tests

update
fix rebase
2025-09-10 01:57:42 +00:00 · 2025-09-10 01:52:21 +00:00 · 2025-09-09 23:30:19 +00:00 · 2025-09-09 23:29:37 +00:00 · 2025-09-09 23:29:37 +00:00 · 2025-09-09 23:29:37 +00:00
129 changed files with 573 additions and 14054 deletions
@@ -198,15 +198,4 @@ steps:
                env:  
                  - TEST_TYPE=inference_vmoba
                agents:
-                  queue: "default"
-            - path:
-                - "fastvideo/**"
-                - "pyproject.toml"
-                - "docker/Dockerfile.python3.12"
-              config:
-                command: "timeout 15m .buildkite/scripts/pr_test.sh"
-                label: "Unit Tests"
-                env:
-                  - TEST_TYPE=unit_test
-                agents:
-                  queue: "default"
+                  queue: "default"
@@ -118,10 +118,6 @@ case "$TEST_TYPE" in
        log "Running V-MoBA precision tests..."
        MODAL_COMMAND="$MODAL_ENV python3 -m modal run $MODAL_TEST_FILE::run_precision_tests_vmoba"
        ;;
-    "unit_test")
-        log "Running unit tests..."
-        MODAL_COMMAND="$MODAL_ENV python3 -m modal run $MODAL_TEST_FILE::run_unit_test"
-        ;;
    *)
        log "Error: Unknown test type: $TEST_TYPE"
        exit 1
@@ -62,8 +62,8 @@ on:
        required: false
        default: false
        type: boolean
-      run_unit_test:
-        description: "Run unit-test"
+      run_nightly_test:
+        description: "Run nightly-test"
        required: false
        default: false
        type: boolean
@@ -93,7 +93,6 @@ jobs:
      inference-test-STA: ${{ steps.filter.outputs.inference-test-STA }}
      precision-test-STA: ${{ steps.filter.outputs.precision-test-STA }}
      precision-test-VSA: ${{ steps.filter.outputs.precision-test-VSA }}
-      unit-test: ${{ steps.filter.outputs.unit-test }}
    steps:
      - uses: actions/checkout@v4
      - uses: dorny/paths-filter@v3
@@ -103,8 +102,6 @@ jobs:
            # Define reusable path patterns
            common-paths: &common-paths
              - 'pyproject.toml'
-              - 'docker/Dockerfile.python3.10'
-              - 'docker/Dockerfile.python3.11'
              - 'docker/Dockerfile.python3.12'
            sta-kernel-paths: &sta-kernel-paths
              - 'csrc/attn/sliding_tile_attn/**'
@@ -158,9 +155,6 @@ jobs:
            precision-test-VSA:
              - *common-paths
              - *vsa-kernel-paths
-            unit-test:
-              - 'fastvideo/**'
-              - *common-paths

  encoder-test:
    needs: change-filter
@@ -339,42 +333,23 @@ jobs:
      RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
      RUNPOD_PRIVATE_KEY: ${{ secrets.RUNPOD_PRIVATE_KEY }}

-  unit-test:
-    needs: change-filter
+  nightly-test:
    if: >-
-      (github.event_name != 'workflow_dispatch' && needs.change-filter.outputs.unit-test == 'true') || 
-      (github.event_name == 'workflow_dispatch' && github.event.inputs.run_unit_test == 'true')
+      (github.event_name == 'workflow_dispatch' && github.event.inputs.run_nightly_test == 'true')
    uses: ./.github/workflows/runpod-test.yml
    with:
-      job_id: "unit-test"
-      gpu_type: "NVIDIA L40S"
-      gpu_count: 1
+      job_id: "nightly-test"
+      gpu_type: "NVIDIA A40"
+      gpu_count: 4
      volume_size: 100
      disk_size: 100
      image: "ghcr.io/${{ github.repository }}/fastvideo-dev:py3.12-latest"
-      test_command: "uv pip install -e .[test] && pytest ./fastvideo/dataset/ -vs && pytest ./fastvideo/workflow/ -vs"
+      test_command: "wandb login $WANDB_API_KEY && uv pip install -e .[test] && pytest ./fastvideo/tests/nightly/test_e2e_overfit_single_sample.py -vs"
      timeout_minutes: 30
    secrets:
      RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
      RUNPOD_PRIVATE_KEY: ${{ secrets.RUNPOD_PRIVATE_KEY }}
-
-  # nightly-test:
-  #   if: >-
-  #     (github.event_name == 'workflow_dispatch' && github.event.inputs.run_nightly_test == 'true')
-  #   uses: ./.github/workflows/runpod-test.yml
-  #   with:
-  #     job_id: "nightly-test"
-  #     gpu_type: "NVIDIA A40"
-  #     gpu_count: 4
-  #     volume_size: 100
-  #     disk_size: 100
-  #     image: "ghcr.io/${{ github.repository }}/fastvideo-dev:py3.12-latest"
-  #     test_command: "wandb login $WANDB_API_KEY && uv pip install -e .[test] && pytest ./fastvideo/tests/nightly/test_e2e_overfit_single_sample.py -vs"
-  #     timeout_minutes: 30
-  #   secrets:
-  #     RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
-  #     RUNPOD_PRIVATE_KEY: ${{ secrets.RUNPOD_PRIVATE_KEY }}
-  #     WANDB_API_KEY: ${{ secrets.WANDB_API_KEY }}
+      WANDB_API_KEY: ${{ secrets.WANDB_API_KEY }}

  runpod-cleanup:
    # Add other jobs to this list as you create them
@@ -64,6 +64,3 @@ docs/source/distillation/examples/
 !docs/source/_static/images/**/*.png
 !comfyui/assets/**/*.png
 !comfyui/assets/**/*.gif
-
-dmd_t2v_output/
-preprocess_output_text/
@@ -20,7 +20,5 @@ setup(
        "License :: OSI Approved :: Apache Software License",
    ],
    python_requires='>=3.12',
-    install_requires=[
-        "flash-attn >= 2.7.1",
-    ]
+    install_requires=[]
 )
@@ -6,16 +6,8 @@ import time
 import os
 import torch
 from typing import Tuple
-try:
-    from flash_attn import flash_attn_varlen_func  # Use the new flash attention function
-    from flash_attn.flash_attn_interface import _flash_attn_varlen_forward, _flash_attn_varlen_backward
-except ImportError:
-    def _unsupported(*args, **kwargs):
-        raise ImportError("flash-attn is not installed. Please install it, e.g., `pip install flash-attn`.")
-    _flash_attn_varlen_forward = _unsupported
-    _flash_attn_varlen_backward = _unsupported
-    flash_attn_varlen_func = _unsupported
-    
+from flash_attn import flash_attn_varlen_func  # Use the new flash attention function
+from flash_attn.flash_attn_interface import _flash_attn_varlen_forward, _flash_attn_varlen_backward
 from functools import lru_cache
 from einops import rearrange

@@ -1,9 +0,0 @@
-# VidProm Dataset
-
-From [Self-Forcing](https://github.com/gdhe17/Self-Forcing) repository.
-
-## Download the dataset
-
-```bash
-./download_dataset.sh
-```
@@ -1,3 +0,0 @@
-#! /bin/bash
-
-huggingface-cli download gdhe17/Self-Forcing vidprom_filtered_extended.txt --local-dir prompts
@@ -1,3 +0,0 @@
-#!/bin/bash
-
-python scripts/huggingface/download_hf.py --repo_id "wlsaidhi/crush-smol-merged" --local_dir "data/crush-smol" --repo_type "dataset"
@@ -1,76 +0,0 @@
-{
-  "data": [
-    {
-      "caption": "A large metal cylinder is seen pressing down on a pile of Oreo cookies, flattening them as if they were under a hydraulic press.",
-      "image_path": null,
-      "video_path": "validation_dataset/yYcK4nANZz4-Scene-034.mp4",
-      "num_inference_steps": 40,
-      "height": 480,
-      "width": 832,
-      "num_frames": 77
-    },
-    {
-      "caption": "A large metal cylinder is seen compressing colorful clay into a compact shape, demonstrating the power of a hydraulic press.",
-      "image_path": null,
-      "video_path": "validation_dataset/yYcK4nANZz4-Scene-027.mp4",
-      "num_inference_steps": 40,
-      "height": 480,
-      "width": 832,
-      "num_frames": 77
-    },
-    {
-      "caption": "A large metal cylinder is seen pressing down on a pile of colorful candies, flattening them as if they were under a hydraulic press. The candies are crushed and broken into small pieces, creating a mess on the table.",
-      "image_path": null,
-      "video_path": "validation_dataset/yYcK4nANZz4-Scene-030.mp4",
-      "num_inference_steps": 40,
-      "height": 480,
-      "width": 832,
-      "num_frames": 77
-    },
-    {
-      "caption": "A watermelon wearing a helmet is crushed by a hydraulic press, causing it to flatten and burst open.",
-      "image_path": null,
-      "video_path": "validation_dataset/1gGQy4nxyUo-Scene-016.mp4",
-      "num_inference_steps": 40,
-      "height": 480,
-      "width": 832,
-      "num_frames": 77
-    },
-    {
-      "caption": "The video shows a green and orange object being flattened as if it were under a hydraulic press, with the press moving down and compressing the object.",
-      "image_path": null,
-      "video_path": "validation_dataset/1gGQy4nxyUo-Scene-056.mp4",
-      "num_inference_steps": 40,
-      "height": 480,
-      "width": 832,
-      "num_frames": 77
-    },
-    {
-      "caption": "The video shows a cylindrical object with a cityscape image being flattened as if it were under a hydraulic press. The object is placed on a metal platform, and a large, striped cylinder presses down on it, causing it to collapse and release a liquid inside. The background features a green wall with a yellow and red warning sign.",
-      "image_path": null,
-      "video_path": "validation_dataset/1gGQy4nxyUo-Scene-059.mp4",
-      "num_inference_steps": 40,
-      "height": 480,
-      "width": 832,
-      "num_frames": 77
-    },
-    {
-      "caption": "The video shows a close-up of an orange being flattened as if it were under a hydraulic press, with the press moving down and compressing the fruit until it is completely flattened.",
-      "image_path": null,
-      "video_path": "validation_dataset/EJqsC21GSBY-Scene-059.mp4",
-      "num_inference_steps": 40,
-      "height": 480,
-      "width": 832,
-      "num_frames": 77
-    },
-    {
-      "caption": "A colorful puzzle ball is being crushed by a large metal cylinder, which flattens the objects as if they were under a hydraulic press.",
-      "image_path": null,
-      "video_path": "validation_dataset/GBSfpTcKegk-Scene-003.mp4",
-      "num_inference_steps": 40,
-      "height": 480,
-      "width": 832,
-      "num_frames": 77
-    }
-  ]
-}
@@ -1,13 +0,0 @@
-{
-  "data": [
-    {
-      "caption": "A watermelon wearing a helmet is crushed by a hydraulic press, causing it to flatten and burst open.",
-      "image_path": null,
-      "video_path": "validation_dataset/1gGQy4nxyUo-Scene-016.mp4",
-      "num_inference_steps": 40,
-      "height": 480,
-      "width": 832,
-      "num_frames": 77
-    }
-  ]
-}
@@ -1,151 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=t2v
-#SBATCH --partition=main
-#SBATCH --nodes=1
-#SBATCH --ntasks=1
-#SBATCH --ntasks-per-node=1
-#SBATCH --gres=gpu:1
-#SBATCH --cpus-per-task=128
-#SBATCH --mem=1440G
-#SBATCH --output=dmd_t2v_output/t2v_%j.out
-#SBATCH --error=dmd_t2v_output/t2v_%j.err
-#SBATCH --exclusive
-
-# Basic Info
-export NCCL_P2P_DISABLE=1
-export TORCH_NCCL_ENABLE_MONITORING=0
-# different cache dir for different processes
-export TRITON_CACHE_DIR=/tmp/triton_cache_${SLURM_PROCID}
-export MASTER_PORT=29501
-export TOKENIZERS_PARALLELISM=false
-export WANDB_API_KEY="50632ebd88ffd970521cec9ab4a1a2d7e85bfc45"
-export WANDB_BASE_URL="https://api.wandb.ai"
-export WANDB_MODE=offline
-export FASTVIDEO_ATTENTION_BACKEND=FLASH_ATTN
-
-# Configs
-NUM_GPUS=4
-
-# Model paths for Self-Forcing DMD distillation:
-GENERATOR_MODEL_PATH="wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers"
-REAL_SCORE_MODEL_PATH="Wan-AI/Wan2.1-T2V-14B-Diffusers"  # Teacher model
-FAKE_SCORE_MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"  # Critic model
-
-DATA_DIR="data/mixkit-64_processed/Node_0_GPU_1_File_1/combined_parquet_dataset"
-VALIDATION_DATASET_FILE="data/mixkit-64_processed/validation.json"
-# export CUDA_VISIBLE_DEVICES=4,5
-# IP=[MASTER NODE IP]
-
-# Training arguments
-training_args=(
-  --tracker_project_name SFwan_t2v_distill_self_forcing_dmd  # Updated for self-forcing DMD
-  --output_dir "/mnt/sharefs/users/hao.zhang/SFwan_t2v_finetune"
-  --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 832
-  --num_frames 81  # Must be divisible by num_frame_per_block (81 % 3 = 0 ✓)
-  --enable_gradient_checkpointing_type "full"
-  --log_visualization
-  --simulate_generator_forward
-  --num_frame_per_block 3  # Frame generation block size for self-forcing
-  --enable_gradient_masking
-  --gradient_mask_last_n_frames 21
-)
-
-# Parallel arguments
-parallel_args=(
-  --num_gpus $NUM_GPUS # 64
-  --sp_size 1
-  --tp_size 1
-  --hsdp_replicate_dim 1 # 64
-  --hsdp_shard_dim $NUM_GPUS
-)
-
-# Model arguments
-model_args=(
-  --model_path $GENERATOR_MODEL_PATH  # TODO: check if you can remove this in this script
-  --pretrained_model_name_or_path $GENERATOR_MODEL_PATH
-  --generator_model_path $GENERATOR_MODEL_PATH
-  --real_score_model_path $REAL_SCORE_MODEL_PATH
-  --fake_score_model_path $FAKE_SCORE_MODEL_PATH
-)
-
-# Dataset arguments
-dataset_args=(
-  --data_path "$DATA_DIR"
-  --dataloader_num_workers 4
-)
-
-# Validation arguments
-validation_args=(
-  --log_validation
-  --validation_dataset_file "$VALIDATION_DATASET_FILE"
-  --validation_steps 100
-  --validation_sampling_steps "4"
-  --validation_guidance_scale "6.0" # not used for dmd inference
-)
-
-# Optimizer arguments
-optimizer_args=(
-  --learning_rate 1e-5
-  --mixed_precision "bf16"
-  --training_state_checkpointing_steps 500
-  --weight_only_checkpointing_steps 500
-  --weight_decay 0.01
-  --betas '0.0,0.999'
-  --max_grad_norm 1.0
-)
-
-# Miscellaneous arguments
-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 100
-  --init_weights_from_safetensors "/mnt/weka/home/hao.zhang/wl/Self-Forcing/diffusers_ode_init/model.safetensors"
-)
-
-# Self-forcing DMD arguments
-dmd_args=(
-  --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 3.0
-  --fake_score_learning_rate 8e-6
-  --fake_score_betas '0.0,0.999'
-  --warp_denoising_step
-)
-
-# Self-forcing specific arguments
-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)
-  --validate_cache_structure False  # Set to True for debugging KV cache issues
-)
-
-torchrun \
--nnodes 1 \
--master_port $MASTER_PORT \
--nproc_per_node $NUM_GPUS \
-    fastvideo/training/wan_self_forcing_distillation_pipeline.py \
-    "${parallel_args[@]}" \
-    "${model_args[@]}" \
-    "${dataset_args[@]}" \
-    "${training_args[@]}" \
-    "${optimizer_args[@]}" \
-    "${validation_args[@]}" \
-    "${miscellaneous_args[@]}" \
-    "${dmd_args[@]}" \
-    "${self_forcing_args[@]}"
@@ -1,3 +0,0 @@
-#!/bin/bash
-
-python scripts/huggingface/download_hf.py --repo_id "wlsaidhi/crush-smol-merged" --local_dir "data/crush-smol" --repo_type "dataset"
@@ -1,24 +0,0 @@
-#!/bin/bash
-
-GPU_NUM=1 # 2,4,8
-MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
-MODEL_TYPE="wan"
-DATA_MERGE_PATH="data/crush-smol/merge.txt"
-OUTPUT_DIR="data/crush-smol_processed_t2v/"
-
-torchrun --nproc_per_node=$GPU_NUM \
-    fastvideo/pipelines/preprocess/v1_preprocess.py \
-    --model_path $MODEL_PATH \
-    --data_merge_path $DATA_MERGE_PATH \
-    --preprocess_video_batch_size 8 \
-    --seed 42 \
-    --max_height 480 \
-    --max_width 832 \
-    --num_frames 81 \
-    --dataloader_num_workers 0 \
-    --output_dir=$OUTPUT_DIR \
-    --train_fps 16 \
-    --samples_per_file 8 \
-    --flush_frequency 8 \
-    --video_length_tolerance_range 5 \
-    --preprocess_task "t2v" 
@@ -1,6 +1,6 @@
 from fastvideo import VideoGenerator

-from fastvideo.configs.sample import SamplingParam
+# from fastvideo.configs.sample import SamplingParam

 OUTPUT_PATH = "video_samples"
 def main():
@@ -17,13 +17,12 @@ def main():
        vae_cpu_offload=False,
        text_encoder_cpu_offload=True,
        pin_cpu_memory=True, # set to false if low CPU RAM or hit obscure "CUDA error: Invalid argument"
-        ti2v_task=True,
        # image_encoder_cpu_offload=False,
    )

-    sampling_param = SamplingParam.from_pretrained("Wan-AI/Wan2.1-T2V-1.3B-Diffusers")
+    # sampling_param = SamplingParam.from_pretrained("Wan-AI/Wan2.1-T2V-1.3B-Diffusers")
    # sampling_param.num_frames = 45
-    sampling_param.image_path = "test.jpg"
+    # sampling_param.image_path = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/astronaut.jpg"
    # Generate videos with the same simple API, regardless of GPU count
    prompt = (
        "A curious raccoon peers through a vibrant field of yellow sunflowers, its eyes "
@@ -45,4 +44,4 @@ def main():


 if __name__ == "__main__":
-    main()
+    main()
@@ -19,15 +19,13 @@ def main():
    )

    sampling_param = SamplingParam.from_pretrained(model_name)
-    sampling_param.num_frames = 81

-    prompts = [
-        "A stylish woman walks down a Tokyo street filled with warm glowing neon and animated city signage. She wears a black leather jacket, a long red dress, and black boots, and carries a black purse. She wears sunglasses and red lipstick. She walks confidently and casually. The street is damp and reflective, creating a mirror effect of the colorful lights. Many pedestrians walk about.",
-        "A white and orange tabby cat is seen happily darting through a dense garden, as if chasing something. Its eyes are wide and happy as it jogs forward, scanning the branches, flowers, and leaves as it walks. The path is narrow as it makes its way between all the plants. the scene is captured from a ground-level angle, following the cat closely, giving a low and intimate perspective. The image is cinematic with warm tones and a grainy texture. The scattered daylight between the leaves and plants above creates a warm contrast, accentuating the cat’s orange fur. The shot is clear and sharp, with a shallow depth of field.",
-    ]
-
-    for prompt in prompts:
-        video = generator.generate_video(prompt, output_path=OUTPUT_PATH, save_video=True, sampling_param=sampling_param)
+    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, sampling_param=sampling_param)

 if __name__ == "__main__":
    main()
@@ -15,7 +15,6 @@ torchrun --nproc_per_node=$GPU_NUM \
    --max_height 480 \
    --max_width 832 \
    --num_frames 81 \
-    --flow_shift 5.0 \
    --dataloader_num_workers 0 \
    --output_dir=$OUTPUT_DIR \
    --train_fps 16 \
@@ -1,99 +0,0 @@
-#!/bin/bash
-
-export WANDB_BASE_URL="https://api.wandb.ai"
-export WANDB_MODE=online
-export TOKENIZERS_PARALLELISM=false
-# export FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA
-
-# MODEL_PATH="wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers"
-MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
-# DATA_DIR="data/crush-smol_processed_t2v_1_3b_ode_init_5/combined_parquet_dataset/"
-DATA_DIR="/mnt/sharefs/users/hao.zhang/klin/preproc/data/test-ode-preprocessing-extended-t2v-1-3b/"
-VALIDATION_DATASET_FILE="$(dirname "$0")/validation.json"
-NUM_GPUS=1
-# export CUDA_VISIBLE_DEVICES=4,5
-# IP=[MASTER NODE IP]
-
-# Training arguments
-training_args=(
-  --tracker_project_name "wan_ode_init"
-  --output_dir "wan_ode_init_70k"
-  --override_transformer_cls_name "CausalWanTransformer3DModel"
-  --wandb_run_name "fixed_wan_ode_init_70k_6e-6"
-  # --resume_from_checkpoint "ode_init_diffusers/"
-  --max_train_steps 6000
-  --train_batch_size 1
-  --train_sp_batch_size 1
-  --gradient_accumulation_steps 1
-  --num_latent_t 21
-  --num_height 480
-  --num_width 832
-  --num_frames 77
-  --warp_denoising_step
-  # --enable_gradient_checkpointing_type "full"
-)
-
-# Parallel arguments
-parallel_args=(
-  --num_gpus $NUM_GPUS
-  --sp_size 1
-  --tp_size 1
-  --hsdp_replicate_dim 1
-  --hsdp_shard_dim 1
-)
-
-# Model arguments
-model_args=(
-  --model_path $MODEL_PATH
-  --pretrained_model_name_or_path $MODEL_PATH
-)
-
-# Dataset arguments
-dataset_args=(
-  --data_path "$DATA_DIR" 
-  --dataloader_num_workers 1
-)
-
-# Validation arguments
-validation_args=(
-  --log_validation
-  --validation_dataset_file "$VALIDATION_DATASET_FILE"
-  --validation_steps 50
-  --validation_sampling_steps "50"
-  --validation_guidance_scale "6.0"
-)
-
-# Optimizer arguments
-optimizer_args=(
-  --learning_rate 6e-6
-  --mixed_precision "bf16"
-  --checkpointing_steps 1000
-  --weight_decay 1e-4
-  --max_grad_norm 1.0
-)
-
-# Miscellaneous arguments
-miscellaneous_args=(
-  --inference_mode False
-  --checkpoints_total_limit 3
-  --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
-torchrun \
-  --nnodes 1 \
-  --nproc_per_node $NUM_GPUS \
-    fastvideo/training/ode_causal_pipeline.py \
-    "${parallel_args[@]}" \
-    "${model_args[@]}" \
-    "${dataset_args[@]}" \
-    "${training_args[@]}" \
-    "${optimizer_args[@]}" \
-    "${validation_args[@]}" \
-    "${miscellaneous_args[@]}"
@@ -1,135 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=1e5B2_16kFV_warp_ode_vidprom
-#SBATCH --partition=main
-#SBATCH --nodes=1
-#SBATCH --ntasks=1
-#SBATCH --ntasks-per-node=1
-#SBATCH --gres=gpu:8
-#SBATCH --cpus-per-task=128
-#SBATCH --mem=1440G
-#SBATCH --output=ode_vidprom16k_warp/Dode_vidprom8b16k_1e-5.out
-#SBATCH --error=ode_vidprom16k_warp/Dode_vidprom8b16k_1e-5.err
-#SBATCH --exclusive
-set -e -x
-
-# Environment Setup
-source ~/conda/miniconda/bin/activate
-conda activate will-fv2
-
-export WANDB_MODE="online"
-export NCCL_P2P_DISABLE=1
-export TORCH_NCCL_ENABLE_MONITORING=0
-# 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 CUDA_VISIBLE_DEVICES=$SLURM_LOCALID
-export TOKENIZERS_PARALLELISM=false
-export WANDB_BASE_URL="https://api.wandb.ai"
-export WANDB_MODE=online
-export WANDB_API_KEY='8d9f4b39abd68eb4e29f6fc010b7ee71a2207cde'
-# export FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA
-
-echo "MASTER_ADDR: $MASTER_ADDR"
-echo "NODE_RANK: $NODE_RANK"
-
-
-# MODEL_PATH="wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers"
-MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
-DATA_DIR="/mnt/sharefs/users/hao.zhang/klin/preproc/data/test-ode-preprocessing-16k-t2v-1-3b-81/"
-VALIDATION_DATASET_FILE="examples/training/consistency_finetune/ode_init/validation.json"
-NUM_GPUS=8
-# export CUDA_VISIBLE_DEVICES=4,5
-# IP=[MASTER NODE IP]
-
-# Training arguments
-training_args=(
-  --tracker_project_name "wan_ode_init"
-  --output_dir "Dwarp_vidprom_8b16k_test_warp_1e-5"
-  --override_transformer_cls_name "CausalWanTransformer3DModel"
-  --wandb_run_name "Dwarp_vidprom_8b16k_wan_ode_init_1e-5"
-  # --resume_from_checkpoint "ode_init_diffusers/"
-  --warp_denoising_step
-  --log_visualization
-  --max_train_steps 6001
-  --train_batch_size 1
-  --train_sp_batch_size 1
-  --gradient_accumulation_steps 1
-  --num_latent_t 21
-  --num_height 480
-  --num_width 832
-  --num_frames 77
-  --dmd_denoising_steps "1000,750,500,250"
-  --enable_gradient_checkpointing_type "full"
-)
-
-# Parallel arguments
-parallel_args=(
-  --num_gpus $NUM_GPUS
-  --sp_size 1
-  --tp_size 1
-  --hsdp_replicate_dim $NUM_GPUS
-  --hsdp_shard_dim 1
-)
-
-# Model arguments
-model_args=(
-  --model_path $MODEL_PATH
-  --pretrained_model_name_or_path $MODEL_PATH
-)
-
-# Dataset arguments
-dataset_args=(
-  --data_path "$DATA_DIR" 
-  --dataloader_num_workers 1
-)
-
-# Validation arguments
-validation_args=(
-  --log_validation
-  --validation_dataset_file "$VALIDATION_DATASET_FILE"
-  --validation_steps 50
-  --validation_sampling_steps "50"
-  --validation_guidance_scale "6.0"
-  # --init_weights_from_safetensors "/mnt/weka/home/hao.zhang/wl/Self-Forcing/diffusers_ode_init/model.safetensors"
-)
-
-# Optimizer arguments
-optimizer_args=(
-  --learning_rate 1e-5
-  --mixed_precision "bf16"
-  --checkpointing_steps 500
-  --weight_decay 1e-4
-  --max_grad_norm 1.0
-)
-
-# Miscellaneous arguments
-miscellaneous_args=(
-  --inference_mode False
-  --checkpoints_total_limit 3
-  --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[@]}"
@@ -1,131 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=ode_vidprom2k
-#SBATCH --partition=main
-#SBATCH --nodes=1
-#SBATCH --ntasks=1
-#SBATCH --ntasks-per-node=1
-#SBATCH --gres=gpu:8
-#SBATCH --cpus-per-task=128
-#SBATCH --mem=1440G
-#SBATCH --output=ode_vidprom2k_output/ode_vidprom2k.out
-#SBATCH --error=ode_vidprom2k_output/ode_vidprom2k.err
-#SBATCH --exclusive
-set -e -x
-
-# Environment Setup
-source ~/conda/miniconda/bin/activate
-conda activate will-fv2
-
-export WANDB_MODE="online"
-export NCCL_P2P_DISABLE=1
-export TORCH_NCCL_ENABLE_MONITORING=0
-# 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 CUDA_VISIBLE_DEVICES=$SLURM_LOCALID
-export TOKENIZERS_PARALLELISM=false
-export WANDB_BASE_URL="https://api.wandb.ai"
-export WANDB_MODE=online
-export WANDB_API_KEY='8d9f4b39abd68eb4e29f6fc010b7ee71a2207cde'
-# export FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA
-
-echo "MASTER_ADDR: $MASTER_ADDR"
-echo "NODE_RANK: $NODE_RANK"
-
-
-# MODEL_PATH="wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers"
-MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
-DATA_DIR="/mnt/sharefs/users/hao.zhang/klin/preproc/data/test-ode-preprocessing/"
-VALIDATION_DATASET_FILE="examples/training/consistency_finetune/ode_init/validation.json"
-NUM_GPUS=8
-# export CUDA_VISIBLE_DEVICES=4,5
-# IP=[MASTER NODE IP]
-
-# Training arguments
-training_args=(
-  --tracker_project_name "wan_ode_init"
-  --output_dir "wan_ode_init_vidprom2k"
-  --override_transformer_cls_name "CausalWanTransformer3DModel"
-  --wandb_run_name "vidprom2k_wan_ode_init_5e-6"
-  # --resume_from_checkpoint "ode_init_diffusers/"
-  --max_train_steps 6001
-  --train_batch_size 1
-  --train_sp_batch_size 1
-  --gradient_accumulation_steps 1
-  --num_latent_t 21
-  --num_height 480
-  --num_width 832
-  --num_frames 77
-  # --enable_gradient_checkpointing_type "full"
-)
-
-# Parallel arguments
-parallel_args=(
-  --num_gpus $NUM_GPUS
-  --sp_size 1
-  --tp_size 1
-  --hsdp_replicate_dim 8
-  --hsdp_shard_dim 1
-)
-
-# Model arguments
-model_args=(
-  --model_path $MODEL_PATH
-  --pretrained_model_name_or_path $MODEL_PATH
-)
-
-# Dataset arguments
-dataset_args=(
-  --data_path "$DATA_DIR" 
-  --dataloader_num_workers 1
-)
-
-# Validation arguments
-validation_args=(
-  --log_validation
-  --validation_dataset_file "$VALIDATION_DATASET_FILE"
-  --validation_steps 100
-  --validation_sampling_steps "50"
-  --validation_guidance_scale "6.0"
-)
-
-# Optimizer arguments
-optimizer_args=(
-  --learning_rate 5e-6
-  --mixed_precision "bf16"
-  --checkpointing_steps 2000
-  --weight_decay 1e-4
-  --max_grad_norm 1.0
-)
-
-# Miscellaneous arguments
-miscellaneous_args=(
-  --inference_mode False
-  --checkpoints_total_limit 3
-  --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[@]}"
@@ -1,132 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=ode_crush
-#SBATCH --partition=main
-#SBATCH --nodes=1
-#SBATCH --ntasks=1
-#SBATCH --ntasks-per-node=1
-#SBATCH --gres=gpu:8
-#SBATCH --cpus-per-task=128
-#SBATCH --mem=1440G
-#SBATCH --output=ode_crush_output/ode_crush.out
-#SBATCH --error=ode_crush_output/ode_crush.err
-#SBATCH --exclusive
-set -e -x
-
-# Environment Setup
-source ~/conda/miniconda/bin/activate
-conda activate will-fv2
-
-export WANDB_MODE="online"
-export NCCL_P2P_DISABLE=1
-export TORCH_NCCL_ENABLE_MONITORING=0
-# 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 CUDA_VISIBLE_DEVICES=$SLURM_LOCALID
-export TOKENIZERS_PARALLELISM=false
-export WANDB_BASE_URL="https://api.wandb.ai"
-export WANDB_MODE=online
-export WANDB_API_KEY='8d9f4b39abd68eb4e29f6fc010b7ee71a2207cde'
-# export FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA
-
-echo "MASTER_ADDR: $MASTER_ADDR"
-echo "NODE_RANK: $NODE_RANK"
-
-
-# MODEL_PATH="wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers"
-MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
-DATA_DIR="data/crush-smol_processed_t2v_1_3b_ode_init_5/combined_parquet_dataset/"
-VALIDATION_DATASET_FILE="examples/training/consistency_finetune/ode_init/validation.json"
-NUM_GPUS=2
-# export CUDA_VISIBLE_DEVICES=4,5
-# IP=[MASTER NODE IP]
-
-# Training arguments
-training_args=(
-  --tracker_project_name "wan_ode_init"
-  --output_dir "wan_ode_init_warp_2"
-  --override_transformer_cls_name "CausalWanTransformer3DModel"
-  --wandb_run_name "2warp_fixed_wan_ode_init_5e-6"
-  # --resume_from_checkpoint "ode_init_diffusers/"
-  # --warp_denoising_step
-  --max_train_steps 6001
-  --train_batch_size 1
-  --train_sp_batch_size 1
-  --gradient_accumulation_steps 1
-  --num_latent_t 21
-  --num_height 480
-  --num_width 832
-  --num_frames 77
-  # --enable_gradient_checkpointing_type "full"
-)
-
-# Parallel arguments
-parallel_args=(
-  --num_gpus $NUM_GPUS
-  --sp_size 1
-  --tp_size 1
-  --hsdp_replicate_dim $NUM_GPUS
-  --hsdp_shard_dim 1
-)
-
-# Model arguments
-model_args=(
-  --model_path $MODEL_PATH
-  --pretrained_model_name_or_path $MODEL_PATH
-)
-
-# Dataset arguments
-dataset_args=(
-  --data_path "$DATA_DIR" 
-  --dataloader_num_workers 1
-)
-
-# Validation arguments
-validation_args=(
-  --log_validation
-  --validation_dataset_file "$VALIDATION_DATASET_FILE"
-  --validation_steps 20
-  --validation_sampling_steps "50"
-  --validation_guidance_scale "6.0"
-)
-
-# Optimizer arguments
-optimizer_args=(
-  --learning_rate 5e-6
-  --mixed_precision "bf16"
-  --checkpointing_steps 2000
-  --weight_decay 1e-4
-  --max_grad_norm 1.0
-)
-
-# Miscellaneous arguments
-miscellaneous_args=(
-  --inference_mode False
-  --checkpoints_total_limit 3
-  --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[@]}"
@@ -1,103 +0,0 @@
-#!/bin/bash
-
-export WANDB_BASE_URL="https://api.wandb.ai"
-export WANDB_MODE=offline
-export TOKENIZERS_PARALLELISM=false
-export MASTER_PORT=29501
-# export FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA
-
-# MODEL_PATH="wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers"
-MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
-DATA_DIR="data/mixkit-64_processed/Node_0_GPU_1_File_1/combined_parquet_dataset"
-# DATA_DIR="/mnt/weka/home/hao.zhang/wl/Self-Forcing/ode_single_lmdb_sf/"
-VALIDATION_DATASET_FILE="$(dirname "$0")/validation.json"
-NUM_GPUS=1
-
-# Training arguments
-training_args=(
-  --tracker_project_name "wan_ode_init"
-  --output_dir "Dwarp_vidprom_8b16k_test_warp_1e-5"
-  --override_transformer_cls_name "CausalWanTransformer3DModel"
-  --wandb_run_name "Dwarp_vidprom_8b16k_wan_ode_init_1e-5"
-  # --resume_from_checkpoint "ode_init_diffusers/"
-  --warp_denoising_step
-  --log_visualization
-  --max_train_steps 10
-  --train_batch_size 1
-  --train_sp_batch_size 1
-  --gradient_accumulation_steps 1
-  --num_latent_t 21
-  --num_height 480
-  --num_width 832
-  --num_frames 77
-  --dmd_denoising_steps "1000,750,500,250"
-  --enable_gradient_checkpointing_type "full"
-)
-
-# Parallel arguments
-parallel_args=(
-  --num_gpus $NUM_GPUS
-  --sp_size 1
-  --tp_size 1
-  --hsdp_replicate_dim $NUM_GPUS
-  --hsdp_shard_dim 1
-)
-
-# Model arguments
-model_args=(
-  --model_path $MODEL_PATH
-  --pretrained_model_name_or_path $MODEL_PATH
-)
-
-# Dataset arguments
-dataset_args=(
-  --data_path "$DATA_DIR" 
-  --dataloader_num_workers 1
-)
-
-# Validation arguments
-validation_args=(
-#   --log_validation
-  --validation_dataset_file "$VALIDATION_DATASET_FILE"
-  --validation_steps 50
-  --validation_sampling_steps "50"
-  --validation_guidance_scale "6.0"
-  # --init_weights_from_safetensors "/mnt/weka/home/hao.zhang/wl/Self-Forcing/diffusers_ode_init/model.safetensors"
-)
-
-# Optimizer arguments
-optimizer_args=(
-  --learning_rate 1e-5
-  --mixed_precision "bf16"
-  --checkpointing_steps 500
-  --weight_decay 1e-4
-  --max_grad_norm 1.0
-)
-
-# Miscellaneous arguments
-miscellaneous_args=(
-  --inference_mode False
-  --checkpoints_total_limit 3
-  --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
-  --seed 1024
-  # --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
-torchrun \
-  --nnodes 1 \
-  --master_port $MASTER_PORT \
-  --nproc_per_node $NUM_GPUS \
-    fastvideo/training/ode_causal_pipeline.py \
-    "${parallel_args[@]}" \
-    "${model_args[@]}" \
-    "${dataset_args[@]}" \
-    "${training_args[@]}" \
-    "${optimizer_args[@]}" \
-    "${validation_args[@]}" \
-    "${miscellaneous_args[@]}"
@@ -1,98 +0,0 @@
-#!/bin/bash
-
-export WANDB_BASE_URL="https://api.wandb.ai"
-export WANDB_MODE=online
-export TOKENIZERS_PARALLELISM=false
-# export FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA
-export WANDB_API_KEY='8d9f4b39abd68eb4e29f6fc010b7ee71a2207cde'
-
-MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
-DATA_DIR="/mnt/weka/home/hao.zhang/wl/FastVideo2/data/crush-smol_processed_t2v_1_3b_ode_init_single"
-VALIDATION_DATASET_FILE="$(dirname "$0")/validation.json"
-NUM_GPUS=1
-# export CUDA_VISIBLE_DEVICES=4,5
-# IP=[MASTER NODE IP]
-
-# Training arguments
-training_args=(
-  --tracker_project_name "wan_ode_init"
-  --output_dir "wan_ode_init_crush_smol"
-  --override_transformer_cls_name "CausalWanTransformer3DModel"
-  --wandb_run_name "overfitwan_ode_init_crush_smol"
-  # --resume_from_checkpoint "ode_init_diffusers/"
-  --max_train_steps 2001
-  # --warp_denoising_step
-  --train_batch_size 1
-  --train_sp_batch_size 1
-  --gradient_accumulation_steps 1
-  --num_latent_t 21
-  --num_height 480
-  --num_width 832
-  --num_frames 77
-  # --enable_gradient_checkpointing_type "full"
-)
-
-# Parallel arguments
-parallel_args=(
-  --num_gpus $NUM_GPUS
-  --sp_size 1
-  --tp_size 1
-  --hsdp_replicate_dim 1
-  --hsdp_shard_dim 1
-)
-
-# Model arguments
-model_args=(
-  --model_path $MODEL_PATH
-  --pretrained_model_name_or_path $MODEL_PATH
-)
-
-# Dataset arguments
-dataset_args=(
-  --data_path "$DATA_DIR" 
-  --dataloader_num_workers 1
-)
-
-# Validation arguments
-validation_args=(
-  --log_validation
-  --validation_dataset_file "$VALIDATION_DATASET_FILE"
-  --validation_steps 20
-  --validation_sampling_steps "50"
-  --validation_guidance_scale "6.0"
-)
-
-# Optimizer arguments
-optimizer_args=(
-  --learning_rate 1e-5
-  --mixed_precision "bf16"
-  --checkpointing_steps 500
-  --weight_decay 1e-4
-  --max_grad_norm 1.0
-)
-
-# Miscellaneous arguments
-miscellaneous_args=(
-  --inference_mode False
-  --checkpoints_total_limit 3
-  --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
-torchrun \
-  --nnodes 1 \
-  --nproc_per_node $NUM_GPUS \
-    fastvideo/training/ode_causal_pipeline.py \
-    "${parallel_args[@]}" \
-    "${model_args[@]}" \
-    "${dataset_args[@]}" \
-    "${training_args[@]}" \
-    "${optimizer_args[@]}" \
-    "${validation_args[@]}" \
-    "${miscellaneous_args[@]}"
@@ -1,100 +0,0 @@
-#!/bin/bash
-
-export WANDB_BASE_URL="https://api.wandb.ai"
-export WANDB_MODE=online
-export TOKENIZERS_PARALLELISM=false
-# export FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA
-
-# MODEL_PATH="wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers"
-MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
-# DATA_DIR="data/crush-smol_processed_t2v_1_3b_ode_init_5/combined_parquet_dataset/"
-DATA_DIR="/mnt/sharefs/users/hao.zhang/klin/preproc/data/test-ode-preprocessing-16k-t2v-1-3b/"
-VALIDATION_DATASET_FILE="$(dirname "$0")/validation.json"
-NUM_GPUS=1
-export WANDB_API_KEY='8d9f4b39abd68eb4e29f6fc010b7ee71a2207cde'
-# export CUDA_VISIBLE_DEVICES=4,5
-# IP=[MASTER NODE IP]
-
-# Training arguments
-training_args=(
-  --tracker_project_name "wan_ode_init"
-  --output_dir "debug_ode_init"
-  --override_transformer_cls_name "CausalWanTransformer3DModel"
-  --wandb_run_name "debug_ode_init"
-  # --resume_from_checkpoint "ode_init_diffusers/"
-  --max_train_steps 1000
-  --train_batch_size 1
-  --train_sp_batch_size 1
-  --gradient_accumulation_steps 1
-  --num_latent_t 21
-  --num_height 480
-  --num_width 832
-  --num_frames 81
-  --warp_denoising_step
-  # --enable_gradient_checkpointing_type "full"
-)
-
-# Parallel arguments
-parallel_args=(
-  --num_gpus $NUM_GPUS
-  --sp_size 1
-  --tp_size 1
-  --hsdp_replicate_dim 1
-  --hsdp_shard_dim 1
-)
-
-# Model arguments
-model_args=(
-  --model_path $MODEL_PATH
-  --pretrained_model_name_or_path $MODEL_PATH
-)
-
-# Dataset arguments
-dataset_args=(
-  --data_path "$DATA_DIR" 
-  --dataloader_num_workers 1
-)
-
-# Validation arguments
-validation_args=(
-  --log_validation
-  --validation_dataset_file "$VALIDATION_DATASET_FILE"
-  --validation_steps 10
-  --validation_sampling_steps "50"
-  --validation_guidance_scale "6.0"
-)
-
-# Optimizer arguments
-optimizer_args=(
-  --learning_rate 1e-5
-  --mixed_precision "bf16"
-  --checkpointing_steps 1000
-  --weight_decay 1e-4
-  --max_grad_norm 1.0
-)
-
-# Miscellaneous arguments
-miscellaneous_args=(
-  --inference_mode False
-  --checkpoints_total_limit 3
-  --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
-torchrun \
-  --nnodes 1 \
-  --nproc_per_node $NUM_GPUS \
-    fastvideo/training/ode_causal_pipeline.py \
-    "${parallel_args[@]}" \
-    "${model_args[@]}" \
-    "${dataset_args[@]}" \
-    "${training_args[@]}" \
-    "${optimizer_args[@]}" \
-    "${validation_args[@]}" \
-    "${miscellaneous_args[@]}"
@@ -1,25 +0,0 @@
-#!/bin/bash
-
-GPU_NUM=1 # 2,4,8
-MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
-MODEL_TYPE="wan"
-DATA_MERGE_PATH="data/vidprom_1.txt"
-OUTPUT_DIR="data/ode_vidprom_1_fv/"
-
-torchrun --nproc_per_node=$GPU_NUM \
-    fastvideo/pipelines/preprocess/v1_preprocess.py \
-    --model_path $MODEL_PATH \
-    --data_merge_path $DATA_MERGE_PATH \
-    --preprocess_video_batch_size 1 \
-    --seed 42 \
-    --max_height 480 \
-    --max_width 832 \
-    --num_frames 81 \
-    --flow_shift 5.0 \
-    --dataloader_num_workers 0 \
-    --output_dir=$OUTPUT_DIR \
-    --train_fps 16 \
-    --samples_per_file 1 \
-    --flush_frequency 1 \
-    --video_length_tolerance_range 5 \
-    --preprocess_task "ode_trajectory" 
@@ -1,76 +0,0 @@
-{
-  "data": [
-    {
-      "caption": "A stylish woman walks down a Tokyo street filled with warm glowing neon and animated city signage. She wears a black leather jacket, a long red dress, and black boots, and carries a black purse. She wears sunglasses and red lipstick. She walks confidently and casually. The street is damp and reflective, creating a mirror effect of the colorful lights. Many pedestrians walk about.",
-      "image_path": null,
-      "video_path": null,
-      "num_inference_steps": 40,
-      "height": 480,
-      "width": 832,
-      "num_frames": 77
-    },
-    {
-      "caption": "A white and orange tabby cat is seen happily darting through a dense garden, as if chasing something. Its eyes are wide and happy as it jogs forward, scanning the branches, flowers, and leaves as it walks. The path is narrow as it makes its way between all the plants. the scene is captured from a ground-level angle, following the cat closely, giving a low and intimate perspective. The image is cinematic with warm tones and a grainy texture. The scattered daylight between the leaves and plants above creates a warm contrast, accentuating the cat’s orange fur. The shot is clear and sharp, with a shallow depth of field.",
-      "image_path": null,
-      "video_path": null,
-      "num_inference_steps": 40,
-      "height": 480,
-      "width": 832,
-      "num_frames": 77
-    },
-    {
-      "caption": "Elon Musk, dressed in a sleek white spacesuit with a reflective visor, walks confidently across the lunar surface. His posture is upright, and he moves steadily with purpose. The moon's rocky terrain and scattered boulders surround him, casting shadows under the dim sunlight. The background shows vast stretches of the moon's barren landscape with craters and dust clouds kicked up by his boots. The scene captures a wide shot, emphasizing the vastness and desolation of the lunar environment. ",
-      "image_path": null,
-      "video_path": null,
-      "num_inference_steps": 40,
-      "height": 480,
-      "width": 832,
-      "num_frames": 77
-    },
-    {
-      "caption": "In a dynamic action-packed sequence set in the Marvel multiverse, Spider-Man and Venom engage in an intense battle. Spider-Man, in his classic red and blue suit, swings and dodges venomous attacks from the black symbiote-covered Venom. Both characters display a range of acrobatic moves and powerful strikes. The environment is a chaotic urban landscape with crumbling buildings and neon lights, reflecting the multiversal theme. The camera captures the epic fight from various angles, including wide shots to show the scale of destruction and close-ups to highlight their fierce expressions and physical combat. ",
-      "image_path": null,
-      "video_path": null,
-      "num_inference_steps": 40,
-      "height": 480,
-      "width": 832,
-      "num_frames": 77
-    },
-    {
-      "caption": "A warm, family-oriented scene depicting a father getting ready to leave the house to buy milk. The father, a middle-aged man with a kind face and a casual outfit, picks up a jacket from the coat rack. His posture is upright as he bends down slightly to put on his shoes. In the background, there are glimpses of a cozy living room with a family photograph on the wall. The camera focuses closely on the father, capturing his gentle smile and reassuring nod towards the camera before he opens the front door and steps outside. Static medium close-up shot. ",
-      "image_path": null,
-      "video_path": null,
-      "num_inference_steps": 40,
-      "height": 480,
-      "width": 832,
-      "num_frames": 77
-    },
-    {
-      "caption": "Close-up shot of a man with a prosthetic hand that functions as a rocket launcher. He looks at his new hand with a mix of amazement and concern, his facial expression showing a blend of curiosity and apprehension. The prosthetic hand is sleek and metallic, with intricate details that resemble a high-tech weapon. The background is a dimly lit laboratory with various scientific equipment and monitors displaying data. The man stands in a relaxed posture, his other hand resting on his hip, as he inspects his new limb. The scene is rendered in a realistic sci-fi style, emphasizing the futuristic technology and the man's emotional response to his new appendage. ",
-      "image_path": null,
-      "video_path": null,
-      "num_inference_steps": 40,
-      "height": 480,
-      "width": 832,
-      "num_frames": 77
-    },
-    {
-      "caption": "Realistic CCTV footage style, Kim Taehyung from the band BTS is involved in a drug deal, caught on camera. Kim Taehyung appears nervous and cautious, wearing casual clothing typical of a public space. He exchanges items discreetly with another person, who is partially obscured. Both individuals maintain a watchful demeanor, occasionally glancing around to ensure no one is watching them. The lighting is dim, with flickering fluorescent lights casting shadows on their faces. The background shows a typical urban setting with blurred figures moving in the distance. Static camera angle, medium close-up shot focusing on the interaction between Taehyung and the other individual. ",
-      "image_path": null,
-      "video_path": null,
-      "num_inference_steps": 40,
-      "height": 480,
-      "width": 832,
-      "num_frames": 77
-    },
-    {
-      "caption": "Photorealistic studio setup with professional lighting, showcasing detailed cubic dissections of experimental plastic and felt-like materials on a pristine white background. Each cube reveals intricate layers and textures of the materials, emphasizing their unique properties. The scene has a shallow depth of field initially, then slowly pulls out to reveal the full arrangement of cubes, maintaining a wide depth of field throughout the transition. ",
-      "image_path": null,
-      "video_path": null,
-      "num_inference_steps": 40,
-      "height": 480,
-      "width": 832,
-      "num_frames": 77
-    }
-  ]
-}
@@ -6,8 +6,8 @@ export TOKENIZERS_PARALLELISM=false
 # export FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA

 MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
-DATA_DIR="data/crush-smol_processed_t2v_old"
-VALIDATION_DATASET_FILE="examples/training/finetune/Wan2.1-Fun-1.3B-InP/crush_smol/validation.json"
+DATA_DIR="data/crush-smol_processed_t2v/combined_parquet_dataset/"
+VALIDATION_DATASET_FILE="$(dirname "$0")/validation.json"
 NUM_GPUS=4
 # export CUDA_VISIBLE_DEVICES=4,5

@@ -52,7 +52,7 @@ dataset_args=(
 validation_args=(
  --log_validation 
  --validation_dataset_file $VALIDATION_DATASET_FILE
-  --validation_steps 50
+  --validation_steps 200
  --validation_sampling_steps "50" 
  --validation_guidance_scale "6.0"
 )
@@ -4,7 +4,7 @@ GPU_NUM=1 # 2,4,8
 MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
 MODEL_TYPE="wan"
 DATA_MERGE_PATH="data/crush-smol/merge.txt"
-OUTPUT_DIR="data/crush-smol_processed_t2v_old/"
+OUTPUT_DIR="data/crush-smol_processed_t2v/"

 torchrun --nproc_per_node=$GPU_NUM \
    fastvideo/pipelines/preprocess/v1_preprocess.py \
@@ -1,6 +1,6 @@
 #!/bin/bash

-GPU_NUM=1 # 2,4,8
+GPU_NUM=2 # 2,4,8
 MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
 DATASET_PATH="data/crush-smol/"
 OUTPUT_DIR="data/crush-smol_processed_t2v/"
@@ -14,7 +14,7 @@ torchrun --nproc_per_node=$GPU_NUM \
    --preprocess.dataset_type merged \
    --preprocess.dataset_path $DATASET_PATH \
    --preprocess.dataset_output_dir $OUTPUT_DIR \
-    --preprocess.preprocess_video_batch_size 8 \
+    --preprocess.preprocess_video_batch_size 2 \
    --preprocess.dataloader_num_workers 0 \
    --preprocess.max_height 480 \
    --preprocess.max_width 832 \
@@ -1,94 +0,0 @@
-#!/bin/bash
-
-export WANDB_BASE_URL="https://api.wandb.ai"
-export WANDB_MODE=online
-export TOKENIZERS_PARALLELISM=false
-# export FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA
-
-MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
-DATA_DIR="data/crush-smol_processed_t2v_old"
-VALIDATION_DATASET_FILE="examples/datasets/crush_smol/validation.json"
-NUM_GPUS=8
-# export CUDA_VISIBLE_DEVICES=4,5
-
-
-# Training arguments
-training_args=(
-  --tracker_project_name "wan_t2v_i2v_finetune"
-  --output_dir "checkpoints/wan_t2v_i2v_finetune"
-  --max_train_steps 5000
-  --train_batch_size 1
-  --train_sp_batch_size 1
-  --gradient_accumulation_steps 2
-  --num_latent_t 20
-  --num_height 480
-  --num_width 832
-  --num_frames 77
-  --enable_gradient_checkpointing_type "full"
-)
-
-# Parallel arguments
-parallel_args=(
-  --num_gpus $NUM_GPUS 
-  --sp_size 4
-  --tp_size 1
-  --hsdp_replicate_dim 2
-  --hsdp_shard_dim 4
-)
-
-# Model arguments
-model_args=(
-  --model_path $MODEL_PATH
-  --pretrained_model_name_or_path $MODEL_PATH
-)
-
-# Dataset arguments
-dataset_args=(
-  --data_path $DATA_DIR
-  --dataloader_num_workers 1
-)
-
-# Validation arguments
-validation_args=(
-  --log_validation 
-  --validation_dataset_file $VALIDATION_DATASET_FILE
-  --validation_steps 50
-  --validation_sampling_steps "50" 
-  --validation_guidance_scale "6.0"
-)
-
-# Optimizer arguments
-optimizer_args=(
-  --learning_rate 5e-5
-  --mixed_precision "bf16"
-  --checkpointing_steps 1000
-  --weight_decay 1e-4
-  --max_grad_norm 1.0
-)
-
-# Miscellaneous arguments
-miscellaneous_args=(
-  --inference_mode False
-  --checkpoints_total_limit 3
-  --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"
-  --t2v_as_i2v_task True
-  # --resume_from_checkpoint "checkpoints/wan_t2v_finetune/checkpoint-2500"
-)
-
-torchrun \
-  --nnodes 1 \
-  --nproc_per_node $NUM_GPUS \
-    fastvideo/training/wan_t2v_i2v_training_pipeline.py \
-    "${parallel_args[@]}" \
-    "${model_args[@]}" \
-    "${dataset_args[@]}" \
-    "${training_args[@]}" \
-    "${optimizer_args[@]}" \
-    "${validation_args[@]}" \
-    "${miscellaneous_args[@]}"
@@ -1,24 +0,0 @@
-#!/bin/bash
-
-GPU_NUM=1 # 2,4,8
-MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
-MODEL_TYPE="wan"
-DATA_MERGE_PATH="data/crush-smol/merge.txt"
-OUTPUT_DIR="data/crush-smol_processed_t2v_i2v_1_3b/"
-
-torchrun --nproc_per_node=$GPU_NUM \
-    fastvideo/pipelines/preprocess/v1_preprocess.py \
-    --model_path $MODEL_PATH \
-    --data_merge_path $DATA_MERGE_PATH \
-    --preprocess_video_batch_size 2 \
-    --seed 42 \
-    --max_height 480 \
-    --max_width 832 \
-    --num_frames 77 \
-    --dataloader_num_workers 0 \
-    --output_dir=$OUTPUT_DIR \
-    --train_fps 16 \
-    --samples_per_file 8 \
-    --flush_frequency 8 \
-    --video_length_tolerance_range 5 \
-    --preprocess_task "t2v_ode_trajectory" 
@@ -5,6 +5,7 @@ from dataclasses import dataclass

 import torch
 from einops import rearrange
+from flash_attn.bert_padding import pad_input

 from csrc.attn.vmoba_attn.vmoba import (moba_attn_varlen, process_moba_input,
                                        process_moba_output)
@@ -133,8 +134,6 @@ class VMOBAAttentionImpl(AttentionImpl):
                 **extra_impl_args) -> None:
        self.prefix = prefix
        self.layer_idx = self._get_layer_idx(prefix)
-        from flash_attn.bert_padding import pad_input
-        self.pad_input = pad_input

    def _get_layer_idx(self, prefix: str) -> int | None:
        match = re.search(r"blocks\.(\d+)", prefix)
@@ -170,6 +169,7 @@ class VMOBAAttentionImpl(AttentionImpl):
            moba_chunk_size = attn_metadata.st_chunk_size
            moba_topk = attn_metadata.st_topk

+        # torch.distributed.breakpoint()
        query, chunk_size = process_moba_input(query,
                                               attn_metadata.patch_resolution,
                                               moba_chunk_size)
@@ -205,8 +205,8 @@ class VMOBAAttentionImpl(AttentionImpl):
            simsum_threshold=attn_metadata.moba_threshold,
            threshold_type=attn_metadata.moba_threshold_type,
        )
-        hidden_states = self.pad_input(hidden_states, indices_q, batch_size,
-                                       sequence_length)
+        hidden_states = pad_input(hidden_states, indices_q, batch_size,
+                                  sequence_length)
        hidden_states = process_moba_output(hidden_states,
                                            attn_metadata.patch_resolution,
                                            moba_chunk_size)
@@ -3,7 +3,6 @@
 import torch
 import torch.nn as nn

-import fastvideo.envs as envs
 from fastvideo.attention.selector import backend_name_to_enum, get_attn_backend
 from fastvideo.distributed.communication_op import (
    sequence_model_parallel_all_gather, sequence_model_parallel_all_to_all_4D)
@@ -37,7 +36,7 @@ class DistributedAttention(nn.Module):
        if num_kv_heads is None:
            num_kv_heads = num_heads

-        dtype = torch.bfloat16 if envs.FASTVIDEO_FORCE_ATTN_BF16 else get_compute_dtype()
+        dtype = get_compute_dtype()
        attn_backend = get_attn_backend(
            head_size,
            dtype,
@@ -222,7 +221,7 @@ class LocalAttention(nn.Module):
        if num_kv_heads is None:
            num_kv_heads = num_heads

-        dtype = torch.bfloat16 if envs.FASTVIDEO_FORCE_ATTN_BF16 else get_compute_dtype()
+        dtype = get_compute_dtype()
        attn_backend = get_attn_backend(
            head_size,
            dtype,
@@ -27,7 +27,6 @@ class DiTArchConfig(ArchConfig):
    num_attention_heads: int = 0
    num_channels_latents: int = 0
    exclude_lora_layers: list[str] = field(default_factory=list)
-    boundary_ratio: float | None = None

    def __post_init__(self) -> None:
        if not self._compile_conditions:
@@ -92,9 +92,6 @@ class WanVideoArchConfig(DiTArchConfig):
    pos_embed_seq_len: int | None = None
    exclude_lora_layers: list[str] = field(default_factory=lambda: ["embedder"])

-    # Wan MoE
-    boundary_ratio: float | None = None
-
    # Causal Wan
    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
@@ -45,13 +45,10 @@ class PipelineConfig:
    embedded_cfg_scale: float = 6.0
    flow_shift: float | None = None
    disable_autocast: bool = False
-    ti2v_task: bool = False
-    t2v_as_i2v_task: bool = False

    # Model configuration
    dit_config: DiTConfig = field(default_factory=DiTConfig)
    dit_precision: str = "bf16"
-    dit_forward_precision: str = "bf16"

    # VAE configuration
    vae_config: VAEConfig = field(default_factory=VAEConfig)
@@ -90,7 +87,6 @@ class PipelineConfig:

    # Wan2.2 TI2V parameters
    ti2v_task: bool = False
-    boundary_ratio: float | None = None

    # Compilation
    # enable_torch_compile: bool = False
@@ -218,24 +214,6 @@ class PipelineConfig:
            "Comma-separated list of denoising steps (e.g., '1000,757,522')",
        )

-        # TI2V task
-        parser.add_argument(
-            f"--{prefix_with_dot}ti2v-task",
-            action=StoreBoolean,
-            dest=f"{prefix_with_dot.replace('-', '_')}ti2v_task",
-            default=PipelineConfig.ti2v_task,
-            help="Enable TI2V",
-        )
-
-        # T2V to I2V task
-        parser.add_argument(
-            f"--{prefix_with_dot}t2v-as-i2v-task",
-            action=StoreBoolean,
-            dest=f"{prefix_with_dot.replace('-', '_')}t2v_as_i2v_task",
-            default=PipelineConfig.t2v_as_i2v_task,
-            help="Enable T2V to I2V task",
-        )
-
        # Add VAE configuration arguments
        from fastvideo.configs.models.vaes.base import VAEConfig
        VAEConfig.add_cli_args(parser, prefix=f"{prefix_with_dot}vae-config")
@@ -267,9 +245,7 @@ class PipelineConfig:
        """
        from fastvideo.configs.pipelines.registry import (
            get_pipeline_config_cls_from_name)
-        logger.info("WTF model_path: %s", model_path)
        pipeline_config_cls = get_pipeline_config_cls_from_name(model_path)
-        logger.info("pipeline_config_cls: %s", pipeline_config_cls)

        return cast(PipelineConfig, pipeline_config_cls(model_path=model_path))

@@ -50,7 +50,6 @@ PIPELINE_DETECTOR: dict[str, Callable[[str], bool]] = {
    "wandmdpipeline": lambda id: "wandmdpipeline" in id.lower(),
    "wancausaldmdpipeline": lambda id: "wancausaldmdpipeline" in id.lower(),
    "stepvideo": lambda id: "stepvideo" in id.lower(),
-    "wancausaldmdpipeline": lambda id: "wancausaldmdpipeline" in id.lower(),
    # Add other pipeline architecture detectors
 }

@@ -82,7 +82,7 @@ class WanI2V480PConfig(WanT2V480PConfig):
        default_factory=CLIPVisionConfig)
    image_encoder_precision: str = "fp32"

-    def __post_init__(self) -> None:
+    def __post_init__(self):
        self.vae_config.load_encoder = True
        self.vae_config.load_decoder = True

@@ -108,17 +108,19 @@ class FastWan2_1_T2V_480P_Config(WanT2V480PConfig):
    dmd_denoising_steps: list[int] | None = field(
        default_factory=lambda: [1000, 757, 522])

+    def __post_init__(self) -> None:
+        self.vae_config.load_encoder = True
+        self.vae_config.load_decoder = True
+

@dataclass
 class Wan2_2_TI2V_5B_Config(WanT2V480PConfig):
    flow_shift: float | None = 5.0
    ti2v_task: bool = True
-    expand_timesteps: bool = True

    def __post_init__(self) -> None:
        self.vae_config.load_encoder = True
        self.vae_config.load_decoder = True
-        self.dit_config.expand_timesteps = self.expand_timesteps


@dataclass
@@ -130,21 +132,12 @@ class FastWan2_2_TI2V_5B_Config(Wan2_2_TI2V_5B_Config):

@dataclass
 class Wan2_2_T2V_A14B_Config(WanT2V480PConfig):
-    flow_shift: float | None = 12.0
-    boundary_ratio: float | None = 0.875
-
-    def __post_init__(self) -> None:
-        self.dit_config.boundary_ratio = self.boundary_ratio
+    pass


@dataclass
-class Wan2_2_I2V_A14B_Config(WanI2V480PConfig):
-    flow_shift: float | None = 5.0
-    boundary_ratio: float | None = 0.900
-
-    def __post_init__(self) -> None:
-        super().__post_init__()
-        self.dit_config.boundary_ratio = self.boundary_ratio
+class Wan2_2_I2V_A14B_Config(WanT2V480PConfig):
+    pass


 # =============================================
@@ -40,7 +40,6 @@ class SamplingParam:
    num_inference_steps: int = 50
    guidance_scale: float = 1.0
    guidance_rescale: float = 0.0
-    boundary_ratio: float | None = None

    # TeaCache parameters
    enable_teacache: bool = False
@@ -170,12 +169,6 @@ class SamplingParam:
            default=SamplingParam.guidance_rescale,
            help="Guidance rescale factor",
        )
-        parser.add_argument(
-            "--boundary-ratio",
-            type=float,
-            default=SamplingParam.boundary_ratio,
-            help="Boundary timestep ratio",
-        )
        parser.add_argument(
            "--save-video",
            action="store_true",
@@ -144,22 +144,18 @@ class Wan2_2_TI2V_5B_SamplingParam(Wan2_2_Base_SamplingParam):

@dataclass
 class Wan2_2_T2V_A14B_SamplingParam(Wan2_2_Base_SamplingParam):
-    guidance_scale: float = 4.0  # high_noise
-    guidance_scale_2: float = 3.0  # low_noise
+    guidance_scale: float = 4.0
+    guidance_scale_2: float = 3.0
    num_inference_steps: int = 40
    fps: int = 16
-    # NOTE(will): default boundary timestep is tracked by PipelineConfig, but
-    # can be overridden during sampling


@dataclass
 class Wan2_2_I2V_A14B_SamplingParam(Wan2_2_Base_SamplingParam):
-    guidance_scale: float = 3.5  # high_noise
-    guidance_scale_2: float = 3.5  # low_noise
+    guidance_scale: float = 3.5
+    guidance_scale_2: float = 3.5
    num_inference_steps: int = 40
    fps: int = 16
-    # NOTE(will): default boundary timestep is tracked by PipelineConfig, but
-    # can be overridden during sampling


 # =============================================
@@ -102,17 +102,3 @@ pyarrow_schema_ode_trajectory_text_only = pa.schema([
    pa.field("caption", pa.string()),
    pa.field("media_type", pa.string()),  # Always 'text' for text-only
 ])
-
-
-pyarrow_schema_text_only = pa.schema([
-    pa.field("id", pa.string()),
-    # --- Text encoder output tensor ---
-    # Tensors are stored as raw bytes with shape and dtype info for loading
-    pa.field("text_embedding_bytes", pa.binary()),
-    # e.g., [SeqLen, Dim]
-    pa.field("text_embedding_shape", pa.list_(pa.int64())),
-    # e.g., 'bfloat16' or 'float32'
-    pa.field("text_embedding_dtype", pa.string()),
-    # --- Metadata ---
-    pa.field("caption", pa.string()),
-])
@@ -1,43 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-from fastvideo.dataset.lmdb_utils import get_array_shape_from_lmdb, retrieve_row_from_lmdb
-from torch.utils.data import Dataset
-import numpy as np
-import torch
-import lmdb
-
-# from Self-Forcing: https://github.com/guandeh17/Self-Forcing/blob/main/utils/dataset.py
-class ODERegressionLMDBDataset(Dataset):
-    def __init__(self, data_path: str, max_pair: int = int(1e8)):
-        print(f"data_path: {data_path}")
-        self.env = lmdb.open(data_path, readonly=True,
-                             lock=False, readahead=False, meminit=False)
-
-        self.latents_shape = get_array_shape_from_lmdb(self.env, 'latents')
-        self.max_pair = max_pair
-
-    def __len__(self):
-        return min(self.latents_shape[0], self.max_pair)
-
-    def __getitem__(self, idx):
-        """
-        Outputs:
-            - prompts: List of Strings
-            - latents: Tensor of shape (num_denoising_steps, num_frames, num_channels, height, width). It is ordered from pure noise to clean image.
-        """
-        latents = retrieve_row_from_lmdb(
-            self.env,
-            "latents", np.float16, idx, shape=self.latents_shape[1:]
-        )
-
-        if len(latents.shape) == 4:
-            latents = latents[None, ...]
-
-        prompts = retrieve_row_from_lmdb(
-            self.env,
-            "prompts", str, idx
-        )
-        return {
-            "prompts": prompts,
-            "ode_latent": torch.tensor(latents, dtype=torch.float32)
-        }
-
@@ -1,75 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# from Self-Forcing: https://github.com/guandeh17/Self-Forcing/blob/main/utils/lmdb.py
-
-import numpy as np
-
-
-def get_array_shape_from_lmdb(env, array_name):
-    with env.begin() as txn:
-        image_shape = txn.get(f"{array_name}_shape".encode()).decode()
-        image_shape = tuple(map(int, image_shape.split()))
-    return image_shape
-
-
-def store_arrays_to_lmdb(env, arrays_dict, start_index=0):
-    """
-    Store rows of multiple numpy arrays in a single LMDB.
-    Each row is stored separately with a naming convention.
-    """
-    with env.begin(write=True) as txn:
-        for array_name, array in arrays_dict.items():
-            for i, row in enumerate(array):
-                # Convert row to bytes
-                if isinstance(row, str):
-                    row_bytes = row.encode()
-                else:
-                    row_bytes = row.tobytes()
-
-                data_key = f'{array_name}_{start_index + i}_data'.encode()
-
-                txn.put(data_key, row_bytes)
-
-
-def process_data_dict(data_dict, seen_prompts):
-    output_dict = {}
-
-    all_videos = []
-    all_prompts = []
-    for prompt, video in data_dict.items():
-        if prompt in seen_prompts:
-            continue
-        else:
-            seen_prompts.add(prompt)
-
-        video = video.half().numpy()
-        all_videos.append(video)
-        all_prompts.append(prompt)
-
-    if len(all_videos) == 0:
-        return {"latents": np.array([]), "prompts": np.array([])}
-
-    all_videos = np.concatenate(all_videos, axis=0)
-
-    output_dict['latents'] = all_videos
-    output_dict['prompts'] = np.array(all_prompts)
-
-    return output_dict
-
-
-def retrieve_row_from_lmdb(lmdb_env, array_name, dtype, row_index, shape=None):
-    """
-    Retrieve a specific row from a specific array in the LMDB.
-    """
-    data_key = f'{array_name}_{row_index}_data'.encode()
-
-    with lmdb_env.begin() as txn:
-        row_bytes = txn.get(data_key)
-
-    if dtype == str:
-        array = row_bytes.decode()
-    else:
-        array = np.frombuffer(row_bytes, dtype=dtype)
-
-    if shape is not None and len(shape) > 0:
-        array = array.reshape(shape)
-    return array
@@ -3,9 +3,6 @@ from typing import Any, cast

 import numpy as np
 import torch
-from fastvideo.logger import init_logger
-
-logger = init_logger(__name__)


 def pad(t: torch.Tensor, padding_length: int) -> tuple[torch.Tensor, torch.Tensor]:
@@ -18,7 +18,6 @@ if TYPE_CHECKING:
    FASTVIDEO_LOGGING_PREFIX: str = ""
    FASTVIDEO_LOGGING_CONFIG_PATH: str | None = None
    FASTVIDEO_TRACE_FUNCTION: int = 0
-    FASTVIDEO_FORCE_ATTN_BF16: bool = False
    FASTVIDEO_ATTENTION_BACKEND: str | None = None
    FASTVIDEO_ATTENTION_CONFIG: str | None = None
    FASTVIDEO_WORKER_MULTIPROC_METHOD: str = "fork"
@@ -169,10 +168,6 @@ environment_variables: dict[str, Callable[[], Any]] = {
    "FASTVIDEO_TRACE_FUNCTION":
    lambda: int(os.getenv("FASTVIDEO_TRACE_FUNCTION", "0")),

-    # if set, fastvideo will force attention to be computed in bfloat16
-    "FASTVIDEO_FORCE_ATTN_BF16":
-    lambda: bool(int(os.getenv("FASTVIDEO_FORCE_ATTN_BF16", "0"))),
-
    # Backend for attention computation
    # Available options:
    # - "TORCH_SDPA": use torch.nn.MultiheadAttention
@@ -158,7 +158,6 @@ class FastVideoArgs:
        "transformer": True,
        "vae": True,
    })
-    override_transformer_cls_name: str | None = None

    # # DMD parameters
    # dmd_denoising_steps: List[int] | None = field(default=None)
@@ -397,12 +396,6 @@ class FastVideoArgs:
            default=FastVideoArgs.enable_stage_verification,
            help="Enable input/output verification for pipeline stages",
        )
-        parser.add_argument(
-            "--override-transformer-cls-name",
-            type=str,
-            default=FastVideoArgs.override_transformer_cls_name,
-            help="Override transformer cls name",
-        )
        # Add pipeline configuration arguments
        PipelineConfig.add_cli_args(parser)

@@ -612,11 +605,6 @@ class TrainingArgs(FastVideoArgs):
    pretrained_model_name_or_path: str = ""
    dit_model_name_or_path: str = ""

-    # DMD model paths - separate paths for each network
-    generator_model_path: str = ""  # path for generator (student) model
-    real_score_model_path: str = ""  # path for real score (teacher) model
-    fake_score_model_path: str = ""  # path for fake score (critic) model
-
    # diffusion setting
    ema_decay: float = 0.0
    ema_start_step: int = 0
@@ -639,7 +627,6 @@ class TrainingArgs(FastVideoArgs):
    checkpoints_total_limit: int = 0
    checkpointing_steps: int = 0
    resume_from_checkpoint: str = ""  # specify the checkpoint folder to resume from
-    init_weights_from_safetensors: str = ""  # path to safetensors file for initial weight loading

    # optimizer & scheduler
    num_train_epochs: int = 0
@@ -671,7 +658,6 @@ class TrainingArgs(FastVideoArgs):
    linear_quadratic_threshold: float = 0.0
    linear_range: float = 0.0
    weight_decay: float = 0.0
-    betas: str = "0.9,0.999"  # betas for optimizer, format: "beta1,beta2"
    use_ema: bool = False
    multi_phased_distill_schedule: str = ""
    pred_decay_weight: float = 0.0
@@ -692,30 +678,16 @@ class TrainingArgs(FastVideoArgs):

    # distillation args
    generator_update_interval: int = 5
-    dfake_gen_update_ratio: int = 5  # self-forcing: how often to train generator vs critic
    min_timestep_ratio: float = 0.2
    max_timestep_ratio: float = 0.98
    real_score_guidance_scale: float = 3.5
    fake_score_learning_rate: float = 0.0  # separate learning rate for fake_score_transformer, if 0.0, use learning_rate
    fake_score_lr_scheduler: str = "constant"  # separate lr scheduler for fake_score_transformer, if not set, use lr_scheduler
-    fake_score_betas: str = "0.9,0.999"  # betas for fake score optimizer, format: "beta1,beta2"
    training_state_checkpointing_steps: int = 0  # for resuming training
    weight_only_checkpointing_steps: int = 0  # for inference
    log_visualization: bool = False
    # simulate generator forward to match inference
    simulate_generator_forward: bool = False
-    warp_denoising_step: bool = False
-    intermediate_latents_visualization: bool = False
-
-    # Self-forcing specific arguments
-    num_frame_per_block: int = 3
-    independent_first_frame: bool = False
-    enable_gradient_masking: bool = True
-    gradient_mask_last_n_frames: int = 21
-    validate_cache_structure: bool = False  # Debug flag for cache validation
-    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

