preprocessing text

[misc] Update Slack invite link (#786 )
Update WeChat Link
2025-09-06 05:56:13 +00:00 · 2025-09-05 12:16:18 -07:00 · 2025-09-05 11:40:47 -07:00 · 2025-09-05 01:23:01 -07:00 · 2025-09-04 17:51:27 -07:00 · 2025-09-03 20:47:33 -07:00
134 changed files with 6329 additions and 813 deletions
@@ -117,11 +117,11 @@ steps:
                  queue: "default"
            - path:
                - "fastvideo/**"
-                - "csrc/attn/vsa/**"
-                - "csrc/attn/tk/**"
-                - "csrc/attn/setup_vsa.py"
-                - "csrc/attn/config_vsa.py"
-                - "csrc/attn/vsa.cpp"
+                - "csrc/attn/video_sparse_attn/**"
+                - "csrc/attn/video_sparse_attn/tk/**"
+                - "csrc/attn/video_sparse_attn/setup.py"
+                - "csrc/attn/video_sparse_attn/config_vsa.py"
+                - "csrc/attn/video_sparse_attn/vsa.cpp"
                - "pyproject.toml"
                - "docker/Dockerfile.python3.12"
              config:
@@ -133,10 +133,10 @@ steps:
                  queue: "default"
            - path:
                - "fastvideo/**"
-                - "csrc/attn/st_attn/**"
-                - "csrc/attn/setup_sta.py"
-                - "csrc/attn/config_sta.py"
-                - "csrc/attn/st_attn.cpp"
+                - "csrc/attn/sliding_tile_attn/**"
+                - "csrc/attn/sliding_tile_attn/setup.py"
+                - "csrc/attn/sliding_tile_attn/config_sta.py"
+                - "csrc/attn/sliding_tile_attn/st_attn.cpp"
                - "pyproject.toml"
                - "docker/Dockerfile.python3.12"
              config:
@@ -147,10 +147,10 @@ steps:
                agents:
                  queue: "default"
            - path:
-                - "csrc/attn/st_attn/**"
-                - "csrc/attn/setup_sta.py"
-                - "csrc/attn/config_sta.py"
-                - "csrc/attn/st_attn.cpp"
+                - "csrc/attn/sliding_tile_attn/**"
+                - "csrc/attn/sliding_tile_attn/setup.py"
+                - "csrc/attn/sliding_tile_attn/config_sta.py"
+                - "csrc/attn/sliding_tile_attn/st_attn.cpp"
                - "pyproject.toml"
                - "docker/Dockerfile.python3.12"
              config:
@@ -161,12 +161,12 @@ steps:
                agents:
                  queue: "default"
            - path:
-                - "csrc/attn/vsa/**"
-                - "csrc/attn/tk/**"
+                - "csrc/attn/video_sparse_attn/**"
+                - "csrc/attn/video_sparse_attn/tk/**"
                - "csrc/attn/tests/test_vsa.py"
-                - "csrc/attn/setup_vsa.py"
-                - "csrc/attn/config_vsa.py"
-                - "csrc/attn/vsa.cpp"
+                - "csrc/attn/video_sparse_attn/setup.py"
+                - "csrc/attn/video_sparse_attn/config_vsa.py"
+                - "csrc/attn/video_sparse_attn/vsa.cpp"
                - "pyproject.toml"
                - "docker/Dockerfile.python3.12"
              config:
@@ -18,6 +18,12 @@ on:
        required: false
        default: false
        type: boolean
+      python_3_12_cuda_12_9:
+        description: 'Build Python 3.12 image Cuda 12.9'
+        required: false
+        default: false
+        type: boolean
+

 permissions:
  contents: read
@@ -49,4 +55,13 @@ jobs:
      python_version: '3.12'
      dockerfile_path: docker/Dockerfile.python3.12
      tag_suffix: py3.12
+    secrets: inherit
+
+  build-python-3-12-cuda-12-9:
+    if: ${{ github.event.inputs.python_3_12_cuda_12_9 == 'true' }}
+    uses: ./.github/workflows/build-image-template.yml
+    with:
+      python_version: '3.12'
+      dockerfile_path: docker/Dockerfile.python3.12.cuda12.9.1
+      tag_suffix: py3.12-cuda12.9.1
    secrets: inherit
@@ -104,16 +104,17 @@ jobs:
              - 'pyproject.toml'
              - 'docker/Dockerfile.python3.12'
            sta-kernel-paths: &sta-kernel-paths
-              - 'csrc/attn/st_attn/**'
-              - 'csrc/attn/setup_sta.py'
-              - 'csrc/attn/config_sta.py'
-              - 'csrc/attn/st_attn.cpp'
+              - 'csrc/attn/sliding_tile_attn/**'
+              - 'csrc/attn/sliding_tile_attn/tk/**'
+              - 'csrc/attn/sliding_tile_attn/setup.py'
+              - 'csrc/attn/sliding_tile_attn/config_sta.py'
+              - 'csrc/attn/sliding_tile_attn/st_attn.cpp'
            vsa-kernel-paths: &vsa-kernel-paths
-              - 'csrc/attn/vsa/**'
-              - 'csrc/attn/tk/**'
-              - 'csrc/attn/setup_vsa.py'
-              - 'csrc/attn/config_vsa.py'
-              - 'csrc/attn/vsa.cpp'
+              - 'csrc/attn/video_sparse_attn/**'
+              - 'csrc/attn/video_sparse_attn/tk/**'
+              - 'csrc/attn/video_sparse_attn/setup.py'
+              - 'csrc/attn/video_sparse_attn/config_vsa.py'
+              - 'csrc/attn/video_sparse_attn/vsa.cpp'
            vsa-paths: &vsa-paths
              - 'fastvideo/**'
              - *common-paths
@@ -234,7 +235,7 @@ jobs:
    secrets:
      RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
      RUNPOD_PRIVATE_KEY: ${{ secrets.RUNPOD_PRIVATE_KEY }}
-  
+
  training-test:
    needs: change-filter
    if: >-
@@ -372,4 +373,4 @@ jobs:
          JOB_IDS: '["encoder-test", "vae-test", "transformer-test", "ssim-test-py3.10", "ssim-test-py3.11", "ssim-test-py3.12", "training-test", "training-test-VSA", "inference-test-STA", "precision-test-STA", "precision-test-VSA"]'
          RUNPOD_API_KEY: ${{ secrets.RUNPOD_API_KEY }}
          GITHUB_RUN_ID: ${{ github.run_id }}
-        run: python .github/scripts/runpod_cleanup.py
+        run: python .github/scripts/runpod_cleanup.py
@@ -5,7 +5,7 @@ on:
    branches:
      - main
    paths:
-      - "csrc/attn/setup_sta.py"
+      - "csrc/attn/sliding_tile_attn/setup.py"
  workflow_dispatch:

 jobs:
@@ -23,13 +23,13 @@ jobs:
      - name: Check if version changed
        id: check-version
        run: |
-          cd csrc/attn
+          cd csrc/attn/sliding_tile_attn
          # Get current commit's version
-          NEW_VERSION=$(grep -oP 'VERSION\s*=\s*"\K[^"]+' setup_sta.py)
+          NEW_VERSION=$(grep -oP 'VERSION\s*=\s*"\K[^"]+' setup.py)
          echo "New version: $NEW_VERSION"
          
          # Get previous version from git history
-          OLD_VERSION=$(git show HEAD~1:./setup_sta.py | grep -oP 'VERSION\s*=\s*"\K[^"]+' || echo "0.0.0")
+          OLD_VERSION=$(git show HEAD~1:./setup.py | grep -oP 'VERSION\s*=\s*"\K[^"]+' || echo "0.0.0")
          echo "Old version: $OLD_VERSION"
          
          if [ "$NEW_VERSION" != "$OLD_VERSION" ]; then
@@ -144,13 +144,13 @@ jobs:
          pip install setuptools
          pip install ninja packaging wheel

-          cd csrc/attn  # Move into the correct folder
+          cd csrc/attn/sliding_tile_attn  # Move into the correct folder
          git submodule update --init --recursive # Ensure ThunderKittens submodule is initialized
-          python setup_sta.py bdist_wheel --dist-dir=dist
+          python setup.py bdist_wheel --dist-dir=dist

      - name: Rename wheel file
        run: |
-          cd csrc/attn
+          cd csrc/attn/sliding_tile_attn

          CUDA_SHORT_VERSION=$(echo ${{ matrix.cuda-version }} | cut -d. -f1,2 | sed 's/\.//g')
          TORCH_SHORT_VERSION=$(echo ${{ matrix.torch-version }} | cut -d. -f1,2)
@@ -165,7 +165,7 @@ jobs:
        uses: actions/upload-artifact@v4
        with:
          name: ${{ env.wheel_name }}
-          path: csrc/attn/dist/*.whl
+          path: csrc/attn/sliding_tile_attn/dist/*.whl
          retention-days: 90

  publish_package:
@@ -239,11 +239,11 @@ jobs:
          pip install setuptools
          pip install ninja packaging wheel

-          cd csrc/attn  # Move into the correct folder
+          cd csrc/attn/sliding_tile_attn  # Move into the correct folder
          git submodule update --init --recursive # Ensure ThunderKittens submodule is initialized
-          python setup_sta.py sdist --dist-dir=dist
+          python setup.py sdist --dist-dir=dist

      - name: Publish release distributions to PyPI
        uses: pypa/gh-action-pypi-publish@release/v1
        with:
-          packages-dir: csrc/attn/dist/
+          packages-dir: csrc/attn/sliding_tile_attn/dist/
@@ -5,7 +5,7 @@ on:
    branches:
      - main
    paths:
-      - "csrc/attn/setup_vsa.py"
+      - "csrc/attn/video_sparse_attn/setup.py"
  workflow_dispatch:

 jobs:
@@ -23,13 +23,13 @@ jobs:
      - name: Check if version changed
        id: check-version
        run: |
-          cd csrc/attn
+          cd csrc/attn/video_sparse_attn
          # Get current commit's version
-          NEW_VERSION=$(grep -oP 'VERSION\s*=\s*"\K[^"]+' setup_vsa.py)
+          NEW_VERSION=$(grep -oP 'VERSION\s*=\s*"\K[^"]+' setup.py)
          echo "New version: $NEW_VERSION"
          
          # Get previous version from git history
-          OLD_VERSION=$(git show HEAD~1:./setup_vsa.py | grep -oP 'VERSION\s*=\s*"\K[^"]+' || echo "0.0.0")
+          OLD_VERSION=$(git show HEAD~1:./setup.py | grep -oP 'VERSION\s*=\s*"\K[^"]+' || echo "0.0.0")
          echo "Old version: $OLD_VERSION"
          
          if [ "$NEW_VERSION" != "$OLD_VERSION" ]; then
@@ -152,13 +152,13 @@ jobs:
          pip install setuptools
          pip install ninja packaging wheel

-          cd csrc/attn  # Move into the correct folder
+          cd csrc/attn/video_sparse_attn  # Move into the correct folder
          git submodule update --init --recursive # Ensure ThunderKittens submodule is initialized
-          python setup_vsa.py bdist_wheel --dist-dir=dist
+          python setup.py bdist_wheel --dist-dir=dist

      - name: Rename wheel file
        run: |
-          cd csrc/attn
+          cd csrc/attn/video_sparse_attn

          CUDA_SHORT_VERSION=$(echo ${{ matrix.torch-cuda.cuda-version }} | cut -d. -f1,2 | sed 's/\.//g')
          TORCH_SHORT_VERSION=$(echo ${{ matrix.torch-cuda.torch-version }} | cut -d. -f1,2)
@@ -173,7 +173,7 @@ jobs:
        uses: actions/upload-artifact@v4
        with:
          name: ${{ env.wheel_name }}
-          path: csrc/attn/dist/*.whl
+          path: csrc/attn/video_sparse_attn/dist/*.whl
          retention-days: 90

  publish_package:
@@ -247,11 +247,11 @@ jobs:
          pip install setuptools
          pip install ninja packaging wheel

-          cd csrc/attn  # Move into the correct folder
+          cd csrc/attn/video_sparse_attn  # Move into the correct folder
          git submodule update --init --recursive # Ensure ThunderKittens submodule is initialized
-          python setup_vsa.py sdist --dist-dir=dist
+          python setup.py sdist --dist-dir=dist

      - name: Publish release distributions to PyPI
        uses: pypa/gh-action-pypi-publish@release/v1
        with:
-          packages-dir: csrc/attn/dist/
+          packages-dir: csrc/attn/video_sparse_attn/dist/
@@ -1,3 +1,7 @@
-[submodule "csrc/attn/tk"]
-	path = csrc/attn/tk
+[submodule "csrc/attn/video_sparse_attn/tk"]
+	path = csrc/attn/video_sparse_attn/tk
+	url = https://github.com/HazyResearch/ThunderKittens.git
+
+[submodule "csrc/attn/sliding_tile_attn/tk"]
+	path = csrc/attn/sliding_tile_attn/tk
 	url = https://github.com/HazyResearch/ThunderKittens.git
@@ -22,6 +22,7 @@ exclude: |
    examples/.*|
    .github/workflows/fastvideo-publish.yml|
    .github/workflows/sta-publish.yml|
+    .github/workflows/vsa-publish.yml|
    .github/workflows/build-image-template.yml|
    docs/source/inference/support_matrix.md
  )
@@ -1,5 +1,5 @@
 <div align="center">
-<img src=assets/logo.png width="30%"/>
+<img src=assets/logos/logo.svg width="30%"/>
 </div>

