute_source_prior_tokens(self, batch: ForwardBatch, inputs) -> None:
-        image_grid_thw = inputs["image_grid_thw"]
-        num_condition_images = image_grid_thw.shape[0] - 1
-        source_grids = image_grid_thw[:num_condition_images]
-        image_features = self.vision_language_encoder.get_image_features(inputs["pixel_values"], source_grids)
-        image_feature_parts = getattr(image_features, "pooler_output", image_features)
-        embed = torch.cat(image_feature_parts, dim=0)
-        src_ids_d32 = self.vision_language_encoder.get_image_tokens(embed, source_grids)
-        split_sizes = source_grids.prod(dim=-1).tolist()
-        upsampled = [
-            _upsample_d32_to_d16(ids, int(grid[1]), int(grid[2])).squeeze(0)
-            for ids, grid in zip(torch.split(src_ids_d32, split_sizes), source_grids, strict=False)
-        ]
-        src_grids_up = source_grids.clone()
-        src_grids_up[:, 1] *= 2
-        src_grids_up[:, 2] *= 2
-        batch.extra["glm_prior_token_image_ids"] = torch.cat(upsampled, dim=0)
-        batch.extra["glm_source_image_grid_thw"] = src_grids_up
-
-    def verify_input(self, batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> VerificationResult:
-        result = VerificationResult()
-        result.add_check("prompt", batch.prompt, V.string_not_empty)
-        return result
-
-    def verify_output(self, batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> VerificationResult:
-        result = VerificationResult()
-        result.add_check("prior_token_id", batch.prior_token_id, V.is_tensor)
-        return result
diff --git a/fastvideo/pipelines/basic/glm_image/stages/condition_encoding.py b/fastvideo/pipelines/basic/glm_image/stages/condition_encoding.py
deleted file mode 100644
index 8fe8ef347..000000000
--- a/fastvideo/pipelines/basic/glm_image/stages/condition_encoding.py
+++ /dev/null
@@ -1,67 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-from __future__ import annotations
-
-import torch
-
-from fastvideo.distributed import get_local_torch_device
-from fastvideo.fastvideo_args import FastVideoArgs
-from fastvideo.forward_context import set_forward_context
-from fastvideo.image_processor import ImageProcessor
-from fastvideo.models.dits.glm_image import GlmImageKVCache
-from fastvideo.pipelines.pipeline_batch_info import ForwardBatch
-from fastvideo.pipelines.stages.base import PipelineStage
-
-_CONDITION_MULTIPLE_OF = 16  # vae_scale_factor (8) * DiT patch_size (2)
-
-
-class GlmImageConditionEncodingStage(PipelineStage):
-
-    def __init__(self, vae, transformer) -> None:
-        super().__init__()
-        self.vae = vae
-        self.transformer = transformer
-        self.image_processor = ImageProcessor(vae_scale_factor=_CONDITION_MULTIPLE_OF)
-
-    @torch.no_grad()
-    def forward(self, batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> ForwardBatch:
-        if batch.pil_image is None:
-            return batch
-
-        device = get_local_torch_device()
-        dtype = torch.bfloat16
-        self.vae.to(device)
-
-        prior_ids = batch.extra["glm_prior_token_image_ids"].to(device)
-        if prior_ids.dim() == 1:
-            prior_ids = prior_ids.unsqueeze(0)
-
-        # Latent patch count must match the source prior tokens; mismatch is fatal.
-        src_grid = batch.extra["glm_source_image_grid_thw"][0]
-        cond_h = int(src_grid[1]) * _CONDITION_MULTIPLE_OF
-        cond_w = int(src_grid[2]) * _CONDITION_MULTIPLE_OF
-        cond_img = self.image_processor.preprocess(batch.pil_image, cond_h, cond_w).to(device=device,
-                                                                                       dtype=torch.float32)
-        latent = self.vae.encode(cond_img).latent_dist.mode()
-        # NOTE: at runtime self.vae.config is a diffusers FrozenDict with flat
-        # latents_mean/latents_std fields. Access the flat fields directly.
-        cfg = self.vae.config
-        mean = torch.tensor(cfg.latents_mean, device=device, dtype=torch.float32).view(1, -1, 1, 1)
-        std = torch.tensor(cfg.latents_std, device=device, dtype=torch.float32).view(1, -1, 1, 1)
-        latent = ((latent - mean) / std).to(dtype)
-
-        kv_caches = GlmImageKVCache(num_layers=self.transformer.num_layers)
-        empty_text = batch.prompt_embeds[0][:1, :0, :].to(device=device, dtype=dtype)
-        with set_forward_context(current_timestep=0, attn_metadata=None, forward_batch=batch):
-            self.transformer(
-                hidden_states=latent,
-                encoder_hidden_states=empty_text,
-                prior_token_id=prior_ids,
-                prior_token_drop=torch.zeros((prior_ids.shape[0], ), dtype=torch.bool, device=device),
-                timestep=torch.zeros((1, ), device=device),
-                target_size=torch.tensor([tuple(cond_img.shape[-2:])], device=device, dtype=torch.long),
-                crop_coords=torch.zeros((1, 2), device=device, dtype=torch.long),
-                kv_caches=kv_caches,
-                kv_caches_mode="write",
-            )
-        batch.extra["glm_kv_caches"] = kv_caches
-        return batch
diff --git a/fastvideo/pipelines/basic/glm_image/stages/decoding.py b/fastvideo/pipelines/basic/glm_image/stages/decoding.py
deleted file mode 100644
index 3b78255c8..000000000
--- a/fastvideo/pipelines/basic/glm_image/stages/decoding.py
+++ /dev/null
@@ -1,35 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-from __future__ import annotations
-
-import torch
-
-from fastvideo.distributed import get_local_torch_device
-from fastvideo.fastvideo_args import FastVideoArgs
-from fastvideo.pipelines.stages.decoding import DecodingStage
-from fastvideo.utils import PRECISION_TO_TYPE
-
-
-class GlmImageDecodingStage(DecodingStage):
-
-    @torch.no_grad()
-    def decode(self, latents: torch.Tensor, fastvideo_args: FastVideoArgs) -> torch.Tensor:
-        self.vae.to(get_local_torch_device())
-        latents = latents.to(get_local_torch_device())
-
-        vae_dtype = PRECISION_TO_TYPE[fastvideo_args.pipeline_config.vae_precision]
-        vae_autocast = (vae_dtype != torch.float32 and not fastvideo_args.disable_autocast)
-
-        latents = self._denormalize_latents(latents, fastvideo_args)
-        if latents.dim() == 5:
-            latents = latents.squeeze(2)
-
-        with torch.autocast(device_type="cuda", dtype=vae_dtype, enabled=vae_autocast):
-            if fastvideo_args.pipeline_config.vae_tiling:
-                self.vae.enable_tiling()
-            if not vae_autocast:
-                latents = latents.to(vae_dtype)
-            decoded = self.vae.decode(latents)
-
-        image = decoded.sample if hasattr(decoded, "sample") else decoded
-        image = (image / 2 + 0.5).clamp(0, 1)
-        return image.unsqueeze(2)
diff --git a/fastvideo/pipelines/basic/glm_image/stages/denoising.py b/fastvideo/pipelines/basic/glm_image/stages/denoising.py
deleted file mode 100644
index 62fd5fe68..000000000
--- a/fastvideo/pipelines/basic/glm_image/stages/denoising.py
+++ /dev/null
@@ -1,198 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""CFG convention (both denoise paths): row 0 conditional (positive), row 1 unconditional."""
-from __future__ import annotations
-
-import torch
-
-from fastvideo.attention.backends.sdpa import SDPAMetadata
-from fastvideo.distributed import get_local_torch_device
-from fastvideo.fastvideo_args import FastVideoArgs
-from fastvideo.forward_context import set_forward_context
-from fastvideo.pipelines.pipeline_batch_info import ForwardBatch
-from fastvideo.pipelines.stages.denoising import DenoisingStage
-from fastvideo.pipelines.stages.validators import StageValidators as V
-from fastvideo.pipelines.stages.validators import VerificationResult
-from fastvideo.platforms import AttentionBackendEnum
-
-
-class GlmImageDenoisingStage(DenoisingStage):
-
-    def verify_input(self, batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> VerificationResult:
-        result = VerificationResult()
-        result.add_check("timesteps", batch.timesteps, [V.is_tensor, V.min_dims(1)])
-        latents = getattr(batch, "latent", getattr(batch, "latents", None))
-        result.add_check("latents", latents, [V.is_tensor, V.with_dims(5)])
-        result.add_check("num_inference_steps", batch.num_inference_steps, V.positive_int)
-        result.add_check("prompt_embeds", batch.prompt_embeds, V.list_not_empty)
-        return result
-
-    def forward(self, batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> ForwardBatch:
-        device = get_local_torch_device()
-        dtype = torch.bfloat16
-        guidance_scale = batch.guidance_scale
-        do_cfg = guidance_scale > 1.0
-
-        latents = getattr(batch, "latent", getattr(batch, "latents", None))
-        if latents is None:
-            raise ValueError("No latents found in batch.")
-        if latents.dim() == 5:
-            latents = latents.squeeze(2)
-
-        prompt_embeds = batch.prompt_embeds[0]
-        text_attention_mask = getattr(batch, "attention_mask", None)
-        timesteps = batch.timesteps
-
-        patch_size = self.transformer.patch_size
-        _, _, h, w = latents.shape
-        image_seq_length = (h // patch_size) * (w // patch_size)
-        text_seq_length = prompt_embeds.shape[1] if prompt_embeds.dim() >= 2 else 0
-
-        first_block = self.transformer.transformer_blocks[0]
-        backend = getattr(first_block.attn1.attn, "backend", None)
-        sdpa = backend == AttentionBackendEnum.TORCH_SDPA and text_attention_mask is not None
-
-        kv_caches = batch.extra.get("glm_kv_caches")
-        if kv_caches is None:
-            self._denoise_t2i(batch, latents, prompt_embeds, text_attention_mask, timesteps, do_cfg, guidance_scale,
-                              text_seq_length, image_seq_length, sdpa, device, dtype)
-        else:
-            self._denoise_i2i(batch, latents, prompt_embeds, text_attention_mask, timesteps, do_cfg, guidance_scale,
-                              text_seq_length, image_seq_length, sdpa, kv_caches, device, dtype)
-        return batch
-
-    def _denoise_t2i(self, batch, latents, prompt_embeds, text_attention_mask, timesteps, do_cfg, guidance_scale,
-                     text_seq_length, image_seq_length, sdpa, device, dtype) -> None:
-        num_inference_steps = batch.num_inference_steps
-        bs = 2 if do_cfg else 1
-        target_size = torch.tensor([[batch.height, batch.width]], device=device, dtype=torch.long).repeat(bs, 1)
-        crop_coords = torch.zeros((bs, 2), device=device, dtype=torch.long)
-
-        prior_token_id = batch.prior_token_id
-        if do_cfg and prior_token_id.shape[0] == 1:
-            prior_token_id = prior_token_id.repeat(2, 1)
-        if do_cfg:
-            prior_token_drop = torch.tensor([False, True], device=device)
-        else:
-            prior_token_drop = getattr(batch, "prior_token_drop", torch.tensor([False], device=device))
-
-        attention_mask_kv = None
-        if sdpa:
-            if (text_attention_mask.shape[0] == 1 and bs > 1):
-                text_attention_mask = text_attention_mask.repeat(bs, 1)
-            mix_attn_mask = torch.ones((bs, text_seq_length + image_seq_length), device=device, dtype=torch.float32)
-            mix_attn_mask[:, :text_seq_length] = (text_attention_mask.float().to(device))
-            attention_mask_kv = (mix_attn_mask > 0).unsqueeze(1).unsqueeze(2)
-
-        with self.progress_bar(total=num_inference_steps) as progress_bar:
-            for i, t in enumerate(timesteps):
-                # Stop if interrupted
-                if getattr(self, "interrupt", False):
-                    break
-                latent_model_input = torch.cat([latents] * 2) if do_cfg else latents
-                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t).to(dtype)
-                t_expand = t.expand(latent_model_input.shape[0]) - 1
-
-                attn_metadata = (SDPAMetadata(current_timestep=i, attn_mask=attention_mask_kv)
-                                 if attention_mask_kv is not None else None)
-
-                with torch.no_grad(), set_forward_context(current_timestep=i,
-                                                          attn_metadata=attn_metadata,
-                                                          forward_batch=batch):
-                    noise_pred = self.transformer(
-                        latent_model_input,
-                        prompt_embeds,
-                        prior_token_id,
-                        prior_token_drop,
-                        t_expand,
-                        target_size,
-                        crop_coords,
-                    )
-
-                    if do_cfg:
-                        noise_pred_cond, noise_pred_uncond = noise_pred.float().chunk(2)
-                        noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_cond - noise_pred_uncond)
-
-                        guidance_rescale = getattr(batch, "guidance_rescale", 0.0)
-                        if guidance_rescale > 0.0:
-                            dims = list(range(1, noise_pred_cond.ndim))
-                            std_text = noise_pred_cond.std(dim=dims, keepdim=True)
-                            std_cfg = noise_pred.std(dim=dims, keepdim=True)
-                            rescaled = noise_pred * (std_text / std_cfg)
-                            noise_pred = (guidance_rescale * rescaled + (1 - guidance_rescale) * noise_pred)
-
-                    latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
-                progress_bar.update()
-
-        batch.latents = latents.unsqueeze(2)
-
-    def _denoise_i2i(self, batch, latents, prompt_embeds, text_attention_mask, timesteps, do_cfg, guidance_scale,
-                     text_seq_length, image_seq_length, sdpa, kv_caches, device, dtype) -> None:
-        """Two separate transformer calls (cond reads the cache, uncond skips it):
-        the cache mode is one global flag with batch-1 k/v, so a 2-row CFG call
-        cannot express both."""
-        num_inference_steps = batch.num_inference_steps
-        target_size = torch.tensor([[batch.height, batch.width]], device=device, dtype=torch.long)
-        crop_coords = torch.zeros((1, 2), device=device, dtype=torch.long)
-        prior_token_id = batch.prior_token_id[:1]
-        drop_keep = torch.zeros((1, ), dtype=torch.bool, device=device)
-        drop_all = torch.ones((1, ), dtype=torch.bool, device=device)
-        cache_len = kv_caches[0].k_cache.shape[1] if kv_caches[0].k_cache is not None else 0
-
-        def _mask(row: int, with_cache: bool):
-            if not sdpa:
-                return None
-            prefix = cache_len if with_cache else 0
-            m = torch.ones((1, prefix + text_seq_length + image_seq_length), device=device, dtype=torch.float32)
-            m[:, prefix:prefix + text_seq_length] = text_attention_mask[row:row + 1].float().to(device)
-            return (m > 0).unsqueeze(1).unsqueeze(2)
-
-        cond_mask = _mask(0, with_cache=True)
-        uncond_mask = _mask(1, with_cache=False) if do_cfg else None
-        pos_embeds = prompt_embeds[:1]
-        neg_embeds = prompt_embeds[1:2] if do_cfg else None
-
-        with self.progress_bar(total=num_inference_steps) as progress_bar:
-            for i, t in enumerate(timesteps):
-                # Stop if interrupted
-                if getattr(self, "interrupt", False):
-                    break
-                latent_model_input = self.scheduler.scale_model_input(latents, t).to(dtype)
-                t_expand = t.expand(1) - 1
-
-                cond_meta = (SDPAMetadata(current_timestep=i, attn_mask=cond_mask) if cond_mask is not None else None)
-                with torch.no_grad(), set_forward_context(current_timestep=i,
-                                                          attn_metadata=cond_meta,
-                                                          forward_batch=batch):
-                    noise_pred = self.transformer(latent_model_input,
-                                                  pos_embeds,
-                                                  prior_token_id,
-                                                  drop_keep,
-                                                  t_expand,
-                                                  target_size,
-                                                  crop_coords,
-                                                  kv_caches=kv_caches,
-                                                  kv_caches_mode="read")
-
-                if do_cfg:
-                    uncond_meta = (SDPAMetadata(current_timestep=i, attn_mask=uncond_mask)
-                                   if uncond_mask is not None else None)
-                    with torch.no_grad(), set_forward_context(current_timestep=i,
-                                                              attn_metadata=uncond_meta,
-                                                              forward_batch=batch):
-                        noise_pred_uncond = self.transformer(latent_model_input,
-                                                             neg_embeds,
-                                                             prior_token_id,
-                                                             drop_all,
-                                                             t_expand,
-                                                             target_size,
-                                                             crop_coords,
-                                                             kv_caches=kv_caches,
-                                                             kv_caches_mode="skip")
-                    noise_pred = noise_pred_uncond.float() + guidance_scale * (noise_pred.float() -
-                                                                               noise_pred_uncond.float())
-
-                latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
-                progress_bar.update()
-
-        kv_caches.clear()
-        batch.latents = latents.unsqueeze(2)
diff --git a/fastvideo/pipelines/basic/longcat/__init__.py b/fastvideo/pipelines/basic/longcat/__init__.py
deleted file mode 100644
index db24fda1d..000000000
--- a/fastvideo/pipelines/basic/longcat/__init__.py
+++ /dev/null
@@ -1,8 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""LongCat pipeline module."""
-
-from fastvideo.pipelines.basic.longcat.longcat_pipeline import LongCatPipeline
-from fastvideo.pipelines.basic.longcat.longcat_i2v_pipeline import LongCatImageToVideoPipeline
-from fastvideo.pipelines.basic.longcat.longcat_vc_pipeline import LongCatVideoContinuationPipeline
-
-__all__ = ["LongCatPipeline", "LongCatImageToVideoPipeline", "LongCatVideoContinuationPipeline"]
diff --git a/fastvideo/pipelines/basic/longcat/longcat_i2v_pipeline.py b/fastvideo/pipelines/basic/longcat/longcat_i2v_pipeline.py
deleted file mode 100644
index ac37f6c6a..000000000
--- a/fastvideo/pipelines/basic/longcat/longcat_i2v_pipeline.py
+++ /dev/null
@@ -1,136 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-LongCat Image-to-Video pipeline implementation.
-
-This module implements I2V (Image-to-Video) generation for LongCat using Tier 3
-conditioning with timestep masking, num_cond_latents support, and RoPE skipping.
-
-Supports:
-- Basic I2V (50 steps, guidance_scale=4.0)
-- Distilled I2V with LoRA (16 steps, guidance_scale=1.0)
-- Refinement I2V for 720p upscaling (with refinement LoRA + BSA)
-"""
-
-from fastvideo.fastvideo_args import FastVideoArgs
-from fastvideo.logger import init_logger
-from fastvideo.pipelines import ComposedPipelineBase, LoRAPipeline
-from fastvideo.pipelines.stages import (
-    DecodingStage,
-    InputValidationStage,
-    TextEncodingStage,
-    TimestepPreparationStage,
-)
-from fastvideo.pipelines.stages.longcat_image_vae_encoding import LongCatImageVAEEncodingStage
-from fastvideo.pipelines.stages.longcat_i2v_latent_preparation import LongCatI2VLatentPreparationStage
-from fastvideo.pipelines.stages.longcat_i2v_denoising import LongCatI2VDenoisingStage
-from fastvideo.pipelines.stages.longcat_refine_init import LongCatRefineInitStage
-from fastvideo.pipelines.stages.longcat_refine_timestep import LongCatRefineTimestepStage
-
-logger = init_logger(__name__)
-
-
-class LongCatImageToVideoPipeline(LoRAPipeline, ComposedPipelineBase):
-    """
-    LongCat Image-to-Video pipeline.
-    
-    Generates video from a single input image using Tier 3 I2V conditioning:
-    - Per-frame timestep masking (timestep[:, 0] = 0)
-    - num_cond_latents parameter to transformer
-    - RoPE skipping for conditioning frames
-    - Selective denoising (skip first frame in scheduler)
-    """
-
-    _required_config_modules = ["text_encoder", "tokenizer", "vae", "transformer", "scheduler"]
-
-    def initialize_pipeline(self, fastvideo_args: FastVideoArgs):
-        """Initialize LongCat-specific components."""
-        # Same BSA initialization as base LongCat pipeline
-        pipeline_config = fastvideo_args.pipeline_config
-        transformer = self.get_module("transformer", None)
-        if transformer is None:
-            return
-
-        # Enable BSA if configured
-        if pipeline_config.enable_bsa:
-            bsa_params_cfg = getattr(pipeline_config, 'bsa_params', None) or {}
-            sparsity = getattr(pipeline_config, 'bsa_sparsity', None)
-            cdf_threshold = getattr(pipeline_config, 'bsa_cdf_threshold', None)
-            chunk_q = getattr(pipeline_config, 'bsa_chunk_q', None)
-            chunk_k = getattr(pipeline_config, 'bsa_chunk_k', None)
-
-            effective_bsa_params = dict(bsa_params_cfg) if isinstance(bsa_params_cfg, dict) else {}
-            if sparsity is not None:
-                effective_bsa_params['sparsity'] = sparsity
-            if cdf_threshold is not None:
-                effective_bsa_params['cdf_threshold'] = cdf_threshold
-            if chunk_q is not None:
-                effective_bsa_params['chunk_3d_shape_q'] = chunk_q
-            if chunk_k is not None:
-                effective_bsa_params['chunk_3d_shape_k'] = chunk_k
-
-            # Provide defaults
-            effective_bsa_params.setdefault('sparsity', 0.9375)
-            effective_bsa_params.setdefault('chunk_3d_shape_q', [4, 4, 4])
-            effective_bsa_params.setdefault('chunk_3d_shape_k', [4, 4, 4])
-
-            if hasattr(transformer, 'enable_bsa'):
-                logger.info("Enabling BSA for LongCat I2V transformer")
-                transformer.enable_bsa()
-                if hasattr(transformer, 'blocks'):
-                    try:
-                        for blk in transformer.blocks:
-                            if hasattr(blk, 'self_attn'):
-                                blk.self_attn.bsa_params = effective_bsa_params
-                    except Exception as e:
-                        logger.warning("Failed to set BSA params: %s", e)
-                logger.info("BSA parameters: %s", effective_bsa_params)
-        else:
-            if hasattr(transformer, 'disable_bsa'):
-                transformer.disable_bsa()
-
-    def create_pipeline_stages(self, fastvideo_args: FastVideoArgs):
-        """Set up I2V-specific pipeline stages."""
-
-        # 1. Input validation
-        self.add_stage(stage_name="input_validation_stage", stage=InputValidationStage())
-
-        # 2. Text encoding (same as T2V)
-        self.add_stage(stage_name="prompt_encoding_stage",
-                       stage=TextEncodingStage(
-                           text_encoders=[self.get_module("text_encoder")],
-                           tokenizers=[self.get_module("tokenizer")],
-                       ))
-
-        # 3. Image VAE encoding (for I2V - skipped in refinement mode)
-        self.add_stage(stage_name="image_vae_encoding_stage",
-                       stage=LongCatImageVAEEncodingStage(vae=self.get_module("vae")))
-
-        # 4. Refinement initialization (skipped if not refining)
-        self.add_stage(stage_name="longcat_refine_init_stage", stage=LongCatRefineInitStage(vae=self.get_module("vae")))
-
-        # 5. Timestep preparation (generic)
-        self.add_stage(stage_name="timestep_preparation_stage",
-                       stage=TimestepPreparationStage(scheduler=self.get_module("scheduler")))
-
-        # 6. Refinement timestep override (skipped if not refining)
-        self.add_stage(stage_name="longcat_refine_timestep_stage",
-                       stage=LongCatRefineTimestepStage(scheduler=self.get_module("scheduler")))
-
-        # 7. Latent preparation with I2V conditioning
-        self.add_stage(stage_name="latent_preparation_stage",
-                       stage=LongCatI2VLatentPreparationStage(scheduler=self.get_module("scheduler"),
-                                                              transformer=self.get_module("transformer")))
-
-        # 8. Denoising with I2V support
-        self.add_stage(stage_name="denoising_stage",
-                       stage=LongCatI2VDenoisingStage(transformer=self.get_module("transformer"),
-                                                      transformer_2=self.get_module("transformer_2", None),
-                                                      scheduler=self.get_module("scheduler"),
-                                                      vae=self.get_module("vae"),
-                                                      pipeline=self))
-
-        # 9. Decoding
-        self.add_stage(stage_name="decoding_stage", stage=DecodingStage(vae=self.get_module("vae"), pipeline=self))
-
-
-EntryClass = LongCatImageToVideoPipeline
diff --git a/fastvideo/pipelines/basic/longcat/longcat_pipeline.py b/fastvideo/pipelines/basic/longcat/longcat_pipeline.py
deleted file mode 100644
index 933660860..000000000
--- a/fastvideo/pipelines/basic/longcat/longcat_pipeline.py
+++ /dev/null
@@ -1,123 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-LongCat video diffusion pipeline implementation.
-
-This module implements the LongCat video diffusion pipeline using FastVideo's
-modular pipeline architecture.
-"""
-
-from fastvideo.fastvideo_args import FastVideoArgs
-from fastvideo.logger import init_logger
-from fastvideo.pipelines import ComposedPipelineBase, LoRAPipeline
-from fastvideo.pipelines.stages import (
-    DecodingStage,
-    InputValidationStage,
-    LatentPreparationStage,
-    TextEncodingStage,
-    TimestepPreparationStage,
-)
-from fastvideo.pipelines.stages.longcat_denoising import LongCatDenoisingStage
-from fastvideo.pipelines.stages.longcat_refine_init import LongCatRefineInitStage
-from fastvideo.pipelines.stages.longcat_refine_timestep import LongCatRefineTimestepStage
-
-logger = init_logger(__name__)
-
-
-class LongCatPipeline(LoRAPipeline, ComposedPipelineBase):
-    """
-    LongCat video diffusion pipeline with LoRA support.
-    """
-
-    _required_config_modules = ["text_encoder", "tokenizer", "vae", "transformer", "scheduler"]
-
-    def initialize_pipeline(self, fastvideo_args: FastVideoArgs):
-        """Initialize LongCat-specific components."""
-
-        # Enable BSA (Block Sparse Attention) if configured
-        pipeline_config = fastvideo_args.pipeline_config
-        transformer = self.get_module("transformer", None)
-        if transformer is None:
-            raise RuntimeError("Transformer module not found during initializing LongCat pipeline.")
-        # If user toggles BSA via CLI/config
-        if pipeline_config.enable_bsa:
-            # Build effective BSA params:
-            # 1) from explicit CLI overrides if provided
-            # 2) else from pipeline_config.bsa_params
-            # 3) else fall back to reasonable defaults
-            bsa_params_cfg = pipeline_config.bsa_params
-            sparsity = pipeline_config.bsa_sparsity
-            cdf_threshold = pipeline_config.bsa_cdf_threshold
-            chunk_q = pipeline_config.bsa_chunk_q
-            chunk_k = pipeline_config.bsa_chunk_k
-
-            effective_bsa_params = dict(bsa_params_cfg) if isinstance(bsa_params_cfg, dict) else {}
-            if sparsity is not None:
-                effective_bsa_params['sparsity'] = sparsity
-            if cdf_threshold is not None:
-                effective_bsa_params['cdf_threshold'] = cdf_threshold
-            if chunk_q is not None:
-                effective_bsa_params['chunk_3d_shape_q'] = chunk_q
-            if chunk_k is not None:
-                effective_bsa_params['chunk_3d_shape_k'] = chunk_k
-            # Provide defaults if still missing
-            effective_bsa_params.setdefault('sparsity', 0.9375)
-            effective_bsa_params.setdefault('chunk_3d_shape_q', [4, 4, 4])
-            effective_bsa_params.setdefault('chunk_3d_shape_k', [4, 4, 4])
-
-            if hasattr(transformer, 'enable_bsa'):
-                logger.info("Enabling Block Sparse Attention (BSA) for LongCat transformer")
-                transformer.enable_bsa()
-                # Propagate params to all attention modules
-                if hasattr(transformer, 'blocks'):
-                    try:
-                        for blk in transformer.blocks:
-                            if hasattr(blk, 'self_attn'):
-                                blk.self_attn.bsa_params = effective_bsa_params
-                    except Exception as e:
-                        logger.warning("Failed to set BSA params on all blocks: %s", e)
-                logger.info("BSA parameters in effect: %s", effective_bsa_params)
-            else:
-                logger.warning("BSA is enabled in config but transformer does not support it")
-        else:
-            # Explicitly disable if present
-            if hasattr(transformer, 'disable_bsa'):
-                transformer.disable_bsa()
-
-    def create_pipeline_stages(self, fastvideo_args: FastVideoArgs) -> None:
-        """Set up pipeline stages with proper dependency injection."""
-
-        self.add_stage(stage_name="input_validation_stage", stage=InputValidationStage())
-
-        self.add_stage(stage_name="prompt_encoding_stage",
-                       stage=TextEncodingStage(
-                           text_encoders=[self.get_module("text_encoder")],
-                           tokenizers=[self.get_module("tokenizer")],
-                       ))
-
-        # Add refine initialization stage (will be skipped if not refining)
-        self.add_stage(stage_name="longcat_refine_init_stage", stage=LongCatRefineInitStage(vae=self.get_module("vae")))
-
-        # First prepare generic timesteps (for non-refine paths)
-        self.add_stage(stage_name="timestep_preparation_stage",
-                       stage=TimestepPreparationStage(scheduler=self.get_module("scheduler")))
-
-        # Then override timesteps for refinement (will be a no-op if not refining),
-        # matching LongCat's generate_refine schedule.
-        self.add_stage(stage_name="longcat_refine_timestep_stage",
-                       stage=LongCatRefineTimestepStage(scheduler=self.get_module("scheduler")))
-
-        self.add_stage(stage_name="latent_preparation_stage",
-                       stage=LatentPreparationStage(scheduler=self.get_module("scheduler"),
-                                                    transformer=self.get_module("transformer", None)))
-
-        self.add_stage(stage_name="denoising_stage",
-                       stage=LongCatDenoisingStage(transformer=self.get_module("transformer"),
-                                                   transformer_2=self.get_module("transformer_2", None),
-                                                   scheduler=self.get_module("scheduler"),
-                                                   vae=self.get_module("vae"),
-                                                   pipeline=self))
-
-        self.add_stage(stage_name="decoding_stage", stage=DecodingStage(vae=self.get_module("vae"), pipeline=self))
-
-
-EntryClass = LongCatPipeline
diff --git a/fastvideo/pipelines/basic/longcat/longcat_vc_pipeline.py b/fastvideo/pipelines/basic/longcat/longcat_vc_pipeline.py
deleted file mode 100644
index 656d16fd1..000000000
--- a/fastvideo/pipelines/basic/longcat/longcat_vc_pipeline.py
+++ /dev/null
@@ -1,157 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-LongCat Video Continuation (VC) pipeline implementation.
-
-This module implements VC (Video Continuation) generation for LongCat with
-KV cache optimization for 2-3x speedup.
-
-Supports:
-- Basic VC (50 steps, guidance_scale=4.0)
-- Distilled VC with LoRA (16 steps, guidance_scale=1.0)
-- KV cache for conditioning frames
-"""
-
-from fastvideo.fastvideo_args import FastVideoArgs
-from fastvideo.logger import init_logger
-from fastvideo.pipelines import ComposedPipelineBase, LoRAPipeline
-from fastvideo.pipelines.stages import (
-    DecodingStage,
-    InputValidationStage,
-    TextEncodingStage,
-    TimestepPreparationStage,
-)
-from fastvideo.pipelines.stages.longcat_video_vae_encoding import LongCatVideoVAEEncodingStage
-from fastvideo.pipelines.stages.longcat_i2v_latent_preparation import LongCatI2VLatentPreparationStage
-from fastvideo.pipelines.stages.longcat_kv_cache_init import LongCatKVCacheInitStage
-from fastvideo.pipelines.stages.longcat_vc_denoising import LongCatVCDenoisingStage
-
-logger = init_logger(__name__)
-
-
-class LongCatVideoContinuationPipeline(LoRAPipeline, ComposedPipelineBase):
-    """
-    LongCat Video Continuation pipeline.
-    
-    Generates video continuation from multiple conditioning frames using
-    optional KV cache for 2-3x speedup.
-    
-    Key features:
-    - Takes video input (13+ frames typically)
-    - Encodes conditioning frames via VAE
-    - Optionally pre-computes KV cache for conditioning
-    - Uses cached K/V during denoising for speedup
-    - Concatenates conditioning back after denoising
-    """
-
-    _required_config_modules = ["text_encoder", "tokenizer", "vae", "transformer", "scheduler"]
-
-    def initialize_pipeline(self, fastvideo_args: FastVideoArgs):
-        """Initialize LongCat-specific components."""
-        pipeline_config = fastvideo_args.pipeline_config
-        transformer = self.get_module("transformer", None)
-        if transformer is None:
-            return
-
-        # Enable BSA if configured (for VC, BSA may not be needed)
-        if getattr(pipeline_config, 'enable_bsa', False):
-            bsa_params_cfg = getattr(pipeline_config, 'bsa_params', None) or {}
-            sparsity = getattr(pipeline_config, 'bsa_sparsity', None)
-            cdf_threshold = getattr(pipeline_config, 'bsa_cdf_threshold', None)
-            chunk_q = getattr(pipeline_config, 'bsa_chunk_q', None)
-            chunk_k = getattr(pipeline_config, 'bsa_chunk_k', None)
-
-            effective_bsa_params = dict(bsa_params_cfg) if isinstance(bsa_params_cfg, dict) else {}
-            if sparsity is not None:
-                effective_bsa_params['sparsity'] = sparsity
-            if cdf_threshold is not None:
-                effective_bsa_params['cdf_threshold'] = cdf_threshold
-            if chunk_q is not None:
-                effective_bsa_params['chunk_3d_shape_q'] = chunk_q
-            if chunk_k is not None:
-                effective_bsa_params['chunk_3d_shape_k'] = chunk_k
-
-            # Provide defaults
-            effective_bsa_params.setdefault('sparsity', 0.9375)
-            effective_bsa_params.setdefault('chunk_3d_shape_q', [4, 4, 4])
-            effective_bsa_params.setdefault('chunk_3d_shape_k', [4, 4, 4])
-
-            if hasattr(transformer, 'enable_bsa'):
-                logger.info("Enabling BSA for LongCat VC transformer")
-                transformer.enable_bsa()
-                if hasattr(transformer, 'blocks'):
-                    try:
-                        for blk in transformer.blocks:
-                            if hasattr(blk, 'self_attn'):
-                                blk.self_attn.bsa_params = effective_bsa_params
-                    except Exception as e:
-                        logger.warning("Failed to set BSA params: %s", e)
-                logger.info("BSA parameters: %s", effective_bsa_params)
-        else:
-            if hasattr(transformer, 'disable_bsa'):
-                transformer.disable_bsa()
-
-    def create_pipeline_stages(self, fastvideo_args: FastVideoArgs):
-        """Set up VC-specific pipeline stages."""
-
-        # 1. Input validation
-        self.add_stage(stage_name="input_validation_stage", stage=InputValidationStage())
-
-        # 2. Text encoding
-        self.add_stage(stage_name="prompt_encoding_stage",
-                       stage=TextEncodingStage(
-                           text_encoders=[self.get_module("text_encoder")],
-                           tokenizers=[self.get_module("tokenizer")],
-                       ))
-
-        # 3. Video VAE encoding (encodes conditioning frames)
-        self.add_stage(stage_name="video_vae_encoding_stage",
-                       stage=LongCatVideoVAEEncodingStage(vae=self.get_module("vae")))
-
-        # 4. Timestep preparation
-        self.add_stage(stage_name="timestep_preparation_stage",
-                       stage=TimestepPreparationStage(scheduler=self.get_module("scheduler")))
-
-        # 5. Latent preparation (reuse I2V stage - it handles video_latent too)
-        self.add_stage(stage_name="latent_preparation_stage",
-                       stage=LongCatVCLatentPreparationStage(scheduler=self.get_module("scheduler"),
-                                                             transformer=self.get_module("transformer")))
-
-        # 6. KV cache initialization (optional, based on config)
-        # This is always added but will skip if use_kv_cache=False
-        self.add_stage(stage_name="kv_cache_init_stage",
-                       stage=LongCatKVCacheInitStage(transformer=self.get_module("transformer")))
-
-        # 7. Denoising with VC and KV cache support
-        self.add_stage(stage_name="denoising_stage",
-                       stage=LongCatVCDenoisingStage(transformer=self.get_module("transformer"),
-                                                     transformer_2=self.get_module("transformer_2", None),
-                                                     scheduler=self.get_module("scheduler"),
-                                                     vae=self.get_module("vae"),
-                                                     pipeline=self))
-
-        # 8. Decoding
-        self.add_stage(stage_name="decoding_stage", stage=DecodingStage(vae=self.get_module("vae"), pipeline=self))
-
-
-class LongCatVCLatentPreparationStage(LongCatI2VLatentPreparationStage):
-    """
-    Prepare latents with video conditioning for first N frames.
-    
-    Extends I2V latent preparation to handle video_latent (multiple frames)
-    instead of image_latent (single frame).
-    """
-
-    def forward(self, batch, fastvideo_args):
-        """Prepare latents with VC conditioning."""
-
-        # Check if we have video_latent (from VC encoding stage)
-        video_latent = getattr(batch, 'video_latent', None)
-        if video_latent is not None:
-            # Set image_latent to video_latent for parent class compatibility
-            batch.image_latent = video_latent
-
-        # Call parent class forward
-        return super().forward(batch, fastvideo_args)
-
-
-EntryClass = LongCatVideoContinuationPipeline
diff --git a/fastvideo/pipelines/basic/longcat/presets.py b/fastvideo/pipelines/basic/longcat/presets.py
deleted file mode 100644
index 39a07652e..000000000
--- a/fastvideo/pipelines/basic/longcat/presets.py
+++ /dev/null
@@ -1,65 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""LongCat model family pipeline presets."""
-from fastvideo.api.presets import InferencePreset, PresetStageSpec
-
-_DENOISE_STAGE = PresetStageSpec(
-    name="denoise",
-    kind="denoising",
-    description="Main denoising pass",
-    allowed_overrides=frozenset({
-        "num_inference_steps",
-        "guidance_scale",
-    }),
-)
-
-LONGCAT_T2V = InferencePreset(
-    name="longcat_t2v",
-    version=1,
-    model_family="longcat",
-    description="LongCat-Video T2V at 480p",
-    workload_type="t2v",
-    stage_schemas=(_DENOISE_STAGE, ),
-    defaults={
-        "height": 480,
-        "width": 848,
-        "num_frames": 121,
-        "fps": 24,
-        "guidance_scale": 1.0,
-        "num_inference_steps": 50,
-    },
-)
-
-LONGCAT_I2V = InferencePreset(
-    name="longcat_i2v",
-    version=1,
-    model_family="longcat",
-    description="LongCat-Video I2V at 480p",
-    workload_type="i2v",
-    stage_schemas=(_DENOISE_STAGE, ),
-    defaults={
-        "height": 480,
-        "width": 848,
-        "num_frames": 121,
-        "fps": 24,
-        "guidance_scale": 1.0,
-        "num_inference_steps": 50,
-    },
-)
-
-LONGCAT_VC = InferencePreset(
-    name="longcat_vc",
-    version=1,
-    model_family="longcat",
-    description="LongCat-Video continuation at 480p",
-    stage_schemas=(_DENOISE_STAGE, ),
-    defaults={
-        "height": 480,
-        "width": 848,
-        "num_frames": 121,
-        "fps": 24,
-        "guidance_scale": 1.0,
-        "num_inference_steps": 50,
-    },
-)
-
-ALL_PRESETS = (LONGCAT_T2V, LONGCAT_I2V, LONGCAT_VC)
diff --git a/fastvideo/pipelines/basic/magi_human/JOURNAL.md b/fastvideo/pipelines/basic/magi_human/JOURNAL.md
index 2485fabe8..20d8e92a7 100644
--- a/fastvideo/pipelines/basic/magi_human/JOURNAL.md
+++ b/fastvideo/pipelines/basic/magi_human/JOURNAL.md
@@ -550,7 +550,7 @@ with hard rules around no-source-residue cleanup.
 | OQ-5 | **Basic-example output mp4 visual quality is impressionistic at 256x448.** Root cause identified: OQ-6 (pre-existing compounding bf16 drift over the 32-step denoise loop). Wave 2-3 investigation confirmed the 4-step pipeline parity shows 18.85x compounding ratio vs expected 4x linear. See OQ-6 for full details and mitigation candidates. | RESOLVED-ROOT-CAUSE-IDENTIFIED (see OQ-6) |
 | OQ-6 | **Video patch packing was spatial-major instead of channel-major.** Wave 14 (2026-05-02) ran upstream E2E and got coherent output; FV produced pure noise at same config. Oracle triage identified the bug in `_img2tokens` rearrange order: FV used `(pT pH pW C)` (spatial-major) but the DiT's `video_embedder` Linear weight was trained on the channel-major `(C pT pH pW)` layout that upstream's `UnfoldNd` (grouped-conv reshape, `unfoldNd/unfold.py:66`) produces. The pipeline parity test imported FV's `build_packed_inputs` for both sides at `test_magi_human_pipeline_parity.py:222`, so it consumed equally-permuted tokens on both sides and reported agreement on garbage. Fixed in `latent_preparation.py:_img2tokens` by changing the einops pattern from `(pT pH pW C)` to `(C pT pH pW)`. Validated end-to-end: `examples/inference/basic/basic_magi_human.py` now produces coherent video matching the prompt (woman on park bench reading a book, green trees). Earlier waves' production-side fixes (negative prompt, tokenizer padding, resolution defaults, silent-audio fallback) all still stand. | RESOLVED — Wave 14 |
 | OQ-11 | **Pipeline parity test imports FV's `build_packed_inputs` for the upstream side.** `tests/local_tests/magi_human/test_magi_human_pipeline_parity.py:222` calls FV's packer for both sides instead of upstream's real `MagiDataProxy.process_input`. This let the channel-major-vs-spatial-major bug (OQ-6, Wave 14) sit silent for weeks because both sides agreed on the wrong layout. Update the parity test to drive the upstream side through `MagiDataProxy.process_input` so future packing-layout regressions are caught at parity time, not at production E2E. | TRACKED FOLLOW-UP |
-| OQ-7 | **Wan VAE shared fp32 op-order drift (MEDIUM PRIORITY).** FV uses `z * std + mean` at decode normalization; upstream uses `z / (1/std) + mean`. Bitwise non-equivalent in fp32. Affects all Wan-family pipelines (`fastvideo/configs/pipelines/wan.py`, `turbodiffusion.py`, `longcat.py`, magi-human). Magi VAE test loosened to `atol=1e-3, rtol=1e-3` (Wave 4) to defer. Tighten back to `atol=1e-4` once the Wan VAE op-order fix lands. Fix should be validated against Wan2.1, Wan2.2, and magi-human. Estimated 0.5-1 day to fix and validate. | TRACKED FOLLOW-UP |
+| OQ-7 | **Wan VAE shared fp32 op-order drift (MEDIUM PRIORITY).** FV uses `z * std + mean` at decode normalization; upstream uses `z / (1/std) + mean`. Bitwise non-equivalent in fp32. Affects all Wan-family pipelines (`fastvideo/configs/pipelines/wan.py`, magi-human). Magi VAE test loosened to `atol=1e-3, rtol=1e-3` (Wave 4) to defer. Tighten back to `atol=1e-4` once the Wan VAE op-order fix lands. Fix should be validated against Wan2.1, Wan2.2, and magi-human. Estimated 0.5-1 day to fix and validate. | TRACKED FOLLOW-UP |
 | OQ-9 | **Upstream `dit_module.py` hardcoded bf16 casts block full fp32 parity validation.** `daVinci-MagiHuman/inference/model/dit/dit_module.py` has hardcoded `.to(torch.bfloat16)` casts at lines 619, 507, 650, 694, 696. FV's DiT forward is now dtype-agnostic (Wave 10), but parity tests against upstream still show bf16-noise residual drift in fp32 runs because upstream's intermediate tensors are bf16. Full fp32-clean parity would require patching the local upstream clone or adding a dtype-config flag upstream. Affects only fp32 parity testing, not production. | TRACKED FOLLOW-UP (LOW PRIORITY) |
 
 ## Troubleshooting
diff --git a/fastvideo/pipelines/basic/turbodiffusion/__init__.py b/fastvideo/pipelines/basic/turbodiffusion/__init__.py
deleted file mode 100644
index 0df303a96..000000000
--- a/fastvideo/pipelines/basic/turbodiffusion/__init__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-from fastvideo.pipelines.basic.turbodiffusion.turbodiffusion_pipeline import (
-    TurboDiffusionPipeline, )
-from fastvideo.pipelines.basic.turbodiffusion.turbodiffusion_i2v_pipeline import (
-    TurboDiffusionI2VPipeline, )
-
-__all__ = ["TurboDiffusionPipeline", "TurboDiffusionI2VPipeline"]
diff --git a/fastvideo/pipelines/basic/turbodiffusion/presets.py b/fastvideo/pipelines/basic/turbodiffusion/presets.py
deleted file mode 100644
index d71e18792..000000000
--- a/fastvideo/pipelines/basic/turbodiffusion/presets.py
+++ /dev/null
@@ -1,73 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""TurboDiffusion model family pipeline presets."""
-from fastvideo.api.presets import InferencePreset, PresetStageSpec
-
-_DENOISE_STAGE = PresetStageSpec(
-    name="denoise",
-    kind="denoising",
-    description="Fast few-step denoising pass",
-    allowed_overrides=frozenset({
-        "num_inference_steps",
-        "guidance_scale",
-    }),
-)
-
-TURBO_T2V_1_3B = InferencePreset(
-    name="turbo_t2v_1_3b",
-    version=1,
-    model_family="turbodiffusion",
-    description="TurboWan 2.1 T2V 1.3B (4-step)",
-    workload_type="t2v",
-    stage_schemas=(_DENOISE_STAGE, ),
-    defaults={
-        "height": 480,
-        "width": 832,
-        "num_frames": 81,
-        "fps": 16,
-        "guidance_scale": 1.0,
-        "num_inference_steps": 4,
-        "negative_prompt": "",
-    },
-)
-
-TURBO_T2V_14B = InferencePreset(
-    name="turbo_t2v_14b",
-    version=1,
-    model_family="turbodiffusion",
-    description="TurboWan 2.1 T2V 14B (4-step)",
-    workload_type="t2v",
-    stage_schemas=(_DENOISE_STAGE, ),
-    defaults={
-        "height": 720,
-        "width": 1280,
-        "num_frames": 81,
-        "fps": 16,
-        "guidance_scale": 1.0,
-        "num_inference_steps": 4,
-        "negative_prompt": "",
-    },
-)
-
-TURBO_I2V_A14B = InferencePreset(
-    name="turbo_i2v_a14b",
-    version=1,
-    model_family="turbodiffusion",
-    description="TurboWan 2.2 I2V A14B (4-step)",
-    workload_type="i2v",
-    stage_schemas=(_DENOISE_STAGE, ),
-    defaults={
-        "height": 720,
-        "width": 1280,
-        "num_frames": 81,
-        "fps": 16,
-        "guidance_scale": 1.0,
-        "num_inference_steps": 4,
-        "negative_prompt": "",
-    },
-)
-
-ALL_PRESETS = (
-    TURBO_T2V_1_3B,
-    TURBO_T2V_14B,
-    TURBO_I2V_A14B,
-)
diff --git a/fastvideo/pipelines/basic/turbodiffusion/turbodiffusion_i2v_pipeline.py b/fastvideo/pipelines/basic/turbodiffusion/turbodiffusion_i2v_pipeline.py
deleted file mode 100644
index 0c591c715..000000000
--- a/fastvideo/pipelines/basic/turbodiffusion/turbodiffusion_i2v_pipeline.py
+++ /dev/null
@@ -1,74 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-TurboDiffusion I2V (Image-to-Video) Pipeline Implementation.
-
-This module contains an implementation of the TurboDiffusion I2V pipeline
-for 1-4 step image-to-video generation using rCM (recurrent Consistency Model)
-sampling with SLA (Sparse-Linear Attention).
-
-Key differences from T2V:
-- Uses dual models (high/low noise) with boundary switching
-- sigma_max=200 (vs 80 for T2V)
-- Mask conditioning with encoded first frame
-"""
-
-from fastvideo.pipelines.basic.wan.stages.denoising import WanDenoisingStage
-from fastvideo.fastvideo_args import FastVideoArgs
-from fastvideo.logger import init_logger
-from fastvideo.models.schedulers.scheduling_rcm import RCMScheduler
-from fastvideo.pipelines import ComposedPipelineBase, LoRAPipeline
-from fastvideo.pipelines.stages import (ConditioningStage, DecodingStage, ImageVAEEncodingStage, InputValidationStage,
-                                        LatentPreparationStage, TextEncodingStage, TimestepPreparationStage)
-
-logger = init_logger(__name__)
-
-
-class TurboDiffusionI2VPipeline(LoRAPipeline, ComposedPipelineBase):
-    """
-    TurboDiffusion I2V pipeline for 1-4 step image-to-video generation.
-
-    Uses RCM scheduler, SLA attention, and dual model switching for
-    high-quality I2V generation.
-    """
-
-    _required_config_modules = ["text_encoder", "tokenizer", "vae", "transformer", "transformer_2", "scheduler"]
-
-    def initialize_pipeline(self, fastvideo_args: FastVideoArgs):
-        # Use RCM scheduler with higher sigma_max for I2V
-        logger.info("Initializing RCM scheduler for TurboDiffusion I2V (sigma_max=200)")
-        self.modules["scheduler"] = RCMScheduler(sigma_max=200.0)
-
-    def create_pipeline_stages(self, fastvideo_args: FastVideoArgs) -> None:
-        """Set up pipeline stages with proper dependency injection."""
-
-        self.add_stage(stage_name="input_validation_stage", stage=InputValidationStage())
-
-        self.add_stage(stage_name="prompt_encoding_stage",
-                       stage=TextEncodingStage(
-                           text_encoders=[self.get_module("text_encoder")],
-                           tokenizers=[self.get_module("tokenizer")],
-                       ))
-
-        self.add_stage(stage_name="conditioning_stage", stage=ConditioningStage())
-
-        self.add_stage(stage_name="timestep_preparation_stage",
-                       stage=TimestepPreparationStage(scheduler=self.get_module("scheduler")))
-
-        self.add_stage(stage_name="latent_preparation_stage",
-                       stage=LatentPreparationStage(scheduler=self.get_module("scheduler"),
-                                                    transformer=self.get_module("transformer", None)))
-
-        # I2V: Encode initial image to latent space
-        self.add_stage(stage_name="image_latent_preparation_stage",
-                       stage=ImageVAEEncodingStage(vae=self.get_module("vae")))
-
-        self.add_stage(stage_name="denoising_stage",
-                       stage=WanDenoisingStage(transformer=self.get_module("transformer"),
-                                               transformer_2=self.get_module("transformer_2", None),
-                                               scheduler=self.get_module("scheduler"),
-                                               pipeline=self))
-
-        self.add_stage(stage_name="decoding_stage", stage=DecodingStage(vae=self.get_module("vae"), pipeline=self))
-
-
-EntryClass = TurboDiffusionI2VPipeline
diff --git a/fastvideo/pipelines/basic/turbodiffusion/turbodiffusion_pipeline.py b/fastvideo/pipelines/basic/turbodiffusion/turbodiffusion_pipeline.py
deleted file mode 100644
index f8a84a9ed..000000000
--- a/fastvideo/pipelines/basic/turbodiffusion/turbodiffusion_pipeline.py
+++ /dev/null
@@ -1,64 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-TurboDiffusion Video Pipeline Implementation.
-
-This module contains an implementation of the TurboDiffusion video diffusion pipeline
-for 1-4 step video generation using rCM (recurrent Consistency Model) sampling
-with SLA (Sparse-Linear Attention).
-"""
-
-from fastvideo.pipelines.basic.wan.stages.denoising import WanDenoisingStage
-from fastvideo.fastvideo_args import FastVideoArgs
-from fastvideo.logger import init_logger
-from fastvideo.models.schedulers.scheduling_rcm import RCMScheduler
-from fastvideo.pipelines import ComposedPipelineBase, LoRAPipeline
-from fastvideo.pipelines.stages import (ConditioningStage, DecodingStage, InputValidationStage, LatentPreparationStage,
-                                        TextEncodingStage, TimestepPreparationStage)
-
-logger = init_logger(__name__)
-
-
-class TurboDiffusionPipeline(LoRAPipeline, ComposedPipelineBase):
-    """
-    TurboDiffusion video pipeline for 1-4 step generation.
-    
-    Uses RCM scheduler and SLA attention for fast, high-quality video generation.
-    """
-
-    _required_config_modules = ["text_encoder", "tokenizer", "vae", "transformer", "scheduler"]
-
-    def initialize_pipeline(self, fastvideo_args: FastVideoArgs):
-        # Use RCM scheduler for TurboDiffusion
-        logger.info("Initializing RCM scheduler for TurboDiffusion")
-        self.modules["scheduler"] = RCMScheduler(sigma_max=80.0)
-
-    def create_pipeline_stages(self, fastvideo_args: FastVideoArgs) -> None:
-        """Set up pipeline stages with proper dependency injection."""
-
-        self.add_stage(stage_name="input_validation_stage", stage=InputValidationStage())
-
-        self.add_stage(stage_name="prompt_encoding_stage",
-                       stage=TextEncodingStage(
-                           text_encoders=[self.get_module("text_encoder")],
-                           tokenizers=[self.get_module("tokenizer")],
-                       ))
-
-        self.add_stage(stage_name="conditioning_stage", stage=ConditioningStage())
-
-        self.add_stage(stage_name="timestep_preparation_stage",
-                       stage=TimestepPreparationStage(scheduler=self.get_module("scheduler")))
-
-        self.add_stage(stage_name="latent_preparation_stage",
-                       stage=LatentPreparationStage(scheduler=self.get_module("scheduler"),
-                                                    transformer=self.get_module("transformer", None)))
-
-        self.add_stage(stage_name="denoising_stage",
-                       stage=WanDenoisingStage(transformer=self.get_module("transformer"),
-                                               transformer_2=self.get_module("transformer_2", None),
-                                               scheduler=self.get_module("scheduler"),
-                                               pipeline=self))
-
-        self.add_stage(stage_name="decoding_stage", stage=DecodingStage(vae=self.get_module("vae"), pipeline=self))
-
-
-EntryClass = TurboDiffusionPipeline
diff --git a/fastvideo/pipelines/basic/zimage/__init__.py b/fastvideo/pipelines/basic/zimage/__init__.py
deleted file mode 100644
index 9428b7d51..000000000
--- a/fastvideo/pipelines/basic/zimage/__init__.py
+++ /dev/null
@@ -1,6 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""Z-Image pipeline."""
-
-from fastvideo.pipelines.basic.zimage.zimage_pipeline import ZImagePipeline
-
-__all__ = ["ZImagePipeline"]
diff --git a/fastvideo/pipelines/basic/zimage/presets.py b/fastvideo/pipelines/basic/zimage/presets.py
deleted file mode 100644
index 59e2e554c..000000000
--- a/fastvideo/pipelines/basic/zimage/presets.py
+++ /dev/null
@@ -1,40 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""Z-Image inference presets."""
-
-from fastvideo.api.presets import InferencePreset, PresetStageSpec
-
-_DENOISE_STAGE = PresetStageSpec(
-    name="denoise",
-    kind="denoising",
-    description="Z-Image denoising pass",
-    allowed_overrides=frozenset({
-        "num_inference_steps",
-        "guidance_scale",
-        "cfg_normalization",
-        "cfg_truncation",
-    }),
-)
-
-ZIMAGE_TURBO = InferencePreset(
-    name="zimage_turbo",
-    version=1,
-    model_family="zimage",
-    description="Z-Image-Turbo text-to-image generation",
-    workload_type="t2i",
-    stage_schemas=(_DENOISE_STAGE, ),
-    defaults={
-        "height": 1024,
-        "width": 1024,
-        "num_frames": 1,
-        "fps": 1,
-        "seed": 42,
-        "guidance_scale": 0.0,
-        "num_inference_steps": 8,
-        "negative_prompt": "",
-        "max_sequence_length": 512,
-        "cfg_normalization": False,
-        "cfg_truncation": 1.0,
-    },
-)
-
-ALL_PRESETS = (ZIMAGE_TURBO, )
diff --git a/fastvideo/pipelines/basic/zimage/stages.py b/fastvideo/pipelines/basic/zimage/stages.py
deleted file mode 100644
index bce1019cd..000000000
--- a/fastvideo/pipelines/basic/zimage/stages.py
+++ /dev/null
@@ -1,340 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""Pipeline stages for the native Z-Image text-to-image path."""
-
-from __future__ import annotations
-
-import inspect
-
-import torch
-
-from fastvideo.distributed import get_local_torch_device
-from fastvideo.fastvideo_args import FastVideoArgs
-from fastvideo.forward_context import set_forward_context
-from fastvideo.hooks.activation_trace import trace_step
-from fastvideo.pipelines.pipeline_batch_info import ForwardBatch
-from fastvideo.pipelines.stages.base import PipelineStage
-from fastvideo.pipelines.stages.input_validation import InputValidationStage
-from fastvideo.utils import PRECISION_TO_TYPE
-
-
-class ZImageInputValidationStage(InputValidationStage):
-    """Validate the image geometry and reproduce the official device RNG."""
-
-    def _generate_seeds(self, batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> None:
-        del fastvideo_args
-        assert batch.seed is not None
-        batch.seeds = [batch.seed]
-        device = get_local_torch_device()
-        batch.generator = torch.Generator(device=device).manual_seed(batch.seed)
-
-    def forward(self, batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> ForwardBatch:
-        if batch.do_classifier_free_guidance and batch.negative_prompt is None and not batch.negative_prompt_embeds:
-            batch.negative_prompt = ""
-        batch = super().forward(batch, fastvideo_args)
-        if batch.num_frames != 1:
-            raise ValueError(f"Z-Image is text-to-image and requires num_frames=1, got {batch.num_frames}")
-        if batch.height is None or batch.width is None:
-            raise ValueError("Z-Image requires height and width")
-        if batch.height % 16 or batch.width % 16:
-            raise ValueError("Z-Image height and width must be divisible by 16; "
-                             f"got {batch.height}x{batch.width}")
-        return batch
-
-
-class ZImageConditioningStage(PipelineStage):
-    """Trim padded Qwen states and materialize variable-length CFG streams."""
-
-    @staticmethod
-    def _trim_embeddings(
-        embeds: torch.Tensor,
-        attention_mask: torch.Tensor | None,
-    ) -> list[torch.Tensor]:
-        if attention_mask is None:
-            return list(embeds.unbind(0))
-        return [
-            sample[mask.to(device=sample.device, dtype=torch.bool)]
-            for sample, mask in zip(embeds, attention_mask, strict=True)
-        ]
-
-    @staticmethod
-    def _repeat(items: list[torch.Tensor], count: int) -> list[torch.Tensor]:
-        return [item for item in items for _ in range(count)]
-
-    @torch.no_grad()
-    def forward(self, batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> ForwardBatch:
-        del fastvideo_args
-        if len(batch.prompt_embeds) != 1:
-            raise ValueError(f"Z-Image expects one text encoder, got {len(batch.prompt_embeds)}")
-
-        prompt_mask = batch.prompt_attention_mask[0] if batch.prompt_attention_mask else None
-        prompt_embeds = self._trim_embeddings(batch.prompt_embeds[0], prompt_mask)
-        batch.extra["zimage_prompt_embeds"] = self._repeat(prompt_embeds, batch.num_videos_per_prompt)
-
-        if batch.do_classifier_free_guidance:
-            if not batch.negative_prompt_embeds:
-                raise ValueError("Z-Image CFG requires negative prompt embeddings")
-            negative_mask = batch.negative_attention_mask[0] if batch.negative_attention_mask else None
-            negative_embeds = self._trim_embeddings(batch.negative_prompt_embeds[0], negative_mask)
-            batch.extra["zimage_negative_prompt_embeds"] = self._repeat(
-                negative_embeds,
-                batch.num_videos_per_prompt,
-            )
-        else:
-            batch.extra["zimage_negative_prompt_embeds"] = []
-        return batch
-
-
-class ZImageLatentPreparationStage(PipelineStage):
-    """Create the official fp32 image latents on the transformer device."""
-
-    def __init__(self, transformer) -> None:
-        self.transformer = transformer
-
-    @staticmethod
-    def _randn(
-        shape: tuple[int, ...],
-        generators: torch.Generator | list[torch.Generator] | None,
-        device: torch.device,
-    ) -> torch.Tensor:
-        if isinstance(generators, list):
-            if len(generators) != shape[0]:
-                raise ValueError(f"generator list length {len(generators)} does not match batch size {shape[0]}")
-            sample_shape = (1, *shape[1:])
-            return torch.cat([
-                torch.randn(sample_shape, generator=generator, device=device, dtype=torch.float32)
-                for generator in generators
-            ])
-        return torch.randn(shape, generator=generators, device=device, dtype=torch.float32)
-
-    @torch.no_grad()
-    def forward(self, batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> ForwardBatch:
-        if batch.height is None or batch.width is None:
-            raise ValueError("Z-Image requires height and width before latent preparation")
-
-        prompt_embeds = batch.extra.get("zimage_prompt_embeds")
-        if not isinstance(prompt_embeds, list) or not prompt_embeds:
-            raise ValueError("Z-Image conditioning must run before latent preparation")
-
-        channels = int(getattr(self.transformer, "in_channels", 16))
-        spatial_ratio = int(fastvideo_args.pipeline_config.vae_config.arch_config.spatial_compression_ratio)
-        shape = (
-            len(prompt_embeds),
-            channels,
-            1,
-            batch.height // spatial_ratio,
-            batch.width // spatial_ratio,
-        )
-        device = get_local_torch_device()
-        if batch.latents is None:
-            latents = self._randn(shape, batch.generator, device)
-        else:
-            latents = batch.latents
-            if latents.ndim == 4:
-                latents = latents.unsqueeze(2)
-            if tuple(latents.shape) != shape:
-                raise ValueError(f"Expected Z-Image latents with shape {shape}, got {tuple(latents.shape)}")
-            latents = latents.to(device=device, dtype=torch.float32)
-
-        batch.latents = latents
-        batch.raw_latent_shape = shape
-        return batch
-
-
-class ZImageTimestepPreparationStage(PipelineStage):
-    """Apply the native scheduler's zero endpoint and discrete schedule."""
-
-    def __init__(self, scheduler) -> None:
-        self.scheduler = scheduler
-
-    @staticmethod
-    def _calculate_shift(
-        image_seq_len: int,
-        base_seq_len: int,
-        max_seq_len: int,
-        base_shift: float,
-        max_shift: float,
-    ) -> float:
-        slope = (max_shift - base_shift) / (max_seq_len - base_seq_len)
-        return image_seq_len * slope + base_shift - slope * base_seq_len
-
-    def forward(self, batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> ForwardBatch:
-        if batch.latents is None:
-            raise ValueError("Z-Image latents must be prepared before timesteps")
-        if batch.timesteps is not None and batch.sigmas is not None:
-            raise ValueError("Only one of timesteps or sigmas may be supplied")
-
-        scheduler = self.scheduler
-        sigma_min = float(fastvideo_args.pipeline_config.scheduler_sigma_min)
-        use_reference_timesteps = bool(fastvideo_args.pipeline_config.scheduler_use_reference_discrete_timesteps)
-        scheduler.sigma_min = sigma_min
-        scheduler.register_to_config(
-            sigma_min=sigma_min,
-            use_reference_discrete_timesteps=use_reference_timesteps,
-        )
-        config = scheduler.config
-        image_seq_len = (batch.latents.shape[-2] // 2) * (batch.latents.shape[-1] // 2)
-        mu = self._calculate_shift(
-            image_seq_len,
-            int(config.get("base_image_seq_len", 256)),
-            int(config.get("max_image_seq_len", 4096)),
-            float(config.get("base_shift", 0.5)),
-            float(config.get("max_shift", 1.15)),
-        )
-        device = get_local_torch_device()
-
-        if batch.timesteps is not None:
-            if "timesteps" not in inspect.signature(scheduler.set_timesteps).parameters:
-                raise ValueError(f"{type(scheduler).__name__} does not accept custom timesteps")
-            scheduler.set_timesteps(timesteps=batch.timesteps, device=device, mu=mu)
-        elif batch.sigmas is not None:
-            if "sigmas" not in inspect.signature(scheduler.set_timesteps).parameters:
-                raise ValueError(f"{type(scheduler).__name__} does not accept custom sigmas")
-            scheduler.set_timesteps(sigmas=batch.sigmas, device=device, mu=mu)
-        else:
-            scheduler.set_timesteps(batch.num_inference_steps, device=device, mu=mu)
-
-        batch.timesteps = scheduler.timesteps
-        batch.num_inference_steps = len(batch.timesteps)
-        return batch
-
-
-class ZImageDenoisingStage(PipelineStage):
-    """Run the native Z-Image flow-matching loop."""
-
-    performance_component_metric = "transformer_time_s"
-
-    def __init__(self, transformer, scheduler) -> None:
-        self.transformer = transformer
-        self.scheduler = scheduler
-
-    @torch.no_grad()
-    def forward(self, batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> ForwardBatch:
-        if batch.latents is None or batch.timesteps is None:
-            raise ValueError("Z-Image denoising requires latents and timesteps")
-
-        latents = batch.latents.float()
-        positive = batch.extra.get("zimage_prompt_embeds")
-        negative = batch.extra.get("zimage_negative_prompt_embeds", [])
-        if not isinstance(positive, list) or not positive:
-            raise ValueError("Z-Image denoising requires prompt embeddings")
-
-        device = get_local_torch_device()
-        target_dtype = PRECISION_TO_TYPE[fastvideo_args.pipeline_config.dit_precision]
-        batch_size = latents.shape[0]
-
-        for index, timestep_value in enumerate(batch.timesteps):
-            # Stop if interrupted
-            if getattr(self, "interrupt", False):
-                break
-            if timestep_value.item() == 0 and index == len(batch.timesteps) - 1:
-                continue
-
-            timestep = timestep_value.expand(batch_size).to(device=device, dtype=torch.float32)
-            timestep = (1000.0 - timestep) / 1000.0
-            current_guidance_scale = float(batch.guidance_scale)
-            if (batch.do_classifier_free_guidance and batch.cfg_truncation is not None and batch.cfg_truncation <= 1.0
-                    and timestep[0].item() > batch.cfg_truncation):
-                current_guidance_scale = 0.0
-            apply_cfg = batch.do_classifier_free_guidance and current_guidance_scale > 0.0
-
-            if apply_cfg:
-                if not isinstance(negative, list) or len(negative) != batch_size:
-                    raise ValueError("Z-Image CFG requires one negative embedding per image")
-                model_latents = latents.to(target_dtype).repeat(2, 1, 1, 1, 1)
-                model_embeddings = positive + negative
-                model_timestep = timestep.repeat(2)
-            else:
-                model_latents = latents.to(target_dtype)
-                model_embeddings = positive
-                model_timestep = timestep
-
-            with (
-                    torch.autocast(
-                        device_type=device.type,
-                        enabled=False,
-                    ),
-                    trace_step(index),
-                    set_forward_context(
-                        current_timestep=index,
-                        attn_metadata=None,
-                        forward_batch=batch,
-                    ),
-            ):
-                model_outputs = self.transformer(
-                    hidden_states=model_latents,
-                    encoder_hidden_states=model_embeddings,
-                    timestep=model_timestep,
-                )[0]
-
-            if apply_cfg:
-                positive_outputs = model_outputs[:batch_size]
-                negative_outputs = model_outputs[batch_size:]
-                guided_outputs = []
-                for positive_output, negative_output in zip(
-                        positive_outputs,
-                        negative_outputs,
-                        strict=True,
-                ):
-                    positive_fp32 = positive_output.float()
-                    prediction = positive_fp32 + current_guidance_scale * (positive_fp32 - negative_output.float())
-                    if batch.cfg_normalization:
-                        positive_norm = torch.linalg.vector_norm(positive_fp32)
-                        prediction_norm = torch.linalg.vector_norm(prediction)
-                        if prediction_norm > positive_norm:
-                            prediction = prediction * (positive_norm / prediction_norm)
-                    guided_outputs.append(prediction)
-                noise_pred = torch.stack(guided_outputs)
-            else:
-                noise_pred = torch.stack([output.float() for output in model_outputs])
-
-            noise_pred = -noise_pred
-            latents = self.scheduler.step(
-                noise_pred,
-                timestep_value,
-                latents,
-                return_dict=False,
-            )[0].float()
-
-        batch.latents = latents
-        return batch
-
-
-class ZImageDecodingStage(PipelineStage):
-    """Apply the official latent transform and decode one image frame."""
-
-    performance_component_metric = "vae_decode_time_s"
-
-    def __init__(self, vae) -> None:
-        self.vae = vae
-
-    @torch.no_grad()
-    def forward(self, batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> ForwardBatch:
-        if batch.latents is None:
-            raise ValueError("Z-Image decoding requires latents")
-        if fastvideo_args.output_type == "latent":
-            # FastVideo standardizes image and video latents as [B,C,T,H,W].
-            # Tongyi's image-only API returns the equivalent tensor with T squeezed.
-            batch.output = batch.latents
-            return batch
-
-        latents = batch.latents
-        if latents.ndim != 5 or latents.shape[2] != 1:
-            raise ValueError(f"Expected Z-Image latents [B,C,1,H,W], got {tuple(latents.shape)}")
-
-        device = get_local_torch_device()
-        self.vae = self.vae.to(device)
-        vae_dtype = getattr(self.vae, "dtype", None)
-        if vae_dtype is None:
-            vae_dtype = next(self.vae.parameters()).dtype
-        config = self.vae.config
-        scaling_factor = float(config.scaling_factor)
-        shift_factor = float(config.shift_factor or 0.0)
-        latents_2d = latents.squeeze(2).to(device=device, dtype=vae_dtype)
-        latents_2d = latents_2d / scaling_factor + shift_factor
-        decoded = self.vae.decode(latents_2d, return_dict=False)[0]
-        decoded = (decoded / 2 + 0.5).clamp(0, 1)
-        batch.output = decoded.unsqueeze(2).float()
-
-        if fastvideo_args.vae_cpu_offload:
-            self.vae.to("cpu")
-        return batch
diff --git a/fastvideo/pipelines/basic/zimage/zimage_pipeline.py b/fastvideo/pipelines/basic/zimage/zimage_pipeline.py
deleted file mode 100644
index ecf897c78..000000000
--- a/fastvideo/pipelines/basic/zimage/zimage_pipeline.py
+++ /dev/null
@@ -1,63 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-
-from __future__ import annotations
-
-from fastvideo.configs.pipelines.zimage import ZImagePipelineConfig
-from fastvideo.fastvideo_args import FastVideoArgs
-from fastvideo.pipelines.composed_pipeline_base import ComposedPipelineBase
-from fastvideo.pipelines.stages.text_encoding import TextEncodingStage
-
-from .stages import (
-    ZImageConditioningStage,
-    ZImageDecodingStage,
-    ZImageDenoisingStage,
-    ZImageInputValidationStage,
-    ZImageLatentPreparationStage,
-    ZImageTimestepPreparationStage,
-)
-
-
-class ZImagePipeline(ComposedPipelineBase):
-    """Native Z-Image text-to-image pipeline."""
-
-    pipeline_config_cls: type[ZImagePipelineConfig] = ZImagePipelineConfig
-    _required_config_modules = [
-        "scheduler",
-        "text_encoder",
-        "tokenizer",
-        "transformer",
-        "vae",
-    ]
-
-    def create_pipeline_stages(self, fastvideo_args: FastVideoArgs) -> None:
-        scheduler = self.get_module("scheduler")
-        transformer = self.get_module("transformer")
-
-        self.add_stage("input_validation_stage", ZImageInputValidationStage())
-        self.add_stage(
-            "text_encoding_stage",
-            TextEncodingStage(
-                text_encoders=[self.get_module("text_encoder")],
-                tokenizers=[self.get_module("tokenizer")],
-            ),
-        )
-        self.add_stage("zimage_conditioning_stage", ZImageConditioningStage())
-        self.add_stage(
-            "latent_preparation_stage",
-            ZImageLatentPreparationStage(transformer=transformer),
-        )
-        self.add_stage(
-            "timestep_preparation_stage",
-            ZImageTimestepPreparationStage(scheduler=scheduler),
-        )
-        self.add_stage(
-            "denoising_stage",
-            ZImageDenoisingStage(transformer=transformer, scheduler=scheduler),
-        )
-        self.add_stage(
-            "decoding_stage",
-            ZImageDecodingStage(vae=self.get_module("vae")),
-        )
-
-
-EntryClass = ZImagePipeline
diff --git a/fastvideo/pipelines/composed_pipeline_base.py b/fastvideo/pipelines/composed_pipeline_base.py
index 7f85149df..c4a16d0e9 100644
--- a/fastvideo/pipelines/composed_pipeline_base.py
+++ b/fastvideo/pipelines/composed_pipeline_base.py
@@ -97,8 +97,8 @@ class ComposedPipelineBase(ABC):
     # component and loading it again is only safe when nothing outside the
     # loader has changed it. Two habits break that and neither raises:
     #
-    #   * mutating a component after load. ``LongCatPipeline.initialize_pipeline``
-    #     turns on block-sparse attention and writes parameters into every
+    #   * mutating a component after load, e.g. an ``initialize_pipeline`` that
+    #     turns on a sparse-attention mode and writes parameters into every
     #     transformer block. That runs once, so a re-materialized component
     #     silently comes back with the feature off.
     #   * reading a component's attributes while building stages. The shared
diff --git a/fastvideo/pipelines/pipeline_batch_info.py b/fastvideo/pipelines/pipeline_batch_info.py
index 0749eb10e..f72546d18 100644
--- a/fastvideo/pipelines/pipeline_batch_info.py
+++ b/fastvideo/pipelines/pipeline_batch_info.py
@@ -112,12 +112,9 @@ class ForwardBatch:
     audio_path: str | None = None
     audio_embeds: torch.Tensor | None = None
 
-    # Refine inputs (LongCat)
-    refine_from: str | None = None
+    # Refine / conditioning-frame inputs
     t_thresh: float = 0.5
-    spatial_refine_only: bool = False
     num_cond_frames: int = 0
-    stage1_video: list[PIL.Image.Image] | None = None  # Loaded frames from refine_from
 
     # Primary encoder embeddings
     prompt_embeds: list[torch.Tensor] = field(default_factory=list)
@@ -166,13 +163,6 @@ class ForwardBatch:
     # Camera control inputs (HYWorld)
     pose: str | None = None  # Camera trajectory: pose string (e.g., 'w-31') or JSON file path
 
-    # Camera/action control inputs (GameCraft)
-    camera_states: torch.Tensor | None = None  # Plücker coordinates [B, T, 6, H, W]
-    gt_latents: torch.Tensor | None = None  # Ground truth latents for conditioning [B, 16, T, H, W]
-    conditioning_mask: torch.Tensor | None = None  # Mask for conditioning [B, 1, T, H, W]
-    camera_trajectory: str | None = None  # Camera trajectory file/identifier
-    action_list: list[str] | None = None  # List of actions (e.g., ['forward', 'left'])
-    action_speed_list: list[float] | None = None  # Speed for each action
     # Camera control inputs (LingBotWorld and LingBotWorld2)
     c2ws_plucker_emb: torch.Tensor | None = None  # Plucker embedding: [B, C, F_lat, H_lat, W_lat]
     action_path: str | None = None  # Directory containing poses.npy and intrinsics.npy
@@ -206,8 +196,6 @@ class ForwardBatch:
     guidance_scale: float = 1.0
     batch_cfg: bool = False
     guidance_scale_2: float | None = None
-    cfg_normalization: bool = False
-    cfg_truncation: float | None = 1.0
     guidance_rescale: float = 0.0
     eta: float = 0.0
     sigmas: list[float] | None = None
diff --git a/fastvideo/pipelines/stages/__init__.py b/fastvideo/pipelines/stages/__init__.py
index 140d47580..1a1dfdedc 100644
--- a/fastvideo/pipelines/stages/__init__.py
+++ b/fastvideo/pipelines/stages/__init__.py
@@ -21,7 +21,6 @@ from fastvideo.pipelines.stages.image_encoding import (ImageEncodingStage, Matri
                                                        MatrixGame3ImageVAEEncodingStage, RefImageEncodingStage,
                                                        ImageVAEEncodingStage, VideoVAEEncodingStage,
                                                        Hy15ImageEncodingStage, HYWorldImageEncodingStage)
-from fastvideo.pipelines.stages.gamecraft_image_encoding import (GameCraftImageVAEEncodingStage)
 from fastvideo.pipelines.stages.input_validation import InputValidationStage
 from fastvideo.pipelines.stages.latent_preparation import (Cosmos25LatentPreparationStage, CosmosLatentPreparationStage,
                                                            Cosmos25AutoLatentPreparationStage,
@@ -41,17 +40,11 @@ from fastvideo.pipelines.stages.kandinsky5 import (Kandinsky5DecodingStage, Kand
 from fastvideo.pipelines.stages.kandinsky6 import (Kandinsky6AudioDecodingStage, Kandinsky6DecodingStage,
                                                    Kandinsky6DenoisingStage, Kandinsky6ImageEncodingStage,
                                                    Kandinsky6LatentPreparationStage)
-from fastvideo.pipelines.stages.gamecraft_denoising import GameCraftDenoisingStage
 from fastvideo.pipelines.stages.gen3c_stages import (Gen3CCFGPolicyStage, Gen3CConditioningStage, Gen3CDenoisingStage,
                                                      Gen3CLatentPreparationStage)
 from fastvideo.pipelines.stages.text_encoding import (Cosmos25TextEncodingStage, TextEncodingStage)
 from fastvideo.pipelines.stages.timestep_preparation import (Cosmos25TimestepPreparationStage, TimestepPreparationStage)
 
-# LongCat stages
-from fastvideo.pipelines.stages.longcat_video_vae_encoding import LongCatVideoVAEEncodingStage
-from fastvideo.pipelines.stages.longcat_kv_cache_init import LongCatKVCacheInitStage
-from fastvideo.pipelines.stages.longcat_vc_denoising import LongCatVCDenoisingStage
-
 __all__ = [
     "AudioEncodingStage",
     "PipelineStage",
@@ -84,7 +77,6 @@ __all__ = [
     "Kandinsky6DenoisingStage",
     "Kandinsky6ImageEncodingStage",
     "Kandinsky6LatentPreparationStage",
-    "GameCraftDenoisingStage",
     "Gen3CCFGPolicyStage",
     "Gen3CConditioningStage",
     "Gen3CLatentPreparationStage",
@@ -110,13 +102,8 @@ __all__ = [
     "RefImageEncodingStage",
     "ImageVAEEncodingStage",
     "VideoVAEEncodingStage",
-    "GameCraftImageVAEEncodingStage",
     "TextEncodingStage",
     "Cosmos25TextEncodingStage",
-    # LongCat stages
-    "LongCatVideoVAEEncodingStage",
-    "LongCatKVCacheInitStage",
-    "LongCatVCDenoisingStage",
 ]
 
 
diff --git a/fastvideo/pipelines/stages/decoding.py b/fastvideo/pipelines/stages/decoding.py
index b95abd287..27b5160a3 100644
--- a/fastvideo/pipelines/stages/decoding.py
+++ b/fastvideo/pipelines/stages/decoding.py
@@ -320,16 +320,6 @@ class DecodingStage(PipelineStage):
         # Convert to float32 for compatibility
         frames = frames.to(torch.float32)
 
-        # Crop padding if this is a LongCat refinement
-        if hasattr(batch, 'num_cond_frames_added') and hasattr(batch, 'new_frame_size_before_padding'):
-            num_cond_frames_added = batch.num_cond_frames_added
-            new_frame_size = batch.new_frame_size_before_padding
-            if num_cond_frames_added > 0 or frames.shape[2] != new_frame_size:
-                # frames is [B, C, T, H, W], crop temporal dimension
-                frames = frames[:, :, num_cond_frames_added:num_cond_frames_added + new_frame_size, :, :]
-                logger.info("Cropped LongCat refinement padding: %s:%s, final shape: %s", num_cond_frames_added,
-                            num_cond_frames_added + new_frame_size, frames.shape)
-
         # Update batch with decoded image
         batch.output = frames
 
diff --git a/fastvideo/pipelines/stages/denoising.py b/fastvideo/pipelines/stages/denoising.py
index 2e4bfb36a..acdc478ae 100644
--- a/fastvideo/pipelines/stages/denoising.py
+++ b/fastvideo/pipelines/stages/denoising.py
@@ -196,26 +196,6 @@ class DenoisingStage(PipelineStage):
             },
         )
 
-        camera_kwargs = self.prepare_extra_func_kwargs(
-            self.transformer.forward,
-            {
-                "camera_states": batch.camera_states,
-            },
-        )
-
-        dreamx_y_camera = batch.extra.get("dreamx_y_camera", batch.extra.get("y_camera"))
-        if isinstance(dreamx_y_camera, dict):
-            dreamx_y_camera = {
-                key: value.to(device=local_device, dtype=target_dtype) if torch.is_tensor(value) else value
-                for key, value in dreamx_y_camera.items()
-            }
-        dreamx_camera_kwargs = self.prepare_extra_func_kwargs(
-            self.transformer.forward,
-            {
-                "y_camera": dreamx_y_camera,
-            },
-        )
-
         for key in ("flux2_txt_ids", "flux2_img_ids"):
             value = batch.extra.get(key)
             if torch.is_tensor(value):
@@ -421,8 +401,6 @@ class DenoisingStage(PipelineStage):
                             **image_kwargs,
                             **pos_cond_kwargs,
                             **action_kwargs,
-                            **camera_kwargs,
-                            **dreamx_camera_kwargs,
                             **timesteps_r_kwarg,
                             **flux2_id_kwargs,
                             **animate_kwargs,
@@ -467,8 +445,6 @@ class DenoisingStage(PipelineStage):
                                     **image_kwargs,
                                     **neg_cond_kwargs,
                                     **action_kwargs,
-                                    **camera_kwargs,
-                                    **dreamx_camera_kwargs,
                                     **timesteps_r_kwarg,
                                     **flux2_id_kwargs,
                                     **animate_uncond_kwargs,
diff --git a/fastvideo/pipelines/stages/gamecraft_denoising.py b/fastvideo/pipelines/stages/gamecraft_denoising.py
deleted file mode 100644
index 3bac8d95a..000000000
--- a/fastvideo/pipelines/stages/gamecraft_denoising.py
+++ /dev/null
@@ -1,318 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-GameCraft denoising stage for camera/action-conditioned video generation.
-
-This stage implements the denoising loop for HunyuanGameCraft, which generates
-game-like videos with camera and action conditioning via:
-1. CameraNet - Encodes Plücker coordinates into features added to image embeddings
-2. Concatenated input - 33 channels (16 latent + 16 gt_latent + 1 mask)
-3. Mask-based conditioning for autoregressive generation
-"""
-
-import torch
-
-from fastvideo.fastvideo_args import FastVideoArgs
-from fastvideo.forward_context import set_forward_context
-from fastvideo.logger import init_logger
-from fastvideo.models.loader.component_loader import TransformerLoader
-from fastvideo.pipelines.pipeline_batch_info import ForwardBatch
-from fastvideo.pipelines.stages.denoising import DenoisingStage
-from fastvideo.pipelines.stages.validators import StageValidators as V
-from fastvideo.pipelines.stages.validators import VerificationResult
-
-logger = init_logger(__name__)
-
-
-class GameCraftDenoisingStage(DenoisingStage):
-    """
-    Denoising stage for HunyuanGameCraft with camera/action conditioning.
-    
-    This stage handles:
-    - Camera state encoding via CameraNet (Plücker coordinates)
-    - Concatenation of latents with gt_latents and mask (33 channels)
-    - Flow matching denoising with camera conditioning
-    - Support for autoregressive generation with history frames
-    """
-
-    def __init__(
-        self,
-        transformer,
-        scheduler,
-        pipeline=None,
-        transformer_2=None,
-        vae=None,
-    ) -> None:
-        super().__init__(transformer, scheduler, pipeline, transformer_2, vae)
-
-    def forward(
-        self,
-        batch: ForwardBatch,
-        fastvideo_args: FastVideoArgs,
-    ) -> ForwardBatch:
-        """
-        Run the denoising loop with camera/action conditioning.
-        
-        Args:
-            batch: The current batch information. Must contain:
-                - latents: Noise latents [B, 16, T, H, W]
-                - camera_states: Plücker coordinates [B, T_video, 6, H_video, W_video]
-                - gt_latents (optional): Ground truth latents for conditioning [B, 16, T, H, W]
-                - conditioning_mask (optional): Mask for conditioning [B, 1, T, H, W]
-            fastvideo_args: The inference arguments.
-            
-        Returns:
-            The batch with denoised latents.
-        """
-        pipeline = self.pipeline() if self.pipeline else None
-        if not fastvideo_args.model_loaded["transformer"]:
-            loader = TransformerLoader()
-            self.transformer = loader.load(fastvideo_args.model_paths["transformer"], fastvideo_args)
-            if pipeline:
-                pipeline.add_module("transformer", self.transformer)
-            fastvideo_args.model_loaded["transformer"] = True
-
-        # Extract GameCraft-specific parameters
-        camera_states = getattr(batch, "camera_states", None)
-        if camera_states is None:
-            camera_states = batch.extra.get("camera_states", None)
-
-        gt_latents = getattr(batch, "gt_latents", None)
-        if gt_latents is None:
-            gt_latents = batch.extra.get("gt_latents", None)
-
-        conditioning_mask = getattr(batch, "conditioning_mask", None)
-        if conditioning_mask is None:
-            conditioning_mask = batch.extra.get("conditioning_mask", None)
-
-        # Prepare extra step kwargs for scheduler
-        extra_step_kwargs = self.prepare_extra_func_kwargs(
-            self.scheduler.step,
-            {
-                "generator": batch.generator,
-                "eta": batch.eta,
-            },
-        )
-
-        # Setup precision and autocast settings
-        target_dtype = torch.bfloat16
-        autocast_enabled = (target_dtype != torch.float32) and not fastvideo_args.disable_autocast
-
-        # Get timesteps
-        timesteps = batch.timesteps
-        if timesteps is None:
-            raise ValueError("Timesteps must be provided")
-        num_inference_steps = batch.num_inference_steps
-        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
-
-        # Prepare image embeddings for I2V generation (if any)
-        image_embeds = batch.image_embeds
-        if len(image_embeds) > 0:
-            assert not torch.isnan(image_embeds[0]).any(), "image_embeds contains nan"
-            image_embeds = [image_embed.to(target_dtype) for image_embed in image_embeds]
-
-        image_kwargs = self.prepare_extra_func_kwargs(
-            self.transformer.forward,
-            {"encoder_hidden_states_image": image_embeds},
-        )
-
-        pos_cond_kwargs = self.prepare_extra_func_kwargs(
-            self.transformer.forward,
-            {
-                "encoder_hidden_states_2": batch.clip_embedding_pos,
-                "encoder_attention_mask": batch.prompt_attention_mask,
-            },
-        )
-
-        neg_cond_kwargs = self.prepare_extra_func_kwargs(
-            self.transformer.forward,
-            {
-                "encoder_hidden_states_2": batch.clip_embedding_neg,
-                "encoder_attention_mask": batch.negative_attention_mask,
-            },
-        )
-
-        # Get latents and embeddings
-        latents = batch.latents
-        prompt_embeds = batch.prompt_embeds
-        assert not torch.isnan(prompt_embeds[0]).any(), "prompt_embeds contains nan"
-
-        if batch.do_classifier_free_guidance:
-            neg_prompt_embeds = batch.negative_prompt_embeds
-            assert neg_prompt_embeds is not None
-            assert not torch.isnan(neg_prompt_embeds[0]).any(), "neg_prompt_embeds contains nan"
-
-        # Prepare gt_latents and mask for concatenation
-        # If not provided, use zeros (for unconditional generation)
-        gt_latents = torch.zeros_like(latents) if gt_latents is None else gt_latents.to(target_dtype)
-
-        if conditioning_mask is None:
-            # Default mask: all zeros (generate everything)
-            conditioning_mask = torch.zeros(
-                latents.shape[0],
-                1,
-                *latents.shape[2:],
-                device=latents.device,
-                dtype=target_dtype,
-            )
-        else:
-            conditioning_mask = conditioning_mask.to(target_dtype)
-
-        # Move camera states to device if provided
-        if camera_states is not None:
-            camera_states = camera_states.to(device=latents.device, dtype=target_dtype)
-
-        # Debug logging
-        logger.debug("[GameCraft DEBUG] latents shape: %s, min/max: %.4f/%.4f", latents.shape, latents.min(),
-                     latents.max())
-        logger.debug("[GameCraft DEBUG] camera_states: %s", camera_states.shape if camera_states is not None else None)
-        logger.debug("[GameCraft DEBUG] prompt_embeds[0] shape: %s", prompt_embeds[0].shape)
-
-        # Initialize lists for trajectory
-        trajectory_timesteps: list[torch.Tensor] = []
-        trajectory_latents: list[torch.Tensor] = []
-
-        # For I2V: extract clean reference latents to inject at every step.
-        # Official GameCraft replaces conditioned frames of the denoising latents
-        # with the clean reference at EVERY timestep (not just the first).
-        # This keeps the conditioned frames noise-free so the model has a strong
-        # reference signal throughout the denoising process.
-        #
-        # ref_latent_for_injection: [B, 16, 1, H, W] or None
-        ref_latent_for_injection = getattr(batch, "_ref_latent_for_injection", None)
-        if (ref_latent_for_injection is None and gt_latents is not None and conditioning_mask.sum() > 0
-                and gt_latents[:, :, 0].abs().sum() > 0):
-            # Extract the clean reference from gt_latents first frame
-            # gt_latents[:, :, 0] should have the VAE-encoded reference image
-            ref_latent_for_injection = gt_latents[:, :, 0:1].clone()  # [B, 16, 1, H, W]
-            logger.info("[GameCraft I2V] Will inject ref latent at conditioned frames each step. "
-                        "ref mean=%.4f",
-                        ref_latent_for_injection.abs().mean())
-
-        # Run denoising loop
-        with self.progress_bar(total=num_inference_steps) as progress_bar:
-            for i, t in enumerate(timesteps):
-                # Skip if interrupted
-                if hasattr(self, "interrupt") and self.interrupt:
-                    continue
-
-                current_model = self.transformer
-                current_guidance_scale = batch.guidance_scale
-
-                # I2V: replace conditioned frames with clean reference latent
-                # (matches official: latents[:,:,0,:,:] = last_latents[:,:,-1,:,:])
-                if ref_latent_for_injection is not None:
-                    # conditioning_mask shape: [B, 1, T, H, W]
-                    # Where mask == 1, replace latents with the clean ref
-                    cond_frames = conditioning_mask[0, 0, :, 0, 0] > 0.5  # [T]
-                    for t_idx in range(cond_frames.shape[0]):
-                        if cond_frames[t_idx]:
-                            latents[:, :, t_idx, :, :] = ref_latent_for_injection[:, :, 0, :, :]
-
-                # Prepare model input: concatenate latents, gt_latents, and mask
-                # [B, 33, T, H, W] = [B, 16, T, H, W] + [B, 16, T, H, W] + [B, 1, T, H, W]
-                latent_model_input = torch.cat(
-                    [latents.to(target_dtype), gt_latents, conditioning_mask],
-                    dim=1,
-                )
-
-                assert not torch.isnan(latent_model_input).any(), "latent_model_input contains nan"
-
-                t_expand = t.repeat(latent_model_input.shape[0])
-
-                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
-
-                # Official GameCraft does NOT use embedded guidance (guidance=None)
-                # It uses standard CFG with guidance_scale instead
-                guidance_expand = None
-
-                # Run transformer with camera conditioning
-                with torch.autocast(
-                        device_type="cuda",
-                        dtype=target_dtype,
-                        enabled=autocast_enabled,
-                ):
-                    batch.is_cfg_negative = False
-                    with set_forward_context(
-                            current_timestep=i,
-                            attn_metadata=None,
-                            forward_batch=batch,
-                    ):
-                        noise_pred = current_model(
-                            latent_model_input,
-                            prompt_embeds,
-                            t_expand,
-                            camera_states=camera_states,
-                            guidance=guidance_expand,
-                            **image_kwargs,
-                            **pos_cond_kwargs,
-                        )
-
-                        # Debug: log first step output
-                        if i == 0:
-                            logger.info("[GameCraft DEBUG] Step 0 noise_pred: shape=%s, min/max=%.4f/%.4f",
-                                        noise_pred.shape, noise_pred.min(), noise_pred.max())
-
-                    # Classifier-free guidance
-                    if batch.do_classifier_free_guidance:
-                        batch.is_cfg_negative = True
-                        with set_forward_context(
-                                current_timestep=i,
-                                attn_metadata=None,
-                                forward_batch=batch,
-                        ):
-                            noise_pred_uncond = current_model(
-                                latent_model_input,
-                                neg_prompt_embeds,
-                                t_expand,
-                                camera_states=camera_states,
-                                guidance=guidance_expand,
-                                **image_kwargs,
-                                **neg_cond_kwargs,
-                            )
-
-                        noise_pred = noise_pred_uncond + current_guidance_scale * (noise_pred - noise_pred_uncond)
-
-                # Compute the previous noisy sample
-                latents = self.scheduler.step(
-                    noise_pred,
-                    t,
-                    latents,
-                    **extra_step_kwargs,
-                    return_dict=False,
-                )[0]
-
-                # Store trajectory if requested
-                if batch.return_trajectory_latents:
-                    trajectory_timesteps.append(t.clone())
-                    trajectory_latents.append(latents.clone())
-
-                # Update progress bar
-                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
-                    progress_bar.update()
-
-        # Debug: log final latents
-        logger.info("[GameCraft DEBUG] Final latents: shape=%s, min/max=%.4f/%.4f", latents.shape, latents.min(),
-                    latents.max())
-
-        # Store final latents and trajectory
-        batch.latents = latents
-        if batch.return_trajectory_latents:
-            batch.trajectory_timesteps = trajectory_timesteps
-            batch.trajectory_latents = torch.stack(trajectory_latents, dim=0)
-
-        return batch
-
-    def verify_input(self, batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> VerificationResult:
-        """Verify that required inputs are present."""
-        result = VerificationResult()
-        result.add_check("timesteps", batch.timesteps, [V.is_tensor, V.min_dims(1)])
-        result.add_check("latents", batch.latents, [V.is_tensor, V.with_dims(5)])
-        result.add_check("prompt_embeds", batch.prompt_embeds, V.list_not_empty)
-        result.add_check("num_inference_steps", batch.num_inference_steps, V.positive_int)
-        return result
-
-    def verify_output(self, batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> VerificationResult:
-        """Verify that outputs are properly set."""
-        result = VerificationResult()
-        result.add_check("latents", batch.latents, [V.is_tensor, V.with_dims(5)])
-        return result
diff --git a/fastvideo/pipelines/stages/gamecraft_image_encoding.py b/fastvideo/pipelines/stages/gamecraft_image_encoding.py
deleted file mode 100644
index 3f58e8bb4..000000000
--- a/fastvideo/pipelines/stages/gamecraft_image_encoding.py
+++ /dev/null
@@ -1,173 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-GameCraft image-to-video encoding stage.
-
-Encodes a reference image into gt_latents and conditioning_mask for
-HunyuanGameCraft I2V generation. For T2V this stage is a no-op.
-"""
-
-import torch
-
-from fastvideo.distributed import get_local_torch_device
-from fastvideo.fastvideo_args import FastVideoArgs
-from fastvideo.logger import init_logger
-from fastvideo.pipelines.pipeline_batch_info import ForwardBatch
-from fastvideo.pipelines.stages.base import PipelineStage
-from fastvideo.pipelines.stages.validators import VerificationResult
-from fastvideo.utils import PRECISION_TO_TYPE
-
-logger = init_logger(__name__)
-
-
-class GameCraftImageVAEEncodingStage(PipelineStage):
-    """
-    Stage for encoding a reference image into gt_latents and conditioning_mask
-    for HunyuanGameCraft image-to-video generation.
-
-    Official GameCraft I2V flow:
-    1. VAE-encode the reference image -> [B, 16, 1, H_lat, W_lat]
-    2. Scale by VAE scaling_factor (0.476986)
-    3. Repeat to all temporal frames
-    4. Zero out non-conditioned frames (first frame only for short videos,
-       first half for longer autoregressive generation)
-    5. Build a binary mask (1 = conditioned, 0 = generate)
-    6. Store gt_latents and conditioning_mask on the batch for the denoising stage
-
-    If no image is provided (T2V mode), this stage is a no-op; the denoising
-    stage already falls back to zero gt_latents and zero mask.
-    """
-
-    def __init__(self, vae) -> None:
-        super().__init__()
-        self.vae = vae
-
-    @torch.no_grad()
-    def forward(
-        self,
-        batch: ForwardBatch,
-        fastvideo_args: FastVideoArgs,
-    ) -> ForwardBatch:
-        """Encode reference image for I2V, or skip for T2V."""
-
-        if batch.pil_image is None:
-            # T2V mode: nothing to do; denoising stage handles the fallback
-            return batch
-
-        device = get_local_torch_device()
-        image = batch.pil_image
-
-        # ------------------------------------------------------------------
-        # 1. Preprocess image to tensor [B, 3, 1, H, W]  (values in [-1, 1])
-        # ------------------------------------------------------------------
-        from torchvision import transforms
-
-        target_height, target_width = batch.height, batch.width
-
-        if isinstance(image, torch.Tensor):
-            # Already a tensor (e.g. from InputValidationStage causal path)
-            if image.dim() == 5:
-                # [B, C, F, H, W] -> take first frame
-                ref_pixel = image[:, :, :1]
-            elif image.dim() == 4:
-                ref_pixel = image.unsqueeze(2)  # [B, C, H, W] -> [B, C, 1, H, W]
-            else:
-                raise ValueError(f"Unexpected image tensor dims: {image.dim()}")
-            ref_pixel = ref_pixel.to(device=device, dtype=torch.float32)
-        else:
-            # PIL Image – resize, center-crop, normalize to [-1, 1]
-            from PIL import Image as PILImage
-            if not isinstance(image, PILImage.Image):
-                import numpy as np
-                if isinstance(image, np.ndarray):
-                    image = PILImage.fromarray(image)
-
-            original_w, original_h = image.size
-            scale = max(target_width / original_w, target_height / original_h)
-            resize_w = int(round(original_w * scale))
-            resize_h = int(round(original_h * scale))
-
-            ref_transform = transforms.Compose([
-                transforms.Resize(
-                    (resize_h, resize_w),
-                    interpolation=transforms.InterpolationMode.LANCZOS,
-                ),
-                transforms.CenterCrop((target_height, target_width)),
-                transforms.ToTensor(),
-                transforms.Normalize([0.5], [0.5]),
-            ])
-            ref_pixel = ref_transform(image)  # [3, H, W]
-            ref_pixel = ref_pixel.unsqueeze(0).unsqueeze(2)  # [1, 3, 1, H, W]
-            ref_pixel = ref_pixel.to(device=device, dtype=torch.float32)
-
-        # ------------------------------------------------------------------
-        # 2. VAE-encode
-        # ------------------------------------------------------------------
-        self.vae = self.vae.to(device)
-
-        vae_dtype = PRECISION_TO_TYPE[fastvideo_args.pipeline_config.vae_precision]
-        vae_autocast_enabled = (vae_dtype != torch.float32) and not fastvideo_args.disable_autocast
-
-        with torch.autocast(device_type="cuda", dtype=vae_dtype, enabled=vae_autocast_enabled):
-            if fastvideo_args.pipeline_config.vae_tiling:
-                self.vae.enable_tiling()
-            if not vae_autocast_enabled:
-                ref_pixel = ref_pixel.to(vae_dtype)
-            encoder_output = self.vae.encode(ref_pixel)
-
-        # Sample from the distribution (official uses .sample())
-        ref_latents = encoder_output.latent_dist.sample().to(dtype=torch.float32)
-        # Scale by VAE scaling factor (0.476986 for GameCraft)
-        ref_latents.mul_(self.vae.config.scaling_factor)
-        # ref_latents: [B, 16, 1, H_lat, W_lat]
-
-        # ------------------------------------------------------------------
-        # 3. Build gt_latents + conditioning_mask
-        # ------------------------------------------------------------------
-        # Get latent temporal dimension from batch latents (set by LatentPreparationStage)
-        latent_frames = batch.latents.shape[2] if batch.latents is not None else ((batch.num_frames - 1) // 4 + 1)
-
-        # Repeat to all frames
-        gt_latents = ref_latents.repeat(1, 1, latent_frames, 1, 1)
-        # [B, 16, T_lat, H_lat, W_lat]
-
-        # Mask construction following official GameCraft logic:
-        #   - Short videos (latent_frames <= 10): first frame conditioned
-        #   - Longer videos: first half conditioned (autoregressive)
-        mask = torch.ones(
-            gt_latents.shape[0],
-            1,
-            gt_latents.shape[2],
-            gt_latents.shape[3],
-            gt_latents.shape[4],
-            device=gt_latents.device,
-            dtype=gt_latents.dtype,
-        )
-
-        if latent_frames <= 10:
-            # I2V: only first frame is conditioned
-            gt_latents[:, :, 1:, :, :] = 0.0
-            mask[:, :, 1:, :, :] = 0.0
-        else:
-            # Autoregressive: first half conditioned
-            half = latent_frames // 2
-            gt_latents[:, :, half:, :, :] = 0.0
-            mask[:, :, half:, :, :] = 0.0
-
-        batch.gt_latents = gt_latents.to(device=device)
-        batch.conditioning_mask = mask.to(device=device)
-
-        # Offload
-        if fastvideo_args.vae_cpu_offload:
-            self.vae.to("cpu")
-
-        return batch
-
-    def verify_input(self, batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> VerificationResult:
-        result = VerificationResult()
-        # Stage is a no-op when pil_image is None, so nothing required
-        return result
-
-    def verify_output(self, batch: ForwardBatch, fastvideo_args: FastVideoArgs) -> VerificationResult:
-        result = VerificationResult()
-        # gt_latents and conditioning_mask are only set for I2V
-        return result
diff --git a/fastvideo/pipelines/stages/latent_preparation.py b/fastvideo/pipelines/stages/latent_preparation.py
index 9cbfd622a..e0116af9a 100644
--- a/fastvideo/pipelines/stages/latent_preparation.py
+++ b/fastvideo/pipelines/stages/latent_preparation.py
@@ -137,12 +137,8 @@ class LatentPreparationStage(PipelineStage):
             if hasattr(self.scheduler, "init_noise_sigma"):
                 latents = latents * self.scheduler.init_noise_sigma
         else:
-            # Pre-initialized latents:
-            # - For LongCat refine (refine_from or stage1_video present), we should not re-scale by init_noise_sigma.
-            # - For other models, keep the original behavior.
             latents = latents.to(device)
-            is_longcat_refine = (batch.refine_from is not None) or (batch.stage1_video is not None)
-            if (not is_longcat_refine) and hasattr(self.scheduler, "init_noise_sigma"):
+            if hasattr(self.scheduler, "init_noise_sigma"):
                 latents = latents * self.scheduler.init_noise_sigma
 
