refactor hy15 sf distill

Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>

.buildkite/pipeline.yml

@@ -61,7 +61,7 @@ steps:
                 - "pyproject.toml"
                 - "docker/Dockerfile.python3.12"
               config:
-                command: "timeout 45m .buildkite/scripts/pr_test.sh"
+                command: "timeout 90m .buildkite/scripts/pr_test.sh"
                 label: "SSIM Tests"
                 env:
                   - TEST_TYPE=ssim
@@ -76,7 +76,7 @@ steps:
                 - "pyproject.toml"
                 - "docker/Dockerfile.python3.12"
               config:
-                command: "timeout 15m .buildkite/scripts/pr_test.sh"
+                command: "timeout 20m .buildkite/scripts/pr_test.sh"
                 label: "LoRA Inference Tests"
                 env:
                   - TEST_TYPE=inference_lora

.github/workflows/fastvideo-kernel-publish.yml

@@ -156,8 +156,23 @@ jobs:
           
           # Fix the wheel to be manylinux compliant
           pip install auditwheel
+          # Point auditwheel at torch libs, but do not vendor them into the wheel.
+          TORCH_LIB_DIR=$(python - <<'PY'
+          import os
+          import torch
+
+          print(os.path.join(os.path.dirname(torch.__file__), "lib"))
+          PY
+          )
+          export LD_LIBRARY_PATH="${TORCH_LIB_DIR}:${LD_LIBRARY_PATH}"
           # Target manylinux_2_35 (Ubuntu 22.04 native)
-          auditwheel repair dist/*.whl --plat manylinux_2_35_x86_64 -w fixed_dist
+          auditwheel repair dist/*.whl --plat manylinux_2_35_x86_64 -w fixed_dist \
+            --exclude libtorch_cuda.so \
+            --exclude libtorch_cpu.so \
+            --exclude libtorch.so \
+            --exclude libc10.so \
+            --exclude libc10_cuda.so \
+            --exclude libtorch_python.so
           # Move fixed wheels back to dist for upload consistency
           rm dist/*.whl
           mv fixed_dist/*.whl dist/

.pre-commit-config.yaml

@@ -68,7 +68,7 @@ repos:
     entry: bash
     args:
       - -c
-      - 'git ls-files | grep -v "^fastvideo/tests/ssim/" | grep -v "^fastvideo/tests/inference/lora/L40S_reference_videos/" | grep " " && echo "Filenames should not contain spaces!" && exit 1 || exit 0'
+      - 'git ls-files | grep -v "^\"*fastvideo/tests/ssim/" | grep -v "^\"*fastvideo/tests/inference/lora/L40S_reference_videos/" | grep " " && echo "Filenames should not contain spaces!" && exit 1 || exit 0'
     language: system
     always_run: true
     pass_filenames: false

README.md

@@ -1,41 +1,47 @@
 <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/c7g1qdD" target="_blank"> <b> WeChat </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://github.com/hao-ai-lab/FastVideo/discussions/982"  target="_blank"><b>Weekly Dev Meeting</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/sv3MMKyv" target="_blank"> <b> WeChat </b> </a> |
 </p>
 
-<div align="center">
-<img src=assets/fastwan.png width="90%"/>
-</div>
+**FastVideo is a unified post-training and inference framework for accelerated video generation.**
 
 ## NEWS
 - ```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/).
+
+<details>
+<summary>More</summary>
+
 - ```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/).
 
+</details>
+
 ## Key Features
 
 FastVideo has the following features:
-- End-to-end post-training support:
-  - [Sparse distillation](https://hao-ai-lab.github.io/blogs/fastvideo_post_training/) for Wan2.1 and Wan2.2 to achineve >50x denoising speedup
-  - Data preprocessing pipeline for video data
+- End-to-end post-training support for bidirectional and autoregressive models:
   - 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
+  - Data preprocessing pipeline for video, image, and text data
+  - Distribution Matching Distillation (DMD2) stepwise distillation.
+  - Sparse attention with [Video Sparse Attention](https://arxiv.org/pdf/2505.13389)
+  - [Sparse distillation](https://hao-ai-lab.github.io/blogs/fastvideo_post_training/) to achineve >50x denoising speedup
+  - Scalable training with FSDP2, sequence parallelism, and selective activation checkpointing.
+  - Causal distillation through Self-Forcing
+  - See this [page](https://hao-ai-lab.github.io/FastVideo/training/overview/) for full list of supported models and recipes.
 - State-of-the-art performance optimizations for inference
-  - [Video Sparse Attention](https://arxiv.org/pdf/2505.13389)
-  - [Sliding Tile Attention](https://arxiv.org/pdf/2502.04507)
-  - [TeaCache](https://arxiv.org/pdf/2411.19108)
-  - [Sage Attention](https://arxiv.org/abs/2410.02367)
+  - Sequence Parallelism for distributed inference
+  - Multiple state-of-the-art attention backends
+  - User-friendly CLI and Python API
+  - See this [page](https://hao-ai-lab.github.io/FastVideo/inference/optimizations/) for full list of supported optimizations.
 - Diverse hardware and OS support
   - Support H100, A100, 4090
   - Support Linux, Windows, MacOS
+  - See this [page](https://hao-ai-lab.github.io/FastVideo/inference/hardware_support/) for full list of supported hardware and OS.
 
 ## Getting Started
 We recommend using an environment manager such as `Conda` to create a clean environment:

docs/attention/developer/index.md

@@ -50,21 +50,25 @@ class MyNewAttnBackend(AttentionBackend):
 FastVideo uses a `ForwardContext` to pass global metadata (like current timestep, batch info, or custom attention configurations) to attention backends without changing the `forward` signature of every layer. **This is optional and only required if your backend needs dynamic per-step information.**
 
 To use this:
+
 1. **Set Context**: In your pipeline or generation loop, use the `set_forward_context` context manager.
 2. **Access Context**: Inside your attention backend, use `get_forward_context()`.
 
-See `docs/attention/sta/index.md` (Sliding Tile Attention) for an example of how complex configuration (window sizes) is passed this way.
+See [`docs/attention/sta/index.md`](../sta/index.md) (Sliding Tile Attention) for an example of how complex configuration (window sizes) is passed this way.
 
 ## 3. Adding Compiled Kernels (C++/CUDA)
 
 If your backend requires custom CUDA kernels, you need to add them to the `fastvideo-kernel` package.
 
 ### A. Add Source Files
+
 Place your kernel implementation files in `fastvideo-kernel/csrc/attention/`.
+
 * `mynew_attn.cu` (CUDA implementation)
 * `mynew_attn.h` (Optional headers)
 
 ### B. Register in Extension
+
 Update `fastvideo-kernel/csrc/common_extension.cpp` to expose your function to Python.
 
 ```cpp
@@ -84,6 +88,7 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
 ```
 
 ### C. Update CMakeLists.txt
+
 Update `fastvideo-kernel/CMakeLists.txt` to compile your new files.
 
 **Case 1: General CUDA Kernel (Runs on all GPUs)**
@@ -111,6 +116,7 @@ endif()
 ```
 
 ### D. Expose in Python Ops
+
 Update `fastvideo-kernel/python/fastvideo_kernel/ops.py` to make the function importable and handle fallbacks gracefully.
 
 ```python
@@ -133,6 +139,7 @@ def my_compiled_attn_func(q, k, v):
 ```
 
 ### E. Expose in Package Init
+
 Update `fastvideo-kernel/python/fastvideo_kernel/__init__.py` to export the function.
 
 ```python

docs/attention/index.md

@@ -41,7 +41,7 @@ Clone the repository and build the kernel:
 
 ```bash
 # Clone recursively to get ThunderKittens submodule
-git clone --recursive https://github.com/hao-ai-lab/FastVideo.git
+git clone https://github.com/hao-ai-lab/FastVideo.git
 cd FastVideo/fastvideo-kernel
 
 # Build and install

docs/attention/vsa/index.md

@@ -13,11 +13,13 @@ from fastvideo_kernel import video_sparse_attn
 
 # q, k, v: [batch_size, num_heads, seq_len, head_dim]
 # variable_block_sizes: Number of valid tokens per block
+# q_variable_block_sizes: Number of valid tokens per q block (can differ from KV for q/k of different lengths)
 # topk: Number of blocks to attend
 
 output = video_sparse_attn(
     q, k, v, 
-    variable_block_sizes=block_sizes, 
+    block_sizes,
+    block_sizes,
     topk=32
 )
 ```

docs/design/overview.md

@@ -4,25 +4,26 @@ This document outlines FastVideo's architecture for developers interested in fra
 
 ## Table of Contents - Directory Structure and Files
 
-- [`fastvideo/pipelines/`](#design-pipeline-system) - Core diffusion pipeline components
-- [`fastvideo/models/`](#design-model-components) - Model implementations
-  - [`dits/`](#design-transformer-models) - Transformer-based diffusion models
-  - [`vaes/`](#design-vae-variational-auto-encoder) - Variational autoencoders
-  - [`encoders/`](#design-text-and-image-encoders) - Text and image encoders
-  - [`schedulers/`](#design-schedulers) - Diffusion schedulers
-- [`fastvideo/attention/`](#design-optimized-attention) - Optimized attention implementations
-- [`fastvideo/distributed/`](#design-distributed-processing) - Distributed computing utilities
-- [`fastvideo/layers/`](#design-tensor-parallelism) - Custom neural network layers
-- [`fastvideo/platforms/`](#design-platforms) - Hardware platform abstractions
-- [`fastvideo/worker/`](#design-executor-and-worker-abstractions) - Multi-GPU process management
-- [`fastvideo/fastvideo_args.py`](#design-fastvideo-args) - Argument handling
-- [`fastvideo/forward_context.py`](#design-forwardcontext) - Forward pass context management
+- [`fastvideo/pipelines/`](#pipeline-system) - Core diffusion pipeline components
+- [`fastvideo/models/`](#model-components) - Model implementations
+  - [`dits/`](#transformer-models) - Transformer-based diffusion models
+  - [`vaes/`](#vae-variational-auto-encoder) - Variational autoencoders
+  - [`encoders/`](#text-and-image-encoders) - Text and image encoders
+  - [`schedulers/`](#schedulers) - Diffusion schedulers
+- [`fastvideo/attention/`](#optimized-attention) - Optimized attention implementations
+- [`fastvideo/distributed/`](#distributed-processing) - Distributed computing utilities
+- [`fastvideo/layers/`](#tensor-parallelism) - Custom neural network layers
+- [`fastvideo/platforms/`](#platforms) - Hardware platform abstractions
+- [`fastvideo/worker/`](#executor-and-worker-system) - Multi-GPU process management
+- [`fastvideo/fastvideo_args.py`](#fastvideoargs) - Argument handling
+- [`fastvideo/forward_context.py`](#forward-context-management) - Forward pass context management
 - `fastvideo/utils.py` - Utility functions
-- [`fastvideo/logger.py`](#design-logger) - Logging infrastructure
+- [`fastvideo/logger.py`](#logger) - Logging infrastructure
 
 ## Core Architecture
 
 FastVideo separates model components from execution logic with these principles:
+
 - **Component Isolation**: Models (encoders, VAEs, transformers) are isolated from execution (pipelines, stages, distributed processing)
 - **Modular Design**: Components can be independently replaced
 - **Distributed Execution**: Supports various parallelism strategies (Tensor, Sequence)
@@ -34,12 +35,14 @@ FastVideo separates model components from execution logic with these principles:
 The `FastVideoArgs` class in `fastvideo/fastvideo_args.py` serves as the central configuration system for FastVideo. It contains all parameters needed to control model loading, inference configuration, performance optimization settings, and more.
 
 Key features include:
+
 - **Command-line Interface**: Automatic conversion between CLI arguments and dataclass fields
 - **Configuration Groups**: Organized by functional areas (model loading, video params, optimization settings)
 - **Context Management**: Global access to current settings via `get_current_fastvideo_args()`
 - **Parameter Validation**: Ensures valid combinations of settings
 
 Common configuration areas:
+
 - **Model paths and loading options**: `model_path`, `trust_remote_code`, `revision`
 - **Distributed execution settings**: `num_gpus`, `tp_size`, `sp_size`
 - **Video generation parameters**: `height`, `width`, `num_frames`, `num_inference_steps`
@@ -90,7 +93,9 @@ class MyCustomPipeline(ComposedPipelineBase):
 ```
 
 ### Pipeline Stages
+
 Each stage handles a specific diffusion process component:
+
 - **Input Validation**: Parameter verification
 - **Text Encoding**: CLIP, LLaMA, or T5-based encoding
 - **Image Encoding**: Image input processing
@@ -133,6 +138,7 @@ Transformer networks perform the actual denoising during diffusion:
   - `HunyuanVideoTransformer3DModel`
 
 Features include:
+
 - Text/image conditioning
 - Standardized interface for model-specific optimizations
 
@@ -161,6 +167,7 @@ VAEs handle conversion between pixel space and latent space:
 These models compress image/video data to a more efficient latent representation (typically 4x-8x smaller in each dimension).
 
 FastVideo's VAE implementations include:
+
 - Efficient video batch processing
 - Memory optimization
 - Optional tiling for large frames
@@ -179,6 +186,7 @@ Encoders process conditioning inputs into embeddings:
   - `CLIPVisionModel`
 
 FastVideo implements optimizations such as:
+
 - Vocab parallelism for distributed processing
 - Caching for common prompts
 - Precision-tuned computation
@@ -193,6 +201,7 @@ Schedulers manage the diffusion sampling process:
   - `FlowMatchEulerDiscreteScheduler`
 
 These components control:
+
 - Diffusion timestep sequences
 - Noise prediction to latent update conversions
 - Quality/speed trade-offs
@@ -219,7 +228,9 @@ This diagram shows how models are discovered, validated, and loaded across entry
 The `fastvideo/attention/` directory contains optimized attention implementations crucial for efficient video diffusion:
 
 ### Attention Backends
+
 Multiple implementations with automatic selection:
+
 - **FLASH_ATTN**: Optimized for supporting hardware
 - **TORCH_SDPA**: Built-in PyTorch scaled dot-product attention
 - **SLIDING_TILE_ATTN**: For very long sequences
@@ -240,7 +251,9 @@ self.attn = LocalAttention(
 ![Attention backend selector design](../assets/images/attention_backend.png)
 
 ### Attention Patterns
+
 Supports various patterns with memory optimization techniques:
+
 - **Cross/Self/Temporal/Global-Local Attention**
 - Chunking, progressive computation, optimized masking
 
@@ -296,6 +309,7 @@ self.attn = DistributedAttention(
 ```
 
 ### Communication Primitives
+
 Efficient distributed operations via AllGather, AllReduce, and synchronization mechanisms.
 
 Efficient communication primitives minimize distributed overhead:
@@ -314,6 +328,7 @@ Defined in `fastvideo/forward_context.py`, `ForwardContext` manages execution-sp
 - **Profiling Data**: Potential hooks for performance metrics collection
 
 This context-based approach enables:
+
 - Dynamic optimization based on execution state (e.g., attention backend selection)
 - Step-specific customizations within model components
 
@@ -339,12 +354,14 @@ FastVideo implements a flexible execution model for distributed processing:
 - **GPU Workers**: Handle actual model execution on individual GPUs
 
 The MultiProcExecutor implementation:
+
 1. Spawns worker processes for each GPU
 2. Establishes communication channels via pipes
 3. Coordinates distributed operations across workers
 4. Handles graceful startup and shutdown of the process group
 
 Each GPU worker:
+
 1. Initializes the distributed environment
 2. Builds the pipeline for the specified model
 3. Executes requested operations on its assigned GPU
@@ -359,11 +376,13 @@ The `fastvideo/platforms/` directory provides hardware platform abstractions tha
 ### Platform Abstraction
 
 FastVideo's platform abstraction layer enables:
+
 - **Hardware Detection**: Automatic detection of available hardware
 - **Backend Selection**: Appropriate selection of compute kernels
 - **Memory Management**: Efficient utilization of hardware-specific memory features
 
 The primary components include:
+
 - **Platform Interface**: Defines the common API for all platform implementations
 - **CUDA Platform**: Optimized implementation for NVIDIA GPUs
 - **Backend Enum**: Used throughout the codebase for feature selection
@@ -383,6 +402,7 @@ else:
 The platform system is designed to be extensible for future hardware targets.
 
 ## Logger
+
 See [PR](https://github.com/hao-ai-lab/FastVideo/pull/356)
 
 *TODO*: (help wanted) Add an environment variable that disables process-aware logging.
@@ -397,6 +417,7 @@ If you're a new contributor, here are some common areas to explore:
 4. **Hardware support**: Extend the `platforms` module for new hardware targets
 
 When adding code, follow these practices:
+
 - Use type hints for better code readability
 - Add appropriate docstrings
 - Maintain the separation between model components and execution logic

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). Set `MODEL_BASE` to your own model path and run:
+First install [VSA](../attention/vsa/index.md). Set `MODEL_BASE` to your own model path and run:
 
 ```bash
 bash scripts/inference/v1_inference_wan_dmd.sh

docs/getting_started/installation.md

@@ -11,15 +11,13 @@ FastVideo supports the following hardware platforms:
 ### Using pip
 
 ```bash
+# Create and activate a new conda environment
+conda create -n fastvideo python=3.12
+conda activate fastvideo
+
 pip install fastvideo
 ```
 
-### Using conda
-
-```bash
-conda install -c conda-forge fastvideo
-```
-
 ### From source
 
 ```bash
@@ -28,6 +26,12 @@ cd FastVideo
 pip install -e .
 ```
 
+Also optionally install flash-attn:
+
+```bash
+pip install flash-attn --no-build-isolation
+```
+
 ## Hardware Requirements
 
 - **NVIDIA GPUs**: CUDA 11.8+ with compute capability 7.0+
@@ -38,4 +42,4 @@ pip install -e .
 
 - [Quick Start Guide](quick_start.md) - Get started with your first video generation
 - [Configuration](../inference/configuration.md) - Learn about configuration options
-- [Examples](../inference/examples/) - Explore example scripts and notebooks
+- [Examples](../inference/examples/examples_inference_index.md) - Explore example scripts and notebooks

docs/getting_started/installation/gpu.md

@@ -84,12 +84,12 @@ pip install flash-attn --no-build-isolation
 
 ## Set up using Docker
 We also have prebuilt docker images with FastVideo dependencies pre-installed:
-[Docker Images](#docker)
+[Docker Images](../../contributing/developer_env/docker.md)
 
 ## Development Environment Setup
 
 If you're planning to contribute to FastVideo please see the following page:
-[Contributor Guide](#developer-overview)
+[Contributor Guide](../../contributing/overview.md)
 
 ## Hardware Requirements
 

docs/getting_started/installation/mps.md

@@ -78,7 +78,7 @@ uv pip install -e .
 ## Development Environment Setup
 
 If you're planning to contribute to FastVideo please see the following page:
-[Contributor Guide](#developer-overview)
+[Contributor Guide](../../contributing/overview.md)
 
 ## Hardware Requirements
 

docs/getting_started/quick_start.md

@@ -15,6 +15,12 @@ conda activate fastvideo
 pip install fastvideo
 ```
 
+Also optionally install flash-attn:
+
+```bash
+pip install flash-attn --no-build-isolation
+```
+
 ## Basic Usage
 
 ### Text-to-Video Generation
@@ -75,4 +81,3 @@ if __name__ == '__main__':
 - [Configuration](../inference/configuration.md) - Learn about configuration options
 - [Examples](../inference/examples/) - Explore more examples
 - [Optimizations](../inference/optimizations.md) - Performance optimization tips
-- [Low VRAM Inference](../inference/low_vram_inference.md) - Memory-saving settings (CPU offload, sharded loading, etc.)

docs/inference/add_pipeline.md

@@ -45,6 +45,7 @@ FastVideo uses the Hugging Face Diffusers format for model organization:
 ### Implementing Modules
 
 Place new modules in the appropriate directories:
+
 - Encoders: `fastvideo/models/encoders/`
 - VAEs: `fastvideo/models/vaes/`
 - Transformer models: `fastvideo/models/dits/`
@@ -53,12 +54,15 @@ Place new modules in the appropriate directories:
 ### Adapting Model Layers
 
 #### Layer Replacements
+
 Replace standard PyTorch layers with FastVideo optimized versions:
+
 - nn.LayerNorm → fastvideo.layers.layernorm.RMSNorm
 - Embedding layers → fastvideo.layers.vocab_parallel_embedding modules
 - Activation functions → versions from fastvideo.layers.activation
 
 #### Distributed Linear Layers
+
 Use appropriate parallel layers for distribution:
 
 ```python
@@ -91,6 +95,7 @@ self.out_proj = RowParallelLinear(
 ```
 
 ### Attention Layers
+
 Replace standard attention with FastVideo's optimized attention:
 
 ```python
@@ -304,6 +309,7 @@ EntryClass = [MyCustomPipeline, MyOtherPipeline]
 ```
 
 The registry will automatically:
+
 1. Scan all packages under `fastvideo/pipelines/`
 2. Look for `EntryClass` variables
 3. Register pipelines using their class names as identifiers

docs/inference/cli.md

@@ -1,7 +1,7 @@
 # FastVideo CLI Inference
 
 The FastVideo CLI provides a quick way to access the FastVideo inference pipeline for video generation. For more advanced usage,
-see the Python interface [here](https://hao-ai-lab.github.io/FastVideo/inference/examples/basic.html).
+see the Python interface [here](examples/basic.md).
 
