checkpoint

Fix runtime errors during training
new branch
2025-09-09 02:48:02 +00:00 · 2025-09-09 01:15:18 +00:00 · 2025-09-09 01:15:18 +00:00 · 2025-09-09 01:15:18 +00:00 · 2025-09-08 19:20:32 +00:00 · 2025-09-07 02:40:19 +00:00
141 changed files with 6014 additions and 3236 deletions
@@ -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
@@ -64,3 +64,6 @@ docs/source/distillation/examples/
 !docs/source/_static/images/**/*.png
 !comfyui/assets/**/*.png
 !comfyui/assets/**/*.gif
+
+dmd_t2v_output/
+sf_output/
@@ -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-38u6p1jqe-yDI1QJOCEnbtkLoaI5bjZQ" target="_blank"> <b>Slack</b> </a> |  🟣💬 <a href="https://ibb.co/rG0QpZdw" target="_blank"> <b> WeChat </b> </a> |
 </p>

 <div align="center">
@@ -0,0 +1,6 @@
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@@ -0,0 +1,6 @@
+<svg width="160" height="93" viewBox="0 0 160 93" fill="none" xmlns="http://www.w3.org/2000/svg">
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+<path d="M1.22217 91.66L30.003 1.86366H31.1543L2.3734 91.66H1.22217Z" fill="#356CFF" stroke="#356CFF" stroke-width="1.15122"/>
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+</svg>
@@ -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.")
@@ -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 && \
+    git submodule update --init --recursive && \
+    python setup_sta.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 && \
+    git submodule update --init --recursive && \
+    python setup_vsa.py install

 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 && \
+    git submodule update --init --recursive && \
+    python setup_sta.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 && \
+    git submodule update --init --recursive && \
+    python setup_vsa.py install

 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 . .

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

@@ -1,3 +0,0 @@
-(vsa-demo)=
-
-# 🎬 Demo
@@ -23,10 +23,10 @@ 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
 ```
@@ -0,0 +1,139 @@
+# Basic Info
+export NCCL_P2P_DISABLE=1
+export TORCH_NCCL_ENABLE_MONITORING=0
+# different cache dir for different processes
+export TRITON_CACHE_DIR=/tmp/triton_cache_${SLURM_PROCID}
+export MASTER_PORT=29500
+export TOKENIZERS_PARALLELISM=false
+export WANDB_BASE_URL="https://api.wandb.ai"
+export WANDB_MODE=offline
+# export FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA
+export WANDB_API_KEY='8d9f4b39abd68eb4e29f6fc010b7ee71a2207cde'
+
+# Configs
+NUM_GPUS=8
+
+# Model paths for DMD distillation:
+GENERATOR_MODEL_PATH="wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers"
+REAL_SCORE_MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"  # Teacher model
+FAKE_SCORE_MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"  # Critic model
+
+DATA_DIR="data/crush-smol_processed_t2v/combined_parquet_dataset/"
+VALIDATION_DATASET_FILE="/mnt/weka/home/hao.zhang/wl/FastVideo/examples/distill/SFWan2.1-I2V/validation_better.json"
+# export CUDA_VISIBLE_DEVICES=4,5
+# IP=[MASTER NODE IP]
+
+# Training arguments
+training_args=(
+  --tracker_project_name SFwan_t2v_distill_self_forcing_dmd
+  --output_dir "/mnt/sharefs/users/hao.zhang/wl/sf_checkpoints/ode0_SFwan_t2v_finetune_sf_${lr}_c${critic_lr}"
+  --wandb_run_name "DEBUG${lr}_c${critic_lr}"
+  --max_train_steps 4000
+  --train_batch_size 1
+  --train_sp_batch_size 1
+  --gradient_accumulation_steps 1
+  --num_latent_t 21
+  --num_height 480
+  --num_width 832
+  --num_frames 81
+  --warp_denoising_step
+  --enable_gradient_checkpointing_type "full"
+  --log_visualization
+  --simulate_generator_forward
+  --num_frame_per_block 3
+  --enable_gradient_masking
+  --gradient_mask_last_n_frames 21
+)
+
+# Parallel arguments
+parallel_args=(
+  --num_gpus 8 # 64
+  --sp_size 1
+  --tp_size 1
+  --hsdp_replicate_dim 1 # 64
+  --hsdp_shard_dim 8
+)
+
+# Model arguments
+model_args=(
+  --model_path $GENERATOR_MODEL_PATH  # TODO: check if you can remove this in this script
+  --pretrained_model_name_or_path $GENERATOR_MODEL_PATH
+  --generator_model_path $GENERATOR_MODEL_PATH
+  --real_score_model_path $REAL_SCORE_MODEL_PATH
+  --fake_score_model_path $FAKE_SCORE_MODEL_PATH
+)
+
+# Dataset arguments
+dataset_args=(
+  --data_path "$DATA_DIR"
+  --dataloader_num_workers 4
+)
+
+# Validation arguments
+validation_args=(
+#   --log_validation
+  --validation_dataset_file "$VALIDATION_DATASET_FILE"
+  --validation_steps 50
+  --validation_sampling_steps "3"
+  --validation_guidance_scale "6.0" # not used for dmd inference
+)
+
+# Optimizer arguments
+optimizer_args=(
+  --learning_rate 1e-5
+  --mixed_precision "bf16"
+  --training_state_checkpointing_steps 50
+  --weight_only_checkpointing_steps 50
+  --weight_decay 0.01
+  --betas '0.0,0.999'
+  --max_grad_norm 1.0
+)
+
+# Miscellaneous arguments
+miscellaneous_args=(
+  --inference_mode False
+  --checkpoints_total_limit 3
+  --training_cfg_rate 0.0
+  --dit_precision "fp32"
+  --flow_shift 5
+  --seed 1000
+  --use_ema True
+  --ema_decay 0.99
+  --ema_start_step 100
+  --init_weights_from_safetensors "/mnt/weka/home/hao.zhang/wl/Self-Forcing/diffusers_ode_init/model.safetensors"
+)
+
+# Self-forcing DMD arguments
+dmd_args=(
+  --dmd_denoising_steps '1000,750,500,250'
+  --min_timestep_ratio 0.02
+  --max_timestep_ratio 0.98
+  --dfake_gen_update_ratio 5
+  --real_score_guidance_scale 3.0
+  --fake_score_learning_rate 8e-6
+  --fake_score_betas '0.0,0.999'
+)
+
+# Self-forcing specific arguments
+self_forcing_args=(
+  --independent_first_frame False  # Whether to treat first frame independently
+  --same_step_across_blocks False  # Whether to use same denoising step across all blocks
+  --last_step_only False  # Whether to only use the last denoising step
+  --context_noise 0  # Amount of noise to add during context caching (0 = no noise)
+  --validate_cache_structure False  # Set to True for debugging KV cache issues
+)
+
+torchrun \
+--nnodes 1 \
+--nproc_per_node $NUM_GPUS \
+--master_port $MASTER_PORT \
+    fastvideo/training/wan_self_forcing_distillation_pipeline.py \
+    "${parallel_args[@]}" \
+    "${model_args[@]}" \
+    "${dataset_args[@]}" \
+    "${training_args[@]}" \
+    "${optimizer_args[@]}" \
+    "${validation_args[@]}" \
+    "${miscellaneous_args[@]}" \
+    "${dmd_args[@]}" \
+    "${self_forcing_args[@]}"
@@ -0,0 +1,165 @@
+#!/bin/bash
+#SBATCH --job-name=t2v
+#SBATCH --partition=main
+#SBATCH --nodes=1
+#SBATCH --ntasks=1
+#SBATCH --ntasks-per-node=1
+#SBATCH --gres=gpu:1
+#SBATCH --cpus-per-task=128
+#SBATCH --mem=1440G
+#SBATCH --output=dmd_t2v_output/t2v_%j.out
+#SBATCH --error=dmd_t2v_output/t2v_%j.err
+#SBATCH --exclusive
+set -e -x
+
+# Environment Setup
+source ~/conda/miniconda/bin/activate
+conda activate wei-fv
+
+# Basic Info
+export WANDB_MODE="online"
+export NCCL_P2P_DISABLE=1
+export TORCH_NCCL_ENABLE_MONITORING=0
+# different cache dir for different processes
+export TRITON_CACHE_DIR=/tmp/triton_cache_${SLURM_PROCID}
+export MASTER_PORT=29500
+export NODE_RANK=$SLURM_PROCID
+nodes=( $(scontrol show hostnames $SLURM_JOB_NODELIST) )
+export MASTER_ADDR=${nodes[0]}
+export CUDA_VISIBLE_DEVICES=$SLURM_LOCALID
+export TOKENIZERS_PARALLELISM=false
+export WANDB_BASE_URL="https://api.wandb.ai"
+export WANDB_MODE=online
+export FASTVIDEO_ATTENTION_BACKEND=FLASH_ATTN
+# export FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA
+
+echo "MASTER_ADDR: $MASTER_ADDR"
+echo "NODE_RANK: $NODE_RANK"
+
+# Configs
+NUM_GPUS=1
+
+# Model paths for Self-Forcing DMD distillation:
+GENERATOR_MODEL_PATH="wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers"
+REAL_SCORE_MODEL_PATH="Wan-AI/Wan2.1-T2V-14B-Diffusers"  # Teacher model
+FAKE_SCORE_MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"  # Critic model
+
+DATA_DIR="data/crush-smol-single_processed_t2v/combined_parquet_dataset/"
+VALIDATION_DATASET_FILE="data/crush-smol-single_processed_t2v/validation.json"
+# export CUDA_VISIBLE_DEVICES=4,5
+# IP=[MASTER NODE IP]
+
+# Training arguments
+training_args=(
+  --tracker_project_name SFwan_t2v_distill_self_forcing_dmd  # Updated for self-forcing DMD
+  --output_dir "checkpoints/SFwan_t2v_finetune"
+  --max_train_steps 500
+  --train_batch_size 1
