remove wan/

debug and temp batch in ode_init
fix ode_init
2025-09-21 23:01:06 +00:00 · 2025-09-21 02:41:14 +00:00 · 2025-09-17 22:02:09 +00:00 · 2025-09-17 03:56:06 +00:00 · 2025-09-16 06:34:09 +00:00 · 2025-09-16 04:27:42 +00:00
177 changed files with 4124 additions and 8160 deletions
@@ -104,18 +104,6 @@ steps:
                  - TEST_TYPE=distillation_dmd
                agents:
                  queue: "default"
-            - path:
-                - "fastvideo/training/*self_forcing_distillation_pipeline.py"
-                - "fastvideo/tests/training/self-forcing/**"
-                - "pyproject.toml"
-                - "docker/Dockerfile.python3.12"
-              config:
-                command: "timeout 15m .buildkite/scripts/pr_test.sh"
-                label: "Self-Forcing Tests"
-                env:
-                  - TEST_TYPE=self_forcing
-                agents:
-                  queue: "default"
            - path:
                - "fastvideo/**"
                - "pyproject.toml"
@@ -110,10 +110,6 @@ case "$TEST_TYPE" in
        MODAL_COMMAND="$MODAL_ENV WANDB_API_KEY=$WANDB_API_KEY python3 -m modal run $MODAL_TEST_FILE::run_distill_dmd_tests"
        ;;
        # run_inference_tests_vmoba
-    "self_forcing")
-        log "Running self-forcing tests..."
-        MODAL_COMMAND="$MODAL_ENV WANDB_API_KEY=$WANDB_API_KEY python3 -m modal run $MODAL_TEST_FILE::run_self_forcing_tests"
-        ;;
    "inference_vmoba")
        log "Running V-MoBA inference tests..."
        MODAL_COMMAND="$MODAL_ENV python3 -m modal run $MODAL_TEST_FILE::run_inference_tests_vmoba"
@@ -352,7 +352,7 @@ jobs:
      volume_size: 100
      disk_size: 100
      image: "ghcr.io/${{ github.repository }}/fastvideo-dev:py3.12-latest"
-      test_command: "uv pip install -e .[test] && pytest ./fastvideo/dataset/ -vs && pytest ./fastvideo/workflow/ -vs && pytest ./fastvideo/entrypoints/ -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 }}
@@ -12,6 +12,9 @@ exclude: |
    scripts/.*|
    fastvideo/data_preprocess/.*|
    fastvideo/dataset/.*|
+    fastvideo/distill/.*|
+    fastvideo/distill\.py|
+    fastvideo/distill_adv\.py|
    fastvideo/models/.*|
    fastvideo/sample/.*|
    fastvideo/train\.py|
@@ -41,10 +44,10 @@ 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/tMwknPLY" 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/S7HLCSTh" 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 Chen, Yongqi and Huang, Haofeng 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 Huang, Haofeng and Chen, Yongqi and Lin, Will and Liu, Zhengzhong and Stoica, Ion and Xing, Eric and Zhang, Hao},
  journal={arXiv preprint arXiv:2505.13389},
  year={2025}
 }
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-</svg>
@@ -1,6 +0,0 @@
-<svg width="160" height="93" viewBox="0 0 160 93" fill="none" xmlns="http://www.w3.org/2000/svg">
-<path d="M28.8511 91.66L57.6319 1.86368H64.5394L35.7585 91.66H28.8511Z" fill="#356CFF" stroke="#356CFF" stroke-width="2.30244"/>
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-</svg>
@@ -20,7 +20,5 @@ setup(
        "License :: OSI Approved :: Apache Software License",
    ],
    python_requires='>=3.12',
-    install_requires=[
-        "flash-attn >= 2.7.1",
-    ]
+    install_requires=[]
 )
@@ -6,16 +6,8 @@ import time
 import os
 import torch
 from typing import Tuple
-try:
-    from flash_attn import flash_attn_varlen_func  # Use the new flash attention function
-    from flash_attn.flash_attn_interface import _flash_attn_varlen_forward, _flash_attn_varlen_backward
-except ImportError:
-    def _unsupported(*args, **kwargs):
-        raise ImportError("flash-attn is not installed. Please install it, e.g., `pip install flash-attn`.")
-    _flash_attn_varlen_forward = _unsupported
-    _flash_attn_varlen_backward = _unsupported
-    flash_attn_varlen_func = _unsupported
-    
+from flash_attn import flash_attn_varlen_func  # Use the new flash attention function
+from flash_attn.flash_attn_interface import _flash_attn_varlen_forward, _flash_attn_varlen_backward
 from functools import lru_cache
 from einops import rearrange

@@ -1,53 +0,0 @@
-# Profiling FastVideo
-
-!!! warning
-    Profiling is only intended for FastVideo developers and maintainers to understand the proportion of time spent in different parts of the codebase. **FastVideo end-users should never turn on profiling** as it will significantly slow down the inference.
-
-## Profiling with PyTorch
-
-FastVideo exposes a process-wide torch profiler that you can enable via environment variables. Set `FASTVIDEO_TORCH_PROFILER_DIR` to an absolute directory path to start collecting traces, and specify the regions you want recorded with `FASTVIDEO_TORCH_PROFILE_REGIONS`:
-
-```bash
-FASTVIDEO_TORCH_PROFILER_DIR=/mnt/traces/fastvideo \
-FASTVIDEO_TORCH_PROFILE_REGIONS="profiler_region_model_loading,profiler_region_training_step"
-```
-
-All profiled regions must be registered in `fastvideo.profiler`; the current list includes:
-
- `profiler_region_model_loading` — pipeline/module loading
- `profiler_region_inference_pre_denoising`
- `profiler_region_inference_denoising`
- `profiler_region_inference_post_denoising`
- `profiler_region_training_checkpoint_saving`
- `profiler_region_training_dit`
- `profiler_region_training_validation`
- `profiler_region_training_epoch`
- `profiler_region_training_step`
- `profiler_region_training_backward`
- `profiler_region_training_optimizer`
- `profiler_region_distillation_teacher_forward`
- `profiler_region_distillation_student_forward`
- `profiler_region_distillation_loss`
- `profiler_region_distillation_update`
-
-While profiling is enabled, FastVideo records additional annotations:
-
- `fastvideo.region::<name>` spans are emitted when entering a region.
- `fastvideo.profiler.enable_collection` / `fastvideo.profiler.disable_collection` events mark when torch profiler collection is toggled on or off.
-
-Only one profiler instance is created per process; subsequent pipelines reuse the same controller. If you set `FASTVIDEO_TORCH_PROFILE_REGIONS` incorrectly (e.g. misspelled name), FastVideo logs a warning and ignores that entry.
-
-Additional knobs:
-
- `FASTVIDEO_TORCH_PROFILER_RECORD_SHAPES`
- `FASTVIDEO_TORCH_PROFILER_WITH_PROFILE_MEMORY`
- `FASTVIDEO_TORCH_PROFILER_WITH_STACK`
- `FASTVIDEO_TORCH_PROFILER_WITH_FLOPS`
-
-Traces can be visualized using <https://ui.perfetto.dev/>.
-
-### Best Practices
-
- Keep the profiled step count small; traces can be large and slow down job shutdown while the profiler flushes data.
- After profiling, clean up trace directories to avoid filling disks.
- When adding new regions, register them in `fastvideo.profiler` and wrap the corresponding code block with `with self.profiler_controller.region("your_region"):` or the `@profile_region` decorator.
@@ -115,7 +115,6 @@ design/overview

 contributing/overview
 contributing/developer_env/index
-contributing/profiling
 :::

 :::{toctree}
@@ -1,40 +1,32 @@
 (inference-optimizations)=
-
 # Optimizations

 This page describes the various options for speeding up generation times in FastVideo.

 ## Table of Contents
-
 - Optimized Attention Backends
-
  - [Flash Attention](#optimizations-flash)
  - [Sliding Tile Attention](#optimizations-sta)
  - [Sage Attention](#optimizations-sage)
-  - [Sage Attention 3](#optimizations-sage3)

 - Caching Techniques
  - [TeaCache](#optimizations-teacache)

 (optimizations-backends)=
-
 ## Attention Backends

 ### Available Backends
-
 - Torch SDPA: `FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA`
 - Flash Attention 2 and 3: `FASTVIDEO_ATTENTION_BACKEND=FLASH_ATTN`
 - Sliding Tile Attention: `FASTVIDEO_ATTENTION_BACKEND=SLIDING_TILE_ATTN`
 - Video Sparse Attention: `FASTVIDEO_ATTENTION_BACKEND=VIDEO_SPARSE_ATTN`
 - Sage Attention: `FASTVIDEO_ATTENTION_BACKEND=SAGE_ATTN`
- Sage Attention 3: `FASTVIDEO_ATTENTION_BACKEND=SAGE_ATTN_THREE`

 ### Configuring Backends

 There are two ways to configure the attention backend in FastVideo.

 #### 1. In Python
-
 In python, set the `FASTVIDEO_ATTENTION_BACKEND` environment variable before instantiating `VideoGenerator` like this:

 ```python
@@ -42,7 +34,6 @@ os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "SLIDING_TILE_ATTN"
 ```

 #### 2. In CLI
-
 You can also set the environment variable on the command line:

 ```bash
@@ -50,7 +41,6 @@ FASTVIDEO_ATTENTION_BACKEND=SAGE_ATTN python example.py
 ```

 (optimizations-flash)=
-
 ### Flash Attention

 **`FLASH_ATTN`**
@@ -67,7 +57,7 @@ And if using a Hopper+ GPU (ie H100), installing [Flash Attention 3](https://git
 git clone https://github.com/Dao-AILab/flash-attention.git && cd flash-attention

 cd hopper
-pip install ninja
+pip install ninja 
 python setup.py install
 ```

@@ -76,9 +66,7 @@ FastVideo will automatically detect and use `FA3` if it is installed when using
 :::

 (optimizations-sta)=
-
 ### Sliding Tile Attention
-
 **`SLIDING_TILE_ATTN`**

 ```bash
@@ -88,9 +76,7 @@ pip install st_attn==0.0.4
 Please see [this page](#sta-installation) for more installation instructions.

 (optimizations-vsa)=
-
 ### Video Sparse Attention
-
 **`VIDEO_SPARSE_ATTN`**

 ```bash
@@ -101,45 +87,19 @@ python setup_vsa.py install
 Please see [this page](#vsa-installation) for more installation instructions.

 (optimizations-sage)=
-
 ### Sage Attention
-
 **`SAGE_ATTN`**

 To use [SageAttention](https://github.com/thu-ml/SageAttention) 2.1.1, please compile from source:

