fix

.buildkite/pipeline.yml

@@ -61,7 +61,7 @@ steps:
                 - "pyproject.toml"
                 - "docker/Dockerfile.python3.12"
               config:
-                command: "timeout 90m .buildkite/scripts/pr_test.sh"
+                command: "timeout 60m .buildkite/scripts/pr_test.sh"
                 label: "SSIM Tests"
                 env:
                   - TEST_TYPE=ssim

.github/workflows/fastvideo-kernel-publish.yml

@@ -156,23 +156,8 @@ jobs:
           
           # Fix the wheel to be manylinux compliant
           pip install auditwheel
-          # Point auditwheel at torch libs, but do not vendor them into the wheel.
-          TORCH_LIB_DIR=$(python - <<'PY'
-          import os
-          import torch
-
-          print(os.path.join(os.path.dirname(torch.__file__), "lib"))
-          PY
-          )
-          export LD_LIBRARY_PATH="${TORCH_LIB_DIR}:${LD_LIBRARY_PATH}"
           # Target manylinux_2_35 (Ubuntu 22.04 native)
-          auditwheel repair dist/*.whl --plat manylinux_2_35_x86_64 -w fixed_dist \
-            --exclude libtorch_cuda.so \
-            --exclude libtorch_cpu.so \
-            --exclude libtorch.so \
-            --exclude libc10.so \
-            --exclude libc10_cuda.so \
-            --exclude libtorch_python.so
+          auditwheel repair dist/*.whl --plat manylinux_2_35_x86_64 -w fixed_dist
           # Move fixed wheels back to dist for upload consistency
           rm dist/*.whl
           mv fixed_dist/*.whl dist/

.pre-commit-config.yaml

@@ -68,7 +68,7 @@ repos:
     entry: bash
     args:
       - -c
-      - 'git ls-files | grep -v "^\"*fastvideo/tests/ssim/" | grep -v "^\"*fastvideo/tests/inference/lora/L40S_reference_videos/" | grep " " && echo "Filenames should not contain spaces!" && exit 1 || exit 0'
+      - 'git ls-files | grep -v "^fastvideo/tests/ssim/" | grep -v "^fastvideo/tests/inference/lora/L40S_reference_videos/" | grep " " && echo "Filenames should not contain spaces!" && exit 1 || exit 0'
     language: system
     always_run: true
     pass_filenames: false

README.md

@@ -3,7 +3,7 @@
 </div>
 
 <p align="center">
-     | <a href="https://hao-ai-lab.github.io/FastVideo"><b>Documentation</b></a> | <a href="https://hao-ai-lab.github.io/FastVideo/inference/inference_quick_start/"><b> Quick Start</b></a> | <a href="https://github.com/hao-ai-lab/FastVideo/discussions/982"  target="_blank"><b>Weekly Dev Meeting</b></a>  | 🟣💬 <a href="https://join.slack.com/t/fastvideo/shared_invite/zt-3f4lao1uq-u~Ipx6Lt4J27AlD2y~IdLQ" target="_blank"> <b>Slack</b> </a> |  🟣💬 <a href="https://ibb.co/sv3MMKyv" target="_blank"> <b> WeChat </b> </a> |
+     | <a href="https://hao-ai-lab.github.io/FastVideo"><b>Documentation</b></a> | <a href="https://hao-ai-lab.github.io/FastVideo/inference/inference_quick_start/"><b> Quick Start</b></a> | <a href="https://github.com/hao-ai-lab/FastVideo/discussions/982"  target="_blank"><b>Weekly Dev Meeting</b></a>  | 🟣💬 <a href="https://join.slack.com/t/fastvideo/shared_invite/zt-3f4lao1uq-u~Ipx6Lt4J27AlD2y~IdLQ" target="_blank"> <b>Slack</b> </a> |  🟣💬 <a href="https://ibb.co/c7g1qdD" target="_blank"> <b> WeChat </b> </a> |
 </p>
 
 **FastVideo is a unified post-training and inference framework for accelerated video generation.**

docs/attention/index.md

@@ -41,7 +41,7 @@ Clone the repository and build the kernel:
 
 ```bash
 # Clone recursively to get ThunderKittens submodule
-git clone https://github.com/hao-ai-lab/FastVideo.git
+git clone --recursive https://github.com/hao-ai-lab/FastVideo.git
 cd FastVideo/fastvideo-kernel
 
 # Build and install

docs/attention/vsa/index.md

@@ -13,13 +13,11 @@ from fastvideo_kernel import video_sparse_attn
 
 # q, k, v: [batch_size, num_heads, seq_len, head_dim]
 # variable_block_sizes: Number of valid tokens per block
-# q_variable_block_sizes: Number of valid tokens per q block (can differ from KV for q/k of different lengths)
 # topk: Number of blocks to attend
 
 output = video_sparse_attn(
     q, k, v, 
-    block_sizes,
-    block_sizes,
+    variable_block_sizes=block_sizes, 
     topk=32
 )
 ```

docs/getting_started/installation.md

@@ -11,13 +11,15 @@ FastVideo supports the following hardware platforms:
 ### Using pip
 
 ```bash
-# Create and activate a new conda environment
-conda create -n fastvideo python=3.12
-conda activate fastvideo
-
 pip install fastvideo
 ```
 
+### Using conda
+
+```bash
+conda install -c conda-forge fastvideo
+```
+
 ### From source
 
 ```bash
@@ -26,12 +28,6 @@ cd FastVideo
 pip install -e .
 ```
 
-Also optionally install flash-attn:
-
-```bash
-pip install flash-attn --no-build-isolation
-```
-
 ## Hardware Requirements
 
 - **NVIDIA GPUs**: CUDA 11.8+ with compute capability 7.0+

docs/getting_started/quick_start.md

@@ -15,12 +15,6 @@ conda activate fastvideo
 pip install fastvideo
 ```
 
-Also optionally install flash-attn:
-
-```bash
-pip install flash-attn --no-build-isolation
-```
-
 ## Basic Usage
 
 ### Text-to-Video Generation

docs/inference/inference_quick_start.md

@@ -106,8 +106,6 @@ If you encounter CUDA out of memory errors:
 - Enable memory optimization with `enable_model_cpu_offload`
 - Try a smaller model or use distilled versions
 - Use `num_gpus` > 1 if multiple GPUs are available
-- Try enabling FSDP inference with `use_fsdp_inference=True` (may slow down generation)
-- Try enabling DiT layerwise offload with `dit_layerwise_offload=True` (now only a few models support this, but may introduce less overhead than FSDP)
 
 ### Slow Generation
 

docs/utilities/lora.md

@@ -1,71 +0,0 @@
-# LoRA Extraction and Merging
-
-Tools for extracting and merging LoRA adapters for FastVideo models.
-
-## Extract LoRA Adapter
-
-```bash
-python scripts/lora_extraction/extract_lora.py \
-  --base Wan-AI/Wan2.2-TI2V-5B-Diffusers \
-  --ft FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers \
-  --out adapter_r32.safetensors \
-  --rank 32
-```
-
-**Options:**
-
-- `--base`: Base model (HuggingFace ID or local path)
-- `--ft`: Fine-tuned model (HuggingFace ID or local path)
-- `--out`: Output adapter file
-- `--rank`: LoRA rank (16, 32, 64, 128)
-- `--full-rank`: Extract full-rank adapter (optional)
-
-## Merge Adapter
-
-```bash
-python scripts/lora_extraction/merge_lora.py \
-  --base Wan-AI/Wan2.2-TI2V-5B-Diffusers \
-  --adapter adapter_r32.safetensors \
-  --ft FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers \
-  --output merged_model
-```
-
-**Options:**
-
-- `--base`: Base model (HuggingFace ID or local path)
-- `--adapter`: LoRA adapter file (.safetensors)
-- `--ft`: Fine-tuned model (for configuration)
-- `--output`: Output directory
-
-## Validate Quality (Optional)
-
-```bash
-python scripts/lora_extraction/lora_inference_comparison.py \
-  --base merged_model \
-  --ft FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers \
-  --adapter NONE \
-  --output-dir results \
-  --prompt "A cat sitting on a windowsill" \
-  --seed 42 \
-  --height 480 \
-  --width 480 \
-  --num-frames 49 \
-  --num-inference-steps 32 \
-  --compute-ssim \
-  --compute-lpips
-```
-
-**Options:**
-
-- `--base`: Merged model or base model path
-- `--ft`: Fine-tuned model (reference)
-- `--adapter`: Path to adapter or NONE
-- `--output-dir`: Output directory
-- `--prompt`: Text prompt (default: "A cat sitting on a windowsill")
-- `--seed`: Random seed (default: 42)
-- `--height`: Video height (default: 480)
-- `--width`: Video width (default: 832)
-- `--num-frames`: Number of frames (default: 49)
-- `--num-inference-steps`: Inference steps (default: 32)
-- `--compute-ssim`: Compute SSIM metric
-- `--compute-lpips`: Compute LPIPS metric

examples/distill/SFHunyuan1.5-T2V/distill_dmd.slurm

@@ -1,169 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=dmd_3333
-#SBATCH --partition=main
-#SBATCH --nodes=4
-#SBATCH --ntasks=4
-#SBATCH --ntasks-per-node=1
-#SBATCH --gres=gpu:8
-#SBATCH --cpus-per-task=128
-#SBATCH --mem=1440G
-#SBATCH --output=dmd_3333_output/dmd_3333_%j.out
-#SBATCH --error=dmd_3333_output/dmd_3333_%j.err
-#SBATCH --exclusive
-
-# Basic Info
-export NCCL_P2P_DISABLE=1
-export TORCH_NCCL_ENABLE_MONITORING=0
-export NCCL_DEBUG_SUBSYS=INIT,NET
-# different cache dir for different processes
-export TRITON_CACHE_DIR=/tmp/triton_cache_${SLURM_PROCID}
-export MASTER_PORT=29500
-export NODE_RANK=$SLURM_PROCID
-nodes=( $(scontrol show hostnames $SLURM_JOB_NODELIST) )
-export MASTER_ADDR=${nodes[0]}
-export TOKENIZERS_PARALLELISM=false
-# export WANDB_API_KEY="8d9f4b39abd68eb4e29f6fc010b7ee71a2207cde"
-export WANDB_API_KEY="50632ebd88ffd970521cec9ab4a1a2d7e85bfc45"
-# export WANDB_API_KEY='your_wandb_api_key_here'
-export WANDB_BASE_URL="https://api.wandb.ai"
-export WANDB_MODE=online
-export FASTVIDEO_ATTENTION_BACKEND=FLASH_ATTN
-
-source ~/conda/miniconda/bin/activate
-conda activate wei-fv-distill
-export HOME="/mnt/weka/home/hao.zhang/wei"
-
-# Configs
-NUM_GPUS=8
-NUM_TOTAL_GPUS=32
-
-# Model paths for Self-Forcing DMD distillation with Hunyuan1.5:
-GENERATOR_MODEL_PATH="weizhou03/SFHunyuanVideo-1.5-Diffusers-480p_t2v"  # Updated to Wan2.2
-REAL_SCORE_MODEL_PATH="hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v" # Teacher model
-FAKE_SCORE_MODEL_PATH="hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v"
-# REAL_SCORE_MODEL_PATH="hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_i2v_step_distilled"
-# FAKE_SCORE_MODEL_PATH="hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_i2v_step_distilled" # Critic model
-
-# DATA_DIR="data/test-text-preprocessing/Node_0_GPU_1_File_1/combined_parquet_dataset/"
-# DATA_DIR="/mnt/weka/home/hao.zhang/matthew/FastVideo/data/test-text-preprocessing"
-DATA_DIR="/mnt/weka/home/hao.zhang/wl/sharefs/wl/release/vidprom_16k_text_embed"
-DATA_DIR_2="/mnt/weka/home/hao.zhang/wl/sharefs/wl/release/fv-ode-preprocessing-16k-hy15-121"
-# DATA_DIR=data/crush-smol_processed_t2v/combined_parquet_dataset
-# DATA_DIR="/mnt/sharefs/users/hao.zhang/Vchitect-2M/Wan-Syn-upload/latents_i2v/train/"
-# VALIDATION_DATASET_FILE="data/crush-smol-single_processed_t2v/validation.json"
-VALIDATION_DATASET_FILE="/mnt/weka/home/hao.zhang/wl/FastVideo/examples/distill/Wan2.2-TI2V-5B-Diffusers/Data-free/validation_64.json"
-
-# export CUDA_VISIBLE_DEVICES=4,5
-# IP=[MASTER NODE IP]
-
-training_args=(
-  --tracker_project_name SFhy1.5_t2v_distill_self_forcing_dmd  # Updated for Wan2.2
-  --output_dir "/mnt/weka/home/hao.zhang/wei/SFhy1.5_distill_3333_1e-5_1e-5_cfg6_corrected_scheduler"
-  --wandb_run_name "self_forcing_3333_context_forcing"
-  --max_train_steps 4000
-  --train_batch_size 1
-  --train_sp_batch_size 1
-  --gradient_accumulation_steps 1
-  --num_latent_t 21
-  --num_height 480
-  --num_width 848
-  --enable_gradient_checkpointing_type "full"
-  --simulate_generator_forward
-  --log_visualization
-  --num_frames 81
-  --num_frame_per_block 3  # Frame generation block size for self-forcing
-  # --resume-from-checkpoint "/mnt/weka/home/hao.zhang/wei/SFhy1.5_distill_1333/checkpoint-300"
-  --init_weights_from_safetensors /mnt/weka/home/hao.zhang/wei/hy15_worldplay_df_init_3333/checkpoint-2000/transformer/diffusion_pytorch_model.safetensors
-  # --init_weights_from_safetensors /mnt/weka/home/hao.zhang/wei/hy15_distilled_ode_init_3333/checkpoint-2400/transformer/diffusion_pytorch_model.safetensors
-)
-
-parallel_args=(
-  --num_gpus $NUM_TOTAL_GPUS # 64
-  --sp_size 1
-  --tp_size 1
-  --hsdp_replicate_dim 1 # 64
-  --hsdp_shard_dim $NUM_TOTAL_GPUS
-)
-
-model_args=(
-  --model_path $GENERATOR_MODEL_PATH
-  --pretrained_model_name_or_path $GENERATOR_MODEL_PATH
-  --real_score_model_path $REAL_SCORE_MODEL_PATH
-  --fake_score_model_path $FAKE_SCORE_MODEL_PATH
-)
-
-dataset_args=(
-  --data_path "$DATA_DIR"
-  # --data_path_2 "$DATA_DIR_2"
-  --dataloader_num_workers 4
-)
-
-validation_args=(
-  --log_validation
-  --validation_dataset_file "$VALIDATION_DATASET_FILE"
-  --validation_steps 50
-  --validation_sampling_steps "8"
-  --validation_guidance_scale "1.0" # not used for dmd inference
-  --text-encoder-cpu-offload
-  # --vae_cpu_offload True
-)
-
-optimizer_args=(
-  --learning_rate 1e-5
-  --mixed_precision "bf16"
-  --training_state_checkpointing_steps 100
-  --weight_only_checkpointing_steps 500
-  --weight_decay 0.01
-  --betas '0.0,0.999'
-  --max_grad_norm 1.0
-)
-
-miscellaneous_args=(
-  --inference_mode False
-  --checkpoints_total_limit 3
-  --training_cfg_rate 0.0
-  --dit_precision "fp32"
-  --flow_shift 5
-  --seed 1000
-  --use_ema True
-  --ema_decay 0.99
-  --ema_start_step 200
-)
-
-dmd_args=(
-  --warp_denoising_step
-  --dmd_denoising_steps '1000,875,750,625,500,375,250,125'
-  # --dmd_denoising_steps '1000,760,520,280'
-  # --dmd_denoising_steps '1000,750,500,250'
-  --min_timestep_ratio 0.02
-  --max_timestep_ratio 0.98
-  --dfake_gen_update_ratio 5
-  --real_score_guidance_scale 3.5
-  --fake_score_learning_rate 5e-6
-  --fake_score_betas '0.0,0.999'
-)
-
-self_forcing_args=(
-  --independent_first_frame False  # Whether to treat first frame independently
-  --same_step_across_blocks True  # Whether to use same denoising step across all blocks
-  --last_step_only False  # Whether to only use the last denoising step
-  --context_noise 0  # Amount of noise to add during context caching (0 = no noise)
-  # --use-context-forcing True
-)
-
-srun torchrun \
---nnodes $SLURM_JOB_NUM_NODES \
---nproc_per_node $NUM_GPUS \
---node_rank $SLURM_PROCID \
---rdzv_backend=c10d \
---rdzv_endpoint="$MASTER_ADDR:$MASTER_PORT" \
-    fastvideo/training/hy15_self_forcing_distillation_pipeline.py \
-    "${parallel_args[@]}" \
-    "${model_args[@]}" \
-    "${dataset_args[@]}" \
-    "${training_args[@]}" \
-    "${optimizer_args[@]}" \
-    "${validation_args[@]}" \
-    "${miscellaneous_args[@]}" \
-    "${dmd_args[@]}" \
-    "${self_forcing_args[@]}"

examples/inference/basic/basic.py

@@ -12,7 +12,7 @@ def main():
         "Wan-AI/Wan2.1-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
+        use_fsdp_inference=True,
         dit_cpu_offload=False,
         vae_cpu_offload=False,
         text_encoder_cpu_offload=True,

examples/inference/basic/basic_cosmos2_5_t2w.py

@@ -1,42 +0,0 @@
-from fastvideo import VideoGenerator
-
-
-def main():
-    # Point this to your local diffusers model dir (or replace with a HF model ID).
-    model_path = "KyleShao/Cosmos-Predict2.5-2B-Diffusers"
-
-    generator = VideoGenerator.from_pretrained(
-        model_path,
-        num_gpus=1,
-        use_fsdp_inference=False,  # set True if GPU is out of memory
-        dit_cpu_offload=False,
-        vae_cpu_offload=False,
-        text_encoder_cpu_offload=True,
-        pin_cpu_memory=True,
-    )
-
-    prompt = (
-       "A high-definition video captures the precision of robotic welding in an industrial setting. The first frame showcases a robotic arm, equipped with a welding torch, positioned over a large metal structure. The welding process is in full swing, with bright sparks and intense light illuminating the scene, creating a vivid display of blue and white hues. A significant amount of smoke billows around the welding area, partially obscuring the view but emphasizing the heat and activity. The background reveals parts of the workshop environment, including a ventilation system and various pieces of machinery, indicating a busy and functional industrial workspace. As the video progresses, the robotic arm maintains its steady position, continuing the welding process and moving to its left. The welding torch consistently emits sparks and light, and the smoke continues to rise, diffusing slightly as it moves upward. The metal surface beneath the torch shows ongoing signs of heating and melting. The scene retains its industrial ambiance, with the welding sparks and smoke dominating the visual field, underscoring the ongoing nature of the welding operation."
-    )
-
-    video = generator.generate_video(
-        prompt,
-        negative_prompt="",
-        height=704,
-        width=1280,
-        num_frames=77,
-        num_inference_steps=35,
-        guidance_scale=7.0,
-        fps=24,
-        output_path="outputs_video/cosmos2_5_t2w.mp4",
-        save_video=True,
-    )
-
-    generator.shutdown()
-
-
-if __name__ == "__main__":
-    main()
-
-
-

examples/inference/basic/basic_dmd.py

@@ -14,13 +14,14 @@ def main():
         model_name,
         # FastVideo will automatically handle distributed setup
         num_gpus=1,
-        use_fsdp_inference=False, # set to True if GPU is out of memory
+        use_fsdp_inference=True,
         # Adjust these offload parameters if you have < 32GB of VRAM
         text_encoder_cpu_offload=True,
         pin_cpu_memory=True, # set to false if low CPU RAM or hit obscure "CUDA error: Invalid argument"
         dit_cpu_offload=False,
         vae_cpu_offload=False,
         VSA_sparsity=0.8,
+        init_weights_from_safetensors="/mnt/weka/home/hao.zhang/wl/release/dmd_distill_1.3_4n_syn/checkpoint-900_weight_only/generator_inference_transformer"
     )
     load_end_time = time.perf_counter()
     load_time = load_end_time - load_start_time

examples/inference/basic/basic_hy15.py

@@ -2,51 +2,31 @@ from fastvideo import VideoGenerator
 import json
 # from fastvideo.configs.sample import SamplingParam
 
-OUTPUT_PATH = "video_samples_hy15_t2v_distilled"
+OUTPUT_PATH = "video_samples_hy15"
 def main():
     # FastVideo will automatically use the optimal default arguments for the
     # model.
     # If a local path is provided, FastVideo will make a best effort
     # attempt to identify the optimal arguments.
     generator = VideoGenerator.from_pretrained(
-        "hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_i2v_step_distilled",
+        "hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v",
         # FastVideo will automatically handle distributed setup
         num_gpus=1,
-        use_fsdp_inference=False, # set to True if GPU is out of memory
+        use_fsdp_inference=True,
         dit_cpu_offload=True,
         vae_cpu_offload=True,
         text_encoder_cpu_offload=True,
         pin_cpu_memory=True, # set to false if low CPU RAM or hit obscure "CUDA error: Invalid argument"
         # image_encoder_cpu_offload=False,
-        # init_weights_from_safetensors="/mnt/weka/home/hao.zhang/wei/SFhy1.5_distill_1333_5e-5_2e-6_cfg3.5/checkpoint-500/ema/generator_ema.safetensors"
     )
 
-    # json_path = "/mnt/weka/home/hao.zhang/wei/FastVideo/data/mixkit_i2v_full_720p.json"
-    # with open(json_path, 'r') as f:
-    #     data_list = json.load(f)["data"]
-    
-    # # Now you can index into data_list however you like
-    # # For example: data_list[0], data_list[1:3], etc.
-    # for data in data_list:
-    #     prompt = data["prompt"]
-    #     image_path = data["image_path"]
-    #     generator.generate_video(prompt, output_path=OUTPUT_PATH, save_video=True, image_path=image_path, num_frames=121, fps=24)
-    # return
-
-    # prompt = (
-    #     "In a brightly lit studio, a photographer wearing a denim jacket focuses intently, capturing shots with a professional camera. Facing him, a model stands gracefully, adjusting her long, flowing hair with delicate movements. The scene is characterized by strong contrasts; the model's soft pink attire and gentle gestures complement the rugged, precise demeanor of the photographer. Positioned against a minimalist backdrop, the pair work seamlessly, with the camera\u2019s lens pointed directly at the model, capturing her elegance. The soft, diffused lighting casts a gentle glow on both subjects, creating an airy and ethereal atmosphere perfect for a high-fashion photo shoot."
-    # )
-
-    # video = generator.generate_video(prompt, output_path=OUTPUT_PATH, save_video=True, negative_prompt="", num_frames=121, fps=24, image_path="data/1.png")
-    # return
-
     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)
+    video = generator.generate_video(prompt, output_path=OUTPUT_PATH, save_video=True, negative_prompt="", num_frames=81, fps=16)
 
     prompt2 = (
         "A majestic lion strides across the golden savanna, its powerful frame "
@@ -55,7 +35,7 @@ def main():
         "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)
+    video2 = generator.generate_video(prompt2, output_path=OUTPUT_PATH, save_video=True, negative_prompt="", num_frames=81, fps=16)
 
 
 if __name__ == "__main__":

examples/inference/basic/basic_hyworld.py

@@ -1,224 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-Basic example for HYWorld (HY-WorldPlay) video generation using FastVideo.
-
-This example replicates the same functionality as HY-WorldPlay/run.sh,
-demonstrating image-to-video generation with camera trajectory control.
-"""
-
-import time
-import math
-import numpy as np
-import imageio
-import torchvision
-from einops import rearrange
-
-from fastvideo import VideoGenerator
-from fastvideo.pipelines import ForwardBatch
-from fastvideo.utils import shallow_asdict, align_to
-from fastvideo.logger import init_logger
-
-from fastvideo.models.dits.hyworld.pose import pose_to_input, compute_latent_num
-from fastvideo.models.dits.hyworld.resolution_utils import get_resolution_from_image
-
-logger = init_logger(__name__)
-
-class HYWorldVideoGenerator(VideoGenerator):
-    """Extended VideoGenerator that adds HYWorld-specific parameters to batch.extra."""
-
-    def _generate_single_video(self, prompt: str, sampling_param=None, **kwargs):
-        """Override to add viewmats, Ks, and action to batch.extra."""
-        fastvideo_args = self.fastvideo_args
-        pipeline_config = fastvideo_args.pipeline_config
-
-        if sampling_param is None:
-            from fastvideo.configs.sample import SamplingParam
-            sampling_param = SamplingParam.from_pretrained(fastvideo_args.model_path)
-
-        # Update sampling param with kwargs
-        if kwargs:
-            for key, value in kwargs.items():
-                if hasattr(sampling_param, key):
-                    setattr(sampling_param, key, value)
-
-        # Get pose string from sampling_param or kwargs
-        pose = kwargs.get('pose', getattr(sampling_param, 'POSE', 'w-31'))
-        num_frames = kwargs.get('num_frames', getattr(sampling_param, 'num_frames', 125))
-
-        # Calculate number of latents
-        latent_num = compute_latent_num(num_frames)
-
-        # Convert pose to viewmats, Ks, and action
-        viewmats, Ks, action = pose_to_input(pose, latent_num)
-
-        # Convert to tensors and add batch dimension
-        viewmats = viewmats.unsqueeze(0)  # (1, T, 4, 4)
-        Ks = Ks.unsqueeze(0)  # (1, T, 3, 3)
-        action = action.unsqueeze(0)  # (1, T)
-
-        # Validate inputs
-        prompt = prompt.strip()
-        sampling_param = sampling_param.__class__(**shallow_asdict(sampling_param))
-        output_path = kwargs.get("output_path", sampling_param.output_path)
-        sampling_param.prompt = prompt
-
-        if sampling_param.negative_prompt is not None:
-            sampling_param.negative_prompt = sampling_param.negative_prompt.strip()
-
-        # Validate dimensions
-        if (sampling_param.height <= 0 or sampling_param.width <= 0 or
-            sampling_param.num_frames <= 0):
-            raise ValueError(
-                f"Height, width, and num_frames must be positive integers")
-
-        temporal_scale_factor = pipeline_config.vae_config.arch_config.temporal_compression_ratio
-        num_frames = sampling_param.num_frames
-        num_gpus = fastvideo_args.num_gpus
-        use_temporal_scaling_frames = pipeline_config.vae_config.use_temporal_scaling_frames
-
-        # Adjust number of frames based on number of GPUs
-        if use_temporal_scaling_frames:
-            orig_latent_num_frames = (num_frames - 1) // temporal_scale_factor + 1
-        else:
-            orig_latent_num_frames = sampling_param.num_frames // 17 * 3
-
-        if orig_latent_num_frames % fastvideo_args.num_gpus != 0:
-            if use_temporal_scaling_frames:
-                new_num_frames = (orig_latent_num_frames - 1) * temporal_scale_factor + 1
-            else:
-                divisor = math.lcm(3, num_gpus)
-                orig_latent_num_frames = (
-                    (orig_latent_num_frames + divisor - 1) // divisor) * divisor
-                new_num_frames = orig_latent_num_frames // 3 * 17
-
-            logger.info(
-                "Adjusting number of frames from %s to %s based on number of GPUs (%s)",
-                sampling_param.num_frames, new_num_frames, fastvideo_args.num_gpus)
-            sampling_param.num_frames = new_num_frames
-
-        # Calculate sizes
-        target_height = align_to(sampling_param.height, 16)
-        target_width = align_to(sampling_param.width, 16)
-
-        # Calculate latent sizes
-        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]
-
-        # Prepare batch
-        batch = ForwardBatch(
-            **shallow_asdict(sampling_param),
-            eta=0.0,
-            n_tokens=n_tokens,
-            VSA_sparsity=fastvideo_args.VSA_sparsity,
-        )
-
-        # Add HYWorld-specific parameters to batch.extra
-        batch.extra['viewmats'] = viewmats
-        batch.extra['Ks'] = Ks
-        batch.extra['action'] = action
-        batch.extra['chunk_latent_frames'] = 16  # For bidirectional model
-
-        # Run inference
-        start_time = time.perf_counter()
-        output_batch = self.executor.execute_forward(batch, fastvideo_args)
-        samples = output_batch.output
-        logging_info = output_batch.logging_info
-
-        gen_time = time.perf_counter() - start_time
-        logger.info("Generated successfully in %.2f seconds", gen_time)
-
-        # Process outputs
-        videos = rearrange(samples, "b c t h w -> t b c h w")
-        frames = []
-        for x in videos:
-            x = torchvision.utils.make_grid(x, nrow=6)
-            x = x.transpose(0, 1).transpose(1, 2).squeeze(-1)
-            frames.append((x * 255).numpy().astype(np.uint8))
-
-        # Save video if requested
-        if batch.save_video:
-            imageio.mimsave(output_path, frames, fps=batch.fps, format="mp4")
-            logger.info("Saved video to %s", output_path)
-
-        if batch.return_frames:
-            return frames
-        else:
-            return {
-                "samples": samples,
-                "frames": frames,
-                "prompts": prompt,
-                "size": (target_height, target_width, batch.num_frames),
-                "generation_time": gen_time,
-                "logging_info": logging_info,
-                "trajectory": output_batch.trajectory_latents,
-                "trajectory_timesteps": output_batch.trajectory_timesteps,
-                "trajectory_decoded": output_batch.trajectory_decoded,
-            }
-
-# Default prompt from HY-WorldPlay run.sh
-DEFAULT_PROMPT = 'A paved pathway leads towards a stone arch bridge spanning a calm body of water.  Lush green trees and foliage line the path and the far bank of the water. A traditional-style pavilion with a tiered, reddish-brown roof sits on the far shore. The water reflects the surrounding greenery and the sky.  The scene is bathed in soft, natural light, creating a tranquil and serene atmosphere. The pathway is composed of large, rectangular stones, and the bridge is constructed of light gray stone.  The overall composition emphasizes the peaceful and harmonious nature of the landscape.'
-DEFAULT_IMAGE = 'https://raw.githubusercontent.com/Tencent-Hunyuan/HY-WorldPlay/main/assets/img/test.png'
-
-def main():
-    import argparse
-
-    # pose: (a, w, s, d) - (15, 31)
-    # num_frames: (61, 125)
-    parser = argparse.ArgumentParser(description="HYWorld video generation with FastVideo")
-    parser.add_argument("--prompt", type=str, default=DEFAULT_PROMPT, help="Text prompt for video generation")
-    parser.add_argument("--image", type=str, default=DEFAULT_IMAGE, help="Path or URL to input image")
-    parser.add_argument("--pose", type=str, default='w-31', help="Pose string (e.g., 'a-31', 'w-31', 's-31', 'd-31')")
-    parser.add_argument("--output_path", type=str, default='video_samples_hyworld', help="Output video path")
-    parser.add_argument("--num-frames", type=int, default=125, help="Number of frames")
-    parser.add_argument("--seed", type=int, default=1, help="Random seed")
-    parser.add_argument("--resolution", type=str, default="480p", help="Only support 480p for now")
-    args = parser.parse_args()
-
-    # Automatically determine resolution from input image
-    HEIGHT, WIDTH = get_resolution_from_image(args.image, args.resolution)
-    print(f"Image: {args.image}")
-    print(f"Pose: {args.pose}")
-    print(f"Resolution: {HEIGHT}x{WIDTH} (from {args.resolution} buckets)")
-    print(f"Num frames: {args.num_frames}")
-    print(f"Output path: {args.output_path}")
-
-    # Initialize generator
-    print("\nInitializing VideoGenerator for HYWorld...")
-    
-    generator = HYWorldVideoGenerator.from_pretrained(
-        "FastVideo/HY-WorldPlay-Bidirectional-Diffusers",
-        num_gpus=1,
-        use_fsdp_inference=True,
-        dit_cpu_offload=True,
-        vae_cpu_offload=True,
-        text_encoder_cpu_offload=True,
-        pin_cpu_memory=True,
-        image_encoder_cpu_offload=True,
-    )
-
-    # Generate video
-    print("\nGenerating video...")
-    start_time = time.time()
-    video = generator.generate_video(
-        prompt=args.prompt,
-        image_path=args.image,
-        output_path=args.output_path,
-        save_video=True,
-        negative_prompt="",
-        num_frames=args.num_frames,
-        fps=24,
-        height=HEIGHT,
-        width=WIDTH,
-        seed=args.seed,
-        pose=args.pose,
-    )
-    elapsed = time.time() - start_time
-
-    print(f"\nVideo generated successfully!")
-    print(f"Saved to: {args.output_path}")
-    print(f"Time: {elapsed:.2f}s")
-
-
-if __name__ == "__main__":
-    main()

examples/inference/basic/basic_longcat_i2v.py

@@ -1,210 +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()
-
-

examples/inference/basic/basic_longcat_t2v.py

@@ -1,198 +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()
-
-

examples/inference/basic/basic_longcat_vc.py

@@ -1,228 +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()
-
-

examples/inference/basic/basic_ltx2.py

@@ -1,34 +0,0 @@
-from fastvideo import VideoGenerator
-
-PROMPT = (
-    "A warm sunny backyard. The camera starts in a tight cinematic close-up "
-    "of a woman and a man in their 30s, facing each other with serious "
-    "expressions. The woman, emotional and dramatic, says softly, \"That's "
-    "it... Dad's lost it. And we've lost Dad.\" The man exhales, slightly "
-    "annoyed: \"Stop being so dramatic, Jess.\" A beat. He glances aside, "
-    "then mutters defensively, \"He's just having fun.\" The camera slowly "
-    "pans right, revealing the grandfather in the garden wearing enormous "
-    "butterfly wings, waving his arms in the air like he's trying to take "
-    "off. He shouts, \"Wheeeew!\" as he flaps his wings with full commitment. "
-    "The woman covers her face, on the verge of tears. The tone is deadpan, "
-    "absurd, and quietly tragic."
-)
-
-
-def main() -> None:
-    generator = VideoGenerator.from_pretrained(
-        "FastVideo/LTX2-Distilled-Diffusers",
-        num_gpus=1,
-    )
-
-    output_path = "outputs_video/ltx2_basic/output_ltx2_distilled_t2v.mp4"
-    generator.generate_video(
-        prompt=PROMPT,
-        output_path=output_path,
-        save_video=True,
-    )
-    generator.shutdown()
-
-
-if __name__ == "__main__":
-    main()

examples/inference/basic/basic_matrixgame.py

@@ -43,7 +43,7 @@ def main():
         config["model_path"],
         # FastVideo will automatically handle distributed setup
         num_gpus=1,
-        use_fsdp_inference=False, # set to True if GPU is out of memory
+        use_fsdp_inference=True,
         dit_cpu_offload=True, # DiT need to be offloaded for MoE
         vae_cpu_offload=False,
         text_encoder_cpu_offload=True,

examples/inference/basic/basic_matrixgame_streaming.py

@@ -46,7 +46,7 @@ async def main():
         config["model_path"],
         # FastVideo will automatically handle distributed setup
         num_gpus=1,
-        use_fsdp_inference=False, # set to True if GPU is out of memory
+        use_fsdp_inference=True,
         dit_cpu_offload=True, # DiT need to be offloaded for MoE
         vae_cpu_offload=False,
         text_encoder_cpu_offload=True,

examples/inference/basic/basic_self_forcing_causal.py

@@ -13,7 +13,7 @@ def main():
         model_name,
         # FastVideo will automatically handle distributed setup
         num_gpus=1,
-        use_fsdp_inference=False, # set to True if GPU is out of memory
+        use_fsdp_inference=True,
         text_encoder_cpu_offload=False,
         dit_cpu_offload=False,
     )

examples/inference/basic/basic_self_forcing_causal_wan2_2_i2v.py

@@ -14,7 +14,7 @@ def main():
         "FastVideo/SFWan2.2-I2V-A14B-Preview-Diffusers",
         # FastVideo will automatically handle distributed setup
         num_gpus=1,
-        use_fsdp_inference=False, # set to True if GPU is out of memory
+        use_fsdp_inference=True,
         dit_cpu_offload=True, # DiT need to be offloaded for MoE
         dit_precision="fp32",
         vae_cpu_offload=False,

examples/inference/basic/basic_self_forcing_causal_wan2_2_t2v.py

@@ -14,7 +14,7 @@ def main():
         "rand0nmr/SFWan2.2-T2V-A14B-Diffusers",
         # FastVideo will automatically handle distributed setup
         num_gpus=1,
-        use_fsdp_inference=False, # set to True if GPU is out of memory
+        use_fsdp_inference=True,
         dit_cpu_offload=True, # DiT need to be offloaded for MoE
         vae_cpu_offload=False,
         text_encoder_cpu_offload=True,

examples/inference/basic/basic_turbodiffusion.py

@@ -15,14 +15,12 @@ def main() -> None:
         "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,
+        # TurboDiffusion uses a custom pipeline with RCM scheduler
+        override_pipeline_cls_name="TurboDiffusionPipeline",
     )
 
     # Generate videos with the same simple API, regardless of GPU count
-    # TurboDiffusion defaults: guidance_scale=1.0 and num_inference_steps=4 (from config)
+    # TurboDiffusion uses guidance_scale=1.0 (no CFG) and only 4 steps
     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 "
@@ -32,7 +30,9 @@ def main() -> None:
         prompt,
         output_path=OUTPUT_PATH,
         save_video=True,
+        num_inference_steps=4,
         seed=42,
+        guidance_scale=1.0,
     )
 
     # Generate another video with a different prompt, without reloading the model!
@@ -47,7 +47,9 @@ def main() -> None:
         prompt2,
         output_path=OUTPUT_PATH,
         save_video=True,
+        num_inference_steps=4,
         seed=42,
+        guidance_scale=1.0,
     )
 
 

examples/inference/basic/basic_turbodiffusion_14b.py

@@ -15,6 +15,8 @@ def main() -> None:
         "loayrashid/TurboWan2.1-T2V-14B-Diffusers",
         # 14B model needs more GPUs
         num_gpus=2,
+        # TurboDiffusion uses a custom pipeline with RCM scheduler
+        override_pipeline_cls_name="TurboDiffusionPipeline",
     )
 
     prompt = (
@@ -26,7 +28,9 @@ def main() -> None:
         prompt,
         output_path=OUTPUT_PATH,
         save_video=True,
+        num_inference_steps=4,
         seed=42,
+        guidance_scale=1.0,
     )
 
     # Generate another video with a different prompt, without reloading the model!
@@ -41,7 +45,9 @@ def main() -> None:
         prompt2,
         output_path=OUTPUT_PATH,
         save_video=True,
+        num_inference_steps=4,
         seed=42,
+        guidance_scale=1.0,
     )
 
 

examples/inference/basic/basic_turbodiffusion_i2v.py

@@ -1,37 +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()

examples/inference/basic/basic_wan2_2.py

@@ -12,7 +12,7 @@ def main():
         "Wan-AI/Wan2.2-T2V-A14B-Diffusers",
         # FastVideo will automatically handle distributed setup
         num_gpus=1,
-        use_fsdp_inference=False, # set to True if GPU is out of memory 
+        use_fsdp_inference=True,
         dit_cpu_offload=True, # DiT need to be offloaded for MoE
         vae_cpu_offload=False,
         text_encoder_cpu_offload=True,

examples/inference/basic/basic_wan2_2_Fun.py

@@ -14,7 +14,7 @@ def main():
         # "alibaba-pai/Wan2.2-Fun-A14B-Control",
         # FastVideo will automatically handle distributed setup
         num_gpus=1,
-        use_fsdp_inference=False, # set to True if GPU is out of memory
+        use_fsdp_inference=True,
         dit_cpu_offload=True, # DiT need to be offloaded for MoE
         vae_cpu_offload=False,
         text_encoder_cpu_offload=True,

examples/inference/basic/basic_wan2_2_i2v.py

@@ -12,7 +12,7 @@ def main():
         "Wan-AI/Wan2.2-I2V-A14B-Diffusers",
         # FastVideo will automatically handle distributed setup
         num_gpus=1,
-        use_fsdp_inference=False, # set to True if GPU is out of memory
+        use_fsdp_inference=True,
         dit_cpu_offload=True, # DiT need to be offloaded for MoE
         vae_cpu_offload=False,
         text_encoder_cpu_offload=True,

examples/inference/basic/basic_wan2_2_ti2v.py

@@ -11,7 +11,7 @@ def main():
         model_name,
         # FastVideo will automatically handle distributed setup
         num_gpus=1,
-        use_fsdp_inference=False, # set to True if GPU is out of memory
+        use_fsdp_inference=True,
         dit_cpu_offload=True,
         vae_cpu_offload=False,
         text_encoder_cpu_offload=True,

examples/training/consistency_finetune/causal_ode_init/finetune_ode_init.sh

@@ -1,38 +1,36 @@
 #!/bin/bash
 
 export WANDB_BASE_URL="https://api.wandb.ai"
-export WANDB_MODE=offline
+export WANDB_MODE=online
 export TOKENIZERS_PARALLELISM=false
 
-MODEL_PATH="hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v"
-DATA_DIR="data/ode-preprocessing-hy15-test/"
-VALIDATION_DATASET_FILE="data/validation_64.json"
+MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
+DATA_DIR="data/crush-smol_processed_t2v_1_3b_ode_init/"
+VALIDATION_DATASET_FILE="$(dirname "$0")/validation.json"
 NUM_GPUS=1
 # IP=[MASTER NODE IP]
 
 # Training arguments
 training_args=(
   --tracker_project_name "wan_ode_init"
-  --output_dir "ode_init_hy15_test"
-  --wandb_run_name "vidprom_bz128_1e-5"
-  # --resume_from_checkpoint "ode_init_diffusers/"
-  # --warp_denoising_step
-  # --log_visualization
-  --max_train_steps 6001
+  --output_dir "wan_ode_init_crush_smol"
+  --override_transformer_cls_name "CausalWanTransformer3DModel"
+  --wandb_run_name "wan_ode_init_crush_smol"
+  --max_train_steps 6000
   --train_batch_size 1
   --train_sp_batch_size 1
   --gradient_accumulation_steps 1
-  --num_latent_t 19
+  --num_latent_t 21
   --num_height 480
   --num_width 832
-  --num_frames 73
-  --dmd_denoising_steps "1000,750,500,250"
+  --num_frames 77
+  --warp_denoising_step
   --enable_gradient_checkpointing_type "full"
 )
 
 # Parallel arguments
 parallel_args=(
-  --num_gpus 1
+  --num_gpus $NUM_GPUS
   --sp_size 1
   --tp_size 1
   --hsdp_replicate_dim 1
@@ -53,17 +51,18 @@ dataset_args=(
 
 # Validation arguments
 validation_args=(
-  # --log_validation
+  --log_validation
   --validation_dataset_file "$VALIDATION_DATASET_FILE"
-  --validation_steps 200
+  --validation_steps 50
   --validation_sampling_steps "50"
   --validation_guidance_scale "6.0"
 )
 
 # Optimizer arguments
 optimizer_args=(
-  --learning_rate 1e-5
+  --learning_rate 6e-6
   --mixed_precision "bf16"
+  --weight_only_checkpointing_steps 1000
   --training_state_checkpointing_steps 1000
   --weight_decay 1e-4
   --max_grad_norm 1.0
@@ -79,7 +78,6 @@ miscellaneous_args=(
   --dit_precision "fp32"
   --num_euler_timesteps 50
   --ema_start_step 0
-  # --enable_gradient_checkpointing_type "full"
 )
 
 # If you do not have 32 GPUs and to fit in memory, you can: 1. increase sp_size. 2. reduce num_latent_t

examples/training/consistency_finetune/causal_ode_init/hy15_finetune_ode_init.slurm

@@ -1,132 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=hy15_ode_3333
-#SBATCH --partition=main
-#SBATCH --nodes=8
-#SBATCH --ntasks=8
-#SBATCH --ntasks-per-node=1
-#SBATCH --gres=gpu:8
-#SBATCH --cpus-per-task=128
-#SBATCH --mem=1440G
-#SBATCH --output=hy15_ode_3333_output/hy15_ode_3333_%j.out
-#SBATCH --error=hy15_ode_3333_output/hy15_ode_3333_%j.err
-#SBATCH --exclusive
-
-# Basic Info
-export NCCL_P2P_DISABLE=1
-export TORCH_NCCL_ENABLE_MONITORING=0
-export NCCL_DEBUG_SUBSYS=INIT,NET
-# different cache dir for different processes
-export TRITON_CACHE_DIR=/tmp/triton_cache_${SLURM_PROCID}
-export MASTER_PORT=29500
-export NODE_RANK=$SLURM_PROCID
-nodes=( $(scontrol show hostnames $SLURM_JOB_NODELIST) )
-export MASTER_ADDR=${nodes[0]}
-export TOKENIZERS_PARALLELISM=false
-# export WANDB_API_KEY="8d9f4b39abd68eb4e29f6fc010b7ee71a2207cde"
-export WANDB_API_KEY="50632ebd88ffd970521cec9ab4a1a2d7e85bfc45"
-# export WANDB_API_KEY='your_wandb_api_key_here'
-export WANDB_BASE_URL="https://api.wandb.ai"
-export WANDB_MODE=online
-export FASTVIDEO_ATTENTION_BACKEND=FLASH_ATTN
-
-source ~/conda/miniconda/bin/activate
-conda activate wei-fv-distill
-export HOME="/mnt/weka/home/hao.zhang/wei"
-
-# Configs
-NUM_GPUS=8
-NUM_TOTAL_GPUS=64
-
-MODEL_PATH="weizhou03/SFHunyuanVideo-1.5-Diffusers-480p_t2v"
-DATA_DIR="/mnt/weka/home/hao.zhang/wl/sharefs/wl/release/fv-ode-preprocessing-16k-hy15-121"
-VALIDATION_DATASET_FILE="/mnt/weka/home/hao.zhang/wl/FastVideo/examples/distill/Wan2.2-TI2V-5B-Diffusers/Data-free/validation_64.json"
-# IP=[MASTER NODE IP]
-
-# Training arguments
-training_args=(
-  --tracker_project_name "hy15_ode_init"
-  --output_dir "/mnt/weka/home/hao.zhang/wei/hy15_ode_init_1333_new"
-  --wandb_run_name "hy15_ode_init_1333_new"
-  --warp_denoising_step
-  --dmd_denoising_steps '1000,760,520,280,0'
-  --log_visualization
-  --visualization_steps 100
-  --max_train_steps 4000
-  --train_batch_size 1
-  --train_sp_batch_size 1
-  --gradient_accumulation_steps 2
-  --num_latent_t 31
-  --num_height 480
-  --num_width 848
-  --num_frames 121
-  --enable_gradient_checkpointing_type "full"
-)
-
-# Parallel arguments
-parallel_args=(
-  --num_gpus $NUM_TOTAL_GPUS # 64
-  --sp_size 1
-  --tp_size 1
-  --hsdp_replicate_dim 1 # 64
-  --hsdp_shard_dim $NUM_TOTAL_GPUS
-)
-
-# Model arguments
-model_args=(
-  --model_path $MODEL_PATH
-  --pretrained_model_name_or_path $MODEL_PATH
-  --text-encoder-cpu-offload
-)
-
-# Dataset arguments
-dataset_args=(
-  --data_path "$DATA_DIR" 
-  --dataloader_num_workers 4
-)
-
-# Optimizer arguments
-optimizer_args=(
-  --learning_rate 1e-5
-  --mixed_precision "bf16"
-  --training_state_checkpointing_steps 400
-  --weight_decay 0.01
-  --max_grad_norm 1.0
-)
-
-validation_args=(
-  # --log_validation
-  --validation_dataset_file "$VALIDATION_DATASET_FILE"
-  --validation_steps 10
-  --validation_sampling_steps "4"
-  --validation_guidance_scale "1.0" # not used for dmd inference
-  # --vae_cpu_offload True
-)
-
-# Miscellaneous arguments
-miscellaneous_args=(
-  --inference_mode False
-  --checkpoints_total_limit 6
-  --training_cfg_rate 0.1
-  --multi_phased_distill_schedule "4000-1"
-  --not_apply_cfg_solver
-  --dit_precision "fp32"
-  --num_euler_timesteps 50
-  --ema_start_step 0
-  # --enable_gradient_checkpointing_type "full"
-)
-
-# If you do not have 32 GPUs and to fit in memory, you can: 1. increase sp_size. 2. reduce num_latent_t
-srun torchrun \
---nnodes $SLURM_JOB_NUM_NODES \
---nproc_per_node $NUM_GPUS \
---node_rank $SLURM_PROCID \
---rdzv_backend=c10d \
---rdzv_endpoint="$MASTER_ADDR:$MASTER_PORT" \
-    fastvideo/training/ode_causal_pipeline.py \
-    "${parallel_args[@]}" \
-    "${model_args[@]}" \
-    "${dataset_args[@]}" \
-    "${training_args[@]}" \
-    "${optimizer_args[@]}" \
-    "${validation_args[@]}" \
-    "${miscellaneous_args[@]}"

fastvideo-kernel/CMakeLists.txt

@@ -10,8 +10,6 @@ if(GPU_BACKEND STREQUAL "ROCM")
     enable_language(HIP)
 else()
     enable_language(CUDA)
-    # Ensure CUDA toolkit targets (CUDA::cudart, CUDA::cuda_driver, etc.) are available.
-    find_package(CUDAToolkit REQUIRED)
 endif()
 
 # Import common utils if needed, but we keep it simple for now
@@ -155,30 +153,6 @@ if(BUILD_CXX_KERNELS)
         $<$<COMPILE_LANGUAGE:CUDA>:${CUDA_FLAGS}>
     )
 
-    # Link against Torch libraries to avoid undefined symbols at import time
-    # (e.g., torch::autograd vtables) when loading the extension module.
-    target_link_libraries(fastvideo_kernel_ops PRIVATE ${TORCH_LIBRARIES})
-
-    # Also link against libtorch_python to satisfy Python-binding symbols
-    # (e.g., torch::PyWarningHandler) required by torch/extension.h.
-    execute_process(
-        COMMAND "${Python_EXECUTABLE}" -c "import torch; from pathlib import Path; p=Path(torch.__file__).parent/'lib'; m=sorted(p.glob('libtorch_python*')); print(str(m[0]) if m else '')"
-        OUTPUT_VARIABLE TORCH_PYTHON_LIBRARY_PATH
-        OUTPUT_STRIP_TRAILING_WHITESPACE
-        ERROR_QUIET
-    )
-    if(TORCH_PYTHON_LIBRARY_PATH)
-        message(STATUS "TORCH_PYTHON_LIBRARY_PATH: ${TORCH_PYTHON_LIBRARY_PATH}")
-        target_link_libraries(fastvideo_kernel_ops PRIVATE "${TORCH_PYTHON_LIBRARY_PATH}")
-    else()
-        message(WARNING "Could not locate libtorch_python; fastvideo_kernel_ops may fail to import.")
-    endif()
-
-    # Link CUDA runtime + driver explicitly (fixes missing symbols like cuGetErrorString at import time)
-    if(NOT GPU_BACKEND STREQUAL "ROCM")
-        target_link_libraries(fastvideo_kernel_ops PRIVATE CUDA::cudart CUDA::cuda_driver)
-    endif()
-
     # We install it to fastvideo_kernel/_C so we can load it to register the ops
     install(TARGETS fastvideo_kernel_ops LIBRARY DESTINATION fastvideo_kernel/_C)
 endif()

fastvideo-kernel/README.md

@@ -34,23 +34,12 @@ from fastvideo_kernel import sliding_tile_attention, video_sparse_attn, moba_att
 out = sliding_tile_attention(q, k, v, window_sizes, text_len)
 
 # Example: Video Sparse Attention (with Triton fallback)
-out = video_sparse_attn(q, k, v, block_sizes, block_sizes, topk=5)
+out = video_sparse_attn(q, k, v, block_sizes, topk=5)
 
 # Example: VMoBA
 out = moba_attn_varlen(q, k, v, cu_seqlens_q, cu_seqlens_k, ...)
 ```
 