    @classmethod
    def from_cli_args(cls, args: argparse.Namespace) -> "TrainingArgs":
@@ -817,20 +789,6 @@ class TrainingArgs(FastVideoArgs):
                            type=str,
                            help="Directory to cache models")

-        # DMD model paths - separate paths for each network
-        parser.add_argument(
-            "--generator-model-path",
-            type=str,
-            help="Path to generator (student) model for DMD distillation")
-        parser.add_argument(
-            "--real-score-model-path",
-            type=str,
-            help="Path to real score (teacher) model for DMD distillation")
-        parser.add_argument(
-            "--fake-score-model-path",
-            type=str,
-            help="Path to fake score (critic) model for DMD distillation")
-
        # Diffusion settings
        parser.add_argument("--ema-decay",
                            type=float,
@@ -901,10 +859,6 @@ class TrainingArgs(FastVideoArgs):
        parser.add_argument("--resume-from-checkpoint",
                            type=str,
                            help="Path to checkpoint to resume from")
-        parser.add_argument(
-            "--init-weights-from-safetensors",
-            type=str,
-            help="Path to safetensors file for initial weight loading")
        parser.add_argument("--logging-dir",
                            type=str,
                            help="Directory for logging")
@@ -1009,10 +963,6 @@ class TrainingArgs(FastVideoArgs):
                            help="Linear quadratic threshold")
        parser.add_argument("--linear-range", type=float, help="Linear range")
        parser.add_argument("--weight-decay", type=float, help="Weight decay")
-        parser.add_argument("--betas",
-                            type=str,
-                            default=TrainingArgs.betas,
-                            help="Betas for optimizer (format: 'beta1,beta2')")
        parser.add_argument("--use-ema",
                            action=StoreBoolean,
                            help="Whether to use EMA")
@@ -1063,13 +1013,6 @@ class TrainingArgs(FastVideoArgs):
                            type=int,
                            default=TrainingArgs.generator_update_interval,
                            help="Ratio of student updates to critic updates.")
-        parser.add_argument(
-            "--dfake-gen-update-ratio",
-            type=int,
-            default=TrainingArgs.dfake_gen_update_ratio,
-            help=
-            "Self-forcing: How often to train generator vs critic (train generator every N steps)."
-        )
        parser.add_argument("--min-timestep-ratio",
                            type=float,
                            default=TrainingArgs.min_timestep_ratio,
@@ -1086,11 +1029,6 @@ class TrainingArgs(FastVideoArgs):
                            type=float,
                            default=TrainingArgs.fake_score_learning_rate,
                            help="Learning rate for fake score transformer")
-        parser.add_argument(
-            "--fake-score-betas",
-            type=str,
-            default=TrainingArgs.fake_score_betas,
-            help="Betas for fake score optimizer (format: 'beta1,beta2')")
        parser.add_argument(
            "--fake-score-lr-scheduler",
            type=str,
@@ -1103,49 +1041,6 @@ class TrainingArgs(FastVideoArgs):
            "--simulate-generator-forward",
            action=StoreBoolean,
            help="Whether to simulate generator forward to match inference")
-        parser.add_argument(
-            "--warp-denoising-step",
-            action=StoreBoolean,
-            help=
-            "Whether to warp denoising step according to the scheduler time shift"
-        )
-
-        # Self-forcing specific arguments
-        parser.add_argument(
-            "--num-frame-per-block",
-            type=int,
-            default=TrainingArgs.num_frame_per_block,
-            help="Number of frames per block for causal generation")
-        parser.add_argument(
-            "--independent-first-frame",
-            action=StoreBoolean,
-            help="Whether the first frame is independent in causal generation")
-        parser.add_argument(
-            "--enable-gradient-masking",
-            action=StoreBoolean,
-            help="Whether to enable frame-level gradient masking")
-        parser.add_argument(
-            "--gradient-mask-last-n-frames",
-            type=int,
-            default=TrainingArgs.gradient_mask_last_n_frames,
-            help="Number of last frames to enable gradients for")
-        parser.add_argument(
-            "--validate-cache-structure",
-            action=StoreBoolean,
-            help="Whether to validate KV cache structure (debug flag)")
-        parser.add_argument(
-            "--same-step-across-blocks",
-            action=StoreBoolean,
-            help="Whether to use the same exit timestep for all blocks")
-        parser.add_argument(
-            "--last-step-only",
-            action=StoreBoolean,
-            help="Whether to only use the last timestep for training")
-        parser.add_argument(
-            "--context-noise",
-            type=int,
-            default=TrainingArgs.context_noise,
-            help="Context noise level for cache updates")

        return parser

@@ -1153,4 +1048,4 @@ class TrainingArgs(FastVideoArgs):
 def parse_int_list(value: str) -> list[int]:
    if not value:
        return []
-    return [int(x.strip()) for x in value.split(",")]
+    return [int(x.strip()) for x in value.split(",")]
@@ -212,9 +212,9 @@ class ScaleResidualLayerNormScaleShift(nn.Module):
            frame_seqlen = normalized.shape[1] // num_frames
            modulated = (
                normalized.unflatten(dim=1, sizes=(num_frames, frame_seqlen)) *
-                (1 + scale) + shift).flatten(1, 2)
+                (1.0 + scale) + shift).flatten(1, 2)
        else:
-            modulated = normalized * (1 + scale) + shift
+            modulated = normalized * (1.0 + scale) + shift
        return modulated, residual_output


@@ -267,11 +267,11 @@ class LayerNormScaleShift(nn.Module):
            frame_seqlen = normalized.shape[1] // num_frames
            output = (
                normalized.unflatten(dim=1, sizes=(num_frames, frame_seqlen)) *
-                (1 + scale) + shift).flatten(1, 2)
+                (1.0 + scale) + shift).flatten(1, 2)
        else:
            # scale.shape: [batch_size, 1, inner_dim]
            # shift.shape: [batch_size, 1, inner_dim]
-            output = normalized * (1 + scale) + shift
+            output = normalized * (1.0 + scale) + shift

        if self.compute_dtype == torch.float32:
            output = output.to(x.dtype)
@@ -77,11 +77,9 @@ class BaseLayerWithLoRA(nn.Module):
            lora_A = self.lora_A.to_local()

        if not self.merged and not self.disable_lora:
-            lora_A_sliced = self.slice_lora_a_weights(
-                lora_A.to(x, non_blocking=True))
-            lora_B_sliced = self.slice_lora_b_weights(
-                lora_B.to(x, non_blocking=True))
-            delta = x @ lora_A_sliced.T @ lora_B_sliced.T
+            delta = x @ (
+                self.slice_lora_b_weights(lora_B.to(x, non_blocking=True))
+                @ self.slice_lora_a_weights(lora_A.to(x, non_blocking=True)))
            if self.lora_alpha != self.lora_rank:
                delta = delta * (
                    self.lora_alpha / self.lora_rank  # type: ignore
@@ -147,9 +147,6 @@ class CausalWanSelfAttention(nn.Module):
                # 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
-                # kv_cache["k"] = kv_cache["k"].detach()
-                # kv_cache["v"] = kv_cache["v"].detach()
-                # logger.info("kv_cache['k'] is in comp graph: %s", kv_cache["k"].requires_grad or kv_cache["k"].grad_fn is not None)
                kv_cache["k"][:, local_start_index:local_end_index] = roped_key
                kv_cache["v"][:, local_start_index:local_end_index] = v
            x = self.attn(
@@ -179,7 +176,7 @@ class CausalWanTransformerBlock(nn.Module):
        super().__init__()

        # 1. Self-attention
-        self.norm1 = nn.LayerNorm(dim, eps, elementwise_affine=False)
+        self.norm1 = FP32LayerNorm(dim, eps, elementwise_affine=False)
        self.to_q = ReplicatedLinear(dim, dim, bias=True)
        self.to_k = ReplicatedLinear(dim, dim, bias=True)
        self.to_v = ReplicatedLinear(dim, dim, bias=True)
@@ -212,7 +209,8 @@ class CausalWanTransformerBlock(nn.Module):
            norm_type="layer",
            eps=eps,
            elementwise_affine=True,
-            dtype=torch.float32)
+            dtype=torch.float32,
+            compute_dtype=torch.float32)

        # 2. Cross-attention
        # Only T2V for now
@@ -225,7 +223,8 @@ class CausalWanTransformerBlock(nn.Module):
            norm_type="layer",
            eps=eps,
            elementwise_affine=False,
-            dtype=torch.float32)
+            dtype=torch.float32,
+            compute_dtype=torch.float32)

        # 3. Feed-forward
        self.ffn = MLP(dim, ffn_dim, act_type="gelu_pytorch_tanh")
@@ -250,34 +249,29 @@ class CausalWanTransformerBlock(nn.Module):
        if hidden_states.dim() == 4:
            hidden_states = hidden_states.squeeze(1)
        num_frames = temb.shape[1]
-        frame_seqlen = hidden_states.shape[1] // num_frames    
+        frame_seqlen = hidden_states.shape[1] // num_frames
        bs, seq_length, _ = hidden_states.shape
        orig_dtype = hidden_states.dtype
        # assert orig_dtype != torch.float32
-        e = self.scale_shift_table + temb
+        e = self.scale_shift_table + temb.float()
        # e.shape: [batch_size, num_frames, 6, inner_dim]
        assert e.shape == (bs, num_frames, 6, self.hidden_dim)
        shift_msa, scale_msa, gate_msa, c_shift_msa, c_scale_msa, c_gate_msa = e.chunk(
            6, dim=2)
        # *_msa.shape: [batch_size, num_frames, 1, inner_dim]
-        # assert shift_msa.dtype == torch.float32
-
-        # logger.info("temb sum: %s, dtype: %s", temb.float().sum().item(), temb.dtype)
-        # logger.info("scale_msa sum: %s, dtype: %s", scale_msa.float().sum().item(), scale_msa.dtype)
-        # logger.info("shift_msa sum: %s, dtype: %s", shift_msa.float().sum().item(), shift_msa.dtype)
+        assert shift_msa.dtype == torch.float32

        # 1. Self-attention
-        norm_hidden_states = (self.norm1(hidden_states).unflatten(dim=1, sizes=(num_frames, frame_seqlen)) *
-                        (1 + scale_msa) + shift_msa).flatten(1, 2)
-        # logger.info("norm_hidden_states sum: %s, shape: %s", norm_hidden_states.float().sum().item(), norm_hidden_states.shape)
+        norm_hidden_states = (self.norm1(hidden_states.float()).unflatten(dim=1, sizes=(num_frames, frame_seqlen)) *
+                        (1 + scale_msa) + shift_msa).flatten(1, 2).to(orig_dtype)
        query, _ = self.to_q(norm_hidden_states)
        key, _ = self.to_k(norm_hidden_states)
        value, _ = self.to_v(norm_hidden_states)

        if self.norm_q is not None:
-            query = self.norm_q.forward_native(query)
+            query = self.norm_q(query)
        if self.norm_k is not None:
-            key = self.norm_k.forward_native(key)
+            key = self.norm_k(key)

        query = query.squeeze(1).unflatten(2, (self.num_attention_heads, -1))
        key = key.squeeze(1).unflatten(2, (self.num_attention_heads, -1))
@@ -291,6 +285,8 @@ class CausalWanTransformerBlock(nn.Module):
        null_shift = null_scale = torch.tensor([0], device=hidden_states.device)
        norm_hidden_states, hidden_states = self.self_attn_residual_norm(
            hidden_states, attn_output, gate_msa, null_shift, null_scale)
+        norm_hidden_states, hidden_states = norm_hidden_states.to(
+            orig_dtype), hidden_states.to(orig_dtype)

        # 2. Cross-attention
        attn_output = self.attn2(norm_hidden_states,
@@ -299,10 +295,13 @@ class CausalWanTransformerBlock(nn.Module):
                                 crossattn_cache=crossattn_cache)
        norm_hidden_states, hidden_states = self.cross_attn_residual_norm(
            hidden_states, attn_output, 1, c_shift_msa, c_scale_msa)
+        norm_hidden_states, hidden_states = norm_hidden_states.to(
+            orig_dtype), hidden_states.to(orig_dtype)

        # 3. Feed-forward
        ff_output = self.ffn(norm_hidden_states)
        hidden_states = self.mlp_residual(hidden_states, ff_output, c_gate_msa)
+        hidden_states = hidden_states.to(orig_dtype)

        return hidden_states

@@ -365,7 +364,8 @@ class CausalWanTransformer3DModel(BaseDiT):
                                            norm_type="layer",
                                            eps=config.eps,
                                            elementwise_affine=False,
-                                            dtype=torch.float32)
+                                            dtype=torch.float32,
+                                            compute_dtype=torch.float32)
        self.proj_out = nn.Linear(
            inner_dim, config.out_channels * math.prod(config.patch_size))
        self.scale_shift_table = nn.Parameter(
@@ -375,7 +375,7 @@ class CausalWanTransformer3DModel(BaseDiT):

        # Causal-specific
        self.block_mask = None
-        self.num_frame_per_block = 3
+        self.num_frame_per_block = 1
        self.independent_first_frame = False

        self.__post_init__()
@@ -487,16 +487,12 @@ class CausalWanTransformer3DModel(BaseDiT):
        )
        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
+        freqs_cis = (freqs_cos.float(),
+                     freqs_sin.float()) if freqs_cos is not None else None

        hidden_states = self.patch_embedding(hidden_states)
-        grid_sizes = torch.stack(
-            [torch.tensor(hidden_states[0].shape[1:], dtype=torch.long)])
        hidden_states = hidden_states.flatten(2).transpose(1, 2)

-        encoder_hidden_states = torch.cat([encoder_hidden_states, encoder_hidden_states.new_zeros(1, self.text_len - encoder_hidden_states.size(1), encoder_hidden_states.size(2))], dim=1)
-
        temb, timestep_proj, encoder_hidden_states, encoder_hidden_states_image = self.condition_embedder(
                        timestep.flatten(), encoder_hidden_states, encoder_hidden_states_image)
        timestep_proj = timestep_proj.unflatten(1, (6, self.hidden_size)).unflatten(dim=0, sizes=timestep.shape)
@@ -543,9 +539,14 @@ class CausalWanTransformer3DModel(BaseDiT):
        hidden_states = self.norm_out(hidden_states, shift, scale)
        hidden_states = self.proj_out(hidden_states)

-        output = self.unpatchify(hidden_states, grid_sizes)
+        hidden_states = hidden_states.reshape(batch_size, post_patch_num_frames,
+                                              post_patch_height,
+                                              post_patch_width, p_t, p_h, p_w,
+                                              -1)
+        hidden_states = hidden_states.permute(0, 7, 1, 4, 2, 5, 3, 6)
+        output = hidden_states.flatten(6, 7).flatten(4, 5).flatten(2, 3)

-        return torch.stack(output)
+        return output

    def _forward_train(self,
                hidden_states: torch.Tensor,
@@ -556,8 +557,6 @@ class CausalWanTransformer3DModel(BaseDiT):
                start_frame: int = 0,
                **kwargs) -> torch.Tensor:

-        logger.info("timestep dtype: %s, timestep sum: %s", timestep.dtype, timestep.float().sum().item())
-
        orig_dtype = hidden_states.dtype
        if not isinstance(encoder_hidden_states, torch.Tensor):
            encoder_hidden_states = encoder_hidden_states[0]
@@ -588,8 +587,8 @@ class CausalWanTransformer3DModel(BaseDiT):
        )
        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
+        freqs_cis = (freqs_cos.float(),
+                     freqs_sin.float()) if freqs_cos is not None else None

        # Construct blockwise causal attn mask
        if self.block_mask is None:
@@ -602,12 +601,8 @@ class CausalWanTransformer3DModel(BaseDiT):
            )

        hidden_states = self.patch_embedding(hidden_states)
-        grid_sizes = torch.stack(
-            [torch.tensor(hidden_states[0].shape[1:], dtype=torch.long)])
        hidden_states = hidden_states.flatten(2).transpose(1, 2)

-        encoder_hidden_states = torch.cat([encoder_hidden_states, encoder_hidden_states.new_zeros(1, self.text_len - encoder_hidden_states.size(1), encoder_hidden_states.size(2))], dim=1)
-
        temb, timestep_proj, encoder_hidden_states, encoder_hidden_states_image = self.condition_embedder(
                        timestep.flatten(), encoder_hidden_states, encoder_hidden_states_image)
        timestep_proj = timestep_proj.unflatten(1, (6, self.hidden_size)).unflatten(dim=0, sizes=timestep.shape)
@@ -642,9 +637,14 @@ class CausalWanTransformer3DModel(BaseDiT):
        hidden_states = self.norm_out(hidden_states, shift, scale)
        hidden_states = self.proj_out(hidden_states)

-        output = self.unpatchify(hidden_states, grid_sizes)
+        hidden_states = hidden_states.reshape(batch_size, post_patch_num_frames,
+                                              post_patch_height,
+                                              post_patch_width, p_t, p_h, p_w,
+                                              -1)
+        hidden_states = hidden_states.permute(0, 7, 1, 4, 2, 5, 3, 6)
+        output = hidden_states.flatten(6, 7).flatten(4, 5).flatten(2, 3)

-        return torch.stack(output)
+        return output

    def forward(
        self,
@@ -652,34 +652,6 @@ class CausalWanTransformer3DModel(BaseDiT):
        **kwargs
    ):
        if kwargs.get('kv_cache', None) is not None:
-            noise_pred = self._forward_inference(*args, **kwargs)
+            return self._forward_inference(*args, **kwargs)
        else:
-            noise_pred = self._forward_train(*args, **kwargs)
-
-        return noise_pred
-
-    def unpatchify(self, x, grid_sizes):
-        r"""
-
-
-        Args:
-            x (List[Tensor]):
-                List of patchified features, each with shape [L, C_out * prod(patch_size)]
-            grid_sizes (Tensor):
-                Original spatial-temporal grid dimensions before patching,
-
-
-        Returns:
-            Tensor:
-                Reconstructed video tensors with shape [B, C_out, F, H / 8, W / 8]
-        """
-
-        c = self.out_channels
-        out = []
-        for u, v in zip(x, grid_sizes.tolist()):
-            u = u[:math.prod(v)].view(*v, *self.patch_size, c)
-            u = u.permute(6, 0, 3, 1, 4, 2, 5)
-            # u = torch.einsum('fhwpqrc->cfphqwr', u.contiguous())
-            u = u.reshape(c, *[i * j for i, j in zip(v, self.patch_size)])
-            out.append(u)
-        return out
+            return self._forward_train(*args, **kwargs)
@@ -1,5 +1,3 @@
-import torch
-import torch.nn as nn
 # SPDX-License-Identifier: Apache-2.0

 import math
@@ -39,14 +37,16 @@ class WanImageEmbedding(torch.nn.Module):
    def __init__(self, in_features: int, out_features: int):
        super().__init__()

-        self.norm1 = nn.LayerNorm(in_features)
+        self.norm1 = FP32LayerNorm(in_features)
        self.ff = MLP(in_features, in_features, out_features, act_type="gelu")
-        self.norm2 = nn.LayerNorm(out_features)
+        self.norm2 = FP32LayerNorm(out_features)

-    def forward(self, encoder_hidden_states_image: torch.Tensor) -> torch.Tensor:
+    def forward(self,
+                encoder_hidden_states_image: torch.Tensor) -> torch.Tensor:
+        dtype = encoder_hidden_states_image.dtype
        hidden_states = self.norm1(encoder_hidden_states_image)
        hidden_states = self.ff(hidden_states)
-        hidden_states = self.norm2(hidden_states)
+        hidden_states = self.norm2(hidden_states).to(dtype)
        return hidden_states


@@ -62,7 +62,7 @@ class WanTimeTextImageEmbedding(nn.Module):
        super().__init__()

        self.time_embedder = TimestepEmbedder(
-            dim, frequency_embedding_size=time_freq_dim, act_layer="silu", freq_dtype=torch.float64)
+            dim, frequency_embedding_size=time_freq_dim, act_layer="silu")
        self.time_modulation = ModulateProjection(dim,
                                                  factor=6,
                                                  act_layer="silu")
@@ -156,12 +156,12 @@ class WanT2VCrossAttention(WanSelfAttention):
        b, n, d = x.size(0), self.num_heads, self.head_dim

        # compute query, key, value
-        q = self.norm_q.forward_native(self.to_q(x)[0]).view(b, -1, n, d)
+        q = self.norm_q(self.to_q(x)[0]).view(b, -1, n, d)

        if crossattn_cache is not None:
            if not crossattn_cache["is_init"]:
                crossattn_cache["is_init"] = True
-                k = self.norm_k.forward_native(self.to_k(context)[0]).view(b, -1, n, d)
+                k = self.norm_k(self.to_k(context)[0]).view(b, -1, n, d)
                v = self.to_v(context)[0].view(b, -1, n, d)
                crossattn_cache["k"] = k
                crossattn_cache["v"] = v
@@ -169,16 +169,11 @@ class WanT2VCrossAttention(WanSelfAttention):
                k = crossattn_cache["k"]
                v = crossattn_cache["v"]
        else:
-            k = self.norm_k.forward_native(self.to_k(context)[0]).view(b, -1, n, d)
+            k = self.norm_k(self.to_k(context)[0]).view(b, -1, n, d)
            v = self.to_v(context)[0].view(b, -1, n, d)

-        if envs.FASTVIDEO_FORCE_ATTN_BF16:
-            out_dtype = v.dtype
-            # compute attention
-            x = self.attn(q.to(torch.bfloat16), k.to(torch.bfloat16), v.to(torch.bfloat16)).to(out_dtype)
-        else:
-            # compute attention
-            x = self.attn(q, k, v)
+        # compute attention
+        x = self.attn(q, k, v)

        # output
        x = x.flatten(2)
@@ -218,10 +213,10 @@ class WanI2VCrossAttention(WanSelfAttention):
        b, n, d = x.size(0), self.num_heads, self.head_dim

        # compute query, key, value
-        q = self.norm_q.forward_native(self.to_q(x)[0]).view(b, -1, n, d)
-        k = self.norm_k.forward_native(self.to_k(context)[0]).view(b, -1, n, d)
+        q = self.norm_q(self.to_q(x)[0]).view(b, -1, n, d)
+        k = self.norm_k(self.to_k(context)[0]).view(b, -1, n, d)
        v = self.to_v(context)[0].view(b, -1, n, d)
-        k_img = self.norm_added_k.forward_native(self.add_k_proj(context_img)[0]).view(
+        k_img = self.norm_added_k(self.add_k_proj(context_img)[0]).view(
            b, -1, n, d)
        v_img = self.add_v_proj(context_img)[0].view(b, -1, n, d)
        img_x = self.attn(q, k_img, v_img)
@@ -252,7 +247,7 @@ class WanTransformerBlock(nn.Module):
        super().__init__()

        # 1. Self-attention
-        self.norm1 = nn.LayerNorm(dim, eps, elementwise_affine=False)
+        self.norm1 = FP32LayerNorm(dim, eps, elementwise_affine=False)
        self.to_q = ReplicatedLinear(dim, dim, bias=True)
        self.to_k = ReplicatedLinear(dim, dim, bias=True)
        self.to_v = ReplicatedLinear(dim, dim, bias=True)
@@ -283,29 +278,29 @@ class WanTransformerBlock(nn.Module):
            norm_type="layer",
            eps=eps,
            elementwise_affine=True,
-            dtype=torch.float32)
+            dtype=torch.float32,
+            compute_dtype=torch.float32)

        # 2. Cross-attention
        if added_kv_proj_dim is not None:
            # I2V
-            self.attn2 = WanI2VCrossAttention(dim, 
-                                              num_heads, 
-                                              qk_norm=qk_norm, 
+            self.attn2 = WanI2VCrossAttention(dim,
+                                              num_heads,
+                                              qk_norm=qk_norm,
                                              eps=eps)
-
        else:
            # T2V
-            self.attn2 = WanT2VCrossAttention(dim, 
-                                              num_heads, 
-                                              qk_norm=qk_norm, 
+            self.attn2 = WanT2VCrossAttention(dim,
+                                              num_heads,
+                                              qk_norm=qk_norm,
                                              eps=eps)
-
        self.cross_attn_residual_norm = ScaleResidualLayerNormScaleShift(
-            dim, 
-            norm_type="layer", 
-            eps=eps, 
-            elementwise_affine=False, 
-            dtype=torch.float32)
+            dim,
+            norm_type="layer",
+            eps=eps,
+            elementwise_affine=False,
+            dtype=torch.float32,
+            compute_dtype=torch.float32)

        # 3. Feed-forward
        self.ffn = MLP(dim, ffn_dim, act_type="gelu_pytorch_tanh")
@@ -324,11 +319,12 @@ class WanTransformerBlock(nn.Module):
            hidden_states = hidden_states.squeeze(1)
        bs, seq_length, _ = hidden_states.shape
        orig_dtype = hidden_states.dtype
+        # assert orig_dtype != torch.float32

        if temb.dim() == 4:
            # temb: batch_size, seq_len, 6, inner_dim (wan2.2 ti2v)
            shift_msa, scale_msa, gate_msa, c_shift_msa, c_scale_msa, c_gate_msa = (
-                self.scale_shift_table.unsqueeze(0) + temb
+                self.scale_shift_table.unsqueeze(0) + temb.float()
            ).chunk(6, dim=2)
            # batch_size, seq_len, 1, inner_dim
            shift_msa = shift_msa.squeeze(2)
@@ -339,20 +335,22 @@ class WanTransformerBlock(nn.Module):
            c_gate_msa = c_gate_msa.squeeze(2)
        else:
            # temb: batch_size, 6, inner_dim (wan2.1/wan2.2 14B)
-            e = self.scale_shift_table + temb
+            e = self.scale_shift_table + temb.float()
            shift_msa, scale_msa, gate_msa, c_shift_msa, c_scale_msa, c_gate_msa = e.chunk(
                6, dim=1)
+        assert shift_msa.dtype == torch.float32

        # 1. Self-attention
-        norm_hidden_states = self.norm1(hidden_states) * (1 + scale_msa) + shift_msa
+        norm_hidden_states = (self.norm1(hidden_states.float()) *
+                              (1 + scale_msa) + shift_msa).to(orig_dtype)
        query, _ = self.to_q(norm_hidden_states)
        key, _ = self.to_k(norm_hidden_states)
        value, _ = self.to_v(norm_hidden_states)

        if self.norm_q is not None:
-            query = self.norm_q.forward_native(query)
+            query = self.norm_q(query)
        if self.norm_k is not None:
-            key = self.norm_k.forward_native(key)
+            key = self.norm_k(key)

        query = query.squeeze(1).unflatten(2, (self.num_attention_heads, -1))
        key = key.squeeze(1).unflatten(2, (self.num_attention_heads, -1))
@@ -364,12 +362,7 @@ class WanTransformerBlock(nn.Module):
                                       is_neox_style=False), _apply_rotary_emb(
                                           key, cos, sin, is_neox_style=False)

-        if envs.FASTVIDEO_FORCE_ATTN_BF16:
-            out_dtype = value.dtype
-            attn_output, _ = self.attn1(query.to(torch.bfloat16), key.to(torch.bfloat16), value.to(torch.bfloat16))
-            attn_output = attn_output.to(out_dtype)
-        else:
-            attn_output, _ = self.attn1(query, key, value)
+        attn_output, _ = self.attn1(query, key, value)
        attn_output = attn_output.flatten(2)
        attn_output, _ = self.to_out(attn_output)
        attn_output = attn_output.squeeze(1)
@@ -377,20 +370,26 @@ class WanTransformerBlock(nn.Module):
        null_shift = null_scale = torch.tensor([0], device=hidden_states.device)
        norm_hidden_states, hidden_states = self.self_attn_residual_norm(
            hidden_states, attn_output, gate_msa, null_shift, null_scale)
+        norm_hidden_states, hidden_states = norm_hidden_states.to(
+            orig_dtype), hidden_states.to(orig_dtype)

        # 2. Cross-attention
-        attn_output = self.attn2(norm_hidden_states, 
-                                 context=encoder_hidden_states, 
+        attn_output = self.attn2(norm_hidden_states,
+                                 context=encoder_hidden_states,
                                 context_lens=None)
        norm_hidden_states, hidden_states = self.cross_attn_residual_norm(
            hidden_states, attn_output, 1, c_shift_msa, c_scale_msa)
+        norm_hidden_states, hidden_states = norm_hidden_states.to(
+            orig_dtype), hidden_states.to(orig_dtype)

        # 3. Feed-forward
        ff_output = self.ffn(norm_hidden_states)
        hidden_states = self.mlp_residual(hidden_states, ff_output, c_gate_msa)
+        hidden_states = hidden_states.to(orig_dtype)

        return hidden_states

+
 class WanTransformerBlock_VSA(nn.Module):

    def __init__(self,
@@ -407,7 +406,7 @@ class WanTransformerBlock_VSA(nn.Module):
        super().__init__()

        # 1. Self-attention
-        self.norm1 = nn.LayerNorm(dim, eps, elementwise_affine=False)
+        self.norm1 = FP32LayerNorm(dim, eps, elementwise_affine=False)
        self.to_q = ReplicatedLinear(dim, dim, bias=True)
        self.to_k = ReplicatedLinear(dim, dim, bias=True)
        self.to_v = ReplicatedLinear(dim, dim, bias=True)
@@ -439,7 +438,8 @@ class WanTransformerBlock_VSA(nn.Module):
            norm_type="layer",
            eps=eps,
            elementwise_affine=True,
-            dtype=torch.float32)
+            dtype=torch.float32,
+            compute_dtype=torch.float32)

        # 2. Cross-attention
        if added_kv_proj_dim is not None:
@@ -459,7 +459,8 @@ class WanTransformerBlock_VSA(nn.Module):
            norm_type="layer",
            eps=eps,
            elementwise_affine=False,
-            dtype=torch.float32)
+            dtype=torch.float32,
+            compute_dtype=torch.float32)

        # 3. Feed-forward
        self.ffn = MLP(dim, ffn_dim, act_type="gelu_pytorch_tanh")
@@ -479,22 +480,23 @@ class WanTransformerBlock_VSA(nn.Module):
        bs, seq_length, _ = hidden_states.shape
        orig_dtype = hidden_states.dtype
        # assert orig_dtype != torch.float32
-        e = self.scale_shift_table + temb
+        e = self.scale_shift_table + temb.float()
        shift_msa, scale_msa, gate_msa, c_shift_msa, c_scale_msa, c_gate_msa = e.chunk(
            6, dim=1)
+        assert shift_msa.dtype == torch.float32

        # 1. Self-attention
-        norm_hidden_states = (self.norm1(hidden_states) *
-                              (1 + scale_msa) + shift_msa)
+        norm_hidden_states = (self.norm1(hidden_states.float()) *
+                              (1 + scale_msa) + shift_msa).to(orig_dtype)
        query, _ = self.to_q(norm_hidden_states)
        key, _ = self.to_k(norm_hidden_states)
        value, _ = self.to_v(norm_hidden_states)
        gate_compress, _ = self.to_gate_compress(norm_hidden_states)

        if self.norm_q is not None:
-            query = self.norm_q.forward_native(query)
+            query = self.norm_q(query)
        if self.norm_k is not None:
-            key = self.norm_k.forward_native(key)
+            key = self.norm_k(key)

        query = query.squeeze(1).unflatten(2, (self.num_attention_heads, -1))
        key = key.squeeze(1).unflatten(2, (self.num_attention_heads, -1))
@@ -519,6 +521,8 @@ class WanTransformerBlock_VSA(nn.Module):
        null_shift = null_scale = torch.tensor([0], device=hidden_states.device)
        norm_hidden_states, hidden_states = self.self_attn_residual_norm(
            hidden_states, attn_output, gate_msa, null_shift, null_scale)
+        norm_hidden_states, hidden_states = norm_hidden_states.to(
+            orig_dtype), hidden_states.to(orig_dtype)

        # 2. Cross-attention
        attn_output = self.attn2(norm_hidden_states,
@@ -526,15 +530,17 @@ class WanTransformerBlock_VSA(nn.Module):
                                 context_lens=None)
        norm_hidden_states, hidden_states = self.cross_attn_residual_norm(
            hidden_states, attn_output, 1, c_shift_msa, c_scale_msa)
+        norm_hidden_states, hidden_states = norm_hidden_states.to(
+            orig_dtype), hidden_states.to(orig_dtype)

        # 3. Feed-forward
        ff_output = self.ffn(norm_hidden_states)
        hidden_states = self.mlp_residual(hidden_states, ff_output, c_gate_msa)
+        hidden_states = hidden_states.to(orig_dtype)

        return hidden_states


-
 class WanTransformer3DModel(CachableDiT):
    _fsdp_shard_conditions = WanVideoConfig()._fsdp_shard_conditions
    _compile_conditions = WanVideoConfig()._compile_conditions
@@ -592,7 +598,8 @@ class WanTransformer3DModel(CachableDiT):
                                            norm_type="layer",
                                            eps=config.eps,
                                            elementwise_affine=False,
-                                            dtype=torch.float32)
+                                            dtype=torch.float32,
+                                            compute_dtype=torch.float32)
        self.proj_out = nn.Linear(
            inner_dim, config.out_channels * math.prod(config.patch_size))
        self.scale_shift_table = nn.Parameter(
@@ -652,12 +659,10 @@ class WanTransformer3DModel(CachableDiT):
            rope_theta=10000)
        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
+        freqs_cis = (freqs_cos.float(),
+                     freqs_sin.float()) if freqs_cos is not None else None

        hidden_states = self.patch_embedding(hidden_states)
-        grid_sizes = torch.stack(
-            [torch.tensor(hidden_states[0].shape[1:], dtype=torch.long)])
        hidden_states = hidden_states.flatten(2).transpose(1, 2)

        # timestep shape: batch_size, or batch_size, seq_len (wan 2.2 ti2v)
@@ -667,8 +672,6 @@ class WanTransformer3DModel(CachableDiT):
        else:
            ts_seq_len = None

-        encoder_hidden_states = torch.cat([encoder_hidden_states, encoder_hidden_states.new_zeros(1, self.text_len - encoder_hidden_states.size(1), encoder_hidden_states.size(2))], dim=1)
-
        temb, timestep_proj, encoder_hidden_states, encoder_hidden_states_image = self.condition_embedder(
            timestep, encoder_hidden_states, encoder_hidden_states_image, timestep_seq_len=ts_seq_len)
        if ts_seq_len is not None:
@@ -725,35 +728,14 @@ class WanTransformer3DModel(CachableDiT):
        hidden_states = self.norm_out(hidden_states, shift, scale)
        hidden_states = self.proj_out(hidden_states)

-        output = self.unpatchify(hidden_states, grid_sizes)
+        hidden_states = hidden_states.reshape(batch_size, post_patch_num_frames,
+                                              post_patch_height,
+                                              post_patch_width, p_t, p_h, p_w,
+                                              -1)
+        hidden_states = hidden_states.permute(0, 7, 1, 4, 2, 5, 3, 6)
+        output = hidden_states.flatten(6, 7).flatten(4, 5).flatten(2, 3)

-        return torch.stack(output)
-    
-    def unpatchify(self, x, grid_sizes):
-        r"""
-
-
-        Args:
-            x (List[Tensor]):
-                List of patchified features, each with shape [L, C_out * prod(patch_size)]
-            grid_sizes (Tensor):
-                Original spatial-temporal grid dimensions before patching,
-
-
-        Returns:
-            Tensor:
-                Reconstructed video tensors with shape [B, C_out, F, H / 8, W / 8]
-        """
-
-        c = self.out_channels
-        out = []
-        for u, v in zip(x, grid_sizes.tolist()):
-            u = u[:math.prod(v)].view(*v, *self.patch_size, c)
-            u = u.permute(6, 0, 3, 1, 4, 2, 5)
-            # u = torch.einsum('fhwpqrc->cfphqwr', u.contiguous())
-            u = u.reshape(c, *[i * j for i, j in zip(v, self.patch_size)])
-            out.append(u)
-        return out
+        return output

    def maybe_cache_states(self, hidden_states: torch.Tensor,
                           original_hidden_states: torch.Tensor) -> None:
@@ -845,4 +827,5 @@ class WanTransformer3DModel(CachableDiT):
        if self.is_even:
            return hidden_states + self.previous_residual_even
        else:
-            return hidden_states + self.previous_residual_odd
+            return hidden_states + self.previous_residual_odd
+        
@@ -238,7 +238,6 @@ class TextEncoderLoader(ComponentLoader):
                1]

        target_device = get_local_torch_device()
-        logger.info("Loading text encoder in %s precision", encoder_precision)
        # TODO(will): add support for other dtypes
        return self.load_model(model_path, encoder_config, target_device,
                               fastvideo_args, encoder_precision)
@@ -416,10 +415,6 @@ class TransformerLoader(ComponentLoader):
            raise ValueError(
                "Model config does not contain a _class_name attribute. "
                "Only diffusers format is supported.")
-        logger.info("transformer cls_name: %s", cls_name)
-        if fastvideo_args.override_transformer_cls_name is not None:
-            cls_name = fastvideo_args.override_transformer_cls_name
-            logger.info("Overriding transformer cls_name to %s", cls_name)

        fastvideo_args.model_paths["transformer"] = model_path

@@ -435,23 +430,11 @@ class TransformerLoader(ComponentLoader):
        if not safetensors_list:
            raise ValueError(f"No safetensors files found in {model_path}")

