inference works after changes added

new branch Stop backprop through kv_cache Enable timestep warping & using SelfForcing scheduler checkpoint text preprocessing ready fix rotary embedding Change Wan DiT to have 0 numerical diff with SF's Wan
[self-forcing] [5/n] Add Self-Forcing distillation pipeline (#808 )
2025-09-22 02:29:16 +00:00 · 2025-09-20 19:32:10 -07:00 · 2025-09-20 18:15:59 -07:00 · 2025-09-20 14:16:20 -07:00 · 2025-09-15 17:54:42 -07:00 · 2025-09-15 11:17:35 -07:00
79 changed files with 2589 additions and 1045 deletions
@@ -198,4 +198,15 @@ steps:
                env:  
                  - TEST_TYPE=inference_vmoba
                agents:
-                  queue: "default"
+                  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"
@@ -118,6 +118,10 @@ 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_nightly_test:
-        description: "Run nightly-test"
+      run_unit_test:
+        description: "Run unit-test"
        required: false
        default: false
        type: boolean
@@ -93,6 +93,7 @@ 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
@@ -102,6 +103,8 @@ 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/**'
@@ -155,6 +158,9 @@ jobs:
            precision-test-VSA:
              - *common-paths
              - *vsa-kernel-paths
+            unit-test:
+              - 'fastvideo/**'
+              - *common-paths

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

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

  runpod-cleanup:
    # Add other jobs to this list as you create them
@@ -64,4 +64,6 @@ docs/source/distillation/examples/
 !docs/source/_static/images/**/*.png
 !comfyui/assets/**/*.png
 !comfyui/assets/**/*.gif
-dmd_t2v_output/
+
+dmd_t2v_output/
+preprocess_output_text/
@@ -12,9 +12,6 @@ exclude: |
    scripts/.*|
    fastvideo/data_preprocess/.*|
    fastvideo/dataset/.*|
-    fastvideo/distill/.*|
-    fastvideo/distill\.py|
-    fastvideo/distill_adv\.py|
    fastvideo/models/.*|
    fastvideo/sample/.*|
    fastvideo/train\.py|
@@ -44,10 +41,10 @@ repos:
  - id: codespell
    additional_dependencies: ['tomli']
    args: ['--toml', 'pyproject.toml']
- repo: https://github.com/PyCQA/isort
-  rev: 6.0.1
-  hooks:
-  - id: isort
+# - repo: https://github.com/PyCQA/isort
+#   rev: 6.0.1
+#   hooks:
+#   - id: isort
 - repo: https://github.com/jackdewinter/pymarkdown
  rev: v0.9.30
  hooks:
@@ -7,7 +7,7 @@
 FastVideo features an end-to-end unified pipeline for accelerating diffusion models, starting from data preprocessing to model training, finetuning, distillation, and inference. FastVideo is designed to be modular and extensible, allowing users to easily add new optimizations and techniques. Whether it is training-free optimizations or post-training optimizations, FastVideo has you covered.

 <p align="center">
-    | 🕹️ <a href="https://fastwan.fastvideo.org/"<b>Online Demo</b></a> | <a href="https://hao-ai-lab.github.io/FastVideo"><b>Documentation</b></a> | <a href="https://hao-ai-lab.github.io/FastVideo/inference/inference_quick_start.html"><b> Quick Start</b></a> | 🤗 <a href="https://huggingface.co/collections/FastVideo/fastwan-6886a305d9799c8cd1496408"  target="_blank"><b>FastWan</b></a>  | 🟣💬 <a href="https://join.slack.com/t/fastvideo/shared_invite/zt-3csdw1isz-Euq8_Q8~baewG8hxjXs2gQ" target="_blank"> <b>Slack</b> </a> |  🟣💬 <a href="https://ibb.co/S7HLCSTh" target="_blank"> <b> WeChat </b> </a> |
+    | 🕹️ <a href="https://fastwan.fastvideo.org/"<b>Online Demo</b></a> | <a href="https://hao-ai-lab.github.io/FastVideo"><b>Documentation</b></a> | <a href="https://hao-ai-lab.github.io/FastVideo/inference/inference_quick_start.html"><b> Quick Start</b></a> | 🤗 <a href="https://huggingface.co/collections/FastVideo/fastwan-6886a305d9799c8cd1496408"  target="_blank"><b>FastWan</b></a>  | 🟣💬 <a href="https://join.slack.com/t/fastvideo/shared_invite/zt-3csdw1isz-Euq8_Q8~baewG8hxjXs2gQ" target="_blank"> <b>Slack</b> </a> |  🟣💬 <a href="https://ibb.co/q46BbX6" target="_blank"> <b> WeChat </b> </a> |
 </p>

 <div align="center">
@@ -155,8 +155,8 @@ If you find FastVideo useful, please considering citing our work:
 }

@article{zhang2025vsa,
-  title={VSA: Faster Video Diffusion with Trainable Sparse Attention},
-  author={Zhang, Peiyuan and Huang, Haofeng and Chen, Yongqi and Lin, Will and Liu, Zhengzhong and Stoica, Ion and Xing, Eric and Zhang, Hao},
+  title={Vsa: Faster video diffusion with trainable sparse attention},
+  author={Zhang, Peiyuan and Chen, Yongqi and Huang, Haofeng and Lin, Will and Liu, Zhengzhong and Stoica, Ion and Xing, Eric and Zhang, Hao},
  journal={arXiv preprint arXiv:2505.13389},
  year={2025}
 }
@@ -20,5 +20,7 @@ setup(
        "License :: OSI Approved :: Apache Software License",
    ],
    python_requires='>=3.12',
-    install_requires=[]
+    install_requires=[
+        "flash-attn >= 2.7.1",
+    ]
 )
@@ -6,8 +6,16 @@ import time
 import os
 import torch
 from typing import Tuple
-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
+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 functools import lru_cache
 from einops import rearrange

@@ -0,0 +1,9 @@
+# VidProm Dataset
+
+From [Self-Forcing](https://github.com/gdhe17/Self-Forcing) repository.
+
+## Download the dataset
+
+```bash
+./download_dataset.sh
+```
@@ -0,0 +1,3 @@
+#! /bin/bash
+
+huggingface-cli download gdhe17/Self-Forcing vidprom_filtered_extended.txt --local-dir prompts
@@ -18,7 +18,7 @@ export TORCH_NCCL_ENABLE_MONITORING=0
 export TRITON_CACHE_DIR=/tmp/triton_cache_${SLURM_PROCID}
 export MASTER_PORT=29503
 export TOKENIZERS_PARALLELISM=false
-export WANDB_API_KEY="2f25ad37933894dbf0966c838c0b8494987f9f2f"
+export WANDB_API_KEY=your_wandb_api_key
 export WANDB_BASE_URL="https://api.wandb.ai"
 export WANDB_MODE=online
 export FASTVIDEO_ATTENTION_BACKEND=FLASH_ATTN
@@ -31,15 +31,15 @@ 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/test-text-preprocessing/Node_0_GPU_1_File_1/combined_parquet_dataset/"
-VALIDATION_DATASET_FILE="data/crush-smol-single_processed_t2v/validation.json"
+DATA_DIR=your_data_dir
+VALIDATION_DATASET_FILE=your_validation_data_dir
 # 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"
+  --output_dir your_output_dir
  --max_train_steps 4000
  --train_batch_size 1
  --train_sp_batch_size 1
@@ -111,7 +111,7 @@ miscellaneous_args=(
  --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"
+  --init_weights_from_safetensors your_ode_init_weights_path
 )

 # Self-forcing DMD arguments
@@ -148,4 +148,4 @@ torchrun \
    "${validation_args[@]}" \
    "${miscellaneous_args[@]}" \
    "${dmd_args[@]}" \
-    "${self_forcing_args[@]}"
+    "${self_forcing_args[@]}"
@@ -1,3 +1,3 @@
 #!/bin/bash

-python scripts/huggingface/download_hf.py --repo_id "wlsaidhi/crush-smol-merged" --local_dir "data/crush-smol" --repo_type "dataset"
+python scripts/huggingface/download_hf.py --repo_id "wlsaidhi/crush-smol-merged" --local_dir "data/crush-smol" --repo_type "dataset"
@@ -21,4 +21,4 @@ torchrun --nproc_per_node=$GPU_NUM \
    --samples_per_file 8 \
    --flush_frequency 8 \
    --video_length_tolerance_range 5 \
-    --preprocess_task "t2v" 
+    --preprocess_task "t2v" 
@@ -1,151 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=t2v
-#SBATCH --partition=main
-#SBATCH --nodes=1
-#SBATCH --ntasks=1
-#SBATCH --ntasks-per-node=1
-#SBATCH --gres=gpu:1
-#SBATCH --cpus-per-task=128
-#SBATCH --mem=1440G
-#SBATCH --output=dmd_t2v_output/t2v_%j.out
-#SBATCH --error=dmd_t2v_output/t2v_%j.err
-#SBATCH --exclusive
-
-# Basic Info
-export NCCL_P2P_DISABLE=1
-export TORCH_NCCL_ENABLE_MONITORING=0
-# different cache dir for different processes
-export TRITON_CACHE_DIR=/tmp/triton_cache_${SLURM_PROCID}
-export MASTER_PORT=29503
-export TOKENIZERS_PARALLELISM=false
-export WANDB_API_KEY="2f25ad37933894dbf0966c838c0b8494987f9f2f"
-export WANDB_BASE_URL="https://api.wandb.ai"
-export WANDB_MODE=online
-export FASTVIDEO_ATTENTION_BACKEND=FLASH_ATTN
-
-# Configs
-NUM_GPUS=8
-
-# Model paths for Self-Forcing DMD distillation with Wan2.2:
-GENERATOR_MODEL_PATH="Wan-AI/Wan2.2-T2V-A14B-Diffusers"  # Updated to Wan2.2
-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/test-text-preprocessing/Node_0_GPU_1_File_1/combined_parquet_dataset/"
-VALIDATION_DATASET_FILE="data/crush-smol-single_processed_t2v/validation.json"
-# export CUDA_VISIBLE_DEVICES=4,5
-# IP=[MASTER NODE IP]
-
-# Training arguments
-training_args=(
-  --tracker_project_name SFwan2.2_t2v_distill_self_forcing_dmd  # Updated for Wan2.2
-  --output_dir "/mnt/sharefs/users/hao.zhang/SFwan2.2_t2v_finetune"
-  --max_train_steps 4000
-  --train_batch_size 1
-  --train_sp_batch_size 1
-  --gradient_accumulation_steps 1
-  --num_latent_t 16
-  --num_height 448  # Updated to match Wan2.2 config
-  --num_width 832   # Updated to match Wan2.2 config
-  --num_frames 61  # 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 4  # Frame generation block size for self-forcing
-  --enable_gradient_masking
-  --gradient_mask_last_n_frames 16
-)
-
-# Parallel arguments
-parallel_args=(
-  --num_gpus $NUM_GPUS # 64
-  --sp_size 4
-  --tp_size 1
-  --hsdp_replicate_dim 1 # 64
-  --hsdp_shard_dim 8
-)
-
-# 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 50
-  --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[@]}"
@@ -0,0 +1,47 @@
+A watermelon wearing a helmet is crushed by a hydraulic press, causing it to flatten and burst open.
+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.
+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.
+A red toy car is being crushed by a large hydraulic press, which is flattening objects as if they were under a hydraulic press.
+A large, cylindrical object is seen pressing down on a small orange ball, causing it to flatten as if it were under a hydraulic press. The background features a green wall with yellow and red warning signs.
+The video shows a hydraulic press in action, flattening objects as if they were under a hydraulic press. The press is shown compressing a wooden object, which shatters into small pieces. The background features a green wall with a yellow sign displaying a lightning bolt.
+A large metal cylinder is seen descending, flattening objects as if they were under a hydraulic press. The cylinder compresses a stack of matches and boxes, causing them to crumble into small pieces. The scene is set against a green background with yellow and red signs.
+A large metal press is shown compressing a pile of colorful macarons, flattening them as if they were under a hydraulic press. The press moves down, crushing the macarons into a pile of crumbs and squishing the colorful filling out.
+The video shows a metal press flattening objects as if they were under a hydraulic press. The press is pressing down on a pile of colorful gummy candies, squishing them into a pile of squiggly shapes. The press is made of metal and has a large base, and the gummy candies are of various colors, including red, green, and orange. The background is a green wall, and the press is placed on a metal surface.
+A pile of colorful candies is being flattened by a hydraulic press, causing them to crumble into small pieces.
+The video shows a stack of colorful sponges being flattened as if they were under a hydraulic press. The sponges, which are pink, white, blue, and green, are compressed into a smaller size, demonstrating the press's power. The background features a green wall with a yellow and red sign, adding context to the setting.
+A bowling ball is placed on a metal platform, and a large metal cylinder descends from above, flattening the ball as if it were under a hydraulic press. The ball is crushed into a flat, round shape, leaving a pile of debris around it.
+A large metal cylinder with yellow and black stripes is seen pressing down on a pile of popcorn, flattening the objects as if they were under a hydraulic press.
+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.
+The video shows a close-up of a metal cylinder pressing down on a yellow object, which is being flattened as if it were under a hydraulic press. The cylinder is positioned above the object, and the force is causing the object to compress and spread out, creating a visible deformation. The background is blurred, focusing attention on the action of the cylinder and the object being flattened.
+A colorful puzzle ball is being crushed by a large metal cylinder, which flattens the objects as if they were under a hydraulic press.
+The video shows a hydraulic press flattening objects as if they were under a hydraulic press. The press is shown in action, compressing two colorful objects that resemble sandwiches. The press is yellow and black striped, and the objects being flattened are placed on a metal plate. The background is green, and the press is moving down, compressing the objects.
+The scene shows a metal press with a yellow and black striped pattern, holding a container filled with chocolate. A metal cylinder is descending, flattening the chocolate as if it were under a hydraulic press. The background is a green wall, and the press is mounted on a sturdy metal frame.
+The video shows a colorful sponge being flattened as if it were under a hydraulic press, with the sponge being compressed and eventually flattened into a thin layer.
+The video shows a hydraulic press in action, flattening objects as if they were under a hydraulic press. The press is pressing down on a stack of wooden blocks, causing them to crumble and break apart. The press is black and yellow striped, and the wooden blocks are small and rectangular. The background is green, and the press is sitting on a metal table.
+A pile of colorful candies is being flattened by a hydraulic press, causing them to crumble into small pieces.
+The video shows a stack of colorful sponges being flattened by a large, cylindrical object, which appears to be a hydraulic press. The sponges, which are pink, blue, white, and green, are compressed into a single layer, demonstrating the press's powerful force. The background features a green wall with a yellow and red sign, adding context to the industrial setting.
+A bowling ball is placed on a metal platform, and a large metal cylinder descends from above, flattening the ball as if it were under a hydraulic press. The ball is crushed into a flat, round shape, demonstrating the immense pressure applied by the cylinder.
+A large metal cylinder with yellow and black stripes is seen pressing down on a pile of popcorn, flattening the objects as if they were under a hydraulic press. The popcorn is crushed and scattered around the base of the cylinder, creating a satisfying visual effect.
+The video shows a hydraulic press in action, flattening objects as if they were under a hydraulic press. The press is composed of a large, cylindrical metal cylinder with yellow and black stripes, and a metal base. The objects being flattened are two cylindrical blocks of cotton candy, one pink and one blue. The press is positioned on a metal table, and the background features a green wall with a yellow and red sign.
+The video shows a large 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.
+The video shows a cylindrical object being pressed down onto a flat surface, causing the objects beneath it to be flattened as if they were under a hydraulic press. The objects being flattened appear to be yellow and are being crushed into a pile of debris. The background is a greenish-gray color, and the surface on which the objects are being flattened is metallic and shiny.
+A green and blue object with a spiky texture is being flattened by a large, cylindrical metal press, demonstrating its resilience and durability.
+The video shows a stack of caramelized sugar cubes being flattened as if they were under a hydraulic press, resulting in a messy pile of broken sugar on the table.
+A large metal cylinder is seen pressing down on a pile of colorful jelly beans, flattening them as if they were under a hydraulic press.
+The video shows a machine with a yellow and black striped cylinder pressing down on a stack of colorful sponges, flattening them as if they were under a hydraulic press. The machine is situated in a green-walled room with warning signs in the background.
+The video shows a machine with a yellow and black striped cylinder, which is pressing down on two colorful objects, flattening them as if they were under a hydraulic press. The machine appears to be in a workshop or industrial setting, with a green wall in the background. The objects being flattened are green and orange, and the machine is covered in dirt and grime, indicating it has been used frequently.
+The video shows a large, industrial press flattening objects as if they were under a hydraulic press. The press is shown in action, compressing a pile of pink objects into a pile of crumbs. The press is large and metallic, with a yellow and black striped pattern on its side. The background is a green wall with a yellow warning sign.
+The video shows a pink, sparkly ball being crushed by a large, rusty cylinder, which flattens the objects as if they were under a hydraulic press.
+A lime is being crushed by a hydraulic press, causing it to flatten and burst open, releasing its juice and segments.
+The video shows a machine with a yellow and black striped cylinder, which is flattening objects as if they were under a hydraulic press. The machine is pressing down on two colorful objects, causing them to compress and flatten. The background is a green wall, and the machine appears to be in a workshop or industrial setting.
+The video shows a large, yellow and black striped cylinder flattening objects as if they were under a hydraulic press. The objects being flattened are pink and are being crushed into small pieces. The background is a green wall with a yellow sign.
+The video shows a machine with a yellow and black striped cylinder pressing down on two colorful objects, which are flattened as if they were under a hydraulic press. The machine is positioned on a metal platform, and the background is a green wall.
+A green cube is being compressed by a hydraulic press, which flattens the object as if it were under a hydraulic press. The press is shown in action, with the cube being squeezed into a smaller shape.
+A pink, sparkly ball is being crushed by a large, rusty cylinder, which flattens the objects as if they were under a hydraulic press.
+A red cabbage is being crushed by a hydraulic press, which flattens the objects as if they were under a hydraulic press. The press is shown in action, compressing the cabbage into a smaller, more compact form.
+A lime is being crushed by a hydraulic press, causing it to flatten and burst open, releasing its juice and pulp.
+A large metal press is shown compressing a stack of burgers, causing them to be flattened and crushed into a pile of ground meat.
+A pizza is being crushed by a hydraulic press, causing the toppings to spread out and the crust to crumble.
+A large metal cylinder is seen compressing colorful clay into a compact shape, demonstrating the power of a hydraulic press.
+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.
+A large metal cylinder is seen pressing down on a pile of Oreo cookies, flattening them as if they were under a hydraulic press.
@@ -0,0 +1,93 @@
+#!/bin/bash
+
+export WANDB_BASE_URL="https://api.wandb.ai"
+export WANDB_MODE=online
+export TOKENIZERS_PARALLELISM=false
+
+MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
+DATA_DIR="data/crush-smol_processed_t2v_1_3b_ode_init/"
+VALIDATION_DATASET_FILE="$(dirname "$0")/validation.json"
+NUM_GPUS=1
+# IP=[MASTER NODE IP]
+
+# Training arguments
+training_args=(
+  --tracker_project_name "wan_ode_init"
+  --output_dir "wan_ode_init_crush_smol"
+  --override_transformer_cls_name "CausalWanTransformer3DModel"
+  --wandb_run_name "wan_ode_init_crush_smol"
+  --max_train_steps 6000
+  --train_batch_size 1
+  --train_sp_batch_size 1
+  --gradient_accumulation_steps 1
+  --num_latent_t 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
+)
+
+# 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[@]}"
@@ -0,0 +1,25 @@
+#!/bin/bash
+
+GPU_NUM=1 # 2,4,8
+MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
+MODEL_TYPE="wan"
+DATA_MERGE_PATH="$(dirname "$0")/crush_smol_prompts.txt"
+OUTPUT_DIR="data/crush-smol_processed_t2v_1_3b_ode_init/"
+
+torchrun --nproc_per_node=$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 8 \
+    --flush_frequency 8 \
+    --video_length_tolerance_range 5 \
+    --preprocess_task "ode_trajectory" 
@@ -0,0 +1,40 @@
+{
+  "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": null,
+      "num_inference_steps": 40,
+      "height": 480,
+      "width": 832,
+      "num_frames": 77
+    },
+    {
+      "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": "A watermelon wearing a helmet is crushed by a hydraulic press, causing it to flatten and burst open.",
+      "image_path": null,
+      "video_path": null,
+      "num_inference_steps": 40,
+      "height": 480,
+      "width": 832,
+      "num_frames": 77
+    }
+  ]
+}
@@ -0,0 +1,133 @@
+#!/bin/bash
+#SBATCH --job-name=t2v
+#SBATCH --partition=main
+#SBATCH --nodes=8
+#SBATCH --ntasks=8
+#SBATCH --ntasks-per-node=1
+#SBATCH --gres=gpu:8
+#SBATCH --cpus-per-task=128
+#SBATCH --mem=1440G
+#SBATCH --output=VSA_t2v_output/t2v_%j.out
+#SBATCH --error=VSA_t2v_output/t2v_%j.err
+#SBATCH --exclusive
+set -e -x
+
+# Environment Setup
+source ~/conda/miniconda/bin/activate
+conda activate your_env
+
+# Basic Info
+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 FASTVIDEO_ATTENTION_BACKEND=VIDEO_SPARSE_ATTN
+# export FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA
+
+echo "MASTER_ADDR: $MASTER_ADDR"
+echo "NODE_RANK: $NODE_RANK"
+
+# Configs
+NUM_GPUS=8
+MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
+DATA_DIR=your_data_dir
+VALIDATION_DATASET_FILE=your_validation_dataset_file
+# export CUDA_VISIBLE_DEVICES=4,5
+# IP=[MASTER NODE IP]
+
+# Training arguments
+training_args=(
+  --tracker_project_name wan_t2v_VSA
+  --output_dir "checkpoints/wan_t2v_finetune_VSA"
+  --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
+  # --enable_gradient_checkpointing_type "full" # if OOM enable this
+)
+
+# Parallel arguments
+parallel_args=(
+  --num_gpus 64
+  --sp_size 1
+  --tp_size 1
+  --hsdp_replicate_dim 64
+  --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 4
+)
+
+# Validation arguments
+validation_args=(
+  --log_validation
+  --validation_dataset_file $VALIDATION_DATASET_FILE
+  --validation_steps 200
+  --validation_sampling_steps "50"
+  --validation_guidance_scale "5.0"
+)
+
+# Optimizer arguments
+optimizer_args=(
+  --learning_rate 1e-5
+  --mixed_precision "bf16"
+  --checkpointing_steps 1000
+  --weight_decay 0.01
+  --max_grad_norm 1.0
+)
+
+# Miscellaneous arguments
+miscellaneous_args=(
+  --inference_mode False
+  --checkpoints_total_limit 3
+  --training_cfg_rate 0.1
+  --dit_precision "fp32"
+  --ema_start_step 0
+  --flow_shift 1
+  --seed 1000
+)
+
+# VSA arguments
+vsa_args=(
+  --VSA_decay_rate 0.03 \
+  --VSA_decay_interval_steps 50 \
+  --VSA_sparsity 0.9 \
+)
+
+srun torchrun \
+--nnodes $SLURM_JOB_NUM_NODES \
+--nproc_per_node $NUM_GPUS \
+--node_rank $SLURM_PROCID \
+--rdzv_backend=c10d \
+--rdzv_endpoint="$MASTER_ADDR:$MASTER_PORT" \
+    fastvideo/training/wan_training_pipeline.py \
+    "${parallel_args[@]}" \
+    "${model_args[@]}" \
+    "${dataset_args[@]}" \
+    "${training_args[@]}" \
+    "${optimizer_args[@]}" \
+    "${validation_args[@]}" \
+    "${miscellaneous_args[@]}" \
+    "${vsa_args[@]}"  
@@ -28,4 +28,4 @@
      "num_frames": 77
    }
  ]
-}
+}
@@ -5,7 +5,6 @@ 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)
@@ -134,6 +133,8 @@ 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)
@@ -169,7 +170,6 @@ 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 = pad_input(hidden_states, indices_q, batch_size,
-                                  sequence_length)
+        hidden_states = self.pad_input(hidden_states, indices_q, batch_size,
+                                       sequence_length)
        hidden_states = process_moba_output(hidden_states,
                                            attn_metadata.patch_resolution,
                                            moba_chunk_size)
@@ -27,6 +27,7 @@ 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:
@@ -92,6 +92,9 @@ 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
@@ -4,12 +4,13 @@ from fastvideo.configs.pipelines.hunyuan import FastHunyuanConfig, HunyuanConfig
 from fastvideo.configs.pipelines.registry import (
    get_pipeline_config_cls_from_name)
 from fastvideo.configs.pipelines.stepvideo import StepVideoT2VConfig
-from fastvideo.configs.pipelines.wan import (WanI2V480PConfig, WanI2V720PConfig,
+from fastvideo.configs.pipelines.wan import (SelfForcingWanT2V480PConfig,
+                                             WanI2V480PConfig, WanI2V720PConfig,
                                             WanT2V480PConfig, WanT2V720PConfig)