-## Benchmark
-
-### VSA (block-sparse) TFLOPs
-
-After building/installing `fastvideo-kernel`, run:
-
-```bash
-cd fastvideo-kernel
-python benchmarks/bench_vsa.py --batch_size 1 --num_heads 16 --head_dim 128 --q_seq_lens 49152 --topk 64
-```
-
 ### TurboDiffusion Kernels
 
 This package also includes kernels from [TurboDiffusion](https://github.com/thu-ml/TurboDiffusion), including INT8 GEMM, Quantization, RMSNorm and LayerNorm.

fastvideo-kernel/benchmarks/bench_vsa.py

@@ -1,166 +0,0 @@
-#!/usr/bin/env python3
-"""
-Benchmark VSA *wrapper* performance (forward + backward) and report TFLOPs.
-
-This script benchmarks the autograd-enabled wrapper:
-  - fastvideo_kernel.block_sparse_attn.block_sparse_attn
-
-So measured time includes wrapper overhead (map->index conversion, dispatch) plus kernel time.
-"""
-
-from __future__ import annotations
-
-import argparse
-import os
-import random
-from typing import Tuple, Callable
-
-import numpy as np
-import torch
-
-try:
-    from triton.testing import do_bench
-except Exception as e:  # pragma: no cover
-    raise ImportError("This benchmark requires triton (for triton.testing.do_bench).") from e
-
-
-BLOCK_M = 64
-BLOCK_N = 64
-
-
-def set_seed(seed: int = 42) -> None:
-    random.seed(seed)
-    np.random.seed(seed)
-    torch.manual_seed(seed)
-    torch.cuda.manual_seed(seed)
-    torch.cuda.manual_seed_all(seed)
-
-
-def parse_arguments() -> argparse.Namespace:
-    p = argparse.ArgumentParser(description="Benchmark FastVideo VSA block-sparse attention")
-    p.add_argument("--batch_size", type=int, default=1)
-    p.add_argument("--num_heads", type=int, default=12)
-    p.add_argument("--head_dim", type=int, default=128, choices=[64, 128])
-    p.add_argument("--topk", type=int, default=None, help="KV blocks per Q block (default: ~90%% sparsity)")
-    p.add_argument("--q_seq_lens", type=int, nargs="+", default=[49152], help="Q sequence lengths (must be /64)")
-    p.add_argument("--kv_seq_lens", type=int, nargs="+", default=None, help="KV sequence lengths (defaults to q_seq_len)")
-    p.add_argument("--warmup", type=int, default=5)
-    p.add_argument("--rep", type=int, default=20)
-    p.add_argument("--seed", type=int, default=42)
-    p.add_argument("--dtype", type=str, default="bf16", choices=["bf16", "fp16"])
-    p.add_argument("--force_triton", action="store_true", help="Force wrapper to use Triton path (if supported by shapes).")
-    return p.parse_args()
-
-
-def create_qkv(batch: int, heads: int, q_len: int, kv_len: int, d: int, dtype: torch.dtype) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-    q = torch.randn(batch, heads, q_len, d, dtype=dtype, device="cuda")
-    k = torch.randn(batch, heads, kv_len, d, dtype=dtype, device="cuda")
-    v = torch.randn(batch, heads, kv_len, d, dtype=dtype, device="cuda")
-    return q, k, v
-
-
-def make_block_map(bs: int, h: int, num_q_blocks: int, num_kv_blocks: int, topk: int) -> torch.Tensor:
-    # block_map: [bs, h, num_q_blocks, num_kv_blocks] bool
-    scores = torch.rand(bs, h, num_q_blocks, num_kv_blocks, device="cuda")
-    topk = min(max(1, topk), num_kv_blocks)
-    idx = torch.topk(scores, topk, dim=-1).indices
-    block_map = torch.zeros(bs, h, num_q_blocks, num_kv_blocks, dtype=torch.bool, device="cuda")
-    block_map.scatter_(-1, idx, True)
-    return block_map
-
-
-def flops_sparse_attention(bs: int, h: int, d: int, q_len: int, topk_blocks: int, block_n: int) -> float:
-    # Approx: QK^T + PV, each is ~2*bs*h*q_len*(topk_blocks*block_n)*d
-    return 4.0 * bs * h * d * q_len * (topk_blocks * block_n)
-
-
-def bench_ms(fn: Callable[[], object], warmup: int, rep: int) -> float:
-    return do_bench(fn, warmup=warmup, rep=rep, quantiles=None)
-
-
-def main() -> None:
-    args = parse_arguments()
-    set_seed(args.seed)
-
-    dtype = torch.bfloat16 if args.dtype == "bf16" else torch.float16
-
-    if args.force_triton:
-        os.environ["FASTVIDEO_KERNEL_VSA_FORCE_TRITON"] = "1"
-
-    from fastvideo_kernel.block_sparse_attn import block_sparse_attn
-
-    bs, h, d = args.batch_size, args.num_heads, args.head_dim
-    kv_seq_lens = args.kv_seq_lens
-    if kv_seq_lens is None:
-        kv_seq_lens = args.q_seq_lens
-    if len(kv_seq_lens) != len(args.q_seq_lens):
-        raise ValueError("kv_seq_lens must have the same number of entries as q_seq_lens (or be omitted).")
-
-    print("VSA Block-Sparse Attention Benchmark (WRAPPER)")
-    print(f"device: {torch.cuda.get_device_name(0)}")
-    print(f"batch={bs}, heads={h}, head_dim={d}, dtype={args.dtype}")
-    print(f"BLOCK_M={BLOCK_M}, BLOCK_N={BLOCK_N}")
-    print("NOTE: timings include wrapper overhead (map->index + dispatch).")
-    if args.force_triton:
-        print("dispatch: forced Triton (FASTVIDEO_KERNEL_VSA_FORCE_TRITON=1)")
-    else:
-        print("dispatch: SM90 if available, else Triton")
-
-    for q_len, kv_len in zip(args.q_seq_lens, kv_seq_lens):
-        if q_len % BLOCK_M != 0 or kv_len % BLOCK_N != 0:
-            print(f"[skip] q_len={q_len}, kv_len={kv_len} must be divisible by 64")
-            continue
-
-        num_q_blocks = q_len // BLOCK_M
-        num_kv_blocks = kv_len // BLOCK_N
-        topk = args.topk if args.topk is not None else max(1, num_kv_blocks // 10)
-        topk = min(topk, num_kv_blocks)
-
-        print("\n" + "=" * 80)
-        print(f"q_len={q_len}, kv_len={kv_len}, num_q_blocks={num_q_blocks}, num_kv_blocks={num_kv_blocks}, topk={topk}")
-
-        q, k, v = create_qkv(bs, h, q_len, kv_len, d, dtype)
-        block_map = make_block_map(bs, h, num_q_blocks, num_kv_blocks, topk)
-
-        # Variable block sizes: default full blocks (64 tokens per KV block)
-        variable_block_sizes = torch.full((num_kv_blocks,), BLOCK_N, dtype=torch.int32, device="cuda")
-
-        def _fwd():
-            return block_sparse_attn(q, k, v, block_map, variable_block_sizes)
-
-        fwd_ms = bench_ms(_fwd, warmup=args.warmup, rep=args.rep)
-
-        # Backward benchmark (wrapper autograd). We build the graph once, then repeatedly run backward
-        # on the retained graph so bwd timing excludes the forward compute.
-        q_ = q.detach().requires_grad_(True)
-        k_ = k.detach().requires_grad_(True)
-        v_ = v.detach().requires_grad_(True)
-        o_, _aux_ = block_sparse_attn(q_, k_, v_, block_map, variable_block_sizes)
-        og = torch.randn_like(o_)
-        loss = (o_ * og).sum()
-
-        for _ in range(max(1, args.warmup // 2)):
-            torch.autograd.grad(loss, (q_, k_, v_), retain_graph=True)
-        torch.cuda.synchronize()
-
-        bwd_ms = bench_ms(
-            lambda: torch.autograd.grad(loss, (q_, k_, v_), retain_graph=True),
-            warmup=0,
-            rep=max(5, args.rep // 2),
-        )
-
-        flops = flops_sparse_attention(bs, h, d, q_len, topk, BLOCK_N)
-        fwd_tflops = flops / fwd_ms * 1e-12 * 1e3
-        # Rough backward multiplier (attention backward typically ~2-3x forward)
-        bwd_tflops = (2.5 * flops) / bwd_ms * 1e-12 * 1e3
-
-        print(f"fwd(wrapper): {fwd_ms:.3f} ms  | {fwd_tflops:.2f} TFLOPs (approx)")
-        print(f"bwd(wrapper): {bwd_ms:.3f} ms  | {bwd_tflops:.2f} TFLOPs (approx)")
-
-
-if __name__ == "__main__":
-    if not torch.cuda.is_available():
-        raise RuntimeError("CUDA is required for this benchmark.")
-    main()
-
-

fastvideo-kernel/csrc/attention/block_sparse_h100.cu

@@ -639,8 +639,7 @@ void bwd_attend_ker(const __grid_constant__ bwd_globals<D> g) {
 
     // store kq and vq
 
-    // ! the following two line seems unnecessary.
-    // tma::store_async_wait(); // ensure qg is finished
+    // ensuring all writes are finished
     __syncthreads();
 
     warpgroup::store(kg_smem[0], kg_reg);
@@ -661,6 +660,145 @@ void bwd_attend_ker(const __grid_constant__ bwd_globals<D> g) {
     tma::store_async_wait(); 
 }
 
+
+template<int D>
+void block_sparse_attention_forward_impl(
+    bf16* d_q, bf16* d_k, bf16* d_v, float* d_l, bf16* d_o,
+    int batch, int qo_heads, int kv_heads, int seq_len, int hr,
+    int max_kv_blocks_per_q, 
+    int32_t* q2k_block_sparse_index_ptr,
+    int32_t* q2k_block_sparse_num_ptr,
+    int32_t* block_size_ptr,
+    cudaStream_t stream
+) {
+    using K = fwd_attend_ker_tile_dims<D>;
+    using q_tile    =         st_bf<K::qo_height, K::tile_width>;
+    using k_tile    =         st_bf<K::kv_height, K::tile_width>;
+    using v_tile    =         st_bf<K::kv_height, K::tile_width>;
+    using l_col_vec = col_vec<st_fl<K::qo_height, K::tile_width>>;
+    using o_tile    =         st_bf<K::qo_height, K::tile_width>;
+
+    using q_global = gl<bf16,  -1, -1, -1, -1, q_tile>;
+    using k_global = gl<bf16,  -1, -1, -1, -1, k_tile>;
+    using v_global = gl<bf16,  -1, -1, -1, -1, v_tile>;
+    using l_global = gl<float, -1, -1, -1, -1, l_col_vec>;
+    using o_global = gl<bf16,  -1, -1, -1, -1, o_tile>;
+
+    using globals      = fwd_globals<D>;
+
+    q_global qg_arg{d_q, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), static_cast<uint32_t>(D)};
+    k_global kg_arg{d_k, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(seq_len), static_cast<uint32_t>(D)};
+    v_global vg_arg{d_v, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(seq_len), static_cast<uint32_t>(D)};
+    l_global lg_arg{d_l, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,   static_cast<unsigned int>(seq_len)};
+    o_global og_arg{d_o, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), static_cast<uint32_t>(D)};
+
+    globals g{
+        qg_arg, kg_arg, vg_arg, lg_arg, og_arg, 
+        static_cast<int>(seq_len), static_cast<int>(hr), static_cast<int>(max_kv_blocks_per_q), 
+        q2k_block_sparse_index_ptr, q2k_block_sparse_num_ptr, block_size_ptr
+    };
+
+    // Shared memory size for the kernel
+    // 54000 bytes is calibrated for H100 shared memory constraints for these tile sizes
+    constexpr int mem_size = 54000;
+
+    dim3 grid(seq_len/(64), qo_heads, batch);
+
+    cudaFuncSetAttribute(
+        fwd_attend_ker<D>,
+        cudaFuncAttributeMaxDynamicSharedMemorySize,
+        mem_size
+    );
+
+    fwd_attend_ker<D><<<grid, (128), mem_size, stream>>>(g);
+}
+
+template<int D>
+void block_sparse_attention_backward_impl(
+    bf16* d_q, bf16* d_k, bf16* d_v, bf16* d_o, bf16* d_og, float* d_l, float* d_d, float* d_qg, float* d_kg, float* d_vg,
+    int batch, int qo_heads, int kv_heads, int seq_len, int hr, int max_q_blocks_per_kv,
+    int32_t* k2q_block_sparse_index_ptr,
+    int32_t* k2q_block_sparse_num_ptr,
+    int32_t* block_size_ptr,
+    cudaStream_t stream
+) {
+    using G = bwd_attend_ker_tile_dims<D>;
+    using og_tile = st_bf<4*16, D>;
+    using o_tile  = st_bf<4*16, D>;
+    using d_tile  = col_vec<st_fl<4*16, D>>;
+
+    using og_global = gl<bf16,  -1, -1, -1, -1, og_tile>;
+    using o_global  = gl<bf16,  -1, -1, -1, -1, o_tile>;
+    using d_global  = gl<float, -1, -1, -1, -1, d_tile>;
+
+    using prep_globals = bwd_prep_globals<D>;
+
+    constexpr int mem_size_prep = kittens::MAX_SHARED_MEMORY;
+    int threads_prep = PREP_NUM_WARPS * kittens::WARP_THREADS;
+    dim3 grid_bwd_prep(seq_len/(PREP_NUM_WARPS*kittens::TILE_ROW_DIM<bf16>*4), qo_heads, batch);
+
+    og_global prep_og_arg{d_og, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), static_cast<uint32_t>(D)};
+    o_global  prep_o_arg {d_o,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), static_cast<uint32_t>(D)};
+    d_global  prep_d_arg {d_d,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,  static_cast<unsigned int>(seq_len)};
+
+    prep_globals bwd_g{prep_og_arg, prep_o_arg, prep_d_arg};
+
+    cudaFuncSetAttribute(
+        bwd_attend_prep_ker<D>,
+        cudaFuncAttributeMaxDynamicSharedMemorySize,
+        mem_size_prep
+    );
+    bwd_attend_prep_ker<D><<<grid_bwd_prep, threads_prep, mem_size_prep, stream>>>(bwd_g);
+
+    using bwd_q_tile    =         st_bf<G::tile_h_qo, G::tile_width>;
+    using bwd_k_tile    =         st_bf<G::tile_h,    G::tile_width>;
+    using bwd_v_tile    =         st_bf<G::tile_h,    G::tile_width>;
+    using bwd_og_tile   =         st_bf<G::tile_h_qo, G::tile_width>;
+    using bwd_qg_tile   =         st_fl<G::tile_h_qo, G::tile_width>;
+    using bwd_kg_tile   =         st_fl<G::tile_h,    G::tile_width>;
+    using bwd_vg_tile   =         st_fl<G::tile_h,    G::tile_width>;
+    using bwd_l_tile    = row_vec<st_fl<G::tile_h_qo, G::tile_h>>;
+    using bwd_d_tile    = row_vec<st_fl<G::tile_h_qo, G::tile_h>>;
+
+    using bwd_q_global  = gl<bf16,  -1, -1, -1, -1, bwd_q_tile>;
+    using bwd_k_global  = gl<bf16,  -1, -1, -1, -1, bwd_k_tile>;
+    using bwd_v_global  = gl<bf16,  -1, -1, -1, -1, bwd_v_tile>;
+    using bwd_og_global = gl<bf16,  -1, -1, -1, -1, bwd_og_tile>;
+    using bwd_qg_global = gl<float, -1, -1, -1, -1, bwd_qg_tile>;
+    using bwd_kg_global = gl<float, -1, -1, -1, -1, bwd_kg_tile>;
+    using bwd_vg_global = gl<float, -1, -1, -1, -1, bwd_vg_tile>;
+    using bwd_l_global  = gl<float, -1, -1, -1, -1, bwd_l_tile>;
+    using bwd_d_global  = gl<float, -1, -1, -1, -1, bwd_d_tile>;
+
+    using bwd_global_args = bwd_globals<D>;
+
+    bwd_q_global  bwd_q_arg {d_q,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), static_cast<uint32_t>(D)};
+    bwd_k_global  bwd_k_arg {d_k,  static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(seq_len), static_cast<uint32_t>(D)};
+    bwd_v_global  bwd_v_arg {d_v,  static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(seq_len), static_cast<uint32_t>(D)};
+    bwd_og_global bwd_og_arg{d_og, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), static_cast<uint32_t>(D)};
+    bwd_qg_global bwd_qg_arg{d_qg, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), static_cast<uint32_t>(D)};
+    bwd_kg_global bwd_kg_arg{d_kg, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(seq_len), static_cast<uint32_t>(D)};
+    bwd_vg_global bwd_vg_arg{d_vg, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(seq_len), static_cast<uint32_t>(D)};
+    bwd_l_global  bwd_l_arg {d_l,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,  static_cast<unsigned int>(seq_len)};
+    bwd_d_global  bwd_d_arg {d_d,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,  static_cast<unsigned int>(seq_len)};
+
+    bwd_global_args bwd_global{bwd_q_arg, bwd_k_arg, bwd_v_arg, bwd_og_arg, bwd_qg_arg, bwd_kg_arg, bwd_vg_arg, bwd_l_arg, bwd_d_arg, 
+                        static_cast<int>(seq_len), static_cast<int>(hr), static_cast<int>(max_q_blocks_per_kv),
+                        k2q_block_sparse_index_ptr, k2q_block_sparse_num_ptr, block_size_ptr};
+
+    dim3 grid_bwd_main(seq_len/64, qo_heads, batch);
+    int threads_main = 128;
+    // Calibrated shared memory sizes for different head dimensions
+    int bwd_mem_size = (D == 64) ? 72000 : 113000;
+
+    cudaFuncSetAttribute(
+        bwd_attend_ker<D>,
+        cudaFuncAttributeMaxDynamicSharedMemorySize,
+        bwd_mem_size
+    );
+    bwd_attend_ker<D><<<grid_bwd_main, threads_main, bwd_mem_size, stream>>>(bwd_global);
+}
+
 #include "pyutils/torch_helpers.cuh"
 #include <ATen/cuda/CUDAContext.h>
 #include <iostream>
@@ -672,32 +810,23 @@ block_sparse_attention_forward(
     torch::Tensor v, 
     torch::Tensor q2k_block_sparse_index, 
     torch::Tensor q2k_block_sparse_num, 
-    torch::Tensor kv_block_size
+    torch::Tensor block_size
 )
 {
     CHECK_INPUT(q);
     CHECK_INPUT(k);
     CHECK_INPUT(v);
 
-    // q shape: (batch, qo_heads, q_seq_len, head_dim)
-    // k shape: (batch, kv_heads, kv_seq_len, head_dim)
-    // v shape: (batch, kv_heads, kv_seq_len, head_dim)
-    // q2k_block_sparse_index shape: (batch, qo_heads, num_q_blocks, max_kv_blocks_per_q)
-    // q2k_block_sparse_num shape: (batch, qo_heads, num_q_blocks)
-    // kv_block_size shape: (num_kv_blocks) This does not need other dimensions because across all batch/heads the padding is the same.
-    
     auto batch    = q.size(0);
-    auto q_seq_len  = q.size(2);
-    auto kv_seq_len = k.size(2);
+    auto seq_len  = q.size(2); 
     auto head_dim = q.size(3); 
     auto qo_heads = q.size(1);
     auto kv_heads = k.size(1);
     auto max_kv_blocks_per_q = q2k_block_sparse_index.size(3);
-    auto num_q_blocks = q2k_block_sparse_index.size(2);
-    auto num_kv_blocks = kv_block_size.size(0);
+    auto num_q_blocks = block_size.size(0);
     TORCH_CHECK(batch==1, "Batch size dim will be removed in the future, please set batch to 1");
-    TORCH_CHECK(num_q_blocks * BLOCK_M == q_seq_len, "This kernel supports variable q block size, but it assumes the input sequence is properly padded.");
-    TORCH_CHECK(num_kv_blocks * BLOCK_M == kv_seq_len, "This kernel supports variable kv block size, but it assumes the input sequence is properly padded.");
+    TORCH_CHECK(num_q_blocks * 64 == seq_len, "This kernel supports variable block size, but it assumes the input sequence is properly padded.");
+    TORCH_CHECK(num_q_blocks == q2k_block_sparse_index.size(2), "Number of Q blocks does not match between q2k_block_sparse_index and block_size");
     // check to see that these dimensions match for all inputs
     TORCH_CHECK(q.size(0) == batch, "Q batch dimension - idx 0 - must match for all inputs");
     TORCH_CHECK(k.size(0) == batch, "K batch dimension - idx 0 - must match for all inputs");
@@ -705,9 +834,11 @@ block_sparse_attention_forward(
     TORCH_CHECK(q2k_block_sparse_index.size(0) == batch, "q2k_block_sparse_index batch dimension - idx 0 - must match for all inputs");
     TORCH_CHECK(q2k_block_sparse_num.size(0) == batch, "q2k_block_sparse_num batch dimension - idx 0 - must match for all inputs");
 
-    TORCH_CHECK(v.size(2) == kv_seq_len, "V sequence length dimension - idx 2 - must match K inputs");
-    TORCH_CHECK(q2k_block_sparse_num.size(2) == num_q_blocks, "q2k_block_sparse_num idx 2 - must match num_q_blocks");
-
+    TORCH_CHECK(q.size(2) == seq_len, "Q sequence length dimension - idx 2 - must match for all inputs");
+    TORCH_CHECK(k.size(2) == seq_len, "K sequence length dimension - idx 2 - must match for all inputs");
+    TORCH_CHECK(v.size(2) == seq_len, "V sequence length dimension - idx 2 - must match for all inputs");
+    TORCH_CHECK(q2k_block_sparse_index.size(2) == seq_len / BLOCK_M, "q2k_block_sparse_index idx 2 - must match seq_len / BLOCK_M");
+    TORCH_CHECK(q2k_block_sparse_num.size(2) == seq_len / BLOCK_M, "q2k_block_sparse_num idx 2 - must match seq_len / BLOCK_M");
 
     TORCH_CHECK(q.size(3) == head_dim, "Q head dimension - idx 3 - must match for all non-vector inputs");
     TORCH_CHECK(k.size(3) == head_dim, "K head dimension - idx 3 - must match for all non-vector inputs");
@@ -733,12 +864,12 @@ block_sparse_attention_forward(
     // for the returned outputs
     torch::Tensor o     = torch::empty({static_cast<const uint>(batch), 
                                         static_cast<const uint>(qo_heads), 
-                                        static_cast<const uint>(q_seq_len), 
+                                        static_cast<const uint>(seq_len), 
                                         static_cast<const uint>(head_dim)}, v.options());
     
     torch::Tensor l_vec = torch::empty({static_cast<const uint>(batch), 
                                         static_cast<const uint>(qo_heads), 
-                                        static_cast<const uint>(q_seq_len), 
+                                        static_cast<const uint>(seq_len), 
                                         static_cast<const uint>(1)}, 
                                         torch::TensorOptions().dtype(torch::kFloat).device(q.device()).memory_format(at::MemoryFormat::Contiguous));
         
@@ -749,110 +880,32 @@ block_sparse_attention_forward(
     float* l_ptr = reinterpret_cast<float*>(l_vec.data_ptr<float>());
     float* d_l   = reinterpret_cast<float*>(l_ptr);
 
-    //cudadevicesynchronize();
     const c10::cuda::OptionalCUDAGuard device_guard(q.device());    
     const cudaStream_t stream = at::cuda::getCurrentCUDAStream().stream(); 
 
+    // Temporated implementation to avoid code duplication between head_dim=64 and 128
     if (head_dim == 64) {
-        using q_tile    =         st_bf<fwd_attend_ker_tile_dims<64>::qo_height, fwd_attend_ker_tile_dims<64>::tile_width>;
-        using k_tile    =         st_bf<fwd_attend_ker_tile_dims<64>::kv_height, fwd_attend_ker_tile_dims<64>::tile_width>;
-        using v_tile    =         st_bf<fwd_attend_ker_tile_dims<64>::kv_height, fwd_attend_ker_tile_dims<64>::tile_width>;
-        using l_col_vec = col_vec<st_fl<fwd_attend_ker_tile_dims<64>::qo_height, fwd_attend_ker_tile_dims<64>::tile_width>>;
-        using o_tile    =         st_bf<fwd_attend_ker_tile_dims<64>::qo_height, fwd_attend_ker_tile_dims<64>::tile_width>;
-
-        using q_global = gl<bf16,  -1, -1, -1, -1, q_tile>;
-        using k_global = gl<bf16,  -1, -1, -1, -1, k_tile>;
-        using v_global = gl<bf16,  -1, -1, -1, -1, v_tile>;
-        using l_global = gl<float, -1, -1, -1, -1, l_col_vec>;
-        using o_global = gl<bf16,  -1, -1, -1, -1, o_tile>;
-
-        using globals      = fwd_globals<64>;
-
-        q_global qg_arg{d_q, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(q_seq_len), 64U};
-        k_global kg_arg{d_k, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(kv_seq_len), 64U};
-        v_global vg_arg{d_v, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(kv_seq_len), 64U};
-        l_global lg_arg{d_l, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,   static_cast<unsigned int>(q_seq_len)};
-        o_global og_arg{d_o, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(q_seq_len), 64U};
-
-        globals g{
-            qg_arg, 
-            kg_arg, 
-            vg_arg, 
-            lg_arg, 
-            og_arg, 
-            static_cast<int>(q_seq_len), 
-            static_cast<int>(hr), 
-            static_cast<int>(max_kv_blocks_per_q), 
-            reinterpret_cast<int32_t*>(q2k_block_sparse_index.data_ptr()), 
+        block_sparse_attention_forward_impl<64>(
+            d_q, d_k, d_v, d_l, d_o,
+            batch, qo_heads, kv_heads, seq_len, hr,
+            max_kv_blocks_per_q,
+            reinterpret_cast<int32_t*>(q2k_block_sparse_index.data_ptr()),
             reinterpret_cast<int32_t*>(q2k_block_sparse_num.data_ptr()),
-            reinterpret_cast<int32_t*>(kv_block_size.data_ptr())
-        };
-
-        constexpr int mem_size = 54000;
-
-        dim3 grid(q_seq_len/(BLOCK_M), qo_heads, batch);
-
-        cudaFuncSetAttribute(
-            fwd_attend_ker<64>,
-            cudaFuncAttributeMaxDynamicSharedMemorySize,
-            mem_size
+            reinterpret_cast<int32_t*>(block_size.data_ptr()),
+            stream
         );
-
-        fwd_attend_ker<64><<<grid, (128), mem_size, stream>>>(g);
-
-        CHECK_CUDA_ERROR(cudaGetLastError());
-        // cudaStreamSynchronize(stream);
-    }
-
-    if (head_dim == 128) {
-        using q_tile    =         st_bf<fwd_attend_ker_tile_dims<128>::qo_height, fwd_attend_ker_tile_dims<128>::tile_width>;
-        using k_tile    =         st_bf<fwd_attend_ker_tile_dims<128>::kv_height, fwd_attend_ker_tile_dims<128>::tile_width>;
-        using v_tile    =         st_bf<fwd_attend_ker_tile_dims<128>::kv_height, fwd_attend_ker_tile_dims<128>::tile_width>;
-        using l_col_vec = col_vec<st_fl<fwd_attend_ker_tile_dims<128>::qo_height, fwd_attend_ker_tile_dims<128>::tile_width>>;
-        using o_tile    =         st_bf<fwd_attend_ker_tile_dims<128>::qo_height, fwd_attend_ker_tile_dims<128>::tile_width>;
-
-        using q_global = gl<bf16,  -1, -1, -1, -1, q_tile>;
-        using k_global = gl<bf16,  -1, -1, -1, -1, k_tile>;
-        using v_global = gl<bf16,  -1, -1, -1, -1, v_tile>;
-        using l_global = gl<float, -1, -1, -1, -1, l_col_vec>;
-        using o_global = gl<bf16,  -1, -1, -1, -1, o_tile>;
-
-        using globals      = fwd_globals<128>;
-
-        q_global qg_arg{d_q, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(q_seq_len), 128U};
-        k_global kg_arg{d_k, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(kv_seq_len), 128U};
-        v_global vg_arg{d_v, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(kv_seq_len), 128U};
-        l_global lg_arg{d_l, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,   static_cast<unsigned int>(q_seq_len)};
-        o_global og_arg{d_o, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(q_seq_len), 128U};
-
-        globals g{
-            qg_arg, 
-            kg_arg, 
-            vg_arg, 
-            lg_arg, 
-            og_arg, 
-            static_cast<int>(q_seq_len), 
-            static_cast<int>(hr), 
-            static_cast<int>(max_kv_blocks_per_q), 
-            reinterpret_cast<int32_t*>(q2k_block_sparse_index.data_ptr()), 
+    } else if (head_dim == 128) {
+        block_sparse_attention_forward_impl<128>(
+            d_q, d_k, d_v, d_l, d_o,
+            batch, qo_heads, kv_heads, seq_len, hr,
+            max_kv_blocks_per_q,
+            reinterpret_cast<int32_t*>(q2k_block_sparse_index.data_ptr()),
             reinterpret_cast<int32_t*>(q2k_block_sparse_num.data_ptr()),
-            reinterpret_cast<int32_t*>(kv_block_size.data_ptr())
-        };
-
-        constexpr int mem_size = 54000;
-
-        dim3 grid(q_seq_len/(BLOCK_M), qo_heads, batch);
-
-        cudaFuncSetAttribute(
-            fwd_attend_ker<128>,
-            cudaFuncAttributeMaxDynamicSharedMemorySize,
-            mem_size
+            reinterpret_cast<int32_t*>(block_size.data_ptr()),
+            stream
         );
-
-        fwd_attend_ker<128><<<grid, (128), mem_size, stream>>>(g);
-
-        CHECK_CUDA_ERROR(cudaGetLastError());
-        // cudaStreamSynchronize(stream);
+    } else {
+        TORCH_CHECK(false, "Unsupported head_dim: ", head_dim, ". Only 64 and 128 are supported.");
     }
 
     return {o, l_vec};
@@ -868,7 +921,7 @@ block_sparse_attention_backward(torch::Tensor q,
                    torch::Tensor og,
                    torch::Tensor k2q_block_sparse_index,
                    torch::Tensor k2q_block_sparse_num,
-                   torch::Tensor kv_block_size)
+                   torch::Tensor block_size)
 {
     CHECK_INPUT(q);
     CHECK_INPUT(k);
@@ -877,23 +930,11 @@ block_sparse_attention_backward(torch::Tensor q,
     CHECK_INPUT(o);
     CHECK_INPUT(og);
 