+  --train_sp_batch_size 1
+  --gradient_accumulation_steps 1
+  --num_latent_t 21
+  --num_height 480
+  --num_width 832
+  --num_frames 81  # Must be divisible by num_frame_per_block (81 % 3 = 0 ✓)
+  --enable_gradient_checkpointing_type "full"
+  --log_visualization
+  --simulate_generator_forward
+  --num_frame_per_block 3  # Frame generation block size for self-forcing
+  --enable_gradient_masking
+  --gradient_mask_last_n_frames 21
+)
+
+# Parallel arguments
+parallel_args=(
+  --num_gpus 1 # 64
+  --sp_size 1
+  --tp_size 1
+  --hsdp_replicate_dim 1 # 64
+  --hsdp_shard_dim 1
+)
+
+# Model arguments
+model_args=(
+  --model_path $GENERATOR_MODEL_PATH  # TODO: check if you can remove this in this script
+  --pretrained_model_name_or_path $GENERATOR_MODEL_PATH
+  --generator_model_path $GENERATOR_MODEL_PATH
+  --real_score_model_path $REAL_SCORE_MODEL_PATH
+  --fake_score_model_path $FAKE_SCORE_MODEL_PATH
+)
+
+# Dataset arguments
+dataset_args=(
+  --data_path "$DATA_DIR"
+  --dataloader_num_workers 4
+)
+
+# Validation arguments
+validation_args=(
+  --log_validation
+  --validation_dataset_file "$VALIDATION_DATASET_FILE"
+  --validation_steps 10
+  --validation_sampling_steps "4"
+  --validation_guidance_scale "6.0" # not used for dmd inference
+)
+
+# Optimizer arguments
+optimizer_args=(
+  --learning_rate 1e-5
+  --mixed_precision "bf16"
+  --training_state_checkpointing_steps 50
+  --weight_only_checkpointing_steps 50
+  --weight_decay 0.01
+  --betas '0.0,0.999'
+  --max_grad_norm 1.0
+)
+
+# Miscellaneous arguments
+miscellaneous_args=(
+  --inference_mode False
+  --checkpoints_total_limit 3
+  --training_cfg_rate 0.0
+  --dit_precision "fp32"
+  --flow_shift 5
+  --seed 1000
+  --use_ema True
+  --ema_decay 0.99
+  --ema_start_step 100
+  --init_weights_from_safetensors "/mnt/weka/home/hao.zhang/wl/Self-Forcing/diffusers_ode_init/model.safetensors"
+)
+
+# Self-forcing DMD arguments
+dmd_args=(
+  --dmd_denoising_steps '1000,750,500,250'
+  --min_timestep_ratio 0.02
+  --max_timestep_ratio 0.98
+  --dfake_gen_update_ratio 5
+  --real_score_guidance_scale 3.0
+  --fake_score_learning_rate 8e-6
+  --fake_score_betas '0.0,0.999'
+)
+
+# Self-forcing specific arguments
+self_forcing_args=(
+  --independent_first_frame False  # Whether to treat first frame independently
+  --same_step_across_blocks False  # Whether to use same denoising step across all blocks
+  --last_step_only False  # Whether to only use the last denoising step
+  --context_noise 0  # Amount of noise to add during context caching (0 = no noise)
+  --validate_cache_structure False  # Set to True for debugging KV cache issues
+)
+
+srun torchrun \
+--nnodes $SLURM_JOB_NUM_NODES \
+--nproc_per_node $NUM_GPUS \
+--node_rank $SLURM_PROCID \
+--rdzv_backend=c10d \
+--rdzv_endpoint="$MASTER_ADDR:$MASTER_PORT" \
+    fastvideo/training/wan_self_forcing_distillation_pipeline.py \
+    "${parallel_args[@]}" \
+    "${model_args[@]}" \
+    "${dataset_args[@]}" \
+    "${training_args[@]}" \
+    "${optimizer_args[@]}" \
+    "${validation_args[@]}" \
+    "${miscellaneous_args[@]}" \
+    "${dmd_args[@]}" \
+    "${self_forcing_args[@]}"
@@ -0,0 +1,43 @@
+#!/bin/bash
+
+# Download the full dataset first
+python scripts/huggingface/download_hf.py --repo_id "wlsaidhi/crush-smol-merged" --local_dir "data/crush-smol" --repo_type "dataset"
+
+# Create a single-example dataset for debugging
+SINGLE_EXAMPLE_DIR="data/crush-smol-single"
+mkdir -p "$SINGLE_EXAMPLE_DIR/videos"
+
+# Copy the specific video that matches the validation.json style (macaron crushing)
+cp "data/crush-smol/videos/7P02AihYkCU-Scene-005.mp4" "$SINGLE_EXAMPLE_DIR/videos/"
+
+# Create a single-line videos.txt
+echo "videos/7P02AihYkCU-Scene-005.mp4" > "$SINGLE_EXAMPLE_DIR/videos.txt"
+
+# Create a single-line prompt.txt with the macaron crushing prompt
+echo "PIKA_CRUSH A large metal press is shown compressing a pile of colorful macarons, flattening them as if they were under a hydraulic press. The press moves down, crushing the macarons into a pile of crumbs and squishing the colorful filling out." > "$SINGLE_EXAMPLE_DIR/prompt.txt"
+
+# Generate the JSON file and merge.txt for the single example
+python scripts/dataset_preparation/prepare_json_file.py --data_folder "$SINGLE_EXAMPLE_DIR" --output "videos2caption.json"
+
+# Create a validation.json that uses the same example for consistency
+cat > "$SINGLE_EXAMPLE_DIR/validation.json" << 'EOF'
+{
+    "data": [
+      {
+        "caption": "A large metal press is shown compressing a pile of colorful macarons, flattening them as if they were under a hydraulic press. The press moves down, crushing the macarons into a pile of crumbs and squishing the colorful filling out.",
+        "image_path": null,
+        "video_path": null,
+        "num_inference_steps": 50,
+        "height": 480,
+        "width": 832,
+        "num_frames": 81
+      }
+    ]
+  }
+EOF
+
+echo "Single example dataset created at $SINGLE_EXAMPLE_DIR"
+echo "Contains:"
+echo "- 1 video: $(cat $SINGLE_EXAMPLE_DIR/videos.txt)"
+echo "- 1 prompt: $(cat $SINGLE_EXAMPLE_DIR/prompt.txt)"
+echo "- Validation file created with the same example for consistency" 
@@ -1,10 +1,10 @@
 #!/bin/bash

 GPU_NUM=1 # 2,4,8
-MODEL_PATH="weizhou03/Wan2.1-Fun-1.3B-InP-Diffusers"
+MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
 MODEL_TYPE="wan"
 DATA_MERGE_PATH="data/crush-smol/merge.txt"
-OUTPUT_DIR="data/crush-smol_processed_i2v_1_3b_inp/"
+OUTPUT_DIR="data/crush-smol_processed_t2v/"

 torchrun --nproc_per_node=$GPU_NUM \
    fastvideo/pipelines/preprocess/v1_preprocess.py \
@@ -14,11 +14,11 @@ torchrun --nproc_per_node=$GPU_NUM \
    --seed 42 \
    --max_height 480 \
    --max_width 832 \
-    --num_frames 77 \
+    --num_frames 81 \
    --dataloader_num_workers 0 \
    --output_dir=$OUTPUT_DIR \
    --train_fps 16 \
    --samples_per_file 8 \
    --flush_frequency 8 \
    --video_length_tolerance_range 5 \
-    --preprocess_task "i2v" 
+    --preprocess_task "t2v" 
@@ -0,0 +1,29 @@
+#!/bin/bash
+
+GPU_NUM=1 # 2,4,8
+MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
+MODEL_TYPE="wan"
+DATA_MERGE_PATH="data/crush-smol-single/merge.txt"
+OUTPUT_DIR="data/crush-smol-single_processed_t2v/"
+
+torchrun --nproc_per_node=$GPU_NUM \
+    fastvideo/pipelines/preprocess/v1_preprocess.py \
+    --model_path $MODEL_PATH \
+    --data_merge_path $DATA_MERGE_PATH \
+    --preprocess_video_batch_size 1 \
+    --seed 42 \
+    --max_height 480 \
+    --max_width 832 \
+    --num_frames 81 \
+    --dataloader_num_workers 0 \
+    --output_dir=$OUTPUT_DIR \
+    --train_fps 16 \
+    --samples_per_file 1 \
+    --flush_frequency 1 \
+    --video_length_tolerance_range 5 \
+    --preprocess_task "t2v"
+
+# Copy the validation.json to the output directory for consistency
+cp "data/crush-smol-single/validation.json" "$OUTPUT_DIR/"
+
+echo "Preprocessing completed. Validation file copied to $OUTPUT_DIR/" 
@@ -0,0 +1,31 @@
+{
+    "data": [
+      {
+        "caption": "A large metal cylinder is seen pressing down on a pile of Oreo cookies, flattening them as if they were under a hydraulic press.",
+        "image_path": null,
+        "video_path": null,
+        "num_inference_steps": 50,
+        "height": 480,
+        "width": 832,
+        "num_frames": 77
+      },
+      {
+        "caption": "A large metal cylinder is seen compressing colorful clay into a compact shape, demonstrating the power of a hydraulic press.",
+        "image_path": null,
+        "video_path": null,
+        "num_inference_steps": 50,
+        "height": 480,
+        "width": 832,
+        "num_frames": 77
+      },
+      {
+        "caption": "A large metal cylinder is seen pressing down on a pile of colorful candies, flattening them as if they were under a hydraulic press. The candies are crushed and broken into small pieces, creating a mess on the table.",
+        "image_path": null,
+        "video_path": null,
+        "num_inference_steps": 50,
+        "height": 480,
+        "width": 832,
+        "num_frames": 77
+      }
+    ]
+  }
@@ -1,16 +0,0 @@
-# Wan2.1-I2V-1.3B-InP Crush-Smol Example
-These are e2e example scripts for finetuning Wan2.1 T2V 1.3B InP on the crush-smol dataset.
