Update preprocess_pipeline_ode_trajectory.py

Co-authored-by: KyleS1016 <kyle.s@gmicloud.ai>

.buildkite/pipeline.yml

@@ -222,3 +222,14 @@ steps:
                   - TEST_TYPE=unit_test
                 agents:
                   queue: "default"
+            - path:
+                - "scripts/lora_extraction/**"
+                - "pyproject.toml"
+                - "docker/Dockerfile.python3.12"
+              config:
+                command: "timeout 90m .buildkite/scripts/pr_test.sh"
+                label: "LoRA Extraction Tests"
+                env:
+                  - TEST_TYPE=lora_extraction
+                agents:
+                  queue: "default"

.buildkite/scripts/pr_test.sh

@@ -75,7 +75,7 @@ case "$TEST_TYPE" in
         ;;
     "ssim")
         log "Running SSIM tests..."
-        MODAL_COMMAND="$MODAL_ENV python3 -m modal run $MODAL_TEST_FILE::run_ssim_tests"
+        MODAL_COMMAND="$MODAL_ENV HF_API_KEY=$HF_API_KEY python3 -m modal run $MODAL_TEST_FILE::run_ssim_tests"
         ;;
     "training")
         log "Running training tests..."
@@ -126,6 +126,10 @@ case "$TEST_TYPE" in
         log "Running unit tests..."
         MODAL_COMMAND="$MODAL_ENV python3 -m modal run $MODAL_TEST_FILE::run_unit_test"
         ;;
+    "lora_extraction")
+        log "Running LoRA extraction tests..."
+        MODAL_COMMAND="$MODAL_ENV HF_API_KEY=$HF_API_KEY python3 -m modal run $MODAL_TEST_FILE::run_lora_extraction_tests"
+        ;;
     *)
         log "Error: Unknown test type: $TEST_TYPE"
         exit 1

.github/workflows/docs.yml

@@ -3,8 +3,18 @@ name: Deploy Documentation
 on:
   push:
     branches: [ main ]
+    paths:
+      - 'docs/**'
+      - 'mkdocs.yml'
+      - 'requirements-mkdocs.txt'
+      - '.github/workflows/docs.yml'
   pull_request:
     branches: [ main ]
+    paths:
+      - 'docs/**'
+      - 'mkdocs.yml'
+      - 'requirements-mkdocs.txt'
+      - '.github/workflows/docs.yml'
 
 permissions:
   contents: read

.pre-commit-config.yaml

@@ -10,6 +10,7 @@ exclude: |
     demo/.*|
     predict\.py|
     scripts/.*|
+    prompts/.*|
     fastvideo/data_preprocess/.*|
     fastvideo/dataset/.*|
     fastvideo/models/.*|

README.md

@@ -1,13 +1,12 @@
 <div align="center">
 <img src=assets/logos/logo.svg width="30%"/>
 </div>
-
 **FastVideo is a unified post-training and inference framework for accelerated video generation.**
 
 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/"><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-3f4lao1uq-u~Ipx6Lt4J27AlD2y~IdLQ" target="_blank"> <b>Slack</b> </a> |  🟣💬 <a href="https://ibb.co/XcY0Cpv" 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/"><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-3f4lao1uq-u~Ipx6Lt4J27AlD2y~IdLQ" target="_blank"> <b>Slack</b> </a> |  🟣💬 <a href="https://ibb.co/c7g1qdD" target="_blank"> <b> WeChat </b> </a> |
 </p>
 
 <div align="center">
@@ -15,7 +14,8 @@ FastVideo features an end-to-end unified pipeline for accelerating diffusion mod
 </div>
 
 ## NEWS
-- ```2025/08/04```: Release [FastWan](https://hao-ai-lab.github.io/FastVideo/distillation/dmd.html) models and [Sparse-Distillation](https://hao-ai-lab.github.io/blogs/fastvideo_post_training/).
+- ```2025/11/19```: Release [CausalWan2.2 I2V A14B Preview](https://huggingface.co/FastVideo/CausalWan2.2-I2V-A14B-Preview-Diffusers) models, [Blog](https://hao-ai-lab.github.io/blogs/fastvideo_causalwan_preview/) and [Inference Code!](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_self_forcing_causal_wan2_2_i2v.py)
+- ```2025/08/04```: Release [FastWan](https://hao-ai-lab.github.io/FastVideo/distillation/dmd) models and [Sparse-Distillation](https://hao-ai-lab.github.io/blogs/fastvideo_post_training/).
 - ```2025/06/14```: Release finetuning and inference code for [VSA](https://arxiv.org/pdf/2505.13389)
 - ```2025/04/24```: [FastVideo V1](https://hao-ai-lab.github.io/blogs/fastvideo/) is released!
 - ```2025/02/18```: Release the inference code for [Sliding Tile Attention](https://hao-ai-lab.github.io/blogs/sta/).
@@ -111,24 +111,21 @@ For a more detailed guide, please see our [inference quick start](https://hao-ai
 - [Distillation Guide](https://hao-ai-lab.github.io/FastVideo/distillation/dmd/)
 <!-- - [Finetuning Guide](https://hao-ai-lab.github.io/FastVideo/training/finetune.html) -->
 
-## 📑 Development Plan
-<!-- - More distillation methods -->
-  <!-- - [ ] Add Distribution Matching Distillation -->
-More FastWan Models Coming Soon!
-- [ ] Add FastWan2.1-T2V-14B
-- [ ] Add FastWan2.2-T2V-14B
-- [ ] Add FastWan2.2-I2V-14B
-<!-- - Optimization features
-- Code updates -->
-  <!-- - [ ] fp8 support -->
-  <!-- - [ ] faster load model and save model support -->
+## Awesome work using FastVideo or our research projects
 
-See details in [development roadmap](https://github.com/hao-ai-lab/FastVideo/issues/468).
+- [SGLang](https://github.com/sgl-project/sglang/tree/main/python/sglang/multimodal_gen): SGLang's diffusion inference functionality is based  on a fork of FastVideo on Sept. 24, 2025. [![Star](https://img.shields.io/github/stars/sgl-project/sglang.svg?style=social&label=Star)](https://github.com/sgl-project/sglang)
+
+- [DanceGRPO](https://github.com/XueZeyue/DanceGRPO): A  unified framework to adapt Group Relative Policy Optimization (GRPO) to visual generation paradigms. Code based on FastVideo. [![Star](https://img.shields.io/github/stars/XueZeyue/DanceGRPO.svg?style=social&label=Star)](https://github.com/XueZeyue/DanceGRPO)
+- [SRPO](https://github.com/Tencent-Hunyuan/SRPO): A method to directly align the full diffusion trajectory with fine-grained human preference. Code based on FastVideo. [![Star](https://img.shields.io/github/stars/Tencent-Hunyuan/SRPO.svg?style=social&label=Star)](https://github.com/Tencent-Hunyuan/SRPO)
+- [DCM](https://github.com/Vchitect/DCM): Dual-expert consistency model for efficient and high-quality video generation. Code based on FastVideo. [![Star](https://img.shields.io/github/stars/Vchitect/DCM.svg?style=social&label=Star)](https://github.com/Vchitect/DCM)
+- [Hunyuan Video 1.5](https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5): A leading lightweight video generation model, where they proposed SSTA based on Sliding Tile Attention. [![Star](https://img.shields.io/github/stars/Tencent-Hunyuan/HunyuanVideo-1.5.svg?style=social&label=Star)](https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5)
+- [Kandinsky-5.0](https://github.com/kandinskylab/kandinsky-5): A family of diffusion models for video & image generation, where their NABLA attention includes a Sliding Tile Attention branch. [![Star](https://img.shields.io/github/stars/kandinskylab/kandinsky-5.svg?style=social&label=Star)](https://github.com/kandinskylab/kandinsky-5)
+- [LongCat Video](https://github.com/meituan-longcat/LongCat-Video): A foundational video generation model with 13.6B parameters with block-sparse attention similar to Video Sparse Attention. [![Star](https://img.shields.io/github/stars/meituan-longcat/LongCat-Video.svg?style=social&label=Star)](https://github.com/meituan-longcat/LongCat-Video)
 
 ## 🤝 Contributing
 
-We welcome all contributions. Please check out our guide [here](https://hao-ai-lab.github.io/FastVideo/contributing/overview/)
-
+We welcome all contributions. Please check out our guide [here](https://hao-ai-lab.github.io/FastVideo/contributing/overview/).
+See details in [development roadmap](https://github.com/hao-ai-lab/FastVideo/issues/899).
 ## Acknowledgement
 We learned and reused code from the following projects:
 - [Wan-Video](https://github.com/Wan-Video)

benchmarks/fvd/README.md

@@ -0,0 +1,103 @@
+# FVD (Fréchet Video Distance) Benchmark
+
+Evaluate generated video quality using FVD with the I3D feature extractor.
+
+## Quick Start
+
+**Run the benchmark:**
+
+```bash
+bash benchmarks/scripts/run.sh
+```
+
+That's it! The script auto-installs dependencies and runs the benchmark.
+
+**To customize:** Edit `benchmarks/fvd/run_fvd.py` to change:
+- Video paths (`real_dir`, `gen_dir`)
+- Number of videos, frames, sampling strategy
+- Device, batch size, caching, etc.
+
+## Advanced Usage (CLI)
+
+For more control without editing Python files, use the CLI.
+
+**First-time setup** (one-time per pod/environment):
+
+```bash
+bash benchmarks/scripts/setup_fvd.sh
+```
+
+Then run any configuration you want:
+
+```bash
+# Custom configuration
+python -m benchmarks.fvd.cli \
+    --real-path data/real/ \
+    --gen-path outputs/gen/ \
+    --num-videos 1024 \
+    --num-frames 32 \
+    --clip-strategy random \
+    --batch-size 32 \
+    --seed 42
+```
+
+**Standard protocols:**
+
+```bash
+# Use predefined protocols
+python -m benchmarks.fvd.cli \
+    --real-path data/real/ \
+    --gen-path outputs/gen/ \
+    --protocol fvd2048_16f  # or fvd2048_128f, quick_test, etc.
+```
+
+**Feature caching** (speed up repeated evaluations):
+
+```bash
+python -m benchmarks.fvd.cli \
+    --real-path data/real/ \
+    --gen-path outputs/gen/ \
+    --protocol fvd2048_16f \
+    --cache-real-features cache/real  # Directory path (will save/load cache/real/real_features.pkl)
+```
+
+Run `python -m benchmarks.fvd.cli --help` for all options.
+
+## Available Protocols
+
+- `fvd2048_16f` - Standard (2048 videos, 16 frames)
+- `fvd2048_128f` - Long videos (128 frames)
+- `fvd2048_128f_subsample8` - Subsampled long videos
+- `quick_test` - Fast testing (10 videos)
+
+## Configuration Options
+
+Key options in `FVDConfig`:
+
+```python
+num_videos=2048,              # Videos to evaluate
+num_frames_per_clip=16,       # Frames per clip
+clip_strategy='beginning',    # beginning|random|uniform|middle|sliding
+frame_stride=1,               # Frame subsampling
+batch_size=32,                # GPU batch size
+device='cuda',                # cuda|cpu
+cache_real_features=None,     # Cache path for speed
+seed=42,                      # Reproducibility
+```
+
+## Programmatic Usage
+
+```python
+from benchmarks.fvd import compute_fvd_with_config, FVDConfig
+
+config = FVDConfig.fvd2048_16f()  # or custom config
+results = compute_fvd_with_config('data/real/', 'outputs/gen/', config)
+print(f"FVD: {results['fvd']:.2f}")
+```
+
+## Notes
+
+- I3D model auto-downloads from Hugging Face on first run
+- Requires minimum 10 frames per clip
+- Supports both video files (.mp4, .avi, etc.) and frame directories
+- `--cache-real-features` expects a **directory path** (e.g., `cache/real`), it will automatically create/load `real_features.pkl` inside that directory

benchmarks/fvd/__init__.py

@@ -0,0 +1,35 @@
+"""
+FastVideo Frechet Video Distance (FVD) Benchmark Module.
+    >>> from fastvideo.benchmarks.fvd import compute_fvd_with_config, FVDConfig
+    >>> config = FVDConfig.fvd2048_16f()  # Standard protocol
+    >>> results = compute_fvd_with_config('data/real/', 'outputs/gen/', config)
+    >>> print(f"FVD: {results['fvd']:.2f}")
+"""
+
+from .fvd import (
+    compute_fvd,
+    compute_fvd_with_config,
+    compute_frechet_distance,
+    compute_statistics,
+    FVDConfig,
+)
+from .i3d_model import I3DFeatureExtractor
+from .video_utils import (
+    load_video_auto,
+    sample_clips_from_video,
+    load_video_clips_streaming,
+    ClipSamplingStrategy,
+)
+
+__all__ = [
+    'compute_fvd',
+    'compute_fvd_with_config',
+    'compute_frechet_distance',
+    'compute_statistics',
+    'FVDConfig',
+    'I3DFeatureExtractor',
+    'load_video_auto',
+    'sample_clips_from_video',
+    'load_video_clips_streaming',
+    'ClipSamplingStrategy',
+]

benchmarks/fvd/cli.py

@@ -0,0 +1,185 @@
+import argparse
+import json
+import sys
+from pathlib import Path
+
+from .fvd import compute_fvd_with_config, FVDConfig
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(
+        description='Compute Fréchet Video Distance (FVD)',
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog="""
+Examples:
+  # Standard FVD2048_16f protocol
+  python -m fastvideo.benchmarks.fvd.cli \\
+      --real-path data/real/ \\
+      --gen-path outputs/gen/ \\
+      --protocol fvd2048_16f
+
+  # Custom configuration
+  python -m fastvideo.benchmarks.fvd.cli \\
+      --real-path data/real/ \\
+      --gen-path outputs/gen/ \\
+      --num-videos 1024 \\
+      --num-frames 32 \\
+      --clip-strategy random \\
+      --frame-stride 2
+        """)
+
+    # Required arguments
+    parser.add_argument('--real-path',
+                        type=str,
+                        required=True,
+                        help='Path to real videos directory')
+    parser.add_argument('--gen-path',
+                        type=str,
+                        required=True,
+                        help='Path to generated videos directory')
+
+    # Reproducibility
+    parser.add_argument(
+        '--seed',
+        type=int,
+        default=None,
+        help='Random seed for reproducibility (np.random, random, torch)')
+
+    # Protocol presets
+    parser.add_argument('--protocol',
+                        type=str,
+                        default=None,
+                        choices=[
+                            'fvd2048_16f', 'fvd2048_128f',
+                            'fvd2048_128f_subsample8', 'quick_test'
+                        ],
+                        help='Use standard protocol (overrides other settings)')
+
+    # Video selection
+    parser.add_argument('--num-videos',
+                        type=int,
+                        default=2048,
+                        help='Number of videos to use (default: 2048)')
+
+    # Clip sampling
+    parser.add_argument('--num-frames',
+                        type=int,
+                        default=16,
+                        help='Number of frames per clip (default: 16)')
+    parser.add_argument('--num-clips',
+                        type=int,
+                        default=1,
+                        help='Number of clips per video (default: 1)')
+    parser.add_argument(
+        '--clip-strategy',
+        type=str,
+        default='beginning',
+        choices=['beginning', 'random', 'uniform', 'middle', 'sliding', 'all'],
+        help='Clip sampling strategy (default: beginning)')
+    parser.add_argument(
+        '--frame-stride',
+        type=int,
+        default=1,
+        help='Frame stride for FPS subsampling (default: 1, no subsampling)')
+    parser.add_argument('--temporal-stride',
+                        type=int,
+                        default=1,
+                        help='Temporal stride for sliding window (default: 1)')
+
+    # Data processing
+    parser.add_argument('--no-frame-dirs',
+                        action='store_true',
+                        help='Disable frame directory support')
+
+    # Computation
+    parser.add_argument('--batch-size',
+                        type=int,
+                        default=32,
+                        help='Batch size for feature extraction (default: 32)')
+    parser.add_argument('--device',
+                        type=str,
+                        default='cuda',
+                        choices=['cuda', 'cpu'],
+                        help='Device to use (default: cuda)')
+
+    # Caching
+    parser.add_argument('--cache-real-features',
+                        type=str,
+                        default=None,
+                        help='Path to cache real video features')
+    parser.add_argument('--i3d-model-path',
+                        type=str,
+                        default=None,
+                        help='Custom cache path for I3D model')
+
+    # Output
+    parser.add_argument('--output',
+                        type=str,
+                        default='fvd_results.json',
+                        help='Output JSON file (default: fvd_results.json)')
+    parser.add_argument('--quiet',
+                        action='store_true',
+                        help='Suppress progress output')
+
+    args = parser.parse_args()
+
+    # Create config
+    if args.protocol:
+        protocol_map = {
+            'fvd2048_16f': FVDConfig.fvd2048_16f,
+            'fvd2048_128f': FVDConfig.fvd2048_128f,
+            'fvd2048_128f_subsample8': FVDConfig.fvd2048_128f_subsample8,
+            'quick_test': FVDConfig.quick_test,
+        }
+        config = protocol_map[args.protocol]()
+
+        # Override device and caching from args
+        config.device = args.device
+        config.cache_real_features = args.cache_real_features
+        config.i3d_model_path = args.i3d_model_path
+        config.batch_size = args.batch_size
+        config.seed = args.seed
+    else:
+        # Custom config from args
+        config = FVDConfig(num_videos=args.num_videos,
+                           num_frames_per_clip=args.num_frames,
+                           num_clips_per_video=args.num_clips,
+                           clip_strategy=args.clip_strategy,
+                           frame_stride=args.frame_stride,
+                           temporal_stride=args.temporal_stride,
+                           support_frame_dirs=not args.no_frame_dirs,
+                           batch_size=args.batch_size,
+                           device=args.device,
+                           cache_real_features=args.cache_real_features,
+                           i3d_model_path=args.i3d_model_path,
+                           seed=args.seed)
+
+    # Compute FVD
+    try:
+        results = compute_fvd_with_config(real_videos=args.real_path,
+                                          gen_videos=args.gen_path,
+                                          config=config,
+                                          verbose=not args.quiet)
+
+        # Save results
+        output_path = Path(args.output)
+        output_path.parent.mkdir(parents=True, exist_ok=True)
+
+        with open(output_path, 'w') as f:
+            json.dump(results, f, indent=2)
+
+        print(f"\nResults saved to {output_path}")
+        print(f"FVD: {results['fvd']:.2f}")
+        print(f"Protocol: {results['protocol']}")
+
+        return 0
+
+    except Exception as e:
+        print(f"Error: {e}", file=sys.stderr)
+        import traceback
+        traceback.print_exc()
+        return 1
+
+
+if __name__ == '__main__':
+    sys.exit(main())

benchmarks/fvd/fvd.py

@@ -0,0 +1,447 @@
+import numpy as np
+import scipy.linalg
+import torch
+from pathlib import Path
+from collections.abc import Iterator
+import pickle
+from dataclasses import dataclass, field
+
+from .i3d_model import I3DFeatureExtractor
+from .video_utils import ClipSamplingStrategy, load_video_clips_streaming
+
+
+def compute_statistics(features: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
+    """Compute mean and covariance."""
+    mu = np.mean(features, axis=0)
+    sigma = np.cov(features, rowvar=False)
+    return mu, sigma
+
+
+def compute_frechet_distance(mu1: np.ndarray,
+                             sigma1: np.ndarray,
+                             mu2: np.ndarray,
+                             sigma2: np.ndarray,
+                             eps: float = 1e-6) -> float:
+    """
+    Compute Fréchet distance between two Gaussians.
+    """
+    sigma1 = sigma1 + eps * np.eye(sigma1.shape[0])
+    sigma2 = sigma2 + eps * np.eye(sigma2.shape[0])
+
+    diff = mu1 - mu2
+    mean_distance = np.sum(diff**2)
+
+    trace_sum = np.trace(sigma1 + sigma2)
+
+    covmean = scipy.linalg.sqrtm(sigma1 @ sigma2)
+
+    if np.iscomplexobj(covmean):
+        if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3):
+            print(
+                f"Warning: Imaginary component: {np.max(np.abs(covmean.imag))}")
+        covmean = covmean.real
+
+    trace_product = np.trace(covmean)
+
+    fvd = mean_distance + trace_sum - 2 * trace_product
+
+    return float(fvd)
+
+
+@dataclass
+class FVDConfig:
+    # default configuration for FVD computation:
+
+    # Video selection
+    num_videos: int = 2048
+
+    # Clip sampling
+    num_frames_per_clip: int = 16
+    num_clips_per_video: int = 1
+    clip_strategy: str | ClipSamplingStrategy = 'beginning'
+
+    # Temporal subsampling
+    frame_stride: int = 1  # 1=no subsampling, 2=every 2nd, 8=every 8th
+    temporal_stride: int = 1  # For sliding window clips
+
+    # Data processing
+    video_extensions: list[str] = field(
+        default_factory=lambda: ['.mp4', '.avi', '.mov', '.mkv'])
+    support_frame_dirs: bool = True
+
+    # Computation
+    batch_size: int = 32
+    device: str = 'cuda'
+
+    use_streaming: bool = True
+    resize_before_extraction: bool = True
+
+    # Caching
+    cache_real_features: str | None = None
+    i3d_model_path: str | None = None
+
+    # Reproducibility
+    seed: int | None = None
+
+    @classmethod
+    def fvd2048_16f(cls) -> 'FVDConfig':
+        """
+        Standard FVD protocol: 2048 videos, 16 frames, beginning clip.
+        
+        most common FVD configuration used in papers
+        """
+        return cls(num_videos=2048,
+                   num_frames_per_clip=16,
+                   clip_strategy='beginning',
+                   use_streaming=True)
+
+    @classmethod
+    def fvd2048_128f(cls) -> 'FVDConfig':
+        """Long video protocol: 2048 videos, 128 frames."""
+        return cls(num_videos=2048,
+                   num_frames_per_clip=128,
+                   clip_strategy='beginning',
+                   use_streaming=True)
+
+    @classmethod
+    def fvd2048_128f_subsample8(cls) -> 'FVDConfig':
+        """
+        Long video with FPS subsampling: 2048 videos, 128 frames (every 8th).
+        Used for very long videos - samples every 8th frame
+        """
+        return cls(num_videos=2048,
+                   num_frames_per_clip=16,
+                   frame_stride=8,
+                   clip_strategy='beginning',
+                   use_streaming=True)
+
+    @classmethod
+    def quick_test(cls) -> 'FVDConfig':
+        """Quick test config: 100 videos, 16 frames."""
+        return cls(num_videos=100,
+                   num_frames_per_clip=16,
+                   clip_strategy='beginning')
+
+    def to_dict(self) -> dict:
+        """Export config to dict for logging"""
+        return {
+            'num_videos': self.num_videos,
+            'num_frames_per_clip': self.num_frames_per_clip,
+            'num_clips_per_video': self.num_clips_per_video,
+            'clip_strategy': str(self.clip_strategy),
+            'frame_stride': self.frame_stride,
+            'temporal_stride': self.temporal_stride,
+            'batch_size': self.batch_size,
+            'device': self.device,
+            'seed': self.seed,
+            'use_streaming': self.use_streaming,
+        }
+
+    def __str__(self) -> str:
+        """Human-readable protocol name"""
+        desc = f"FVD{self.num_videos}_{self.num_frames_per_clip}f"
+        if self.frame_stride > 1:
+            desc += f"_subsample{self.frame_stride}"
+        if self.num_clips_per_video > 1:
+            desc += f"_{self.num_clips_per_video}clips"
+        if self.clip_strategy != 'beginning':
+            desc += f"_{self.clip_strategy}"
+        return desc
+
+
+def extract_features_streaming(video_generator: Iterator[torch.Tensor],
+                               extractor: I3DFeatureExtractor,
+                               batch_size: int = 32,
+                               max_clips: int | None = None,
+                               verbose: bool = True) -> np.ndarray:
+    """
+    Extract features from a video clip generator using streaming.
+
+    Args:
+        video_generator: Iterator yielding clips [T, C, H, W]
+        extractor: I3D feature extractor
+        batch_size: Batch size for processing
+        max_clips: Maximum clips to process (for validation)
+        verbose: Show progress
+    
+    Returns:
+        features: [N, 400] numpy array
+    """
+    all_features = []
+    batch = []
+    clip_count = 0
+
+    if verbose:
+        print(f"Extracting features with batch_size={batch_size}...")
+
+    for clip_count, clip in enumerate(video_generator):
+        batch.append(clip)
+
+        # Process batch when full
+        if len(batch) == batch_size:
+            batch_tensor = torch.stack(batch).to(extractor.device)
+            features = extractor.extract_features(batch_tensor,
+                                                  batch_size=batch_size,
+                                                  verbose=False)
+            all_features.append(features.cpu().numpy())
+
+            batch = []  # Clear batch
+
+            if verbose and clip_count % (batch_size * 10) == 0:
+                print(f"Processed {clip_count} clips...")
+
+        # Stop if we've reached max_clips
+        if max_clips is not None and clip_count >= max_clips:
+            break
+
+    # Process remaining clips
+    if len(batch) > 0:
+        batch_tensor = torch.stack(batch).to(extractor.device)
+        features = extractor.extract_features(batch_tensor,
+                                              batch_size=len(batch),
+                                              verbose=False)
+        all_features.append(features.cpu().numpy())
+
+    if len(all_features) == 0:
+        raise RuntimeError("No features extracted - check video loading")
+
+    features = np.concatenate(all_features, axis=0)
+
+    if verbose:
+        print(f"Extracted {len(features)} feature vectors")
+
+    return features
+
+
+def load_or_compute_features(videos: str | Path | torch.Tensor,
+                             extractor: I3DFeatureExtractor,
+                             config: FVDConfig,
+                             cache_path: str | None = None,
+                             cache_name: str = "real_features") -> np.ndarray:
+    """Load features from cache or compute (with streaming support)"""
+
+    if cache_path is not None:
+        cache_file = Path(cache_path) / f"{cache_name}.pkl"
+        if cache_file.exists():
+            print(f"Loading cached features from {cache_file}")
+            with open(cache_file, 'rb') as f:
+                features = pickle.load(f)
+
+            # Validate and limit based on config
+            max_features = config.num_videos * config.num_clips_per_video
+            if len(features) < max_features:
+                print(
+                    f"WARNING: Cache has {len(features)} features but need {max_features}"
+                )
+                print("Recomputing features...")
+            elif len(features) > max_features:
+                features = features[:max_features]
+                return features
+            else:
+                return features
+
+    # Compute features
+    if isinstance(videos, str | Path):
+        target_size = (224, 224) if config.resize_before_extraction else None
+
+        video_generator = load_video_clips_streaming(
+            videos,
+            num_frames=config.num_frames_per_clip,
+            max_videos=config.num_videos,
+            clip_strategy=config.clip_strategy,
+            frame_stride=config.frame_stride,
+            num_clips_per_video=config.num_clips_per_video,
+            video_extensions=config.video_extensions,
+            support_frame_dirs=config.support_frame_dirs,
+            target_size=target_size,
+            verbose=True)
+
+        max_clips = config.num_videos * config.num_clips_per_video
+        features = extract_features_streaming(video_generator,
+                                              extractor,
+                                              batch_size=config.batch_size,
+                                              max_clips=max_clips,
+                                              verbose=True)
+
+    else:
+        # Already a tensor
+        print(f"Extracting features from {len(videos)} video tensors...")
+        features = extractor.extract_features(videos,
+                                              batch_size=config.batch_size,
+                                              verbose=True)
+        features = features.numpy()
+
+    # Validate feature count
+    expected_count = config.num_videos * config.num_clips_per_video
+    if len(features) < expected_count:
+        raise ValueError(
+            f"ERROR: Only extracted {len(features)} features, but need {expected_count}!\n"
+            f"Found fewer videos than expected. Check your video directory.")
+    elif len(features) > expected_count:
+        print(f"Truncating {len(features)} features to {expected_count}")
+        features = features[:expected_count]
+
+    # Cache features if requested
+    if cache_path is not None:
+        cache_dir = Path(cache_path)
+        cache_dir.mkdir(parents=True, exist_ok=True)
+        cache_file = cache_dir / f"{cache_name}.pkl"
+        print(f"Caching features to {cache_file}")
+        with open(cache_file, 'wb') as f:
+            pickle.dump(features, f)
+
+    return features
+
+
+def compute_fvd(real_videos: str | Path | torch.Tensor,
+                gen_videos: str | Path | torch.Tensor,
+                num_frames: int = 16,
+                batch_size: int = 32,
+                device: str = 'cuda',
+                num_videos: int | None = 2048,
+                cache_real_features: str | None = None,
+                i3d_model_path: str | None = None,
+                seed: int | None = None,
+                verbose: bool = True) -> float:
+    """
+    Compute Fréchet Video Distance (FVD)
+    
+    For advanced control, use compute_fvd_with_config() instead.
+    
+    Args:
+        real_videos: Path to real videos or tensor [N, T, C, H, W]
+        gen_videos: Path to generated videos or tensor [N, T, C, H, W]
+        num_frames: Frames per video (default: 16)
+        batch_size: Batch size (default: 32)
+        device: 'cuda' or 'cpu' (default: 'cuda')
+        num_videos: Max videos (default: 2048)
+        cache_real_features: Cache path for real features
+        i3d_model_path: Custom I3D model cache path
+        seed: Random seed for reproducibility
+        verbose: Print progress
+    
+    Returns:
+        FVD score (float). Lower is better.
+    """
+    num_videos = num_videos if num_videos is not None else 2048
+
+    config = FVDConfig(
+        num_videos=num_videos,
+        num_frames_per_clip=num_frames,
+        batch_size=batch_size,
+        device=device,
+        cache_real_features=cache_real_features,
+        i3d_model_path=i3d_model_path,
+        seed=seed,
+    )
+
+    result = compute_fvd_with_config(real_videos, gen_videos, config, verbose)
+    return result['fvd']
+
+
+def compute_fvd_with_config(real_videos: str | Path | torch.Tensor,
+                            gen_videos: str | Path | torch.Tensor,
+                            config: FVDConfig,
+                            verbose: bool = True) -> dict:
+    """
+    Compute FVD using a standardized configuration.
+    
+    This is the recommended way to compute FVD for reproducibility.
+    
+    Args:
+        real_videos: Path or tensors
+        gen_videos: Path or tensors
+        config: FVDConfig specifying protocol
+        verbose: Print progress
+    
+    Returns:
+        results: Dictionary with:
+            - 'fvd': FVD score (float)
+            - 'protocol': Protocol name (str)
+            - 'config': Configuration dict
+    
+    Example:
+        >>> config = FVDConfig.fvd2048_16f()
+        >>> results = compute_fvd_with_config('data/real/', 'outputs/gen/', config)
+        >>> print(f"FVD: {results['fvd']:.2f}")
+        >>> print(f"Protocol: {results['protocol']}")  # "FVD2048_16f"
+    """
+    # Seed for reproducibility
+    if config.seed is not None:
+        import random as _rnd
+        _rnd.seed(config.seed)
+        np.random.seed(config.seed)
+        torch.manual_seed(config.seed)
+        if torch.cuda.is_available():
+            torch.cuda.manual_seed_all(config.seed)
+
+    if verbose:
+        print("=" * 70)
+        print(f"Computing FVD with protocol: {config}")
+        print("=" * 70)
+        print("\nConfiguration:")
+        for key, value in config.to_dict().items():
+            print(f"  {key}: {value}")
+        print()
+
+    # Initialize I3D
+    if verbose:
+        print(f"\nInitializing I3D model on {config.device}...")
+
+    extractor = I3DFeatureExtractor(device=config.device,
+                                    cache_dir=config.i3d_model_path)
+
+    # Extract features
+    if verbose:
+        print(f"\n{'='*70}")
+        print("Extracting REAL video features...")
+        print(f"{'='*70}")
+
+    real_features = load_or_compute_features(
+        videos=real_videos,
+        extractor=extractor,
+        config=config,
+        cache_path=config.cache_real_features,
+        cache_name="real_features")
+
+    if verbose:
+        print(f"\n{'='*70}")
+        print("Extracting GENERATED video features...")
+        print(f"{'='*70}")
+
+    gen_features = load_or_compute_features(videos=gen_videos,
+                                            extractor=extractor,
+                                            config=config,
+                                            cache_path=None,
+                                            cache_name="gen_features")
+
+    if verbose:
+        print(f"\nReal videos/clips: {len(real_features)}")
+        print(f"Generated videos/clips: {len(gen_features)}")
+        print(f"\n{'='*70}")
+        print("Computing statistics...")
+        print(f"{'='*70}")
+
+    mu_real, sigma_real = compute_statistics(real_features)
+    mu_gen, sigma_gen = compute_statistics(gen_features)
+
+    if verbose:
+        print(f"\n{'='*70}")
+        print("Computing Fréchet distance...")
+        print(f"{'='*70}")
+
+    fvd = compute_frechet_distance(mu_real, sigma_real, mu_gen, sigma_gen)
+
+    if verbose:
+        print(f"\n{'='*70}")
+        print(f"FVD Score: {fvd:.4f}")
+        print(f"Protocol: {config}")
+        print(f"{'='*70}\n")
+
+    results = {
+        'fvd': fvd,
+        'protocol': str(config),
+        'config': config.to_dict(),
+    }
+
+    return results

benchmarks/fvd/i3d_model.py

@@ -0,0 +1,142 @@
+"""I3D Feature Extractor for FVD Computation"""
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from pathlib import Path
+from huggingface_hub import hf_hub_download
+from tqdm import tqdm
+from contextlib import suppress
+
+
+class I3DFeatureExtractor(nn.Module):
+    """
+    I3D feature extractor for FVD computation.
+    Extracts 400-dimensional features from videos using I3D model
+    trained on Kinetics-400.
+    """
+
+    REPO_ID = 'flateon/FVD-I3D-torchscript'
+    MODEL_FILENAME = 'i3d_torchscript.pt'
+
+    def __init__(self,
+                 device: str = 'cuda',
+                 cache_dir: str | Path | None = None):
+        super().__init__()
+
+        self.device_str = device
+        if device == 'cuda' and not torch.cuda.is_available():
+            print(
+                "Warning: CUDA requested but not available – falling back to CPU"
+            )
+            self.device = torch.device('cpu')
+        else:
+            self.device = torch.device(device)
+
+        self.cache_dir: str | None
+        if cache_dir is not None:
+            self.cache_dir = str(Path(cache_dir).resolve())
+        else:
+            self.cache_dir = None  # Use HF default cache
+
+        self.model = self._load_model()
+        self.model.eval()
+
+        with suppress(Exception):
+            self.model.to(self.device)
+
+    def _load_model(self) -> torch.nn.Module:
+        """Download and load I3D TorchScript model from Hugging Face Hub."""
+        print(f"Loading I3D model from Hugging Face Hub ({self.REPO_ID})...")
+
+        try:
+            # Download model from Hugging Face Hub
+            model_path = hf_hub_download(repo_id=self.REPO_ID,
+                                         filename=self.MODEL_FILENAME,
+                                         cache_dir=self.cache_dir)
+
+            # Load directly to chosen device
+            model = torch.jit.load(model_path, map_location=self.device)
+            print("I3D model loaded successfully")
+            return model
+
+        except Exception as e:
+            raise RuntimeError(
+                f"Failed to load I3D model from Hugging Face Hub. Error: {e}\n"
+                f"Ensure you have internet connection and huggingface_hub installed:\n"
+                f"pip install huggingface_hub") from e
+
+    def preprocess(self, videos: torch.Tensor) -> torch.Tensor:
+        """
+        Preprocess videos for I3D.
+        
+        Args:
+            videos: [B, T, C, H, W], values in [0, 255]
+        
+        Returns:
+            Preprocessed videos [B, C, T, 224, 224] (normalized and resized)
+        """
+        B, T, C, H, W = videos.shape
+
+        if T < 10:
+            raise ValueError(f"I3D requires at least 10 frames, got {T}")
+
+        # Normalize to [0, 1] if needed
+        if videos.max() > 1.0:
+            videos = videos / 255.0
+
+        # Resize to 224x224 if needed
+        if H != 224 or W != 224:
+            videos = videos.reshape(B * T, C, H, W)
+            videos = F.interpolate(videos,
+                                   size=(224, 224),
+                                   mode='bilinear',
+                                   align_corners=False)
+            videos = videos.reshape(B, T, C, 224, 224)
+
+        # Convert to [B, C, T, H, W] format
+        videos = videos.permute(0, 2, 1, 3, 4).contiguous()
+
+        return videos
+
+    @torch.no_grad()
+    def extract_features(self,
+                         videos: torch.Tensor,
+                         batch_size: int = 32,
+                         verbose: bool = True) -> torch.Tensor:
+        """
+        Extract I3D features
+        
+        Args:
+            videos: [N, T, C, H, W], values in [0, 255]
+            batch_size: Batch size for processing
+            verbose: Show progress bar
+        
+        Returns:
+            Features [N, 400]
+        """
+        N = len(videos)
+        all_features = []
+
+        iterator = range(0, N, batch_size)
+        if verbose:
+            iterator = tqdm(iterator, desc="Extracting I3D features")
+
+        for i in iterator:
+            batch = videos[i:i + batch_size].to(self.device)
+            batch = self.preprocess(batch)  # Now returns [B, C, T, H, W]
+
+            # Use the HF model without rescale/resize (we handle it in preprocess)
+            features = self.model(batch,
+                                  rescale=False,
+                                  resize=False,
+                                  return_features=True)
+
+            all_features.append(features.cpu())
+
+        return torch.cat(all_features, dim=0)
+
+    def __call__(self,
+                 videos: torch.Tensor,
+                 batch_size: int = 32) -> torch.Tensor:
+        return self.extract_features(videos, batch_size=batch_size)

benchmarks/fvd/run_fvd.py

@@ -0,0 +1,34 @@
+import sys
+from pathlib import Path
+from benchmarks.fvd.fvd import FVDConfig, compute_fvd_with_config
+
+root_dir = Path(__file__).parent.parent.parent
+sys.path.insert(0, str(root_dir))
+
+
+def main() -> None:
+    # Get script directory
+    script_dir = Path(__file__).parent.resolve()
+
+    clip_strategy = 'beginning'  # Options: 'uniform', 'random', 'beginning', 'end', 'all'
+    cfg = FVDConfig(
+        num_videos=650,
+        num_frames_per_clip=16,
+        num_clips_per_video=1,
+        clip_strategy=clip_strategy,
+        frame_stride=1,
+        batch_size=32,
+        device='cuda',
+        seed=42,
+        cache_real_features=str(script_dir / f'fvd-cache/{clip_strategy}'),
+    )
+
+    real_dir = "benchmarks/data/real_videos"
+    gen_dir = "benchmarks/data/generated_videos"
+
+    results = compute_fvd_with_config(real_dir, gen_dir, cfg, verbose=True)
+    print(f"FVD = {results['fvd']:.2f}")
+
+
+if __name__ == '__main__':
+    main()

benchmarks/fvd/validate_fvd.py

@@ -0,0 +1,97 @@
+#!/usr/bin/env python3
+import sys
+from pathlib import Path
+import shutil
+import random
+from fvd import compute_fvd_with_config, FVDConfig
+
+script_path = Path(__file__).resolve()
+fastvideo_root = script_path.parent.parent.parent
+sys.path.insert(0, str(fastvideo_root))
+
+
+def split_videos(video_dir: Path, n_per_subset: int = 128, seed: int = 42):
+    subset_a = video_dir.parent / 'bair_full_subset_A'
+    subset_b = video_dir.parent / 'bair_full_subset_B'
+
+    if subset_a.exists():
+        shutil.rmtree(subset_a)
+    if subset_b.exists():
+        shutil.rmtree(subset_b)
+
+    subset_a.mkdir(parents=True)
+    subset_b.mkdir(parents=True)
+
+    videos = sorted(video_dir.glob('*.mp4'))
+
+    random.seed(seed)
+    shuffled = list(videos)
+    random.shuffle(shuffled)
+
+    needed = n_per_subset * 2
+    if len(shuffled) > needed:
+        shuffled = shuffled[:needed]
+
+    mid = len(shuffled) // 2
+
+    print(f"\nSplitting {len(shuffled)} BAIR FULL videos:")
+    print(f"  Subset A: {mid} videos")
+    print(f"  Subset B: {len(shuffled) - mid} videos")
+
+    for v in shuffled[:mid]:
+        shutil.copy2(v, subset_a / v.name)
+
+    for v in shuffled[mid:]:
+        shutil.copy2(v, subset_b / v.name)
+
+    return subset_a, subset_b, mid
+
+
+def validate_fvd(subset_a: Path, subset_b: Path, num_videos: int):
+    config = FVDConfig(num_videos=num_videos,
+                       num_frames_per_clip=16,
+                       clip_strategy='beginning',
+                       batch_size=8,
+                       device='cuda',
+                       seed=42)
+
+    print("\n" + "=" * 70)
+    print("TEST 1: Identity Test")
+    print("=" * 70)
+
+    result1 = compute_fvd_with_config(real_videos=str(subset_a),
+                                      gen_videos=str(subset_a),
+                                      config=config,
+                                      verbose=False)
+    fvd_identity = result1['fvd']
+    print(f"\nIdentity FVD: {fvd_identity:.2f}")
+
+    print("\n" + "=" * 70)
+    print("TEST 2: Real vs Real")
+    print("=" * 70)
+
+    result2 = compute_fvd_with_config(real_videos=str(subset_a),
+                                      gen_videos=str(subset_b),
+                                      config=config,
+                                      verbose=False)
+    fvd_real = result2['fvd']
+    print(f"\nReal vs Real FVD: {fvd_real:.2f}")
+
+    print("\n" + "=" * 70)
+    print("RESULTS")
+    print("=" * 70)
+    print(f"Identity:        {fvd_identity:.2f}")
+    print(f"Real vs Real:    {fvd_real:.2f}")
+
+
+def main() -> None:
+    bair_dir = Path('benchmarks/data/bair_full_videos')
+
+    subset_a, subset_b, count = split_videos(bair_dir,
+                                             n_per_subset=128,
+                                             seed=42)
+    validate_fvd(subset_a, subset_b, count)
+
+
+if __name__ == '__main__':
+    main()

benchmarks/fvd/video_utils.py

@@ -0,0 +1,490 @@
+import torch
+import cv2
+import numpy as np
+from pathlib import Path
+from collections.abc import Iterator
+from tqdm import tqdm
+from enum import Enum
+
+
+class ClipSamplingStrategy(Enum):
+    """Clip sampling strategies for FVD evaluation."""
+    BEGINNING = 'beginning'  # Take first N frames (most common)
+    RANDOM = 'random'  # Random N consecutive frames
+    UNIFORM = 'uniform'  # Uniformly spaced frames across video
+    MIDDLE = 'middle'  # Middle N frames
+    SLIDING = 'sliding'  # Multiple sliding windows
+    ALL = 'all'  # All possible clips
+
+
+def _load_video_cv2(video_path: str | Path,
+                    num_frames: int | None = 16,
+                    sample_strategy: str = 'uniform') -> torch.Tensor:
+    """
+    Load video from video file using OpenCV.
+    
+    Args:
+        video_path: Path to video file (MP4, AVI, MOV, MKV)
+        num_frames: Number of frames to extract
+        sample_strategy: 'uniform' or 'random'
+    
+    Returns:
+        video: [T, C, H, W]
+    """
+    video_path = str(video_path)
+    cap = cv2.VideoCapture(video_path)
+
+    if not cap.isOpened():
+        raise RuntimeError(f"Cannot open video: {video_path}")
+
+    frames = []
+    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+
+    if num_frames is None:
+        # Read all available frames
+        while True:
+            ret, frame = cap.read()
+            if not ret:
+                break
+            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+            frames.append(frame)
+
+        cap.release()
+        if len(frames) == 0:
+            raise RuntimeError(f"Video has 0 frames: {video_path}")
+
+        frames = np.stack(frames)  # [T, H, W, C]
+        frames = torch.from_numpy(frames).permute(0, 3, 1,
+                                                  2).float()  # [T, C, H, W]
+        return frames
+
+    if total_frames == 0:
+        raise RuntimeError(f"Video has 0 frames: {video_path}")
+
+    # Determine frame indices for sampling
+    if total_frames < num_frames:
+        frame_indices = list(range(
+            total_frames)) + [total_frames - 1] * (num_frames - total_frames)
+    elif sample_strategy == 'uniform':
+        frame_indices = np.linspace(0, total_frames - 1, num_frames,
+                                    dtype=int).tolist()
+    elif sample_strategy == 'random':
+        frame_indices = sorted(
+            np.random.choice(total_frames, num_frames, replace=False))
+    else:
+        raise ValueError(f"Unknown sample_strategy: {sample_strategy}")
+
+    # Extract frames
+    for idx in frame_indices:
+        cap.set(cv2.CAP_PROP_POS_FRAMES, idx)
+        ret, frame = cap.read()
+
+        if not ret:
+            if len(frames) > 0:
+                frames.append(frames[-1].copy())
+            else:
+                h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+                w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+                frames.append(np.zeros((h, w, 3), dtype=np.uint8))
+            continue
+
+        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        frames.append(frame)
+
+    cap.release()
+
+    frames = np.stack(frames)  # [T, H, W, C]
+    frames = torch.from_numpy(frames).permute(0, 3, 1,
+                                              2).float()  # [T, C, H, W]
+
+    return frames
+
+
+def _load_video_from_frames(
+        frame_dir: str | Path,
+        num_frames: int | None = 16,
+        sample_strategy: str = 'uniform',
+        frame_extensions: list[str] | None = None) -> torch.Tensor:
+    """
+    Load video from directory of frame images.
+    
+    Args:
+        frame_dir: Directory containing frames
+        num_frames: Number of frames to sample
+        sample_strategy: 'uniform' or 'random'
+        frame_extensions: Image file extensions to look for
+    
+    Returns:
+        video: [T, C, H, W]
+    """
+    if frame_extensions is None:
+        frame_extensions = ['.jpg', '.png', '.jpeg', '.bmp']
+
+    frame_dir = Path(frame_dir)
+
+    if not frame_dir.exists():
+        raise FileNotFoundError(f"Frame directory not found: {frame_dir}")
+
+    # Find all frames
+    frame_files: list[Path] = []
+    for ext in frame_extensions:
+        frame_files.extend(frame_dir.glob(f"*{ext}"))
+
+    if len(frame_files) == 0:
+        raise ValueError(
+            f"No frames found in {frame_dir} with extensions {frame_extensions}"
+        )
+
+    frame_files = sorted(frame_files, key=lambda x: x.name)
+    total_frames = len(frame_files)
+
+    # Determine frame indices
+    if num_frames is None:
+        frame_indices = list(range(total_frames))
+    else:
+        if total_frames < num_frames:
+            frame_indices = list(range(total_frames)) + [total_frames - 1] * (
+                num_frames - total_frames)
+        elif sample_strategy == 'uniform':
+            frame_indices = np.linspace(0,
+                                        total_frames - 1,
+                                        num_frames,
+                                        dtype=int).tolist()
+        elif sample_strategy == 'random':
+            frame_indices = sorted(
+                np.random.choice(total_frames, num_frames, replace=False))
+        else:
+            raise ValueError(f"Unknown sample_strategy: {sample_strategy}")
+
+    # Load frames
+    frames = []
+    for idx in frame_indices:
+        frame_path = frame_files[idx]
+        frame = cv2.imread(str(frame_path))
+
+        if frame is None:
+            raise RuntimeError(f"Failed to load frame: {frame_path}")
+
+        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        frames.append(frame)
+
+    # Stack and convert to tensor
+    frames = np.stack(frames)  # [T, H, W, C]
+    frames = torch.from_numpy(frames).permute(0, 3, 1,
+                                              2).float()  # [T, C, H, W]
+
+    return frames
+
+
+def _detect_video_format(path: str | Path) -> str:
+    """
+    Detect if path is a video file or frame directory.
+    
+    Returns:
+        'video_file', 'frame_directory', or 'unknown'
+    """
+    path = Path(path)
+
+    if path.is_file():
+        return 'video_file'
+    elif path.is_dir():
+        # Check if contains image files
+        image_extensions = ['.jpg', '.jpeg', '.png', '.bmp']
+        for ext in image_extensions:
+            if list(path.glob(f"*{ext}")):
+                return 'frame_directory'
+        return 'unknown'
+    else:
+        raise ValueError(f"Path does not exist: {path}")
+
+
+def load_video_auto(video_path: str | Path,
+                    num_frames: int | None = 16,
+                    sample_strategy: str = 'uniform') -> torch.Tensor:
+    """
+    Automatically detect format and load video.
+    
+    Supports:
+    - Video files (MP4, AVI, MOV, MKV)
+    - Frame directories (JPG, PNG)
+    
+    Args:
+        video_path: Path to video file or frame directory
+        num_frames: Number of frames to extract
+        sample_strategy: 'uniform' or 'random'
+    
+    Returns:
+        video: [T, C, H, W]
+    """
+    format_type = _detect_video_format(video_path)
+
+    if format_type == 'video_file':
+        return _load_video_cv2(video_path, num_frames, sample_strategy)
+    elif format_type == 'frame_directory':
+        return _load_video_from_frames(video_path, num_frames, sample_strategy)
+    else:
+        raise ValueError(f"Unknown video format at {video_path}")
+
+
+def sample_clips_from_video(
+        video: torch.Tensor,
+        num_frames_per_clip: int = 16,
+        num_clips: int = 1,
+        strategy: str | ClipSamplingStrategy = ClipSamplingStrategy.BEGINNING,
+        frame_stride: int = 1,
+        temporal_stride: int = 1) -> list[torch.Tensor]:
+    """
+    Sample clips from a video with various strategies.
+        
+    Args:
+        video: [T, C, H, W] full video
+        num_frames_per_clip: Frames per clip
+        num_clips: Number of clips to extract
+        strategy: ClipSamplingStrategy or string ('beginning', 'random', etc.)
+        frame_stride: Skip frames (FPS control: 1=all, 2=every 2nd, 8=every 8th)
+        temporal_stride: Stride between clips for sliding window
+    
+    Returns:
+        List of clips, each [num_frames_per_clip, C, H, W]
+    
+    Examples:
+        >>> # Beginning clip (most common for FVD)
+        >>> clips = sample_clips_from_video(video, 16, strategy='beginning')
+        
+        >>> # Multiple random clips
+        >>> clips = sample_clips_from_video(video, 16, num_clips=4, strategy='random')
+        
+        >>> # Subsample FPS by 2x (every 2nd frame)
+        >>> clips = sample_clips_from_video(video, 16, frame_stride=2)
+        
+        >>> # Sliding window with overlap
+        >>> clips = sample_clips_from_video(video, 16, strategy='sliding', temporal_stride=8)
+    """
+    # Convert string to enum if needed
+    if isinstance(strategy, str):
+        strategy = ClipSamplingStrategy(strategy)
+
+    T, C, H, W = video.shape
+
+    # Apply frame stride (FPS subsampling)
+    if frame_stride > 1:
+        video = video[::frame_stride]
+        T = len(video)
+
+    effective_clip_length = num_frames_per_clip
+
+    # Handle videos shorter than clip length
+    if effective_clip_length > T:
+        pad_length = effective_clip_length - T
+        last_frame = video[-1:].repeat(pad_length, 1, 1, 1)
+        video = torch.cat([video, last_frame], dim=0)
+        T = len(video)
+
+    clips = []
+
+    if strategy == ClipSamplingStrategy.BEGINNING:
+        # Take first clip (most common for FVD evaluation)
+        clip = video[:effective_clip_length]
+        clips.append(clip)
+
+    elif strategy == ClipSamplingStrategy.MIDDLE:
+        # Take middle clip
+        start = (T - effective_clip_length) // 2
+        clip = video[start:start + effective_clip_length]
+        clips.append(clip)
+
+    elif strategy == ClipSamplingStrategy.RANDOM:
+        # Sample N random clips
+        for _ in range(num_clips):
+            if effective_clip_length == T:
+                start = 0
+            else:
+                start = np.random.randint(0, T - effective_clip_length + 1)
+            clip = video[start:start + effective_clip_length]
+            clips.append(clip)
+
+    elif strategy == ClipSamplingStrategy.UNIFORM:
+        # Uniformly spaced clips
+        if num_clips == 1:
+            # Single clip from middle
+            start = (T - effective_clip_length) // 2
+            clip = video[start:start + effective_clip_length]
+            clips.append(clip)
+        else:
+            # Multiple uniformly spaced clips
+            step = (T - effective_clip_length) / (num_clips -
+                                                  1) if num_clips > 1 else 0
+            for i in range(num_clips):
+                start = int(i * step)
+                start = min(start, T - effective_clip_length)
+                clip = video[start:start + effective_clip_length]
+                clips.append(clip)
+
+    elif strategy == ClipSamplingStrategy.SLIDING:
+        # Sliding window with stride
+        for start in range(0, T - effective_clip_length + 1, temporal_stride):
+            clip = video[start:start + effective_clip_length]
+            clips.append(clip)
+            if len(clips) >= num_clips:
+                break
+
+    elif strategy == ClipSamplingStrategy.ALL:
+        # All possible clips (overlapping)
+        for start in range(T - effective_clip_length + 1):
+            clip = video[start:start + effective_clip_length]
+            clips.append(clip)
+
+    else:
+        raise ValueError(f"Unknown strategy: {strategy}")
+
+    return clips
+
+
+def load_video_clips_streaming(directory: str | Path,
+                               num_frames: int = 16,
+                               max_videos: int | None = None,
+                               clip_strategy: str
+                               | ClipSamplingStrategy = 'beginning',
+                               frame_stride: int = 1,
+                               num_clips_per_video: int = 1,
+                               video_extensions: list[str] | None = None,
+                               support_frame_dirs: bool = True,
+                               target_size: tuple[int, int] | None = (224, 224),
+                               verbose: bool = True) -> Iterator[torch.Tensor]:
+    """
+    This generator yields clips one-by-one instead of loading all videos into RAM.
+    Perfect for large datasets where memory is limited.
+    
+    Args:
+        directory: Path to directory with videos
+        num_frames: Frames per clip
+        max_videos: Max videos to load
+        clip_strategy: 'beginning', 'random', 'uniform', etc.
+        frame_stride: Frame skip (1=all, 2=every 2nd, 8=every 8th)
+        num_clips_per_video: Number of clips per video
+        video_extensions: Video file extensions
+        support_frame_dirs: Also load frame directories
+        target_size: Resize clips to (H, W). If None, keep original size.
+        verbose: Show progress
+    
+    Yields:
+        clip: [T, C, H, W] individual clips
+    
+    Example:
+        >>> for clip in load_video_clips_streaming('data/videos/', num_frames=16):
+        >>>     features = model.extract_features(clip.unsqueeze(0))
+        >>>     # Process one clip at a time - low memory usage!
+    """
+    if video_extensions is None:
+        video_extensions = ['.mp4', '.avi', '.mov', '.mkv']
+
+    directory = Path(directory)
+
+    if not directory.exists():
+        raise FileNotFoundError(f"Directory not found: {directory}")
+
+    # Find video paths
+    video_paths: list[Path] = []
+
+    # Find video files
+    for ext in video_extensions:
+        video_paths.extend(directory.glob(f"**/*{ext}"))
+
+    # Find frame directories if enabled
+    if support_frame_dirs:
+        for subdir in directory.iterdir():
+            if subdir.is_dir():
+                # Check if it contains frames
+                image_extensions = ['.jpg', '.jpeg', '.png', '.bmp']
+                for ext in image_extensions:
+                    if list(subdir.glob(f"*{ext}")):
+                        video_paths.append(subdir)
+                        break
+
+    if len(video_paths) == 0:
+        raise ValueError(f"No videos found in {directory}")
+
+    video_paths = sorted(video_paths)
+
+    if max_videos is not None:
+        video_paths = video_paths[:max_videos]
+
+    if verbose:
+        print(f"Found {len(video_paths)} videos in {directory}")
+        if num_clips_per_video > 1:
+            print(f"Extracting {num_clips_per_video} clips per video...")
+        if frame_stride > 1:
+            print(f"Subsampling frames with stride {frame_stride}...")
+        if target_size:
+            print(f"Resizing clips to {target_size}...")
+
+    # Track statistics
+    failed_count = 0
+    total_clips = 0
+
+    iterator = tqdm(video_paths,
+                    desc="Loading videos") if verbose else video_paths
+
+    for video_path in iterator:
+        try:
+            # Load full video
+            video = load_video_auto(video_path,
+                                    num_frames=None,
+                                    sample_strategy='uniform')
+
+            # Sample clips from video
+            clips = sample_clips_from_video(video,
+                                            num_frames_per_clip=num_frames,
+                                            num_clips=num_clips_per_video,
+                                            strategy=clip_strategy,
+                                            frame_stride=frame_stride)
+
+            if target_size is not None:
+                resized_clips = []
+                for clip in clips:
+                    T, C, H, W = clip.shape
+                    if target_size != (H, W):
+                        # Resize to target size
+                        clip = clip.contiguous(
+                        )  # Fix non-contiguous tensors first
+                        clip_flat = clip.view(T * C, H,
+                                              W).unsqueeze(0)  # [1, T*C, H, W]
+                        clip_resized = torch.nn.functional.interpolate(
+                            clip_flat,
+                            size=target_size,
+                            mode='bilinear',
+                            align_corners=False)
+                        clip = clip_resized.squeeze(0).view(
+                            T, C, target_size[0],
+                            target_size[1])  # Back to [T, C, H, W]
+                    resized_clips.append(clip)
+                clips = resized_clips
+
+            # Yield clips one by one
+            for clip in clips:
+                yield clip
+                total_clips += 1
+
+            # Free memory
+            del video, clips
+
+        except Exception as e:
+            failed_count += 1
+            if verbose:
+                print(f"\nWarning: Failed to load {video_path}: {e}")
+            continue
+
+    # Validate
+    if total_clips == 0:
+        raise RuntimeError(f"Failed to load any videos from {directory}")
+
+    failure_rate = failed_count / len(video_paths)
+    if failure_rate > 0.1:  # More than 10% failed
+        print(
+            f"\nWARNING: {failure_rate:.1%} of videos failed to load ({failed_count}/{len(video_paths)})"
+        )
+
+    if verbose:
+        print(
+            f"\nSuccessfully loaded {total_clips} clips from {len(video_paths) - failed_count} videos"
+        )

benchmarks/scripts/run.sh

@@ -0,0 +1,7 @@
+#!/bin/bash
+
+# 1. Install missing dependency
+pip install -q opencv-python-headless
+
+# 2. Run FVD script
+python benchmarks/fvd/run_fvd.py

benchmarks/scripts/setup_fvd.sh

@@ -0,0 +1,4 @@
+#!/bin/bash
+
+# 1. Install missing dependency
+pip install -q opencv-python-headless

csrc/attn/tests/test_vsa.py

@@ -34,16 +34,16 @@ 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):
+def block_sparse_kernel_test(Q, K, V, block_sparse_mask, variable_block_sizes, q_non_pad_index, kv_non_pad_index, q_num_blocks, kv_num_blocks, dO):
     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)
-    v_padded = vsa_pad(V, non_pad_index, variable_block_sizes.shape[0], BLOCK_M)
+    q_padded = vsa_pad(Q, q_non_pad_index, q_num_blocks, BLOCK_M)
+    k_padded = vsa_pad(K, kv_non_pad_index, kv_num_blocks, BLOCK_M)
+    v_padded = vsa_pad(V, kv_non_pad_index, kv_num_blocks, BLOCK_M)
     output, _= block_sparse_attn(q_padded, k_padded, v_padded, block_sparse_mask, variable_block_sizes)
-    output = output[:, :, non_pad_index, :]
+    output = output[:, :, q_non_pad_index, :]
     output.backward(dO)
     return output, Q.grad, K.grad, V.grad
 
@@ -64,7 +64,7 @@ def generate_tensor(shape, dtype, device):
     tensor = torch.randn(shape, dtype=dtype, device=device)
     return tensor
 
-def generate_variable_block_sizes(num_blocks, min_size=32, max_size=64, device="cuda"):
+def generate_variable_block_sizes(num_blocks, min_size=16, max_size=64, device="cuda"):
     return torch.randint(min_size, max_size + 1, (num_blocks,), device=device, dtype=torch.int32)
 
 
@@ -86,19 +86,21 @@ def check_correctness(h, d, num_blocks, k,  num_iterations=20, error_mode='all')
     S = int(variable_block_sizes.sum().item())
     padded_S = num_blocks * BLOCK_M
     non_pad_index = get_non_pad_index(variable_block_sizes, num_blocks, BLOCK_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)
+    block_mask = generate_block_sparse_mask_for_function(h, num_blocks, num_blocks, k, device)
+    full_mask = create_full_mask_from_block_mask(block_mask, variable_block_sizes, variable_block_sizes, device)
     for _ in range(num_iterations):
         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)
+        
+        # print(Q.shape, K.shape, V.shape, dO.shape)
 
         # dO_padded = torch.zeros_like(dO_padded)
         # dO_padded[:, :, non_pad_index, :] = dO
 
         pt_o, pt_qg, pt_kg, pt_vg = pytorch_test(Q, K, V, full_mask, dO)
-        bs_o, bs_qg, bs_kg, bs_vg = block_sparse_kernel_test(Q, K, V, block_mask.unsqueeze(0), variable_block_sizes,non_pad_index, dO)
+        bs_o, bs_qg, bs_kg, bs_vg = block_sparse_kernel_test(Q, K, V, block_mask.unsqueeze(0), variable_block_sizes, non_pad_index, non_pad_index, num_blocks, num_blocks, dO)
         for name, (pt, bs) in zip(['gQ', 'gK', 'gV', 'gO'], [(pt_qg, bs_qg), (pt_kg, bs_kg), (pt_vg, bs_vg), (pt_o, bs_o)]):
             if bs is not None:
                 diff = pt - bs
@@ -118,6 +120,60 @@ def check_correctness(h, d, num_blocks, k,  num_iterations=20, error_mode='all')
 
     return results
 
+def check_correctness_qkdiff(h, d, num_q_blocks, num_kv_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},
+        'gK': {'sum_diff': 0.0, 'sum_abs': 0.0, 'max_diff': 0.0},
+        'gV': {'sum_diff': 0.0, 'sum_abs': 0.0, 'max_diff': 0.0},
+    }
+
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    q_variable_block_sizes = generate_variable_block_sizes(num_q_blocks, device=device)
+    kv_variable_block_sizes = generate_variable_block_sizes(num_kv_blocks, device=device)
+    
+    S_q = int(q_variable_block_sizes.sum().item())
+    S_kv = int(kv_variable_block_sizes.sum().item())
+    
+    q_non_pad_index = get_non_pad_index(q_variable_block_sizes, num_q_blocks, BLOCK_M)
+    kv_non_pad_index = get_non_pad_index(kv_variable_block_sizes, num_kv_blocks, BLOCK_M)
+    
+    block_mask = generate_block_sparse_mask_for_function(h, num_q_blocks, num_kv_blocks, k, device)
+    full_mask = create_full_mask_from_block_mask(block_mask, q_variable_block_sizes, kv_variable_block_sizes, device)
+    
+    for _ in range(num_iterations):
+        Q = generate_tensor((1, h, S_q, d),  torch.bfloat16, device)
+        K = generate_tensor((1, h, S_kv, d), torch.bfloat16, device)
+        V = generate_tensor((1, h, S_kv, d),  torch.bfloat16, device)
+        dO = generate_tensor((1, h, S_q, d), torch.bfloat16, device)
+        
+        # print(Q.shape, K.shape, V.shape, dO.shape)
+
+        pt_o, pt_qg, pt_kg, pt_vg = pytorch_test(Q, K, V, full_mask, dO)
+        bs_o, bs_qg, bs_kg, bs_vg = block_sparse_kernel_test(Q, K, V, block_mask.unsqueeze(0), kv_variable_block_sizes, q_non_pad_index, kv_non_pad_index, num_q_blocks, num_kv_blocks, dO)
+        
+        for name, (pt, bs) in zip(['gQ', 'gK', 'gV', 'gO'], [(pt_qg, bs_qg), (pt_kg, bs_kg), (pt_vg, bs_vg), (pt_o, bs_o)]):
+            if bs is not None:
+                diff = pt - bs
+                abs_diff = torch.abs(diff)
+                results[name]['sum_diff'] += torch.sum(abs_diff).item()
+                results[name]['sum_abs'] += torch.sum(torch.abs(pt)).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()
+
+    total_elements_q = h * S_q * d * num_iterations
+    total_elements_kv = h * S_kv * d * num_iterations
+    
+    for name, data in results.items():
+        total_elements = total_elements_q if name in ['gQ', 'gO'] else total_elements_kv
+        avg_diff = data['sum_diff'] / total_elements
+        max_diff = data['max_diff']
+        results[name] = {'avg_diff': avg_diff, 'max_diff': max_diff}
+
+    return results
+
 def generate_error_graphs(h, d, error_mode='all'):
     test_configs = [
         {"num_blocks": 16, "k": 2, "description": "Small sequence"},
@@ -147,10 +203,43 @@ def generate_error_graphs(h, d, error_mode='all'):
 
     print("-" * 150)
 
+def generate_error_graphs_qkdiff(h, d, error_mode='all'):
+    test_configs = [
+        {"num_q_blocks": 16, "num_kv_blocks": 32, "k": 2, "description": "Small Q, Med KV"},
+        {"num_q_blocks": 32, "num_kv_blocks": 16, "k": 4, "description": "Med Q, Small KV"},
+        {"num_q_blocks": 53, "num_kv_blocks": 32, "k": 6, "description": "Large Q, Med KV"},
+        {"num_q_blocks": 16, "num_kv_blocks": 48, "k": 2, "description": "Small Q, Large KV"},
+        {"num_q_blocks": 48, "num_kv_blocks": 16, "k": 2, "description": "Large Q, Small KV"},
+    ]
+    
+    print(f"\nError Analysis (QK Diff) for h={h}, d={d}, mode={error_mode}")
+    print("=" * 150)
+    print(f"{'Config':<20} {'Q Blks':<8} {'KV Blks':<8} {'K':<4} "
+          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_q_blocks = config["num_q_blocks"]
+        num_kv_blocks = config["num_kv_blocks"]
+        k = config["k"]
+        description = config["description"]
+        results = check_correctness_qkdiff(h, d, num_q_blocks, num_kv_blocks, k, error_mode=error_mode)
+        print(f"{description:<20} {num_q_blocks:<8} {num_kv_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} "
+              f"{results['gV']['avg_diff']:<12.6e} {results['gV']['max_diff']:<12.6e} "
+              f"{results['gO']['avg_diff']:<12.6e} {results['gO']['max_diff']:<12.6e}")
+
+    print("-" * 150)
+
 if __name__ == "__main__":
     h, d = 16, 128
     print("Block Sparse Attention with Variable Block Sizes Analysis")
     print("=" * 60)
     for mode in ['backward']:
         generate_error_graphs(h, d, error_mode=mode)
-    print("\nAnalysis completed for all modes.")
+        generate_error_graphs_qkdiff(h, d, error_mode=mode)
+    print("\nAnalysis completed for all modes.")

csrc/attn/tests/test_vsa_forward.py

@@ -0,0 +1,236 @@
+import os
+import sys
+from typing import Tuple
+
+import torch
+
+# Make sure we can import from the project root (`vsa`, `tests.utils`, etc.)
+CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
+PROJECT_ROOT = os.path.dirname(CURRENT_DIR)
+if PROJECT_ROOT not in sys.path:
+    sys.path.append(PROJECT_ROOT)
+if CURRENT_DIR not in sys.path:
+    sys.path.append(CURRENT_DIR)
+
+from tests.utils import (
+    generate_block_sparse_mask_for_function,
+    create_full_mask_from_block_mask,
+)
+from vsa import block_sparse_attn, BLOCK_M
+import test_vsa as ref  # reuse helper functions from backward test
+
+
+def pytorch_forward(
+    Q: torch.Tensor,
+    K: torch.Tensor,
+    V: torch.Tensor,
+    block_sparse_mask: torch.Tensor,
+) -> torch.Tensor:
+    """
+    Dense PyTorch reference forward:
+    - Q: [1, h, S_q, d]
+    - K,V: [1, h, S_kv, d]
+    - block_sparse_mask: [h, S_q, S_kv] bool
+    """
+    q = Q.clone().float()
+    k = K.clone().float()
+    v = V.clone().float()
+
+    attn = torch.matmul(q, k.transpose(-2, -1))  # [1, h, S_q, S_kv]
+    attn = attn / (q.size(-1) ** 0.5)
+    attn = attn.masked_fill(~block_sparse_mask.unsqueeze(0), float("-inf"))
+    attn = torch.nn.functional.softmax(attn, dim=-1)
+    out = torch.matmul(attn, v)  # [1, h, S_q, d]
+    return out.to(torch.bfloat16)
+
+
+def block_sparse_forward_test(
+    Q: torch.Tensor,
+    K: torch.Tensor,
+    V: torch.Tensor,
+    block_sparse_mask: torch.Tensor,
+    variable_block_sizes: torch.Tensor,
+    q_non_pad_index: torch.Tensor,
+    kv_non_pad_index: torch.Tensor,
+    q_num_blocks: int,
+    kv_num_blocks: int,
+) -> torch.Tensor:
+    """
+    Forward-only wrapper around `block_sparse_attn`, mirroring `block_sparse_kernel_test`
+    but without any backward / grad logic.
+    """
+    Q = Q.detach()
+    K = K.detach()
+    V = V.detach()
+
+    q_padded = ref.vsa_pad(Q, q_non_pad_index, q_num_blocks, BLOCK_M)
+    k_padded = ref.vsa_pad(K, kv_non_pad_index, kv_num_blocks, BLOCK_M)
+    v_padded = ref.vsa_pad(V, kv_non_pad_index, kv_num_blocks, BLOCK_M)
+
+    out_padded, _ = block_sparse_attn(
+        q_padded, k_padded, v_padded, block_sparse_mask, variable_block_sizes
+    )
+    # Remove padding on the query side
+    out = out_padded[:, :, q_non_pad_index, :]
+    return out
+
+
+def run_forward_equal_qk(
+    h: int = 16,
+    d: int = 128,
+    num_blocks: int = 16,
+    k: int = 2,
+    num_iterations: int = 5,
+) -> Tuple[float, float]:
+    """
+    Forward-only correctness test for the case S_q == S_kv.
+    Mirrors `check_correctness` but only compares forward outputs.
+    """
+    assert torch.cuda.is_available(), "VSA kernels require CUDA"
+    device = "cuda"
+
+    variable_block_sizes = ref.generate_variable_block_sizes(
+        num_blocks, device=device
+    )
+    S = int(variable_block_sizes.sum().item())
+    non_pad_index = ref.get_non_pad_index(
+        variable_block_sizes, num_blocks, BLOCK_M
+    )
+
+    block_mask = generate_block_sparse_mask_for_function(
+        h, num_blocks, num_blocks, k, device
+    )
+    full_mask = create_full_mask_from_block_mask(
+        block_mask, variable_block_sizes, variable_block_sizes, device
+    )
+    print(f"[qkequal] h: {h}, d: {d}, num_blocks: {num_blocks}, k: {k}")
+    print(f"[qkequal] variable_block_sizes: {variable_block_sizes}, non_pad_index: {non_pad_index.shape}, block_mask: {block_mask.shape}, full_mask: {full_mask.shape}")
+    sum_diff = 0.0
+    sum_abs = 0.0
+    max_rel_diff = 0.0
+
+    for i in range(num_iterations):
+        Q = ref.generate_tensor((1, h, S, d), torch.bfloat16, device)
+        K = ref.generate_tensor((1, h, S, d), torch.bfloat16, device)
+        V = ref.generate_tensor((1, h, S, d), torch.bfloat16, device)
+
+        if i == 0: print(f"[qkequal] Q: {Q.shape}, K: {K.shape}, V: {V.shape}, full_mask: {full_mask.shape}")
+        if i == 0: print(f"[qkequal] block_mask: {block_mask.shape}")
+
+        pt_o = pytorch_forward(Q, K, V, full_mask)
+        bs_o = block_sparse_forward_test(
+            Q,
+            K,
+            V,
+            block_mask.unsqueeze(0),
+            variable_block_sizes,
+            non_pad_index,
+            non_pad_index,
+            num_blocks,
+            num_blocks,
+        )
+
+        diff = (pt_o - bs_o).abs()
+        sum_diff += diff.sum().item()
+        sum_abs += pt_o.abs().sum().item()
+        rel_max = diff.max() / (pt_o.abs().mean() + 1e-6)
+        max_rel_diff = max(max_rel_diff, rel_max.item())
+
+    total_elems = h * S * d * num_iterations
+    avg_abs_err = sum_diff / total_elems
+    return avg_abs_err, max_rel_diff
+
+
+def run_forward_qk_diff(
+    h: int = 16,
+    d: int = 128,
+    num_q_blocks: int = 16,
+    num_kv_blocks: int = 32,
+    k: int = 2,
+    num_iterations: int = 5,
+) -> Tuple[float, float]:
+    """
+    Forward-only correctness test for the case S_q != S_kv.
+
+    NOTE:
+      - The Triton backend supports different Q/KV logical lengths via padding.
+      - The SM90 (H100) CUDA backend currently assumes the same number of blocks
+        for Q and KV, so we skip this test there.
+    """
+    assert torch.cuda.is_available(), "VSA kernels require CUDA"
+
+    device = "cuda"
+
+    q_variable_block_sizes = ref.generate_variable_block_sizes(
+        num_q_blocks, device=device
+    )
+    kv_variable_block_sizes = ref.generate_variable_block_sizes(
+        num_kv_blocks, device=device
+    )
+
+    S_q = int(q_variable_block_sizes.sum().item())
+    S_kv = int(kv_variable_block_sizes.sum().item())
+
+    q_non_pad_index = ref.get_non_pad_index(
+        q_variable_block_sizes, num_q_blocks, BLOCK_M
+    )
+    kv_non_pad_index = ref.get_non_pad_index(
+        kv_variable_block_sizes, num_kv_blocks, BLOCK_M
+    )
+
+    block_mask = generate_block_sparse_mask_for_function(
+        h, num_q_blocks, num_kv_blocks, k, device
+    )
+    full_mask = create_full_mask_from_block_mask(
+        block_mask, q_variable_block_sizes, kv_variable_block_sizes, device
+    )
+
+    sum_diff = 0.0
+    sum_abs = 0.0
+    max_rel_diff = 0.0
+
+    for _ in range(num_iterations):
+        Q = ref.generate_tensor((1, h, S_q, d), torch.bfloat16, device)
+        K = ref.generate_tensor((1, h, S_kv, d), torch.bfloat16, device)
+        V = ref.generate_tensor((1, h, S_kv, d), torch.bfloat16, device)
+
+        pt_o = pytorch_forward(Q, K, V, full_mask)
+        bs_o = block_sparse_forward_test(
+            Q,
+            K,
+            V,
+            block_mask.unsqueeze(0),
+            kv_variable_block_sizes,
+            q_non_pad_index,
+            kv_non_pad_index,
+            num_q_blocks,
+            num_kv_blocks,
+        )
+
+        diff = (pt_o - bs_o).abs()
+        sum_diff += diff.sum().item()
+        sum_abs += pt_o.abs().sum().item()
+        rel_max = diff.max() / (pt_o.abs().mean() + 1e-6)
+        max_rel_diff = max(max_rel_diff, rel_max.item())
+
+    total_elems = h * S_q * d * num_iterations
+    avg_abs_err = sum_diff / total_elems
+    return avg_abs_err, max_rel_diff
+
+
+if __name__ == "__main__":
+    h, d = 16, 128
+    print("Forward Block Sparse Attention Check (QK Equal)")
+    print("=" * 80)
+    avg_err_eq, max_rel_eq = run_forward_equal_qk(h, d, num_blocks=32, k=2)
+    print(f"QK equal: avg |ΔO| = {avg_err_eq:.6e}, max rel ΔO = {max_rel_eq:.6e}")
+
+    print("\nForward Block Sparse Attention Check (QK Different)")
+    print("=" * 80)
+    avg_err_diff, max_rel_diff = run_forward_qk_diff(
+        h, d, num_q_blocks=32, num_kv_blocks=48, k=2
+    )
+    print(
+        f"QK diff:  avg |ΔO| = {avg_err_diff:.6e}, max rel ΔO = {max_rel_diff:.6e}"
+    )
+

csrc/attn/tests/utils.py

@@ -1,54 +1,60 @@
 import torch
 
-def generate_block_sparse_mask_for_function(h, num_blocks, k, device="cuda"):
+def generate_block_sparse_mask_for_function(h, num_q_blocks, num_kv_blocks, k, device="cuda"):
     """
-    Generate block sparse mask of shape [h, num_blocks, num_blocks].
+    Generate block sparse mask of shape [h, num_q_blocks, num_kv_blocks].
     
     Args:
         h: number of heads
-        num_blocks: number of blocks
+        num_q_blocks: number of query blocks
+        num_kv_blocks: number of key/value blocks
         k: number of kv blocks each q block attends to
         device: device to create tensors on
 
     Returns:
-        block_sparse_mask: [h, num_blocks, num_blocks] bool tensor
+        block_sparse_mask: [h, num_q_blocks, num_kv_blocks] bool tensor
     """
-    k = min(k, num_blocks)
-    scores = torch.rand(h, num_blocks, num_blocks, device=device)
+    k = min(k, num_kv_blocks)
+    scores = torch.rand(h, num_q_blocks, num_kv_blocks, device=device)
     _, indices = torch.topk(scores, k, dim=-1)
-    block_sparse_mask = torch.zeros(h, num_blocks, num_blocks, dtype=torch.bool, device=device)
+    block_sparse_mask = torch.zeros(h, num_q_blocks, num_kv_blocks, dtype=torch.bool, device=device)
 
     block_sparse_mask = block_sparse_mask.scatter_(2, indices, 1).bool()
     return block_sparse_mask
 
 
-def create_full_mask_from_block_mask(block_sparse_mask, variable_block_sizes, device="cuda"):
+def create_full_mask_from_block_mask(block_sparse_mask, q_variable_block_sizes, 
+                                     kv_variable_block_sizes, device="cuda"):
     """
     Convert block-level sparse mask to full attention mask.
     
     Args:
-        block_sparse_mask: [h, num_blocks, num_blocks] bool tensor
-        variable_block_sizes: [num_blocks] tensor
+        block_sparse_mask: [h, num_q_blocks, num_kv_blocks] bool tensor
+        q_variable_block_sizes: [num_q_blocks] tensor
+        kv_variable_block_sizes: [num_kv_blocks] tensor
         device: device to create tensors on
 
     Returns:
-        full_mask: [h, S, S] bool tensor where S = total sequence length
+        full_mask: [h, S_q, S_kv] bool tensor where S = total sequence length
     """
-    h, num_blocks, _ = block_sparse_mask.shape
-    total_seq_len = variable_block_sizes.sum().item()
-    cumsum = torch.cat([torch.tensor([0], device=device), variable_block_sizes.cumsum(dim=0)[:-1]])
+    h, num_q_blocks, num_kv_blocks = block_sparse_mask.shape
+    total_q_seq_len = q_variable_block_sizes.sum().item()
+    total_kv_seq_len = kv_variable_block_sizes.sum().item()
+    
+    q_cumsum = torch.cat([torch.tensor([0], device=device), q_variable_block_sizes.cumsum(dim=0)[:-1]])
+    kv_cumsum = torch.cat([torch.tensor([0], device=device), kv_variable_block_sizes.cumsum(dim=0)[:-1]])
 
-    full_mask = torch.zeros(h, total_seq_len, total_seq_len, dtype=torch.bool, device=device)
+    full_mask = torch.zeros(h, total_q_seq_len, total_kv_seq_len, dtype=torch.bool, device=device)
 
     for head in range(h):
-        for q_block in range(num_blocks):
-            q_start = cumsum[q_block]
-            q_end = q_start + variable_block_sizes[q_block]
+        for q_block in range(num_q_blocks):
+            q_start = q_cumsum[q_block]
+            q_end = q_start + q_variable_block_sizes[q_block]
 
-            for kv_block in range(num_blocks):
+            for kv_block in range(num_kv_blocks):
                 if block_sparse_mask[head, q_block, kv_block]:
-                    kv_start = cumsum[kv_block]
-                    kv_end = kv_start + variable_block_sizes[kv_block]
+                    kv_start = kv_cumsum[kv_block]
+                    kv_end = kv_start + kv_variable_block_sizes[kv_block]
                     full_mask[head, q_start:q_end, kv_start:kv_end] = True
 
     return full_mask

csrc/attn/video_sparse_attn/vsa/block_sparse_attn_triton.py

@@ -247,11 +247,12 @@ def _attn_bwd_dq(dq, q, K, V,  #
 
     kv_blocks = tl.load(q2k_num  + meta_base)                 # int32
     kv_ptr    = q2k_index + meta_base * max_kv_blks           # ptr to list
-    block_size = tl.load(variable_block_sizes + q_blk)
     
     
     for blk_idx in range(kv_blocks*2):
-        block_sparse_offset = (tl.load(kv_ptr + blk_idx//2).to(tl.int32)*2 + blk_idx%2) *step_n * stride_tok
+        kv_idx = tl.load(kv_ptr + blk_idx//2).to(tl.int32)
+        block_size = tl.load(variable_block_sizes + kv_idx) -  (blk_idx % 2) * step_n
+        block_sparse_offset = (kv_idx*2 + blk_idx%2) * step_n * stride_tok
         kT = tl.load(kT_ptrs + block_sparse_offset)
         vT = tl.load(vT_ptrs + block_sparse_offset)
         qk = tl.dot(q, kT)

csrc/attn/video_sparse_attn/vsa/block_sparse_h100.cu

@@ -672,23 +672,32 @@ block_sparse_attention_forward(
     torch::Tensor v, 
     torch::Tensor q2k_block_sparse_index, 
     torch::Tensor q2k_block_sparse_num, 
-    torch::Tensor block_size
+    torch::Tensor kv_block_size
 )
 {
     CHECK_INPUT(q);
     CHECK_INPUT(k);
     CHECK_INPUT(v);
 
+    // q shape: (batch, qo_heads, q_seq_len, head_dim)
+    // k shape: (batch, kv_heads, kv_seq_len, head_dim)
+    // v shape: (batch, kv_heads, kv_seq_len, head_dim)
+    // q2k_block_sparse_index shape: (batch, qo_heads, num_q_blocks, max_kv_blocks_per_q)
+    // q2k_block_sparse_num shape: (batch, qo_heads, num_q_blocks)
+    // kv_block_size shape: (num_kv_blocks) This does not need other dimensions because across all batch/heads the padding is the same.
+    
     auto batch    = q.size(0);
-    auto seq_len  = q.size(2); 
+    auto q_seq_len  = q.size(2);
+    auto kv_seq_len = k.size(2);
     auto head_dim = q.size(3); 
     auto qo_heads = q.size(1);
     auto kv_heads = k.size(1);
     auto max_kv_blocks_per_q = q2k_block_sparse_index.size(3);
-    auto num_q_blocks = block_size.size(0);
+    auto num_q_blocks = q2k_block_sparse_index.size(2);
+    auto num_kv_blocks = kv_block_size.size(0);
     TORCH_CHECK(batch==1, "Batch size dim will be removed in the future, please set batch to 1");
-    TORCH_CHECK(num_q_blocks * 64 == seq_len, "This kernel supports variable block size, but it assumes the input sequence is properly padded.");
-    TORCH_CHECK(num_q_blocks == q2k_block_sparse_index.size(2), "Number of Q blocks does not match between q2k_block_sparse_index and block_size");
+    TORCH_CHECK(num_q_blocks * BLOCK_M == q_seq_len, "This kernel supports variable q block size, but it assumes the input sequence is properly padded.");
+    TORCH_CHECK(num_kv_blocks * BLOCK_M == kv_seq_len, "This kernel supports variable kv block size, but it assumes the input sequence is properly padded.");
     // check to see that these dimensions match for all inputs
     TORCH_CHECK(q.size(0) == batch, "Q batch dimension - idx 0 - must match for all inputs");
     TORCH_CHECK(k.size(0) == batch, "K batch dimension - idx 0 - must match for all inputs");
@@ -696,11 +705,8 @@ block_sparse_attention_forward(
     TORCH_CHECK(q2k_block_sparse_index.size(0) == batch, "q2k_block_sparse_index batch dimension - idx 0 - must match for all inputs");
     TORCH_CHECK(q2k_block_sparse_num.size(0) == batch, "q2k_block_sparse_num batch dimension - idx 0 - must match for all inputs");
 
-    TORCH_CHECK(q.size(2) == seq_len, "Q sequence length dimension - idx 2 - must match for all inputs");
-    TORCH_CHECK(k.size(2) == seq_len, "K sequence length dimension - idx 2 - must match for all inputs");
-    TORCH_CHECK(v.size(2) == seq_len, "V sequence length dimension - idx 2 - must match for all inputs");
-    TORCH_CHECK(q2k_block_sparse_index.size(2) == seq_len / BLOCK_M, "q2k_block_sparse_index idx 2 - must match seq_len / BLOCK_M");
-    TORCH_CHECK(q2k_block_sparse_num.size(2) == seq_len / BLOCK_M, "q2k_block_sparse_num idx 2 - must match seq_len / BLOCK_M");
+    TORCH_CHECK(v.size(2) == kv_seq_len, "V sequence length dimension - idx 2 - must match K inputs");
+    TORCH_CHECK(q2k_block_sparse_num.size(2) == num_q_blocks, "q2k_block_sparse_num idx 2 - must match num_q_blocks");
 
 
     TORCH_CHECK(q.size(3) == head_dim, "Q head dimension - idx 3 - must match for all non-vector inputs");
@@ -727,12 +733,12 @@ block_sparse_attention_forward(
     // for the returned outputs
     torch::Tensor o     = torch::empty({static_cast<const uint>(batch), 
                                         static_cast<const uint>(qo_heads), 
-                                        static_cast<const uint>(seq_len), 
+                                        static_cast<const uint>(q_seq_len), 
                                         static_cast<const uint>(head_dim)}, v.options());
     
     torch::Tensor l_vec = torch::empty({static_cast<const uint>(batch), 
                                         static_cast<const uint>(qo_heads), 
-                                        static_cast<const uint>(seq_len), 
+                                        static_cast<const uint>(q_seq_len), 
                                         static_cast<const uint>(1)}, 
                                         torch::TensorOptions().dtype(torch::kFloat).device(q.device()).memory_format(at::MemoryFormat::Contiguous));
         
@@ -762,11 +768,11 @@ block_sparse_attention_forward(
 
         using globals      = fwd_globals<64>;
 
-        q_global qg_arg{d_q, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), 64U};
-        k_global kg_arg{d_k, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(seq_len), 64U};
-        v_global vg_arg{d_v, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(seq_len), 64U};
-        l_global lg_arg{d_l, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,   static_cast<unsigned int>(seq_len)};
-        o_global og_arg{d_o, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), 64U};
+        q_global qg_arg{d_q, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(q_seq_len), 64U};
+        k_global kg_arg{d_k, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(kv_seq_len), 64U};
+        v_global vg_arg{d_v, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(kv_seq_len), 64U};
+        l_global lg_arg{d_l, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,   static_cast<unsigned int>(q_seq_len)};
+        o_global og_arg{d_o, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(q_seq_len), 64U};
 
         globals g{
             qg_arg, 
@@ -774,17 +780,17 @@ block_sparse_attention_forward(
             vg_arg, 
             lg_arg, 
             og_arg, 
-            static_cast<int>(seq_len), 
+            static_cast<int>(q_seq_len), 
             static_cast<int>(hr), 
             static_cast<int>(max_kv_blocks_per_q), 
             reinterpret_cast<int32_t*>(q2k_block_sparse_index.data_ptr()), 
             reinterpret_cast<int32_t*>(q2k_block_sparse_num.data_ptr()),
-            reinterpret_cast<int32_t*>(block_size.data_ptr())
+            reinterpret_cast<int32_t*>(kv_block_size.data_ptr())
         };
 
         constexpr int mem_size = 54000;
 
-        dim3 grid(seq_len/(64), qo_heads, batch);
+        dim3 grid(q_seq_len/(BLOCK_M), qo_heads, batch);
 
         cudaFuncSetAttribute(
             fwd_attend_ker<64>,
@@ -813,11 +819,11 @@ block_sparse_attention_forward(
 
         using globals      = fwd_globals<128>;
 
-        q_global qg_arg{d_q, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), 128U};
-        k_global kg_arg{d_k, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(seq_len), 128U};
-        v_global vg_arg{d_v, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(seq_len), 128U};
-        l_global lg_arg{d_l, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,   static_cast<unsigned int>(seq_len)};
-        o_global og_arg{d_o, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), 128U};
+        q_global qg_arg{d_q, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(q_seq_len), 128U};
+        k_global kg_arg{d_k, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(kv_seq_len), 128U};
+        v_global vg_arg{d_v, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(kv_seq_len), 128U};
+        l_global lg_arg{d_l, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,   static_cast<unsigned int>(q_seq_len)};
+        o_global og_arg{d_o, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(q_seq_len), 128U};
 
         globals g{
             qg_arg, 
@@ -825,17 +831,17 @@ block_sparse_attention_forward(
             vg_arg, 
             lg_arg, 
             og_arg, 
-            static_cast<int>(seq_len), 
+            static_cast<int>(q_seq_len), 
             static_cast<int>(hr), 
             static_cast<int>(max_kv_blocks_per_q), 
             reinterpret_cast<int32_t*>(q2k_block_sparse_index.data_ptr()), 
             reinterpret_cast<int32_t*>(q2k_block_sparse_num.data_ptr()),
-            reinterpret_cast<int32_t*>(block_size.data_ptr())
+            reinterpret_cast<int32_t*>(kv_block_size.data_ptr())
         };
 
         constexpr int mem_size = 54000;
 
-        dim3 grid(seq_len/(64), qo_heads, batch);
+        dim3 grid(q_seq_len/(BLOCK_M), qo_heads, batch);
 
         cudaFuncSetAttribute(
             fwd_attend_ker<128>,
@@ -862,7 +868,7 @@ block_sparse_attention_backward(torch::Tensor q,
                    torch::Tensor og,
                    torch::Tensor k2q_block_sparse_index,
                    torch::Tensor k2q_block_sparse_num,
-                   torch::Tensor block_size)
+                   torch::Tensor kv_block_size)
 {
     CHECK_INPUT(q);
     CHECK_INPUT(k);
@@ -871,11 +877,23 @@ block_sparse_attention_backward(torch::Tensor q,
     CHECK_INPUT(o);
     CHECK_INPUT(og);
 
+    // q: [batch, qo_heads, q_seq_len, head_dim]
+    // k: [batch, kv_heads, kv_seq_len, head_dim]
+    // v: [batch, kv_heads, kv_seq_len, head_dim]
+    // o: [batch, qo_heads, q_seq_len, head_dim]
+    // l_vec: [batch, qo_heads, q_seq_len, 1]
+    // og: [batch, qo_heads, q_seq_len, head_dim]
+    // k2q_block_sparse_index: [batch, kv_heads, num_kv_blocks, max_num_q_blocks]
+    // k2q_block_sparse_num: [batch, kv_heads, num_kv_blocks]
+    // kv_block_size: [num_kv_blocks]
+
     auto batch    = q.size(0);
-    auto seq_len  = q.size(2);
+    auto q_seq_len  = q.size(2);
+    auto kv_seq_len = k.size(2);
     auto head_dim = q.size(3);
     auto max_q_blocks_per_kv = k2q_block_sparse_index.size(3);
-    TORCH_CHECK(k2q_block_sparse_index.size(2) == block_size.size(0), "k2q_block_sparse_index.size(2) must match block_size.size(0)");
+    auto num_kv_blocks = kv_block_size.size(0);
+    TORCH_CHECK(k2q_block_sparse_index.size(2) == num_kv_blocks, "k2q_block_sparse_index.size(2) must match num_kv_blocks (kv_block_size.size(0))");
     // check to see that these dimensions match for all inputs
     TORCH_CHECK(q.size(0)     == batch, "Q  batch dimension - idx 0 - must match for all inputs");
     TORCH_CHECK(k.size(0)     == batch, "K  batch dimension - idx 0 - must match for all inputs");
@@ -886,23 +904,18 @@ block_sparse_attention_backward(torch::Tensor q,
     TORCH_CHECK(k2q_block_sparse_index.size(0) == batch, "k2q_block_sparse_index batch dimension - idx 0 - must match for all inputs");
     TORCH_CHECK(k2q_block_sparse_num.size(0) == batch, "k2q_block_sparse_num batch dimension - idx 0 - must match for all inputs");
 
-
-    TORCH_CHECK(q.size(2)     == seq_len, "Q  sequence length dimension - idx 2 - must match for all inputs");
-    TORCH_CHECK(k.size(2)     == seq_len, "K  sequence length dimension - idx 2 - must match for all inputs");
-    TORCH_CHECK(v.size(2)     == seq_len, "V  sequence length dimension - idx 2 - must match for all inputs");
-    TORCH_CHECK(l_vec.size(2) == seq_len, "L  sequence length dimension - idx 2 - must match for all inputs");
-    TORCH_CHECK(o.size(2)     == seq_len, "O  sequence length dimension - idx 2 - must match for all inputs");
-    TORCH_CHECK(og.size(2)    == seq_len, "OG sequence length dimension - idx 2 - must match for all inputs");
-    TORCH_CHECK(k2q_block_sparse_index.size(2) == seq_len / BLOCK_N, "k2q_block_sparse_index idx 2 - must match seq_len / BLOCK_N");
-    TORCH_CHECK(k2q_block_sparse_num.size(2) == seq_len / BLOCK_N, "k2q_block_sparse_num idx 2 - must match seq_len / BLOCK_N");
+    TORCH_CHECK(v.size(2)     == kv_seq_len, "V  sequence length dimension - idx 2 - must match K sequence length");
+    TORCH_CHECK(l_vec.size(2) == q_seq_len, "L  sequence length dimension - idx 2 - must match Q sequence length");
+    TORCH_CHECK(o.size(2)     == q_seq_len, "O  sequence length dimension - idx 2 - must match Q sequence length");
+    TORCH_CHECK(og.size(2)    == q_seq_len, "OG sequence length dimension - idx 2 - must match Q sequence length");
+    TORCH_CHECK(k2q_block_sparse_index.size(2) == num_kv_blocks, "k2q_block_sparse_index idx 2 - must match num_kv_blocks (kv_block_size.size(0))");
+    TORCH_CHECK(k2q_block_sparse_num.size(2) == num_kv_blocks, "k2q_block_sparse_num idx 2 - must match num_kv_blocks (kv_block_size.size(0))");
 
     TORCH_CHECK(q.size(3)  == head_dim, "Q  head dimension - idx 3 - must match for all non-vector inputs");
     TORCH_CHECK(k.size(3)  == head_dim, "K  head dimension - idx 3 - must match for all non-vector inputs");
     TORCH_CHECK(v.size(3)  == head_dim, "V  head dimension - idx 3 - must match for all non-vector inputs");
     TORCH_CHECK(o.size(3)  == head_dim, "O  head dimension - idx 3 - must match for all non-vector inputs");
     TORCH_CHECK(og.size(3) == head_dim, "OG head dimension - idx 3 - must match for all non-vector inputs");
-    
-
 
     auto qo_heads = q.size(1);
     auto kv_heads = k.size(1);
@@ -929,20 +942,20 @@ block_sparse_attention_backward(torch::Tensor q,
 
     torch::Tensor qg = torch::zeros({static_cast<const uint>(batch), 
                                      static_cast<const uint>(qo_heads), 
-                                     static_cast<const uint>(seq_len), 
+                                     static_cast<const uint>(q_seq_len), 
                                      static_cast<const uint>(head_dim)},   l_vec.options());
     torch::Tensor kg = torch::zeros({static_cast<const uint>(batch), 
                                      static_cast<const uint>(kv_heads), 
-                                     static_cast<const uint>(seq_len), 
+                                     static_cast<const uint>(kv_seq_len), 
                                      static_cast<const uint>(head_dim)},   l_vec.options());
     torch::Tensor vg = torch::zeros({static_cast<const uint>(batch), 
                                      static_cast<const uint>(kv_heads), 
-                                     static_cast<const uint>(seq_len), 
+                                     static_cast<const uint>(kv_seq_len), 
                                      static_cast<const uint>(head_dim)},   l_vec.options());
     
     torch::Tensor d_vec = torch::empty({static_cast<const uint>(batch), 
                                         static_cast<const uint>(qo_heads), 
-                                        static_cast<const uint>(seq_len), 
+                                        static_cast<const uint>(q_seq_len), 
                                         static_cast<const uint>(1)},       l_vec.options());
 
     float*         qg_ptr = qg.data_ptr<float>();
@@ -971,7 +984,7 @@ block_sparse_attention_backward(torch::Tensor q,
     //  cudaStreamSynchronize(stream);
 
     // TORCH_CHECK(seq_len % (4*kittens::TILE_DIM*4) == 0, "sequence length must be divisible by 256");
-    dim3 grid_bwd(seq_len/(PREP_NUM_WARPS*kittens::TILE_ROW_DIM<bf16>*4), qo_heads, batch);
+    dim3 grid_bwd(q_seq_len/(PREP_NUM_WARPS*kittens::TILE_ROW_DIM<bf16>*4), qo_heads, batch);
 
     if (head_dim == 64)  {
         using og_tile = st_bf<4*16, 64>;
@@ -984,9 +997,9 @@ block_sparse_attention_backward(torch::Tensor q,
 
         using bwd_prep_globals = bwd_prep_globals<64>;
 
-        og_global prep_og_arg{d_og, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), 64U};
-        o_global  prep_o_arg {d_o,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), 64U};
-        d_global  prep_d_arg {d_d,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,  static_cast<unsigned int>(seq_len)};
+        og_global prep_og_arg{d_og, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(q_seq_len), 64U};
+        o_global  prep_o_arg {d_o,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(q_seq_len), 64U};
+        d_global  prep_d_arg {d_d,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,  static_cast<unsigned int>(q_seq_len)};
 
         bwd_prep_globals bwd_g{prep_og_arg, prep_o_arg, prep_d_arg};
 
@@ -1023,15 +1036,15 @@ block_sparse_attention_backward(torch::Tensor q,
 
         using bwd_global_args = bwd_globals<64>;
 
-        bwd_q_global  bwd_q_arg {d_q,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), 64U};
-        bwd_k_global  bwd_k_arg {d_k,  static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(seq_len), 64U};
-        bwd_v_global  bwd_v_arg {d_v,  static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(seq_len), 64U};
-        bwd_og_global bwd_og_arg{d_og, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), 64U};
-        bwd_qg_global bwd_qg_arg{d_qg, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), 64U};
-        bwd_kg_global bwd_kg_arg{d_kg, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(seq_len), 64U};
-        bwd_vg_global bwd_vg_arg{d_vg, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(seq_len), 64U};
-        bwd_l_global  bwd_l_arg {d_l,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,  static_cast<unsigned int>(seq_len)};
-        bwd_d_global  bwd_d_arg {d_d,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,  static_cast<unsigned int>(seq_len)};
+        bwd_q_global  bwd_q_arg {d_q,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(q_seq_len), 64U};
+        bwd_k_global  bwd_k_arg {d_k,  static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(kv_seq_len), 64U};
+        bwd_v_global  bwd_v_arg {d_v,  static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(kv_seq_len), 64U};
+        bwd_og_global bwd_og_arg{d_og, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(q_seq_len), 64U};
+        bwd_qg_global bwd_qg_arg{d_qg, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(q_seq_len), 64U};
+        bwd_kg_global bwd_kg_arg{d_kg, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(kv_seq_len), 64U};
+        bwd_vg_global bwd_vg_arg{d_vg, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(kv_seq_len), 64U};
+        bwd_l_global  bwd_l_arg {d_l,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,  static_cast<unsigned int>(q_seq_len)};
+        bwd_d_global  bwd_d_arg {d_d,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,  static_cast<unsigned int>(q_seq_len)};
 
         bwd_global_args bwd_global{bwd_q_arg, 
                         bwd_k_arg, 
@@ -1042,14 +1055,14 @@ block_sparse_attention_backward(torch::Tensor q,
                         bwd_vg_arg, 
                         bwd_l_arg, 
                         bwd_d_arg, 
-                        static_cast<int>(seq_len), 
+                        static_cast<int>(kv_seq_len), // N is not used in the kernel
                         static_cast<int>(hr),
                         static_cast<int>(max_q_blocks_per_kv),
                         reinterpret_cast<int32_t*>(k2q_block_sparse_index.data_ptr()),
                         reinterpret_cast<int32_t*>(k2q_block_sparse_num.data_ptr()),
-                        reinterpret_cast<int32_t*>(block_size.data_ptr())};
+                        reinterpret_cast<int32_t*>(kv_block_size.data_ptr())};
 
-        dim3 grid_bwd_2(seq_len/64, qo_heads, batch);
+        dim3 grid_bwd_2(kv_seq_len/BLOCK_N, qo_heads, batch);
         threads = 128;
 
         //cudadevicesynchronize();
@@ -1088,9 +1101,9 @@ block_sparse_attention_backward(torch::Tensor q,
 
         using bwd_prep_globals = bwd_prep_globals<128>;
 
-        og_global prep_og_arg{d_og, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), 128U};
-        o_global  prep_o_arg {d_o,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), 128U};
-        d_global  prep_d_arg {d_d,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,  static_cast<unsigned int>(seq_len)};
+        og_global prep_og_arg{d_og, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(q_seq_len), 128U};
+        o_global  prep_o_arg {d_o,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(q_seq_len), 128U};
+        d_global  prep_d_arg {d_d,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,  static_cast<unsigned int>(q_seq_len)};
 
         bwd_prep_globals bwd_g{prep_og_arg, prep_o_arg, prep_d_arg};
 
@@ -1127,15 +1140,15 @@ block_sparse_attention_backward(torch::Tensor q,
 
         using bwd_global_args = bwd_globals<128>;
 
-        bwd_q_global  bwd_q_arg {d_q,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), 128U};
-        bwd_k_global  bwd_k_arg {d_k,  static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(seq_len), 128U};
-        bwd_v_global  bwd_v_arg {d_v,  static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(seq_len), 128U};
-        bwd_og_global bwd_og_arg{d_og, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), 128U};
-        bwd_qg_global bwd_qg_arg{d_qg, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), 128U};
-        bwd_kg_global bwd_kg_arg{d_kg, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(seq_len), 128U};
-        bwd_vg_global bwd_vg_arg{d_vg, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(seq_len), 128U};
-        bwd_l_global  bwd_l_arg {d_l,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,  static_cast<unsigned int>(seq_len)};
-        bwd_d_global  bwd_d_arg {d_d,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,  static_cast<unsigned int>(seq_len)};
+        bwd_q_global  bwd_q_arg {d_q,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(q_seq_len), 128U};
+        bwd_k_global  bwd_k_arg {d_k,  static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(kv_seq_len), 128U};
+        bwd_v_global  bwd_v_arg {d_v,  static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(kv_seq_len), 128U};
+        bwd_og_global bwd_og_arg{d_og, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(q_seq_len), 128U};
+        bwd_qg_global bwd_qg_arg{d_qg, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(q_seq_len), 128U};
+        bwd_kg_global bwd_kg_arg{d_kg, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(kv_seq_len), 128U};
+        bwd_vg_global bwd_vg_arg{d_vg, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(kv_seq_len), 128U};
+        bwd_l_global  bwd_l_arg {d_l,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,  static_cast<unsigned int>(q_seq_len)};
+        bwd_d_global  bwd_d_arg {d_d,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), 1U,  static_cast<unsigned int>(q_seq_len)};
 
         bwd_global_args bwd_global{bwd_q_arg, 
                         bwd_k_arg, 
@@ -1146,14 +1159,14 @@ block_sparse_attention_backward(torch::Tensor q,
                         bwd_vg_arg, 
                         bwd_l_arg, 
                         bwd_d_arg, 
-                        static_cast<int>(seq_len), 
+                        static_cast<int>(kv_seq_len), // N is not used in the kernel
                         static_cast<int>(hr),
                         static_cast<int>(max_q_blocks_per_kv),
                         reinterpret_cast<int32_t*>(k2q_block_sparse_index.data_ptr()),
                         reinterpret_cast<int32_t*>(k2q_block_sparse_num.data_ptr()),
-                        reinterpret_cast<int32_t*>(block_size.data_ptr())};
+                        reinterpret_cast<int32_t*>(kv_block_size.data_ptr())};
 
-        dim3 grid_bwd_2(seq_len/64, qo_heads, batch);
+        dim3 grid_bwd_2(kv_seq_len/BLOCK_N, qo_heads, batch);
         threads = 128;
 
         //cudadevicesynchronize();

docker/Dockerfile.python3.10

@@ -1,4 +1,4 @@
-FROM nvidia/cuda:12.8.0-devel-ubuntu22.04
+FROM nvidia/cuda:12.8.0-cudnn-devel-ubuntu22.04
 
 ENV DEBIAN_FRONTEND=noninteractive
 
@@ -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==2.8.3 --no-build-isolation
+    uv pip install --no-cache-dir https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.5.4/flash_attn-2.8.3%2Bcu128torch2.9-cp310-cp310-linux_x86_64.whl
 
 COPY . .
 

docker/Dockerfile.python3.11

@@ -1,4 +1,4 @@
-FROM nvidia/cuda:12.8.0-devel-ubuntu22.04
+FROM nvidia/cuda:12.8.0-cudnn-devel-ubuntu22.04
 
 ENV DEBIAN_FRONTEND=noninteractive
 
@@ -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==2.8.3 --no-build-isolation
+    uv pip install --no-cache-dir https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.5.4/flash_attn-2.8.3%2Bcu128torch2.9-cp311-cp311-linux_x86_64.whl
 
 COPY . .
 

docker/Dockerfile.python3.12

@@ -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==2.8.3 --no-build-isolation
+    uv pip install --no-cache-dir https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.5.4/flash_attn-2.8.3%2Bcu128torch2.9-cp312-cp312-linux_x86_64.whl
 
 COPY . .
 

docs/contributing/overview.md

@@ -70,3 +70,7 @@ uv pip install ninja
 
 python setup.py install
 ```
+
+## Testing
+
+Please refer to the [Testing Guide](testing.md) for more information on how to add and run tests in FastVideo.