-    // q: [batch, qo_heads, q_seq_len, head_dim]
-    // k: [batch, kv_heads, kv_seq_len, head_dim]
-    // v: [batch, kv_heads, kv_seq_len, head_dim]
-    // o: [batch, qo_heads, q_seq_len, head_dim]
-    // l_vec: [batch, qo_heads, q_seq_len, 1]
-    // og: [batch, qo_heads, q_seq_len, head_dim]
-    // k2q_block_sparse_index: [batch, kv_heads, num_kv_blocks, max_num_q_blocks]
-    // k2q_block_sparse_num: [batch, kv_heads, num_kv_blocks]
-    // kv_block_size: [num_kv_blocks]
-
     auto batch    = q.size(0);
-    auto q_seq_len  = q.size(2);
-    auto kv_seq_len = k.size(2);
+    auto seq_len  = q.size(2);
     auto head_dim = q.size(3);
     auto max_q_blocks_per_kv = k2q_block_sparse_index.size(3);
-    auto num_kv_blocks = kv_block_size.size(0);
-    TORCH_CHECK(k2q_block_sparse_index.size(2) == num_kv_blocks, "k2q_block_sparse_index.size(2) must match num_kv_blocks (kv_block_size.size(0))");
+    TORCH_CHECK(k2q_block_sparse_index.size(2) == block_size.size(0), "k2q_block_sparse_index.size(2) must match block_size.size(0)");
     // check to see that these dimensions match for all inputs
     TORCH_CHECK(q.size(0)     == batch, "Q  batch dimension - idx 0 - must match for all inputs");
     TORCH_CHECK(k.size(0)     == batch, "K  batch dimension - idx 0 - must match for all inputs");
@@ -904,18 +945,23 @@ block_sparse_attention_backward(torch::Tensor q,
     TORCH_CHECK(k2q_block_sparse_index.size(0) == batch, "k2q_block_sparse_index batch dimension - idx 0 - must match for all inputs");
     TORCH_CHECK(k2q_block_sparse_num.size(0) == batch, "k2q_block_sparse_num batch dimension - idx 0 - must match for all inputs");
 
-    TORCH_CHECK(v.size(2)     == kv_seq_len, "V  sequence length dimension - idx 2 - must match K sequence length");
-    TORCH_CHECK(l_vec.size(2) == q_seq_len, "L  sequence length dimension - idx 2 - must match Q sequence length");
-    TORCH_CHECK(o.size(2)     == q_seq_len, "O  sequence length dimension - idx 2 - must match Q sequence length");
-    TORCH_CHECK(og.size(2)    == q_seq_len, "OG sequence length dimension - idx 2 - must match Q sequence length");
-    TORCH_CHECK(k2q_block_sparse_index.size(2) == num_kv_blocks, "k2q_block_sparse_index idx 2 - must match num_kv_blocks (kv_block_size.size(0))");
-    TORCH_CHECK(k2q_block_sparse_num.size(2) == num_kv_blocks, "k2q_block_sparse_num idx 2 - must match num_kv_blocks (kv_block_size.size(0))");
+
+    TORCH_CHECK(q.size(2)     == seq_len, "Q  sequence length dimension - idx 2 - must match for all inputs");
+    TORCH_CHECK(k.size(2)     == seq_len, "K  sequence length dimension - idx 2 - must match for all inputs");
+    TORCH_CHECK(v.size(2)     == seq_len, "V  sequence length dimension - idx 2 - must match for all inputs");
+    TORCH_CHECK(l_vec.size(2) == seq_len, "L  sequence length dimension - idx 2 - must match for all inputs");
+    TORCH_CHECK(o.size(2)     == seq_len, "O  sequence length dimension - idx 2 - must match for all inputs");
+    TORCH_CHECK(og.size(2)    == seq_len, "OG sequence length dimension - idx 2 - must match for all inputs");
+    TORCH_CHECK(k2q_block_sparse_index.size(2) == seq_len / BLOCK_N, "k2q_block_sparse_index idx 2 - must match seq_len / BLOCK_N");
+    TORCH_CHECK(k2q_block_sparse_num.size(2) == seq_len / BLOCK_N, "k2q_block_sparse_num idx 2 - must match seq_len / BLOCK_N");
 
     TORCH_CHECK(q.size(3)  == head_dim, "Q  head dimension - idx 3 - must match for all non-vector inputs");
     TORCH_CHECK(k.size(3)  == head_dim, "K  head dimension - idx 3 - must match for all non-vector inputs");
     TORCH_CHECK(v.size(3)  == head_dim, "V  head dimension - idx 3 - must match for all non-vector inputs");
     TORCH_CHECK(o.size(3)  == head_dim, "O  head dimension - idx 3 - must match for all non-vector inputs");
     TORCH_CHECK(og.size(3) == head_dim, "OG head dimension - idx 3 - must match for all non-vector inputs");
+    
+
 
     auto qo_heads = q.size(1);
     auto kv_heads = k.size(1);
@@ -942,20 +988,20 @@ block_sparse_attention_backward(torch::Tensor q,
 
     torch::Tensor qg = torch::zeros({static_cast<const uint>(batch), 
                                      static_cast<const uint>(qo_heads), 
-                                     static_cast<const uint>(q_seq_len), 
+                                     static_cast<const uint>(seq_len), 
                                      static_cast<const uint>(head_dim)},   l_vec.options());
     torch::Tensor kg = torch::zeros({static_cast<const uint>(batch), 
                                      static_cast<const uint>(kv_heads), 
-                                     static_cast<const uint>(kv_seq_len), 
+                                     static_cast<const uint>(seq_len), 
                                      static_cast<const uint>(head_dim)},   l_vec.options());
     torch::Tensor vg = torch::zeros({static_cast<const uint>(batch), 
                                      static_cast<const uint>(kv_heads), 
-                                     static_cast<const uint>(kv_seq_len), 
+                                     static_cast<const uint>(seq_len), 
                                      static_cast<const uint>(head_dim)},   l_vec.options());
     
     torch::Tensor d_vec = torch::empty({static_cast<const uint>(batch), 
                                         static_cast<const uint>(qo_heads), 
-                                        static_cast<const uint>(q_seq_len), 
+                                        static_cast<const uint>(seq_len), 
                                         static_cast<const uint>(1)},       l_vec.options());
 
     float*         qg_ptr = qg.data_ptr<float>();
@@ -984,7 +1030,7 @@ block_sparse_attention_backward(torch::Tensor q,
     //  cudaStreamSynchronize(stream);
 
     // TORCH_CHECK(seq_len % (4*kittens::TILE_DIM*4) == 0, "sequence length must be divisible by 256");
-    dim3 grid_bwd(q_seq_len/(PREP_NUM_WARPS*kittens::TILE_ROW_DIM<bf16>*4), qo_heads, batch);
+    dim3 grid_bwd(seq_len/(PREP_NUM_WARPS*kittens::TILE_ROW_DIM<bf16>*4), qo_heads, batch);
 
     if (head_dim == 64)  {
         using og_tile = st_bf<4*16, 64>;
@@ -997,9 +1043,9 @@ block_sparse_attention_backward(torch::Tensor q,
 
         using bwd_prep_globals = bwd_prep_globals<64>;
 
-        og_global prep_og_arg{d_og, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(q_seq_len), 64U};
-        o_global  prep_o_arg {d_o,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(q_seq_len), 64U};
-        d_global  prep_d_arg {d_d,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,  static_cast<unsigned int>(q_seq_len)};
+        og_global prep_og_arg{d_og, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), 64U};
+        o_global  prep_o_arg {d_o,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), 64U};
+        d_global  prep_d_arg {d_d,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,  static_cast<unsigned int>(seq_len)};
 
         bwd_prep_globals bwd_g{prep_og_arg, prep_o_arg, prep_d_arg};
 
@@ -1036,15 +1082,15 @@ block_sparse_attention_backward(torch::Tensor q,
 
         using bwd_global_args = bwd_globals<64>;
 
-        bwd_q_global  bwd_q_arg {d_q,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(q_seq_len), 64U};
-        bwd_k_global  bwd_k_arg {d_k,  static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(kv_seq_len), 64U};
-        bwd_v_global  bwd_v_arg {d_v,  static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(kv_seq_len), 64U};
-        bwd_og_global bwd_og_arg{d_og, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(q_seq_len), 64U};
-        bwd_qg_global bwd_qg_arg{d_qg, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(q_seq_len), 64U};
-        bwd_kg_global bwd_kg_arg{d_kg, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(kv_seq_len), 64U};
-        bwd_vg_global bwd_vg_arg{d_vg, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(kv_seq_len), 64U};
-        bwd_l_global  bwd_l_arg {d_l,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,  static_cast<unsigned int>(q_seq_len)};
-        bwd_d_global  bwd_d_arg {d_d,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,  static_cast<unsigned int>(q_seq_len)};
+        bwd_q_global  bwd_q_arg {d_q,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), 64U};
+        bwd_k_global  bwd_k_arg {d_k,  static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(seq_len), 64U};
+        bwd_v_global  bwd_v_arg {d_v,  static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(seq_len), 64U};
+        bwd_og_global bwd_og_arg{d_og, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), 64U};
+        bwd_qg_global bwd_qg_arg{d_qg, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), 64U};
+        bwd_kg_global bwd_kg_arg{d_kg, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(seq_len), 64U};
+        bwd_vg_global bwd_vg_arg{d_vg, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(seq_len), 64U};
+        bwd_l_global  bwd_l_arg {d_l,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,  static_cast<unsigned int>(seq_len)};
+        bwd_d_global  bwd_d_arg {d_d,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,  static_cast<unsigned int>(seq_len)};
 
         bwd_global_args bwd_global{bwd_q_arg, 
                         bwd_k_arg, 
@@ -1055,14 +1101,14 @@ block_sparse_attention_backward(torch::Tensor q,
                         bwd_vg_arg, 
                         bwd_l_arg, 
                         bwd_d_arg, 
-                        static_cast<int>(kv_seq_len), // N is not used in the kernel
+                        static_cast<int>(seq_len), 
                         static_cast<int>(hr),
                         static_cast<int>(max_q_blocks_per_kv),
                         reinterpret_cast<int32_t*>(k2q_block_sparse_index.data_ptr()),
                         reinterpret_cast<int32_t*>(k2q_block_sparse_num.data_ptr()),
-                        reinterpret_cast<int32_t*>(kv_block_size.data_ptr())};
+                        reinterpret_cast<int32_t*>(block_size.data_ptr())};
 
-        dim3 grid_bwd_2(kv_seq_len/BLOCK_N, qo_heads, batch);
+        dim3 grid_bwd_2(seq_len/64, qo_heads, batch);
         threads = 128;
 
         //cudadevicesynchronize();
@@ -1101,9 +1147,9 @@ block_sparse_attention_backward(torch::Tensor q,
 
         using bwd_prep_globals = bwd_prep_globals<128>;
 
-        og_global prep_og_arg{d_og, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(q_seq_len), 128U};
-        o_global  prep_o_arg {d_o,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(q_seq_len), 128U};
-        d_global  prep_d_arg {d_d,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,  static_cast<unsigned int>(q_seq_len)};
+        og_global prep_og_arg{d_og, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), 128U};
+        o_global  prep_o_arg {d_o,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), 128U};
+        d_global  prep_d_arg {d_d,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,  static_cast<unsigned int>(seq_len)};
 
         bwd_prep_globals bwd_g{prep_og_arg, prep_o_arg, prep_d_arg};
 
@@ -1140,15 +1186,15 @@ block_sparse_attention_backward(torch::Tensor q,
 
         using bwd_global_args = bwd_globals<128>;
 
-        bwd_q_global  bwd_q_arg {d_q,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(q_seq_len), 128U};
-        bwd_k_global  bwd_k_arg {d_k,  static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(kv_seq_len), 128U};
-        bwd_v_global  bwd_v_arg {d_v,  static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(kv_seq_len), 128U};
-        bwd_og_global bwd_og_arg{d_og, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(q_seq_len), 128U};
-        bwd_qg_global bwd_qg_arg{d_qg, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(q_seq_len), 128U};
-        bwd_kg_global bwd_kg_arg{d_kg, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(kv_seq_len), 128U};
-        bwd_vg_global bwd_vg_arg{d_vg, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(kv_seq_len), 128U};
-        bwd_l_global  bwd_l_arg {d_l,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,  static_cast<unsigned int>(q_seq_len)};
-        bwd_d_global  bwd_d_arg {d_d,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,  static_cast<unsigned int>(q_seq_len)};
+        bwd_q_global  bwd_q_arg {d_q,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), 128U};
+        bwd_k_global  bwd_k_arg {d_k,  static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(seq_len), 128U};
+        bwd_v_global  bwd_v_arg {d_v,  static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(seq_len), 128U};
+        bwd_og_global bwd_og_arg{d_og, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), 128U};
+        bwd_qg_global bwd_qg_arg{d_qg, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), 128U};
+        bwd_kg_global bwd_kg_arg{d_kg, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(seq_len), 128U};
+        bwd_vg_global bwd_vg_arg{d_vg, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(seq_len), 128U};
+        bwd_l_global  bwd_l_arg {d_l,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,  static_cast<unsigned int>(seq_len)};
+        bwd_d_global  bwd_d_arg {d_d,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,  static_cast<unsigned int>(seq_len)};
 
         bwd_global_args bwd_global{bwd_q_arg, 
                         bwd_k_arg, 
@@ -1159,14 +1205,14 @@ block_sparse_attention_backward(torch::Tensor q,
                         bwd_vg_arg, 
                         bwd_l_arg, 
                         bwd_d_arg, 
-                        static_cast<int>(kv_seq_len), // N is not used in the kernel
+                        static_cast<int>(seq_len), 
                         static_cast<int>(hr),
                         static_cast<int>(max_q_blocks_per_kv),
                         reinterpret_cast<int32_t*>(k2q_block_sparse_index.data_ptr()),
                         reinterpret_cast<int32_t*>(k2q_block_sparse_num.data_ptr()),
-                        reinterpret_cast<int32_t*>(kv_block_size.data_ptr())};
+                        reinterpret_cast<int32_t*>(block_size.data_ptr())};
 
-        dim3 grid_bwd_2(kv_seq_len/BLOCK_N, qo_heads, batch);
+        dim3 grid_bwd_2(seq_len/64, qo_heads, batch);
         threads = 128;
 
         //cudadevicesynchronize();
@@ -1187,4 +1233,4 @@ block_sparse_attention_backward(torch::Tensor q,
 
     return {qg, kg, vg};
     //cudadevicesynchronize();
-}
+}

fastvideo-kernel/csrc/turbodiffusion/norm/layernorm.cu

@@ -4,7 +4,6 @@
 #include <torch/all.h>
 #include <torch/python.h>
 #include <cutlass/cutlass.h>
-#include <cutlass/numeric_types.h>
 #include "common/common.hpp"
 #include "norm/layernorm.hpp"
 
@@ -15,6 +14,10 @@ auto layer_norm(
   std::optional<at::Tensor const> const B,
   std::optional<at::Tensor> Output
 ) {
+  using ElementIn = float;
+  using ElementOut = float;
+  using ElementWeight = float;
+
   int64_t const m = Input.size(0);
   int64_t const n = Input.size(1);
   torch::Device const input_device = Input.device();
@@ -23,70 +26,31 @@ auto layer_norm(
     Output.emplace(
       torch::empty(
         {m, n},
-        torch::TensorOptions().device(input_device).dtype(Input.scalar_type())
+        torch::TensorOptions().device(input_device).dtype(torch::kFloat32)
       )
     );
   }
 
-  TORCH_CHECK(Output.value().scalar_type() == Input.scalar_type(),
-              "Output dtype must match Input dtype. Got Output=",
-              Output.value().scalar_type(), ", Input=", Input.scalar_type());
-  if (W.has_value()) {
-    TORCH_CHECK(W.value().scalar_type() == Input.scalar_type(),
-                "W dtype must match Input dtype. Got W=",
-                W.value().scalar_type(), ", Input=", Input.scalar_type());
-  }
-  if (B.has_value()) {
-    TORCH_CHECK(B.value().scalar_type() == Input.scalar_type(),
-                "B dtype must match Input dtype. Got B=",
-                B.value().scalar_type(), ", Input=", Input.scalar_type());
-  }
 
   void *Iptr = Input.data_ptr();
   void *Wptr = W.has_value() ? W.value().data_ptr() : nullptr;
   void *Bptr = B.has_value() ? B.value().data_ptr() : nullptr;
   void *Optr = Output.value().data_ptr();
 
-  auto stream = at::cuda::getCurrentCUDAStream().stream();
-  if (Input.scalar_type() == at::kHalf) {
-    using ElementIn = cutlass::half_t;
-    using ElementOut = cutlass::half_t;
-    using ElementWeight = cutlass::half_t;
-    BOOL_SWITCH(B.has_value(), BIAS, [&]{
-      BOOL_SWITCH(W.has_value(), AFFINE, [&]{
-        CONFIG_SWITCH(n, [&]{
-          layernorm<ElementIn, ElementOut, ElementWeight, AFFINE, BIAS, MAX_HIDDEN_SIZE, NUM_THR_PER_CTA>(
-              Iptr, Wptr, Bptr, Optr, eps, m, n, stream);
-        });
+  BOOL_SWITCH(B.has_value(), BIAS, [&]{
+    BOOL_SWITCH(W.has_value(), AFFINE, [&]{
+    CONFIG_SWITCH(n, [&]{
+    layernorm<
+    ElementIn, ElementOut, ElementWeight,
+    AFFINE, BIAS,
+    MAX_HIDDEN_SIZE, NUM_THR_PER_CTA> (
+      Iptr, Wptr, Bptr,
+      Optr, eps, m, n,
+      at::cuda::getCurrentCUDAStream().stream()
+        );
       });
     });
-  } else if (Input.scalar_type() == at::kBFloat16) {
-    using ElementIn = cutlass::bfloat16_t;
-    using ElementOut = cutlass::bfloat16_t;
-    using ElementWeight = cutlass::bfloat16_t;
-    BOOL_SWITCH(B.has_value(), BIAS, [&]{
-      BOOL_SWITCH(W.has_value(), AFFINE, [&]{
-        CONFIG_SWITCH(n, [&]{
-          layernorm<ElementIn, ElementOut, ElementWeight, AFFINE, BIAS, MAX_HIDDEN_SIZE, NUM_THR_PER_CTA>(
-              Iptr, Wptr, Bptr, Optr, eps, m, n, stream);
-        });
-      });
-    });
-  } else if (Input.scalar_type() == at::kFloat) {
-    using ElementIn = float;
-    using ElementOut = float;
-    using ElementWeight = float;
-    BOOL_SWITCH(B.has_value(), BIAS, [&]{
-      BOOL_SWITCH(W.has_value(), AFFINE, [&]{
-        CONFIG_SWITCH(n, [&]{
-          layernorm<ElementIn, ElementOut, ElementWeight, AFFINE, BIAS, MAX_HIDDEN_SIZE, NUM_THR_PER_CTA>(
-              Iptr, Wptr, Bptr, Optr, eps, m, n, stream);
-        });
-      });
-    });
-  } else {
-    TORCH_CHECK(false, "Unsupported dtype for layer_norm_cuda: ", Input.scalar_type());
-  }
+  });
   
     
 

fastvideo-kernel/csrc/turbodiffusion/norm/layernorm.hpp

@@ -68,22 +68,9 @@ public:
     // mean reduction
     float u = _reduce_sum(x, shared_data) / params.n;
 
-    // IMPORTANT:
-    // Loader pads out-of-range lanes with 0. That is OK for the sum, but after
-    // subtracting mean, those padded lanes become -u and would incorrectly
-    // contribute to the variance. Mask them back to 0 before variance reduction.
-    // We launch exactly NumThrPerCta threads for a 1xMaxHiddenSize tile,
-    // so each thread is responsible for a contiguous chunk in N.
-    int thr_n_offset = tidx * NumElementPerThread;
     CUTLASS_PRAGMA_UNROLL
-    for (int i = 0; i < NumElementPerThread; ++i) {
-      int idx = thr_n_offset + i;
-      if (idx < params.n) {
-        x[i] -= u;
-      } else {
-        x[i] = 0.f;
-      }
-    }
+    for (int i = 0; i < NumElementPerThread; ++i)
+      x[i] -= u;
 
     __syncthreads();
     // var reduction

fastvideo-kernel/csrc/turbodiffusion/norm/rmsnorm.cu

@@ -4,7 +4,6 @@
 #include <torch/all.h>
 #include <torch/python.h>
 #include <cutlass/cutlass.h>
-#include <cutlass/numeric_types.h>
 #include <pybind11/pybind11.h>
 
 #include "common/common.hpp"
@@ -17,6 +16,10 @@ auto rms_norm(
   std::optional<at::Tensor>& Output
 ) {
   
+  using ElementIn = float;
+  using ElementOut = float;
+  using ElementWeight = float;
+  
   int64_t const m = Input.size(0);
   int64_t const n = Input.size(1);
   torch::Device const input_device = Input.device();
@@ -25,51 +28,27 @@ auto rms_norm(
     Output.emplace(
       torch::empty(
         {m, n},
-        torch::TensorOptions().device(input_device).dtype(Input.scalar_type())
+        torch::TensorOptions().device(input_device).dtype(torch::kFloat32)
       )
     );
   }
 
-  TORCH_CHECK(Output.value().scalar_type() == Input.scalar_type(),
-              "Output dtype must match Input dtype. Got Output=",
-              Output.value().scalar_type(), ", Input=", Input.scalar_type());
-  if (Weight.has_value()) {
-    TORCH_CHECK(Weight.value().scalar_type() == Input.scalar_type(),
-                "Weight dtype must match Input dtype. Got Weight=",
-                Weight.value().scalar_type(), ", Input=", Input.scalar_type());
-  }
-
   void *Iptr = Input.data_ptr();
   void *Wptr = Weight.has_value() ? Weight.value().data_ptr() : nullptr;
   void *Optr = Output.value().data_ptr();
 
-  if (Input.scalar_type() == at::kHalf) {
-    using ElementIn = cutlass::half_t;
-    using ElementOut = cutlass::half_t;
-    using ElementWeight = cutlass::half_t;
-    CONFIG_SWITCH(n, [&]{
-      rmsnorm<ElementIn, ElementOut, ElementWeight, MAX_HIDDEN_SIZE, NUM_THR_PER_CTA>(
-          Iptr, Wptr, Optr, eps, m, n, at::cuda::getCurrentCUDAStream().stream());
-    });
-  } else if (Input.scalar_type() == at::kBFloat16) {
-    using ElementIn = cutlass::bfloat16_t;
-    using ElementOut = cutlass::bfloat16_t;
-    using ElementWeight = cutlass::bfloat16_t;
-    CONFIG_SWITCH(n, [&]{
-      rmsnorm<ElementIn, ElementOut, ElementWeight, MAX_HIDDEN_SIZE, NUM_THR_PER_CTA>(
-          Iptr, Wptr, Optr, eps, m, n, at::cuda::getCurrentCUDAStream().stream());
-    });
-  } else if (Input.scalar_type() == at::kFloat) {
-    using ElementIn = float;
-    using ElementOut = float;
-    using ElementWeight = float;
-    CONFIG_SWITCH(n, [&]{
-      rmsnorm<ElementIn, ElementOut, ElementWeight, MAX_HIDDEN_SIZE, NUM_THR_PER_CTA>(
-          Iptr, Wptr, Optr, eps, m, n, at::cuda::getCurrentCUDAStream().stream());
-    });
-  } else {
-    TORCH_CHECK(false, "Unsupported dtype for rms_norm_cuda: ", Input.scalar_type());
-  }
+
+  CONFIG_SWITCH(n, [&]{
+    rmsnorm<
+    ElementIn, ElementOut, ElementWeight,
+    MAX_HIDDEN_SIZE, NUM_THR_PER_CTA
+    > (
+      Iptr, Wptr,
+      Optr, 
+      eps, m, n, 
+      at::cuda::getCurrentCUDAStream().stream()
+    );
+});
   
 
   return Output;

fastvideo-kernel/pyproject.toml

@@ -9,7 +9,7 @@ build-backend = "scikit_build_core.build"
 
 [project]
 name = "fastvideo-kernel"
-version = "0.2.5"
+version = "0.2.1"
 description = "Unified CUDA kernels for FastVideo"
 readme = "README.md"
 requires-python = ">=3.10"

fastvideo-kernel/python/fastvideo_kernel/block_sparse_attn.py

@@ -1,295 +0,0 @@
-from __future__ import annotations
-
-import os
-from typing import Tuple
-
-import torch
-
-
-def _get_sm90_ops():
-    try:
-        from fastvideo_kernel._C import fastvideo_kernel_ops  # type: ignore
-    except Exception:
-        return None, None
-    return (
-        getattr(fastvideo_kernel_ops, "block_sparse_fwd", None),
-        getattr(fastvideo_kernel_ops, "block_sparse_bwd", None),
-    )
-
-
-def _is_sm90() -> bool:
-    if not torch.cuda.is_available():
-        return False
-    major, minor = torch.cuda.get_device_capability(0)
-    return major == 9 and minor == 0
-
-
-def _force_triton() -> bool:
-    # Force Triton even on SM90 and even if the compiled extension is available.
-    # Useful for CI / debugging / parity testing.
-    return os.environ.get("FASTVIDEO_KERNEL_VSA_FORCE_TRITON", "0") == "1"
-
-
-def _map_to_index(block_map: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
-    """
-    Preferred map->index conversion used by the wrapper.
-
-    This wrapper **requires** the Triton implementation.
-    If Triton (or the Triton map_to_index module) is not available, it raises.
-    """
-    if block_map.dim() == 3:
-        block_map = block_map.unsqueeze(0)
-    if block_map.dim() != 4:
-        raise ValueError(f"block_map must be [B,H,Q,KV] (or [H,Q,KV]), got shape={tuple(block_map.shape)}")
-    if block_map.dtype != torch.bool:
-        block_map = block_map.to(torch.bool)
-
-    if not block_map.is_cuda:
-        raise RuntimeError("block_map must be a CUDA tensor (Triton map_to_index required).")
-
-    try:
-        from fastvideo_kernel.triton_kernels.index import map_to_index as triton_map_to_index  # local import
-    except Exception as e:
-        raise ImportError(
-            "Triton map_to_index is required but not available. "
-            "Ensure Triton is installed and fastvideo_kernel.triton_kernels.index is importable."
-        ) from e
-    return triton_map_to_index(block_map)
-
-
-@torch.library.custom_op(
-    "fastvideo_kernel::block_sparse_attn_triton",
-    mutates_args=(),
-    device_types="cuda",
-)
-def block_sparse_attn_triton(
-    q: torch.Tensor,
-    k: torch.Tensor,
-    v: torch.Tensor,
-    block_map: torch.Tensor,
-    variable_block_sizes: torch.Tensor,
-) -> Tuple[torch.Tensor, torch.Tensor]:
-    q = q.contiguous()
-    k = k.contiguous()
-    v = v.contiguous()
-    block_map = block_map.to(torch.bool)
-    q2k_idx, q2k_num = _map_to_index(block_map)
-
-    from fastvideo_kernel.triton_kernels.block_sparse_attn_triton import (  # local import
-        triton_block_sparse_attn_forward,
-    )
-
-    o, M = triton_block_sparse_attn_forward(q, k, v, q2k_idx, q2k_num, variable_block_sizes)
-    return o, M
-
-
-
-@torch.library.register_fake("fastvideo_kernel::block_sparse_attn_triton")
-def _block_sparse_attn_triton_fake(
-    q: torch.Tensor,
-    k: torch.Tensor,
-    v: torch.Tensor,
-    block_map: torch.Tensor,
-    variable_block_sizes: torch.Tensor,
-) -> Tuple[torch.Tensor, torch.Tensor]:
-    o = torch.empty_like(q)
-    M = torch.empty((q.shape[0], q.shape[1], q.shape[2]), device=q.device, dtype=torch.float32)
-    return o, M
-
-
-@torch.library.custom_op(
-    "fastvideo_kernel::block_sparse_attn_backward_triton",
-    mutates_args=(),
-    device_types="cuda",
-)
-def block_sparse_attn_backward_triton(
-    grad_output: torch.Tensor,
-    q: torch.Tensor,
-    k: torch.Tensor,
-    v: torch.Tensor,
-    o: torch.Tensor,
-    M: torch.Tensor,
-    block_map: torch.Tensor,
-    variable_block_sizes: torch.Tensor,
-) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-    grad_output = grad_output.contiguous()
-    block_map = block_map.to(torch.bool)
-    q2k_idx, q2k_num = _map_to_index(block_map)
-    k2q_idx, k2q_num = _map_to_index(block_map.transpose(-1, -2).contiguous())
-
-    from fastvideo_kernel.triton_kernels.block_sparse_attn_triton import (  # local import
-        triton_block_sparse_attn_backward,
-    )
-
-    dq, dk, dv = triton_block_sparse_attn_backward(
-        grad_output, q, k, v, o, M, q2k_idx, q2k_num, k2q_idx, k2q_num, variable_block_sizes
-    )
-    return dq, dk, dv
-
-
-@torch.library.register_fake("fastvideo_kernel::block_sparse_attn_backward_triton")
-def _block_sparse_attn_backward_triton_fake(
-    grad_output: torch.Tensor,
-    q: torch.Tensor,
-    k: torch.Tensor,
-    v: torch.Tensor,
-    o: torch.Tensor,
-    M: torch.Tensor,
-    block_map: torch.Tensor,
-    variable_block_sizes: torch.Tensor,
-) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-    dq = torch.empty_like(q)
-    dk = torch.empty_like(k)
-    dv = torch.empty_like(v)
-    return dq, dk, dv
-
-
-def _backward_triton(ctx, grad_o, grad_M):
-    q, k, v, o, M, block_map, variable_block_sizes = ctx.saved_tensors
-    dq, dk, dv = block_sparse_attn_backward_triton(grad_o, q, k, v, o, M, block_map, variable_block_sizes)
-    return dq, dk, dv, None, None
-
-
-def _setup_context_triton(ctx, inputs, output):
-    q, k, v, block_map, variable_block_sizes = inputs
-    o, M = output
-    ctx.save_for_backward(q, k, v, o, M, block_map, variable_block_sizes)
-
-
-block_sparse_attn_triton.register_autograd(_backward_triton, setup_context=_setup_context_triton)
-
-
-@torch.library.custom_op(
-    "fastvideo_kernel::block_sparse_attn_sm90",
-    mutates_args=(),
-    device_types="cuda",
-)
-def block_sparse_attn_sm90(
-    q_padded: torch.Tensor,
-    k_padded: torch.Tensor,
-    v_padded: torch.Tensor,
-    block_map: torch.Tensor,
-    variable_block_sizes: torch.Tensor,
-) -> Tuple[torch.Tensor, torch.Tensor]:
-    block_sparse_fwd, _ = _get_sm90_ops()
-    if block_sparse_fwd is None:
-        raise ImportError("fastvideo_kernel_ops.block_sparse_fwd is not available")
-
-    q_padded = q_padded.contiguous()
-    k_padded = k_padded.contiguous()
-    v_padded = v_padded.contiguous()
-    block_map = block_map.to(torch.bool)
-    q2k_idx, q2k_num = _map_to_index(block_map)
-
-    o_padded, lse_padded = block_sparse_fwd(
-        q_padded, k_padded, v_padded, q2k_idx, q2k_num, variable_block_sizes.int()
-    )
-    return o_padded, lse_padded
-
-
-@torch.library.register_fake("fastvideo_kernel::block_sparse_attn_sm90")
-def _block_sparse_attn_sm90_fake(
-    q_padded: torch.Tensor,
-    k_padded: torch.Tensor,
-    v_padded: torch.Tensor,
-    block_map: torch.Tensor,
-    variable_block_sizes: torch.Tensor,
-) -> Tuple[torch.Tensor, torch.Tensor]:
-    o = torch.empty_like(q_padded)
-    lse = torch.empty((q_padded.shape[0], q_padded.shape[1], q_padded.shape[2], 1), device=q_padded.device, dtype=torch.float32)
-    return o, lse
-
-
-@torch.library.custom_op(
-    "fastvideo_kernel::block_sparse_attn_backward_sm90",
-    mutates_args=(),
-    device_types="cuda",
-)
-def block_sparse_attn_backward_sm90(
-    grad_output_padded: torch.Tensor,
-    q_padded: torch.Tensor,
-    k_padded: torch.Tensor,
-    v_padded: torch.Tensor,
-    o_padded: torch.Tensor,
-    lse_padded: torch.Tensor,
-    block_map: torch.Tensor,
-    variable_block_sizes: torch.Tensor,
-) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-    _, block_sparse_bwd = _get_sm90_ops()
-    if block_sparse_bwd is None:
-        raise ImportError("fastvideo_kernel_ops.block_sparse_bwd is not available")
-
-    grad_output_padded = grad_output_padded.contiguous()
-    block_map = block_map.to(torch.bool)
-    k2q_idx, k2q_num = _map_to_index(block_map.transpose(-1, -2).contiguous())
-
-    dq, dk, dv = block_sparse_bwd(
-        q_padded,
-        k_padded,
-        v_padded,
-        o_padded,
-        lse_padded,
-        grad_output_padded,
-        k2q_idx,
-        k2q_num,
-        variable_block_sizes.int(),
-    )
-    # C++ kernel returns fp32 grads; cast back to match PyTorch convention if needed
-    return dq.to(grad_output_padded.dtype), dk.to(grad_output_padded.dtype), dv.to(grad_output_padded.dtype)
-
-
-@torch.library.register_fake("fastvideo_kernel::block_sparse_attn_backward_sm90")
-def _block_sparse_attn_backward_sm90_fake(
-    grad_output_padded: torch.Tensor,
-    q_padded: torch.Tensor,
-    k_padded: torch.Tensor,
-    v_padded: torch.Tensor,
-    o_padded: torch.Tensor,
-    lse_padded: torch.Tensor,
-    block_map: torch.Tensor,
-    variable_block_sizes: torch.Tensor,
-) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-    dq = torch.empty_like(q_padded)
-    dk = torch.empty_like(k_padded)
-    dv = torch.empty_like(v_padded)
-    return dq, dk, dv
-
-
-def _backward_sm90(ctx, grad_o, grad_lse):
-    q, k, v, o, lse, block_map, variable_block_sizes = ctx.saved_tensors
-    dq, dk, dv = block_sparse_attn_backward_sm90(
-        grad_o, q, k, v, o, lse, block_map, variable_block_sizes
-    )
-    return dq, dk, dv, None, None
-
-
-def _setup_context_sm90(ctx, inputs, output):
-    q, k, v, block_map, variable_block_sizes = inputs
-    o, lse = output
-    ctx.save_for_backward(q, k, v, o, lse, block_map, variable_block_sizes)
-
-
-block_sparse_attn_sm90.register_autograd(_backward_sm90, setup_context=_setup_context_sm90)
-
-
-def block_sparse_attn(
-    q: torch.Tensor,
-    k: torch.Tensor,
-    v: torch.Tensor,
-    block_map: torch.Tensor,
-    variable_block_sizes: torch.Tensor,
-) -> Tuple[torch.Tensor, torch.Tensor]:
-    """
-    Unified block-sparse attention op with autograd support.
-    - On SM90 with compiled extension present: uses fastvideo_kernel_ops.block_sparse_fwd/bwd.
-    - Otherwise: uses Triton implementation (requires q/k/v to have same padded length today).
-    """
-    block_sparse_fwd, block_sparse_bwd = _get_sm90_ops()
-    if (not _force_triton()) and _is_sm90() and (block_sparse_fwd is not None) and (block_sparse_bwd is not None):
-        return block_sparse_attn_sm90(q, k, v, block_map, variable_block_sizes)
-    # Triton path: generally assumes q/k/v share the same padded length
-    if q.shape[2] != k.shape[2] or q.shape[2] != v.shape[2]:
-        raise RuntimeError("Triton fallback requires q/k/v to have the same padded length.")
-    return block_sparse_attn_triton(q, k, v, block_map, variable_block_sizes)
-
-

fastvideo-kernel/python/fastvideo_kernel/ops.py

@@ -1,6 +1,5 @@
 import math
 import torch
-from .block_sparse_attn import block_sparse_attn
 from .triton_kernels.block_sparse_attn_triton import triton_block_sparse_attn_forward
 from .triton_kernels.st_attn_triton import sliding_tile_attention_triton
 from .triton_kernels.index import map_to_index
@@ -46,22 +45,14 @@ def sliding_tile_attention(
     flag = shape_map[seq_shape]
 
     for head_idx, (t, h, w) in enumerate(window_size):
-        # Per-head slices are not contiguous in the batch dimension when batch>1
-        # (they keep the original head-stride). The TK kernel assumes contiguous
-        # [B, H, S, D] layout, so we materialize a contiguous [B,1,S,D] view.
-        q_h = q[:, head_idx:head_idx + 1].contiguous()
-        k_h = k[:, head_idx:head_idx + 1].contiguous()
-        v_h = v[:, head_idx:head_idx + 1].contiguous()
-        o_h = torch.empty_like(q_h)
         sta_fwd(
-            q_h, k_h,
-            v_h, o_h,
+            q[:, head_idx:head_idx + 1], k[:, head_idx:head_idx + 1],
+            v[:, head_idx:head_idx + 1], output[:, head_idx:head_idx + 1],
             t, h, w, text_length, False, has_text, flag
         )
-        output[:, head_idx:head_idx + 1] = o_h
 
     if has_text:
-        sta_fwd(q.contiguous(), k.contiguous(), v.contiguous(), output, 3, 3, 3, text_length, True, True, flag)
+        sta_fwd(q, k, v, output, 3, 3, 3, text_length, True, True, flag)
 
     return output[:, :, :seq_length]
 