 ## Basic Usage
 

docs/inference/configuration.md

@@ -74,4 +74,4 @@ if __name__ == '__main__':
 
 ## Performance Optimization
 
-For configuring optimizations, please see our [optimizations guide](#inference-optimizations)
+For configuring optimizations, please see our [optimizations guide](optimizations.md)

docs/inference/inference_quick_start.md

@@ -3,6 +3,7 @@
 This page contains step-by-step instructions to get you quickly started with video generation using FastVideo.
 
 ## Requirements
+
 - **OS**: Linux (Tested on Ubuntu 22.04+)
 - **Python**: 3.10-3.12
 - **CUDA**: 12.8
@@ -21,9 +22,10 @@ conda activate fastvideo
 pip install fastvideo
 ```
 
-For advanced installation options, see the [Installation Guide](installation.md).
+For advanced installation options, see the [Installation Guide](../getting_started/installation.md).
 
 ## Generating Your First Video
+
 Here's a minimal example to generate a video using the default settings. Create a file called `example.py` with the following code:
 
 ```python
@@ -60,9 +62,10 @@ python example.py
 The generated video will be saved in the current directory under `my_videos/`  
 
 More inference example scripts can be found in `scripts/inference/`
+
 ## Available Models
 
-Please see the [support matrix](#support-matrix) for the list of supported models and their available optimizations.
+Please see the [support matrix](support_matrix.md) for the list of supported models and their available optimizations.
 
 ## Image-to-Video Generation
 
@@ -96,20 +99,28 @@ if __name__ == '__main__':
 Common issues and their solutions:
 
 ### Out of Memory Errors
+
 If you encounter CUDA out of memory errors:
+
 - Reduce `num_frames` or video resolution
-- Enable memory optimization with CPU-offload and sharded loading flags (see [Low VRAM Inference](low_vram_inference.md))
+- Enable memory optimization with `enable_model_cpu_offload`
 - Try a smaller model or use distilled versions
 - Use `num_gpus` > 1 if multiple GPUs are available
+- Try enabling FSDP inference with `use_fsdp_inference=True` (may slow down generation)
+- Try enabling DiT layerwise offload with `dit_layerwise_offload=True` (now only a few models support this, but may introduce less overhead than FSDP)
 
 ### Slow Generation
+
 To speed up generation:
+
 - Reduce `num_inference_steps` (20-30 is usually sufficient)
 - Use half precision (`fp16`) for the VAE
 - Use multiple GPUs if available
 
 ### Unexpected Results
+
 If the generated video doesn't match your prompt:
+
 - Try increasing `guidance_scale` (7.0-9.0 works well)
 - Make your prompt more detailed and specific
 - Experiment with different random seeds
@@ -117,8 +128,8 @@ If the generated video doesn't match your prompt:
 
 ## Next Steps
 
-- Learn about [Advanced Inference Configurations](#inference-configuration)
-- Learn about using [Optimizations](#inference-optimizations)
-- See [Examples](../examples/examples_inference_index.md) for more usage scenarios
+- Learn about [Advanced Inference Configurations](configuration.md)
+- Learn about using [Optimizations](optimizations.md)
+- See [Examples](examples/examples_inference_index.md) for more usage scenarios
 - Join our [Community Discord](https://discord.gg/JA7cksDz86).
 - Join our [Community Slack](https://join.slack.com/t/fastvideo/shared_invite/zt-38u6p1jqe-yDI1QJOCEnbtkLoaI5bjZQ).

docs/inference/optimizations.md

@@ -7,13 +7,13 @@ This page describes the various options for speeding up generation times in Fast
 
 - Optimized Attention Backends
 
-  - [Flash Attention](#optimizations-flash)
-  - [Sliding Tile Attention](#optimizations-sta)
-  - [Sage Attention](#optimizations-sage)
-  - [Sage Attention 3](#optimizations-sage3)
+  - [Flash Attention](#flash-attention)
+  - [Sliding Tile Attention](#sliding-tile-attention)
+  - [Sage Attention](#sage-attention)
+  - [Sage Attention 3](#sage-attention-3)
 
 - Caching Techniques
-  - [TeaCache](#optimizations-teacache)
+  - [TeaCache](#teacache)
 
 ## Attention Backends
 
@@ -74,7 +74,7 @@ python setup.py install
 pip install st_attn==0.0.4
 ```
 
-Please see [this page](#sta-installation) for more installation instructions.
+Please see [this page](../attention/sta/index.md) for more installation instructions.
 
 ### Video Sparse Attention
 
@@ -85,7 +85,7 @@ git submodule update --init --recursive
 python setup_vsa.py install
 ```
 
-Please see [this page](#vsa-installation) for more installation instructions.
+Please see [this page](../attention/vsa/index.md) for more installation instructions.
 
 ### Sage Attention
 

docs/inference/support_matrix.md

@@ -40,20 +40,26 @@ The `HuggingFace Model ID` can be directly pass to `from_pretrained()` methods a
   }
 </style>
 
-| Model Name | HuggingFace Model ID | Resolutions | TeaCache | Sliding Tile Attn | Sage Attn | VSA |
-|------------|---------------------|-------------|----------|-------------------|-----------|-----|
-| FastWan2.1 T2V 1.3B | `FastVideo/FastWan2.1-T2V-1.3B-Diffusers` | 480P | ⭕ | ⭕ | ⭕ | ✅ |
-| FastWan2.2 TI2V 5B Full Attn* | `FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers` | 720P | ⭕ | ⭕ | ⭕ | ✅ |
-| Wan2.2 TI2V 5B | `Wan-AI/Wan2.2-TI2V-5B-Diffusers` | 720P | ⭕ | ⭕ | ✅ | ⭕ |
-| Wan2.2 T2V A14B | `Wan-AI/Wan2.2-T2V-A14B-Diffusers` | 480P<br>720P | ❌ | ❌ | ✅ | ⭕ |
-| Wan2.2 I2V A14B | `Wan-AI/Wan2.2-I2V-A14B-Diffusers` | 480P<br>720P | ❌ | ❌ | ✅ | ⭕ |
-| HunyuanVideo | `hunyuanvideo-community/HunyuanVideo` | 720px1280p<br>544px960p | ❌ | ✅ | ✅ | ⭕ |
-| FastHunyuan | `FastVideo/FastHunyuan-diffusers` | 720px1280p<br>544px960p | ❌ | ✅ | ✅ | ⭕ |
-| Wan2.1 T2V 1.3B | `Wan-AI/Wan2.1-T2V-1.3B-Diffusers` | 480P | ✅ | ✅* | ✅ | ⭕ |
-| Wan2.1 T2V 14B | `Wan-AI/Wan2.1-T2V-14B-Diffusers` | 480P, 720P | ✅ | ✅* | ✅ | ⭕ |
-| Wan2.1 I2V 480P | `Wan-AI/Wan2.1-I2V-14B-480P-Diffusers` | 480P | ✅ | ✅* | ✅ | ⭕ |
-| Wan2.1 I2V 720P | `Wan-AI/Wan2.1-I2V-14B-720P-Diffusers` | 720P | ✅ | ✅ | ✅ | ⭕ |
-| StepVideo T2V | `FastVideo/stepvideo-t2v-diffusers` | 768px768px204f<br>544px992px204f<br>544px992px136f | ❌ | ❌ | ✅ | ⭕ |
+| Model Name | HuggingFace Model ID | Resolutions | TeaCache | Sliding Tile Attn | Sage Attn | VSA | BSA |
+|------------|---------------------|-------------|----------|-------------------|-----------|-----|-----|
+| FastWan2.1 T2V 1.3B | `FastVideo/FastWan2.1-T2V-1.3B-Diffusers` | 480P | ⭕ | ⭕ | ⭕ | ✅ | ⭕ |
+| FastWan2.2 TI2V 5B Full Attn* | `FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers` | 720P | ⭕ | ⭕ | ⭕ | ✅ | ⭕ |
+| Wan2.2 TI2V 5B | `Wan-AI/Wan2.2-TI2V-5B-Diffusers` | 720P | ⭕ | ⭕ | ✅ | ⭕ | ⭕ |
+| Wan2.2 T2V A14B | `Wan-AI/Wan2.2-T2V-A14B-Diffusers` | 480P<br>720P | ❌ | ❌ | ✅ | ⭕ | ⭕ |
+| Wan2.2 I2V A14B | `Wan-AI/Wan2.2-I2V-A14B-Diffusers` | 480P<br>720P | ❌ | ❌ | ✅ | ⭕ | ⭕ |
+| HunyuanVideo | `hunyuanvideo-community/HunyuanVideo` | 720px1280p<br>544px960p | ❌ | ✅ | ✅ | ⭕ | ⭕ |
+| FastHunyuan | `FastVideo/FastHunyuan-diffusers` | 720px1280p<br>544px960p | ❌ | ✅ | ✅ | ⭕ | ⭕ |
+| Wan2.1 T2V 1.3B | `Wan-AI/Wan2.1-T2V-1.3B-Diffusers` | 480P | ✅ | ✅* | ✅ | ⭕ | ⭕ |
+| Wan2.1 T2V 14B | `Wan-AI/Wan2.1-T2V-14B-Diffusers` | 480P, 720P | ✅ | ✅* | ✅ | ⭕ | ⭕ |
+| Wan2.1 I2V 480P | `Wan-AI/Wan2.1-I2V-14B-480P-Diffusers` | 480P | ✅ | ✅* | ✅ | ⭕ | ⭕ |
+| Wan2.1 I2V 720P | `Wan-AI/Wan2.1-I2V-14B-720P-Diffusers` | 720P | ✅ | ✅ | ✅ | ⭕ | ⭕ |
+| StepVideo T2V | `FastVideo/stepvideo-t2v-diffusers` | 768px768px204f<br>544px992px204f<br>544px992px136f | ❌ | ❌ | ✅ | ⭕ | ⭕ |
+| TurboWan2.1 T2V 1.3B | `loayrashid/TurboWan2.1-T2V-1.3B-Diffusers` | 480P | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ |
+| TurboWan2.1 T2V 14B | `loayrashid/TurboWan2.1-T2V-14B-Diffusers` | 480P, 720P | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ |
+| LongCat T2V 13.6B | See note** | 480P<br>720P | ❌ | ❌ | ❌ | ⭕ | ✅ |
+| Matrix Game 2.0 Base | `FastVideo/Matrix-Game-2.0-Base-Diffusers` | 352x640 | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ |
+| Matrix Game 2.0 GTA | `FastVideo/Matrix-Game-2.0-GTA-Diffusers` | 352x640 | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ |
+| Matrix Game 2.0 TempleRun | `FastVideo/Matrix-Game-2.0-TempleRun-Diffusers` | 352x640 | ⭕ | ⭕ | ⭕ | ⭕ | ⭕ |
 
 **Note**: Wan2.2 TI2V 5B has some quality issues when performing I2V generation. We are working on fixing this issue.
 
@@ -64,3 +70,13 @@ The `HuggingFace Model ID` can be directly pass to `from_pretrained()` methods a
 
 ### Sliding Tile Attention
 - Currently only Hopper GPUs (H100s) are supported.
+
+### TurboWan2.1 (TurboDiffusion)
+- Uses TurboDiffusionPipeline with RCM scheduler for 1-4 step generation
+- Requires SLA attention backend: `export FASTVIDEO_ATTENTION_BACKEND=SLA_ATTN`
+- Uses `guidance_scale=1.0` (no classifier-free guidance)
+
+### Matrix Game 2.0
+- Image-to-video game world models with keyboard/mouse control input
+- Three variants available: Base (universal), GTA, and TempleRun
+- Each variant has different keyboard dimensions for control inputs

docs/training/data_preprocess.md

@@ -1,45 +1,130 @@
+# 🧱 Data Preprocessing
 
-# 🧱 Data Preprocess
+To save GPU memory during training, FastVideo precomputes text embeddings and VAE latents. This eliminates the need to load the text encoder and VAE during training.
 
-To save GPU memory, we precompute text embeddings and VAE latents to eliminate the need to load the text encoder and VAE during training.
+## Quick Start
 
-We provide a sample dataset to help you get started. Download the source media using the following command:
+Download the sample dataset and run preprocessing:
 
 ```bash
-python scripts/huggingface/download_hf.py --repo_id=FastVideo/mini_i2v_dataset --local_dir=data/mini_i2v_dataset --repo_type=dataset
+# Download the crush-smol dataset
+python scripts/huggingface/download_hf.py \
+    --repo_id "wlsaidhi/crush-smol-merged" \
+    --local_dir "data/crush-smol" \
+    --repo_type "dataset"
+
+# Run preprocessing
+bash examples/training/finetune/wan_t2v_1.3B/crush_smol/preprocess_wan_data_t2v_new.sh
 ```
 
-The folder `crush-smol_raw/` contains raw videos and captions for testing preprocessing, while `crush-smol_preprocessed/` contains latents prepared for testing training.
+## Preprocessing Pipeline
 
-To preprocess the dataset for fine-tuning or distillation, run:
+The new preprocessing pipeline supports multiple dataset formats and video loaders:
 
-```
-bash scripts/preprocess/v1_preprocess_wan_data_t2v # for wan
+```bash
+GPU_NUM=2
+MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
+DATASET_PATH="data/crush-smol/"
+OUTPUT_DIR="data/crush-smol_processed_t2v/"
+
+torchrun --nproc_per_node=$GPU_NUM \
+    -m fastvideo.pipelines.preprocess.v1_preprocessing_new \
+    --model_path $MODEL_PATH \
+    --mode preprocess \
+    --workload_type t2v \
+    --preprocess.video_loader_type torchvision \
+    --preprocess.dataset_type merged \
+    --preprocess.dataset_path $DATASET_PATH \
+    --preprocess.dataset_output_dir $OUTPUT_DIR \
+    --preprocess.preprocess_video_batch_size 2 \
+    --preprocess.dataloader_num_workers 0 \
+    --preprocess.max_height 480 \
+    --preprocess.max_width 832 \
+    --preprocess.num_frames 77 \
+    --preprocess.train_fps 16 \
+    --preprocess.samples_per_file 8 \
+    --preprocess.flush_frequency 8 \
+    --preprocess.video_length_tolerance_range 5
 ```
 
-## Process your own dataset
+### Key Parameters
 
-If you wish to create your own dataset for finetuning or distillation, please refer `mini_i2v_dataset/crush-smol_raw/` to structure you video dataset in the following format:
+| Parameter | Description |
+|-----------|-------------|
+| `--workload_type` | Task type: `t2v` (text-to-video) or `i2v` (image-to-video) |
+| `--preprocess.dataset_type` | Input format: `hf` (HuggingFace) or `merged` (local folder) |
+| `--preprocess.dataset_path` | Path to dataset (HF repo ID or local folder) |
+| `--preprocess.dataset_output_dir` | Output directory for Parquet files |
+| `--preprocess.video_loader_type` | Video decoder: `torchcodec` or `torchvision` |
+| `--preprocess.max_height` / `max_width` | Target resolution for videos |
+| `--preprocess.num_frames` | Number of frames to extract per video |
+| `--preprocess.train_fps` | Target FPS for frame extraction |
+
+## Dataset Formats
+
+### Merged Dataset (Local Folder)
+
+Structure your dataset as follows:
 
 ```
-path_to_your_dataset_folder/
+your_dataset/
+├── videos/
+│   ├── video_001.mp4
+│   ├── video_002.mp4
+│   └── ...
+└── videos2caption.json
+```
+
+The `videos2caption.json` maps video filenames to captions:
+
+```json
+[
+  {"path": "video_001.mp4", "cap": "A cat playing with yarn..."},
+  {"path": "video_002.mp4", "cap": "Ocean waves at sunset..."}
+]
+```
+
+### HuggingFace Dataset
+
+Use `--preprocess.dataset_type hf` and point `--preprocess.dataset_path` to a HuggingFace dataset with `video` and `caption` columns.
+
+## Creating Your Own Dataset
+
+If you have raw videos and captions in separate files, generate the `videos2caption.json`:
+
+```bash
+python scripts/dataset_preparation/prepare_json_file.py \
+    --data_folder path/to/your_raw_data/ \
+    --output path/to/output_folder
+```
+
+Your raw data folder should contain:
+
+```
+your_raw_data/
 ├── videos/
 │   ├── 0.mp4
 │   ├── 1.mp4
-├── videos.txt
-└── prompt.txt
+│   └── ...
+├── videos.txt    # list of video filenames
+└── prompt.txt    # corresponding captions (one per line)
 ```
 
-To generate the `videos2caption.json` and `merge.txt`, run
+## Output Format
 
-``` python
-python scripts/dataset_preparation/prepare_json_file.py --data_folder mini_i2v_dataset/crush-smol_raw/ --output your_output_folder
-```
+Preprocessing outputs Parquet files in the `combined_parquet_dataset/` subdirectory containing:
 
-Adjust the `DATA_MERGE_PATH` and `OUTPUT_DIR` in `scripts/preprocess/v1_preprocess_****.sh` accordingly and run:
+- `vae_latent_bytes` — VAE-encoded video latent
+- `text_embedding_bytes` — text encoder output
+- `clip_feature_bytes` — CLIP image features (I2V only)
+- `first_frame_latent_bytes` — first frame latent (I2V only)
+- Metadata: shapes, dtypes, and sample identifiers
 
-```
-bash scripts/preprocess/v1_preprocess_****.sh
-```
+## Examples
 
-The preprocessed data will be put into the `OUTPUT_DIR` and the `videos2caption.json` can be used in finetune and distill scripts.
+See ready-to-run preprocessing scripts in the training examples:
+
+- **T2V**: `examples/training/finetune/wan_t2v_1.3B/crush_smol/preprocess_wan_data_t2v_new.sh`
+- **I2V**: `examples/training/finetune/wan_i2v_14B_480p/crush_smol/preprocess_wan_data_i2v_new.sh`
+
+**→ [Browse all training examples](examples/examples_training_index.md)**

docs/training/finetune.md

@@ -1,78 +1,176 @@
+# 🧠 Finetuning
 
-# 🧠 Finetune
-## ⚡ Full Finetune
-Ensure your data is prepared and preprocessed in the format specified in [data_preprocess.md](#v0-data-preprocess). For convenience, we also provide a mochi preprocessed Black Myth Wukong data that can be downloaded directly:
+This guide covers finetuning video diffusion models with FastVideo, including full finetuning and LoRA.
+
+## Training Arguments
+
+FastVideo training scripts use several argument groups:
+
+### Training Arguments
+
+| Argument | Description |
+|----------|-------------|
+| `--max_train_steps` | Total training steps |
+| `--train_batch_size` | Batch size per GPU |
+| `--gradient_accumulation_steps` | Steps to accumulate before optimizer update |
+| `--num_latent_t` | Temporal latent dimension (reduce to save memory) |
+| `--num_height` / `--num_width` | Video resolution |
+| `--num_frames` | Number of frames per video |
+| `--output_dir` | Directory for checkpoints |
+
+### Parallelism Arguments
+
+| Argument | Description |
+|----------|-------------|
+| `--num_gpus` | Total number of GPUs |
+| `--sp_size` | Sequence parallel size (increase to reduce memory per GPU) |
+| `--tp_size` | Tensor parallel size |
+| `--hsdp_replicate_dim` | HSDP replication dimension |
+| `--hsdp_shard_dim` | HSDP sharding dimension |
+
+### Optimizer Arguments
+
+| Argument | Description |
+|----------|-------------|
+| `--learning_rate` | Base learning rate |
+| `--mixed_precision` | Precision mode (`bf16` recommended) |
+| `--weight_decay` | Weight decay for regularization |
+| `--max_grad_norm` | Gradient clipping threshold |
+
+### Validation Arguments
+
+| Argument | Description |
+|----------|-------------|
+| `--log_validation` | Enable validation logging |
+| `--validation_dataset_file` | JSON file with validation prompts |
+| `--validation_steps` | Run validation every N steps |
+| `--validation_sampling_steps` | Inference steps for validation |
+| `--validation_guidance_scale` | CFG scale for validation |
+
+## Full Finetuning
+
+Full finetuning updates all model weights. This provides the best quality but requires more GPU memory.
 