docs/contributing/testing.md

@@ -0,0 +1,129 @@
+# Testing in FastVideo
+
+This guide explains how to add and run tests in FastVideo. The testing suite is divided into several categories to ensure correctness across components, training workflows, and inference quality.
+
+## Test Types
+
+* **Unit Tests**: Located in `fastvideo/tests/dataset`, `fastvideo/tests/entrypoints`, and `fastvideo/tests/workflow`. These test individual functions and classes.
+* **Component Tests**: Located in `fastvideo/tests/encoders`, `fastvideo/tests/transformers`, and `fastvideo/tests/vaes`. These verify the loading and basic functionality of model components.
+* **SSIM Tests**: Located in `fastvideo/tests/ssim`. These are regression tests that compare generated videos against reference videos using the Structural Similarity Index Measure (SSIM) to detect quality degradation.
+* **Training Tests**: Located in `fastvideo/tests/training`. These validate training loops, loss calculations, and specific training techniques like LoRA, Distillation, and VSA.
+* **Inference Tests**: Located in `fastvideo/tests/inference`. These test specialized inference pipelines and optimizations (e.g., STA, V-MoBA).
+
+For now, we will focus on **SSIM Tests**.
+
+## SSIM Tests
+
+SSIM tests are located in `fastvideo/tests/ssim`. These tests generate videos using specific models and parameters, and compare them against reference videos to ensure that changes in the codebase do not degrade generation quality or alter the output unexpectedly.
+
+!!! note
+    If you are adding an SSIM test, this serves as a safeguard. Any future code changes that break or cause errors with the specific arguments and configurations you defined will trigger a failure. Therefore, it is important to include multiple settings and arguments that cover the core features of your new pipeline to ensure robust regression testing.
+
+### Directory Structure
+
+```
+fastvideo/tests/ssim/
+├── <GPU>_reference_videos/   # Reference videos organized by GPU type (e.g., L40S_reference_videos)
+│   ├── <Model_Name>/
+│   │   ├── <Backend>/        # e.g., FLASH_ATTN, TORCH_SDPA
+│   │   │   └── <Video_File>
+├── test_causal_similarity.py
+├── test_inference_similarity.py
+├── update_reference_videos.sh
+└── ...
+```
+
+### Adding a New SSIM Test
+
+To add a new SSIM test, follow these steps:
+
+1. **Create or Update a Test File**: You can add a new test function to an existing file (like `test_inference_similarity.py`) or create a new one if testing a distinct category of models.
+
+2. **Define Model Parameters**: Define the configuration for the model you want to test. This includes model path, dimensions, inference steps, and other generation parameters. **Note:** Consider using lower `num_inference_steps` or reduced resolution (e.g., 480p instead of 720p) to keep test execution time reasonable, provided it doesn't compromise the test's ability to detect regression.
+
+   ```python
+   MY_MODEL_PARAMS = {
+       "num_gpus": 1,
+       "model_path": "organization/model-name",
+       "height": 480,
+       "width": 832,
+       "num_frames": 45,
+       "num_inference_steps": 20,
+       # ... other parameters
+   }
+   ```
+
+3. **Implement the Test Function**:
+   * Use `pytest.mark.parametrize` to run the test with different prompts, backends, and models.
+   * Set the attention backend environment variable.
+   * Initialize the `VideoGenerator`.
+   * Generate the video.
+   * Compare the generated video with the reference video using `compute_video_ssim_torchvision`.
+
+   Example structure:
+
+   ```python
+   @pytest.mark.parametrize("prompt", TEST_PROMPTS)
+   @pytest.mark.parametrize("ATTENTION_BACKEND", ["FLASH_ATTN"])
+   def test_my_model_similarity(prompt, ATTENTION_BACKEND):
+       # Setup output directories
+       # ...
+
+       # Initialize Generator
+       generator = VideoGenerator.from_pretrained(...)
+       generator.generate_video(prompt, ...)
+
+       # Compare with Reference
+       ssim_values = compute_video_ssim_torchvision(reference_path, generated_path, use_ms_ssim=True)
+        assert ssim_values[0] >= 0.98  # Threshold
+    ```
+
+4. **Reference Videos**:
+   * When running the test for the first time (or when updating the reference), the test will fail because the reference video is missing. The generated video will be saved in `fastvideo/tests/ssim/generated_videos`.
+   * Inspect the generated video to ensure it meets quality expectations.
+   * Move the generated video to the appropriate reference folder: `fastvideo/tests/ssim/<GPU>_reference_videos/<Model>/<Backend>/`.
+   * You can use the helper script `update_reference_videos.sh` to automate copying videos from `generated_videos` to `L40S_reference_videos`. Note: Check the script to ensure paths match your environment (it defaults to `L40S_reference_videos`).
+
+### Running Tests Locally
+
+To run the SSIM tests locally:
+
+```bash
+pytest fastvideo/tests/ssim/ -vs
+```
+
+Ensure you have the necessary GPUs available as defined in your test parameters.
+
+## Modal Workflow
+
+FastVideo uses [Modal](https://modal.com/) for running tests in a CI environment. The workflow scripts are located in `fastvideo/tests/modal/`.
+
+### `pr_test.py`
+
+The main entry point for CI tests is `fastvideo/tests/modal/pr_test.py`. This script defines Modal functions that execute the pytest suites on specific hardware (e.g., L40S, H100).
+
+### Updating Modal Configuration
+
+If you add a new test that requires:
+* **Different GPU Hardware**: You may need to change the `@app.function(gpu=...)` decorator.
+* **Longer Execution Time**: Increase the `timeout` parameter.
+* **New Environment Variables/Secrets**: Add them to `secrets=[...]` or the image environment. For example, if your model is gated on Hugging Face, ensure `HF_API_KEY` is passed.
+
+For SSIM tests, the `run_ssim_tests` function in `pr_test.py` currently runs:
+
+```python
+@app.function(gpu="L40S:2", image=image, timeout=2700, secrets=[modal.Secret.from_dict({"HF_API_KEY": os.environ.get("HF_API_KEY", "")})])
+def run_ssim_tests():
+    run_test("hf auth login --token $HF_API_KEY && pytest ./fastvideo/tests/ssim -vs")
+```
+
+If your new test file is inside `fastvideo/tests/ssim`, it will automatically be picked up by this command. However, ensure that the `gpu="L40S:2"` configuration is sufficient for your model. If your model requires more GPUs (e.g., 4 or 8), you might need to create a separate Modal function or update the existing one.
+
+### Workflow Scripts
+
+The shell script that triggers these tests in the CI pipeline is located at `.buildkite/scripts/pr_test.sh`. If you add a new test category (e.g., a new folder outside of `ssim`), you will need to:
+1. Add a new function in `fastvideo/tests/modal/pr_test.py`.
+2. Add a new case in `.buildkite/scripts/pr_test.sh` to handle the new test type.
+
+!!! note
+    If you are a maintainer, you'll need to finally manually update the workflow script in Buildkite. Otherwise, a maintainer will help you update.

docs/distillation/dmd.md

@@ -13,7 +13,7 @@ We provide two distilled models:
 Both models are trained on **61×448×832** resolution but support generating videos with **any resolution** (1.3B  model mainly support 480P, 14B model support 480P and 720P, quality may degrade for different resolutions).
 