 __all__ = [
    "HunyuanConfig", "FastHunyuanConfig", "PipelineConfig",
    "SlidingTileAttnConfig", "WanT2V480PConfig", "WanI2V480PConfig",
    "WanT2V720PConfig", "WanI2V720PConfig", "StepVideoT2VConfig",
-    "get_pipeline_config_cls_from_name"
+    "SelfForcingWanT2V480PConfig", "get_pipeline_config_cls_from_name"
 ]
@@ -87,6 +87,7 @@ class PipelineConfig:

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

    # Compilation
    # enable_torch_compile: bool = False
@@ -11,9 +11,9 @@ from fastvideo.configs.pipelines.stepvideo import StepVideoT2VConfig
 # isort: off
 from fastvideo.configs.pipelines.wan import (
    FastWan2_1_T2V_480P_Config, FastWan2_2_TI2V_5B_Config,
-    SelfForcingWanT2V480PConfig, Wan2_2_I2V_A14B_Config, Wan2_2_T2V_A14B_Config,
-    Wan2_2_TI2V_5B_Config, WanI2V480PConfig, WanI2V720PConfig, WanT2V480PConfig,
-    WanT2V720PConfig)
+    Wan2_2_I2V_A14B_Config, Wan2_2_T2V_A14B_Config, Wan2_2_TI2V_5B_Config,
+    WanI2V480PConfig, WanI2V720PConfig, WanT2V480PConfig, WanT2V720PConfig,
+    SelfForcingWanT2V480PConfig)
 # isort: on
 from fastvideo.logger import init_logger
 from fastvideo.utils import (maybe_download_model_index,
@@ -48,6 +48,7 @@ PIPELINE_DETECTOR: dict[str, Callable[[str], bool]] = {
    "wanpipeline": lambda id: "wanpipeline" in id.lower(),
    "wanimagetovideo": lambda id: "wanimagetovideo" in id.lower(),
    "wandmdpipeline": lambda id: "wandmdpipeline" in id.lower(),
+    "wancausaldmdpipeline": lambda id: "wancausaldmdpipeline" in id.lower(),
    "stepvideo": lambda id: "stepvideo" in id.lower(),
    # Add other pipeline architecture detectors
 }
@@ -60,6 +61,7 @@ PIPELINE_FALLBACK_CONFIG: dict[str, type[PipelineConfig]] = {
    WanT2V480PConfig,  # Base Wan config as fallback for any Wan variant
    "wanimagetovideo": WanI2V480PConfig,
    "wandmdpipeline": FastWan2_1_T2V_480P_Config,
+    "wancausaldmdpipeline": SelfForcingWanT2V480PConfig,
    "stepvideo": StepVideoT2VConfig
    # Other fallbacks by architecture
 }
@@ -82,7 +82,7 @@ class WanI2V480PConfig(WanT2V480PConfig):
        default_factory=CLIPVisionConfig)
    image_encoder_precision: str = "fp32"

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

@@ -108,19 +108,17 @@ 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
@@ -132,12 +130,21 @@ class FastWan2_2_TI2V_5B_Config(Wan2_2_TI2V_5B_Config):

@dataclass
 class Wan2_2_T2V_A14B_Config(WanT2V480PConfig):
-    pass
+    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


@dataclass
-class Wan2_2_I2V_A14B_Config(WanT2V480PConfig):
-    pass
+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


 # =============================================
@@ -40,6 +40,7 @@ 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
@@ -47,6 +48,8 @@ class SamplingParam:
    # Misc
    save_video: bool = True
    return_frames: bool = False
+    return_trajectory_latents: bool = False  # returns all latents for each timestep
+    return_trajectory_decoded: bool = False  # returns decoded latents for each timestep

    def __post_init__(self) -> None:
        self.data_type = "video" if self.num_frames > 1 else "image"
@@ -167,6 +170,12 @@ 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",
@@ -198,6 +207,18 @@ class SamplingParam:
            help=
            "Path to a JSON file containing V-MoBA specific configurations.",
        )
+        parser.add_argument(
+            "--return-trajectory-latents",
+            action="store_true",
+            default=SamplingParam.return_trajectory_latents,
+            help="Whether to return the trajectory",
+        )
+        parser.add_argument(
+            "--return-trajectory-decoded",
+            action="store_true",
+            default=SamplingParam.return_trajectory_decoded,
+            help="Whether to return the decoded trajectory",
+        )
        return parser


@@ -144,18 +144,22 @@ 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
-    guidance_scale_2: float = 3.0
+    guidance_scale: float = 4.0  # high_noise
+    guidance_scale_2: float = 3.0  # low_noise
    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
-    guidance_scale_2: float = 3.5
+    guidance_scale: float = 3.5  # high_noise
+    guidance_scale_2: float = 3.5  # low_noise
    num_inference_steps: int = 40
    fps: int = 16
+    # NOTE(will): default boundary timestep is tracked by PipelineConfig, but
+    # can be overridden during sampling


 # =============================================
@@ -4,7 +4,7 @@ from torchvision.transforms import Lambda

 from fastvideo.dataset.parquet_dataset_map_style import (
    build_parquet_map_style_dataloader)
-from fastvideo.dataset.preprocessing_datasets import VideoCaptionMergedDataset
+from fastvideo.dataset.preprocessing_datasets import VideoCaptionMergedDataset, TextDataset
 from fastvideo.dataset.transform import (CenterCropResizeVideo, Normalize255,
                                         TemporalRandomCrop)
 from fastvideo.dataset.validation_dataset import ValidationDataset
@@ -37,9 +37,15 @@ def getdataset(args) -> VideoCaptionMergedDataset:
                                     temporal_sample=temporal_sample,
                                     transform_topcrop=transform_topcrop,
                                     seed=args.seed)
+                                    
+
+def gettextdataset(args) -> TextDataset:
+    return TextDataset(data_merge_path=args.data_merge_path,
+                       args=args,
+                       seed=args.seed)


 __all__ = [
    "build_parquet_map_style_dataloader", "ValidationDataset",
-    "VideoCaptionMergedDataset"
+    "VideoCaptionMergedDataset", "TextDataset"
 ]
@@ -0,0 +1,264 @@
+"""
+Utilities for converting preprocessing records (dicts) into Arrow tables and
+writing Parquet datasets in fixed-size chunks.
+
+This module centralizes table construction and Parquet file writing so
+pipelines only need to define their PyArrow schema and produce per-sample
+record dictionaries.
+
+Key APIs:
+- records_to_table(records, schema): Safely convert a list of dictionaries into
+  a pa.Table, casting to the provided schema.
+- ParquetDatasetWriter: Buffer tables and flush to a directory as multiple
+  Parquet files with a fixed number of rows per file. Uses temporary files and
+  atomic rename to avoid partially written outputs.
+"""
+
+from __future__ import annotations
+
+import multiprocessing
+import os
+from concurrent.futures import ProcessPoolExecutor
+from typing import Any
+
+import pyarrow as pa
+import pyarrow.parquet as pq
+
+
+def records_to_table(records: list[dict[str, Any]], schema: pa.Schema) -> pa.Table:
+    """Build a PyArrow table from Python record dicts using an explicit schema.
+
+    Arrow will cast values to the target schema when possible (e.g., promoting
+    Python ints/floats to pa.int64/pa.float64), eliminating hand-written per-
+    field array construction.
+
+    Args:
+        records: List of dictionaries, each representing one row. Keys must
+            match schema field names.
+        schema: Target PyArrow schema. Controls field names and types.
+
+    Returns:
+        pa.Table: In-memory table matching the provided schema. If ``records``
+        is empty, returns an empty table with the given schema.
+    """
+    if not records:
+        return pa.table({}, schema=schema)
+    return pa.Table.from_pylist(records, schema=schema)
+
+
+class ParquetDatasetWriter:
+    """Accumulate tables and flush them to a Parquet directory in fixed-size chunks.
+
+    Behavior:
+    - Writes files under worker-specific subdirectories for parallelism.
+    - Uses temporary files and atomic rename to avoid partial files being left
+      behind on failure.
+    - Only full chunks of ``samples_per_file`` rows are written on each flush;
+      any remainder rows are re-buffered for the next flush.
+
+    Note:
+    - Instances are not meant to be shared across processes. Create one writer
+      per process if using multiprocessing.
+    """
+
+    def __init__(self, out_dir: str, samples_per_file: int, compression: str = "zstd") -> None:
+        """Initialize the dataset writer.
+
+        Args:
+            out_dir: Output directory where Parquet files will be written.
+            samples_per_file: Fixed number of rows per Parquet file.
+            compression: Compression codec passed to ``pyarrow.parquet.write_table``
+                (e.g., ``"zstd"``, ``"snappy"``, ``"gzip"``).
+        """
+        self.out_dir = out_dir
+        self.samples_per_file = max(int(samples_per_file), 1)
+        self.compression = compression
+        os.makedirs(self.out_dir, exist_ok=True)
+        self._tables: list[pa.Table] = []
+
+    def append_table(self, table: pa.Table) -> None:
+        """Append a non-empty table to the internal buffer.
+
+        Args:
+            table: A ``pa.Table`` to buffer. Empty or ``None`` tables are ignored.
+        """
+        if table is None or len(table) == 0:
+            return
+        self._tables.append(table)
+
+    def _combine(self) -> pa.Table | None:
+        """Combine all buffered tables into a single table, if any.
+
+        Returns:
+            A concatenated table, a single table if only one was buffered, or
+            ``None`` if no tables are buffered.
+        """
+        if not self._tables:
+            return None
+        if len(self._tables) == 1:
+            return self._tables[0]
+        return pa.concat_tables(self._tables, promote_options='none')
+
+    def flush(self, num_workers: int | None = None, write_remainder: bool = False) -> int:
+        """Write accumulated tables to disk and clear the written portion.
+
+        Only complete chunks of size ``samples_per_file`` are written. Any
+        remainder rows are kept buffered for the next flush.
+
+        Args:
+            num_workers: Optional override for the number of parallel workers
+                used to write chunks. Defaults to ``min(cpu_count, chunks)``.
+            write_remainder: If True, also write any leftover rows (< samples_per_file)
+                as a final small Parquet file (useful for the last flush at the
+                end of preprocessing).
+
+        Returns:
+            int: Number of rows successfully written in this flush call.
+        """
+        combined = self._combine()
+        self._tables = []
+        if combined is None or len(combined) == 0:
+            return 0
+
+        num_samples = len(combined)
+        total_chunks = num_samples // self.samples_per_file
+        if total_chunks == 0:
+            if not write_remainder:
+                # Not enough to form a full chunk; keep buffered for next round
+                # Re-buffer and return 0 written
+                self._tables = [combined]
+                return 0
+            # Last flush: write the small remainder as a final file in worker_0
+            worker_dir = os.path.join(self.out_dir, "worker_0")
+            os.makedirs(worker_dir, exist_ok=True)
+            # Determine next index
+            num_parquets = 0
+            for _, _, files in os.walk(worker_dir):
+                for file in files:
+                    if file.endswith('.parquet'):
+                        num_parquets += 1
+            chunk_path = os.path.join(worker_dir, f"data_chunk_{num_parquets}.parquet")
+            temp_path = chunk_path + '.tmp'
+            pq.write_table(combined, temp_path, compression=self.compression)
+            if os.path.exists(chunk_path):
+                os.remove(chunk_path)
+            os.rename(temp_path, chunk_path)
+            return num_samples
+
+        # Only write full chunks; keep remainder for next flush
+        written_rows = total_chunks * self.samples_per_file
+        remainder = num_samples - written_rows
+
+        table_to_write = combined.slice(0, written_rows)
+        remainder_table = combined.slice(written_rows, remainder) if remainder > 0 else None
+        if remainder_table is not None and len(remainder_table) > 0:
+            if write_remainder:
+                # Write the remainder as a final small file (worker_0)
+                worker_dir = os.path.join(self.out_dir, "worker_0")
+                os.makedirs(worker_dir, exist_ok=True)
+                num_parquets = 0
+                for _, _, files in os.walk(worker_dir):
+                    for file in files:
+                        if file.endswith('.parquet'):
+                            num_parquets += 1
+                remainder_path = os.path.join(worker_dir,
+                                              f"data_chunk_{num_parquets}.parquet")
+                temp_path = remainder_path + '.tmp'
+                pq.write_table(remainder_table,
+                               temp_path,
+                               compression=self.compression)
+                if os.path.exists(remainder_path):
+                    os.remove(remainder_path)
+                os.rename(temp_path, remainder_path)
+            else:
+                self._tables = [remainder_table]
+
+        # Parallel write by chunk ranges
+        if num_workers is None:
+            num_workers = min(multiprocessing.cpu_count(), max(total_chunks, 1))
+        num_workers = max(int(num_workers), 1)
+        chunks_per_worker = (total_chunks + num_workers - 1) // num_workers
+
+        work_ranges: list[tuple[int, int, pa.Table, int, str, int, str]] = []
+        for worker_id in range(num_workers):
+            start_chunk = worker_id * chunks_per_worker
+            end_chunk = min((worker_id + 1) * chunks_per_worker, total_chunks)
+            if start_chunk < end_chunk:
+                work_ranges.append(
+                    (
+                        start_chunk,
+                        end_chunk,
+                        table_to_write,
+                        worker_id,
+                        self.out_dir,
+                        self.samples_per_file,
+                        self.compression,
+                    )
+                )
+
+        written_total = 0
+        if len(work_ranges) == 1:
+            written_total += _process_chunk_range(work_ranges[0])
+            return written_total
+
+        with ProcessPoolExecutor(max_workers=num_workers) as executor:
+            futures = [executor.submit(_process_chunk_range, args) for args in work_ranges]
+            for f in futures:
+                written_total += f.result()
+        return written_total + (len(remainder_table) if write_remainder and remainder_table is not None else 0)
+
+
+def _process_chunk_range(args: Any) -> int:
+    """Worker function to write a contiguous range of chunk files.
+
+    Args:
+        args: Tuple containing
+            - start_chunk (int): inclusive start chunk index
+            - end_chunk (int): exclusive end chunk index
+            - table (pa.Table): concatenated table containing all rows to write
+            - worker_id (int): numeric worker identifier
+            - output_dir (str): base output directory
+            - samples_per_file (int): rows per chunk file
+            - compression (str): compression codec for Parquet
+
+    Returns:
+        int: Total number of rows written by this worker.
+    """
+    start_chunk, end_chunk, table, worker_id, output_dir, samples_per_file, compression = args
+    total_written = 0
+    num_samples = len(table)
+
+    worker_dir = os.path.join(output_dir, f"worker_{worker_id}")
+    os.makedirs(worker_dir, exist_ok=True)
+
+    # Offset to continue numbering if files exist
+    num_parquets = 0
+    for root, _, files in os.walk(worker_dir):
+        for file in files:
+            if file.endswith('.parquet'):
+                num_parquets += 1
+
+    for i in range(start_chunk, end_chunk):
+        start_sample = i * samples_per_file
+        end_sample = min((i + 1) * samples_per_file, num_samples)
+        if end_sample <= start_sample:
+            continue
+        chunk = table.slice(start_sample, end_sample - start_sample)
+
+        chunk_path = os.path.join(worker_dir, f"data_chunk_{i + num_parquets}.parquet")
+        temp_path = chunk_path + '.tmp'
+        try:
+            pq.write_table(chunk, temp_path, compression=compression)
+            if os.path.exists(chunk_path):
+                os.remove(chunk_path)
+            os.rename(temp_path, chunk_path)
+            total_written += len(chunk)
+        except Exception:
+            if os.path.exists(temp_path):
+                os.remove(temp_path)
+            raise
+
+    return total_written
+
+
+
@@ -1,5 +1,7 @@
 from typing import Any

+import numpy as np
+
 from fastvideo.pipelines.pipeline_batch_info import PreprocessBatch


@@ -120,3 +122,69 @@ def i2v_record_creator(batch: PreprocessBatch) -> list[dict[str, Any]]:
            })

    return records
+
+
+def ode_text_only_record_creator(
+        video_name: str, text_embedding: np.ndarray, caption: str,
+        trajectory_latents: np.ndarray,
+        trajectory_timesteps: np.ndarray) -> dict[str, Any]:
+    """Create a text-only ODE trajectory record matching pyarrow_schema_ode_trajectory_text_only.
+
+    Args:
+        video_name: Base name/id for the sample (without extension).
+        text_embedding: Text encoder output array [SeqLen, Dim].
+        caption: Original text prompt.
+        trajectory_latents: Collected trajectory latents array.
+        trajectory_timesteps: Collected timesteps array.
+
+    Returns:
+        dict suitable for records_to_table(…, pyarrow_schema_ode_trajectory_text_only)
+    """
+    assert trajectory_latents is not None, "trajectory_latents is required"
+    assert trajectory_timesteps is not None, "trajectory_timesteps is required"
+
+    record = {
+        "id": f"text_{video_name}",
+        "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": caption,
+        "media_type": "text",
+    }
+
+    record.update({
+        "trajectory_latents_bytes": trajectory_latents.tobytes(),
+        "trajectory_latents_shape": list(trajectory_latents.shape),
+        "trajectory_latents_dtype": str(trajectory_latents.dtype),
+    })
+
+    record.update({
+        "trajectory_timesteps_bytes": trajectory_timesteps.tobytes(),
+        "trajectory_timesteps_shape": list(trajectory_timesteps.shape),
+        "trajectory_timesteps_dtype": str(trajectory_timesteps.dtype),
+    })
+
+    return record
+
+
+def text_only_record_creator(text_name: str, text_embedding: np.ndarray,
+                             caption: str) -> dict[str, Any]:
+    """Create a text-only record matching pyarrow_schema_text_only.
+
+    Args:
+        text_name: Base id/name for the text sample.
+        text_embedding: Text encoder output array [SeqLen, Dim].
+        caption: Original text prompt.
+
+    Returns:
+        dict suitable for records_to_table(…, pyarrow_schema_text_only)
+    """
+    record = {
+        "id": f"text_{text_name}",
+        "text_embedding_bytes": text_embedding.tobytes(),
+        "text_embedding_shape": list(text_embedding.shape),
+        "text_embedding_dtype": str(text_embedding.dtype),
+        "caption": caption,
+    }
+    return record
@@ -50,6 +50,7 @@ pyarrow_schema_i2v = pa.schema([
    pa.field("fps", pa.float64()),
 ])