@@ -71,7 +62,6 @@ def video_sparse_attn(
     k: torch.Tensor,
     v: torch.Tensor,
     variable_block_sizes: torch.Tensor,
-    q_variable_block_sizes: torch.Tensor,
     topk: int,
     block_size: int | tuple = 64,
     compress_attn_weight: torch.Tensor = None,
@@ -80,42 +70,14 @@ def video_sparse_attn(
         block_size = (block_size, block_size, block_size)
 
     block_elements = block_size[0] * block_size[1] * block_size[2]
-    batch, heads, q_seq_len, dim = q.shape
-    kv_seq_len = k.shape[2]
-    if v.shape[2] != kv_seq_len:
-        raise ValueError(
-            f"Expected k and v to have the same sequence length, got "
-            f"k.shape[2]={kv_seq_len}, v.shape[2]={v.shape[2]}"
-        )
-    if k.shape[0] != batch or v.shape[0] != batch or k.shape[1] != heads or v.shape[1] != heads:
-        raise ValueError("Expected q/k/v to have the same batch and head dimensions.")
-
-    if q_seq_len % block_elements != 0 or kv_seq_len % block_elements != 0:
-        raise ValueError(
-            f"q_seq_len and kv_seq_len must be divisible by block_elements={block_elements}, "
-            f"got q_seq_len={q_seq_len}, kv_seq_len={kv_seq_len}"
-        )
-    q_num_blocks = q_seq_len // block_elements
-    kv_num_blocks = kv_seq_len // block_elements
-
-    if variable_block_sizes.numel() != kv_num_blocks:
-        raise ValueError(
-            f"variable_block_sizes must have length kv_num_blocks={kv_num_blocks}, "
-            f"got {variable_block_sizes.numel()}"
-        )
-
-    if q_variable_block_sizes.numel() != q_num_blocks:
-        raise ValueError(
-            f"q_variable_block_sizes must have length q_num_blocks={q_num_blocks}, "
-            f"got {q_variable_block_sizes.numel()}"
-        )
+    batch, heads, seq_len, dim = q.shape
 
     # Compression branch
-    q_c = q.view(batch, heads, q_num_blocks, block_elements, dim)
-    k_c = k.view(batch, heads, kv_num_blocks, block_elements, dim)
-    v_c = v.view(batch, heads, kv_num_blocks, block_elements, dim)
+    q_c = q.view(batch, heads, seq_len // block_elements, block_elements, dim)
+    k_c = k.view(batch, heads, seq_len // block_elements, block_elements, dim)
+    v_c = v.view(batch, heads, seq_len // block_elements, block_elements, dim)
 
-    q_c = (q_c.float().sum(dim=3) / q_variable_block_sizes.view(1, 1, -1, 1)).to(
+    q_c = (q_c.float().sum(dim=3) / variable_block_sizes.view(1, 1, -1, 1)).to(
         q.dtype)
     k_c = (k_c.float().sum(dim=3) / variable_block_sizes.view(1, 1, -1, 1)).to(
         k.dtype)
@@ -126,9 +88,9 @@ def video_sparse_attn(
     attn = torch.softmax(scores, dim=-1)
     out_c = torch.matmul(attn, v_c)
 
-    out_c = out_c.view(batch, heads, q_num_blocks, 1, dim)
+    out_c = out_c.view(batch, heads, seq_len // block_elements, 1, dim)
     out_c = out_c.repeat(1, 1, 1, block_elements,
-                         1).view(batch, heads, q_seq_len, dim)
+                         1).view(batch, heads, seq_len, dim)
 
     # Sparse branch
     topk_idx = torch.topk(scores, topk, dim=-1).indices
@@ -138,17 +100,12 @@ def video_sparse_attn(
     idx, num = map_to_index(mask)
     
     if block_sparse_fwd is not None:
-        # Use autograd-enabled wrapper so backward works (and still uses SM90 kernel when available)
-        out_s = block_sparse_attn(q, k, v, mask, variable_block_sizes)[0]
+        out_s = block_sparse_fwd(
+            q, k, v, idx, num, variable_block_sizes.int()
+        )[0] # block_sparse_fwd returns vector<Tensor>
     else:
-        if q_seq_len != kv_seq_len:
-            raise RuntimeError(
-                "q/k have different lengths, but the compiled CUDA kernel (block_sparse_fwd) "
-                "is not available. The Triton fallback currently requires q and k/v to have "
-                "the same padded length."
-            )
-        # Triton-only forward (kept for environments without the wrapper deps)
-        out_s, _ = triton_block_sparse_attn_forward(q, k, v, idx, num, variable_block_sizes)
+        out_s, _ = triton_block_sparse_attn_forward(q, k, v, idx, num,
+                                                    variable_block_sizes)
 
     if compress_attn_weight is not None:
         return out_c * compress_attn_weight + out_s

fastvideo-kernel/python/fastvideo_kernel/version.py

@@ -1 +1 @@
-__version__ = "0.2.5"
+__version__ = "0.2.1"

fastvideo-kernel/tests/test_vsa.py

@@ -42,13 +42,37 @@ def block_sparse_kernel_test(Q, K, V, block_sparse_mask, variable_block_sizes, q
     q_padded = vsa_pad(Q, q_non_pad_index, q_num_blocks, BLOCK_M)
     k_padded = vsa_pad(K, kv_non_pad_index, kv_num_blocks, BLOCK_M)
     v_padded = vsa_pad(V, kv_non_pad_index, kv_num_blocks, BLOCK_M)
-    # Use autograd-enabled wrapper (internally dispatches to SM90 kernel or Triton)
-    from fastvideo_kernel.block_sparse_attn import block_sparse_attn
-    output_padded, _aux = block_sparse_attn(
-        q_padded, k_padded, v_padded, block_sparse_mask, variable_block_sizes
-    )
+    # Use raw kernel or triton
+    try:
+        from fastvideo_kernel._C import fastvideo_kernel_ops
+        raw_kernel = getattr(fastvideo_kernel_ops, "block_sparse_fwd", None)
+    except ImportError:
+        raw_kernel = None
 
-    output = output_padded[:, :, q_non_pad_index, :]
+    from fastvideo_kernel.triton_kernels.index import map_to_index
+
+    
+    # Convert mask to indices
+    # block_sparse_mask is [H, M, N] bool
+    # We need to map it to index.
+    # block_sparse_mask needs to be expanded/reshaped? 
+    # generate_block_sparse_mask_for_function returns [H, NumBlocksQ, NumBlocksKV]
+    
+    # Ops.py logic:
+    # mask = torch.zeros_like(scores, dtype=torch.bool).scatter_(-1, topk_idx, True)
+    # idx, num = map_to_index(mask)
+    
+    idx, num = map_to_index(block_sparse_mask.unsqueeze(0)) # Add batch dim [1, H, M, N]
+    
+    if raw_kernel:
+        out_s = raw_kernel(q_padded, k_padded, v_padded, idx, num, variable_block_sizes.int())
+        output = out_s[0]
+    else:
+        # Fallback to triton testing if C++ not available
+        from fastvideo_kernel.triton_kernels.block_sparse_attn_triton import triton_block_sparse_attn_forward
+        output, _ = triton_block_sparse_attn_forward(q_padded, k_padded, v_padded, idx, num, variable_block_sizes)
+
+    output = output[:, :, q_non_pad_index, :]
     output.backward(dO)
     return output, Q.grad, K.grad, V.grad
 
@@ -240,6 +264,7 @@ def generate_error_graphs_qkdiff(h, d, error_mode='all'):
 
     print("-" * 150)
 
+@pytest.mark.skip()
 def test_video_sparse_attention_backward():
     if not torch.cuda.is_available():
         return

fastvideo-kernel/tests/test_vsa_forward.py

@@ -3,7 +3,6 @@ import sys
 from typing import Tuple
 
 import torch
-import pytest
 
 from .utils import (
     generate_block_sparse_mask_for_function,
@@ -58,14 +57,23 @@ def block_sparse_forward_test(
     k_padded = ref.vsa_pad(K, kv_non_pad_index, kv_num_blocks, BLOCK_M)
     v_padded = ref.vsa_pad(V, kv_non_pad_index, kv_num_blocks, BLOCK_M)
 
-    # Use autograd-enabled wrapper (internally dispatches SM90 C++ vs Triton)
-    from fastvideo_kernel.block_sparse_attn import block_sparse_attn
+    # Use raw kernel or triton
     try:
-        out_padded, _aux = block_sparse_attn(
-            q_padded, k_padded, v_padded, block_sparse_mask, variable_block_sizes
+        from fastvideo_kernel._C import fastvideo_kernel_ops
+        raw_kernel = getattr(fastvideo_kernel_ops, "block_sparse_fwd", None)
+    except ImportError:
+        raw_kernel = None
+        
+    from fastvideo_kernel.triton_kernels.index import map_to_index
+    idx, num = map_to_index(block_sparse_mask)
+    
+    if raw_kernel:
+        out_padded = raw_kernel(q_padded, k_padded, v_padded, idx, num, variable_block_sizes.int())[0]
+    else:
+        from fastvideo_kernel.triton_kernels.block_sparse_attn_triton import triton_block_sparse_attn_forward
+        out_padded, _ = triton_block_sparse_attn_forward(
+            q_padded, k_padded, v_padded, idx, num, variable_block_sizes
         )
-    except RuntimeError as e:
-        pytest.skip(str(e))
 
     # Remove padding on the query side
     out = out_padded[:, :, q_non_pad_index, :]
@@ -148,6 +156,11 @@ def run_forward_qk_diff(
 ) -> Tuple[float, float]:
     """
     Forward-only correctness test for the case S_q != S_kv.
+
+    NOTE:
+      - The Triton backend supports different Q/KV logical lengths via padding.
+      - The SM90 (H100) CUDA backend currently assumes the same number of blocks
+        for Q and KV, so we skip this test there.
     """
     assert torch.cuda.is_available(), "VSA kernels require CUDA"
 

fastvideo/attention/backends/video_sparse_attn.py

@@ -276,9 +276,8 @@ class VideoSparseAttentionImpl(AttentionImpl):
             query,
             key,
             value,
-            attn_metadata.variable_block_sizes,
-            attn_metadata.variable_block_sizes,
-            cur_topk,
+            variable_block_sizes=attn_metadata.variable_block_sizes,
+            topk=cur_topk,
             block_size=VSA_TILE_SIZE,
             compress_attn_weight=gate_compress).transpose(1, 2)
 

fastvideo/configs/models/__init__.py

@@ -2,16 +2,5 @@ from fastvideo.configs.models.base import ModelConfig
 from fastvideo.configs.models.dits.base import DiTConfig
 from fastvideo.configs.models.encoders.base import EncoderConfig
 from fastvideo.configs.models.vaes.base import VAEConfig
-from fastvideo.configs.models.audio import (LTX2AudioDecoderConfig,
-                                            LTX2AudioEncoderConfig,
-                                            LTX2VocoderConfig)
 
-__all__ = [
-    "ModelConfig",
-    "VAEConfig",
-    "DiTConfig",
-    "EncoderConfig",
-    "LTX2AudioEncoderConfig",
-    "LTX2AudioDecoderConfig",
-    "LTX2VocoderConfig",
-]
+__all__ = ["ModelConfig", "VAEConfig", "DiTConfig", "EncoderConfig"]

fastvideo/configs/models/audio/__init__.py

@@ -1,13 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-
-from fastvideo.configs.models.audio.ltx2_audio_vae import (
-    LTX2AudioDecoderConfig,
-    LTX2AudioEncoderConfig,
-    LTX2VocoderConfig,
-)
-
-__all__ = [
-    "LTX2AudioEncoderConfig",
-    "LTX2AudioDecoderConfig",
-    "LTX2VocoderConfig",
-]

fastvideo/configs/models/audio/ltx2_audio_vae.py

@@ -1,31 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-LTX-2 audio VAE and vocoder configuration.
-"""
-
-from dataclasses import dataclass, field
-
-from fastvideo.configs.models.base import ArchConfig, ModelConfig
-
-
-@dataclass
-class LTX2AudioArchConfig(ArchConfig):
-    architectures: list[str] = field(default_factory=list)
-
-
-@dataclass
-class LTX2AudioEncoderConfig(ModelConfig):
-    arch_config: ArchConfig = field(default_factory=lambda: LTX2AudioArchConfig(
-        architectures=["LTX2AudioEncoder"]))
-
-
-@dataclass
-class LTX2AudioDecoderConfig(ModelConfig):
-    arch_config: ArchConfig = field(default_factory=lambda: LTX2AudioArchConfig(
-        architectures=["LTX2AudioDecoder"]))
-
-
-@dataclass
-class LTX2VocoderConfig(ModelConfig):
-    arch_config: ArchConfig = field(default_factory=lambda: LTX2AudioArchConfig(
-        architectures=["LTX2Vocoder"]))

fastvideo/configs/models/dits/__init__.py

@@ -3,13 +3,11 @@ from fastvideo.configs.models.dits.cosmos2_5 import Cosmos25VideoConfig
 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.stepvideo import StepVideoConfig
 from fastvideo.configs.models.dits.wanvideo import WanVideoConfig
-from fastvideo.configs.models.dits.hyworld import HYWorldConfig
 
 __all__ = [
     "HunyuanVideoConfig", "HunyuanVideo15Config", "WanVideoConfig",
     "StepVideoConfig", "CosmosVideoConfig", "Cosmos25VideoConfig",
-    "LongCatVideoConfig", "LTX2VideoConfig", "HYWorldConfig"
+    "LongCatVideoConfig"
 ]

fastvideo/configs/models/dits/hunyuanvideo15.py

@@ -26,9 +26,6 @@ class HunyuanVideo15ArchConfig(DiTArchConfig):
 
     param_names_mapping: dict = field(
         default_factory=lambda: {
-            r"^cond_type_embed\.(.*)$":
-            r"cond_type_embed.\1",
-
             # 1. context_embedder.time_text_embed submodules (specific rules, applied first):
             r"^context_embedder\.time_text_embed\.timestep_embedder\.linear_1\.(.*)$":
             r"txt_in.t_embedder.mlp.fc_in.\1",
@@ -58,16 +55,6 @@ class HunyuanVideo15ArchConfig(DiTArchConfig):
             r"txt_in.refiner_blocks.\1.mlp.fc_out.\2",
             r"^context_embedder\.token_refiner\.refiner_blocks\.(\d+)\.norm_out\.linear\.(.*)$":
             r"txt_in.refiner_blocks.\1.adaLN_modulation.linear.\2",
-            r"^context_embedder\.token_refiner\.refiner_blocks\.(\d+)\.self_attn_qkv\.(.*)$":
-            r"txt_in.refiner_blocks.\1.self_attn_qkv.\2",
-            r"^image_embedder\.linear_1\.(.*)$":
-            r"image_embedder.linear_1.\1",
-            r"^image_embedder\.linear_2\.(.*)$":
-            r"image_embedder.linear_2.\1",
-            r"^image_embedder\.norm_in\.(.*)$":
-            r"image_embedder.norm_in.\1",
-            r"^image_embedder\.norm_out\.(.*)$":
-            r"image_embedder.norm_out.\1",
 
             # 2. txt_in_2 mapping:
             r"^context_embedder_2\.(.*)$":
@@ -157,12 +144,6 @@ class HunyuanVideo15ArchConfig(DiTArchConfig):
     exclude_lora_layers: list[str] = field(
         default_factory=lambda: ["img_in", "txt_in", "time_in", "vector_in"])
 
-    # Causal HunyuanVideo1.5
-    local_attn_size: int = -1  # Window size for temporal local attention (-1 indicates global attention)
-    sink_size: int = 0  # Size of the attention sink, we keep the first `sink_size` frames unchanged when rolling the KV cache
-    num_frames_per_block: int = 3
-    sliding_window_num_frames: int = 31
-
     def __post_init__(self):
         super().__post_init__()
         self.hidden_size: int = self.attention_head_dim * self.num_attention_heads

fastvideo/configs/models/dits/hyworld.py

@@ -1,207 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-from dataclasses import dataclass, field
-
-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"
-
-
-@dataclass
-class HYWorldArchConfig(DiTArchConfig):
-    _fsdp_shard_conditions: list = field(
-        default_factory=lambda: [is_double_block, is_single_block])
-
-    _compile_conditions: list = field(
-        default_factory=lambda: [is_double_block, is_single_block, is_txt_in])
-
-    param_names_mapping: dict = field(
-        default_factory=lambda: {
-            # 1. txt_in submodules (text embedder, refiner blocks):
-            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",
-            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",
-            r"^txt_in\.individual_token_refiner\.blocks\.(\d+)\.norm1\.(.*)$":
-            r"txt_in.refiner_blocks.\1.norm1.\2",
-            r"^txt_in\.individual_token_refiner\.blocks\.(\d+)\.norm2\.(.*)$":
-            r"txt_in.refiner_blocks.\1.norm2.\2",
-            r"^txt_in\.individual_token_refiner\.blocks\.(\d+)\.self_attn_qkv\.(.*)$":
-            r"txt_in.refiner_blocks.\1.self_attn_qkv.\2",
-            r"^txt_in\.individual_token_refiner\.blocks\.(\d+)\.self_attn_proj\.(.*)$":
-            r"txt_in.refiner_blocks.\1.self_attn_proj.\2",
-            r"^txt_in\.individual_token_refiner\.blocks\.(\d+)\.mlp\.fc1\.(.*)$":
-            r"txt_in.refiner_blocks.\1.mlp.fc_in.\2",
-            r"^txt_in\.individual_token_refiner\.blocks\.(\d+)\.mlp\.fc2\.(.*)$":
-            r"txt_in.refiner_blocks.\1.mlp.fc_out.\2",
-            r"^txt_in\.individual_token_refiner\.blocks\.(\d+)\.adaLN_modulation\.1\.(.*)$":
-            r"txt_in.refiner_blocks.\1.adaLN_modulation.linear.\2",
-
-            # 2. time_in mappings (HYWorld uses TimestepEmbedder directly,
-            #    but FastVideo model inherits HunyuanVideo15TimeEmbedding with timestep_embedder):
-            r"^time_in\.mlp\.0\.(.*)$":
-            r"time_in.timestep_embedder.mlp.fc_in.\1",
-            r"^time_in\.mlp\.2\.(.*)$":
-            r"time_in.timestep_embedder.mlp.fc_out.\1",
-
-            # 3. action_in mappings:
-            r"^action_in\.mlp\.0\.(.*)$":
-            r"action_in.mlp.fc_in.\1",
-            r"^action_in\.mlp\.2\.(.*)$":
-            r"action_in.mlp.fc_out.\1",
-
-            # 4. byt5_in -> txt_in_2 mappings:
-            r"^byt5_in\.layernorm\.(.*)$":
-            r"txt_in_2.norm.\1",
-            r"^byt5_in\.fc1\.(.*)$":
-            r"txt_in_2.linear_1.\1",
-            r"^byt5_in\.fc2\.(.*)$":
-            r"txt_in_2.linear_2.\1",
-            r"^byt5_in\.fc3\.(.*)$":
-            r"txt_in_2.linear_3.\1",
-
-            # 5. cond_type_embedding -> cond_type_embed:
-            r"^cond_type_embedding\.(.*)$":
-            r"cond_type_embed.\1",
-
-            # 6. vision_in -> image_embedder mappings:
-            r"^vision_in\.proj\.0\.(.*)$":
-            r"image_embedder.norm_in.\1",
-            r"^vision_in\.proj\.1\.(.*)$":
-            r"image_embedder.linear_1.\1",
-            r"^vision_in\.proj\.3\.(.*)$":
-            r"image_embedder.linear_2.\1",
-            r"^vision_in\.proj\.4\.(.*)$":
-            r"image_embedder.norm_out.\1",
-
-            # 7. double_blocks mapping:
-            r"^double_blocks\.(\d+)\.img_attn_q\.(.*)$":
-            (r"double_blocks.\1.img_attn_qkv.\2", 0, 3),
-            r"^double_blocks\.(\d+)\.img_attn_k\.(.*)$":
-            (r"double_blocks.\1.img_attn_qkv.\2", 1, 3),
-            r"^double_blocks\.(\d+)\.img_attn_v\.(.*)$":
-            (r"double_blocks.\1.img_attn_qkv.\2", 2, 3),
-            r"^double_blocks\.(\d+)\.txt_attn_q\.(.*)$":
-            (r"double_blocks.\1.txt_attn_qkv.\2", 0, 3),
-            r"^double_blocks\.(\d+)\.txt_attn_k\.(.*)$":
-            (r"double_blocks.\1.txt_attn_qkv.\2", 1, 3),
-            r"^double_blocks\.(\d+)\.txt_attn_v\.(.*)$":
-            (r"double_blocks.\1.txt_attn_qkv.\2", 2, 3),
-            r"^double_blocks\.(\d+)\.img_mlp\.fc1\.(.*)$":
-            r"double_blocks.\1.img_mlp.fc_in.\2",
-            r"^double_blocks\.(\d+)\.img_mlp\.fc2\.(.*)$":
-            r"double_blocks.\1.img_mlp.fc_out.\2",
-            r"^double_blocks\.(\d+)\.txt_mlp\.fc1\.(.*)$":
-            r"double_blocks.\1.txt_mlp.fc_in.\2",
-            r"^double_blocks\.(\d+)\.txt_mlp\.fc2\.(.*)$":
-            r"double_blocks.\1.txt_mlp.fc_out.\2",
-
-            # 8. Final layer mapping:
-            r"^final_layer\.adaLN_modulation\.1\.(.*)$":
-            r"final_layer.adaLN_modulation.linear.\1",
-        })
-
-    # Reverse mapping for saving checkpoints: custom -> hf
-    reverse_param_names_mapping: dict = field(default_factory=lambda: {})
-
-    # Parameters from HY-WorldPlay config.json (loaded from checkpoint)
-    patch_size: list | tuple | int = field(default_factory=lambda: [1, 1, 1])
-    # Base latent channels - will be expanded in __post_init__ if concat_condition=True
-    in_channels: int = 32
-    concat_condition: bool = True
-    out_channels: int = 32
-    hidden_size: int = 2048
-    heads_num: int = 16
-    mlp_width_ratio: float = 4.0
-    mlp_act_type: str = "gelu_tanh"
-    mm_double_blocks_depth: int = 54
-    mm_single_blocks_depth: int = 0
-    rope_dim_list: list | tuple = field(default_factory=lambda: [16, 56, 56])
-    qkv_bias: bool = True
-    qk_norm: bool | str = True
-    qk_norm_type: str = "rms"
-    guidance_embed: bool = False
-    use_meanflow: bool = False
-    text_projection: str = "single_refiner"
-    use_attention_mask: bool = True
-    text_states_dim: int = 3584
-    text_states_dim_2: int | None = None
-    text_pool_type: str | None = None
-    rope_theta: float = 256.0
-    attn_mode: str = "flash"
-    attn_param: str | None = None
-    glyph_byT5_v2: bool = True
-    vision_projection: str = "linear"
-    vision_states_dim: int = 1152
-    is_reshape_temporal_channels: bool = False
-    use_cond_type_embedding: bool = True
-    ideal_resolution: str = "480p"
-    ideal_task: str = "i2v"
-    task_type: str = "i2v"
-    exclude_lora_layers: list[str] = field(
-        default_factory=lambda: ["img_in", "txt_in", "time_in", "vector_in"])
-
-    def __post_init__(self):
-        super().__post_init__()
-        # Convert HY-WorldPlay naming to FastVideo naming conventions
-        self.num_attention_heads: int = self.heads_num
-        self.attention_head_dim: int = self.hidden_size // self.heads_num
-        self.num_layers: int = self.mm_double_blocks_depth
-        self.num_single_layers: int = self.mm_single_blocks_depth
-        self.num_refiner_layers: int = 2  # Default for HYWorld
-        self.mlp_ratio: float = float(self.mlp_width_ratio)
-        self.text_embed_dim: int = self.text_states_dim
-        self.text_embed_2_dim: int = self.text_states_dim_2 if self.text_states_dim_2 else 1472
-        self.image_embed_dim: int = self.vision_states_dim
-        self.rope_axes_dim: tuple[int, ...] = tuple(self.rope_dim_list)
-        self.num_channels_latents: int = self.out_channels
-        self.target_size: int = 640
-
-        # Handle concat_condition: when True, actual in_channels = base * 2 + 1
-        # (base latent + condition latent + mask channel)
-        # config.json has base in_channels (32), but img_in needs full (65)
-        if self.concat_condition and self.in_channels == 32:
-            if self.is_reshape_temporal_channels:
-                self.in_channels = self.in_channels + self.in_channels // 2 + 1
-            else:
-                self.in_channels = self.in_channels * 2 + 1  # 32 * 2 + 1 = 65
-
-        # Handle patch_size (can be list/tuple or int)
-        if isinstance(self.patch_size, list | tuple):
-            self.patch_size_t: int = self.patch_size[0]
-            # assume square patch size for height and width
-            patch_size_hw: int = self.patch_size[1]
-            object.__setattr__(self, 'patch_size', patch_size_hw)
-        else:
-            self.patch_size_t = 1
-
-        # Convert qk_norm to string format
-        if isinstance(self.qk_norm, bool):
-            if self.qk_norm:
-                self.qk_norm = "rms_norm" if self.qk_norm_type == "rms" else self.qk_norm_type
-            else:
-                self.qk_norm = "none"
-
-
-@dataclass
-class HYWorldConfig(DiTConfig):
-    arch_config: DiTArchConfig = field(default_factory=HYWorldArchConfig)
-
-    prefix: str = "HYWorld"

fastvideo/configs/models/dits/longcat.py

@@ -26,6 +26,7 @@ class LongCatVideoArchConfig(DiTArchConfig):
         default_factory=lambda: [is_longcat_blocks])
 
     # Parameter name mapping for weight conversion
+    # Maps original LongCat third_party names -> native FastVideo names
     param_names_mapping: dict = field(
         default_factory=lambda: {
             # Embedders

fastvideo/configs/models/dits/ltx2.py

@@ -1,84 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-LTX-2 Transformer configuration for native FastVideo integration.
-"""
-
-from dataclasses import dataclass, field
-
-from fastvideo.configs.models.dits.base import DiTArchConfig, DiTConfig
-
-
-def is_ltx2_blocks(name: str, _module) -> bool:
-    """FSDP shard condition for LTX-2 transformer blocks."""
-    return "transformer_blocks" in name
-
-
-@dataclass
-class LTX2VideoArchConfig(DiTArchConfig):
-    """Architecture configuration for LTX-2 video transformer."""
-
-    _fsdp_shard_conditions: list = field(
-        default_factory=lambda: [is_ltx2_blocks])
-    _compile_conditions: list = field(default_factory=lambda: [is_ltx2_blocks])
-
-    # Parameter name mapping for weight conversion (hf/comfy -> FastVideo)
-    param_names_mapping: dict = field(
-        default_factory=lambda: {
-            r"^model\.diffusion_model\.(.*)$": r"model.\1",
-            r"^diffusion_model\.(.*)$": r"model.\1",
-            r"^model\.(.*)$": r"model.\1",
-            r"^(.*)$": r"model.\1",
-        })
-
-    reverse_param_names_mapping: dict = field(default_factory=lambda: {})
-    lora_param_names_mapping: dict = field(default_factory=lambda: {})
-
-    # Core transformer settings (defaults from LTX-2 metadata)
-    num_attention_heads: int = 32
-    attention_head_dim: int = 128
-    num_layers: int = 48
-    cross_attention_dim: int = 4096
-    caption_channels: int = 3840
-    norm_eps: float = 1e-6
-    attention_type: str = "default"
-    rope_type: str = "split"
-    double_precision_rope: bool = True
-
-    positional_embedding_theta: float = 10000.0
-    positional_embedding_max_pos: list[int] = field(
-        default_factory=lambda: [20, 2048, 2048])
-    timestep_scale_multiplier: int = 1000
-    use_middle_indices_grid: bool = True
-
-    # Patchification (video-only path)
-    patch_size: tuple[int, int, int] = (1, 1, 1)
-    num_channels_latents: int = 128
-    in_channels: int | None = None
-    out_channels: int | None = None
-
-    # Audio defaults (reserved for joint AV ports)
-    audio_num_attention_heads: int = 32
-    audio_attention_head_dim: int = 64
-    audio_in_channels: int = 128
-    audio_out_channels: int = 128
-    audio_cross_attention_dim: int = 2048
-    audio_positional_embedding_max_pos: list[int] = field(
-        default_factory=lambda: [20])
-    av_ca_timestep_scale_multiplier: int = 1
-
-    def __post_init__(self):
-        super().__post_init__()
-        patch_volume = self.patch_size[0] * self.patch_size[
-            1] * self.patch_size[2]
-        if self.in_channels is None:
-            self.in_channels = self.num_channels_latents * patch_volume
-        if self.out_channels is None:
-            self.out_channels = self.in_channels
-
-
-@dataclass
-class LTX2VideoConfig(DiTConfig):
-    """Main configuration for LTX-2 transformer."""
-
-    arch_config: DiTArchConfig = field(default_factory=LTX2VideoArchConfig)
-    prefix: str = "ltx2"

fastvideo/configs/models/encoders/__init__.py

@@ -7,14 +7,10 @@ from fastvideo.configs.models.encoders.clip import (
 from fastvideo.configs.models.encoders.llama import LlamaConfig
 from fastvideo.configs.models.encoders.t5 import T5Config, T5LargeConfig
 from fastvideo.configs.models.encoders.qwen2_5 import Qwen2_5_VLConfig
-from fastvideo.configs.models.encoders.siglip import SiglipVisionConfig
-from fastvideo.configs.models.encoders.reason1 import Reason1ArchConfig, Reason1Config
-from fastvideo.configs.models.encoders.gemma import LTX2GemmaConfig
 
 __all__ = [
     "EncoderConfig", "TextEncoderConfig", "ImageEncoderConfig",
     "BaseEncoderOutput", "CLIPTextConfig", "CLIPVisionConfig",
     "WAN2_1ControlCLIPVisionConfig", "LlamaConfig", "T5Config", "T5LargeConfig",
-    "Qwen2_5_VLConfig", "Reason1ArchConfig", "Reason1Config", "LTX2GemmaConfig",
-    "SiglipVisionConfig"
+    "Qwen2_5_VLConfig"
 ]

fastvideo/configs/models/encoders/gemma.py

@@ -1,48 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-from dataclasses import dataclass, field
-
-from fastvideo.configs.models.encoders.base import (
-    TextEncoderArchConfig,
-    TextEncoderConfig,
-)
-
-
-@dataclass
-class LTX2GemmaArchConfig(TextEncoderArchConfig):
-    architectures: list[str] = field(
-        default_factory=lambda: ["LTX2GemmaTextEncoderModel"])
-    hidden_size: int = 3840
-    num_hidden_layers: int = 48
-    num_attention_heads: int = 30
-    text_len: int = 1024
-    pad_token_id: int = 0
-    eos_token_id: int = 2
-
-    gemma_model_path: str = ""
-    gemma_dtype: str = "bfloat16"
-    padding_side: str = "left"
-
-    feature_extractor_in_features: int = 3840 * 49
-    feature_extractor_out_features: int = 3840
-
-    connector_num_attention_heads: int = 30
-    connector_attention_head_dim: int = 128
-    connector_num_layers: int = 2
-    connector_positional_embedding_theta: float = 10000.0
-    connector_positional_embedding_max_pos: list[int] = field(
-        default_factory=lambda: [4096])
-    connector_rope_type: str = "split"
-    connector_double_precision_rope: bool = False
-    connector_num_learnable_registers: int | None = 128
-
-    def __post_init__(self) -> None:
-        super().__post_init__()
-        self.tokenizer_kwargs["padding"] = "max_length"
-
-
-@dataclass
-class LTX2GemmaConfig(TextEncoderConfig):
-    arch_config: TextEncoderArchConfig = field(
-        default_factory=LTX2GemmaArchConfig)
-
-    prefix: str = "ltx2_gemma"

fastvideo/configs/models/encoders/reason1.py

@@ -1,72 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""Config for Reason1 (Qwen2.5-VL) text encoder."""
-
-from dataclasses import dataclass, field
-from typing import Any
-
-from fastvideo.configs.models.encoders.base import TextEncoderArchConfig, TextEncoderConfig
-
-
-@dataclass
-class Reason1ArchConfig(TextEncoderArchConfig):
-    """Architecture settings (defaults match Qwen2.5-VL-7B-Instruct)."""
-
-    architectures: list[str] = field(
-        default_factory=lambda: ["Qwen2_5_VLForConditionalGeneration"])
-    model_type: str = "qwen2_5_vl"
-
-    vocab_size: int = 152064
-    hidden_size: int = 3584
-    num_hidden_layers: int = 28
-    num_attention_heads: int = 28
-    num_key_value_heads: int = 4
-    intermediate_size: int = 18944
-
-    text_len: int = 512
-    hidden_state_skip_layer: int = 0
-    bos_token_id: int = 151643
-    pad_token_id: int = 151643
-    eos_token_id: int = 151645
-
-    image_token_id: int = 151655
-    video_token_id: int = 151656
-    vision_token_id: int = 151654
-    vision_start_token_id: int = 151652
-    vision_end_token_id: int = 151653
-
-    vision_config: dict[str, Any] | None = None
-
-    rope_theta: float = 1000000.0
-    rope_scaling: dict[str, Any] | None = field(default_factory=lambda: {
-        "type": "mrope",
-        "mrope_section": [16, 24, 24]
-    })
-    max_position_embeddings: int = 128000
-    max_window_layers: int = 28
-
-    embedding_concat_strategy: str = "mean_pooling"
-    n_layers_per_group: int = 5
-    num_embedding_padding_tokens: int = 512
-
-    attention_dropout: float = 0.0
-    hidden_act: str = "silu"
-    initializer_range: float = 0.02
-    rms_norm_eps: float = 1e-6
-
-    use_sliding_window: bool = False
-    sliding_window: int = 32768
-
-    tie_word_embeddings: bool = False
-    use_cache: bool = False
-    output_hidden_states: bool = True
-
-    torch_dtype: str = "bfloat16"
-    _attn_implementation: str = "flash_attention_2"
-
-
-@dataclass
-class Reason1Config(TextEncoderConfig):
-    """Reason1 text encoder config."""
-
-    arch_config: Reason1ArchConfig = field(default_factory=Reason1ArchConfig)
-    tokenizer_type: str = "Qwen/Qwen2.5-VL-7B-Instruct"

fastvideo/configs/models/encoders/siglip.py

@@ -1,53 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""SigLIP vision encoder configuration for FastVideo."""
-
-from dataclasses import dataclass, field
-
-from fastvideo.configs.models.encoders.base import (ImageEncoderArchConfig,
-                                                    ImageEncoderConfig)
-
-
-@dataclass
-class SiglipVisionArchConfig(ImageEncoderArchConfig):
-    """Architecture configuration for SigLIP vision encoder.
-    
-    Fields match the config.json from HuggingFace SigLIP checkpoints.
-    """
-
-    # From config.json
-    architectures: list[str] = field(
-        default_factory=lambda: ["SiglipVisionModel"])
-    attention_dropout: float = 0.0
-    dtype: str | None = None
-    hidden_act: str = "gelu_pytorch_tanh"
-    hidden_size: int = 1152
-    image_size: int = 384
-    intermediate_size: int = 4304
-    layer_norm_eps: float = 1e-6
-    model_type: str = "siglip_vision_model"
-    num_attention_heads: int = 16
-    num_channels: int = 3
-    num_hidden_layers: int = 27
-    patch_size: int = 14
-
-    # FastVideo specific - QKV fusion mapping
-    stacked_params_mapping: list = field(default_factory=lambda: [
-        ("qkv_proj", "q_proj", "q"),
-        ("qkv_proj", "k_proj", "k"),
-        ("qkv_proj", "v_proj", "v"),
-    ])
-
-
-@dataclass
-class SiglipVisionConfig(ImageEncoderConfig):
-    """Configuration for SigLIP vision encoder."""
-
-    arch_config: ImageEncoderArchConfig = field(
-        default_factory=SiglipVisionArchConfig)
-
-    # FastVideo specific
-    num_hidden_layers_override: int | None = None
-    require_post_norm: bool | None = None
-    enable_scale: bool = True
-    is_causal: bool = False
-    prefix: str = "siglip"

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.hunyuanvae import HunyuanVAEConfig
 from fastvideo.configs.models.vaes.hunyuan15vae import Hunyuan15VAEConfig
-from fastvideo.configs.models.vaes.ltx2vae import LTX2VAEConfig
 from fastvideo.configs.models.vaes.stepvideovae import StepVideoVAEConfig
 from fastvideo.configs.models.vaes.wanvae import WanVAEConfig
 