 ```bash
-python scripts/huggingface/download_hf.py --repo_id=FastVideo/Mochi-Black-Myth --local_dir=data/Mochi-Black-Myth --repo_type=dataset
+# Example: Wan2.1 T2V 1.3B full finetune (4 GPUs)
+bash examples/training/finetune/wan_t2v_1.3B/crush_smol/finetune_t2v.sh
 ```
 
-Download the original model weights as specified in the [Distillation Section](../distillation/dmd.md):
+**Typical settings:**
 
-Then you can run the finetune with:
+- Learning rate: `1e-5` to `5e-5`
+- Gradient checkpointing: `--enable_gradient_checkpointing_type "full"`
+- Memory scaling: Increase `--sp_size` or reduce `--num_latent_t` to fit in memory
 
-```
-bash scripts/finetune/finetune_mochi.sh # for mochi
-```
+## LoRA Finetuning
 
-**Note that for finetuning, we did not tune the hyperparameters in the provided script.**
-## ⚡ Finetune with VSA
-Follow [data_preprocess.md](#v0-data-preprocess) to get parquet files for preproccessed latent, and then run:
+LoRA (Low-Rank Adaptation) trains lightweight adapters while keeping the base model frozen. This significantly reduces memory usage and training time.
+
+### LoRA-Specific Arguments
+
+| Argument | Description |
+|----------|-------------|
+| `--lora_training True` | Enable LoRA mode |
+| `--lora_rank` | Rank of LoRA adapters (16, 32, 64, 128) |
+
+### Learning Rate for LoRA
+
+**Important:** LoRA typically requires a **10–20× higher learning rate** than full finetuning because only the low-rank adapters are being trained while the base model is frozen.
+
+| Training Mode | Recommended Learning Rate |
+|---------------|---------------------------|
+| Full finetune | `1e-5` to `5e-5` |
+| LoRA | `1e-4` to `2e-4` |
+
+### Example LoRA Training
 
 ```bash
-bash scripts/finetune/finetune_v1_VSA.sh
+# Example: Wan2.1 T2V 1.3B LoRA finetune (1 GPU)
+bash examples/training/finetune/wan_t2v_1.3B/crush_smol/finetune_t2v_lora.sh
 ```
 
-## ⚡ Lora Finetune
+Key differences from full finetune:
 
-Hunyuan supports Lora fine-tuning of videos up to 720p. Demos and prompts of Black-Myth-Wukong can be found in [here](https://huggingface.co/FastVideo/Hunyuan-Black-Myth-Wukong-lora-weight). You can download the Lora weight through:
+- Add `--lora_training True --lora_rank 32`
+- Use higher learning rate (10–20× full finetune)
+- Can run on fewer GPUs (even single GPU)
+- Outputs adapter weights instead of full model
+
+## LoRA Extraction and Merging
+
+FastVideo provides tools to extract LoRA adapters from finetuned models and merge them back.
+
+### Extract LoRA Adapter
+
+Extract a LoRA adapter by comparing a finetuned model to its base:
 
 ```bash
-python scripts/huggingface/download_hf.py --repo_id=FastVideo/Hunyuan-Black-Myth-Wukong-lora-weight --local_dir=data/Hunyuan-Black-Myth-Wukong-lora-weight --repo_type=model
+python scripts/lora_extraction/extract_lora.py \
+  --base Wan-AI/Wan2.1-T2V-1.3B-Diffusers \
+  --ft path/to/your/finetuned_model \
+  --out adapter_r32.safetensors \
+  --rank 32
 ```
 
-### Minimum Hardware Requirement
-- 40 GB GPU memory each for 2 GPUs with lora.
-- 30 GB GPU memory each for 2 GPUs with CPU offload and lora.
+| Argument | Description |
+|----------|-------------|
+| `--base` | Base model (HuggingFace ID or local path) |
+| `--ft` | Finetuned model path |
+| `--out` | Output adapter file (.safetensors) |
+| `--rank` | LoRA rank (16, 32, 64, 128) |
+| `--full-rank` | Extract full-rank adapter (optional) |
 
-Currently, both Mochi and Hunyuan models support Lora finetuning through diffusers. To generate personalized videos from your own dataset, you'll need to follow three main steps: dataset preparation, finetuning, and inference.
+### Merge LoRA Adapter
 
-### Dataset Preparation
-We provide scripts to better help you get started to train on your own characters!
-You can run this to organize your dataset to get the videos2caption.json before preprocess. Specify your video folder and corresponding caption folder (caption files should be .txt files and have the same name with its video):
-
-```
-python scripts/dataset_preparation/prepare_json_file.py --video_dir data/input_videos/ --prompt_dir data/captions/ --output_path data/output_folder/videos2caption.json --verbose
-```
-
-Also, we provide script to resize your videos:
-
-```
-python scripts/data_preprocess/resize_videos.py
-```
-
-### Finetuning
-After basic dataset preparation and preprocess, you can start to finetune your model using Lora:
-
-```
-bash scripts/finetune/finetune_hunyuan_hf_lora.sh
-```
-
-### Inference
-For inference with Lora checkpoint, you can run the following scripts with additional parameter `--lora_checkpoint_dir`:
-
-```
-bash scripts/inference/inference_hunyuan_hf.sh
-```
-
-**We also provide scripts for Mochi in the same directory.**
-
-### Finetune with Both Image and Video
-Our codebase support finetuning with both image and video.
+Merge an adapter back into a base model:
 