 ```bash
 git clone https://github.com/thu-ml/SageAttention.git
-cd sageattention
+cd sageattention 
 python setup.py install  # or pip install -e .
 ```

-(optimizations-sage3)=
-
-### Sage Attention 3
-
-**`SAGE_ATTN_THREE`**
-
-[SageAttention 3](https://huggingface.co/jt-zhang/SageAttention3) is an advanced attention mechanism that leverages FP4 quantization and Blackwell GPU Tensor Cores for significant performance improvements.
-
-#### Hardware Requirements
-
- RTX5090
-
-#### Installation
-
-Note that Sage Attention 3 requires `python>=3.13`, `torch>=2.8.0`, `CUDA >=12.8`. If you are using `uv` and using `torch==2.8.0` make sure that `sentencepiece==0.2.1` in the pyproject.toml file.
-
-To use Sage Attention 3 in FastVideo, first get access to the SageAttention3 code, then move `sageattn/` and `setup.py` to the directory `fastvideo/attention/backends`, then install from using:
-
-```bash
-python setup.py install
-```
-
 (optimizations-teacache)=
-
 ## Teacache
-
 TeaCache is an optimization technique supported in FastVideo that can significantly speed up video generation by skipping redundant calculations across diffusion steps. This guide explains how to enable and configure TeaCache for optimal performance in FastVideo.

 ### What is TeaCache?
@@ -0,0 +1,3 @@
+#!/bin/bash
+
+python scripts/huggingface/download_hf.py --repo_id "wlsaidhi/crush-smol-merged" --local_dir "data/crush-smol" --repo_type "dataset"
@@ -0,0 +1,76 @@
+{
+  "data": [
+    {
+      "caption": "A large metal cylinder is seen pressing down on a pile of Oreo cookies, flattening them as if they were under a hydraulic press.",
+      "image_path": null,
+      "video_path": "validation_dataset/yYcK4nANZz4-Scene-034.mp4",
+      "num_inference_steps": 40,
+      "height": 480,
+      "width": 832,
+      "num_frames": 77
+    },
+    {
+      "caption": "A large metal cylinder is seen compressing colorful clay into a compact shape, demonstrating the power of a hydraulic press.",
+      "image_path": null,
+      "video_path": "validation_dataset/yYcK4nANZz4-Scene-027.mp4",
+      "num_inference_steps": 40,
+      "height": 480,
+      "width": 832,
+      "num_frames": 77
+    },
+    {
+      "caption": "A large metal cylinder is seen pressing down on a pile of colorful candies, flattening them as if they were under a hydraulic press. The candies are crushed and broken into small pieces, creating a mess on the table.",
+      "image_path": null,
+      "video_path": "validation_dataset/yYcK4nANZz4-Scene-030.mp4",
+      "num_inference_steps": 40,
+      "height": 480,
+      "width": 832,
+      "num_frames": 77
+    },
+    {
+      "caption": "A watermelon wearing a helmet is crushed by a hydraulic press, causing it to flatten and burst open.",
+      "image_path": null,
+      "video_path": "validation_dataset/1gGQy4nxyUo-Scene-016.mp4",
+      "num_inference_steps": 40,
+      "height": 480,
+      "width": 832,
+      "num_frames": 77
+    },
+    {
+      "caption": "The video shows a green and orange object being flattened as if it were under a hydraulic press, with the press moving down and compressing the object.",
+      "image_path": null,
+      "video_path": "validation_dataset/1gGQy4nxyUo-Scene-056.mp4",
+      "num_inference_steps": 40,
+      "height": 480,
+      "width": 832,
+      "num_frames": 77
+    },
+    {
+      "caption": "The video shows a cylindrical object with a cityscape image being flattened as if it were under a hydraulic press. The object is placed on a metal platform, and a large, striped cylinder presses down on it, causing it to collapse and release a liquid inside. The background features a green wall with a yellow and red warning sign.",
+      "image_path": null,
+      "video_path": "validation_dataset/1gGQy4nxyUo-Scene-059.mp4",
+      "num_inference_steps": 40,
+      "height": 480,
+      "width": 832,
+      "num_frames": 77
+    },
+    {
+      "caption": "The video shows a close-up of an orange being flattened as if it were under a hydraulic press, with the press moving down and compressing the fruit until it is completely flattened.",
+      "image_path": null,
+      "video_path": "validation_dataset/EJqsC21GSBY-Scene-059.mp4",
+      "num_inference_steps": 40,
+      "height": 480,
+      "width": 832,
+      "num_frames": 77
+    },
+    {
+      "caption": "A colorful puzzle ball is being crushed by a large metal cylinder, which flattens the objects as if they were under a hydraulic press.",
+      "image_path": null,
+      "video_path": "validation_dataset/GBSfpTcKegk-Scene-003.mp4",
+      "num_inference_steps": 40,
+      "height": 480,
+      "width": 832,
+      "num_frames": 77
+    }
+  ]
+}
@@ -0,0 +1,13 @@
+{
+  "data": [
+    {
+      "caption": "A watermelon wearing a helmet is crushed by a hydraulic press, causing it to flatten and burst open.",
+      "image_path": null,
+      "video_path": "validation_dataset/1gGQy4nxyUo-Scene-016.mp4",
+      "num_inference_steps": 40,
+      "height": 480,
+      "width": 832,
+      "num_frames": 77
+    }
+  ]
+}
@@ -16,29 +16,30 @@ 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 MASTER_PORT=29501
 export TOKENIZERS_PARALLELISM=false
-export WANDB_API_KEY=your_wandb_api_key
+export WANDB_API_KEY="50632ebd88ffd970521cec9ab4a1a2d7e85bfc45"
 export WANDB_BASE_URL="https://api.wandb.ai"
-export WANDB_MODE=online
+export WANDB_MODE=offline
 export FASTVIDEO_ATTENTION_BACKEND=FLASH_ATTN

 # Configs
-NUM_GPUS=1
+NUM_GPUS=4

 # Model paths for Self-Forcing DMD distillation:
 GENERATOR_MODEL_PATH="wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers"
 REAL_SCORE_MODEL_PATH="Wan-AI/Wan2.1-T2V-14B-Diffusers"  # Teacher model
 FAKE_SCORE_MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"  # Critic model

-DATA_DIR=your_data_dir
-VALIDATION_DATASET_FILE=your_validation_data_dir
+DATA_DIR="data/mixkit-64_processed/Node_0_GPU_1_File_1/combined_parquet_dataset"
+VALIDATION_DATASET_FILE="data/mixkit-64_processed/validation.json"
 # export CUDA_VISIBLE_DEVICES=4,5
 # IP=[MASTER NODE IP]

+# Training arguments
 training_args=(
-  --tracker_project_name SFwan_t2v_distill_self_forcing_dmd  
-  --output_dir your_output_dir
+  --tracker_project_name SFwan_t2v_distill_self_forcing_dmd  # Updated for self-forcing DMD
+  --output_dir "/mnt/sharefs/users/hao.zhang/SFwan_t2v_finetune"
  --max_train_steps 4000
  --train_batch_size 1
  --train_sp_batch_size 1
@@ -46,15 +47,16 @@ training_args=(
  --num_latent_t 21
  --num_height 480
  --num_width 832
+  --num_frames 81  # Must be divisible by num_frame_per_block (81 % 3 = 0 ✓)
  --enable_gradient_checkpointing_type "full"
  --log_visualization
  --simulate_generator_forward
-  --num_frames 81
  --num_frame_per_block 3  # Frame generation block size for self-forcing
  --enable_gradient_masking
  --gradient_mask_last_n_frames 21
 )

+# Parallel arguments
 parallel_args=(
  --num_gpus $NUM_GPUS # 64
  --sp_size 1
@@ -63,26 +65,31 @@ parallel_args=(
  --hsdp_shard_dim $NUM_GPUS
 )

+# Model arguments
 model_args=(
  --model_path $GENERATOR_MODEL_PATH  # TODO: check if you can remove this in this script
  --pretrained_model_name_or_path $GENERATOR_MODEL_PATH
+  --generator_model_path $GENERATOR_MODEL_PATH
  --real_score_model_path $REAL_SCORE_MODEL_PATH
  --fake_score_model_path $FAKE_SCORE_MODEL_PATH
 )

+# Dataset arguments
 dataset_args=(
  --data_path "$DATA_DIR"
  --dataloader_num_workers 4
 )

+# Validation arguments
 validation_args=(
  --log_validation
  --validation_dataset_file "$VALIDATION_DATASET_FILE"
-  --validation_steps 50
+  --validation_steps 100
  --validation_sampling_steps "4"
  --validation_guidance_scale "6.0" # not used for dmd inference
 )

+# Optimizer arguments
 optimizer_args=(
  --learning_rate 1e-5
  --mixed_precision "bf16"
@@ -93,6 +100,7 @@ optimizer_args=(
  --max_grad_norm 1.0
 )

+# Miscellaneous arguments
 miscellaneous_args=(
  --inference_mode False
  --checkpoints_total_limit 3
@@ -103,9 +111,10 @@ miscellaneous_args=(
  --use_ema True
  --ema_decay 0.99
  --ema_start_step 100
-  --init_weights_from_safetensors your_ode_init_weights_path
+  --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
@@ -117,11 +126,13 @@ dmd_args=(
  --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 \
@@ -137,4 +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=$GPU_NUM \
    --samples_per_file 8 \
    --flush_frequency 8 \
    --video_length_tolerance_range 5 \
-    --preprocess_task "t2v" 
+    --preprocess_task "t2v" 
@@ -1,157 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=t2v
-#SBATCH --partition=main
-#SBATCH --nodes=4
-#SBATCH --ntasks=4
-#SBATCH --ntasks-per-node=1
-#SBATCH --gres=gpu:8
-#SBATCH --cpus-per-task=128
-#SBATCH --mem=1440G
-#SBATCH --output=dmd_t2v_output/t2v_%j.out
-#SBATCH --error=dmd_t2v_output/t2v_%j.err
-#SBATCH --exclusive
-
-# Basic Info
-export NCCL_P2P_DISABLE=1
-export TORCH_NCCL_ENABLE_MONITORING=0
-export NCCL_DEBUG_SUBSYS=INIT,NET
-# different cache dir for different processes
-export TRITON_CACHE_DIR=/tmp/triton_cache_${SLURM_PROCID}
-export MASTER_PORT=29500
-export NODE_RANK=$SLURM_PROCID
-nodes=( $(scontrol show hostnames $SLURM_JOB_NODELIST) )
-export MASTER_ADDR=${nodes[0]}
-export TOKENIZERS_PARALLELISM=false
-export WANDB_API_KEY="2f25ad37933894dbf0966c838c0b8494987f9f2f"
-# export WANDB_API_KEY='your_wandb_api_key_here'
-export WANDB_BASE_URL="https://api.wandb.ai"
-export WANDB_MODE=online
-export FASTVIDEO_ATTENTION_BACKEND=FLASH_ATTN
-
-# 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/Wan2.2-T2V-A14B-Diffusers" # Teacher model
-# FAKE_SCORE_MODEL_PATH="Wan-AI/Wan2.2-T2V-A14B-Diffusers" # Critic model
-GENERATOR_MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"  # Updated to Wan2.2
-REAL_SCORE_MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-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/"
-DATA_DIR="/mnt/weka/home/hao.zhang/matthew/FastVideo/data/test-text-preprocessing"
-# DATA_DIR=data/crush-smol_processed_t2v/combined_parquet_dataset
-# DATA_DIR="/mnt/sharefs/users/hao.zhang/Vchitect-2M/Wan-Syn-upload/latents_i2v/train/"
-# VALIDATION_DATASET_FILE="data/crush-smol-single_processed_t2v/validation.json"
-VALIDATION_DATASET_FILE="/mnt/weka/home/hao.zhang/wl/FastVideo/examples/distill/Wan2.2-TI2V-5B-Diffusers/Data-free/validation_64.json"
-
-# export CUDA_VISIBLE_DEVICES=4,5
-# IP=[MASTER NODE IP]
-
-training_args=(
-  --tracker_project_name SFwan2.2_t2v_distill_self_forcing_dmd  # Updated for Wan2.2
-  --output_dir "/mnt/sharefs/users/hao.zhang/SFwan2.2_t2v_finetune"
-  --override_transformer_cls_name "CausalWanTransformer3DModel"
-  --max_train_steps 4000
-  --train_batch_size 1
-  --train_sp_batch_size 1
-  --gradient_accumulation_steps 1
-  --num_latent_t 21
-  --num_height 448  # Updated to match Wan2.2 config
-  --num_width 832   # Updated to match Wan2.2 config
-  --enable_gradient_checkpointing_type "full"
-  --simulate_generator_forward
-  # --log_visualization
-  --num_frames 81
-  --num_frame_per_block 3  # Frame generation block size for self-forcing
-  --enable_gradient_masking
-  --gradient_mask_last_n_frames 21
-  # --init_weights_from_safetensors /mnt/sharefs/users/hao.zhang/wl/models/sf_ode_init_wan22_checkpoints/high/3k/
-  # --init_weights_from_safetensors_2 /mnt/sharefs/users/hao.zhang/wl/models/sf_ode_init_wan22_checkpoints/low/3k/
-)
-
-parallel_args=(
-  --num_gpus 32 # 64
-  --sp_size 1
-  --tp_size 1
-  --hsdp_replicate_dim 1 # 64
-  --hsdp_shard_dim 32
-)
-
-model_args=(
-  --model_path $GENERATOR_MODEL_PATH
-  --pretrained_model_name_or_path $GENERATOR_MODEL_PATH
-  --real_score_model_path $REAL_SCORE_MODEL_PATH
-  --fake_score_model_path $FAKE_SCORE_MODEL_PATH
-)
-
-dataset_args=(
-  --data_path "$DATA_DIR"
-  --dataloader_num_workers 4
-)
-
-validation_args=(
-  --log_validation
-  --validation_dataset_file "$VALIDATION_DATASET_FILE"
-  --validation_steps 20
-  --validation_sampling_steps "4"
-  --validation_guidance_scale "6.0" # not used for dmd inference
-)
-
-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_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
-)
-
-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_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)
-)
-
-srun torchrun \
--nnodes $SLURM_JOB_NUM_NODES \
--nproc_per_node $NUM_GPUS \
--node_rank $SLURM_PROCID \
--rdzv_backend=c10d \
--rdzv_endpoint="$MASTER_ADDR:$MASTER_PORT" \
-    fastvideo/training/wan_self_forcing_distillation_pipeline.py \
-    "${parallel_args[@]}" \
-    "${model_args[@]}" \
-    "${dataset_args[@]}" \
-    "${training_args[@]}" \
-    "${optimizer_args[@]}" \
-    "${validation_args[@]}" \
-    "${miscellaneous_args[@]}" \
-    "${dmd_args[@]}" \
-    "${self_forcing_args[@]}"
@@ -39,18 +39,15 @@ echo "NODE_RANK: $NODE_RANK"
 # Configs
 NUM_GPUS=8
 MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
-REAL_SCORE_MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
-FAKE_SCORE_MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
 DATA_DIR=your_data_dir
 VALIDATION_DATASET_FILE=your_validation_dataset_file
-OUTPUT_DIR="checkpoints/wan_t2v_finetune"
 # export CUDA_VISIBLE_DEVICES=4,5
 # IP=[MASTER NODE IP]

 # Training arguments
 training_args=(
  --tracker_project_name wan_t2v_distill_dmd_VSA
-  --output_dir $OUTPUT_DIR
+  --output_dir"checkpoints/wan_t2v_finetune"
  --max_train_steps 4000
  --train_batch_size 1
  --train_sp_batch_size 1
@@ -75,8 +72,6 @@ parallel_args=(
 model_args=(
  --model_path $MODEL_PATH
  --pretrained_model_name_or_path $MODEL_PATH
-  --real_score_model_path $REAL_SCORE_MODEL_PATH
-  --fake_score_model_path $FAKE_SCORE_MODEL_PATH
 )

 # Dataset arguments
@@ -96,7 +91,7 @@ validation_args=(

 # Optimizer arguments
 optimizer_args=(
-  --learning_rate 2e-6
+  --learning_rate 1e-5
  --mixed_precision "bf16"
  --training_state_checkpointing_steps 500
  --weight_only_checkpointing_steps 500
@@ -139,4 +134,4 @@ srun torchrun \
    "${optimizer_args[@]}" \
    "${validation_args[@]}" \
    "${miscellaneous_args[@]}" \
-    "${dmd_args[@]}"
+    "${dmd_args[@]}"
@@ -39,18 +39,15 @@ echo "NODE_RANK: $NODE_RANK"
 # Configs
 NUM_GPUS=8
 MODEL_PATH="Wan-AI/Wan2.1-T2V-14B-Diffusers"
-REAL_SCORE_MODEL_PATH="Wan-AI/Wan2.1-T2V-14B-Diffusers"
-FAKE_SCORE_MODEL_PATH="Wan-AI/Wan2.1-T2V-14B-Diffusers"
 DATA_DIR=your_data_dir
 VALIDATION_DATASET_FILE=your_validation_dataset_file
-OUTPUT_DIR="checkpoints/wan_t2v_finetune"
 # export CUDA_VISIBLE_DEVICES=4,5
 # IP=[MASTER NODE IP]

 # Training arguments
 training_args=(
  --tracker_project_name wan_t2v_distill_dmd_VSA
-  --output_dir "$OUTPUT_DIR"
+  --output_dir "checkpoints/wan_t2v_finetune"
  --max_train_steps 4000
  --train_batch_size 1
  --train_sp_batch_size 1
@@ -75,8 +72,6 @@ parallel_args=(
 model_args=(
  --model_path $MODEL_PATH
  --pretrained_model_name_or_path $MODEL_PATH
-  --real_score_model_path $REAL_SCORE_MODEL_PATH
-  --fake_score_model_path $FAKE_SCORE_MODEL_PATH
 )

 # Dataset arguments
@@ -96,7 +91,7 @@ validation_args=(

 # Optimizer arguments
 optimizer_args=(
-  --learning_rate 2e-6
+  --learning_rate 1e-5
  --mixed_precision "bf16"
  --training_state_checkpointing_steps 500
  --weight_only_checkpointing_steps 500
@@ -139,4 +134,4 @@ srun torchrun \
    "${optimizer_args[@]}" \
    "${validation_args[@]}" \
    "${miscellaneous_args[@]}" \
-    "${dmd_args[@]}"
+    "${dmd_args[@]}"
@@ -39,18 +39,15 @@ echo "NODE_RANK: $NODE_RANK"
 # Configs
 NUM_GPUS=8
 MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
-REAL_SCORE_MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
-FAKE_SCORE_MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
 DATA_DIR=your_data_dir
 VALIDATION_DATASET_FILE=your_validation_dataset_file
-OUTPUT_DIR="checkpoints/wan_t2v_finetune"
 # export CUDA_VISIBLE_DEVICES=4,5
 # IP=[MASTER NODE IP]

 # Training arguments
 training_args=(
  --tracker_project_name wan_t2v_distill_dmd
-  --output_dir "$OUTPUT_DIR"
+  --output_dir "checkpoints/wan_t2v_finetune"
  --max_train_steps 4000
  --train_batch_size 1
  --train_sp_batch_size 1
@@ -75,8 +72,6 @@ parallel_args=(
 model_args=(
  --model_path $MODEL_PATH
  --pretrained_model_name_or_path $MODEL_PATH
-  --real_score_model_path $REAL_SCORE_MODEL_PATH
-  --fake_score_model_path $FAKE_SCORE_MODEL_PATH
 )

 # Dataset arguments
@@ -96,7 +91,7 @@ validation_args=(

 # Optimizer arguments
 optimizer_args=(
-  --learning_rate 2e-6
+  --learning_rate 1e-5
  --mixed_precision "bf16"
  --training_state_checkpointing_steps 500
  --weight_only_checkpointing_steps 500
@@ -138,4 +133,4 @@ srun torchrun \
    "${optimizer_args[@]}" \
    "${validation_args[@]}" \
    "${miscellaneous_args[@]}" \
-    "${dmd_args[@]}"
+    "${dmd_args[@]}"
@@ -1,3 +1,3 @@
 #!/bin/bash

-python scripts/huggingface/download_hf.py --repo_id "FastVideo/Wan-Syn_77x448x832_600k" --local_dir "FastVideo/Wan-Syn_77x448x832_600k" --repo_type "dataset"
+python scripts/huggingface/download_hf.py --repo_id "FastVideo/Wan-Syn_77x448x832_600k" --local_dir "FastVideo/Wan-Syn_77x448x832_600k" --repo_type "dataset"
@@ -40,18 +40,15 @@ echo "NODE_RANK: $NODE_RANK"
 # Configs
 NUM_GPUS=8
 MODEL_PATH="Wan-AI/Wan2.2-TI2V-5B-Diffusers"
-REAL_SCORE_MODEL_PATH="Wan-AI/Wan2.2-TI2V-5B-Diffusers"
-FAKE_SCORE_MODEL_PATH="Wan-AI/Wan2.2-TI2V-5B-Diffusers"
 DATA_DIR=your_data_dir
 VALIDATION_DIR=your_validation_path  #(example:validation_64.json)
-OUTPUT_DIR="checkpoints/wan_t2v_finetune"
 # export CUDA_VISIBLE_DEVICES=4,5
 # IP=[MASTER NODE IP]

 # Training arguments
 training_args=(
  --tracker_project_name Wan_distillation
-  --output_dir "$OUTPUT_DIR"
+  --output_dir "your_output_dir"
  --max_train_steps 4000
  --train_batch_size 1
  --train_sp_batch_size 1
@@ -76,8 +73,6 @@ parallel_args=(
 model_args=(
  --model_path $MODEL_PATH
  --pretrained_model_name_or_path $MODEL_PATH
-  --real_score_model_path $REAL_SCORE_MODEL_PATH
-  --fake_score_model_path $FAKE_SCORE_MODEL_PATH
 )

 # Dataset arguments
@@ -97,11 +92,11 @@ validation_args=(

 # Optimizer arguments
 optimizer_args=(
-  --learning_rate 4e-6
+  --learning_rate 2e-5
  --lr_scheduler "cosine_with_min_lr"
  --min_lr_ratio 0.5
  --lr_warmup_steps 100
-  --fake_score_learning_rate 2e-6
+  --fake_score_learning_rate 1e-5
  --fake_score_lr_scheduler "cosine_with_min_lr"
  --mixed_precision "bf16"
  --training_state_checkpointing_steps 500
@@ -146,4 +141,4 @@ srun torchrun \
    "${optimizer_args[@]}" \
    "${validation_args[@]}" \
    "${miscellaneous_args[@]}" \
-    "${dmd_args[@]}"
+    "${dmd_args[@]}"
@@ -40,8 +40,6 @@ echo "NODE_RANK: $NODE_RANK"
 # Configs
 NUM_GPUS=8
 MODEL_PATH="Wan-AI/Wan2.2-TI2V-5B-Diffusers"
-REAL_SCORE_MODEL_PATH="Wan-AI/Wan2.2-TI2V-5B-Diffusers"
-FAKE_SCORE_MODEL_PATH="Wan-AI/Wan2.2-TI2V-5B-Diffusers"
 DATA_DIR=your_data_dir
 VALIDATION_DIR=your_validation_path  #(example:validation_64.json)
 # export CUDA_VISIBLE_DEVICES=4,5
@@ -75,8 +73,6 @@ parallel_args=(
 model_args=(
  --model_path $MODEL_PATH
  --pretrained_model_name_or_path $MODEL_PATH
-  --real_score_model_path $REAL_SCORE_MODEL_PATH
-  --fake_score_model_path $FAKE_SCORE_MODEL_PATH
 )

 # Dataset arguments
@@ -96,11 +92,11 @@ validation_args=(

 # Optimizer arguments
 optimizer_args=(
-  --learning_rate 4e-6
+  --learning_rate 2e-5
  --lr_scheduler "cosine_with_min_lr"
  --min_lr_ratio 0.5
  --lr_warmup_steps 100
-  --fake_score_learning_rate 2e-6
+  --fake_score_learning_rate 1e-5
  --fake_score_lr_scheduler "cosine_with_min_lr"
  --mixed_precision "bf16"
  --training_state_checkpointing_steps 500
@@ -146,4 +142,4 @@ srun torchrun \
    "${optimizer_args[@]}" \
    "${validation_args[@]}" \
    "${miscellaneous_args[@]}" \
-    "${dmd_args[@]}"
+    "${dmd_args[@]}"
@@ -14,29 +14,26 @@ export FASTVIDEO_ATTENTION_BACKEND=VIDEO_SPARSE_ATTN
 # Configs
 NUM_GPUS=1
 MODEL_PATH="Wan-AI/Wan2.2-TI2V-5B-Diffusers"
-REAL_SCORE_MODEL_PATH="Wan-AI/Wan2.2-TI2V-5B-Diffusers" 
-FAKE_SCORE_MODEL_PATH="Wan-AI/Wan2.2-TI2V-5B-Diffusers"
 DATA_DIR="data/crush-smol_processed_ti2v/combined_parquet_dataset/"
 VALIDATION_DATASET_FILE="examples/distill/Wan2.2-TI2V-5B-Diffusers/crush_smol/validation.json"
-OUTPUT_DIR="checkpoints/wan_t2v_finetune"
 # export CUDA_VISIBLE_DEVICES=4,5
 # IP=[MASTER NODE IP]

 # Training arguments
 training_args=(
  --tracker_project_name wan_t2v_distill_dmd_VSA
-  --output_dir "$OUTPUT_DIR"
-  --max_train_steps 4000
-  --train_batch_size 1
+  --output_dir="checkpoints/wan_t2v_finetune"
+  --max_train_steps=4000
+  --train_batch_size=1
  --train_sp_batch_size 1
-  --gradient_accumulation_steps 1
+  --gradient_accumulation_steps=1
  --num_latent_t 31
  --num_height 704
  --num_width 1280
  --num_frames 121
  --enable_gradient_checkpointing_type "full"
-  --training_state_checkpointing_steps 500
-  --weight_only_checkpointing_steps 500
+  --training_state_checkpointing_steps=500
+  --weight_only_checkpointing_steps=500
 )

 # Parallel arguments
@@ -52,8 +49,6 @@ parallel_args=(
 model_args=(
  --model_path $MODEL_PATH
  --pretrained_model_name_or_path $MODEL_PATH
-  --real_score_model_path $REAL_SCORE_MODEL_PATH
-  --fake_score_model_path $FAKE_SCORE_MODEL_PATH
 )

 # Dataset arguments
@@ -73,8 +68,8 @@ validation_args=(

 # Optimizer arguments
 optimizer_args=(
-  --learning_rate 2e-6
-  --mixed_precision "bf16"
+  --learning_rate=1e-5
+  --mixed_precision="bf16"
  --weight_decay 0.01
  --max_grad_norm 1.0
 )
@@ -112,4 +107,4 @@ torchrun \
    "${optimizer_args[@]}" \
    "${validation_args[@]}" \
    "${miscellaneous_args[@]}" \
-    "${dmd_args[@]}"
+    "${dmd_args[@]}"
@@ -14,8 +14,6 @@ export FASTVIDEO_ATTENTION_BACKEND=VIDEO_SPARSE_ATTN
 # Configs
 NUM_GPUS=1
 MODEL_PATH="Wan-AI/Wan2.2-TI2V-5B-Diffusers"
-REAL_SCORE_MODEL_PATH="Wan-AI/Wan2.2-TI2V-5B-Diffusers"
-FAKE_SCORE_MODEL_PATH="Wan-AI/Wan2.2-TI2V-5B-Diffusers"
 DATA_DIR="data/crush-smol_processed_ti2v/combined_parquet_dataset/"
 VALIDATION_DATASET_FILE="examples/distill/Wan2.2-TI2V-5B-Diffusers/crush_smol/validation.json"
 # export CUDA_VISIBLE_DEVICES=4,5
@@ -53,8 +51,6 @@ parallel_args=(
 model_args=(
  --model_path $MODEL_PATH
  --pretrained_model_name_or_path $MODEL_PATH
-  --real_score_model_path $REAL_SCORE_MODEL_PATH
-  --fake_score_model_path $FAKE_SCORE_MODEL_PATH
 )

 # Dataset arguments
@@ -113,4 +109,4 @@ torchrun \
    "${optimizer_args[@]}" \
    "${validation_args[@]}" \
    "${miscellaneous_args[@]}" \
-    "${dmd_args[@]}"
+    "${dmd_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" 
@@ -1,6 +1,6 @@
 from fastvideo import VideoGenerator

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

 OUTPUT_PATH = "video_samples"
 def main():
@@ -17,31 +17,30 @@ def main():
        vae_cpu_offload=False,
        text_encoder_cpu_offload=True,