         # Update batch with prepared latents
diff --git a/fastvideo/pipelines/stages/longcat_denoising.py b/fastvideo/pipelines/stages/longcat_denoising.py
deleted file mode 100644
index dec765373..000000000
--- a/fastvideo/pipelines/stages/longcat_denoising.py
+++ /dev/null
@@ -1,164 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-LongCat-specific denoising stage implementing CFG-zero optimized guidance.
-"""
-
-import torch
-from tqdm import tqdm
-
-from fastvideo.fastvideo_args import FastVideoArgs
-from fastvideo.logger import init_logger
-from fastvideo.pipelines.pipeline_batch_info import ForwardBatch
-from fastvideo.pipelines.stages.denoising import DenoisingStage
-from fastvideo.forward_context import set_forward_context
-
-logger = init_logger(__name__)
-
-
-class LongCatDenoisingStage(DenoisingStage):
-    """
-    LongCat denoising stage with CFG-zero optimized guidance scale.
-    
-    Implements:
-    1. Optimized CFG scale from CFG-zero paper
-    2. Negation of noise prediction before scheduler step (flow matching convention)
-    3. Batched CFG computation (unlike standard FastVideo separate passes)
-    """
-
-    def optimized_scale(self, positive_flat, negative_flat) -> torch.Tensor:
-        """
-        Calculate optimized scale from CFG-zero paper.
-        
-        st_star = (v_cond^T * v_uncond) / ||v_uncond||^2
-        
-        Args:
-            positive_flat: Conditional prediction, flattened [B, -1]
-            negative_flat: Unconditional prediction, flattened [B, -1]
-        
-        Returns:
-            st_star: Optimized scale [B, 1]
-        """
-        # Calculate dot product
-        dot_product = torch.sum(positive_flat * negative_flat, dim=1, keepdim=True)
-        # Squared norm of uncondition
-        squared_norm = torch.sum(negative_flat**2, dim=1, keepdim=True) + 1e-8
-        # st_star = v_cond^T * v_uncond / ||v_uncond||^2
-        st_star = dot_product / squared_norm
-        return st_star
-
-    def forward(
-        self,
-        batch: ForwardBatch,
-        fastvideo_args: FastVideoArgs,
-    ) -> ForwardBatch:
-        """
-        Run LongCat denoising loop with optimized CFG.
-        
-        Args:
-            batch: The current batch information.
-            fastvideo_args: The inference arguments.
-            
-        Returns:
-            The batch with denoised latents.
-        """
-        if not fastvideo_args.model_loaded["transformer"]:
-            from fastvideo.models.loader.component_loader import TransformerLoader
-            loader = TransformerLoader()
-            self.transformer = loader.load(fastvideo_args.model_paths["transformer"], fastvideo_args)
-            pipeline = self.pipeline() if self.pipeline else None
-            if pipeline:
-                pipeline.add_module("transformer", self.transformer)
-            fastvideo_args.model_loaded["transformer"] = True
-
-        # Inference dtype. We hardcode bf16 (matching the WanDenoisingStage
-        # pattern) rather than reading transformer.parameters().dtype: when
-        # a model is loaded with default_dtype=fp32 but its FSDP-wrapped
-        # submodules compute in bf16, the parameter-dtype heuristic
-        # mismatches the Conv3d weight/bias dtype and the patch_embed
-        # forward fails with "Input type (float) and bias type
-        # (c10::BFloat16) should be the same".
-        target_dtype = torch.bfloat16
-        autocast_enabled = (target_dtype != torch.float32) and not fastvideo_args.disable_autocast
-
-        # Extract batch parameters
-        latents = batch.latents
-        timesteps = batch.timesteps
-        prompt_embeds = batch.prompt_embeds[0]  # LongCat uses single encoder
-        prompt_attention_mask = batch.prompt_attention_mask[0] if batch.prompt_attention_mask else None
-        guidance_scale = batch.guidance_scale
-        do_classifier_free_guidance = batch.do_classifier_free_guidance
-
-        # Get negative prompts if doing CFG
-        if do_classifier_free_guidance:
-            negative_prompt_embeds = batch.negative_prompt_embeds[0]
-            negative_prompt_attention_mask = (batch.negative_attention_mask[0]
-                                              if batch.negative_attention_mask else None)
-            # Concatenate for batched processing
-            prompt_embeds_combined = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
-            if prompt_attention_mask is not None:
-                prompt_attention_mask_combined = torch.cat([negative_prompt_attention_mask, prompt_attention_mask],
-                                                           dim=0)
-            else:
-                prompt_attention_mask_combined = None
-        else:
-            prompt_embeds_combined = prompt_embeds
-            prompt_attention_mask_combined = prompt_attention_mask
-
-        # Denoising loop
-        num_inference_steps = len(timesteps)
-        with tqdm(total=num_inference_steps, desc="LongCat Denoising") as progress_bar:
-            for i, t in enumerate(timesteps):
-                # Stop if interrupted
-                if getattr(self, "interrupt", False):
-                    break
-                # Expand latents for CFG
-                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
-
-                latent_model_input = latent_model_input.to(target_dtype)
-
-                # Expand timestep to match batch size
-                timestep = t.expand(latent_model_input.shape[0]).to(target_dtype)
-
-                # Run transformer with context
-                batch.is_cfg_negative = False
-                with set_forward_context(
-                        current_timestep=i,
-                        attn_metadata=None,
-                        forward_batch=batch,
-                ), torch.autocast(device_type='cuda', dtype=target_dtype, enabled=autocast_enabled):
-                    noise_pred = self.transformer(
-                        hidden_states=latent_model_input,
-                        encoder_hidden_states=prompt_embeds_combined,
-                        timestep=timestep,
-                        encoder_attention_mask=prompt_attention_mask_combined,
-                    )
-
-                # Apply CFG with optimized scale
-                if do_classifier_free_guidance:
-                    noise_pred_uncond, noise_pred_cond = noise_pred.chunk(2)
-
-                    B = noise_pred_cond.shape[0]
-                    positive = noise_pred_cond.reshape(B, -1)
-                    negative = noise_pred_uncond.reshape(B, -1)
-
-                    # Calculate optimized scale (CFG-zero)
-                    st_star = self.optimized_scale(positive, negative)
-
-                    # Reshape for broadcasting
-                    st_star = st_star.view(B, 1, 1, 1, 1)
-
-                    # Apply optimized CFG formula
-                    noise_pred = (noise_pred_uncond * st_star + guidance_scale *
-                                  (noise_pred_cond - noise_pred_uncond * st_star))
-
-                # CRITICAL: Negate noise prediction for flow matching scheduler
-                noise_pred = -noise_pred
-
-                # Compute previous noisy sample x_t -> x_t-1
-                latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
-
-                progress_bar.update()
-
-        # Update batch with denoised latents
-        batch.latents = latents
-        return batch
diff --git a/fastvideo/pipelines/stages/longcat_i2v_denoising.py b/fastvideo/pipelines/stages/longcat_i2v_denoising.py
deleted file mode 100644
index 25a46c1d1..000000000
--- a/fastvideo/pipelines/stages/longcat_i2v_denoising.py
+++ /dev/null
@@ -1,154 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-LongCat I2V Denoising Stage with conditioning support.
-
-This stage implements Tier 3 I2V denoising:
-1. Per-frame timestep masking (timestep[:, :num_cond_latents] = 0)
-2. Passes num_cond_latents to transformer (for RoPE skipping)
-3. Selective denoising (only updates non-conditioned frames)
-4. CFG-zero optimized guidance
-"""
-
-import torch
-from tqdm import tqdm
-
-from fastvideo.fastvideo_args import FastVideoArgs
-from fastvideo.forward_context import set_forward_context
-from fastvideo.logger import init_logger
-from fastvideo.models.loader.component_loader import TransformerLoader
-from fastvideo.pipelines.pipeline_batch_info import ForwardBatch
-from fastvideo.pipelines.stages.longcat_denoising import LongCatDenoisingStage
-
-logger = init_logger(__name__)
-
-
-class LongCatI2VDenoisingStage(LongCatDenoisingStage):
-    """
-    LongCat denoising with I2V conditioning support.
-    
-    Key modifications from base LongCat denoising:
-    1. Sets timestep=0 for conditioning frames
-    2. Passes num_cond_latents to transformer
-    3. Only applies scheduler step to non-conditioned frames
-    """
-
-    def forward(
-        self,
-        batch: ForwardBatch,
-        fastvideo_args: FastVideoArgs,
-    ) -> ForwardBatch:
-        """Run denoising loop with I2V conditioning."""
-
-        # Load transformer if needed
-        if not fastvideo_args.model_loaded["transformer"]:
-            loader = TransformerLoader()
-            self.transformer = loader.load(fastvideo_args.model_paths["transformer"], fastvideo_args)
-            fastvideo_args.model_loaded["transformer"] = True
-
-        # Setup
-        target_dtype = torch.bfloat16
-        autocast_enabled = (target_dtype != torch.float32) and not fastvideo_args.disable_autocast
-
-        latents = batch.latents
-        timesteps = batch.timesteps
-        prompt_embeds = batch.prompt_embeds[0]
-        prompt_attention_mask = (batch.prompt_attention_mask[0] if batch.prompt_attention_mask else None)
-        guidance_scale = batch.guidance_scale
-        do_classifier_free_guidance = batch.do_classifier_free_guidance
-
-        # Get num_cond_latents from batch
-        num_cond_latents = getattr(batch, 'num_cond_latents', 0)
-
-        if num_cond_latents > 0:
-            logger.info("I2V Denoising: num_cond_latents=%s, latent_shape=%s", num_cond_latents, latents.shape)
-
-        # Prepare negative prompts for CFG
-        if do_classifier_free_guidance:
-            negative_prompt_embeds = batch.negative_prompt_embeds[0]
-            negative_prompt_attention_mask = (batch.negative_attention_mask[0]
-                                              if batch.negative_attention_mask else None)
-
-            prompt_embeds_combined = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
-            if prompt_attention_mask is not None:
-                prompt_attention_mask_combined = torch.cat([negative_prompt_attention_mask, prompt_attention_mask],
-                                                           dim=0)
-            else:
-                prompt_attention_mask_combined = None
-        else:
-            prompt_embeds_combined = prompt_embeds
-            prompt_attention_mask_combined = prompt_attention_mask
-
-        # Denoising loop
-        num_inference_steps = len(timesteps)
-
-        with tqdm(total=num_inference_steps, desc="I2V Denoising") as progress_bar:
-            for i, t in enumerate(timesteps):
-                # Stop if interrupted
-                if getattr(self, "interrupt", False):
-                    break
-
-                # 1. Expand latents for CFG
-                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
-
-                latent_model_input = latent_model_input.to(target_dtype)
-
-                # 2. Expand timestep to match batch size
-                timestep = t.expand(latent_model_input.shape[0]).to(target_dtype)
-
-                # 3. CRITICAL: Expand timestep to temporal dimension
-                # and set conditioning frames to timestep=0
-                timestep = timestep.unsqueeze(-1).repeat(1, latent_model_input.shape[2])
-
-                # Mark conditioning frames as clean (timestep=0)
-                if num_cond_latents > 0:
-                    timestep[:, :num_cond_latents] = 0
-
-                # 4. Run transformer with num_cond_latents
-                batch.is_cfg_negative = False
-                with set_forward_context(
-                        current_timestep=i,
-                        attn_metadata=None,
-                        forward_batch=batch,
-                ), torch.autocast(device_type='cuda', dtype=target_dtype, enabled=autocast_enabled):
-                    noise_pred = self.transformer(
-                        hidden_states=latent_model_input,
-                        encoder_hidden_states=prompt_embeds_combined,
-                        timestep=timestep,
-                        encoder_attention_mask=prompt_attention_mask_combined,
-                        num_cond_latents=num_cond_latents,
-                    )
-
-                # 5. Apply CFG with optimized scale
-                if do_classifier_free_guidance:
-                    noise_pred_uncond, noise_pred_cond = noise_pred.chunk(2)
-
-                    B = noise_pred_cond.shape[0]
-                    positive = noise_pred_cond.reshape(B, -1)
-                    negative = noise_pred_uncond.reshape(B, -1)
-
-                    # CFG-zero optimized scale
-                    st_star = self.optimized_scale(positive, negative)
-                    st_star = st_star.view(B, 1, 1, 1, 1)
-
-                    noise_pred = (noise_pred_uncond * st_star + guidance_scale *
-                                  (noise_pred_cond - noise_pred_uncond * st_star))
-
-                # 6. CRITICAL: Negate for flow matching scheduler
-                noise_pred = -noise_pred
-
-                # 7. CRITICAL: Only update non-conditioned frames
-                # The conditioning frames stay FIXED throughout denoising
-                if num_cond_latents > 0:
-                    latents[:, :, num_cond_latents:] = self.scheduler.step(noise_pred[:, :, num_cond_latents:],
-                                                                           t,
-                                                                           latents[:, :, num_cond_latents:],
-                                                                           return_dict=False)[0]
-                else:
-                    # No conditioning, update all frames
-                    latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
-
-                progress_bar.update()
-
-        # Update batch with denoised latents
-        batch.latents = latents
-        return batch
diff --git a/fastvideo/pipelines/stages/longcat_i2v_latent_preparation.py b/fastvideo/pipelines/stages/longcat_i2v_latent_preparation.py
deleted file mode 100644
index a49f1cb93..000000000
--- a/fastvideo/pipelines/stages/longcat_i2v_latent_preparation.py
+++ /dev/null
@@ -1,95 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-LongCat I2V Latent Preparation Stage.
-
-This stage prepares latents with image conditioning for the first frame.
-"""
-
-import torch
-
-from fastvideo.distributed import get_local_torch_device
-from fastvideo.fastvideo_args import FastVideoArgs
-from fastvideo.logger import init_logger
-from fastvideo.pipelines.pipeline_batch_info import ForwardBatch
-from fastvideo.pipelines.stages.latent_preparation import LatentPreparationStage
-
-logger = init_logger(__name__)
-
-
-class LongCatI2VLatentPreparationStage(LatentPreparationStage):
-    """
-    Prepare latents with image conditioning for first frame.
-    
-    This stage:
-    1. Generates random noise for all frames
-    2. Replaces first latent frame with encoded image latent
-    3. Marks conditioning information in batch
-    """
-
-    # Uses parent __init__ - no need for additional constructor
-
-    def forward(
-        self,
-        batch: ForwardBatch,
-        fastvideo_args: FastVideoArgs,
-    ) -> ForwardBatch:
-        """Prepare latents with I2V conditioning."""
-
-        # IMPORTANT: Skip if latents already prepared (e.g., by refinement init stage)
-        # The refine_init stage encodes stage1 video and mixes with noise - don't overwrite!
-        if batch.latents is not None:
-            logger.info(
-                "I2V Latent Prep: Skipping - latents already prepared "
-                "(shape=%s), likely from refinement stage", batch.latents.shape)
-            return batch
-
-        # 1. Calculate dimensions
-        num_frames = batch.num_frames
-        height = batch.height
-        width = batch.width
-
-        # Get VAE compression factors
-        # IMPORTANT: Use VAE's temporal compression (4), NOT transformer's patch_size[0] (1)
-        vae_temporal_scale = fastvideo_args.pipeline_config.vae_config.arch_config.scale_factor_temporal
-        vae_spatial_scale = fastvideo_args.pipeline_config.vae_config.arch_config.scale_factor_spatial
-
-        num_latent_frames = (num_frames - 1) // vae_temporal_scale + 1
-        latent_height = height // vae_spatial_scale
-        latent_width = width // vae_spatial_scale
-
-        num_channels = self.transformer.config.in_channels
-
-        logger.info(
-            "I2V Latent Prep: num_frames=%s, num_latent_frames=%s "
-            "(vae_temporal_scale=%s), latent_shape=(%s, %s)", num_frames, num_latent_frames, vae_temporal_scale,
-            latent_height, latent_width)
-
-        # 2. Generate random noise for all frames
-        # batch_size might not be set, default to 1
-        batch_size = batch.batch_size if batch.batch_size is not None else 1
-        shape = (batch_size, num_channels, num_latent_frames, latent_height, latent_width)
-
-        # Handle generator - may be a list for batch handling
-        generator = batch.generator
-        if isinstance(generator, list):
-            generator = generator[0] if generator else None
-
-        # torch.randn requires specific argument order: size, generator, dtype
-        latents = torch.randn(*shape, generator=generator).to(get_local_torch_device(), dtype=torch.float32)
-
-        # 3. Replace first frame with conditioned image latent
-        if batch.image_latent is not None:
-            num_cond_latents = batch.num_cond_latents
-            latents[:, :, :num_cond_latents] = batch.image_latent[:, :, :num_cond_latents]
-
-            logger.info("I2V: Replaced first %s latent frame(s) with image conditioning", num_cond_latents)
-        else:
-            logger.warning("No image_latent found in batch, proceeding without conditioning")
-
-        # 4. Store in batch
-        batch.latents = latents
-
-        # Required by base class output validator
-        batch.raw_latent_shape = (num_latent_frames, latent_height, latent_width)
-
-        return batch
diff --git a/fastvideo/pipelines/stages/longcat_image_vae_encoding.py b/fastvideo/pipelines/stages/longcat_image_vae_encoding.py
deleted file mode 100644
index 4642f6fea..000000000
--- a/fastvideo/pipelines/stages/longcat_image_vae_encoding.py
+++ /dev/null
@@ -1,149 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-LongCat Image VAE Encoding Stage for I2V generation.
-
-This stage handles encoding a single input image to latent space with
-LongCat-specific normalization for I2V conditioning.
-"""
-
-import PIL
-import torch
-
-from fastvideo.distributed import get_local_torch_device
-from fastvideo.fastvideo_args import FastVideoArgs
-from fastvideo.logger import init_logger
-from fastvideo.models.vision_utils import (normalize, numpy_to_pt, pil_to_numpy, resize)
-from fastvideo.pipelines.pipeline_batch_info import ForwardBatch
-from fastvideo.pipelines.stages.base import PipelineStage
-from fastvideo.utils import PRECISION_TO_TYPE
-
-logger = init_logger(__name__)
-
-
-class LongCatImageVAEEncodingStage(PipelineStage):
-    """
-    Encode input image to latent space for I2V conditioning.
-    
-    This stage:
-    1. Preprocesses image to match target dimensions
-    2. Encodes via VAE to latent space
-    3. Applies LongCat-specific normalization
-    4. Stores latent and calculates num_cond_latents
-    """
-
-    def __init__(self, vae):
-        super().__init__()
-        self.vae = vae
-
-    def forward(
-        self,
-        batch: ForwardBatch,
-        fastvideo_args: FastVideoArgs,
-    ) -> ForwardBatch:
-        """Encode image to latent for I2V conditioning."""
-
-        # Skip image encoding for refinement tasks - we're refining an existing video
-        if getattr(batch, 'stage1_video', None) is not None or getattr(batch, 'refine_from', None) is not None:
-            logger.info("Skipping image encoding - refinement mode (using stage1_video)")
-            return batch
-
-        # 1. Get image from batch
-        image = batch.pil_image  # PIL.Image
-        if image is None:
-            raise ValueError("pil_image must be provided for I2V")
-
-        if not isinstance(image, PIL.Image.Image):
-            raise TypeError(f"pil_image must be PIL.Image, got {type(image)}")
-
-        # 2. Get target dimensions
-        height = batch.height
-        width = batch.width
-
-        if height is None or width is None:
-            raise ValueError("height and width must be set for I2V")
-
-        # 3. Preprocess image
-        image = resize(image, height, width, resize_mode="default")
-        image = pil_to_numpy(image)
-        image = numpy_to_pt(image)
-        image = normalize(image)  # to [-1, 1]
-
-        # 4. Add temporal dimension
-        # After numpy_to_pt: [1, C, H, W] (batch already added by pil_to_numpy)
-        # Add T dimension: [1, C, H, W] -> [1, C, 1, H, W] = [B, C, T, H, W]
-        image = image.unsqueeze(2)
-        image = image.to(get_local_torch_device(), dtype=torch.float32)
-
-        # 5. Encode via VAE
-        self.vae = self.vae.to(get_local_torch_device())
-
-        # Setup VAE precision
-        vae_dtype = PRECISION_TO_TYPE[fastvideo_args.pipeline_config.vae_precision]
-        vae_autocast_enabled = (vae_dtype != torch.float32) and not fastvideo_args.disable_autocast
-
-        with torch.autocast(device_type="cuda", dtype=vae_dtype, enabled=vae_autocast_enabled):
-            if fastvideo_args.pipeline_config.vae_tiling:
-                self.vae.enable_tiling()
-
-            if not vae_autocast_enabled:
-                image = image.to(vae_dtype)
-
-            with torch.no_grad():
-                encoder_output = self.vae.encode(image)
-                latent = self.retrieve_latents(encoder_output, batch.generator)
-
-        # 6. Apply LongCat-specific normalization
-        # Formula: (latents - mean) / std
-        latent = self.normalize_latents(latent)
-
-        # 7. Calculate num_cond_latents
-        # Formula: 1 + (num_cond_frames - 1) // vae_temporal_scale
-        # For single image (num_cond_frames=1): always 1 latent frame
-        num_cond_frames = 1  # Single image
-        vae_temporal_scale = self.vae.config.scale_factor_temporal
-        batch.num_cond_latents = 1 + (num_cond_frames - 1) // vae_temporal_scale
-
-        # 8. Store in batch
-        batch.image_latent = latent
-        batch.num_cond_frames = 1
-
-        logger.info("I2V: Encoded image to latent shape %s, num_cond_latents=%s", latent.shape, batch.num_cond_latents)
-
-        # Offload VAE if needed
-        if fastvideo_args.vae_cpu_offload:
-            self.vae.to("cpu")
-
-        return batch
-
-    def retrieve_latents(self, encoder_output: object, generator: torch.Generator | None) -> torch.Tensor:
-        """Sample from VAE posterior."""
-        # WAN VAE returns an object with .sample() method
-        if hasattr(encoder_output, 'sample'):
-            return encoder_output.sample(generator)
-        elif hasattr(encoder_output, 'latent_dist'):
-            return encoder_output.latent_dist.sample(generator)
-        elif hasattr(encoder_output, 'latents'):
-            return encoder_output.latents
-        else:
-            raise AttributeError("Could not access latents from encoder output")
-
-    def normalize_latents(self, latents: torch.Tensor) -> torch.Tensor:
-        """
-        Apply LongCat-specific latent normalization.
-        
-        Formula: (latents - mean) / std
-        
-        This matches the original LongCat implementation and is DIFFERENT
-        from standard VAE scaling (which uses scaling_factor).
-        """
-        if not hasattr(self.vae.config, 'latents_mean') or not hasattr(self.vae.config, 'latents_std'):
-            raise ValueError("VAE config must have 'latents_mean' and 'latents_std' "
-                             "for LongCat normalization")
-
-        latents_mean = torch.tensor(self.vae.config.latents_mean).view(1, self.vae.config.z_dim, 1, 1,
-                                                                       1).to(latents.device, latents.dtype)
-
-        latents_std = torch.tensor(self.vae.config.latents_std).view(1, self.vae.config.z_dim, 1, 1,
-                                                                     1).to(latents.device, latents.dtype)
-
-        return (latents - latents_mean) / latents_std
diff --git a/fastvideo/pipelines/stages/longcat_kv_cache_init.py b/fastvideo/pipelines/stages/longcat_kv_cache_init.py
deleted file mode 100644
index c85d0ce2b..000000000
--- a/fastvideo/pipelines/stages/longcat_kv_cache_init.py
+++ /dev/null
@@ -1,112 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-LongCat KV Cache Initialization Stage for Video Continuation (VC).
-
-This stage pre-computes K/V cache for conditioning frames.
-
-"""
-
-import torch
-
-from fastvideo.fastvideo_args import FastVideoArgs
-from fastvideo.forward_context import set_forward_context
-from fastvideo.logger import init_logger
-from fastvideo.pipelines.pipeline_batch_info import ForwardBatch
-from fastvideo.pipelines.stages.base import PipelineStage
-
-logger = init_logger(__name__)
-
-
-class LongCatKVCacheInitStage(PipelineStage):
-    """
-    Pre-compute KV cache for conditioning frames.
-
-    After this stage:
-    - batch.kv_cache_dict contains {block_idx: (k, v)}
-    - batch.cond_latents contains the conditioning latents
-    - batch.latents contains ONLY noise latents
-    """
-
-    def __init__(self, transformer):
-        super().__init__()
-        self.transformer = transformer
-
-    def forward(
-        self,
-        batch: ForwardBatch,
-        fastvideo_args: FastVideoArgs,
-    ) -> ForwardBatch:
-        """Initialize KV cache from conditioning latents."""
-
-        # Check if KV cache is enabled
-        use_kv_cache = getattr(fastvideo_args.pipeline_config, 'use_kv_cache', True)
-        if not use_kv_cache:
-            batch.kv_cache_dict = {}
-            batch.use_kv_cache = False
-            logger.info("KV cache disabled, skipping initialization")
-            return batch
-
-        batch.use_kv_cache = True
-        offload_kv_cache = getattr(fastvideo_args.pipeline_config, 'offload_kv_cache', False)
-
-        # Get conditioning latents
-        num_cond_latents = batch.num_cond_latents
-        if num_cond_latents <= 0:
-            batch.kv_cache_dict = {}
-            logger.warning("num_cond_latents <= 0, skipping KV cache init")
-            return batch
-
-        # Extract conditioning latents
-        cond_latents = batch.latents[:, :, :num_cond_latents].clone()
-
-        logger.info("Initializing KV cache for %d conditioning latents, shape: %s", num_cond_latents,
-                    cond_latents.shape)
-
-        # Timestep = 0 for conditioning (they are "clean")
-        B = cond_latents.shape[0]
-        T_cond = cond_latents.shape[2]
-        timestep = torch.zeros(B, T_cond, device=cond_latents.device, dtype=cond_latents.dtype)
-
-        # Empty prompt embeddings (cross-attn will be skipped)
-        max_seq_len = 512
-        # Get caption dimension from transformer config
-        caption_dim = self.transformer.config.caption_channels
-        empty_embeds = torch.zeros(B, max_seq_len, caption_dim, device=cond_latents.device, dtype=cond_latents.dtype)
-
-        # Get transformer dtype
-        if hasattr(self.transformer, 'module'):
-            transformer_dtype = next(self.transformer.module.parameters()).dtype
-        else:
-            transformer_dtype = next(self.transformer.parameters()).dtype
-
-        # Run transformer with return_kv=True, skip_crs_attn=True
-        with (
-                torch.no_grad(),
-                set_forward_context(
-                    current_timestep=0,
-                    attn_metadata=None,
-                    forward_batch=batch,
-                ),
-                torch.autocast(device_type='cuda', dtype=transformer_dtype),
-        ):
-            _, kv_cache_dict = self.transformer(
-                hidden_states=cond_latents.to(transformer_dtype),
-                encoder_hidden_states=empty_embeds.to(transformer_dtype),
-                timestep=timestep.to(transformer_dtype),
-                return_kv=True,
-                skip_crs_attn=True,
-                offload_kv_cache=offload_kv_cache,
-            )
-
-        # Store cache and save cond_latents for later concatenation
-        batch.kv_cache_dict = kv_cache_dict
-        batch.cond_latents = cond_latents
-
-        # Remove conditioning latents from main latents
-        # After this, batch.latents contains ONLY noise frames
-        batch.latents = batch.latents[:, :, num_cond_latents:]
-
-        logger.info("KV cache initialized: %d blocks, offload=%s, remaining latents shape: %s", len(kv_cache_dict),
-                    offload_kv_cache, batch.latents.shape)
-
-        return batch
diff --git a/fastvideo/pipelines/stages/longcat_refine_init.py b/fastvideo/pipelines/stages/longcat_refine_init.py
deleted file mode 100644
index ba26cd273..000000000
--- a/fastvideo/pipelines/stages/longcat_refine_init.py
+++ /dev/null
@@ -1,273 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-LongCat refinement initialization stage.
-
-This stage prepares the latent variables for LongCat's 480p->720p refinement by:
-1. Loading the stage1 (480p) video
-2. Upsampling it to 720p resolution
-3. Encoding it with VAE
-4. Mixing with noise according to t_thresh
-"""
-
-import math
-from pathlib import Path
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-from PIL import Image
-
-from fastvideo.distributed import get_sp_group
-from fastvideo.fastvideo_args import FastVideoArgs
-from fastvideo.logger import init_logger
-from fastvideo.models.vision_utils import load_video
-from fastvideo.pipelines.pipeline_batch_info import ForwardBatch
-from fastvideo.pipelines.stages.base import PipelineStage
-from fastvideo.configs.pipelines.longcat import get_bucket_config
-
-logger = init_logger(__name__)
-
-
-class LongCatRefineInitStage(PipelineStage):
-    """
-    Stage for initializing LongCat refinement from a stage1 (480p) video.
-    
-    This replicates the logic from LongCatVideoPipeline.generate_refine():
-    - Load stage1_video frames
-    - Upsample spatially and temporally
-    - VAE encode and normalize
-    - Mix with noise according to t_thresh
-    """
-
-    def __init__(self, vae) -> None:
-        super().__init__()
-        self.vae = vae
-
-    def forward(
-        self,
-        batch: ForwardBatch,
-        fastvideo_args: FastVideoArgs,
-    ) -> ForwardBatch:
-        """
-        Initialize latents for refinement.
-        
-        Args:
-            batch: The current batch information.
-            fastvideo_args: The inference arguments.
-            
-        Returns:
-            The batch with initialized latents for refinement.
-        """
-        refine_from = batch.refine_from
-        in_memory_stage1 = batch.stage1_video
-
-        # Only run for refinement tasks: either a path (refine_from) or in-memory video is provided
-        if refine_from is None and in_memory_stage1 is None:
-            # Not a refinement task, skip
-            return batch
-
-        # ------------------------------------------------------------------
-        # 1. Obtain stage1 frames (either from disk or from in-memory input)
-        # ------------------------------------------------------------------
-        if in_memory_stage1 is not None:
-            # User provided stage1 frames directly (e.g., from distilled stage output)
-            if len(in_memory_stage1) == 0:
-                raise ValueError("stage1_video is empty; expected a non-empty list of frames")
-
-            if isinstance(in_memory_stage1[0], Image.Image):
-                pil_images = in_memory_stage1
-            else:
-                # Assume numpy arrays or torch tensors with shape [H, W, C]
-                pil_images = [Image.fromarray(np.array(frame)) for frame in in_memory_stage1]
-
-            logger.info("Initializing LongCat refinement from in-memory stage1_video (%s frames)", len(pil_images))
-        else:
-            # Path-based refine: load video from disk (original design)
-            logger.info("Initializing LongCat refinement from file: %s", refine_from)
-            stage1_video_path = Path(refine_from)
-            if not stage1_video_path.exists():
-                raise FileNotFoundError(f"Stage1 video not found: {refine_from}")
-
-            # Load video frames as PIL Images
-            pil_images, original_fps = load_video(str(stage1_video_path), return_fps=True)
-            logger.info("Loaded stage1 video: %s frames @ %s fps", len(pil_images), original_fps)
-
-        # Store in batch for reference (use PIL images, same as official demo)
-        batch.stage1_video = pil_images
-
-        # Get parameters from batch
-        num_frames = len(pil_images)
-        spatial_refine_only = batch.spatial_refine_only
-        t_thresh = batch.t_thresh
-        num_cond_frames = batch.num_cond_frames if hasattr(batch, 'num_cond_frames') else 0
-
-        # Calculate new frame count (temporal upsampling if not spatial_refine_only)
-        new_num_frames = num_frames if spatial_refine_only else 2 * num_frames
-        logger.info("Refine mode: %s", 'spatial only' if spatial_refine_only else 'spatial + temporal')
-
-        # Update batch.num_frames to reflect the upsampled count
-        batch.num_frames = new_num_frames
-
-        # Use bucket system to select resolution (exactly like LongCat)
-        # Calculate the global spatial alignment required by BSA.
-        sp_size = fastvideo_args.sp_size if fastvideo_args.sp_size > 0 else 1
-        vae_scale_factor_spatial = 8  # VAE spatial downsampling
-        patch_size_spatial = 2  # LongCat patch size
-        bsa_latent_granularity = 4
-        scale_factor_spatial = vae_scale_factor_spatial * patch_size_spatial * bsa_latent_granularity  # 64
-
-        # Refinement SP restores global time/height/width order before BSA, so a
-        # shard's chunks line up with the global grid. The token grid must still be
-        # divisible by the BSA chunk_3d_shape (flash_attn_bsa_3d asserts this) and by
-        # the SP split factors (LongCatSPLayout raises otherwise). The F64 buckets
-        # below satisfy that for the documented chunk [4,4,4] at sp_size<=16, but
-        # coarser bsa_chunk_q/k (e.g. wq=8, which needs width%128==0) or sp_size=32
-        # reject some buckets. Keep bucket selection independent of GPU count so
-        # changing SP does not change the video size.
-
-        # Get bucket config and find closest bucket for the input aspect ratio
-        bucket_config = get_bucket_config('720p', scale_factor_spatial)
-
-        # Get input aspect ratio from stage1 video
-        input_height, input_width = pil_images[0].height, pil_images[0].width
-        input_ratio = input_height / input_width
-
-        # Find closest bucket
-        closest_ratio = min(bucket_config.keys(), key=lambda x: abs(float(x) - input_ratio))
-        height, width = bucket_config[closest_ratio][0]
-
-        logger.info("Input aspect ratio: %.2f (%sx%s)", input_ratio, input_width, input_height)
-        logger.info("Matched bucket ratio: %s -> resolution: %sx%s", closest_ratio, width, height)
-        logger.info("Target: %sx%s @ %s frames (sp_size=%s, scale_factor=%s)", width, height, new_num_frames, sp_size,
-                    scale_factor_spatial)
-
-        # Override batch height/width with bucket-selected resolution
-        batch.height = height
-        batch.width = width
-
-        # Convert PIL images to tensor [T, C, H, W]
-        stage1_video_tensor = torch.stack([
-            torch.from_numpy(np.array(img)).permute(2, 0, 1)  # HWC -> CHW
-            for img in pil_images
-        ]).float()  # [T, C, H, W]
-
-        device = batch.prompt_embeds[0].device
-        dtype = batch.prompt_embeds[0].dtype
-        stage1_video_tensor = stage1_video_tensor.to(device=device, dtype=dtype)
-
-        # Replicate LongCat's exact preprocessing (lines 1227-1235 in pipeline_longcat_video.py)
-        # First: spatial interpolation to target (height, width) on [T, C, H, W]
-        video_down = F.interpolate(stage1_video_tensor, size=(height, width), mode='bilinear', align_corners=True)
-
-        # Rearrange to [C, T, H, W] and add batch dimension -> [1, C, T, H, W]
-        video_down = video_down.permute(1, 0, 2, 3).unsqueeze(0)  # [1, C, T, H, W]
-        video_down = video_down / 255.0  # Normalize to [0, 1]
-
-        # Then: temporal+spatial interpolation to (new_num_frames, height, width)
-        video_up = F.interpolate(video_down, size=(new_num_frames, height, width), mode='trilinear', align_corners=True)
-
-        # Rescale to [-1, 1] for VAE
-        video_up = video_up * 2.0 - 1.0
-
-        logger.info("Upsampled video shape: %s", video_up.shape)
-
-        # Padding logic (exactly like LongCat lines 1237-1255)
-        # Only pad temporal dimension to ensure BSA compatibility
-        vae_scale_factor_temporal = 4
-        num_noise_frames = video_up.shape[2] - num_cond_frames
-
-        num_cond_latents = 0
-        num_cond_frames_added = 0
-        if num_cond_frames > 0:
-            num_cond_latents = 1 + math.ceil((num_cond_frames - 1) / vae_scale_factor_temporal)
-            num_cond_latents = math.ceil(num_cond_latents / bsa_latent_granularity) * bsa_latent_granularity
-            num_cond_frames_added = 1 + (num_cond_latents - 1) * vae_scale_factor_temporal - num_cond_frames
-            num_cond_frames = num_cond_frames + num_cond_frames_added
-
-        num_noise_latents = math.ceil(num_noise_frames / vae_scale_factor_temporal)
-        num_noise_latents = math.ceil(num_noise_latents / bsa_latent_granularity) * bsa_latent_granularity
-        num_noise_frames_added = num_noise_latents * vae_scale_factor_temporal - num_noise_frames
-
-        if num_cond_frames_added > 0 or num_noise_frames_added > 0:
-            logger.info("Padding temporal dimension for BSA: cond_frames+=%s, noise_frames+=%s", num_cond_frames_added,
-                        num_noise_frames_added)
-            pad_front = video_up[:, :, 0:1].repeat(1, 1, num_cond_frames_added, 1, 1)
-            pad_back = video_up[:, :, -1:].repeat(1, 1, num_noise_frames_added, 1, 1)
-            video_up = torch.cat([pad_front, video_up, pad_back], dim=2)
-            logger.info("Padded video shape: %s", video_up.shape)
-
-        # Update batch with actual frame count after padding
-        batch.num_frames = video_up.shape[2]
-
-        # Store padding info for later cropping (CRITICAL for correct output!)
-        batch.num_cond_frames_added = num_cond_frames_added
-        batch.num_noise_frames_added = num_noise_frames_added
-        batch.new_frame_size_before_padding = new_num_frames
-
-        # Store num_cond_latents for denoising stage
-        if num_cond_latents > 0:
-            batch.num_cond_latents = num_cond_latents
-            logger.info("Will use num_cond_latents=%s during denoising", num_cond_latents)
-
-        logger.info("Padding info: cond+=%s, noise+=%s, original=%s", num_cond_frames_added, num_noise_frames_added,
-                    new_num_frames)
-
-        # VAE encode with tiling for memory efficiency
-        logger.info("Encoding stage1 video with VAE (tiling enabled)...")
-        vae_dtype = next(self.vae.parameters()).dtype
-        vae_device = next(self.vae.parameters()).device
-        video_up = video_up.to(dtype=vae_dtype, device=vae_device)
-
-        # Enable tiling for large video encoding
-        if hasattr(self.vae, 'enable_tiling'):
-            self.vae.enable_tiling()
-
-        with torch.no_grad():
-            latent_dist = self.vae.encode(video_up)
-            # Extract tensor from latent distribution
-            if hasattr(latent_dist, 'latent_dist'):
-                # Nested distribution wrapper
-                latent_up = latent_dist.latent_dist.sample()
-            elif hasattr(latent_dist, 'sample'):
-                # DiagonalGaussianDistribution or similar
-                latent_up = latent_dist.sample()
-            elif hasattr(latent_dist, 'latents'):
-                # Direct latents tensor
-                latent_up = latent_dist.latents
-            else:
-                # Assume it's already a tensor
-                latent_up = latent_dist
-
-        # Normalize latents using VAE config (exactly like LongCat)
-        if hasattr(self.vae.config, 'latents_mean') and hasattr(self.vae.config, 'latents_std'):
-            latents_mean = torch.tensor(self.vae.config.latents_mean).view(1, self.vae.config.z_dim, 1, 1,
-                                                                           1).to(latent_up.device, latent_up.dtype)
-            # LongCat uses: 1.0 / latents_std (equivalent to dividing by latents_std)
-            latents_std = 1.0 / torch.tensor(self.vae.config.latents_std).view(1, self.vae.config.z_dim, 1, 1, 1).to(
-                latent_up.device, latent_up.dtype)
-            # LongCat: (latents - mean) * (1/std)
-            latent_up = (latent_up - latents_mean) * latents_std
-
-        logger.info("Encoded latent shape: %s", latent_up.shape)
-
-        # Mix with noise according to t_thresh
-        # latent_up = (1 - t_thresh) * latent_up + t_thresh * noise
-        noise = torch.randn_like(latent_up).contiguous()
-        latent_up = (1 - t_thresh) * latent_up + t_thresh * noise
-
-        logger.info("Applied t_thresh=%s noise mixing", t_thresh)
-
-        # Store in batch - ensure correct dtype and device
-        # The latents need to be on the same device as the transformer (CUDA)
-        target_device = batch.prompt_embeds[0].device
-        batch.latents = latent_up.to(device=target_device, dtype=dtype)
-        if sp_size > 1:
-            # Posterior/noise sampling uses worker-local RNG. All SP ranks
-            # must start the scheduler from the same full latent tensor.
-            batch.latents = get_sp_group().broadcast(batch.latents, src=0)
-        batch.raw_latent_shape = latent_up.shape
-
-        logger.info("Latents device: %s, dtype: %s", batch.latents.device, batch.latents.dtype)
-        logger.info("LongCat refinement initialization complete")
-
-        return batch
diff --git a/fastvideo/pipelines/stages/longcat_refine_timestep.py b/fastvideo/pipelines/stages/longcat_refine_timestep.py
deleted file mode 100644
index 0b292fd86..000000000
--- a/fastvideo/pipelines/stages/longcat_refine_timestep.py
+++ /dev/null
@@ -1,96 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-LongCat refinement timestep preparation stage.
-
-This stage prepares special timesteps for LongCat refinement that start from t_thresh.
-"""
-
-import torch
-
-from fastvideo.distributed import get_local_torch_device
-from fastvideo.fastvideo_args import FastVideoArgs
-from fastvideo.logger import init_logger
-from fastvideo.pipelines.pipeline_batch_info import ForwardBatch
-from fastvideo.pipelines.stages.base import PipelineStage
-
-logger = init_logger(__name__)
-
-
-class LongCatRefineTimestepStage(PipelineStage):
-    """
-    Stage for preparing timesteps specific to LongCat refinement.
-    
-    For refinement, we need to start from t_thresh instead of t=1.0, so we:
-    1. Generate normal timesteps for num_inference_steps
-    2. Filter to only keep timesteps < t_thresh * 1000
-    3. Prepend t_thresh * 1000 as the first timestep
-    """
-
-    def __init__(self, scheduler) -> None:
-        super().__init__()
-        self.scheduler = scheduler
-
-    def forward(
-        self,
-        batch: ForwardBatch,
-        fastvideo_args: FastVideoArgs,
-    ) -> ForwardBatch:
-        """
-        Prepare refinement-specific timesteps.
-        
-        Args:
-            batch: The current batch information.
-            fastvideo_args: The inference arguments.
-            
-        Returns:
-            The batch with refinement timesteps.
-        """
-        # Only apply if this is a refinement task
-        # Trigger when either a refine_from path or in-memory stage1_video is provided
-        if batch.refine_from is None and batch.stage1_video is None:
-            return batch
-
-        device = get_local_torch_device()
-        num_inference_steps = batch.num_inference_steps
-        t_thresh = batch.t_thresh
-
-        logger.info("Preparing LongCat refinement timesteps (t_thresh=%s)", t_thresh)
-
-        # ------------------------------------------------------------------
-        # 1) Match LongCatVideoPipeline.get_timesteps_sigmas (non-distill):
-        #    sigmas = linspace(1, 0.001, num_inference_steps) on CPU
-        # ------------------------------------------------------------------
-        base_sigmas = torch.linspace(
-            1.0,
-            0.001,
-            num_inference_steps,
-            dtype=torch.float32,
-            device="cpu",  # scheduler.set_timesteps expects CPU-convertible sigmas
-        )
-        # Let the scheduler build its internal timestep schedule from sigmas
-        self.scheduler.set_timesteps(num_inference_steps, sigmas=base_sigmas, device=device)
-        base_timesteps = self.scheduler.timesteps
-
-        # ------------------------------------------------------------------
-        # 2) Apply t_thresh cropping exactly like generate_refine:
-        #    timesteps = [t_thresh*1000] + [t for t in base_timesteps if t < t_thresh*1000]
-        #    sigmas = timesteps / 1000  (with trailing zero)
-        # ------------------------------------------------------------------
-        t_thresh_value = t_thresh * 1000.0
-        t_thresh_tensor = torch.tensor(t_thresh_value, dtype=base_timesteps.dtype, device=device)
-        filtered_timesteps = base_timesteps[base_timesteps < t_thresh_tensor]
-
-        timesteps = torch.cat([t_thresh_tensor.unsqueeze(0), filtered_timesteps])
-
-        # Update scheduler with these custom timesteps and corresponding sigmas
-        self.scheduler.timesteps = timesteps
-        sigmas = torch.cat([timesteps / 1000.0, torch.zeros(1, device=device)])
-        self.scheduler.sigmas = sigmas
-
-        logger.info("Refinement timesteps: %s steps starting from t=%s", len(timesteps), t_thresh)
-        logger.info("First few timesteps: %s", timesteps[:5].tolist())
-
-        # Store in batch so downstream stages (denoising) use the same schedule
-        batch.timesteps = timesteps
-
-        return batch
diff --git a/fastvideo/pipelines/stages/longcat_vc_denoising.py b/fastvideo/pipelines/stages/longcat_vc_denoising.py
deleted file mode 100644
index 0c298c709..000000000
--- a/fastvideo/pipelines/stages/longcat_vc_denoising.py
+++ /dev/null
@@ -1,194 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-LongCat VC Denoising Stage with KV cache support.
-
-This stage extends the I2V denoising stage to support:
-1. KV cache for conditioning frames
-2. Video continuation with multiple conditioning frames
-"""
-
-import time
-import torch
-from tqdm import tqdm
-
-from fastvideo.fastvideo_args import FastVideoArgs
-from fastvideo.forward_context import set_forward_context
-from fastvideo.logger import init_logger
-from fastvideo.models.loader.component_loader import TransformerLoader
-from fastvideo.pipelines.pipeline_batch_info import ForwardBatch
-from fastvideo.pipelines.stages.longcat_denoising import LongCatDenoisingStage
-
-logger = init_logger(__name__)
-
-
-class LongCatVCDenoisingStage(LongCatDenoisingStage):
-    """
-    LongCat denoising with Video Continuation and KV cache support.
-    
-    Key differences from I2V denoising:
-    - Supports KV cache (reuses cached K/V from conditioning frames)
-    - Handles larger num_cond_latents
-    - Concatenates conditioning latents back after denoising
-    
-    When use_kv_cache=True:
-    - batch.latents contains ONLY noise frames (cond removed by KV cache init)
-    - batch.kv_cache_dict contains cached K/V
-    - batch.cond_latents contains conditioning latents for post-concat
-    
-    When use_kv_cache=False:
-    - batch.latents contains ALL frames (cond + noise)
-    - Timestep masking: timestep[:, :num_cond_latents] = 0
-    - Selective denoising: only update noise frames
-    """
-
-    def forward(
-        self,
-        batch: ForwardBatch,
-        fastvideo_args: FastVideoArgs,
-    ) -> ForwardBatch:
-        """Run denoising loop with VC conditioning and optional KV cache."""
-
-        # Load transformer if needed
-        if not fastvideo_args.model_loaded["transformer"]:
-            loader = TransformerLoader()
-            self.transformer = loader.load(fastvideo_args.model_paths["transformer"], fastvideo_args)
-            fastvideo_args.model_loaded["transformer"] = True
-
-        # Setup
-        target_dtype = torch.bfloat16
-        autocast_enabled = (target_dtype != torch.float32) and not fastvideo_args.disable_autocast
-
-        latents = batch.latents
-        timesteps = batch.timesteps
-        prompt_embeds = batch.prompt_embeds[0]
-        prompt_attention_mask = (batch.prompt_attention_mask[0] if batch.prompt_attention_mask else None)
-        guidance_scale = batch.guidance_scale
-        do_classifier_free_guidance = batch.do_classifier_free_guidance
-
-        # Get VC-specific parameters
-        num_cond_latents = getattr(batch, 'num_cond_latents', 0)
-        use_kv_cache = getattr(batch, 'use_kv_cache', False)
-        kv_cache_dict = getattr(batch, 'kv_cache_dict', {})
-
-        logger.info("VC Denoising: num_cond_latents=%d, use_kv_cache=%s, latent_shape=%s", num_cond_latents,
-                    use_kv_cache, latents.shape)
-
-        # Prepare negative prompts for CFG
-        if do_classifier_free_guidance:
-            negative_prompt_embeds = batch.negative_prompt_embeds[0]
-            negative_prompt_attention_mask = (batch.negative_attention_mask[0]
-                                              if batch.negative_attention_mask else None)
-
-            prompt_embeds_combined = torch.cat([negative_prompt_embeds, prompt_embeds], dim=0)
-            if prompt_attention_mask is not None:
-                prompt_attention_mask_combined = torch.cat([negative_prompt_attention_mask, prompt_attention_mask],
-                                                           dim=0)
-            else:
-                prompt_attention_mask_combined = None
-        else:
-            prompt_embeds_combined = prompt_embeds
-            prompt_attention_mask_combined = prompt_attention_mask
-
-        # Denoising loop
-        num_inference_steps = len(timesteps)
-        step_times = []
-
-        with tqdm(total=num_inference_steps, desc="VC Denoising") as progress_bar:
-            for i, t in enumerate(timesteps):
-                # Stop if interrupted
-                if getattr(self, "interrupt", False):
-                    break
-                step_start = time.time()
-
-                # 1. Expand latents for CFG
-                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
-
-                latent_model_input = latent_model_input.to(target_dtype)
-
-                # 2. Expand timestep to match batch size
-                timestep = t.expand(latent_model_input.shape[0]).to(target_dtype)
-
-                # 3. Expand timestep to temporal dimension
-                timestep = timestep.unsqueeze(-1).repeat(1, latent_model_input.shape[2])
-
-                # 4. Timestep masking (only when NOT using KV cache)
-                if not use_kv_cache and num_cond_latents > 0:
-                    timestep[:, :num_cond_latents] = 0
-
-                # 5. Prepare transformer kwargs
-                # IMPORTANT: num_cond_latents is ALWAYS passed - needed for RoPE position offset
-                transformer_kwargs = {
-                    'num_cond_latents': num_cond_latents,
-                }
-                if use_kv_cache:
-                    transformer_kwargs['kv_cache_dict'] = kv_cache_dict
-
-                # 6. Run transformer
-                batch.is_cfg_negative = False
-                with set_forward_context(
-                        current_timestep=i,
-                        attn_metadata=None,
-                        forward_batch=batch,
-                ), torch.autocast(device_type='cuda', dtype=target_dtype, enabled=autocast_enabled):
-                    noise_pred = self.transformer(
-                        hidden_states=latent_model_input,
-                        encoder_hidden_states=prompt_embeds_combined,
-                        timestep=timestep,
-                        encoder_attention_mask=prompt_attention_mask_combined,
-                        **transformer_kwargs,
-                    )
-
-                # 7. Apply CFG with optimized scale (CFG-zero)
-                if do_classifier_free_guidance:
-                    noise_pred_uncond, noise_pred_cond = noise_pred.chunk(2)
-
-                    B = noise_pred_cond.shape[0]
-                    positive = noise_pred_cond.reshape(B, -1)
-                    negative = noise_pred_uncond.reshape(B, -1)
-
-                    st_star = self.optimized_scale(positive, negative)
-                    st_star = st_star.view(B, 1, 1, 1, 1)
-
-                    noise_pred = (noise_pred_uncond * st_star + guidance_scale *
-                                  (noise_pred_cond - noise_pred_uncond * st_star))
-
-                # 8. Negate for flow matching scheduler
-                noise_pred = -noise_pred
-
-                # 9. Scheduler step
-                if use_kv_cache:
-                    # All latents are noise frames (conditioning is in cache)
-                    latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
-                else:
-                    # Only update noise frames (skip conditioning)
-                    if num_cond_latents > 0:
-                        latents[:, :, num_cond_latents:] = self.scheduler.step(
-                            noise_pred[:, :, num_cond_latents:],
-                            t,
-                            latents[:, :, num_cond_latents:],
-                            return_dict=False,
-                        )[0]
-                    else:
-                        latents = self.scheduler.step(noise_pred, t, latents, return_dict=False)[0]
-
-                step_time = time.time() - step_start
-                step_times.append(step_time)
-
-                # Log timing for first few steps
-                if i < 3:
-                    logger.info("Step %d: %.2fs", i, step_time)
-
-                progress_bar.update()
-
-        # 10. If using KV cache, concatenate conditioning latents back
-        if use_kv_cache and hasattr(batch, 'cond_latents') and batch.cond_latents is not None:
-            latents = torch.cat([batch.cond_latents, latents], dim=2)
-            logger.info("Concatenated conditioning latents back, final shape: %s", latents.shape)
-
-        # Log average timing
-        avg_time = sum(step_times) / len(step_times)
-        logger.info("Average step time: %.2fs (total: %.1fs)", avg_time, sum(step_times))
-
-        # Update batch with denoised latents
-        batch.latents = latents
-        return batch
diff --git a/fastvideo/pipelines/stages/longcat_video_vae_encoding.py b/fastvideo/pipelines/stages/longcat_video_vae_encoding.py
deleted file mode 100644
index 8905dc082..000000000
--- a/fastvideo/pipelines/stages/longcat_video_vae_encoding.py
+++ /dev/null
@@ -1,163 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-LongCat Video VAE Encoding Stage for Video Continuation (VC) generation.
-
-This stage handles encoding multiple video frames to latent space with
-LongCat-specific normalization for VC conditioning.
-"""
-
-from typing import Any
-
-import PIL.Image
-import torch
-
-from fastvideo.distributed import get_local_torch_device
-from fastvideo.fastvideo_args import FastVideoArgs
-from fastvideo.logger import init_logger
-from fastvideo.models.vision_utils import normalize, numpy_to_pt, pil_to_numpy, resize
-from fastvideo.pipelines.pipeline_batch_info import ForwardBatch
-from fastvideo.pipelines.stages.base import PipelineStage
-from fastvideo.utils import PRECISION_TO_TYPE
-
-logger = init_logger(__name__)
-
-
-class LongCatVideoVAEEncodingStage(PipelineStage):
-    """
-    Encode video frames to latent space for VC conditioning.
-    
-    This stage:
-    1. Loads video frames from path or uses provided frames
-    2. Takes the last num_cond_frames from the video
-    3. Preprocesses and stacks frames
-    4. Encodes via VAE to latent space
-    5. Applies LongCat-specific normalization
-    6. Calculates num_cond_latents
-    """
-
-    def __init__(self, vae):
-        super().__init__()
-        self.vae = vae
-
-    def forward(
-        self,
-        batch: ForwardBatch,
-        fastvideo_args: FastVideoArgs,
-    ) -> ForwardBatch:
-        """Encode video frames to latent for VC conditioning."""
-
-        # Get video from batch - can be path, list of PIL images, or already loaded
-        video = getattr(batch, 'video_frames', None) or getattr(batch, 'video_path', None)
-        num_cond_frames = getattr(batch, 'num_cond_frames', 13)  # Default 13 for VC
-
-        if video is None:
-            raise ValueError("video_frames or video_path must be provided for VC")
-
-        # Load video if path
-        if isinstance(video, str):
-            from diffusers.utils import load_video
-            video = load_video(video)
-            logger.info("Loaded video from path: %d frames", len(video))
-
-        # Take last num_cond_frames
-        if len(video) > num_cond_frames:
-            video = video[-num_cond_frames:]
-            logger.info("Using last %d frames for conditioning", num_cond_frames)
-        elif len(video) < num_cond_frames:
-            logger.warning("Video has only %d frames, less than num_cond_frames=%d", len(video), num_cond_frames)
-            num_cond_frames = len(video)
-
-        # Get target dimensions
-        height = batch.height
-        width = batch.width
-
-        if height is None or width is None:
-            raise ValueError("height and width must be set for VC")
-
-        # Preprocess and stack frames
-        processed_frames = []
-        for frame in video:
-            if not isinstance(frame, PIL.Image.Image):
-                raise TypeError(f"Frame must be PIL.Image, got {type(frame)}")
-
-            frame = resize(frame, height, width, resize_mode="default")
-            frame = pil_to_numpy(frame)  # Returns [1, H, W, C] then converted
-            frame = numpy_to_pt(frame)  # Returns [1, C, H, W]
-            frame = normalize(frame)  # to [-1, 1]
-            processed_frames.append(frame)
-
-        # Stack frames: [num_frames, C, H, W] -> [1, C, T, H, W]
-        video_tensor = torch.cat(processed_frames, dim=0)  # [T, C, H, W]
-        video_tensor = video_tensor.permute(1, 0, 2, 3).unsqueeze(0)  # [1, C, T, H, W]
-        video_tensor = video_tensor.to(get_local_torch_device(), dtype=torch.float32)
-
-        logger.info("VC: Preprocessed video tensor shape: %s", video_tensor.shape)
-
-        # Encode via VAE
-        self.vae = self.vae.to(get_local_torch_device())
-
-        # Setup VAE precision
-        vae_dtype = PRECISION_TO_TYPE[fastvideo_args.pipeline_config.vae_precision]
-        vae_autocast_enabled = (vae_dtype != torch.float32) and not fastvideo_args.disable_autocast
-
-        with torch.autocast(device_type="cuda", dtype=vae_dtype, enabled=vae_autocast_enabled):
-            if fastvideo_args.pipeline_config.vae_tiling:
-                self.vae.enable_tiling()
-
-            if not vae_autocast_enabled:
-                video_tensor = video_tensor.to(vae_dtype)
-
-            with torch.no_grad():
-                encoder_output = self.vae.encode(video_tensor)
-                latent = self.retrieve_latents(encoder_output, batch.generator)
-
-        # Apply LongCat-specific normalization
-        latent = self.normalize_latents(latent)
-
-        # Calculate num_cond_latents
-        # Formula: 1 + (num_cond_frames - 1) // vae_temporal_scale
-        vae_temporal_scale = self.vae.config.scale_factor_temporal
-        num_cond_latents = 1 + (num_cond_frames - 1) // vae_temporal_scale
-
-        # Store in batch
-        batch.video_latent = latent
-        batch.num_cond_frames = num_cond_frames
-        batch.num_cond_latents = num_cond_latents
-
-        logger.info("VC: Encoded %d frames to latent shape %s, num_cond_latents=%d", num_cond_frames, latent.shape,
-                    num_cond_latents)
-
-        # Offload VAE if needed
-        if fastvideo_args.vae_cpu_offload:
-            self.vae.to("cpu")
-
-        return batch
-
-    def retrieve_latents(self, encoder_output: Any, generator: torch.Generator | None) -> torch.Tensor:
-        """Sample from VAE posterior."""
-        if hasattr(encoder_output, 'sample'):
-            return encoder_output.sample(generator)
-        elif hasattr(encoder_output, 'latent_dist'):
-            return encoder_output.latent_dist.sample(generator)
-        elif hasattr(encoder_output, 'latents'):
-            return encoder_output.latents
-        else:
-            raise AttributeError("Could not access latents from encoder output")
-
-    def normalize_latents(self, latents: torch.Tensor) -> torch.Tensor:
-        """
-        Apply LongCat-specific latent normalization.
-        
-        Formula: (latents - mean) / std
-        """
-        if not hasattr(self.vae.config, 'latents_mean') or not hasattr(self.vae.config, 'latents_std'):
-            raise ValueError("VAE config must have 'latents_mean' and 'latents_std' "
-                             "for LongCat normalization")
-
-        latents_mean = torch.tensor(self.vae.config.latents_mean).view(1, self.vae.config.z_dim, 1, 1,
-                                                                       1).to(latents.device, latents.dtype)
-
-        latents_std = torch.tensor(self.vae.config.latents_std).view(1, self.vae.config.z_dim, 1, 1,
-                                                                     1).to(latents.device, latents.dtype)
-
-        return (latents - latents_mean) / latents_std
diff --git a/fastvideo/registry.py b/fastvideo/registry.py
index f72cb4d70..f9962885e 100644
--- a/fastvideo/registry.py
+++ b/fastvideo/registry.py
@@ -20,9 +20,7 @@ from fastvideo.configs.pipelines.cosmos2_5 import (
     Cosmos25Config,
     Cosmos25_14BConfig,
 )
-from fastvideo.configs.pipelines.dreamx_world import DreamXWorld5BARPipelineConfig, DreamXWorld5BCamPipelineConfig
 from fastvideo.configs.pipelines.hunyuan import FastHunyuanConfig, HunyuanConfig
-from fastvideo.configs.pipelines.hunyuangamecraft import HunyuanGameCraftPipelineConfig
 from fastvideo.configs.pipelines.gen3c import Gen3CConfig
 from fastvideo.configs.pipelines.helios import HeliosPipelineConfig
 from fastvideo.configs.pipelines.hunyuan15 import (Hunyuan15T2V480PConfig, Hunyuan15I2V480PStepDistilledConfig,
@@ -36,7 +34,6 @@ from fastvideo.configs.pipelines.lingbot_video import LingBotVideoT2VConfig
 from fastvideo.configs.pipelines.lingbotworld import LingBotWorldI2V480PConfig
 from fastvideo.configs.pipelines.lingbotworld2 import LingBotWorld2CausalFastI2V480PConfig
 from fastvideo.configs.pipelines.lingbotworld_fast import LingBotWorldFastI2V480PConfig
-from fastvideo.configs.pipelines.longcat import LongCatT2V480PConfig
 from fastvideo.pipelines.basic.ltx2.pipeline_configs import LTX2T2VConfig
 from fastvideo.configs.pipelines.flux_2 import (
     Flux2KleinPipelineConfig,
@@ -53,18 +50,11 @@ from fastvideo.configs.pipelines.mmaudio import (
     MMAudioSmall44kV2AConfig,
     MMAudioV2AConfig,
 )
-from fastvideo.configs.pipelines.turbodiffusion import (
-    TurboDiffusionI2V_A14B_Config,
-    TurboDiffusionT2V_14B_Config,
-    TurboDiffusionT2V_1_3B_Config,
-)
 from fastvideo.models.wan import pipeline_config as wan_pipeline_config
-from fastvideo.models.wan.definition import WAN_MODEL_DEFINITION_GROUPS, WanModelDefinition
-from fastvideo.configs.pipelines.glm_image import GlmImageConfig
+from fastvideo.models.wan.definition import WAN_MODEL_DEFINITIONS, WanModelDefinition
 from fastvideo.configs.pipelines.flux import FluxPipelineConfig
 from fastvideo.configs.pipelines.sd35 import SD35Config
 from fastvideo.configs.pipelines.stable_audio import (StableAudioOpenSmallConfig, StableAudioT2AConfig)
-from fastvideo.configs.pipelines.zimage import ZImagePipelineConfig
 from fastvideo.api.sampling_param import SamplingParam
 from fastvideo.api.matrixgame2 import MatrixGame2SamplingParam
 from fastvideo.api.matrixgame3 import MatrixGame3SamplingParam
@@ -631,7 +621,7 @@ def _register_configs() -> None:
         default_preset="hunyuan15_sr_1080p",
     )
 
-    # Hunyuan (excludes gamecraft, hyworld, and versioned models)
+    # Hunyuan (excludes hyworld, versioned models, and removed GameCraft checkpoints)
     register_configs(
         sampling_param_cls=None,
         pipeline_config_cls=HunyuanConfig,
@@ -670,18 +660,6 @@ def _register_configs() -> None:
         default_preset="hyworld_t2v",
     )
 
-    # HunyuanGameCraft
-    register_configs(
-        sampling_param_cls=None,
-        pipeline_config_cls=HunyuanGameCraftPipelineConfig,
-        workload_types=(WorkloadType.I2V, ),
-        hf_model_paths=[
-            "FastVideo/HunyuanGameCraft-Diffusers",
-        ],
-        model_detectors=[lambda path: "gamecraft" in path.lower()],
-        model_family="gamecraft",
-        default_preset="gamecraft_i2v",
-    )
     # LingBotWorld2 causal-fast
     register_configs(
         sampling_param_cls=None,
@@ -1021,43 +999,6 @@ def _register_configs() -> None:
         pipeline_cls_name="Kandinsky6TI2VAPipeline",
     )
 
-    # LongCat (T2V, I2V, VC use same config; workload varies by path)
-    register_configs(
-        sampling_param_cls=None,
-        pipeline_config_cls=LongCatT2V480PConfig,
-        workload_types=(WorkloadType.T2V, ),
-        hf_model_paths=["FastVideo/LongCat-Video-T2V-Diffusers"],
-        model_detectors=[
-            lambda path: "longcat" in path.lower() and "i2v" not in path.lower() and "imagetovideo" not in path.lower()
-            and "vc" not in path.lower() and "videocontinuation" not in path.lower(),
-        ],
-        model_family="longcat",
-        default_preset="longcat_t2v",
-    )
-    register_configs(
-        sampling_param_cls=None,
-        pipeline_config_cls=LongCatT2V480PConfig,
-        workload_types=(WorkloadType.I2V, ),
-        hf_model_paths=["FastVideo/LongCat-Video-I2V-Diffusers"],
-        model_detectors=[
-            lambda path: "longcatimagetovideo" in path.lower() or ("longcat" in path.lower() and "i2v" in path.lower()),
-        ],
-        model_family="longcat",
-        default_preset="longcat_i2v",
-    )
-    register_configs(
-        sampling_param_cls=None,
-        pipeline_config_cls=LongCatT2V480PConfig,
-        workload_types=(),
-        hf_model_paths=["FastVideo/LongCat-Video-VC-Diffusers"],
-        model_detectors=[
-            lambda path: "longcatvideocontinuation" in path.lower() or
-            ("longcat" in path.lower() and "vc" in path.lower()),
-        ],
-        model_family="longcat",
-        default_preset="longcat_vc",
-    )
-
     # MatrixGame 2.0 (I2V)
     register_configs(
         sampling_param_cls=MatrixGame2SamplingParam,
@@ -1198,76 +1139,7 @@ def _register_configs() -> None:
         default_preset="cosmos_predict2_2b",
     )
 
-    # TurboDiffusion
-    register_configs(
-        sampling_param_cls=None,
-        pipeline_config_cls=TurboDiffusionT2V_1_3B_Config,
-        workload_types=(WorkloadType.T2V, ),
-        hf_model_paths=[
-            "loayrashid/TurboWan2.1-T2V-1.3B-Diffusers",
-        ],
-        model_detectors=[lambda path: "turbodiffusion" in path.lower() or "turbowan" in path.lower()],
-        model_family="turbodiffusion",
-        default_preset="turbo_t2v_1_3b",
-    )
-    register_configs(
-        sampling_param_cls=None,
-        pipeline_config_cls=TurboDiffusionT2V_14B_Config,
-        workload_types=(WorkloadType.T2V, ),
-        hf_model_paths=[
-            "loayrashid/TurboWan2.1-T2V-14B-Diffusers",
-        ],
-        model_family="turbodiffusion",
-        default_preset="turbo_t2v_14b",
-    )
-    register_configs(
-        sampling_param_cls=None,
-        pipeline_config_cls=TurboDiffusionI2V_A14B_Config,
-        workload_types=(WorkloadType.I2V, ),
-        hf_model_paths=[
-            "loayrashid/TurboWan2.2-I2V-A14B-Diffusers",
-        ],
-        model_family="turbodiffusion",
-        default_preset="turbo_i2v_a14b",
-    )
-
-    # Preserve first-match ordering around the DreamX registrations below.
-    _register_wan_configs(WAN_MODEL_DEFINITION_GROUPS[0])
-
-    register_configs(
-        sampling_param_cls=None,
-        pipeline_config_cls=DreamXWorld5BCamPipelineConfig,
-        workload_types=(WorkloadType.I2V, ),
-        hf_model_paths=[
-            "FastVideo/DreamX-World-5B-Cam-Diffusers",
-        ],
-        model_detectors=[
-            # Mutually exclusive with the AR detector below: Cam requires an
-            # explicit "cam" marker so hyphenated AR local paths (e.g.
-            # /ckpts/dreamx-world-5b-converted) don't first-match here —
-            # detector resolution is first-match in registration order.
-            lambda path:
-            ("dreamx-world" in path.lower() and "cam" in path.lower()) or "dreamxworldpipeline" in path.lower()
-        ],
-        model_family="dreamx_world",
-        default_preset="dreamx_world_5b_cam",
-    )
-    register_configs(
-        sampling_param_cls=None,
-        pipeline_config_cls=DreamXWorld5BARPipelineConfig,
-        workload_types=(WorkloadType.I2V, ),
-        hf_model_paths=[
-            "FastVideo/DreamX-World-5B-Diffusers",
-        ],
-        model_detectors=[
-            lambda path:
-            ("dreamx-world-5b" in path.lower() and "cam" not in path.lower()) or "dreamxworldarpipeline" in path.lower(
-            )
-        ],
-        model_family="dreamx_world",
-        default_preset="dreamx_world_5b_ar",
-    )
-    _register_wan_configs(WAN_MODEL_DEFINITION_GROUPS[1])
+    _register_wan_configs(WAN_MODEL_DEFINITIONS)
 
     # MiniMax H3
     register_configs(
@@ -1314,18 +1186,6 @@ def _register_configs() -> None:
         default_preset="sd35_medium",
     )
 
-    # GLM-Image
-    register_configs(
-        sampling_param_cls=None,
-        pipeline_config_cls=GlmImageConfig,
-        hf_model_paths=[
-            "zai-org/GLM-Image",
-        ],
-        model_detectors=[lambda path: "glmimage" in path.lower() or "glm-image" in path.lower()],
-        workload_types=(WorkloadType.T2I, ),
-        model_family="glm_image",
-    )
-
     # FLUX.1-dev (Diffusers)
     register_configs(
         sampling_param_cls=FluxSamplingParam,
@@ -1341,19 +1201,6 @@ def _register_configs() -> None:
         ],
     )
 
-    # Z-Image-Turbo
-    register_configs(
-        sampling_param_cls=None,
-        pipeline_config_cls=ZImagePipelineConfig,
-        workload_types=(WorkloadType.T2I, ),
-        hf_model_paths=["Tongyi-MAI/Z-Image-Turbo"],
-        model_detectors=[
-            lambda path: "zimagepipeline" in path or "z-image" in path or "z_image" in path,
-        ],
-        model_family="zimage",
-        default_preset="zimage_turbo",
-    )
-
 
 # --- Part 3: Main Resolver ---
 
@@ -1445,10 +1292,6 @@ def _register_presets() -> None:
     from fastvideo.api.presets import register_preset
     from fastvideo.pipelines.basic.cosmos.presets import (
         ALL_PRESETS as COSMOS_PRESETS, )
-    from fastvideo.pipelines.basic.dreamx_world.presets import (
-        ALL_PRESETS as DREAMX_WORLD_PRESETS, )
-    from fastvideo.pipelines.basic.gamecraft.presets import (
-        ALL_PRESETS as GAMECRAFT_PRESETS, )
     from fastvideo.pipelines.basic.gen3c.presets import (
         ALL_PRESETS as GEN3C_PRESETS, )
     from fastvideo.pipelines.basic.hunyuan.presets import (
@@ -1473,8 +1316,6 @@ def _register_presets() -> None:
         ALL_PRESETS as LINGBOTWORLD_FAST_PRESETS, )
     from fastvideo.pipelines.basic.lingbot_video.presets import (
         ALL_PRESETS as LINGBOT_VIDEO_PRESETS, )
-    from fastvideo.pipelines.basic.longcat.presets import (
-        ALL_PRESETS as LONGCAT_PRESETS, )
     from fastvideo.pipelines.basic.ltx2.presets import (
         ALL_PRESETS as LTX2_PRESETS, )
     from fastvideo.pipelines.basic.magi_human.presets import (
@@ -1491,20 +1332,14 @@ def _register_presets() -> None:
         ALL_PRESETS as SD35_PRESETS, )
     from fastvideo.pipelines.basic.stable_audio.presets import (
         ALL_PRESETS as STABLE_AUDIO_PRESETS, )
-    from fastvideo.pipelines.basic.turbodiffusion.presets import (
-        ALL_PRESETS as TURBODIFFUSION_PRESETS, )
     from fastvideo.pipelines.basic.wan.presets import (
         ALL_PRESETS as WAN_PRESETS, )
-    from fastvideo.pipelines.basic.zimage.presets import (
-        ALL_PRESETS as ZIMAGE_PRESETS, )
     from fastvideo.pipelines.basic.flux_2.presets import (
         ALL_PRESETS as FLUX2_PRESETS, )
 
     all_preset_groups = (
         COSMOS_PRESETS,
-        DREAMX_WORLD_PRESETS,
         FLUX2_PRESETS,
-        GAMECRAFT_PRESETS,
         GEN3C_PRESETS,
         HUNYUAN_PRESETS,
         HUNYUAN15_PRESETS,
@@ -1517,7 +1352,6 @@ def _register_presets() -> None:
         LINGBOTWORLD_PRESETS,
         LINGBOTWORLD2_PRESETS,
         LINGBOTWORLD_FAST_PRESETS,
-        LONGCAT_PRESETS,
         LTX2_PRESETS,
         MAGI_HUMAN_PRESETS,
         MATRIXGAME2_PRESETS,
@@ -1526,9 +1360,7 @@ def _register_presets() -> None:
         MMAUDIO_PRESETS,
         SD35_PRESETS,
         STABLE_AUDIO_PRESETS,
-        TURBODIFFUSION_PRESETS,
         WAN_PRESETS,
-        ZIMAGE_PRESETS,
     )
     for group in all_preset_groups:
         for preset in group:
diff --git a/fastvideo/tests/api/test_ltx2_continuation.py b/fastvideo/tests/api/test_ltx2_continuation.py
index 0ffca3473..d0714ea06 100644
--- a/fastvideo/tests/api/test_ltx2_continuation.py
+++ b/fastvideo/tests/api/test_ltx2_continuation.py
@@ -175,7 +175,7 @@ class TestValidation:
     """Invalid payloads error cleanly."""
 
     def test_wrong_kind_rejected(self):
-        envelope = ContinuationState(kind="longcat.v1", payload={})
+        envelope = ContinuationState(kind="other.v1", payload={})
         with pytest.raises(ValueError, match="Expected ContinuationState.kind"):
             LTX2ContinuationState.from_continuation_state(envelope)
 
diff --git a/fastvideo/tests/api/test_parser.py b/fastvideo/tests/api/test_parser.py
index 0c2df14fc..27412cc0d 100644
--- a/fastvideo/tests/api/test_parser.py
+++ b/fastvideo/tests/api/test_parser.py
@@ -215,8 +215,6 @@ def test_load_run_config_supports_yaml_roundtrip(tmp_path) -> None:
                 "grid_sizes": None,
                 "c2ws_plucker_emb": None,
                 "action_path": None,
-                "refine_from": None,
-                "stage1_video": None,
             },
             "sampling": {
                 "num_videos_per_prompt": 1,
@@ -233,8 +231,6 @@ def test_load_run_config_supports_yaml_roundtrip(tmp_path) -> None:
                 "guidance_scale": 1.0,
                 "batch_cfg": False,
                 "guidance_scale_2": None,
-                "cfg_normalization": False,
-                "cfg_truncation": 1.0,
                 "guidance_rescale": 0.0,
                 "true_cfg_scale": None,
                 "use_embedded_guidance": None,
diff --git a/fastvideo/tests/api/test_presets.py b/fastvideo/tests/api/test_presets.py
index 166492bdb..3f0ec560f 100644
--- a/fastvideo/tests/api/test_presets.py
+++ b/fastvideo/tests/api/test_presets.py
@@ -484,6 +484,22 @@ class TestHunyuanPresets:
         p = get_preset("fast_hunyuan_t2v", "hunyuan")
         assert p.defaults["num_inference_steps"] == 6
 
+    def test_removed_gamecraft_checkpoint_is_not_detected_as_hunyuan(self, tmp_path) -> None:
+        from fastvideo.registry import get_model_family
+
+        def make_checkpoint(name: str, class_name: str) -> str:
+            model_dir = tmp_path / name
+            model_dir.mkdir()
+            (model_dir / "model_index.json").write_text(
+                json.dumps({
+                    "_class_name": class_name,
+                    "_diffusers_version": "0.33.1"
+                }))
+            return str(model_dir)
+
+        assert get_model_family(make_checkpoint("HunyuanVideo-Custom", "HunyuanVideoPipeline")) == "hunyuan"
+        assert get_model_family(make_checkpoint("Hunyuan-GameCraft-Diffusers", "HunyuanGameCraftPipeline")) is None
+
 
 # -------------------------------------------------------------------
 # Hunyuan15 preset integration (includes two-stage SR)
@@ -591,30 +607,6 @@ class TestCosmosPresets:
         assert sp.fps == 24
 
 
-# -------------------------------------------------------------------
-# TurboDiffusion preset integration
-# -------------------------------------------------------------------
-
-
-class TestTurboDiffusionPresets:
-
-    def test_turbo_presets_registered(self) -> None:
-        import fastvideo.registry  # noqa: F401
-        presets = get_presets_for_family("turbodiffusion")
-        names = {p.name for p in presets}
-        assert names == {
-            "turbo_t2v_1_3b",
-            "turbo_t2v_14b",
-            "turbo_i2v_a14b",
-        }
-
-    def test_turbo_4_step(self) -> None:
-        import fastvideo.registry  # noqa: F401
-        p = get_preset("turbo_t2v_14b", "turbodiffusion")
-        assert p.defaults["num_inference_steps"] == 4
-        assert p.defaults["guidance_scale"] == 1.0
-
-
 # -------------------------------------------------------------------
 # SD35 preset integration
 # -------------------------------------------------------------------
@@ -816,12 +808,6 @@ class TestSinglePresetFamilies:
         p = get_preset("hyworld_t2v", "hyworld")
         assert p.workload_type == "t2v"
 
-    def test_gamecraft_registered(self) -> None:
-        import fastvideo.registry  # noqa: F401
-        p = get_preset("gamecraft_i2v", "gamecraft")
-        assert p.workload_type == "i2v"
-        assert p.defaults["num_frames"] == 33
-
     def test_gen3c_registered(self) -> None:
         import fastvideo.registry  # noqa: F401
         p = get_preset("gen3c_cosmos_7b", "gen3c")
@@ -836,12 +822,6 @@ class TestSinglePresetFamilies:
         assert p3.defaults["num_inference_steps"] == 3
         assert p3.defaults["height"] == 720
 
-    def test_longcat_presets_registered(self) -> None:
-        import fastvideo.registry  # noqa: F401
-        presets = get_presets_for_family("longcat")
-        names = {p.name for p in presets}
-        assert names == {"longcat_t2v", "longcat_i2v", "longcat_vc"}
-
 
 # -------------------------------------------------------------------
 # Cross-family: total preset count
@@ -871,8 +851,8 @@ class TestPresetDefaultTypes:
         ``ltx2_cfg_scale_video`` or ``ltx2_cfg_scale_audio`` is != 1.0,
         so any non-1.0 default silently forces CFG on for every model
         family that doesn't explicitly override these fields. Guard
-        against the regression that surfaced as the TurboDiffusion I2V
-        SSIM crash (``text_encoding.py:81`` assertion on
+        against the regression that surfaced as a Wan-based I2V SSIM
+        crash (``text_encoding.py:81`` assertion on
         ``negative_prompt``)."""
         from fastvideo.api.sampling_param import SamplingParam
         sp = SamplingParam()
diff --git a/fastvideo/tests/api/test_wan_definitions.py b/fastvideo/tests/api/test_wan_definitions.py
index 5c63e210f..540c4e1a8 100644
--- a/fastvideo/tests/api/test_wan_definitions.py
+++ b/fastvideo/tests/api/test_wan_definitions.py
@@ -92,8 +92,6 @@ def test_legacy_aliases_and_sampling_defaults(model_path, config_name, preset_na
     # during a relocation, including when path and class detectors disagree.
     ("lucy-edit-custom", "WanPipeline", "wan", "wan_t2v_1_3b"),
     ("sfwan2.2-i2v-custom", "WanCausalDMDPipeline", "wan", "sf_wan_t2v_1_3b"),
-    ("dreamx-world-5b-lucy-edit-custom", "UnknownPipeline", "dreamx_world", "dreamx_world_5b_ar"),
-    ("dreamx-world-5b-custom", "WanPipeline", "wan", "wan_t2v_1_3b"),
 ])
 def test_local_manifest_detectors_keep_first_match(tmp_path, directory, manifest_class, expected_family, expected_preset):
     checkpoint = tmp_path / directory
diff --git a/fastvideo/tests/contract/test_env_policy.py b/fastvideo/tests/contract/test_env_policy.py
index 79a05ec5c..697bfc30a 100644
--- a/fastvideo/tests/contract/test_env_policy.py
+++ b/fastvideo/tests/contract/test_env_policy.py
@@ -191,7 +191,7 @@ KNOWN_VIOLATIONS: dict[str, int] = {
     'fastvideo/tests/train/methods/test_minimax_h3_finetune.py: whole-environ': 2,
     'fastvideo/tests/train/models/test_load_kandinsky5.py: write FASTVIDEO_ATTENTION_BACKEND': 1,
     'fastvideo/tests/training/distill/test_anyflow_smoke.py: whole-environ': 2,
-    'fastvideo/train/entrypoint/train.py: write FASTVIDEO_ATTENTION_BACKEND': 2,
+    'fastvideo/train/entrypoint/train.py: write FASTVIDEO_ATTENTION_BACKEND': 1,
     'fastvideo/utils.py: read <dynamic>': 5,
     'fastvideo/utils.py: write <dynamic>': 1,
     'fastvideo/worker/ray_distributed_executor.py: read <dynamic>': 2,
diff --git a/fastvideo/tests/contract/test_merge_ci_plan.py b/fastvideo/tests/contract/test_merge_ci_plan.py
index f68c57c4b..7b33d81cc 100644
--- a/fastvideo/tests/contract/test_merge_ci_plan.py
+++ b/fastvideo/tests/contract/test_merge_ci_plan.py
@@ -123,6 +123,28 @@ def test_changed_ssim_test_selects_only_that_file():
     assert plan.encoded_ssim_tests() == "test_flux_t2i_similarity.py"
 
 
+def test_removed_golden_and_ssim_tests_are_not_selected():
+    removed = [
+        "fastvideo/tests/golden_gate/test_longcat.py",
+        "fastvideo/tests/ssim/test_longcat_similarity.py",
+    ]
+    plan = PLAN_MERGE_CI.classify_paths(removed, removed_paths=removed)
+
+    assert plan.encoded_lanes() == ",none,"
+    assert plan.encoded_golden_tests() == "none"
+    assert plan.encoded_ssim_tests() == "none"
+
+
+def test_renamed_golden_test_selects_only_the_new_name():
+    plan = PLAN_MERGE_CI.classify_paths(
+        ["fastvideo/tests/golden_gate/test_old_name.py", "fastvideo/tests/golden_gate/test_new_name.py"],
+        removed_paths=["fastvideo/tests/golden_gate/test_old_name.py"],
+    )
+
+    assert plan.encoded_lanes() == ",golden-gate,"
+    assert plan.encoded_golden_tests() == "test_new_name.py"
+
+
 def test_shared_golden_harness_or_reference_requires_full_golden_lane():
     plan = PLAN_MERGE_CI.classify_paths(["fastvideo/tests/golden_gate/_harness.py"])
 
diff --git a/fastvideo/tests/distributed/test_sp_longcat_layout.py b/fastvideo/tests/distributed/test_sp_longcat_layout.py
deleted file mode 100644
index a2f9289b8..000000000
--- a/fastvideo/tests/distributed/test_sp_longcat_layout.py
+++ /dev/null
@@ -1,90 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""CPU regression tests for LongCatSPLayout (LongCat BSA sequence-parallel sharding).
-
-These pin the layout math the multi-GPU BSA-refinement SP path depends on
-(fastvideo/models/dits/longcat.py ``LongCatSPLayout``): the ``split_h``/``split_w``
-factors, the rank<->(row, col) mapping in ``shard``, and the global<->rank-major
-permutes in ``rank_to_global``/``global_to_rank``. A future edit to any of these
-that breaks the single-GPU-equivalent token ordering would otherwise ship with no
-signal and surface only as a wrong or crashing video on multi-GPU hardware.
-
-Single-process and CPU-resident (plain tensor reshape/permute; no torchrun, no
-GPU compute). It imports ``fastvideo.models.dits.longcat``, which pulls in the
-standard FastVideo dev/GPU stack (torch + triton via the BSA interface) -- the
-same import the existing ``test_sp_*.py`` model tests perform.
-"""
-from __future__ import annotations
-
-import pytest
-import torch
-
-from fastvideo.models.dits.longcat import LongCatSPLayout
-
-
-def _case(t: int, h: int, w: int, size: int):
-    return pytest.param((t, h, w), size, id=f"grid{t}x{h}x{w}-sp{size}")
-
-
-# Grids whose token dims divide the SP split factors (h % split_h == 0 and
-# w % split_w == 0), across the SP sizes LongCat refinement is expected to run.
-_VALID = [
-    _case(4, 8, 8, 2),
-    _case(4, 8, 8, 4),
-    _case(4, 16, 16, 8),
-    _case(4, 8, 16, 4),
-    _case(8, 4, 8, 2),
-    _case(4, 32, 32, 16),
-    _case(2, 4, 4, 2),
-]
-
-
-@pytest.mark.parametrize("grid,size", _VALID)
-def test_rank_global_roundtrip(grid, size):
-    t, h, w = grid
-    layout = LongCatSPLayout(grid, size)
-    n = t * h * w
-    x = torch.arange(n).reshape(1, n, 1)
-    # global -> rank-major -> global is the identity
-    assert torch.equal(layout.rank_to_global(layout.global_to_rank(x)), x)
-    # rank-major -> global -> rank-major is the identity
-    y = layout.global_to_rank(x)
-    assert torch.equal(layout.global_to_rank(layout.rank_to_global(y)), y)
-
-
-@pytest.mark.parametrize("grid,size", _VALID)
-def test_shard_blocks_reconstruct_global(grid, size):
-    t, h, w = grid
-    layout = LongCatSPLayout(grid, size)
-    n = t * h * w
-    x = torch.arange(n).reshape(1, n, 1)
-    shards = [layout.shard(x, rank) for rank in range(size)]
-    for s in shards:
-        assert s.shape == (1, n // size, 1)
-    # Concatenating per-rank shards in rank order and mapping back to global
-    # reproduces the input exactly (shard is consistent with global_to_rank).
-    cat = torch.cat(shards, dim=1)
-    assert torch.equal(layout.rank_to_global(cat), x)
-
-
-def test_split_factors():
-    assert (LongCatSPLayout((4, 8, 8), 2).split_h, LongCatSPLayout((4, 8, 8), 2).split_w) == (1, 2)
-    assert (LongCatSPLayout((4, 8, 8), 4).split_h, LongCatSPLayout((4, 8, 8), 4).split_w) == (2, 2)
-    assert (LongCatSPLayout((4, 8, 8), 8).split_h, LongCatSPLayout((4, 8, 8), 8).split_w) == (2, 4)
-    assert (LongCatSPLayout((4, 32, 32), 16).split_h, LongCatSPLayout((4, 32, 32), 16).split_w) == (4, 4)
-    assert (LongCatSPLayout((4, 32, 32), 32).split_h, LongCatSPLayout((4, 32, 32), 32).split_w) == (4, 8)
-
-
-def test_rejects_unsplittable_grid():
-    # sp=32 -> split_h=4, split_w=8; w=6 is not divisible by split_w=8
-    with pytest.raises(ValueError):
-        LongCatSPLayout((4, 8, 6), 32)
-    # sp=4 -> split_h=2, split_w=2; h=3 is not divisible by split_h=2
-    with pytest.raises(ValueError):
-        LongCatSPLayout((4, 3, 4), 4)
-
-
-def test_rejects_bad_args():
-    with pytest.raises(ValueError):
-        LongCatSPLayout((4, 8, 8), 0)  # size < 1
-    with pytest.raises(ValueError):
-        LongCatSPLayout((4, 8), 4)  # len(grid) != 3
\ No newline at end of file
diff --git a/fastvideo/tests/golden_gate/test_dreamx.py b/fastvideo/tests/golden_gate/test_dreamx.py
deleted file mode 100644
index a61e21c90..000000000
--- a/fastvideo/tests/golden_gate/test_dreamx.py
+++ /dev/null
@@ -1,127 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""Golden-gate: DreamX-World-5B-Cam transformer block 0 (Wan2.2-TI2V-5B derivative
-with PRoPE camera control, bidirectional diffusion variant).
-
-Spec notes: DreamXWorldArchConfig class defaults inherit Wan-14B dims and do NOT
-match the 5B checkpoint — geometry comes from make_dreamx_world_5b_cam_dit_config()
-(configs/pipelines/dreamx_world.py:19-34), verified equal to the mirror repo's
-transformer/config.json. Checkpoint index is model.safetensors.index.json (saved
-via save_torch_state_dict), hence weight_file="model.safetensors". temb is the 4D
-[B, seq, 6, inner_dim] per-token shape (expand_timesteps=True pipeline path,
-dreamx_world.py:273-282). y_camera dtype must equal hidden_states dtype (mixed
-dtype einsum in _dreamx_prope_qkv errors); the camera builder is deterministic
-(camera_conditioning.py:201-228). The ssim test runs TORCH_SDPA only. Forward
-returns a plain [1, seq, 3072] tensor — no postprocess. The AR variant
-(DreamX-World-5B, CausalWanAttentionBlock) is a different implementation and
-needs its own gate recipe; not covered by this spec.
-"""
-
-from __future__ import annotations
-
-import torch
-
-from fastvideo.tests.golden_gate._harness import GateSpec, distributed_runtime, run_gate
-
-__all__ = ["distributed_runtime"]
-
-INNER_DIM = 3072  # 24 heads x 128, DreamX-World-5B-Cam transformer/config.json
-TEXT_LEN = 512
-
-
-def _build_block(layer: int) -> torch.nn.Module:
-    from fastvideo.configs.pipelines.dreamx_world import (make_dreamx_world_5b_cam_dit_config)
-    from fastvideo.models.dits.dreamx_world import DreamXWorldTransformerBlock
-
-    arch = make_dreamx_world_5b_cam_dit_config().arch_config
-    # exactly as DreamXWorldTransformer3DModel.__init__ builds each block
-    # (dreamx_world.py:374-391)
-    return DreamXWorldTransformerBlock(
-        arch.num_attention_heads * arch.attention_head_dim,  # 3072
-        arch.ffn_dim,  # 14336
-        arch.num_attention_heads,  # 24
-        arch.qk_norm,  # "rms_norm_across_heads"
-        arch.cross_attn_norm,  # True
-        arch.eps,  # 1e-6
-        arch.added_kv_proj_dim,  # None -> T2V cross-attn
-        arch._supported_attention_backends,
-        quant_config=None,
-        prefix=f"Wan.blocks.{layer}",
-        add_control_adapter=arch.add_control_adapter,  # True
-        cam_method=arch.cam_method,  # "prope"
-        attn_compress=arch.attn_compress,  # 1
-        cam_self_attn_layers=arch.cam_self_attn_layers,  # None -> cam attn everywhere
-        layer_idx=layer,
-    )
-
-
-def _make_inputs(device: torch.device, seed: int) -> dict:
-    """Fixed batch mirroring the SSIM defaults (9 frames @ 64x64):
-    latent (3, 4, 4) -> patch (1,2,2) -> seq = 3*2*2 = 12, cameras = 3 latent frames."""
-    from fastvideo.layers.rotary_embedding import get_rotary_pos_embed
-    from fastvideo.pipelines.basic.dreamx_world.camera_conditioning import (build_dreamx_camera_condition)
-
-    generator = torch.Generator(device="cpu").manual_seed(seed)
-    num_frames, height, width = 9, 64, 64
-    post_t = (num_frames - 1) // 4 + 1  # VAE 4x temporal
-    post_h, post_w = height // 16 // 2, width // 16 // 2  # VAE 16x spatial, patch (1,2,2)
-    seq = post_t * post_h * post_w  # 12
-
-    # RoPE exactly as the model forward builds it (dreamx_world.py:429-439)
-    d = INNER_DIM // 24  # head_dim 128
-    rope_dim_list = [d - 4 * (d // 6), 2 * (d // 6), 2 * (d // 6)]  # [44, 42, 42]
-    freqs_cos, freqs_sin = get_rotary_pos_embed((post_t, post_h, post_w),
-                                                INNER_DIM,
-                                                24,
-                                                rope_dim_list,
-                                                rope_theta=10000,
-                                                dtype=torch.float64)
-    freqs_cis = (freqs_cos.to(device).float(), freqs_sin.to(device).float())
-
-    hidden = torch.randn(1, seq, INNER_DIM, generator=generator, dtype=torch.float32)
-    encoder = torch.randn(1, TEXT_LEN, INNER_DIM, generator=generator, dtype=torch.float32)
-    # expand_timesteps=True pipeline -> per-token timesteps -> 4D temb (B, seq, 6, dim)
-    temb = torch.randn(1, seq, 6, INNER_DIM, generator=generator, dtype=torch.float32)
-
-    # Deterministic camera condition, same builder + keys as the pipeline stage
-    # (stages.py:55-64). dtype MUST match hidden_states.
-    camera = build_dreamx_camera_condition(["w"], [4.0],
-                                           num_frames=num_frames,
-                                           height=height,
-                                           width=width,
-                                           dtype=torch.bfloat16,
-                                           device=device)
-    y_camera = {k: v.unsqueeze(0) for k, v in camera.items()}  # viewmats (1,3,4,4), K (1,3,3,3)
-
-    return {
-        "hidden_states": hidden.to(device=device, dtype=torch.bfloat16),
-        "encoder_hidden_states": encoder.to(device=device, dtype=torch.bfloat16),
-        "temb": temb.to(device=device, dtype=torch.bfloat16),
-        "freqs_cis": freqs_cis,
-        "original_seq_len": seq,
-        "y_camera": y_camera,
-    }
-
-
-SPEC = GateSpec(
-    name="dreamx_world_5b_cam",
-    repo_id="FastVideo/DreamX-World-5B-Cam-Diffusers",
-    build_block=_build_block,
-    make_inputs=_make_inputs,
-    prefix_template="blocks.{N}.",
-    renames=(
-        # to_out rename must run before the generic attn1 strip
-        (r"\.attn1\.to_out\.0\.", ".to_out."),
-        (r"\.attn1\.", "."),  # to_q/to_k/to_v/norm_q/norm_k -> block level
-        (r"\.attn2\.to_out\.0\.", ".attn2.to_out."),
-        (r"\.ffn\.net\.0\.proj\.", ".ffn.fc_in."),
-        (r"\.ffn\.net\.2\.", ".ffn.fc_out."),
-        (r"\.norm2\.", ".self_attn_residual_norm.norm."),
-        # identity: attn2.{to_q,to_k,to_v,norm_q,norm_k}, cam_self_attn.*, scale_shift_table
-    ),
-    attention_backend="TORCH_SDPA",
-    weight_file="model.safetensors",
-)
-
-
-def test_dreamx_world_cam_golden_gate(distributed_runtime) -> None:
-    run_gate(SPEC)
diff --git a/fastvideo/tests/golden_gate/test_gamecraft.py b/fastvideo/tests/golden_gate/test_gamecraft.py
deleted file mode 100644
index d9c5f36ac..000000000
--- a/fastvideo/tests/golden_gate/test_gamecraft.py
+++ /dev/null
@@ -1,87 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""Golden-gate: HunyuanGameCraft double-stream block 0 (Hunyuan-video arch).
-
-Spec notes: the repeated block is MMDoubleStreamBlock from hunyuanvideo.py
-(imported by hunyuangamecraft.py:24-28); ctor mirrors
-HunyuanGameCraftTransformer3DModel.__init__ (hunyuangamecraft.py:252-261) with
-hidden_size 3072 = 24 heads x 128. Checkpoint is a SINGLE FILE (no index.json)
-under transformer/ and its keys are already in FastVideo form, so renames is
-empty. RoPE is built over the video grid only, rope_dim_list (16,56,56),
-rope_theta 256 (hunyuangamecraft.py:340-348) — applied inside
-DistributedAttention to the img qkv, so freqs_cis length must equal the img
-sequence length. Forward returns an (img, txt) TUPLE (hunyuanvideo.py:280);
-postprocess concatenates along the sequence dim into one tensor.
-"""
-
-from __future__ import annotations
-
-import torch
-
-import fastvideo.envs as envs
-from fastvideo.tests.golden_gate._harness import GateSpec, distributed_runtime, run_gate
-
-__all__ = ["distributed_runtime"]
-
-HIDDEN = 3072  # 24 heads x 128, configs/models/dits/hunyuangamecraft.py:125
-
-
-def _build_block(layer: int) -> torch.nn.Module:
-    from fastvideo.configs.models.dits.hunyuangamecraft import HunyuanGameCraftArchConfig
-    from fastvideo.models.dits.hunyuanvideo import MMDoubleStreamBlock
-
-    arch = HunyuanGameCraftArchConfig()
-    return MMDoubleStreamBlock(
-        hidden_size=arch.hidden_size,  # 3072
-        num_attention_heads=arch.num_attention_heads,  # 24
-        mlp_ratio=arch.mlp_ratio,  # 4.0
-        dtype=arch.dtype,  # None
-        supported_attention_backends=arch._supported_attention_backends,
-        prefix=f"hunyuan_gamecraft.double_blocks.{layer}",
-    )
-
-
-def _make_inputs(device: torch.device, seed: int) -> dict:
-    """3x4x5 = 60 video tokens + 32 text tokens, hidden 3072.
-
-    Shapes per MMDoubleStreamBlock.forward (hunyuanvideo.py:193-200):
-    img [B, S_img, 3072], txt [B, S_txt, 3072], vec [B, 3072],
-    freqs_cis = (cos, sin) each [S_img, 128].
-    """
-    from fastvideo.layers.rotary_embedding import get_rotary_pos_embed
-
-    generator = torch.Generator(device="cpu").manual_seed(seed)
-    tt, th, tw = 3, 4, 5
-    img_seq, txt_seq = tt * th * tw, 32
-
-    freqs_cos, freqs_sin = get_rotary_pos_embed((tt, th, tw), HIDDEN, 24, [16, 56, 56], 256)  # -> [60, 128] fp32 each
-
-    img = torch.randn(1, img_seq, HIDDEN, generator=generator, dtype=torch.float32)
-    txt = torch.randn(1, txt_seq, HIDDEN, generator=generator, dtype=torch.float32)
-    vec = torch.randn(1, HIDDEN, generator=generator, dtype=torch.float32)
-
-    return {
-        "img": img.to(device=device, dtype=torch.bfloat16),
-        "txt": txt.to(device=device, dtype=torch.bfloat16),
-        "vec": vec.to(device=device, dtype=torch.bfloat16),
-        "freqs_cis": (freqs_cos.to(device=device,
-                                   dtype=torch.bfloat16), freqs_sin.to(device=device, dtype=torch.bfloat16)),
-    }
-
-
-SPEC = GateSpec(
-    name="gamecraft",
-    repo_id="FastVideo/HunyuanGameCraft-Diffusers",
-    build_block=_build_block,
-    make_inputs=_make_inputs,
-    prefix_template="double_blocks.{N}.",
-    renames=(),  # checkpoint keys already match the block's state_dict 1:1
-    attention_backend="FLASH_ATTN",
-    postprocess=lambda out: torch.cat(out, dim=1),  # (img, txt) tuple -> one tensor
-    # The registry default is the Hugging Face repo id, so only an explicitly set value names a local checkout.
-    model_root=lambda: (envs.FASTVIDEO_TEST_GAMECRAFT_MODEL_PATH.get()
-                        if envs.FASTVIDEO_TEST_GAMECRAFT_MODEL_PATH.is_set() else None),
-)
-
-
-def test_gamecraft_golden_gate(distributed_runtime) -> None:
-    run_gate(SPEC)
diff --git a/fastvideo/tests/golden_gate/test_glm_image.py b/fastvideo/tests/golden_gate/test_glm_image.py
deleted file mode 100644
index 1ecc65ab1..000000000
--- a/fastvideo/tests/golden_gate/test_glm_image.py
+++ /dev/null
@@ -1,86 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""Golden-gate: GLM-Image (GlmImageTransformer2DModel) transformer block 0.
-
-Spec notes: inner_dim = 32 heads x 128 = 4096 (glm_image.py:631, arch config
-defaults). RoPE is built outside the block by GlmImageRotaryPosEmbed
-(glm_image.py:727-729) from only the shape/device of the pre-patchify latent
-(B, 16, H, W); cos/sin are fp32 (image_seq, 64). temb is passed post-SiLU in
-the hidden dtype (glm_image.py:747-750), so bf16 here. The block forward
-returns a TUPLE (hidden_states, encoder_hidden_states) (glm_image.py:502) —
-postprocess cats encoder first along seq, matching the attention layout
-(glm_image.py:343). attn1.to_out is nn.ModuleList so "attn1.to_out.0.*"
-matches natively; only the two ff.net renames apply. The SSIM test pins
-TORCH_SDPA and loads local weights via FASTVIDEO_TEST_GLM_IMAGE_MODEL_DIR.
-"""
-
-from __future__ import annotations
-
-import torch
-
-import fastvideo.envs as envs
-from fastvideo.tests.golden_gate._harness import GateSpec, distributed_runtime, run_gate
-
-__all__ = ["distributed_runtime"]
-
-INNER_DIM = 4096  # 32 heads x 128, GlmImageDiTArchConfig defaults
-TEXT_LEN = 32
-LATENT_H = LATENT_W = 64  # patch_size=2 -> 32x32 = 1024 image tokens
-
-
-def _build_block(layer: int) -> torch.nn.Module:
-    from fastvideo.configs.models.dits.glm_image import GlmImageDiTArchConfig
-    from fastvideo.models.dits.glm_image import (GlmImageTransformer2DModel, GlmImageTransformerBlock)
-
-    arch = GlmImageDiTArchConfig()
-    return GlmImageTransformerBlock(
-        arch.num_attention_heads * arch.attention_head_dim,  # dim = 4096
-        arch.num_attention_heads,  # 32
-        arch.attention_head_dim,  # 128
-        arch.time_embed_dim,  # 512
-        supported_attention_backends=GlmImageTransformer2DModel._supported_attention_backends,
-        prefix=f"transformer_blocks.{layer}",
-    )
-
-
-def _make_inputs(device: torch.device, seed: int) -> dict:
-    from fastvideo.models.dits.glm_image import GlmImageRotaryPosEmbed
-
-    generator = torch.Generator(device="cpu").manual_seed(seed)
-    n_image = (LATENT_H // 2) * (LATENT_W // 2)  # 1024 tokens
-
-    # rope consumes only shape+device of the raw latent (glm_image.py:518-522)
-    rope = GlmImageRotaryPosEmbed(dim=128, patch_size=2, theta=10000.0)
-    cos, sin = rope(torch.empty(1, 16, LATENT_H, LATENT_W, device=device))
-
-    hidden = torch.randn(1, n_image, INNER_DIM, generator=generator, dtype=torch.float32)
-    encoder = torch.randn(1, TEXT_LEN, INNER_DIM, generator=generator, dtype=torch.float32)
-    temb = torch.randn(1, 512, generator=generator, dtype=torch.float32)
-
-    return {
-        "hidden_states": hidden.to(device=device, dtype=torch.bfloat16),
-        "encoder_hidden_states": encoder.to(device=device, dtype=torch.bfloat16),
-        "temb": temb.to(device=device, dtype=torch.bfloat16),
-        "image_rotary_emb": (cos, sin),  # fp32, already on device
-    }
-
-
-SPEC = GateSpec(
-    name="glm_image",
-    repo_id="zai-org/GLM-Image",
-    build_block=_build_block,
-    make_inputs=_make_inputs,
-    prefix_template="transformer_blocks.{N}.",
-    renames=(
-        (r"\.ff\.net\.0\.proj\.", ".ff.fc_in."),
-        (r"\.ff\.net\.2\.", ".ff.fc_out."),
-    ),
-    attention_backend="TORCH_SDPA",
-    # forward returns (hidden_states, encoder_hidden_states); encoder first,
-    # like the attention concat layout (glm_image.py:343)
-    postprocess=lambda out: torch.cat([out[1], out[0]], dim=1),
-    model_root=lambda: envs.FASTVIDEO_TEST_GLM_IMAGE_MODEL_DIR.get(),
-)
-
-
-def test_glm_image_golden_gate(distributed_runtime) -> None:
-    run_gate(SPEC)
diff --git a/fastvideo/tests/golden_gate/test_longcat.py b/fastvideo/tests/golden_gate/test_longcat.py
deleted file mode 100644
index 004950aa6..000000000
--- a/fastvideo/tests/golden_gate/test_longcat.py
+++ /dev/null
@@ -1,80 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""Golden-gate: LongCat-Video (native FastVideo impl) transformer block 0.
-
-Spec notes: LongCatVideoConfig() arch defaults match the mirror's
-transformer/config.json (hidden 4096, 32 heads, mlp_ratio 4, adaln 512).
-The block __init__ needs the FULL LongCatVideoConfig, not the arch config —
-self_attn reads config._supported_attention_backends (longcat.py:273) while
-cross_attn reads config.arch_config._supported_attention_backends (:548).
-Checkpoint is a SINGLE fp32 file (transformer/model.safetensors, no
-index.json) with zero renames — the mirror is pre-converted, keys match
-module names. 3D RoPE is built inside the block from latent_shape; t is
-per-latent-frame temb [B, T, 512]. Defaults (return_kv=False) return one
-plain tensor, so no postprocess. Backend FLASH_ATTN per the ssim test
-(tests/ssim/test_longcat_similarity.py:200).
-"""
-
-from __future__ import annotations
-
-
-import torch
-
-import fastvideo.envs as envs
-from fastvideo.tests.golden_gate._harness import GateSpec, distributed_runtime, run_gate
-
-__all__ = ["distributed_runtime"]
-
-HIDDEN_SIZE = 4096  # configs/models/dits/longcat.py:77
-ADALN_DIM = 512  # configs/models/dits/longcat.py:93
-
-
-def _build_block(layer: int) -> torch.nn.Module:
-    from fastvideo.configs.models.dits.longcat import LongCatVideoConfig
-    from fastvideo.models.dits.longcat import LongCatTransformerBlock
-
-    config = LongCatVideoConfig()
-    arch = config.arch_config
-    return LongCatTransformerBlock(
-        hidden_size=arch.hidden_size,
-        num_heads=arch.num_attention_heads,
-        mlp_ratio=arch.mlp_ratio,
-        adaln_tembed_dim=arch.adaln_tembed_dim,
-        config=config,
-    )
-
-
-def _make_inputs(device: torch.device, seed: int) -> dict:
-    # forward signature (longcat.py:773-783): x [B, N, 4096], context
-    # [B, N_text, 4096], t [B, T, 512], latent_shape (T, H, W), N == T*H*W.
-    generator = torch.Generator(device="cpu").manual_seed(seed)
-    t_frames, h, w = 3, 8, 8
-    seq = t_frames * h * w
-    n_text = 32  # production pads to 512; any length works
-
-    hidden = torch.randn(1, seq, HIDDEN_SIZE, generator=generator, dtype=torch.float32)
-    context = torch.randn(1, n_text, HIDDEN_SIZE, generator=generator, dtype=torch.float32)
-    temb = torch.randn(1, t_frames, ADALN_DIM, generator=generator, dtype=torch.float32)
-
-    return {
-        "x": hidden.to(device=device, dtype=torch.bfloat16),
-        "context": context.to(device=device, dtype=torch.bfloat16),
-        "t": temb.to(device=device, dtype=torch.bfloat16),
-        "latent_shape": (t_frames, h, w),
-    }
-
-
-SPEC = GateSpec(
-    name="longcat_t2v",
-    repo_id="FastVideo/LongCat-Video-T2V-Diffusers",
-    build_block=_build_block,
-    make_inputs=_make_inputs,
-    prefix_template="blocks.{N}.",
-    renames=(),
-    attention_backend="FLASH_ATTN",
-    model_root=lambda: envs.FASTVIDEO_TEST_LONGCAT_MODEL_ROOT.get(),
-    weight_file="model.safetensors",  # single 54.3 GB fp32 file, no index.json
-)
-
-
-def test_longcat_golden_gate(distributed_runtime) -> None:
-    run_gate(SPEC)
diff --git a/fastvideo/tests/golden_gate/test_wan_t2v.py b/fastvideo/tests/golden_gate/test_wan_t2v.py
index 4bea1ee25..9fcc8dc7c 100644
--- a/fastvideo/tests/golden_gate/test_wan_t2v.py
+++ b/fastvideo/tests/golden_gate/test_wan_t2v.py
@@ -1,5 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
-"""Golden-gate: Wan2.1-T2V-1.3B transformer block 0 (also the SFWan/TurboWan arch).
+"""Golden-gate: Wan2.1-T2V-1.3B transformer block 0 (also the SFWan arch).
 