-        # Check if we should use custom initialization weights
-        custom_weights_path = getattr(fastvideo_args, 'init_weights_from_safetensors', None)
-        use_custom_weights = (custom_weights_path and os.path.exists(custom_weights_path) and 
-                            fastvideo_args.training_mode and 
-                            not hasattr(fastvideo_args, '_loading_teacher_critic_model'))
-
-        if use_custom_weights:
-            logger.info("Using custom initialization weights from: %s", custom_weights_path)
-            safetensors_list = [custom_weights_path]
-
        logger.info("Loading model from %s safetensors files in %s",
                    len(safetensors_list), model_path)

        default_dtype = PRECISION_TO_TYPE[
            fastvideo_args.pipeline_config.dit_precision]
-        param_dtype = PRECISION_TO_TYPE[
-            fastvideo_args.pipeline_config.dit_forward_precision]

        # Load the model using FSDP loader
        logger.info("Loading model from %s, default_dtype: %s", cls_name,
@@ -471,8 +454,7 @@ class TransformerLoader(ComponentLoader):
            pin_cpu_memory=fastvideo_args.pin_cpu_memory,
            fsdp_inference=fastvideo_args.use_fsdp_inference,
            # TODO(will): make these configurable
-            default_dtype=default_dtype,
-            param_dtype=param_dtype,
+            param_dtype=torch.bfloat16,
            reduce_dtype=torch.float32,
            output_dtype=None,
            training_mode=fastvideo_args.training_mode)
@@ -481,11 +463,9 @@ class TransformerLoader(ComponentLoader):
        total_params = sum(p.numel() for p in model.parameters())
        logger.info("Loaded model with %.2fB parameters", total_params / 1e9)

-        # Need to convert the model to the default_dtype
-        # Otherwise, the model master weights will be in param_dtype, and the gradients will also be in param_dtype
-        # This means the param update will be in lower precision, causing precision loss
-        logger.info("Converting model to dtype: %s", default_dtype)
-        model = model.to(default_dtype)
+        dtypes = set(param.dtype for param in model.parameters())
+        if len(dtypes) > 1:
+            model = model.to(default_dtype)
        model = model.eval()
        return model

@@ -62,7 +62,6 @@ def maybe_load_fsdp_model(
    device: torch.device,
    hsdp_replicate_dim: int,
    hsdp_shard_dim: int,
-    default_dtype: torch.dtype,
    param_dtype: torch.dtype,
    reduce_dtype: torch.dtype,
    cpu_offload: bool = False,
@@ -88,7 +87,7 @@ def maybe_load_fsdp_model(
        mp_policy=mp_policy,
    )

-    with set_default_dtype(default_dtype), torch.device("meta"):
+    with set_default_dtype(param_dtype), torch.device("meta"):
        model = model_cls(**init_params)

    # Check if we should use FSDP
@@ -126,7 +125,7 @@ def maybe_load_fsdp_model(
        model,
        weight_iterator,
        device,
-        default_dtype,
+        param_dtype,
        strict=True,
        cpu_offload=cpu_offload,
        param_names_mapping=param_names_mapping_fn,
@@ -635,31 +635,8 @@ class FlowMatchEulerDiscreteScheduler(SchedulerMixin, ConfigMixin,
        noise: torch.Tensor,
        timestep: torch.IntTensor,
    ) -> torch.Tensor:
-
-        """
-        Args:
-            clean_latent: the clean latent with shape [B, C, H, W],
-                where B is batch_size or batch_size * num_frames
-            noise: the noise with shape [B, C, H, W]
-            timestep: the timestep with shape [1] or [bs * num_frames] or [bs, num_frames]
-
-        Returns:
-            the corrupted latent with shape [B, C, H, W]
-        """
-        # If timestep is [bs, num_frames]
-        if timestep.ndim == 2:
-            timestep = timestep.flatten(0, 1)
-            assert timestep.numel() == clean_latent.shape[0]
-        elif timestep.ndim == 1:
-            # If timestep is [1]
-            if timestep.shape[0] == 1:
-                timestep = timestep.expand(clean_latent.shape[0])
-            else:
-                assert timestep.numel() == clean_latent.shape[0]
-        else:
-            raise ValueError(f"[add_noise] Invalid timestep shape: {timestep.shape}")
-        # timestep shape should be [B]
        self.sigmas = self.sigmas.to(noise.device)
+        timestep = timestep.expand(clean_latent.shape[0])
        self.timesteps = self.timesteps.to(noise.device)
        timestep_id = torch.argmin(
            (self.timesteps.unsqueeze(0) - timestep.unsqueeze(1)).abs(), dim=1)
@@ -22,10 +22,8 @@ class SelfForcingFlowMatchSchedulerOutput(BaseOutput):
    prev_sample: torch.FloatTensor

 class SelfForcingFlowMatchScheduler(BaseScheduler, ConfigMixin, SchedulerMixin):
-    
-    config_name = "scheduler_config.json"
+
    order = 1
-    @register_to_config
    def __init__(self, num_inference_steps=100, num_train_timesteps=1000, shift=3.0, sigma_max=1.0, sigma_min=0.003 / 1.002, inverse_timesteps=False, extra_one_step=False, reverse_sigmas=False, training=False):
        self.num_train_timesteps = num_train_timesteps
        self.shift = shift
@@ -64,15 +62,8 @@ class SelfForcingFlowMatchScheduler(BaseScheduler, ConfigMixin, SchedulerMixin):
    def step(self, model_output: torch.FloatTensor, timestep: torch.FloatTensor, sample: torch.FloatTensor, to_final=False, return_dict=False, **kwargs):
        if timestep.ndim == 2:
            timestep = timestep.flatten(0, 1)
-        elif timestep.ndim == 0:
-            # handles the case where timestep is a scalar, this occurs when we
-            # use this scheduler for ODE trajectory
-            timestep = timestep.unsqueeze(0)
-
        self.sigmas = self.sigmas.to(model_output.device)
        self.timesteps = self.timesteps.to(model_output.device)
-        timestep = timestep.to(model_output.device)
-
        timestep_id = torch.argmin(
            (self.timesteps.unsqueeze(0) - timestep.unsqueeze(1)).abs(), dim=1)
        sigma = self.sigmas[timestep_id].reshape(-1, 1, 1, 1)
@@ -171,34 +171,12 @@ def pred_noise_to_pred_video(pred_noise: torch.Tensor,
    # timestep shape should be [B]
    dtype = pred_noise.dtype
    device = pred_noise.device
-    pred_noise = pred_noise.double().to(device)
-    noise_input_latent = noise_input_latent.double().to(device)
-    sigmas = scheduler.sigmas.double().to(device)
-    timesteps = scheduler.timesteps.double().to(device)
+    pred_noise = pred_noise.float().to(device)
+    noise_input_latent = noise_input_latent.float().to(device)
+    sigmas = scheduler.sigmas.float().to(device)
+    timesteps = scheduler.timesteps.float().to(device)
    timestep_id = torch.argmin(
        (timesteps.unsqueeze(0) - timestep.unsqueeze(1)).abs(), dim=1)
    sigma_t = sigmas[timestep_id].reshape(-1, 1, 1, 1)
    pred_video = noise_input_latent - sigma_t * pred_noise
    return pred_video.to(dtype)
-
-def pred_video_to_pred_noise(x0_pred: torch.Tensor, xt: torch.Tensor, timestep: torch.Tensor, scheduler: Any) -> torch.Tensor:
-    """
-    Convert x0 prediction to flow matching's prediction.
-    x0_pred: the x0 prediction with shape [B, C, H, W]
-    xt: the input noisy data with shape [B, C, H, W]
-    timestep: the timestep with shape [B]
-
-    pred = (x_t - x_0) / sigma_t
-    """
-    # use higher precision for calculations
-    original_dtype = x0_pred.dtype
-    x0_pred, xt, sigmas, timesteps = map(
-        lambda x: x.double().to(x0_pred.device), [x0_pred, xt,
-                                                    scheduler.sigmas,
-                                                    scheduler.timesteps]
-    )
-    timestep_id = torch.argmin(
-        (timesteps.unsqueeze(0) - timestep.unsqueeze(1)).abs(), dim=1)
-    sigma_t = sigmas[timestep_id].reshape(-1, 1, 1, 1)
-    flow_pred = (xt - x0_pred) / sigma_t
-    return flow_pred.to(original_dtype)
@@ -28,6 +28,10 @@ class WanCausalDMDPipeline(LoRAPipeline, ComposedPipelineBase):
        "text_encoder", "tokenizer", "vae", "transformer", "scheduler"
    ]

+    def initialize_pipeline(self, fastvideo_args: FastVideoArgs):
+        self.modules["scheduler"] = FlowMatchEulerDiscreteScheduler(
+            shift=fastvideo_args.pipeline_config.flow_shift)
+
    def create_pipeline_stages(self, fastvideo_args: FastVideoArgs) -> None:
        """Set up pipeline stages with proper dependency injection."""

@@ -121,25 +121,14 @@ class ComposedPipelineBase(ABC):
                        model_path: str,
                        device: str | None = None,
                        torch_dtype: torch.dtype | None = None,
-                        pipeline_config: PipelineConfig | None = None,
+                        pipeline_config: str | PipelineConfig | None = None,
                        args: argparse.Namespace | None = None,
                        required_config_modules: list[str] | None = None,
                        loaded_modules: dict[str, torch.nn.Module]
                        | None = None,
                        **kwargs) -> "ComposedPipelineBase":
        """
-        Load a pipeline from a pretrained model. 
-        Few different patterns are supported:
-        - Only provide model_path:
-            - This will load the pipeline in inference mode.
-            - The pipeline will be initialized with the default config.
-            - The pipeline will be initialized with the default modules.
-            - The pipeline will be initialized with the default stages.
-            - The pipeline will be initialized with the default stages.
-        - override the default config using pipeline_config or args or kwargs
-        - override the default modules using loaded_modules
-        - override the pipelineconfig
-
+        Load a pipeline from a pretrained model.
        loaded_modules: Optional[Dict[str, torch.nn.Module]] = None,
        If provided, loaded_modules will be used instead of loading from config/pretrained weights.
        """
@@ -147,18 +136,9 @@ class ComposedPipelineBase(ABC):

            kwargs['model_path'] = model_path
            fastvideo_args = FastVideoArgs.from_kwargs(**kwargs)
-            if pipeline_config is not None:
-                fastvideo_args.pipeline_config = pipeline_config
-            if fastvideo_args.override_transformer_cls_name is not None:
-                pipeline_config = PipelineConfig.from_pretrained("wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers")
-                fastvideo_args.pipeline_config = pipeline_config
        else:
            assert args is not None, "args must be provided for training mode"
            fastvideo_args = TrainingArgs.from_cli_args(args)
-            if fastvideo_args.override_transformer_cls_name is not None:
-                pipeline_config = PipelineConfig.from_pretrained("wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers")
-                fastvideo_args.pipeline_config = pipeline_config
-                logger.info("in 2 Overriding transformer cls name to %s", fastvideo_args.override_transformer_cls_name)
            # TODO(will): fix this so that its not so ugly
            fastvideo_args.model_path = model_path
            for key, value in kwargs.items():
@@ -169,8 +149,7 @@ class ComposedPipelineBase(ABC):
            # model is loaded with the correct precision. Subsequently we will
            # use FSDP2's MixedPrecisionPolicy to set the precision for the
            # fwd, bwd, and other operations' precision.
-            fastvideo_args.pipeline_config.dit_precision = 'fp32'
-            # assert fastvideo_args.pipeline_config.dit_precision == 'fp32', 'only fp32 is supported for training'
+            assert fastvideo_args.pipeline_config.dit_precision == 'fp32', 'only fp32 is supported for training'

        logger.info("fastvideo_args in from_pretrained: %s", fastvideo_args)

@@ -258,19 +237,20 @@ class ComposedPipelineBase(ABC):
        # remove keys that are not pipeline modules
        model_index.pop("_class_name")
        model_index.pop("_diffusers_version")
+        # @TODO(Wei): Temporary hack
        if "boundary_ratio" in model_index and model_index[
                "boundary_ratio"] is not None:
            logger.info(
                "MoE pipeline detected. Adding transformer_2 to self.required_config_modules..."
            )
            self.required_config_modules.append("transformer_2")
-            logger.info("MoE pipeline detected. Setting boundary ratio to %s",
-                        model_index["boundary_ratio"])
-            fastvideo_args.pipeline_config.dit_config.boundary_ratio = model_index[
-                "boundary_ratio"]
+            if fastvideo_args.boundary_ratio is None:
+                logger.info(
+                    "MoE pipeline detected. Setting boundary ratio to %s",
+                    model_index["boundary_ratio"])
+                fastvideo_args.boundary_ratio = model_index["boundary_ratio"]

        model_index.pop("boundary_ratio", None)
-        # used by Wan2.2 ti2v
        model_index.pop("expand_timesteps", None)

        # some sanity checks
@@ -303,8 +283,8 @@ class ComposedPipelineBase(ABC):
                          architecture) in model_index.items():
            if transformers_or_diffusers is None:
                logger.warning(
-                    "Module %s in model_index.json has null value, removing from required_config_modules",
-                    module_name)
+                    "Module in model_index.json has null value, removing from required_config_modules"
+                )
                if module_name in self.required_config_modules:
                    self.required_config_modules.remove(module_name)
                continue
@@ -129,7 +129,6 @@ class ForwardBatch:
    timesteps: torch.Tensor | None = None
    timestep: torch.Tensor | float | int | None = None
    step_index: int | None = None
-    boundary_ratio: float | None = None

    # Scheduler parameters
    num_inference_steps: int = 50
@@ -151,7 +150,7 @@ class ForwardBatch:
    output: torch.Tensor | None = None
    return_trajectory_latents: bool = False
    return_trajectory_decoded: bool = False
-    trajectory_timesteps: list[int] | None = None
+    trajectory_timesteps: list[torch.Tensor] | None = None
    trajectory_latents: torch.Tensor | None = None
    trajectory_decoded: list[torch.Tensor] | None = None

@@ -246,7 +245,6 @@ class TrainingBatch:
    fake_score_loss: float = 0.0

    dmd_latent_vis_dict: dict[str, Any] = field(default_factory=dict)
-    latent_vis_dict: dict[str, torch.Tensor] = field(default_factory=dict)
    fake_score_latent_vis_dict: dict[str, Any] = field(default_factory=dict)


@@ -10,8 +10,6 @@ from torch.utils.data import DataLoader
 from tqdm import tqdm

 from fastvideo.dataset import getdataset
-from fastvideo.dataset.dataloader.parquet_io import (ParquetDatasetWriter,
-                                                     records_to_table)
 from fastvideo.dataset.preprocessing_datasets import PreprocessBatch
 from fastvideo.distributed import get_local_torch_device
 from fastvideo.fastvideo_args import FastVideoArgs
@@ -19,6 +17,8 @@ from fastvideo.logger import init_logger
 from fastvideo.pipelines.composed_pipeline_base import ComposedPipelineBase
 from fastvideo.pipelines.pipeline_batch_info import ForwardBatch
 from fastvideo.pipelines.stages import TextEncodingStage
+from fastvideo.workflow.preprocess.parquet_io import (ParquetDatasetWriter,
+                                                      records_to_table)

 logger = init_logger(__name__)

@@ -423,3 +423,7 @@ class BasePreprocessPipeline(ComposedPipelineBase):
                written = self.dataset_writer.flush()
                logger.info("Flushed %s samples to parquet", written)
                num_processed_samples = 0
+
+    def _final_flush_if_any(self):
+        if hasattr(self, 'dataset_writer'):
+            self.dataset_writer.flush()
@@ -15,17 +15,12 @@ from typing import Any
 import numpy as np
 import pyarrow as pa
 import torch
-from PIL import Image
 from torch.utils.data import DataLoader
 from torchdata.stateful_dataloader import StatefulDataLoader
 from tqdm import tqdm

 from fastvideo.configs.sample import SamplingParam
 from fastvideo.dataset import gettextdataset
-from fastvideo.dataset.dataloader.parquet_io import (ParquetDatasetWriter,
-                                                     records_to_table)
-from fastvideo.dataset.dataloader.record_schema import (
-    ode_text_only_record_creator)
 from fastvideo.dataset.dataloader.schema import (
    pyarrow_schema_ode_trajectory_text_only)
 from fastvideo.fastvideo_args import FastVideoArgs
@@ -41,11 +36,8 @@ from fastvideo.pipelines.stages import (DecodingStage, DenoisingStage,
                                        TextEncodingStage,
                                        TimestepPreparationStage)
 from fastvideo.utils import save_decoded_latents_as_video, shallow_asdict
-
-from fastvideo.forward_context import set_forward_context
-from fastvideo.models.utils import pred_noise_to_pred_video, pred_video_to_pred_noise
-from fastvideo.models.loader.component_loader import TransformerLoader
-from fastvideo.distributed import get_local_torch_device
+from fastvideo.workflow.preprocess.parquet_io import (ParquetDatasetWriter,
+                                                      records_to_table)

 logger = init_logger(__name__)

@@ -67,22 +59,26 @@ class PreprocessPipeline_ODE_Trajectory(BasePreprocessPipeline):

    def create_pipeline_stages(self, fastvideo_args: FastVideoArgs):
        """Set up pipeline stages with proper dependency injection."""
+        logger.info('WTF flow_shift: %s',
+                    fastvideo_args.pipeline_config.flow_shift)
        assert fastvideo_args.pipeline_config.flow_shift == 5
+        # self.modules["scheduler"] = FlowMatchEulerDiscreteScheduler(
+        # shift=fastvideo_args.pipeline_config.flow_shift)
        self.modules["scheduler"] = SelfForcingFlowMatchScheduler(
            shift=fastvideo_args.pipeline_config.flow_shift,
            sigma_min=0.0,
            extra_one_step=True)
        self.modules["scheduler"].set_timesteps(num_inference_steps=48,
                                                denoising_strength=1.0)
-
-        fastvideo_args.model_loaded["transformer"] = False
-        loader = TransformerLoader()
-        fastvideo_args.pipeline_config.dit_precision = "fp32" # Overwrite the precision to fp32 for transformer
-        fastvideo_args.pipeline_config.dit_forward_precision = "fp32"
-        self.transformer = loader.load(
-            fastvideo_args.model_paths["transformer"], fastvideo_args)
-        self.add_module("transformer", self.transformer)
-        fastvideo_args.model_loaded["transformer"] = True
+        # logger.info('WTF scheduler timesteps: %s',
+        #             self.modules["scheduler"].timesteps)
+        # scheduler = FlowMatchScheduler(
+        #     shift=8.0, sigma_min=0.0, extra_one_step=True)
+        # device = get_local_torch_device()
+        # # scheduler.num_train_timesteps = 100
+        # scheduler.set_timesteps(num_inference_steps=50, denoising_strength=1.0)
+        # scheduler.sigmas = scheduler.sigmas.to(device)
+        # self.modules["scheduler"] = scheduler

        self.add_stage(stage_name="input_validation_stage",
                       stage=InputValidationStage())
@@ -112,7 +108,6 @@ class PreprocessPipeline_ODE_Trajectory(BasePreprocessPipeline):
        """Preprocess text-only data and generate trajectory information."""

        for batch_idx, data in enumerate(self.pbar):
-            # logger.info("transformer weight sum: %s", sum(p.float().sum().item() for p in self.transformer.parameters()))
            if data is None:
                continue

@@ -143,7 +138,6 @@ class PreprocessPipeline_ODE_Trajectory(BasePreprocessPipeline):
                    ]

                batch_captions = valid_data["text"]
-                self.prompt_encoding_stage.text_encoders[0] = self.prompt_encoding_stage.text_encoders[0].to(dtype=torch.bfloat16).to(dtype=torch.float32)
                # Encode text using the standalone TextEncodingStage API
                prompt_embeds_list, prompt_masks_list = self.prompt_encoding_stage.encode_text(
                    batch_captions,
@@ -152,25 +146,22 @@ class PreprocessPipeline_ODE_Trajectory(BasePreprocessPipeline):
                    return_attention_mask=True,
                )
                prompt_embeds = prompt_embeds_list[0]
-                logger.info("prompt_embeds sum: %s, prompt_embeds shape: %s, prompt_embeds dtype: %s", prompt_embeds.float().sum(), prompt_embeds.shape, prompt_embeds.dtype)
                prompt_attention_masks = prompt_masks_list[0]
                assert prompt_embeds.shape[0] == prompt_attention_masks.shape[0]

                sampling_params = SamplingParam.from_pretrained(args.model_path)

-                negative_prompt = '色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走'
-
                # encode negative prompt for trajectory collection
                if sampling_params.guidance_scale > 1 and sampling_params.negative_prompt is not None:
                    negative_prompt_embeds_list, negative_prompt_masks_list = self.prompt_encoding_stage.encode_text(
-                        negative_prompt,
+                        sampling_params.negative_prompt,
                        fastvideo_args,
                        encoder_index=[0],
                        return_attention_mask=True,
                    )
-                    negative_prompt_embed = negative_prompt_embeds_list[0]
+                    negative_prompt_embed = negative_prompt_embeds_list[0][0]
                    negative_prompt_attention_mask = negative_prompt_masks_list[
-                        0]
+                        0][0]
                else:
                    negative_prompt_embed = None
                    negative_prompt_attention_mask = None
@@ -195,131 +186,30 @@ class PreprocessPipeline_ODE_Trajectory(BasePreprocessPipeline):
                    ]
                    batch.num_inference_steps = 48
                    batch.return_trajectory_latents = True
-                    # Enabling this will save the decoded trajectory videos.
-                    # Used for debugging.
-                    batch.return_trajectory_decoded = True
+                    batch.return_trajectory_decoded = False
                    batch.height = args.max_height
                    batch.width = args.max_width
                    batch.fps = args.train_fps
-                    batch.guidance_scale = 3.0
+                    batch.guidance_scale = 6.0
                    batch.do_classifier_free_guidance = True

                    result_batch = self.input_validation_stage(
                        batch, fastvideo_args)
                    result_batch = self.timestep_preparation_stage(
                        batch, fastvideo_args)
-                    # result_batch = self.latent_preparation_stage(
-                    #     result_batch, fastvideo_args)
-                    # result_batch = self.denoising_stage(result_batch,
-                    #                                     fastvideo_args)
-                    noisy_input = []
-                    # latents = result_batch.latents.permute(0, 2, 1, 3, 4)
-                    latents = torch.randn(
-                        [1, 21, 16, 60, 104], dtype=torch.float32, device=get_local_torch_device()
-                    )
-                    # logger.info("transformer weight sum: %s", sum(p.float().sum().item() for p in self.transformer.parameters()))
-                    logger.info("latents sum: %s, latents shape: %s, latents dtype: %s", latents.float().sum(), latents.shape, latents.dtype)
-
-                    logger.info("scheduler timesteps: %s", self.get_module("scheduler").timesteps)
-                    for progress_id, t in enumerate(tqdm(self.get_module("scheduler").timesteps)):
-                        timestep = t * \
-                            torch.ones([1, 21], device=latents.device, dtype=torch.float32)
-
-                        noisy_input.append(latents)
-
-                        with set_forward_context(
-                            current_timestep=0,
-                            attn_metadata=None,
-                            forward_batch=None,
-                        ):
-                            # logger.info("prompt_embed sum: %s, prompt_embed shape: %s, prompt_embed dtype: %s", prompt_embed.float().sum(), prompt_embed.shape, prompt_embed.dtype)
-                            # logger.info("timestep: %s", timestep[:, 0])
-                            # Run transformer
-                            cond_pred_noise_btchw = self.transformer(
-                                hidden_states=latents.permute(0, 2, 1, 3, 4),
-                                encoder_hidden_states=prompt_embed,
-                                timestep=timestep[:, 0]
-                            ).permute(0, 2, 1, 3, 4)
-
-                        # logger.info("cond_pred_noise_btchw sum: %s, cond_pred_noise_btchw shape: %s, cond_pred_noise_btchw dtype: %s", cond_pred_noise_btchw.float().sum(), cond_pred_noise_btchw.shape, cond_pred_noise_btchw.dtype)
-
-                        cond_pred_video_btchw = pred_noise_to_pred_video(
-                            pred_noise=cond_pred_noise_btchw.flatten(0, 1),
-                            noise_input_latent=latents.flatten(0, 1),
-                            timestep=timestep.flatten(0, 1),
-                            scheduler=self.get_module("scheduler")).unflatten(
-                                0, cond_pred_noise_btchw.shape[:2])
-
-                        # logger.info("cond_pred_video_btchw sum: %s, cond_pred_video_btchw shape: %s, cond_pred_video_btchw dtype: %s", cond_pred_video_btchw.float().sum(), cond_pred_video_btchw.shape, cond_pred_video_btchw.dtype)
-
-                        with set_forward_context(
-                            current_timestep=t,
-                            attn_metadata=None,
-                            forward_batch=result_batch,
-                        ):
-                            # logger.info("latents sum: %s, latents shape: %s, latents dtype: %s", latents.float().sum(), latents.shape, latents.dtype)
-                            # Run transformer
-                            uncond_pred_noise_btchw = self.transformer(
-                                latents.permute(0, 2, 1, 3, 4),
-                                negative_prompt_embed,
-                                timestep[:, 0]
-                            ).permute(0, 2, 1, 3, 4)
-
-                        # logger.info("uncond_pred_noise_btchw sum: %s, uncond_pred_noise_btchw shape: %s, uncond_pred_noise_btchw dtype: %s", uncond_pred_noise_btchw.float().sum(), uncond_pred_noise_btchw.shape, uncond_pred_noise_btchw.dtype)
-
-                        uncond_pred_video_btchw = pred_noise_to_pred_video(
-                            pred_noise=uncond_pred_noise_btchw.flatten(0, 1),
-                            noise_input_latent=latents.flatten(0, 1),
-                            timestep=timestep.flatten(0, 1),
-                            scheduler=self.get_module("scheduler")).unflatten(
-                                0, uncond_pred_noise_btchw.shape[:2])
-
-                        pred_video_btchw = uncond_pred_video_btchw + batch.guidance_scale * (
-                            cond_pred_video_btchw - uncond_pred_video_btchw
-                        )
-
-                        # logger.info("pred_video_btchw sum: %s, pred_video_btchw shape: %s, pred_video_btchw dtype: %s", pred_video_btchw.float().sum(), pred_video_btchw.shape, pred_video_btchw.dtype)
-
-                        pred_noise_btchw = pred_video_to_pred_noise(
-                            x0_pred=pred_video_btchw.flatten(0, 1),
-                            xt=latents.flatten(0, 1),
-                            timestep=timestep.flatten(0, 1),
-                            scheduler=self.get_module("scheduler")).unflatten(
-                                0, pred_video_btchw.shape[:2])
-
-                        # logger.info("pred_noise_btchw sum: %s, pred_noise_btchw shape: %s, pred_noise_btchw dtype: %s", pred_noise_btchw.float().sum(), pred_noise_btchw.shape, pred_noise_btchw.dtype)
-
-                        latents = self.get_module("scheduler").step(
-                            pred_noise_btchw.flatten(0, 1),
-                            self.get_module("scheduler").timesteps[progress_id] * torch.ones(
-                                [1, 21], device=latents.device, dtype=torch.long).flatten(0, 1),
-                            latents.flatten(0, 1)
-                        )[0].unflatten(dim=0, sizes=pred_noise_btchw.shape[:2])
-
-                        # logger.info("latents sum: %s, latents shape: %s, latents dtype: %s", latents.float().sum(), latents.shape, latents.dtype)
-
-                    noisy_input.append(latents)
-
-                    noisy_inputs = torch.stack(noisy_input, dim=1)
-
-                    noisy_inputs = noisy_inputs[:, [0, 12, 24, 36, -1]].half()
-
-                    logger.info("noisy inputs sum: %s, noisy inputs shape: %s, noisy inputs dtype: %s", noisy_inputs.float().sum(), noisy_inputs.shape, noisy_inputs.dtype)
-
-                    result_batch.trajectory_latents = noisy_inputs.permute(0, 1, 3, 2, 4, 5)
-                    result_batch.trajectory_timesteps = torch.tensor([self.get_module("scheduler").timesteps[i] for i in [0, 12, 24, 36, -1]])
-                    result_batch.latents = latents.permute(0, 2, 1, 3, 4)
+                    result_batch = self.latent_preparation_stage(
+                        result_batch, fastvideo_args)
+                    result_batch = self.denoising_stage(result_batch,
+                                                        fastvideo_args)
                    result_batch = self.decoding_stage(result_batch,
                                                       fastvideo_args)
+
                    trajectory_latents.append(
                        result_batch.trajectory_latents.cpu())
                    trajectory_timesteps.append(
                        result_batch.trajectory_timesteps.cpu())
                    trajectory_decoded.append(result_batch.trajectory_decoded)

-                trajectory_latents = torch.stack(trajectory_latents, dim=0).squeeze(0)
-                # trajecotry_latents = trajectory_latents[:, [0, 12, 24, 36, -1]]
-
                # Prepare extra features for text-only processing
                extra_features = {
                    "trajectory_latents": trajectory_latents,
@@ -335,7 +225,7 @@ class PreprocessPipeline_ODE_Trajectory(BasePreprocessPipeline):
                                args.train_fps)

                # Prepare batch data for Parquet dataset
-                batch_data: list[dict[str, Any]] = []
+                batch_data = []

                # Add progress bar for saving outputs
                save_pbar = tqdm(enumerate(valid_data["path"]),
@@ -364,16 +254,14 @@ class PreprocessPipeline_ODE_Trajectory(BasePreprocessPipeline):
                                else:
                                    sample_extra_features[key] = value[idx]

-                    # Create record for Parquet dataset (text-only ODE schema)
-                    record: dict[str, Any] = ode_text_only_record_creator(
+                    # Create record for Parquet dataset (without VAE latents for text-only)
+                    record = self.create_text_only_record(
+                        args,
                        video_name=video_name,
                        text_embedding=text_embedding,
-                        caption=valid_data["text"][idx],
-                        trajectory_latents=sample_extra_features[
-                            "trajectory_latents"],
-                        trajectory_timesteps=sample_extra_features[
-                            "trajectory_timesteps"],
-                    )
+                        valid_data=valid_data,
+                        idx=idx,
+                        extra_features=sample_extra_features)
                    batch_data.append(record)

                if batch_data:
@@ -401,10 +289,78 @@ class PreprocessPipeline_ODE_Trajectory(BasePreprocessPipeline):

        # Final flush for any remaining samples
        if hasattr(self, 'dataset_writer'):
-            written = self.dataset_writer.flush(write_remainder=True)
+            written = self.dataset_writer.flush()
            if written:
                logger.info("Final flush wrote %s samples", written)

+    def create_text_only_record(
+            self,
+            args,
+            video_name: str,
+            text_embedding: np.ndarray,
+            valid_data: dict[str, Any],
+            idx: int,
+            extra_features: dict[str, Any] | None = None) -> dict[str, Any]:
+        """Create a record for text-only preprocessing using text-only schema."""
+
+        # Create base record using only fields from text-only schema
+        record = {
+            "id": f"text_{video_name}_{idx}",
+            "text_embedding_bytes": text_embedding.tobytes(),
+            "text_embedding_shape": list(text_embedding.shape),
+            "text_embedding_dtype": str(text_embedding.dtype),
+            "file_name": video_name,
+            "caption": valid_data["text"][idx],
+            "media_type": "text",
+        }
+
+        assert extra_features is not None, "extra_features is required"
+        assert "trajectory_latents" in extra_features, "trajectory_latents is required"
+        assert "trajectory_timesteps" in extra_features, "trajectory_timesteps is required"
+
+        # Add trajectory data if available
+        if extra_features and "trajectory_latents" in extra_features:
+            trajectory_latents = extra_features[
+                "trajectory_latents"][idx] if isinstance(
+                    extra_features["trajectory_latents"],
+                    list) else extra_features["trajectory_latents"]
+            record.update({
+                "trajectory_latents_bytes":
+                trajectory_latents.tobytes(),
+                "trajectory_latents_shape":
+                list(trajectory_latents.shape),
+                "trajectory_latents_dtype":
+                str(trajectory_latents.dtype),
+            })
+        else:
+            record.update({
+                "trajectory_latents_bytes": b"",
+                "trajectory_latents_shape": [],
+                "trajectory_latents_dtype": "",
+            })
+
+        if extra_features and "trajectory_timesteps" in extra_features:
+            trajectory_timesteps = extra_features[
+                "trajectory_timesteps"][idx] if isinstance(
+                    extra_features["trajectory_timesteps"],
+                    list) else extra_features["trajectory_timesteps"]
+            record.update({
+                "trajectory_timesteps_bytes":
+                trajectory_timesteps.tobytes(),
+                "trajectory_timesteps_shape":
+                list(trajectory_timesteps.shape),
+                "trajectory_timesteps_dtype":
+                str(trajectory_timesteps.dtype),
+            })
+        else:
+            record.update({
+                "trajectory_timesteps_bytes": b"",
+                "trajectory_timesteps_shape": [],
+                "trajectory_timesteps_dtype": "",
+            })
+
+        return record
+
    def forward(self, batch: ForwardBatch, fastvideo_args: FastVideoArgs, args):
        if not self.post_init_called:
            self.post_init()
@@ -1,184 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""
-Text-only Data Preprocessing pipeline implementation.
-
-This module contains an implementation of the Text-only Data Preprocessing pipeline
-using the modular pipeline architecture, based on the ODE Trajectory preprocessing.
-"""
-
-import os
-from collections.abc import Iterator
-from typing import Any
-
-import torch
-from torch.utils.data import DataLoader
-from torchdata.stateful_dataloader import StatefulDataLoader
-from tqdm import tqdm
-
-from fastvideo.dataset import gettextdataset
-from fastvideo.dataset.dataloader.parquet_io import (ParquetDatasetWriter,
-                                                     records_to_table)
-from fastvideo.dataset.dataloader.record_schema import text_only_record_creator
-from fastvideo.dataset.dataloader.schema import pyarrow_schema_text_only
-from fastvideo.fastvideo_args import FastVideoArgs
-from fastvideo.logger import init_logger
-from fastvideo.pipelines.pipeline_batch_info import ForwardBatch
-from fastvideo.pipelines.preprocess.preprocess_pipeline_base import (
-    BasePreprocessPipeline)
-from fastvideo.pipelines.stages import TextEncodingStage
-
-logger = init_logger(__name__)
-
-
-class PreprocessPipeline_Text(BasePreprocessPipeline):
-    """Text-only preprocessing pipeline implementation."""
-
-    _required_config_modules = ["text_encoder", "tokenizer"]
-    preprocess_dataloader: StatefulDataLoader
-    preprocess_loader_iter: Iterator[dict[str, Any]]
-    pbar: Any
-    num_processed_samples: int = 0
-
-    def get_pyarrow_schema(self):
-        """Return the PyArrow schema for text-only pipeline."""
-        return pyarrow_schema_text_only
-
-    def create_pipeline_stages(self, fastvideo_args: FastVideoArgs):
-        """Set up pipeline stages with proper dependency injection."""
-        self.add_stage(stage_name="prompt_encoding_stage",
-                       stage=TextEncodingStage(
-                           text_encoders=[self.get_module("text_encoder")],
-                           tokenizers=[self.get_module("tokenizer")],
-                       ))
-
-    def preprocess_text_only(self, fastvideo_args: FastVideoArgs, args):
-        """Preprocess text-only data."""
-
-        for batch_idx, data in enumerate(self.pbar):
-            if data is None:
-                continue
-
-            with torch.inference_mode():
-                # For text-only processing, we only need text data
-                # Filter out samples without text
-                valid_indices = []
-                for i, text in enumerate(data["text"]):
-                    if text and text.strip():  # Check if text is not empty
-                        valid_indices.append(i)
-                self.num_processed_samples += len(valid_indices)
-
-                if not valid_indices:
-                    continue
-
-                # Create new batch with only valid samples (text-only)
-                valid_data = {
-                    "text": [data["text"][i] for i in valid_indices],
-                    "path": [data["path"][i] for i in valid_indices],
-                }
-
-                batch_captions = valid_data["text"]
-                # Encode text using the standalone TextEncodingStage API
-                prompt_embeds_list, prompt_masks_list = self.prompt_encoding_stage.encode_text(
-                    batch_captions,
-                    fastvideo_args,
-                    encoder_index=[0],
-                    return_attention_mask=True,
-                )
-                prompt_embeds = prompt_embeds_list[0]
-                prompt_attention_masks = prompt_masks_list[0]
-                assert prompt_embeds.shape[0] == prompt_attention_masks.shape[0]
-
-                logger.info("===== prompt_embeds: %s", prompt_embeds.shape)
-                logger.info("===== prompt_attention_masks: %s",
-                            prompt_attention_masks.shape)
-
-                # Prepare batch data for Parquet dataset
-                batch_data = []
-
-                # Add progress bar for saving outputs
-                save_pbar = tqdm(enumerate(valid_data["path"]),
-                                 desc="Saving outputs",
-                                 unit="item",
-                                 leave=False)
-
-                for idx, text_path in save_pbar:
-                    text_name = os.path.basename(text_path).split(".")[0]
-
-                    # Convert tensors to numpy arrays
-                    text_embedding = prompt_embeds[idx].cpu().numpy()
-
-                    # Create record for Parquet dataset (text-only schema)
-                    record = text_only_record_creator(
-                        text_name=text_name,
-                        text_embedding=text_embedding,
-                        caption=valid_data["text"][idx],
-                    )
-                    batch_data.append(record)
-
-                if batch_data:
-                    write_pbar = tqdm(total=1,
-                                      desc="Writing to Parquet dataset",
-                                      unit="batch")
-                    table = records_to_table(batch_data,
-                                             pyarrow_schema_text_only)
-                    write_pbar.update(1)
-                    write_pbar.close()
-
-                    if not hasattr(self, 'dataset_writer'):
-                        self.dataset_writer = ParquetDatasetWriter(
-                            out_dir=self.combined_parquet_dir,
-                            samples_per_file=args.samples_per_file,
-                        )
-                    self.dataset_writer.append_table(table)
-
-                    logger.info("Collected batch with %s samples", len(table))
-
-                if self.num_processed_samples >= args.flush_frequency:
-                    written = self.dataset_writer.flush()
-                    logger.info("Flushed %s samples to parquet", written)
-                    self.num_processed_samples = 0
-
-        # Final flush for any remaining samples
-        if hasattr(self, 'dataset_writer'):
-            written = self.dataset_writer.flush(write_remainder=True)
-            if written:
-                logger.info("Final flush wrote %s samples", written)
-
-    # Text-only record creation moved to fastvideo.dataset.dataloader.record_schema
-
-    def forward(self, batch: ForwardBatch, fastvideo_args: FastVideoArgs, args):
-        if not self.post_init_called:
-            self.post_init()
-
-        self.local_rank = int(os.getenv("RANK", 0))
-        os.makedirs(args.output_dir, exist_ok=True)
-        # Create directory for combined data
-        self.combined_parquet_dir = os.path.join(args.output_dir,
-                                                 "combined_parquet_dataset")
-        os.makedirs(self.combined_parquet_dir, exist_ok=True)
-
-        # Loading text dataset
-        train_dataset = gettextdataset(args)
-
-        self.preprocess_dataloader = DataLoader(
-            train_dataset,
-            batch_size=args.preprocess_video_batch_size,
-            num_workers=args.dataloader_num_workers,
-        )
-
-        self.preprocess_loader_iter = iter(self.preprocess_dataloader)
-
-        self.num_processed_samples = 0
-        # Add progress bar for text preprocessing
-        self.pbar = tqdm(self.preprocess_loader_iter,
-                         desc="Processing text",
-                         unit="batch",
-                         disable=self.local_rank != 0)
-
-        # Initialize class variables for data sharing
-        self.text_data: dict[str, Any] = {}  # Store text metadata and paths
-
-        self.preprocess_text_only(fastvideo_args, args)
-
-
-EntryClass = PreprocessPipeline_Text
@@ -1,6 +1,5 @@
 import argparse
 import os
-from typing import Any