        pin_cpu_memory=True, # set to false if low CPU RAM or hit obscure "CUDA error: Invalid argument"
+        ti2v_task=True,
        # image_encoder_cpu_offload=False,
    )

-    # sampling_param = SamplingParam.from_pretrained("Wan-AI/Wan2.1-T2V-1.3B-Diffusers")
+    sampling_param = SamplingParam.from_pretrained("Wan-AI/Wan2.1-T2V-1.3B-Diffusers")
    # sampling_param.num_frames = 45
-    # sampling_param.image_path = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/astronaut.jpg"
+    sampling_param.image_path = "test.jpg"
    # Generate videos with the same simple API, regardless of GPU count
    prompt = (
-        "A curious raccoon peers through a vibrant field of yellow sunflowers, its eyes "
-        "wide with interest. The playful yet serene atmosphere is complemented by soft "
-        "natural light filtering through the petals. Mid-shot, warm and cheerful tones."
+        "A girl is packing a suitcase when stuff suddently starts flying around the room."
    )
    video = generator.generate_video(prompt, output_path=OUTPUT_PATH, save_video=True)
    # video = generator.generate_video(prompt, sampling_param=sampling_param, output_path="wan_t2v_videos/")

    # Generate another video with a different prompt, without reloading the
    # model!
-    prompt2 = (
-        "A majestic lion strides across the golden savanna, its powerful frame "
-        "glistening under the warm afternoon sun. The tall grass ripples gently in "
-        "the breeze, enhancing the lion's commanding presence. The tone is vibrant, "
-        "embodying the raw energy of the wild. Low angle, steady tracking shot, "
-        "cinematic.")
-    video2 = generator.generate_video(prompt2, output_path=OUTPUT_PATH, save_video=True)
+    # prompt2 = (
+    #     "A majestic lion strides across the golden savanna, its powerful frame "
+    #     "glistening under the warm afternoon sun. The tall grass ripples gently in "
+    #     "the breeze, enhancing the lion's commanding presence. The tone is vibrant, "
+    #     "embodying the raw energy of the wild. Low angle, steady tracking shot, "
+    #     "cinematic.")
+    # video2 = generator.generate_video(prompt2, output_path=OUTPUT_PATH, save_video=True)


 if __name__ == "__main__":
-    main()
+    main()
@@ -1,44 +0,0 @@
-from fastvideo import VideoGenerator
-
-# from fastvideo.configs.sample import SamplingParam
-
-OUTPUT_PATH = "video_samples"
-def main():
-    # FastVideo will automatically use the optimal default arguments for the
-    # model.
-    # If a local path is provided, FastVideo will make a best effort
-    # attempt to identify the optimal arguments.
-    generator = VideoGenerator.from_pretrained(
-        "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
-        # FastVideo will automatically handle distributed setup
-        num_gpus=2,
-        use_fsdp_inference=True,
-        dit_cpu_offload=False,
-        vae_cpu_offload=False,
-        text_encoder_cpu_offload=True,
-        pin_cpu_memory=True, # set to false if low CPU RAM or hit obscure "CUDA error: Invalid argument"
-        distributed_executor_backend="ray",
-        # image_encoder_cpu_offload=False,
-    )
-
-    # Generate videos with the same simple API, regardless of GPU count
-    prompt = (
-        "A curious raccoon peers through a vibrant field of yellow sunflowers, its eyes "
-        "wide with interest. The playful yet serene atmosphere is complemented by soft "
-        "natural light filtering through the petals. Mid-shot, warm and cheerful tones."
-    )
-    video = generator.generate_video(prompt, output_path=OUTPUT_PATH, save_video=True)
-
-    # Generate another video with a different prompt, without reloading the
-    # model!
-    prompt2 = (
-        "A majestic lion strides across the golden savanna, its powerful frame "
-        "glistening under the warm afternoon sun. The tall grass ripples gently in "
-        "the breeze, enhancing the lion's commanding presence. The tone is vibrant, "
-        "embodying the raw energy of the wild. Low angle, steady tracking shot, "
-        "cinematic.")
-    video2 = generator.generate_video(prompt2, output_path=OUTPUT_PATH, save_video=True)
-
-
-if __name__ == "__main__":
-    main()
@@ -19,13 +19,15 @@ def main():
    )

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

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

 if __name__ == "__main__":
    main()
@@ -1,36 +0,0 @@
-from fastvideo import VideoGenerator
-
-# from fastvideo.configs.sample import SamplingParam
-
-OUTPUT_PATH = "video_samples_wan2_1_Fun"
-OUTPUT_NAME = "wan2.1_test"
-def main():
-    # FastVideo will automatically use the optimal default arguments for the
-    # model.
-    # If a local path is provided, FastVideo will make a best effort
-    # attempt to identify the optimal arguments.
-    generator = VideoGenerator.from_pretrained(
-        "IRMChen/Wan2.1-Fun-1.3B-Control-Diffusers",
-        # "alibaba-pai/Wan2.2-Fun-A14B-Control",
-        # FastVideo will automatically handle distributed setup
-        num_gpus=1,
-        use_fsdp_inference=True,
-        dit_cpu_offload=True, # DiT need to be offloaded for MoE
-        vae_cpu_offload=False,
-        text_encoder_cpu_offload=True,
-        # Set pin_cpu_memory to false if CPU RAM is limited and there're no frequent CPU-GPU transfer
-        pin_cpu_memory=True,
-        # image_encoder_cpu_offload=False,
-    )
-
-    prompt = "一位年轻女性穿着一件粉色的连衣裙，裙子上有白色的装饰和粉色的纽扣。她的头发是紫色的，头上戴着一个红色的大蝴蝶结，显得非常可爱和精致。她还戴着一个红色的领结，整体造型充满了少女感和活力。她的表情温柔，双手轻轻交叉放在身前，姿态优雅。背景是简单的灰色，没有任何多余的装饰，使得人物更加突出。她的妆容清淡自然，突显了她的清新气质。整体画面给人一种甜美、梦幻的感觉，仿佛置身于童话世界中。"
-    negative_prompt = "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走"
-    # prompt                  = "A young woman with beautiful, clear eyes and blonde hair stands in the forest, wearing a white dress and a crown. Her expression is serene, reminiscent of a movie star, with fair and youthful skin. Her brown long hair flows in the wind. The video quality is very high, with a clear view. High quality, masterpiece, best quality, high resolution, ultra-fine, fantastical."
-    # negative_prompt         = "Twisted body, limb deformities, text captions, comic, static, ugly, error, messy code."
-    image_path = "https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/wan_fun/asset_Wan2_2/v1.0/8.png"
-    control_video_path = "https://pai-aigc-photog.oss-cn-hangzhou.aliyuncs.com/wan_fun/asset_Wan2_2/v1.0/pose.mp4"
-
-    video = generator.generate_video(prompt, negative_prompt=negative_prompt, image_path=image_path, video_path=control_video_path, output_path=OUTPUT_PATH, output_video_name=OUTPUT_NAME, save_video=True)
-
-if __name__ == "__main__":
-    main()
@@ -1,56 +0,0 @@
-# FastVideo Gradio Local Demo
-
-This is a Gradio-based web interface for generating videos using the FastVideo framework. The demo allows users to create videos from text prompts with various customization options.
-
-## Overview
-
-The demo uses the FastVideo framework to generate videos based on text prompts. It provides a simple web interface built with Gradio that allows users to:
-
- Enter text prompts to generate videos
- Customize video parameters (dimensions, number of frames, etc.)
- Use negative prompts to guide the generation process
- Set or randomize seeds for reproducibility
-
---
-
-## Usage
-
-Run the demo with:
-
-```bash
-python examples/inference/gradio/local/gradio_local_demo.py
-```
-
-This will start a web server at `http://0.0.0.0:7860` where you can access the interface.
-
---
-
-## Model Initialization
-
-This demo initializes a `VideoGenerator` with the minimum required arguments for inference. Users can seamlessly adjust inference options between generations, including prompts, resolution, video length, *without ever needing to reload the model*.
-
-## Video Generation
-
-The core functionality is in the `generate_video` function, which:
-1. Processes user inputs
-2. Uses the FastVideo VideoGenerator from earlier to run inference (`generator.generate_video()`)
-
-## Gradio Interface
-
-The interface is built with several components:
- A text input for the prompt
- A video display for the result
- Inference options in a collapsible accordion:
-  - Height and width sliders
-  - Number of frames slider
-  - Guidance scale slider
-  - Negative prompt options
-  - Seed controls
-
-### Inference Options
-
- **Height/Width**: Control the resolution of the generated video
- **Number of Frames**: Set how many frames to generate
- **Guidance Scale**: Control how closely the generation follows the prompt
- **Negative Prompt**: Specify what you don't want to see in the video
- **Seed**: Control randomness for reproducible results
@@ -1,656 +0,0 @@
-import argparse
-import os
-import base64
-import time
-
-import gradio as gr
-from fastvideo.entrypoints.video_generator import VideoGenerator
-from fastvideo.configs.sample.base import SamplingParam
-from copy import deepcopy
-
-
-MODEL_PATH_MAPPING = {
-    "FastWan2.1-T2V-1.3B": "FastVideo/FastWan2.1-T2V-1.3B-Diffusers",
-    # "FastWan2.2-TI2V-5B-FullAttn": "FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers",
-}
-
-def create_timing_display(inference_time, total_time, stage_execution_times, num_frames):
-    dit_denoising_time = f"{stage_execution_times[5]:.2f}s" if len(stage_execution_times) > 5 else "N/A"
-    
-    timing_html = f"""
-    <div style="margin: 10px 0;">
-        <h3 style="text-align: center; margin-bottom: 10px;">⏱️ Timing Breakdown</h3>
-        <div style="display: grid; grid-template-columns: repeat(5, 1fr); gap: 10px; margin-bottom: 10px;">
-            <div class="timing-card timing-card-highlight">
-                <div style="font-size: 20px;">🚀</div>
-                <div style="font-weight: bold; margin: 3px 0; font-size: 14px;">DiT Denoising</div>
-                <div style="font-size: 16px; color: #ffa200; font-weight: bold;">{dit_denoising_time}</div>
-            </div>
-            <div class="timing-card">
-                <div style="font-size: 20px;">🧠</div>
-                <div style="font-weight: bold; margin: 3px 0; font-size: 14px;">E2E (w. vae/text encoder)</div>
-                <div style="font-size: 16px; color: #2563eb;">{inference_time:.2f}s</div>
-            </div>
-            <div class="timing-card">
-                <div style="font-size: 20px;">🎬</div>
-                <div style="font-weight: bold; margin: 3px 0; font-size: 14px;">Video Encoding</div>
-                <div style="font-size: 16px; color: #dc2626;">N/A</div>
-            </div>
-            <div class="timing-card">
-                <div style="font-size: 20px;">🌐</div>
-                <div style="font-weight: bold; margin: 3px 0; font-size: 14px;">Network Transfer</div>
-                <div style="font-size: 16px; color: #059669;">N/A</div>
-            </div>
-            <div class="timing-card">
-                <div style="font-size: 20px;">📊</div>
-                <div style="font-weight: bold; margin: 3px 0; font-size: 14px;">Total Processing</div>
-                <div style="font-size: 18px; color: #0277bd;">{total_time:.2f}s</div>
-            </div>
-        </div>"""
-    
-    if inference_time > 0:
-        fps = num_frames / inference_time
-        timing_html += f"""
-        <div class="performance-card" style="margin-top: 15px;">
-            <span style="font-weight: bold;">Generation Speed: </span>
-            <span style="font-size: 18px; color: #6366f1; font-weight: bold;">{fps:.1f} frames/second</span>
-        </div>"""
-    
-    return timing_html + "</div>"
-def setup_model_environment(model_path: str) -> None:
-    if "fullattn" in model_path.lower():
-        os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "FLASH_ATTN"
-    else:
-        os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "VIDEO_SPARSE_ATTN"
-    os.environ["FASTVIDEO_STAGE_LOGGING"] = "1"
-
-def load_example_prompts():
-    def contains_chinese(text):
-        return any('\u4e00' <= char <= '\u9fff' for char in text)
-    
-    def load_from_file(filepath):
-        prompts, labels = [], []
-        try:
-            with open(filepath, "r", encoding='utf-8') as f:
-                for line in f:
-                    line = line.strip()
-                    if line and not contains_chinese(line):
-                        label = line[:100] + "..." if len(line) > 100 else line
-                        labels.append(label)
-                        prompts.append(line)
-        except Exception as e:
-            print(f"Warning: Could not read {filepath}: {e}")
-        return prompts, labels
-    
-    examples, example_labels = load_from_file("examples/inference/gradio/local/prompts_final.txt")
-    
-    if not examples:
-        examples = ["A crowded rooftop bar buzzes with energy, the city skyline twinkling like a field of stars in the background."]
-        example_labels = ["Crowded rooftop bar at night"]
-    
-    return examples, example_labels
-
-
-def create_gradio_interface(default_params: dict[str, SamplingParam], generators: dict[str, VideoGenerator]):
-    def generate_video(
-        prompt, negative_prompt, use_negative_prompt, seed, guidance_scale,
-        num_frames, height, width, randomize_seed, model_selection, progress
-    ):
-        model_path = MODEL_PATH_MAPPING.get(model_selection, "FastVideo/FastWan2.1-T2V-1.3B-Diffusers")
-        setup_model_environment(model_path)
-        try:
-            if progress:
-                progress(0.1, desc="Loading model for local inference...")
-            
-            generator = generators[model_path]
-            params = deepcopy(default_params[model_path])
-            total_start_time = time.time()
-            if progress:
-                progress(0.2, desc="Configuring parameters...")
-
-            params.prompt = prompt
-            params.seed = int(seed)
-            params.guidance_scale = guidance_scale
-            params.num_frames = int(num_frames)
-            params.height = int(height)
-            params.width = int(width)
-
-            if randomize_seed:
-                params.seed = torch.randint(0, 1000000, (1, )).item()
-
-            if use_negative_prompt and negative_prompt:
-                params.negative_prompt = negative_prompt
-            else:
-                params.negative_prompt = default_params[model_path].negative_prompt
-
-            if progress:
-                progress(0.4, desc="Generating video locally...")
-
-            output_dir = "outputs/"
-            os.makedirs(output_dir, exist_ok=True)
-            start_time = time.time()
-            result = generator.generate_video(prompt=prompt, sampling_param=params, save_video=True, return_frames=False)
-            inference_time = time.time() - start_time
-            logging_info = result.get("logging_info", None)
-            if logging_info:
-                stage_names = logging_info.get_execution_order()
-                stage_execution_times = [
-                    logging_info.get_stage_info(stage_name).get("execution_time", 0.0) 
-                    for stage_name in stage_names
-                ]
-            else:
-                stage_names = []
-                stage_execution_times = []
-            total_time = time.time() - total_start_time
-            timing_details=create_timing_display(inference_time=inference_time, total_time=total_time, stage_execution_times=stage_execution_times, num_frames=params.num_frames)
-            safe_prompt = params.prompt[:100].replace(' ', '_').replace('/', '_').replace('\\', '_')
-            video_filename = f"{params.prompt[:100]}.mp4"
-            output_path = os.path.join(output_dir, video_filename)
-
-            if progress:
-                progress(1.0, desc="Generation complete!")
-
-            return output_path, params.seed, timing_details
-
-        except Exception as e:
-            print(f"An error occurred during local generation: {e}")
-            return None, f"Generation failed: {str(e)}", ""
-
-    examples, example_labels = load_example_prompts()
-    
-    theme = gr.themes.Base().set(
-        button_primary_background_fill="#2563eb",
-        button_primary_background_fill_hover="#1d4ed8",
-        button_primary_text_color="white",
-        slider_color="#2563eb",
-        checkbox_background_color_selected="#2563eb",
-    )
-    
-    def get_default_values(model_name):
-        model_path = MODEL_PATH_MAPPING.get(model_name)
-        if model_path and model_path in default_params:
-            params = default_params[model_path]
-            return {
-                'height': params.height,
-                'width': params.width,
-                'num_frames': params.num_frames,
-                'guidance_scale': params.guidance_scale,
-                'seed': params.seed,
-            }
-        
-        return {
-            'height': 448,
-            'width': 832,
-            'num_frames': 61,
-            'guidance_scale': 3.0,
-            'seed': 1024,
-        }
-    
-    initial_values = get_default_values("FastWan2.1-T2V-1.3B")
-    
-    with gr.Blocks(title="FastWan", theme=theme) as demo:
-        gr.Image("assets/full.svg", show_label=False, container=False, height=80)
-
-        gr.HTML("""
-        <div style="text-align: center; margin-bottom: 10px;">
-            <p style="font-size: 18px;"> Make Video Generation Go Blurrrrrrr </p>
-            <p style="font-size: 18px;"> <a href="https://github.com/hao-ai-lab/FastVideo/tree/main" target="_blank">Code</a> | <a href="https://hao-ai-lab.github.io/blogs/fastvideo_post_training/" target="_blank">Blog</a> | <a href="https://hao-ai-lab.github.io/FastVideo/" target="_blank">Docs</a>  </p>
-        </div>
-        """)
-        
-        with gr.Accordion("🎥 What Is FastVideo?", open=False):
-            gr.HTML("""
-            <div style="padding: 20px; line-height: 1.6;">
-                <p style="font-size: 16px; margin-bottom: 15px;">
-                    FastVideo is an inference and post-training framework for diffusion models. It 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>
-            </div>
-            """)
-        
-        with gr.Row():
-            model_selection = gr.Dropdown(
-                choices=list(MODEL_PATH_MAPPING.keys()),
-                value="FastWan2.1-T2V-1.3B",
-                label="Select Model",
-                interactive=True
-            )
-
-        with gr.Row():
-            example_dropdown = gr.Dropdown(
-                choices=example_labels,
-                label="Example Prompts",
-                value=None,
-                interactive=True,
-                allow_custom_value=False
-            )
-        
-        with gr.Row():
-            with gr.Column(scale=6):
-                prompt = gr.Text(
-                    label="Prompt",
-                    show_label=False,
-                    max_lines=3,
-                    placeholder="Describe your scene...",
-                    container=False,
-                    lines=3,
-                    autofocus=True,
-                )
-            with gr.Column(scale=1, min_width=120, elem_classes="center-button"):
-                run_button = gr.Button("Run", variant="primary", size="lg")
-        
-        with gr.Row():
-            with gr.Column():
-                error_output = gr.Text(label="Error", visible=False)
-                timing_display = gr.Markdown(label="Timing Breakdown", visible=False)
-
-        with gr.Row(equal_height=True, elem_classes="main-content-row"):
-            with gr.Column(scale=1, elem_classes="advanced-options-column"):
-                with gr.Group():
-                    gr.HTML("<div style='margin: 0 0 15px 0; text-align: center; font-size: 16px;'>Advanced Options</div>")
-                    with gr.Row():
-                        height = gr.Number(
-                            label="Height",
-                            value=initial_values['height'],
-                            interactive=False,
-                            container=True
-                        )
-                        width = gr.Number(
-                            label="Width",
-                            value=initial_values['width'],
-                            interactive=False,
-                            container=True
-                        )
-                    
-                    with gr.Row():
-                        num_frames = gr.Number(
-                            label="Number of Frames",
-                            value=initial_values['num_frames'],
-                            interactive=False,
-                            container=True
-                        )
-                        guidance_scale = gr.Slider(
-                            label="Guidance Scale",
-                            minimum=1,
-                            maximum=12,
-                            value=initial_values['guidance_scale'],
-                        )
-                    
-                    with gr.Row():
-                        use_negative_prompt = gr.Checkbox(
-                            label="Use negative prompt", value=False)
-                        negative_prompt = gr.Text(
-                            label="Negative prompt",
-                            max_lines=3,
-                            lines=3,
-                            placeholder="Enter a negative prompt",
-                            visible=False,
-                        )
-
-                    seed = gr.Slider(
-                        label="Seed",
-                        minimum=0,
-                        maximum=1000000,
-                        step=1,
-                        value=initial_values['seed'],
-                    )
-                    randomize_seed = gr.Checkbox(label="Randomize seed", value=False)
-                    seed_output = gr.Number(label="Used Seed")
-        
-            with gr.Column(scale=1, elem_classes="video-column"):
-                result = gr.Video(
-                    label="Generated Video", 
-                    show_label=True,
-                    height=466,
-                    width=600,
-                    container=True,
-                    elem_classes="video-component"
-                )
-        
-        gr.HTML("""
-        <style>
-        .center-button {
-            display: flex !important;
-            justify-content: center !important;
-            height: 100% !important;
-            padding-top: 1.4em !important;
-        }
-        
-        .gradio-container {
-            max-width: 1200px !important;
-            margin: 0 auto !important;
-        }
-        
-        .main {
-            max-width: 1200px !important;