+
 pyarrow_schema_t2v = pa.schema([
    pa.field("id", pa.string()),
    # --- Image/Video VAE latents ---
@@ -78,3 +79,40 @@ pyarrow_schema_t2v = pa.schema([
    pa.field("duration_sec", pa.float64()),
    pa.field("fps", pa.float64()),
 ])
+
+
+pyarrow_schema_ode_trajectory_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()),
+    # --- ODE Trajectory ---
+    pa.field("trajectory_latents_bytes", pa.binary()),
+    pa.field("trajectory_latents_shape", pa.list_(pa.int64())),
+    pa.field("trajectory_latents_dtype", pa.string()),
+    pa.field("trajectory_timesteps_bytes", pa.binary()),
+    pa.field("trajectory_timesteps_shape", pa.list_(pa.int64())),
+    pa.field("trajectory_timesteps_dtype", pa.string()),
+    # --- Metadata ---
+    pa.field("file_name", pa.string()),
+    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()),
+])
@@ -628,3 +628,134 @@ class VideoCaptionMergedDataset(torch.utils.data.IterableDataset,
    def load_state_dict(self, state_dict: dict[str, Any]) -> None:
        """Load state dict from checkpoint."""
        self.processed_batches = state_dict["processed_batches"]
+
+
+class TextDataset(torch.utils.data.IterableDataset,
+                  torch.distributed.checkpoint.stateful.Stateful):
+    """
+    Text-only dataset for processing prompts from a simple text file.
+    
+    Assumes that data_merge_path is a text file with one prompt per line:
+    A cat playing with a ball
+    A dog running in the park
+    A person cooking dinner
+    ...
+    
+    This dataset processes text data through text encoding stages only.
+    """
+
+    def __init__(self,
+                 data_merge_path: str,
+                 args,
+                 start_idx: int = 0,
+                 seed: int = 42):
+        self.data_merge_path = data_merge_path
+        self.start_idx = start_idx
+        self.args = args
+        self.seed = seed
+
+        # Initialize tokenizer
+        tokenizer_path = os.path.join(args.model_path, "tokenizer")
+        tokenizer = AutoTokenizer.from_pretrained(tokenizer_path,
+                                                  cache_dir=args.cache_dir)
+
+        # Initialize text encoding stage
+        self.text_encoding_stage = TextEncodingStage(
+            tokenizer=tokenizer,
+            text_max_length=args.text_max_length,
+            cfg_rate=getattr(args, 'training_cfg_rate', 0.0),
+            seed=self.seed)
+
+        # Process text data
+        self.processed_batches = self._process_text_data()
+
+    def _load_text_data(self) -> list[str]:
+        """Load text prompts from file."""
+        prompts = []
+        with open(self.data_merge_path, 'r', encoding='utf-8') as f:
+            for line in f:
+                line = line.strip()
+                if line:  # Skip empty lines
+                    prompts.append(line)
+        
+        logger.info(f"Loaded {len(prompts)} text prompts from {self.data_merge_path}")
+        return prompts
+
+    def _process_text_data(self) -> list[PreprocessBatch]:
+        """Process the text prompts through text encoding stage."""
+        raw_prompts = self._load_text_data()
+        processed_batches = []
+
+        for idx, prompt in enumerate(raw_prompts):
+            # Create a text-only batch with dummy path
+            batch = PreprocessBatch(
+                path=f"text_prompt_{idx}",
+                cap=[prompt],  # TextEncodingStage expects a list
+                resolution=None,
+                fps=None,
+                duration=None,
+                num_frames=0,
+                sample_frame_index=None,
+                sample_num_frames=0
+            )
+            
+            processed_batches.append(batch)
+
+        logger.info(f"Processed {len(processed_batches)} text batches")
+        return processed_batches
+
+    def __iter__(self):
+        """Iterator for the dataset."""
+        # Set up distributed sampling if needed
+        if torch.distributed.is_available() and torch.distributed.is_initialized():
+            rank = torch.distributed.get_rank()
+            world_size = torch.distributed.get_world_size()
+        else:
+            rank = 0
+            world_size = 1
+
+        # Calculate chunk for this rank
+        total_items = len(self.processed_batches)
+        items_per_rank = math.ceil(total_items / world_size)
+        start_idx = rank * items_per_rank + self.start_idx
+        end_idx = min(start_idx + items_per_rank, total_items)
+
+        # Yield items for this rank
+        for idx in range(start_idx, end_idx):
+            if idx < len(self.processed_batches):
+                yield self._get_item(idx)
+
+    def _get_item(self, idx: int) -> dict:
+        """Get a single processed text item."""
+        batch = self.processed_batches[idx]
+
+        # Apply text encoding stage
+        batch = self.text_encoding_stage.process(batch)
+
+        # Build result dictionary for text-only processing with required schema fields
+        result = {
+            "text": batch.text,
+            "input_ids": batch.input_ids,
+            "cond_mask": batch.cond_mask,
+            "path": batch.path,
+            # Required schema fields for ODE trajectory processing
+            "id": f"text_{idx}",
+            "file_name": batch.path,
+            "caption": batch.text,
+            "media_type": "text",
+            "width": 1,
+            "height": 1,
+            "num_frames": 0,
+            "duration_sec": 0.0,
+            "fps": 0.0,
+        }
+
+        return result
+
+    def state_dict(self) -> dict[str, Any]:
+        """Return state dict for checkpointing."""
+        return {"processed_batches": self.processed_batches}
+
+    def load_state_dict(self, state_dict: dict[str, Any]) -> None:
+        """Load state dict from checkpoint."""
+        self.processed_batches = state_dict["processed_batches"]
@@ -5,7 +5,7 @@ import numpy as np
 import torch


-def pad(t: torch.Tensor, padding_length: int) -> torch.Tensor:
+def pad(t: torch.Tensor, padding_length: int) -> tuple[torch.Tensor, torch.Tensor]:
    """
    Pad or crop an embedding [L, D] to exactly padding_length tokens.
    Return:
@@ -344,6 +344,9 @@ class VideoGenerator:
                "size": (target_height, target_width, batch.num_frames),
                "generation_time": gen_time,
                "logging_info": logging_info,
+                "trajectory": output_batch.trajectory_latents,
+                "trajectory_timesteps": output_batch.trajectory_timesteps,
+                "trajectory_decoded": output_batch.trajectory_decoded,
            }

    def set_lora_adapter(self,
@@ -1133,11 +1133,10 @@ class TrainingArgs(FastVideoArgs):
            "--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")
+        parser.add_argument("--context-noise",
+                            type=int,
+                            default=TrainingArgs.context_noise,
+                            help="Context noise level for cache updates")

        return parser

@@ -1145,4 +1144,4 @@ 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(",")]
@@ -77,9 +77,11 @@ class BaseLayerWithLoRA(nn.Module):
            lora_A = self.lora_A.to_local()

        if not self.merged and not self.disable_lora:
-            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)))
+            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
            if self.lora_alpha != self.lora_rank:
                delta = delta * (
                    self.lora_alpha / self.lora_rank  # type: ignore
@@ -147,8 +147,8 @@ 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()
+                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
@@ -464,6 +464,7 @@ 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=torch.bfloat16,
            reduce_dtype=torch.float32,
            output_dtype=None,
@@ -473,9 +474,8 @@ class TransformerLoader(ComponentLoader):
        total_params = sum(p.numel() for p in model.parameters())
        logger.info("Loaded model with %.2fB parameters", total_params / 1e9)

-        dtypes = set(param.dtype for param in model.parameters())
-        if len(dtypes) > 1:
-            model = model.to(default_dtype)
+        assert next(model.parameters()).dtype == default_dtype, "Model dtype does not match default dtype"
+
        model = model.eval()
        return model

@@ -62,6 +62,7 @@ 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,
@@ -87,7 +88,8 @@ def maybe_load_fsdp_model(
        mp_policy=mp_policy,
    )

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

    # Check if we should use FSDP
@@ -125,7 +127,7 @@ def maybe_load_fsdp_model(
        model,
        weight_iterator,
        device,
-        param_dtype,
+        default_dtype,
        strict=True,
        cpu_offload=cpu_offload,
        param_names_mapping=param_names_mapping_fn,
@@ -64,8 +64,15 @@ 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)
@@ -7,8 +7,6 @@ This module wires the causal DMD denoising stage into the modular pipeline.

 from fastvideo.fastvideo_args import FastVideoArgs
 from fastvideo.logger import init_logger
-from fastvideo.models.schedulers.scheduling_flow_match_euler_discrete import (
-    FlowMatchEulerDiscreteScheduler)
 from fastvideo.pipelines import ComposedPipelineBase, LoRAPipeline

 # isort: off
@@ -40,7 +40,7 @@ class ComposedPipelineBase(ABC):
    _extra_config_module_map: dict[str, str] = {}
    training_args: TrainingArgs | None = None
    fastvideo_args: FastVideoArgs | TrainingArgs | None = None
-    modules: dict[str, torch.nn.Module] = {}
+    modules: dict[str, Any] = {}
    post_init_called: bool = False

    # TODO(will): args should support both inference args and training args
@@ -237,20 +237,19 @@ 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")
-            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"]
+            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"]

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

        # some sanity checks
@@ -283,8 +282,8 @@ class ComposedPipelineBase(ABC):
                          architecture) in model_index.items():
            if transformers_or_diffusers is None:
                logger.warning(
-                    "Module in model_index.json has null value, removing from required_config_modules"
-                )
+                    "Module %s in model_index.json has null value, removing from required_config_modules",
+                    module_name)
                if module_name in self.required_config_modules:
                    self.required_config_modules.remove(module_name)
                continue
@@ -129,6 +129,7 @@ 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
@@ -147,7 +148,12 @@ class ForwardBatch:
    modules: dict[str, Any] = field(default_factory=dict)

    # Final output (after pipeline completion)
-    output: Any = None
+    output: torch.Tensor | None = None
+    return_trajectory_latents: bool = False
+    return_trajectory_decoded: bool = False
+    trajectory_timesteps: list[torch.Tensor] | None = None
+    trajectory_latents: torch.Tensor | None = None
+    trajectory_decoded: list[torch.Tensor] | None = None

    # Extra parameters that might be needed by specific pipeline implementations
    extra: dict[str, Any] = field(default_factory=dict)
@@ -206,6 +212,10 @@ class TrainingBatch:
    infos: list[dict[str, Any]] | None = None
    mask_lat_size: torch.Tensor | None = None

+    # ODE trajectory supervision
+    trajectory_latents: torch.Tensor | None = None
+    trajectory_timesteps: torch.Tensor | None = None
+
    # Transformer inputs
    noisy_model_input: torch.Tensor | None = None
    timesteps: torch.Tensor | None = None
@@ -1,7 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
-import multiprocessing
 import os
-from concurrent.futures import ProcessPoolExecutor
 from typing import Any

 import numpy as np
@@ -12,6 +10,8 @@ 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
@@ -54,10 +54,14 @@ class BasePreprocessPipeline(ComposedPipelineBase):
        """Get additional features specific to the pipeline type. Override in subclasses."""
        return {}

-    def get_schema_fields(self) -> list[str]:
-        """Get the schema fields for the pipeline type. Override in subclasses."""
+    def get_pyarrow_schema(self) -> pa.Schema:
+        """Return the PyArrow schema for this pipeline. Must be overridden."""
        raise NotImplementedError

+    def get_schema_fields(self) -> list[str]:
+        """Get the schema fields for the pipeline type."""
+        return [f.name for f in self.get_pyarrow_schema()]
+
    def create_record_for_schema(self,
                                 preprocess_batch: PreprocessBatch,
                                 schema: pa.Schema,
@@ -400,166 +404,22 @@ class BasePreprocessPipeline(ComposedPipelineBase):
                batch_data.append(record)

            if batch_data:
-                # Add progress bar for writing to Parquet dataset
                write_pbar = tqdm(total=1,
                                  desc="Writing to Parquet dataset",
                                  unit="batch")
-                # Convert batch data to PyArrow arrays
-                arrays = []
-                for field in self.get_schema_fields():
-                    if field.endswith('_bytes'):
-                        arrays.append(
-                            pa.array([record[field] for record in batch_data],
-                                     type=pa.binary()))
-                    elif field.endswith('_shape'):
-                        arrays.append(
-                            pa.array([record[field] for record in batch_data],
-                                     type=pa.list_(pa.int32())))
-                    elif field in ['width', 'height', 'num_frames']:
-                        arrays.append(
-                            pa.array([record[field] for record in batch_data],
-                                     type=pa.int32()))
-                    elif field in ['duration_sec', 'fps']:
-                        arrays.append(
-                            pa.array([record[field] for record in batch_data],
-                                     type=pa.float32()))
-                    else:
-                        arrays.append(
-                            pa.array([record[field] for record in batch_data]))
-
-                table = pa.Table.from_arrays(arrays,
-                                             names=self.get_schema_fields())
+                table = records_to_table(batch_data, self.get_pyarrow_schema())
                write_pbar.update(1)
                write_pbar.close()

-                # Store the table in a list for later processing
-                if not hasattr(self, 'all_tables'):
-                    self.all_tables = []
-                self.all_tables.append(table)
-
+                if not hasattr(self, 'dataset_writer'):
+                    self.dataset_writer = ParquetDatasetWriter(
+                        out_dir=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 num_processed_samples >= args.flush_frequency:
-                self._flush_tables(num_processed_samples, args,
-                                   combined_parquet_dir)
+                written = self.dataset_writer.flush()
+                logger.info("Flushed %s samples to parquet", written)
                num_processed_samples = 0
-                self.all_tables = []
-
-    def _flush_tables(self, num_processed_samples: int, args,
-                      combined_parquet_dir: str):
-        """Flush collected tables to disk."""
-        assert hasattr(self, 'all_tables') and self.all_tables
-        print(f"Combining {len(self.all_tables)} batches...")
-        combined_table = pa.concat_tables(self.all_tables)
-        assert len(combined_table) == num_processed_samples
-        print(f"Total samples collected: {len(combined_table)}")
-
-        # Calculate total number of chunks needed, discarding remainder
-        total_chunks = max(num_processed_samples // args.samples_per_file, 1)
-
-        print(f"Fixed samples per parquet file: {args.samples_per_file}")
-        print(f"Total number of parquet files: {total_chunks}")
-        print(
-            f"Total samples to be processed: {total_chunks * args.samples_per_file} (discarding {num_processed_samples % args.samples_per_file} samples)"
-        )
-
-        # Split work among processes
-        num_workers = int(min(multiprocessing.cpu_count(), total_chunks))
-        chunks_per_worker = (total_chunks + num_workers - 1) // num_workers
-
-        print(f"Using {num_workers} workers to process {total_chunks} chunks")
-        logger.info("Chunks per worker: %s", chunks_per_worker)
-
-        # Prepare work ranges
-        work_ranges = []
-        for i in range(num_workers):
-            start_idx = i * chunks_per_worker
-            end_idx = min((i + 1) * chunks_per_worker, total_chunks)
-            if start_idx < total_chunks:
-                work_ranges.append(
-                    (start_idx, end_idx, combined_table, i,
-                     combined_parquet_dir, args.samples_per_file))
-
-        total_written = 0
-        failed_ranges = []
-        with ProcessPoolExecutor(max_workers=num_workers) as executor:
-            futures = {
-                executor.submit(self.process_chunk_range, work_range):
-                work_range
-                for work_range in work_ranges
-            }
-            for future in tqdm(futures, desc="Processing chunks"):
-                try:
-                    written = future.result()
-                    total_written += written
-                    logger.info("Processed chunk with %s samples", written)
-                except Exception as e:
-                    work_range = futures[future]
-                    failed_ranges.append(work_range)
-                    logger.error("Failed to process range %s-%s: %s",
-                                 work_range[0], work_range[1], str(e))
-
-        # Retry failed ranges sequentially
-        if failed_ranges:
-            logger.warning("Retrying %s failed ranges sequentially",
-                           len(failed_ranges))
-            for work_range in failed_ranges:
-                try:
-                    total_written += self.process_chunk_range(work_range)
-                except Exception as e:
-                    logger.error(
-                        "Failed to process range %s-%s after retry: %s",
-                        work_range[0], work_range[1], str(e))
-
-        logger.info("Total samples written: %s", total_written)
-
-    @staticmethod
-    def process_chunk_range(args: Any) -> int:
-        start_idx, end_idx, table, worker_id, output_dir, samples_per_file = args
-        try:
-            total_written = 0
-            num_samples = len(table)
-
-            # Create worker-specific subdirectory
-            worker_dir = os.path.join(output_dir, f"worker_{worker_id}")
-            os.makedirs(worker_dir, exist_ok=True)
-
-            # Check how many files there are already in the dir, and update i accordingly
-            num_parquets = 0
-            for root, _, files in os.walk(worker_dir):
-                for file in files:
-                    if file.endswith('.parquet'):
-                        num_parquets += 1
-
-            for i in range(start_idx, end_idx):
-                start_sample = i * samples_per_file
-                end_sample = min((i + 1) * samples_per_file, num_samples)
-                chunk = table.slice(start_sample, end_sample - start_sample)
-
-                # Create chunk file in worker's directory
-                chunk_path = os.path.join(
-                    worker_dir, f"data_chunk_{i + num_parquets}.parquet")
-                temp_path = chunk_path + '.tmp'
-
-                try:
-                    # Write to temporary file
-                    pq.write_table(chunk, temp_path, compression='zstd')
-
-                    # Rename temporary file to final file
-                    if os.path.exists(chunk_path):
-                        os.remove(
-                            chunk_path)  # Remove existing file if it exists
-                    os.rename(temp_path, chunk_path)
-
-                    total_written += len(chunk)
-                except Exception as e:
-                    # Clean up temporary file if it exists
-                    if os.path.exists(temp_path):
-                        os.remove(temp_path)
-                    raise e
-
-            return total_written
-        except Exception as e:
-            logger.error("Error processing chunks %s-%s for worker %s: %s",
-                         start_idx, end_idx, worker_id, str(e))
-            raise
@@ -40,9 +40,9 @@ class PreprocessPipeline_I2V(BasePreprocessPipeline):
                           image_processor=self.get_module("image_processor"),
                       ))