 from fastvideo import PipelineConfig
 from fastvideo.configs.models.vaes import WanVAEConfig
@@ -14,8 +13,6 @@ from fastvideo.pipelines.preprocess.preprocess_pipeline_ode_trajectory import (
    PreprocessPipeline_ODE_Trajectory)
 from fastvideo.pipelines.preprocess.preprocess_pipeline_t2v import (
    PreprocessPipeline_T2V)
-from fastvideo.pipelines.preprocess.preprocess_pipeline_text import (
-    PreprocessPipeline_Text)
 from fastvideo.utils import maybe_download_model

 logger = init_logger(__name__)
@@ -26,22 +23,13 @@ def main(args) -> None:
    maybe_init_distributed_environment_and_model_parallel(1, 1)
    num_gpus = int(os.environ["WORLD_SIZE"])
    assert num_gpus == 1, "Only support 1 GPU"
-
    pipeline_config = PipelineConfig.from_pretrained(args.model_path)
-
-    kwargs: dict[str, Any] = {}
-    if args.preprocess_task == "text_only":
-        kwargs = {
-            "text_encoder_cpu_offload": False,
-        }
-    else:
-        # Full config for video/image processing
-        kwargs = {
-            "vae_precision": "fp32",
-            "vae_config": WanVAEConfig(load_encoder=True, load_decoder=True),
-        }
+    kwargs = {
+        "vae_precision": "fp32",
+        "vae_config": WanVAEConfig(load_encoder=True, load_decoder=True),
+        "flow_shift": 5,
+    }
    pipeline_config.update_config_from_dict(kwargs)
-
    fastvideo_args = FastVideoArgs(
        model_path=args.model_path,
        num_gpus=get_world_size(),
@@ -54,19 +42,13 @@ def main(args) -> None:
        PreprocessPipeline = PreprocessPipeline_T2V
    elif args.preprocess_task == "i2v":
        PreprocessPipeline = PreprocessPipeline_I2V
-    elif args.preprocess_task == "text_only":
-        PreprocessPipeline = PreprocessPipeline_Text
    elif args.preprocess_task == "ode_trajectory":
-        assert args.flow_shift is not None, "flow_shift is required for ode_trajectory"
-        fastvideo_args.pipeline_config.flow_shift = args.flow_shift
        PreprocessPipeline = PreprocessPipeline_ODE_Trajectory
    else:
-        raise ValueError(f"Invalid preprocess task: {args.preprocess_task}. "
-                         f"Valid options: t2v, i2v, ode_trajectory, text_only")
+        raise ValueError(f"Invalid preprocess task: {args.preprocess_task}")

    logger.info("Preprocess task: %s using %s", args.preprocess_task,
                PreprocessPipeline.__name__)
-
    pipeline = PreprocessPipeline(args.model_path, fastvideo_args)
    pipeline.forward(batch=None, fastvideo_args=fastvideo_args, args=args)

@@ -105,12 +87,7 @@ if __name__ == "__main__":
    parser.add_argument("--video_length_tolerance_range", type=int, default=2.0)
    parser.add_argument("--group_frame", action="store_true")  # TODO
    parser.add_argument("--group_resolution", action="store_true")  # TODO
-    parser.add_argument("--flow_shift", type=float, default=None)
-    parser.add_argument("--preprocess_task",
-                        type=str,
-                        default="t2v",
-                        choices=["t2v", "i2v", "text_only", "ode_trajectory"],
-                        help="Type of preprocessing task to run")
+    parser.add_argument("--preprocess_task", type=str, default="t2v")
    parser.add_argument("--train_fps", type=int, default=30)
    parser.add_argument("--use_image_num", type=int, default=0)
    parser.add_argument("--text_max_length", type=int, default=256)
@@ -78,8 +78,6 @@ class CausalDMDDenosingStage(DenoisingStage):
                                             torch.tensor([0],
                                                          dtype=torch.float32)))
            timesteps = scheduler_timesteps[1000 - timesteps]
-        else:
-            assert False, "warp_denoising_step must be true"
        timesteps = timesteps.to(get_local_torch_device())
        logger.info("Using timesteps: %s", timesteps)

@@ -140,12 +140,11 @@ class DenoisingStage(PipelineStage):
            latents = latents[:, :, rank_in_sp_group, :, :, :]
            batch.latents = latents
            if batch.image_latent is not None:
-                if not fastvideo_args.pipeline_config.ti2v_task and not fastvideo_args.pipeline_config.t2v_as_i2v_task:
-                    image_latent = rearrange(batch.image_latent,
-                                             "b c (n t) h w -> b c n t h w",
-                                             n=sp_world_size).contiguous()
-                    image_latent = image_latent[:, :, rank_in_sp_group, :, :, :]
-                    batch.image_latent = image_latent
+                image_latent = rearrange(batch.image_latent,
+                                         "b c (n t) h w -> b c n t h w",
+                                         n=sp_world_size).contiguous()
+                image_latent = image_latent[:, :, rank_in_sp_group, :, :, :]
+                batch.image_latent = image_latent
        # Get timesteps and calculate warmup steps
        timesteps = batch.timesteps
        # TODO(will): remove this once we add input/output validation for stages
@@ -205,14 +204,8 @@ class DenoisingStage(PipelineStage):
                neg_prompt_embeds[0]).any(), "neg_prompt_embeds contains nan"

        # (Wan2.2) Calculate timestep to switch from high noise expert to low noise expert
-        boundary_ratio = fastvideo_args.pipeline_config.dit_config.boundary_ratio
-        if batch.boundary_ratio is not None:
-            logger.info("Overriding boundary ratio from %s to %s",
-                        boundary_ratio, batch.boundary_ratio)
-            boundary_ratio = batch.boundary_ratio
-
-        if boundary_ratio is not None:
-            boundary_timestep = boundary_ratio * self.scheduler.num_train_timesteps
+        if fastvideo_args.boundary_ratio is not None:
+            boundary_timestep = fastvideo_args.boundary_ratio * self.scheduler.num_train_timesteps
        else:
            boundary_timestep = None
        latent_model_input = latents.to(target_dtype)
@@ -254,7 +247,8 @@ class DenoisingStage(PipelineStage):
                                                             patch_size[2])
            seq_len = int(math.ceil(seq_len / sp_world_size)) * sp_world_size

-        trajectory_timesteps: list[int] = []
+        # Initialize lists for ODE trajectory
+        trajectory_timesteps: list[torch.Tensor] = []
        trajectory_latents: list[torch.Tensor] = []

        # Run denoising loop
@@ -284,27 +278,14 @@ class DenoisingStage(PipelineStage):

                # Expand latents for I2V
                latent_model_input = latents.to(target_dtype)
-                if batch.image_latent is not None and not fastvideo_args.pipeline_config.t2v_as_i2v_task:
+                if batch.image_latent is not None:
                    assert not fastvideo_args.pipeline_config.ti2v_task, "image latents should not be provided for TI2V task"
                    latent_model_input = torch.cat(
                        [latent_model_input, batch.image_latent],
                        dim=1).to(target_dtype)
-                elif batch.image_latent is not None and fastvideo_args.pipeline_config.t2v_as_i2v_task:
-                    assert batch.image_latent is not None, "image latents should be provided for T2V to I2V task"
-                    if rank_in_sp_group == 0:
-                        logger.info("latent_model_input.shape: %s",
-                                    latent_model_input.shape)
-                        latent_model_input = torch.cat([
-                            batch.image_latent,
-                            latent_model_input[:, :, 1:, :, :],
-                        ],
-                                                       dim=2).to(target_dtype)
-                        logger.info("latent_model_input.shape: %s",
-                                    latent_model_input.shape)

                assert not torch.isnan(
                    latent_model_input).any(), "latent_model_input contains nan"
-
                if fastvideo_args.pipeline_config.ti2v_task and batch.pil_image is not None:
                    timestep = torch.stack([t]).to(get_local_torch_device())
                    temp_ts = (mask2[0][0][:, ::2, ::2] * timestep).flatten()
@@ -319,13 +300,6 @@ class DenoisingStage(PipelineStage):

                latent_model_input = self.scheduler.scale_model_input(
                    latent_model_input, t)
-                if fastvideo_args.pipeline_config.t2v_as_i2v_task:
-                    if rank_in_sp_group == 0:
-                        latent_model_input = torch.cat([
-                            batch.image_latent,
-                            latent_model_input[:, :, 1:, :, :],
-                        ],
-                                                       dim=2).to(target_dtype)

                # Prepare inputs for transformer
                guidance_expand = (
@@ -488,17 +462,10 @@ class DenoisingStage(PipelineStage):
                trajectory_tensor = sequence_model_parallel_all_gather(
                    trajectory_tensor, dim=3)

-        if trajectory_tensor is not None:
-            batch.trajectory_timesteps = torch.tensor(trajectory_timesteps).cpu()
+        if trajectory_tensor is not None and trajectory_timesteps_tensor is not None:
+            batch.trajectory_timesteps = trajectory_timesteps_tensor.cpu()
            batch.trajectory_latents = trajectory_tensor.cpu()

-        if fastvideo_args.pipeline_config.t2v_as_i2v_task:
-            latents = torch.cat([
-                batch.image_latent,
-                latents[:, :, 1:, :, :],
-            ],
-                                dim=2)
-
        # Update batch with final latents
        batch.latents = latents

@@ -105,81 +105,6 @@ class ImageVAEEncodingStage(PipelineStage):
    def __init__(self, vae: ParallelTiledVAE) -> None:
        self.vae: ParallelTiledVAE = vae

-    def encode_image(self,
-                     image: PIL.Image.Image,
-                     height: int,
-                     width: int,
-                     fastvideo_args: FastVideoArgs,
-                     generator: torch.Generator | None = None) -> torch.Tensor:
-        """
-        Encode image into latent space.
-        """
-        image = self.preprocess(
-            image,
-            vae_scale_factor=self.vae.spatial_compression_ratio,
-            height=height,
-            width=width).to(get_local_torch_device(), dtype=torch.float32)
-
-        # (B, C, H, W) -> (B, C, 1, H, W)
-        print(f"image.shape: {image.shape}")
-        image = image.unsqueeze(2)
-        print(f"after unsqueeze image.shape: {image.shape}")
-        return self.encode_tensor(image, fastvideo_args, generator)
-
-    def encode_tensor(self,
-                      video_condition: torch.Tensor,
-                      fastvideo_args: FastVideoArgs,
-                      generator: torch.Generator | None = None) -> torch.Tensor:
-        """
-        Encode frames into latent space.
-        """
-        self.vae = self.vae.to(get_local_torch_device())
-        video_condition = video_condition.to(device=get_local_torch_device(),
-                                             dtype=torch.float32)
-
-        # Setup VAE precision
-        vae_dtype = PRECISION_TO_TYPE[
-            fastvideo_args.pipeline_config.vae_precision]
-        vae_autocast_enabled = (
-            vae_dtype != torch.float32) and not fastvideo_args.disable_autocast
-
-        # Encode Image
-        with torch.autocast(device_type="cuda",
-                            dtype=vae_dtype,
-                            enabled=vae_autocast_enabled):
-            if fastvideo_args.pipeline_config.vae_tiling:
-                self.vae.enable_tiling()
-            # if fastvideo_args.vae_sp:
-            #     self.vae.enable_parallel()
-            if not vae_autocast_enabled:
-                video_condition = video_condition.to(vae_dtype)
-            encoder_output = self.vae.encode(video_condition)
-
-        if fastvideo_args.mode == ExecutionMode.PREPROCESS:
-            latent_condition = encoder_output.mean
-        else:
-            generator = generator
-            if generator is None:
-                raise ValueError("Generator must be provided")
-            latent_condition = self.retrieve_latents(encoder_output, generator)
-
-        # Apply shifting if needed
-        if (hasattr(self.vae, "shift_factor")
-                and self.vae.shift_factor is not None):
-            if isinstance(self.vae.shift_factor, torch.Tensor):
-                latent_condition -= self.vae.shift_factor.to(
-                    latent_condition.device, latent_condition.dtype)
-            else:
-                latent_condition -= self.vae.shift_factor
-
-        if isinstance(self.vae.scaling_factor, torch.Tensor):
-            latent_condition = latent_condition * self.vae.scaling_factor.to(
-                latent_condition.device, latent_condition.dtype)
-        else:
-            latent_condition = latent_condition * self.vae.scaling_factor
-
-        return latent_condition
-
    def forward(
        self,
        batch: ForwardBatch,
@@ -232,28 +157,57 @@ class ImageVAEEncodingStage(PipelineStage):
        # (B, C, H, W) -> (B, C, 1, H, W)
        image = image.unsqueeze(2)

-        if fastvideo_args.pipeline_config.t2v_as_i2v_task:
-            # repeat the image self.vae.temporal_compression_ratio times
-            video_condition = image.repeat(1, 1,
-                                           self.vae.temporal_compression_ratio,
-                                           1, 1)
-            # video_condition = image
-            logger.info("video_condition.shape: %s", video_condition.shape)
-        else:
-            video_condition = torch.cat([
-                image,
-                image.new_zeros(image.shape[0], image.shape[1], num_frames - 1,
-                                image.shape[3], image.shape[4])
-            ],
-                                        dim=2)
+        video_condition = torch.cat([
+            image,
+            image.new_zeros(image.shape[0], image.shape[1], num_frames - 1,
+                            image.shape[3], image.shape[4])
+        ],
+                                    dim=2)
+        video_condition = video_condition.to(device=get_local_torch_device(),
+                                             dtype=torch.float32)

-        latent_condition = self.encode_tensor(video_condition, fastvideo_args,
-                                              batch.generator)
+        # Setup VAE precision
+        vae_dtype = PRECISION_TO_TYPE[
+            fastvideo_args.pipeline_config.vae_precision]
+        vae_autocast_enabled = (
+            vae_dtype != torch.float32) and not fastvideo_args.disable_autocast
+
+        # Encode Image
+        with torch.autocast(device_type="cuda",
+                            dtype=vae_dtype,
+                            enabled=vae_autocast_enabled):
+            if fastvideo_args.pipeline_config.vae_tiling:
+                self.vae.enable_tiling()
+            # if fastvideo_args.vae_sp:
+            #     self.vae.enable_parallel()
+            if not vae_autocast_enabled:
+                video_condition = video_condition.to(vae_dtype)
+            encoder_output = self.vae.encode(video_condition)
+
+        if fastvideo_args.mode == ExecutionMode.PREPROCESS:
+            latent_condition = encoder_output.mean
+        else:
+            generator = batch.generator
+            if generator is None:
+                raise ValueError("Generator must be provided")
+            latent_condition = self.retrieve_latents(encoder_output, generator)
+
+        # Apply shifting if needed
+        if (hasattr(self.vae, "shift_factor")
+                and self.vae.shift_factor is not None):
+            if isinstance(self.vae.shift_factor, torch.Tensor):
+                latent_condition -= self.vae.shift_factor.to(
+                    latent_condition.device, latent_condition.dtype)
+            else:
+                latent_condition -= self.vae.shift_factor
+
+        if isinstance(self.vae.scaling_factor, torch.Tensor):
+            latent_condition = latent_condition * self.vae.scaling_factor.to(
+                latent_condition.device, latent_condition.dtype)
+        else:
+            latent_condition = latent_condition * self.vae.scaling_factor

        if fastvideo_args.mode == ExecutionMode.PREPROCESS:
-            batch.image_latent = latent_condition
-        elif fastvideo_args.pipeline_config.t2v_as_i2v_task:
-            logger.info("latent_condition.shape: %s", latent_condition.shape)
            batch.image_latent = latent_condition
        else:
            mask_lat_size = torch.ones(1, 1, num_frames, latent_height,
@@ -35,15 +35,9 @@ class InputValidationStage(PipelineStage):
        """Generate seeds for the inference"""
        seed = batch.seed
        num_videos_per_prompt = batch.num_videos_per_prompt
-        if isinstance(batch.prompt, list):
-            num_prompts = len(batch.prompt)
-        else:
-            num_prompts = 1
-
-        total_num_videos = num_prompts * num_videos_per_prompt

        assert seed is not None
-        seeds = [seed + i for i in range(total_num_videos)]
+        seeds = [seed + i for i in range(num_videos_per_prompt)]
        batch.seeds = seeds
        # Peiyuan: using GPU seed will cause A100 and H100 to generate different results...
        batch.generator = [
@@ -3,7 +3,6 @@
 Latent preparation stage for diffusion pipelines.
 """

-import torch
 from diffusers.utils.torch_utils import randn_tensor

 from fastvideo.distributed import get_local_torch_device
@@ -94,7 +93,7 @@ class LatentPreparationStage(PipelineStage):
        # Generate or use provided latents
        if latents is None:
            latents = randn_tensor(shape,
-                                   generator=torch.Generator(device="cuda").manual_seed(1024),
+                                   generator=generator,
                                   device=device,
                                   dtype=dtype)
        else:
@@ -82,8 +82,8 @@ def rocm_platform_plugin() -> str | None:
                logger.info("ROCm platform is available")
        finally:
            amdsmi.amdsmi_shut_down()
-    except Exception:
-        pass
+    except Exception as e:
+        logger.info("ROCm platform is unavailable: %s", e)

    return "fastvideo.platforms.rocm.RocmPlatform" if is_rocm else None

@@ -1,123 +0,0 @@
-import numpy as np
-
-from fastvideo.dataset.dataloader.record_schema import (
-    basic_t2v_record_creator,
-    i2v_record_creator,
-    ode_text_only_record_creator,
-    text_only_record_creator,
-)
-from fastvideo.pipelines.pipeline_batch_info import PreprocessBatch
-
-
-def _mk_basic_batch(N: int) -> PreprocessBatch:
-    batch = PreprocessBatch(data_type="video")
-    batch.video_file_name = [f"vid_{i}" for i in range(N)]
-    batch.prompt = [f"caption_{i}" for i in range(N)]
-    batch.width = [640 for _ in range(N)]
-    batch.height = [360 for _ in range(N)]
-    batch.fps = [4 for _ in range(N)]
-    batch.num_frames = [2 for _ in range(N)]
-    # Latents: shape (N, C, T, H, W); per-record use latents[idx]
-    batch.latents = np.zeros((N, 4, 2, 8, 8), dtype=np.float32)
-    # Prompt embeds: list of per-record arrays [Seq, Dim]
-    batch.prompt_embeds = [np.ones((6, 16), dtype=np.float32) for _ in range(N)]
-    return batch
-
-
-def test_basic_t2v_record_creator_fields():
-    N = 2
-    batch = _mk_basic_batch(N)
-
-    records = basic_t2v_record_creator(batch)
-    assert isinstance(records, list) and len(records) == N
-
-    for i, rec in enumerate(records):
-        assert rec["id"] == batch.video_file_name[i]
-        # Latents bytes/shape/dtype
-        assert isinstance(rec["vae_latent_bytes"], (bytes, bytearray))
-        assert rec["vae_latent_shape"] == list(batch.latents[i].shape)
-        assert rec["vae_latent_dtype"] == str(batch.latents[i].dtype)
-        # Text embedding
-        assert isinstance(rec["text_embedding_bytes"], (bytes, bytearray))
-        assert rec["text_embedding_shape"] == list(batch.prompt_embeds[i].shape)
-        assert rec["text_embedding_dtype"] == str(batch.prompt_embeds[i].dtype)
-        # Meta
-        assert rec["caption"] == batch.prompt[i]
-        assert rec["media_type"] == "video"
-        assert rec["width"] == int(batch.width[i])
-        assert rec["height"] == int(batch.height[i])
-        assert rec["num_frames"] == batch.latents[i].shape[1]
-
-
-def test_i2v_record_creator_additional_fields():
-    N = 3
-    batch = _mk_basic_batch(N)
-    # image_embeds is a list of length 1, with an array of shape [N, D]
-    batch.image_embeds = [np.ones((N, 32), dtype=np.float32)]
-    # first frame latent per record
-    batch.image_latent = np.zeros((N, 4, 1, 8, 8), dtype=np.float32)
-    # pil image per record
-    batch.pil_image = np.zeros((N, 8, 8, 3), dtype=np.uint8)
-
-    records = i2v_record_creator(batch)
-    assert isinstance(records, list) and len(records) == N
-
-    for i, rec in enumerate(records):
-        # clip feature
-        assert isinstance(rec["clip_feature_bytes"], (bytes, bytearray))
-        assert rec["clip_feature_shape"] == list(batch.image_embeds[0][i].shape)
-        assert rec["clip_feature_dtype"] == str(batch.image_embeds[0][i].dtype)
-        # first frame latent
-        assert isinstance(rec["first_frame_latent_bytes"], (bytes, bytearray))
-        assert rec["first_frame_latent_shape"] == list(batch.image_latent[i].shape)
-        assert rec["first_frame_latent_dtype"] == str(batch.image_latent[i].dtype)
-        # pil image
-        assert isinstance(rec["pil_image_bytes"], (bytes, bytearray))
-        assert rec["pil_image_shape"] == list(batch.pil_image[i].shape)
-        assert rec["pil_image_dtype"] == str(batch.pil_image[i].dtype)
-
-
-def test_ode_text_only_record_creator():
-    video_name = "ex"
-    caption = "a prompt"
-    text_embedding = np.ones((6, 16), dtype=np.float32)
-    traj = np.ones((5, 4, 2, 2), dtype=np.float32)
-    tsteps = np.arange(5, dtype=np.float32)
-
-    rec = ode_text_only_record_creator(
-        video_name=video_name,
-        text_embedding=text_embedding,
-        caption=caption,
-        trajectory_latents=traj,
-        trajectory_timesteps=tsteps,
-    )
-    assert rec["id"] == f"text_{video_name}"
-    assert isinstance(rec["text_embedding_bytes"], (bytes, bytearray))
-    assert rec["text_embedding_shape"] == list(text_embedding.shape)
-    assert rec["text_embedding_dtype"] == str(text_embedding.dtype)
-    assert rec["file_name"] == video_name
-    assert rec["caption"] == caption
-    assert rec["media_type"] == "text"
-    # Trajectory fields
-    assert isinstance(rec["trajectory_latents_bytes"], (bytes, bytearray))
-    assert rec["trajectory_latents_shape"] == list(traj.shape)
-    assert rec["trajectory_latents_dtype"] == str(traj.dtype)
-    assert isinstance(rec["trajectory_timesteps_bytes"], (bytes, bytearray))
-    assert rec["trajectory_timesteps_shape"] == list(tsteps.shape)
-    assert rec["trajectory_timesteps_dtype"] == str(tsteps.dtype)
-
-
-def test_text_only_record_creator():
-    text_name = "note1"
-    caption = "a prompt"
-    text_embedding = np.ones((7, 16), dtype=np.float32)
-    rec = text_only_record_creator(
-        text_name=text_name,
-        text_embedding=text_embedding,
-        caption=caption,
-    )
-    assert rec["id"] == f"text_{text_name}"
-    assert isinstance(rec["text_embedding_bytes"], (bytes, bytearray))
-    assert rec["text_embedding_shape"] == list(text_embedding.shape)
-    assert rec["text_embedding_dtype"] == str(text_embedding.dtype)
-    assert rec["caption"] == caption
@@ -8,9 +8,6 @@ from torch.distributed.tensor import DTensor
 from torch.testing import assert_close
 from transformers import AutoConfig, AutoTokenizer, UMT5EncoderModel

-from fastvideo.wan.modules.tokenizers import HuggingfaceTokenizer
-from fastvideo.wan.modules.t5 import umt5_xxl
-
 from fastvideo.configs.pipelines import PipelineConfig
 from fastvideo.forward_context import set_forward_context
 from fastvideo.logger import init_logger
@@ -41,22 +38,9 @@ def test_t5_encoder():
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
    precision_str = "fp32"
    precision = PRECISION_TO_TYPE[precision_str]
-    # model1 = UMT5EncoderModel.from_pretrained(TEXT_ENCODER_PATH).to(
-    #     precision).to(device).eval()
-    # tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_PATH)
-    model1 = umt5_xxl(
-        encoder_only=True,
-        return_tokenizer=False,
-        dtype=torch.float32,
-        device=device,
-    ).eval().requires_grad_(False)
-    model1.load_state_dict(
-        torch.load("/mnt/weka/home/hao.zhang/wei/Self-Forcing-clean/wan_models/Wan2.1-T2V-1.3B/models_t5_umt5-xxl-enc-bf16.pth",
-                    map_location='cpu', weights_only=False)
-    )
-
-    tokenizer1 = HuggingfaceTokenizer(
-        name="/mnt/weka/home/hao.zhang/wei/Self-Forcing-clean/wan_models/Wan2.1-T2V-1.3B/google/umt5-xxl/", seq_len=512, clean='whitespace')
+    model1 = UMT5EncoderModel.from_pretrained(TEXT_ENCODER_PATH).to(
+        precision).to(device).eval()
+    tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_PATH)


    args = FastVideoArgs(model_path=TEXT_ENCODER_PATH,
@@ -65,9 +49,8 @@ def test_t5_encoder():
                        pin_cpu_memory=False)
    loader = TextEncoderLoader()
    model2 = loader.load(TEXT_ENCODER_PATH, args)
-    model2 = model2.to(dtype=torch.bfloat16).to(precision)
+    model2 = model2.to(precision)
    model2.eval()
-    tokenizer2 = AutoTokenizer.from_pretrained(TOKENIZER_PATH)

    # Sanity check weights between the two models
    logger.info("Comparing model weights for sanity check...")
@@ -78,13 +61,8 @@ def test_t5_encoder():
    logger.info("Model1 has %s parameters", len(params1))
    logger.info("Model2 has %s parameters", len(params2))

-    model1_weight_sum = sum(p.float().sum().item() for p in model1.parameters())
-    model2_weight_sum = sum(p.float().sum().item() for p in model2.parameters())
-    logger.info("Model1 weight sum: %s", model1_weight_sum)
-    logger.info("Model2 weight sum: %s", model2_weight_sum)
-
-    # weight_diffs = []
-    # # check if embed_tokens are the same
+    weight_diffs = []
+    # check if embed_tokens are the same
    weights = ["encoder.block.{}.layer.0.layer_norm.weight", "encoder.block.{}.layer.0.SelfAttention.relative_attention_bias.weight", \
               "encoder.block.{}.layer.0.SelfAttention.o.weight", "encoder.block.{}.layer.1.DenseReluDense.wi_0.weight", "encoder.block.{}.layer.1.DenseReluDense.wi_1.weight",\
                "encoder.block.{}.layer.1.DenseReluDense.wo.weight", \
@@ -92,21 +70,18 @@ def test_t5_encoder():
    
    for idx in range(hf_config.num_hidden_layers):
        for w in weights:
-            # name1 = w.format(idx)
+            name1 = w.format(idx)
            name2 = w.format(idx)
-            # p1 = params1[name1]
+            p1 = params1[name1]
            p2 = params2[name2]
-            p2 = (p2.to_local() if isinstance(p2, DTensor) else p2).to(device)
-            # assert_close(p1, p2, atol=1e-4, rtol=1e-4)
+            p2 = (p2.to_local() if isinstance(p2, DTensor) else p2).to(p1)
+            assert_close(p1, p2, atol=1e-4, rtol=1e-4)
    

    # Test with some sample prompts
-    # prompts = [
-    #     "Once upon a time", "The quick brown fox jumps over",
-    #     "In a galaxy far, far away"
-    # ]
    prompts = [
-        "A vibrant scene of Kenyan golfers at a lush green golf course on a sunny day. The golfers, dressed in casual yet stylish attire, are teeing off with animated expressions, showcasing their enthusiasm for the game. Rolling hills and pristine greens stretch out behind them, creating a picturesque backdrop. In the foreground, a golf buggy and a caddy stand ready, adding to the serene atmosphere. The camera captures the action from a mid-shot angle, focusing on the golfers' dynamic motions as they swing their clubs."
+        "Once upon a time", "The quick brown fox jumps over",
+        "In a galaxy far, far away"
    ]

    logger.info("Testing T5 encoder with sample prompts")
@@ -116,8 +91,7 @@ def test_t5_encoder():
            logger.info("Testing prompt: %s", prompt)

            # Tokenize the prompt
-            tokens1, mask = tokenizer1(prompt, return_mask=True, add_special_tokens=True)
-            tokens2 = tokenizer2(prompt,
+            tokens = tokenizer(prompt,
                               padding="max_length",
                               max_length=512,
                               truncation=True,
@@ -127,23 +101,22 @@ def test_t5_encoder():
            # filter out padding input_ids
            # tokens.input_ids = tokens.input_ids[tokens.attention_mask==1]
            # tokens.attention_mask = tokens.attention_mask[tokens.attention_mask==1]
-            outputs1 = model1(tokens1.to(device),
-                              mask.to(device))
+            outputs1 = model1(input_ids=tokens.input_ids,
+                              attention_mask=tokens.attention_mask,
+                              output_hidden_states=True).last_hidden_state
            print("--------------------------------")
            logger.info("Testing model2")

            # Get outputs from our implementation
            with set_forward_context(current_timestep=0, attn_metadata=None):
                outputs2 = model2(
-                    input_ids=tokens2.input_ids,
-                    attention_mask=tokens2.attention_mask,
+                    input_ids=tokens.input_ids,
+                    attention_mask=tokens.attention_mask,
                ).last_hidden_state