 ```bash
-bash scripts/finetune/finetune_hunyuan.sh
-bash scripts/finetune/finetune_mochi_lora_mix.sh
+python scripts/lora_extraction/merge_lora.py \
+  --base Wan-AI/Wan2.1-T2V-1.3B-Diffusers \
+  --adapter adapter_r32.safetensors \
+  --ft path/to/your/finetuned_model \
+  --output merged_model
 ```
 
-For Image-Video Mixture Fine-tuning, make sure to enable the `--group_frame` option in your script.
+| Argument | Description |
+|----------|-------------|
+| `--base` | Base model path |
+| `--adapter` | LoRA adapter file |
+| `--ft` | Finetuned model (for config reference) |
+| `--output` | Output directory for merged model |
+
+### Validate Merged Model
+
+Compare the merged model against the original finetuned model:
+
+```bash
+python scripts/lora_extraction/lora_inference_comparison.py \
+  --base merged_model \
+  --ft path/to/your/finetuned_model \
+  --adapter NONE \
+  --output-dir results \
+  --prompt "A cat sitting on a windowsill" \
+  --compute-ssim \
+  --compute-lpips
+```
+
+## Training Examples
+
+Ready-to-run training scripts are available for multiple models:
+
+**→ [Browse all training examples](examples/examples_training_index.md)**
+
+| Model | Type | Example |
+|-------|------|---------|
+| Wan2.1 T2V 1.3B | T2V | `examples/training/finetune/wan_t2v_1.3B/crush_smol/` |
+| Wan2.1 I2V 14B | I2V | `examples/training/finetune/wan_i2v_14B_480p/crush_smol/` |
+| Wan2.1-Fun 1.3B InP | I2V | `examples/training/finetune/Wan2.1-Fun-1.3B-InP/crush_smol/` |
+| Wan2.1 VSA | T2V/I2V | `examples/training/finetune/Wan2.1-VSA/Wan-Syn-Data/` |
+
+Each example includes:
+
+- `download_dataset.sh` — download sample data
+- `preprocess_*.sh` — run preprocessing
+- `finetune_*.sh` — full finetune launcher
+- `finetune_*_lora.sh` — LoRA finetune launcher
+- `validation.json` — validation prompts

docs/training/overview.md

@@ -0,0 +1,67 @@
+# Training Overview
+
+FastVideo supports finetuning video diffusion models on custom datasets. This page explains what data you need and how to get started.
+
+## Data Requirements
+
+To save GPU memory during training, FastVideo precomputes embeddings and latents ahead of time. This eliminates the need to load the text encoder and VAE during training, significantly reducing memory usage.
+
+### Text-to-Video (T2V) Finetuning
+
+For T2V models, you need:
+
+| Component | Description |
+|-----------|-------------|
+| **Text embeddings** | Precomputed embeddings from the model's text encoder (e.g., T5 or LLaMA). Stored as numpy arrays in Parquet files. |
+| **Video latents** | VAE-encoded representations of your training videos. Each video is encoded into a compressed latent tensor. |
+
+### Image-to-Video (I2V) Finetuning
+
+For I2V models, you need everything from T2V plus additional image conditioning. Note that not all I2V architectures require encoded images—this depends on how the model conditions on the input frame. Wan2.1 and Wan2.2 A14B I2V models do require these additional components:
+
+| Component | Description |
+|-----------|-------------|
+| **Text embeddings** | Same as T2V—precomputed from the text encoder. |
+| **Video latents** | Same as T2V—VAE-encoded video representations. |
+| **First frame latent** | VAE-encoded representation of the first frame, used as the conditioning image. |
+| **CLIP features** | Image embeddings from a CLIP vision encoder for the conditioning frame. |
+
+## Preprocessing
+
+Before training, you need to preprocess your raw videos and captions into Parquet files containing precomputed latents and embeddings.
+
+FastVideo supports two input formats:
+
+- **HuggingFace datasets** — load directly from HF Hub or local HF datasets
+- **Merged datasets** — local folder with videos and a `videos2caption.json` metadata file
+
+**→ See [Data Preprocessing](data_preprocess.md) for full details and examples.**
+
+## Training Examples
+
+Ready-to-run examples with preprocessing scripts, training launchers, and validation configs are available for multiple models and datasets:
+
+**→ [Browse all training examples](examples/examples_training_index.md)**
+
+Each example includes:
+
+- `download_dataset.sh` — download sample data
+- `preprocess_*.sh` — run preprocessing
+- `finetune_*.sh` — launch training (full finetune or LoRA)
+- `validation.json` — validation prompts for checkpoints
+
+## Training Methods
+
+FastVideo supports several training approaches:
+
+| Method | Use Case |
+|--------|----------|
+| **Full finetune** | Adapt entire model to a new domain or style |
+| **LoRA finetune** | Lightweight adaptation with frozen base weights |
+| **VSA finetune** | Finetune with Variable Sparse Attention for efficiency |
+
+## Next Steps
+
+1. **Get started**: Pick an example from the [training examples index](examples/examples_training_index.md)
+2. **Prepare data**: Follow [data preprocessing](data_preprocess.md) for your own dataset
+3. **Run inference**: After training, see [inference examples](../inference/examples/examples_inference_index.md)

docs/utilities/lora.md

@@ -0,0 +1,71 @@
+# LoRA Extraction and Merging
+
+Tools for extracting and merging LoRA adapters for FastVideo models.
+
+## Extract LoRA Adapter
+
+```bash
+python scripts/lora_extraction/extract_lora.py \
+  --base Wan-AI/Wan2.2-TI2V-5B-Diffusers \
+  --ft FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers \
+  --out adapter_r32.safetensors \
+  --rank 32
+```
+
+**Options:**
+
+- `--base`: Base model (HuggingFace ID or local path)
+- `--ft`: Fine-tuned model (HuggingFace ID or local path)
+- `--out`: Output adapter file
+- `--rank`: LoRA rank (16, 32, 64, 128)
+- `--full-rank`: Extract full-rank adapter (optional)
+
+## Merge Adapter
+
+```bash
+python scripts/lora_extraction/merge_lora.py \
+  --base Wan-AI/Wan2.2-TI2V-5B-Diffusers \
+  --adapter adapter_r32.safetensors \
+  --ft FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers \
+  --output merged_model
+```
+
+**Options:**
+
+- `--base`: Base model (HuggingFace ID or local path)
+- `--adapter`: LoRA adapter file (.safetensors)
+- `--ft`: Fine-tuned model (for configuration)
+- `--output`: Output directory
+
+## Validate Quality (Optional)
+
+```bash
+python scripts/lora_extraction/lora_inference_comparison.py \
+  --base merged_model \
+  --ft FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers \
+  --adapter NONE \
+  --output-dir results \
+  --prompt "A cat sitting on a windowsill" \
+  --seed 42 \
+  --height 480 \
+  --width 480 \
+  --num-frames 49 \
+  --num-inference-steps 32 \
+  --compute-ssim \
+  --compute-lpips
+```
+
+**Options:**
+
+- `--base`: Merged model or base model path
+- `--ft`: Fine-tuned model (reference)
+- `--adapter`: Path to adapter or NONE
+- `--output-dir`: Output directory
+- `--prompt`: Text prompt (default: "A cat sitting on a windowsill")
+- `--seed`: Random seed (default: 42)
+- `--height`: Video height (default: 480)
+- `--width`: Video width (default: 832)
+- `--num-frames`: Number of frames (default: 49)
+- `--num-inference-steps`: Inference steps (default: 32)
+- `--compute-ssim`: Compute SSIM metric
+- `--compute-lpips`: Compute LPIPS metric

examples/distill/SFHunyuan1.5-T2V/distill_dmd.slurm

@@ -0,0 +1,169 @@
+#!/bin/bash
+#SBATCH --job-name=dmd_3333
+#SBATCH --partition=main
+#SBATCH --nodes=4
+#SBATCH --ntasks=4
+#SBATCH --ntasks-per-node=1
+#SBATCH --gres=gpu:8
+#SBATCH --cpus-per-task=128
+#SBATCH --mem=1440G
+#SBATCH --output=dmd_3333_output/dmd_3333_%j.out
+#SBATCH --error=dmd_3333_output/dmd_3333_%j.err
+#SBATCH --exclusive
+
+# Basic Info
+export NCCL_P2P_DISABLE=1
+export TORCH_NCCL_ENABLE_MONITORING=0
+export NCCL_DEBUG_SUBSYS=INIT,NET
+# different cache dir for different processes
+export TRITON_CACHE_DIR=/tmp/triton_cache_${SLURM_PROCID}
+export MASTER_PORT=29500
+export NODE_RANK=$SLURM_PROCID
+nodes=( $(scontrol show hostnames $SLURM_JOB_NODELIST) )
+export MASTER_ADDR=${nodes[0]}
+export TOKENIZERS_PARALLELISM=false
+# export WANDB_API_KEY="8d9f4b39abd68eb4e29f6fc010b7ee71a2207cde"
+export WANDB_API_KEY="50632ebd88ffd970521cec9ab4a1a2d7e85bfc45"
+# export WANDB_API_KEY='your_wandb_api_key_here'
+export WANDB_BASE_URL="https://api.wandb.ai"
+export WANDB_MODE=online
+export FASTVIDEO_ATTENTION_BACKEND=FLASH_ATTN
+
+source ~/conda/miniconda/bin/activate
+conda activate wei-fv-distill
+export HOME="/mnt/weka/home/hao.zhang/wei"
+
+# Configs
+NUM_GPUS=8
+NUM_TOTAL_GPUS=32
+
+# Model paths for Self-Forcing DMD distillation with Hunyuan1.5:
+GENERATOR_MODEL_PATH="weizhou03/SFHunyuanVideo-1.5-Diffusers-480p_t2v"  # Updated to Wan2.2
+REAL_SCORE_MODEL_PATH="hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v" # Teacher model
+FAKE_SCORE_MODEL_PATH="hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v"
+# REAL_SCORE_MODEL_PATH="hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_i2v_step_distilled"
+# FAKE_SCORE_MODEL_PATH="hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_i2v_step_distilled" # Critic model
+
+# DATA_DIR="data/test-text-preprocessing/Node_0_GPU_1_File_1/combined_parquet_dataset/"
+# DATA_DIR="/mnt/weka/home/hao.zhang/matthew/FastVideo/data/test-text-preprocessing"
+DATA_DIR="/mnt/weka/home/hao.zhang/wl/sharefs/wl/release/vidprom_16k_text_embed"
+DATA_DIR_2="/mnt/weka/home/hao.zhang/wl/sharefs/wl/release/fv-ode-preprocessing-16k-hy15-121"
+# DATA_DIR=data/crush-smol_processed_t2v/combined_parquet_dataset
+# DATA_DIR="/mnt/sharefs/users/hao.zhang/Vchitect-2M/Wan-Syn-upload/latents_i2v/train/"
+# VALIDATION_DATASET_FILE="data/crush-smol-single_processed_t2v/validation.json"
+VALIDATION_DATASET_FILE="/mnt/weka/home/hao.zhang/wl/FastVideo/examples/distill/Wan2.2-TI2V-5B-Diffusers/Data-free/validation_64.json"
+
+# export CUDA_VISIBLE_DEVICES=4,5
+# IP=[MASTER NODE IP]
+
+training_args=(
+  --tracker_project_name SFhy1.5_t2v_distill_self_forcing_dmd  # Updated for Wan2.2
+  --output_dir "/mnt/weka/home/hao.zhang/wei/SFhy1.5_distill_3333_1e-5_1e-5_cfg6_corrected_scheduler"
+  --wandb_run_name "self_forcing_3333_context_forcing"
+  --max_train_steps 4000
+  --train_batch_size 1
+  --train_sp_batch_size 1
+  --gradient_accumulation_steps 1
+  --num_latent_t 21
+  --num_height 480
+  --num_width 848
+  --enable_gradient_checkpointing_type "full"
+  --simulate_generator_forward
+  --log_visualization
+  --num_frames 81
+  --num_frame_per_block 3  # Frame generation block size for self-forcing
+  # --resume-from-checkpoint "/mnt/weka/home/hao.zhang/wei/SFhy1.5_distill_1333/checkpoint-300"
+  --init_weights_from_safetensors /mnt/weka/home/hao.zhang/wei/hy15_worldplay_df_init_3333/checkpoint-2000/transformer/diffusion_pytorch_model.safetensors
+  # --init_weights_from_safetensors /mnt/weka/home/hao.zhang/wei/hy15_distilled_ode_init_3333/checkpoint-2400/transformer/diffusion_pytorch_model.safetensors
+)
+
+parallel_args=(
+  --num_gpus $NUM_TOTAL_GPUS # 64
+  --sp_size 1
+  --tp_size 1
+  --hsdp_replicate_dim 1 # 64
+  --hsdp_shard_dim $NUM_TOTAL_GPUS
+)
+
+model_args=(
+  --model_path $GENERATOR_MODEL_PATH
+  --pretrained_model_name_or_path $GENERATOR_MODEL_PATH
+  --real_score_model_path $REAL_SCORE_MODEL_PATH
+  --fake_score_model_path $FAKE_SCORE_MODEL_PATH
+)
+
+dataset_args=(
+  --data_path "$DATA_DIR"
+  # --data_path_2 "$DATA_DIR_2"
+  --dataloader_num_workers 4
+)
+
+validation_args=(
+  --log_validation
+  --validation_dataset_file "$VALIDATION_DATASET_FILE"
+  --validation_steps 50
+  --validation_sampling_steps "8"
+  --validation_guidance_scale "1.0" # not used for dmd inference
+  --text-encoder-cpu-offload
+  # --vae_cpu_offload True
+)
+
+optimizer_args=(
+  --learning_rate 1e-5
+  --mixed_precision "bf16"
+  --training_state_checkpointing_steps 100
+  --weight_only_checkpointing_steps 500
+  --weight_decay 0.01
+  --betas '0.0,0.999'
+  --max_grad_norm 1.0
+)
+
+miscellaneous_args=(
+  --inference_mode False
+  --checkpoints_total_limit 3
+  --training_cfg_rate 0.0
+  --dit_precision "fp32"
+  --flow_shift 5
+  --seed 1000
+  --use_ema True
+  --ema_decay 0.99
+  --ema_start_step 200
+)
+
+dmd_args=(
+  --warp_denoising_step
+  --dmd_denoising_steps '1000,875,750,625,500,375,250,125'
+  # --dmd_denoising_steps '1000,760,520,280'
+  # --dmd_denoising_steps '1000,750,500,250'
+  --min_timestep_ratio 0.02
+  --max_timestep_ratio 0.98
+  --dfake_gen_update_ratio 5
+  --real_score_guidance_scale 3.5
+  --fake_score_learning_rate 5e-6
+  --fake_score_betas '0.0,0.999'
+)
+
+self_forcing_args=(
+  --independent_first_frame False  # Whether to treat first frame independently
+  --same_step_across_blocks True  # Whether to use same denoising step across all blocks
+  --last_step_only False  # Whether to only use the last denoising step
+  --context_noise 0  # Amount of noise to add during context caching (0 = no noise)
+  # --use-context-forcing True
+)
+
+srun torchrun \
+--nnodes $SLURM_JOB_NUM_NODES \
+--nproc_per_node $NUM_GPUS \
+--node_rank $SLURM_PROCID \
+--rdzv_backend=c10d \
+--rdzv_endpoint="$MASTER_ADDR:$MASTER_PORT" \
+    fastvideo/training/hy15_self_forcing_distillation_pipeline.py \
+    "${parallel_args[@]}" \
+    "${model_args[@]}" \
+    "${dataset_args[@]}" \
+    "${training_args[@]}" \
+    "${optimizer_args[@]}" \
+    "${validation_args[@]}" \
+    "${miscellaneous_args[@]}" \
+    "${dmd_args[@]}" \
+    "${self_forcing_args[@]}"

examples/distill/SFWan2.1-T2V/README.md

@@ -0,0 +1,19 @@
+# Self-Forcing Distillation for SFWan2.1 T2V 1.3B
+
+These scripts demonstrate self-forcing distillation (SFwan) for the causal Wan2.1 T2V 1.3B model. The workflow mirrors DMD2 while injecting self-forcing blocks so the student can autoregressively refine later frames.
+
+## Run the recipe
+1. Download the preprocessed text-video dataset:
+   ```bash
+   bash examples/distill/SFWan2.1-T2V/download_dataset.sh
+   ```
+2. (Optional) Regenerate parquet shards locally:
+   ```bash
+   bash examples/distill/SFWan2.1-T2V/preprocess_data.sh
+   ```
+3. Launch self-forcing distillation with your cluster settings:
+   ```bash
+   sbatch examples/distill/SFWan2.1-T2V/distill_dmd_t2v_1.3B.sh
+   ```
+
+Update the dataset paths and wandb credentials inside the script before running on your environment.

examples/inference/basic/basic.py

@@ -12,7 +12,7 @@ def main():
         "Wan-AI/Wan2.1-T2V-1.3B-Diffusers",
         # FastVideo will automatically handle distributed setup
         num_gpus=1,
-        use_fsdp_inference=True,
+        use_fsdp_inference=False, # set to True if GPU is out of memory
         dit_cpu_offload=False,
         vae_cpu_offload=False,
         text_encoder_cpu_offload=True,

examples/inference/basic/basic_cosmos2_5_t2w.py

@@ -0,0 +1,42 @@
+from fastvideo import VideoGenerator
+
+
+def main():
+    # Point this to your local diffusers model dir (or replace with a HF model ID).
+    model_path = "KyleShao/Cosmos-Predict2.5-2B-Diffusers"
+
+    generator = VideoGenerator.from_pretrained(
+        model_path,
+        num_gpus=1,
+        use_fsdp_inference=False,  # set True if GPU is out of memory
+        dit_cpu_offload=False,
+        vae_cpu_offload=False,
+        text_encoder_cpu_offload=True,
+        pin_cpu_memory=True,
+    )
+
+    prompt = (
+       "A high-definition video captures the precision of robotic welding in an industrial setting. The first frame showcases a robotic arm, equipped with a welding torch, positioned over a large metal structure. The welding process is in full swing, with bright sparks and intense light illuminating the scene, creating a vivid display of blue and white hues. A significant amount of smoke billows around the welding area, partially obscuring the view but emphasizing the heat and activity. The background reveals parts of the workshop environment, including a ventilation system and various pieces of machinery, indicating a busy and functional industrial workspace. As the video progresses, the robotic arm maintains its steady position, continuing the welding process and moving to its left. The welding torch consistently emits sparks and light, and the smoke continues to rise, diffusing slightly as it moves upward. The metal surface beneath the torch shows ongoing signs of heating and melting. The scene retains its industrial ambiance, with the welding sparks and smoke dominating the visual field, underscoring the ongoing nature of the welding operation."
+    )
+
+    video = generator.generate_video(
+        prompt,
+        negative_prompt="",
+        height=704,
+        width=1280,
+        num_frames=77,
+        num_inference_steps=35,
+        guidance_scale=7.0,
+        fps=24,
+        output_path="outputs_video/cosmos2_5_t2w.mp4",
+        save_video=True,
+    )
+
+    generator.shutdown()
+
+
+if __name__ == "__main__":
+    main()
+
+
+

examples/inference/basic/basic_dmd.py

@@ -14,7 +14,7 @@ def main():
         model_name,
         # FastVideo will automatically handle distributed setup
         num_gpus=1,
-        use_fsdp_inference=True,
+        use_fsdp_inference=False, # set to True if GPU is out of memory
         # Adjust these offload parameters if you have < 32GB of VRAM
         text_encoder_cpu_offload=True,
         pin_cpu_memory=True, # set to false if low CPU RAM or hit obscure "CUDA error: Invalid argument"

examples/inference/basic/basic_hy15.py

@@ -2,31 +2,51 @@ from fastvideo import VideoGenerator
 import json
 # from fastvideo.configs.sample import SamplingParam
 
-OUTPUT_PATH = "video_samples_hy15"
+OUTPUT_PATH = "video_samples_hy15_t2v_distilled"
 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",
+        "hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_i2v_step_distilled",
         # FastVideo will automatically handle distributed setup
         num_gpus=1,
-        use_fsdp_inference=True,
+        use_fsdp_inference=False, # set to True if GPU is out of memory
         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,
+        # init_weights_from_safetensors="/mnt/weka/home/hao.zhang/wei/SFhy1.5_distill_1333_5e-5_2e-6_cfg3.5/checkpoint-500/ema/generator_ema.safetensors"
     )
 
+    # json_path = "/mnt/weka/home/hao.zhang/wei/FastVideo/data/mixkit_i2v_full_720p.json"
+    # with open(json_path, 'r') as f:
+    #     data_list = json.load(f)["data"]
+    
+    # # Now you can index into data_list however you like
+    # # For example: data_list[0], data_list[1:3], etc.
+    # for data in data_list:
+    #     prompt = data["prompt"]
+    #     image_path = data["image_path"]
+    #     generator.generate_video(prompt, output_path=OUTPUT_PATH, save_video=True, image_path=image_path, num_frames=121, fps=24)
+    # return
+
+    # prompt = (
+    #     "In a brightly lit studio, a photographer wearing a denim jacket focuses intently, capturing shots with a professional camera. Facing him, a model stands gracefully, adjusting her long, flowing hair with delicate movements. The scene is characterized by strong contrasts; the model's soft pink attire and gentle gestures complement the rugged, precise demeanor of the photographer. Positioned against a minimalist backdrop, the pair work seamlessly, with the camera\u2019s lens pointed directly at the model, capturing her elegance. The soft, diffused lighting casts a gentle glow on both subjects, creating an airy and ethereal atmosphere perfect for a high-fashion photo shoot."
+    # )
+
+    # video = generator.generate_video(prompt, output_path=OUTPUT_PATH, save_video=True, negative_prompt="", num_frames=121, fps=24, image_path="data/1.png")
+    # return
+
     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)
+    video = generator.generate_video(prompt, output_path=OUTPUT_PATH, save_video=True)
 
     prompt2 = (
         "A majestic lion strides across the golden savanna, its powerful frame "
@@ -35,7 +55,7 @@ def main():
         "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)
+    video2 = generator.generate_video(prompt2, output_path=OUTPUT_PATH, save_video=True)
 
 
 if __name__ == "__main__":

examples/inference/basic/basic_hyworld.py

@@ -0,0 +1,224 @@
+# SPDX-License-Identifier: Apache-2.0
+"""
+Basic example for HYWorld (HY-WorldPlay) video generation using FastVideo.
+
+This example replicates the same functionality as HY-WorldPlay/run.sh,
+demonstrating image-to-video generation with camera trajectory control.
+"""
+
+import time
+import math
+import numpy as np
+import imageio
+import torchvision
+from einops import rearrange
+
+from fastvideo import VideoGenerator
+from fastvideo.pipelines import ForwardBatch
+from fastvideo.utils import shallow_asdict, align_to
+from fastvideo.logger import init_logger
+
+from fastvideo.models.dits.hyworld.pose import pose_to_input, compute_latent_num
+from fastvideo.models.dits.hyworld.resolution_utils import get_resolution_from_image
+
+logger = init_logger(__name__)
+
+class HYWorldVideoGenerator(VideoGenerator):
+    """Extended VideoGenerator that adds HYWorld-specific parameters to batch.extra."""
+
+    def _generate_single_video(self, prompt: str, sampling_param=None, **kwargs):
+        """Override to add viewmats, Ks, and action to batch.extra."""
+        fastvideo_args = self.fastvideo_args
+        pipeline_config = fastvideo_args.pipeline_config
+
+        if sampling_param is None:
+            from fastvideo.configs.sample import SamplingParam
+            sampling_param = SamplingParam.from_pretrained(fastvideo_args.model_path)
+
+        # Update sampling param with kwargs
+        if kwargs:
+            for key, value in kwargs.items():
+                if hasattr(sampling_param, key):
+                    setattr(sampling_param, key, value)
+
+        # Get pose string from sampling_param or kwargs
+        pose = kwargs.get('pose', getattr(sampling_param, 'POSE', 'w-31'))
+        num_frames = kwargs.get('num_frames', getattr(sampling_param, 'num_frames', 125))
+
+        # Calculate number of latents
+        latent_num = compute_latent_num(num_frames)
+
+        # Convert pose to viewmats, Ks, and action
+        viewmats, Ks, action = pose_to_input(pose, latent_num)
+
+        # Convert to tensors and add batch dimension
+        viewmats = viewmats.unsqueeze(0)  # (1, T, 4, 4)
+        Ks = Ks.unsqueeze(0)  # (1, T, 3, 3)
+        action = action.unsqueeze(0)  # (1, T)
+
+        # Validate inputs
+        prompt = prompt.strip()
+        sampling_param = sampling_param.__class__(**shallow_asdict(sampling_param))
+        output_path = kwargs.get("output_path", sampling_param.output_path)
+        sampling_param.prompt = prompt
+
+        if sampling_param.negative_prompt is not None:
+            sampling_param.negative_prompt = sampling_param.negative_prompt.strip()
+
+        # Validate dimensions
+        if (sampling_param.height <= 0 or sampling_param.width <= 0 or
+            sampling_param.num_frames <= 0):
+            raise ValueError(
+                f"Height, width, and num_frames must be positive integers")
+
+        temporal_scale_factor = pipeline_config.vae_config.arch_config.temporal_compression_ratio
+        num_frames = sampling_param.num_frames
+        num_gpus = fastvideo_args.num_gpus
+        use_temporal_scaling_frames = pipeline_config.vae_config.use_temporal_scaling_frames
+
+        # Adjust number of frames based on number of GPUs
+        if use_temporal_scaling_frames:
+            orig_latent_num_frames = (num_frames - 1) // temporal_scale_factor + 1
+        else:
+            orig_latent_num_frames = sampling_param.num_frames // 17 * 3
+
+        if orig_latent_num_frames % fastvideo_args.num_gpus != 0:
+            if use_temporal_scaling_frames:
+                new_num_frames = (orig_latent_num_frames - 1) * temporal_scale_factor + 1
+            else:
+                divisor = math.lcm(3, num_gpus)
+                orig_latent_num_frames = (
+                    (orig_latent_num_frames + divisor - 1) // divisor) * divisor
+                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)",
+                sampling_param.num_frames, new_num_frames, fastvideo_args.num_gpus)
+            sampling_param.num_frames = new_num_frames
+
+        # Calculate sizes
+        target_height = align_to(sampling_param.height, 16)
+        target_width = align_to(sampling_param.width, 16)
+
+        # Calculate latent sizes
+        latents_size = [(sampling_param.num_frames - 1) // 4 + 1,
+                        sampling_param.height // 8, sampling_param.width // 8]
+        n_tokens = latents_size[0] * latents_size[1] * latents_size[2]
+
+        # Prepare batch
+        batch = ForwardBatch(