 Spec notes: arch-config defaults are the 14B model, so the 1.3B geometry is
 pinned explicitly from the checkpoint's transformer/config.json. RoPE is built
diff --git a/fastvideo/tests/golden_gate/test_zimage.py b/fastvideo/tests/golden_gate/test_zimage.py
deleted file mode 100644
index 9271a5b83..000000000
--- a/fastvideo/tests/golden_gate/test_zimage.py
+++ /dev/null
@@ -1,89 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""Golden-gate: Z-Image-Turbo (Tongyi-MAI) main-stack transformer block 0.
-
-Spec notes: ZImageDiTArchConfig defaults match the checkpoint's
-transformer/config.json (dim=3840, n_heads=n_kv_heads=30, adaln_embed_dim=256).
-Checkpoint keys match FastVideo module names exactly — no renames. Rotary is
-built OUTSIDE the block: freqs_cis is complex64 from RopeEmbedder(theta=256,
-axes_dims=(32,48,48)) and must stay complex64 (rotary math runs fp32 inside
-attention, zimage.py:104-108). adaln_input is width min(dim, adaln_embed_dim)
-= 256, not dim. Attention is plain F.scaled_dot_product_attention with a bool
-[B, S] key-padding mask (True=attend); the block ignores fastvideo attention
-backends, but TORCH_SDPA matches the ssim test and keys the golden filename.
-Block returns a bare tensor [B, S, dim], no postprocess needed.
-"""
-
-from __future__ import annotations
-
-import torch
-
-import fastvideo.envs as envs
-from fastvideo.tests.golden_gate._harness import GateSpec, distributed_runtime, run_gate
-
-__all__ = ["distributed_runtime"]
-
-DIM = 3840  # 30 heads x 128, Z-Image-Turbo transformer/config.json
-
-
-def _build_block(layer: int) -> torch.nn.Module:
-    from fastvideo.configs.models.dits.zimage import ZImageDiTArchConfig
-    from fastvideo.models.dits.zimage import ZImageTransformerBlock
-
-    arch = ZImageDiTArchConfig()
-    return ZImageTransformerBlock(
-        layer,  # main `layers` stack uses the plain index (zimage.py:337-338)
-        arch.dim,
-        arch.n_heads,
-        arch.n_kv_heads,
-        arch.norm_eps,
-        arch.qk_norm,
-        arch.adaln_embed_dim,
-        modulation=True,  # main-stack blocks are modulated (zimage.py:337-338)
-    )
-
-
-def _make_inputs(device: torch.device, seed: int) -> dict:
-    """Fixed padded batch: 2 sequences over an 8x8 image grid, lengths [64, 48]."""
-    from fastvideo.models.dits.zimage import RopeEmbedder, ZImageTransformer2DModel
-
-    generator = torch.Generator(device="cpu").manual_seed(seed)
-    batch, seq = 2, 64
-    lengths = [64, 48]
-
-    # freqs_cis exactly as the model builds it (zimage.py:342, 527-531); image
-    # position ids start at frame index cap_len+1 -> use start=(1, 0, 0).
-    rope = RopeEmbedder(theta=256.0, axes_dims=(32, 48, 48), axes_lens=(1536, 512, 512))
-    pos_ids = ZImageTransformer2DModel.create_coordinate_grid((1, 8, 8), start=(1, 0, 0)).flatten(0, 2)  # [64, 3] int32
-    freqs_cis = rope(pos_ids).unsqueeze(0).expand(batch, seq, 64).contiguous()  # complex64 [2, 64, 64]
-
-    attn_mask = torch.zeros(batch, seq, dtype=torch.bool)
-    for i, length in enumerate(lengths):
-        attn_mask[i, :length] = True
-
-    x = torch.randn(batch, seq, DIM, generator=generator, dtype=torch.float32)
-    adaln = torch.randn(batch, 256, generator=generator, dtype=torch.float32)
-
-    return {
-        "x": x.to(device=device, dtype=torch.bfloat16),
-        "attn_mask": attn_mask.to(device),
-        "freqs_cis": freqs_cis.to(device),  # stays complex64, never cast to bf16
-        "adaln_input": adaln.to(device=device, dtype=torch.bfloat16),
-    }
-
-
-SPEC = GateSpec(
-    name="zimage_turbo",
-    repo_id="Tongyi-MAI/Z-Image-Turbo",
-    build_block=_build_block,
-    make_inputs=_make_inputs,
-    prefix_template="layers.{N}.",
-    renames=(),
-    attention_backend="TORCH_SDPA",
-    # The registry default is the Hugging Face repo id, so only an explicitly set value names a local checkout.
-    model_root=lambda: (envs.FASTVIDEO_TEST_ZIMAGE_MODEL_DIR.get()
-                        if envs.FASTVIDEO_TEST_ZIMAGE_MODEL_DIR.is_set() else None),
-)
-
-
-def test_zimage_golden_gate(distributed_runtime) -> None:
-    run_gate(SPEC)
diff --git a/fastvideo/tests/inference/lora/test_lora_inference_similarity.py b/fastvideo/tests/inference/lora/test_lora_inference_similarity.py
index 4e2ec6800..add16e12e 100644
--- a/fastvideo/tests/inference/lora/test_lora_inference_similarity.py
+++ b/fastvideo/tests/inference/lora/test_lora_inference_similarity.py
@@ -60,9 +60,8 @@ LORA_CONFIGS = [{
 }
                 # TODO: Add a LoRA with lora_alpha values to test alpha scaling
                 #
-                # Context: This change is mainly for an in-progress ticket porting over LongCat-Video,
-                # where they used an alpha value that is two times smaller than their rank. This fix
-                # ensures that LoRA weights are correctly scaled by the alpha/rank ratio when merged.
+                # Context: Some LoRAs use an alpha value that is two times smaller than their rank.
+                # This fix ensures that LoRA weights are correctly scaled by the alpha/rank ratio when merged.
                 #
                 # Issue: Currently, we cannot add a test for LoRA adapters with alpha values because:
                 # - The existing public LoRAs for Wan-AI/Wan2.1-T2V-1.3B-Diffusers don't store lora_alpha
@@ -70,7 +69,7 @@ LORA_CONFIGS = [{
                 # - This is why the alpha/rank scaling bug wasn't caught by existing tests
                 #
                 # The fix has been validated with:
-                # - LongCat-Video distilled LoRA (which includes alpha values)
+                # - A distilled LoRA that includes alpha values
                 # - Manual testing shows correct alpha/rank scaling behavior
                 # - Backward compatibility confirmed with LoRAs without alpha values
                 #
diff --git a/fastvideo/tests/loader/test_wan_family_imports.py b/fastvideo/tests/loader/test_wan_family_imports.py
index 8ec1d747b..452b0b944 100644
--- a/fastvideo/tests/loader/test_wan_family_imports.py
+++ b/fastvideo/tests/loader/test_wan_family_imports.py
@@ -117,19 +117,14 @@ def test_legacy_vae_cache_context_is_shared_and_restored_after_errors():
 
 @pytest.mark.parametrize("module_name", [
     "fastvideo.models.dits.causal_wanvideo",
-    "fastvideo.models.dits.dreamx_world",
     "fastvideo.models.dits.matrixgame2.model",
     "fastvideo.models.dits.matrixgame2.causal_model",
     "fastvideo.models.dits.matrixgame3.model",
     "fastvideo.models.dits.lingbotworld.model",
-    "fastvideo.configs.models.dits.dreamx_world",
     "fastvideo.configs.models.dits.matrixgame2",
     "fastvideo.configs.models.dits.matrixgame3",
     "fastvideo.configs.pipelines.wan",
-    "fastvideo.configs.pipelines.dreamx_world",
-    "fastvideo.configs.pipelines.longcat",
     "fastvideo.configs.pipelines.lingbot_video",
-    "fastvideo.configs.pipelines.turbodiffusion",
     "fastvideo.configs.pipelines.lingbotworld2",
 ])
 def test_downstream_wan_consumers_import(module_name):
diff --git a/fastvideo/tests/ssim/test_dreamx_world_similarity.py b/fastvideo/tests/ssim/test_dreamx_world_similarity.py
deleted file mode 100644
index e8648382e..000000000
--- a/fastvideo/tests/ssim/test_dreamx_world_similarity.py
+++ /dev/null
@@ -1,186 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-from __future__ import annotations
-
-import os
-from pathlib import Path
-
-import pytest
-from PIL import Image, ImageDraw
-
-import fastvideo.envs as envs
-from fastvideo.logger import init_logger
-from fastvideo.tests.ssim.inference_similarity_utils import (
-    resolve_inference_device_reference_folder,
-    run_image_to_video_similarity_test,
-    run_text_to_video_similarity_test,
-)
-from fastvideo.tests.ssim.reference_utils import with_model_path
-
-logger = init_logger(__name__)
-
-REQUIRED_GPUS = 1
-pytestmark = pytest.mark.skip(reason="Disabled pending removal of DreamX World support.")
-
-device_reference_folder = resolve_inference_device_reference_folder(logger)
-
-# The tests add "model_path" from FASTVIDEO_TEST_DREAMX_WORLD_SSIM_MODEL_PATH and
-# FASTVIDEO_TEST_DREAMX_WORLD_AR_SSIM_MODEL_PATH.
-DREAMX_WORLD_PARAMS = {
-    "num_gpus": 1,
-    "height": 64,
-    "width": 64,
-    "num_frames": 9,
-    "num_inference_steps": 1,
-    "guidance_scale": 1.0,
-    "seed": 1024,
-    "fps": 16,
-}
-
-DREAMX_WORLD_FULL_QUALITY_PARAMS = {
-    **DREAMX_WORLD_PARAMS,
-    "height": 480,
-    "width": 832,
-    "num_frames": 161,
-    "num_inference_steps": 30,
-    "guidance_scale": 5.0,
-}
-
-DREAMX_WORLD_AR_PARAMS = {
-    "num_gpus": 1,
-    "height": 192,
-    "width": 192,
-    "num_frames": 81,
-    "num_inference_steps": 4,
-    "guidance_scale": 1.0,
-    "seed": 2048,
-    "fps": 16,
-}
-
-DREAMX_WORLD_AR_FULL_QUALITY_PARAMS = {
-    **DREAMX_WORLD_AR_PARAMS,
-    "height": 704,
-    "width": 1280,
-    "num_frames": 1005,
-}
-
-DREAMX_WORLD_MODEL_TO_PARAMS = {
-    "DreamX-World-5B-Cam": DREAMX_WORLD_PARAMS,
-}
-
-DREAMX_WORLD_AR_MODEL_TO_PARAMS = {
-    "DreamX-World-5B": DREAMX_WORLD_AR_PARAMS,
-}
-
-FULL_QUALITY_DREAMX_WORLD_MODEL_TO_PARAMS = {
-    "DreamX-World-5B-Cam": DREAMX_WORLD_FULL_QUALITY_PARAMS,
-}
-
-FULL_QUALITY_DREAMX_WORLD_AR_MODEL_TO_PARAMS = {
-    "DreamX-World-5B": DREAMX_WORLD_AR_FULL_QUALITY_PARAMS,
-}
-
-DREAMX_WORLD_TEST_CASES = [
-    (
-        "A cinematic first-person drive through a futuristic coastal city at sunrise, "
-        "reflective glass towers, clean streets, soft volumetric light.",
-        ("w", "d", "w"),
-        (4.0, 2.0, 4.0),
-    ),
-]
-
-DREAMX_WORLD_AR_TEST_CASES = [
-    (
-        "A long autonomous drive through a futuristic coastal city at sunrise, "
-        "smooth forward camera motion, reflective glass towers, clean streets.",
-        ("w", "d", "w", "a"),
-        (2.0, 1.0, 2.0, 1.0),
-    ),
-]
-
-
-def _write_deterministic_reference_image(path: Path) -> None:
-    path.parent.mkdir(parents=True, exist_ok=True)
-    image = Image.new("RGB", (96, 96), color=(42, 76, 112))
-    draw = ImageDraw.Draw(image)
-    draw.rectangle((0, 56, 96, 96), fill=(36, 44, 52))
-    draw.polygon([(0, 56), (48, 30), (96, 56)], fill=(120, 142, 158))
-    draw.rectangle((34, 42, 62, 72), fill=(178, 198, 212))
-    draw.line((0, 80, 96, 66), fill=(238, 209, 124), width=3)
-    image.save(path)
-
-
-@pytest.mark.parametrize(("prompt", "action_list", "action_speed_list"), DREAMX_WORLD_TEST_CASES)
-@pytest.mark.parametrize("attention_backend_name", ["TORCH_SDPA"])
-@pytest.mark.parametrize("model_id", list(DREAMX_WORLD_MODEL_TO_PARAMS.keys()))
-def test_dreamx_world_inference_similarity(
-    prompt: str,
-    action_list: tuple[str, ...],
-    action_speed_list: tuple[float, ...],
-    attention_backend_name: str,
-    model_id: str,
-    tmp_path: Path,
-) -> None:
-    image_path = tmp_path / "dreamx_world_ssim_input.png"
-    _write_deterministic_reference_image(image_path)
-    model_path = envs.FASTVIDEO_TEST_DREAMX_WORLD_SSIM_MODEL_PATH.get()
-
-    run_image_to_video_similarity_test(
-        logger=logger,
-        script_dir=os.path.dirname(os.path.abspath(__file__)),
-        device_reference_folder=device_reference_folder,
-        prompt=prompt,
-        image_path=str(image_path),
-        attention_backend_name=attention_backend_name,
-        model_id=model_id,
-        default_params_map=with_model_path(DREAMX_WORLD_MODEL_TO_PARAMS, model_path),
-        full_quality_params_map=with_model_path(FULL_QUALITY_DREAMX_WORLD_MODEL_TO_PARAMS, model_path),
-        min_acceptable_ssim=0.98,
-        init_kwargs_override={
-            "use_fsdp_inference": False,
-            "dit_cpu_offload": False,
-            "vae_cpu_offload": True,
-            "text_encoder_cpu_offload": True,
-            "pin_cpu_memory": False,
-            "override_pipeline_cls_name": "DreamXWorldPipeline",
-        },
-        generation_kwargs_override={
-            "action_list": list(action_list),
-            "action_speed_list": list(action_speed_list),
-        },
-    )
-
-
-@pytest.mark.parametrize(("prompt", "action_list", "action_speed_list"), DREAMX_WORLD_AR_TEST_CASES)
-@pytest.mark.parametrize("attention_backend_name", ["TORCH_SDPA"])
-@pytest.mark.parametrize("model_id", list(DREAMX_WORLD_AR_MODEL_TO_PARAMS.keys()))
-def test_dreamx_world_ar_inference_similarity(
-    prompt: str,
-    action_list: tuple[str, ...],
-    action_speed_list: tuple[float, ...],
-    attention_backend_name: str,
-    model_id: str,
-) -> None:
-    model_path = envs.FASTVIDEO_TEST_DREAMX_WORLD_AR_SSIM_MODEL_PATH.get()
-    run_text_to_video_similarity_test(
-        logger=logger,
-        script_dir=os.path.dirname(os.path.abspath(__file__)),
-        device_reference_folder=device_reference_folder,
-        prompt=prompt,
-        attention_backend_name=attention_backend_name,
-        model_id=model_id,
-        default_params_map=with_model_path(DREAMX_WORLD_AR_MODEL_TO_PARAMS, model_path),
-        full_quality_params_map=with_model_path(FULL_QUALITY_DREAMX_WORLD_AR_MODEL_TO_PARAMS, model_path),
-        min_acceptable_ssim=0.98,
-        init_kwargs_override={
-            "use_fsdp_inference": False,
-            "dit_cpu_offload": False,
-            "vae_cpu_offload": True,
-            "text_encoder_cpu_offload": True,
-            "pin_cpu_memory": False,
-            "override_pipeline_cls_name": "DreamXWorldARPipeline",
-        },
-        generation_kwargs_override={
-            "action_list": list(action_list),
-            "action_speed_list": list(action_speed_list),
-        },
-    )
diff --git a/fastvideo/tests/ssim/test_gamecraft_similarity.py b/fastvideo/tests/ssim/test_gamecraft_similarity.py
deleted file mode 100644
index 5f0424b41..000000000
--- a/fastvideo/tests/ssim/test_gamecraft_similarity.py
+++ /dev/null
@@ -1,382 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-SSIM regression test for HunyuanGameCraft (T2V and I2V).
-
-Generates a video with deterministic seed and camera trajectory,
-then compares against a device-specific reference video via MS-SSIM.
-
-Reference videos must be pre-generated and stored under:
-    reference_videos/<quality-tier>/<device>_reference_videos/HunyuanGameCraft/
-    <ATTENTION_BACKEND>/
-
-To create initial reference videos, run this test once and copy the
-generated videos into the appropriate reference folder.
-"""
-import os
-
-import torch
-import pytest
-
-import fastvideo.envs as envs
-from fastvideo import VideoGenerator
-from fastvideo.api.sampling_param import SamplingParam
-from fastvideo.logger import init_logger
-from fastvideo.tests.ssim.reference_utils import (
-    build_generated_output_dir,
-    build_reference_folder_path,
-    get_cuda_device_name,
-    resolve_device_reference_folder,
-    select_ssim_params,
-    with_model_path,
-)
-from fastvideo.tests.utils import (
-    compute_video_ssim_torchvision,
-    write_ssim_results,
-)
-
-logger = init_logger(__name__)
-
-REQUIRED_GPUS = 1
-pytestmark = pytest.mark.skip(reason="Disabled pending removal of HunyuanGameCraft support.")
-
-# ---------------------------------------------------------------------------
-# Device-dependent reference folder
-# ---------------------------------------------------------------------------
-device_reference_folder = resolve_device_reference_folder(
-    (
-        ("A40", "A40"),
-        ("L40S", "L40S"),
-        ("A100", "A100"),
-        ("H100", "H100"),
-        ("GB200", "GB200"),
-        ("H200", "H200"),
-    ),
-    device_name=get_cuda_device_name(),
-    unknown_device_prefix="Unknown",
-    logger=logger,
-)
-
-# ---------------------------------------------------------------------------
-# Helpers – camera trajectory (self-contained, no official repo dependency)
-# ---------------------------------------------------------------------------
-from fastvideo.models.camera import create_camera_trajectory as _create_camera_trajectory
-
-
-def _shutdown_executor(generator: VideoGenerator | None) -> None:
-    if generator is None:
-        return
-    generator.executor.shutdown()
-
-
-# ---------------------------------------------------------------------------
-# Model parameters
-# ---------------------------------------------------------------------------
-# Same as basic_gamecraft.py: default HF path; set FASTVIDEO_TEST_GAMECRAFT_MODEL_PATH for local weights.
-# The tests add "model_path" from FASTVIDEO_TEST_GAMECRAFT_MODEL_PATH.
-GAMECRAFT_T2V_PARAMS = {
-    "num_gpus": 1,
-    "height": 480,
-    "width": 832,
-    "num_frames": 33,
-    "num_inference_steps": 20,
-    "guidance_scale": 6.0,
-    "seed": 1024,
-    "action": "forward",
-    "action_speed": 0.2,
-    "negative_prompt": "",
-}
-
-
-def _gamecraft_t2v_full_quality_params(model_path: str) -> dict[str, object]:
-    """Return the full-quality T2V params, taken from the default sampling params of ``model_path``."""
-    full_quality_defaults = SamplingParam.from_pretrained(model_path)
-    return {
-        "num_gpus": GAMECRAFT_T2V_PARAMS["num_gpus"],
-        "model_path": model_path,
-        "height": full_quality_defaults.height,
-        "width": full_quality_defaults.width,
-        "num_frames": GAMECRAFT_T2V_PARAMS["num_frames"],  # default num_frames: 33
-        "num_inference_steps": full_quality_defaults.num_inference_steps,
-        "guidance_scale": full_quality_defaults.guidance_scale,
-        "seed": full_quality_defaults.seed,
-        "action": GAMECRAFT_T2V_PARAMS["action"],
-        "action_speed": GAMECRAFT_T2V_PARAMS["action_speed"],
-        "negative_prompt": full_quality_defaults.negative_prompt,
-    }
-
-
-GAMECRAFT_I2V_PARAMS = {
-    **GAMECRAFT_T2V_PARAMS,
-    "image_path": ("https://huggingface.co/datasets/huggingface/documentation-images/"
-                   "resolve/main/diffusers/astronaut.jpg"),
-}
-
-
-def _gamecraft_i2v_full_quality_params(model_path: str) -> dict[str, object]:
-    return {
-        **_gamecraft_t2v_full_quality_params(model_path),
-        "image_path": GAMECRAFT_I2V_PARAMS["image_path"],
-    }
-
-
-MODEL_TO_PARAMS = {
-    "HunyuanGameCraft-T2V": GAMECRAFT_T2V_PARAMS,
-}
-
-I2V_MODEL_TO_PARAMS = {
-    "HunyuanGameCraft-I2V": GAMECRAFT_I2V_PARAMS,
-}
-
-TEST_PROMPTS = [
-    "A majestic ancient temple stands under a clear blue sky, its grandeur highlighted by towering Doric columns and intricate architectural details.",
-]
-
-I2V_TEST_PROMPTS = [
-    "An astronaut hatching from an egg, on the surface of the moon, the darkness and depth of space realised in the background.",
-]
-
-# ---------------------------------------------------------------------------
-# T2V SSIM test
-# ---------------------------------------------------------------------------
-
-
-@pytest.mark.parametrize("prompt", TEST_PROMPTS)
-@pytest.mark.parametrize("ATTENTION_BACKEND", ["FLASH_ATTN"])
-@pytest.mark.parametrize("model_id", list(MODEL_TO_PARAMS.keys()))
-def test_gamecraft_t2v_similarity(prompt, ATTENTION_BACKEND, model_id, env_overrides):
-    """Generate a T2V video with GameCraft and compare to reference via SSIM."""
-    env_overrides.enter_context(envs.FASTVIDEO_ATTENTION_BACKEND.override(ATTENTION_BACKEND))
-
-    script_dir = os.path.dirname(os.path.abspath(__file__))
-    output_dir = build_generated_output_dir(
-        script_dir,
-        device_reference_folder,
-        model_id,
-        ATTENTION_BACKEND,
-    )
-    output_video_name = f"{prompt[:100].strip()}.mp4"
-
-    os.makedirs(output_dir, exist_ok=True)
-
-    model_path = envs.FASTVIDEO_TEST_GAMECRAFT_MODEL_PATH.get()
-    params_map = select_ssim_params(
-        with_model_path(MODEL_TO_PARAMS, model_path),
-        {model_id: _gamecraft_t2v_full_quality_params(model_path)},
-    )
-    BASE_PARAMS = params_map[model_id]
-    num_inference_steps = BASE_PARAMS["num_inference_steps"]
-
-    # Build camera trajectory
-    camera_states = _create_camera_trajectory(
-        action=BASE_PARAMS["action"],
-        height=BASE_PARAMS["height"],
-        width=BASE_PARAMS["width"],
-        num_frames=BASE_PARAMS["num_frames"],
-        action_speed=BASE_PARAMS["action_speed"],
-        dtype=torch.bfloat16,
-    )
-
-    init_kwargs = {
-        "num_gpus": BASE_PARAMS["num_gpus"],
-        "use_fsdp_inference": True,
-        "dit_cpu_offload": True,
-        "vae_cpu_offload": True,
-        "text_encoder_cpu_offload": True,
-        "pin_cpu_memory": True,
-    }
-
-    generation_kwargs = {
-        "num_inference_steps": num_inference_steps,
-        "output_path": output_dir,
-        "height": BASE_PARAMS["height"],
-        "width": BASE_PARAMS["width"],
-        "num_frames": BASE_PARAMS["num_frames"],
-        "guidance_scale": BASE_PARAMS["guidance_scale"],
-        "seed": BASE_PARAMS["seed"],
-        "fps": 24,
-        "camera_states": camera_states,
-        "negative_prompt": BASE_PARAMS.get("negative_prompt", ""),
-        "save_video": True,
-    }
-
-    generator: VideoGenerator | None = None
-    try:
-        generator = VideoGenerator.from_pretrained(model_path=BASE_PARAMS["model_path"], **init_kwargs)
-        generator.generate_video(prompt, **generation_kwargs)
-    finally:
-        _shutdown_executor(generator)
-
-    assert os.path.exists(output_dir), (f"Output video was not generated at {output_dir}")
-
-    # Compare to reference
-    reference_folder = build_reference_folder_path(
-        script_dir,
-        device_reference_folder,
-        model_id,
-        ATTENTION_BACKEND,
-    )
-
-    if not os.path.exists(reference_folder):
-        logger.error("Reference folder missing")
-        raise FileNotFoundError(f"Reference video folder does not exist: {reference_folder}")
-
-    reference_video_name = None
-    for filename in os.listdir(reference_folder):
-        if filename.endswith(".mp4") and prompt[:100].strip() in filename:
-            reference_video_name = filename
-            break
-
-    if not reference_video_name:
-        logger.error(f"Reference video not found for prompt: {prompt} "
-                     f"with backend: {ATTENTION_BACKEND}")
-        raise FileNotFoundError("Reference video missing")
-
-    reference_video_path = os.path.join(reference_folder, reference_video_name)
-    generated_video_path = os.path.join(output_dir, output_video_name)
-
-    logger.info(f"Computing SSIM between {reference_video_path} and {generated_video_path}")
-    ssim_values = compute_video_ssim_torchvision(reference_video_path, generated_video_path, use_ms_ssim=True)
-
-    mean_ssim = ssim_values[0]
-    logger.info(f"SSIM mean value: {mean_ssim}")
-    logger.info(f"Writing SSIM results to directory: {output_dir}")
-
-    success = write_ssim_results(
-        output_dir,
-        ssim_values,
-        reference_video_path,
-        generated_video_path,
-        num_inference_steps,
-        prompt,
-    )
-
-    if not success:
-        logger.error("Failed to write SSIM results to file")
-
-    min_acceptable_ssim = 0.93
-    assert mean_ssim >= min_acceptable_ssim, (f"SSIM value {mean_ssim} is below threshold {min_acceptable_ssim} "
-                                              f"for {model_id} with backend {ATTENTION_BACKEND}")
-
-
-# ---------------------------------------------------------------------------
-# I2V SSIM test
-# ---------------------------------------------------------------------------
-
-
-@pytest.mark.parametrize("prompt", I2V_TEST_PROMPTS)
-@pytest.mark.parametrize("ATTENTION_BACKEND", ["FLASH_ATTN"])
-@pytest.mark.parametrize("model_id", list(I2V_MODEL_TO_PARAMS.keys()))
-def test_gamecraft_i2v_similarity(prompt, ATTENTION_BACKEND, model_id, env_overrides):
-    """Generate an I2V video with GameCraft and compare to reference via SSIM."""
-    env_overrides.enter_context(envs.FASTVIDEO_ATTENTION_BACKEND.override(ATTENTION_BACKEND))
-
-    script_dir = os.path.dirname(os.path.abspath(__file__))
-    output_dir = build_generated_output_dir(
-        script_dir,
-        device_reference_folder,
-        model_id,
-        ATTENTION_BACKEND,
-    )
-    output_video_name = f"{prompt[:100].strip()}.mp4"
-
-    os.makedirs(output_dir, exist_ok=True)
-
-    model_path = envs.FASTVIDEO_TEST_GAMECRAFT_MODEL_PATH.get()
-    params_map = select_ssim_params(
-        with_model_path(I2V_MODEL_TO_PARAMS, model_path),
-        {model_id: _gamecraft_i2v_full_quality_params(model_path)},
-    )
-    BASE_PARAMS = params_map[model_id]
-    num_inference_steps = BASE_PARAMS["num_inference_steps"]
-
-    # Build camera trajectory
-    camera_states = _create_camera_trajectory(
-        action=BASE_PARAMS["action"],
-        height=BASE_PARAMS["height"],
-        width=BASE_PARAMS["width"],
-        num_frames=BASE_PARAMS["num_frames"],
-        action_speed=BASE_PARAMS["action_speed"],
-        dtype=torch.bfloat16,
-    )
-
-    init_kwargs = {
-        "num_gpus": BASE_PARAMS["num_gpus"],
-        "use_fsdp_inference": True,
-        "dit_cpu_offload": True,
-        "vae_cpu_offload": True,
-        "text_encoder_cpu_offload": True,
-        "pin_cpu_memory": True,
-    }
-
-    generation_kwargs = {
-        "num_inference_steps": num_inference_steps,
-        "output_path": output_dir,
-        "image_path": BASE_PARAMS["image_path"],
-        "height": BASE_PARAMS["height"],
-        "width": BASE_PARAMS["width"],
-        "num_frames": BASE_PARAMS["num_frames"],
-        "guidance_scale": BASE_PARAMS["guidance_scale"],
-        "seed": BASE_PARAMS["seed"],
-        "fps": 24,
-        "camera_states": camera_states,
-        "negative_prompt": BASE_PARAMS.get("negative_prompt", ""),
-        "save_video": True,
-    }
-
-    generator: VideoGenerator | None = None
-    try:
-        generator = VideoGenerator.from_pretrained(model_path=BASE_PARAMS["model_path"], **init_kwargs)
-        generator.generate_video(prompt, **generation_kwargs)
-    finally:
-        _shutdown_executor(generator)
-
-    assert os.path.exists(output_dir), (f"Output video was not generated at {output_dir}")
-
-    # Compare to reference
-    reference_folder = build_reference_folder_path(
-        script_dir,
-        device_reference_folder,
-        model_id,
-        ATTENTION_BACKEND,
-    )
-
-    if not os.path.exists(reference_folder):
-        logger.error("Reference folder missing")
-        raise FileNotFoundError(f"Reference video folder does not exist: {reference_folder}")
-
-    reference_video_name = None
-    for filename in os.listdir(reference_folder):
-        if filename.endswith(".mp4") and prompt[:100].strip() in filename:
-            reference_video_name = filename
-            break
-
-    if not reference_video_name:
-        logger.error(f"Reference video not found for prompt: {prompt} "
-                     f"with backend: {ATTENTION_BACKEND}")
-        raise FileNotFoundError("Reference video missing")
-
-    reference_video_path = os.path.join(reference_folder, reference_video_name)
-    generated_video_path = os.path.join(output_dir, output_video_name)
-
-    logger.info(f"Computing SSIM between {reference_video_path} and {generated_video_path}")
-    ssim_values = compute_video_ssim_torchvision(reference_video_path, generated_video_path, use_ms_ssim=True)
-
-    mean_ssim = ssim_values[0]
-    logger.info(f"SSIM mean value: {mean_ssim}")
-    logger.info(f"Writing SSIM results to directory: {output_dir}")
-
-    success = write_ssim_results(
-        output_dir,
-        ssim_values,
-        reference_video_path,
-        generated_video_path,
-        num_inference_steps,
-        prompt,
-    )
-
-    if not success:
-        logger.error("Failed to write SSIM results to file")
-
-    min_acceptable_ssim = 0.93
-    assert mean_ssim >= min_acceptable_ssim, (f"SSIM value {mean_ssim} is below threshold {min_acceptable_ssim} "
-                                              f"for {model_id} with backend {ATTENTION_BACKEND}")
diff --git a/fastvideo/tests/ssim/test_glm_image_similarity.py b/fastvideo/tests/ssim/test_glm_image_similarity.py
deleted file mode 100644
index 31834733e..000000000
--- a/fastvideo/tests/ssim/test_glm_image_similarity.py
+++ /dev/null
@@ -1,144 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""SSIM-based regression test for GLM-Image generation."""
-from __future__ import annotations
-
-import os
-from pathlib import Path
-
-import pytest
-import torch
-
-import fastvideo.envs as envs
-from fastvideo.logger import init_logger
-from fastvideo.tests.ssim.inference_similarity_utils import (
-    run_text_to_video_similarity_test, )
-from fastvideo.tests.ssim.reference_utils import (
-    get_cuda_device_name,
-    resolve_device_reference_folder,
-    with_model_path,
-)
-
-logger = init_logger(__name__)
-
-REQUIRED_GPUS = 1
-pytestmark = pytest.mark.skip(reason="Disabled pending removal of GLM-Image support.")
-
-REPO_ROOT = Path(__file__).resolve().parents[3]
-
-device_reference_folder = resolve_device_reference_folder(
-    (
-        ("A40", "A40"),
-        ("L40S", "L40S"),
-        ("H100", "H100"),
-        ("H200", "H200"),
-        # GB200 must precede B200: the lookup is a substring scan and
-        # "B200" matches "NVIDIA GB200", so the coarser pattern would
-        # otherwise claim every Grace-Blackwell host.
-        ("GB200", "GB200"),
-        ("B200", "B200"),
-    ),
-    device_name=get_cuda_device_name(),
-    fallback_device_prefix="L40S",
-    logger=logger,
-)
-
-MODEL_ID = "zai-org__GLM-Image"
-
-TEST_PROMPTS = [
-    "A beautiful landscape photography with rolling hills, "
-    "a winding river, and a vibrant sunset in the background. "
-    "Warm golden light, photorealistic style.",
-]
-
-# The test adds "model_path" from FASTVIDEO_TEST_GLM_IMAGE_MODEL_DIR.
-GLM_IMAGE_PARAMS = {
-    "num_gpus": 1,
-    "sp_size": 1,
-    "tp_size": 1,
-    "height": 256,
-    "width": 256,
-    "num_frames": 1,
-    "fps": 1,
-    "num_inference_steps": 4,
-    "guidance_scale": 1.5,
-    "seed": 0,
-    "neg_prompt": "",
-}
-
-GLM_IMAGE_FULL_QUALITY_PARAMS = {
-    "num_gpus": 1,
-    "sp_size": 1,
-    "tp_size": 1,
-    "height": 1024,
-    "width": 1024,
-    "num_frames": 1,
-    "fps": 1,
-    "num_inference_steps": 50,
-    "guidance_scale": 1.5,
-    "seed": 0,
-    "neg_prompt": "",
-}
-
-GLM_IMAGE_MODEL_TO_PARAMS = {
-    MODEL_ID: GLM_IMAGE_PARAMS,
-}
-GLM_IMAGE_FULL_QUALITY_MODEL_TO_PARAMS = {
-    MODEL_ID: GLM_IMAGE_FULL_QUALITY_PARAMS,
-}
-
-
-def _local_weights_dir() -> Path:
-    return Path(envs.FASTVIDEO_TEST_GLM_IMAGE_LOCAL_WEIGHTS_DIR.get() or REPO_ROOT / "official_weights" / "glm_image")
-
-
-def _has_weights() -> bool:
-    required = ["transformer", "vae", "text_encoder", "vision_language_encoder", "processor", "tokenizer", "scheduler"]
-    return all((_local_weights_dir() / r).exists() for r in required)
-
-
-def _upstream_glm_image_available() -> bool:
-    try:
-        import transformers
-    except ImportError:
-        return False
-    return hasattr(transformers, "GlmImageForConditionalGeneration")
-
-
-@pytest.mark.skipif(
-    not torch.cuda.is_available(),
-    reason="GLM-Image SSIM test requires CUDA",
-)
-@pytest.mark.skipif(
-    not _upstream_glm_image_available(),
-    reason="GLM-Image needs transformers>=5.0.0rc0 (ships the AR encoder).",
-)
-@pytest.mark.parametrize("prompt", TEST_PROMPTS)
-@pytest.mark.parametrize("attention_backend_name", ["TORCH_SDPA"])
-@pytest.mark.parametrize("model_id", list(GLM_IMAGE_MODEL_TO_PARAMS.keys()))
-def test_glm_image_similarity(
-    prompt: str,
-    attention_backend_name: str,
-    model_id: str,
-) -> None:
-    if not _has_weights():
-        pytest.skip(f"GLM-Image full weights not found at {_local_weights_dir()}.")
-    model_path = envs.FASTVIDEO_TEST_GLM_IMAGE_MODEL_DIR.get() or str(_local_weights_dir())
-
-    run_text_to_video_similarity_test(
-        logger=logger,
-        script_dir=os.path.dirname(os.path.abspath(__file__)),
-        device_reference_folder=device_reference_folder,
-        prompt=prompt,
-        attention_backend_name=attention_backend_name,
-        model_id=model_id,
-        default_params_map=with_model_path(GLM_IMAGE_MODEL_TO_PARAMS, model_path),
-        full_quality_params_map=with_model_path(GLM_IMAGE_FULL_QUALITY_MODEL_TO_PARAMS, model_path),
-        min_acceptable_ssim=0.98,
-        init_kwargs_override={
-            "trust_remote_code": True,
-            "use_fsdp_inference": False,
-        },
-        generation_kwargs_override={
-            "save_video": True,
-        },
-    )
diff --git a/fastvideo/tests/ssim/test_longcat_similarity.py b/fastvideo/tests/ssim/test_longcat_similarity.py
deleted file mode 100644
index c68eddb6e..000000000
--- a/fastvideo/tests/ssim/test_longcat_similarity.py
+++ /dev/null
@@ -1,470 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-SSIM-based similarity tests for LongCat video generation.
-
-Tests three LongCat modes:
-- T2V (Text-to-Video): 480p video from text prompt
-- I2V (Image-to-Video): 480p video from image + text prompt  
-- VC (Video Continuation): 480p video continuation from input video + text prompt
-
-Sampling parameters are derived from:
-- examples/inference/basic/basic_longcat_t2v.py
-- examples/inference/basic/basic_longcat_i2v.py
-- examples/inference/basic/basic_longcat_vc.py
-
-Note: num_inference_steps is reduced for CI speed (4 steps vs 50 in examples).
-"""
-import os
-
-import pytest
-import torch
-
-import fastvideo.envs as envs
-from fastvideo import VideoGenerator
-from fastvideo.api.sampling_param import SamplingParam
-from fastvideo.logger import init_logger
-from fastvideo.tests.ssim.reference_utils import (
-    build_generated_output_dir,
-    build_reference_folder_path,
-    get_cuda_device_name,
-    resolve_device_reference_folder,
-    select_ssim_params,
-)
-from fastvideo.tests.utils import compute_video_ssim_torchvision, write_ssim_results
-
-logger = init_logger(__name__)
-
-REQUIRED_GPUS = 1
-pytestmark = pytest.mark.skip(reason="Disabled pending removal of LongCat support.")
-
-# Device-specific reference folder
-device_reference_folder = resolve_device_reference_folder(
-    (
-        ("A40", "A40"),
-        ("L40S", "L40S"),
-        ("H100", "H100"),
-        ("GB200", "GB200"),
-        ("H200", "H200"),
-    ),
-    device_name=get_cuda_device_name(),
-    logger=logger,
-)
-
-# Common negative prompt from example scripts
-NEGATIVE_PROMPT = ("Bright tones, overexposed, static, blurred details, subtitles, style, works, "
-                   "paintings, images, static, overall gray, worst quality, low quality, JPEG compression "
-                   "residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, "
-                   "deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, "
-                   "three legs, many people in the background, walking backwards")
-
-# =============================================================================
-# LongCat T2V Parameters (from basic_longcat_t2v.py)
-# =============================================================================
-LONGCAT_T2V_PARAMS = {
-    "num_gpus": 1,
-    "model_path": "FastVideo/LongCat-Video-T2V-Diffusers",
-    "height": 480,
-    "width": 480,
-    "num_frames": 43,
-    "num_inference_steps": 4,  # Reduced from 50 for CI speed
-    "guidance_scale": 4.0,
-    "fps": 15,
-    "seed": 42,
-    "negative_prompt": NEGATIVE_PROMPT,
-}
-_LONGCAT_T2V_FULL_QUALITY_DEFAULTS = SamplingParam.from_pretrained(LONGCAT_T2V_PARAMS["model_path"])
-LONGCAT_T2V_FULL_QUALITY_PARAMS = {
-    "num_gpus": LONGCAT_T2V_PARAMS["num_gpus"],
-    "model_path": LONGCAT_T2V_PARAMS["model_path"],
-    "height": _LONGCAT_T2V_FULL_QUALITY_DEFAULTS.height,
-    "width": _LONGCAT_T2V_FULL_QUALITY_DEFAULTS.width,
-    "num_frames": LONGCAT_T2V_PARAMS["num_frames"],  # default num_frames: 125
-    "num_inference_steps": _LONGCAT_T2V_FULL_QUALITY_DEFAULTS.num_inference_steps,
-    "guidance_scale": _LONGCAT_T2V_FULL_QUALITY_DEFAULTS.guidance_scale,
-    "fps": _LONGCAT_T2V_FULL_QUALITY_DEFAULTS.fps,
-    "seed": _LONGCAT_T2V_FULL_QUALITY_DEFAULTS.seed,
-    "negative_prompt": _LONGCAT_T2V_FULL_QUALITY_DEFAULTS.negative_prompt,
-}
-
-# =============================================================================
-# LongCat I2V Parameters (from basic_longcat_i2v.py)
-# =============================================================================
-LONGCAT_I2V_PARAMS = {
-    "num_gpus": 1,
-    "model_path": "FastVideo/LongCat-Video-I2V-Diffusers",
-    "height": 480,
-    "width": 480,  # Square for I2V
-    "num_frames": 43,
-    "num_inference_steps": 4,  # Reduced from 50 for CI speed
-    "guidance_scale": 4.0,
-    "fps": 15,
-    "seed": 42,
-    "negative_prompt": NEGATIVE_PROMPT,
-}
-_LONGCAT_I2V_FULL_QUALITY_DEFAULTS = SamplingParam.from_pretrained(LONGCAT_I2V_PARAMS["model_path"])
-LONGCAT_I2V_FULL_QUALITY_PARAMS = {
-    "num_gpus": LONGCAT_I2V_PARAMS["num_gpus"],
-    "model_path": LONGCAT_I2V_PARAMS["model_path"],
-    "height": _LONGCAT_I2V_FULL_QUALITY_DEFAULTS.height,
-    "width": _LONGCAT_I2V_FULL_QUALITY_DEFAULTS.width,
-    "num_frames": LONGCAT_I2V_PARAMS["num_frames"],  # default num_frames: 125
-    "num_inference_steps": _LONGCAT_I2V_FULL_QUALITY_DEFAULTS.num_inference_steps,
-    "guidance_scale": _LONGCAT_I2V_FULL_QUALITY_DEFAULTS.guidance_scale,
-    "fps": _LONGCAT_I2V_FULL_QUALITY_DEFAULTS.fps,
-    "seed": _LONGCAT_I2V_FULL_QUALITY_DEFAULTS.seed,
-    "negative_prompt": _LONGCAT_I2V_FULL_QUALITY_DEFAULTS.negative_prompt,
-}
-
-# =============================================================================
-# LongCat VC Parameters (from basic_longcat_vc.py)
-# =============================================================================
-LONGCAT_VC_PARAMS = {
-    "num_gpus": 1,
-    "model_path": "FastVideo/LongCat-Video-VC-Diffusers",
-    "height": 480,
-    "width": 480,
-    "num_frames": 43,
-    "num_inference_steps": 4,  # Reduced from 50 for CI speed
-    "guidance_scale": 4.0,
-    "fps": 15,
-    "seed": 42,
-    "num_cond_frames": 13,
-    "negative_prompt": NEGATIVE_PROMPT,
-}
-_LONGCAT_VC_FULL_QUALITY_DEFAULTS = SamplingParam.from_pretrained(LONGCAT_VC_PARAMS["model_path"])
-LONGCAT_VC_FULL_QUALITY_PARAMS = {
-    "num_gpus": LONGCAT_VC_PARAMS["num_gpus"],
-    "model_path": LONGCAT_VC_PARAMS["model_path"],
-    "height": _LONGCAT_VC_FULL_QUALITY_DEFAULTS.height,
-    "width": _LONGCAT_VC_FULL_QUALITY_DEFAULTS.width,
-    "num_frames": LONGCAT_VC_PARAMS["num_frames"],  # default num_frames: 125
-    "num_inference_steps": _LONGCAT_VC_FULL_QUALITY_DEFAULTS.num_inference_steps,
-    "guidance_scale": _LONGCAT_VC_FULL_QUALITY_DEFAULTS.guidance_scale,
-    "fps": _LONGCAT_VC_FULL_QUALITY_DEFAULTS.fps,
-    "seed": _LONGCAT_VC_FULL_QUALITY_DEFAULTS.seed,
-    "num_cond_frames": LONGCAT_VC_PARAMS["num_cond_frames"],
-    "negative_prompt": _LONGCAT_VC_FULL_QUALITY_DEFAULTS.negative_prompt,
-}
-
-# Test prompts
-T2V_TEST_PROMPTS = [
-    "In a realistic photography style, a white boy around seven or eight years old "
-    "sits on a park bench, wearing a light blue T-shirt, denim shorts, and white sneakers. "
-    "He holds an ice cream cone with vanilla and chocolate flavors, and beside him is a "
-    "medium-sized golden Labrador. Smiling, the boy offers the ice cream to the dog, "
-    "who eagerly licks it with its tongue. The sun is shining brightly, and the background "
-    "features a green lawn and several tall trees, creating a warm and loving scene.",
-]
-
-I2V_TEST_PROMPTS = [
-    "A woman sits at a wooden table by the window in a cozy café. She reaches out "
-    "with her right hand, picks up the white coffee cup from the saucer, and gently "
-    "brings it to her lips to take a sip. After drinking, she places the cup back on "
-    "the table and looks out the window, enjoying the peaceful atmosphere.",
-]
-
-I2V_IMAGE_PATHS = [
-    "assets/girl.png",
-]
-
-VC_TEST_PROMPTS = [
-    "A person rides a motorcycle along a long, straight road that stretches between "
-    "a body of water and a forested hillside. The rider steadily accelerates, keeping "
-    "the motorcycle centered between the guardrails, while the scenery passes by on "
-    "both sides. The video captures the journey from the rider's perspective, emphasizing "
-    "the sense of motion and adventure.",
-]
-
-VC_VIDEO_PATHS = [
-    "assets/motorcycle.mp4",
-]
-
-
-def _resolve_asset_path(asset_path: str) -> str:
-    """Resolve asset path relative to FastVideo root."""
-    # Check if absolute or already exists
-    if os.path.isabs(asset_path) or os.path.exists(asset_path):
-        return asset_path
-    # Try relative to workspace root
-    script_dir = os.path.dirname(os.path.abspath(__file__))
-    repo_root = os.path.abspath(os.path.join(script_dir, "..", "..", ".."))
-    return os.path.join(repo_root, asset_path)
-
-
-@pytest.mark.parametrize("prompt", T2V_TEST_PROMPTS)
-@pytest.mark.parametrize("ATTENTION_BACKEND", ["FLASH_ATTN"])
-def test_longcat_t2v_similarity(prompt: str, ATTENTION_BACKEND: str, env_overrides):
-    """
-    Test LongCat T2V inference and compare output to reference videos using SSIM.
-    
-    Parameters derived from examples/inference/basic/basic_longcat_t2v.py
-    """
-    env_overrides.enter_context(envs.FASTVIDEO_ATTENTION_BACKEND.override(ATTENTION_BACKEND))
-
-    params = select_ssim_params(LONGCAT_T2V_PARAMS, LONGCAT_T2V_FULL_QUALITY_PARAMS)
-
-    script_dir = os.path.dirname(os.path.abspath(__file__))
-    model_id = "LongCat-Video-T2V"
-
-    output_dir = build_generated_output_dir(
-        script_dir,
-        device_reference_folder,
-        model_id,
-        ATTENTION_BACKEND,
-    )
-    output_video_name = f"{prompt[:100].strip()}.mp4"
-    os.makedirs(output_dir, exist_ok=True)
-
-    init_kwargs = {
-        "num_gpus": params["num_gpus"],
-        "use_fsdp_inference": True,
-        "dit_cpu_offload": True,
-        "vae_cpu_offload": True,
-        "text_encoder_cpu_offload": True,
-        "enable_bsa": False,
-    }
-
-    generation_kwargs = {
-        "output_path": output_dir,
-        "height": params["height"],
-        "width": params["width"],
-        "num_frames": params["num_frames"],
-        "num_inference_steps": params["num_inference_steps"],
-        "guidance_scale": params["guidance_scale"],
-        "fps": params["fps"],
-        "seed": params["seed"],
-        "negative_prompt": params["negative_prompt"],
-    }
-
-    generator = VideoGenerator.from_pretrained(model_path=params["model_path"], **init_kwargs)
-    generator.generate_video(prompt, **generation_kwargs)
-    generator.shutdown()
-
-    generated_video_path = os.path.join(output_dir, output_video_name)
-    assert os.path.exists(generated_video_path), (f"Output video was not generated at {generated_video_path}")
-
-    # Find reference video
-    reference_folder = build_reference_folder_path(
-        script_dir,
-        device_reference_folder,
-        model_id,
-        ATTENTION_BACKEND,
-    )
-    if not os.path.exists(reference_folder):
-        raise FileNotFoundError(f"Reference video folder does not exist: {reference_folder}")
-
-    reference_video_name = None
-    for filename in os.listdir(reference_folder):
-        if filename.endswith(".mp4") and prompt[:100].strip() in filename:
-            reference_video_name = filename
-            break
-
-    if not reference_video_name:
-        raise FileNotFoundError(
-            f"Reference video not found for prompt: {prompt[:50]}... with backend: {ATTENTION_BACKEND}")
-
-    reference_video_path = os.path.join(reference_folder, reference_video_name)
-
-    logger.info(f"Computing SSIM between {reference_video_path} and {generated_video_path}")
-    ssim_values = compute_video_ssim_torchvision(reference_video_path, generated_video_path, use_ms_ssim=True)
-
-    mean_ssim = ssim_values[0]
-    logger.info(f"SSIM mean value: {mean_ssim}")
-
-    write_ssim_results(output_dir, ssim_values, reference_video_path, generated_video_path,
-                       params["num_inference_steps"], prompt)
-
-    min_acceptable_ssim = 0.90
-    assert mean_ssim >= min_acceptable_ssim, (f"SSIM value {mean_ssim} is below threshold {min_acceptable_ssim} "
-                                              f"for {model_id} with backend {ATTENTION_BACKEND}")
-
-
-@pytest.mark.parametrize("prompt", I2V_TEST_PROMPTS)
-@pytest.mark.parametrize("ATTENTION_BACKEND", ["FLASH_ATTN"])
-def test_longcat_i2v_similarity(prompt: str, ATTENTION_BACKEND: str, env_overrides):
-    """
-    Test LongCat I2V inference and compare output to reference videos using SSIM.
-    
-    Parameters derived from examples/inference/basic/basic_longcat_i2v.py
-    """
-    env_overrides.enter_context(envs.FASTVIDEO_ATTENTION_BACKEND.override(ATTENTION_BACKEND))
-
-    params = select_ssim_params(LONGCAT_I2V_PARAMS, LONGCAT_I2V_FULL_QUALITY_PARAMS)
-
-    script_dir = os.path.dirname(os.path.abspath(__file__))
-    model_id = "LongCat-Video-I2V"
-
-    output_dir = build_generated_output_dir(
-        script_dir,
-        device_reference_folder,
-        model_id,
-        ATTENTION_BACKEND,
-    )
-    output_video_name = f"{prompt[:100].strip()}.mp4"
-    os.makedirs(output_dir, exist_ok=True)
-
-    # Get image path for this prompt
-    prompt_idx = I2V_TEST_PROMPTS.index(prompt)
-    image_path = _resolve_asset_path(I2V_IMAGE_PATHS[prompt_idx])
-
-    init_kwargs = {
-        "num_gpus": params["num_gpus"],
-        "use_fsdp_inference": True,
-        "dit_cpu_offload": True,
-        "vae_cpu_offload": True,
-        "text_encoder_cpu_offload": True,
-        "enable_bsa": False,
-    }
-
-    generation_kwargs = {
-        "output_path": output_dir,
-        "image_path": image_path,
-        "height": params["height"],
-        "width": params["width"],
-        "num_frames": params["num_frames"],
-        "num_inference_steps": params["num_inference_steps"],
-        "guidance_scale": params["guidance_scale"],
-        "fps": params["fps"],
-        "seed": params["seed"],
-        "negative_prompt": params["negative_prompt"],
-    }
-
-    generator = VideoGenerator.from_pretrained(model_path=params["model_path"], **init_kwargs)
-    generator.generate_video(prompt, **generation_kwargs)
-    generator.shutdown()
-
-    generated_video_path = os.path.join(output_dir, output_video_name)
-    assert os.path.exists(generated_video_path), (f"Output video was not generated at {generated_video_path}")
-
-    # Find reference video
-    reference_folder = build_reference_folder_path(
-        script_dir,
-        device_reference_folder,
-        model_id,
-        ATTENTION_BACKEND,
-    )
-    if not os.path.exists(reference_folder):
-        raise FileNotFoundError(f"Reference video folder does not exist: {reference_folder}")
-
-    reference_video_name = None
-    for filename in os.listdir(reference_folder):
-        if filename.endswith(".mp4") and prompt[:100].strip() in filename:
-            reference_video_name = filename
-            break
-
-    if not reference_video_name:
-        raise FileNotFoundError(
-            f"Reference video not found for prompt: {prompt[:50]}... with backend: {ATTENTION_BACKEND}")
-
-    reference_video_path = os.path.join(reference_folder, reference_video_name)
-
-    logger.info(f"Computing SSIM between {reference_video_path} and {generated_video_path}")
-    ssim_values = compute_video_ssim_torchvision(reference_video_path, generated_video_path, use_ms_ssim=True)
-
-    mean_ssim = ssim_values[0]
-    logger.info(f"SSIM mean value: {mean_ssim}")
-
-    write_ssim_results(output_dir, ssim_values, reference_video_path, generated_video_path,
-                       params["num_inference_steps"], prompt)
-
-    min_acceptable_ssim = 0.90
-    assert mean_ssim >= min_acceptable_ssim, (f"SSIM value {mean_ssim} is below threshold {min_acceptable_ssim} "
-                                              f"for {model_id} with backend {ATTENTION_BACKEND}")
-
-
-@pytest.mark.parametrize("prompt", VC_TEST_PROMPTS)
-@pytest.mark.parametrize("ATTENTION_BACKEND", ["FLASH_ATTN"])
-def test_longcat_vc_similarity(prompt: str, ATTENTION_BACKEND: str, env_overrides):
-    """
-    Test LongCat VC (Video Continuation) inference and compare output to reference videos using SSIM.
-    
-    Parameters derived from examples/inference/basic/basic_longcat_vc.py
-    """
-    env_overrides.enter_context(envs.FASTVIDEO_ATTENTION_BACKEND.override(ATTENTION_BACKEND))
-
-    params = select_ssim_params(LONGCAT_VC_PARAMS, LONGCAT_VC_FULL_QUALITY_PARAMS)
-
-    script_dir = os.path.dirname(os.path.abspath(__file__))
-    model_id = "LongCat-Video-VC"
-
-    output_dir = build_generated_output_dir(
-        script_dir,
-        device_reference_folder,
-        model_id,
-        ATTENTION_BACKEND,
-    )
-    output_video_name = f"{prompt[:100].strip()}.mp4"
-    os.makedirs(output_dir, exist_ok=True)
-
-    # Get video path for this prompt
-    prompt_idx = VC_TEST_PROMPTS.index(prompt)
-    video_path = _resolve_asset_path(VC_VIDEO_PATHS[prompt_idx])
-
-    if not os.path.exists(video_path):
-        pytest.skip(f"Input video not found at {video_path}")
-
-    init_kwargs = {
-        "num_gpus": params["num_gpus"],
-        "use_fsdp_inference": False,
-        "dit_cpu_offload": False,
-        "vae_cpu_offload": True,
-        "text_encoder_cpu_offload": True,
-        "pin_cpu_memory": False,
-        "enable_bsa": False,
-    }
-
-    generation_kwargs = {
-        "output_path": output_dir,
-        "video_path": video_path,
-        "num_cond_frames": params["num_cond_frames"],
-        "height": params["height"],
-        "width": params["width"],
-        "num_frames": params["num_frames"],
-        "num_inference_steps": params["num_inference_steps"],
-        "guidance_scale": params["guidance_scale"],
-        "fps": params["fps"],
-        "seed": params["seed"],
-        "negative_prompt": params["negative_prompt"],
-    }
-
-    generator = VideoGenerator.from_pretrained(model_path=params["model_path"], **init_kwargs)
-    generator.generate_video(prompt, **generation_kwargs)
-    generator.shutdown()
-
-    generated_video_path = os.path.join(output_dir, output_video_name)
-    assert os.path.exists(generated_video_path), (f"Output video was not generated at {generated_video_path}")
-
-    # Find reference video
-    reference_folder = build_reference_folder_path(
-        script_dir,
-        device_reference_folder,
-        model_id,
-        ATTENTION_BACKEND,
-    )
-    if not os.path.exists(reference_folder):
-        raise FileNotFoundError(f"Reference video folder does not exist: {reference_folder}")
-
-    reference_video_name = None
-    for filename in os.listdir(reference_folder):
-        if filename.endswith(".mp4") and prompt[:100].strip() in filename:
-            reference_video_name = filename
-            break
-
-    if not reference_video_name:
-        raise FileNotFoundError(
-            f"Reference video not found for prompt: {prompt[:50]}... with backend: {ATTENTION_BACKEND}")
-
-    reference_video_path = os.path.join(reference_folder, reference_video_name)
-
-    logger.info(f"Computing SSIM between {reference_video_path} and {generated_video_path}")
-    ssim_values = compute_video_ssim_torchvision(reference_video_path, generated_video_path, use_ms_ssim=True)
-
-    mean_ssim = ssim_values[0]
-    logger.info(f"SSIM mean value: {mean_ssim}")
-
-    write_ssim_results(output_dir, ssim_values, reference_video_path, generated_video_path,
-                       params["num_inference_steps"], prompt)
-
-    min_acceptable_ssim = 0.90
-    assert mean_ssim >= min_acceptable_ssim, (f"SSIM value {mean_ssim} is below threshold {min_acceptable_ssim} "
-                                              f"for {model_id} with backend {ATTENTION_BACKEND}")
diff --git a/fastvideo/tests/ssim/test_turbodiffusion_similarity.py b/fastvideo/tests/ssim/test_turbodiffusion_similarity.py
deleted file mode 100644
index f73feea6b..000000000
--- a/fastvideo/tests/ssim/test_turbodiffusion_similarity.py
+++ /dev/null
@@ -1,172 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-import os
-
-import pytest
-
-from fastvideo.api.sampling_param import SamplingParam
-from fastvideo.logger import init_logger
-from fastvideo.tests.ssim.inference_similarity_utils import (
-    DEVICE_MAPPINGS,
-    resolve_inference_device_reference_folder,
-    run_image_to_video_similarity_test,
-    run_text_to_video_similarity_test,
-)
-from fastvideo.tests.ssim.reference_utils import (
-    get_cuda_device_name,
-    resolve_device_reference_folder,
-)
-
-logger = init_logger(__name__)
-
-REQUIRED_GPUS = 4
-pytestmark = pytest.mark.skip(reason="Disabled pending removal of TurboDiffusion and TurboWan support.")
-
-device_name = get_cuda_device_name()
-device_reference_folder = resolve_device_reference_folder(
-    DEVICE_MAPPINGS,
-    device_name=device_name,
-)
-if device_reference_folder is None:
-    raise ValueError(f"Unsupported device for ssim tests: {device_name}")
-
-TURBODIFFUSION_PARAMS = {
-    "num_gpus": 4,
-    "model_path": "loayrashid/TurboWan2.1-T2V-1.3B-Diffusers",
-    "height": 480,
-    "width": 832,
-    "num_frames": 81,
-    "num_inference_steps": 4,
-    "guidance_scale": 1.0,
-    "seed": 42,
-    "sp_size": 4,
-    "tp_size": 1,
-    "fps": 24,
-}
-_TURBODIFFUSION_FULL_QUALITY_DEFAULTS = SamplingParam.from_pretrained(TURBODIFFUSION_PARAMS["model_path"])
-TURBODIFFUSION_FULL_QUALITY_PARAMS = {
-    "num_gpus": TURBODIFFUSION_PARAMS["num_gpus"],
-    "model_path": TURBODIFFUSION_PARAMS["model_path"],
-    "height": _TURBODIFFUSION_FULL_QUALITY_DEFAULTS.height,
-    "width": _TURBODIFFUSION_FULL_QUALITY_DEFAULTS.width,
-    "num_frames": TURBODIFFUSION_PARAMS["num_frames"],
-    "num_inference_steps": (_TURBODIFFUSION_FULL_QUALITY_DEFAULTS.num_inference_steps),
-    "guidance_scale": (_TURBODIFFUSION_FULL_QUALITY_DEFAULTS.guidance_scale),
-    "seed": _TURBODIFFUSION_FULL_QUALITY_DEFAULTS.seed,
-    "sp_size": TURBODIFFUSION_PARAMS["sp_size"],
-    "tp_size": TURBODIFFUSION_PARAMS["tp_size"],
-    "fps": _TURBODIFFUSION_FULL_QUALITY_DEFAULTS.fps,
-}
-
-TURBODIFFUSION_MODEL_TO_PARAMS = {
-    "TurboWan2.1-T2V-1.3B-Diffusers": TURBODIFFUSION_PARAMS,
-}
-FULL_QUALITY_TURBODIFFUSION_MODEL_TO_PARAMS = {
-    "TurboWan2.1-T2V-1.3B-Diffusers": (TURBODIFFUSION_FULL_QUALITY_PARAMS),
-}
-
-TURBODIFFUSION_TEST_PROMPTS = [
-    "Will Smith casually eats noodles, his relaxed demeanor contrasting "
-    "with the energetic background of a bustling street food market. "
-    "The scene captures a mix of humor and authenticity. Mid-shot "
-    "framing, vibrant lighting.",
-]
-
-
-@pytest.mark.parametrize("prompt", TURBODIFFUSION_TEST_PROMPTS)
-@pytest.mark.parametrize(
-    "model_id",
-    list(TURBODIFFUSION_MODEL_TO_PARAMS.keys()),
-)
-def test_turbodiffusion_inference_similarity(
-    prompt: str,
-    model_id: str,
-) -> None:
-    run_text_to_video_similarity_test(
-        logger=logger,
-        script_dir=os.path.dirname(os.path.abspath(__file__)),
-        device_reference_folder=device_reference_folder,
-        prompt=prompt,
-        attention_backend_name="SLA_ATTN",
-        model_id=model_id,
-        default_params_map=TURBODIFFUSION_MODEL_TO_PARAMS,
-        full_quality_params_map=(FULL_QUALITY_TURBODIFFUSION_MODEL_TO_PARAMS),
-        min_acceptable_ssim=0.95,
-        init_kwargs_override={
-            "override_pipeline_cls_name": "TurboDiffusionPipeline",
-        },
-    )
-
-
-TURBODIFFUSION_I2V_PARAMS = {
-    "num_gpus": 4,
-    "model_path": "loayrashid/TurboWan2.2-I2V-A14B-Diffusers",
-    "height": 480,
-    "width": 832,
-    "num_frames": 45,
-    "num_inference_steps": 4,
-    "guidance_scale": 1.0,
-    "seed": 42,
-    "sp_size": 4,
-    "tp_size": 1,
-    "fps": 24,
-}
-_TURBODIFFUSION_I2V_FULL_QUALITY_DEFAULTS = SamplingParam.from_pretrained(TURBODIFFUSION_I2V_PARAMS["model_path"])
-TURBODIFFUSION_I2V_FULL_QUALITY_PARAMS = {
-    "num_gpus": TURBODIFFUSION_I2V_PARAMS["num_gpus"],
-    "model_path": TURBODIFFUSION_I2V_PARAMS["model_path"],
-    "height": _TURBODIFFUSION_I2V_FULL_QUALITY_DEFAULTS.height,
-    "width": _TURBODIFFUSION_I2V_FULL_QUALITY_DEFAULTS.width,
-    "num_frames": TURBODIFFUSION_I2V_PARAMS["num_frames"],
-    "num_inference_steps": (_TURBODIFFUSION_I2V_FULL_QUALITY_DEFAULTS.num_inference_steps),
-    "guidance_scale": (_TURBODIFFUSION_I2V_FULL_QUALITY_DEFAULTS.guidance_scale),
-    "seed": _TURBODIFFUSION_I2V_FULL_QUALITY_DEFAULTS.seed,
-    "sp_size": TURBODIFFUSION_I2V_PARAMS["sp_size"],
-    "tp_size": TURBODIFFUSION_I2V_PARAMS["tp_size"],
-    "fps": _TURBODIFFUSION_I2V_FULL_QUALITY_DEFAULTS.fps,
-}
-
-TURBODIFFUSION_I2V_MODEL_TO_PARAMS = {
-    "TurboWan2.2-I2V-A14B-Diffusers": TURBODIFFUSION_I2V_PARAMS,
-}
-FULL_QUALITY_TURBODIFFUSION_I2V_MODEL_TO_PARAMS = {
-    "TurboWan2.2-I2V-A14B-Diffusers": (TURBODIFFUSION_I2V_FULL_QUALITY_PARAMS),
-}
-
-TURBODIFFUSION_I2V_TEST_PROMPTS = [
-    "An astronaut hatching from an egg, on the surface of the moon, "
-    "the darkness and depth of space realised in the background. "
-    "High quality, ultrarealistic detail and breath-taking "
-    "movie-like camera shot.",
-]
-
-TURBODIFFUSION_I2V_IMAGE_PATHS = [
-    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/astronaut.jpg",
-]
-
-
-@pytest.mark.skip(reason="Disabled: causes OOM too often in CI")
-@pytest.mark.parametrize("prompt", TURBODIFFUSION_I2V_TEST_PROMPTS)
-@pytest.mark.parametrize(
-    "model_id",
-    list(TURBODIFFUSION_I2V_MODEL_TO_PARAMS.keys()),
-)
-def test_turbodiffusion_i2v_inference_similarity(
-    prompt: str,
-    model_id: str,
-) -> None:
-    image_path = TURBODIFFUSION_I2V_IMAGE_PATHS[TURBODIFFUSION_I2V_TEST_PROMPTS.index(prompt)]
-    run_image_to_video_similarity_test(
-        logger=logger,
-        script_dir=os.path.dirname(os.path.abspath(__file__)),
-        device_reference_folder=device_reference_folder,
-        prompt=prompt,
-        image_path=image_path,
-        attention_backend_name="SLA_ATTN",
-        model_id=model_id,
-        default_params_map=TURBODIFFUSION_I2V_MODEL_TO_PARAMS,
-        full_quality_params_map=(FULL_QUALITY_TURBODIFFUSION_I2V_MODEL_TO_PARAMS),
-        min_acceptable_ssim=0.95,
-        init_kwargs_override={
-            "override_pipeline_cls_name": ("TurboDiffusionI2VPipeline"),
-        },
-    )
diff --git a/fastvideo/tests/ssim/test_zimage_similarity.py b/fastvideo/tests/ssim/test_zimage_similarity.py
deleted file mode 100644
index f739e9cbf..000000000
--- a/fastvideo/tests/ssim/test_zimage_similarity.py
+++ /dev/null
@@ -1,117 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""PNG regression for Tongyi-MAI/Z-Image@26f23eda's native Turbo recipe."""
-
-from __future__ import annotations
-
-import os
-
-import pytest
-import torch
-
-import fastvideo.envs as envs
-from fastvideo.logger import init_logger
-from fastvideo.tests.ssim.inference_similarity_utils import (
-    run_text_to_video_similarity_test, )
-from fastvideo.tests.ssim.reference_utils import (
-    get_cuda_device_name,
-    resolve_device_reference_folder,
-    with_model_path,
-)
-
-logger = init_logger(__name__)
-
-REQUIRED_GPUS = 1
-pytestmark = pytest.mark.skip(reason="Disabled pending removal of Z-Image support.")
-ZIMAGE_MIN_SSIM = 0.98
-
-TEST_PROMPTS = [
-    "Young Chinese woman in red Hanfu, intricate embroidery. Impeccable makeup, red floral forehead pattern. "
-    "Elaborate high bun, golden phoenix headdress, red flowers, beads. Holds round folding fan with lady, trees, bird. "
-    "Neon lightning-bolt lamp (⚡️), bright yellow glow, above extended left palm. Soft-lit outdoor night background, "
-    "silhouetted tiered pagoda (西安大雁塔), blurred colorful distant lights.",
-]
-
-device_reference_folder = resolve_device_reference_folder(
-    (
-        ("A40", "A40"),
-        ("L40S", "L40S"),
-        ("H100", "H100"),
-        ("H200", "H200"),
-        # GB200 must precede B200: the lookup is a substring scan and
-        # "B200" matches "NVIDIA GB200", so the coarser pattern would
-        # otherwise claim every Grace-Blackwell host.
-        ("GB200", "GB200"),
-        ("B200", "B200"),
-    ),
-    device_name=get_cuda_device_name(),
-    fallback_device_prefix="L40S",
-    logger=logger,
-)
-
-# The test adds "model_path" from FASTVIDEO_TEST_ZIMAGE_MODEL_DIR.
-ZIMAGE_MODEL_TO_PARAMS: dict[str, dict[str, object]] = {
-    "Tongyi-MAI__Z-Image-Turbo": {
-        "num_gpus": 1,
-        "sp_size": 1,
-        "tp_size": 1,
-        "height": 1024,
-        "width": 1024,
-        "num_frames": 1,
-        "fps": 1,
-        "num_inference_steps": 8,
-        "guidance_scale": 0.0,
-        "seed": 42,
-    },
-}
-
-ZIMAGE_FULL_QUALITY_MODEL_TO_PARAMS = {model_id: dict(params) for model_id, params in ZIMAGE_MODEL_TO_PARAMS.items()}
-
-
-@pytest.mark.skipif(
-    not torch.cuda.is_available(),
-    reason="Z-Image SSIM test requires CUDA",
-)
-@pytest.mark.parametrize("prompt", TEST_PROMPTS)
-@pytest.mark.parametrize("attention_backend_name", ["TORCH_SDPA"])
-@pytest.mark.parametrize("model_id", list(ZIMAGE_MODEL_TO_PARAMS))
-def test_zimage_similarity(
-    prompt: str,
-    attention_backend_name: str,
-    model_id: str,
-) -> None:
-    zimage_model_path = envs.FASTVIDEO_TEST_ZIMAGE_MODEL_DIR.get()
-    is_hf_repo = "/" in zimage_model_path and not zimage_model_path.startswith("/")
-    if not is_hf_repo and not os.path.isdir(zimage_model_path):
-        pytest.skip(f"Z-Image weights not found at {zimage_model_path} "
-                    "(set FASTVIDEO_TEST_ZIMAGE_MODEL_DIR to override)")
-
-    run_text_to_video_similarity_test(
-        logger=logger,
-        script_dir=os.path.dirname(os.path.abspath(__file__)),
-        device_reference_folder=device_reference_folder,
-        prompt=prompt,
-        attention_backend_name=attention_backend_name,
-        model_id=model_id,
-        default_params_map=with_model_path(ZIMAGE_MODEL_TO_PARAMS, zimage_model_path),
-        full_quality_params_map=with_model_path(ZIMAGE_FULL_QUALITY_MODEL_TO_PARAMS, zimage_model_path),
-        min_acceptable_ssim=ZIMAGE_MIN_SSIM,
-        media_extension=".png",
-        init_kwargs_override={
-            "revision": envs.FASTVIDEO_TEST_ZIMAGE_MODEL_REVISION.get(),
-            "workload_type": "t2i",
-            "trust_remote_code": True,
-            "use_fsdp_inference": False,
-            "dit_cpu_offload": False,
-            "text_encoder_cpu_offload": False,
-            "vae_cpu_offload": False,
-            "image_encoder_cpu_offload": False,
-            "pin_cpu_memory": False,
-        },
-        generation_kwargs_override={
-            "save_video": True,
-            "negative_prompt": "",
-            "max_sequence_length": 512,
-            "cfg_normalization": False,
-            "cfg_truncation": 1.0,
-        },
-    )
diff --git a/fastvideo/tests/stages/test_wan_pipeline_wiring.py b/fastvideo/tests/stages/test_wan_pipeline_wiring.py
index 900f9aa59..74db356d9 100644
--- a/fastvideo/tests/stages/test_wan_pipeline_wiring.py
+++ b/fastvideo/tests/stages/test_wan_pipeline_wiring.py
@@ -19,9 +19,6 @@ from fastvideo.tests.stages._denoising_fixtures import RecordingDenoiser, TinyVA
     ("wan.wan_i2v_dmd_pipeline", "DmdDenoisingStage", False),
     ("wan.wan_causal_pipeline", "CausalDenoisingStage", False),
     ("wan.wan_causal_dmd_pipeline", "CausalDMDDenosingStage", True),
-    ("dreamx_world.dreamx_world_pipeline", "WanDenoisingStage", True),
-    ("turbodiffusion.turbodiffusion_pipeline", "WanDenoisingStage", False),
-    ("turbodiffusion.turbodiffusion_i2v_pipeline", "WanDenoisingStage", False),
 ])
 def test_pipeline_wires_family_sampler_and_owns_scheduler(monkeypatch, env_overrides, module_name, sampler,
                                                           first_frame):
diff --git a/fastvideo/tests/train/fixtures/longcat_t2v_min.yaml b/fastvideo/tests/train/fixtures/longcat_t2v_min.yaml
deleted file mode 100644
index 7969d8fad..000000000
--- a/fastvideo/tests/train/fixtures/longcat_t2v_min.yaml
+++ /dev/null
@@ -1,23 +0,0 @@
-# Minimum config to instantiate LongCatModel for the loading + forward
-# smoke test. Real LongCat-Video-T2V checkpoint (~13.6B at bf16) loaded
-# via load_module_from_path. flow_shift=12.0 matches the released
-# LongCat scheduler config (0.0 would collapse FlowMatch timesteps).
-#
-# Forward-only: a single-GPU backward of the 13.6B transformer exceeds
-# the L40S CI runner, so LongCat is covered by this loading smoke only
-# (no per-method grad-norm test).
-
-models:
-  student:
-    _target_: fastvideo.train.models.longcat.LongCatModel
-    init_from: FastVideo/LongCat-Video-T2V-Diffusers
-    trainable: false
-
-method:
-  _target_: fastvideo.train.methods.fine_tuning.finetune.FineTuneMethod
-
-training:
-  dit_precision: bf16
-
-pipeline:
-  flow_shift: 12.0
diff --git a/fastvideo/tests/train/methods/test_matrixgame2_finetune.py b/fastvideo/tests/train/methods/test_matrixgame2_finetune.py
index 5c5f33d8d..e353439e6 100644
--- a/fastvideo/tests/train/methods/test_matrixgame2_finetune.py
+++ b/fastvideo/tests/train/methods/test_matrixgame2_finetune.py
@@ -13,8 +13,7 @@ receive gradients. The goal is to validate the training grad path
 Matrix-Game-2.0-Base is ~14B at bf16. A single-GPU backward (params +
 grads, no FSDP) needs well over the L40S CI runner's 48 GB, so this
 test skips on memory-constrained GPUs and only exercises the grad path
-on larger dev GPUs (H200 / GB200). LongCat (13.6B) is likewise covered
-by a loading-only smoke for the same reason.
+on larger dev GPUs (H200 / GB200).
 """
 
 from __future__ import annotations
diff --git a/fastvideo/tests/train/models/test_load_longcat.py b/fastvideo/tests/train/models/test_load_longcat.py
deleted file mode 100644
index 7bf0f8a47..000000000
--- a/fastvideo/tests/train/models/test_load_longcat.py
+++ /dev/null
@@ -1,83 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""GPU loading + forward smoke test for ``LongCatModel``.
-
-Loads the real LongCat-Video-T2V checkpoint (~13.6B at bf16) via
-``LongCatModel.__init__`` and runs one transformer forward pass on
-synthetic inputs. Catches loader or forward-signature regressions in
-``fastvideo.train.models.longcat.LongCatModel`` and the underlying
-``LongCatTransformer3DModel``.
-
-Forward-only (``trainable=False``, ``torch.no_grad``): a single-GPU
-backward of the 13.6B transformer exceeds the L40S CI runner, so
-LongCat training is not covered by a per-method grad-norm test. The
-forward fits (the ~13B HunyuanModel loading test already runs on the
-same runner).
-"""
-
-from __future__ import annotations
-
-import fastvideo.envs as envs
-
-# Required by the ``distributed_setup`` fixture pulled from
-# ``fastvideo/tests/conftest.py``.  Set at import, before the fixture runs.
-envs.setdefault_external("MASTER_ADDR", "localhost")
-envs.setdefault_external("MASTER_PORT", "29519")
-
-from pathlib import Path
-
-import pytest
-import torch
-
-from fastvideo.forward_context import set_forward_context
-from fastvideo.train.models.longcat import LongCatModel
-from fastvideo.train.utils.config import load_run_config
-
-_FIXTURE = str(Path(__file__).resolve().parent.parent / "fixtures" / "longcat_t2v_min.yaml")
-
-# LongCat caption_channels (text embedding width).
-_LONGCAT_TEXT_DIM = 4096
-
-
-@pytest.mark.usefixtures("distributed_setup")
-def test_longcat_model_loads_and_forwards():
-    if not torch.cuda.is_available():
-        pytest.skip("requires CUDA")
-
-    cfg = load_run_config(_FIXTURE)
-    model = LongCatModel(
-        init_from=cfg.models["student"]["init_from"],
-        training_config=cfg.training,
-        trainable=False,
-    )
-
-    transformer = model.transformer
-    assert isinstance(transformer, torch.nn.Module)
-    assert sum(p.numel() for p in transformer.parameters()) > 0
-
-    device = torch.device("cuda:0")
-    dtype = torch.bfloat16
-    transformer = transformer.to(device=device, dtype=dtype).eval()
-
-    # LongCat transformer takes [B, C, T, H, W] (in_channels=16,
-    # patch_size=(1,2,2)) and a [B, N_text, 4096] text embedding.
-    hidden_states = torch.randn(1, 16, 4, 32, 32, device=device, dtype=dtype)
-    encoder_hidden_states = torch.randn(1, 16, _LONGCAT_TEXT_DIM, device=device, dtype=dtype)
-    encoder_attention_mask = torch.ones(1, 16, device=device, dtype=dtype)
-    timestep = torch.tensor([500], device=device, dtype=dtype)
-
-    with torch.no_grad(), set_forward_context(
-            current_timestep=0,
-            attn_metadata=None,
-    ):
-        out = transformer(
-            hidden_states=hidden_states,
-            encoder_hidden_states=encoder_hidden_states,
-            timestep=timestep,
-            encoder_attention_mask=encoder_attention_mask,
-        )
-
-    if isinstance(out, tuple):
-        out = out[0]
-    assert out.shape == hidden_states.shape, (f"output shape {tuple(out.shape)} != input shape "
-                                              f"{tuple(hidden_states.shape)}")
-    assert torch.isfinite(out).all().item(), "output contains NaN/Inf"
diff --git a/fastvideo/tests/train/models/test_wan_override_contract.py b/fastvideo/tests/train/models/test_wan_override_contract.py
index 30dd7759a..8e6fa4889 100644
--- a/fastvideo/tests/train/models/test_wan_override_contract.py
+++ b/fastvideo/tests/train/models/test_wan_override_contract.py
@@ -7,11 +7,10 @@ import pytest
 
 from fastvideo.train.models.hunyuan.hunyuan import HunyuanModel
 from fastvideo.train.models.hunyuan15.hunyuan15 import Hunyuan15Model
-from fastvideo.train.models.longcat.longcat import LongCatModel
 from fastvideo.train.models.matrixgame2.matrixgame2 import MatrixGame2Model
 
 
-@pytest.mark.parametrize("model_cls", [HunyuanModel, Hunyuan15Model, LongCatModel, MatrixGame2Model])
+@pytest.mark.parametrize("model_cls", [HunyuanModel, Hunyuan15Model, MatrixGame2Model])
 def test_distill_kwargs_accept_wan_teacher_forcing_keywords(model_cls: type) -> None:
     parameters = inspect.signature(model_cls._build_distill_input_kwargs).parameters
 
@@ -19,7 +18,7 @@ def test_distill_kwargs_accept_wan_teacher_forcing_keywords(model_cls: type) ->
     assert "aug_t" in parameters
 
 
-@pytest.mark.parametrize("model_cls", [Hunyuan15Model, LongCatModel, MatrixGame2Model])
+@pytest.mark.parametrize("model_cls", [Hunyuan15Model, MatrixGame2Model])
 def test_predict_noise_override_preserves_wan_teacher_forcing_keywords(model_cls: type) -> None:
     parameters = inspect.signature(model_cls.predict_noise).parameters
 
diff --git a/fastvideo/tests/train/utils/test_finetune_config_construction.py b/fastvideo/tests/train/utils/test_finetune_config_construction.py
index 75435b4a6..4d0db79cb 100644
--- a/fastvideo/tests/train/utils/test_finetune_config_construction.py
+++ b/fastvideo/tests/train/utils/test_finetune_config_construction.py
@@ -47,4 +47,4 @@ def test_fine_tuning_recipe_constructs(recipe: Path) -> None:
 
 def test_recipe_glob_found_recipes() -> None:
     # Guard against the glob silently matching nothing after a move.
-    assert len(_RECIPES) >= 20
+    assert len(_RECIPES) >= 18
diff --git a/fastvideo/tests/transformers/test_hunyuangamecraft.py b/fastvideo/tests/transformers/test_hunyuangamecraft.py
deleted file mode 100644
index 66864a79f..000000000
--- a/fastvideo/tests/transformers/test_hunyuangamecraft.py
+++ /dev/null
@@ -1,144 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-Regression test for HunyuanGameCraft transformer.
-
-Tests that the FastVideo HunyuanGameCraft implementation produces consistent outputs.
-"""
-import os
-
-import pytest
-import torch
-
-import fastvideo.envs as envs
-from fastvideo.configs.models.dits.hunyuangamecraft import HunyuanGameCraftConfig
-from fastvideo.configs.pipelines import PipelineConfig
-from fastvideo.distributed.parallel_state import (
-    get_sp_parallel_rank,
-    get_sp_world_size,
-)
-from fastvideo.fastvideo_args import FastVideoArgs
-from fastvideo.forward_context import set_forward_context
-from fastvideo.logger import init_logger
-from fastvideo.models.loader.component_loader import TransformerLoader
-from fastvideo.pipelines.pipeline_batch_info import ForwardBatch
-
-logger = init_logger(__name__)
-
-envs.setdefault_external("MASTER_ADDR", "localhost")
-envs.setdefault_external("MASTER_PORT", "29507")
-
-# Path to converted weights (local path, not HuggingFace)
-TRANSFORMER_PATH = "official_weights/hunyuan-gamecraft/transformer"
-
-# Reference latent computed from FastVideo HunyuanGameCraft model with:
-# - seed=42, batch=1, frames=9, H=44, W=80, text_seq=32
-# - 33 camera frames at 352x640 resolution
-# - timestep=500
-REFERENCE_LATENT = 42351.12903189659
-
-
-@pytest.mark.usefixtures("distributed_setup")
-def test_hunyuangamecraft_transformer(env_overrides):
-    """Test HunyuanGameCraft transformer regression."""
-
-    if not os.path.exists(TRANSFORMER_PATH):
-        pytest.skip(f"Weights not found at {TRANSFORMER_PATH}")
-
-    # Keep this model-specific override local to this test. Setting
-    # TORCHDYNAMO_DISABLE during module collection disables torch.compile for
-    # every later transformer test in the same pytest process; FakeTensor
-    # compile checks then execute Triton kernels against fake CUDA pointers.
-    env_overrides.enter_context(envs.override_external("TORCHDYNAMO_DISABLE", "1"))
-
-    sp_rank = get_sp_parallel_rank()
-    sp_world_size = get_sp_world_size()
-
-    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-    precision = torch.bfloat16
-    precision_str = "bf16"
-
-    args = FastVideoArgs(
-        model_path=TRANSFORMER_PATH,
-        dit_cpu_offload=False,
-        use_fsdp_inference=False,
-        pipeline_config=PipelineConfig(dit_config=HunyuanGameCraftConfig(), dit_precision=precision_str),
-    )
-    args.device = device
-
-    loader = TransformerLoader()
-    model = loader.load(TRANSFORMER_PATH, args).to(device, dtype=precision)
-    model.eval()
-
-    total_params = sum(p.numel() for p in model.parameters())
-    weight_sum = sum(p.to(torch.float64).sum().item() for p in model.parameters())
-    weight_mean = weight_sum / total_params
-    logger.info("Total parameters: %s", total_params)
-    logger.info("Weight sum: %s", weight_sum)
-    logger.info("Weight mean: %s", weight_mean)
-
-    torch.manual_seed(42)
-
-    batch_size = 1
-    latent_frames = 9  # GameCraft uses 9 latent frames
-    latent_height = 44
-    latent_width = 80
-    text_seq_len = 32
-
-    # Input latents [B, 33, T, H, W] - 16 latent + 16 gt_latent + 1 mask
-    hidden_states = torch.randn(batch_size,
-                                33,
-                                latent_frames,
-                                latent_height,
-                                latent_width,
-                                device=device,
-                                dtype=precision)
-
-    if sp_world_size > 1:
-        chunk_per_rank = hidden_states.shape[2] // sp_world_size
-        hidden_states = hidden_states[:, :, sp_rank * chunk_per_rank:(sp_rank + 1) * chunk_per_rank]
-
-    # Text embeddings (LLaMA)
-    text_states = torch.randn(batch_size, text_seq_len, 4096, device=device, dtype=precision)
-
-    # CLIP pooled embeddings
-    text_states_2 = torch.randn(batch_size, 768, device=device, dtype=precision)
-
-    # Text mask
-    text_mask = torch.ones(batch_size, text_seq_len, device=device, dtype=torch.long)
-
-    # Timestep
-    timestep = torch.tensor([500], device=device, dtype=precision)
-
-    # Camera states [B, num_frames, 6, H, W] - 33 video frames at full resolution
-    # For 9 latent frames, we use 33 camera frames (matching official model)
-    camera_states = torch.randn(batch_size, 33, 6, 352, 640, device=device, dtype=precision)
-
-    encoder_hidden_states = [text_states, text_states_2]
-
-    forward_batch = ForwardBatch(data_type="video")
-
-    with torch.no_grad():
-        with torch.amp.autocast('cuda', dtype=precision):
-            with set_forward_context(current_timestep=0, attn_metadata=None, forward_batch=forward_batch):
-                output = model(
-                    hidden_states,
-                    encoder_hidden_states,
-                    timestep,
-                    camera_states=camera_states,
-                    encoder_attention_mask=[text_mask],
-                )
-
-    latent = output.double().sum().item()
-    logger.info(f"Output shape: {output.shape}")
-    logger.info(f"Current latent: {latent}")
-
-    if REFERENCE_LATENT is not None:
-        diff = abs(REFERENCE_LATENT - latent)
-        relative_diff = diff / abs(REFERENCE_LATENT)
-        logger.info(f"Reference latent: {REFERENCE_LATENT}")
-        logger.info(f"Absolute diff: {diff}, Relative diff: {relative_diff * 100:.4f}%")
-
-        assert relative_diff < 0.005, \
-            f"Output latents differ significantly: relative diff = {relative_diff * 100:.4f}% (max allowed: 0.5%)"
-    else:
-        logger.info(f"No reference latent set. To enable regression testing, set REFERENCE_LATENT = {latent}")
diff --git a/fastvideo/third_party/longcat_video/block_sparse_attention/__init__.py b/fastvideo/third_party/longcat_video/block_sparse_attention/__init__.py
deleted file mode 100644
index e69de29bb..000000000
diff --git a/fastvideo/third_party/longcat_video/block_sparse_attention/bsa_interface.py b/fastvideo/third_party/longcat_video/block_sparse_attention/bsa_interface.py
deleted file mode 100644
index 1b6bbd6c9..000000000
--- a/fastvideo/third_party/longcat_video/block_sparse_attention/bsa_interface.py
+++ /dev/null
@@ -1,737 +0,0 @@
-import os
-import torch
-import torch.nn.functional as F
-import triton
-import triton.language as tl
-import math
-
-from .common import _attn_fwd_gating, _attn_bwd_preprocess, configs_gating_preset
-from .flash_attn_bsa_varlen_mask import (_attn_fwd_bsa_varlen, _attn_fwd_bsa_varlen_align,
-                                         _attn_bwd_dkdv_bsa_varlen_wrapper, _attn_bwd_dq_bsa_varlen_wrapper,
-                                         _attn_bwd_dq_bsa_varlen_align_wrapper, configs_fwd_bsa_varlen_preset,
-                                         configs_fwd_bsa_varlen_align_preset, configs_bwd_dkdv_bsa_varlen_preset,
-                                         configs_bwd_dq_bsa_varlen_preset, configs_bwd_dq_bsa_varlen_align_preset)
-
-torch._dynamo.config.cache_size_limit = 32
-
-
-def is_cuda():
-    return triton.runtime.driver.active.get_current_target().backend == "cuda"
-
-
-def supports_tma():
-    return is_cuda() and torch.cuda.get_device_capability()[0] >= 9
-
-
-HAS_TMA_DESC = "nv_tma_desc_type" in dir(tl)
-
-if HAS_TMA_DESC:
-    print("TMA benchmarks will be running with experimental grid constant TMA descriptor.", )
-else:
-    print("TMA benchmarks will be running without grid constant TMA descriptor.", )
-
-
-# TmaAutoTuneHelper used in htyu's PR #5622
-class TmaAutoTuneHelper:
-
-    # duck typing wrapper to implement the same interface as TmaDescKernelParam in Triton PR #4498
-    class KernelParamWrapper:
-
-        def __init__(self, desc):
-            self.desc = desc
-
-        def tma_desc_cpu_ptr(self):
-            return self.desc.data_ptr()
-
-    TMA_SIZE = 128
-
-    def __init__(self):
-        self.fill_1d_tma_descriptor_inner = (triton.runtime.driver.active.utils.fill_1d_tma_descriptor)
-        self.fill_2d_tma_descriptor_inner = (triton.runtime.driver.active.utils.fill_2d_tma_descriptor)
-        if HAS_TMA_DESC:
-            self.descriptors = {}
-        else:
-            self.cuda_descriptors = {}
-
-    # Call this method outside of the lambda function for grid size
-    def init_tma_descriptor(self, name):
-        if HAS_TMA_DESC:
-            self.descriptors[name] = torch.empty(TmaAutoTuneHelper.TMA_SIZE, device="cpu", dtype=torch.int8)
-        else:
-            self.cuda_descriptors[name] = torch.empty(TmaAutoTuneHelper.TMA_SIZE, device="cuda", dtype=torch.int8)
-
-    # Call this method inside the lambda function for grid size
-    def fill_1d_tma_descriptor(self, name, ptr, dim, block_dim, element_size):
-        if HAS_TMA_DESC:
-            desc_x = self.descriptors[name]
-            assert desc_x.data_ptr() % 64 == 0
-            self.fill_1d_tma_descriptor_inner(ptr, dim, block_dim, element_size, desc_x.data_ptr())
-        else:
-            desc_x = self.cuda_descriptors[name]
-            buf_x = torch.empty_like(desc_x, device="cpu", pin_memory=True)
-            self.fill_1d_tma_descriptor_inner(ptr, dim, block_dim, element_size, buf_x.data_ptr())
-            desc_x.copy_(buf_x, non_blocking=True)
-
-    # Call this method inside the lambda function for grid size
-    def fill_2d_tma_descriptor(self, name, ptr, dim1, dim0, block_dim1, block_dim0, element_size):
-        if HAS_TMA_DESC:
-            desc_x = self.descriptors[name]
-            assert desc_x.data_ptr() % 64 == 0
-            self.fill_2d_tma_descriptor_inner(ptr, dim1, dim0, block_dim1, block_dim0, element_size, desc_x.data_ptr())
-        else:
-            desc_x = self.cuda_descriptors[name]
-            buf_x = torch.empty_like(desc_x, device="cpu", pin_memory=True)
-            self.fill_2d_tma_descriptor_inner(ptr, dim1, dim0, block_dim1, block_dim0, element_size, buf_x.data_ptr())
-            desc_x.copy_(buf_x, non_blocking=True)
-
-    def get_tma_descriptor_kernel_param(self, name):
-        if HAS_TMA_DESC:
-            assert self.descriptors[name] is not None
-            return self.KernelParamWrapper(self.descriptors[name])
-        else:
-            assert self.cuda_descriptors[name] is not None
-            return self.cuda_descriptors[name]
-
-
-@triton.jit
-def create_mask_from_indices_kernel(
-    block_indices,
-    block_mask,
-    stride_bz,
-    stride_bh,
-    stride_bm,
-    stride_bs,
-    stride_mz,
-    stride_mh,
-    stride_mm,
-    stride_mn,
-    H,
-):
-    i_zh, i_m, i_s = tl.program_id(0), tl.program_id(1), tl.program_id(2)
-    i_z, i_h = i_zh // H, i_zh % H
-
-    off_b = i_z.to(tl.int64) * stride_bz + i_h.to(tl.int64) * stride_bh + i_m.to(tl.int64) * stride_bm + i_s.to(
-        tl.int64) * stride_bs
-
-    b_i = tl.load(block_indices + off_b)
-
-    off_m = i_z.to(tl.int64) * stride_mz + i_h.to(tl.int64) * stride_mh + i_m.to(tl.int64) * stride_mm + b_i.to(
-        tl.int64) * stride_mn
-
-    b_m = 1
-    tl.store(block_mask + off_m, b_m.to(block_mask.dtype.element_ty))
-
-
-def create_mask_from_indices_triton(block_indices, N_cols):
-    B, H, N_rows, S = block_indices.shape
-    block_mask = torch.zeros((B, H, N_rows, N_cols), dtype=torch.bool, device=block_indices.device)
-    create_mask_from_indices_kernel[(B * H, N_rows, S)](
-        block_indices,
-        block_mask,
-        block_indices.stride(0),
-        block_indices.stride(1),
-        block_indices.stride(2),
-        block_indices.stride(3),
-        block_mask.stride(0),
-        block_mask.stride(1),
-        block_mask.stride(2),
-        block_mask.stride(3),
-        H,
-    )
-    return block_mask
-
-
-@torch.compile
-def create_mask_from_indices_varlen(block_indices, N_cols_mask):
-
-    B, H, M, _ = block_indices.shape
-    device = block_indices.device
-
-    mask = torch.zeros((B, H, M, N_cols_mask), dtype=torch.bool, device=device)
-
-    valid = block_indices < N_cols_mask
-
-    b_idx = torch.arange(B, device=device)[:, None, None, None].expand_as(block_indices)
-    h_idx = torch.arange(H, device=device)[None, :, None, None].expand_as(block_indices)
-    m_idx = torch.arange(M, device=device)[None, None, :, None].expand_as(block_indices)
-
-    valid_coords = (b_idx[valid], h_idx[valid], m_idx[valid], block_indices[valid])
-
-    mask[valid_coords] = True
-
-    return mask
-
-
-@torch.compile
-def create_indices_k_from_indices_q_varlen(
-    block_indices,
-    N_cols_mask  # indicate the number of the last dimension of the bool mask, since this information cannot be determined by block_indices, which may contain invalid elements
-):
-    block_mask_qk = create_mask_from_indices_varlen(block_indices, N_cols_mask)
-    B, H, M, N = block_mask_qk.shape
-    block_mask_kq = block_mask_qk.permute(0, 1, 3, 2)
-    indices = torch.arange(M, device=block_indices.device).view(1, 1, 1, -1).expand_as(block_mask_kq)
-    block_indices_k = torch.where(block_mask_kq, indices, M)
-    block_indices_k, _ = torch.sort(block_indices_k, dim=-1)
-
-    block_indices_k_lens = (block_indices_k < M).sum(dim=-1)
-
-    return block_indices_k, block_indices_k_lens
-
-
-@torch.compile
-def mean_pooling_compression(x: torch.Tensor, block_size: int) -> torch.Tensor:
-    B, H, S = x.shape[:3]
-    num_block = math.ceil(S / block_size)
-    if S % block_size != 0:
-        x = F.pad(x, (0, 0, 0, num_block * block_size - S))
-    x_cmp = x.view(B, H, num_block, block_size, -1).mean(dim=3)
-    return x_cmp
-
-
-@torch.compile
-def cal_score(q, k):
-    k_transposed = k.transpose(-1, -2)  # [b, h, d, s_k]
-    score = torch.matmul(q, k_transposed)  # [b, h, s_q, s_k]
-    return score
-
-
-def cal_score_triton(q, k):
-    B, H, s_q, D = q.shape
-    s_k = k.shape[2]
-
-    score = torch.empty(B, H, s_q, s_k, device=q.device, dtype=q.dtype)
-
-    kernel_config = {} if os.environ.get('TRITON_AUTOTUNE_ENBALE', '0') == '1' else configs_gating_preset['default']
-
-    grid = lambda args: (triton.cdiv(s_q, args["BLOCK_M"]), B * H, 1)
-    _attn_fwd_gating[grid](q,
-                           k,
-                           score,
-                           q.stride(0),
-                           q.stride(1),
-                           q.stride(2),
-                           q.stride(3),
-                           k.stride(0),
-                           k.stride(1),
-                           k.stride(2),
-                           k.stride(3),
-                           score.stride(0),
-                           score.stride(1),
-                           score.stride(2),
-                           score.stride(3),
-                           H,
-                           s_q,
-                           s_k,
-                           HEAD_DIM=D,
-                           **kernel_config)
-    return score
-
-
-@torch.compile
-def get_select_indices_topk(q, k, sparsity):
-    score = cal_score(q, k)
-    block_indices, block_indices_lens = get_select_indices_topk_from_score(score, sparsity)
-    return block_indices, block_indices_lens
-
-
-@torch.compile
-def get_select_indices_topk_from_score(score, sparsity):
-    num_selected = int((1 - sparsity) * score.shape[-1])
-    block_indices = torch.topk(score, num_selected)[1]
-
-    block_indices_lens = torch.full((block_indices.shape[0], block_indices.shape[1], block_indices.shape[2]),
-                                    num_selected,
-                                    dtype=torch.int32,
-                                    device=block_indices.device)
-
-    return block_indices, block_indices_lens
-
-
-@torch.compile
-def get_select_indices_cdf(q, k, cdf_threshold):
-    score = cal_score(q, k)
-    head_dim = q.shape[-1]
-    block_indices, block_indices_lens = get_select_indices_cdf_from_score(score, cdf_threshold, 1 / head_dim**0.5)
-    return block_indices, block_indices_lens
-
-
-@torch.compile
-def get_select_indices_cdf_from_score(score, cdf_threshold, sm_scale):
-    weights = torch.softmax(score * sm_scale, dim=-1)
-
-    B, H, Sq, Sk = weights.shape
-    cdf_threshold = torch.full((H, ), cdf_threshold, device=weights.device).view(1, H, 1, 1).expand(B, -1, Sq, -1)
-    weights_sorted = torch.sort(weights, dim=-1, descending=True)
-    cdf = torch.cumsum(weights_sorted.values, dim=-1)
-    num_selected = torch.searchsorted(cdf, cdf_threshold, right=True)
-
-    return weights_sorted.indices, num_selected.squeeze(-1)
-
-
-@torch.compile
-def get_select_indices_cdf_topk(q, k, sparsity, cdf_threshold):
-    score = cal_score(q, k)
-    head_dim = q.shape[-1]
-    block_indices, block_indices_lens = get_select_indices_cdf_topk_from_score(score, sparsity, cdf_threshold,
-                                                                               1 / head_dim**0.5)
-    return block_indices, block_indices_lens
-
-
-@torch.compile
-def get_select_indices_cdf_topk_from_score(score, sparsity, cdf_threshold, sm_scale):
-    weights = torch.softmax(score * sm_scale, dim=-1)
-
-    B, H, Sq, Sk = weights.shape
-    cdf_threshold = torch.full((H, ), cdf_threshold, device=weights.device).view(1, H, 1, 1).expand(B, -1, Sq, -1)
-    weights_sorted = torch.sort(weights, dim=-1, descending=True)
-    cdf = torch.cumsum(weights_sorted.values, dim=-1)
-    num_selected = torch.searchsorted(cdf, cdf_threshold, right=True)
-
-    # max(cdf, topk)
-    num_selected_topk = int((1 - sparsity) * score.shape[-1])
-    num_selected[num_selected < num_selected_topk] = num_selected_topk
-
-    return weights_sorted.indices, num_selected.squeeze(-1)
-
-
-def get_select_indices(q, k, sparsity, cdf_threshold):
-    if sparsity is not None and cdf_threshold is None:
-        block_indices, block_indices_lens = get_select_indices_topk(q, k, sparsity)
-    elif sparsity is None and cdf_threshold is not None:
-        block_indices, block_indices_lens = get_select_indices_cdf(q, k, cdf_threshold)
-    elif sparsity is not None and cdf_threshold is not None:
-        block_indices, block_indices_lens = get_select_indices_cdf_topk(q, k, sparsity, cdf_threshold)
-    else:
-        raise ValueError
-    return block_indices, block_indices_lens
-
-
-def get_select_indices_from_score(score, sparsity, cdf_threshold):
-    if sparsity is not None and cdf_threshold is None:
-        block_indices, block_indices_lens = get_select_indices_topk_from_score(score, sparsity)
-    elif sparsity is None and cdf_threshold is not None:
-        block_indices, block_indices_lens = get_select_indices_cdf_from_score(score, cdf_threshold)
-    elif sparsity is not None and cdf_threshold is not None:
-        block_indices, block_indices_lens = get_select_indices_cdf_topk_from_score(score, sparsity, cdf_threshold)
-    else:
-        raise ValueError
-    return block_indices, block_indices_lens
-
-
-def attn_fwd_bsa_varlen_triton(q, k, v, sm_scale, block_indices, block_indices_lens, chunk_size_q, chunk_size_k,
-                               sparsity):
-
-    B, H, Seq, D = q.shape
-
-    o = torch.empty_like(q)
-    M = torch.empty((q.shape[0], q.shape[1], q.shape[2]), device=q.device, dtype=torch.float32)
-
-    grid = lambda args: (triton.cdiv(q.shape[2], args["BLOCK_M"]), q.shape[0] * q.shape[1], 1)
-
-    config_key = 'BLOCK_N_LG=64' if chunk_size_k == 64 else 'default'
-    if chunk_size_k > 128:
-        fwd_func = _attn_fwd_bsa_varlen
-        kernel_config = {} if os.environ.get('TRITON_AUTOTUNE_ENBALE',
-                                             '0') == '1' else configs_fwd_bsa_varlen_preset[config_key]
-    else:
-        fwd_func = _attn_fwd_bsa_varlen_align
-        kernel_config = {} if os.environ.get('TRITON_AUTOTUNE_ENBALE',
-                                             '0') == '1' else configs_fwd_bsa_varlen_align_preset[config_key]
-
-    block_indices = block_indices.contiguous()
-    block_indices_lens = block_indices_lens.contiguous()
-
-    fwd_func[grid](
-        q,
-        k,
-        v,
-        sm_scale,
-        M,
-        o,
-        block_indices,  # [B, H, M_COMPRESS, S]
-        block_indices_lens,  # [B, H, M_COMPRESS, S_MAX]
-        q.stride(0),
-        q.stride(1),
-        q.stride(2),
-        q.stride(3),
-        k.stride(0),
-        k.stride(1),
-        k.stride(2),
-        k.stride(3),
-        v.stride(0),
-        v.stride(1),
-        v.stride(2),
-        v.stride(3),
-        o.stride(0),
-        o.stride(1),
-        o.stride(2),
-        o.stride(3),
-        block_indices.stride(0),
-        block_indices.stride(1),
-        block_indices.stride(2),
-        block_indices.stride(3),
-        block_indices_lens.stride(0),
-        block_indices_lens.stride(1),
-        block_indices_lens.stride(2),
-        H,
-        Seq,
-        D,
-        BLOCK_M=chunk_size_q,
-        BLOCK_N_LG=chunk_size_k,
-        SPARSITY=sparsity,
-        **kernel_config)
-
-    LN2 = 0.6931471824645996
-    lse = M * LN2  # convert back to natural units (M is of base 2)
-
-    return o, lse
-
-
-def attn_bwd_bsa_varlen_triton(do, q, k, v, o, dq, dk, dv, sm_scale, M, block_indices, block_indices_lens, chunk_size_q,
-                               chunk_size_k, sparsity):
-    RCP_LN2 = 1.4426950408889634
-    M = M * RCP_LN2  # ln -> log2
-
-    do = do.contiguous()
-    # assert q.stride() == k.stride() == v.stride() == o.stride() == do.stride()
-
-    BATCH, N_HEAD, N_CTX, HEAD_DIM = q.shape
-    N_CTX_KV = k.shape[-2]
-
-    RCP_LN2 = 1.4426950408889634  # = 1.0 / ln(2) # reciprocal
-    arg_k = k
-    arg_k = arg_k * (sm_scale * RCP_LN2)
-
-    PRE_BLOCK = 128 if min(chunk_size_q, chunk_size_k) >= 128 else min(chunk_size_q, chunk_size_k)
-
-    assert N_CTX % PRE_BLOCK == 0
-    pre_grid = (N_CTX // PRE_BLOCK, BATCH * N_HEAD)
-    delta = torch.empty_like(M)
-    _attn_bwd_preprocess[pre_grid](o, do, delta, N_CTX, BLOCK_M=PRE_BLOCK, HEAD_DIM=HEAD_DIM)
-
-    block_indices_k, block_indices_k_lens = create_indices_k_from_indices_q_varlen(block_indices=block_indices,
-                                                                                   N_cols_mask=N_CTX_KV // chunk_size_k)
-
-    block_indices = block_indices.contiguous()
-    block_indices_lens = block_indices_lens.contiguous()
-    block_indices_k = block_indices_k.contiguous()
-    block_indices_k_lens = block_indices_k_lens.contiguous()
-
-    config_key = 'BLOCK_N_DQ_LG=64' if chunk_size_k == 64 else 'default'
-    kernel_config = {} if os.environ.get('TRITON_AUTOTUNE_ENBALE',
-                                         '0') == '1' else configs_bwd_dkdv_bsa_varlen_preset[config_key]
-
-    grid_dkdv = lambda args: (triton.cdiv(arg_k.shape[2], args["BLOCK_N"]), 1, arg_k.shape[0] * arg_k.shape[1])
-    _attn_bwd_dkdv_bsa_varlen_wrapper[grid_dkdv](
-        q,
-        arg_k,
-        v,
-        sm_scale,  # softmax scale
-        do,
-        dk,
-        dv,
-        M,  # lse (log2)
-        delta,
-        block_indices_k,
-        block_indices_k_lens,
-        q.stride(0),
-        q.stride(1),
-        q.stride(2),
-        q.stride(3),
-        k.stride(0),
-        k.stride(1),
-        k.stride(2),
-        k.stride(3),
-        v.stride(0),
-        v.stride(1),
-        v.stride(2),
-        v.stride(3),
-        dk.stride(0),
-        dk.stride(1),
-        dk.stride(2),
-        dk.stride(3),
-        dv.stride(0),
-        dv.stride(1),
-        dv.stride(2),
-        dv.stride(3),
-        do.stride(0),
-        do.stride(1),
-        do.stride(2),
-        do.stride(3),
-        M.stride(0),
-        M.stride(1),
-        M.stride(2),
-        delta.stride(0),
-        delta.stride(1),
-        delta.stride(2),
-        block_indices_k.stride(0),
-        block_indices_k.stride(1),
-        block_indices_k.stride(2),
-        block_indices_k.stride(3),
-        block_indices_k_lens.stride(0),
-        block_indices_k_lens.stride(1),
-        block_indices_k_lens.stride(2),
-        N_HEAD,
-        N_CTX,
-        BLOCK_M=chunk_size_q,
-        BLOCK_N_DQ_LG=chunk_size_k,
-        HEAD_DIM=HEAD_DIM,
-        SPARSITY=sparsity,
-        **kernel_config)
-
-    config_key = 'BLOCK_N_DQ_LG=64' if chunk_size_k == 64 else 'default'
-    if chunk_size_k > 128:
-        bwd_dq_func = _attn_bwd_dq_bsa_varlen_wrapper
-        kernel_config = {} if os.environ.get('TRITON_AUTOTUNE_ENBALE',
-                                             '0') == '1' else configs_bwd_dq_bsa_varlen_preset[config_key]
-    else:
-        bwd_dq_func = _attn_bwd_dq_bsa_varlen_align_wrapper
-        kernel_config = {} if os.environ.get('TRITON_AUTOTUNE_ENBALE',
-                                             '0') == '1' else configs_bwd_dq_bsa_varlen_align_preset[config_key]
-
-    grid_dq = lambda args: (triton.cdiv(q.shape[2], args["BLOCK_M"]), 1, q.shape[0] * q.shape[1])
-    bwd_dq_func[grid_dq](
-        q,
-        arg_k,
-        v,
-        do,
-        dq,
-        M,  # lse (log2)
-        delta,
-        block_indices,
-        block_indices_lens,
-        q.stride(0),
-        q.stride(1),
-        q.stride(2),
-        q.stride(3),
-        k.stride(0),
-        k.stride(1),
-        k.stride(2),
-        k.stride(3),
-        v.stride(0),
-        v.stride(1),
-        v.stride(2),
-        v.stride(3),
-        dq.stride(0),
-        dq.stride(1),
-        dq.stride(2),
-        dq.stride(3),
-        do.stride(0),
-        do.stride(1),
-        do.stride(2),
-        do.stride(3),
-        M.stride(0),
-        M.stride(1),
-        M.stride(2),
-        delta.stride(0),
-        delta.stride(1),
-        delta.stride(2),
-        block_indices.stride(0),
-        block_indices.stride(1),
-        block_indices.stride(2),
-        block_indices.stride(3),
-        block_indices_lens.stride(0),
-        block_indices_lens.stride(1),
-        block_indices_lens.stride(2),
-        N_HEAD,
-        N_CTX,
-        BLOCK_M=chunk_size_q,
-        BLOCK_N_DQ_LG=chunk_size_k,
-        HEAD_DIM=HEAD_DIM,
-        SPARSITY=sparsity,
-        **kernel_config)
-
-
-@torch.compile
-def make_block_indices_varlen_cp_list(block_indices, cp_size, num_blocks_k_full):
-    """
-    Args:
-        block_indices: [B, H, num_blocks_q_per_cp_rank, num_blocks_k_full]
-    
-    Return:
-        a list of [block_indices, block_indices_lens] for k from each cp_rank
-            - each block_indices starts from zero
-            - block_indices_lens indicates the valid number of elements in the last dimension of block_indices
-    """
-    res = []
-    num_blocks_per_rank = num_blocks_k_full // cp_size
-    for i in range(cp_size):
-        block_indices_tmp = block_indices.clone()
-        min_block_idx = i * num_blocks_per_rank
-        block_indices_tmp -= min_block_idx
-        block_indices_tmp[
-            block_indices_tmp <
-            0] = num_blocks_per_rank  # block_indices_tmp < 0 indicate invalid indices, set them to num_blocks_per_rank in order to sort them to the tail, so that the first N elements of the block_indices indicated by block_indices_lens are valid
-
-        block_indices_tmp, _ = torch.sort(block_indices_tmp, dim=-1)
-
-        block_indices_tmp_lens = (block_indices_tmp < num_blocks_per_rank).sum(dim=-1)
-
-        res.append([block_indices_tmp, block_indices_tmp_lens])
-
-    return res
-
-
-@torch.compile
-def flash_attn_fwd_softmax_lse_correction(
-    softmax_lse: torch.Tensor,
-    softmax_lse_per_step: torch.Tensor,
-):
-    """Merge softmax stats of each step in Attention with context parallelism"""
-    max_scale = torch.max(softmax_lse, softmax_lse_per_step)
-    min_scale = torch.min(softmax_lse, softmax_lse_per_step)
-    lse_diff = min_scale - max_scale
-    lse_diff = lse_diff.nan_to_num(
-        nan=0.
-    )  # handle cases: tensor(-inf) - tensor(-inf) = tensor(nan); In the current cp implementation, it is possible that lses of 2 cp ranks are both -inf, if no block is selected from both cp ranks. In such cases, the finally corrected lse should remain -inf.
-    new_scale = max_scale + torch.log1p(
-        torch.exp(lse_diff))  # a + ln(1 + e^(b - a)) = ln(e^a) + ln(1 + e^(b - a)) = ln(e^a + e^b)
-    softmax_lse.copy_(new_scale)
-
-
-@torch.compile
-def flash_attn_fwd_out_correction_init(
-    out_init_step: torch.Tensor,  # b h s d
-    softmax_lse: torch.Tensor,  # b h s
-    softmax_lse_init_step: torch.Tensor,
-):
-    """Merge partial outputs of the first step in Attention with context parallelism"""
-    softmax_lse_corrected_exp = torch.exp(softmax_lse_init_step - softmax_lse)
-    softmax_lse_corrected_exp = softmax_lse_corrected_exp.unsqueeze(-1)
-    out_corrected = out_init_step * softmax_lse_corrected_exp
-    return out_corrected.to(out_init_step.dtype)
-
-
-@torch.compile
-def flash_attn_fwd_out_correction(
-    out: torch.Tensor,
-    out_per_step: torch.Tensor,
-    softmax_lse: torch.Tensor,
-    softmax_lse_per_step: torch.Tensor,
-):
-    """Merge partial outputs of each step in Attention with context parallelism"""
-    softmax_lse_corrected_exp = torch.exp(softmax_lse_per_step - softmax_lse)
-    softmax_lse_corrected_exp = softmax_lse_corrected_exp.unsqueeze(-1)
-    out_corrected = out_per_step * softmax_lse_corrected_exp
-    out.add_(out_corrected)
-
-
-@torch.compile
-def topk_sort(score, num_chunks_selected):
-    block_indices = torch.topk(score, num_chunks_selected)[1]
-    block_indices, _ = torch.sort(block_indices, dim=-1)
-    return block_indices
-
-
-class _attention_bsa(torch.autograd.Function):
-
-    @staticmethod
-    def forward(ctx, q, k, v, chunk_size_q, chunk_size_k, sparsity, cdf_threshold, sm_scale, use_tma=False):
-        # shape constraints
-        HEAD_DIM_Q, HEAD_DIM_K = q.shape[-1], k.shape[-1]
-        # when v is in float8_e5m2 it is transposed.
-        HEAD_DIM_V = v.shape[-1]
-        assert HEAD_DIM_Q == HEAD_DIM_K and HEAD_DIM_K == HEAD_DIM_V
-        assert HEAD_DIM_K in {16, 32, 64, 128, 256}
-
-        # ---------------------- gating ----------------------
-        q_cmp = mean_pooling_compression(q, chunk_size_q)
-        k_cmp = mean_pooling_compression(k, chunk_size_k)
-        block_indices, block_indices_lens = get_select_indices(q_cmp, k_cmp, sparsity, cdf_threshold)
-
-        # ---------------------- bsa ----------------------
-
-        o, lse = attn_fwd_bsa_varlen_triton(q, k, v, sm_scale, block_indices, block_indices_lens, chunk_size_q,
-                                            chunk_size_k, sparsity)
-
-        ctx.save_for_backward(q, k, v, o, lse, block_indices, block_indices_lens)
-        ctx.sm_scale = sm_scale
-        ctx.HEAD_DIM = HEAD_DIM_K
-        ctx.chunk_size_q = chunk_size_q
-        ctx.chunk_size_k = chunk_size_k
-        ctx.use_tma = use_tma
-        ctx.sparsity = sparsity
-
-        return o
-
-    @staticmethod
-    def backward(ctx, do):
-        q, k, v, o, lse, block_indices, block_indices_lens = ctx.saved_tensors
-
-        dq = torch.empty_like(q)
-        dk = torch.empty_like(k)
-        dv = torch.empty_like(v)
-
-        attn_bwd_bsa_varlen_triton(do, q, k, v, o, dq, dk, dv, ctx.sm_scale, lse, block_indices, block_indices_lens,
-                                   ctx.chunk_size_q, ctx.chunk_size_k, ctx.sparsity)
-
-        return dq, dk, dv, None, None, None, None, None, None
-
-
-flash_attn_bsa = _attention_bsa.apply
-
-
-def rearrange_THW_to_3d_block(x, Nt, Nh, Nw, t, h, w, D):
-    B, H, _, D = x.shape
-    x = x.view(B, H, Nt, t, Nh, h, Nw, w, D)
-    x = x.permute(0, 1, 2, 4, 6, 3, 5, 7, 8)  # B H Nt Nh Nw t h w D
-    return x.contiguous().view(B, H, Nt * Nh * Nw * t * h * w, D)
-
-
-def rearrange_3d_block_to_THW(x, Nt, Nh, Nw, t, h, w, D):
-    B, H, _, D = x.shape
-    x = x.view(B, H, Nt, Nh, Nw, t, h, w, D)
-    x = x.permute(0, 1, 2, 5, 3, 6, 4, 7, 8)  # B H Nt t Nh h Nw w D
-    return x.contiguous().view(B, H, Nt * t * Nh * h * Nw * w, D)
-
-
-def flash_attn_bsa_3d(
-    q: torch.Tensor,  # [B, H, Sq, D]
-    k: torch.Tensor,  # [B, H, Skv, D]
-    v: torch.Tensor,  # [B, H, Skv, D]
-    latent_shape_q,
-    latent_shape_k,
-    # bsa_params
-    sparsity=0.875,
-    cdf_threshold=None,
-    chunk_3d_shape_q=[4, 4, 8],
-    chunk_3d_shape_k=[4, 4, 8],
-) -> torch.Tensor:
-    _, _, Sq, head_dim_q = q.shape
-    _, _, Sk, head_dim_k = k.shape
-
-    assert head_dim_q == head_dim_k
-    head_dim = head_dim_q
-
-    Tq, Hq, Wq = latent_shape_q
-    Tk, Hk, Wk = latent_shape_k
-
-    assert Tq * Hq * Wq == Sq
-    assert Tk * Hk * Wk == Sk
-
-    tq, hq, wq = chunk_3d_shape_q
-    tk, hk, wk = chunk_3d_shape_k
-
-    assert Tq % tq == 0 and Hq % hq == 0 and Wq % wq == 0
-    assert Tk % tk == 0 and Hk % hk == 0 and Wk % wk == 0
-
-    Ntq = Tq // tq
-    Nhq = Hq // hq
-    Nwq = Wq // wq
-
-    Ntk = Tk // tk
-    Nhk = Hk // hk
-    Nwk = Wk // wk
-
-    q = rearrange_THW_to_3d_block(q, Ntq, Nhq, Nwq, tq, hq, wq, q.shape[-1])
-    k = rearrange_THW_to_3d_block(k, Ntk, Nhk, Nwk, tk, hk, wk, k.shape[-1])
-    v = rearrange_THW_to_3d_block(v, Ntk, Nhk, Nwk, tk, hk, wk, v.shape[-1])
-
-    chunk_size_q = tq * hq * wq
-    chunk_size_k = tk * hk * wk
-
-    output = flash_attn_bsa(q, k, v, chunk_size_q, chunk_size_k, sparsity, cdf_threshold, 1 / head_dim**0.5)
-
-    output = rearrange_3d_block_to_THW(output, Ntq, Nhq, Nwq, tq, hq, wq, output.shape[-1])
-    return output
diff --git a/fastvideo/third_party/longcat_video/block_sparse_attention/common.py b/fastvideo/third_party/longcat_video/block_sparse_attention/common.py
deleted file mode 100644
index 09502b67f..000000000
--- a/fastvideo/third_party/longcat_video/block_sparse_attention/common.py
+++ /dev/null
@@ -1,130 +0,0 @@
-import triton
-import triton.language as tl
-import os
-
-if os.environ.get('TRITON_AUTOTUNE_ENBALE', '0') == '1':
-    autotune = triton.autotune
-else:
-
-    def autotune(*args, **kwargs):
-
-        def decorator(func):
-            return func
-
-        return decorator
-
-
-configs_gating_preset = {
-    'default': {
-        'BLOCK_M': 64,
-        'BLOCK_N': 64,
-        'num_stages': 3,
-        'num_warps': 8,
-    }
-}
-
-configs_gating = [
-    triton.Config({'BLOCK_M': BM, 'BLOCK_N': BN}, num_stages=s, num_warps=w) \
-    for BM in [64, 128] \
-    for BN in [32, 64] \
-    for s in [2, 3, 4, 5] \
-    for w in [4, 8] \
-]
-
-gating_reevaluate_keys = ["M", "N"] if os.environ.get('TRITON_REEVALUATE_KEY', '0') == '1' else []
-
-
-@autotune(configs_gating, key=gating_reevaluate_keys)
-@triton.jit
-def _attn_fwd_gating(
-    Q,
-    K,
-    Out,
-    stride_qz,
-    stride_qh,
-    stride_qm,
-    stride_qk,
-    stride_kz,
-    stride_kh,
-    stride_kn,
-    stride_kk,
-    stride_oz,
-    stride_oh,
-    stride_om,
-    stride_on,
-    H,
-    M,
-    N,
-    HEAD_DIM: tl.constexpr,
-    BLOCK_M: tl.constexpr,
-    BLOCK_N: tl.constexpr,
-):
-
-    tl.static_assert(BLOCK_N <= HEAD_DIM)
-    start_m = tl.program_id(0)
-    off_hz = tl.program_id(1)
-    off_z = off_hz // H
-    off_h = off_hz % H
-    q_offset = off_z.to(tl.int64) * stride_qz + off_h.to(tl.int64) * stride_qh
-    k_offset = off_z.to(tl.int64) * stride_kz + off_h.to(tl.int64) * stride_kh
-    o_offset = off_z.to(tl.int64) * stride_oz + off_h.to(tl.int64) * stride_oh
-
-    # block pointers
-    Q_block_ptr = tl.make_block_ptr(
-        base=Q + q_offset,
-        shape=(M, HEAD_DIM),
-        strides=(stride_qm, stride_qk),
-        offsets=(start_m * BLOCK_M, 0),
-        block_shape=(BLOCK_M, HEAD_DIM),
-        order=(1, 0),
-    )
-
-    K_block_ptr = tl.make_block_ptr(
-        base=K + k_offset,
-        shape=(HEAD_DIM, N),
-        strides=(stride_kk, stride_kn),
-        offsets=(0, 0),
-        block_shape=(HEAD_DIM, BLOCK_N),
-        order=(0, 1),
-    )
-    O_block_ptr = tl.make_block_ptr(
-        base=Out + o_offset,
-        shape=(M, N),
-        strides=(stride_om, stride_on),
-        offsets=(start_m * BLOCK_M, 0),
-        block_shape=(BLOCK_M, BLOCK_N),
-        order=(1, 0),
-    )
-
-    # load q: it will stay in SRAM throughout
-    q = tl.load(Q_block_ptr, boundary_check=(0, ))
-    for start_n in range(0, N, BLOCK_N):
-        start_n = tl.multiple_of(start_n, BLOCK_N)
-        # -- compute qk ----
-        k = tl.load(K_block_ptr, boundary_check=(1, ))
-        qk = tl.dot(q, k)
-
-        tl.store(O_block_ptr, qk.to(Out.type.element_ty), boundary_check=(0, 1))
-
-        K_block_ptr = tl.advance(K_block_ptr, (0, BLOCK_N))
-        O_block_ptr = tl.advance(O_block_ptr, (0, BLOCK_N))
-
-
-@triton.jit
-def _attn_bwd_preprocess(
-        O,
-        DO,
-        Delta,  # output
-        N_CTX,
-        BLOCK_M: tl.constexpr,
-        HEAD_DIM: tl.constexpr):
-
-    off_m = tl.program_id(0) * BLOCK_M + tl.arange(0, BLOCK_M)
-    off_hz = tl.program_id(1)
-    off_n = tl.arange(0, HEAD_DIM)
-    # load
-    o = tl.load(O + off_hz * HEAD_DIM * N_CTX + off_m[:, None] * HEAD_DIM + off_n[None, :])
-    do = tl.load(DO + off_hz * HEAD_DIM * N_CTX + off_m[:, None] * HEAD_DIM + off_n[None, :]).to(tl.float32)
-    delta = tl.sum(o * do, axis=1)
-    # write-back
-    tl.store(Delta + off_hz * N_CTX + off_m, delta)
diff --git a/fastvideo/third_party/longcat_video/block_sparse_attention/communicate.py b/fastvideo/third_party/longcat_video/block_sparse_attention/communicate.py
deleted file mode 100644
index afbab5e1c..000000000
--- a/fastvideo/third_party/longcat_video/block_sparse_attention/communicate.py
+++ /dev/null
@@ -1,33 +0,0 @@
-import torch
-
-
-def p2p_communicate(rank, send_tensor, send_dst, recv_tensor, recv_src, cp_group, batch_p2p_comm):
-    """Point-to-point communications of KV and dKV in Attention with context parallelism"""
-    send_recv_ops = []
-
-    if batch_p2p_comm:  # int(os.getenv("NVTE_BATCH_MHA_P2P_COMM", "0")) or (cp_size == 2) 为啥呢
-        if rank % 2 == 0:
-            send_op = torch.distributed.P2POp(torch.distributed.isend, send_tensor, send_dst, cp_group)
-            recv_op = torch.distributed.P2POp(torch.distributed.irecv, recv_tensor, recv_src, cp_group)
-            send_recv_ops.append(send_op)
-            send_recv_ops.append(recv_op)
-        else:
-            recv_op = torch.distributed.P2POp(torch.distributed.irecv, recv_tensor, recv_src, cp_group)
-            send_op = torch.distributed.P2POp(torch.distributed.isend, send_tensor, send_dst, cp_group)
-            send_recv_ops.append(recv_op)
-            send_recv_ops.append(send_op)
-        send_recv_reqs = torch.distributed.batch_isend_irecv(send_recv_ops)
-    else:
-        if rank % 2 == 0:
-            send_op = torch.distributed.isend(send_tensor, send_dst, cp_group)
-            recv_op = torch.distributed.irecv(recv_tensor, recv_src, cp_group)
-            send_recv_ops.append(send_op)
-            send_recv_ops.append(recv_op)
-        else:
-            recv_op = torch.distributed.irecv(recv_tensor, recv_src, cp_group)
-            send_op = torch.distributed.isend(send_tensor, send_dst, cp_group)
-            send_recv_ops.append(recv_op)
-            send_recv_ops.append(send_op)
-        send_recv_reqs = send_recv_ops
-
-    return send_recv_reqs
diff --git a/fastvideo/third_party/longcat_video/block_sparse_attention/flash_attn_bsa_varlen_mask.py b/fastvideo/third_party/longcat_video/block_sparse_attention/flash_attn_bsa_varlen_mask.py
deleted file mode 100644
index ea52e096f..000000000
--- a/fastvideo/third_party/longcat_video/block_sparse_attention/flash_attn_bsa_varlen_mask.py
+++ /dev/null
@@ -1,1130 +0,0 @@
-import triton
-import triton.language as tl
-import os
-
-from .common import autotune
-"""
-TRITON_REEVALUATE_KEY=1
-- autotune whenever params in reevaluate keys change 
-- use in benchmark script to fine the best config
-
-TRITON_AUTOTUNE_ENBALE=1
-- if set to 0, autotune will not work, and the related params must be passed to the function call.
-"""
-
-configs_fwd_bsa_varlen_preset = {
-    'default': {
-        'BLOCK_N': 64,
-        'num_stages': 3,
-        'num_warps': 8,
-    },
-    'BLOCK_N_LG=64': {
-        'BLOCK_N': 64,
-        'num_stages': 3,
-        'num_warps': 4,
-    },
-}
-configs_fwd_bsa_varlen = [
-    triton.Config({'BLOCK_N': BN}, num_stages=s, num_warps=w) \
-    for BN in [32, 64, 128] \
-    for s in [2, 3, 4, 5] \
-    for w in [4, 8] \
-]
-
-fwd_bsa_reevaluate_varlen_keys = ['N_CTX', 'BLOCK_M', 'BLOCK_N_LG', 'SPARSITY'] if os.environ.get(
-    'TRITON_REEVALUATE_KEY', '0') == '1' else []
-
-
-@autotune(list(configs_fwd_bsa_varlen), key=fwd_bsa_reevaluate_varlen_keys)
-@triton.jit
-def _attn_fwd_bsa_varlen(
-        Q,
-        K,
-        V,
-        sm_scale,
-        M,
-        Out,
-        block_indices,  # [B, H, M_COMPRESS, S_MAX]
-        block_indices_lens,  # [B, H, M_COMPRESS]
-        stride_qz,
-        stride_qh,
-        stride_qm,
-        stride_qk,
-        stride_kz,
-        stride_kh,
-        stride_kn,
-        stride_kk,
-        stride_vz,
-        stride_vh,
-        stride_vn,
-        stride_vk,
-        stride_oz,
-        stride_oh,
-        stride_om,
-        stride_ok,
-        stride_bz,
-        stride_bh,
-        stride_bm,
-        stride_bs,
-        stride_lz,
-        stride_lh,
-        stride_lm,
-        H,
-        N_CTX,
-        HEAD_DIM: tl.constexpr,
-        BLOCK_M: tl.constexpr,
-        BLOCK_N_LG: tl.constexpr,
-        BLOCK_N: tl.constexpr,
-        SPARSITY: tl.constexpr,  # not used; just for trigger reevaluate for benchmarking
-):
-
-    start_m = tl.program_id(0)
-    off_hz = tl.program_id(1)
-    off_z = off_hz // H
-    off_h = off_hz % H
-
-    q_offset = off_z.to(tl.int64) * stride_qz + off_h.to(tl.int64) * stride_qh
-    k_offset = off_z.to(tl.int64) * stride_kz + off_h.to(tl.int64) * stride_kh
-    v_offset = off_z.to(tl.int64) * stride_vz + off_h.to(tl.int64) * stride_vh
-    o_offset = off_z.to(tl.int64) * stride_oz + off_h.to(tl.int64) * stride_oh
-    b_offset = off_z.to(tl.int64) * stride_bz + off_h.to(tl.int64) * stride_bh
-    l_offset = off_z.to(tl.int64) * stride_lz + off_h.to(tl.int64) * stride_lh
-
-    # block pointers
-    Q_block_ptr = tl.make_block_ptr(
-        base=Q + q_offset,
-        shape=(N_CTX, HEAD_DIM),
-        strides=(stride_qm, stride_qk),
-        offsets=(start_m * BLOCK_M, 0),
-        block_shape=(BLOCK_M, HEAD_DIM),
-        order=(1, 0),
-    )
-    V_block_ptr = tl.make_block_ptr(
-        base=V + v_offset,
-        shape=(N_CTX, HEAD_DIM),
-        strides=(stride_vn, stride_vk),
-        offsets=(0, 0),
-        block_shape=(BLOCK_N, HEAD_DIM),
-        order=(1, 0),
-    )
-    KT_block_ptr = tl.make_block_ptr(
-        base=K + k_offset,
-        shape=(HEAD_DIM, N_CTX),
-        strides=(stride_kk, stride_kn),
-        offsets=(0, 0),
-        block_shape=(HEAD_DIM, BLOCK_N),
-        order=(0, 1),
-    )
-    O_block_ptr = tl.make_block_ptr(
-        base=Out + o_offset,
-        shape=(N_CTX, HEAD_DIM),
-        strides=(stride_om, stride_ok),
-        offsets=(start_m * BLOCK_M, 0),
-        block_shape=(BLOCK_M, HEAD_DIM),
-        order=(1, 0),
-    )
-    block_indices += b_offset + start_m * stride_bm
-    block_indices_lens += l_offset + start_m * stride_lm
-    # initialize offsets
-    offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M)
-
-    # initialize pointer to m and l
-    m_i = tl.zeros([BLOCK_M], dtype=tl.float32) - float("inf")
-    l_i = tl.zeros([BLOCK_M], dtype=tl.float32) + 1.0
-    acc = tl.zeros([BLOCK_M, HEAD_DIM], dtype=tl.float32)
-    # load scales
-    qk_scale = sm_scale
-    qk_scale *= 1.44269504  # 1/ln2; exp2(x/ln2) == exp2(ln(e^x) / ln2) == exp2(log2(e^x)) == exp(x)
-    # load q: it will stay in SRAM throughout
-    q = tl.load(Q_block_ptr)
-    S = tl.load(block_indices_lens)
-    for i in range(S):
-        block_id = tl.load(block_indices + i * stride_bs).to(tl.int32)
-        lo, hi = block_id * BLOCK_N_LG, (block_id + 1) * BLOCK_N_LG
-        lo = tl.multiple_of(lo, BLOCK_N)
-        KT_block_ptr_i = tl.advance(KT_block_ptr, (0, lo))
-        V_block_ptr_i = tl.advance(V_block_ptr, (lo, 0))
-
-        # loop over k, v and update accumulator
-        for start_n in range(lo, hi, BLOCK_N):
-            start_n = tl.multiple_of(start_n, BLOCK_N)
-            # -- compute qk ----
-            kT = tl.load(KT_block_ptr_i)
-            qkT = tl.dot(q, kT)
-
-            m_ij = tl.maximum(m_i, tl.max(qkT, 1) * qk_scale)
-            qkT = qkT * qk_scale - m_ij[:, None]
-            p = tl.math.exp2(qkT)
-
-            # -- update m_i and l_i
-            alpha = tl.math.exp2(m_i - m_ij)
-            l_ij = tl.sum(p, 1)
-            # -- update output accumulator --
-            acc = acc * alpha[:, None]
-            # update acc
-            v = tl.load(V_block_ptr_i)
-            acc = tl.dot(p.to(v.dtype), v, acc)
-            # update m_i and l_i
-            # place this at the end of the loop to reduce register pressure: https://github.com/triton-lang/triton/commit/ee6abd9
-            l_i = l_i * alpha + l_ij
-            m_i = m_ij
-            V_block_ptr_i = tl.advance(V_block_ptr_i, (BLOCK_N, 0))
-            KT_block_ptr_i = tl.advance(KT_block_ptr_i, (0, BLOCK_N))
-
-    # epilogue
-    m_i += tl.math.log2(l_i)
-    acc = acc / l_i[:, None]
-    m_ptrs = M + off_hz * N_CTX + offs_m
-    tl.store(m_ptrs, m_i)
-    tl.store(O_block_ptr, acc.to(Out.type.element_ty))
-
-
-configs_fwd_bsa_varlen_align_preset = {
-    'default': {
-        'num_stages': 3,
-        'num_warps': 8,
-    },
-    'BLOCK_N_LG=64': {
-        'num_stages': 3,
-        'num_warps': 4,
-    },
-}
-configs_fwd_bsa_varlen_align = [
-    triton.Config({}, num_stages=s, num_warps=w) \
-    for s in [2, 3, 4, 5] \
-    for w in [4, 8] \
-]
-
-fwd_bsa_reevaluate_varlen_align_keys = ['N_CTX', 'BLOCK_M', 'BLOCK_N_LG', 'SPARSITY'] if os.environ.get(
-    'TRITON_REEVALUATE_KEY', '0') == '1' else []
-
-
-@autotune(list(configs_fwd_bsa_varlen_align), key=fwd_bsa_reevaluate_varlen_align_keys)
-@triton.jit
-def _attn_fwd_bsa_varlen_align(
-        Q,
-        K,
-        V,
-        sm_scale,
-        M,
-        Out,
-        block_indices,  # [B, H, M_COMPRESS, S_MAX]
-        block_indices_lens,  # [B, H, M_COMPRESS]
-        stride_qz,
-        stride_qh,
-        stride_qm,
-        stride_qk,
-        stride_kz,
-        stride_kh,
-        stride_kn,
-        stride_kk,
-        stride_vz,
-        stride_vh,
-        stride_vn,
-        stride_vk,
-        stride_oz,
-        stride_oh,
-        stride_om,
-        stride_on,
-        stride_bz,
-        stride_bh,
-        stride_bm,
-        stride_bs,
-        stride_lz,
-        stride_lh,
-        stride_lm,
-        H,
-        N_CTX,
-        HEAD_DIM: tl.constexpr,
-        BLOCK_M: tl.constexpr,
-        BLOCK_N_LG: tl.constexpr,
-        SPARSITY: tl.constexpr,  # not used; just for trigger reevaluate for benchmarking
-):
-
-    start_m = tl.program_id(0)
-    off_hz = tl.program_id(1)
-    off_z = off_hz // H
-    off_h = off_hz % H
-
-    q_offset = off_z.to(tl.int64) * stride_qz + off_h.to(tl.int64) * stride_qh
-    k_offset = off_z.to(tl.int64) * stride_kz + off_h.to(tl.int64) * stride_kh
-    v_offset = off_z.to(tl.int64) * stride_vz + off_h.to(tl.int64) * stride_vh
-    o_offset = off_z.to(tl.int64) * stride_oz + off_h.to(tl.int64) * stride_oh
-    b_offset = off_z.to(tl.int64) * stride_bz + off_h.to(tl.int64) * stride_bh
-    l_offset = off_z.to(tl.int64) * stride_lz + off_h.to(tl.int64) * stride_lh
-
-    # block pointers
-    Q_block_ptr = tl.make_block_ptr(
-        base=Q + q_offset,
-        shape=(N_CTX, HEAD_DIM),
-        strides=(stride_qm, stride_qk),
-        offsets=(start_m * BLOCK_M, 0),
-        block_shape=(BLOCK_M, HEAD_DIM),
-        order=(1, 0),
-    )
-    V_block_ptr = tl.make_block_ptr(
-        base=V + v_offset,
-        shape=(N_CTX, HEAD_DIM),
-        strides=(stride_vn, stride_vk),
-        offsets=(0, 0),
-        block_shape=(BLOCK_N_LG, HEAD_DIM),
-        order=(1, 0),
-    )
-    KT_block_ptr = tl.make_block_ptr(
-        base=K + k_offset,
-        shape=(HEAD_DIM, N_CTX),
-        strides=(stride_kk, stride_kn),
-        offsets=(0, 0),
-        block_shape=(HEAD_DIM, BLOCK_N_LG),
-        order=(0, 1),
-    )
-    O_block_ptr = tl.make_block_ptr(
-        base=Out + o_offset,
-        shape=(N_CTX, HEAD_DIM),
-        strides=(stride_om, stride_on),
-        offsets=(start_m * BLOCK_M, 0),
-        block_shape=(BLOCK_M, HEAD_DIM),
-        order=(1, 0),
-    )
-    block_indices += b_offset + start_m * stride_bm
-    block_indices_lens += l_offset + start_m * stride_lm
-    # initialize offsets
-    offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M)
-
-    # initialize pointer to m and l
-    m_i = tl.zeros([BLOCK_M], dtype=tl.float32) - float("inf")
-    l_i = tl.zeros([BLOCK_M], dtype=tl.float32) + 1.0
-    acc = tl.zeros([BLOCK_M, HEAD_DIM], dtype=tl.float32)
-    # load scales
-    qk_scale = sm_scale
-    qk_scale *= 1.44269504  # 1/ln2; exp2(x/ln2) == exp2(ln(e^x) / ln2) == exp2(log2(e^x)) == exp(x)；乘1/ln2后，exp2(x/ln2) == exp(x)，exp2速度更快
-    # load q: it will stay in SRAM throughout
-    q = tl.load(Q_block_ptr)
-    S = tl.load(block_indices_lens)
-    for i in range(S):
-        block_id = tl.load(block_indices + i * stride_bs).to(tl.int32)
-        lo = block_id * BLOCK_N_LG
-        lo = tl.multiple_of(lo, BLOCK_N_LG)
-        KT_block_ptr_i = tl.advance(KT_block_ptr, (0, lo))
-        V_block_ptr_i = tl.advance(V_block_ptr, (lo, 0))
-
-        # -- compute qk ----
-        kT = tl.load(KT_block_ptr_i)
-        qkT = tl.dot(q, kT)
-
-        m_ij = tl.maximum(m_i, tl.max(qkT, 1) * qk_scale)
-        qkT = qkT * qk_scale - m_ij[:, None]
-        p = tl.math.exp2(qkT)
-
-        # -- update m_i and l_i
-        alpha = tl.math.exp2(m_i - m_ij)
-        l_ij = tl.sum(p, 1)
-        # -- update output accumulator --
-        acc = acc * alpha[:, None]
-        # update acc
-        v = tl.load(V_block_ptr_i)
-        acc = tl.dot(p.to(v.dtype), v, acc)  # 没除se，fa2引入的优化
-        # update m_i and l_i
-        # place this at the end of the loop to reduce register pressure: https://github.com/triton-lang/triton/commit/ee6abd9
-        l_i = l_i * alpha + l_ij  # 当前总se
-        m_i = m_ij
-
-    # epilogue
-    m_i += tl.math.log2(l_i)
-    acc = acc / l_i[:, None]
-    m_ptrs = M + off_hz * N_CTX + offs_m
-    tl.store(m_ptrs, m_i)
-    tl.store(O_block_ptr, acc.to(Out.type.element_ty))
-
-
-# The main inner-loop logic for computing dK and dV.
-@triton.jit
-def _attn_bwd_dkdv_bsa_varlen(
-    dk,
-    dv,
-    k,
-    v,
-    Q,
-    DO,
-    M,
-    D,
-    block_indices,
-    block_indices_lens,
-    # shared by Q/K/V/DO.
-    # stride_tok, stride_d,
-    stride_qm,
-    stride_qk,
-    stride_dom,
-    stride_dok,
-    stride_mm,
-    stride_dm,
-    stride_bm,
-    N_CTX,
-    BLOCK_M1: tl.constexpr,
-    HEAD_DIM: tl.constexpr,
-):
-
-    QT_block_ptr = tl.make_block_ptr(
-        base=Q,
-        shape=(HEAD_DIM, N_CTX),
-        strides=(stride_qk, stride_qm),
-        offsets=(0, 0),
-        block_shape=(HEAD_DIM, BLOCK_M1),
-        order=(0, 1),
-    )
-
-    DO_block_ptr = tl.make_block_ptr(
-        base=DO,
-        shape=(N_CTX, HEAD_DIM),
-        strides=(stride_dom, stride_dok),
-        offsets=(0, 0),
-        block_shape=(BLOCK_M1, HEAD_DIM),
-        order=(1, 0),
-    )
-
-    S = tl.load(block_indices_lens)
-    for i in range(S):
-        block_id = tl.load(block_indices + i * stride_bm).to(tl.int32)
-        start_m = block_id * BLOCK_M1
-        start_m = tl.multiple_of(start_m, BLOCK_M1)
-
-        QT_block_ptr_i = tl.advance(QT_block_ptr, (0, start_m))
-        DO_block_ptr_i = tl.advance(DO_block_ptr, (start_m, 0))
-
-        qT = tl.load(QT_block_ptr_i)
-        # Load m before computing qk to reduce pipeline stall.
-        offs_m = start_m + tl.arange(0, BLOCK_M1) * stride_mm
-        m = tl.load(M + offs_m)
-        kqT = tl.dot(k, qT)
-        pT = tl.math.exp2(kqT - m[None, :])
-
-        do = tl.load(DO_block_ptr_i)
-        # Compute dV.
-        ppT = pT
-        ppT = ppT.to(v.dtype)
-        dv += tl.dot(ppT, do)
-        # D (= delta) is pre-divided by ds_scale.
-        offs_d = start_m + tl.arange(0, BLOCK_M1) * stride_dm
-        Di = tl.load(D + offs_d)
-        # Compute dP and dS.
-        dpT = tl.dot(v, tl.trans(do)).to(tl.float32)
-        dsT = pT * (dpT - Di[None, :])
-        dsT = dsT.to(v.dtype)
-        dk += tl.dot(dsT, tl.trans(qT))
-
-    return dk, dv
-
-
-# the main inner-loop logic for computing dQ
-@triton.jit
-def _attn_bwd_dq_bsa_varlen(
-    dq,
-    q,
-    do,
-    m,
-    d,
-    K,
-    V,
-    N_CTX,
-    BLOCK_N2: tl.constexpr,
-    BLOCK_N_LG: tl.constexpr,
-    HEAD_DIM: tl.constexpr,
-    block_indices,
-    block_indices_lens,
-    stride_bn,
-    # stride_tok, stride_d,
-    stride_kn,
-    stride_kk,
-    stride_vn,
-    stride_vk,
-):
-
-    VT_block_ptr = tl.make_block_ptr(
-        base=V,
-        shape=(HEAD_DIM, N_CTX),
-        strides=(stride_vk, stride_vn),
-        offsets=(0, 0),
-        block_shape=(HEAD_DIM, BLOCK_N2),
-        order=(0, 1),
-    )
-    KT_block_ptr = tl.make_block_ptr(
-        base=K,
-        shape=(HEAD_DIM, N_CTX),
-        strides=(stride_kk, stride_kn),
-        offsets=(0, 0),
-        block_shape=(HEAD_DIM, BLOCK_N2),
-        order=(0, 1),
-    )
-
-    S = tl.load(block_indices_lens)
-    for i in range(S):
-        block_id = tl.load(block_indices + i * stride_bn).to(tl.int32)
-        lo, hi = block_id * BLOCK_N_LG, (block_id + 1) * BLOCK_N_LG
-        lo = tl.multiple_of(lo, BLOCK_N2)
-
-        KT_block_ptr_i = tl.advance(KT_block_ptr, (0, lo))
-        VT_block_ptr_i = tl.advance(VT_block_ptr, (0, lo))
-
-        for start_n in range(lo, hi, BLOCK_N2):
-            start_n = tl.multiple_of(start_n, BLOCK_N2)
-
-            kT = tl.load(KT_block_ptr_i)
-            vT = tl.load(VT_block_ptr_i)
-            qkT = tl.dot(q, kT)
-            p = tl.math.exp2(qkT - m)
-            # Compute dP and dS.
-            dp = tl.dot(do, vT).to(tl.float32)
-            ds = p * (dp - d)
-            ds = ds.to(
-                kT.dtype
-            )  # https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/flash_attn_triton.py: Converting ds to q.dtype here reduces register pressure and makes it much faster for BLOCK_HEADDIM=128
-            # Compute dQ.
-            # NOTE: We need to de-scale dq in the end, because kT was pre-scaled.
-            dq += tl.dot(ds, tl.trans(kT))
-            # Increment pointers.
-            KT_block_ptr_i = tl.advance(KT_block_ptr_i, (0, BLOCK_N2))
-            VT_block_ptr_i = tl.advance(VT_block_ptr_i, (0, BLOCK_N2))
-
-    return dq
-
-
-# the main inner-loop logic for computing dQ
-@triton.jit
-def _attn_bwd_dq_bsa_varlen_align(
-    dq,
-    q,
-    do,
-    m,
-    d,
-    K,
-    V,
-    N_CTX,
-    BLOCK_N_LG: tl.constexpr,
-    HEAD_DIM: tl.constexpr,
-    block_indices,
-    block_indices_lens,
-    stride_bn,
-    stride_kn,
-    stride_kk,
-    stride_vn,
-    stride_vk,
-):
-
-    VT_block_ptr = tl.make_block_ptr(
-        base=V,
-        shape=(HEAD_DIM, N_CTX),
-        strides=(stride_vk, stride_vn),
-        offsets=(0, 0),
-        block_shape=(HEAD_DIM, BLOCK_N_LG),
-        order=(0, 1),
-    )
-    KT_block_ptr = tl.make_block_ptr(
-        base=K,
-        shape=(HEAD_DIM, N_CTX),
-        strides=(stride_kk, stride_kn),
-        offsets=(0, 0),
-        block_shape=(HEAD_DIM, BLOCK_N_LG),
-        order=(0, 1),
-    )
-
-    S = tl.load(block_indices_lens)
-    for i in range(S):
-        block_id = tl.load(block_indices + i * stride_bn).to(tl.int32)
-        lo = block_id * BLOCK_N_LG
-        lo = tl.multiple_of(lo, BLOCK_N_LG)
-
-        KT_block_ptr_i = tl.advance(KT_block_ptr, (0, lo))
-        VT_block_ptr_i = tl.advance(VT_block_ptr, (0, lo))
-
-        kT = tl.load(KT_block_ptr_i)
-        vT = tl.load(VT_block_ptr_i)
-        qkT = tl.dot(q, kT)
-        p = tl.math.exp2(qkT - m)
-        # Compute dP and dS.
-        dp = tl.dot(do, vT).to(tl.float32)
-        ds = p * (dp - d)
-        ds = ds.to(
-            kT.dtype
-        )  # https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/flash_attn_triton.py: Converting ds to q.dtype here reduces register pressure and makes it much faster for BLOCK_HEADDIM=128
-        # Compute dQ.
-        # NOTE: We need to de-scale dq in the end, because kT was pre-scaled.
-        dq += tl.dot(ds, tl.trans(kT))
-
-    return dq
-
-
-configs_bwd_dkdv_bsa_varlen_preset = {
-    'default': {
-        'BLOCK_N': 128,
-        'num_stages': 2,
-        'num_warps': 8,
-    },
-    'BLOCK_N_DQ_LG=64': {
-        'BLOCK_N': 64,
-        'num_stages': 2,
-        'num_warps': 4,
-    }
-}
-configs_bwd_dkdv_bsa_varlen = [
-    triton.Config({'BLOCK_N': BN}, num_stages=s, num_warps=w) \
-    for BN in [32, 64, 128] \
-    for s in [2, 3, 4, 5] \
-    for w in [4, 8] \
-]
-bwd_dkdv_bsa_varlen_reevaluate_keys = ['N_CTX', 'BLOCK_M', 'BLOCK_N_DQ_LG', 'SPARSITY'] if os.environ.get(
-    'TRITON_REEVALUATE_KEY', '0') == '1' else []
-
-
-@autotune(list(configs_bwd_dkdv_bsa_varlen), key=bwd_dkdv_bsa_varlen_reevaluate_keys)
-@triton.jit
-def _attn_bwd_dkdv_bsa_varlen_wrapper(
-        Q,
-        K,
-        V,
-        sm_scale,  # softmax scale
-        DO,
-        DK,
-        DV,
-        M,  # lse (log2)
-        D,
-        block_indices,
-        block_indices_lens,
-        # stride_z, stride_h, stride_tok, stride_d, # shared by Q/K/V/DO.
-        # qkv
-        stride_qz,
-        stride_qh,
-        stride_qm,
-        stride_qk,
-        stride_kz,
-        stride_kh,
-        stride_kn,
-        stride_kk,
-        stride_vz,
-        stride_vh,
-        stride_vn,
-        stride_vk,
-        # dk dv do
-        stride_dkz,
-        stride_dkh,
-        stride_dkn,
-        stride_dkk,
-        stride_dvz,
-        stride_dvh,
-        stride_dvn,
-        stride_dvk,
-        stride_doz,
-        stride_doh,
-        stride_dom,
-        stride_dok,
-        # m, d
-        stride_mz,
-        stride_mh,
-        stride_mm,
-        stride_dz,
-        stride_dh,
-        stride_dm,
-        #
-        stride_bz,
-        stride_bh,
-        stride_bn,
-        stride_bm,  # block_indices
-        stride_lz,
-        stride_lh,
-        stride_ln,  # block_indices_lens
-        #
-    H,
-        N_CTX,
-        BLOCK_M: tl.constexpr,
-        BLOCK_N: tl.constexpr,
-        BLOCK_N_DQ_LG: tl.constexpr,  # logical block size
-        HEAD_DIM: tl.constexpr,
-        SPARSITY: tl.constexpr,  # not used; just for trigger reevaluate for benchmarking
-):
-
-    tl.static_assert(BLOCK_N_DQ_LG % BLOCK_N == 0)
-    start_n = tl.program_id(0)
-
-    off_hz = tl.program_id(2)
-    off_z = off_hz // H
-    off_h = off_hz % H
-
-    off_q = off_z.to(tl.int64) * stride_qz + off_h.to(tl.int64) * stride_qh
-    off_k = off_z.to(tl.int64) * stride_kz + off_h.to(tl.int64) * stride_kh
-    off_v = off_z.to(tl.int64) * stride_vz + off_h.to(tl.int64) * stride_vh
-
-    off_dk = off_z.to(tl.int64) * stride_dkz + off_h.to(tl.int64) * stride_dkh
-    off_dv = off_z.to(tl.int64) * stride_dvz + off_h.to(tl.int64) * stride_dvh
-    off_do = off_z.to(tl.int64) * stride_doz + off_h.to(tl.int64) * stride_doh
-
-    off_m = off_z.to(tl.int64) * stride_mz + off_h.to(tl.int64) * stride_mh
-    off_d = off_z.to(tl.int64) * stride_dz + off_h.to(tl.int64) * stride_dh
-
-    off_block_incides = off_z.to(tl.int64) * stride_bz + off_h.to(tl.int64) * stride_bh
-    off_block_incides_lens = off_z.to(tl.int64) * stride_lz + off_h.to(tl.int64) * stride_lh
-
-    # offset pointers for batch/head
-    Q += off_q
-    K += off_k
-    V += off_v
-    DO += off_do
-    DK += off_dk
-    DV += off_dv
-
-    M += off_m
-    D += off_d
-    block_indices += off_block_incides
-    block_indices_lens += off_block_incides_lens
-
-    # ---------------------------------------- [DKDV] ----------------------------------------
-
-    dv = tl.zeros([BLOCK_N, HEAD_DIM], dtype=tl.float32)
-    dk = tl.zeros([BLOCK_N, HEAD_DIM], dtype=tl.float32)
-
-    # load K and V: they stay in SRAM throughout the inner loop.
-
-    K_block_ptr = tl.make_block_ptr(
-        base=K,
-        shape=(N_CTX, HEAD_DIM),
-        strides=(stride_kn, stride_kk),
-        offsets=(start_n * BLOCK_N, 0),
-        block_shape=(BLOCK_N, HEAD_DIM),
-        order=(1, 0),
-    )
-    V_block_ptr = tl.make_block_ptr(
-        base=V,
-        shape=(N_CTX, HEAD_DIM),
-        strides=(stride_vn, stride_vk),
-        offsets=(start_n * BLOCK_N, 0),
-        block_shape=(BLOCK_N, HEAD_DIM),
-        order=(1, 0),
-    )
-    DK_block_ptr = tl.make_block_ptr(
-        base=DK,
-        shape=(N_CTX, HEAD_DIM),
-        strides=(stride_dkn, stride_dkk),
-        offsets=(start_n * BLOCK_N, 0),
-        block_shape=(BLOCK_N, HEAD_DIM),
-        order=(1, 0),
-    )
-    DV_block_ptr = tl.make_block_ptr(
-        base=DV,
-        shape=(N_CTX, HEAD_DIM),
-        strides=(stride_dvn, stride_dvk),
-        offsets=(start_n * BLOCK_N, 0),
-        block_shape=(BLOCK_N, HEAD_DIM),
-        order=(1, 0),
-    )
-
-    k = tl.load(K_block_ptr)
-    v = tl.load(V_block_ptr)
-
-    k_compress_idx = start_n * BLOCK_N // BLOCK_N_DQ_LG
-    block_indices_i = block_indices + k_compress_idx * stride_bn
-    block_indices_lens_i = block_indices_lens + k_compress_idx * stride_ln
-
-    dk, dv = _attn_bwd_dkdv_bsa_varlen(
-        dk,
-        dv,
-        k,
-        v,
-        Q,
-        DO,
-        M,
-        D,
-        block_indices_i,
-        block_indices_lens_i,
-        # shared by Q/K/V/DO.
-        stride_qm,
-        stride_qk,
-        stride_dom,
-        stride_dok,
-        stride_mm,
-        stride_dm,
-        #
-        stride_bm,
-        N_CTX,
-        BLOCK_M,
-        HEAD_DIM,
-    )
-
-    # Write back dk
-    dk *= sm_scale  # S = scale * QKT; dK = scale * QdST
-    tl.store(DK_block_ptr, dk.to(k.dtype))
-
-    # Write back dv
-    tl.store(DV_block_ptr, dv.to(v.dtype))
-
-
-configs_bwd_dq_bsa_varlen_preset = {
-    'default': {
-        'BLOCK_N_DQ': 64,
-        'num_stages': 2,
-        'num_warps': 8,
-    },
-    'BLOCK_N_DQ_LG=64': {
-        'BLOCK_N_DQ': 64,
-        'num_stages': 2,
-        'num_warps': 4,
-    },
-}
-configs_bwd_dq_bsa_varlen = [
-    triton.Config({'BLOCK_N_DQ': BN}, num_stages=s, num_warps=w) \
-    for BN in [32, 64, 128] \
-    for s in [2, 3, 4, 5] \
-    for w in [4, 8] \
-]
-bwd_dq_bsa_varlen_reevaluate_keys = ['N_CTX', 'BLOCK_M', 'BLOCK_N_DQ_LG', 'SPARSITY'] if os.environ.get(
-    'TRITON_REEVALUATE_KEY', '0') == '1' else []
-
-
-@autotune(list(configs_bwd_dq_bsa_varlen), key=bwd_dq_bsa_varlen_reevaluate_keys)
-@triton.jit
-def _attn_bwd_dq_bsa_varlen_wrapper(
-        Q,
-        K,
-        V,  # softmax scale
-        DO,
-        DQ,
-        M,  # lse (log2)
-        D,
-        block_indices,
-        block_indices_lens,
-        # stride_z, stride_h, stride_tok, stride_d, # shared by Q/K/V/DO.
-        # qkv
-        stride_qz,
-        stride_qh,
-        stride_qm,
-        stride_qk,
-        stride_kz,
-        stride_kh,
-        stride_kn,
-        stride_kk,
-        stride_vz,
-        stride_vh,
-        stride_vn,
-        stride_vk,
-        # dq do
-        stride_dqz,
-        stride_dqh,
-        stride_dqm,
-        stride_dqk,
-        stride_doz,
-        stride_doh,
-        stride_dom,
-        stride_dok,
-        # m, d
-        stride_mz,
-        stride_mh,
-        stride_mm,
-        stride_dz,
-        stride_dh,
-        stride_dm,
-        #
-        stride_bz,
-        stride_bh,
-        stride_bm,
-        stride_bn,  # block_indices
-        stride_lz,
-        stride_lh,
-        stride_lm,  # block_indices_lens
-        #
-    H,
-        N_CTX,
-        BLOCK_M: tl.constexpr,
-        BLOCK_N_DQ_LG: tl.constexpr,  # logical block size
-        BLOCK_N_DQ: tl.constexpr,
-        HEAD_DIM: tl.constexpr,
-        SPARSITY: tl.constexpr,  # not used; just for trigger reevaluate for benchmarking
-):
-
-    tl.static_assert(BLOCK_N_DQ_LG % BLOCK_N_DQ == 0)
-    tl.static_assert(BLOCK_N_DQ_LG % BLOCK_M == 0)
-
-    LN2: tl.constexpr = 0.6931471824645996  # = ln(2)
-
-    start_m = tl.program_id(0)
-
-    off_hz = tl.program_id(2)
-    off_z = off_hz // H
-    off_h = off_hz % H
-
-    off_q = off_z.to(tl.int64) * stride_qz + off_h.to(tl.int64) * stride_qh
-    off_k = off_z.to(tl.int64) * stride_kz + off_h.to(tl.int64) * stride_kh
-    off_v = off_z.to(tl.int64) * stride_vz + off_h.to(tl.int64) * stride_vh
-
-    off_dq = off_z.to(tl.int64) * stride_dqz + off_h.to(tl.int64) * stride_dqh
-    off_do = off_z.to(tl.int64) * stride_doz + off_h.to(tl.int64) * stride_doh
-
-    off_m = off_z.to(tl.int64) * stride_mz + off_h.to(tl.int64) * stride_mh
-    off_d = off_z.to(tl.int64) * stride_dz + off_h.to(tl.int64) * stride_dh
-
-    off_block_incides = off_z.to(tl.int64) * stride_bz + off_h.to(tl.int64) * stride_bh
-    off_block_incides_lens = off_z.to(tl.int64) * stride_lz + off_h.to(tl.int64) * stride_lh
-
-    # offset pointers for batch/head
-    Q += off_q
-    K += off_k
-    V += off_v
-    DO += off_do
-    DQ += off_dq
-
-    M += off_m
-    D += off_d
-    block_indices += off_block_incides
-    block_indices_lens += off_block_incides_lens
-
-    # ---------------------------------------- [DQ] ----------------------------------------
-
-    Q_block_ptr = tl.make_block_ptr(
-        base=Q,
-        shape=(N_CTX, HEAD_DIM),
-        strides=(stride_qm, stride_qk),
-        offsets=(start_m * BLOCK_M, 0),
-        block_shape=(BLOCK_M, HEAD_DIM),
-        order=(1, 0),
-    )
-    DO_block_ptr = tl.make_block_ptr(
-        base=DO,
-        shape=(N_CTX, HEAD_DIM),
-        strides=(stride_dom, stride_dok),
-        offsets=(start_m * BLOCK_M, 0),
-        block_shape=(BLOCK_M, HEAD_DIM),
-        order=(1, 0),
-    )
-    DQ_block_ptr = tl.make_block_ptr(
-        base=DQ,
-        shape=(N_CTX, HEAD_DIM),
-        strides=(stride_dqm, stride_dqk),
-        offsets=(start_m * BLOCK_M, 0),
-        block_shape=(BLOCK_M, HEAD_DIM),
-        order=(1, 0),
-    )
-
-    q = tl.load(Q_block_ptr)
-    do = tl.load(DO_block_ptr)
-
-    dq = tl.zeros([BLOCK_M, HEAD_DIM], dtype=tl.float32)
-
-    start_m = start_m * BLOCK_M
-
-    offs_m = start_m + tl.arange(0, BLOCK_M) * stride_mm
-    offs_d = start_m + tl.arange(0, BLOCK_M) * stride_dm
-
-    m = tl.load(M + offs_m)
-    m = m[:, None]
-
-    d = tl.load(D + offs_d)
-    d = d[:, None]
-
-    block_indices_m = block_indices + (start_m // BLOCK_M) * stride_bm
-    block_indices_lens_m = block_indices_lens + (start_m // BLOCK_M) * stride_lm
-
-    dq = _attn_bwd_dq_bsa_varlen(
-        dq,
-        q,
-        do,
-        m,
-        d,
-        K,
-        V,
-        N_CTX,
-        BLOCK_N_DQ,
-        BLOCK_N_DQ_LG,
-        HEAD_DIM,
-        block_indices_m,
-        block_indices_lens_m,
-        stride_bn,
-        stride_kn,
-        stride_kk,
-        stride_vn,
-        stride_vk,
-    )
-
-    # Write back dQ.
-    dq *= LN2
-    tl.store(DQ_block_ptr, dq.to(q.dtype))
-
-
-configs_bwd_dq_bsa_varlen_align_preset = {
-    'default': {
-        'num_stages': 2,
-        'num_warps': 8,
-    },
-    'BLOCK_N_DQ_LG=64': {
-        'num_stages': 2,
-        'num_warps': 4,
-    },
-}
-configs_bwd_dq_bsa_varlen_align = [
-    triton.Config({}, num_stages=s, num_warps=w) \
-    for s in [2, 3, 4, 5] \
-    for w in [4, 8] \
-]
-bwd_dq_bsa_varlen_align_reevaluate_keys = ['N_CTX', 'BLOCK_M', 'BLOCK_N_DQ_LG', 'SPARSITY'] if os.environ.get(
-    'TRITON_REEVALUATE_KEY', '0') == '1' else []
-
-
-@autotune(list(configs_bwd_dq_bsa_varlen_align), key=bwd_dq_bsa_varlen_align_reevaluate_keys)
-@triton.jit
-def _attn_bwd_dq_bsa_varlen_align_wrapper(
-        Q,
-        K,
-        V,  # softmax scale
-        DO,
-        DQ,
-        M,  # lse (log2)
-        D,
-        block_indices,
-        block_indices_lens,
-        # qkv
-        stride_qz,
-        stride_qh,
-        stride_qm,
-        stride_qk,
-        stride_kz,
-        stride_kh,
-        stride_kn,
-        stride_kk,
-        stride_vz,
-        stride_vh,
-        stride_vn,
-        stride_vk,
-        # dq do
-        stride_dqz,
-        stride_dqh,
-        stride_dqm,
-        stride_dqk,
-        stride_doz,
-        stride_doh,
-        stride_dom,
-        stride_dok,
-        # m, d
-        stride_mz,
-        stride_mh,
-        stride_mm,
-        stride_dz,
-        stride_dh,
-        stride_dm,
-        #
-        stride_bz,
-        stride_bh,
-        stride_bm,
-        stride_bn,  # block_indices
-        stride_lz,
-        stride_lh,
-        stride_lm,  # block_indices_lens
-        #
-    H,
-        N_CTX,
-        BLOCK_M: tl.constexpr,
-        BLOCK_N_DQ_LG: tl.constexpr,  # logical block size
-        HEAD_DIM: tl.constexpr,
-        SPARSITY: tl.constexpr,  # not used; just for trigger reevaluate for benchmarking
-):
-
-    tl.static_assert(BLOCK_N_DQ_LG % BLOCK_N_DQ_LG == 0)
-    tl.static_assert(BLOCK_N_DQ_LG % BLOCK_M == 0)
-
-    LN2: tl.constexpr = 0.6931471824645996  # = ln(2)
-
-    start_m = tl.program_id(0)
-
-    off_hz = tl.program_id(2)
-    off_z = off_hz // H
-    off_h = off_hz % H
-
-    off_q = off_z.to(tl.int64) * stride_qz + off_h.to(tl.int64) * stride_qh
-    off_k = off_z.to(tl.int64) * stride_kz + off_h.to(tl.int64) * stride_kh
-    off_v = off_z.to(tl.int64) * stride_vz + off_h.to(tl.int64) * stride_vh
-
-    off_dq = off_z.to(tl.int64) * stride_dqz + off_h.to(tl.int64) * stride_dqh
-    off_do = off_z.to(tl.int64) * stride_doz + off_h.to(tl.int64) * stride_doh
-
-    off_m = off_z.to(tl.int64) * stride_mz + off_h.to(tl.int64) * stride_mh
-    off_d = off_z.to(tl.int64) * stride_dz + off_h.to(tl.int64) * stride_dh
-
-    off_block_incides = off_z.to(tl.int64) * stride_bz + off_h.to(tl.int64) * stride_bh
-    off_block_incides_lens = off_z.to(tl.int64) * stride_lz + off_h.to(tl.int64) * stride_lh
-
-    # offset pointers for batch/head
-    Q += off_q
-    K += off_k
-    V += off_v
-    DO += off_do
-    DQ += off_dq
-
-    M += off_m
-    D += off_d
-    block_indices += off_block_incides
-    block_indices_lens += off_block_incides_lens
-
-    # ---------------------------------------- [DQ] ----------------------------------------
-
-    Q_block_ptr = tl.make_block_ptr(
-        base=Q,
-        shape=(N_CTX, HEAD_DIM),
-        strides=(stride_qm, stride_qk),
-        offsets=(start_m * BLOCK_M, 0),
-        block_shape=(BLOCK_M, HEAD_DIM),
-        order=(1, 0),
-    )
-    DO_block_ptr = tl.make_block_ptr(
-        base=DO,
-        shape=(N_CTX, HEAD_DIM),
-        strides=(stride_dom, stride_dok),
-        offsets=(start_m * BLOCK_M, 0),
-        block_shape=(BLOCK_M, HEAD_DIM),
-        order=(1, 0),
-    )
-    DQ_block_ptr = tl.make_block_ptr(
-        base=DQ,
-        shape=(N_CTX, HEAD_DIM),
-        strides=(stride_dqm, stride_dqk),
-        offsets=(start_m * BLOCK_M, 0),
-        block_shape=(BLOCK_M, HEAD_DIM),
-        order=(1, 0),
-    )
-
-    q = tl.load(Q_block_ptr)
-    do = tl.load(DO_block_ptr)
-
-    dq = tl.zeros([BLOCK_M, HEAD_DIM], dtype=tl.float32)
-
-    start_m = start_m * BLOCK_M
-
-    offs_m = start_m + tl.arange(0, BLOCK_M) * stride_mm
-    offs_d = start_m + tl.arange(0, BLOCK_M) * stride_dm
-
-    m = tl.load(M + offs_m)
-    m = m[:, None]
-
-    # D (= delta) is pre-divided by ds_scale.
-    d = tl.load(D + offs_d)
-    d = d[:, None]
-
-    block_indices_m = block_indices + (start_m // BLOCK_M) * stride_bm
-    block_indices_lens_m = block_indices_lens + (start_m // BLOCK_M) * stride_lm
-
-    dq = _attn_bwd_dq_bsa_varlen_align(
-        dq,
-        q,
-        do,
-        m,
-        d,
-        K,
-        V,
-        N_CTX,
-        BLOCK_N_DQ_LG,
-        HEAD_DIM,
-        block_indices_m,
-        block_indices_lens_m,
-        stride_bn,
-        stride_kn,
-        stride_kk,
-        stride_vn,
-        stride_vk,
-    )
-
-    # Write back dQ.
-    dq *= LN2
-    tl.store(DQ_block_ptr, dq.to(q.dtype))
diff --git a/fastvideo/train/AGENTS.md b/fastvideo/train/AGENTS.md
index 045e36d14..210fb337a 100644
--- a/fastvideo/train/AGENTS.md
+++ b/fastvideo/train/AGENTS.md
@@ -19,7 +19,7 @@ train/
 │   └── rl/                      #   RL methods, sampling helpers, rewards
 ├── models/
 │   ├── base.py             #   ModelBase / CausalModelBase wrappers
-│   ├── wan/, hunyuan/, cosmos/, kandinsky5/, longcat/, ltx2/, matrixgame2/, minimax_h3/  # Per-family wrappers
+│   ├── wan/, hunyuan/, cosmos/, kandinsky5/, ltx2/, matrixgame2/, minimax_h3/  # Per-family wrappers
 ├── callbacks/              # callback.py base + ema, grad_clip, validation
 └── utils/
     ├── training_config.py  #   Hierarchical YAML config dataclasses
diff --git a/fastvideo/train/entrypoint/train.py b/fastvideo/train/entrypoint/train.py
index 3f71e2a72..8533e5c1f 100644
--- a/fastvideo/train/entrypoint/train.py
+++ b/fastvideo/train/entrypoint/train.py
@@ -54,20 +54,13 @@ def run_training_from_config(
     cfg = load_run_config(config_path, overrides=overrides)
     tc = cfg.training
 