-    def get_schema_fields(self) -> list[str]:
-        """Get the schema fields for I2V pipeline."""
-        return [f.name for f in pyarrow_schema_i2v]
+    def get_pyarrow_schema(self):
+        """Return the PyArrow schema for I2V pipeline."""
+        return pyarrow_schema_i2v

    def get_extra_features(self, valid_data: dict[str, Any],
                           fastvideo_args: FastVideoArgs) -> dict[str, Any]:
@@ -0,0 +1,323 @@
+# SPDX-License-Identifier: Apache-2.0
+"""
+ODE Trajectory Data Preprocessing pipeline implementation.
+
+This module contains an implementation of the ODE Trajectory Data Preprocessing pipeline
+using the modular pipeline architecture.
+
+Sec 4.3 of CausVid paper: https://arxiv.org/pdf/2412.07772
+"""
+
+import os
+from collections.abc import Iterator
+from typing import Any
+
+import pyarrow as pa
+import torch
+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
+from fastvideo.logger import init_logger
+from fastvideo.models.schedulers.scheduling_self_forcing_flow_match import (
+    SelfForcingFlowMatchScheduler)
+from fastvideo.pipelines.pipeline_batch_info import ForwardBatch
+from fastvideo.pipelines.preprocess.preprocess_pipeline_base import (
+    BasePreprocessPipeline)
+from fastvideo.pipelines.stages import (DecodingStage, DenoisingStage,
+                                        InputValidationStage,
+                                        LatentPreparationStage,
+                                        TextEncodingStage,
+                                        TimestepPreparationStage)
+from fastvideo.utils import save_decoded_latents_as_video, shallow_asdict
+
+logger = init_logger(__name__)
+
+
+class PreprocessPipeline_ODE_Trajectory(BasePreprocessPipeline):
+    """ODE Trajectory preprocessing pipeline implementation."""
+
+    _required_config_modules = [
+        "text_encoder", "tokenizer", "vae", "transformer", "scheduler"
+    ]
+    preprocess_dataloader: StatefulDataLoader
+    preprocess_loader_iter: Iterator[dict[str, Any]]
+    pbar: Any
+    num_processed_samples: int
+
+    def get_pyarrow_schema(self) -> pa.Schema:
+        """Return the PyArrow schema for ODE Trajectory pipeline."""
+        return pyarrow_schema_ode_trajectory_text_only
+
+    def create_pipeline_stages(self, fastvideo_args: FastVideoArgs):
+        """Set up pipeline stages with proper dependency injection."""
+        assert fastvideo_args.pipeline_config.flow_shift == 5
+        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)
+
+        self.add_stage(stage_name="input_validation_stage",
+                       stage=InputValidationStage())
+        self.add_stage(stage_name="prompt_encoding_stage",
+                       stage=TextEncodingStage(
+                           text_encoders=[self.get_module("text_encoder")],
+                           tokenizers=[self.get_module("tokenizer")],
+                       ))
+        self.add_stage(stage_name="timestep_preparation_stage",
+                       stage=TimestepPreparationStage(
+                           scheduler=self.get_module("scheduler")))
+        self.add_stage(stage_name="latent_preparation_stage",
+                       stage=LatentPreparationStage(
+                           scheduler=self.get_module("scheduler"),
+                           transformer=self.get_module("transformer", None)))
+        self.add_stage(stage_name="denoising_stage",
+                       stage=DenoisingStage(
+                           transformer=self.get_module("transformer"),
+                           scheduler=self.get_module("scheduler"),
+                           pipeline=self,
+                       ))
+        self.add_stage(stage_name="decoding_stage",
+                       stage=DecodingStage(vae=self.get_module("vae")))
+
+    def preprocess_text_and_trajectory(self, fastvideo_args: FastVideoArgs,
+                                       args):
+        """Preprocess text-only data and generate trajectory information."""
+
+        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],
+                }
+
+                # Add fps and duration if available in data
+                if "fps" in data:
+                    valid_data["fps"] = [data["fps"][i] for i in valid_indices]
+                if "duration" in data:
+                    valid_data["duration"] = [
+                        data["duration"][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]
+
+                sampling_params = SamplingParam.from_pretrained(args.model_path)
+
+                # 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(
+                        sampling_params.negative_prompt,
+                        fastvideo_args,
+                        encoder_index=[0],
+                        return_attention_mask=True,
+                    )
+                    negative_prompt_embed = negative_prompt_embeds_list[0][0]
+                    negative_prompt_attention_mask = negative_prompt_masks_list[
+                        0][0]
+                else:
+                    negative_prompt_embed = None
+                    negative_prompt_attention_mask = None
+
+                trajectory_latents = []
+                trajectory_timesteps = []
+                trajectory_decoded = []
+
+                for i, (prompt_embed, prompt_attention_mask) in enumerate(
+                        zip(prompt_embeds, prompt_attention_masks,
+                            strict=False)):
+                    prompt_embed = prompt_embed.unsqueeze(0)
+                    prompt_attention_mask = prompt_attention_mask.unsqueeze(0)
+
+                    # Collect the trajectory data (text-to-video generation)
+                    batch = ForwardBatch(**shallow_asdict(sampling_params), )
+                    batch.prompt_embeds = [prompt_embed]
+                    batch.prompt_attention_mask = [prompt_attention_mask]
+                    batch.negative_prompt_embeds = [negative_prompt_embed]
+                    batch.negative_attention_mask = [
+                        negative_prompt_attention_mask
+                    ]
+                    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 = False
+                    batch.height = args.max_height
+                    batch.width = args.max_width
+                    batch.fps = args.train_fps
+                    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)
+                    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)
+
+                # Prepare extra features for text-only processing
+                extra_features = {
+                    "trajectory_latents": trajectory_latents,
+                    "trajectory_timesteps": trajectory_timesteps
+                }
+
+                if batch.return_trajectory_decoded:
+                    for i, decoded_frames in enumerate(trajectory_decoded):
+                        for j, decoded_frame in enumerate(decoded_frames):
+                            save_decoded_latents_as_video(
+                                decoded_frame,
+                                f"decoded_videos/trajectory_decoded_{i}_{j}.mp4",
+                                args.train_fps)
+
+                # Prepare batch data for Parquet dataset
+                batch_data: list[dict[str, Any]] = []
+
+                # Add progress bar for saving outputs
+                save_pbar = tqdm(enumerate(valid_data["path"]),
+                                 desc="Saving outputs",
+                                 unit="item",
+                                 leave=False)
+
+                for idx, video_path in save_pbar:
+                    video_name = os.path.basename(video_path).split(".")[0]
+
+                    # Convert tensors to numpy arrays
+                    text_embedding = prompt_embeds[idx].cpu().numpy()
+
+                    # Get extra features for this sample
+                    sample_extra_features = {}
+                    if extra_features:
+                        for key, value in extra_features.items():
+                            if isinstance(value, torch.Tensor):
+                                sample_extra_features[key] = value[idx].cpu(
+                                ).numpy()
+                            else:
+                                assert isinstance(value, list)
+                                if isinstance(value[idx], torch.Tensor):
+                                    sample_extra_features[key] = value[idx].cpu(
+                                    ).float().numpy()
+                                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(
+                        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"],
+                    )
+                    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,
+                                             self.get_pyarrow_schema())
+                    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)
+
+    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 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 video preprocessing
+        self.pbar = tqdm(self.preprocess_loader_iter,
+                         desc="Processing videos",
+                         unit="batch",
+                         disable=self.local_rank != 0)
+
+        # Initialize class variables for data sharing
+        self.video_data: dict[str, Any] = {}  # Store video metadata and paths
+        self.latent_data: dict[str, Any] = {}  # Store latent tensors
+        self.preprocess_text_and_trajectory(fastvideo_args, args)
+
+
+EntryClass = PreprocessPipeline_ODE_Trajectory
@@ -15,9 +15,9 @@ class PreprocessPipeline_T2V(BasePreprocessPipeline):

    _required_config_modules = ["text_encoder", "tokenizer", "vae"]

-    def get_schema_fields(self):
-        """Get the schema fields for T2V pipeline."""
-        return [f.name for f in pyarrow_schema_t2v]
+    def get_pyarrow_schema(self):
+        """Return the PyArrow schema for T2V pipeline."""
+        return pyarrow_schema_t2v


 EntryClass = PreprocessPipeline_T2V
@@ -0,0 +1,184 @@
+# 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
@@ -1,5 +1,6 @@
 import argparse
 import os
+from typing import Any

 from fastvideo import PipelineConfig
 from fastvideo.configs.models.vaes import WanVAEConfig
@@ -9,8 +10,12 @@ from fastvideo.fastvideo_args import FastVideoArgs
 from fastvideo.logger import init_logger
 from fastvideo.pipelines.preprocess.preprocess_pipeline_i2v import (
    PreprocessPipeline_I2V)
+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__)
@@ -21,12 +26,22 @@ 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 = {
-        "vae_precision": "fp32",
-        "vae_config": WanVAEConfig(load_encoder=True, load_decoder=False),
-    }
+
+    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),
+        }
    pipeline_config.update_config_from_dict(kwargs)
+
    fastvideo_args = FastVideoArgs(
        model_path=args.model_path,
        num_gpus=get_world_size(),
@@ -35,7 +50,23 @@ def main(args) -> None:
        text_encoder_cpu_offload=False,
        pipeline_config=pipeline_config,
    )
-    PreprocessPipeline = PreprocessPipeline_I2V if args.preprocess_task == "i2v" else PreprocessPipeline_T2V
+    if args.preprocess_task == "t2v":
+        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")
+
+    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)

@@ -74,7 +105,12 @@ 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("--preprocess_task", type=str, default="t2v")
+    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("--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)
@@ -50,6 +50,63 @@ class DecodingStage(PipelineStage):
        result.add_check("output", batch.output, [V.is_tensor, V.with_dims(5)])
        return result

+    @torch.no_grad()
+    def decode(self, latents: torch.Tensor,
+               fastvideo_args: FastVideoArgs) -> torch.Tensor:
+        """
+        Decode latent representations into pixel space using VAE.
+        
+        Args:
+            latents: Input latent tensor with shape (batch, channels, frames, height_latents, width_latents)
+            fastvideo_args: Configuration containing:
+                - disable_autocast: Whether to disable automatic mixed precision (default: False)
+                - pipeline_config.vae_precision: VAE computation precision ("fp32", "fp16", "bf16")
+                - pipeline_config.vae_tiling: Whether to enable VAE tiling for memory efficiency
+            
+        Returns:
+            Decoded video tensor with shape (batch, channels, frames, height, width), 
+            normalized to [0, 1] range and moved to CPU as float32
+        """
+        self.vae = self.vae.to(get_local_torch_device())
+        latents = latents.to(get_local_torch_device())
+
+        # Setup VAE precision
+        vae_dtype = PRECISION_TO_TYPE[
+            fastvideo_args.pipeline_config.vae_precision]
+        vae_autocast_enabled = (
+            vae_dtype != torch.float32) and not fastvideo_args.disable_autocast
+
+        if isinstance(self.vae.scaling_factor, torch.Tensor):
+            latents = latents / self.vae.scaling_factor.to(
+                latents.device, latents.dtype)
+        else:
+            latents = latents / self.vae.scaling_factor
+
+        # 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):
+                latents += self.vae.shift_factor.to(latents.device,
+                                                    latents.dtype)
+            else:
+                latents += self.vae.shift_factor
+
+        # Decode latents
+        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:
+                latents = latents.to(vae_dtype)
+            image = self.vae.decode(latents)
+
+        # Normalize image to [0, 1] range
+        image = (image / 2 + 0.5).clamp(0, 1)
+        return image
+
    @torch.no_grad()
    def forward(
        self,
@@ -59,13 +116,28 @@ class DecodingStage(PipelineStage):
        """
        Decode latent representations into pixel space.
        
+        This method processes the batch through the VAE decoder, converting latent
+        representations to pixel-space video/images. It also optionally decodes
+        trajectory latents for visualization purposes.
+        
        Args:
-            batch: The current batch information.
-            fastvideo_args: The inference arguments.
+            batch: The current batch containing:
+                - latents: Tensor to decode (batch, channels, frames, height_latents, width_latents)
+                - return_trajectory_decoded (optional): Flag to decode trajectory latents
+                - trajectory_latents (optional): Latents at different timesteps
+                - trajectory_timesteps (optional): Corresponding timesteps
+            fastvideo_args: Configuration containing:
+                - output_type: "latent" to skip decoding, otherwise decode to pixels
+                - vae_cpu_offload: Whether to offload VAE to CPU after decoding
+                - model_loaded: Track VAE loading state
+                - model_paths: Path to VAE model if loading needed
            
        Returns:
-            The batch with decoded outputs.
+            Modified batch with:
+                - output: Decoded frames (batch, channels, frames, height, width) as CPU float32
+                - trajectory_decoded (if requested): List of decoded frames per timestep
        """
+        # load vae if not already loaded (used for memory constrained devices)
        pipeline = self.pipeline() if self.pipeline else None
        if not fastvideo_args.model_loaded["vae"]:
            loader = VAELoader()
@@ -75,58 +147,29 @@ class DecodingStage(PipelineStage):
                pipeline.add_module("vae", self.vae)
            fastvideo_args.model_loaded["vae"] = True

-        self.vae = self.vae.to(get_local_torch_device())
-
-        latents = batch.latents
-        # TODO(will): remove this once we add input/output validation for stages
-        if latents is None:
-            raise ValueError("Latents must be provided")
-
-        # Skip decoding if output type is latent
        if fastvideo_args.output_type == "latent":
-            image = latents
+            frames = batch.latents
        else:
-            # 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
+            frames = self.decode(batch.latents, fastvideo_args)

-            if isinstance(self.vae.scaling_factor, torch.Tensor):
-                latents = latents / self.vae.scaling_factor.to(
-                    latents.device, latents.dtype)
-            else:
-                latents = latents / self.vae.scaling_factor
-
-            # 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):
-                    latents += self.vae.shift_factor.to(latents.device,
-                                                        latents.dtype)
-                else:
-                    latents += self.vae.shift_factor
-
-            # Decode latents
-            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:
-                    latents = latents.to(vae_dtype)
-                image = self.vae.decode(latents)
-
-        # Normalize image to [0, 1] range
-        image = (image / 2 + 0.5).clamp(0, 1)
+        # decode trajectory latents if needed
+        if batch.return_trajectory_decoded:
+            batch.trajectory_decoded = []
+            assert batch.trajectory_latents is not None, "batch should have trajectory latents"
+            for idx in range(batch.trajectory_latents.shape[1]):
+                # batch.trajectory_latents is [batch_size, timesteps, channels, frames, height, width]
+                cur_latent = batch.trajectory_latents[:, idx, :, :, :, :]
+                cur_timestep = batch.trajectory_timesteps[idx]
+                logger.info("decoding trajectory latent for timestep: %s",
+                            cur_timestep)
+                decoded_frames = self.decode(cur_latent, fastvideo_args)
+                batch.trajectory_decoded.append(decoded_frames.cpu().float())

        # Convert to CPU float32 for compatibility
-        image = image.cpu().float()
+        frames = frames.cpu().float()

        # Update batch with decoded image
-        batch.output = image
+        batch.output = frames

        # Offload models if needed
        if hasattr(self, 'maybe_free_model_hooks'):
@@ -204,8 +204,14 @@ 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
-        if fastvideo_args.boundary_ratio is not None:
-            boundary_timestep = fastvideo_args.boundary_ratio * self.scheduler.num_train_timesteps
+        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
        else:
            boundary_timestep = None
        latent_model_input = latents.to(target_dtype)
@@ -247,6 +253,10 @@ class DenoisingStage(PipelineStage):
                                                             patch_size[2])
            seq_len = int(math.ceil(seq_len / sp_world_size)) * sp_world_size

+        # Initialize lists for ODE trajectory
+        trajectory_timesteps: list[torch.Tensor] = []
+        trajectory_latents: list[torch.Tensor] = []
+
        # Run denoising loop
        with self.progress_bar(total=num_inference_steps) as progress_bar:
            for i, t in enumerate(timesteps):
@@ -427,6 +437,11 @@ class DenoisingStage(PipelineStage):
                        latents = (1. - mask2[0]) * z + mask2[0] * latents
                        # latents = latents.unsqueeze(0)

+                # save trajectory latents if needed
+                if batch.return_trajectory_latents:
+                    trajectory_timesteps.append(t)
+                    trajectory_latents.append(latents)
+
                # Update progress bar
                if i == len(timesteps) - 1 or (
                    (i + 1) > num_warmup_steps and
@@ -434,9 +449,28 @@ class DenoisingStage(PipelineStage):
                        and progress_bar is not None):
                    progress_bar.update()

+        # Gather results if using sequence parallelism
+        trajectory_tensor: torch.Tensor | None = None
+        if trajectory_latents:
+            trajectory_tensor = torch.stack(trajectory_latents, dim=1)
+            trajectory_timesteps_tensor = torch.stack(trajectory_timesteps,
+                                                      dim=0)
+        else:
+            trajectory_tensor = None
+            trajectory_timesteps_tensor = None
+
        # Gather results if using sequence parallelism
        if sp_group:
            latents = sequence_model_parallel_all_gather(latents, dim=2)
+            if batch.return_trajectory_latents:
+                trajectory_tensor = trajectory_tensor.to(
+                    get_local_torch_device())
+                trajectory_tensor = sequence_model_parallel_all_gather(
+                    trajectory_tensor, dim=3)
+
+        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()