@@ -11,7 +9,5 @@ __all__ = [
     "WanVAEConfig",
     "StepVideoVAEConfig",
     "CosmosVAEConfig",
-    "Cosmos25VAEConfig",
     "Hunyuan15VAEConfig",
-    "LTX2VAEConfig",
 ]

fastvideo/configs/models/vaes/cosmos2_5vae.py

@@ -1,223 +0,0 @@
-"""Cosmos 2.5 (Wan2.1-style) VAE config and checkpoint-key mapping."""
-
-from __future__ import annotations
-
-import re
-from dataclasses import dataclass, field
-
-import torch
-
-from fastvideo.configs.models.vaes.base import VAEArchConfig, VAEConfig
-
-
-@dataclass
-class Cosmos25VAEArchConfig(VAEArchConfig):
-    _name_or_path: str = ""
-    base_dim: int = 96
-    decoder_base_dim: int | None = None
-    z_dim: int = 16
-    dim_mult: tuple[int, ...] = (1, 2, 4, 4)
-    num_res_blocks: int = 2
-    attn_scales: tuple[float, ...] = ()
-    temperal_downsample: tuple[bool, ...] = (False, True, True)
-    dropout: float = 0.0
-    is_residual: bool = False
-    in_channels: int = 3
-    out_channels: int = 3
-    patch_size: int | None = None
-    scale_factor_temporal: int = 4
-    scale_factor_spatial: int = 8
-    clip_output: bool = True
-
-    latents_mean: tuple[float, ...] = (
-        -0.7571,
-        -0.7089,
-        -0.9113,
-        0.1075,
-        -0.1745,
-        0.9653,
-        -0.1517,
-        1.5508,
-        0.4134,
-        -0.0715,
-        0.5517,
-        -0.3632,
-        -0.1922,
-        -0.9497,
-        0.2503,
-        -0.2921,
-    )
-    latents_std: tuple[float, ...] = (
-        2.8184,
-        1.4541,
-        2.3275,
-        2.6558,
-        1.2196,
-        1.7708,
-        2.6052,
-        2.0743,
-        3.2687,
-        2.1526,
-        2.8652,
-        1.5579,
-        1.6382,
-        1.1253,
-        2.8251,
-        1.9160,
-    )
-
-    # Simple 1:1 renames. More complex decoder remapping is handled by
-    # `map_official_key()`.
-    param_names_mapping: dict[str, str] = field(
-        default_factory=lambda: {
-            r"^conv1\.(.*)$": r"quant_conv.\1",
-            r"^conv2\.(.*)$": r"post_quant_conv.\1",
-            r"^encoder\.conv1\.(.*)$": r"encoder.conv_in.\1",
-            r"^decoder\.conv1\.(.*)$": r"decoder.conv_in.\1",
-            r"^encoder\.head\.0\.gamma$": r"encoder.norm_out.gamma",
-            r"^encoder\.head\.2\.(.*)$": r"encoder.conv_out.\1",
-            r"^decoder\.head\.0\.gamma$": r"decoder.norm_out.gamma",
-            r"^decoder\.head\.2\.(.*)$": r"decoder.conv_out.\1",
-        })
-
-    @staticmethod
-    def map_official_key(key: str) -> str | None:
-        """Map a single official checkpoint key into FastVideo key space."""
-
-        def map_residual_subkey(prefix: str, sub: str) -> str | None:
-            if re.match(r"^residual\.0\.gamma$", sub):
-                return f"{prefix}.norm1.gamma"
-            m = re.match(r"^residual\.2\.(weight|bias)$", sub)
-            if m:
-                return f"{prefix}.conv1.{m.group(1)}"
-            if re.match(r"^residual\.3\.gamma$", sub):
-                return f"{prefix}.norm2.gamma"
-            m = re.match(r"^residual\.6\.(weight|bias)$", sub)
-            if m:
-                return f"{prefix}.conv2.{m.group(1)}"
-            m = re.match(r"^shortcut\.(weight|bias)$", sub)
-            if m:
-                return f"{prefix}.conv_shortcut.{m.group(1)}"
-            return None
-
-        def map_attn_subkey(prefix: str, sub: str) -> str | None:
-            if re.match(r"^norm\.gamma$", sub):
-                return f"{prefix}.norm.gamma"
-            m = re.match(r"^to_qkv\.(weight|bias)$", sub)
-            if m:
-                return f"{prefix}.to_qkv.{m.group(1)}"
-            m = re.match(r"^proj\.(weight|bias)$", sub)
-            if m:
-                return f"{prefix}.proj.{m.group(1)}"
-            return None
-
-        def map_resample_subkey(prefix: str, sub: str) -> str | None:
-            m = re.match(r"^resample\.1\.(weight|bias)$", sub)
-            if m:
-                return f"{prefix}.resample.1.{m.group(1)}"
-            m = re.match(r"^time_conv\.(weight|bias)$", sub)
-            if m:
-                return f"{prefix}.time_conv.{m.group(1)}"
-            return None
-
-        m = re.match(r"^conv1\.(weight|bias)$", key)
-        if m:
-            return f"quant_conv.{m.group(1)}"
-        m = re.match(r"^conv2\.(weight|bias)$", key)
-        if m:
-            return f"post_quant_conv.{m.group(1)}"
-        m = re.match(r"^(encoder|decoder)\.conv1\.(weight|bias)$", key)
-        if m:
-            return f"{m.group(1)}.conv_in.{m.group(2)}"
-        m = re.match(r"^(encoder|decoder)\.head\.0\.gamma$", key)
-        if m:
-            return f"{m.group(1)}.norm_out.gamma"
-        m = re.match(r"^(encoder|decoder)\.head\.2\.(weight|bias)$", key)
-        if m:
-            return f"{m.group(1)}.conv_out.{m.group(2)}"
-
-        m = re.match(r"^(encoder|decoder)\.middle\.0\.(.*)$", key)
-        if m:
-            return map_residual_subkey(f"{m.group(1)}.mid_block.resnets.0",
-                                       m.group(2))
-        m = re.match(r"^(encoder|decoder)\.middle\.1\.(.*)$", key)
-        if m:
-            return map_attn_subkey(f"{m.group(1)}.mid_block.attentions.0",
-                                   m.group(2))
-        m = re.match(r"^(encoder|decoder)\.middle\.2\.(.*)$", key)
-        if m:
-            return map_residual_subkey(f"{m.group(1)}.mid_block.resnets.1",
-                                       m.group(2))
-
-        m = re.match(r"^encoder\.downsamples\.(\d+)\.(.*)$", key)
-        if m:
-            idx = int(m.group(1))
-            sub = m.group(2)
-            if sub.startswith("residual.") or sub.startswith("shortcut."):
-                return map_residual_subkey(f"encoder.down_blocks.{idx}", sub)
-            if sub.startswith("resample.") or sub.startswith("time_conv."):
-                return map_resample_subkey(f"encoder.down_blocks.{idx}", sub)
-            return None
-
-        m = re.match(r"^decoder\.upsamples\.(\d+)\.(.*)$", key)
-        if m:
-            uidx = int(m.group(1))
-            sub = m.group(2)
-
-            if uidx in (0, 1, 2):
-                block_i, res_i = 0, uidx
-            elif uidx == 3:
-                block_i, res_i = 0, None
-            elif uidx in (4, 5, 6):
-                block_i, res_i = 1, uidx - 4
-            elif uidx == 7:
-                block_i, res_i = 1, None
-            elif uidx in (8, 9, 10):
-                block_i, res_i = 2, uidx - 8
-            elif uidx == 11:
-                block_i, res_i = 2, None
-            elif uidx in (12, 13, 14):
-                block_i, res_i = 3, uidx - 12
-            else:
-                return None
-
-            if res_i is None:
-                return map_resample_subkey(
-                    f"decoder.up_blocks.{block_i}.upsamplers.0",
-                    sub,
-                )
-
-            return map_residual_subkey(
-                f"decoder.up_blocks.{block_i}.resnets.{res_i}",
-                sub,
-            )
-
-        return None
-
-    temporal_compression_ratio: int = 4
-    spatial_compression_ratio: int = 8
-
-    def __post_init__(self):
-        self.scaling_factor: torch.Tensor = 1.0 / torch.tensor(
-            self.latents_std).view(1, self.z_dim, 1, 1, 1)
-        self.shift_factor: torch.Tensor = torch.tensor(self.latents_mean).view(
-            1, self.z_dim, 1, 1, 1)
-        self.temporal_compression_ratio = self.scale_factor_temporal
-        self.spatial_compression_ratio = self.scale_factor_spatial
-
-
-@dataclass
-class Cosmos25VAEConfig(VAEConfig):
-    """Cosmos2.5 VAE config."""
-
-    arch_config: Cosmos25VAEArchConfig = field(
-        default_factory=Cosmos25VAEArchConfig)
-
-    use_feature_cache: bool = True
-    use_tiling: bool = False
-    use_temporal_tiling: bool = False
-    use_parallel_tiling: bool = False
-
-    def __post_init__(self):
-        self.blend_num_frames = (self.tile_sample_min_num_frames -
-                                 self.tile_sample_stride_num_frames) * 2

fastvideo/configs/models/vaes/ltx2vae.py

@@ -1,45 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-LTX-2 VAE configuration.
-"""
-
-from dataclasses import dataclass, field
-
-from fastvideo.configs.models.vaes.base import VAEArchConfig, VAEConfig
-
-
-@dataclass
-class LTX2VAEArchConfig(VAEArchConfig):
-    # Mirrors LTX-2 safetensors metadata config under "vae"
-    _class_name: str = "CausalVideoAutoencoder"
-    dims: int = 3
-    in_channels: int = 3
-    out_channels: int = 3
-    latent_channels: int = 128
-    encoder_blocks: list = field(default_factory=list)
-    decoder_blocks: list = field(default_factory=list)
-    patch_size: int = 4
-    norm_layer: str = "pixel_norm"
-    latent_log_var: str = "uniform"
-    encoder_spatial_padding_mode: str = "zeros"
-    decoder_spatial_padding_mode: str = "reflect"
-    causal_decoder: bool = False
-    timestep_conditioning: bool = True
-    use_quant_conv: bool = False
-    scaling_factor: float = 1.0
-    normalize_latent_channels: bool = False
-
-    # Match FastVideo naming for compression ratios (LTX-2 default)
-    temporal_compression_ratio: int = 8
-    spatial_compression_ratio: int = 32
-
-
-@dataclass
-class LTX2VAEConfig(VAEConfig):
-    arch_config: VAEArchConfig = field(default_factory=LTX2VAEArchConfig)
-
-    # LTX-2 tiling defaults (match ltx_core.video_vae.TilingConfig.default()).
-    ltx2_spatial_tile_size_in_pixels: int = 512
-    ltx2_spatial_tile_overlap_in_pixels: int = 64
-    ltx2_temporal_tile_size_in_frames: int = 64
-    ltx2_temporal_tile_overlap_in_frames: int = 24

fastvideo/configs/pipelines/__init__.py

@@ -1,11 +1,8 @@
 from fastvideo.configs.pipelines.base import (PipelineConfig,
                                               SlidingTileAttnConfig)
 from fastvideo.configs.pipelines.cosmos import CosmosConfig
-from fastvideo.configs.pipelines.cosmos2_5 import Cosmos25Config
 from fastvideo.configs.pipelines.hunyuan import FastHunyuanConfig, HunyuanConfig
 from fastvideo.configs.pipelines.hunyuan15 import Hunyuan15T2V480PConfig, Hunyuan15T2V720PConfig
-from fastvideo.configs.pipelines.hyworld import HYWorldConfig
-from fastvideo.configs.pipelines.ltx2 import LTX2T2VConfig
 from fastvideo.configs.pipelines.registry import (
     get_pipeline_config_cls_from_name)
 from fastvideo.configs.pipelines.stepvideo import StepVideoT2VConfig
@@ -18,6 +15,5 @@ __all__ = [
     "Hunyuan15T2V480PConfig", "Hunyuan15T2V720PConfig", "SlidingTileAttnConfig",
     "WanT2V480PConfig", "WanI2V480PConfig", "WanT2V720PConfig",
     "WanI2V720PConfig", "StepVideoT2VConfig", "SelfForcingWanT2V480PConfig",
-    "CosmosConfig", "Cosmos25Config", "LTX2T2VConfig", "HYWorldConfig",
-    "get_pipeline_config_cls_from_name"
+    "CosmosConfig", "get_pipeline_config_cls_from_name"
 ]

fastvideo/configs/pipelines/cosmos2_5.py

@@ -1,89 +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 import Cosmos25VideoConfig
-from fastvideo.configs.models.dits.cosmos2_5 import Cosmos25ArchConfig
-from fastvideo.configs.models.encoders import BaseEncoderOutput
-from fastvideo.configs.models.encoders.reason1 import Reason1Config, Reason1ArchConfig
-from fastvideo.configs.models.vaes import Cosmos25VAEConfig
-from fastvideo.configs.pipelines.base import PipelineConfig, STA_Mode
-
-
-def _identity_preprocess_text(prompt: str) -> str:
-    return prompt
-
-
-def reason1_postprocess_text(outputs: BaseEncoderOutput) -> torch.Tensor:
-    hidden_states = getattr(outputs, "hidden_states", None)
-    if hidden_states is None:
-        raise ValueError("Reason1 postprocess requires outputs.hidden_states")
-
-    hs = list(hidden_states)[1:]
-    normed = []
-    for h in hs:
-        h = h.float()
-        h = (h - h.mean(dim=-1, keepdim=True)) / (h.std(dim=-1, keepdim=True) +
-                                                  1e-8)
-        normed.append(h)
-    return torch.cat(normed, dim=-1).to(hidden_states[0].dtype)
-
-
-@dataclass
-class Cosmos25Config(PipelineConfig):
-    """Configuration for Cosmos 2.5 (Predict2.5) video generation pipeline."""
-
-    dit_config: DiTConfig = field(default_factory=lambda: Cosmos25VideoConfig(
-        arch_config=Cosmos25ArchConfig(
-            num_attention_heads=16,
-            attention_head_dim=128,
-            in_channels=16,
-            out_channels=16,
-            num_layers=28,
-            patch_size=[1, 2, 2],
-            max_size=[128, 240, 240],
-            rope_scale=[1.0, 3.0, 3.0],
-            text_embed_dim=1024,
-            mlp_ratio=4.0,
-            adaln_lora_dim=256,
-            use_adaln_lora=True,
-            concat_padding_mask=True,
-            extra_pos_embed_type=None,
-            use_crossattn_projection=True,
-            rope_enable_fps_modulation=False,
-            qk_norm="rms_norm",
-        )))
-
-    vae_config: VAEConfig = field(default_factory=Cosmos25VAEConfig)
-
-    text_encoder_configs: tuple[EncoderConfig, ...] = field(
-        default_factory=lambda: (Reason1Config(arch_config=Reason1ArchConfig(
-            embedding_concat_strategy="full_concat")), ))
-
-    preprocess_text_funcs: tuple[Callable[[str], str], ...] = field(
-        default_factory=lambda: (_identity_preprocess_text, ))
-    postprocess_text_funcs: tuple[Callable[[BaseEncoderOutput], torch.Tensor],
-                                  ...] = field(default_factory=lambda:
-                                               (reason1_postprocess_text, ))
-
-    dit_precision: str = "bf16"
-    vae_precision: str = "bf16"
-    text_encoder_precisions: tuple[str, ...] = field(
-        default_factory=lambda: ("bf16", ))
-
-    embedded_cfg_scale: float = 0.0
-    flow_shift: float = 5.0
-
-    vae_tiling: bool = False
-    vae_sp: bool = False
-
-    STA_mode: STA_Mode = STA_Mode.NONE
-    skip_time_steps: int = 0
-
-    def __post_init__(self):
-        self.vae_config.load_encoder = True
-        self.vae_config.load_decoder = True
-        self._vae_latent_dim = 16

fastvideo/configs/pipelines/hunyuan15.py

@@ -127,29 +127,13 @@ class Hunyuan15T2V480PConfig(PipelineConfig):
     vae_tiling: bool = True
 
     def __post_init__(self):
-        self.vae_config.load_encoder = True
+        self.vae_config.load_encoder = False
         self.vae_config.load_decoder = True
 
 
-@dataclass
-class Hunyuan15DistilledI2V480PConfig(Hunyuan15T2V480PConfig):
-    flow_shift: int = 7
-
-
 @dataclass
 class Hunyuan15T2V720PConfig(Hunyuan15T2V480PConfig):
     """Base configuration for HunYuan pipeline architecture."""
 
     # HunyuanConfig-specific parameters with defaults
     flow_shift: int = 9
-
-
-@dataclass
-class SelfForcingHunyuan15T2V480PConfig(Hunyuan15T2V480PConfig):
-    flow_shift: int = 5
-    is_causal: bool = True
-    # dmd_denoising_steps: list[int] | None = field(
-    #     default_factory=lambda: [1000, 750, 500, 250])
-    dmd_denoising_steps: list[int] | None = field(
-        default_factory=lambda: [1000, 875, 750, 625, 500, 375, 250, 125])
-    warp_denoising_step: bool = True

fastvideo/configs/pipelines/hyworld.py

@@ -1,29 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-from dataclasses import dataclass, field
-
-from fastvideo.configs.models import DiTConfig, EncoderConfig
-from fastvideo.configs.models.dits import HYWorldConfig as HYWorldDiTConfig
-from fastvideo.configs.models.encoders import SiglipVisionConfig
-from fastvideo.configs.pipelines.hunyuan15 import Hunyuan15T2V480PConfig
-
-
-@dataclass
-class HYWorldConfig(Hunyuan15T2V480PConfig):
-    """Base configuration for HYWorld pipeline architecture."""
-
-    # HYWorldConfig-specific parameters with defaults
-    dit_config: DiTConfig = field(default_factory=HYWorldDiTConfig)
-
-    # SigLIP image encoder for I2V
-    image_encoder_config: EncoderConfig = field(
-        default_factory=SiglipVisionConfig)
-    image_encoder_precision: str = "fp16"
-    # vae_precision: str = "fp32"
-
-    # Text encoding
-    text_encoder_precisions: tuple[str, ...] = field(
-        default_factory=lambda: ("fp16", "fp32"))
-
-    def __post_init__(self):
-        self.vae_config.load_encoder = True
-        self.vae_config.load_decoder = True

fastvideo/configs/pipelines/longcat.py

@@ -17,7 +17,11 @@ from fastvideo.configs.pipelines.base import PipelineConfig
 
 @dataclass
 class LongCatDiTArchConfig(DiTArchConfig):
-    """Extended DiTArchConfig with LongCat-specific fields."""
+    """Extended DiTArchConfig with LongCat-specific fields.
+    
+    NOTE: This is for Phase 1 wrapper compatibility. For native model (Phase 2),
+    use LongCatVideoConfig from fastvideo.configs.models.dits.longcat instead.
+    """
     # LongCat-specific architecture parameters
     adaln_tembed_dim: int = 512
     caption_channels: int = 4096
@@ -84,16 +88,20 @@ def umt5_postprocess_text(outputs: BaseEncoderOutput) -> torch.Tensor:
 
 @dataclass
 class LongCatT2V480PConfig(PipelineConfig):
-    """Configuration for LongCat pipeline (480p).
+    """Configuration for LongCat pipeline (480p) aligned to LongCat-Video modules.
 
     Components expected by loaders:
       - tokenizer: AutoTokenizer
       - text_encoder: UMT5EncoderModel
-      - transformer: LongCatTransformer3DModel
+      - transformer: LongCatVideoTransformer3DModel (Phase 1 wrapper)
+                  OR LongCatTransformer3DModel (Phase 2 native)
       - vae: AutoencoderKLWan (Wan VAE, 4x8 compression)
       - scheduler: FlowMatchEulerDiscreteScheduler
     """
 
+    # DiT config with LongCat-specific arch_config
+    # NOTE: For Phase 1 wrapper, uses LongCatDiTArchConfig
+    # For Phase 2 native model, can use LongCatVideoConfig directly
     dit_config: DiTConfig = field(
         default_factory=lambda: DiTConfig(arch_config=LongCatDiTArchConfig()))
 

fastvideo/configs/pipelines/ltx2.py

@@ -1,50 +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, ModelConfig,
-                                      LTX2AudioDecoderConfig, LTX2VocoderConfig,
-                                      VAEConfig)
-from fastvideo.configs.models.dits import LTX2VideoConfig
-from fastvideo.configs.models.encoders import BaseEncoderOutput, LTX2GemmaConfig
-from fastvideo.configs.models.vaes import LTX2VAEConfig
-from fastvideo.configs.pipelines.base import PipelineConfig, preprocess_text
-
-
-def ltx2_postprocess_text(outputs: BaseEncoderOutput) -> torch.Tensor:
-    return outputs.last_hidden_state
-
-
-@dataclass
-class LTX2T2VConfig(PipelineConfig):
-    """Configuration for LTX-2 T2V pipeline."""
-
-    dit_config: DiTConfig = field(default_factory=LTX2VideoConfig)
-    vae_config: VAEConfig = field(default_factory=LTX2VAEConfig)
-    vae_tiling: bool = True
-    vae_sp: bool = False
-
-    text_encoder_configs: tuple[EncoderConfig, ...] = field(
-        default_factory=lambda: (LTX2GemmaConfig(), ))
-    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:
-                                               (ltx2_postprocess_text, ))
-
-    dit_precision: str = "bf16"
-    vae_precision: str = "bf16"
-    text_encoder_precisions: tuple[str, ...] = field(
-        default_factory=lambda: ("bf16", ))
-
-    audio_decoder_config: ModelConfig = field(
-        default_factory=LTX2AudioDecoderConfig)
-    vocoder_config: ModelConfig = field(default_factory=LTX2VocoderConfig)
-    audio_decoder_precision: str = "bf16"
-    vocoder_precision: str = "bf16"
-
-    def __post_init__(self) -> None:
-        self.vae_config.load_encoder = False
-        self.vae_config.load_decoder = True

fastvideo/configs/pipelines/registry.py

@@ -6,16 +6,10 @@ from collections.abc import Callable
 
 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.hunyuan import FastHunyuanConfig, HunyuanConfig
-from fastvideo.configs.pipelines.hyworld import HYWorldConfig
-from fastvideo.configs.pipelines.ltx2 import LTX2T2VConfig
-from fastvideo.configs.pipelines.hunyuan15 import Hunyuan15T2V480PConfig, Hunyuan15T2V720PConfig, SelfForcingHunyuan15T2V480PConfig, Hunyuan15DistilledI2V480PConfig
+from fastvideo.configs.pipelines.hunyuan15 import Hunyuan15T2V480PConfig, Hunyuan15T2V720PConfig
 from fastvideo.configs.pipelines.stepvideo import StepVideoT2VConfig
 from fastvideo.configs.pipelines.longcat import LongCatT2V480PConfig
-from fastvideo.configs.pipelines.turbodiffusion import (
-    TurboDiffusionT2V_1_3B_Config, TurboDiffusionT2V_14B_Config,
-    TurboDiffusionI2V_A14B_Config)
 
 # isort: off
 from fastvideo.configs.pipelines.wan import (
@@ -35,15 +29,10 @@ logger = init_logger(__name__)
 PIPE_NAME_TO_CONFIG: dict[str, type[PipelineConfig]] = {
     "FastVideo/FastHunyuan-diffusers": FastHunyuanConfig,
     "hunyuanvideo-community/HunyuanVideo": HunyuanConfig,
-    "weizhou03/SFHunyuanVideo-1.5-Diffusers-480p_t2v":
-    SelfForcingHunyuan15T2V480PConfig,
-    "hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_i2v_step_distilled":
-    Hunyuan15DistilledI2V480PConfig,
     "hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v":
     Hunyuan15T2V480PConfig,
     "hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_t2v":
     Hunyuan15T2V720PConfig,
-    "FastVideo/HY-WorldPlay-Bidirectional-Diffusers": HYWorldConfig,
     "Wan-AI/Wan2.1-T2V-1.3B-Diffusers": WanT2V480PConfig,
     "weizhou03/Wan2.1-Fun-1.3B-InP-Diffusers": WanI2V480PConfig,
     "IRMChen/Wan2.1-Fun-1.3B-Control-Diffusers": WANV2VConfig,
@@ -63,38 +52,18 @@ PIPE_NAME_TO_CONFIG: dict[str, type[PipelineConfig]] = {
     "Wan-AI/Wan2.2-T2V-A14B-Diffusers": Wan2_2_T2V_A14B_Config,
     "Wan-AI/Wan2.2-I2V-A14B-Diffusers": Wan2_2_I2V_A14B_Config,
     "nvidia/Cosmos-Predict2-2B-Video2World": CosmosConfig,
-    "KyleShao/Cosmos-Predict2.5-2B-Diffusers": Cosmos25Config,
     "FastVideo/Matrix-Game-2.0-Base-Diffusers": MatrixGameI2V480PConfig,
     "FastVideo/Matrix-Game-2.0-GTA-Diffusers": MatrixGameI2V480PConfig,
     "FastVideo/Matrix-Game-2.0-TempleRun-Diffusers": MatrixGameI2V480PConfig,
-    # LongCat Video models
-    "FastVideo/LongCat-Video-T2V-Diffusers": LongCatT2V480PConfig,
-    "FastVideo/LongCat-Video-I2V-Diffusers": LongCatT2V480PConfig,
-    "FastVideo/LongCat-Video-VC-Diffusers": LongCatT2V480PConfig,
-    # LTX-2 models
-    "Lightricks/LTX-2": LTX2T2VConfig,
-    "converted/ltx2_diffusers": LTX2T2VConfig,
-    # TurboDiffusion models
-    "loayrashid/TurboWan2.1-T2V-1.3B-Diffusers": TurboDiffusionT2V_1_3B_Config,
-    "loayrashid/TurboWan2.1-T2V-14B-Diffusers": TurboDiffusionT2V_14B_Config,
-    "loayrashid/TurboWan2.2-I2V-A14B-Diffusers": TurboDiffusionI2V_A14B_Config,
     # Add other specific weight variants
 }
 
 # For determining pipeline type from model ID
 PIPELINE_DETECTOR: dict[str, Callable[[str], bool]] = {
-    "longcatimagetovideo":
-    lambda id: "longcatimagetovideo" in id.lower(),
-    "longcatvideocontinuation":
-    lambda id: "longcatvideocontinuation" in id.lower(),
-    "longcat":
-    lambda id: "longcat" in id.lower(),
     "hunyuan":
     lambda id: "hunyuan" in id.lower(),
     "hunyuan15":
     lambda id: "hunyuan15" in id.lower(),
-    "hyworld":
-    lambda id: "hyworld" in id.lower(),
     "matrixgame":
     lambda id: "matrix-game" in id.lower() or "matrixgame" in id.lower(),
     "wanpipeline":
@@ -108,38 +77,26 @@ PIPELINE_DETECTOR: dict[str, Callable[[str], bool]] = {
     "stepvideo":
     lambda id: "stepvideo" in id.lower(),
     "cosmos":
-    lambda id: "cosmos" in id.lower() and ("2.5" not in id.lower(
-    ) and "2_5" not in id.lower() and "25" not in id.lower()),
-    "cosmos25":
-    lambda id: "cosmos25" in id.lower(),
-    "turbodiffusion":
-    lambda id: "turbodiffusion" in id.lower() or "turbowan" in id.lower(),
-    "ltx2":
-    lambda id: "ltx2" in id.lower() or "ltx-2" in id.lower(),
+    lambda id: "cosmos" in id.lower(),
+    "longcat":
+    lambda id: "longcat" in id.lower(),
     # Add other pipeline architecture detectors
 }
 
 # Fallback configs when exact match isn't found but architecture is detected
 PIPELINE_FALLBACK_CONFIG: dict[str, type[PipelineConfig]] = {
-    "longcatimagetovideo": LongCatT2V480PConfig,
-    "longcatvideocontinuation": LongCatT2V480PConfig,
     "longcat": LongCatT2V480PConfig,
-    "cosmos25": Cosmos25Config,
     "hunyuan":
     HunyuanConfig,  # Base Hunyuan config as fallback for any Hunyuan variant
     "matrixgame": MatrixGameI2V480PConfig,
     "hunyuan15":
     Hunyuan15T2V480PConfig,  # Base Hunyuan15 config as fallback for any Hunyuan15 variant
-    "hyworld":
-    HYWorldConfig,  # HYWorld-specific config as fallback for any HYWorld variant
     "wanpipeline":
     WanT2V480PConfig,  # Base Wan config as fallback for any Wan variant
     "wanimagetovideo": WanI2V480PConfig,
     "wandmdpipeline": FastWan2_1_T2V_480P_Config,
     "wancausaldmdpipeline": SelfForcingWanT2V480PConfig,
-    "stepvideo": StepVideoT2VConfig,
-    "turbodiffusion": TurboDiffusionT2V_1_3B_Config,
-    "ltx2": LTX2T2VConfig,
+    "stepvideo": StepVideoT2VConfig
     # Other fallbacks by architecture
 }
 

fastvideo/configs/pipelines/turbodiffusion.py

@@ -1,128 +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.configs.pipelines.wan 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

fastvideo/configs/sample/base.py

@@ -223,7 +223,7 @@ class SamplingParam:
             help="Path to input image for image-to-video generation",
         )
         parser.add_argument(
-            "--video-path",
+            "--video_path",
             type=str,
             default=SamplingParam.video_path,
             help="Path to input video for video-to-video generation",

fastvideo/configs/sample/cosmos2_5.py

@@ -1,19 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-from dataclasses import dataclass
-
-from fastvideo.configs.sample.base import SamplingParam
-
-
-@dataclass
-class Cosmos_Predict2_5_2B_Diffusers_SamplingParam(SamplingParam):
-    """Defaults for Cosmos 2.5 (Predict2.5) text-to-video diffusers-format model."""
-
-    height: int = 480
-    width: int = 832
-    num_frames: int = 121
-    fps: int = 24
-
-    guidance_scale: float = 7.0
-    # Official Cosmos2.5 sampling uses empty string as unconditional.
-    negative_prompt: str = ""
-    num_inference_steps: int = 35

fastvideo/configs/sample/hunyuan15.py

@@ -17,24 +17,11 @@ class Hunyuan15_480P_SamplingParam(SamplingParam):
     guidance_scale: float = 6.0
     prompt_attention_mask: list = field(default_factory=list)
     negative_attention_mask: list = field(default_factory=list)
-    # sigmas: list[float] | None = field(
-    #     default_factory=lambda: list(np.linspace(1.0, 0.0, 50 + 1)[:-1]))
+    sigmas: list[float] | None = field(
+        default_factory=lambda: list(np.linspace(1.0, 0.0, 50 + 1)[:-1]))
 
     negative_prompt: str = ""
 
-    def __post_init__(self):
-        super().__post_init__()
-        self.sigmas = list(
-            np.linspace(1.0, 0.0, self.num_inference_steps + 1)[:-1])
-
-
-@dataclass
-class Hunyuan15_480P_Distilled_SamplingParam(Hunyuan15_480P_SamplingParam):
-    num_inference_steps: int = 8
-    fps: int = 24
-
-    guidance_scale: float = 1.0
-
 
 @dataclass
 class Hunyuan15_720P_SamplingParam(Hunyuan15_480P_SamplingParam):

fastvideo/configs/sample/hyworld.py

@@ -1,26 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-from dataclasses import dataclass, field
-
-from fastvideo.configs.sample.base import SamplingParam
-import numpy as np
-
-
-@dataclass
-class HYWorld_SamplingParam(SamplingParam):
-    num_inference_steps: int = 50
-
-    num_frames: int = 125
-    height: int = 480
-    width: int = 832
-    fps: int = 24
-
-    # Camera trajectory: pose string (e.g., 'w-31' means generating [1 + 31] latents) or JSON file path
-    pose: str = 'w-31'
-
-    guidance_scale: float = 6.0
-    prompt_attention_mask: list = field(default_factory=list)
-    negative_attention_mask: list = field(default_factory=list)
-    sigmas: list[float] | None = field(
-        default_factory=lambda: list(np.linspace(1.0, 0.0, 50 + 1)[:-1]))
-
-    negative_prompt: str = ""

fastvideo/configs/sample/ltx2.py

@@ -1,20 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-from dataclasses import dataclass
-
-from fastvideo.configs.sample.base import SamplingParam
-
-
-@dataclass
-class LTX2SamplingParam(SamplingParam):
-    """Default sampling parameters for LTX-2 distilled T2V.
-    """
-
-    seed: int = 10
-    num_frames: int = 121
-    height: int = 1024
-    width: int = 1536
-    fps: int = 24
-    num_inference_steps: int = 8
-    guidance_scale: float = 1.0
-    # No default negative_prompt for distilled models
-    negative_prompt: str = ""

fastvideo/configs/sample/registry.py

@@ -5,13 +5,10 @@ from typing import Any
 
 from fastvideo.configs.sample.hunyuan import (FastHunyuanSamplingParam,
                                               HunyuanSamplingParam)
-from fastvideo.configs.sample.hyworld import HYWorld_SamplingParam
-from fastvideo.configs.sample.hunyuan15 import Hunyuan15_480P_SamplingParam, Hunyuan15_720P_SamplingParam, Hunyuan15_480P_Distilled_SamplingParam
+from fastvideo.configs.sample.hunyuan15 import Hunyuan15_480P_SamplingParam, Hunyuan15_720P_SamplingParam
 from fastvideo.configs.sample.stepvideo import StepVideoT2VSamplingParam
 
 from fastvideo.configs.sample.cosmos import Cosmos_Predict2_2B_Video2World_SamplingParam
-from fastvideo.configs.sample.cosmos2_5 import Cosmos_Predict2_5_2B_Diffusers_SamplingParam
-from fastvideo.configs.sample.ltx2 import LTX2SamplingParam
 
 # isort: off
 from fastvideo.configs.sample.wan import (
@@ -29,11 +26,6 @@ from fastvideo.configs.sample.wan import (
     SelfForcingWan2_2_T2V_A14B_480P_SamplingParam,
     MatrixGame2_SamplingParam,
 )
-from fastvideo.configs.sample.turbodiffusion import (
-    TurboDiffusionT2V_1_3B_SamplingParam,
-    TurboDiffusionT2V_14B_SamplingParam,
-    TurboDiffusionI2V_A14B_SamplingParam,
-)
 # isort: on
 from fastvideo.logger import init_logger
 from fastvideo.utils import (maybe_download_model_index,
@@ -44,15 +36,10 @@ logger = init_logger(__name__)
 SAMPLING_PARAM_REGISTRY: dict[str, Any] = {
     "FastVideo/FastHunyuan-diffusers": FastHunyuanSamplingParam,
     "hunyuanvideo-community/HunyuanVideo": HunyuanSamplingParam,
-    "weizhou03/SFHunyuanVideo-1.5-Diffusers-480p_t2v":
-    Hunyuan15_480P_SamplingParam,
-    "hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_i2v_step_distilled":
-    Hunyuan15_480P_Distilled_SamplingParam,
     "hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v":
     Hunyuan15_480P_SamplingParam,
     "hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_t2v":
     Hunyuan15_720P_SamplingParam,
-    "FastVideo/HY-WorldPlay-Bidirectional-Diffusers": HYWorld_SamplingParam,
     "FastVideo/stepvideo-t2v-diffusers": StepVideoT2VSamplingParam,
 
     # Wan2.1
@@ -92,27 +79,11 @@ SAMPLING_PARAM_REGISTRY: dict[str, Any] = {
     "nvidia/Cosmos-Predict2-2B-Video2World":
     Cosmos_Predict2_2B_Video2World_SamplingParam,
 
-    # Cosmos2.5
-    "KyleShao/Cosmos-Predict2.5-2B-Diffusers":
-    Cosmos_Predict2_5_2B_Diffusers_SamplingParam,
-
     # MatrixGame2.0 models
     "FastVideo/Matrix-Game-2.0-Base-Diffusers": MatrixGame2_SamplingParam,
     "FastVideo/Matrix-Game-2.0-GTA-Diffusers": MatrixGame2_SamplingParam,
     "FastVideo/Matrix-Game-2.0-TempleRun-Diffusers": MatrixGame2_SamplingParam,
 
-    # TurboDiffusion models
-    "loayrashid/TurboWan2.1-T2V-1.3B-Diffusers":
-    TurboDiffusionT2V_1_3B_SamplingParam,
-    "loayrashid/TurboWan2.1-T2V-14B-Diffusers":
-    TurboDiffusionT2V_14B_SamplingParam,
-    "loayrashid/TurboWan2.2-I2V-A14B-Diffusers":
-    TurboDiffusionI2V_A14B_SamplingParam,
-
-    # LTX-2 models
-    "Lightricks/LTX-2": LTX2SamplingParam,
-    "FastVideo/LTX2-Distilled-Diffusers": LTX2SamplingParam,
-
     # Add other specific weight variants
 }
 
@@ -122,8 +93,6 @@ SAMPLING_PARAM_DETECTOR: dict[str, Callable[[str], bool]] = {
     lambda id: "hunyuan" in id.lower(),
     "hunyuan15":
     lambda id: "hunyuan15" in id.lower(),
-    "hyworld":
-    lambda id: "hyworld" in id.lower(),
     "wanpipeline":
     lambda id: "wanpipeline" in id.lower(),
     "wanimagetovideo":
@@ -136,14 +105,6 @@ SAMPLING_PARAM_DETECTOR: dict[str, Callable[[str], bool]] = {
     lambda id: "wancausaldmdpipeline" in id.lower(),
     "matrixgame":
     lambda id: "matrixgame" in id.lower() or "matrix-game" in id.lower(),
-    "turbodiffusion":
-    lambda id: "turbodiffusion" in id.lower() or "turbowan" in id.lower(),
-    "cosmos25":
-    lambda id: "cosmos2_5" in id.lower(),
-    "cosmos":
-    lambda id: "cosmos" in id.lower() and "2_5" not in id.lower(),
-    "ltx2":
-    lambda id: "ltx2" in id.lower() or "ltx-2" in id.lower(),
     # Add other pipeline architecture detectors
 }
 
@@ -153,8 +114,6 @@ SAMPLING_FALLBACK_PARAM: dict[str, Any] = {
     HunyuanSamplingParam,  # Base Hunyuan config as fallback for any Hunyuan variant
     "hunyuan15":
     Hunyuan15_480P_SamplingParam,  # Base Hunyuan15 config as fallback for any Hunyuan15 variant
-    "hyworld":
-    HYWorld_SamplingParam,  # HYWorld-specific config as fallback for any HYWorld variant
     "wanpipeline":
     WanT2V_1_3B_SamplingParam,  # Base Wan config as fallback for any Wan variant
     "wanimagetovideo": WanI2V_14B_480P_SamplingParam,
@@ -162,17 +121,13 @@ SAMPLING_FALLBACK_PARAM: dict[str, Any] = {
     "wancausaldmdpipeline": SelfForcingWan2_1_T2V_1_3B_480P_SamplingParam,
     "stepvideo": StepVideoT2VSamplingParam,
     "matrixgame": MatrixGame2_SamplingParam,
-    "turbodiffusion":
-    TurboDiffusionT2V_1_3B_SamplingParam,  # Default to T2V for fallback
-    "cosmos25": Cosmos_Predict2_5_2B_Diffusers_SamplingParam,
-    "cosmos": Cosmos_Predict2_2B_Video2World_SamplingParam,
-    "ltx2": LTX2SamplingParam,
     # Other fallbacks by architecture
 }
 
 
 def get_sampling_param_cls_for_name(pipeline_name_or_path: str) -> Any | None:
     """Get the appropriate sampling param for specific pretrained weights."""
+
     # First try exact match for specific weights
     if pipeline_name_or_path in SAMPLING_PARAM_REGISTRY:
         return SAMPLING_PARAM_REGISTRY[pipeline_name_or_path]
@@ -189,6 +144,9 @@ def get_sampling_param_cls_for_name(pipeline_name_or_path: str) -> Any | None:
 
     if os.path.exists(pipeline_name_or_path):
         config = verify_model_config_and_directory(pipeline_name_or_path)
+        logger.warning(
+            "FastVideo may not correctly identify the optimal sampling param for this model, as the local directory may have been renamed."
+        )
     else:
         config = maybe_download_model_index(pipeline_name_or_path)
 

fastvideo/configs/sample/turbodiffusion.py

@@ -1,73 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-TurboDiffusion sampling parameters.
-
-TurboDiffusion uses RCM (recurrent Consistency Model) scheduler for
-1-4 step video generation with no classifier-free guidance.
-"""
-from dataclasses import dataclass
-
-from fastvideo.configs.sample.base import SamplingParam
-
-
-@dataclass
-class TurboDiffusionT2V_1_3B_SamplingParam(SamplingParam):
-    """Sampling parameters for TurboDiffusion T2V 1.3B model.
-    
-    Uses 4-step RCM sampling with guidance_scale=1.0 (no CFG).
-    """
-    # Video parameters
-    height: int = 480
-    width: int = 832
-    num_frames: int = 81
-    fps: int = 16
-
-    # Denoising stage - TurboDiffusion uses 1-4 steps with no CFG
-    guidance_scale: float = 1.0
-    num_inference_steps: int = 4
-
-    # No negative prompt needed for TurboDiffusion (no CFG)
-    negative_prompt: str | None = None
-
-
-@dataclass
-class TurboDiffusionT2V_14B_SamplingParam(SamplingParam):
-    """Sampling parameters for TurboDiffusion T2V 14B model.
-    
-    Uses 4-step RCM sampling with guidance_scale=1.0 (no CFG).
-    """
-    # Video parameters (720p for 14B)
-    height: int = 720
-    width: int = 1280
-    num_frames: int = 81
-    fps: int = 16
-
-    # Denoising stage - TurboDiffusion uses 1-4 steps with no CFG
-    guidance_scale: float = 1.0
-    num_inference_steps: int = 4
-
-    # No negative prompt needed for TurboDiffusion (no CFG)
-    negative_prompt: str | None = None
-
-
-@dataclass
-class TurboDiffusionI2V_A14B_SamplingParam(SamplingParam):
-    """Sampling parameters for TurboDiffusion I2V A14B model.
-    
-    Uses 4-step RCM sampling with dual-model switching (high/low noise).
-    """
-    # Video parameters (720p for A14B I2V)
-    height: int = 720
-    width: int = 1280
-    num_frames: int = 81
-    fps: int = 16
-
-    # Denoising stage - TurboDiffusion uses 1-4 steps with no CFG
-    guidance_scale: float = 1.0
-    num_inference_steps: int = 4
-
-    # Note: boundary_ratio is set in the pipeline config (TurboDiffusionI2VConfig),
-    # not here. This keeps sampling params and pipeline config separate.
-
-    # No negative prompt needed for TurboDiffusion (no CFG)
-    negative_prompt: str | None = None

fastvideo/dataset/dataloader/record_schema.py

@@ -127,10 +127,7 @@ def i2v_record_creator(batch: PreprocessBatch) -> list[dict[str, Any]]:
 def ode_text_only_record_creator(
         video_name: str, text_embedding: np.ndarray, caption: str,
         trajectory_latents: np.ndarray,
-        trajectory_timesteps: np.ndarray,
-        text_mask: np.ndarray | None = None,
-        text_embedding_2: np.ndarray | None = None,
-        text_mask_2: np.ndarray | None = None) -> dict[str, Any]:
+        trajectory_timesteps: np.ndarray) -> dict[str, Any]:
     """Create a text-only ODE trajectory record matching pyarrow_schema_ode_trajectory_text_only.
 