            # Compare last hidden states
-            last_hidden_state1 = outputs1[mask == 1]
-            last_hidden_state2 = outputs2[tokens2.attention_mask == 1]
-            logger.info("last_hidden_state1 sum: %s", last_hidden_state1.float().sum())
-            logger.info("last_hidden_state2 sum: %s", last_hidden_state2.float().sum())
+            last_hidden_state1 = outputs1[tokens.attention_mask == 1]
+            last_hidden_state2 = outputs2[tokens.attention_mask == 1]

            assert last_hidden_state1.shape == last_hidden_state2.shape, \
                f"Hidden state shapes don't match: {last_hidden_state1.shape} vs {last_hidden_state2.shape}"
@@ -117,7 +117,3 @@ def run_inference_lora_tests():
@app.function(gpu="L40S:2", image=image, timeout=900)
 def run_distill_dmd_tests():
    run_test("pytest ./fastvideo/tests/training/distill/test_distill_dmd.py -vs")
-
-@app.function(gpu="L40S:1", image=image, timeout=900)
-def run_unit_test():
-    run_test("pytest ./fastvideo/tests/dataset/ ./fastvideo/tests/workflow/ -vs")
@@ -1,292 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-import os
-import math
-
-import numpy as np
-import pytest
-import torch
-from fastvideo.wan.modules.causal_model import CausalWanModel
-
-from fastvideo.configs.pipelines import PipelineConfig
-from fastvideo.forward_context import set_forward_context
-from fastvideo.fastvideo_args import FastVideoArgs
-from fastvideo.logger import init_logger
-from fastvideo.models.loader.component_loader import TransformerLoader
-from fastvideo.models.dits.causal_wanvideo import CausalWanTransformer3DModel
-from fastvideo.utils import maybe_download_model
-from fastvideo.configs.models.dits import WanVideoConfig
-from fastvideo.pipelines.pipeline_batch_info import ForwardBatch
-
-
-logger = init_logger(__name__)
-
-os.environ["MASTER_ADDR"] = "localhost"
-os.environ["MASTER_PORT"] = "29503"
-
-BASE_MODEL_PATH = "wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers"
-MODEL_PATH = maybe_download_model(BASE_MODEL_PATH,
-                                  local_dir=os.path.join(
-                                      'data', BASE_MODEL_PATH))
-TRANSFORMER_PATH = os.path.join(MODEL_PATH, "transformer")
-
-
-@pytest.mark.usefixtures("distributed_setup")
-def test_ori_causal_wan_transformer():
-    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-    precision = torch.bfloat16
-    precision_str = "bf16"
-    args = FastVideoArgs(model_path=TRANSFORMER_PATH,
-                         dit_cpu_offload=True,
-                         pipeline_config=PipelineConfig(dit_config=WanVideoConfig(), dit_precision=precision_str))
-    args.device = device
-
-    loader = TransformerLoader()
-    model2 = loader.load(TRANSFORMER_PATH, args).to(dtype=precision)
-
-    model1 = CausalWanModel.from_pretrained(
-        "/mnt/weka/home/hao.zhang/wei/Self-Forcing/wan_models/Wan2.1-T2V-1.3B", device=device,
-        torch_dtype=precision).to(device, dtype=precision).requires_grad_(False)
-    causal_state_dict = torch.load("/mnt/weka/home/hao.zhang/wei/Self-Forcing/checkpoints/self_forcing_dmd.pt")["generator_ema"]
-    new_state_dict = {}
-    for k, v in causal_state_dict.items():
-        if k.startswith("model."):
-            new_state_dict[k.replace("model.", "")] = v
-    causal_state_dict = new_state_dict
-    model1.load_state_dict(causal_state_dict)
-
-    total_params = sum(p.numel() for p in model1.parameters())
-    # Calculate weight sum for model1 (converting to float64 to avoid overflow)
-    weight_sum_model1 = sum(
-        p.to(torch.float64).sum().item() for p in model1.parameters())
-    # Also calculate mean for more stable comparison
-    weight_mean_model1 = weight_sum_model1 / total_params
-    logger.info("Model 1 weight sum: %s", weight_sum_model1)
-    logger.info("Model 1 weight mean: %s", weight_mean_model1)
-
-    # Calculate weight sum for model2 (converting to float64 to avoid overflow)
-    total_params_model2 = sum(p.numel() for p in model2.parameters())
-    weight_sum_model2 = sum(
-        p.to(torch.float64).sum().item() for p in model2.parameters())
-    # Also calculate mean for more stable comparison
-    weight_mean_model2 = weight_sum_model2 / total_params_model2
-    logger.info("Model 2 weight sum: %s", weight_sum_model2)
-    logger.info("Model 2 weight mean: %s", weight_mean_model2)
-
-    weight_sum_diff = abs(weight_sum_model1 - weight_sum_model2)
-    logger.info("Weight sum difference: %s", weight_sum_diff)
-    weight_mean_diff = abs(weight_mean_model1 - weight_mean_model2)
-    logger.info("Weight mean difference: %s", weight_mean_diff)
-
-    # Set both models to eval mode
-    model1 = model1.eval()
-    model2 = model2.eval()
-
-    # Create identical inputs for both models
-    batch_size = 1
-    text_seq_len = 30
-
-    # Video latents [B, C, T, H, W]
-    hidden_states = torch.randn(batch_size,
-                                16,
-                                12,
-                                160,
-                                90,
-                                device=device,
-                                dtype=precision)
-    block_sizes = [3 for _ in range(4)]
-    timesteps = [1000, 750, 500, 250]
-
-    # Text embeddings [B, L, D] (including global token)
-    encoder_hidden_states = torch.randn(batch_size,
-                                        text_seq_len + 1,
-                                        4096,
-                                        device=device,
-                                        dtype=precision)
-
-    output1 = _causal_inference(model1, hidden_states.clone(), encoder_hidden_states.clone(), block_sizes, timesteps, precision)
-    logger.info("Finish inference for model1")
-    output2 = _causal_inference(model2, hidden_states.clone(), encoder_hidden_states.clone(), block_sizes, timesteps, precision)
-
-    # Check if outputs have the same shape
-    assert output1.shape == output2.shape, f"Output shapes don't match: {output1.shape} vs {output2.shape}"
-    assert output1.dtype == output2.dtype, f"Output dtype don't match: {output1.dtype} vs {output2.dtype}"
-
-    logger.info("Output 1 Sum: %s", output1.float().sum().item())
-    logger.info("Output 2 Sum: %s", output2.float().sum().item())
-
-    # Check if outputs are similar (allowing for small numerical differences)
-    max_diff = torch.max(torch.abs(output1 - output2))
-    mean_diff = torch.mean(torch.abs(output1 - output2))
-    logger.info("Max Diff: %s", max_diff.item())
-    logger.info("Mean Diff: %s", mean_diff.item())
-    assert max_diff < 1e-4, f"Maximum difference between outputs: {max_diff.item()}"
-    # mean diff
-    assert mean_diff < 1e-4, f"Mean difference between outputs: {mean_diff.item()}"
-
-def _causal_inference(transformer, latents, prompt_embeds, block_sizes, timesteps, target_dtype):
-    forward_batch = ForwardBatch(
-        data_type="dummy",
-    )
-    start_index = 0
-    pos_start_base = 0
-    frame_seq_length = latents.shape[-1] * latents.shape[-2] // (WanVideoConfig().arch_config.patch_size[-1] * WanVideoConfig().arch_config.patch_size[-2])
-    seq_len = frame_seq_length * latents.shape[2]
-    kv_cache1 = _initialize_kv_cache(transformer, batch_size=latents.shape[0],
-                                      kv_cache_size=frame_seq_length * latents.shape[2],
-                                      dtype=target_dtype,
-                                      device=latents.device)
-    crossattn_cache = _initialize_crossattn_cache(
-                transformer,
-                batch_size=latents.shape[0],
-                max_text_len=WanVideoConfig().arch_config.text_len,
-                dtype=target_dtype,
-                device=latents.device)
-    for current_num_frames, t_cur in zip(block_sizes, timesteps):
-        # logger.info(f"Current frame idx: {start_index}, Current timestep: {t_cur}")
-        # logger.info(f"k cache sum: {sum(kv_cache['k'].float().sum().item() for kv_cache in kv_cache1)}, v cache sum: {sum(kv_cache['v'].float().sum().item() for kv_cache in kv_cache1)}")
-        # logger.info(f"latents sum: {latents.float().sum().item()}, encoder_hidden_states sum: {prompt_embeds.float().sum().item()}")
-        current_latents = latents[:, :, start_index:start_index +
-                                    current_num_frames, :, :]
-
-        attn_metadata = None
-
-        with set_forward_context(current_timestep=0,
-                                attn_metadata=attn_metadata,
-                                forward_batch=forward_batch):
-            # Run transformer; follow DMD stage pattern
-            t_expanded_noise = t_cur * torch.ones(
-                (current_latents.shape[0], 1),
-                device=current_latents.device,
-                dtype=torch.long)
-            if isinstance(transformer, CausalWanModel):
-                pred_noise_btchw = transformer(
-                    x=current_latents,
-                    context=prompt_embeds,
-                    t=t_expanded_noise,
-                    seq_len=seq_len,
-                    kv_cache=kv_cache1,
-                    crossattn_cache=crossattn_cache,
-                    current_start=(pos_start_base + start_index) *
-                    frame_seq_length
-                )
-            elif isinstance(transformer, CausalWanTransformer3DModel):
-                pred_noise_btchw = transformer(
-                    current_latents,
-                    prompt_embeds,
-                    t_expanded_noise,
-                    kv_cache=kv_cache1,
-                    crossattn_cache=crossattn_cache,
-                    current_start=(pos_start_base + start_index) *
-                    frame_seq_length,
-                    start_frame=start_index
-                )
-
-        # Write back and advance
-        latents[:, :, start_index:start_index +
-                current_num_frames, :, :] = pred_noise_btchw.clone()
-
-        # Re-run with context timestep to update KV cache using clean context
-        context_noise = 0
-        t_context = torch.ones([latents.shape[0]],
-                                device=latents.device,
-                                dtype=torch.long) * int(context_noise)
-        context_bcthw = pred_noise_btchw.to(target_dtype)
-        with set_forward_context(current_timestep=0,
-                                attn_metadata=attn_metadata,
-                                forward_batch=forward_batch):
-            t_expanded_context = t_context.unsqueeze(1)
-            if isinstance(transformer, CausalWanModel):
-                _ = transformer(
-                    x=context_bcthw,
-                    context=prompt_embeds,
-                    t=t_expanded_context,
-                    seq_len=seq_len,
-                    kv_cache=kv_cache1,
-                    crossattn_cache=crossattn_cache,
-                    current_start=(pos_start_base + start_index) *
-                    frame_seq_length
-                )
-            elif isinstance(transformer, CausalWanTransformer3DModel):
-                _ = transformer(
-                    context_bcthw,
-                    prompt_embeds,
-                    t_expanded_context,
-                    kv_cache=kv_cache1,
-                    crossattn_cache=crossattn_cache,
-                    current_start=(pos_start_base + start_index) *
-                    frame_seq_length,
-                    start_frame=start_index
-                )
-        start_index += current_num_frames
-
-    return latents
-
-def _initialize_kv_cache(transformer, batch_size, kv_cache_size, dtype, device) -> None:
-    """
-    Initialize a Per-GPU KV cache aligned with the Wan model assumptions.
-    """
-    kv_cache1 = []
-    if isinstance(transformer, CausalWanModel):
-        num_attention_heads = transformer.num_heads
-        attention_head_dim = transformer.dim // transformer.num_heads
-    elif isinstance(transformer, CausalWanTransformer3DModel):
-        num_attention_heads = transformer.num_attention_heads
-        attention_head_dim = transformer.attention_head_dim
-
-    for _ in range(len(transformer.blocks)):
-        kv_cache1.append({
-            "k":
-            torch.zeros([
-                batch_size, kv_cache_size, num_attention_heads,
-                attention_head_dim
-            ],
-                        dtype=dtype,
-                        device=device),
-            "v":
-            torch.zeros([
-                batch_size, kv_cache_size, num_attention_heads,
-                attention_head_dim
-            ],
-                        dtype=dtype,
-                        device=device),
-            "global_end_index":
-            torch.tensor([0], dtype=torch.long, device=device),
-            "local_end_index":
-            torch.tensor([0], dtype=torch.long, device=device),
-        })
-
-    return kv_cache1
-
-def _initialize_crossattn_cache(transformer, batch_size, max_text_len, dtype,
-                                    device) -> None:
-    """
-    Initialize a Per-GPU cross-attention cache aligned with the Wan model assumptions.
-    """
-    crossattn_cache = []
-    if isinstance(transformer, CausalWanModel):
-        num_attention_heads = transformer.num_heads
-        attention_head_dim = transformer.dim // transformer.num_heads
-    elif isinstance(transformer, CausalWanTransformer3DModel):
-        num_attention_heads = transformer.num_attention_heads
-        attention_head_dim = transformer.attention_head_dim
-
-    for _ in range(len(transformer.blocks)):
-        crossattn_cache.append({
-            "k":
-            torch.zeros([
-                batch_size, max_text_len, num_attention_heads,
-                attention_head_dim
-            ],
-                        dtype=dtype,
-                        device=device),
-            "v":
-            torch.zeros([
-                batch_size, max_text_len, num_attention_heads,
-                attention_head_dim
-            ],
-                        dtype=dtype,
-                        device=device),
-            "is_init":
-            False,
-        })
-    return crossattn_cache
@@ -1,144 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-import os
-import math
-
-import numpy as np
-import pytest
-import torch
-from fastvideo.wan.modules.model import WanModel
-
-from fastvideo.configs.pipelines import PipelineConfig
-from fastvideo.forward_context import set_forward_context
-from fastvideo.fastvideo_args import FastVideoArgs
-from fastvideo.logger import init_logger
-from fastvideo.models.loader.component_loader import TransformerLoader
-from fastvideo.utils import maybe_download_model
-from fastvideo.configs.models.dits import WanVideoConfig
-from fastvideo.pipelines.pipeline_batch_info import ForwardBatch
-
-
-logger = init_logger(__name__)
-
-os.environ["MASTER_ADDR"] = "localhost"
-os.environ["MASTER_PORT"] = "29503"
-
-os.environ["FASTVIDEO_FORCE_ATTN_BF16"] = "1"
-BASE_MODEL_PATH = "Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
-MODEL_PATH = maybe_download_model(BASE_MODEL_PATH,
-                                  local_dir=os.path.join(
-                                      'data', BASE_MODEL_PATH))
-TRANSFORMER_PATH = os.path.join(MODEL_PATH, "transformer")
-
-
-@pytest.mark.usefixtures("distributed_setup")
-def test_ori_wan_transformer():
-    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-    precision = torch.float32
-    precision_str = "fp32"
-    args = FastVideoArgs(model_path=TRANSFORMER_PATH,
-                         dit_cpu_offload=True,
-                         pipeline_config=PipelineConfig(dit_config=WanVideoConfig(), dit_precision=precision_str, dit_forward_precision=precision_str))
-    args.device = device
-
-    loader = TransformerLoader()
-    model2 = loader.load(TRANSFORMER_PATH, args)
-
-    model1 = WanModel.from_pretrained(
-        "/mnt/weka/home/hao.zhang/wei/Self-Forcing-clean/wan_models/Wan2.1-T2V-1.3B")
-    model1.eval()
-    model1 = model1.to(device).to(precision)
-    model1.requires_grad_(False)
-
-    total_params = sum(p.numel() for p in model1.parameters())
-    # Calculate weight sum for model1 (converting to float64 to avoid overflow)
-    weight_sum_model1 = sum(
-        p.to(torch.float64).sum().item() for p in model1.parameters())
-    # Also calculate mean for more stable comparison
-    weight_mean_model1 = weight_sum_model1 / total_params
-    logger.info("Model 1 weight sum: %s", weight_sum_model1)
-    logger.info("Model 1 weight mean: %s", weight_mean_model1)
-
-    # Calculate weight sum for model2 (converting to float64 to avoid overflow)
-    total_params_model2 = sum(p.numel() for p in model2.parameters())
-    weight_sum_model2 = sum(
-        p.to(torch.float64).sum().item() for p in model2.parameters())
-    # Also calculate mean for more stable comparison
-    weight_mean_model2 = weight_sum_model2 / total_params_model2
-    logger.info("Model 2 weight sum: %s", weight_sum_model2)
-    logger.info("Model 2 weight mean: %s", weight_mean_model2)
-
-    weight_sum_diff = abs(weight_sum_model1 - weight_sum_model2)
-    logger.info("Weight sum difference: %s", weight_sum_diff)
-    weight_mean_diff = abs(weight_mean_model1 - weight_mean_model2)
-    logger.info("Weight mean difference: %s", weight_mean_diff)
-
-    # Set both models to eval mode
-    model1 = model1.eval()
-    model2 = model2.eval()
-
-    # Create identical inputs for both models
-    batch_size = 1
-    text_seq_len = 120
-    seq_len = math.ceil((104 * 60) /
-                            (2 * 2) *
-                            21)
-
-    # Video latents [B, C, T, H, W]
-    hidden_states = torch.randn(batch_size,
-                                21,
-                                16,
-                                60,
-                                104,
-                                device=device,
-                                generator=torch.Generator("cuda").manual_seed(1024),
-                                dtype=precision)
-
-    logger.info("Hidden states sum: %s, Hidden states shape: %s, Hidden states dtype: %s", hidden_states.float().sum(), hidden_states.shape, hidden_states.dtype)
-
-    # Text embeddings [B, L, D] (including global token)
-    # encoder_hidden_states = torch.randn(batch_size,
-    #                                     text_seq_len + 1,
-    #                                     4096,
-    #                                     device=device,
-    #                                     dtype=precision)
-    encoder_hidden_states = torch.load("../sf_cond_prompt_embeds.pt").to(device, dtype=precision)
-
-    logger.info("Encoder hidden states sum: %s, Encoder hidden states shape: %s, Encoder hidden states dtype: %s", encoder_hidden_states.float().sum(), encoder_hidden_states.shape, encoder_hidden_states.dtype)
-
-    # Timestep
-    timestep = torch.tensor([995.7627], device=device, dtype=precision)
-
-    forward_batch = ForwardBatch(
-        data_type="dummy",
-    )
-
-    # with torch.amp.autocast('cuda', dtype=precision):
-    output1 = model1(
-        x=hidden_states.permute(0, 2, 1, 3, 4),
-        context=encoder_hidden_states,
-        t=timestep,
-        seq_len=seq_len,
-    ).permute(0, 2, 1, 3, 4)
-    with set_forward_context(
-            current_timestep=0,
-            attn_metadata=None,
-            forward_batch=forward_batch,
-    ):
-        output2 = model2(hidden_states=hidden_states.permute(0, 2, 1, 3, 4),
-                            encoder_hidden_states=encoder_hidden_states,
-                            timestep=timestep).permute(0, 2, 1, 3, 4)
-
-    # Check if outputs have the same shape
-    assert output1.shape == output2.shape, f"Output shapes don't match: {output1.shape} vs {output2.shape}"
-    assert output1.dtype == output2.dtype, f"Output dtype don't match: {output1.dtype} vs {output2.dtype}"
-
-    # Check if outputs are similar (allowing for small numerical differences)
-    max_diff = torch.max(torch.abs(output1 - output2))
-    mean_diff = torch.mean(torch.abs(output1 - output2))
-    logger.info("Max Diff: %s", max_diff.item())
-    logger.info("Mean Diff: %s", mean_diff.item())
-    logger.info("Output 1 sum: %s", output1.float().sum())
-    logger.info("Output 2 sum: %s", output2.float().sum())
-    assert max_diff < 1e-4, f"Maximum difference between outputs: {max_diff.item()}"
-    # mean diff
-    assert mean_diff < 1e-4, f"Mean difference between outputs: {mean_diff.item()}"
@@ -1,144 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-import os
-import math
-
-import numpy as np
-import pytest
-import torch
-from fastvideo.wan.modules.causal_model import CausalWanModel
-
-from fastvideo.configs.pipelines import PipelineConfig
-from fastvideo.forward_context import set_forward_context
-from fastvideo.fastvideo_args import FastVideoArgs
-from fastvideo.logger import init_logger
-from fastvideo.models.loader.component_loader import TransformerLoader
-from fastvideo.utils import maybe_download_model
-from fastvideo.configs.models.dits import WanVideoConfig
-from fastvideo.pipelines.pipeline_batch_info import ForwardBatch
-
-
-logger = init_logger(__name__)
-
-os.environ["MASTER_ADDR"] = "localhost"
-os.environ["MASTER_PORT"] = "29503"
-
-BASE_MODEL_PATH = "wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers"
-MODEL_PATH = maybe_download_model(BASE_MODEL_PATH,
-                                  local_dir=os.path.join(
-                                      'data', BASE_MODEL_PATH))
-TRANSFORMER_PATH = os.path.join(MODEL_PATH, "transformer")
-
-
-@pytest.mark.usefixtures("distributed_setup")
-def test_train_ori_causal_wan_transformer():
-    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-    precision = torch.bfloat16
-    precision_str = "bf16"
-    args = FastVideoArgs(model_path=TRANSFORMER_PATH,
-                         dit_cpu_offload=True,
-                         pipeline_config=PipelineConfig(dit_config=WanVideoConfig(), dit_precision=precision_str))
-    args.device = device
-
-    loader = TransformerLoader()
-    model2 = loader.load(TRANSFORMER_PATH, args).to(dtype=precision)
-
-    model1 = CausalWanModel.from_pretrained(
-        "/mnt/weka/home/hao.zhang/wei/Self-Forcing/wan_models/Wan2.1-T2V-1.3B", device=device,
-        torch_dtype=precision).to(device, dtype=precision).requires_grad_(False)
-    causal_state_dict = torch.load("/mnt/weka/home/hao.zhang/wei/Self-Forcing/checkpoints/self_forcing_dmd.pt")["generator_ema"]
-    new_state_dict = {}
-    for k, v in causal_state_dict.items():
-        if k.startswith("model."):
-            new_state_dict[k.replace("model.", "")] = v
-    causal_state_dict = new_state_dict
-    model1.load_state_dict(causal_state_dict)
-
-    model1.num_frame_per_block = 3
-    model2.num_frame_per_block = 3
-
-    total_params = sum(p.numel() for p in model1.parameters())
-    # Calculate weight sum for model1 (converting to float64 to avoid overflow)
-    weight_sum_model1 = sum(
-        p.to(torch.float64).sum().item() for p in model1.parameters())
-    # Also calculate mean for more stable comparison
-    weight_mean_model1 = weight_sum_model1 / total_params
-    logger.info("Model 1 weight sum: %s", weight_sum_model1)
-    logger.info("Model 1 weight mean: %s", weight_mean_model1)
-
-    # Calculate weight sum for model2 (converting to float64 to avoid overflow)
-    total_params_model2 = sum(p.numel() for p in model2.parameters())
-    weight_sum_model2 = sum(
-        p.to(torch.float64).sum().item() for p in model2.parameters())
-    # Also calculate mean for more stable comparison
-    weight_mean_model2 = weight_sum_model2 / total_params_model2
-    logger.info("Model 2 weight sum: %s", weight_sum_model2)
-    logger.info("Model 2 weight mean: %s", weight_mean_model2)
-
-    weight_sum_diff = abs(weight_sum_model1 - weight_sum_model2)
-    logger.info("Weight sum difference: %s", weight_sum_diff)
-    weight_mean_diff = abs(weight_mean_model1 - weight_mean_model2)
-    logger.info("Weight mean difference: %s", weight_mean_diff)
-
-    # Set both models to eval mode
-    model1 = model1.eval()
-    model2 = model2.eval()
-
-    # Create identical inputs for both models
-    batch_size = 1
-    text_seq_len = 30
-    seq_len = math.ceil((160 * 90) /
-                            (2 * 2) *
-                            21)
-
-    # Video latents [B, C, T, H, W]
-    hidden_states = torch.randn(batch_size,
-                                16,
-                                21,
-                                160,
-                                90,
-                                device=device,
-                                dtype=precision)
-
-    # Text embeddings [B, L, D] (including global token)
-    encoder_hidden_states = torch.randn(batch_size,
-                                        text_seq_len + 1,
-                                        4096,
-                                        device=device,
-                                        dtype=precision)
-
-    # Timestep
-    timestep = torch.randint(0, 1000, (batch_size, 21), device=device, dtype=torch.long)
-    logger.info("timestep: %s", timestep)
-
-    forward_batch = ForwardBatch(
-        data_type="dummy",
-    )
-
-    # with torch.amp.autocast('cuda', dtype=precision):
-    output1 = model1(
-        x=hidden_states,
-        context=encoder_hidden_states,
-        t=timestep,
-        seq_len=seq_len,
-    )
-    with set_forward_context(
-            current_timestep=0,
-            attn_metadata=None,
-            forward_batch=forward_batch,
-    ):
-        output2 = model2(hidden_states=hidden_states,
-                            encoder_hidden_states=encoder_hidden_states,
-                            timestep=timestep)
-
-    # Check if outputs have the same shape
-    assert output1.shape == output2.shape, f"Output shapes don't match: {output1.shape} vs {output2.shape}"
-    assert output1.dtype == output2.dtype, f"Output dtype don't match: {output1.dtype} vs {output2.dtype}"
-
-    # Check if outputs are similar (allowing for small numerical differences)
-    max_diff = torch.max(torch.abs(output1 - output2))
-    mean_diff = torch.mean(torch.abs(output1 - output2))
-    logger.info("Max Diff: %s", max_diff.item())
-    logger.info("Mean Diff: %s", mean_diff.item())
-    assert max_diff < 1e-4, f"Maximum difference between outputs: {max_diff.item()}"
-    # mean diff
-    assert mean_diff < 1e-4, f"Mean difference between outputs: {mean_diff.item()}"
@@ -38,8 +38,7 @@ def test_parquet_dataset_saver_flush_and_last(tmp_path: Path):
        data_type="video",
        latents=torch.randn(B, 2),
        prompt_embeds=[torch.randn(B, 1, 1)],
-        # Attention mask should be integer dtype in real pipelines
-        prompt_attention_mask=[torch.ones(B, 1, dtype=torch.int64)],
+        prompt_attention_mask=[torch.ones(B, 1)],
    )
    batch.video_file_name = [f"vid_{i}" for i in range(B)]

@@ -53,14 +52,16 @@ def test_parquet_dataset_saver_flush_and_last(tmp_path: Path):
    out_dir = tmp_path / "saver_out"
    saver.save_and_write_parquet_batch(batch, str(out_dir))
    # First flush: should write one full file (3 rows), keep 2 in buffer
-    saver.flush_tables()
+    saver.flush_tables(str(out_dir))
    files = sorted(out_dir.rglob("*.parquet"))
    assert len(files) == 1
    assert pq.read_table(str(files[0])).num_rows == 3

    # Final flush: write remainder 2 rows
-    saver.flush_tables(write_remainder=True)
+    saver.flush_last(str(out_dir))
    files2 = sorted(out_dir.rglob("*.parquet"))
    assert len(files2) == 2
    total = sum(pq.read_table(str(f)).num_rows for f in files2)
    assert total == 5
+
+
@@ -1,7 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 import copy
 import gc
-import json
 import os
 import time
 from abc import abstractmethod
@@ -12,7 +11,6 @@ from typing import Any
 import imageio
 import numpy as np
 import torch
-import torch.distributed as dist
 import torch.nn.functional as F
 import torchvision
 from einops import rearrange
@@ -38,11 +36,10 @@ from fastvideo.training.activation_checkpoint import (
    apply_activation_checkpointing)
 from fastvideo.training.training_pipeline import TrainingPipeline
 from fastvideo.training.training_utils import (
-    EMA_FSDP, clip_grad_norm_while_handling_failing_dtensor_cases,
+    clip_grad_norm_while_handling_failing_dtensor_cases, count_trainable,
    get_scheduler, load_distillation_checkpoint, save_distillation_checkpoint,
    shift_timestep)
-from fastvideo.utils import (is_vsa_available, maybe_download_model,
-                             set_random_seed, verify_model_config_and_directory)
+from fastvideo.utils import is_vsa_available, set_random_seed

 import wandb  # isort: skip

@@ -72,11 +69,18 @@ class DistillationPipeline(TrainingPipeline):
    current_trainstep: int
    video_latent_shape: tuple[int, ...]
    video_latent_shape_sp: tuple[int, ...]
+    real_score_transformer: torch.nn.Module
+    fake_score_transformer: torch.nn.Module

    def create_pipeline_stages(self, fastvideo_args: FastVideoArgs):
        raise RuntimeError(
            "create_pipeline_stages should not be called for training pipeline")

+    def set_trainable(self) -> None:
+        super().set_trainable()
+        self.modules["real_score_transformer"].requires_grad_(False)
+        self.modules["vae"].requires_grad_(False)
+
    def initialize_training_pipeline(self, training_args: TrainingArgs):
        """Initialize the distillation training pipeline with multiple models."""
        logger.info("Initializing distillation pipeline...")
@@ -85,37 +89,14 @@ class DistillationPipeline(TrainingPipeline):

        self.noise_scheduler = self.get_module("scheduler")
        self.vae = self.get_module("vae")
-        self.vae.requires_grad_(False)
-
        self.timestep_shift = self.training_args.pipeline_config.flow_shift
        self.noise_scheduler = FlowMatchEulerDiscreteScheduler(
            shift=self.timestep_shift)

-        if training_args.real_score_model_path:
-            logger.info(
-                f"Loading real score transformer from: {training_args.real_score_model_path}"
-            )
-            self.real_score_transformer = self.load_module_from_path(
-                training_args.real_score_model_path, "transformer",
-                training_args)
-        else:
-            self.real_score_transformer = self.get_module(
-                "real_score_transformer")
-
-        if training_args.fake_score_model_path:
-            logger.info(
-                f"Loading fake score transformer from: {training_args.fake_score_model_path}"
-            )
-            self.fake_score_transformer = self.load_module_from_path(
-                training_args.fake_score_model_path, "transformer",
-                training_args)
-        else:
-            self.fake_score_transformer = self.get_module(
-                "fake_score_transformer")
-
-        self.real_score_transformer.requires_grad_(False)
+        # self.transformer is the generator model
+        self.real_score_transformer = self.get_module("real_score_transformer")
+        self.fake_score_transformer = self.get_module("fake_score_transformer")
        self.real_score_transformer.eval()
-        self.fake_score_transformer.requires_grad_(True)
        self.fake_score_transformer.train()

        if training_args.enable_gradient_checkpointing_type is not None:
@@ -138,13 +119,10 @@ class DistillationPipeline(TrainingPipeline):
        if fake_score_lr == 0.0:
            fake_score_lr = training_args.learning_rate

-        betas_str = training_args.fake_score_betas
-        betas = tuple(float(x.strip()) for x in betas_str.split(","))
-
        self.fake_score_optimizer = torch.optim.AdamW(
            fake_score_params,
            lr=fake_score_lr,
-            betas=betas,
+            betas=(0.9, 0.999),
            weight_decay=training_args.weight_decay,
            eps=1e-8,
        )
@@ -172,19 +150,8 @@ class DistillationPipeline(TrainingPipeline):
            self.training_args.pipeline_config.dmd_denoising_steps,
            dtype=torch.long,
            device=get_local_torch_device())
-
-        if training_args.warp_denoising_step:  # Warp the denoising step according to the scheduler time shift
-            timesteps = torch.cat((self.noise_scheduler.timesteps.cpu(),
-                                   torch.tensor([0],
-                                                dtype=torch.float32))).cuda()
-            self.denoising_step_list = timesteps[1000 -
-                                                 self.denoising_step_list]
-            logger.info("Warping denoising_step_list")
-
-        self.denoising_step_list = self.denoising_step_list.to(
-            get_local_torch_device())
-        logger.info("Distillation generator model to %s denoising steps: %s",
-                    len(self.denoising_step_list), self.denoising_step_list)
+        logger.info("Distillation generator model to %s denoising steps",
+                    len(self.denoising_step_list))
        self.num_train_timestep = self.noise_scheduler.num_train_timesteps

        self.min_timestep = int(self.training_args.min_timestep_ratio *
@@ -194,82 +161,6 @@ class DistillationPipeline(TrainingPipeline):

        self.real_score_guidance_scale = self.training_args.real_score_guidance_scale

-        self.generator_ema = None
-        if (self.training_args.ema_decay
-                is not None) and (self.training_args.ema_decay > 0.0):
-            self.generator_ema = EMA_FSDP(self.transformer,
-                                          decay=self.training_args.ema_decay)
-            logger.info(
-                f"Initialized generator EMA with decay={self.training_args.ema_decay}"
-            )
-        else:
-            logger.info("Generator EMA disabled (ema_decay <= 0.0)")
-
-    def load_module_from_path(self, model_path: str, module_type: str,
-                              training_args: "TrainingArgs"):
-        """
-        Load a module from a specific path using the same loading logic as the pipeline.
-        
-        Args:
-            model_path: Path to the model
-            module_type: Type of module to load (e.g., "transformer")
-            training_args: Training arguments
-            
-        Returns:
-            The loaded module
-        """
-        logger.info(f"Loading {module_type} from custom path: {model_path}")
-        # Set flag to prevent custom weight loading for teacher/critic models
-        training_args._loading_teacher_critic_model = True
-
-        try:
-            from fastvideo.models.loader.component_loader import (
-                PipelineComponentLoader)
-
-            # Download the model if it's a Hugging Face model ID
-            local_model_path = maybe_download_model(model_path)
-            logger.info(f"Model downloaded/found at: {local_model_path}")
-            config = verify_model_config_and_directory(local_model_path)
-
-            if module_type not in config:
-                if hasattr(self, '_extra_config_module_map'
-                           ) and module_type in self._extra_config_module_map:
-                    extra_module = self._extra_config_module_map[module_type]
-                    if extra_module in config:
-                        module_type = extra_module
-                        logger.info(f"Using {extra_module} for {module_type}")
-                    else:
-                        raise ValueError(
-                            f"Module {module_type} not found in config at {local_model_path}"
-                        )
-                else:
-                    raise ValueError(
-                        f"Module {module_type} not found in config at {local_model_path}"
-                    )
-
-            module_info = config[module_type]
-            if module_info is None:
-                raise ValueError(
-                    f"Module {module_type} has null value in config at {local_model_path}"
-                )
-
-            transformers_or_diffusers, architecture = module_info
-            component_path = os.path.join(local_model_path, module_type)
-            module = PipelineComponentLoader.load_module(
-                module_name=module_type,
-                component_model_path=component_path,
-                transformers_or_diffusers=transformers_or_diffusers,
-                fastvideo_args=training_args,
-            )
-
-            logger.info(
-                f"Successfully loaded {module_type} from {component_path}")
-            return module
-        finally:
-            # Always clean up the flag
-            if hasattr(training_args, '_loading_teacher_critic_model'):
-                delattr(training_args, '_loading_teacher_critic_model')
-
    @abstractmethod
    def initialize_validation_pipeline(self, training_args: TrainingArgs):
        """Initialize validation pipeline - must be implemented by subclasses."""
@@ -279,117 +170,11 @@ class DistillationPipeline(TrainingPipeline):
    def _prepare_distillation(self,
                              training_batch: TrainingBatch) -> TrainingBatch:
        """Prepare training environment for distillation."""
-        self.transformer.requires_grad_(True)
        self.transformer.train()
-        self.fake_score_transformer.requires_grad_(True)
        self.fake_score_transformer.train()

        return training_batch

-    def apply_ema_to_model(self, model):
-        """Apply EMA weights to the model for validation or inference."""
-        if self.generator_ema is not None:
-            with self.generator_ema.apply_to_model(model):
-                return model
-        return model
-
-    def get_ema_model_copy(self):
-        """Get a copy of the model with EMA weights applied."""
-        if self.generator_ema is not None:
-            ema_model = copy.deepcopy(self.transformer)
-            self.generator_ema.copy_to_unwrapped(ema_model)
-            return ema_model
-        return None
-
-    def is_ema_ready(self, current_step: int = None):
-        """Check if EMA is ready for use (after ema_start_step)."""
-        if current_step is None:
-            current_step = getattr(self, 'current_trainstep', 0)
-        return (self.generator_ema is not None
-                and current_step >= self.training_args.ema_start_step)
-
-    def save_ema_weights(self, output_dir: str, step: int):
-        """Save EMA weights separately for inference purposes."""
-        if self.generator_ema is None:
-            logger.warning("Cannot save EMA weights: EMA not initialized")
-            return
-
-        if not self.is_ema_ready():
-            logger.warning(
-                "Cannot save EMA weights: EMA not ready yet (step < ema_start_step)"
-            )
-            return
-
-        try:
-            ema_model = self.get_ema_model_copy()
-            if ema_model is None:
-                logger.warning("Failed to create EMA model copy")
-                return
-
-            ema_save_dir = os.path.join(output_dir, f"ema_checkpoint-{step}")
-            os.makedirs(ema_save_dir, exist_ok=True)
-
-            # save as diffusers format
-            from safetensors.torch import save_file
-
-            from fastvideo.training.training_utils import (
-                custom_to_hf_state_dict, gather_state_dict_on_cpu_rank0)
-            cpu_state = gather_state_dict_on_cpu_rank0(ema_model, device=None)
-
-            if self.global_rank == 0:
-                weight_path = os.path.join(
-                    ema_save_dir, "diffusion_pytorch_model.safetensors")
-                diffusers_state_dict = custom_to_hf_state_dict(
-                    cpu_state, ema_model.reverse_param_names_mapping)
-                save_file(diffusers_state_dict, weight_path)
-
-                config_dict = ema_model.hf_config
-                if "dtype" in config_dict:
-                    del config_dict["dtype"]
-                config_path = os.path.join(ema_save_dir, "config.json")
-                with open(config_path, "w") as f:
-                    json.dump(config_dict, f, indent=4)
-
-                logger.info(f"EMA weights saved to {weight_path}")
-
-            del ema_model
-
-        except Exception as e:
-            logger.error(f"Failed to save EMA weights: {str(e)}")
-
-    def get_ema_stats(self):
-        """Get EMA statistics for monitoring."""
-        if self.generator_ema is None:
-            return {
-                "ema_enabled": False,
-                "ema_decay": None,
-                "ema_start_step": self.training_args.ema_start_step,
-                "ema_ready": False,
-                "ema_step": self.current_trainstep,
-            }
-
-        return {
-            "ema_enabled": True,
-            "ema_decay": self.training_args.ema_decay,
-            "ema_start_step": self.training_args.ema_start_step,
-            "ema_ready": self.is_ema_ready(),
-            "ema_step": self.current_trainstep,
-        }
-
-    def reset_ema(self):
-        """Reset EMA to current model weights."""
-        if self.generator_ema is not None:
-            logger.info("Resetting EMA to current model weights")
-            self.generator_ema.update(self.transformer)
-            # Force update to current weights by setting decay to 0 temporarily
-            original_decay = self.generator_ema.decay
-            self.generator_ema.decay = 0.0
-            self.generator_ema.update(self.transformer)
-            self.generator_ema.decay = original_decay
-            logger.info("EMA reset completed")
-        else:
-            logger.warning("Cannot reset EMA: EMA not initialized")
-
    def _build_distill_input_kwargs(
            self, noise_input: torch.Tensor, timestep: torch.Tensor,
            text_dict: dict[str, torch.Tensor] | None,
@@ -546,7 +331,6 @@ class DistillationPipeline(TrainingPipeline):
    def _dmd_forward(self, generator_pred_video: torch.Tensor,
                     training_batch: TrainingBatch) -> torch.Tensor:
        """Compute DMD (Diffusion Model Distillation) loss."""
-        original_latent = generator_pred_video
        with torch.no_grad():
            timestep = torch.randint(0,
                                     self.num_train_timestep, [1],
@@ -571,7 +355,7 @@ class DistillationPipeline(TrainingPipeline):

            noisy_latent = self.noise_scheduler.add_noise(
                generator_pred_video.flatten(0, 1), noise.flatten(0, 1),
-                timestep).detach().unflatten(0, (1, generator_pred_video.shape[1]))
+                timestep).unflatten(0, (1, generator_pred_video.shape[1]))

            # fake_score_transformer forward
            training_batch = self._build_distill_input_kwargs(
@@ -620,24 +404,24 @@ class DistillationPipeline(TrainingPipeline):
                pred_real_video_uncond) * self.real_score_guidance_scale

            grad = (faker_score_pred_video - real_score_pred_video) / torch.abs(
-                original_latent - real_score_pred_video).mean()
+                generator_pred_video - real_score_pred_video).mean()
            grad = torch.nan_to_num(grad)

        dmd_loss = 0.5 * F.mse_loss(
-            original_latent.float(),
-            (original_latent.float() - grad.float()).detach())
+            generator_pred_video.float(),
+            (generator_pred_video.float() - grad.float()).detach())

        training_batch.dmd_latent_vis_dict.update({
            "training_batch_dmd_fwd_clean_latent":
            training_batch.latents,
            "generator_pred_video":
-            original_latent.detach(),
+            generator_pred_video,
            "real_score_pred_video":
-            real_score_pred_video.detach(),
+            real_score_pred_video,
            "faker_score_pred_video":
-            faker_score_pred_video.detach(),
+            faker_score_pred_video,
            "dmd_timestep":
-            timestep.detach(),
+            timestep,
        })

        return dmd_loss
@@ -734,12 +518,12 @@ class DistillationPipeline(TrainingPipeline):
            "encoder_hidden_states": self.negative_prompt_embeds,
            "encoder_attention_mask": self.negative_prompt_attention_mask,
        }
-        training_batch.unconditional_dict = unconditional_dict

        training_batch.dmd_latent_vis_dict = {}
        training_batch.fake_score_latent_vis_dict = {}

        training_batch.conditional_dict = conditional_dict
+        training_batch.unconditional_dict = unconditional_dict
        training_batch.raw_latent_shape = training_batch.latents.shape
        training_batch.latents = training_batch.latents.permute(0, 2, 1, 3, 4)
        self.video_latent_shape = training_batch.latents.shape
@@ -802,15 +586,8 @@ class DistillationPipeline(TrainingPipeline):
                    (dmd_loss / gradient_accumulation_steps).backward()
                total_dmd_loss += dmd_loss.detach().item()
            self._clip_model_grad_norm_(batch_gen, self.transformer)
-            for param in self.transformer.parameters():
-                # check if the gradient is not None and not zero
-                assert param.grad is not None and param.grad.abs().sum() > 0
            self.optimizer.step()
            self.optimizer.zero_grad(set_to_none=True)
-
-            if self.generator_ema is not None:
-                self.generator_ema.update(self.transformer)
-
            avg_dmd_loss = torch.tensor(total_dmd_loss /
                                        gradient_accumulation_steps,
                                        device=self.device)
@@ -834,9 +611,6 @@ class DistillationPipeline(TrainingPipeline):
            fake_score_latent_vis_dict.update(
                batch_fake.fake_score_latent_vis_dict)
        self._clip_model_grad_norm_(batch_fake, self.fake_score_transformer)
-        for param in self.fake_score_transformer.parameters():
-            # check if the gradient is not None and not zero
-            assert param.grad is not None and param.grad.abs().sum() > 0
        self.fake_score_optimizer.step()
        self.fake_score_lr_scheduler.step()
        self.lr_scheduler.step()
@@ -864,8 +638,7 @@ class DistillationPipeline(TrainingPipeline):
            self.transformer, self.fake_score_transformer, self.global_rank,
            self.training_args.resume_from_checkpoint, self.optimizer,
            self.fake_score_optimizer, self.train_dataloader, self.lr_scheduler,
-            self.fake_score_lr_scheduler, self.noise_random_generator,
-            self.generator_ema)
+            self.fake_score_lr_scheduler, self.noise_random_generator)

        if resumed_step > 0:
            self.init_steps = resumed_step
@@ -896,14 +669,6 @@ class DistillationPipeline(TrainingPipeline):
            sum(p.numel()
                for p in self.fake_score_transformer.parameters()) / 1e9)