        # Update batch with final latents
        batch.latents = latents
@@ -0,0 +1,108 @@
+import os
+from pathlib import Path
+
+import pyarrow as pa
+import pyarrow.parquet as pq
+
+from fastvideo.dataset.dataloader.parquet_io import (
+    ParquetDatasetWriter,
+    records_to_table,
+)
+
+
+def test_records_to_table_types():
+    schema = pa.schema([
+        pa.field("id", pa.string()),
+        pa.field("vae_latent_bytes", pa.binary()),
+        pa.field("vae_latent_shape", pa.list_(pa.int64())),
+        pa.field("duration_sec", pa.float64()),
+        pa.field("width", pa.int64()),
+    ])
+    records = [{
+        "id": "a",
+        "vae_latent_bytes": b"\x00\x01",
+        "vae_latent_shape": [1, 2, 3],
+        "duration_sec": 1.5,
+        "width": 640,
+    }]
+
+    table = records_to_table(records, schema)
+    assert table.schema == schema
+    assert table.num_rows == 1
+    cols = {name: table.column(name).to_pylist()[0] for name in schema.names}
+    assert cols["id"] == "a"
+    assert isinstance(cols["vae_latent_bytes"], (bytes, bytearray))
+    assert cols["vae_latent_shape"] == [1, 2, 3]
+    assert abs(cols["duration_sec"] - 1.5) < 1e-6
+    assert cols["width"] == 640
+
+
+def test_writer_flush_and_remainder(tmp_path: Path):
+    schema = pa.schema([pa.field("id", pa.string())])
+    records = [{"id": str(i)} for i in range(25)]
+    table = records_to_table(records, schema)
+
+    out_dir = tmp_path / "out"
+    writer = ParquetDatasetWriter(str(out_dir), samples_per_file=10)
+    writer.append_table(table)
+    written = writer.flush(num_workers=1)
+    assert written == 20
+
+    files = sorted(out_dir.rglob("*.parquet"))
+    assert len(files) == 2
+    total_rows = sum(pq.read_table(str(f)).num_rows for f in files)
+    assert total_rows == 20
+
+    # Append remainder to complete another chunk
+    extra = records_to_table([{"id": str(i)} for i in range(5)], schema)
+    writer.append_table(extra)
+    written2 = writer.flush(num_workers=1)
+    assert written2 == 10
+    files2 = sorted(out_dir.rglob("*.parquet"))
+    assert len(files2) == 3
+    total_rows2 = sum(pq.read_table(str(f)).num_rows for f in files2)
+    assert total_rows2 == 30
+
+
+def test_writer_flush_write_remainder(tmp_path: Path):
+    schema = pa.schema([pa.field("id", pa.string())])
+    # 25 rows, 10 per file => 2 full files + 1 remainder(5)
+    records = [{"id": str(i)} for i in range(25)]
+    table = records_to_table(records, schema)
+
+    out_dir = tmp_path / "out_last"
+    writer = ParquetDatasetWriter(str(out_dir), samples_per_file=10)
+    writer.append_table(table)
+    # First flush writes 20
+    written1 = writer.flush(num_workers=1)
+    assert written1 == 20
+    # Final flush with remainder
+    written2 = writer.flush(num_workers=1, write_remainder=True)
+    assert written2 == 5
+    files = sorted(out_dir.rglob("*.parquet"))
+    assert len(files) == 3
+    total_rows = sum(pq.read_table(str(f)).num_rows for f in files)
+    assert total_rows == 25
+
+
+def test_writer_parallel_workers(tmp_path: Path):
+    schema = pa.schema([pa.field("id", pa.string())])
+    # 40 rows, 10 per file => 4 files
+    records = [{"id": str(i)} for i in range(40)]
+    table = records_to_table(records, schema)
+
+    out_dir = tmp_path / "out_parallel"
+    writer = ParquetDatasetWriter(str(out_dir), samples_per_file=10)
+    writer.append_table(table)
+    written = writer.flush(num_workers=2)
+    assert written == 40
+
+    # Ensure files exist under worker subdirs
+    worker_dirs = [p for p in out_dir.iterdir() if p.is_dir() and p.name.startswith("worker_")]
+    assert len(worker_dirs) >= 1
+    files = sorted(out_dir.rglob("*.parquet"))
+    assert len(files) == 4
+    total_rows = sum(pq.read_table(str(f)).num_rows for f in files)
+    assert total_rows == 40
+
+
@@ -0,0 +1,123 @@
+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
@@ -117,3 +117,7 @@ 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")
@@ -0,0 +1,184 @@
+import os
+from pathlib import Path
+from huggingface_hub import snapshot_download
+import subprocess
+import sys
+from fastvideo.tests.ssim.test_inference_similarity import compute_video_ssim_torchvision
+import shutil
+
+# Import the training pipeline
+sys.path.append(str(Path(__file__).parent.parent.parent.parent.parent))
+
+NUM_NODES = "1"
+MODEL_PATH = "Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
+
+# preprocessing
+DATA_DIR = "data"
+LOCAL_RAW_DATA_DIR = Path(os.path.join(DATA_DIR, "crush-smol"))
+NUM_GPUS_PER_NODE_PREPROCESSING = "1"
+PREPROCESSING_ENTRY_FILE_PATH = "fastvideo/pipelines/preprocess/v1_preprocess.py"
+
+LOCAL_PREPROCESSED_DATA_DIR = Path(os.path.join(DATA_DIR, "crush-smol_processed_t2v"))
+
+
+# training
+NUM_GPUS_PER_NODE_TRAINING = "4"
+TRAINING_ENTRY_FILE_PATH = "fastvideo/training/wan_distillation_pipeline.py"
+LOCAL_TRAINING_DATA_DIR = os.path.join(LOCAL_PREPROCESSED_DATA_DIR, "combined_parquet_dataset")
+LOCAL_VALIDATION_DATASET_FILE = "examples/training/finetune/Wan2.1-Fun-1.3B-InP/crush_smol/validation.json"
+LOCAL_OUTPUT_DIR = Path(os.path.join(DATA_DIR, "outputs"))
+
+def download_data():
+    # create the data dir if it doesn't exist
+    data_dir = Path(DATA_DIR)
+
+    print(f"Creating data directory at {data_dir}")
+    os.makedirs(data_dir, exist_ok=True)
+
+    print(f"Downloading raw dataset to {LOCAL_RAW_DATA_DIR}...")
+    try:
+        result = snapshot_download(
+            repo_id="wlsaidhi/crush-smol-merged",
+            local_dir=str(LOCAL_RAW_DATA_DIR),
+            repo_type="dataset",
+            resume_download=True,
+            token=os.environ.get("HF_TOKEN"),  # In case authentication is needed
+        )
+        print(f"Download completed successfully. Files downloaded to: {result}")
+        
+        # Verify the download
+        if not LOCAL_RAW_DATA_DIR.exists():
+            raise RuntimeError(f"Download appeared to succeed but {LOCAL_RAW_DATA_DIR} does not exist")
+            
+        # List downloaded files
+        print("Downloaded files:")
+        for file in LOCAL_RAW_DATA_DIR.rglob("*"):
+            if file.is_file():
+                print(f"  - {file.relative_to(LOCAL_RAW_DATA_DIR)}")
+                
+    except Exception as e:
+        print(f"Error during download: {str(e)}")
+        raise
+
+
+def run_preprocessing():
+    # remove the local_preprocessed_data_dir if it exists
+    if LOCAL_PREPROCESSED_DATA_DIR.exists():
+        print(f"Removing local_preprocessed_data_dir: {LOCAL_PREPROCESSED_DATA_DIR}")
+        shutil.rmtree(LOCAL_PREPROCESSED_DATA_DIR)
+
+    # Run torchrun command
+    cmd = [
+        "torchrun",
+        "--nnodes", NUM_NODES,
+        "--nproc_per_node", NUM_GPUS_PER_NODE_PREPROCESSING,
+        PREPROCESSING_ENTRY_FILE_PATH,
+        "--model_path", MODEL_PATH,
+        "--seed", "42",
+        "--data_merge_path", os.path.join(LOCAL_RAW_DATA_DIR, "merge.txt"),
+        "--preprocess_video_batch_size", "1",
+        "--max_height", "480",
+        "--max_width", "832",
+        "--num_frames", "81",
+        "--dataloader_num_workers", "0",
+        "--output_dir", LOCAL_PREPROCESSED_DATA_DIR,
+        "--train_fps", "16",
+        "--samples_per_file", "1",
+        "--flush_frequency", "1",
+        "--video_length_tolerance_range", "5",
+        "--preprocess_task", "t2v",
+    ]
+
+    process = subprocess.run(cmd, check=True)
+
+
+def run_training():
+    cmd = [
+        "torchrun",
+        "--nnodes", NUM_NODES,
+        "--nproc_per_node", NUM_GPUS_PER_NODE_TRAINING,
+        TRAINING_ENTRY_FILE_PATH,
+        "--model_path", MODEL_PATH,
+        "--inference_mode", "False",
+        "--pretrained_model_name_or_path", MODEL_PATH,
+        "--data_path", LOCAL_TRAINING_DATA_DIR,
+        "--validation_dataset_file", LOCAL_VALIDATION_DATASET_FILE,
+        "--train_batch_size", "1",
+        "--num_latent_t", "8",
+        "--num_gpus", NUM_GPUS_PER_NODE_TRAINING,
+        "--sp_size", "1",
+        "--tp_size", "1",
+        "--hsdp_replicate_dim", "1",
+        "--hsdp_shard_dim", NUM_GPUS_PER_NODE_TRAINING,
+        "--train_sp_batch_size", "1",
+        "--dataloader_num_workers", "10",
+        "--gradient_accumulation_steps", "1",
+        "--max_train_steps", "501",
+        "--learning_rate", "2e-6",
+        "--fake_score_learning_rate", "2e-6",
+        "--mixed_precision", "bf16",
+        "--training_state_checkpointing_steps", "1000",
+        "--weight_only_checkpointing_steps", "1000",
+        "--validation_steps", "50",
+        "--validation_sampling_steps", "3",
+        "--log_validation",
+        "--checkpoints_total_limit", "3",
+        "--ema_start_step", "0",
+        "--training_cfg_rate", "0.0",
+        "--output_dir", LOCAL_OUTPUT_DIR,
+        "--tracker_project_name", "ci_wan_t2v_dmd_overfit",
+        "--num_height", "480",
+        "--num_width", "832",
+        "--num_frames", "81",
+        "--flow_shift", "8",
+        "--validation_guidance_scale", "6.0",
+        "--weight_decay", "0.01",
+        "--generator_update_interval", "5",
+        "--dmd_denoising_steps", "1000,757,522",
+        "--min_timestep_ratio", "0.02",
+        "--max_timestep_ratio", "0.98",
+        "--seed", "1000",
+        "--real_score_guidance_scale", "3.5",
+        "--dit_precision", "fp32",
+        "--max_grad_norm", "1.0",
+        "--enable_gradient_checkpointing_type", "full",
+    ]
+
+    print(f"Running training with command: {cmd}")
+    process = subprocess.run(cmd, check=True)
+
+
+def test_e2e_overfit_single_sample():
+    os.environ["WANDB_MODE"] = "online"
+
+    download_data()
+    run_preprocessing()
+    run_training()
+
+    reference_video_file = os.path.join(os.path.dirname(__file__), "reference_video_1_sample_v0.mp4")
+    print(f"reference_video_file: {reference_video_file}")
+    final_validation_video_file = os.path.join(LOCAL_OUTPUT_DIR, "validation_step_900_inference_steps_50_video_0.mp4")
+    print(f"final_validation_video_file: {final_validation_video_file}")
+
+
+    # Ensure both files exist
+    assert os.path.exists(reference_video_file), f"Reference video not found at {reference_video_file}"
+    assert os.path.exists(final_validation_video_file), f"Validation video not found at {final_validation_video_file}"
+
+    # Compute SSIM
+    mean_ssim, min_ssim, max_ssim = compute_video_ssim_torchvision(
+        reference_video_file,
+        final_validation_video_file,
+        use_ms_ssim=True  # Using MS-SSIM for better quality assessment
+    )
+
+    print("\n===== SSIM Results for Step 900 Validation =====")
+    print(f"Mean MS-SSIM: {mean_ssim:.4f}")
+    print(f"Min MS-SSIM: {min_ssim:.4f}")
+    print(f"Max MS-SSIM: {max_ssim:.4f}")
+
+    assert max_ssim > 0.5, f"Max SSIM is below 0.5: {max_ssim}"
+
+
+if __name__ == "__main__":
+    test_e2e_overfit_single_sample()
@@ -62,6 +62,11 @@ def download_data():


 def run_preprocessing():
+    # remove the local_preprocessed_data_dir if it exists
+    if LOCAL_PREPROCESSED_DATA_DIR.exists():
+        print(f"Removing local_preprocessed_data_dir: {LOCAL_PREPROCESSED_DATA_DIR}")
+        shutil.rmtree(LOCAL_PREPROCESSED_DATA_DIR)
+
    # Run torchrun command
    cmd = [
        "torchrun",
@@ -1 +1 @@
-{"step_time":0.6983645600266755,"_wandb":{"runtime":107},"grad_norm":0.50390625,"avg_step_time":1.002151239803061,"_step":5,"validation_videos_50_steps":{"captions":false,"_type":"videos","count":8,"videos":[{"size":159131,"path":"media/videos/validation_videos_50_steps_0_dc447599dbe48350e9c9.mp4","_type":"video-file","sha256":"dc447599dbe48350e9c920f4971e1786bde580dd20d52b4aa147ae8d3dc564d6"},{"_type":"video-file","sha256":"4e283876ddfbf5a2cb6f8aca07a39f832b5f806fbefde8854bc73ed904ff20ee","size":160315,"path":"media/videos/validation_videos_50_steps_0_4e283876ddfbf5a2cb6f.mp4"},{"size":135225,"path":"media/videos/validation_videos_50_steps_0_78185c41e1935306e93c.mp4","_type":"video-file","sha256":"78185c41e1935306e93c2d416ee40b31d038abffb029cb5bfb11c2a634eb2fcf"},{"_type":"video-file","sha256":"27e9819d002d3f63c8918bbdc5bf2857b5effe0caf5d5b8374b9c590fc6432eb","size":197873,"path":"media/videos/validation_videos_50_steps_0_27e9819d002d3f63c891.mp4"},{"_type":"video-file","sha256":"46fe548e86144ca60a9396fcd15e8788d3a05c066c6819ccdd6a9041feaaec8f","size":170601,"path":"media/videos/validation_videos_50_steps_0_46fe548e86144ca60a93.mp4"},{"sha256":"91ec338774bec870b9c5c81be4330a8f0f2535124cb372e881c3703e6b65ed77","size":164462,"path":"media/videos/validation_videos_50_steps_0_91ec338774bec870b9c5.mp4","_type":"video-file"},{"_type":"video-file","sha256":"ee4e811080a619215fd7541b39203dfb69c2db4cdadbfdd7a234a2cff684f6f3","size":139435,"path":"media/videos/validation_videos_50_steps_0_ee4e811080a619215fd7.mp4"},{"sha256":"22e31e048ba5e5b9d6587d7306904d6edc6c618b8f77c3ceb05ba2d602309274","size":147072,"path":"media/videos/validation_videos_50_steps_0_22e31e048ba5e5b9d658.mp4","_type":"video-file"}]},"_timestamp":1.75118195270901e+09,"vsa_sparsity":0.05,"learning_rate":1e-05,"train_loss":0.19960195198655128,"_runtime":107.325113071}
+{"step_time":0.6983645600266755,"_wandb":{"runtime":107},"grad_norm":1.260593056678772,"avg_step_time":1.002151239803061,"_step":5,"validation_videos_50_steps":{"captions":false,"_type":"videos","count":8,"videos":[{"size":159131,"path":"media/videos/validation_videos_50_steps_0_dc447599dbe48350e9c9.mp4","_type":"video-file","sha256":"dc447599dbe48350e9c920f4971e1786bde580dd20d52b4aa147ae8d3dc564d6"},{"_type":"video-file","sha256":"4e283876ddfbf5a2cb6f8aca07a39f832b5f806fbefde8854bc73ed904ff20ee","size":160315,"path":"media/videos/validation_videos_50_steps_0_4e283876ddfbf5a2cb6f.mp4"},{"size":135225,"path":"media/videos/validation_videos_50_steps_0_78185c41e1935306e93c.mp4","_type":"video-file","sha256":"78185c41e1935306e93c2d416ee40b31d038abffb029cb5bfb11c2a634eb2fcf"},{"_type":"video-file","sha256":"27e9819d002d3f63c8918bbdc5bf2857b5effe0caf5d5b8374b9c590fc6432eb","size":197873,"path":"media/videos/validation_videos_50_steps_0_27e9819d002d3f63c891.mp4"},{"_type":"video-file","sha256":"46fe548e86144ca60a9396fcd15e8788d3a05c066c6819ccdd6a9041feaaec8f","size":170601,"path":"media/videos/validation_videos_50_steps_0_46fe548e86144ca60a93.mp4"},{"sha256":"91ec338774bec870b9c5c81be4330a8f0f2535124cb372e881c3703e6b65ed77","size":164462,"path":"media/videos/validation_videos_50_steps_0_91ec338774bec870b9c5.mp4","_type":"video-file"},{"_type":"video-file","sha256":"ee4e811080a619215fd7541b39203dfb69c2db4cdadbfdd7a234a2cff684f6f3","size":139435,"path":"media/videos/validation_videos_50_steps_0_ee4e811080a619215fd7.mp4"},{"sha256":"22e31e048ba5e5b9d6587d7306904d6edc6c618b8f77c3ceb05ba2d602309274","size":147072,"path":"media/videos/validation_videos_50_steps_0_22e31e048ba5e5b9d658.mp4","_type":"video-file"}]},"_timestamp":1.75118195270901e+09,"vsa_sparsity":0.05,"learning_rate":1e-05,"train_loss":0.2620866410434246,"_runtime":107.325113071}
@@ -110,7 +110,7 @@ def test_distributed_training():
        'avg_step_time': 1.0,
        'grad_norm': 0.1,
        'step_time': 1.0,
-        'train_loss': 0.001
+        'train_loss': 0.005
    }

    failures = []
@@ -1 +1 @@
-{"train_loss":0.10545400530099869,"_step":5,"_wandb":{"runtime":35},"step_time":2.2575189135968685,"grad_norm":0.53125,"_runtime":35.701958502,"avg_step_time":2.4754387199878694,"_timestamp":1.7525528420185745e+09,"learning_rate":1e-06,"vsa_sparsity":0,"validation_videos_8_steps":{"videos":[{"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.","_type":"video-file","sha256":"ee83e83df073a648f89dcd288cccaed9af765e11fc35c77a6e0cb2ebaa1be5b0","size":475248,"path":"media/videos/validation_videos_8_steps_0_ee83e83df073a648f89d.mp4"},{"size":341490,"path":"media/videos/validation_videos_8_steps_0_d0b2758549d5c82845ca.mp4","caption":"A large metal cylinder is seen compressing colorful clay into a compact shape, demonstrating the power of a hydraulic press.","_type":"video-file","sha256":"d0b2758549d5c82845ca3c1ac0db6812877631495e77ac041acdc8ea31f0a5ee"},{"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.","_type":"video-file","sha256":"08638381f1607d6ab10684772be38a58b18628fd402a208b25f6af96e765d454","size":436814,"path":"media/videos/validation_videos_8_steps_0_08638381f1607d6ab106.mp4"}],"captions":["A large metal cylinder is seen pressing down on a pile of Oreo cookies, flattening them as if they were under a hydraulic press.","A large metal cylinder is seen compressing colorful clay into a compact shape, demonstrating the power of a hydraulic press.","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."],"_type":"videos","count":3}}
+{"train_loss":0.1021774671971798,"_step":5,"_wandb":{"runtime":35},"step_time":2.2575189135968685,"grad_norm":0.11582941561937332,"_runtime":35.701958502,"avg_step_time":2.4754387199878694,"_timestamp":1.7525528420185745e+09,"learning_rate":1e-06,"vsa_sparsity":0,"validation_videos_8_steps":{"videos":[{"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.","_type":"video-file","sha256":"ee83e83df073a648f89dcd288cccaed9af765e11fc35c77a6e0cb2ebaa1be5b0","size":475248,"path":"media/videos/validation_videos_8_steps_0_ee83e83df073a648f89d.mp4"},{"size":341490,"path":"media/videos/validation_videos_8_steps_0_d0b2758549d5c82845ca.mp4","caption":"A large metal cylinder is seen compressing colorful clay into a compact shape, demonstrating the power of a hydraulic press.","_type":"video-file","sha256":"d0b2758549d5c82845ca3c1ac0db6812877631495e77ac041acdc8ea31f0a5ee"},{"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.","_type":"video-file","sha256":"08638381f1607d6ab10684772be38a58b18628fd402a208b25f6af96e765d454","size":436814,"path":"media/videos/validation_videos_8_steps_0_08638381f1607d6ab106.mp4"}],"captions":["A large metal cylinder is seen pressing down on a pile of Oreo cookies, flattening them as if they were under a hydraulic press.","A large metal cylinder is seen compressing colorful clay into a compact shape, demonstrating the power of a hydraulic press.","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."],"_type":"videos","count":3}}
@@ -0,0 +1,66 @@
+from pathlib import Path
+
+import numpy as np
+import pyarrow as pa
+import pyarrow.parquet as pq
+import torch
+
+from fastvideo.workflow.preprocess.components import ParquetDatasetSaver
+from fastvideo.pipelines.pipeline_batch_info import PreprocessBatch
+
+
+def _simple_record_creator(batch: PreprocessBatch) -> list[dict]:
+    # batch.latents will be converted to numpy by the saver before this call
+    assert isinstance(batch.latents, np.ndarray)
+    num = len(batch.video_file_name)
+    records = []
+    for i in range(num):
+        arr = batch.latents[i]
+        records.append({
+            "id": batch.video_file_name[i],
+            "data_bytes": arr.tobytes(),
+            "data_shape": list(arr.shape),
+        })
+    return records
+
+
+def test_parquet_dataset_saver_flush_and_last(tmp_path: Path):
+    # Schema for the simple record creator
+    schema = pa.schema([
+        pa.field("id", pa.string()),
+        pa.field("data_bytes", pa.binary()),
+        pa.field("data_shape", pa.list_(pa.int64())),
+    ])
+
+    B = 5
+    # Build a minimal PreprocessBatch
+    batch = PreprocessBatch(
+        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)],
+    )
+    batch.video_file_name = [f"vid_{i}" for i in range(B)]
+
+    saver = ParquetDatasetSaver(
+        flush_frequency=10,  # higher than B to avoid auto-flush
+        samples_per_file=3,
+        schema=schema,
+        record_creator=_simple_record_creator,
+    )
+
+    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()
+    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)
+    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
@@ -40,7 +40,7 @@ from fastvideo.training.training_pipeline import TrainingPipeline
 from fastvideo.training.training_utils import (
    EMA_FSDP, clip_grad_norm_while_handling_failing_dtensor_cases,
    get_scheduler, load_distillation_checkpoint, save_distillation_checkpoint,
-    shift_timestep, compute_density_for_timestep_sampling, get_sigmas)
+    shift_timestep)
 from fastvideo.utils import (is_vsa_available, maybe_download_model,
                             set_random_seed, verify_model_config_and_directory)

@@ -91,13 +91,9 @@ class DistillationPipeline(TrainingPipeline):
        self.noise_scheduler = FlowMatchEulerDiscreteScheduler(
            shift=self.timestep_shift)

-        self.transformer_2 = self.get_module("transformer_2", None)
-        self.boundary_timestep = self.training_args.boundary_ratio * self.noise_scheduler.num_train_timesteps
-        
        if training_args.real_score_model_path:
-            logger.info(
-                f"Loading real score transformer from: {training_args.real_score_model_path}"
-            )
+            logger.info("Loading real score transformer from: %s",
+                        training_args.real_score_model_path)
            self.real_score_transformer = self.load_module_from_path(
                training_args.real_score_model_path, "transformer",
                training_args)
@@ -106,9 +102,8 @@ class DistillationPipeline(TrainingPipeline):
                "real_score_transformer")

        if training_args.fake_score_model_path:
-            logger.info(
-                f"Loading fake score transformer from: {training_args.fake_score_model_path}"
-            )
+            logger.info("Loading fake score transformer from: %s",
+                        training_args.fake_score_model_path)
            self.fake_score_transformer = self.load_module_from_path(
                training_args.fake_score_model_path, "transformer",
                training_args)
@@ -130,39 +125,6 @@ class DistillationPipeline(TrainingPipeline):
                self.real_score_transformer,
                checkpointing_type=training_args.
                enable_gradient_checkpointing_type)
-            if self.transformer_2 is not None:
-                self.transformer_2 = apply_activation_checkpointing(
-                    self.transformer_2,
-                    checkpointing_type=training_args.
-                    enable_gradient_checkpointing_type)
-
-        if self.transformer_2 is not None:
-            self.transformer_2.train()
-            self.transformer_2.requires_grad_(True)
-            params_to_optimize_2 = self.transformer_2.parameters()
-            params_to_optimize_2 = list(
-                filter(lambda p: p.requires_grad, params_to_optimize_2))
-            
-            betas_str = training_args.betas
-            betas = tuple(float(x.strip()) for x in betas_str.split(","))
-            
-            self.optimizer_2 = torch.optim.AdamW(
-                params_to_optimize_2,
-                lr=training_args.learning_rate,
-                betas=betas,
-                weight_decay=training_args.weight_decay,
-                eps=1e-8,
-            )
-            self.lr_scheduler_2 = get_scheduler(
-                training_args.lr_scheduler,
-                optimizer=self.optimizer_2,
-                num_warmup_steps=training_args.lr_warmup_steps,
-                num_training_steps=training_args.max_train_steps,
-                num_cycles=training_args.lr_num_cycles,
-                power=training_args.lr_power,
-                min_lr_ratio=training_args.min_lr_ratio,
-                last_epoch=self.init_steps - 1,
-            )

        # Initialize optimizers
        fake_score_params = list(
@@ -230,14 +192,13 @@ class DistillationPipeline(TrainingPipeline):

        self.real_score_guidance_scale = self.training_args.real_score_guidance_scale

-        self.generator_ema = None
+        self.generator_ema: EMA_FSDP | None = 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}"
-            )
+            logger.info("Initialized generator EMA with decay=%s",
+                        self.training_args.ema_decay)
        else:
            logger.info("Generator EMA disabled (ema_decay <= 0.0)")

@@ -254,7 +215,7 @@ class DistillationPipeline(TrainingPipeline):
        Returns:
            The loaded module
        """
-        logger.info(f"Loading {module_type} from custom path: {model_path}")
+        logger.info("Loading %s from custom path: %s", module_type, model_path)
        # Set flag to prevent custom weight loading for teacher/critic models
        training_args._loading_teacher_critic_model = True