     Args:
@@ -168,25 +165,6 @@ def ode_text_only_record_creator(
         "trajectory_timesteps_dtype": str(trajectory_timesteps.dtype),
     })
 
-    if text_embedding_2 is not None:
-        record.update({
-            "text_embedding_2_bytes": text_embedding_2.tobytes(),
-            "text_embedding_2_shape": list(text_embedding_2.shape),
-            "text_embedding_2_dtype": str(text_embedding_2.dtype),
-        })
-    if text_mask is not None:
-        record.update({
-            "text_mask_bytes": text_mask.tobytes(),
-            "text_mask_shape": list(text_mask.shape),
-            "text_mask_dtype": str(text_mask.dtype),
-        })
-    if text_mask_2 is not None:
-        record.update({
-            "text_mask_2_bytes": text_mask_2.tobytes(),
-            "text_mask_2_shape": list(text_mask_2.shape),
-            "text_mask_2_dtype": str(text_mask_2.dtype),
-        })
-        
     return record
 
 
@@ -209,4 +187,4 @@ def text_only_record_creator(text_name: str, text_embedding: np.ndarray,
         "text_embedding_dtype": str(text_embedding.dtype),
         "caption": caption,
     }
-    return record
+    return record

fastvideo/dataset/dataloader/schema.py

@@ -90,15 +90,6 @@ pyarrow_schema_ode_trajectory_text_only = pa.schema([
     pa.field("text_embedding_shape", pa.list_(pa.int64())),
     # e.g., 'bfloat16' or 'float32'
     pa.field("text_embedding_dtype", pa.string()),
-    pa.field("text_embedding_2_bytes", pa.binary()),
-    pa.field("text_embedding_2_shape", pa.list_(pa.int64())),
-    pa.field("text_embedding_2_dtype", pa.string()),
-    pa.field("text_mask_bytes", pa.binary()),
-    pa.field("text_mask_shape", pa.list_(pa.int64())),
-    pa.field("text_mask_dtype", pa.string()),
-    pa.field("text_mask_2_bytes", pa.binary()),
-    pa.field("text_mask_2_shape", pa.list_(pa.int64())),
-    pa.field("text_mask_2_dtype", pa.string()),
     # --- ODE Trajectory ---
     pa.field("trajectory_latents_bytes", pa.binary()),
     pa.field("trajectory_latents_shape", pa.list_(pa.int64())),
@@ -124,4 +115,4 @@ pyarrow_schema_text_only = pa.schema([
     pa.field("text_embedding_dtype", pa.string()),
     # --- Metadata ---
     pa.field("caption", pa.string()),
-])
+])

fastvideo/dataset/preprocessing_datasets.py

@@ -17,7 +17,6 @@ from PIL import Image
 from transformers import AutoTokenizer
 
 from fastvideo.logger import init_logger
-from fastvideo.utils import maybe_download_model
 
 logger = init_logger(__name__)
 
@@ -656,7 +655,7 @@ class TextDataset(torch.utils.data.IterableDataset,
         self.seed = seed
 
         # Initialize tokenizer
-        tokenizer_path = os.path.join(maybe_download_model(args.model_path), "tokenizer")
+        tokenizer_path = os.path.join(args.model_path, "tokenizer")
         tokenizer = AutoTokenizer.from_pretrained(tokenizer_path,
                                                   cache_dir=args.cache_dir)
 
@@ -759,4 +758,4 @@ class TextDataset(torch.utils.data.IterableDataset,
 
     def load_state_dict(self, state_dict: dict[str, Any]) -> None:
         """Load state dict from checkpoint."""
-        self.processed_batches = state_dict["processed_batches"]
+        self.processed_batches = state_dict["processed_batches"]

fastvideo/dataset/utils.py

@@ -154,14 +154,8 @@ def collate_rows_from_parquet_schema(rows,
                     ) if rng else random.random()) < cfg_rate:
                         data = np.zeros((512, 4096), dtype=np.float32)
                     else:
-                        if row[f"{tensor_name}_dtype"] == "float32":
-                            data = np.frombuffer(
-                                bytes_data, dtype=np.float32).reshape(shape).copy()
-                        elif row[f"{tensor_name}_dtype"] == "int64":
-                            data = np.frombuffer(
-                                bytes_data, dtype=np.int64).reshape(shape).copy()
-                        else:
-                            raise ValueError(f"Unsupported dtype: {row[f"{tensor_name}_dtype"]}")
+                        data = np.frombuffer(
+                            bytes_data, dtype=np.float32).reshape(shape).copy()
                     tensor = torch.from_numpy(data)
                     # if len(data.shape) == 3:
                     #     B, L, D = tensor.shape
@@ -174,7 +168,7 @@ def collate_rows_from_parquet_schema(rows,
                 tensor_list.append(torch.zeros(0, dtype=torch.bfloat16))
 
         # Stack tensors with special handling for text embeddings
-        if tensor_name == 'null':
+        if tensor_name == 'text_embedding':
             # Handle text embeddings with padding
             padded_tensors = []
             attention_masks = []

fastvideo/distributed/device_communicators/base_device_communicator.py

@@ -57,9 +57,6 @@ class DistributedAutograd:
             ctx.dim = dim
             ctx.input_shape = input_.shape
 
-            # NCCL all_gather_into_tensor requires contiguous tensors.
-            if not input_.is_contiguous():
-                input_ = input_.contiguous()
             input_size = input_.size()
             output_size = (input_size[0] * world_size, ) + input_size[1:]
             output_tensor = torch.empty(output_size,

fastvideo/entrypoints/video_generator.py

@@ -18,8 +18,6 @@ import numpy as np
 import torch
 import torchvision
 from einops import rearrange
-import shutil
-import tempfile
 
 from fastvideo.configs.sample import SamplingParam
 from fastvideo.fastvideo_args import FastVideoArgs
@@ -391,11 +389,6 @@ class VideoGenerator:
         if batch.save_video:
             imageio.mimsave(output_path, frames, fps=batch.fps, format="mp4")
             logger.info("Saved video to %s", output_path)
-            audio = output_batch.extra.get("audio")
-            audio_sample_rate = output_batch.extra.get("audio_sample_rate")
-            if (audio is not None and audio_sample_rate is not None and
-                    not self._mux_audio(output_path, audio, audio_sample_rate)):
-                logger.warning("Audio mux failed; saved video without audio.")
 
         if batch.return_frames:
             return frames
@@ -403,7 +396,6 @@ class VideoGenerator:
             return {
                 "samples": samples,
                 "frames": frames,
-                "audio": output_batch.extra.get("audio"),
                 "prompts": prompt,
                 "size": (target_height, target_width, batch.num_frames),
                 "generation_time": gen_time,
@@ -413,98 +405,6 @@ class VideoGenerator:
                 "trajectory_decoded": output_batch.trajectory_decoded,
             }
 
-    @staticmethod
-    def _mux_audio(
-        video_path: str,
-        audio: torch.Tensor | np.ndarray,
-        sample_rate: int,
-    ) -> bool:
-        """Mux audio into video using PyAV."""
-        try:
-            import av
-        except ImportError:
-            logger.warning("PyAV not installed; cannot mux audio. "
-                           "Install with: pip install av")
-            return False
-
-        if torch.is_tensor(audio):
-            audio_np = audio.detach().cpu().float().numpy()
-        else:
-            audio_np = np.asarray(audio, dtype=np.float32)
-
-        if audio_np.ndim == 1:
-            audio_np = audio_np[:, None]
-        elif audio_np.ndim == 2:
-            if audio_np.shape[0] <= 8 and audio_np.shape[1] > audio_np.shape[0]:
-                audio_np = audio_np.T
-        else:
-            logger.warning("Unexpected audio shape %s; skipping mux.",
-                           audio_np.shape)
-            return False
-
-        audio_np = np.clip(audio_np, -1.0, 1.0)
-        audio_int16 = (audio_np * 32767.0).astype(np.int16)
-        num_channels = audio_int16.shape[1]
-        layout = "stereo" if num_channels == 2 else "mono"
-
-        try:
-            import wave
-            with tempfile.TemporaryDirectory() as tmpdir:
-                out_path = os.path.join(tmpdir, "muxed.mp4")
-                wav_path = os.path.join(tmpdir, "audio.wav")
-
-                # Write audio to WAV file
-                with wave.open(wav_path, "wb") as wav_file:
-                    wav_file.setnchannels(num_channels)
-                    wav_file.setsampwidth(2)
-                    wav_file.setframerate(sample_rate)
-                    wav_file.writeframes(audio_int16.tobytes())
-
-                # Open input video and audio
-                input_video = av.open(video_path)
-                input_audio = av.open(wav_path)
-
-                # Create output with both streams
-                output = av.open(out_path, mode="w")
-
-                # Add video stream (copy codec from input)
-                in_video_stream = input_video.streams.video[0]
-                out_video_stream = output.add_stream(
-                    codec_name=in_video_stream.codec_context.name,
-                    rate=in_video_stream.average_rate,
-                )
-                out_video_stream.width = in_video_stream.width
-                out_video_stream.height = in_video_stream.height
-                out_video_stream.pix_fmt = in_video_stream.pix_fmt
-
-                # Add audio stream (AAC)
-                out_audio_stream = output.add_stream("aac", rate=sample_rate)
-                out_audio_stream.layout = layout
-
-                # Remux video (decode and re-encode to be safe)
-                for frame in input_video.decode(video=0):
-                    for packet in out_video_stream.encode(frame):
-                        output.mux(packet)
-                for packet in out_video_stream.encode():
-                    output.mux(packet)
-
-                # Encode audio
-                for frame in input_audio.decode(audio=0):
-                    frame.pts = None  # Let encoder assign PTS
-                    for packet in out_audio_stream.encode(frame):
-                        output.mux(packet)
-                for packet in out_audio_stream.encode():
-                    output.mux(packet)
-
-                input_video.close()
-                input_audio.close()
-                output.close()
-                shutil.move(out_path, video_path)
-            return True
-        except Exception as e:
-            logger.warning("Audio mux failed: %s", e)
-            return False
-
     def set_lora_adapter(self,
                          lora_nickname: str,
                          lora_path: str | None = None) -> None:

fastvideo/fastvideo_args.py

@@ -132,8 +132,8 @@ class FastVideoArgs:
 
     # CPU offload parameters
     dit_cpu_offload: bool = True
-    use_fsdp_inference: bool = False
-    dit_layerwise_offload: bool = True
+    use_fsdp_inference: bool = True
+    dit_layerwise_offload: bool = False
     text_encoder_cpu_offload: bool = True
     image_encoder_cpu_offload: bool = True
     vae_cpu_offload: bool = True
@@ -166,14 +166,6 @@ class FastVideoArgs:
     # Prompt text file for batch processing
     prompt_txt: str | None = None
 
-    # LTX-2 VAE tiling overrides
-    ltx2_vae_tiling: bool | None = None
-    ltx2_vae_spatial_tile_size_in_pixels: int | None = None
-    ltx2_vae_spatial_tile_overlap_in_pixels: int | None = None
-    ltx2_vae_temporal_tile_size_in_frames: int | None = None
-    ltx2_vae_temporal_tile_overlap_in_frames: int | None = None
-    ltx2_initial_latent_path: str | None = None
-
     # model paths for correct deallocation
     model_paths: dict[str, str] = field(default_factory=dict)
     model_loaded: dict[str, bool] = field(default_factory=lambda: {
@@ -211,44 +203,8 @@ class FastVideoArgs:
                 logger.error("Failed to load V-MoBA config from %s: %s",
                              self.moba_config_path, e)
                 raise
-        self._apply_ltx2_vae_overrides()
         self.check_fastvideo_args()
 
-    def _apply_ltx2_vae_overrides(self) -> None:
-        if self.pipeline_config is None:
-            return
-        vae_config = self.pipeline_config.vae_config
-        has_any = any(value is not None for value in (
-            self.ltx2_vae_spatial_tile_size_in_pixels,
-            self.ltx2_vae_spatial_tile_overlap_in_pixels,
-            self.ltx2_vae_temporal_tile_size_in_frames,
-            self.ltx2_vae_temporal_tile_overlap_in_frames,
-        ))
-        if self.ltx2_vae_tiling is not None and hasattr(self.pipeline_config,
-                                                        "vae_tiling"):
-            self.pipeline_config.vae_tiling = self.ltx2_vae_tiling
-        elif has_any and hasattr(self.pipeline_config, "vae_tiling"):
-            self.pipeline_config.vae_tiling = True
-
-        if hasattr(vae_config, "ltx2_spatial_tile_size_in_pixels"
-                   ) and self.ltx2_vae_spatial_tile_size_in_pixels is not None:
-            vae_config.ltx2_spatial_tile_size_in_pixels = (
-                self.ltx2_vae_spatial_tile_size_in_pixels)
-        if hasattr(
-                vae_config, "ltx2_spatial_tile_overlap_in_pixels"
-        ) and self.ltx2_vae_spatial_tile_overlap_in_pixels is not None:
-            vae_config.ltx2_spatial_tile_overlap_in_pixels = (
-                self.ltx2_vae_spatial_tile_overlap_in_pixels)
-        if hasattr(vae_config, "ltx2_temporal_tile_size_in_frames"
-                   ) and self.ltx2_vae_temporal_tile_size_in_frames is not None:
-            vae_config.ltx2_temporal_tile_size_in_frames = (
-                self.ltx2_vae_temporal_tile_size_in_frames)
-        if hasattr(
-                vae_config, "ltx2_temporal_tile_overlap_in_frames"
-        ) and self.ltx2_vae_temporal_tile_overlap_in_frames is not None:
-            vae_config.ltx2_temporal_tile_overlap_in_frames = (
-                self.ltx2_vae_temporal_tile_overlap_in_frames)
-
     @staticmethod
     def add_cli_args(parser: FlexibleArgumentParser) -> FlexibleArgumentParser:
         # Model and path configuration
@@ -369,44 +325,6 @@ class FastVideoArgs:
             "Path to a text file containing prompts (one per line) for batch processing",
         )
 
-        # LTX-2 VAE tiling overrides
-        parser.add_argument(
-            "--ltx2-vae-tiling",
-            action=StoreBoolean,
-            default=FastVideoArgs.ltx2_vae_tiling,
-            help="Enable LTX-2 VAE tiling overrides.",
-        )
-        parser.add_argument(
-            "--ltx2-vae-spatial-tile-size-in-pixels",
-            type=int,
-            default=FastVideoArgs.ltx2_vae_spatial_tile_size_in_pixels,
-            help="LTX-2 VAE spatial tile size in pixels.",
-        )
-        parser.add_argument(
-            "--ltx2-vae-spatial-tile-overlap-in-pixels",
-            type=int,
-            default=FastVideoArgs.ltx2_vae_spatial_tile_overlap_in_pixels,
-            help="LTX-2 VAE spatial tile overlap in pixels.",
-        )
-        parser.add_argument(
-            "--ltx2-vae-temporal-tile-size-in-frames",
-            type=int,
-            default=FastVideoArgs.ltx2_vae_temporal_tile_size_in_frames,
-            help="LTX-2 VAE temporal tile size in frames.",
-        )
-        parser.add_argument(
-            "--ltx2-vae-temporal-tile-overlap-in-frames",
-            type=int,
-            default=FastVideoArgs.ltx2_vae_temporal_tile_overlap_in_frames,
-            help="LTX-2 VAE temporal tile overlap in frames.",
-        )
-        parser.add_argument(
-            "--ltx2-initial-latent-path",
-            type=str,
-            default=FastVideoArgs.ltx2_initial_latent_path,
-            help="Path to load/save a precomputed LTX-2 initial latent.",
-        )
-
         # LoRA parameters (inference-time adapter loading)
         parser.add_argument(
             "--lora-path",
@@ -513,9 +431,7 @@ class FastVideoArgs:
             "--use-fsdp-inference",
             action=StoreBoolean,
             help=
-            "Use FSDP for inference by sharding the model weights. FSDP helps reduce GPU memory usage but may introduce"
-            +
-            " weight transfer overhead depending on the specific setup. Enable if run out of memory.",
+            "Use FSDP for inference by sharding the model weights. Latency is very low due to prefetch--enable if run out of memory.",
         )
         parser.add_argument(
             "--text-encoder-cpu-offload",
@@ -830,7 +746,6 @@ class TrainingArgs(FastVideoArgs):
     precedence.
     """
     data_path: str = ""
-    data_path_2: str | None = None
     dataloader_num_workers: int = 0
     num_height: int = 0
     num_width: int = 0
@@ -860,7 +775,6 @@ class TrainingArgs(FastVideoArgs):
     validation_sampling_steps: str = ""
     validation_guidance_scale: str = ""
     validation_steps: float = 0.0
-    visualization_steps: float = 0.0
     log_validation: bool = False
     trackers: list[str] = dataclasses.field(default_factory=list)
     tracker_project_name: str = ""
@@ -876,7 +790,6 @@ class TrainingArgs(FastVideoArgs):
     num_train_epochs: int = 0
     max_train_steps: int = 0
     gradient_accumulation_steps: int = 0
-    optimizer_type: str = "adamw"
     learning_rate: float = 0.0
     scale_lr: bool = False
     lr_scheduler: str = "constant"
@@ -946,8 +859,6 @@ class TrainingArgs(FastVideoArgs):
     same_step_across_blocks: bool = False  # Use same exit timestep for all blocks
     last_step_only: bool = False  # Only use the last timestep for training
     context_noise: int = 0  # Context noise level for cache updates
-    use_context_forcing: bool = False
-    use_ode_init: bool = False
 
     @classmethod
     def from_cli_args(cls, args: argparse.Namespace) -> "TrainingArgs":
@@ -1003,10 +914,6 @@ class TrainingArgs(FastVideoArgs):
                             type=str,
                             required=True,
                             help="Path to parquet files")
-        parser.add_argument("--data-path-2",
-                            type=str,
-                            required=False,
-                            help="Path to parquet files")
         parser.add_argument("--dataloader-num-workers",
                             type=int,
                             required=True,
@@ -1100,9 +1007,6 @@ class TrainingArgs(FastVideoArgs):
         parser.add_argument("--validation-steps",
                             type=float,
                             help="Number of validation steps")
-        parser.add_argument("--visualization-steps",
-                            type=float,
-                            help="Number of visualization steps")
         parser.add_argument("--log-validation",
                             action=StoreBoolean,
                             help="Whether to log validation results")
@@ -1151,10 +1055,6 @@ class TrainingArgs(FastVideoArgs):
         parser.add_argument("--gradient-accumulation-steps",
                             type=int,
                             help="Number of steps to accumulate gradients")
-        parser.add_argument("--optimizer-type",
-                            type=str,
-                            choices=["adamw", "muon"],
-                            help="Optimizer type")
         parser.add_argument("--learning-rate",
                             type=float,
                             required=True,
@@ -1381,12 +1281,6 @@ class TrainingArgs(FastVideoArgs):
                             type=int,
                             default=TrainingArgs.context_noise,
                             help="Context noise level for cache updates")
-        parser.add_argument("--use-context-forcing",
-                            action=StoreBoolean,
-                            help="Whether to use context forcing")
-        parser.add_argument("--use-ode-init",
-                            action=StoreBoolean,
-                            help="Whether to use ODE init")
 
         return parser
 

fastvideo/hooks/hooks.py

@@ -1,122 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-
-import functools
-from typing import Any
-from torch import nn
-
-
-class ForwardHook:
-    """
-    Base class for forward hooks.
-    Hooks are used in the way:
-        modified_args, modified_kwargs = hook.pre_forward(module, *args, **kwargs)
-        output = module.forward(*modified_args, **modified_kwargs)
-        modified_output = hook.post_forward(module, output)
-    """
-
-    @classmethod
-    def name(cls) -> str:
-        raise NotImplementedError
-
-    def on_attach(self, module: nn.Module):  # noqa: B027
-        """Called once when the hook is attached to the module."""
-        pass
-
-    def on_detach(self, module: nn.Module):  # noqa: B027
-        """
-        Called once when the hook is detached from the module.
-        Note: this function is not guaranteed to be called if the module is
-              deleted before the hook is detached.
-        """
-        pass
-
-    def pre_forward(self, module: nn.Module, *args,
-                    **kwargs) -> tuple[tuple[Any, ...], dict[str, Any]]:
-        """Called before the module's forward method is executed."""
-        return args, kwargs
-
-    def post_forward(self, module: nn.Module, output: Any) -> Any:
-        """Called after the module's forward method is executed."""
-        return output
-
-
-class ModuleHookManager:
-    module_hook_attribute = "_hook_manager"
-
-    def __init__(self, module: nn.Module):
-        self.module = module
-        self.forward_hooks: dict[str, ForwardHook] = {}
-        self.original_forward = module.forward
-
-    @classmethod
-    def get_from(cls, module: nn.Module) -> "ModuleHookManager | None":
-        if hasattr(module, cls.module_hook_attribute):
-            return getattr(module, cls.module_hook_attribute)
-        return None
-
-    @classmethod
-    def get_from_or_default(cls, module: nn.Module) -> "ModuleHookManager":
-        if not hasattr(module, cls.module_hook_attribute):
-            setattr(module, cls.module_hook_attribute, cls(module))
-
-            def forward_hook_wrapper(mod: nn.Module, *args, **kwargs):
-                manager: ModuleHookManager = getattr(mod,
-                                                     cls.module_hook_attribute)
-                for hook in manager.forward_hooks.values():
-                    args, kwargs = hook.pre_forward(mod, *args, **kwargs)
-                output = manager.original_forward(*args, **kwargs)
-                for hook in reversed(manager.forward_hooks.values()):
-                    output = hook.post_forward(mod, output)
-                return output
-
-            module.forward = functools.partial(forward_hook_wrapper, module)
-
-        return getattr(module, cls.module_hook_attribute)
-
-    @staticmethod
-    def remove_from_manager(module: nn.Module) -> None:
-        if hasattr(module, ModuleHookManager.module_hook_attribute):
-            manager: ModuleHookManager = getattr(
-                module, ModuleHookManager.module_hook_attribute)
-            module.forward = manager.original_forward
-            delattr(module, ModuleHookManager.module_hook_attribute)
-
-    def _check_manager_attached(self) -> None:
-        if not hasattr(self.module, self.module_hook_attribute):
-            raise ValueError("ModuleHookManager is not attached to the module.")
-        if getattr(self.module, self.module_hook_attribute) is not self:
-            raise ValueError(
-                "ModuleHookManager attached to the module is different.")
-
-    def append_forward_hook(self, hook: ForwardHook):
-        self._check_manager_attached()
-        if hook.name() in self.forward_hooks:
-            raise ValueError(
-                f"Hook with name {hook.name()} is already registered.")
-        # after python 3.7, dicts maintain insertion order
-        self.forward_hooks[hook.name()] = hook
-        hook.on_attach(self.module)
-
-    def replace_forward_hook(self,
-                             hook_name: str,
-                             new_hook: ForwardHook,
-                             run_on_attach: bool = True):
-        self._check_manager_attached()
-        if hook_name not in self.forward_hooks:
-            raise ValueError(f"No hook with name {hook_name} found.")
-        old_hook = self.forward_hooks[hook_name]
-        if run_on_attach:
-            old_hook.on_detach(self.module)
-        self.forward_hooks[hook_name] = new_hook
-        new_hook.on_attach(self.module)
-
-    def remove_forward_hook(self, hook_name: str, run_detach: bool = True):
-        self._check_manager_attached()
-        if hook_name not in self.forward_hooks:
-            raise ValueError(f"No hook with name {hook_name} found.")
-        if run_detach:
-            self.forward_hooks[hook_name].on_detach(self.module)
-        del self.forward_hooks[hook_name]
-
-    def get_forward_hook(self, hook_name: str) -> ForwardHook | None:
-        return self.forward_hooks.get(hook_name, None)

fastvideo/hooks/layerwise_offload.py

@@ -1,164 +0,0 @@
-from contextlib import contextmanager
-from typing import Any
-import torch
-from torch import nn
-from fastvideo.hooks.hooks import ForwardHook, ModuleHookManager
-from fastvideo.logger import init_logger
-
-logger = init_logger(__name__)
-
-
-def _tensor_placeholder(tensor: torch.Tensor,
-                        device: torch.device) -> torch.Tensor:
-    """Create a rank-preserving empty placeholder on the specified device."""
-    shape = (0, ) if tensor.ndim <= 0 else (0, ) * tensor.ndim
-    return torch.empty(shape, device=device, dtype=tensor.dtype)
-
-
-class LayerwiseOffloadState:
-
-    def __init__(
-        self,
-        async_copy_stream: torch.cuda.Stream,
-        device: torch.device,
-        next_state: "LayerwiseOffloadState | None" = None,
-    ) -> None:
-        self.async_copy_stream = async_copy_stream
-        self.next_state = next_state
-        self.gpu_named_parameters: dict[str, torch.Tensor] = {}
-        self.cpu_named_parameters: dict[str, torch.Tensor] = {}
-        self.module_ref: nn.Module = None  # type: ignore
-        self.device: torch.device = device
-
-    def _will_offload(self, name: str) -> bool:
-        return True
-
-    @torch.compiler.disable
-    def on_init(self, module: nn.Module):
-        self.module_ref = module
-        for name, param in self.module_ref.named_parameters():
-            if self._will_offload(name):
-                self.cpu_named_parameters[name] = (
-                    param.data.detach().to("cpu").pin_memory())
-                param.data = _tensor_placeholder(param.data, self.device)
-
-    @torch.compiler.disable
-    def wait_and_replace_params(self):
-        torch.cuda.current_stream().wait_stream(self.async_copy_stream)
-        # now gpu_named_parameters are ready
-        for name, param in self.module_ref.named_parameters():
-            if not self._will_offload(name):
-                continue
-            if name not in self.gpu_named_parameters:
-                # first load with blocking load
-                self.gpu_named_parameters[name] = self.cpu_named_parameters[
-                    name].to(self.device)
-            param.data = self.gpu_named_parameters[name]
-
-    @torch.compiler.disable
-    def prefetch_params(self):
-        compute_stream = torch.cuda.current_stream()
-        with torch.cuda.stream(self.async_copy_stream):
-            for name, param in self.module_ref.named_parameters():
-                if not self._will_offload(name):
-                    continue
-                assert name not in self.gpu_named_parameters
-                gpu_param = self.cpu_named_parameters[name].to(
-                    self.device, non_blocking=True)
-                gpu_param.record_stream(
-                    compute_stream
-                )  # ensure tensor will not be freed until forward is completed
-                self.gpu_named_parameters[name] = gpu_param
-
-    @torch.compiler.disable
-    def release_gpu_params(self):
-        for name, param in self.module_ref.named_parameters():
-            if self._will_offload(name):
-                param.data = _tensor_placeholder(param.data, self.device)
-                del self.gpu_named_parameters[name]
-        assert len(self.gpu_named_parameters) == 0
-
-
-class LayerwiseOffloadHook(ForwardHook):
-    """A hook that enables layerwise CPU offloading during forward pass."""
-
-    def __init__(self, state: LayerwiseOffloadState) -> None:
-        self.state = state
-
-    def on_attach(self, module: nn.Module):
-        self.state.on_init(module)  # pyright: ignore
-
-    def on_detach(self, module: nn.Module):
-        named_parameters = dict(module.named_parameters())
-        for name, cpu_tensor in self.state.cpu_named_parameters.items():
-            if name not in self.state.gpu_named_parameters:
-                if name in named_parameters:
-                    named_parameters[name].data = cpu_tensor.to(
-                        device=self.state.device)
-                else:
-                    logger.warning(
-                        "Parameter {} not found in module during detachment.",
-                        name,
-                    )
-
-    @classmethod
-    def name(cls) -> str:
-        return "LayerwiseOffloadHook"
-
-    def pre_forward(self, module: nn.Module, *args, **kwargs):
-        self.state.wait_and_replace_params()  # pyright: ignore
-        if self.state.next_state is not None:
-            self.state.next_state.prefetch_params()  # pyright: ignore
-        return args, kwargs
-
-    def post_forward(self, module: torch.nn.Module, output: Any):
-        self.state.release_gpu_params()  # pyright: ignore
-        return output
-
-    @contextmanager
-    def mutate_params_scope(self):
-        try:
-            # load params to GPU and keep them there
-            self.state.wait_and_replace_params()  # pyright: ignore
-            yield
-        finally:
-            # instead of releasing, we should overwrite the original params since they have been modified
-            self.state.cpu_named_parameters.clear()
-            self.state.gpu_named_parameters.clear()
-            self.state.on_init(self.state.module_ref)  # pyright: ignore
-
-
-def enable_layerwise_offload(model: nn.Module, is_replace: bool = False):
-    if torch.cuda.is_available():
-        device = torch.device("cuda", torch.cuda.current_device())
-    else:
-        logger.warning(
-            "CUDA is not available. Layerwise offloading is disabled.")
-        return
-    state_list = []
-    async_stream = torch.cuda.Stream()
-    for name, submodule in model.named_children():
-        if isinstance(submodule, nn.ModuleList):
-            for idx, module_entry in enumerate(submodule):
-                state = LayerwiseOffloadState(async_copy_stream=async_stream,
-                                              device=device)
-                state_list.append(state)
-                hook_mgr = ModuleHookManager.get_from_or_default(module_entry)
-                hook = LayerwiseOffloadHook(state)
-                if is_replace:
-                    existing_hook = hook_mgr.forward_hooks.get(hook.name())
-                    if existing_hook is not None:
-                        hook_mgr.replace_forward_hook(hook.name(), hook)
-                    else:
-                        raise AssertionError(
-                            f"Expect hook exists in {name} for replacement.")
-                else:
-                    hook_mgr.append_forward_hook(hook)
-            break
-    if len(state_list) == 0:
-        raise ValueError(
-            "No nn.ModuleList found in the model for layerwise offloading.")
-
-    # circular linking of states
-    for i in range(len(state_list)):
-        state_list[i].next_state = state_list[(i + 1) % len(state_list)]

fastvideo/layers/layernorm.py

@@ -168,15 +168,9 @@ class ScaleResidualLayerNormScaleShift(nn.Module):
         else:
             raise NotImplementedError(f"Norm type {norm_type} not implemented")
 
-    def forward(
-        self,
-        residual: torch.Tensor,
-        x: torch.Tensor,
-        gate: torch.Tensor | int,
-        shift: torch.Tensor,
-        scale: torch.Tensor,
-        convert_modulation_dtype: bool = False
-    ) -> tuple[torch.Tensor, torch.Tensor]:
+    def forward(self, residual: torch.Tensor, x: torch.Tensor,
+                gate: torch.Tensor | int, shift: torch.Tensor,
+                scale: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
         """
         Apply gated residual connection, followed by layernorm and 
         scale/shift in a single fused operation.
@@ -211,11 +205,6 @@ class ScaleResidualLayerNormScaleShift(nn.Module):
 
         # Apply normalization
         normalized = self.norm(residual_output)
-
-        if convert_modulation_dtype:
-            scale = scale.to(normalized.dtype)
-            shift = shift.to(normalized.dtype)
-
         # Apply scale and shift
         if isinstance(scale, torch.Tensor) and scale.dim() == 4:
             # scale.shape: [batch_size, num_frames, 1, inner_dim]
@@ -265,21 +254,14 @@ class LayerNormScaleShift(nn.Module):
         else:
             raise NotImplementedError(f"Norm type {norm_type} not implemented")
 
-    def forward(self,
-                x: torch.Tensor,
-                shift: torch.Tensor,
-                scale: torch.Tensor,
-                convert_modulation_dtype: bool = False) -> torch.Tensor:
+    def forward(self, x: torch.Tensor, shift: torch.Tensor,
+                scale: torch.Tensor) -> torch.Tensor:
         """Apply ln followed by scale and shift in a single fused operation."""
         # x.shape: [batch_size, seq_len, inner_dim]
         normalized = self.norm(x)
         if self.compute_dtype == torch.float32:
             normalized = normalized.float()
 
-        if convert_modulation_dtype:
-            scale = scale.to(normalized.dtype)
-            shift = shift.to(normalized.dtype)
-
         if scale.dim() == 4:
             # scale.shape: [batch_size, num_frames, 1, inner_dim]
             num_frames = scale.shape[1]

fastvideo/models/audio/__init__.py

@@ -1,9 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-
-from fastvideo.models.audio.ltx2_audio_vae import (
-    LTX2AudioDecoder,
-    LTX2AudioEncoder,
-    LTX2Vocoder,
-)
-
-__all__ = ["LTX2AudioEncoder", "LTX2AudioDecoder", "LTX2Vocoder"]