-    model_path_lower = str(tc.model_path).lower()
-
-    # Auto-set attention backend for model families that require a specific
-    # backend at load time, unless the user already overrode it explicitly.
+    # Auto-set the VSA attention backend when VSA sparsity is enabled,
+    # unless the user already overrode it explicitly.
     if tc.vsa_sparsity > 0.0:
         os.environ.setdefault(
             "FASTVIDEO_ATTENTION_BACKEND",
             "VIDEO_SPARSE_ATTN",
         )
-    elif ("turbodiffusion" in model_path_lower or "turbowan" in model_path_lower):
-        os.environ.setdefault(
-            "FASTVIDEO_ATTENTION_BACKEND",
-            "SLA_ATTN",
-        )
 
     maybe_init_distributed_environment_and_model_parallel(
         tc.distributed.tp_size,
diff --git a/fastvideo/train/models/longcat/__init__.py b/fastvideo/train/models/longcat/__init__.py
deleted file mode 100644
index c9aacfd95..000000000
--- a/fastvideo/train/models/longcat/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""LongCat model plugin package."""
-
-from fastvideo.train.models.longcat.longcat import (
-    LongCatModel as LongCatModel, )
diff --git a/fastvideo/train/models/longcat/longcat.py b/fastvideo/train/models/longcat/longcat.py
deleted file mode 100644
index 8e78d10a8..000000000
--- a/fastvideo/train/models/longcat/longcat.py
+++ /dev/null
@@ -1,140 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""LongCat model plugin (per-role instance)."""
-
-from __future__ import annotations
-
-from typing import Any, Literal, TYPE_CHECKING
-
-import torch
-
-from fastvideo.pipelines import TrainingBatch
-from fastvideo.train.models.wan.wan import WanModel
-
-if TYPE_CHECKING:
-    from fastvideo.train.utils.training_config import TrainingConfig
-
-
-class LongCatModel(WanModel):
-    """LongCat per-role model for training and distillation."""
-
-    _transformer_cls_name: str = "LongCatTransformer3DModel"
-
-    @staticmethod
-    def _validate_flow_shift(flow_shift: float | None) -> float:
-        if flow_shift is None:
-            return 12.0
-
-        validated = float(flow_shift)
-        if validated == 0.0:
-            raise ValueError("LongCat training does not support flow_shift=0.0 because "
-                             "it collapses FlowMatch training timesteps. Use 12.0 to "
-                             "match the released LongCat scheduler config.")
-        return validated
-
-    def __init__(
-        self,
-        *,
-        init_from: str,
-        training_config: TrainingConfig,
-        trainable: bool = True,
-        disable_custom_init_weights: bool = False,
-        flow_shift: float = 12.0,
-        enable_gradient_checkpointing_type: str | None = None,
-        transformer_override_safetensor: str | None = None,
-    ) -> None:
-        super().__init__(
-            init_from=init_from,
-            training_config=training_config,
-            trainable=trainable,
-            disable_custom_init_weights=disable_custom_init_weights,
-            flow_shift=self._validate_flow_shift(flow_shift),
-            enable_gradient_checkpointing_type=enable_gradient_checkpointing_type,
-            transformer_override_safetensor=transformer_override_safetensor,
-        )
-
-    def _init_timestep_mechanics(self) -> None:
-        assert self.training_config is not None
-        tc = self.training_config
-        flow_shift = getattr(tc.pipeline_config, "flow_shift", None)  # type: ignore[union-attr]
-        self.timestep_shift = self._validate_flow_shift(flow_shift)
-        self.num_train_timestep = int(self.noise_scheduler.num_train_timesteps)
-        self.min_timestep = 0
-        self.max_timestep = self.num_train_timestep
-
-    def _build_attention_metadata(self, training_batch: TrainingBatch) -> TrainingBatch:
-        training_batch.attn_metadata = None
-        return training_batch
-
-    def _build_distill_input_kwargs(
-        self,
-        noise_input: torch.Tensor,
-        timestep: torch.Tensor,
-        text_dict: dict[str, torch.Tensor] | None,
-        clean_x: torch.Tensor | None = None,
-        aug_t: torch.Tensor | None = None,
-    ) -> dict[str, Any]:
-        if clean_x is not None or aug_t is not None:
-            raise NotImplementedError("LongCatModel does not support clean-history teacher forcing")
-        if text_dict is None:
-            raise ValueError("text_dict cannot be None for LongCat distillation")
-
-        batch_size = int(noise_input.shape[0])
-        if timestep.ndim == 0:
-            timestep = timestep.view(1).expand(batch_size)
-        elif timestep.ndim == 1 and int(timestep.shape[0]) == 1 and batch_size > 1:
-            timestep = timestep.expand(batch_size)
-
-        return {
-            "hidden_states": noise_input.permute(0, 2, 1, 3, 4),
-            "encoder_hidden_states": text_dict["encoder_hidden_states"],
-            "encoder_attention_mask": text_dict["encoder_attention_mask"],
-            "timestep": timestep,
-        }
-
-    def predict_noise(
-        self,
-        noisy_latents: torch.Tensor,
-        timestep: torch.Tensor,
-        batch: TrainingBatch,
-        *,
-        conditional: bool,
-        cfg_uncond: dict[str, Any] | None = None,
-        attn_kind: Literal["dense", "vsa"] = "dense",
-        clean_x: torch.Tensor | None = None,
-        aug_t: torch.Tensor | None = None,
-    ) -> torch.Tensor:
-        """Adapt LongCat's sign convention to FineTuneMethod's target.
-
-        ``LongCatTransformer3DModel`` is pretrained to output the
-        ``clean - noise`` direction; ``LongCatDenoisingStage`` (the
-        bidirectional inference pipeline) explicitly negates the
-        transformer output before handing it to
-        ``FlowMatchEulerDiscreteScheduler.step``. Training methods on
-        the other hand (``FineTuneMethod``,
-        ``DiffusionForcingSFTMethod``) target ``noise - clean``
-        directly (the standard rectified-flow velocity Wan uses).
-
-        Without the negation here, the loss MSE pushes the transformer
-        toward ``noise - clean``, flipping its native output sign over
-        training. Inference then applies its own negation on top, so
-        the scheduler receives the wrong direction and produces noise
-        even while the training loss is dropping. Verified empirically
-        on a 100-step LongCat overfit run: step 0 generated meaningful
-        video, step 100 was pure noise despite low loss.
-
-        Negating in ``predict_noise`` keeps the transformer's
-        pretrained sign convention intact while presenting the
-        training methods with a Wan-compatible
-        ``pred ≈ noise - clean`` for MSE.
-        """
-        pred = super().predict_noise(
-            noisy_latents,
-            timestep,
-            batch,
-            conditional=conditional,
-            cfg_uncond=cfg_uncond,
-            attn_kind=attn_kind,
-            clean_x=clean_x,
-            aug_t=aug_t,
-        )
-        return -pred
diff --git a/mkdocs.yml b/mkdocs.yml
index ba9ca60b3..3d670bb61 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -180,12 +180,8 @@ nav:
       - Hunyuan: cookbook/hunyuan.md
       - Kandinsky 5: cookbook/kandinsky5.md
       - Kandinsky 6: cookbook/kandinsky6.md
-      - LongCat: cookbook/longcat.md
-      - TurboDiffusion: cookbook/turbodiffusion.md
     - Image:
       - FLUX: cookbook/flux.md
-      - GLM-Image: cookbook/glm-image.md
-      - Z-Image: cookbook/z-image.md
       - Stable Diffusion: cookbook/stable-diffusion.md
     - Audio:
       - Stable Audio: cookbook/stable-audio.md
diff --git a/pyproject.toml b/pyproject.toml
index dbe0fa608..fb0c012bd 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -21,10 +21,9 @@ dependencies = [
     "requests>=2.32.2",
 
     # Machine Learning & Transformers
-    # GLM-Image's AR encoder (GlmImageForConditionalGeneration) first ships in
-    # transformers 5.0.0. Floor raised from >=5.0.0 to >=5.15.0 for the public
-    # vision interpolation helper (get_vision_interpolation_indices_and_weights)
-    # that the MiniMax-H3 Qwen3-VL encoder cross-checks against.
+    # Floor raised from >=5.0.0 to >=5.15.0 for the public vision interpolation
+    # helper (get_vision_interpolation_indices_and_weights) that the MiniMax-H3
+    # Qwen3-VL encoder cross-checks against.
     # LTX-2.5's packed Gemma 4 unified encoder also requires transformers 5.15.0+
     # for correct mapping and loading of the Gemma 4 architecture.
     "transformers>=5.15.0",
diff --git a/pyproject_other.toml b/pyproject_other.toml
index 129bbf236..db95d8aa6 100644
--- a/pyproject_other.toml
+++ b/pyproject_other.toml
@@ -21,8 +21,7 @@ dependencies = [
     "requests>=2.32.2",
 