-            margin: 0 auto !important;
-        }
-        
-        .gr-form, .gr-box, .gr-group {
-            max-width: 1200px !important;
-        }
-        
-        .gr-video {
-            max-width: 500px !important;
-            margin: 0 auto !important;
-        }
-        
-        .main-content-row {
-            display: flex !important;
-            align-items: flex-start !important;
-            min-height: 500px !important;
-            gap: 20px !important;
-        }
-        
-        .advanced-options-column,
-        .video-column {
-            display: flex !important;
-            flex-direction: column !important;
-            flex: 1 !important;
-            min-height: 400px !important;
-            align-items: stretch !important;
-        }
-        
-        .video-column > * {
-            margin-top: 0 !important;
-        }
-        
-        .video-column .gr-video,
-        .video-component {
-            margin-top: 0 !important;
-            padding-top: 0 !important;
-        }
-        
-        .video-column .gr-video .gr-form {
-            margin-top: 0 !important;
-        }
-        
-        .advanced-options-column .gr-group,
-        .video-column .gr-video {
-            margin-top: 0 !important;
-            vertical-align: top !important;
-        }
-        
-        .advanced-options-column > *:last-child,
-        .video-column > *:last-child {
-            flex-grow: 0 !important;
-        }
-        
-        @media (max-width: 1400px) {
-            .main-content-row {
-                min-height: 600px !important;
-            }
-            
-            .advanced-options-column,
-            .video-column {
-                min-height: 600px !important;
-            }
-        }
-        
-        @media (max-width: 1200px) {
-            .main-content-row {
-                flex-direction: column !important;
-                align-items: stretch !important;
-            }
-            
-            .advanced-options-column,
-            .video-column {
-                min-height: auto !important;
-                width: 100% !important;
-            }
-        }
-        
-        .timing-card {
-            background: var(--background-fill-secondary) !important;
-            border: 1px solid var(--border-color-primary) !important;
-            color: var(--body-text-color) !important;
-            padding: 10px;
-            border-radius: 8px;
-            text-align: center;
-            min-height: 80px;
-            display: flex;
-            flex-direction: column;
-            justify-content: center;
-        }
-        
-        .timing-card-highlight {
-            background: var(--background-fill-primary) !important;
-            border: 2px solid var(--color-accent) !important;
-        }
-        
-        .performance-card {
-            background: var(--background-fill-secondary) !important;
-            border: 1px solid var(--border-color-primary) !important;
-            color: var(--body-text-color) !important;
-            padding: 10px;
-            border-radius: 6px;
-            text-align: center;
-        }
-        
-        .gr-number input[readonly] {
-            background-color: var(--background-fill-secondary) !important;
-            border: 1px solid var(--border-color-primary) !important;
-            color: var(--body-text-color-subdued) !important;
-            cursor: default !important;
-            text-align: center !important;
-            font-weight: 500 !important;
-        }
-        </style>
-        """)
-        
-        def on_example_select(example_label):
-            if example_label and example_label in example_labels:
-                index = example_labels.index(example_label)
-                return examples[index]
-            return ""
-        
-        example_dropdown.change(
-            fn=on_example_select,
-            inputs=example_dropdown,
-            outputs=prompt,
-        )
-        
-        gr.HTML("""
-        <div style="text-align: center; margin-top: 10px; margin-bottom: 15px;">
-            <p style="font-size: 16px; margin: 0;">Note that this demo is meant to showcase FastWan's quality and that under a large number of requests, generation speed may be affected.</p>
-        </div>
-        """)
-        
-        use_negative_prompt.change(
-            fn=lambda x: gr.update(visible=x),
-            inputs=use_negative_prompt,
-            outputs=negative_prompt,
-        )
-        
-        def on_model_selection_change(selected_model):
-            if not selected_model:
-                selected_model = "FastWan2.1-T2V-1.3B"
-            
-            model_path = MODEL_PATH_MAPPING.get(selected_model)
-            
-            if model_path and model_path in default_params:
-                params = default_params[model_path]
-                return (
-                    gr.update(value=params.height),
-                    gr.update(value=params.width),
-                    gr.update(value=params.num_frames),
-                    gr.update(value=params.guidance_scale),
-                    gr.update(value=params.seed),
-                )
-            
-            return (
-                gr.update(value=448),
-                gr.update(value=832),
-                gr.update(value=61),
-                gr.update(value=3.0),
-                gr.update(value=1024),
-            )
-        
-        model_selection.change(
-            fn=on_model_selection_change,
-            inputs=model_selection,
-            outputs=[height, width, num_frames, guidance_scale, seed],
-        )
-        
-        def handle_generation(*args, progress=None, request: gr.Request = None):
-            model_selection, prompt, negative_prompt, use_negative_prompt, seed, guidance_scale, num_frames, height, width, randomize_seed = args
-            
-            result_path, seed_or_error, timing_details = generate_video(
-                prompt, negative_prompt, use_negative_prompt, seed, guidance_scale, 
-                num_frames, height, width, randomize_seed, model_selection, progress
-            )
-            if result_path and os.path.exists(result_path):
-                return (
-                    result_path, 
-                    seed_or_error, 
-                    gr.update(visible=False),
-                    gr.update(visible=True, value=timing_details),
-                )
-            else:
-                return (
-                    None, 
-                    seed_or_error, 
-                    gr.update(visible=True, value=seed_or_error),
-                    gr.update(visible=False),
-                )
-        
-        run_button.click(
-            fn=handle_generation,
-            inputs=[
-                model_selection,
-                prompt,
-                negative_prompt,
-                use_negative_prompt,
-                seed,
-                guidance_scale,
-                num_frames,
-                height,
-                width,
-                randomize_seed,
-            ],
-            outputs=[result, seed_output, error_output, timing_display],
-            concurrency_limit=20,
-        )
-    
-    return demo
-
-
-def main():
-    parser = argparse.ArgumentParser(description="FastVideo Gradio Local Demo")
-    parser.add_argument("--t2v_model_paths", type=str,
-                        default="FastVideo/FastWan2.1-T2V-1.3B-Diffusers",
-                        help="Comma separated list of paths to the T2V model(s)")
-    parser.add_argument("--host", type=str, default="0.0.0.0",
-                        help="Host to bind to")
-    parser.add_argument("--port", type=int, default=7860,
-                        help="Port to bind to")
-    args = parser.parse_args()
-    generators = {}
-    default_params = {}
-    model_paths = args.t2v_model_paths.split(",")
-    for model_path in model_paths:
-        print(f"Loading model: {model_path}")
-        setup_model_environment(model_path)
-        generators[model_path] = VideoGenerator.from_pretrained(model_path)
-        default_params[model_path] = SamplingParam.from_pretrained(model_path)
-    demo = create_gradio_interface(default_params, generators)
-    print(f"Starting Gradio frontend at http://{args.host}:{args.port}")
-    print(f"T2V Models: {args.t2v_model_paths}")
-    
-    from fastapi import FastAPI, Request, HTTPException
-    from fastapi.responses import HTMLResponse, FileResponse
-    import uvicorn
-    
-    app = FastAPI()
-    
-    @app.get("/logo.png")
-    def get_logo():
-        return FileResponse(
-            "assets/full.svg",
-            media_type="image/svg+xml",
-            headers={
-                "Cache-Control": "public, max-age=3600",
-                "Access-Control-Allow-Origin": "*"
-            }
-        )
-    
-    @app.get("/favicon.ico")
-    def get_favicon():
-        favicon_path = "assets/icon-simple.svg"
-        
-        if os.path.exists(favicon_path):
-            return FileResponse(
-                favicon_path, 
-                media_type="image/svg+xml",
-                headers={
-                    "Cache-Control": "public, max-age=3600",
-                    "Access-Control-Allow-Origin": "*"
-                }
-            )
-        else:
-            raise HTTPException(status_code=404, detail="Favicon not found")
-    
-    @app.get("/", response_class=HTMLResponse)
-    def index(request: Request):
-        base_url = str(request.base_url).rstrip('/')
-        return f"""
-        <!DOCTYPE html>
-        <html lang="en">
-        <head>
-            <meta charset="UTF-8" />
-            <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-            
-            <title>FastWan</title>
-            <meta name="title" content="FastWan">
-            <meta name="description" content="Make video generation go blurrrrrrr">
-            <meta name="keywords" content="FastVideo, video generation, AI, machine learning, FastWan">
-            
-            <meta property="og:type" content="website">
-            <meta property="og:url" content="{base_url}/">
-            <meta property="og:title" content="FastWan">
-            <meta property="og:description" content="Make video generation go blurrrrrrr">
-            <meta property="og:image" content="{base_url}/logo.png">
-            <meta property="og:image:width" content="1200">
-            <meta property="og:image:height" content="630">
-            <meta property="og:site_name" content="FastWan">
-            
-            <meta property="twitter:card" content="summary_large_image">
-            <meta property="twitter:url" content="{base_url}/">
-            <meta property="twitter:title" content="FastWan">
-            <meta property="twitter:description" content="Make video generation go blurrrrrrr">
-            <meta property="twitter:image" content="{base_url}/logo.png">
-            <link rel="icon" type="image/png" sizes="32x32" href="/favicon.ico">
-            <link rel="icon" type="image/png" sizes="16x16" href="/favicon.ico">
-            <link rel="apple-touch-icon" href="/favicon.ico">
-            <style>
-                body, html {{
-                    margin: 0;
-                    padding: 0;
-                    height: 100%;
-                    overflow: hidden;
-                }}
-                iframe {{
-                    width: 100%;
-                    height: 100vh;
-                    border: none;
-                }}
-            </style>
-        </head>
-        <body>
-            <iframe src="/gradio" width="100%" height="100%" style="border: none;"></iframe>
-        </body>
-        </html>
-        """
-    
-    app = gr.mount_gradio_app(
-        app, 
-        demo, 
-        path="/gradio",
-        allowed_paths=[os.path.abspath("outputs"), os.path.abspath("fastvideo-logos")]
-    )
-    
-    uvicorn.run(app, host=args.host, port=args.port)
-
-
-if __name__ == "__main__":
-    
-    main() 
@@ -1,11 +0,0 @@
-A dynamic shot of a sleek black motorcycle accelerating down an empty highway at sunset. The bike's engine roars as it gains speed, smoke trailing from the tires. The rider, wearing a black leather jacket and helmet, leans forward with determination, gripping the handlebars tightly. The camera follows the motorcycle from a distance, capturing the dust kicked up behind it, then zooms in to show the intense focus on the rider's face. The background showcases the endless road stretching into the horizon with vibrant orange and pink hues of the setting sun. Medium shot transitioning to close-up.
-A Jedi Master Yoda, recognizable by his green skin, large ears, and wise wrinkles, is performing on a small stage, strumming a guitar with great concentration. Yoda wears a casual robe and sits on a stool, his eyes closed as he plays, fully immersed in the music. The stage is dimly lit with spotlights highlighting Yoda, creating a mystical atmosphere. The background shows a live audience watching intently. Medium close-up shot focusing on Yoda's expressive face and hands moving gracefully over the guitar strings.
-A cute, fluffy panda bear is preparing a meal in a cozy, modern kitchen. The panda is standing at a wooden countertop, wearing a white chef’s hat and apron. It skillfully stirs a pot on the stove with one hand while holding a spatula in the other. The kitchen is well-lit, with appliances and cabinets in pastel colors, creating a warm and inviting atmosphere. The panda moves gracefully, with a focused and determined expression, as steam rises from the pot. Medium shot focusing on the panda’s actions at the stove.
-In a futuristic Tokyo rooftop during a heavy rainstorm, a robotic DJ stands behind a turntable, spinning vinyl records in a cyberpunk night setting. The robot has metallic, sleek body parts with glowing blue LED lights, and it moves gracefully with the beat. Raindrops create a shimmering effect as they hit the ground and the DJ. The surrounding environment features neon signs, towering skyscrapers, and a dark, misty atmosphere. The camera starts with a wide shot of the city skyline before zooming in on the DJ performing. Sci-fi, fantasy.
-A realistic animated scene featuring a polar bear playing a guitar. The polar bear is standing upright, wearing a cozy fur vest and fingerless gloves. It holds the guitar with both hands, strumming the strings with one hand while plucking them with the other, showcasing natural, fluid motions. The polar bear's expressive face shows concentration and joy as it plays. The background is a snowy Arctic landscape with icebergs and a clear blue sky. The scene captures the bear from a mid-shot angle, focusing on its interaction with the guitar.
-The scene opens to a breathtaking view of a tranquil ocean horizon at dusk, displaying a vibrant tapestry of oranges, pinks, and purples as the sun sets. In the foreground, tall, swaying palm trees frame the scene, their silhouettes stark against the colorful sky. The ocean itself shimmers with reflections of the sunset, creating a peaceful, almost ethereal atmosphere. A small boat can be seen in the distance, centered on the horizon, adding a sense of scale and solitude to the scene. The waves gently lap the shore, creating faint patterns on the sandy beach, which stretches across the foreground. Above, the sky is dotted with scattered clouds that catch the last light of the day, enhancing the drama and beauty of the scene. The overall mood is serene and contemplative, capturing a perfect moment of nature’s grandeur.
-A large, modern semi-truck accelerating down an empty highway, gaining speed with each second. The truck's powerful engine roars as it moves forward, smoke billowing from the tires. The camera starts from a wide shot, capturing the truck in the distance, then smoothly zooms in to follow the vehicle as it speeds up. The truck's headlights illuminate the road ahead, casting a bright glow. The truck driver can be seen through the windshield, focused and determined. The background shows the vast openness of the highway stretching into the horizon under a clear blue sky. Medium to close-up shots of the truck as it accelerates.
-Soft blue light pulses from the blade’s rune-etched hilt, illuminating nearby moss-covered roots and ferns. The surrounding trees are tall and gnarled, their branches curling like claws overhead. Fog swirls gently at ground level, parting slightly as a figure in a cloak approaches from the distance. Medium shot slowly zooming toward the sword, emphasizing its mystical aura.
-The video opens with a tranquil scene in the heart of a dense forest, emphasizing two large, textured tree trunks in the foreground framing the view. Sunlight filters through the canopy above, casting intricate patterns of light and shadow on the trees and the ground. Between the tree trunks, a clear view of a calm, muddy river unfolds, its surface shimmering under the gentle sunlight. The riverbank is decorated with a variety of small bushes and vibrant foliage, subtly transitioning into the deep greens of tall, leafy plants. In the background, the dense forest looms, filled with dark, towering trees, their branches intertwining to form an intricate canopy. The scene is bathed in the soft glow of the sun, creating a serene and picturesque setting. Occasional sunbeams pierce through the foliage, adding a magical aura to the landscape. The vibrant reds and oranges of the smaller plants add contrast, bringing warmth to the earthy tones of the scenery. Overall, this harmonious blend of natural elements creates a peaceful and idyllic forest setting.
-A lone figure stands on a large, moss-covered rock, surrounded by the soft rush of a nearby stream. The figure is wearing white sneakers and shorts, with a plaid shirt that hangs loosely in the breeze. The lighting creates dramatic shadows, enhancing the textures of the rock and the subtle movement of the water below. In the background, a waterfall cascades into the stream, completing this tranquil and serene nature scene.
-In an industrial setting, a person leans casually against a railing, exuding a sense of confidence and composure. They are wearing a striking outfit, consisting of a vibrant, patterned jacket over a simple white crop top, creating a bold contrast. The atmosphere is infused with warm, ambient lighting that casts soft shadows on the concrete walls and metallic surfaces. Intricate wiring and pipes form an intricate backdrop, enhancing the urban aesthetic. Their relaxed posture and direct, engaging gaze suggest a sense of ease in this industrial environment. This scene encapsulates a blend of modern fashion and gritty, urban architecture, creating a visually compelling narrative.
@@ -1,47 +0,0 @@
-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.
@@ -3,19 +3,24 @@
 export WANDB_BASE_URL="https://api.wandb.ai"
 export WANDB_MODE=online
 export TOKENIZERS_PARALLELISM=false
+# export FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA

+# MODEL_PATH="wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers"
 MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
-DATA_DIR="data/crush-smol_processed_t2v_1_3b_ode_init/"
+# DATA_DIR="data/crush-smol_processed_t2v_1_3b_ode_init_5/combined_parquet_dataset/"
+DATA_DIR="/mnt/sharefs/users/hao.zhang/klin/preproc/data/test-ode-preprocessing-extended-t2v-1-3b/"
 VALIDATION_DATASET_FILE="$(dirname "$0")/validation.json"
 NUM_GPUS=1
+# export CUDA_VISIBLE_DEVICES=4,5
 # IP=[MASTER NODE IP]

 # Training arguments
 training_args=(
  --tracker_project_name "wan_ode_init"
-  --output_dir "wan_ode_init_crush_smol"
+  --output_dir "wan_ode_init_70k"
  --override_transformer_cls_name "CausalWanTransformer3DModel"
-  --wandb_run_name "wan_ode_init_crush_smol"
+  --wandb_run_name "fixed_wan_ode_init_70k_6e-6"
+  # --resume_from_checkpoint "ode_init_diffusers/"
  --max_train_steps 6000
  --train_batch_size 1
  --train_sp_batch_size 1
@@ -25,7 +30,7 @@ training_args=(
  --num_width 832
  --num_frames 77
  --warp_denoising_step
-  --enable_gradient_checkpointing_type "full"
+  # --enable_gradient_checkpointing_type "full"
 )

 # Parallel arguments
@@ -62,8 +67,7 @@ validation_args=(
 optimizer_args=(
  --learning_rate 6e-6
  --mixed_precision "bf16"
-  --weight_only_checkpointing_steps 1000
-  --training_state_checkpointing_steps 1000
+  --checkpointing_steps 1000
  --weight_decay 1e-4
  --max_grad_norm 1.0
 )
@@ -78,6 +82,7 @@ miscellaneous_args=(
  --dit_precision "fp32"
  --num_euler_timesteps 50
  --ema_start_step 0
+  --enable_gradient_checkpointing_type "full"
 )

 # If you do not have 32 GPUs and to fit in memory, you can: 1. increase sp_size. 2. reduce num_latent_t
@@ -1,5 +1,5 @@
 #!/bin/bash
-#SBATCH --job-name=2e6B8_16kFV_ode_vidprom
+#SBATCH --job-name=1e5B2_16kFV_warp_ode_vidprom
 #SBATCH --partition=main
 #SBATCH --nodes=1
 #SBATCH --ntasks=1
@@ -7,14 +7,14 @@
 #SBATCH --gres=gpu:8
 #SBATCH --cpus-per-task=128
 #SBATCH --mem=1440G
-#SBATCH --output=ode_vidprom16k/ode_vidprom8b16k_2e-6.out
-#SBATCH --error=ode_vidprom16k/ode_vidprom8b16k_2e-6.err
+#SBATCH --output=ode_vidprom16k_warp/Dode_vidprom8b16k_1e-5.out
+#SBATCH --error=ode_vidprom16k_warp/Dode_vidprom8b16k_1e-5.err
 #SBATCH --exclusive
 set -e -x

 # Environment Setup
 source ~/conda/miniconda/bin/activate
-conda activate your-conda-env
+conda activate will-fv2

 export WANDB_MODE="online"
 export NCCL_P2P_DISABLE=1
@@ -28,18 +28,18 @@ 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_API_KEY=your-wandb-api-key
+export WANDB_MODE=online
+export WANDB_API_KEY='8d9f4b39abd68eb4e29f6fc010b7ee71a2207cde'
 # export FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA

 echo "MASTER_ADDR: $MASTER_ADDR"
 echo "NODE_RANK: $NODE_RANK"


-MODEL_PATH="wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers"
-DATA_DIR="your-data-dir"
-VALIDATION_DATASET_FILE="examples/training/consistency_finetune/causal_ode_init/validation.json"
-OUTPUT_DIR="your-output-dir"
-INIT_WEIGHTS_FROM_SAFETENSORS="your-init-weights-from-safetensors" # bidirectional weights from Wan2.1-T2V-1.3B-Diffusers
+# MODEL_PATH="wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers"
+MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
+DATA_DIR="/mnt/sharefs/users/hao.zhang/klin/preproc/data/test-ode-preprocessing-16k-t2v-1-3b-81/"
+VALIDATION_DATASET_FILE="examples/training/consistency_finetune/ode_init/validation.json"
 NUM_GPUS=8
 # export CUDA_VISIBLE_DEVICES=4,5
 # IP=[MASTER NODE IP]
@@ -47,9 +47,9 @@ NUM_GPUS=8
 # Training arguments
 training_args=(
  --tracker_project_name "wan_ode_init"
-  --output_dir $OUTPUT_DIR
+  --output_dir "Dwarp_vidprom_8b16k_test_warp_1e-5"