-
-## Execute the following commands from `FastVideo/` to run training:
-
-### Download crush-smol dataset:
-
-`bash examples/training/finetune/wan_i2v_14b_480p/crush_smol/download_dataset.sh`
-
-### Preprocess the videos and captions into latents:
-
-`bash examples/training/finetune/wan_i2v_14b_480p/crush_smol/preprocess_wan_data_i2v.sh`
-
-### Edit the following file and run finetuning:
-
-`bash examples/training/finetune/wan_i2v_14b_480p/crush_smol/finetune_i2v.sh`
@@ -1,111 +0,0 @@
-#!/bin/bash
-
-export WANDB_BASE_URL="https://api.wandb.ai"
-export WANDB_MODE=online
-export TOKENIZERS_PARALLELISM=false
-# export FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA
-
-MODEL_PATH="weizhou03/Wan2.1-Fun-1.3B-InP-Diffusers"
-DATA_DIR="data/crush-smol_processed_i2v_1_3b_inp_single/combined_parquet_dataset/"
-VALIDATION_DATASET_FILE="examples/distill/Wan2.1-Fun-1.3B-InP/crush_smol/validation.json"
-NUM_GPUS=8
-export WANDB_API_KEY='8d9f4b39abd68eb4e29f6fc010b7ee71a2207cde'
-# export CUDA_VISIBLE_DEVICES=4,5
-# IP=[MASTER NODE IP]
-
-# Training arguments
-training_args=(
-  --tracker_project_name "wan_i2v_distill"
-  --output_dir "checkpoints/wan_i2v_distill"
-  --wandb_run_name "wan_i2v_distill"
-  --max_train_steps 2000
-  --train_batch_size 1
-  --train_sp_batch_size 1
-  --gradient_accumulation_steps 2
-  --num_latent_t 8
-  --num_height 480
-  --num_width 832
-  --num_frames 77
-  # --enable_gradient_checkpointing_type "full"
-)
-
-# Parallel arguments
-parallel_args=(
-  --num_gpus $NUM_GPUS
-  --sp_size 1
-  --tp_size 1
-  --hsdp_replicate_dim 1
-  --hsdp_shard_dim 8
-)
-
-# Model arguments
-model_args=(
-  --model_path $MODEL_PATH
-  --pretrained_model_name_or_path $MODEL_PATH
-)
-
-# Dataset arguments
-dataset_args=(
-  --data_path "$DATA_DIR"
-  --dataloader_num_workers 1
-)
-
-# Validation arguments
-validation_args=(
-  --log_validation
-  # --log_visualization
-  --log_visualization
-  --validation_dataset_file "$VALIDATION_DATASET_FILE"
-  --validation_steps 50
-  --validation_sampling_steps "4"
-  --validation_guidance_scale "1.0" # not used for dmd inference
-)
-
-# Optimizer arguments
-optimizer_args=(
-  --learning_rate 4e-6
-  --lr_scheduler "constant"
-  --fake_score_learning_rate 8e-7
-  --fake_score_lr_scheduler "constant"
-  --mixed_precision "bf16"
-  --training_state_checkpointing_steps 2000
-  --weight_only_checkpointing_steps 2000
-  --weight_decay 0.01
-  --max_grad_norm 1.0
-)
-
-# Miscellaneous arguments
-miscellaneous_args=(
-  --inference_mode False
-  --checkpoints_total_limit 3
-  --training_cfg_rate 0.0
-  --dit_precision "fp32"
-  --ema_start_step 0
-  --flow_shift 8
-  --seed 1000
-  # --enable_gradient_checkpointing_type "full"
-)
-
-dmd_args=(
-  --dmd_denoising_steps '1000,750,500,250'
-  --min_timestep_ratio 0.02
-  --max_timestep_ratio 0.98
-  --generator_update_interval 5
-  --simulate_generator_forward
-  --real_score_guidance_scale 5
-  --VSA_sparsity 0.8
-)
-
-# If you do not have 32 GPUs and to fit in memory, you can: 1. increase sp_size. 2. reduce num_latent_t
-torchrun \
-  --nnodes 1 \
-  --nproc_per_node $NUM_GPUS \
-    fastvideo/training/wan_i2v_distillation_pipeline.py \
-    "${parallel_args[@]}" \
-    "${model_args[@]}" \
-    "${dataset_args[@]}" \
-    "${training_args[@]}" \
-    "${optimizer_args[@]}" \
-    "${validation_args[@]}" \
-    "${miscellaneous_args[@]}" \
-    "${dmd_args[@]}"
@@ -1,111 +0,0 @@
-#!/bin/bash
-
-export WANDB_BASE_URL="https://api.wandb.ai"
-export WANDB_MODE=online
-export TOKENIZERS_PARALLELISM=false
-# export FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA
-
-MODEL_PATH="Wan-AI/Wan2.1-I2V-14B-480P-Diffusers"
-DATA_DIR="data/crush-smol_processed_i2v_1_3b_inp/combined_parquet_dataset/"
-VALIDATION_DATASET_FILE="examples/distill/Wan2.1-Fun-1.3B-InP/crush_smol/validation.json"
-NUM_GPUS=8
-export WANDB_API_KEY='8d9f4b39abd68eb4e29f6fc010b7ee71a2207cde'
-export HF_HUB_ENABLE_HF_TRANSFER=1
-# export CUDA_VISIBLE_DEVICES=4,5
-# IP=[MASTER NODE IP]
-
-# Training arguments
-training_args=(
-  --tracker_project_name "wan_i2v_distill"
-  --output_dir "checkpoints/wan_i2v_distill"
-  --wandb_run_name "wan_i2v_distill"
-  --max_train_steps 2000
-  --train_batch_size 1
-  --train_sp_batch_size 1
-  --gradient_accumulation_steps 2
-  --num_latent_t 8
-  --num_height 480
-  --num_width 832
-  --num_frames 77
-  # --enable_gradient_checkpointing_type "full"
-)
-
-# Parallel arguments
-parallel_args=(
-  --num_gpus $NUM_GPUS
-  --sp_size 1
-  --tp_size 1
-  --hsdp_replicate_dim 1
-  --hsdp_shard_dim 8
-)
-
-# Model arguments
-model_args=(
-  --model_path $MODEL_PATH
-  --pretrained_model_name_or_path $MODEL_PATH
-)
-
-# Dataset arguments
-dataset_args=(
-  --data_path "$DATA_DIR"
-  --dataloader_num_workers 1
-)
-
-# Validation arguments
-validation_args=(
-  --log_validation
-  # --log_visualization
-  --log_visualization
-  --validation_dataset_file "$VALIDATION_DATASET_FILE"
-  --validation_steps 50
-  --validation_sampling_steps "50"
-  --validation_guidance_scale "1.0" # not used for dmd inference
-)
-
-# Optimizer arguments
-optimizer_args=(
-  --learning_rate 4e-6
-  --lr_scheduler "constant"
-  --fake_score_learning_rate 8e-7
-  --fake_score_lr_scheduler "constant"
-  --mixed_precision "bf16"
-  --checkpointing_steps 2000
-  --weight_decay 0.01
-  --max_grad_norm 1.0
-)
-
-# Miscellaneous arguments
-miscellaneous_args=(
-  --inference_mode False
-  --checkpoints_total_limit 3
-  --training_cfg_rate 0.0
-  --dit_precision "fp32"
-  --ema_start_step 0
-  --flow_shift 8
-  --seed 1000
-  # --enable_gradient_checkpointing_type "full"
-)
-
-dmd_args=(
-  --dmd_denoising_steps '1000,757,522'
-  --min_timestep_ratio 0.02
-  --max_timestep_ratio 0.98
-  --generator_update_interval 5
-  --simulate_generator_forward
-  --real_score_guidance_scale 3.5
-  --VSA_sparsity 0.8
-)
-
-# If you do not have 32 GPUs and to fit in memory, you can: 1. increase sp_size. 2. reduce num_latent_t
-torchrun \
-  --nnodes 1 \
-  --nproc_per_node $NUM_GPUS \
-    fastvideo/training/wan_i2v_training_pipeline.py \
-    "${parallel_args[@]}" \
-    "${model_args[@]}" \
-    "${dataset_args[@]}" \
-    "${training_args[@]}" \
-    "${optimizer_args[@]}" \
-    "${validation_args[@]}" \
-    "${miscellaneous_args[@]}" \
-    "${dmd_args[@]}"
@@ -1,129 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=i2v
-#SBATCH --partition=main
-#SBATCH --nodes=4
-#SBATCH --ntasks=4
-#SBATCH --ntasks-per-node=1
-#SBATCH --gres=gpu:8
-#SBATCH --cpus-per-task=128
-#SBATCH --mem=1440G
-#SBATCH --output=i2v_output/i2v_%j.out
-#SBATCH --error=i2v_output/i2v_%j.err
-#SBATCH --exclusive
-set -e -x
-
-# Environment Setup
-source ~/conda/miniconda/bin/activate
-conda activate will-fv
-
-# Basic Info
-export WANDB_MODE="online"
-export NCCL_P2P_DISABLE=1
-export TORCH_NCCL_ENABLE_MONITORING=0
-# different cache dir for different processes
-export TRITON_CACHE_DIR=/tmp/triton_cache_${SLURM_PROCID}
-export MASTER_PORT=29500
-export NODE_RANK=$SLURM_PROCID
-nodes=( $(scontrol show hostnames $SLURM_JOB_NODELIST) )
-export MASTER_ADDR=${nodes[0]}
-export CUDA_VISIBLE_DEVICES=$SLURM_LOCALID
-export TOKENIZERS_PARALLELISM=false
-export WANDB_BASE_URL="https://api.wandb.ai"
-export WANDB_MODE=online
-# export FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA
-
-echo "MASTER_ADDR: $MASTER_ADDR"
-echo "NODE_RANK: $NODE_RANK"
-
-# Configs
-NUM_GPUS=8
-
-MODEL_PATH="weizhou03/Wan2.1-Fun-1.3B-InP-Diffusers"
-DATA_DIR="data/crush-smol_processed_i2v_1_3b_inp/combined_parquet_dataset/"
-VALIDATION_DATASET_FILE="examples/training/finetune/Wan2.1-Fun-1.3B-InP/crush_smol/validation.json"
-# export CUDA_VISIBLE_DEVICES=4,5
-# IP=[MASTER NODE IP]
-
-# If you do not have 32 GPUs and to fit in memory, you can: 1. increase sp_size. 2. reduce num_latent_t
-
-# Training arguments
-training_args=(
-  --tracker_project_name wan_i2v_finetune
-  --output_dir "checkpoints/wan_i2v_finetune"
-  --max_train_steps 2000
-  --train_batch_size 2
-  --train_sp_batch_size 1
-  --gradient_accumulation_steps 1
-  --num_latent_t 8
-  --num_height 480
-  --num_width 832
-  --num_frames 77
-  --enable_gradient_checkpointing_type "full"
-)
-
-# Parallel arguments
-parallel_args=(
-  --num_gpus $NUM_GPUS
-  --sp_size $NUM_GPUS
-  --tp_size $NUM_GPUS
-  --hsdp_replicate_dim $SLURM_JOB_NUM_NODES
-  --hsdp_shard_dim $NUM_GPUS
-)
-
-# Model arguments
-model_args=(
-  --model_path $MODEL_PATH
-  --pretrained_model_name_or_path $MODEL_PATH
-)
-
-# Dataset arguments
-dataset_args=(
-  --data_path "$DATA_DIR"
-  --dataloader_num_workers 10
-)
-
-# Validation arguments
-validation_args=(
-  --log_validation
-  --validation_dataset_file "$VALIDATION_DATASET_FILE"
-  --validation_steps 100
-  --validation_sampling_steps "40"
-  --validation_guidance_scale "6.0"
-)
-
-# Optimizer arguments
-optimizer_args=(
-  --learning_rate 1e-5
-  --mixed_precision "bf16"
-  --checkpointing_steps 1000
-  --weight_decay 1e-4
-  --max_grad_norm 1.0
-)
-
-# Miscellaneous arguments
-miscellaneous_args=(
-  --inference_mode False
-  --checkpoints_total_limit 3
-  --training_cfg_rate 0.1
-  --multi_phased_distill_schedule "4000-1"
-  --not_apply_cfg_solver
-  --dit_precision "fp32"
-  --num_euler_timesteps 50
-  --ema_start_step 0
-  --enable_gradient_checkpointing_type "full"
-)
-
-srun torchrun \
--nnodes $SLURM_JOB_NUM_NODES \
--nproc_per_node $NUM_GPUS \
--node_rank $SLURM_PROCID \
--rdzv_backend=c10d \
--rdzv_endpoint="$MASTER_ADDR:$MASTER_PORT" \
-    fastvideo/training/wan_i2v_training_pipeline.py \
-    "${parallel_args[@]}" \
-    "${model_args[@]}" \
-    "${dataset_args[@]}" \
-    "${training_args[@]}" \
-    "${optimizer_args[@]}" \
-    "${validation_args[@]}" \
-    "${miscellaneous_args[@]}"
@@ -1,76 +0,0 @@
-{
-  "data": [
-    {
-      "caption": "A large metal cylinder is seen pressing down on a pile of Oreo cookies, flattening them as if they were under a hydraulic press.",
-      "image_path": null,
-      "video_path": "validation_dataset/yYcK4nANZz4-Scene-034.mp4",
-      "num_inference_steps": 40,
-      "height": 480,
-      "width": 832,
-      "num_frames": 77
-    },
-    {
-      "caption": "A large metal cylinder is seen compressing colorful clay into a compact shape, demonstrating the power of a hydraulic press.",
-      "image_path": null,
-      "video_path": "validation_dataset/yYcK4nANZz4-Scene-027.mp4",
-      "num_inference_steps": 40,
-      "height": 480,
-      "width": 832,
-      "num_frames": 77
-    },
-    {
-      "caption": "A large metal cylinder is seen pressing down on a pile of colorful candies, flattening them as if they were under a hydraulic press. The candies are crushed and broken into small pieces, creating a mess on the table.",
-      "image_path": null,
-      "video_path": "validation_dataset/yYcK4nANZz4-Scene-030.mp4",
-      "num_inference_steps": 40,
-      "height": 480,
-      "width": 832,
-      "num_frames": 77
-    },
-    {
-      "caption": "A watermelon wearing a helmet is crushed by a hydraulic press, causing it to flatten and burst open.",
-      "image_path": null,
-      "video_path": "validation_dataset/1gGQy4nxyUo-Scene-016.mp4",
-      "num_inference_steps": 40,
-      "height": 480,
-      "width": 832,
-      "num_frames": 77
-    },
-    {
-      "caption": "The video shows a green and orange object being flattened as if it were under a hydraulic press, with the press moving down and compressing the object.",
-      "image_path": null,
-      "video_path": "validation_dataset/1gGQy4nxyUo-Scene-056.mp4",
-      "num_inference_steps": 40,
-      "height": 480,
-      "width": 832,
-      "num_frames": 77
-    },
-    {
-      "caption": "The video shows a cylindrical object with a cityscape image being flattened as if it were under a hydraulic press. The object is placed on a metal platform, and a large, striped cylinder presses down on it, causing it to collapse and release a liquid inside. The background features a green wall with a yellow and red warning sign.",
-      "image_path": null,
-      "video_path": "validation_dataset/1gGQy4nxyUo-Scene-059.mp4",
-      "num_inference_steps": 40,
-      "height": 480,
-      "width": 832,
-      "num_frames": 77
-    },
-    {
-      "caption": "The video shows a close-up of an orange being flattened as if it were under a hydraulic press, with the press moving down and compressing the fruit until it is completely flattened.",
-      "image_path": null,
-      "video_path": "validation_dataset/EJqsC21GSBY-Scene-059.mp4",
-      "num_inference_steps": 40,
-      "height": 480,
-      "width": 832,
-      "num_frames": 77
-    },
-    {
-      "caption": "A colorful puzzle ball is being crushed by a large metal cylinder, which flattens the objects as if they were under a hydraulic press.",
-      "image_path": null,
-      "video_path": "validation_dataset/GBSfpTcKegk-Scene-003.mp4",
-      "num_inference_steps": 40,
-      "height": 480,
-      "width": 832,
-      "num_frames": 77
-    }
-  ]
-}
@@ -1,31 +0,0 @@
-{
-  "data": [
-    {
-      "caption": "A large metal cylinder is seen pressing down on a pile of Oreo cookies, flattening them as if they were under a hydraulic press.",
-      "image_path": null,
-      "video_path": "validation_dataset/yYcK4nANZz4-Scene-034.mp4",
-      "num_inference_steps": 40,
-      "height": 480,
-      "width": 832,
-      "num_frames": 77
-    },
-    {
-      "caption": "A large metal cylinder is seen compressing colorful clay into a compact shape, demonstrating the power of a hydraulic press.",
-      "image_path": null,
-      "video_path": "validation_dataset/yYcK4nANZz4-Scene-027.mp4",
-      "num_inference_steps": 40,
-      "height": 480,
-      "width": 832,
-      "num_frames": 77
-    },
-    {
-      "caption": "A large metal cylinder is seen pressing down on a pile of colorful candies, flattening them as if they were under a hydraulic press. The candies are crushed and broken into small pieces, creating a mess on the table.",
-      "image_path": null,
-      "video_path": "validation_dataset/yYcK4nANZz4-Scene-030.mp4",
-      "num_inference_steps": 40,
-      "height": 480,
-      "width": 832,
-      "num_frames": 77
-    }
-  ]
-}
@@ -1,16 +0,0 @@
-# Wan2.1-I2V-1.3B-InP Crush-Smol Example
-These are e2e example scripts for finetuning Wan2.1 T2V 1.3B InP on the crush-smol dataset.