     # Machine Learning & Transformers
-    # GLM-Image's AR encoder (GlmImageForConditionalGeneration) first ships in
-    # transformers 5.0.0; floor bumped from >=4.57.3 to >=5.0.0 (stable, not rc).
+    # Floor bumped from >=4.57.3 to >=5.0.0 (stable, not rc).
     # Kept in lockstep with pyproject.toml: >=5.15.0 for the MiniMax-H3 vision
     # interpolation helper and LTX-2.5's packed Gemma 4 unified encoder mapping.
     "transformers>=5.15.0",
diff --git a/scripts/checkpoint_conversion/AGENTS.md b/scripts/checkpoint_conversion/AGENTS.md
index b11755c8d..daeb7d693 100644
--- a/scripts/checkpoint_conversion/AGENTS.md
+++ b/scripts/checkpoint_conversion/AGENTS.md
@@ -9,20 +9,12 @@
 
 ```
 checkpoint_conversion/
-├── convert_gamecraft_full.py            # Combined DiT + VAE
-├── convert_gamecraft_vae.py             # VAE only
-├── convert_gamecraft_weights.py         # DiT only
 ├── convert_gen3c_to_fastvideo.py
 ├── convert_ltx2_weights.py
 ├── convert_minimax_h3_adaln_rank.py     # Rank-reduces AdaLN in place; same family
 ├── quantize_minimax_h3_text_encoder_fp8.py  # Block-FP8 H3 text encoder (Hopper residency)
-├── convert_turbodiffusion_to_diffusers.py
-├── convert_turbodiffusion_i2v_to_diffusers.py
-├── extract_llava_text_encoder.py        # Encoder extraction from a multimodal repo
-├── longcat_to_fastvideo.py
 ├── stable_audio_to_diffusers.py
 ├── wan_to_diffusers.py
-├── validate_longcat_weights.py          # Post-conversion validation
 ├── pt_to_safetensors.py                 # Generic format flip
 └── create_hf_repo.py                    # Push to HF after conversion
 ```
diff --git a/scripts/checkpoint_conversion/convert_gamecraft_full.py b/scripts/checkpoint_conversion/convert_gamecraft_full.py
deleted file mode 100644
index 20057c26e..000000000
--- a/scripts/checkpoint_conversion/convert_gamecraft_full.py
+++ /dev/null
@@ -1,239 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-Convert the full Hunyuan-GameCraft-1.0 model to FastVideo diffusers format.
-
-This script converts:
-1. Transformer (DiT) weights from DeepSpeed format
-2. VAE weights
-3. Text encoders (LLaVA-LLaMA-3-8B and CLIP)
-4. Creates model_index.json and scheduler config
-
-Usage:
-    python scripts/checkpoint_conversion/convert_gamecraft_full.py \
-        --input-dir Hunyuan-GameCraft-1.0/weights \
-        --output-dir official_weights/hunyuan-gamecraft-diffusers
-
-    # Or specify individual paths:
-    python scripts/checkpoint_conversion/convert_gamecraft_full.py \
-        --transformer Hunyuan-GameCraft-1.0/weights/gamecraft_models/mp_rank_00_model_states.pt \
-        --vae Hunyuan-GameCraft-1.0/weights/stdmodels/vae_3d/hyvae/checkpoint-step-270000.ckpt \
-        --text-encoder Hunyuan-GameCraft-1.0/weights/stdmodels/llava-llama-3-8b-v1_1-transformers \
-        --text-encoder-2 Hunyuan-GameCraft-1.0/weights/stdmodels/openai_clip-vit-large-patch14 \
-        --output-dir official_weights/hunyuan-gamecraft-diffusers
-"""
-from __future__ import annotations
-
-import argparse
-import json
-import shutil
-from pathlib import Path
-
-import torch
-from safetensors.torch import save_file
-
-# Import conversion functions
-from convert_gamecraft_weights import convert_weights as convert_transformer
-from convert_gamecraft_vae import convert_gamecraft_vae
-
-
-def create_model_index(output_dir: Path) -> None:
-    """Create model_index.json for the pipeline."""
-    model_index = {
-        "_class_name": "HunyuanGameCraftPipeline",
-        "_diffusers_version": "0.30.0",
-        "scheduler": ["diffusers", "FlowMatchEulerDiscreteScheduler"],
-        "text_encoder": ["transformers", "LlamaModel"],
-        "text_encoder_2": ["transformers", "CLIPTextModel"],
-        "tokenizer": ["transformers", "AutoTokenizer"],
-        "tokenizer_2": ["transformers", "CLIPTokenizer"],
-        "transformer": ["fastvideo", "HunyuanGameCraftTransformer3DModel"],
-        "vae": ["fastvideo", "AutoencoderKLCausal3D"]
-    }
-
-    with open(output_dir / "model_index.json", "w") as f:
-        json.dump(model_index, f, indent=2)
-    print(f"Created model_index.json")
-
-
-def create_scheduler_config(output_dir: Path) -> None:
-    """Create scheduler config for FlowMatchEulerDiscreteScheduler."""
-    scheduler_dir = output_dir / "scheduler"
-    scheduler_dir.mkdir(parents=True, exist_ok=True)
-
-    scheduler_config = {
-        "_class_name": "FlowMatchEulerDiscreteScheduler",
-        "_diffusers_version": "0.30.0",
-        "base_image_seq_len": 256,
-        "base_shift": 0.5,
-        "invert_sigmas": False,
-        "max_shift": 1.15,
-        "num_train_timesteps": 1000,
-        "shift": 7.0,
-        "use_dynamic_shifting": True
-    }
-
-    with open(scheduler_dir / "scheduler_config.json", "w") as f:
-        json.dump(scheduler_config, f, indent=2)
-    print(f"Created scheduler config")
-
-
-def copy_text_encoder(src_dir: Path, dst_dir: Path, name: str) -> None:
-    """Copy text encoder files to output directory."""
-    if not src_dir.exists():
-        print(f"Warning: {name} not found at {src_dir}")
-        return
-
-    dst_dir.mkdir(parents=True, exist_ok=True)
-
-    # Copy all files
-    for src_file in src_dir.iterdir():
-        if src_file.is_file():
-            shutil.copy2(src_file, dst_dir / src_file.name)
-
-    print(f"Copied {name} to {dst_dir}")
-
-
-def copy_tokenizer(src_dir: Path, dst_dir: Path, name: str) -> None:
-    """Copy tokenizer files to output directory."""
-    if not src_dir.exists():
-        print(f"Warning: {name} not found at {src_dir}")
-        return
-
-    dst_dir.mkdir(parents=True, exist_ok=True)
-
-    # Copy tokenizer-related files
-    tokenizer_files = [
-        "tokenizer.json",
-        "tokenizer_config.json",
-        "special_tokens_map.json",
-        "vocab.json",
-        "merges.txt",
-        "added_tokens.json",
-    ]
-
-    for filename in tokenizer_files:
-        src_file = src_dir / filename
-        if src_file.exists():
-            shutil.copy2(src_file, dst_dir / filename)
-
-    print(f"Copied {name} to {dst_dir}")
-
-
-def convert_full_model(
-        transformer_path: Path | None = None,
-        vae_path: Path | None = None,
-        text_encoder_path: Path | None = None,
-        text_encoder_2_path: Path | None = None,
-        input_dir: Path | None = None,
-        output_dir: Path = Path("official_weights/hunyuan-gamecraft-diffusers"),
-        verbose: bool = False,
-) -> None:
-    """Convert all components of GameCraft to diffusers format."""
-
-    # Resolve paths from input_dir if individual paths not provided
-    if input_dir is not None:
-        if transformer_path is None:
-            transformer_path = input_dir / "gamecraft_models" / "mp_rank_00_model_states.pt"
-        if vae_path is None:
-            vae_path = input_dir / "stdmodels" / "vae_3d" / "hyvae" / "checkpoint-step-270000.ckpt"
-            if not vae_path.exists():
-                vae_path = input_dir / "stdmodels" / "vae_3d" / "hyvae" / "pytorch_model.pt"
-        if text_encoder_path is None:
-            text_encoder_path = input_dir / "stdmodels" / "llava-llama-3-8b-v1_1-transformers"
-        if text_encoder_2_path is None:
-            text_encoder_2_path = input_dir / "stdmodels" / "openai_clip-vit-large-patch14"
-
-    output_dir.mkdir(parents=True, exist_ok=True)
-    print(f"Converting GameCraft to diffusers format at {output_dir}")
-    print("=" * 60)
-
-    # 1. Convert transformer
-    if transformer_path and transformer_path.exists():
-        print(f"\n[1/5] Converting transformer from {transformer_path}")
-        convert_transformer(
-            input_path=transformer_path,
-            output_dir=output_dir,
-            save_config=True,
-            verbose=verbose,
-        )
-    else:
-        print(f"\n[1/5] Skipping transformer (not found: {transformer_path})")
-
-    # 2. Convert VAE
-    if vae_path and vae_path.exists():
-        print(f"\n[2/5] Converting VAE from {vae_path}")
-        convert_gamecraft_vae(
-            input_path=vae_path,
-            output_dir=output_dir / "vae",
-            copy_config=True,
-        )
-    else:
-        print(f"\n[2/5] Skipping VAE (not found: {vae_path})")
-
-    # 3. Copy text encoder (LLaMA)
-    if text_encoder_path and text_encoder_path.exists():
-        print(f"\n[3/5] Copying text encoder from {text_encoder_path}")
-        copy_text_encoder(text_encoder_path, output_dir / "text_encoder", "text_encoder")
-        copy_tokenizer(text_encoder_path, output_dir / "tokenizer", "tokenizer")
-    else:
-        print(f"\n[3/5] Skipping text_encoder (not found: {text_encoder_path})")
-
-    # 4. Copy text encoder 2 (CLIP)
-    if text_encoder_2_path and text_encoder_2_path.exists():
-        print(f"\n[4/5] Copying text encoder 2 from {text_encoder_2_path}")
-        copy_text_encoder(text_encoder_2_path, output_dir / "text_encoder_2", "text_encoder_2")
-        copy_tokenizer(text_encoder_2_path, output_dir / "tokenizer_2", "tokenizer_2")
-    else:
-        print(f"\n[4/5] Skipping text_encoder_2 (not found: {text_encoder_2_path})")
-
-    # 5. Create model_index.json and scheduler
-    print(f"\n[5/5] Creating model_index.json and scheduler config")
-    create_model_index(output_dir)
-    create_scheduler_config(output_dir)
-
-    print("\n" + "=" * 60)
-    print(f"Conversion complete! Output: {output_dir}")
-    print("\nDirectory structure:")
-    for item in sorted(output_dir.rglob("*")):
-        if item.is_file():
-            rel_path = item.relative_to(output_dir)
-            size_mb = item.stat().st_size / (1024 * 1024)
-            print(f"  {rel_path} ({size_mb:.1f} MB)")
-
-
-def main():
-    parser = argparse.ArgumentParser(
-        description="Convert full Hunyuan-GameCraft-1.0 model to FastVideo diffusers format.")
-    parser.add_argument("--input-dir", type=str, default=None, help="Path to Hunyuan-GameCraft-1.0/weights directory")
-    parser.add_argument("--transformer",
-                        type=str,
-                        default=None,
-                        help="Path to transformer checkpoint (mp_rank_00_model_states.pt)")
-    parser.add_argument("--vae", type=str, default=None, help="Path to VAE checkpoint")
-    parser.add_argument("--text-encoder", type=str, default=None, help="Path to text encoder (LLaVA-LLaMA) directory")
-    parser.add_argument("--text-encoder-2", type=str, default=None, help="Path to text encoder 2 (CLIP) directory")
-    parser.add_argument("--output-dir",
-                        type=str,
-                        default="official_weights/hunyuan-gamecraft-diffusers",
-                        help="Output directory for converted model")
-    parser.add_argument("--verbose", "-v", action="store_true", help="Print detailed conversion info")
-
-    args = parser.parse_args()
-
-    # Require either input-dir or individual paths
-    if args.input_dir is None and args.transformer is None:
-        parser.error("Either --input-dir or individual component paths must be provided")
-
-    convert_full_model(
-        transformer_path=Path(args.transformer) if args.transformer else None,
-        vae_path=Path(args.vae) if args.vae else None,
-        text_encoder_path=Path(args.text_encoder) if args.text_encoder else None,
-        text_encoder_2_path=Path(args.text_encoder_2) if args.text_encoder_2 else None,
-        input_dir=Path(args.input_dir) if args.input_dir else None,
-        output_dir=Path(args.output_dir),
-        verbose=args.verbose,
-    )
-
-
-if __name__ == "__main__":
-    main()
diff --git a/scripts/checkpoint_conversion/convert_gamecraft_vae.py b/scripts/checkpoint_conversion/convert_gamecraft_vae.py
deleted file mode 100644
index de44fb8a6..000000000
--- a/scripts/checkpoint_conversion/convert_gamecraft_vae.py
+++ /dev/null
@@ -1,93 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-Convert GameCraft VAE weights to FastVideo safetensors format.
-
-The official GameCraft VAE (checkpoint-step-270000.ckpt or pytorch_model.pt) uses
-a "vae." prefix. This script extracts and converts for FastVideo GameCraftVAE.
-
-Usage:
-    python scripts/checkpoint_conversion/convert_gamecraft_vae.py \
-        --input Hunyuan-GameCraft-1.0/weights/stdmodels/vae_3d/hyvae/checkpoint-step-270000.ckpt \
-        --output official_weights/hunyuan-gamecraft/vae
-"""
-from __future__ import annotations
-
-import argparse
-import json
-import shutil
-from pathlib import Path
-
-import torch
-from safetensors.torch import save_file
-
-
-def convert_gamecraft_vae(
-    input_path: Path,
-    output_dir: Path,
-    copy_config: bool = True,
-) -> dict:
-    """Convert official GameCraft VAE checkpoint to FastVideo format."""
-    ckpt = torch.load(input_path, map_location="cpu", weights_only=True)
-    if "state_dict" in ckpt:
-        state_dict = ckpt["state_dict"]
-    else:
-        state_dict = ckpt
-
-    # Extract VAE weights and strip "vae." prefix
-    vae_sd = {k.replace("vae.", ""): v for k, v in state_dict.items() if k.startswith("vae.")}
-    print(f"Extracted {len(vae_sd)} VAE parameters")
-
-    output_dir.mkdir(parents=True, exist_ok=True)
-    save_file(vae_sd, output_dir / "diffusion_pytorch_model.safetensors")
-    print(f"Saved to {output_dir / 'diffusion_pytorch_model.safetensors'}")
-
-    if copy_config:
-        config_src = input_path.parent / "config.json"
-        if config_src.exists():
-            config_dst = output_dir / "config.json"
-            config = json.loads(config_src.read_text())
-            config["_class_name"] = "AutoencoderKLCausal3D"
-            with open(config_dst, "w") as f:
-                json.dump(config, f, indent=2)
-            print(f"Saved config to {config_dst}")
-        else:
-            print(f"Warning: config.json not found at {config_src}")
-
-    return {"total": len(vae_sd)}
-
-
-def main():
-    parser = argparse.ArgumentParser(description="Convert GameCraft VAE weights to FastVideo safetensors format.")
-    parser.add_argument(
-        "--input",
-        type=str,
-        required=True,
-        help="Path to official VAE checkpoint (.ckpt or .pt)",
-    )
-    parser.add_argument(
-        "--output",
-        type=str,
-        default="official_weights/hunyuan-gamecraft/vae",
-        help="Output directory for converted weights",
-    )
-    parser.add_argument(
-        "--no-config",
-        action="store_true",
-        help="Don't copy config.json",
-    )
-    args = parser.parse_args()
-
-    input_path = Path(args.input)
-    if not input_path.exists():
-        raise FileNotFoundError(f"Input not found: {input_path}")
-
-    stats = convert_gamecraft_vae(
-        input_path=input_path,
-        output_dir=Path(args.output),
-        copy_config=not args.no_config,
-    )
-    print(f"\nConversion complete! Total parameters: {stats['total']}")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/scripts/checkpoint_conversion/convert_gamecraft_weights.py b/scripts/checkpoint_conversion/convert_gamecraft_weights.py
deleted file mode 100644
index 97a208454..000000000
--- a/scripts/checkpoint_conversion/convert_gamecraft_weights.py
+++ /dev/null
@@ -1,242 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-Convert Hunyuan-GameCraft-1.0 weights from DeepSpeed format to FastVideo format.
-
-The official GameCraft checkpoint (mp_rank_00_model_states.pt) uses slightly different
-naming conventions than FastVideo. This script converts the weights to be compatible
-with FastVideo's HunyuanGameCraft model.
-
-Usage:
-    python scripts/checkpoint_conversion/convert_gamecraft_weights.py \
-        --input Hunyuan-GameCraft-1.0/weights/gamecraft_models/mp_rank_00_model_states.pt \
-        --output official_weights/hunyuan-gamecraft/transformer/diffusion_pytorch_model.safetensors
-"""
-
-from __future__ import annotations
-
-import argparse
-import json
-import re
-from collections import OrderedDict
-from pathlib import Path
-
-import torch
-from safetensors.torch import save_file
-
-# Mapping from official GameCraft naming to FastVideo naming
-# GameCraft weights are already close to FastVideo format, but need minor adjustments
-PARAM_NAME_MAP: dict[str, str] = {
-    # MLP naming: fc1 -> fc_in, fc2 -> fc_out
-    r"^(.*)\.img_mlp\.fc1\.(.*)$": r"\1.img_mlp.fc_in.\2",
-    r"^(.*)\.img_mlp\.fc2\.(.*)$": r"\1.img_mlp.fc_out.\2",
-    r"^(.*)\.txt_mlp\.fc1\.(.*)$": r"\1.txt_mlp.fc_in.\2",
-    r"^(.*)\.txt_mlp\.fc2\.(.*)$": r"\1.txt_mlp.fc_out.\2",
-
-    # Single block MLP naming
-    r"^single_blocks\.(\d+)\.mlp\.fc1\.(.*)$": r"single_blocks.\1.mlp.fc_in.\2",
-    r"^single_blocks\.(\d+)\.mlp\.fc2\.(.*)$": r"single_blocks.\1.mlp.fc_out.\2",
-
-    # Token refiner naming - rename individual_token_refiner.blocks to refiner_blocks
-    # This is applied first, then subsequent mappings handle the sub-components
-    r"^txt_in\.individual_token_refiner\.blocks\.(\d+)\.(.*)$": r"txt_in.refiner_blocks.\1.\2",
-
-    # Vector in naming
-    r"^vector_in\.in_layer\.(.*)$": r"vector_in.fc_in.\1",
-    r"^vector_in\.out_layer\.(.*)$": r"vector_in.fc_out.\1",
-
-    # Time embedder naming
-    r"^time_in\.mlp\.0\.(.*)$": r"time_in.mlp.fc_in.\1",
-    r"^time_in\.mlp\.2\.(.*)$": r"time_in.mlp.fc_out.\1",
-
-    # Guidance embedder naming (if present)
-    r"^guidance_in\.mlp\.0\.(.*)$": r"guidance_in.mlp.fc_in.\1",
-    r"^guidance_in\.mlp\.2\.(.*)$": r"guidance_in.mlp.fc_out.\1",
-
-    # Final layer adaLN modulation
-    r"^final_layer\.adaLN_modulation\.1\.(.*)$": r"final_layer.adaLN_modulation.linear.\1",
-
-    # Text in c_embedder (context embedder) naming
-    r"^txt_in\.c_embedder\.linear_1\.(.*)$": r"txt_in.c_embedder.fc_in.\1",
-    r"^txt_in\.c_embedder\.linear_2\.(.*)$": r"txt_in.c_embedder.fc_out.\1",
-
-    # Text in t_embedder (time embedder) naming
-    r"^txt_in\.t_embedder\.mlp\.0\.(.*)$": r"txt_in.t_embedder.mlp.fc_in.\1",
-    r"^txt_in\.t_embedder\.mlp\.2\.(.*)$": r"txt_in.t_embedder.mlp.fc_out.\1",
-
-    # Refiner block MLP naming - need to match the actual checkpoint names
-    r"^txt_in\.refiner_blocks\.(\d+)\.mlp\.fc1\.(.*)$": r"txt_in.refiner_blocks.\1.mlp.fc_in.\2",
-    r"^txt_in\.refiner_blocks\.(\d+)\.mlp\.fc2\.(.*)$": r"txt_in.refiner_blocks.\1.mlp.fc_out.\2",
-
-    # Refiner block adaLN modulation naming - matches .adaLN_modulation.1.weight/bias
-    r"^txt_in\.refiner_blocks\.(\d+)\.adaLN_modulation\.1\.(.*)$":
-    r"txt_in.refiner_blocks.\1.adaLN_modulation.linear.\2",
-
-    # Camera net - keep as-is (already correct)
-    # r"^camera_net\.(.*)$": r"camera_net.\1",
-}
-
-
-def apply_mapping(key: str) -> str:
-    """Apply name mapping to convert official key to FastVideo key.
-    
-    Mappings are applied repeatedly until no more changes occur.
-    This allows for chained transformations.
-    """
-    prev_key = None
-    while prev_key != key:
-        prev_key = key
-        for pattern, replacement in PARAM_NAME_MAP.items():
-            if re.match(pattern, key):
-                key = re.sub(pattern, replacement, key)
-                break  # Apply only one mapping per iteration
-    return key
-
-
-def load_deepspeed_checkpoint(path: Path) -> dict[str, torch.Tensor]:
-    """Load weights from a DeepSpeed checkpoint file."""
-    checkpoint = torch.load(path, map_location="cpu")
-
-    if "module" in checkpoint:
-        state_dict = checkpoint["module"]
-    elif "state_dict" in checkpoint:
-        state_dict = checkpoint["state_dict"]
-    elif "model" in checkpoint:
-        state_dict = checkpoint["model"]
-    else:
-        state_dict = checkpoint
-
-    if not isinstance(state_dict, dict):
-        raise TypeError(f"Expected dict state_dict, got {type(state_dict)}")
-
-    return state_dict
-
-
-def convert_weights(
-    input_path: Path,
-    output_dir: Path,
-    save_config: bool = True,
-    verbose: bool = False,
-) -> dict[str, int]:
-    """
-    Convert GameCraft weights to FastVideo format.
-    
-    Returns:
-        dict with conversion statistics
-    """
-    print(f"Loading checkpoint from {input_path}")
-    state_dict = load_deepspeed_checkpoint(input_path)
-
-    print(f"Found {len(state_dict)} parameters")
-
-    # Convert weights
-    converted_state_dict: OrderedDict[str, torch.Tensor] = OrderedDict()
-    renamed_count = 0
-    unchanged_count = 0
-
-    for key, value in state_dict.items():
-        new_key = apply_mapping(key)
-        if new_key != key:
-            if verbose:
-                print(f"  {key} -> {new_key}")
-            renamed_count += 1
-        else:
-            unchanged_count += 1
-        converted_state_dict[new_key] = value
-
-    print(f"Renamed {renamed_count} parameters, {unchanged_count} unchanged")
-
-    # Create output directory structure
-    transformer_dir = output_dir / "transformer"
-    transformer_dir.mkdir(parents=True, exist_ok=True)
-
-    # Save as safetensors
-    output_path = transformer_dir / "diffusion_pytorch_model.safetensors"
-    print(f"Saving converted weights to {output_path}")
-    save_file(converted_state_dict, str(output_path))
-
-    # Save config if requested
-    if save_config:
-        config = {
-            "_class_name": "HunyuanGameCraftTransformer3DModel",
-            "_fastvideo_version": "0.1.0",
-            "in_channels": 33,  # 16 + 16 + 1 for concat mode
-            "out_channels": 16,
-            "hidden_size": 3072,
-            "num_attention_heads": 24,
-            "attention_head_dim": 128,
-            "num_layers": 20,
-            "num_single_layers": 40,
-            "num_refiner_layers": 2,
-            "patch_size": [1, 2, 2],
-            "rope_axes_dim": [16, 56, 56],
-            "text_embed_dim": 4096,
-            "pooled_projection_dim": 768,
-            "mlp_ratio": 4.0,
-            "guidance_embeds": False,
-            "camera_net": True,
-        }
-        config_path = transformer_dir / "config.json"
-        with open(config_path, "w") as f:
-            json.dump(config, f, indent=2)
-        print(f"Saved config to {config_path}")
-
-    # Verify camera_net weights
-    camera_keys = [k for k in converted_state_dict.keys() if "camera_net" in k]
-    print(f"\nCamera net weights ({len(camera_keys)}):")
-    for k in camera_keys:
-        print(f"  {k}: {converted_state_dict[k].shape}")
-
-    # Verify img_in weights (should be 33 channels)
-    if "img_in.proj.weight" in converted_state_dict:
-        img_in_shape = converted_state_dict["img_in.proj.weight"].shape
-        print(f"\nimg_in.proj.weight shape: {img_in_shape}")
-        expected_in_channels = 33
-        actual_in_channels = img_in_shape[1]
-        if actual_in_channels != expected_in_channels:
-            print(f"  WARNING: Expected {expected_in_channels} input channels, got {actual_in_channels}")
-        else:
-            print(f"  OK: 33 input channels (16 latent + 16 gt_latent + 1 mask)")
-
-    return {
-        "total": len(converted_state_dict),
-        "renamed": renamed_count,
-        "unchanged": unchanged_count,
-    }
-
-
-def main():
-    parser = argparse.ArgumentParser(description="Convert Hunyuan-GameCraft-1.0 weights to FastVideo format.")
-    parser.add_argument("--input",
-                        type=str,
-                        required=True,
-                        help="Path to official GameCraft checkpoint (mp_rank_00_model_states.pt)")
-    parser.add_argument("--output",
-                        type=str,
-                        default="official_weights/hunyuan-gamecraft",
-                        help="Output directory for converted weights")
-    parser.add_argument("--no-config", action="store_true", help="Don't save config.json")
-    parser.add_argument("--verbose", "-v", action="store_true", help="Print all renamed parameters")
-
-    args = parser.parse_args()
-
-    input_path = Path(args.input)
-    output_dir = Path(args.output)
-
-    if not input_path.exists():
-        raise FileNotFoundError(f"Input file not found: {input_path}")
-
-    stats = convert_weights(
-        input_path=input_path,
-        output_dir=output_dir,
-        save_config=not args.no_config,
-        verbose=args.verbose,
-    )
-
-    print(f"\nConversion complete!")
-    print(f"  Total parameters: {stats['total']}")
-    print(f"  Renamed: {stats['renamed']}")
-    print(f"  Unchanged: {stats['unchanged']}")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/scripts/checkpoint_conversion/convert_turbodiffusion_i2v_to_diffusers.py b/scripts/checkpoint_conversion/convert_turbodiffusion_i2v_to_diffusers.py
deleted file mode 100644
index ccc997457..000000000
--- a/scripts/checkpoint_conversion/convert_turbodiffusion_i2v_to_diffusers.py
+++ /dev/null
@@ -1,197 +0,0 @@
-#!/usr/bin/env python3
-"""
-Convert TurboDiffusion I2V .pth checkpoint to Diffusers safetensors format.
-
-TurboDiffusion I2V uses two models: high-noise and low-noise.
-This script converts both checkpoints to Diffusers format.
-
-Usage:
-    python convert_turbodiffusion_i2v_to_diffusers.py \
-        --high_noise_path /path/to/TurboWan2.2-I2V-A14B-high-720P.pth \
-        --low_noise_path /path/to/TurboWan2.2-I2V-A14B-low-720P.pth \
-        --output_dir /path/to/output \
-        --reference_repo Wan-AI/Wan2.1-I2V-14B-720P-Diffusers
-"""
-
-import argparse
-import os
-import re
-import json
-import torch
-import shutil
-import glob
-from safetensors import safe_open
-from safetensors.torch import save_file
-from huggingface_hub import snapshot_download
-
-# Weight mapping from TurboDiffusion -> Diffusers/FastVideo format
-# Same as T2V but may need additional I2V-specific mappings
-TURBODIFFUSION_WEIGHT_MAPPING = {
-    # Self attention
-    r"^blocks\.(\d+)\.self_attn\.q\.(.*)$": r"blocks.\1.to_q.\2",
-    r"^blocks\.(\d+)\.self_attn\.k\.(.*)$": r"blocks.\1.to_k.\2",
-    r"^blocks\.(\d+)\.self_attn\.v\.(.*)$": r"blocks.\1.to_v.\2",
-    r"^blocks\.(\d+)\.self_attn\.o\.(.*)$": r"blocks.\1.to_out.\2",
-    r"^blocks\.(\d+)\.self_attn\.norm_q\.(.*)$": r"blocks.\1.norm_q.\2",
-    r"^blocks\.(\d+)\.self_attn\.norm_k\.(.*)$": r"blocks.\1.norm_k.\2",
-    # Cross attention
-    r"^blocks\.(\d+)\.cross_attn\.q\.(.*)$": r"blocks.\1.attn2.to_q.\2",
-    r"^blocks\.(\d+)\.cross_attn\.k\.(.*)$": r"blocks.\1.attn2.to_k.\2",
-    r"^blocks\.(\d+)\.cross_attn\.v\.(.*)$": r"blocks.\1.attn2.to_v.\2",
-    r"^blocks\.(\d+)\.cross_attn\.o\.(.*)$": r"blocks.\1.attn2.to_out.\2",
-    r"^blocks\.(\d+)\.cross_attn\.norm_q\.(.*)$": r"blocks.\1.attn2.norm_q.\2",
-    r"^blocks\.(\d+)\.cross_attn\.norm_k\.(.*)$": r"blocks.\1.attn2.norm_k.\2",
-    # I2V-specific cross attention (add_k/v_proj for image context)
-    r"^blocks\.(\d+)\.cross_attn\.add_k\.(.*)$": r"blocks.\1.attn2.add_k_proj.\2",
-    r"^blocks\.(\d+)\.cross_attn\.add_v\.(.*)$": r"blocks.\1.attn2.add_v_proj.\2",
-    r"^blocks\.(\d+)\.cross_attn\.norm_add_k\.(.*)$": r"blocks.\1.attn2.norm_added_k.\2",
-    r"^blocks\.(\d+)\.cross_attn\.norm_add_q\.(.*)$": r"blocks.\1.attn2.norm_added_q.\2",
-    # Norms and FFN
-    r"^blocks\.(\d+)\.norm1\.(.*)$": r"blocks.\1.norm1.\2",
-    r"^blocks\.(\d+)\.norm3\.(.*)$": r"blocks.\1.self_attn_residual_norm.norm.\2",
-    r"^blocks\.(\d+)\.norm2\.(.*)$": r"blocks.\1.norm3.\2",
-    r"^blocks\.(\d+)\.ffn\.0\.(.*)$": r"blocks.\1.ffn.fc_in.\2",
-    r"^blocks\.(\d+)\.ffn\.2\.(.*)$": r"blocks.\1.ffn.fc_out.\2",
-    r"^blocks\.(\d+)\.modulation$": r"blocks.\1.scale_shift_table",
-    # Embeddings
-    r"^text_embedding\.0\.(.*)$": r"condition_embedder.text_embedder.fc_in.\1",
-    r"^text_embedding\.2\.(.*)$": r"condition_embedder.text_embedder.fc_out.\1",
-    r"^time_embedding\.0\.(.*)$": r"condition_embedder.time_embedder.mlp.fc_in.\1",
-    r"^time_embedding\.2\.(.*)$": r"condition_embedder.time_embedder.mlp.fc_out.\1",
-    r"^time_projection\.1\.(.*)$": r"condition_embedder.time_modulation.linear.\1",
-    # Head
-    r"^head\.head\.(.*)$": r"proj_out.\1",
-    r"^head\.norm\.(.*)$": r"norm_out.\1",
-    r"^head\.modulation$": r"scale_shift_table",
-    # SLA proj_l weights
-    r"^blocks\.(\d+)\.self_attn\.attn_op\.local_attn\.proj_l\.(.*)$": r"blocks.\1.attn1.attn_impl.proj_l.\2",
-}
-
-SKIP_PATTERNS = []
-
-
-def should_skip_key(key: str) -> bool:
-    for pattern in SKIP_PATTERNS:
-        if re.match(pattern, key):
-            return True
-    return False
-
-
-def convert_key(turbo_key: str) -> str:
-    for pattern, replacement in TURBODIFFUSION_WEIGHT_MAPPING.items():
-        if re.match(pattern, turbo_key):
-            return re.sub(pattern, replacement, turbo_key)
-    return turbo_key
-
-
-def reshape_patch_embedding(tensor: torch.Tensor, target_shape: tuple) -> torch.Tensor:
-    if len(tensor.shape) == 2 and len(target_shape) == 5:
-        return tensor.view(target_shape)
-    return tensor
-
-
-def get_reference_shapes(reference_repo: str) -> dict:
-    print(f"Downloading reference model shapes from {reference_repo}...")
-
-    local_dir = snapshot_download(
-        repo_id=reference_repo,
-        allow_patterns=["transformer/config.json", "transformer/diffusion_pytorch_model*.safetensors"],
-        local_dir_use_symlinks=False)
-
-    weight_files = glob.glob(os.path.join(local_dir, "transformer", "*.safetensors"))
-    shapes = {}
-
-    for wf in weight_files:
-        with safe_open(wf, framework="pt") as f:
-            for key in f.keys():
-                shapes[key] = f.get_tensor(key).shape
-
-    return shapes, local_dir
-
-
-def convert_checkpoint(input_path: str, output_dir: str, ref_shapes: dict, model_name: str) -> None:
-    """Convert a single TurboDiffusion checkpoint to Diffusers format."""
-    print(f"\n{'='*60}")
-    print(f"Converting {model_name}: {input_path}")
-    print(f"{'='*60}")
-
-    turbo_state_dict = torch.load(input_path, map_location="cpu", weights_only=True)
-    print(f"Loaded {len(turbo_state_dict)} keys")
-
-    converted_state_dict = {}
-    skipped_keys = []
-
-    for turbo_key, tensor in turbo_state_dict.items():
-        if should_skip_key(turbo_key):
-            skipped_keys.append(turbo_key)
-            continue
-
-        new_key = convert_key(turbo_key)
-
-        if "patch_embedding" in new_key and new_key in ref_shapes:
-            target_shape = ref_shapes[new_key]
-            if tensor.shape != target_shape:
-                print(f"Reshaping {new_key}: {tensor.shape} -> {target_shape}")
-                tensor = reshape_patch_embedding(tensor, target_shape)
-
-        if new_key in ref_shapes:
-            if tensor.shape != ref_shapes[new_key]:
-                print(f"WARNING: Shape mismatch for {new_key}: got {tensor.shape}, expected {ref_shapes[new_key]}")
-
-        converted_state_dict[new_key] = tensor
-
-    print(f"Converted: {len(converted_state_dict)} keys, Skipped: {len(skipped_keys)} keys")
-
-    os.makedirs(output_dir, exist_ok=True)
-    output_path = os.path.join(output_dir, "diffusion_pytorch_model.safetensors")
-    print(f"Saving to {output_path}...")
-    save_file(converted_state_dict, output_path)
-
-    return converted_state_dict
-
-
-def main():
-    parser = argparse.ArgumentParser(description="Convert TurboDiffusion I2V checkpoints to Diffusers format")
-    parser.add_argument("--high_noise_path",
-                        type=str,
-                        required=True,
-                        help="Path to high-noise TurboDiffusion .pth checkpoint")
-    parser.add_argument("--low_noise_path",
-                        type=str,
-                        required=True,
-                        help="Path to low-noise TurboDiffusion .pth checkpoint")
-    parser.add_argument("--output_dir", type=str, required=True, help="Output directory for converted safetensors")
-    parser.add_argument("--reference_repo",
-                        type=str,
-                        default="Wan-AI/Wan2.2-I2V-A14B-Diffusers",
-                        help="Reference HF repo to get expected tensor shapes")
-
-    args = parser.parse_args()
-
-    # Get reference shapes
-    ref_shapes, ref_local_dir = get_reference_shapes(args.reference_repo)
-    print(f"Got {len(ref_shapes)} reference shapes")
-
-    # Convert high-noise model
-    high_noise_output = os.path.join(args.output_dir, "transformer_high")
-    convert_checkpoint(args.high_noise_path, high_noise_output, ref_shapes, "high-noise")
-
-    # Convert low-noise model
-    low_noise_output = os.path.join(args.output_dir, "transformer_low")
-    convert_checkpoint(args.low_noise_path, low_noise_output, ref_shapes, "low-noise")
-
-    # Copy config.json to both
-    src_config = os.path.join(ref_local_dir, "transformer", "config.json")
-    shutil.copy(src_config, os.path.join(high_noise_output, "config.json"))
-    shutil.copy(src_config, os.path.join(low_noise_output, "config.json"))
-    print("Copied config.json to both transformer directories")
-
-    print(f"\n{'='*60}")
-    print(f"Conversion complete!")
-    print(f"High-noise model: {high_noise_output}")
-    print(f"Low-noise model: {low_noise_output}")
-    print(f"{'='*60}")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/scripts/checkpoint_conversion/convert_turbodiffusion_to_diffusers.py b/scripts/checkpoint_conversion/convert_turbodiffusion_to_diffusers.py
deleted file mode 100644
index 72f405a29..000000000
--- a/scripts/checkpoint_conversion/convert_turbodiffusion_to_diffusers.py
+++ /dev/null
@@ -1,204 +0,0 @@
-#!/usr/bin/env python3
-"""
-Convert TurboDiffusion .pth checkpoint to Diffusers safetensors format.
-
-This script:
-1. Loads the TurboDiffusion .pth checkpoint
-2. Applies weight key renaming to match FastVideo/Diffusers format
-3. Reshapes patch_embedding from [D, C*P] to [D, C, P_t, P_h, P_w]
-4. Saves as sharded safetensors files compatible with diffusers
-
-Usage:
-    python convert_turbodiffusion_to_diffusers.py \
-        --input_path /path/to/TurboWan2.1-T2V-1.3B-480P.pth \
-        --output_dir /path/to/output/transformer \
-        --reference_repo Wan-AI/Wan2.1-T2V-1.3B-Diffusers
-"""
-
-import argparse
-import os
-import re
-import json
-import torch
-import shutil
-import glob
-from safetensors import safe_open
-from safetensors.torch import save_file
-from huggingface_hub import snapshot_download
-
-# Weight mapping from TurboDiffusion -> Diffusers/FastVideo format
-TURBODIFFUSION_WEIGHT_MAPPING = {
-    # Self attention
-    r"^blocks\.(\d+)\.self_attn\.q\.(.*)$": r"blocks.\1.to_q.\2",
-    r"^blocks\.(\d+)\.self_attn\.k\.(.*)$": r"blocks.\1.to_k.\2",
-    r"^blocks\.(\d+)\.self_attn\.v\.(.*)$": r"blocks.\1.to_v.\2",
-    r"^blocks\.(\d+)\.self_attn\.o\.(.*)$": r"blocks.\1.to_out.\2",
-    r"^blocks\.(\d+)\.self_attn\.norm_q\.(.*)$": r"blocks.\1.norm_q.\2",
-    r"^blocks\.(\d+)\.self_attn\.norm_k\.(.*)$": r"blocks.\1.norm_k.\2",
-    # Cross attention
-    r"^blocks\.(\d+)\.cross_attn\.q\.(.*)$": r"blocks.\1.attn2.to_q.\2",
-    r"^blocks\.(\d+)\.cross_attn\.k\.(.*)$": r"blocks.\1.attn2.to_k.\2",
-    r"^blocks\.(\d+)\.cross_attn\.v\.(.*)$": r"blocks.\1.attn2.to_v.\2",
-    r"^blocks\.(\d+)\.cross_attn\.o\.(.*)$": r"blocks.\1.attn2.to_out.\2",
-    r"^blocks\.(\d+)\.cross_attn\.norm_q\.(.*)$": r"blocks.\1.attn2.norm_q.\2",
-    r"^blocks\.(\d+)\.cross_attn\.norm_k\.(.*)$": r"blocks.\1.attn2.norm_k.\2",
-    # Norms and FFN
-    r"^blocks\.(\d+)\.norm1\.(.*)$": r"blocks.\1.norm1.\2",
-    r"^blocks\.(\d+)\.norm3\.(.*)$": r"blocks.\1.self_attn_residual_norm.norm.\2",
-    r"^blocks\.(\d+)\.norm2\.(.*)$": r"blocks.\1.norm3.\2",
-    r"^blocks\.(\d+)\.ffn\.0\.(.*)$": r"blocks.\1.ffn.fc_in.\2",
-    r"^blocks\.(\d+)\.ffn\.2\.(.*)$": r"blocks.\1.ffn.fc_out.\2",
-    r"^blocks\.(\d+)\.modulation$": r"blocks.\1.scale_shift_table",
-    # Embeddings - DON'T add .proj here! WanVideoArchConfig's param_names_mapping will add it
-    # patch_embedding.weight stays as patch_embedding.weight (HF format needs this)
-    r"^text_embedding\.0\.(.*)$": r"condition_embedder.text_embedder.fc_in.\1",
-    r"^text_embedding\.2\.(.*)$": r"condition_embedder.text_embedder.fc_out.\1",
-    r"^time_embedding\.0\.(.*)$": r"condition_embedder.time_embedder.mlp.fc_in.\1",
-    r"^time_embedding\.2\.(.*)$": r"condition_embedder.time_embedder.mlp.fc_out.\1",
-    r"^time_projection\.1\.(.*)$": r"condition_embedder.time_modulation.linear.\1",
-    # Head
-    r"^head\.head\.(.*)$": r"proj_out.\1",
-    r"^head\.norm\.(.*)$": r"norm_out.\1",
-    r"^head\.modulation$": r"scale_shift_table",
-    # SLA proj_l weights - include them! They're the distilled attention weights
-    r"^blocks\.(\d+)\.self_attn\.attn_op\.local_attn\.proj_l\.(.*)$": r"blocks.\1.attn1.attn_impl.proj_l.\2",
-}
-
-# No keys to skip - we want all weights including proj_l
-SKIP_PATTERNS = []
-
-
-def should_skip_key(key: str) -> bool:
-    """Check if a key should be skipped (SLA-specific weights)."""
-    for pattern in SKIP_PATTERNS:
-        if re.match(pattern, key):
-            return True
-    return False
-
-
-def convert_key(turbo_key: str) -> str:
-    """Convert TurboDiffusion key to Diffusers format."""
-    for pattern, replacement in TURBODIFFUSION_WEIGHT_MAPPING.items():
-        if re.match(pattern, turbo_key):
-            return re.sub(pattern, replacement, turbo_key)
-    return turbo_key  # Return unchanged if no pattern matches
-
-
-def reshape_patch_embedding(tensor: torch.Tensor, target_shape: tuple) -> torch.Tensor:
-    """Reshape patch_embedding from [D, C*P_t*P_h*P_w] to [D, C, P_t, P_h, P_w]."""
-    if len(tensor.shape) == 2 and len(target_shape) == 5:
-        return tensor.view(target_shape)
-    return tensor
-
-
-def get_reference_shapes(reference_repo: str) -> dict:
-    """Download reference model and get expected shapes for patch_embedding."""
-    print(f"Downloading reference model shapes from {reference_repo}...")
-
-    # Download just the transformer config and a weight file to get shapes
-    local_dir = snapshot_download(
-        repo_id=reference_repo,
-        allow_patterns=["transformer/config.json", "transformer/diffusion_pytorch_model*.safetensors"],
-        local_dir_use_symlinks=False)
-
-    # Load the first safetensors file to get shapes
-    weight_files = glob.glob(os.path.join(local_dir, "transformer", "*.safetensors"))
-    shapes = {}
-
-    for wf in weight_files:
-        with safe_open(wf, framework="pt") as f:
-            for key in f.keys():
-                shapes[key] = f.get_tensor(key).shape
-
-    return shapes
-
-
-def main():
-    parser = argparse.ArgumentParser(description="Convert TurboDiffusion checkpoint to Diffusers format")
-    parser.add_argument("--input_path", type=str, required=True, help="Path to TurboDiffusion .pth checkpoint")
-    parser.add_argument("--output_dir", type=str, required=True, help="Output directory for converted safetensors")
-    parser.add_argument("--reference_repo",
-                        type=str,
-                        default="Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
-                        help="Reference HF repo to get expected tensor shapes")
-    parser.add_argument("--skip_sla_weights",
-                        action="store_true",
-                        default=False,
-                        help="Skip SLA-specific weights (proj_l) that aren't in base model")
-
-    args = parser.parse_args()
-
-    # Load TurboDiffusion checkpoint
-    print(f"Loading TurboDiffusion checkpoint from {args.input_path}...")
-    turbo_state_dict = torch.load(args.input_path, map_location="cpu", weights_only=True)
-    print(f"Loaded {len(turbo_state_dict)} keys")
-
-    # Get reference shapes for reshaping
-    ref_shapes = get_reference_shapes(args.reference_repo)
-    print(f"Got {len(ref_shapes)} reference shapes")
-
-    # Convert keys and reshape tensors
-    converted_state_dict = {}
-    skipped_keys = []
-
-    for turbo_key, tensor in turbo_state_dict.items():
-        # Skip SLA-specific weights if requested
-        if args.skip_sla_weights and should_skip_key(turbo_key):
-            skipped_keys.append(turbo_key)
-            continue
-
-        # Convert key name
-        new_key = convert_key(turbo_key)
-
-        # Reshape patch_embedding if needed
-        if "patch_embedding" in new_key and new_key in ref_shapes:
-            target_shape = ref_shapes[new_key]
-            if tensor.shape != target_shape:
-                print(f"Reshaping {new_key}: {tensor.shape} -> {target_shape}")
-                tensor = reshape_patch_embedding(tensor, target_shape)
-
-        # Verify shape matches reference if available
-        if new_key in ref_shapes:
-            if tensor.shape != ref_shapes[new_key]:
-                print(f"WARNING: Shape mismatch for {new_key}: got {tensor.shape}, expected {ref_shapes[new_key]}")
-
-        converted_state_dict[new_key] = tensor
-
-    print(f"\nConversion summary:")
-    print(f"  Converted: {len(converted_state_dict)} keys")
-    print(f"  Skipped (SLA): {len(skipped_keys)} keys")
-
-    if skipped_keys:
-        print(f"\nSkipped SLA keys (first 5):")
-        for k in skipped_keys[:5]:
-            print(f"  - {k}")
-
-    # Create output directory
-    os.makedirs(args.output_dir, exist_ok=True)
-
-    # Save as safetensors
-    output_path = os.path.join(args.output_dir, "diffusion_pytorch_model.safetensors")
-    print(f"\nSaving to {output_path}...")
-    save_file(converted_state_dict, output_path)
-
-    # Copy config.json from reference
-    ref_local = snapshot_download(repo_id=args.reference_repo,
-                                  allow_patterns=["transformer/config.json"],
-                                  local_dir_use_symlinks=False)
-    src_config = os.path.join(ref_local, "transformer", "config.json")
-    dst_config = os.path.join(args.output_dir, "config.json")
-    shutil.copy(src_config, dst_config)
-    print(f"Copied config.json")
-
-    print(f"\nDone! Converted weights saved to: {args.output_dir}")
-    print(f"\nNext steps:")
-    print(f"  1. Use create_hf_repo.py to create a complete diffusers repo:")
-    print(f"     python scripts/checkpoint_conversion/create_hf_repo.py \\")
-    print(f"       --repo_id Wan-AI/Wan2.1-T2V-1.3B-Diffusers \\")
-    print(f"       --local_dir /tmp/turbodiffusion-wan \\")
-    print(f"       --checkpoint_dir {args.output_dir} \\")
-    print(f"       --push_to_hub --upload_repo_id YOUR_USERNAME/TurboWan-Diffusers")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/scripts/checkpoint_conversion/dreamx_world_ar_to_diffusers.py b/scripts/checkpoint_conversion/dreamx_world_ar_to_diffusers.py
deleted file mode 100755
index 5f3206c4e..000000000
--- a/scripts/checkpoint_conversion/dreamx_world_ar_to_diffusers.py
+++ /dev/null
@@ -1,124 +0,0 @@
-#!/usr/bin/env python3
-# SPDX-License-Identifier: Apache-2.0
-"""Convert DreamX-World-5B autoregressive weights to FastVideo layout.
-
-The HF repository stores one raw official ``model.safetensors`` whose keys match
-FastVideo's native ``DreamXWorldARTransformer3DModel``. The converter writes a
-Diffusers-like root with ``transformer/config.json`` and reusable Wan2.2
-components. Use ``--symlink-transformer`` locally to avoid duplicating the 21GB
-AR tensor file.
-"""
-
-from __future__ import annotations
-
-import argparse
-import json
-import shutil
-from pathlib import Path
-
-TRANSFORMER_CONFIG: dict[str, object] = {
-    "_class_name": "DreamXWorldARTransformer3DModel",
-    "model_type": "ti2v",
-    "patch_size": [1, 2, 2],
-    "text_len": 512,
-    "num_attention_heads": 24,
-    "attention_head_dim": 128,
-    "in_channels": 48,
-    "out_channels": 48,
-    "text_dim": 4096,
-    "freq_dim": 256,
-    "ffn_dim": 14336,
-    "num_layers": 30,
-    "local_attn_size": 12,
-    "sink_size": 3,
-    "cross_attn_norm": True,
-    "qk_norm": True,
-    "eps": 1e-6,
-    "add_control_adapter": True,
-    "cam_method": "prope",
-    "attn_compress": 4,
-    "cam_self_attn_layers": list(range(30)),
-    "num_frames_per_block": 3,
-}
-
-MODEL_INDEX: dict[str, object] = {
-    "_class_name": "DreamXWorldARPipeline",
-    "_diffusers_version": "0.31.0",
-    "scheduler": ["diffusers", "FlowMatchEulerDiscreteScheduler"],
-    "text_encoder": ["transformers", "UMT5EncoderModel"],
-    "tokenizer": ["transformers", "AutoTokenizer"],
-    "transformer": ["diffusers", "DreamXWorldARTransformer3DModel"],
-    "vae": ["diffusers", "AutoencoderKLWan"],
-}
-
-REUSED_COMPONENTS = ("scheduler", "text_encoder", "tokenizer", "vae")
-
-
-def _source_safetensors(source: Path) -> Path:
-    if source.is_file():
-        return source
-    path = source / "model.safetensors"
-    if not path.exists():
-        raise FileNotFoundError(f"Missing AR model.safetensors under {source}")
-    return path
-
-
-def convert_transformer(source: Path, output: Path, symlink_transformer: bool) -> None:
-    src = _source_safetensors(source)
-    transformer_dir = output / "transformer"
-    transformer_dir.mkdir(parents=True, exist_ok=True)
-    (transformer_dir / "config.json").write_text(json.dumps(TRANSFORMER_CONFIG, indent=2) + "\n")
-    dst = transformer_dir / "model.safetensors"
-    if dst.is_symlink() and not dst.exists():
-        dst.unlink()  # dangling symlink: replace so re-conversion self-heals
-    if dst.exists():
-        return
-    if symlink_transformer:
-        dst.symlink_to(src.resolve())
-    else:
-        shutil.copy2(src, dst)
-
-
-def _copy_or_link_component(component: str, component_source: Path, output: Path, symlink: bool) -> None:
-    src = component_source / component
-    dst = output / component
-    if not src.exists():
-        raise FileNotFoundError(f"Missing reused component source: {src}")
-    if dst.is_symlink() and not dst.exists():
-        dst.unlink()  # dangling symlink: replace so re-conversion self-heals
-    if dst.exists():
-        return
-    if symlink:
-        dst.symlink_to(src.resolve(), target_is_directory=src.is_dir())
-    elif src.is_dir():
-        shutil.copytree(src, dst)
-    else:
-        shutil.copy2(src, dst)
-
-
-def write_model_index(output: Path, component_source: Path | None, symlink_components: bool) -> None:
-    if component_source is not None:
-        for component in REUSED_COMPONENTS:
-            _copy_or_link_component(component, component_source, output, symlink_components)
-    missing = [component for component in REUSED_COMPONENTS if not (output / component).exists()]
-    if missing:
-        print("skipping model_index.json; missing reused components: " + ", ".join(missing))
-        return
-    (output / "model_index.json").write_text(json.dumps(MODEL_INDEX, indent=2) + "\n")
-
-
-def main() -> None:
-    parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument("--source", type=Path, required=True)
-    parser.add_argument("--output", type=Path, required=True)
-    parser.add_argument("--component-source", type=Path)
-    parser.add_argument("--symlink-components", action="store_true")
-    parser.add_argument("--symlink-transformer", action="store_true")
-    args = parser.parse_args()
-
-    convert_transformer(args.source, args.output, args.symlink_transformer)
-    write_model_index(args.output, args.component_source, args.symlink_components)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/scripts/checkpoint_conversion/dreamx_world_to_diffusers.py b/scripts/checkpoint_conversion/dreamx_world_to_diffusers.py
deleted file mode 100644
index 449f51afe..000000000
--- a/scripts/checkpoint_conversion/dreamx_world_to_diffusers.py
+++ /dev/null
@@ -1,226 +0,0 @@
-#!/usr/bin/env python3
-# SPDX-License-Identifier: Apache-2.0
-"""Convert DreamX-World-5B-Cam raw transformer weights to FastVideo-loadable format.
-
-The GD-ML/DreamX-World-5B-Cam repository stores the transformer as raw
-DreamX/Wan official shards. FastVideo's TransformerLoader expects a Diffusers-like
-transformer folder with a config.json and safetensors whose keys can be mapped by
-WanVideoConfig.param_names_mapping. This script performs the raw official ->
-Diffusers-like key rename and writes the DreamX 5B-Cam transformer config.
-
-Example:
-    python scripts/checkpoint_conversion/dreamx_world_to_diffusers.py \
-        --source official_weights/dreamx_world \
-        --output converted_weights/dreamx_world
-"""
-
-from __future__ import annotations
-
-import argparse
-import json
-import re
-import shutil
-from collections import OrderedDict
-from pathlib import Path
-
-import torch
-from huggingface_hub import save_torch_state_dict
-from safetensors import safe_open
-from safetensors.torch import load_file
-
-OFFICIAL_TO_DIFFUSERS_MAPPING: dict[str, str] = {
-    r"^text_embedding\.0\.(.*)$": r"condition_embedder.text_embedder.linear_1.\1",
-    r"^text_embedding\.2\.(.*)$": r"condition_embedder.text_embedder.linear_2.\1",
-    r"^time_embedding\.0\.(.*)$": r"condition_embedder.time_embedder.linear_1.\1",
-    r"^time_embedding\.2\.(.*)$": r"condition_embedder.time_embedder.linear_2.\1",
-    r"^time_projection\.1\.(.*)$": r"condition_embedder.time_proj.\1",
-    r"^img_emb\.proj\.0\.(.*)$": r"condition_embedder.image_embedder.norm1.\1",
-    r"^img_emb\.proj\.1\.(.*)$": r"condition_embedder.image_embedder.ff.net.0.proj.\1",
-    r"^img_emb\.proj\.3\.(.*)$": r"condition_embedder.image_embedder.ff.net.2.\1",
-    r"^img_emb\.proj\.4\.(.*)$": r"condition_embedder.image_embedder.norm2.\1",
-    r"^head\.modulation": r"scale_shift_table",
-    r"^head\.head\.(.*)$": r"proj_out.\1",
-    r"^blocks\.(\d+)\.self_attn\.q\.(.*)$": r"blocks.\1.attn1.to_q.\2",
-    r"^blocks\.(\d+)\.self_attn\.k\.(.*)$": r"blocks.\1.attn1.to_k.\2",
-    r"^blocks\.(\d+)\.self_attn\.v\.(.*)$": r"blocks.\1.attn1.to_v.\2",
-    r"^blocks\.(\d+)\.self_attn\.o\.(.*)$": r"blocks.\1.attn1.to_out.0.\2",
-    r"^blocks\.(\d+)\.self_attn\.norm_q\.(.*)$": r"blocks.\1.attn1.norm_q.\2",
-    r"^blocks\.(\d+)\.self_attn\.norm_k\.(.*)$": r"blocks.\1.attn1.norm_k.\2",
-    r"^blocks\.(\d+)\.cross_attn\.q\.(.*)$": r"blocks.\1.attn2.to_q.\2",
-    r"^blocks\.(\d+)\.cross_attn\.k\.(.*)$": r"blocks.\1.attn2.to_k.\2",
-    r"^blocks\.(\d+)\.cross_attn\.k_img\.(.*)$": r"blocks.\1.attn2.add_k_proj.\2",
-    r"^blocks\.(\d+)\.cross_attn\.v\.(.*)$": r"blocks.\1.attn2.to_v.\2",
-    r"^blocks\.(\d+)\.cross_attn\.v_img\.(.*)$": r"blocks.\1.attn2.add_v_proj.\2",
-    r"^blocks\.(\d+)\.cross_attn\.o\.(.*)$": r"blocks.\1.attn2.to_out.0.\2",
-    r"^blocks\.(\d+)\.cross_attn\.norm_q\.(.*)$": r"blocks.\1.attn2.norm_q.\2",
-    r"^blocks\.(\d+)\.cross_attn\.norm_k\.(.*)$": r"blocks.\1.attn2.norm_k.\2",
-    r"^blocks\.(\d+)\.cross_attn\.norm_k_img\.(.*)$": r"blocks.\1.attn2.norm_added_k.\2",
-    r"^blocks\.(\d+)\.ffn\.0\.(.*)$": r"blocks.\1.ffn.net.0.proj.\2",
-    r"^blocks\.(\d+)\.ffn\.2\.(.*)$": r"blocks.\1.ffn.net.2.\2",
-    r"^blocks\.(\d+)\.modulation": r"blocks.\1.scale_shift_table",
-    r"^blocks\.(\d+)\.norm3\.(.*)$": r"blocks.\1.norm2.\2",
-}
-
-TRANSFORMER_CONFIG: dict[str, object] = {
-    "_class_name": "DreamXWorldTransformer3DModel",
-    "patch_size": [1, 2, 2],
-    "text_len": 512,
-    "num_attention_heads": 24,
-    "attention_head_dim": 128,
-    "in_channels": 48,
-    "out_channels": 48,
-    "text_dim": 4096,
-    "freq_dim": 256,
-    "ffn_dim": 14336,
-    "num_layers": 30,
-    "cross_attn_norm": True,
-    "qk_norm": "rms_norm_across_heads",
-    "eps": 1e-6,
-    "image_dim": None,
-    "added_kv_proj_dim": None,
-    "rope_max_seq_len": 1024,
-    "add_control_adapter": True,
-    "cam_method": "prope",
-    "attn_compress": 1,
-    "cam_self_attn_layers": None,
-}
-
-MODEL_INDEX: dict[str, object] = {
-    "_class_name": "DreamXWorldPipeline",
-    "_diffusers_version": "0.31.0",
-    "scheduler": ["diffusers", "FlowMatchEulerDiscreteScheduler"],
-    "text_encoder": ["transformers", "UMT5EncoderModel"],
-    "tokenizer": ["transformers", "AutoTokenizer"],
-    "transformer": ["diffusers", "DreamXWorldTransformer3DModel"],
-    "vae": ["diffusers", "AutoencoderKLWan"],
-}
-
-REUSED_COMPONENTS = ("scheduler", "text_encoder", "tokenizer", "vae")
-
-
-def map_transformer_key(key: str) -> str:
-    for pattern, replacement in OFFICIAL_TO_DIFFUSERS_MAPPING.items():
-        if re.match(pattern, key):
-            return re.sub(pattern, replacement, key)
-    return key
-
-
-def _safetensor_files(source: Path) -> list[Path]:
-    if source.is_file():
-        if source.suffix != ".safetensors":
-            raise ValueError(f"Only .safetensors files are supported, got {source}")
-        return [source]
-
-    index_path = source / "diffusion_pytorch_model.safetensors.index.json"
-    if index_path.exists():
-        index = json.loads(index_path.read_text())
-        return sorted({source / shard for shard in index["weight_map"].values()})
-
-    files = sorted(source.glob("*.safetensors"))
-    if not files:
-        raise FileNotFoundError(f"No safetensors files found under {source}")
-    return files
-
-
-def convert_transformer(source: Path, output: Path, max_shard_size: str) -> None:
-    transformer_dir = output / "transformer"
-    transformer_dir.mkdir(parents=True, exist_ok=True)
-    (transformer_dir / "config.json").write_text(json.dumps(TRANSFORMER_CONFIG, indent=2) + "\n")
-
-    converted: OrderedDict[str, torch.Tensor] = OrderedDict()
-    for shard in _safetensor_files(source):
-        print(f"loading {shard}")
-        for key, tensor in load_file(shard, device="cpu").items():
-            new_key = map_transformer_key(key)
-            if new_key in converted:
-                raise ValueError(f"Duplicate converted key: {new_key}")
-            converted[new_key] = tensor
-
-    print(f"saving {len(converted)} tensors to {transformer_dir}")
-    save_torch_state_dict(converted, transformer_dir, max_shard_size=max_shard_size)
-
-
-def _copy_or_link_component(component: str, component_source: Path, output: Path, symlink: bool) -> None:
-    src = component_source / component
-    dst = output / component
-    if not src.exists():
-        raise FileNotFoundError(f"Missing reused component source: {src}")
-    if dst.is_symlink() and not dst.exists():
-        dst.unlink()  # dangling symlink: replace so re-conversion self-heals
-    if dst.exists():
-        print(f"keeping existing {dst}")
-        return
-    if symlink:
-        dst.symlink_to(src.resolve(), target_is_directory=src.is_dir())
-        print(f"linked {dst} -> {src}")
-    elif src.is_dir():
-        shutil.copytree(src, dst)
-        print(f"copied {src} -> {dst}")
-    else:
-        shutil.copy2(src, dst)
-        print(f"copied {src} -> {dst}")
-
-
-def write_model_index(output: Path, component_source: Path | None, symlink_components: bool) -> None:
-    if component_source is not None:
-        for component in REUSED_COMPONENTS:
-            _copy_or_link_component(component, component_source, output, symlink_components)
-    missing = [component for component in REUSED_COMPONENTS if not (output / component).exists()]
-    if missing:
-        print("skipping model_index.json; missing reused components: " + ", ".join(missing))
-        print("pass --component-source <Wan2.2 Diffusers root> to copy or link reused components")
-        return
-    (output / "model_index.json").write_text(json.dumps(MODEL_INDEX, indent=2) + "\n")
-    print(f"wrote {output / 'model_index.json'}")
-
-
-def analyze(source: Path) -> None:
-    total = 0
-    unchanged = 0
-    examples: list[tuple[str, str]] = []
-    for shard in _safetensor_files(source):
-        with safe_open(shard, framework="pt", device="cpu") as tensors:
-            for key in tensors:
-                total += 1
-                new_key = map_transformer_key(key)
-                unchanged += int(new_key == key)
-                if len(examples) < 20 and new_key != key:
-                    examples.append((key, new_key))
-    print(f"total_keys={total} unchanged_keys={unchanged}")
-    for old, new in examples:
-        print(f"{old} -> {new}")
-
-
-def main() -> None:
-    parser = argparse.ArgumentParser(description=__doc__)
-    parser.add_argument("--source",
-                        type=Path,
-                        required=True,
-                        help="DreamX raw transformer directory or safetensors file")
-    parser.add_argument("--output",
-                        type=Path,
-                        required=True,
-                        help="Output model root; transformer/ is created inside it")
-    parser.add_argument("--max-shard-size", default="10GB")
-    parser.add_argument(
-        "--component-source",
-        type=Path,
-        help="Optional Wan2.2 Diffusers root whose scheduler/text_encoder/tokenizer/vae components are reused.",
-    )
-    parser.add_argument(
-        "--symlink-components",
-        action="store_true",
-        help="Symlink reused components from --component-source instead of copying them.",
-    )
-    parser.add_argument("--analyze", action="store_true", help="Only print key mapping summary")
-    args = parser.parse_args()
-
-    if args.analyze:
-        analyze(args.source)
-    else:
-        convert_transformer(args.source, args.output, args.max_shard_size)
-        write_model_index(args.output, args.component_source, args.symlink_components)
-
-
-if __name__ == "__main__":
-    main()
diff --git a/scripts/checkpoint_conversion/extract_llava_text_encoder.py b/scripts/checkpoint_conversion/extract_llava_text_encoder.py
deleted file mode 100644
index b202aec4c..000000000
--- a/scripts/checkpoint_conversion/extract_llava_text_encoder.py
+++ /dev/null
@@ -1,232 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-Extract LlamaModel text encoder from LLaVA weights for GameCraft/HunyuanVideo.
-
-LLaVA stores the text encoder as `language_model.model.XXX`, but FastVideo/HunyuanVideo
-expects just `XXX` (e.g., `layers.0.self_attn.q_proj.weight` instead of 
-`language_model.model.layers.0.self_attn.q_proj.weight`).
-
-This script extracts just the LlamaModel weights and renames them.
-
-Usage:
-    python scripts/checkpoint_conversion/extract_llava_text_encoder.py \
-        --input Hunyuan-GameCraft-1.0/weights/stdmodels/llava-llama-3-8b-v1_1-transformers \
-        --output official_weights/hunyuan-gamecraft-diffusers/text_encoder
-"""
-from __future__ import annotations
-
-import argparse
-import json
-from pathlib import Path
-from collections import OrderedDict
-
-import torch
-from safetensors import safe_open
-from safetensors.torch import save_file
-
-
-def extract_text_encoder(
-    input_dir: Path,
-    output_dir: Path,
-) -> dict:
-    """Extract LlamaModel from LLaVA weights."""
-
-    # Find all safetensor shards
-    shard_files = sorted(input_dir.glob("model-*.safetensors"))
-    if not shard_files:
-        raise FileNotFoundError(f"No safetensor files found in {input_dir}")
-
-    print(f"Found {len(shard_files)} weight shards")
-
-    # Collect all weights, filtering for language_model.model prefix
-    extracted_weights: OrderedDict[str, torch.Tensor] = OrderedDict()
-
-    for shard_file in shard_files:
-        print(f"Processing {shard_file.name}...")
-        with safe_open(str(shard_file), framework="pt") as f:
-            for key in f.keys():
-                # Extract only language_model.model weights
-                if key.startswith("language_model.model."):
-                    # Strip the prefix
-                    new_key = key.replace("language_model.model.", "")
-                    extracted_weights[new_key] = f.get_tensor(key)
-
-    print(f"Extracted {len(extracted_weights)} weights")
-
-    # Save to output directory
-    output_dir.mkdir(parents=True, exist_ok=True)
-
-    # Save as single file (it should be <10GB which is fine for safetensors)
-    output_file = output_dir / "model.safetensors"
-    print(f"Saving to {output_file}...")
-    save_file(extracted_weights, str(output_file))
-
-    # Create config.json for LlamaModel
-    config = {
-        "_name_or_path": "llava-llama-3-8b-v1_1-text_encoder",
-        "architectures": ["LlamaModel"],
-        "attention_bias": False,
-        "attention_dropout": 0.0,
-        "bos_token_id": 128000,
-        "eos_token_id": 128001,
-        "head_dim": 128,
-        "hidden_act": "silu",
-        "hidden_size": 4096,
-        "initializer_range": 0.02,
-        "intermediate_size": 14336,
-        "max_position_embeddings": 8192,
-        "mlp_bias": False,
-        "model_type": "llama",
-        "num_attention_heads": 32,
-        "num_hidden_layers": 32,
-        "num_key_value_heads": 8,
-        "pretraining_tp": 1,
-        "rms_norm_eps": 1e-05,
-        "rope_scaling": None,
-        "rope_theta": 500000.0,
-        "tie_word_embeddings": False,
-        "torch_dtype": "float16",
-        "transformers_version": "4.48.0",
-        "use_cache": True,
-        "vocab_size": 128320
-    }
-
-    with open(output_dir / "config.json", "w") as f:
-        json.dump(config, f, indent=2)
-    print(f"Saved config.json")
-
-    # Copy tokenizer files if they exist
-    tokenizer_files = [
-        "tokenizer.json",
-        "tokenizer_config.json",
-        "special_tokens_map.json",
-    ]
-    for fname in tokenizer_files:
-        src = input_dir / fname
-        if src.exists():
-            import shutil
-            shutil.copy2(src, output_dir / fname)
-            print(f"Copied {fname}")
-
-    return {"total": len(extracted_weights)}
-
-
-def extract_clip_text_encoder(
-    input_dir: Path,
-    output_dir: Path,
-) -> dict:
-    """Extract CLIPTextModel from full CLIP model weights."""
-
-    # Find weight file
-    weight_files = list(input_dir.glob("model.safetensors")) + \
-                   list(input_dir.glob("pytorch_model.bin"))
-
-    if not weight_files:
-        raise FileNotFoundError(f"No weight files found in {input_dir}")
-
-    weight_file = weight_files[0]
-    print(f"Loading {weight_file}...")
-
-    if weight_file.suffix == ".safetensors":
-        from safetensors.torch import load_file
-        state_dict = load_file(str(weight_file))
-    else:
-        state_dict = torch.load(weight_file, map_location="cpu")
-
-    # Extract only text_model weights
-    extracted_weights: OrderedDict[str, torch.Tensor] = OrderedDict()
-
-    for key, value in state_dict.items():
-        # CLIP full model has text_model.XXX, we want just XXX
-        if key.startswith("text_model."):
-            new_key = key.replace("text_model.", "")
-            extracted_weights[new_key] = value
-
-    if not extracted_weights:
-        # Maybe it's already just the text model
-        extracted_weights = OrderedDict(state_dict)
-
-    print(f"Extracted {len(extracted_weights)} weights")
-
-    # Save to output directory
-    output_dir.mkdir(parents=True, exist_ok=True)
-
-    output_file = output_dir / "model.safetensors"
-    print(f"Saving to {output_file}...")
-    save_file(extracted_weights, str(output_file))
-
-    # Create config.json for CLIPTextModel
-    config = {
-        "_name_or_path": "openai/clip-vit-large-patch14",
-        "architectures": ["CLIPTextModel"],
-        "attention_dropout": 0.0,
-        "bos_token_id": 0,
-        "dropout": 0.0,
-        "eos_token_id": 2,
-        "hidden_act": "quick_gelu",
-        "hidden_size": 768,
-        "initializer_factor": 1.0,
-        "initializer_range": 0.02,
-        "intermediate_size": 3072,
-        "layer_norm_eps": 1e-05,
-        "max_position_embeddings": 77,
-        "model_type": "clip_text_model",
-        "num_attention_heads": 12,
-        "num_hidden_layers": 12,
-        "pad_token_id": 1,
-        "projection_dim": 768,
-        "torch_dtype": "float16",
-        "transformers_version": "4.48.0",
-        "vocab_size": 49408
-    }
-
-    with open(output_dir / "config.json", "w") as f:
-        json.dump(config, f, indent=2)
-    print(f"Saved config.json")
-
-    # Copy tokenizer files if they exist
-    tokenizer_files = [
-        "tokenizer.json",
-        "tokenizer_config.json",
-        "special_tokens_map.json",
-        "vocab.json",
-        "merges.txt",
-    ]
-    for fname in tokenizer_files:
-        src = input_dir / fname
-        if src.exists():
-            import shutil
-            shutil.copy2(src, output_dir / fname)
-            print(f"Copied {fname}")
-
-    return {"total": len(extracted_weights)}
-
-
-def main():
-    parser = argparse.ArgumentParser(description="Extract text encoder from LLaVA or CLIP model weights.")
-    parser.add_argument("--input", type=str, required=True, help="Input directory containing model weights")
-    parser.add_argument("--output", type=str, required=True, help="Output directory for extracted weights")
-    parser.add_argument("--type",
-                        type=str,
-                        choices=["llama", "clip"],
-                        default="llama",
-                        help="Type of text encoder to extract")
-
-    args = parser.parse_args()
-
-    input_dir = Path(args.input)
-    output_dir = Path(args.output)
-
-    if not input_dir.exists():
-        raise FileNotFoundError(f"Input directory not found: {input_dir}")
-
-    if args.type == "llama":
-        stats = extract_text_encoder(input_dir, output_dir)
-    else:
-        stats = extract_clip_text_encoder(input_dir, output_dir)
-
-    print(f"\nExtraction complete! Total weights: {stats['total']}")
-
-
-if __name__ == "__main__":
-    main()
diff --git a/scripts/checkpoint_conversion/longcat_to_fastvideo.py b/scripts/checkpoint_conversion/longcat_to_fastvideo.py
deleted file mode 100644
index 19a80601c..000000000
--- a/scripts/checkpoint_conversion/longcat_to_fastvideo.py
+++ /dev/null
@@ -1,706 +0,0 @@
-#!/usr/bin/env python3
-# SPDX-License-Identifier: Apache-2.0
-"""
-Convert LongCat weights to FastVideo native format.
-
-This script performs a complete conversion from original LongCat weights
-to FastVideo native implementation in a single step:
-
-1. Converts transformer weights (with QKV/KV splitting)
-2. Copies other components (VAE, text encoder, tokenizer, scheduler)
-3. Converts LoRA weights (cfg_step_lora, refinement_lora)
-4. Updates config files to point to native model
-
-Usage:
-    python scripts/checkpoint_conversion/longcat_to_fastvideo.py \
-        --source /path/to/LongCat-Video/weights/LongCat-Video \
-        --output weights/longcat-native \
-        --validate
-"""
-
-import argparse
-import glob
-import json
-import re
-import shutil
-from pathlib import Path
-from collections import OrderedDict
-
-import torch
-from safetensors.torch import load_file, save_file
-from tqdm import tqdm
-
-
-def split_qkv(qkv_weight: torch.Tensor, qkv_bias: torch.Tensor | None = None):
-    """Split fused QKV projection into separate Q, K, V."""
-    dim = qkv_weight.shape[0] // 3
-    q, k, v = torch.chunk(qkv_weight, 3, dim=0)
-
-    if qkv_bias is not None:
-        q_bias, k_bias, v_bias = torch.chunk(qkv_bias, 3, dim=0)
-    else:
-        q_bias = k_bias = v_bias = None
-
-    return (q, k, v), (q_bias, k_bias, v_bias)
-
-
-def split_kv(kv_weight: torch.Tensor, kv_bias: torch.Tensor | None = None):
-    """Split fused KV projection into separate K, V."""
-    dim = kv_weight.shape[0] // 2
-    k, v = torch.chunk(kv_weight, 2, dim=0)
-
-    if kv_bias is not None:
-        k_bias, v_bias = torch.chunk(kv_bias, 2, dim=0)
-    else:
-        k_bias = v_bias = None
-
-    return (k, v), (k_bias, v_bias)
-
-
-def convert_transformer_weights(source_weights: dict[str, torch.Tensor]) -> dict[str, torch.Tensor]:
-    """
-    Convert LongCat transformer weights to native FastVideo format.
-    
-    Main transformations:
-    1. Split fused QKV projections (self-attention)
-    2. Split fused KV projections (cross-attention)
-    3. Rename parameters according to mapping
-    """
-    converted = OrderedDict()
-    processed_keys = set()
-
-    print("  Converting transformer weights...")
-
-    for key, value in tqdm(source_weights.items(), desc="  Processing parameters"):
-        if key in processed_keys:
-            continue
-
-        # === Embedders ===
-        if key.startswith("x_embedder."):
-            new_key = key.replace("x_embedder.", "patch_embed.")
-            converted[new_key] = value
-
-        elif key.startswith("t_embedder.mlp.0."):
-            new_key = key.replace("t_embedder.mlp.0.", "time_embedder.linear_1.")
-            converted[new_key] = value
-
-        elif key.startswith("t_embedder.mlp.2."):
-            new_key = key.replace("t_embedder.mlp.2.", "time_embedder.linear_2.")
-            converted[new_key] = value
-
-        elif key.startswith("y_embedder.y_proj.0."):
-            new_key = key.replace("y_embedder.y_proj.0.", "caption_embedder.linear_1.")
-            converted[new_key] = value
-
-        elif key.startswith("y_embedder.y_proj.2."):
-            new_key = key.replace("y_embedder.y_proj.2.", "caption_embedder.linear_2.")
-            converted[new_key] = value
-
-        # === Self-Attention QKV Splitting ===
-        elif ".attn.qkv.weight" in key:
-            block_idx = key.split(".")[1]
-            qkv_weight = value
-            qkv_bias_key = key.replace(".weight", ".bias")
-            qkv_bias = source_weights.get(qkv_bias_key)
-
-            (q, k, v), (q_bias, k_bias, v_bias) = split_qkv(qkv_weight, qkv_bias)
-
-            converted[f"blocks.{block_idx}.self_attn.to_q.weight"] = q
-            converted[f"blocks.{block_idx}.self_attn.to_k.weight"] = k
-            converted[f"blocks.{block_idx}.self_attn.to_v.weight"] = v
-
-            if q_bias is not None:
-                converted[f"blocks.{block_idx}.self_attn.to_q.bias"] = q_bias
-                converted[f"blocks.{block_idx}.self_attn.to_k.bias"] = k_bias
-                converted[f"blocks.{block_idx}.self_attn.to_v.bias"] = v_bias
-
-            processed_keys.add(key)
-            if qkv_bias is not None:
-                processed_keys.add(qkv_bias_key)
-
-        elif ".attn.qkv.bias" in key:
-            continue
-
-        elif ".attn.proj." in key:
-            new_key = key.replace(".attn.proj.", ".self_attn.to_out.")
-            converted[new_key] = value
-
-        elif ".attn.q_norm." in key or ".attn.k_norm." in key:
-            new_key = key.replace(".attn.", ".self_attn.")
-            converted[new_key] = value
-
-        # === Cross-Attention ===
-        elif ".cross_attn.q_linear." in key:
-            new_key = key.replace(".cross_attn.q_linear.", ".cross_attn.to_q.")
-            converted[new_key] = value
-
-        elif ".cross_attn.kv_linear.weight" in key:
-            block_idx = key.split(".")[1]
-            kv_weight = value
-            kv_bias_key = key.replace(".weight", ".bias")
-            kv_bias = source_weights.get(kv_bias_key)
-
-            (k, v), (k_bias, v_bias) = split_kv(kv_weight, kv_bias)
-
-            converted[f"blocks.{block_idx}.cross_attn.to_k.weight"] = k
-            converted[f"blocks.{block_idx}.cross_attn.to_v.weight"] = v
-
-            if k_bias is not None:
-                converted[f"blocks.{block_idx}.cross_attn.to_k.bias"] = k_bias
-                converted[f"blocks.{block_idx}.cross_attn.to_v.bias"] = v_bias
-
-            processed_keys.add(key)
-            if kv_bias is not None:
-                processed_keys.add(kv_bias_key)
-
-        elif ".cross_attn.kv_linear.bias" in key:
-            continue
-
-        elif ".cross_attn.proj." in key:
-            new_key = key.replace(".cross_attn.proj.", ".cross_attn.to_out.")
-            converted[new_key] = value
-
-        elif ".cross_attn.q_norm." in key or ".cross_attn.k_norm." in key:
-            converted[key] = value
-
-        # === Final Layer (must come BEFORE general transformer block patterns) ===
-        elif key.startswith("final_layer.adaLN_modulation.1."):
-            new_key = key.replace("final_layer.adaLN_modulation.1.", "final_layer.adaln_linear.")
-            converted[new_key] = value
-
-        # === Transformer Block AdaLN ===
-        elif ".adaLN_modulation.1." in key:
-            new_key = key.replace(".adaLN_modulation.1.", ".adaln_linear_1.")
-            converted[new_key] = value
-
-        # === Transformer Block Normalization ===
-        elif ".mod_norm_attn." in key or ".mod_norm_ffn." in key:
-            continue
-
-        elif ".pre_crs_attn_norm.weight" in key:
-            new_key = key.replace(".pre_crs_attn_norm.", ".norm_cross.")
-            converted[new_key] = value
-
-        elif ".pre_crs_attn_norm.bias" in key:
-            new_key = key.replace(".pre_crs_attn_norm.", ".norm_cross.")
-            converted[new_key] = value
-
-        # === FFN (SwiGLU) ===
-        elif ".ffn.w1." in key or ".ffn.w2." in key or ".ffn.w3." in key:
-            converted[key] = value
-
-        elif key.startswith("final_layer.norm_final."):
-            continue
-
-        elif key.startswith("final_layer.linear."):
-            new_key = key.replace("final_layer.linear.", "final_layer.proj.")
-            converted[new_key] = value
-
-        else:
-            print(f"    ⚠️  Unknown key: {key}")
-            converted[key] = value
-
-    return converted
-
-
-def validate_conversion(original: dict, converted: dict) -> bool:
-    """Validate that conversion preserved all parameters correctly."""
-    print("\n  Validating conversion...")
-
-    orig_count = sum(p.numel() for p in original.values())
-    conv_count = sum(p.numel() for p in converted.values())
-
-    dropped_count = 0
-    for key, value in original.items():
-        if ".mod_norm_attn." in key or ".mod_norm_ffn." in key:
-            dropped_count += value.numel()
-        elif "final_layer.norm_final." in key:
-            dropped_count += value.numel()
-
-    expected_conv_count = orig_count - dropped_count
-
-    print(f"    Original parameters: {orig_count:,}")
-    print(f"    Converted parameters: {conv_count:,}")
-    print(f"    Dropped parameters (norms without params): {dropped_count:,}")
-
-    if conv_count != expected_conv_count:
-        print(f"    ⚠️  Parameter count mismatch!")
-        return False
-
-    print(f"    ✓ Parameter count matches")
-
-    # Verify QKV/KV splits
-    print("\n  Verifying QKV/KV splits...")
-    num_blocks = 48
-
-    for i in range(num_blocks):
-        orig_qkv_weight = original.get(f"blocks.{i}.attn.qkv.weight")
-        if orig_qkv_weight is not None:
-            conv_q = converted[f"blocks.{i}.self_attn.to_q.weight"]
-            conv_k = converted[f"blocks.{i}.self_attn.to_k.weight"]
-            conv_v = converted[f"blocks.{i}.self_attn.to_v.weight"]
-            reconstructed = torch.cat([conv_q, conv_k, conv_v], dim=0)
-            if not torch.allclose(orig_qkv_weight, reconstructed):
-                print(f"    ❌ QKV weight mismatch in block {i}")
-                return False
-
-        orig_kv_weight = original.get(f"blocks.{i}.cross_attn.kv_linear.weight")
-        if orig_kv_weight is not None:
-            conv_k = converted[f"blocks.{i}.cross_attn.to_k.weight"]
-            conv_v = converted[f"blocks.{i}.cross_attn.to_v.weight"]
-            reconstructed = torch.cat([conv_k, conv_v], dim=0)
-            if not torch.allclose(orig_kv_weight, reconstructed):
-                print(f"    ❌ KV weight mismatch in block {i}")
-                return False
-
-    print(f"    ✓ All splits verified successfully")
-    return True
-
-
-def copy_component(source_dir: Path, output_dir: Path, component: str, mapping: dict = None) -> bool:
-    """Copy a component directory, optionally with name mapping."""
-    source_name = mapping.get(component, component) if mapping else component
-    source_path = source_dir / source_name
-
-    if source_path.exists():
-        output_path = output_dir / component
-        if output_path.exists():
-            shutil.rmtree(output_path)
-        shutil.copytree(source_path, output_path)
-        print(f"  ✓ {component} copied")
-        return True
-    else:
-        print(f"  ⚠️  {component} not found, skipping")
-        return False
-
-
-def create_model_index():
-    """Create model_index.json for FastVideo native model."""
-    return {
-        "_class_name": "LongCatPipeline",
-        "_diffusers_version": "0.32.0",
-        "workload_type": "video-generation",
-        "tokenizer": ["transformers", "AutoTokenizer"],
-        "text_encoder": ["transformers", "UMT5EncoderModel"],
-        "vae": ["diffusers", "AutoencoderKLWan"],
-        "scheduler": ["diffusers", "FlowMatchEulerDiscreteScheduler"],
-        "transformer": ["diffusers", "LongCatTransformer3DModel"]  # Native model
-    }
-
-
-def update_transformer_config(transformer_dir: Path):
-    """Update transformer config.json to point to native model."""
-    config_path = transformer_dir / "config.json"
-    if not config_path.exists():
-        print("  ⚠️  Transformer config not found, skipping")
-        return
-
-    with open(config_path, 'r') as f:
-        config = json.load(f)
-
-    if '_class_name' in config:
-        old_class = config['_class_name']
-        config['_class_name'] = 'LongCatTransformer3DModel'
-        print(f"  Updated _class_name: {old_class} → LongCatTransformer3DModel")
-    else:
-        config['_class_name'] = 'LongCatTransformer3DModel'
-        print(f"  Added _class_name: LongCatTransformer3DModel")
-
-    # Fix num_heads -> num_attention_heads for FastVideo compatibility
-    if 'num_heads' in config and 'num_attention_heads' not in config:
-        config['num_attention_heads'] = config.pop('num_heads')
-        print(f"  Updated num_heads → num_attention_heads")
-
-    with open(config_path, 'w') as f:
-        json.dump(config, f, indent=2)
-
-    print("  ✓ Transformer config updated")
-
-
-# ============================================================================
-# LoRA Conversion Functions
-# ============================================================================
-
-
-def parse_lora_key(key: str) -> tuple[str, str]:
-    """Parse LongCat LoRA key into module path and weight type."""
-    if key.startswith("lora___lorahyphen___"):
-        key = key[len("lora___lorahyphen___"):]
-
-    key = key.replace("___lorahyphen___", ".")
-
-    if ".lora_down.weight" in key:
-        return key.replace(".lora_down.weight", ""), "lora_down.weight"
-    elif ".lora_up.weight" in key:
-        return key.replace(".lora_up.weight", ""), "lora_up.weight"
-    elif ".lora_up.blocks." in key:
-        match = re.match(r"(.+)\.lora_up\.blocks\.(\d+)\.weight", key)
-        if match:
-            return match.group(1), f"lora_up.blocks.{match.group(2)}.weight"
-    elif ".alpha_scale" in key:
-        return key.replace(".alpha_scale", ""), "alpha_scale"
-    elif ".lora_alpha" in key:
-        return key.replace(".lora_alpha", ""), "lora_alpha"
-
-    raise ValueError(f"Unknown LoRA key format: {key}")
-
-
-def map_lora_module(module_path: str) -> list[tuple[str, str]]:
-    """Map LongCat module path to FastVideo paths. Returns [(path, component)]."""
-    # Self-attention QKV → Q, K, V
-    match = re.match(r"blocks\.(\d+)\.attn\.qkv", module_path)
-    if match:
-        b = match.group(1)
-        return [(f"blocks.{b}.self_attn.to_q", "q"), (f"blocks.{b}.self_attn.to_k", "k"),
-                (f"blocks.{b}.self_attn.to_v", "v")]
-
-    # Self-attention output
-    match = re.match(r"blocks\.(\d+)\.attn\.proj", module_path)
-    if match:
-        return [(f"blocks.{match.group(1)}.self_attn.to_out", "single")]
-
-    # Cross-attention Q
-    match = re.match(r"blocks\.(\d+)\.cross_attn\.q_linear", module_path)
-    if match:
-        return [(f"blocks.{match.group(1)}.cross_attn.to_q", "single")]
-
-    # Cross-attention KV → K, V
-    match = re.match(r"blocks\.(\d+)\.cross_attn\.kv_linear", module_path)
-    if match:
-        b = match.group(1)
-        return [(f"blocks.{b}.cross_attn.to_k", "k"), (f"blocks.{b}.cross_attn.to_v", "v")]
-
-    # FFN
-    match = re.match(r"blocks\.(\d+)\.ffn\.(w[123])", module_path)
-    if match:
-        return [(f"blocks.{match.group(1)}.ffn.{match.group(2)}", "single")]
-
-    # AdaLN modulation
-    match = re.match(r"blocks\.(\d+)\.adaLN_modulation\.1", module_path)
-    if match:
-        return [(f"blocks.{match.group(1)}.adaln_linear_1", "single")]
-
-    # Final layer
-    if module_path == "final_layer.adaLN_modulation.1":
-        return [("final_layer.adaln_linear", "single")]
-    if module_path == "final_layer.linear":
-        return [("final_layer.proj", "single")]
-
-    raise ValueError(f"Unknown LoRA module: {module_path}")
-
-
-def convert_lora_weights(source_weights: dict[str, torch.Tensor], lora_name: str) -> dict[str, torch.Tensor]:
-    """Convert LongCat LoRA to FastVideo format."""
-    print(f"  Converting {lora_name}...")
-    print(f"    Source keys: {len(source_weights)}")
-
-    converted = OrderedDict()
-
-    # Group by module
-    modules = {}
-    for key in source_weights.keys():
-        try:
-            module_path, weight_type = parse_lora_key(key)
-            if module_path not in modules:
-                modules[module_path] = {}
-            modules[module_path][weight_type] = key
-        except ValueError:
-            continue
-
-    # Process each module
-    for module_path, weight_keys in modules.items():
-        try:
-            targets = map_lora_module(module_path)
-        except ValueError:
-            continue
-
-        # Get alpha_scale if present (defaults to 1.0 if missing)
-        alpha_scale = 1.0
-        if "alpha_scale" in weight_keys:
-            alpha_scale_tensor = source_weights[weight_keys["alpha_scale"]]
-            alpha_scale = alpha_scale_tensor.item() if alpha_scale_tensor.numel() == 1 else float(
-                alpha_scale_tensor.mean())
-
-        # Handle lora_down (lora_A)
-        if "lora_down.weight" in weight_keys:
-            lora_down = source_weights[weight_keys["lora_down.weight"]]
-
-            if len(targets) == 1:
-                converted[f"{targets[0][0]}.lora_A"] = lora_down
-                # Compute alpha from alpha_scale and rank
-                rank = lora_down.shape[0]
-                alpha = alpha_scale * rank
-                converted[f"{targets[0][0]}.lora_alpha"] = torch.tensor(alpha, dtype=torch.float32)
-            else:
-                # Split for fused projections
-                n = len(targets)
-                rank = lora_down.shape[0] // n
-                for i, (path, _) in enumerate(targets):
-                    converted[f"{path}.lora_A"] = lora_down[i * rank:(i + 1) * rank, :]
-                    # Compute alpha from alpha_scale and rank for each split
-                    alpha = alpha_scale * rank
-                    converted[f"{path}.lora_alpha"] = torch.tensor(alpha, dtype=torch.float32)
-
-        # Handle lora_up (lora_B) - may have multiple blocks
-        lora_up_blocks = []
-        i = 0
-        while f"lora_up.blocks.{i}.weight" in weight_keys:
-            lora_up_blocks.append(source_weights[weight_keys[f"lora_up.blocks.{i}.weight"]])
-            i += 1
-
-        if lora_up_blocks:
-            # Multi-block LoRA: construct block-diagonal lora_B
-            # This is equivalent to the multi-block computation without modifying fastvideo
-            n_blocks = len(lora_up_blocks)
-            out_per_block, rank_per_block = lora_up_blocks[0].shape  # e.g., [4096, 128]
-
-            if len(targets) == 1:
-                # Single layer with multi-block: create block-diagonal matrix
-                total_out = out_per_block * n_blocks
-                total_rank = rank_per_block * n_blocks
-                lora_B_blockdiag = torch.zeros(total_out, total_rank, dtype=lora_up_blocks[0].dtype)
-
-                for i in range(n_blocks):
-                    lora_B_blockdiag[i * out_per_block:(i + 1) * out_per_block,
-                                     i * rank_per_block:(i + 1) * rank_per_block] = lora_up_blocks[i]
-
-                converted[f"{targets[0][0]}.lora_B"] = lora_B_blockdiag
-                # Note: rank for alpha calculation should be total_rank (will be computed from lora_A.shape[0])
-            else:
-                # Multi-block with split targets (e.g., QKV split)
-                # Each target gets one block
-                for i, (path, _) in enumerate(targets):
-                    if i < n_blocks:
-                        converted[f"{path}.lora_B"] = lora_up_blocks[i]
-        elif "lora_up.weight" in weight_keys:
-            lora_up = source_weights[weight_keys["lora_up.weight"]]
-            # Split if needed
-            if len(targets) == 1:
-                converted[f"{targets[0][0]}.lora_B"] = lora_up
-            else:
-                n = len(targets)
-                out_dim = lora_up.shape[0] // n
-                for i, (path, _) in enumerate(targets):
-                    converted[f"{path}.lora_B"] = lora_up[i * out_dim:(i + 1) * out_dim, :]
-        else:
-            continue
-
-    print(f"    Output keys: {len(converted)} (including lora_alpha)")
-    # Count how many lora_alpha values were added
-    alpha_count = sum(1 for k in converted.keys() if "lora_alpha" in k)
-    print(f"    Alpha values saved: {alpha_count}")
-    return converted
-
-
-def convert_loras(source_dir: Path, output_dir: Path) -> bool:
-    """Convert all LoRA files in source directory."""
-    lora_source = source_dir / "lora"
-    if not lora_source.exists():
-        print("  No LoRA directory found, skipping")
-        return False
-
-    lora_files = list(lora_source.glob("*.safetensors"))
-    if not lora_files:
-        print("  No LoRA files found, skipping")
-        return False
-
-    print(f"  Found {len(lora_files)} LoRA file(s)")
-
-    # Map LoRA filenames to subdirectory names for FastVideo compatibility
-    # Each LoRA gets its own directory under lora/
-    lora_subdir_mapping = {
-        "cfg_step_lora.safetensors": "distilled",
-        "refinement_lora.safetensors": "refinement",
-    }
-
-    for lora_file in lora_files:
-        try:
-            # Determine output subdirectory - use mapping if available, otherwise generic name
-            if lora_file.name in lora_subdir_mapping:
-                lora_subdir_name = lora_subdir_mapping[lora_file.name]
-            else:
-                # For unknown LoRAs, create subdirectory based on filename
-                lora_subdir_name = lora_file.stem
-
-            lora_output = output_dir / "lora" / lora_subdir_name
-            lora_output.mkdir(parents=True, exist_ok=True)
-
-            # Load
-            source_weights = load_file(str(lora_file))
-
-            # Convert
-            converted = convert_lora_weights(source_weights, lora_file.stem)
-
-            # Save
-            output_file = lora_output / lora_file.name
-            save_file(converted, str(output_file))
-
-            size_mb = output_file.stat().st_size / (1024**2)
-            print(f"    ✓ {lora_file.name} → lora/{lora_subdir_name}/ ({size_mb:.1f} MB)")
-
-        except Exception as e:
-            print(f"    ❌ Failed to convert {lora_file.name}: {e}")
-            return False
-
-    return True
-
-
-def main():
-    parser = argparse.ArgumentParser(description="Convert LongCat weights to FastVideo native format")
-    parser.add_argument(
-        "--source",
-        type=str,
-        required=True,
-        help="Path to original LongCat weights (LongCat-Video/weights/LongCat-Video/)",
-    )
-    parser.add_argument(
-        "--output",
-        type=str,
-        required=True,
-        help="Path to output directory for native weights",
-    )
-    parser.add_argument(
-        "--validate",
-        action="store_true",
-        help="Run validation after conversion",
-    )
-
-    args = parser.parse_args()
-
-    source_dir = Path(args.source)
-    output_dir = Path(args.output)
-
-    # Check source directory
-    if not source_dir.exists():
-        print(f"❌ Error: Source directory not found: {source_dir}")
-        return 1
-
-    # Check for dit/transformer directory (original uses 'dit', we output to 'transformer')
-    transformer_source = source_dir / "dit"
-    if not transformer_source.exists():
-        print(f"❌ Error: DiT directory not found in source")
-        return 1
-
-    print("=" * 60)
-    print("LongCat → FastVideo Native Conversion")
-    print("=" * 60)
-    print(f"Source: {source_dir}")
-    print(f"Output: {output_dir}")
-    print()
-
-    # Step 1: Convert transformer weights
-    print("[Step 1/4] Converting transformer weights...")
-
-    # Load source weights
-    shard_files = sorted(glob.glob(str(transformer_source / "*.safetensors")))
-    if not shard_files:
-        print(f"❌ Error: No safetensors files found in {transformer_source}")
-        return 1
-
-    print(f"  Found {len(shard_files)} shard(s)")
-    source_weights = {}
-    for shard_file in shard_files:
-        print(f"  Loading {Path(shard_file).name}...")
-        source_weights.update(load_file(shard_file))
-
-    print(f"  Loaded {len(source_weights)} parameters")
-
-    # Convert
-    converted_weights = convert_transformer_weights(source_weights)
-    print(f"  Converted to {len(converted_weights)} parameters")
-
-    # Validate if requested
-    if args.validate:
-        if not validate_conversion(source_weights, converted_weights):
-            print("\n❌ Validation failed!")
-            return 1
-        print("\n✓ Validation passed!")
-
-    # Save
-    transformer_output = output_dir / "transformer"
-    transformer_output.mkdir(parents=True, exist_ok=True)
-    output_file = transformer_output / "model.safetensors"
-    print(f"\n  Saving to {output_file}...")
-    save_file(converted_weights, str(output_file))
-    size_gb = output_file.stat().st_size / (1024**3)
-    print(f"  ✓ Saved ({size_gb:.2f} GB)")
-    print()
-
-    # Step 2: Copy other components
-    print("[Step 2/5] Copying other components...")
-    output_dir.mkdir(parents=True, exist_ok=True)
-
-    components = ["vae", "text_encoder", "tokenizer", "scheduler"]
-    for component in components:
-        copy_component(source_dir, output_dir, component)
-
-    print()
-
-    # Step 3: Convert LoRA weights
-    print("[Step 3/5] Converting LoRA weights...")
-    convert_loras(source_dir, output_dir)
-    print()
-
-    # Step 4: Update transformer config
-    print("[Step 4/5] Updating transformer config...")
-
-    # Copy config.json from source if exists
-    source_config = transformer_source / "config.json"
-    output_config = transformer_output / "config.json"
-    if source_config.exists():
-        shutil.copy(source_config, output_config)
-        print(f"  Copied config.json")
-
-    update_transformer_config(transformer_output)
-    print()
-
-    # Step 5: Create model_index.json
-    print("[Step 5/5] Creating model_index.json...")
-    model_index_path = output_dir / "model_index.json"
-    with open(model_index_path, 'w') as f:
-        json.dump(create_model_index(), f, indent=2)
-    print(f"  ✓ Created {model_index_path}")
-    print()
-
-    print("=" * 60)
-    print("✓ Conversion Complete!")
-    print("=" * 60)
-    print(f"Native weights ready at: {output_dir}")
-    print()
-    print("Converted components:")
-    print("  ✓ Transformer (native FastVideo implementation)")
-    print("  ✓ VAE, text encoder, tokenizer, scheduler")
-    if (output_dir / "lora").exists():
-        lora_dirs = [d for d in (output_dir / "lora").iterdir() if d.is_dir()]
-        if lora_dirs:
-            print(f"  ✓ LoRA weights ({len(lora_dirs)} adapters)")
-            for lora_dir in sorted(lora_dirs):
-                print(f"    - lora/{lora_dir.name}/")
-    print()
-    print("Next steps:")
-    print()
-    print("  1. Test basic generation:")
-    print("     from fastvideo import VideoGenerator")
-    print(f"     generator = VideoGenerator.from_pretrained('{output_dir}')")
-    print("     video = generator.generate_video(")
-    print("         prompt='A cat playing piano',")
-    print("         num_inference_steps=50")
-    print("     )")
-    print()
-    if (output_dir / "lora" / "distilled").exists():
-        print("  2. Test distilled generation (16 steps with LoRA):")
-        print(f"     generator = VideoGenerator.from_pretrained('{output_dir}',")
-        print(f"         lora_path='{output_dir}/lora/distilled',")
-        print("         lora_nickname='distilled')")
-        print("     video = generator.generate_video(")
-        print("         prompt='A cat playing piano',")
-        print("         num_inference_steps=16,")
-        print("         guidance_scale=1.0)")
-    print()
-    print()
-
-    return 0
-
-
-if __name__ == "__main__":
-    exit(main())
diff --git a/scripts/checkpoint_conversion/validate_longcat_weights.py b/scripts/checkpoint_conversion/validate_longcat_weights.py
deleted file mode 100644
index d0416dc21..000000000
--- a/scripts/checkpoint_conversion/validate_longcat_weights.py
+++ /dev/null
@@ -1,237 +0,0 @@
-import os
-import json
-import argparse
-from pathlib import Path
-from safetensors import safe_open
-
-
-def validate_components(model_path):
-    """Validate all model components exist."""
-    print("=" * 60)
-    print("VALIDATING COMPONENTS")
-    print("=" * 60)
-
-    components = {
-        "tokenizer": ["special_tokens_map.json", "tokenizer_config.json"],
-        "text_encoder": ["config.json", "model.safetensors.index.json"],
-        "vae": ["config.json", "diffusion_pytorch_model.safetensors"],
-        "scheduler": ["scheduler_config.json"],
-        "transformer": ["config.json", "diffusion_pytorch_model.safetensors.index.json"]
-    }
-
-    all_valid = True
-    for component, required_files in components.items():
-        component_path = os.path.join(model_path, component)
-        print(f"\n{component}:")
-
-        if not os.path.exists(component_path):
-            print(f"  ✗ Directory not found")
-            all_valid = False
-            continue
-
-        for req_file in required_files:
-            file_path = os.path.join(component_path, req_file)
-            exists = os.path.exists(file_path)
-            symbol = "✓" if exists else "✗"
-            print(f"  {symbol} {req_file}")
-            if not exists:
-                all_valid = False
-
-    return all_valid
-
-
-def validate_dit_weights(dit_path):
-    """Validate DiT weights structure."""
-    print("\n" + "=" * 60)
-    print("VALIDATING DiT WEIGHTS")
-    print("=" * 60)
-
-    # Load config
-    config_path = os.path.join(dit_path, "config.json")
-    with open(config_path) as f:
-        config = json.load(f)
-
-    hidden_size = config["hidden_size"]
-    depth = config["depth"]
-    num_heads = config["num_heads"]
-
-    print(f"\nArchitecture:")
-    print(f"  - hidden_size: {hidden_size}")
-    print(f"  - depth: {depth}")
-    print(f"  - num_heads: {num_heads}")
-
-    # Load weight index
-    index_path = os.path.join(dit_path, "diffusion_pytorch_model.safetensors.index.json")
-    with open(index_path) as f:
-        index = json.load(f)
-
-    weight_map = index["weight_map"]
-    all_keys = list(weight_map.keys())
-
-    print(f"\nWeight statistics:")
-    print(f"  - Total keys: {len(all_keys)}")
-    print(f"  - Total size: {index['metadata']['total_size'] / 1e9:.2f} GB")
-
-    # Check structure
-    embedder_keys = [k for k in all_keys if 'embedder' in k]
-    block_keys = [k for k in all_keys if k.startswith('blocks.')]
-    final_keys = [k for k in all_keys if k.startswith('final_layer.')]
-
-    print(f"\nKey distribution:")
-    print(f"  - Embedder layers: {len(embedder_keys)}")
-    print(f"  - Transformer blocks: {len(block_keys)}")
-    print(f"  - Final layer: {len(final_keys)}")
-
-    # Verify all blocks present
-    block_nums = set()
-    for key in block_keys:
-        if key.startswith('blocks.'):
-            block_num = int(key.split('.')[1])
-            block_nums.add(block_num)
-
-    expected_blocks = set(range(depth))
-    missing_blocks = expected_blocks - block_nums
-
-    if missing_blocks:
-        print(f"\n✗ Missing blocks: {sorted(missing_blocks)}")
-        return False
-    else:
-        print(f"\n✓ All {depth} blocks present (0-{depth-1})")
-
-    # Sample weights
-    first_shard = os.path.join(dit_path, "diffusion_pytorch_model-00001-of-00006.safetensors")
-    print(f"\nSampling weights from first shard:")
-
-    with safe_open(first_shard, framework="pt", device="cpu") as f:
-        sample_keys = [k for k in f.keys() if k in all_keys][:5]
-        for key in sample_keys:
-            tensor = f.get_tensor(key)
-            print(f"  - {key}")
-            print(f"    Shape: {tuple(tensor.shape)}, Dtype: {tensor.dtype}")
-
-    return True
-
-
-def validate_shapes(dit_path):
-    """Validate weight shapes match expected architecture."""
-    print("\n" + "=" * 60)
-    print("VALIDATING WEIGHT SHAPES")
-    print("=" * 60)
-
-    # Load config
-    config_path = os.path.join(dit_path, "config.json")
-    with open(config_path) as f:
-        config = json.load(f)
-
-    hidden_size = config["hidden_size"]
-    num_heads = config["num_heads"]
-    head_dim = hidden_size // num_heads
-    adaln_dim = config.get("adaln_tembed_dim", 512)
-    mlp_ratio = config.get("mlp_ratio", 4)
-
-    # Calculate FFN hidden_dim using SwiGLU formula from blocks.py
-    # hidden_dim = int(2 * (hidden_size * mlp_ratio) / 3)
-    # rounded to multiple_of=256
-    multiple_of = 256
-    ffn_hidden = int(2 * hidden_size * mlp_ratio / 3)
-    ffn_hidden = multiple_of * ((ffn_hidden + multiple_of - 1) // multiple_of)
-
-    # Expected shapes
-    expected = {
-        "x_embedder.proj.weight": (hidden_size, 16, 1, 2, 2),
-        "x_embedder.proj.bias": (hidden_size, ),
-        "t_embedder.mlp.0.weight": (adaln_dim, 256),
-        "t_embedder.mlp.2.weight": (adaln_dim, adaln_dim),
-        "y_embedder.y_proj.0.weight": (hidden_size, 4096),
-        "blocks.0.attn.qkv.weight": (3 * hidden_size, hidden_size),
-        "blocks.0.attn.q_norm.weight": (head_dim, ),
-        "blocks.0.attn.proj.weight": (hidden_size, hidden_size),
-        "blocks.0.cross_attn.q_linear.weight": (hidden_size, hidden_size),
-        "blocks.0.cross_attn.kv_linear.weight": (2 * hidden_size, hidden_size),
-        "blocks.0.ffn.w1.weight": (ffn_hidden, hidden_size),
-        "blocks.0.adaLN_modulation.1.weight": (6 * hidden_size, adaln_dim),
-        "final_layer.linear.weight": (64, hidden_size),
-    }
-
-    # Load and check
-    first_shard = os.path.join(dit_path, "diffusion_pytorch_model-00001-of-00006.safetensors")
-
-    all_valid = True
-    with safe_open(first_shard, framework="pt", device="cpu") as f:
-        for key, expected_shape in expected.items():
-            if key in f.keys():
-                tensor = f.get_tensor(key)
-                actual_shape = tuple(tensor.shape)
-
-                if actual_shape == expected_shape:
-                    print(f"✓ {key}: {actual_shape}")
-                else:
-                    print(f"✗ {key}: expected {expected_shape}, got {actual_shape}")
-                    all_valid = False
-
-    return all_valid
-
-
-def validate_model_index(model_path):
-    """Validate model_index.json exists and is correct."""
-    print("\n" + "=" * 60)
-    print("VALIDATING MODEL INDEX")
-    print("=" * 60)
-
-    model_index_path = os.path.join(model_path, "model_index.json")
-
-    if not os.path.exists(model_index_path):
-        print("✗ model_index.json not found")
-        return False
-
-    with open(model_index_path) as f:
-        index = json.load(f)
-
-    required_keys = ["_class_name", "workload_type", "tokenizer", "text_encoder", "vae", "scheduler", "transformer"]
-
-    all_valid = True
-    for key in required_keys:
-        if key in index:
-            print(f"✓ {key}: {index[key]}")
-        else:
-            print(f"✗ {key}: missing")
-            all_valid = False
-
-    return all_valid
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Validate LongCat weights for FastVideo")
-    parser.add_argument("--model-path", type=str, required=True, help="Path to LongCat model directory")
-    parser.add_argument("--check-shapes", action="store_true", help="Also validate weight shapes (slower)")
-
-    args = parser.parse_args()
-
-    print(f"\nValidating: {args.model_path}\n")
-
-    # Run validations
-    components_valid = validate_components(args.model_path)
-    model_index_valid = validate_model_index(args.model_path)
-    dit_valid = validate_dit_weights(os.path.join(args.model_path, "transformer"))
-
-    if args.check_shapes:
-        shapes_valid = validate_shapes(os.path.join(args.model_path, "transformer"))
-    else:
-        shapes_valid = True
-        print("\nSkipping shape validation (use --check-shapes to enable)")
-
-    # Summary
-    print("\n" + "=" * 60)
-    print("VALIDATION SUMMARY")
-    print("=" * 60)
-
-    all_valid = components_valid and model_index_valid and dit_valid and shapes_valid
-
-    if all_valid:
-        print("✓ All validations passed!")
-        print("✓ Model ready for FastVideo")
-    else:
-        print("✗ Some validations failed")
-        print("✗ Please check errors above")
-
-    print("=" * 60)
diff --git a/scripts/inference/inference_longcat.yaml b/scripts/inference/inference_longcat.yaml
deleted file mode 100644
index 7887a28a7..000000000
--- a/scripts/inference/inference_longcat.yaml
+++ /dev/null
@@ -1,43 +0,0 @@
-# Run with:
-#   fastvideo generate --config scripts/inference/inference_longcat.yaml
-generator:
-  model_path: FastVideo/LongCat-Video-T2V-Diffusers
-  engine:
-    num_gpus: 1
-    parallelism:
-      tp_size: 1
-      sp_size: 1
-    offload:
-      dit: false
-      text_encoder: false
-      vae: false
-      pin_cpu_memory: false
-  pipeline:
-    experimental:
-      enable_bsa: false
-request:
-  prompt: >-
-    In a realistic photography style, a white boy around seven or eight years
-    old sits on a park bench, wearing a light blue T-shirt, denim shorts, and
-    white sneakers. He holds an ice cream cone with vanilla and chocolate
-    flavors, and beside him is a medium-sized golden Labrador. Smiling, the
-    boy offers the ice cream to the dog, who eagerly licks it with its tongue.
-    The sun is shining brightly, and the background features a green lawn and
-    several tall trees, creating a warm and loving scene.
-  negative_prompt: >-
-    Bright tones, overexposed, static, blurred details, subtitles, style,
-    works, paintings, images, static, overall gray, worst quality, low
-    quality, JPEG compression residue, ugly, incomplete, extra fingers,
-    poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen
-    limbs, fused fingers, still picture, messy background, three legs, many
-    people in the background, walking backwards
-  sampling:
-    seed: 42
-    num_frames: 93
-    height: 480
-    width: 832
-    fps: 15
-    num_inference_steps: 50
-    guidance_scale: 4.0
-  output:
-    output_path: outputs_video/longcat_t2v
diff --git a/scripts/inference/inference_longcat_distill.yaml b/scripts/inference/inference_longcat_distill.yaml
deleted file mode 100644
index 8e6b77d3d..000000000
--- a/scripts/inference/inference_longcat_distill.yaml
+++ /dev/null
@@ -1,44 +0,0 @@
-# Run with:
-#   fastvideo generate --config scripts/inference/inference_longcat_distill.yaml
-generator:
-  model_path: FastVideo/LongCat-Video-T2V-Diffusers
-  engine:
-    num_gpus: 1
-    parallelism:
-      tp_size: 1
-      sp_size: 1
-    offload:
-      dit: false
-      pin_cpu_memory: false
-  pipeline:
-    components:
-      lora_path: FastVideo/LongCat-Video-T2V-Distilled-LoRA
-    experimental:
-      enable_bsa: false
-      lora_nickname: distilled
-request:
-  prompt: >-
-    In a realistic photography style, a white boy around seven or eight years
-    old sits on a park bench, wearing a light blue T-shirt, denim shorts, and
-    white sneakers. He holds an ice cream cone with vanilla and chocolate
-    flavors, and beside him is a medium-sized golden Labrador. Smiling, the
-    boy offers the ice cream to the dog, who eagerly licks it with its tongue.
-    The sun is shining brightly, and the background features a green lawn and
-    several tall trees, creating a warm and loving scene.
-  negative_prompt: >-
-    Bright tones, overexposed, static, blurred details, subtitles, style,
-    works, paintings, images, static, overall gray, worst quality, low
-    quality, JPEG compression residue, ugly, incomplete, extra fingers,
-    poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen
-    limbs, fused fingers, still picture, messy background, three legs, many
-    people in the background, walking backwards
-  sampling:
-    seed: 42
-    num_frames: 93
-    height: 480
-    width: 832
-    fps: 15
-    num_inference_steps: 16
-    guidance_scale: 1.0
-  output:
-    output_path: outputs_video/longcat_distill
diff --git a/scripts/inference/inference_longcat_i2v.yaml b/scripts/inference/inference_longcat_i2v.yaml
deleted file mode 100644
index 7ab21af18..000000000
--- a/scripts/inference/inference_longcat_i2v.yaml
+++ /dev/null
@@ -1,42 +0,0 @@
-# Run with:
-#   fastvideo generate --config scripts/inference/inference_longcat_i2v.yaml
-generator:
-  model_path: FastVideo/LongCat-Video-I2V-Diffusers
-  engine:
-    num_gpus: 1
-    parallelism:
-      tp_size: 1
-      sp_size: 1
-    offload:
-      dit: false
-      pin_cpu_memory: false
-  pipeline:
-    workload_type: i2v
-    experimental:
-      enable_bsa: false
-request:
-  prompt: >-
-    A woman sits at a wooden table by the window in a cozy café. She reaches
-    out with her right hand, picks up the white coffee cup from the saucer,
-    and gently brings it to her lips to take a sip. After drinking, she places
-    the cup back on the table and looks out the window, enjoying the peaceful
-    atmosphere.
-  negative_prompt: >-
-    Bright tones, overexposed, static, blurred details, subtitles, style,
-    works, paintings, images, static, overall gray, worst quality, low
-    quality, JPEG compression residue, ugly, incomplete, extra fingers,
-    poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen
-    limbs, fused fingers, still picture, messy background, three legs, many
-    people in the background, walking backwards
-  inputs:
-    image_path: assets/girl.png
-  sampling:
-    seed: 42
-    num_frames: 93
-    height: 480
-    width: 480
-    fps: 15
-    num_inference_steps: 50
-    guidance_scale: 4.0
-  output:
-    output_path: outputs_video/longcat_i2v
diff --git a/scripts/inference/inference_longcat_refine_fromvideo.yaml b/scripts/inference/inference_longcat_refine_fromvideo.yaml
deleted file mode 100644
index 29046b684..000000000
--- a/scripts/inference/inference_longcat_refine_fromvideo.yaml
+++ /dev/null
@@ -1,51 +0,0 @@
-# Run with:
-#   fastvideo generate --config scripts/inference/inference_longcat_refine_fromvideo.yaml
-generator:
-  model_path: FastVideo/LongCat-Video-T2V-Diffusers
-  engine:
-    num_gpus: 1
-    parallelism:
-      tp_size: 1
-      sp_size: 1
-    offload:
-      pin_cpu_memory: false
-  pipeline:
-    components:
-      lora_path: FastVideo/LongCat-Video-T2V-Refinement-LoRA
-    experimental:
-      enable_bsa: true
-      bsa_sparsity: 0.875
-      bsa_chunk_q: [4, 4, 8]
-      bsa_chunk_k: [4, 4, 8]
-      lora_nickname: refinement
-request:
-  prompt: >-
-    In a realistic photography style, a white boy around seven or eight years
-    old sits on a park bench, wearing a light blue T-shirt, denim shorts, and
-    white sneakers. He holds an ice cream cone with vanilla and chocolate
-    flavors, and beside him is a medium-sized golden Labrador. Smiling, the
-    boy offers the ice cream to the dog, who eagerly licks it with its tongue.
-    The sun is shining brightly, and the background features a green lawn and
-    several tall trees, creating a warm and loving scene.
-  negative_prompt: >-
-    Bright tones, overexposed, static, blurred details, subtitles, style,
-    works, paintings, images, static, overall gray, worst quality, low
-    quality, JPEG compression residue, ugly, incomplete, extra fingers,
-    poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen
-    limbs, fused fingers, still picture, messy background, three legs, many
-    people in the background, walking backwards
-  inputs:
-    refine_from: outputs_video/longcat_distill/In a realistic photography style, a white boy around seven or eight years old sits on a park bench,.mp4
-  sampling:
-    seed: 42
-    height: 720
-    width: 1280
-    fps: 30
-    num_inference_steps: 50
-    guidance_scale: 1.0
-  extensions:
-    t_thresh: 0.5
-    spatial_refine_only: false
-    num_cond_frames: 0
-  output:
-    output_path: outputs_video/longcat_refine_720p
diff --git a/scripts/inference/inference_longcat_vc.yaml b/scripts/inference/inference_longcat_vc.yaml
deleted file mode 100644
index 0fe5c78b3..000000000
--- a/scripts/inference/inference_longcat_vc.yaml
+++ /dev/null
@@ -1,43 +0,0 @@
-# Run with:
-#   fastvideo generate --config scripts/inference/inference_longcat_vc.yaml
-generator:
-  model_path: FastVideo/LongCat-Video-VC-Diffusers
-  engine:
-    num_gpus: 1
-    parallelism:
-      tp_size: 1
-      sp_size: 1
-    offload:
-      dit: false
-      pin_cpu_memory: false
-  pipeline:
-    experimental:
-      enable_bsa: false
-request:
-  prompt: >-
-    A person rides a motorcycle along a long, straight road that stretches
-    between a body of water and a forested hillside. The rider steadily
-    accelerates, keeping the motorcycle centered between the guardrails, while
-    the scenery passes by on both sides. The video captures the journey from
-    the rider's perspective, emphasizing the sense of motion and adventure.
-  negative_prompt: >-
-    Bright tones, overexposed, static, blurred details, subtitles, style,
-    works, paintings, images, static, overall gray, worst quality, low
-    quality, JPEG compression residue, ugly, incomplete, extra fingers,
-    poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen
-    limbs, fused fingers, still picture, messy background, three legs, many
-    people in the background, walking backwards
-  inputs:
-    video_path: assets/motorcycle.mp4
-  sampling:
-    seed: 42
-    num_frames: 93
-    height: 480
-    width: 832
-    fps: 15
-    num_inference_steps: 50
-    guidance_scale: 4.0
-  extensions:
-    num_cond_frames: 13
-  output:
-    output_path: outputs_video/longcat_vc
diff --git a/tests/local_tests/README.md b/tests/local_tests/README.md
index 35bf2dde7..1e15754e7 100644
--- a/tests/local_tests/README.md
+++ b/tests/local_tests/README.md
@@ -24,7 +24,6 @@ tests/local_tests/<family>/
 | Family | Workload | Dir |
 |---|---|---|
 | Flux2 | T2I | [`flux2/`](./flux2/) |
-| Hunyuan GameCraft | T2V / I2V | [`gamecraft/`](./gamecraft/) |
 | GEN3C | T2V | [`gen3c/`](./gen3c/) |
 | Kandinsky-5 | T2V | [`kandinsky5/`](./kandinsky5/) |
 | Kandinsky-6 | TI2VA (T2V/I2V + audio) | [`kandinsky6/`](./kandinsky6/) |
diff --git a/tests/local_tests/dreamx_world/PORT_STATUS.md b/tests/local_tests/dreamx_world/PORT_STATUS.md
deleted file mode 100644
index 986a33e31..000000000
--- a/tests/local_tests/dreamx_world/PORT_STATUS.md
+++ /dev/null
@@ -1,189 +0,0 @@
-# DreamX World Port Status
-
-## Summary
-
-- model_family: `dreamx_world`
-- workload_types: `I2V camera-control compatibility shim`; `I2V autoregressive camera-control forcing`
-- official_ref: `https://github.com/AMAP-ML/DreamX-World`
-- official_ref_dir: `DreamX-World/`
-- hf_weights_path: `GD-ML/DreamX-World-5B-Cam`
-- local_weights_dir: `official_weights/dreamx_world`
-- source_layout: `raw_official`
-- local_tests_readme: `tests/local_tests/dreamx_world/README.md`
-
-## Current Phase
-
-- phase: `phase_11_post_parity_handoff`
-- status: `complete`
-- owner: `orchestrator`
-- last_updated: `2026-07-02`
-
-## Component Matrix
-
-| Component | Type | Reuse/Port | Official Definition | Official Instantiation | FastVideo Target | Prototype | Conversion | Parity | Open Issues |
-|---|---|---|---|---|---|---|---|---|---|
-| transformer | dit | ported_dedicated | `DreamX-World/models/wan_transformer3d.py`; PRoPE helpers in `DreamX-World/models/prope_utils.py` | `DreamX-World/inference_dreamx5b.py::setup_models`, `Wan2_2Transformer3DModel.from_pretrained(... cam_method=prope, add_control_adapter=True)` | `fastvideo/models/dits/dreamx_world.py`; `fastvideo/configs/models/dits/dreamx_world.py`; DreamX pipeline config helper | native_prope_pass | real_conversion_pass | strict_load_and_forward_parity_pass | none |
-| vae | vae | reuse_pending | `DreamX-World/models/wan_vae3_8.py` | `DreamX-World/inference_dreamx5b.py::setup_models`, `AutoencoderKLWan3_8.from_pretrained(Wan2.2_VAE.pth)` | `fastvideo/models/vaes/wanvae.py`; DreamX VAE config helper | config_smoke_pass | raw_key_mapping_pass | encode_parity_pass | none |
-| text_encoder/tokenizer | encoder | reuse_pending | `DreamX-World/models/wan_text_encoder.py`; tokenizer via Wan2.2 base model | `DreamX-World/inference_dreamx5b.py::setup_models`, `WanT5EncoderModel` + tokenizer subpaths | `fastvideo/models/encoders/t5.py::UMT5EncoderModel`; DreamX UMT5 config helper | config_smoke_pass | staged_weight_load_pass | hidden_state_parity_pass | none |
-| scheduler | generic | reuse_proven | Diffusers `FlowMatchEulerDiscreteScheduler` | `DreamX-World/inference_dreamx5b.py::setup_models`, default `sampler_name=Flow` | `fastvideo/models/schedulers/scheduling_flow_match_euler_discrete.py` | pass | not_required | non_skip_pass | Q003 |
-| camera_conditioning | generic | port_pending | `DreamX-World/utils/inference_utils.py`, `DreamX-World/models/prope_utils.py`, `DreamX-World/wan/modules/camera_prope.py` | `DreamX-World/inference_dreamx5b.py::get_camera_sequence`, `pipeline(... control_camera_video=...)` | `fastvideo/pipelines/basic/dreamx_world/camera_conditioning.py` | pass | not_required | non_skip_pass | none |
-| pipeline | pipeline | port_complete | `DreamX-World/pipeline/pipeline_dreamxworld.py` | `DreamX-World/inference_dreamx5b.py::process_inference_from_json` | `fastvideo/pipelines/basic/dreamx_world/` plus config/preset/registry | pipeline_load_generate_smoke_pass | model_index_and_config_consistency_smoke_pass | pipeline_api_vs_worker_forward_parity_pass | none |
-| ar_transformer | dit | port_complete | `DreamX-World/wan/modules/causal_camera_model_2_2_prope_infinity.py::CausalWanModel` | `DreamX-World/inference_ar_forcing.py::load_pipeline` | `fastvideo/models/dits/dreamx_world_ar.py`; `fastvideo/configs/models/dits/dreamx_world.py::DreamXWorldARConfig` | tiny_official_forward_parity_pass | identity_conversion_pass | real_5b_strict_load_pass | none |
-| ar_pipeline | pipeline | port_complete | `DreamX-World/pipeline/pipeline_causal_camera.py` | `DreamX-World/inference_ar_forcing.py::main` | `fastvideo/pipelines/basic/dreamx_world/dreamx_world_ar_pipeline.py`; `fastvideo/pipelines/basic/dreamx_world/ar_denoising.py`; registry/preset/config | config_registry_pass | symlink_model_index_pass | short_full_generation_pass | none |
-
-## Conversion State
-
-- conversion_script: `scripts/checkpoint_conversion/dreamx_world_to_diffusers.py`
-- converted_weights_dir: `converted_weights/dreamx_world`
-- source_layout: `raw_official`
-- strict_load_status: `pass`
-- conversion_script_status: `transformer_model_index_and_config_consistency_smoke_pass`
-- model_index_status: `smoke_pass`
-- passthrough_components: `Wan2.2 Diffusers scheduler, tokenizer, and text encoder are symlinked from official_weights/Wan2.2-TI2V-5B-Diffusers; VAE parity uses raw Wan2.2_VAE.pth with an explicit DreamX raw-to-FastVideo key mapper because official encode returns normalized latents.`
-- retry_history: `none`
-
-## Parity Commands
-
-| Scope | Command | Last Result | Notes |
-|---|---|---|---|
-| transformer | `python -m pytest tests/local_tests/dreamx_world/test_dreamx_world_transformer_parity.py -v -s` | strict_load_and_forward_parity_pass | 2026-07-01: converted real 5B-Cam transformer shards strict-load into dedicated `DreamXWorldTransformer3DModel` with 0 shape mismatches; official-vs-FastVideo small-input fp32 forward parity passes on CUDA (`diff_max=0.072533`, `diff_mean=0.008014`). |
-| vae | `python -m pytest tests/local_tests/dreamx_world/test_dreamx_world_vae_parity.py -v -s` | encode_parity_pass | 2026-06-30: official DreamX raw `Wan2.2_VAE.pth` maps 196/196 keys into FastVideo Wan VAE and encode parity passes after applying the same official latent normalization (`(mu - mean) / std`). |
-| text_encoder | `python -m pytest tests/local_tests/dreamx_world/test_dreamx_world_text_encoder_parity.py -v -s` | hidden_state_parity_pass | 2026-06-30: official `WanT5EncoderModel` vs FastVideo `UMT5EncoderModel` hidden-state parity passes on CUDA using staged Wan2.2 text encoder/tokenizer weights and reference-only `xfuser` stubs. |
-| scheduler | `python -m pytest tests/local_tests/dreamx_world/test_dreamx_world_scheduler_parity.py -v -s` | non_skip_pass | 2026-06-30: FastVideo FlowMatch scheduler matches official Diffusers timesteps and step output for DreamX default Flow sampler; `DreamXWorldPipeline` initializes FlowMatch with official `shift=3.0`. |
-| camera_conditioning | `python -m pytest tests/local_tests/dreamx_world/test_dreamx_world_camera_conditioning_parity.py -v -s` | non_skip_pass | 2026-06-29: 3 parameterized cases passed against official reference on CPU. |
-| pipeline_config | `python -m pytest tests/local_tests/dreamx_world/test_dreamx_world_pipeline_config.py -v -s` | pipeline_entry_preset_scheduler_modelinfo_and_camera_stage_smoke_pass | DreamX 5B-Cam PipelineConfig wires DiT/VAE/UMT5/Flow/TI2V settings and official `shift=3.0`; default preset is registered for `GD-ML/DreamX-World-5B-Cam`; local converted-style `model_index.json` resolves to `DreamXWorldPipeline`; the pipeline initializes FlowMatch, camera conditioning writes `batch.extra["dreamx_y_camera"]`, and generic denoising can pass it as `y_camera`. |
-| ar_transformer | `PYTHONPATH=/workspace/FastVideo python -m pytest tests/local_tests/dreamx_world/test_dreamx_world_ar_conversion.py tests/local_tests/dreamx_world/test_dreamx_world_ar_transformer_parity.py -q -rs` | 6_passed_0_skipped | 2026-07-02: AR converter writes symlinked transformer layout/model_index; tiny official `CausalWanModel` vs FastVideo `DreamXWorldARTransformer3DModel` forward parity passes; real 5B `model.safetensors` strict-loads with zero missing/unexpected keys from `/tmp/converted_dreamx_world_ar`. |
-| ar_pipeline_config | `PYTHONPATH=/workspace/FastVideo python -m pytest tests/local_tests/dreamx_world/test_dreamx_world_pipeline_config.py -q -rs` | 10_passed_0_skipped | 2026-07-02: `DreamXWorld5BARPipelineConfig`, `DreamXWorldARPipeline`, registry config selection for `GD-ML/DreamX-World-5B`, and `dreamx_world_5b_ar` preset pass. |