 ## ⚙️ Inference
-First install [VSA](https://hao-ai-lab.github.io/FastVideo/video_sparse_attention/installation.html). Set `MODEL_BASE` to your own model path and run:
+First install [VSA](https://hao-ai-lab.github.io/FastVideo/video_sparse_attention/installation). Set `MODEL_BASE` to your own model path and run:
 
 ```bash
 bash scripts/inference/v1_inference_wan_dmd.sh

docs/index.md

@@ -24,12 +24,13 @@ FastVideo is an inference and post-training framework for diffusion models. It f
 ## Key Features
 
 FastVideo has the following features:
+
 - State-of-the-art performance optimizations for inference
   - [Sliding Tile Attention](https://arxiv.org/pdf/2502.04507)
   - [TeaCache](https://arxiv.org/pdf/2411.19108)
   - [Sage Attention](https://arxiv.org/abs/2410.02367)
 - E2E post-training support
-  - Data preprocessing pipeline for video data.
+  - Data preprocessing pipeline for video data
   - [Sparse distillation](https://hao-ai-lab.github.io/blogs/fastvideo_post_training/) for Wan2.1 and Wan2.2 using [Video Sparse Attention](https://arxiv.org/pdf/2505.13389) and [Distribution Matching Distillation](https://tianweiy.github.io/dmd2/)
   - Support full finetuning and LoRA finetuning for state-of-the-art open video DiTs.
   - Scalable training with FSDP2, sequence parallelism, and selective activation checkpointing, with near linear scaling to 64 GPUs.
@@ -42,7 +43,7 @@ Use the navigation menu on the left to explore different sections:
 
 - **Getting Started**: Installation and quick start guides
 - **Inference**: Learn how to use FastVideo for video generation
-- **Training**: Data preprocessing and fine-tuning workflows  
+- **Training**: Data preprocessing and fine-tuning workflows
 - **Distillation**: Post-training optimization techniques
 - **Sliding Tile Attention**: Advanced attention mechanisms
 - **Video Sparse Attention**: Efficient attention for video models

docs/seed_parameter_behavior.md

@@ -1,51 +0,0 @@
-# Seed Parameter Behavior in vLLM
-
-## Overview
-
-The `seed` parameter in vLLM is used to control the random states for various random number generators. This parameter can affect the behavior of random operations in user code, especially when working with models in vLLM.
-
-## Default Behavior
-
-By default, the `seed` parameter is set to `None`. When the `seed` parameter is `None`, the global random states for `random`, `np.random`, and `torch.manual_seed` are not set. This means that the random operations will behave as expected, without any fixed random states.
-
-## Specifying a Seed
-
-If a specific seed value is provided, the global random states for `random`, `np.random`, and `torch.manual_seed` will be set accordingly. This can be useful for reproducibility, as it ensures that the random operations produce the same results across multiple runs.
-
-## Example Usage
-
-### Without Specifying a Seed
-
-```python
-import random
-from vllm import LLM
-
-# Initialize a vLLM model without specifying a seed
-model = LLM(model="Qwen/Qwen2.5-0.5B-Instruct")
-
-# Try generating random numbers
-print(random.randint(0, 100))  # Outputs different numbers across runs
-```
-
-### Specifying a Seed
-
-```python
-import random
-from vllm import LLM
-
-# Initialize a vLLM model with a specific seed
-model = LLM(model="Qwen/Qwen2.5-0.5B-Instruct", seed=42)
-
-# Try generating random numbers
-print(random.randint(0, 100))  # Outputs the same number across runs
-```
-
-## Important Notes
-
-- If the `seed` parameter is not specified, the behavior of global random states remains unaffected.
-- If a specific seed value is provided, the global random states for `random`, `np.random`, and `torch.manual_seed` will be set to that value.
-- This behavior can be useful for reproducibility but may lead to non-intuitive behavior if the user is not explicitly aware of it.
-
-## Conclusion
-
-Understanding the behavior of the `seed` parameter in vLLM is crucial for ensuring the expected behavior of random operations in your code. By default, the `seed` parameter is set to `None`, which means that the global random states are not affected. However, specifying a seed value can help achieve reproducibility in your experiments.

examples/inference/basic/README.md

@@ -6,7 +6,7 @@ The `VideoGenerator` class provides the primary Python interface for doing offli
 - Python 3.10-3.12
 
 ## Installation
-If you have not installed FastVideo, please following these [instructions](https://hao-ai-lab.github.io/FastVideo/getting_started/installation.html) first.
+If you have not installed FastVideo, please following these [instructions](https://hao-ai-lab.github.io/FastVideo/getting_started/installation) first.
 
 ## Usage
 The first script in this example shows the most basic usage of FastVideo. If you are new to Python and FastVideo, you should start here.

examples/inference/basic/basic_hy15.py

@@ -0,0 +1,42 @@
+from fastvideo import VideoGenerator
+import json
+# from fastvideo.configs.sample import SamplingParam
+
+OUTPUT_PATH = "video_samples_hy15"
+def main():
+    # FastVideo will automatically use the optimal default arguments for the
+    # model.
+    # If a local path is provided, FastVideo will make a best effort
+    # attempt to identify the optimal arguments.
+    generator = VideoGenerator.from_pretrained(
+        "hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v",
+        # FastVideo will automatically handle distributed setup
+        num_gpus=1,
+        use_fsdp_inference=True,
+        dit_cpu_offload=True,
+        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"
+        # image_encoder_cpu_offload=False,
+    )
+
+    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, negative_prompt="", num_frames=81, fps=16)
+
+    prompt2 = (
+        "A majestic lion strides across the golden savanna, its powerful frame "
+        "glistening under the warm afternoon sun. The tall grass ripples gently in "
+        "the breeze, enhancing the lion's commanding presence. The tone is vibrant, "
+        "embodying the raw energy of the wild. Low angle, steady tracking shot, "
+        "cinematic.")
+
+    video2 = generator.generate_video(prompt2, output_path=OUTPUT_PATH, save_video=True, negative_prompt="", num_frames=81, fps=16)
+
+
+if __name__ == "__main__":
+    main()

examples/inference/basic/basic_matrixgame.py

@@ -0,0 +1,75 @@
+from fastvideo import VideoGenerator
+from fastvideo.configs.pipelines.wan import MatrixGameI2V480PConfig
+from fastvideo.models.dits.matrix_game.utils import create_action_presets
+
+import torch
+
+# Available variants: "base_distilled_model", "gta_distilled_model", "templerun_distilled_model"
+# Each variant has different keyboard_dim:
+#   - base_distilled_model: keyboard_dim=4
+#   - gta_distilled_model: keyboard_dim=2
+#   - templerun_distilled_model: keyboard_dim=7 (keyboard only, no mouse)
+MODEL_VARIANT = "base_distilled_model"
+
+# Variant-specific settings
+VARIANT_CONFIG = {
+    "base_distilled_model": {
+        "model_path": "FastVideo/Matrix-Game-2.0-Base-Diffusers",
+        "keyboard_dim": 4,
+        "image_url": "https://raw.githubusercontent.com/SkyworkAI/Matrix-Game/main/Matrix-Game-2/demo_images/universal/0000.png",
+    },
+    "gta_distilled_model": {
+        "model_path": "FastVideo/Matrix-Game-2.0-GTA-Diffusers",
+        "keyboard_dim": 2,
+        "image_url": "https://raw.githubusercontent.com/SkyworkAI/Matrix-Game/main/Matrix-Game-2/demo_images/gta_drive/0000.png",
+    },
+    "templerun_distilled_model": {
+        "model_path": "FastVideo/Matrix-Game-2.0-TempleRun-Diffusers",
+        "keyboard_dim": 7,
+        "image_url": "https://raw.githubusercontent.com/SkyworkAI/Matrix-Game/main/Matrix-Game-2/demo_images/temple_run/0000.png",
+    },
+}
+
+
+OUTPUT_PATH = "video_samples_matrixgame2"
+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.
+    config = VARIANT_CONFIG[MODEL_VARIANT]
+
+    generator = VideoGenerator.from_pretrained(
+        config["model_path"],
+        # FastVideo will automatically handle distributed setup
+        num_gpus=1,
+        use_fsdp_inference=True,
+        dit_cpu_offload=True, # DiT need to be offloaded for MoE
+        vae_cpu_offload=False,
+        text_encoder_cpu_offload=True,
+        # Set pin_cpu_memory to false if CPU RAM is limited and there're no frequent CPU-GPU transfer
+        pin_cpu_memory=True,
+        # image_encoder_cpu_offload=False,
+    )
+
+    num_frames = 597
+    actions = create_action_presets(num_frames, keyboard_dim=config["keyboard_dim"])
+    grid_sizes = torch.tensor([150, 44, 80])
+
+    generator.generate_video(
+        prompt="",
+        image_path=config["image_url"],
+        mouse_cond=actions["mouse"].unsqueeze(0),
+        keyboard_cond=actions["keyboard"].unsqueeze(0),
+        grid_sizes=grid_sizes,
+        num_frames=num_frames,
+        height=352,
+        width=640,
+        num_inference_steps=50,
+        output_path=OUTPUT_PATH,
+        save_video=True,
+    )
+
+
+if __name__ == "__main__":
+    main()

examples/inference/basic/basic_self_forcing_causal_wan2_2_i2v.py

@@ -11,7 +11,7 @@ def main():
     # If a local path is provided, FastVideo will make a best effort
     # attempt to identify the optimal arguments.
     generator = VideoGenerator.from_pretrained(
-        "rand0nmr/SFWan2.2-T2V-A14B-Diffusers",
+        "FastVideo/SFWan2.2-I2V-A14B-Preview-Diffusers",
         # FastVideo will automatically handle distributed setup
         num_gpus=1,
         use_fsdp_inference=True,
@@ -22,13 +22,11 @@ def main():
         dmd_denoising_steps=[1000, 850, 700, 550, 350, 275, 200, 125],
         # Set pin_cpu_memory to false if CPU RAM is limited and there're no frequent CPU-GPU transfer
         pin_cpu_memory=True,
-        init_weights_from_safetensors="/mnt/sharefs/users/hao.zhang/wei/SFwan2.2_distill_self_forcing_release_x0_CacheAppend_1333/checkpoint-442_weight_only/ema/generator_ema.safetensors",
-        init_weights_from_safetensors_2="/mnt/sharefs/users/hao.zhang/wei/SFwan2.2_distill_self_forcing_release_x0_CacheAppend_1333/checkpoint-442_weight_only/ema/generator_ema_2.safetensors",
         # image_encoder_cpu_offload=False,
     )
 
-    sampling_param = SamplingParam.from_pretrained("rand0nmr/SFWan2.2-T2V-A14B-Diffusers")
-    sampling_param.num_frames = 73
+    sampling_param = SamplingParam.from_pretrained("FastVideo/SFWan2.2-I2V-A14B-Preview-Diffusers")
+    sampling_param.num_frames = 81
     sampling_param.width = 832
     sampling_param.height = 480
     sampling_param.seed = 1000
@@ -43,4 +41,4 @@ def main():
 
 
 if __name__ == "__main__":
-    main()
+    main()

examples/inference/gradio/serving/gradio_frontend.py

@@ -3,6 +3,9 @@ import os
 import requests
 import base64
 import time
+import json
+from pathlib import Path
+import tempfile
 
 import gradio as gr
 
@@ -12,6 +15,7 @@ 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",
+    "CausalWan2.2-I2V-A14B-Preview": "FastVideo/SFWan2.2-I2V-A14B-Preview-Diffusers",
 }
 
 
@@ -37,7 +41,7 @@ class RayServeClient:
                 f"{self.backend_url}/generate_video",
                 json=request_data,
                 headers=headers,
-                timeout=300
+                timeout=900  # 15 minutes timeout for longer video generation
             )
             
             round_trip_time = time.time() - start_time
@@ -81,49 +85,78 @@ def save_video_from_base64(video_data: str, output_dir: str, prompt: str) -> str
         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"
+def encode_image_to_base64(image_path: str) -> str:
+    """Encode an image file to base64 string."""
+    if not image_path or not os.path.exists(image_path):
+        return None
     
-    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>"
+    try:
+        with open(image_path, 'rb') as f:
+            image_bytes = f.read()
+        
+        image_base64 = base64.b64encode(image_bytes).decode('utf-8')
+        
+        # Determine image type from extension
+        ext = os.path.splitext(image_path)[1].lower()
+        mime_types = {
+            '.jpg': 'image/jpeg',
+            '.jpeg': 'image/jpeg',
+            '.png': 'image/png',
+            '.gif': 'image/gif',
+            '.webp': 'image/webp',
+        }
+        mime_type = mime_types.get(ext, 'image/jpeg')
+        
+        return f"data:{mime_type};base64,{image_base64}"
+        
+    except Exception as e:
+        print(f"Failed to encode image: {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():
@@ -144,26 +177,83 @@ def load_example_prompts():
             print(f"Warning: Could not read {filepath}: {e}")
         return prompts, labels
     
-    examples, example_labels = load_from_file("prompts/prompts_final.txt")
+    # Load prompts from prompts.txt
+    examples, example_labels = load_from_file("examples/inference/gradio/serving/prompts.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
+    # Load image mappings from JSON file
+    prompt_to_image = {}
+    # Try to find the JSON file relative to project root
+    possible_json_paths = [
+        Path("prompts/mixkit_i2v.jsonl"),
+        Path(__file__).parent.parent.parent.parent / "prompts" / "mixkit_i2v.jsonl",
+    ]
+    json_path = None
+    for path in possible_json_paths:
+        if path.exists():
+            json_path = path
+            break
+    
+    if json_path and json_path.exists():
+        try:
+            with open(json_path, "r", encoding='utf-8') as f:
+                data = json.load(f)
+                # Get the project root directory (parent of prompts directory)
+                project_root = json_path.parent.parent
+                for item in data:
+                    prompt_text = item.get("prompt", "").strip()
+                    image_path = item.get("image_path", "")
+                    if prompt_text and image_path:
+                        # Resolve image path relative to project root
+                        full_image_path = project_root / image_path
+                        if full_image_path.exists():
+                            prompt_to_image[prompt_text] = str(full_image_path.absolute())
+        except Exception as e:
+            print(f"Warning: Could not load image mappings from {json_path}: {e}")
+    
+    # Create image paths list matching the prompts
+    example_images = []
+    for prompt in examples:
+        # Try exact match first
+        image_path = prompt_to_image.get(prompt)
+        if not image_path:
+            # Try fuzzy match (case-insensitive, whitespace normalized)
+            normalized_prompt = " ".join(prompt.split())
+            for json_prompt, img_path in prompt_to_image.items():
+                normalized_json = " ".join(json_prompt.split())
+                if normalized_prompt.lower() == normalized_json.lower():
+                    image_path = img_path
+                    break
+        example_images.append(image_path if image_path and os.path.exists(image_path) else None)
+    
+    return examples, example_labels, example_images
 
 
 def create_gradio_interface(backend_url: str, default_params: dict[str, SamplingParam]):
     
     client = RayServeClient(backend_url)
     
+    def is_i2v_model(model_name: str) -> bool:
+        """Check if the model is an I2V model."""
+        return "I2V" in model_name
+    
     def generate_video(
-        prompt, negative_prompt, use_negative_prompt, seed, guidance_scale,
-        num_frames, height, width, randomize_seed, model_selection, progress
+        prompt, negative_prompt, use_negative_prompt, guidance_scale,
+        num_frames, height, width, model_selection, input_image, progress
     ):
+        # Use default seed value (randomize_seed disabled)
+        seed = 1000
+        randomize_seed = False
         if not client.check_health():
             return None, f"Backend is not available. Please check if Ray Serve is running at {backend_url}", ""
         
+        # Check if I2V model requires an image
+        if is_i2v_model(model_selection) and not input_image:
+            return None, "I2V models require an input image. Please upload an image.", ""
+        
         # Validate dimensions
         max_pixels = 720 * 1280
         if height * width > max_pixels:
@@ -172,6 +262,15 @@ def create_gradio_interface(backend_url: str, default_params: dict[str, Sampling
         if progress:
             progress(0.1, desc="Checking backend health...")
         
+        # Encode image if provided
+        image_data = None
+        if input_image:
+            if progress:
+                progress(0.2, desc="Encoding input image...")
+            image_data = encode_image_to_base64(input_image)
+            if not image_data:
+                return None, "Failed to encode input image", ""
+        
         request_data = {
             "prompt": prompt,
             "negative_prompt": negative_prompt,
@@ -183,7 +282,7 @@ def create_gradio_interface(backend_url: str, default_params: dict[str, Sampling
             "width": width,
             "randomize_seed": randomize_seed,
             "return_frames": False,
-            "image_path": None,
+            "image_data": image_data,
             "model_path": MODEL_PATH_MAPPING.get(model_selection, "FastVideo/FastWan2.1-T2V-1.3B-Diffusers")
         }
         
@@ -198,16 +297,16 @@ def create_gradio_interface(backend_url: str, default_params: dict[str, Sampling
         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", [])
+            # 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
-            )
+            # timing_details = create_timing_display(
+            #     inference_time, encoding_time, network_time, total_time, 
+            #     stage_execution_times, num_frames
+            # )
             
             if video_data:
                 if progress:
@@ -219,7 +318,7 @@ def create_gradio_interface(backend_url: str, default_params: dict[str, Sampling
                     progress(1.0, desc="Generation complete!")
                 
                 if video_path and os.path.exists(video_path):
-                    return video_path, used_seed, timing_details
+                    return video_path, used_seed, ""
                 else:
                     return None, "Failed to save video", ""
             else:
@@ -228,7 +327,7 @@ def create_gradio_interface(backend_url: str, default_params: dict[str, Sampling
             error_msg = response.get("error_message", "Unknown error occurred")
             return None, f"Generation failed: {error_msg}", ""
     
-    examples, example_labels = load_example_prompts()
+    examples, example_labels, example_images = load_example_prompts()
     
     theme = gr.themes.Base().set(
         button_primary_background_fill="#2563eb",
@@ -239,33 +338,39 @@ def create_gradio_interface(backend_url: str, default_params: dict[str, Sampling
     )
     
     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,
-            }
+        # 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,
+        #     }
         
         return {
-            'height': 448,
+            'height': 480,
             'width': 832,
-            'num_frames': 61,
-            'guidance_scale': 3.0,
-            'seed': 1024,
+            'num_frames': 73,
         }
     
-    initial_values = get_default_values("FastWan2.1-T2V-1.3B")
+    # Get available models based on what's loaded
+    available_models = []
+    for model_name, model_path in MODEL_PATH_MAPPING.items():
+        if model_path in default_params:
+            available_models.append(model_name)
     
-    with gr.Blocks(title="FastWan", theme=theme) as demo:
+    # Select first available model as default
+    default_model = available_models[0] if available_models else "FastWan2.1-T2V-1.3B"
+    initial_values = get_default_values(default_model)
+    initial_show_image = is_i2v_model(default_model)
+    
+    with gr.Blocks(title="CausalWan", 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>
+            <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_causalwan_preview/" target="_blank">Blog</a> | <a href="https://hao-ai-lab.github.io/FastVideo/" target="_blank">Docs</a>  </p>
         </div>
         """)
         
@@ -280,8 +385,8 @@ def create_gradio_interface(backend_url: str, default_params: dict[str, Sampling
         
         with gr.Row():
             model_selection = gr.Dropdown(
-                choices=list(MODEL_PATH_MAPPING.keys()),
-                value="FastWan2.1-T2V-1.3B",
+                choices=available_models,
+                value=default_model,
                 label="Select Model",
                 interactive=True
             )
@@ -312,69 +417,70 @@ def create_gradio_interface(backend_url: str, default_params: dict[str, Sampling
         with gr.Row():
             with gr.Column():
                 error_output = gr.Text(label="Error", visible=False)
-                timing_display = gr.Markdown(label="Timing Breakdown", 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(equal_height=False):
+            with gr.Column(scale=1):
+                with gr.Tabs():
+                    with gr.Tab("Input Image", visible=initial_show_image) as image_tab:
+                        gr.Markdown("**Please make sure you upload a 480x832 image**")
+                        input_image = gr.Image(
+                            label="",
+                            type="filepath",
+                            height=400,
                         )
                     
-                    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,
-                        )
+                    with gr.Tab("Advanced Options"):
+                        with gr.Group():
+                            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.Number(
+                                    label="Guidance Scale",
+                                    value=1.0,
+                                    interactive=False,
+                                    container=True
+                                )
+                            
+                            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")
+                            # randomize_seed = gr.Checkbox(label="Randomize seed", value=False)
+                            seed_output = gr.Number(label="Used Seed", value=1000)
         
-            with gr.Column(scale=1, elem_classes="video-column"):
+            with gr.Column(scale=1):
                 result = gr.Video(
                     label="Generated Video", 
                     show_label=True,
-                    height=466,
-                    width=600,
+                    height=500,
                     container=True,
-                    elem_classes="video-component"
+                    autoplay=True,
                 )
         
         gr.HTML("""
@@ -387,116 +493,10 @@ def create_gradio_interface(backend_url: str, default_params: dict[str, Sampling
         }
         
         .gradio-container {
-            max-width: 1200px !important;
+            max-width: 1400px !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;
@@ -511,18 +511,20 @@ def create_gradio_interface(backend_url: str, default_params: dict[str, Sampling
         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 ""
+                selected_prompt = examples[index]
+                selected_image = example_images[index] if index < len(example_images) else None
+                return selected_prompt, selected_image
+            return "", None
         
         example_dropdown.change(
             fn=on_example_select,
             inputs=example_dropdown,
-            outputs=prompt,
+            outputs=[prompt, input_image],
         )
         
         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>
+            <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 as a preview of our distilled I2V model. Outside of few-step distillation, we have not yet fully optimized it for speed. Stay tuned for updates!</p>
         </div>
         """)
         
@@ -537,6 +539,7 @@ def create_gradio_interface(backend_url: str, default_params: dict[str, Sampling
                 selected_model = "FastWan2.1-T2V-1.3B"
             
             model_path = MODEL_PATH_MAPPING.get(selected_model)
+            show_image_input = is_i2v_model(selected_model)
             
             if model_path and model_path in default_params:
                 params = default_params[model_path]
@@ -545,29 +548,29 @@ def create_gradio_interface(backend_url: str, default_params: dict[str, Sampling
                     gr.update(value=params.width),
                     gr.update(value=params.num_frames),
                     gr.update(value=params.guidance_scale),
-                    gr.update(value=params.seed),
+                    gr.update(visible=show_image_input),
                 )
             
             return (
                 gr.update(value=448),
                 gr.update(value=832),
-                gr.update(value=61),
+                gr.update(value=20),
                 gr.update(value=3.0),
-                gr.update(value=1024),
+                gr.update(visible=show_image_input),
             )
         
         model_selection.change(
             fn=on_model_selection_change,
             inputs=model_selection,
-            outputs=[height, width, num_frames, guidance_scale, seed],
+            outputs=[height, width, num_frames, guidance_scale, image_tab],
         )
         
         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
+            model_selection, prompt, negative_prompt, use_negative_prompt, guidance_scale, num_frames, height, width, input_image = 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
+            result_path, seed_or_error, _ = generate_video(
+                prompt, negative_prompt, use_negative_prompt, guidance_scale, 
+                num_frames, height, width, model_selection, input_image, progress
             )
             
             if result_path and os.path.exists(result_path):
@@ -575,14 +578,12 @@ def create_gradio_interface(backend_url: str, default_params: dict[str, Sampling
                     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(
@@ -592,14 +593,14 @@ def create_gradio_interface(backend_url: str, default_params: dict[str, Sampling
                 prompt,
                 negative_prompt,
                 use_negative_prompt,
-                seed,
                 guidance_scale,
                 num_frames,
                 height,
                 width,
-                randomize_seed,
+                # randomize_seed,
+                input_image,
             ],
-            outputs=[result, seed_output, error_output, timing_display],
+            outputs=[result, seed_output, error_output],  # timing_display removed
             concurrency_limit=20,
         )
     
@@ -611,8 +612,11 @@ def main():
     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",
+                        default="",
                         help="Comma separated list of paths to the T2V model(s)")
+    parser.add_argument("--i2v_model_paths", type=str,
+                        default="FastVideo/SFWan2.2-I2V-A14B-Preview-Diffusers",
+                        help="Comma separated list of paths to the I2V 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,
@@ -621,8 +625,15 @@ def main():
     args = parser.parse_args()
     
     default_params = {}
-    model_paths = args.t2v_model_paths.split(",")
-    for model_path in model_paths:
+    
+    # Load T2V model params
+    t2v_paths = [p.strip() for p in args.t2v_model_paths.split(",") if p.strip()]
+    for model_path in t2v_paths:
+        default_params[model_path] = SamplingParam.from_pretrained(model_path)
+    
+    # Load I2V model params
+    i2v_paths = [p.strip() for p in args.i2v_model_paths.split(",") if p.strip()]
+    for model_path in i2v_paths:
         default_params[model_path] = SamplingParam.from_pretrained(model_path)
     
     demo = create_gradio_interface(args.backend_url, default_params)
@@ -630,6 +641,8 @@ def main():
     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}")
+    if args.i2v_model_paths:
+        print(f"I2V Models: {args.i2v_model_paths}")
     
     from fastapi import FastAPI, Request, HTTPException
     from fastapi.responses import HTMLResponse, FileResponse
@@ -674,23 +687,23 @@ def main():
             <meta charset="UTF-8" />
             <meta name="viewport" content="width=device-width, initial-scale=1.0" />
             
-            <title>FastWan</title>
-            <meta name="title" content="FastWan">
+            <title>CausalWan</title>
+            <meta name="title" content="CausalWan">
             <meta name="description" content="Make video generation go blurrrrrrr">
-            <meta name="keywords" content="FastVideo, video generation, AI, machine learning, FastWan">
+            <meta name="keywords" content="FastVideo, video generation, AI, machine learning, CausalWan">
             
             <meta property="og:type" content="website">
             <meta property="og:url" content="{base_url}/">
-            <meta property="og:title" content="FastWan">
+            <meta property="og:title" content="CausalWan">
             <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="og:site_name" content="CausalWan">
             
             <meta property="twitter:card" content="summary_large_image">
             <meta property="twitter:url" content="{base_url}/">
-            <meta property="twitter:title" content="FastWan">
+            <meta property="twitter:title" content="CausalWan">
             <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">
@@ -720,7 +733,14 @@ def main():
         app, 
         demo, 
         path="/gradio",
-        allowed_paths=[os.path.abspath("outputs"), os.path.abspath("fastvideo-logos")]
+        allowed_paths=[
+            os.path.abspath("outputs"), 
+            os.path.abspath("fastvideo-logos"),
+            os.path.abspath("prompts"),
+            os.path.abspath("images"),
+            os.path.abspath(tempfile.gettempdir()),
+            os.path.abspath(os.path.join(tempfile.gettempdir(), "gradio")),
+        ]
     )
     
     uvicorn.run(app, host=args.host, port=args.port)

examples/inference/gradio/serving/prompts.txt

@@ -0,0 +1,15 @@
+Some friends dancing and having fun together in circles, at a party surrounded by colored lights at a party, in a fancy old place, in a view from below them.
+A man wearing grey shorts jumps rope in a gym, weights and gym equipment in the background.
+Flying over a peninsula covered in bushy trees, while discovering the sea around it, painted a beautiful turquoise blue, on a sunny day.
+Skillful cyclist doing a wheelie on a bike while riding through a forest, on a dirt road, surrounded by many trees, in the morning.
+In the video, a lone rider guides a majestic horse across an expansive, open field as the sun sets in the background. The rider, dressed in a classic blue shirt and wide-brimmed hat, sits confidently in the saddle, silhouetted against the warm glow of the evening sky. The horse moves gracefully, its mane and tail flowing with each step, creating a sense of harmony between horse and rider. Surrounding the pair, towering trees form a natural border, their leaves gently rustling in the breeze. The shadows lengthen on the ground, accentuating the serene and timeless feel of the scene. The distant hills and wooden fences frame the horizon, adding depth to the tranquil landscape. A few horses graze peacefully in the background, blending into the pastoral setting. The overall ambiance evokes a sense of calmness and quietude, capturing a perfect moment in the golden light of dusk.
+A silver SUV drives along a winding, snow-covered mountain road, with dense pine trees blanketed in snow lining both sides. The scene is serene, with the vehicle moving smoothly, possibly on a winter journey or vacation. As the SUV disappears around the bend, another, darker SUV follows, creating a sense of motion and perspective on the snow-dusted asphalt. The towering, snow-laden rock formation to the right contrasts with the dark green of the pines, highlighting the peacefulness of the wintry landscape.
+A man and a woman playing in a field with grass, during a bright afternoon, while cars pass by in the distance.
+A saxophonist wearing a blazer dances while playing a song in a park.
+Romantic couple embracing and looking at each other in the middle of a forest, during a break on a road trip through nature.
+Young woman cleaning her house decorated with plants and decorations, while dancing happily to music in her headphones.
+Man dressed in 80's style dances very happily in his kitchen while listening to music on his radio and drinking wine.
+Pair of jazz musicians performing a song with their saxophone and trombone on an abandoned train.
+A young woman with short hair wearing pink sunglasses chews gum and makes a bubble gum with the city in the background.
+Natural aerial landscape with a relief covered with abundant trees and vegetation and a thick layer of mist.
+Loving couple sitting on a log on the shore of a lake outside, sharing an affectionate hug.

examples/inference/gradio/serving/ray_serve_backend.py

@@ -26,6 +26,7 @@ SEED_RANGE_MAX = 1_000_000
 SUPPORTED_MODELS = [
     "FastVideo/FastWan2.1-T2V-1.3B-Diffusers", 
     "FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers",
+    "FastVideo/SFWan2.2-I2V-A14B-Preview-Diffusers",
 ]
 
 MODEL_CONFIGS = {
@@ -42,6 +43,13 @@ MODEL_CONFIGS = {
         "dit_cpu_offload": True,
         "vae_cpu_offload": False,
         "VSA_sparsity": 0.9,
+    },
+    "I2V-A14B": {
+        "num_cpus": 15,
+        "text_encoder_cpu_offload": True,
+        "dit_cpu_offload": True,
+        "vae_cpu_offload": False,
+        "VSA_sparsity": 0.0,
     }
 }
 
@@ -58,6 +66,7 @@ class VideoGenerationRequest(BaseModel):
     randomize_seed: bool = False
     return_frames: bool = False
     model_path: Optional[str] = None
+    image_data: Optional[str] = None  # Base64 encoded image for I2V
 
 
 class VideoGenerationResponse(BaseModel):
@@ -91,11 +100,38 @@ def encode_video_to_base64(frames: List[np.ndarray], fps: int = DEFAULT_FPS) ->
         return ""
 
 
+def save_image_from_base64(image_data: str, output_dir: str) -> Optional[str]:
+    """Save base64 image data to a temporary file and return the path."""
+    if not image_data:
+        return None
+    
+    try:
+        # Remove data URL prefix if present
+        if image_data.startswith('data:image/'):
+            image_data = image_data.split(',')[1]
+        
+        image_bytes = base64.b64decode(image_data)
+        
+        # Save to temporary file
+        os.makedirs(output_dir, exist_ok=True)
+        temp_image_path = os.path.join(output_dir, f"temp_input_{int(time.time() * 1000)}.png")
+        
+        with open(temp_image_path, 'wb') as f:
+            f.write(image_bytes)
+        
+        return temp_image_path
+        
+    except Exception as e:
+        print(f"Warning: Failed to save image: {e}")
+        return None
+
+
 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"
+    # 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_ATTENTION_BACKEND"] = "FLASH_ATTN"
     os.environ["FASTVIDEO_STAGE_LOGGING"] = "1"
 
 
@@ -157,22 +193,41 @@ class BaseModelDeployment:
             num_gpus=1,
             use_fsdp_inference=True,
             text_encoder_cpu_offload=config["text_encoder_cpu_offload"],
+            dmd_denoising_steps=[1000, 850, 700, 550, 350, 275, 200, 125], # TODO: hardocde for I2V
+            dit_precision="fp32",  # TODO: hardocde for I2V
             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)
+        self.default_params.seed = 1000
+        self.default_params.num_frames = 73
+        self.default_params.width = 832
+        self.default_params.height = 480
 
     def generate_video(self, video_request: VideoGenerationRequest) -> VideoGenerationResponse:
         total_start_time = time.time()
         
         params = prepare_sampling_params(video_request, self.default_params)
 
+        # Save image if provided (for I2V)
+        image_path = None
+        if video_request.image_data:
+            image_path = save_image_from_base64(video_request.image_data, self.output_path)
+            if image_path is None:
+                return VideoGenerationResponse(
+                    video_data=None,
+                    seed=params.seed,
+                    success=False,
+                    error_message="Failed to save input image",
+                )
+
         inference_start_time = time.time()
         result = self.generator.generate_video(
             prompt=video_request.prompt,
             sampling_param=params,
+            image_path=image_path,
             save_video=False,
             return_frames=False,
         )
@@ -185,6 +240,13 @@ class BaseModelDeployment:
         encoding_time = time.time() - encoding_start_time
         
         total_time = time.time() - total_start_time
+        
+        # Clean up temporary image file
+        if image_path and os.path.exists(image_path):
+            try:
+                os.remove(image_path)
+            except Exception as e:
+                print(f"Warning: Failed to remove temporary image file {image_path}: {e}")
 
         return VideoGenerationResponse(
             video_data=video_data,
@@ -200,7 +262,7 @@ class BaseModelDeployment:
 
 
 @serve.deployment(
-    ray_actor_options={"num_cpus": 2, "num_gpus": 1, "runtime_env": {"conda": "fv"}},
+    ray_actor_options={"num_cpus": 2, "num_gpus": 1, "runtime_env": {"conda": "demo-fv"}},
 )
 class T2VModelDeployment(BaseModelDeployment):
     def __init__(self, t2v_model_path: str, output_path: str = "outputs"):
@@ -210,7 +272,7 @@ class T2VModelDeployment(BaseModelDeployment):
 
 
 @serve.deployment(
-    ray_actor_options={"num_cpus": 16, "num_gpus": 1, "runtime_env": {"conda": "fv"}},
+    ray_actor_options={"num_cpus": 16, "num_gpus": 1, "runtime_env": {"conda": "demo-fv"}},
 )
 class T2V14BModelDeployment(BaseModelDeployment):
     def __init__(self, t2v_14b_model_path: str, output_path: str = "outputs"):
@@ -221,18 +283,32 @@ class T2V14BModelDeployment(BaseModelDeployment):
         print("✅ T2V 14B model initialized successfully")
 
 
+@serve.deployment(
+    ray_actor_options={"num_cpus": 15, "num_gpus": 1, "runtime_env": {"conda": "demo-fv"}},
+)
+class I2VModelDeployment(BaseModelDeployment):
+    def __init__(self, i2v_model_path: str, output_path: str = "outputs"):
+        super().__init__(i2v_model_path, output_path)
+        # Override environment for I2V model
+        os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "FLASH_ATTN"
+        self._initialize_generator(MODEL_CONFIGS["I2V-A14B"])
+        print("✅ I2V 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.deployment(num_replicas=1, ray_actor_options={"num_cpus": 1})
 @serve.ingress(app)
 class FastVideoAPI:
 
-    def __init__(self, t2v_deployments: Dict[str, DeploymentHandle]):
+    def __init__(self, t2v_deployments: Dict[str, DeploymentHandle], i2v_deployments: Dict[str, DeploymentHandle] = None):
         self.t2v_deployments = t2v_deployments
+        self.i2v_deployments = i2v_deployments or {}
+        self.all_deployments = {**self.t2v_deployments, **self.i2v_deployments}
         
         # Initialize Prometheus metrics
         self.request_count = Counter('fastvideo_requests_total', 'Total FastVideo requests', ['model_type', 'status'])
@@ -257,10 +333,10 @@ class FastVideoAPI:
         model_name = self._get_model_name(video_request.model_path)
         
         try:
-            if video_request.model_path not in self.t2v_deployments:
+            if video_request.model_path not in self.all_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_ref = self.all_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)
@@ -291,18 +367,21 @@ class FastVideoAPI:
 
 
 def validate_configuration(model_paths: List[str], replicas: List[int]) -> None:
+    assert len(model_paths) > 0, "At least one model must be specified"
     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 model in SUPPORTED_MODELS, f"Model {model} not supported. Supported models: {SUPPORTED_MODELS}"
         assert replica_count > 0, f"Replicas must be greater than 0"
 
 
 def start_ray_serve(
     *,
-    t2v_model_paths: str,
-    t2v_model_replicas: str,
+    t2v_model_paths: str = "",
+    t2v_model_replicas: str = "",
+    i2v_model_paths: str = "",
+    i2v_model_replicas: str = "",
     output_path: str = "outputs",
     host: str = "0.0.0.0",
     port: int = 8000,
@@ -310,21 +389,39 @@ def start_ray_serve(
     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)
+    # Parse T2V models
+    t2v_paths = [p.strip() for p in t2v_model_paths.split(",") if p.strip()]
+    t2v_reps = [int(r.strip()) for r in t2v_model_replicas.split(",") if r.strip()] if t2v_model_replicas else []
+    
+    # Parse I2V models
+    i2v_paths = [p.strip() for p in i2v_model_paths.split(",") if p.strip()]
+    i2v_reps = [int(r.strip()) for r in i2v_model_replicas.split(",") if r.strip()] if i2v_model_replicas else []
+    
+    # Validate configurations
+    all_paths = t2v_paths + i2v_paths
+    all_replicas = t2v_reps + i2v_reps
+    validate_configuration(all_paths, all_replicas)
 
+    # Create T2V deployments
     t2v_deps = {}
-    for model_path, replica_count in zip(model_paths, replicas):
+    for model_path, replica_count in zip(t2v_paths, t2v_reps):
         t2v_dep = T2VModelDeployment.options(num_replicas=replica_count).bind(model_path, output_path)
         t2v_deps[model_path] = t2v_dep
 
-    api = FastVideoAPI.bind(t2v_deps)
+    # Create I2V deployments
+    i2v_deps = {}
+    for model_path, replica_count in zip(i2v_paths, i2v_reps):
+        i2v_dep = I2VModelDeployment.options(num_replicas=replica_count).bind(model_path, output_path)
+        i2v_deps[model_path] = i2v_dep
+
+    api = FastVideoAPI.bind(t2v_deps, i2v_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):
+    for model_path, replica_count in zip(t2v_paths, t2v_reps):
         print(f"T2V Model: {model_path} | Replicas: {replica_count}")
+    for model_path, replica_count in zip(i2v_paths, i2v_reps):
+        print(f"I2V Model: {model_path} | Replicas: {replica_count}")
     print(f"Health check: http://{host}:{port}/health")
     print(f"Video generation endpoint: http://{host}:{port}/generate_video")
 
@@ -340,12 +437,20 @@ if __name__ == "__main__":
     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",
+                        default="",
                         help="Comma separated list of paths to the T2V model(s)")
     parser.add_argument("--t2v_model_replicas",
                         type=str,
-                        default="4,4",
+                        default="",
                         help="Comma separated list of number of replicas for the T2V model(s)")
+    parser.add_argument("--i2v_model_paths",
+                        type=str,
+                        default="FastVideo/SFWan2.2-I2V-A14B-Preview-Diffusers",
+                        help="Comma separated list of paths to the I2V model(s)")
+    parser.add_argument("--i2v_model_replicas",
+                        type=str,
+                        default="1",
+                        help="Comma separated list of number of replicas for the I2V model(s)")
     parser.add_argument("--output_path",
                         type=str,
                         default="outputs",
@@ -361,13 +466,21 @@ if __name__ == "__main__":
     
     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)
+    # Parse and validate all models
+    t2v_paths = [p.strip() for p in args.t2v_model_paths.split(",") if p.strip()]
+    t2v_reps = [int(r.strip()) for r in args.t2v_model_replicas.split(",") if r.strip()] if args.t2v_model_replicas else []
+    i2v_paths = [p.strip() for p in args.i2v_model_paths.split(",") if p.strip()]
+    i2v_reps = [int(r.strip()) for r in args.i2v_model_replicas.split(",") if r.strip()] if args.i2v_model_replicas else []
+    
+    all_paths = t2v_paths + i2v_paths
+    all_replicas = t2v_reps + i2v_reps
+    validate_configuration(all_paths, all_replicas)
     
     start_ray_serve(
         t2v_model_paths=args.t2v_model_paths,
         t2v_model_replicas=args.t2v_model_replicas,
+        i2v_model_paths=args.i2v_model_paths,
+        i2v_model_replicas=args.i2v_model_replicas,
         output_path=args.output_path,
         host=args.host,
         port=args.port,
@@ -376,4 +489,4 @@ if __name__ == "__main__":
     setup_signal_handlers()
     print("✅ FastVideo backend is running. Press Ctrl-C to stop.")
     while True:
-        time.sleep(3600) 
+        time.sleep(3600) 

examples/inference/gradio/serving/start.sh

@@ -1,3 +1,5 @@
-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"
+python examples/inference/gradio/serving/start_ray_serve_app.py \
+--t2v_model_paths "" \
+--t2v_model_replicas "" \
+--i2v_model_paths "FastVideo/SFWan2.2-I2V-A14B-Preview-Diffusers" \
+--i2v_model_replicas "1"

examples/inference/gradio/serving/start_ray_serve_app.py

@@ -20,8 +20,10 @@ 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"
+DEFAULT_T2V_MODELS = ""
+DEFAULT_T2V_REPLICAS = ""
+DEFAULT_I2V_MODELS = "FastVideo/SFWan2.2-I2V-A14B-Preview-Diffusers"
+DEFAULT_I2V_REPLICAS = "1"
 
 HEALTH_CHECK_TIMEOUT = 5
 HEALTH_CHECK_MAX_RETRIES = 100
@@ -100,6 +102,12 @@ class ServiceManager:
             "port": self.args.backend_port
         }
         
+        # Add I2V parameters if provided
+        if self.args.i2v_model_paths:
+            backend_args["i2v_model_paths"] = self.args.i2v_model_paths
+        if self.args.i2v_model_replicas:
+            backend_args["i2v_model_replicas"] = self.args.i2v_model_replicas
+        
         self.backend_process = self._start_service("ray_serve_backend.py", backend_args, "backend")
         return self.backend_process
     
@@ -111,6 +119,10 @@ class ServiceManager:
             "port": self.args.frontend_port
         }
         
+        # Add I2V parameters if provided
+        if self.args.i2v_model_paths:
+            frontend_args["i2v_model_paths"] = self.args.i2v_model_paths
+        
         self.frontend_process = self._start_service("gradio_frontend.py", frontend_args, "frontend")
         return self.frontend_process
     
@@ -173,6 +185,9 @@ def print_startup_info(args: argparse.Namespace) -> None:
     print("=" * 50)
     print(f"T2V Models: {args.t2v_model_paths}")
     print(f"T2V Model Replicas: {args.t2v_model_replicas}")
+    if args.i2v_model_paths:
+        print(f"I2V Models: {args.i2v_model_paths}")
+        print(f"I2V Model Replicas: {args.i2v_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}")
@@ -190,6 +205,14 @@ def parse_arguments() -> argparse.Namespace:
                         type=str, 
                         default=DEFAULT_T2V_REPLICAS,
                         help="Comma separated list of number of replicas for the T2V model(s)")
+    parser.add_argument("--i2v_model_paths", 
+                        type=str,
+                        default=DEFAULT_I2V_MODELS,
+                        help="Comma separated list of paths to the I2V model(s)")
+    parser.add_argument("--i2v_model_replicas", 
+                        type=str, 
+                        default=DEFAULT_I2V_REPLICAS,
+                        help="Comma separated list of number of replicas for the I2V model(s)")
     parser.add_argument("--output_path", 
                         type=str, 
                         default=DEFAULT_OUTPUT_PATH,

examples/training/finetune/wan_t2v_1.3B/crush_smol/README.md

@@ -5,12 +5,12 @@ These are e2e example scripts for finetuning Wan2.1 T2V 1.3B on the crush-smol d
 
 ### Download crush-smol dataset:
 
-`bash examples/training/finetune/wan_t2v_1_3b/crush_smol/download_dataset.sh`
+`bash examples/training/finetune/wan_t2v_1.3B/crush_smol/download_dataset.sh`
 
 ### Preprocess the videos and captions into latents:
 
-`bash examples/training/finetune/wan_t2v_1_3b/crush_smol/preprocess_wan_data_t2v.sh`
+`bash examples/training/finetune/wan_t2v_1.3B/crush_smol/preprocess_wan_data_t2v.sh`
 
 ### Edit the following file and run finetuning:
 
-`bash examples/training/finetune/wan_t2v_1_3b/crush_smol/finetune_t2v.sh`
+`bash examples/training/finetune/wan_t2v_1.3B/crush_smol/finetune_t2v.sh`

examples/training/finetune/wan_t2v_1.3B/crush_smol/finetune_t2v.sh

@@ -54,7 +54,7 @@ validation_args=(
   --validation_dataset_file $VALIDATION_DATASET_FILE
   --validation_steps 200
   --validation_sampling_steps "50" 
-  --validation_guidance_scale "6.0"
+  --validation_guidance_scale "3.0"
 )
 
 # Optimizer arguments

examples/training/finetune/wan_t2v_1.3B/crush_smol/profile_finetune_t2v.sh

@@ -0,0 +1,107 @@
+#!/bin/bash
+
+export WANDB_BASE_URL="https://api.wandb.ai"
+export WANDB_MODE=online
+export TOKENIZERS_PARALLELISM=false
+# export FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA
+
+MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
+DATA_DIR="data/crush-smol_processed_t2v/combined_parquet_dataset/"
+VALIDATION_DATASET_FILE="$(dirname "$0")/validation.json"
+NUM_GPUS=4
+# export CUDA_VISIBLE_DEVICES=4,5
+
+mkdir -p ../profiler_traces/wan_t2v_finetune/
+
+# Torch Profiler Configuration
+export FASTVIDEO_TORCH_PROFILE_REGIONS="profiler_region_training_train_one_step"
+export FASTVIDEO_TORCH_PROFILER_RECORD_SHAPES=1
+export FASTVIDEO_TORCH_PROFILER_WITH_STACK=1  
+export FASTVIDEO_TORCH_PROFILER_WITH_FLOPS=1
+export FASTVIDEO_TORCH_PROFILER_WITH_PROFILE_MEMORY=1
+export FASTVIDEO_TORCH_PROFILER_WAIT_STEPS=2
+export FASTVIDEO_TORCH_PROFILER_WARMUP_STEPS=1
+export FASTVIDEO_TORCH_PROFILER_ACTIVE_STEPS=1
+export FASTVIDEO_TORCH_PROFILER_DIR="../profiler_traces/wan_t2v_finetune/"
+
+# Training arguments
+training_args=(
+  --tracker_project_name "wan_t2v_finetune"
+  --output_dir "checkpoints/wan_t2v_finetune"
+  --max_train_steps 5000
+  --train_batch_size 1
+  --train_sp_batch_size 1
+  --gradient_accumulation_steps 8
+  --num_latent_t 20
+  --num_height 480
+  --num_width 832
+  --num_frames 77
+  --enable_gradient_checkpointing_type "full"
+)
+
+# Parallel arguments
+parallel_args=(
+  --num_gpus $NUM_GPUS 
+  --sp_size $NUM_GPUS 
+  --tp_size 1
+  --hsdp_replicate_dim 1
+  --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 1
+)
+
+# Validation arguments
+validation_args=(
+  --log_validation 
+  --validation_dataset_file $VALIDATION_DATASET_FILE
+  --validation_steps 200
+  --validation_sampling_steps "50" 
+  --validation_guidance_scale "6.0"
+)
+
+# Optimizer arguments
+optimizer_args=(
+  --learning_rate 5e-5
+  --mixed_precision "bf16"
+  --weight_only_checkpointing_steps 1000
+  --training_state_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"
+  # --resume_from_checkpoint "checkpoints/wan_t2v_finetune/checkpoint-2500"
+)
+
+torchrun \
+  --nnodes 1 \
+  --nproc_per_node $NUM_GPUS \
+  --master_port 29502 \
+    fastvideo/training/wan_training_pipeline.py \
+    "${parallel_args[@]}" \
+    "${model_args[@]}" \
+    "${dataset_args[@]}" \
+    "${training_args[@]}" \
+    "${optimizer_args[@]}" \
+    "${validation_args[@]}" \
+    "${miscellaneous_args[@]}"

fastvideo/__init__.py

@@ -3,4 +3,4 @@ from fastvideo.configs.sample import SamplingParam
 from fastvideo.entrypoints.video_generator import VideoGenerator
 from fastvideo.version import __version__
 
-__all__ = ["VideoGenerator", "PipelineConfig", "SamplingParam", "__version__"]
+__all__ = ["VideoGenerator", "PipelineConfig", "SamplingParam", "__version__"]

fastvideo/attention/backends/flash_attn.py

@@ -1,8 +1,9 @@
 # SPDX-License-Identifier: Apache-2.0
 
 import torch
+import torch.nn.functional as F
 from flash_attn import flash_attn_func as flash_attn_2_func
-
+from dataclasses import dataclass
 try:
     from flash_attn_interface import flash_attn_func as flash_attn_3_func
 
@@ -46,6 +47,29 @@ class FlashAttentionBackend(AttentionBackend):
         raise NotImplementedError
 
 
+@dataclass
+class FlashAttnMetadata(AttentionMetadata):
+    current_timestep: int
+    attn_mask: torch.Tensor | None = None
+
+
+class FlashAttnMetadataBuilder(AttentionMetadataBuilder):
+
+    def __init__(self):
+        pass
+
+    def prepare(self):
+        pass
+
+    def build(  # type: ignore
+        self,
+        current_timestep: int,
+        attn_mask: torch.Tensor,
+    ) -> FlashAttnMetadata:
+        return FlashAttnMetadata(current_timestep=current_timestep,
+                                 attn_mask=attn_mask)
+
+
 class FlashAttentionImpl(AttentionImpl):
 
     def __init__(
@@ -66,12 +90,27 @@ class FlashAttentionImpl(AttentionImpl):
         query: torch.Tensor,
         key: torch.Tensor,
         value: torch.Tensor,
-        attn_metadata: AttentionMetadata,
+        attn_metadata: FlashAttnMetadata,
     ):
-        output = flash_attn_func(
-            query,  # type: ignore[no-untyped-call]
-            key,
-            value,
-            softmax_scale=self.softmax_scale,
-            causal=self.causal)
+        if attn_metadata is not None and hasattr(
+                attn_metadata,
+                "attn_mask") and attn_metadata.attn_mask is not None:
+            from fastvideo.attention.utils.flash_attn_no_pad import flash_attn_no_pad
+            attn_mask = attn_metadata.attn_mask
+            qkv = torch.stack([query, key, value], dim=2)
+
+            attn_mask = F.pad(attn_mask, (qkv.shape[1] - attn_mask.shape[1], 0),
+                              value=True)
+            output = flash_attn_no_pad(qkv,
+                                       attn_mask,
+                                       causal=False,
+                                       dropout_p=0,
+                                       softmax_scale=None)
+        else:
+            output = flash_attn_func(
+                query,  # type: ignore[no-untyped-call]
+                key,
+                value,
+                softmax_scale=self.softmax_scale,
+                causal=self.causal)
         return output

fastvideo/attention/backends/sdpa.py

@@ -1,9 +1,10 @@
 # SPDX-License-Identifier: Apache-2.0
 
 import torch
-
+from dataclasses import dataclass
 from fastvideo.attention.backends.abstract import (  # FlashAttentionMetadata,
-    AttentionBackend, AttentionImpl, AttentionMetadata)
+    AttentionBackend, AttentionImpl, AttentionMetadata,
+    AttentionMetadataBuilder)
 from fastvideo.logger import init_logger
 
 logger = init_logger(__name__)
@@ -30,6 +31,29 @@ class SDPABackend(AttentionBackend):
     #     return FlashAttentionMetadata
 
 
+@dataclass
+class SDPAMetadata(AttentionMetadata):
+    current_timestep: int
+    attn_mask: torch.Tensor | None = None
+
+
+class SDPAMetadataBuilder(AttentionMetadataBuilder):
+
+    def __init__(self):
+        pass
+
+    def prepare(self):
+        pass
+
+    def build(  # type: ignore
+        self,
+        current_timestep: int,
+        attn_mask: torch.Tensor,
+    ) -> SDPAMetadata:
+        return SDPAMetadata(current_timestep=current_timestep,
+                            attn_mask=attn_mask)
+
+
 class SDPAImpl(AttentionImpl):
 
     def __init__(
@@ -51,14 +75,15 @@ class SDPAImpl(AttentionImpl):
         query: torch.Tensor,
         key: torch.Tensor,
         value: torch.Tensor,
-        attn_metadata: AttentionMetadata,
+        attn_metadata: SDPAMetadata,
     ) -> torch.Tensor:
         # transpose to bs, heads, seq_len, head_dim
         query = query.transpose(1, 2)
         key = key.transpose(1, 2)
         value = value.transpose(1, 2)
+        attn_mask = attn_metadata.attn_mask if attn_metadata is not None else None
         attn_kwargs = {
-            "attn_mask": None,
+            "attn_mask": attn_mask,
             "dropout_p": self.dropout,
             "is_causal": self.causal,
             "scale": self.softmax_scale

fastvideo/attention/layer.py

@@ -11,6 +11,7 @@ from fastvideo.distributed.parallel_state import (get_sp_parallel_rank,
 from fastvideo.forward_context import ForwardContext, get_forward_context
 from fastvideo.platforms import AttentionBackendEnum
 from fastvideo.utils import get_compute_dtype
+from fastvideo.layers.rotary_embedding import _apply_rotary_emb
 
 
 class DistributedAttention(nn.Module):
@@ -64,6 +65,8 @@ class DistributedAttention(nn.Module):
         replicated_q: torch.Tensor | None = None,
         replicated_k: torch.Tensor | None = None,
         replicated_v: torch.Tensor | None = None,
+        freqs_cis: tuple[torch.Tensor, torch.Tensor] | None = None,
+        attention_mask: torch.Tensor | None = None,
     ) -> tuple[torch.Tensor, torch.Tensor | None]:
         """Forward pass for distributed attention.
         
@@ -74,6 +77,7 @@ class DistributedAttention(nn.Module):