fastvideo/models/dits/causal_hunyuanvideo15.py

@@ -1,808 +0,0 @@
-# Copyright 2025 The Hunyuan Team and The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from typing import Any, Dict, Optional, List
-import math
-
-import torch
-# import torch._dynamo
-# torch._dynamo.config.cache_size_limit = 128
-# try:
-#     torch._dynamo.config.recompile_limit = 128
-# except AttributeError:
-#     pass
-import torch.nn as nn
-import torch.nn.functional as F
-
-from torch.nn.attention.flex_attention import create_block_mask, flex_attention
-from torch.nn.attention.flex_attention import BlockMask
-flex_attention = torch.compile(
-    flex_attention, dynamic=False, mode="default")
-import torch.distributed as dist
-
-from fastvideo.attention import LocalAttention
-from fastvideo.forward_context import set_forward_context
-from fastvideo.configs.models.dits import HunyuanVideo15Config
-from fastvideo.layers.layernorm import (LayerNormScaleShift, ScaleResidual,
-                                        ScaleResidualLayerNormScaleShift)
-from fastvideo.layers.linear import ReplicatedLinear
-# TODO(will-PY-refactor): RMSNorm ....
-from fastvideo.layers.mlp import MLP
-from fastvideo.layers.rotary_embedding import get_rotary_pos_embed, _apply_rotary_emb
-from fastvideo.layers.visual_embedding import (ModulateProjection, PatchEmbed,
-                                               unpatchify)
-from fastvideo.models.dits.base import CachableDiT
-from fastvideo.platforms import AttentionBackendEnum
-from fastvideo.logger import init_logger
-
-from fastvideo.models.dits.hunyuanvideo15 import (
-    HunyuanRMSNorm, 
-    HunyuanVideo15TimeEmbedding, 
-    HunyuanVideo15ByT5TextProjection,
-    HunyuanVideo15ImageProjection,
-    SingleTokenRefiner,
-    FinalLayer)
-
-logger = init_logger(__name__)
-
-class MMDoubleStreamBlock(nn.Module):
-    """
-    A multimodal DiT block with separate modulation for text and image/video,
-    using distributed attention and linear layers.
-    """
-
-    def __init__(
-        self,
-        hidden_size: int,
-        num_attention_heads: int,
-        mlp_ratio: float,
-        local_attn_size: int = -1,
-        sink_size: int = 0,
-        dtype: torch.dtype | None = None,
-        supported_attention_backends: tuple[AttentionBackendEnum, ...]
-        | None = None,
-        prefix: str = "",
-    ):
-        super().__init__()
-
-        self.deterministic = False
-        self.num_attention_heads = num_attention_heads
-        self.head_dim = hidden_size // num_attention_heads
-        self.local_attn_size = local_attn_size
-        self.sink_size = sink_size
-        mlp_hidden_dim = int(hidden_size * mlp_ratio)
-
-        # Image modulation components
-        self.img_mod = ModulateProjection(
-            hidden_size,
-            factor=6,
-            act_layer="silu",
-            dtype=dtype,
-            prefix=f"{prefix}.img_mod",
-        )
-
-        # Fused operations for image stream
-        self.img_attn_norm = LayerNormScaleShift(hidden_size,
-                                                 norm_type="layer",
-                                                 elementwise_affine=False,
-                                                 dtype=dtype)
-        self.img_attn_residual_mlp_norm = ScaleResidualLayerNormScaleShift(
-            hidden_size,
-            norm_type="layer",
-            elementwise_affine=False,
-            dtype=dtype)
-        self.img_mlp_residual = ScaleResidual()
-
-        # Image attention components
-        self.img_attn_qkv = ReplicatedLinear(hidden_size,
-                                             hidden_size * 3,
-                                             bias=True,
-                                             params_dtype=dtype,
-                                             prefix=f"{prefix}.img_attn_qkv")
-
-        self.img_attn_q_norm = HunyuanRMSNorm(self.head_dim, eps=1e-6, dtype=dtype)
-        self.img_attn_k_norm = HunyuanRMSNorm(self.head_dim, eps=1e-6, dtype=dtype)
-
-        self.img_attn_proj = ReplicatedLinear(hidden_size,
-                                              hidden_size,
-                                              bias=True,
-                                              params_dtype=dtype,
-                                              prefix=f"{prefix}.img_attn_proj")
-
-        self.img_mlp = MLP(hidden_size,
-                           mlp_hidden_dim,
-                           bias=True,
-                           dtype=dtype,
-                           prefix=f"{prefix}.img_mlp")
-
-        # Text modulation components
-        self.txt_mod = ModulateProjection(
-            hidden_size,
-            factor=6,
-            act_layer="silu",
-            dtype=dtype,
-            prefix=f"{prefix}.txt_mod",
-        )
-
-        # Fused operations for text stream
-        self.txt_attn_norm = LayerNormScaleShift(hidden_size,
-                                                 norm_type="layer",
-                                                 elementwise_affine=False,
-                                                 dtype=dtype)
-        self.txt_attn_residual_mlp_norm = ScaleResidualLayerNormScaleShift(
-            hidden_size,
-            norm_type="layer",
-            elementwise_affine=False,
-            dtype=dtype)
-        self.txt_mlp_residual = ScaleResidual()
-
-        # Text attention components
-        self.txt_attn_qkv = ReplicatedLinear(hidden_size,
-                                             hidden_size * 3,
-                                             bias=True,
-                                             params_dtype=dtype)
-
-        # QK norm layers for text
-        self.txt_attn_q_norm = HunyuanRMSNorm(self.head_dim, eps=1e-6, dtype=dtype)
-        self.txt_attn_k_norm = HunyuanRMSNorm(self.head_dim, eps=1e-6, dtype=dtype)
-
-        self.txt_attn_proj = ReplicatedLinear(hidden_size,
-                                              hidden_size,
-                                              bias=True,
-                                              params_dtype=dtype)
-
-        self.txt_mlp = MLP(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype)
-
-        self.max_attention_size = 21 * 1590 if local_attn_size == -1 else local_attn_size * 1590
-
-        self.attn = LocalAttention(
-            num_heads=self.num_attention_heads,
-            head_size=self.head_dim,
-            supported_attention_backends=supported_attention_backends,
-            prefix=f"{prefix}.attn"
-        )
-
-    def forward_txt(
-        self,
-        txt: torch.Tensor,
-        vec: torch.Tensor,
-        cache_txt: bool = False,
-    ):
-        txt_mod_outputs = self.txt_mod(vec)
-        (
-            txt_attn_shift,
-            txt_attn_scale,
-            txt_attn_gate,
-            txt_mlp_shift,
-            txt_mlp_scale,
-            txt_mlp_gate,
-        ) = torch.chunk(txt_mod_outputs, 6, dim=-1)
-
-        # Prepare text for attention using fused operation
-        txt_attn_input = self.txt_attn_norm(txt, txt_attn_shift, txt_attn_scale)
-
-        # Get QKV for text
-        txt_qkv, _ = self.txt_attn_qkv(txt_attn_input)
-        batch_size, text_seq_len = txt_qkv.shape[0], txt_qkv.shape[1]
-
-        # Split QKV
-        txt_qkv = txt_qkv.view(batch_size, text_seq_len, 3,
-                               self.num_attention_heads, -1)
-        txt_q, txt_k, txt_v = txt_qkv[:, :, 0], txt_qkv[:, :, 1], txt_qkv[:, :,
-                                                                          2]
-        # Apply QK-Norm if needed
-        txt_q = self.txt_attn_q_norm(txt_q).to(txt_q.dtype)
-        txt_k = self.txt_attn_k_norm(txt_k).to(txt_k.dtype)
-
-        t_kv = {}
-        if cache_txt:
-            t_kv["k_txt"] = txt_k
-            t_kv["v_txt"] = txt_v
-
-        txt_attn = self.attn(txt_q, txt_k, txt_v)
-        # Process text attention output
-        txt_attn_out, _ = self.txt_attn_proj(
-            txt_attn.reshape(batch_size, text_seq_len, -1))
-
-        # Use fused operation for residual connection, normalization, and modulation
-        txt_mlp_input, txt_residual = self.txt_attn_residual_mlp_norm(
-            txt, txt_attn_out, txt_attn_gate, txt_mlp_shift, txt_mlp_scale)
-
-        # Process text MLP
-        txt_mlp_out = self.txt_mlp(txt_mlp_input)
-        txt = self.txt_mlp_residual(txt_residual, txt_mlp_out, txt_mlp_gate)
-
-        return txt, t_kv
-
-    def forward_vision(
-        self,
-        img: torch.Tensor,
-        vec: torch.Tensor,
-        freqs_cis: tuple,
-        block_mask: BlockMask,
-        kv_cache: dict | None = None,
-        txt_kv_cache: list | None = None,
-        current_start: int = 0,
-    ) -> tuple[torch.Tensor, torch.Tensor]:
-        # Process modulation vectors
-        if vec.dim() == 3:
-            img_mod_outputs = self.img_mod(vec).unflatten(dim=-1, sizes=(6, -1))
-            (
-                img_attn_shift,
-                img_attn_scale,
-                img_attn_gate,
-                img_mlp_shift,
-                img_mlp_scale,
-                img_mlp_gate,
-            ) = torch.chunk(img_mod_outputs, 6, dim=2)
-        else:
-            img_mod_outputs = self.img_mod(vec)
-            (
-                img_attn_shift,
-                img_attn_scale,
-                img_attn_gate,
-                img_mlp_shift,
-                img_mlp_scale,
-                img_mlp_gate,
-            ) = torch.chunk(img_mod_outputs, 6, dim=-1)
-
-        # Prepare image for attention using fused operation
-        img_attn_input = self.img_attn_norm(img, img_attn_shift, img_attn_scale)
-        # Get QKV for image
-        img_qkv, _ = self.img_attn_qkv(img_attn_input)
-        batch_size, image_seq_len = img_qkv.shape[0], img_qkv.shape[1]
-
-        # Split QKV
-        img_qkv = img_qkv.view(batch_size, image_seq_len, 3,
-                               self.num_attention_heads, -1)
-        img_q, img_k, img_v = img_qkv[:, :, 0], img_qkv[:, :, 1], img_qkv[:, :,
-                                                                          2]
-
-        # Apply QK-Norm if needed
-        img_q = self.img_attn_q_norm(img_q).to(img_v)
-        img_k = self.img_attn_k_norm(img_k).to(img_v)
-
-        # Apply rotary embeddings
-        cos, sin = freqs_cis
-        img_q = _apply_rotary_emb(img_q, cos, sin, is_neox_style=False)
-        img_k = _apply_rotary_emb(img_k, cos, sin, is_neox_style=False)
-
-        # Apply flex_attention
-        # Does not support SP padding for now
-        if kv_cache is None:
-            q = img_q
-            k = torch.cat([img_k, txt_kv_cache["k_txt"]], dim=1)
-            v = torch.cat([img_v, txt_kv_cache["v_txt"]], dim=1)
-            # Padding for flex attention
-            padded_length = math.ceil(q.shape[1] / 128) * 128 - q.shape[1]
-            padded_kv_length = math.ceil(k.shape[1] / 128) * 128 - k.shape[1]
-            padded_roped_query = torch.cat(
-                [q,
-                    torch.zeros([q.shape[0], padded_length, q.shape[2], q.shape[3]],
-                                device=q.device, dtype=v.dtype)],
-                dim=1
-            )
-
-            padded_roped_key = torch.cat(
-                [k, torch.zeros([k.shape[0], padded_kv_length, k.shape[2], k.shape[3]],
-                                        device=k.device, dtype=v.dtype)],
-                dim=1
-            )
-
-            padded_v = torch.cat(
-                [v, torch.zeros([v.shape[0], padded_kv_length, v.shape[2], v.shape[3]],
-                                device=v.device, dtype=v.dtype)],
-                dim=1
-            )
-
-            img_attn = flex_attention(
-                query=padded_roped_query.transpose(2, 1),
-                key=padded_roped_key.transpose(2, 1),
-                value=padded_v.transpose(2, 1),
-                block_mask=block_mask
-            )[:, :, :-padded_length].transpose(2, 1)
-
-            assert img_attn.shape[1] == image_seq_len
-            updated_kv_cache = None
-        else:
-            current_end = current_start + img_q.shape[1]
-            num_new_tokens = img_q.shape[1]
-            sink_tokens = self.sink_size * 1590
-            # If we are using local attention and the current KV cache size is larger than the local attention size, we need to truncate the KV cache
-            kv_cache_size = self.max_attention_size
-            
-            # Clone cache to avoid in-place modification during gradient checkpointing
-            k_cache = kv_cache["k"].clone()
-            v_cache = kv_cache["v"].clone()
-            
-            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):
-                # Calculate the number of new tokens added in this step
-                # Shift existing cache content left to discard oldest tokens
-                # Clone the source slice to avoid overlapping memory error
-                num_evicted_tokens = num_new_tokens + kv_cache["local_end_index"].item() - kv_cache_size
-                num_rolled_tokens = kv_cache_size - num_new_tokens - sink_tokens
-                k_cache[:, sink_tokens:sink_tokens + num_rolled_tokens] = \
-                    k_cache[:, sink_tokens + num_evicted_tokens:sink_tokens + num_evicted_tokens + num_rolled_tokens].clone()
-                v_cache[:, sink_tokens:sink_tokens + num_rolled_tokens] = \
-                    v_cache[:, sink_tokens + num_evicted_tokens:sink_tokens + num_evicted_tokens + num_rolled_tokens].clone()
-                # Insert the new keys/values at the end
-                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
-                assert local_end_index == self.max_attention_size
-            else:
-                # Assign new keys/values directly up to current_end
-                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
-
-            assert local_start_index >= 0
-            q = img_q
-            k = torch.cat([k_cache[:, :local_start_index], img_k, txt_kv_cache["k_txt"]], dim=1)
-            v = torch.cat([v_cache[:, :local_start_index], img_v, txt_kv_cache["v_txt"]], dim=1)
-            img_attn = self.attn(q, k, v)
-
-            k_cache[:, local_start_index:local_end_index] = img_k
-            v_cache[:, local_start_index:local_end_index] = img_v
-            
-            updated_kv_cache = {
-                "k": k_cache,
-                "v": v_cache,
-                "global_end_index": torch.tensor([current_end], dtype=torch.long, device=k_cache.device),
-                "local_end_index": torch.tensor([local_end_index], dtype=torch.long, device=k_cache.device)
-            }
-        
-        img_attn_out, _ = self.img_attn_proj(
-            img_attn.view(batch_size, image_seq_len, -1))
-        # Use fused operation for residual connection, normalization, and modulation
-        img_mlp_input, img_residual = self.img_attn_residual_mlp_norm(
-            img, img_attn_out, img_attn_gate, img_mlp_shift, img_mlp_scale)
-
-        # Process image MLP
-        img_mlp_out = self.img_mlp(img_mlp_input)
-        img = self.img_mlp_residual(img_residual, img_mlp_out, img_mlp_gate)
-
-        return img, updated_kv_cache
-
-    def forward(
-        self,
-        txt_inference=False,
-        vision_inference=False,
-        **kwargs
-    ):
-        if txt_inference:
-            return self.forward_txt(**kwargs)
-        elif vision_inference:
-            return self.forward_vision(**kwargs)
-        else:
-            raise ValueError("txt_inference and vision_inference cannot be both False")
-
-
-class CausalHunyuanVideo15Transformer3DModel(CachableDiT):
-    r"""
-    A Transformer model for video-like data used in [HunyuanVideo1.5](https://huggingface.co/tencent/HunyuanVideo1.5).
-    """
-
-    # shard single stream, double stream blocks, and refiner_blocks
-    _fsdp_shard_conditions = HunyuanVideo15Config()._fsdp_shard_conditions
-    _compile_conditions = HunyuanVideo15Config()._compile_conditions
-    _supported_attention_backends = HunyuanVideo15Config(
-    )._supported_attention_backends
-    param_names_mapping = HunyuanVideo15Config().param_names_mapping
-    reverse_param_names_mapping = HunyuanVideo15Config(
-    ).reverse_param_names_mapping
-    lora_param_names_mapping = HunyuanVideo15Config().lora_param_names_mapping
-
-    def __init__(
-        self,
-        config: HunyuanVideo15Config,
-        hf_config: dict[str, Any],
-    ) -> None:
-        super().__init__(config=config, hf_config=hf_config)
-
-        self.hidden_size = config.hidden_size
-        self.num_attention_heads = config.num_attention_heads
-        self.num_channels_latents = config.num_channels_latents
-        self.out_channels = config.out_channels or config.in_channels
-        self.patch_size = (config.patch_size_t, config.patch_size, config.patch_size)
-
-        # 1. Latent and condition embedders
-        self.img_in = PatchEmbed(self.patch_size, 
-                                    config.in_channels, 
-                                    self.hidden_size,
-                                    prefix=f"{config.prefix}.img_in")
-        self.image_embedder = HunyuanVideo15ImageProjection(config.image_embed_dim, self.hidden_size)
-
-        self.txt_in = SingleTokenRefiner(config.text_embed_dim,
-                                                   self.hidden_size,
-                                                   config.num_attention_heads,
-                                                   depth=config.num_refiner_layers,
-                                                   dtype=None,
-                                                   prefix=f"{config.prefix}.txt_in")
-
-        self.txt_in_2 = HunyuanVideo15ByT5TextProjection(config.text_embed_2_dim, 2048, self.hidden_size)
-
-        self.time_in = HunyuanVideo15TimeEmbedding(self.hidden_size, use_meanflow=config.use_meanflow)
-
-        self.cond_type_embed = nn.Embedding(3, self.hidden_size)
-
-        # 3. Dual stream transformer blocks
-
-        self.double_blocks = nn.ModuleList(
-            [
-                MMDoubleStreamBlock(
-                    hidden_size=self.hidden_size,
-                    num_attention_heads=config.num_attention_heads,
-                    mlp_ratio=config.mlp_ratio,
-                    local_attn_size=config.local_attn_size,
-                    sink_size=config.sink_size,
-                    dtype=None,
-                    supported_attention_backends=self._supported_attention_backends,
-                    prefix=f"{config.prefix}.double_blocks.{i}"
-                )
-                for i in range(config.num_layers)
-            ]
-        )
-
-        # 5. Output projection
-        self.final_layer = FinalLayer(self.hidden_size,
-                                self.patch_size,
-                                self.out_channels,
-                                prefix=f"{config.prefix}.final_layer")
-
-        self.gradient_checkpointing = False
-
-        self.num_frame_per_block = config.num_frames_per_block
-        self.local_attn_size = config.local_attn_size
-        self.block_mask = None
-
-        self.__post_init__()
-
-    def get_text_and_mask(
-        self,
-        encoder_hidden_states: torch.Tensor,
-        encoder_hidden_states_2: torch.Tensor,
-        encoder_attention_mask: torch.Tensor,
-        encoder_attention_mask_2: torch.Tensor,
-        encoder_hidden_states_image: torch.Tensor,
-        timestep: torch.Tensor,
-    ):
-        batch_size, txt_seq_len = encoder_hidden_states.shape[0], encoder_hidden_states.shape[1]
-        # qwen text embedding
-        encoder_hidden_states = self.txt_in(encoder_hidden_states, timestep, encoder_attention_mask)
-
-        encoder_hidden_states_cond_emb = self.cond_type_embed(
-            torch.zeros_like(encoder_hidden_states[:, :, 0], dtype=torch.long)
-        )
-        encoder_hidden_states = encoder_hidden_states + encoder_hidden_states_cond_emb
-
-        # byt5 text embedding
-        encoder_hidden_states_2 = self.txt_in_2(encoder_hidden_states_2)
-
-        encoder_hidden_states_2_cond_emb = self.cond_type_embed(
-            torch.ones_like(encoder_hidden_states_2[:, :, 0], dtype=torch.long)
-        )
-        encoder_hidden_states_2 = encoder_hidden_states_2 + encoder_hidden_states_2_cond_emb
-
-        # image embed
-        encoder_hidden_states_3 = self.image_embedder(encoder_hidden_states_image)
-        is_t2v = torch.all(encoder_hidden_states_image == 0)
-        if is_t2v:
-            encoder_hidden_states_3 = encoder_hidden_states_3 * 0.0
-            encoder_attention_mask_3 = torch.zeros(
-                (batch_size, encoder_hidden_states_3.shape[1]),
-                dtype=encoder_attention_mask.dtype,
-                device=encoder_attention_mask.device,
-            )
-        else:
-            encoder_attention_mask_3 = torch.ones(
-                (batch_size, encoder_hidden_states_3.shape[1]),
-                dtype=encoder_attention_mask.dtype,
-                device=encoder_attention_mask.device,
-            )
-        encoder_hidden_states_3_cond_emb = self.cond_type_embed(
-            2
-            * torch.ones_like(
-                encoder_hidden_states_3[:, :, 0],
-                dtype=torch.long,
-            )
-        )
-        encoder_hidden_states_3 = encoder_hidden_states_3 + encoder_hidden_states_3_cond_emb
-
-        # reorder and combine text tokens: combine valid tokens first, then padding
-        encoder_attention_mask = encoder_attention_mask.bool()
-        encoder_attention_mask_2 = encoder_attention_mask_2.bool()
-        encoder_attention_mask_3 = encoder_attention_mask_3.bool()
-        new_encoder_hidden_states = []
-        new_encoder_attention_mask = []
-
-        for text, text_mask, text_2, text_mask_2, image, image_mask in zip(
-            encoder_hidden_states,
-            encoder_attention_mask,
-            encoder_hidden_states_2,
-            encoder_attention_mask_2,
-            encoder_hidden_states_3,
-            encoder_attention_mask_3,
-        ):
-            # Concatenate: [valid_image, valid_byt5, valid_mllm, invalid_image, invalid_byt5, invalid_mllm]
-            new_encoder_hidden_states.append(
-                torch.cat(
-                    [
-                        image[image_mask],  # valid image
-                        text_2[text_mask_2],  # valid byt5
-                        text[text_mask],  # valid mllm
-                        image[~image_mask],  # invalid image (zeroed)
-                        torch.zeros_like(text_2[~text_mask_2]),  # invalid byt5 (zeroed)
-                        torch.zeros_like(text[~text_mask]),  # invalid mllm (zeroed)
-                    ],
-                    dim=0,
-                )
-            )
-            # Apply same reordering to attention masks
-            new_encoder_attention_mask.append(
-                torch.cat(
-                    [
-                        image_mask[image_mask],
-                        text_mask_2[text_mask_2],
-                        text_mask[text_mask],
-                        image_mask[~image_mask],
-                        text_mask_2[~text_mask_2],
-                        text_mask[~text_mask],
-                    ],
-                    dim=0,
-                )
-            )
-
-        encoder_hidden_states = torch.stack(new_encoder_hidden_states)
-        encoder_attention_mask = torch.stack(new_encoder_attention_mask)
-        assert encoder_hidden_states.shape[0] == 1
-        return encoder_hidden_states, encoder_attention_mask
-
-    
-    @staticmethod
-    def _prepare_blockwise_causal_attn_mask(
-        device: torch.device | str, num_frames: int = 21,
-        frame_seqlen: int = 1560, num_frame_per_block=1, local_attn_size=-1,
-        text_seq_len: int = 0
-    ) -> BlockMask:
-        """
-        we will divide the token sequence into the following format
-        [1 latent frame] [1 latent frame] ... [1 latent frame]
-        We use flexattention to construct the attention mask
-        """
-        total_length = num_frames * frame_seqlen
-        total_kv_length = total_length + text_seq_len
-
-        total_length_tensor = torch.tensor(total_length, device=device)
-        total_kv_length_tensor = torch.tensor(total_kv_length, device=device)
-
-        # we do right padding to get to a multiple of 128
-        padded_length = math.ceil(total_length / 128) * 128 - total_length
-        kv_padded_length = math.ceil(total_kv_length / 128) * 128 - total_kv_length
-
-        ends = torch.zeros(total_length + padded_length,
-                           device=device, dtype=torch.long)
-
-        # Block-wise causal mask will attend to all elements that are before the end of the current chunk
-        frame_indices = torch.arange(
-            # start=frame_seqlen,
-            start=0,
-            end=total_length,
-            step=frame_seqlen * num_frame_per_block,
-            device=device
-        )
-        # frame_indices = torch.cat([torch.tensor([0], device=device), frame_indices])
-
-        for i, tmp in enumerate(frame_indices):
-            # if i == 0:
-            #     ends[tmp:tmp + frame_seqlen] = tmp + frame_seqlen
-            # else:
-            ends[tmp:tmp + frame_seqlen * num_frame_per_block] = tmp + \
-                frame_seqlen * num_frame_per_block
-
-        def attention_mask(b, h, q_idx, kv_idx):
-            if local_attn_size == -1:
-                return (kv_idx < ends[q_idx]) | (q_idx == kv_idx) | ((kv_idx >= total_length_tensor) & (kv_idx < total_kv_length_tensor))
-            else:
-                return ((kv_idx < ends[q_idx]) & (kv_idx >= (ends[q_idx] - local_attn_size * frame_seqlen))) | (q_idx == kv_idx) | ((kv_idx >= total_length_tensor) & (kv_idx < total_kv_length_tensor))
-            # return ((kv_idx < total_length) & (q_idx < total_length))  | (q_idx == kv_idx) # bidirectional mask
-
-        block_mask = create_block_mask(attention_mask, B=None, H=None, Q_LEN=total_length + padded_length,
-                                       KV_LEN=total_kv_length + kv_padded_length, _compile=False, device=device)
-
-        # if not dist.is_initialized() or dist.get_rank() == 0:
-        #     print(
-        #         f" cache a block wise causal mask with block size of {num_frame_per_block} frames")
-        #     print(block_mask)
-
-        # import imageio
-        # import numpy as np
-        # from torch.nn.attention.flex_attention import create_mask
-
-        # mask = create_mask(attention_mask, B=None, H=None, Q_LEN=total_length +
-        #                    padded_length, KV_LEN=total_length + padded_length, device=device)
-        # import cv2
-        # mask = cv2.resize(mask[0, 0].cpu().float().numpy(), (1024, 1024))
-        # imageio.imwrite("mask_%d.jpg" % (0), np.uint8(255. * mask))
-
-        return block_mask
-
-    def forward_txt(
-        self,
-        encoder_hidden_states: List[torch.Tensor],
-        timestep: torch.LongTensor,
-        encoder_hidden_states_image: Optional[List[torch.Tensor]] = None,
-        encoder_attention_mask: Optional[List[torch.Tensor]] = None,
-        guidance: Optional[torch.Tensor] = None,
-        timestep_r: Optional[torch.LongTensor] = None,
-        attention_kwargs: Optional[Dict[str, Any]] = None,
-        cache_txt: bool = False,
-        **kwargs
-    ):
-        # Check that the timestep is only consisted of 0s
-        assert torch.all(timestep == 0), "Timestep for txt must be only consisted of 0s"
-
-        if cache_txt:
-            _kv_cache_new = []
-            transformer_num_layers = len(self.double_blocks)
-            for _ in range(transformer_num_layers):
-                _kv_cache_new.append(
-                    {"k_vision": None, "v_vision": None, "k_txt": None, "v_txt": None}
-                )
-
-        encoder_hidden_states_image = encoder_hidden_states_image[0]
-        encoder_hidden_states, encoder_hidden_states_2 = encoder_hidden_states
-        encoder_attention_mask, encoder_attention_mask_2 = encoder_attention_mask
-
-        # 2. Conditional embeddings
-        if timestep.dim() == 2:
-            ts_seq_len = timestep.shape[1]
-            temb = self.time_in(timestep.flatten(), timestep_r=timestep_r, timestep_seq_len=ts_seq_len)
-        else:
-            temb = self.time_in(timestep, timestep_r=timestep_r)
-        # temb is [bs, seq_len, inner_dim] if ts_seq_len is not None, otherwise [bs, inner_dim]
-
-        encoder_hidden_states, encoder_attention_mask = self.get_text_and_mask(
-            encoder_hidden_states,
-            encoder_hidden_states_2,
-            encoder_attention_mask,
-            encoder_attention_mask_2,
-            encoder_hidden_states_image,
-            timestep
-        )
-        
-        encoder_hidden_states = encoder_hidden_states[encoder_attention_mask.bool().to(encoder_hidden_states.device)].unsqueeze(0)
-
-        # 4. Transformer blocks
-        for index, block in enumerate(self.double_blocks):
-            encoder_hidden_states, t_kv = block(
-                txt_inference=True,
-                vision_inference=False,
-                txt=encoder_hidden_states,
-                vec=temb,
-                cache_txt=cache_txt,
-            )
-
-            if cache_txt:
-                _kv_cache_new[index]["k_txt"] = t_kv["k_txt"]
-                _kv_cache_new[index]["v_txt"] = t_kv["v_txt"]
-
-        if cache_txt:
-            return _kv_cache_new
-
-    def forward_vision(
-        self,
-        hidden_states: torch.Tensor,
-        timestep: torch.LongTensor,
-        guidance: Optional[torch.Tensor] = None,
-        timestep_r: Optional[torch.LongTensor] = None,
-        attention_kwargs: Optional[Dict[str, Any]] = None,
-        kv_cache: dict | None = None,
-        txt_kv_cache: list | None = None,
-        current_start: int = 0,
-        rope_start_idx: int = 0,
-    ):
-        assert txt_kv_cache is not None, "txt_kv_cache must be provided"
-
-        batch_size, num_channels, num_frames, height, width = hidden_states.shape
-        p_t, p_h, p_w = self.config.patch_size_t, self.config.patch_size, self.config.patch_size
-        post_patch_num_frames = num_frames // p_t
-        post_patch_height = height // p_h
-        post_patch_width = width // p_w
-
-        # 1. RoPE
-        # Get rotary embeddings
-        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, self.config.rope_axes_dim, self.config.rope_theta, start_frame=rope_start_idx)
-        freqs_cos = freqs_cos.to(hidden_states.device)
-        freqs_sin = freqs_sin.to(hidden_states.device)
-        freqs_cis = (freqs_cos, freqs_sin) if freqs_cos is not None else None
-
-        # 2. Conditional embeddings
-        if timestep.dim() == 2:
-            ts_seq_len = timestep.shape[1]
-            temb = self.time_in(timestep.flatten(), timestep_r=timestep_r, timestep_seq_len=ts_seq_len)
-        else:
-            temb = self.time_in(timestep, timestep_r=timestep_r)
-        # temb is [bs, seq_len, inner_dim] if ts_seq_len is not None, otherwise [bs, inner_dim]
-
-        hidden_states = self.img_in(hidden_states)
-
-        # Prepare block-wise causal attention mask
-        if kv_cache is None:
-            self.block_mask = self._prepare_blockwise_causal_attn_mask(
-                device=hidden_states.device,
-                num_frames=num_frames,
-                frame_seqlen=post_patch_height * post_patch_width,
-                num_frame_per_block=self.num_frame_per_block,
-                local_attn_size=self.local_attn_size,
-                text_seq_len=txt_kv_cache[0]["k_txt"].shape[1]
-            )
-
-        # 4. Transformer blocks
-        if torch.is_grad_enabled() and self.gradient_checkpointing:
-            for block_index, block in enumerate(self.double_blocks):
-                hidden_states, new_cache = self._gradient_checkpointing_func(
-                    block,
-                    txt_inference=False,
-                    vision_inference=True,
-                    img=hidden_states,
-                    vec=temb,
-                    freqs_cis=freqs_cis,
-                    block_mask=self.block_mask,
-                    kv_cache=kv_cache[block_index] if kv_cache is not None else None,
-                    txt_kv_cache=txt_kv_cache[block_index],
-                    current_start=current_start
-                )
-                if new_cache is not None and kv_cache is not None:
-                    for k in new_cache.keys():
-                        kv_cache[block_index][k] = new_cache[k].clone()
-
-        else:
-            for block_index, block in enumerate(self.double_blocks):
-                hidden_states, new_cache = block(
-                    txt_inference=False,
-                    vision_inference=True,
-                    img=hidden_states,
-                    vec=temb,
-                    freqs_cis=freqs_cis,
-                    block_mask=self.block_mask,
-                    kv_cache=kv_cache[block_index] if kv_cache is not None else None,
-                    txt_kv_cache=txt_kv_cache[block_index],
-                    current_start=current_start
-                )
-                if new_cache is not None and kv_cache is not None:
-                    for k in new_cache.keys():
-                        kv_cache[block_index][k] = new_cache[k].clone()
-
-        # Final layer processing
-        hidden_states = self.final_layer(hidden_states, temb)
-        # Unpatchify to get original shape
-        hidden_states = unpatchify(hidden_states, post_patch_num_frames, post_patch_height, post_patch_width, self.patch_size, self.out_channels)
-
-        return hidden_states, kv_cache
-
-    def forward(
-        self,
-        txt_inference=False,
-        vision_inference=False,
-        **kwargs,
-    ):
-        if txt_inference:
-            return self.forward_txt(**kwargs)
-        elif vision_inference:
-            return self.forward_vision(**kwargs)
-        else:
-            raise ValueError("txt_inference and vision_inference cannot be both False")

fastvideo/models/dits/hunyuanvideo15.py

@@ -127,9 +127,8 @@ class HunyuanVideo15TimeEmbedding(nn.Module):
         self,
         timestep: torch.Tensor,
         timestep_r: Optional[torch.Tensor] = None,
-        timestep_seq_len: int | None = None,
     ) -> torch.Tensor:
-        timesteps_emb = self.timestep_embedder(timestep, timestep_seq_len)
+        timesteps_emb = self.timestep_embedder(timestep)
 
         if timestep_r is not None:
             timesteps_emb_r = self.timestep_embedder_r(timestep_r)
@@ -474,7 +473,6 @@ class HunyuanVideo15Transformer3DModel(CachableDiT):
         guidance: Optional[torch.Tensor] = None,
         timestep_r: Optional[torch.LongTensor] = None,
         attention_kwargs: Optional[Dict[str, Any]] = None,
-        **kwargs
     ):
         encoder_hidden_states_image = encoder_hidden_states_image[0]
         encoder_hidden_states, encoder_hidden_states_2 = encoder_hidden_states
@@ -496,12 +494,7 @@ class HunyuanVideo15Transformer3DModel(CachableDiT):
         freqs_cis = (freqs_cos, freqs_sin) if freqs_cos is not None else None
 
         # 2. Conditional embeddings
-        if timestep.dim() == 2:
-            ts_seq_len = timestep.shape[1]
-            temb = self.time_in(timestep.flatten(), timestep_r=timestep_r, timestep_seq_len=ts_seq_len)
-        else:
-            temb = self.time_in(timestep, timestep_r=timestep_r)
-        # temb is [bs, seq_len, inner_dim] if ts_seq_len is not None, otherwise [bs, inner_dim]
+        temb = self.time_in(timestep, timestep_r=timestep_r)
 
         hidden_states = self.img_in(hidden_states)
         hidden_states, original_seq_len = sequence_model_parallel_shard(hidden_states, dim=1)
@@ -633,8 +626,6 @@ class HunyuanVideo15Transformer3DModel(CachableDiT):
                 )
 
         # Final layer processing
-        if get_sp_world_size() > 1:
-            hidden_states = hidden_states.contiguous()
         hidden_states = sequence_model_parallel_all_gather_with_unpad(hidden_states, original_seq_len, dim=1)
         hidden_states = self.final_layer(hidden_states, temb)
         # Unpatchify to get original shape
@@ -705,7 +696,6 @@ class SingleTokenRefiner(nn.Module):
         else:
             mask_float = mask.float().unsqueeze(-1)  # [B, L, 1]
             context_aware_representations = (x * mask_float).sum(dim=1) / mask_float.sum(dim=1)
-        context_aware_representations = context_aware_representations.to(original_dtype)
 
         context_aware_representations = self.c_embedder(
             context_aware_representations)
@@ -858,13 +848,6 @@ class FinalLayer(nn.Module):
     def forward(self, x, c):
         # What the heck HF? Why you change the scale and shift order here???
         scale, shift = self.adaLN_modulation(c).chunk(2, dim=-1)
-        if c.dim() == 3:
-            # [bs, seq_len, inner_dim]
-            num_frames = scale.shape[1]
-            frame_seqlen = x.shape[1] // num_frames
-            x = (self.norm_final(x).unflatten(dim=1, sizes=(num_frames, frame_seqlen)) * (1.0 + scale.unsqueeze(2)) + shift.unsqueeze(2)).flatten(1, 2)
-        else:
-            # [bs, inner_dim]
-            x = self.norm_final(x) * (1.0 + scale.unsqueeze(1)) + shift.unsqueeze(1)
+        x = self.norm_final(x) * (1.0 + scale.unsqueeze(1)) + shift.unsqueeze(1)
         x, _ = self.linear(x)
         return x

fastvideo/models/dits/hyworld/__init__.py

@@ -1,24 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-HYWorld (HY-WorldPlay) model components for FastVideo.
-
-This module provides:
-- HYWorldTransformer3DModel: The main transformer model with ProPE and action conditioning
-- HYWorldVideoGenerator: Extended VideoGenerator for HYWorld inference
-- Utilities for pose processing and camera trajectory generation
-"""
-
-from .hyworld import HYWorldTransformer3DModel, HYWorldDoubleStreamBlock
-
-# Inference utilities (used by examples)
-from .resolution_utils import (
-    get_resolution_from_image,
-)
-
-__all__ = [
-    # Model (used by model registry)
-    "HYWorldTransformer3DModel",
-    "HYWorldDoubleStreamBlock",
-    # Inference utilities (used by examples)
-    "get_resolution_from_image",
-]

fastvideo/models/dits/hyworld/camera_rope.py

@@ -1,261 +0,0 @@
-# HY-WorldPlay/hyvideo/prope/camera_rope.py
-
-# MIT License
-#
-# Copyright (c) Authors of
-# "PRoPE: Projective Positional Encoding for Multiview Transformers"
-#
-# Permission is hereby granted, free of charge, to any person obtaining a copy
-# of this software and associated documentation files (the "Software"), to deal
-# in the Software without restriction, including without limitation the rights
-# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-# copies of the Software, and to permit persons to whom the Software is
-# furnished to do so, subject to the following conditions:
-#
-# The above copyright notice and this permission notice shall be included in all
-# copies or substantial portions of the Software.
-#
-# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-# SOFTWARE.
-
-# How to use PRoPE attention for self-attention:
-#
-# 1. Easiest way (fast):
-#    attn = PropeDotProductAttention(...)
-#    o = attn(q, k, v, viewmats, Ks)
-#
-# 2. More flexible way (fast):
-#    attn = PropeDotProductAttention(...)
-#    attn._precompute_and_cache_apply_fns(viewmats, Ks)
-#    q = attn._apply_to_q(q)
-#    k = attn._apply_to_kv(k)
-#    v = attn._apply_to_kv(v)
-#    o = F.scaled_dot_product_attention(q, k, v, **kwargs)
-#    o = attn._apply_to_o(o)
-#
-# 3. The most flexible way (but slower because repeated computation of RoPE coefficients):
-#    o = prope_dot_product_attention(q, k, v, ...)
-#
-# How to use PRoPE attention for cross-attention:
-#
-#    attn_src = PropeDotProductAttention(...)
-#    attn_tgt = PropeDotProductAttention(...)
-#    attn_src._precompute_and_cache_apply_fns(viewmats_src, Ks_src)
-#    attn_tgt._precompute_and_cache_apply_fns(viewmats_tgt, Ks_tgt)
-#    q_src = attn_src._apply_to_q(q_src)
-#    k_tgt = attn_tgt._apply_to_kv(k_tgt)
-#    v_tgt = attn_tgt._apply_to_kv(v_tgt)
-#    o_src = F.scaled_dot_product_attention(q_src, k_tgt, v_tgt, **kwargs)
-#    o_src = attn_src._apply_to_o(o_src)
-
-from functools import partial
-from typing import Callable, Optional, Tuple, List
-
-import torch
-import torch.nn.functional as F
-
-
-def prope_qkv(
-    q: torch.Tensor,  # (batch, num_heads, seqlen, head_dim)
-    k: torch.Tensor,  # (batch, num_heads, seqlen, head_dim)
-    v: torch.Tensor,  # (batch, num_heads, seqlen, head_dim)
-    *,
-    viewmats: torch.Tensor,  # (batch, cameras, 4, 4)
-    Ks: Optional[torch.Tensor],  # (batch, cameras, 3, 3)
-    patches_x: int = None,  # How many patches wide is each image?
-    patches_y: int = None,  # How many patches tall is each image?
-    image_width: int = None,  # Width of the image. Used to normalize intrinsics.
-    image_height: int = None,  # Height of the image. Used to normalize intrinsics.
-    coeffs_x: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
-    coeffs_y: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
-    mask: Optional[torch.Tensor] = None,
-    kv_cache=None,
-    is_cache: bool = False,
-    **kwargs,
-) -> torch.Tensor:
-    """Similar to torch.nn.functional.scaled_dot_product_attention, but applies PRoPE-style
-    positional encoding.
-
-    Currently, we assume that the sequence length is equal to:
-
-        cameras * patches_x * patches_y
-
-    And token ordering allows the `(seqlen,)` axis to be reshaped into
-    `(cameras, patches_x, patches_y)`.
-    """
-    # We're going to assume self-attention: all inputs are the same shape.
-    (batch, num_heads, seqlen, head_dim) = q.shape
-    cameras = viewmats.shape[1]
-    assert q.shape == k.shape == v.shape
-    assert viewmats.shape == (batch, cameras, 4, 4)
-    assert Ks is None or Ks.shape == (batch, cameras, 3, 3)
-    # assert seqlen == cameras * patches_x * patches_y
-
-    apply_fn_q, apply_fn_kv, apply_fn_o = _prepare_apply_fns_all_dim(
-        head_dim=head_dim,
-        viewmats=viewmats,
-        Ks=Ks,
-        patches_x=patches_x,
-        patches_y=patches_y,
-        image_width=image_width,
-        image_height=image_height,
-        coeffs_x=coeffs_x,
-        coeffs_y=coeffs_y,
-    )
-
-    query = apply_fn_q(q)
-    key = apply_fn_kv(k)
-    value = apply_fn_kv(v)
-
-    return query, key, value, apply_fn_o
-
-
-def _prepare_apply_fns_all_dim(
-    head_dim: int,  # Q/K/V will have this last dimension
-    viewmats: torch.Tensor,  # (batch, cameras, 4, 4)
-    Ks: Optional[torch.Tensor],  # (batch, cameras, 3, 3)
-    patches_x: int,  # How many patches wide is each image?
-    patches_y: int,  # How many patches tall is each image?
-    image_width: int,  # Width of the image. Used to normalize intrinsics.
-    image_height: int,  # Height of the image. Used to normalize intrinsics.
-    coeffs_x: Optional[torch.Tensor] = None,
-    coeffs_y: Optional[torch.Tensor] = None,
-) -> Tuple[
-    Callable[[torch.Tensor], torch.Tensor],
-    Callable[[torch.Tensor], torch.Tensor],
-    Callable[[torch.Tensor], torch.Tensor],
-]:
-    """Prepare transforms for PRoPE-style positional encoding."""
-    device = viewmats.device
-    (batch, cameras, _, _) = viewmats.shape
-
-    # Normalize camera intrinsics.
-    if Ks is not None:
-        Ks_norm = torch.zeros_like(Ks)
-        Ks_norm[..., 0, 0] = Ks[..., 0, 0]
-        Ks_norm[..., 1, 1] = Ks[..., 1, 1]
-        Ks_norm[..., 0, 2] = 0
-        Ks_norm[..., 1, 2] = 0
-        Ks_norm[..., 2, 2] = 1.0
-        Ks_norm = Ks_norm.to(dtype=Ks.dtype)
-        del Ks
-
-        # Compute the camera projection matrices we use in PRoPE.
-        # - K is an `image<-camera` transform.
-        # - viewmats is a `camera<-world` transform.
-        # - P = lift(K) @ viewmats is an `image<-world` transform.
-        P = torch.einsum("...ij,...jk->...ik", _lift_K(Ks_norm), viewmats)
-        P_T = P.transpose(-1, -2).to(dtype=viewmats.dtype)
-        P_inv = torch.einsum(
-            "...ij,...jk->...ik",
-            _invert_SE3(viewmats),
-            _lift_K(_invert_K(Ks_norm)),
-        ).to(dtype=viewmats.dtype)
-
-    else:
-        # GTA formula. P is `camera<-world` transform.
-        P = viewmats
-        P_T = P.transpose(-1, -2)
-        P_inv = _invert_SE3(viewmats)
-
-    assert P.shape == P_inv.shape == (batch, cameras, 4, 4)
-
-    # Block-diagonal transforms to the inputs and outputs of the attention operator.
-    assert head_dim % 4 == 0
-    transforms_q = [
-        (partial(_apply_tiled_projmat, matrix=P_T), head_dim),
-    ]
-    transforms_kv = [
-        (partial(_apply_tiled_projmat, matrix=P_inv), head_dim),
-    ]
-    transforms_o = [
-        (partial(_apply_tiled_projmat, matrix=P), head_dim),
-    ]
-
-    apply_fn_q = partial(_apply_block_diagonal, func_size_pairs=transforms_q)
-    apply_fn_kv = partial(_apply_block_diagonal, func_size_pairs=transforms_kv)
-    apply_fn_o = partial(_apply_block_diagonal, func_size_pairs=transforms_o)
-    return apply_fn_q, apply_fn_kv, apply_fn_o
-
-
-def _apply_tiled_projmat(
-    feats: torch.Tensor,  # (batch, num_heads, seqlen, feat_dim)
-    matrix: torch.Tensor,  # (batch, cameras, D, D)
-) -> torch.Tensor:
-    """Apply projection matrix to features."""
-    # - seqlen => (cameras, patches_x * patches_y)
-    # - feat_dim => (feat_dim // 4, 4)
-    (batch, num_heads, seqlen, feat_dim) = feats.shape
-    cameras = matrix.shape[1]
-    assert seqlen >= cameras and seqlen % cameras == 0
-    D = matrix.shape[-1]
-    assert matrix.shape == (batch, cameras, D, D)
-    assert feat_dim % D == 0
-    return torch.einsum(
-        "bcij,bncpkj->bncpki",
-        matrix,
-        feats.reshape((batch, num_heads, cameras, -1, feat_dim // D, D)),
-    ).reshape(feats.shape)
-
-
-def _apply_block_diagonal(
-    feats: torch.Tensor,  # (..., dim)
-    func_size_pairs: List[Tuple[Callable[[torch.Tensor], torch.Tensor], int]],
-) -> torch.Tensor:
-    """Apply a block-diagonal function to an input array.
-
-    Each function is specified as a tuple with form:
-
-        ((Tensor) -> Tensor, int)
-
-    Where the integer is the size of the input to the function.
-    """
-    funcs, block_sizes = zip(*func_size_pairs)
-    assert feats.shape[-1] == sum(block_sizes)
-    x_blocks = torch.split(feats, block_sizes, dim=-1)
-    out = torch.cat(
-        [f(x_block) for f, x_block in zip(funcs, x_blocks)],
-        dim=-1,
-    )
-    assert out.shape == feats.shape, "Input/output shapes should match."
-    return out
-
-
-def _invert_SE3(transforms: torch.Tensor) -> torch.Tensor:
-    """Invert a 4x4 SE(3) matrix."""
-    assert transforms.shape[-2:] == (4, 4)
-    Rinv = transforms[..., :3, :3].transpose(-1, -2)
-    out = torch.zeros_like(transforms)
-    out[..., :3, :3] = Rinv
-    out[..., :3, 3] = -torch.einsum("...ij,...j->...i", Rinv, transforms[..., :3, 3])
-    out[..., 3, 3] = 1.0
-    out = out.to(dtype=transforms.dtype)
-    return out
-
-
-def _lift_K(Ks: torch.Tensor) -> torch.Tensor:
-    """Lift 3x3 matrices to homogeneous 4x4 matrices."""
-    assert Ks.shape[-2:] == (3, 3)
-    out = torch.zeros(Ks.shape[:-2] + (4, 4), device=Ks.device)
-    out[..., :3, :3] = Ks
-    out[..., 3, 3] = 1.0
-    out = out.to(dtype=Ks.dtype)
-    return out
-
-
-def _invert_K(Ks: torch.Tensor) -> torch.Tensor:
-    """Invert 3x3 intrinsics matrices. Assumes no skew."""
-    assert Ks.shape[-2:] == (3, 3)
-    out = torch.zeros_like(Ks)
-    out[..., 0, 0] = 1.0 / Ks[..., 0, 0]
-    out[..., 1, 1] = 1.0 / Ks[..., 1, 1]
-    out[..., 0, 2] = -Ks[..., 0, 2] / Ks[..., 0, 0]
-    out[..., 1, 2] = -Ks[..., 1, 2] / Ks[..., 1, 1]
-    out[..., 2, 2] = 1.0
-    out = out.to(dtype=Ks.dtype)
-    return out