-| ar_full_generation | `PYTHONPATH=/workspace/FastVideo python /tmp/run_dreamx_ar_video_smoke.py` | generated_video_pass | 2026-07-02: A40 short full-generation smoke passed from `/tmp/converted_dreamx_world_ar` with 64x64, 9 frames, 4 denoise steps, `output_type=pil`, `save_video=True`; MP4 saved at `outputs_video/dreamx_world_ar_video_smoke/a quiet road through a futuristic coastal city at sunrise.mp4` and decoded as 9 frames of `(64, 64, 3)` uint8. |
-| ar_long_horizon | `PYTHONPATH=/workspace/FastVideo FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA python /tmp/run_dreamx_ar_long_horizon.py` | generated_video_pass | 2026-07-02: A40 long-horizon AR generation passed from `/tmp/converted_dreamx_world_ar` with 64x64, 1005 frames, 4 denoise steps, seed 4096; MP4 saved at `outputs_video/dreamx_world_ar_long_horizon/A long autonomous drive through a futuristic coastal city at sunrise, smooth forward camera motion,.mp4` and decoded as 1005 frames of `(64, 64, 3)`; end-to-end generation latency was 231.68s after load. |
-| ar_ssim_default | `PYTHONPATH=/workspace/FastVideo FASTVIDEO_SSIM_MODEL_ID=DreamX-World-5B FASTVIDEO_TEST_DREAMX_WORLD_AR_SSIM_MODEL_PATH=/tmp/converted_dreamx_world_ar python -m pytest fastvideo/tests/ssim/test_dreamx_world_similarity.py -q -rs --skip-ssim-reference-download` | 1_passed_0_skipped | 2026-07-02: A40 default AR SSIM reference seeded locally at `fastvideo/tests/ssim/reference_videos/default/A40_reference_videos/DreamX-World-5B/TORCH_SDPA/`; helper removes stale generated base MP4 before generation; default params are 192x192, 81 frames, 4 steps, seed 2048, min SSIM 0.98. |
-| ar_ssim_modal_l40s | `modal run /tmp/modal_dreamx_ar_ssim_git.py` | 1_passed_0_skipped | 2026-07-02: Modal L40S run checked out `post-fix dreamx-world-5b-cam branch commit`, downloaded default `L40S_reference_videos` via `reference_videos_cli.py download`, used cached converted AR weights under `/root/data/dreamx_world_ar_converted`, seeded the missing AR L40S reference from generated output, reran a fresh generated-vs-reference compare successfully (`mean_ssim=1.0`), and exported the reference to Modal volume `hf-model-weights:dreamx_ar_ssim_l40s`. Downloaded local reference decodes as 81 frames of `(192, 192, 3)`. A40-vs-L40S reference spot check after deterministic AR noise fix: mean SSIM 0.7770, min 0.6139, max 0.9944. |
-| pipeline_smoke | `python -m pytest tests/local_tests/pipelines/test_dreamx_world_pipeline_smoke.py tests/local_tests/pipelines/test_dreamx_world_pipeline_parity.py -q -rs` | 4_passed_0_skipped | 2026-06-30: combined smoke/parity passed. 2026-07-01: smoke alone passed with real `image_path` TI2V coverage (`3 passed`), validating image load, TI2V preprocessing, VAE first-frame encode under CPU offload, camera conditioning, and 1-step latent generation from `converted_weights/dreamx_world`. Tests force `FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA` to avoid the local FlashAttention-4 cute ABI mismatch. |
-| basic_example | `PYTHONPATH=/workspace/FastVideo FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA DREAMX_WORLD_MODEL_DIR=converted_weights/dreamx_world DREAMX_WORLD_IMAGE_PATH= DREAMX_WORLD_HEIGHT=64 DREAMX_WORLD_WIDTH=64 DREAMX_WORLD_NUM_FRAMES=9 DREAMX_WORLD_STEPS=1 DREAMX_WORLD_GUIDANCE=1.0 DREAMX_WORLD_OUTPUT_PATH=outputs_video/dreamx_world_example_smoke python examples/inference/basic/basic_dreamx_world.py` | generated_video_pass | 2026-06-30: example saved an MP4 under `outputs_video/dreamx_world_example_smoke`; imageio/ffmpeg decoded frame 0 as `(64, 64, 3)` uint8, fps 16, duration 0.56s. |
-
-## Open Questions
-
-| ID | Question | Owner | Needed By Phase | Status | Resolution |
-|---|---|---|---|---|---|
-| Q001 | Should first PR expose only the `DreamX-World-5B-Cam` 5s camera-control mode and exclude AR long-horizon forcing? | user | prep | resolved | User approved starting with `DreamX-World-5B-Cam`; AR long-horizon is out of first-PR scope. |
-| Q002 | Does FastVideo's existing Wan2.2 TI2V transformer support DreamX PRoPE/control adapter with a small extension, or is a DreamX-specific DiT required? | component:transformer | Phase 3 | resolved | Project guidance prefers a separate DreamX DiT for maintainability. DreamX PRoPE/control adapter now lives in `fastvideo/models/dits/dreamx_world.py`; Wan DiT/config have no DreamX-specific fields or `y_camera` signature. |
-| Q003 | Which sampler is in first-PR scope: official default `Flow` only, or also `Flow_Unipc` and `Flow_DPM++`? | orchestrator | Phase 3 | resolved | First PR should support official default `Flow` only. FastVideo FlowMatch scheduler parity is non-skip PASS; optional `Flow_Unipc` and `Flow_DPM++` are out of first-PR scope. |
-| Q004 | Which HF token env var should be used if rate limits or gated Wan2.2 base weights require auth? | user | Phase 5 | resolved | No auth was required for the completed local downloads; keep using env var names only if future gated repos require auth. |
-| Q005 | Should native FastVideo production code depend on DreamX reference-only packages such as `xfuser` or OpenCV? | user | Phase 3 | resolved | No. These packages may be used only for official reference/local parity setup; native FastVideo integration must remove that runtime requirement. |
-| Q006 | Should AR handoff require a full generated long-horizon video in this no-HF-token/no-GPU-budget pass? | user/runtime | pipeline | resolved | A40 long-horizon generation passes with 1005 frames at 64x64/4 steps. AR default SSIM passes locally on A40 and on Modal L40S with a 192x192/81-frame reference. HF upload/publication remains a separate operation if the reference dataset should be updated upstream. |
-| Q007 | Can A40 references stand in for L40S CI references? | quality | release | resolved | No. After deterministic AR noise fix, A40-vs-L40S reference mean SSIM is 0.7770, below the 0.98 same-device threshold. Publish the L40S-specific reference for CI. |
-
-## Issues And Blockers
-
-| ID | Phase | Component | Severity | Issue | Evidence | Owner | Status | Resolution |
-|---|---|---|---|---|---|---|---|---|
-| I001 | prep | official_env | medium | Official import initially failed because `xfuser` was missing. | `ModuleNotFoundError: No module named 'xfuser'` from `python -c "import sys; sys.path.insert(0, 'DreamX-World'); import inference_dreamx5b"` | prep | resolved | Installed `xfuser==0.4.1`; import progressed. |
-| I002 | prep | official_env | medium | Official import then failed because GUI OpenCV required missing system `libxcb.so.1`. | `ImportError: libxcb.so.1: cannot open shared object file` through `cv2` import in Diffusers ConsisID path. | prep | resolved | Installed `opencv-python-headless`; `import inference_dreamx5b` passed. |
-| I003 | prep | weights | medium | HF repo has raw official transformer shards and no Diffusers `model_index.json`. | `inspect_hf_layout.py GD-ML/DreamX-World-5B-Cam --json` returned `source_layout=raw_official`, `needs_conversion=yes`, `model_index_class=null`. | conversion | resolved | Downloaded raw DreamX shards to `official_weights/dreamx_world`; converted transformer to `converted_weights/dreamx_world/transformer`; symlinked reusable Wan2.2 Diffusers components; real 5B transformer strict-load passes. |
-| I004 | prep | dependencies | high | Official reference import required extra packages in the local environment, but FastVideo native runtime should not inherit those dependencies. | `xfuser==0.4.1` and `opencv-python-headless` were installed only to make `DreamX-World/inference_dreamx5b.py` import for reference/parity. | pipeline | resolved | Production DreamX FastVideo code uses native camera/image/video utilities and has no runtime `xfuser` or OpenCV import requirement; those packages remain reference-only local parity dependencies. |
-| I005 | parity | transformer | medium | Transformer full forward parity initially failed in bf16 official harness. | Official CUDA bf16 LayerNorm path was unstable; fp32 small-input harness avoids that dtype issue and compares against FastVideo with single-process SP identity patches. | component:transformer | resolved | Official-vs-FastVideo forward parity now passes on CUDA with `diff_max=0.072533`, `diff_mean=0.008014`. |
-| I006 | parity | vae/text_encoder | medium | VAE/text parity initially remained skipped after weights were staged. | Text official import needed a reference-only `xfuser` stub; VAE comparison initially used raw official normalized latents against FastVideo raw mu. | component:vae,component:text_encoder | resolved | Text hidden-state parity passes. VAE encode parity passes after raw key mapping and applying the official latent normalization to FastVideo output. |
-| I007 | quality | pipeline_ti2v | medium | Real image-path TI2V smoke initially failed when `vae_cpu_offload=True` because DenoisingStage encoded the first frame while the VAE weights remained on CPU. | DreamX SSIM first run failed with `RuntimeError: Input type (torch.cuda.FloatTensor) and weight type (torch.FloatTensor) should be the same` at `fastvideo/pipelines/stages/denoising.py` VAE encode. | pipeline | resolved | DenoisingStage now moves the VAE to `local_device` before TI2V first-frame encode; image-path pipeline smoke and DreamX SSIM both pass. |
-| I008 | quality | ssim_helper | high | SSIM helper could compare against a stale generated base MP4 when a rerun saved the new video as `_1.mp4`. | Existing generated outputs made AR reference seeding appear to pass before a fresh generated-vs-reference compare. | quality | resolved | `run_text_to_video_similarity_test` and `run_image_to_video_similarity_test` now remove the stale generated base MP4 before generation. A40 and Modal L40S AR SSIM were rerun after the fix. |
-| I009 | quality | ar_denoising | high | AR denoising added CUDA noise without using the request seed when the original generator was CPU-backed. | Fresh reruns against old AR references produced mean SSIM near 0.05. | pipeline | resolved | `DreamXWorldARCausalDenoisingStage` now derives a device-local generator from the request seed for AR noise. Fresh same-device A40 and L40S reruns pass with mean SSIM 1.0 after reseeding references. |
-
-## Escape Hatches
-
-| ID | Phase | Decision Type | Question | Recommended Option | Status | Resolution |
-|---|---|---|---|---|---|---|
-
-## Decisions
-
-| Date | Decision | Rationale | Impact |
-|---|---|---|---|
-| 2026-06-29 | First PR scope is `DreamX-World-5B-Cam` only. | Cam mode is closest to existing Wan2.2 TI2V support; AR forcing needs separate causal/KV pipeline work. | Component inventory and parity focus on `inference_dreamx5b.py` and `pipeline_dreamxworld.py`. |
-| 2026-06-29 | Do not install full DreamX requirements during prep. | Full requirements pin core FastVideo stack packages. | Installed only `xfuser==0.4.1` and `opencv-python-headless` to make official imports work. |
-| 2026-06-29 | Treat HF DreamX-World-5B-Cam weights as raw official transformer layout requiring conversion. | HF inspection found no `model_index.json`. | Phase 5 must create `scripts/checkpoint_conversion/dreamx_world_to_diffusers.py` after component prototype/key dumps. |
-| 2026-06-29 | Do not add DreamX reference-only dependencies to FastVideo production requirements. | The current environment should remain the FastVideo environment; extra packages are only for official reference parity. | Native DreamX integration must avoid runtime `xfuser` and OpenCV requirements unless explicitly approved later. |
-| 2026-06-29 | Implement DreamX camera conditioning as native FastVideo utility. | It is weightless and removes the need to import DreamX reference utilities at production runtime. | `fastvideo/pipelines/basic/dreamx_world/camera_conditioning.py` now has non-skip parity against official action-to-PRoPE tensors. |
-| 2026-06-29 | First PR supports DreamX default `Flow` sampler only. | FastVideo FlowMatch Euler scheduler matches the official Diffusers scheduler for DreamX defaults. | Pipeline work can use FastVideo native FlowMatch scheduler; UniPC and DPM++ are out of first-PR scope. |
-| 2026-07-01 | Keep DreamX PRoPE/control adapter in a dedicated DreamX DiT class. | Project guidance is that putting too much DreamX behavior into Wan makes the model hard to manage. | `fastvideo/models/dits/dreamx_world.py` defines `DreamXWorldTransformer3DModel`, `DreamXWorldTransformerBlock`, and `DreamXPropeSelfAttention`; `fastvideo/configs/models/dits/dreamx_world.py` owns DreamX adapter config fields; Wan DiT/config are unchanged from DreamX. |
-| 2026-06-30 | Camera parity test loads official camera functions by file instead of importing the official `utils` package. | Official package initialization pulls unrelated dependencies that can require GUI OpenCV system libraries. | Camera parity remains non-skip without adding DreamX reference-only dependencies to FastVideo production requirements. |
-| 2026-06-30 | Add DreamX-World-5B-Cam model and pipeline config helpers plus a conversion script. | Official HF DreamX 5B-Cam transformer config is 30 layers, hidden size 3072, 24 heads, 48 latent channels, plus Wan2.2 48-channel VAE and UMT5-XXL text encoder. | DreamX helpers wire DiT/VAE/UMT5/Flow/TI2V settings; `dreamx_world_to_diffusers.py` writes a FastVideo-loadable transformer config plus renamed safetensors; strict-load smoke passes on a tiny official DreamX transformer and the real 5B converted shards. |
-| 2026-06-30 | Pass DreamX camera PRoPE condition through the FastVideo batch/denoising path. | DreamX transformer expects `y_camera={"viewmats", "K"}` at denoising time. | `DreamXWorldPipeline` is registered as a basic pipeline entry and initializes the official default FlowMatch scheduler; `dreamx_world_5b_cam` preset mirrors official 5B-Cam defaults; `DreamXWorldCameraConditioningStage` writes `batch.extra["dreamx_y_camera"]`; generic denoising filters and forwards it as `y_camera` only for compatible transformers. |
-
-## Handoff Notes
-
-- Prep, component parity, pipeline smoke/parity, and the basic example validation are complete for `DreamX-World-5B-Cam`.
-- Official reference clone is staged at `DreamX-World/` and ignored by git.
-- Workspace-local weights are staged: DreamX raw transformer shards under `official_weights/dreamx_world`, Wan2.2 raw base artifacts under `official_weights/Wan2.2-TI2V-5B`, and Wan2.2 Diffusers reusable components under `official_weights/Wan2.2-TI2V-5B-Diffusers`.
-- Camera conditioning parity is active and passing without weights.
-- Default Flow scheduler parity is active and passing without weights.
-- Transformer has corrected official 5B-Cam architecture in dedicated DreamX DiT/config files, native PRoPE/control-adapter, conversion mapping, real converted 5B strict-load, and official-vs-FastVideo forward parity passing on CUDA. VAE encode parity and text hidden-state parity pass on CUDA. Pipeline entry/registry, local model_info resolution, preset, config, FlowMatch scheduler init, camera stage, denoising `y_camera` kwarg smokes, independent CUDA pipeline smoke/parity, and a small saved-video basic example pass.
-- Full local DreamX component suite is non-skip PASS: `python -m pytest tests/local_tests/dreamx_world/ -q -rs` returned `26 passed` on 2026-07-01. Pipeline smoke/parity and SSIM quality regression are also non-skip PASS locally.
-- Keep `xfuser` and OpenCV as reference-only parity dependencies. Do not add them
-  to FastVideo requirements or production imports.
-
-
-## Quality Regression
-
-- status: `added`
-- test: `fastvideo/tests/ssim/test_dreamx_world_similarity.py`
-- command: `PYTHONPATH=/workspace/FastVideo FASTVIDEO_SSIM_MODEL_ID=DreamX-World-5B-Cam python -m pytest fastvideo/tests/ssim/test_dreamx_world_similarity.py -q -rs`
-- result: `1 passed, 0 skipped` on 2026-07-01
-- reference: Local A40/TORCH_SDPA reference seeded from the generated candidate under `fastvideo/tests/ssim/reference_videos/default/A40_reference_videos/DreamX-World-5B-Cam/TORCH_SDPA/`. The test uses a deterministic generated input image, 64x64 request dimensions, 9 frames, 1 denoise step, seed 1024, and min SSIM 0.98. Full-quality params are present for 480x832/161 frames/30 steps.
-- note: Modal L40S seeding passed using the configured Modal profile and unauthenticated HF public downloads. HF upload/publication still requires `HF_API_KEY`, `HUGGINGFACE_HUB_TOKEN`, or `HF_TOKEN` with write access; no token values were used or recorded.
-
-## Final Handoff
-
-```text
-final_handoff:
-  prep_handoff_complete: yes
-  conversion_status: pass
-  components:
-    - name: transformer
-      reuse_or_port: ported_dedicated_dit
-      parity_test: tests/local_tests/dreamx_world/test_dreamx_world_transformer_parity.py
-      parity_status: non_skip_pass
-      concerns_or_unknowns: none
-    - name: vae
-      reuse_or_port: reused
-      parity_test: tests/local_tests/dreamx_world/test_dreamx_world_vae_parity.py
-      parity_status: non_skip_pass
-      concerns_or_unknowns: none
-    - name: text_encoder_tokenizer
-      reuse_or_port: reused
-      parity_test: tests/local_tests/dreamx_world/test_dreamx_world_text_encoder_parity.py
-      parity_status: non_skip_pass
-      concerns_or_unknowns: none
-    - name: scheduler
-      reuse_or_port: reused
-      parity_test: tests/local_tests/dreamx_world/test_dreamx_world_scheduler_parity.py
-      parity_status: non_skip_pass
-      concerns_or_unknowns: none
-    - name: camera_conditioning
-      reuse_or_port: ported
-      parity_test: tests/local_tests/dreamx_world/test_dreamx_world_camera_conditioning_parity.py
-      parity_status: non_skip_pass
-      concerns_or_unknowns: none
-    - name: ar_transformer
-      reuse_or_port: ported
-      parity_test: tests/local_tests/dreamx_world/test_dreamx_world_ar_transformer_parity.py
-      parity_status: non_skip_pass
-      concerns_or_unknowns: none
-    - name: ar_pipeline
-      reuse_or_port: ported
-      parity_test: tests/local_tests/dreamx_world/test_dreamx_world_pipeline_config.py plus AR smoke/SSIM commands listed above
-      parity_status: non_skip_pass
-      concerns_or_unknowns: none
-  pipeline_smoke: pass
-  pipeline_parity: pass
-  example_status: pass
-  quality_regression: added
-  local_tests_readme: tests/local_tests/dreamx_world/README.md
-  port_state_file: tests/local_tests/dreamx_world/PORT_STATUS.md
-  token_values_committed: no
-  runtime_third_party_model_imports: none
-  blockers: none
-  escape_hatch: none
-```
-
-| 2026-07-02 | Add DreamX-World-5B autoregressive support. | Official AR repo is raw single-safetensors layout and needs a dedicated causal/KV stage. | Added native AR DiT/config, identity converter, AR pipeline config/preset/registry, and targeted non-skip tests. Raw AR weights are staged at `/tmp/dreamx_world_ar_weights`; converted symlink layout at `/tmp/converted_dreamx_world_ar`. |
-| 2026-07-02 | Validate DreamX-World-5B AR short full generation on A40. | Targeted parity/config tests prove components, but end-to-end runtime can still fail at scheduler, RoPE cache, device, or decode boundaries. | `DreamXWorldARPipeline` generated and saved a 64x64/9-frame/4-step MP4 from `/tmp/converted_dreamx_world_ar`; the saved MP4 decodes to 9 frames. |
-| 2026-07-02 | Validate DreamX-World-5B AR long-horizon and default SSIM on A40. | AR needs coverage beyond the 9-frame smoke to exercise longer KV/cache progression and a quality regression path. | 1005-frame 64x64/4-step generation passes and decodes; default AR SSIM test uses 192x192/81 frames because MS-SSIM requires short side >160. |
-| 2026-07-02 | Validate DreamX-World-5B AR default SSIM on Modal L40S. | CI references are device-specific; A40 alone is not enough for L40S reference coverage. | Modal checked out `post-fix dreamx-world-5b-cam branch commit`, downloaded existing default L40S references, seeded the missing AR reference, reran SSIM successfully (`mean_ssim=1.0`), and exported the L40S reference back to the workspace. |
diff --git a/tests/local_tests/dreamx_world/README.md b/tests/local_tests/dreamx_world/README.md
deleted file mode 100644
index 2a836b17b..000000000
--- a/tests/local_tests/dreamx_world/README.md
+++ /dev/null
@@ -1,254 +0,0 @@
-# DreamX World Local Tests
-
-Local-only parity and smoke tests for the `dreamx_world` FastVideo port. These
-tests compare FastVideo against the official DreamX-World reference
-implementation and are not expected to run in CI unless explicitly promoted
-later.
-
-Port progress, open questions, issues, and handoff notes live in
-`tests/local_tests/dreamx_world/PORT_STATUS.md`.
-
-## Reference Assets
-
-| Field | Value |
-|---|---|
-| Model family | `dreamx_world` |
-| First-PR scope | `DreamX-World-5B-Cam`; follow-up scope now includes `DreamX-World-5B` autoregressive forcing |
-| Out-of-scope variants | none for the DreamX-World 5B/Cam paths currently ported |
-| Workload types | I2V camera-control compatibility shim: image + prompt + action sequence to video |
-| Official reference | `https://github.com/AMAP-ML/DreamX-World` |
-| Local reference dir | `DreamX-World/` |
-| Official commit/version | `221875811ba31f7eac6c3025b215c09ad2cefd1d` |
-| HF weights | `GD-ML/DreamX-World-5B-Cam` |
-| HF revision | default |
-| Local weights dir | `official_weights/dreamx_world` |
-| Source layout | `raw_official` |
-| Needs conversion | `yes` |
-
-Do not write token values in this file. Current token env var detected during
-prep: `none`.
-
-## Shared Environment Setup
-
-Run from the FastVideo repo root in the same conda/env used for FastVideo. Do
-not create a separate upstream environment for parity tests.
-
-```bash
-python ".agents/skills/add-model-01-prep/scripts/clone_reference_repo.py" \
-    "https://github.com/AMAP-ML/DreamX-World.git" \
-    "DreamX-World" \
-    --commit "221875811ba31f7eac6c3025b215c09ad2cefd1d" \
-    --update-gitignore
-```
-
-DreamX-World does not expose a packaging file for editable install. During prep
-the official import check used `sys.path.insert(0, "DreamX-World")`.
-
-Additional official deps installed into the current environment for imports:
-
-```bash
-uv pip install xfuser==0.4.1
-uv pip install opencv-python-headless
-```
-
-These packages are for running the official DreamX reference during local
-parity only. They must not become FastVideo production/runtime dependencies for
-the native `dreamx_world` pipeline.
-
-Do not install the full `DreamX-World/requirements.txt` without explicit
-approval. It pins core FastVideo stack packages including `torch`, `torchvision`,
-`triton`, `flash_attn`, and `diffusers`.
-
-## Official Environment Status
-
-```text
-dependency_changes: installed official deps in current env
-official_env_status: imports_ok
-private_dep_stubs: none
-blocked_on: none
-```
-
-Import check used during prep:
-
-```bash
-python -c "import sys; sys.path.insert(0, 'DreamX-World'); import inference_dreamx5b; print('imports_ok')"
-```
-
-## Weight Setup
-
-HF layout inspection found no root `model_index.json`; the repo contains
-`config.json`, a safetensors index, and three transformer safetensors shards.
-This is a raw official transformer layout and requires conversion before
-FastVideo can load it through `VideoGenerator.from_pretrained`.
-
-```bash
-python ".agents/skills/add-model-01-prep/scripts/inspect_hf_layout.py" \
-    "GD-ML/DreamX-World-5B-Cam" \
-    --json
-```
-
-Weights have been staged workspace-locally. The raw DreamX transformer repo lives at `official_weights/dreamx_world`; Wan2.2 raw base artifacts live at `official_weights/Wan2.2-TI2V-5B`; Wan2.2 Diffusers reusable components live at `official_weights/Wan2.2-TI2V-5B-Diffusers`. To reproduce the DreamX download:
-
-```bash
-python ".agents/skills/add-model-01-prep/scripts/download_hf_weights.py" \
-    "GD-ML/DreamX-World-5B-Cam" \
-    "official_weights/dreamx_world"
-```
-
-
-
-### DreamX-World-5B Autoregressive Setup
-
-The AR repository `GD-ML/DreamX-World-5B` is also raw official layout: no
-`model_index.json`, root `config.json`, and a single `model.safetensors`. The
-current environment has the raw AR checkpoint staged outside the workspace at
-`/tmp/dreamx_world_ar_weights` to avoid workspace quota pressure. The converted
-FastVideo layout is staged at `/tmp/converted_dreamx_world_ar` with the 21GB
-transformer safetensors symlinked instead of copied.
-
-```bash
-python ".agents/skills/add-model-01-prep/scripts/download_hf_weights.py" \
-    "GD-ML/DreamX-World-5B" \
-    "/tmp/dreamx_world_ar_weights"
-
-python scripts/checkpoint_conversion/dreamx_world_ar_to_diffusers.py \
-    --source /tmp/dreamx_world_ar_weights \
-    --output /tmp/converted_dreamx_world_ar \
-    --component-source official_weights/Wan2.2-TI2V-5B-Diffusers \
-    --symlink-components \
-    --symlink-transformer
-```
-
-AR production code uses `DreamXWorldARTransformer3DModel`,
-`DreamXWorld5BARPipelineConfig`, `DreamXWorldARPipeline`, and
-`DreamXWorldARCausalDenoisingStage`. The AR DiT is a native FastVideo port of
-the official Apache-2.0 `CausalWanModel`; it has no production DreamX, Diffusers
-model-class, Transformers model-class, `xfuser`, or OpenCV import.
-
-## Prototype And Conversion Artifacts
-
-State-dict key/shape dumps are generated after FastVideo native prototypes exist
-and are used to build the conversion mapping.
-
-```text
-official_key_dumps:
-  transformer: converted_weights/dreamx_world/_mapping/transformer_official_keys.json
-fastvideo_key_dumps:
-  transformer: converted_weights/dreamx_world/_mapping/transformer_fastvideo_keys.json
-conversion_script: scripts/checkpoint_conversion/dreamx_world_to_diffusers.py
-conversion_script_status: transformer_model_index_and_config_consistency_smoke_pass
-conversion_source_layout: raw_official
-converted_weights_dir: converted_weights/dreamx_world
-model_index_status: smoke_pass
-strict_load_status: pass
-```
-
-The converter writes `transformer/` from raw DreamX shards. To create a full
-FastVideo-loadable diffusers-style root, pass a Wan2.2 Diffusers directory as the
-component source so reusable components are copied or symlinked before
-`model_index.json` is emitted:
-
-```bash
-python scripts/checkpoint_conversion/dreamx_world_to_diffusers.py \
-    --source official_weights/dreamx_world \
-    --output converted_weights/dreamx_world \
-    --component-source /path/to/Wan2.2-TI2V-5B-Diffusers \
-    --symlink-components
-```
-
-## Expected Parity Tests
-
-Planned local tests for this family:
-
-| Component | Official files / args | Test | Concerns | Status |
-|---|---|---|---|---|
-| transformer | `DreamX-World/models/wan_transformer3d.py`; instantiated in `DreamX-World/inference_dreamx5b.py` with `cam_method=prope`, `add_control_adapter=True` | `tests/local_tests/dreamx_world/test_dreamx_world_transformer_parity.py` | FastVideo uses dedicated `DreamXWorldTransformer3DModel`/`DreamXWorldConfig` files for DreamX 5B-Cam config, PRoPE, conversion mapping, real converted 5B strict-load PASS, and official-vs-FastVideo small-input forward parity PASS on CUDA. Wan DiT/config have no DreamX-specific adapter fields. | strict_load_and_forward_parity_pass |
-| vae | `DreamX-World/models/wan_vae3_8.py`; `vae_type=AutoencoderKLWan3_8`, `vae_subpath=Wan2.2_VAE.pth` | `tests/local_tests/dreamx_world/test_dreamx_world_vae_parity.py` | DreamX raw `Wan2.2_VAE.pth` maps 196/196 keys into FastVideo Wan VAE; encode parity passes after applying official latent normalization. | encode_parity_pass |
-| text_encoder/tokenizer | `DreamX-World/models/wan_text_encoder.py`; T5 path from Wan2.2 base model | `tests/local_tests/dreamx_world/test_dreamx_world_text_encoder_parity.py` | Official `WanT5EncoderModel` vs FastVideo `UMT5EncoderModel` hidden-state parity passes on CUDA with staged Wan2.2 weights/tokenizer. | hidden_state_parity_pass |
-| scheduler | Diffusers `FlowMatchEulerDiscreteScheduler`; selected by default `sampler_name=Flow` | `tests/local_tests/dreamx_world/test_dreamx_world_scheduler_parity.py` | First PR can support official default `Flow`; optional `Flow_Unipc` and `Flow_DPM++` are out of first-PR scope. | non_skip_pass |
-| camera_conditioning | `DreamX-World/utils/inference_utils.py`, `models/prope_utils.py`, `wan/modules/camera_prope.py` | `tests/local_tests/dreamx_world/test_dreamx_world_camera_conditioning_parity.py` | Action sequence to PRoPE/control input must match official tensor shapes and values. | non_skip_pass |
-| pipeline | `DreamX-World/pipeline/pipeline_dreamxworld.py`; call path in `DreamX-World/inference_dreamx5b.py` | `tests/local_tests/dreamx_world/test_dreamx_world_pipeline_config.py`; `tests/local_tests/pipelines/test_dreamx_world_pipeline_smoke.py`; `tests/local_tests/pipelines/test_dreamx_world_pipeline_parity.py` | DreamX PipelineConfig wires first-scope DiT/VAE/UMT5/Flow/TI2V settings, official `shift=3.0`, default preset values, FlowMatch scheduler initialization, and local `model_index.json` resolution; independent pipeline smoke covers real CUDA local load + latent generation, and parity compares public API output to worker-side explicit ForwardBatch execution. | pipeline_smoke_and_parity_pass |
-| ar_transformer | `DreamX-World/wan/modules/causal_camera_model_2_2_prope_infinity.py`; instantiated by `DreamX-World/inference_ar_forcing.py` as `CausalWanModel` with `local_attn_size=12`, `sink_size=3`, `attn_compress=4` | `tests/local_tests/dreamx_world/test_dreamx_world_ar_transformer_parity.py` | Native `DreamXWorldARTransformer3DModel` keeps official identity key layout; tiny official-vs-FastVideo forward parity passes; real 5B AR safetensors strict-load passes from `/tmp/converted_dreamx_world_ar`. | tiny_forward_parity_and_real_strict_load_pass |
-| ar_pipeline | `DreamX-World/pipeline/pipeline_causal_camera.py`; AR block/KV/context-noise loop | `tests/local_tests/dreamx_world/test_dreamx_world_pipeline_config.py`; A40 short full-generation smoke | Dedicated `DreamXWorldARCausalDenoisingStage` implements blockwise KV forcing; raw HF repo must be converted before `VideoGenerator.from_pretrained` because it has no `model_index.json`; converted AR layout generated a 64x64/9-frame/4-step MP4 on A40. | config_registry_and_short_full_generation_pass |
-
-Include reused components in parity. Reuse is accepted only after the FastVideo
-component definition and official instantiation arguments have both been checked
-and the component parity test passes non-skip.
-
-Run the relevant tests with:
-
-```bash
-python -m pytest tests/local_tests/dreamx_world/test_dreamx_world_camera_conditioning_parity.py -v -s
-python -m pytest tests/local_tests/dreamx_world/test_dreamx_world_scheduler_parity.py -v -s
-python -m pytest tests/local_tests/dreamx_world/test_dreamx_world_transformer_parity.py -v -s
-python -m pytest tests/local_tests/dreamx_world/test_dreamx_world_vae_parity.py -v -s
-python -m pytest tests/local_tests/dreamx_world/test_dreamx_world_text_encoder_parity.py -v -s
-python -m pytest tests/local_tests/dreamx_world/test_dreamx_world_pipeline_config.py -v -s
-python -m pytest tests/local_tests/dreamx_world/test_dreamx_world_conversion.py -v -s
-python -m pytest tests/local_tests/pipelines/test_dreamx_world_pipeline_smoke.py tests/local_tests/pipelines/test_dreamx_world_pipeline_parity.py -q -rs
-```
-
-## Current Local Results
-
-```bash
-python -m pytest tests/local_tests/dreamx_world/ -v -s
-# 2026-07-01: 26 passed, 0 skipped
-# 2026-07-02: AR targeted suite passed: 16 passed, 0 skipped
-
-PYTHONPATH=/workspace/FastVideo python /tmp/run_dreamx_ar_video_smoke.py
-# 2026-07-02: DreamX-World-5B AR short full-generation smoke passed on A40
-# output: outputs_video/dreamx_world_ar_video_smoke/a quiet road through a futuristic coastal city at sunrise.mp4
-# decoded: 9 frames, (64, 64, 3), uint8
-
-PYTHONPATH=/workspace/FastVideo FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA python /tmp/run_dreamx_ar_long_horizon.py
-# 2026-07-02: DreamX-World-5B AR long-horizon generation passed on A40
-# output: outputs_video/dreamx_world_ar_long_horizon/A long autonomous drive through a futuristic coastal city at sunrise, smooth forward camera motion,.mp4
-# decoded: 1005 frames, (64, 64, 3)
-
-PYTHONPATH=/workspace/FastVideo FASTVIDEO_SSIM_MODEL_ID=DreamX-World-5B FASTVIDEO_TEST_DREAMX_WORLD_AR_SSIM_MODEL_PATH=/tmp/converted_dreamx_world_ar python -m pytest fastvideo/tests/ssim/test_dreamx_world_similarity.py -q -rs --skip-ssim-reference-download
-# 2026-07-02: DreamX-World-5B AR default SSIM passed: 1 passed, 0 skipped
-# local A40 reference: fastvideo/tests/ssim/reference_videos/default/A40_reference_videos/DreamX-World-5B/TORCH_SDPA/
-
-modal run /tmp/modal_dreamx_ar_ssim_git.py
-# 2026-07-02: Modal L40S default SSIM passed: 1 passed, 0 skipped
-# checked out post-fix dreamx-world-5b-cam branch commit
-# first downloaded default L40S references with reference_videos_cli.py download
-# seeded missing AR L40S reference, then reran a fresh generated-vs-reference compare
-# Modal JSON: mean_ssim=1.0, min_ssim=1.0, max_ssim=1.0
-# local L40S reference: fastvideo/tests/ssim/reference_videos/default/L40S_reference_videos/DreamX-World-5B/TORCH_SDPA/
-# A40-vs-L40S reference spot check after deterministic AR noise fix: mean SSIM 0.7770, min 0.6139, max 0.9944
-
-python -m pytest tests/local_tests/pipelines/test_dreamx_world_pipeline_smoke.py tests/local_tests/pipelines/test_dreamx_world_pipeline_parity.py -q -rs
-# 2026-06-30: 4 passed, 0 skipped
-# 2026-07-01: smoke image-path TI2V coverage passed separately with 3 passed, 0 skipped
-
-PYTHONPATH=/workspace/FastVideo FASTVIDEO_SSIM_MODEL_ID=DreamX-World-5B-Cam python -m pytest fastvideo/tests/ssim/test_dreamx_world_similarity.py -q -rs
-# 2026-07-01: 1 passed, 0 skipped
-```
-
-`camera_conditioning`, default `Flow` scheduler, DreamX component/pipeline configs, default preset, pipeline entry/registry, FlowMatch scheduler initialization, DreamX camera stage, denoising `y_camera` pass-through, conversion model-index/config-consistency checks, real converted 5B dedicated DreamX transformer strict-load and forward parity on CUDA, VAE encode parity on CUDA, text hidden-state parity on CUDA, native DreamX PRoPE branch structure smoke, AR transformer parity/strict-load, AR config/registry, and AR short full-generation smoke are non-skip PASS results. The full local DreamX component suite, independent pipeline smoke/parity suite, image-path TI2V smoke, and SSIM quality regression currently have zero skips. The basic 5B-Cam example was run against `converted_weights/dreamx_world` with a 64x64/9-frame/1-step saved-video smoke, and imageio decoded the generated MP4 successfully; the AR pipeline separately saved and decoded a 64x64/9-frame/4-step MP4 from `/tmp/converted_dreamx_world_ar`.
-
-## Review Notes
-
-- Required before handoff: non-skip PASS for each required component parity
-  test, including reused components that own weights or numerical behavior.
-- First PR scope originally targeted `DreamX-World-5B-Cam`; scope was later
-  expanded to include `DreamX-World-5B` AR support with a separate causal/KV
-  pipeline.
-- AR support has targeted parity/config coverage, short full-generation smoke,
-  1005-frame A40 long-horizon generation, local A40 default SSIM coverage,
-  and Modal L40S default SSIM coverage. L40S validation was rerun after fixing
-  stale generated-output comparison in the SSIM helper and deterministic AR
-  noise seeding. HF reference publication remains a separate token-gated
-  release operation.
-- FastVideo production code must not require `xfuser` or OpenCV just because the
-  official reference import needed them. Port camera/action preprocessing and
-  sequence-parallel behavior into existing FastVideo-native utilities or keep
-  reference-only imports inside local parity tests.
-- Review agents should verify setup commands still match the PR, then run the
-  listed parity tests or report the exact blocker.
-
-
-## Quality Regression
-
-Quality regression is added in `fastvideo/tests/ssim/test_dreamx_world_similarity.py`. The 5B-Cam default test uses the workspace converted model root, `TORCH_SDPA`, a deterministic generated conditioning image, 9 frames, 1 denoise step, seed 1024, and min SSIM 0.98. A local A40 reference was seeded under `fastvideo/tests/ssim/reference_videos/default/A40_reference_videos/DreamX-World-5B-Cam/TORCH_SDPA/`, and the test passed non-skip locally on 2026-07-01. The AR default test uses `/tmp/converted_dreamx_world_ar` or `/root/data/dreamx_world_ar_converted`, `TORCH_SDPA`, 192x192, 81 frames, 4 steps, seed 2048, and min SSIM 0.98; local A40 and Modal L40S references were seeded under `fastvideo/tests/ssim/reference_videos/default/{A40,L40S}_reference_videos/DreamX-World-5B/TORCH_SDPA/`, and both tests passed non-skip on 2026-07-02 after fresh generated-output cleanup was added to the SSIM helper. Modal L40S validation checked out `post-fix dreamx-world-5b-cam branch commit`, seeded the missing AR L40S reference, reran the test, and wrote `mean_ssim=1.0`. A cross-device A40-vs-L40S reference spot check produced mean SSIM 0.7770, so CI should use the L40S-specific reference rather than the A40 artifact. Full-quality params are present for 5B-Cam 480x832/161 frames/30 steps and AR 704x1280/1005 frames/4 steps; publishing references to the HF dataset remains a release operation requiring a write-capable HF token env var, never a raw token value.
diff --git a/tests/local_tests/dreamx_world/__init__.py b/tests/local_tests/dreamx_world/__init__.py
deleted file mode 100644
index 988131360..000000000
--- a/tests/local_tests/dreamx_world/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
diff --git a/tests/local_tests/dreamx_world/test_dreamx_world_ar_conversion.py b/tests/local_tests/dreamx_world/test_dreamx_world_ar_conversion.py
deleted file mode 100644
index f80c41917..000000000
--- a/tests/local_tests/dreamx_world/test_dreamx_world_ar_conversion.py
+++ /dev/null
@@ -1,58 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""DreamX-World-5B autoregressive conversion smoke tests."""
-
-import json
-
-from fastvideo.configs.pipelines.dreamx_world import make_dreamx_world_5b_ar_dit_config
-from fastvideo.models.registry import _LEGACY_FAST_VIDEO_MODELS
-from scripts.checkpoint_conversion.dreamx_world_ar_to_diffusers import (
-    MODEL_INDEX,
-    REUSED_COMPONENTS,
-    TRANSFORMER_CONFIG,
-    convert_transformer,
-    write_model_index,
-)
-
-
-def test_dreamx_world_ar_converter_writes_symlinked_transformer_and_model_index(tmp_path):
-    source = tmp_path / "raw"
-    source.mkdir()
-    raw_tensor = source / "model.safetensors"
-    raw_tensor.write_bytes(b"placeholder")
-    component_source = tmp_path / "wan22"
-    output = tmp_path / "dreamx_ar"
-
-    for component in REUSED_COMPONENTS:
-        component_dir = component_source / component
-        component_dir.mkdir(parents=True)
-        (component_dir / "config.json").write_text("{}\n")
-
-    convert_transformer(source, output, symlink_transformer=True)
-    write_model_index(output, component_source, symlink_components=True)
-
-    assert (output / "transformer" / "model.safetensors").is_symlink()
-    model_index = json.loads((output / "model_index.json").read_text())
-    assert model_index == MODEL_INDEX
-    assert model_index["_class_name"] == "DreamXWorldARPipeline"
-    assert model_index["transformer"] == ["diffusers", "DreamXWorldARTransformer3DModel"]
-
-
-def test_dreamx_world_ar_transformer_config_matches_pipeline_dit_config():
-    dit_config = make_dreamx_world_5b_ar_dit_config()
-    assert TRANSFORMER_CONFIG["_class_name"] == "DreamXWorldARTransformer3DModel"
-    assert TRANSFORMER_CONFIG["num_attention_heads"] == dit_config.num_attention_heads
-    assert TRANSFORMER_CONFIG["attention_head_dim"] == dit_config.attention_head_dim
-    assert TRANSFORMER_CONFIG["in_channels"] == dit_config.in_channels
-    assert TRANSFORMER_CONFIG["out_channels"] == dit_config.out_channels
-    assert TRANSFORMER_CONFIG["ffn_dim"] == dit_config.ffn_dim
-    assert TRANSFORMER_CONFIG["num_layers"] == dit_config.num_layers
-    assert TRANSFORMER_CONFIG["local_attn_size"] == dit_config.local_attn_size
-    assert TRANSFORMER_CONFIG["sink_size"] == dit_config.sink_size
-    assert TRANSFORMER_CONFIG["attn_compress"] == dit_config.attn_compress
-    assert tuple(TRANSFORMER_CONFIG["cam_self_attn_layers"]) == dit_config.cam_self_attn_layers
-
-
-def test_dreamx_world_ar_model_index_component_classes_are_registered():
-    for component in ("scheduler", "text_encoder", "transformer", "vae"):
-        class_name = MODEL_INDEX[component][1]
-        assert class_name in _LEGACY_FAST_VIDEO_MODELS
diff --git a/tests/local_tests/dreamx_world/test_dreamx_world_ar_transformer_parity.py b/tests/local_tests/dreamx_world/test_dreamx_world_ar_transformer_parity.py
deleted file mode 100644
index 6d0f59518..000000000
--- a/tests/local_tests/dreamx_world/test_dreamx_world_ar_transformer_parity.py
+++ /dev/null
@@ -1,189 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""DreamX-World-5B autoregressive transformer parity.
-
-Coverage scope: both. The tiny forward parity compares FastVideo's native AR DiT
-against the official DreamX ``CausalWanModel`` implementation with identical
-weights. The real 5B checkpoint gate strict-loads the downloaded safetensors
-through the FastVideo model class.
-"""
-from __future__ import annotations
-
-import os
-from pathlib import Path
-import sys
-
-import pytest
-import torch
-from torch.testing import assert_close
-
-from fastvideo.configs.models.dits.dreamx_world import DreamXWorldARArchConfig, DreamXWorldARConfig
-from fastvideo.configs.pipelines.dreamx_world import make_dreamx_world_5b_ar_dit_config
-from fastvideo.models.dits.dreamx_world_ar import DreamXWorldARTransformer3DModel
-from fastvideo.models.loader.fsdp_load import load_model_from_full_model_state_dict
-from fastvideo.models.loader.utils import get_param_names_mapping
-from fastvideo.models.loader.weight_utils import resolve_safetensors_files, safetensors_weights_iterator
-
-REPO_ROOT = Path(__file__).resolve().parents[3]
-OFFICIAL_REF_DIR = Path(os.getenv("DREAMX_WORLD_OFFICIAL_REF_DIR", REPO_ROOT / "DreamX-World"))
-CONVERTED_AR_DIR = Path(os.getenv("DREAMX_WORLD_AR_CONVERTED_DIR", "/tmp/converted_dreamx_world_ar"))
-PARITY_SCOPE = "both"
-
-
-def _tiny_config() -> DreamXWorldARConfig:
-    return DreamXWorldARConfig(arch_config=DreamXWorldARArchConfig(
-        num_attention_heads=1,
-        attention_head_dim=8,
-        in_channels=4,
-        out_channels=4,
-        ffn_dim=16,
-        num_layers=1,
-        text_dim=8,
-        freq_dim=8,
-        text_len=4,
-        local_attn_size=2,
-        sink_size=1,
-        attn_compress=1,
-        cam_self_attn_layers=(0, ),
-    ))
-
-
-def _load_official_tiny():
-    if not OFFICIAL_REF_DIR.exists():
-        pytest.skip(f"Official DreamX reference missing: {OFFICIAL_REF_DIR}")
-    sys.path.insert(0, str(OFFICIAL_REF_DIR))
-    try:
-        from wan.modules import attention as official_attention
-        from wan.modules import causal_camera_model_2_2_prope_infinity as causal_module
-        from wan.modules import model_2_2 as official_model_2_2
-        from wan.modules.causal_camera_model_2_2_prope_infinity import CausalWanModel
-    except Exception as exc:  # noqa: BLE001
-        pytest.skip(f"Cannot import official AR transformer: {exc}")
-    official_attention.FLASH_ATTN_2_AVAILABLE = False
-    official_attention.FLASH_ATTN_3_AVAILABLE = False
-
-    def _sdpa_same_dtype(q, k, v, **kwargs):
-        del kwargs
-        out = torch.nn.functional.scaled_dot_product_attention(q.transpose(1, 2),
-                                                               k.transpose(1, 2),
-                                                               v.transpose(1, 2),
-                                                               dropout_p=0.0)
-        return out.transpose(1, 2).contiguous()
-
-    official_attention.attention = _sdpa_same_dtype
-    official_attention.flash_attention = _sdpa_same_dtype
-    official_model_2_2.flash_attention = _sdpa_same_dtype
-    causal_module.attention = _sdpa_same_dtype
-    return CausalWanModel(
-        model_type="ti2v",
-        patch_size=(1, 2, 2),
-        text_len=4,
-        in_dim=4,
-        dim=8,
-        ffn_dim=16,
-        freq_dim=8,
-        text_dim=8,
-        out_dim=4,
-        num_heads=1,
-        num_layers=1,
-        local_attn_size=2,
-        sink_size=1,
-        qk_norm=True,
-        cross_attn_norm=True,
-        add_control_adapter=True,
-        cam_method="prope",
-        attn_compress=1,
-        cam_self_attn_layers=(0, ),
-    ).eval()
-
-
-def _make_inputs():
-    torch.manual_seed(123)
-    x = [torch.randn(4, 1, 4, 4)]
-    t = torch.zeros(1, 4, dtype=torch.long)
-    context = [torch.randn(2, 8)]
-    camera = {
-        "viewmats": torch.eye(4).reshape(1, 1, 4, 4).repeat(1, 4, 1, 1),
-        "K": torch.eye(3).reshape(1, 1, 3, 3).repeat(1, 4, 1, 1),
-    }
-    kv_cache = [{
-        "k": torch.zeros(1, 8, 1, 8),
-        "v": torch.zeros(1, 8, 1, 8),
-        "global_end_index": torch.tensor([0]),
-        "local_end_index": torch.tensor([0]),
-        "prope_k": torch.zeros(1, 8, 1, 8),
-        "prope_v": torch.zeros(1, 8, 1, 8),
-        "prope_global_end_index": torch.tensor([0]),
-        "prope_local_end_index": torch.tensor([0]),
-    }]
-    cross_cache = [{
-        "k": torch.zeros(1, 4, 1, 8),
-        "v": torch.zeros(1, 4, 1, 8),
-        "is_init": False,
-    }]
-    return x, t, context, camera, kv_cache, cross_cache
-
-
-def test_dreamx_world_ar_tiny_forward_matches_official():
-    official = _load_official_tiny()
-    # The official init_weights zero-inits the output head (head.head.weight and
-    # biases), so both models would output exactly zero and the comparison would
-    # pass vacuously. Randomize the head (deterministically) before copying the
-    # state dict so the outputs reflect the internal computation.
-    generator = torch.Generator().manual_seed(7)
-    with torch.no_grad():
-        official.head.head.weight.normal_(std=0.5, generator=generator)
-        official.head.head.bias.normal_(std=0.5, generator=generator)
-    fastvideo = DreamXWorldARTransformer3DModel(_tiny_config(), {}).eval()
-    fastvideo.load_state_dict(official.state_dict(), strict=True)
-
-    x, t, context, camera, kv_cache, cross_cache = _make_inputs()
-    official_out = official(x=x,
-                            t=t,
-                            context=context,
-                            seq_len=4,
-                            y_camera=camera,
-                            kv_cache=kv_cache,
-                            crossattn_cache=cross_cache).detach()
-    x, t, context, camera, kv_cache, cross_cache = _make_inputs()
-    fastvideo_out = fastvideo(x=x,
-                              t=t,
-                              context=context,
-                              seq_len=4,
-                              y_camera=camera,
-                              kv_cache=kv_cache,
-                              crossattn_cache=cross_cache).detach()
-    assert official_out.abs().max() > 0, "official output is all-zero; parity comparison is vacuous"
-    assert_close(fastvideo_out, official_out, atol=1e-5, rtol=1e-5)
-
-
-def test_dreamx_world_ar_5b_config_matches_official_shape():
-    config = make_dreamx_world_5b_ar_dit_config()
-    assert config.num_layers == 30
-    assert config.num_attention_heads == 24
-    assert config.attention_head_dim == 128
-    assert config.hidden_size == 3072
-    assert config.ffn_dim == 14336
-    assert config.local_attn_size == 12
-    assert config.sink_size == 3
-    assert config.attn_compress == 4
-    assert config.cam_self_attn_layers == tuple(range(30))
-
-
-def test_dreamx_world_ar_converted_5b_transformer_strict_loads():
-    transformer_dir = CONVERTED_AR_DIR / "transformer"
-    if not transformer_dir.exists():
-        pytest.skip(f"Converted AR transformer missing: {transformer_dir}")
-    with torch.device("meta"):
-        model = DreamXWorldARTransformer3DModel(make_dreamx_world_5b_ar_dit_config(), {})
-    incompatible = load_model_from_full_model_state_dict(
-        model,
-        safetensors_weights_iterator(resolve_safetensors_files(str(transformer_dir)), to_cpu=True),
-        device=torch.device("cpu"),
-        param_dtype=torch.bfloat16,
-        strict=True,
-        param_names_mapping=get_param_names_mapping(model.param_names_mapping),
-        training_mode=False,
-    )
-    assert incompatible.missing_keys == []
-    assert incompatible.unexpected_keys == []
-    assert not any(param.is_meta for param in model.parameters())
diff --git a/tests/local_tests/dreamx_world/test_dreamx_world_camera_conditioning_parity.py b/tests/local_tests/dreamx_world/test_dreamx_world_camera_conditioning_parity.py
deleted file mode 100644
index 09b9e5c2a..000000000
--- a/tests/local_tests/dreamx_world/test_dreamx_world_camera_conditioning_parity.py
+++ /dev/null
@@ -1,95 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""DreamX-World camera-conditioning parity against the official reference.
-
-Coverage scope: implementation_subcomponent. This verifies the weightless
-action-sequence to PRoPE camera tensor path used by DreamX-World-5B-Cam.
-"""
-from __future__ import annotations
-
-import os
-from pathlib import Path
-import pytest
-import torch
-from torch.testing import assert_close
-
-from fastvideo.pipelines.basic.dreamx_world.camera_conditioning import build_dreamx_camera_condition
-
-REPO_ROOT = Path(__file__).resolve().parents[3]
-OFFICIAL_REF_DIR = Path(os.getenv("DREAMX_WORLD_OFFICIAL_REF_DIR", REPO_ROOT / "DreamX-World"))
-PARITY_SCOPE = "implementation_subcomponent"
-
-
-def _load_official_functions():
-    if not OFFICIAL_REF_DIR.exists():
-        pytest.skip(f"Official reference missing: {OFFICIAL_REF_DIR}")
-    try:
-        import importlib.util
-
-        pose_path = OFFICIAL_REF_DIR / "utils" / "pose_utils.py"
-        pose_spec = importlib.util.spec_from_file_location("dreamx_world_pose_utils", pose_path)
-        if pose_spec is None or pose_spec.loader is None:
-            raise RuntimeError(f"Cannot load DreamX pose_utils: {pose_path}")
-        pose_module = importlib.util.module_from_spec(pose_spec)
-        pose_spec.loader.exec_module(pose_module)
-
-        source = (OFFICIAL_REF_DIR / "utils" / "inference_utils.py").read_text()
-        source = source.replace("from .pose_utils import interpolate_camera_poses\n", "")
-        namespace = {"interpolate_camera_poses": pose_module.interpolate_camera_poses}
-        exec(compile(source, str(OFFICIAL_REF_DIR / "utils" / "inference_utils.py"), "exec"), namespace)
-    except Exception as exc:  # noqa: BLE001 - local parity should skip missing reference deps.
-        pytest.skip(f"Cannot load DreamX camera reference: {exc}")
-    return namespace["ActionToPoseFromID"], namespace["GetPoseEmbedsFromPosesPrope"]
-
-
-def _official_camera_condition(
-    action_seq: list[str],
-    action_speed_list: list[float],
-    *,
-    num_frames: int,
-    height: int,
-    width: int,
-    dtype: torch.dtype,
-):
-    action_to_pose, get_pose_embeds = _load_official_functions()
-    duration = -(-num_frames // len(action_seq))
-    poses = action_to_pose(action_seq, action_speed_list, duration=duration)[:num_frames]
-    condition, _ = get_pose_embeds(poses, height, width, len(poses), False, 0, dtype=dtype, device="cpu")
-    return condition
-
-
-@pytest.mark.parametrize(
-    ("action_seq", "action_speed_list", "num_frames"),
-    [
-        (["w"], [4], 81),
-        (["wj", "d"], [4, 6], 121),
-        (["i", "k", "l"], [3, 5, 2], 85),
-    ],
-)
-def test_dreamx_world_camera_conditioning_matches_official(action_seq, action_speed_list, num_frames):
-    dtype = torch.float32
-    official = _official_camera_condition(
-        action_seq,
-        action_speed_list,
-        num_frames=num_frames,
-        height=704,
-        width=1280,
-        dtype=dtype,
-    )
-    fastvideo = build_dreamx_camera_condition(
-        action_seq,
-        action_speed_list,
-        num_frames=num_frames,
-        height=704,
-        width=1280,
-        dtype=dtype,
-        device="cpu",
-    )
-
-    assert official.keys() == fastvideo.keys() == {"viewmats", "K"}
-    for key in ("viewmats", "K"):
-        assert official[key].shape == fastvideo[key].shape
-        diff = (official[key] - fastvideo[key]).abs()
-        print(
-            f"{key}: shape={tuple(fastvideo[key].shape)} diff_max={diff.max().item():.8f} diff_mean={diff.mean().item():.8f}"
-        )
-        assert_close(fastvideo[key], official[key], atol=1e-5, rtol=1e-5)
diff --git a/tests/local_tests/dreamx_world/test_dreamx_world_conversion.py b/tests/local_tests/dreamx_world/test_dreamx_world_conversion.py
deleted file mode 100644
index dbc3a26ab..000000000
--- a/tests/local_tests/dreamx_world/test_dreamx_world_conversion.py
+++ /dev/null
@@ -1,79 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""DreamX-World conversion script smoke tests."""
-
-import json
-
-from fastvideo.configs.pipelines.dreamx_world import make_dreamx_world_5b_cam_dit_config
-from fastvideo.models.registry import _LEGACY_FAST_VIDEO_MODELS
-
-from scripts.checkpoint_conversion.dreamx_world_to_diffusers import (
-    MODEL_INDEX,
-    REUSED_COMPONENTS,
-    TRANSFORMER_CONFIG,
-    _copy_or_link_component,
-    write_model_index,
-)
-
-
-def test_dreamx_world_converter_writes_full_model_index_with_reused_components(tmp_path):
-    component_source = tmp_path / "wan22"
-    output = tmp_path / "dreamx"
-    output.mkdir()
-
-    for component in REUSED_COMPONENTS:
-        component_dir = component_source / component
-        component_dir.mkdir(parents=True)
-        (component_dir / "config.json").write_text("{}\n")
-
-    write_model_index(output, component_source, symlink_components=True)
-
-    model_index = json.loads((output / "model_index.json").read_text())
-    assert model_index == MODEL_INDEX
-    assert model_index["_class_name"] == "DreamXWorldPipeline"
-    assert model_index["transformer"] == ["diffusers", "DreamXWorldTransformer3DModel"]
-    for component in REUSED_COMPONENTS:
-        assert (output / component).is_symlink()
-
-
-def test_dreamx_world_transformer_config_has_camera_adapter_enabled():
-    assert TRANSFORMER_CONFIG["_class_name"] == "DreamXWorldTransformer3DModel"
-    assert TRANSFORMER_CONFIG["add_control_adapter"] is True
-    assert TRANSFORMER_CONFIG["cam_method"] == "prope"
-    assert TRANSFORMER_CONFIG["num_layers"] == 30
-
-
-def test_dreamx_world_converter_transformer_config_matches_pipeline_dit_config():
-    dit_config = make_dreamx_world_5b_cam_dit_config()
-
-    assert TRANSFORMER_CONFIG["num_attention_heads"] == dit_config.num_attention_heads
-    assert TRANSFORMER_CONFIG["attention_head_dim"] == dit_config.attention_head_dim
-    assert TRANSFORMER_CONFIG["in_channels"] == dit_config.in_channels
-    assert TRANSFORMER_CONFIG["out_channels"] == dit_config.out_channels
-    assert TRANSFORMER_CONFIG["ffn_dim"] == dit_config.ffn_dim
-    assert TRANSFORMER_CONFIG["num_layers"] == dit_config.num_layers
-    assert TRANSFORMER_CONFIG["cross_attn_norm"] == dit_config.cross_attn_norm
-    assert TRANSFORMER_CONFIG["qk_norm"] == dit_config.qk_norm
-    assert TRANSFORMER_CONFIG["add_control_adapter"] == dit_config.add_control_adapter
-    assert TRANSFORMER_CONFIG["cam_method"] == dit_config.cam_method
-    assert TRANSFORMER_CONFIG["attn_compress"] == dit_config.attn_compress
-    assert TRANSFORMER_CONFIG["cam_self_attn_layers"] == dit_config.cam_self_attn_layers
-
-
-def test_dreamx_world_model_index_component_classes_are_registered():
-    for component in ("scheduler", "text_encoder", "transformer", "vae"):
-        class_name = MODEL_INDEX[component][1]
-        assert class_name in _LEGACY_FAST_VIDEO_MODELS
-
-
-def test_dreamx_world_copy_or_link_component_keeps_broken_symlink(tmp_path):
-    component_source = tmp_path / "wan22"
-    src = component_source / "scheduler"
-    src.mkdir(parents=True)
-    output = tmp_path / "dreamx"
-    output.mkdir()
-    dst = output / "scheduler"
-    dst.symlink_to(tmp_path / "missing_scheduler", target_is_directory=True)
-
-    _copy_or_link_component("scheduler", component_source, output, symlink=True)
-
-    assert dst.is_symlink()
diff --git a/tests/local_tests/dreamx_world/test_dreamx_world_pipeline_config.py b/tests/local_tests/dreamx_world/test_dreamx_world_pipeline_config.py
deleted file mode 100644
index da8f8bf53..000000000
--- a/tests/local_tests/dreamx_world/test_dreamx_world_pipeline_config.py
+++ /dev/null
@@ -1,324 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""DreamX-World pipeline config and conditioning smoke tests."""
-
-from types import SimpleNamespace
-import json
-
-import numpy as np
-import pytest
-import torch
-
-from fastvideo.api.presets import get_preset
-from fastvideo.fastvideo_args import WorkloadType
-from fastvideo.models.schedulers.scheduling_flow_match_euler_discrete import FlowMatchEulerDiscreteScheduler
-from fastvideo.pipelines.pipeline_batch_info import ForwardBatch
-from fastvideo.pipelines.pipeline_registry import PipelineType, import_pipeline_classes
-from fastvideo.pipelines.basic.dreamx_world.camera_conditioning import (
-    DreamXCamera,
-    _interpolate_camera_poses,
-    build_dreamx_camera_condition,
-)
-from fastvideo.configs.pipelines.dreamx_world import DreamXWorld5BARPipelineConfig, DreamXWorld5BCamPipelineConfig
-from fastvideo.pipelines.basic.dreamx_world.ar_denoising import DreamXWorldARCausalDenoisingStage
-from fastvideo.pipelines.basic.dreamx_world.dreamx_world_ar_pipeline import DreamXWorldARPipeline
-from fastvideo.pipelines.basic.dreamx_world.dreamx_world_pipeline import DreamXWorldPipeline
-from fastvideo.pipelines.basic.dreamx_world.stages import (
-    DREAMX_Y_CAMERA_KEY,
-    DreamXWorldCameraConditioningStage,
-)
-from fastvideo.pipelines.stages.denoising import DenoisingStage
-from fastvideo.registry import get_default_preset, get_model_info, get_pipeline_config_cls_from_name
-
-
-@pytest.fixture
-def dreamx_model_manifests(monkeypatch):
-    """Registry/config checks do not need network access or model weights."""
-    names = {
-        "GD-ML/DreamX-World-5B-Cam": "DreamXWorldPipeline",
-        "GD-ML/DreamX-World-5B": "DreamXWorldARPipeline",
-    }
-
-    def manifest(model_path, revision=None):
-        name = names[model_path]
-        return {"_class_name": name, "pipeline_name": name, "_diffusers_version": "0.31.0"}
-
-    monkeypatch.setattr("fastvideo.registry.maybe_download_model_index", manifest)
-
-
-def test_dreamx_world_5b_cam_pipeline_config_wires_first_scope_components():
-    config = DreamXWorld5BCamPipelineConfig()
-
-    assert config.flow_shift == 3.0
-    assert config.ti2v_task is True
-    assert config.expand_timesteps is True
-    assert config.dit_config.expand_timesteps is True
-    assert config.dit_config.num_layers == 30
-    assert config.dit_config.add_control_adapter is True
-    assert config.dit_config.cam_method == "prope"
-
-    assert config.vae_config.load_encoder is True
-    assert config.vae_config.load_decoder is True
-    assert config.vae_config.z_dim == 48
-    assert config.vae_config.scale_factor_temporal == 4
-    assert config.vae_config.scale_factor_spatial == 16
-
-    assert len(config.text_encoder_configs) == 1
-    text_config = config.text_encoder_configs[0]
-    assert text_config.prefix == "umt5"
-    assert text_config.vocab_size == 256384
-    assert text_config.d_model == 4096
-    assert config.text_encoder_precisions == ("bf16", )
-
-
-def test_dreamx_world_pipeline_registry_discovers_entrypoint():
-    pipelines = import_pipeline_classes(PipelineType.BASIC)
-
-    assert pipelines["basic"]["DreamXWorldPipeline"] is DreamXWorldPipeline
-
-
-def test_dreamx_world_local_model_index_resolves_model_info(tmp_path):
-    model_dir = tmp_path / "DreamX-World-5B-Cam-converted"
-    model_dir.mkdir()
-    for component in ("scheduler", "text_encoder", "tokenizer", "transformer", "vae"):
-        (model_dir / component).mkdir()
-    (model_dir / "model_index.json").write_text(
-        json.dumps({
-            "_class_name": "DreamXWorldPipeline",
-            "_diffusers_version": "0.31.0",
-            "scheduler": ["diffusers", "FlowMatchEulerDiscreteScheduler"],
-            "text_encoder": ["transformers", "UMT5EncoderModel"],
-            "tokenizer": ["transformers", "AutoTokenizer"],
-            "transformer": ["diffusers", "DreamXWorldTransformer3DModel"],
-            "vae": ["diffusers", "AutoencoderKLWan"],
-        }) + "\n")
-
-    info = get_model_info(str(model_dir), pipeline_type=PipelineType.BASIC, workload_type=WorkloadType.I2V)
-
-    assert info.pipeline_cls is DreamXWorldPipeline
-    assert info.pipeline_config_cls is DreamXWorld5BCamPipelineConfig
-
-
-def test_dreamx_world_model_path_resolves_pipeline_config(dreamx_model_manifests):
-    assert get_pipeline_config_cls_from_name("GD-ML/DreamX-World-5B-Cam") is DreamXWorld5BCamPipelineConfig
-
-
-def test_dreamx_world_default_preset_is_registered(dreamx_model_manifests):
-    preset_name = get_default_preset("GD-ML/DreamX-World-5B-Cam")
-    preset = get_preset(preset_name, "dreamx_world")
-
-    assert preset.name == "dreamx_world_5b_cam"
-    assert preset.workload_type == "i2v"
-    assert preset.defaults["height"] == 480
-    assert preset.defaults["width"] == 832
-    assert preset.defaults["num_frames"] == 161
-    assert preset.defaults["num_inference_steps"] == 30
-    assert preset.defaults["guidance_scale"] == 5.0
-
-
-def test_dreamx_world_pipeline_initializes_official_flow_scheduler():
-    pipeline = DreamXWorldPipeline.__new__(DreamXWorldPipeline)
-    pipeline.modules = {}
-    fastvideo_args = SimpleNamespace(pipeline_config=DreamXWorld5BCamPipelineConfig())
-
-    pipeline.initialize_pipeline(fastvideo_args)
-
-    scheduler = pipeline.modules["scheduler"]
-    assert isinstance(scheduler, FlowMatchEulerDiscreteScheduler)
-    assert scheduler.config.shift == 3.0
-
-
-def test_dreamx_world_camera_conditioning_stage_sets_y_camera_extra():
-    batch = ForwardBatch(
-        data_type="t2v",
-        action_list=["wj", "d"],
-        action_speed_list=[4, 6],
-        num_frames=17,
-        height=704,
-        width=1280,
-        latents=torch.zeros(1, 16, 5, 44, 80),
-    )
-    stage = DreamXWorldCameraConditioningStage()
-
-    out = stage.forward(batch, fastvideo_args=object())
-    y_camera = out.extra[DREAMX_Y_CAMERA_KEY]
-    expected = build_dreamx_camera_condition(
-        ["wj", "d"],
-        [4, 6],
-        num_frames=17,
-        height=704,
-        width=1280,
-        dtype=torch.float32,
-        device="cpu",
-    )
-
-    assert set(y_camera) == {"viewmats", "K"}
-    for key, expected_value in expected.items():
-        assert y_camera[key].shape == (1, *expected_value.shape)
-        torch.testing.assert_close(y_camera[key][0], expected_value)
-    assert stage.verify_output(out, fastvideo_args=object()).is_valid()
-
-
-def test_dreamx_world_denoising_kwargs_filter_for_y_camera():
-    y_camera = {"viewmats": torch.eye(4).reshape(1, 1, 4, 4), "K": torch.eye(3).reshape(1, 1, 3, 3)}
-    stage = DenoisingStage.__new__(DenoisingStage)
-
-    def accepts_y_camera(hidden_states, encoder_hidden_states, timestep, y_camera=None):
-        return y_camera
-
-    def no_y_camera(hidden_states, encoder_hidden_states, timestep):
-        return hidden_states
-
-    assert stage.prepare_extra_func_kwargs(accepts_y_camera, {"y_camera": y_camera}) == {"y_camera": y_camera}
-    assert stage.prepare_extra_func_kwargs(no_y_camera, {"y_camera": y_camera}) == {}
-
-
-def test_dreamx_world_ar_pipeline_config_wires_components():
-    config = DreamXWorld5BARPipelineConfig()
-    assert config.is_causal is True
-    assert config.flow_shift == 5.0
-    assert config.dmd_denoising_steps == (1000, 750, 500, 250)
-    assert config.warp_denoising_step is True
-    assert config.context_noise == 0.1
-    assert config.dit_config.arch_config.local_attn_size == 12
-    assert config.dit_config.arch_config.sink_size == 3
-    assert config.dit_config.arch_config.attn_compress == 4
-
-
-def test_dreamx_world_ar_pipeline_registry_and_preset(dreamx_model_manifests):
-    from fastvideo.api.presets import get_preset
-    from fastvideo.registry import get_pipeline_config_cls_from_name, get_preset_selection
-
-    assert get_pipeline_config_cls_from_name("GD-ML/DreamX-World-5B") is DreamXWorld5BARPipelineConfig
-    preset_name, family = get_preset_selection("GD-ML/DreamX-World-5B")
-    assert (preset_name, family) == ("dreamx_world_5b_ar", "dreamx_world")
-    preset = get_preset("dreamx_world_5b_ar", "dreamx_world")
-    assert preset.defaults["num_inference_steps"] == 4
-    assert DreamXWorldARPipeline.pipeline_config_cls is DreamXWorld5BARPipelineConfig
-
-
-def test_dreamx_world_camera_conditioning_stage_expands_scalar_speed():
-    batch = ForwardBatch(
-        data_type="t2v",
-        action_list=["w", "d"],
-        action_speed_list=2.0,
-        num_frames=17,
-        height=704,
-        width=1280,
-        latents=torch.zeros(1, 16, 5, 44, 80),
-    )
-    stage = DreamXWorldCameraConditioningStage()
-
-    out = stage.forward(batch, fastvideo_args=object())
-
-    assert set(out.extra[DREAMX_Y_CAMERA_KEY]) == {"viewmats", "K"}
-
-
-def test_dreamx_world_camera_interpolation_handles_single_camera():
-    camera = DreamXCamera(
-        fx=0.8,
-        fy=0.8,
-        cx=0.5,
-        cy=0.5,
-        w2c_mat=np.eye(4, dtype=np.float64),
-    )
-
-    out = _interpolate_camera_poses(
-        [camera],
-        src_indices=np.array([0.0]),
-        tgt_indices=np.array([0.0, 1.0, 2.0]),
-    )
-
-    assert out == [camera, camera, camera]
-
-
-def test_dreamx_world_ar_cache_initializes_camera_self_attention_entries():
-    cam_self_attn = SimpleNamespace(num_heads=3, head_dim=5)
-    transformer = SimpleNamespace(
-        blocks=[SimpleNamespace(cam_self_attn=cam_self_attn),
-                SimpleNamespace(cam_self_attn=cam_self_attn)],
-        num_attention_heads=2,
-        attention_head_dim=4,
-    )
-    stage = DreamXWorldARCausalDenoisingStage.__new__(DreamXWorldARCausalDenoisingStage)
-    stage.transformer = transformer
-    stage.num_transformer_blocks = 2
-    stage.local_attn_size = 6
-
-    caches = stage._initialize_kv_cache(
-        batch_size=1,
-        dtype=torch.float32,
-        device=torch.device("cpu"),
-        frame_seq_length=7,
-    )
-
-    assert len(caches) == 2
-    assert caches[0]["k"].shape == (1, 42, 2, 4)
-    assert caches[0]["prope_k"].shape == (1, 42, 3, 5)
-    assert caches[0]["prope_v"].shape == (1, 42, 3, 5)
-    assert int(caches[0]["prope_global_end_index"].item()) == 0
-    assert int(caches[0]["prope_local_end_index"].item()) == 0
-
-
-def test_dreamx_world_ar_context_noise_fraction_maps_to_scheduler_timestep():
-    assert DreamXWorldARCausalDenoisingStage._context_noise_timestep(0.1) == 100
-    assert DreamXWorldARCausalDenoisingStage._context_noise_timestep(100) == 100
-
-
-def test_dreamx_world_ar_context_update_advances_camera_cache_indices():
-
-    class DummyTransformer:
-
-        def __call__(self, *, hidden_states, encoder_hidden_states, timestep, y_camera, kv_cache, crossattn_cache,
-                     current_start):
-            del encoder_hidden_states, y_camera, crossattn_cache
-            assert current_start == 0
-            assert timestep.unique().tolist() == [100]
-            new_tokens = timestep.shape[1]
-            for cache in kv_cache:
-                cache["local_end_index"] += new_tokens
-                cache["global_end_index"] += new_tokens
-                cache["prope_local_end_index"] += new_tokens
-                cache["prope_global_end_index"] += new_tokens
-                cache["k"][:, :new_tokens] = 1
-                cache["prope_k"][:, :new_tokens] = 1
-            return hidden_states
-
-    cam_self_attn = SimpleNamespace(num_heads=3, head_dim=5)
-    cache_transformer = SimpleNamespace(
-        blocks=[SimpleNamespace(cam_self_attn=cam_self_attn)],
-        num_attention_heads=2,
-        attention_head_dim=4,
-    )
-    stage = DreamXWorldARCausalDenoisingStage.__new__(DreamXWorldARCausalDenoisingStage)
-    stage.transformer = cache_transformer
-    stage.num_transformer_blocks = 1
-    stage.local_attn_size = 6
-    caches = stage._initialize_kv_cache(
-        batch_size=1,
-        dtype=torch.float32,
-        device=torch.device("cpu"),
-        frame_seq_length=2,
-    )
-    # Keep the cache allocation source separate from the callable transformer used by _update_context_cache.
-    stage.transformer = DummyTransformer()
-
-    stage._update_context_cache(
-        block_latents=torch.zeros(1, 4, 3, 2, 2),
-        context=[torch.zeros(2, 4)],
-        camera_block={
-            "viewmats": torch.eye(4).reshape(1, 1, 4, 4),
-            "K": torch.eye(3).reshape(1, 1, 3, 3)
-        },
-        kv_cache=caches,
-        crossattn_cache=[{}],
-        start=0,
-        frame_seq_length=2,
-        target_dtype=torch.float32,
-        autocast_enabled=False,
-        context_noise=0.1,
-    )
-
-    assert int(caches[0]["local_end_index"].item()) == 6
-    assert int(caches[0]["prope_local_end_index"].item()) == 6
-    assert caches[0]["k"][:, :6].sum().item() == 48
-    assert caches[0]["prope_k"][:, :6].sum().item() == 90
diff --git a/tests/local_tests/dreamx_world/test_dreamx_world_scheduler_parity.py b/tests/local_tests/dreamx_world/test_dreamx_world_scheduler_parity.py
deleted file mode 100644
index 367542995..000000000
--- a/tests/local_tests/dreamx_world/test_dreamx_world_scheduler_parity.py
+++ /dev/null
@@ -1,52 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""DreamX-World default Flow scheduler parity.
-
-Coverage scope: implementation_subcomponent. DreamX-World-5B-Cam defaults to
-Diffusers FlowMatchEulerDiscreteScheduler for sampler_name=Flow. This test
-checks that FastVideo's native FlowMatchEulerDiscreteScheduler matches the
-timestep schedule and Euler step used by the official default path.
-"""
-from __future__ import annotations
-
-from pathlib import Path
-import inspect
-
-import torch
-from diffusers import FlowMatchEulerDiscreteScheduler as OfficialFlowMatchEulerDiscreteScheduler
-from omegaconf import OmegaConf
-from torch.testing import assert_close
-
-from fastvideo.models.schedulers.scheduling_flow_match_euler_discrete import (
-    FlowMatchEulerDiscreteScheduler as FastVideoFlowMatchEulerDiscreteScheduler, )
-
-REPO_ROOT = Path(__file__).resolve().parents[3]
-PARITY_SCOPE = "implementation_subcomponent"
-
-
-def _scheduler_kwargs(cls):
-    config_path = REPO_ROOT / "DreamX-World" / "configs" / "wan2.2" / "wan_ti2v_5b.yaml"
-    config = OmegaConf.load(config_path)
-    raw_kwargs = OmegaConf.to_container(config["scheduler_kwargs"])
-    signature = inspect.signature(cls)
-    return {key: value for key, value in raw_kwargs.items() if key in signature.parameters}
-
-
-def test_dreamx_world_flow_scheduler_timesteps_and_step_match():
-    official = OfficialFlowMatchEulerDiscreteScheduler(**_scheduler_kwargs(OfficialFlowMatchEulerDiscreteScheduler))
-    fastvideo = FastVideoFlowMatchEulerDiscreteScheduler(**_scheduler_kwargs(FastVideoFlowMatchEulerDiscreteScheduler))
-
-    official.set_timesteps(50, device="cpu", mu=1)
-    fastvideo.set_timesteps(50, device="cpu", mu=1)
-    assert_close(fastvideo.timesteps, official.timesteps, atol=0, rtol=0)
-    assert_close(fastvideo.sigmas, official.sigmas, atol=0, rtol=0)
-
-    torch.manual_seed(7)
-    sample = torch.randn(1, 4, 2, 8, 8)
-    model_output = torch.randn_like(sample)
-    timestep = official.timesteps[3]
-
-    official_prev = official.step(model_output, timestep, sample, return_dict=False)[0]
-    fastvideo_prev = fastvideo.step(model_output, fastvideo.timesteps[3], sample, return_dict=False)[0]
-    diff = (official_prev - fastvideo_prev).abs()
-    print(f"scheduler diff_max={diff.max().item():.8f} diff_mean={diff.mean().item():.8f}")
-    assert_close(fastvideo_prev, official_prev, atol=0, rtol=0)
diff --git a/tests/local_tests/dreamx_world/test_dreamx_world_text_encoder_parity.py b/tests/local_tests/dreamx_world/test_dreamx_world_text_encoder_parity.py
deleted file mode 100644
index 9b3147581..000000000
--- a/tests/local_tests/dreamx_world/test_dreamx_world_text_encoder_parity.py
+++ /dev/null
@@ -1,162 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""DreamX-World Wan T5 encoder reuse parity scaffold.
-
-Coverage scope: implementation_subcomponent. It records the official
-WanT5EncoderModel loading path and FastVideo T5 target for later activation
-with staged Wan2.2 base text encoder/tokenizer weights.
-"""
-from __future__ import annotations
-
-import os
-from pathlib import Path
-import sys
-import types
-
-import pytest
-import torch
-from omegaconf import OmegaConf
-from torch.testing import assert_close
-from transformers import AutoTokenizer
-
-from fastvideo.fastvideo_args import FastVideoArgs
-from fastvideo.configs.pipelines.dreamx_world import (
-    DreamXWorld5BCamPipelineConfig,
-    make_dreamx_world_5b_cam_text_encoder_config,
-)
-from fastvideo.models.loader.component_loader import TextEncoderLoader
-
-REPO_ROOT = Path(__file__).resolve().parents[3]
-OFFICIAL_REF_DIR = Path(os.getenv("DREAMX_WORLD_OFFICIAL_REF_DIR", REPO_ROOT / "DreamX-World"))
-WAN_BASE_DIR = Path(os.getenv("DREAMX_WORLD_WAN_BASE_DIR", REPO_ROOT / "official_weights" / "Wan2.2-TI2V-5B"))
-WAN_DIFFUSERS_DIR = Path(
-    os.getenv("DREAMX_WORLD_WAN_DIFFUSERS_DIR", REPO_ROOT / "official_weights" / "Wan2.2-TI2V-5B-Diffusers"))
-PARITY_SCOPE = "implementation_subcomponent"
-
-
-def _add_official_to_path():
-    if not OFFICIAL_REF_DIR.exists():
-        pytest.skip(f"Official reference missing: {OFFICIAL_REF_DIR}")
-    if str(OFFICIAL_REF_DIR) not in sys.path:
-        sys.path.insert(0, str(OFFICIAL_REF_DIR))
-
-
-def _install_xfuser_stub() -> None:
-    if "xfuser" in sys.modules:
-        return
-    xfuser = types.ModuleType("xfuser")
-    core = types.ModuleType("xfuser.core")
-    distributed = types.ModuleType("xfuser.core.distributed")
-    long_ctx = types.ModuleType("xfuser.core.long_ctx_attention")
-    distributed.get_sequence_parallel_rank = lambda: 0
-    distributed.get_sequence_parallel_world_size = lambda: 1
-    distributed.get_sp_group = lambda: None
-    distributed.get_world_group = lambda: types.SimpleNamespace(local_rank=0, rank=0)
-    distributed.init_distributed_environment = lambda *args, **kwargs: None
-    distributed.initialize_model_parallel = lambda *args, **kwargs: None
-    distributed.model_parallel_is_initialized = lambda: False
-
-    class XFuserLongContextAttention:
-
-        def __call__(self, *args, **kwargs):
-            raise RuntimeError("xfuser stub cannot execute attention")
-
-    long_ctx.xFuserLongContextAttention = XFuserLongContextAttention
-    sys.modules.update({
-        "xfuser": xfuser,
-        "xfuser.core": core,
-        "xfuser.core.distributed": distributed,
-        "xfuser.core.long_ctx_attention": long_ctx,
-    })
-
-
-def _text_kwargs():
-    config = OmegaConf.load(OFFICIAL_REF_DIR / "configs" / "wan2.2" / "wan_ti2v_5b.yaml")
-    return OmegaConf.to_container(config["text_encoder_kwargs"])
-
-
-def _patch_single_process_text_parallel(monkeypatch):
-    import fastvideo.layers.linear as fastvideo_linear
-    import fastvideo.layers.vocab_parallel_embedding as fastvideo_embedding
-    import fastvideo.models.encoders.t5 as fastvideo_t5
-
-    for module in (fastvideo_t5, fastvideo_embedding, fastvideo_linear):
-        if hasattr(module, "get_tp_rank"):
-            monkeypatch.setattr(module, "get_tp_rank", lambda: 0)
-        if hasattr(module, "get_tp_world_size"):
-            monkeypatch.setattr(module, "get_tp_world_size", lambda: 1)
-    monkeypatch.setattr(fastvideo_embedding, "tensor_model_parallel_all_reduce", lambda x: x)
-
-
-def _load_official_text_encoder(device, dtype):
-    _add_official_to_path()
-    _install_xfuser_stub()
-    text_path = WAN_BASE_DIR / "models_t5_umt5-xxl-enc-bf16.pth"
-    if not text_path.exists():
-        pytest.skip(f"Wan2.2 text encoder weights missing: {text_path}")
-    try:
-        from models import WanT5EncoderModel
-    except Exception as exc:  # noqa: BLE001
-        pytest.skip(f"Cannot import official DreamX text encoder: {exc}")
-    model = WanT5EncoderModel.from_pretrained(str(text_path),
-                                              additional_kwargs=_text_kwargs(),
-                                              low_cpu_mem_usage=True,
-                                              torch_dtype=dtype)
-    return model.to(device=device, dtype=dtype).eval()
-
-
-def _load_fastvideo_text_encoder(device, dtype, monkeypatch):
-    text_encoder_path = WAN_DIFFUSERS_DIR / "text_encoder"
-    if not text_encoder_path.exists():
-        pytest.skip(f"Wan2.2 Diffusers text encoder missing: {text_encoder_path}")
-    _patch_single_process_text_parallel(monkeypatch)
-    pipeline_config = DreamXWorld5BCamPipelineConfig()
-    pipeline_config.text_encoder_configs[0]._fsdp_shard_conditions = []
-    args = FastVideoArgs(
-        model_path=str(text_encoder_path),
-        pipeline_config=pipeline_config,
-        text_encoder_cpu_offload=(device.type == "cpu"),
-    )
-    args.model_paths = {}
-    return TextEncoderLoader().load(str(text_encoder_path), args).to(device=device, dtype=dtype).eval()
-
-
-def test_dreamx_world_text_encoder_config_matches_umt5_xxl_shape():
-    config = make_dreamx_world_5b_cam_text_encoder_config()
-    assert config.vocab_size == 256384
-    assert config.d_model == 4096
-    assert config.d_kv == 64
-    assert config.d_ff == 10240
-    assert config.num_heads == 64
-    assert config.num_layers == 24
-    assert config.relative_attention_num_buckets == 32
-    assert config.dropout_rate == 0.0
-    assert config.text_len == 512
-    assert config.prefix == "umt5"
-
-
-def test_dreamx_world_fastvideo_text_encoder_loads_staged_weights(monkeypatch):
-    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-    model = _load_fastvideo_text_encoder(device, torch.bfloat16, monkeypatch)
-    assert model.__class__.__name__ == "UMT5EncoderModel"
-    assert next(model.parameters()).device.type == device.type
-    assert next(model.parameters()).dtype == torch.bfloat16
-
-
-@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA required for text encoder parity.")
-def test_dreamx_world_text_encoder_parity_scaffold(monkeypatch):
-    device = torch.device("cuda:0")
-    dtype = torch.bfloat16
-    official = _load_official_text_encoder(device, dtype)
-    fastvideo = _load_fastvideo_text_encoder(device, dtype, monkeypatch)
-    tokenizer_path = WAN_BASE_DIR / "google" / "umt5-xxl"
-    if not tokenizer_path.exists():
-        pytest.skip(f"Wan2.2 tokenizer missing: {tokenizer_path}")
-    tokenizer = AutoTokenizer.from_pretrained(str(tokenizer_path))
-    batch = tokenizer(["A quiet forest trail at sunrise."], padding="max_length", max_length=512, return_tensors="pt")
-    input_ids = batch.input_ids.to(device)
-    attention_mask = batch.attention_mask.to(device)
-    with torch.inference_mode():
-        official_hidden = official(input_ids, attention_mask=attention_mask)[0].float().cpu()
-        fastvideo_hidden = fastvideo(input_ids, attention_mask=attention_mask).last_hidden_state.float().cpu()
-    assert official_hidden.shape == fastvideo_hidden.shape
-    assert_close(fastvideo_hidden, official_hidden, atol=1e-3, rtol=1e-3)
diff --git a/tests/local_tests/dreamx_world/test_dreamx_world_transformer_parity.py b/tests/local_tests/dreamx_world/test_dreamx_world_transformer_parity.py
deleted file mode 100644
index f7908fb1b..000000000
--- a/tests/local_tests/dreamx_world/test_dreamx_world_transformer_parity.py
+++ /dev/null
@@ -1,330 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""DreamX-World transformer parity scaffold.
-
-Coverage scope: both. The official side loads DreamX-World-5B-Cam through
-Wan2_2Transformer3DModel.from_pretrained with PRoPE camera control enabled.
-The FastVideo side strict-loads the converted DreamX transformer weights into
-the native DreamX-World DiT implementation.
-"""
-from __future__ import annotations
-
-import math
-import os
-from pathlib import Path
-import sys
-import types
-
-import pytest
-import torch
-from omegaconf import OmegaConf
-from torch.testing import assert_close
-
-from fastvideo.forward_context import set_forward_context
-from fastvideo.configs.models.dits.dreamx_world import (DreamXWorldArchConfig, DreamXWorldConfig)
-from fastvideo.pipelines.basic.dreamx_world.camera_conditioning import build_dreamx_camera_condition
-from fastvideo.configs.pipelines.dreamx_world import make_dreamx_world_5b_cam_dit_config
-from fastvideo.platforms import AttentionBackendEnum
-from fastvideo.models.dits.dreamx_world import (DreamXPropeSelfAttention, DreamXWorldTransformer3DModel,
-                                                DreamXWorldTransformerBlock)
-from fastvideo.models.loader.fsdp_load import load_model_from_full_model_state_dict
-from fastvideo.models.loader.utils import get_param_names_mapping
-from fastvideo.models.loader.weight_utils import resolve_safetensors_files, safetensors_weights_iterator
-from scripts.checkpoint_conversion.dreamx_world_to_diffusers import map_transformer_key
-
-REPO_ROOT = Path(__file__).resolve().parents[3]
-OFFICIAL_REF_DIR = Path(os.getenv("DREAMX_WORLD_OFFICIAL_REF_DIR", REPO_ROOT / "DreamX-World"))
-LOCAL_WEIGHTS_DIR = Path(os.getenv("DREAMX_WORLD_LOCAL_WEIGHTS_DIR", REPO_ROOT / "official_weights" / "dreamx_world"))
-WAN_BASE_DIR = Path(os.getenv("DREAMX_WORLD_WAN_BASE_DIR", REPO_ROOT / "official_weights" / "Wan2.2-TI2V-5B"))
-CONVERTED_WEIGHTS_DIR = Path(
-    os.getenv("DREAMX_WORLD_CONVERTED_WEIGHTS_DIR", REPO_ROOT / "converted_weights" / "dreamx_world"))
-PARITY_SCOPE = "both"
-
-
-def _install_xfuser_stub() -> None:
-    if "xfuser" in sys.modules:
-        return
-    xfuser = types.ModuleType("xfuser")
-    core = types.ModuleType("xfuser.core")
-    distributed = types.ModuleType("xfuser.core.distributed")
-    long_ctx = types.ModuleType("xfuser.core.long_ctx_attention")
-    distributed.get_sequence_parallel_rank = lambda: 0
-    distributed.get_sequence_parallel_world_size = lambda: 1
-    distributed.get_sp_group = lambda: None
-    distributed.get_world_group = lambda: types.SimpleNamespace(local_rank=0, rank=0)
-    distributed.init_distributed_environment = lambda *args, **kwargs: None
-    distributed.initialize_model_parallel = lambda *args, **kwargs: None
-    distributed.model_parallel_is_initialized = lambda: False
-
-    class XFuserLongContextAttention:
-
-        def __call__(self, *args, **kwargs):
-            raise RuntimeError("xfuser stub cannot execute attention")
-
-    long_ctx.xFuserLongContextAttention = XFuserLongContextAttention
-    sys.modules.update({
-        "xfuser": xfuser,
-        "xfuser.core": core,
-        "xfuser.core.distributed": distributed,
-        "xfuser.core.long_ctx_attention": long_ctx,
-    })
-
-
-def _make_tiny_dreamx_config() -> DreamXWorldConfig:
-    return DreamXWorldConfig(arch_config=DreamXWorldArchConfig(
-        num_attention_heads=1,
-        attention_head_dim=8,
-        in_channels=16,
-        out_channels=16,
-        ffn_dim=32,
-        num_layers=1,
-        cross_attn_norm=True,
-        qk_norm="rms_norm_across_heads",
-        add_control_adapter=True,
-        cam_method="prope",
-        attn_compress=1,
-        cam_self_attn_layers=None,
-    ))
-
-
-def _add_official_to_path() -> None:
-    if not OFFICIAL_REF_DIR.exists():
-        pytest.skip(f"Official reference missing: {OFFICIAL_REF_DIR}")
-    if str(OFFICIAL_REF_DIR) not in sys.path:
-        sys.path.insert(0, str(OFFICIAL_REF_DIR))
-
-
-def _official_transformer_kwargs() -> dict:
-    config_path = OFFICIAL_REF_DIR / "configs" / "wan2.2" / "wan_ti2v_5b.yaml"
-    if not config_path.exists():
-        pytest.skip(f"DreamX Wan config missing: {config_path}")
-    config = OmegaConf.load(config_path)
-    kwargs = OmegaConf.to_container(config["transformer_additional_kwargs"])
-    kwargs["cam_method"] = "prope"
-    kwargs["add_control_adapter"] = True
-    return kwargs
-
-
-def _load_official_transformer(device: torch.device, dtype: torch.dtype) -> torch.nn.Module:
-    _add_official_to_path()
-    _install_xfuser_stub()
-    if not LOCAL_WEIGHTS_DIR.exists():
-        pytest.skip(f"DreamX transformer weights missing: {LOCAL_WEIGHTS_DIR}")
-    try:
-        from models import Wan2_2Transformer3DModel
-    except Exception as exc:  # noqa: BLE001 - local parity should skip missing refs.
-        pytest.skip(f"Cannot import official DreamX transformer: {exc}")
-    model = Wan2_2Transformer3DModel.from_pretrained(
-        str(LOCAL_WEIGHTS_DIR),
-        transformer_additional_kwargs=_official_transformer_kwargs(),
-        torch_dtype=dtype,
-    )
-    return model.to(device=device, dtype=dtype).eval()
-
-
-def _load_fastvideo_transformer(device: torch.device, dtype: torch.dtype) -> torch.nn.Module:
-    model = _load_fastvideo_transformer_strict(torch.device("cpu"), dtype)
-    return model.to(device=device, dtype=dtype).eval()
-
-
-def _load_fastvideo_transformer_strict(device: torch.device, dtype: torch.dtype) -> torch.nn.Module:
-    transformer_dir = CONVERTED_WEIGHTS_DIR / "transformer"
-    if not transformer_dir.exists():
-        pytest.skip(f"Converted DreamX transformer missing: {transformer_dir}")
-    safetensors_files = resolve_safetensors_files(str(transformer_dir))
-    config = make_dreamx_world_5b_cam_dit_config()
-    import fastvideo.models.dits.dreamx_world as fastvideo_dreamx
-    original_get_sp_world_size = fastvideo_dreamx.get_sp_world_size
-    fastvideo_dreamx.get_sp_world_size = lambda: 1
-    try:
-        with torch.device("meta"):
-            model = DreamXWorldTransformer3DModel(config=config, hf_config={})
-    finally:
-        fastvideo_dreamx.get_sp_world_size = original_get_sp_world_size
-    incompatible = load_model_from_full_model_state_dict(
-        model,
-        safetensors_weights_iterator(safetensors_files, to_cpu=True),
-        device=device,
-        param_dtype=dtype,
-        strict=True,
-        param_names_mapping=get_param_names_mapping(model.param_names_mapping),
-        training_mode=False,
-    )
-    assert incompatible.missing_keys == []
-    assert incompatible.unexpected_keys == []
-    assert not any(param.is_meta for param in model.parameters())
-    return model.to(device=device, dtype=dtype).eval()
-
-
-def _make_inputs(device: torch.device, dtype: torch.dtype):
-    torch.manual_seed(1234)
-    num_frames = 5
-    height = 64
-    width = 64
-    latent_frames = (num_frames - 1) // 4 + 1
-    latent_h = height // 16
-    latent_w = width // 16
-    x = torch.randn(1, 48, latent_frames, latent_h, latent_w, device=device, dtype=dtype)
-    context = [torch.randn(16, 4096, device=device, dtype=dtype)]
-    seq_len = math.ceil((latent_h * latent_w) / 4 * latent_frames)
-    timestep = torch.full((1, seq_len), 250, device=device, dtype=torch.long)
-    camera = build_dreamx_camera_condition(["w"], [4],
-                                           num_frames=num_frames,
-                                           height=height,
-                                           width=width,
-                                           dtype=dtype,
-                                           device=device)
-    camera = {key: value.unsqueeze(0) for key, value in camera.items()}
-    return {"x": [x[0]], "context": context, "t": timestep, "seq_len": seq_len, "y_camera": camera}
-
-
-def _run_official(model: torch.nn.Module, inputs: dict) -> torch.Tensor:
-    with torch.inference_mode():
-        output = model(**inputs)
-    if isinstance(output, list):
-        output = torch.stack(output, dim=0)
-    assert torch.is_tensor(output), f"official output is not a tensor: {type(output)}"
-    return output.detach().float().cpu()
-
-
-def _run_fastvideo(model: torch.nn.Module, inputs: dict) -> torch.Tensor:
-    hidden_states = torch.stack(inputs["x"], dim=0)
-    encoder_hidden_states = torch.stack(
-        [torch.cat([inputs["context"][0], inputs["context"][0].new_zeros(512 - inputs["context"][0].shape[0], 4096)])])
-    with torch.inference_mode(), set_forward_context(current_timestep=0, attn_metadata=None):
-        output = model(
-            hidden_states=hidden_states,
-            encoder_hidden_states=encoder_hidden_states,
-            timestep=inputs["t"],
-            y_camera=inputs["y_camera"],
-        )
-    assert torch.is_tensor(output), f"FastVideo output is not a tensor: {type(output)}"
-    return output.detach().float().cpu()
-
-
-def test_dreamx_world_conversion_mapping_strict_load_smoke(monkeypatch):
-    _add_official_to_path()
-    _install_xfuser_stub()
-    try:
-        from models import Wan2_2Transformer3DModel
-    except Exception as exc:  # noqa: BLE001
-        pytest.skip(f"Cannot import official DreamX transformer: {exc}")
-
-    official = Wan2_2Transformer3DModel(
-        dim=8,
-        ffn_dim=32,
-        num_heads=1,
-        num_layers=1,
-        add_control_adapter=True,
-        cam_method="prope",
-    )
-    official_state = official.state_dict()
-    diffusers_like_state = {map_transformer_key(key): value.detach().clone() for key, value in official_state.items()}
-
-    import fastvideo.models.dits.dreamx_world as fastvideo_dreamx
-    monkeypatch.setattr(fastvideo_dreamx, "get_sp_world_size", lambda: 1)
-    with torch.device("meta"):
-        fastvideo = DreamXWorldTransformer3DModel(config=_make_tiny_dreamx_config(), hf_config={})
-
-    incompatible = load_model_from_full_model_state_dict(
-        fastvideo,
-        iter(diffusers_like_state.items()),
-        device=torch.device("cpu"),
-        param_dtype=torch.float32,
-        strict=True,
-        param_names_mapping=get_param_names_mapping(fastvideo.param_names_mapping),
-        training_mode=False,
-    )
-    assert incompatible.missing_keys == []
-    assert incompatible.unexpected_keys == []
-    assert not any(param.is_meta for param in fastvideo.parameters())
-
-
-def test_dreamx_world_5b_cam_dit_config_matches_official_shape():
-    config = make_dreamx_world_5b_cam_dit_config()
-    assert config.num_layers == 30
-    assert config.num_attention_heads == 24
-    assert config.attention_head_dim == 128
-    assert config.hidden_size == 3072
-    assert config.ffn_dim == 14336
-    assert config.add_control_adapter is True
-    assert config.cam_method == "prope"
-    assert config.attn_compress == 1
-
-
-def test_dreamx_world_converted_5b_transformer_strict_loads():
-    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-    _load_fastvideo_transformer_strict(device, torch.bfloat16)
-
-
-def test_dreamx_world_fastvideo_prope_branch_smoke():
-    block = DreamXWorldTransformerBlock(
-        8,
-        32,
-        1,
-        cross_attn_norm=True,
-        add_control_adapter=True,
-        cam_method="prope",
-        attn_compress=1,
-        layer_idx=0,
-        supported_attention_backends=(AttentionBackendEnum.TORCH_SDPA, ),
-    )
-    assert block.cam_self_attn is not None
-    assert [name for name, _ in block.named_parameters() if name.startswith("cam_self_attn.")][:8] == [
-        "cam_self_attn.q_proj.weight",
-        "cam_self_attn.q_proj.bias",
-        "cam_self_attn.k_proj.weight",
-        "cam_self_attn.k_proj.bias",
-        "cam_self_attn.v_proj.weight",
-        "cam_self_attn.v_proj.bias",
-        "cam_self_attn.out_proj.weight",
-        "cam_self_attn.out_proj.bias",
-    ]
-
-    module = DreamXPropeSelfAttention(
-        dim=8,
-        attn_dim=8,
-        num_heads=1,
-        qk_norm="rms_norm_across_heads",
-    ).eval()
-    assert module.num_heads == 1
-    assert module.head_dim == 8
-    assert tuple(module.out_proj.weight.shape) == (8, 8)
-    assert torch.count_nonzero(module.out_proj.weight) == 0
-
-
-@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA required for transformer parity.")
-def test_dreamx_world_transformer_parity_scaffold(monkeypatch):
-    device = torch.device("cuda:0")
-    dtype = torch.float32
-    inputs = _make_inputs(device, dtype)
-
-    official = _load_official_transformer(device, dtype)
-    official_out = _run_official(official, inputs)
-    del official
-    torch.cuda.empty_cache()
-
-    import fastvideo.attention.layer as attention_layer
-    import fastvideo.distributed.communication_op as communication_op
-    import fastvideo.models.dits.dreamx_world as fastvideo_dreamx
-    monkeypatch.setattr(attention_layer, "get_sp_parallel_rank", lambda: 0)
-    monkeypatch.setattr(attention_layer, "get_sp_world_size", lambda: 1)
-    monkeypatch.setattr(attention_layer,
-                        "sequence_model_parallel_all_to_all_4D",
-                        lambda tensor, scatter_dim=2, gather_dim=1: tensor)
-    monkeypatch.setattr(attention_layer, "sequence_model_parallel_all_gather", lambda tensor, dim=-1: tensor)
-    monkeypatch.setattr(fastvideo_dreamx, "get_sp_world_size", lambda: 1)
-    monkeypatch.setattr(communication_op, "get_sp_world_size", lambda: 1)
-    monkeypatch.setattr(fastvideo_dreamx,
-                        "sequence_model_parallel_shard",
-                        lambda tensor, dim=1: (tensor, tensor.shape[dim]))
-    monkeypatch.setattr(
-        fastvideo_dreamx,
-        "sequence_model_parallel_all_gather_with_unpad",
-        lambda tensor, original_seq_len, dim=1: tensor.narrow(dim, 0, original_seq_len),
-    )
-    fastvideo = _load_fastvideo_transformer(device, dtype)
-    fastvideo_out = _run_fastvideo(fastvideo, inputs)
-    assert official_out.shape == fastvideo_out.shape
-    diff = (official_out - fastvideo_out).abs()
-    print(f"diff_max={diff.max().item():.6f} diff_mean={diff.mean().item():.6f}")
-    assert_close(fastvideo_out, official_out, atol=1e-1, rtol=1e-1)
diff --git a/tests/local_tests/dreamx_world/test_dreamx_world_vae_parity.py b/tests/local_tests/dreamx_world/test_dreamx_world_vae_parity.py
deleted file mode 100644
index 114897b44..000000000
--- a/tests/local_tests/dreamx_world/test_dreamx_world_vae_parity.py
+++ /dev/null
@@ -1,221 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""DreamX-World Wan2.2 VAE reuse parity scaffold.
-
-Coverage scope: implementation_subcomponent. The official side uses
-AutoencoderKLWan3_8 from DreamX, while the FastVideo side targets the native
-Wan VAE. This remains a scaffold until Wan2.2 base VAE weights are staged.
-"""
-from __future__ import annotations
-
-import os
-from pathlib import Path
-import sys
-import re
-import types
-
-import pytest
-import torch
-from omegaconf import OmegaConf
-from torch.testing import assert_close
-
-from fastvideo.configs.pipelines.dreamx_world import make_dreamx_world_5b_cam_vae_config
-from fastvideo.models.wan.vae import AutoencoderKLWan
-
-REPO_ROOT = Path(__file__).resolve().parents[3]
-OFFICIAL_REF_DIR = Path(os.getenv("DREAMX_WORLD_OFFICIAL_REF_DIR", REPO_ROOT / "DreamX-World"))
-WAN_BASE_DIR = Path(os.getenv("DREAMX_WORLD_WAN_BASE_DIR", REPO_ROOT / "official_weights" / "Wan2.2-TI2V-5B"))
-PARITY_SCOPE = "implementation_subcomponent"
-
-
-def _add_official_to_path():
-    if not OFFICIAL_REF_DIR.exists():
-        pytest.skip(f"Official reference missing: {OFFICIAL_REF_DIR}")
-    if str(OFFICIAL_REF_DIR) not in sys.path:
-        sys.path.insert(0, str(OFFICIAL_REF_DIR))
-
-
-def _install_xfuser_stub() -> None:
-    if "xfuser" in sys.modules:
-        return
-    xfuser = types.ModuleType("xfuser")
-    core = types.ModuleType("xfuser.core")
-    distributed = types.ModuleType("xfuser.core.distributed")
-    long_ctx = types.ModuleType("xfuser.core.long_ctx_attention")
-    distributed.get_sequence_parallel_rank = lambda: 0
-    distributed.get_sequence_parallel_world_size = lambda: 1
-    distributed.get_sp_group = lambda: None
-    distributed.get_world_group = lambda: types.SimpleNamespace(local_rank=0, rank=0)
-    distributed.init_distributed_environment = lambda *args, **kwargs: None
-    distributed.initialize_model_parallel = lambda *args, **kwargs: None
-    distributed.model_parallel_is_initialized = lambda: False
-
-    class XFuserLongContextAttention:
-
-        def __call__(self, *args, **kwargs):
-            raise RuntimeError("xfuser stub cannot execute attention")
-
-    long_ctx.xFuserLongContextAttention = XFuserLongContextAttention
-    sys.modules.update({
-        "xfuser": xfuser,
-        "xfuser.core": core,
-        "xfuser.core.distributed": distributed,
-        "xfuser.core.long_ctx_attention": long_ctx,
-    })
-
-
-def _map_residual_subkey(prefix: str, sub: str) -> str | None:
-    if sub == "residual.0.gamma":
-        return f"{prefix}.norm1.gamma"
-    match = re.match(r"^residual\.2\.(weight|bias)$", sub)
-    if match:
-        return f"{prefix}.conv1.{match.group(1)}"
-    if sub == "residual.3.gamma":
-        return f"{prefix}.norm2.gamma"
-    match = re.match(r"^residual\.6\.(weight|bias)$", sub)
-    if match:
-        return f"{prefix}.conv2.{match.group(1)}"
-    match = re.match(r"^shortcut\.(weight|bias)$", sub)
-    if match:
-        return f"{prefix}.conv_shortcut.{match.group(1)}"
-    return None
-
-
-def _map_attention_subkey(prefix: str, sub: str) -> str | None:
-    if sub == "norm.gamma":
-        return f"{prefix}.norm.gamma"
-    match = re.match(r"^(to_qkv|proj)\.(weight|bias)$", sub)
-    if match:
-        return f"{prefix}.{match.group(1)}.{match.group(2)}"
-    return None
-
-
-def _map_resample_subkey(prefix: str, sub: str) -> str | None:
-    match = re.match(r"^resample\.1\.(weight|bias)$", sub)
-    if match:
-        return f"{prefix}.resample.1.{match.group(1)}"
-    match = re.match(r"^time_conv\.(weight|bias)$", sub)
-    if match:
-        return f"{prefix}.time_conv.{match.group(1)}"
-    return None
-
-
-def _map_dreamx_raw_vae_key(key: str) -> str | None:
-    match = re.match(r"^(conv1|conv2)\.(weight|bias)$", key)
-    if match:
-        prefix = "quant_conv" if match.group(1) == "conv1" else "post_quant_conv"
-        return f"{prefix}.{match.group(2)}"
-    match = re.match(r"^(encoder|decoder)\.conv1\.(weight|bias)$", key)
-    if match:
-        return f"{match.group(1)}.conv_in.{match.group(2)}"
-    match = re.match(r"^(encoder|decoder)\.head\.0\.gamma$", key)
-    if match:
-        return f"{match.group(1)}.norm_out.gamma"
-    match = re.match(r"^(encoder|decoder)\.head\.2\.(weight|bias)$", key)
-    if match:
-        return f"{match.group(1)}.conv_out.{match.group(2)}"
-    match = re.match(r"^(encoder|decoder)\.middle\.0\.(.*)$", key)
-    if match:
-        return _map_residual_subkey(f"{match.group(1)}.mid_block.resnets.0", match.group(2))
-    match = re.match(r"^(encoder|decoder)\.middle\.1\.(.*)$", key)
-    if match:
-        return _map_attention_subkey(f"{match.group(1)}.mid_block.attentions.0", match.group(2))
-    match = re.match(r"^(encoder|decoder)\.middle\.2\.(.*)$", key)
-    if match:
-        return _map_residual_subkey(f"{match.group(1)}.mid_block.resnets.1", match.group(2))
-    match = re.match(r"^encoder\.downsamples\.(\d+)\.downsamples\.(\d+)\.(.*)$", key)
-    if match:
-        stage = int(match.group(1))
-        block = int(match.group(2))
-        sub = match.group(3)
-        if block in (0, 1):
-            return _map_residual_subkey(f"encoder.down_blocks.{stage}.resnets.{block}", sub)
-        if block == 2:
-            return _map_resample_subkey(f"encoder.down_blocks.{stage}.downsampler", sub)
-        return None
-    match = re.match(r"^decoder\.upsamples\.(\d+)\.upsamples\.(\d+)\.(.*)$", key)
-    if match:
-        stage = int(match.group(1))
-        block = int(match.group(2))
-        sub = match.group(3)
-        if block in (0, 1, 2):
-            return _map_residual_subkey(f"decoder.up_blocks.{stage}.resnets.{block}", sub)
-        if block == 3:
-            return _map_resample_subkey(f"decoder.up_blocks.{stage}.upsampler", sub)
-        return None
-    return None
-
-
-def _vae_kwargs():
-    config = OmegaConf.load(OFFICIAL_REF_DIR / "configs" / "wan2.2" / "wan_ti2v_5b.yaml")
-    return OmegaConf.to_container(config["vae_kwargs"])
-
-
-def _load_official_vae(device, dtype):
-    _add_official_to_path()
-    _install_xfuser_stub()
-    vae_path = WAN_BASE_DIR / "Wan2.2_VAE.pth"
-    if not vae_path.exists():
-        pytest.skip(f"Wan2.2 base VAE weights missing: {vae_path}")
-    try:
-        from models import AutoencoderKLWan3_8
-    except Exception as exc:  # noqa: BLE001
-        pytest.skip(f"Cannot import official DreamX VAE: {exc}")
-    model = AutoencoderKLWan3_8.from_pretrained(str(vae_path), additional_kwargs=_vae_kwargs())
-    return model.to(device=device, dtype=dtype).eval()
-
-
-def _load_fastvideo_vae(device, dtype):
-    vae_path = WAN_BASE_DIR / "Wan2.2_VAE.pth"
-    if not vae_path.exists():
-        pytest.skip(f"Wan2.2 raw VAE weights missing: {vae_path}")
-    config = make_dreamx_world_5b_cam_vae_config()
-    config.load_encoder = True
-    config.load_decoder = True
-    model = AutoencoderKLWan(config).to(device=device, dtype=dtype)
-    raw_state = torch.load(str(vae_path), map_location="cpu", weights_only=True)
-    mapped_state = {}
-    for key, value in raw_state.items():
-        mapped_key = _map_dreamx_raw_vae_key(key)
-        if mapped_key is None:
-            raise AssertionError(f"Unmapped DreamX raw VAE key: {key}")
-        mapped_state[mapped_key] = value
-    model.load_state_dict(mapped_state, strict=True)
-    return model.eval()
-
-
-def _normalize_fastvideo_vae_latent(latent: torch.Tensor) -> torch.Tensor:
-    config = make_dreamx_world_5b_cam_vae_config()
-    mean = torch.tensor(config.latents_mean, device=latent.device, dtype=latent.dtype).view(1, -1, 1, 1, 1)
-    std = torch.tensor(config.latents_std, device=latent.device, dtype=latent.dtype).view(1, -1, 1, 1, 1)
-    return (latent - mean) / std
-
-
-def test_dreamx_world_vae_config_matches_wan22_shape():
-    config = make_dreamx_world_5b_cam_vae_config()
-    assert config.z_dim == 48
-    assert config.in_channels == 12
-    assert config.out_channels == 12
-    assert config.base_dim == 160
-    assert config.decoder_base_dim == 256
-    assert config.scale_factor_temporal == 4
-    assert config.scale_factor_spatial == 16
-    assert config.patch_size == 2
-    assert config.is_residual is True
-    assert config.clip_output is False
-    assert len(config.latents_mean) == 48
-    assert len(config.latents_std) == 48
-
-
-@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA required for VAE parity.")
-def test_dreamx_world_vae_encode_parity_scaffold():
-    device = torch.device("cuda:0")
-    dtype = torch.bfloat16
-    official = _load_official_vae(device, dtype)
-    fastvideo = _load_fastvideo_vae(device, dtype)
-    torch.manual_seed(123)
-    video = torch.randn(1, 3, 5, 64, 64, device=device, dtype=dtype).clamp(-1, 1)
-    with torch.inference_mode():
-        official_latent = official.encode(video).latent_dist.mean.float().cpu()
-        fastvideo_latent = _normalize_fastvideo_vae_latent(fastvideo.encode(video).mean).float().cpu()
-    assert official_latent.shape == fastvideo_latent.shape
-    assert_close(fastvideo_latent, official_latent, atol=5e-2, rtol=5e-2)
diff --git a/tests/local_tests/gamecraft/README.md b/tests/local_tests/gamecraft/README.md
deleted file mode 100644
index e11aa2f13..000000000
--- a/tests/local_tests/gamecraft/README.md
+++ /dev/null
@@ -1,67 +0,0 @@
-# Hunyuan GameCraft Local Tests
-
-Local-only parity tests for the `gamecraft` FastVideo port. They compare
-FastVideo against the official `Hunyuan-GameCraft-1.0` implementation and are
-skipped in CI; run locally on a single GPU (~40 GB+ memory).
-
-## Reference Assets
-
-| Field | Value |
-|---|---|
-| Model family | `gamecraft` |
-| Workload types | `T2V`, `I2V` |
-| Official reference | `Hunyuan-GameCraft-1.0` (local clone, see test files) |
-| Local reference dir | `Hunyuan-GameCraft-1.0/` (env: `GAMECRAFT_OFFICIAL_PATH`) |
-| Official commit/version | `<TODO>` |
-| HF weights | `<TODO>` |
-| HF revision | `<TODO>` |
-| Local weights dir | `Hunyuan-GameCraft-1.0/weights/` (env: `GAMECRAFT_WEIGHTS_PATH`) |
-| Source layout | `<TODO>` |
-| Needs conversion | `<TODO>` |
-
-> Use only the env-var **name** for tokens (e.g., `HF_TOKEN`). Never paste a token value.
-
-## Shared Environment Setup
-
-Run from the FastVideo repo root in the same env used for FastVideo. See the
-`add-model-prep` skill for canonical clone + install commands.
-
-```bash
-# Clone the official reference under the FastVideo repo root.
-git clone <official_repo_url> Hunyuan-GameCraft-1.0
-uv pip install --no-deps -e ./Hunyuan-GameCraft-1.0
-```
-
-Do not change core dependency versions (`torch`, `diffusers`, `transformers`,
-`flash-attn`, `triton`, CUDA packages) without explicit approval.
-
-## Official Environment Status
-
-```text
-dependency_changes: <TODO>
-official_env_status: <TODO: imports_ok | private_deps_need_stubs | blocked>
-private_dep_stubs: <TODO>
-blocked_on: <TODO>
-```
-
-## Tests in this directory
-
-Run the whole family:
-
-```bash
-DISABLE_SP=1 pytest tests/local_tests/gamecraft/ -v -s
-```
-
-| Component | Test | Concerns | Status |
-|---|---|---|---|
-| `text encoders` (LLaVA-LLaMA-3-8B + CLIP ViT-L/14) | [`test_gamecraft_encoders_parity.py`](./test_gamecraft_encoders_parity.py) | `<TODO>` | `<TODO>` |
-| `transformer/DiT` | [`test_gamecraft_parity.py`](./test_gamecraft_parity.py) | `<TODO>` | `<TODO>` |
-| `vae` (`AutoencoderKLCausal3D`) | [`test_gamecraft_vae_parity.py`](./test_gamecraft_vae_parity.py) | `<TODO>` | `<TODO>` |
-| `pipeline` | [`test_gamecraft_pipeline_parity.py`](./test_gamecraft_pipeline_parity.py) | text encoding -> denoising -> VAE decode | `<TODO>` |
-
-## Review Notes
-
-- Required before handoff: non-skip PASS for each component parity test,
-  including reused components that own weights or numerical behavior.
-- Pipeline parity may start as a scaffold; final handoff requires non-skip
-  PASS or an explicit blocker accepted via the escape-hatch process.
diff --git a/tests/local_tests/gamecraft/__init__.py b/tests/local_tests/gamecraft/__init__.py
deleted file mode 100644
index e69de29bb..000000000
diff --git a/tests/local_tests/gamecraft/test_gamecraft_encoders_parity.py b/tests/local_tests/gamecraft/test_gamecraft_encoders_parity.py
deleted file mode 100644
index 65640bd7a..000000000
--- a/tests/local_tests/gamecraft/test_gamecraft_encoders_parity.py
+++ /dev/null
@@ -1,244 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-Numerical parity test for GameCraft text encoders.
-
-GameCraft uses:
-1. LLaVA-LLaMA-3-8B for primary text encoding (4096-dim)
-2. CLIP ViT-L/14 for secondary text encoding (768-dim pooled)
-
-Usage:
-    DISABLE_SP=1 pytest tests/local_tests/gamecraft/test_gamecraft_encoders_parity.py -v
-"""
-import os
-import sys
-from pathlib import Path
-
-import pytest
-import torch
-from torch.testing import assert_close
-
-os.environ.setdefault("MASTER_ADDR", "localhost")
-os.environ.setdefault("MASTER_PORT", "29516")
-os.environ.setdefault("DISABLE_SP", "1")
-
-repo_root = Path(__file__).resolve().parents[3]
-
-
-@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA required")
-def test_gamecraft_llama_encoder():
-    """Test LLaVA-LLaMA-3-8B text encoder."""
-    torch.manual_seed(42)
-
-    llama_path = Path(
-        os.getenv("GAMECRAFT_LLAMA_PATH",
-                  repo_root / "Hunyuan-GameCraft-1.0" / "weights" / "stdmodels" / "llava-llama-3-8b-v1_1-transformers"))
-
-    if not llama_path.exists():
-        pytest.skip(f"LLaMA encoder not found at {llama_path}")
-
-    # Check for model weights
-    model_files = list(llama_path.glob("*.safetensors")) + list(llama_path.glob("*.bin"))
-    if not model_files:
-        pytest.skip(f"No model weights found in {llama_path}")
-
-    device = torch.device("cuda:0")
-    dtype = torch.float16  # LLaMA uses fp16
-
-    print(f"\n[LLAMA TEST] Loading LLaVA-LLaMA-3-8B from {llama_path}")
-
-    try:
-        from transformers import LlavaForConditionalGeneration, LlamaTokenizerFast
-
-        # Load model
-        model = LlavaForConditionalGeneration.from_pretrained(
-            llama_path,
-            low_cpu_mem_usage=True,
-            torch_dtype=dtype,
-        )
-        model = model.to(device)
-        model.eval()
-
-        # Load tokenizer
-        tokenizer = LlamaTokenizerFast.from_pretrained(llama_path, padding_side="right")
-
-        print(f"[LLAMA TEST] Model loaded successfully")
-        print(f"[LLAMA TEST] Model dtype: {model.dtype}")
-
-        # Test encoding
-        test_prompt = "A medieval village with cobblestone streets"
-        inputs = tokenizer(
-            test_prompt,
-            return_tensors="pt",
-            padding="max_length",
-            max_length=256,
-            truncation=True,
-        )
-        inputs = {k: v.to(device) for k, v in inputs.items()}
-
-        with torch.no_grad():
-            outputs = model(
-                input_ids=inputs["input_ids"],
-                attention_mask=inputs["attention_mask"],
-                output_hidden_states=True,
-            )
-
-        # Get last hidden state
-        last_hidden_state = outputs.hidden_states[-1]
-
-        print(f"[LLAMA TEST] Input tokens: {inputs['input_ids'].shape}")
-        print(f"[LLAMA TEST] Output hidden state shape: {last_hidden_state.shape}")
-        print(f"[LLAMA TEST] Hidden dim: {last_hidden_state.shape[-1]} (expected: 4096)")
-        print(f"[LLAMA TEST] Output stats: mean={last_hidden_state.mean():.6f}, std={last_hidden_state.std():.6f}")
-
-        assert last_hidden_state.shape[-1] == 4096, f"Expected hidden dim 4096, got {last_hidden_state.shape[-1]}"
-
-        print("[LLAMA TEST] LLaMA encoder test passed!")
-
-    except ImportError as e:
-        pytest.skip(f"transformers not available: {e}")
-    except Exception as e:
-        pytest.fail(f"LLaMA encoder test failed: {e}")
-
-
-@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA required")
-def test_gamecraft_clip_encoder():
-    """Test CLIP ViT-L/14 text encoder."""
-    torch.manual_seed(42)
-
-    clip_path = Path(
-        os.getenv("GAMECRAFT_CLIP_PATH",
-                  repo_root / "Hunyuan-GameCraft-1.0" / "weights" / "stdmodels" / "openai_clip-vit-large-patch14"))
-
-    if not clip_path.exists():
-        pytest.skip(f"CLIP encoder not found at {clip_path}")
-
-    device = torch.device("cuda:0")
-    dtype = torch.float32  # CLIP typically uses fp32
-
-    print(f"\n[CLIP TEST] Loading CLIP ViT-L/14 from {clip_path}")
-
-    try:
-        from transformers import CLIPTextModel, CLIPTokenizer
-
-        # Load model
-        model = CLIPTextModel.from_pretrained(clip_path)
-        model = model.to(device=device, dtype=dtype)
-        model.eval()
-
-        # Load tokenizer
-        tokenizer = CLIPTokenizer.from_pretrained(clip_path, max_length=77)
-
-        print(f"[CLIP TEST] Model loaded successfully")
-        print(f"[CLIP TEST] Model dtype: {model.dtype}")
-
-        # Test encoding
-        test_prompt = "A medieval village with cobblestone streets"
-        inputs = tokenizer(
-            test_prompt,
-            return_tensors="pt",
-            padding="max_length",
-            max_length=77,
-            truncation=True,
-        )
-        inputs = {k: v.to(device) for k, v in inputs.items()}
-
-        with torch.no_grad():
-            outputs = model(
-                input_ids=inputs["input_ids"],
-                attention_mask=inputs["attention_mask"],
-            )
-
-        # Get pooled output (used for text_states_2)
-        pooler_output = outputs.pooler_output
-        last_hidden_state = outputs.last_hidden_state
-
-        print(f"[CLIP TEST] Input tokens: {inputs['input_ids'].shape}")
-        print(f"[CLIP TEST] Pooled output shape: {pooler_output.shape} (expected: [B, 768])")
-        print(f"[CLIP TEST] Last hidden state shape: {last_hidden_state.shape}")
-        print(f"[CLIP TEST] Pooled output stats: mean={pooler_output.mean():.6f}, std={pooler_output.std():.6f}")
-
-        assert pooler_output.shape[-1] == 768, f"Expected pooled dim 768, got {pooler_output.shape[-1]}"
-
-        print("[CLIP TEST] CLIP encoder test passed!")
-
-    except ImportError as e:
-        pytest.skip(f"transformers not available: {e}")
-    except Exception as e:
-        pytest.fail(f"CLIP encoder test failed: {e}")
-
-
-@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA required")
-@pytest.mark.skip(
-    reason=
-    "Official TextEncoder wrapper incompatible with installed transformers version (language_model attribute removed from LlavaForConditionalGeneration)"
-)
-def test_gamecraft_text_encoder_wrapper():
-    """Test the official GameCraft TextEncoder wrapper."""
-    torch.manual_seed(42)
-
-    official_path = Path(os.getenv("GAMECRAFT_OFFICIAL_PATH", repo_root / "Hunyuan-GameCraft-1.0"))
-
-    if not official_path.exists():
-        pytest.skip(f"Official GameCraft repo not found at {official_path}")
-
-    sys.path.insert(0, str(official_path))
-
-    llama_path = official_path / "weights" / "stdmodels" / "llava-llama-3-8b-v1_1-transformers"
-    clip_path = official_path / "weights" / "stdmodels" / "openai_clip-vit-large-patch14"
-
-    if not llama_path.exists() or not clip_path.exists():
-        pytest.skip("Text encoder weights not found")
-
-    device = torch.device("cuda:0")
-
-    try:
-        from hymm_sp.text_encoder import TextEncoder
-
-        # Test LLaMA encoder wrapper
-        print("\n[WRAPPER TEST] Testing LLaVA-LLaMA-3-8B wrapper...")
-        llama_encoder = TextEncoder(
-            text_encoder_type="llava-llama-3-8b",
-            max_length=256,
-            text_encoder_precision="fp16",
-            text_encoder_path=str(llama_path),
-            tokenizer_path=str(llama_path),
-            use_attention_mask=True,
-            device=device,
-        )
-
-        test_prompt = "A medieval village with cobblestone streets"
-        llama_output = llama_encoder(test_prompt)
-
-        print(f"[WRAPPER TEST] LLaMA hidden state: {llama_output.hidden_state.shape}")
-        print(
-            f"[WRAPPER TEST] LLaMA attention mask: {llama_output.attention_mask.shape if llama_output.attention_mask is not None else 'None'}"
-        )
-
-        # Test CLIP encoder wrapper
-        print("\n[WRAPPER TEST] Testing CLIP wrapper...")
-        clip_encoder = TextEncoder(
-            text_encoder_type="clipL",
-            max_length=77,
-            text_encoder_precision="fp32",
-            text_encoder_path=str(clip_path),
-            tokenizer_path=str(clip_path),
-            use_attention_mask=True,
-            device=device,
-        )
-
-        clip_output = clip_encoder(test_prompt)
-
-        print(f"[WRAPPER TEST] CLIP pooled output: {clip_output.hidden_state.shape}")
-
-        print("[WRAPPER TEST] TextEncoder wrapper test passed!")
-
-    except ImportError as e:
-        pytest.skip(f"Failed to import TextEncoder: {e}")
-    except Exception as e:
-        pytest.fail(f"TextEncoder wrapper test failed: {e}")
-
-
-if __name__ == "__main__":
-    test_gamecraft_clip_encoder()
-    test_gamecraft_llama_encoder()
-    # test_gamecraft_text_encoder_wrapper()  # Requires full model weights
diff --git a/tests/local_tests/gamecraft/test_gamecraft_parity.py b/tests/local_tests/gamecraft/test_gamecraft_parity.py
deleted file mode 100644
index b470f57d5..000000000
--- a/tests/local_tests/gamecraft/test_gamecraft_parity.py
+++ /dev/null
@@ -1,753 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-Numerical parity test for HunyuanGameCraft transformer.
-
-This test compares the FastVideo implementation against the official
-Hunyuan-GameCraft-1.0 implementation to ensure numerical alignment.
-
-Usage:
-    # Run with default paths
-    pytest tests/local_tests/gamecraft/test_gamecraft_parity.py -v
-    
-    # Run with custom paths
-    GAMECRAFT_OFFICIAL_PATH=path/to/Hunyuan-GameCraft-1.0 \
-    GAMECRAFT_WEIGHTS_PATH=path/to/weights/gamecraft_models/mp_rank_00_model_states.pt \
-    pytest tests/local_tests/gamecraft/test_gamecraft_parity.py -v
-    
-    # Enable debug logging
-    GAMECRAFT_DEBUG_LOGS=1 pytest tests/local_tests/gamecraft/test_gamecraft_parity.py -v
-"""
-import os
-import sys
-from pathlib import Path
-
-import pytest
-import torch
-from torch.testing import assert_close
-
-# Set up distributed environment defaults
-os.environ.setdefault("MASTER_ADDR", "localhost")
-os.environ.setdefault("MASTER_PORT", "29514")
-os.environ.setdefault("DISABLE_SP", "1")  # Disable sequence parallelism for testing
-
-repo_root = Path(__file__).resolve().parents[3]
-
-
-def _add_official_to_path():
-    """Add official GameCraft implementation to Python path."""
-    official_path = Path(os.getenv("GAMECRAFT_OFFICIAL_PATH", repo_root / "Hunyuan-GameCraft-1.0"))
-    if official_path.exists() and str(official_path) not in sys.path:
-        sys.path.insert(0, str(official_path))
-    return official_path
-
-
-def _format_sum(tensor: torch.Tensor | None) -> str:
-    """Format tensor sum for logging."""
-    if tensor is None:
-        return "None"
-    return f"{tensor.float().sum().item():.6f}"
-
-
-def _attach_block_logging(
-    model: torch.nn.Module,
-    log_path: Path,
-    label: str,
-    enabled: bool,
-) -> None:
-    """Attach forward hooks to log block outputs."""
-    if not enabled:
-        return
-
-    log_path.parent.mkdir(parents=True, exist_ok=True)
-    if log_path.exists():
-        log_path.unlink()
-
-    def _hook_factory(block_idx: int, block_type: str):
-
-        def _hook(_module, _inputs, outputs):
-            if isinstance(outputs, tuple):
-                img_out, txt_out = outputs
-                img_sum = _format_sum(img_out)
-                txt_sum = _format_sum(txt_out)
-                out_str = f"img_sum={img_sum},txt_sum={txt_sum}"
-            else:
-                out_str = f"out_sum={_format_sum(outputs)}"
-            with log_path.open("a", encoding="utf-8") as f:
-                f.write(f"{label}:{block_type}:{block_idx}:{out_str}\n")
-
-        return _hook
-
-    # Attach to double blocks
-    if hasattr(model, "double_blocks"):
-        for idx, block in enumerate(model.double_blocks):
-            block.register_forward_hook(_hook_factory(idx, "double"))
-
-    # Attach to single blocks
-    if hasattr(model, "single_blocks"):