  --override_transformer_cls_name "CausalWanTransformer3DModel"
-  --wandb_run_name "vidprom_8b16k_ode_init_2e-6"
+  --wandb_run_name "Dwarp_vidprom_8b16k_wan_ode_init_1e-5"
  # --resume_from_checkpoint "ode_init_diffusers/"
  --warp_denoising_step
  --log_visualization
@@ -60,7 +60,7 @@ training_args=(
  --num_latent_t 21
  --num_height 480
  --num_width 832
-  --num_frames 81
+  --num_frames 77
  --dmd_denoising_steps "1000,750,500,250"
  --enable_gradient_checkpointing_type "full"
 )
@@ -93,15 +93,14 @@ validation_args=(
  --validation_steps 50
  --validation_sampling_steps "50"
  --validation_guidance_scale "6.0"
-  --init_weights_from_safetensors $INIT_WEIGHTS_FROM_SAFETENSORS
+  # --init_weights_from_safetensors "/mnt/weka/home/hao.zhang/wl/Self-Forcing/diffusers_ode_init/model.safetensors"
 )

 # Optimizer arguments
 optimizer_args=(
-  --learning_rate 2e-6
+  --learning_rate 1e-5
  --mixed_precision "bf16"
-  --weight_only_checkpointing_steps 500
-  --training_state_checkpointing_steps 500
+  --checkpointing_steps 500
  --weight_decay 1e-4
  --max_grad_norm 1.0
 )
@@ -1,22 +1,21 @@
 #!/bin/bash
-#SBATCH --job-name=t2v
+#SBATCH --job-name=ode_vidprom2k
 #SBATCH --partition=main
-#SBATCH --nodes=8
-#SBATCH --ntasks=8
+#SBATCH --nodes=1
+#SBATCH --ntasks=1
 #SBATCH --ntasks-per-node=1
 #SBATCH --gres=gpu:8
 #SBATCH --cpus-per-task=128
 #SBATCH --mem=1440G
-#SBATCH --output=VSA_t2v_output/t2v_%j.out
-#SBATCH --error=VSA_t2v_output/t2v_%j.err
+#SBATCH --output=ode_vidprom2k_output/ode_vidprom2k.out
+#SBATCH --error=ode_vidprom2k_output/ode_vidprom2k.err
 #SBATCH --exclusive
 set -e -x

 # Environment Setup
 source ~/conda/miniconda/bin/activate
-conda activate your_env
+conda activate will-fv2

-# Basic Info
 export WANDB_MODE="online"
 export NCCL_P2P_DISABLE=1
 export TORCH_NCCL_ENABLE_MONITORING=0
@@ -30,41 +29,45 @@ 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 WANDB_API_KEY='8d9f4b39abd68eb4e29f6fc010b7ee71a2207cde'
 # export FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA

 echo "MASTER_ADDR: $MASTER_ADDR"
 echo "NODE_RANK: $NODE_RANK"

-# Configs
-NUM_GPUS=8
+
+# MODEL_PATH="wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers"
 MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
-DATA_DIR=your_data_dir
-VALIDATION_DATASET_FILE=your_validation_dataset_file
+DATA_DIR="/mnt/sharefs/users/hao.zhang/klin/preproc/data/test-ode-preprocessing/"
+VALIDATION_DATASET_FILE="examples/training/consistency_finetune/ode_init/validation.json"
+NUM_GPUS=8
 # export CUDA_VISIBLE_DEVICES=4,5
 # IP=[MASTER NODE IP]

 # Training arguments
 training_args=(
-  --tracker_project_name wan_t2v_VSA
-  --output_dir "checkpoints/wan_t2v_finetune_VSA"
-  --max_train_steps 4000
+  --tracker_project_name "wan_ode_init"
+  --output_dir "wan_ode_init_vidprom2k"
+  --override_transformer_cls_name "CausalWanTransformer3DModel"
+  --wandb_run_name "vidprom2k_wan_ode_init_5e-6"
+  # --resume_from_checkpoint "ode_init_diffusers/"
+  --max_train_steps 6001
  --train_batch_size 1
  --train_sp_batch_size 1
  --gradient_accumulation_steps 1
  --num_latent_t 21
  --num_height 480
  --num_width 832
-  --num_frames 81
-  # --enable_gradient_checkpointing_type "full" # if OOM enable this
+  --num_frames 77
+  # --enable_gradient_checkpointing_type "full"
 )

 # Parallel arguments
 parallel_args=(
-  --num_gpus 64
+  --num_gpus $NUM_GPUS
  --sp_size 1
  --tp_size 1
-  --hsdp_replicate_dim 64
+  --hsdp_replicate_dim 8
  --hsdp_shard_dim 1
 )

@@ -76,26 +79,25 @@ model_args=(

 # Dataset arguments
 dataset_args=(
-  --data_path "$DATA_DIR"
-  --dataloader_num_workers 4
+  --data_path "$DATA_DIR" 
+  --dataloader_num_workers 1
 )

 # Validation arguments
 validation_args=(
  --log_validation
-  --validation_dataset_file $VALIDATION_DATASET_FILE
-  --validation_steps 200
+  --validation_dataset_file "$VALIDATION_DATASET_FILE"
+  --validation_steps 100
  --validation_sampling_steps "50"
-  --validation_guidance_scale "5.0"
+  --validation_guidance_scale "6.0"
 )

 # Optimizer arguments
 optimizer_args=(
-  --learning_rate 1e-6
+  --learning_rate 5e-6
  --mixed_precision "bf16"
-  --weight_only_checkpointing_steps 1000
-  --training_state_checkpointing_steps 1000
-  --weight_decay 0.01
+  --checkpointing_steps 2000
+  --weight_decay 1e-4
  --max_grad_norm 1.0
 )

@@ -104,31 +106,26 @@ 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
-  --flow_shift 1
-  --seed 1000
-)
-
-# VSA arguments
-vsa_args=(
-  --VSA_decay_rate 0.03 \
-  --VSA_decay_interval_steps 50 \
-  --VSA_sparsity 0.9 \
+  --enable_gradient_checkpointing_type "full"
 )

+# If you do not have 32 GPUs and to fit in memory, you can: 1. increase sp_size. 2. reduce num_latent_t
 srun torchrun \
--nnodes $SLURM_JOB_NUM_NODES \
--nproc_per_node $NUM_GPUS \
--node_rank $SLURM_PROCID \
--rdzv_backend=c10d \
--rdzv_endpoint="$MASTER_ADDR:$MASTER_PORT" \
-    fastvideo/training/wan_training_pipeline.py \
+  --nnodes $SLURM_JOB_NUM_NODES \
+  --nproc_per_node $NUM_GPUS \
+  --node_rank $SLURM_PROCID \
+  --rdzv_backend=c10d \
+  --rdzv_endpoint="$MASTER_ADDR:$MASTER_PORT" \
+    fastvideo/training/ode_causal_pipeline.py \
    "${parallel_args[@]}" \
    "${model_args[@]}" \
    "${dataset_args[@]}" \
    "${training_args[@]}" \
    "${optimizer_args[@]}" \
    "${validation_args[@]}" \
-    "${miscellaneous_args[@]}" \
-    "${vsa_args[@]}"  
+    "${miscellaneous_args[@]}"
@@ -0,0 +1,132 @@
+#!/bin/bash
+#SBATCH --job-name=ode_crush
+#SBATCH --partition=main
+#SBATCH --nodes=1
+#SBATCH --ntasks=1
+#SBATCH --ntasks-per-node=1
+#SBATCH --gres=gpu:8
+#SBATCH --cpus-per-task=128
+#SBATCH --mem=1440G
+#SBATCH --output=ode_crush_output/ode_crush.out
+#SBATCH --error=ode_crush_output/ode_crush.err
+#SBATCH --exclusive
+set -e -x
+
+# Environment Setup
+source ~/conda/miniconda/bin/activate
+conda activate will-fv2
+
+export WANDB_MODE="online"
+export NCCL_P2P_DISABLE=1
+export TORCH_NCCL_ENABLE_MONITORING=0
+# different cache dir for different processes
+export TRITON_CACHE_DIR=/tmp/triton_cache_${SLURM_PROCID}
+export MASTER_PORT=29500
+export NODE_RANK=$SLURM_PROCID
+nodes=( $(scontrol show hostnames $SLURM_JOB_NODELIST) )
+export MASTER_ADDR=${nodes[0]}
+export CUDA_VISIBLE_DEVICES=$SLURM_LOCALID
+export TOKENIZERS_PARALLELISM=false
+export WANDB_BASE_URL="https://api.wandb.ai"
+export WANDB_MODE=online
+export WANDB_API_KEY='8d9f4b39abd68eb4e29f6fc010b7ee71a2207cde'
+# export FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA
+
+echo "MASTER_ADDR: $MASTER_ADDR"
+echo "NODE_RANK: $NODE_RANK"
+
+
+# MODEL_PATH="wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers"
+MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
+DATA_DIR="data/crush-smol_processed_t2v_1_3b_ode_init_5/combined_parquet_dataset/"
+VALIDATION_DATASET_FILE="examples/training/consistency_finetune/ode_init/validation.json"
+NUM_GPUS=2
+# export CUDA_VISIBLE_DEVICES=4,5
+# IP=[MASTER NODE IP]
+
+# Training arguments
+training_args=(
+  --tracker_project_name "wan_ode_init"
+  --output_dir "wan_ode_init_warp_2"
+  --override_transformer_cls_name "CausalWanTransformer3DModel"
+  --wandb_run_name "2warp_fixed_wan_ode_init_5e-6"
+  # --resume_from_checkpoint "ode_init_diffusers/"
+  # --warp_denoising_step
+  --max_train_steps 6001
+  --train_batch_size 1
+  --train_sp_batch_size 1
+  --gradient_accumulation_steps 1
+  --num_latent_t 21
+  --num_height 480
+  --num_width 832
+  --num_frames 77
+  # --enable_gradient_checkpointing_type "full"
+)
+
+# Parallel arguments
+parallel_args=(
+  --num_gpus $NUM_GPUS
+  --sp_size 1
+  --tp_size 1
+  --hsdp_replicate_dim $NUM_GPUS
+  --hsdp_shard_dim 1
+)
+
+# Model arguments
+model_args=(
+  --model_path $MODEL_PATH
+  --pretrained_model_name_or_path $MODEL_PATH
+)
+
+# Dataset arguments
+dataset_args=(
+  --data_path "$DATA_DIR" 
+  --dataloader_num_workers 1
+)
+
+# Validation arguments
+validation_args=(
+  --log_validation
+  --validation_dataset_file "$VALIDATION_DATASET_FILE"
+  --validation_steps 20
+  --validation_sampling_steps "50"
+  --validation_guidance_scale "6.0"
+)
+
+# Optimizer arguments
+optimizer_args=(
+  --learning_rate 5e-6
+  --mixed_precision "bf16"
+  --checkpointing_steps 2000
+  --weight_decay 1e-4
+  --max_grad_norm 1.0
+)
+
+# Miscellaneous arguments
+miscellaneous_args=(
+  --inference_mode False
+  --checkpoints_total_limit 3
+  --training_cfg_rate 0.1
+  --multi_phased_distill_schedule "4000-1"
+  --not_apply_cfg_solver
+  --dit_precision "fp32"
+  --num_euler_timesteps 50
+  --ema_start_step 0
+  --enable_gradient_checkpointing_type "full"
+)
+
+# If you do not have 32 GPUs and to fit in memory, you can: 1. increase sp_size. 2. reduce num_latent_t
+srun torchrun \
+  --nnodes $SLURM_JOB_NUM_NODES \
+  --nproc_per_node $NUM_GPUS \
+  --node_rank $SLURM_PROCID \
+  --rdzv_backend=c10d \
+  --rdzv_endpoint="$MASTER_ADDR:$MASTER_PORT" \
+    fastvideo/training/ode_causal_pipeline.py \
+    "${parallel_args[@]}" \
+    "${model_args[@]}" \
+    "${dataset_args[@]}" \
+    "${training_args[@]}" \
+    "${optimizer_args[@]}" \
+    "${validation_args[@]}" \
+    "${miscellaneous_args[@]}"
@@ -0,0 +1,98 @@
+#!/bin/bash
+
+export WANDB_BASE_URL="https://api.wandb.ai"
+export WANDB_MODE=online
+export TOKENIZERS_PARALLELISM=false
+# export FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA
+export WANDB_API_KEY='8d9f4b39abd68eb4e29f6fc010b7ee71a2207cde'
+
+MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
+DATA_DIR="/mnt/weka/home/hao.zhang/wl/FastVideo2/data/crush-smol_processed_t2v_1_3b_ode_init_single"
+VALIDATION_DATASET_FILE="$(dirname "$0")/validation.json"
+NUM_GPUS=1
+# export CUDA_VISIBLE_DEVICES=4,5
+# IP=[MASTER NODE IP]
+
+# Training arguments
+training_args=(
+  --tracker_project_name "wan_ode_init"
+  --output_dir "wan_ode_init_crush_smol"
+  --override_transformer_cls_name "CausalWanTransformer3DModel"
+  --wandb_run_name "overfitwan_ode_init_crush_smol"
+  # --resume_from_checkpoint "ode_init_diffusers/"
+  --max_train_steps 2001
+  # --warp_denoising_step
+  --train_batch_size 1
+  --train_sp_batch_size 1
+  --gradient_accumulation_steps 1
+  --num_latent_t 21
+  --num_height 480
+  --num_width 832
+  --num_frames 77
+  # --enable_gradient_checkpointing_type "full"
+)
+
+# Parallel arguments
+parallel_args=(
+  --num_gpus $NUM_GPUS
+  --sp_size 1
+  --tp_size 1
+  --hsdp_replicate_dim 1
+  --hsdp_shard_dim 1
+)
+
+# Model arguments
+model_args=(
+  --model_path $MODEL_PATH
+  --pretrained_model_name_or_path $MODEL_PATH
+)
+
+# Dataset arguments
+dataset_args=(
+  --data_path "$DATA_DIR" 
+  --dataloader_num_workers 1
+)
+
+# Validation arguments
+validation_args=(
+  --log_validation
+  --validation_dataset_file "$VALIDATION_DATASET_FILE"
+  --validation_steps 20
+  --validation_sampling_steps "50"
+  --validation_guidance_scale "6.0"
+)
+
+# Optimizer arguments
+optimizer_args=(
+  --learning_rate 1e-5
+  --mixed_precision "bf16"
+  --checkpointing_steps 500
+  --weight_decay 1e-4
+  --max_grad_norm 1.0
+)
+
+# Miscellaneous arguments
+miscellaneous_args=(
+  --inference_mode False
+  --checkpoints_total_limit 3
+  --training_cfg_rate 0.1
+  --multi_phased_distill_schedule "4000-1"
+  --not_apply_cfg_solver
+  --dit_precision "fp32"
+  --num_euler_timesteps 50
+  --ema_start_step 0
+  --enable_gradient_checkpointing_type "full"
+)
+
+# If you do not have 32 GPUs and to fit in memory, you can: 1. increase sp_size. 2. reduce num_latent_t
+torchrun \
+  --nnodes 1 \
+  --nproc_per_node $NUM_GPUS \
+    fastvideo/training/ode_causal_pipeline.py \
+    "${parallel_args[@]}" \
+    "${model_args[@]}" \
+    "${dataset_args[@]}" \
+    "${training_args[@]}" \
+    "${optimizer_args[@]}" \
+    "${validation_args[@]}" \
+    "${miscellaneous_args[@]}"
@@ -0,0 +1,100 @@
+#!/bin/bash
+
+export WANDB_BASE_URL="https://api.wandb.ai"
+export WANDB_MODE=online
+export TOKENIZERS_PARALLELISM=false
+# export FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA
+
+# MODEL_PATH="wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers"
+MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
+# DATA_DIR="data/crush-smol_processed_t2v_1_3b_ode_init_5/combined_parquet_dataset/"
+DATA_DIR="/mnt/sharefs/users/hao.zhang/klin/preproc/data/test-ode-preprocessing-16k-t2v-1-3b/"
+VALIDATION_DATASET_FILE="$(dirname "$0")/validation.json"
+NUM_GPUS=1
+export WANDB_API_KEY='8d9f4b39abd68eb4e29f6fc010b7ee71a2207cde'
+# export CUDA_VISIBLE_DEVICES=4,5
+# IP=[MASTER NODE IP]
+
+# Training arguments
+training_args=(
+  --tracker_project_name "wan_ode_init"
+  --output_dir "debug_ode_init"
+  --override_transformer_cls_name "CausalWanTransformer3DModel"
+  --wandb_run_name "debug_ode_init"
+  # --resume_from_checkpoint "ode_init_diffusers/"
+  --max_train_steps 1000
+  --train_batch_size 1
+  --train_sp_batch_size 1
+  --gradient_accumulation_steps 1
+  --num_latent_t 21
+  --num_height 480
+  --num_width 832
+  --num_frames 81
+  --warp_denoising_step
+  # --enable_gradient_checkpointing_type "full"
+)
+
+# Parallel arguments
+parallel_args=(
+  --num_gpus $NUM_GPUS
+  --sp_size 1
+  --tp_size 1
+  --hsdp_replicate_dim 1
+  --hsdp_shard_dim 1
+)
+
+# Model arguments
+model_args=(
+  --model_path $MODEL_PATH
+  --pretrained_model_name_or_path $MODEL_PATH
+)
+
+# Dataset arguments
+dataset_args=(
+  --data_path "$DATA_DIR" 
+  --dataloader_num_workers 1
+)
+
+# Validation arguments
+validation_args=(
+  --log_validation
+  --validation_dataset_file "$VALIDATION_DATASET_FILE"
+  --validation_steps 10
+  --validation_sampling_steps "50"
+  --validation_guidance_scale "6.0"
+)
+
+# Optimizer arguments
+optimizer_args=(
+  --learning_rate 1e-5
+  --mixed_precision "bf16"
+  --checkpointing_steps 1000
+  --weight_decay 1e-4
+  --max_grad_norm 1.0
+)
+
+# Miscellaneous arguments
+miscellaneous_args=(
+  --inference_mode False
+  --checkpoints_total_limit 3
+  --training_cfg_rate 0.1
+  --multi_phased_distill_schedule "4000-1"
+  --not_apply_cfg_solver
+  --dit_precision "fp32"
+  --num_euler_timesteps 50
+  --ema_start_step 0
+  --enable_gradient_checkpointing_type "full"
+)
+
+# If you do not have 32 GPUs and to fit in memory, you can: 1. increase sp_size. 2. reduce num_latent_t
+torchrun \
+  --nnodes 1 \
+  --nproc_per_node $NUM_GPUS \
+    fastvideo/training/ode_causal_pipeline.py \
+    "${parallel_args[@]}" \
+    "${model_args[@]}" \
+    "${dataset_args[@]}" \
+    "${training_args[@]}" \
+    "${optimizer_args[@]}" \
+    "${validation_args[@]}" \
+    "${miscellaneous_args[@]}"
@@ -3,8 +3,8 @@
 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/"
+DATA_MERGE_PATH="data/crush-smol_single/merge.txt"
+OUTPUT_DIR="data/crush-smol_processed_t2v_1_3b_ode_init_single/"

 torchrun --nproc_per_node=$GPU_NUM \
    fastvideo/pipelines/preprocess/v1_preprocess.py \
@@ -15,11 +15,10 @@ torchrun --nproc_per_node=$GPU_NUM \
    --max_height 480 \
    --max_width 832 \
    --num_frames 81 \
-    --flow_shift 5.0 \
    --dataloader_num_workers 0 \
    --output_dir=$OUTPUT_DIR \
    --train_fps 16 \
-    --samples_per_file 8 \
-    --flush_frequency 8 \
+    --samples_per_file 1 \
+    --flush_frequency 1 \
    --video_length_tolerance_range 5 \
    --preprocess_task "ode_trajectory" 
@@ -61,8 +61,7 @@ validation_args=(
 optimizer_args=(
  --learning_rate 2e-5
  --mixed_precision "bf16"
-  --weight_only_checkpointing_steps 2000
-  --training_state_checkpointing_steps 2000
+  --checkpointing_steps 2000
  --weight_decay 1e-4
  --max_grad_norm 1.0
 )
@@ -95,8 +95,7 @@ validation_args=(
 optimizer_args=(
  --learning_rate 1e-5
  --mixed_precision "bf16"
-  --weight_only_checkpointing_steps 1000
-  --training_state_checkpointing_steps 1000
+  --checkpointing_steps 1000
  --weight_decay 1e-4
  --max_grad_norm 1.0
 )
@@ -93,8 +93,7 @@ validation_args=(
 optimizer_args=(
  --learning_rate 1e-6
  --mixed_precision "bf16"
-  --weight_only_checkpointing_steps 1000
-  --training_state_checkpointing_steps 1000
+  --checkpointing_steps 1000
  --weight_decay 0.01
  --max_grad_norm 1.0
 )
@@ -91,10 +91,9 @@ validation_args=(

 # Optimizer arguments
 optimizer_args=(
-  --learning_rate 1e-6
+  --learning_rate 1e-5
  --mixed_precision "bf16"
-  --weight_only_checkpointing_steps 1000
-  --training_state_checkpointing_steps 1000
+  --checkpointing_steps 1000
  --weight_decay 0.01
  --max_grad_norm 1.0
 )
@@ -61,8 +61,7 @@ validation_args=(
 optimizer_args=(
  --learning_rate 1e-5
  --mixed_precision "bf16"
-  --weight_only_checkpointing_steps 1000
-  --training_state_checkpointing_steps 1000
+  --checkpointing_steps 1000
  --weight_decay 1e-4
  --max_grad_norm 1.0
 )
@@ -95,8 +95,7 @@ validation_args=(
 optimizer_args=(
  --learning_rate 1e-5
  --mixed_precision "bf16"
-  --weight_only_checkpointing_steps 1000
-  --training_state_checkpointing_steps 1000
+  --checkpointing_steps 1000
  --weight_decay 1e-4
  --max_grad_norm 1.0
 )
@@ -61,8 +61,7 @@ validation_args=(
 optimizer_args=(
  --learning_rate 1e-5
  --mixed_precision "bf16"
-  --weight_only_checkpointing_steps 1000
-  --training_state_checkpointing_steps 1000
+  --checkpointing_steps 1000
  --weight_decay 1e-4
  --max_grad_norm 1.0
 )
@@ -6,8 +6,8 @@ export TOKENIZERS_PARALLELISM=false
 # export FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA

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

@@ -52,7 +52,7 @@ dataset_args=(
 validation_args=(
  --log_validation 
  --validation_dataset_file $VALIDATION_DATASET_FILE
-  --validation_steps 200
+  --validation_steps 50
  --validation_sampling_steps "50" 
  --validation_guidance_scale "6.0"
 )
@@ -61,8 +61,7 @@ validation_args=(
 optimizer_args=(
  --learning_rate 5e-5
  --mixed_precision "bf16"
-  --weight_only_checkpointing_steps 1000
-  --training_state_checkpointing_steps 1000
+  --checkpointing_steps 1000
  --weight_decay 1e-4
  --max_grad_norm 1.0
 )
@@ -92,8 +92,7 @@ validation_args=(
 optimizer_args=(
  --learning_rate 5e-5
  --mixed_precision "bf16"
-  --weight_only_checkpointing_steps 400
-  --training_state_checkpointing_steps 400
+  --checkpointing_steps 400
  --weight_decay 1e-4
  --max_grad_norm 1.0
 )
@@ -61,8 +61,7 @@ validation_args=(
 optimizer_args=(
  --learning_rate 5e-5
  --mixed_precision "bf16"
-  --weight_only_checkpointing_steps 400
-  --training_state_checkpointing_steps 400
+  --checkpointing_steps 400
  --weight_decay 1e-4
  --max_grad_norm 1.0
 )
@@ -4,7 +4,7 @@ GPU_NUM=1 # 2,4,8
 MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
 MODEL_TYPE="wan"
 DATA_MERGE_PATH="data/crush-smol/merge.txt"
-OUTPUT_DIR="data/crush-smol_processed_t2v/"
+OUTPUT_DIR="data/crush-smol_processed_t2v_old/"

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

-GPU_NUM=2 # 2,4,8
+GPU_NUM=1 # 2,4,8
 MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
 DATASET_PATH="data/crush-smol/"
 OUTPUT_DIR="data/crush-smol_processed_t2v/"
@@ -14,7 +14,7 @@ torchrun --nproc_per_node=$GPU_NUM \
    --preprocess.dataset_type merged \
    --preprocess.dataset_path $DATASET_PATH \
    --preprocess.dataset_output_dir $OUTPUT_DIR \
-    --preprocess.preprocess_video_batch_size 2 \
+    --preprocess.preprocess_video_batch_size 8 \
    --preprocess.dataloader_num_workers 0 \
    --preprocess.max_height 480 \
    --preprocess.max_width 832 \
@@ -28,4 +28,4 @@
      "num_frames": 77
    }
  ]
-}
+}
@@ -0,0 +1,94 @@
+#!/bin/bash
+
+export WANDB_BASE_URL="https://api.wandb.ai"
+export WANDB_MODE=online
+export TOKENIZERS_PARALLELISM=false
+# export FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA
+
+MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
+DATA_DIR="data/crush-smol_processed_t2v_old"
+VALIDATION_DATASET_FILE="examples/datasets/crush_smol/validation.json"
+NUM_GPUS=8
+# export CUDA_VISIBLE_DEVICES=4,5
+
+
+# Training arguments
+training_args=(
+  --tracker_project_name "wan_t2v_i2v_finetune"
+  --output_dir "checkpoints/wan_t2v_i2v_finetune"
+  --max_train_steps 5000
+  --train_batch_size 1
+  --train_sp_batch_size 1
+  --gradient_accumulation_steps 2
+  --num_latent_t 20
+  --num_height 480
+  --num_width 832
+  --num_frames 77
+  --enable_gradient_checkpointing_type "full"
+)
+
+# Parallel arguments
+parallel_args=(
+  --num_gpus $NUM_GPUS 
+  --sp_size 4
+  --tp_size 1
+  --hsdp_replicate_dim 2
+  --hsdp_shard_dim 4
+)
+
+# Model arguments
+model_args=(
+  --model_path $MODEL_PATH
+  --pretrained_model_name_or_path $MODEL_PATH
+)
+
+# Dataset arguments
+dataset_args=(
+  --data_path $DATA_DIR
+  --dataloader_num_workers 1
+)
+
+# Validation arguments
+validation_args=(
+  --log_validation 
+  --validation_dataset_file $VALIDATION_DATASET_FILE
+  --validation_steps 50
+  --validation_sampling_steps "50" 
+  --validation_guidance_scale "6.0"
+)
+
+# Optimizer arguments
+optimizer_args=(
+  --learning_rate 5e-5
+  --mixed_precision "bf16"
+  --checkpointing_steps 1000
+  --weight_decay 1e-4
+  --max_grad_norm 1.0
+)
+
+# Miscellaneous arguments
+miscellaneous_args=(
+  --inference_mode False
+  --checkpoints_total_limit 3
+  --training_cfg_rate 0.1
+  --multi_phased_distill_schedule "4000-1"
+  --not_apply_cfg_solver
+  --dit_precision "fp32"
+  --num_euler_timesteps 50
+  --ema_start_step 0
+  --enable_gradient_checkpointing_type "full"
+  --t2v_as_i2v_task True
+  # --resume_from_checkpoint "checkpoints/wan_t2v_finetune/checkpoint-2500"
+)
+
+torchrun \
+  --nnodes 1 \
+  --nproc_per_node $NUM_GPUS \
+    fastvideo/training/wan_t2v_i2v_training_pipeline.py \
+    "${parallel_args[@]}" \
+    "${model_args[@]}" \
+    "${dataset_args[@]}" \
+    "${training_args[@]}" \
+    "${optimizer_args[@]}" \
+    "${validation_args[@]}" \
+    "${miscellaneous_args[@]}"
@@ -0,0 +1,24 @@
+#!/bin/bash
+
+GPU_NUM=1 # 2,4,8
+MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
+MODEL_TYPE="wan"
+DATA_MERGE_PATH="data/crush-smol/merge.txt"
+OUTPUT_DIR="data/crush-smol_processed_t2v_i2v_1_3b/"
+
+torchrun --nproc_per_node=$GPU_NUM \
+    fastvideo/pipelines/preprocess/v1_preprocess.py \
+    --model_path $MODEL_PATH \
+    --data_merge_path $DATA_MERGE_PATH \
+    --preprocess_video_batch_size 2 \
+    --seed 42 \
+    --max_height 480 \
+    --max_width 832 \
+    --num_frames 77 \
+    --dataloader_num_workers 0 \
+    --output_dir=$OUTPUT_DIR \
+    --train_fps 16 \
+    --samples_per_file 8 \
+    --flush_frequency 8 \
+    --video_length_tolerance_range 5 \
+    --preprocess_task "t2v_ode_trajectory" 
@@ -1,72 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-
-import torch
-from fastvideo.attention.backends.sageattn.api import sageattn_blackwell
-
-from fastvideo.attention.backends.abstract import (AttentionBackend,
-                                                   AttentionImpl,
-                                                   AttentionMetadata,
-                                                   AttentionMetadataBuilder)