+            **shallow_asdict(sampling_param),
+            eta=0.0,
+            n_tokens=n_tokens,
+            VSA_sparsity=fastvideo_args.VSA_sparsity,
+        )
+
+        # Add HYWorld-specific parameters to batch.extra
+        batch.extra['viewmats'] = viewmats
+        batch.extra['Ks'] = Ks
+        batch.extra['action'] = action
+        batch.extra['chunk_latent_frames'] = 16  # For bidirectional model
+
+        # Run inference
+        start_time = time.perf_counter()
+        output_batch = self.executor.execute_forward(batch, fastvideo_args)
+        samples = output_batch.output
+        logging_info = output_batch.logging_info
+
+        gen_time = time.perf_counter() - start_time
+        logger.info("Generated successfully in %.2f seconds", gen_time)
+
+        # Process outputs
+        videos = rearrange(samples, "b c t h w -> t b c h w")
+        frames = []
+        for x in videos:
+            x = torchvision.utils.make_grid(x, nrow=6)
+            x = x.transpose(0, 1).transpose(1, 2).squeeze(-1)
+            frames.append((x * 255).numpy().astype(np.uint8))
+
+        # Save video if requested
+        if batch.save_video:
+            imageio.mimsave(output_path, frames, fps=batch.fps, format="mp4")
+            logger.info("Saved video to %s", output_path)
+
+        if batch.return_frames:
+            return frames
+        else:
+            return {
+                "samples": samples,
+                "frames": frames,
+                "prompts": prompt,
+                "size": (target_height, target_width, batch.num_frames),
+                "generation_time": gen_time,
+                "logging_info": logging_info,
+                "trajectory": output_batch.trajectory_latents,
+                "trajectory_timesteps": output_batch.trajectory_timesteps,
+                "trajectory_decoded": output_batch.trajectory_decoded,
+            }
+
+# Default prompt from HY-WorldPlay run.sh
+DEFAULT_PROMPT = 'A paved pathway leads towards a stone arch bridge spanning a calm body of water.  Lush green trees and foliage line the path and the far bank of the water. A traditional-style pavilion with a tiered, reddish-brown roof sits on the far shore. The water reflects the surrounding greenery and the sky.  The scene is bathed in soft, natural light, creating a tranquil and serene atmosphere. The pathway is composed of large, rectangular stones, and the bridge is constructed of light gray stone.  The overall composition emphasizes the peaceful and harmonious nature of the landscape.'
+DEFAULT_IMAGE = 'https://raw.githubusercontent.com/Tencent-Hunyuan/HY-WorldPlay/main/assets/img/test.png'
+
+def main():
+    import argparse
+
+    # pose: (a, w, s, d) - (15, 31)
+    # num_frames: (61, 125)
+    parser = argparse.ArgumentParser(description="HYWorld video generation with FastVideo")
+    parser.add_argument("--prompt", type=str, default=DEFAULT_PROMPT, help="Text prompt for video generation")
+    parser.add_argument("--image", type=str, default=DEFAULT_IMAGE, help="Path or URL to input image")
+    parser.add_argument("--pose", type=str, default='w-31', help="Pose string (e.g., 'a-31', 'w-31', 's-31', 'd-31')")
+    parser.add_argument("--output_path", type=str, default='video_samples_hyworld', help="Output video path")
+    parser.add_argument("--num-frames", type=int, default=125, help="Number of frames")
+    parser.add_argument("--seed", type=int, default=1, help="Random seed")
+    parser.add_argument("--resolution", type=str, default="480p", help="Only support 480p for now")
+    args = parser.parse_args()
+
+    # Automatically determine resolution from input image
+    HEIGHT, WIDTH = get_resolution_from_image(args.image, args.resolution)
+    print(f"Image: {args.image}")
+    print(f"Pose: {args.pose}")
+    print(f"Resolution: {HEIGHT}x{WIDTH} (from {args.resolution} buckets)")
+    print(f"Num frames: {args.num_frames}")
+    print(f"Output path: {args.output_path}")
+
+    # Initialize generator
+    print("\nInitializing VideoGenerator for HYWorld...")
+    
+    generator = HYWorldVideoGenerator.from_pretrained(
+        "FastVideo/HY-WorldPlay-Bidirectional-Diffusers",
+        num_gpus=1,
+        use_fsdp_inference=True,
+        dit_cpu_offload=True,
+        vae_cpu_offload=True,
+        text_encoder_cpu_offload=True,
+        pin_cpu_memory=True,
+        image_encoder_cpu_offload=True,
+    )
+
+    # Generate video
+    print("\nGenerating video...")
+    start_time = time.time()
+    video = generator.generate_video(
+        prompt=args.prompt,
+        image_path=args.image,
+        output_path=args.output_path,
+        save_video=True,
+        negative_prompt="",
+        num_frames=args.num_frames,
+        fps=24,
+        height=HEIGHT,
+        width=WIDTH,
+        seed=args.seed,
+        pose=args.pose,
+    )
+    elapsed = time.time() - start_time
+
+    print(f"\nVideo generated successfully!")
+    print(f"Saved to: {args.output_path}")
+    print(f"Time: {elapsed:.2f}s")
+
+
+if __name__ == "__main__":
+    main()

examples/inference/basic/basic_longcat_i2v.py

@@ -0,0 +1,210 @@
+"""
+LongCat Image-to-Video (I2V) Example Script
+
+This script demonstrates LongCat I2V inference using the FastVideo Python API.
+LongCat I2V takes an input image and generates a video from it.
+
+It runs both basic generation (50 steps) and distill+refine generation
+(16 steps distill + 50 steps refinement to 720p with BSA).
+
+Usage:
+    python examples/inference/basic/basic_longcat_i2v.py
+
+Note:
+    Refinement uses 768x768 dimensions where latent (48x48) is divisible by 8,
+    compatible with BSA chunks [4, 4, 8].
+"""
+
+import glob
+import os
+
+from fastvideo import VideoGenerator
+
+# Common prompts and settings matching the shell script examples
+PROMPT = (
+    "A woman sits at a wooden table by the window in a cozy café. She reaches out "
+    "with her right hand, picks up the white coffee cup from the saucer, and gently "
+    "brings it to her lips to take a sip. After drinking, she places the cup back on "
+    "the table and looks out the window, enjoying the peaceful atmosphere."
+)
+
+NEGATIVE_PROMPT = (
+    "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"
+)
+
+# Input image path
+IMAGE_PATH = "assets/girl.png"
+
+SEED = 42
+
+
+def basic_generation():
+    """
+    Run basic LongCat I2V generation (50 steps at 480p).
+    
+    This uses the full 50-step denoising process for highest quality.
+    """
+    print("=" * 60)
+    print("LongCat I2V: Basic Generation (50 steps, 480p)")
+    print("=" * 60)
+    
+    generator = VideoGenerator.from_pretrained(
+        "FastVideo/LongCat-Video-I2V-Diffusers",
+        num_gpus=1,
+        use_fsdp_inference=False, # set to True if GPU is out of memory
+        dit_cpu_offload=False,
+        vae_cpu_offload=True,
+        text_encoder_cpu_offload=True,
+        pin_cpu_memory=False,
+        enable_bsa=False,
+    )
+    
+    output_path = "outputs_video/longcat_i2v_basic"
+    
+    generator.generate_video(
+        prompt=PROMPT,
+        negative_prompt=NEGATIVE_PROMPT,
+        image_path=IMAGE_PATH,
+        output_path=output_path,
+        save_video=True,
+        height=480,
+        width=480,  # Square
+        num_frames=93,
+        num_inference_steps=50,
+        fps=15,
+        guidance_scale=4.0,
+        seed=SEED,
+    )
+    
+    print(f"\nBasic generation complete! Video saved to: {output_path}")
+    generator.shutdown()
+
+
+def distill_refine_generation():
+    """
+    Run LongCat I2V with distill+refine pipeline (16 steps + refinement to 768p).
+    
+    This uses the distilled LoRA for fast 480p generation (16 steps),
+    then refines to 768p using the refinement LoRA with BSA enabled.
+    """
+    print("\n" + "=" * 60)
+    print("LongCat I2V: Distill + Refine Pipeline")
+    print("=" * 60)
+    
+    # Stage 1: Distilled generation (16 steps at 480p)
+    print("\n[Stage 1] Distilled generation (16 steps, 480p)")
+    print("-" * 40)
+    
+    generator = VideoGenerator.from_pretrained(
+        "FastVideo/LongCat-Video-I2V-Diffusers",
+        num_gpus=1,
+        use_fsdp_inference=True,
+        dit_cpu_offload=False,
+        vae_cpu_offload=True,
+        text_encoder_cpu_offload=True,
+        pin_cpu_memory=False,
+        enable_bsa=False,
+        lora_path="FastVideo/LongCat-Video-T2V-Distilled-LoRA",
+        lora_nickname="distilled",
+    )
+    
+    distill_output_path = "outputs_video/longcat_i2v_distill"
+    
+    generator.generate_video(
+        prompt=PROMPT,
+        negative_prompt=NEGATIVE_PROMPT,
+        image_path=IMAGE_PATH,
+        output_path=distill_output_path,
+        save_video=True,
+        height=480,
+        width=480,  # Square
+        num_frames=93,
+        num_inference_steps=16,
+        fps=15,
+        guidance_scale=1.0,
+        seed=SEED,
+    )
+    
+    print(f"Distilled generation complete! Video saved to: {distill_output_path}")
+    generator.shutdown()
+    
+    # Stage 2: Refinement (480p -> 768p)
+    print("\n[Stage 2] Refinement (480p -> 768p with BSA)")
+    print("-" * 40)
+    
+    # Find the actual saved video file from stage 1
+    video_files = glob.glob(os.path.join(distill_output_path, "*.mp4"))
+    if not video_files:
+        raise FileNotFoundError(f"No video file found in {distill_output_path}")
+    # Use the most recently created video file
+    distill_video_path = max(video_files, key=os.path.getmtime)
+    print(f"Using stage 1 video: {distill_video_path}")
+    
+    # Create a new generator with refinement LoRA and BSA enabled
+    # Note: Refinement uses the T2V model (not I2V) since it's upscaling the generated video
+    # For BSA [4, 4, 8]: latent must be divisible by 8
+    # 768x768: latent 48x48, 48%8=0 ✓
+    refine_generator = VideoGenerator.from_pretrained(
+        "FastVideo/LongCat-Video-T2V-Diffusers",
+        num_gpus=1,
+        use_fsdp_inference=True,
+        dit_cpu_offload=True,
+        vae_cpu_offload=True,
+        text_encoder_cpu_offload=True,
+        pin_cpu_memory=False,
+        enable_bsa=True,
+        bsa_sparsity=0.875,
+        bsa_chunk_q=[4, 4, 4],
+        bsa_chunk_k=[4, 4, 4],
+        lora_path="FastVideo/LongCat-Video-T2V-Refinement-LoRA",
+        lora_nickname="refinement",
+    )
+    
+    refine_output_path = "outputs_video/longcat_i2v_refine_720p"
+    
+    refine_generator.generate_video(
+        prompt=PROMPT,
+        negative_prompt=NEGATIVE_PROMPT,
+        output_path=refine_output_path,
+        save_video=True,
+        refine_from=distill_video_path,
+        t_thresh=0.5,
+        spatial_refine_only=False,
+        num_cond_frames=0,
+        height=720,
+        width=720,
+        num_inference_steps=50,
+        fps=30,
+        guidance_scale=1.0,
+        seed=SEED,
+    )
+    
+    print(f"Refinement complete! Video saved to: {refine_output_path}")
+    refine_generator.shutdown()
+
+
+def main():
+    """Run both basic and distill+refine generation pipelines."""
+    print("\n" + "=" * 60)
+    print("LongCat Image-to-Video Example")
+    print("=" * 60 + "\n")
+    
+    # Run basic generation
+    basic_generation()
+    
+    # Run distill+refine pipeline
+    distill_refine_generation()
+    
+    print("\n" + "=" * 60)
+    print("All generations complete!")
+    print("=" * 60)
+
+
+if __name__ == "__main__":
+    main()
+
+

examples/inference/basic/basic_longcat_t2v.py

@@ -0,0 +1,198 @@
+"""
+LongCat Text-to-Video (T2V) Example Script
+
+This script demonstrates LongCat T2V inference using the FastVideo Python API.
+It runs both basic generation (50 steps) and distill+refine generation
+(16 steps distill + 50 steps refinement to 720p).
+
+Usage:
+    python examples/inference/basic/basic_longcat_t2v.py
+"""
+
+import glob
+import os
+
+from fastvideo import VideoGenerator
+
+# Common prompts and settings matching the shell script examples
+PROMPT = (
+    "In a realistic photography style, a white boy around seven or eight years old "
+    "sits on a park bench, wearing a light blue T-shirt, denim shorts, and white sneakers. "
+    "He holds an ice cream cone with vanilla and chocolate flavors, and beside him is a "
+    "medium-sized golden Labrador. Smiling, the boy offers the ice cream to the dog, "
+    "who eagerly licks it with its tongue. The sun is shining brightly, and the background "
+    "features a green lawn and several tall trees, creating a warm and loving scene."
+)
+
+NEGATIVE_PROMPT = (
+    "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"
+)
+
+SEED = 42
+
+
+def basic_generation():
+    """
+    Run basic LongCat T2V generation (50 steps at 480p).
+    
+    This uses the full 50-step denoising process for highest quality.
+    """
+    print("=" * 60)
+    print("LongCat T2V: Basic Generation (50 steps, 480p)")
+    print("=" * 60)
+    
+    generator = VideoGenerator.from_pretrained(
+        "FastVideo/LongCat-Video-T2V-Diffusers",
+        num_gpus=1,
+        use_fsdp_inference=False, # set to True if GPU is out of memory
+        dit_cpu_offload=False,
+        vae_cpu_offload=True,
+        text_encoder_cpu_offload=True,
+        pin_cpu_memory=False,
+        enable_bsa=False,
+    )
+    
+    output_path = "outputs_video/longcat_t2v_basic"
+    
+    generator.generate_video(
+        prompt=PROMPT,
+        negative_prompt=NEGATIVE_PROMPT,
+        output_path=output_path,
+        save_video=True,
+        height=480,
+        width=832,
+        num_frames=93,
+        num_inference_steps=50,
+        fps=15,
+        guidance_scale=4.0,
+        seed=SEED,
+    )
+    
+    print(f"\nBasic generation complete! Video saved to: {output_path}")
+    generator.shutdown()
+
+
+def distill_refine_generation():
+    """
+    Run LongCat T2V with distill+refine pipeline (16 steps + refinement to 720p).
+    
+    This uses the distilled LoRA for fast 480p generation (16 steps),
+    then refines to 720p using the refinement LoRA with BSA enabled.
+    """
+    print("\n" + "=" * 60)
+    print("LongCat T2V: Distill + Refine Pipeline")
+    print("=" * 60)
+    
+    # Stage 1: Distilled generation (16 steps at 480p)
+    print("\n[Stage 1] Distilled generation (16 steps, 480p)")
+    print("-" * 40)
+    
+    generator = VideoGenerator.from_pretrained(
+        "FastVideo/LongCat-Video-T2V-Diffusers",
+        num_gpus=1,
+        use_fsdp_inference=True,
+        dit_cpu_offload=False,
+        vae_cpu_offload=True,
+        text_encoder_cpu_offload=True,
+        pin_cpu_memory=False,
+        enable_bsa=False,
+        lora_path="FastVideo/LongCat-Video-T2V-Distilled-LoRA",
+        lora_nickname="distilled",
+    )
+    
+    distill_output_path = "outputs_video/longcat_t2v_distill"
+    
+    generator.generate_video(
+        prompt=PROMPT,
+        negative_prompt=NEGATIVE_PROMPT,
+        output_path=distill_output_path,
+        save_video=True,
+        height=480,
+        width=832,
+        num_frames=93,
+        num_inference_steps=16,
+        fps=15,
+        guidance_scale=1.0,
+        seed=SEED,
+    )
+    
+    print(f"Distilled generation complete! Video saved to: {distill_output_path}")
+    generator.shutdown()
+    
+    # Stage 2: Refinement (480p -> 720p)
+    print("\n[Stage 2] Refinement (480p -> 720p with BSA)")
+    print("-" * 40)
+    
+    # Find the actual saved video file from stage 1
+    video_files = glob.glob(os.path.join(distill_output_path, "*.mp4"))
+    if not video_files:
+        raise FileNotFoundError(f"No video file found in {distill_output_path}")
+    # Use the most recently created video file
+    distill_video_path = max(video_files, key=os.path.getmtime)
+    print(f"Using stage 1 video: {distill_video_path}")
+    
+    # Create a new generator with refinement LoRA and BSA enabled
+    refine_generator = VideoGenerator.from_pretrained(
+        "FastVideo/LongCat-Video-T2V-Diffusers",
+        num_gpus=1,
+        use_fsdp_inference=True,
+        dit_cpu_offload=True,
+        vae_cpu_offload=True,
+        text_encoder_cpu_offload=True,
+        pin_cpu_memory=False,
+        enable_bsa=True,
+        bsa_sparsity=0.875,
+        bsa_chunk_q=[4, 4, 8],
+        bsa_chunk_k=[4, 4, 8],
+        lora_path="FastVideo/LongCat-Video-T2V-Refinement-LoRA",
+        lora_nickname="refinement",
+    )
+    
+    refine_output_path = "outputs_video/longcat_t2v_refine_720p"
+    
+    refine_generator.generate_video(
+        prompt=PROMPT,
+        negative_prompt=NEGATIVE_PROMPT,
+        output_path=refine_output_path,
+        save_video=True,
+        refine_from=distill_video_path,
+        t_thresh=0.5,
+        spatial_refine_only=False,
+        num_cond_frames=0,
+        height=720,
+        width=1280,
+        num_inference_steps=50,
+        fps=30,
+        guidance_scale=1.0,
+        seed=SEED,
+    )
+    
+    print(f"Refinement complete! Video saved to: {refine_output_path}")
+    refine_generator.shutdown()
+
+
+def main():
+    """Run both basic and distill+refine generation pipelines."""
+    print("\n" + "=" * 60)
+    print("LongCat Text-to-Video Example")
+    print("=" * 60 + "\n")
+    
+    # Run basic generation
+    basic_generation()
+    
+    # Run distill+refine pipeline
+    distill_refine_generation()
+    
+    print("\n" + "=" * 60)
+    print("All generations complete!")
+    print("=" * 60)
+
+
+if __name__ == "__main__":
+    main()
+
+

examples/inference/basic/basic_longcat_vc.py

@@ -0,0 +1,228 @@
+"""
+LongCat Video Continuation (VC) Example Script
+
+This script demonstrates LongCat VC inference using the FastVideo Python API.
+LongCat VC takes an input video and generates a continuation of it.
+
+It runs both basic generation (50 steps) and distill+refine generation
+(16 steps distill + 50 steps refinement to 720p).
+
+Usage:
+    python examples/inference/basic/basic_longcat_vc.py
+
+Prerequisites:
+    - Ensure the input video exists at assets/motorcycle.mp4
+      (or provide your own video)
+"""
+
+import glob
+import os
+
+from fastvideo import VideoGenerator
+
+# Common prompts and settings matching the shell script examples
+PROMPT = (
+    "A person rides a motorcycle along a long, straight road that stretches between "
+    "a body of water and a forested hillside. The rider steadily accelerates, keeping "
+    "the motorcycle centered between the guardrails, while the scenery passes by on "
+    "both sides. The video captures the journey from the rider's perspective, emphasizing "
+    "the sense of motion and adventure."
+)
+
+NEGATIVE_PROMPT = (
+    "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"
+)
+
+# Input video path
+VIDEO_PATH = "assets/motorcycle.mp4"
+
+# Number of conditioning frames from the input video
+NUM_COND_FRAMES = 13
+
+SEED = 42
+
+
+def basic_generation():
+    """
+    Run basic LongCat VC generation (50 steps at 480p).
+    
+    This uses the full 50-step denoising process for highest quality.
+    """
+    print("=" * 60)
+    print("LongCat VC: Basic Generation (50 steps, 480p)")
+    print("=" * 60)
+    
+    # Check if video exists
+    if not os.path.exists(VIDEO_PATH):
+        raise FileNotFoundError(
+            f"Video not found at {VIDEO_PATH}. "
+            "Please provide a valid video path."
+        )
+    
+    generator = VideoGenerator.from_pretrained(
+        "FastVideo/LongCat-Video-VC-Diffusers",
+        num_gpus=1,
+        use_fsdp_inference=False, # set to True if GPU is out of memory
+        dit_cpu_offload=False,
+        vae_cpu_offload=True,
+        text_encoder_cpu_offload=True,
+        pin_cpu_memory=False,
+        enable_bsa=False,
+    )
+    
+    output_path = "outputs_video/longcat_vc_basic"
+    
+    generator.generate_video(
+        prompt=PROMPT,
+        negative_prompt=NEGATIVE_PROMPT,
+        video_path=VIDEO_PATH,
+        num_cond_frames=NUM_COND_FRAMES,
+        output_path=output_path,
+        save_video=True,
+        height=480,
+        width=832,
+        num_frames=93,
+        num_inference_steps=50,
+        fps=15,
+        guidance_scale=4.0,
+        seed=SEED,
+    )
+    
+    print(f"\nBasic generation complete! Video saved to: {output_path}")
+    generator.shutdown()
+
+
+def distill_refine_generation():
+    """
+    Run LongCat VC with distill+refine pipeline (16 steps + refinement to 720p).
+    
+    This uses the distilled LoRA for fast 480p generation (16 steps),
+    then refines to 720p using the refinement LoRA with BSA enabled.
+    """
+    print("\n" + "=" * 60)
+    print("LongCat VC: Distill + Refine Pipeline")
+    print("=" * 60)
+    
+    # Check if video exists
+    if not os.path.exists(VIDEO_PATH):
+        raise FileNotFoundError(
+            f"Video not found at {VIDEO_PATH}. "
+            "Please provide a valid video path."
+        )
+    
+    # Stage 1: Distilled generation (16 steps at 480p)
+    print("\n[Stage 1] Distilled generation (16 steps, 480p)")
+    print("-" * 40)
+    
+    generator = VideoGenerator.from_pretrained(
+        "FastVideo/LongCat-Video-VC-Diffusers",
+        num_gpus=1,
+        use_fsdp_inference=True,
+        dit_cpu_offload=False,
+        vae_cpu_offload=True,
+        text_encoder_cpu_offload=True,
+        pin_cpu_memory=False,
+        enable_bsa=False,
+        lora_path="FastVideo/LongCat-Video-T2V-Distilled-LoRA",
+        lora_nickname="distilled",
+    )
+    
+    distill_output_path = "outputs_video/longcat_vc_distill"
+    
+    generator.generate_video(
+        prompt=PROMPT,
+        negative_prompt=NEGATIVE_PROMPT,
+        video_path=VIDEO_PATH,
+        num_cond_frames=NUM_COND_FRAMES,
+        output_path=distill_output_path,
+        save_video=True,
+        height=480,
+        width=832,
+        num_frames=93,
+        num_inference_steps=16,
+        fps=15,
+        guidance_scale=1.0,
+        seed=SEED,
+    )
+    
+    print(f"Distilled generation complete! Video saved to: {distill_output_path}")
+    generator.shutdown()
+    
+    # Stage 2: Refinement (480p -> 720p)
+    print("\n[Stage 2] Refinement (480p -> 720p with BSA)")
+    print("-" * 40)
+    
+    # Find the actual saved video file from stage 1
+    video_files = glob.glob(os.path.join(distill_output_path, "*.mp4"))
+    if not video_files:
+        raise FileNotFoundError(f"No video file found in {distill_output_path}")
+    # Use the most recently created video file
+    distill_video_path = max(video_files, key=os.path.getmtime)
+    print(f"Using stage 1 video: {distill_video_path}")
+    
+    # Create a new generator with refinement LoRA and BSA enabled
+    # Note: Refinement uses the T2V model (not VC) since it's upscaling the generated video
+    refine_generator = VideoGenerator.from_pretrained(
+        "FastVideo/LongCat-Video-T2V-Diffusers",
+        num_gpus=1,
+        use_fsdp_inference=True,
+        dit_cpu_offload=True,
+        vae_cpu_offload=True,
+        text_encoder_cpu_offload=True,
+        pin_cpu_memory=False,
+        enable_bsa=True,
+        bsa_sparsity=0.875,
+        bsa_chunk_q=[4, 4, 8],
+        bsa_chunk_k=[4, 4, 8],
+        lora_path="FastVideo/LongCat-Video-T2V-Refinement-LoRA",
+        lora_nickname="refinement",
+    )
+    
+    refine_output_path = "outputs_video/longcat_vc_refine_720p"
+    
+    refine_generator.generate_video(
+        prompt=PROMPT,
+        negative_prompt=NEGATIVE_PROMPT,
+        output_path=refine_output_path,
+        save_video=True,
+        refine_from=distill_video_path,
+        t_thresh=0.5,
+        spatial_refine_only=False,
+        num_cond_frames=0,  # For refinement, no conditioning frames
+        height=720,
+        width=1280,
+        num_inference_steps=50,
+        fps=30,
+        guidance_scale=1.0,
+        seed=SEED,
+    )
+    
+    print(f"Refinement complete! Video saved to: {refine_output_path}")
+    refine_generator.shutdown()
+
+
+def main():
+    """Run both basic and distill+refine generation pipelines."""
+    print("\n" + "=" * 60)
+    print("LongCat Video Continuation Example")
+    print("=" * 60 + "\n")
+    
+    # Run basic generation
+    basic_generation()
+    
+    # Run distill+refine pipeline
+    distill_refine_generation()
+    
+    print("\n" + "=" * 60)
+    print("All generations complete!")
+    print("=" * 60)
+
+
+if __name__ == "__main__":
+    main()
+
+

examples/inference/basic/basic_ltx2.py

@@ -0,0 +1,34 @@
+from fastvideo import VideoGenerator
+