-        for idx, block in enumerate(model.single_blocks):
-            block.register_forward_hook(_hook_factory(idx, "single"))
-
-
-def _load_official_model(official_path: Path, weights_path: Path, device: torch.device, dtype: torch.dtype):
-    """Load the official GameCraft model."""
-    try:
-        # Add official implementation to path
-        sys.path.insert(0, str(official_path))
-
-        from hymm_sp.modules.models import HYVideoDiffusionTransformer, HUNYUAN_VIDEO_CONFIG
-        from hymm_sp.config import parse_args
-
-        # Create a minimal args object
-        class MinimalArgs:
-            text_projection = "single_refiner"
-            text_states_dim = 4096
-            use_attention_mask = False
-            text_states_dim_2 = 768
-
-        args = MinimalArgs()
-        config = HUNYUAN_VIDEO_CONFIG['HYVideo-T/2']
-
-        # Initialize model
-        model = HYVideoDiffusionTransformer(
-            args=args,
-            patch_size=[1, 2, 2],
-            in_channels=16,  # Will be adjusted for concat mode
-            out_channels=16,
-            hidden_size=config['hidden_size'],
-            mlp_width_ratio=config['mlp_width_ratio'],
-            num_heads=config['num_heads'],
-            depth_double_blocks=config['depth_double_blocks'],
-            depth_single_blocks=config['depth_single_blocks'],
-            rope_dim_list=config['rope_dim_list'],
-            guidance_embed=False,
-            dtype=dtype,
-            device='cpu',  # Load on CPU first
-            multitask_mask_training_type="concat",  # GameCraft uses concat mode
-            camera_in_channels=6,
-            camera_down_coef=8,
-        )
-
-        # Load weights
-        checkpoint = torch.load(weights_path, map_location='cpu')
-        state_dict = checkpoint['module']
-        model.load_state_dict(state_dict, strict=True)
-        model = model.to(device=device, dtype=dtype)
-        model.eval()
-
-        return model
-
-    except ImportError as e:
-        pytest.skip(f"Failed to import official GameCraft: {e}")
-    except Exception as e:
-        pytest.skip(f"Failed to load official GameCraft model: {e}")
-
-
-def _create_test_inputs(batch_size: int, device: torch.device, dtype: torch.dtype):
-    """Create test inputs for the transformer."""
-    # Video latent shape: [B, C, T, H, W]
-    # For GameCraft: C=33 (16 latent + 16 gt_latent + 1 mask)
-    frames = 9  # 9 latent frames = 33 video frames with temporal compression
-    height = 88  # 704 / 8
-    width = 160  # 1280 / 8
-
-    # Pure latents (what we're denoising)
-    latents = torch.randn(batch_size, 16, frames, height, width, device=device, dtype=dtype)
-
-    # Ground truth latents (history/reference)
-    gt_latents = torch.randn(batch_size, 16, frames, height, width, device=device, dtype=dtype)
-
-    # Mask (1 = use gt, 0 = generate)
-    mask = torch.zeros(batch_size, 1, frames, height, width, device=device, dtype=dtype)
-    mask[:, :, :frames // 2] = 1.0  # First half is conditioned
-
-    # Concatenated input
-    x = torch.cat([latents, gt_latents, mask], dim=1)  # [B, 33, T, H, W]
-
-    # Timestep
-    t = torch.tensor([500.0] * batch_size, device=device, dtype=dtype)
-
-    # Text embeddings (LLaMA)
-    text_states = torch.randn(batch_size, 77, 4096, device=device, dtype=dtype)
-    text_mask = torch.ones(batch_size, 77, device=device, dtype=dtype)
-
-    # CLIP embeddings
-    text_states_2 = torch.randn(batch_size, 768, device=device, dtype=dtype)
-
-    # Camera/action states: [B, T_video, 6, H_video, W_video]
-    # For 9 latent frames with temporal 4x compression -> ~36 video frames
-    # But camera net expects raw video resolution
-    video_frames = 33  # (9-1)*4 + 1 for 884 VAE
-    cam_latents = torch.randn(batch_size, video_frames, 6, 704, 1280, device=device, dtype=dtype)
-
-    return {
-        'x': x,
-        't': t,
-        'text_states': text_states,
-        'text_mask': text_mask,
-        'text_states_2': text_states_2,
-        'cam_latents': cam_latents,
-    }
-
-
-def _get_rotary_embeddings(shape, hidden_size, num_heads, rope_dim_list, device, dtype):
-    """Generate rotary position embeddings."""
-    # Simplified RoPE generation for testing
-    # In production, this uses the full RoPE implementation
-    t, h, w = shape
-    seq_len = t * h * w
-    head_dim = hidden_size // num_heads
-
-    freqs_cos = torch.ones(seq_len, head_dim, device=device, dtype=dtype)
-    freqs_sin = torch.zeros(seq_len, head_dim, device=device, dtype=dtype)
-
-    return freqs_cos, freqs_sin
-
-
-@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA required")
-def test_gamecraft_transformer_parity():
-    """Test numerical parity between official and FastVideo GameCraft implementations."""
-    torch.manual_seed(42)
-
-    # Initialize distributed environment for FastVideo
-    from fastvideo.distributed.parallel_state import init_distributed_environment, initialize_model_parallel
-    init_distributed_environment(world_size=1, rank=0, local_rank=0)
-    initialize_model_parallel(tensor_model_parallel_size=1, sequence_model_parallel_size=1)
-
-    # Get paths
-    official_path = Path(os.getenv("GAMECRAFT_OFFICIAL_PATH", repo_root / "Hunyuan-GameCraft-1.0"))
-    weights_path = Path(
-        os.getenv("GAMECRAFT_WEIGHTS_PATH",
-                  repo_root / "Hunyuan-GameCraft-1.0" / "weights" / "gamecraft_models" / "mp_rank_00_model_states.pt"))
-
-    if not official_path.exists():
-        pytest.skip(f"Official GameCraft repo not found at {official_path}")
-    if not weights_path.exists():
-        pytest.skip(f"GameCraft weights not found at {weights_path}")
-
-    device = torch.device("cuda:0")
-    dtype = torch.bfloat16
-    batch_size = 1
-
-    # Load official model
-    print(f"\n[GameCraft TEST] Loading official model from {weights_path}")
-    official_model = _load_official_model(official_path, weights_path, device, dtype)
-
-    # Enable debug logging if requested
-    debug_logs = os.getenv("GAMECRAFT_DEBUG_LOGS", "0") == "1"
-    _attach_block_logging(
-        official_model,
-        repo_root / "gamecraft_debug" / "official.log",
-        "official",
-        debug_logs,
-    )
-
-    # Create test inputs
-    # IMPORTANT: latent_len must be 9, 10, or 18 for official model's camera conditioning
-    frames = 9  # Valid latent temporal length
-    height = 44  # 352 / 8 (small spatial resolution for testing)
-    width = 80  # 640 / 8
-
-    latents = torch.randn(batch_size, 16, frames, height, width, device=device, dtype=dtype)
-    gt_latents = torch.randn(batch_size, 16, frames, height, width, device=device, dtype=dtype)
-    mask = torch.zeros(batch_size, 1, frames, height, width, device=device, dtype=dtype)
-    mask[:, :, :4] = 1.0  # First 4 frames are conditioned (history)
-
-    x = torch.cat([latents, gt_latents, mask], dim=1)  # [B, 33, T, H, W]
-    t = torch.tensor([500.0], device=device, dtype=dtype)
-
-    text_states = torch.randn(batch_size, 32, 4096, device=device, dtype=dtype)
-    text_mask = torch.ones(batch_size, 32, device=device, dtype=dtype)
-    text_states_2 = torch.randn(batch_size, 768, device=device, dtype=dtype)
-
-    # Camera states: for 9 latent frames -> 33 video frames with 884 VAE
-    video_frames = 33
-    cam_latents = torch.randn(batch_size, video_frames, 6, 352, 640, device=device, dtype=dtype)
-
-    # Get RoPE embeddings - after patchify with patch_size=[1,2,2]: T, H/2, W/2
-    tt, th, tw = frames, height // 2, width // 2
-
-    # Use FastVideo's RoPE implementation for both models
-    from fastvideo.layers.rotary_embedding import get_rotary_pos_embed
-
-    hidden_size = 3072
-    num_heads = 24
-    rope_dim_list = [16, 56, 56]  # From GameCraft config
-    rope_theta = 256.0
-
-    freqs_cos, freqs_sin = get_rotary_pos_embed(
-        (tt, th, tw),
-        hidden_size,
-        num_heads,
-        rope_dim_list,
-        rope_theta,
-    )
-    freqs_cos = freqs_cos.to(device=device, dtype=dtype)
-    freqs_sin = freqs_sin.to(device=device, dtype=dtype)
-
-    print(f"[GameCraft TEST] Input shapes:")
-    print(f"  x: {x.shape}")
-    print(f"  text_states: {text_states.shape}")
-    print(f"  cam_latents: {cam_latents.shape}")
-
-    # Run official model
-    with torch.no_grad():
-        official_output = official_model(
-            x=x,
-            t=t,
-            text_states=text_states,
-            text_mask=text_mask,
-            text_states_2=text_states_2,
-            freqs_cos=freqs_cos,
-            freqs_sin=freqs_sin,
-            cam_latents=cam_latents,
-            return_dict=True,
-        )
-        if isinstance(official_output, dict):
-            official_out = official_output['x']
-        else:
-            official_out = official_output
-
-    print(f"[GameCraft TEST] Official output shape: {official_out.shape}")
-    print(f"[GameCraft TEST] Official output stats: mean={official_out.mean():.6f}, std={official_out.std():.6f}")
-
-    # Load FastVideo GameCraft model
-    print("[GameCraft TEST] Loading FastVideo GameCraft model...")
-    from fastvideo.configs.models.dits.hunyuangamecraft import HunyuanGameCraftConfig
-    from fastvideo.models.dits.hunyuangamecraft import HunyuanGameCraftTransformer3DModel
-
-    # Create FastVideo config
-    fastvideo_config = HunyuanGameCraftConfig()
-    fastvideo_config.dtype = dtype
-
-    # Load converted weights
-    converted_weights_path = repo_root / "official_weights" / "hunyuan-gamecraft" / "transformer"
-    if not converted_weights_path.exists():
-        # Try to convert weights on-the-fly
-        print(f"[GameCraft TEST] Converting weights from {weights_path}...")
-        from scripts.checkpoint_conversion.convert_gamecraft_weights import convert_gamecraft_weights
-        convert_gamecraft_weights(
-            input_path=str(weights_path),
-            output_dir=str(converted_weights_path),
-        )
-
-    # Load hf_config from converted weights
-    import json
-    config_path = converted_weights_path / "config.json"
-    if config_path.exists():
-        with open(config_path) as f:
-            hf_config = json.load(f)
-    else:
-        # Create minimal hf_config
-        hf_config = {
-            "hidden_size": 3072,
-            "num_attention_heads": 24,
-            "num_layers": 20,
-            "num_single_layers": 40,
-        }
-
-    # Initialize FastVideo model
-    fastvideo_model = HunyuanGameCraftTransformer3DModel(
-        config=fastvideo_config,
-        hf_config=hf_config,
-    )
-
-    # Load weights
-    from safetensors.torch import load_file
-    safetensor_files = list(converted_weights_path.glob("*.safetensors"))
-    if safetensor_files:
-        state_dict = {}
-        for sf_file in safetensor_files:
-            state_dict.update(load_file(sf_file))
-
-        # Apply param_names_mapping in reverse
-        reverse_mapping = fastvideo_config.reverse_param_names_mapping
-        mapped_state_dict = {}
-        for key, value in state_dict.items():
-            mapped_key = key
-            for pattern, replacement in reverse_mapping.items():
-                import re
-                if re.match(pattern, key):
-                    mapped_key = re.sub(pattern, replacement, key)
-                    break
-            mapped_state_dict[mapped_key] = value
-
-        missing, unexpected = fastvideo_model.load_state_dict(mapped_state_dict, strict=False)
-        print(f"[GameCraft TEST] Missing keys: {len(missing)}")
-        print(f"[GameCraft TEST] Unexpected keys: {len(unexpected)}")
-        if missing:
-            print(f"[GameCraft TEST] First 5 missing: {missing[:5]}")
-        if unexpected:
-            print(f"[GameCraft TEST] First 5 unexpected: {unexpected[:5]}")
-
-    fastvideo_model = fastvideo_model.to(device=device, dtype=dtype)
-    fastvideo_model.eval()
-
-    _attach_block_logging(
-        fastvideo_model,
-        repo_root / "gamecraft_debug" / "fastvideo.log",
-        "fastvideo",
-        debug_logs,
-    )
-
-    # Prepare FastVideo inputs
-    # FastVideo expects encoder_hidden_states as [text_states, text_states_2]
-    fastvideo_encoder_hidden_states = [text_states, text_states_2]
-
-    # Import forward context
-    from fastvideo.forward_context import set_forward_context
-    from fastvideo.pipelines.pipeline_batch_info import ForwardBatch
-
-    forward_batch = ForwardBatch(data_type="video")
-
-    # Run FastVideo model
-    with torch.no_grad():
-        with set_forward_context(current_timestep=0, attn_metadata=None, forward_batch=forward_batch):
-            fastvideo_out = fastvideo_model(
-                x,  # Already concatenated [B, 33, T, H, W]
-                fastvideo_encoder_hidden_states,
-                t,
-                camera_states=cam_latents,
-                encoder_attention_mask=[text_mask],
-            )
-
-    print(f"[GameCraft TEST] FastVideo output shape: {fastvideo_out.shape}")
-    print(f"[GameCraft TEST] FastVideo output stats: mean={fastvideo_out.mean():.6f}, std={fastvideo_out.std():.6f}")
-
-    # Compare outputs using appropriate metrics for numerical parity
-    print("[GameCraft TEST] Comparing outputs...")
-
-    # Compute various metrics
-    diff = (official_out - fastvideo_out).abs()
-    max_diff = diff.max().item()
-    mean_diff = diff.mean().item()
-
-    # Correlation (most important for numerical alignment)
-    corr = torch.corrcoef(torch.stack([official_out.flatten().float(), fastvideo_out.flatten().float()]))[0, 1].item()
-
-    # Cosine similarity
-    cos_sim = torch.nn.functional.cosine_similarity(official_out.flatten().unsqueeze(0).float(),
-                                                    fastvideo_out.flatten().unsqueeze(0).float()).item()
-
-    # Sum comparison (should match exactly)
-    off_sum = official_out.double().sum().item()
-    fv_sum = fastvideo_out.double().sum().item()
-    sum_diff_pct = abs(off_sum - fv_sum) / (abs(off_sum) + 1e-10) * 100
-
-    print(f"[GameCraft TEST] Max diff: {max_diff:.4f}")
-    print(f"[GameCraft TEST] Mean diff: {mean_diff:.6f}")
-    print(f"[GameCraft TEST] Correlation: {corr:.6f}")
-    print(f"[GameCraft TEST] Cosine similarity: {cos_sim:.6f}")
-    print(f"[GameCraft TEST] Sum diff: {sum_diff_pct:.4f}%")
-
-    # Pass criteria: correlation > 0.99 and cosine_sim > 0.99 and sum_diff < 10%
-    # Sum diff can be larger due to floating point accumulation differences
-    assert corr > 0.99, f"Correlation too low: {corr}"
-    assert cos_sim > 0.99, f"Cosine similarity too low: {cos_sim}"
-    assert sum_diff_pct < 10.0, f"Sum difference too high: {sum_diff_pct}%"
-
-    print("[GameCraft TEST] PASSED: Outputs are numerically aligned!")
-    print("[GameCraft TEST] Parity test complete!")
-
-
-@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA required")
-def test_gamecraft_layer_by_layer():
-    """Test layer-by-layer parity to find divergence point."""
-    import os
-    os.environ['DISABLE_SP'] = '1'
-
-    torch.manual_seed(42)
-
-    from fastvideo.distributed.parallel_state import init_distributed_environment, initialize_model_parallel
-    init_distributed_environment(world_size=1, rank=0, local_rank=0)
-    initialize_model_parallel(tensor_model_parallel_size=1, sequence_model_parallel_size=1)
-
-    official_path = Path(os.getenv("GAMECRAFT_OFFICIAL_PATH", repo_root / "Hunyuan-GameCraft-1.0"))
-    weights_path = Path(
-        os.getenv("GAMECRAFT_WEIGHTS_PATH",
-                  repo_root / "Hunyuan-GameCraft-1.0" / "weights" / "gamecraft_models" / "mp_rank_00_model_states.pt"))
-
-    if not official_path.exists():
-        pytest.skip(f"Official GameCraft repo not found at {official_path}")
-    if not weights_path.exists():
-        pytest.skip(f"GameCraft weights not found at {weights_path}")
-
-    device = torch.device("cuda:0")
-    dtype = torch.bfloat16
-    batch_size = 1
-    frames = 9
-    height, width = 44, 80
-    text_seq_len = 32
-
-    # Load official model
-    print("\n[LAYER TEST] Loading official model...")
-    official_model = _load_official_model(official_path, weights_path, device, dtype)
-
-    # Load FastVideo model
-    print("[LAYER TEST] Loading FastVideo model...")
-    from fastvideo.configs.models.dits.hunyuangamecraft import HunyuanGameCraftConfig
-    from fastvideo.models.dits.hunyuangamecraft import HunyuanGameCraftTransformer3DModel
-
-    fastvideo_config = HunyuanGameCraftConfig()
-    converted_weights_path = repo_root / "official_weights" / "hunyuan-gamecraft" / "transformer"
-
-    import json
-    config_path = converted_weights_path / "config.json"
-    hf_config = {}
-    if config_path.exists():
-        with open(config_path) as f:
-            hf_config = json.load(f)
-
-    fastvideo_model = HunyuanGameCraftTransformer3DModel(config=fastvideo_config, hf_config=hf_config)
-
-    from safetensors.torch import load_file
-    safetensor_files = list(converted_weights_path.glob("*.safetensors"))
-    if safetensor_files:
-        state_dict = {}
-        for sf_file in safetensor_files:
-            state_dict.update(load_file(sf_file))
-
-        reverse_mapping = fastvideo_config.reverse_param_names_mapping
-        mapped_state_dict = {}
-        for key, value in state_dict.items():
-            mapped_key = key
-            for pattern, replacement in reverse_mapping.items():
-                import re
-                if re.match(pattern, key):
-                    mapped_key = re.sub(pattern, replacement, key)
-                    break
-            mapped_state_dict[mapped_key] = value
-
-        fastvideo_model.load_state_dict(mapped_state_dict, strict=False)
-
-    fastvideo_model = fastvideo_model.to(device=device, dtype=dtype)
-    fastvideo_model.eval()
-
-    # Create inputs
-    torch.manual_seed(42)
-    x = torch.randn(batch_size, 33, frames, height, width, device=device, dtype=dtype)
-    t = torch.tensor([500.0], device=device, dtype=dtype)
-    text_states = torch.randn(batch_size, text_seq_len, 4096, device=device, dtype=dtype)
-    text_states_2 = torch.randn(batch_size, 768, device=device, dtype=dtype)
-    cam_latents = torch.randn(batch_size, 33, 6, 352, 640, device=device, dtype=dtype)
-
-    # RoPE
-    tt, th, tw = frames, height // 2, width // 2
-    from fastvideo.layers.rotary_embedding import get_rotary_pos_embed
-    freqs_cos, freqs_sin = get_rotary_pos_embed((tt, th, tw), 3072, 24, [16, 56, 56], 256.0)
-    freqs_cos = freqs_cos.to(device=device, dtype=dtype)
-    freqs_sin = freqs_sin.to(device=device, dtype=dtype)
-
-    print("[LAYER TEST] Testing embedding layers...")
-
-    # Set up FastVideo context early (needed for txt_in)
-    from fastvideo.forward_context import set_forward_context
-    from fastvideo.attention.backends.flash_attn import FlashAttnMetadataBuilder
-    attn_metadata = FlashAttnMetadataBuilder().build(current_timestep=0, attn_mask=None)
-
-    with torch.no_grad(), set_forward_context(current_timestep=0, attn_metadata=attn_metadata):
-        # === img_in ===
-        off_img = official_model.img_in(x)
-        fv_img = fastvideo_model.img_in(x)
-        diff = (off_img - fv_img).abs().max().item()
-        print(f"  img_in: max_diff={diff:.6f} {'OK' if diff < 1e-3 else 'DIFF!'}")
-
-        # === time_in ===
-        off_time = official_model.time_in(t)
-        fv_time = fastvideo_model.time_in(t)
-        diff = (off_time - fv_time).abs().max().item()
-        print(f"  time_in: max_diff={diff:.6f} {'OK' if diff < 1e-3 else 'DIFF!'}")
-
-        # === vector_in ===
-        off_vec = official_model.vector_in(text_states_2)
-        fv_vec = fastvideo_model.vector_in(text_states_2)
-        diff = (off_vec - fv_vec).abs().max().item()
-        print(f"  vector_in: max_diff={diff:.6f} {'OK' if diff < 1e-3 else 'DIFF!'}")
-
-        # === txt_in ===
-        off_txt = official_model.txt_in(text_states, t, None)
-        fv_txt = fastvideo_model.txt_in(text_states, t)
-        diff = (off_txt - fv_txt).abs().max().item()
-        print(f"  txt_in: max_diff={diff:.6f} {'OK' if diff < 1e-3 else 'DIFF!'}")
-
-        # === camera_net ===
-        off_cam = official_model.camera_net(cam_latents)
-        fv_cam = fastvideo_model.camera_net(cam_latents)
-        diff = (off_cam - fv_cam).abs().max().item()
-        print(f"  camera_net: max_diff={diff:.6f} {'OK' if diff < 1e-3 else 'DIFF!'}")
-
-        # Combined embeddings
-        off_img_combined = off_img + off_cam
-        fv_img_combined = fv_img + fv_cam
-        off_vec_combined = off_time + off_vec
-        fv_vec_combined = fv_time + fv_vec
-
-        print("\n[LAYER TEST] Testing double blocks (first 5)...")
-
-        # cu_seqlens for official model
-        seq_len = off_img_combined.shape[1] + off_txt.shape[1]
-        cu_seqlens = torch.tensor([0, seq_len, seq_len], device=device, dtype=torch.int32)
-
-        off_img_cur, off_txt_cur = off_img_combined, off_txt
-        fv_img_cur, fv_txt_cur = fv_img_combined, fv_txt
-
-        for i in range(min(5, len(official_model.double_blocks))):
-            # Official
-            off_img_out, off_txt_out = official_model.double_blocks[i](off_img_cur,
-                                                                       off_txt_cur,
-                                                                       off_vec_combined,
-                                                                       cu_seqlens_q=cu_seqlens,
-                                                                       cu_seqlens_kv=cu_seqlens,
-                                                                       max_seqlen_q=seq_len,
-                                                                       max_seqlen_kv=seq_len,
-                                                                       freqs_cis=(freqs_cos, freqs_sin),
-                                                                       use_sage=False)
-
-            # FastVideo (context already set above)
-            fv_img_out, fv_txt_out = fastvideo_model.double_blocks[i](fv_img_cur,
-                                                                      fv_txt_cur,
-                                                                      fv_vec_combined,
-                                                                      freqs_cis=(freqs_cos, freqs_sin))
-
-            img_diff = (off_img_out - fv_img_out).abs().max().item()
-            txt_diff = (off_txt_out - fv_txt_out).abs().max().item()
-            img_mean_diff = (off_img_out - fv_img_out).abs().mean().item()
-
-            status = 'OK' if img_diff < 0.1 else 'DIFF!'
-            print(
-                f"  double_block[{i}]: img_max_diff={img_diff:.4f}, img_mean_diff={img_mean_diff:.6f}, txt_max_diff={txt_diff:.4f} {status}"
-            )
-
-            # Use outputs for next layer
-            off_img_cur, off_txt_cur = off_img_out, off_txt_out
-            fv_img_cur, fv_txt_cur = fv_img_out, fv_txt_out
-
-            if img_diff > 1.0:
-                print(f"  [STOPPING] Large divergence at double_block[{i}]")
-                break
-
-    print("\n[LAYER TEST] Done.")
-
-
-@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA required")
-def test_gamecraft_cameranet_parity():
-    """Test CameraNet component in isolation."""
-    torch.manual_seed(42)
-
-    official_path = Path(os.getenv("GAMECRAFT_OFFICIAL_PATH", repo_root / "Hunyuan-GameCraft-1.0"))
-    weights_path = Path(
-        os.getenv("GAMECRAFT_WEIGHTS_PATH",
-                  repo_root / "Hunyuan-GameCraft-1.0" / "weights" / "gamecraft_models" / "mp_rank_00_model_states.pt"))
-
-    if not official_path.exists():
-        pytest.skip(f"Official GameCraft repo not found at {official_path}")
-    if not weights_path.exists():
-        pytest.skip(f"GameCraft weights not found at {weights_path}")
-
-    device = torch.device("cuda:0")
-    dtype = torch.bfloat16
-
-    # Add official to path
-    _add_official_to_path()
-
-    try:
-        from hymm_sp.modules.cameranet import CameraNet
-    except ImportError as e:
-        pytest.skip(f"Failed to import CameraNet: {e}")
-
-    # Load CameraNet weights
-    checkpoint = torch.load(weights_path, map_location='cpu')
-    state_dict = checkpoint['module']
-
-    # Extract CameraNet weights
-    camera_state_dict = {}
-    for k, v in state_dict.items():
-        if k.startswith('camera_net.'):
-            new_key = k.replace('camera_net.', '')
-            camera_state_dict[new_key] = v
-
-    # Initialize CameraNet
-    camera_net = CameraNet(
-        in_channels=6,
-        downscale_coef=8,
-        out_channels=16,
-        patch_size=[1, 2, 2],
-        hidden_size=3072,
-    )
-    camera_net.load_state_dict(camera_state_dict, strict=True)
-    camera_net = camera_net.to(device=device, dtype=dtype)
-    camera_net.eval()
-
-    # Test input
-    batch_size = 1
-    num_frames = 17
-    cam_input = torch.randn(batch_size, num_frames, 6, 352, 640, device=device, dtype=dtype)
-
-    with torch.no_grad():
-        cam_output = camera_net(cam_input)
-
-    print(f"[CameraNet TEST] Official input shape: {cam_input.shape}")
-    print(f"[CameraNet TEST] Official output shape: {cam_output.shape}")
-    print(f"[CameraNet TEST] Official output stats: mean={cam_output.mean():.6f}, std={cam_output.std():.6f}")
-
-    # Load FastVideo CameraNet
-    print("[CameraNet TEST] Loading FastVideo CameraNet...")
-    from fastvideo.models.dits.hunyuangamecraft import CameraNet as FastVideoCameraNet
-
-    # Initialize FastVideo CameraNet
-    fastvideo_camera_net = FastVideoCameraNet(
-        in_channels=6,
-        downscale_coef=8,
-        out_channels=16,
-        patch_size=[1, 2, 2],
-        hidden_size=3072,
-        dtype=dtype,
-        prefix="camera_net",
-    )
-
-    # Map official weights to FastVideo format
-    fastvideo_camera_state_dict = {}
-    for k, v in camera_state_dict.items():
-        new_key = k
-        # Map camera_in -> PatchEmbed format
-        if "camera_in.proj" in k:
-            new_key = k  # Keep as-is for now
-        fastvideo_camera_state_dict[new_key] = v
-
-    missing, unexpected = fastvideo_camera_net.load_state_dict(fastvideo_camera_state_dict, strict=False)
-    print(f"[CameraNet TEST] Missing keys: {len(missing)}, Unexpected keys: {len(unexpected)}")
-    if missing:
-        print(f"[CameraNet TEST] Missing: {missing}")
-    if unexpected:
-        print(f"[CameraNet TEST] Unexpected: {unexpected}")
-
-    fastvideo_camera_net = fastvideo_camera_net.to(device=device, dtype=dtype)
-    fastvideo_camera_net.eval()
-
-    # Run FastVideo CameraNet
-    with torch.no_grad():
-        fastvideo_cam_output = fastvideo_camera_net(cam_input)
-
-    print(f"[CameraNet TEST] FastVideo output shape: {fastvideo_cam_output.shape}")
-    print(
-        f"[CameraNet TEST] FastVideo output stats: mean={fastvideo_cam_output.mean():.6f}, std={fastvideo_cam_output.std():.6f}"
-    )
-
-    # Compare outputs
-    print("[CameraNet TEST] Comparing outputs...")
-    try:
-        assert_close(cam_output, fastvideo_cam_output, atol=0.0, rtol=0.0)
-        print("[CameraNet TEST] PASSED: CameraNet outputs match!")
-    except AssertionError as e:
-        diff = (cam_output - fastvideo_cam_output).abs()
-        print(f"[CameraNet TEST] Max diff: {diff.max():.6f}")
-        print(f"[CameraNet TEST] Mean diff: {diff.mean():.6f}")
-        print(f"[CameraNet TEST] FAILED: {e}")
-        raise
-
-    print("[CameraNet TEST] CameraNet parity test complete!")
-
-
-if __name__ == "__main__":
-    # Run tests directly
-    test_gamecraft_cameranet_parity()
-    test_gamecraft_transformer_parity()
diff --git a/tests/local_tests/gamecraft/test_gamecraft_pipeline_parity.py b/tests/local_tests/gamecraft/test_gamecraft_pipeline_parity.py
deleted file mode 100644
index 9cf21adec..000000000
--- a/tests/local_tests/gamecraft/test_gamecraft_pipeline_parity.py
+++ /dev/null
@@ -1,508 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-End-to-end pipeline parity test for HunyuanGameCraft.
-
-This test compares the FastVideo pipeline against the official
-Hunyuan-GameCraft-1.0 implementation to ensure numerical alignment
-across the complete pipeline (text encoding -> denoising -> VAE decode).
-
-Usage:
-    # Run with default paths
-    DISABLE_SP=1 pytest tests/local_tests/gamecraft/test_gamecraft_pipeline_parity.py -v -s
-    
-    # Run with custom paths
-    GAMECRAFT_OFFICIAL_PATH=path/to/Hunyuan-GameCraft-1.0 \
-    GAMECRAFT_WEIGHTS_PATH=path/to/weights \
-    DISABLE_SP=1 pytest tests/local_tests/gamecraft/test_gamecraft_pipeline_parity.py -v -s
-
-Notes:
-    - This test requires the official Hunyuan-GameCraft-1.0 repo
-    - This test requires significant GPU memory (~40GB+)
-    - The test compares denoised latents, not final decoded video (for speed)
-"""
-import math
-import os
-import sys
-from pathlib import Path
-
-import pytest
-import torch
-import numpy as np
-from torch.testing import assert_close
-
-# Set up distributed environment defaults
-os.environ.setdefault("MASTER_ADDR", "localhost")
-os.environ.setdefault("MASTER_PORT", "29520")
-os.environ.setdefault("DISABLE_SP", "1")
-
-repo_root = Path(__file__).resolve().parents[3]
-
-
-def _log_tensor_stats(label: str, tensor: torch.Tensor) -> None:
-    """Log tensor statistics for debugging."""
-    tensor_f32 = tensor.float()
-    print(f"[GAMECRAFT PIPELINE] {label}: shape={tuple(tensor.shape)} "
-          f"dtype={tensor.dtype} device={tensor.device} "
-          f"min={tensor_f32.min().item():.6f} max={tensor_f32.max().item():.6f} "
-          f"mean={tensor_f32.mean().item():.6f} std={tensor_f32.std().item():.6f}")
-
-
-def _add_official_to_path():
-    """Add official GameCraft implementation to Python path."""
-    official_path = Path(os.getenv("GAMECRAFT_OFFICIAL_PATH", repo_root / "Hunyuan-GameCraft-1.0"))
-    if official_path.exists() and str(official_path) not in sys.path:
-        sys.path.insert(0, str(official_path))
-    return official_path
-
-
-def _create_camera_trajectory_from_action(
-        action_id: str,
-        height: int,
-        width: int,
-        num_frames: int,
-        action_speed: float = 0.2,
-        device: torch.device = torch.device("cpu"),
-        dtype: torch.dtype = torch.bfloat16,
-):
-    """
-    Create camera trajectory (Plücker coordinates) from an action ID.
-    
-    This replicates the official GameCraft camera trajectory generation.
-    
-    Args:
-        action_id: One of 'w' (forward), 'a' (left), 'd' (right), 's' (backward),
-                   'left_rot', 'right_rot', 'up_rot', 'down_rot'
-        height: Video height in pixels
-        width: Video width in pixels  
-        num_frames: Number of video frames
-        action_speed: Speed of motion
-        device: Torch device
-        dtype: Torch dtype
-        
-    Returns:
-        plucker_embedding: [1, num_frames, 6, height, width]
-        uncond_plucker_embedding: [1, num_frames, 6, height, width]
-    """
-    official_path = _add_official_to_path()
-
-    try:
-        from hymm_sp.sample_inference import (
-            ActionToPoseFromID,
-            GetPoseEmbedsFromPoses,
-        )
-    except ImportError as e:
-        pytest.skip(f"Cannot import official camera trajectory functions: {e}")
-
-    # Generate poses from action
-    poses = ActionToPoseFromID(action_id, value=action_speed, duration=num_frames)
-
-    # Convert to Plücker embeddings
-    plucker_embedding, uncond_plucker_embedding, _ = GetPoseEmbedsFromPoses(poses,
-                                                                            height,
-                                                                            width,
-                                                                            num_frames,
-                                                                            flip=False,
-                                                                            start_index=0)
-
-    # Add batch dimension and convert to target dtype/device
-    plucker_embedding = plucker_embedding.unsqueeze(0).to(device=device, dtype=dtype)
-    uncond_plucker_embedding = uncond_plucker_embedding.unsqueeze(0).to(device=device, dtype=dtype)
-
-    return plucker_embedding, uncond_plucker_embedding
-
-
-def _run_official_pipeline(
-    prompt: str,
-    action_id: str,
-    height: int,
-    width: int,
-    num_frames: int,
-    num_inference_steps: int,
-    seed: int,
-    device: torch.device,
-    dtype: torch.dtype,
-    official_path: Path,
-    weights_path: Path,
-):
-    """
-    Run the official GameCraft pipeline and return denoised latents.
-    
-    Returns:
-        denoised_latents: The final denoised latents
-        timesteps: The timesteps used
-    """
-    sys.path.insert(0, str(official_path))
-
-    try:
-        from hymm_sp.config import parse_args
-        from hymm_sp.sample_inference import HunyuanVideoSampler
-        from hymm_sp.constants import PRECISION_TO_TYPE
-        from hymm_sp.vae import load_vae
-        from hymm_sp.modules import load_model
-        from hymm_sp.text_encoder import TextEncoder
-        from hymm_sp.constants import PROMPT_TEMPLATE
-    except ImportError as e:
-        pytest.skip(f"Cannot import official GameCraft modules: {e}")
-
-    # Build args for official model - we need to parse default args
-    # and override specific values
-    sys.argv = [
-        "test",
-        "--model",
-        "HYVideo-T/2",
-        "--prompt",
-        prompt,
-        "--vae",
-        "884-16c-hy",
-        "--vae-precision",
-        "fp16",
-        "--precision",
-        "bf16",
-        "--dit-weight",
-        str(weights_path / "gamecraft_models" / "mp_rank_00_model_states.pt"),
-        "--video-length",
-        str(num_frames + 1),  # Official uses +1
-        "--video-size",
-        str(height),
-        str(width),
-        "--seed",
-        str(seed),
-        "--infer-steps",
-        str(num_inference_steps),
-        "--flow-shift",
-        "7.0",
-        "--embedded-cfg-scale",
-        "6.0",
-        "--cfg-scale",
-        "1.0",
-        "--text-encoder",
-        str(weights_path / "text_encoder"),
-        "--text-encoder-2",
-        str(weights_path / "text_encoder_2"),
-        "--tokenizer",
-        str(weights_path / "text_encoder"),
-        "--tokenizer-2",
-        str(weights_path / "text_encoder_2"),
-        "--prompt-template-video",
-        "dit-llm-encode-video",
-        "--load-key",
-        "module",
-    ]
-
-    args = parse_args()
-    args.cpu_offload = 0
-    args.use_fp8 = False
-
-    # Create the sampler
-    sampler = HunyuanVideoSampler.from_pretrained(
-        pretrained_model_path=str(weights_path / "gamecraft_models"),
-        args=args,
-        device=device,
-    )
-
-    # Run inference
-    result = sampler.predict(
-        prompt=prompt,
-        is_image=True,  # 33 frames mode
-        size=(height, width),
-        video_length=num_frames + 1,
-        seed=seed,
-        infer_steps=num_inference_steps,
-        guidance_scale=1.0,  # CFG scale
-        flow_shift=7.0,
-        action_id=action_id,
-        action_speed=0.2,
-        start_index=0,
-        return_latents=True,
-        output_type="latent",
-    )
-
-    denoised_latents = result.get("denoised_lantents")  # Note: typo in official code
-    timesteps = result.get("timesteps")
-
-    # Clean up
-    del sampler
-    torch.cuda.empty_cache()
-
-    return denoised_latents, timesteps
-
-
-@pytest.mark.skipif(
-    not torch.cuda.is_available(),
-    reason="GameCraft pipeline test requires CUDA.",
-)
-def test_gamecraft_pipeline_latent_parity():
-    """
-    Test that FastVideo GameCraft pipeline produces matching latents.
-    
-    This test runs both pipelines with identical:
-    - Prompt
-    - Camera trajectory (action)
-    - Random seed
-    - Number of inference steps
-    
-    And compares the denoised latents (before VAE decode) for numerical parity.
-    """
-    # Check for required paths
-    official_path = _add_official_to_path()
-    if not official_path.exists():
-        pytest.skip(f"Official GameCraft repo not found at {official_path}")
-
-    weights_path = Path(
-        os.getenv("GAMECRAFT_WEIGHTS_PATH", repo_root / "Hunyuan-GameCraft-1.0" / "weights" / "stdmodels"))
-    if not weights_path.exists():
-        pytest.skip(f"GameCraft weights not found at {weights_path}")
-
-    # Check for required weight files
-    dit_weights = weights_path / "gamecraft_models" / "mp_rank_00_model_states.pt"
-    if not dit_weights.exists():
-        pytest.skip(f"DiT weights not found at {dit_weights}")
-
-    # Test parameters - small size for faster testing
-    device = torch.device("cuda:0")
-    dtype = torch.bfloat16
-    prompt = "A first-person view of walking through a lush forest."
-    action_id = "w"  # Forward motion
-    height = 256  # Smaller for testing
-    width = 256
-    num_frames = 33  # Standard GameCraft frame count
-    num_inference_steps = 5  # Reduced for testing
-    seed = 42
-
-    print(f"\n[GAMECRAFT PIPELINE TEST] Configuration:")
-    print(f"  - Prompt: {prompt}")
-    print(f"  - Action: {action_id} (forward)")
-    print(f"  - Size: {height}x{width}")
-    print(f"  - Frames: {num_frames}")
-    print(f"  - Steps: {num_inference_steps}")
-    print(f"  - Seed: {seed}")
-
-    # =========================================================================
-    # Run Official Pipeline
-    # =========================================================================
-    print("\n[GAMECRAFT PIPELINE TEST] Running official pipeline...")
-
-    try:
-        official_latents, official_timesteps = _run_official_pipeline(
-            prompt=prompt,
-            action_id=action_id,
-            height=height,
-            width=width,
-            num_frames=num_frames,
-            num_inference_steps=num_inference_steps,
-            seed=seed,
-            device=device,
-            dtype=dtype,
-            official_path=official_path,
-            weights_path=weights_path,
-        )
-        _log_tensor_stats("Official latents", official_latents)
-    except Exception as e:
-        pytest.skip(f"Failed to run official pipeline: {e}")
-
-    # Clear memory
-    torch.cuda.empty_cache()
-
-    # =========================================================================
-    # Run FastVideo Pipeline
-    # =========================================================================
-    print("\n[GAMECRAFT PIPELINE TEST] Running FastVideo pipeline...")
-
-    # Import FastVideo components
-    from fastvideo import VideoGenerator
-
-    # Get diffusers-format model path
-    diffusers_path = os.getenv("GAMECRAFT_DIFFUSERS_PATH", str(repo_root / "official_weights" / "hunyuan-gamecraft"))
-    if not os.path.exists(diffusers_path):
-        pytest.skip(f"FastVideo GameCraft weights not found at {diffusers_path}")
-
-    # Create camera trajectory for FastVideo
-    plucker_embedding, uncond_plucker_embedding = _create_camera_trajectory_from_action(
-        action_id=action_id,
-        height=height,
-        width=width,
-        num_frames=num_frames,
-        action_speed=0.2,
-        device=device,
-        dtype=dtype,
-    )
-
-    # Create generator
-    generator = VideoGenerator.from_pretrained(
-        diffusers_path,
-        num_gpus=1,
-        use_fsdp_inference=False,
-        dit_cpu_offload=False,
-        vae_cpu_offload=True,  # Save memory
-        text_encoder_cpu_offload=True,
-        pin_cpu_memory=False,
-    )
-
-    # Run generation
-    result = generator.generate_video(
-        prompt=prompt,
-        output_path="outputs_video/gamecraft_test",
-        save_video=False,
-        height=height,
-        width=width,
-        num_frames=num_frames,
-        num_inference_steps=num_inference_steps,
-        seed=seed,
-        guidance_scale=1.0,
-        camera_states=plucker_embedding,
-    )
-
-    fastvideo_latents = result.get("samples")
-
-    _log_tensor_stats("FastVideo latents", fastvideo_latents)
-
-    generator.shutdown()
-
-    # =========================================================================
-    # Compare Results
-    # =========================================================================
-    print("\n[GAMECRAFT PIPELINE TEST] Comparing latents...")
-
-    # Ensure same shape
-    assert official_latents.shape == fastvideo_latents.shape, (f"Shape mismatch: official {official_latents.shape} vs "
-                                                               f"fastvideo {fastvideo_latents.shape}")
-
-    # Compute differences
-    official_f32 = official_latents.float()
-    fastvideo_f32 = fastvideo_latents.float()
-
-    abs_diff = (official_f32 - fastvideo_f32).abs()
-    max_diff = abs_diff.max().item()
-    mean_diff = abs_diff.mean().item()
-
-    # Compute correlation
-    official_flat = official_f32.flatten()
-    fastvideo_flat = fastvideo_f32.flatten()
-    correlation = torch.corrcoef(torch.stack([official_flat, fastvideo_flat]))[0, 1].item()
-
-    print(f"\n[GAMECRAFT PIPELINE TEST] Results:")
-    print(f"  - Max absolute difference: {max_diff:.6f}")
-    print(f"  - Mean absolute difference: {mean_diff:.6f}")
-    print(f"  - Correlation: {correlation:.6f}")
-
-    # Assertions
-    # For full pipeline, we expect higher tolerance due to accumulated errors
-    assert correlation > 0.99, f"Correlation {correlation} is too low (expected > 0.99)"
-    assert max_diff < 0.5, f"Max diff {max_diff} is too high (expected < 0.5)"
-
-    print("\n[GAMECRAFT PIPELINE TEST] PASSED!")
-
-
-@pytest.mark.skipif(
-    not torch.cuda.is_available(),
-    reason="GameCraft pipeline test requires CUDA.",
-)
-def test_gamecraft_pipeline_camera_trajectory():
-    """
-    Test that camera trajectory (Plücker embedding) generation matches.
-    
-    This is a simpler test that just validates camera conditioning matches
-    between official and FastVideo implementations.
-    """
-    official_path = _add_official_to_path()
-    if not official_path.exists():
-        pytest.skip(f"Official GameCraft repo not found at {official_path}")
-
-    device = torch.device("cuda:0")
-    dtype = torch.bfloat16
-
-    # Test parameters
-    height = 256
-    width = 256
-    num_frames = 33
-    action_id = "w"
-    action_speed = 0.2
-
-    print("\n[CAMERA TEST] Generating camera trajectories...")
-
-    # Generate using official code path
-    plucker, uncond_plucker = _create_camera_trajectory_from_action(
-        action_id=action_id,
-        height=height,
-        width=width,
-        num_frames=num_frames,
-        action_speed=action_speed,
-        device=device,
-        dtype=dtype,
-    )
-
-    _log_tensor_stats("Plücker embedding", plucker)
-    _log_tensor_stats("Uncond Plücker embedding", uncond_plucker)
-
-    # Basic shape validation
-    expected_shape = (1, num_frames, 6, height, width)
-    assert plucker.shape == expected_shape, (f"Plücker shape {plucker.shape} != expected {expected_shape}")
-    assert uncond_plucker.shape == expected_shape, (
-        f"Uncond Plücker shape {uncond_plucker.shape} != expected {expected_shape}")
-
-    # Validate uncond is identity (zeros for translation, identity for rotation)
-    # In practice, uncond should have specific structure
-    assert not torch.allclose(plucker, uncond_plucker), ("Conditional and unconditional embeddings should differ")
-
-    print("\n[CAMERA TEST] PASSED!")
-
-
-@pytest.mark.skipif(
-    not torch.cuda.is_available(),
-    reason="GameCraft pipeline test requires CUDA.",
-)
-def test_gamecraft_pipeline_smoke():
-    """
-    Smoke test for GameCraft pipeline - just verify it runs without errors.
-    
-    This is a lighter test that doesn't require the official implementation,
-    just validates that the FastVideo pipeline can run.
-    """
-    diffusers_path = os.getenv("GAMECRAFT_DIFFUSERS_PATH", str(repo_root / "official_weights" / "hunyuan-gamecraft"))
-
-    if not os.path.exists(diffusers_path):
-        pytest.skip(f"FastVideo GameCraft weights not found at {diffusers_path}")
-
-    if not os.path.isfile(os.path.join(diffusers_path, "model_index.json")):
-        pytest.skip(f"model_index.json not found in {diffusers_path}")
-
-    device = torch.device("cuda:0")
-
-    print("\n[SMOKE TEST] Running FastVideo GameCraft pipeline...")
-
-    from fastvideo import VideoGenerator
-
-    # Very small test
-    height = 128
-    width = 128
-    num_frames = 9  # Minimum
-    num_inference_steps = 2
-
-    generator = VideoGenerator.from_pretrained(
-        diffusers_path,
-        num_gpus=1,
-        use_fsdp_inference=False,
-        dit_cpu_offload=True,
-        vae_cpu_offload=True,
-        text_encoder_cpu_offload=True,
-        pin_cpu_memory=False,
-    )
-
-    result = generator.generate_video(
-        prompt="A test video.",
-        output_path="outputs_video/gamecraft_smoke",
-        save_video=False,
-        height=height,
-        width=width,
-        num_frames=num_frames,
-        num_inference_steps=num_inference_steps,
-        seed=123,
-        guidance_scale=1.0,
-    )
-
-    assert result is not None, "Pipeline returned None"
-    assert "samples" in result or "latents" in result, "No output in result"
-
-    generator.shutdown()
-
-    print("\n[SMOKE TEST] PASSED!")
diff --git a/tests/local_tests/gamecraft/test_gamecraft_vae_parity.py b/tests/local_tests/gamecraft/test_gamecraft_vae_parity.py
deleted file mode 100644
index 878a9bffd..000000000
--- a/tests/local_tests/gamecraft/test_gamecraft_vae_parity.py
+++ /dev/null
@@ -1,274 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-Numerical parity test for GameCraft VAE (AutoencoderKLCausal3D).
-
-Ported from official Hunyuan-GameCraft-1.0/hymm_sp/vae. Compares FastVideo's
-GameCraftVAE against the official implementation.
-
-Follows the same pattern as test_ltx2_vae.py:
-- Loads same weights into both models (strict key checking)
-- Encodes same input, compares latents
-- Decodes same latents through both decoders, compares output
-- Checks for non-finite values
-
-Usage:
-    DISABLE_SP=1 pytest tests/local_tests/gamecraft/test_gamecraft_vae_parity.py -v
-"""
-import json
-import os
-import sys
-from pathlib import Path
-
-import pytest
-import torch
-from torch.testing import assert_close
-
-os.environ.setdefault("MASTER_ADDR", "localhost")
-os.environ.setdefault("MASTER_PORT", "29515")
-os.environ.setdefault("DISABLE_SP", "1")
-
-repo_root = Path(__file__).resolve().parents[3]
-if str(repo_root) not in sys.path:
-    sys.path.insert(0, str(repo_root))
-
-
-def _load_weights(weights_path: Path) -> dict[str, torch.Tensor]:
-    """Load VAE weights from checkpoint and strip 'vae.' prefix."""
-    print(f"[VAE TEST] Loading weights from {weights_path}")
-    state_dict = torch.load(weights_path, map_location="cpu", weights_only=True)
-    if "state_dict" in state_dict:
-        state_dict = state_dict["state_dict"]
-    # Strip 'vae.' prefix
-    vae_sd = {k.replace("vae.", ""): v for k, v in state_dict.items() if k.startswith("vae.")}
-    print(f"[VAE TEST] Loaded {len(vae_sd)} VAE tensors")
-    return vae_sd
-
-
-def _load_into_model(model: torch.nn.Module, weights: dict[str, torch.Tensor], name: str) -> tuple[int, list[str]]:
-    """Load weights into a model, checking for missing/unexpected keys."""
-    model_state = model.state_dict()
-    filtered = {k: v for k, v in weights.items() if k in model_state and model_state[k].shape == v.shape}
-    missing = [k for k in model_state.keys() if k not in filtered]
-    unexpected = [k for k in weights.keys() if k not in model_state]
-    print(f"[VAE TEST] {name}: Loading {len(filtered)}/{len(model_state)} tensors "
-          f"({len(missing)} missing, {len(unexpected)} unexpected)")
-    if missing:
-        print(f"[VAE TEST] {name} missing keys: {missing[:5]}{'...' if len(missing) > 5 else ''}")
-    if not filtered:
-        return 0, missing
-    model.load_state_dict(filtered, strict=False)
-    return len(filtered), missing
-
-
-def _load_official_vae(official_path: Path, vae_sd: dict[str, torch.Tensor], device: torch.device, dtype: torch.dtype):
-    """Load the official GameCraft VAE from hymm_sp/vae."""
-    sys.path.insert(0, str(official_path))
-
-    try:
-        from hymm_sp.vae.autoencoder_kl_causal_3d import AutoencoderKLCausal3D
-    except ImportError as e:
-        pytest.skip(f"Failed to import official VAE: {e}")
-
-    config_path = (official_path / "weights" / "stdmodels" / "vae_3d" / "hyvae" / "config.json")
-    if not config_path.exists():
-        pytest.skip(f"VAE config not found at {config_path}")
-
-    with open(config_path) as f:
-        config = json.load(f)
-
-    vae = AutoencoderKLCausal3D(
-        in_channels=config.get("in_channels", 3),
-        out_channels=config.get("out_channels", 3),
-        down_block_types=tuple(config.get("down_block_types", [])),
-        up_block_types=tuple(config.get("up_block_types", [])),
-        block_out_channels=tuple(config.get("block_out_channels", [])),
-        layers_per_block=config.get("layers_per_block", 2),
-        act_fn=config.get("act_fn", "silu"),
-        latent_channels=config.get("latent_channels", 16),
-        norm_num_groups=config.get("norm_num_groups", 32),
-        sample_size=config.get("sample_size", 256),
-        sample_tsize=config.get("sample_tsize", 64),
-        scaling_factor=config.get("scaling_factor", 0.476986),
-        time_compression_ratio=config.get("time_compression_ratio", 4),
-        mid_block_add_attention=config.get("mid_block_add_attention", True),
-        mid_block_causal_attn=config.get("mid_block_causal_attn", True),
-    )
-
-    loaded, missing = _load_into_model(vae, vae_sd, "Official VAE")
-    if loaded == 0:
-        pytest.skip("Failed to load weights into official VAE")
-    if missing:
-        pytest.skip(f"Official VAE has {len(missing)} missing keys; cannot ensure parity")
-
-    if not hasattr(vae, "use_trt_decoder"):
-        vae.use_trt_decoder = False
-    return vae.to(device=device, dtype=dtype).eval()
-
-
-def _load_fastvideo_vae(vae_sd: dict[str, torch.Tensor], device: torch.device, dtype: torch.dtype):
-    """Load FastVideo GameCraftVAE with official weights."""
-    from fastvideo.configs.models.vaes import GameCraftVAEConfig
-    from fastvideo.models.vaes.gamecraftvae import GameCraftVAE
-
-    config = GameCraftVAEConfig()
-    vae = GameCraftVAE(config)
-
-    loaded, missing = _load_into_model(vae, vae_sd, "FastVideo VAE")
-    if loaded == 0:
-        pytest.skip("Failed to load weights into FastVideo VAE")
-    if missing:
-        pytest.skip(f"FastVideo VAE has {len(missing)} missing keys; cannot ensure parity")
-
-    return vae.to(device=device, dtype=dtype).eval()
-
-
-def test_gamecraft_vae_parity():
-    """Test VAE encode/decode parity between official and FastVideo.
-    
-    Key: decodes the SAME latents through both decoders to isolate
-    decode-only differences (following LTX2 VAE test pattern).
-    """
-    torch.manual_seed(42)
-
-    official_path = Path(os.getenv("GAMECRAFT_OFFICIAL_PATH", repo_root / "Hunyuan-GameCraft-1.0"))
-    vae_weights_path = Path(
-        os.getenv(
-            "GAMECRAFT_VAE_PATH",
-            repo_root / "Hunyuan-GameCraft-1.0" / "weights" / "stdmodels" / "vae_3d" / "hyvae" / "pytorch_model.pt",
-        ))
-
-    if not official_path.exists():
-        pytest.skip(f"Official GameCraft repo not found at {official_path}")
-    if not vae_weights_path.exists():
-        alt_paths = [
-            vae_weights_path.parent / "checkpoint-step-270000.ckpt",
-            vae_weights_path.parent / "diffusion_pytorch_model.safetensors",
-        ]
-        for alt in alt_paths:
-            if alt.exists():
-                vae_weights_path = alt
-                break
-        else:
-            pytest.skip(f"VAE weights not found. Tried: {vae_weights_path}, {alt_paths}")
-
-    # Use fp32 for precise comparison (bf16 amplifies small differences)
-    use_cuda = torch.cuda.is_available()
-    try:
-        if use_cuda:
-            _ = torch.zeros(1, device="cuda:0")
-            del _
-            torch.cuda.empty_cache()
-    except Exception:
-        use_cuda = False
-    device = torch.device("cuda:0" if use_cuda else "cpu")
-    # Use fp32 for parity test precision (bf16 gives ~0.2 max diff due to precision)
-    dtype = torch.float32
-
-    # Load weights once, share between both models
-    vae_sd = _load_weights(vae_weights_path)
-
-    batch_size = 1
-    frames = 5
-    height = 64
-    width = 64
-    video_input = torch.randn(batch_size, 3, frames, height, width, device=device, dtype=dtype)
-    print(f"\n[VAE TEST] Input shape: {video_input.shape}, dtype: {dtype}")
-
-    # --- Encode comparison ---
-    print("\n[VAE TEST] Loading official VAE...")
-    official_vae = _load_official_vae(official_path, vae_sd, device, dtype)
-    with torch.no_grad():
-        official_latent_dist = official_vae.encode(video_input)
-        if hasattr(official_latent_dist, "latent_dist"):
-            official_latent_dist = official_latent_dist.latent_dist
-        official_latents = official_latent_dist.mode()
-
-    print("[VAE TEST] Loading FastVideo GameCraftVAE...")
-    fastvideo_vae = _load_fastvideo_vae(vae_sd, device, dtype)
-    with torch.no_grad():
-        fv_latent_dist = fastvideo_vae.encode(video_input)
-        fv_latents = fv_latent_dist.latent_dist.mode()
-
-    print(f"[VAE TEST] Official latents: {official_latents.shape}")
-    print(f"[VAE TEST] FastVideo latents: {fv_latents.shape}")
-    assert official_latents.shape == fv_latents.shape, "Latent shape mismatch"
-    assert torch.isfinite(official_latents).all(), "Official encoder produced non-finite latents"
-    assert torch.isfinite(fv_latents).all(), "FastVideo encoder produced non-finite latents"
-
-    enc_diff = (official_latents - fv_latents).abs().max().item()
-    enc_mean = (official_latents - fv_latents).abs().mean().item()
-    print(f"[VAE TEST] Encode max diff: {enc_diff:.6f}, mean diff: {enc_mean:.6f}")
-    assert_close(official_latents, fv_latents, atol=1e-2, rtol=1e-2)
-
-    # --- Decode comparison (SAME latents through both decoders) ---
-    # Use official latents as the shared input to isolate decode-only differences
-    shared_latents = official_latents
-
-    with torch.no_grad():
-        official_decoded = official_vae.decode(shared_latents)
-        if hasattr(official_decoded, "sample"):
-            official_decoded = official_decoded.sample
-
-    # Free official VAE memory before loading FastVideo decode
-    del official_vae
-    if torch.cuda.is_available():
-        torch.cuda.empty_cache()
-
-    with torch.no_grad():
-        fv_decoded = fastvideo_vae.decode(shared_latents)
-        if hasattr(fv_decoded, "sample"):
-            fv_decoded = fv_decoded.sample
-
-    print(f"[VAE TEST] Official decoded: {official_decoded.shape}")
-    print(f"[VAE TEST] FastVideo decoded: {fv_decoded.shape}")
-    assert official_decoded.shape == fv_decoded.shape, "Decoded shape mismatch"
-    assert torch.isfinite(official_decoded).all(), "Official decoder produced non-finite output"
-    assert torch.isfinite(fv_decoded).all(), "FastVideo decoder produced non-finite output"
-
-    dec_diff = (official_decoded - fv_decoded).abs().max().item()
-    dec_mean = (official_decoded - fv_decoded).abs().mean().item()
-    print(f"[VAE TEST] Decode max diff: {dec_diff:.6f}, mean diff: {dec_mean:.6f}")
-    assert_close(official_decoded, fv_decoded, atol=1e-2, rtol=1e-2)
-
-    print("[VAE TEST] PASSED: Numerical parity between official and FastVideo!")
-
-
-@pytest.mark.skipif(not torch.cuda.is_available(), reason="CUDA required")
-def test_gamecraft_vae_config_compatibility():
-    """Test that GameCraft VAE config is compatible with FastVideo's GameCraftVAE."""
-    vae_config_path = (repo_root / "Hunyuan-GameCraft-1.0" / "weights" / "stdmodels" / "vae_3d" / "hyvae" /
-                       "config.json")
-
-    if not vae_config_path.exists():
-        pytest.skip(f"VAE config not found at {vae_config_path}")
-
-    with open(vae_config_path) as f:
-        gc_config = json.load(f)
-
-    print("\n[VAE CONFIG TEST] GameCraft VAE config:")
-    for k, v in gc_config.items():
-        print(f"  {k}: {v}")
-
-    # Expected HunyuanVideo VAE config
-    expected_config = {
-        "in_channels": 3,
-        "out_channels": 3,
-        "latent_channels": 16,
-        "block_out_channels": [128, 256, 512, 512],
-        "time_compression_ratio": 4,
-        "scaling_factor": 0.476986,
-    }
-
-    # Verify compatibility
-    for key, expected in expected_config.items():
-        if key in gc_config:
-            actual = gc_config[key]
-            assert actual == expected, f"Config mismatch for {key}: expected {expected}, got {actual}"
-            print(f"[VAE CONFIG TEST] {key}: OK (matches expected)")
-
-    print("[VAE CONFIG TEST] Config compatibility check passed!")
-
-
-if __name__ == "__main__":
-    test_gamecraft_vae_config_compatibility()
-    test_gamecraft_vae_parity()
diff --git a/tests/local_tests/glm_image/README.md b/tests/local_tests/glm_image/README.md
deleted file mode 100644
index 32037b77e..000000000
--- a/tests/local_tests/glm_image/README.md
+++ /dev/null
@@ -1,74 +0,0 @@
-# GLM-Image Local Tests
-
-Local-only parity and smoke tests for the `glm_image` port: FastVideo vs the
-official HF `transformers` + `diffusers` GLM-Image implementation. These are
-**not run in CI** — they need the full weights, CUDA, and (for the reference
-side) the diffusers GLM-Image classes; each test skips with an actionable
-message when a prerequisite is missing.
-
-## What you need
-
-| | |
-|---|---|
-| Weights | `zai-org/GLM-Image` → `official_weights/glm_image` (~30 GB: AR encoder 4 shards, transformer 3 shards, vae + text_encoder) |
-| Runtime deps | `transformers>=5.15.0` (repo floor; the AR encoder `GlmImageForConditionalGeneration` first ships in 5.0.0) and `diffusers>=0.38.0` — both committed in `pyproject.toml` |
-| Test-only deps | the diffusers `GlmImageTransformer2DModel` / `GlmImagePipeline` reference classes need `diffusers>=0.37.0.dev0` (used **only** by the parity tests) |
-
-Download weights (from the repo root; cache via `HF_HOME` if `/` is small):
-
-```bash
-python ".agents/skills/add-model-01-prep/scripts/download_hf_weights.py" \
-    "zai-org/GLM-Image" "official_weights/glm_image"
-```
-
-Optionally install the diffusers reference classes to run the parity side:
-
-```bash
-uv pip install -U "git+https://github.com/huggingface/diffusers.git@ff3b86b4"
-```
-
-Parity was checked against transformers `a65bf6c9` + diffusers `ff3b86b4`.
-
-## Parity results (GB200)
-
-| Component | Test | Result |
-|---|---|---|
-| `transformer` (DiT) | `test_transformer_parity.py` | fp32 cosine `1.000000` (100% within 5e-3); bf16 cosine `0.999969` (element-wise rounding over 30 layers vs a different SDPA kernel — not a bug; RoPE proven exactly equivalent). fp32 + bf16 + strict-load |
-| `vae` (`AutoencoderKL`) | `test_vae_parity.py` | decode + encode + config, 3/3 |
-| `text_encoder` (T5) + **ByT5** tokenizer | `test_t5_parity.py` | tokenizer + glyph extract + embeds, 3/3 |
-| `vision_language_encoder` (GLM-4-9B AR) | `test_ar_parity.py` | surface + to/eval + greedy determinism, 3/3 |
-| `pipeline` (T2I) | `test_pipeline_parity.py` | injected-input determinism: latent cosine `0.999997`, decoded-image MAE `0.159/255`. validity + deterministic parity, 2/2 |
-| `pipeline` (I2I / edit) | `test_edit_pipeline_parity.py` | validity / wiring gate (condition image enters via a KV-cache write pass, not noise-the-latent). diffusers distribution parity deferred (stochastic AR) |
-
-`processor` (`GlmImageProcessor`) and `scheduler` (`FlowMatchEulerDiscreteScheduler`,
-shared flow-matching) are exercised via the AR and pipeline tests.
-
-Run:
-
-```bash
-pytest tests/local_tests/glm_image/test_transformer_parity.py -v -s
-pytest tests/local_tests/glm_image/test_vae_parity.py -v -s
-pytest tests/local_tests/glm_image/test_t5_parity.py -v -s
-pytest tests/local_tests/glm_image/test_ar_parity.py -v -s
-pytest tests/local_tests/glm_image/test_pipeline_parity.py -v -s
-pytest tests/local_tests/glm_image/test_edit_pipeline_parity.py -v -s
-```
-
-## Design notes (load-bearing — don't regress)
-
-- **Pipeline parity is by injection.** Full-pipeline pixel parity is ill-posed
-  (stochastic AR + independent latent RNG), so the pipeline tests inject matched
-  `(prompt_embeds, prior_token_ids, latents)` into both the diffusers pipeline
-  and the real `GlmImageDenoisingStage`, then compare denoised latents + decoded
-  image — not raw end-to-end pixels.
-- **HF imports are confined** to the lazy-wrapper loader
-  `fastvideo/models/encoders/glm_image_ar_loader.py`, never the production
-  loaders (production-boundary rule).
-- **Tokenizer is ByT5**, matching the diffusers reference (not plain T5);
-  `compute_glyph_embeds` mirrors diffusers `_get_glyph_embeds`.
-- **Model-specific stages** live under
-  `fastvideo/pipelines/basic/glm_image/stages/` per the `add-model` Files Map.
-- **Param mapping** is handled in-place at load via `param_names_mapping`
-  (`fastvideo/configs/models/dits/glm_image.py`) + the native VAE/encoder
-  configs — no conversion script; the loader raises on any unmatched param and
-  `test_transformer_strict_load.py` asserts completeness with `strict=True`.
diff --git a/tests/local_tests/glm_image/test_ar_parity.py b/tests/local_tests/glm_image/test_ar_parity.py
deleted file mode 100644
index 3c4f4f0cf..000000000
--- a/tests/local_tests/glm_image/test_ar_parity.py
+++ /dev/null
@@ -1,107 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-from __future__ import annotations
-
-import os
-from pathlib import Path
-
-import pytest
-import torch
-
-import fastvideo.envs as envs
-
-os.environ.setdefault("MASTER_ADDR", "localhost")
-os.environ.setdefault("MASTER_PORT", "29519")
-os.environ.setdefault("DISABLE_SP", "1")
-
-REPO_ROOT = Path(__file__).resolve().parents[3]
-FAMILY = "glm_image"
-LOCAL_WEIGHTS_DIR = Path(envs.FASTVIDEO_TEST_GLM_IMAGE_LOCAL_WEIGHTS_DIR.get() or
-                         REPO_ROOT / "official_weights" / FAMILY)
-AR_DIR = LOCAL_WEIGHTS_DIR / "vision_language_encoder"
-PROCESSOR_DIR = LOCAL_WEIGHTS_DIR / "processor"
-
-
-def _has_weights() -> bool:
-    return AR_DIR.exists() and any(AR_DIR.glob("*.safetensors"))
-
-
-def _has_glm_image_transformers() -> bool:
-    import importlib.util
-    spec = importlib.util.find_spec("transformers")
-    if spec is None:
-        return False
-    import transformers
-    return hasattr(transformers, "GlmImageForConditionalGeneration")
-
-
-pytestmark = [
-    pytest.mark.skipif(
-        not _has_weights(),
-        reason=f"GLM-Image AR encoder weights not found at {AR_DIR}.",
-    ),
-    pytest.mark.skipif(
-        not _has_glm_image_transformers(),
-        reason=("transformers in this env lacks "
-                "`GlmImageForConditionalGeneration` (main pins 4.57.3; needs "
-                "5.0.0rc0+). Bump transformers locally to exercise the AR "
-                "lazy-wrapper."),
-    ),
-]
-
-SAMPLE_PROMPT = ("A beautiful landscape photography with rolling hills, a winding river, "
-                 "and a vibrant sunset in the background. Photorealistic style.")
-
-
-@pytest.fixture(scope="module")
-def device() -> torch.device:
-    if not torch.cuda.is_available():
-        pytest.skip("CUDA required for GLM-Image AR encoder parity.")
-    return torch.device("cuda")
-
-
-@pytest.fixture(scope="module")
-def fastvideo_ar(device):
-    pytest.importorskip("fastvideo")
-    try:
-        from fastvideo.models.encoders.glm_image_ar_loader import (GlmImageARLoader)
-    except ImportError as e:
-        pytest.skip(f"FastVideo lazy-wrapper not yet at target path: {e}")
-    loader = GlmImageARLoader(str(AR_DIR), str(PROCESSOR_DIR), torch_dtype=torch.bfloat16)
-    loader.to(device).eval()
-    return loader
-
-
-def test_ar_wrapper_exposes_expected_surface(fastvideo_ar):
-    assert callable(getattr(fastvideo_ar, "generate", None))
-    assert getattr(fastvideo_ar, "processor", None) is not None
-    assert getattr(fastvideo_ar, "config", None) is not None
-    assert getattr(fastvideo_ar, "generation_config", None) is not None
-    from transformers import GlmImageForConditionalGeneration
-    assert isinstance(fastvideo_ar._model, GlmImageForConditionalGeneration)
-
-
-def test_ar_to_and_eval_propagate(fastvideo_ar, device):
-    assert next(fastvideo_ar._model.parameters()).device.type == device.type
-    assert not fastvideo_ar._model.training
-
-
-def test_ar_generate_is_deterministic_under_do_sample_false(fastvideo_ar, device):
-    processor = fastvideo_ar.processor
-    messages = [{
-        "role": "user",
-        "content": [{
-            "type": "text",
-            "text": SAMPLE_PROMPT
-        }],
-    }]
-    inputs = processor.apply_chat_template(messages,
-                                           tokenize=True,
-                                           target_h=1024,
-                                           target_w=1024,
-                                           return_dict=True,
-                                           return_tensors="pt").to(device)
-    with torch.no_grad():
-        out_a = fastvideo_ar.generate(**inputs, max_new_tokens=32, do_sample=False)
-        out_b = fastvideo_ar.generate(**inputs, max_new_tokens=32, do_sample=False)
-    assert out_a.shape == out_b.shape
-    assert torch.equal(out_a, out_b)
diff --git a/tests/local_tests/glm_image/test_edit_pipeline_parity.py b/tests/local_tests/glm_image/test_edit_pipeline_parity.py
deleted file mode 100644
index a727d7516..000000000
--- a/tests/local_tests/glm_image/test_edit_pipeline_parity.py
+++ /dev/null
@@ -1,124 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""End-to-end scaffold for the GLM-Image image-to-image (edit) pipeline.
-
-Drives FastVideo `VideoGenerator` in edit mode (a condition image is passed) and
-checks that the unified pipeline routes through the condition-encoding stage +
-KV-cache denoising path and emits a well-formed image. Like the T2I parity test,
-pixel-exact parity vs diffusers is NOT asserted: the AR prior is sampled
-(`do_sample=True`) and the diffusion latents draw from independent RNG streams,
-so the codebases produce different valid samples of the same edit. We gate on
-validity (and, when diffusers is present, a comparable brightness regime).
-
-Skips cleanly until weights are available; GPU-heavy.
-"""
-from __future__ import annotations
-
-import os
-from pathlib import Path
-
-import numpy as np
-import pytest
-import torch
-from PIL import Image
-
-import fastvideo.envs as envs
-
-os.environ.setdefault("MASTER_ADDR", "localhost")
-os.environ.setdefault("MASTER_PORT", "29520")
-os.environ.setdefault("DISABLE_SP", "1")
-os.environ.setdefault("FASTVIDEO_ATTENTION_BACKEND", "TORCH_SDPA")
-
-REPO_ROOT = Path(__file__).resolve().parents[3]
-FAMILY = "glm_image"
-LOCAL_WEIGHTS_DIR = Path(envs.FASTVIDEO_TEST_GLM_IMAGE_LOCAL_WEIGHTS_DIR.get() or
-                         REPO_ROOT / "official_weights" / FAMILY)
-CONDITION_IMAGE = REPO_ROOT / "assets" / "images" / "couple.jpg"
-
-
-def _has_weights() -> bool:
-    required = ["transformer", "vae", "text_encoder", "vision_language_encoder", "processor", "tokenizer", "scheduler"]
-    return all((LOCAL_WEIGHTS_DIR / r).exists() for r in required)
-
-
-def _upstream_glm_image_available() -> bool:
-    try:
-        import transformers
-    except ImportError:
-        return False
-    return hasattr(transformers, "GlmImageForConditionalGeneration")
-
-
-pytestmark = [
-    pytest.mark.skipif(
-        not _has_weights(),
-        reason=f"GLM-Image full weights not found at {LOCAL_WEIGHTS_DIR}.",
-    ),
-    pytest.mark.skipif(
-        not _upstream_glm_image_available(),
-        reason=("Edit pipeline needs transformers>=5.0.0rc0 "
-                "(GlmImageForConditionalGeneration); main pin predates it. "
-                "Bump locally to run."),
-    ),
-]
-
-EDIT_PROMPT = "Make the scene a snowy winter landscape."
-SEED = 0
-HEIGHT = 512
-WIDTH = 512
-STEPS = 8  # low step count keeps the test under 5 min on a single GPU
-
-
-@pytest.fixture(scope="module")
-def device():
-    if not torch.cuda.is_available():
-        pytest.skip("CUDA required for GLM-Image edit pipeline.")
-    return torch.device("cuda")
-
-
-def _to_uint8_hwc(a) -> np.ndarray:
-    """Coerce a frame buffer (np or torch, [0,1] float or uint8, with optional
-    leading batch/time dims, HWC or CHW) to a single (H, W, 3) uint8 image."""
-    if torch.is_tensor(a):
-        a = a.float().cpu().numpy()
-    a = np.asarray(a)
-    while a.ndim > 3:
-        a = a[0]
-    if a.ndim == 3 and a.shape[0] in (1, 3) and a.shape[-1] not in (1, 3):
-        a = np.transpose(a, (1, 2, 0))
-    if a.dtype != np.uint8:
-        scale = 255.0 if float(a.max()) <= 1.5 else 1.0
-        a = np.clip(a * scale, 0, 255).astype(np.uint8)
-    return a
-
-
-def _fastvideo_edit_image(device) -> np.ndarray:
-    pytest.importorskip("fastvideo")
-    try:
-        from fastvideo import VideoGenerator
-    except ImportError as e:
-        pytest.skip(f"FastVideo VideoGenerator unavailable: {e}")
-    condition = Image.open(CONDITION_IMAGE).convert("RGB")
-    gen = VideoGenerator.from_pretrained(str(LOCAL_WEIGHTS_DIR), num_gpus=1, trust_remote_code=True)
-    result = gen.generate_video(prompt=EDIT_PROMPT,
-                                pil_image=condition,
-                                save_video=False,
-                                return_frames=True,
-                                height=HEIGHT,
-                                width=WIDTH,
-                                num_inference_steps=STEPS,
-                                guidance_scale=1.5,
-                                seed=SEED)
-    gen.shutdown()
-    return _to_uint8_hwc(result["frames"][0])
-
-
-def test_edit_pipeline_produces_valid_image(device):
-    """Wiring gate for the edit path: passing a condition image routes through
-    the condition-encoding (KV-cache write) stage and the read/skip denoising
-    path, and the pipeline emits a well-formed, non-degenerate image of the
-    requested size. Component numerical correctness is covered by the (green)
-    DiT / VAE / T5 / AR component-parity tests."""
-    fv = _fastvideo_edit_image(device)
-    assert fv.shape == (HEIGHT, WIDTH, 3), f"unexpected shape {fv.shape}"
-    assert fv.dtype == np.uint8 and np.isfinite(fv).all()
-    assert fv.std() > 10.0, f"image is near-constant (std={fv.std():.2f})"
diff --git a/tests/local_tests/glm_image/test_pipeline_parity.py b/tests/local_tests/glm_image/test_pipeline_parity.py
deleted file mode 100644
index 2ce23a6e6..000000000
--- a/tests/local_tests/glm_image/test_pipeline_parity.py
+++ /dev/null
@@ -1,308 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-from __future__ import annotations
-
-import os
-from pathlib import Path
-
-import numpy as np
-import pytest
-import torch
-
-import fastvideo.envs as envs
-
-os.environ.setdefault("MASTER_ADDR", "localhost")
-os.environ.setdefault("MASTER_PORT", "29519")
-os.environ.setdefault("DISABLE_SP", "1")
-os.environ.setdefault("FASTVIDEO_ATTENTION_BACKEND", "TORCH_SDPA")
-
-REPO_ROOT = Path(__file__).resolve().parents[3]
-FAMILY = "glm_image"
-LOCAL_WEIGHTS_DIR = Path(envs.FASTVIDEO_TEST_GLM_IMAGE_LOCAL_WEIGHTS_DIR.get() or
-                         REPO_ROOT / "official_weights" / FAMILY)
-TRANSFORMER_DIR = LOCAL_WEIGHTS_DIR / "transformer"
-
-
-def _has_weights() -> bool:
-    required = ["transformer", "vae", "text_encoder", "vision_language_encoder", "processor", "tokenizer", "scheduler"]
-    return all((LOCAL_WEIGHTS_DIR / r).exists() for r in required)
-
-
-def _upstream_glm_image_available() -> bool:
-    try:
-        import transformers
-        import diffusers
-    except ImportError:
-        return False
-    return (hasattr(transformers, "GlmImageForConditionalGeneration") and hasattr(diffusers, "GlmImagePipeline"))
-
-
-pytestmark = [
-    pytest.mark.skipif(
-        not _has_weights(),
-        reason=f"GLM-Image full weights not found at {LOCAL_WEIGHTS_DIR}.",
-    ),
-    pytest.mark.skipif(
-        not _upstream_glm_image_available(),
-        reason=("Pipeline parity needs transformers>=5.0.0rc0 and "
-                "diffusers>=0.37.0.dev0; main pins predate both. Bump locally "
-                "to run."),
-    ),
-]
-
-SAMPLE_PROMPT = ("A landscape photo with rolling green hills under a clear blue sky.")
-SEED = 0
-HEIGHT = 512
-WIDTH = 512
-STEPS = 8
-
-# bf16 + a different SDPA kernel across 30 DiT layers x STEPS steps leaves a
-# small residual; a real wiring/schedule bug is far larger than these bounds.
-LATENT_COSINE_MIN = 0.995
-IMAGE_MAE_MAX = 5.0  # /255
-
-
-@pytest.fixture(scope="module")
-def device():
-    if not torch.cuda.is_available():
-        pytest.skip("CUDA required for GLM-Image pipeline parity.")
-    return torch.device("cuda")
-
-
-def _to_uint8_hwc(a) -> np.ndarray:
-    if torch.is_tensor(a):
-        a = a.detach().float().cpu().numpy()
-    a = np.asarray(a)
-    while a.ndim > 3:
-        a = a[0]
-    if a.ndim == 3 and a.shape[0] in (1, 3) and a.shape[-1] not in (1, 3):
-        a = np.transpose(a, (1, 2, 0))
-    if a.dtype != np.uint8:
-        scale = 255.0 if float(a.max()) <= 1.5 else 1.0
-        a = np.clip(a * scale, 0, 255).astype(np.uint8)
-    return a
-
-
-# --------------------------------------------------------------------------- #
-# FastVideo native DiT loader (mirrors test_transformer_parity.py).
-# --------------------------------------------------------------------------- #
-def _load_state_dict(dir_path: Path) -> dict[str, torch.Tensor]:
-    safetensors = pytest.importorskip("safetensors.torch")
-    sd: dict[str, torch.Tensor] = {}
-    for shard in sorted(dir_path.glob("*.safetensors")):
-        sd.update(safetensors.load_file(str(shard)))
-    return sd
-
-
-def _apply_param_mapping(sd, mapping):
-    import re
-    out = {}
-    for k, v in sd.items():
-        new_k = k
-        for pat, repl in mapping.items():
-            if re.match(pat, k):
-                new_k = re.sub(pat, repl, k)
-                break
-        out[new_k] = v
-    return out
-
-
-def _ensure_distributed():
-    """The denoising stage calls get_local_torch_device(), which needs
-    FastVideo's world/TP groups. Initialize a single-rank (world_size=1) group
-    in-process (mirrors tests/local_tests/sd35/test_sd35_component_parity.py)."""
-    import torch.distributed as dist
-    from fastvideo.distributed.parallel_state import (get_tp_group, init_distributed_environment,
-                                                      initialize_model_parallel)
-    try:
-        get_tp_group()
-        return
-    except Exception:
-        pass
-    if not dist.is_initialized():
-        os.environ.setdefault("RANK", "0")
-        os.environ.setdefault("WORLD_SIZE", "1")
-        os.environ.setdefault("LOCAL_RANK", "0")
-        torch.cuda.set_device(int(os.environ["LOCAL_RANK"]))
-        store_path = f"/tmp/fastvideo_glm_pg_{os.getpid()}.store"
-        dist.init_process_group(backend="nccl", init_method=f"file://{store_path}", rank=0, world_size=1)
-    init_distributed_environment(world_size=1, rank=0, local_rank=0, distributed_init_method="env://")
-    try:
-        get_tp_group()
-    except Exception:
-        initialize_model_parallel(tensor_model_parallel_size=1, sequence_model_parallel_size=1, data_parallel_size=1)
-
-
-def _load_fastvideo_transformer(device, dtype):
-    from fastvideo.configs.models.dits.glm_image import GlmImageDiTConfig
-    from fastvideo.models.dits.glm_image import GlmImageTransformer2DModel
-    cfg = GlmImageDiTConfig()
-    model = GlmImageTransformer2DModel(cfg, {"_class_name": "GlmImageTransformer2DModel"})
-    sd = _load_state_dict(TRANSFORMER_DIR)
-    sd = _apply_param_mapping(sd, cfg.arch_config.param_names_mapping)
-    missing, unexpected = model.load_state_dict(sd, strict=False)
-    assert not missing, f"FastVideo DiT missing keys: {missing[:10]}"
-    assert not unexpected, f"FastVideo DiT unexpected keys: {unexpected[:10]}"
-    return model.to(device, dtype=dtype).eval()
-
-
-def _fastvideo_denoise_latents(device, dtype, *, prompt_embeds, prior_token_ids, init_latents):
-    """Drive the real GlmImageDenoisingStage with injected, matched inputs and
-    return the denoised latents (1, 16, H/8, W/8)."""
-    from fastvideo.models.schedulers.scheduling_flow_match_euler_discrete import (FlowMatchEulerDiscreteScheduler)
-    from fastvideo.pipelines.basic.glm_image.stages.before_denoising import (calculate_shift)
-    from fastvideo.pipelines.basic.glm_image.stages.denoising import (GlmImageDenoisingStage)
-    from fastvideo.pipelines.pipeline_batch_info import ForwardBatch
-
-    _ensure_distributed()
-    transformer = _load_fastvideo_transformer(device, dtype)
-    scheduler = FlowMatchEulerDiscreteScheduler(shift=1.0)
-    stage = GlmImageDenoisingStage(transformer=transformer, scheduler=scheduler)
-
-    # Reproduce the production timestep/sigma schedule (before_denoising.py):
-    # integer-cast linspace timesteps, resolution-dependent shift on sigmas.
-    ntt = scheduler.config.num_train_timesteps
-    patch = transformer.patch_size
-    image_seq_len = ((HEIGHT // 8) * (WIDTH // 8)) // (patch**2)
-    sched_t = np.linspace(ntt, 1.0, STEPS + 1)[:-1].astype(np.int64).astype(np.float32)
-    scheduler.set_shift(calculate_shift(image_seq_len))
-    scheduler.set_timesteps(STEPS, device=device, sigmas=(sched_t / ntt).tolist(), timesteps=sched_t.tolist())
-
-    batch = ForwardBatch(data_type="image")
-    batch.prompt_embeds = [prompt_embeds.to(device, dtype)]
-    batch.attention_mask = None  # match diffusers (no text padding mask)
-    batch.prior_token_id = prior_token_ids.to(device)
-    batch.prior_token_drop = torch.zeros_like(prior_token_ids, dtype=torch.bool, device=device)
-    batch.latents = init_latents.clone().unsqueeze(2).to(device, dtype)
-    batch.timesteps = scheduler.timesteps
-    batch.height, batch.width = HEIGHT, WIDTH
-    batch.num_inference_steps = STEPS
-    batch.guidance_scale = 1.5
-    batch.do_classifier_free_guidance = True
-    batch.seed = SEED
-    batch.extra = {}  # no glm_kv_caches -> T2I denoise path
-
-    out = stage.forward(batch, fastvideo_args=None)
-    latents = out.latents
-    if latents.dim() == 5:
-        latents = latents.squeeze(2)
-    latents = latents.detach().float().cpu()
-    del transformer, stage
-    torch.cuda.empty_cache()
-    import torch.distributed as dist
-    if dist.is_initialized():
-        dist.destroy_process_group()
-    return latents
-
-
-def _fastvideo_image(device) -> np.ndarray:
-    pytest.importorskip("fastvideo")
-    try:
-        from fastvideo import VideoGenerator
-    except ImportError as e:
-        pytest.skip(f"FastVideo VideoGenerator unavailable: {e}")
-    gen = VideoGenerator.from_pretrained(str(LOCAL_WEIGHTS_DIR), num_gpus=1, trust_remote_code=True)
-    result = gen.generate_video(prompt=SAMPLE_PROMPT,
-                                save_video=False,
-                                return_frames=True,
-                                height=HEIGHT,
-                                width=WIDTH,
-                                num_inference_steps=STEPS,
-                                guidance_scale=1.5,
-                                seed=SEED)
-    gen.shutdown()
-    return _to_uint8_hwc(result["frames"][0])
-
-
-def test_fastvideo_pipeline_produces_valid_image(device):
-    fv = _fastvideo_image(device)
-    assert fv.shape == (HEIGHT, WIDTH, 3), f"unexpected shape {fv.shape}"
-    assert fv.dtype == np.uint8 and np.isfinite(fv).all()
-    assert fv.std() > 10.0, f"image is near-constant (std={fv.std():.2f})"
-
-
-def test_pipeline_denoise_parity_deterministic(device):
-    """Inject identical (prompt_embeds, prior_token_ids, init_latents) into the
-    diffusers pipeline and FastVideo's GlmImageDenoisingStage, then compare the
-    denoised latents and the decoded images. With the stochastic AR and the
-    independent latent RNG removed, the two implementations must agree to within
-    bf16/SDPA-kernel noise."""
-    # Inference only: the T5 glyph encoder leaves a grad graph on the embeds,
-    # which later breaks image_processor.postprocess()'s .numpy() on the decoded
-    # image. Disabling grad for the whole test also trims memory.
-    torch.set_grad_enabled(False)
-    diffusers = pytest.importorskip("diffusers")
-    from diffusers.utils.torch_utils import randn_tensor
-
-    dtype = torch.bfloat16
-    pipe = diffusers.GlmImagePipeline.from_pretrained(str(LOCAL_WEIGHTS_DIR), torch_dtype=dtype).to(device)
-
-    gen = torch.Generator(device=device).manual_seed(SEED)
-
-    # --- shared, deterministic inputs (computed once, fed to both) ---------- #
-    pos, neg = pipe.encode_prompt(SAMPLE_PROMPT, do_classifier_free_guidance=True, device=device, dtype=dtype)
-    assert pos.shape[1] == neg.shape[1], (f"quote-free prompt should give equal pos/neg glyph lengths, got "
-                                          f"{pos.shape[1]} vs {neg.shape[1]}")
-    prior_token_ids, _, _ = pipe.generate_prior_tokens(SAMPLE_PROMPT,
-                                                       HEIGHT,
-                                                       WIDTH,
-                                                       image=None,
-                                                       device=device,
-                                                       generator=gen)
-    latent_ch = pipe.transformer.config.in_channels
-    init_latents = randn_tensor((1, latent_ch, HEIGHT // 8, WIDTH // 8), generator=gen, device=device, dtype=dtype)
-
-    # --- official denoised latents ----------------------------------------- #
-    # prompt=None: diffusers check_inputs rejects passing prompt + prompt_embeds
-    # together; the embeds (and prior tokens) fully specify the run.
-    official = pipe(prompt=None,
-                    prompt_embeds=pos,
-                    negative_prompt_embeds=neg,
-                    prior_token_ids=prior_token_ids,
-                    latents=init_latents.clone(),
-                    height=HEIGHT,
-                    width=WIDTH,
-                    num_inference_steps=STEPS,
-                    guidance_scale=1.5,
-                    output_type="latent").images.float().cpu()
-
-    # FastVideo packs CFG as a single [pos; neg] 2-row tensor (no padding here
-    # because L_pos == L_neg, asserted above).
-    packed = torch.cat([pos, neg], dim=0)
-
-    # --- FastVideo denoised latents (real GlmImageDenoisingStage) ---------- #
-    fv = _fastvideo_denoise_latents(device,
-                                    dtype,
-                                    prompt_embeds=packed,
-                                    prior_token_ids=prior_token_ids,
-                                    init_latents=init_latents)
-
-    assert official.shape == fv.shape, (f"latent shape mismatch: {official.shape} vs {fv.shape}")
-
-    # --- latent-level agreement -------------------------------------------- #
-    cos = torch.nn.functional.cosine_similarity(official.flatten(), fv.flatten(), dim=0).item()
-    lat_mae = (official - fv).abs().mean().item()
-    lat_scale = official.abs().mean().item()
-    print(f"[glm-image denoise parity] latent cosine={cos:.6f} "
-          f"MAE={lat_mae:.5f} (|latent| mean={lat_scale:.5f}, "
-          f"rel={lat_mae / max(lat_scale, 1e-6):.4f})")
-
-    # --- decoded-image agreement (same VAE both sides isolates denoise) ----- #
-    def _decode(lat):
-        lat = lat.to(device, dtype)
-        mean = torch.tensor(pipe.vae.config.latents_mean).view(1, -1, 1, 1).to(device, dtype)
-        std = torch.tensor(pipe.vae.config.latents_std).view(1, -1, 1, 1).to(device, dtype)
-        img = pipe.vae.decode((lat * std + mean), return_dict=False)[0]
-        return _to_uint8_hwc(pipe.image_processor.postprocess(img, output_type="np")[0])
-
-    off_img, fv_img = _decode(official), _decode(fv)
-    img_mae = np.abs(off_img.astype(np.float32) - fv_img.astype(np.float32)).mean()
-    print(f"[glm-image denoise parity] decoded image MAE={img_mae:.3f}/255 "
-          f"(diffusers mean={off_img.mean():.1f}, fv mean={fv_img.mean():.1f})")
-
-    del pipe
-    torch.cuda.empty_cache()
-
-    assert cos > LATENT_COSINE_MIN, (f"denoised-latent cosine {cos:.6f} < {LATENT_COSINE_MIN}: the FastVideo "
-                                     "and official denoise diverge well beyond kernel noise (wiring bug).")
-    assert img_mae < IMAGE_MAE_MAX, (f"decoded-image MAE {img_mae:.3f}/255 >= {IMAGE_MAE_MAX}: outputs are "
-                                     "not equivalent under matched inputs (wiring/schedule bug).")
diff --git a/tests/local_tests/glm_image/test_t5_parity.py b/tests/local_tests/glm_image/test_t5_parity.py
deleted file mode 100644
index ef189eca3..000000000
--- a/tests/local_tests/glm_image/test_t5_parity.py
+++ /dev/null
@@ -1,161 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""Parity scaffold for GLM-Image glyph T5 encoder.
-
-The text_encoder is a small byte-level T5 (d_model=1472, gated-gelu, ByT5
-tokenizer, vocab_size=384). This test exercises the glyph-only encoding path
-that the diffusers pipeline implements as `_get_glyph_embeds` and compares
-against the FastVideo glyph-encoding stage logic. Until the FastVideo stage is
-refactored to mirror diffusers (per-prompt loop, even-length pad-prefix,
-attention-mask flattening, left-padded batch), the parity test asserts the
-high-impact pieces individually so failures point to the exact path that
-diverged.
-"""
-from __future__ import annotations
-
-import os
-from pathlib import Path
-
-import pytest
-import torch
-
-import fastvideo.envs as envs
-
-os.environ.setdefault("MASTER_ADDR", "localhost")
-os.environ.setdefault("MASTER_PORT", "29519")
-os.environ.setdefault("DISABLE_SP", "1")
-
-REPO_ROOT = Path(__file__).resolve().parents[3]
-FAMILY = "glm_image"
-LOCAL_WEIGHTS_DIR = Path(envs.FASTVIDEO_TEST_GLM_IMAGE_LOCAL_WEIGHTS_DIR.get() or
-                         REPO_ROOT / "official_weights" / FAMILY)
-TEXT_ENCODER_DIR = LOCAL_WEIGHTS_DIR / "text_encoder"
-TOKENIZER_DIR = LOCAL_WEIGHTS_DIR / "tokenizer"
-
-
-def _has_weights() -> bool:
-    return (TEXT_ENCODER_DIR / "model.safetensors").exists() and TOKENIZER_DIR.exists()
-
-
-pytestmark = pytest.mark.skipif(
-    not _has_weights(),
-    reason=f"GLM-Image text_encoder/tokenizer not found under {LOCAL_WEIGHTS_DIR}.",
-)
-
-SAMPLE_PROMPT = ('A photo of a coffee shop with a wooden sign reading "Daily Grind" '
-                 "and a chalk menu next to it")
-
-
-@pytest.fixture(scope="module")
-def device() -> torch.device:
-    if not torch.cuda.is_available():
-        pytest.skip("CUDA required for GLM-Image glyph encoder parity.")
-    return torch.device("cuda")
-
-
-@pytest.fixture(scope="module")
-def official_tokenizer():
-    transformers = pytest.importorskip("transformers")
-    return transformers.ByT5Tokenizer.from_pretrained(str(TOKENIZER_DIR))
-
-
-@pytest.fixture(scope="module")
-def official_text_encoder(device):
-    transformers = pytest.importorskip("transformers")
-    return transformers.T5EncoderModel.from_pretrained(str(TEXT_ENCODER_DIR),
-                                                       torch_dtype=torch.float32).to(device).eval()
-
-
-def test_tokenizer_is_byt5(official_tokenizer):
-    """Glyph tokenizer must be ByT5 (byte-level), not generic T5."""
-    assert type(official_tokenizer).__name__ == "ByT5Tokenizer"
-    assert official_tokenizer.vocab_size <= 400, ("ByT5 vocab is byte-level (~256+special)")
-
-
-def test_glyph_text_extraction():
-    """Glyph extraction must pull single+double+CJK quoted spans."""
-    pytest.importorskip("fastvideo")
-    try:
-        from fastvideo.pipelines.basic.glm_image.stages.before_denoising import (  # noqa
-            get_glyph_texts)
-    except ImportError as e:
-        pytest.skip(f"FastVideo glyph extractor not yet at target path: {e}")
-    out = get_glyph_texts(SAMPLE_PROMPT)
-    assert "Daily Grind" in out
-
-
-def _diffusers_reference_get_glyph_embeds(prompts, tokenizer, text_encoder, device, dtype, max_sequence_length=2048):
-    """Inlined copy of diffusers `_get_glyph_embeds` + `get_glyph_texts`.
-
-    Source: `reference/diffusers/src/diffusers/pipelines/glm_image/`
-    `pipeline_glm_image.py::GlmImagePipeline._get_glyph_embeds` at commit
-    ff3b86b4755b46a7b5656dfcf84d25bd25ad4740 (diffusers main, 0.37.0.dev0).
-    Inlined here because the installed diffusers package (`__version__`
-    typically <0.37.0) does not yet ship `GlmImagePipeline`. This function is
-    the reference oracle for FastVideo's `compute_glyph_embeds`.
-    """
-    import re
-
-    def get_glyph_texts(prompt):
-        if isinstance(prompt, str):
-            prompt = [prompt]
-        out = []
-        for p in prompt:
-            out.append(
-                re.findall(r"'([^']*)'", p) + re.findall(r"“([^“”]*)”", p) + re.findall(r'"([^"]*)"', p) +
-                re.findall(r"「([^「」]*)」", p))
-        return out
-
-    all_glyph_texts = get_glyph_texts(prompts)
-    all_glyph_embeds = []
-    for glyph_texts in all_glyph_texts:
-        if len(glyph_texts) == 0:
-            glyph_texts = [""]
-        input_ids = tokenizer(
-            glyph_texts,
-            max_length=max_sequence_length,
-            truncation=True,
-        ).input_ids
-        input_ids = [[tokenizer.pad_token_id] * ((len(input_ids) + 1) % 2) + ids for ids in input_ids]
-        max_length = max(len(ids) for ids in input_ids)
-        attention_mask = torch.tensor(
-            [[1] * len(ids) + [0] * (max_length - len(ids)) for ids in input_ids],
-            device=device,
-        )
-        input_ids_t = torch.tensor(
-            [ids + [tokenizer.pad_token_id] * (max_length - len(ids)) for ids in input_ids],
-            device=device,
-        )
-        outputs = text_encoder(input_ids_t, attention_mask=attention_mask)
-        glyph_embeds = outputs.last_hidden_state[attention_mask.bool()].unsqueeze(0)
-        all_glyph_embeds.append(glyph_embeds)
-
-    max_seq_len = max(emb.size(1) for emb in all_glyph_embeds)
-    padded = []
-    for emb in all_glyph_embeds:
-        if emb.size(1) < max_seq_len:
-            pad = torch.zeros(emb.size(0), max_seq_len - emb.size(1), emb.size(2), device=device, dtype=emb.dtype)
-            emb = torch.cat([pad, emb], dim=1)
-        padded.append(emb)
-    return torch.cat(padded, dim=0).to(device=device, dtype=dtype)
-
-
-def test_get_glyph_embeds_matches_diffusers(official_tokenizer, official_text_encoder, device):
-    """FastVideo `compute_glyph_embeds` must equal the diffusers reference."""
-    pytest.importorskip("fastvideo")
-    try:
-        from fastvideo.pipelines.basic.glm_image.stages.before_denoising import (  # noqa
-            compute_glyph_embeds)
-    except ImportError as e:
-        pytest.skip(f"FastVideo glyph encoder not yet at target path: {e}")
-
-    with torch.no_grad():
-        official_embeds = _diffusers_reference_get_glyph_embeds([SAMPLE_PROMPT], official_tokenizer,
-                                                                official_text_encoder, device, torch.float32)
-        fv_embeds = compute_glyph_embeds([SAMPLE_PROMPT],
-                                         tokenizer=official_tokenizer,
-                                         text_encoder=official_text_encoder,
-                                         device=device,
-                                         dtype=torch.float32,
-                                         max_sequence_length=2048)
-    assert fv_embeds.shape == official_embeds.shape, (f"shape mismatch: {fv_embeds.shape} vs {official_embeds.shape}")
-    torch.testing.assert_close(fv_embeds, official_embeds, atol=1e-4, rtol=1e-4)
diff --git a/tests/local_tests/glm_image/test_transformer_parity.py b/tests/local_tests/glm_image/test_transformer_parity.py
deleted file mode 100644
index 95aac33e0..000000000
--- a/tests/local_tests/glm_image/test_transformer_parity.py
+++ /dev/null
@@ -1,193 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""Component parity scaffold for GLM-Image DiT (GlmImageTransformer2DModel).
-
-Compares the FastVideo-native port at `fastvideo.models.dits.glm_image` against
-the diffusers reference `GlmImageTransformer2DModel` from
-`diffusers.models.transformers.transformer_glm_image`.
-
-Skips cleanly until both the FastVideo class and the local weights exist; the
-test is real (loads weights, forwards a fixed input, compares output tensors).
-"""
-from __future__ import annotations
-
-import os
-from pathlib import Path
-
-import pytest
-import torch
-
-import fastvideo.envs as envs
-
-os.environ.setdefault("MASTER_ADDR", "localhost")
-os.environ.setdefault("MASTER_PORT", "29519")
-os.environ.setdefault("DISABLE_SP", "1")
-os.environ.setdefault("FASTVIDEO_ATTENTION_BACKEND", "TORCH_SDPA")
-
-REPO_ROOT = Path(__file__).resolve().parents[3]
-FAMILY = "glm_image"
-LOCAL_WEIGHTS_DIR = Path(envs.FASTVIDEO_TEST_GLM_IMAGE_LOCAL_WEIGHTS_DIR.get() or
-                         REPO_ROOT / "official_weights" / FAMILY)
-TRANSFORMER_DIR = LOCAL_WEIGHTS_DIR / "transformer"
-
-
-def _has_weights() -> bool:
-    return TRANSFORMER_DIR.exists() and any(TRANSFORMER_DIR.glob("*.safetensors"))
-
-
-def _has_glm_image_diffusers() -> bool:
-    """`GlmImageTransformer2DModel` lands in diffusers>=0.37.0.dev0.
-
-    FastVideo main pins `diffusers>=0.33.1`; the installed env is 0.36.0. The
-    numerical parity test needs the diffusers class as an oracle — until the
-    installed diffusers is upgraded, this test skips. The structural surface
-    is still verified by `test_glm_image_transformer_strict_load.py`.
-    """
-    import importlib.util
-    spec = importlib.util.find_spec("diffusers")
-    if spec is None:
-        return False
-    import diffusers
-    return hasattr(diffusers, "GlmImageTransformer2DModel")
-
-
-pytestmark = [
-    pytest.mark.skipif(
-        not _has_weights(),
-        reason=(f"GLM-Image transformer weights not found at {TRANSFORMER_DIR}. "
-                "Download via "
-                "`python .agents/skills/add-model-01-prep/scripts/download_hf_weights.py "
-                "zai-org/GLM-Image official_weights/glm_image`."),
-    ),
-    pytest.mark.skipif(
-        not _has_glm_image_diffusers(),
-        reason=("diffusers in this env lacks `GlmImageTransformer2DModel` "
-                "(main allows >=0.33.1; installed 0.36.0; class lands in "
-                "0.37.0.dev0). Bump diffusers locally to run numerical parity. "
-                "Structural surface is covered by "
-                "`test_glm_image_transformer_strict_load.py`."),
-    ),
-]
-
-
-@pytest.fixture(scope="module")
-def device() -> torch.device:
-    if not torch.cuda.is_available():
-        pytest.skip("CUDA required for GLM-Image transformer parity.")
-    return torch.device("cuda")
-
-
-def _load_official(device, dtype):
-    diffusers = pytest.importorskip("diffusers")
-    cls = diffusers.GlmImageTransformer2DModel
-    return cls.from_pretrained(str(TRANSFORMER_DIR), torch_dtype=dtype).to(device).eval()
-
-
-def _load_fastvideo(device, dtype):
-    pytest.importorskip("fastvideo")
-    try:
-        from fastvideo.configs.models.dits.glm_image import GlmImageDiTConfig
-        from fastvideo.models.dits.glm_image import GlmImageTransformer2DModel
-    except ImportError as e:
-        pytest.skip(f"FastVideo GLM-Image DiT not yet ported: {e}")
-    cfg = GlmImageDiTConfig()
-    model = GlmImageTransformer2DModel(cfg, {"_class_name": "GlmImageTransformer2DModel"})
-    sd = _load_state_dict(TRANSFORMER_DIR)
-    sd = _apply_param_mapping(sd, cfg.arch_config.param_names_mapping)
-    missing, unexpected = model.load_state_dict(sd, strict=False)
-    assert not missing, f"FastVideo DiT missing keys: {missing[:10]}"
-    assert not unexpected, f"FastVideo DiT unexpected keys: {unexpected[:10]}"
-    return model.to(device, dtype=dtype).eval()
-
-
-def _forward_pair(device, dtype):
-    """Run both DiTs on identical synthetic inputs; return (fv_out, ref_out)."""
-    from fastvideo.forward_context import set_forward_context
-    ref = _load_official(device, dtype)
-    fv = _load_fastvideo(device, dtype)
-    inputs = _make_inputs(device, dtype=dtype)
-    with torch.no_grad():
-        ref_out = ref(**inputs, return_dict=False)[0]
-        with set_forward_context(current_timestep=0, attn_metadata=None, forward_batch=None):
-            fv_out = fv(**inputs)
-        if isinstance(fv_out, tuple):
-            fv_out = fv_out[0]
-    assert ref_out.shape == fv_out.shape, (f"shape mismatch: {ref_out.shape} vs {fv_out.shape}")
-    # Free the two 7B models before the next dtype runs (single-GPU budget).
-    del ref, fv
-    torch.cuda.empty_cache()
-    return fv_out.float(), ref_out.float()
-
-
-def _load_state_dict(dir_path: Path) -> dict[str, torch.Tensor]:
-    safetensors = pytest.importorskip("safetensors.torch")
-    sd: dict[str, torch.Tensor] = {}
-    for shard in sorted(dir_path.glob("*.safetensors")):
-        sd.update(safetensors.load_file(str(shard)))
-    return sd
-
-
-def _apply_param_mapping(sd, mapping):
-    import re
-    out = {}
-    for k, v in sd.items():
-        new_k = k
-        for pat, repl in mapping.items():
-            if re.match(pat, k):
-                new_k = re.sub(pat, repl, k)
-                break
-        out[new_k] = v
-    return out
-
-
-def _make_inputs(device,
-                 batch_size=1,
-                 height=64,
-                 width=64,
-                 text_seq_len=32,
-                 dtype=torch.bfloat16) -> dict[str, torch.Tensor]:
-    """Synthetic inputs matching the diffusers `GlmImageTransformer2DModel.forward`
-    contract. Latent shape is `(B, 16, H, W)`; post-patch grid is `(H/2, W/2)`
-    so `prior_token_id` has `(H/2)*(W/2)` entries per batch element."""
-    torch.manual_seed(0)
-    patch_size = 2
-    num_patches = (height // patch_size) * (width // patch_size)
-    return {
-        "hidden_states": torch.randn(batch_size, 16, height, width, device=device, dtype=dtype),
-        "encoder_hidden_states": torch.randn(batch_size, text_seq_len, 1472, device=device, dtype=dtype),
-        "prior_token_id": torch.randint(0, 16384, (batch_size, num_patches), device=device, dtype=torch.long),
-        "prior_token_drop": torch.zeros(batch_size, device=device, dtype=torch.bool),
-        "timestep": torch.tensor([500] * batch_size, device=device, dtype=torch.long),
-        "target_size": torch.tensor([[height * 8, width * 8]] * batch_size, device=device, dtype=torch.long),
-        "crop_coords": torch.zeros(batch_size, 2, device=device, dtype=torch.long),
-    }
-
-
-# fp32 is the structural-correctness gate: with identical math the two
-# implementations agree to ~1e-3 (residual is SDPA reduction-order noise).
-ATOL_FP32 = 1e-2
-RTOL_FP32 = 1e-2
-# bf16 is the inference dtype. Rounding accumulates across 30 layers and the
-# attention kernels differ, so element-wise atol is meaningless; cosine
-# similarity is the right statistical bar for "same function, bf16 noise".
-COS_MIN_BF16 = 0.999
-
-
-def test_transformer_forward_matches_diffusers_fp32(device):
-    """Structural gate: in fp32 the FastVideo DiT is numerically faithful to
-    the diffusers reference (cosine ~1.0, element-wise close to ~1e-3)."""
-    fv_out, ref_out = _forward_pair(device, torch.float32)
-    cos = torch.nn.functional.cosine_similarity(fv_out.flatten(), ref_out.flatten(), dim=0)
-    assert cos > 0.9999, f"fp32 cosine similarity too low: {cos:.6f}"
-    torch.testing.assert_close(fv_out, ref_out, atol=ATOL_FP32, rtol=RTOL_FP32)
-
-
-def test_transformer_forward_matches_diffusers_bf16(device):
-    """Inference-dtype sanity: bf16 output tracks the diffusers reference up to
-    precision accumulation (high cosine similarity). Element-wise atol is not a
-    meaningful bar for a 30-layer bf16 transformer vs a different attention
-    kernel (~28% of elements exceed atol=5e-3 purely from rounding) — see
-    PORT_STATUS I020."""
-    fv_out, ref_out = _forward_pair(device, torch.bfloat16)
-    cos = torch.nn.functional.cosine_similarity(fv_out.flatten(), ref_out.flatten(), dim=0)
-    assert cos > COS_MIN_BF16, (f"bf16 cosine similarity {cos:.6f} below {COS_MIN_BF16}; "
-                                "indicates a structural divergence, not just precision.")
diff --git a/tests/local_tests/glm_image/test_transformer_strict_load.py b/tests/local_tests/glm_image/test_transformer_strict_load.py
deleted file mode 100644
index 0aab4d32b..000000000
--- a/tests/local_tests/glm_image/test_transformer_strict_load.py
+++ /dev/null
@@ -1,93 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""Strict-load smoke for GLM-Image DiT.
-
-Loads all three transformer shards from `official_weights/glm_image/transformer/`,
-applies the FastVideo `param_names_mapping`, and calls
-`GlmImageTransformer2DModel.load_state_dict(..., strict=True)`. Asserts the
-config-driven model instantiates and every checkpoint key has a matching
-FastVideo parameter with the same shape and dtype.
-
-This is weight-load verification only — it does not run a forward pass. Numerical
-parity against the diffusers reference lives in
-`test_glm_image_transformer_parity.py` and requires `diffusers >= 0.37.0.dev0`.
-"""
-from __future__ import annotations
-
-import os
-import re
-from pathlib import Path
-
-import pytest
-import torch
-
-import fastvideo.envs as envs
-
-os.environ.setdefault("MASTER_ADDR", "localhost")
-os.environ.setdefault("MASTER_PORT", "29519")
-os.environ.setdefault("DISABLE_SP", "1")
-os.environ.setdefault("FASTVIDEO_ATTENTION_BACKEND", "TORCH_SDPA")
-
-REPO_ROOT = Path(__file__).resolve().parents[3]
-FAMILY = "glm_image"
-LOCAL_WEIGHTS_DIR = Path(envs.FASTVIDEO_TEST_GLM_IMAGE_LOCAL_WEIGHTS_DIR.get() or
-                         REPO_ROOT / "official_weights" / FAMILY)
-TRANSFORMER_DIR = LOCAL_WEIGHTS_DIR / "transformer"
-
-
-def _has_shards() -> bool:
-    return TRANSFORMER_DIR.exists() and any(TRANSFORMER_DIR.glob("*.safetensors"))
-
-
-pytestmark = pytest.mark.skipif(
-    not _has_shards(),
-    reason=f"GLM-Image transformer shards not at {TRANSFORMER_DIR}.",
-)
-
-
-def test_dit_strict_load_against_hf_checkpoint():
-    safetensors = pytest.importorskip("safetensors.torch")
-    from fastvideo.configs.models.dits.glm_image import GlmImageDiTConfig
-    from fastvideo.models.dits.glm_image import GlmImageTransformer2DModel
-
-    cfg = GlmImageDiTConfig()
-    mapping = cfg.arch_config.param_names_mapping
-
-    # 1. Build the FastVideo model on CPU/meta-free path.
-    torch.set_default_device("cpu")
-    hf_config = {"_class_name": "GlmImageTransformer2DModel"}
-    model = GlmImageTransformer2DModel(cfg, hf_config)
-
-    # 2. Load every shard and apply param_names_mapping.
-    raw_sd: dict[str, torch.Tensor] = {}
-    for shard in sorted(TRANSFORMER_DIR.glob("*.safetensors")):
-        raw_sd.update(safetensors.load_file(str(shard)))
-
-    def rename(k: str) -> str:
-        for pat, repl in mapping.items():
-            if re.match(pat, k):
-                return re.sub(pat, repl, k)
-        return k
-
-    renamed_sd = {rename(k): v for k, v in raw_sd.items()}
-
-    # 3. Compare key sets (sanity, redundant with strict=True but gives a
-    # clearer error message if it fails).
-    fv_keys = set(model.state_dict().keys())
-    ckpt_keys = set(renamed_sd.keys())
-    missing = fv_keys - ckpt_keys
-    unexpected = ckpt_keys - fv_keys
-    assert not missing, f"FastVideo DiT missing {len(missing)} keys, e.g. {sorted(missing)[:5]}"
-    assert not unexpected, f"checkpoint has {len(unexpected)} unexpected keys, e.g. {sorted(unexpected)[:5]}"
-
-    # 4. Shape and dtype sanity — strict_load with shape mismatch raises early.
-    fv_sd = model.state_dict()
-    shape_mismatches = []
-    for k, v in renamed_sd.items():
-        if v.shape != fv_sd[k].shape:
-            shape_mismatches.append((k, tuple(fv_sd[k].shape), tuple(v.shape)))
-    assert not shape_mismatches, (f"shape mismatches: {shape_mismatches[:5]}")
-
-    # 5. Strict load.
-    incompatible = model.load_state_dict(renamed_sd, strict=True)
-    assert not incompatible.missing_keys
-    assert not incompatible.unexpected_keys
diff --git a/tests/local_tests/glm_image/test_vae_parity.py b/tests/local_tests/glm_image/test_vae_parity.py
deleted file mode 100644
index 673e724c8..000000000
--- a/tests/local_tests/glm_image/test_vae_parity.py
+++ /dev/null
@@ -1,104 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""Component parity scaffold for GLM-Image VAE (AutoencoderKL).
-
-Compares the FastVideo-native AutoencoderKL port against diffusers'
-`AutoencoderKL` loaded from `zai-org/GLM-Image/vae`. The published checkpoint
-uses `block_out_channels=[128, 512, 1024, 1024]`, `latent_channels=16`, and
-per-channel `latents_mean[16]` + `latents_std[16]` normalization (not a scalar
-`scaling_factor`).
-
-Skips cleanly until both the FastVideo class and the VAE shard exist.
-"""
-from __future__ import annotations
-
-import json
-import os
-from pathlib import Path
-
-import pytest
-import torch
-
-import fastvideo.envs as envs
-
-os.environ.setdefault("MASTER_ADDR", "localhost")
-os.environ.setdefault("MASTER_PORT", "29519")
-os.environ.setdefault("DISABLE_SP", "1")
-
-REPO_ROOT = Path(__file__).resolve().parents[3]
-FAMILY = "glm_image"
-LOCAL_WEIGHTS_DIR = Path(envs.FASTVIDEO_TEST_GLM_IMAGE_LOCAL_WEIGHTS_DIR.get() or
-                         REPO_ROOT / "official_weights" / FAMILY)
-VAE_DIR = LOCAL_WEIGHTS_DIR / "vae"
-
-
-def _has_weights() -> bool:
-    return (VAE_DIR / "diffusion_pytorch_model.safetensors").exists()
-
-
-pytestmark = pytest.mark.skipif(
-    not _has_weights(),
-    reason=f"GLM-Image VAE weights not found at {VAE_DIR}.",
-)
-
-
-@pytest.fixture(scope="module")
-def device() -> torch.device:
-    if not torch.cuda.is_available():
-        pytest.skip("CUDA required for GLM-Image VAE parity.")
-    return torch.device("cuda")
-
-
-@pytest.fixture(scope="module")
-def vae_config():
-    with open(VAE_DIR / "config.json") as f:
-        return json.load(f)
-
-
-@pytest.fixture(scope="module")
-def official_vae(device):
-    diffusers = pytest.importorskip("diffusers")
-    vae = diffusers.AutoencoderKL.from_pretrained(str(VAE_DIR), torch_dtype=torch.float32).to(device).eval()
-    return vae
-
-
-@pytest.fixture(scope="module")
-def fastvideo_vae(device):
-    pytest.importorskip("fastvideo")
-    try:
-        from fastvideo.configs.models.vaes.glm_image import GlmImageVAEConfig
-        from fastvideo.models.vaes.autoencoder_kl import AutoencoderKL
-    except ImportError as e:
-        pytest.skip(f"FastVideo native AutoencoderKL not yet ported: {e}")
-    cfg = GlmImageVAEConfig()
-    vae = AutoencoderKL(cfg)
-    safetensors = pytest.importorskip("safetensors.torch")
-    sd = safetensors.load_file(str(VAE_DIR / "diffusion_pytorch_model.safetensors"))
-    missing, unexpected = vae.load_state_dict(sd, strict=False)
-    assert not missing, f"FastVideo VAE missing keys: {missing[:10]}"
-    assert not unexpected, f"FastVideo VAE unexpected keys: {unexpected[:10]}"
-    return vae.to(device, dtype=torch.float32).eval()
-
-
-def test_vae_config_matches_real_checkpoint(vae_config):
-    assert vae_config["block_out_channels"] == [128, 512, 1024, 1024]
-    assert vae_config["latent_channels"] == 16
-    assert "latents_mean" in vae_config and len(vae_config["latents_mean"]) == 16
-    assert "latents_std" in vae_config and len(vae_config["latents_std"]) == 16
-
-
-def test_vae_decode_matches_diffusers(official_vae, fastvideo_vae, device):
-    torch.manual_seed(0)
-    latents = torch.randn(1, 16, 32, 32, device=device, dtype=torch.float32)
-    with torch.no_grad():
-        official_out = official_vae.decode(latents, return_dict=False)[0]
-        fv_out = fastvideo_vae.decode(latents, return_dict=False)[0]
-    torch.testing.assert_close(fv_out, official_out, atol=1e-3, rtol=1e-3)
-
-
-def test_vae_encode_matches_diffusers(official_vae, fastvideo_vae, device):
-    torch.manual_seed(0)
-    pixels = torch.randn(1, 3, 256, 256, device=device, dtype=torch.float32)
-    with torch.no_grad():
-        official_z = official_vae.encode(pixels).latent_dist.mode()
-        fv_z = fastvideo_vae.encode(pixels).latent_dist.mode()
-    torch.testing.assert_close(fv_z, official_z, atol=1e-3, rtol=1e-3)
diff --git a/tests/local_tests/pipelines/test_dreamx_world_pipeline_parity.py b/tests/local_tests/pipelines/test_dreamx_world_pipeline_parity.py
deleted file mode 100644
index f10288994..000000000
--- a/tests/local_tests/pipelines/test_dreamx_world_pipeline_parity.py
+++ /dev/null
@@ -1,105 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""DreamX-World pipeline parity checks.
-
-This test compares the FastVideo pipeline's DreamX-specific conditioning and
-single-step scheduler path against an explicit hand-rolled pass using the same
-loaded modules. It is intentionally local and deterministic: component parity
-against the official DreamX repository lives in ``tests/local_tests/dreamx_world``.
-"""
-from __future__ import annotations
-
-import gc
-import os
-from pathlib import Path
-from typing import Any, cast
-
-import pytest
-import torch
-from torch.testing import assert_close
-
-os.environ.setdefault("FASTVIDEO_ATTENTION_BACKEND", "TORCH_SDPA")
-
-MODEL_DIR = Path(os.getenv("DREAMX_WORLD_MODEL_DIR", "converted_weights/dreamx_world"))
-
-pytestmark = pytest.mark.skipif(
-    not torch.cuda.is_available(),
-    reason="DreamX-World pipeline parity requires CUDA",
-)
-
-
-def _run_worker_forward_batch(worker_wrapper: Any, request_kwargs: dict[str, Any]) -> torch.Tensor:
-    from fastvideo.api.sampling_param import SamplingParam
-    from fastvideo.pipelines.pipeline_batch_info import ForwardBatch
-    from fastvideo.utils import shallow_asdict
-
-    fastvideo_args = worker_wrapper.worker.fastvideo_args
-    sampling_param = SamplingParam.from_pretrained(fastvideo_args.model_path)
-    sampling_param.update({key: value for key, value in request_kwargs.items() if key not in {"prompt", "output_path"}})
-    sampling_param.prompt = request_kwargs["prompt"]
-
-    latents_size = [
-        (sampling_param.num_frames - 1) // 4 + 1,
-        sampling_param.height // 8,
-        sampling_param.width // 8,
-    ]
-    n_tokens = latents_size[0] * latents_size[1] * latents_size[2]
-    batch = ForwardBatch(
-        **shallow_asdict(sampling_param),
-        eta=0.0,
-        n_tokens=n_tokens,
-        VSA_sparsity=fastvideo_args.VSA_sparsity,
-    )
-    output_batch = worker_wrapper.worker.pipeline.forward(batch, fastvideo_args)
-    assert output_batch.output is not None
-    return output_batch.output.detach().cpu()
-
-
-def _close_generator(generator: Any) -> None:
-    generator.shutdown()
-    gc.collect()
-    torch.cuda.empty_cache()
-
-
-def test_dreamx_world_one_step_pipeline_latent_matches_manual_pass() -> None:
-    if not MODEL_DIR.exists():
-        pytest.fail(f"DreamX-World converted model directory is missing: {MODEL_DIR}")
-
-    from fastvideo import VideoGenerator
-    common_kwargs = dict(
-        prompt="a quiet road through a futuristic city at sunrise",
-        output_path="outputs_video/dreamx_world_parity",
-        save_video=False,
-        return_frames=True,
-        height=64,
-        width=64,
-        num_frames=9,
-        num_inference_steps=1,
-        guidance_scale=1.0,
-        action_list=["w"],
-        action_speed_list=[2.0],
-        seed=123,
-    )
-
-    generator = VideoGenerator.from_pretrained(
-        str(MODEL_DIR),
-        num_gpus=1,
-        use_fsdp_inference=False,
-        dit_cpu_offload=False,
-        vae_cpu_offload=True,
-        text_encoder_cpu_offload=True,
-        pin_cpu_memory=False,
-        output_type="latent",
-        override_pipeline_cls_name="DreamXWorldPipeline",
-    )
-    try:
-        result = cast(dict[str, Any], generator.generate_video(**common_kwargs))
-        pipeline_latents = cast(torch.Tensor, result["samples"]).detach().cpu()
-
-        manual_latents = generator.executor.collective_rpc(
-            _run_worker_forward_batch,
-            kwargs={"request_kwargs": common_kwargs},
-        )[0]
-    finally:
-        _close_generator(generator)
-
-    assert_close(pipeline_latents, manual_latents, atol=0.0, rtol=0.0)
diff --git a/tests/local_tests/pipelines/test_dreamx_world_pipeline_smoke.py b/tests/local_tests/pipelines/test_dreamx_world_pipeline_smoke.py
deleted file mode 100644
index 29b80036b..000000000
--- a/tests/local_tests/pipelines/test_dreamx_world_pipeline_smoke.py
+++ /dev/null
@@ -1,155 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""Smoke tests for the DreamX-World-5B-Cam pipeline."""
-from __future__ import annotations
-
-import os
-from pathlib import Path
-from typing import Any, cast
-
-import pytest
-import torch
-from PIL import Image, ImageDraw
-
-os.environ.setdefault("FASTVIDEO_ATTENTION_BACKEND", "TORCH_SDPA")
-
-MODEL_DIR = Path(os.getenv("DREAMX_WORLD_MODEL_DIR", "converted_weights/dreamx_world"))
-
-
-def _write_smoke_image(path: Path) -> None:
-    image = Image.new("RGB", (96, 96), color=(42, 76, 112))
-    draw = ImageDraw.Draw(image)
-    draw.rectangle((0, 56, 96, 96), fill=(36, 44, 52))
-    draw.polygon([(0, 56), (48, 30), (96, 56)], fill=(120, 142, 158))
-    draw.rectangle((34, 42, 62, 72), fill=(178, 198, 212))
-    image.save(path)
-
-
-pytestmark = pytest.mark.skipif(
-    not torch.cuda.is_available(),
-    reason="DreamX-World pipeline smoke requires CUDA",
-)
-
-
-def test_dreamx_world_typed_surface_preflight() -> None:
-    import fastvideo.registry as registry
-    from fastvideo.api.presets import get_preset, get_presets_for_family
-    from fastvideo.configs.pipelines.dreamx_world import DreamXWorld5BCamPipelineConfig
-    from fastvideo.fastvideo_args import WorkloadType
-    from fastvideo.pipelines.basic.dreamx_world.dreamx_world_pipeline import (
-        DreamXWorldPipeline,
-        EntryClass,
-    )
-
-    assert DreamXWorldPipeline.__name__ == "DreamXWorldPipeline"
-    assert EntryClass is DreamXWorldPipeline
-    assert DreamXWorldPipeline._required_config_modules == [
-        "text_encoder",
-        "tokenizer",
-        "vae",
-        "transformer",
-        "scheduler",
-    ]
-
-    default_preset, model_family = registry.get_preset_selection("GD-ML/DreamX-World-5B-Cam")
-    assert model_family == "dreamx_world"
-    assert default_preset == "dreamx_world_5b_cam"
-
-    info = registry.get_model_info(
-        "GD-ML/DreamX-World-5B-Cam",
-        workload_type=WorkloadType.I2V,
-        override_pipeline_cls_name="DreamXWorldPipeline",
-    )
-    assert info.pipeline_cls is DreamXWorldPipeline
-    assert info.pipeline_config_cls is DreamXWorld5BCamPipelineConfig
-
-    names = {p.name for p in get_presets_for_family("dreamx_world")}
-    assert "dreamx_world_5b_cam" in names
-    preset = get_preset("dreamx_world_5b_cam", "dreamx_world")
-    assert preset.defaults["num_inference_steps"] == 30
-    assert preset.defaults["height"] == 480
-    assert preset.defaults["width"] == 832
-    assert preset.defaults["num_frames"] == 161
-    assert preset.defaults["guidance_scale"] == 5.0
-
-    cfg = DreamXWorld5BCamPipelineConfig()
-    assert cfg.flow_shift == 3.0
-    assert cfg.ti2v_task is True
-    assert cfg.expand_timesteps is True
-    assert cfg.dit_config.arch_config.add_control_adapter is True
-    assert cfg.dit_config.arch_config.cam_method == "prope"
-
-
-def test_dreamx_world_camera_stage_writes_y_camera() -> None:
-    from types import SimpleNamespace
-
-    from fastvideo.pipelines.basic.dreamx_world.stages import (
-        DREAMX_Y_CAMERA_KEY,
-        DreamXWorldCameraConditioningStage,
-    )
-    from fastvideo.pipelines.pipeline_batch_info import ForwardBatch
-
-    batch = ForwardBatch(
-        data_type="video",
-        prompt="camera smoke",
-        latents=torch.zeros(1, 48, 3, 8, 8, dtype=torch.bfloat16, device="cuda"),
-        num_frames=9,
-        height=64,
-        width=64,
-        action_list=["w", "d"],
-        action_speed_list=[2.0, 1.0],
-    )
-    out = DreamXWorldCameraConditioningStage().forward(batch, cast(Any, SimpleNamespace()))
-
-    y_camera = out.extra[DREAMX_Y_CAMERA_KEY]
-    assert set(y_camera) == {"viewmats", "K"}
-    assert y_camera["viewmats"].shape == (1, 3, 4, 4)
-    assert y_camera["K"].shape == (1, 3, 3, 3)
-    assert y_camera["viewmats"].device.type == "cuda"
-    assert y_camera["viewmats"].dtype == torch.bfloat16
-
-
-def test_dreamx_world_pipeline_load_generate_latent_smoke(tmp_path: Path) -> None:
-    if not MODEL_DIR.exists():
-        pytest.fail(f"DreamX-World converted model directory is missing: {MODEL_DIR}")
-
-    from fastvideo import VideoGenerator
-
-    image_path = tmp_path / "dreamx_world_smoke_input.png"
-    _write_smoke_image(image_path)
-
-    generator = VideoGenerator.from_pretrained(
-        str(MODEL_DIR),
-        num_gpus=1,
-        use_fsdp_inference=False,
-        dit_cpu_offload=False,
-        vae_cpu_offload=True,
-        text_encoder_cpu_offload=True,
-        pin_cpu_memory=False,
-        output_type="latent",
-        override_pipeline_cls_name="DreamXWorldPipeline",
-    )
-    try:
-        result = generator.generate_video(
-            prompt="a quiet road through a futuristic city at sunrise",
-            output_path="outputs_video/dreamx_world_smoke",
-            save_video=False,
-            return_frames=True,
-            height=64,
-            width=64,
-            num_frames=9,
-            num_inference_steps=1,
-            guidance_scale=1.0,
-            image_path=str(image_path),
-            action_list=["w"],
-            action_speed_list=[2.0],
-            seed=0,
-        )
-    finally:
-        generator.shutdown()
-
-    assert isinstance(result, dict)
-    samples = cast(dict[str, Any], result)["samples"]
-    assert torch.is_tensor(samples)
-    assert samples.ndim == 5
-    assert samples.shape[1] == 48
-    assert torch.isfinite(samples).all()
diff --git a/tests/local_tests/pipelines/test_zimage_pipeline_parity.py b/tests/local_tests/pipelines/test_zimage_pipeline_parity.py
deleted file mode 100644
index b3aadddb2..000000000
--- a/tests/local_tests/pipelines/test_zimage_pipeline_parity.py
+++ /dev/null
@@ -1,326 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""Native Z-Image stage contract and pinned-repository pipeline parity."""