 **FastVideo is a unified post-training and inference framework for accelerated video generation.**
@@ -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-38u6p1jqe-yDI1QJOCEnbtkLoaI5bjZQ" target="_blank"> <b>Slack</b> </a> |  🟣💬 <a href="https://ibb.co/qqPzbrw" 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">
@@ -0,0 +1,6 @@
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+<path d="M15.2524 48.2724L30.0988 1H33.6619L18.8156 48.2724H15.2524Z" fill="#356CFF" stroke="#356CFF" stroke-width="1.18771"/>
+<path d="M8.12646 48.2724L22.9728 1H24.1605L9.31417 48.2724H8.12646Z" fill="#356CFF" stroke="#356CFF" stroke-width="1.18771"/>
+<path d="M1 48.2724L15.8463 1H16.4402L1.59385 48.2724H1Z" fill="#356CFF" stroke="#356CFF" stroke-width="0.593853"/>
+<path d="M85.3271 55.5457H67.5116L87 12.7363L44.3513 68.2729H58.6038L43.1636 101L85.3271 55.5457Z" fill="#FDC717" stroke="#FDC717" stroke-width="1.18771" stroke-miterlimit="16"/>
+</svg>
@@ -0,0 +1,6 @@
+<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"/>
+<path d="M15.0376 91.66L43.8185 1.86368H46.1209L17.3401 91.66H15.0376Z" fill="#356CFF" stroke="#356CFF" stroke-width="2.30244"/>
+<path d="M1.22217 91.66L30.003 1.86366H31.1543L2.3734 91.66H1.22217Z" fill="#356CFF" stroke="#356CFF" stroke-width="1.15122"/>
+<path d="M71.4465 1.86483L42.666 91.6599H69.144L78.3538 58.2746H123.251L129.007 39.855H84.1099L89.866 22.5868H152.032L157.788 1.86483H71.4465Z" fill="#356CFF" stroke="#356CFF" stroke-width="2.30244"/>
+</svg>
@@ -25,9 +25,9 @@ sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-11 100 --slave
 sudo apt update
 sudo apt install clang-11
 ```
-(If you use CUDA12.4)
+(If you use CUDA12.8)
 ```bash
-export CUDA_HOME=/usr/local/cuda-12.4
+export CUDA_HOME=/usr/local/cuda-12.8
 export PATH=${CUDA_HOME}/bin:${PATH} 
 export LD_LIBRARY_PATH=${CUDA_HOME}/lib64:$LD_LIBRARY_PATH
 ```
@@ -1,4 +0,0 @@
-    off_hz = tl.program_id(2)
-    b = off_hz // H
-    h = off_hz % H
-    meta_base = ((b * H + h) * q_tiles + q_blk)
@@ -1,2 +1,2 @@
 recursive-include tk *
-include config.py
+include config_sta.py
@@ -0,0 +1,87 @@
+
+# Attention Kernel Used in FastVideo
+
+## Sliding Tile Attention (STA)
+We only support H100 for STA.
+
+### Installation
+```bash
+pip install st_attn
+``` 
+
+Install from source:
+
+```bash
+git submodule update --init --recursive
+python setup.py install
+```
+
+If you encounter error during installation, try below:
+Install C++20 for ThunderKittens:
+```bash
+sudo apt update
+sudo apt install gcc-11 g++-11
+
+sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-11 100 --slave /usr/bin/g++ g++ /usr/bin/g++-11
+
+sudo apt update
+sudo apt install clang-11
+```
+(If you use CUDA12.8)
+```bash
+export CUDA_HOME=/usr/local/cuda-12.8
+export PATH=${CUDA_HOME}/bin:${PATH} 
+export LD_LIBRARY_PATH=${CUDA_HOME}/lib64:$LD_LIBRARY_PATH
+```
+
+###  Usage
+End-2-end inference with FastVideo:
+```bash
+bash scripts/inference/v1_inference_wan_STA.sh
+```
+
+If you want to use sliding tile attention in your custom model:
+```python
+from st_attn import sliding_tile_attention
+# assuming video size (T, H, W) = (30, 48, 80), text tokens = 256 with padding. 
+# q, k, v: [batch_size, num_heads, seq_length, head_dim], seq_length = T*H*W + 256
+# a tile is a cube of size (6, 8, 8)
+# window_size in tiles: [(window_t, window_h, window_w), (..)...]. For example, window size (3, 3, 3) means a query can attend to (3x6, 3x8, 3x8) = (18, 24, 24) tokens out of the total 30x48x80 video.
+# text_length: int ranging from 0 to 256
+# If your attention contains text token (Hunyuan)
+out = sliding_tile_attention(q, k, v, window_size, text_length)
+# If your attention does not contain text token (StepVideo)
+out = sliding_tile_attention(q, k, v, window_size, 0, False)
+```
+
+
+### Test
+```bash
+python ../tests/test_sta.py # test STA
+python ../tests/test_vsa.py # test VSA
+```
+### Benchmark
+```bash
+python ../benchmarks/bench_sta.py
+```
+
+
+### How Does STA Work?
+We give a demo for 2D STA with window size (6,6) operating on a (10, 10) image. 
+
+
+https://github.com/user-attachments/assets/f3b6dd79-7b43-4b60-a0fa-3d6495ec5747
+
+## Why is STA Fast?
+2D/3D Sliding Window Attention (SWA) creates many mixed blocks in the attention map. Even though mixed blocks have less output value,a mixed block is significantly slower than a dense block due to the GPU-unfriendly masking operation. 
+
+STA removes mixed blocks.
+
+
+<div align="center">
+<img src=../../../assets/sliding_tile_attn_map.png width="80%"/>
+</div>
+
+## Acknowledgement
+
+We learned or reuse code from FlexAtteniton, NATEN, and ThunderKittens.
@@ -1,7 +1,7 @@
 import os
 import subprocess

-from csrc.attn.config_sta import kernels, sources, target
+from config_sta import kernels, sources, target
 from setuptools import find_packages, setup
 from torch.utils.cpp_extension import BuildExtension, CUDAExtension

@@ -9,7 +9,7 @@ target = target.lower()

 # Package metadata
 PACKAGE_NAME = "st_attn"
-VERSION = "0.0.4"
+VERSION = "0.0.6"
 AUTHOR = "Hao AI Lab"
 DESCRIPTION = "Sliding Tile Atteniton Kernel Used in FastVideo"
 URL = "https://github.com/hao-ai-lab/FastVideo/tree/main/csrc/sliding_tile_attention"
@@ -13,9 +13,9 @@ BLOCK_M = 64
 BLOCK_N = 64

 def pytorch_test(Q, K, V, block_sparse_mask, dO):
-    q_ = Q.clone().requires_grad_()
-    k_ = K.clone().requires_grad_()
-    v_ = V.clone().requires_grad_()
+    q_ = Q.clone().float().requires_grad_()
+    k_ = K.clone().float().requires_grad_()
+    v_ = V.clone().float().requires_grad_()

    QK = torch.matmul(q_, k_.transpose(-2, -1))
    QK /= (q_.size(-1) ** 0.5)
@@ -35,9 +35,9 @@ def pytorch_test(Q, K, V, block_sparse_mask, dO):


 def block_sparse_kernel_test(Q, K, V, block_sparse_mask, variable_block_sizes, non_pad_index, dO):
-    Q = Q.clone().requires_grad_()
-    K = K.clone().requires_grad_()
-    V = V.clone().requires_grad_()
+    Q = Q.detach().requires_grad_()
+    K = K.detach().requires_grad_()
+    V = V.detach().requires_grad_()
    
    q_padded = vsa_pad(Q, non_pad_index, variable_block_sizes.shape[0], BLOCK_M)
    k_padded = vsa_pad(K, non_pad_index, variable_block_sizes.shape[0], BLOCK_M)
@@ -60,11 +60,9 @@ def get_non_pad_index(

    return index_pad[index_mask]

-def generate_tensor(shape, mean, std, dtype, device):
+def generate_tensor(shape, dtype, device):
    tensor = torch.randn(shape, dtype=dtype, device=device)
-    magnitude = torch.norm(tensor, dim=-1, keepdim=True)
-    scaled_tensor = tensor * (torch.randn(magnitude.shape, dtype=dtype, device=device) * std + mean) / magnitude
-    return scaled_tensor.contiguous()
+    return tensor

 def generate_variable_block_sizes(num_blocks, min_size=32, max_size=64, device="cuda"):
    return torch.randint(min_size, max_size + 1, (num_blocks,), device=device, dtype=torch.int32)
@@ -75,7 +73,7 @@ def vsa_pad(x, non_pad_index, num_blocks, block_size):
    padded_x[:, :, non_pad_index, :] = x
    return padded_x

-def check_correctness(h, d, num_blocks, k, mean, std, num_iterations=20, error_mode='all'):
+def check_correctness(h, d, num_blocks, k,  num_iterations=20, error_mode='all'):
    results = {
        'gO': {'sum_diff': 0.0, 'sum_abs': 0.0, 'max_diff': 0.0},
        'gQ': {'sum_diff': 0.0, 'sum_abs': 0.0, 'max_diff': 0.0},
@@ -91,10 +89,10 @@ def check_correctness(h, d, num_blocks, k, mean, std, num_iterations=20, error_m
    block_mask = generate_block_sparse_mask_for_function(h, num_blocks, k, device)
    full_mask = create_full_mask_from_block_mask(block_mask, variable_block_sizes, device)
    for _ in range(num_iterations):
-        Q = generate_tensor((1, h, S, d), mean, std, torch.bfloat16, device)
-        K = generate_tensor((1, h, S, d), mean, std, torch.bfloat16, device)
-        V = generate_tensor((1, h, S, d), mean, std, torch.bfloat16, device)
-        dO = generate_tensor((1, h, S, d), mean, std, torch.bfloat16, device)
+        Q = generate_tensor((1, h, S, d),  torch.bfloat16, device)
+        K = generate_tensor((1, h, S, d), torch.bfloat16, device)
+        V = generate_tensor((1, h, S, d),  torch.bfloat16, device)
+        dO = generate_tensor((1, h, S, d), torch.bfloat16, device)

        # dO_padded = torch.zeros_like(dO_padded)
        # dO_padded[:, :, non_pad_index, :] = dO
@@ -107,7 +105,8 @@ def check_correctness(h, d, num_blocks, k, mean, std, num_iterations=20, error_m
                abs_diff = torch.abs(diff)
                results[name]['sum_diff'] += torch.sum(abs_diff).item()
                results[name]['sum_abs'] += torch.sum(torch.abs(pt)).item()
-                results[name]['max_diff'] = max(results[name]['max_diff'], torch.max(abs_diff).item())
+                rel_max_diff = torch.max(abs_diff) / torch.mean(torch.abs(pt))
+                results[name]['max_diff'] = max(results[name]['max_diff'], rel_max_diff.item())
        if torch.cuda.is_available():
            torch.cuda.empty_cache()

@@ -119,27 +118,27 @@ def check_correctness(h, d, num_blocks, k, mean, std, num_iterations=20, error_m

    return results

-def generate_error_graphs(h, d, mean, std, error_mode='all'):
+def generate_error_graphs(h, d, error_mode='all'):
    test_configs = [
        {"num_blocks": 16, "k": 2, "description": "Small sequence"},
        {"num_blocks": 32, "k": 4, "description": "Medium sequence"},
        {"num_blocks": 53, "k": 6, "description": "Large sequence"},
    ]
    
-    print(f"\nError Analysis for h={h}, d={d}, mean={mean}, std={std}, mode={error_mode}")
+    print(f"\nError Analysis for h={h}, d={d}, mode={error_mode}")
    print("=" * 150)
    print(f"{'Config':<20} {'Blocks':<8} {'K':<4} "
-          f"{'gQ Avg':<12} {'gQ Max':<12} "
-          f"{'gK Avg':<12} {'gK Max':<12} "
-          f"{'gV Avg':<12} {'gV Max':<12} "
-          f"{'gO Avg':<12} {'gO Max':<12}")
+          f"{'gQ Avg':<12} {'Rel gQ Max':<12} "
+          f"{'gK Avg':<12} {'Rel gK Max':<12} "
+          f"{'gV Avg':<12} {'Rel gV Max':<12} "
+          f"{'gO Avg':<12} {'Rel gO Max':<12}")
    print("-" * 150)

    for config in test_configs:
        num_blocks = config["num_blocks"]
        k = config["k"]
        description = config["description"]
-        results = check_correctness(h, d, num_blocks, k, mean, std, error_mode=error_mode)
+        results = check_correctness(h, d, num_blocks, k, error_mode=error_mode)
        print(f"{description:<20} {num_blocks:<8} {k:<4} "
              f"{results['gQ']['avg_diff']:<12.6e} {results['gQ']['max_diff']:<12.6e} "
              f"{results['gK']['avg_diff']:<12.6e} {results['gK']['max_diff']:<12.6e} "
@@ -150,10 +149,8 @@ def generate_error_graphs(h, d, mean, std, error_mode='all'):

 if __name__ == "__main__":
    h, d = 16, 128
-    mean = 0.0
-    std = 1
    print("Block Sparse Attention with Variable Block Sizes Analysis")
    print("=" * 60)
    for mode in ['backward']:
-        generate_error_graphs(h, d, mean, std, error_mode=mode)
+        generate_error_graphs(h, d, error_mode=mode)
    print("\nAnalysis completed for all modes.")
@@ -0,0 +1,2 @@
+recursive-include tk *
+include config_vsa.py
@@ -0,0 +1,61 @@
+
+
+# Attention Kernel Used in FastVideo
+
+## Video Sparse Attention (VSA)
+
+### Installation
+We support H100 (via TK) and any other GPU (via triton) for VSA.
+
+```bash
+pip install vsa
+``` 
+
+Install from source:
+
+```bash
+git submodule update --init --recursive
+python setup.py install
+```
+
+
+If you encounter error during installation, try below:
+Install C++20 for ThunderKittens:
+```bash
+sudo apt update
+sudo apt install gcc-11 g++-11
+
+sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-11 100 --slave /usr/bin/g++ g++ /usr/bin/g++-11
+
+sudo apt update
+sudo apt install clang-11
+```
+(If you use CUDA12.8)
+```bash
+export CUDA_HOME=/usr/local/cuda-12.8
+export PATH=${CUDA_HOME}/bin:${PATH} 
+export LD_LIBRARY_PATH=${CUDA_HOME}/lib64:$LD_LIBRARY_PATH
+```
+
+### Verify if you have successfully installed
+
+```bash
+# test numerical
+python ../tests/test_vsa.py
+# (For H100) test speed
+python ../benchmarks/bench_vsa_hopper.py
+```
+
+bench_vsa_hopper.py should print something like this:
+
+```bash
+Using topk=76 kv blocks per q block (out of 768 total kv blocks)
+
+=== BLOCK SPARSE ATTENTION BENCHMARK ===
+Block Sparse Forward  - TFLOPS: 5622.26
+Block Sparse Backward - TFLOPS: 3865.68
+```
+
+## Acknowledgement
+
+We learned or reuse code from FlexAtteniton, NATEN, and ThunderKittens.
@@ -9,10 +9,10 @@ target = target.lower()

 # Package metadata
 PACKAGE_NAME = "vsa"
-VERSION = "0.0.1"
+VERSION = "0.0.3"
 AUTHOR = "Hao AI Lab"
 DESCRIPTION = "Video Sparse Attention Kernel Used in FastVideo"
-URL = "https://github.com/hao-ai-lab/FastVideo/tree/main/csrc/attn"
+URL = "https://github.com/hao-ai-lab/FastVideo/tree/main/csrc/attn/video_sparse_attn"

 # Set environment variables
 tk_root = os.getenv('THUNDERKITTENS_ROOT', os.path.abspath(os.path.join(os.getcwd(), 'tk/')))
@@ -568,7 +568,6 @@ void bwd_attend_ker(const __grid_constant__ bwd_globals<D> g) {
        __syncthreads(); // wait for sd_smem shared memory write
        warpgroup::mm_AtB(qg_reg, ds_smem_t[0], k_smem[0]); //delat dQ = dSK
        warpgroup::mma_commit_group();
-        tma::store_async_wait();
        warpgroup::mma_async_wait();
        // store qg to shared memory
        warpgroup::store(qg_smem, qg_reg);
@@ -578,6 +577,7 @@ void bwd_attend_ker(const __grid_constant__ bwd_globals<D> g) {
        if (threadIdx.x / 32 == 0) {
            coord<qg_tile> tile_idx = {blockIdx.z, blockIdx.y, store_qg_block_index, 0};
            tma::store_add_async(g.qg, qg_smem, tile_idx);
+            tma::store_async_wait();
        }
    }

@@ -624,7 +624,6 @@ void bwd_attend_ker(const __grid_constant__ bwd_globals<D> g) {
        __syncthreads(); // wait for sd_smem shared memory write
        warpgroup::mm_AtB(qg_reg, ds_smem_t[0], k_smem[0]); //delat dQ = dSK
        warpgroup::mma_commit_group();
-        tma::store_async_wait();
        warpgroup::mma_async_wait();
        // store qg to shared memory
        warpgroup::store(qg_smem, qg_reg);
@@ -634,13 +633,14 @@ void bwd_attend_ker(const __grid_constant__ bwd_globals<D> g) {
        if (threadIdx.x / 32 == 0) {
            coord<qg_tile> tile_idx = {blockIdx.z, blockIdx.y, store_qg_block_index, 0};
            tma::store_add_async(g.qg, qg_smem, tile_idx);
+            tma::store_async_wait();
        }
    }

    // store kq and vq

    // ! the following two line seems unnecessary.
-    tma::store_async_wait(); // ensure qg is finished
+    // tma::store_async_wait(); // ensure qg is finished
    __syncthreads();

    warpgroup::store(kg_smem[0], kg_reg);
@@ -1174,4 +1174,4 @@ block_sparse_attention_backward(torch::Tensor q,

    return {qg, kg, vg};
    //cudadevicesynchronize();
-}
+}
@@ -1,7 +1,9 @@
-FROM nvidia/cuda:12.4.1-devel-ubuntu20.04
+FROM nvidia/cuda:12.8.0-devel-ubuntu22.04

 ENV DEBIAN_FRONTEND=noninteractive

+SHELL ["/bin/bash", "-c"]
+
 WORKDIR /FastVideo

 RUN apt-get update && apt-get install -y --no-install-recommends \
@@ -9,17 +11,25 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
    git \
    ca-certificates \
    openssh-server \
+    zsh \
+    vim \
+    curl \
+    gcc-11 \
+    g++-11 \
+    clang-11 \
    && rm -rf /var/lib/apt/lists/*

-RUN wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh && \
-    bash Miniconda3-latest-Linux-x86_64.sh -b -p /opt/conda && \
-    rm Miniconda3-latest-Linux-x86_64.sh
+# Set up C++20 compilers for ThunderKittens
+RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-11 100 --slave /usr/bin/g++ g++ /usr/bin/g++-11

-ENV PATH=/opt/conda/bin:$PATH
+# Set CUDA environment variables
+ENV CUDA_HOME=/usr/local/cuda-12.8
+ENV PATH=${CUDA_HOME}/bin:${PATH}
+ENV LD_LIBRARY_PATH=${CUDA_HOME}/lib64:$LD_LIBRARY_PATH

-RUN conda create --name fastvideo-dev python=3.10.0 -y
-
-SHELL ["/bin/bash", "-c"]
+# Install uv and source its environment
+RUN curl -LsSf https://astral.sh/uv/install.sh | sh && \
+    echo 'source $HOME/.local/bin/env' >> /root/.bashrc

 # Copy just the pyproject.toml first to leverage Docker cache
 COPY pyproject.toml ./
@@ -27,22 +37,36 @@ COPY pyproject.toml ./
 # Create a dummy README to satisfy the installation
 RUN echo "# Placeholder" > README.md

-RUN conda run -n fastvideo-dev pip install --no-cache-dir --upgrade pip && \
-    conda run -n fastvideo-dev pip install --no-cache-dir .[dev] && \
-    conda run -n fastvideo-dev pip install --no-cache-dir flash-attn==2.7.4.post1 --no-build-isolation && \
-    conda clean -afy
+# Create and activate virtual environment with specific Python version and seed
+RUN source $HOME/.local/bin/env && \
+    uv venv --python 3.10 --seed /opt/venv && \
+    source /opt/venv/bin/activate && \
+    uv pip install --no-cache-dir --upgrade pip && \
+    uv pip install --no-cache-dir .[dev] && \
+    uv pip install --no-cache-dir flash-attn==2.8.3 --no-build-isolation

 COPY . .

-RUN conda run -n fastvideo-dev pip install --no-cache-dir -e .[dev]
+# Install dependencies using uv and set up shell configuration
+RUN source $HOME/.local/bin/env && \
+    source /opt/venv/bin/activate && \
+    uv pip install --no-cache-dir -e .[dev] && \
+    git config --unset-all http.https://github.com/.extraheader || true && \
+    echo 'source /opt/venv/bin/activate' >> /root/.bashrc && \
+    echo 'if [ -n "$ZSH_VERSION" ] && [ -f ~/.zshrc ]; then . ~/.zshrc; elif [ -f ~/.bashrc ]; then . ~/.bashrc; fi' > /root/.profile

-# Remove authentication headers
-RUN git config --unset-all http.https://github.com/.extraheader || true
+# Install STA (Sliding Tile Attention)
+RUN source $HOME/.local/bin/env && \
+    source /opt/venv/bin/activate && \
+    cd csrc/attn/sliding_tile_attn && \
+    git submodule update --init --recursive && \
+    python setup.py install

-# Set up automatic conda environment activation for all shells
-RUN echo 'source /opt/conda/etc/profile.d/conda.sh' >> /root/.bashrc && \
-    echo 'conda activate fastvideo-dev' >> /root/.bashrc && \
-    # Ensure .bashrc is sourced for SSH login shells
-    echo 'if [ -f ~/.bashrc ]; then . ~/.bashrc; fi' > /root/.profile
+# Install VSA 
+RUN source $HOME/.local/bin/env && \
+    source /opt/venv/bin/activate && \
+    cd csrc/attn/video_sparse_attn && \
+    git submodule update --init --recursive && \
+    python setup.py install

-EXPOSE 22
+EXPOSE 22
@@ -1,7 +1,9 @@
-FROM nvidia/cuda:12.4.1-devel-ubuntu20.04
+FROM nvidia/cuda:12.8.0-devel-ubuntu22.04

 ENV DEBIAN_FRONTEND=noninteractive

+SHELL ["/bin/bash", "-c"]
+
 WORKDIR /FastVideo

 RUN apt-get update && apt-get install -y --no-install-recommends \
@@ -9,17 +11,25 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
    git \
    ca-certificates \
    openssh-server \
+    zsh \
+    vim \
+    curl \
+    gcc-11 \
+    g++-11 \
+    clang-11 \
    && rm -rf /var/lib/apt/lists/*

-RUN wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh && \
-    bash Miniconda3-latest-Linux-x86_64.sh -b -p /opt/conda && \
-    rm Miniconda3-latest-Linux-x86_64.sh
+# Set up C++20 compilers for ThunderKittens
+RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-11 100 --slave /usr/bin/g++ g++ /usr/bin/g++-11

-ENV PATH=/opt/conda/bin:$PATH
+# Set CUDA environment variables
+ENV CUDA_HOME=/usr/local/cuda-12.8
+ENV PATH=${CUDA_HOME}/bin:${PATH}
+ENV LD_LIBRARY_PATH=${CUDA_HOME}/lib64:$LD_LIBRARY_PATH

-RUN conda create --name fastvideo-dev python=3.11.11 -y
-
-SHELL ["/bin/bash", "-c"]
+# Install uv and source its environment
+RUN curl -LsSf https://astral.sh/uv/install.sh | sh && \
+    echo 'source $HOME/.local/bin/env' >> /root/.bashrc

 # Copy just the pyproject.toml first to leverage Docker cache
 COPY pyproject.toml ./
@@ -27,22 +37,36 @@ COPY pyproject.toml ./
 # Create a dummy README to satisfy the installation
 RUN echo "# Placeholder" > README.md

-RUN conda run -n fastvideo-dev pip install --no-cache-dir --upgrade pip && \
-    conda run -n fastvideo-dev pip install --no-cache-dir .[dev] && \
-    conda run -n fastvideo-dev pip install --no-cache-dir flash-attn==2.7.4.post1 --no-build-isolation && \
-    conda clean -afy
+# Create and activate virtual environment with specific Python version and seed
+RUN source $HOME/.local/bin/env && \
+    uv venv --python 3.11 --seed /opt/venv && \
+    source /opt/venv/bin/activate && \
+    uv pip install --no-cache-dir --upgrade pip && \
+    uv pip install --no-cache-dir .[dev] && \
+    uv pip install --no-cache-dir flash-attn==2.8.3 --no-build-isolation

 COPY . .

-RUN conda run -n fastvideo-dev pip install --no-cache-dir -e .[dev]
+# Install dependencies using uv and set up shell configuration
+RUN source $HOME/.local/bin/env && \
+    source /opt/venv/bin/activate && \
+    uv pip install --no-cache-dir -e .[dev] && \
+    git config --unset-all http.https://github.com/.extraheader || true && \
+    echo 'source /opt/venv/bin/activate' >> /root/.bashrc && \
+    echo 'if [ -n "$ZSH_VERSION" ] && [ -f ~/.zshrc ]; then . ~/.zshrc; elif [ -f ~/.bashrc ]; then . ~/.bashrc; fi' > /root/.profile

-# Remove authentication headers
-RUN git config --unset-all http.https://github.com/.extraheader || true
+# Install STA (Sliding Tile Attention)
+RUN source $HOME/.local/bin/env && \
+    source /opt/venv/bin/activate && \
+    cd csrc/attn/sliding_tile_attn && \
+    git submodule update --init --recursive && \
+    python setup.py install

-# Set up automatic conda environment activation for all shells
-RUN echo 'source /opt/conda/etc/profile.d/conda.sh' >> /root/.bashrc && \
-    echo 'conda activate fastvideo-dev' >> /root/.bashrc && \
-    # Ensure .bashrc is sourced for SSH login shells
-    echo 'if [ -f ~/.bashrc ]; then . ~/.bashrc; fi' > /root/.profile
+# Install VSA 
+RUN source $HOME/.local/bin/env && \
+    source /opt/venv/bin/activate && \
+    cd csrc/attn/video_sparse_attn && \
+    git submodule update --init --recursive && \
+    python setup.py install

-EXPOSE 22
+EXPOSE 22
@@ -43,7 +43,7 @@ RUN source $HOME/.local/bin/env && \
    source /opt/venv/bin/activate && \
    uv pip install --no-cache-dir --upgrade pip && \
    uv pip install --no-cache-dir .[dev] && \
-    uv pip install --no-cache-dir flash-attn==2.8.0.post2 --no-build-isolation
+    uv pip install --no-cache-dir flash-attn==2.8.3 --no-build-isolation

 COPY . .

@@ -58,15 +58,15 @@ RUN source $HOME/.local/bin/env && \
 # Install STA (Sliding Tile Attention)
 RUN source $HOME/.local/bin/env && \
    source /opt/venv/bin/activate && \
-    cd csrc/attn && \
+    cd csrc/attn/sliding_tile_attn && \
    git submodule update --init --recursive && \
-    python setup_sta.py install
+    python setup.py install

 # Install VSA 
 RUN source $HOME/.local/bin/env && \
    source /opt/venv/bin/activate && \
-    cd csrc/attn && \
+    cd csrc/attn/video_sparse_attn && \
    git submodule update --init --recursive && \
-    python setup_vsa.py install
+    python setup.py install

-EXPOSE 22
+EXPOSE 22
@@ -0,0 +1,72 @@
+FROM nvidia/cuda:12.9.1-cudnn-devel-ubuntu22.04
+
+ENV DEBIAN_FRONTEND=noninteractive
+
+SHELL ["/bin/bash", "-c"]
+
+WORKDIR /FastVideo
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    wget \
+    git \
+    ca-certificates \
+    openssh-server \
+    zsh \
+    vim \
+    curl \
+    gcc-11 \
+    g++-11 \
+    clang-11 \
+    && rm -rf /var/lib/apt/lists/*
+
+# Set up C++20 compilers for ThunderKittens
+RUN update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-11 100 --slave /usr/bin/g++ g++ /usr/bin/g++-11
+
+# Set CUDA environment variables
+ENV CUDA_HOME=/usr/local/cuda-12.9
+ENV PATH=${CUDA_HOME}/bin:${PATH}
+ENV LD_LIBRARY_PATH=${CUDA_HOME}/lib64:$LD_LIBRARY_PATH
+
+# Install uv and source its environment
+RUN curl -LsSf https://astral.sh/uv/install.sh | sh && \
+    echo 'source $HOME/.local/bin/env' >> /root/.bashrc
+
+# Copy just the pyproject.toml first to leverage Docker cache
+COPY pyproject.toml ./
+
+# Create a dummy README to satisfy the installation
+RUN echo "# Placeholder" > README.md
+
+# Create and activate virtual environment with specific Python version and seed
+RUN source $HOME/.local/bin/env && \
+    uv venv --python 3.12 --seed /opt/venv && \
+    source /opt/venv/bin/activate && \
+    uv pip install --no-cache-dir --upgrade pip && \
+    uv pip install --no-cache-dir .[dev] && \
+    uv pip install --no-cache-dir flash-attn==2.8.3 --no-build-isolation
+
+COPY . .
+
+# Install dependencies using uv and set up shell configuration
+RUN source $HOME/.local/bin/env && \
+    source /opt/venv/bin/activate && \
+    uv pip install --no-cache-dir -e .[dev] && \
+    git config --unset-all http.https://github.com/.extraheader || true && \
+    echo 'source /opt/venv/bin/activate' >> /root/.bashrc && \
+    echo 'if [ -n "$ZSH_VERSION" ] && [ -f ~/.zshrc ]; then . ~/.zshrc; elif [ -f ~/.bashrc ]; then . ~/.bashrc; fi' > /root/.profile
+
+# Install STA (Sliding Tile Attention)
+RUN source $HOME/.local/bin/env && \
+    source /opt/venv/bin/activate && \
+    cd csrc/attn/sliding_tile_attn && \
+    git submodule update --init --recursive && \
+    python setup.py install
+
+# Install VSA 
+RUN source $HOME/.local/bin/env && \
+    source /opt/venv/bin/activate && \
+    cd csrc/attn/video_sparse_attn && \
+    git submodule update --init --recursive && \
+    python setup.py install
+
+EXPOSE 22
@@ -96,8 +96,7 @@ copybutton_prompt_is_regexp = True
 #
 html_title = project
 html_theme = 'sphinx_book_theme'
-html_logo = '../../assets/logo.jpg'
-#html_favicon = 'assets/logos/vllm-logo-only-light.ico'
+html_logo = '../../assets/logos/icon_simple.svg'
 html_theme_options = {
    'path_to_docs': 'docs/source',
    'repository_url': 'https://github.com/hao-ai-lab/FastVideo/',
@@ -3,12 +3,12 @@

 If you prefer a containerized development environment or want to avoid managing dependencies manually, you can use our prebuilt Docker image:

-**Image:** [`ghcr.io/hao-ai-lab/fastvideo/fastvideo-dev:latest`](https://ghcr.io/hao-ai-lab/fastvideo/fastvideo-dev)
+**Images:** [`ghcr.io/hao-ai-lab/fastvideo/fastvideo-dev:py3.12-latest`](https://ghcr.io/hao-ai-lab/fastvideo/fastvideo-dev)

 ## Starting the container

 ```bash
-docker run --gpus all -it ghcr.io/hao-ai-lab/fastvideo/fastvideo-dev:latest
+docker run --gpus all -it ghcr.io/hao-ai-lab/fastvideo/fastvideo-dev:py3.12-latest
 ```

 This will:
@@ -6,7 +6,7 @@ You can easily use the FastVideo Docker image as a custom container on [RunPod](

 ## Creating a new pod

-Choose a GPU that supports CUDA 12.4
+Choose a GPU that supports CUDA 12.8

 Pick 1 or 2 L40S GPU(s)

@@ -22,10 +22,20 @@ source ~/.bashrc
 Create and activate a Conda environment for FastVideo:

 ```
-conda create -n fastvideo python=3.10 -y
+conda create -n fastvideo python=3.12 -y
 conda activate fastvideo
 ```

+Install `uv` (optional, but recommended):
+
+From instructions on [uv](https://astral.sh/uv/):
+
+```
+curl -LsSf https://astral.sh/uv/install.sh | sh
+# or 
+wget -qO- https://astral.sh/uv/install.sh | sh
+```
+
 Clone the FastVideo repository and go to the FastVideo directory:

 ```
@@ -36,10 +46,10 @@ git clone https://github.com/hao-ai-lab/FastVideo.git && cd FastVideo
 Now you can install FastVideo and setup git hooks for running linting. By using `pre-commit`, the linters will run and have to pass before you'll be able to make a commit.

 ```bash
-pip install -e .[dev]
+uv pip install -e .[dev]

 # Can also install flash-attn (optional)
-pip install flash-attn==2.7.4.post1 --no-build-isolation 
+uv pip install flash-attn --no-build-isolation 

 # Linting, formatting and static type checking
 pre-commit install --hook-type pre-commit --hook-type commit-msg
@@ -50,3 +60,14 @@ pre-commit run --all-files
 # Unit tests
 pytest tests/
 ```
+
+If you are on a Hopper GPU, you should also install [FA3](https://github.com/Dao-AILab/flash-attention) for much better performance:
+
+```
+git clone https://github.com/Dao-AILab/flash-attention.git && cd flash-attention/hopper
+
+# make sure you have ninja installed
+uv pip install ninja
+
+python setup.py install
+```
@@ -6,7 +6,7 @@ Instructions to install FastVideo for NVIDIA CUDA GPUs.

 - **OS: Linux or Windows WSL**
 - **Python: 3.10-3.12**
- **CUDA 12.4**
+- **CUDA 12.8**
 - **At least 1 NVIDIA GPU**

 ## Set up using Python
@@ -38,6 +38,7 @@ conda activate fastvideo

 :::{tip}
 We highly recommend using `uv` to install FastVideo. In our experience, `uv` speeds up installation by at least 3x.
+Note that you can also use `uv` to install FastVideo in a Conda environment.
 :::

 Or you can create a new Python environment using [uv](https://docs.astral.sh/uv/), a very fast Python environment manager. Please follow the [documentation](https://docs.astral.sh/uv/#getting-started) to install `uv`. After installing `uv`, you can create a new Python environment using the following command:
@@ -60,7 +61,7 @@ uv pip install fastvideo
 Also optionally install flash-attn:

 ```bash
-pip install flash-attn==2.7.4.post1 --no-build-isolation
+pip install flash-attn --no-build-isolation
 ```

 ### Installation from Source
@@ -87,7 +88,7 @@ uv pip install -e .
 #### Flash Attention

 ```bash
-pip install flash-attn==2.7.4.post1 --no-build-isolation
+pip install flash-attn --no-build-isolation
 ```

 ## Set up using Docker
@@ -102,7 +103,7 @@ If you're planning to contribute to FastVideo please see the following page:
 ## Hardware Requirements

 ### For Basic Inference
- NVIDIA GPU with CUDA 12.4 support
+- NVIDIA GPU with CUDA 12.8 support

 ### For Lora Finetuning
 - 40GB GPU memory each for 2 GPUs with lora
@@ -39,6 +39,7 @@ conda activate fastvideo

 :::{tip}
 We highly recommend using `uv` to install FastVideo. In our experience, `uv` speeds up installation by at least 3x.
+Note that you can also use `uv` to install FastVideo in a Conda environment.
 :::

 Or you can create a new Python environment using [uv](https://docs.astral.sh/uv/), a very fast Python environment manager. Please follow the [documentation](https://docs.astral.sh/uv/#getting-started) to install `uv`. After installing `uv`, you can create a new Python environment using the following command:
@@ -1,6 +1,6 @@
 # Welcome to FastVideo

-:::{figure} ../../assets/logo.png
+:::{figure} ../../assets/logos/logo.svg
 :align: center
 :alt: FastVideo
 :class: no-scaled-link
@@ -101,7 +101,6 @@ sliding_tile_attention/demo
 :maxdepth: 1

 video_sparse_attention/installation
-video_sparse_attention/demo
 :::

 :::{toctree}
@@ -5,7 +5,7 @@ This page contains step-by-step instructions to get you quickly started with vid
 ## Requirements
 - **OS**: Linux (Tested on Ubuntu 22.04+)
 - **Python**: 3.10-3.12
- **CUDA**: 12.4
+- **CUDA**: 12.8
 - **GPU**: At least one NVIDIA GPU

 ## Installation
@@ -6,6 +6,7 @@ The symbols used have the following meanings:

 - ✅ = Full compatibility
 - ❌ = No compatibility
+- ⭕ = Does not apply to this model

 ## Models x Optimization
 The `HuggingFace Model ID` can be directly pass to `from_pretrained()` methods and FastVideo will use the optimal default parameters when initializing and generating videos.
@@ -37,51 +38,94 @@ The `HuggingFace Model ID` can be directly pass to `from_pretrained()` methods a
  * TeaCache
  * Sliding Tile Attn
  * Sage Attn
+  * Video Sparse Attention (VSA)
+- * FastWan2.1 T2V 1.3B
+  * `FastVideo/FastWan2.1-T2V-1.3B-Diffusers`
+  * 480P
+  * ⭕
+  * ⭕
+  * ⭕
+  * ✅
+- * FastWan2.2 TI2V 5B Full Attn*
+  * `FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers`
+  * 720P
+  * ⭕
+  * ⭕
+  * ⭕
+  * ✅
+- * Wan2.2 TI2V 5B 
+  * `Wan-AI/Wan2.2-TI2V-5B-Diffusers`
+  * 720P
+  * ⭕
+  * ⭕
+  * ✅
+  * ⭕
+- * Wan2.2 T2V A14B
+  * `Wan-AI/Wan2.2-T2V-A14B-Diffusers`
+  * 480P<br>720P
+  * ❌
+  * ❌
+  * ✅
+  * ⭕
+- * Wan2.2 I2V A14B
+  * `Wan-AI/Wan2.2-I2V-A14B-Diffusers`
+  * 480P<br>720P
+  * ❌
+  * ❌
+  * ✅
+  * ⭕
 - * HunyuanVideo
  * `hunyuanvideo-community/HunyuanVideo`
  * 720px1280p<br>544px960p
  * ❌
  * ✅
  * ✅
+  * ⭕
 - * FastHunyuan
  * `FastVideo/FastHunyuan-diffusers`
  * 720px1280p<br>544px960p
  * ❌
  * ✅
  * ✅
- * Wan T2V 1.3B
+  * ⭕
+- * Wan2.1 T2V 1.3B
  * `Wan-AI/Wan2.1-T2V-1.3B-Diffusers`
  * 480P
  * ✅
  * ✅*
  * ✅
- * Wan T2V 14B
+  * ⭕
+- * Wan2.1 T2V 14B
  * `Wan-AI/Wan2.1-T2V-14B-Diffusers`
  * 480P, 720P
  * ✅
  * ✅*
  * ✅
- * Wan I2V 480P
+  * ⭕
+- * Wan2.1 I2V 480P
  * `Wan-AI/Wan2.1-I2V-14B-480P-Diffusers`
  * 480P
  * ✅
  * ✅*
  * ✅
- * Wan I2V 720P
+  * ⭕
+- * Wan2.1 I2V 720P
  * `Wan-AI/Wan2.1-I2V-14B-720P-Diffusers`
  * 720P
  * ✅
-  * ✅*
  * ✅
+  * ✅
+  * ⭕
 - * StepVideo T2V
  * `FastVideo/stepvideo-t2v-diffusers`
  * 768px768px204f<br>544px992px204f<br>544px992px136f
  * ❌
  * ❌
  * ✅
+  * ⭕
 :::

-**Note**: there are some known quality issues with Wan2.1 + Sliding Tile Attn. We are working on fixing this issue.
+**Note**: Wan2.2 TI2V 5B has some quality issues when performing I2V generation. We are working on fixing this issue.