             replicated_q (Optional[torch.Tensor]): Replicated query tensor, typically for text tokens
             replicated_k (Optional[torch.Tensor]): Replicated key tensor
             replicated_v (Optional[torch.Tensor]): Replicated value tensor
+            attention_mask (Optional[torch.Tensor]): Attention mask [batch_size, seq_len]
             
         Returns:
             Tuple[torch.Tensor, Optional[torch.Tensor]]: A tuple containing:
@@ -91,12 +95,30 @@ class DistributedAttention(nn.Module):
         ctx_attn_metadata = forward_context.attn_metadata
 
         # Stack QKV
-        qkv = torch.cat([q, k, v], dim=0)  # [3, seq_len, num_heads, head_dim]
+        qkv = torch.cat([q, k, v],
+                        dim=0)  # [3*batch, seq_len, num_heads, head_dim]
 
         # Redistribute heads across sequence dimension
         qkv = sequence_model_parallel_all_to_all_4D(qkv,
                                                     scatter_dim=2,
                                                     gather_dim=1)
+
+        # After all-to-all, each rank has the full sequence but only a subset of heads
+        # The attention mask should now apply to the full sequence length
+        # Since mask is [batch, full_seq_len], it's already in the correct format
+
+        # LOAY TODO, instead of slicing repeatedly maintain an original qkv and rewrite into that
+        valid_seq_len = None
+        if attention_mask is not None:
+            valid_seq_len = (attention_mask[0] == 1).sum().item()
+            qkv = qkv[:, :valid_seq_len, :, :]
+
+        if freqs_cis is not None:
+            cos, sin = freqs_cis
+            qkv[:batch_size * 2] = _apply_rotary_emb(qkv[:batch_size * 2],
+                                                     cos,
+                                                     sin,
+                                                     is_neox_style=False)
         # Apply backend-specific preprocess_qkv
         qkv = self.attn_impl.preprocess_qkv(qkv, ctx_attn_metadata)
 
@@ -119,17 +141,23 @@ class DistributedAttention(nn.Module):
         # Redistribute back if using sequence parallelism
         replicated_output = None
         if replicated_q is not None:
-            replicated_output = output[:, seq_len * world_size:]
-            output = output[:, :seq_len * world_size]
+            split_idx = seq_len * world_size if valid_seq_len is None else valid_seq_len
+            replicated_output = output[:, split_idx:]
+            output = output[:, :split_idx]
             # TODO: make this asynchronous
             replicated_output = sequence_model_parallel_all_gather(
                 replicated_output.contiguous(), dim=2)
         # Apply backend-specific postprocess_output
         output = self.attn_impl.postprocess_output(output, ctx_attn_metadata)
 
+        if attention_mask is not None:
+            pad_len = (attention_mask[0] == 0).sum().item()
+            output = torch.nn.functional.pad(output, (0, 0, 0, 0, 0, pad_len))
+
         output = sequence_model_parallel_all_to_all_4D(output,
                                                        scatter_dim=1,
                                                        gather_dim=2)
+
         return output, replicated_output
 
 
@@ -147,6 +175,8 @@ class DistributedAttention_VSA(DistributedAttention):
         replicated_k: torch.Tensor | None = None,
         replicated_v: torch.Tensor | None = None,
         gate_compress: torch.Tensor | None = None,
+        freqs_cis: tuple[torch.Tensor, torch.Tensor] | None = None,
+        attention_mask: torch.Tensor | None = None,
     ) -> tuple[torch.Tensor, torch.Tensor | None]:
         """Forward pass for distributed attention.
         
@@ -158,6 +188,7 @@ class DistributedAttention_VSA(DistributedAttention):
             replicated_q (Optional[torch.Tensor]): Replicated query tensor, typically for text tokens
             replicated_k (Optional[torch.Tensor]): Replicated key tensor
             replicated_v (Optional[torch.Tensor]): Replicated value tensor
+            attention_mask (Optional[torch.Tensor]): Attention mask [batch_size, seq_len]
             
         Returns:
             Tuple[torch.Tensor, Optional[torch.Tensor]]: A tuple containing:
@@ -173,15 +204,32 @@ class DistributedAttention_VSA(DistributedAttention):
         forward_context: ForwardContext = get_forward_context()
         ctx_attn_metadata = forward_context.attn_metadata
 
+        batch_size, seq_len, num_heads, head_dim = q.shape
         # Stack QKV
         qkvg = torch.cat([q, k, v, gate_compress],
-                         dim=0)  # [3, seq_len, num_heads, head_dim]
+                         dim=0)  # [4*batch, seq_len, num_heads, head_dim]
 
         # Redistribute heads across sequence dimension
+        # Before: [4*batch, shard_seq_len, num_heads, head_dim]
+        # After:  [4*batch, full_seq_len, shard_num_heads, head_dim]
         qkvg = sequence_model_parallel_all_to_all_4D(qkvg,
                                                      scatter_dim=2,
                                                      gather_dim=1)
 
+        # After all-to-all, each rank has the full sequence but only a subset of heads
+        # The attention mask should now apply to the full sequence length
+
+        if attention_mask is not None:
+            valid_seq_len = (attention_mask[0] == 1).sum().item()
+            qkvg = qkvg[:, :valid_seq_len, :, :]
+
+        if freqs_cis is not None:
+            cos, sin = freqs_cis
+            qkvg[:batch_size * 2] = _apply_rotary_emb(qkvg[:batch_size * 2],
+                                                      cos,
+                                                      sin,
+                                                      is_neox_style=False)
+
         qkvg = self.attn_impl.preprocess_qkv(qkvg, ctx_attn_metadata)
 
         q, k, v, gate_compress = qkvg.chunk(4, dim=0)
@@ -194,6 +242,10 @@ class DistributedAttention_VSA(DistributedAttention):
         # Apply backend-specific postprocess_output
         output = self.attn_impl.postprocess_output(output, ctx_attn_metadata)
 
+        if attention_mask is not None:
+            pad_len = (attention_mask[0] == 0).sum().item()
+            output = torch.nn.functional.pad(output, (0, 0, 0, 0, 0, pad_len))
+
         output = sequence_model_parallel_all_to_all_4D(output,
                                                        scatter_dim=1,
                                                        gather_dim=2)
@@ -244,6 +296,7 @@ class LocalAttention(nn.Module):
         q: torch.Tensor,
         k: torch.Tensor,
         v: torch.Tensor,
+        freqs_cis: tuple[torch.Tensor, torch.Tensor] | None = None,
     ) -> torch.Tensor:
         """
         Apply local attention between query, key and value tensors.
@@ -263,5 +316,10 @@ class LocalAttention(nn.Module):
         forward_context: ForwardContext = get_forward_context()
         ctx_attn_metadata = forward_context.attn_metadata
 
+        if freqs_cis is not None:
+            cos, sin = freqs_cis
+            q = _apply_rotary_emb(q, cos, sin, is_neox_style=False)
+            k = _apply_rotary_emb(k, cos, sin, is_neox_style=False)
+
         output = self.attn_impl.forward(q, k, v, ctx_attn_metadata)
         return output

fastvideo/attention/utils/flash_attn_no_pad.py

@@ -0,0 +1,99 @@
+# Licensed under the TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5/blob/main/LICENSE
+#
+# Unless and only to the extent required by applicable law, the Tencent Hunyuan works and any
+# output and results there from are provided "AS IS" without any express or implied warranties of
+# any kind including any warranties of title, merchantability, noninfringement, course of dealing,
+# usage of trade, or fitness for a particular purpose. You are solely responsible for determining the
+# appropriateness of using, reproducing, modifying, performing, displaying or distributing any of
+# the Tencent Hunyuan works or outputs and assume any and all risks associated with your or a
+# third party's use or distribution of any of the Tencent Hunyuan works or outputs and your exercise
+# of rights and permissions under this agreement.
+# See the License for the specific language governing permissions and limitations under the License.
+
+from einops import rearrange
+
+
+def flash_attn_no_pad(qkv,
+                      key_padding_mask,
+                      causal=False,
+                      dropout_p=0.0,
+                      softmax_scale=None,
+                      deterministic=False):
+    from flash_attn import flash_attn_varlen_qkvpacked_func
+    from flash_attn.bert_padding import pad_input, unpad_input
+    batch_size = qkv.shape[0]
+    seqlen = qkv.shape[1]
+    nheads = qkv.shape[-2]
+    x = rearrange(qkv, "b s three h d -> b s (three h d)")
+    x_unpad, indices, cu_seqlens, max_s, used_seqlens_in_batch = unpad_input(
+        x, key_padding_mask)
+
+    x_unpad = rearrange(x_unpad,
+                        "nnz (three h d) -> nnz three h d",
+                        three=3,
+                        h=nheads)
+    output_unpad = flash_attn_varlen_qkvpacked_func(
+        x_unpad,
+        cu_seqlens,
+        max_s,
+        dropout_p,
+        softmax_scale=softmax_scale,
+        causal=causal,
+        deterministic=deterministic,
+    )
+    output = rearrange(
+        pad_input(rearrange(output_unpad, "nnz h d -> nnz (h d)"), indices,
+                  batch_size, seqlen),
+        "b s (h d) -> b s h d",
+        h=nheads,
+    )
+    return output
+
+
+def flash_attn_no_pad_v3(qkv,
+                         key_padding_mask,
+                         causal=False,
+                         dropout_p=0.0,
+                         softmax_scale=None,
+                         deterministic=False):
+    from flash_attn.bert_padding import pad_input, unpad_input
+    from flash_attn_interface import flash_attn_varlen_func as flash_attn_varlen_func_v3
+
+    if flash_attn_varlen_func_v3 is None:
+        raise ImportError("FlashAttention V3 backend not available")
+
+    batch_size, seqlen, _, nheads, head_dim = qkv.shape
+    query, key, value = qkv.unbind(dim=2)
+
+    query_unpad, indices, cu_seqlens_q, max_seqlen_q, _ = unpad_input(
+        rearrange(query, "b s h d -> b s (h d)"), key_padding_mask)
+    key_unpad, _, cu_seqlens_k, _, _ = unpad_input(
+        rearrange(key, "b s h d -> b s (h d)"), key_padding_mask)
+    value_unpad, _, _, _, _ = unpad_input(
+        rearrange(value, "b s h d -> b s (h d)"), key_padding_mask)
+
+    query_unpad = rearrange(query_unpad, "nnz (h d) -> nnz h d", h=nheads)
+    key_unpad = rearrange(key_unpad, "nnz (h d) -> nnz h d", h=nheads)
+    value_unpad = rearrange(value_unpad, "nnz (h d) -> nnz h d", h=nheads)
+
+    output_unpad = flash_attn_varlen_func_v3(query_unpad,
+                                             key_unpad,
+                                             value_unpad,
+                                             cu_seqlens_q,
+                                             cu_seqlens_k,
+                                             max_seqlen_q,
+                                             max_seqlen_q,
+                                             softmax_scale=softmax_scale,
+                                             causal=causal,
+                                             deterministic=deterministic)
+
+    output = rearrange(pad_input(
+        rearrange(output_unpad, "nnz h d -> nnz (h d)"), indices, batch_size,
+        seqlen),
+                       "b s (h d) -> b s h d",
+                       h=nheads)
+    return output

fastvideo/configs/models/dits/__init__.py

@@ -1,9 +1,11 @@
 from fastvideo.configs.models.dits.cosmos import CosmosVideoConfig
+from fastvideo.configs.models.dits.cosmos2_5 import Cosmos25VideoConfig
 from fastvideo.configs.models.dits.hunyuanvideo import HunyuanVideoConfig
+from fastvideo.configs.models.dits.hunyuanvideo15 import HunyuanVideo15Config
 from fastvideo.configs.models.dits.stepvideo import StepVideoConfig
 from fastvideo.configs.models.dits.wanvideo import WanVideoConfig
 
 __all__ = [
-    "HunyuanVideoConfig", "WanVideoConfig", "StepVideoConfig",
-    "CosmosVideoConfig"
+    "HunyuanVideoConfig", "HunyuanVideo15Config", "WanVideoConfig",
+    "StepVideoConfig", "CosmosVideoConfig", "Cosmos25VideoConfig"
 ]

fastvideo/configs/models/dits/base.py

@@ -23,6 +23,8 @@ class DiTArchConfig(ArchConfig):
     hidden_size: int = 0
     num_attention_heads: int = 0
     num_channels_latents: int = 0
+    in_channels: int = 0
+    out_channels: int = 0
     exclude_lora_layers: list[str] = field(default_factory=list)
     boundary_ratio: float | None = None
 

fastvideo/configs/models/dits/cosmos2_5.py

@@ -0,0 +1,181 @@
+# SPDX-License-Identifier: Apache-2.0
+from dataclasses import dataclass, field
+
+from fastvideo.configs.models.dits.base import DiTArchConfig, DiTConfig
+
+
+def is_transformer_blocks(n: str, m) -> bool:
+    return "transformer_blocks" in n and str.isdigit(n.split(".")[-1])
+
+
+@dataclass
+class Cosmos25ArchConfig(DiTArchConfig):
+    """Configuration for Cosmos 2.5 architecture (MiniTrainDIT)."""
+
+    _fsdp_shard_conditions: list = field(
+        default_factory=lambda: [is_transformer_blocks])
+
+    param_names_mapping: dict = field(
+        default_factory=lambda: {
+            # Remove "net." prefix and map official structure to FastVideo
+            # Patch embedding: net.x_embedder.proj.1.weight -> patch_embed.proj.weight
+            r"^net\.x_embedder\.proj\.1\.(.*)$":
+            r"patch_embed.proj.\1",
+
+            # Time embedding: net.t_embedder.1.linear_1.weight -> time_embed.t_embedder.linear_1.weight
+            r"^net\.t_embedder\.1\.linear_1\.(.*)$":
+            r"time_embed.t_embedder.linear_1.\1",
+            r"^net\.t_embedder\.1\.linear_2\.(.*)$":
+            r"time_embed.t_embedder.linear_2.\1",
+            # Time embedding norm: net.t_embedding_norm.weight -> time_embed.norm.weight
+            # Note: This also handles _extra_state if present
+            r"^net\.t_embedding_norm\.(.*)$":
+            r"time_embed.norm.\1",
+
+            # Cross-attention projection (optional): net.crossattn_proj.0.weight -> crossattn_proj.0.weight
+            r"^net\.crossattn_proj\.0\.weight$":
+            r"crossattn_proj.0.weight",
+            r"^net\.crossattn_proj\.0\.bias$":
+            r"crossattn_proj.0.bias",
+
+            # Transformer blocks: net.blocks.N -> transformer_blocks.N
+            # Self-attention (self_attn -> attn1)
+            r"^net\.blocks\.(\d+)\.self_attn\.q_proj\.(.*)$":
+            r"transformer_blocks.\1.attn1.to_q.\2",
+            r"^net\.blocks\.(\d+)\.self_attn\.k_proj\.(.*)$":
+            r"transformer_blocks.\1.attn1.to_k.\2",
+            r"^net\.blocks\.(\d+)\.self_attn\.v_proj\.(.*)$":
+            r"transformer_blocks.\1.attn1.to_v.\2",
+            r"^net\.blocks\.(\d+)\.self_attn\.output_proj\.(.*)$":
+            r"transformer_blocks.\1.attn1.to_out.\2",
+            r"^net\.blocks\.(\d+)\.self_attn\.q_norm\.weight$":
+            r"transformer_blocks.\1.attn1.norm_q.weight",
+            r"^net\.blocks\.(\d+)\.self_attn\.k_norm\.weight$":
+            r"transformer_blocks.\1.attn1.norm_k.weight",
+            # RMSNorm _extra_state keys (internal PyTorch state, will be recomputed automatically)
+            r"^net\.blocks\.(\d+)\.self_attn\.q_norm\._extra_state$":
+            r"transformer_blocks.\1.attn1.norm_q._extra_state",
+            r"^net\.blocks\.(\d+)\.self_attn\.k_norm\._extra_state$":
+            r"transformer_blocks.\1.attn1.norm_k._extra_state",
+
+            # Cross-attention (cross_attn -> attn2)
+            r"^net\.blocks\.(\d+)\.cross_attn\.q_proj\.(.*)$":
+            r"transformer_blocks.\1.attn2.to_q.\2",
+            r"^net\.blocks\.(\d+)\.cross_attn\.k_proj\.(.*)$":
+            r"transformer_blocks.\1.attn2.to_k.\2",
+            r"^net\.blocks\.(\d+)\.cross_attn\.v_proj\.(.*)$":
+            r"transformer_blocks.\1.attn2.to_v.\2",
+            r"^net\.blocks\.(\d+)\.cross_attn\.output_proj\.(.*)$":
+            r"transformer_blocks.\1.attn2.to_out.\2",
+            r"^net\.blocks\.(\d+)\.cross_attn\.q_norm\.weight$":
+            r"transformer_blocks.\1.attn2.norm_q.weight",
+            r"^net\.blocks\.(\d+)\.cross_attn\.k_norm\.weight$":
+            r"transformer_blocks.\1.attn2.norm_k.weight",
+            # RMSNorm _extra_state keys for cross-attention
+            r"^net\.blocks\.(\d+)\.cross_attn\.q_norm\._extra_state$":
+            r"transformer_blocks.\1.attn2.norm_q._extra_state",
+            r"^net\.blocks\.(\d+)\.cross_attn\.k_norm\._extra_state$":
+            r"transformer_blocks.\1.attn2.norm_k._extra_state",
+
+            # MLP: net.blocks.N.mlp.layer1 -> transformer_blocks.N.mlp.fc_in
+            r"^net\.blocks\.(\d+)\.mlp\.layer1\.(.*)$":
+            r"transformer_blocks.\1.mlp.fc_in.\2",
+            r"^net\.blocks\.(\d+)\.mlp\.layer2\.(.*)$":
+            r"transformer_blocks.\1.mlp.fc_out.\2",
+
+            # AdaLN-LoRA modulations: net.blocks.N.adaln_modulation_* -> transformer_blocks.N.adaln_modulation_*
+            # These are now at the block level, not inside norm layers
+            r"^net\.blocks\.(\d+)\.adaln_modulation_self_attn\.1\.(.*)$":
+            r"transformer_blocks.\1.adaln_modulation_self_attn.1.\2",
+            r"^net\.blocks\.(\d+)\.adaln_modulation_self_attn\.2\.(.*)$":
+            r"transformer_blocks.\1.adaln_modulation_self_attn.2.\2",
+            r"^net\.blocks\.(\d+)\.adaln_modulation_cross_attn\.1\.(.*)$":
+            r"transformer_blocks.\1.adaln_modulation_cross_attn.1.\2",
+            r"^net\.blocks\.(\d+)\.adaln_modulation_cross_attn\.2\.(.*)$":
+            r"transformer_blocks.\1.adaln_modulation_cross_attn.2.\2",
+            r"^net\.blocks\.(\d+)\.adaln_modulation_mlp\.1\.(.*)$":
+            r"transformer_blocks.\1.adaln_modulation_mlp.1.\2",
+            r"^net\.blocks\.(\d+)\.adaln_modulation_mlp\.2\.(.*)$":
+            r"transformer_blocks.\1.adaln_modulation_mlp.2.\2",
+
+            # Layer norms: net.blocks.N.layer_norm_* -> transformer_blocks.N.norm*.norm
+            r"^net\.blocks\.(\d+)\.layer_norm_self_attn\._extra_state$":
+            r"transformer_blocks.\1.norm1.norm._extra_state",
+            r"^net\.blocks\.(\d+)\.layer_norm_cross_attn\._extra_state$":
+            r"transformer_blocks.\1.norm2.norm._extra_state",
+            r"^net\.blocks\.(\d+)\.layer_norm_mlp\._extra_state$":
+            r"transformer_blocks.\1.norm3.norm._extra_state",
+
+            # Final layer: net.final_layer.linear -> final_layer.proj_out
+            r"^net\.final_layer\.linear\.(.*)$":
+            r"final_layer.proj_out.\1",
+            # Final layer AdaLN-LoRA: net.final_layer.adaln_modulation -> final_layer.linear_*
+            r"^net\.final_layer\.adaln_modulation\.1\.(.*)$":
+            r"final_layer.linear_1.\1",
+            r"^net\.final_layer\.adaln_modulation\.2\.(.*)$":
+            r"final_layer.linear_2.\1",
+
+            # Note: The following keys from official checkpoint are NOT mapped and can be safely ignored:
+            # - net.pos_embedder.* (seq, dim_spatial_range, dim_temporal_range) - These are computed dynamically
+            #   in FastVideo's Cosmos25RotaryPosEmbed forward() method, so they don't need to be loaded.
+            # - net.accum_* keys (training metadata) - These are skipped during checkpoint loading.
+        })
+
+    lora_param_names_mapping: dict = field(
+        default_factory=lambda: {
+            r"^transformer_blocks\.(\d+)\.attn1\.to_q\.(.*)$":
+            r"transformer_blocks.\1.attn1.to_q.\2",
+            r"^transformer_blocks\.(\d+)\.attn1\.to_k\.(.*)$":
+            r"transformer_blocks.\1.attn1.to_k.\2",
+            r"^transformer_blocks\.(\d+)\.attn1\.to_v\.(.*)$":
+            r"transformer_blocks.\1.attn1.to_v.\2",
+            r"^transformer_blocks\.(\d+)\.attn1\.to_out\.(.*)$":
+            r"transformer_blocks.\1.attn1.to_out.\2",
+            r"^transformer_blocks\.(\d+)\.attn2\.to_q\.(.*)$":
+            r"transformer_blocks.\1.attn2.to_q.\2",
+            r"^transformer_blocks\.(\d+)\.attn2\.to_k\.(.*)$":
+            r"transformer_blocks.\1.attn2.to_k.\2",
+            r"^transformer_blocks\.(\d+)\.attn2\.to_v\.(.*)$":
+            r"transformer_blocks.\1.attn2.to_v.\2",
+            r"^transformer_blocks\.(\d+)\.attn2\.to_out\.(.*)$":
+            r"transformer_blocks.\1.attn2.to_out.\2",
+            r"^transformer_blocks\.(\d+)\.mlp\.(.*)$":
+            r"transformer_blocks.\1.mlp.\2",
+        })
+
+    # Cosmos 2.5 specific config parameters
+    in_channels: int = 16
+    out_channels: int = 16
+    num_attention_heads: int = 16
+    attention_head_dim: int = 128  # 2048 / 16
+    num_layers: int = 28
+    mlp_ratio: float = 4.0
+    text_embed_dim: int = 1024
+    adaln_lora_dim: int = 256
+    use_adaln_lora: bool = True
+    max_size: tuple[int, int, int] = (128, 240, 240)
+    patch_size: tuple[int, int, int] = (1, 2, 2)
+    rope_scale: tuple[float, float, float] = (1.0, 3.0, 3.0)  # T, H, W scaling
+    concat_padding_mask: bool = True
+    extra_pos_embed_type: str | None = None  # "learnable" or None
+    # Note: Official checkpoint has use_crossattn_projection=True with 100K-dim input from Qwen 7B.
+    # When enabled, must provide 100,352-dim embeddings to match the projection layer in checkpoint.
+    use_crossattn_projection: bool = False
+    crossattn_proj_in_channels: int = 100352  # Qwen 7B embedding dimension
+    rope_enable_fps_modulation: bool = True
+    qk_norm: str = "rms_norm"
+    eps: float = 1e-6
+    exclude_lora_layers: list[str] = field(default_factory=lambda: ["embedder"])
+
+    def __post_init__(self):
+        super().__post_init__()
+        self.out_channels = self.out_channels or self.in_channels
+        self.hidden_size = self.num_attention_heads * self.attention_head_dim
+        self.num_channels_latents = self.in_channels
+
+
+@dataclass
+class Cosmos25VideoConfig(DiTConfig):
+    """Configuration for Cosmos 2.5 video generation model."""
+    arch_config: DiTArchConfig = field(default_factory=Cosmos25ArchConfig)
+    prefix: str = "Cosmos25"

fastvideo/configs/models/dits/hunyuanvideo15.py

@@ -0,0 +1,157 @@
+# SPDX-License-Identifier: Apache-2.0
+from dataclasses import dataclass, field
+
+from fastvideo.configs.models.dits.base import DiTArchConfig, DiTConfig
+
+
+def is_double_block(n: str, m) -> bool:
+    return "double_blocks" in n and str.isdigit(n.split(".")[-1])
+
+
+def is_refiner_block(n: str, m) -> bool:
+    return "refiner" in n and str.isdigit(n.split(".")[-1])
+
+
+def is_txt_in(n: str, m) -> bool:
+    return n.split(".")[-1] == "txt_in"
+
+
+@dataclass
+class HunyuanVideo15ArchConfig(DiTArchConfig):
+    _fsdp_shard_conditions: list = field(
+        default_factory=lambda: [is_double_block, is_refiner_block])
+
+    _compile_conditions: list = field(
+        default_factory=lambda: [is_double_block, is_refiner_block, is_txt_in])
+
+    param_names_mapping: dict = field(
+        default_factory=lambda: {
+            # 1. context_embedder.time_text_embed submodules (specific rules, applied first):
+            r"^context_embedder\.time_text_embed\.timestep_embedder\.linear_1\.(.*)$":
+            r"txt_in.t_embedder.mlp.fc_in.\1",
+            r"^context_embedder\.time_text_embed\.timestep_embedder\.linear_2\.(.*)$":
+            r"txt_in.t_embedder.mlp.fc_out.\1",
+            r"^context_embedder\.proj_in\.(.*)$":
+            r"txt_in.input_embedder.\1",
+            r"^context_embedder\.time_text_embed\.text_embedder\.linear_1\.(.*)$":
+            r"txt_in.c_embedder.fc_in.\1",
+            r"^context_embedder\.time_text_embed\.text_embedder\.linear_2\.(.*)$":
+            r"txt_in.c_embedder.fc_out.\1",
+            r"^context_embedder\.token_refiner\.refiner_blocks\.(\d+)\.norm1\.(.*)$":
+            r"txt_in.refiner_blocks.\1.norm1.\2",
+            r"^context_embedder\.token_refiner\.refiner_blocks\.(\d+)\.norm2\.(.*)$":
+            r"txt_in.refiner_blocks.\1.norm2.\2",
+            r"^context_embedder\.token_refiner\.refiner_blocks\.(\d+)\.attn\.to_q\.(.*)$":
+            (r"txt_in.refiner_blocks.\1.self_attn_qkv.\2", 0, 3),
+            r"^context_embedder\.token_refiner\.refiner_blocks\.(\d+)\.attn\.to_k\.(.*)$":
+            (r"txt_in.refiner_blocks.\1.self_attn_qkv.\2", 1, 3),
+            r"^context_embedder\.token_refiner\.refiner_blocks\.(\d+)\.attn\.to_v\.(.*)$":
+            (r"txt_in.refiner_blocks.\1.self_attn_qkv.\2", 2, 3),
+            r"^context_embedder\.token_refiner\.refiner_blocks\.(\d+)\.attn\.to_out\.0\.(.*)$":
+            r"txt_in.refiner_blocks.\1.self_attn_proj.\2",
+            r"^context_embedder\.token_refiner\.refiner_blocks\.(\d+)\.ff\.net\.0(?:\.proj)?\.(.*)$":
+            r"txt_in.refiner_blocks.\1.mlp.fc_in.\2",
+            r"^context_embedder\.token_refiner\.refiner_blocks\.(\d+)\.ff\.net\.2(?:\.proj)?\.(.*)$":
+            r"txt_in.refiner_blocks.\1.mlp.fc_out.\2",
+            r"^context_embedder\.token_refiner\.refiner_blocks\.(\d+)\.norm_out\.linear\.(.*)$":
+            r"txt_in.refiner_blocks.\1.adaLN_modulation.linear.\2",
+
+            # 2. txt_in_2 mapping:
+            r"^context_embedder_2\.(.*)$":
+            r"txt_in_2.\1",
+
+            # 3. x_embedder mapping:
+            r"^x_embedder\.proj\.(.*)$":
+            r"img_in.proj.\1",
+
+            # 4. Top-level time_text_embed mappings:
+            r"^time_embed\.timestep_embedder\.linear_1\.(.*)$":
+            r"time_in.timestep_embedder.mlp.fc_in.\1",
+            r"^time_embed\.timestep_embedder\.linear_2\.(.*)$":
+            r"time_in.timestep_embedder.mlp.fc_out.\1",
+            r"^time_embed\.timestep_embedder_r\.linear_1\.(.*)$":
+            r"time_in.timestep_embedder_r.mlp.fc_in.\1",
+            r"^time_embed\.timestep_embedder_r\.linear_2\.(.*)$":
+            r"time_in.timestep_embedder_r.mlp.fc_out.\1",
+
+            # 5. transformer_blocks mapping:
+            r"^transformer_blocks\.(\d+)\.norm1\.linear\.(.*)$":
+            r"double_blocks.\1.img_mod.linear.\2",
+            r"^transformer_blocks\.(\d+)\.norm1_context\.linear\.(.*)$":
+            r"double_blocks.\1.txt_mod.linear.\2",
+            r"^transformer_blocks\.(\d+)\.attn\.norm_q\.(.*)$":
+            r"double_blocks.\1.img_attn_q_norm.\2",
+            r"^transformer_blocks\.(\d+)\.attn\.norm_k\.(.*)$":
+            r"double_blocks.\1.img_attn_k_norm.\2",
+            r"^transformer_blocks\.(\d+)\.attn\.to_q\.(.*)$":
+            (r"double_blocks.\1.img_attn_qkv.\2", 0, 3),
+            r"^transformer_blocks\.(\d+)\.attn\.to_k\.(.*)$":
+            (r"double_blocks.\1.img_attn_qkv.\2", 1, 3),
+            r"^transformer_blocks\.(\d+)\.attn\.to_v\.(.*)$":
+            (r"double_blocks.\1.img_attn_qkv.\2", 2, 3),
+            r"^transformer_blocks\.(\d+)\.attn\.add_q_proj\.(.*)$":
+            (r"double_blocks.\1.txt_attn_qkv.\2", 0, 3),
+            r"^transformer_blocks\.(\d+)\.attn\.add_k_proj\.(.*)$":
+            (r"double_blocks.\1.txt_attn_qkv.\2", 1, 3),
+            r"^transformer_blocks\.(\d+)\.attn\.add_v_proj\.(.*)$":
+            (r"double_blocks.\1.txt_attn_qkv.\2", 2, 3),
+            r"^transformer_blocks\.(\d+)\.attn\.to_out\.0\.(.*)$":
+            r"double_blocks.\1.img_attn_proj.\2",
+            # Corrected: merge attn.to_add_out into the main projection.
+            r"^transformer_blocks\.(\d+)\.attn\.to_add_out\.(.*)$":
+            r"double_blocks.\1.txt_attn_proj.\2",
+            r"^transformer_blocks\.(\d+)\.attn\.norm_added_q\.(.*)$":
+            r"double_blocks.\1.txt_attn_q_norm.\2",
+            r"^transformer_blocks\.(\d+)\.attn\.norm_added_k\.(.*)$":
+            r"double_blocks.\1.txt_attn_k_norm.\2",
+            r"^transformer_blocks\.(\d+)\.ff\.net\.0(?:\.proj)?\.(.*)$":
+            r"double_blocks.\1.img_mlp.fc_in.\2",
+            r"^transformer_blocks\.(\d+)\.ff\.net\.2(?:\.proj)?\.(.*)$":
+            r"double_blocks.\1.img_mlp.fc_out.\2",
+            r"^transformer_blocks\.(\d+)\.ff_context\.net\.0(?:\.proj)?\.(.*)$":
+            r"double_blocks.\1.txt_mlp.fc_in.\2",
+            r"^transformer_blocks\.(\d+)\.ff_context\.net\.2(?:\.proj)?\.(.*)$":
+            r"double_blocks.\1.txt_mlp.fc_out.\2",
+
+            # 7. Final layers mapping:
+            r"^norm_out\.linear\.(.*)$":
+            r"final_layer.adaLN_modulation.linear.\1",
+            r"^proj_out\.(.*)$":
+            r"final_layer.linear.\1",
+        })
+
+    # Reverse mapping for saving checkpoints: custom -> hf
+    reverse_param_names_mapping: dict = field(default_factory=lambda: {})
+
+    in_channels: int = 65
+    out_channels: int = 32
+    num_attention_heads: int = 16
+    attention_head_dim: int = 128
+    num_layers: int = 54
+    num_refiner_layers: int = 2
+    mlp_ratio: float = 4.0
+    patch_size: int = 1
+    patch_size_t: int = 1
+    qk_norm: str = "rms_norm"
+    text_embed_dim: int = 3584
+    text_embed_2_dim: int = 1472
+    image_embed_dim: int = 1152
+    rope_theta: float = 256.0
+    rope_axes_dim: tuple[int, ...] = (16, 56, 56)
+    target_size: int = 640
+    task_type: str = "i2v"
+    use_meanflow: bool = False
+    exclude_lora_layers: list[str] = field(
+        default_factory=lambda: ["img_in", "txt_in", "time_in", "vector_in"])
+
+    def __post_init__(self):
+        super().__post_init__()
+        self.hidden_size: int = self.attention_head_dim * self.num_attention_heads
+        self.num_channels_latents: int = self.out_channels
+
+
+@dataclass
+class HunyuanVideo15Config(DiTConfig):
+    arch_config: DiTArchConfig = field(default_factory=HunyuanVideo15ArchConfig)
+
+    prefix: str = "Hunyuan15"

fastvideo/configs/models/dits/matrixgame.py

@@ -0,0 +1,83 @@
+from dataclasses import dataclass, field
+from fastvideo.configs.models.dits.wanvideo import WanVideoArchConfig, WanVideoConfig
+
+
+@dataclass
+class MatrixGameWanVideoArchConfig(WanVideoArchConfig):
+    # Override param_names_mapping to remove patch_embedding transformation
+    # because MatrixGame checkpoints already have patch_embedding.proj format
+    param_names_mapping: dict = field(
+        default_factory=lambda: {
+            # Removed: r"^patch_embedding\.(.*)$": r"patch_embedding.proj.\1"
+            # because checkpoint already has correct format
+            r"^condition_embedder\.text_embedder\.linear_1\.(.*)$":
+            r"condition_embedder.text_embedder.fc_in.\1",
+            r"^condition_embedder\.text_embedder\.linear_2\.(.*)$":
+            r"condition_embedder.text_embedder.fc_out.\1",
+            r"^condition_embedder\.time_embedder\.linear_1\.(.*)$":
+            r"condition_embedder.time_embedder.mlp.fc_in.\1",
+            r"^condition_embedder\.time_embedder\.linear_2\.(.*)$":
+            r"condition_embedder.time_embedder.mlp.fc_out.\1",
+            r"^condition_embedder\.time_proj\.(.*)$":
+            r"condition_embedder.time_modulation.linear.\1",
+            r"^condition_embedder\.image_embedder\.ff\.net\.0\.proj\.(.*)$":
+            r"condition_embedder.image_embedder.ff.fc_in.\1",
+            r"^condition_embedder\.image_embedder\.ff\.net\.2\.(.*)$":
+            r"condition_embedder.image_embedder.ff.fc_out.\1",
+            r"^blocks\.(\d+)\.attn1\.to_q\.(.*)$":
+            r"blocks.\1.to_q.\2",
+            r"^blocks\.(\d+)\.attn1\.to_k\.(.*)$":
+            r"blocks.\1.to_k.\2",
+            r"^blocks\.(\d+)\.attn1\.to_v\.(.*)$":
+            r"blocks.\1.to_v.\2",
+            r"^blocks\.(\d+)\.attn1\.to_out\.0\.(.*)$":
+            r"blocks.\1.to_out.\2",
+            r"^blocks\.(\d+)\.attn1\.norm_q\.(.*)$":
+            r"blocks.\1.norm_q.\2",
+            r"^blocks\.(\d+)\.attn1\.norm_k\.(.*)$":
+            r"blocks.\1.norm_k.\2",
+            r"^blocks\.(\d+)\.attn2\.to_out\.0\.(.*)$":
+            r"blocks.\1.attn2.to_out.\2",
+            r"^blocks\.(\d+)\.ffn\.net\.0\.proj\.(.*)$":
+            r"blocks.\1.ffn.fc_in.\2",
+            r"^blocks\.(\d+)\.ffn\.net\.2\.(.*)$":
+            r"blocks.\1.ffn.fc_out.\2",
+            r"^blocks\.(\d+)\.norm2\.(.*)$":
+            r"blocks.\1.self_attn_residual_norm.norm.\2",
+        })
+
+    action_config: dict = field(
+        default_factory=lambda: {
+            "blocks": list(range(15)),
+            "enable_mouse": True,
+            "enable_keyboard": True,
+            "heads_num": 16,
+            "hidden_size": 128,
+            "img_hidden_size": 1536,
+            "keyboard_dim_in": 4,
+            "keyboard_hidden_dim": 1024,
+            "mouse_dim_in": 2,
+            "mouse_hidden_dim": 1024,
+            "mouse_qk_dim_list": [8, 28, 28],
+            "patch_size": [1, 2, 2],
+            "qk_norm": True,
+            "qkv_bias": False,
+            "rope_dim_list": [8, 28, 28],
+            "rope_theta": 256,
+            "vae_time_compression_ratio": 4,
+            "windows_size": 3,
+        })
+
+    local_attn_size: int = -1
+    sink_size: int = 0
+    num_frames_per_block: int = 3
+    text_len: int = 512
+    text_dim: int = 0
+    image_dim: int = 1280
+
+
+@dataclass
+class MatrixGameWanVideoConfig(WanVideoConfig):
+    arch_config: MatrixGameWanVideoArchConfig = field(
+        default_factory=MatrixGameWanVideoArchConfig)
+    prefix: str = "Wan"

fastvideo/configs/models/encoders/__init__.py

@@ -6,9 +6,11 @@ from fastvideo.configs.models.encoders.clip import (
     CLIPTextConfig, CLIPVisionConfig, WAN2_1ControlCLIPVisionConfig)
 from fastvideo.configs.models.encoders.llama import LlamaConfig
 from fastvideo.configs.models.encoders.t5 import T5Config, T5LargeConfig
+from fastvideo.configs.models.encoders.qwen2_5 import Qwen2_5_VLConfig
 
 __all__ = [
     "EncoderConfig", "TextEncoderConfig", "ImageEncoderConfig",
     "BaseEncoderOutput", "CLIPTextConfig", "CLIPVisionConfig",
-    "WAN2_1ControlCLIPVisionConfig", "LlamaConfig", "T5Config", "T5LargeConfig"
+    "WAN2_1ControlCLIPVisionConfig", "LlamaConfig", "T5Config", "T5LargeConfig",
+    "Qwen2_5_VLConfig"
 ]

fastvideo/configs/models/encoders/base.py

@@ -72,6 +72,7 @@ class EncoderConfig(ModelConfig):
 @dataclass
 class TextEncoderConfig(EncoderConfig):
     arch_config: ArchConfig = field(default_factory=TextEncoderArchConfig)
+    is_chat_model: bool = False
 
 
 @dataclass

fastvideo/configs/models/encoders/qwen2_5.py

@@ -0,0 +1,93 @@
+# SPDX-License-Identifier: Apache-2.0
+from dataclasses import dataclass, field
+
+from fastvideo.configs.models.encoders.base import (TextEncoderArchConfig,
+                                                    TextEncoderConfig)
+
+
+def _is_transformer_layer(n: str, m) -> bool:
+    return "layers" in n and str.isdigit(n.split(".")[-1])
+
+
+def _is_embeddings(n: str, m) -> bool:
+    return n.endswith("embed_tokens")
+
+
+def _is_final_norm(n: str, m) -> bool:
+    return n.endswith("norm")
+
+
+@dataclass
+class Qwen2_5_VLArchConfig(TextEncoderArchConfig):
+    vocab_size: int = 152064
+    hidden_size: int = 8192
+    intermediate_size: int = 29568
+    num_hidden_layers: int = 80
+    num_attention_heads: int = 64
+    num_key_value_heads: int = 8
+    hidden_act: str = "silu"
+    max_position_embeddings: int = 32768
+    initializer_range: float = 0.02
+    rms_norm_eps: float = 1e-05
+    use_cache: bool = True
+    tie_word_embeddings: bool = False
+    rope_theta: float = 1000000.0
+    use_sliding_window: bool = False
+    sliding_window: int | None = 4096
+    max_window_layers: int = 80
+    layer_types: list = field(default_factory=list)
+    attention_dropout: float = 0.0
+    rope_scaling: dict | None = None
+    bos_token_id: int | None = None
+    eos_token_id: int | None = None
+    pad_token_id: int | None = None
+    vision_token_id: int = 151654
+    model_type: str = "qwen2_5_vl_text"
+    dtype: str = "bfloat16"
+
+    stacked_params_mapping: list[tuple[str, str, str
+                                       | int]] = field(default_factory=lambda: [
+                                           (".qkv_proj", ".q_proj", "q"),
+                                           (".qkv_proj", ".k_proj", "k"),
+                                           (".qkv_proj", ".v_proj", "v"),
+                                           (".gate_up_proj", ".gate_proj", 0),
+                                           (".gate_up_proj", ".up_proj", 1),
+                                       ])
+    _fsdp_shard_conditions: list = field(
+        default_factory=lambda:
+        [_is_transformer_layer, _is_embeddings, _is_final_norm])
+
+    def __post_init__(self):
+        super().__post_init__()
+        self.sliding_window = self.sliding_window if self.use_sliding_window else None
+        # for backward compatibility
+        if self.num_key_value_heads is None:
+            self.num_key_value_heads = self.num_attention_heads
+        if self.layer_types is None:
+            self.layer_types = [
+                "sliding_attention" if self.sliding_window is not None
+                and i >= self.max_window_layers else "full_attention"
+                for i in range(self.num_hidden_layers)
+            ]
+        if self.rope_scaling is not None and "type" in self.rope_scaling:
+            if self.rope_scaling["type"] == "mrope":
+                self.rope_scaling["type"] = "default"
+            self.rope_scaling["rope_type"] = self.rope_scaling["type"]
+
+        self.tokenizer_kwargs = {
+            "add_generation_prompt": True,
+            "tokenize": True,
+            "return_dict": True,
+            "padding": "max_length",
+            "max_length": 1000 + 108,
+            "truncation": True,
+            "return_tensors": "pt",
+        }
+
+
+@dataclass
+class Qwen2_5_VLConfig(TextEncoderConfig):
+    arch_config: TextEncoderArchConfig = field(
+        default_factory=Qwen2_5_VLArchConfig)
+    prefix: str = "qwen2_5_vl"
+    is_chat_model: bool = True

fastvideo/configs/models/encoders/t5.py

@@ -40,6 +40,8 @@ class T5ArchConfig(TextEncoderArchConfig):
     eos_token_id: int = 1
     classifier_dropout: float = 0.0
     text_len: int = 512
+    dtype: str | None = None
+    gradient_checkpointing: bool = False
     stacked_params_mapping: list[tuple[str, str,
                                        str]] = field(default_factory=lambda: [
                                            # (param_name, shard_name, shard_id)
@@ -68,6 +70,7 @@ class T5ArchConfig(TextEncoderArchConfig):
             "return_attention_mask": True,
             "return_tensors": "pt",
         }
+        self.hidden_size = self.d_model
 
 
 @dataclass

fastvideo/configs/models/vaes/__init__.py

@@ -1,5 +1,6 @@
 from fastvideo.configs.models.vaes.cosmosvae import CosmosVAEConfig
 from fastvideo.configs.models.vaes.hunyuanvae import HunyuanVAEConfig
+from fastvideo.configs.models.vaes.hunyuan15vae import Hunyuan15VAEConfig
 from fastvideo.configs.models.vaes.stepvideovae import StepVideoVAEConfig
 from fastvideo.configs.models.vaes.wanvae import WanVAEConfig
 
@@ -8,4 +9,5 @@ __all__ = [
     "WanVAEConfig",
     "StepVideoVAEConfig",
     "CosmosVAEConfig",
+    "Hunyuan15VAEConfig",
 ]

fastvideo/configs/models/vaes/hunyuan15vae.py

@@ -0,0 +1,27 @@
+# SPDX-License-Identifier: Apache-2.0
+from dataclasses import dataclass, field
+
+from fastvideo.configs.models.vaes.base import VAEArchConfig, VAEConfig
+
+
+@dataclass
+class Hunyuan15VAEArchConfig(VAEArchConfig):
+    in_channels: int = 3
+    out_channels: int = 3
+    latent_channels: int = 32
+    block_out_channels: tuple[int, ...] = (128, 256, 512, 1024, 1024)
+    layers_per_block: int = 2
+    spatial_compression_ratio: int = 16
+    temporal_compression_ratio: int = 4
+    downsample_match_channel: bool = True
+    upsample_match_channel: bool = True
+    scaling_factor: float = 1.03682
+
+    def __post_init__(self):
+        self.spatial_compression_ratio: int = 2**(len(self.block_out_channels) -
+                                                  1)
+
+
+@dataclass
+class Hunyuan15VAEConfig(VAEConfig):
+    arch_config: VAEArchConfig = field(default_factory=Hunyuan15VAEArchConfig)

fastvideo/configs/pipelines/__init__.py

@@ -2,6 +2,7 @@ from fastvideo.configs.pipelines.base import (PipelineConfig,
                                               SlidingTileAttnConfig)
 from fastvideo.configs.pipelines.cosmos import CosmosConfig
 from fastvideo.configs.pipelines.hunyuan import FastHunyuanConfig, HunyuanConfig
+from fastvideo.configs.pipelines.hunyuan15 import Hunyuan15T2V480PConfig, Hunyuan15T2V720PConfig
 from fastvideo.configs.pipelines.registry import (
     get_pipeline_config_cls_from_name)
 from fastvideo.configs.pipelines.stepvideo import StepVideoT2VConfig
@@ -11,8 +12,8 @@ from fastvideo.configs.pipelines.wan import (SelfForcingWanT2V480PConfig,
 
 __all__ = [
     "HunyuanConfig", "FastHunyuanConfig", "PipelineConfig",
-    "SlidingTileAttnConfig", "WanT2V480PConfig", "WanI2V480PConfig",
-    "WanT2V720PConfig", "WanI2V720PConfig", "StepVideoT2VConfig",
-    "SelfForcingWanT2V480PConfig", "CosmosConfig",
-    "get_pipeline_config_cls_from_name"
+    "Hunyuan15T2V480PConfig", "Hunyuan15T2V720PConfig", "SlidingTileAttnConfig",
+    "WanT2V480PConfig", "WanI2V480PConfig", "WanT2V720PConfig",
+    "WanI2V720PConfig", "StepVideoT2VConfig", "SelfForcingWanT2V480PConfig",
+    "CosmosConfig", "get_pipeline_config_cls_from_name"
 ]

fastvideo/configs/pipelines/base.py

@@ -45,6 +45,7 @@ class PipelineConfig:
     embedded_cfg_scale: float = 6.0
     flow_shift: float | None = None
     disable_autocast: bool = False
+    is_causal: bool = False
 
     # Model configuration
     dit_config: DiTConfig = field(default_factory=DiTConfig)

fastvideo/configs/pipelines/hunyuan15.py

@@ -0,0 +1,139 @@
+# SPDX-License-Identifier: Apache-2.0
+from collections.abc import Callable
+from dataclasses import dataclass, field
+from typing import Any
+import re
+
+import torch
+
+from fastvideo.configs.models import DiTConfig, EncoderConfig, VAEConfig
+from fastvideo.configs.models.dits import HunyuanVideo15Config
+from fastvideo.configs.models.encoders import (BaseEncoderOutput,
+                                               Qwen2_5_VLConfig, T5Config)
+from fastvideo.configs.models.vaes import Hunyuan15VAEConfig
+from fastvideo.configs.pipelines.base import PipelineConfig
+
+PROMPT_TEMPLATE_TOKEN_LENGTH = 108
+
+PROMPT_TEMPLATE_ENCODE_VIDEO = "You are a helpful assistant. Describe the video by detailing the following aspects: \
+        1. The main content and theme of the video. \
+        2. The color, shape, size, texture, quantity, text, and spatial relationships of the objects. \
+        3. Actions, events, behaviors temporal relationships, physical movement changes of the objects. \
+        4. background environment, light, style and atmosphere. \
+        5. camera angles, movements, and transitions used in the video."
+
+
+def extract_glyph_texts(prompt: str) -> str | None:
+    """
+    Extract glyph texts from prompt using regex pattern.
+
+    Args:
+        prompt: Input prompt string
+
+    Returns:
+        List of extracted glyph texts
+    """
+    pattern = r"\"(.*?)\"|“(.*?)”"
+    matches = re.findall(pattern, prompt)
+    result = [match[0] or match[1] for match in matches]
+    result = list(dict.fromkeys(result)) if len(result) > 1 else result
+
+    if result:
+        formatted_result = ". ".join([f'Text "{text}"'
+                                      for text in result]) + ". "
+    else:
+        formatted_result = None
+
+    return formatted_result
+
+
+def format_text_input(prompt: str, system_message: str) -> list[dict[str, Any]]:
+    """
+    Apply text to template.
+
+    Args:
+        prompt (List[str]): Input text.
+        system_message (str): System message.
+
+    Returns:
+        List[Dict[str, Any]]: List of chat conversation.
+    """
+
+    template = [{
+        "role": "system",
+        "content": system_message
+    }, {
+        "role": "user",
+        "content": prompt if prompt else " "
+    }]
+
+    return template
+
+
+def qwen_preprocess_text(prompt: str) -> list[dict[str, Any]]:
+    output = format_text_input(prompt, PROMPT_TEMPLATE_ENCODE_VIDEO)
+    return output
+
+
+def qwen_postprocess_text(
+        outputs: BaseEncoderOutput,
+        mask: torch.tensor) -> tuple[torch.tensor, torch.tensor]:
+    assert outputs.hidden_states is not None
+    output = outputs.hidden_states[-3]
+    output = output[:, PROMPT_TEMPLATE_TOKEN_LENGTH:]
+    mask = mask[:, PROMPT_TEMPLATE_TOKEN_LENGTH:]
+    return output, mask
+
+
+def byt5_preprocess_text(prompt: str) -> str | None:
+    prompts = [prompt] if isinstance(prompt, str) else prompt
+    glyph_texts = [extract_glyph_texts(p) for p in prompts]
+    return glyph_texts[0]
+
+
+def byt5_postprocess_text(outputs: BaseEncoderOutput) -> torch.tensor:
+    return outputs.last_hidden_state
+
+
+@dataclass
+class Hunyuan15T2V480PConfig(PipelineConfig):
+    """Base configuration for HunYuan pipeline architecture."""
+
+    # HunyuanConfig-specific parameters with defaults
+    # DiT
+    dit_config: DiTConfig = field(default_factory=HunyuanVideo15Config)
+    # VAE
+    vae_config: VAEConfig = field(default_factory=Hunyuan15VAEConfig)
+    # Denoising stage
+    flow_shift: int = 5
+
+    # Text encoding stage
+    text_encoder_configs: tuple[EncoderConfig, ...] = field(
+        default_factory=lambda: (Qwen2_5_VLConfig(), T5Config()))
+    preprocess_text_funcs: tuple[Callable[[Any], Any], ...] = field(
+        default_factory=lambda: (qwen_preprocess_text, byt5_preprocess_text))
+    postprocess_text_funcs: tuple[Callable[..., Any], ...] = field(
+        default_factory=lambda: (qwen_postprocess_text, byt5_postprocess_text))
+
+    # Precision for each component
+    dit_precision: str = "bf16"
+    vae_precision: str = "fp16"
+    text_encoder_precisions: tuple[str, ...] = field(
+        default_factory=lambda: ("bf16", "fp32"))
+    text_encoder_crop_start: int = PROMPT_TEMPLATE_TOKEN_LENGTH
+    text_encoder_max_lengths: tuple[int, ...] = field(
+        default_factory=lambda: (1000 + PROMPT_TEMPLATE_TOKEN_LENGTH, 256))
+
+    vae_tiling: bool = True
+
+    def __post_init__(self):
+        self.vae_config.load_encoder = False
+        self.vae_config.load_decoder = True
+
+
+@dataclass
+class Hunyuan15T2V720PConfig(Hunyuan15T2V480PConfig):
+    """Base configuration for HunYuan pipeline architecture."""
+
+    # HunyuanConfig-specific parameters with defaults
+    flow_shift: int = 9

fastvideo/configs/pipelines/registry.py

@@ -7,6 +7,7 @@ from collections.abc import Callable
 from fastvideo.configs.pipelines.base import PipelineConfig
 from fastvideo.configs.pipelines.cosmos import CosmosConfig
 from fastvideo.configs.pipelines.hunyuan import FastHunyuanConfig, HunyuanConfig
+from fastvideo.configs.pipelines.hunyuan15 import Hunyuan15T2V480PConfig, Hunyuan15T2V720PConfig
 from fastvideo.configs.pipelines.stepvideo import StepVideoT2VConfig
 
 # isort: off
@@ -14,7 +15,8 @@ from fastvideo.configs.pipelines.wan import (
     FastWan2_1_T2V_480P_Config, FastWan2_2_TI2V_5B_Config,
     Wan2_2_I2V_A14B_Config, Wan2_2_T2V_A14B_Config, Wan2_2_TI2V_5B_Config,
     WanI2V480PConfig, WanI2V720PConfig, WanT2V480PConfig, WanT2V720PConfig,
-    SelfForcingWanT2V480PConfig, WANV2VConfig, SelfForcingWan2_2_T2V480PConfig)
+    SelfForcingWanT2V480PConfig, WANV2VConfig, SelfForcingWan2_2_T2V480PConfig,
+    MatrixGameI2V480PConfig)
 # isort: on
 from fastvideo.logger import init_logger
 from fastvideo.utils import (maybe_download_model_index,
@@ -26,6 +28,10 @@ logger = init_logger(__name__)
 PIPE_NAME_TO_CONFIG: dict[str, type[PipelineConfig]] = {
     "FastVideo/FastHunyuan-diffusers": FastHunyuanConfig,
     "hunyuanvideo-community/HunyuanVideo": HunyuanConfig,
+    "hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v":
+    Hunyuan15T2V480PConfig,
+    "hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_t2v":
+    Hunyuan15T2V720PConfig,
     "Wan-AI/Wan2.1-T2V-1.3B-Diffusers": WanT2V480PConfig,
     "weizhou03/Wan2.1-Fun-1.3B-InP-Diffusers": WanI2V480PConfig,
     "IRMChen/Wan2.1-Fun-1.3B-Control-Diffusers": WANV2VConfig,
@@ -39,22 +45,38 @@ PIPE_NAME_TO_CONFIG: dict[str, type[PipelineConfig]] = {
     "FastVideo/Wan2.1-VSA-T2V-14B-720P-Diffusers": WanT2V720PConfig,
     "wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers": SelfForcingWanT2V480PConfig,
     "rand0nmr/SFWan2.2-T2V-A14B-Diffusers": SelfForcingWan2_2_T2V480PConfig,
+    "FastVideo/SFWan2.2-I2V-A14B-Preview-Diffusers":
+    SelfForcingWan2_2_T2V480PConfig,
     "Wan-AI/Wan2.2-TI2V-5B-Diffusers": Wan2_2_TI2V_5B_Config,
     "Wan-AI/Wan2.2-T2V-A14B-Diffusers": Wan2_2_T2V_A14B_Config,
     "Wan-AI/Wan2.2-I2V-A14B-Diffusers": Wan2_2_I2V_A14B_Config,
     "nvidia/Cosmos-Predict2-2B-Video2World": CosmosConfig,
+    "FastVideo/Matrix-Game-2.0-Base-Diffusers": MatrixGameI2V480PConfig,
+    "FastVideo/Matrix-Game-2.0-GTA-Diffusers": MatrixGameI2V480PConfig,
+    "FastVideo/Matrix-Game-2.0-TempleRun-Diffusers": MatrixGameI2V480PConfig,
     # Add other specific weight variants
 }
 
 # For determining pipeline type from model ID
 PIPELINE_DETECTOR: dict[str, Callable[[str], bool]] = {
-    "hunyuan": lambda id: "hunyuan" in id.lower(),
-    "wanpipeline": lambda id: "wanpipeline" in id.lower(),
-    "wanimagetovideo": lambda id: "wanimagetovideo" in id.lower(),
-    "wandmdpipeline": lambda id: "wandmdpipeline" in id.lower(),
-    "wancausaldmdpipeline": lambda id: "wancausaldmdpipeline" in id.lower(),
-    "stepvideo": lambda id: "stepvideo" in id.lower(),
-    "cosmos": lambda id: "cosmos" in id.lower(),
+    "hunyuan":
+    lambda id: "hunyuan" in id.lower(),
+    "hunyuan15":
+    lambda id: "hunyuan15" in id.lower(),
+    "matrixgame":
+    lambda id: "matrix-game" in id.lower() or "matrixgame" in id.lower(),
+    "wanpipeline":
+    lambda id: "wanpipeline" in id.lower(),
+    "wanimagetovideo":
+    lambda id: "wanimagetovideo" in id.lower(),
+    "wandmdpipeline":
+    lambda id: "wandmdpipeline" in id.lower(),
+    "wancausaldmdpipeline":
+    lambda id: "wancausaldmdpipeline" in id.lower(),
+    "stepvideo":
+    lambda id: "stepvideo" in id.lower(),
+    "cosmos":
+    lambda id: "cosmos" in id.lower(),
     # Add other pipeline architecture detectors
 }
 
@@ -62,6 +84,9 @@ PIPELINE_DETECTOR: dict[str, Callable[[str], bool]] = {
 PIPELINE_FALLBACK_CONFIG: dict[str, type[PipelineConfig]] = {
     "hunyuan":
     HunyuanConfig,  # Base Hunyuan config as fallback for any Hunyuan variant
+    "matrixgame": MatrixGameI2V480PConfig,
+    "hunyuan15":
+    Hunyuan15T2V480PConfig,  # Base Hunyuan15 config as fallback for any Hunyuan15 variant
     "wanpipeline":
     WanT2V480PConfig,  # Base Wan config as fallback for any Wan variant
     "wanimagetovideo": WanI2V480PConfig,
@@ -107,6 +132,7 @@ def get_pipeline_config_cls_from_name(
     # First try exact match for specific weights
     if pipeline_name_or_path in PIPE_NAME_TO_CONFIG:
         pipeline_config_cls = PIPE_NAME_TO_CONFIG[pipeline_name_or_path]
+        return pipeline_config_cls
 
     # Try partial matches (for local paths that might include the weight ID)
     for registered_id, config_class in PIPE_NAME_TO_CONFIG.items():

fastvideo/configs/pipelines/wan.py

@@ -6,6 +6,7 @@ import torch
 
 from fastvideo.configs.models import DiTConfig, EncoderConfig, VAEConfig
 from fastvideo.configs.models.dits import WanVideoConfig
+from fastvideo.configs.models.dits.matrixgame import MatrixGameWanVideoConfig
 from fastvideo.configs.models.encoders import (BaseEncoderOutput,
                                                CLIPVisionConfig, T5Config,
                                                WAN2_1ControlCLIPVisionConfig)
@@ -190,3 +191,23 @@ class SelfForcingWan2_2_T2V480PConfig(Wan2_2_T2V_A14B_Config):
     def __post_init__(self) -> None:
         self.vae_config.load_encoder = True
         self.vae_config.load_decoder = True
+
+
+# =============================================
+# ============= Matrix Game ===================
+# =============================================
+@dataclass
+class MatrixGameI2V480PConfig(WanI2V480PConfig):
+    dit_config: DiTConfig = field(default_factory=MatrixGameWanVideoConfig)
+
+    image_encoder_config: EncoderConfig = field(
+        default_factory=WAN2_1ControlCLIPVisionConfig)
+
+    is_causal: bool = True
+    flow_shift: float | None = 5.0
+    dmd_denoising_steps: list[int] | None = field(
+        default_factory=lambda: [1000, 666, 333])
+    warp_denoising_step: bool = True
+    context_noise: int = 0
+    num_frames_per_block: int = 3
+    # sliding_window_num_frames: int = 15

fastvideo/configs/sample/base.py

@@ -17,10 +17,16 @@ class SamplingParam:
 
     # Image inputs
     image_path: str | None = None
+    pil_image: Any | None = None
 
     # Video inputs
     video_path: str | None = None
 
+    # Action control inputs (Matrix-Game)
+    mouse_cond: Any | None = None  # Shape: (B, T, 2)
+    keyboard_cond: Any | None = None  # Shape: (B, T, K)
+    grid_sizes: Any | None = None  # Shape: (3,) [F,H,W]
+
     # Text inputs
     prompt: str | list[str] | None = None
     negative_prompt: str = "Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards"
@@ -44,6 +50,7 @@ class SamplingParam:
     guidance_scale: float = 1.0
     guidance_rescale: float = 0.0
     boundary_ratio: float | None = None
+    sigmas: list[float] | None = None
 
     # TeaCache parameters
     enable_teacache: bool = False

fastvideo/configs/sample/hunyuan15.py

@@ -0,0 +1,29 @@
+# SPDX-License-Identifier: Apache-2.0
+import numpy as np
+from dataclasses import dataclass, field
+
+from fastvideo.configs.sample.base import SamplingParam
+
+
+@dataclass
+class Hunyuan15_480P_SamplingParam(SamplingParam):
+    num_inference_steps: int = 50
+
+    num_frames: int = 121
+    height: int = 480
+    width: int = 848
+    fps: int = 24
+
+    guidance_scale: float = 6.0
+    prompt_attention_mask: list = field(default_factory=list)
+    negative_attention_mask: list = field(default_factory=list)
+    sigmas: list[float] | None = field(
+        default_factory=lambda: list(np.linspace(1.0, 0.0, 50 + 1)[:-1]))
+
+    negative_prompt: str = ""
+
+
+@dataclass
+class Hunyuan15_720P_SamplingParam(Hunyuan15_480P_SamplingParam):
+    height: int = 720
+    width: int = 1280

fastvideo/configs/sample/registry.py

@@ -5,6 +5,7 @@ from typing import Any
 
 from fastvideo.configs.sample.hunyuan import (FastHunyuanSamplingParam,
                                               HunyuanSamplingParam)
+from fastvideo.configs.sample.hunyuan15 import Hunyuan15_480P_SamplingParam, Hunyuan15_720P_SamplingParam
 from fastvideo.configs.sample.stepvideo import StepVideoT2VSamplingParam
 
 from fastvideo.configs.sample.cosmos import Cosmos_Predict2_2B_Video2World_SamplingParam
@@ -23,6 +24,7 @@ from fastvideo.configs.sample.wan import (
     Wan2_1_Fun_1_3B_Control_SamplingParam,
     SelfForcingWan2_1_T2V_1_3B_480P_SamplingParam,
     SelfForcingWan2_2_T2V_A14B_480P_SamplingParam,
+    MatrixGame2_SamplingParam,
 )
 # isort: on
 from fastvideo.logger import init_logger
@@ -32,44 +34,36 @@ from fastvideo.utils import (maybe_download_model_index,
 logger = init_logger(__name__)
 # Registry maps specific model weights to their config classes
 SAMPLING_PARAM_REGISTRY: dict[str, Any] = {
-    "FastVideo/FastHunyuan-diffusers":
-    FastHunyuanSamplingParam,
-    "hunyuanvideo-community/HunyuanVideo":
-    HunyuanSamplingParam,
-    "FastVideo/stepvideo-t2v-diffusers":
-    StepVideoT2VSamplingParam,
+    "FastVideo/FastHunyuan-diffusers": FastHunyuanSamplingParam,
+    "hunyuanvideo-community/HunyuanVideo": HunyuanSamplingParam,
+    "hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v":
+    Hunyuan15_480P_SamplingParam,
+    "hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_t2v":
+    Hunyuan15_720P_SamplingParam,
+    "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,
+    "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,
     "weizhou03/Wan2.1-Fun-1.3B-InP-Diffusers":
     Wan2_1_Fun_1_3B_InP_SamplingParam,
     "IRMChen/Wan2.1-Fun-1.3B-Control-Diffusers":
     Wan2_1_Fun_1_3B_Control_SamplingParam,
 
     # Wan2.2
-    "Wan-AI/Wan2.2-TI2V-5B-Diffusers":
-    Wan2_2_TI2V_5B_SamplingParam,
+    "Wan-AI/Wan2.2-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,
+    "Wan-AI/Wan2.2-T2V-A14B-Diffusers": Wan2_2_T2V_A14B_SamplingParam,
+    "Wan-AI/Wan2.2-I2V-A14B-Diffusers": Wan2_2_I2V_A14B_SamplingParam,
 
     # FastWan2.1
-    "FastVideo/FastWan2.1-T2V-1.3B-Diffusers":
-    FastWanT2V480P_SamplingParam,
+    "FastVideo/FastWan2.1-T2V-1.3B-Diffusers": FastWanT2V480P_SamplingParam,
 
     # FastWan2.2
-    "FastVideo/FastWan2.2-TI2V-5B-Diffusers":
-    Wan2_2_TI2V_5B_SamplingParam,
+    "FastVideo/FastWan2.2-TI2V-5B-Diffusers": Wan2_2_TI2V_5B_SamplingParam,
 