-        if self.generator_ema is not None:
-            logger.info("  Generator EMA enabled with decay: %s",
-                        self.training_args.ema_decay)
-            logger.info("  Generator EMA start step: %s",
-                        self.training_args.ema_start_step)
-        else:
-            logger.info("  Generator EMA disabled")
-
    @torch.no_grad()
    def _log_validation(self, transformer, training_args, global_step) -> None:
        training_args.inference_mode = True
@@ -935,18 +700,6 @@ class DistillationPipeline(TrainingPipeline):

        transformer.eval()

-        # Optionally use EMA model for validation if available and ready
-        use_ema_for_validation = (self.training_args.use_ema
-                                  and self.is_ema_ready(global_step))
-        if use_ema_for_validation:
-            logger.info("Using EMA model for validation")
-            validation_transformer = self.transformer
-            ema_context = self.generator_ema.apply_to_model(
-                validation_transformer)
-        else:
-            validation_transformer = transformer
-            ema_context = None
-
        validation_steps = training_args.validation_sampling_steps.split(",")
        validation_steps = [int(step) for step in validation_steps]
        validation_steps = [step for step in validation_steps if step > 0]
@@ -962,98 +715,50 @@ class DistillationPipeline(TrainingPipeline):
            step_videos: list[np.ndarray] = []
            step_captions: list[str] = []

-            if ema_context is not None:
-                with ema_context:
-                    for validation_batch in validation_dataloader:
-                        batch = self._prepare_validation_batch(
-                            sampling_param, training_args, validation_batch,
-                            num_inference_steps)
+            for validation_batch in validation_dataloader:
+                batch = self._prepare_validation_batch(sampling_param,
+                                                       training_args,
+                                                       validation_batch,
+                                                       num_inference_steps)

-                        negative_prompt = batch.negative_prompt
-                        batch_negative = ForwardBatch(
-                            data_type="video",
-                            prompt=negative_prompt,
-                            prompt_embeds=[],
-                            prompt_attention_mask=[],
-                        )
-                        result_batch = self.validation_pipeline.prompt_encoding_stage(  # type: ignore
-                            batch_negative, training_args)
-                        self.negative_prompt_embeds, self.negative_prompt_attention_mask = result_batch.prompt_embeds[
-                            0], result_batch.prompt_attention_mask[0]
+                negative_prompt = batch.negative_prompt
+                batch_negative = ForwardBatch(
+                    data_type="video",
+                    prompt=negative_prompt,
+                    prompt_embeds=[],
+                    prompt_attention_mask=[],
+                )
+                result_batch = self.validation_pipeline.prompt_encoding_stage(  # type: ignore
+                    batch_negative, training_args)
+                self.negative_prompt_embeds, self.negative_prompt_attention_mask = result_batch.prompt_embeds[
+                    0], result_batch.prompt_attention_mask[0]

-                        logger.info(
-                            "rank: %s: rank_in_sp_group: %s, batch.prompt: %s",
+                logger.info("rank: %s: rank_in_sp_group: %s, batch.prompt: %s",
                            self.global_rank,
                            self.rank_in_sp_group,
                            batch.prompt,
                            local_main_process_only=False)

-                        assert batch.prompt is not None and isinstance(
-                            batch.prompt, str)
-                        step_captions.append(batch.prompt)
+                assert batch.prompt is not None and isinstance(
+                    batch.prompt, str)
+                step_captions.append(batch.prompt)

-                        # Run validation inference
-                        with torch.no_grad():
-                            output_batch = self.validation_pipeline.forward(
-                                batch, training_args)
-                        samples = output_batch.output
-                        if self.rank_in_sp_group != 0:
-                            continue
+                # Run validation inference
+                with torch.no_grad():
+                    output_batch = self.validation_pipeline.forward(
+                        batch, training_args)
+                samples = output_batch.output
+                if self.rank_in_sp_group != 0:
+                    continue

-                        # Process outputs
-                        video = rearrange(samples, "b c t h w -> t b c h w")
-                        frames = []
-                        for x in video:
-                            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))
-                        step_videos.append(frames)
-            else:
-                # Use original transformer without EMA
-                for validation_batch in validation_dataloader:
-                    batch = self._prepare_validation_batch(
-                        sampling_param, training_args, validation_batch,
-                        num_inference_steps)
-
-                    negative_prompt = batch.negative_prompt
-                    batch_negative = ForwardBatch(
-                        data_type="video",
-                        prompt=negative_prompt,
-                        prompt_embeds=[],
-                        prompt_attention_mask=[],
-                    )
-                    result_batch = self.validation_pipeline.prompt_encoding_stage(  # type: ignore
-                        batch_negative, training_args)
-                    self.negative_prompt_embeds, self.negative_prompt_attention_mask = result_batch.prompt_embeds[
-                        0], result_batch.prompt_attention_mask[0]
-
-                    logger.info(
-                        "rank: %s: rank_in_sp_group: %s, batch.prompt: %s",
-                        self.global_rank,
-                        self.rank_in_sp_group,
-                        batch.prompt,
-                        local_main_process_only=False)
-
-                    assert batch.prompt is not None and isinstance(
-                        batch.prompt, str)
-                    step_captions.append(batch.prompt)
-
-                    # Run validation inference
-                    with torch.no_grad():
-                        output_batch = self.validation_pipeline.forward(
-                            batch, training_args)
-                    samples = output_batch.output
-                    if self.rank_in_sp_group != 0:
-                        continue
-
-                    # Process outputs
-                    video = rearrange(samples, "b c t h w -> t b c h w")
-                    frames = []
-                    for x in video:
-                        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))
-                    step_videos.append(frames)
+                # Process outputs
+                video = rearrange(samples, "b c t h w -> t b c h w")
+                frames = []
+                for x in video:
+                    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))
+                step_videos.append(frames)

            # Log validation results for this step
            world_group = get_world_group()
@@ -1130,16 +835,16 @@ class DistillationPipeline(TrainingPipeline):
                                                        latents.dtype)
                else:
                    latents += self.vae.shift_factor
-                with torch.autocast("cuda", dtype=torch.bfloat16):
-                    video = self.vae.decode(latents)
-                video = (video / 2 + 0.5).clamp(0, 1)
-                video = video.cpu().float()
-                video = video.permute(0, 2, 1, 3, 4)
-                video = (video * 255).numpy().astype(np.uint8)
-                wandb_loss_dict[latent_key] = wandb.Video(
-                    video, fps=24, format="mp4")  # change to 16 for Wan2.1
-                # Clean up references
-                del video, latents
+            with torch.autocast("cuda", dtype=torch.bfloat16):
+                video = self.vae.decode(latents)
+            video = (video / 2 + 0.5).clamp(0, 1)
+            video = video.cpu().float()
+            video = video.permute(0, 2, 1, 3, 4)
+            video = (video * 255).numpy().astype(np.uint8)
+            wandb_loss_dict[latent_key] = wandb.Video(
+                video, fps=24, format="mp4")  # change to 16 for Wan2.1
+            # Clean up references
+            del video, latents

        # Process DMD training data if available - use decode_stage instead of self.vae.decode
        if 'generator_pred_video' in dmd_latents_vis_dict:
@@ -1191,6 +896,14 @@ class DistillationPipeline(TrainingPipeline):
        else:
            set_random_seed(seed + self.global_rank)

+        # Check trainable params
+        num_trainable_generator = round(
+            count_trainable(self.transformer) / 1e9, 3)
+        num_trainable_critic = round(
+            count_trainable(self.fake_score_transformer) / 1e9, 3)
+        logger.info(
+            "rank: %s: # of trainable params in generator: %sB, # of trainable params in critic: %sB",
+            self.global_rank, num_trainable_generator, num_trainable_critic)
        # Set random seeds for deterministic training
        self.noise_random_generator = torch.Generator(device="cpu").manual_seed(
            self.seed)
@@ -1200,10 +913,6 @@ class DistillationPipeline(TrainingPipeline):
            device="cpu").manual_seed(self.seed)
        logger.info("Initialized random seeds with seed: %s", seed)

-        # Initialize current_trainstep for EMA ready checks
-        #TODO: check if needed
-        self.current_trainstep = self.init_steps
-
        # Resume from checkpoint if specified (this will restore random states)
        if self.training_args.resume_from_checkpoint:
            self._resume_from_checkpoint()
@@ -1247,14 +956,6 @@ class DistillationPipeline(TrainingPipeline):
            self.current_trainstep = step
            training_batch.current_vsa_sparsity = current_vsa_sparsity

-            if (step >= self.training_args.ema_start_step) and \
-                    (self.generator_ema is None) and (self.training_args.ema_decay > 0):
-                self.generator_ema = EMA_FSDP(
-                    self.transformer, decay=self.training_args.ema_decay)
-                logger.info(
-                    f"Created generator EMA at step {step} with decay={self.training_args.ema_decay}"
-                )
-
            with torch.autocast("cuda", dtype=torch.bfloat16):
                training_batch = self.train_one_step(training_batch)

@@ -1268,19 +969,11 @@ class DistillationPipeline(TrainingPipeline):
            avg_step_time = sum(step_times) / len(step_times)

            progress_bar.set_postfix({
-                "total_loss":
-                f"{total_loss:.4f}",
-                "generator_loss":
-                f"{generator_loss:.4f}",
-                "fake_score_loss":
-                f"{fake_score_loss:.4f}",
-                "step_time":
-                f"{step_time:.2f}s",
-                "grad_norm":
-                grad_norm,
-                "ema":
-                "✓" if (self.generator_ema is not None and self.is_ema_ready())
-                else "✗",
+                "total_loss": f"{total_loss:.4f}",
+                "generator_loss": f"{generator_loss:.4f}",
+                "fake_score_loss": f"{fake_score_loss:.4f}",
+                "step_time": f"{step_time:.2f}s",
+                "grad_norm": grad_norm,
            })
            progress_bar.update(1)

@@ -1308,15 +1001,6 @@ class DistillationPipeline(TrainingPipeline):
                if use_vsa:
                    log_data["VSA_train_sparsity"] = current_vsa_sparsity

-                if self.generator_ema is not None:
-                    log_data["ema_enabled"] = True
-                    log_data["ema_decay"] = self.training_args.ema_decay
-                else:
-                    log_data["ema_enabled"] = False
-
-                ema_stats = self.get_ema_stats()
-                log_data.update(ema_stats)
-
                if training_batch.dmd_latent_vis_dict:
                    dmd_additional_logs = {
                        "generator_timestep":
@@ -1348,8 +1032,7 @@ class DistillationPipeline(TrainingPipeline):
                    self.global_rank, self.training_args.output_dir, step,
                    self.optimizer, self.fake_score_optimizer,
                    self.train_dataloader, self.lr_scheduler,
-                    self.fake_score_lr_scheduler, self.noise_random_generator,
-                    self.generator_ema)
+                    self.fake_score_lr_scheduler, self.noise_random_generator)

                if self.transformer:
                    self.transformer.train()
@@ -1366,11 +1049,7 @@ class DistillationPipeline(TrainingPipeline):
                                             self.global_rank,
                                             self.training_args.output_dir,
                                             f"{step}_weight_only",
-                                             only_save_generator_weight=True,
-                                             generator_ema=self.generator_ema)
-
-                if self.training_args.use_ema and self.is_ema_ready():
-                    self.save_ema_weights(self.training_args.output_dir, step)
+                                             only_save_generator_weight=True)

            if self.training_args.log_validation and step % self.training_args.validation_steps == 0:
                if self.training_args.log_visualization:
@@ -1390,11 +1069,7 @@ class DistillationPipeline(TrainingPipeline):
            self.training_args.output_dir, self.training_args.max_train_steps,
            self.optimizer, self.fake_score_optimizer, self.train_dataloader,
            self.lr_scheduler, self.fake_score_lr_scheduler,
-            self.noise_random_generator, self.generator_ema)
-
-        if self.training_args.use_ema and self.is_ema_ready():
-            self.save_ema_weights(self.training_args.output_dir,
-                                  self.training_args.max_train_steps)
+            self.noise_random_generator)

        if get_sp_group():
-            cleanup_dist_env_and_memory()
+            cleanup_dist_env_and_memory()
@@ -1,492 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-import sys
-from copy import deepcopy
-from typing import cast
-
-import numpy as np
-import torch
-import torch.nn.functional as F
-
-import wandb
-from fastvideo.dataset.dataloader.schema import (
-    pyarrow_schema_ode_trajectory_text_only)
-from fastvideo.distributed import get_local_torch_device
-from fastvideo.fastvideo_args import FastVideoArgs, TrainingArgs
-from fastvideo.forward_context import set_forward_context
-from fastvideo.logger import init_logger
-from fastvideo.models.schedulers.scheduling_self_forcing_flow_match import (
-    SelfForcingFlowMatchScheduler)
-from fastvideo.pipelines.basic.wan.wan_causal_dmd_pipeline import (
-    WanCausalDMDPipeline)
-from fastvideo.pipelines.pipeline_batch_info import TrainingBatch
-from fastvideo.training.training_pipeline import TrainingPipeline
-from fastvideo.training.training_utils import (
-    clip_grad_norm_while_handling_failing_dtensor_cases)
-
-logger = init_logger(__name__)
-
-
-class ODEInitTrainingPipeline(TrainingPipeline):
-    """
-    Training pipeline for ODE-init using precomputed denoising trajectories.
-
-    Supervision: predict the next latent in the stored trajectory by
-    - feeding current latent at timestep t into the transformer to predict noise
-    - stepping the scheduler with the predicted noise
-    - minimizing MSE to the stored next latent at timestep t_next
-    """
-
-    _required_config_modules = ["scheduler", "transformer", "vae"]
-
-    def initialize_pipeline(self, fastvideo_args: FastVideoArgs):
-        # Match the preprocess/generation scheduler for consistent stepping
-        self.modules["scheduler"] = SelfForcingFlowMatchScheduler(
-            shift=fastvideo_args.pipeline_config.flow_shift,
-            sigma_min=0.0,
-            extra_one_step=True)
-        self.modules["scheduler"].set_timesteps(num_inference_steps=1000,
-                                                training=True)
-
-    def set_schemas(self):
-        self.train_dataset_schema = pyarrow_schema_ode_trajectory_text_only
-
-    def initialize_training_pipeline(self, training_args: TrainingArgs):
-        super().initialize_training_pipeline(training_args)
-
-        self.noise_scheduler = self.get_module("scheduler")
-        self.vae = self.get_module("vae")
-        self.vae.requires_grad_(False)
-
-        self.timestep_shift = self.training_args.pipeline_config.flow_shift
-        assert self.timestep_shift == 5.0, "flow_shift must be 5.0"
-        self.noise_scheduler = SelfForcingFlowMatchScheduler(
-            shift=self.timestep_shift, sigma_min=0.0, extra_one_step=True)
-        self.noise_scheduler.set_timesteps(num_inference_steps=1000,
-                                           training=True)
-
-        # logger.info(f"ARG dmd_denoising_steps: {training_args.pipeline_config.dmd_denoising_steps}")
-        logger.info(
-            f"ARG dmd_denoising_steps: {self.training_args.pipeline_config.dmd_denoising_steps}"
-        )
-        self.dmd_denoising_steps = torch.tensor([1000, 750, 500, 250],
-                                                dtype=torch.long,
-                                                device=get_local_torch_device())
-        # self.dmd_denoising_steps = torch.tensor([1000, 750, 500, 250], dtype=torch.long, device=get_local_torch_device())
-        if training_args.warp_denoising_step:  # Warp the denoising step according to the scheduler time shift
-            timesteps = torch.cat((self.noise_scheduler.timesteps.cpu(),
-                                   torch.tensor([0],
-                                                dtype=torch.float32))).cuda()
-            logger.info(f"timesteps: {timesteps}")
-            self.dmd_denoising_steps = timesteps[1000 -
-                                                 self.dmd_denoising_steps]
-            logger.info(
-                f"warped self.dmd_denoising_steps: {self.dmd_denoising_steps}")
-            # assert False, "warp_denoising_step must be false"
-        else:
-            assert False, "warp_denoising_step must be true"
-            logger.info("not warped")
-        self.dmd_denoising_steps = self.dmd_denoising_steps.to(
-            get_local_torch_device())
-
-        logger.info(f"denoising_step_list: {self.dmd_denoising_steps}")
-
-        logger.info(
-            "Initialized ODE-init training pipeline with %s denoising steps",
-            len(self.dmd_denoising_steps))
-        # Cache for nearest trajectory index per DMD step (computed lazily on first batch)
-        self._cached_closest_idx_per_dmd = None
-        self.num_train_timestep = self.noise_scheduler.num_train_timesteps
-        # self.min_timestep = int(self.training_args.min_timestep_ratio *
-        #                         self.num_train_timestep)
-        # self.max_timestep = int(self.training_args.max_timestep_ratio *
-        #                         self.num_train_timestep)
-        # self.real_score_guidance_scale = self.training_args.real_score_guidance_scale
-
-    def initialize_validation_pipeline(self, training_args: TrainingArgs):
-        logger.info("Initializing validation pipeline...")
-        args_copy = deepcopy(training_args)
-        args_copy.inference_mode = True
-        # Warm start validation with current transformer
-        self.validation_pipeline = WanCausalDMDPipeline.from_pretrained(
-            # training_args.model_path,
-            "wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers",
-            args=args_copy,  # type: ignore
-            inference_mode=True,
-            loaded_modules={
-                "transformer": self.get_module("transformer"),
-            },
-            tp_size=training_args.tp_size,
-            sp_size=training_args.sp_size,
-            num_gpus=training_args.num_gpus,
-            pin_cpu_memory=training_args.pin_cpu_memory,
-            dit_cpu_offload=True)
-
-    def _get_next_batch(self, training_batch):  # type: ignore[override]
-        # batch = next(self.train_loader_iter, None)  # type: ignore
-        # if batch is None:
-        #     self.current_epoch += 1
-        #     logger.info("Starting epoch %s", self.current_epoch)
-        #     self.train_loader_iter = iter(self.train_dataloader)
-        #     batch = next(self.train_loader_iter)
-
-        # # Required fields from parquet (ODE trajectory schema)
-        # encoder_hidden_states = batch['text_embedding']
-        # encoder_attention_mask = batch['text_attention_mask']
-        # infos = batch['info_list']
-
-        # # Trajectory tensors may include a leading singleton batch dim per row
-        # trajectory_latents = batch['trajectory_latents']
-        # if trajectory_latents.dim() == 7:
-        #     # [B, 1, S, C, T, H, W] -> [B, S, C, T, H, W]
-        #     trajectory_latents = trajectory_latents[:, 0]
-        # elif trajectory_latents.dim() == 6:
-        #     # already [B, S, C, T, H, W]
-        #     pass
-        # else:
-        #     raise ValueError(
-        #         f"Unexpected trajectory_latents dim: {trajectory_latents.dim()}"
-        #     )
-
-        # trajectory_timesteps = batch['trajectory_timesteps']
-        # if trajectory_timesteps.dim() == 3:
-        #     # [B, 1, S] -> [B, S]
-        #     trajectory_timesteps = trajectory_timesteps[:, 0]
-        # elif trajectory_timesteps.dim() == 2:
-        #     # [B, S]
-        #     pass
-        # else:
-        #     raise ValueError(
-        #         f"Unexpected trajectory_timesteps dim: {trajectory_timesteps.dim()}"
-        #     )
-        # # [B, S, C, T, H, W] -> [B, S, T, C, H, W] to match self-forcing
-        # trajectory_latents = trajectory_latents.permute(0, 1, 3, 2, 4, 5)
-
-        # # Move to device
-        device = get_local_torch_device()
-        # training_batch.encoder_hidden_states = encoder_hidden_states.to(
-        #     device, dtype=torch.bfloat16)
-        # training_batch.encoder_attention_mask = encoder_attention_mask.to(
-        #     device, dtype=torch.bfloat16)
-        # training_batch.infos = infos
-
-        # return training_batch, trajectory_latents.to(
-        #     device, dtype=torch.bfloat16), trajectory_timesteps.to(device)
-
-        ## TEMP
-        path = "/mnt/weka/home/hao.zhang/wei/FastVideo/data/ode_vidprom_1_fv/00000.pt"
-        b = torch.load(path)
-        for k, v in b.items():
-            logger.info(f"b[{k}]: {type(v)}")
-            if isinstance(v, torch.Tensor):
-                logger.info(f"b[{k}]: {v.shape}")
-            else:
-                logger.info(f"b[{k}]: {v}")
-        training_batch.encoder_hidden_states = b["text_embedding"][0].unsqueeze(
-            0).to(device, dtype=torch.bfloat16)
-        trajectory_latents = b["ode_latent"].to(device, dtype=torch.bfloat16)
-        logger.info(f"trajectory_latents: {trajectory_latents.shape}")
-        logger.info(
-            f"encoder_hidden_states: {training_batch.encoder_hidden_states.shape}"
-        )
-
-        return training_batch, trajectory_latents.to(device,
-                                                     dtype=torch.bfloat16), None
-
-    def _get_timestep(self,
-                      min_timestep: int,
-                      max_timestep: int,
-                      batch_size: int,
-                      num_frame: int,
-                      num_frame_per_block: int,
-                      uniform_timestep: bool = False) -> torch.Tensor:
-        if uniform_timestep:
-            timestep = torch.randint(min_timestep,
-                                     max_timestep, [batch_size, 1],
-                                     device=self.device,
-                                     dtype=torch.long).repeat(1, num_frame)
-            return timestep
-        else:
-            timestep = torch.randint(min_timestep,
-                                     max_timestep, [batch_size, num_frame],
-                                     device=self.device,
-                                     dtype=torch.long)
-            # logger.info(f"individual timestep: {timestep}")
-            # make the noise level the same within every block
-            timestep = timestep.reshape(timestep.shape[0], -1,
-                                        num_frame_per_block)
-            timestep[:, :, 1:] = timestep[:, :, 0:1]
-            timestep = timestep.reshape(timestep.shape[0], -1)
-            return timestep
-
-    def _step_predict_next_latent(
-        self, traj_latents: torch.Tensor, traj_timesteps: torch.Tensor,
-        encoder_hidden_states: torch.Tensor,
-        encoder_attention_mask: torch.Tensor
-    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, dict[str,
-                                                              torch.Tensor]]:
-        latent_vis_dict = {}
-        device = get_local_torch_device()
-        target_latent = traj_latents[:, -1]
-
-        # logger.info(f"traj_latents: {traj_latents.shape}")
-        # logger.info(f"traj_timesteps: {traj_timesteps.shape}")
-
-        # Shapes: traj_latents [B, S, C, T, H, W], traj_timesteps [B, S]
-        B, S, num_frames, num_channels, height, width = traj_latents.shape
-
-        # Lazily cache nearest trajectory index per DMD step based on the (fixed) S timesteps
-        if self._cached_closest_idx_per_dmd is None:
-            # Use the first sample's trajectory timesteps; assumed identical across batches
-            # s_steps = traj_timesteps[0].to(torch.long)  # [S]
-            # dmd = cast(torch.Tensor, self.dmd_denoising_steps).to(s_steps.device)  # [K]
-            # distances_ks: [K, S] = |s_steps - dmd|
-            # distances_ks = (s_steps.unsqueeze(0) - dmd.unsqueeze(1)).abs()
-            # self._cached_closest_idx_per_dmd = distances_ks.argmin(dim=1).to(torch.long).cpu()  # [K]
-            self._cached_closest_idx_per_dmd = torch.tensor(
-                [0, 12, 24, 36], dtype=torch.long).cpu()
-            logger.info(
-                f"self._cached_closest_idx_per_dmd: {self._cached_closest_idx_per_dmd}"
-            )
-            logger.info(
-                f"corresponding timesteps: {self.noise_scheduler.timesteps[self._cached_closest_idx_per_dmd]}"
-            )
-
-        # logger.info(f"traj_latents: {traj_latents.shape}")
-        # Select the K indexes from traj_latents using self._cached_closest_idx_per_dmd
-        # traj_latents: [B, S, C, T, H, W], self._cached_closest_idx_per_dmd: [K]
-        # Output: [B, K, C, T, H, W]
-        # relevant_traj_latents = torch.index_select(
-        #     traj_latents,
-        #     dim=1,
-        #     index=self._cached_closest_idx_per_dmd.to(traj_latents.device))
-        relevant_traj_latents = traj_latents
-        logger.info(f"relevant_traj_latents sum: {relevant_traj_latents.float().sum().item()}, relevant_traj_latents: {relevant_traj_latents.shape}")
-        # assert relevant_traj_latents.shape[0] == 1
-
-        indexes = self._get_timestep(  # [B, num_frames]
-            0,
-            len(self.dmd_denoising_steps),
-            B,
-            num_frames,
-            3,
-            uniform_timestep=False)
-        logger.info(f"indexes: {indexes.shape}")
-        logger.info(f"indexes: {indexes}")
-        # noisy_input = relevant_traj_latents[indexes]
-        noisy_input = torch.gather(
-            relevant_traj_latents,
-            dim=1,
-            index=indexes.reshape(B, 1, num_frames, 1, 1,
-                                  1).expand(-1, -1, -1, num_channels, height,
-                                            width).to(self.device)).squeeze(1)
-        # noisy_input = noisy_input.unsqueeze(0)
-
-        # # Sample a single DMD step for the whole batch and fetch its cached nearest S-index
-        # K = len(self.dmd_denoising_steps)
-        # dmd_idx = torch.randint(0, K, (1,), device=device)
-        # logger.info(f"dmd_idx: {dmd_idx}")
-        # assert self._cached_closest_idx_per_dmd is not None
-        # nearest_s_idx = int(self._cached_closest_idx_per_dmd[int(dmd_idx.item())])
-        # nearest_idx = torch.full((B,), nearest_s_idx, device=device, dtype=torch.long)
-
-        # batch_indices = torch.arange(B, device=device)
-        # noisy_input = traj_latents[batch_indices, nearest_idx]  # [B, C, T, H, W]
-        # target_latent = traj_latents[batch_indices, -1]  # [B, C, T, H, W]
-        # t = traj_timesteps[batch_indices, nearest_idx]  # [B]
-
-        # Scale model input as in inference for consistency with stored trajectories
-        # noisy_input = self.modules["scheduler"].scale_model_input(noisy_input, t)
-        # logger.info(f"indexes: {indexes.shape}")
-        # logger.info(f"indexes: {indexes}")
-        timestep = self.dmd_denoising_steps[indexes]
-        # logger.info(f"timestep: {timestep.shape}")
-        # logger.info(f"timestep: {timestep}")
-
-        # Prepare inputs for transformer
-        latent_vis_dict["noisy_input"] = noisy_input.permute(
-            0, 2, 1, 3, 4).detach().clone().cpu()
-        latent_vis_dict["x0"] = target_latent.permute(0, 2, 1, 3,
-                                                      4).detach().clone().cpu()
-
-        logger.info("noisy input sum: %s, noisy input shape: %s, noisy input dtype: %s", noisy_input.float().sum().item(), noisy_input.shape, noisy_input.dtype)
-        logger.info("encoder hidden states sum: %s, encoder hidden states shape: %s, encoder hidden states dtype: %s", encoder_hidden_states.float().sum().item(), encoder_hidden_states.shape, encoder_hidden_states.dtype)
-        logger.info("timestep sum: %s, timestep shape: %s, timestep dtype: %s", timestep.float().sum().item(), timestep.shape, timestep.dtype)
-        logger.info("model dtype set: %s. model weight sum: %s", set(p.dtype for p in self.transformer.parameters()), sum(p.float().sum().item() for p in self.transformer.parameters()))
-
-        # model_dtype = next(self.transformer.parameters()).dtype
-        model_dtype = torch.bfloat16
-        input_kwargs = {
-            "hidden_states": noisy_input.permute(0, 2, 1, 3, 4),
-            "encoder_hidden_states": encoder_hidden_states,
-            "timestep": timestep,
-            "return_dict": False,
-            "scheduler": self.modules["scheduler"]
-        }
-        # Predict noise and step the scheduler to obtain next latent
-        with set_forward_context(current_timestep=timestep,
-                                 attn_metadata=None,
-                                 forward_batch=None):
-            pred_video = self.transformer(**input_kwargs)
-            # logger.info(f"noise_pred: {noise_pred.shape}")
-        # logger.info("noise pred sum: %s, noise pred shape: %s, noise pred dtype: %s", noise_pred.float().sum().item(), noise_pred.shape, noise_pred.dtype)
-        # if isinstance(noise_pred, (tuple, list)):
-        #     noise_pred = noise_pred[0]
-
-        # from fastvideo.models.utils import pred_noise_to_pred_video
-        # pred_video = pred_noise_to_pred_video(
-        #     pred_noise=noise_pred.flatten(0, 1),
-        #     noise_input_latent=noisy_input.flatten(0, 1),
-        #     timestep=timestep.to(dtype=model_dtype).flatten(0, 1),
-        #     scheduler=self.modules["scheduler"]).unflatten(
-        #         0, noise_pred.shape[:2])
-        logger.info("pred video sum: %s, pred video shape: %s, pred video dtype: %s", pred_video.float().sum().item(), pred_video.shape, pred_video.dtype)
-
-        latent_vis_dict["pred_video"] = pred_video.permute(
-            0, 2, 1, 3, 4).detach().clone().cpu()
-
-        # noisy_input = pred_noise_to_pred_video(noise_pred, noisy_input, t, self.modules["scheduler"])
-        # next_latent_pred = self.modules["scheduler"].step(
-        #     noise_pred, t, current_latents, return_dict=False)[0]
-        return pred_video, target_latent, timestep, latent_vis_dict
-
-    def train_one_step(self, training_batch):  # type: ignore[override]
-        self.transformer.train()
-        self.optimizer.zero_grad()
-        training_batch.total_loss = 0.0
-        args = cast(TrainingArgs, self.training_args)
-
-        # Using cached nearest index per DMD step; computation happens in _step_predict_next_latent
-
-        for _ in range(args.gradient_accumulation_steps):
-            training_batch, traj_latents, traj_timesteps = self._get_next_batch(
-                training_batch)
-            text_embeds = training_batch.encoder_hidden_states
-            text_attention_mask = training_batch.encoder_attention_mask
-            assert traj_latents.shape[0] == 1
-
-            # Shapes: traj_latents [B, S, C, T, H, W], traj_timesteps [B, S]
-            B, S = traj_latents.shape[0], traj_latents.shape[1]
-            if S < 2:
-                raise ValueError("Trajectory must contain at least 2 steps")
-
-            # Sample per-sample current step i in [0, S-2]
-
-            # idx = torch.randint(low=0, high=S - 1, size=(B, ),
-            #                     device=traj_latents.device)
-
-            # Gather current latents and next latents
-            # batch_indices = torch.arange(B, device=traj_latents.device)
-            # current_latents = traj_latents[batch_indices, idx]  # [B, C, T,H,W]
-            # current_latent = traj_timesteps[:, -1, :, :, :, :]
-            # target_latents = traj_latents[:, -1, :, :, :, :]
-
-            # Corresponding timesteps t (long) -> cast per sample
-            # t = traj_timesteps[:, -1, :, :, :, :]
-            # if t.dtype != torch.long:
-            #     t = t.long()
-
-            # Forward to predict next latent by stepping scheduler with predicted noise
-            noise_pred, target_latent, t, latent_vis_dict = self._step_predict_next_latent(
-                traj_latents, training_batch.current_timestep, text_embeds, text_attention_mask)
-
-            training_batch.latent_vis_dict.update(latent_vis_dict)
-
-            mask = t != 0
-
-            # Compute loss
-            loss = F.mse_loss(noise_pred[mask],
-                              target_latent[mask],
-                              reduction="mean")
-            loss = loss / args.gradient_accumulation_steps
-
-            with set_forward_context(current_timestep=t,
-                                     attn_metadata=None,
-                                     forward_batch=None):
-                loss.backward()
-
-            logger.info("Loss sum: %s, Loss dtype: %s", loss.float().sum().item(), loss.dtype)
-            assert self.transformer.blocks[0].to_q.weight.dtype == torch.float32
-            logger.info("blocks[0].to_q param sum before backprop: %s", self.transformer.blocks[0].to_q.weight.float().sum().item())
-            logger.info("blocks[0].to_q param grad sum: %s", self.transformer.blocks[0].to_q.weight.grad.float().sum().item())
-            logger.info("Transformer grad dtype: %s", set(p.grad.dtype for p in self.transformer.parameters()))
-            logger.info("Transformer param dtype before backprop: %s", set(p.dtype for p in self.transformer.parameters()))
-            avg_loss = loss.detach().clone()
-            training_batch.total_loss += avg_loss.item()
-
-        # Clip grad and step optimizers
-        # grad_norm = clip_grad_norm_while_handling_failing_dtensor_cases(
-        #     [p for p in self.transformer.parameters() if p.requires_grad],
-        #     args.max_grad_norm if args.max_grad_norm is not None else 0.0)
-        grad_norm = None
-
-        self.optimizer.step()
-        logger.info("Transformer param dtype after backprop: %s", set(p.dtype for p in self.transformer.parameters()))
-        logger.info("blocks[0].to_q param param after backprop sum: %s", self.transformer.blocks[0].to_q.weight.float().sum().item())
-
-        assert False
-        # self.lr_scheduler.step()
-
-        if grad_norm is None:
-            grad_value = 0.0
-        else:
-            try:
-                if isinstance(grad_norm, torch.Tensor):
-                    grad_value = float(grad_norm.detach().float().item())
-                else:
-                    grad_value = float(grad_norm)
-            except Exception:
-                grad_value = 0.0
-        training_batch.grad_norm = grad_value
-        return training_batch
-
-    def visualize_intermediate_latents(self, training_batch: TrainingBatch,
-                                       training_args: TrainingArgs, step: int):
-        """Add visualization data to wandb logging and save frames to disk."""
-        wandb_loss_dict = {}
-        latents_vis_dict = training_batch.latent_vis_dict
-        latent_log_keys = ['noisy_input', 'x0', 'pred_video']
-        for latent_key in latent_log_keys:
-            assert latent_key in latents_vis_dict and latents_vis_dict[
-                latent_key] is not None
-            latent = latents_vis_dict[latent_key]
-            pixel_latent = self.validation_pipeline.decoding_stage.decode(
-                latent, training_args)
-
-            video = pixel_latent.cpu().float()
-            video = video.permute(0, 2, 1, 3, 4)
-            video = (video * 255).numpy().astype(np.uint8)
-            wandb_loss_dict[latent_key] = wandb.Video(
-                video, fps=16, format="mp4")  # change to 16 for Wan2.1
-            # Clean up references
-            del video, pixel_latent, latent
-
-        # Log to wandb
-        if self.global_rank == 0:
-            wandb.log(wandb_loss_dict, step=step)
-
-        # dmd_latents_vis_dict = training_batch.dmd_latent_vis_dict
-        # fake_score_latents_vis_dict = training_batch.fake_score_latent_vis_dict
-        # fake_score_log_keys = ['generator_pred_video']
-        # dmd_log_keys = ['faker_score_pred_video', 'real_score_pred_video']
-
-
-def main(args) -> None:
-    logger.info("Starting ODE-init training pipeline...")
-    logger.info(f"ARG dmd_denoising_steps: {args.dmd_denoising_steps}")
-    pipeline = ODEInitTrainingPipeline.from_pretrained(
-        args.pretrained_model_name_or_path, args=args)
-    args = pipeline.training_args
-    pipeline.train()
-    logger.info("ODE-init training pipeline done")
-
-
-if __name__ == "__main__":
-    argv = sys.argv
-    from fastvideo.fastvideo_args import TrainingArgs
-    from fastvideo.utils import FlexibleArgumentParser
-    parser = FlexibleArgumentParser()
-    parser = TrainingArgs.add_cli_args(parser)
-    parser = FastVideoArgs.add_cli_args(parser)
-    args = parser.parse_args()
-    args.dit_cpu_offload = False
-    main(args)
@@ -22,7 +22,7 @@ from tqdm.auto import tqdm
 import fastvideo.envs as envs
 from fastvideo.attention.backends.video_sparse_attn import (
    VideoSparseAttentionMetadataBuilder)
-# from fastvideo.attention.backends.vmoba import VideoMobaAttentionMetadataBuilder
+from fastvideo.attention.backends.vmoba import VideoMobaAttentionMetadataBuilder
 from fastvideo.configs.sample import SamplingParam
 from fastvideo.dataset import build_parquet_map_style_dataloader
 from fastvideo.dataset.dataloader.schema import pyarrow_schema_t2v
@@ -39,26 +39,20 @@ from fastvideo.training.activation_checkpoint import (
    apply_activation_checkpointing)
 from fastvideo.training.training_utils import (
    clip_grad_norm_while_handling_failing_dtensor_cases,
-    compute_density_for_timestep_sampling, get_scheduler, get_sigmas,
-    load_checkpoint, normalize_dit_input, save_checkpoint,
+    compute_density_for_timestep_sampling, count_trainable, get_scheduler,
+    get_sigmas, load_checkpoint, normalize_dit_input, save_checkpoint,
    shard_latents_across_sp)
-# from fastvideo.utils import (is_vmoba_available, is_vsa_available,
-#                              set_random_seed, shallow_asdict)
-from fastvideo.utils import (is_vsa_available,
+from fastvideo.utils import (is_vmoba_available, is_vsa_available,
                             set_random_seed, shallow_asdict)

 import wandb  # isort: skip

 vsa_available = is_vsa_available()
-# vmoba_available = is_vmoba_available()
+vmoba_available = is_vmoba_available()

 logger = init_logger(__name__)