 ## Special requirements

@@ -4,7 +4,7 @@
 You can install the Sliding Tile Attention package using

 ```
-pip install st_attn==0.0.4
+pip install st_attn
 ``` 

 # Building from Source
@@ -12,7 +12,6 @@ We test our code on Pytorch 2.5.0 and CUDA>=12.4. Currently we only have impleme
 First, install C++20 for ThunderKittens:

 ```bash
-cd csrc/sliding_tile_attention/
 sudo apt update
 sudo apt install gcc-11 g++-11

@@ -22,14 +21,20 @@ sudo apt update
 sudo apt install clang-11
 ```

-Install STA:
+Set up CUDA environment (if using CUDA 12.4):

 ```bash
 export CUDA_HOME=/usr/local/cuda-12.4
 export PATH=${CUDA_HOME}/bin:${PATH} 
 export LD_LIBRARY_PATH=${CUDA_HOME}/lib64:$LD_LIBRARY_PATH
+```
+
+Install STA:
+
+```bash
+cd csrc/attn/sliding_tile_attn/
 git submodule update --init --recursive
-python setup_sta.py install
+python setup.py install
 ```

 # 🧪 Test
@@ -1,3 +0,0 @@
-(vsa-demo)=
-
-# 🎬 Demo
@@ -4,8 +4,7 @@
 You can install the Video Sparse Attention package using

 ```bash
-git submodule update --init --recursive
-python setup_vsa.py install
+pip install vsa
 ```

 # Building from Source
@@ -23,10 +22,10 @@ sudo apt update
 sudo apt install clang-11
 ```

-Set up CUDA environment (if using CUDA 12.4):
+Set up CUDA environment (if using CUDA 12.8):

 ```bash
-export CUDA_HOME=/usr/local/cuda-12.4
+export CUDA_HOME=/usr/local/cuda-12.8
 export PATH=${CUDA_HOME}/bin:${PATH} 
 export LD_LIBRARY_PATH=${CUDA_HOME}/lib64:$LD_LIBRARY_PATH
 ```
@@ -34,9 +33,9 @@ export LD_LIBRARY_PATH=${CUDA_HOME}/lib64:$LD_LIBRARY_PATH
 Install VSA:

 ```bash
-cd csrc/attn/
+cd csrc/attn/video_sparse_attn/
 git submodule update --init --recursive
-python setup_vsa.py install
+python setup.py install
 ```

 # 🧪 Test
@@ -4,9 +4,7 @@ These are end-to-end example scripts for distilling Wan2.1 T2V 1.3B model using
 ### 0. Make sure you have installed VSA

 ```bash
-cd csrc/attn
-git submodule update --init --recursive
-python setup_vsa.py install
+pip install vsa
 ```

 ### 1. Download dataset:
@@ -4,9 +4,7 @@ These are end-to-end example scripts for distilling Wan2.2 TI2V 5B model DMD+VSA
 ### 0. Make sure you have installed VSA

 ```bash
-cd csrc/attn
-git submodule update --init --recursive
-python setup_vsa.py install
+pip install vsa
 ```

 ### Data-free Distillation
@@ -4,9 +4,7 @@ These are end-to-end example scripts for distilling Wan2.2 TI2V 5B model DMD+VSA
 ### 0. Make sure you have installed VSA

 ```bash
-cd csrc/attn
-git submodule update --init --recursive
-python setup_vsa.py install
+pip install vsa
 ```

 ### 1. Download dataset:
@@ -0,0 +1,31 @@
+import os
+import time
+from fastvideo import VideoGenerator, SamplingParam
+
+OUTPUT_PATH = "video_samples_causal"
+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.
+    model_name = "wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers"
+    generator = VideoGenerator.from_pretrained(
+        model_name,
+        # FastVideo will automatically handle distributed setup
+        num_gpus=1,
+        use_fsdp_inference=True,
+        text_encoder_cpu_offload=False,
+        dit_cpu_offload=False,
+    )
+
+    sampling_param = SamplingParam.from_pretrained(model_name)
+
+    prompt = (
+        "A curious raccoon peers through a vibrant field of yellow sunflowers, its eyes "
+        "wide with interest. The playful yet serene atmosphere is complemented by soft "
+        "natural light filtering through the petals. Mid-shot, warm and cheerful tones."
+    )
+    video = generator.generate_video(prompt, output_path=OUTPUT_PATH, save_video=True, sampling_param=sampling_param)
+
+if __name__ == "__main__":
+    main()
@@ -0,0 +1,41 @@
+from fastvideo import VideoGenerator
+
+OUTPUT_PATH = "video_samples_wan2_2_5B_ti2v"
+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.
+    model_name = "Wan-AI/Wan2.2-TI2V-5B-Diffusers"
+    generator = VideoGenerator.from_pretrained(
+        model_name,
+        # FastVideo will automatically handle distributed setup
+        num_gpus=1,
+        use_fsdp_inference=True,
+        dit_cpu_offload=True,
+        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"
+        # image_encoder_cpu_offload=False,
+    )
+
+    # I2V is triggered just by passing in an image_path argument
+    prompt = "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside."
+    image_path = "https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/wan_i2v_input.JPG"
+    video = generator.generate_video(prompt, output_path=OUTPUT_PATH, save_video=True, image_path=image_path)
+
+    # Generate another video with a different prompt, without reloading the
+    # model!
+
+    # T2V mode
+    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()
@@ -1,59 +0,0 @@
-# FastVideo Gradio 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/gradio_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, or even the number of inference steps, *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()`)
-3. Returns an output path that Gradio uses to display the generated 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
-  - Inference steps 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
- **Inference Steps**: More steps can improve quality but take longer
- **Negative Prompt**: Specify what you don't want to see in the video
- **Seed**: Control randomness for reproducible results
@@ -1,169 +0,0 @@
-import argparse
-import os
-from copy import deepcopy
-
-import gradio as gr
-import torch
-
-from fastvideo import VideoGenerator
-from fastvideo.configs.sample.base import SamplingParam
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="FastVideo Gradio Demo")
-    parser.add_argument("--model_path",
-                        type=str,
-                        default="FastVideo/FastHunyuan-diffusers",
-                        help="Path to the model")
-    parser.add_argument("--num_gpus",
-                        type=int,
-                        default=1,
-                        help="Number of GPUs to use")
-    parser.add_argument("--output_path",
-                        type=str,
-                        default="outputs",
-                        help="Path to save generated videos")
-    parsed_args = parser.parse_args()
-
-    # args = FastVideoArgs(model_path="FastVideo/FastHunyuan-Diffusers", num_gpus=2)
-
-    generator = VideoGenerator.from_pretrained(
-        model_path=parsed_args.model_path, num_gpus=parsed_args.num_gpus)
-
-    default_params = SamplingParam.from_pretrained(parsed_args.model_path)
-
-    def generate_video(
-        prompt,
-        negative_prompt,
-        use_negative_prompt,
-        seed,
-        guidance_scale,
-        num_frames,
-        height,
-        width,
-        num_inference_steps,
-        randomize_seed=False,
-    ):
-        params = deepcopy(default_params)
-        params.prompt = prompt
-        params.negative_prompt = negative_prompt
-        params.seed = seed
-        params.guidance_scale = guidance_scale
-        params.num_frames = num_frames
-        params.height = height
-        params.width = width
-        params.num_inference_steps = num_inference_steps
-
-        if randomize_seed:
-            params.seed = torch.randint(0, 1000000, (1, )).item()
-
-        if not use_negative_prompt:
-            params.negative_prompt = None
-
-        generator.generate_video(prompt=prompt, sampling_param=params)
-
-        output_path = os.path.join(parsed_args.output_path,
-                                   f"{params.prompt[:100]}.mp4")
-
-        return output_path, params.seed
-
-    examples = [
-        "A hand enters the frame, pulling a sheet of plastic wrap over three balls of dough placed on a wooden surface. The plastic wrap is stretched to cover the dough more securely. The hand adjusts the wrap, ensuring that it is tight and smooth over the dough. The scene focuses on the hand’s movements as it secures the edges of the plastic wrap. No new objects appear, and the camera remains stationary, focusing on the action of covering the dough.",
-        "A vintage train snakes through the mountains, its plume of white steam rising dramatically against the jagged peaks. The cars glint in the late afternoon sun, their deep crimson and gold accents lending a touch of elegance. The tracks carve a precarious path along the cliffside, revealing glimpses of a roaring river far below. Inside, passengers peer out the large windows, their faces lit with awe as the landscape unfolds.",
-        "A crowded rooftop bar buzzes with energy, the city skyline twinkling like a field of stars in the background. Strings of fairy lights hang above, casting a warm, golden glow over the scene. Groups of people gather around high tables, their laughter blending with the soft rhythm of live jazz. The aroma of freshly mixed cocktails and charred appetizers wafts through the air, mingling with the cool night breeze.",
-    ]
-
-    with gr.Blocks() as demo:
-        gr.Markdown("# FastVideo Inference Demo")
-
-        with gr.Group():
-            with gr.Row():
-                prompt = gr.Text(
-                    label="Prompt",
-                    show_label=False,
-                    max_lines=1,
-                    placeholder="Enter your prompt",
-                    container=False,
-                )
-                run_button = gr.Button("Run", scale=0)
-            result = gr.Video(label="Result", show_label=False)
-
-        with gr.Accordion("Advanced options", open=False):
-            with gr.Group():
-                with gr.Row():
-                    height = gr.Slider(
-                        label="Height",
-                        minimum=256,
-                        maximum=1024,
-                        step=32,
-                        value=default_params.height,
-                    )
-                    width = gr.Slider(label="Width",
-                                      minimum=256,
-                                      maximum=1024,
-                                      step=32,
-                                      value=default_params.width)
-
-                with gr.Row():
-                    num_frames = gr.Slider(
-                        label="Number of Frames",
-                        minimum=21,
-                        maximum=163,
-                        value=default_params.num_frames,
-                    )
-                    guidance_scale = gr.Slider(
-                        label="Guidance Scale",
-                        minimum=1,
-                        maximum=12,
-                        value=default_params.guidance_scale,
-                    )
-                    num_inference_steps = gr.Slider(
-                        label="Inference Steps",
-                        minimum=4,
-                        maximum=100,
-                        value=default_params.num_inference_steps,
-                    )
-
-                with gr.Row():
-                    use_negative_prompt = gr.Checkbox(
-                        label="Use negative prompt", value=False)
-                    negative_prompt = gr.Text(
-                        label="Negative prompt",
-                        max_lines=1,
-                        placeholder="Enter a negative prompt",
-                        visible=False,
-                    )
-
-                seed = gr.Slider(label="Seed",
-                                 minimum=0,
-                                 maximum=1000000,
-                                 step=1,
-                                 value=default_params.seed)
-                randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-                seed_output = gr.Number(label="Used Seed")
-
-        gr.Examples(examples=examples, inputs=prompt)
-
-        use_negative_prompt.change(
-            fn=lambda x: gr.update(visible=x),
-            inputs=use_negative_prompt,
-            outputs=default_params.negative_prompt,
-        )
-
-        run_button.click(
-            fn=generate_video,
-            inputs=[
-                prompt,
-                negative_prompt,
-                use_negative_prompt,
-                seed,
-                guidance_scale,
-                num_frames,
-                height,
-                width,
-                num_inference_steps,
-                randomize_seed,
-            ],
-            outputs=[result, seed_output],
-        )
-
-    demo.queue(max_size=20).launch(server_name="0.0.0.0", server_port=7860)
@@ -0,0 +1,730 @@
+import argparse
+import os
+import requests
+import base64
+import time
+
+import gradio as gr
+
+from fastvideo.configs.sample.base import SamplingParam
+
+
+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",
+}
+
+
+class RayServeClient:
+    def __init__(self, backend_url: str):
+        self.backend_url = backend_url
+        self.session = requests.Session()
+    
+    def check_health(self) -> bool:
+        try:
+            response = self.session.get(f"{self.backend_url}/health", timeout=5)
+            return response.status_code == 200
+        except requests.exceptions.RequestException:
+            return False
+    
+    def generate_video(self, request_data: dict) -> dict:
+        start_time = time.time()
+        
+        try:
+            headers = {"Content-Type": "application/json"}
+            
+            response = self.session.post(
+                f"{self.backend_url}/generate_video",
+                json=request_data,
+                headers=headers,
+                timeout=300
+            )
+            
+            round_trip_time = time.time() - start_time
+            
+            if response.status_code == 200:
+                result = response.json()
+                backend_total = result.get("total_time", 0)
+                network_time = round_trip_time - backend_total
+                result["network_time"] = network_time
+                return result
+            else:
+                return {"success": False, "error_message": f"HTTP {response.status_code}: {response.text}"}
+                
+        except requests.exceptions.RequestException as e:
+            return {"success": False, "error_message": f"Request failed: {str(e)}"}
+
+
+def save_video_from_base64(video_data: str, output_dir: str, prompt: str) -> str:
+    if not video_data:
+        return None
+    
+    try:
+        if video_data.startswith('data:video/'):
+            video_data = video_data.split(',')[1]
+        
+        video_bytes = base64.b64decode(video_data)
+        
+        safe_prompt = prompt[:50].replace(' ', '_').replace('/', '_').replace('\\', '_')
+        video_filename = f"{safe_prompt}.mp4"
+        video_path = os.path.join(output_dir, video_filename)
+        
+        os.makedirs(output_dir, exist_ok=True)
+        
+        with open(video_path, 'wb') as f:
+            f.write(video_bytes)
+        
+        return video_path
+        
+    except Exception as e:
+        print(f"Failed to save video: {e}")
+        return None
+
+
+def create_timing_display(inference_time, encoding_time, network_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;">{encoding_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;">Network Transfer</div>
+                <div style="font-size: 16px; color: #059669;">{network_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;">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 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("prompts/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(backend_url: str, default_params: dict[str, SamplingParam]):
+    
+    client = RayServeClient(backend_url)
+    
+    def generate_video(
+        prompt, negative_prompt, use_negative_prompt, seed, guidance_scale,
+        num_frames, height, width, randomize_seed, model_selection, progress
+    ):
+        if not client.check_health():
+            return None, f"Backend is not available. Please check if Ray Serve is running at {backend_url}", ""
+        
+        # Validate dimensions
+        max_pixels = 720 * 1280
+        if height * width > max_pixels:
+            return None, f"Video dimensions too large. Maximum: 720x1280 pixels", ""
+        
+        if progress:
+            progress(0.1, desc="Checking backend health...")
+        
+        request_data = {
+            "prompt": prompt,
+            "negative_prompt": negative_prompt,
+            "use_negative_prompt": use_negative_prompt,
+            "seed": seed,
+            "guidance_scale": guidance_scale,
+            "num_frames": num_frames,
+            "height": height,
+            "width": width,
+            "randomize_seed": randomize_seed,
+            "return_frames": False,
+            "image_path": None,
+            "model_path": MODEL_PATH_MAPPING.get(model_selection, "FastVideo/FastWan2.1-T2V-1.3B-Diffusers")
+        }
+        
+        if progress:
+            progress(0.4, desc="Generating video...")
+        
+        response = client.generate_video(request_data)
+        
+        if progress:
+            progress(0.8, desc="Processing response...")
+        
+        if response.get("success", False):
+            video_data = response.get("video_data", "")
+            used_seed = response.get("seed", seed)
+            inference_time = response.get("inference_time", 0.0)
+            encoding_time = response.get("encoding_time", 0.0)
+            total_time = response.get("total_time", 0.0)
+            network_time = response.get("network_time", 0.0)
+            stage_execution_times = response.get("stage_execution_times", [])
+            
+            timing_details = create_timing_display(
+                inference_time, encoding_time, network_time, total_time, 
+                stage_execution_times, num_frames
+            )
+            
+            if video_data:
+                if progress:
+                    progress(0.9, desc="Saving video...")
+                
+                video_path = save_video_from_base64(video_data, "outputs", prompt)
+                
+                if progress:
+                    progress(1.0, desc="Generation complete!")
+                
+                if video_path and os.path.exists(video_path):
+                    return video_path, used_seed, timing_details
+                else:
+                    return None, "Failed to save video", ""
+            else:
+                return None, "No video data received from backend", ""
+        else:
+            error_msg = response.get("error_message", "Unknown error occurred")
+            return None, f"Generation failed: {error_msg}", ""
+    
+    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/logos/logo.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;">The compute for this demo is generously provided by <a href="https://www.gmicloud.ai/" target="_blank">GMI Cloud</a>. Note that this demo is meant to showcase FastWan's quality and that under a large number of requests, generation speed may be affected. We are also rate-limiting users to 3 requests per minute.</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 Frontend")
+    parser.add_argument("--backend_url", type=str, default="http://localhost:8000",
+                        help="URL of the Ray Serve backend")
+    parser.add_argument("--t2v_model_paths", type=str,
+                        default="FastVideo/FastWan2.1-T2V-1.3B-Diffusers,FastVideo/FastWan2.1-T2V-14B-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()
+    
+    default_params = {}
+    model_paths = args.t2v_model_paths.split(",")
+    for model_path in model_paths:
+        default_params[model_path] = SamplingParam.from_pretrained(model_path)
+    
+    demo = create_gradio_interface(args.backend_url, default_params)
+    
+    print(f"Starting Gradio frontend at http://{args.host}:{args.port}")
+    print(f"Backend URL: {args.backend_url}")
+    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.svg")
+    def get_logo():
+        return FileResponse(
+            "assets/logos/logo.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/logos/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.svg">
+            <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.svg">
+            <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() 
@@ -0,0 +1,379 @@
+import time
+import os
+import torch
+import base64
+import io
+from copy import deepcopy
+from typing import Dict, Any, Optional, List
+import signal
+import sys
+
+import ray
+from ray import serve
+from fastapi import FastAPI, Request, Response
+from pydantic import BaseModel
+import numpy as np
+from slowapi import Limiter, _rate_limit_exceeded_handler
+from slowapi.util import get_remote_address
+from slowapi.errors import RateLimitExceeded
+import imageio
+from ray.serve.handle import DeploymentHandle
+from prometheus_client import Counter, Histogram, generate_latest
+
+NUM_GPUS = 16
+DEFAULT_FPS = 16
+SEED_RANGE_MAX = 1_000_000
+SUPPORTED_MODELS = [
+    "FastVideo/FastWan2.1-T2V-1.3B-Diffusers", 
+    "FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers",
+]
+
+MODEL_CONFIGS = {
+    "1.3B": {
+        "num_cpus": 2,
+        "text_encoder_cpu_offload": False,
+        "dit_cpu_offload": False,
+        "vae_cpu_offload": False,
+        "VSA_sparsity": 0.8,
+    },
+    "14B": {
+        "num_cpus": 16,
+        "text_encoder_cpu_offload": True,
+        "dit_cpu_offload": True,
+        "vae_cpu_offload": False,
+        "VSA_sparsity": 0.9,
+    }
+}
+
+
+class VideoGenerationRequest(BaseModel):
+    prompt: str
+    negative_prompt: Optional[str] = None
+    use_negative_prompt: bool = False
+    seed: int = 42
+    guidance_scale: float = 7.5
+    num_frames: int = 21
+    height: int = 448
+    width: int = 832
+    randomize_seed: bool = False
+    return_frames: bool = False
+    model_path: Optional[str] = None
+
+
+class VideoGenerationResponse(BaseModel):
+    video_data: Optional[str] = None
+    seed: int
+    success: bool
+    error_message: Optional[str] = None
+    generation_time: Optional[float] = None
+    model_load_time: Optional[float] = None
+    inference_time: Optional[float] = None
+    encoding_time: Optional[float] = None
+    total_time: Optional[float] = None
+    stage_names: Optional[List[str]] = None
+    stage_execution_times: Optional[List[float]] = None
+
+
+def encode_video_to_base64(frames: List[np.ndarray], fps: int = DEFAULT_FPS) -> str:
+    if not frames:
+        return ""
+    
+    try:
+        buffer = io.BytesIO()
+        imageio.mimsave(buffer, frames, fps=fps, format="mp4")
+        buffer.seek(0)
+        
+        video_base64 = base64.b64encode(buffer.getvalue()).decode('utf-8')
+        return f"data:video/mp4;base64,{video_base64}"
+        
+    except Exception as e:
+        print(f"Warning: Failed to encode video: {e}")
+        return ""
+
+
+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 process_generation_result(result: Any) -> tuple[List[np.ndarray], float, List[str], List[float]]:
+    frames = result if isinstance(result, list) else result.get("frames", [])
+    generation_time = result.get("generation_time", 0.0) if isinstance(result, dict) else 0.0
+    
+    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 = []
+    
+    return frames, generation_time, stage_names, stage_execution_times
+
+
+def prepare_sampling_params(video_request: VideoGenerationRequest, default_params: Any) -> Any:
+    params = deepcopy(default_params)
+    params.prompt = video_request.prompt
+    
+    if video_request.use_negative_prompt:
+        params.negative_prompt = video_request.negative_prompt
+
+    params.seed = (video_request.seed if not video_request.randomize_seed 
+                  else torch.randint(0, SEED_RANGE_MAX, (1,)).item())
+    params.randomize_seed = video_request.randomize_seed
+    params.guidance_scale = video_request.guidance_scale
+    params.num_frames = video_request.num_frames
+    params.height = video_request.height
+    params.width = video_request.width
+    params.save_video = False
+    params.return_frames = False
+    
+    return params
+
+
+class BaseModelDeployment:
+    def __init__(self, model_path: str, output_path: str = "outputs"):
+        self.model_path = model_path
+        self.output_path = output_path
+        self.generator = None
+        self.default_params = None
+
+        os.makedirs(self.output_path, exist_ok=True)
+        setup_model_environment(self.model_path)
+
+    def _initialize_generator(self, config: Dict[str, Any]) -> None:
+        from fastvideo.entrypoints.video_generator import VideoGenerator
+        from fastvideo.configs.sample.base import SamplingParam
+
+        print(f"Initializing model: {self.model_path}")
+        self.generator = VideoGenerator.from_pretrained(
+            model_path=self.model_path,
+            num_gpus=1,
+            use_fsdp_inference=True,
+            text_encoder_cpu_offload=config["text_encoder_cpu_offload"],
+            dit_cpu_offload=config["dit_cpu_offload"],
+            vae_cpu_offload=config["vae_cpu_offload"],
+            VSA_sparsity=config["VSA_sparsity"],
+            enable_stage_verification=False,
+        )
+        self.default_params = SamplingParam.from_pretrained(self.model_path)
+
+    def generate_video(self, video_request: VideoGenerationRequest) -> VideoGenerationResponse:
+        total_start_time = time.time()
+        
+        params = prepare_sampling_params(video_request, self.default_params)
+
+        inference_start_time = time.time()
+        result = self.generator.generate_video(
+            prompt=video_request.prompt,
+            sampling_param=params,
+            save_video=False,
+            return_frames=False,
+        )
+        inference_time = time.time() - inference_start_time
+
+        frames, generation_time, stage_names, stage_execution_times = process_generation_result(result)
+
+        encoding_start_time = time.time()
+        video_data = encode_video_to_base64(frames, fps=DEFAULT_FPS)
+        encoding_time = time.time() - encoding_start_time
+        
+        total_time = time.time() - total_start_time
+
+        return VideoGenerationResponse(
+            video_data=video_data,
+            seed=params.seed,
+            success=True,
+            generation_time=generation_time,
+            inference_time=inference_time,
+            encoding_time=encoding_time,
+            total_time=total_time,
+            stage_names=stage_names,
+            stage_execution_times=stage_execution_times,
+        )
+
+
+@serve.deployment(
+    ray_actor_options={"num_cpus": 2, "num_gpus": 1, "runtime_env": {"conda": "fv"}},
+)
+class T2VModelDeployment(BaseModelDeployment):
+    def __init__(self, t2v_model_path: str, output_path: str = "outputs"):
+        super().__init__(t2v_model_path, output_path)
+        self._initialize_generator(MODEL_CONFIGS["1.3B"])
+        print("✅ T2V model initialized successfully")
+
+
+@serve.deployment(
+    ray_actor_options={"num_cpus": 16, "num_gpus": 1, "runtime_env": {"conda": "fv"}},
+)
+class T2V14BModelDeployment(BaseModelDeployment):
+    def __init__(self, t2v_14b_model_path: str, output_path: str = "outputs"):
+        super().__init__(t2v_14b_model_path, output_path)
+        # Override environment for 14B model
+        os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "VIDEO_SPARSE_ATTN"
+        self._initialize_generator(MODEL_CONFIGS["14B"])
+        print("✅ T2V 14B model initialized successfully")
+
+
+app = FastAPI()
+limiter = Limiter(key_func=get_remote_address)
+app.state.limiter = limiter
+app.add_exception_handler(RateLimitExceeded, _rate_limit_exceeded_handler)
+
+
+@serve.deployment(num_replicas=50, ray_actor_options={"num_cpus": 2})
+@serve.ingress(app)
+class FastVideoAPI:
+
+    def __init__(self, t2v_deployments: Dict[str, DeploymentHandle]):
+        self.t2v_deployments = t2v_deployments
+        
+        # Initialize Prometheus metrics
+        self.request_count = Counter('fastvideo_requests_total', 'Total FastVideo requests', ['model_type', 'status'])
+        self.request_duration = Histogram('fastvideo_request_duration_seconds', 'FastVideo request duration', ['model_type'])
+        self.video_generation_time = Histogram('fastvideo_video_generation_seconds', 'Video generation time', ['model_type'])
+    
+    def _get_model_name(self, model_path: Optional[str]) -> str:
+        return model_path.split('/')[-1] if model_path else "unknown"
+    
+    def _record_metrics(self, model_name: str, status: str, duration: float, response: Optional[VideoGenerationResponse] = None) -> None:
+        self.request_count.labels(model_type=model_name, status=status).inc()
+        self.request_duration.labels(model_type=model_name).observe(duration)
+        
+        if response and hasattr(response, 'generation_time') and response.generation_time:
+            self.video_generation_time.labels(model_type=model_name).observe(response.generation_time)
+    
+    @app.post("/generate_video", response_model=VideoGenerationResponse)
+    @limiter.limit("10/minute")
+    async def generate_video(self, request: Request, video_request: VideoGenerationRequest) -> VideoGenerationResponse:
+        """Route the request to the appropriate model deployment based on model_path."""
+        start_time = time.time()
+        model_name = self._get_model_name(video_request.model_path)
+        
+        try:
+            if video_request.model_path not in self.t2v_deployments:
+                raise ValueError(f"Model {video_request.model_path} not found")
+            
+            response_ref = self.t2v_deployments[video_request.model_path].generate_video.remote(video_request)
+            response = await response_ref
+            
+            self._record_metrics(model_name, "success", time.time() - start_time, response)
+            return response
+
+        except Exception as e:
+            self._record_metrics(model_name, "error", time.time() - start_time)
+            
+            return VideoGenerationResponse(
+                video_data=None,
+                seed=video_request.seed,
+                success=False,
+                error_message=str(e),
+                generation_time=0,
+                inference_time=0,
+                encoding_time=0,
+                total_time=0,
+            )
+    
+    @app.get("/health")
+    @limiter.limit("10/minute")
+    async def health_check(self, request: Request) -> Dict[str, str]:
+        return {"status": "healthy"}
+    
+    @app.get("/metrics")
+    async def metrics(self) -> Response:
+        return Response(generate_latest(), media_type="text/plain")
+
+
+def validate_configuration(model_paths: List[str], replicas: List[int]) -> None:
+    assert len(model_paths) == len(replicas), "Number of models and replicas must match"
+    assert sum(replicas) <= NUM_GPUS, f"Total replicas ({sum(replicas)}) must be <= {NUM_GPUS}"
+    
+    for model, replica_count in zip(model_paths, replicas):
+        assert model in SUPPORTED_MODELS, f"Model {model} not supported"
+        assert replica_count > 0, f"Replicas must be greater than 0"
+
+
+def start_ray_serve(
+    *,
+    t2v_model_paths: str,
+    t2v_model_replicas: str,
+    output_path: str = "outputs",
+    host: str = "0.0.0.0",
+    port: int = 8000,
+) -> None:
+    if not ray.is_initialized():
+        ray.init()
+
+    model_paths = t2v_model_paths.split(",")
+    replicas = [int(r) for r in t2v_model_replicas.split(",")]
+    validate_configuration(model_paths, replicas)
+
+    t2v_deps = {}
+    for model_path, replica_count in zip(model_paths, replicas):
+        t2v_dep = T2VModelDeployment.options(num_replicas=replica_count).bind(model_path, output_path)
+        t2v_deps[model_path] = t2v_dep
+
+    api = FastVideoAPI.bind(t2v_deps)
+    serve.run(api, route_prefix="/", name="fast_video")
+
+    print(f"Ray Serve backend started at http://{host}:{port}")
+    for model_path, replica_count in zip(model_paths, replicas):
+        print(f"T2V Model: {model_path} | Replicas: {replica_count}")
+    print(f"Health check: http://{host}:{port}/health")
+    print(f"Video generation endpoint: http://{host}:{port}/generate_video")
+
+
+def setup_signal_handlers() -> None:
+    signal.signal(signal.SIGINT, lambda *_: sys.exit(0))
+    signal.signal(signal.SIGTERM, lambda *_: sys.exit(0))
+
+
+if __name__ == "__main__":
+    import argparse
+    
+    parser = argparse.ArgumentParser(description="FastVideo Ray Serve Backend")
+    parser.add_argument("--t2v_model_paths",
+                        type=str,
+                        default="FastVideo/FastWan2.1-T2V-1.3B-Diffusers,FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers",
+                        help="Comma separated list of paths to the T2V model(s)")
+    parser.add_argument("--t2v_model_replicas",
+                        type=str,
+                        default="4,4",
+                        help="Comma separated list of number of replicas for the T2V model(s)")
+    parser.add_argument("--output_path",
+                        type=str,
+                        default="outputs",
+                        help="Path to save generated videos")
+    parser.add_argument("--host",
+                        type=str,
+                        default="0.0.0.0",
+                        help="Host to bind to")
+    parser.add_argument("--port",
+                        type=int,
+                        default=8000,
+                        help="Port to bind to")
+    
+    args = parser.parse_args()
+
+    model_paths = args.t2v_model_paths.split(",")
+    replicas = [int(r) for r in args.t2v_model_replicas.split(",")]
+    validate_configuration(model_paths, replicas)
+    
+    start_ray_serve(
+        t2v_model_paths=args.t2v_model_paths,
+        t2v_model_replicas=args.t2v_model_replicas,
+        output_path=args.output_path,
+        host=args.host,
+        port=args.port,
+    )
+
+    setup_signal_handlers()
+    print("✅ FastVideo backend is running. Press Ctrl-C to stop.")
+    while True:
+        time.sleep(3600) 
@@ -0,0 +1,3 @@
+python examples/inference/gradio/start_ray_serve_app.py \
+--t2v_model_paths "FastVideo/FastWan2.1-T2V-1.3B-Diffusers,FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers" \
+--t2v_model_replicas "4,4"
@@ -0,0 +1,257 @@
+"""
+Startup script for FastVideo with Ray Serve backend and Gradio frontend.
+This script starts both the backend and frontend services.
+"""
+
+import argparse
+import os
+import subprocess
+import sys
+import time
+import threading
+import signal
+import requests
+from pathlib import Path
+from typing import Dict, Any, Optional
+
+
+DEFAULT_BACKEND_HOST = "0.0.0.0"
+DEFAULT_BACKEND_PORT = 8000
+DEFAULT_FRONTEND_HOST = "0.0.0.0"
+DEFAULT_FRONTEND_PORT = 7860
+DEFAULT_OUTPUT_PATH = "outputs"
+DEFAULT_T2V_MODELS = "FastVideo/FastWan2.1-T2V-1.3B-Diffusers,FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers"
+DEFAULT_T2V_REPLICAS = "4,4"
+
+HEALTH_CHECK_TIMEOUT = 5
+HEALTH_CHECK_MAX_RETRIES = 100
+HEALTH_CHECK_INTERVAL = 2
+PROCESS_SHUTDOWN_TIMEOUT = 5
+PROCESS_MONITOR_INTERVAL = 1
+
+PROJECT_ROOT = Path(__file__).parent.parent.parent.parent
+sys.path.insert(0, str(PROJECT_ROOT))
+
+
+class ServiceManager:
+    
+    def __init__(self, args: argparse.Namespace):
+        self.args = args
+        self.backend_process: Optional[subprocess.Popen] = None
+        self.frontend_process: Optional[subprocess.Popen] = None
+        self.backend_url = f"http://{args.backend_host}:{args.backend_port}"
+    
+    def check_backend_health(self, max_retries: int = HEALTH_CHECK_MAX_RETRIES) -> bool:
+        health_url = f"{self.backend_url}/health"
+        
+        for attempt in range(max_retries):
+            try:
+                response = requests.get(health_url, timeout=HEALTH_CHECK_TIMEOUT)
+                if response.status_code == 200:
+                    print(f"✅ Backend is healthy at {self.backend_url}")
+                    return True
+            except requests.exceptions.RequestException:
+                pass
+            
+            if attempt < max_retries - 1:
+                print(f"⏳ Waiting for backend to start... ({attempt + 1}/{max_retries})")
+                time.sleep(HEALTH_CHECK_INTERVAL)
+        
+        print(f"❌ Backend failed to start within {max_retries * HEALTH_CHECK_INTERVAL} seconds")
+        return False
+    
+    def _create_monitor_thread(self, process: subprocess.Popen, service_name: str) -> threading.Thread:
+        def monitor():
+            if process.stdout:
+                for line in process.stdout:
+                    print(f"[{service_name}] {line.rstrip()}")
+        
+        thread = threading.Thread(target=monitor, daemon=True)
+        thread.start()
+        return thread
+    
+    def _start_service(self, script_name: str, args_dict: Dict[str, Any], service_name: str) -> subprocess.Popen:
+        script_path = Path(__file__).parent / script_name
+        
+        cmd = [sys.executable, str(script_path)]
+        for key, value in args_dict.items():
+            cmd.extend([f"--{key}", str(value)])
+        
+        print(f"🚀 Starting {service_name}...")
+        print(f"Command: {' '.join(cmd)}")
+        
+        process = subprocess.Popen(
+            cmd,
+            stdout=subprocess.PIPE,
+            stderr=subprocess.STDOUT,
+            universal_newlines=True,
+            bufsize=1
+        )
+        
+        self._create_monitor_thread(process, service_name.upper())
+        return process
+    
+    def start_backend(self) -> subprocess.Popen:
+        backend_args = {
+            "t2v_model_paths": self.args.t2v_model_paths,
+            "t2v_model_replicas": self.args.t2v_model_replicas,
+            "output_path": self.args.output_path,
+            "host": self.args.backend_host,
+            "port": self.args.backend_port
+        }
+        
+        self.backend_process = self._start_service("ray_serve_backend.py", backend_args, "backend")
+        return self.backend_process
+    
+    def start_frontend(self) -> subprocess.Popen:
+        frontend_args = {
+            "backend_url": self.backend_url,
+            "t2v_model_paths": self.args.t2v_model_paths,
+            "host": self.args.frontend_host,
+            "port": self.args.frontend_port
+        }
+        
+        self.frontend_process = self._start_service("gradio_frontend.py", frontend_args, "frontend")
+        return self.frontend_process
+    
+    def shutdown_services(self) -> None:
+        print("\n🛑 Shutting down services...")
+        
+        processes = []
+        if self.frontend_process:
+            self.frontend_process.terminate()
+            processes.append(("frontend", self.frontend_process))
+        
+        if self.backend_process:
+            self.backend_process.terminate()
+            processes.append(("backend", self.backend_process))
+        
+        for name, process in processes:
+            try:
+                process.wait(timeout=PROCESS_SHUTDOWN_TIMEOUT)
+                print(f"✅ {name.capitalize()} stopped gracefully")
+            except subprocess.TimeoutExpired:
+                print(f"⚠️  Force killing {name} process...")
+                process.kill()
+        
+        print("✅ All services stopped")
+    
+    def monitor_processes(self) -> None:
+        if not self.backend_process or not self.frontend_process:
+            print("❌ Processes not properly initialized")
+            return
+        
+        try:
+            while True:
+                if self.frontend_process.poll() is not None:
+                    print("❌ Frontend process died unexpectedly")
+                    break
+                
+                if self.backend_process.poll() is not None:
+                    print("❌ Backend process died unexpectedly")
+                    break
+                
+                time.sleep(PROCESS_MONITOR_INTERVAL)
+        
+        except KeyboardInterrupt:
+            pass
+        
+        self.shutdown_services()
+
+
+def setup_signal_handlers(service_manager: ServiceManager) -> None:
+    def signal_handler(signum: int, frame: Any) -> None:
+        service_manager.shutdown_services()
+        sys.exit(0)
+    
+    signal.signal(signal.SIGINT, signal_handler)
+    signal.signal(signal.SIGTERM, signal_handler)
+
+
+def print_startup_info(args: argparse.Namespace) -> None:
+    print("🎬 FastVideo Ray Serve App")
+    print("=" * 50)
+    print(f"T2V Models: {args.t2v_model_paths}")
+    print(f"T2V Model Replicas: {args.t2v_model_replicas}")
+    print(f"Output: {args.output_path}")
+    print(f"Backend: http://{args.backend_host}:{args.backend_port}")
+    print(f"Frontend: http://{args.frontend_host}:{args.frontend_port}")
+    print("=" * 50)
+
+
+def parse_arguments() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="FastVideo Ray Serve App")
+    
+    parser.add_argument("--t2v_model_paths", 
+                        type=str,
+                        default=DEFAULT_T2V_MODELS,
+                        help="Comma separated list of paths to the T2V model(s)")
+    parser.add_argument("--t2v_model_replicas", 
+                        type=str, 
+                        default=DEFAULT_T2V_REPLICAS,
+                        help="Comma separated list of number of replicas for the T2V model(s)")
+    parser.add_argument("--output_path", 
+                        type=str, 
+                        default=DEFAULT_OUTPUT_PATH,
+                        help="Path to save generated videos")
+    
+    parser.add_argument("--backend_host", 
+                        type=str, 
+                        default=DEFAULT_BACKEND_HOST,
+                        help="Backend host to bind to")
+    parser.add_argument("--backend_port", 
+                        type=int, 
+                        default=DEFAULT_BACKEND_PORT,
+                        help="Backend port to bind to")
+    
+    parser.add_argument("--frontend_host", 
+                        type=str, 
+                        default=DEFAULT_FRONTEND_HOST,
+                        help="Frontend host to bind to")
+    parser.add_argument("--frontend_port", 
+                        type=int, 
+                        default=DEFAULT_FRONTEND_PORT,
+                        help="Frontend port to bind to")
+    
+    parser.add_argument("--skip_backend_check", 
+                        action="store_true",
+                        help="Skip backend health check")
+    
+    return parser.parse_args()
+
+
+def main() -> None:
+    args = parse_arguments()
+    
+    os.makedirs(args.output_path, exist_ok=True)
+    print_startup_info(args)
+    
+    service_manager = ServiceManager(args)
+    setup_signal_handlers(service_manager)
+    
+    try:
+        service_manager.start_backend()
+        
+        if not args.skip_backend_check:
+            if not service_manager.check_backend_health():
+                print("❌ Backend failed to start. Terminating...")
+                service_manager.shutdown_services()
+                sys.exit(1)
+        
+        service_manager.start_frontend()
+        
+        print("\n🎉 Both services are starting up!")
+        print(f"📺 Frontend will be available at: http://{args.frontend_host}:{args.frontend_port}")
+        print(f"🔧 Backend API will be available at: http://{args.backend_host}:{args.backend_port}")
+        print("\nPress Ctrl+C to stop both services...")
+        
+        service_manager.monitor_processes()
+    
+    except Exception as e:
+        print(f"❌ Unexpected error: {e}")
+        service_manager.shutdown_services()
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()
@@ -14,9 +14,10 @@ def main():
        vae_cpu_offload=True,
        text_encoder_cpu_offload=True,
        pin_cpu_memory=True, # set to false if low CPU RAM or hit obscure "CUDA error: Invalid argument" 
-        lora_path="checkpoints/wan_t2v_finetune_lora/checkpoint-1000/transformer",
+        lora_path="checkpoints/wan_t2v_finetune_lora/checkpoint-160/transformer",
        lora_nickname="crush_smol"
    )
+    generator.unmerge_lora_weights()
    kwargs = {
        "height": 480,
        "width": 832,
@@ -7,9 +7,7 @@ These are e2e example scripts for finetuning Wan2.1 T2V with VSA to accelerate i
 ## Make sure you have installed VSA