-
-from __future__ import annotations
-
-import gc
-import importlib
-import os
-import subprocess
-import sys
-from contextlib import contextmanager
-from pathlib import Path
-from types import SimpleNamespace
-
-import pytest
-import torch
-from torch.testing import assert_close
-
-import fastvideo.envs as envs
-from fastvideo.configs.pipelines.zimage import ZImagePipelineConfig
-from fastvideo.models.schedulers.scheduling_flow_match_euler_discrete import (
-    FlowMatchEulerDiscreteScheduler, )
-from fastvideo.pipelines.basic.zimage.stages import (
-    ZImageConditioningStage,
-    ZImageDecodingStage,
-    ZImageDenoisingStage,
-    ZImageInputValidationStage,
-    ZImageLatentPreparationStage,
-    ZImageTimestepPreparationStage,
-)
-from fastvideo.pipelines.pipeline_batch_info import ForwardBatch
-
-REPO_ROOT = Path(__file__).resolve().parents[3]
-REFERENCE_REVISION = "26f23eda626ffadda020b04ff79488e1d72004cd"
-REFERENCE_REPO = Path(os.getenv("ZIMAGE_REFERENCE_REPO", REPO_ROOT / "Z-Image"))
-MODEL_DIR = Path(envs.FASTVIDEO_TEST_ZIMAGE_MODEL_DIR.get() if envs.FASTVIDEO_TEST_ZIMAGE_MODEL_DIR.is_set() else
-                 REPO_ROOT / "official_weights" / "Z-Image")
-
-
-class _ConstantTransformer:
-    in_channels = 16
-
-    def __init__(self) -> None:
-        self.calls: list[tuple[torch.Tensor, list[torch.Tensor], torch.Tensor]] = []
-
-    def __call__(
-        self,
-        *,
-        hidden_states: torch.Tensor,
-        encoder_hidden_states: list[torch.Tensor],
-        timestep: torch.Tensor,
-    ) -> tuple[list[torch.Tensor], dict]:
-        self.calls.append((hidden_states, encoder_hidden_states, timestep))
-        return [torch.ones_like(sample) for sample in hidden_states.unbind(0)], {}
-
-
-class _RecordingVAE(torch.nn.Module):
-
-    def __init__(self) -> None:
-        super().__init__()
-        self.anchor = torch.nn.Parameter(torch.zeros(()))
-        self.config = SimpleNamespace(scaling_factor=0.3611, shift_factor=0.1159)
-        self.decode_input: torch.Tensor | None = None
-
-    def decode(self, latents: torch.Tensor, return_dict: bool = False) -> tuple[torch.Tensor]:
-        assert return_dict is False
-        self.decode_input = latents.detach().clone()
-        batch, _, height, width = latents.shape
-        return (torch.zeros(batch, 3, height * 8, width * 8, device=latents.device), )
-
-
-def _stage_args(*, output_type: str = "pil") -> SimpleNamespace:
-    return SimpleNamespace(
-        pipeline_config=ZImagePipelineConfig(),
-        output_type=output_type,
-        vae_cpu_offload=False,
-        disable_autocast=False,
-    )
-
-
-def test_zimage_native_default_stage_math(monkeypatch: pytest.MonkeyPatch) -> None:
-    """Exercise the pinned native 1-step formulas without model assets."""
-    autocast_calls: list[dict[str, object]] = []
-
-    @contextmanager
-    def record_autocast(**kwargs):
-        autocast_calls.append(kwargs)
-        yield
-
-    monkeypatch.setattr(
-        "fastvideo.pipelines.basic.zimage.stages.get_local_torch_device",
-        lambda: torch.device("cpu"),
-    )
-    monkeypatch.setattr("fastvideo.pipelines.basic.zimage.stages.torch.autocast", record_autocast)
-    args = _stage_args()
-    transformer = _ConstantTransformer()
-    scheduler = FlowMatchEulerDiscreteScheduler(shift=3.0)
-    vae = _RecordingVAE()
-    batch = ForwardBatch(
-        data_type="image",
-        prompt="test prompt",
-        negative_prompt="",
-        prompt_embeds=[torch.arange(32, dtype=torch.float32).reshape(1, 4, 8)],
-        prompt_attention_mask=[torch.tensor([[True, True, False, False]])],
-        height=64,
-        width=64,
-        num_frames=1,
-        num_inference_steps=1,
-        guidance_scale=0.0,
-        seed=42,
-        latents=torch.zeros(1, 16, 1, 8, 8),
-    )
-
-    ZImageInputValidationStage().forward(batch, args)
-    ZImageConditioningStage().forward(batch, args)
-    ZImageLatentPreparationStage(transformer).forward(batch, args)
-    ZImageTimestepPreparationStage(scheduler).forward(batch, args)
-
-    assert len(batch.extra["zimage_prompt_embeds"]) == 1
-    assert batch.extra["zimage_prompt_embeds"][0].shape == (2, 8)
-    assert_close(batch.timesteps, torch.tensor([1000.0]))
-    assert scheduler.sigma_min == 0.0
-    assert scheduler.config.use_reference_discrete_timesteps is True
-
-    ZImageDenoisingStage(transformer, scheduler).forward(batch, args)
-    assert autocast_calls == [{"device_type": "cpu", "enabled": False}]
-    assert_close(batch.latents, torch.ones_like(batch.latents))
-    _, encoded, normalized_timestep = transformer.calls[0]
-    assert encoded[0].shape == (2, 8)
-    assert_close(normalized_timestep, torch.tensor([0.0]))
-
-    ZImageDecodingStage(vae).forward(batch, args)
-    assert vae.decode_input is not None
-    expected_vae_input = torch.full((1, 16, 8, 8), 1 / 0.3611 + 0.1159)
-    assert_close(vae.decode_input, expected_vae_input)
-    assert batch.output is not None
-    assert batch.output.shape == (1, 3, 1, 64, 64)
-    assert_close(batch.output, torch.full_like(batch.output, 0.5))
-
-
-def test_zimage_batch_rng_matches_one_native_generator(monkeypatch: pytest.MonkeyPatch) -> None:
-    monkeypatch.setattr(
-        "fastvideo.pipelines.basic.zimage.stages.get_local_torch_device",
-        lambda: torch.device("cpu"),
-    )
-    args = _stage_args(output_type="latent")
-    transformer = _ConstantTransformer()
-    batch = ForwardBatch(
-        data_type="image",
-        prompt=["one", "two"],
-        negative_prompt="",
-        height=64,
-        width=64,
-        num_frames=1,
-        num_inference_steps=1,
-        guidance_scale=0.0,
-        seed=42,
-    )
-    ZImageInputValidationStage().forward(batch, args)
-    batch.extra["zimage_prompt_embeds"] = [torch.zeros(1, 8), torch.zeros(1, 8)]
-    ZImageLatentPreparationStage(transformer).forward(batch, args)
-
-    expected = torch.randn(
-        (2, 16, 1, 8, 8),
-        generator=torch.Generator("cpu").manual_seed(42),
-    )
-    assert_close(batch.latents, expected)
-
-
-def test_zimage_text_stage_honors_request_max_length(monkeypatch: pytest.MonkeyPatch) -> None:
-    from fastvideo.pipelines.stages.text_encoding import TextEncodingStage
-
-    args = _stage_args()
-    stage = TextEncodingStage(text_encoders=[object()], tokenizers=[object()])
-    observed: list[int | None] = []
-
-    def fake_encode_text(
-        text,
-        fastvideo_args,
-        encoder_index,
-        return_attention_mask,
-        max_length=None,
-    ):
-        del text, fastvideo_args, encoder_index, return_attention_mask
-        observed.append(max_length)
-        return [torch.zeros(1, 4, 8)], [torch.ones(1, 4, dtype=torch.long)]
-
-    monkeypatch.setattr(stage, "encode_text", fake_encode_text)
-    batch = ForwardBatch(
-        data_type="image",
-        prompt="test",
-        negative_prompt="",
-        max_sequence_length=64,
-        guidance_scale=0.0,
-    )
-    stage.forward(batch, args)
-    assert observed == [64]
-
-
-def test_zimage_cfg_none_uses_native_empty_negative_prompt(monkeypatch: pytest.MonkeyPatch) -> None:
-    monkeypatch.setattr(
-        "fastvideo.pipelines.basic.zimage.stages.get_local_torch_device",
-        lambda: torch.device("cpu"),
-    )
-    batch = ForwardBatch(
-        data_type="image",
-        prompt="test",
-        negative_prompt=None,
-        height=64,
-        width=64,
-        num_frames=1,
-        num_inference_steps=1,
-        guidance_scale=2.0,
-        seed=42,
-    )
-    ZImageInputValidationStage().forward(batch, _stage_args())
-    assert batch.negative_prompt == ""
-
-
-def _require_pinned_reference() -> Path:
-    if not REFERENCE_REPO.is_dir():
-        pytest.skip(f"Pinned Tongyi Z-Image clone not found: {REFERENCE_REPO}")
-    result = subprocess.run(
-        ["git", "-C", str(REFERENCE_REPO), "rev-parse", "HEAD"],
-        check=True,
-        capture_output=True,
-        text=True,
-    )
-    actual = result.stdout.strip()
-    assert actual == REFERENCE_REVISION, (f"Tongyi Z-Image oracle must be pinned at {REFERENCE_REVISION}, got {actual}")
-    return REFERENCE_REPO / "src"
-
-
-def _import_reference_modules() -> tuple[object, object]:
-    source_root = _require_pinned_reference().resolve()
-    if str(source_root) not in sys.path:
-        sys.path.insert(0, str(source_root))
-    pipeline_module = importlib.import_module("zimage.pipeline")
-    utils_module = importlib.import_module("utils")
-    for module in (pipeline_module, utils_module):
-        module_path = Path(module.__file__ or "").resolve()
-        assert module_path.is_relative_to(source_root), (
-            f"Z-Image oracle import escaped the pinned repository: {module_path}")
-    return pipeline_module, utils_module
-
-
-@pytest.mark.skipif(not torch.cuda.is_available(), reason="Full Z-Image pipeline parity requires CUDA")
-def test_zimage_pipeline_latents_match_pinned_native_repo(tmp_path: Path) -> None:
-    """Compare one complete native step; the oracle never imports Diffusers' pipeline."""
-    if not (MODEL_DIR / "model_index.json").is_file():
-        pytest.skip(f"Pinned Z-Image weights not found: {MODEL_DIR}")
-    pipeline_module, utils_module = _import_reference_modules()
-    prompt = "Young Chinese woman in red Hanfu, intricate embroidery."
-
-    previous_fastvideo_backend = os.environ.get("FASTVIDEO_ATTENTION_BACKEND")
-    try:
-        # Exercise the shared PyTorch SDPA path with each process's native
-        # kernel selection; the official and FastVideo implementations both
-        # pass the same padding mask into scaled_dot_product_attention.
-        utils_module.set_attention_backend(None)
-        components = utils_module.load_from_local_dir(
-            MODEL_DIR,
-            device="cuda",
-            dtype=torch.bfloat16,
-            compile=False,
-        )
-        reference = pipeline_module.generate(
-            prompt=prompt,
-            **components,
-            height=64,
-            width=64,
-            num_inference_steps=1,
-            guidance_scale=0.0,
-            negative_prompt="",
-            max_sequence_length=512,
-            generator=torch.Generator("cuda").manual_seed(42),
-            output_type="latent",
-        ).detach().float().cpu()
-        del components
-        gc.collect()
-        torch.cuda.empty_cache()
-
-        os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "TORCH_SDPA"
-        from fastvideo import VideoGenerator
-
-        generator = VideoGenerator.from_pretrained(
-            str(MODEL_DIR),
-            num_gpus=1,
-            tp_size=1,
-            sp_size=1,
-            use_fsdp_inference=False,
-            dit_cpu_offload=False,
-            dit_layerwise_offload=False,
-            text_encoder_cpu_offload=False,
-            vae_cpu_offload=False,
-            pin_cpu_memory=False,
-            output_type="latent",
-        )
-        try:
-            result = generator.generate_video(
-                prompt,
-                negative_prompt="",
-                output_path=str(tmp_path),
-                save_video=False,
-                return_frames=True,
-                height=64,
-                width=64,
-                num_frames=1,
-                fps=1,
-                num_inference_steps=1,
-                guidance_scale=0.0,
-                max_sequence_length=512,
-                seed=42,
-            )
-        finally:
-            generator.shutdown()
-        actual = result["samples"].squeeze(2).float()
-        assert actual.shape == reference.shape
-        diff = (actual - reference).abs()
-        print(f"Z-Image pipeline latent parity: max={diff.max():.6f}, mean={diff.mean():.6f}")
-        assert_close(actual, reference, atol=1e-2, rtol=1e-2)
-    finally:
-        if previous_fastvideo_backend is None:
-            os.environ.pop("FASTVIDEO_ATTENTION_BACKEND", None)
-        else:
-            os.environ["FASTVIDEO_ATTENTION_BACKEND"] = previous_fastvideo_backend
diff --git a/tests/local_tests/pipelines/test_zimage_pipeline_smoke.py b/tests/local_tests/pipelines/test_zimage_pipeline_smoke.py
deleted file mode 100644
index 61f4ccc49..000000000
--- a/tests/local_tests/pipelines/test_zimage_pipeline_smoke.py
+++ /dev/null
@@ -1,155 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""Import/registry preflight and optional real Z-Image-Turbo smoke."""
-
-from __future__ import annotations
-
-from contextlib import nullcontext
-from pathlib import Path
-from typing import Any, cast
-
-import pytest
-import torch
-
-import fastvideo.envs as envs
-
-MODEL_DIR_ENV = envs.FASTVIDEO_TEST_ZIMAGE_MODEL_DIR.get() if envs.FASTVIDEO_TEST_ZIMAGE_MODEL_DIR.is_set() else None
-
-
-def test_zimage_typed_surface_preflight() -> None:
-    from fastvideo.api import GeneratorConfig, PipelineSelection
-    from fastvideo.api.compat import generator_config_to_fastvideo_args
-    import fastvideo.registry as registry
-    from fastvideo.api.presets import get_preset, get_presets_for_family
-    from fastvideo.configs.models.dits.zimage import ZImageDiTConfig
-    from fastvideo.configs.models.encoders.qwen3 import Qwen3TextConfig
-    from fastvideo.configs.models.vaes.autoencoder_kl import AutoencoderKLVAEConfig
-    from fastvideo.configs.pipelines.zimage import ZImagePipelineConfig
-    from fastvideo.fastvideo_args import WorkloadType
-    from fastvideo.pipelines.basic.zimage.zimage_pipeline import EntryClass, ZImagePipeline
-
-    assert ZImagePipeline.__name__ == "ZImagePipeline"
-    assert EntryClass is ZImagePipeline
-    assert ZImagePipeline.pipeline_config_cls is ZImagePipelineConfig
-    assert ZImagePipeline._required_config_modules == [
-        "scheduler",
-        "text_encoder",
-        "tokenizer",
-        "transformer",
-        "vae",
-    ]
-
-    default_preset, model_family = registry.get_preset_selection("Tongyi-MAI/Z-Image-Turbo")
-    assert (default_preset, model_family) == ("zimage_turbo", "zimage")
-    info = registry.get_model_info(
-        "Tongyi-MAI/Z-Image-Turbo",
-        workload_type=WorkloadType.T2I,
-        override_pipeline_cls_name="ZImagePipeline",
-    )
-    assert info.pipeline_cls is ZImagePipeline
-    assert info.pipeline_config_cls is ZImagePipelineConfig
-
-    assert {preset.name for preset in get_presets_for_family("zimage")} == {"zimage_turbo"}
-    preset = get_preset("zimage_turbo", "zimage")
-    assert preset.defaults == {
-        "height": 1024,
-        "width": 1024,
-        "num_frames": 1,
-        "fps": 1,
-        "seed": 42,
-        "guidance_scale": 0.0,
-        "num_inference_steps": 8,
-        "negative_prompt": "",
-        "max_sequence_length": 512,
-        "cfg_normalization": False,
-        "cfg_truncation": 1.0,
-    }
-
-    config = ZImagePipelineConfig()
-    assert isinstance(config.dit_config, ZImageDiTConfig)
-    assert isinstance(config.vae_config, AutoencoderKLVAEConfig)
-    assert isinstance(config.text_encoder_configs[0], Qwen3TextConfig)
-    assert config.text_encoder_configs[0].chat_template_enable_thinking is True
-    assert config.text_encoder_configs[0].output_hidden_states is True
-    assert config.text_encoder_configs[0].tokenizer_kwargs["max_length"] == 512
-    assert config.scheduler_sigma_min == 0.0
-    assert config.scheduler_use_reference_discrete_timesteps is True
-
-    args = generator_config_to_fastvideo_args(
-        GeneratorConfig(
-            model_path="Tongyi-MAI/Z-Image-Turbo",
-            pipeline=PipelineSelection(workload_type="t2i"),
-        ))
-    assert isinstance(args.pipeline_config, ZImagePipelineConfig)
-    assert args.workload_type is WorkloadType.T2I
-
-
-def test_model_download_honors_pinned_revision(monkeypatch: pytest.MonkeyPatch, tmp_path: Path) -> None:
-    import fastvideo.utils as utils
-
-    observed: dict[str, object] = {}
-
-    def fake_snapshot_download(**kwargs):
-        observed.update(kwargs)
-        return str(tmp_path)
-
-    monkeypatch.setattr(utils, "snapshot_download", fake_snapshot_download)
-    monkeypatch.setattr(utils, "get_lock", lambda _key: nullcontext())
-    resolved = utils.maybe_download_model(
-        "Tongyi-MAI/Z-Image-Turbo",
-        revision="f332072aa78be7aecdf3ee76d5c247082da564a6",
-    )
-
-    assert resolved == str(tmp_path)
-    assert observed["repo_id"] == "Tongyi-MAI/Z-Image-Turbo"
-    assert observed["revision"] == "f332072aa78be7aecdf3ee76d5c247082da564a6"
-
-
-@pytest.mark.skipif(not torch.cuda.is_available(), reason="Z-Image load/generate smoke requires CUDA")
-def test_zimage_pipeline_load_generate_smoke() -> None:
-    if MODEL_DIR_ENV is None:
-        pytest.skip("Set FASTVIDEO_TEST_ZIMAGE_MODEL_DIR to activate the real load/generate smoke")
-    model_dir = Path(MODEL_DIR_ENV)
-    if not (model_dir / "model_index.json").is_file():
-        pytest.skip(f"Z-Image model_index.json not found under {model_dir}")
-
-    from fastvideo import VideoGenerator
-
-    generator = VideoGenerator.from_pretrained(
-        str(model_dir),
-        num_gpus=1,
-        tp_size=1,
-        sp_size=1,
-        use_fsdp_inference=False,
-        dit_cpu_offload=False,
-        dit_layerwise_offload=False,
-        text_encoder_cpu_offload=False,
-        vae_cpu_offload=False,
-        pin_cpu_memory=False,
-        output_type="latent",
-    )
-    try:
-        result = generator.generate_video(
-            prompt="a red panda reading a book",
-            negative_prompt="",
-            output_path="outputs/zimage/smoke",
-            save_video=False,
-            return_frames=True,
-            height=64,
-            width=64,
-            num_frames=1,
-            fps=1,
-            num_inference_steps=1,
-            guidance_scale=0.0,
-            max_sequence_length=64,
-            cfg_normalization=False,
-            cfg_truncation=1.0,
-            seed=42,
-        )
-    finally:
-        generator.shutdown()
-
-    result_dict = cast(dict[str, Any], result)
-    samples = result_dict["samples"]
-    assert torch.is_tensor(samples)
-    assert samples.ndim in (4, 5)
-    assert torch.isfinite(samples).all()
diff --git a/tests/local_tests/zimage/PORT_STATUS.md b/tests/local_tests/zimage/PORT_STATUS.md
deleted file mode 100644
index 1a3b7d085..000000000
--- a/tests/local_tests/zimage/PORT_STATUS.md
+++ /dev/null
@@ -1,162 +0,0 @@
-# Z-Image Port Status
-
-## Summary
-
-- model_family: `zimage`
-- variant: `Z-Image-Turbo`
-- workload_types: `T2I`
-- PR scope: required components plus same-PR FastVideo-native pipeline, example/parity, SSIM test, and L40S seed workflow
-- official_ref: `https://github.com/Tongyi-MAI/Z-Image`
-- official_ref_dir: `<repo_root>/Z-Image/src`
-- official_ref_commit: `26f23eda626ffadda020b04ff79488e1d72004cd`
-- hf_weights_path: `Tongyi-MAI/Z-Image-Turbo@f332072aa78be7aecdf3ee76d5c247082da564a6`
-- local_weights_dir: `<repo_root>/official_weights/Z-Image/`
-- source_layout: official repo-native `src/zimage/`; HF component subfolders provide assets only
-- weight_layout: HF per-component subfolders; these are assets, not the reference implementation
-- local_tests_readme: `tests/local_tests/zimage/README.md`
-
-The pinned text encoder has 36 decoder blocks, hidden size 2560,
-intermediate size 9728, 32 attention heads, 8 KV heads, and head dimension
-128. FastVideo obtains those values from the pinned `config.json`.
-
-## Current Phase
-
-- phase: Phase 11 (post-parity review and handoff); quality reference published
-- status: external_ci_gate
-- owner: infra/reviewer
-- last_updated: 2026-07-15
-
-## Pinned Official Pipeline Context
-
-- official_pipeline: `Tongyi-MAI/Z-Image@26f23eda626ffadda020b04ff79488e1d72004cd`,
-  `src/zimage/pipeline.py::generate` and `src/config/inference.py`
-- oracle_policy: official Z-Image source only; Diffusers is not the implementation oracle
-- workload_and_output: `T2I`; prompt plus optional negative prompt to PIL images, or latents when requested
-- model_index_contract: `_class_name=ZImagePipeline`; required modules are `transformer`, `vae`,
-  `text_encoder`, `tokenizer`, and `scheduler`
-- official_defaults: `height=1024`, `width=1024`, `num_inference_steps=8`,
-  `guidance_scale=0.0`, `cfg_truncation=1.0`, `max_sequence_length=512`
-- reproducible_rng: `torch.Generator("cuda").manual_seed(42)`; the API accepts a generator and has no fixed seed default
-- scheduler_contract: `use_reference_discrete_timesteps=True`, runtime `sigma_min=0.0`, endpoint-preserving
-  `num_steps + 1` schedule, and no DiT call for the final zero timestep
-- decode_contract: apply `(latents / 0.3611) + 0.1159` before official VAE decode
-- implementation_status: native pipeline/config/preset/registry/example and the 8-step 1024x1024 PNG SSIM test are
-  implemented; one-step native GPU parity is bit-exact; the L40S candidate generated at inference head `9a6dbcadc`
-  was author-approved and published at dataset revision `7b27d00004b723ef0cf4923e77ae57128573f01c`
-
-## Component Matrix
-
-| Component | Type | Reuse/Port | Official Definition / Instantiation | FastVideo Target | Prototype | Conversion | Parity | Open Issues |
-|---|---|---|---|---|---|---|---|---|
-| Production text encoder (Qwen3 body) | text_encoder | reused production passthrough | Official `src/utils/loader.py` uses Transformers with typed load then device-only move; official pipeline consumes `hidden_states[-2]` | `TextEncoderLoader` -> `Qwen3ForCausalLM.from_pretrained_local` -> body-only Transformers `AutoModel` | DONE | not_needed for current HF subfolder | PINNED GB200 PASS; reference/production outputs exact | none |
-| Native Qwen quality | implementation_subcomponent | shared native implementation | Same pinned text-encoder weights and official `hidden_states[-2]` boundary; not loaded by the pipeline | direct `fastvideo.models.encoders.qwen3.Qwen3ForCausalLM` construction | DONE | not_needed | PINNED GB200 PASS; fp32 `atol=2e-4`, `rtol=1e-4`; bf16 thresholds unchanged | none |
-| Tokenizer | tokenizer | reused | Official loader's `AutoTokenizer` from `<weights>/tokenizer/`; official thinking template and length 512 | `TokenizerLoader` | DONE | not_needed | PINNED GB200 PASS (2 passed) | none |
-| VAE | vae | reused with accepted wrapper exception | Pinned official `src/zimage/autoencoder.py`; raw decode is the component boundary | Existing Diffusers-backed `fastvideo.models.vaes.autoencoder_kl.AutoencoderKL` through production `VAELoader`, plus direct raw-decode coverage | DONE | not_needed | PINNED GB200 PASS (2 passed) | none |
-| Scheduler | scheduler | reused with Z-Image extension | Pinned official `src/zimage/scheduler.py`; official pipeline sets `sigma_min=0.0` | `FlowMatchEulerDiscreteScheduler(use_reference_discrete_timesteps=True, sigma_min=0.0)` | DONE | not_needed | PINNED GB200 PASS (5 passed) | none |
-| Transformer (`ZImageTransformer2DModel`) | dit | native port | Pinned official `src/zimage/transformer.py`; HF `transformer/` supplies weights only | `fastvideo.models.dits.zimage.ZImageTransformer2DModel` through `TransformerLoader` | DONE | not_needed; exact 521-key surface | PINNED GB200 PASS (5 passed; real-weight full forward exact) | none |
-| Pipeline | pipeline | native same-PR implementation | Pinned official `src/zimage/pipeline.py::generate` | FastVideo `ZImagePipeline` plus config/preset/registry/example/parity | DONE | not_needed | RTX 5090 ONE-STEP LATENTS BIT-EXACT; L40S REFERENCE PUBLISHED | I005 (CI verification only) |
-
-## Conversion State
-
-- conversion_script: none for the reused components or native transformer
-- converted_weights_dir: none
-- source_layout: official repo-native Python (`Tongyi-MAI/Z-Image`)
-- hf_weight_layout: per-component subfolders; not a Diffusers implementation oracle
-- transformer_conversion: not_needed; official, HF index, and native names align exactly
-- transformer_key_surface: 521 tensors; sorted-key SHA256 `3a9216f208c1873b2cf06394411a53e1e95e10fae3b01dca0f7223556e47c354`
-- strict_load_status: transformer real-weight strict load passed; native Qwen strictness regressions passed
-- passthrough_components: body-only Qwen3 `AutoModel`, tokenizer assets
-- direct_load_components: native Qwen3 parity target, VAE, scheduler
-- native_qwen_exclusion: `lm_head.weight` only; the native encoder owns the body, not an LM head
-- native_qwen_fused_contract: every Q/K/V and gate/up shard is required unless the exact fused destination is present
-- native_qwen_auxiliary_contract: mapped quantization auxiliaries, including scale parameters, must load and may not be silently discarded
-- retry_history: none
-
-## Parity Commands
-
-| Scope | Command | Last Result | Notes |
-|---|---|---|---|
-| Scheduler | `pytest tests/local_tests/zimage/test_zimage_scheduler_parity.py -v -s` | 5 PASSED, no skips, GB200, 2026-07-15 | Enforces official-source SHA/import origin, fp32 scalar contract, exact zero endpoint, and positional/default regressions. |
-| Tokenizer | `pytest tests/local_tests/zimage/test_zimage_tokenizer_parity.py -v -s` | 2 PASSED as part of the pinned full GB200 run, 2026-07-15 | Uses `enable_thinking=True`, `max_length=512`, and fails if chat templating is unavailable. |
-| VAE | `pytest tests/local_tests/zimage/test_zimage_vae_parity.py -v -s` | 2 PASSED, no skips, GB200, 2026-07-15 | Direct raw decode plus production `VAELoader`; config values are checked, while latent scale/shift application is pipeline behavior. |
-| Production text encoder | `pytest tests/local_tests/zimage/test_zimage_encoder_parity.py::test_zimage_qwen3_encoder_parity_forward[fp32] -v -s` | REFERENCE/PRODUCTION CHECKS EXACT, GB200, 2026-07-15 | The required pipeline path matches an independent official-loader materialization exactly. |
-| Native Qwen fp32 quality | `pytest tests/local_tests/zimage/test_zimage_encoder_parity.py::test_zimage_qwen3_encoder_parity_forward[fp32] -v -s` | PASS, GB200, 2026-07-15 | Z-Image-only `atol=2e-4`, `rtol=1e-4`; production equality and shared Qwen math are unchanged. |
-| Native Qwen bf16 quality | `pytest tests/local_tests/zimage/test_zimage_encoder_parity.py::test_zimage_qwen3_encoder_parity_forward[bf16] -v -s` | PASS, GB200, 2026-07-15 | Existing mean/median distribution thresholds pass unchanged against an independent materialization of the official loader contract. |
-| Native Qwen per-layer bf16 diagnostic | `pytest tests/local_tests/zimage/test_zimage_encoder_parity.py::test_zimage_qwen3_encoder_per_layer_bf16_diagnostic -v -s` | PASS, GB200, 2026-07-15 | Pinned architecture has 36 blocks and 37 hidden-state entries under the tested contract. |
-| Transformer parity | `ZIMAGE_OFFICIAL_REF_DIR=<pinned-clone> pytest tests/local_tests/zimage/test_zimage_transformer_parity.py -v -s` | 5 PASSED, no skips, GB200, 2026-07-15 | Production 521-key/shape surface, tiny CPU parity, mask behavior, SP guard, real-weight strict load, and 6.15B full forward pass; real-weight output was exact (`max=0`, `mean=0`). |
-| Complete component closure | `ZIMAGE_OFFICIAL_REF_DIR=<pinned-clone> ZIMAGE_TRANSFORMER_DIR=<pinned-transformer> pytest tests/local_tests/zimage/ -v -s` | 34 PASSED, no skips, GB200, 2026-07-15, `f310aacb1` | Official source `26f23eda...`, HF snapshot `f332072a...`; production/native encoder, scheduler, tokenizer, transformer, and VAE all passed. |
-| Pipeline typed surface | `pytest tests/local_tests/pipelines/test_zimage_pipeline_smoke.py::test_zimage_typed_surface_preflight -v` | PASS, hardware-free, 2026-07-14 | Covers exact `EntryClass`, config, preset, registry, model detection, and official defaults. |
-| Native stage contract | `pytest tests/local_tests/pipelines/test_zimage_pipeline_parity.py::test_zimage_native_default_stage_math -v` | PASS, hardware-free, 2026-07-14 | Covers conditioning trim, endpoint-preserving timestep setup, denoising sign/timestep math, and VAE scale/shift. |
-| Full pipeline parity | `ZIMAGE_REFERENCE_REPO=<pinned-clone> FASTVIDEO_TEST_ZIMAGE_MODEL_DIR=<pinned-weights> DISABLE_SP=1 pytest tests/local_tests/pipelines/test_zimage_pipeline_parity.py::test_zimage_pipeline_latents_match_pinned_native_repo -v -s` | PASS, RTX 5090, `fec2baa6c` | Real FastVideo one-step latents match pinned native Tongyi exactly (`max=0`, `mean=0`). Later commits through `f310aacb1` change only API inventory/tests, not inference math. |
-| PNG SSIM quality | `pytest fastvideo/tests/ssim/test_zimage_similarity.py -v -s` | L40S REFERENCE UPLOADED; END-TO-END SSIM CI PENDING | Dataset revision `7b27d00004b723ef0cf4923e77ae57128573f01c`; remote SHA-256 `6c2be648...f7a6`; upload scoped to `Tongyi-MAI__Z-Image-Turbo/TORCH_SDPA`. |
-
-## Open Questions
-
-| ID | Question | Owner | Needed By Phase | Status | Resolution |
-|---|---|---|---|---|---|
-| Q001 | Which exact Z-Image source revision is the parity oracle? | prep | Phase 1 | resolved | Pinned `Tongyi-MAI/Z-Image@26f23eda626ffadda020b04ff79488e1d72004cd`; scheduler/VAE tests fail on a wrong HEAD or import origin. |
-| Q002 | Which immutable HF snapshot supplies component weights? | prep | Phase 1 | resolved | `Tongyi-MAI/Z-Image-Turbo@f332072aa78be7aecdf3ee76d5c247082da564a6`. |
-| Q003 | What prompt/tokenization/hidden-state contract does the official pipeline use? | pipeline | Phase 6 | resolved | `apply_chat_template(tokenize=False, add_generation_prompt=True, enable_thinking=True)`, tokenization length 512, and valid-token `hidden_states[-2]`. |
-| Q004 | Does the native transformer require conversion? | conversion | Phase 5 | resolved | No. Pinned HF index, official production meta model, and FastVideo production meta model expose the same 521 keys; official/native key shapes match exactly. |
-
-## Issues And Blockers
-
-| ID | Phase | Component | Severity | Issue | Evidence | Owner | Status | Resolution |
-|---|---|---|---|---|---|---|---|---|
-| I001 | Phase 6 | native_qwen_quality | medium | The optional native fp32 `hidden_states[-2]` tail narrowly exceeded `atol=1e-4` after smooth cross-kernel accumulation. | Exact layer trace: embeddings exact, layer-0 max `3.815e-6`, first misses at layer 33, largest failing boundary difference `1.686811e-4`; final RMSNorm passes. The required production `AutoModel` path is exact. | parity | resolved | Author approved Z-Image-only `_FP32_ATOL=2e-4` with `_FP32_RTOL=1e-4`; complete pinned GB200 suite passed 34/34 with bf16 and all other gates unchanged. |
-| I002 | Phase 7 | scheduler/pipeline | high | Published `scheduler_config.json` omits the reference-schedule flag and official zero endpoint. | The native stage applies both values; hardware-free stage coverage and pinned native GPU parity pass. | pipeline | resolved | The Z-Image pipeline applies `use_reference_discrete_timesteps=True` and `sigma_min=0.0`; one-step latents match official Tongyi exactly. |
-| I003 | Phase 6 | tokenizer/text_encoder | medium | Prompt formatting and target length previously differed from the official call. | Pinned source uses thinking chat template, 512 tokens, and `hidden_states[-2]`. | parity | resolved | Corrected behavior passed the pinned GB200 run. |
-| I004 | Phase 4 | transformer | high | `ZImageTransformer2DModel` was not ported. | Native config/model/registry and deterministic parity test now exist. | port | resolved | Production meta surface matches all 521 pinned HF keys; tiny full-forward parity passes exactly. |
-| I005 | Phase 10 | pipeline/quality | high | Verify the published L40S PNG through end-to-end SSIM CI. | Author approved the reviewed candidate; the guarded upload published exactly one file at dataset revision `7b27d000...f01c`, and remote bytes match SHA-256 `6c2be648...f7a6`. | infra | external_ci_gate | Reference publication is resolved. The full-suite launcher remains blocked by the external-fork documentation workflow's `action_required` state; merge is separate. |
-| I006 | Phase 6 | text_encoder | high | HF passthrough loading inherited an ambient target-device context, and the earlier test harness incorrectly cast float32 RoPE buffers during bf16 placement. | HF loading now occurs outside the ambient context; typed load is followed by device-only movement, matching official `src/utils/loader.py`; production/independent outputs match exactly. | encoder | resolved | Hardware-free placement/default-dtype tests and the pinned GB200 production path pass. |
-| I007 | Phase 6 | text_encoder | high | Lenient native loading could miss one fused source shard or an auxiliary quantization scale. | Loader completeness covers every destination, required split shard, exact fused destination, and mapped scale parameter. | encoder | resolved | Strictness regressions and pinned checkpoint loading pass. |
-| I008 | Phase 6 | vae | medium | Direct decode alone did not cover production registry/config/device/strict-load behavior; the shared target is a runtime Diffusers wrapper. | VAE parity now covers direct raw decode and production `VAELoader` against official `src/zimage/autoencoder.py`. | parity | resolved | Pinned GB200 subset passed 2/2. The wrapper is an implementation exception, not the oracle; official latent normalization remains Phase 7 pipeline scope. |
-| I009 | Phase 2 | reference assets | high | Adding a nonexistent clone path to `sys.path` could import another installed `zimage` package and yield false parity. | Tests now verify exact clone HEAD and module paths under `Z-Image/src`; `/Z-Image/` is ignored. | prep | resolved | Wrong SHA/path/import is a failure; absent local assets may skip. |
-| I010 | Phase 6 | tokenizer | medium | Missing `apply_chat_template` previously skipped and could hide an incompatible tokenizer. | Tokenizer parity now asserts the API and exact official kwargs. | parity | resolved | Missing chat-template behavior fails; pinned asset-backed parity passed. |
-| I011 | Phase 6 | transformer | high | Production real-weight strict load and full-forward parity were not verified. | Pinned 24.6 GB weights and official source were exercised on GB200. | parity | resolved | Strict load and full forward passed exactly (`max=0`, `mean=0`). |
-| I012 | Phase 6 | required_components | high | Record a complete non-skip run after the loader/oracle corrections and approved native-Qwen tolerance. | Exact PR head `f310aacb1`, pinned official source `26f23eda...`, immutable HF snapshot `f332072a...`, GB200. | parity | resolved | Complete suite passed 34/34 in 94.21s with no skips; real-weight transformer output was exact (`max=0`, `mean=0`). |
-
-## Escape Hatches
-
-| ID | Phase | Decision Type | Question | Recommended Option | Status | Resolution |
-|---|---|---|---|---|---|---|
-| E001 | Phase 6 | cost | Approve one standard 8-hour 4×GB200 hold to run the complete pinned component and real-weight transformer parity suite at PR #1339? | Approve `model/r9`; reuse a suitable existing allocation if available. | resolved | Author approved. Existing job `1545932` was reused, so no new hold was created. Excludes Modal, SSIM seeding/upload, and merge. |
-| E002 | Phase 6 | numerical | Accept the traced Z-Image-only native encoder fp32 tail without weakening shared Qwen or bf16 gates? | Approve `model/r10`: change only `_FP32_ATOL` from `1e-4` to `2e-4`, retain `_FP32_RTOL=1e-4`, then rerun native parity at the exact resulting head. | resolved | Author approved. The one-line test threshold changed at `f310aacb1`; complete pinned GB200 suite passed 34/34 with production equality, bf16, strict-load, and official-oracle checks unchanged. |
-
-## Decisions
-
-| Date | Decision | Rationale | Impact |
-|---|---|---|---|
-| 2026-07-15 | Publish the visually approved L40S PNG reference to `FastVideo/ssim-reference-videos`. | The author confirmed the staged image looks good and explicitly requested the HF upload. | Guarded no-force upload added one Z-Image PNG at dataset revision `7b27d000...f01c`; remote bytes match the reviewed artifact. |
-| 2026-07-15 | Accept the traced Z-Image-only native encoder fp32 absolute tolerance of `2e-4` while retaining `rtol=1e-4`. | Exact tracing showed gradual fused/split-kernel accumulation rather than a semantic break; the largest failing absolute tail was `1.686811e-4`. | One test constant changed. The full pinned suite passed 34/34; production equality, bf16 thresholds, strict loading, and official Tongyi oracle are unchanged. |
-| 2026-07-14 | Carry the native FastVideo pipeline, example/parity, SSIM test, and L40S seed workflow on PR #1339. | The author explicitly requested the pipeline and quality regression on the same PR rather than a follow-up. | Implementation, component closure, native GPU parity, and reference publication are complete. Later commits do not change inference math. |
-| 2026-07-14 | Treat the exact production `AutoModel` loader as the required pipeline text encoder and direct native-Qwen parity as a separate quality row. | `TextEncoderLoader` calls `Qwen3ForCausalLM.from_pretrained_local`, which returns body-only Transformers `AutoModel`; the pipeline does not direct-construct FastVideo-native Qwen. | The production component is exact. The separately approved native-Qwen tolerance changed no production math. |
-| 2026-07-13 | Use pinned `Tongyi-MAI/Z-Image@26f23eda...` as the implementation oracle; treat HF subfolders only as weight/config assets. | The official repo defines transformer, VAE, scheduler, pipeline, and loader semantics. A runtime component may inherit Diffusers without making Diffusers the reference. | Tests verify official clone SHA/import origin; docs distinguish the oracle from storage/runtime implementation details. |
-| 2026-07-13 | Keep official latent scale/shift application out of the VAE component assertion. | `(latents / 0.3611) + 0.1159` is pipeline-stage behavior, while VAE component parity compares raw decode and production loading. | The Z-Image stage implements the exact formula; pinned native GPU parity passed. |
-| 2026-07-13 | Preserve the published transformer key surface and use raw masked SDPA at SP=1. | The checkpoint already matches the official class, while padded variable-length attention needs a key mask unavailable in current distributed wrappers. | No converter is needed; runtime rejects SP>1 until a mask-aware distributed path has parity evidence. |
-| 2026-07-04 | Pin both external sources and treat all earlier GPU results as historical until rerun. | Earlier evidence did not record an immutable HF revision, and source imports were not origin-checked. | README has executable clone/download commands; tests enforce the source SHA; all asset-backed parity rows were revalidated. |
-| 2026-07-04 | Use a body-only Transformers `AutoModel` for production and a separate `AutoModel` object to materialize the official loader contract independently. | The pinned official Z-Image source is the oracle. Every FastVideo consumer needs hidden states, not full-vocabulary logits or an LM head, and comparing an object with itself is not independent evidence. | Production avoids the LM head while parity covers production loading and native implementation separately. |
-| 2026-07-04 | Make fused-source and quantization-scale completeness part of native Qwen strict loading. | A loaded destination name alone did not prove that every required Q/K/V or gate/up shard and auxiliary scale arrived. | Missing split shards or mapped scale parameters fail; an exact fused destination remains valid. |
-| 2026-07-04 | Reuse the existing Diffusers-backed AutoencoderKL wrapper for the Z-Image VAE component. | The target is established shared code with production-loader parity against pinned official `src/zimage/autoencoder.py`. | The wrapper remains an implementation detail for this VAE only; official Z-Image source defines pipeline behavior and numerical parity. |
-| 2026-07-04 | Treat absent chat-template behavior as incompatibility, not an optional test environment. | Thinking-format prompt construction is part of the official numerical contract. | Tokenizer parity fails when `apply_chat_template` is unavailable. |
-| 2026-07-04 | Record the pinned Qwen architecture as 36 blocks, hidden size 2560, intermediate size 9728, 32 attention heads, and 8 KV heads. | Earlier notes described a different architecture and could mis-size the native target. | Config/review evidence now matches `f332072aa78be7aecdf3ee76d5c247082da564a6`; expected hidden-state length is 37. |
-| 2026-06-21 | Expand generated Qwen `position_ids` to `[batch_size, seq_len]`. | The rotary layer flattens positions; `[1, seq_len]` misaligned RoPE for batch sizes greater than one. | Batch-one behavior is unchanged; pinned batch-two parity passed. |
-| 2026-06-21 | Reuse the shared native `Qwen3ForCausalLM` implementation rather than maintain a second Z-Image encoder. | The shared implementation supports the same config-driven GQA, fused QKV/gate-up, and hidden-state output contract. | Registry/native parity uses the shared class; architecture values come from the pinned config. |
-| 2026-05-12 | Use mean and median drift checks for deep bf16 encoder parity. | Cross-kernel fused/unfused bf16 arithmetic produces a long max-error tail; fp32 and per-layer diagnostics distinguish this from structural divergence. | Existing thresholds passed unchanged on the pinned 36-block snapshot. |
-| 2026-05-12 | Forward the complete scheduler config except loader metadata. | Hand-picking three fields could silently omit future scheduler behavior. | Scheduler parity consumes all on-disk constructor fields and explicitly supplies Z-Image runtime overrides. |
-
-## Handoff Notes
-
-- The implementation oracle is the pinned official `Tongyi-MAI/Z-Image` clone,
-  never Diffusers. Transformers is used for text components only because the
-  official loader does so; HF component subfolders provide immutable assets.
-- The required production text encoder is the exact body-only `AutoModel` path.
-  `model/r10` is resolved: only the Z-Image test's fp32 absolute tolerance is
-  `2e-4`; relative tolerance, shared Qwen math, bf16, and production loading are unchanged.
-- I012 is closed by the exact-head 34/34 non-skip GB200 run at `f310aacb1`.
-- Native pipeline/config/preset/registry/example, parity tests, and the 8-step
-  1024x1024 PNG SSIM test are implemented. Native RTX 5090 one-step parity is
-  bit-exact. The reviewed L40S PNG is live at dataset revision
-  `7b27d00004b723ef0cf4923e77ae57128573f01c` with matching remote bytes.
-- End-to-end SSIM/full-suite execution remains an infra/reviewer gate; reference
-  publication does not authorize PR approval or merge.
diff --git a/tests/local_tests/zimage/README.md b/tests/local_tests/zimage/README.md
deleted file mode 100644
index e7c24ff3c..000000000
--- a/tests/local_tests/zimage/README.md
+++ /dev/null
@@ -1,200 +0,0 @@
-# Z-Image Local Tests
-
-Local component coverage and quality handoff for the `zimage` FastVideo port
-(`T2I`, Z-Image-Turbo only). The same PR is authorized to carry the components,
-FastVideo-native `ZImagePipeline`, example, pipeline parity, SSIM test, and L40S
-reference seed. The native pipeline surface and 8-step 1024x1024 PNG SSIM test
-are implemented. Native one-step GPU parity is bit-exact, and the L40S candidate
-has been published to `FastVideo/ssim-reference-videos`.
-
-> **Status:** Implementation and quality-reference seeding complete; Phase 11
-> final handoff. The
-> Phase 6 exact-head closure is complete: the full pinned component suite passed
-> 34/34 with no skips on GB200 at `f310aacb1`. The required production text
-> encoder is the official-loader-compatible Transformers `AutoModel` path, which
-> matches an independently materialized reference exactly. Scheduler, tokenizer,
-> VAE, native Qwen fp32/bf16, and the 6.15B real-weight transformer all pass.
-> Native one-step pipeline parity is bit-exact on RTX 5090. The reviewed
-> 1024x1024 L40S PNG generated at inference head `9a6dbcadc` is published at
-> dataset revision `7b27d00004b723ef0cf4923e77ae57128573f01c`. See
-> [`PORT_STATUS.md`](./PORT_STATUS.md) for the live state.
-
-## Reference assets and scope
-
-| Field | Value |
-|---|---|
-| Model family / variant | `zimage` / `Z-Image-Turbo` |
-| Workload | `T2I` |
-| PR scope | required components, native pipeline, example/parity, SSIM test, and L40S seed |
-| Required component scope | production `AutoModel` text encoder, tokenizer, VAE, scheduler, native transformer |
-| Additional quality scope | direct FastVideo-native Qwen parity (`model/r10`; not used by the pipeline loader) |
-| Official reference | `https://github.com/Tongyi-MAI/Z-Image` |
-| Local reference dir | `<repo_root>/Z-Image/src` |
-| Official commit | `26f23eda626ffadda020b04ff79488e1d72004cd` |
-| HF weights | `Tongyi-MAI/Z-Image-Turbo@f332072aa78be7aecdf3ee76d5c247082da564a6` |
-| Local weights dir | `<repo_root>/official_weights/Z-Image/` (component subfolders; transformer is 24.6 GB) |
-| Reference implementation layout | Official repo-native `src/zimage/` Python; Diffusers is not the parity oracle |
-| Weight layout | HF per-component subfolders (`text_encoder/`, `vae/`, `scheduler/`, `transformer/`) |
-| Conversion | not needed; the native and official production transformer expose the same 521 checkpoint keys/shapes |
-| HF token env | `HF_TOKEN` (the pinned repository is public; never record a token value) |
-
-The pinned text-encoder config declares `Qwen3ForCausalLM` with 36 decoder
-blocks, hidden size 2560, intermediate size 9728, 32 attention heads, 8 KV
-heads, and head dimension 128. FastVideo loads those values from
-`text_encoder/config.json`; they are not hard-coded in the Z-Image integration.
-
-## Shared environment setup
-
-Run from the FastVideo repository root in the same environment used for
-FastVideo. Keep the reference clone and weights inside the ignored paths shown
-below.
-
-Clone and pin the official reference:
-
-```bash
-python .agents/skills/add-model-01-prep/scripts/clone_reference_repo.py \
-  https://github.com/Tongyi-MAI/Z-Image.git \
-  Z-Image \
-  --commit 26f23eda626ffadda020b04ff79488e1d72004cd \
-  --update-gitignore
-git -C Z-Image rev-parse HEAD
-```
-
-The final command must print
-`26f23eda626ffadda020b04ff79488e1d72004cd`. The scheduler and VAE tests also
-enforce that exact HEAD and verify that imported `zimage` modules resolve under
-`<repo_root>/Z-Image/src`. A missing clone may skip local parity; a wrong SHA or
-wrong import origin fails rather than silently testing another installation.
-
-Download the immutable HF snapshot:
-
-```bash
-python .agents/skills/add-model-01-prep/scripts/download_hf_weights.py \
-  Tongyi-MAI/Z-Image-Turbo \
-  official_weights/Z-Image \
-  --revision f332072aa78be7aecdf3ee76d5c247082da564a6 \
-  --allow-pattern 'text_encoder/*' \
-  --allow-pattern 'tokenizer/*' \
-  --allow-pattern 'vae/*' \
-  --allow-pattern 'scheduler/*'
-```
-
-The real-weight transformer test additionally needs `transformer/*` from the
-same pinned revision (24.6 GB). Do not download it without approving that cost;
-point `ZIMAGE_TRANSFORMER_DIR` at an existing pinned transformer subfolder.
-
-Do not change core dependency versions (`torch`, `diffusers`, `transformers`,
-`flash-attn`, `triton`, or CUDA packages) without explicit approval.
-
-```text
-dependency_changes: none
-official_env_status: official source pinned and imported; GB200 component validation run completed
-private_dep_stubs: none
-blocked_on: repo-wide full-suite/SSIM launch gate and PR review; model implementation work is complete
-```
-
-## Run the tests
-
-```bash
-pytest tests/local_tests/zimage/ -v -s
-```
-
-| Component | Coverage scope and contract | Status |
-|---|---|---|
-| Scheduler ([`test_zimage_scheduler_parity.py`](./test_zimage_scheduler_parity.py)) | `implementation_subcomponent`; exact official `sigma_min=0.0` plus `use_reference_discrete_timesteps=True`; positional/default-path regressions remain asset-free | PINNED GB200 PASS (5 passed) |
-| Tokenizer ([`test_zimage_tokenizer_parity.py`](./test_zimage_tokenizer_parity.py)) | `production_loader`; exact `apply_chat_template(tokenize=False, add_generation_prompt=True, enable_thinking=True)` and `max_length=512`; absence of `apply_chat_template` is a failure, not a skip | PINNED GB200 PASS (2 passed) |
-| VAE ([`test_zimage_vae_parity.py`](./test_zimage_vae_parity.py)) | `both`; direct raw decode plus production `VAELoader`, config values, and raw decode | PINNED GB200 PASS (2 passed) |
-| Production text encoder ([`test_zimage_encoder_parity.py`](./test_zimage_encoder_parity.py)) | `production_loader`; `TextEncoderLoader` materializes a body-only Transformers `AutoModel` matching official `src/utils/loader.py`; required by the pipeline | PINNED GB200 PASS (reference/production outputs exact) |
-| Native Qwen quality ([`test_zimage_encoder_parity.py`](./test_zimage_encoder_parity.py)) | `implementation_subcomponent`; direct FastVideo-native construction, strict loading, and fp32/bf16 diagnostics; not loaded by the pipeline | PINNED GB200 PASS; fp32 `atol=2e-4`, `rtol=1e-4`; bf16 thresholds unchanged |
-| Transformer ([`test_zimage_transformer_parity.py`](./test_zimage_transformer_parity.py)) | `both`; pinned production meta key/shape surface, additive-mask attention parity, deterministic tiny CPU full-forward parity, explicit `sp_size=1` guard, and real-weight production-loader/full-forward parity | PINNED GB200 PASS (5 passed; real-weight full forward exact) |
-
-## Pinned official pipeline context
-
-| Field | Contract |
-|---|---|
-| Oracle | `Tongyi-MAI/Z-Image@26f23eda626ffadda020b04ff79488e1d72004cd`, `src/zimage/pipeline.py::generate` and `src/config/inference.py`; never Diffusers |
-| Pipeline/modules | `_class_name=ZImagePipeline`; `transformer`, `vae`, `text_encoder`, `tokenizer`, `scheduler` |
-| Inputs/outputs | text-to-image prompt with optional negative prompt; PIL images by default or latents when requested |
-| Official defaults | `1024x1024`, `num_inference_steps=8`, `guidance_scale=0.0`, `cfg_truncation=1.0`, `max_sequence_length=512` |
-| Reproducible RNG | `torch.Generator("cuda").manual_seed(42)`; `generate()` accepts the generator rather than fixing a seed internally |
-| Scheduler | `use_reference_discrete_timesteps=True`, runtime `sigma_min=0.0`, and the endpoint-preserving `num_steps + 1` schedule with the final zero-timestep DiT call skipped |
-| Decode | apply `(latents / 0.3611) + 0.1159` before official VAE decode |
-| FastVideo status | Native pipeline/config/preset/registry/example and PNG SSIM test are implemented. One-step RTX 5090 parity is bit-exact; the reviewed L40S PNG generated at `9a6dbcadc` is published at dataset revision `7b27d000...f01c`. Later PR changes affect only tests/docs. |
-
-## Pipeline and quality verification
-
-| Scope | Command | Status |
-|---|---|---|
-| Hardware-free pipeline surface | `pytest tests/local_tests/pipelines/test_zimage_pipeline_smoke.py::test_zimage_typed_surface_preflight -v` | PASS |
-| Hardware-free native stage contract | `pytest tests/local_tests/pipelines/test_zimage_pipeline_parity.py::test_zimage_native_default_stage_math -v` | PASS |
-| Pinned native full GPU pipeline parity | `ZIMAGE_REFERENCE_REPO=<pinned-clone> FASTVIDEO_TEST_ZIMAGE_MODEL_DIR=<pinned-weights> DISABLE_SP=1 pytest tests/local_tests/pipelines/test_zimage_pipeline_parity.py::test_zimage_pipeline_latents_match_pinned_native_repo -v -s` | PASS on RTX 5090; one-step latents bit-exact (`max=0`, `mean=0`) |
-| 8-step 1024x1024 PNG SSIM | `pytest fastvideo/tests/ssim/test_zimage_similarity.py -v -s` | Reviewed L40S PNG uploaded; remote SHA-256 `6c2be648...f7a6`; end-to-end SSIM CI pending |
-
-## Component contracts
-
-### Text encoder and tokenizer
-
-- The production `TextEncoderLoader` path deliberately loads Transformers
-  `AutoModel`, even when the checkpoint advertises `Qwen3ForCausalLM`. FastVideo
-  consumes hidden states only, so the production path is body-only and does not
-  materialize or execute an LM head.
-- The parity oracle is the pinned official Z-Image source and pipeline contract.
-  A separate Transformers `AutoModel` instance materializes the official
-  loader behavior independently; it is not the object returned by FastVideo's
-  production loader. The native `Qwen3ForCausalLM` implementation is compared
-  separately.
-- CPU/MPS text-encoder offload remains on the requested device. The loader records
-  `_fastvideo_input_device`, and `TextEncodingStage` sends token tensors there.
-- Native loading must account for every destination parameter and every required
-  fused source shard. Q/K/V and gate/up shards are all required unless the exact
-  destination is supplied already fused; quantized auxiliary parameters such as
-  `scale_weight` are not silently ignored.
-- The pinned official prompt path enables thinking, tokenizes to 512, and consumes
-  `hidden_states[-2]` at valid-token positions. A 36-block Qwen model exposes 37
-  hidden-state entries in this contract (embedding/intermediate entries plus the
-  final normalized state).
-- `TextEncoderConfig.chat_template_enable_thinking` is keyword-only and defaults
-  to `False` for existing model families. The Z-Image pipeline config opts in
-  with `True`.
-
-### VAE
-
-The test covers both direct implementation parity and production `VAELoader`
-resolution/strict loading, confirms the official config's scaling/shift values,
-and compares raw decode outputs. The official pipeline's
-`(latents / 0.3611) + 0.1159` transformation is pipeline behavior, so it remains
-a Phase 7 Z-Image pipeline-stage and pipeline-parity requirement. This port
-reuses the
-existing shared `fastvideo.models.vaes.autoencoder_kl.AutoencoderKL` wrapper,
-which subclasses Diffusers `AutoencoderKL`, as a narrowly scoped exception to
-the native-component boundary. That runtime inheritance does not make Diffusers
-the reference implementation: parity is against pinned
-`Tongyi-MAI/Z-Image/src/zimage/autoencoder.py`. This is not precedent for the
-transformer or pipeline implementation.
-
-### Scheduler
-
-The pinned official pipeline mutates `scheduler.sigma_min = 0.0` before building
-its `num_steps + 1` schedule. The Z-Image pipeline applies both
-`use_reference_discrete_timesteps=True` and `sigma_min=0.0`; the published
-`scheduler_config.json` alone does not encode the complete runtime contract.
-
-### Transformer
-
-- The FastVideo-native transformer preserves all 521 published checkpoint keys.
-  The sorted key digest from the pinned HF safetensors index is
-  `3a9216f208c1873b2cf06394411a53e1e95e10fae3b01dca0f7223556e47c354`.
-- Tiny CPU parity uses the pinned official class, identical deterministic state,
-  variable-length image/text batches, and concrete full-forward output checks.
-- Attention uses raw torch SDPA because the padded variable-length stream needs
-  a key mask not exposed by FastVideo's distributed wrappers. Runtime rejects
-  initialized sequence-parallel world sizes above one.
-- The production-loader/full-forward test runs when the pinned transformer
-  subfolder and CUDA are available. The pinned GB200 run strict-loaded the
-  6.15B model and matched the official full-forward output exactly.
-
-## Remaining work
-
-- Run the end-to-end SSIM/full suite after the external-fork documentation
-  approval gate permits the repo-wide launcher.
-- Keep PR approval and merge separate from the completed reference upload.
diff --git a/tests/local_tests/zimage/__init__.py b/tests/local_tests/zimage/__init__.py
deleted file mode 100644
index 988131360..000000000
--- a/tests/local_tests/zimage/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
diff --git a/tests/local_tests/zimage/test_zimage_encoder_parity.py b/tests/local_tests/zimage/test_zimage_encoder_parity.py
deleted file mode 100644
index caf04902f..000000000
--- a/tests/local_tests/zimage/test_zimage_encoder_parity.py
+++ /dev/null
@@ -1,716 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-Parity test for Z-Image Qwen3 text encoder support in FastVideo.
-
-This compares:
-1) direct transformers AutoModel output, and
-2) FastVideo's production ``TextEncoderLoader`` passthrough, and
-3) FastVideo's shared native Qwen3 encoder (``Qwen3ForCausalLM``, added for Flux2
-   Klein and reused here — Z-Image-Turbo's ``Qwen3Model`` checkpoint routes
-   to it via the model registry),
-
-using identical local checkpoint, tokenization, and inputs.
-
-Usage:
-    pytest tests/local_tests/zimage/test_zimage_encoder_parity.py -v -s
-"""
-
-from __future__ import annotations
-
-from pathlib import Path
-from types import SimpleNamespace
-import gc
-import json
-import os
-
-import pytest
-import torch
-
-
-def _reclaim_vram() -> None:
-    """Actually reclaim VRAM after the caller has ``del``'d its model refs.
-
-    HF transformer models hold reference cycles, so ``del`` alone does not free
-    them — a ``gc.collect()`` is required before the CUDA caching allocator will
-    release the blocks. Without this the parity test keeps two full encoder
-    copies resident and OOMs on a 44 GB GPU.
-    """
-    gc.collect()
-    if torch.cuda.is_available():
-        torch.cuda.empty_cache()
-
-
-def _gpu_mem_mb() -> float:
-    if torch.cuda.is_available():
-        return torch.cuda.memory_allocated() / (1024**2)
-    return 0.0
-
-
-from torch.testing import assert_close
-from transformers import AutoModel, AutoModelForCausalLM, AutoTokenizer
-from safetensors.torch import safe_open
-
-from fastvideo.configs.models.encoders.qwen3 import Qwen3TextConfig
-from fastvideo.distributed.parallel_state import cleanup_dist_env_and_memory, maybe_init_distributed_environment_and_model_parallel
-from fastvideo.layers.quantization.absmax_fp8 import AbsMaxFP8MergedParameter
-from fastvideo.models.encoders.qwen3 import Qwen3ForCausalLM
-from fastvideo.models.loader.component_loader import TextEncoderLoader
-from fastvideo.models.loader.utils import set_default_torch_dtype
-from fastvideo.models.registry import ModelRegistry
-
-PARITY_SCOPE = "both"
-
-# Strict-load contract: the shared Qwen3 encoder is body-only (embed_tokens +
-# layers + norm, no lm_head). Z-Image-Turbo ships a full Qwen3 checkpoint, so
-# `lm_head.weight` is the only key that may go unmatched; anything else means a
-# real silent drop. Enforced here in the test since the shared encoder's loader
-# is intentionally lenient (it's used by multiple models).
-_ALLOWED_UNEXPECTED_KEYS = {"lm_head.weight"}
-
-REPO_ROOT = Path(__file__).resolve().parents[3]
-ZIMAGE_TEXT_ENCODER_DIR = REPO_ROOT / "official_weights" / "Z-Image" / "text_encoder"
-ZIMAGE_TOKENIZER_DIR = REPO_ROOT / "official_weights" / "Z-Image" / "tokenizer"
-
-
-@pytest.fixture(scope="module", autouse=True)
-def _init_dist_and_tp_groups():
-    os.environ.setdefault("MASTER_ADDR", "localhost")
-    os.environ.setdefault("MASTER_PORT", "29531")
-    os.environ.setdefault("RANK", "0")
-    os.environ.setdefault("WORLD_SIZE", "1")
-    os.environ.setdefault("LOCAL_RANK", "0")
-
-    maybe_init_distributed_environment_and_model_parallel(1, 1)
-    yield
-    cleanup_dist_env_and_memory()
-
-
-def _load_json(path: Path) -> dict:
-    with path.open("r", encoding="utf-8") as f:
-        return json.load(f)
-
-
-def _iter_safetensors(path: Path):
-    with safe_open(str(path), framework="pt", device="cpu") as f:
-        for k in f.keys():
-            yield k, f.get_tensor(k)
-
-
-def _iter_pretrained_safetensors(model_dir: Path):
-    single = model_dir / "model.safetensors"
-    if single.exists():
-        yield from _iter_safetensors(single)
-        return
-
-    index = model_dir / "model.safetensors.index.json"
-    if index.exists():
-        idx = _load_json(index)
-        shard_names = sorted(set(idx["weight_map"].values()))
-        for shard in shard_names:
-            yield from _iter_safetensors(model_dir / shard)
-        return
-
-    raise FileNotFoundError(
-        f"Missing safetensors checkpoint in {model_dir} (expected model.safetensors or model.safetensors.index.json)")
-
-
-def _pretrained_safetensor_keys(model_dir: Path) -> set[str]:
-    return {name for name, _ in _iter_pretrained_safetensors(model_dir)}
-
-
-def _load_qwen3_config() -> Qwen3TextConfig:
-    cfg_raw = _load_json(ZIMAGE_TEXT_ENCODER_DIR / "config.json")
-    for key in ("_name_or_path", "transformers_version", "model_type", "torch_dtype"):
-        cfg_raw.pop(key, None)
-    config = Qwen3TextConfig()
-    config.update_model_arch(cfg_raw)
-    assert config.num_hidden_layers == 36
-    assert config.hidden_size == 2560
-    assert config.intermediate_size == 9728
-    assert config.num_attention_heads == 32
-    assert config.num_key_value_heads == 8
-    return config
-
-
-def _loader_args(cpu_offload: bool) -> SimpleNamespace:
-    return SimpleNamespace(
-        text_encoder_cpu_offload=cpu_offload,
-        override_text_encoder_quant=None,
-        override_text_encoder_safetensors=None,
-        pin_cpu_memory=False,
-        disable_offload_on_unified_memory=lambda device_id, offload_flag=None: False,
-    )
-
-
-def _precision_name(dtype: torch.dtype) -> str:
-    if dtype == torch.float32:
-        return "fp32"
-    if dtype == torch.bfloat16:
-        return "bf16"
-    raise ValueError(f"Unsupported parity dtype: {dtype}")
-
-
-def _tokenize_official_prompts(tokenizer, prompts: list[str]):
-    formatted = [
-        tokenizer.apply_chat_template(
-            [{
-                "role": "user",
-                "content": prompt
-            }],
-            tokenize=False,
-            add_generation_prompt=True,
-            enable_thinking=True,
-        ) for prompt in prompts
-    ]
-    return tokenizer(
-        formatted,
-        padding="max_length",
-        truncation=True,
-        max_length=512,
-        return_tensors="pt",
-    )
-
-
-def _assert_native_load_surface(
-    model,
-    checkpoint_keys: set[str],
-    loaded_params: set[str],
-) -> None:
-    param_names = {name for name, _ in model.named_parameters()}
-    missing = param_names - loaded_params
-    assert not missing, f"FastVideo parameters not loaded: {sorted(missing)}"
-
-    normalized_keys = {name[len("model."):] if name.startswith("model.") else name for name in checkpoint_keys}
-    mapped_keys: set[str] = set()
-    for checkpoint_name in normalized_keys:
-        for param_name, weight_name, _ in model.config.arch_config.stacked_params_mapping:
-            if weight_name in checkpoint_name:
-                mapped_keys.add(checkpoint_name.replace(weight_name, param_name))
-                break
-        else:
-            mapped_keys.add(checkpoint_name)
-
-    unexpected = mapped_keys - param_names - {
-        "rotary_emb.inv_freq",
-        "rotary_emb.cos_cached",
-        "rotary_emb.sin_cached",
-    }
-    assert unexpected <= _ALLOWED_UNEXPECTED_KEYS, ("Unexpected checkpoint keys not in allowlist: "
-                                                    f"{sorted(unexpected - _ALLOWED_UNEXPECTED_KEYS)}")
-
-
-def _fake_qwen3_weight_target(include_quant_scales: bool = False):
-    stacked = Qwen3TextConfig().arch_config.stacked_params_mapping
-    params = {
-        "layers.0.self_attn.qkv_proj.weight": torch.nn.Parameter(torch.empty(1)),
-        "layers.0.mlp.gate_up_proj.weight": torch.nn.Parameter(torch.empty(1)),
-    }
-    if include_quant_scales:
-        qkv_scale = AbsMaxFP8MergedParameter(torch.zeros(3), requires_grad=False)
-        qkv_scale.output_partition_sizes = [1, 1, 1]
-        gate_up_scale = AbsMaxFP8MergedParameter(torch.zeros(2), requires_grad=False)
-        gate_up_scale.output_partition_sizes = [1, 1]
-        params.update({
-            "layers.0.self_attn.qkv_proj.scale_weight": qkv_scale,
-            "layers.0.mlp.gate_up_proj.scale_weight": gate_up_scale,
-        })
-    for param in params.values():
-        if not hasattr(param, "weight_loader"):
-            param.weight_loader = (lambda target, loaded, *args: target.data.copy_(loaded))
-
-    model = SimpleNamespace(
-        config=SimpleNamespace(arch_config=SimpleNamespace(stacked_params_mapping=stacked)),
-        named_parameters=lambda: params.items(),
-    )
-    return model, params, stacked
-
-
-# The provisional bf16 thresholds below come from the historical unpinned
-# 35-layer run. Per add-model-02-parity's calibration block, bf16 max-based
-# asserts are meaningless for deep encoders — fused-vs-unfused linear order
-# alone produces a long tail, so the test uses mean + median instead. The
-# pinned snapshot has 36 blocks and must be rerun before these thresholds are
-# treated as current evidence.
-#
-# Historical empirical numbers (unrecorded snapshot, 35 layers, CUDA):
-#   last_hidden_state (post-norm):  mean=0.0152, median=0.0117
-#   hidden_states[-2] (pre-norm):   mean=0.0754, median=0.0625
-# Thresholds below add ~1.6x headroom over observed.
-_BF16_MEAN_DRIFT_POST_NORM = 0.025
-_BF16_MEAN_DRIFT_PRE_NORM = 0.120
-_BF16_MEDIAN_DRIFT_POST_NORM = 0.020
-_BF16_MEDIAN_DRIFT_PRE_NORM = 0.100
-_FP32_ATOL = 2e-4
-_FP32_RTOL = 1e-4
-
-
-def _print_diag(label: str, ref: torch.Tensor, fv: torch.Tensor) -> torch.Tensor:
-    diff = (ref.float() - fv.float()).abs()
-    flat = diff.flatten()
-    p99 = flat.kthvalue(max(1, int(0.99 * flat.numel()))).values
-    print(
-        f"[{label}] max_diff={diff.max():.4f}  mean_diff={diff.mean():.4f}  "
-        f"median_diff={diff.median():.4f}  p99_diff={p99:.4f}",
-        flush=True,
-    )
-    return diff
-
-
-@pytest.mark.parametrize(
-    ("cpu_offload", "target_device", "expected_device"),
-    [
-        (True, torch.device("cuda"), torch.device("cpu")),
-        (False, torch.device("meta"), torch.device("meta")),
-    ],
-)
-def test_qwen3_production_loader_avoids_device_context_and_honors_placement(
-    monkeypatch: pytest.MonkeyPatch,
-    tmp_path: Path,
-    cpu_offload: bool,
-    target_device: torch.device,
-    expected_device: torch.device,
-):
-    """HF loading stays ambient while final placement honors target/offload."""
-    import fastvideo.platforms as platforms
-
-    placements: list[torch.device] = []
-    auto_model_calls: list[dict] = []
-
-    class FakeHFModel:
-
-        def eval(self):
-            return self
-
-        def to(self, device: torch.device):
-            placements.append(torch.device(device))
-            return self
-
-    fake_model = FakeHFModel()
-
-    ambient_device = torch.empty(0).device
-    default_devices: list[torch.device] = []
-
-    def fake_auto_model_from_pretrained(*args, **kwargs):
-        auto_model_calls.append(kwargs)
-        default_devices.append(torch.empty(0).device)
-        return fake_model
-
-    def fail_causal_lm_from_pretrained(*args, **kwargs):
-        raise AssertionError("Qwen text encoding must not load AutoModelForCausalLM")
-
-    monkeypatch.setattr(
-        AutoModel,
-        "from_pretrained",
-        staticmethod(fake_auto_model_from_pretrained),
-    )
-    monkeypatch.setattr(
-        AutoModelForCausalLM,
-        "from_pretrained",
-        staticmethod(fail_causal_lm_from_pretrained),
-    )
-    monkeypatch.setattr(
-        platforms,
-        "_current_platform",
-        SimpleNamespace(
-            is_mps=lambda: False,
-            verify_model_arch=lambda _arch: None,
-        ),
-    )
-    monkeypatch.setattr(torch.cuda, "is_available", lambda: True)
-    monkeypatch.setattr("fastvideo.models.loader.component_loader.get_local_torch_device",
-                        lambda: torch.device("cuda"))
-
-    config = Qwen3TextConfig()
-    # The pinned checkpoint names the causal wrapper even though the official
-    # Z-Image loader intentionally asks Transformers for the body-only model.
-    config.arch_config.architectures = ["Qwen3ForCausalLM"]
-    loaded = TextEncoderLoader().load_model(
-        str(tmp_path),
-        config,
-        target_device,
-        _loader_args(cpu_offload=cpu_offload),
-        dtype="fp32",
-        use_text_encoder_override=True,
-    )
-
-    assert loaded is fake_model
-    assert auto_model_calls == [{
-        "local_files_only": True,
-        "dtype": torch.float32,
-        "low_cpu_mem_usage": True,
-    }]
-    assert default_devices == [ambient_device]
-    assert placements == [expected_device]
-    assert loaded._fastvideo_input_device == expected_device
-
-
-def test_set_default_torch_dtype_restores_after_exception():
-    original = torch.get_default_dtype()
-
-    with pytest.raises(RuntimeError, match="expected failure"):
-        with set_default_torch_dtype(torch.bfloat16):
-            raise RuntimeError("expected failure")
-
-    assert torch.get_default_dtype() == original
-
-
-@pytest.mark.parametrize("checkpoint_prefix", ["", "model."])
-def test_qwen3_native_load_accepts_already_fused_weights_and_scales(checkpoint_prefix: str, ):
-    """Direct FastVideo/state-dict keys satisfy each fused target atomically."""
-    fake_model, params, _ = _fake_qwen3_weight_target(include_quant_scales=True)
-    expected = {name: torch.arange(1, param.numel() + 1, dtype=param.dtype) for name, param in params.items()}
-    weights = [(f"{checkpoint_prefix}{name}", expected[name]) for name in params]
-
-    loaded = Qwen3ForCausalLM.load_weights(fake_model, weights)
-
-    assert loaded == set(params)
-    for name, param in params.items():
-        assert torch.equal(param.data, expected[name])
-    _assert_native_load_surface(
-        fake_model,
-        {name
-         for name, _ in weights},
-        loaded,
-    )
-
-
-def test_qwen3_native_load_rejects_incompatible_fused_quant_scale_shape():
-    fake_model, params, _ = _fake_qwen3_weight_target(include_quant_scales=True)
-    weights = [(name, torch.ones_like(param)) for name, param in params.items()
-               if name != "layers.0.self_attn.qkv_proj.scale_weight"]
-    weights.append(("layers.0.self_attn.qkv_proj.scale_weight", torch.ones(1)))
-
-    with pytest.raises(AssertionError, match="Attempted to load weight"):
-        Qwen3ForCausalLM.load_weights(fake_model, weights)
-
-
-def test_qwen3_native_load_uses_real_shard_loader_for_split_quant_scales():
-    fake_model, params, stacked = _fake_qwen3_weight_target(include_quant_scales=True)
-    weights = [(name, torch.ones_like(param)) for name, param in params.items() if name.endswith(".weight")]
-    weights.extend((f"model.layers.0.self_attn{weight_name}.scale_weight", torch.tensor(float(index + 1)))
-                   for index, (_, weight_name, _) in enumerate(stacked[:3]))
-    weights.extend((f"model.layers.0.mlp{weight_name}.scale_weight", torch.tensor(float(index + 4)))
-                   for index, (_, weight_name, _) in enumerate(stacked[3:]))
-
-    loaded = Qwen3ForCausalLM.load_weights(fake_model, weights)
-
-    assert loaded == set(params)
-    assert torch.equal(
-        params["layers.0.self_attn.qkv_proj.scale_weight"].data,
-        torch.tensor([1.0, 2.0, 3.0]),
-    )
-    assert torch.equal(
-        params["layers.0.mlp.gate_up_proj.scale_weight"].data,
-        torch.tensor([4.0, 5.0]),
-    )
-
-
-@pytest.mark.parametrize(
-    ("missing_weight_name", "missing_shard_id"),
-    [
-        (".q_proj", "q"),
-        (".k_proj", "k"),
-        (".v_proj", "v"),
-        (".gate_proj", 0),
-        (".up_proj", 1),
-    ],
-)
-def test_qwen3_native_load_rejects_missing_fused_weight_shard(
-    missing_weight_name: str,
-    missing_shard_id: str | int,
-):
-    fake_model, _, stacked = _fake_qwen3_weight_target()
-    weights = [(f"model.layers.0.self_attn{weight_name}.weight", torch.empty(1)) for _, weight_name, _ in stacked[:3]
-               if weight_name != missing_weight_name]
-    weights.extend((f"model.layers.0.mlp{weight_name}.weight", torch.empty(1)) for _, weight_name, _ in stacked[3:]
-                   if weight_name != missing_weight_name)
-
-    with pytest.raises(ValueError, match="Missing required stacked checkpoint shards") as exc_info:
-        Qwen3ForCausalLM.load_weights(fake_model, weights)
-
-    destination = ("layers.0.self_attn.qkv_proj.weight"
-                   if isinstance(missing_shard_id, str) else "layers.0.mlp.gate_up_proj.weight")
-    assert f"{destination}[{missing_shard_id}]" in str(exc_info.value)
-
-
-@pytest.mark.parametrize(
-    ("missing_weight_name", "missing_shard_id"),
-    [
-        (".q_proj", "q"),
-        (".k_proj", "k"),
-        (".v_proj", "v"),
-        (".gate_proj", 0),
-        (".up_proj", 1),
-    ],
-)
-def test_qwen3_native_load_rejects_missing_fused_quant_scale_shard(
-    missing_weight_name: str,
-    missing_shard_id: str | int,
-):
-    fake_model, params, stacked = _fake_qwen3_weight_target(include_quant_scales=True)
-    weights = [(name, torch.empty(1)) for name in params if name.endswith(".weight")]
-    weights.extend((f"model.layers.0.self_attn{weight_name}.scale_weight", torch.empty(1))
-                   for _, weight_name, _ in stacked[:3] if weight_name != missing_weight_name)
-    weights.extend((f"model.layers.0.mlp{weight_name}.scale_weight", torch.empty(1))
-                   for _, weight_name, _ in stacked[3:] if weight_name != missing_weight_name)
-
-    with pytest.raises(ValueError, match="Missing required stacked checkpoint shards") as exc_info:
-        Qwen3ForCausalLM.load_weights(fake_model, weights)
-
-    destination = ("layers.0.self_attn.qkv_proj.scale_weight"
-                   if isinstance(missing_shard_id, str) else "layers.0.mlp.gate_up_proj.scale_weight")
-    assert f"{destination}[{missing_shard_id}]" in str(exc_info.value)
-
-
-@pytest.mark.skipif(not ZIMAGE_TEXT_ENCODER_DIR.exists(), reason="Z-Image text encoder checkpoint required")
-@pytest.mark.parametrize(
-    "dtype",
-    [
-        pytest.param(torch.float32, id="fp32"),
-        pytest.param(
-            torch.bfloat16,
-            id="bf16",
-            marks=pytest.mark.skipif(
-                not (torch.cuda.is_available() and torch.cuda.is_bf16_supported()),
-                reason="bf16 parity requires a bf16-capable CUDA device",
-            ),
-        ),
-    ],
-)
-def test_zimage_qwen3_encoder_parity_forward(dtype: torch.dtype):
-    if not ZIMAGE_TOKENIZER_DIR.exists():
-        pytest.skip(f"Z-Image tokenizer dir not found: {ZIMAGE_TOKENIZER_DIR}")
-
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    torch.manual_seed(11)
-
-    tokenizer = AutoTokenizer.from_pretrained(str(ZIMAGE_TOKENIZER_DIR), local_files_only=True)
-
-    prompts = [
-        "A cinematic shot of a rainy neon street at night.",
-        "A watercolor illustration of a fox in autumn leaves.",
-    ]
-    toks = _tokenize_official_prompts(tokenizer, prompts)
-    input_ids = toks["input_ids"].to(device)
-    attention_mask = toks["attention_mask"].to(device)
-    config = _load_qwen3_config()
-
-    ref = AutoModel.from_pretrained(
-        str(ZIMAGE_TEXT_ENCODER_DIR),
-        local_files_only=True,
-        trust_remote_code=True,
-        dtype=dtype,
-        low_cpu_mem_usage=True,
-    ).eval().to(device=device)
-
-    with torch.no_grad():
-        ref_out = ref(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            output_hidden_states=True,
-            return_dict=True,
-        )
-        ref_last = ref_out.last_hidden_state.detach().float().cpu()
-        assert ref_out.hidden_states is not None
-        ref_hs_m2 = ref_out.hidden_states[-2].detach().float().cpu()
-
-    print(f"[mem] after HF ref forward: {_gpu_mem_mb():.0f} MiB", flush=True)
-    # Keep only CPU outputs so each full encoder is resident one at a time.
-    del ref, ref_out
-    _reclaim_vram()
-    print(f"[mem] after freeing HF ref:  {_gpu_mem_mb():.0f} MiB", flush=True)
-
-    production = TextEncoderLoader().load_model(
-        str(ZIMAGE_TEXT_ENCODER_DIR),
-        config,
-        device,
-        _loader_args(cpu_offload=False),
-        dtype=_precision_name(dtype),
-        use_text_encoder_override=True,
-    )
-    assert production.__class__.__name__ == "Qwen3Model"
-    assert not hasattr(production, "lm_head")
-    assert production._fastvideo_input_device == device
-
-    with torch.no_grad():
-        production_out = production(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            output_hidden_states=True,
-            return_dict=True,
-        )
-        production_last = production_out.last_hidden_state.detach().float().cpu()
-        assert production_out.hidden_states is not None
-        production_hs_m2 = production_out.hidden_states[-2].detach().float().cpu()
-
-    # Production loading must preserve the independent Transformers oracle
-    # exactly; native-kernel drift is assessed separately below.
-    assert_close(ref_last, production_last, atol=0.0, rtol=0.0)
-    assert_close(ref_hs_m2, production_hs_m2, atol=0.0, rtol=0.0)
-    del production, production_out, production_last, production_hs_m2
-    _reclaim_vram()
-
-    fv_cls, _ = ModelRegistry.resolve_model_cls("Qwen3Model")
-    fv = fv_cls(config).eval()
-    loaded = fv.load_weights(_iter_pretrained_safetensors(ZIMAGE_TEXT_ENCODER_DIR))
-    assert loaded, "No Qwen3 weights were loaded into FastVideo model"
-    fv = fv.to(device=device, dtype=dtype)
-    print(f"[mem] after FastVideo load:  {_gpu_mem_mb():.0f} MiB", flush=True)
-
-    _assert_native_load_surface(
-        fv,
-        _pretrained_safetensor_keys(ZIMAGE_TEXT_ENCODER_DIR),
-        loaded,
-    )
-
-    with torch.no_grad():
-        fv_out = fv(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            output_hidden_states=True,
-        )
-        fv_last = fv_out.last_hidden_state.detach().float().cpu()
-        assert fv_out.hidden_states is not None
-        fv_hs_m2 = fv_out.hidden_states[-2].detach().float().cpu()
-
-    # Z-Image uses only valid token embeddings selected by attention mask.
-    # Compare parity on that exact path to avoid padded-token artifacts.
-    mask_cpu = attention_mask.detach().bool().cpu()
-    is_bf16 = dtype == torch.bfloat16
-
-    for i in range(mask_cpu.shape[0]):
-        valid = mask_cpu[i]
-        ref_last_v = ref_last[i][valid]
-        fv_last_v = fv_last[i][valid]
-        ref_hs_m2_v = ref_hs_m2[i][valid]
-        fv_hs_m2_v = fv_hs_m2[i][valid]
-
-        last_diff = _print_diag(f"batch{i} last_hidden_state {dtype}", ref_last_v, fv_last_v)
-        hs_m2_diff = _print_diag(f"batch{i} hidden_states[-2] {dtype}", ref_hs_m2_v, fv_hs_m2_v)
-
-        if is_bf16:
-            # Distribution checks instead of element-wise assert_close: cross-
-            # kernel bf16 produces a long max tail (fused vs unfused linears),
-            # but a real op bug pushes mean *and* median high. Mean catches
-            # systematic drift; median catches "wrong weights / swapped
-            # layers" where most elements diverge. Pre-norm tensors get a
-            # looser bound because the final RMSNorm compresses drift ~5x.
-            assert last_diff.mean().item() < _BF16_MEAN_DRIFT_POST_NORM, (
-                f"batch{i} last_hidden_state bf16 mean drift "
-                f"{last_diff.mean():.4f} >= {_BF16_MEAN_DRIFT_POST_NORM}")
-            assert last_diff.median().item() < _BF16_MEDIAN_DRIFT_POST_NORM, (
-                f"batch{i} last_hidden_state bf16 median drift "
-                f"{last_diff.median():.4f} >= {_BF16_MEDIAN_DRIFT_POST_NORM}")
-            assert hs_m2_diff.mean().item() < _BF16_MEAN_DRIFT_PRE_NORM, (
-                f"batch{i} hidden_states[-2] bf16 mean drift "
-                f"{hs_m2_diff.mean():.4f} >= {_BF16_MEAN_DRIFT_PRE_NORM}")
-            assert hs_m2_diff.median().item() < _BF16_MEDIAN_DRIFT_PRE_NORM, (
-                f"batch{i} hidden_states[-2] bf16 median drift "
-                f"{hs_m2_diff.median():.4f} >= {_BF16_MEDIAN_DRIFT_PRE_NORM}")
-        else:
-            assert_close(ref_last_v, fv_last_v, atol=_FP32_ATOL, rtol=_FP32_RTOL)
-            assert_close(ref_hs_m2_v, fv_hs_m2_v, atol=_FP32_ATOL, rtol=_FP32_RTOL)
-
-
-@pytest.mark.skipif(not ZIMAGE_TEXT_ENCODER_DIR.exists(), reason="Z-Image text encoder checkpoint required")
-@pytest.mark.skipif(
-    not (torch.cuda.is_available() and torch.cuda.is_bf16_supported()),
-    reason="bf16 per-layer diagnostic requires a bf16-capable CUDA device",
-)
-def test_zimage_qwen3_encoder_per_layer_bf16_diagnostic():
-    """Diagnostic-only: prints per-layer FastVideo-vs-HF drift for the bf16
-    forward to distinguish monotonic accumulation (bf16 tail) from a
-    single-layer spike (real op mismatch).
-
-    This test always passes; review the captured stdout to interpret.
-    Expected pattern for healthy bf16 accumulation: max/mean/median grow
-    smoothly with layer depth. Suspect pattern: one layer's diff is
-    >>2x the previous, suggesting a real bug at that block.
-    """
-    if not ZIMAGE_TOKENIZER_DIR.exists():
-        pytest.skip(f"Z-Image tokenizer dir not found: {ZIMAGE_TOKENIZER_DIR}")
-
-    device = torch.device("cuda")
-    dtype = torch.bfloat16
-    torch.manual_seed(11)
-
-    tokenizer = AutoTokenizer.from_pretrained(str(ZIMAGE_TOKENIZER_DIR), local_files_only=True)
-    toks = _tokenize_official_prompts(
-        tokenizer,
-        ["A cinematic shot of a rainy neon street at night."],
-    )
-    input_ids = toks["input_ids"].to(device)
-    attention_mask = toks["attention_mask"].to(device)
-
-    ref = AutoModel.from_pretrained(
-        str(ZIMAGE_TEXT_ENCODER_DIR),
-        local_files_only=True,
-        trust_remote_code=True,
-        dtype=dtype,
-        low_cpu_mem_usage=True,
-    ).eval().to(device=device)
-
-    with torch.no_grad():
-        ref_out = ref(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            output_hidden_states=True,
-            return_dict=True,
-        )
-        # Cache the reference hidden states on CPU, then free the HF model so
-        # only one encoder is resident when FastVideo runs (avoids OOM).
-        ref_hidden_cpu = [h.detach().float().cpu() for h in ref_out.hidden_states]
-    del ref, ref_out
-    _reclaim_vram()
-
-    fv_cls, _ = ModelRegistry.resolve_model_cls("Qwen3Model")
-    cfg_raw = _load_json(ZIMAGE_TEXT_ENCODER_DIR / "config.json")
-    for k in ("_name_or_path", "transformers_version", "model_type", "torch_dtype"):
-        cfg_raw.pop(k, None)
-    cfg = Qwen3TextConfig()
-    cfg.update_model_arch(cfg_raw)
-    fv = fv_cls(cfg).eval()
-    fv.load_weights(_iter_pretrained_safetensors(ZIMAGE_TEXT_ENCODER_DIR))
-    fv = fv.to(device=device, dtype=dtype)
-
-    with torch.no_grad():
-        fv_out = fv(
-            input_ids=input_ids,
-            attention_mask=attention_mask,
-            output_hidden_states=True,
-        )
-        fv_hidden_cpu = [h.detach().float().cpu() for h in fv_out.hidden_states]
-
-    assert fv_out.hidden_states is not None
-    assert len(ref_hidden_cpu) == 37, (f"expected embeddings plus 36 block outputs, got {len(ref_hidden_cpu)} entries")
-    assert len(ref_hidden_cpu) == len(fv_hidden_cpu), (
-        f"len mismatch: HF={len(ref_hidden_cpu)} FV={len(fv_hidden_cpu)}")
-
-    mask_cpu = attention_mask.detach().bool().cpu()[0]
-    print(
-        f"\n[per-layer bf16 diagnostic] num_hidden_states={len(ref_hidden_cpu)}  "
-        f"valid_tokens={mask_cpu.sum().item()}/{mask_cpu.numel()}",
-        flush=True,
-    )
-    print(
-        f"{'idx':>3}  {'kind':<10}  {'max':>8}  {'mean':>8}  {'median':>8}  {'p99':>8}",
-        flush=True,
-    )
-    for idx, (ref_h, fv_h) in enumerate(zip(ref_hidden_cpu, fv_hidden_cpu)):
-        ref_v = ref_h[0][mask_cpu]
-        fv_v = fv_h[0][mask_cpu]
-        diff = (ref_v - fv_v).abs()
-        flat = diff.flatten()
-        p99 = torch.quantile(flat, 0.99).item()
-        if idx == 0:
-            kind = "embedding"
-        elif idx == len(ref_hidden_cpu) - 1:
-            kind = "post-norm"
-        else:
-            kind = f"layer{idx - 1}-out"
-        print(
-            f"{idx:>3}  {kind:<10}  "
-            f"{diff.max().item():>8.4f}  {diff.mean().item():>8.4f}  "
-            f"{diff.median().item():>8.4f}  {p99:>8.4f}",
-            flush=True,
-        )
diff --git a/tests/local_tests/zimage/test_zimage_scheduler_parity.py b/tests/local_tests/zimage/test_zimage_scheduler_parity.py
deleted file mode 100644
index ef4807929..000000000
--- a/tests/local_tests/zimage/test_zimage_scheduler_parity.py
+++ /dev/null
@@ -1,224 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-Parity tests for Z-Image FlowMatchEulerDiscreteScheduler.
-
-These tests compare FastVideo's scheduler implementation against the
-reference scheduler shipped in the local Z-Image repository.
-
-Usage:
-    pytest tests/local_tests/zimage/test_zimage_scheduler_parity.py -v
-"""
-
-from __future__ import annotations
-
-import importlib
-import json
-import subprocess
-import sys
-from pathlib import Path
-
-import numpy as np
-import pytest
-import torch
-from torch.testing import assert_close
-
-from fastvideo.models.schedulers.scheduling_flow_match_euler_discrete import (
-    FlowMatchEulerDiscreteScheduler as FastVideoScheduler, )
-
-REPO_ROOT = Path(__file__).resolve().parents[3]
-ZIMAGE_REPO = REPO_ROOT / "Z-Image"
-ZIMAGE_SRC = REPO_ROOT / "Z-Image" / "src"
-ZIMAGE_SCHEDULER_CFG = (REPO_ROOT / "official_weights" / "Z-Image" / "scheduler" / "scheduler_config.json")
-ZIMAGE_REFERENCE_REVISION = "26f23eda626ffadda020b04ff79488e1d72004cd"
-PARITY_SCOPE = "implementation_subcomponent"
-
-
-def _require_pinned_reference_module(module_name: str, source_file: Path):
-    if not ZIMAGE_REPO.exists():
-        pytest.skip(f"Pinned Z-Image reference clone not found: {ZIMAGE_REPO}")
-    if not source_file.is_file():
-        pytest.fail(f"Z-Image reference clone is incomplete; missing {source_file}")
-
-    try:
-        result = subprocess.run(
-            ["git", "-C", str(ZIMAGE_REPO), "rev-parse", "HEAD"],
-            check=True,
-            capture_output=True,
-            text=True,
-        )
-    except (FileNotFoundError, subprocess.CalledProcessError) as exc:
-        pytest.fail(f"Cannot verify Z-Image reference revision: {exc}")
-
-    actual_revision = result.stdout.strip()
-    assert actual_revision == ZIMAGE_REFERENCE_REVISION, (
-        "Z-Image reference clone is not at the pinned revision: "
-        f"expected {ZIMAGE_REFERENCE_REVISION}, got {actual_revision}")
-
-    if str(ZIMAGE_SRC) not in sys.path:
-        sys.path.insert(0, str(ZIMAGE_SRC))
-    try:
-        module = importlib.import_module(module_name)
-    except Exception as exc:
-        pytest.fail(f"Cannot import pinned Z-Image module {module_name}: {exc}")
-
-    module_file = Path(module.__file__ or "").resolve()
-    assert module_file.is_relative_to(
-        ZIMAGE_SRC.resolve()), (f"{module_name} resolved outside the pinned clone: {module_file}")
-    return module
-
-
-@pytest.fixture(scope="module")
-def reference_scheduler_cls():
-    module = _require_pinned_reference_module(
-        "zimage.scheduler",
-        ZIMAGE_SRC / "zimage" / "scheduler.py",
-    )
-    return module.FlowMatchEulerDiscreteScheduler
-
-
-@pytest.fixture(scope="module")
-def scheduler_config() -> dict:
-    if not ZIMAGE_SCHEDULER_CFG.exists():
-        pytest.skip(f"Z-Image scheduler config not found: {ZIMAGE_SCHEDULER_CFG}")
-    with ZIMAGE_SCHEDULER_CFG.open("r", encoding="utf-8") as f:
-        return json.load(f)
-
-
-# Diffusers-style scheduler-config keys we do NOT want to forward — they are
-# loader/state metadata, not constructor kwargs. Anything else in
-# scheduler_config.json (including future keys like `time_shift_type`,
-# `invert_sigmas`, etc.) must be forwarded so parity actually exercises the
-# config-on-disk and not a hand-curated subset.
-_SCHEDULER_CONFIG_LOADER_KEYS = frozenset({"_class_name", "_diffusers_version", "_name_or_path"})
-
-
-def _scheduler_kwargs_from_config(scheduler_config: dict) -> dict:
-    return {k: v for k, v in scheduler_config.items() if k not in _SCHEDULER_CONFIG_LOADER_KEYS}
-
-
-def _make_scheduler_pair(reference_scheduler_cls, scheduler_config: dict, **overrides):
-    kwargs = _scheduler_kwargs_from_config(scheduler_config)
-    kwargs.update(overrides)
-
-    ref = reference_scheduler_cls(**kwargs)
-    # The pinned Z-Image pipeline mutates this immediately before scheduling.
-    ref.sigma_min = 0.0
-
-    # These values must be serialized by the future production pipeline config.
-    # setdefault also allows this test to consume that config once it lands.
-    fv_kwargs = dict(kwargs)
-    fv_kwargs.setdefault("use_reference_discrete_timesteps", True)
-    fv_kwargs.setdefault("sigma_min", 0.0)
-    fv = FastVideoScheduler(**fv_kwargs)
-    return ref, fv
-
-
-@pytest.fixture(scope="module")
-def scheduler_pair(reference_scheduler_cls, scheduler_config: dict):
-    return _make_scheduler_pair(reference_scheduler_cls, scheduler_config)
-
-
-def _run_step_loop(ref_scheduler, fv_scheduler, sample_shape=(2, 4, 32, 32)):
-    torch.manual_seed(123)
-
-    ref_sample = torch.randn(sample_shape, dtype=torch.float32)
-    fv_sample = ref_sample.clone()
-
-    for t in ref_scheduler.timesteps:
-        model_output = torch.randn_like(ref_sample)
-
-        ref_next = ref_scheduler.step(
-            model_output,
-            t,
-            ref_sample,
-            return_dict=False,
-        )[0]
-        fv_next = fv_scheduler.step(
-            model_output,
-            t,
-            fv_sample,
-            return_dict=False,
-        )[0]
-
-        assert_close(ref_next, fv_next, atol=1e-6, rtol=1e-6)
-
-        ref_sample = ref_next
-        fv_sample = fv_next
-
-
-def test_zimage_scheduler_parity_default_schedule_and_step(scheduler_pair):
-    ref, fv = scheduler_pair
-
-    num_inference_steps = 8
-    ref.set_timesteps(num_inference_steps=num_inference_steps, device="cpu")
-    fv.set_timesteps(num_inference_steps=num_inference_steps, device="cpu")
-
-    assert_close(ref.timesteps, fv.timesteps, atol=1e-4, rtol=1e-6)
-    assert_close(ref.sigmas, fv.sigmas, atol=1e-7, rtol=1e-6)
-
-    _run_step_loop(ref, fv)
-
-
-def test_scheduler_positional_args_keep_existing_bindings_and_default_schedule():
-    scheduler = FastVideoScheduler(1000, 1.0, False, 0.7)
-
-    assert scheduler.config.base_shift == 0.7
-    assert scheduler.config.use_reference_discrete_timesteps is False
-
-    scheduler.set_timesteps(num_inference_steps=8, device="cpu")
-    assert_close(
-        scheduler.sigmas[-2],
-        torch.tensor(scheduler.sigma_min, dtype=torch.float32),
-    )
-
-
-def test_scheduler_honors_explicit_zero_sigma_min():
-    scheduler = FastVideoScheduler(
-        sigma_min=0.0,
-        use_reference_discrete_timesteps=True,
-    )
-
-    assert scheduler.sigma_min == 0.0
-    scheduler.set_timesteps(num_inference_steps=8, device="cpu")
-    assert_close(
-        scheduler.timesteps[-1],
-        torch.tensor(125.0, dtype=torch.float32),
-    )
-
-
-def test_reference_schedule_preserves_float64_linspace_rounding():
-    scheduler = FastVideoScheduler(
-        sigma_min=0.0,
-        use_reference_discrete_timesteps=True,
-    )
-    scheduler.set_timesteps(num_inference_steps=9, device="cpu")
-
-    expected = torch.from_numpy(np.linspace(1000.0, 0.0, 10)[:-1] / 1000.0, ).to(torch.float32) * 1000.0
-    float32_regression = torch.from_numpy(
-        np.linspace(1000.0, 0.0, 10, dtype=np.float32)[:-1] / np.float32(1000.0), ) * 1000.0
-
-    # The fifth timestep differs by one float32 ULP depending on where the
-    # linspace is rounded, so this assertion fails if dtype=np.float32 returns.
-    assert expected[4].item() != float32_regression[4].item()
-    assert scheduler.timesteps[4].item() == expected[4].item()
-
-
-def test_zimage_scheduler_parity_dynamic_shifting_with_mu(
-    reference_scheduler_cls,
-    scheduler_config: dict,
-):
-    ref, fv = _make_scheduler_pair(
-        reference_scheduler_cls,
-        scheduler_config,
-        use_dynamic_shifting=True,
-    )
-
-    mu = 0.75
-    num_inference_steps = 8
-    ref.set_timesteps(num_inference_steps=num_inference_steps, device="cpu", mu=mu)
-    fv.set_timesteps(num_inference_steps=num_inference_steps, device="cpu", mu=mu)
-
-    assert_close(ref.timesteps, fv.timesteps, atol=1e-4, rtol=1e-6)
-    assert_close(ref.sigmas, fv.sigmas, atol=1e-7, rtol=1e-6)
-
-    _run_step_loop(ref, fv)
diff --git a/tests/local_tests/zimage/test_zimage_tokenizer_parity.py b/tests/local_tests/zimage/test_zimage_tokenizer_parity.py
deleted file mode 100644
index 32e9160bb..000000000
--- a/tests/local_tests/zimage/test_zimage_tokenizer_parity.py
+++ /dev/null
@@ -1,127 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-Parity test for Z-Image tokenizer loading and tokenization behavior.
-
-This compares:
-1) direct transformers AutoTokenizer loading from local tokenizer dir, and
-2) FastVideo TokenizerLoader loading path,
-
-using the exact chat-template and tokenization settings from the pinned
-Z-Image pipeline.
-
-Usage:
-    pytest tests/local_tests/zimage/test_zimage_tokenizer_parity.py -v
-"""
-
-from __future__ import annotations
-
-from dataclasses import dataclass, field
-from pathlib import Path
-
-import pytest
-import torch
-from torch.testing import assert_close
-from transformers import AutoTokenizer
-
-from fastvideo.models.loader.component_loader import TokenizerLoader
-
-REPO_ROOT = Path(__file__).resolve().parents[3]
-ZIMAGE_TOKENIZER_DIR = REPO_ROOT / "official_weights" / "Z-Image" / "tokenizer"
-PARITY_SCOPE = "production_loader"
-OFFICIAL_MAX_SEQUENCE_LENGTH = 512
-OFFICIAL_CHAT_TEMPLATE_KWARGS = {
-    "tokenize": False,
-    "add_generation_prompt": True,
-    "enable_thinking": True,
-}
-
-
-@dataclass
-class _DummyPipelineConfig:
-    text_encoder_configs: tuple = field(default_factory=tuple)
-
-
-@dataclass
-class _DummyFastVideoArgs:
-    pipeline_config: _DummyPipelineConfig = field(default_factory=_DummyPipelineConfig)
-
-
-def _load_reference_tokenizer():
-    if not ZIMAGE_TOKENIZER_DIR.exists():
-        pytest.skip(f"Z-Image tokenizer dir not found: {ZIMAGE_TOKENIZER_DIR}")
-    return AutoTokenizer.from_pretrained(str(ZIMAGE_TOKENIZER_DIR), local_files_only=True)
-
-
-def _load_fastvideo_tokenizer():
-    if not ZIMAGE_TOKENIZER_DIR.exists():
-        pytest.skip(f"Z-Image tokenizer dir not found: {ZIMAGE_TOKENIZER_DIR}")
-    loader = TokenizerLoader()
-    return loader.load(str(ZIMAGE_TOKENIZER_DIR), _DummyFastVideoArgs())
-
-
-def test_zimage_tokenizer_loader_and_tokenization_parity():
-    ref_tok = _load_reference_tokenizer()
-    fv_tok = _load_fastvideo_tokenizer()
-
-    prompts = [
-        "A close-up portrait of a cat in warm studio lighting.",
-        "An oil painting of a lighthouse at sunset.",
-    ]
-
-    tok_kwargs = {
-        "padding": "max_length",
-        "max_length": OFFICIAL_MAX_SEQUENCE_LENGTH,
-        "truncation": True,
-        "return_tensors": "pt",
-    }
-
-    ref_out = ref_tok(prompts, **tok_kwargs)
-    fv_out = fv_tok(prompts, **tok_kwargs)
-
-    assert_close(ref_out["input_ids"], fv_out["input_ids"], atol=0, rtol=0)
-    assert_close(ref_out["attention_mask"], fv_out["attention_mask"], atol=0, rtol=0)
-
-    # Compare key tokenizer attributes that affect generation-time behavior.
-    assert ref_tok.padding_side == fv_tok.padding_side
-    assert ref_tok.pad_token_id == fv_tok.pad_token_id
-    assert ref_tok.eos_token_id == fv_tok.eos_token_id
-
-
-@pytest.mark.skipif(not ZIMAGE_TOKENIZER_DIR.exists(), reason="Z-Image tokenizer assets are required")
-def test_zimage_tokenizer_chat_template_parity():
-    ref_tok = _load_reference_tokenizer()
-    fv_tok = _load_fastvideo_tokenizer()
-
-    assert callable(getattr(ref_tok, "apply_chat_template",
-                            None)), ("Pinned Z-Image tokenizer must expose apply_chat_template")
-    assert callable(getattr(fv_tok, "apply_chat_template",
-                            None)), ("FastVideo TokenizerLoader dropped the required apply_chat_template API")
-
-    messages = [{"role": "user", "content": "Describe a futuristic city skyline."}]
-
-    ref_text = ref_tok.apply_chat_template(messages, **OFFICIAL_CHAT_TEMPLATE_KWARGS)
-    fv_text = fv_tok.apply_chat_template(messages, **OFFICIAL_CHAT_TEMPLATE_KWARGS)
-
-    assert ref_text == fv_text
-
-    # Z-Image deliberately enables Qwen's thinking template. Guard against a
-    # pipeline silently using the generic non-thinking prompt path.
-    non_thinking_text = fv_tok.apply_chat_template(
-        messages,
-        tokenize=False,
-        add_generation_prompt=True,
-        enable_thinking=False,
-    )
-    assert fv_text != non_thinking_text
-
-    tokenization_kwargs = {
-        "padding": "max_length",
-        "max_length": OFFICIAL_MAX_SEQUENCE_LENGTH,
-        "truncation": True,
-        "return_tensors": "pt",
-    }
-    ref_tokens = ref_tok(ref_text, **tokenization_kwargs)
-    fv_tokens = fv_tok(fv_text, **tokenization_kwargs)
-
-    assert_close(ref_tokens["input_ids"], fv_tokens["input_ids"], atol=0, rtol=0)
-    assert_close(ref_tokens["attention_mask"], fv_tokens["attention_mask"], atol=0, rtol=0)
diff --git a/tests/local_tests/zimage/test_zimage_transformer_parity.py b/tests/local_tests/zimage/test_zimage_transformer_parity.py
deleted file mode 100644
index d67537394..000000000
--- a/tests/local_tests/zimage/test_zimage_transformer_parity.py
+++ /dev/null
@@ -1,343 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""Z-Image transformer parity.
-
-Coverage scope: both. The tiny CPU tests compare the pinned implementation and
-FastVideo model directly. The production-loader test activates when the real
-transformer subfolder is available.
-"""
-
-from __future__ import annotations
-
-import hashlib
-import importlib.util
-import json
-import os
-from pathlib import Path
-import subprocess
-import sys
-from types import ModuleType
-
-import pytest
-import torch
-from torch.testing import assert_close
-
-from fastvideo.configs.models.dits import ZImageDiTConfig
-from fastvideo.models.dits.zimage import ZImageAttention, ZImageTransformer2DModel, _prepare_attention_mask
-
-REPO_ROOT = Path(__file__).resolve().parents[3]
-OFFICIAL_REF_DIR = Path(os.getenv("ZIMAGE_OFFICIAL_REF_DIR", REPO_ROOT / "Z-Image"))
-OFFICIAL_SRC = OFFICIAL_REF_DIR / "src"
-TRANSFORMER_DIR = Path(os.getenv("ZIMAGE_TRANSFORMER_DIR", REPO_ROOT / "official_weights" / "Z-Image" / "transformer"))
-REFERENCE_REVISION = "26f23eda626ffadda020b04ff79488e1d72004cd"
-HF_REVISION = "f332072aa78be7aecdf3ee76d5c247082da564a6"
-HF_KEY_COUNT = 521
-HF_KEY_SHA256 = "3a9216f208c1873b2cf06394411a53e1e95e10fae3b01dca0f7223556e47c354"
-PARITY_SCOPE = "both"
-
-PRODUCTION_CONFIG = {
-    "all_patch_size": (2, ),
-    "all_f_patch_size": (1, ),
-    "in_channels": 16,
-    "dim": 3840,
-    "n_layers": 30,
-    "n_refiner_layers": 2,
-    "n_heads": 30,
-    "n_kv_heads": 30,
-    "norm_eps": 1e-5,
-    "qk_norm": True,
-    "cap_feat_dim": 2560,
-    "rope_theta": 256.0,
-    "t_scale": 1000.0,
-    "axes_dims": (32, 48, 48),
-    "axes_lens": (1536, 512, 512),
-}
-
-TINY_CONFIG = {
-    "all_patch_size": (2, ),
-    "all_f_patch_size": (1, ),
-    "in_channels": 2,
-    "dim": 12,
-    "n_layers": 1,
-    "n_refiner_layers": 1,
-    "n_heads": 2,
-    "n_kv_heads": 2,
-    "norm_eps": 1e-5,
-    "qk_norm": True,
-    "cap_feat_dim": 8,
-    "rope_theta": 256.0,
-    "t_scale": 1000.0,
-    "axes_dims": (2, 2, 2),
-    "axes_lens": (192, 16, 16),
-}
-
-
-def _load_source_module(name: str, path: Path):
-    spec = importlib.util.spec_from_file_location(name, path)
-    if spec is None or spec.loader is None:
-        pytest.fail(f"Cannot build import spec for {path}")
-    module = importlib.util.module_from_spec(spec)
-    sys.modules[name] = module
-    spec.loader.exec_module(module)
-    return module
-
-
-@pytest.fixture(scope="module")
-def reference_transformer_cls():
-    source_file = OFFICIAL_SRC / "zimage" / "transformer.py"
-    if not OFFICIAL_REF_DIR.exists():
-        pytest.skip(f"Pinned Z-Image reference clone not found: {OFFICIAL_REF_DIR}")
-    if not source_file.is_file():
-        pytest.fail(f"Z-Image reference clone is incomplete; missing {source_file}")
-
-    try:
-        revision = subprocess.run(
-            ["git", "-C", str(OFFICIAL_REF_DIR), "rev-parse", "HEAD"],
-            check=True,
-            capture_output=True,
-            text=True,
-        ).stdout.strip()
-    except (FileNotFoundError, subprocess.CalledProcessError) as exc:
-        pytest.fail(f"Cannot verify Z-Image reference revision: {exc}")
-    assert revision == REFERENCE_REVISION, (f"expected Z-Image reference {REFERENCE_REVISION}, got {revision}")
-
-    module_names = (
-        "config",
-        "config.inference",
-        "config.model",
-        "utils",
-        "utils.import_utils",
-        "utils.attention",
-        "_zimage_reference_transformer",
-    )
-    saved_modules = {name: sys.modules.get(name) for name in module_names}
-    added_path = str(OFFICIAL_SRC) not in sys.path
-    if added_path:
-        sys.path.insert(0, str(OFFICIAL_SRC))
-
-    try:
-        for name in module_names:
-            sys.modules.pop(name, None)
-        utils_package = ModuleType("utils")
-        utils_package.__path__ = [str(OFFICIAL_SRC / "utils")]
-        sys.modules["utils"] = utils_package
-        _load_source_module("utils.import_utils", OFFICIAL_SRC / "utils" / "import_utils.py")
-        _load_source_module("utils.attention", OFFICIAL_SRC / "utils" / "attention.py")
-        module = _load_source_module("_zimage_reference_transformer", source_file)
-        assert Path(module.__file__).resolve() == source_file.resolve()
-        yield module.ZImageTransformer2DModel
-    finally:
-        for name in module_names:
-            sys.modules.pop(name, None)
-            if saved_modules[name] is not None:
-                sys.modules[name] = saved_modules[name]
-        if added_path:
-            sys.path.remove(str(OFFICIAL_SRC))
-
-
-def _build_fastvideo(config_values: dict) -> ZImageTransformer2DModel:
-    config = ZImageDiTConfig()
-    config.update_model_arch(config_values)
-    return ZImageTransformer2DModel(
-        config=config,
-        hf_config={
-            "_class_name": "ZImageTransformer2DModel",
-            **config_values
-        },
-    )
-
-
-def _state_shapes(model: torch.nn.Module) -> dict[str, tuple[int, ...]]:
-    return {name: tuple(tensor.shape) for name, tensor in model.state_dict().items()}
-
-
-def test_zimage_transformer_production_meta_key_surface(reference_transformer_cls):
-    from fastvideo.models.registry import ModelRegistry
-
-    model_cls, _ = ModelRegistry.resolve_model_cls("ZImageTransformer2DModel")
-    assert model_cls is ZImageTransformer2DModel
-
-    with torch.device("meta"):
-        reference = reference_transformer_cls(**PRODUCTION_CONFIG)
-        fastvideo = _build_fastvideo(PRODUCTION_CONFIG)
-
-    reference_shapes = _state_shapes(reference)
-    fastvideo_shapes = _state_shapes(fastvideo)
-    assert fastvideo_shapes == reference_shapes
-    keys = sorted(reference_shapes)
-    key_digest = hashlib.sha256(("\n".join(keys) + "\n").encode()).hexdigest()
-    assert len(keys) == HF_KEY_COUNT
-    assert key_digest == HF_KEY_SHA256
-    print(f"Z-Image production transformer key/shape surface: {len(reference_shapes)} tensors")
-
-
-def test_zimage_transformer_tiny_cpu_parity(reference_transformer_cls):
-    reference = reference_transformer_cls(**TINY_CONFIG).eval()
-    generator = torch.Generator(device="cpu").manual_seed(123)
-    with torch.no_grad():
-        for parameter in reference.parameters():
-            parameter.copy_(torch.randn(parameter.shape, generator=generator, dtype=parameter.dtype) * 0.02)
-
-    fastvideo = _build_fastvideo(TINY_CONFIG).eval()
-    assert _state_shapes(fastvideo) == _state_shapes(reference)
-    fastvideo.load_state_dict(reference.state_dict(), strict=True)
-
-    generator.manual_seed(456)
-    images = [
-        torch.randn((2, 1, 4, 4), generator=generator),
-        torch.randn((2, 1, 8, 10), generator=generator),
-    ]
-    caption_features = [
-        torch.randn((3, 8), generator=generator),
-        torch.randn((35, 8), generator=generator),
-    ]
-    timestep = torch.tensor([0.25, 0.75], dtype=torch.float32)
-
-    with torch.inference_mode(), torch.nn.attention.sdpa_kernel(torch.nn.attention.SDPBackend.MATH):
-        reference_outputs = reference(images, timestep, caption_features)[0]
-        positional_outputs = fastvideo(images, caption_features, timestep)[0]
-        keyword_outputs = fastvideo(
-            hidden_states=images,
-            encoder_hidden_states=caption_features,
-            timestep=timestep,
-        )[0]
-
-    assert len(positional_outputs) == len(keyword_outputs) == len(reference_outputs) == 2
-    for positional, actual, expected in zip(positional_outputs, keyword_outputs, reference_outputs):
-        assert actual.shape == expected.shape
-        assert expected.abs().max() > 0
-        assert_close(positional, expected, atol=1e-6, rtol=1e-6)
-        assert_close(actual, expected, atol=1e-6, rtol=1e-6)
-
-
-def test_zimage_attention_additive_mask_parity(reference_transformer_cls):
-    reference_module = sys.modules[reference_transformer_cls.__module__]
-    reference = reference_module.ZImageAttention(12, 2, 2).eval()
-    generator = torch.Generator(device="cpu").manual_seed(321)
-    with torch.no_grad():
-        for parameter in reference.parameters():
-            parameter.copy_(torch.randn(parameter.shape, generator=generator, dtype=parameter.dtype) * 0.02)
-
-    fastvideo = ZImageAttention(12, 2, 2).eval()
-    fastvideo.load_state_dict(reference.state_dict(), strict=True)
-    hidden_states = torch.randn((2, 5, 12), generator=generator)
-    attention_mask = torch.tensor([[True, True, True, False, False], [True, True, True, True, True]])
-    additive_mask = _prepare_attention_mask(attention_mask, hidden_states.dtype)
-    assert additive_mask is not None
-    assert additive_mask.dtype == hidden_states.dtype
-    assert additive_mask.shape == (2, 1, 1, 5)
-    assert additive_mask[0, 0, 0, 0] == 0
-    assert torch.isneginf(additive_mask[0, 0, 0, -1])
-
-    with torch.inference_mode(), torch.nn.attention.sdpa_kernel(torch.nn.attention.SDPBackend.MATH):
-        expected = reference(hidden_states, attention_mask=attention_mask)
-        actual = fastvideo(hidden_states, attention_mask=attention_mask)
-    assert_close(actual, expected, atol=1e-6, rtol=1e-6)
-
-
-def test_zimage_transformer_rejects_sequence_parallelism(monkeypatch: pytest.MonkeyPatch):
-    model = _build_fastvideo(TINY_CONFIG)
-    monkeypatch.setattr("fastvideo.models.dits.zimage.model_parallel_is_initialized", lambda: True)
-    monkeypatch.setattr("fastvideo.models.dits.zimage.get_sp_world_size", lambda: 2)
-
-    with pytest.raises(NotImplementedError, match="sp_size=1"):
-        model(
-            hidden_states=[torch.zeros(2, 1, 4, 4)],
-            encoder_hidden_states=[torch.zeros(3, 8)],
-            timestep=torch.tensor([0.5]),
-        )
-
-
-def _has_real_weights() -> bool:
-    return any(TRANSFORMER_DIR.glob("*.safetensors"))
-
-
-def _load_real_config() -> dict:
-    with (TRANSFORMER_DIR / "config.json").open(encoding="utf-8") as file:
-        config = json.load(file)
-    assert config.pop("_class_name") == "ZImageTransformer2DModel"
-    config.pop("_diffusers_version", None)
-    config.pop("_name_or_path", None)
-    for key, expected in PRODUCTION_CONFIG.items():
-        actual = config[key]
-        if isinstance(expected, tuple):
-            actual = tuple(actual)
-        assert actual == expected, f"unexpected production config {key}={actual!r}"
-    assert set(config) == set(PRODUCTION_CONFIG)
-    return config
-
-
-def _run_fastvideo_production(inputs: tuple, dtype: torch.dtype) -> list[torch.Tensor]:
-    from fastvideo.configs.pipelines.base import PipelineConfig
-    from fastvideo.fastvideo_args import FastVideoArgs
-    from fastvideo.models.loader.component_loader import TransformerLoader
-
-    precision = "bf16" if dtype == torch.bfloat16 else "fp16"
-    args = FastVideoArgs(
-        model_path=str(TRANSFORMER_DIR),
-        dit_cpu_offload=False,
-        dit_layerwise_offload=False,
-        use_fsdp_inference=False,
-        pipeline_config=PipelineConfig(dit_config=ZImageDiTConfig(), dit_precision=precision),
-    )
-    model = TransformerLoader().load(str(TRANSFORMER_DIR), args).eval()
-    assert isinstance(model, ZImageTransformer2DModel)
-    assert args.model_paths["transformer"] == str(TRANSFORMER_DIR)
-    assert next(model.parameters()).device.type == "cuda"
-    with torch.inference_mode(), torch.nn.attention.sdpa_kernel(torch.nn.attention.SDPBackend.MATH):
-        outputs = model(
-            hidden_states=inputs[0],
-            encoder_hidden_states=inputs[1],
-            timestep=inputs[2],
-        )[0]
-    result = [output.detach().float().cpu() for output in outputs]
-    del model
-    torch.cuda.empty_cache()
-    return result
-
-
-def _run_reference_production(reference_transformer_cls, config: dict, inputs: tuple,
-                              dtype: torch.dtype) -> list[torch.Tensor]:
-    from safetensors.torch import load_file
-
-    state_dict = {}
-    for shard in sorted(TRANSFORMER_DIR.glob("*.safetensors")):
-        state_dict.update(load_file(str(shard), device="cpu"))
-    with torch.device("meta"):
-        model = reference_transformer_cls(**config)
-    model.load_state_dict(state_dict, strict=True, assign=True)
-    del state_dict
-    model = model.to(device="cuda", dtype=dtype).eval()
-    with torch.inference_mode(), torch.nn.attention.sdpa_kernel(torch.nn.attention.SDPBackend.MATH):
-        outputs = model(inputs[0], inputs[2], inputs[1])[0]
-    result = [output.detach().float().cpu() for output in outputs]
-    del model
-    torch.cuda.empty_cache()
-    return result
-
-
-@pytest.mark.skipif(
-    not _has_real_weights(),
-    reason=("Pinned Z-Image transformer weights are absent; set ZIMAGE_TRANSFORMER_DIR "
-            f"to Tongyi-MAI/Z-Image-Turbo@{HF_REVISION}/transformer"),
-)
-@pytest.mark.skipif(not torch.cuda.is_available(), reason="Real-weight Z-Image transformer parity requires CUDA")
-def test_zimage_transformer_production_loader_forward_parity(reference_transformer_cls):
-    config = _load_real_config()
-    dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16
-    generator = torch.Generator(device="cuda").manual_seed(789)
-    inputs = (
-        [torch.randn((16, 1, 4, 4), device="cuda", dtype=dtype, generator=generator)],
-        [torch.randn((3, 2560), device="cuda", dtype=dtype, generator=generator)],
-        torch.tensor([0.25], device="cuda", dtype=dtype),
-    )
-
-    fastvideo_outputs = _run_fastvideo_production(inputs, dtype)
-    reference_outputs = _run_reference_production(reference_transformer_cls, config, inputs, dtype)
-    assert len(fastvideo_outputs) == len(reference_outputs) == 1
-
-    actual, expected = fastvideo_outputs[0], reference_outputs[0]
-    diff = (actual - expected).abs()
-    mean_scale = expected.abs().mean().clamp_min(1e-6)
-    print(f"Z-Image real-weight parity: max={diff.max():.6f}, mean={diff.mean():.6f}")
-    assert diff.mean() / mean_scale < 0.01
-    assert_close(actual, expected, atol=1e-2, rtol=1e-2)
diff --git a/tests/local_tests/zimage/test_zimage_vae_parity.py b/tests/local_tests/zimage/test_zimage_vae_parity.py
deleted file mode 100644
index 5c71bd674..000000000
--- a/tests/local_tests/zimage/test_zimage_vae_parity.py
+++ /dev/null
@@ -1,195 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-Parity test for Z-Image VAE (AutoencoderKL decode path).
-
-This compares FastVideo's AutoencoderKL wrapper against the Z-Image
-reference AutoencoderKL implementation using the same local weights.
-
-Usage:
-    pytest tests/local_tests/zimage/test_zimage_vae_parity.py -v
-"""
-
-from __future__ import annotations
-
-import importlib
-import json
-import subprocess
-import sys
-from pathlib import Path
-from types import SimpleNamespace
-
-import pytest
-import torch
-from safetensors.torch import load_file as safetensors_load_file
-from torch.testing import assert_close
-
-from fastvideo.configs.models.vaes.autoencoder_kl import AutoencoderKLVAEConfig
-from fastvideo.models.loader.component_loader import VAELoader
-from fastvideo.models.vaes.autoencoder_kl import AutoencoderKL as FastVideoAutoencoderKL
-
-REPO_ROOT = Path(__file__).resolve().parents[3]
-ZIMAGE_REPO = REPO_ROOT / "Z-Image"
-ZIMAGE_SRC = REPO_ROOT / "Z-Image" / "src"
-ZIMAGE_VAE_DIR = REPO_ROOT / "official_weights" / "Z-Image" / "vae"
-ZIMAGE_VAE_CFG = ZIMAGE_VAE_DIR / "config.json"
-ZIMAGE_VAE_WEIGHTS = ZIMAGE_VAE_DIR / "diffusion_pytorch_model.safetensors"
-ZIMAGE_REFERENCE_REVISION = "26f23eda626ffadda020b04ff79488e1d72004cd"
-ZIMAGE_VAE_SCALING_FACTOR = 0.3611
-ZIMAGE_VAE_SHIFT_FACTOR = 0.1159
-PARITY_SCOPE = "both"
-
-
-def _require_pinned_reference_module(module_name: str, source_file: Path):
-    if not ZIMAGE_REPO.exists():
-        pytest.skip(f"Pinned Z-Image reference clone not found: {ZIMAGE_REPO}")
-    if not source_file.is_file():
-        pytest.fail(f"Z-Image reference clone is incomplete; missing {source_file}")
-
-    try:
-        result = subprocess.run(
-            ["git", "-C", str(ZIMAGE_REPO), "rev-parse", "HEAD"],
-            check=True,
-            capture_output=True,
-            text=True,
-        )
-    except (FileNotFoundError, subprocess.CalledProcessError) as exc:
-        pytest.fail(f"Cannot verify Z-Image reference revision: {exc}")
-
-    actual_revision = result.stdout.strip()
-    assert actual_revision == ZIMAGE_REFERENCE_REVISION, (
-        "Z-Image reference clone is not at the pinned revision: "
-        f"expected {ZIMAGE_REFERENCE_REVISION}, got {actual_revision}")
-
-    if str(ZIMAGE_SRC) not in sys.path:
-        sys.path.insert(0, str(ZIMAGE_SRC))
-    try:
-        module = importlib.import_module(module_name)
-    except Exception as exc:
-        pytest.fail(f"Cannot import pinned Z-Image module {module_name}: {exc}")
-
-    module_file = Path(module.__file__ or "").resolve()
-    assert module_file.is_relative_to(
-        ZIMAGE_SRC.resolve()), (f"{module_name} resolved outside the pinned clone: {module_file}")
-    return module
-
-
-@pytest.fixture(scope="module")
-def reference_autoencoder_cls():
-    module = _require_pinned_reference_module(
-        "zimage.autoencoder",
-        ZIMAGE_SRC / "zimage" / "autoencoder.py",
-    )
-    return module.AutoencoderKL
-
-
-def _load_cfg() -> dict:
-    if not ZIMAGE_VAE_CFG.exists():
-        pytest.skip(f"Z-Image VAE config not found: {ZIMAGE_VAE_CFG}")
-    with ZIMAGE_VAE_CFG.open("r", encoding="utf-8") as f:
-        cfg = json.load(f)
-    cfg.pop("_class_name", None)
-    cfg.pop("_diffusers_version", None)
-    cfg.pop("_name_or_path", None)
-    return cfg
-
-
-def _load_weights() -> dict[str, torch.Tensor]:
-    if not ZIMAGE_VAE_WEIGHTS.exists():
-        pytest.skip(f"Z-Image VAE weights not found: {ZIMAGE_VAE_WEIGHTS}")
-    return safetensors_load_file(str(ZIMAGE_VAE_WEIGHTS), device="cpu")
-
-
-def _build_reference(reference_autoencoder_cls, cfg: dict, weights: dict[str, torch.Tensor]) -> torch.nn.Module:
-    ref = reference_autoencoder_cls(
-        in_channels=cfg["in_channels"],
-        out_channels=cfg["out_channels"],
-        down_block_types=tuple(cfg["down_block_types"]),
-        up_block_types=tuple(cfg["up_block_types"]),
-        block_out_channels=tuple(cfg["block_out_channels"]),
-        layers_per_block=cfg["layers_per_block"],
-        latent_channels=cfg["latent_channels"],
-        norm_num_groups=cfg["norm_num_groups"],
-        scaling_factor=cfg["scaling_factor"],
-        shift_factor=cfg["shift_factor"],
-        use_quant_conv=cfg["use_quant_conv"],
-        use_post_quant_conv=cfg["use_post_quant_conv"],
-        mid_block_add_attention=cfg["mid_block_add_attention"],
-    ).eval()
-    ref.load_state_dict(weights, strict=True)
-    return ref
-
-
-def _build_fastvideo(cfg: dict, weights: dict[str, torch.Tensor]) -> torch.nn.Module:
-    fv_cfg = AutoencoderKLVAEConfig()
-    fv_cfg.update_model_arch(cfg)
-    fv = FastVideoAutoencoderKL(fv_cfg).eval()
-    fv.load_state_dict(weights, strict=True)
-    return fv
-
-
-def _load_fastvideo_production_vae(monkeypatch: pytest.MonkeyPatch):
-    monkeypatch.setattr(
-        "fastvideo.models.loader.component_loader.get_local_torch_device",
-        lambda: torch.device("cpu"),
-    )
-    pipeline_config = SimpleNamespace(
-        vae_config=AutoencoderKLVAEConfig(),
-        vae_precision="fp32",
-    )
-    fastvideo_args = SimpleNamespace(
-        model_paths={},
-        pipeline_config=pipeline_config,
-        vae_cpu_offload=False,
-    )
-    vae = VAELoader().load(str(ZIMAGE_VAE_DIR), fastvideo_args)
-    assert isinstance(vae, FastVideoAutoencoderKL)
-    assert fastvideo_args.model_paths["vae"] == str(ZIMAGE_VAE_DIR)
-    return vae
-
-
-def test_zimage_vae_decode_parity(reference_autoencoder_cls):
-    torch.manual_seed(7)
-
-    cfg = _load_cfg()
-    weights = _load_weights()
-
-    ref = _build_reference(reference_autoencoder_cls, cfg, weights)
-    fv = _build_fastvideo(cfg, weights)
-
-    # Decode-only parity is the critical path for Z-Image pipeline integration.
-    latents = torch.randn(1, cfg["latent_channels"], 8, 8, dtype=torch.float32)
-
-    with torch.no_grad():
-        ref_out = ref.decode(latents, return_dict=False)[0].detach().float()
-        fv_out = fv.decode(latents, return_dict=False)[0].detach().float()
-
-    assert ref_out.shape == fv_out.shape
-    assert_close(ref_out, fv_out, atol=1e-4, rtol=1e-4)
-
-
-def test_zimage_vae_production_loader_and_raw_decode_parity(
-    reference_autoencoder_cls,
-    monkeypatch: pytest.MonkeyPatch,
-):
-    torch.manual_seed(17)
-
-    cfg = _load_cfg()
-    weights = _load_weights()
-    assert cfg["scaling_factor"] == ZIMAGE_VAE_SCALING_FACTOR
-    assert cfg["shift_factor"] == ZIMAGE_VAE_SHIFT_FACTOR
-    ref = _build_reference(reference_autoencoder_cls, cfg, weights)
-    fv = _load_fastvideo_production_vae(monkeypatch)
-
-    assert ref.config.scaling_factor == cfg["scaling_factor"]
-    assert fv.config.scaling_factor == cfg["scaling_factor"]
-    assert ref.config.shift_factor == cfg["shift_factor"]
-    assert fv.config.shift_factor == cfg["shift_factor"]
-
-    decode_latents = torch.randn(1, cfg["latent_channels"], 8, 8, dtype=torch.float32)
-
-    with torch.no_grad():
-        ref_out = ref.decode(decode_latents, return_dict=False)[0].detach().float()
-        fv_out = fv.decode(decode_latents, return_dict=False)[0].detach().float()
-
-    assert ref_out.shape == fv_out.shape
-    assert_close(ref_out, fv_out, atol=1e-4, rtol=1e-4)