     # Causal Self-Forcing Wan2.1
     "wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers":
@@ -78,22 +72,39 @@ SAMPLING_PARAM_REGISTRY: dict[str, Any] = {
     # Causal Self-Forcing Wan2.2
     "rand0nmr/SFWan2.2-T2V-A14B-Diffusers":
     SelfForcingWan2_2_T2V_A14B_480P_SamplingParam,
+    "FastVideo/SFWan2.2-I2V-A14B-Preview-Diffusers":
+    SelfForcingWan2_2_T2V_A14B_480P_SamplingParam,
 
     # Cosmos2
     "nvidia/Cosmos-Predict2-2B-Video2World":
     Cosmos_Predict2_2B_Video2World_SamplingParam,
 
+    # MatrixGame2.0 models
+    "FastVideo/Matrix-Game-2.0-Base-Diffusers": MatrixGame2_SamplingParam,
+    "FastVideo/Matrix-Game-2.0-GTA-Diffusers": MatrixGame2_SamplingParam,
+    "FastVideo/Matrix-Game-2.0-TempleRun-Diffusers": MatrixGame2_SamplingParam,
+
     # Add other specific weight variants
 }
 
 # For determining pipeline type from model ID
 SAMPLING_PARAM_DETECTOR: dict[str, Callable[[str], bool]] = {
-    "hunyuan": lambda id: "hunyuan" in id.lower(),
-    "wanpipeline": lambda id: "wanpipeline" in id.lower(),
-    "wanimagetovideo": lambda id: "wanimagetovideo" in id.lower(),
-    "stepvideo": lambda id: "stepvideo" in id.lower(),
-    "wandmdpipeline": lambda id: "wandmdpipeline" in id.lower(),
-    "wancausaldmdpipeline": lambda id: "wancausaldmdpipeline" in id.lower(),
+    "hunyuan":
+    lambda id: "hunyuan" in id.lower(),
+    "hunyuan15":
+    lambda id: "hunyuan15" in id.lower(),
+    "wanpipeline":
+    lambda id: "wanpipeline" in id.lower(),
+    "wanimagetovideo":
+    lambda id: "wanimagetovideo" in id.lower(),
+    "stepvideo":
+    lambda id: "stepvideo" in id.lower(),
+    "wandmdpipeline":
+    lambda id: "wandmdpipeline" in id.lower(),
+    "wancausaldmdpipeline":
+    lambda id: "wancausaldmdpipeline" in id.lower(),
+    "matrixgame":
+    lambda id: "matrixgame" in id.lower() or "matrix-game" in id.lower(),
     # Add other pipeline architecture detectors
 }
 
@@ -101,12 +112,15 @@ SAMPLING_PARAM_DETECTOR: dict[str, Callable[[str], bool]] = {
 SAMPLING_FALLBACK_PARAM: dict[str, Any] = {
     "hunyuan":
     HunyuanSamplingParam,  # Base Hunyuan config as fallback for any Hunyuan variant
+    "hunyuan15":
+    Hunyuan15_480P_SamplingParam,  # Base Hunyuan15 config as fallback for any Hunyuan15 variant
     "wanpipeline":
     WanT2V_1_3B_SamplingParam,  # Base Wan config as fallback for any Wan variant
     "wanimagetovideo": WanI2V_14B_480P_SamplingParam,
     "wandmdpipeline": FastWanT2V480P_SamplingParam,
     "wancausaldmdpipeline": SelfForcingWan2_1_T2V_1_3B_480P_SamplingParam,
     "stepvideo": StepVideoT2VSamplingParam,
+    "matrixgame": MatrixGame2_SamplingParam,
     # Other fallbacks by architecture
 }
 
@@ -114,6 +128,20 @@ SAMPLING_FALLBACK_PARAM: dict[str, Any] = {
 def get_sampling_param_cls_for_name(pipeline_name_or_path: str) -> Any | None:
     """Get the appropriate sampling param for specific pretrained weights."""
 
+    # First try exact match for specific weights
+    if pipeline_name_or_path in SAMPLING_PARAM_REGISTRY:
+        return SAMPLING_PARAM_REGISTRY[pipeline_name_or_path]
+
+    # Try partial matches (for local paths that might include the weight ID)
+    for registered_id, config_class in SAMPLING_PARAM_REGISTRY.items():
+        if registered_id in pipeline_name_or_path:
+            return config_class
+
+    matrixgame_patterns = ["Matrix-Game", "Skywork--Matrix-Game", "matrixgame"]
+    for pattern in matrixgame_patterns:
+        if pattern.lower() in pipeline_name_or_path.lower():
+            return MatrixGame2_SamplingParam
+
     if os.path.exists(pipeline_name_or_path):
         config = verify_model_config_and_directory(pipeline_name_or_path)
         logger.warning(
@@ -124,15 +152,6 @@ def get_sampling_param_cls_for_name(pipeline_name_or_path: str) -> Any | None:
 
     pipeline_name = config["_class_name"]
 
-    # First try exact match for specific weights
-    if pipeline_name_or_path in SAMPLING_PARAM_REGISTRY:
-        return SAMPLING_PARAM_REGISTRY[pipeline_name_or_path]
-
-    # Try partial matches (for local paths that might include the weight ID)
-    for registered_id, config_class in SAMPLING_PARAM_REGISTRY.items():
-        if registered_id in pipeline_name_or_path:
-            return config_class
-
     # If no match, try to use the fallback config
     fallback_config = None
     # Try to determine pipeline architecture for fallback

fastvideo/configs/sample/wan.py

@@ -196,3 +196,14 @@ class SelfForcingWan2_2_T2V_A14B_480P_SamplingParam(
     height: int = 448
     width: int = 832
     fps: int = 16
+
+
+@dataclass
+class MatrixGame2_SamplingParam(SamplingParam):
+    height: int = 352
+    width: int = 640
+    num_frames: int = 57
+    fps: int = 25
+    guidance_scale: float = 1.0
+    num_inference_steps: int = 3
+    negative_prompt: str | None = None

fastvideo/dataset/dataloader/record_schema.py

@@ -127,7 +127,10 @@ def i2v_record_creator(batch: PreprocessBatch) -> list[dict[str, Any]]:
 def ode_text_only_record_creator(
         video_name: str, text_embedding: np.ndarray, caption: str,
         trajectory_latents: np.ndarray,
-        trajectory_timesteps: np.ndarray) -> dict[str, Any]:
+        trajectory_timesteps: np.ndarray,
+        text_mask: np.ndarray | None = None,
+        text_embedding_2: np.ndarray | None = None,
+        text_mask_2: np.ndarray | None = None) -> dict[str, Any]:
     """Create a text-only ODE trajectory record matching pyarrow_schema_ode_trajectory_text_only.
 
     Args:
@@ -165,6 +168,25 @@ def ode_text_only_record_creator(
         "trajectory_timesteps_dtype": str(trajectory_timesteps.dtype),
     })
 
+    if text_embedding_2 is not None:
+        record.update({
+            "text_embedding_2_bytes": text_embedding_2.tobytes(),
+            "text_embedding_2_shape": list(text_embedding_2.shape),
+            "text_embedding_2_dtype": str(text_embedding_2.dtype),
+        })
+    if text_mask is not None:
+        record.update({
+            "text_mask_bytes": text_mask.tobytes(),
+            "text_mask_shape": list(text_mask.shape),
+            "text_mask_dtype": str(text_mask.dtype),
+        })
+    if text_mask_2 is not None:
+        record.update({
+            "text_mask_2_bytes": text_mask_2.tobytes(),
+            "text_mask_2_shape": list(text_mask_2.shape),
+            "text_mask_2_dtype": str(text_mask_2.dtype),
+        })
+        
     return record
 
 

fastvideo/dataset/dataloader/schema.py

@@ -90,6 +90,15 @@ pyarrow_schema_ode_trajectory_text_only = pa.schema([
     pa.field("text_embedding_shape", pa.list_(pa.int64())),
     # e.g., 'bfloat16' or 'float32'
     pa.field("text_embedding_dtype", pa.string()),
+    pa.field("text_embedding_2_bytes", pa.binary()),
+    pa.field("text_embedding_2_shape", pa.list_(pa.int64())),
+    pa.field("text_embedding_2_dtype", pa.string()),
+    pa.field("text_mask_bytes", pa.binary()),
+    pa.field("text_mask_shape", pa.list_(pa.int64())),
+    pa.field("text_mask_dtype", pa.string()),
+    pa.field("text_mask_2_bytes", pa.binary()),
+    pa.field("text_mask_2_shape", pa.list_(pa.int64())),
+    pa.field("text_mask_2_dtype", pa.string()),
     # --- ODE Trajectory ---
     pa.field("trajectory_latents_bytes", pa.binary()),
     pa.field("trajectory_latents_shape", pa.list_(pa.int64())),

fastvideo/dataset/preprocessing_datasets.py

@@ -17,6 +17,7 @@ from PIL import Image
 from transformers import AutoTokenizer
 
 from fastvideo.logger import init_logger
+from fastvideo.utils import maybe_download_model
 
 logger = init_logger(__name__)
 
@@ -655,7 +656,7 @@ class TextDataset(torch.utils.data.IterableDataset,
         self.seed = seed
 
         # Initialize tokenizer
-        tokenizer_path = os.path.join(args.model_path, "tokenizer")
+        tokenizer_path = os.path.join(maybe_download_model(args.model_path), "tokenizer")
         tokenizer = AutoTokenizer.from_pretrained(tokenizer_path,
                                                   cache_dir=args.cache_dir)
 

fastvideo/distributed/communication_op.py

@@ -4,7 +4,13 @@
 import torch
 import torch.distributed
 
-from fastvideo.distributed.parallel_state import get_sp_group, get_tp_group
+from fastvideo.distributed.parallel_state import (get_sp_group,
+                                                  get_sp_parallel_rank,
+                                                  get_sp_world_size,
+                                                  get_tp_group)
+from fastvideo.distributed.utils import (unpad_sequence_tensor,
+                                         compute_padding_for_sp,
+                                         pad_sequence_tensor)
 
 
 def tensor_model_parallel_all_reduce(input_: torch.Tensor) -> torch.Tensor:
@@ -30,3 +36,68 @@ def sequence_model_parallel_all_gather(input_: torch.Tensor,
                                        dim: int = -1) -> torch.Tensor:
     """All-gather the input tensor across model parallel group."""
     return get_sp_group().all_gather(input_, dim)
+
+
+def sequence_model_parallel_all_gather_with_unpad(
+        input_: torch.Tensor,
+        original_seq_len: int,
+        dim: int = -1) -> torch.Tensor:
+    """All-gather the input tensor and remove padding.
+    
+    Args:
+        input_: Sharded (and possibly padded) tensor to gather
+        original_seq_len: Original sequence length before padding
+        dim: Dimension to gather along (default: -1)
+        
+    Returns:
+        Tensor: Gathered and unpadded tensor
+    """
+
+    # First gather across all ranks
+    gathered = get_sp_group().all_gather(input_, dim)
+
+    current_seq_len = gathered.shape[dim]
+    if current_seq_len > original_seq_len:
+        gathered = unpad_sequence_tensor(gathered,
+                                         original_seq_len,
+                                         seq_dim=dim)
+
+    return gathered
+
+
+def sequence_model_parallel_shard(input_: torch.Tensor,
+                                  dim: int = 1) -> tuple[torch.Tensor, int]:
+    """Shard the input tensor across model parallel group with optional padding.
+    
+    Args:
+        input_: Input tensor to shard
+        dim: Dimension to shard along (default: 1)
+        
+    Returns:
+        tuple: (sharded_tensor, original_seq_len)
+            - sharded_tensor: The sharded (and possibly padded) tensor
+            - original_seq_len: Original sequence length before padding
+    """
+
+    sp_rank = get_sp_parallel_rank()
+    sp_world_size = get_sp_world_size()
+
+    original_seq_len = input_.shape[dim]
+
+    # Compute padding if needed
+    padded_seq_len, padding_amount = compute_padding_for_sp(
+        original_seq_len, sp_world_size)
+
+    # Pad if necessary
+    if padding_amount > 0:
+        input_ = pad_sequence_tensor(input_, padded_seq_len, seq_dim=dim)
+
+    elements_per_rank = padded_seq_len // sp_world_size
+
+    # Sharding along dim
+    input_ = input_.movedim(dim, 0)
+    input_ = input_[sp_rank * elements_per_rank:(sp_rank + 1) *
+                    elements_per_rank]
+    input_ = input_.movedim(0, dim)
+
+    return input_, original_seq_len

fastvideo/distributed/utils.py

@@ -61,6 +61,129 @@ def split_tensor_along_last_dim(
     return tuple(tensor_list)
 
 
+def compute_padding_for_sp(seq_len: int, sp_world_size: int) -> tuple[int, int]:
+    """
+    Compute padding needed for sequence parallel.
+    
+    Args:
+        seq_len: Original sequence length
+        sp_world_size: Sequence parallel world size
+        
+    Returns:
+        tuple: (padded_seq_len, padding_amount)
+    """
+    if seq_len % sp_world_size == 0:
+        return seq_len, 0
+
+    padding_amount = sp_world_size - (seq_len % sp_world_size)
+    padded_seq_len = seq_len + padding_amount
+
+    return padded_seq_len, padding_amount
+
+
+def create_attention_mask_for_padding(
+    seq_len: int,
+    padded_seq_len: int,
+    batch_size: int,
+    device: torch.device,
+    dtype: torch.dtype = torch.bool,
+) -> torch.Tensor | None:
+    """
+    Create attention mask to ignore padded tokens.
+    
+    Args:
+        seq_len: Original sequence length (before padding)
+        padded_seq_len: Padded sequence length
+        batch_size: Batch size
+        device: Device to create mask on
+        dtype: Data type for the mask (default: bool)
+        
+    Returns:
+        Tensor: Boolean mask [B, padded_seq_len] where True = valid token,
+                or None if no padding is needed
+    """
+    if seq_len == padded_seq_len:
+        return None
+
+    # Create mask: True for valid tokens, False for padding
+    attention_mask = torch.ones(
+        (batch_size, padded_seq_len),
+        dtype=dtype,
+        device=device,
+    )
+
+    # Mask out padding tokens
+    attention_mask[:, seq_len:] = 0
+
+    return attention_mask
+
+
+def pad_sequence_tensor(
+    tensor: torch.Tensor,
+    target_seq_len: int,
+    seq_dim: int = 1,
+    pad_value: float = 0.0,
+) -> torch.Tensor:
+    """
+    Pad a tensor along the sequence dimension.
+    
+    Args:
+        tensor: Input tensor to pad
+        target_seq_len: Target sequence length after padding
+        seq_dim: Dimension to pad along (default: 1)
+        pad_value: Value to use for padding (default: 0.0)
+        
+    Returns:
+        Tensor: Padded tensor
+    """
+    current_seq_len = tensor.shape[seq_dim]
+
+    if current_seq_len >= target_seq_len:
+        return tensor
+
+    padding_amount = target_seq_len - current_seq_len
+
+    # Create padding shape
+    pad_shape = list(tensor.shape)
+    pad_shape[seq_dim] = padding_amount
+
+    # Create padding tensor
+    padding = torch.full(
+        pad_shape,
+        pad_value,
+        dtype=tensor.dtype,
+        device=tensor.device,
+    )
+
+    # Concatenate along sequence dimension
+    padded_tensor = torch.cat([tensor, padding], dim=seq_dim)
+
+    return padded_tensor
+
+
+def unpad_sequence_tensor(
+    tensor: torch.Tensor,
+    original_seq_len: int,
+    seq_dim: int = 1,
+) -> torch.Tensor:
+    """
+    Remove padding from a tensor along the sequence dimension.
+    
+    Args:
+        tensor: Padded tensor
+        original_seq_len: Original sequence length (before padding)
+        seq_dim: Dimension to unpad along (default: 1)
+        
+    Returns:
+        Tensor: Unpadded tensor
+    """
+    # Use slice to remove padding
+    indices = [slice(None)] * tensor.dim()
+    indices[seq_dim] = slice(0, original_seq_len)
+
+    return tensor[tuple(indices)]
+
+
 @dataclasses.dataclass
 class StatelessProcessGroup:
     """A dataclass to hold a metadata store, and the rank, world_size of the

fastvideo/entrypoints/video_generator.py

@@ -102,6 +102,11 @@ class VideoGenerator:
         self,
         prompt: str | None = None,
         sampling_param: SamplingParam | None = None,
+        # Action control inputs (Matrix-Game)
+        mouse_cond: torch.Tensor | None = None,
+        keyboard_cond: torch.Tensor | None = None,
+        grid_sizes: tuple[int, int, int] | list[int] | torch.Tensor
+        | None = None,
         **kwargs,
     ) -> dict[str, Any] | list[np.ndarray] | list[dict[str, Any]]:
         """
@@ -131,6 +136,15 @@ class VideoGenerator:
         if sampling_param is None:
             sampling_param = SamplingParam.from_pretrained(
                 self.fastvideo_args.model_path)
+
+        # Add action control inputs to kwargs if provided
+        if mouse_cond is not None:
+            kwargs['mouse_cond'] = mouse_cond
+        if keyboard_cond is not None:
+            kwargs['keyboard_cond'] = keyboard_cond
+        if grid_sizes is not None:
+            kwargs['grid_sizes'] = grid_sizes
+
         sampling_param.update(kwargs)
 
         if self.fastvideo_args.prompt_txt is not None or sampling_param.prompt_path is not None:
@@ -297,27 +311,19 @@ class VideoGenerator:
             orig_latent_num_frames = sampling_param.num_frames // 17 * 3
 
         if orig_latent_num_frames % fastvideo_args.num_gpus != 0:
-            # Adjust latent frames to be divisible by number of GPUs
-            if sampling_param.num_frames_round_down:
-                # Ensure we have at least 1 batch per GPU
-                new_latent_num_frames = max(
-                    1, (orig_latent_num_frames // num_gpus)) * num_gpus
-            else:
-                new_latent_num_frames = math.ceil(
-                    orig_latent_num_frames / num_gpus) * num_gpus
 
             if use_temporal_scaling_frames:
                 # Convert back to number of frames, ensuring num_frames-1 is a multiple of temporal_scale_factor
-                new_num_frames = (new_latent_num_frames -
+                new_num_frames = (orig_latent_num_frames -
                                   1) * temporal_scale_factor + 1
             else:  # stepvideo only
                 # Find the least common multiple of 3 and num_gpus
                 divisor = math.lcm(3, num_gpus)
                 # Round up to the nearest multiple of this LCM
-                new_latent_num_frames = (
-                    (new_latent_num_frames + divisor - 1) // divisor) * divisor
+                orig_latent_num_frames = (
+                    (orig_latent_num_frames + divisor - 1) // divisor) * divisor
                 # Convert back to actual frames using the StepVideo formula
-                new_num_frames = new_latent_num_frames // 3 * 17
+                new_num_frames = orig_latent_num_frames // 3 * 17
 
             logger.info(
                 "Adjusting number of frames from %s to %s based on number of GPUs (%s)",

fastvideo/envs.py

@@ -32,6 +32,9 @@ if TYPE_CHECKING:
     FASTVIDEO_TORCH_PROFILER_WITH_PROFILE_MEMORY: bool = False
     FASTVIDEO_TORCH_PROFILER_WITH_STACK: bool = True
     FASTVIDEO_TORCH_PROFILER_WITH_FLOPS: bool = False
+    FASTVIDEO_TORCH_PROFILER_WAIT_STEPS: int = 2
+    FASTVIDEO_TORCH_PROFILER_WARMUP_STEPS: int = 1
+    FASTVIDEO_TORCH_PROFILER_ACTIVE_STEPS: int = 2
     FASTVIDEO_TORCH_PROFILE_REGIONS: str = ""
     FASTVIDEO_SERVER_DEV_MODE: bool = False
     FASTVIDEO_STAGE_LOGGING: bool = False
@@ -247,6 +250,18 @@ environment_variables: dict[str, Callable[[], Any]] = {
     # not profile flops.
     "FASTVIDEO_TORCH_PROFILER_WITH_FLOPS":
     lambda: bool(os.getenv("FASTVIDEO_TORCH_PROFILER_WITH_FLOPS", "0") != "0"),
+    # Wait steps per profiling cycle (torch.profiler.schedule wait parameter)
+    # Defaults to 2 if not set.
+    "FASTVIDEO_TORCH_PROFILER_WAIT_STEPS":
+    lambda: int(os.getenv("FASTVIDEO_TORCH_PROFILER_WAIT_STEPS", "2")),
+    # Warmup steps per profiling cycle (torch.profiler.schedule warmup parameter)
+    # Defaults to 1 if not set.
+    "FASTVIDEO_TORCH_PROFILER_WARMUP_STEPS":
+    lambda: int(os.getenv("FASTVIDEO_TORCH_PROFILER_WARMUP_STEPS", "1")),
+    # Active steps per profiling cycle (torch.profiler.schedule active parameter)
+    # Defaults to 2 if not set.
+    "FASTVIDEO_TORCH_PROFILER_ACTIVE_STEPS":
+    lambda: int(os.getenv("FASTVIDEO_TORCH_PROFILER_ACTIVE_STEPS", "2")),
     "FASTVIDEO_TORCH_PROFILE_REGIONS":
     lambda: os.getenv("FASTVIDEO_TORCH_PROFILE_REGIONS", ""),
 

fastvideo/fastvideo_args.py

@@ -174,6 +174,8 @@ class FastVideoArgs:
     init_weights_from_safetensors: str = ""  # path to safetensors file for initial weight loading
     init_weights_from_safetensors_2: str = ""  # path to safetensors file for initial weight loading for transformer_2
 
+    override_pipeline_cls_name: str | None = None
+
     # # DMD parameters
     # dmd_denoising_steps: List[int] | None = field(default=None)
 
@@ -424,6 +426,12 @@ class FastVideoArgs:
             default=FastVideoArgs.override_transformer_cls_name,
             help="Override transformer cls name",
         )
+        parser.add_argument(
+            "--override-pipeline-cls-name",
+            type=str,
+            default=FastVideoArgs.override_pipeline_cls_name,
+            help="Override pipeline cls name",
+        )
         parser.add_argument(
             "--init-weights-from-safetensors",
             type=str,

fastvideo/layers/activation.py

@@ -87,6 +87,7 @@ _ACTIVATION_REGISTRY = {
     "gelu_pytorch_tanh": lambda: nn.GELU(approximate="tanh"),
     "relu": nn.ReLU,
     "silu": nn.SiLU,
+    "swish": nn.SiLU,
     "quick_gelu": QuickGELU,
 }
 

fastvideo/layers/lora/linear.py

@@ -101,12 +101,19 @@ class BaseLayerWithLoRA(nn.Module):
     def set_lora_weights(self,
                          A: torch.Tensor,
                          B: torch.Tensor,
+                         lora_alpha: float | None = None,
                          training_mode: bool = False,
                          lora_path: str | None = None) -> None:
         self.lora_A = torch.nn.Parameter(
             A)  # share storage with weights in the pipeline
         self.lora_B = torch.nn.Parameter(B)
         self.disable_lora = False
+
+        # Store rank and alpha directly
+        rank = A.shape[0]  # rank is the first dimension of A
+        self.lora_rank = rank
+        self.lora_alpha = int(lora_alpha) if lora_alpha is not None else rank
+
         if not training_mode:
             self.merge_lora_weights()
         self.lora_path = lora_path
@@ -134,8 +141,13 @@ class BaseLayerWithLoRA(nn.Module):
             current_device = self.base_layer.weight.data.device
             data = self.base_layer.weight.data.to(
                 get_local_torch_device()).full_tensor()
-            data += (self.slice_lora_b_weights(self.lora_B).to(data)
-                     @ self.slice_lora_a_weights(self.lora_A).to(data))
+
+            # Apply LoRA with alpha scaling
+            lora_delta = (self.slice_lora_b_weights(self.lora_B).to(data)
+                          @ self.slice_lora_a_weights(self.lora_A).to(data))
+            if self.lora_alpha and self.lora_rank and self.lora_alpha != self.lora_rank:
+                lora_delta *= (self.lora_alpha / self.lora_rank)
+            data += lora_delta
             unsharded_base_layer.weight = nn.Parameter(data.to(current_device))
             if isinstance(getattr(self.base_layer, "bias", None), DTensor):
                 unsharded_base_layer.bias = nn.Parameter(
@@ -154,8 +166,13 @@ class BaseLayerWithLoRA(nn.Module):
         else:
             current_device = self.base_layer.weight.data.device
             data = self.base_layer.weight.data.to(get_local_torch_device())
-            data += \
-                (self.slice_lora_b_weights(self.lora_B.to(data)) @ self.slice_lora_a_weights(self.lora_A.to(data)))
+
+            # Apply LoRA with alpha scaling
+            lora_delta = (self.slice_lora_b_weights(self.lora_B.to(data))
+                          @ self.slice_lora_a_weights(self.lora_A.to(data)))
+            if self.lora_alpha and self.lora_rank and self.lora_alpha != self.lora_rank:
+                lora_delta *= (self.lora_alpha / self.lora_rank)
+            data += lora_delta
             self.base_layer.weight.data = data.to(current_device,
                                                   non_blocking=True)
 

fastvideo/layers/rotary_embedding.py

@@ -429,6 +429,7 @@ def get_rotary_pos_embed(
     theta_rescale_factor=1.0,
     interpolation_factor=1.0,
     shard_dim: int = 0,
+    do_sp_sharding: bool = False,
     dtype: torch.dtype = torch.float32,
     start_frame: int = 0,
 ) -> tuple[torch.Tensor, torch.Tensor]:
@@ -444,6 +445,7 @@ def get_rotary_pos_embed(
         theta_rescale_factor: Rescale factor for theta. Defaults to 1.0
         interpolation_factor: Factor to scale positions. Defaults to 1.0
         shard_dim: Which dimension to shard for sequence parallelism. Defaults to 0.
+        do_sp_sharding: Whether to shard the positional embeddings for sequence parallelism. Defaults to False.
         
     Returns:
         Tuple of (cos, sin) tensors for rotary embeddings
@@ -460,9 +462,13 @@ def get_rotary_pos_embed(
     ) == head_dim, "sum(rope_dim_list) should equal to head_dim of attention layer"
 
     # Get SP info
-    sp_group = get_sp_group()
-    sp_rank = sp_group.rank_in_group
-    sp_world_size = sp_group.world_size
+    if do_sp_sharding:
+        sp_group = get_sp_group()
+        sp_rank = sp_group.rank_in_group
+        sp_world_size = sp_group.world_size
+    else:
+        sp_rank = 0
+        sp_world_size = 1
 
     freqs_cos, freqs_sin = get_nd_rotary_pos_embed(
         rope_dim_list,

fastvideo/models/dits/cosmos2_5.py

@@ -0,0 +1,961 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import math
+from typing import Any
+
+import numpy as np
+import torch
+import torch.nn as nn
+from torchvision import transforms
+
+from fastvideo.attention import DistributedAttention, LocalAttention
+from fastvideo.configs.models.dits.cosmos2_5 import Cosmos25VideoConfig
+from fastvideo.forward_context import get_forward_context
+from fastvideo.layers.layernorm import RMSNorm
+from fastvideo.layers.mlp import MLP
+from fastvideo.layers.rotary_embedding import apply_rotary_emb
+from fastvideo.layers.visual_embedding import Timesteps
+from fastvideo.models.dits.base import BaseDiT
+from fastvideo.platforms import AttentionBackendEnum
+
+
+
+class Cosmos25PatchEmbed(nn.Module):
+    """
+    COSMOS 2.5 patch embedding - converts video (B, C, T, H, W) to patches (B, T', H', W', D).
+    Uses linear projection after rearranging patches.
+    """
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        patch_size: tuple[int, int, int],
+    ) -> None:
+        super().__init__()
+        self.patch_size = patch_size
+        self.dim = in_channels * patch_size[0] * patch_size[1] * patch_size[2]
+
+        self.proj = nn.Linear(self.dim, out_channels, bias=False)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        """
+        Args:
+            hidden_states: (B, C, T, H, W)
+        Returns:
+            (B, T', H', W', D) where T'=T//pt, H'=H//ph, W'=W//pw
+        """
+        batch_size, num_channels, num_frames, height, width = hidden_states.shape
+        p_t, p_h, p_w = self.patch_size
+
+        # Rearrange: b c (t pt) (h ph) (w pw) -> b t h w (c pt ph pw)
+        hidden_states = hidden_states.reshape(
+            batch_size, num_channels,
+            num_frames // p_t, p_t,
+            height // p_h, p_h,
+            width // p_w, p_w
+        )
+        hidden_states = hidden_states.permute(0, 2, 4, 6, 1, 3, 5, 7)
+        hidden_states = hidden_states.flatten(4, 7)  # Flatten patch dimensions
+        
+        # Project to model dimension
+        hidden_states = self.proj(hidden_states)
+        return hidden_states
+
+
+class Cosmos25TimestepEmbedding(nn.Module):
+    """
+    COSMOS 2.5 timestep embedding with AdaLN-LoRA support.
+    Generates both standard embedding and AdaLN-LoRA parameters.
+    """
+
+    def __init__(
+        self,
+        in_features: int,
+        out_features: int,
+        use_adaln_lora: bool = True,
+        adaln_lora_dim: int = 256,
+    ) -> None:
+        super().__init__()
+        self.use_adaln_lora = use_adaln_lora
+        
+        self.linear_1 = nn.Linear(in_features, out_features, bias=False)
+        self.activation = nn.SiLU()
+        
+        if use_adaln_lora:
+            self.linear_2 = nn.Linear(out_features, 3 * out_features, bias=False)
+        else:
+            self.linear_2 = nn.Linear(out_features, out_features, bias=False)
+
+    def forward(self, sample: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor | None]:
+        """
+        Returns:
+            emb: Standard embedding (B, T, D)
+            adaln_lora: AdaLN-LoRA parameters (B, T, 3D) or None
+        """
+        emb = self.linear_1(sample)
+        emb = self.activation(emb)
+        emb = self.linear_2(emb)
+
+        if self.use_adaln_lora:
+            adaln_lora = emb  # (B, T, 3D)
+            emb_standard = sample  # Use input as standard embedding
+        else:
+            emb_standard = emb
+            adaln_lora = None
+
+        return emb_standard, adaln_lora
+
+
+class Cosmos25Embedding(nn.Module):
+    """
+    COSMOS 2.5 timestep conditioning embedding.
+    Generates sinusoidal embeddings and processes them through MLP.
+    """
+
+    def __init__(
+        self,
+        embedding_dim: int,
+        condition_dim: int,
+        use_adaln_lora: bool = True,
+        adaln_lora_dim: int = 256,
+    ) -> None:
+        super().__init__()
+
+        self.time_proj = Timesteps(embedding_dim, flip_sin_to_cos=True, downscale_freq_shift=0.0)
+        self.t_embedder = Cosmos25TimestepEmbedding(
+            embedding_dim,
+            condition_dim,
+            use_adaln_lora=use_adaln_lora,
+            adaln_lora_dim=adaln_lora_dim,
+        )
+        self.norm = RMSNorm(embedding_dim, eps=1e-6)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        timestep: torch.Tensor,
+    ) -> tuple[torch.Tensor, torch.Tensor | None]:
+        """
+        Args:
+            timestep: (B, T) tensor of timesteps
+            
+        Returns:
+            embedded_timestep: Normalized timestep embedding (B, T, D)
+            adaln_lora: AdaLN-LoRA parameters (B, T, 3D) or None
+        """
+        # Handle 2D timestep input (B, T) like the official model
+        assert timestep.ndim == 2, f"Expected 2D timestep, got {timestep.ndim}D with shape {timestep.shape}"
+        B, T = timestep.shape
+        
+        # Flatten for Timesteps layer which expects 1D, then reshape back
+        timestep_flat = timestep.flatten()  # (B*T,)
+        timesteps_proj = self.time_proj(timestep_flat).type_as(hidden_states)  # (B*T, D)
+        timesteps_proj = timesteps_proj.reshape(B, T, -1)  # (B, T, D)
+        
+        embedded_timestep, adaln_lora = self.t_embedder(timesteps_proj)
+        embedded_timestep = self.norm(embedded_timestep)
+        
+        return embedded_timestep, adaln_lora
+
+
+class Cosmos25AdaLayerNormZero(nn.Module):
+    """
+    COSMOS 2.5 Adaptive Layer Normalization with zero initialization and gate.
+    This is a simplified version that expects pre-computed shift/scale/gate parameters.
+    """
+
+    def __init__(
+        self,
+        in_features: int,
+    ) -> None:
+        super().__init__()
+        self.norm = nn.LayerNorm(in_features, elementwise_affine=False, eps=1e-6)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        shift: torch.Tensor,
+        scale: torch.Tensor,
+    ) -> torch.Tensor:
+        """
+        Args:
+            hidden_states: Input tensor
+            shift: Shift parameter for modulation
+            scale: Scale parameter for modulation
+            
+        Returns:
+            normalized_hidden_states: Modulated normalized hidden states
+        """
+        # Apply layer norm and modulation
+        hidden_states = self.norm(hidden_states)
+        hidden_states = hidden_states * (1 + scale) + shift
+        return hidden_states
+
+
+class Cosmos25SelfAttention(nn.Module):
+    """
+    COSMOS 2.5 self-attention with QK normalization and RoPE.
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int,
+        qk_norm: bool = True,
+        eps: float = 1e-6,
+        supported_attention_backends: tuple[AttentionBackendEnum, ...] | None = None,
+    ) -> None:
+        assert dim % num_heads == 0
+        super().__init__()
+        self.dim = dim
+        self.num_heads = num_heads
+        self.head_dim = dim // num_heads
+
+        self.to_q = nn.Linear(dim, dim, bias=False)
+        self.to_k = nn.Linear(dim, dim, bias=False)
+        self.to_v = nn.Linear(dim, dim, bias=False)
+        self.to_out = nn.Linear(dim, dim, bias=False)
+
+        self.norm_q = RMSNorm(self.head_dim, eps=eps) if qk_norm else nn.Identity()
+        self.norm_k = RMSNorm(self.head_dim, eps=eps) if qk_norm else nn.Identity()
+
+        # Use DistributedAttention for flexible backend support (torch SDPA / FlashAttention)
+        # For single-GPU (non-distributed), use LocalAttention to avoid distributed requirements
+        if supported_attention_backends is None:
+            supported_attention_backends = (AttentionBackendEnum.FLASH_ATTN, AttentionBackendEnum.TORCH_SDPA)
+        
+        # Always use DistributedAttention (requires distributed environment to be initialized)
+        self.attn = DistributedAttention(
+            num_heads=num_heads,
+            head_size=self.head_dim,
+            causal=False,
+            supported_attention_backends=supported_attention_backends,
+            prefix="self_attn"
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        rope_emb: tuple[torch.Tensor, torch.Tensor] | None = None,
+    ) -> torch.Tensor:
+        """
+        Args:
+            hidden_states: (B, S, D) where S = T*H*W
+            rope_emb: Tuple of (cos, sin) for RoPE
+        """
+        # Get QKV
+        query = self.to_q(hidden_states)
+        key = self.to_k(hidden_states)
+        value = self.to_v(hidden_states)
+
+        # Reshape for multi-head attention: (B, S, D) -> (B, S, H, D_h) -> (B, H, S, D_h)
+        query = query.unflatten(-1, (self.num_heads, self.head_dim)).transpose(1, 2)
+        key = key.unflatten(-1, (self.num_heads, self.head_dim)).transpose(1, 2)
+        value = value.unflatten(-1, (self.num_heads, self.head_dim)).transpose(1, 2)
+
+        # Apply QK normalization
+        query = self.norm_q(query)
+        key = self.norm_k(key)
+
+        # Apply RoPE if provided (query/key are now in (B, H, S, D_h) format)
+        if rope_emb is not None:
+            cos, sin = rope_emb
+            query = apply_rotary_emb(query, (cos, sin), use_real=True, use_real_unbind_dim=-2)
+            key = apply_rotary_emb(key, (cos, sin), use_real=True, use_real_unbind_dim=-2)
+
+        # Attention computation using DistributedAttention or LocalAttention
+        # Both expect (B, S, H, D_h), so transpose first
+        query = query.transpose(1, 2)  # (B, H, S, D_h) -> (B, S, H, D_h)
+        key = key.transpose(1, 2)
+        value = value.transpose(1, 2)
+        
+        
+        attn_output, _ = self.attn(query, key, value)
+        # Reshape back: (B, S, H, D_h) -> (B, S, H*D_h)
+        attn_output = attn_output.flatten(-2, -1)
+
+        # Output projection
+        attn_output = self.to_out(attn_output)
+        return attn_output
+
+
+class Cosmos25CrossAttention(nn.Module):
+    """
+    COSMOS 2.5 cross-attention for text conditioning.
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        cross_attention_dim: int,
+        num_heads: int,
+        qk_norm: bool = True,
+        eps: float = 1e-6,
+        supported_attention_backends: tuple[AttentionBackendEnum, ...] | None = None,
+    ) -> None:
+        assert dim % num_heads == 0
+        super().__init__()
+        self.dim = dim
+        self.cross_attention_dim = cross_attention_dim
+        self.num_heads = num_heads
+        self.head_dim = dim // num_heads
+
+        self.to_q = nn.Linear(dim, dim, bias=False)
+        self.to_k = nn.Linear(cross_attention_dim, dim, bias=False)
+        self.to_v = nn.Linear(cross_attention_dim, dim, bias=False)
+        self.to_out = nn.Linear(dim, dim, bias=False)
+
+        self.norm_q = RMSNorm(self.head_dim, eps=eps) if qk_norm else nn.Identity()
+        self.norm_k = RMSNorm(self.head_dim, eps=eps) if qk_norm else nn.Identity()
+
+        if supported_attention_backends is None:
+            supported_attention_backends = (AttentionBackendEnum.FLASH_ATTN, AttentionBackendEnum.TORCH_SDPA)
+        
+        # Use LocalAttention for cross-attention since text embeddings are not sharded
+        # in sequence parallelism (replicated across ranks)
+        self.attn = LocalAttention(
+            num_heads=num_heads,
+            head_size=self.head_dim,
+            causal=False,
+            supported_attention_backends=supported_attention_backends,
+        )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor,
+        attention_mask: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        """
+        Args:
+            hidden_states: (B, S, D)
+            encoder_hidden_states: (B, N, D_text)
+        """
+        # Get QKV
+        query = self.to_q(hidden_states)
+        key = self.to_k(encoder_hidden_states)
+        value = self.to_v(encoder_hidden_states)
+
+        # Reshape for multi-head attention
+        query = query.unflatten(-1, (self.num_heads, self.head_dim))
+        key = key.unflatten(-1, (self.num_heads, self.head_dim))
+        value = value.unflatten(-1, (self.num_heads, self.head_dim))
+
+        # Apply QK normalization
+        query = self.norm_q(query)
+        key = self.norm_k(key)
+
+        # LocalAttention expects (B, S, H, D_h), which is what we already have
+        attn_output = self.attn(query, key, value)
+
+        # Reshape back: (B, S, H, D_h) -> (B, S, H*D_h)
+        attn_output = attn_output.flatten(-2, -1)
+
+        # Output projection
+        attn_output = self.to_out(attn_output)
+        return attn_output
+
+
+class Cosmos25TransformerBlock(nn.Module):
+    """
+    COSMOS 2.5 transformer block with self-attention, cross-attention, and MLP.
+    Uses AdaLN-LoRA for conditioning.
+    Matches the official architecture where modulation parameters are computed once per block.
+    """
+
+    def __init__(
+        self,
+        num_attention_heads: int,
+        attention_head_dim: int,
+        cross_attention_dim: int,
+        mlp_ratio: float = 4.0,
+        adaln_lora_dim: int = 256,
+        use_adaln_lora: bool = True,
+        qk_norm: bool = True,
+        supported_attention_backends: tuple[AttentionBackendEnum, ...] | None = None,
+    ) -> None:
+        super().__init__()
+
+        hidden_size = num_attention_heads * attention_head_dim
+        self.use_adaln_lora = use_adaln_lora
+
+        # Layer norms (no modulation logic inside)
+        self.norm1 = Cosmos25AdaLayerNormZero(hidden_size)
+        self.norm2 = Cosmos25AdaLayerNormZero(hidden_size)
+        self.norm3 = Cosmos25AdaLayerNormZero(hidden_size)
+
+        # Attention and MLP layers
+        self.attn1 = Cosmos25SelfAttention(
+            dim=hidden_size,
+            num_heads=num_attention_heads,
+            qk_norm=qk_norm,
+            supported_attention_backends=supported_attention_backends,
+        )
+        self.attn2 = Cosmos25CrossAttention(
+            dim=hidden_size,
+            cross_attention_dim=cross_attention_dim,
+            num_heads=num_attention_heads,
+            qk_norm=qk_norm,
+            supported_attention_backends=supported_attention_backends,
+        )
+        self.mlp = MLP(hidden_size, int(hidden_size * mlp_ratio), act_type="gelu", bias=False)
+
+        # AdaLN modulation layers (compute shift/scale/gate for each sub-layer)
+        # These match the official model's adaln_modulation_* layers
+        if use_adaln_lora:
+            self.adaln_modulation_self_attn = nn.Sequential(
+                nn.SiLU(),
+                nn.Linear(hidden_size, adaln_lora_dim, bias=False),
+                nn.Linear(adaln_lora_dim, 3 * hidden_size, bias=False),
+            )
+            self.adaln_modulation_cross_attn = nn.Sequential(
+                nn.SiLU(),
+                nn.Linear(hidden_size, adaln_lora_dim, bias=False),
+                nn.Linear(adaln_lora_dim, 3 * hidden_size, bias=False),
+            )
+            self.adaln_modulation_mlp = nn.Sequential(
+                nn.SiLU(),
+                nn.Linear(hidden_size, adaln_lora_dim, bias=False),
+                nn.Linear(adaln_lora_dim, 3 * hidden_size, bias=False),
+            )
+        else:
+            self.adaln_modulation_self_attn = nn.Sequential(
+                nn.SiLU(), nn.Linear(hidden_size, 3 * hidden_size, bias=False)
+            )
+            self.adaln_modulation_cross_attn = nn.Sequential(
+                nn.SiLU(), nn.Linear(hidden_size, 3 * hidden_size, bias=False)
+            )
+            self.adaln_modulation_mlp = nn.Sequential(
+                nn.SiLU(), nn.Linear(hidden_size, 3 * hidden_size, bias=False)
+            )
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor,
+        embedded_timestep: torch.Tensor,
+        adaln_lora: torch.Tensor | None = None,
+        rope_emb: tuple[torch.Tensor, torch.Tensor] | None = None,
+        extra_pos_emb: torch.Tensor | None = None,
+        attention_mask: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        """
+        Args:
+            hidden_states: (B, T, H, W, D)
+            encoder_hidden_states: (B, N, D_text)
+            embedded_timestep: (B, T, D)
+            adaln_lora: (B, T, 3D) AdaLN-LoRA parameters
+            rope_emb: Tuple of (cos, sin) for RoPE
+            extra_pos_emb: Optional learnable positional embeddings
+        """
+        # Add extra positional embeddings if provided
+        if extra_pos_emb is not None:
+            hidden_states = hidden_states + extra_pos_emb
+
+        B, T, H, W, D = hidden_states.shape
+
+        # Step 1: Compute ALL modulation parameters once (matches official model)
+        if self.use_adaln_lora and adaln_lora is not None:
+            shift_self_attn, scale_self_attn, gate_self_attn = (
+                self.adaln_modulation_self_attn(embedded_timestep) + adaln_lora
+            ).chunk(3, dim=-1)
+            shift_cross_attn, scale_cross_attn, gate_cross_attn = (
+                self.adaln_modulation_cross_attn(embedded_timestep) + adaln_lora
+            ).chunk(3, dim=-1)
+            shift_mlp, scale_mlp, gate_mlp = (
+                self.adaln_modulation_mlp(embedded_timestep) + adaln_lora
+            ).chunk(3, dim=-1)
+        else:
+            shift_self_attn, scale_self_attn, gate_self_attn = self.adaln_modulation_self_attn(
+                embedded_timestep
+            ).chunk(3, dim=-1)
+            shift_cross_attn, scale_cross_attn, gate_cross_attn = self.adaln_modulation_cross_attn(
+                embedded_timestep
+            ).chunk(3, dim=-1)
+            shift_mlp, scale_mlp, gate_mlp = self.adaln_modulation_mlp(embedded_timestep).chunk(3, dim=-1)
+
+        # Reshape modulation parameters from (B, T, D) to (B, T, 1, 1, D) for broadcasting
+        shift_self_attn = shift_self_attn.unsqueeze(2).unsqueeze(2).type_as(hidden_states)
+        scale_self_attn = scale_self_attn.unsqueeze(2).unsqueeze(2).type_as(hidden_states)
+        gate_self_attn = gate_self_attn.unsqueeze(2).unsqueeze(2).type_as(hidden_states)
+
+        shift_cross_attn = shift_cross_attn.unsqueeze(2).unsqueeze(2).type_as(hidden_states)
+        scale_cross_attn = scale_cross_attn.unsqueeze(2).unsqueeze(2).type_as(hidden_states)
+        gate_cross_attn = gate_cross_attn.unsqueeze(2).unsqueeze(2).type_as(hidden_states)
+
+        shift_mlp = shift_mlp.unsqueeze(2).unsqueeze(2).type_as(hidden_states)
+        scale_mlp = scale_mlp.unsqueeze(2).unsqueeze(2).type_as(hidden_states)
+        gate_mlp = gate_mlp.unsqueeze(2).unsqueeze(2).type_as(hidden_states)
+
+        # Step 2: Self-attention block
+        norm_hidden_states = self.norm1(hidden_states, shift_self_attn, scale_self_attn)
+        # Flatten for attention: (B, T, H, W, D) -> (B, THW, D)
+        norm_hidden_states_flat = norm_hidden_states.flatten(1, 3)
+        
+        attn_output = self.attn1(norm_hidden_states_flat, rope_emb=rope_emb)
+        
+        # Reshape back and apply residual
+        attn_output = attn_output.unflatten(1, (T, H, W))  # (B, T, H, W, D)
+        hidden_states = hidden_states + gate_self_attn * attn_output
+
+        # Step 3: Cross-attention block
+        norm_hidden_states = self.norm2(hidden_states, shift_cross_attn, scale_cross_attn)
+        norm_hidden_states_flat = norm_hidden_states.flatten(1, 3)
+        
+        attn_output = self.attn2(
+            norm_hidden_states_flat,
+            encoder_hidden_states=encoder_hidden_states,
+            attention_mask=attention_mask,
+        )
+        
+        attn_output = attn_output.unflatten(1, (T, H, W))
+        hidden_states = hidden_states + gate_cross_attn * attn_output
+
+        # Step 4: MLP block
+        norm_hidden_states = self.norm3(hidden_states, shift_mlp, scale_mlp)
+        norm_hidden_states_flat = norm_hidden_states.flatten(1, 3)
+        
+        mlp_output = self.mlp(norm_hidden_states_flat)
+        
+        mlp_output = mlp_output.unflatten(1, (T, H, W))
+        hidden_states = hidden_states + gate_mlp * mlp_output
+
+        return hidden_states
+
+
+class Cosmos25RotaryPosEmbed(nn.Module):
+    """
+    COSMOS 2.5 3D Rotary Position Embedding with NTK-aware extrapolation.
+    """
+
+    def __init__(
+        self,
+        hidden_size: int,
+        max_size: tuple[int, int, int] = (128, 240, 240),
+        patch_size: tuple[int, int, int] = (1, 2, 2),
+        base_fps: int = 24,
+        rope_scale: tuple[float, float, float] = (1.0, 1.0, 1.0),
+        enable_fps_modulation: bool = True,
+    ) -> None:
+        super().__init__()
+
+        self.max_size = [size // patch for size, patch in zip(max_size, patch_size, strict=True)]
+        self.patch_size = patch_size
+        self.base_fps = base_fps
+        self.enable_fps_modulation = enable_fps_modulation
+
+        # Split dimensions: 1/3 for T, 1/3 for H, 1/3 for W
+        self.dim_h = hidden_size // 6 * 2
+        self.dim_w = hidden_size // 6 * 2
+        self.dim_t = hidden_size - self.dim_h - self.dim_w
+
+        # NTK-aware extrapolation factors
+        self.h_ntk_factor = rope_scale[1] ** (self.dim_h / (self.dim_h - 2))
+        self.w_ntk_factor = rope_scale[2] ** (self.dim_w / (self.dim_w - 2))
+        self.t_ntk_factor = rope_scale[0] ** (self.dim_t / (self.dim_t - 2))
+
+    def forward(
+        self, hidden_states: torch.Tensor, fps: int | None = None
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        """
+        Generate 3D RoPE embeddings.
+        
+        Args:
+            hidden_states: (B, T, H, W, D) - patch-embedded features
+            fps: Frames per second for temporal scaling
+            
+        Returns:
+            cos, sin: RoPE embeddings (THW, D)
+        """
+        batch_size, T, H, W, input_dim = hidden_states.shape
+        device = hidden_states.device
+
+        # T, H, W are already patch dimensions after patch_embed
+        # No need to divide by patch_size
+
+        # Generate frequency scales with NTK
+        h_theta = 10000.0 * self.h_ntk_factor
+        w_theta = 10000.0 * self.w_ntk_factor
+        t_theta = 10000.0 * self.t_ntk_factor
+
+        seq = torch.arange(max(self.max_size), device=device, dtype=torch.float32)
+
+        # Use self.dim_h/w/t which were set during initialization
+        dim_h_range = torch.arange(0, self.dim_h, 2, device=device, dtype=torch.float32)[: (self.dim_h // 2)] / self.dim_h
+        dim_w_range = torch.arange(0, self.dim_w, 2, device=device, dtype=torch.float32)[: (self.dim_w // 2)] / self.dim_w
+        dim_t_range = torch.arange(0, self.dim_t, 2, device=device, dtype=torch.float32)[: (self.dim_t // 2)] / self.dim_t
+
+        h_spatial_freqs = 1.0 / (h_theta ** dim_h_range)
+        w_spatial_freqs = 1.0 / (w_theta ** dim_w_range)
+        temporal_freqs = 1.0 / (t_theta ** dim_t_range)
+
+        # Generate positional embeddings
+        half_emb_h = torch.outer(seq[:H], h_spatial_freqs)
+        half_emb_w = torch.outer(seq[:W], w_spatial_freqs)
+
+        if self.enable_fps_modulation and fps is not None:
+            # Apply FPS scaling
+            half_emb_t = torch.outer(seq[:T] / fps * self.base_fps, temporal_freqs)
+        else:
+            half_emb_t = torch.outer(seq[:T], temporal_freqs)
+
+        # Broadcast and concatenate embeddings
+        emb_t = half_emb_t[:, None, None, :].repeat(1, H, W, 1)
+        emb_h = half_emb_h[None, :, None, :].repeat(T, 1, W, 1)
+        emb_w = half_emb_w[None, None, :, :].repeat(T, H, 1, 1)
+
+        # Concatenate [t, h, w, t, h, w] for sin/cos pairs
+        freqs = torch.cat([emb_t, emb_h, emb_w] * 2, dim=-1)
+        freqs = freqs.flatten(0, 2).float()  # (THW, D)
+
+        cos = torch.cos(freqs)  # (THW, D)
+        sin = torch.sin(freqs)  # (THW, D)
+        
+        return cos, sin
+
+
+class Cosmos25LearnablePositionalEmbed(nn.Module):
+    """
+    COSMOS 2.5 learnable absolute positional embeddings (optional).
+    """
+
+    def __init__(
+        self,
+        hidden_size: int,
+        max_size: tuple[int, int, int],
+        patch_size: tuple[int, int, int],
+        eps: float = 1e-6,
+    ) -> None:
+        super().__init__()
+
+        self.max_size = [size // patch for size, patch in zip(max_size, patch_size, strict=True)]
+        self.patch_size = patch_size
+        self.eps = eps
+
+        self.pos_emb_t = nn.Parameter(torch.zeros(self.max_size[0], hidden_size))
+        self.pos_emb_h = nn.Parameter(torch.zeros(self.max_size[1], hidden_size))
+        self.pos_emb_w = nn.Parameter(torch.zeros(self.max_size[2], hidden_size))
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        """
+        Args:
+            hidden_states: (B, T, H, W, D)
+        Returns:
+            pos_emb: (B, T, H, W, D)
+        """
+        B, T, H, W, D = hidden_states.shape
+
+        emb_t = self.pos_emb_t[:T][None, :, None, None, :].repeat(B, 1, H, W, 1)
+        emb_h = self.pos_emb_h[:H][None, None, :, None, :].repeat(B, T, 1, W, 1)
+        emb_w = self.pos_emb_w[:W][None, None, None, :, :].repeat(B, T, H, 1, 1)
+
+        emb = emb_t + emb_h + emb_w
+
+        # Normalize
+        norm = torch.linalg.vector_norm(emb, dim=-1, keepdim=True, dtype=torch.float32)
+        norm = torch.add(self.eps, norm, alpha=np.sqrt(norm.numel() / emb.numel()))
+        return (emb / norm).type_as(hidden_states)
+
+
+class Cosmos25FinalLayer(nn.Module):
+    """
+    COSMOS 2.5 final layer with AdaLN modulation and unpatchification.
+    """
+
+    def __init__(
+        self,
+        hidden_size: int,
+        out_channels: int,
+        patch_size: tuple[int, int, int],
+        adaln_lora_dim: int = 256,
+        use_adaln_lora: bool = True,
+    ) -> None:
+        super().__init__()
+        self.hidden_size = hidden_size
+        self.use_adaln_lora = use_adaln_lora
+
+        self.norm = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.activation = nn.SiLU()
+
+        if use_adaln_lora:
+            self.linear_1 = nn.Linear(hidden_size, adaln_lora_dim, bias=False)
+            self.linear_2 = nn.Linear(adaln_lora_dim, 2 * hidden_size, bias=False)
+        else:
+            self.linear_1 = nn.Identity()
+            self.linear_2 = nn.Linear(hidden_size, 2 * hidden_size, bias=False)
+
+        # Output projection
+        output_dim = out_channels * patch_size[0] * patch_size[1] * patch_size[2]
+        self.proj_out = nn.Linear(hidden_size, output_dim, bias=False)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        embedded_timestep: torch.Tensor,
+        adaln_lora: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        """
+        Args:
+            hidden_states: (B, T, H, W, D)
+            embedded_timestep: (B, T, D) or (B, D)
+            adaln_lora: (B, T, 3D) or None
+        """
+        # Generate modulation parameters
+        embedded_timestep = self.activation(embedded_timestep)
+        embedded_timestep = self.linear_1(embedded_timestep)
+        embedded_timestep = self.linear_2(embedded_timestep)
+
+        if self.use_adaln_lora and adaln_lora is not None:
+            # Use first 2*hidden_size elements for shift/scale
+            embedded_timestep = embedded_timestep + adaln_lora[..., : 2 * self.hidden_size]
+
+        shift, scale = embedded_timestep.chunk(2, dim=-1)
+
+        # Apply normalization and modulation
+        hidden_states = self.norm(hidden_states)
+
+        # Reshape for broadcasting if needed
+        if embedded_timestep.ndim == 2:
+            shift, scale = (x.unsqueeze(1) for x in (shift, scale))
+        elif embedded_timestep.ndim == 3 and hidden_states.ndim == 5:
+            shift, scale = (x.unsqueeze(2).unsqueeze(2) for x in (shift, scale))
+
+        hidden_states = hidden_states * (1 + scale) + shift
+
+        # Project to output
+        hidden_states = self.proj_out(hidden_states)
+
+        return hidden_states
+
+
+class Cosmos25Transformer3DModel(BaseDiT):
+    """
+    COSMOS 2.5 DiT - MiniTrainDIT architecture adapted for FastVideo.
+    
+    Key features:
+    - AdaLN-LoRA conditioning
+    - 3D RoPE with NTK-aware extrapolation
+    - Optional learnable positional embeddings
+    - QK normalization
+    - Cross-attention projection (optional)
+    """
+    
+    _fsdp_shard_conditions = Cosmos25VideoConfig()._fsdp_shard_conditions
+    _compile_conditions = Cosmos25VideoConfig()._compile_conditions
+    param_names_mapping = Cosmos25VideoConfig().param_names_mapping
+    lora_param_names_mapping = Cosmos25VideoConfig().lora_param_names_mapping
+
+    def __init__(self, config: Cosmos25VideoConfig, 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 = inner_dim
+        self.num_attention_heads = config.num_attention_heads
+        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.max_size = config.max_size
+        self.rope_scale = config.rope_scale
+        self.concat_padding_mask = config.concat_padding_mask
+        self.use_adaln_lora = getattr(config, "use_adaln_lora", True)
+        self.adaln_lora_dim = getattr(config, "adaln_lora_dim", 256)
+        self.extra_pos_embed_type = getattr(config, "extra_pos_embed_type", None)
+        self.use_crossattn_projection = getattr(config, "use_crossattn_projection", False)
+
+        # 1. Patch Embedding
+        # Account for: VAE channels + condition_mask (1) + padding_mask (1 if concat_padding_mask)
+        patch_embed_in_channels = config.in_channels  # Base VAE channels (16)
+        patch_embed_in_channels += 1  # Always add 1 for condition_mask
+        if config.concat_padding_mask:
+            patch_embed_in_channels += 1  # Add 1 for padding_mask
+        # Total: 16 + 1 + 1 = 18 (with concat_padding_mask=True)
+        
+        self.patch_embed = Cosmos25PatchEmbed(
+            patch_embed_in_channels, inner_dim, config.patch_size
+        )
+
+        # 2. Positional Embeddings
+        self.rope = Cosmos25RotaryPosEmbed(
+            hidden_size=config.attention_head_dim,
+            max_size=config.max_size,
+            patch_size=config.patch_size,
+            rope_scale=config.rope_scale,
+            enable_fps_modulation=getattr(config, "rope_enable_fps_modulation", True),
+        )
+
+        self.learnable_pos_embed = None
+        if self.extra_pos_embed_type == "learnable":
+            self.learnable_pos_embed = Cosmos25LearnablePositionalEmbed(
+                hidden_size=inner_dim,
+                max_size=config.max_size,
+                patch_size=config.patch_size,
+            )
+
+        # 3. Time Embedding
+        self.time_embed = Cosmos25Embedding(
+            inner_dim,
+            inner_dim,
+            use_adaln_lora=self.use_adaln_lora,
+            adaln_lora_dim=self.adaln_lora_dim,
+        )
+
+        # 4. Cross-attention projection (optional)
+        if self.use_crossattn_projection:
+            crossattn_proj_in_channels = getattr(config, "crossattn_proj_in_channels", config.text_embed_dim)
+            self.crossattn_proj = nn.Sequential(
+                nn.Linear(crossattn_proj_in_channels, config.text_embed_dim, bias=True),
+                nn.GELU(),
+            )
+
+        # 5. Transformer Blocks
+        self.transformer_blocks = nn.ModuleList([
+            Cosmos25TransformerBlock(
+                num_attention_heads=config.num_attention_heads,
+                attention_head_dim=config.attention_head_dim,
+                cross_attention_dim=config.text_embed_dim,
+                mlp_ratio=config.mlp_ratio,
+                adaln_lora_dim=self.adaln_lora_dim,
+                use_adaln_lora=self.use_adaln_lora,
+                qk_norm=(config.qk_norm == "rms_norm"),
+                supported_attention_backends=config._supported_attention_backends,
+            )
+            for i in range(config.num_layers)
+        ])
+
+        # 6. Final Layer
+        self.final_layer = Cosmos25FinalLayer(
+            hidden_size=inner_dim,
+            out_channels=config.out_channels,
+            patch_size=config.patch_size,
+            adaln_lora_dim=self.adaln_lora_dim,
+            use_adaln_lora=self.use_adaln_lora,
+        )
+
+        self.gradient_checkpointing = False
+        self.__post_init__()
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        timestep: torch.Tensor,
+        encoder_hidden_states: torch.Tensor | list[torch.Tensor],
+        attention_mask: torch.Tensor | None = None,
+        fps: int | None = None,
+        condition_mask: torch.Tensor | None = None,
+        padding_mask: torch.Tensor | None = None,
+        **kwargs,
+    ) -> torch.Tensor:
+        """
+        Args:
+            hidden_states: (B, C, T, H, W) latent video
+            timestep: (B,) or (B, T) diffusion timesteps