-
-## Execute the following commands from `FastVideo/` to run training:
-
-### Download crush-smol dataset:
-
-`bash examples/training/finetune/wan_i2v_14b_480p/crush_smol/download_dataset.sh`
-
-### Preprocess the videos and captions into latents:
-
-`bash examples/training/finetune/wan_i2v_14b_480p/crush_smol/preprocess_wan_data_i2v.sh`
-
-### Edit the following file and run finetuning:
-
-`bash examples/training/finetune/wan_i2v_14b_480p/crush_smol/finetune_i2v.sh`
@@ -1,122 +0,0 @@
-#!/bin/bash
-
-export WANDB_BASE_URL="https://api.wandb.ai"
-export WANDB_MODE=online
-export TOKENIZERS_PARALLELISM=false
-# export FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA
-
-MODEL_PATH="Wan-AI/Wan2.1-I2V-14B-480P-Diffusers"
-# MODEL_PATH="weizhou03/Wan2.1-Fun-1.3B-InP-Diffusers"
-DATA_DIR="data/crush-smol_processed_wan21_i2v_14b/combined_parquet_dataset/"
-# DATA_DIR="data/crush-smol_processed_i2v_1_3b_inp/combined_parquet_dataset/"
-VALIDATION_DATASET_FILE="examples/distill/Wan2.1-I2V/crush_smol/validation_orig.json"
-NUM_GPUS=8
-export WANDB_API_KEY='8d9f4b39abd68eb4e29f6fc010b7ee71a2207cde'
-# export CUDA_VISIBLE_DEVICES=4,5
-# IP=[MASTER NODE IP]
-
-# Training arguments
-training_args=(
-  --tracker_project_name "wan_i2v_distill"
-  --output_dir "checkpoints/wan_i2v_distill"
-  --wandb_run_name "14b_i2v_cm"
-  --max_train_steps 1500
-  --train_batch_size 1
-  --train_sp_batch_size 1
-  --gradient_accumulation_steps 1
-  --num_latent_t 8
-  --num_height 480
-  --num_width 832
-  --num_frames 77
-  --enable_gradient_checkpointing_type "full"
-)
-
-# Parallel arguments
-parallel_args=(
-  --num_gpus $NUM_GPUS
-  --sp_size 1
-  --tp_size 1
-  --hsdp_replicate_dim 1
-  --hsdp_shard_dim 8
-)
-
-# Model arguments
-model_args=(
-  --model_path $MODEL_PATH
-  --pretrained_model_name_or_path $MODEL_PATH
-)
-
-# Dataset arguments
-dataset_args=(
-  --data_path "$DATA_DIR"
-  --dataloader_num_workers 1
-)
-
-# Validation arguments
-validation_args=(
-  --log_validation
-  # --log_visualization
-  --log_visualization
-  --validation_dataset_file "$VALIDATION_DATASET_FILE"
-  --validation_steps 20
-  --validation_sampling_steps "3"
-  --validation_guidance_scale "1.0" # not used for dmd inference
-)
-
-# Optimizer arguments
-optimizer_args=(
-  --learning_rate 4e-6
-  --lr_scheduler "constant"
-  # --min_lr_ratio 0.5
-  # --lr_warmup_steps 50
-  --fake_score_learning_rate 7e-7
-  --fake_score_lr_scheduler "constant"
-  --mixed_precision "bf16"
-  --training_state_checkpointing_steps 2000
-  --weight_only_checkpointing_steps 2000
-  --weight_decay 0.01
-  --max_grad_norm 1.0
-)
-
-# Miscellaneous arguments
-miscellaneous_args=(
-  --inference_mode False
-  --checkpoints_total_limit 3
-  --training_cfg_rate 0.0
-  --dit_precision "fp32"
-  --ema_start_step 0
-  --flow_shift 8
-  --seed 1000
-  # --enable_gradient_checkpointing_type "full"
-)
-
-dmd_args=(
-  --dmd_denoising_steps '1000,750,500,250'
-  --warp_denoising_step True
-  --min_timestep_ratio 0.02
-  --max_timestep_ratio 0.96
-  --generator_update_interval 5
-  --simulate_generator_forward
-  --simulate_forward_interval 1
-  --real_score_guidance_scale 5
-  --VSA_sparsity 0.8
-  --regression_loss_weight 0.01
-  --use_regression_loss False
-  --cm_loss_weight 1
-  --ema_decay 0.999
-  --cm_use_ema_teacher
-)
-
-# If you do not have 32 GPUs and to fit in memory, you can: 1. increase sp_size. 2. reduce num_latent_t
-torchrun \
-  --nnodes 1 \
-  --nproc_per_node $NUM_GPUS \
-    fastvideo/training/wan_i2v_distillation_pipeline.py \
-    "${parallel_args[@]}" \
-    "${model_args[@]}" \
-    "${dataset_args[@]}" \
-    "${training_args[@]}" \
-    "${optimizer_args[@]}" \
-    "${validation_args[@]}" \
-    "${miscellaneous_args[@]}" \
-    "${dmd_args[@]}"
@@ -1,3 +0,0 @@
-#!/bin/bash
-
-python scripts/huggingface/download_hf.py --repo_id "wlsaidhi/crush-smol-merged" --local_dir "data/crush-smol" --repo_type "dataset"
@@ -1,111 +0,0 @@
-#!/bin/bash
-
-export WANDB_BASE_URL="https://api.wandb.ai"
-export WANDB_MODE=online
-export TOKENIZERS_PARALLELISM=false
-# export FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA
-
-MODEL_PATH="Wan-AI/Wan2.1-I2V-14B-480P-Diffusers"
-DATA_DIR="data/crush-smol_processed_wan21_i2v_14b/combined_parquet_dataset/"
-VALIDATION_DATASET_FILE="examples/distill/Wan2.1-I2V/crush_smol/validation.json"
-NUM_GPUS=8
-export WANDB_API_KEY='8d9f4b39abd68eb4e29f6fc010b7ee71a2207cde'
-export HF_HUB_ENABLE_HF_TRANSFER=1
-# export CUDA_VISIBLE_DEVICES=4,5
-# IP=[MASTER NODE IP]
-
-# Training arguments
-training_args=(
-  --tracker_project_name "wan_i2v_distill"
-  --output_dir "checkpoints/wan_i2v_distill"
-  --wandb_run_name "wan21_i2v_14b_ode_50steps"
-  --max_train_steps 2000
-  --train_batch_size 1
-  --train_sp_batch_size 1
-  --gradient_accumulation_steps 2
-  --num_latent_t 8
-  --num_height 480
-  --num_width 832
-  --num_frames 77
-  # --enable_gradient_checkpointing_type "full"
-)
-
-# Parallel arguments
-parallel_args=(
-  --num_gpus $NUM_GPUS
-  --sp_size 8
-  --tp_size 1
-  --hsdp_replicate_dim 1
-  --hsdp_shard_dim 8
-)
-
-# Model arguments
-model_args=(
-  --model_path $MODEL_PATH
-  --pretrained_model_name_or_path $MODEL_PATH
-)
-
-# Dataset arguments
-dataset_args=(
-  --data_path "$DATA_DIR"
-  --dataloader_num_workers 1
-)
-
-# Validation arguments
-validation_args=(
-  --log_validation
-  # --log_visualization
-  --log_visualization
-  --validation_dataset_file "$VALIDATION_DATASET_FILE"
-  --validation_steps 50
-  --validation_sampling_steps "50"
-  --validation_guidance_scale "1.0" # not used for dmd inference
-)
-
-# Optimizer arguments
-optimizer_args=(
-  --learning_rate 4e-6
-  --lr_scheduler "constant"
-  --fake_score_learning_rate 8e-7
-  --fake_score_lr_scheduler "constant"
-  --mixed_precision "bf16"
-  --checkpointing_steps 2000
-  --weight_decay 0.01
-  --max_grad_norm 1.0
-)
-
-# Miscellaneous arguments
-miscellaneous_args=(
-  --inference_mode False
-  --checkpoints_total_limit 3
-  --training_cfg_rate 0.0
-  --dit_precision "fp32"
-  --ema_start_step 0
-  --flow_shift 8
-  --seed 1000
-  # --enable_gradient_checkpointing_type "full"
-)
-
-dmd_args=(
-  --dmd_denoising_steps '1000,757,522'
-  --min_timestep_ratio 0.02
-  --max_timestep_ratio 0.98
-  --generator_update_interval 5
-  --simulate_generator_forward
-  --real_score_guidance_scale 3.5
-  --VSA_sparsity 0.8
-)
-
-# If you do not have 32 GPUs and to fit in memory, you can: 1. increase sp_size. 2. reduce num_latent_t
-torchrun \
-  --nnodes 1 \
-  --nproc_per_node $NUM_GPUS \
-    fastvideo/training/wan_i2v_training_pipeline.py \
-    "${parallel_args[@]}" \
-    "${model_args[@]}" \
-    "${dataset_args[@]}" \
-    "${training_args[@]}" \
-    "${optimizer_args[@]}" \
-    "${validation_args[@]}" \
-    "${miscellaneous_args[@]}" \
-    "${dmd_args[@]}"
@@ -1,129 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=i2v
-#SBATCH --partition=main
-#SBATCH --nodes=4
-#SBATCH --ntasks=4
-#SBATCH --ntasks-per-node=1
-#SBATCH --gres=gpu:8
-#SBATCH --cpus-per-task=128
-#SBATCH --mem=1440G
-#SBATCH --output=i2v_output/i2v_%j.out
-#SBATCH --error=i2v_output/i2v_%j.err
-#SBATCH --exclusive
-set -e -x
-
-# Environment Setup
-source ~/conda/miniconda/bin/activate
-conda activate will-fv
-
-# Basic Info
-export WANDB_MODE="online"
-export NCCL_P2P_DISABLE=1
-export TORCH_NCCL_ENABLE_MONITORING=0
-# different cache dir for different processes
-export TRITON_CACHE_DIR=/tmp/triton_cache_${SLURM_PROCID}
-export MASTER_PORT=29500
-export NODE_RANK=$SLURM_PROCID
-nodes=( $(scontrol show hostnames $SLURM_JOB_NODELIST) )
-export MASTER_ADDR=${nodes[0]}
-export CUDA_VISIBLE_DEVICES=$SLURM_LOCALID
-export TOKENIZERS_PARALLELISM=false
-export WANDB_BASE_URL="https://api.wandb.ai"
-export WANDB_MODE=online
-# export FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA
-
-echo "MASTER_ADDR: $MASTER_ADDR"
-echo "NODE_RANK: $NODE_RANK"
-
-# Configs
-NUM_GPUS=8
-
-MODEL_PATH="weizhou03/Wan2.1-Fun-1.3B-InP-Diffusers"
-DATA_DIR="data/crush-smol_processed_i2v_1_3b_inp/combined_parquet_dataset/"
-VALIDATION_DATASET_FILE="examples/training/finetune/Wan2.1-Fun-1.3B-InP/crush_smol/validation.json"
-# export CUDA_VISIBLE_DEVICES=4,5
-# IP=[MASTER NODE IP]
-
-# If you do not have 32 GPUs and to fit in memory, you can: 1. increase sp_size. 2. reduce num_latent_t
-
-# Training arguments
-training_args=(
-  --tracker_project_name wan_i2v_finetune
-  --output_dir "checkpoints/wan_i2v_finetune"
-  --max_train_steps 2000
-  --train_batch_size 2
-  --train_sp_batch_size 1
-  --gradient_accumulation_steps 1
-  --num_latent_t 8
-  --num_height 480
-  --num_width 832
-  --num_frames 77
-  --enable_gradient_checkpointing_type "full"
-)
-
-# Parallel arguments
-parallel_args=(
-  --num_gpus $NUM_GPUS
-  --sp_size $NUM_GPUS
-  --tp_size $NUM_GPUS
-  --hsdp_replicate_dim $SLURM_JOB_NUM_NODES
-  --hsdp_shard_dim $NUM_GPUS
-)
-
-# Model arguments
-model_args=(
-  --model_path $MODEL_PATH
-  --pretrained_model_name_or_path $MODEL_PATH
-)
-
-# Dataset arguments
-dataset_args=(
-  --data_path "$DATA_DIR"
-  --dataloader_num_workers 10
-)
-
-# Validation arguments
-validation_args=(
-  --log_validation
-  --validation_dataset_file "$VALIDATION_DATASET_FILE"
-  --validation_steps 100
-  --validation_sampling_steps "40"
-  --validation_guidance_scale "6.0"
-)
-
-# Optimizer arguments
-optimizer_args=(
-  --learning_rate 1e-5
-  --mixed_precision "bf16"
-  --checkpointing_steps 1000
-  --weight_decay 1e-4
-  --max_grad_norm 1.0
-)
-
-# Miscellaneous arguments
-miscellaneous_args=(
-  --inference_mode False
-  --checkpoints_total_limit 3
-  --training_cfg_rate 0.1
-  --multi_phased_distill_schedule "4000-1"
-  --not_apply_cfg_solver
-  --dit_precision "fp32"
-  --num_euler_timesteps 50