 ```bash
-cd csrc/attn
-git submodule update --init --recursive
-python setup_vsa.py install
+pip install vsa
 ```

 ### Download the synthetic dataset:
@@ -0,0 +1,24 @@
+#!/bin/bash
+
+GPU_NUM=2 # 2,4,8
+MODEL_PATH="Wan-AI/Wan2.1-I2V-14B-480P-Diffusers"
+DATASET_PATH="data/crush-smol/"
+OUTPUT_DIR="data/crush-smol_processed_i2v/"
+
+torchrun --nproc_per_node=$GPU_NUM \
+    -m fastvideo.pipelines.preprocess.v1_preprocessing_new \
+    --model_path $MODEL_PATH \
+    --mode preprocess \
+    --workload_type i2v \
+    --preprocess.dataset_type merged \
+    --preprocess.dataset_path $DATASET_PATH \
+    --preprocess.dataset_output_dir $OUTPUT_DIR \
+    --preprocess.preprocess_video_batch_size 2 \
+    --preprocess.dataloader_num_workers 0 \
+    --preprocess.max_height 480 \
+    --preprocess.max_width 832 \
+    --preprocess.num_frames 77 \
+    --preprocess.train_fps 16 \
+    --preprocess.samples_per_file 8 \
+    --preprocess.flush_frequency 8 \
+    --preprocess.video_length_tolerance_range 5
@@ -7,7 +7,7 @@ export WANDB_MODE=online
 MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
 DATA_DIR="data/crush-smol_processed_t2v/combined_parquet_dataset/"
 VALIDATION_DATASET_FILE="$(dirname "$0")/validation.json"
-NUM_GPUS=2
+NUM_GPUS=1
 # export CUDA_VISIBLE_DEVICES=4,5


@@ -76,6 +76,7 @@ miscellaneous_args=(
  --dit_precision "fp32"
  --num_euler_timesteps 50
  --ema_start_step 0
+  --resume_from_checkpoint "checkpoints/wan_t2v_finetune_lora/checkpoint-160"
 )

 torchrun \
@@ -1,19 +1,20 @@
 #!/bin/bash

-GPU_NUM=1 # 2,4,8
+GPU_NUM=2 # 2,4,8
 MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
-MODEL_TYPE="wan"
-DATASET_PATH="data/crush-smol-test/"
+DATASET_PATH="data/crush-smol/"
 OUTPUT_DIR="data/crush-smol_processed_t2v/"

 torchrun --nproc_per_node=$GPU_NUM \
-    fastvideo/pipelines/preprocess/v1_preprocessing_new.py \
+    -m fastvideo.pipelines.preprocess.v1_preprocessing_new \
    --model_path $MODEL_PATH \
    --mode preprocess \
    --workload_type t2v \
+    --preprocess.video_loader_type torchvision \
+    --preprocess.dataset_type merged \
    --preprocess.dataset_path $DATASET_PATH \
    --preprocess.dataset_output_dir $OUTPUT_DIR \
-    --preprocess.preprocess_video_batch_size 4 \
+    --preprocess.preprocess_video_batch_size 2 \
    --preprocess.dataloader_num_workers 0 \
    --preprocess.max_height 480 \
    --preprocess.max_width 832 \
@@ -1,9 +1,59 @@
 import dataclasses
+from enum import Enum
 from typing import Any, Optional

 from fastvideo.configs.utils import update_config_from_args
+from fastvideo.logger import init_logger
 from fastvideo.utils import FlexibleArgumentParser, StoreBoolean

+logger = init_logger(__name__)
+
+
+class DatasetType(str, Enum):
+    """
+    Enumeration for different dataset types.
+    """
+    HF = "hf"
+    MERGED = "merged"
+
+    @classmethod
+    def from_string(cls, value: str) -> "DatasetType":
+        """Convert string to DatasetType enum."""
+        try:
+            return cls(value.lower())
+        except ValueError:
+            raise ValueError(
+                f"Invalid dataset type: {value}. Must be one of: {', '.join([m.value for m in cls])}"
+            ) from None
+
+    @classmethod
+    def choices(cls) -> list[str]:
+        """Get all available choices as strings for argparse."""
+        return [dataset_type.value for dataset_type in cls]
+
+
+class VideoLoaderType(str, Enum):
+    """
+    Enumeration for different video loaders.
+    """
+    TORCHCODEC = "torchcodec"
+    TORCHVISION = "torchvision"
+
+    @classmethod
+    def from_string(cls, value: str) -> "VideoLoaderType":
+        """Convert string to VideoLoader enum."""
+        try:
+            return cls(value.lower())
+        except ValueError:
+            raise ValueError(
+                f"Invalid video loader: {value}. Must be one of: {', '.join([m.value for m in cls])}"
+            ) from None
+
+    @classmethod
+    def choices(cls) -> list[str]:
+        """Get all available choices as strings for argparse."""
+        return [video_loader.value for video_loader in cls]
+

@dataclasses.dataclass
 class PreprocessConfig:
@@ -12,6 +62,7 @@ class PreprocessConfig:
    # Model and dataset configuration
    model_path: str = ""
    dataset_path: str = ""
+    dataset_type: DatasetType = DatasetType.HF
    dataset_output_dir: str = "./output"

    # Dataloader configuration
@@ -23,6 +74,7 @@ class PreprocessConfig:
    flush_frequency: int = 256

    # Video processing parameters
+    video_loader_type: VideoLoaderType = VideoLoaderType.TORCHCODEC
    max_height: int = 480
    max_width: int = 848
    num_frames: int = 163
@@ -35,6 +87,9 @@ class PreprocessConfig:
    # Model configuration
    training_cfg_rate: float = 0.0

+    # framework configuration
+    seed: int = 42
+
    @staticmethod
    def add_cli_args(parser: FlexibleArgumentParser,
                     prefix: str = "preprocess") -> FlexibleArgumentParser:
@@ -52,6 +107,12 @@ class PreprocessConfig:
            type=str,
            default=PreprocessConfig.dataset_path,
            help="Path to the dataset directory for preprocessing")
+        preprocess_args.add_argument(
+            f"--{prefix_with_dot}dataset-type",
+            type=str,
+            choices=DatasetType.choices(),
+            default=PreprocessConfig.dataset_type.value,
+            help="Type of the dataset")
        preprocess_args.add_argument(
            f"--{prefix_with_dot}dataset-output-dir",
            type=str,
@@ -83,6 +144,12 @@ class PreprocessConfig:
                                     help="How often to save to parquet files")

        # Video processing parameters
+        preprocess_args.add_argument(
+            f"--{prefix_with_dot}video-loader-type",
+            type=str,
+            choices=VideoLoaderType.choices(),
+            default=PreprocessConfig.video_loader_type.value,
+            help="Type of the video loader")
        preprocess_args.add_argument(f"--{prefix_with_dot}max-height",
                                     type=int,
                                     default=PreprocessConfig.max_height,
@@ -123,6 +190,10 @@ class PreprocessConfig:
                                     type=float,
                                     default=PreprocessConfig.training_cfg_rate,
                                     help="Training CFG rate")
+        preprocess_args.add_argument(f"--{prefix_with_dot}seed",
+                                     type=int,
+                                     default=PreprocessConfig.seed,
+                                     help="Seed for random number generator")

        return parser

@@ -130,6 +201,14 @@ class PreprocessConfig:
    def from_kwargs(cls, kwargs: dict[str,
                                      Any]) -> Optional["PreprocessConfig"]:
        """Create PreprocessConfig from keyword arguments."""
+        if 'dataset_type' in kwargs and isinstance(kwargs['dataset_type'], str):
+            kwargs['dataset_type'] = DatasetType.from_string(
+                kwargs['dataset_type'])
+        if 'video_loader_type' in kwargs and isinstance(
+                kwargs['video_loader_type'], str):
+            kwargs['video_loader_type'] = VideoLoaderType.from_string(
+                kwargs['video_loader_type'])
+
        preprocess_config = cls()
        if not update_config_from_args(
                preprocess_config, kwargs, prefix="preprocess", pop_args=True):
@@ -92,6 +92,12 @@ class WanVideoArchConfig(DiTArchConfig):
    pos_embed_seq_len: int | None = None
    exclude_lora_layers: list[str] = field(default_factory=lambda: ["embedder"])

+    # Causal Wan
+    local_attn_size: int = -1  # Window size for temporal local attention (-1 indicates global attention)
+    sink_size: int = 0  # Size of the attention sink, we keep the first `sink_size` frames unchanged when rolling the KV cache
+    num_frames_per_block: int = 3
+    sliding_window_num_frames: int = 21
+
    def __post_init__(self):
        super().__post_init__()
        self.out_channels = self.out_channels or self.in_channels
@@ -85,6 +85,9 @@ class PipelineConfig:
    # DMD parameters
    dmd_denoising_steps: list[int] | None = field(default=None)

+    # Wan2.2 TI2V parameters
+    ti2v_task: bool = False
+
    # Compilation
    # enable_torch_compile: bool = False

@@ -7,10 +7,14 @@ from collections.abc import Callable
 from fastvideo.configs.pipelines.base import PipelineConfig
 from fastvideo.configs.pipelines.hunyuan import FastHunyuanConfig, HunyuanConfig
 from fastvideo.configs.pipelines.stepvideo import StepVideoT2VConfig
-from fastvideo.configs.pipelines.wan import (FastWan2_1_T2V_480P_Config,
-                                             FastWan2_2_TI2V_5B_Config,
-                                             WanI2V480PConfig, WanI2V720PConfig,
-                                             WanT2V480PConfig, WanT2V720PConfig)
+
+# isort: off
+from fastvideo.configs.pipelines.wan import (
+    FastWan2_1_T2V_480P_Config, FastWan2_2_TI2V_5B_Config,
+    SelfForcingWanT2V480PConfig, Wan2_2_I2V_A14B_Config, Wan2_2_T2V_A14B_Config,
+    Wan2_2_TI2V_5B_Config, WanI2V480PConfig, WanI2V720PConfig, WanT2V480PConfig,
+    WanT2V720PConfig)
+# isort: on
 from fastvideo.logger import init_logger
 from fastvideo.utils import (maybe_download_model_index,
                             verify_model_config_and_directory)
@@ -31,9 +35,10 @@ PIPE_NAME_TO_CONFIG: dict[str, type[PipelineConfig]] = {
    "FastVideo/FastWan2.2-TI2V-5B-Diffusers": FastWan2_2_TI2V_5B_Config,
    "FastVideo/stepvideo-t2v-diffusers": StepVideoT2VConfig,
    "FastVideo/Wan2.1-VSA-T2V-14B-720P-Diffusers": WanT2V720PConfig,
-    "Wan-AI/Wan2.2-TI2V-5B-Diffusers": WanT2V720PConfig,
-    "Wan-AI/Wan2.2-T2V-A14B-Diffusers": WanT2V480PConfig,
-    "Wan-AI/Wan2.2-I2V-A14B-Diffusers": WanI2V480PConfig,
+    "wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers": SelfForcingWanT2V480PConfig,
+    "Wan-AI/Wan2.2-TI2V-5B-Diffusers": Wan2_2_TI2V_5B_Config,
+    "Wan-AI/Wan2.2-T2V-A14B-Diffusers": Wan2_2_T2V_A14B_Config,
+    "Wan-AI/Wan2.2-I2V-A14B-Diffusers": Wan2_2_I2V_A14B_Config,
    # Add other specific weight variants
 }

@@ -138,3 +138,13 @@ class Wan2_2_T2V_A14B_Config(WanT2V480PConfig):
@dataclass
 class Wan2_2_I2V_A14B_Config(WanT2V480PConfig):
    pass
+
+
+# =============================================
+# ============= Causal Self-Forcing =============
+# =============================================
+@dataclass
+class SelfForcingWanT2V480PConfig(WanT2V480PConfig):
+    is_causal: bool = True
+    dmd_denoising_steps: list[int] | None = field(
+        default_factory=lambda: [1000, 750, 500, 250])
@@ -10,6 +10,7 @@ from fastvideo.configs.sample.stepvideo import StepVideoT2VSamplingParam
 # isort: off
 from fastvideo.configs.sample.wan import (
    FastWanT2V480PConfig,
+    Wan2_1_Fun_1_3B_InP_SamplingParam,
    Wan2_2_I2V_A14B_SamplingParam,
    Wan2_2_T2V_A14B_SamplingParam,
    Wan2_2_TI2V_5B_SamplingParam,
@@ -17,6 +18,7 @@ from fastvideo.configs.sample.wan import (
    WanI2V_14B_720P_SamplingParam,
    WanT2V_1_3B_SamplingParam,
    WanT2V_14B_SamplingParam,
+    SelfForcingWanT2V480PConfig,
 )
 # isort: on
 from fastvideo.logger import init_logger
@@ -28,16 +30,31 @@ logger = init_logger(__name__)
 SAMPLING_PARAM_REGISTRY: dict[str, Any] = {
    "FastVideo/FastHunyuan-diffusers": FastHunyuanSamplingParam,
    "hunyuanvideo-community/HunyuanVideo": HunyuanSamplingParam,
+    "FastVideo/stepvideo-t2v-diffusers": StepVideoT2VSamplingParam,
+
+    # Wan2.1
    "Wan-AI/Wan2.1-T2V-1.3B-Diffusers": WanT2V_1_3B_SamplingParam,
    "Wan-AI/Wan2.1-T2V-14B-Diffusers": WanT2V_14B_SamplingParam,
    "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers": WanI2V_14B_480P_SamplingParam,
    "Wan-AI/Wan2.1-I2V-14B-720P-Diffusers": WanI2V_14B_720P_SamplingParam,
-    "FastVideo/stepvideo-t2v-diffusers": StepVideoT2VSamplingParam,
-    "FastVideo/FastWan2.1-T2V-1.3B-Diffusers": FastWanT2V480PConfig,
+    "weizhou03/Wan2.1-Fun-1.3B-InP-Diffusers":
+    Wan2_1_Fun_1_3B_InP_SamplingParam,
+
+    # Wan2.2
    "Wan-AI/Wan2.2-TI2V-5B-Diffusers": Wan2_2_TI2V_5B_SamplingParam,
-    "FastVideo/FastWan2.2-TI2V-5B-Diffusers": Wan2_2_TI2V_5B_SamplingParam,
+    "FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers":
+    Wan2_2_TI2V_5B_SamplingParam,
    "Wan-AI/Wan2.2-T2V-A14B-Diffusers": Wan2_2_T2V_A14B_SamplingParam,
    "Wan-AI/Wan2.2-I2V-A14B-Diffusers": Wan2_2_I2V_A14B_SamplingParam,
+
+    # FastWan2.1
+    "FastVideo/FastWan2.1-T2V-1.3B-Diffusers": FastWanT2V480PConfig,
+
+    # FastWan2.2
+    "FastVideo/FastWan2.2-TI2V-5B-Diffusers": Wan2_2_TI2V_5B_SamplingParam,
+
+    # Causal Self-Forcing Wan2.1
+    "wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers": SelfForcingWanT2V480PConfig,
    # Add other specific weight variants
 }

@@ -107,6 +107,21 @@ class FastWanT2V480PConfig(WanT2V_1_3B_SamplingParam):
    fps: int = 16


+# =============================================
+# ============= Wan2.1 Fun Models =============
+# =============================================
+@dataclass
+class Wan2_1_Fun_1_3B_InP_SamplingParam(SamplingParam):
+    """Sampling parameters for Wan2.1 Fun 1.3B InP model."""
+    height: int = 480
+    width: int = 832
+    num_frames: int = 81
+    fps: int = 16
+    negative_prompt: str | None = "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"
+    guidance_scale: float = 6.0
+    num_inference_steps: int = 50
+
+
 # =============================================
 # ============= Wan2.2 TI2V Models =============
 # =============================================
@@ -141,3 +156,11 @@ class Wan2_2_I2V_A14B_SamplingParam(Wan2_2_Base_SamplingParam):
    guidance_scale_2: float = 3.5
    num_inference_steps: int = 40
    fps: int = 16
+
+
+# =============================================
+# ============= Causal Self-Forcing =============
+# =============================================
+@dataclass
+class SelfForcingWanT2V480PConfig(WanT2V_1_3B_SamplingParam):
+    pass
@@ -4,7 +4,7 @@ from torchvision.transforms import Lambda

 from fastvideo.dataset.parquet_dataset_map_style import (
    build_parquet_map_style_dataloader)
-from fastvideo.dataset.preprocessing_datasets import VideoCaptionMergedDataset
+from fastvideo.dataset.preprocessing_datasets import VideoCaptionMergedDataset, TextDataset
 from fastvideo.dataset.transform import (CenterCropResizeVideo, Normalize255,
                                         TemporalRandomCrop)
 from fastvideo.dataset.validation_dataset import ValidationDataset
@@ -39,7 +39,13 @@ def getdataset(args) -> VideoCaptionMergedDataset:
                                     seed=args.seed)


+def gettextdataset(args) -> TextDataset:
+    return TextDataset(data_merge_path=args.data_merge_path,
+                       args=args,
+                       seed=args.seed)
+
+
 __all__ = [
    "build_parquet_map_style_dataloader", "ValidationDataset",
-    "VideoCaptionMergedDataset"
-]
+    "VideoCaptionMergedDataset", "TextDataset"
+]
@@ -78,3 +78,76 @@ pyarrow_schema_t2v = pa.schema([
    pa.field("duration_sec", pa.float64()),
    pa.field("fps", pa.float64()),
 ])
+
+pyarrow_schema_ode_trajectory = pa.schema([
+    pa.field("id", pa.string()),
+    # --- Image/Video VAE latents ---
+    # Tensors are stored as raw bytes with shape and dtype info for loading
+    pa.field("vae_latent_bytes", pa.binary()),
+    # e.g., [C, T, H, W] or [C, H, W]
+    pa.field("vae_latent_shape", pa.list_(pa.int64())),
+    # e.g., 'float32'
+    pa.field("vae_latent_dtype", pa.string()),
+    # --- Text encoder output tensor ---
+    # Tensors are stored as raw bytes with shape and dtype info for loading
+    pa.field("text_embedding_bytes", pa.binary()),
+    # e.g., [SeqLen, Dim]
+    pa.field("text_embedding_shape", pa.list_(pa.int64())),
+    # e.g., 'bfloat16' or 'float32'
+    pa.field("text_embedding_dtype", pa.string()),
+    # I2V
+    pa.field("image_condition_latents_bytes", pa.binary()),
+    pa.field("image_condition_latents_shape", pa.list_(pa.int64())),
+    pa.field("image_condition_latents_dtype", pa.string()),
+    # --- ODE Trajectory ---
+    pa.field("trajectory_latents_bytes", pa.binary()),
+    pa.field("trajectory_latents_shape", pa.list_(pa.int64())),
+    pa.field("trajectory_latents_dtype", pa.string()),
+    pa.field("trajectory_timesteps_bytes", pa.binary()),
+    pa.field("trajectory_timesteps_shape", pa.list_(pa.int64())),
+    pa.field("trajectory_timesteps_dtype", pa.string()),
+    # --- Metadata ---
+    pa.field("file_name", pa.string()),
+    pa.field("caption", pa.string()),
+    pa.field("media_type", pa.string()),  # 'image' or 'video'
+    pa.field("width", pa.int64()),
+    pa.field("height", pa.int64()),
+    # -- Video-specific (can be null/default for images) ---
+    # Number of frames processed (e.g., 1 for image, N for video)
+    pa.field("num_frames", pa.int64()),
+    pa.field("duration_sec", pa.float64()),
+    pa.field("fps", pa.float64()),
+])
+
+pyarrow_schema_ode_trajectory_text_only = pa.schema([
+    pa.field("id", pa.string()),
+    # --- Text encoder output tensor ---
+    # Tensors are stored as raw bytes with shape and dtype info for loading
+    pa.field("text_embedding_bytes", pa.binary()),
+    # e.g., [SeqLen, Dim]
+    pa.field("text_embedding_shape", pa.list_(pa.int64())),
+    # e.g., 'bfloat16' or 'float32'
+    pa.field("text_embedding_dtype", pa.string()),
+    # --- ODE Trajectory ---
+    pa.field("trajectory_latents_bytes", pa.binary()),
+    pa.field("trajectory_latents_shape", pa.list_(pa.int64())),
+    pa.field("trajectory_latents_dtype", pa.string()),
+    pa.field("trajectory_timesteps_bytes", pa.binary()),
+    pa.field("trajectory_timesteps_shape", pa.list_(pa.int64())),
+    pa.field("trajectory_timesteps_dtype", pa.string()),
+    # --- Metadata ---
+    pa.field("file_name", pa.string()),
+    pa.field("caption", pa.string()),
+    pa.field("media_type", pa.string()),  # Always 'text' for text-only
+])
+
+pyarrow_schema_text_only = pa.schema([
+    pa.field("id", pa.string()),
+    # --- Text encoder output tensor ---
+    # Tensors are stored as raw bytes with shape and dtype info for loading
+    pa.field("text_embedding_bytes", pa.binary()),
+    # e.g., [SeqLen, Dim]
+    pa.field("text_embedding_shape", pa.list_(pa.int64())),
+    # e.g., 'bfloat16' or 'float32'
+    pa.field("text_embedding_dtype", pa.string()),
+])
@@ -628,3 +628,134 @@ class VideoCaptionMergedDataset(torch.utils.data.IterableDataset,
    def load_state_dict(self, state_dict: dict[str, Any]) -> None:
        """Load state dict from checkpoint."""
        self.processed_batches = state_dict["processed_batches"]
+
+
+class TextDataset(torch.utils.data.IterableDataset,
+                  torch.distributed.checkpoint.stateful.Stateful):
+    """
+    Text-only dataset for processing prompts from a simple text file.
+    
+    Assumes that data_merge_path is a text file with one prompt per line:
+    A cat playing with a ball
+    A dog running in the park
+    A person cooking dinner
+    ...
+    
+    This dataset processes text data through text encoding stages only.
+    """
+
+    def __init__(self,
+                 data_merge_path: str,
+                 args,
+                 start_idx: int = 0,
+                 seed: int = 42):
+        self.data_merge_path = data_merge_path
+        self.start_idx = start_idx
+        self.args = args
+        self.seed = seed
+
+        # Initialize tokenizer
+        tokenizer_path = os.path.join(args.model_path, "tokenizer")
+        tokenizer = AutoTokenizer.from_pretrained(tokenizer_path,
+                                                  cache_dir=args.cache_dir)
+
+        # Initialize text encoding stage
+        self.text_encoding_stage = TextEncodingStage(
+            tokenizer=tokenizer,
+            text_max_length=args.text_max_length,
+            cfg_rate=getattr(args, 'training_cfg_rate', 0.0),
+            seed=self.seed)
+
+        # Process text data
+        self.processed_batches = self._process_text_data()
+
+    def _load_text_data(self) -> list[str]:
+        """Load text prompts from file."""
+        prompts = []
+        with open(self.data_merge_path, 'r', encoding='utf-8') as f:
+            for line in f:
+                line = line.strip()
+                if line:  # Skip empty lines
+                    prompts.append(line)
+        
+        logger.info(f"Loaded {len(prompts)} text prompts from {self.data_merge_path}")
+        return prompts
+
+    def _process_text_data(self) -> list[PreprocessBatch]:
+        """Process the text prompts through text encoding stage."""
+        raw_prompts = self._load_text_data()
+        processed_batches = []
+
+        for idx, prompt in enumerate(raw_prompts):
+            # Create a text-only batch with dummy path
+            batch = PreprocessBatch(
+                path=f"text_prompt_{idx}",
+                cap=[prompt],  # TextEncodingStage expects a list
+                resolution=None,
+                fps=None,
+                duration=None,
+                num_frames=0,
+                sample_frame_index=None,
+                sample_num_frames=0
+            )
+            
+            processed_batches.append(batch)
+
+        logger.info(f"Processed {len(processed_batches)} text batches")
+        return processed_batches
+
+    def __iter__(self):
+        """Iterator for the dataset."""
+        # Set up distributed sampling if needed
+        if torch.distributed.is_available() and torch.distributed.is_initialized():
+            rank = torch.distributed.get_rank()
+            world_size = torch.distributed.get_world_size()
+        else:
+            rank = 0
+            world_size = 1
+
+        # Calculate chunk for this rank
+        total_items = len(self.processed_batches)
+        items_per_rank = math.ceil(total_items / world_size)
+        start_idx = rank * items_per_rank + self.start_idx
+        end_idx = min(start_idx + items_per_rank, total_items)
+
+        # Yield items for this rank
+        for idx in range(start_idx, end_idx):
+            if idx < len(self.processed_batches):
+                yield self._get_item(idx)
+
+    def _get_item(self, idx: int) -> dict:
+        """Get a single processed text item."""
+        batch = self.processed_batches[idx]
+
+        # Apply text encoding stage
+        batch = self.text_encoding_stage.process(batch)
+
+        # Build result dictionary for text-only processing with required schema fields
+        result = {
+            "text": batch.text,
+            "input_ids": batch.input_ids,
+            "cond_mask": batch.cond_mask,
+            "path": batch.path,
+            # Required schema fields for ODE trajectory processing
+            "id": f"text_{idx}",
+            "file_name": batch.path,
+            "caption": batch.text,
+            "media_type": "text",
+            "width": 1,
+            "height": 1,
+            "num_frames": 0,
+            "duration_sec": 0.0,
+            "fps": 0.0,
+        }
+
+        return result
+
+    def state_dict(self) -> dict[str, Any]:
+        """Return state dict for checkpointing."""
+        return {"processed_batches": self.processed_batches}
+
+    def load_state_dict(self, state_dict: dict[str, Any]) -> None:
+        """Load state dict from checkpoint."""
+        self.processed_batches = state_dict["processed_batches"]
@@ -9,6 +9,7 @@ diffusion models.
 import math
 import os
 import time
+from copy import deepcopy
 from typing import Any

 import imageio
@@ -202,6 +203,8 @@ class VideoGenerator:
        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)
@@ -275,6 +278,7 @@ class VideoGenerator:
                       width: {target_width}
                video_length: {sampling_param.num_frames}
                      prompt: {prompt}
+                      image_path: {sampling_param.image_path}
                  neg_prompt: {sampling_param.negative_prompt}
                        seed: {sampling_param.seed}
                 infer_steps: {sampling_param.num_inference_steps}
@@ -304,7 +308,8 @@ class VideoGenerator:
        # Run inference
        start_time = time.perf_counter()
        output_batch = self.executor.execute_forward(batch, fastvideo_args)
-        samples = output_batch
+        samples = output_batch.output
+        logging_info = output_batch.logging_info

        gen_time = time.perf_counter() - start_time
        logger.info("Generated successfully in %.2f seconds", gen_time)
@@ -334,9 +339,11 @@ class VideoGenerator:
        else:
            return {
                "samples": samples,
+                "frames": frames,
                "prompts": prompt,
                "size": (target_height, target_width, batch.num_frames),
-                "generation_time": gen_time
+                "generation_time": gen_time,
+                "logging_info": logging_info,
            }

    def set_lora_adapter(self,
@@ -344,6 +351,16 @@ class VideoGenerator:
                         lora_path: str | None = None) -> None:
        self.executor.set_lora_adapter(lora_nickname, lora_path)

+    def unmerge_lora_weights(self) -> None:
+        """
+        Use unmerged weights for inference to produce videos that align with 
+        validation videos generated during training.
+        """
+        self.executor.unmerge_lora_weights()
+
+    def merge_lora_weights(self) -> None:
+        self.executor.merge_lora_weights()
+
    def shutdown(self):
        """
        Shutdown the video generator.
@@ -76,7 +76,7 @@ class BaseLayerWithLoRA(nn.Module):
            lora_B = self.lora_B.to_local()
            lora_A = self.lora_A.to_local()

-        if (self.training_mode or not self.merged) and not self.disable_lora:
+        if not self.merged and not self.disable_lora:
            delta = x @ (
                self.slice_lora_b_weights(lora_B.to(x, non_blocking=True))
                @ self.slice_lora_a_weights(lora_A.to(x, non_blocking=True)))
@@ -29,6 +29,9 @@ import torch

 from fastvideo.distributed.parallel_state import get_sp_group
 from fastvideo.layers.custom_op import CustomOp
+from fastvideo.logger import init_logger
+
+logger = init_logger(__name__)


 def _rotate_neox(x: torch.Tensor) -> torch.Tensor:
@@ -267,6 +270,7 @@ def get_nd_rotary_pos_embed(
    sp_rank: int = 0,
    sp_world_size: int = 1,
    dtype: torch.dtype = torch.float32,
+    start_frame: int = 0,
 ) -> tuple[torch.Tensor, torch.Tensor]:
    """
    This is a n-d version of precompute_freqs_cis, which is a RoPE for tokens with n-d structure.
@@ -292,6 +296,9 @@ def get_nd_rotary_pos_embed(
    full_grid = get_meshgrid_nd(
        start, *args, dim=len(rope_dim_list))  # [3, W, H, D] / [2, W, H]

+    if start_frame > 0:
+        full_grid[0] += start_frame
+
    # Shard the grid if using sequence parallelism (sp_world_size > 1)
    assert shard_dim < len(
        rope_dim_list
@@ -370,6 +377,7 @@ def get_rotary_pos_embed(
    interpolation_factor=1.0,
    shard_dim: int = 0,
    dtype: torch.dtype = torch.float32,
+    start_frame: int = 0,
 ) -> tuple[torch.Tensor, torch.Tensor]:
    """
    Generate rotary positional embeddings for the given sizes.
@@ -413,6 +421,7 @@ def get_rotary_pos_embed(
        sp_rank=sp_rank,
        sp_world_size=sp_world_size,
        dtype=dtype,
+        start_frame=start_frame,
    )
    return freqs_cos, freqs_sin

@@ -86,12 +86,16 @@ class TimestepEmbedder(nn.Module):
                       dtype=dtype)
        self.freq_dtype = freq_dtype

-    def forward(self, t: torch.Tensor) -> torch.Tensor:
+    def forward(self,