+PROMPT = (
+    "A warm sunny backyard. The camera starts in a tight cinematic close-up "
+    "of a woman and a man in their 30s, facing each other with serious "
+    "expressions. The woman, emotional and dramatic, says softly, \"That's "
+    "it... Dad's lost it. And we've lost Dad.\" The man exhales, slightly "
+    "annoyed: \"Stop being so dramatic, Jess.\" A beat. He glances aside, "
+    "then mutters defensively, \"He's just having fun.\" The camera slowly "
+    "pans right, revealing the grandfather in the garden wearing enormous "
+    "butterfly wings, waving his arms in the air like he's trying to take "
+    "off. He shouts, \"Wheeeew!\" as he flaps his wings with full commitment. "
+    "The woman covers her face, on the verge of tears. The tone is deadpan, "
+    "absurd, and quietly tragic."
+)
+
+
+def main() -> None:
+    generator = VideoGenerator.from_pretrained(
+        "FastVideo/LTX2-Distilled-Diffusers",
+        num_gpus=1,
+    )
+
+    output_path = "outputs_video/ltx2_basic/output_ltx2_distilled_t2v.mp4"
+    generator.generate_video(
+        prompt=PROMPT,
+        output_path=output_path,
+        save_video=True,
+    )
+    generator.shutdown()
+
+
+if __name__ == "__main__":
+    main()

examples/inference/basic/basic_matrixgame.py

@@ -43,7 +43,7 @@ def main():
         config["model_path"],
         # FastVideo will automatically handle distributed setup
         num_gpus=1,
-        use_fsdp_inference=True,
+        use_fsdp_inference=False, # set to True if GPU is out of memory
         dit_cpu_offload=True, # DiT need to be offloaded for MoE
         vae_cpu_offload=False,
         text_encoder_cpu_offload=True,

examples/inference/basic/basic_matrixgame_streaming.py

@@ -0,0 +1,98 @@
+from fastvideo.entrypoints.streaming_generator import StreamingVideoGenerator
+from fastvideo.models.dits.matrix_game.utils import get_current_action_async, expand_action_to_frames
+
+import torch
+import asyncio
+
+# 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,
+        "mode": "universal",
+        "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,
+        "mode": "gta_drive",
+        "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,
+        "mode": "templerun",
+        "image_url": "https://raw.githubusercontent.com/SkyworkAI/Matrix-Game/main/Matrix-Game-2/demo_images/temple_run/0000.png",
+    },
+}
+
+
+OUTPUT_PATH = "video_samples_matrixgame2"
+async 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 = StreamingVideoGenerator.from_pretrained(
+        config["model_path"],
+        # FastVideo will automatically handle distributed setup
+        num_gpus=1,
+        use_fsdp_inference=False, # set to True if GPU is out of memory
+        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,
+    )
+
+    max_blocks = 50
+    num_frames = 597    
+    actions = {
+        "keyboard": torch.zeros((num_frames, config["keyboard_dim"])),
+        "mouse": torch.zeros((num_frames, 2))
+    }
+    grid_sizes = torch.tensor([150, 44, 80])
+    mode = config["mode"]
+
+    generator.reset(
+        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,
+    )
+    print("Initialization complete.")
+
+    for block_id in range(max_blocks):
+        print(f"\n=== Block {block_id + 1}/{max_blocks} ===")
+        
+        action = await get_current_action_async(mode)
+        keyboard_cond, mouse_cond = expand_action_to_frames(action, 12)
+        await generator.step_async(keyboard_cond, mouse_cond)
+        
+        if (await asyncio.to_thread(input, "\nContinue? (y/n): ")).lower() == 'n':
+            break
+
+    # Save final video
+    generator.finalize()
+    generator.shutdown()
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

examples/inference/basic/basic_self_forcing_causal.py

@@ -13,7 +13,7 @@ def main():
         model_name,
         # FastVideo will automatically handle distributed setup
         num_gpus=1,
-        use_fsdp_inference=True,
+        use_fsdp_inference=False, # set to True if GPU is out of memory
         text_encoder_cpu_offload=False,
         dit_cpu_offload=False,
     )

examples/inference/basic/basic_self_forcing_causal_wan2_2_i2v.py

@@ -14,7 +14,7 @@ def main():
         "FastVideo/SFWan2.2-I2V-A14B-Preview-Diffusers",
         # FastVideo will automatically handle distributed setup
         num_gpus=1,
-        use_fsdp_inference=True,
+        use_fsdp_inference=False, # set to True if GPU is out of memory
         dit_cpu_offload=True, # DiT need to be offloaded for MoE
         dit_precision="fp32",
         vae_cpu_offload=False,

examples/inference/basic/basic_self_forcing_causal_wan2_2_t2v.py

@@ -14,7 +14,7 @@ def main():
         "rand0nmr/SFWan2.2-T2V-A14B-Diffusers",
         # FastVideo will automatically handle distributed setup
         num_gpus=1,
-        use_fsdp_inference=True,
+        use_fsdp_inference=False, # set to True if GPU is out of memory
         dit_cpu_offload=True, # DiT need to be offloaded for MoE
         vae_cpu_offload=False,
         text_encoder_cpu_offload=True,

examples/inference/basic/basic_turbodiffusion.py

@@ -0,0 +1,55 @@
+import os
+
+# Set SLA attention backend BEFORE fastvideo imports
+os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "SLA_ATTN"
+
+from fastvideo import VideoGenerator
+
+OUTPUT_PATH = "video_samples_turbodiffusion"
+
+
+def main() -> None:
+    # TurboDiffusion: 1-4 step video generation using RCM scheduler + SLA attention
+    # FastVideo will automatically use TurboDiffusionPipeline when specified
+    generator = VideoGenerator.from_pretrained(
+        "loayrashid/TurboWan2.1-T2V-1.3B-Diffusers",
+        # FastVideo will automatically handle distributed setup
+        num_gpus=1,
+        use_fsdp_inference=False, # set to True if GPU is out of memory
+
+        # set to false if using RTX 4090 
+        # pin_cpu_memory=False,
+    )
+
+    # Generate videos with the same simple API, regardless of GPU count
+    # TurboDiffusion defaults: guidance_scale=1.0 and num_inference_steps=4 (from config)
+    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,
+        seed=42,
+    )
+
+    # Generate another video with a different prompt, without reloading the model!
+    prompt2 = (
+        "A majestic lion strides across the golden savanna, its powerful frame "
+        "glistening under the warm afternoon sun. The tall grass ripples gently in "
+        "the breeze, enhancing the lion's commanding presence. The tone is vibrant, "
+        "embodying the raw energy of the wild. Low angle, steady tracking shot, "
+        "cinematic."
+    )
+    video2 = generator.generate_video(
+        prompt2,
+        output_path=OUTPUT_PATH,
+        save_video=True,
+        seed=42,
+    )
+
+
+if __name__ == "__main__":
+    main()

examples/inference/basic/basic_turbodiffusion_14b.py

@@ -0,0 +1,49 @@
+import os
+
+# Set SLA attention backend BEFORE fastvideo imports
+os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "SLA_ATTN"
+
+from fastvideo import VideoGenerator
+
+OUTPUT_PATH = "video_samples_turbodiffusion_14B"
+
+
+def main() -> None:
+    # TurboDiffusion 14B: 1-4 step video generation using RCM scheduler + SLA attention
+    # FastVideo will automatically use TurboDiffusionPipeline when specified
+    generator = VideoGenerator.from_pretrained(
+        "loayrashid/TurboWan2.1-T2V-14B-Diffusers",
+        # 14B model needs more GPUs
+        num_gpus=2,
+    )
+
+    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,
+        seed=42,
+    )
+
+    # Generate another video with a different prompt, without reloading the model!
+    prompt2 = (
+        "A majestic lion strides across the golden savanna, its powerful frame "
+        "glistening under the warm afternoon sun. The tall grass ripples gently in "
+        "the breeze, enhancing the lion's commanding presence. The tone is vibrant, "
+        "embodying the raw energy of the wild. Low angle, steady tracking shot, "
+        "cinematic."
+    )
+    video2 = generator.generate_video(
+        prompt2,
+        output_path=OUTPUT_PATH,
+        save_video=True,
+        seed=42,
+    )
+
+
+if __name__ == "__main__":
+    main()

examples/inference/basic/basic_turbodiffusion_i2v.py

@@ -0,0 +1,37 @@
+import os
+
+# Set SLA attention backend BEFORE fastvideo imports
+os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "SLA_ATTN"
+
+from fastvideo import VideoGenerator
+
+# Use local model path
+MODEL_PATH = "loayrashid/TurboWan2.2-I2V-A14B-Diffusers"
+OUTPUT_PATH = "video_samples_turbodiffusion_i2v"
+
+
+def main() -> None:
+    # TurboDiffusion I2V: 1-4 step image-to-video generation
+    generator = VideoGenerator.from_pretrained(
+        MODEL_PATH,
+        num_gpus=2,
+    )
+
+    # Example prompt and image for I2V
+    prompt = ("Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside.")
+
+    # Use an example image path
+    image_path = "https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/wan_i2v_input.JPG"
+
+
+    video = generator.generate_video(
+        prompt,
+        image_path=image_path,
+        output_path=OUTPUT_PATH,
+        save_video=True,
+        seed=42,
+    )
+
+
+if __name__ == "__main__":
+    main()

examples/inference/basic/basic_wan2_2.py

@@ -12,7 +12,7 @@ def main():
         "Wan-AI/Wan2.2-T2V-A14B-Diffusers",
         # FastVideo will automatically handle distributed setup
         num_gpus=1,
-        use_fsdp_inference=True,
+        use_fsdp_inference=False, # set to True if GPU is out of memory 
         dit_cpu_offload=True, # DiT need to be offloaded for MoE
         vae_cpu_offload=False,
         text_encoder_cpu_offload=True,

examples/inference/basic/basic_wan2_2_Fun.py

@@ -14,7 +14,7 @@ def main():
         # "alibaba-pai/Wan2.2-Fun-A14B-Control",
         # FastVideo will automatically handle distributed setup
         num_gpus=1,
-        use_fsdp_inference=True,
+        use_fsdp_inference=False, # set to True if GPU is out of memory
         dit_cpu_offload=True, # DiT need to be offloaded for MoE
         vae_cpu_offload=False,
         text_encoder_cpu_offload=True,

examples/inference/basic/basic_wan2_2_i2v.py

@@ -12,7 +12,7 @@ def main():
         "Wan-AI/Wan2.2-I2V-A14B-Diffusers",
         # FastVideo will automatically handle distributed setup
         num_gpus=1,
-        use_fsdp_inference=True,
+        use_fsdp_inference=False, # set to True if GPU is out of memory
         dit_cpu_offload=True, # DiT need to be offloaded for MoE
         vae_cpu_offload=False,
         text_encoder_cpu_offload=True,

examples/inference/basic/basic_wan2_2_ti2v.py

@@ -11,7 +11,7 @@ def main():
         model_name,
         # FastVideo will automatically handle distributed setup
         num_gpus=1,
-        use_fsdp_inference=True,
+        use_fsdp_inference=False, # set to True if GPU is out of memory
         dit_cpu_offload=True,
         vae_cpu_offload=False,
         text_encoder_cpu_offload=True,

examples/inference/gradio/local/gradio_local_demo_matrixgame.py

@@ -0,0 +1,684 @@
+import argparse
+import asyncio
+import os
+import time
+
+import gradio as gr
+import torch
+import uvicorn
+from fastapi import FastAPI, Request, HTTPException
+from fastapi.responses import HTMLResponse, FileResponse
+
+from fastvideo.entrypoints.streaming_generator import StreamingVideoGenerator
+from fastvideo.models.dits.matrix_game.utils import expand_action_to_frames
+
+
+VARIANT_CONFIG = {
+    "Matrix-Game-2.0-Base": {
+        "model_path": "FastVideo/Matrix-Game-2.0-Base-Diffusers",
+        "keyboard_dim": 4,
+        "mode": "universal",
+        "image_url": "https://raw.githubusercontent.com/SkyworkAI/Matrix-Game/main/Matrix-Game-2/demo_images/universal/0000.png",
+    },
+    "Matrix-Game-2.0-GTA": {
+        "model_path": "FastVideo/Matrix-Game-2.0-GTA-Diffusers",
+        "keyboard_dim": 2,
+        "mode": "gta_drive",
+        "image_url": "https://raw.githubusercontent.com/SkyworkAI/Matrix-Game/main/Matrix-Game-2/demo_images/gta_drive/0000.png",
+    },
+    "Matrix-Game-2.0-TempleRun": {
+        "model_path": "FastVideo/Matrix-Game-2.0-TempleRun-Diffusers",
+        "keyboard_dim": 7,
+        "mode": "templerun",
+        "image_url": "https://raw.githubusercontent.com/SkyworkAI/Matrix-Game/main/Matrix-Game-2/demo_images/temple_run/0000.png",
+    },
+}
+
+MODEL_PATH_MAPPING = {
+    name: config["model_path"] for name, config in VARIANT_CONFIG.items()
+}
+
+
+CAM_VALUE = 0.1
+KEYBOARD_MAP_UNIVERSAL = {
+    "W (Forward)": [1, 0, 0, 0],
+    "S (Back)": [0, 1, 0, 0],
+    "A (Left)": [0, 0, 1, 0],
+    "D (Right)": [0, 0, 0, 1],
+    "Q (Stop)": [0, 0, 0, 0],
+}
+KEYBOARD_MAP_GTA = {
+    "W (Forward)": [1, 0],
+    "S (Back)": [0, 1],
+    "Q (Stop)": [0, 0],
+}
+KEYBOARD_MAP_TEMPLERUN = {
+    "Q (Run)": [1, 0, 0, 0, 0, 0, 0],
+    "W (Jump)": [0, 1, 0, 0, 0, 0, 0],
+    "S (Slide)": [0, 0, 1, 0, 0, 0, 0],
+    "Z (Turn Left)": [0, 0, 0, 1, 0, 0, 0],
+    "C (Turn Right)": [0, 0, 0, 0, 1, 0, 0],
+    "A (Left)": [0, 0, 0, 0, 0, 1, 0],
+    "D (Right)": [0, 0, 0, 0, 0, 0, 1],
+}
+
+
+CAMERA_MAP_UNIVERSAL = {
+    "U (Center)": [0, 0],
+    "I (Up)": [CAM_VALUE, 0],
+    "K (Down)": [-CAM_VALUE, 0],
+    "J (Left)": [0, -CAM_VALUE],
+    "L (Right)": [0, CAM_VALUE],
+}
+CAMERA_MAP_GTA = {
+    "Q (Straight)": [0, 0],
+    "A (Steer Left)": [0, -CAM_VALUE],
+    "D (Steer Right)": [0, CAM_VALUE],
+}
+
+def setup_model_environment(model_path: str) -> None:
+    # if "fullattn" in model_path.lower():
+    #     os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "FLASH_ATTN"
+    # else:
+    #     os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "VIDEO_SPARSE_ATTN"
+    os.environ["FASTVIDEO_ATTENTION_BACKEND"] = "FLASH_ATTN"
+    os.environ["FASTVIDEO_STAGE_LOGGING"] = "1"
+
+def create_timing_display(inference_time, total_time, stage_execution_times, num_frames):
+    dit_denoising_time = f"{stage_execution_times[5]:.2f}s" if len(stage_execution_times) > 5 else "N/A"
+    
+    timing_html = f"""
+    <div style="margin: 10px 0;">
+        <h3 style="text-align: center; margin-bottom: 10px;">⏱️ Timing Breakdown</h3>
+        <div style="display: grid; grid-template-columns: repeat(5, 1fr); gap: 10px; margin-bottom: 10px;">
+            <div class="timing-card timing-card-highlight">
+                <div style="font-size: 20px;">🚀</div>
+                <div style="font-weight: bold; margin: 3px 0; font-size: 14px;">DiT Denoising</div>
+                <div style="font-size: 16px; color: #ffa200; font-weight: bold;">{dit_denoising_time}</div>
+            </div>
+            <div class="timing-card">
+                <div style="font-size: 20px;">🧠</div>
+                <div style="font-weight: bold; margin: 3px 0; font-size: 14px;">E2E (w. vae/text encoder)</div>
+                <div style="font-size: 16px; color: #2563eb;">{inference_time:.2f}s</div>
+            </div>
+            <div class="timing-card">
+                <div style="font-size: 20px;">🎬</div>
+                <div style="font-weight: bold; margin: 3px 0; font-size: 14px;">Video Encoding</div>
+                <div style="font-size: 16px; color: #dc2626;">N/A</div>
+            </div>
+            <div class="timing-card">
+                <div style="font-size: 20px;">🌐</div>
+                <div style="font-weight: bold; margin: 3px 0; font-size: 14px;">Network Transfer</div>
+                <div style="font-size: 16px; color: #059669;">N/A</div>
+            </div>
+            <div class="timing-card">
+                <div style="font-size: 20px;">📊</div>
+                <div style="font-weight: bold; margin: 3px 0; font-size: 14px;">Total Processing</div>
+                <div style="font-size: 18px; color: #0277bd;">{total_time:.2f}s</div>
+            </div>
+        </div>"""
+    
+    if inference_time > 0:
+        fps = num_frames / inference_time
+        timing_html += f"""
+        <div class="performance-card" style="margin-top: 15px;">
+            <span style="font-weight: bold;">Generation Speed: </span>
+            <span style="font-size: 18px; color: #6366f1; font-weight: bold;">{fps:.1f} frames/second</span>
+        </div>"""
+    
+    return timing_html + "</div>"
+
+def get_action_tensors(mode: str, keyboard_key: str, mouse_key: str | None):
+    if mode == "universal":
+        keyboard = torch.tensor(KEYBOARD_MAP_UNIVERSAL.get(keyboard_key, [0, 0, 0, 0])).cuda()
+        mouse = torch.tensor(CAMERA_MAP_UNIVERSAL.get(mouse_key, [0, 0])).cuda()
+    elif mode == "gta_drive":
+        keyboard = torch.tensor(KEYBOARD_MAP_GTA.get(keyboard_key, [0, 0])).cuda()
+        mouse = torch.tensor(CAMERA_MAP_GTA.get(mouse_key, [0, 0])).cuda()
+    elif mode == "templerun":
+        keyboard = torch.tensor(KEYBOARD_MAP_TEMPLERUN.get(keyboard_key, [1, 0, 0, 0, 0, 0, 0])).cuda()
+        mouse = None
+    else:
+        raise ValueError(f"Unknown mode: {mode}")
+    
+    return {"keyboard": keyboard, "mouse": mouse}
+
+def create_gradio_interface(generators: dict[str, StreamingVideoGenerator], loaded_model_name: str):
+    initial_config = VARIANT_CONFIG.get(loaded_model_name, VARIANT_CONFIG["Matrix-Game-2.0-Base"])
+    initial_mode = initial_config["mode"]
+    
+    if initial_mode == "universal":
+        initial_kb_choices = list(KEYBOARD_MAP_UNIVERSAL.keys())
+        initial_mouse_choices = list(CAMERA_MAP_UNIVERSAL.keys())
+        initial_mouse_visible = True
+    elif initial_mode == "gta_drive":
+        initial_kb_choices = list(KEYBOARD_MAP_GTA.keys())
+        initial_mouse_choices = list(CAMERA_MAP_GTA.keys())
+        initial_mouse_visible = True
+    else:  # templerun
+        initial_kb_choices = list(KEYBOARD_MAP_TEMPLERUN.keys())
+        initial_mouse_choices = []
+        initial_mouse_visible = False
+    
+    theme = gr.themes.Base().set(
+        button_primary_background_fill="#2563eb",
+        button_primary_background_fill_hover="#1d4ed8",
+        button_primary_text_color="white",
+        slider_color="#2563eb",
+        checkbox_background_color_selected="#2563eb",
+    )
+    
+    with gr.Blocks(title="FastVideo - Matrix Game 2.0", theme=theme) as demo:
+        game_state = gr.State({
+            "initialized": False,
+            "current_model": None,
+            "block_idx": 0,
+            "max_blocks": 50,
+        })
+        
+        # Header
+        gr.Image("assets/full.svg", show_label=False, container=False, height=80)
+        
+        gr.HTML("""
+        <div style="text-align: center; margin-bottom: 10px;">
+            <p style="font-size: 18px;"> Make Video Generation Go Blurrrrrrr </p>
+            <p style="font-size: 18px;"> <a href="https://github.com/hao-ai-lab/FastVideo/tree/main" target="_blank">Code</a> | <a href="https://hao-ai-lab.github.io/blogs/fastvideo_post_training/" target="_blank">Blog</a> | <a href="https://hao-ai-lab.github.io/FastVideo/" target="_blank">Docs</a>  </p>
+        </div>
+        """)
+        
+        with gr.Accordion("🎥 What Is FastVideo?", open=False):
+            gr.HTML("""
+            <div style="padding: 20px; line-height: 1.6;">
+                <p style="font-size: 16px; margin-bottom: 15px;">
+                    FastVideo is an inference and post-training framework for diffusion models. It features an end-to-end unified pipeline for accelerating diffusion models, starting from data preprocessing to model training, finetuning, distillation, and inference. FastVideo is designed to be modular and extensible, allowing users to easily add new optimizations and techniques. Whether it is training-free optimizations or post-training optimizations, FastVideo has you covered.
+                </p>
+            </div>
+            """)
+        
+        # Model Selection
+        with gr.Row():
+            model_selection = gr.Dropdown(
+                choices=[loaded_model_name],
+                value=loaded_model_name,
+                label="Select Model",
+                interactive=False
+            )
+        
+
+        # Main Layout
+        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;'>Game Controls</div>")
+                    
+                    with gr.Group():
+                        gr.HTML("<div style='font-size: 14px; margin-bottom: 5px; font-weight: bold;'>🎮 Keyboard Control</div>")
+                        keyboard_action = gr.Radio(
+                            choices=initial_kb_choices,
+                            value=initial_kb_choices[0] if initial_kb_choices else None,
+                            label="Movement",
+                            show_label=False,
+                            interactive=True
+                        )
+                    
+                    with gr.Group(visible=initial_mouse_visible) as mouse_group:
+                        gr.HTML("<div style='font-size: 14px; margin-bottom: 5px; font-weight: bold;'>🖱️ Mouse/Camera Control</div>")
+                        mouse_action = gr.Radio(
+                            choices=initial_mouse_choices if initial_mouse_visible else [],
+                            value=initial_mouse_choices[0] if initial_mouse_choices else None,
+                            label="Camera",
+                            show_label=False,
+                            interactive=True
+                        )
+                    
+                    with gr.Row():
+                        action_btn = gr.Button("Start", variant="primary")
+                        stop_btn = gr.Button("Stop", variant="stop")
+                    
+                    gr.HTML("<div style='margin-top: 15px;'></div>")
+                    
+                    seed = gr.Slider(
+                        label="Seed",
+                        minimum=0,
+                        maximum=1000000,
+                        step=1,
+                        value=1024,
+                    )
+                    randomize_seed = gr.Checkbox(label="Randomize seed", value=False)
+                    seed_output = gr.Number(label="Used Seed")
+                    
+                    block_counter = gr.Textbox(label="Progress", value="Block: 0 / 50", interactive=False, lines=1)
+
+
+            # Right Column: Video Output
+            with gr.Column(scale=1, elem_classes="video-column"):
+                video_output = gr.Video(
+                    label="Generated Video",
+                    show_label=True,
+                    height=466,
+                    width=600,
+                    container=True,
+                    elem_classes="video-component",
+                    autoplay=True
+                )
+
+        # Styles
+        gr.HTML("""
+        <style>
+        .center-button {
+            display: flex !important;
+            justify-content: center !important;
+            height: 100% !important;
+            padding-top: 1.4em !important;
+        }
+        
+        .gradio-container {
+            max-width: 1200px !important;
+            margin: 0 auto !important;
+        }
+        
+        .main {
+            max-width: 1200px !important;
+            margin: 0 auto !important;
+        }
+        
+        .gr-form, .gr-box, .gr-group {
+            max-width: 1200px !important;
+        }
+        
+        .gr-video {
+            max-width: 500px !important;
+            margin: 0 auto !important;
+        }
+        
+        .main-content-row {
+            display: flex !important;
+            align-items: flex-start !important;
+            min-height: 500px !important;
+            gap: 20px !important;
+        }
+        
+        .advanced-options-column,
+        .video-column {
+            display: flex !important;
+            flex-direction: column !important;
+            flex: 1 !important;
+            min-height: 400px !important;
+            align-items: stretch !important;
+        }
+        
+        .video-column > * {
+            margin-top: 0 !important;
+        }
+        
+        .video-column .gr-video,
+        .video-component {
+            margin-top: 0 !important;
+            padding-top: 0 !important;
+        }
+        
+        .video-column .gr-video .gr-form {
+            margin-top: 0 !important;
+        }
+        
+        .advanced-options-column .gr-group,
+        .video-column .gr-video {
+            margin-top: 0 !important;
+            vertical-align: top !important;
+        }
+        
+        .advanced-options-column > *:last-child,