@@ -264,7 +225,7 @@ class DistillationPipeline(TrainingPipeline):

            # 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}")
+            logger.info("Model downloaded/found at: %s", local_model_path)
            config = verify_model_config_and_directory(local_model_path)

            if module_type not in config:
@@ -273,7 +234,8 @@ class DistillationPipeline(TrainingPipeline):
                    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}")
+                        logger.info("Using %s for %s", extra_module,
+                                    module_type)
                    else:
                        raise ValueError(
                            f"Module {module_type} not found in config at {local_model_path}"
@@ -298,8 +260,8 @@ class DistillationPipeline(TrainingPipeline):
                fastvideo_args=training_args,
            )

-            logger.info(
-                f"Successfully loaded {module_type} from {component_path}")
+            logger.info("Successfully loaded %s from %s", module_type,
+                        component_path)
            return module
        finally:
            # Always clean up the flag
@@ -317,9 +279,6 @@ class DistillationPipeline(TrainingPipeline):
        """Prepare training environment for distillation."""
        self.transformer.requires_grad_(True)
        self.transformer.train()
-        if self.transformer_2 is not None:
-            self.transformer_2.requires_grad_(True)
-            self.transformer_2.train()
        self.fake_score_transformer.requires_grad_(True)
        self.fake_score_transformer.train()

@@ -332,7 +291,7 @@ class DistillationPipeline(TrainingPipeline):
                return model
        return model

-    def get_ema_model_copy(self):
+    def get_ema_model_copy(self) -> torch.nn.Module | None:
        """Get a copy of the model with EMA weights applied."""
        if self.generator_ema is not None:
            ema_model = copy.deepcopy(self.transformer)
@@ -340,7 +299,7 @@ class DistillationPipeline(TrainingPipeline):
            return ema_model
        return None

-    def is_ema_ready(self, current_step: int = None):
+    def is_ema_ready(self, current_step: int | None = None):
        """Check if EMA is ready for use (after ema_start_step)."""
        if current_step is None:
            current_step = getattr(self, 'current_trainstep', 0)
@@ -389,14 +348,14 @@ class DistillationPipeline(TrainingPipeline):
                with open(config_path, "w") as f:
                    json.dump(config_dict, f, indent=4)

-                logger.info(f"EMA weights saved to {weight_path}")
+                logger.info("EMA weights saved to %s", weight_path)

            del ema_model

        except Exception as e:
-            logger.error(f"Failed to save EMA weights: {str(e)}")
+            logger.error("Failed to save EMA weights: %s", str(e))

-    def get_ema_stats(self):
+    def get_ema_stats(self) -> dict[str, Any]:
        """Get EMA statistics for monitoring."""
        if self.generator_ema is None:
            return {
@@ -475,9 +434,7 @@ class DistillationPipeline(TrainingPipeline):
        training_batch = self._build_distill_input_kwargs(
            noisy_latent, timestep, training_batch.conditional_dict,
            training_batch)
-        
-        current_model = self.transformer_2 if self.train_transformer_2 else self.transformer
-        pred_noise = current_model(**training_batch.input_kwargs).permute(
+        pred_noise = self.transformer(**training_batch.input_kwargs).permute(
            0, 2, 1, 3, 4)
        pred_video = pred_noise_to_pred_video(
            pred_noise=pred_noise.flatten(0, 1),
@@ -519,7 +476,6 @@ class DistillationPipeline(TrainingPipeline):
        max_target_idx = len(self.denoising_step_list) - 1
        noise_latents = []
        noise_latent_index = target_timestep_idx_int - 1
-        current_model = self.transformer_2 if self.train_transformer_2 else self.transformer
        if max_target_idx > 0:
            # Run student model for all steps before the target timestep
            with torch.no_grad():
@@ -531,7 +487,7 @@ class DistillationPipeline(TrainingPipeline):
                    training_batch_temp = self._build_distill_input_kwargs(
                        current_noise_latents, current_timestep_tensor,
                        training_batch.conditional_dict, training_batch)
-                    pred_flow = current_model(
+                    pred_flow = self.transformer(
                        **training_batch_temp.input_kwargs).permute(
                            0, 2, 1, 3, 4)
                    pred_clean = pred_noise_to_pred_video(
@@ -574,7 +530,7 @@ class DistillationPipeline(TrainingPipeline):
        training_batch = self._build_distill_input_kwargs(
            noisy_input, target_timestep, training_batch.conditional_dict,
            training_batch)
-        pred_noise = current_model(**training_batch.input_kwargs).permute(
+        pred_noise = self.transformer(**training_batch.input_kwargs).permute(
            0, 2, 1, 3, 4)
        pred_video = pred_noise_to_pred_video(
            pred_noise=pred_noise.flatten(0, 1),
@@ -613,7 +569,8 @@ 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).detach().unflatten(0,
+                                             (1, generator_pred_video.shape[1]))

            # fake_score_transformer forward
            training_batch = self._build_distill_input_kwargs(
@@ -816,9 +773,6 @@ class DistillationPipeline(TrainingPipeline):
            batches.append(batch)

        self.optimizer.zero_grad()
-        # TODO: confirm this
-        if self.transformer_2 is not None:
-            self.optimizer_2.zero_grad()
        total_dmd_loss = 0.0
        dmd_latent_vis_dict = {}
        fake_score_latent_vis_dict = {}
@@ -847,31 +801,15 @@ class DistillationPipeline(TrainingPipeline):
                        attn_metadata=batch_gen.attn_metadata_vsa):
                    (dmd_loss / gradient_accumulation_steps).backward()
                total_dmd_loss += dmd_loss.detach().item()
-            
-            # Only clip gradients for the model that is currently training
-            if self.train_transformer_2 and self.transformer_2 is not None:
-                self._clip_model_grad_norm_(batch_gen, self.transformer_2)
-                for param in self.transformer_2.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_2.step()
-                self.optimizer_2.zero_grad(set_to_none=True)
-            else:
-                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)
+            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:
-                # TODO: support EMA for transformer_2?
-                if self.train_transformer_2 and self.transformer_2 is not None:
-                    # Note: EMA currently only supports the main transformer
-                    # Could be extended to support transformer_2 in the future
-                    pass
-                else:
-                    self.generator_ema.update(self.transformer)
+                self.generator_ema.update(self.transformer)

            avg_dmd_loss = torch.tensor(total_dmd_loss /
                                        gradient_accumulation_steps,
@@ -901,13 +839,7 @@ class DistillationPipeline(TrainingPipeline):
            assert param.grad is not None and param.grad.abs().sum() > 0
        self.fake_score_optimizer.step()
        self.fake_score_lr_scheduler.step()
-        
-        # Step the appropriate scheduler
-        if self.train_transformer_2 and self.transformer_2 is not None:
-            self.lr_scheduler_2.step()
-        else:
-            self.lr_scheduler.step()
-            
+        self.lr_scheduler.step()
        self.fake_score_optimizer.zero_grad(set_to_none=True)
        avg_fake_score_loss = torch.tensor(total_fake_score_loss /
                                           gradient_accumulation_steps,
@@ -1319,9 +1251,8 @@ class DistillationPipeline(TrainingPipeline):
                    (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}"
-                )
+                logger.info("Created generator EMA at step %s with decay=%s",
+                            step, self.training_args.ema_decay)

            with torch.autocast("cuda", dtype=torch.bfloat16):
                training_batch = self.train_one_step(training_batch)
@@ -12,12 +12,14 @@ from tqdm.auto import tqdm

 import fastvideo.envs as envs
 import wandb
-from fastvideo.distributed import get_local_torch_device, get_world_group
+from fastvideo.distributed import (cleanup_dist_env_and_memory,
+                                   get_local_torch_device, get_sp_group,
+                                   get_world_group)
 from fastvideo.fastvideo_args import 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.models.schedulers.scheduling_self_forcing_flow_match import SelfForcingFlowMatchScheduler
 from fastvideo.models.utils import pred_noise_to_pred_video
 from fastvideo.pipelines import TrainingBatch
 from fastvideo.training.distillation_pipeline import DistillationPipeline
@@ -45,6 +47,8 @@ class SelfForcingDistillationPipeline(DistillationPipeline):
        """Initialize the self-forcing training pipeline."""
        logger.info("Initializing self-forcing distillation pipeline...")

+        self.generator_ema: EMA_FSDP | None = None
+
        super().initialize_training_pipeline(training_args)
        self.noise_scheduler = SelfForcingFlowMatchScheduler(
            num_inference_steps=1000,
@@ -69,14 +73,14 @@ class SelfForcingDistillationPipeline(DistillationPipeline):
        self.frame_seq_length = 1560  # TODO: Calculate this dynamically based on patch size

        # Cache references (will be initialized per forward pass)
-        self.kv_cache1 = None
-        self.crossattn_cache = None
+        self.kv_cache1: list[dict[str, Any]] | None = None
+        self.crossattn_cache: list[dict[str, Any]] | None = None

-        logger.info(
-            f"Self-forcing generator update ratio: {self.dfake_gen_update_ratio}"
-        )
+        logger.info("Self-forcing generator update ratio: %s",
+                    self.dfake_gen_update_ratio)

-    def generate_and_sync_list(self, num_blocks, num_denoising_steps, device):
+    def generate_and_sync_list(self, num_blocks: int, num_denoising_steps: int,
+                               device: torch.device) -> list[int]:
        """Generate and synchronize random exit flags across distributed processes."""
        rank = dist.get_rank() if dist.is_initialized() else 0

@@ -134,7 +138,7 @@ class SelfForcingDistillationPipeline(DistillationPipeline):
        updated_batch, flow_matching_loss = self.faker_score_forward(
            training_batch)
        training_batch.fake_score_latent_vis_dict = updated_batch.fake_score_latent_vis_dict
-        log_dict = {}
+        log_dict: dict[str, Any] = {}

        return flow_matching_loss, log_dict

@@ -180,13 +184,10 @@ class SelfForcingDistillationPipeline(DistillationPipeline):
            noisy_latent, timestep, training_batch.conditional_dict,
            training_batch)

-        # Use the appropriate transformer
-        current_model = self.transformer_2 if self.train_transformer_2 else self.transformer
-
        # Step 5: Forward pass with KV cache if available
-        if hasattr(current_model, '_forward_inference'):
+        if hasattr(self.transformer, '_forward_inference'):
            # Use causal inference forward with KV cache
-            pred_noise = current_model(
+            pred_noise = self.transformer(
                hidden_states=training_batch.input_kwargs['hidden_states'],
                encoder_hidden_states=training_batch.
                input_kwargs['encoder_hidden_states'],
@@ -199,7 +200,7 @@ class SelfForcingDistillationPipeline(DistillationPipeline):
                cache_start=0).permute(0, 2, 1, 3, 4)
        else:
            # Fallback to regular forward
-            pred_noise = current_model(
+            pred_noise = self.transformer(
                **training_batch.input_kwargs).permute(0, 2, 1, 3, 4)

        # Step 6: Convert noise prediction to video prediction
@@ -212,19 +213,6 @@ class SelfForcingDistillationPipeline(DistillationPipeline):
        self._reset_simulation_caches(kv_cache, crossattn_cache)

        return pred_video
-    
-    def _enable_training(self, model: torch.nn.Module, optimizer: torch.optim.Optimizer) -> None:
-        """Enable training mode and gradients for the specified model."""
-        for param in model.parameters():
-            param.requires_grad = True
-        model.train()
-        optimizer.zero_grad()
-
-    def _disable_training(self, model: torch.nn.Module, optimizer: torch.optim.Optimizer) -> None:
-        """Disable training mode and gradients for the specified model."""
-        for param in model.parameters():
-            param.requires_grad = False
-        optimizer.zero_grad(set_to_none=True)

    def _generator_multi_step_simulation_forward(
            self,
@@ -321,9 +309,8 @@ class SelfForcingDistillationPipeline(DistillationPipeline):
                training_batch_temp = self._build_distill_input_kwargs(
                    initial_latent, timestep * 0,
                    training_batch.conditional_dict, training_batch)
-            # we process the image latent with self.transformer_2 (low-noise expert)
-                current_model = self.transformer_2 if self.transformer_2 is not None else self.transformer
-                current_model(
+
+                self.transformer(
                    hidden_states=training_batch_temp.
                    input_kwargs['hidden_states'],
                    encoder_hidden_states=training_batch_temp.
@@ -362,16 +349,6 @@ class SelfForcingDistillationPipeline(DistillationPipeline):
                timestep = torch.ones([batch_size, current_num_frames],
                                      device=noise.device,
                                      dtype=torch.int64) * current_timestep
-                
-                if current_timestep <= self.boundary_timestep and self.transformer_2 is not None:
-                    current_model = self.transformer_2
-                    self._enable_training(self.transformer_2, self.optimizer_2)
-                    self._disable_training(self.transformer, self.optimizer)
-                else:
-                    current_model = self.transformer
-                    self._enable_training(self.transformer, self.optimizer)
-                    if self.transformer_2 is not None:
-                        self._disable_training(self.transformer_2, self.optimizer_2)

                if not exit_flag:
                    with torch.no_grad():
@@ -380,7 +357,7 @@ class SelfForcingDistillationPipeline(DistillationPipeline):
                            noisy_input, timestep,
                            training_batch.conditional_dict, training_batch)

-                        pred_flow = current_model(
+                        pred_flow = self.transformer(
                            hidden_states=training_batch_temp.
                            input_kwargs['hidden_states'],
                            encoder_hidden_states=training_batch_temp.
@@ -420,7 +397,7 @@ class SelfForcingDistillationPipeline(DistillationPipeline):
                                noisy_input, timestep,
                                training_batch.conditional_dict, training_batch)

-                            pred_flow = current_model(
+                            pred_flow = self.transformer(
                                hidden_states=training_batch_temp.
                                input_kwargs['hidden_states'],
                                encoder_hidden_states=training_batch_temp.
@@ -440,7 +417,7 @@ class SelfForcingDistillationPipeline(DistillationPipeline):
                            noisy_input, timestep,
                            training_batch.conditional_dict, training_batch)

-                        pred_flow = current_model(
+                        pred_flow = self.transformer(
                            hidden_states=training_batch_temp.
                            input_kwargs['hidden_states'],
                            encoder_hidden_states=training_batch_temp.
@@ -480,9 +457,7 @@ class SelfForcingDistillationPipeline(DistillationPipeline):
                    denoised_pred, context_timestep,
                    training_batch.conditional_dict, training_batch)

-                # context_timestep is 0 so we use transformer_2
-                current_model = self.transformer_2 if self.transformer_2 is not None else self.transformer
-                current_model(
+                self.transformer(
                    hidden_states=training_batch_temp.
                    input_kwargs['hidden_states'],
                    encoder_hidden_states=training_batch_temp.
@@ -507,7 +482,7 @@ class SelfForcingDistillationPipeline(DistillationPipeline):
        gradient_mask = None
        if pred_image_or_video.shape[1] > 21:
            with torch.no_grad():
-                # Reencode to get image latent
+                # Re-encode to get image latent
                latent_to_decode = pred_image_or_video[:, :-20, ...]
                # Decode to video
                latent_to_decode = latent_to_decode.permute(
@@ -582,12 +557,15 @@ class SelfForcingDistillationPipeline(DistillationPipeline):
            training_batch.dmd_latent_vis_dict["min_num_frames"] = torch.tensor(
                min_num_frames, dtype=torch.float32, device=self.device)

+        assert self.kv_cache1 is not None
+        assert self.crossattn_cache is not None
        self._reset_simulation_caches(self.kv_cache1, self.crossattn_cache)

        return final_output if gradient_mask is not None else pred_image_or_video

-    def _initialize_simulation_caches(self, batch_size: int, dtype: torch.dtype,
-                                      device: torch.device):
+    def _initialize_simulation_caches(
+        self, batch_size: int, dtype: torch.dtype, device: torch.device
+    ) -> tuple[list[dict[str, Any]], list[dict[str, Any]]]:
        """Initialize KV cache and cross-attention cache for multi-step simulation."""
        num_transformer_blocks = len(self.transformer.blocks)

@@ -605,7 +583,7 @@ class SelfForcingDistillationPipeline(DistillationPipeline):
        frame_seq_length = post_patch_height * post_patch_width

        # Get local attention size from transformer config
-        local_attn_size = getattr(self.transformer, 'local_attn_size', -1)
+        # local_attn_size = getattr(self.transformer, 'local_attn_size', -1)

        # Get model configuration parameters - handle FSDP wrapping
        if hasattr(self.transformer, 'config'):
@@ -671,7 +649,8 @@ class SelfForcingDistillationPipeline(DistillationPipeline):

        return kv_cache, crossattn_cache

-    def _reset_simulation_caches(self, kv_cache, crossattn_cache):
+    def _reset_simulation_caches(self, kv_cache: list[dict[str, Any]],
+                                 crossattn_cache: list[dict[str, Any]]) -> None:
        """Reset KV cache and cross-attention cache to clean state."""
        if kv_cache is not None:
            for cache_dict in kv_cache:
@@ -802,10 +781,7 @@ class SelfForcingDistillationPipeline(DistillationPipeline):
        training_batch.fake_score_latent_vis_dict = {}

        if train_generator:
-            logger.debug(f"Training generator at step {self.current_trainstep}")
            self.optimizer.zero_grad()
-            if self.transformer_2 is not None:
-                self.optimizer_2.zero_grad()
            total_generator_loss = 0.0
            generator_log_dict = {}

@@ -837,23 +813,12 @@ class SelfForcingDistillationPipeline(DistillationPipeline):
                    training_batch.dmd_latent_vis_dict.update(
                        batch_gen.dmd_latent_vis_dict)

-            # Only clip gradients and step optimizer for the model that is currently training
-            if hasattr(self, 'train_transformer_2') and self.train_transformer_2 and self.transformer_2 is not None:
-                self._clip_model_grad_norm_(batch_gen, self.transformer_2)
-                self.optimizer_2.step()
-                self.lr_scheduler_2.step()
-            else:
-                self._clip_model_grad_norm_(batch_gen, self.transformer)
-                self.optimizer.step()
-                self.lr_scheduler.step()
+            self._clip_model_grad_norm_(batch_gen, self.transformer)
+            self.optimizer.step()
+            self.lr_scheduler.step()

            if self.generator_ema is not None:
-                if hasattr(self, 'train_transformer_2') and self.train_transformer_2 and self.transformer_2 is not None:
-                    # Note: EMA currently only supports the main transformer
-                    # Could be extended to support transformer_2 in the future
-                    pass
-                else:
-                    self.generator_ema.update(self.transformer)
+                self.generator_ema.update(self.transformer)

            avg_generator_loss = torch.tensor(total_generator_loss /
                                              gradient_accumulation_steps,
@@ -865,7 +830,6 @@ class SelfForcingDistillationPipeline(DistillationPipeline):
        else:
            training_batch.generator_loss = 0.0

-        logger.debug(f"Training critic at step {self.current_trainstep}")
        self.fake_score_optimizer.zero_grad()
        total_critic_loss = 0.0
        critic_log_dict = {}
@@ -926,15 +890,13 @@ class SelfForcingDistillationPipeline(DistillationPipeline):
        wandb_loss_dict = {}

        # Debug logging
-        logger.info(f"Step {step}: Starting visualization")
+        logger.info("Step %s: Starting visualization", step)
        if hasattr(training_batch, 'dmd_latent_vis_dict'):
-            logger.info(
-                f"DMD latent keys: {list(training_batch.dmd_latent_vis_dict.keys())}"
-            )
+            logger.info("DMD latent keys: %s",
+                        list(training_batch.dmd_latent_vis_dict.keys()))
        if hasattr(training_batch, 'fake_score_latent_vis_dict'):
-            logger.info(
-                f"Fake score latent keys: {list(training_batch.fake_score_latent_vis_dict.keys())}"
-            )
+            logger.info("Fake score latent keys: %s",
+                        list(training_batch.fake_score_latent_vis_dict.keys()))

        # Process generator predictions if available
        if hasattr(
@@ -948,12 +910,11 @@ class SelfForcingDistillationPipeline(DistillationPipeline):

            for latent_key in dmd_log_keys:
                if latent_key in dmd_latents_vis_dict:
-                    logger.info(f"Processing DMD latent: {latent_key}")
+                    logger.info("Processing DMD latent: %s", latent_key)
                    latents = dmd_latents_vis_dict[latent_key]
                    if not isinstance(latents, torch.Tensor):
-                        logger.warning(
-                            f"Expected tensor for {latent_key}, got {type(latents)}"
-                        )
+                        logger.warning("Expected tensor for %s, got %s",
+                                       latent_key, type(latents))
                        continue

                    latents = latents.detach()
@@ -982,12 +943,11 @@ class SelfForcingDistillationPipeline(DistillationPipeline):
                        video = (video * 255).numpy().astype(np.uint8)
                        wandb_loss_dict[f"dmd_{latent_key}"] = wandb.Video(
                            video, fps=24, format="mp4")
-                        logger.info(
-                            f"Successfully processed DMD latent: {latent_key}")
+                        logger.info("Successfully processed DMD latent: %s",
+                                    latent_key)
                    except Exception as e:
-                        logger.error(
-                            f"Error processing DMD latent {latent_key}: {str(e)}"
-                        )
+                        logger.error("Error processing DMD latent %s: %s",
+                                     latent_key, str(e))
                    del video, latents