-def _get_trainable_params(model: torch.nn.Module) -> int:
-    return sum(p.numel() for p in model.parameters() if p.requires_grad)
-
-
 class TrainingPipeline(LoRAPipeline, ABC):
    """
    A pipeline for training a model. All training pipelines should inherit from this class.
@@ -118,18 +112,15 @@ class TrainingPipeline(LoRAPipeline, ABC):
                enable_gradient_checkpointing_type)

        noise_scheduler = self.modules["scheduler"]
+        # Set grads for proper modules based on the training mode (Distill, LoRA, etc.)
        self.set_trainable()
        params_to_optimize = self.transformer.parameters()
        params_to_optimize = list(
            filter(lambda p: p.requires_grad, params_to_optimize))
-        # Parse betas from string format "beta1,beta2"
-        betas_str = training_args.betas
-        betas = tuple(float(x.strip()) for x in betas_str.split(","))
-        
        self.optimizer = torch.optim.AdamW(
            params_to_optimize,
            lr=training_args.learning_rate,
-            betas=betas,
+            betas=(0.9, 0.999),
            weight_decay=training_args.weight_decay,
            eps=1e-8,
        )
@@ -281,20 +272,20 @@ class TrainingPipeline(LoRAPipeline, ABC):
                patch_size=patch_size,
                VSA_sparsity=current_vsa_sparsity,
                device=get_local_torch_device())
-        # elif vmoba_available and envs.FASTVIDEO_ATTENTION_BACKEND == "VMOBA_ATTN":
-        #     moba_params = self.training_args.moba_config.copy()
-        #     moba_params.update({
-        #         "current_timestep":
-        #         training_batch.timesteps,
-        #         "raw_latent_shape":
-        #         training_batch.raw_latent_shape[2:5],
-        #         "patch_size":
-        #         self.training_args.pipeline_config.dit_config.patch_size,
-        #         "device":
-        #         get_local_torch_device(),
-        #     })
-        #     training_batch.attn_metadata = VideoMobaAttentionMetadataBuilder(
-        #     ).build(**moba_params)
+        elif vmoba_available and envs.FASTVIDEO_ATTENTION_BACKEND == "VMOBA_ATTN":
+            moba_params = self.training_args.moba_config.copy()
+            moba_params.update({
+                "current_timestep":
+                training_batch.timesteps,
+                "raw_latent_shape":
+                training_batch.raw_latent_shape[2:5],
+                "patch_size":
+                self.training_args.pipeline_config.dit_config.patch_size,
+                "device":
+                get_local_torch_device(),
+            })
+            training_batch.attn_metadata = VideoMobaAttentionMetadataBuilder(
+            ).build(**moba_params)
        else:
            training_batch.attn_metadata = None

@@ -319,8 +310,7 @@ class TrainingPipeline(LoRAPipeline, ABC):

    def _transformer_forward_and_compute_loss(
            self, training_batch: TrainingBatch) -> TrainingBatch:
-        # if vsa_available and envs.FASTVIDEO_ATTENTION_BACKEND == "VIDEO_SPARSE_ATTN" or vmoba_available and envs.FASTVIDEO_ATTENTION_BACKEND == "VMOBA_ATTN":
-        if vsa_available and envs.FASTVIDEO_ATTENTION_BACKEND == "VIDEO_SPARSE_ATTN":
+        if vsa_available and envs.FASTVIDEO_ATTENTION_BACKEND == "VIDEO_SPARSE_ATTN" or vmoba_available and envs.FASTVIDEO_ATTENTION_BACKEND == "VMOBA_ATTN":
            assert training_batch.attn_metadata is not None
        else:
            assert training_batch.attn_metadata is None
@@ -441,7 +431,7 @@ class TrainingPipeline(LoRAPipeline, ABC):
                    local_main_process_only=False)
        if not self.post_init_called:
            self.post_init()
-        num_trainable_params = _get_trainable_params(self.transformer)
+        num_trainable_params = count_trainable(self.transformer)
        logger.info("Starting training with %s B trainable parameters",
                    round(num_trainable_params / 1e9, 3))

@@ -486,9 +476,9 @@ class TrainingPipeline(LoRAPipeline, ABC):
                current_decay_times = min(step // vsa_decay_interval_steps,
                                          vsa_sparsity // vsa_decay_rate)
                current_vsa_sparsity = current_decay_times * vsa_decay_rate
-            # elif vmoba_available:
-            #     # TODO: add vmoba sparsity scheduling here
-            #     current_vsa_sparsity = 0.0
+            elif vmoba_available:
+                # TODO: add vmoba sparsity scheduling here
+                pass
            else:
                current_vsa_sparsity = 0.0

@@ -530,14 +520,10 @@ class TrainingPipeline(LoRAPipeline, ABC):
                self.transformer.train()
                self.sp_group.barrier()
            if self.training_args.log_validation and step % self.training_args.validation_steps == 0:
-                if self.training_args.log_visualization:
-                    self.visualize_intermediate_latents(training_batch,
-                                                        self.training_args,
-                                                        step)
                self._log_validation(self.transformer, self.training_args, step)
                gpu_memory_usage = torch.cuda.memory_allocated() / 1024**2
                trainable_params = round(
-                    _get_trainable_params(self.transformer) / 1e9, 3)
+                    count_trainable(self.transformer) / 1e9, 3)
                logger.info(
                    "GPU memory usage after validation: %s MB, trainable params: %sB",
                    gpu_memory_usage, trainable_params)
@@ -573,7 +559,7 @@ class TrainingPipeline(LoRAPipeline, ABC):
        logger.info("  Total optimization steps = %s",
                    self.training_args.max_train_steps)
        logger.info("  Total training parameters per FSDP shard = %s B",
-                    round(_get_trainable_params(self.transformer) / 1e9, 3))
+                    round(count_trainable(self.transformer) / 1e9, 3))
        # print dtype
        logger.info("  Master weight dtype: %s",
                    self.transformer.parameters().__next__().dtype)
@@ -641,7 +627,6 @@ class TrainingPipeline(LoRAPipeline, ABC):
        validation_dataloader = DataLoader(validation_dataset,
                                           batch_size=None,
                                           num_workers=0)
-
        transformer.eval()

        validation_steps = training_args.validation_sampling_steps.split(",")
@@ -735,8 +720,3 @@ class TrainingPipeline(LoRAPipeline, ABC):
        # Re-enable gradients for training
        training_args.inference_mode = False
        transformer.train()
-    
-    def visualize_intermediate_latents(self, training_batch: TrainingBatch,
-                                       training_args: TrainingArgs, step: int):
-        """Add visualization data to wandb logging and save frames to disk."""
-        raise NotImplementedError("Visualize intermediate latents is not implemented for training pipeline")
@@ -202,7 +202,6 @@ def save_distillation_checkpoint(generator_transformer,
                                 generator_scheduler=None,
                                 fake_score_scheduler=None,
                                 noise_generator=None,
-                                 generator_ema=None,
                                 only_save_generator_weight=False) -> None:
    """
    Save distillation checkpoint with both generator and fake_score models.
@@ -234,8 +233,6 @@ def save_distillation_checkpoint(generator_transformer,
        if generator_scheduler is not None:
            generator_states["scheduler"] = SchedulerWrapper(
                generator_scheduler)
-        if generator_ema is not None:
-            generator_states["ema"] = generator_ema.state_dict()

        generator_dcp_dir = os.path.join(save_dir, "distributed_checkpoint",
                                         "generator")
@@ -349,14 +346,10 @@ def load_checkpoint(transformer,
    """
    if not os.path.exists(checkpoint_path):
        logger.warning("Checkpoint path %s does not exist", checkpoint_path)
-        assert False
        return 0

    # Extract step number from checkpoint path
-    try:
-        step = int(os.path.basename(checkpoint_path).split('-')[-1])
-    except:
-        step = 1
+    step = int(os.path.basename(checkpoint_path).split('-')[-1])

    if rank == 0:
        logger.info("Loading checkpoint from step %s", step)
@@ -409,8 +402,7 @@ def load_distillation_checkpoint(generator_transformer,
                                 dataloader=None,
                                 generator_scheduler=None,
                                 fake_score_scheduler=None,
-                                 noise_generator=None,
-                                 generator_ema=None) -> int:
+                                 noise_generator=None) -> int:
    """
    Load distillation checkpoint with both generator and fake_score models.
    Returns the step number from which training should resume.
@@ -464,18 +456,6 @@ def load_distillation_checkpoint(generator_transformer,
        end_time - begin_time,
        local_main_process_only=False)

-    # Load EMA state if available and generator_ema is provided
-    if generator_ema is not None:
-        try:
-            ema_state = generator_states.get("ema")
-            if ema_state is not None:
-                generator_ema.load_state_dict(ema_state)
-                logger.info("rank: %s, generator EMA state loaded successfully", rank)
-            else:
-                logger.info("rank: %s, no EMA state found in checkpoint", rank)
-        except Exception as e:
-            logger.warning("rank: %s, failed to load EMA state: %s", rank, str(e))
-
    # Load critic distributed checkpoint
    critic_dcp_dir = os.path.join(checkpoint_path, "distributed_checkpoint",
                                  "critic")
@@ -1300,154 +1280,5 @@ def get_scheduler(
                         last_epoch=last_epoch)


-class EMA_FSDP:
-    """
-    FSDP2-friendly EMA with two modes:
-      - mode="local_shard" (default): maintain float32 CPU EMA of local parameter shards on every rank.
-        Provides a context manager to temporarily swap EMA weights into the live model for teacher forward.
-      - mode="rank0_full": maintain a consolidated float32 CPU EMA of full parameters on rank 0 only
-        using gather_state_dict_on_cpu_rank0(). Useful for checkpoint export; not for teacher forward.
-
-    Usage (local_shard for CM teacher):
-      ema = EMA_FSDP(model, decay=0.999, mode="local_shard")
-      for step in ...:
-          ema.update(model)
-      with ema.apply_to_model(model):
-          with torch.no_grad():
-              y_teacher = model(...)
-
-    Usage (rank0_full for export):
-      ema = EMA_FSDP(model, decay=0.999, mode="rank0_full")
-      ema.update(model)
-      ema.state_dict()  # on rank 0
-    """
-    def __init__(self, module, decay: float = 0.999, mode: str = "local_shard"):
-        self.decay = float(decay)
-        self.mode = mode
-        self.shadow: dict[str, torch.Tensor] = {}
-        self.rank = dist.get_rank() if dist.is_initialized() else 0
-        if self.mode not in {"local_shard", "rank0_full"}:
-            raise ValueError(f"Unsupported EMA_FSDP mode: {self.mode}")
-        self._init_shadow(module)
-
-    @staticmethod
-    def _to_local_tensor(t: torch.Tensor) -> torch.Tensor:
-        # DTensor-aware to_local fetch; fall back to raw tensor
-        try:
-            from torch.distributed.tensor import DTensor  # type: ignore
-            if isinstance(t, DTensor):
-                return t.to_local()
-        except Exception:
-            pass
-        return t
-
-    @torch.no_grad()
-    def _init_shadow(self, module):
-        if self.mode == "rank0_full":
-            cpu_state = gather_state_dict_on_cpu_rank0(module, device=None)
-            if self.rank == 0:
-                self.shadow = {k: v.detach().clone().float().cpu() for k, v in cpu_state.items()}
-            else:
-                self.shadow = {}
-            return
-
-        # local_shard: maintain EMA of local shards for requires_grad params
-        self.shadow = {}
-        for name, p in module.named_parameters():
-            if not p.requires_grad:
-                continue
-            local = self._to_local_tensor(p.detach())
-            self.shadow[name] = local.clone().float().cpu()
-
-    @torch.no_grad()
-    def update(self, module):
-        d = self.decay
-        if self.mode == "rank0_full":
-            if self.rank != 0:
-                return
-            cpu_state = gather_state_dict_on_cpu_rank0(module, device=None)
-            for n, v in cpu_state.items():
-                v_cpu = v.detach().float().cpu()
-                if n not in self.shadow:
-                    self.shadow[n] = v_cpu.clone()
-                else:
-                    self.shadow[n].mul_(d).add_(v_cpu, alpha=1.0 - d)
-            return
-
-        # local_shard: update local shard EMA on every rank
-        for name, p in module.named_parameters():
-            if not p.requires_grad:
-                continue
-            local = self._to_local_tensor(p.detach())
-            v_cpu = local.float().cpu()
-            if name not in self.shadow:
-                self.shadow[name] = v_cpu.clone()
-            else:
-                self.shadow[name].mul_(d).add_(v_cpu, alpha=1.0 - d)
-
-    def state_dict(self) -> dict[str, torch.Tensor]:
-        if self.mode == "rank0_full":
-            return {k: v.clone() for k, v in self.shadow.items()} if self.rank == 0 else {}
-        return {k: v.clone() for k, v in self.shadow.items()}
-
-    def load_state_dict(self, sd: dict[str, torch.Tensor]):
-        self.shadow = {k: v.clone() for k, v in sd.items()}
-
-    @torch.no_grad()
-    def copy_to_unwrapped(self, module) -> None:
-        """
-        Copy EMA weights into a non-sharded (unwrapped) module. Intended for export/eval.
-        For mode="rank0_full", only rank 0 has the full EMA state.
-        """
-        if self.mode == "rank0_full" and self.rank != 0:
-            return
-        name_to_param = dict(module.named_parameters())
-        for n, w in self.shadow.items():
-            if n in name_to_param:
-                p = name_to_param[n]
-                p.data.copy_(w.to(dtype=p.dtype, device=p.device))
-
-    class _ApplyEMACtx:
-        def __init__(self, ema: "EMA_FSDP", module):
-            self.ema = ema
-            self.module = module
-            self.saved: dict[str, torch.Tensor] = {}
-
-        def __enter__(self):
-            if self.ema.mode != "local_shard":
-                raise RuntimeError("EMA apply_to_model is only supported for mode='local_shard'")
-            with torch.no_grad():
-                for name, p in self.module.named_parameters():
-                    if not p.requires_grad:
-                        continue
-                    # Save local shard
-                    p_local = EMA_FSDP._to_local_tensor(p.detach())
-                    if p_local.numel() == 0:
-                        # Nothing to swap on this rank for this param
-                        continue
-                    self.saved[name] = p_local.clone().to(device=p_local.device, dtype=p_local.dtype)
-                    if name in self.ema.shadow:
-                        ema_cpu = self.ema.shadow[name]
-                        if ema_cpu.numel() != p_local.numel():
-                            # Shard shape mismatch (e.g., empty shard here), skip
-                            continue
-                        # Copy EMA shard into local param shard
-                        p_local.copy_(ema_cpu.to(dtype=p_local.dtype, device=p_local.device))
-            return self.module
-
-        def __exit__(self, exc_type, exc, tb):
-            with torch.no_grad():
-                for name, p in self.module.named_parameters():
-                    if name in self.saved:
-                        p_local = EMA_FSDP._to_local_tensor(p.detach())
-                        if p_local.numel() == 0:
-                            continue
-                        saved_local = self.saved[name]
-                        if saved_local.numel() != p_local.numel():
-                            continue
-                        p_local.copy_(saved_local)
-            self.saved.clear()
-            return False
-
-    def apply_to_model(self, module):
-        return EMA_FSDP._ApplyEMACtx(self, module)
+def count_trainable(model: torch.nn.Module) -> int:
+    return sum(p.numel() for p in model.parameters() if p.requires_grad)
@@ -1,72 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-import sys
-from copy import deepcopy
-
-from fastvideo.fastvideo_args import FastVideoArgs, TrainingArgs
-from fastvideo.logger import init_logger
-from fastvideo.models.schedulers.scheduling_flow_match_euler_discrete import (
-    FlowMatchEulerDiscreteScheduler)
-from fastvideo.pipelines.basic.wan.wan_causal_dmd_pipeline import WanCausalDMDPipeline
-from fastvideo.training.self_forcing_distillation_pipeline import SelfForcingDistillationPipeline
-from fastvideo.utils import is_vsa_available
-
-vsa_available = is_vsa_available()
-
-logger = init_logger(__name__)
-
-
-class WanSelfForcingDistillationPipeline(SelfForcingDistillationPipeline):
-    """
-    A self-forcing distillation pipeline for Wan that uses the self-forcing methodology
-    with DMD for video generation.
-    """
-    _required_config_modules = [
-        "scheduler", "transformer", "vae", "real_score_transformer",
-        "fake_score_transformer"
-    ]
-
-    def create_training_stages(self, training_args: TrainingArgs):
-        """
-        May be used in future refactors.
-        """
-        pass
-
-    def initialize_validation_pipeline(self, training_args: TrainingArgs):
-        logger.info("Initializing validation pipeline...")
-        args_copy = deepcopy(training_args)
-
-        args_copy.inference_mode = True
-        validation_pipeline = WanCausalDMDPipeline.from_pretrained(
-            training_args.model_path,
-            args=args_copy,  # type: ignore
-            inference_mode=True,
-            loaded_modules={"transformer": self.get_module("transformer")},
-            tp_size=training_args.tp_size,
-            sp_size=training_args.sp_size,
-            num_gpus=training_args.num_gpus,
-            pin_cpu_memory=training_args.pin_cpu_memory,
-            dit_cpu_offload=True)
-
-        self.validation_pipeline = validation_pipeline
-
-
-def main(args) -> None:
-    logger.info("Starting Wan self-forcing distillation pipeline...")
-
-    pipeline = WanSelfForcingDistillationPipeline.from_pretrained(
-        args.pretrained_model_name_or_path, args=args)
-
-    args = pipeline.training_args
-    pipeline.train()
-    logger.info("Wan self-forcing distillation pipeline completed")
-
-
-if __name__ == "__main__":
-    argv = sys.argv
-    from fastvideo.fastvideo_args import TrainingArgs
-    from fastvideo.utils import FlexibleArgumentParser
-    parser = FlexibleArgumentParser()
-    parser = TrainingArgs.add_cli_args(parser)
-    parser = FastVideoArgs.add_cli_args(parser)
-    args = parser.parse_args()
-    main(args) 
@@ -1,211 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-import sys
-from copy import deepcopy
-from typing import Any
-
-import torch
-
-from fastvideo.configs.pipelines.base import PipelineConfig
-from fastvideo.configs.sample import SamplingParam
-from fastvideo.dataset.dataloader.schema import pyarrow_schema_t2v
-from fastvideo.distributed import get_local_torch_device
-from fastvideo.fastvideo_args import FastVideoArgs, TrainingArgs
-from fastvideo.logger import init_logger
-from fastvideo.models.schedulers.scheduling_flow_unipc_multistep import (
-    FlowUniPCMultistepScheduler)
-from fastvideo.pipelines.basic.wan.wan_i2v_pipeline import (
-    WanImageToVideoPipeline)
-from fastvideo.pipelines.pipeline_batch_info import ForwardBatch, TrainingBatch
-from fastvideo.training.training_pipeline import TrainingPipeline
-from fastvideo.utils import is_vsa_available, shallow_asdict
-
-vsa_available = is_vsa_available()
-
-logger = init_logger(__name__)
-
-
-class WanT2VI2VTrainingPipeline(TrainingPipeline):
-    """
-    A training pipeline for Wan.
-    """
-    _required_config_modules = ["scheduler", "transformer", "vae"]
-
-    def initialize_pipeline(self, fastvideo_args: FastVideoArgs):
-        self.modules["scheduler"] = FlowUniPCMultistepScheduler(
-            shift=fastvideo_args.pipeline_config.flow_shift)
-
-    def create_training_stages(self, training_args: TrainingArgs):
-        """
-        May be used in future refactors.
-        """
-        pass
-
-    def set_schemas(self):
-        self.train_dataset_schema = pyarrow_schema_t2v
-
-    def initialize_validation_pipeline(self, training_args: TrainingArgs):
-        logger.info("Initializing validation pipeline...")
-        args_copy = deepcopy(training_args)
-
-        args_copy.inference_mode = True
-        args_copy.dit_cpu_offload = True
-        # args_copy.pipeline_config.vae_config.load_encoder = False
-        # validation_pipeline = WanImageToVideoValidationPipeline.from_pretrained(
-        pipeline_config = PipelineConfig.from_pretrained(
-            training_args.model_path)
-        pipeline_config.vae_config.load_encoder = True
-        self.validation_pipeline = WanImageToVideoPipeline.from_pretrained(
-            training_args.model_path,
-            args=None,
-            inference_mode=True,
-            pipeline_config=pipeline_config,
-            loaded_modules={
-                "transformer": self.get_module("transformer"),
-            },
-            required_config_modules=[
-                "scheduler", "transformer", "vae", "text_encoder", "tokenizer"
-            ],
-            tp_size=training_args.tp_size,
-            sp_size=training_args.sp_size,
-            num_gpus=training_args.num_gpus,
-            dit_cpu_offload=True,
-        )
-
-    def _get_next_batch(self, training_batch: TrainingBatch) -> TrainingBatch:
-        batch = next(self.train_loader_iter, None)  # type: ignore
-        if batch is None:
-            self.current_epoch += 1
-            logger.info("Starting epoch %s", self.current_epoch)
-            # Reset iterator for next epoch
-            self.train_loader_iter = iter(self.train_dataloader)
-            # Get first batch of new epoch
-            batch = next(self.train_loader_iter)
-
-        latents = batch['vae_latent']
-        latents = latents[:, :, :self.training_args.num_latent_t]
-        encoder_hidden_states = batch['text_embedding']
-        encoder_attention_mask = batch['text_attention_mask']
-        # clip_features = batch['clip_feature']
-        # image_latents = batch['first_frame_latent']
-        # image_latents = image_latents[:, :, :self.training_args.num_latent_t]
-        # pil_image = batch['pil_image']
-        infos = batch['info_list']
-
-        training_batch.latents = latents.to(get_local_torch_device(),
-                                            dtype=torch.bfloat16)
-        training_batch.encoder_hidden_states = encoder_hidden_states.to(
-            get_local_torch_device(), dtype=torch.bfloat16)
-        training_batch.encoder_attention_mask = encoder_attention_mask.to(
-            get_local_torch_device(), dtype=torch.bfloat16)
-        # training_batch.preprocessed_image = pil_image.to(
-        #     get_local_torch_device())
-        # training_batch.image_embeds = clip_features.to(get_local_torch_device())
-        # training_batch.image_latents = image_latents.to(
-        #     get_local_torch_device())
-        training_batch.infos = infos
-
-        return training_batch
-
-    def _prepare_dit_inputs(self,
-                            training_batch: TrainingBatch) -> TrainingBatch:
-        """Override to properly handle I2V concatenation - call parent first, then concatenate image conditioning."""
-
-        # First, call parent method to prepare noise, timesteps, etc. for video latents
-        training_batch = super()._prepare_dit_inputs(training_batch)
-
-        latents = training_batch.latents
-        logger.info("latents.shape: %s", latents.shape)
-        first_frame_latent = latents[:, :, 0, :, :]
-
-        logger.info("first_frame_latent.shape: %s", first_frame_latent.shape)
-        logger.info("training_batch.noisy_model_input.shape: %s",
-                    training_batch.noisy_model_input.shape)
-
-        training_batch.noisy_model_input = torch.cat([
-            first_frame_latent.unsqueeze(2),
-            training_batch.noisy_model_input[:, :, 1:, :, :]
-        ],
-                                                     dim=2)
-
-        return training_batch
-
-    def _build_input_kwargs(self,
-                            training_batch: TrainingBatch) -> TrainingBatch:
-
-        # Image Embeds for conditioning
-        # image_embeds = training_batch.image_embeds
-        # assert torch.isnan(image_embeds).sum() == 0
-        # image_embeds = image_embeds.to(get_local_torch_device(),
-        #                                dtype=torch.bfloat16)
-        # encoder_hidden_states_image = image_embeds
-
-        # NOTE: noisy_model_input already contains concatenated image_latents from _prepare_dit_inputs
-        training_batch.input_kwargs = {
-            "hidden_states":
-            training_batch.noisy_model_input,
-            "encoder_hidden_states":
-            training_batch.encoder_hidden_states,
-            "timestep":
-            training_batch.timesteps.to(get_local_torch_device(),
-                                        dtype=torch.bfloat16),
-            "encoder_attention_mask":
-            training_batch.encoder_attention_mask,
-            # "encoder_hidden_states_image":
-            # encoder_hidden_states_image,
-            "return_dict":
-            False,
-        }
-        return training_batch
-
-    def _prepare_validation_batch(self, sampling_param: SamplingParam,
-                                  training_args: TrainingArgs,
-                                  validation_batch: dict[str, Any],
-                                  num_inference_steps: int) -> ForwardBatch:
-        sampling_param.prompt = validation_batch['prompt']
-        sampling_param.height = training_args.num_height
-        sampling_param.width = training_args.num_width
-        sampling_param.image_path = validation_batch['video_path']
-        sampling_param.num_inference_steps = num_inference_steps
-        sampling_param.data_type = "video"
-        assert self.seed is not None
-        sampling_param.seed = self.seed
-
-        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]
-        temporal_compression_factor = training_args.pipeline_config.vae_config.arch_config.temporal_compression_ratio
-        num_frames = (training_args.num_latent_t -
-                      1) * temporal_compression_factor + 1
-        sampling_param.num_frames = num_frames
-        batch = ForwardBatch(
-            **shallow_asdict(sampling_param),
-            latents=None,
-            generator=torch.Generator(device="cpu").manual_seed(self.seed),
-            n_tokens=n_tokens,
-            eta=0.0,
-            VSA_sparsity=training_args.VSA_sparsity,
-        )
-
-        return batch
-
-
-def main(args) -> None:
-    logger.info("Starting training pipeline...")
-
-    pipeline = WanT2VI2VTrainingPipeline.from_pretrained(
-        args.pretrained_model_name_or_path, args=args)
-    args = pipeline.training_args
-    pipeline.train()
-    logger.info("Training pipeline done")
-
-
-if __name__ == "__main__":
-    argv = sys.argv
-    from fastvideo.fastvideo_args import TrainingArgs
-    from fastvideo.utils import FlexibleArgumentParser
-    parser = FlexibleArgumentParser()
-    parser = TrainingArgs.add_cli_args(parser)
-    parser = FastVideoArgs.add_cli_args(parser)
-    args = parser.parse_args()
-    args.dit_cpu_offload = False
-    main(args)
@@ -2,10 +2,6 @@
 # Adapted from vllm: https://github.com/vllm-project/vllm/blob/v0.7.3/vllm/utils.py

 import argparse
-from einops import rearrange
-import torchvision
-import numpy as np
-import imageio
 import ctypes
 import hashlib
 import importlib
@@ -1,2 +0,0 @@
-Code in this folder is modified from https://github.com/Wan-Video/Wan2.1
-Apache-2.0 License 
@@ -1,3 +0,0 @@
-from . import configs, distributed, modules
-from .image2video import WanI2V
-from .text2video import WanT2V
@@ -1,42 +0,0 @@
-# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
-from .wan_t2v_14B import t2v_14B
-from .wan_t2v_1_3B import t2v_1_3B
-from .wan_i2v_14B import i2v_14B
-import copy
-import os
-
-os.environ['TOKENIZERS_PARALLELISM'] = 'false'
-
-
-# the config of t2i_14B is the same as t2v_14B
-t2i_14B = copy.deepcopy(t2v_14B)
-t2i_14B.__name__ = 'Config: Wan T2I 14B'
-
-WAN_CONFIGS = {
-    't2v-14B': t2v_14B,
-    't2v-1.3B': t2v_1_3B,
-    'i2v-14B': i2v_14B,
-    't2i-14B': t2i_14B,
-}
-
-SIZE_CONFIGS = {
-    '720*1280': (720, 1280),
-    '1280*720': (1280, 720),
-    '480*832': (480, 832),
-    '832*480': (832, 480),
-    '1024*1024': (1024, 1024),
-}
-
-MAX_AREA_CONFIGS = {
-    '720*1280': 720 * 1280,
-    '1280*720': 1280 * 720,
-    '480*832': 480 * 832,
-    '832*480': 832 * 480,
-}
-
-SUPPORTED_SIZES = {
-    't2v-14B': ('720*1280', '1280*720', '480*832', '832*480'),
-    't2v-1.3B': ('480*832', '832*480'),
-    'i2v-14B': ('720*1280', '1280*720', '480*832', '832*480'),
-    't2i-14B': tuple(SIZE_CONFIGS.keys()),
-}
@@ -1,19 +0,0 @@
-# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
-import torch
-from easydict import EasyDict
-
-# ------------------------ Wan shared config ------------------------#
-wan_shared_cfg = EasyDict()
-
-# t5
-wan_shared_cfg.t5_model = 'umt5_xxl'
-wan_shared_cfg.t5_dtype = torch.bfloat16
-wan_shared_cfg.text_len = 512
-
-# transformer
-wan_shared_cfg.param_dtype = torch.bfloat16
-
-# inference
-wan_shared_cfg.num_train_timesteps = 1000
-wan_shared_cfg.sample_fps = 16
-wan_shared_cfg.sample_neg_prompt = '色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走'
@@ -1,35 +0,0 @@
-# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
-import torch
-from easydict import EasyDict
-
-from .shared_config import wan_shared_cfg
-
-# ------------------------ Wan I2V 14B ------------------------#
-
-i2v_14B = EasyDict(__name__='Config: Wan I2V 14B')
-i2v_14B.update(wan_shared_cfg)
-
-i2v_14B.t5_checkpoint = 'models_t5_umt5-xxl-enc-bf16.pth'
-i2v_14B.t5_tokenizer = 'google/umt5-xxl'
-
-# clip
-i2v_14B.clip_model = 'clip_xlm_roberta_vit_h_14'
-i2v_14B.clip_dtype = torch.float16
-i2v_14B.clip_checkpoint = 'models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth'
-i2v_14B.clip_tokenizer = 'xlm-roberta-large'
-
-# vae
-i2v_14B.vae_checkpoint = 'Wan2.1_VAE.pth'
-i2v_14B.vae_stride = (4, 8, 8)
-
-# transformer
-i2v_14B.patch_size = (1, 2, 2)
-i2v_14B.dim = 5120
-i2v_14B.ffn_dim = 13824
-i2v_14B.freq_dim = 256
-i2v_14B.num_heads = 40
-i2v_14B.num_layers = 40
-i2v_14B.window_size = (-1, -1)
-i2v_14B.qk_norm = True
-i2v_14B.cross_attn_norm = True
-i2v_14B.eps = 1e-6
@@ -1,29 +0,0 @@
-# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
-from easydict import EasyDict
-
-from .shared_config import wan_shared_cfg
-
-# ------------------------ Wan T2V 14B ------------------------#
-
-t2v_14B = EasyDict(__name__='Config: Wan T2V 14B')
-t2v_14B.update(wan_shared_cfg)
-
-# t5
-t2v_14B.t5_checkpoint = 'models_t5_umt5-xxl-enc-bf16.pth'
-t2v_14B.t5_tokenizer = 'google/umt5-xxl'
-
-# vae
-t2v_14B.vae_checkpoint = 'Wan2.1_VAE.pth'
-t2v_14B.vae_stride = (4, 8, 8)
-
-# transformer
-t2v_14B.patch_size = (1, 2, 2)
-t2v_14B.dim = 5120
-t2v_14B.ffn_dim = 13824
-t2v_14B.freq_dim = 256
-t2v_14B.num_heads = 40
-t2v_14B.num_layers = 40
-t2v_14B.window_size = (-1, -1)
-t2v_14B.qk_norm = True
-t2v_14B.cross_attn_norm = True
-t2v_14B.eps = 1e-6
@@ -1,29 +0,0 @@
-# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
-from easydict import EasyDict
-
-from .shared_config import wan_shared_cfg
-
-# ------------------------ Wan T2V 1.3B ------------------------#
-
-t2v_1_3B = EasyDict(__name__='Config: Wan T2V 1.3B')
-t2v_1_3B.update(wan_shared_cfg)
-
-# t5
-t2v_1_3B.t5_checkpoint = 'models_t5_umt5-xxl-enc-bf16.pth'
-t2v_1_3B.t5_tokenizer = 'google/umt5-xxl'
-
-# vae
-t2v_1_3B.vae_checkpoint = 'Wan2.1_VAE.pth'
-t2v_1_3B.vae_stride = (4, 8, 8)
-
-# transformer
-t2v_1_3B.patch_size = (1, 2, 2)
-t2v_1_3B.dim = 1536
-t2v_1_3B.ffn_dim = 8960
-t2v_1_3B.freq_dim = 256
-t2v_1_3B.num_heads = 12
-t2v_1_3B.num_layers = 30
-t2v_1_3B.window_size = (-1, -1)
-t2v_1_3B.qk_norm = True
-t2v_1_3B.cross_attn_norm = True
-t2v_1_3B.eps = 1e-6
@@ -1,33 +0,0 @@
-# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
-from functools import partial
-
-import torch
-from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
-from torch.distributed.fsdp import MixedPrecision, ShardingStrategy
-from torch.distributed.fsdp.wrap import lambda_auto_wrap_policy
-
-
-def shard_model(
-    model,
-    device_id,
-    param_dtype=torch.bfloat16,
-    reduce_dtype=torch.float32,
-    buffer_dtype=torch.float32,
-    process_group=None,
-    sharding_strategy=ShardingStrategy.FULL_SHARD,
-    sync_module_states=True,
-):
-    model = FSDP(
-        module=model,
-        process_group=process_group,
-        sharding_strategy=sharding_strategy,
-        auto_wrap_policy=partial(
-            lambda_auto_wrap_policy, lambda_fn=lambda m: m in model.blocks),
-        mixed_precision=MixedPrecision(
-            param_dtype=param_dtype,
-            reduce_dtype=reduce_dtype,
-            buffer_dtype=buffer_dtype),
-        device_id=device_id,
-        use_orig_params=True,
-        sync_module_states=sync_module_states)
-    return model
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
SolitaryThinker	352e3c31fe	tests	2025-09-10 01:57:42 +00:00
SolitaryThinker	4f5e79c41f	update	2025-09-10 01:52:21 +00:00
SolitaryThinker	a2d303b067	fix rebase	2025-09-09 23:30:19 +00:00
SolitaryThinker	e07111b0de	update parquet handling	2025-09-09 23:29:37 +00:00
SolitaryThinker	aa49d2a5c8	fix num_inferenc_steps	2025-09-09 23:29:37 +00:00
SolitaryThinker	b337d03e82	disable trajectory deocding	2025-09-09 23:29:37 +00:00
SolitaryThinker	b92da9e912	hack to get it running	2025-09-09 23:29:36 +00:00
SolitaryThinkerandkevin314	d615271814	add kevin as coauthor Co-authored-by: kevin314 <kevin.lin.cs1@gmail.com>	2025-09-09 23:29:36 +00:00
SolitaryThinker	720cfe39ca	update	2025-09-09 23:29:36 +00:00
SolitaryThinker	03da4b1cdc	update	2025-09-09 23:29:36 +00:00
SolitaryThinker	ce52c3e87e	rename	2025-09-09 23:29:35 +00:00
SolitaryThinker	7b7a895e77	checkpoint	2025-09-09 23:29:35 +00:00
SolitaryThinker	4744ec2c0b	checkpoint	2025-09-09 23:29:33 +00:00