+        .video-column > *:last-child {
+            flex-grow: 0 !important;
+        }
+        
+        @media (max-width: 1400px) {
+            .main-content-row {
+                min-height: 600px !important;
+            }
+            
+            .advanced-options-column,
+            .video-column {
+                min-height: 600px !important;
+            }
+        }
+        
+        @media (max-width: 1200px) {
+            .main-content-row {
+                flex-direction: column !important;
+                align-items: stretch !important;
+            }
+            
+            .advanced-options-column,
+            .video-column {
+                min-height: auto !important;
+                width: 100% !important;
+            }
+        }
+        
+        .timing-card {
+            background: var(--background-fill-secondary) !important;
+            border: 1px solid var(--border-color-primary) !important;
+            color: var(--body-text-color) !important;
+            padding: 10px;
+            border-radius: 8px;
+            text-align: center;
+            min-height: 80px;
+            display: flex;
+            flex-direction: column;
+            justify-content: center;
+        }
+        
+        .timing-card-highlight {
+            background: var(--background-fill-primary) !important;
+            border: 2px solid var(--color-accent) !important;
+        }
+        
+        .performance-card {
+            background: var(--background-fill-secondary) !important;
+            border: 1px solid var(--border-color-primary) !important;
+            color: var(--body-text-color) !important;
+            padding: 10px;
+            border-radius: 6px;
+            text-align: center;
+        }
+
+        .gr-number input[readonly] {
+            background-color: var(--background-fill-secondary) !important;
+            border: 1px solid var(--border-color-primary) !important;
+            color: var(--body-text-color-subdued) !important;
+            cursor: default !important;
+            text-align: center !important;
+            font-weight: 500 !important;
+        }
+        </style>
+        """)
+        
+        # UI update based on model selection
+        def on_model_change(model_name):
+            config = VARIANT_CONFIG.get(model_name, VARIANT_CONFIG["Matrix-Game-2.0-Base"])
+            mode = config["mode"]
+            
+            if mode == "universal":
+                kb_choices = list(KEYBOARD_MAP_UNIVERSAL.keys())
+                mouse_choices = list(CAMERA_MAP_UNIVERSAL.keys())
+                mouse_visible = True
+            elif mode == "gta_drive":
+                kb_choices = list(KEYBOARD_MAP_GTA.keys())
+                mouse_choices = list(CAMERA_MAP_GTA.keys())
+                mouse_visible = True
+            else:  # templerun
+                kb_choices = list(KEYBOARD_MAP_TEMPLERUN.keys())
+                mouse_choices = []
+                mouse_visible = False
+            
+            return (
+                gr.update(choices=kb_choices, value=kb_choices[0] if kb_choices else None),
+                gr.update(choices=mouse_choices, value=mouse_choices[0] if mouse_choices else None, visible=mouse_visible),
+                gr.update(visible=mouse_visible),
+            )
+        
+        model_selection.change(
+            fn=on_model_change,
+            inputs=model_selection,
+            outputs=[keyboard_action, mouse_action, mouse_group]
+        )
+
+        def start_game(model_name, seed_val, randomize, state):
+            if randomize:
+                seed_val = torch.randint(0, 1000000, (1,)).item()
+            
+            config = VARIANT_CONFIG.get(model_name)
+            if not config:
+                return state, seed_val, "Block: 0 / 50", None, "", gr.update(), gr.update()
+            
+            generator = generators.get(config["model_path"])
+            if not generator:
+                return state, seed_val, "Block: 0 / 50", None, "", gr.update(), gr.update()
+            
+            # If already initialized, clean up first
+            if state.get("initialized"):
+                try:
+                    # Clear accumulated frames without saving
+                    generator.accumulated_frames = []
+                    generator.executor.execute_streaming_clear()
+                except Exception as e:
+                    print(f"Warning: cleanup error: {e}")
+            
+            # Streaming parameters
+            num_latent_frames_per_block = 3
+            max_blocks = 50
+            total_latent_frames = num_latent_frames_per_block * max_blocks
+            num_frames = (total_latent_frames - 1) * 4 + 1
+            
+            actions = {
+                "keyboard": torch.zeros((num_frames, config["keyboard_dim"])),
+                "mouse": torch.zeros((num_frames, 2))
+            }
+            grid_sizes = torch.tensor([150, 44, 80])
+            
+            output_dir = os.path.abspath("outputs/matrixgame")
+            os.makedirs(output_dir, exist_ok=True)
+            video_path = os.path.join(output_dir, f"video_{int(time.time())}.mp4")
+            
+            generator.reset(
+                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=video_path,
+            )
+            
+            new_state = {
+                "initialized": True,
+                "current_model": model_name,
+                "block_idx": 0,
+                "max_blocks": max_blocks,
+                "video_path": video_path,
+                "frames_per_block": num_latent_frames_per_block * 4,
+                "mode": config["mode"],
+                "seed": seed_val,
+            }
+            
+            return new_state, seed_val, "Block: 0 / 50", None, gr.update(value="Step"), gr.update(interactive=True)
+        
+        async def step_game(keyboard_key, mouse_key, model_name, state):
+            if not state.get("initialized"):
+                return state, state.get("seed", 0), "Block: 0 / 50", None, gr.update(), gr.update()
+            
+            # total_start_time = time.time()
+            config = VARIANT_CONFIG.get(model_name)
+            generator = generators.get(config["model_path"])
+            mode = state["mode"]
+            frames_per_block = state["frames_per_block"]
+
+            # Parse inputs to tensors
+            action = get_action_tensors(mode, keyboard_key, mouse_key)
+            keyboard_cond, mouse_cond = expand_action_to_frames(action, frames_per_block)
+            
+            # run step async
+            # inference_start_time = time.time()
+            frames, block_future = await generator.step_async(keyboard_cond, mouse_cond)
+            # inference_time = time.time() - inference_start_time
+            
+            # wait for block file to be written
+            block_path = await asyncio.to_thread(block_future.result) if block_future else None
+            state["block_idx"] = generator.block_idx
+            block_str = f"Block: {state['block_idx']} / {state['max_blocks']}"
+            
+            # total_time = time.time() - total_start_time
+            
+            # Timing breakdown
+            # timing_html = create_timing_display(inference_time, total_time, [], frames_per_block)
+
+            return state, state.get("seed", 0), block_str, block_path, gr.update(), gr.update()
+
+        def stop_game(model_name, state):
+            if not state.get("initialized"):
+                return {"initialized": False}, 0, "Block: 0 / 50", None, gr.update(value="Start"), gr.update(interactive=False)
+            
+            config = VARIANT_CONFIG.get(model_name)
+            generator = generators.get(config["model_path"])
+            
+            final_path = state.get("video_path")
+            generator.finalize(final_path)
+            
+            return {"initialized": False}, state.get("seed", 0), "Block: 0 / 50", final_path, gr.update(value="Start"), gr.update(interactive=False)
+        
+        async def handle_action(keyboard_key, mouse_key, model_name, seed_val, randomize, state):
+            if not state.get("initialized"):
+                return start_game(model_name, seed_val, randomize, state)
+            else:
+                return await step_game(keyboard_key, mouse_key, model_name, state)
+        
+        action_btn.click(
+            fn=handle_action,
+            inputs=[keyboard_action, mouse_action, model_selection, seed, randomize_seed, game_state],
+            outputs=[game_state, seed_output, block_counter, video_output, action_btn, stop_btn]
+        )
+        
+        stop_btn.click(
+            fn=stop_game,
+            inputs=[model_selection, game_state],
+            outputs=[game_state, seed_output, block_counter, video_output, action_btn, stop_btn]
+        )
+        
+        gr.HTML("""
+        <div style="text-align: center; margin-top: 10px; margin-bottom: 15px;">
+            <p style="font-size: 16px; margin: 0;">Note that this demo is meant to showcase Matrix Game's quality and that under a large number of requests, generation speed may be affected.</p>
+        </div>
+        """)
+    
+    return demo
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Matrix Game Gradio Demo")
+    parser.add_argument("--model", type=str, default="Matrix-Game-2.0-Base",
+                        choices=list(VARIANT_CONFIG.keys()),
+                        help="Model variant to load")
+    parser.add_argument("--host", type=str, default="0.0.0.0")
+    parser.add_argument("--port", type=int, default=7860)
+    args = parser.parse_args()
+    
+    # Load the selected model
+    config = VARIANT_CONFIG[args.model]
+    model_path = config["model_path"]
+    
+    print(f"Loading model: {model_path}")
+    setup_model_environment(model_path)
+    generator = StreamingVideoGenerator.from_pretrained(
+        model_path,
+        num_gpus=1,
+        use_fsdp_inference=True,
+        dit_cpu_offload=True,
+        vae_cpu_offload=False,
+        text_encoder_cpu_offload=True,
+        pin_cpu_memory=True,
+    )
+    
+    generators = {model_path: generator}
+    
+    demo = create_gradio_interface(generators, args.model)
+    
+    print(f"Starting Gradio at http://{args.host}:{args.port}")
+    
+    # FastAPI Wrapper
+    app = FastAPI()
+    
+    @app.get("/logo.png")
+    def get_logo():
+        return FileResponse(
+            "assets/full.svg",
+            media_type="image/svg+xml",
+            headers={
+                "Cache-Control": "public, max-age=3600",
+                "Access-Control-Allow-Origin": "*"
+            }
+        )
+
+    @app.get("/favicon.ico")
+    def get_favicon():
+        favicon_path = "assets/icon-simple.svg"
+        
+        if os.path.exists(favicon_path):
+            return FileResponse(
+                favicon_path, 
+                media_type="image/svg+xml",
+                headers={
+                    "Cache-Control": "public, max-age=3600",
+                    "Access-Control-Allow-Origin": "*"
+                }
+            )
+        else:
+            raise HTTPException(status_code=404, detail="Favicon not found")
+    
+    @app.get("/", response_class=HTMLResponse)
+    def index(request: Request):
+        base_url = str(request.base_url).rstrip('/')
+        return f"""
+        <!DOCTYPE html>
+        <html lang="en">
+        <head>
+            <meta charset="UTF-8" />
+            <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+            
+            <title>FastVideo - Matrix Game 2.0</title>
+            <meta name="title" content="MatrixGame2.0">
+            <meta name="description" content="Make video generation go blurrrrrrr">
+            <meta name="keywords" content="FastVideo, video generation, AI, machine learning, Matrix Game 2.0">
+            
+            <meta property="og:type" content="website">
+            <meta property="og:url" content="{base_url}/">
+            <meta property="og:title" content="FastVideo - Matrix Game 2.0">
+            <meta property="og:description" content="Make video generation go blurrrrrrr">
+            <meta property="og:image" content="{base_url}/logo.png">
+            <meta property="og:image:width" content="1200">
+            <meta property="og:image:height" content="630">
+            <meta property="og:site_name" content="MatrixGame2.0">
+            
+            <meta property="twitter:card" content="summary_large_image">
+            <meta property="twitter:url" content="{base_url}/">
+            <meta property="twitter:title" content="MatrixGame2.0">
+            <meta property="twitter:description" content="Make video generation go blurrrrrrr">
+            <meta property="twitter:image" content="{base_url}/logo.png">
+            <link rel="icon" type="image/png" sizes="32x32" href="/favicon.ico">
+            <link rel="icon" type="image/png" sizes="16x16" href="/favicon.ico">
+            <link rel="apple-touch-icon" href="/favicon.ico">
+            <style>
+                body, html {{
+                    margin: 0;
+                    padding: 0;
+                    height: 100%;
+                    overflow: hidden;
+                }}
+                iframe {{
+                    width: 100%;
+                    height: 100vh;
+                    border: none;
+                }}
+            </style>
+        </head>
+        <body>
+            <iframe src="/gradio" width="100%" height="100%" style="border: none;"></iframe>
+        </body>
+        </html>
+        """
+
+    app = gr.mount_gradio_app(
+        app, 
+        demo, 
+        path="/gradio",
+        allowed_paths=[os.path.abspath("outputs"), os.path.abspath("fastvideo-logos")]
+    )
+    
+    uvicorn.run(app, host=args.host, port=args.port)
+
+
+if __name__ == "__main__":
+    main()

examples/inference/optimizations/text_encoder_quant_example.py

@@ -0,0 +1,46 @@
+from fastvideo import VideoGenerator
+import argparse
+
+OUTPUT_PATH = "video_samples_wan2_2_5B_ti2v"
+
+
+def main(text_encoder_path: str):
+    # FastVideo will automatically use the optimal default arguments for the
+    # model.
+    # If a local path is provided, FastVideo will make a best effort
+    # attempt to identify the optimal arguments.
+    model_name = "Wan-AI/Wan2.2-TI2V-5B-Diffusers"
+    generator = VideoGenerator.from_pretrained(
+        model_name,
+        # FastVideo will automatically handle distributed setup
+        num_gpus=1,
+        use_fsdp_inference=True,
+        dit_cpu_offload=True,
+        vae_cpu_offload=False,
+        text_encoder_cpu_offload=False,
+        # AbsMaxFP8 is the quantization method used by ComfyUI; 
+        # check fastvideo/layers/quantization/* for more quantization methods
+        override_text_encoder_quant="AbsMaxFP8",
+        # for Wan 2.2, this is the path to "umt5_xxl_fp8_e4m3fn_scaled.safetensors"
+        override_text_encoder_safetensors=text_encoder_path,
+        pin_cpu_memory=True,  # set to false if low CPU RAM or hit obscure "CUDA error: Invalid argument"
+    )
+
+    # I2V is triggered just by passing in an image_path argument
+    prompt = "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside."
+    image_path = "https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/wan_i2v_input.JPG"
+    video = generator.generate_video(
+        prompt, output_path=OUTPUT_PATH, save_video=True, image_path=image_path
+    )
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--text_encoder_path",
+        type=str,
+        required=True,
+        help="Path to the quantized text encoder safetensors file.",
+    )
+    args = parser.parse_args()
+    main(args.text_encoder_path)

examples/training/consistency_finetune/causal_ode_init/finetune_ode_init.sh

@@ -1,36 +1,38 @@
 #!/bin/bash
 
 export WANDB_BASE_URL="https://api.wandb.ai"
-export WANDB_MODE=online
+export WANDB_MODE=offline
 export TOKENIZERS_PARALLELISM=false
 
-MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
-DATA_DIR="data/crush-smol_processed_t2v_1_3b_ode_init/"
-VALIDATION_DATASET_FILE="$(dirname "$0")/validation.json"
+MODEL_PATH="hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v"
+DATA_DIR="data/ode-preprocessing-hy15-test/"
+VALIDATION_DATASET_FILE="data/validation_64.json"
 NUM_GPUS=1
 # IP=[MASTER NODE IP]
 
 # Training arguments
 training_args=(
   --tracker_project_name "wan_ode_init"
-  --output_dir "wan_ode_init_crush_smol"
-  --override_transformer_cls_name "CausalWanTransformer3DModel"
-  --wandb_run_name "wan_ode_init_crush_smol"
-  --max_train_steps 6000
+  --output_dir "ode_init_hy15_test"
+  --wandb_run_name "vidprom_bz128_1e-5"
+  # --resume_from_checkpoint "ode_init_diffusers/"
+  # --warp_denoising_step
+  # --log_visualization
+  --max_train_steps 6001
   --train_batch_size 1
   --train_sp_batch_size 1
   --gradient_accumulation_steps 1
-  --num_latent_t 21
+  --num_latent_t 19
   --num_height 480
   --num_width 832
-  --num_frames 77
-  --warp_denoising_step
+  --num_frames 73
+  --dmd_denoising_steps "1000,750,500,250"
   --enable_gradient_checkpointing_type "full"
 )
 
 # Parallel arguments
 parallel_args=(
-  --num_gpus $NUM_GPUS
+  --num_gpus 1
   --sp_size 1
   --tp_size 1
   --hsdp_replicate_dim 1
@@ -51,18 +53,17 @@ dataset_args=(
 
 # Validation arguments
 validation_args=(
-  --log_validation
+  # --log_validation
   --validation_dataset_file "$VALIDATION_DATASET_FILE"
-  --validation_steps 50
+  --validation_steps 200
   --validation_sampling_steps "50"
   --validation_guidance_scale "6.0"
 )
 
 # Optimizer arguments
 optimizer_args=(
-  --learning_rate 6e-6
+  --learning_rate 1e-5
   --mixed_precision "bf16"
-  --weight_only_checkpointing_steps 1000
   --training_state_checkpointing_steps 1000
   --weight_decay 1e-4
   --max_grad_norm 1.0
@@ -78,6 +79,7 @@ miscellaneous_args=(
   --dit_precision "fp32"
   --num_euler_timesteps 50
   --ema_start_step 0
+  # --enable_gradient_checkpointing_type "full"
 )
 
 # If you do not have 32 GPUs and to fit in memory, you can: 1. increase sp_size. 2. reduce num_latent_t

examples/training/consistency_finetune/causal_ode_init/hy15_finetune_ode_init.slurm

@@ -0,0 +1,132 @@
+#!/bin/bash
+#SBATCH --job-name=hy15_ode_3333
+#SBATCH --partition=main
+#SBATCH --nodes=8
+#SBATCH --ntasks=8
+#SBATCH --ntasks-per-node=1
+#SBATCH --gres=gpu:8
+#SBATCH --cpus-per-task=128
+#SBATCH --mem=1440G
+#SBATCH --output=hy15_ode_3333_output/hy15_ode_3333_%j.out
+#SBATCH --error=hy15_ode_3333_output/hy15_ode_3333_%j.err
+#SBATCH --exclusive
+
+# Basic Info
+export NCCL_P2P_DISABLE=1
+export TORCH_NCCL_ENABLE_MONITORING=0
+export NCCL_DEBUG_SUBSYS=INIT,NET
+# different cache dir for different processes
+export TRITON_CACHE_DIR=/tmp/triton_cache_${SLURM_PROCID}
+export MASTER_PORT=29500
+export NODE_RANK=$SLURM_PROCID
+nodes=( $(scontrol show hostnames $SLURM_JOB_NODELIST) )
+export MASTER_ADDR=${nodes[0]}
+export TOKENIZERS_PARALLELISM=false
+# export WANDB_API_KEY="8d9f4b39abd68eb4e29f6fc010b7ee71a2207cde"
+export WANDB_API_KEY="50632ebd88ffd970521cec9ab4a1a2d7e85bfc45"
+# export WANDB_API_KEY='your_wandb_api_key_here'
+export WANDB_BASE_URL="https://api.wandb.ai"
+export WANDB_MODE=online
+export FASTVIDEO_ATTENTION_BACKEND=FLASH_ATTN
+
+source ~/conda/miniconda/bin/activate
+conda activate wei-fv-distill
+export HOME="/mnt/weka/home/hao.zhang/wei"
+
+# Configs
+NUM_GPUS=8
+NUM_TOTAL_GPUS=64
+
+MODEL_PATH="weizhou03/SFHunyuanVideo-1.5-Diffusers-480p_t2v"
+DATA_DIR="/mnt/weka/home/hao.zhang/wl/sharefs/wl/release/fv-ode-preprocessing-16k-hy15-121"
+VALIDATION_DATASET_FILE="/mnt/weka/home/hao.zhang/wl/FastVideo/examples/distill/Wan2.2-TI2V-5B-Diffusers/Data-free/validation_64.json"
+# IP=[MASTER NODE IP]
+
+# Training arguments
+training_args=(
+  --tracker_project_name "hy15_ode_init"
+  --output_dir "/mnt/weka/home/hao.zhang/wei/hy15_ode_init_1333_new"
+  --wandb_run_name "hy15_ode_init_1333_new"
+  --warp_denoising_step
+  --dmd_denoising_steps '1000,760,520,280,0'
+  --log_visualization
+  --visualization_steps 100
+  --max_train_steps 4000
+  --train_batch_size 1
+  --train_sp_batch_size 1
+  --gradient_accumulation_steps 2
+  --num_latent_t 31
+  --num_height 480
+  --num_width 848
+  --num_frames 121
+  --enable_gradient_checkpointing_type "full"
+)
+
+# Parallel arguments
+parallel_args=(
+  --num_gpus $NUM_TOTAL_GPUS # 64
+  --sp_size 1
+  --tp_size 1
+  --hsdp_replicate_dim 1 # 64
+  --hsdp_shard_dim $NUM_TOTAL_GPUS
+)
+
+# Model arguments
+model_args=(
+  --model_path $MODEL_PATH
+  --pretrained_model_name_or_path $MODEL_PATH
+  --text-encoder-cpu-offload
+)
+
+# Dataset arguments
+dataset_args=(
+  --data_path "$DATA_DIR" 
+  --dataloader_num_workers 4
+)
+
+# Optimizer arguments
+optimizer_args=(
+  --learning_rate 1e-5
+  --mixed_precision "bf16"
+  --training_state_checkpointing_steps 400
+  --weight_decay 0.01
+  --max_grad_norm 1.0
+)
+
+validation_args=(
+  # --log_validation
+  --validation_dataset_file "$VALIDATION_DATASET_FILE"
+  --validation_steps 10
+  --validation_sampling_steps "4"
+  --validation_guidance_scale "1.0" # not used for dmd inference
+  # --vae_cpu_offload True
+)
+
+# Miscellaneous arguments
+miscellaneous_args=(
+  --inference_mode False
+  --checkpoints_total_limit 6
+  --training_cfg_rate 0.1
+  --multi_phased_distill_schedule "4000-1"
+  --not_apply_cfg_solver
+  --dit_precision "fp32"
+  --num_euler_timesteps 50
+  --ema_start_step 0
+  # --enable_gradient_checkpointing_type "full"
+)
+
+# If you do not have 32 GPUs and to fit in memory, you can: 1. increase sp_size. 2. reduce num_latent_t
+srun torchrun \
+--nnodes $SLURM_JOB_NUM_NODES \
+--nproc_per_node $NUM_GPUS \
+--node_rank $SLURM_PROCID \
+--rdzv_backend=c10d \
+--rdzv_endpoint="$MASTER_ADDR:$MASTER_PORT" \
+    fastvideo/training/ode_causal_pipeline.py \
+    "${parallel_args[@]}" \
+    "${model_args[@]}" \
+    "${dataset_args[@]}" \
+    "${training_args[@]}" \
+    "${optimizer_args[@]}" \
+    "${validation_args[@]}" \
+    "${miscellaneous_args[@]}"

fastvideo-kernel/CMakeLists.txt

@@ -10,6 +10,8 @@ if(GPU_BACKEND STREQUAL "ROCM")
     enable_language(HIP)
 else()
     enable_language(CUDA)
+    # Ensure CUDA toolkit targets (CUDA::cudart, CUDA::cuda_driver, etc.) are available.
+    find_package(CUDAToolkit REQUIRED)
 endif()
 
 # Import common utils if needed, but we keep it simple for now
@@ -153,6 +155,30 @@ if(BUILD_CXX_KERNELS)
         $<$<COMPILE_LANGUAGE:CUDA>:${CUDA_FLAGS}>
     )
 
+    # Link against Torch libraries to avoid undefined symbols at import time
+    # (e.g., torch::autograd vtables) when loading the extension module.
+    target_link_libraries(fastvideo_kernel_ops PRIVATE ${TORCH_LIBRARIES})
+
+    # Also link against libtorch_python to satisfy Python-binding symbols
+    # (e.g., torch::PyWarningHandler) required by torch/extension.h.
+    execute_process(
+        COMMAND "${Python_EXECUTABLE}" -c "import torch; from pathlib import Path; p=Path(torch.__file__).parent/'lib'; m=sorted(p.glob('libtorch_python*')); print(str(m[0]) if m else '')"
+        OUTPUT_VARIABLE TORCH_PYTHON_LIBRARY_PATH
+        OUTPUT_STRIP_TRAILING_WHITESPACE
+        ERROR_QUIET
+    )
+    if(TORCH_PYTHON_LIBRARY_PATH)
+        message(STATUS "TORCH_PYTHON_LIBRARY_PATH: ${TORCH_PYTHON_LIBRARY_PATH}")
+        target_link_libraries(fastvideo_kernel_ops PRIVATE "${TORCH_PYTHON_LIBRARY_PATH}")
+    else()
+        message(WARNING "Could not locate libtorch_python; fastvideo_kernel_ops may fail to import.")
+    endif()
+
+    # Link CUDA runtime + driver explicitly (fixes missing symbols like cuGetErrorString at import time)
+    if(NOT GPU_BACKEND STREQUAL "ROCM")
+        target_link_libraries(fastvideo_kernel_ops PRIVATE CUDA::cudart CUDA::cuda_driver)
+    endif()
+
     # We install it to fastvideo_kernel/_C so we can load it to register the ops
     install(TARGETS fastvideo_kernel_ops LIBRARY DESTINATION fastvideo_kernel/_C)
 endif()

fastvideo-kernel/README.md

@@ -34,12 +34,23 @@ from fastvideo_kernel import sliding_tile_attention, video_sparse_attn, moba_att
 out = sliding_tile_attention(q, k, v, window_sizes, text_len)
 
 # Example: Video Sparse Attention (with Triton fallback)
-out = video_sparse_attn(q, k, v, block_sizes, topk=5)
+out = video_sparse_attn(q, k, v, block_sizes, block_sizes, topk=5)
 
 # Example: VMoBA
 out = moba_attn_varlen(q, k, v, cu_seqlens_q, cu_seqlens_k, ...)
 ```
 