+            encoder_hidden_states: (B, N, D_text) text embeddings
+            attention_mask: Optional attention mask
+            fps: Frames per second
+            condition_mask: (B, 1, T, H, W) conditioning mask
+            padding_mask: (B, 1, H, W) padding mask
+        """
+        if not isinstance(encoder_hidden_states, torch.Tensor):
+            encoder_hidden_states = encoder_hidden_states[0]
+
+        batch_size, num_channels, num_frames, height, width = hidden_states.shape
+
+        # 1. Concatenate condition mask if provided
+        if condition_mask is not None:
+            hidden_states = torch.cat([hidden_states, condition_mask], dim=1)
+
+        # 2. Concatenate padding mask if needed
+        if self.concat_padding_mask and padding_mask is not None:
+            padding_mask = transforms.functional.resize(
+                padding_mask,
+                list(hidden_states.shape[-2:]),
+                interpolation=transforms.InterpolationMode.NEAREST,
+            )
+            hidden_states = torch.cat(
+                [hidden_states, padding_mask.unsqueeze(2).repeat(1, 1, num_frames, 1, 1)],
+                dim=1,
+            )
+
+        # 3. Patchify input
+        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
+        
+        
+        hidden_states = self.patch_embed(hidden_states)  # (B, T', H', W', D)
+       
+
+        # 4. Generate RoPE embeddings (after patchify, using patch dimensions)
+        rope_emb = self.rope(hidden_states, fps=fps)
+
+        # 5. Generate learnable positional embeddings (if used)
+        extra_pos_emb = None
+        if self.learnable_pos_embed is not None:
+            extra_pos_emb = self.learnable_pos_embed(hidden_states)
+
+        # 6. Timestep embeddings
+        # Official model expects timestep in (B, T) format, so ensure it has 2D shape
+        if timestep.ndim == 1:
+            # Scalar timestep per sample: (B,) -> (B, 1)
+            timestep = timestep.unsqueeze(1)
+        elif timestep.ndim == 2:
+            # Already in (B, T) format
+            pass
+        else:
+            raise ValueError(f"Unsupported timestep shape: {timestep.shape}")
+        
+        # Now timestep is always (B, T), pass directly to time_embed
+        embedded_timestep, adaln_lora = self.time_embed(hidden_states, timestep)
+
+        # 7. Apply cross-attention projection (if used)
+        if self.use_crossattn_projection:
+            encoder_hidden_states = self.crossattn_proj(encoder_hidden_states)
+            
+        
+        
+
+        # Prepare attention mask
+        if attention_mask is not None:
+            attention_mask = attention_mask.unsqueeze(1).unsqueeze(1)  # (B, 1, 1, N)
+
+        # 8. Transformer blocks
+        if torch.is_grad_enabled() and self.gradient_checkpointing:
+            for block in self.transformer_blocks:
+                hidden_states = self._gradient_checkpointing_func(
+                    block,
+                    hidden_states,
+                    encoder_hidden_states,
+                    embedded_timestep,
+                    adaln_lora,
+                    rope_emb,
+                    extra_pos_emb,
+                    attention_mask,
+                )
+        else:
+            for i, block in enumerate(self.transformer_blocks):
+                hidden_states = block(
+                    hidden_states=hidden_states,
+                    encoder_hidden_states=encoder_hidden_states,
+                    embedded_timestep=embedded_timestep,
+                    adaln_lora=adaln_lora,
+                    rope_emb=rope_emb,
+                    extra_pos_emb=extra_pos_emb,
+                    attention_mask=attention_mask,
+                )
+
+        # 9. Final layer - output norm & projection
+        hidden_states = self.final_layer(hidden_states, embedded_timestep, adaln_lora)
+
+        # 10. Unpatchify: (B, T', H', W', P) -> (B, C, T, H, W)
+        # After unflatten: (B, T', H', W', p_t, p_h, p_w, C) with dims [0,1,2,3,4,5,6,7]
+        hidden_states = hidden_states.unflatten(-1, (p_t, p_h, p_w, self.out_channels))
+        # Permute to: (B, C, T', p_t, H', p_h, W', p_w)
+        hidden_states = hidden_states.permute(0, 7, 1, 4, 2, 5, 3, 6)
+        # Flatten pairs to get (B, C, T, H, W)
+        hidden_states = hidden_states.flatten(2, 3).flatten(3, 4).flatten(4, 5)
+
+        return hidden_states
+

fastvideo/models/dits/hunyuanvideo.py

@@ -23,6 +23,7 @@ from fastvideo.layers.visual_embedding import (ModulateProjection, PatchEmbed,
 from fastvideo.models.dits.base import CachableDiT
 from fastvideo.models.utils import modulate
 from fastvideo.platforms import AttentionBackendEnum
+from fastvideo.distributed.communication_op import sequence_model_parallel_shard, sequence_model_parallel_all_gather
 
 
 class HunyuanRMSNorm(nn.Module):
@@ -239,14 +240,7 @@ class MMDoubleStreamBlock(nn.Module):
 
         img_q = self.img_attn_q_norm(img_q).to(img_v)
         img_k = self.img_attn_k_norm(img_k).to(img_v)
-        # Apply rotary embeddings
-        cos, sin = freqs_cis
-        img_q, img_k = _apply_rotary_emb(
-            img_q, cos, sin,
-            is_neox_style=False), _apply_rotary_emb(img_k,
-                                                    cos,
-                                                    sin,
-                                                    is_neox_style=False)
+
         # Prepare text for attention using fused operation
         txt_attn_input = self.txt_attn_norm(txt, txt_attn_shift, txt_attn_scale)
 
@@ -265,7 +259,7 @@ class MMDoubleStreamBlock(nn.Module):
         txt_k = self.txt_attn_k_norm(txt_k).to(txt_k.dtype)
 
         # Run distributed attention
-        img_attn, txt_attn = self.attn(img_q, img_k, img_v, txt_q, txt_k, txt_v)
+        img_attn, txt_attn = self.attn(img_q, img_k, img_v, txt_q, txt_k, txt_v, freqs_cis=freqs_cis)
         img_attn_out, _ = self.img_attn_proj(
             img_attn.view(batch_size, image_seq_len, -1))
         # Use fused operation for residual connection, normalization, and modulation
@@ -395,18 +389,11 @@ class MMSingleStreamBlock(nn.Module):
         img_q, txt_q = q[:, :-txt_len], q[:, -txt_len:]
         img_k, txt_k = k[:, :-txt_len], k[:, -txt_len:]
         img_v, txt_v = v[:, :-txt_len], v[:, -txt_len:]
-        # Apply rotary embeddings to image parts
-        cos, sin = freqs_cis
-        img_q, img_k = _apply_rotary_emb(
-            img_q, cos, sin,
-            is_neox_style=False), _apply_rotary_emb(img_k,
-                                                    cos,
-                                                    sin,
-                                                    is_neox_style=False)
+
 
         # Run distributed attention
         img_attn_output, txt_attn_output = self.attn(img_q, img_k, img_v, txt_q,
-                                                     txt_k, txt_v)
+                                                     txt_k, txt_v, freqs_cis = freqs_cis)
         attn_output = torch.cat((img_attn_output, txt_attn_output),
                                 dim=1).view(batch_size, seq_len, -1)
         # Process MLP activation
@@ -593,7 +580,7 @@ class HunyuanVideoTransformer3DModel(CachableDiT):
 
         # Get rotary embeddings
         freqs_cos, freqs_sin = get_rotary_pos_embed(
-            (tt * get_sp_world_size(), th, tw), self.hidden_size,
+            (tt, th, tw), self.hidden_size,
             self.num_attention_heads, self.rope_dim_list, self.rope_theta)
         freqs_cos = freqs_cos.to(x.device)
         freqs_sin = freqs_sin.to(x.device)
@@ -608,6 +595,7 @@ class HunyuanVideoTransformer3DModel(CachableDiT):
             vec = vec + self.guidance_in(guidance)
         # Embed image and text
         img = self.img_in(img)
+        img, _ = sequence_model_parallel_shard(img, dim=1)
         txt = self.txt_in(txt, t)
         txt_seq_len = txt.shape[1]
         img_seq_len = img.shape[1]
@@ -648,6 +636,7 @@ class HunyuanVideoTransformer3DModel(CachableDiT):
                 self.maybe_cache_states(img, original_img)
 
         # Final layer processing
+        img = sequence_model_parallel_all_gather(img, dim=1)
         img = self.final_layer(img, vec)
         # Unpatchify to get original shape
         img = unpatchify(img, tt, th, tw, self.patch_size, self.out_channels)

fastvideo/models/dits/hunyuanvideo15.py

@@ -0,0 +1,853 @@
+# Copyright 2025 The Hunyuan Team and The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import Any, Dict, Optional, List
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from fastvideo.attention import DistributedAttention, LocalAttention
+from fastvideo.distributed.communication_op import (
+    sequence_model_parallel_all_gather_with_unpad,
+    sequence_model_parallel_shard)
+from fastvideo.configs.models.dits import HunyuanVideo15Config
+from fastvideo.layers.layernorm import (LayerNormScaleShift, ScaleResidual,
+                                        ScaleResidualLayerNormScaleShift)
+from fastvideo.layers.linear import ReplicatedLinear
+# TODO(will-PY-refactor): RMSNorm ....
+from fastvideo.layers.mlp import MLP
+from fastvideo.layers.rotary_embedding import get_rotary_pos_embed
+from fastvideo.layers.visual_embedding import (ModulateProjection, PatchEmbed,
+                                               TimestepEmbedder, unpatchify)
+from fastvideo.models.dits.base import CachableDiT
+from fastvideo.platforms import AttentionBackendEnum
+from fastvideo.logger import init_logger
+from fastvideo.forward_context import set_forward_context
+from fastvideo.attention.backends.abstract import AttentionMetadata
+
+from fastvideo.distributed.parallel_state import get_sp_world_size
+from fastvideo.distributed.utils import create_attention_mask_for_padding
+
+logger = init_logger(__name__)
+
+class HunyuanRMSNorm(nn.Module):
+
+    def __init__(
+        self,
+        dim: int,
+        elementwise_affine=True,
+        eps: float = 1e-6,
+        device=None,
+        dtype=None,
+    ):
+        """
+        Initialize the RMSNorm normalization layer.
+
+        Args:
+            dim (int): The dimension of the input tensor.
+            eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6.
+
+        Attributes:
+            eps (float): A small value added to the denominator for numerical stability.
+            weight (nn.Parameter): Learnable scaling parameter.
+
+        """
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__()
+        self.eps = eps
+        if elementwise_affine:
+            self.weight = nn.Parameter(torch.ones(dim, **factory_kwargs))
+
+    def _norm(self, x) -> torch.Tensor:
+        """
+        Apply the RMSNorm normalization to the input tensor.
+
+        Args:
+            x (torch.Tensor): The input tensor.
+
+        Returns:
+            torch.Tensor: The normalized tensor.
+
+        """
+        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
+
+    def forward(self, x):
+        """
+        Forward pass through the RMSNorm layer.
+
+        Args:
+            x (torch.Tensor): The input tensor.
+
+        Returns:
+            torch.Tensor: The output tensor after applying RMSNorm.
+
+        """
+        output = self._norm(x.float()).type_as(x)
+        if hasattr(self, "weight"):
+            output = output * self.weight
+        return output
+
+
+class HunyuanVideo15TimeEmbedding(nn.Module):
+    r"""
+    Time embedding for HunyuanVideo 1.5.
+
+    Supports standard timestep embedding and optional reference timestep embedding for MeanFlow-based super-resolution
+    models.
+
+    Args:
+        embedding_dim (`int`):
+            The dimension of the output embedding.
+    """
+
+    def __init__(self, embedding_dim: int, use_meanflow: bool = False):
+        super().__init__()
+
+        self.timestep_embedder = TimestepEmbedder(hidden_size=embedding_dim)
+
+        self.use_meanflow = use_meanflow
+        self.time_proj_r = None
+        self.timestep_embedder_r = None
+        if use_meanflow:
+            self.timestep_embedder_r = TimestepEmbedder(hidden_size=embedding_dim)
+
+    def forward(
+        self,
+        timestep: torch.Tensor,
+        timestep_r: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        timesteps_emb = self.timestep_embedder(timestep)
+
+        if timestep_r is not None:
+            timesteps_emb_r = self.timestep_embedder_r(timestep_r)
+            timesteps_emb = timesteps_emb + timesteps_emb_r
+
+        return timesteps_emb
+
+
+class HunyuanVideo15ByT5TextProjection(nn.Module):
+    def __init__(self, in_features: int, hidden_size: int, out_features: int):
+        super().__init__()
+        self.norm = nn.LayerNorm(in_features)
+        self.linear_1 = nn.Linear(in_features, hidden_size)
+        self.linear_2 = nn.Linear(hidden_size, hidden_size)
+        self.linear_3 = nn.Linear(hidden_size, out_features)
+        self.act_fn = nn.GELU()
+
+    def forward(self, encoder_hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.norm(encoder_hidden_states)
+        hidden_states = self.linear_1(hidden_states)
+        hidden_states = self.act_fn(hidden_states)
+        hidden_states = self.linear_2(hidden_states)
+        hidden_states = self.act_fn(hidden_states)
+        hidden_states = self.linear_3(hidden_states)
+        return hidden_states
+
+
+class HunyuanVideo15ImageProjection(nn.Module):
+    def __init__(self, in_channels: int, hidden_size: int):
+        super().__init__()
+        self.norm_in = nn.LayerNorm(in_channels)
+        self.linear_1 = nn.Linear(in_channels, in_channels)
+        self.act_fn = nn.GELU()
+        self.linear_2 = nn.Linear(in_channels, hidden_size)
+        self.norm_out = nn.LayerNorm(hidden_size)
+
+    def forward(self, image_embeds: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.norm_in(image_embeds)
+        hidden_states = self.linear_1(hidden_states)
+        hidden_states = self.act_fn(hidden_states)
+        hidden_states = self.linear_2(hidden_states)
+        hidden_states = self.norm_out(hidden_states)
+        return hidden_states
+
+
+class MMDoubleStreamBlock(nn.Module):
+    """
+    A multimodal DiT block with separate modulation for text and image/video,
+    using distributed attention and linear layers.
+    """
+
+    def __init__(
+        self,
+        hidden_size: int,
+        num_attention_heads: int,
+        mlp_ratio: float,
+        dtype: torch.dtype | None = None,
+        supported_attention_backends: tuple[AttentionBackendEnum, ...]
+        | None = None,
+        prefix: str = "",
+    ):
+        super().__init__()
+
+        self.deterministic = False
+        self.num_attention_heads = num_attention_heads
+        head_dim = hidden_size // num_attention_heads
+        mlp_hidden_dim = int(hidden_size * mlp_ratio)
+
+        # Image modulation components
+        self.img_mod = ModulateProjection(
+            hidden_size,
+            factor=6,
+            act_layer="silu",
+            dtype=dtype,
+            prefix=f"{prefix}.img_mod",
+        )
+
+        # Fused operations for image stream
+        self.img_attn_norm = LayerNormScaleShift(hidden_size,
+                                                 norm_type="layer",
+                                                 elementwise_affine=False,
+                                                 dtype=dtype)
+        self.img_attn_residual_mlp_norm = ScaleResidualLayerNormScaleShift(
+            hidden_size,
+            norm_type="layer",
+            elementwise_affine=False,
+            dtype=dtype)
+        self.img_mlp_residual = ScaleResidual()
+
+        # Image attention components
+        self.img_attn_qkv = ReplicatedLinear(hidden_size,
+                                             hidden_size * 3,
+                                             bias=True,
+                                             params_dtype=dtype,
+                                             prefix=f"{prefix}.img_attn_qkv")
+
+        self.img_attn_q_norm = HunyuanRMSNorm(head_dim, eps=1e-6, dtype=dtype)
+        self.img_attn_k_norm = HunyuanRMSNorm(head_dim, eps=1e-6, dtype=dtype)
+
+        self.img_attn_proj = ReplicatedLinear(hidden_size,
+                                              hidden_size,
+                                              bias=True,
+                                              params_dtype=dtype,
+                                              prefix=f"{prefix}.img_attn_proj")
+
+        self.img_mlp = MLP(hidden_size,
+                           mlp_hidden_dim,
+                           bias=True,
+                           dtype=dtype,
+                           prefix=f"{prefix}.img_mlp")
+
+        # Text modulation components
+        self.txt_mod = ModulateProjection(
+            hidden_size,
+            factor=6,
+            act_layer="silu",
+            dtype=dtype,
+            prefix=f"{prefix}.txt_mod",
+        )
+
+        # Fused operations for text stream
+        self.txt_attn_norm = LayerNormScaleShift(hidden_size,
+                                                 norm_type="layer",
+                                                 elementwise_affine=False,
+                                                 dtype=dtype)
+        self.txt_attn_residual_mlp_norm = ScaleResidualLayerNormScaleShift(
+            hidden_size,
+            norm_type="layer",
+            elementwise_affine=False,
+            dtype=dtype)
+        self.txt_mlp_residual = ScaleResidual()
+
+        # Text attention components
+        self.txt_attn_qkv = ReplicatedLinear(hidden_size,
+                                             hidden_size * 3,
+                                             bias=True,
+                                             params_dtype=dtype)
+
+        # QK norm layers for text
+        self.txt_attn_q_norm = HunyuanRMSNorm(head_dim, eps=1e-6, dtype=dtype)
+        self.txt_attn_k_norm = HunyuanRMSNorm(head_dim, eps=1e-6, dtype=dtype)
+
+        self.txt_attn_proj = ReplicatedLinear(hidden_size,
+                                              hidden_size,
+                                              bias=True,
+                                              params_dtype=dtype)
+
+        self.txt_mlp = MLP(hidden_size, mlp_hidden_dim, bias=True, dtype=dtype)
+
+        # Distributed attention
+        self.attn = DistributedAttention(
+            num_heads=num_attention_heads,
+            head_size=head_dim,
+            causal=False,
+            supported_attention_backends=supported_attention_backends,
+            prefix=f"{prefix}.attn")
+
+    def forward(
+        self,
+        img: torch.Tensor,
+        txt: torch.Tensor,
+        encoder_attention_mask: torch.Tensor,
+        vec: torch.Tensor,
+        freqs_cis: tuple,
+        seq_attention_mask: torch.Tensor,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        # Process modulation vectors
+        img_mod_outputs = self.img_mod(vec)
+        (
+            img_attn_shift,
+            img_attn_scale,
+            img_attn_gate,
+            img_mlp_shift,
+            img_mlp_scale,
+            img_mlp_gate,
+        ) = torch.chunk(img_mod_outputs, 6, dim=-1)
+
+        txt_mod_outputs = self.txt_mod(vec)
+        (
+            txt_attn_shift,
+            txt_attn_scale,
+            txt_attn_gate,
+            txt_mlp_shift,
+            txt_mlp_scale,
+            txt_mlp_gate,
+        ) = torch.chunk(txt_mod_outputs, 6, dim=-1)
+
+        # Prepare image for attention using fused operation
+        img_attn_input = self.img_attn_norm(img, img_attn_shift, img_attn_scale)
+        # Get QKV for image
+        img_qkv, _ = self.img_attn_qkv(img_attn_input)
+        batch_size, image_seq_len = img_qkv.shape[0], img_qkv.shape[1]
+
+        # Split QKV
+        img_qkv = img_qkv.view(batch_size, image_seq_len, 3,
+                               self.num_attention_heads, -1)
+        img_q, img_k, img_v = img_qkv[:, :, 0], img_qkv[:, :, 1], img_qkv[:, :,
+                                                                          2]
+
+        # Apply QK-Norm if needed
+        img_q = self.img_attn_q_norm(img_q).to(img_v)
+        img_k = self.img_attn_k_norm(img_k).to(img_v)
+
+        # Prepare text for attention using fused operation
+        txt_attn_input = self.txt_attn_norm(txt, txt_attn_shift, txt_attn_scale)
+
+        # Get QKV for text
+        txt_qkv, _ = self.txt_attn_qkv(txt_attn_input)
+        batch_size, text_seq_len = txt_qkv.shape[0], txt_qkv.shape[1]
+
+        # Split QKV
+        txt_qkv = txt_qkv.view(batch_size, text_seq_len, 3,
+                               self.num_attention_heads, -1)
+        txt_q, txt_k, txt_v = txt_qkv[:, :, 0], txt_qkv[:, :, 1], txt_qkv[:, :,
+                                                                          2]
+        # Apply QK-Norm if needed
+        txt_q = self.txt_attn_q_norm(txt_q).to(txt_q.dtype)
+        txt_k = self.txt_attn_k_norm(txt_k).to(txt_k.dtype)
+
+        # seq_len = txt_q.shape[1] + img_q.shape[1]
+        # attention_mask = F.pad(encoder_attention_mask, (seq_len - encoder_attention_mask.shape[1], 0), value=True)
+        # attention_mask = attention_mask.bool()
+        # self_attn_mask_1 = attention_mask.view(batch_size, 1, 1, seq_len).repeat(1, 1, seq_len, 1)
+        # self_attn_mask_2 = self_attn_mask_1.transpose(2, 3)
+        # attention_mask = (self_attn_mask_1 & self_attn_mask_2).bool()
+
+        from fastvideo.attention.backends.flash_attn import FlashAttnMetadataBuilder
+        attn_metadata = FlashAttnMetadataBuilder().build(
+            current_timestep=0,
+            attn_mask=encoder_attention_mask,
+        )
+        # Run distributed attention
+        with set_forward_context(current_timestep=0, attn_metadata=attn_metadata):
+            img_attn, txt_attn = self.attn(img_q, img_k, img_v, txt_q, txt_k, txt_v, freqs_cis=freqs_cis, attention_mask=seq_attention_mask)
+        
+        img_attn_out, _ = self.img_attn_proj(
+            img_attn.view(batch_size, image_seq_len, -1))
+        # Use fused operation for residual connection, normalization, and modulation
+        img_mlp_input, img_residual = self.img_attn_residual_mlp_norm(
+            img, img_attn_out, img_attn_gate, img_mlp_shift, img_mlp_scale)
+
+        # Process image MLP
+        img_mlp_out = self.img_mlp(img_mlp_input)
+        img = self.img_mlp_residual(img_residual, img_mlp_out, img_mlp_gate)
+
+        # Process text attention output
+        txt_attn_out, _ = self.txt_attn_proj(
+            txt_attn.reshape(batch_size, text_seq_len, -1))
+
+        # Use fused operation for residual connection, normalization, and modulation
+        txt_mlp_input, txt_residual = self.txt_attn_residual_mlp_norm(
+            txt, txt_attn_out, txt_attn_gate, txt_mlp_shift, txt_mlp_scale)
+
+        # Process text MLP
+        txt_mlp_out = self.txt_mlp(txt_mlp_input)
+        txt = self.txt_mlp_residual(txt_residual, txt_mlp_out, txt_mlp_gate)
+
+        return img, txt
+
+
+class HunyuanVideo15Transformer3DModel(CachableDiT):
+    r"""
+    A Transformer model for video-like data used in [HunyuanVideo1.5](https://huggingface.co/tencent/HunyuanVideo1.5).
+    """
+
+    # shard single stream, double stream blocks, and refiner_blocks
+    _fsdp_shard_conditions = HunyuanVideo15Config()._fsdp_shard_conditions
+    _compile_conditions = HunyuanVideo15Config()._compile_conditions
+    _supported_attention_backends = HunyuanVideo15Config(
+    )._supported_attention_backends
+    param_names_mapping = HunyuanVideo15Config().param_names_mapping
+    reverse_param_names_mapping = HunyuanVideo15Config(
+    ).reverse_param_names_mapping
+    lora_param_names_mapping = HunyuanVideo15Config().lora_param_names_mapping
+
+    def __init__(
+        self,
+        config: HunyuanVideo15Config,
+        hf_config: dict[str, Any],
+    ) -> None:
+        super().__init__(config=config, hf_config=hf_config)
+
+        self.hidden_size = config.hidden_size
+        self.num_attention_heads = config.num_attention_heads
+        self.num_channels_latents = config.num_channels_latents
+        self.out_channels = config.out_channels or config.in_channels
+        self.patch_size = (config.patch_size_t, config.patch_size, config.patch_size)
+
+        # 1. Latent and condition embedders
+        self.img_in = PatchEmbed(self.patch_size, 
+                                    config.in_channels, 
+                                    self.hidden_size,
+                                    prefix=f"{config.prefix}.img_in")
+        self.image_embedder = HunyuanVideo15ImageProjection(config.image_embed_dim, self.hidden_size)
+
+        self.txt_in = SingleTokenRefiner(config.text_embed_dim,
+                                                   self.hidden_size,
+                                                   config.num_attention_heads,
+                                                   depth=config.num_refiner_layers,
+                                                   dtype=None,
+                                                   prefix=f"{config.prefix}.txt_in")
+
+        self.txt_in_2 = HunyuanVideo15ByT5TextProjection(config.text_embed_2_dim, 2048, self.hidden_size)
+
+        self.time_in = HunyuanVideo15TimeEmbedding(self.hidden_size, use_meanflow=config.use_meanflow)
+
+        self.cond_type_embed = nn.Embedding(3, self.hidden_size)
+
+        # 3. Dual stream transformer blocks
+
+        self.double_blocks = nn.ModuleList(
+            [
+                MMDoubleStreamBlock(
+                    hidden_size=self.hidden_size,
+                    num_attention_heads=config.num_attention_heads,
+                    mlp_ratio=config.mlp_ratio,
+                    dtype=None,
+                    supported_attention_backends=self._supported_attention_backends,
+                    prefix=f"{config.prefix}.double_blocks.{i}"
+                )
+                for i in range(config.num_layers)
+            ]
+        )
+
+        # 5. Output projection
+        self.final_layer = FinalLayer(self.hidden_size,
+                                self.patch_size,
+                                self.out_channels,
+                                prefix=f"{config.prefix}.final_layer")
+
+        self.gradient_checkpointing = False
+
+        self.__post_init__()
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: List[torch.Tensor],
+        timestep: torch.LongTensor,
+        encoder_hidden_states_image: List[torch.Tensor],
+        encoder_attention_mask: List[torch.Tensor],
+        guidance: Optional[torch.Tensor] = None,
+        timestep_r: Optional[torch.LongTensor] = None,
+        attention_kwargs: Optional[Dict[str, Any]] = None,
+    ):
+        encoder_hidden_states_image = encoder_hidden_states_image[0]
+        encoder_hidden_states, encoder_hidden_states_2 = encoder_hidden_states
+        encoder_attention_mask, encoder_attention_mask_2 = encoder_attention_mask
+
+        batch_size, num_channels, num_frames, height, width = hidden_states.shape
+        p_t, p_h, p_w = self.config.patch_size_t, self.config.patch_size, self.config.patch_size
+        post_patch_num_frames = num_frames // p_t
+        post_patch_height = height // p_h
+        post_patch_width = width // p_w
+
+        # 1. RoPE
+        # Get rotary embeddings
+        freqs_cos, freqs_sin = get_rotary_pos_embed(
+            (post_patch_num_frames, post_patch_height, post_patch_width), self.hidden_size,
+            self.num_attention_heads, self.config.rope_axes_dim, self.config.rope_theta)
+        freqs_cos = freqs_cos.to(hidden_states.device)
+        freqs_sin = freqs_sin.to(hidden_states.device)
+        freqs_cis = (freqs_cos, freqs_sin) if freqs_cos is not None else None
+
+        # 2. Conditional embeddings
+        temb = self.time_in(timestep, timestep_r=timestep_r)
+
+        hidden_states = self.img_in(hidden_states)
+        hidden_states, original_seq_len = sequence_model_parallel_shard(hidden_states, dim=1)
+
+        current_seq_len = hidden_states.shape[1]
+        sp_world_size = get_sp_world_size()
+        padded_seq_len = current_seq_len * sp_world_size
+        
+        if padded_seq_len > original_seq_len:
+            seq_attention_mask = create_attention_mask_for_padding(
+                seq_len=original_seq_len,
+                padded_seq_len=padded_seq_len,
+                batch_size=batch_size,
+                device=hidden_states.device,
+            )
+        else:
+            seq_attention_mask = None
+
+        # qwen text embedding
+        encoder_hidden_states = self.txt_in(encoder_hidden_states, timestep, encoder_attention_mask)
+
+        encoder_hidden_states_cond_emb = self.cond_type_embed(
+            torch.zeros_like(encoder_hidden_states[:, :, 0], dtype=torch.long)
+        )
+        encoder_hidden_states = encoder_hidden_states + encoder_hidden_states_cond_emb
+
+        # byt5 text embedding
+        encoder_hidden_states_2 = self.txt_in_2(encoder_hidden_states_2)
+
+        encoder_hidden_states_2_cond_emb = self.cond_type_embed(
+            torch.ones_like(encoder_hidden_states_2[:, :, 0], dtype=torch.long)
+        )
+        encoder_hidden_states_2 = encoder_hidden_states_2 + encoder_hidden_states_2_cond_emb
+
+        # image embed
+        encoder_hidden_states_3 = self.image_embedder(encoder_hidden_states_image)
+        is_t2v = torch.all(encoder_hidden_states_image == 0)
+        if is_t2v:
+            encoder_hidden_states_3 = encoder_hidden_states_3 * 0.0
+            encoder_attention_mask_3 = torch.zeros(
+                (batch_size, encoder_hidden_states_3.shape[1]),
+                dtype=encoder_attention_mask.dtype,
+                device=encoder_attention_mask.device,
+            )
+        else:
+            encoder_attention_mask_3 = torch.ones(
+                (batch_size, encoder_hidden_states_3.shape[1]),
+                dtype=encoder_attention_mask.dtype,
+                device=encoder_attention_mask.device,
+            )
+        encoder_hidden_states_3_cond_emb = self.cond_type_embed(
+            2
+            * torch.ones_like(
+                encoder_hidden_states_3[:, :, 0],
+                dtype=torch.long,
+            )
+        )
+        encoder_hidden_states_3 = encoder_hidden_states_3 + encoder_hidden_states_3_cond_emb
+
+        # reorder and combine text tokens: combine valid tokens first, then padding
+        encoder_attention_mask = encoder_attention_mask.bool()
+        encoder_attention_mask_2 = encoder_attention_mask_2.bool()
+        encoder_attention_mask_3 = encoder_attention_mask_3.bool()
+        new_encoder_hidden_states = []
+        new_encoder_attention_mask = []
+
+        for text, text_mask, text_2, text_mask_2, image, image_mask in zip(
+            encoder_hidden_states,
+            encoder_attention_mask,
+            encoder_hidden_states_2,
+            encoder_attention_mask_2,
+            encoder_hidden_states_3,
+            encoder_attention_mask_3,
+        ):
+            # Concatenate: [valid_image, valid_byt5, valid_mllm, invalid_image, invalid_byt5, invalid_mllm]
+            new_encoder_hidden_states.append(
+                torch.cat(
+                    [
+                        image[image_mask],  # valid image
+                        text_2[text_mask_2],  # valid byt5
+                        text[text_mask],  # valid mllm
+                        image[~image_mask],  # invalid image (zeroed)
+                        torch.zeros_like(text_2[~text_mask_2]),  # invalid byt5 (zeroed)
+                        torch.zeros_like(text[~text_mask]),  # invalid mllm (zeroed)
+                    ],
+                    dim=0,
+                )
+            )
+            # Apply same reordering to attention masks
+            new_encoder_attention_mask.append(
+                torch.cat(
+                    [
+                        image_mask[image_mask],
+                        text_mask_2[text_mask_2],
+                        text_mask[text_mask],
+                        image_mask[~image_mask],
+                        text_mask_2[~text_mask_2],
+                        text_mask[~text_mask],
+                    ],
+                    dim=0,
+                )
+            )
+
+        encoder_hidden_states = torch.stack(new_encoder_hidden_states)
+        encoder_attention_mask = torch.stack(new_encoder_attention_mask)
+
+        # 4. Transformer blocks
+        if torch.is_grad_enabled() and self.gradient_checkpointing:
+            for block in self.double_blocks:
+                hidden_states, encoder_hidden_states = self._gradient_checkpointing_func(
+                    block,
+                    hidden_states,
+                    encoder_hidden_states,
+                    encoder_attention_mask,
+                    temb,
+                    freqs_cis,
+                    seq_attention_mask
+                )
+
+        else:
+            for block in self.double_blocks:
+                hidden_states, encoder_hidden_states = block(
+                    hidden_states,
+                    encoder_hidden_states,
+                    encoder_attention_mask,
+                    temb,
+                    freqs_cis,
+                    seq_attention_mask
+                )
+
+        # Final layer processing
+        hidden_states = sequence_model_parallel_all_gather_with_unpad(hidden_states, original_seq_len, dim=1)
+        hidden_states = self.final_layer(hidden_states, temb)
+        # Unpatchify to get original shape
+        hidden_states = unpatchify(hidden_states, post_patch_num_frames, post_patch_height, post_patch_width, self.patch_size, self.out_channels)
+
+        return hidden_states
+
+class SingleTokenRefiner(nn.Module):
+    """
+    A token refiner that processes text embeddings with attention to improve
+    their representation for cross-attention with image features.
+    """
+
+    def __init__(
+        self,
+        in_channels,
+        hidden_size,
+        num_attention_heads,
+        depth=2,
+        qkv_bias=True,
+        dtype=None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+
+        # Input projection
+        # self.input_embedder = ReplicatedLinear(
+        #     in_channels,
+        #     hidden_size,
+        #     bias=True,
+        #     params_dtype=dtype,
+        #     prefix=f"{prefix}.input_embedder")
+        self.input_embedder = nn.Linear(in_channels, hidden_size, bias=True)
+
+        # Timestep embedding
+        self.t_embedder = TimestepEmbedder(hidden_size,
+                                           act_layer="silu",
+                                           dtype=dtype,
+                                           prefix=f"{prefix}.t_embedder")
+
+        # Context embedding
+        self.c_embedder = MLP(in_channels,
+                              hidden_size,
+                              hidden_size,
+                              act_type="silu",
+                              dtype=dtype,
+                              prefix=f"{prefix}.c_embedder")
+
+        # Refiner blocks
+        self.refiner_blocks = nn.ModuleList([
+            IndividualTokenRefinerBlock(
+                hidden_size,
+                num_attention_heads,
+                qkv_bias=qkv_bias,
+                dtype=dtype,
+                prefix=f"{prefix}.refiner_blocks.{i}",
+            ) for i in range(depth)
+        ])
+
+    def forward(self, x, t, mask=None):
+        # Get timestep embeddings
+        timestep_aware_representations = self.t_embedder(t)
+
+        # Get context-aware representations
+        original_dtype = x.dtype
+        if mask is None:
+            context_aware_representations = x.mean(dim=1)
+        else:
+            mask_float = mask.float().unsqueeze(-1)  # [B, L, 1]
+            context_aware_representations = (x * mask_float).sum(dim=1) / mask_float.sum(dim=1)
+
+        context_aware_representations = self.c_embedder(
+            context_aware_representations)
+        c = timestep_aware_representations + context_aware_representations
+        # Project input
+        x = self.input_embedder(x)
+        # Process through refiner blocks
+        for block in self.refiner_blocks:
+            x = block(x, c, mask)
+        return x
+
+class IndividualTokenRefinerBlock(nn.Module):
+    """
+    A transformer block for refining individual tokens with self-attention.
+    """
+
+    def __init__(
+        self,
+        hidden_size,
+        num_attention_heads,
+        mlp_ratio=4.0,
+        qkv_bias=True,
+        dtype=None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__()
+        self.num_attention_heads = num_attention_heads
+        mlp_hidden_dim = int(hidden_size * mlp_ratio)
+
+        # Normalization and attention
+        self.norm1 = nn.LayerNorm(hidden_size,
+                                  eps=1e-6,
+                                  elementwise_affine=True,
+                                  dtype=dtype)
+
+        self.self_attn_qkv = ReplicatedLinear(hidden_size,
+                                              hidden_size * 3,
+                                              bias=qkv_bias,
+                                              params_dtype=dtype,
+                                              prefix=f"{prefix}.self_attn_qkv")
+
+        self.self_attn_proj = ReplicatedLinear(
+            hidden_size,
+            hidden_size,
+            bias=qkv_bias,
+            params_dtype=dtype,
+            prefix=f"{prefix}.self_attn_proj")
+
+        # MLP
+        self.norm2 = nn.LayerNorm(hidden_size,
+                                  eps=1e-6,
+                                  elementwise_affine=True,
+                                  dtype=dtype)
+        self.mlp = MLP(hidden_size,
+                       mlp_hidden_dim,
+                       bias=True,
+                       act_type="silu",
+                       dtype=dtype,
+                       prefix=f"{prefix}.mlp")
+
+        # Modulation
+        self.adaLN_modulation = ModulateProjection(
+            hidden_size,
+            factor=2,
+            act_layer="silu",
+            dtype=dtype,
+            prefix=f"{prefix}.adaLN_modulation")
+
+        # Scaled dot product attention
+        self.attn = LocalAttention(
+            num_heads=num_attention_heads,
+            head_size=hidden_size // num_attention_heads,
+            # TODO: remove hardcode; remove STA
+            supported_attention_backends=(AttentionBackendEnum.FLASH_ATTN,
+                                          AttentionBackendEnum.TORCH_SDPA),
+        )
+
+    def forward(self, x, c, mask=None):
+        if mask is not None:
+            mask = mask.clone().bool()
+            mask[:, 0] = True  # Prevent attention weights from becoming NaN
+
+        # Get modulation parameters
+        gate_msa, gate_mlp = self.adaLN_modulation(c).chunk(2, dim=-1)
+        # Self-attention
+        norm_x = self.norm1(x)
+        qkv, _ = self.self_attn_qkv(norm_x)
+
+        batch_size, seq_len = qkv.shape[0], qkv.shape[1]
+        qkv = qkv.view(batch_size, seq_len, 3, self.num_attention_heads, -1)
+        q, k, v = qkv[:, :, 0], qkv[:, :, 1], qkv[:, :, 2]
+
+        # Run scaled dot product attention
+        from fastvideo.attention.backends.flash_attn import FlashAttnMetadataBuilder
+        attn_metadata = FlashAttnMetadataBuilder().build(
+            current_timestep=0,
+            attn_mask=mask,
+        )
+        # Run distributed attention
+        with set_forward_context(current_timestep=0, attn_metadata=attn_metadata):
+            attn_output = self.attn(q, k, v)  # [B, L, H, D]
+        attn_output = attn_output.reshape(batch_size, seq_len,
+                                          -1)  # [B, L, H*D]
+
+        # Project and apply residual connection with gating
+        attn_out, _ = self.self_attn_proj(attn_output)
+        x = x + attn_out * gate_msa.unsqueeze(1)
+
+        # MLP
+        mlp_out = self.mlp(self.norm2(x))
+        x = x + mlp_out * gate_mlp.unsqueeze(1)
+
+        return x
+
+class FinalLayer(nn.Module):
+    """
+    The final layer of DiT that projects features to pixel space.
+    """
+
+    def __init__(self,
+                 hidden_size,
+                 patch_size,
+                 out_channels,
+                 dtype=None,
+                 prefix: str = "") -> None:
+        super().__init__()
+
+        # Normalization
+        self.norm_final = nn.LayerNorm(hidden_size,
+                                       eps=1e-6,
+                                       elementwise_affine=False,
+                                       dtype=dtype)
+
+        output_dim = patch_size[0] * patch_size[1] * patch_size[2] * out_channels
+
+        self.linear = ReplicatedLinear(hidden_size,
+                                       output_dim,
+                                       bias=True,
+                                       params_dtype=dtype,
+                                       prefix=f"{prefix}.linear")
+
+        # Modulation
+        self.adaLN_modulation = ModulateProjection(
+            hidden_size,
+            factor=2,
+            act_layer="silu",
+            dtype=dtype,
+            prefix=f"{prefix}.adaLN_modulation")
+
+    def forward(self, x, c):
+        # What the heck HF? Why you change the scale and shift order here???
+        scale, shift = self.adaLN_modulation(c).chunk(2, dim=-1)
+        x = self.norm_final(x) * (1.0 + scale.unsqueeze(1)) + shift.unsqueeze(1)
+        x, _ = self.linear(x)
+        return x

fastvideo/models/dits/matrix_game/__init__.py

@@ -0,0 +1,11 @@
+from .model import MatrixGameWanModel, MatrixGameTransformerBlock
+from .causal_model import CausalMatrixGameWanModel, CausalMatrixGameTransformerBlock
+from .action_module import ActionModule
+
+__all__ = [
+    "MatrixGameWanModel",
+    "MatrixGameTransformerBlock",
+    "CausalMatrixGameWanModel",
+    "CausalMatrixGameTransformerBlock",
+    "ActionModule",
+]

fastvideo/models/dits/matrix_game/action_module.py

@@ -0,0 +1,567 @@
+# SPDX-License-Identifier: Apache-2.0
+# Adapted from Matrix-Game: https://github.com/SkyworkAI/Matrix-Game/blob/main/Matrix-Game-2/wan/modules/action_module.py
+
+from einops import rearrange
+import torch
+import torch.nn as nn
+import math
+from torch.nn.attention.flex_attention import flex_attention
+
+from fastvideo.attention import LocalAttention
+from fastvideo.layers.linear import ReplicatedLinear
+from fastvideo.layers.layernorm import FP32LayerNorm, RMSNorm
+from fastvideo.layers.rotary_embedding import (
+    get_nd_rotary_pos_embed as _fv_get_nd_rotary_pos_embed,
+    _apply_rotary_emb,
+)
+from fastvideo.platforms import AttentionBackendEnum
+
+
+DISABLE_COMPILE = False
+flex_attention = torch.compile(
+    flex_attention, dynamic=False, mode="max-autotune-no-cudagraphs")
+
+
+def _get_nd_rotary_pos_embed_matrixgame(
+    rope_dim_list,
+    rope_sizes,
+    theta: float = 10000.0,
+    theta_rescale_factor: float = 1.0,
+):
+    cos, sin = _fv_get_nd_rotary_pos_embed(
+        rope_dim_list,
+        rope_sizes,
+        theta=theta,
+        theta_rescale_factor=theta_rescale_factor,
+        dtype=torch.float32,
+    )
+    # convert from [S, D/2] to [S, D] format
+    cos = cos.repeat_interleave(2, dim=1)
+    sin = sin.repeat_interleave(2, dim=1)
+    return cos, sin
+
+
+def _apply_rotary_emb_qk(
+    xq: torch.Tensor,
+    xk: torch.Tensor,
+    freqs_cos: torch.Tensor,
+    freqs_sin: torch.Tensor,
+    start_offset: int = 0,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    seq_len = xq.shape[1]
+    
+    # Slice frequencies based on offset
+    cos = freqs_cos[start_offset:start_offset + seq_len]  # [S, D]
+    sin = freqs_sin[start_offset:start_offset + seq_len]  # [S, D]
+    
+    # Move to device
+    cos = cos.to(xq.device)
+    sin = sin.to(xq.device)
+    
+    # Convert from [S, D] (interleaved) back to [S, D/2]
+    cos_half = cos[:, ::2]  # [S, D/2]
+    sin_half = sin[:, ::2]  # [S, D/2]
+
+    # xq/xk are [B, S, H, D], need to reshape for each batch
+    B, S, H, D = xq.shape
+
+    xq_out = _apply_rotary_emb(xq, cos_half, sin_half, is_neox_style=False)
+    xk_out = _apply_rotary_emb(xk, cos_half, sin_half, is_neox_style=False)
+    
+    return xq_out, xk_out
+
+class ActionModule(nn.Module):
+    """
+    action module from https://arxiv.org/pdf/2501.08325
+    """
+
+    def __init__(
+        self, 
+        mouse_dim_in: int = 2,
+        keyboard_dim_in: int = 6,
+        hidden_size: int = 128,
+        img_hidden_size: int = 1536,
+        keyboard_hidden_dim: int = 1024,
+        mouse_hidden_dim: int = 1024,
+        vae_time_compression_ratio: int = 4, 
+        windows_size: int = 3,
+        heads_num: int = 16,
+        patch_size: list | None = None,
+        qk_norm: bool = True,
+        qkv_bias: bool = False,
+        rope_dim_list: list | None = None,
+        rope_theta = 256,
+        mouse_qk_dim_list: list | None = None,
+        enable_mouse = True,
+        enable_keyboard = True,
+        local_attn_size = 6,
+        blocks: list | None = None,
+    ):
+        super().__init__()
+        # Initialize mutable defaults
+        patch_size = patch_size if patch_size is not None else [1, 2, 2]
+        rope_dim_list = rope_dim_list if rope_dim_list is not None else [8, 28, 28]
+        mouse_qk_dim_list = mouse_qk_dim_list if mouse_qk_dim_list is not None else [8, 28, 28]
+        blocks = blocks if blocks is not None else []
+        self.local_attn_size = local_attn_size
+        self.enable_mouse = enable_mouse
+        self.enable_keyboard = enable_keyboard
+
+        self.rope_dim_list = rope_dim_list
+        self.rope_theta = rope_theta
+        if self.enable_keyboard:
+            self.keyboard_embed = nn.Sequential(
+                nn.Linear(keyboard_dim_in, hidden_size, bias=True),
+                nn.SiLU(),
+                nn.Linear(hidden_size, hidden_size, bias=True)
+            )
+
+        self.mouse_qk_dim_list = mouse_qk_dim_list
+        self.heads_num = heads_num
+        if self.enable_mouse:
+            c = mouse_hidden_dim
+            self.mouse_mlp = nn.Sequential(
+                nn.Linear(mouse_dim_in * vae_time_compression_ratio * windows_size + img_hidden_size, c, bias=True),
+                nn.GELU(approximate="tanh"),
+                nn.Linear(c, c),
+                FP32LayerNorm(c, elementwise_affine=True),
+            )
+            
+            head_dim = c // heads_num
+            self.t_qkv = ReplicatedLinear(c, c*3, bias=qkv_bias)
+            self.img_attn_q_norm = (
+                RMSNorm(head_dim, eps=1e-6)
+                if qk_norm
+                else nn.Identity()
+            )
+            self.img_attn_k_norm = (
+                RMSNorm(head_dim, eps=1e-6)
+                if qk_norm
+                else nn.Identity()
+            )
+            self.proj_mouse = ReplicatedLinear(c, img_hidden_size, bias=qkv_bias)
+
+        if self.enable_keyboard:
+            head_dim_key = keyboard_hidden_dim // heads_num
+            self.key_attn_q_norm = (
+                RMSNorm(head_dim_key, eps=1e-6)
+                if qk_norm
+                else nn.Identity()
+            )
+            self.key_attn_k_norm = (
+                RMSNorm(head_dim_key, eps=1e-6)
+                if qk_norm
+                else nn.Identity()
+            )
+            
+            self.mouse_attn_q = ReplicatedLinear(img_hidden_size, keyboard_hidden_dim, bias=qkv_bias)
+            self.keyboard_attn_kv = ReplicatedLinear(hidden_size * windows_size * vae_time_compression_ratio, keyboard_hidden_dim * 2, bias=qkv_bias)
+            self.proj_keyboard = ReplicatedLinear(keyboard_hidden_dim, img_hidden_size, bias=qkv_bias)
+
+        self.mouse_attn_layer = LocalAttention(
+            num_heads=heads_num,
+            head_size=mouse_hidden_dim // heads_num,
+            causal=False,
+            supported_attention_backends=(AttentionBackendEnum.FLASH_ATTN,
+                                          AttentionBackendEnum.TORCH_SDPA)
+        ) if self.enable_mouse else None
+
+        self.keyboard_attn_layer = LocalAttention(
+            num_heads=heads_num,
+            head_size=keyboard_hidden_dim // heads_num,
+            causal=False,
+            supported_attention_backends=(AttentionBackendEnum.FLASH_ATTN,
+                                          AttentionBackendEnum.TORCH_SDPA)
+        ) if self.enable_keyboard else None
+
+        self.vae_time_compression_ratio = vae_time_compression_ratio
+        self.windows_size = windows_size
+        self.patch_size = patch_size
+        # Lazy initialization: freqs will be created on first forward pass
+        self._freqs_cos = None
+        self._freqs_sin = None
+
+    def patchify(self, x, patch_size):
+        """
+        x : (N C T H W)
+        """
+        pt, ph, pw = self.patch_size
+        t, h, w = x.shape[2] //  pt, x.shape[3] // ph, x.shape[4] // pw
+        c = x.shape[1]
+        x = x.reshape(shape=(x.shape[0], c, t , pt, h , ph, w , pw))
+        x = torch.einsum("nctohpwq->nthwcopq", x)
+        x = x.reshape(shape=(x.shape[0], t*h*w,  c*pt*ph*pw))
+        return x
+
+    def unpatchify(self, x, t, h, w, patch_size):
+        """
+        x: (N, T, patch_size**2 * C)
+        imgs: (N, H, W, C)
+        """
+        c =  x.shape[2] // patch_size #self.unpatchify_channels
+        pt, ph, pw = self.patch_size
+        assert t * h * w == x.shape[1]
+
+        x = x.reshape(shape=(x.shape[0], t, h, w, c, pt, ph, pw))
+        x = torch.einsum("nthwcopq->nctohpwq", x)
+        imgs = x.reshape(shape=(x.shape[0], c, t * pt, h * ph, w * pw))
+
+        return imgs
+
+    def get_rotary_pos_embed(self, video_length, height, width, head_dim, rope_dim_list = None, start_offset=0):
+        target_ndim = 3
+        ndim = 5 - 2
+        
+        latents_size = [video_length+start_offset, height, width]
+
+        if isinstance(self.patch_size, int):
+            assert all(s % self.patch_size == 0 for s in latents_size), (
+                f"Latent size(last {ndim} dimensions) should be divisible by patch size({self.patch_size}), "
+                f"but got {latents_size}."
+            )
+            rope_sizes = [s // self.patch_size for s in latents_size]
+        elif isinstance(self.patch_size, list):
+            assert all(
+                s % self.patch_size[idx] == 0
+                for idx, s in enumerate(latents_size)
+            ), (
+                f"Latent size(last {ndim} dimensions) should be divisible by patch size({self.patch_size}), "
+                f"but got {latents_size}."
+            )
+            rope_sizes = [
+                s // self.patch_size[idx] for idx, s in enumerate(latents_size)
+            ]
+
+        if len(rope_sizes) != target_ndim:
+            rope_sizes = [1] * (target_ndim - len(rope_sizes)) + rope_sizes  # time axis
+        
+        if rope_dim_list is None:
+            rope_dim_list = [head_dim // target_ndim for _ in range(target_ndim)]
+        assert (
+            sum(rope_dim_list) == head_dim
+        ), "sum(rope_dim_list) should equal to head_dim of attention layer"
+        # Use Matrix-Game wrapper for FastVideo's function
+        freqs_cos, freqs_sin = _get_nd_rotary_pos_embed_matrixgame(
+            rope_dim_list,
+            rope_sizes,
+            theta=self.rope_theta,
+            theta_rescale_factor=1,
+        )
+        return freqs_cos[-video_length*rope_sizes[1]*rope_sizes[2]//self.patch_size[0]:], freqs_sin[-video_length*rope_sizes[1]*rope_sizes[2]//self.patch_size[0]:]
+
+    def forward(self, x, tt, th, tw, mouse_condition=None, keyboard_condition=None, block_mask_mouse=None, block_mask_keyboard=None, is_causal=False, kv_cache_mouse=None, kv_cache_keyboard=None, start_frame=0, use_rope_keyboard=True, num_frame_per_block=3):
+        '''
+        hidden_states: B, tt*th*tw, C
+        mouse_condition: B, N_frames, C1
+        keyboard_condition: B, N_frames, C2
+        '''
+        assert use_rope_keyboard
+
+        B, N_frames, C = keyboard_condition.shape
+        assert tt*th*tw == x.shape[1]
+        assert ((N_frames - 1) + self.vae_time_compression_ratio) % self.vae_time_compression_ratio == 0
+        N_feats = int((N_frames - 1) / self.vae_time_compression_ratio) + 1
+        
+        # Lazy initialization of freqs on first forward pass
+        if self._freqs_cos is None or self._freqs_sin is None:
+            self._freqs_cos, self._freqs_sin = self.get_rotary_pos_embed(
+                7500, self.patch_size[1], self.patch_size[2], 64, 
+                self.mouse_qk_dim_list, start_offset=0
+            )
+        
+        # Defined freqs_cis early so it's available for both mouse and keyboard
+        freqs_cis = (self._freqs_cos, self._freqs_sin)
+
+        assert (N_feats == tt and ((is_causal and kv_cache_mouse is None) or not is_causal)) or ((N_frames - 1) // self.vae_time_compression_ratio + 1 == start_frame + num_frame_per_block and is_causal)
+        
+        if self.enable_mouse and mouse_condition is not None:
+            hidden_states = rearrange(x, "B (T S) C -> (B S) T C", T=tt, S=th*tw) # 65*272*480 -> 17*(272//16)*(480//16) -> 8670
+            B, N_frames, C = mouse_condition.shape
+        else:
+            hidden_states = x
+        # padding
+        
+        pad_t = self.vae_time_compression_ratio * self.windows_size
+        if self.enable_mouse and mouse_condition is not None:
+            pad = mouse_condition[:, 0:1, :].expand(-1, pad_t, -1)
+            mouse_condition = torch.cat([pad, mouse_condition], dim=1)
+            if is_causal and kv_cache_mouse is not None: 
+                mouse_condition = mouse_condition[:, self.vae_time_compression_ratio*(N_feats - num_frame_per_block - self.windows_size) + pad_t:, :] 
+                group_mouse = [mouse_condition[:, self.vae_time_compression_ratio*(i - self.windows_size) + pad_t:i * self.vae_time_compression_ratio + pad_t,:] for i in range(num_frame_per_block)]
+            else:
+                group_mouse = [mouse_condition[:, self.vae_time_compression_ratio*(i - self.windows_size) + pad_t:i * self.vae_time_compression_ratio + pad_t,:] for i in range(N_feats)]
+                
+            group_mouse = torch.stack(group_mouse, dim = 1)
+
+            S = th * tw 
+            group_mouse = group_mouse.unsqueeze(-1).expand(B, num_frame_per_block, pad_t, C, S)
+            group_mouse = group_mouse.permute(0, 4, 1, 2, 3).reshape(B * S, num_frame_per_block, pad_t * C) 
+
+            group_mouse = torch.cat([hidden_states, group_mouse], dim = -1)
+            group_mouse = self.mouse_mlp(group_mouse)
+            # qkv
+            mouse_qkv, _ = self.t_qkv(group_mouse)
+            q, k, v = rearrange(mouse_qkv, "B L (K H D) -> K B L H D", K=3, H=self.heads_num) # BHW F H C
+            q = self.img_attn_q_norm(q).to(v)
+            k = self.img_attn_k_norm(k).to(v)        
+            # rope embd
+
+            
+            # freqs_cis = (self.freqs_cos, self.freqs_sin)
+            
+            
+            q, k = _apply_rotary_emb_qk(q, k, freqs_cis[0], freqs_cis[1], start_offset=start_frame)
+            ## TODO: adding cache here
+            if is_causal:
+                if kv_cache_mouse is None:
+                    assert q.shape[0] ==  k.shape[0] and q.shape[0] % 880 == 0 # == 880, f"{q.shape[0]},{k.shape[0]}"
+                    padded_length = math.ceil(q.shape[1] / 32) * 32 - q.shape[1]
+                    padded_q = torch.cat(
+                        [q,
+                            torch.zeros([q.shape[0], padded_length, q.shape[2], q.shape[3]],
+                                        device=q.device, dtype=v.dtype)],
+                        dim=1
+                    )
+                    padded_k = torch.cat(
+                        [k, 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
+                    )
+                    attn = flex_attention(
+                        query=padded_q.transpose(2, 1), # after: B, HW, F, C
+                        key=padded_k.transpose(2, 1),
+                        value=padded_v.transpose(2, 1),
+                        block_mask=block_mask_mouse
+                    )[:, :, :-padded_length].transpose(2, 1)
+                else:
+                    current_start = start_frame
+                    current_end = current_start + q.shape[1]
+                    
+                    assert q.shape[1] == num_frame_per_block
+                    sink_size = 0
+                    max_attention_size = self.local_attn_size
+                    sink_tokens = sink_size * 1
+                    kv_cache_size = kv_cache_mouse["k"].shape[1]
+                    num_new_tokens = q.shape[1]
+                    
+
+                    if (current_end > kv_cache_mouse["global_end_index"].item()) and (