+                t: torch.Tensor,
+                timestep_seq_len: int | None = None) -> torch.Tensor:
        t_freq = timestep_embedding(t,
                                    self.frequency_embedding_size,
                                    self.max_period,
                                    dtype=self.freq_dtype).to(
                                        self.mlp.fc_in.weight.dtype)
+        if timestep_seq_len is not None:
+            t_freq = t_freq.unflatten(0, (1, timestep_seq_len))
        # t_freq = t_freq.to(self.mlp.fc_in.weight.dtype)
        t_emb = self.mlp(t_freq)
        return t_emb
@@ -120,14 +120,14 @@ def _info(logger: Logger,

    if not _warned_local_main_process and local_main_process_only:
        logger.warning(
-            '%s is_local_main_process is set to True, logging only from the local main process.%s',
+            '%s By default, logger.info(..) will only log from the local main process. Set logger.info(..., is_local_main_process=False) to log from all processes.%s',
            GREEN,
            RESET,
        )
        _warned_local_main_process = True
    if not _warned_main_process and main_process_only:
        logger.warning(
-            '%s is_main_process_only is set to True, logging only from the main process.%s',
+            '%s is_main_process_only is set to True, logging only from the main (RANK==0) process.%s',
            GREEN,
            RESET,
        )
@@ -0,0 +1,648 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import math
+from typing import Any
+
+import numpy as np
+import torch
+import torch.nn as nn
+
+from torch.nn.attention.flex_attention import create_block_mask, flex_attention
+from torch.nn.attention.flex_attention import BlockMask
+# wan 1.3B model has a weird channel / head configurations and require max-autotune to work with flexattention
+# see https://github.com/pytorch/pytorch/issues/133254
+# change to default for other models
+flex_attention = torch.compile(
+    flex_attention, dynamic=False, mode="max-autotune-no-cudagraphs")
+import torch.distributed as dist
+
+import fastvideo.envs as envs
+from fastvideo.attention import (DistributedAttention,
+                                 LocalAttention)
+from fastvideo.configs.models.dits import WanVideoConfig
+from fastvideo.distributed.parallel_state import get_sp_world_size
+from fastvideo.forward_context import get_forward_context
+from fastvideo.layers.layernorm import (FP32LayerNorm, LayerNormScaleShift,
+                                        RMSNorm, ScaleResidual,
+                                        ScaleResidualLayerNormScaleShift)
+from fastvideo.layers.linear import ReplicatedLinear
+from fastvideo.layers.mlp import MLP
+from fastvideo.layers.rotary_embedding import (_apply_rotary_emb,
+                                               get_rotary_pos_embed)
+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, current_platform
+
+logger = init_logger(__name__)
+class CausalWanSelfAttention(nn.Module):
+
+    def __init__(self,
+                 dim: int,
+                 num_heads: int,
+                 local_attn_size: int = -1,
+                 sink_size: int = 0,
+                 qk_norm=True,
+                 eps=1e-6,
+                 parallel_attention=False) -> None:
+        assert dim % num_heads == 0
+        super().__init__()
+        self.dim = dim
+        self.num_heads = num_heads
+        self.head_dim = dim // num_heads
+        self.local_attn_size = local_attn_size
+        self.sink_size = sink_size
+        self.qk_norm = qk_norm
+        self.eps = eps
+        self.parallel_attention = parallel_attention
+        self.max_attention_size = 32760 if local_attn_size == -1 else local_attn_size * 1560
+
+        # Scaled dot product attention
+        self.attn = LocalAttention(
+            num_heads=num_heads,
+            head_size=self.head_dim,
+            dropout_rate=0,
+            softmax_scale=None,
+            causal=False,
+            supported_attention_backends=(AttentionBackendEnum.FLASH_ATTN,
+                                          AttentionBackendEnum.TORCH_SDPA))
+
+    def forward(self, 
+                q: torch.Tensor,
+                k: torch.Tensor,
+                v: torch.Tensor,
+                freqs_cis: tuple[torch.Tensor, torch.Tensor],
+                block_mask: BlockMask,
+                kv_cache: dict | None = None,
+                current_start: int = 0,
+                cache_start: int | None = None):
+        r"""
+        Args:
+            x(Tensor): Shape [B, L, num_heads, C / num_heads]
+            seq_lens(Tensor): Shape [B]
+            grid_sizes(Tensor): Shape [B, 3], the second dimension contains (F, H, W)
+            freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2]
+        """
+        if cache_start is None:
+            cache_start = current_start
+
+        cos, sin = freqs_cis
+        roped_query = _apply_rotary_emb(q, cos, sin, is_neox_style=False).type_as(v)
+        roped_key = _apply_rotary_emb(k, cos, sin, is_neox_style=False).type_as(v)
+
+        if kv_cache is None:
+            # Padding for flex attention
+            padded_length = math.ceil(q.shape[1] / 128) * 128 - q.shape[1]
+            padded_roped_query = torch.cat(
+                [roped_query,
+                    torch.zeros([q.shape[0], padded_length, q.shape[2], q.shape[3]],
+                                device=q.device, dtype=v.dtype)],
+                dim=1
+            )
+
+            padded_roped_key = torch.cat(
+                [roped_key, torch.zeros([k.shape[0], padded_length, k.shape[2], k.shape[3]],
+                                        device=k.device, dtype=v.dtype)],
+                dim=1
+            )
+
+            padded_v = torch.cat(
+                [v, torch.zeros([v.shape[0], padded_length, v.shape[2], v.shape[3]],
+                                device=v.device, dtype=v.dtype)],
+                dim=1
+            )
+
+            x = flex_attention(
+                query=padded_roped_query.transpose(2, 1),
+                key=padded_roped_key.transpose(2, 1),
+                value=padded_v.transpose(2, 1),
+                block_mask=block_mask
+            )[:, :, :-padded_length].transpose(2, 1)
+        else:
+            frame_seqlen = q.shape[1]
+            current_end = current_start + roped_query.shape[1]
+            sink_tokens = self.sink_size * frame_seqlen
+            # If we are using local attention and the current KV cache size is larger than the local attention size, we need to truncate the KV cache
+            kv_cache_size = kv_cache["k"].shape[1]
+            num_new_tokens = roped_query.shape[1]
+            if self.local_attn_size != -1 and (current_end > kv_cache["global_end_index"].item()) and (
+                    num_new_tokens + kv_cache["local_end_index"].item() > kv_cache_size):
+                # Calculate the number of new tokens added in this step
+                # Shift existing cache content left to discard oldest tokens
+                # Clone the source slice to avoid overlapping memory error
+                num_evicted_tokens = num_new_tokens + kv_cache["local_end_index"].item() - kv_cache_size
+                num_rolled_tokens = kv_cache["local_end_index"].item() - num_evicted_tokens - sink_tokens
+                kv_cache["k"][:, sink_tokens:sink_tokens + num_rolled_tokens] = \
+                    kv_cache["k"][:, sink_tokens + num_evicted_tokens:sink_tokens + num_evicted_tokens + num_rolled_tokens].clone()
+                kv_cache["v"][:, sink_tokens:sink_tokens + num_rolled_tokens] = \
+                    kv_cache["v"][:, sink_tokens + num_evicted_tokens:sink_tokens + num_evicted_tokens + num_rolled_tokens].clone()
+                # Insert the new keys/values at the end
+                local_end_index = kv_cache["local_end_index"].item() + current_end - \
+                    kv_cache["global_end_index"].item() - num_evicted_tokens
+                local_start_index = local_end_index - num_new_tokens
+                kv_cache["k"][:, local_start_index:local_end_index] = roped_key
+                kv_cache["v"][:, local_start_index:local_end_index] = v
+            else:
+                # Assign new keys/values directly up to current_end
+                local_end_index = kv_cache["local_end_index"].item() + current_end - kv_cache["global_end_index"].item()
+                local_start_index = local_end_index - num_new_tokens
+                kv_cache["k"][:, local_start_index:local_end_index] = roped_key
+                kv_cache["v"][:, local_start_index:local_end_index] = v
+            x = self.attn(
+                roped_query,
+                kv_cache["k"][:, max(0, local_end_index - self.max_attention_size):local_end_index],
+                kv_cache["v"][:, max(0, local_end_index - self.max_attention_size):local_end_index]
+            )
+            kv_cache["global_end_index"].fill_(current_end)
+            kv_cache["local_end_index"].fill_(local_end_index)
+
+        return x
+
+class CausalWanTransformerBlock(nn.Module):
+
+    def __init__(self,
+                 dim: int,
+                 ffn_dim: int,
+                 num_heads: int,
+                 local_attn_size: int = -1,
+                 sink_size: int = 0,
+                 qk_norm: str = "rms_norm_across_heads",
+                 cross_attn_norm: bool = False,
+                 eps: float = 1e-6,
+                 added_kv_proj_dim: int | None = None,
+                 supported_attention_backends: tuple[AttentionBackendEnum, ...] | None = None,
+                 prefix: str = ""):
+        super().__init__()
+
+        # 1. Self-attention
+        self.norm1 = FP32LayerNorm(dim, eps, elementwise_affine=False)
+        self.to_q = ReplicatedLinear(dim, dim, bias=True)
+        self.to_k = ReplicatedLinear(dim, dim, bias=True)
+        self.to_v = ReplicatedLinear(dim, dim, bias=True)
+
+        self.to_out = ReplicatedLinear(dim, dim, bias=True)
+        self.attn1 = CausalWanSelfAttention(
+            dim,
+            num_heads,
+            local_attn_size=local_attn_size,
+            sink_size=sink_size,
+            qk_norm=qk_norm,
+            eps=eps)
+        self.hidden_dim = dim
+        self.num_attention_heads = num_heads
+        self.local_attn_size = local_attn_size
+        dim_head = dim // num_heads
+        if qk_norm == "rms_norm":
+            self.norm_q = RMSNorm(dim_head, eps=eps)
+            self.norm_k = RMSNorm(dim_head, eps=eps)
+        elif qk_norm == "rms_norm_across_heads":
+            # LTX applies qk norm across all heads
+            self.norm_q = RMSNorm(dim, eps=eps)
+            self.norm_k = RMSNorm(dim, eps=eps)
+        else:
+            print("QK Norm type not supported")
+            raise Exception
+        assert cross_attn_norm is True
+        self.self_attn_residual_norm = ScaleResidualLayerNormScaleShift(
+            dim,
+            norm_type="layer",
+            eps=eps,
+            elementwise_affine=True,
+            dtype=torch.float32,
+            compute_dtype=torch.float32)
+
+        # 2. Cross-attention
+        # Only T2V for now
+        self.attn2 = WanT2VCrossAttention(dim,
+                                            num_heads,
+                                            qk_norm=qk_norm,
+                                            eps=eps)
+        self.cross_attn_residual_norm = ScaleResidualLayerNormScaleShift(
+            dim,
+            norm_type="layer",
+            eps=eps,
+            elementwise_affine=False,
+            dtype=torch.float32,
+            compute_dtype=torch.float32)
+
+        # 3. Feed-forward
+        self.ffn = MLP(dim, ffn_dim, act_type="gelu_pytorch_tanh")
+        self.mlp_residual = ScaleResidual()
+
+        self.scale_shift_table = nn.Parameter(torch.randn(1, 6, dim) / dim**0.5)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor,
+        temb: torch.Tensor,
+        freqs_cis: tuple[torch.Tensor, torch.Tensor],
+        block_mask: BlockMask,
+        kv_cache: dict | None = None,
+        crossattn_cache: dict | None = None,
+        current_start: int = 0,
+        cache_start: int | None = None,
+    ) -> torch.Tensor:
+        if hidden_states.dim() == 4:
+            hidden_states = hidden_states.squeeze(1)
+        bs, seq_length, _ = hidden_states.shape
+        orig_dtype = hidden_states.dtype
+        # assert orig_dtype != torch.float32
+        e = self.scale_shift_table + temb.float()
+        shift_msa, scale_msa, gate_msa, c_shift_msa, c_scale_msa, c_gate_msa = e.chunk(
+            6, dim=1)
+        assert shift_msa.dtype == torch.float32
+
+        # 1. Self-attention
+        norm_hidden_states = (self.norm1(hidden_states.float()) *
+                              (1 + scale_msa) + shift_msa).to(orig_dtype)
+        query, _ = self.to_q(norm_hidden_states)
+        key, _ = self.to_k(norm_hidden_states)
+        value, _ = self.to_v(norm_hidden_states)
+
+        if self.norm_q is not None:
+            query = self.norm_q(query)
+        if self.norm_k is not None:
+            key = self.norm_k(key)
+
+        query = query.squeeze(1).unflatten(2, (self.num_attention_heads, -1))
+        key = key.squeeze(1).unflatten(2, (self.num_attention_heads, -1))
+        value = value.squeeze(1).unflatten(2, (self.num_attention_heads, -1))
+
+        attn_output = self.attn1(query, key, value, freqs_cis, block_mask, kv_cache, current_start, cache_start)
+        attn_output = attn_output.flatten(2)
+        attn_output, _ = self.to_out(attn_output)
+        attn_output = attn_output.squeeze(1)
+
+        null_shift = null_scale = torch.tensor([0], device=hidden_states.device)
+        norm_hidden_states, hidden_states = self.self_attn_residual_norm(
+            hidden_states, attn_output, gate_msa, null_shift, null_scale)
+        norm_hidden_states, hidden_states = norm_hidden_states.to(
+            orig_dtype), hidden_states.to(orig_dtype)
+
+        # 2. Cross-attention
+        attn_output = self.attn2(norm_hidden_states,
+                                 context=encoder_hidden_states,
+                                 context_lens=None,
+                                 crossattn_cache=crossattn_cache)
+        norm_hidden_states, hidden_states = self.cross_attn_residual_norm(
+            hidden_states, attn_output, 1, c_shift_msa, c_scale_msa)
+        norm_hidden_states, hidden_states = norm_hidden_states.to(
+            orig_dtype), hidden_states.to(orig_dtype)
+
+        # 3. Feed-forward
+        ff_output = self.ffn(norm_hidden_states)
+        hidden_states = self.mlp_residual(hidden_states, ff_output, c_gate_msa)
+        hidden_states = hidden_states.to(orig_dtype)
+
+        return hidden_states
+
+class CausalWanTransformer3DModel(BaseDiT):
+    _fsdp_shard_conditions = WanVideoConfig()._fsdp_shard_conditions
+    _compile_conditions = WanVideoConfig()._compile_conditions
+    _supported_attention_backends = WanVideoConfig(
+    )._supported_attention_backends
+    param_names_mapping = WanVideoConfig().param_names_mapping
+    reverse_param_names_mapping = WanVideoConfig().reverse_param_names_mapping
+    lora_param_names_mapping = WanVideoConfig().lora_param_names_mapping
+
+    def __init__(self, config: WanVideoConfig, hf_config: dict[str,
+                                                               Any]) -> None:
+        super().__init__(config=config, hf_config=hf_config)
+
+        inner_dim = config.num_attention_heads * config.attention_head_dim
+        self.hidden_size = config.hidden_size
+        self.num_attention_heads = config.num_attention_heads
+        self.attention_head_dim = config.attention_head_dim
+        self.in_channels = config.in_channels
+        self.out_channels = config.out_channels
+        self.num_channels_latents = config.num_channels_latents
+        self.patch_size = config.patch_size
+        self.text_len = config.text_len
+        self.local_attn_size = config.local_attn_size
+
+        # 1. Patch & position embedding
+        self.patch_embedding = PatchEmbed(in_chans=config.in_channels,
+                                          embed_dim=inner_dim,
+                                          patch_size=config.patch_size,
+                                          flatten=False)
+
+        # 2. Condition embeddings
+        self.condition_embedder = WanTimeTextImageEmbedding(
+            dim=inner_dim,
+            time_freq_dim=config.freq_dim,
+            text_embed_dim=config.text_dim,
+            image_embed_dim=config.image_dim,
+        )
+
+        # 3. Transformer blocks
+        self.blocks = nn.ModuleList([
+            CausalWanTransformerBlock(inner_dim,
+                              config.ffn_dim,
+                              config.num_attention_heads,
+                              config.local_attn_size,
+                              config.sink_size,
+                              config.qk_norm,
+                              config.cross_attn_norm,
+                              config.eps,
+                              config.added_kv_proj_dim,
+                              self._supported_attention_backends,
+                              prefix=f"{config.prefix}.blocks.{i}")
+            for i in range(config.num_layers)
+        ])
+
+        # 4. Output norm & projection
+        self.norm_out = LayerNormScaleShift(inner_dim,
+                                            norm_type="layer",
+                                            eps=config.eps,
+                                            elementwise_affine=False,
+                                            dtype=torch.float32,
+                                            compute_dtype=torch.float32)
+        self.proj_out = nn.Linear(
+            inner_dim, config.out_channels * math.prod(config.patch_size))
+        self.scale_shift_table = nn.Parameter(
+            torch.randn(1, 2, inner_dim) / inner_dim**0.5)
+
+        self.gradient_checkpointing = False
+
+        # Causal-specific
+        self.block_mask = None
+        self.num_frame_per_block = 1
+        self.independent_first_frame = False
+
+        self.__post_init__()
+
+    @staticmethod
+    def _prepare_blockwise_causal_attn_mask(
+        device: torch.device | str, num_frames: int = 21,
+        frame_seqlen: int = 1560, num_frame_per_block=1, local_attn_size=-1
+    ) -> BlockMask:
+        """
+        we will divide the token sequence into the following format
+        [1 latent frame] [1 latent frame] ... [1 latent frame]
+        We use flexattention to construct the attention mask
+        """
+        total_length = num_frames * frame_seqlen
+
+        # we do right padding to get to a multiple of 128
+        padded_length = math.ceil(total_length / 128) * 128 - total_length
+
+        ends = torch.zeros(total_length + padded_length,
+                           device=device, dtype=torch.long)
+
+        # Block-wise causal mask will attend to all elements that are before the end of the current chunk
+        frame_indices = torch.arange(
+            start=0,
+            end=total_length,
+            step=frame_seqlen * num_frame_per_block,
+            device=device
+        )
+
+        for tmp in frame_indices:
+            ends[tmp:tmp + frame_seqlen * num_frame_per_block] = tmp + \
+                frame_seqlen * num_frame_per_block
+
+        def attention_mask(b, h, q_idx, kv_idx):
+            if local_attn_size == -1:
+                return (kv_idx < ends[q_idx]) | (q_idx == kv_idx)
+            else:
+                return ((kv_idx < ends[q_idx]) & (kv_idx >= (ends[q_idx] - local_attn_size * frame_seqlen))) | (q_idx == kv_idx)
+            # return ((kv_idx < total_length) & (q_idx < total_length))  | (q_idx == kv_idx) # bidirectional mask
+
+        block_mask = create_block_mask(attention_mask, B=None, H=None, Q_LEN=total_length + padded_length,
+                                       KV_LEN=total_length + padded_length, _compile=False, device=device)
+
+        if not dist.is_initialized() or dist.get_rank() == 0:
+            print(
+                f" cache a block wise causal mask with block size of {num_frame_per_block} frames")
+            print(block_mask)
+
+        # import imageio
+        # import numpy as np
+        # from torch.nn.attention.flex_attention import create_mask
+
+        # mask = create_mask(attention_mask, B=None, H=None, Q_LEN=total_length +
+        #                    padded_length, KV_LEN=total_length + padded_length, device=device)
+        # import cv2
+        # mask = cv2.resize(mask[0, 0].cpu().float().numpy(), (1024, 1024))
+        # imageio.imwrite("mask_%d.jpg" % (0), np.uint8(255. * mask))
+
+        return block_mask
+
+    def _forward_inference(
+                self,
+                hidden_states: torch.Tensor,
+                encoder_hidden_states: torch.Tensor | list[torch.Tensor],
+                timestep: torch.LongTensor,
+                encoder_hidden_states_image: torch.Tensor | list[torch.Tensor]
+                | None = None,
+                kv_cache: dict = None,
+                crossattn_cache: dict = None,
+                current_start: int = 0,
+                cache_start: int = 0,
+                start_frame: int = 0,
+                **kwargs) -> torch.Tensor:
+        r"""
+        Run the diffusion model with kv caching.
+        See Algorithm 2 of CausVid paper https://arxiv.org/abs/2412.07772 for details.
+        This function will be run for num_frame times.
+        Process the latent frames one by one (1560 tokens each)
+        """
+
+        orig_dtype = hidden_states.dtype
+        if not isinstance(encoder_hidden_states, torch.Tensor):
+            encoder_hidden_states = encoder_hidden_states[0]
+        if isinstance(encoder_hidden_states_image,
+                      list) and len(encoder_hidden_states_image) > 0:
+            encoder_hidden_states_image = encoder_hidden_states_image[0]
+        else:
+            encoder_hidden_states_image = None
+
+        batch_size, num_channels, num_frames, height, width = hidden_states.shape
+        p_t, p_h, p_w = self.patch_size
+        post_patch_num_frames = num_frames // p_t
+        post_patch_height = height // p_h
+        post_patch_width = width // p_w
+
+        # Get rotary embeddings
+        d = self.hidden_size // self.num_attention_heads
+        rope_dim_list = [d - 4 * (d // 6), 2 * (d // 6), 2 * (d // 6)]
+        freqs_cos, freqs_sin = get_rotary_pos_embed(
+            (post_patch_num_frames * get_sp_world_size(), post_patch_height,
+             post_patch_width),
+            self.hidden_size,
+            self.num_attention_heads,
+            rope_dim_list,
+            dtype=torch.float32 if current_platform.is_mps() else torch.float64,
+            rope_theta=10000,
+            start_frame=start_frame # Assume that start_frame is 0 when kv_cache is None
+        )
+        freqs_cos = freqs_cos.to(hidden_states.device)
+        freqs_sin = freqs_sin.to(hidden_states.device)
+        freqs_cis = (freqs_cos.float(),
+                     freqs_sin.float()) if freqs_cos is not None else None
+
+        hidden_states = self.patch_embedding(hidden_states)
+        hidden_states = hidden_states.flatten(2).transpose(1, 2)
+
+        temb, timestep_proj, encoder_hidden_states, encoder_hidden_states_image = self.condition_embedder(
+            timestep, encoder_hidden_states, encoder_hidden_states_image)
+        timestep_proj = timestep_proj.unflatten(1, (6, -1))
+
+        if encoder_hidden_states_image is not None:
+            encoder_hidden_states = torch.concat(
+                [encoder_hidden_states_image, encoder_hidden_states], dim=1)
+
+        encoder_hidden_states = encoder_hidden_states.to(
+            orig_dtype) if current_platform.is_mps(
+            ) else encoder_hidden_states  # cast to orig_dtype for MPS
+
+        assert encoder_hidden_states.dtype == orig_dtype
+
+        # 4. Transformer blocks
+        for block_index, block in enumerate(self.blocks):
+            if torch.is_grad_enabled() and self.gradient_checkpointing:
+                causal_kwargs = {
+                    "kv_cache": kv_cache[block_index],
+                    "current_start": current_start,
+                    "cache_start": cache_start,
+                    "block_mask": self.block_mask
+                }
+                hidden_states = self._gradient_checkpointing_func(
+                    block, hidden_states, encoder_hidden_states,
+                    timestep_proj, freqs_cis,
+                    **causal_kwargs)
+            else:
+                causal_kwargs = {
+                    "kv_cache": kv_cache[block_index],
+                    "crossattn_cache": crossattn_cache[block_index],
+                    "current_start": current_start,
+                    "cache_start": cache_start,
+                    "block_mask": self.block_mask
+                }
+                hidden_states = block(hidden_states, encoder_hidden_states,
+                                        timestep_proj, freqs_cis,
+                                        **causal_kwargs)
+
+        # 5. Output norm, projection & unpatchify
+        shift, scale = (self.scale_shift_table + temb.unsqueeze(1)).chunk(2,
+                                                                          dim=1)
+        hidden_states = self.norm_out(hidden_states, shift, scale)
+        hidden_states = self.proj_out(hidden_states)
+
+        hidden_states = hidden_states.reshape(batch_size, post_patch_num_frames,
+                                              post_patch_height,
+                                              post_patch_width, p_t, p_h, p_w,
+                                              -1)
+        hidden_states = hidden_states.permute(0, 7, 1, 4, 2, 5, 3, 6)
+        output = hidden_states.flatten(6, 7).flatten(4, 5).flatten(2, 3)
+
+        return output
+
+    def _forward_train(self,
+                hidden_states: torch.Tensor,
+                encoder_hidden_states: torch.Tensor | list[torch.Tensor],
+                timestep: torch.LongTensor,
+                encoder_hidden_states_image: torch.Tensor | list[torch.Tensor]
+                | None = None,
+                start_frame: int = 0,
+                **kwargs) -> torch.Tensor:
+
+        orig_dtype = hidden_states.dtype
+        if not isinstance(encoder_hidden_states, torch.Tensor):
+            encoder_hidden_states = encoder_hidden_states[0]
+        if isinstance(encoder_hidden_states_image,
+                      list) and len(encoder_hidden_states_image) > 0:
+            encoder_hidden_states_image = encoder_hidden_states_image[0]
+        else:
+            encoder_hidden_states_image = None
+
+        batch_size, num_channels, num_frames, height, width = hidden_states.shape
+        p_t, p_h, p_w = self.patch_size
+        post_patch_num_frames = num_frames // p_t
+        post_patch_height = height // p_h
+        post_patch_width = width // p_w
+
+        # Get rotary embeddings
+        d = self.hidden_size // self.num_attention_heads
+        rope_dim_list = [d - 4 * (d // 6), 2 * (d // 6), 2 * (d // 6)]
+        freqs_cos, freqs_sin = get_rotary_pos_embed(
+            (post_patch_num_frames * get_sp_world_size(), post_patch_height,
+             post_patch_width),
+            self.hidden_size,
+            self.num_attention_heads,
+            rope_dim_list,
+            dtype=torch.float32 if current_platform.is_mps() else torch.float64,
+            rope_theta=10000,
+            start_frame=start_frame