-from fastvideo.logger import init_logger
-
-logger = init_logger(__name__)
-
-
-class SageAttention3Backend(AttentionBackend):
-
-    accept_output_buffer: bool = True
-
-    @staticmethod
-    def get_supported_head_sizes() -> list[int]:
-        return [64, 128, 256]
-
-    @staticmethod
-    def get_name() -> str:
-        return "SAGE_ATTN_THREE"
-
-    @staticmethod
-    def get_impl_cls() -> type["SageAttention3Impl"]:
-        return SageAttention3Impl
-
-    @staticmethod
-    def get_metadata_cls() -> type["AttentionMetadata"]:
-        raise NotImplementedError
-
-    @staticmethod
-    def get_builder_cls() -> type["AttentionMetadataBuilder"]:
-        raise NotImplementedError
-
-    # @staticmethod
-    # def get_metadata_cls() -> Type["AttentionMetadata"]:
-    #     return FlashAttentionMetadata
-
-
-class SageAttention3Impl(AttentionImpl):
-
-    def __init__(
-        self,
-        num_heads: int,
-        head_size: int,
-        causal: bool,
-        softmax_scale: float,
-        num_kv_heads: int | None = None,
-        prefix: str = "",
-        **extra_impl_args,
-    ) -> None:
-        self.causal = causal
-        self.softmax_scale = softmax_scale
-        self.dropout = extra_impl_args.get("dropout_p", 0.0)
-
-    def forward(
-        self,
-        query: torch.Tensor,
-        key: torch.Tensor,
-        value: torch.Tensor,
-        attn_metadata: AttentionMetadata,
-    ) -> torch.Tensor:
-        query = query.transpose(1, 2)
-        key = key.transpose(1, 2)
-        value = value.transpose(1, 2)
-        output = sageattn_blackwell(query, key, value, is_causal=self.causal)
-        output = output.transpose(1, 2)
-        return output
@@ -5,6 +5,7 @@ from dataclasses import dataclass

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

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

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

+        # torch.distributed.breakpoint()
        query, chunk_size = process_moba_input(query,
                                               attn_metadata.patch_resolution,
                                               moba_chunk_size)
@@ -205,8 +205,8 @@ class VMOBAAttentionImpl(AttentionImpl):
            simsum_threshold=attn_metadata.moba_threshold,
            threshold_type=attn_metadata.moba_threshold_type,
        )
-        hidden_states = self.pad_input(hidden_states, indices_q, batch_size,
-                                       sequence_length)
+        hidden_states = pad_input(hidden_states, indices_q, batch_size,
+                                  sequence_length)
        hidden_states = process_moba_output(hidden_states,
                                            attn_metadata.patch_resolution,
                                            moba_chunk_size)
@@ -12,7 +12,7 @@ import torch
 import fastvideo.envs as envs
 from fastvideo.attention.backends.abstract import AttentionBackend
 from fastvideo.logger import init_logger
-from fastvideo.platforms import AttentionBackendEnum
+from fastvideo.platforms import AttentionBackendEnum, current_platform
 from fastvideo.utils import STR_BACKEND_ENV_VAR, resolve_obj_by_qualname

 logger = init_logger(__name__)
@@ -117,8 +117,6 @@ def _cached_get_attn_backend(
            selected_backend = backend_name_to_enum(backend_by_env_var)

    # get device-specific attn_backend
-    from fastvideo.platforms import current_platform
-
    if selected_backend not in supported_attention_backends:
        selected_backend = None
    attention_cls = current_platform.get_attn_backend_cls(
@@ -15,16 +15,18 @@ class DiTArchConfig(ArchConfig):
    reverse_param_names_mapping: dict = field(default_factory=dict)
    lora_param_names_mapping: dict = field(default_factory=dict)
    _supported_attention_backends: tuple[AttentionBackendEnum, ...] = (
-        AttentionBackendEnum.SLIDING_TILE_ATTN, AttentionBackendEnum.SAGE_ATTN,
-        AttentionBackendEnum.FLASH_ATTN, AttentionBackendEnum.TORCH_SDPA,
-        AttentionBackendEnum.VIDEO_SPARSE_ATTN, AttentionBackendEnum.VMOBA_ATTN,
-        AttentionBackendEnum.SAGE_ATTN_THREE)
+        AttentionBackendEnum.SLIDING_TILE_ATTN,
+        AttentionBackendEnum.SAGE_ATTN,
+        AttentionBackendEnum.FLASH_ATTN,
+        AttentionBackendEnum.TORCH_SDPA,
+        AttentionBackendEnum.VIDEO_SPARSE_ATTN,
+        AttentionBackendEnum.VMOBA_ATTN,
+    )

    hidden_size: int = 0
    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:
@@ -2,13 +2,13 @@ from fastvideo.configs.models.encoders.base import (BaseEncoderOutput,
                                                    EncoderConfig,
                                                    ImageEncoderConfig,
                                                    TextEncoderConfig)
-from fastvideo.configs.models.encoders.clip import (
-    CLIPTextConfig, CLIPVisionConfig, WAN2_1ControlCLIPVisionConfig)
+from fastvideo.configs.models.encoders.clip import (CLIPTextConfig,
+                                                    CLIPVisionConfig)
 from fastvideo.configs.models.encoders.llama import LlamaConfig
 from fastvideo.configs.models.encoders.t5 import T5Config

 __all__ = [
    "EncoderConfig", "TextEncoderConfig", "ImageEncoderConfig",
-    "BaseEncoderOutput", "CLIPTextConfig", "CLIPVisionConfig",
-    "WAN2_1ControlCLIPVisionConfig", "LlamaConfig", "T5Config"
+    "BaseEncoderOutput", "CLIPTextConfig", "CLIPVisionConfig", "LlamaConfig",
+    "T5Config"
 ]
@@ -77,8 +77,6 @@ class CLIPTextConfig(TextEncoderConfig):

    num_hidden_layers_override: int | None = None
    require_post_norm: bool | None = None
-    enable_scale: bool = True
-    is_causal: bool = True
    prefix: str = "clip"


@@ -89,14 +87,4 @@ class CLIPVisionConfig(ImageEncoderConfig):

    num_hidden_layers_override: int | None = None
    require_post_norm: bool | None = None
-    enable_scale: bool = True
-    is_causal: bool = True
    prefix: str = "clip"
-
-
-@dataclass
-class WAN2_1ControlCLIPVisionConfig(CLIPVisionConfig):
-    num_hidden_layers_override: int | None = 31
-    require_post_norm: bool | None = False
-    enable_scale: bool = False
-    is_causal: bool = False
@@ -4,13 +4,12 @@ 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 (SelfForcingWanT2V480PConfig,
-                                             WanI2V480PConfig, WanI2V720PConfig,
+from fastvideo.configs.pipelines.wan import (WanI2V480PConfig, WanI2V720PConfig,
                                             WanT2V480PConfig, WanT2V720PConfig)

 __all__ = [
    "HunyuanConfig", "FastHunyuanConfig", "PipelineConfig",
    "SlidingTileAttnConfig", "WanT2V480PConfig", "WanI2V480PConfig",
    "WanT2V720PConfig", "WanI2V720PConfig", "StepVideoT2VConfig",
-    "SelfForcingWanT2V480PConfig", "get_pipeline_config_cls_from_name"
+    "get_pipeline_config_cls_from_name"
 ]
@@ -45,6 +45,8 @@ class PipelineConfig:
    embedded_cfg_scale: float = 6.0
    flow_shift: float | None = None
    disable_autocast: bool = False
+    ti2v_task: bool = False
+    t2v_as_i2v_task: bool = False

    # Model configuration
    dit_config: DiTConfig = field(default_factory=DiTConfig)
@@ -85,10 +87,6 @@ class PipelineConfig:
    # DMD parameters
    dmd_denoising_steps: list[int] | None = field(default=None)

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

@@ -215,6 +213,24 @@ class PipelineConfig:
            "Comma-separated list of denoising steps (e.g., '1000,757,522')",
        )

+        # TI2V task
+        parser.add_argument(
+            f"--{prefix_with_dot}ti2v-task",
+            action=StoreBoolean,
+            dest=f"{prefix_with_dot.replace('-', '_')}ti2v_task",
+            default=PipelineConfig.ti2v_task,
+            help="Enable TI2V",
+        )
+
+        # T2V to I2V task
+        parser.add_argument(
+            f"--{prefix_with_dot}t2v-as-i2v-task",
+            action=StoreBoolean,
+            dest=f"{prefix_with_dot.replace('-', '_')}t2v_as_i2v_task",
+            default=PipelineConfig.t2v_as_i2v_task,
+            help="Enable T2V to I2V task",
+        )
+
        # Add VAE configuration arguments
        from fastvideo.configs.models.vaes.base import VAEConfig
        VAEConfig.add_cli_args(parser, prefix=f"{prefix_with_dot}vae-config")
@@ -246,7 +262,9 @@ class PipelineConfig:
        """
        from fastvideo.configs.pipelines.registry import (
            get_pipeline_config_cls_from_name)
+        logger.info("WTF model_path: %s", model_path)
        pipeline_config_cls = get_pipeline_config_cls_from_name(model_path)
+        logger.info("pipeline_config_cls: %s", pipeline_config_cls)

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

@@ -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,
-    Wan2_2_I2V_A14B_Config, Wan2_2_T2V_A14B_Config, Wan2_2_TI2V_5B_Config,
-    WanI2V480PConfig, WanI2V720PConfig, WanT2V480PConfig, WanT2V720PConfig,
-    SelfForcingWanT2V480PConfig, WANV2VConfig)
+    SelfForcingWanT2V480PConfig, 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,
@@ -27,7 +27,6 @@ PIPE_NAME_TO_CONFIG: dict[str, type[PipelineConfig]] = {
    "hunyuanvideo-community/HunyuanVideo": HunyuanConfig,
    "Wan-AI/Wan2.1-T2V-1.3B-Diffusers": WanT2V480PConfig,
    "weizhou03/Wan2.1-Fun-1.3B-InP-Diffusers": WanI2V480PConfig,
-    "IRMChen/Wan2.1-Fun-1.3B-Control-Diffusers": WANV2VConfig,
    "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers": WanI2V480PConfig,
    "Wan-AI/Wan2.1-I2V-14B-720P-Diffusers": WanI2V720PConfig,
    "Wan-AI/Wan2.1-T2V-14B-Diffusers": WanT2V720PConfig,
@@ -49,8 +48,8 @@ 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(),
+    "wancausaldmdpipeline": lambda id: "wancausaldmdpipeline" in id.lower(),
    # Add other pipeline architecture detectors
 }

@@ -62,8 +61,8 @@ 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,
+    "stepvideo": StepVideoT2VConfig,
    "wancausaldmdpipeline": SelfForcingWanT2V480PConfig,
-    "stepvideo": StepVideoT2VConfig
    # Other fallbacks by architecture
 }

@@ -7,8 +7,7 @@ import torch
 from fastvideo.configs.models import DiTConfig, EncoderConfig, VAEConfig
 from fastvideo.configs.models.dits import WanVideoConfig
 from fastvideo.configs.models.encoders import (BaseEncoderOutput,
-                                               CLIPVisionConfig, T5Config,
-                                               WAN2_1ControlCLIPVisionConfig)
+                                               CLIPVisionConfig, T5Config)
 from fastvideo.configs.models.vaes import WanVAEConfig
 from fastvideo.configs.pipelines.base import PipelineConfig

@@ -55,9 +54,6 @@ class WanT2V480PConfig(PipelineConfig):
    text_encoder_precisions: tuple[str, ...] = field(
        default_factory=lambda: ("fp32", ))

-    # self-forcing params
-    warp_denoising_step: bool = True
-
    # WanConfig-specific added parameters

    def __post_init__(self):
@@ -101,16 +97,6 @@ class WanI2V720PConfig(WanI2V480PConfig):
    flow_shift: float | None = 5.0


-@dataclass
-class WANV2VConfig(WanI2V480PConfig):
-    """Configuration for WAN2.1 1.3B Control pipeline."""
-
-    image_encoder_config: EncoderConfig = field(
-        default_factory=WAN2_1ControlCLIPVisionConfig)
-    # CLIP encoder precision
-    image_encoder_precision: str = 'bf16'
-
-
@dataclass
 class FastWan2_1_T2V_480P_Config(WanT2V480PConfig):
    """Base configuration for FastWan T2V 1.3B 480P pipeline architecture with DMD"""
@@ -147,11 +133,6 @@ class Wan2_2_T2V_A14B_Config(WanT2V480PConfig):
    flow_shift: float | None = 12.0
    boundary_ratio: float | None = 0.875

-    # self-forcing params
-    dmd_denoising_steps: list[int] | None = field(
-        default_factory=lambda: [1000, 750, 500, 250])
-    warp_denoising_step: bool = True
-
    def __post_init__(self) -> None:
        self.dit_config.boundary_ratio = self.boundary_ratio

@@ -18,9 +18,6 @@ class SamplingParam:
    # Image inputs
    image_path: str | None = None

-    # Video inputs
-    video_path: str | None = None
-
    # Text inputs
    prompt: str | list[str] | None = None
    negative_prompt: str = "Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards"
@@ -203,12 +200,6 @@ class SamplingParam:
            default=SamplingParam.image_path,
            help="Path to input image for image-to-video generation",
        )
-        parser.add_argument(
-            "--video_path",
-            type=str,
-            default=SamplingParam.video_path,
-            help="Path to input video for video-to-video generation",
-        )
        parser.add_argument(
            "--moba-config-path",
            type=str,
@@ -18,7 +18,6 @@ from fastvideo.configs.sample.wan import (
    WanI2V_14B_720P_SamplingParam,
    WanT2V_1_3B_SamplingParam,
    WanT2V_14B_SamplingParam,
-    Wan2_1_Fun_1_3B_Control_SamplingParam,
    SelfForcingWanT2V480PConfig,
 )
 # isort: on
@@ -40,8 +39,6 @@ SAMPLING_PARAM_REGISTRY: dict[str, Any] = {
    "Wan-AI/Wan2.1-I2V-14B-720P-Diffusers": WanI2V_14B_720P_SamplingParam,
    "weizhou03/Wan2.1-Fun-1.3B-InP-Diffusers":
    Wan2_1_Fun_1_3B_InP_SamplingParam,
-    "IRMChen/Wan2.1-Fun-1.3B-Control-Diffusers":
-    Wan2_1_Fun_1_3B_Control_SamplingParam,

    # Wan2.2
    "Wan-AI/Wan2.2-TI2V-5B-Diffusers": Wan2_2_TI2V_5B_SamplingParam,
@@ -122,17 +122,6 @@ class Wan2_1_Fun_1_3B_InP_SamplingParam(SamplingParam):
    num_inference_steps: int = 50


-@dataclass
-class Wan2_1_Fun_1_3B_Control_SamplingParam(SamplingParam):
-    fps: int = 16
-    num_frames: int = 49
-    height: int = 832
-    width: int = 480
-    guidance_scale: float = 6.0
-    teacache_params: WanTeaCacheParams = field(
-        default_factory=lambda: WanTeaCacheParams(teacache_thresh=0.1, ))
-
-
 # =============================================
 # ============= Wan2.2 TI2V Models =============
 # =============================================
@@ -173,12 +162,6 @@ class Wan2_2_I2V_A14B_SamplingParam(Wan2_2_Base_SamplingParam):
    # can be overridden during sampling


-@dataclass
-class Wan2_2_Fun_A14B_Control_SamplingParam(
-        Wan2_1_Fun_1_3B_Control_SamplingParam):
-    num_frames: int = 81
-
-
 # =============================================
 # ============= Causal Self-Forcing =============
 # =============================================
@@ -37,7 +37,7 @@ 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,
@@ -0,0 +1,43 @@
+# SPDX-License-Identifier: Apache-2.0
+from fastvideo.dataset.lmdb_utils import get_array_shape_from_lmdb, retrieve_row_from_lmdb
+from torch.utils.data import Dataset
+import numpy as np
+import torch
+import lmdb
+
+# from Self-Forcing: https://github.com/guandeh17/Self-Forcing/blob/main/utils/dataset.py
+class ODERegressionLMDBDataset(Dataset):
+    def __init__(self, data_path: str, max_pair: int = int(1e8)):
+        print(f"data_path: {data_path}")
+        self.env = lmdb.open(data_path, readonly=True,
+                             lock=False, readahead=False, meminit=False)
+
+        self.latents_shape = get_array_shape_from_lmdb(self.env, 'latents')
+        self.max_pair = max_pair
+
+    def __len__(self):
+        return min(self.latents_shape[0], self.max_pair)
+
+    def __getitem__(self, idx):
+        """
+        Outputs:
+            - prompts: List of Strings
+            - latents: Tensor of shape (num_denoising_steps, num_frames, num_channels, height, width). It is ordered from pure noise to clean image.
+        """
+        latents = retrieve_row_from_lmdb(
+            self.env,
+            "latents", np.float16, idx, shape=self.latents_shape[1:]
+        )
+
+        if len(latents.shape) == 4:
+            latents = latents[None, ...]
+
+        prompts = retrieve_row_from_lmdb(
+            self.env,
+            "prompts", str, idx
+        )
+        return {
+            "prompts": prompts,
+            "ode_latent": torch.tensor(latents, dtype=torch.float32)
+        }
+
@@ -0,0 +1,75 @@
+# SPDX-License-Identifier: Apache-2.0
+# from Self-Forcing: https://github.com/guandeh17/Self-Forcing/blob/main/utils/lmdb.py
+
+import numpy as np
+
+
+def get_array_shape_from_lmdb(env, array_name):
+    with env.begin() as txn:
+        image_shape = txn.get(f"{array_name}_shape".encode()).decode()
+        image_shape = tuple(map(int, image_shape.split()))
+    return image_shape
+
+
+def store_arrays_to_lmdb(env, arrays_dict, start_index=0):
+    """
+    Store rows of multiple numpy arrays in a single LMDB.
+    Each row is stored separately with a naming convention.
+    """
+    with env.begin(write=True) as txn:
+        for array_name, array in arrays_dict.items():
+            for i, row in enumerate(array):
+                # Convert row to bytes
+                if isinstance(row, str):
+                    row_bytes = row.encode()
+                else:
+                    row_bytes = row.tobytes()
+
+                data_key = f'{array_name}_{start_index + i}_data'.encode()
+
+                txn.put(data_key, row_bytes)
+
+
+def process_data_dict(data_dict, seen_prompts):
+    output_dict = {}
+
+    all_videos = []
+    all_prompts = []
+    for prompt, video in data_dict.items():
+        if prompt in seen_prompts:
+            continue
+        else:
+            seen_prompts.add(prompt)
+
+        video = video.half().numpy()
+        all_videos.append(video)
+        all_prompts.append(prompt)
+
+    if len(all_videos) == 0:
+        return {"latents": np.array([]), "prompts": np.array([])}
+
+    all_videos = np.concatenate(all_videos, axis=0)
+
+    output_dict['latents'] = all_videos
+    output_dict['prompts'] = np.array(all_prompts)
+
+    return output_dict
+
+
+def retrieve_row_from_lmdb(lmdb_env, array_name, dtype, row_index, shape=None):
+    """
+    Retrieve a specific row from a specific array in the LMDB.
+    """
+    data_key = f'{array_name}_{row_index}_data'.encode()
+
+    with lmdb_env.begin() as txn:
+        row_bytes = txn.get(data_key)
+
+    if dtype == str:
+        array = row_bytes.decode()
+    else:
+        array = np.frombuffer(row_bytes, dtype=dtype)
+
+    if shape is not None and len(shape) > 0:
+        array = array.reshape(shape)
+    return array
@@ -12,7 +12,6 @@ import tqdm
 # Dataset
 from torch.utils.data import Dataset, Sampler
 from torchdata.stateful_dataloader import StatefulDataLoader
-from fastvideo.platforms import current_platform

 from fastvideo.dataset.utils import collate_rows_from_parquet_schema
 from fastvideo.distributed import (get_sp_world_size, get_world_group,
@@ -343,7 +342,6 @@ def build_parquet_map_style_dataloader(
        collate_fn=passthrough,
        num_workers=num_data_workers,
        pin_memory=True,
-        pin_memory_device=current_platform.device_name,
        persistent_workers=num_data_workers > 0,
    )
    return dataset, loader
@@ -3,6 +3,9 @@ from typing import Any, cast

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


 def pad(t: torch.Tensor, padding_length: int) -> tuple[torch.Tensor, torch.Tensor]:
@@ -6,6 +6,7 @@ import os
 import torch
 from torch.distributed import ProcessGroup

+from fastvideo.platforms import current_platform
 from fastvideo.platforms.interface import CpuArchEnum

 from .base_device_communicator import DeviceCommunicatorBase
@@ -21,8 +22,6 @@ class CpuCommunicator(DeviceCommunicatorBase):
        super().__init__(cpu_group, device, device_group, unique_name)
        self.dist_module = torch.distributed

-        from fastvideo.platforms import current_platform
-
        if (current_platform.get_cpu_architecture()
                == CpuArchEnum.X86) and hasattr(
                    torch.ops._C,
@@ -1,71 +0,0 @@
-import torch
-from torch.distributed import ProcessGroup
-
-from fastvideo.distributed.device_communicators.base_device_communicator import (
-    DeviceCommunicatorBase)
-
-
-class NpuCommunicator(DeviceCommunicatorBase):
-
-    def __init__(self,
-                 cpu_group: ProcessGroup,
-                 device: torch.device | None = None,
-                 device_group: ProcessGroup | None = None,
-                 unique_name: str = ""):
-        super().__init__(cpu_group, device, device_group, unique_name)
-
-        from fastvideo.distributed.device_communicators.pyhccl import (
-            PyHcclCommunicator)
-
-        self.pyhccl_comm: PyHcclCommunicator | None = None
-        if self.world_size > 1:
-            self.pyhccl_comm = PyHcclCommunicator(
-                group=self.cpu_group,
-                device=self.device,
-            )
-
-    def all_reduce(self, input_, op: torch.distributed.ReduceOp | None = None):
-        pyhccl_comm = self.pyhccl_comm
-        assert pyhccl_comm is not None, "pyhccl_comm should not be None"
-        out = pyhccl_comm.all_reduce(input_, op=op)
-        if out is None:
-            # fall back to the default all-reduce using PyTorch.
-            # this usually happens during testing.
-            # when we run the model, allreduce only happens for the TP
-            # group, where we always have either custom allreduce or pyhccl.
-            out = input_.clone()
-            torch.distributed.all_reduce(out, group=self.device_group, op=op)
-        return out
-
-    def send(self, tensor: torch.Tensor, dst: int | None = None) -> None:
-        """Sends a tensor to the destination rank in a non-blocking way"""
-        """NOTE: `dst` is the local rank of the destination rank."""
-        if dst is None:
-            dst = (self.rank_in_group + 1) % self.world_size
-
-        pyhccl_comm = self.pyhccl_comm
-        if pyhccl_comm is not None and not pyhccl_comm.disabled:
-            pyhccl_comm.send(tensor, dst)
-        else:
-            torch.distributed.send(tensor, self.ranks[dst], self.device_group)
-
-    def recv(self,
-             size: torch.Size,
-             dtype: torch.dtype,
-             src: int | None = None) -> torch.Tensor:
-        """Receives a tensor from the source rank."""
-        """NOTE: `src` is the local rank of the source rank."""
-        if src is None:
-            src = (self.rank_in_group - 1) % self.world_size
-
-        tensor = torch.empty(size, dtype=dtype, device=self.device)
-        pyhccl_comm = self.pyhccl_comm
-        if pyhccl_comm is not None and not pyhccl_comm.disabled:
-            pyhccl_comm.recv(tensor, src)
-        else:
-            torch.distributed.recv(tensor, self.ranks[src], self.device_group)
-        return tensor
-
-    def destroy(self) -> None:
-        if self.pyhccl_comm is not None:
-            self.pyhccl_comm = None
@@ -1,146 +0,0 @@
-import torch
-import torch.distributed as dist
-from torch.distributed import ProcessGroup, ReduceOp
-
-from fastvideo.distributed.device_communicators.pyhccl_wrapper import (
-    HCCLLibrary, aclrtStream_t, buffer_type, hcclComm_t, hcclDataTypeEnum,