+                        num_new_tokens + kv_cache_mouse["local_end_index"].item() > kv_cache_size):
+                        num_evicted_tokens = num_new_tokens + kv_cache_mouse["local_end_index"].item() - kv_cache_size
+                        num_rolled_tokens = kv_cache_mouse["local_end_index"].item() - num_evicted_tokens - sink_tokens
+                        kv_cache_mouse["k"][:, sink_tokens:sink_tokens + num_rolled_tokens] = \
+                            kv_cache_mouse["k"][:, sink_tokens + num_evicted_tokens:sink_tokens + num_evicted_tokens + num_rolled_tokens].clone()
+                        kv_cache_mouse["v"][:, sink_tokens:sink_tokens + num_rolled_tokens] = \
+                            kv_cache_mouse["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_mouse["local_end_index"].item() + current_end - \
+                            kv_cache_mouse["global_end_index"].item() - num_evicted_tokens
+                        local_start_index = local_end_index - num_new_tokens
+                    else:
+                        local_end_index = kv_cache_mouse["local_end_index"].item() + current_end - kv_cache_mouse["global_end_index"].item()
+                        local_start_index = local_end_index - num_new_tokens
+                    kv_cache_mouse["k"][:, local_start_index:local_end_index] = k
+                    kv_cache_mouse["v"][:, local_start_index:local_end_index] = v
+
+                    attn = self.mouse_attn_layer(
+                        q,
+                        kv_cache_mouse["k"][:, max(0, local_end_index - max_attention_size):local_end_index],
+                        kv_cache_mouse["v"][:, max(0, local_end_index - max_attention_size):local_end_index],
+                    )
+                    kv_cache_mouse["global_end_index"].fill_(current_end)
+                    kv_cache_mouse["local_end_index"].fill_(local_end_index)
+            else:
+                attn = self.mouse_attn_layer(q, k, v)
+            # Compute cu_squlens and max_seqlen for flash attention
+            # qk norm
+            attn = rearrange(attn, '(b S) T h d -> b (T S) (h d)',b=B)
+            
+            hidden_states = rearrange(x, "(B S) T C -> B (T S) C", B=B)
+            attn, _ = self.proj_mouse(attn)
+            
+            hidden_states = hidden_states + attn
+        
+        if self.enable_keyboard and keyboard_condition is not None:
+            pad = keyboard_condition[:, 0:1, :].expand(-1, pad_t, -1)
+            keyboard_condition = torch.cat([pad, keyboard_condition], dim=1)
+            if is_causal and kv_cache_keyboard is not None:
+                keyboard_condition = keyboard_condition[:, self.vae_time_compression_ratio*(N_feats - num_frame_per_block - self.windows_size) + pad_t:, :] # keyboard_condition[:, self.vae_time_compression_ratio*(start_frame - self.windows_size) + pad_t:start_frame * self.vae_time_compression_ratio + pad_t,:]
+                keyboard_condition = self.keyboard_embed(keyboard_condition)
+                group_keyboard = [keyboard_condition[:, self.vae_time_compression_ratio*(i - self.windows_size) + pad_t:i * self.vae_time_compression_ratio + pad_t,:] for i in range(num_frame_per_block)]
+            else:
+                keyboard_condition = self.keyboard_embed(keyboard_condition)
+                group_keyboard = [keyboard_condition[:, self.vae_time_compression_ratio*(i - self.windows_size) + pad_t:i * self.vae_time_compression_ratio + pad_t,:] for i in range(N_feats)]
+            group_keyboard = torch.stack(group_keyboard, dim = 1) # B F RW C
+            group_keyboard = group_keyboard.reshape(shape=(group_keyboard.shape[0],group_keyboard.shape[1],-1))
+            # apply cross attn
+            mouse_q, _ = self.mouse_attn_q(hidden_states)
+            keyboard_kv, _ = self.keyboard_attn_kv(group_keyboard)
+
+            B, L, HD = mouse_q.shape
+            D = HD // self.heads_num
+            q = mouse_q.view(B, L, self.heads_num, D)
+
+            B, L, KHD = keyboard_kv.shape
+            k, v = keyboard_kv.view(B, L, 2, self.heads_num, D).permute(2, 0, 1, 3, 4)
+        
+            # Compute cu_squlens and max_seqlen for flash attention
+            # qk norm
+            
+            q = self.key_attn_q_norm(q).to(v)
+            k = self.key_attn_k_norm(k).to(v)
+            S = th * tw
+            assert S == 880
+            # position embed 
+            if use_rope_keyboard: 
+                B, TS, H, D = q.shape
+                T_ = TS // S 
+                q = q.view(B, T_, S, H, D).transpose(1, 2).reshape(B * S, T_, H, D)
+                q, k = _apply_rotary_emb_qk(q, k, freqs_cis[0], freqs_cis[1], start_offset=start_frame)
+
+                k1, k2, k3, k4 = k.shape
+                k = k.expand(S, k2, k3, k4)
+                v = v.expand(S, k2, k3, k4)
+
+
+                if is_causal:
+                    if kv_cache_keyboard is None:
+                        assert q.shape[0] == k.shape[0] and q.shape[0] % 880 == 0 
+
+                        padded_length = math.ceil(q.shape[1] / 32) * 32 - q.shape[1]
+                        padded_q = torch.cat(
+                            [q,
+                                torch.zeros([q.shape[0], padded_length, q.shape[2], q.shape[3]],
+                                            device=q.device, dtype=v.dtype)],
+                            dim=1
+                        )
+                        padded_k = torch.cat(
+                            [k, 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
+                        )
+                        attn = flex_attention(
+                            query=padded_q.transpose(2, 1), # after: B, HW, F, C
+                            key=padded_k.transpose(2, 1),
+                            value=padded_v.transpose(2, 1),
+                            block_mask=block_mask_keyboard
+                        )[:, :, :-padded_length].transpose(2, 1)
+                    else:
+                        current_start = start_frame
+                        current_end = current_start + k.shape[1]
+                        assert k.shape[1] == num_frame_per_block
+                        sink_size = 0
+                        max_attention_size = self.local_attn_size
+                        sink_tokens = sink_size * 1
+                        kv_cache_size = kv_cache_keyboard["k"].shape[1]
+                        num_new_tokens = k.shape[1]
+
+                        if (current_end > kv_cache_keyboard["global_end_index"].item()) and (
+                            num_new_tokens + kv_cache_keyboard["local_end_index"].item() > kv_cache_size):
+                            num_evicted_tokens = num_new_tokens + kv_cache_keyboard["local_end_index"].item() - kv_cache_size
+                            num_rolled_tokens = kv_cache_keyboard["local_end_index"].item() - num_evicted_tokens - sink_tokens
+                            kv_cache_keyboard["k"][:, sink_tokens:sink_tokens + num_rolled_tokens] = \
+                                kv_cache_keyboard["k"][:, sink_tokens + num_evicted_tokens:sink_tokens + num_evicted_tokens + num_rolled_tokens].clone()
+                            kv_cache_keyboard["v"][:, sink_tokens:sink_tokens + num_rolled_tokens] = \
+                                kv_cache_keyboard["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_keyboard["local_end_index"].item() + current_end - \
+                                kv_cache_keyboard["global_end_index"].item() - num_evicted_tokens
+                            local_start_index = local_end_index - num_new_tokens
+                        else:
+                            local_end_index = kv_cache_keyboard["local_end_index"].item() + current_end - kv_cache_keyboard["global_end_index"].item()
+                            local_start_index = local_end_index - num_new_tokens
+                        assert k.shape[0] == 880 # BS == 1 or the cache should not be saved/ load method should be modified
+                        kv_cache_keyboard["k"][:, local_start_index:local_end_index] = k[:1]
+                        kv_cache_keyboard["v"][:, local_start_index:local_end_index] = v[:1]
+
+                        attn = self.keyboard_attn_layer(
+                            q,
+                            kv_cache_keyboard["k"][:, max(0, local_end_index - max_attention_size):local_end_index].repeat(S, 1, 1, 1),
+                            kv_cache_keyboard["v"][:, max(0, local_end_index - max_attention_size):local_end_index].repeat(S, 1, 1, 1),
+                        )
+
+                        kv_cache_keyboard["global_end_index"].fill_(current_end)
+                        kv_cache_keyboard["local_end_index"].fill_(local_end_index)
+                else:
+                    attn = self.keyboard_attn_layer(q, k, v)
+                attn = rearrange(attn, '(B S) T H D -> B (T S) (H D)', S=S)
+            else:
+                if is_causal:
+                    if kv_cache_keyboard is None:
+                        
+                        padded_length = math.ceil(q.shape[1] / 32) * 32 - q.shape[1]
+                        padded_q = torch.cat(
+                            [q,
+                                torch.zeros([q.shape[0], padded_length, q.shape[2], q.shape[3]],
+                                            device=q.device, dtype=v.dtype)],
+                            dim=1
+                        )
+                        padded_k = torch.cat(
+                            [k, 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
+                        )
+                        attn = flex_attention(
+                            query=padded_q.transpose(2, 1), # after: B, HW, F, C
+                            key=padded_k.transpose(2, 1),
+                            value=padded_v.transpose(2, 1),
+                            block_mask=block_mask_keyboard
+                        )[:, :, :-padded_length].transpose(2, 1)
+                    else:
+                        current_start = start_frame
+                        current_end = current_start + k.shape[1]
+                        assert k.shape[1] == num_frame_per_block
+                        sink_size = 0
+                        max_attention_size = self.local_attn_size
+                        sink_tokens = sink_size * 1
+                        kv_cache_size = kv_cache_keyboard["k"].shape[1]
+                        num_new_tokens = k.shape[1]
+
+
+                        if (current_end > kv_cache_keyboard["global_end_index"].item()) and (
+                            num_new_tokens + kv_cache_keyboard["local_end_index"].item() > kv_cache_size):
+                            num_evicted_tokens = num_new_tokens + kv_cache_keyboard["local_end_index"].item() - kv_cache_size
+                            num_rolled_tokens = kv_cache_keyboard["local_end_index"].item() - num_evicted_tokens - sink_tokens
+                            kv_cache_keyboard["k"][:, sink_tokens:sink_tokens + num_rolled_tokens] = \
+                                kv_cache_keyboard["k"][:, sink_tokens + num_evicted_tokens:sink_tokens + num_evicted_tokens + num_rolled_tokens].clone()
+                            kv_cache_keyboard["v"][:, sink_tokens:sink_tokens + num_rolled_tokens] = \
+                                kv_cache_keyboard["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_keyboard["local_end_index"].item() + current_end - \
+                                kv_cache_keyboard["global_end_index"].item() - num_evicted_tokens
+                            local_start_index = local_end_index - num_new_tokens
+
+                            
+                        else:
+                            local_end_index = kv_cache_keyboard["local_end_index"].item() + current_end - kv_cache_keyboard["global_end_index"].item()
+                            local_start_index = local_end_index - num_new_tokens
+                        kv_cache_keyboard["k"][:, local_start_index:local_end_index] = k
+                        kv_cache_keyboard["v"][:, local_start_index:local_end_index] = v
+                        attn = self.keyboard_attn_layer(
+                            q,
+                            kv_cache_keyboard["k"][:, max(0, local_end_index - max_attention_size):local_end_index],
+                            kv_cache_keyboard["v"][:, max(0, local_end_index - max_attention_size):local_end_index],
+                        )
+                        kv_cache_keyboard["global_end_index"].fill_(current_end)
+                        kv_cache_keyboard["local_end_index"].fill_(local_end_index)
+                else:
+                    attn = self.keyboard_attn_layer(q, k, v)
+                attn = rearrange(attn, 'B L H D -> B L (H D)')
+            attn, _ = self.proj_keyboard(attn)
+            hidden_states = hidden_states + attn
+        return hidden_states

fastvideo/models/dits/matrix_game/model.py

@@ -0,0 +1,469 @@
+# SPDX-License-Identifier: Apache-2.0
+
+import math
+from typing import Any
+
+import torch
+import torch.nn as nn
+
+from fastvideo.attention import DistributedAttention
+from fastvideo.configs.models.dits.matrixgame import MatrixGameWanVideoConfig
+from fastvideo.distributed.parallel_state import get_sp_world_size
+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, TimestepEmbedder, ModulateProjection
+from fastvideo.logger import init_logger
+from fastvideo.models.dits.base import BaseDiT
+from fastvideo.models.dits.wanvideo import (WanSelfAttention,
+                                           WanI2VCrossAttention,
+                                           WanT2VCrossAttention,
+                                           WanImageEmbedding)
+from fastvideo.platforms import AttentionBackendEnum, current_platform
+
+# Import ActionModule
+from .action_module import ActionModule
+
+logger = init_logger(__name__)
+
+
+class MatrixGameTimeImageEmbedding(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        time_freq_dim: int,
+        image_embed_dim: int | None = None,
+    ):
+        super().__init__()
+
+        self.time_embedder = TimestepEmbedder(
+            dim, frequency_embedding_size=time_freq_dim, act_layer="silu")
+        self.time_modulation = ModulateProjection(dim,
+                                                  factor=6,
+                                                  act_layer="silu")
+        
+        self.image_embedder = None
+        if image_embed_dim is not None:
+            self.image_embedder = WanImageEmbedding(image_embed_dim, dim)
+
+    def forward(
+        self,
+        timestep: torch.Tensor,
+        encoder_hidden_states: torch.Tensor, # Kept for interface compatibility
+        encoder_hidden_states_image: torch.Tensor | None = None,
+        timestep_seq_len: int | None = None,
+    ):
+        temb = self.time_embedder(timestep, timestep_seq_len)
+        timestep_proj = self.time_modulation(temb)
+
+        # MatrixGame does not use text embeddings, so we ignore encoder_hidden_states
+        # and return None for the text embedding part
+        
+        if encoder_hidden_states_image is not None:
+            assert self.image_embedder is not None
+            encoder_hidden_states_image = self.image_embedder(
+                encoder_hidden_states_image)
+
+        return temb, timestep_proj, None, encoder_hidden_states_image
+
+
+class MatrixGameCrossAttention(WanSelfAttention):
+    def forward(self, x, context, context_lens=None, crossattn_cache=None):
+        r"""
+        Args:
+            x(Tensor): Shape [B, L1, C]
+            context(Tensor): Shape [B, L2, C] - typically 257 image tokens
+            context_lens(Tensor): Shape [B]
+            crossattn_cache(dict): Optional cache for k/v during inference
+        """
+        b, n, d = x.size(0), self.num_heads, self.head_dim
+
+        # compute query, key, value
+        q = self.norm_q(self.to_q(x)[0]).view(b, -1, n, d)
+        
+        if crossattn_cache is not None:
+            if not crossattn_cache["is_init"]:
+                crossattn_cache["is_init"] = True
+                k = self.norm_k(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)
+
+        # output
+        x = x.flatten(2)
+        x, _ = self.to_out(x)
+        return x
+
+
+class MatrixGameTransformerBlock(nn.Module):
+    
+    def __init__(self,
+                 dim: int,
+                 ffn_dim: int,
+                 num_heads: int,
+                 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 = "",
+                 action_config: dict | None = None):
+        super().__init__()
+        action_config = action_config or {}
+
+        # 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 = DistributedAttention(
+            num_heads=num_heads,
+            head_size=dim // num_heads,
+            causal=False,
+            supported_attention_backends=supported_attention_backends,
+            prefix=f"{prefix}.attn1")
+        self.hidden_dim = dim
+        self.num_attention_heads = num_heads
+        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
+        if added_kv_proj_dim is not None:
+            # I2V
+            self.attn2 = WanI2VCrossAttention(dim,
+                                              num_heads,
+                                              qk_norm=qk_norm,
+                                              eps=eps)
+        else:
+            # T2V
+            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)
+
+        # 2.1. Action Module Integration
+        self.use_action_module = len(action_config) > 0
+        if self.use_action_module:
+            self.action_model = ActionModule(**action_config)
+        else:
+            self.action_model = None
+
+        # 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],
+        # Action Module specific args
+        grid_sizes: torch.Tensor | None = None,
+        mouse_cond: torch.Tensor | None = None,
+        keyboard_cond: torch.Tensor | None = None,
+    ) -> torch.Tensor:
+        if hidden_states.dim() == 4:
+            hidden_states = hidden_states.squeeze(1)
+        orig_dtype = hidden_states.dtype
+
+        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
+        norm_hidden_states = (self.norm1(hidden_states.float()) *
+                              (1 + scale_msa) + shift_msa).to(orig_dtype)
+        query, _ = self.to_q(norm_hidden_states)
+        key, _ = self.to_k(norm_hidden_states)
+        value, _ = self.to_v(norm_hidden_states)
+
+        if self.norm_q is not None:
+            query = self.norm_q(query)
+        if self.norm_k is not None:
+            key = self.norm_k(key)
+
+        query = query.squeeze(1).unflatten(2, (self.num_attention_heads, -1))
+        key = key.squeeze(1).unflatten(2, (self.num_attention_heads, -1))
+        value = value.squeeze(1).unflatten(2, (self.num_attention_heads, -1))
+
+        # Apply rotary embeddings
+        cos, sin = freqs_cis
+        query, key = _apply_rotary_emb(query, cos, sin,
+                                       is_neox_style=False), _apply_rotary_emb(
+                                           key, cos, sin, is_neox_style=False)
+
+        attn_output, _ = self.attn1(query, key, value)
+        attn_output = attn_output.flatten(2)
+        attn_output, _ = self.to_out(attn_output)
+        attn_output = attn_output.squeeze(1)
+
+        null_shift = null_scale = torch.tensor([0], device=hidden_states.device)
+        norm_hidden_states, hidden_states = self.self_attn_residual_norm(
+            hidden_states, attn_output, gate_msa, null_shift, null_scale)
+        norm_hidden_states, hidden_states = norm_hidden_states.to(
+            orig_dtype), hidden_states.to(orig_dtype)
+
+        # 2. Cross-attention
+        attn_output = self.attn2(norm_hidden_states,
+                                 context=encoder_hidden_states,
+                                 context_lens=None)
+        norm_hidden_states, hidden_states = self.cross_attn_residual_norm(
+            hidden_states, attn_output, 1, c_shift_msa, c_scale_msa)
+        norm_hidden_states, hidden_states = norm_hidden_states.to(
+            orig_dtype), hidden_states.to(orig_dtype)
+
+        # ================= Action Module =================
+        if self.action_model is not None:
+            if mouse_cond is not None or keyboard_cond is not None:
+                # grid_sizes is expected to be [F, H, W]
+                # ActionModule implementation takes hidden_states directly
+                hidden_states = self.action_model(
+                    hidden_states,
+                    grid_sizes[0], grid_sizes[1], grid_sizes[2],
+                    mouse_cond, keyboard_cond,
+                    num_frame_per_block=grid_sizes[0],
+                )
+        # =================================================
+
+        # 3. Feed-forward
+        ff_output = self.ffn(norm_hidden_states)
+        hidden_states = self.mlp_residual(hidden_states, ff_output, c_gate_msa)
+        hidden_states = hidden_states.to(orig_dtype)
+
+        return hidden_states
+
+_DEFAULT_MATRIXGAME_CONFIG = MatrixGameWanVideoConfig()
+
+
+class MatrixGameWanModel(BaseDiT):
+    # Marker for action input support (Matrix-Game)
+    supports_action_input = True
+
+    _fsdp_shard_conditions = _DEFAULT_MATRIXGAME_CONFIG._fsdp_shard_conditions
+    _compile_conditions = _DEFAULT_MATRIXGAME_CONFIG._compile_conditions
+    _supported_attention_backends = _DEFAULT_MATRIXGAME_CONFIG._supported_attention_backends
+    param_names_mapping = _DEFAULT_MATRIXGAME_CONFIG.param_names_mapping
+    reverse_param_names_mapping = _DEFAULT_MATRIXGAME_CONFIG.reverse_param_names_mapping
+    lora_param_names_mapping = _DEFAULT_MATRIXGAME_CONFIG.lora_param_names_mapping
+
+    def __init__(self,
+                 config: MatrixGameWanVideoConfig,
+                 hf_config: dict[str, Any],
+                 **kwargs) -> 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.patch_size = config.patch_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 = MatrixGameTimeImageEmbedding(
+            dim=inner_dim,
+            time_freq_dim=config.freq_dim,
+            image_embed_dim=config.image_dim,
+        )
+
+        # 2.1. Get action config
+        self.action_config = getattr(config, 'action_config', {})
+
+        # 3. Transformer blocks
+        self.blocks = nn.ModuleList([
+            MatrixGameTransformerBlock(
+                inner_dim,
+                config.ffn_dim,
+                config.num_attention_heads,
+                config.qk_norm,
+                config.cross_attn_norm,
+                config.eps,
+                config.added_kv_proj_dim,
+                self._supported_attention_backends,
+                prefix=f"{getattr(config, 'prefix', 'Wan')}.blocks.{i}",
+                action_config=self.action_config)
+            for i in range(config.num_layers)
+        ])
+
+        # 4. Output norm & projection
+        self.norm_out = LayerNormScaleShift(inner_dim,
+                                            norm_type="layer",
+                                            eps=config.eps,
+                                            elementwise_affine=False,
+                                            dtype=torch.float32,
+                                            compute_dtype=torch.float32)
+        self.proj_out = nn.Linear(
+            inner_dim, config.out_channels * math.prod(config.patch_size))
+        self.scale_shift_table = nn.Parameter(
+            torch.randn(1, 2, inner_dim) / inner_dim**0.5)
+ 
+        self.gradient_checkpointing = False
+
+    def forward(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,
+                # Action inputs
+                mouse_cond: torch.Tensor | None = None,
+                keyboard_cond: torch.Tensor | None = None,
+                **kwargs) -> torch.Tensor:
+        if encoder_hidden_states is not None and 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)
+        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)
+
+        if timestep.dim() == 2:
+            timestep = timestep.flatten()
+
+        temb, timestep_proj, encoder_hidden_states, encoder_hidden_states_image = self.condition_embedder(
+            timestep, encoder_hidden_states, encoder_hidden_states_image)
+        timestep_proj = timestep_proj.unflatten(1, (6, -1))
+
+        if encoder_hidden_states is not None:
+            if isinstance(encoder_hidden_states, list):
+                encoder_hidden_states = encoder_hidden_states[0]
+            elif encoder_hidden_states.ndim == 2:
+                encoder_hidden_states = encoder_hidden_states.unsqueeze(0)
+        else:
+            # encoder_hidden_states is None (e.g. no text encoder)
+            # MatrixGame uses image-action cross-attn.
+            pass
+
+        if encoder_hidden_states_image is not None:
+            if encoder_hidden_states is not None:
+                encoder_hidden_states = torch.concat(
+                    [encoder_hidden_states_image, encoder_hidden_states], dim=1)
+            else:
+                encoder_hidden_states = encoder_hidden_states_image
+
+        # This is [F, H, W] for the ActionModule
+        grid_sizes = torch.tensor([
+            post_patch_num_frames, post_patch_height, post_patch_width
+        ],
+                                 device=hidden_states.device)
+
+        # Blocks
+        for block in self.blocks:
+            if torch.is_grad_enabled() and self.gradient_checkpointing:
+                hidden_states = self._gradient_checkpointing_func(
+                    block, hidden_states, encoder_hidden_states, timestep_proj,
+                    freqs_cis,
+                    grid_sizes=grid_sizes,
+                    mouse_cond=mouse_cond,
+                    keyboard_cond=keyboard_cond)
+            else:
+                hidden_states = block(
+                    hidden_states,
+                    encoder_hidden_states,
+                    timestep_proj,
+                    freqs_cis,
+                    grid_sizes=grid_sizes,
+                    mouse_cond=mouse_cond,
+                    keyboard_cond=keyboard_cond)
+
+        # Output
+        shift, scale = (self.scale_shift_table + temb.unsqueeze(1)).chunk(2,
+                                                                          dim=1)
+        hidden_states = self.norm_out(hidden_states, shift, scale)
+        hidden_states = self.proj_out(hidden_states)
+
+        hidden_states = hidden_states.reshape(batch_size, post_patch_num_frames,
+                                              post_patch_height,
+                                              post_patch_width, p_t, p_h, p_w,
+                                              -1)
+        hidden_states = hidden_states.permute(0, 7, 1, 4, 2, 5, 3, 6)
+        output = hidden_states.flatten(6, 7).flatten(4, 5).flatten(2, 3)
+
+        return output

fastvideo/models/dits/matrix_game/utils.py

@@ -0,0 +1,307 @@
+from __future__ import annotations
+
+import os
+import random
+
+# import cv2
+import numpy as np
+import torch
+from diffusers.utils import export_to_video
+from PIL import Image
+
+from fastvideo.utils import logger
+
+
+CAM_VALUE = 0.1
+CAMERA_MAP = {
+    "i": [CAM_VALUE, 0], "k": [-CAM_VALUE, 0],
+    "j": [0, -CAM_VALUE], "l": [0, CAM_VALUE], "u": [0, 0]
+}
+
+KEYBOARD_MAP_4 = {  # base_distilled_model (universal): W/S/A/D
+    "w": [1, 0, 0, 0], "s": [0, 1, 0, 0],
+    "a": [0, 0, 1, 0], "d": [0, 0, 0, 1], "q": [0, 0, 0, 0]
+}
+KEYBOARD_MAP_2 = {  # gta_distilled_model: W/S only (steering via mouse)
+    "w": [1, 0], "s": [0, 1], "q": [0, 0]
+}
+KEYBOARD_MAP_7 = {  # templerun_distilled_model: still/w/s/left/right/a/d
+    "q": [1, 0, 0, 0, 0, 0, 0],  # still
+    "w": [0, 1, 0, 0, 0, 0, 0],  # forward
+    "s": [0, 0, 1, 0, 0, 0, 0],  # back
+    "j": [0, 0, 0, 1, 0, 0, 0],  # left (swipe)
+    "l": [0, 0, 0, 0, 1, 0, 0],  # right (swipe)
+    "a": [0, 0, 0, 0, 0, 1, 0],  # a
+    "d": [0, 0, 0, 0, 0, 0, 1],  # d
+}
+KEYBOARD_MAP = KEYBOARD_MAP_4  # Default for backward compatibility
+
+
+def load_initial_image(image_path: str = None) -> Image.Image:
+    if image_path and os.path.exists(image_path):
+        return Image.open(image_path).convert("RGB")
+    logger.warning("No image provided, creating placeholder...")
+    return Image.new("RGB", (640, 352), (128, 128, 128))
+
+
+def create_action_presets(num_frames: int, keyboard_dim: int = 4, seed: int = None):
+    if keyboard_dim not in (2, 4, 7):
+        raise ValueError(f"keyboard_dim must be 2, 4, or 7, got {keyboard_dim}")
+    if num_frames % 4 != 1:
+        raise ValueError("Matrix-Game conditioning expects num_frames to be 4k+1.")
+
+    # Set seed for reproducibility if provided
+    if seed is not None:
+        random.seed(seed)
+
+    num_samples_per_action = 4
+    
+    # Define actions based on keyboard_dim
+    if keyboard_dim == 4:
+        # Universal model: W, S, A, D
+        actions_single_action = ["forward", "left", "right"]
+        actions_double_action = ["forward_left", "forward_right"]
+        actions_single_camera = ["camera_l", "camera_r"]
+        keyboard_idx = {"forward": 0, "back": 1, "left": 2, "right": 3}
+    elif keyboard_dim == 2:
+        # GTA model: W, S only (steering via mouse)
+        actions_single_action = ["forward", "back"]
+        actions_double_action = []
+        actions_single_camera = ["camera_l", "camera_r"]
+        keyboard_idx = {"forward": 0, "back": 1}
+    else:  # keyboard_dim == 7
+        # Temple Run model: still, w, s, left, right, a, d (no mouse)
+        actions_single_action = ["forward", "back", "left", "right"]
+        actions_double_action = []
+        actions_single_camera = []  # No mouse for Temple Run
+        keyboard_idx = {"still": 0, "forward": 1, "back": 2, "left": 3, "right": 4, "a": 5, "d": 6}
+
+    actions_to_test = (
+        actions_double_action * 5 + actions_single_camera * 5 + actions_single_action * 5
+    )
+    for action in (actions_single_action + actions_double_action):
+        for camera in actions_single_camera:
+            actions_to_test.append(f"{action}_{camera}")
+
+    # Ensure we have at least some actions
+    if not actions_to_test:
+        actions_to_test = actions_single_action * 5
+
+    base_action = actions_single_action + actions_single_camera
+    cam_value = 0.1
+    camera_value_map = {
+        "camera_up": [cam_value, 0],
+        "camera_down": [-cam_value, 0],
+        "camera_l": [0, -cam_value],
+        "camera_r": [0, cam_value],
+        "camera_ur": [cam_value, cam_value],
+        "camera_ul": [cam_value, -cam_value],
+        "camera_dr": [-cam_value, cam_value],
+        "camera_dl": [-cam_value, -cam_value],
+    }
+
+    data = []
+    for action_name in actions_to_test:
+        keyboard_condition = torch.zeros((num_samples_per_action, keyboard_dim))
+        mouse_condition = torch.zeros((num_samples_per_action, 2))
+
+        for sub_act in base_action:
+            if sub_act not in action_name:
+                continue
+            if sub_act in camera_value_map:
+                mouse_condition = torch.tensor(
+                    [camera_value_map[sub_act] for _ in range(num_samples_per_action)],
+                    dtype=mouse_condition.dtype,
+                )
+            elif sub_act in keyboard_idx:
+                keyboard_condition[:, keyboard_idx[sub_act]] = 1
+
+        data.append({
+            "keyboard_condition": keyboard_condition,
+            "mouse_condition": mouse_condition,
+        })
+
+    keyboard_condition = torch.zeros((num_frames, keyboard_dim))
+    mouse_condition = torch.zeros((num_frames, 2))
+    current_frame = 0
+    selections = [12]
+
+    while current_frame < num_frames:
+        rd_frame = selections[random.randint(0, len(selections) - 1)]
+        entry = data[random.randint(0, len(data) - 1)]
+        key_seq = entry["keyboard_condition"]
+        mouse_seq = entry["mouse_condition"]
+
+        if current_frame == 0:
+            keyboard_condition[:1] = key_seq[:1]
+            mouse_condition[:1] = mouse_seq[:1]
+            current_frame = 1
+        else:
+            rd_frame = min(rd_frame, num_frames - current_frame)
+            repeat_time = rd_frame // 4
+            keyboard_condition[current_frame:current_frame + rd_frame] = key_seq.repeat(repeat_time, 1)
+            mouse_condition[current_frame:current_frame + rd_frame] = mouse_seq.repeat(repeat_time, 1)
+            current_frame += rd_frame
+
+    return {"keyboard": keyboard_condition, "mouse": mouse_condition}
+
+
+def parse_config(config, mode="universal"):
+    assert mode in ['universal', 'gta_drive', 'templerun']
+    key_data = {}
+    mouse_data = {}
+    if mode != 'templerun':
+        key, mouse = config
+    else:
+        key = config
+
+    for i in range(len(key)):
+        if mode == 'templerun':
+            still, w, s, left, right, a, d = key[i]
+        elif mode == 'universal':
+            w, s, a, d = key[i]
+        else:
+            w, s, a, d = key[i][0], key[i][1], mouse[i][1] < 0, mouse[i][1] > 0
+        if mode == 'universal':
+            mouse_y, mouse_x = mouse[i]
+            mouse_y = -1 * mouse_y
+
+        key_data[i] = {"W": bool(w), "A": bool(a), "S": bool(s), "D": bool(d)}
+        if mode == 'templerun':
+            key_data[i].update({"left": bool(left), "right": bool(right)})
+
+        if mode == 'universal':
+            if i == 0:
+                mouse_data[i] = (320, 352 // 2)
+            else:
+                global_scale_factor = 0.1
+                mouse_scale_x = 15 * global_scale_factor
+                mouse_scale_y = 15 * 4 * global_scale_factor
+                mouse_data[i] = (
+                    mouse_data[i - 1][0] + mouse_x * mouse_scale_x,
+                    mouse_data[i - 1][1] + mouse_y * mouse_scale_y,
+                )
+    return key_data, mouse_data
+
+
+# NOTE: drawing functions are commented out to avoid cv2/libGL dependency.
+#
+# def draw_rounded_rectangle(image, top_left, bottom_right, color, radius=10, alpha=0.5):
+#     overlay = image.copy()
+#     x1, y1 = top_left
+#     x2, y2 = bottom_right
+#
+#     cv2.rectangle(overlay, (x1 + radius, y1), (x2 - radius, y2), color, -1)
+#     cv2.rectangle(overlay, (x1, y1 + radius), (x2, y2 - radius), color, -1)
+#     cv2.ellipse(overlay, (x1 + radius, y1 + radius), (radius, radius), 180, 0, 90, color, -1)
+#     cv2.ellipse(overlay, (x2 - radius, y1 + radius), (radius, radius), 270, 0, 90, color, -1)
+#     cv2.ellipse(overlay, (x1 + radius, y2 - radius), (radius, radius), 90, 0, 90, color, -1)
+#     cv2.ellipse(overlay, (x2 - radius, y2 - radius), (radius, radius), 0, 0, 90, color, -1)
+#     cv2.addWeighted(overlay, alpha, image, 1 - alpha, 0, image)
+#
+#
+# def draw_keys_on_frame(frame, keys, key_size=(80, 50), spacing=20, bottom_margin=30, mode='universal'):
+#     h, w, _ = frame.shape
+#     horison_shift = 90
+#     vertical_shift = -20
+#     horizon_shift_all = 50
+#     key_positions = {
+#         "W": (w // 2 - key_size[0] // 2 - horison_shift - horizon_shift_all, 
+#               h - bottom_margin - key_size[1] * 2 + vertical_shift - 20),
+#         "A": (w // 2 - key_size[0] * 2 + 5 - horison_shift - horizon_shift_all, 
+#               h - bottom_margin - key_size[1] + vertical_shift),
+#         "S": (w // 2 - key_size[0] // 2 - horison_shift - horizon_shift_all, 
+#               h - bottom_margin - key_size[1] + vertical_shift),
+#         "D": (w // 2 + key_size[0] - 5 - horison_shift - horizon_shift_all, 
+#               h - bottom_margin - key_size[1] + vertical_shift),
+#     }
+#     key_icon = {"W": "W", "A": "A", "S": "S", "D": "D", "left": "left", "right": "right"}
+#     if mode == 'templerun':
+#         key_positions.update({
+#             "left": (w // 2 + key_size[0] * 2 + spacing * 2 - horison_shift - horizon_shift_all, 
+#                      h - bottom_margin - key_size[1] + vertical_shift),
+#             "right": (w // 2 + key_size[0] * 3 + spacing * 7 - horison_shift - horizon_shift_all, 
+#                       h - bottom_margin - key_size[1] + vertical_shift)
+#         })
+#
+#     for key, (x, y) in key_positions.items():
+#         is_pressed = keys.get(key, False)
+#         top_left = (x, y)
+#         if key in ["left", "right"]:
+#             bottom_right = (x + key_size[0] + 40, y + key_size[1])
+#         else:
+#             bottom_right = (x + key_size[0], y + key_size[1])
+#
+#         color = (0, 255, 0) if is_pressed else (200, 200, 200)
+#         alpha = 0.8 if is_pressed else 0.5
+#         draw_rounded_rectangle(frame, top_left, bottom_right, color, radius=10, alpha=alpha)
+#
+#         text_size = cv2.getTextSize(key, cv2.FONT_HERSHEY_SIMPLEX, 0.8, 2)[0]
+#         if key in ["left", "right"]:
+#             text_x = x + (key_size[0] + 40 - text_size[0]) // 2
+#         else:
+#             text_x = x + (key_size[0] - text_size[0]) // 2
+#         text_y = y + (key_size[1] + text_size[1]) // 2
+#         cv2.putText(frame, key_icon[key], (text_x, text_y), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 0, 0), 2)
+#
+#
+# def overlay_icon(frame, icon, position, scale=1.0, rotation=0):
+#     x, y = position
+#     h, w, _ = icon.shape
+#
+#     scaled_width = int(w * scale)
+#     scaled_height = int(h * scale)
+#     icon_resized = cv2.resize(icon, (scaled_width, scaled_height), interpolation=cv2.INTER_AREA)
+#
+#     center = (scaled_width // 2, scaled_height // 2)
+#     rotation_matrix = cv2.getRotationMatrix2D(center, rotation, 1.0)
+#     icon_rotated = cv2.warpAffine(
+#         icon_resized, rotation_matrix, (scaled_width, scaled_height),
+#         flags=cv2.INTER_LINEAR, borderMode=cv2.BORDER_CONSTANT, borderValue=(0, 0, 0, 0)
+#     )
+#
+#     h, w, _ = icon_rotated.shape
+#     frame_h, frame_w, _ = frame.shape
+#
+#     top_left_x = max(0, int(x - w // 2))
+#     top_left_y = max(0, int(y - h // 2))
+#     bottom_right_x = min(frame_w, int(x + w // 2))
+#     bottom_right_y = min(frame_h, int(y + h // 2))
+#
+#     icon_x_start = max(0, int(-x + w // 2))
+#     icon_y_start = max(0, int(-y + h // 2))
+#     icon_x_end = icon_x_start + (bottom_right_x - top_left_x)
+#     icon_y_end = icon_y_start + (bottom_right_y - top_left_y)
+#
+#     icon_region = icon_rotated[icon_y_start:icon_y_end, icon_x_start:icon_x_end]
+#     alpha = icon_region[:, :, 3] / 255.0
+#     icon_rgb = icon_region[:, :, :3]
+#
+#     frame_region = frame[top_left_y:bottom_right_y, top_left_x:bottom_right_x]
+#     for c in range(3):
+#         frame_region[:, :, c] = (1 - alpha) * frame_region[:, :, c] + alpha * icon_rgb[:, :, c]
+#     frame[top_left_y:bottom_right_y, top_left_x:bottom_right_x] = frame_region
+#
+#
+# def process_video(input_video, output_video, config, mouse_icon_path, 
+#                   mouse_scale=1.0, mouse_rotation=0, process_icon=True, mode='universal'):
+#     key_data, mouse_data = parse_config(config, mode=mode)
+#     fps = 12
+#
+#     mouse_icon = cv2.imread(mouse_icon_path, cv2.IMREAD_UNCHANGED)
+#
+#     out_video = []
+#     for frame_idx, frame in enumerate(input_video):
+#         frame = np.ascontiguousarray(frame)
+#         if process_icon:
+#             keys = key_data.get(frame_idx, {"W": False, "A": False, "S": False, "D": False, "left": False, "right": False})
+#             draw_keys_on_frame(frame, keys, key_size=(50, 50), spacing=10, bottom_margin=20, mode=mode)
+#             if mode == 'universal':
+#                 frame_width = frame.shape[1]
+#                 frame_height = frame.shape[0]
+#                 mouse_position = mouse_data.get(frame_idx, (frame_width // 2, frame_height // 2))
+#                 overlay_icon(frame, mouse_icon, mouse_position, scale=mouse_scale, rotation=mouse_rotation)
+#         out_video.append(frame / 255)
+#     
+#     export_to_video(out_video, output_video, fps=fps)
+#     logger.info(f"Video saved to {output_video}")

fastvideo/models/dits/wanvideo.py

@@ -12,7 +12,10 @@ from fastvideo.attention import (DistributedAttention, DistributedAttention_VSA,
                                  LocalAttention)
 from fastvideo.configs.models.dits import WanVideoConfig
 from fastvideo.configs.sample.wan import WanTeaCacheParams
-from fastvideo.distributed.parallel_state import get_sp_world_size
+from fastvideo.distributed.communication_op import (
+    sequence_model_parallel_all_gather,
+    sequence_model_parallel_all_gather_with_unpad,
+    sequence_model_parallel_shard)
 from fastvideo.forward_context import get_forward_context
 from fastvideo.layers.layernorm import (FP32LayerNorm, LayerNormScaleShift,
                                         RMSNorm, ScaleResidual,
@@ -21,14 +24,16 @@ from fastvideo.layers.linear import ReplicatedLinear
 # from torch.nn import RMSNorm
 # TODO: RMSNorm ....
 from fastvideo.layers.mlp import MLP
-from fastvideo.layers.rotary_embedding import (_apply_rotary_emb,
-                                               get_rotary_pos_embed)
+from fastvideo.layers.rotary_embedding import get_rotary_pos_embed
 from fastvideo.layers.visual_embedding import (ModulateProjection, PatchEmbed,
                                                TimestepEmbedder)
 from fastvideo.logger import init_logger
 from fastvideo.models.dits.base import CachableDiT
 from fastvideo.platforms import AttentionBackendEnum, current_platform
 
+from fastvideo.distributed.parallel_state import get_sp_world_size
+from fastvideo.distributed.utils import create_attention_mask_for_padding
+
 logger = init_logger(__name__)
 
 
@@ -314,6 +319,7 @@ class WanTransformerBlock(nn.Module):
         encoder_hidden_states: torch.Tensor,
         temb: torch.Tensor,
         freqs_cis: tuple[torch.Tensor, torch.Tensor],
+        attention_mask: torch.Tensor | None = None,
     ) -> torch.Tensor:
         if hidden_states.dim() == 4:
             hidden_states = hidden_states.squeeze(1)
@@ -356,13 +362,7 @@ class WanTransformerBlock(nn.Module):
         key = key.squeeze(1).unflatten(2, (self.num_attention_heads, -1))
         value = value.squeeze(1).unflatten(2, (self.num_attention_heads, -1))
 
-        # Apply rotary embeddings
-        cos, sin = freqs_cis
-        query, key = _apply_rotary_emb(query, cos, sin,
-                                       is_neox_style=False), _apply_rotary_emb(
-                                           key, cos, sin, is_neox_style=False)
-
-        attn_output, _ = self.attn1(query, key, value)
+        attn_output, _ = self.attn1(query, key, value, freqs_cis=freqs_cis, attention_mask=attention_mask)
         attn_output = attn_output.flatten(2)
         attn_output, _ = self.to_out(attn_output)
         attn_output = attn_output.squeeze(1)
@@ -474,6 +474,7 @@ class WanTransformerBlock_VSA(nn.Module):
         encoder_hidden_states: torch.Tensor,
         temb: torch.Tensor,
         freqs_cis: tuple[torch.Tensor, torch.Tensor],
+        attention_mask: torch.Tensor | None = None,
     ) -> torch.Tensor:
         if hidden_states.dim() == 4:
             hidden_states = hidden_states.squeeze(1)
@@ -504,16 +505,12 @@ class WanTransformerBlock_VSA(nn.Module):
         gate_compress = gate_compress.squeeze(1).unflatten(
             2, (self.num_attention_heads, -1))
 
-        # Apply rotary embeddings
-        cos, sin = freqs_cis
-        query, key = _apply_rotary_emb(query, cos, sin,
-                                       is_neox_style=False), _apply_rotary_emb(
-                                           key, cos, sin, is_neox_style=False)
-
         attn_output, _ = self.attn1(query,
                                     key,
                                     value,
-                                    gate_compress=gate_compress)
+                                    freqs_cis = freqs_cis,
+                                    gate_compress=gate_compress,
+                                    attention_mask=attention_mask)
         attn_output = attn_output.flatten(2)
         attn_output, _ = self.to_out(attn_output)
         attn_output = attn_output.squeeze(1)
@@ -563,6 +560,8 @@ class WanTransformer3DModel(CachableDiT):
         self.patch_size = config.patch_size
         self.text_len = config.text_len
 
+        assert config.num_attention_heads % get_sp_world_size() == 0, f"The number of attention heads ({config.num_attention_heads}) must be divisible by the sequence parallel size ({get_sp_world_size()})"
+
         # 1. Patch & position embedding
         self.patch_embedding = PatchEmbed(in_chans=config.in_channels,
                                           embed_dim=inner_dim,
@@ -606,6 +605,7 @@ class WanTransformer3DModel(CachableDiT):
             torch.randn(1, 2, inner_dim) / inner_dim**0.5)
 
         self.gradient_checkpointing = False
+        self._logged_attention_mask = False
 
         # For type checking
         self.previous_e0_even = None
@@ -650,21 +650,43 @@ class WanTransformer3DModel(CachableDiT):
         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_num_frames, 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)
-        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
+        freqs_cis = (freqs_cos.to(hidden_states.device).float(),
+                     freqs_sin.to(hidden_states.device).float())
 
         hidden_states = self.patch_embedding(hidden_states)
         hidden_states = hidden_states.flatten(2).transpose(1, 2)
 
+        # Shard with padding support - returns (sharded_tensor, original_seq_len)
+        hidden_states, original_seq_len = sequence_model_parallel_shard(hidden_states, dim=1)
+        
+        # Create attention mask for padded tokens if padding was applied
+        current_seq_len = hidden_states.shape[1]
+        sp_world_size = get_sp_world_size()
+        padded_seq_len = current_seq_len * sp_world_size
+        
+        if padded_seq_len > original_seq_len:
+            if not self._logged_attention_mask:
+                logger.info(f"Padding applied, original seq len: {original_seq_len}, padded seq len: {padded_seq_len}")
+                self._logged_attention_mask = True
+            attention_mask = create_attention_mask_for_padding(
+                seq_len=original_seq_len,
+                padded_seq_len=padded_seq_len,
+                batch_size=batch_size,
+                device=hidden_states.device,
+            )
+        else:
+            if not self._logged_attention_mask:
+                logger.info(f"Padding not applied")
+                self._logged_attention_mask = True
+            attention_mask = None
+
         # timestep shape: batch_size, or batch_size, seq_len (wan 2.2 ti2v)
         if timestep.dim() == 2:
             ts_seq_len = timestep.shape[1]
@@ -698,6 +720,7 @@ class WanTransformer3DModel(CachableDiT):
             timestep_proj=timestep_proj, temb=temb)
 
         if should_skip_forward:
+            print("skipping forward, cached")
             hidden_states = self.retrieve_cached_states(hidden_states)
         else:
             # if teacache is enabled, we need to cache the original hidden states
@@ -708,12 +731,13 @@ class WanTransformer3DModel(CachableDiT):
                 for block in self.blocks:
                     hidden_states = self._gradient_checkpointing_func(
                         block, hidden_states, encoder_hidden_states,
-                        timestep_proj, freqs_cis)
+                        timestep_proj, freqs_cis, attention_mask)
             else:
                 for block in self.blocks:
                     hidden_states = block(hidden_states, encoder_hidden_states,
-                                          timestep_proj, freqs_cis)
+                                          timestep_proj, freqs_cis, attention_mask)
             # if teacache is enabled, we need to cache the original hidden states
+
             if enable_teacache:
                 self.maybe_cache_states(hidden_states, original_hidden_states)
         # 5. Output norm, projection & unpatchify
@@ -726,7 +750,12 @@ class WanTransformer3DModel(CachableDiT):
             # 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)
+
+        # Gather and unpad in one operation
+        hidden_states = sequence_model_parallel_all_gather_with_unpad(
+            hidden_states, original_seq_len, dim=1)
         hidden_states = self.proj_out(hidden_states)
 
         hidden_states = hidden_states.reshape(batch_size, post_patch_num_frames,

fastvideo/models/encoders/qwen2_5.py

@@ -0,0 +1,387 @@
+# SPDX-License-Identifier: Apache-2.0
+# Adapted from transformers: https://github.com/huggingface/transformers/blob/main/src/transformers/models/qwen2_5_vl/modeling_qwen2_5_vl.py
+
+import math
+from typing import Any, Optional, Tuple, Union, List, Callable, Iterable
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from fastvideo.configs.models.encoders import BaseEncoderOutput, Qwen2_5_VLConfig
+from fastvideo.distributed import get_tp_rank, get_tp_world_size
+from fastvideo.layers.activation import get_act_fn, SiluAndMul
+from fastvideo.layers.layernorm import RMSNorm
+from fastvideo.layers.linear import MergedColumnParallelLinear, QKVParallelLinear, RowParallelLinear
+from fastvideo.layers.quantization import QuantizationConfig
+from fastvideo.layers.vocab_parallel_embedding import VocabParallelEmbedding
+from fastvideo.models.encoders.base import TextEncoder
+from fastvideo.models.loader.weight_utils import default_weight_loader
+from fastvideo.models.mask_utils import sdpa_mask
+from fastvideo.logger import init_logger
+
+logger = init_logger(__name__)
+
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+    """
+    This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+    num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+    """
+    batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+    if n_rep == 1:
+        return hidden_states
+    hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+    return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+def sdpa_attention_forward(
+    module: torch.nn.Module,
+    query: torch.Tensor,
+    key: torch.Tensor,
+    value: torch.Tensor,
+    attention_mask: Optional[torch.Tensor],
+    dropout: float = 0.0,
+    scaling: Optional[float] = None,
+    is_causal: Optional[bool] = None,
+    **kwargs,
+) -> tuple[torch.Tensor, None]:
+    if kwargs.get("output_attentions", False) or kwargs.get("head_mask") is not None:
+        logger.warning_once(
+            "`sdpa` attention does not support `output_attentions=True` or `head_mask`."
+            " Please set your attention to `eager` if you want any of these features."
+        )
+
+    if hasattr(module, "num_key_value_groups"):
+        key = repeat_kv(key, module.num_key_value_groups)
+        value = repeat_kv(value, module.num_key_value_groups)
+
+    if attention_mask is not None and attention_mask.ndim == 4:
+        attention_mask = attention_mask[:, :, :, : key.shape[-2]]
+    # If attention_mask is not None, convert it to boolean type
+    if attention_mask is not None and attention_mask.dtype != torch.bool:
+        attention_mask = attention_mask.bool()
+
+    attn_output = torch.nn.functional.scaled_dot_product_attention(
+        query,
+        key,
+        value,
+        attn_mask=attention_mask,
+        dropout_p=dropout,
+        scale=scaling,
+        is_causal=is_causal,
+    )
+    attn_output = attn_output.transpose(1, 2).contiguous()
+
+    return attn_output, None
+
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2 :]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_multimodal_rotary_pos_emb(q, k, cos, sin, mrope_section, unsqueeze_dim=1):
+    mrope_section = [s * 2 for s in mrope_section]
+    cos = torch.cat([m[i % 3] for i, m in enumerate(cos.split(mrope_section, dim=-1))], dim=-1).unsqueeze(
+        unsqueeze_dim
+    )
+    sin = torch.cat([m[i % 3] for i, m in enumerate(sin.split(mrope_section, dim=-1))], dim=-1).unsqueeze(
+        unsqueeze_dim
+    )
+
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    k_embed = (k * cos) + (rotate_half(k) * sin)
+    return q_embed, k_embed
+
+
+class Qwen2_5_VLRotaryEmbedding(nn.Module):
+    def __init__(self, config: Qwen2_5_VLConfig, device=None):
+        super().__init__()
+        self.max_seq_len_cached = config.max_position_embeddings
+        self.original_max_seq_len = config.max_position_embeddings
+        self.config = config
+        
+        self.rope_type = config.rope_scaling.get("rope_type", "default")
+        self.base = config.rope_theta
+        
+        # Simplified initialization
+        head_dim = config.hidden_size // config.num_attention_heads
+        dim = head_dim
+        self.attention_scaling = 1.0
+
+        inv_freq = 1.0 / (
+            self.base ** (torch.arange(0, dim, 2, dtype=torch.int64).float().to(device) / dim)
+        )
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+        self.original_inv_freq = inv_freq
+
+    def forward(self, x, position_ids):
+        # In contrast to other models, Qwen2_5_VL has different position ids for the grids
+        # So we expand the inv_freq to shape (3, ...)
+        inv_freq_expanded = self.inv_freq[None, None, :, None].float().expand(3, position_ids.shape[1], -1, 1)
+        position_ids_expanded = position_ids[:, :, None, :].float()  # shape (3, bs, 1, positions)
+
+        device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != "mps" else "cpu"
+        with torch.autocast(device_type=device_type, enabled=False):  # Force float32
+            freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(2, 3)
+            emb = torch.cat((freqs, freqs), dim=-1)
+            cos = emb.cos() * self.attention_scaling
+            sin = emb.sin() * self.attention_scaling
+
+        return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)
+
+
+class Qwen2_5_VLMLP(nn.Module):
+    def __init__(self, config: Qwen2_5_VLConfig, quant_config: QuantizationConfig | None = None, prefix: str = ""):
+        super().__init__()
+        self.gate_up_proj = MergedColumnParallelLinear(
+            input_size=config.hidden_size,
+            output_sizes=[config.intermediate_size] * 2,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.gate_up_proj",
+        )
+        self.down_proj = RowParallelLinear(
+            input_size=config.intermediate_size,
+            output_size=config.hidden_size,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.down_proj",
+        )
+        self.act_fn = SiluAndMul()
+
+    def forward(self, x):
+        x, _ = self.gate_up_proj(x)
+        x = self.act_fn(x)
+        x, _ = self.down_proj(x)
+        return x
+
+
+class Qwen2_5_VLAttention(nn.Module):
+    def __init__(self, config: Qwen2_5_VLConfig, layer_idx: int, quant_config: QuantizationConfig | None = None, prefix: str = ""):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.hidden_size // self.num_heads
+        self.num_key_value_heads = config.num_key_value_heads
+        self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+        
+        tp_size = get_tp_world_size()
+        self.total_num_heads = self.num_heads
+        assert self.total_num_heads % tp_size == 0
+        self.num_heads = self.total_num_heads // tp_size
+        
+        self.total_num_kv_heads = self.num_key_value_heads
+        if self.total_num_kv_heads >= tp_size:
+             assert self.total_num_kv_heads % tp_size == 0
+             self.num_kv_heads = self.total_num_kv_heads // tp_size
+        else:
+             assert tp_size % self.total_num_kv_heads == 0
+             self.num_kv_heads = 1 
+
+        self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
+        
+        self.q_size = self.num_heads * self.head_dim
+        self.kv_size = self.num_kv_heads * self.head_dim
+        self.scaling = self.head_dim**-0.5
+
+        self.qkv_proj = QKVParallelLinear(
+            hidden_size=self.hidden_size,
+            head_size=self.head_dim,
+            total_num_heads=self.total_num_heads,
+            total_num_kv_heads=self.total_num_kv_heads,
+            bias=True,
+            quant_config=quant_config,
+            prefix=f"{prefix}.qkv_proj",
+        )
+        
+        self.o_proj = RowParallelLinear(
+            input_size=self.total_num_heads * self.head_dim,
+            output_size=self.hidden_size,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.o_proj",
+        )
+        
+        self.layer_type = config.layer_types[layer_idx] if config.layer_types else "full_attention"
+        self.sliding_window = config.sliding_window if self.layer_type == "sliding_attention" else None
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
+        output_attentions: bool = False,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
+        bsz, q_len, _ = hidden_states.size()
+
+        qkv, _ = self.qkv_proj(hidden_states)
+        query_states, key_states, value_states = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
+