        # Process critic predictions
@@ -998,12 +958,11 @@ class SelfForcingDistillationPipeline(DistillationPipeline):

            for latent_key in fake_score_log_keys:
                if latent_key in fake_score_latents_vis_dict:
-                    logger.info(f"Processing critic latent: {latent_key}")
+                    logger.info("Processing critic latent: %s", latent_key)
                    latents = fake_score_latents_vis_dict[latent_key]
                    if not isinstance(latents, torch.Tensor):
-                        logger.warning(
-                            f"Expected tensor for {latent_key}, got {type(latents)}"
-                        )
+                        logger.warning("Expected tensor for %s, got %s",
+                                       latent_key, type(latents))
                        continue

                    latents = latents.detach()
@@ -1032,13 +991,11 @@ class SelfForcingDistillationPipeline(DistillationPipeline):
                        video = (video * 255).numpy().astype(np.uint8)
                        wandb_loss_dict[f"critic_{latent_key}"] = wandb.Video(
                            video, fps=24, format="mp4")
-                        logger.info(
-                            f"Successfully processed critic latent: {latent_key}"
-                        )
+                        logger.info("Successfully processed critic latent: %s",
+                                    latent_key)
                    except Exception as e:
-                        logger.error(
-                            f"Error processing critic latent {latent_key}: {str(e)}"
-                        )
+                        logger.error("Error processing critic latent %s: %s",
+                                     latent_key, str(e))
                    del video, latents

        # Log metadata
@@ -1054,16 +1011,16 @@ class SelfForcingDistillationPipeline(DistillationPipeline):
                    "dmd_timestep"] = training_batch.dmd_latent_vis_dict[
                        "dmd_timestep"].item()

-        if hasattr(training_batch, 'fake_score_latent_vis_dict'
-                   ) and training_batch.fake_score_latent_vis_dict:
-            if "fake_score_timestep" in training_batch.fake_score_latent_vis_dict:
-                wandb_loss_dict[
-                    "fake_score_timestep"] = training_batch.fake_score_latent_vis_dict[
-                        "fake_score_timestep"].item()
+        if hasattr(
+                training_batch, 'fake_score_latent_vis_dict'
+        ) and training_batch.fake_score_latent_vis_dict and "fake_score_timestep" in training_batch.fake_score_latent_vis_dict:
+            wandb_loss_dict[
+                "fake_score_timestep"] = training_batch.fake_score_latent_vis_dict[
+                    "fake_score_timestep"].item()

        # Log final dict contents
-        logger.info(
-            f"Final wandb_loss_dict keys: {list(wandb_loss_dict.keys())}")
+        logger.info("Final wandb_loss_dict keys: %s",
+                    list(wandb_loss_dict.keys()))

        if self.global_rank == 0:
            wandb.log(wandb_loss_dict, step=step)
@@ -1101,7 +1058,7 @@ class SelfForcingDistillationPipeline(DistillationPipeline):

        self.train_loader_iter = iter(self.train_dataloader)

-        step_times = deque(maxlen=100)
+        step_times: deque[float] = deque(maxlen=100)

        self._log_training_info()
        self._log_validation(self.transformer, self.training_args,
@@ -1139,9 +1096,8 @@ class SelfForcingDistillationPipeline(DistillationPipeline):
                    (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}"
-                )
+                logger.info("Created generator EMA at step %s with decay=%s",
+                            step, self.training_args.ema_decay)

            with torch.autocast("cuda", dtype=torch.bfloat16):
                training_batch = self.train_one_step(training_batch)
@@ -1223,10 +1179,10 @@ class SelfForcingDistillationPipeline(DistillationPipeline):

                wandb.log(log_data, step=step)

-                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)
+                if self.training_args.log_validation and step % self.training_args.validation_steps == 0 and self.training_args.log_visualization:
+                    self.visualize_intermediate_latents(training_batch,
+                                                        self.training_args,
+                                                        step)

            if (self.training_args.training_state_checkpointing_steps > 0
                    and step %
@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-import gc
 import dataclasses
 import math
 import os
@@ -70,7 +69,6 @@ class TrainingPipeline(LoRAPipeline, ABC):
    train_dataloader: StatefulDataLoader
    train_loader_iter: Iterator[dict[str, Any]]
    current_epoch: int = 0
-    train_transformer_2: bool = False

    def __init__(
            self,
@@ -106,7 +104,6 @@ class TrainingPipeline(LoRAPipeline, ABC):
        self.sp_world_size = self.sp_group.world_size
        self.local_rank = world_group.local_rank
        self.transformer = self.get_module("transformer")
-        self.transformer_2 = self.get_module("transformer_2", None)
        self.seed = training_args.seed
        self.set_schemas()

@@ -119,11 +116,6 @@ class TrainingPipeline(LoRAPipeline, ABC):
                self.transformer,
                checkpointing_type=training_args.
                enable_gradient_checkpointing_type)
-            if self.transformer_2 is not None:
-                self.transformer_2 = apply_activation_checkpointing(
-                    self.transformer_2,
-                    checkpointing_type=training_args.
-                    enable_gradient_checkpointing_type)

        noise_scheduler = self.modules["scheduler"]
        self.set_trainable()
@@ -155,30 +147,6 @@ class TrainingPipeline(LoRAPipeline, ABC):
            min_lr_ratio=training_args.min_lr_ratio,
            last_epoch=self.init_steps - 1,
        )
-        if self.transformer_2 is not None:
-            # Ensure transformer_2 has trainable parameters before creating optimizer
-            self.transformer_2.train()
-            self.transformer_2.requires_grad_(True)
-            params_to_optimize_2 = self.transformer_2.parameters()
-            params_to_optimize_2 = list(
-                filter(lambda p: p.requires_grad, params_to_optimize_2))
-            self.optimizer_2 = torch.optim.AdamW(
-                params_to_optimize_2,
-                lr=training_args.learning_rate,
-                betas=(0.9, 0.999),
-                weight_decay=training_args.weight_decay,
-                eps=1e-8,
-            )
-            self.lr_scheduler_2 = get_scheduler(
-                training_args.lr_scheduler,
-                optimizer=self.optimizer_2,
-                num_warmup_steps=training_args.lr_warmup_steps,
-                num_training_steps=training_args.max_train_steps,
-                num_cycles=training_args.lr_num_cycles,
-                power=training_args.lr_power,
-                min_lr_ratio=training_args.min_lr_ratio,
-                last_epoch=self.init_steps - 1,
-            )

        self.train_dataset, self.train_dataloader = build_parquet_map_style_dataloader(
            training_args.data_path,
@@ -193,7 +161,7 @@ class TrainingPipeline(LoRAPipeline, ABC):
            seed=self.seed)

        self.noise_scheduler = noise_scheduler
-        self.boundary_timestep = self.training_args.boundary_ratio * self.noise_scheduler.num_train_timesteps
+
        self.num_update_steps_per_epoch = math.ceil(
            len(self.train_dataloader) /
            training_args.gradient_accumulation_steps * training_args.sp_size /
@@ -219,25 +187,9 @@ class TrainingPipeline(LoRAPipeline, ABC):
    def _prepare_training(self, training_batch: TrainingBatch) -> TrainingBatch:
        self.transformer.train()
        self.optimizer.zero_grad()
-        if self.transformer_2 is not None:
-            self.transformer_2.train()
-            self.optimizer_2.zero_grad()
        training_batch.total_loss = 0.0
        return training_batch

-    def _enable_training(self, model: torch.nn.Module, optimizer: torch.optim.Optimizer) -> None:
-        """Enable training mode and gradients for the specified model."""
-        for param in model.parameters():
-            param.requires_grad = True
-        model.train()
-        optimizer.zero_grad()
-
-    def _disable_training(self, model: torch.nn.Module, optimizer: torch.optim.Optimizer) -> None:
-        """Disable training mode and gradients for the specified model."""
-        for param in model.parameters():
-            param.requires_grad = False
-        optimizer.zero_grad(set_to_none=True)
-
    def _get_next_batch(self, training_batch: TrainingBatch) -> TrainingBatch:
        batch = next(self.train_loader_iter, None)  # type: ignore
        if batch is None:
@@ -281,17 +233,17 @@ class TrainingPipeline(LoRAPipeline, ABC):
                            generator=self.noise_gen_cuda,
                            device=latents.device,
                            dtype=latents.dtype)
-        timesteps = self._sample_timesteps(batch_size, latents.device)
-
-        # Enable training for the model that will be trained next and disable the other
-        if self.train_transformer_2:
-            self._enable_training(self.transformer_2, self.optimizer_2)
-            self._disable_training(self.transformer, self.optimizer)
-        else:
-            self._enable_training(self.transformer, self.optimizer)
-            if self.transformer_2 is not None:
-                self._disable_training(self.transformer_2, self.optimizer_2)
-
+        u = compute_density_for_timestep_sampling(
+            weighting_scheme=self.training_args.weighting_scheme,
+            batch_size=batch_size,
+            generator=self.noise_random_generator,
+            logit_mean=self.training_args.logit_mean,
+            logit_std=self.training_args.logit_std,
+            mode_scale=self.training_args.mode_scale,
+        )
+        indices = (u * self.noise_scheduler.config.num_train_timesteps).long()
+        timesteps = self.noise_scheduler.timesteps[indices].to(
+            device=latents.device)
        if self.training_args.sp_size > 1:
            # Make sure that the timesteps are the same across all sp processes.
            sp_group = get_sp_group()
@@ -314,38 +266,6 @@ class TrainingPipeline(LoRAPipeline, ABC):

        return training_batch

-    def _sample_timesteps(self, batch_size, device):
-        # Determine which model to train based on the boundary timestep
-        if (self.transformer_2 is not None and self.boundary_timestep is not None and
-                torch.rand(1, generator=self.noise_random_generator).item() <= self.training_args.boundary_ratio):
-            self.train_transformer_2 = True
-        else:
-            self.train_transformer_2 = False
-
-        # Broadcast the decision to all processes
-        decision = torch.tensor(1.0 if self.train_transformer_2 else 0.0, device=self.device)
-        dist.broadcast(decision, src=0)
-        self.train_transformer_2 = decision.item() == 1.0
-
-        # Sample u from the appropriate range
-        u = compute_density_for_timestep_sampling(
-            weighting_scheme=self.training_args.weighting_scheme,
-            batch_size=batch_size,
-            generator=self.noise_random_generator,
-            logit_mean=self.training_args.logit_mean,
-            logit_std=self.training_args.logit_std,
-            mode_scale=self.training_args.mode_scale,
-        )
-
-        boundary_ratio = self.training_args.boundary_ratio
-        if self.train_transformer_2:
-            u = (1 - boundary_ratio) + u * boundary_ratio # min: 1 - boundary_ratio, max: 1
-        else:
-            u = u * (1 - boundary_ratio) # min: 0, max: 1 - boundary_ratio
-
-        indices = (u * self.noise_scheduler.config.num_train_timesteps).long()
-        return self.noise_scheduler.timesteps[indices].to(device=device)
-        
    def _build_attention_metadata(
            self, training_batch: TrainingBatch) -> TrainingBatch:
        latents_shape = training_batch.raw_latent_shape
@@ -361,6 +281,20 @@ 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)
        else:
            training_batch.attn_metadata = None

@@ -385,6 +319,7 @@ 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":
            assert training_batch.attn_metadata is not None
        else:
@@ -396,12 +331,11 @@ class TrainingPipeline(LoRAPipeline, ABC):
        #         [1000.0],
        #         device=training_batch.noisy_model_input.device,
        #         dtype=torch.bfloat16)
-        current_model = self.transformer_2 if self.train_transformer_2 else self.transformer

        with set_forward_context(
                current_timestep=training_batch.current_timestep,
                attn_metadata=training_batch.attn_metadata):
-            model_pred = current_model(**input_kwargs)
+            model_pred = self.transformer(**input_kwargs)
            if self.training_args.precondition_outputs:
                assert training_batch.sigmas is not None
                model_pred = training_batch.noisy_model_input - model_pred * training_batch.sigmas
@@ -432,12 +366,7 @@ class TrainingPipeline(LoRAPipeline, ABC):
        # the following:
        # grad_norm = transformer.clip_grad_norm_(max_grad_norm)
        if max_grad_norm is not None:
-            # Only clip gradients for the model that is currently training
-            if self.train_transformer_2 and self.transformer_2 is not None:
-                model_parts = [self.transformer_2]
-            else:
-                model_parts = [self.transformer]
-            
+            model_parts = [self.transformer]
            grad_norm = clip_grad_norm_while_handling_failing_dtensor_cases(
                [p for m in model_parts for p in m.parameters()],
                max_grad_norm,
@@ -482,14 +411,9 @@ class TrainingPipeline(LoRAPipeline, ABC):

        training_batch = self._clip_grad_norm(training_batch)

-        # Only step the optimizer and scheduler for the model that is currently training
-        if self.train_transformer_2 and self.transformer_2 is not None:
-            self.optimizer_2.step()
-            self.lr_scheduler_2.step()
-        else:
-            self.optimizer.step()
-            self.lr_scheduler.step()
-            
+        self.optimizer.step()
+        self.lr_scheduler.step()
+
        training_batch.total_loss = training_batch.total_loss
        training_batch.grad_norm = training_batch.grad_norm
        return training_batch
@@ -521,11 +445,6 @@ class TrainingPipeline(LoRAPipeline, ABC):
        logger.info("Starting training with %s B trainable parameters",
                    round(num_trainable_params / 1e9, 3))

-        if getattr(self, "transformer_2", None) is not None:
-            num_trainable_params = _get_trainable_params(self.transformer_2)
-            logger.info("Transformer 2: Starting training with %s B trainable parameters",
-                        round(num_trainable_params / 1e9, 3))
-
        # Set random seeds for deterministic training
        self.noise_random_generator = torch.Generator(device="cpu").manual_seed(
            self.seed)
@@ -546,7 +465,7 @@ class TrainingPipeline(LoRAPipeline, ABC):

        self._log_training_info()

-        self._log_validation(self.training_args,
+        self._log_validation(self.transformer, self.training_args,
                             self.init_steps)

        # Train!
@@ -567,6 +486,9 @@ 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
+            #     pass
            else:
                current_vsa_sparsity = 0.0

@@ -608,7 +530,7 @@ 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:
-                self._log_validation(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)
@@ -689,12 +611,12 @@ class TrainingPipeline(LoRAPipeline, ABC):
        return batch

    @torch.no_grad()
-    def _log_validation(self, training_args, global_step) -> None:
+    def _log_validation(self, transformer, training_args, global_step) -> None:
        """
        Generate a validation video and log it to wandb to check the quality during training.
        """
        training_args.inference_mode = True
-        training_args.dit_cpu_offload = False
+        training_args.dit_cpu_offload = True
        if not training_args.log_validation:
            return
        if self.validation_pipeline is None:
@@ -716,9 +638,7 @@ class TrainingPipeline(LoRAPipeline, ABC):
                                           batch_size=None,
                                           num_workers=0)

-        self.transformer.eval()
-        if getattr(self, "transformer_2", None) is not None:
-            self.transformer_2.eval()
+        transformer.eval()

        validation_steps = training_args.validation_sampling_steps.split(",")
        validation_steps = [int(step) for step in validation_steps]
@@ -810,6 +730,4 @@ class TrainingPipeline(LoRAPipeline, ABC):

        # Re-enable gradients for training
        training_args.inference_mode = False
-        self.transformer.train()
-        if getattr(self, "transformer_2", None) is not None:
-            self.transformer_2.train()
+        transformer.train()
@@ -466,11 +466,13 @@ def load_distillation_checkpoint(generator_transformer,
            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)
+                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))
+            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",
@@ -1296,6 +1298,10 @@ def get_scheduler(
                         last_epoch=last_epoch)


+def count_trainable(model: torch.nn.Module) -> int:
+    return sum(p.numel() for p in model.parameters() if p.requires_grad)
+
+
 class EMA_FSDP:
    """
    FSDP2-friendly EMA with two modes:
@@ -1342,7 +1348,10 @@ class EMA_FSDP:
        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()}
+                self.shadow = {
+                    k: v.detach().clone().float().cpu()
+                    for k, v in cpu_state.items()
+                }
            else:
                self.shadow = {}
            return
@@ -1383,7 +1392,10 @@ class EMA_FSDP:

    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()
+            } 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]):
@@ -1411,7 +1423,9 @@ class EMA_FSDP:

        def __enter__(self):
            if self.ema.mode != "local_shard":
-                raise RuntimeError("EMA apply_to_model is only supported for 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:
@@ -1421,14 +1435,17 @@ class EMA_FSDP:
                    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)
+                    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))
+                        p_local.copy_(
+                            ema_cpu.to(dtype=p_local.dtype,
+                                       device=p_local.device))
            return self.module

        def __exit__(self, exc_type, exc, tb):
@@ -1446,4 +1463,4 @@ class EMA_FSDP:
            return False

    def apply_to_model(self, module):
-        return EMA_FSDP._ApplyEMACtx(self, module)
+        return EMA_FSDP._ApplyEMACtx(self, module)
@@ -4,10 +4,10 @@ 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.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()
@@ -69,4 +69,4 @@ if __name__ == "__main__":
    parser = TrainingArgs.add_cli_args(parser)
    parser = FastVideoArgs.add_cli_args(parser)
    args = parser.parse_args()
-    main(args) 
+    main(args)
@@ -23,10 +23,14 @@ from typing import Any, TypeVar, cast

 import cloudpickle
 import filelock
+import imageio
+import numpy as np
 import torch
+import torchvision
 import yaml
 from diffusers.loaders.lora_base import (
    _best_guess_weight_name)  # watch out for potetential removal from diffusers
+from einops import rearrange
 from huggingface_hub import snapshot_download
 from remote_pdb import RemotePdb
 from torch.distributed.fsdp import MixedPrecisionPolicy
@@ -886,3 +890,17 @@ def best_output_size(w, h, dw, dh, expected_area):
        return ow1, oh1
    else:
        return ow2, oh2
+
+
+def save_decoded_latents_as_video(decoded_latents: list[torch.Tensor],
+                                  output_path: str, fps: int):
+    # Process outputs
+    videos = rearrange(decoded_latents, "b c t h w -> t b c h w")
+    frames = []
+    for x in videos:
+        x = torchvision.utils.make_grid(x, nrow=6)
+        x = x.transpose(0, 1).transpose(1, 2).squeeze(-1)
+        frames.append((x * 255).numpy().astype(np.uint8))
+
+    os.makedirs(os.path.dirname(output_path), exist_ok=True)
+    imageio.mimsave(output_path, frames, fps=fps, format="mp4")
@@ -1,20 +1,19 @@
 import dataclasses
 import gc
-import multiprocessing
 import os
 import random
 from collections.abc import Callable
-from concurrent.futures import ProcessPoolExecutor
 from typing import Any

 import numpy as np
 import pyarrow as pa
-import pyarrow.parquet as pq
 import torch
 from datasets import Dataset, Video, load_dataset

 from fastvideo.configs.configs import (DatasetType, PreprocessConfig,
                                       VideoLoaderType)
+from fastvideo.dataset.dataloader.parquet_io import (ParquetDatasetWriter,
+                                                     records_to_table)
 from fastvideo.distributed.parallel_state import get_world_rank, get_world_size
 from fastvideo.logger import init_logger
 from fastvideo.pipelines.pipeline_batch_info import PreprocessBatch
@@ -155,31 +154,17 @@ class VideoForwardBatchBuilder:


 class ParquetDatasetSaver:
-    """Component for saving and writing Parquet datasets"""
+    """Component for saving and writing Parquet datasets using shared parquet_io."""

-    def __init__(self,
-                 flush_frequency: int,
-                 samples_per_file: int,
-                 schema_fields: list[str],
-                 record_creator: Callable[..., list[dict[str, Any]]],
-                 file_writer_fn: Callable | None = None):
-        """
-        Initialize ParquetDatasetSaver
-        
-        Args:
-            schema_fields: schema fields list
-            record_creator: Function for creating records
-            file_writer_fn: Function for writing records to files, uses default implementation if None
-        """
+    def __init__(self, flush_frequency: int, samples_per_file: int,
+                 schema: pa.Schema,
+                 record_creator: Callable[..., list[dict[str, Any]]]):
        self.flush_frequency = flush_frequency
        self.samples_per_file = samples_per_file
-        self.schema_fields = schema_fields
+        self.schema = schema
        self.create_records_from_batch = record_creator
-        self.file_writer_fn: Callable[
-            [tuple], int] = file_writer_fn or self._default_file_writer_fn
-        self.all_tables: list[pa.Table] = []
        self.num_processed_samples: int = 0
-        self.num_saved_files: int = 0
+        self._writer: ParquetDatasetWriter | None = None

    def save_and_write_parquet_batch(
            self,
@@ -227,16 +212,17 @@ class ParquetDatasetSaver:

        if batch_data:
            self.num_processed_samples += len(batch_data)
-            # Convert batch data to PyArrow arrays
-            table = self._convert_batch_to_pyarrow_table(batch_data)
-
-            # Store the table in a list for later processing
-            self.all_tables.append(table)
+            table = records_to_table(batch_data, self.schema)
+            if self._writer is None:
+                os.makedirs(output_dir, exist_ok=True)
+                self._writer = ParquetDatasetWriter(
+                    out_dir=output_dir, samples_per_file=self.samples_per_file)
+            self._writer.append_table(table)
            logger.debug("Collected batch with %s samples", len(table))

        # If flush is needed
        if self.num_processed_samples >= self.flush_frequency:
-            self.flush_tables(output_dir)
+            self.flush_tables()

    def _process_non_padded_embeddings(
            self, prompt_embeds: torch.Tensor,
@@ -259,146 +245,31 @@ class ParquetDatasetSaver:

        return non_padded_embeds

-    def _convert_batch_to_pyarrow_table(self,
-                                        batch_data: list[dict]) -> pa.Table:
-        """Convert batch data to PyArrow table"""
-        arrays = []
+    def flush_tables(self, write_remainder: bool = False):
+        """Flush buffered records to disk.