-  --ema_start_step 0
-  --enable_gradient_checkpointing_type "full"
-)
-
-srun torchrun \
--nnodes $SLURM_JOB_NUM_NODES \
--nproc_per_node $NUM_GPUS \
--node_rank $SLURM_PROCID \
--rdzv_backend=c10d \
--rdzv_endpoint="$MASTER_ADDR:$MASTER_PORT" \
-    fastvideo/training/wan_i2v_training_pipeline.py \
-    "${parallel_args[@]}" \
-    "${model_args[@]}" \
-    "${dataset_args[@]}" \
-    "${training_args[@]}" \
-    "${optimizer_args[@]}" \
-    "${validation_args[@]}" \
-    "${miscellaneous_args[@]}"
@@ -1,26 +0,0 @@
-#!/bin/bash
-
-GPU_NUM=1 # 2,4,8
-MODEL_PATH="Wan-AI/Wan2.1-T2V-14B-Diffusers"
-MODEL_TYPE="wan"
-# DATA_MERGE_PATH="data/single/merge.txt"
-DATA_MERGE_PATH="data/crush-smol/merge.txt"
-OUTPUT_DIR="data/crush-smol_processed_wan21_t2v_14b/"
-# OUTPUT_DIR="data/single_processed_i2v_14b/"
-
-torchrun --nproc_per_node=$GPU_NUM \
-    fastvideo/pipelines/preprocess/v1_preprocess.py \
-    --model_path $MODEL_PATH \
-    --data_merge_path $DATA_MERGE_PATH \
-    --preprocess_video_batch_size 8 \
-    --seed 42 \
-    --max_height 480 \
-    --max_width 832 \
-    --num_frames 77 \
-    --dataloader_num_workers 0 \
-    --output_dir=$OUTPUT_DIR \
-    --train_fps 16 \
-    --samples_per_file 8\
-    --flush_frequency 8 \
-    --video_length_tolerance_range 5 \
-    --preprocess_task "t2v" 
@@ -1,13 +0,0 @@
-{
-  "data": [
-    {
-      "caption": "A watermelon wearing a helmet is crushed by a hydraulic press, causing it to flatten and burst open.",
-      "image_path": null,
-      "video_path": "validation_dataset/1gGQy4nxyUo-Scene-016.mp4",
-      "num_inference_steps": 40,
-      "height": 480,
-      "width": 832,
-      "num_frames": 77
-    }
-  ]
-}
@@ -1,76 +0,0 @@
-{
-  "data": [
-    {
-      "caption": "A large metal cylinder is seen pressing down on a pile of Oreo cookies, flattening them as if they were under a hydraulic press.",
-      "image_path": null,
-      "video_path": "validation_dataset/yYcK4nANZz4-Scene-034.mp4",
-      "num_inference_steps": 40,
-      "height": 480,
-      "width": 832,
-      "num_frames": 77
-    },
-    {
-      "caption": "A large metal cylinder is seen compressing colorful clay into a compact shape, demonstrating the power of a hydraulic press.",
-      "image_path": null,
-      "video_path": "validation_dataset/yYcK4nANZz4-Scene-027.mp4",
-      "num_inference_steps": 40,
-      "height": 480,
-      "width": 832,
-      "num_frames": 77
-    },
-    {
-      "caption": "A large metal cylinder is seen pressing down on a pile of colorful candies, flattening them as if they were under a hydraulic press. The candies are crushed and broken into small pieces, creating a mess on the table.",
-      "image_path": null,
-      "video_path": "validation_dataset/yYcK4nANZz4-Scene-030.mp4",
-      "num_inference_steps": 40,
-      "height": 480,
-      "width": 832,
-      "num_frames": 77
-    },
-    {
-      "caption": "A watermelon wearing a helmet is crushed by a hydraulic press, causing it to flatten and burst open.",
-      "image_path": null,
-      "video_path": "validation_dataset/1gGQy4nxyUo-Scene-016.mp4",
-      "num_inference_steps": 40,
-      "height": 480,
-      "width": 832,
-      "num_frames": 77
-    },
-    {
-      "caption": "The video shows a green and orange object being flattened as if it were under a hydraulic press, with the press moving down and compressing the object.",
-      "image_path": null,
-      "video_path": "validation_dataset/1gGQy4nxyUo-Scene-056.mp4",
-      "num_inference_steps": 40,
-      "height": 480,
-      "width": 832,
-      "num_frames": 77
-    },
-    {
-      "caption": "The video shows a cylindrical object with a cityscape image being flattened as if it were under a hydraulic press. The object is placed on a metal platform, and a large, striped cylinder presses down on it, causing it to collapse and release a liquid inside. The background features a green wall with a yellow and red warning sign.",
-      "image_path": null,
-      "video_path": "validation_dataset/1gGQy4nxyUo-Scene-059.mp4",
-      "num_inference_steps": 40,
-      "height": 480,
-      "width": 832,
-      "num_frames": 77
-    },
-    {
-      "caption": "The video shows a close-up of an orange being flattened as if it were under a hydraulic press, with the press moving down and compressing the fruit until it is completely flattened.",
-      "image_path": null,
-      "video_path": "validation_dataset/EJqsC21GSBY-Scene-059.mp4",
-      "num_inference_steps": 40,
-      "height": 480,
-      "width": 832,
-      "num_frames": 77
-    },
-    {
-      "caption": "A colorful puzzle ball is being crushed by a large metal cylinder, which flattens the objects as if they were under a hydraulic press.",
-      "image_path": null,
-      "video_path": "validation_dataset/GBSfpTcKegk-Scene-003.mp4",
-      "num_inference_steps": 40,
-      "height": 480,
-      "width": 832,
-      "num_frames": 77
-    }
-  ]
-}
@@ -1,118 +0,0 @@
-#!/bin/bash
-
-export WANDB_BASE_URL="https://api.wandb.ai"
-export WANDB_MODE=online
-export TOKENIZERS_PARALLELISM=false
-# export FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA
-
-MODEL_PATH="Wan-AI/Wan2.1-T2V-14B-Diffusers"
-# MODEL_PATH="weizhou03/Wan2.1-Fun-1.3B-InP-Diffusers"
-DATA_DIR="data/crush-smol_processed_wan21_t2v_14b/combined_parquet_dataset/"
-# DATA_DIR="data/crush-smol_processed_i2v_1_3b_inp/combined_parquet_dataset/"
-VALIDATION_DATASET_FILE="examples/distill/Wan2.1-I2V/crush_smol/validation_orig.json"
-NUM_GPUS=8
-export WANDB_API_KEY='8d9f4b39abd68eb4e29f6fc010b7ee71a2207cde'
-# export CUDA_VISIBLE_DEVICES=4,5
-# IP=[MASTER NODE IP]
-
-# Training arguments
-training_args=(
-  --tracker_project_name "wan_i2v_distill"
-  --output_dir "checkpoints/wan_i2v_distill"
-  --wandb_run_name "cm_t2v"
-  --max_train_steps 1500
-  --train_batch_size 1
-  --train_sp_batch_size 1
-  --gradient_accumulation_steps 1
-  --num_latent_t 8
-  --num_height 480
-  --num_width 832
-  --num_frames 77
-  --enable_gradient_checkpointing_type "full"
-)
-
-# Parallel arguments
-parallel_args=(
-  --num_gpus $NUM_GPUS
-  --sp_size 1
-  --tp_size 1
-  --hsdp_replicate_dim 1
-  --hsdp_shard_dim 8
-)
-
-# Model arguments
-model_args=(
-  --model_path $MODEL_PATH
-  --pretrained_model_name_or_path $MODEL_PATH
-)
-
-# Dataset arguments
-dataset_args=(
-  --data_path "$DATA_DIR"
-  --dataloader_num_workers 1
-)
-
-# Validation arguments
-validation_args=(
-  --log_validation
-  # --log_visualization
-  --log_visualization
-  --validation_dataset_file "$VALIDATION_DATASET_FILE"
-  --validation_steps 20
-  --validation_sampling_steps "3"
-  --validation_guidance_scale "1.0" # not used for dmd inference
-)
-
-# Optimizer arguments
-optimizer_args=(
-  --learning_rate 4e-6
-  --lr_scheduler "constant"
-  # --min_lr_ratio 0.5
-  # --lr_warmup_steps 50
-  --mixed_precision "bf16"
-  --training_state_checkpointing_steps 2000
-  --weight_only_checkpointing_steps 2000
-  --weight_decay 0.01
-  --max_grad_norm 1.0
-)
-
-# Miscellaneous arguments
-miscellaneous_args=(
-  --inference_mode False
-  --checkpoints_total_limit 3
-  --training_cfg_rate 0.0
-  --dit_precision "fp32"
-  --ema_start_step 0
-  --flow_shift 8
-  --seed 1000
-  # --enable_gradient_checkpointing_type "full"
-)
-
-dmd_args=(
-  --dmd_denoising_steps '1000,750,500,250'
-  --warp_denoising_step True
-  --min_timestep_ratio 0.02
-  --max_timestep_ratio 0.96
-  --real_score_guidance_scale 5
-  --VSA_sparsity 0.8
-  --regression_loss_weight 0.01
-  --use_regression_loss False
-  --cm_loss_weight 1
-  --ema_decay 0.999
-  --cm_weighing_function "constant"
-  # --cm_use_ema_teacher
-)
-
-# If you do not have 32 GPUs and to fit in memory, you can: 1. increase sp_size. 2. reduce num_latent_t
-torchrun \
-  --nnodes 1 \
-  --nproc_per_node $NUM_GPUS \
-    fastvideo/training/wan_cm_distillation_pipeline.py \
-    "${parallel_args[@]}" \
-    "${model_args[@]}" \
-    "${dataset_args[@]}" \
-    "${training_args[@]}" \
-    "${optimizer_args[@]}" \
-    "${validation_args[@]}" \
-    "${miscellaneous_args[@]}" \
-    "${dmd_args[@]}"
@@ -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()
@@ -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,
@@ -9,19 +9,17 @@ MODEL_PATH="weizhou03/Wan2.1-Fun-1.3B-InP-Diffusers"
 DATA_DIR="data/crush-smol_processed_i2v_1_3b_inp/combined_parquet_dataset/"
 VALIDATION_DATASET_FILE="examples/training/finetune/Wan2.1-Fun-1.3B-InP/crush_smol/validation.json"
 NUM_GPUS=8
-export WANDB_API_KEY='8d9f4b39abd68eb4e29f6fc010b7ee71a2207cde'
 # export CUDA_VISIBLE_DEVICES=4,5
 # IP=[MASTER NODE IP]

 # Training arguments
 training_args=(
  --tracker_project_name "wan_i2v_finetune"
-  --output_dir "outputs/wan_i2v_finetune"
-  --wandb_run_name "wan_i2v_finetune"
+  --output_dir "$DATA_DIR/outputs/wan_i2v_finetune"
  --max_train_steps 2000
-  --train_batch_size 1
+  --train_batch_size 4
  --train_sp_batch_size 1
-  --gradient_accumulation_steps 2
+  --gradient_accumulation_steps 1
  --num_latent_t 8
  --num_height 480
  --num_width 832
@@ -32,10 +30,10 @@ training_args=(
 # Parallel arguments
 parallel_args=(
  --num_gpus $NUM_GPUS
-  --sp_size 1
-  --tp_size 1
-  --hsdp_replicate_dim 1
-  --hsdp_shard_dim 8
+  --sp_size 4
+  --tp_size 4
+  --hsdp_replicate_dim 2
+  --hsdp_shard_dim 4
 )

 # Model arguments
@@ -61,7 +59,7 @@ validation_args=(

 # Optimizer arguments
 optimizer_args=(
-  --learning_rate 1e-5
+  --learning_rate 2e-5
  --mixed_precision "bf16"
  --checkpointing_steps 2000
  --weight_decay 1e-4
@@ -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 +1,19 @@
 #!/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.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 +1,36 @@
 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]