+        query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        key_states = key_states.view(bsz, q_len, self.num_kv_heads, self.head_dim).transpose(1, 2)
+        value_states = value_states.view(bsz, q_len, self.num_kv_heads, self.head_dim).transpose(1, 2)
+
+        cos, sin = position_embeddings
+        query_states, key_states = apply_multimodal_rotary_pos_emb(
+            query_states, key_states, cos, sin, self.config.rope_scaling["mrope_section"]
+        )
+
+        attn_output = sdpa_attention_forward(self, query_states, key_states, value_states, attention_mask, dropout=self.config.attention_dropout, scaling=self.scaling, is_causal=False)[0].reshape(bsz, q_len, -1)
+        attn_output, _ = self.o_proj(attn_output)
+        
+        return attn_output
+
+class Qwen2_5_VLDecoderLayer(nn.Module):
+    def __init__(self, config: Qwen2_5_VLConfig, layer_idx: int, quant_config: QuantizationConfig | None = None, prefix: str = ""):
+        super().__init__()
+        self.self_attn = Qwen2_5_VLAttention(config, layer_idx, quant_config=quant_config, prefix=f"{prefix}.self_attn")
+        self.mlp = Qwen2_5_VLMLP(config, quant_config=quant_config, prefix=f"{prefix}.mlp")
+        self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        position_embeddings: Optional[tuple[torch.Tensor, torch.Tensor]] = None,
+        output_attentions: Optional[bool] = False,
+    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+        
+        hidden_states = self.self_attn(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            position_embeddings=position_embeddings,
+            output_attentions=output_attentions,
+        )
+        hidden_states = residual + hidden_states
+        
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states
+        
+        outputs = (hidden_states,)
+            
+        return outputs
+
+class Qwen2_5_VLTextModel(TextEncoder):
+    def __init__(self, config: Qwen2_5_VLConfig):
+        super().__init__(config)
+
+        quant_config = None
+
+        self.embed_tokens = VocabParallelEmbedding(
+            config.vocab_size,
+            config.hidden_size,
+            org_num_embeddings=config.vocab_size
+        )
+        
+        self.layers = nn.ModuleList([
+            Qwen2_5_VLDecoderLayer(config, layer_idx, quant_config=quant_config, prefix=f"{config.prefix}.layers.{layer_idx}")
+            for layer_idx in range(config.num_hidden_layers)
+        ])
+        
+        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.rotary_emb = Qwen2_5_VLRotaryEmbedding(config)
+
+    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
+        return self.embed_tokens(input_ids)
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        output_hidden_states: Optional[bool] = None,
+        **kwargs,
+    ) -> BaseEncoderOutput:
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        
+        if inputs_embeds is None:
+            inputs_embeds = self.get_input_embeddings(input_ids)
+            
+        hidden_states = inputs_embeds
+        
+        if position_ids is None:
+            seq_length = hidden_states.shape[1]
+            cache_position = torch.arange(seq_length, device=hidden_states.device)
+            position_ids = cache_position.view(1, 1, -1).expand(3, hidden_states.shape[0], -1)
+
+        mask_kwargs = {
+            "batch_size": hidden_states.shape[0],
+            "cache_position": cache_position,
+            "kv_length": attention_mask.shape[-1],
+            "kv_offset": 0,
+            "attention_mask": attention_mask
+        }
+
+        position_embeddings = self.rotary_emb(hidden_states, position_ids)
+        
+        all_hidden_states = () if output_hidden_states else None
+        
+        for decoder_layer in self.layers:
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+                
+            layer_outputs = decoder_layer(
+                hidden_states,
+                attention_mask=sdpa_mask(**mask_kwargs),
+                position_ids=position_ids,
+                position_embeddings=position_embeddings,
+                output_attentions=False,
+            )
+            hidden_states = layer_outputs[0]
+            
+        hidden_states = self.norm(hidden_states)
+        
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+            
+        return BaseEncoderOutput(
+            last_hidden_state=hidden_states,
+            hidden_states=all_hidden_states,
+        )
+
+    def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
+        params_dict = dict(self.named_parameters())
+        loaded_params: set[str] = set()
+        for name, loaded_weight in weights:
+            if "rotary_emb.inv_freq" in name:
+                continue
+            for param_name, weight_name, shard_id in self.config.stacked_params_mapping:
+                if weight_name not in name:
+                    continue
+                name = name.replace(weight_name, param_name)
+                # Skip loading extra bias for GPTQ models.
+                # if name.endswith(".bias") and name not in params_dict:
+                #     continue
+                if name not in params_dict:
+                    continue
+                param = params_dict[name]
+                weight_loader = param.weight_loader
+                weight_loader(param, loaded_weight, shard_id)
+                break
+            else:
+                # Skip loading extra bias for GPTQ models.
+                # if name.endswith(".bias") and name not in params_dict:
+                #     continue
+                if name not in params_dict:
+                    continue
+
+                param = params_dict[name]
+                weight_loader = getattr(param, "weight_loader", default_weight_loader)
+                weight_loader(param, loaded_weight)
+            loaded_params.add(name)
+        return loaded_params

fastvideo/models/encoders/stepllm.py

@@ -19,7 +19,8 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 from einops import rearrange
-from transformers.modeling_utils import PretrainedConfig, PreTrainedModel
+from transformers import PretrainedConfig
+from transformers.modeling_utils import PreTrainedModel
 
 from fastvideo.models.dits.stepvideo import StepVideoRMSNorm
 

fastvideo/models/loader/component_loader.py

@@ -412,11 +412,9 @@ class VAELoader(ComponentLoader):
         # Find all safetensors files
         safetensors_list = glob.glob(
             os.path.join(str(model_path), "*.safetensors"))
-        # TODO(PY)
-        assert len(
-            safetensors_list
-        ) == 1, f"Found {len(safetensors_list)} safetensors files in {model_path}"
-        loaded = safetensors_load_file(safetensors_list[0])
+        loaded = {}
+        for sf_file in safetensors_list:
+            loaded.update(safetensors_load_file(sf_file))
         vae.load_state_dict(
             loaded, strict=False)  # We might only load encoder or decoder
 
@@ -478,8 +476,6 @@ class TransformerLoader(ComponentLoader):
             fastvideo_args.pipeline_config.dit_precision]
 
         # Load the model using FSDP loader
-        logger.info("Loading model from %s, default_dtype: %s", cls_name,
-                    default_dtype)
         assert fastvideo_args.hsdp_shard_dim is not None
         model = maybe_load_fsdp_model(
             model_cls=model_cls,

fastvideo/models/mask_utils.py

@@ -0,0 +1,201 @@
+import torch
+from typing import Callable, Optional
+
+def and_masks(*mask_functions: Callable) -> Callable:
+    """Returns a mask function that is the intersection of provided mask functions"""
+    if not all(callable(arg) for arg in mask_functions):
+        raise RuntimeError(f"All inputs should be callable mask_functions: {mask_functions}")
+
+    def and_mask(batch_idx, head_idx, q_idx, kv_idx):
+        result = q_idx.new_ones((), dtype=torch.bool)
+        for mask in mask_functions:
+            result = result & mask(batch_idx, head_idx, q_idx, kv_idx).to(result.device)
+        return result
+
+    return and_mask
+
+def causal_mask_function(batch_idx: int, head_idx: int, q_idx: int, kv_idx: int) -> bool:
+    """
+    This creates a basic lower-diagonal causal mask.
+    """
+    return kv_idx <= q_idx
+
+def padding_mask_function(padding_mask: torch.Tensor) -> Callable:
+    """
+    This return the mask_function function corresponding to a 2D padding mask.
+    """
+
+    def inner_mask(batch_idx: int, head_idx: int, q_idx: int, kv_idx: int) -> bool:
+        # Note that here the mask should ALWAYS be at least of the max `kv_index` size in the dimension 1. This is because
+        # we cannot pad it here in the mask_function as we don't know the final size, and we cannot try/except, as it is not
+        # vectorizable on accelerator devices
+        return padding_mask[batch_idx, kv_idx]
+
+    return inner_mask
+
+def prepare_padding_mask(
+    attention_mask: Optional[torch.Tensor], kv_length: int, kv_offset: int
+) -> Optional[torch.Tensor]:
+    """
+    From the 2D attention mask, prepare the correct padding mask to use by potentially padding it.
+    """
+    local_padding_mask = attention_mask
+    if attention_mask is not None:
+        # Pad it if necessary
+        if (padding_length := kv_length + kv_offset - attention_mask.shape[-1]) > 0:
+            local_padding_mask = torch.nn.functional.pad(attention_mask, (0, padding_length))
+    return local_padding_mask
+
+def _non_vmap_expansion_sdpa(
+    batch_indices: torch.Tensor, head_indices: torch.Tensor, q_indices: torch.Tensor, kv_indices: torch.Tensor
+):
+    """
+    Used to broadcast our mask_functions over the all 4 dimensions (b_idx, h_idx, q_idx, kv_idx) of the inputs.
+    Allows the usage of any index-based mask function without relying on vmap.
+
+    NOTE: This is limited to index based functions only and is not guaranteed to work otherwise.
+
+    Reference:
+        - https://github.com/huggingface/optimum-onnx/blob/c123e8f4fab61b54a8e0e31ce74462bcacca576e/optimum/exporters/onnx/model_patcher.py#L362-L365
+    """
+    batch_indices = batch_indices[:, None, None, None]
+    head_indices = head_indices[None, :, None, None]
+    q_indices = q_indices[None, None, :, None]
+    kv_indices = kv_indices[None, None, None, :]
+    return batch_indices, head_indices, q_indices, kv_indices
+
+def sdpa_mask(
+    batch_size: int,
+    cache_position: torch.Tensor,
+    kv_length: int,
+    kv_offset: int = 0,
+    mask_function: Callable = causal_mask_function,
+    attention_mask: Optional[torch.Tensor] = None,
+    local_size: Optional[int] = None,
+    allow_is_causal_skip: bool = True,
+    allow_is_bidirectional_skip: bool = False,
+    allow_torch_fix: bool = True,
+    use_vmap: bool = False,
+    **kwargs,
+) -> Optional[torch.Tensor]:
+    """
+    Create a 4D boolean mask of shape `(batch_size, 1, query_length, kv_length)` where a value of True indicates that
+    the element should take part in the attention computation, and False that it should not.
+    This function can only be used with torch>=2.5, as the context manager is otherwise not available.
+
+    Args:
+        batch_size (`int`):
+            The batch size of the input sequence.
+        cache_position (`torch.Tensor`):
+            A tensor of shape (query_length,) indicating the current indices of the input sequence elements.
+        kv_length (`int`):
+            The size that the key and value states will have during the attention computation.
+        kv_offset (`int`, optional):
+            An optional offset to indicate at which first position the key and values states will refer to.
+        mask_function (`Callable`):
+            The mask factory function describing the mask pattern.
+        attention_mask (`torch.Tensor`, optional):
+            The 2D attention mask corresponding to padded tokens of shape (batch_size, number_of_seen_tokens+q_length)
+        local_size (`int`, optional):
+            The size of the local attention, if we do not use full attention. This is used only if `allow_is_causal_skip=True`
+            to try to skip mask creation if possible.
+        allow_is_causal_skip (`bool`, optional):
+            Whether to allow to return `None` for the mask under conditions where we can use the `is_causal` argument in
+            `torch.sdpa` instead. Default to `True`.
+        allow_is_bidirectional_skip (`bool`, optional):
+            Whether to allow to return `None` for the mask under conditions where we do not have to add any bias,
+            i.e. full attention without any padding. Default to `False`.
+        allow_torch_fix (`bool`, optional):
+            Whether to update the mask in case a query is not attending to any tokens, to solve a bug in torch's older
+            versions. We need an arg to skip it when using eager. By default `True`.
+        use_vmap (`bool`, optional):
+            Whether to use `vmap` during the mask construction or not. Allows powerful custom patterns that may not be
+            index-based (for the cost of speed performance). By default `False`.
+
+
+    ## Creating a simple causal mask:
+
+    To create the following causal mask:
+
+        0 ■ ⬚ ⬚ ⬚ ⬚
+        1 ■ ■ ⬚ ⬚ ⬚
+        2 ■ ■ ■ ⬚ ⬚
+        3 ■ ■ ■ ■ ⬚
+        4 ■ ■ ■ ■ ■
+
+    You can do
+
+    ```python
+    >>> sdpa_mask(batch_size=1, cache_position=torch.arange(5), kv_length=5)
+    >>> tensor([[[[ True, False, False, False, False],
+                  [ True,  True, False, False, False],
+                  [ True,  True,  True, False, False],
+                  [ True,  True,  True,  True, False],
+                  [ True,  True,  True,  True,  True]]]])
+    ```
+
+    ## Creating a sliding window mask:
+
+    To create the following sliding window mask (`sliding_window=3`):
+
+        0 ■ ⬚ ⬚ ⬚ ⬚
+        1 ■ ■ ⬚ ⬚ ⬚
+        2 ■ ■ ■ ⬚ ⬚
+        3 ⬚ ■ ■ ■ ⬚
+        4 ⬚ ⬚ ■ ■ ■
+
+    You can do
+
+    ```python
+    >>> sdpa_mask(batch_size=1, cache_position=torch.arange(5), kv_length=5, mask_function=sliding_window_causal_mask_function(3))
+    >>> tensor([[[[ True, False, False, False, False],
+                  [ True,  True, False, False, False],
+                  [ True,  True,  True, False, False],
+                  [False,  True,  True,  True, False],
+                  [False, False,  True,  True,  True]]]])
+    ```
+
+    ## Creating a chunked attention mask
+
+    To create the following chunked attention mask (`chunk_size=3`):
+
+        0 ■ ⬚ ⬚ ⬚ ⬚
+        1 ■ ■ ⬚ ⬚ ⬚
+        2 ■ ■ ■ ⬚ ⬚
+        3 ⬚ ⬚ ⬚ ■ ⬚
+        4 ⬚ ⬚ ⬚ ■ ■
+
+    You can do
+
+    ```python
+    >>> sdpa_mask(batch_size=1, cache_position=torch.arange(5), kv_length=5, mask_function=chunked_causal_mask_function(3, torch.zeros(1, dtype=int)))
+    >>> tensor([[[[ True, False, False, False, False],
+                [ True,  True, False, False, False],
+                [ True,  True,  True, False, False],
+                [False, False, False,  True, False],
+                [False, False, False,  True,  True]]]])
+    ```
+
+    """
+    q_length = cache_position.shape[0]
+
+    # Potentially pad the 2D mask
+    padding_mask = prepare_padding_mask(attention_mask, kv_length, kv_offset)
+
+    # Potentially add the padding 2D mask
+    if padding_mask is not None:
+        mask_function = and_masks(mask_function, padding_mask_function(padding_mask))
+
+    batch_arange = torch.arange(batch_size, device=cache_position.device)
+    head_arange = torch.arange(1, device=cache_position.device)
+    # Similar to `kv_arange = torch.arange(start=kv_offset, end=kv_offset + kv_length, device=cache_position.device)`
+    # but without data-dependent slicing (i.e. torch.compile friendly)
+    kv_arange = torch.arange(kv_length, device=cache_position.device) + kv_offset
+
+    # Actual mask creation
+    # Apply mask function element-wise through broadcasting
+    attention_mask = mask_function(*_non_vmap_expansion_sdpa(batch_arange, head_arange, cache_position, kv_arange))
+    # Expand the mask to match batch size and query length if they weren't used in the mask function
+    attention_mask = attention_mask.expand(batch_size, -1, q_length, kv_length)
+
+    return attention_mask

fastvideo/models/registry.py

@@ -24,6 +24,8 @@ logger = init_logger(__name__)
 _TEXT_TO_VIDEO_DIT_MODELS = {
     "HunyuanVideoTransformer3DModel":
     ("dits", "hunyuanvideo", "HunyuanVideoTransformer3DModel"),
+    "HunyuanVideo15Transformer3DModel":
+    ("dits", "hunyuanvideo15", "HunyuanVideo15Transformer3DModel"),
     "WanTransformer3DModel": ("dits", "wanvideo", "WanTransformer3DModel"),
     "CausalWanTransformer3DModel": ("dits", "causal_wanvideo", "CausalWanTransformer3DModel"),
     "StepVideoModel": ("dits", "stepvideo", "StepVideoModel"),
@@ -34,6 +36,8 @@ _IMAGE_TO_VIDEO_DIT_MODELS = {
     # "HunyuanVideoTransformer3DModel": ("dits", "hunyuanvideo", "HunyuanVideoDiT"),
     "WanTransformer3DModel": ("dits", "wanvideo", "WanTransformer3DModel"),
     "CausalWanTransformer3DModel": ("dits", "causal_wanvideo", "CausalWanTransformer3DModel"),
+    "MatrixGameWanModel": ("dits", "matrix_game", "MatrixGameWanModel"),
+    "CausalMatrixGameWanModel": ("dits", "matrix_game", "CausalMatrixGameWanModel"),
 }
 
 _TEXT_ENCODER_MODELS = {
@@ -43,16 +47,19 @@ _TEXT_ENCODER_MODELS = {
     "T5EncoderModel": ("encoders", "t5", "T5EncoderModel"),
     "STEP1TextEncoder": ("encoders", "stepllm", "STEP1TextEncoder"),
     "BertModel": ("encoders", "clip", "CLIPTextModel"),
+    "Qwen2_5_VLTextModel": ("encoders", "qwen2_5", "Qwen2_5_VLTextModel"),
 }
 
 _IMAGE_ENCODER_MODELS: dict[str, tuple] = {
     # "HunyuanVideoTransformer3DModel": ("image_encoder", "hunyuanvideo", "HunyuanVideoImageEncoder"),
     "CLIPVisionModelWithProjection": ("encoders", "clip", "CLIPVisionModel"),
+    "CLIPVisionModel": ("encoders", "clip", "CLIPVisionModel"),
 }
 
 _VAE_MODELS = {
     "AutoencoderKLHunyuanVideo":
     ("vaes", "hunyuanvae", "AutoencoderKLHunyuanVideo"),
+    "AutoencoderKLHunyuanVideo15": ("vaes", "hunyuan15vae", "AutoencoderKLHunyuanVideo15"),
     "AutoencoderKLWan": ("vaes", "wanvae", "AutoencoderKLWan"),
     "AutoencoderKLStepvideo": ("vaes", "stepvideovae", "AutoencoderKLStepvideo")
 }

fastvideo/models/vaes/hunyuan15vae.py

@@ -0,0 +1,703 @@
+# SPDX-License-Identifier: Apache-2.0
+# Adapted from diffusers
+
+# Copyright 2025 The Hunyuan Team and The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import Optional, Tuple, Union
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.checkpoint
+
+from fastvideo.layers.activation import get_act_fn
+from fastvideo.configs.models.vaes import Hunyuan15VAEConfig
+from fastvideo.models.vaes.common import ParallelTiledVAE
+
+class HunyuanVideo15CausalConv3d(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: Union[int, Tuple[int, int, int]] = 3,
+        stride: Union[int, Tuple[int, int, int]] = 1,
+        padding: Union[int, Tuple[int, int, int]] = 0,
+        dilation: Union[int, Tuple[int, int, int]] = 1,
+        bias: bool = True,
+        pad_mode: str = "replicate",
+    ) -> None:
+        super().__init__()
+
+        kernel_size = (kernel_size, kernel_size, kernel_size) if isinstance(kernel_size, int) else kernel_size
+
+        self.pad_mode = pad_mode
+        self.time_causal_padding = (
+            kernel_size[0] // 2,
+            kernel_size[0] // 2,
+            kernel_size[1] // 2,
+            kernel_size[1] // 2,
+            kernel_size[2] - 1,
+            0,
+        )
+
+        self.conv = nn.Conv3d(in_channels, out_channels, kernel_size, stride, padding, dilation, bias=bias)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = F.pad(hidden_states, self.time_causal_padding, mode=self.pad_mode)
+        return self.conv(hidden_states)
+
+
+class HunyuanVideo15RMS_norm(nn.Module):
+    r"""
+    A custom RMS normalization layer.
+
+    Args:
+        dim (int): The number of dimensions to normalize over.
+        channel_first (bool, optional): Whether the input tensor has channels as the first dimension.
+            Default is True.
+        images (bool, optional): Whether the input represents image data. Default is True.
+        bias (bool, optional): Whether to include a learnable bias term. Default is False.
+    """
+
+    def __init__(self, dim: int, channel_first: bool = True, images: bool = True, bias: bool = False) -> None:
+        super().__init__()
+        broadcastable_dims = (1, 1, 1) if not images else (1, 1)
+        shape = (dim, *broadcastable_dims) if channel_first else (dim,)
+
+        self.channel_first = channel_first
+        self.scale = dim**0.5
+        self.gamma = nn.Parameter(torch.ones(shape))
+        self.bias = nn.Parameter(torch.zeros(shape)) if bias else 0.0
+
+    def forward(self, x):
+        return F.normalize(x, dim=(1 if self.channel_first else -1)) * self.scale * self.gamma + self.bias
+
+
+class HunyuanVideo15AttnBlock(nn.Module):
+    def __init__(self, in_channels: int):
+        super().__init__()
+        self.in_channels = in_channels
+
+        self.norm = HunyuanVideo15RMS_norm(in_channels, images=False)
+
+        self.to_q = nn.Conv3d(in_channels, in_channels, kernel_size=1)
+        self.to_k = nn.Conv3d(in_channels, in_channels, kernel_size=1)
+        self.to_v = nn.Conv3d(in_channels, in_channels, kernel_size=1)
+        self.proj_out = nn.Conv3d(in_channels, in_channels, kernel_size=1)
+
+    @staticmethod
+    def prepare_causal_attention_mask(n_frame: int, n_hw: int, dtype, device, batch_size: int = None):
+        """Prepare a causal attention mask for 3D videos.
+
+        Args:
+            n_frame (int): Number of frames (temporal length).
+            n_hw (int): Product of height and width.
+            dtype: Desired mask dtype.
+            device: Device for the mask.
+            batch_size (int, optional): If set, expands for batch.
+
+        Returns:
+            torch.Tensor: Causal attention mask.
+        """
+        seq_len = n_frame * n_hw
+        mask = torch.full((seq_len, seq_len), float("-inf"), dtype=dtype, device=device)
+        for i in range(seq_len):
+            i_frame = i // n_hw
+            mask[i, : (i_frame + 1) * n_hw] = 0
+        if batch_size is not None:
+            mask = mask.unsqueeze(0).expand(batch_size, -1, -1)
+        return mask
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        identity = x
+
+        x = self.norm(x)
+
+        query = self.to_q(x)
+        key = self.to_k(x)
+        value = self.to_v(x)
+
+        batch_size, channels, frames, height, width = query.shape
+
+        query = query.reshape(batch_size, channels, frames * height * width).permute(0, 2, 1).unsqueeze(1).contiguous()
+        key = key.reshape(batch_size, channels, frames * height * width).permute(0, 2, 1).unsqueeze(1).contiguous()
+        value = value.reshape(batch_size, channels, frames * height * width).permute(0, 2, 1).unsqueeze(1).contiguous()
+
+        attention_mask = self.prepare_causal_attention_mask(
+            frames, height * width, query.dtype, query.device, batch_size=batch_size
+        )
+
+        x = nn.functional.scaled_dot_product_attention(query, key, value, attn_mask=attention_mask)
+
+        # batch_size, 1, frames * height * width, channels
+
+        x = x.squeeze(1).reshape(batch_size, frames, height, width, channels).permute(0, 4, 1, 2, 3)
+        x = self.proj_out(x)
+
+        return x + identity
+
+
+class HunyuanVideo15Upsample(nn.Module):
+    def __init__(self, in_channels: int, out_channels: int, add_temporal_upsample: bool = True):
+        super().__init__()
+        factor = 2 * 2 * 2 if add_temporal_upsample else 1 * 2 * 2
+        self.conv = HunyuanVideo15CausalConv3d(in_channels, out_channels * factor, kernel_size=3)
+
+        self.add_temporal_upsample = add_temporal_upsample
+        self.repeats = factor * out_channels // in_channels
+
+    @staticmethod
+    def _dcae_upsample_rearrange(tensor, r1=1, r2=2, r3=2):
+        """
+        Convert (b, r1*r2*r3*c, f, h, w) -> (b, c, r1*f, r2*h, r3*w)
+
+        Args:
+            tensor: Input tensor of shape (b, r1*r2*r3*c, f, h, w)
+            r1: temporal upsampling factor
+            r2: height upsampling factor
+            r3: width upsampling factor
+        """
+        b, packed_c, f, h, w = tensor.shape
+        factor = r1 * r2 * r3
+        c = packed_c // factor
+
+        tensor = tensor.view(b, r1, r2, r3, c, f, h, w)
+        tensor = tensor.permute(0, 4, 5, 1, 6, 2, 7, 3)
+        return tensor.reshape(b, c, f * r1, h * r2, w * r3)
+
+    def forward(self, x: torch.Tensor):
+        r1 = 2 if self.add_temporal_upsample else 1
+        h = self.conv(x)
+        if self.add_temporal_upsample:
+            h_first = h[:, :, :1, :, :]
+            h_first = self._dcae_upsample_rearrange(h_first, r1=1, r2=2, r3=2)
+            h_first = h_first[:, : h_first.shape[1] // 2]
+            h_next = h[:, :, 1:, :, :]
+            h_next = self._dcae_upsample_rearrange(h_next, r1=r1, r2=2, r3=2)
+            h = torch.cat([h_first, h_next], dim=2)
+
+            # shortcut computation
+            x_first = x[:, :, :1, :, :]
+            x_first = self._dcae_upsample_rearrange(x_first, r1=1, r2=2, r3=2)
+            x_first = x_first.repeat_interleave(repeats=self.repeats // 2, dim=1)
+
+            x_next = x[:, :, 1:, :, :]
+            x_next = self._dcae_upsample_rearrange(x_next, r1=r1, r2=2, r3=2)
+            x_next = x_next.repeat_interleave(repeats=self.repeats, dim=1)
+            shortcut = torch.cat([x_first, x_next], dim=2)
+
+        else:
+            h = self._dcae_upsample_rearrange(h, r1=r1, r2=2, r3=2)
+            shortcut = x.repeat_interleave(repeats=self.repeats, dim=1)
+            shortcut = self._dcae_upsample_rearrange(shortcut, r1=r1, r2=2, r3=2)
+        return h + shortcut
+
+
+class HunyuanVideo15Downsample(nn.Module):
+    def __init__(self, in_channels: int, out_channels: int, add_temporal_downsample: bool = True):
+        super().__init__()
+        factor = 2 * 2 * 2 if add_temporal_downsample else 1 * 2 * 2
+        self.conv = HunyuanVideo15CausalConv3d(in_channels, out_channels // factor, kernel_size=3)
+
+        self.add_temporal_downsample = add_temporal_downsample
+        self.group_size = factor * in_channels // out_channels
+
+    @staticmethod
+    def _dcae_downsample_rearrange(tensor, r1=1, r2=2, r3=2):
+        """
+        Convert (b, c, r1*f, r2*h, r3*w) -> (b, r1*r2*r3*c, f, h, w)
+
+        This packs spatial/temporal dimensions into channels (opposite of upsample)
+        """
+        b, c, packed_f, packed_h, packed_w = tensor.shape
+        f, h, w = packed_f // r1, packed_h // r2, packed_w // r3
+
+        tensor = tensor.view(b, c, f, r1, h, r2, w, r3)
+        tensor = tensor.permute(0, 3, 5, 7, 1, 2, 4, 6)
+        return tensor.reshape(b, r1 * r2 * r3 * c, f, h, w)
+
+    def forward(self, x: torch.Tensor):
+        r1 = 2 if self.add_temporal_downsample else 1
+        h = self.conv(x)
+        if self.add_temporal_downsample:
+            h_first = h[:, :, :1, :, :]
+            h_first = self._dcae_downsample_rearrange(h_first, r1=1, r2=2, r3=2)
+            h_first = torch.cat([h_first, h_first], dim=1)
+            h_next = h[:, :, 1:, :, :]
+            h_next = self._dcae_downsample_rearrange(h_next, r1=r1, r2=2, r3=2)
+            h = torch.cat([h_first, h_next], dim=2)
+
+            # shortcut computation
+            x_first = x[:, :, :1, :, :]
+            x_first = self._dcae_downsample_rearrange(x_first, r1=1, r2=2, r3=2)
+            B, C, T, H, W = x_first.shape
+            x_first = x_first.view(B, h.shape[1], self.group_size // 2, T, H, W).mean(dim=2)
+            x_next = x[:, :, 1:, :, :]
+            x_next = self._dcae_downsample_rearrange(x_next, r1=r1, r2=2, r3=2)
+            B, C, T, H, W = x_next.shape
+            x_next = x_next.view(B, h.shape[1], self.group_size, T, H, W).mean(dim=2)
+            shortcut = torch.cat([x_first, x_next], dim=2)
+        else:
+            h = self._dcae_downsample_rearrange(h, r1=r1, r2=2, r3=2)
+            shortcut = self._dcae_downsample_rearrange(x, r1=r1, r2=2, r3=2)
+            B, C, T, H, W = shortcut.shape
+            shortcut = shortcut.view(B, h.shape[1], self.group_size, T, H, W).mean(dim=2)
+
+        return h + shortcut
+
+
+class HunyuanVideo15ResnetBlock(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: Optional[int] = None,
+        non_linearity: str = "swish",
+    ) -> None:
+        super().__init__()
+        out_channels = out_channels or in_channels
+
+        self.nonlinearity = get_act_fn(non_linearity)
+
+        self.norm1 = HunyuanVideo15RMS_norm(in_channels, images=False)
+        self.conv1 = HunyuanVideo15CausalConv3d(in_channels, out_channels, kernel_size=3)
+
+        self.norm2 = HunyuanVideo15RMS_norm(out_channels, images=False)
+        self.conv2 = HunyuanVideo15CausalConv3d(out_channels, out_channels, kernel_size=3)
+
+        self.conv_shortcut = None
+        if in_channels != out_channels:
+            self.conv_shortcut = nn.Conv3d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        residual = hidden_states
+
+        hidden_states = self.norm1(hidden_states)
+        hidden_states = self.nonlinearity(hidden_states)
+        hidden_states = self.conv1(hidden_states)
+
+        hidden_states = self.norm2(hidden_states)
+        hidden_states = self.nonlinearity(hidden_states)
+        hidden_states = self.conv2(hidden_states)
+
+        if self.conv_shortcut is not None:
+            residual = self.conv_shortcut(residual)
+
+        return hidden_states + residual
+
+
+class HunyuanVideo15MidBlock(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        num_layers: int = 1,
+        add_attention: bool = True,
+    ) -> None:
+        super().__init__()
+        self.add_attention = add_attention
+
+        # There is always at least one resnet
+        resnets = [
+            HunyuanVideo15ResnetBlock(
+                in_channels=in_channels,
+                out_channels=in_channels,
+            )
+        ]
+        attentions = []
+
+        for _ in range(num_layers):
+            if self.add_attention:
+                attentions.append(HunyuanVideo15AttnBlock(in_channels))
+            else:
+                attentions.append(None)
+
+            resnets.append(
+                HunyuanVideo15ResnetBlock(
+                    in_channels=in_channels,
+                    out_channels=in_channels,
+                )
+            )
+
+        self.attentions = nn.ModuleList(attentions)
+        self.resnets = nn.ModuleList(resnets)
+
+        self.gradient_checkpointing = False
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.resnets[0](hidden_states)
+
+        for attn, resnet in zip(self.attentions, self.resnets[1:]):
+            if attn is not None:
+                hidden_states = attn(hidden_states)
+            hidden_states = resnet(hidden_states)
+
+        return hidden_states
+
+
+class HunyuanVideo15DownBlock3D(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        num_layers: int = 1,
+        downsample_out_channels: Optional[int] = None,
+        add_temporal_downsample: int = True,
+    ) -> None:
+        super().__init__()
+        resnets = []
+
+        for i in range(num_layers):
+            in_channels = in_channels if i == 0 else out_channels
+            resnets.append(
+                HunyuanVideo15ResnetBlock(
+                    in_channels=in_channels,
+                    out_channels=out_channels,
+                )
+            )
+
+        self.resnets = nn.ModuleList(resnets)
+
+        if downsample_out_channels is not None:
+            self.downsamplers = nn.ModuleList(
+                [
+                    HunyuanVideo15Downsample(
+                        out_channels,
+                        out_channels=downsample_out_channels,
+                        add_temporal_downsample=add_temporal_downsample,
+                    )
+                ]
+            )
+        else:
+            self.downsamplers = None
+
+        self.gradient_checkpointing = False
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        for resnet in self.resnets:
+            hidden_states = resnet(hidden_states)
+
+        if self.downsamplers is not None:
+            for downsampler in self.downsamplers:
+                hidden_states = downsampler(hidden_states)
+
+        return hidden_states
+
+
+class HunyuanVideo15UpBlock3D(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        num_layers: int = 1,
+        upsample_out_channels: Optional[int] = None,
+        add_temporal_upsample: bool = True,
+    ) -> None:
+        super().__init__()
+        resnets = []
+
+        for i in range(num_layers):
+            input_channels = in_channels if i == 0 else out_channels
+
+            resnets.append(
+                HunyuanVideo15ResnetBlock(
+                    in_channels=input_channels,
+                    out_channels=out_channels,
+                )
+            )
+
+        self.resnets = nn.ModuleList(resnets)
+
+        if upsample_out_channels is not None:
+            self.upsamplers = nn.ModuleList(
+                [
+                    HunyuanVideo15Upsample(
+                        out_channels,
+                        out_channels=upsample_out_channels,
+                        add_temporal_upsample=add_temporal_upsample,
+                    )
+                ]
+            )
+        else:
+            self.upsamplers = None
+
+        self.gradient_checkpointing = False
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        if torch.is_grad_enabled() and self.gradient_checkpointing:
+            for resnet in self.resnets:
+                hidden_states = self._gradient_checkpointing_func(resnet, hidden_states)
+
+        else:
+            for resnet in self.resnets:
+                hidden_states = resnet(hidden_states)
+
+        if self.upsamplers is not None:
+            for upsampler in self.upsamplers:
+                hidden_states = upsampler(hidden_states)
+
+        return hidden_states
+
+
+class HunyuanVideo15Encoder3D(nn.Module):
+    r"""
+    3D vae encoder for HunyuanImageRefiner.
+    """
+
+    def __init__(
+        self,
+        in_channels: int = 3,
+        out_channels: int = 64,
+        block_out_channels: Tuple[int, ...] = (128, 256, 512, 1024, 1024),
+        layers_per_block: int = 2,
+        temporal_compression_ratio: int = 4,
+        spatial_compression_ratio: int = 16,
+        downsample_match_channel: bool = True,
+    ) -> None:
+        super().__init__()
+
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.group_size = block_out_channels[-1] // self.out_channels
+
+        self.conv_in = HunyuanVideo15CausalConv3d(in_channels, block_out_channels[0], kernel_size=3)
+        self.mid_block = None
+        self.down_blocks = nn.ModuleList([])
+
+        input_channel = block_out_channels[0]
+        for i in range(len(block_out_channels)):
+            add_spatial_downsample = i < np.log2(spatial_compression_ratio)
+            output_channel = block_out_channels[i]
+            if not add_spatial_downsample:
+                down_block = HunyuanVideo15DownBlock3D(
+                    num_layers=layers_per_block,
+                    in_channels=input_channel,
+                    out_channels=output_channel,
+                    downsample_out_channels=None,
+                    add_temporal_downsample=False,
+                )
+                input_channel = output_channel
+            else:
+                add_temporal_downsample = i >= np.log2(spatial_compression_ratio // temporal_compression_ratio)
+                downsample_out_channels = block_out_channels[i + 1] if downsample_match_channel else output_channel
+                down_block = HunyuanVideo15DownBlock3D(
+                    num_layers=layers_per_block,
+                    in_channels=input_channel,
+                    out_channels=output_channel,
+                    downsample_out_channels=downsample_out_channels,
+                    add_temporal_downsample=add_temporal_downsample,
+                )
+                input_channel = downsample_out_channels
+
+            self.down_blocks.append(down_block)
+
+        self.mid_block = HunyuanVideo15MidBlock(in_channels=block_out_channels[-1])
+
+        self.norm_out = HunyuanVideo15RMS_norm(block_out_channels[-1], images=False)
+        self.conv_act = nn.SiLU()
+        self.conv_out = HunyuanVideo15CausalConv3d(block_out_channels[-1], out_channels, kernel_size=3)
+
+        self.gradient_checkpointing = False
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.conv_in(hidden_states)
+
+        if torch.is_grad_enabled() and self.gradient_checkpointing:
+            for down_block in self.down_blocks:
+                hidden_states = self._gradient_checkpointing_func(down_block, hidden_states)
+
+            hidden_states = self._gradient_checkpointing_func(self.mid_block, hidden_states)
+        else:
+            for down_block in self.down_blocks:
+                hidden_states = down_block(hidden_states)
+
+            hidden_states = self.mid_block(hidden_states)
+
+        batch_size, _, frame, height, width = hidden_states.shape
+        short_cut = hidden_states.view(batch_size, -1, self.group_size, frame, height, width).mean(dim=2)
+
+        hidden_states = self.norm_out(hidden_states)
+        hidden_states = self.conv_act(hidden_states)
+        hidden_states = self.conv_out(hidden_states)
+
+        hidden_states += short_cut
+
+        return hidden_states
+
+
+class HunyuanVideo15Decoder3D(nn.Module):
+    r"""
+    Causal decoder for 3D video-like data used for HunyuanImage-1.5 Refiner.
+    """
+
+    def __init__(
+        self,
+        in_channels: int = 32,
+        out_channels: int = 3,
+        block_out_channels: Tuple[int, ...] = (1024, 1024, 512, 256, 128),
+        layers_per_block: int = 2,
+        spatial_compression_ratio: int = 16,
+        temporal_compression_ratio: int = 4,
+        upsample_match_channel: bool = True,
+    ):
+        super().__init__()
+        self.layers_per_block = layers_per_block
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.repeat = block_out_channels[0] // self.in_channels
+
+        self.conv_in = HunyuanVideo15CausalConv3d(self.in_channels, block_out_channels[0], kernel_size=3)
+        self.up_blocks = nn.ModuleList([])
+
+        # mid
+        self.mid_block = HunyuanVideo15MidBlock(in_channels=block_out_channels[0])
+
+        # up
+        input_channel = block_out_channels[0]
+        for i in range(len(block_out_channels)):
+            output_channel = block_out_channels[i]
+
+            add_spatial_upsample = i < np.log2(spatial_compression_ratio)
+            add_temporal_upsample = i < np.log2(temporal_compression_ratio)
+            if add_spatial_upsample or add_temporal_upsample:
+                upsample_out_channels = block_out_channels[i + 1] if upsample_match_channel else output_channel
+                up_block = HunyuanVideo15UpBlock3D(
+                    num_layers=self.layers_per_block + 1,
+                    in_channels=input_channel,
+                    out_channels=output_channel,
+                    upsample_out_channels=upsample_out_channels,
+                    add_temporal_upsample=add_temporal_upsample,
+                )
+                input_channel = upsample_out_channels
+            else:
+                up_block = HunyuanVideo15UpBlock3D(
+                    num_layers=self.layers_per_block + 1,
+                    in_channels=input_channel,
+                    out_channels=output_channel,
+                    upsample_out_channels=None,
+                    add_temporal_upsample=False,
+                )
+                input_channel = output_channel
+
+            self.up_blocks.append(up_block)
+
+        # out
+        self.norm_out = HunyuanVideo15RMS_norm(block_out_channels[-1], images=False)
+        self.conv_act = nn.SiLU()
+        self.conv_out = HunyuanVideo15CausalConv3d(block_out_channels[-1], out_channels, kernel_size=3)
+
+        self.gradient_checkpointing = False
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        hidden_states = self.conv_in(hidden_states) + hidden_states.repeat_interleave(repeats=self.repeat, dim=1)
+
+        if torch.is_grad_enabled() and self.gradient_checkpointing:
+            hidden_states = self._gradient_checkpointing_func(self.mid_block, hidden_states)
+
+            for up_block in self.up_blocks:
+                hidden_states = self._gradient_checkpointing_func(up_block, hidden_states)
+        else:
+            hidden_states = self.mid_block(hidden_states)
+
+            for up_block in self.up_blocks:
+                hidden_states = up_block(hidden_states)
+
+        # post-process
+        hidden_states = self.norm_out(hidden_states)
+        hidden_states = self.conv_act(hidden_states)
+        hidden_states = self.conv_out(hidden_states)
+        return hidden_states
+
+
+class AutoencoderKLHunyuanVideo15(nn.Module, ParallelTiledVAE):
+    r"""
+    A VAE model with KL loss for encoding videos into latents and decoding latent representations into videos. Used for
+    HunyuanVideo-1.5.
+
+    This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
+    for all models (such as downloading or saving).
+    """
+
+    _supports_gradient_checkpointing = True
+
+    def __init__(
+        self,
+        config: Hunyuan15VAEConfig,
+    ) -> None:
+        nn.Module.__init__(self)
+        ParallelTiledVAE.__init__(self, config)
+
+        if config.load_encoder:
+            self.encoder = HunyuanVideo15Encoder3D(
+                in_channels=config.in_channels,
+                out_channels=config.latent_channels * 2,
+                block_out_channels=config.block_out_channels,
+                layers_per_block=config.layers_per_block,
+                temporal_compression_ratio=config.temporal_compression_ratio,
+                spatial_compression_ratio=config.spatial_compression_ratio,
+                downsample_match_channel=config.downsample_match_channel,
+            )
+
+        if config.load_decoder:
+            self.decoder = HunyuanVideo15Decoder3D(
+                in_channels=config.latent_channels,
+                out_channels=config.out_channels,
+                block_out_channels=list(reversed(config.block_out_channels)),
+                layers_per_block=config.layers_per_block,
+                temporal_compression_ratio=config.temporal_compression_ratio,
+                spatial_compression_ratio=config.spatial_compression_ratio,
+                upsample_match_channel=config.upsample_match_channel,
+            )
+
+        # When decoding spatially large video latents, the memory requirement is very high. By breaking the video latent
+        # frames spatially into smaller tiles and performing multiple forward passes for decoding, and then blending the
+        # intermediate tiles together, the memory requirement can be lowered.
+        self.use_tiling = False
+
+        # The minimal tile height and width for spatial tiling to be used
+        self.tile_sample_min_height = 256
+        self.tile_sample_min_width = 256
+        self.tile_sample_min_num_frames = 2000 # Fill in a random large number, as hy1.5 vae does not use temporal tiling
+
+    def _encode(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.encoder(x)
+        return x
+
+    def _decode(self, z: torch.Tensor) -> torch.Tensor:
+        dec = self.decoder(z)
+        return dec
+
+    def forward(
+        self,
+        sample: torch.Tensor,
+        sample_posterior: bool = False,
+        return_dict: bool = True,
+        generator: Optional[torch.Generator] = None,
+    ) -> torch.Tensor:
+        r"""
+        Args:
+            sample (`torch.Tensor`): Input sample.
+            sample_posterior (`bool`, *optional*, defaults to `False`):
+                Whether to sample from the posterior.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`DecoderOutput`] instead of a plain tuple.
+        """
+        x = sample
+        posterior = self.encode(x).latent_dist
+        if sample_posterior:
+            z = posterior.sample(generator=generator)
+        else:
+            z = posterior.mode()
+        dec = self.decode(z)
+        return dec

fastvideo/pipelines/__init__.py

@@ -44,6 +44,12 @@ def build_pipeline(
 
     config = verify_model_config_and_directory(model_path)
     pipeline_name = config.get("_class_name")
+
+    if fastvideo_args.override_pipeline_cls_name:
+        logger.info("Overriding pipeline class name from %s to %s",
+                    pipeline_name, fastvideo_args.override_pipeline_cls_name)
+        pipeline_name = fastvideo_args.override_pipeline_cls_name
+
     if pipeline_name is None:
         raise ValueError(
             "Model config does not contain a _class_name attribute. "

fastvideo/pipelines/basic/hunyuan15/hunyuan15_pipeline.py

@@ -0,0 +1,74 @@
+# SPDX-License-Identifier: Apache-2.0
+"""
+Hunyuan video diffusion pipeline implementation.
+
+This module contains an implementation of the Hunyuan video diffusion pipeline
+using the modular pipeline architecture.
+"""
+
+from fastvideo.fastvideo_args import FastVideoArgs
+from fastvideo.logger import init_logger
+from fastvideo.pipelines.composed_pipeline_base import ComposedPipelineBase
+from fastvideo.pipelines.stages import (ConditioningStage, DecodingStage,
+                                        DenoisingStage, InputValidationStage,
+                                        LatentPreparationStage,
+                                        TextEncodingStage,
+                                        TimestepPreparationStage,
+                                        Hy15ImageEncodingStage)
+
+# TODO(will): move PRECISION_TO_TYPE to better place
+
+logger = init_logger(__name__)
+
+
+class HunyuanVideo15Pipeline(ComposedPipelineBase):
+
+    _required_config_modules = [
+        "text_encoder", "text_encoder_2", "tokenizer", "tokenizer_2", "vae",
+        "transformer", "scheduler"
+    ]
+
+    def create_pipeline_stages(self, fastvideo_args: FastVideoArgs):
+        """Set up pipeline stages with proper dependency injection."""
+
+        self.add_stage(stage_name="input_validation_stage",
+                       stage=InputValidationStage())
+
+        self.add_stage(stage_name="prompt_encoding_stage_primary",
+                       stage=TextEncodingStage(
+                           text_encoders=[
+                               self.get_module("text_encoder"),
+                               self.get_module("text_encoder_2")
+                           ],
+                           tokenizers=[
+                               self.get_module("tokenizer"),
+                               self.get_module("tokenizer_2")
+                           ],
+                       ))
+
+        self.add_stage(stage_name="conditioning_stage",
+                       stage=ConditioningStage())
+
+        self.add_stage(stage_name="timestep_preparation_stage",
+                       stage=TimestepPreparationStage(
+                           scheduler=self.get_module("scheduler")))
+
+        self.add_stage(stage_name="latent_preparation_stage",
+                       stage=LatentPreparationStage(
+                           scheduler=self.get_module("scheduler"),
+                           transformer=self.get_module("transformer")))
+
+        self.add_stage(stage_name="image_encoding_stage",
+                       stage=Hy15ImageEncodingStage(image_encoder=None,
+                                                    image_processor=None))
+
+        self.add_stage(stage_name="denoising_stage",
+                       stage=DenoisingStage(
+                           transformer=self.get_module("transformer"),
+                           scheduler=self.get_module("scheduler")))
+
+        self.add_stage(stage_name="decoding_stage",
+                       stage=DecodingStage(vae=self.get_module("vae")))
+
+
+EntryClass = HunyuanVideo15Pipeline

fastvideo/pipelines/basic/matrixgame/__init__.py

@@ -0,0 +1,7 @@
+# SPDX-License-Identifier: Apache-2.0
+from fastvideo.pipelines.basic.matrixgame.matrixgame_i2v_pipeline import (
+    MatrixGamePipeline)
+from fastvideo.pipelines.basic.matrixgame.matrixgame_causal_dmd_pipeline import (
+    MatrixGameCausalDMDPipeline)
+
+__all__ = ["MatrixGamePipeline", "MatrixGameCausalDMDPipeline"]

fastvideo/pipelines/basic/matrixgame/matrixgame_causal_dmd_pipeline.py

@@ -0,0 +1,73 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Matrix-Game causal DMD pipeline implementation."""
+
+from fastvideo.fastvideo_args import FastVideoArgs
+from fastvideo.logger import init_logger
+from fastvideo.pipelines import ComposedPipelineBase, LoRAPipeline
+
+from fastvideo.pipelines.stages import (ConditioningStage, DecodingStage,
+                                        InputValidationStage,
+                                        LatentPreparationStage,
+                                        TextEncodingStage,
+                                        MatrixGameImageEncodingStage,
+                                        MatrixGameCausalDenoisingStage)
+from fastvideo.pipelines.stages.image_encoding import (
+    MatrixGameImageVAEEncodingStage)
+
+logger = init_logger(__name__)
+
+
+class MatrixGameCausalDMDPipeline(LoRAPipeline, ComposedPipelineBase):
+    _required_config_modules = [
+        "vae", "transformer", "scheduler", "image_encoder", "image_processor"
+    ]
+
+    def create_pipeline_stages(self, fastvideo_args: FastVideoArgs) -> None:
+        self.add_stage(stage_name="input_validation_stage",
+                       stage=InputValidationStage())
+
+        if (self.get_module("text_encoder", None) is not None
+                and self.get_module("tokenizer", None) is not None):
+            self.add_stage(stage_name="prompt_encoding_stage",
+                           stage=TextEncodingStage(
+                               text_encoders=[self.get_module("text_encoder")],
+                               tokenizers=[self.get_module("tokenizer")],
+                           ))
+
+        if (self.get_module("image_encoder", None) is not None
+                and self.get_module("image_processor", None) is not None):
+            self.add_stage(
+                stage_name="image_encoding_stage",
+                stage=MatrixGameImageEncodingStage(
+                    image_encoder=self.get_module("image_encoder"),
+                    image_processor=self.get_module("image_processor"),
+                ))
+
+        self.add_stage(stage_name="conditioning_stage",
+                       stage=ConditioningStage())
+
+        self.add_stage(stage_name="latent_preparation_stage",
+                       stage=LatentPreparationStage(
+                           scheduler=self.get_module("scheduler"),
+                           transformer=self.get_module("transformer", None)))
+
+        self.add_stage(
+            stage_name="image_latent_preparation_stage",
+            stage=MatrixGameImageVAEEncodingStage(vae=self.get_module("vae")))
+
+        self.add_stage(stage_name="denoising_stage",
+                       stage=MatrixGameCausalDenoisingStage(
+                           transformer=self.get_module("transformer"),
+                           transformer_2=self.get_module("transformer_2", None),
+                           scheduler=self.get_module("scheduler"),
+                           pipeline=self,
+                           vae=self.get_module("vae")))
+
+        self.add_stage(stage_name="decoding_stage",
+                       stage=DecodingStage(vae=self.get_module("vae")))
+
+        logger.info(
+            "MatrixGameCausalDMDPipeline initialized with action support")
+
+
+EntryClass = [MatrixGameCausalDMDPipeline]

fastvideo/pipelines/basic/matrixgame/matrixgame_i2v_pipeline.py

@@ -0,0 +1,79 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Matrix-Game I2V pipeline implementation."""
+
+from fastvideo.fastvideo_args import FastVideoArgs
+from fastvideo.logger import init_logger
+from fastvideo.pipelines.composed_pipeline_base import ComposedPipelineBase
+from fastvideo.pipelines.lora_pipeline import LoRAPipeline
+
+from fastvideo.pipelines.stages import (MatrixGameImageEncodingStage,
+                                        ConditioningStage, DecodingStage,
+                                        DenoisingStage, InputValidationStage,
+                                        LatentPreparationStage,
+                                        TextEncodingStage,
+                                        TimestepPreparationStage)
+from fastvideo.pipelines.stages.image_encoding import (
+    MatrixGameImageVAEEncodingStage)
+from fastvideo.models.schedulers.scheduling_flow_unipc_multistep import (
+    FlowUniPCMultistepScheduler)
+
+logger = init_logger(__name__)
+
+
+class MatrixGamePipeline(LoRAPipeline, ComposedPipelineBase):
+    _required_config_modules = [
+        "vae", "transformer", "scheduler", "image_encoder", "image_processor"
+    ]
+
+    def initialize_pipeline(self, fastvideo_args: FastVideoArgs):
+        self.modules["scheduler"] = FlowUniPCMultistepScheduler(
+            shift=fastvideo_args.pipeline_config.flow_shift)
+
+    def create_pipeline_stages(self, fastvideo_args: FastVideoArgs):
+        self.add_stage(stage_name="input_validation_stage",
+                       stage=InputValidationStage())
+
+        if (self.get_module("text_encoder", None) is not None
+                and self.get_module("tokenizer", None) is not None):
+            self.add_stage(stage_name="prompt_encoding_stage",
+                           stage=TextEncodingStage(
+                               text_encoders=[self.get_module("text_encoder")],
+                               tokenizers=[self.get_module("tokenizer")],
+                           ))
+
+        if (self.get_module("image_encoder", None) is not None
+                and self.get_module("image_processor", None) is not None):
+            self.add_stage(
+                stage_name="image_encoding_stage",
+                stage=MatrixGameImageEncodingStage(
+                    image_encoder=self.get_module("image_encoder"),
+                    image_processor=self.get_module("image_processor"),
+                ))
+
+        self.add_stage(stage_name="conditioning_stage",
+                       stage=ConditioningStage())
+
+        self.add_stage(stage_name="timestep_preparation_stage",
+                       stage=TimestepPreparationStage(
+                           scheduler=self.get_module("scheduler")))
+
+        self.add_stage(stage_name="latent_preparation_stage",
+                       stage=LatentPreparationStage(
+                           scheduler=self.get_module("scheduler"),
+                           transformer=self.get_module("transformer")))
+
+        self.add_stage(
+            stage_name="image_latent_preparation_stage",
+            stage=MatrixGameImageVAEEncodingStage(vae=self.get_module("vae")))
+
+        self.add_stage(stage_name="denoising_stage",
+                       stage=DenoisingStage(
+                           transformer=self.get_module("transformer"),
+                           transformer_2=self.get_module("transformer_2", None),
+                           scheduler=self.get_module("scheduler")))
+
+        self.add_stage(stage_name="decoding_stage",
+                       stage=DecodingStage(vae=self.get_module("vae")))
+
+
+EntryClass = [MatrixGamePipeline]

fastvideo/pipelines/basic/wan/wan_dmd_pipeline.py

@@ -59,7 +59,8 @@ class WanDMDPipeline(LoRAPipeline, ComposedPipelineBase):
         self.add_stage(stage_name="latent_preparation_stage",
                        stage=LatentPreparationStage(
                            scheduler=self.get_module("scheduler"),
-                           transformer=self.get_module("transformer", None)))
+                           transformer=self.get_module("transformer", None),
+                           use_btchw_layout=True))
 
         self.add_stage(stage_name="denoising_stage",
                        stage=DmdDenoisingStage(

fastvideo/pipelines/basic/wan/wan_i2v_dmd_pipeline.py

@@ -62,7 +62,8 @@ class WanImageToVideoDmdPipeline(LoRAPipeline, ComposedPipelineBase):
         self.add_stage(stage_name="latent_preparation_stage",
                        stage=LatentPreparationStage(
                            scheduler=self.get_module("scheduler"),
-                           transformer=self.get_module("transformer")))
+                           transformer=self.get_module("transformer"),
+                           use_btchw_layout=True))
 
         self.add_stage(stage_name="image_latent_preparation_stage",
                        stage=ImageVAEEncodingStage(vae=self.get_module("vae")))