-        for field in self.schema_fields:
-            if field.endswith('_bytes'):
-                arrays.append(
-                    pa.array([record[field] for record in batch_data],
-                             type=pa.binary()))
-            elif field.endswith('_shape'):
-                arrays.append(
-                    pa.array([record[field] for record in batch_data],
-                             type=pa.list_(pa.int32())))
-            elif field in ['width', 'height', 'num_frames']:
-                arrays.append(
-                    pa.array([record[field] for record in batch_data],
-                             type=pa.int32()))
-            elif field in ['duration_sec', 'fps']:
-                arrays.append(
-                    pa.array([record[field] for record in batch_data],
-                             type=pa.float32()))
-            else:
-                arrays.append(pa.array([record[field]
-                                        for record in batch_data]))
-
-        return pa.Table.from_arrays(arrays, names=self.schema_fields)
-
-    def flush_tables(self, output_dir: str):
-        """Flush collected tables to disk"""
-        if not hasattr(self, 'all_tables') or not self.all_tables:
+        Args:
+            output_dir: Directory where parquet files are written. Kept for API
+                symmetry (writer already configured with this path).
+            write_remainder: If True, also write any leftover rows smaller than
+                ``samples_per_file`` as a final small file. Useful for the last flush.
+        """
+        if self._writer is None:
            return
-
-        logger.debug("Combining %d batches...", len(self.all_tables))
-        combined_table = pa.concat_tables(self.all_tables)
-        assert len(combined_table) == self.num_processed_samples
-        logger.debug("Total samples collected: %d", len(combined_table))
-
-        # Calculate total number of chunks needed, putting remainder into self.all_tables
-        total_files = max(self.num_processed_samples // self.samples_per_file,
-                          1)
-
-        logger.debug("Fixed samples per parquet file: %d",
-                     self.samples_per_file)
-        logger.debug("Total number of parquet files: %d", total_files)
-        logger.debug(
-            "Total samples to be processed: %d (putting %d samples into self.all_tables)",
-            total_files * self.samples_per_file,
-            self.num_processed_samples % self.samples_per_file)
-
-        # Split work among processes
-        num_workers = int(min(multiprocessing.cpu_count(), total_files))
-        files_per_worker = (total_files + num_workers - 1) // num_workers
-
-        logger.debug("Using %d workers to process %d files", num_workers,
-                     total_files)
-        logger.debug("Files per worker: %s", files_per_worker)
-
-        # Prepare work ranges
-        work_ranges = []
-        for i in range(num_workers):
-            start_idx = i * files_per_worker
-            end_idx = min((i + 1) * files_per_worker, total_files)
-            if start_idx < total_files:
-                work_ranges.append((start_idx, end_idx, combined_table, i,
-                                    output_dir, self.samples_per_file))
-
-        total_written = 0
-        failed_ranges = []
-        with ProcessPoolExecutor(max_workers=num_workers) as executor:
-            futures = {
-                executor.submit(self.file_writer_fn, work_range): work_range
-                for work_range in work_ranges
-            }
-            for future in futures:
-                try:
-                    written = future.result()
-                    total_written += written
-                    logger.info("Processed file with %s samples", written)
-                except Exception as e:
-                    work_range = futures[future]
-                    failed_ranges.append(work_range)
-                    logger.error("Failed to process range %s-%s: %s",
-                                 work_range[0], work_range[1], str(e))
-
-        # Retry failed ranges sequentially
-        if failed_ranges:
-            logger.warning("Retrying %s failed ranges sequentially",
-                           len(failed_ranges))
-            for work_range in failed_ranges:
-                try:
-                    total_written += self.file_writer_fn(work_range)
-                except Exception as e:
-                    logger.error(
-                        "Failed to process range %s-%s after retry: %s",
-                        work_range[0], work_range[1], str(e))
-
-        self.num_saved_files += total_files
-
-        # Clear tables list
-        self.all_tables = []
-        if self.num_processed_samples > self.samples_per_file:
-            saved_samples = total_files * self.samples_per_file
-            self.all_tables.append(combined_table.slice(saved_samples))
-            self.num_processed_samples -= saved_samples
-        else:
-            self.num_processed_samples = 0
-
-        del combined_table
-        gc.collect()
+        _ = self._writer.flush(write_remainder=write_remainder)
+        # Reset processed sample count modulo samples_per_file
+        remainder = self.num_processed_samples % self.samples_per_file
+        self.num_processed_samples = 0 if write_remainder else remainder

    def clean_up(self) -> None:
        """Clean up all tables"""
-        self.all_tables = []
+        self.flush_tables(write_remainder=True)
+        self._writer = None
        self.num_processed_samples = 0
-        self.num_saved_files = 0
        gc.collect()

-    def _default_file_writer_fn(self, args_tuple: tuple) -> int:
-        """Default chunk processing implementation"""
-        start_idx, end_idx, combined_table, worker_id, output_dir, samples_per_file = args_tuple
-
-        written_count = 0
-        for file_idx in range(start_idx, end_idx):
-            start_row = file_idx * samples_per_file
-            end_row = min(start_row + samples_per_file, len(combined_table))
-
-            if start_row >= len(combined_table):
-                break
-
-            chunk_table = combined_table.slice(start_row, end_row - start_row)
-
-            # Write to file
-            output_file = os.path.join(
-                output_dir,
-                f"chunk_{file_idx + self.num_saved_files:06d}.parquet")
-            pq.write_table(chunk_table, output_file)
-            written_count += len(chunk_table)
-
-        return written_count
+    def __del__(self):
+        self.clean_up()


 def build_dataset(preprocess_config: PreprocessConfig, split: str,
@@ -4,6 +4,8 @@ from typing import cast
 from torch.utils.data import DataLoader

 from fastvideo.configs.configs import PreprocessConfig
+from fastvideo.dataset.dataloader.record_schema import (
+    basic_t2v_record_creator, i2v_record_creator)
 from fastvideo.dataset.dataloader.schema import (pyarrow_schema_i2v,
                                                 pyarrow_schema_t2v)
 from fastvideo.distributed.parallel_state import get_world_rank
@@ -13,8 +15,6 @@ from fastvideo.pipelines.pipeline_registry import PipelineType
 from fastvideo.workflow.preprocess.components import (
    ParquetDatasetSaver, PreprocessingDataValidator, VideoForwardBatchBuilder,
    build_dataset)
-from fastvideo.workflow.preprocess.record_schema import (
-    basic_t2v_record_creator, i2v_record_creator)
 from fastvideo.workflow.workflow_base import WorkflowBase

 logger = init_logger(__name__)
@@ -85,16 +85,16 @@ class PreprocessWorkflow(WorkflowBase):
        # record creator
        if self.fastvideo_args.workload_type == WorkloadType.I2V:
            record_creator = i2v_record_creator
-            schema_fields = [f.name for f in pyarrow_schema_i2v]
+            schema = pyarrow_schema_i2v
        else:
            record_creator = basic_t2v_record_creator
-            schema_fields = [f.name for f in pyarrow_schema_t2v]
+            schema = pyarrow_schema_t2v
        processed_dataset_saver = ParquetDatasetSaver(
            flush_frequency=self.fastvideo_args.preprocess_config.
            flush_frequency,
            samples_per_file=self.fastvideo_args.preprocess_config.
            samples_per_file,
-            schema_fields=schema_fields,
+            schema=schema,
            record_creator=record_creator,
        )
        self.add_component("processed_dataset_saver", processed_dataset_saver)
@@ -35,8 +35,7 @@ class PreprocessWorkflowI2V(PreprocessWorkflow):
            self.processed_dataset_saver.save_and_write_parquet_batch(
                forward_batch, self.training_dataset_output_dir)

-        self.processed_dataset_saver.flush_tables(
-            self.training_dataset_output_dir)
+        self.processed_dataset_saver.flush_tables()
        self.processed_dataset_saver.clean_up()

        # Validation dataset preprocessing
@@ -51,6 +50,5 @@ class PreprocessWorkflowI2V(PreprocessWorkflow):

                self.processed_dataset_saver.save_and_write_parquet_batch(
                    forward_batch, self.validation_dataset_output_dir)
-            self.processed_dataset_saver.flush_tables(
-                self.validation_dataset_output_dir)
+            self.processed_dataset_saver.flush_tables()
            self.processed_dataset_saver.clean_up()
@@ -35,8 +35,7 @@ class PreprocessWorkflowT2V(PreprocessWorkflow):
            self.processed_dataset_saver.save_and_write_parquet_batch(
                forward_batch, self.training_dataset_output_dir)

-        self.processed_dataset_saver.flush_tables(
-            self.training_dataset_output_dir)
+        self.processed_dataset_saver.flush_tables()
        self.processed_dataset_saver.clean_up()

        # Validation dataset preprocessing
@@ -51,6 +50,5 @@ class PreprocessWorkflowT2V(PreprocessWorkflow):

                self.processed_dataset_saver.save_and_write_parquet_batch(
                    forward_batch, self.validation_dataset_output_dir)
-            self.processed_dataset_saver.flush_tables(
-                self.validation_dataset_output_dir)
+            self.processed_dataset_saver.flush_tables()
            self.processed_dataset_saver.clean_up()
@@ -34,7 +34,7 @@ dependencies = [

    # Miscellaneous Utilities
    "tqdm", "pytest", "PyYAML==6.0.1", "protobuf>=5.28.3",
-    "gradio>=5.22.0", "moviepy>=2.0.0", "flask",
+    "gradio==5.41.0", "moviepy>=2.0.0", "flask",
    "flask_restful", "aiohttp", "huggingface_hub", "cloudpickle",
    # System & Monitoring Tools
    "gpustat", "watch", "remote-pdb",
@@ -0,0 +1,21 @@
+#!/bin/bash
+
+# Create output directory if it doesn't exist
+mkdir -p preprocess_output_text
+
+# Launch 8 jobs, one for each node
+# Each node processes 8 consecutive files (64 total files / 8 nodes = 8 files per node)
+for node_id in {0..7}; do
+    # Calculate the starting file number for this node
+    start_file=$((node_id * 8 + 1))
+    
+    echo "Launching text-only node $node_id with files v2m_${start_file}.txt to v2m_$((start_file + 7)).txt"
+    echo "sbatch --job-name=text-${node_id} --output=preprocess_output_text/preprocess-text-node-${node_id}.out --error=preprocess_output_text/preprocess-text-node-${node_id}.err scripts/preprocess/syn_text.slurm $start_file $node_id"
+    
+    sbatch --job-name=text-${node_id} \
+           --output=preprocess_output_text/preprocess-text-node-${node_id}.out \
+           --error=preprocess_output_text/preprocess-text-node-${node_id}.err \
+           scripts/preprocess/syn_text.slurm $start_file $node_id
+done
+
+echo "All 8 text-only nodes launched successfully!" 
@@ -0,0 +1,88 @@
+#!/bin/bash
+#SBATCH --partition=main
+#SBATCH --nodes=1
+#SBATCH --ntasks-per-node=8
+#SBATCH --gres=gpu:8
+#SBATCH --cpus-per-task=16
+#SBATCH --mem=960G
+#SBATCH --exclusive
+#SBATCH --time=72:00:00
+
+# conda init
+# source ~/conda/miniconda/bin/activate
+# PYTHON_VIRTUAL_ENVIRONMENT=fastvideo-train-yq
+# conda activate $PYTHON_VIRTUAL_ENVIRONMENT
+nvidia-smi
+nvidia-smi --query-compute-apps=pid,process_name,used_memory --format=csv
+
+echo " "
+echo " Number of nodes:= " $SLURM_JOB_NUM_NODES
+echo " GPUs per node:= " $SLURM_JOB_GPUS
+echo " Running on multiple nodes/GPU devices for TEXT-ONLY preprocessing"
+echo ""
+echo " Run started at:- "
+date
+
+# Accept parameters from launch script
+START_FILE=${1:-1}  # Starting file number for this node
+NODE_ID=${2:-0}  # Node identifier (0-7)
+
+num_gpus=1
+export MODEL_BASE=Wan-AI/Wan2.1-T2V-1.3B-Diffusers
+# Start port number - we'll increment for each job
+base_port=$((29603 + NODE_ID * 100))  # Different port range per node
+
+# Create an array of CUDA device IDs
+gpu_ids=(0 1 2 3 4 5 6 7)
+
+GPU_NUM=1
+MODEL_TYPE="wan"
+
+echo "NODE_ID: $NODE_ID"
+echo "START_FILE: $START_FILE"
+echo "Base port for this node: $base_port"
+echo "Processing TEXT-ONLY data"
+
+# Run 8 parallel preprocessing jobs on this node
+for i in {1..8}; do
+    # Calculate port for this job
+    port=$((base_port + i))
+    
+    # Get GPU ID using modulo to cycle through available GPUs
+    gpu=${gpu_ids[((i-1))]}
+    
+    # Calculate which file this GPU should process
+    file_num=$((START_FILE + i - 1))
+    DATA_MERGE_PATH="prompts/v2m_${file_num}.txt"
+    
+    # Create unique output directory based on node and GPU for text-only processing
+    OUTPUT_DIR="data/test-text-preprocessing/Node_${NODE_ID}_GPU_${i}_File_${file_num}"
+    
+    start_cpu=$(( (i-1)*2 ))  # Reduced CPU allocation for 8 nodes
+    end_cpu=$(( start_cpu+1 ))
+    
+    echo "Starting GPU $gpu processing text-only file v2m_${file_num}.txt on port $port, output: $OUTPUT_DIR"
+    
+    # Run the text-only preprocessing command in background
+    CUDA_VISIBLE_DEVICES=$gpu taskset -c ${start_cpu}-${end_cpu} torchrun --nnodes=1 --nproc_per_node=$GPU_NUM --master_port $port \
+        fastvideo/pipelines/preprocess/v1_preprocess.py \
+        --model_path $MODEL_BASE \
+        --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 "text_only" &
+done
+
+# Wait for all jobs on this node to complete
+wait
+
+echo "All text-only processing blocks completed!" 
@@ -21,4 +21,4 @@ torchrun --nproc_per_node=$GPU_NUM \
    --validation_dataset_file $VALIDATION_PATH \
    --samples_per_file 8 \
    --flush_frequency 8 \
-    --preprocess_task "i2v" 
+    --preprocess_task "i2v" 
@@ -22,4 +22,4 @@ torchrun --nproc_per_node=$GPU_NUM \
    --samples_per_file 1 \
    --flush_frequency 1 \
    --video_length_tolerance_range 5 \
-    --preprocess_task "t2v"
+    --preprocess_task "t2v"
@@ -1,57 +0,0 @@
-export WANDB_BASE_URL="https://api.wandb.ai"
-export WANDB_MODE=online
-export WANDB_API_KEY='2f25ad37933894dbf0966c838c0b8494987f9f2f'
-# export FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA
-export TRITON_CACHE_DIR=/tmp/triton_cache
-# DATA_DIR=/mnt/sharefs/users/hao.zhang/Vchitect-2M/Wan-Syn-upload/latents_i2v/train/
-DATA_DIR=/mnt/weka/home/hao.zhang/wei/FastVideo/data/crush-smol_processed_t2v/combined_parquet_dataset
-# VALIDATION_DIR=/mnt/sharefs/users/hao.zhang/Vchitect-2M/mixkit/validation_8.json
-VALIDATION_DIR=/mnt/weka/home/hao.zhang/wei/FastVideo/data/crush-smol-single_processed_t2v/validation.json
-NUM_GPUS=8
-export FASTVIDEO_ATTENTION_BACKEND=FLASH_ATTN
-# export FASTVIDEO_ATTENTION_BACKEND=FLASH_ATTN
-# export CUDA_VISIBLE_DEVICES=4,5
-# IP=[MASTER NODE IP]
-CHECKPOINT_PATH="outputs_train_test/wan_finetune/checkpoint-10"
-# 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 $NUM_GPUS \
-    fastvideo/training/wan_training_pipeline.py \
-    --model_path Wan-AI/Wan2.2-T2V-A14B-Diffusers \
-    --inference_mode False\
-    --pretrained_model_name_or_path Wan-AI/Wan2.2-T2V-A14B-Diffusers \
-    --cache_dir "/home/ray/.cache" \
-    --data_path "$DATA_DIR" \
-    --validation_dataset_file  "$VALIDATION_DIR" \
-    --train_batch_size 1 \
-    --num_latent_t 16\
-    --sp_size 4 \
-    --tp_size 1 \
-    --num_gpus $NUM_GPUS \
-    --hsdp_replicate_dim 1 \
-    --hsdp-shard-dim 8 \
-    --train_sp_batch_size 1 \
-    --dataloader_num_workers 4 \
-    --gradient_accumulation_steps 1 \
-    --max_train_steps 30000 \
-    --learning_rate 1e-5 \
-    --mixed_precision "bf16" \
-    --checkpointing_steps 1000 \
-    --validation_steps 30 \
-    --validation_sampling_steps "40" \
-    --log_validation True \
-    --checkpoints_total_limit 3 \
-    --ema_start_step 0 \
-    --training_cfg_rate 0.1 \
-    --seed 1024 \
-    --output_dir "outputs_train_test/wan_finetune_v1" \
-    --tracker_project_name VSA_finetune \
-    --num_height 448 \
-    --num_width 832 \
-    --num_frames 61 \
-    --flow_shift 5 \
-    --validation_guidance_scale "5.0" \
-    --num_euler_timesteps 50 \
-    --master_weight_type "fp32" \
-    --dit_precision "fp32" \
-    --weight_decay 0.01 \
-    --max_grad_norm 1.0
Author	SHA1	Message	Date
RandNMR73	eddc7efc64	inference works after changes added new branch Stop backprop through kv_cache Enable timestep warping & using SelfForcing scheduler checkpoint text preprocessing ready fix rotary embedding Change Wan DiT to have 0 numerical diff with SF's Wan	2025-09-22 02:29:16 +00:00
Wei Zhou RandNMR73 SolitaryThinker	31f000d1cc	[self-forcing] [5/n] Add Self-Forcing distillation pipeline (#808 ) Co-authored-by: RandNMR73 <notomatthew31@gmail.com> Co-authored-by: SolitaryThinker <wlsaidhi@gmail.com>	2025-09-20 19:32:10 -07:00
Yongqi Chen	cd32b3e02f	Update example files and readme (#809 )	2025-09-20 18:15:59 -07:00
Zhang Peiyuan	bf27908095	Update WeChat Link	2025-09-20 14:16:20 -07:00
William Lin	c5f9ea53b2	[self-forcing] [4/n] Preprocessing for collecting ODE trajectory (#788 )	2025-09-15 17:54:42 -07:00
William Lin	d32a7184da	[bugfix] Wan2.2 Boundary ratio (#804 )	2025-09-15 11:17:35 -07:00
Wenxuan Tanandgemini-code-assist[bot]	2930abe456	[Bugfix] Fix VMoba requirements (#802 ) Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>	2025-09-14 18:28:52 -07:00
William Lin	b93ef4289d	[bugfix] Fix empty PipelineConfigs for Wan2.2 A14B (#800 )	2025-09-13 17:31:38 -07:00
William Lin RandNMR73 JerryZhou54 kevin314	401bdbd316	[self-forcing] [3/n] Text embed only preprocessing (#797 ) Co-authored-by: RandNMR73 <notomatthew31@gmail.com> Co-authored-by: JerryZhou54 <zhouw.jerry2017@outlook.com> Co-authored-by: kevin314 <kevin.lin.cs1@gmail.com>	2025-09-13 14:03:53 -07:00
William Lin	1048d79cf8	[bugfix] pin gradio version and set current_vsa_sparsity in TrainingPipeline (#798 )	2025-09-11 17:04:47 -07:00
Zhaobin Chu zbchu2 William Lin	1e8406162d	[bugfix] Fix delta calculation (#796 ) Co-authored-by: zbchu2 <zbchu2@iflytek.com> Co-authored-by: William Lin <SolitaryThinker@users.noreply.github.com>	2025-09-11 16:31:23 -07:00
William Lin	03edd35c83	[preprocessing] [self-forcing] [2/n] Improve preprocessing and add ode trajectory dataset schema (#794 )	2025-09-10 17:33:57 -07:00