+

@dataclasses.dataclass
 class PreprocessConfig:
@@ -12,6 +39,7 @@ class PreprocessConfig:
    # Model and dataset configuration
    model_path: str = ""
    dataset_path: str = ""
+    dataset_type: DatasetType = DatasetType.HF
    dataset_output_dir: str = "./output"

    # Dataloader configuration
@@ -35,6 +63,9 @@ 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 +83,12 @@ 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,
@@ -123,6 +160,10 @@ 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 +171,10 @@ 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'])
+
        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
@@ -103,4 +109,4 @@ class WanVideoArchConfig(DiTArchConfig):
 class WanVideoConfig(DiTConfig):
    arch_config: DiTArchConfig = field(default_factory=WanVideoArchConfig)

-    prefix: str = "Wan"
+    prefix: str = "Wan"
@@ -9,6 +9,7 @@ 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,
+                                             SelfForcingWanT2V480PConfig,
                                             WanI2V480PConfig, WanI2V720PConfig,
                                             WanT2V480PConfig, WanT2V720PConfig)
 from fastvideo.logger import init_logger
@@ -34,7 +35,7 @@ PIPE_NAME_TO_CONFIG: dict[str, type[PipelineConfig]] = {
    "Wan-AI/Wan2.2-TI2V-5B-Diffusers": WanT2V720PConfig,
    "Wan-AI/Wan2.2-T2V-A14B-Diffusers": WanT2V480PConfig,
    "Wan-AI/Wan2.2-I2V-A14B-Diffusers": WanI2V480PConfig,
-    "weizhou03/Wan2.1-Fun-1.3B-InP-Diffusers": WanI2V480PConfig,
+    "wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers": SelfForcingWanT2V480PConfig,
    # Add other specific weight variants
 }

@@ -138,3 +138,14 @@ 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
+    flow_shift: int = 5
+    dmd_denoising_steps: list[int] | None = field(
+        default_factory=lambda: [1000, 750, 500, 250])
@@ -17,7 +17,7 @@ from fastvideo.configs.sample.wan import (
    WanI2V_14B_720P_SamplingParam,
    WanT2V_1_3B_SamplingParam,
    WanT2V_14B_SamplingParam,
-    Wan2_1_Fun_1_3B_InP_SamplingParam,
+    SelfForcingWanT2V480PConfig,
 )
 # isort: on
 from fastvideo.logger import init_logger
@@ -29,18 +29,29 @@ 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,
+
+    # 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,
-    "weizhou03/Wan2.1-Fun-1.3B-InP-Diffusers":
-    Wan2_1_Fun_1_3B_InP_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
 }

@@ -107,28 +107,13 @@ 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 = "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 =============
 # =============================================
@dataclass
 class Wan2_2_Base_SamplingParam(SamplingParam):
    """Sampling parameters for Wan2.2 TI2V 5B model."""
-    negative_prompt: str = "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"
+    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"


@dataclass
@@ -156,3 +141,11 @@ 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
@@ -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.
@@ -161,9 +161,6 @@ class FastVideoArgs:
    # MoE parameters used by Wan2.2
    boundary_ratio: float | None = None

-    # scheduler parameters
-    warp_denoising_step: bool = False
-
    @property
    def training_mode(self) -> bool:
        return not self.inference_mode
@@ -385,15 +382,6 @@ class FastVideoArgs:
            default=FastVideoArgs.enable_stage_verification,
            help="Enable input/output verification for pipeline stages",
        )
-
-        # scheduler parameters
-        parser.add_argument(
-            "--warp-denoising-step",
-            action=StoreBoolean,
-            default=FastVideoArgs.warp_denoising_step,
-            help="Warp denoising step for scheduler",
-        )
-
        # Add pipeline configuration arguments
        PipelineConfig.add_cli_args(parser)

@@ -603,7 +591,14 @@ class TrainingArgs(FastVideoArgs):
    pretrained_model_name_or_path: str = ""
    dit_model_name_or_path: str = ""

+    # DMD model paths - separate paths for each network
+    generator_model_path: str = ""  # path for generator (student) model
+    real_score_model_path: str = ""  # path for real score (teacher) model
+    fake_score_model_path: str = ""  # path for fake score (critic) model
+
    # diffusion setting
+    ema_decay: float = 0.0
+    ema_start_step: int = 0
    training_cfg_rate: float = 0.0
    precondition_outputs: bool = False

@@ -623,6 +618,7 @@ class TrainingArgs(FastVideoArgs):
    checkpoints_total_limit: int = 0
    checkpointing_steps: int = 0
    resume_from_checkpoint: str = ""  # specify the checkpoint folder to resume from
+    init_weights_from_safetensors: str = ""  # path to safetensors file for initial weight loading

    # optimizer & scheduler
    num_train_epochs: int = 0
@@ -654,6 +650,7 @@ class TrainingArgs(FastVideoArgs):
    linear_quadratic_threshold: float = 0.0
    linear_range: float = 0.0
    weight_decay: float = 0.0
+    betas: str = "0.9,0.999"  # betas for optimizer, format: "beta1,beta2"
    use_ema: bool = False
    multi_phased_distill_schedule: str = ""
    pred_decay_weight: float = 0.0
@@ -674,26 +671,29 @@ class TrainingArgs(FastVideoArgs):

    # distillation args
    generator_update_interval: int = 5
+    dfake_gen_update_ratio: int = 5  # self-forcing: how often to train generator vs critic
    min_timestep_ratio: float = 0.2
    max_timestep_ratio: float = 0.98
    real_score_guidance_scale: float = 3.5
    fake_score_learning_rate: float = 0.0  # separate learning rate for fake_score_transformer, if 0.0, use learning_rate
    fake_score_lr_scheduler: str = "constant"  # separate lr scheduler for fake_score_transformer, if not set, use lr_scheduler
+    fake_score_betas: str = "0.9,0.999"  # betas for fake score optimizer, format: "beta1,beta2"
    training_state_checkpointing_steps: int = 0  # for resuming training
    weight_only_checkpointing_steps: int = 0  # for inference
    log_visualization: bool = False
    # simulate generator forward to match inference
    simulate_generator_forward: bool = False
-    simulate_forward_interval: int = 1
-    regression_loss_weight: float = 0.0
-    use_regression_loss: bool = False
+    warp_denoising_step: bool = False

-    # consistency model parameters
-    cm_loss_weight: float = 0.0
-    cm_use_ema_teacher: bool = False
-    ema_decay: float = 0.999
-    ema_start_step: int = 0
-    cm_weighing_function: str = "constant"
+    # Self-forcing specific arguments
+    num_frame_per_block: int = 3
+    independent_first_frame: bool = False
+    enable_gradient_masking: bool = True
+    gradient_mask_last_n_frames: int = 21
+    validate_cache_structure: bool = False  # Debug flag for cache validation
+    same_step_across_blocks: bool = False  # Use same exit timestep for all blocks
+    last_step_only: bool = False  # Only use the last timestep for training
+    context_noise: int = 0  # Context noise level for cache updates

    @classmethod
    def from_cli_args(cls, args: argparse.Namespace) -> "TrainingArgs":
@@ -795,7 +795,29 @@ class TrainingArgs(FastVideoArgs):
                            type=str,
                            help="Directory to cache models")

+        # DMD model paths - separate paths for each network
+        parser.add_argument(
+            "--generator-model-path",
+            type=str,
+            help="Path to generator (student) model for DMD distillation")
+        parser.add_argument(
+            "--real-score-model-path",
+            type=str,
+            help="Path to real score (teacher) model for DMD distillation")
+        parser.add_argument(
+            "--fake-score-model-path",
+            type=str,
+            help="Path to fake score (critic) model for DMD distillation")
+
        # Diffusion settings
+        parser.add_argument("--ema-decay",
+                            type=float,
+                            default=0.999,
+                            help="EMA decay rate")
+        parser.add_argument("--ema-start-step",
+                            type=int,
+                            default=0,
+                            help="Step to start EMA")
        parser.add_argument("--training-cfg-rate",
                            type=float,
                            help="Classifier-free guidance scale")
@@ -857,6 +879,10 @@ class TrainingArgs(FastVideoArgs):
        parser.add_argument("--resume-from-checkpoint",
                            type=str,
                            help="Path to checkpoint to resume from")
+        parser.add_argument(
+            "--init-weights-from-safetensors",
+            type=str,
+            help="Path to safetensors file for initial weight loading")
        parser.add_argument("--logging-dir",
                            type=str,
                            help="Directory for logging")
@@ -961,6 +987,10 @@ class TrainingArgs(FastVideoArgs):
                            help="Linear quadratic threshold")
        parser.add_argument("--linear-range", type=float, help="Linear range")
        parser.add_argument("--weight-decay", type=float, help="Weight decay")
+        parser.add_argument("--betas",
+                            type=str,
+                            default=TrainingArgs.betas,
+                            help="Betas for optimizer (format: 'beta1,beta2')")
        parser.add_argument("--use-ema",
                            action=StoreBoolean,
                            help="Whether to use EMA")
@@ -1002,6 +1032,13 @@ class TrainingArgs(FastVideoArgs):
                            type=int,
                            default=TrainingArgs.generator_update_interval,
                            help="Ratio of student updates to critic updates.")