-    hcclRedOpTypeEnum, hcclUniqueId)
-from fastvideo.distributed.utils import StatelessProcessGroup
-from fastvideo.logger import init_logger
-from fastvideo.utils import current_stream
-
-logger = init_logger(__name__)
-
-
-class PyHcclCommunicator:
-
-    def __init__(
-        self,
-        group: ProcessGroup | StatelessProcessGroup,
-        device: int | str | torch.device,
-        library_path: str | None = None,
-    ):
-        """
-        Args:
-            group: the process group to work on. If None, it will use the
-                default process group.
-            device: the device to bind the PyHcclCommunicator to. If None,
-                it will be bind to f"npu:{local_rank}".
-            library_path: the path to the HCCL library. If None, it will
-                use the default library path.
-        It is the caller's responsibility to make sure each communicator
-        is bind to a unique device.
-        """
-
-        if not isinstance(group, StatelessProcessGroup):
-            assert dist.is_initialized()
-            assert dist.get_backend(group) != dist.Backend.HCCL, (
-                "PyHcclCommunicator should be attached to a non-HCCL group.")
-            # note: this rank is the rank in the group
-            self.rank = dist.get_rank(group)
-            self.world_size = dist.get_world_size(group)
-        else:
-            self.rank = group.rank
-            self.world_size = group.world_size
-
-        self.group = group
-
-        # if world_size == 1, no need to create communicator
-        if self.world_size == 1:
-            self.available = False
-            self.disabled = True
-            return
-
-        try:
-            self.hccl = HCCLLibrary(library_path)
-        except Exception:
-            logger.warning("disable hccl because of missing HCCL library")
-            # disable because of missing HCCL library
-            # e.g. in a non-NPU environment
-            self.available = False
-            self.disabled = True
-            return
-
-        self.available = True
-        self.disabled = False
-
-        logger.info("FastVideo is using pyhccl")
-
-        if isinstance(device, int):
-            device = torch.device(f"npu:{device}")
-        elif isinstance(device, str):
-            device = torch.device(device)
-        # now `device` is a `torch.device` object
-        assert isinstance(device, torch.device)
-        self.device = device
-
-        if self.rank == 0:
-            # get the unique id from HCCL
-            with torch.npu.device(device):
-                self.unique_id = self.hccl.hcclGetUniqueId()
-        else:
-            # construct an empty unique id
-            self.unique_id = hcclUniqueId()
-
-        if not isinstance(group, StatelessProcessGroup):
-            tensor = torch.ByteTensor(list(self.unique_id.internal))
-            ranks = dist.get_process_group_ranks(group)
-            # arg `src` in `broadcast` is the global rank
-            dist.broadcast(tensor, src=ranks[0], group=group)
-            byte_list = tensor.tolist()
-            for i, byte in enumerate(byte_list):
-                self.unique_id.internal[i] = byte
-        else:
-            self.unique_id = group.broadcast_obj(self.unique_id, src=0)
-
-        # hccl communicator and stream will use this device
-        # `torch.npu.device` is a context manager that changes the
-        # current npu device to the specified one
-        with torch.npu.device(device):
-            self.comm: hcclComm_t = self.hccl.hcclCommInitRank(
-                self.world_size, self.unique_id, self.rank)
-
-            stream = current_stream()
-            # A small all_reduce for warmup.
-            data = torch.zeros(1, device=device)
-            self.all_reduce(data)
-            stream.synchronize()
-            del data
-
-    def all_reduce(self,
-                   in_tensor: torch.Tensor,
-                   op: ReduceOp = ReduceOp.SUM,
-                   stream=None) -> torch.Tensor:
-        if self.disabled:
-            return None
-        # hccl communicator created on a specific device
-        # will only work on tensors on the same device
-        # otherwise it will cause "illegal memory access"
-        assert in_tensor.device == self.device, (
-            f"this hccl communicator is created to work on {self.device}, "
-            f"but the input tensor is on {in_tensor.device}")
-
-        out_tensor = torch.empty_like(in_tensor)
-
-        if stream is None:
-            stream = current_stream()
-        self.hccl.hcclAllReduce(buffer_type(in_tensor.data_ptr()),
-                                buffer_type(out_tensor.data_ptr()),
-                                in_tensor.numel(),
-                                hcclDataTypeEnum.from_torch(in_tensor.dtype),
-                                hcclRedOpTypeEnum.from_torch(op), self.comm,
-                                aclrtStream_t(stream.npu_stream))
-        return out_tensor
-
-    def broadcast(self, tensor: torch.Tensor, src: int, stream=None):
-        if self.disabled:
-            return
-        assert tensor.device == self.device, (
-            f"this hccl communicator is created to work on {self.device}, "
-            f"but the input tensor is on {tensor.device}")
-        if stream is None:
-            stream = current_stream()
-        buffer = buffer_type(tensor.data_ptr())
-        self.hccl.hcclBroadcast(buffer, tensor.numel(),
-                                hcclDataTypeEnum.from_torch(tensor.dtype), src,
-                                self.comm, aclrtStream_t(stream.npu_stream))
@@ -1,208 +0,0 @@
-import ctypes
-import platform
-from dataclasses import dataclass
-from typing import Any
-
-import torch
-from torch.distributed import ReduceOp
-
-from fastvideo.logger import init_logger
-from fastvideo.utils import find_hccl_library
-
-logger = init_logger(__name__)
-
-hcclResult_t = ctypes.c_int
-hcclComm_t = ctypes.c_void_p
-
-
-class hcclUniqueId(ctypes.Structure):
-    _fields_ = [("internal", ctypes.c_byte * 4108)]
-
-
-aclrtStream_t = ctypes.c_void_p
-buffer_type = ctypes.c_void_p
-
-hcclDataType_t = ctypes.c_int
-
-
-class hcclDataTypeEnum:
-    hcclInt8 = 0
-    hcclInt16 = 1
-    hcclInt32 = 2
-    hcclFloat16 = 3
-    hcclFloat32 = 4
-    hcclInt64 = 5
-    hcclUint64 = 6
-    hcclUint8 = 7
-    hcclUint16 = 8
-    hcclUint32 = 9
-    hcclFloat64 = 10
-    hcclBfloat16 = 11
-    hcclInt128 = 12
-
-    @classmethod
-    def from_torch(cls, dtype: torch.dtype) -> int:
-        if dtype == torch.int8:
-            return cls.hcclInt8
-        if dtype == torch.uint8:
-            return cls.hcclUint8
-        if dtype == torch.int32:
-            return cls.hcclInt32
-        if dtype == torch.int64:
-            return cls.hcclInt64
-        if dtype == torch.float16:
-            return cls.hcclFloat16
-        if dtype == torch.float32:
-            return cls.hcclFloat32
-        if dtype == torch.float64:
-            return cls.hcclFloat64
-        if dtype == torch.bfloat16:
-            return cls.hcclBfloat16
-        raise ValueError(f"Unsupported dtype: {dtype}")
-
-
-hcclRedOp_t = ctypes.c_int
-
-
-class hcclRedOpTypeEnum:
-    hcclSum = 0
-    hcclProd = 1
-    hcclMax = 2
-    hcclMin = 3
-
-    @classmethod
-    def from_torch(cls, op: ReduceOp) -> int:
-        if op == ReduceOp.SUM:
-            return cls.hcclSum
-        if op == ReduceOp.PRODUCT:
-            return cls.hcclProd
-        if op == ReduceOp.MAX:
-            return cls.hcclMax
-        if op == ReduceOp.MIN:
-            return cls.hcclMin
-        raise ValueError(f"Unsupported op: {op}")
-
-
-@dataclass
-class Function:
-    name: str
-    restype: Any
-    argtypes: list[Any]
-
-
-class HCCLLibrary:
-    exported_functions = [
-        Function("HcclGetErrorString", ctypes.c_char_p, [hcclResult_t]),
-        Function("HcclGetRootInfo", hcclResult_t,
-                 [ctypes.POINTER(hcclUniqueId)]),
-        Function("HcclCommInitRootInfo", hcclResult_t, [
-            ctypes.c_int,
-            ctypes.POINTER(hcclUniqueId),
-            ctypes.c_int,
-            ctypes.POINTER(hcclComm_t),
-        ]),
-        Function("HcclAllReduce", hcclResult_t, [
-            buffer_type,
-            buffer_type,
-            ctypes.c_size_t,
-            hcclDataType_t,
-            hcclRedOp_t,
-            hcclComm_t,
-            aclrtStream_t,
-        ]),
-        Function("HcclBroadcast", hcclResult_t, [
-            buffer_type,
-            ctypes.c_size_t,
-            hcclDataType_t,
-            ctypes.c_int,
-            hcclComm_t,
-            aclrtStream_t,
-        ]),
-        Function("HcclCommDestroy", hcclResult_t, [hcclComm_t]),
-    ]
-
-    # class attribute to store the mapping from the path to the library
-    # to avoid loading the same library multiple times
-    path_to_library_cache: dict[str, Any] = {}
-
-    # class attribute to store the mapping from library path
-    # to the correspongding directory
-    path_to_dict_mapping: dict[str, dict[str, Any]] = {}
-
-    def __init__(self, so_file: str | None = None):
-
-        so_file = so_file or find_hccl_library()
-
-        try:
-            if so_file not in HCCLLibrary.path_to_dict_mapping:
-                lib = ctypes.CDLL(so_file)
-                HCCLLibrary.path_to_library_cache[so_file] = lib
-            self.lib = HCCLLibrary.path_to_library_cache[so_file]
-        except Exception as e:
-            logger.error(
-                "Failed to load HCCL library from %s. "
-                "It is expected if you are not running on Ascend NPUs."
-                "Otherwise, the hccl library might not exist, be corrupted "
-                "or it does not support the current platform %s. "
-                "If you already have the library, please set the "
-                "environment variable HCCL_SO_PATH"
-                " to point to the correct hccl library path.", so_file,
-                platform.platform())
-            raise e
-
-        if so_file not in HCCLLibrary.path_to_dict_mapping:
-            _funcs: dict[str, Any] = {}
-            for func in HCCLLibrary.exported_functions:
-                f = getattr(self.lib, func.name)
-                f.restype = func.restype
-                f.argtypes = func.argtypes
-                _funcs[func.name] = f
-            HCCLLibrary.path_to_dict_mapping[so_file] = _funcs
-        self._funcs = HCCLLibrary.path_to_dict_mapping[so_file]
-
-    def hcclGetErrorString(self, result: hcclResult_t) -> str:
-        return self._funcs["HcclGetErrorString"](result).decode("utf-8")
-
-    def HCCL_CHECK(self, result: hcclResult_t) -> None:
-        if result != 0:
-            error_str = self.hcclGetErrorString(result)
-            raise RuntimeError(f"HCCL error: {error_str}")
-
-    def hcclGetUniqueId(self) -> hcclUniqueId:
-        unique_id = hcclUniqueId()
-        self.HCCL_CHECK(self._funcs["HcclGetRootInfo"](ctypes.byref(unique_id)))
-        return unique_id
-
-    def hcclCommInitRank(self, world_size: int, unique_id: hcclUniqueId,
-                         rank: int) -> hcclComm_t:
-        comm = hcclComm_t()
-        self.HCCL_CHECK(self._funcs["HcclCommInitRootInfo"](
-            world_size, ctypes.byref(unique_id), rank, ctypes.byref(comm)))
-        return comm
-
-    def hcclAllReduce(self, sendbuff: buffer_type, recvbuff: buffer_type,
-                      count: int, datatype: int, op: int, comm: hcclComm_t,
-                      stream: aclrtStream_t) -> None:
-        self.HCCL_CHECK(self._funcs["HcclAllReduce"](sendbuff, recvbuff, count,
-                                                     datatype, op, comm,
-                                                     stream))
-
-    def hcclBroadcast(self, buf: buffer_type, count: int, datatype: int,
-                      root: int, comm: hcclComm_t,
-                      stream: aclrtStream_t) -> None:
-        self.HCCL_CHECK(self._funcs["HcclBroadcast"](buf, count, datatype, root,
-                                                     comm, stream))
-
-    def hcclCommDestroy(self, comm: hcclComm_t) -> None:
-        self.HCCL_CHECK(self._funcs["HcclCommDestroy"](comm))
-
-
-__all__ = [
-    "HCCLLibrary",
-    "hcclDataTypeEnum",
-    "hcclRedOpTypeEnum",
-    "hcclUniqueId",
-    "hcclComm_t",
-    "aclrtStream_t",
-    "buffer_type",
-]
@@ -45,6 +45,7 @@ from fastvideo.distributed.device_communicators.cpu_communicator import (
    CpuCommunicator)
 from fastvideo.distributed.utils import StatelessProcessGroup
 from fastvideo.logger import init_logger
+from fastvideo.platforms import current_platform

 logger = init_logger(__name__)

@@ -189,6 +190,7 @@ class GroupCoordinator:
        self.device = get_local_torch_device()

        self.use_device_communicator = use_device_communicator
+
        self.device_communicator: DeviceCommunicatorBase = None  # type: ignore
        if use_device_communicator and self.world_size > 1:
            # Platform-aware device communicator selection
@@ -201,15 +203,6 @@ class GroupCoordinator:
                    device_group=self.device_group,
                    unique_name=self.unique_name,
                )
-            elif current_platform.is_npu():
-                from fastvideo.distributed.device_communicators.npu_communicator import (
-                    NpuCommunicator)
-                self.device_communicator = NpuCommunicator(
-                    cpu_group=self.cpu_group,
-                    device=self.device,
-                    device_group=self.device_group,
-                    unique_name=self.unique_name,
-                )
            else:
                # For MPS and CPU, use the CPU communicator
                self.device_communicator = CpuCommunicator(
@@ -783,13 +776,8 @@ def init_distributed_environment(
 ):
    # Determine the appropriate backend based on the platform
    from fastvideo.platforms import current_platform
-    backend = "nccl"
-    if current_platform.is_cuda_alike():
-        logger.info("Using nccl backend for CUDA platform")
-    elif current_platform.is_npu():
-        backend = "hccl"
-        logger.info("Using hccl backend for NPU platform")
-    else:
+    if backend == "nccl" and not current_platform.is_cuda_alike():
+        # Use gloo backend for non-CUDA platforms (MPS, CPU)
        backend = "gloo"
        logger.info("Using gloo backend for %s platform",
                    current_platform.device_name)
@@ -803,11 +791,21 @@ def init_distributed_environment(
            "distributed_init_method must be provided when initializing "
            "distributed environment")

-        torch.distributed.init_process_group(
-            backend=backend,
-            init_method=distributed_init_method,
-            world_size=world_size,
-            rank=rank)
+        # For MPS, don't pass device_id as it doesn't support device indices
+        if current_platform.is_mps():
+            torch.distributed.init_process_group(
+                backend=backend,
+                init_method=distributed_init_method,
+                world_size=world_size,
+                rank=rank)
+        else:
+            # this backend is used for WORLD
+            torch.distributed.init_process_group(
+                backend=backend,
+                init_method=distributed_init_method,
+                world_size=world_size,
+                rank=rank,
+                device_id=device_id)
    # set the local rank
    # local_rank is not available in torch ProcessGroup,
    # see https://github.com/pytorch/pytorch/issues/122816
@@ -950,14 +948,9 @@ def get_dp_rank() -> int:

 def get_local_torch_device() -> torch.device:
    """Return the torch device for the current rank."""
-    from fastvideo.platforms import current_platform
-    if current_platform.is_npu():
-        device = torch.device(f"npu:{envs.LOCAL_RANK}")
-    elif current_platform.is_cuda_alike() or current_platform.is_cuda():
-        device = torch.device(f"cuda:{envs.LOCAL_RANK}")
-    else:
-        device = torch.device("mps")
-    return device
+    return torch.device(f"cuda:{envs.LOCAL_RANK}"
+                        ) if current_platform.is_cuda_alike() else torch.device(
+                            "mps")


 def maybe_init_distributed_environment_and_model_parallel(
@@ -975,9 +968,7 @@ def maybe_init_distributed_environment_and_model_parallel(
    device = get_local_torch_device()
    logger.info(
        "Initializing distributed environment with world_size=%d, device=%s",
-        world_size,
-        device,
-        local_main_process_only=False)
+        world_size, device)

    init_distributed_environment(
        world_size=world_size,
@@ -988,11 +979,10 @@ def maybe_init_distributed_environment_and_model_parallel(
    initialize_model_parallel(tensor_model_parallel_size=tp_size,
                              sequence_model_parallel_size=sp_size)

-    # set device if we're on a CUDA/NPU platform
-    from fastvideo.platforms import current_platform
-    device_type = current_platform.device_type
-    device = torch.device(f"{device_type}:{local_rank}")
-    current_platform.get_torch_device().set_device(device)
+    # Only set CUDA device if we're on a CUDA platform
+    if current_platform.is_cuda_alike():
+        device = torch.device(f"cuda:{local_rank}")
+        torch.cuda.set_device(device)


 def model_parallel_is_initialized() -> bool:
@@ -8,7 +8,6 @@ diffusion models.

 import math
 import os
-import re
 import time
 from copy import deepcopy
 from typing import Any
@@ -111,7 +110,7 @@ class VideoGenerator:
            prompt: The prompt to use for generation (optional if prompt_txt is provided)
            negative_prompt: The negative prompt to use (overrides the one in fastvideo_args)
            output_path: Path to save the video (overrides the one in fastvideo_args)
-            prompt_path: Path to prompt file
+            output_video_name: Name of the video file to save. Default is the first 100 characters of the prompt.
            save_video: Whether to save the video to disk
            return_frames: Whether to return the raw frames
            num_inference_steps: Number of denoising steps (overrides fastvideo_args)
@@ -128,13 +127,8 @@ class VideoGenerator:
            Either the output dictionary, list of frames, or list of results for batch processing
        """
        # Handle batch processing from text file
-        if sampling_param is None:
-            sampling_param = SamplingParam.from_pretrained(
-                self.fastvideo_args.model_path)
-        sampling_param.update(kwargs)
-
-        if self.fastvideo_args.prompt_txt is not None or sampling_param.prompt_path is not None:
-            prompt_txt_path = sampling_param.prompt_path or self.fastvideo_args.prompt_txt
+        if self.fastvideo_args.prompt_txt is not None:
+            prompt_txt_path = self.fastvideo_args.prompt_txt
            if not os.path.exists(prompt_txt_path):
                raise FileNotFoundError(
                    f"Prompt text file not found: {prompt_txt_path}")
@@ -148,19 +142,22 @@ class VideoGenerator:

            logger.info("Found %d prompts in %s", len(prompts), prompt_txt_path)

+            if sampling_param is not None:
+                original_output_video_name = sampling_param.output_video_name
+            else:
+                original_output_video_name = None
+
            results = []
            for i, batch_prompt in enumerate(prompts):
                logger.info("Processing prompt %d/%d: %s...", i + 1,
                            len(prompts), batch_prompt[:100])
+
                try:
                    # Generate video for this prompt using the same logic below
-                    output_path = self._prepare_output_path(
-                        sampling_param.output_path, batch_prompt)
-                    kwargs["output_path"] = output_path
+                    if sampling_param is not None and original_output_video_name is not None:
+                        sampling_param.output_video_name = original_output_video_name + f"_{i}"
                    result = self._generate_single_video(
-                        prompt=batch_prompt,
-                        sampling_param=sampling_param,
-                        **kwargs)
+                        batch_prompt, sampling_param, **kwargs)

                    # Add prompt info to result
                    if isinstance(result, dict):
@@ -184,73 +181,8 @@ class VideoGenerator:
        # Single prompt generation (original behavior)
        if prompt is None:
            raise ValueError("Either prompt or prompt_txt must be provided")
-        output_path = self._prepare_output_path(sampling_param.output_path,
-                                                prompt)
-        kwargs["output_path"] = output_path
-        return self._generate_single_video(prompt=prompt,
-                                           sampling_param=sampling_param,
-                                           **kwargs)

-    def _prepare_output_path(
-        self,
-        output_path: str,
-        prompt: str,
-    ) -> str:
-        """Build a unique, sanitized .mp4 output file path.
-
-        - If `output_path` ends with .mp4 (case-insensitive), treat it as a file path.
-        - Otherwise, treat `output_path` as a directory and derive the filename
-          from the prompt.
-        - Invalid filename characters are removed; if the name changes, a
-          warning is logged.
-        - If the target path already exists, a numeric suffix is appended.
-        """
-
-        def _sanitize_filename_component(name: str) -> str:
-            # Remove characters invalid on common filesystems, strip spaces/dots
-            sanitized = re.sub(r'[\\/:*?"<>|]', '', name)
-            sanitized = sanitized.strip().strip('.')
-            sanitized = re.sub(r'\s+', ' ', sanitized)
-            return sanitized or "video"
-
-        base_path, extension = os.path.splitext(output_path)
-        extension_lower = extension.lower()
-
-        if extension_lower == ".mp4":
-            output_dir = os.path.dirname(output_path)
-            base_name = os.path.basename(
-                base_path)  # filename without extension
-            sanitized_base = _sanitize_filename_component(base_name)
-            if sanitized_base != base_name:
-                logger.warning(
-                    "The video name '%s' contained invalid characters. It has been renamed to '%s.mp4'",
-                    os.path.basename(output_path),
-                    sanitized_base,
-                )
-            video_name = f"{sanitized_base}.mp4"
-        else:
-            # Treat as directory; inform if an unexpected extension was provided.
-            if extension:
-                logger.info(
-                    "Output path '%s' has non-mp4 extension '%s'; treating it as a directory and using a .mp4 filename derived from the prompt",
-                    output_path,
-                    extension,
-                )
-            output_dir = output_path
-            prompt_component = _sanitize_filename_component(prompt[:100])
-            video_name = f"{prompt_component}.mp4"
-
-        if output_dir:
-            os.makedirs(output_dir, exist_ok=True)
-
-        new_output_path = os.path.join(output_dir, video_name)
-        counter = 1
-        while os.path.exists(new_output_path):
-            name_part, ext_part = os.path.splitext(video_name)
-            new_video_name = f"{name_part}_{counter}{ext_part}"
-            new_output_path = os.path.join(output_dir, new_video_name)
-            counter += 1
-        return new_output_path
+        return self._generate_single_video(prompt, sampling_param, **kwargs)

    def _generate_single_video(
        self,
@@ -268,9 +200,15 @@ class VideoGenerator:
            raise TypeError(
                f"`prompt` must be a string, but got {type(prompt)}")
        prompt = prompt.strip()
-        sampling_param = deepcopy(sampling_param)
-        output_path = kwargs["output_path"]
-        sampling_param.prompt = prompt
+        if sampling_param is None:
+            sampling_param = SamplingParam.from_pretrained(
+                fastvideo_args.model_path)
+        else:
+            sampling_param = deepcopy(sampling_param)
+
+        kwargs["prompt"] = prompt
+        sampling_param.update(kwargs)
+
        # Process negative prompt
        if sampling_param.negative_prompt is not None:
            sampling_param.negative_prompt = sampling_param.negative_prompt.strip(
@@ -339,7 +277,7 @@ class VideoGenerator:
                      height: {target_height}
                       width: {target_width}
                video_length: {sampling_param.num_frames}
-                      prompt: {sampling_param.prompt}
+                      prompt: {prompt}
                      image_path: {sampling_param.image_path}
                  neg_prompt: {sampling_param.negative_prompt}
                        seed: {sampling_param.seed}
@@ -350,7 +288,7 @@ class VideoGenerator:
                  flow_shift: {fastvideo_args.pipeline_config.flow_shift}
     embedded_guidance_scale: {fastvideo_args.pipeline_config.embedded_cfg_scale}
                  save_video: {sampling_param.save_video}
-                  output_path: {output_path}
+                  output_path: {sampling_param.output_path}
        """ # type: ignore[attr-defined]
        logger.info(debug_str)

@@ -360,8 +298,13 @@ class VideoGenerator:
            eta=0.0,
            n_tokens=n_tokens,
            VSA_sparsity=fastvideo_args.VSA_sparsity,
+            extra={},
        )

+        # Use prompt[:100] for video name
+        if batch.output_video_name is None:
+            batch.output_video_name = prompt[:100]
+
        # Run inference
        start_time = time.perf_counter()
        output_batch = self.executor.execute_forward(batch, fastvideo_args)
@@ -381,8 +324,15 @@ class VideoGenerator:

        # Save video if requested
        if batch.save_video:
-            imageio.mimsave(output_path, frames, fps=batch.fps, format="mp4")
-            logger.info("Saved video to %s", output_path)
+            output_path = batch.output_path
+            if output_path:
+                os.makedirs(output_path, exist_ok=True)
+                video_path = os.path.join(output_path,
+                                          f"{batch.output_video_name}.mp4")
+                imageio.mimsave(video_path, frames, fps=batch.fps, format="mp4")
+                logger.info("Saved video to %s", video_path)
+            else:
+                logger.warning("No output path provided, video not saved")

        if batch.return_frames:
            return frames
@@ -14,29 +14,20 @@ if TYPE_CHECKING:
    FASTVIDEO_CACHE_ROOT: str = os.path.expanduser("~/.cache/fastvideo")
    FASTVIDEO_CONFIG_ROOT: str = os.path.expanduser("~/.config/fastvideo")
    FASTVIDEO_CONFIGURE_LOGGING: int = 1
-    FASTVIDEO_RAY_PER_WORKER_GPUS: float = 1.0
    FASTVIDEO_LOGGING_LEVEL: str = "INFO"
    FASTVIDEO_LOGGING_PREFIX: str = ""
    FASTVIDEO_LOGGING_CONFIG_PATH: str | None = None
    FASTVIDEO_TRACE_FUNCTION: int = 0
    FASTVIDEO_ATTENTION_BACKEND: str | None = None
    FASTVIDEO_ATTENTION_CONFIG: str | None = None
-    FASTVIDEO_WORKER_MULTIPROC_METHOD: str = "spawn"
+    FASTVIDEO_WORKER_MULTIPROC_METHOD: str = "fork"
    FASTVIDEO_TARGET_DEVICE: str = "cuda"
    MAX_JOBS: str | None = None
    NVCC_THREADS: str | None = None
    CMAKE_BUILD_TYPE: str | None = None
    VERBOSE: bool = False
-    FASTVIDEO_TORCH_PROFILER_DIR: str | None = None
-    FASTVIDEO_TORCH_PROFILER_RECORD_SHAPES: bool = False
-    FASTVIDEO_TORCH_PROFILER_WITH_PROFILE_MEMORY: bool = False
-    FASTVIDEO_TORCH_PROFILER_WITH_STACK: bool = True
-    FASTVIDEO_TORCH_PROFILER_WITH_FLOPS: bool = False
-    FASTVIDEO_TORCH_PROFILE_REGIONS: str = ""
    FASTVIDEO_SERVER_DEV_MODE: bool = False
    FASTVIDEO_STAGE_LOGGING: bool = False
-    FASTVIDEO_HOST_IP: str = ""
-    FASTVIDEO_LOOPBACK_IP: str = ""


 def get_default_cache_root() -> str:
@@ -122,23 +113,6 @@ environment_variables: dict[str, Callable[[], Any]] = {
            os.path.join(get_default_cache_root(), "fastvideo"),
        )),

-    # used in distributed environment to determine the ip address
-    # of the current node, when the node has multiple network interfaces.
-    # If you are using multi-node inference, you should set this differently
-    # on each node.
-    "FASTVIDEO_HOST_IP":
-    lambda: os.getenv("FASTVIDEO_HOST_IP", ""),
-
-    # Used to force set up loopback IP
-    "FASTVIDEO_LOOPBACK_IP":
-    lambda: os.getenv("FASTVIDEO_LOOPBACK_IP", ""),
-
-    # Number of GPUs per worker in Ray, if it is set to be a fraction,
-    # it allows ray to schedule multiple actors on a single GPU,
-    # so that users can colocate other actors on the same GPUs as FastVideo.
-    "FASTVIDEO_RAY_PER_WORKER_GPUS":
-    lambda: float(os.getenv("FASTVIDEO_RAY_PER_WORKER_GPUS", "1.0")),
-
    # Interval in seconds to log a warning message when the ring buffer is full
    "FASTVIDEO_RINGBUFFER_WARNING_INTERVAL":
    lambda: int(os.environ.get("FASTVIDEO_RINGBUFFER_WARNING_INTERVAL", "60")),
@@ -201,7 +175,6 @@ environment_variables: dict[str, Callable[[], Any]] = {
    # - "SLIDING_TILE_ATTN" : use Sliding Tile Attention
    # - "VIDEO_SPARSE_ATTN": use Video Sparse Attention
    # - "SAGE_ATTN": use Sage Attention
-    # - "SAGE_ATTN_THREE": use Sage Attention 3
    "FASTVIDEO_ATTENTION_BACKEND":
    lambda: os.getenv("FASTVIDEO_ATTENTION_BACKEND", None),

@@ -212,8 +185,9 @@ environment_variables: dict[str, Callable[[], Any]] = {
             os.path.expanduser(os.getenv("FASTVIDEO_ATTENTION_CONFIG", "."))),

    # Use dedicated multiprocess context for workers.
+    # Both spawn and fork work
    "FASTVIDEO_WORKER_MULTIPROC_METHOD":
-    lambda: os.getenv("FASTVIDEO_WORKER_MULTIPROC_METHOD", "spawn"),
+    lambda: os.getenv("FASTVIDEO_WORKER_MULTIPROC_METHOD", "fork"),

    # Enables torch profiler if set. Path to the directory where torch profiler
    # traces are saved. Note that it must be an absolute path.
@@ -222,34 +196,6 @@ environment_variables: dict[str, Callable[[], Any]] = {
             if os.getenv("FASTVIDEO_TORCH_PROFILER_DIR", None) is None else os.
             path.expanduser(os.getenv("FASTVIDEO_TORCH_PROFILER_DIR", "."))),

-    # Enable torch profiler to record shapes if set
-    # FASTVIDEO_TORCH_PROFILER_RECORD_SHAPES=1. If not set, torch profiler will
-    # not record shapes.
-    "FASTVIDEO_TORCH_PROFILER_RECORD_SHAPES":
-    lambda: bool(
-        os.getenv("FASTVIDEO_TORCH_PROFILER_RECORD_SHAPES", "0") != "0"),
-
-    # Enable torch profiler to profile memory if set
-    # FASTVIDEO_TORCH_PROFILER_WITH_PROFILE_MEMORY=1. If not set, torch profiler
-    # will not profile memory.
-    "FASTVIDEO_TORCH_PROFILER_WITH_PROFILE_MEMORY":
-    lambda: bool(
-        os.getenv("FASTVIDEO_TORCH_PROFILER_WITH_PROFILE_MEMORY", "0") != "0"),
-
-    # Enable torch profiler to profile stack if set
-    # FASTVIDEO_TORCH_PROFILER_WITH_STACK=1. If not set, torch profiler WILL
-    # profile stack by default.
-    "FASTVIDEO_TORCH_PROFILER_WITH_STACK":
-    lambda: bool(os.getenv("FASTVIDEO_TORCH_PROFILER_WITH_STACK", "1") != "0"),
-
-    # Enable torch profiler to profile flops if set
-    # FASTVIDEO_TORCH_PROFILER_WITH_FLOPS=1. If not set, torch profiler will
-    # not profile flops.
-    "FASTVIDEO_TORCH_PROFILER_WITH_FLOPS":
-    lambda: bool(os.getenv("FASTVIDEO_TORCH_PROFILER_WITH_FLOPS", "0") != "0"),
-    "FASTVIDEO_TORCH_PROFILE_REGIONS":
-    lambda: os.getenv("FASTVIDEO_TORCH_PROFILE_REGIONS", ""),
-
    # If set, fastvideo will run in development mode, which will enable
    # some additional endpoints for developing and debugging,
    # e.g. `/reset_prefix_cache`
@@ -7,21 +7,15 @@ import json
 from contextlib import contextmanager
 from dataclasses import field
 from enum import Enum
-from typing import Any, TYPE_CHECKING
+from typing import Any

 from fastvideo.configs.configs import PreprocessConfig
 from fastvideo.configs.pipelines.base import PipelineConfig, STA_Mode
 from fastvideo.configs.utils import clean_cli_args
 from fastvideo.logger import init_logger
+from fastvideo.platforms import current_platform
 from fastvideo.utils import FlexibleArgumentParser, StoreBoolean

-if TYPE_CHECKING:
-    from ray.runtime_env import RuntimeEnv
-    from ray.util.placement_group import PlacementGroup
-else:
-    RuntimeEnv = Any
-    PlacementGroup = Any
-
 logger = init_logger(__name__)


@@ -97,11 +91,6 @@ class FastVideoArgs:
    # Distributed executor backend
    distributed_executor_backend: str = "mp"

-    # a few attributes for ray related
-    ray_placement_group: PlacementGroup | None = None
-
-    ray_runtime_env: RuntimeEnv | None = None
-
    inference_mode: bool = True  # if False == training mode

    # HuggingFace specific parameters
@@ -144,7 +133,6 @@ class FastVideoArgs:

    # Compilation
    enable_torch_compile: bool = False
-    torch_compile_kwargs: dict[str, Any] = field(default_factory=dict)

    disable_autocast: bool = False

@@ -171,14 +159,12 @@ class FastVideoArgs:
        "vae": True,
    })
    override_transformer_cls_name: str | None = None
-    init_weights_from_safetensors: str = ""  # path to safetensors file for initial weight loading
-    init_weights_from_safetensors_2: str = ""  # path to safetensors file for initial weight loading for transformer_2

    # # DMD parameters
    # dmd_denoising_steps: List[int] | None = field(default=None)

    # MoE parameters used by Wan2.2
-    boundary_ratio: float | None = 0.875
+    boundary_ratio: float | None = None

    @property
    def training_mode(self) -> bool:
@@ -344,13 +330,6 @@ class FastVideoArgs:
            help="Use torch.compile to speed up DiT inference." +
            "However, will likely cause precision drifts. See (https://github.com/pytorch/pytorch/issues/145213)",
        )
-        parser.add_argument(
-            "--torch-compile-kwargs",
-            type=str,
-            default=None,
-            help=
-            "JSON string of kwargs to pass to torch.compile. Example: '{\"backend\":\"inductor\",\"mode\":\"reduce-overhead\"}'",
-        )

        parser.add_argument(
            "--dit-cpu-offload",
@@ -424,15 +403,6 @@ class FastVideoArgs:
            default=FastVideoArgs.override_transformer_cls_name,
            help="Override transformer cls name",
        )
-        parser.add_argument(
-            "--init-weights-from-safetensors",
-            type=str,
-            help="Path to safetensors file for initial weight loading")
-        parser.add_argument(
-            "--init-weights-from-safetensors-2",
-            type=str,
-            help="Path to safetensors file for initial weight loading")
-
        # Add pipeline configuration arguments
        PipelineConfig.add_cli_args(parser)

@@ -461,21 +431,6 @@ class FastVideoArgs:
                mode_value = getattr(args, attr, FastVideoArgs.mode.value)
                kwargs['mode'] = ExecutionMode.from_string(
                    mode_value) if isinstance(mode_value, str) else mode_value
-            elif attr == 'torch_compile_kwargs':
-                # Parse JSON string for torch.compile kwargs
-                torch_compile_kwargs_str = getattr(args, 'torch_compile_kwargs',
-                                                   None)
-                if torch_compile_kwargs_str:
-                    try:
-                        import json
-                        kwargs['torch_compile_kwargs'] = json.loads(
-                            torch_compile_kwargs_str)
-                    except json.JSONDecodeError as e:
-                        raise ValueError(
-                            f"Invalid JSON for torch_compile_kwargs: {e}"
-                        ) from e
-                else:
-                    kwargs['torch_compile_kwargs'] = {}
            elif attr == 'workload_type':
                # Convert string to WorkloadType enum
                workload_type_value = getattr(args, 'workload_type',
@@ -517,8 +472,6 @@ class FastVideoArgs:

    def check_fastvideo_args(self) -> None:
        """Validate inference arguments for consistency"""
-        from fastvideo.platforms import current_platform
-
        if current_platform.is_mps():
            self.use_fsdp_inference = False

@@ -657,8 +610,10 @@ class TrainingArgs(FastVideoArgs):

    # text encoder & vae & diffusion model
    pretrained_model_name_or_path: str = ""
+    dit_model_name_or_path: str = ""

    # DMD model paths - separate paths for each network
+    generator_model_path: str = ""  # path for generator (student) model
    real_score_model_path: str = ""  # path for real score (teacher) model
    fake_score_model_path: str = ""  # path for fake score (critic) model

@@ -675,7 +630,6 @@ class TrainingArgs(FastVideoArgs):
    validation_guidance_scale: str = ""
    validation_steps: float = 0.0
    log_validation: bool = False
-    trackers: list[str] = dataclasses.field(default_factory=list)
    tracker_project_name: str = ""
    wandb_run_name: str = ""
    seed: int | None = None
@@ -683,7 +637,9 @@ class TrainingArgs(FastVideoArgs):
    # output
    output_dir: str = ""
    checkpoints_total_limit: int = 0
+    checkpointing_steps: int = 0
    resume_from_checkpoint: str = ""  # specify the checkpoint folder to resume from
+    init_weights_from_safetensors: str = ""  # path to safetensors file for initial weight loading

    # optimizer & scheduler
    num_train_epochs: int = 0
@@ -749,12 +705,14 @@ class TrainingArgs(FastVideoArgs):
    # simulate generator forward to match inference
    simulate_generator_forward: bool = False
    warp_denoising_step: bool = False
+    intermediate_latents_visualization: bool = False

    # Self-forcing specific arguments
    num_frame_per_block: int = 3
    independent_first_frame: bool = False
    enable_gradient_masking: bool = True
    gradient_mask_last_n_frames: int = 21
+    validate_cache_structure: bool = False  # Debug flag for cache validation
    same_step_across_blocks: bool = False  # Use same exit timestep for all blocks
    last_step_only: bool = False  # Only use the last timestep for training
    context_noise: int = 0  # Context noise level for cache updates
@@ -928,6 +886,9 @@ class TrainingArgs(FastVideoArgs):
        parser.add_argument("--checkpoints-total-limit",
                            type=int,
                            help="Maximum number of checkpoints to keep")
+        parser.add_argument("--checkpointing-steps",
+                            type=int,
+                            help="Steps between checkpoints")
        parser.add_argument(
            "--training-state-checkpointing-steps",
            type=int,
@@ -940,6 +901,10 @@ class TrainingArgs(FastVideoArgs):
        parser.add_argument("--resume-from-checkpoint",
                            type=str,
                            help="Path to checkpoint to resume from")
+        parser.add_argument(
+            "--init-weights-from-safetensors",
+            type=str,
+            help="Path to safetensors file for initial weight loading")
        parser.add_argument("--logging-dir",
                            type=str,
                            help="Directory for logging")
@@ -7,6 +7,7 @@ import torch.nn as nn
 import torch.nn.functional as F

 from fastvideo.layers.custom_op import CustomOp
+from fastvideo.platforms import current_platform


@CustomOp.register("rms_norm")
@@ -33,8 +34,6 @@ class RMSNorm(CustomOp):
                                       else var_hidden_size)
        self.has_weight = has_weight

-        from fastvideo.platforms import current_platform
-
        self.weight = torch.ones(hidden_size) if current_platform.is_cuda_alike(
        ) else torch.ones(hidden_size, dtype=dtype)
        if self.has_weight:
@@ -213,9 +212,9 @@ class ScaleResidualLayerNormScaleShift(nn.Module):
            frame_seqlen = normalized.shape[1] // num_frames
            modulated = (
                normalized.unflatten(dim=1, sizes=(num_frames, frame_seqlen)) *
-                (1.0 + scale) + shift).flatten(1, 2)
+                (1 + scale) + shift).flatten(1, 2)
        else:
-            modulated = normalized * (1.0 + scale) + shift
+            modulated = normalized * (1 + scale) + shift
        return modulated, residual_output


@@ -268,11 +267,11 @@ class LayerNormScaleShift(nn.Module):
            frame_seqlen = normalized.shape[1] // num_frames
            output = (
                normalized.unflatten(dim=1, sizes=(num_frames, frame_seqlen)) *
-                (1.0 + scale) + shift).flatten(1, 2)
+                (1 + scale) + shift).flatten(1, 2)
        else:
            # scale.shape: [batch_size, 1, inner_dim]
            # shift.shape: [batch_size, 1, inner_dim]
-            output = normalized * (1.0 + scale) + shift
+            output = normalized * (1 + scale) + shift

        if self.compute_dtype == torch.float32:
            output = output.to(x.dtype)
@@ -33,7 +33,7 @@ from fastvideo.layers.visual_embedding import (PatchEmbed)
 from fastvideo.logger import init_logger
 from fastvideo.models.dits.base import BaseDiT
 from fastvideo.models.dits.wanvideo import WanT2VCrossAttention, WanTimeTextImageEmbedding
-from fastvideo.platforms import AttentionBackendEnum
+from fastvideo.platforms import AttentionBackendEnum, current_platform

 logger = init_logger(__name__)
 class CausalWanSelfAttention(nn.Module):
@@ -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
@@ -262,9 +262,14 @@ class CausalWanTransformerBlock(nn.Module):
        # *_msa.shape: [batch_size, num_frames, 1, inner_dim]
        # assert shift_msa.dtype == torch.float32

+        # logger.info("temb sum: %s, dtype: %s", temb.float().sum().item(), temb.dtype)
+        # logger.info("scale_msa sum: %s, dtype: %s", scale_msa.float().sum().item(), scale_msa.dtype)
+        # logger.info("shift_msa sum: %s, dtype: %s", shift_msa.float().sum().item(), shift_msa.dtype)
+
        # 1. Self-attention
        norm_hidden_states = (self.norm1(hidden_states).unflatten(dim=1, sizes=(num_frames, frame_seqlen)) *
                        (1 + scale_msa) + shift_msa).flatten(1, 2)
+        # logger.info("norm_hidden_states sum: %s, shape: %s", norm_hidden_states.float().sum().item(), norm_hidden_states.shape)
        query, _ = self.to_q(norm_hidden_states)
        key, _ = self.to_k(norm_hidden_states)
        value, _ = self.to_v(norm_hidden_states)
@@ -370,8 +375,7 @@ class CausalWanTransformer3DModel(BaseDiT):

        # Causal-specific
        self.block_mask = None
-        self.num_frame_per_block = config.arch_config.num_frames_per_block
-        assert self.num_frame_per_block <= 3
+        self.num_frame_per_block = 3
        self.independent_first_frame = False

        self.__post_init__()
@@ -452,7 +456,6 @@ class CausalWanTransformer3DModel(BaseDiT):
        This function will be run for num_frame times.
        Process the latent frames one by one (1560 tokens each)
        """
-        from fastvideo.platforms import current_platform

        orig_dtype = hidden_states.dtype
        if not isinstance(encoder_hidden_states, torch.Tensor):
@@ -676,4 +679,4 @@ class CausalWanTransformer3DModel(BaseDiT):
            # u = torch.einsum('fhwpqrc->cfphqwr', u.contiguous())
            u = u.reshape(c, *[i * j for i, j in zip(v, self.patch_size)])
            out.append(u)
-        return out
+        return out
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
SolitaryThinker	2e5302817f	remove wan/	2025-09-21 23:01:06 +00:00
SolitaryThinker	33376b7347	debug and temp batch in ode_init	2025-09-21 02:41:14 +00:00
SolitaryThinker	d91c8c5541	fix ode_init	2025-09-17 22:02:09 +00:00
JerryZhou54	96683c2fa9	Resolve timestep mismatch between dit forward and pred_noise_to_pred_video	2025-09-17 03:56:06 +00:00
SolitaryThinker	907616e412	debug	2025-09-16 06:34:09 +00:00
SolitaryThinker	7e2c8f8d49	comment out vmoba	2025-09-16 04:27:42 +00:00
SolitaryThinker	421dcbb50b	Merge branch 'wei/dit_debug' into will/ode_init	2025-09-16 02:14:14 +00:00
SolitaryThinker	b9662bf882	lmdb datasets	2025-09-16 02:06:43 +00:00
JerryZhou54	140fb9f20e	Fix test for forward_train	2025-09-15 23:23:33 +00:00
JerryZhou54	2078876b98	Ensure 0 numerical diff for forward_train	2025-09-15 22:49:52 +00:00
JerryZhou54	a953f46bd6	Add test for _forward_train	2025-09-15 22:37:50 +00:00
JerryZhou54	adae957008	Fix numerical diff between causal_wanvideo.py and SF's causal wan	2025-09-14 08:09:57 +00:00
SolitaryThinker	fa40553afb	t2v to i2v finetune checkpoint ode checkpoint fix t2v to i2v lint chekpt checkpoint hacked but working ode_init scripts WIP fixing time embedding WIP fixing time embedding checkpoint update fix revert revert revert update update visualize	2025-09-14 02:23:42 +00:00
RandNMR73	93ebd15a0d	text preprocessing ready	2025-09-10 11:12:48 +00:00
JerryZhou54	1110474065	checkpoint	2025-09-10 08:57:54 +00:00
JerryZhou54	80baffd540	Enable timestep warping & using SelfForcing scheduler	2025-09-09 23:30:55 +00:00
JerryZhou54	918180048e	Stop backprop through kv_cache	2025-09-09 10:02:03 +00:00
RandNMR73	b7dbd7cb9e	new branch	2025-09-09 10:02:00 +00:00
RandNMR73	71159b6416	inference works after changes added	2025-09-09 10:01:31 +00:00