+        )
+        freqs_cos = freqs_cos.to(hidden_states.device)
+        freqs_sin = freqs_sin.to(hidden_states.device)
+        freqs_cis = (freqs_cos.float(),
+                     freqs_sin.float()) if freqs_cos is not None else None
+
+        # Construct blockwise causal attn mask
+        if self.block_mask is None:
+            self.block_mask = self._prepare_blockwise_causal_attn_mask(
+                device=hidden_states.device,
+                num_frames=num_frames,
+                frame_seqlen=post_patch_height * post_patch_width,
+                num_frame_per_block=self.num_frame_per_block,
+                local_attn_size=self.local_attn_size
+            )
+
+        hidden_states = self.patch_embedding(hidden_states)
+        hidden_states = hidden_states.flatten(2).transpose(1, 2)
+
+        temb, timestep_proj, encoder_hidden_states, encoder_hidden_states_image = self.condition_embedder(
+            timestep, encoder_hidden_states, encoder_hidden_states_image)
+        timestep_proj = timestep_proj.unflatten(1, (6, -1))
+
+        if encoder_hidden_states_image is not None:
+            encoder_hidden_states = torch.concat(
+                [encoder_hidden_states_image, encoder_hidden_states], dim=1)
+
+        encoder_hidden_states = encoder_hidden_states.to(
+            orig_dtype) if current_platform.is_mps(
+            ) else encoder_hidden_states  # cast to orig_dtype for MPS
+
+        assert encoder_hidden_states.dtype == orig_dtype
+
+        # 4. Transformer blocks
+        if torch.is_grad_enabled() and self.gradient_checkpointing:
+            for block in self.blocks:
+                hidden_states = self._gradient_checkpointing_func(
+                    block, hidden_states, encoder_hidden_states,
+                    timestep_proj, freqs_cis,
+                    block_mask=self.block_mask)
+        else:
+            for block in self.blocks:
+                hidden_states = block(hidden_states, encoder_hidden_states,
+                                        timestep_proj, freqs_cis,
+                                        block_mask=self.block_mask)
+
+        # 5. Output norm, projection & unpatchify
+        shift, scale = (self.scale_shift_table + temb.unsqueeze(1)).chunk(2,
+                                                                          dim=1)
+        hidden_states = self.norm_out(hidden_states, shift, scale)
+        hidden_states = self.proj_out(hidden_states)
+
+        hidden_states = hidden_states.reshape(batch_size, post_patch_num_frames,
+                                              post_patch_height,
+                                              post_patch_width, p_t, p_h, p_w,
+                                              -1)
+        hidden_states = hidden_states.permute(0, 7, 1, 4, 2, 5, 3, 6)
+        output = hidden_states.flatten(6, 7).flatten(4, 5).flatten(2, 3)
+
+        return output
+
+    def forward(
+        self,
+        *args,
+        **kwargs
+    ):
+        if kwargs.get('kv_cache', None) is not None:
+            return self._forward_inference(*args, **kwargs)
+        else:
+            return self._forward_train(*args, **kwargs)
@@ -81,8 +81,9 @@ class WanTimeTextImageEmbedding(nn.Module):
        timestep: torch.Tensor,
        encoder_hidden_states: torch.Tensor,
        encoder_hidden_states_image: torch.Tensor | None = None,
+        timestep_seq_len: int | None = None,
    ):
-        temb = self.time_embedder(timestep)
+        temb = self.time_embedder(timestep, timestep_seq_len)
        timestep_proj = self.time_modulation(temb)

        encoder_hidden_states = self.text_embedder(encoder_hidden_states)
@@ -145,7 +146,7 @@ class WanSelfAttention(nn.Module):

 class WanT2VCrossAttention(WanSelfAttention):

-    def forward(self, x, context, context_lens):
+    def forward(self, x, context, context_lens, crossattn_cache=None):
        r"""
        Args:
            x(Tensor): Shape [B, L1, C]
@@ -156,8 +157,20 @@ class WanT2VCrossAttention(WanSelfAttention):

        # compute query, key, value
        q = self.norm_q(self.to_q(x)[0]).view(b, -1, n, d)
-        k = self.norm_k(self.to_k(context)[0]).view(b, -1, n, d)
-        v = self.to_v(context)[0].view(b, -1, n, d)
+
+        if crossattn_cache is not None:
+            if not crossattn_cache["is_init"]:
+                crossattn_cache["is_init"] = True
+                k = self.norm_k(self.to_k(context)[0]).view(b, -1, n, d)
+                v = self.to_v(context)[0].view(b, -1, n, d)
+                crossattn_cache["k"] = k
+                crossattn_cache["v"] = v
+            else:
+                k = crossattn_cache["k"]
+                v = crossattn_cache["v"]
+        else:
+            k = self.norm_k(self.to_k(context)[0]).view(b, -1, n, d)
+            v = self.to_v(context)[0].view(b, -1, n, d)

        # compute attention
        x = self.attn(q, k, v)
@@ -307,9 +320,24 @@ class WanTransformerBlock(nn.Module):
        bs, seq_length, _ = hidden_states.shape
        orig_dtype = hidden_states.dtype
        # assert orig_dtype != torch.float32
-        e = self.scale_shift_table + temb.float()
-        shift_msa, scale_msa, gate_msa, c_shift_msa, c_scale_msa, c_gate_msa = e.chunk(
-            6, dim=1)
+
+        if temb.dim() == 4:
+            # temb: batch_size, seq_len, 6, inner_dim (wan2.2 ti2v)
+            shift_msa, scale_msa, gate_msa, c_shift_msa, c_scale_msa, c_gate_msa = (
+                self.scale_shift_table.unsqueeze(0) + temb.float()
+            ).chunk(6, dim=2)
+            # batch_size, seq_len, 1, inner_dim
+            shift_msa = shift_msa.squeeze(2)
+            scale_msa = scale_msa.squeeze(2)
+            gate_msa = gate_msa.squeeze(2)
+            c_shift_msa = c_shift_msa.squeeze(2)
+            c_scale_msa = c_scale_msa.squeeze(2)
+            c_gate_msa = c_gate_msa.squeeze(2)
+        else:
+            # temb: batch_size, 6, inner_dim (wan2.1/wan2.2 14B)
+            e = self.scale_shift_table + temb.float()
+            shift_msa, scale_msa, gate_msa, c_shift_msa, c_scale_msa, c_gate_msa = e.chunk(
+                6, dim=1)
        assert shift_msa.dtype == torch.float32

        # 1. Self-attention
@@ -637,9 +665,21 @@ class WanTransformer3DModel(CachableDiT):
        hidden_states = self.patch_embedding(hidden_states)
        hidden_states = hidden_states.flatten(2).transpose(1, 2)

+        # timestep shape: batch_size, or batch_size, seq_len (wan 2.2 ti2v)
+        if timestep.dim() == 2:
+            ts_seq_len = timestep.shape[1]
+            timestep = timestep.flatten()  # batch_size * seq_len
+        else:
+            ts_seq_len = None
+
        temb, timestep_proj, encoder_hidden_states, encoder_hidden_states_image = self.condition_embedder(
-            timestep, encoder_hidden_states, encoder_hidden_states_image)
-        timestep_proj = timestep_proj.unflatten(1, (6, -1))
+            timestep, encoder_hidden_states, encoder_hidden_states_image, timestep_seq_len=ts_seq_len)
+        if ts_seq_len is not None:
+            # batch_size, seq_len, 6, inner_dim
+            timestep_proj = timestep_proj.unflatten(2, (6, -1))
+        else:
+            # batch_size, 6, inner_dim
+            timestep_proj = timestep_proj.unflatten(1, (6, -1))

        if encoder_hidden_states_image is not None:
            encoder_hidden_states = torch.concat(
@@ -676,8 +716,15 @@ class WanTransformer3DModel(CachableDiT):
            if enable_teacache:
                self.maybe_cache_states(hidden_states, original_hidden_states)
        # 5. Output norm, projection & unpatchify
-        shift, scale = (self.scale_shift_table + temb.unsqueeze(1)).chunk(2,
-                                                                          dim=1)
+        if temb.dim() == 3:
+            # batch_size, seq_len, inner_dim (wan 2.2 ti2v)
+            shift, scale = (self.scale_shift_table.unsqueeze(0) + temb.unsqueeze(2)).chunk(2, dim=2)
+            shift = shift.squeeze(2)
+            scale = scale.squeeze(2)
+        else:
+            # batch_size, inner_dim
+            shift, scale = (self.scale_shift_table + temb.unsqueeze(1)).chunk(2, dim=1)
+            
        hidden_states = self.norm_out(hidden_states, shift, scale)
        hidden_states = self.proj_out(hidden_states)

@@ -781,3 +828,4 @@ class WanTransformer3DModel(CachableDiT):
            return hidden_states + self.previous_residual_even
        else:
            return hidden_states + self.previous_residual_odd
+        
@@ -25,12 +25,14 @@ _TEXT_TO_VIDEO_DIT_MODELS = {
    "HunyuanVideoTransformer3DModel":
    ("dits", "hunyuanvideo", "HunyuanVideoTransformer3DModel"),
    "WanTransformer3DModel": ("dits", "wanvideo", "WanTransformer3DModel"),
+    "CausalWanTransformer3DModel": ("dits", "causal_wanvideo", "CausalWanTransformer3DModel"),
    "StepVideoModel": ("dits", "stepvideo", "StepVideoModel")
 }

 _IMAGE_TO_VIDEO_DIT_MODELS = {
    # "HunyuanVideoTransformer3DModel": ("dits", "hunyuanvideo", "HunyuanVideoDiT"),
    "WanTransformer3DModel": ("dits", "wanvideo", "WanTransformer3DModel"),
+    "CausalWanTransformer3DModel": ("dits", "causal_wanvideo", "CausalWanTransformer3DModel"),
 }

 _TEXT_ENCODER_MODELS = {
@@ -137,3 +137,24 @@ def modulate(x: torch.Tensor,
    else:
        return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(
            1)  # type: ignore[union-attr]
+
+
+def pred_noise_to_pred_video(pred_noise: torch.Tensor,
+                             noise_input_latent: torch.Tensor,
+                             timestep: torch.Tensor,
+                             scheduler: Any) -> torch.Tensor:
+    """
+    Convert predicted noise to clean latent.
+    """
+    timestep = timestep.expand(noise_input_latent.shape[0])
+    dtype = pred_noise.dtype
+    device = pred_noise.device
+    pred_noise = pred_noise.float().to(device)
+    noise_input_latent = noise_input_latent.float().to(device)
+    sigmas = scheduler.sigmas.float().to(device)
+    timesteps = scheduler.timesteps.float().to(device)
+    timestep_id = torch.argmin(
+        (timesteps.unsqueeze(0) - timestep.unsqueeze(1)).abs(), dim=1)
+    sigma_t = sigmas[timestep_id].reshape(-1, 1, 1, 1)
+    pred_video = noise_input_latent - sigma_t * pred_noise
+    return pred_video.to(dtype)
@@ -0,0 +1,69 @@
+# SPDX-License-Identifier: Apache-2.0
+"""
+Wan causal DMD pipeline implementation.
+
+This module wires the causal DMD denoising stage into the modular pipeline.
+"""
+
+from fastvideo.fastvideo_args import FastVideoArgs
+from fastvideo.logger import init_logger
+from fastvideo.models.schedulers.scheduling_flow_match_euler_discrete import (
+    FlowMatchEulerDiscreteScheduler)
+from fastvideo.pipelines import ComposedPipelineBase, LoRAPipeline
+
+# isort: off
+from fastvideo.pipelines.stages import (ConditioningStage, DecodingStage,
+                                        CausalDMDDenosingStage,
+                                        InputValidationStage,
+                                        LatentPreparationStage,
+                                        TextEncodingStage,
+                                        TimestepPreparationStage)
+# isort: on
+
+logger = init_logger(__name__)
+
+
+class WanCausalDMDPipeline(LoRAPipeline, ComposedPipelineBase):
+
+    _required_config_modules = [
+        "text_encoder", "tokenizer", "vae", "transformer", "scheduler"
+    ]
+
+    def initialize_pipeline(self, fastvideo_args: FastVideoArgs):
+        self.modules["scheduler"] = FlowMatchEulerDiscreteScheduler(
+            shift=fastvideo_args.pipeline_config.flow_shift)
+
+    def create_pipeline_stages(self, fastvideo_args: FastVideoArgs) -> None:
+        """Set up pipeline stages with proper dependency injection."""
+
+        self.add_stage(stage_name="input_validation_stage",
+                       stage=InputValidationStage())
+
+        self.add_stage(stage_name="prompt_encoding_stage",
+                       stage=TextEncodingStage(
+                           text_encoders=[self.get_module("text_encoder")],
+                           tokenizers=[self.get_module("tokenizer")],
+                       ))
+
+        self.add_stage(stage_name="conditioning_stage",
+                       stage=ConditioningStage())
+
+        self.add_stage(stage_name="timestep_preparation_stage",
+                       stage=TimestepPreparationStage(
+                           scheduler=self.get_module("scheduler")))
+
+        self.add_stage(stage_name="latent_preparation_stage",
+                       stage=LatentPreparationStage(
+                           scheduler=self.get_module("scheduler"),
+                           transformer=self.get_module("transformer", None)))
+
+        self.add_stage(stage_name="denoising_stage",
+                       stage=CausalDMDDenosingStage(
+                           transformer=self.get_module("transformer"),
+                           scheduler=self.get_module("scheduler")))
+
+        self.add_stage(stage_name="decoding_stage",
+                       stage=DecodingStage(vae=self.get_module("vae")))
+
+
+EntryClass = WanCausalDMDPipeline
@@ -12,12 +12,10 @@ from fastvideo.pipelines.composed_pipeline_base import ComposedPipelineBase
 from fastvideo.pipelines.lora_pipeline import LoRAPipeline

 # isort: off
-from fastvideo.pipelines.stages import (ImageEncodingStage, ConditioningStage,
-                                        DecodingStage, DmdDenoisingStage,
-                                        EncodingStage, InputValidationStage,
-                                        LatentPreparationStage,
-                                        TextEncodingStage,
-                                        TimestepPreparationStage)
+from fastvideo.pipelines.stages import (
+    ImageEncodingStage, ConditioningStage, DecodingStage, DmdDenoisingStage,
+    ImageVAEEncodingStage, InputValidationStage, LatentPreparationStage,
+    TextEncodingStage, TimestepPreparationStage)
 # isort: on
 from fastvideo.models.schedulers.scheduling_flow_match_euler_discrete import (
    FlowMatchEulerDiscreteScheduler)
@@ -67,7 +65,7 @@ class WanImageToVideoDmdPipeline(LoRAPipeline, ComposedPipelineBase):
                           transformer=self.get_module("transformer")))

        self.add_stage(stage_name="image_latent_preparation_stage",
-                       stage=EncodingStage(vae=self.get_module("vae")))
+                       stage=ImageVAEEncodingStage(vae=self.get_module("vae")))

        self.add_stage(stage_name="denoising_stage",
                       stage=DmdDenoisingStage(
@@ -63,6 +63,7 @@ class WanPipeline(LoRAPipeline, ComposedPipelineBase):
                           transformer=self.get_module("transformer"),
                           transformer_2=self.get_module("transformer_2", None),
                           scheduler=self.get_module("scheduler"),
+                           vae=self.get_module("vae"),
                           pipeline=self))

        self.add_stage(stage_name="decoding_stage",
@@ -282,7 +282,11 @@ class ComposedPipelineBase(ABC):
        for module_name, (transformers_or_diffusers,
                          architecture) in model_index.items():
            if transformers_or_diffusers is None:
-                self.required_config_modules.remove(module_name)
+                logger.warning(
+                    "Module in model_index.json has null value, removing from required_config_modules"
+                )
+                if module_name in self.required_config_modules:
+                    self.required_config_modules.remove(module_name)
                continue
            if module_name not in required_modules:
                logger.info("Skipping module %s", module_name)
@@ -217,3 +217,11 @@ class LoRAPipeline(ComposedPipelineBase):
                layer.disable_lora = True
        logger.info("Rank %d: LoRA adapter %s applied to %d layers", rank,
                    lora_path, adapted_count)
+
+    def merge_lora_weights(self) -> None:
+        for name, layer in self.lora_layers.items():
+            layer.merge_lora_weights()
+
+    def unmerge_lora_weights(self) -> None:
+        for name, layer in self.lora_layers.items():
+            layer.unmerge_lora_weights()
@@ -17,10 +17,47 @@ import torch
 if TYPE_CHECKING:
    from torchcodec.decoders import VideoDecoder

+import time
+from collections import OrderedDict
+
 from fastvideo.attention import AttentionMetadata
 from fastvideo.configs.sample.teacache import TeaCacheParams, WanTeaCacheParams


+class PipelineLoggingInfo:
+    """Simple approach using OrderedDict to track stage metrics."""
+
+    def __init__(self):
+        # OrderedDict preserves insertion order and allows easy access
+        self.stages: OrderedDict[str, dict[str, Any]] = OrderedDict()
+
+    def add_stage_execution_time(self, stage_name: str, execution_time: float):
+        """Add execution time for a stage."""
+        if stage_name not in self.stages:
+            self.stages[stage_name] = {}
+        self.stages[stage_name]['execution_time'] = execution_time
+        self.stages[stage_name]['timestamp'] = time.time()
+
+    def add_stage_metric(self, stage_name: str, metric_name: str, value: Any):
+        """Add any metric for a stage."""
+        if stage_name not in self.stages:
+            self.stages[stage_name] = {}
+        self.stages[stage_name][metric_name] = value
+
+    def get_stage_info(self, stage_name: str) -> dict[str, Any]:
+        """Get all info for a specific stage."""
+        return self.stages.get(stage_name, {})
+
+    def get_execution_order(self) -> list[str]:
+        """Get stages in execution order."""
+        return list(self.stages.keys())
+
+    def get_total_execution_time(self) -> float:
+        """Get total pipeline execution time."""
+        return sum(
+            stage.get('execution_time', 0) for stage in self.stages.values())
+
+
@dataclass
 class ForwardBatch:
    """
@@ -40,7 +77,7 @@ class ForwardBatch:
    # Image inputs
    image_path: str | None = None
    image_embeds: list[torch.Tensor] = field(default_factory=list)
-    pil_image: PIL.Image.Image | None = None
+    pil_image: torch.Tensor | PIL.Image.Image | None = None
    preprocessed_image: torch.Tensor | None = None

    # Text inputs
@@ -132,6 +169,10 @@ class ForwardBatch:
    # VSA parameters
    VSA_sparsity: float = 0.0

+    # Logging info
+    logging_info: PipelineLoggingInfo = field(
+        default_factory=PipelineLoggingInfo)
+
    def __post_init__(self):
        """Initialize dependent fields after dataclass initialization."""

@@ -200,5 +241,5 @@ class TrainingBatch:

@dataclass
 class PreprocessBatch(ForwardBatch):
-    video_loader: list["VideoDecoder"] = field(default_factory=list)
+    video_loader: list["VideoDecoder"] | list[str] = field(default_factory=list)
    video_file_name: list[str] = field(default_factory=list)
@@ -21,10 +21,16 @@ _PIPELINE_NAME_TO_ARCHITECTURE_NAME: dict[str, str] = {
    "WanPipeline": "wan",
    "WanDMDPipeline": "wan",
    "WanImageToVideoPipeline": "wan",
+    "WanCausalDMDPipeline": "wan",
    "StepVideoPipeline": "stepvideo",
    "HunyuanVideoPipeline": "hunyuan",
 }

+_PREPROCESS_WORKLOAD_TYPE_TO_PIPELINE_NAME: dict[WorkloadType, str] = {
+    WorkloadType.I2V: "PreprocessPipelineI2V",
+    WorkloadType.T2V: "PreprocessPipelineT2V",
+}
+

 class PipelineType(str, Enum):
    """
@@ -68,12 +74,8 @@ class _PipelineRegistry:
    def _load_preprocess_pipeline_cls(
            self, workload_type: WorkloadType,
            arch: str) -> type[ComposedPipelineBase] | None:
-        if workload_type == WorkloadType.I2V:
-            pipeline_name = "I2VPreprocessPipeline"
-        elif workload_type == WorkloadType.T2V:
-            pipeline_name = "T2VPreprocessPipeline"
-        else:
-            raise ValueError(f"Invalid workload type: {workload_type.value}")
+        pipeline_name = _PREPROCESS_WORKLOAD_TYPE_TO_PIPELINE_NAME[
+            workload_type]

        return self.pipelines[
            PipelineType.PREPROCESS.value][arch][pipeline_name]
@@ -0,0 +1,762 @@
+# SPDX-License-Identifier: Apache-2.0
+"""
+ODE Trajectory Data Preprocessing pipeline implementation.
+
+This module contains an implementation of the ODE Trajectory Data Preprocessing pipeline
+using the modular pipeline architecture.
+
+Sec 4.3 of CausVid paper: https://arxiv.org/pdf/2412.07772
+"""
+
+import os
+from collections.abc import Iterator
+from typing import Any
+
+import numpy as np
+import pyarrow as pa
+import torch
+from PIL import Image
+from torch.utils.data import DataLoader
+from torchdata.stateful_dataloader import StatefulDataLoader
+from tqdm import tqdm
+
+from fastvideo.configs.sample import SamplingParam
+from fastvideo.dataset import getdataset, gettextdataset
+from fastvideo.dataset.dataloader.schema import pyarrow_schema_ode_trajectory, pyarrow_schema_ode_trajectory_text_only
+from fastvideo.distributed import get_local_torch_device
+from fastvideo.fastvideo_args import FastVideoArgs
+from fastvideo.logger import init_logger
+from fastvideo.utils import shallow_asdict, save_decoded_latents_as_video
+from fastvideo.pipelines.pipeline_batch_info import ForwardBatch
+from fastvideo.pipelines.preprocess.preprocess_pipeline_base import (
+    BasePreprocessPipeline)
+from fastvideo.pipelines.stages import (DenoisingStage, ImageVAEEncodingStage,
+                                        InputValidationStage,
+                                        LatentPreparationStage,
+                                        TextEncodingStage,
+                                        TimestepPreparationStage,
+                                        DecodingStage)
+
+logger = init_logger(__name__)
+
+
+class PreprocessPipeline_ODE_Trajectory(BasePreprocessPipeline):
+    """ODE Trajectory preprocessing pipeline implementation."""
+
+    _required_config_modules = [
+        "text_encoder", "tokenizer", "vae", "transformer", "scheduler"
+    ]
+    preprocess_dataloader: StatefulDataLoader
+    preprocess_loader_iter: Iterator[dict[str, Any]]
+
+    def get_schema_fields(self):
+        """Get the schema fields for ODE Trajectory pipeline."""
+        # Check if we're using text dataset by checking if the dataset is TextDataset
+        if hasattr(self, 'preprocess_dataloader') and hasattr(self.preprocess_dataloader.dataset, '_process_text_data'):
+            return [f.name for f in pyarrow_schema_ode_trajectory_text_only]
+        return [f.name for f in pyarrow_schema_ode_trajectory]
+
+    def create_pipeline_stages(self, fastvideo_args: FastVideoArgs):
+        """Set up pipeline stages with proper dependency injection."""
+        self.add_stage(stage_name="input_validation_stage",
+                       stage=InputValidationStage())
+        self.add_stage(stage_name="prompt_encoding_stage",
+                       stage=TextEncodingStage(
+                           text_encoders=[self.get_module("text_encoder")],
+                           tokenizers=[self.get_module("tokenizer")],
+                       ))
+        self.add_stage(stage_name="vae_encoding_stage",
+                       stage=ImageVAEEncodingStage(
+                           vae=self.get_module("vae"), ))
+        self.add_stage(stage_name="timestep_preparation_stage",
+                       stage=TimestepPreparationStage(
+                           scheduler=self.get_module("scheduler")))
+        self.add_stage(stage_name="latent_preparation_stage",
+                       stage=LatentPreparationStage(
+                           scheduler=self.get_module("scheduler"),
+                           transformer=self.get_module("transformer", None)))
+        self.add_stage(stage_name="denoising_stage",
+                       stage=DenoisingStage(
+                           transformer=self.get_module("transformer"),
+                           transformer_2=self.get_module("transformer_2", None),
+                           scheduler=self.get_module("scheduler"),
+                           pipeline=self,
+                       ))
+        self.add_stage(stage_name="decoding_stage",
+                       stage=DecodingStage(vae=self.get_module("vae")))
+
+    def preprocess_video_and_text_and_trajectory(self,
+                                                 fastvideo_args: FastVideoArgs,
+                                                 args):
+
+        for batch_idx, data in enumerate(self.pbar):
+            if data is None:
+                continue
+
+            with torch.inference_mode():
+                # Filter out invalid samples (those with all zeros)
+                valid_indices = []
+                for i, pixel_values in enumerate(data["pixel_values"]):
+                    if not torch.all(
+                            pixel_values == 0):  # Check if all values are zero
+                        valid_indices.append(i)
+                self.num_processed_samples += len(valid_indices)
+
+                if not valid_indices:
+                    continue
+
+                # Create new batch with only valid samples
+                valid_data = {
+                    "pixel_values":
+                    torch.stack(
+                        [data["pixel_values"][i] for i in valid_indices]),
+                    "text": [data["text"][i] for i in valid_indices],
+                    "path": [data["path"][i] for i in valid_indices],
+                    "fps": [data["fps"][i] for i in valid_indices],
+                    "duration": [data["duration"][i] for i in valid_indices],
+                }
+
+                # VAE
+                with torch.autocast("cuda", dtype=torch.float32):
+                    latents = self.get_module("vae").encode(
+                        valid_data["pixel_values"].to(
+                            get_local_torch_device())).mean
+
+                # Get extra features if needed
+                extra_features = self.get_extra_features(
+                    valid_data, fastvideo_args)
+
+                batch_captions = valid_data["text"]
+                # Encode text using the standalone TextEncodingStage API
+                prompt_embeds_list, prompt_masks_list = self.prompt_encoding_stage.encode_text(
+                    batch_captions,
+                    fastvideo_args,
+                    encoder_index=[0],
+                    return_attention_mask=True,
+                )
+                prompt_embeds = prompt_embeds_list[0]
+                prompt_attention_masks = prompt_masks_list[0]
+                assert prompt_embeds.shape[0] == prompt_attention_masks.shape[0]
+
+                # # Get sequence lengths from attention masks (number of 1s)
+                # seq_lens = prompt_attention_mask.sum(dim=1)
+
+                # non_padded_embeds = []
+                # non_padded_masks = []
+
+                # # Process each item in the batch
+                # for i in range(prompt_embeds.size(0)):
+                #     seq_len = seq_lens[i].item()
+                #     # Slice the embeddings and masks to keep only non-padding parts
+                #     non_padded_embeds.append(prompt_embeds[i, :seq_len])
+                #     non_padded_masks.append(prompt_attention_mask[i, :seq_len])
+
+                # Update the tensors with non-padded versions
+                # prompt_embeds = non_padded_embeds
+                # prompt_attention_masks = non_padded_masks
+                # prompt_embeds = prompt_embeds
+
+                # logger.info(f"===== prompt_embeds: {prompt_embeds[0].shape}")
+                # logger.info(f"===== prompt_attention_masks: {prompt_attention_masks[0].shape}")