+        parser.add_argument(
+            "--dfake-gen-update-ratio",
+            type=int,
+            default=TrainingArgs.dfake_gen_update_ratio,
+            help=
+            "Self-forcing: How often to train generator vs critic (train generator every N steps)."
+        )
        parser.add_argument("--min-timestep-ratio",
                            type=float,
                            default=TrainingArgs.min_timestep_ratio,
@@ -1018,6 +1055,11 @@ class TrainingArgs(FastVideoArgs):
                            type=float,
                            default=TrainingArgs.fake_score_learning_rate,
                            help="Learning rate for fake score transformer")
+        parser.add_argument(
+            "--fake-score-betas",
+            type=str,
+            default=TrainingArgs.fake_score_betas,
+            help="Betas for fake score optimizer (format: 'beta1,beta2')")
        parser.add_argument(
            "--fake-score-lr-scheduler",
            type=str,
@@ -1030,40 +1072,49 @@ class TrainingArgs(FastVideoArgs):
            "--simulate-generator-forward",
            action=StoreBoolean,
            help="Whether to simulate generator forward to match inference")
-        parser.add_argument("--simulate-forward-interval",
-                            type=int,
-                            default=TrainingArgs.simulate_forward_interval,
-                            help="Ratio of steps to simulate generator forward")
-        parser.add_argument("--regression-loss-weight",
-                            type=float,
-                            default=TrainingArgs.regression_loss_weight,
-                            help="Weight for regression loss")
-        parser.add_argument("--use-regression-loss",
-                            action=StoreBoolean,
-                            help="Whether to use regression loss")
-
-        # Consistency model arguments
-        parser.add_argument("--ema-decay",
-                            type=float,
-                            default=0.999,
-                            help="EMA decay rate")
-        parser.add_argument("--ema-start-step",
-                            type=int,
-                            default=0,
-                            help="Step to start EMA")
-        parser.add_argument("--cm-loss-weight",
-                            type=float,
-                            default=TrainingArgs.cm_loss_weight,
-                            help="Weight for consistency loss")
        parser.add_argument(
-            "--cm-use-ema-teacher",
+            "--warp-denoising-step",
            action=StoreBoolean,
-            help="Whether to use EMA teacher for consistency loss")
-        parser.add_argument("--cm-weighing-function",
-                            type=str,
-                            choices=["constant", "sigma_sqrt"],
-                            default=TrainingArgs.cm_weighing_function,
-                            help="Weighting function for consistency loss")
+            help=
+            "Whether to warp denoising step according to the scheduler time shift"
+        )
+
+        # Self-forcing specific arguments
+        parser.add_argument(
+            "--num-frame-per-block",
+            type=int,
+            default=TrainingArgs.num_frame_per_block,
+            help="Number of frames per block for causal generation")
+        parser.add_argument(
+            "--independent-first-frame",
+            action=StoreBoolean,
+            help="Whether the first frame is independent in causal generation")
+        parser.add_argument(
+            "--enable-gradient-masking",
+            action=StoreBoolean,
+            help="Whether to enable frame-level gradient masking")
+        parser.add_argument(
+            "--gradient-mask-last-n-frames",
+            type=int,
+            default=TrainingArgs.gradient_mask_last_n_frames,
+            help="Number of last frames to enable gradients for")
+        parser.add_argument(
+            "--validate-cache-structure",
+            action=StoreBoolean,
+            help="Whether to validate KV cache structure (debug flag)")
+        parser.add_argument(
+            "--same-step-across-blocks",
+            action=StoreBoolean,
+            help="Whether to use the same exit timestep for all blocks")
+        parser.add_argument(
+            "--last-step-only",
+            action=StoreBoolean,
+            help="Whether to only use the last timestep for training")
+        parser.add_argument(
+            "--context-noise",
+            type=int,
+            default=TrainingArgs.context_noise,
+            help="Context noise level for cache updates")

        return parser

@@ -9,6 +9,9 @@ import torch.nn.functional as F
 from fastvideo.layers.custom_op import CustomOp
 from fastvideo.platforms import current_platform

+from fastvideo.logger import init_logger
+
+logger = init_logger(__name__)

@CustomOp.register("rms_norm")
 class RMSNorm(CustomOp):
@@ -100,7 +103,13 @@ class ScaleResidual(nn.Module):
    def forward(self, residual: torch.Tensor, x: torch.Tensor,
                gate: torch.Tensor) -> torch.Tensor:
        """Apply gated residual connection."""
-        return residual + x * gate
+        # logger.info("x.shape: %s", x.shape)
+        # if isinstance(gate, torch.Tensor):
+            # logger.info("gate.shape: %s", gate.shape)
+
+        num_frames = gate.shape[1]
+        frame_seqlen = x.shape[1] // num_frames
+        return residual + (x.unflatten(dim=1, sizes=(num_frames, frame_seqlen)) * gate).flatten(1, 2)


 # adapted from Diffusers: https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/normalization.py
@@ -172,11 +181,35 @@ class ScaleResidualLayerNormScaleShift(nn.Module):
              but before normalization)
        """
        # Apply residual connection with gating
-        residual_output = residual + x * gate
+        # logger.info("x.shape: %s", x.shape)
+        if isinstance(gate, int): 
+            # used by cross-attention, should be 1
+            assert gate == 1
+            residual_output = residual + x * gate
+        elif isinstance(gate, torch.Tensor):
+            # logger.info("gate.shape: %s", gate.shape)
+            if gate.dim() == 3:
+                # used by bidirectional self attention
+                residual_output = residual + x * gate
+            else:
+                assert gate.dim() == 4 
+                num_frames = gate.shape[1]
+                frame_seqlen = x.shape[1] // num_frames
+                residual_output = residual + (x.unflatten(dim=1, sizes=(num_frames, frame_seqlen)) * gate).flatten(1, 2)
+                # residual_output = residual + x * gate
+        else:
+            raise ValueError(f"Gate type {type(gate)} not supported")
+        # logger.info("residual_output.shape: %s", residual_output.shape)
+
        # Apply normalization
        normalized = self.norm(residual_output)
        # Apply scale and shift
-        modulated = normalized * (1.0 + scale) + shift
+        if isinstance(scale, torch.Tensor) and scale.dim() == 4:
+            num_frames = scale.shape[1]
+            frame_seqlen = normalized.shape[1] // num_frames
+            modulated = (normalized.unflatten(dim=1, sizes=(num_frames, frame_seqlen)) * (1.0 + scale) + shift).flatten(1, 2)
+        else:
+            modulated = normalized * (1.0 + scale) + shift
        return modulated, residual_output


@@ -219,7 +252,15 @@ class LayerNormScaleShift(nn.Module):
                scale: torch.Tensor) -> torch.Tensor:
        """Apply ln followed by scale and shift in a single fused operation."""