fastvideo/models/dits/hyworld/data_utils.py

@@ -1,76 +0,0 @@
-# HY-WorldPlay/hyvideo/utils/data_utils.py
-
-# Licensed under the TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5/blob/main/LICENSE
-#
-# Unless and only to the extent required by applicable law, the Tencent Hunyuan works and any
-# output and results therefrom are provided "AS IS" without any express or implied warranties of
-# any kind including any warranties of title, merchantability, noninfringement, course of dealing,
-# usage of trade, or fitness for a particular purpose. You are solely responsible for determining the
-# appropriateness of using, reproducing, modifying, performing, displaying or distributing any of
-# the Tencent Hunyuan works or outputs and assume any and all risks associated with your or a
-# third party's use or distribution of any of the Tencent Hunyuan works or outputs and your exercise
-# of rights and permissions under this agreement.
-# See the License for the specific language governing permissions and limitations under the License.
-
-import numpy as np
-from PIL import Image
-
-
-def resize_and_center_crop(image, target_width, target_height):
-    if target_height == image.shape[0] and target_width == image.shape[1]:
-        return image
-
-    pil_image = Image.fromarray(image)
-    original_width, original_height = pil_image.size
-    scale_factor = max(target_width / original_width, target_height / original_height)
-    resized_width = int(round(original_width * scale_factor))
-    resized_height = int(round(original_height * scale_factor))
-    resized_image = pil_image.resize((resized_width, resized_height), Image.LANCZOS)
-    left = (resized_width - target_width) / 2
-    top = (resized_height - target_height) / 2
-    right = (resized_width + target_width) / 2
-    bottom = (resized_height + target_height) / 2
-    cropped_image = resized_image.crop((left, top, right, bottom))
-    return np.array(cropped_image)
-
-
-def get_closest_ratio(height: float, width: float, ratios: list, buckets: list):
-    """
-    Get the closest ratio in the buckets.
-
-    Args:
-        height (float): video height
-        width (float): video width
-        ratios (list): video aspect ratio
-        buckets (list): buckets generated by `generate_crop_size_list`
-
-    Returns:
-        the closest size in the buckets and the corresponding ratio
-    """
-    aspect_ratio = float(height) / float(width)
-
-    ratios_array = np.array(ratios)
-    closest_ratio_id = np.abs(ratios_array - aspect_ratio).argmin()
-    closest_size = buckets[closest_ratio_id]
-    closest_ratio = ratios_array[closest_ratio_id]
-
-    return closest_size, closest_ratio
-
-
-def generate_crop_size_list(base_size=256, patch_size=16, max_ratio=4.0):
-    num_patches = round((base_size / patch_size) ** 2)
-    assert max_ratio >= 1.0
-    crop_size_list = []
-    wp, hp = num_patches, 1
-    while wp > 0:
-        if max(wp, hp) / min(wp, hp) <= max_ratio:
-            crop_size_list.append((wp * patch_size, hp * patch_size))
-        if (hp + 1) * wp <= num_patches:
-            hp += 1
-        else:
-            wp -= 1
-    return crop_size_list

fastvideo/models/dits/hyworld/hyworld.py

@@ -1,569 +0,0 @@
-# Licensed under the TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5/blob/main/LICENSE
-#
-# Unless and only to the extent required by applicable law, the Tencent Hunyuan works and any
-# output and results therefrom are provided "AS IS" without any express or implied warranties of
-# any kind including any warranties of title, merchantability, noninfringement, course of dealing,
-# usage of trade, or fitness for a particular purpose. You are solely responsible for determining the
-# appropriateness of using, reproducing, modifying, performing, displaying or distributing any of
-# the Tencent Hunyuan works or outputs and assume any and all risks associated with your or a
-# third party's use or distribution of any of the Tencent Hunyuan works or outputs and your exercise
-# of rights and permissions under this agreement.
-# See the License for the specific language governing permissions and limitations under the License.
-
-from typing import Any, Optional
-
-import torch
-import torch.nn as nn
-from einops import rearrange, repeat
-
-from fastvideo.distributed.communication_op import (
-    sequence_model_parallel_all_gather_with_unpad,
-    sequence_model_parallel_shard)
-from fastvideo.configs.models.dits import HYWorldConfig
-from fastvideo.layers.linear import ReplicatedLinear
-from fastvideo.layers.rotary_embedding import get_rotary_pos_embed
-from fastvideo.layers.visual_embedding import TimestepEmbedder, unpatchify
-from fastvideo.models.dits.hunyuanvideo15 import (
-    MMDoubleStreamBlock,
-    HunyuanVideo15Transformer3DModel,
-)
-from fastvideo.platforms import AttentionBackendEnum
-from fastvideo.logger import init_logger
-from fastvideo.forward_context import set_forward_context
-from fastvideo.distributed.parallel_state import get_sp_world_size
-from fastvideo.distributed.utils import create_attention_mask_for_padding
-
-from .camera_rope import prope_qkv
-
-logger = init_logger(__name__)
-
-
-class HYWorldDoubleStreamBlock(MMDoubleStreamBlock):
-    """
-    Extended MMDoubleStreamBlock with ProPE (Projective Positional Encoding) support
-    for camera-aware attention in HY-World/WorldPlay models.
-    """
-
-    def __init__(
-        self,
-        hidden_size: int,
-        num_attention_heads: int,
-        mlp_ratio: float,
-        dtype: torch.dtype | None = None,
-        supported_attention_backends: tuple[AttentionBackendEnum, ...] | None = None,
-        prefix: str = "",
-    ):
-        super().__init__(
-            hidden_size=hidden_size,
-            num_attention_heads=num_attention_heads,
-            mlp_ratio=mlp_ratio,
-            dtype=dtype,
-            supported_attention_backends=supported_attention_backends,
-            prefix=prefix,
-        )
-        self.hidden_size = hidden_size
-
-        # Add ProPE projection layer for camera-aware attention
-        self.img_attn_prope_proj = ReplicatedLinear(
-            hidden_size,
-            hidden_size,
-            bias=True,
-            params_dtype=dtype,
-            prefix=f"{prefix}.img_attn_prope_proj"
-        )
-        # Zero-initialize ProPE projection (starts as identity)
-        nn.init.zeros_(self.img_attn_prope_proj.weight)
-        if self.img_attn_prope_proj.bias is not None:
-            nn.init.zeros_(self.img_attn_prope_proj.bias)
-
-    def forward(
-        self,
-        img: torch.Tensor,
-        txt: torch.Tensor,
-        encoder_attention_mask: torch.Tensor,
-        vec: torch.Tensor,
-        vec_txt: torch.Tensor,
-        freqs_cis: tuple,
-        seq_attention_mask: torch.Tensor,
-        viewmats: torch.Tensor,
-        Ks: torch.Tensor,
-    ) -> tuple[torch.Tensor, torch.Tensor]:
-        """
-        Forward pass with ProPE camera conditioning.
-
-        Args:
-            img: Image/video tokens
-            txt: Text tokens
-            encoder_attention_mask: Text attention mask
-            vec: Modulation vector
-            freqs_cis: Rotary embedding frequencies
-            seq_attention_mask: Sequence attention mask
-            viewmats: Camera view matrices for ProPE [B, T, 4, 4]
-            Ks: Camera intrinsics for ProPE [B, T, 3, 3]
-
-        Returns:
-            Tuple of (img, txt) output tokens
-        """
-        # Process modulation vectors (inherited from parent)
-        img_mod_outputs = self.img_mod(vec)
-        (
-            img_attn_shift,
-            img_attn_scale,
-            img_attn_gate,
-            img_mlp_shift,
-            img_mlp_scale,
-            img_mlp_gate,
-        ) = torch.chunk(img_mod_outputs, 6, dim=-1)
-
-        txt_mod_outputs = self.txt_mod(vec_txt)
-        (
-            txt_attn_shift,
-            txt_attn_scale,
-            txt_attn_gate,
-            txt_mlp_shift,
-            txt_mlp_scale,
-            txt_mlp_gate,
-        ) = torch.chunk(txt_mod_outputs, 6, dim=-1)
-
-        # Prepare image for attention using fused operation
-        img_attn_input = self.img_attn_norm(img, img_attn_shift, img_attn_scale, convert_modulation_dtype=True)
-        # Get QKV for image
-        img_qkv, _ = self.img_attn_qkv(img_attn_input)
-        batch_size, image_seq_len = img_qkv.shape[0], img_qkv.shape[1]
-
-        # Split QKV
-        img_qkv = img_qkv.view(batch_size, image_seq_len, 3,
-                               self.num_attention_heads, -1)
-        img_q, img_k, img_v = img_qkv[:, :, 0], img_qkv[:, :, 1], img_qkv[:, :,
-                                                                          2]
-
-        # Apply QK-Norm if needed
-        img_q = self.img_attn_q_norm(img_q).to(img_v)
-        img_k = self.img_attn_k_norm(img_k).to(img_v)
-
-        # Prepare text for attention using fused operation
-        txt_attn_input = self.txt_attn_norm(txt, txt_attn_shift, txt_attn_scale, convert_modulation_dtype=True)
-
-        # Get QKV for text
-        txt_qkv, _ = self.txt_attn_qkv(txt_attn_input)
-        batch_size, text_seq_len = txt_qkv.shape[0], txt_qkv.shape[1]
-
-        # Split QKV
-        txt_qkv = txt_qkv.view(batch_size, text_seq_len, 3,
-                               self.num_attention_heads, -1)
-        txt_q, txt_k, txt_v = txt_qkv[:, :, 0], txt_qkv[:, :, 1], txt_qkv[:, :,
-                                                                          2]
-        # Apply QK-Norm if needed
-        txt_q = self.txt_attn_q_norm(txt_q).to(txt_q.dtype)
-        txt_k = self.txt_attn_k_norm(txt_k).to(txt_k.dtype)
-
-        # begin hyworld: add camera pose through prope
-        img_q_prope, img_k_prope, img_v_prope, apply_fn_o = prope_qkv(
-            img_q.permute(0, 2, 1, 3),
-            img_k.permute(0, 2, 1, 3),
-            img_v.permute(0, 2, 1, 3),
-            viewmats=viewmats,
-            Ks=Ks,
-        )  # [batch, num_heads, seqlen, head_dim]
-        img_q_prope = img_q_prope.permute(0, 2, 1, 3)  # [batch, seqlen, num_heads, head_dim]
-        img_k_prope = img_k_prope.permute(0, 2, 1, 3)  # [batch, seqlen, num_heads, head_dim]
-        img_v_prope = img_v_prope.permute(0, 2, 1, 3)  # [batch, seqlen, num_heads, head_dim]
-        # end hyworld
-
-        from fastvideo.attention.backends.flash_attn import FlashAttnMetadataBuilder
-        attn_metadata = FlashAttnMetadataBuilder().build(
-            current_timestep=0,
-            attn_mask=encoder_attention_mask,
-        )
-        # Run distributed attention
-        with set_forward_context(current_timestep=0, attn_metadata=attn_metadata):
-            img_attn, txt_attn = self.attn(img_q, img_k, img_v, txt_q, txt_k, txt_v, freqs_cis=freqs_cis, attention_mask=seq_attention_mask)
-        
-        # begin hyworld
-        # attention with prope
-        from fastvideo.attention.backends.flash_attn import FlashAttnMetadataBuilder
-        attn_metadata_prope = FlashAttnMetadataBuilder().build(
-            current_timestep=0,
-            attn_mask=encoder_attention_mask,
-        )       
-        # NOTE: Do NOT pass freqs_cis to prope attention - HY-WorldPlay does not apply RoPE to prope
-        with set_forward_context(current_timestep=0, attn_metadata=attn_metadata_prope):
-            img_attn_prope, _ = self.attn(
-                img_q_prope, img_k_prope, img_v_prope, txt_q, txt_k, txt_v, 
-                freqs_cis=None, attention_mask=seq_attention_mask  # No RoPE for prope attention
-            )
-            img_attn_prope = img_attn_prope.reshape(batch_size, image_seq_len, -1)
-            img_attn_prope = rearrange(
-                img_attn_prope, "B L (H D) -> B H L D", H=self.num_attention_heads
-            )
-            img_attn_prope = apply_fn_o(img_attn_prope) # [batch, num_heads, seqlen, head_dim]
-            img_attn_prope = rearrange(img_attn_prope, "B H L D -> B L (H D)")
-
-        # add prope to img_attn
-        img_attn_out, _ = self.img_attn_proj(img_attn.view(batch_size, image_seq_len, -1))
-        img_attn_prope_out, _ = self.img_attn_prope_proj(img_attn_prope)
-        img_attn_out = img_attn_out + img_attn_prope_out
-        # end hyworld
-
-        # Use fused operation for residual connection, normalization, and modulation
-        img_mlp_input, img_residual = self.img_attn_residual_mlp_norm(
-            img, img_attn_out, img_attn_gate, img_mlp_shift, img_mlp_scale, convert_modulation_dtype=True)
-
-        # Process image MLP
-        img_mlp_out = self.img_mlp(img_mlp_input)
-        img = self.img_mlp_residual(img_residual, img_mlp_out, img_mlp_gate)
-
-        # Process text attention output
-        txt_attn_out, _ = self.txt_attn_proj(
-            txt_attn.reshape(batch_size, text_seq_len, -1))
-
-        # Use fused operation for residual connection, normalization, and modulation
-        txt_mlp_input, txt_residual = self.txt_attn_residual_mlp_norm(
-            txt, txt_attn_out, txt_attn_gate, txt_mlp_shift, txt_mlp_scale, convert_modulation_dtype=True)
-
-        # Process text MLP
-        txt_mlp_out = self.txt_mlp(txt_mlp_input)
-        txt = self.txt_mlp_residual(txt_residual, txt_mlp_out, txt_mlp_gate)
-
-        return img, txt
-
-
-class HYWorldFinalLayer(nn.Module):
-    """
-    Final layer for HYWorld that uses modulate() to handle per-token conditioning.
-    This matches HY-WorldPlay's FinalLayer behavior.
-    """
-
-    def __init__(self,
-                 hidden_size,
-                 patch_size,
-                 out_channels,
-                 dtype=None,
-                 prefix: str = "") -> None:
-        super().__init__()
-        from fastvideo.layers.linear import ReplicatedLinear
-        from fastvideo.layers.visual_embedding import ModulateProjection
-        from fastvideo.layers.layernorm import LayerNormScaleShift
-
-        self.norm_final = LayerNormScaleShift(
-            hidden_size,
-            norm_type="layer",
-            eps=1e-6,
-            elementwise_affine=False,
-            dtype=dtype,
-            prefix=f"{prefix}.norm_final")
-
-        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")
-
-        # Modulation projection to get shift/scale
-        self.adaLN_modulation = ModulateProjection(
-            hidden_size,
-            factor=2,
-            act_layer="silu",
-            dtype=dtype,
-            prefix=f"{prefix}.adaLN_modulation")
-
-    def forward(self, x, c):
-        shift, scale = self.adaLN_modulation(c).chunk(2, dim=-1)
-        x = self.norm_final(x, shift, scale, convert_modulation_dtype=True)
-        x, _ = self.linear(x)
-        return x
-
-
-class HYWorldTransformer3DModel(HunyuanVideo15Transformer3DModel):
-    r"""
-    HY-World Transformer extending HunyuanVideo15 with:
-    - ProPE (Projective Positional Encoding) for camera-aware attention
-    - Action conditioning for interactive video generation
-    """
-    
-    # Class attributes for weight loading - use HYWorld-specific mapping
-    _fsdp_shard_conditions = HYWorldConfig().arch_config._fsdp_shard_conditions
-    _compile_conditions = HYWorldConfig().arch_config._compile_conditions
-    param_names_mapping = HYWorldConfig().arch_config.param_names_mapping
-    reverse_param_names_mapping = HYWorldConfig().arch_config.reverse_param_names_mapping
-
-    def __init__(
-        self,
-        config: HYWorldConfig,
-        hf_config: dict[str, Any],
-    ) -> None:
-        super().__init__(config=config, hf_config=hf_config)
-
-        # Replace double_blocks with HY-World version that supports ProPE
-        self.double_blocks = nn.ModuleList([
-            HYWorldDoubleStreamBlock(
-                hidden_size=self.hidden_size,
-                num_attention_heads=self.num_attention_heads,
-                mlp_ratio=config.arch_config.mlp_ratio,
-                dtype=None,
-                supported_attention_backends=self._supported_attention_backends,
-                prefix=f"{config.prefix}.double_blocks.{i}"
-            )
-            for i in range(config.arch_config.num_layers)
-        ])
-
-        # Add action conditioning module
-        self.action_in = TimestepEmbedder(
-            self.hidden_size,
-            act_layer="silu",
-            dtype=None,
-            prefix=f"{config.prefix}.action_in"
-        )
-        # Zero-initialize action embedding (starts with no effect)
-        nn.init.zeros_(self.action_in.mlp.fc_out.weight)
-        if self.action_in.mlp.fc_out.bias is not None:
-            nn.init.zeros_(self.action_in.mlp.fc_out.bias)
-
-        # Override final_layer with HYWorld version that uses per-token modulate()
-        self.final_layer = HYWorldFinalLayer(
-            hidden_size=self.hidden_size,
-            patch_size=self.patch_size,
-            out_channels=self.out_channels,
-            dtype=None,
-            prefix=f"{config.prefix}.final_layer"
-        )
-
-        self.gradient_checkpointing = False
-
-    def forward(
-        self,
-        hidden_states: torch.Tensor,
-        encoder_hidden_states: list[torch.Tensor],
-        timestep: torch.LongTensor,
-        encoder_hidden_states_image: list[torch.Tensor],
-        encoder_attention_mask: list[torch.Tensor],
-        action: torch.Tensor,
-        viewmats: torch.Tensor,
-        Ks: torch.Tensor,
-        timestep_txt: torch.LongTensor,
-        guidance: Optional[torch.Tensor] = None,
-        timestep_r: Optional[torch.LongTensor] = None,
-        attention_kwargs: Optional[dict[str, Any]] = None,
-    ):
-        """
-        Forward pass with action and camera conditioning.
-
-        Args:
-            action: Action tensor for action conditioning [B, T] or [B*T]
-            viewmats: Camera view matrices [B, T, 4, 4]
-            Ks: Camera intrinsics [B, T, 3, 3]
-            ... (other args same as parent)
-        """
-        encoder_hidden_states_image = encoder_hidden_states_image[0]
-        encoder_hidden_states, encoder_hidden_states_2 = encoder_hidden_states
-        encoder_attention_mask, encoder_attention_mask_2 = encoder_attention_mask
-
-        batch_size, num_channels, num_frames, height, width = hidden_states.shape
-        p_t, p_h, p_w = self.config.patch_size_t, self.config.patch_size, self.config.patch_size
-        post_patch_num_frames = num_frames // p_t
-        post_patch_height = height // p_h
-        post_patch_width = width // p_w
-
-        # 1. RoPE
-        # Get rotary embeddings
-        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,
-            self.config.rope_axes_dim,
-            self.config.rope_theta
-        )
-        freqs_cos = freqs_cos.to(hidden_states.device)
-        freqs_sin = freqs_sin.to(hidden_states.device)
-        # NOTE: freqs_cis does NOT need sharding because FastVideo's DistributedAttention
-        # uses all-to-all to gather the full sequence before applying RoPE
-        freqs_cis = (freqs_cos, freqs_sin) if freqs_cos is not None else None
-
-        # 2. Conditional embeddings
-        temb = self.time_in(timestep, timestep_r=timestep_r)
-        temb_txt = self.time_in(timestep_txt, timestep_r=timestep_r)
-
-        # Add action conditioning if provided
-        # temb shape: [B*T, C] where T = num_frames
-        temb = temb + self.action_in(action.reshape(-1))
-        
-        # Broadcast timestep embedding for transformer blocks (one per spatial token)
-        # [B*T, C] -> [B, T*H*W, C] -> [B*T*H*W, C]
-        temb = repeat(temb, "(B T) C -> B (T H W) C", B=batch_size, H=post_patch_height, W=post_patch_width)
-
-        hidden_states = self.img_in(hidden_states)
-        hidden_states, original_seq_len = sequence_model_parallel_shard(hidden_states, dim=1)
-
-        current_seq_len = hidden_states.shape[1]
-        sp_world_size = get_sp_world_size()
-        padded_seq_len = current_seq_len * sp_world_size
-
-        if padded_seq_len > original_seq_len:
-            seq_attention_mask = create_attention_mask_for_padding(
-                seq_len=original_seq_len,
-                padded_seq_len=padded_seq_len,
-                batch_size=batch_size,
-                device=hidden_states.device,
-            )
-        else:
-            seq_attention_mask = None
-
-        viewmats_seq = repeat(
-            viewmats, "B T M N->B (T H W) M N",
-            H=post_patch_height,
-            W=post_patch_width
-        )
-        Ks_seq = repeat(
-            Ks, "B T M N->B (T H W) M N",
-            H=post_patch_height,
-            W=post_patch_width
-        )
-        
-        # Shard viewmats, Ks, and temb for sequence parallelism (shard along sequence dim=1)
-        # Note that temb in HY1.5 does not need sharding because it is per-sample modulation
-        # In HYWorld, temb is per-token modulation.
-        if sp_world_size > 1:
-            viewmats_seq, _ = sequence_model_parallel_shard(viewmats_seq, dim=1)
-            Ks_seq, _ = sequence_model_parallel_shard(Ks_seq, dim=1)
-            temb, _ = sequence_model_parallel_shard(temb, dim=1)
-        
-        # Rearrange temb after sharding to match expected shape
-        temb = rearrange(temb, "B S C -> (B S) C")
-
-        # qwen text embedding
-        encoder_hidden_states = self.txt_in(encoder_hidden_states, timestep_txt, encoder_attention_mask)
-
-        encoder_hidden_states_cond_emb = self.cond_type_embed(
-            torch.zeros_like(encoder_hidden_states[:, :, 0], dtype=torch.long)
-        )
-        encoder_hidden_states = encoder_hidden_states + encoder_hidden_states_cond_emb
-
-        # byt5 text embedding
-        encoder_hidden_states_2 = self.txt_in_2(encoder_hidden_states_2)
-
-        encoder_hidden_states_2_cond_emb = self.cond_type_embed(
-            torch.ones_like(encoder_hidden_states_2[:, :, 0], dtype=torch.long)
-        )
-        encoder_hidden_states_2 = encoder_hidden_states_2 + encoder_hidden_states_2_cond_emb
-
-        # image embed
-        encoder_hidden_states_3 = self.image_embedder(encoder_hidden_states_image)
-        is_t2v = torch.all(encoder_hidden_states_image == 0)
-        if is_t2v:
-            encoder_hidden_states_3 = encoder_hidden_states_3 * 0.0
-            encoder_attention_mask_3 = torch.zeros(
-                (batch_size, encoder_hidden_states_3.shape[1]),
-                dtype=encoder_attention_mask.dtype,
-                device=encoder_attention_mask.device,
-            )
-        else:
-            encoder_attention_mask_3 = torch.ones(
-                (batch_size, encoder_hidden_states_3.shape[1]),
-                dtype=encoder_attention_mask.dtype,
-                device=encoder_attention_mask.device,
-            )
-        encoder_hidden_states_3_cond_emb = self.cond_type_embed(
-            2
-            * torch.ones_like(
-                encoder_hidden_states_3[:, :, 0],
-                dtype=torch.long,
-            )
-        )
-        encoder_hidden_states_3 = encoder_hidden_states_3 + encoder_hidden_states_3_cond_emb
-
-        # reorder and combine text tokens: combine valid tokens first, then padding
-        encoder_attention_mask = encoder_attention_mask.bool()
-        encoder_attention_mask_2 = encoder_attention_mask_2.bool()
-        encoder_attention_mask_3 = encoder_attention_mask_3.bool()
-        new_encoder_hidden_states = []
-        new_encoder_attention_mask = []
-
-        for text, text_mask, text_2, text_mask_2, image, image_mask in zip(
-            encoder_hidden_states,
-            encoder_attention_mask,
-            encoder_hidden_states_2,
-            encoder_attention_mask_2,
-            encoder_hidden_states_3,
-            encoder_attention_mask_3,
-        ):
-            # Concatenate: [valid_image, valid_byt5, valid_mllm, invalid_image, invalid_byt5, invalid_mllm]
-            new_encoder_hidden_states.append(
-                torch.cat(
-                    [
-                        image[image_mask],  # valid image
-                        text_2[text_mask_2],  # valid byt5
-                        text[text_mask],  # valid mllm
-                        image[~image_mask],  # invalid image (zeroed)
-                        torch.zeros_like(text_2[~text_mask_2]),  # invalid byt5 (zeroed)
-                        torch.zeros_like(text[~text_mask]),  # invalid mllm (zeroed)
-                    ],
-                    dim=0,
-                )
-            )
-            # Apply same reordering to attention masks
-            new_encoder_attention_mask.append(
-                torch.cat(
-                    [
-                        image_mask[image_mask],
-                        text_mask_2[text_mask_2],
-                        text_mask[text_mask],
-                        image_mask[~image_mask],
-                        text_mask_2[~text_mask_2],
-                        text_mask[~text_mask],
-                    ],
-                    dim=0,
-                )
-            )
-
-        encoder_hidden_states = torch.stack(new_encoder_hidden_states)
-        encoder_attention_mask = torch.stack(new_encoder_attention_mask)
-
-        # 4. Transformer blocks
-        if torch.is_grad_enabled() and self.gradient_checkpointing:
-            for block in self.double_blocks:
-                hidden_states, encoder_hidden_states = self._gradient_checkpointing_func(
-                    block,
-                    hidden_states,
-                    encoder_hidden_states,
-                    encoder_attention_mask,
-                    temb,
-                    temb_txt,
-                    freqs_cis,
-                    seq_attention_mask,
-                    viewmats_seq, # hyworld
-                    Ks_seq, # hyworld
-                )
-        else:
-            for block in self.double_blocks:
-                hidden_states, encoder_hidden_states = block(
-                    hidden_states,
-                    encoder_hidden_states,
-                    encoder_attention_mask,
-                    temb,
-                    temb_txt,
-                    freqs_cis,
-                    seq_attention_mask,
-                    viewmats=viewmats_seq, # hyworld
-                    Ks=Ks_seq, # hyworld
-                )
-
-
-        # Final layer processing (per-token conditioning via HYWorldFinalLayer)
-        # Apply final_layer on sharded data first, then gather
-        hidden_states = self.final_layer(hidden_states, temb)
-        
-        # Gather the output from all ranks
-        if get_sp_world_size() > 1:
-            hidden_states = hidden_states.contiguous()
-        hidden_states = sequence_model_parallel_all_gather_with_unpad(hidden_states, original_seq_len, dim=1)
-        
-        # Unpatchify to get original shape
-        hidden_states = unpatchify(hidden_states, post_patch_num_frames, post_patch_height, post_patch_width, self.patch_size, self.out_channels)
-
-        return hidden_states

fastvideo/models/dits/hyworld/pose.py

@@ -1,413 +0,0 @@
-# Some functions from HY-WorldPlay/hyvideo/generate.py
-
-"""
-Pose processing utilities for HYWorld video generation.
-
-This module provides functions to convert camera poses to model input tensors,
-including viewmats, intrinsics, and action labels.
-
-Adapted from HY-WorldPlay: https://github.com/Tencent-Hunyuan/HY-WorldPlay
-"""
-
-import json
-import numpy as np
-import torch
-from scipy.spatial.transform import Rotation as R
-from typing import Union, Optional
-
-from .trajectory import generate_camera_trajectory_local
-
-
-# Mapping from one-hot action encoding to single label
-mapping = {
-    (0, 0, 0, 0): 0,
-    (1, 0, 0, 0): 1,
-    (0, 1, 0, 0): 2,
-    (0, 0, 1, 0): 3,
-    (0, 0, 0, 1): 4,
-    (1, 0, 1, 0): 5,
-    (1, 0, 0, 1): 6,
-    (0, 1, 1, 0): 7,
-    (0, 1, 0, 1): 8,
-}
-
-# Default camera intrinsic matrix (for 1920x1080 resolution)
-DEFAULT_INTRINSIC = [
-    [969.6969696969696, 0.0, 960.0],
-    [0.0, 969.6969696969696, 540.0],
-    [0.0, 0.0, 1.0],
-]
-
-# Default movement speeds
-DEFAULT_FORWARD_SPEED = 0.08  # units per frame
-DEFAULT_YAW_SPEED = np.deg2rad(3)  # radians per frame
-DEFAULT_PITCH_SPEED = np.deg2rad(3)  # radians per frame
-
-
-def one_hot_to_one_dimension(one_hot: torch.Tensor) -> torch.Tensor:
-    """Convert one-hot action encoding to single dimension labels."""
-    return torch.tensor([mapping[tuple(row.tolist())] for row in one_hot])
-
-
-def parse_pose_string(
-    pose_string: str,
-    forward_speed: float = DEFAULT_FORWARD_SPEED,
-    yaw_speed: float = DEFAULT_YAW_SPEED,
-    pitch_speed: float = DEFAULT_PITCH_SPEED,
-) -> list[dict]:
-    """
-    Parse pose string to motions list.
-    
-    Format: "w-3, right-0.5, d-4"
-    - w: forward movement
-    - s: backward movement
-    - a: left movement
-    - d: right movement
-    - up: pitch up rotation
-    - down: pitch down rotation
-    - left: yaw left rotation
-    - right: yaw right rotation
-    - number after dash: duration in frames/latents
-    
-    Args:
-        pose_string: Comma-separated pose commands
-        forward_speed: Movement amount per frame
-        yaw_speed: Yaw rotation amount per frame (radians)
-        pitch_speed: Pitch rotation amount per frame (radians)
-        
-    Returns:
-        List of motion dictionaries for generate_camera_trajectory_local
-    """
-    motions = []
-    commands = [cmd.strip() for cmd in pose_string.split(",")]
-
-    for cmd in commands:
-        if not cmd:
-            continue
-
-        parts = cmd.split("-")
-        if len(parts) != 2:
-            raise ValueError(
-                f"Invalid pose command: {cmd}. Expected format: 'action-duration'"
-            )
-
-        action = parts[0].strip()
-        try:
-            duration = float(parts[1].strip())
-        except ValueError:
-            raise ValueError(f"Invalid duration in command: {cmd}")
-
-        num_frames = int(duration)
-
-        # Parse action and create motion dicts
-        if action == "w":
-            # Forward
-            for _ in range(num_frames):
-                motions.append({"forward": forward_speed})
-        elif action == "s":
-            # Backward
-            for _ in range(num_frames):
-                motions.append({"forward": -forward_speed})
-        elif action == "a":
-            # Left
-            for _ in range(num_frames):
-                motions.append({"right": -forward_speed})
-        elif action == "d":
-            # Right
-            for _ in range(num_frames):
-                motions.append({"right": forward_speed})
-        elif action == "up":
-            # Pitch up
-            for _ in range(num_frames):
-                motions.append({"pitch": pitch_speed})
-        elif action == "down":
-            # Pitch down
-            for _ in range(num_frames):
-                motions.append({"pitch": -pitch_speed})
-        elif action == "left":
-            # Yaw left
-            for _ in range(num_frames):
-                motions.append({"yaw": -yaw_speed})
-        elif action == "right":
-            # Yaw right
-            for _ in range(num_frames):
-                motions.append({"yaw": yaw_speed})
-        else:
-            raise ValueError(
-                f"Unknown action: {action}. "
-                f"Supported actions: w, s, a, d, up, down, left, right"
-            )
-
-    return motions
-
-def pose_string_to_json(
-    pose_string: str,
-    intrinsic: Optional[list[list[float]]] = None,
-) -> dict:
-    """
-    Convert pose string to pose JSON format.
-    
-    Args:
-        pose_string: Comma-separated pose commands
-        intrinsic: Camera intrinsic matrix (default: DEFAULT_INTRINSIC from trajectory)
-        
-    Returns:
-        Dict with frame indices as keys, containing extrinsic and K (intrinsic) matrices
-    """
-    if intrinsic is None:
-        intrinsic = DEFAULT_INTRINSIC
-    
-    motions = parse_pose_string(pose_string)
-    poses = generate_camera_trajectory_local(motions)
-
-    pose_json = {}
-    for i, p in enumerate(poses):
-        pose_json[str(i)] = {"extrinsic": p.tolist(), "K": intrinsic}
-
-    return pose_json
-
-def pose_to_input(
-    pose_data: Union[str, dict],
-    latent_num: int,
-    tps: bool = False,
-) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
-    """
-    Convert pose data to model input tensors.
-    
-    Args:
-        pose_data: One of:
-            - str ending with '.json': path to JSON file
-            - str: pose string (e.g., "w-3, right-0.5, d-4")
-            - dict: pose JSON data
-        latent_num: Number of latents (frames in latent space)
-        tps: Third-person mode flag
-        
-    Returns:
-        Tuple of (viewmats, intrinsics, action_labels):
-            - viewmats: World-to-camera matrices [T, 4, 4]
-            - intrinsics: Normalized camera intrinsics [T, 3, 3]
-            - action_labels: Action labels for each frame [T]
-    """
-    # Handle different input types
-    if isinstance(pose_data, str):
-        if pose_data.endswith(".json"):
-            # Load from JSON file
-            with open(pose_data, "r") as f:
-                pose_json = json.load(f)
-        else:
-            # Parse pose string
-            pose_json = pose_string_to_json(pose_data)
-    elif isinstance(pose_data, dict):
-        pose_json = pose_data
-    else:
-        raise ValueError(
-            f"Invalid pose_data type: {type(pose_data)}. Expected str or dict."
-        )
-
-    pose_keys = list(pose_json.keys())
-    latent_num_from_pose = len(pose_keys)
-    assert latent_num_from_pose == latent_num, (
-        f"pose corresponds to {latent_num_from_pose * 4 - 3} frames, num_frames "
-        f"must be set to {latent_num_from_pose * 4 - 3} to ensure alignment."
-    )
-
-    intrinsic_list = []
-    w2c_list = []
-    for i in range(latent_num):
-        t_key = pose_keys[i]
-        c2w = np.array(pose_json[t_key]["extrinsic"])
-        w2c = np.linalg.inv(c2w)
-        w2c_list.append(w2c)
-        
-        # Normalize intrinsics
-        intrinsic = np.array(pose_json[t_key]["K"])
-        intrinsic[0, 0] /= intrinsic[0, 2] * 2
-        intrinsic[1, 1] /= intrinsic[1, 2] * 2
-        intrinsic[0, 2] = 0.5
-        intrinsic[1, 2] = 0.5
-        intrinsic_list.append(intrinsic)
-
-    w2c_list = np.array(w2c_list)
-    intrinsic_list = torch.tensor(np.array(intrinsic_list))
-
-    # Compute relative camera-to-world transforms
-    c2ws = np.linalg.inv(w2c_list)
-    C_inv = np.linalg.inv(c2ws[:-1])
-    relative_c2w = np.zeros_like(c2ws)
-    relative_c2w[0, ...] = c2ws[0, ...]
-    relative_c2w[1:, ...] = C_inv @ c2ws[1:, ...]
-    
-    # Initialize one-hot action encodings
-    trans_one_hot = np.zeros((relative_c2w.shape[0], 4), dtype=np.int32)
-    rotate_one_hot = np.zeros((relative_c2w.shape[0], 4), dtype=np.int32)
-
-    move_norm_valid = 0.0001
-    for i in range(1, relative_c2w.shape[0]):
-        move_dirs = relative_c2w[i, :3, 3]  # direction vector
-        move_norms = np.linalg.norm(move_dirs)
-        
-        if move_norms > move_norm_valid:  # threshold for movement
-            move_norm_dirs = move_dirs / move_norms
-            angles_rad = np.arccos(move_norm_dirs.clip(-1.0, 1.0))
-            trans_angles_deg = angles_rad * (180.0 / np.pi)  # convert to degrees
-        else:
-            trans_angles_deg = np.zeros(3)
-
-        R_rel = relative_c2w[i, :3, :3]
-        r = R.from_matrix(R_rel)
-        rot_angles_deg = r.as_euler("xyz", degrees=True)
-
-        # Determine movement and rotation actions
-        if move_norms > move_norm_valid:  # threshold for movement
-            if (not tps) or (
-                tps and abs(rot_angles_deg[1]) < 5e-2 and abs(rot_angles_deg[0]) < 5e-2
-            ):
-                if trans_angles_deg[2] < 60:
-                    trans_one_hot[i, 0] = 1  # forward
-                elif trans_angles_deg[2] > 120:
-                    trans_one_hot[i, 1] = 1  # backward
-
-                if trans_angles_deg[0] < 60:
-                    trans_one_hot[i, 2] = 1  # right
-                elif trans_angles_deg[0] > 120:
-                    trans_one_hot[i, 3] = 1  # left
-
-        if rot_angles_deg[1] > 5e-2:
-            rotate_one_hot[i, 0] = 1  # right
-        elif rot_angles_deg[1] < -5e-2:
-            rotate_one_hot[i, 1] = 1  # left
-
-        if rot_angles_deg[0] > 5e-2:
-            rotate_one_hot[i, 2] = 1  # up
-        elif rot_angles_deg[0] < -5e-2:
-            rotate_one_hot[i, 3] = 1  # down
-
-    trans_one_hot = torch.tensor(trans_one_hot)
-    rotate_one_hot = torch.tensor(rotate_one_hot)
-
-    # Convert one-hot to single-dimension labels
-    trans_one_label = one_hot_to_one_dimension(trans_one_hot)
-    rotate_one_label = one_hot_to_one_dimension(rotate_one_hot)
-    action_one_label = trans_one_label * 9 + rotate_one_label
-
-    return (
-        torch.as_tensor(w2c_list),
-        torch.as_tensor(intrinsic_list),
-        action_one_label,
-    )
-
-
-def camera_center_normalization(w2c: np.ndarray) -> np.ndarray:
-    """Normalize camera centers relative to the first camera."""
-    c2w = np.linalg.inv(w2c)
-    C0_inv = np.linalg.inv(c2w[0])
-    c2w_aligned = np.array([C0_inv @ C for C in c2w])
-    return np.linalg.inv(c2w_aligned)
-
-
-
-def parse_pose_string_to_actions(pose_string: str, fps: int = 24) -> list[dict]:
-    """
-    Parse pose string to frame-level action timeline.
-    
-    Format: pose string uses latent counts, where:
-    - 1 latent = 4 frames
-    - Special rule: first frame of entire video is extra (frame 0)
-    - Example: "w-4,d-4" means:
-      - w-4: forward for frames 0-16 (17 frames total: 1 extra + 4*4)
-      - d-4: right for frames 17-32 (16 frames total: 4*4)
-    
-    Args:
-        pose_string: Comma-separated pose commands (e.g., "w-4,d-4")
-        fps: Frames per second for video (default: 24)
-        
-    Returns:
-        List of dicts with action values for each frame
-    """
-    commands = [cmd.strip() for cmd in pose_string.split(",")]
-
-    frame_actions = []
-    is_first_command = True
-
-    for cmd in commands:
-        if not cmd:
-            continue
-
-        parts = cmd.split("-")
-        if len(parts) != 2:
-            raise ValueError(
-                f"Invalid pose command: {cmd}. Expected format: 'action-duration'"
-            )
-
-        action = parts[0].strip()
-        try:
-            num_latents = int(parts[1].strip())
-        except ValueError:
-            raise ValueError(f"Invalid duration in command: {cmd}")
-
-        # Convert latents to frames
-        # First command gets 1 extra frame (the special frame 0)
-        if is_first_command:
-            num_frames = 1 + num_latents * 4
-            is_first_command = False
-        else:
-            num_frames = num_latents * 4
-
-        # Map action to action values
-        action_values = {"forward": 0, "left": 0, "yaw": 0, "pitch": 0}
-
-        if action == "w":
-            action_values["forward"] = 1
-        elif action == "s":
-            action_values["forward"] = -1
-        elif action == "a":
-            action_values["left"] = 1
-        elif action == "d":
-            action_values["left"] = -1
-        elif action == "up":
-            action_values["pitch"] = 1
-        elif action == "down":
-            action_values["pitch"] = -1
-        elif action == "left":
-            action_values["yaw"] = -1
-        elif action == "right":
-            action_values["yaw"] = 1
-        else:
-            raise ValueError(f"Unknown action: {action}")
-
-        # Add frame-level actions
-        for _ in range(num_frames):
-            frame_actions.append(action_values.copy())
-
-    return frame_actions
-
-
-def compute_latent_num(num_frames: int) -> int:
-    """
-    Compute the number of latents from number of frames.
-    
-    Formula: num_frames = (latent_num - 1) * 4 + 1
-    So: latent_num = (num_frames - 1) // 4 + 1
-    
-    Args:
-        num_frames: Number of video frames
-        
-    Returns:
-        Number of latents
-    """
-    return (num_frames - 1) // 4 + 1
-
-
-def compute_num_frames(latent_num: int) -> int:
-    """
-    Compute the number of frames from number of latents.
-    
-    Formula: num_frames = (latent_num - 1) * 4 + 1
-    
-    Args:
-        latent_num: Number of latents
-        
-    Returns:
-        Number of video frames
-    """
-    return (latent_num - 1) * 4 + 1