+
+                sampling_params = SamplingParam.from_pretrained(
+                    args.model_path)
+
+                # encode negative prompt for trajectory collection
+                if sampling_params.guidance_scale > 1 and sampling_params.negative_prompt is not None:
+                    negative_prompt_embeds_list, negative_prompt_masks_list = self.prompt_encoding_stage.encode_text(
+                        sampling_params.negative_prompt,
+                        fastvideo_args,
+                        encoder_index=[0],
+                        return_attention_mask=True,
+                    )
+                    negative_prompt_embed = negative_prompt_embeds_list[0][0]
+                    negative_prompt_attention_mask = negative_prompt_masks_list[0][0]
+                else:
+                    negative_prompt_embed = None
+                    negative_prompt_attention_mask = None
+
+                trajectory_latents = []
+                trajectory_timesteps = []
+                trajectory_decoded = []
+                for i, (prompt_embed, prompt_attention_mask) in enumerate(zip(prompt_embeds, prompt_attention_masks)):
+                    prompt_embed = prompt_embed.unsqueeze(0)
+                    prompt_attention_mask = prompt_attention_mask.unsqueeze(0)
+                    logger.info(f"what")
+                    logger.info(f"===== prompt_embed: {prompt_embed.shape}")
+                    logger.info(f"===== prompt_attention_mask: {prompt_attention_mask.shape}")
+                    # Collect the trajectory data
+                    batch = ForwardBatch(
+                        **shallow_asdict(sampling_params),
+                        # data_type="video",
+                        # seed=args.seed,
+                        # prompt=batch_captions[i],
+                        # prompt_embeds=[prompt_embed],
+                        # prompt_attention_mask=[prompt_attention_mask],
+                        # height=args.max_height,
+                        # width=args.max_width,
+                        # num_frames=81,
+                        # fps=args.train_fps,
+                        # return_trajectory_latents=True,
+                        # guidance_scale=3.0,
+                        # do_classifier_free_guidance=True,
+                    )
+                    batch.prompt_embeds = [prompt_embed]
+                    batch.prompt_attention_mask = [prompt_attention_mask]
+                    batch.negative_prompt_embeds = [negative_prompt_embed]
+                    batch.negative_attention_mask = [negative_prompt_attention_mask]
+                    batch.return_trajectory_latents = True
+                    batch.return_trajectory_decoded = False
+                    batch.height = args.max_height
+                    batch.width = args.max_width
+                    # batch.num_frames = 81
+                    batch.fps = args.train_fps
+                    batch.guidance_scale = 3.0
+                    batch.do_classifier_free_guidance = True
+                    # fastvideo_args.pipeline_config.ti2v_task = True
+
+                    result_batch = self.input_validation_stage(
+                        batch, fastvideo_args)
+                    # result_batch = self.prompt_encoding_stage(result_batch, fastvideo_args)
+                    # result_batch = self.vae_encoding_stage(result_batch, fastvideo_args)
+                    result_batch = self.timestep_preparation_stage(
+                        batch, fastvideo_args)
+                    result_batch = self.latent_preparation_stage(
+                        result_batch, fastvideo_args)
+                    result_batch = self.denoising_stage(result_batch,
+                                                        fastvideo_args)
+                    result_batch = self.decoding_stage(result_batch, fastvideo_args)
+                    # trajectory_latents = result_batch.trajectory_latents
+                    trajectory_latents.append(result_batch.trajectory_latents.cpu())
+                    trajectory_timesteps.append(result_batch.trajectory_timesteps.cpu())
+                    trajectory_decoded.append(result_batch.trajectory_decoded)
+
+            extra_features["trajectory_latents"] = trajectory_latents
+            extra_features["trajectory_timesteps"] = trajectory_timesteps
+            logger.info(f"===== trajectory_latents: {trajectory_latents[0].shape}")
+            logger.info(f"===== trajectory_latents len: {len(trajectory_latents)}")
+            logger.info(f"===== trajectory_timesteps: {trajectory_timesteps}")
+            logger.info(f"===== trajectory_timesteps len: {len(trajectory_timesteps)}")
+
+            if batch.return_trajectory_decoded:
+                logger.info(f"===== SAVING TRAJECTORY DECODED")
+                for i, decoded_frames in enumerate(trajectory_decoded):
+                    for j, decoded_frame in enumerate(decoded_frames):
+                        logger.info(f"===== SAVING TRAJECTORY DECODED {i} for prompt {batch_captions[i]}")
+                        save_decoded_latents_as_video(decoded_frame, f"decoded_videos/trajectory_decoded_{i}_{j}.mp4", args.train_fps)
+            # assert False
+            # Prepare batch data for Parquet dataset
+            batch_data = []
+
+            # Add progress bar for saving outputs
+            save_pbar = tqdm(enumerate(valid_data["path"]),
+                             desc="Saving outputs",
+                             unit="item",
+                             leave=False)
+            for idx, video_path in save_pbar:
+                # Get the corresponding latent and info using video name
+                latent = latents[idx].cpu()
+                video_name = os.path.basename(video_path).split(".")[0]
+
+                # Convert tensors to numpy arrays
+                vae_latent = latent.cpu().numpy()
+                text_embedding = prompt_embeds[idx].cpu().numpy()
+
+                # Get extra features for this sample if needed
+                sample_extra_features = {}
+                if extra_features:
+                    for key, value in extra_features.items():
+                        logger.info(f"===== key: {key}")
+                        if isinstance(value, torch.Tensor):
+                            logger.info(f"===== value: {value[idx].shape}")
+                            sample_extra_features[key] = value[idx].cpu().numpy(
+                            )
+                        else:
+                            assert isinstance(value, list)
+                            if isinstance(value[idx], torch.Tensor):
+                                logger.info(f"===== value in list: {value[idx].shape}")
+                                sample_extra_features[key] = value[idx].cpu().float().numpy(
+                                )
+                            else:
+                                logger.info(f"===== value in list: not tensor")
+                                sample_extra_features[key] = value[idx]
+                            # logger.info(f"===== value: not tensor")
+                            # sample_extra_features[key] = value[idx]
+
+                # Create record for Parquet dataset
+                record = self.create_record(
+                    video_name=video_name,
+                    vae_latent=vae_latent,
+                    text_embedding=text_embedding,
+                    valid_data=valid_data,
+                    idx=idx,
+                    extra_features=sample_extra_features)
+                batch_data.append(record)
+
+            if batch_data:
+                # Add progress bar for writing to Parquet dataset
+                write_pbar = tqdm(total=1,
+                                  desc="Writing to Parquet dataset",
+                                  unit="batch")
+                # Convert batch data to PyArrow arrays
+                arrays = []
+                for field in self.get_schema_fields():
+                    if field.endswith('_bytes'):
+                        arrays.append(
+                            pa.array([record[field] for record in batch_data],
+                                     type=pa.binary()))
+                    elif field.endswith('_shape'):
+                        arrays.append(
+                            pa.array([record[field] for record in batch_data],
+                                     type=pa.list_(pa.int32())))
+                    elif field in ['width', 'height', 'num_frames']:
+                        arrays.append(
+                            pa.array([record[field] for record in batch_data],
+                                     type=pa.int32()))
+                    elif field in ['duration_sec', 'fps']:
+                        arrays.append(
+                            pa.array([record[field] for record in batch_data],
+                                     type=pa.float32()))
+                    else:
+                        arrays.append(
+                            pa.array([record[field] for record in batch_data]))
+
+                table = pa.Table.from_arrays(arrays,
+                                             names=self.get_schema_fields())
+                write_pbar.update(1)
+                write_pbar.close()
+
+                # Store the table in a list for later processing
+                if not hasattr(self, 'all_tables'):
+                    self.all_tables = []
+                self.all_tables.append(table)
+
+                logger.info("Collected batch with %s samples", len(table))
+
+            if self.num_processed_samples >= args.flush_frequency:
+                self._flush_tables(self.num_processed_samples, args,
+                                   self.combined_parquet_dir)
+                self.num_processed_samples = 0
+                self.all_tables = []
+
+    def preprocess_text_and_trajectory(self,
+                                     fastvideo_args: FastVideoArgs,
+                                     args):
+        """Preprocess text-only data and generate trajectory information."""
+        
+        for batch_idx, data in enumerate(self.pbar):
+            if data is None:
+                continue
+
+            with torch.inference_mode():
+                # For text-only processing, we only need text data
+                # Filter out samples without text
+                valid_indices = []
+                for i, text in enumerate(data["text"]):
+                    if text and text.strip():  # Check if text is not empty
+                        valid_indices.append(i)
+                self.num_processed_samples += len(valid_indices)
+
+                if not valid_indices:
+                    continue
+
+                # Create new batch with only valid samples (text-only)
+                valid_data = {
+                    "text": [data["text"][i] for i in valid_indices],
+                    "path": [data["path"][i] for i in valid_indices],
+                }
+                
+                # Add fps and duration if available in data
+                if "fps" in data:
+                    valid_data["fps"] = [data["fps"][i] for i in valid_indices]
+                if "duration" in data:
+                    valid_data["duration"] = [data["duration"][i] for i in valid_indices]
+
+                batch_captions = valid_data["text"]
+                # Encode text using the standalone TextEncodingStage API
+                prompt_embeds_list, prompt_masks_list = self.prompt_encoding_stage.encode_text(
+                    batch_captions,
+                    fastvideo_args,
+                    encoder_index=[0],
+                    return_attention_mask=True,
+                )
+                prompt_embeds = prompt_embeds_list[0]
+                prompt_attention_masks = prompt_masks_list[0]
+                assert prompt_embeds.shape[0] == prompt_attention_masks.shape[0]
+
+                sampling_params = SamplingParam.from_pretrained(
+                    args.model_path)
+
+                # encode negative prompt for trajectory collection
+                if sampling_params.guidance_scale > 1 and sampling_params.negative_prompt is not None:
+                    negative_prompt_embeds_list, negative_prompt_masks_list = self.prompt_encoding_stage.encode_text(
+                        sampling_params.negative_prompt,
+                        fastvideo_args,
+                        encoder_index=[0],
+                        return_attention_mask=True,
+                    )
+                    negative_prompt_embed = negative_prompt_embeds_list[0][0]
+                    negative_prompt_attention_mask = negative_prompt_masks_list[0][0]
+                else:
+                    negative_prompt_embed = None
+                    negative_prompt_attention_mask = None
+
+                trajectory_latents = []
+                trajectory_timesteps = []
+                trajectory_decoded = []
+                
+                for i, (prompt_embed, prompt_attention_mask) in enumerate(zip(prompt_embeds, prompt_attention_masks)):
+                    prompt_embed = prompt_embed.unsqueeze(0)
+                    prompt_attention_mask = prompt_attention_mask.unsqueeze(0)
+                    
+                    # Collect the trajectory data (text-to-video generation)
+                    batch = ForwardBatch(
+                        **shallow_asdict(sampling_params),
+                    )
+                    batch.prompt_embeds = [prompt_embed]
+                    batch.prompt_attention_mask = [prompt_attention_mask]
+                    batch.negative_prompt_embeds = [negative_prompt_embed]
+                    batch.negative_attention_mask = [negative_prompt_attention_mask]
+                    batch.return_trajectory_latents = True
+                    batch.return_trajectory_decoded = False
+                    batch.height = args.max_height
+                    batch.width = args.max_width
+                    batch.fps = args.train_fps
+                    batch.guidance_scale = 3.0
+                    batch.do_classifier_free_guidance = True
+
+                    result_batch = self.input_validation_stage(
+                        batch, fastvideo_args)
+                    result_batch = self.timestep_preparation_stage(
+                        batch, fastvideo_args)
+                    result_batch = self.latent_preparation_stage(
+                        result_batch, fastvideo_args)
+                    result_batch = self.denoising_stage(result_batch,
+                                                        fastvideo_args)
+                    result_batch = self.decoding_stage(result_batch, fastvideo_args)
+                    
+                    trajectory_latents.append(result_batch.trajectory_latents.cpu())
+                    trajectory_timesteps.append(result_batch.trajectory_timesteps.cpu())
+                    trajectory_decoded.append(result_batch.trajectory_decoded)
+
+                # Prepare extra features for text-only processing
+                extra_features = {
+                    "trajectory_latents": trajectory_latents,
+                    "trajectory_timesteps": trajectory_timesteps
+                }
+
+                logger.info(f"===== trajectory_latents: {trajectory_latents[0].shape}")
+                logger.info(f"===== trajectory_latents len: {len(trajectory_latents)}")
+                logger.info(f"===== trajectory_timesteps: {trajectory_timesteps}")
+                logger.info(f"===== trajectory_timesteps len: {len(trajectory_timesteps)}")
+
+                if batch.return_trajectory_decoded:
+                    logger.info(f"===== SAVING TRAJECTORY DECODED")
+                    for i, decoded_frames in enumerate(trajectory_decoded):
+                        for j, decoded_frame in enumerate(decoded_frames):
+                            logger.info(f"===== SAVING TRAJECTORY DECODED {i} for prompt {batch_captions[i]}")
+                            save_decoded_latents_as_video(decoded_frame, f"decoded_videos/trajectory_decoded_{i}_{j}.mp4", args.train_fps)
+
+                # Prepare batch data for Parquet dataset
+                batch_data = []
+
+                # Add progress bar for saving outputs
+                save_pbar = tqdm(enumerate(valid_data["path"]),
+                                 desc="Saving outputs",
+                                 unit="item",
+                                 leave=False)
+                
+                for idx, video_path in save_pbar:
+                    video_name = os.path.basename(video_path).split(".")[0]
+
+                    # Convert tensors to numpy arrays
+                    text_embedding = prompt_embeds[idx].cpu().numpy()
+
+                    # Get extra features for this sample
+                    sample_extra_features = {}
+                    if extra_features:
+                        for key, value in extra_features.items():
+                            logger.info(f"===== key: {key}")
+                            if isinstance(value, torch.Tensor):
+                                logger.info(f"===== value: {value[idx].shape}")
+                                sample_extra_features[key] = value[idx].cpu().numpy()
+                            else:
+                                assert isinstance(value, list)
+                                if isinstance(value[idx], torch.Tensor):
+                                    logger.info(f"===== value in list: {value[idx].shape}")
+                                    sample_extra_features[key] = value[idx].cpu().float().numpy()
+                                else:
+                                    logger.info(f"===== value in list: not tensor")
+                                    sample_extra_features[key] = value[idx]
+
+                    # Create record for Parquet dataset (without VAE latents for text-only)
+                    record = self.create_text_only_record(
+                        args,
+                        video_name=video_name,
+                        text_embedding=text_embedding,
+                        valid_data=valid_data,
+                        idx=idx,
+                        extra_features=sample_extra_features)
+                    batch_data.append(record)
+
+                if batch_data:
+                    # Add progress bar for writing to Parquet dataset
+                    write_pbar = tqdm(total=1,
+                                      desc="Writing to Parquet dataset",
+                                      unit="batch")
+                    # Convert batch data to PyArrow arrays
+                    arrays = []
+                    for field in self.get_schema_fields():
+                        if field.endswith('_bytes'):
+                            arrays.append(
+                                pa.array([record[field] for record in batch_data],
+                                         type=pa.binary()))
+                        elif field.endswith('_shape'):
+                            arrays.append(
+                                pa.array([record[field] for record in batch_data],
+                                         type=pa.list_(pa.int32())))
+                        elif field in ['width', 'height', 'num_frames']:
+                            arrays.append(
+                                pa.array([record[field] for record in batch_data],
+                                         type=pa.int32()))
+                        elif field in ['duration_sec', 'fps']:
+                            arrays.append(
+                                pa.array([record[field] for record in batch_data],
+                                         type=pa.float32()))
+                        else:
+                            arrays.append(
+                                pa.array([record[field] for record in batch_data]))
+
+                    table = pa.Table.from_arrays(arrays,
+                                                 names=self.get_schema_fields())
+                    write_pbar.update(1)
+                    write_pbar.close()
+
+                    # Store the table in a list for later processing
+                    if not hasattr(self, 'all_tables'):
+                        self.all_tables = []
+                    self.all_tables.append(table)
+
+                    logger.info("Collected batch with %s samples", len(table))
+
+                if self.num_processed_samples >= args.flush_frequency:
+                    self._flush_tables(self.num_processed_samples, args,
+                                       self.combined_parquet_dir)
+                    self.num_processed_samples = 0
+                    self.all_tables = []
+        
+        # Final flush for any remaining samples
+        if hasattr(self, 'all_tables') and self.all_tables and self.num_processed_samples > 0:
+            logger.info(f"Final flush with {self.num_processed_samples} remaining samples")
+            self._flush_tables(self.num_processed_samples, args, self.combined_parquet_dir)
+            self.num_processed_samples = 0
+            self.all_tables = []
+
+    def create_text_only_record(
+            self,
+            args,
+            video_name: str,
+            text_embedding: np.ndarray,
+            valid_data: dict[str, Any],
+            idx: int,
+            extra_features: dict[str, Any] | None = None) -> dict[str, Any]:
+        """Create a record for text-only preprocessing using text-only schema."""
+        
+        # Create base record using only fields from text-only schema
+        record = {
+            "id": f"text_{video_name}_{idx}",
+            "text_embedding_bytes": text_embedding.tobytes(),
+            "text_embedding_shape": list(text_embedding.shape),
+            "text_embedding_dtype": str(text_embedding.dtype),
+            "file_name": video_name,
+            "caption": valid_data["text"][idx],
+            "media_type": "text",
+        }
+
+        # Add trajectory data if available
+        if extra_features and "trajectory_latents" in extra_features:
+            trajectory_latents = extra_features["trajectory_latents"][idx] if isinstance(extra_features["trajectory_latents"], list) else extra_features["trajectory_latents"]
+            record.update({
+                "trajectory_latents_bytes": trajectory_latents.tobytes(),
+                "trajectory_latents_shape": list(trajectory_latents.shape),
+                "trajectory_latents_dtype": str(trajectory_latents.dtype),
+            })
+        else:
+            record.update({
+                "trajectory_latents_bytes": b"",
+                "trajectory_latents_shape": [],
+                "trajectory_latents_dtype": "",
+            })
+
+        if extra_features and "trajectory_timesteps" in extra_features:
+            trajectory_timesteps = extra_features["trajectory_timesteps"][idx] if isinstance(extra_features["trajectory_timesteps"], list) else extra_features["trajectory_timesteps"]
+            record.update({
+                "trajectory_timesteps_bytes": trajectory_timesteps.tobytes(),
+                "trajectory_timesteps_shape": list(trajectory_timesteps.shape),
+                "trajectory_timesteps_dtype": str(trajectory_timesteps.dtype),
+            })
+        else:
+            record.update({
+                "trajectory_timesteps_bytes": b"",
+                "trajectory_timesteps_shape": [],
+                "trajectory_timesteps_dtype": "",
+            })
+
+        return record
+
+
+    def get_extra_features(self, valid_data: dict[str, Any],
+                           fastvideo_args: FastVideoArgs) -> dict[str, Any]:
+
+        # TODO(will): move these to cpu at some point
+        self.get_module("vae").to(get_local_torch_device())
+
+        # generator = torch.Generator(device=get_local_torch_device(), seed=42)
+        generator = torch.Generator("cpu").manual_seed(42)
+
+        features = {}
+        """Get CLIP features from the first frame of each video."""
+        first_frame = valid_data["pixel_values"][:, :, 0, :, :].permute(
+            0, 2, 3, 1)  # (B, C, T, H, W) -> (B, H, W, C)
+        _, _, num_frames, height, width = valid_data["pixel_values"].shape
+        # latent_height = height // self.get_module(
+        #     "vae").spatial_compression_ratio
+        # latent_width = width // self.get_module("vae").spatial_compression_ratio
+
+        unprocessed_images = []
+        pil_images = []
+        # Frame has values between -1 and 1
+        for frame in first_frame:
+            frame = (frame + 1) * 127.5
+            frame_pil = Image.fromarray(frame.cpu().numpy().astype(np.uint8))
+            pil_images.append(frame_pil)
+            # processed_img = self.get_module("image_processor")(
+            #     images=frame_pil, return_tensors="pt")
+            unprocessed_images.append(frame_pil)
+        """Get VAE features from the first frame of each video"""
+        video_conditions = []
+        for frame in unprocessed_images:
+
+            latent = self.vae_encoding_stage.encode_image(
+                frame, height, width, fastvideo_args, generator)
+            video_conditions.append(latent)
+
+        features["image_condition_latents"] = video_conditions
+        features["pil_images"] = pil_images
+        return features
+
+    def create_record(
+            self,
+            video_name: str,
+            vae_latent: np.ndarray,
+            text_embedding: np.ndarray,
+            valid_data: dict[str, Any],
+            idx: int,
+            extra_features: dict[str, Any] | None = None) -> dict[str, Any]:
+        """Create a record for the Parquet dataset with CLIP features."""
+        record = super().create_record(video_name=video_name,
+                                       vae_latent=vae_latent,
+                                       text_embedding=text_embedding,
+                                       valid_data=valid_data,
+                                       idx=idx,
+                                       extra_features=extra_features)
+
+        if extra_features and "image_condition_latents" in extra_features:
+            image_condition_latents = extra_features["image_condition_latents"]
+            record.update({
+                "image_condition_latents_bytes":
+                image_condition_latents.tobytes(),
+                "image_condition_latents_shape":
+                list(image_condition_latents.shape),
+                "image_condition_latents_dtype":
+                str(image_condition_latents.dtype),
+            })
+        else:
+            record.update({
+                "image_condition_latents_bytes": b"",
+                "image_condition_latents_shape": [],
+                "image_condition_latents_dtype": "",
+            })
+
+        if extra_features and "trajectory_latents" in extra_features:
+            trajectory_latents = extra_features["trajectory_latents"]
+            record.update({
+                "trajectory_latents_bytes": trajectory_latents.tobytes(),
+                "trajectory_latents_shape": list(trajectory_latents.shape),
+                "trajectory_latents_dtype": str(trajectory_latents.dtype),
+            })
+        else:
+            record.update({
+                "trajectory_latents_bytes": b"",
+                "trajectory_latents_shape": [],
+                "trajectory_latents_dtype": "",
+            })
+
+        if extra_features and "trajectory_timesteps" in extra_features:
+            trajectory_timesteps = extra_features["trajectory_timesteps"]
+            record.update({
+                "trajectory_timesteps_bytes": trajectory_timesteps.tobytes(),
+                "trajectory_timesteps_shape": list(trajectory_timesteps.shape),
+                "trajectory_timesteps_dtype": str(trajectory_timesteps.dtype),
+            })
+        else:
+            record.update({
+                "trajectory_timesteps_bytes": b"",
+                "trajectory_timesteps_shape": [],
+                "trajectory_timesteps_dtype": "",
+            })
+
+        if extra_features and "pil_image" in extra_features:
+            pil_image = extra_features["pil_image"]
+            record.update({
+                "pil_image_bytes": pil_image.tobytes(),
+                "pil_image_shape": list(pil_image.shape),
+                "pil_image_dtype": str(pil_image.dtype),
+            })
+        else:
+            record.update({
+                "pil_image_bytes": b"",
+                "pil_image_shape": [],
+                "pil_image_dtype": "",
+            })
+
+        return record
+
+    def forward(self, batch: ForwardBatch, fastvideo_args: FastVideoArgs, args):
+        if not self.post_init_called:
+            self.post_init()
+
+        self.local_rank = int(os.getenv("RANK", 0))
+        os.makedirs(args.output_dir, exist_ok=True)
+        # Create directory for combined data
+        self.combined_parquet_dir = os.path.join(args.output_dir,
+                                                 "combined_parquet_dataset")
+        os.makedirs(self.combined_parquet_dir, exist_ok=True)
+
+        # Loading dataset
+        #train_dataset = getdataset(args)
+        train_dataset = gettextdataset(args)
+
+        self.preprocess_dataloader = DataLoader(
+            train_dataset,
+            batch_size=args.preprocess_video_batch_size,
+            num_workers=args.dataloader_num_workers,
+        )
+
+        self.preprocess_loader_iter = iter(self.preprocess_dataloader)
+
+        self.num_processed_samples = 0
+        # Add progress bar for video preprocessing
+        self.pbar = tqdm(self.preprocess_loader_iter,
+                         desc="Processing videos",
+                         unit="batch",
+                         disable=self.local_rank != 0)
+
+        # Initialize class variables for data sharing
+        self.video_data: dict[str, Any] = {}  # Store video metadata and paths
+        self.latent_data: dict[str, Any] = {}  # Store latent tensors
+        #self.preprocess_video_and_text_and_trajectory(fastvideo_args, args)
+        self.preprocess_text_and_trajectory(fastvideo_args, args)
+
+
+EntryClass = PreprocessPipeline_ODE_Trajectory
@@ -1,14 +1,17 @@
+import random
 from collections.abc import Callable
 from typing import cast

 import numpy as np
 import torch
+import torchvision
 from einops import rearrange
 from torchvision import transforms

+from fastvideo.configs.configs import VideoLoaderType
 from fastvideo.dataset.transform import (CenterCropResizeVideo,
                                         TemporalRandomCrop)
-from fastvideo.fastvideo_args import FastVideoArgs
+from fastvideo.fastvideo_args import FastVideoArgs, WorkloadType
 from fastvideo.pipelines.pipeline_batch_info import (ForwardBatch,
                                                     PreprocessBatch)
 from fastvideo.pipelines.stages.base import PipelineStage
@@ -60,7 +63,16 @@ class VideoTransformStage(PipelineStage):
                else:
                    frame_indices = frame_indices[:self.num_frames]

-            video = batch.video_loader[i].get_frames_at(frame_indices).data
+            if fastvideo_args.preprocess_config.video_loader_type == VideoLoaderType.TORCHCODEC:
+                video = batch.video_loader[i].get_frames_at(frame_indices).data
+            elif fastvideo_args.preprocess_config.video_loader_type == VideoLoaderType.TORCHVISION:
+                video, _, _ = torchvision.io.read_video(batch.video_loader[i],
+                                                        output_format="TCHW")
+                video = video[frame_indices]
+            else:
+                raise ValueError(
+                    f"Invalid video loader type: {fastvideo_args.preprocess_config.video_loader_type}"
+                )
            video = self.video_transform(video)
            video_pixel_batch.append(video)

@@ -68,6 +80,39 @@ class VideoTransformStage(PipelineStage):
        video_pixel_values = rearrange(video_pixel_values,
                                       "b t c h w -> b c t h w")
        video_pixel_values = video_pixel_values.to(torch.uint8)
+
+        if fastvideo_args.workload_type == WorkloadType.I2V:
+            batch.pil_image = video_pixel_values[:, :, 0, :, :]
+
        video_pixel_values = video_pixel_values.float() / 255.0
        batch.latents = video_pixel_values
+        batch.num_frames = [video_pixel_values.shape[2]] * len(
+            batch.video_loader)
+        batch.height = [video_pixel_values.shape[3]] * len(batch.video_loader)
+        batch.width = [video_pixel_values.shape[4]] * len(batch.video_loader)
+        return cast(ForwardBatch, batch)
+
+
+class TextTransformStage(PipelineStage):
+    """
+    Process text data according to the cfg rate.
+    """
+
+    def __init__(self, cfg_uncondition_drop_rate: float, seed: int) -> None:
+        self.cfg_rate = cfg_uncondition_drop_rate
+        self.rng = random.Random(seed)
+
+    def forward(self, batch: ForwardBatch,
+                fastvideo_args: FastVideoArgs) -> ForwardBatch:
+        batch = cast(PreprocessBatch, batch)
+
+        prompts = []
+        for prompt in batch.prompt:
+            if not isinstance(prompt, list):
+                prompt = [prompt]
+            prompt = self.rng.choice(prompt)
+            prompt = prompt if self.rng.random() > self.cfg_rate else ""
+            prompts.append(prompt)
+
+        batch.prompt = prompts
        return cast(ForwardBatch, batch)
@@ -9,8 +9,11 @@ from fastvideo.fastvideo_args import FastVideoArgs
 from fastvideo.logger import init_logger
 from fastvideo.pipelines.preprocess.preprocess_pipeline_i2v import (
    PreprocessPipeline_I2V)
+from fastvideo.pipelines.preprocess.preprocess_pipeline_ode_trajectory import (
+    PreprocessPipeline_ODE_Trajectory)
 from fastvideo.pipelines.preprocess.preprocess_pipeline_t2v import (
    PreprocessPipeline_T2V)
+from fastvideo.pipelines.preprocess_text import PreprocessPipeline_Text
 from fastvideo.utils import maybe_download_model

 logger = init_logger(__name__)
@@ -21,12 +24,22 @@ def main(args) -> None:
    maybe_init_distributed_environment_and_model_parallel(1, 1)
    num_gpus = int(os.environ["WORLD_SIZE"])
    assert num_gpus == 1, "Only support 1 GPU"
-    pipeline_config = PipelineConfig.from_pretrained(args.model_path)
-    kwargs = {
-        "vae_precision": "fp32",
-        "vae_config": WanVAEConfig(load_encoder=True, load_decoder=False),
-    }
-    pipeline_config.update_config_from_dict(kwargs)
+    
+    if args.preprocess_task == "text_only":
+        pipeline_config = PipelineConfig.from_pretrained(args.model_path)
+        kwargs = {
+            "text_encoder_cpu_offload": False,
+        }
+        pipeline_config.update_config_from_dict(kwargs)
+    else:
+        # Full config for video/image processing
+        pipeline_config = PipelineConfig.from_pretrained(args.model_path)
+        kwargs = {
+            "vae_precision": "fp32",
+            "vae_config": WanVAEConfig(load_encoder=True, load_decoder=True),
+        }
+        pipeline_config.update_config_from_dict(kwargs)
+    
    fastvideo_args = FastVideoArgs(
        model_path=args.model_path,
        num_gpus=get_world_size(),
@@ -35,7 +48,25 @@ def main(args) -> None:
        text_encoder_cpu_offload=False,
        pipeline_config=pipeline_config,
    )
-    PreprocessPipeline = PreprocessPipeline_I2V if args.preprocess_task == "i2v" else PreprocessPipeline_T2V
+    
+    if args.preprocess_task == "t2v":
+        PreprocessPipeline = PreprocessPipeline_T2V
+    elif args.preprocess_task == "i2v":
+        PreprocessPipeline = PreprocessPipeline_I2V
+    elif args.preprocess_task == "ode_trajectory":
+        print("Preprocess pipeline...")
+        PreprocessPipeline = PreprocessPipeline_ODE_Trajectory
+    elif args.preprocess_task == "text_only":
+        print("Text-only preprocessing pipeline...")
+        PreprocessPipeline = PreprocessPipeline_Text
+    else:
+        raise ValueError(f"Invalid preprocess task: {args.preprocess_task}. "
+                        f"Valid options: t2v, i2v, ode_trajectory, text_only")
+
+    logger.info(
+        f"Preprocess task: {args.preprocess_task} using {PreprocessPipeline.__name__}"
+    )
+
    pipeline = PreprocessPipeline(args.model_path, fastvideo_args)
    pipeline.forward(batch=None, fastvideo_args=fastvideo_args, args=args)

@@ -74,7 +105,11 @@ if __name__ == "__main__":
    parser.add_argument("--video_length_tolerance_range", type=int, default=2.0)
    parser.add_argument("--group_frame", action="store_true")  # TODO
    parser.add_argument("--group_resolution", action="store_true")  # TODO
-    parser.add_argument("--preprocess_task", type=str, default="t2v")
+    parser.add_argument("--preprocess_task", 
+                        type=str, 
+                        default="t2v",
+                        choices=["t2v", "i2v", "ode_trajectory", "text_only"],
+                        help="Type of preprocessing task to run")
    parser.add_argument("--train_fps", type=int, default=30)
    parser.add_argument("--use_image_num", type=int, default=0)
    parser.add_argument("--text_max_length", type=int, default=256)
@@ -98,4 +133,4 @@ if __name__ == "__main__":
    )

    args = parser.parse_args()
-    main(args)
+    main(args)
@@ -1,21 +1,17 @@
-import os
-
 from fastvideo.distributed import (
    maybe_init_distributed_environment_and_model_parallel)
 from fastvideo.fastvideo_args import FastVideoArgs
 from fastvideo.logger import init_logger
 from fastvideo.utils import FlexibleArgumentParser
-from fastvideo.workflow.preprocess.preprocess_workflow_t2v import (
-    PreprocessWorkflowT2V)
+from fastvideo.workflow.workflow_base import WorkflowBase

 logger = init_logger(__name__)


 def main(fastvideo_args: FastVideoArgs) -> None:
    maybe_init_distributed_environment_and_model_parallel(1, 1)
-    num_gpus = int(os.environ["WORLD_SIZE"])
-    assert num_gpus == 1, "Only support 1 GPU"
-    preprocess_workflow = PreprocessWorkflowT2V(fastvideo_args)
+    preprocess_workflow_cls = WorkflowBase.get_workflow_cls(fastvideo_args)
+    preprocess_workflow = preprocess_workflow_cls(fastvideo_args)
    preprocess_workflow.run()


@@ -1,36 +1,66 @@
 from fastvideo.fastvideo_args import FastVideoArgs
 from fastvideo.pipelines.composed_pipeline_base import ComposedPipelineBase
-from fastvideo.pipelines.preprocess.preprocess_stages import VideoTransformStage
+from fastvideo.pipelines.preprocess.preprocess_stages import (
+    TextTransformStage, VideoTransformStage)
 from fastvideo.pipelines.stages import (EncodingStage, ImageEncodingStage,
                                        TextEncodingStage)
+from fastvideo.pipelines.stages.image_encoding import ImageVAEEncodingStage


-class I2VPreprocessPipeline(ComposedPipelineBase):
+class PreprocessPipelineI2V(ComposedPipelineBase):
    _required_config_modules = [
        "image_encoder", "image_processor", "text_encoder", "tokenizer", "vae"
    ]

    def create_pipeline_stages(self, fastvideo_args: FastVideoArgs):
+        assert fastvideo_args.preprocess_config is not None
+        self.add_stage(stage_name="text_transform_stage",
+                       stage=TextTransformStage(
+                           cfg_uncondition_drop_rate=fastvideo_args.
+                           preprocess_config.training_cfg_rate,
+                           seed=fastvideo_args.preprocess_config.seed,
+                       ))
        self.add_stage(stage_name="prompt_encoding_stage",
                       stage=TextEncodingStage(
                           text_encoders=[self.get_module("text_encoder")],
                           tokenizers=[self.get_module("tokenizer")],
                       ))
-        self.add_stage(stage_name="image_encoding_stage",
-                       stage=ImageEncodingStage(
-                           image_encoder=self.get_module("image_encoder"),
-                           image_processor=self.get_module("image_processor"),
-                       ))
+        self.add_stage(
+            stage_name="video_transform_stage",
+            stage=VideoTransformStage(
+                train_fps=fastvideo_args.preprocess_config.train_fps,
+                num_frames=fastvideo_args.preprocess_config.num_frames,
+                max_height=fastvideo_args.preprocess_config.max_height,
+                max_width=fastvideo_args.preprocess_config.max_width,
+                do_temporal_sample=fastvideo_args.preprocess_config.
+                do_temporal_sample,
+            ))
+        if (self.get_module("image_encoder") is not None
+                and self.get_module("image_processor") is not None):
+            self.add_stage(
+                stage_name="image_encoding_stage",
+                stage=ImageEncodingStage(
+                    image_encoder=self.get_module("image_encoder"),
+                    image_processor=self.get_module("image_processor"),
+                ))
+        self.add_stage(stage_name="image_vae_encoding_stage",
+                       stage=ImageVAEEncodingStage(
+                           vae=self.get_module("vae"), ))
        self.add_stage(stage_name="video_encoding_stage",
                       stage=EncodingStage(vae=self.get_module("vae"), ))


-class T2VPreprocessPipeline(ComposedPipelineBase):
+class PreprocessPipelineT2V(ComposedPipelineBase):
    _required_config_modules = ["text_encoder", "tokenizer", "vae"]

    def create_pipeline_stages(self, fastvideo_args: FastVideoArgs):
        assert fastvideo_args.preprocess_config is not None
-
+        self.add_stage(stage_name="text_transform_stage",
+                       stage=TextTransformStage(
+                           cfg_uncondition_drop_rate=fastvideo_args.
+                           preprocess_config.training_cfg_rate,
+                           seed=fastvideo_args.preprocess_config.seed,
+                       ))
        self.add_stage(stage_name="prompt_encoding_stage",
                       stage=TextEncodingStage(
                           text_encoders=[self.get_module("text_encoder")],
@@ -50,4 +80,4 @@ class T2VPreprocessPipeline(ComposedPipelineBase):
                       stage=EncodingStage(vae=self.get_module("vae"), ))


-EntryClass = [I2VPreprocessPipeline, T2VPreprocessPipeline]
+EntryClass = [PreprocessPipelineI2V, PreprocessPipelineT2V]
@@ -0,0 +1,216 @@
+# SPDX-License-Identifier: Apache-2.0
+"""
+Text-only Data Preprocessing pipeline implementation.
+
+This module contains an implementation of the Text-only Data Preprocessing pipeline
+using the modular pipeline architecture, based on the ODE Trajectory preprocessing.
+"""
+
+import os
+from collections.abc import Iterator
+from typing import Any
+
+import numpy as np
+import pyarrow as pa
+import torch
+from torch.utils.data import DataLoader
+from torchdata.stateful_dataloader import StatefulDataLoader
+from tqdm import tqdm
+
+from fastvideo.dataset import gettextdataset
+from fastvideo.dataset.dataloader.schema import pyarrow_schema_text_only
+from fastvideo.fastvideo_args import FastVideoArgs
+from fastvideo.logger import init_logger
+from fastvideo.pipelines.pipeline_batch_info import ForwardBatch
+from fastvideo.pipelines.preprocess.preprocess_pipeline_base import (
+    BasePreprocessPipeline)
+from fastvideo.pipelines.stages import (TextEncodingStage)
+
+logger = init_logger(__name__)
+
+
+class PreprocessPipeline_Text(BasePreprocessPipeline):
+    """Text-only preprocessing pipeline implementation."""
+
+    _required_config_modules = [
+        "text_encoder", "tokenizer"
+    ]
+    preprocess_dataloader: StatefulDataLoader
+    preprocess_loader_iter: Iterator[dict[str, Any]]
+
+    def get_schema_fields(self):
+        """Get the schema fields for text-only pipeline."""
+        return [f.name for f in pyarrow_schema_text_only]
+
+    def create_pipeline_stages(self, fastvideo_args: FastVideoArgs):
+        """Set up pipeline stages with proper dependency injection."""
+        self.add_stage(stage_name="prompt_encoding_stage",
+                       stage=TextEncodingStage(
+                           text_encoders=[self.get_module("text_encoder")],
+                           tokenizers=[self.get_module("tokenizer")],
+                       ))
+
+    def preprocess_text_only(self,
+                            fastvideo_args: FastVideoArgs,
+                            args):
+        """Preprocess text-only data."""
+        
+        for batch_idx, data in enumerate(self.pbar):
+            if data is None:
+                continue
+
+            with torch.inference_mode():
+                # For text-only processing, we only need text data
+                # Filter out samples without text
+                valid_indices = []
+                for i, text in enumerate(data["text"]):
+                    if text and text.strip():  # Check if text is not empty
+                        valid_indices.append(i)
+                self.num_processed_samples += len(valid_indices)
+
+                if not valid_indices:
+                    continue
+
+                # Create new batch with only valid samples (text-only)
+                valid_data = {
+                    "text": [data["text"][i] for i in valid_indices],
+                    "path": [data["path"][i] for i in valid_indices],
+                }
+
+                batch_captions = valid_data["text"]
+                # Encode text using the standalone TextEncodingStage API
+                prompt_embeds_list, prompt_masks_list = self.prompt_encoding_stage.encode_text(
+                    batch_captions,
+                    fastvideo_args,
+                    encoder_index=[0],
+                    return_attention_mask=True,
+                )
+                prompt_embeds = prompt_embeds_list[0]
+                prompt_attention_masks = prompt_masks_list[0]
+                assert prompt_embeds.shape[0] == prompt_attention_masks.shape[0]
+
+                logger.info(f"===== prompt_embeds: {prompt_embeds.shape}")
+                logger.info(f"===== prompt_attention_masks: {prompt_attention_masks.shape}")
+
+                # Prepare batch data for Parquet dataset
+                batch_data = []
+
+                # Add progress bar for saving outputs
+                save_pbar = tqdm(enumerate(valid_data["path"]),
+                                 desc="Saving outputs",
+                                 unit="item",
+                                 leave=False)
+                
+                for idx, text_path in save_pbar:
+                    text_name = os.path.basename(text_path).split(".")[0]
+
+                    # Convert tensors to numpy arrays
+                    text_embedding = prompt_embeds[idx].cpu().numpy()
+
+                    # Create record for Parquet dataset (text-only)
+                    record = self.create_text_only_record(
+                        text_name=text_name,
+                        text_embedding=text_embedding,
+                        valid_data=valid_data,
+                        idx=idx)
+                    batch_data.append(record)
+
+                if batch_data:
+                    # Add progress bar for writing to Parquet dataset
+                    write_pbar = tqdm(total=1,
+                                      desc="Writing to Parquet dataset",
+                                      unit="batch")
+                    # Convert batch data to PyArrow arrays
+                    arrays = []
+                    for field in self.get_schema_fields():
+                        if field.endswith('_bytes'):
+                            arrays.append(
+                                pa.array([record[field] for record in batch_data],
+                                         type=pa.binary()))
+                        elif field.endswith('_shape'):
+                            arrays.append(
+                                pa.array([record[field] for record in batch_data],
+                                         type=pa.list_(pa.int32())))
+                        else:
+                            arrays.append(
+                                pa.array([record[field] for record in batch_data]))
+
+                    table = pa.Table.from_arrays(arrays,
+                                                 names=self.get_schema_fields())
+                    write_pbar.update(1)
+                    write_pbar.close()
+
+                    # Store the table in a list for later processing
+                    if not hasattr(self, 'all_tables'):
+                        self.all_tables = []
+                    self.all_tables.append(table)
+
+                    logger.info("Collected batch with %s samples", len(table))
+
+                if self.num_processed_samples >= args.flush_frequency:
+                    self._flush_tables(self.num_processed_samples, args,
+                                       self.combined_parquet_dir)
+                    self.num_processed_samples = 0
+                    self.all_tables = []
+        
+        # Final flush for any remaining samples
+        if hasattr(self, 'all_tables') and self.all_tables and self.num_processed_samples > 0:
+            logger.info(f"Final flush with {self.num_processed_samples} remaining samples")
+            self._flush_tables(self.num_processed_samples, args, self.combined_parquet_dir)
+            self.num_processed_samples = 0
+            self.all_tables = []
+
+    def create_text_only_record(
+            self,
+            text_name: str,
+            text_embedding: np.ndarray,
+            valid_data: dict[str, Any],
+            idx: int) -> dict[str, Any]:
+        """Create a record for text-only preprocessing using text-only schema."""
+        
+        # Create base record using only fields from text-only schema
+        record = {
+            "id": f"text_{text_name}_{idx}",
+            "text_embedding_bytes": text_embedding.tobytes(),
+            "text_embedding_shape": list(text_embedding.shape),
+            "text_embedding_dtype": str(text_embedding.dtype),
+        }
+
+        return record
+
+    def forward(self, batch: ForwardBatch, fastvideo_args: FastVideoArgs, args):
+        if not self.post_init_called:
+            self.post_init()
+
+        self.local_rank = int(os.getenv("RANK", 0))
+        os.makedirs(args.output_dir, exist_ok=True)
+        # Create directory for combined data
+        self.combined_parquet_dir = os.path.join(args.output_dir,
+                                                 "combined_parquet_dataset")
+        os.makedirs(self.combined_parquet_dir, exist_ok=True)
+
+        # Loading text dataset
+        train_dataset = gettextdataset(args)
+
+        self.preprocess_dataloader = DataLoader(
+            train_dataset,
+            batch_size=args.preprocess_video_batch_size,
+            num_workers=args.dataloader_num_workers,
+        )
+
+        self.preprocess_loader_iter = iter(self.preprocess_dataloader)
+
+        self.num_processed_samples = 0
+        # Add progress bar for text preprocessing
+        self.pbar = tqdm(self.preprocess_loader_iter,
+                         desc="Processing text",
+                         unit="batch",
+                         disable=self.local_rank != 0)
+
+        # Initialize class variables for data sharing
+        self.text_data: dict[str, Any] = {}  # Store text metadata and paths
+        
+        self.preprocess_text_only(fastvideo_args, args)
+
+
+EntryClass = PreprocessPipeline_Text
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
RandNMR73	da5ca94091	preprocessing text	2025-09-06 05:56:13 +00:00
William Lin	d3ceb67e66	[misc] Update Slack invite link (#786 )	2025-09-05 12:16:18 -07:00
Zhang Peiyuan	7ac153a5ca	Update WeChat Link	2025-09-05 11:40:47 -07:00
William Lin	d1e7aa0abd	[CI] Add ssim test for causal inference (#784 )	2025-09-05 01:23:01 -07:00
William Lin	2d846c55a1	[misc] Improve text encoding stage (#774 )	2025-09-04 17:51:27 -07:00
Jinzhe Pan	b318063c0a	[Preprocess][Fix] video quality issue (#773 )	2025-09-03 20:47:33 -07:00
Jinzhe Pan	4aa307be55	[Preprocess][Feat] support torchvision to load video in new preprocessing (#761 )	2025-09-01 23:37:01 -07:00
William Lin	055e52e5ea	[misc] [VSA] [STA] fix tk_root in setup.py for VSA and STA (#772 )	2025-08-29 01:13:37 -07:00
William Lin	7d2069596b	[bugfix] [VSA] [STA] Fix MANIFEST.in for VSA and STA; Move tk into both directories (#771 )	2025-08-29 00:51:05 -07:00
William Lin	c45009c9a4	[bugfix] fix STA install setup.py import (#770 )	2025-08-28 23:02:53 -07:00
William LinandPeiyuan Zhang	b91020b407	[VSA] [STA] Fix directory structure for pypi publishing (#769 ) Co-authored-by: Peiyuan Zhang <a1286225768@gmail.com>	2025-08-28 22:34:03 -07:00
William Lin	2dcc5ea4f6	[chore] Release 0.1.6 (#768 )	2025-08-28 20:56:21 -07:00
Wei ZhouandSolitaryThinker	359151d9a0	[Feature] Add wan2.2 5b i2v (#760 ) Co-authored-by: SolitaryThinker <wlsaidhi@gmail.com>	2025-08-28 18:15:59 -07:00
Wei ZhouandSolitaryThinker	ce67cd3729	[Feat] Support Self-Forcing's Causal Inference for Wan2.1 T2V 1.3B (#766 ) Co-authored-by: SolitaryThinker <wlsaidhi@gmail.com>	2025-08-28 16:47:49 -07:00
Zhang Peiyuan	7c554e5da8	Update Community Link (#765 )	2025-08-27 16:12:47 -07:00
William Lin	663ea33ff1	[bugfix] Fix wrong HF model string for FastWan2.2 5B (#763 )	2025-08-26 22:05:40 -07:00
William Lin	3ef04f1654	[misc] [docs] Various fixes for logging and docs (#758 )	2025-08-23 21:13:50 -07:00
Jinzhe Pan	0eced76a41	[Feat][Preprocess] support multi-gpus (#753 )	2025-08-23 11:34:42 +08:00
Jinzhe Pan	3ab6470d1a	[Feat][Preprocess] support merged dataset (#752 )	2025-08-22 15:29:33 -07:00
Wenxuan Tan	989a03532c	Optionally use unmerged weights for inference (#745 )	2025-08-22 15:20:31 -07:00
William Lin	fa15369a02	[bugfix] Check that model_index.json module is in required_modules list before removing (#756 )	2025-08-22 14:36:44 -07:00
Zhang Peiyuan	78a9cb88d8	[Fix] fix seed in dmd denoising loop (#736 )	2025-08-21 18:06:16 -07:00
Peng Xiaoand肖鹏	a0bff12746	[bugfix] [dmd] Align backward simulation with dmd2 sample back (#744 ) Co-authored-by: 肖鹏 <xiaopeng1@aishi.ai>	2025-08-20 22:25:33 -07:00
William Lin	98f2af94e5	[bugfix] Missing Docker file for cuda12.9 (#750 )	2025-08-20 15:34:31 -07:00
William Lin	46f7b6d574	[Docker] add 12.9 docker image and also fix py3.10 and py3.11 dockerfile (#749 )	2025-08-20 15:31:15 -07:00
Jinzhe Pan	911a6a6a35	[Feat][Preprocessing] i2v preprocessing workflow (#737 )	2025-08-14 20:47:25 -07:00
Zhang Peiyuan	38c7949d5c	Update WeChat group link (#739 )	2025-08-14 15:03:35 -07:00
Jinzhe Pan	7e7a0dba9d	feat: preprocess validation dataset only when exist (#734 )	2025-08-12 02:16:31 -07:00
Zhang Peiyuan	f62e210ae6	Fix vsa backward gQ (#735 )	2025-08-11 21:43:13 -07:00
William Lin	6ceb4942a0	[bugfix] [dmd] Fix backward simulation and also naming in wan_i2v_dmd_pipeline (#731 )	2025-08-10 21:13:30 -07:00
William LinandRandNMR73	8cae5e4708	[feature] add Gradio live serving demo code (#727 ) Co-authored-by: RandNMR73 <notomatthew31@gmail.com>	2025-08-10 15:34:03 -07:00