        normalized = self.norm(x)
-        if self.compute_dtype == torch.float32:
-            return (normalized.float() * (1.0 + scale) + shift).to(x.dtype)
+        if scale.dim() == 4:
+            num_frames = scale.shape[1]
+            frame_seqlen = normalized.shape[1] // num_frames
+            if self.compute_dtype == torch.float32:
+                return (normalized.float().unflatten(dim=1, sizes=(num_frames, frame_seqlen)) * (1.0 + scale) + shift).flatten(1, 2).to(x.dtype)
+            else:
+                return (normalized.unflatten(dim=1, sizes=(num_frames, frame_seqlen)) * (1.0 + scale) + shift).flatten(1, 2)
        else:
-            return normalized * (1.0 + scale) + shift
+            if self.compute_dtype == torch.float32:
+                return (normalized.float() * (1.0 + scale) + shift).to(x.dtype)
+            else:
+                return normalized * (1.0 + scale) + shift
@@ -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)))
@@ -29,7 +29,11 @@ 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__)
+
+logger = init_logger(__name__)

 def _rotate_neox(x: torch.Tensor) -> torch.Tensor:
    x1 = x[..., :x.shape[-1] // 2]
@@ -267,6 +271,7 @@ 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 +297,9 @@ 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 +378,7 @@ 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 +422,7 @@ 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
@@ -172,4 +176,4 @@ def unpatchify(x, t, h, w, patch_size, channels) -> torch.Tensor:
    x = torch.einsum("nthwcopq->nctohpwq", x)
    imgs = x.reshape(shape=(x.shape[0], c, t * pt, h * ph, w * pw))

-    return imgs
+    return imgs
@@ -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 +1,710 @@
+# 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"] = kv_cache["k"].clone()
+                kv_cache["v"] = kv_cache["v"].clone()
+                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:
+        # logger.info("temb.shape: %s", temb.shape)
+        num_frames = temb.shape[1]
+        # logger.info("first hidden_states.shape: %s", hidden_states.shape)
+        # logger.info("num_frames: %s", num_frames)
+        if hidden_states.dim() == 4:
+            hidden_states = hidden_states.squeeze(1)
+        frame_seqlen = hidden_states.shape[1] // temb.shape[1]
+        # logger.info("frame_seqlen: %s", frame_seqlen)
+        bs, seq_length, _ = hidden_states.shape
+        orig_dtype = hidden_states.dtype
+        # assert orig_dtype != torch.float32
+        e = self.scale_shift_table + temb.float()
+        # logger.info("e.shape: %s", e.shape)
+        shift_msa, scale_msa, gate_msa, c_shift_msa, c_scale_msa, c_gate_msa = e.chunk(
+            6, dim=2)
+        assert shift_msa.dtype == torch.float32
+
+        # 1. Self-attention
+        # logger.info("hidden_states.shape: %s", hidden_states.shape)
+        # logger.info("scale_msa.shape: %s", scale_msa.shape)
+        # logger.info("shift_msa.shape: %s", shift_msa.shape)
+        
+        norm_hidden_states_unflattened = self.norm1(hidden_states.float()).unflatten(dim=1, sizes=(num_frames, frame_seqlen))
+        # logger.info("norm_hidden_states_unflattened.shape: %s", norm_hidden_states_unflattened.shape)
+        
+        # norm_hidden_states = (self.norm1(hidden_states.float()) *
+        #                       (1 + scale_msa) + shift_msa).to(orig_dtype)
+        norm_hidden_states = (norm_hidden_states_unflattened *
+                              (1 + scale_msa) + shift_msa).flatten(1, 2).to(orig_dtype)
+        # logger.info("1 norm_hidden_states.shape: %s", norm_hidden_states.shape)
+        query, _ = self.to_q(norm_hidden_states)
+        key, _ = self.to_k(norm_hidden_states)
+        value, _ = self.to_v(norm_hidden_states)
+
+        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)
+        # logger.info("after self_attn_residual_norm norm_hidden_states.shape: %s", norm_hidden_states.shape)
+        # logger.info("after self_attn_residual_norm hidden_states.shape: %s", hidden_states.shape)
+        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)
+        # logger.info("after cross_attn_residual_norm norm_hidden_states.shape: %s", norm_hidden_states.shape)
+        # logger.info("after cross_attn_residual_norm hidden_states.shape: %s", hidden_states.shape)
+        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)
+        # logger.info("after mlp_residual norm_hidden_states.shape: %s", norm_hidden_states.shape)
+        # logger.info("after mlp_residual hidden_states.shape: %s", hidden_states.shape)
+        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)
+        # Debug: Log configuration values
+        proj_out_dim = config.out_channels * math.prod(config.patch_size)
+        
+        self.proj_out = nn.Linear(inner_dim, proj_out_dim)
+        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)
+        """
+        # logger.info("forward inference hidden_states.shape: %s", hidden_states.shape)
+
+        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)
+        # logger.info("forward inference flattened and transposed hidden_states.shape: %s", hidden_states.shape)
+
+        # logger.info("timestep shape: %s", timestep.shape)
+
+        temb, timestep_proj, encoder_hidden_states, encoder_hidden_states_image = self.condition_embedder(
+            timestep.flatten(), encoder_hidden_states, encoder_hidden_states_image)
+        timestep_proj = timestep_proj.unflatten(1, (6, self.hidden_size)).unflatten(dim=0, sizes=timestep.shape)
+
+        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
+        # logger.info("===== INFERENCE 5. Output norm, projection & unpatchify")
+        # logger.info("hidden_states.shape: %s", hidden_states.shape)
+        # logger.info("temb.shape: %s", temb.shape)
+        temb = temb.unflatten(dim=0, sizes=timestep.shape).unsqueeze(2)
+        # logger.info("WTFWTF train temb.shape: %s", temb.shape)
+        # logger.info("WTFWTF train self.scale_shift_table.shape: %s", self.scale_shift_table.shape)
+        shift, scale = (self.scale_shift_table.unsqueeze(1) + temb).chunk(2,
+                                                                          dim=2)
+        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:
+
+        # logger.info("===== forward train hidden_states.shape: %s", hidden_states.shape)
+        # logger.info("===== forward train timestep.shape: %s", timestep.shape)
+        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)
+        # logger.info("forward train flattened and transposed hidden_states.shape: %s", hidden_states.shape)
+
+        temb, timestep_proj, encoder_hidden_states, encoder_hidden_states_image = self.condition_embedder(
+            timestep.flatten(), encoder_hidden_states, encoder_hidden_states_image)
+        # logger.info("forward train timestep_proj.shape: %s", timestep_proj.shape)
+        # logger.info("forward train timestep.shape: %s", timestep.shape)
+        # logger.info("forward train temb.shape: %s", temb.shape)
+        timestep_proj = timestep_proj.unflatten(1, (6, self.hidden_size)).unflatten(dim=0, sizes=timestep.shape)
+
+        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_index, block in enumerate(self.blocks):
+                logger.info("===== TRAIN block %d", block_index)
+                logger.info("hidden_states.shape: %s", hidden_states.shape)
+                # logger.info("encoder_hidden_states.shape: %s", encoder_hidden_states.shape)
+                logger.info("timestep_proj.shape: %s", timestep_proj.shape)
+                # logger.info("freqs_cis.shape: %s", freqs_cis.shape)
+                # logger.info("block_mask.shape: %s", self.block_mask.shape)
+                hidden_states = block(hidden_states, encoder_hidden_states,
+                                        timestep_proj, freqs_cis,
+                                        block_mask=self.block_mask)
+
+        # 5. Output norm, projection & unpatchify
+        # logger.info("===== TRAIN 5. Output norm, projection & unpatchify")
+        # logger.info("hidden_states.shape: %s", hidden_states.shape)
+        # logger.info("temb.shape: %s", temb.shape)
+        # shift, scale = (self.scale_shift_table + temb.unsqueeze(1)).chunk(2,
+        temb = temb.unflatten(dim=0, sizes=timestep.shape).unsqueeze(2)
+        # logger.info("WTFWTF train temb.shape: %s", temb.shape)
+        # logger.info("WTFWTF train self.scale_shift_table.shape: %s", self.scale_shift_table.shape)
+        shift, scale = (self.scale_shift_table.unsqueeze(1) + temb).chunk(2,
+                                                                          dim=2)
+        # logger.info("DEBUG scale.shape: %s", scale.shape)
+        # logger.info("DEBUG shift.shape: %s", shift.shape)
+        hidden_states = self.norm_out(hidden_states, shift, scale)
+        hidden_states = self.proj_out(hidden_states)
+        # logger.info("DEBUG after proj_out hidden_states.shape: %s", hidden_states.shape)
+        # logger.info(f"DEBUG reshape dimensions: batch_size={batch_size}, post_patch_num_frames={post_patch_num_frames}")
+        # logger.info(f"DEBUG reshape dimensions: post_patch_height={post_patch_height}, post_patch_width={post_patch_width}")
+        # logger.info(f"DEBUG patch dimensions: p_t={p_t}, p_h={p_h}, p_w={p_w}")
+
+        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
+        
@@ -430,6 +430,16 @@ class TransformerLoader(ComponentLoader):
        if not safetensors_list:
            raise ValueError(f"No safetensors files found in {model_path}")

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

--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
JerryZhou54	32711250ad	checkpoint	2025-09-09 02:48:02 +00:00
JerryZhou54	3d6cac57fc	Fix runtime errors during training	2025-09-09 01:15:18 +00:00
Matthew Noto	57fd3d8159	new branch	2025-09-09 01:15:18 +00:00
JerryZhou54	1eacdd80de	new branch	2025-09-09 01:15:18 +00:00
Matthew Noto	2ca24b3288	new branch	2025-09-08 19:20:32 +00:00
RandNMR73	328eb611c4	training refactor	2025-09-07 02:40:19 +00:00
RandNMR73	3408e20d7a	Merge branch 'matthew/causal' of github.com:hao-ai-lab/FastVideo into matthew/causal	2025-09-07 00:17:20 +00:00
SolitaryThinker	4328fe1ebf	fix warping	2025-09-05 10:20:10 +00:00
SolitaryThinker	6d0eba5789	warp timestep	2025-09-05 09:54:35 +00:00
RandNMR73	b3e76ae7dd	Merge branch 'matthew/causal' of github.com:hao-ai-lab/FastVideo into matthew/causal	2025-09-02 00:57:20 +00:00
Matthew Noto	1ffd80ee51	training experiments in progress	2025-09-02 00:03:58 +00:00
Matthew Noto	e84fdaedde	add single example preprocessing	2025-09-01 03:49:33 +00:00
Matthew Noto	dd0fe401c9	fix kv-cache + training loop	2025-09-01 01:52:11 +00:00
Matthew Noto	60eac9f18b	clean-up + kv cache debugging	2025-08-31 08:31:19 +00:00
Matthew Noto	a75bccb75d	debugging kv cache	2025-08-31 05:30:21 +00:00
RandNMR73	c52ff91747	Merge branch 'matthew/causal' of github.com:hao-ai-lab/FastVideo into matthew/causal	2025-08-28 12:21:18 +00:00
RandNMR73	36bb0935a8	val data path fix	2025-08-28 12:18:41 +00:00
RandNMR73	d1e26abd63	Merge branch 'matthew/causal' of github.com:hao-ai-lab/FastVideo into matthew/causal	2025-08-28 11:53:46 +00:00
RandNMR73	b6f187f338	working training script	2025-08-28 11:32:53 +00:00
RandNMR73	de4938c3a7	working training script	2025-08-28 11:27:18 +00:00
RandNMR73	a9f7407228	debugging training	2025-08-28 08:06:31 +00:00
RandNMR73	258d1da0d3	training script testing	2025-08-28 05:03:12 +00:00
RandNMR73	17f6dff632	training branch	2025-08-28 04:49:51 +00:00
RandNMR73	e66f16057f	training script testing	2025-08-28 04:42:51 +00:00
JerryZhou54	8acc7c3655	small fix	2025-08-28 04:10:51 +00:00
JerryZhou54	8dd0b6536d	Fix pre-commit tests	2025-08-28 04:07:11 +00:00
JerryZhou54	1a79b30ea4	Fix inter-block issues	2025-08-28 03:56:37 +00:00
JerryZhou54	772ead0d34	Wan2.1 causal, few-step inference runnable	2025-08-28 03:56:37 +00:00
SolitaryThinker	1575102965	debugging model	2025-08-28 03:56:37 +00:00
SolitaryThinker	b198ba5607	checkpoint	2025-08-28 03:56:34 +00:00
William Lin	cf1942fd47	[dev] Will/causal infer (#764 )	2025-08-28 03:55:16 +00:00
Wei Zhou	b5519f1f91	Delete fastvideo/configs/models/dits/causal_wanvideo.py	2025-08-28 03:55:16 +00:00
JerryZhou54	a464f96b95	Fix file dir	2025-08-28 03:55:16 +00:00
JerryZhou54	363cf0d173	First version of causalwan	2025-08-28 03:55:15 +00:00
JerryZhou54	36371c5689	checkpoint	2025-08-28 03:55:15 +00:00
Zhang Peiyuan	7c554e5da8	Update Community Link (#765 )	2025-08-27 16:12:47 -07:00
SolitaryThinker	8fea7c02b5	debugging model	2025-08-27 02:56:31 -07:00
SolitaryThinker	e2b6f49879	checkpoint	2025-08-27 02:12:43 -07:00
William Lin	9f24aef7cf	[dev] Will/causal infer (#764 )	2025-08-26 23:13:20 -07:00
William Lin	663ea33ff1	[bugfix] Fix wrong HF model string for FastWan2.2 5B (#763 )	2025-08-26 22:05:40 -07:00
Wei Zhou	7a489da74d	Delete fastvideo/configs/models/dits/causal_wanvideo.py	2025-08-26 16:33:25 -07:00
JerryZhou54	cf230dcccd	Fix file dir	2025-08-26 23:32:30 +00:00
JerryZhou54	c4521e8953	First version of causalwan	2025-08-26 23:31:46 +00:00
JerryZhou54	026ee8d9f4	checkpoint	2025-08-26 19:49:57 +00:00
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