+## Benchmark
+
+### VSA (block-sparse) TFLOPs
+
+After building/installing `fastvideo-kernel`, run:
+
+```bash
+cd fastvideo-kernel
+python benchmarks/bench_vsa.py --batch_size 1 --num_heads 16 --head_dim 128 --q_seq_lens 49152 --topk 64
+```
+
 ### TurboDiffusion Kernels
 
 This package also includes kernels from [TurboDiffusion](https://github.com/thu-ml/TurboDiffusion), including INT8 GEMM, Quantization, RMSNorm and LayerNorm.

fastvideo-kernel/benchmarks/bench_vsa.py

@@ -0,0 +1,166 @@
+#!/usr/bin/env python3
+"""
+Benchmark VSA *wrapper* performance (forward + backward) and report TFLOPs.
+
+This script benchmarks the autograd-enabled wrapper:
+  - fastvideo_kernel.block_sparse_attn.block_sparse_attn
+
+So measured time includes wrapper overhead (map->index conversion, dispatch) plus kernel time.
+"""
+
+from __future__ import annotations
+
+import argparse
+import os
+import random
+from typing import Tuple, Callable
+
+import numpy as np
+import torch
+
+try:
+    from triton.testing import do_bench
+except Exception as e:  # pragma: no cover
+    raise ImportError("This benchmark requires triton (for triton.testing.do_bench).") from e
+
+
+BLOCK_M = 64
+BLOCK_N = 64
+
+
+def set_seed(seed: int = 42) -> None:
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+
+
+def parse_arguments() -> argparse.Namespace:
+    p = argparse.ArgumentParser(description="Benchmark FastVideo VSA block-sparse attention")
+    p.add_argument("--batch_size", type=int, default=1)
+    p.add_argument("--num_heads", type=int, default=12)
+    p.add_argument("--head_dim", type=int, default=128, choices=[64, 128])
+    p.add_argument("--topk", type=int, default=None, help="KV blocks per Q block (default: ~90%% sparsity)")
+    p.add_argument("--q_seq_lens", type=int, nargs="+", default=[49152], help="Q sequence lengths (must be /64)")
+    p.add_argument("--kv_seq_lens", type=int, nargs="+", default=None, help="KV sequence lengths (defaults to q_seq_len)")
+    p.add_argument("--warmup", type=int, default=5)
+    p.add_argument("--rep", type=int, default=20)
+    p.add_argument("--seed", type=int, default=42)
+    p.add_argument("--dtype", type=str, default="bf16", choices=["bf16", "fp16"])
+    p.add_argument("--force_triton", action="store_true", help="Force wrapper to use Triton path (if supported by shapes).")
+    return p.parse_args()
+
+
+def create_qkv(batch: int, heads: int, q_len: int, kv_len: int, d: int, dtype: torch.dtype) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    q = torch.randn(batch, heads, q_len, d, dtype=dtype, device="cuda")
+    k = torch.randn(batch, heads, kv_len, d, dtype=dtype, device="cuda")
+    v = torch.randn(batch, heads, kv_len, d, dtype=dtype, device="cuda")
+    return q, k, v
+
+
+def make_block_map(bs: int, h: int, num_q_blocks: int, num_kv_blocks: int, topk: int) -> torch.Tensor:
+    # block_map: [bs, h, num_q_blocks, num_kv_blocks] bool
+    scores = torch.rand(bs, h, num_q_blocks, num_kv_blocks, device="cuda")
+    topk = min(max(1, topk), num_kv_blocks)
+    idx = torch.topk(scores, topk, dim=-1).indices
+    block_map = torch.zeros(bs, h, num_q_blocks, num_kv_blocks, dtype=torch.bool, device="cuda")
+    block_map.scatter_(-1, idx, True)
+    return block_map
+
+
+def flops_sparse_attention(bs: int, h: int, d: int, q_len: int, topk_blocks: int, block_n: int) -> float:
+    # Approx: QK^T + PV, each is ~2*bs*h*q_len*(topk_blocks*block_n)*d
+    return 4.0 * bs * h * d * q_len * (topk_blocks * block_n)
+
+
+def bench_ms(fn: Callable[[], object], warmup: int, rep: int) -> float:
+    return do_bench(fn, warmup=warmup, rep=rep, quantiles=None)
+
+
+def main() -> None:
+    args = parse_arguments()
+    set_seed(args.seed)
+
+    dtype = torch.bfloat16 if args.dtype == "bf16" else torch.float16
+
+    if args.force_triton:
+        os.environ["FASTVIDEO_KERNEL_VSA_FORCE_TRITON"] = "1"
+
+    from fastvideo_kernel.block_sparse_attn import block_sparse_attn
+
+    bs, h, d = args.batch_size, args.num_heads, args.head_dim
+    kv_seq_lens = args.kv_seq_lens
+    if kv_seq_lens is None:
+        kv_seq_lens = args.q_seq_lens
+    if len(kv_seq_lens) != len(args.q_seq_lens):
+        raise ValueError("kv_seq_lens must have the same number of entries as q_seq_lens (or be omitted).")
+
+    print("VSA Block-Sparse Attention Benchmark (WRAPPER)")
+    print(f"device: {torch.cuda.get_device_name(0)}")
+    print(f"batch={bs}, heads={h}, head_dim={d}, dtype={args.dtype}")
+    print(f"BLOCK_M={BLOCK_M}, BLOCK_N={BLOCK_N}")
+    print("NOTE: timings include wrapper overhead (map->index + dispatch).")
+    if args.force_triton:
+        print("dispatch: forced Triton (FASTVIDEO_KERNEL_VSA_FORCE_TRITON=1)")
+    else:
+        print("dispatch: SM90 if available, else Triton")
+
+    for q_len, kv_len in zip(args.q_seq_lens, kv_seq_lens):
+        if q_len % BLOCK_M != 0 or kv_len % BLOCK_N != 0:
+            print(f"[skip] q_len={q_len}, kv_len={kv_len} must be divisible by 64")
+            continue
+
+        num_q_blocks = q_len // BLOCK_M
+        num_kv_blocks = kv_len // BLOCK_N
+        topk = args.topk if args.topk is not None else max(1, num_kv_blocks // 10)
+        topk = min(topk, num_kv_blocks)
+
+        print("\n" + "=" * 80)
+        print(f"q_len={q_len}, kv_len={kv_len}, num_q_blocks={num_q_blocks}, num_kv_blocks={num_kv_blocks}, topk={topk}")
+
+        q, k, v = create_qkv(bs, h, q_len, kv_len, d, dtype)
+        block_map = make_block_map(bs, h, num_q_blocks, num_kv_blocks, topk)
+
+        # Variable block sizes: default full blocks (64 tokens per KV block)
+        variable_block_sizes = torch.full((num_kv_blocks,), BLOCK_N, dtype=torch.int32, device="cuda")
+
+        def _fwd():
+            return block_sparse_attn(q, k, v, block_map, variable_block_sizes)
+
+        fwd_ms = bench_ms(_fwd, warmup=args.warmup, rep=args.rep)
+
+        # Backward benchmark (wrapper autograd). We build the graph once, then repeatedly run backward
+        # on the retained graph so bwd timing excludes the forward compute.
+        q_ = q.detach().requires_grad_(True)
+        k_ = k.detach().requires_grad_(True)
+        v_ = v.detach().requires_grad_(True)
+        o_, _aux_ = block_sparse_attn(q_, k_, v_, block_map, variable_block_sizes)
+        og = torch.randn_like(o_)
+        loss = (o_ * og).sum()
+
+        for _ in range(max(1, args.warmup // 2)):
+            torch.autograd.grad(loss, (q_, k_, v_), retain_graph=True)
+        torch.cuda.synchronize()
+
+        bwd_ms = bench_ms(
+            lambda: torch.autograd.grad(loss, (q_, k_, v_), retain_graph=True),
+            warmup=0,
+            rep=max(5, args.rep // 2),
+        )
+
+        flops = flops_sparse_attention(bs, h, d, q_len, topk, BLOCK_N)
+        fwd_tflops = flops / fwd_ms * 1e-12 * 1e3
+        # Rough backward multiplier (attention backward typically ~2-3x forward)
+        bwd_tflops = (2.5 * flops) / bwd_ms * 1e-12 * 1e3
+
+        print(f"fwd(wrapper): {fwd_ms:.3f} ms  | {fwd_tflops:.2f} TFLOPs (approx)")
+        print(f"bwd(wrapper): {bwd_ms:.3f} ms  | {bwd_tflops:.2f} TFLOPs (approx)")
+
+
+if __name__ == "__main__":
+    if not torch.cuda.is_available():
+        raise RuntimeError("CUDA is required for this benchmark.")
+    main()
+
+

fastvideo-kernel/csrc/attention/block_sparse_h100.cu

@@ -639,7 +639,8 @@ void bwd_attend_ker(const __grid_constant__ bwd_globals<D> g) {
 
     // store kq and vq
 
-    // ensuring all writes are finished
+    // ! the following two line seems unnecessary.
+    // tma::store_async_wait(); // ensure qg is finished
     __syncthreads();
 
     warpgroup::store(kg_smem[0], kg_reg);
@@ -660,145 +661,6 @@ void bwd_attend_ker(const __grid_constant__ bwd_globals<D> g) {
     tma::store_async_wait(); 
 }
 
-
-template<int D>
-void block_sparse_attention_forward_impl(
-    bf16* d_q, bf16* d_k, bf16* d_v, float* d_l, bf16* d_o,
-    int batch, int qo_heads, int kv_heads, int seq_len, int hr,
-    int max_kv_blocks_per_q, 
-    int32_t* q2k_block_sparse_index_ptr,
-    int32_t* q2k_block_sparse_num_ptr,
-    int32_t* block_size_ptr,
-    cudaStream_t stream
-) {
-    using K = fwd_attend_ker_tile_dims<D>;
-    using q_tile    =         st_bf<K::qo_height, K::tile_width>;
-    using k_tile    =         st_bf<K::kv_height, K::tile_width>;
-    using v_tile    =         st_bf<K::kv_height, K::tile_width>;
-    using l_col_vec = col_vec<st_fl<K::qo_height, K::tile_width>>;
-    using o_tile    =         st_bf<K::qo_height, K::tile_width>;
-
-    using q_global = gl<bf16,  -1, -1, -1, -1, q_tile>;
-    using k_global = gl<bf16,  -1, -1, -1, -1, k_tile>;
-    using v_global = gl<bf16,  -1, -1, -1, -1, v_tile>;
-    using l_global = gl<float, -1, -1, -1, -1, l_col_vec>;
-    using o_global = gl<bf16,  -1, -1, -1, -1, o_tile>;
-
-    using globals      = fwd_globals<D>;
-
-    q_global qg_arg{d_q, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), static_cast<uint32_t>(D)};
-    k_global kg_arg{d_k, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(seq_len), static_cast<uint32_t>(D)};
-    v_global vg_arg{d_v, static_cast<unsigned int>(batch), static_cast<unsigned int>(kv_heads), static_cast<unsigned int>(seq_len), static_cast<uint32_t>(D)};
-    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), static_cast<uint32_t>(D)};
-
-    globals g{
-        qg_arg, kg_arg, vg_arg, lg_arg, og_arg, 
-        static_cast<int>(seq_len), static_cast<int>(hr), static_cast<int>(max_kv_blocks_per_q), 
-        q2k_block_sparse_index_ptr, q2k_block_sparse_num_ptr, block_size_ptr
-    };
-
-    // Shared memory size for the kernel
-    // 54000 bytes is calibrated for H100 shared memory constraints for these tile sizes
-    constexpr int mem_size = 54000;
-
-    dim3 grid(seq_len/(64), qo_heads, batch);
-
-    cudaFuncSetAttribute(
-        fwd_attend_ker<D>,
-        cudaFuncAttributeMaxDynamicSharedMemorySize,
-        mem_size
-    );
-
-    fwd_attend_ker<D><<<grid, (128), mem_size, stream>>>(g);
-}
-
-template<int D>
-void block_sparse_attention_backward_impl(
-    bf16* d_q, bf16* d_k, bf16* d_v, bf16* d_o, bf16* d_og, float* d_l, float* d_d, float* d_qg, float* d_kg, float* d_vg,
-    int batch, int qo_heads, int kv_heads, int seq_len, int hr, int max_q_blocks_per_kv,
-    int32_t* k2q_block_sparse_index_ptr,
-    int32_t* k2q_block_sparse_num_ptr,
-    int32_t* block_size_ptr,
-    cudaStream_t stream
-) {
-    using G = bwd_attend_ker_tile_dims<D>;
-    using og_tile = st_bf<4*16, D>;
-    using o_tile  = st_bf<4*16, D>;
-    using d_tile  = col_vec<st_fl<4*16, D>>;
-
-    using og_global = gl<bf16,  -1, -1, -1, -1, og_tile>;
-    using o_global  = gl<bf16,  -1, -1, -1, -1, o_tile>;
-    using d_global  = gl<float, -1, -1, -1, -1, d_tile>;
-
-    using prep_globals = bwd_prep_globals<D>;
-
-    constexpr int mem_size_prep = kittens::MAX_SHARED_MEMORY;
-    int threads_prep = PREP_NUM_WARPS * kittens::WARP_THREADS;
-    dim3 grid_bwd_prep(seq_len/(PREP_NUM_WARPS*kittens::TILE_ROW_DIM<bf16>*4), qo_heads, batch);
-
-    og_global prep_og_arg{d_og, static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), static_cast<uint32_t>(D)};
-    o_global  prep_o_arg {d_o,  static_cast<unsigned int>(batch), static_cast<unsigned int>(qo_heads), static_cast<unsigned int>(seq_len), static_cast<uint32_t>(D)};
-    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)};
-
-    prep_globals bwd_g{prep_og_arg, prep_o_arg, prep_d_arg};
-
-    cudaFuncSetAttribute(
-        bwd_attend_prep_ker<D>,
-        cudaFuncAttributeMaxDynamicSharedMemorySize,
-        mem_size_prep
-    );
-    bwd_attend_prep_ker<D><<<grid_bwd_prep, threads_prep, mem_size_prep, stream>>>(bwd_g);
-
-    using bwd_q_tile    =         st_bf<G::tile_h_qo, G::tile_width>;
-    using bwd_k_tile    =         st_bf<G::tile_h,    G::tile_width>;
-    using bwd_v_tile    =         st_bf<G::tile_h,    G::tile_width>;
-    using bwd_og_tile   =         st_bf<G::tile_h_qo, G::tile_width>;
-    using bwd_qg_tile   =         st_fl<G::tile_h_qo, G::tile_width>;
-    using bwd_kg_tile   =         st_fl<G::tile_h,    G::tile_width>;
-    using bwd_vg_tile   =         st_fl<G::tile_h,    G::tile_width>;
-    using bwd_l_tile    = row_vec<st_fl<G::tile_h_qo, G::tile_h>>;
-    using bwd_d_tile    = row_vec<st_fl<G::tile_h_qo, G::tile_h>>;
-
-    using bwd_q_global  = gl<bf16,  -1, -1, -1, -1, bwd_q_tile>;
-    using bwd_k_global  = gl<bf16,  -1, -1, -1, -1, bwd_k_tile>;
-    using bwd_v_global  = gl<bf16,  -1, -1, -1, -1, bwd_v_tile>;
-    using bwd_og_global = gl<bf16,  -1, -1, -1, -1, bwd_og_tile>;
-    using bwd_qg_global = gl<float, -1, -1, -1, -1, bwd_qg_tile>;
-    using bwd_kg_global = gl<float, -1, -1, -1, -1, bwd_kg_tile>;
-    using bwd_vg_global = gl<float, -1, -1, -1, -1, bwd_vg_tile>;
-    using bwd_l_global  = gl<float, -1, -1, -1, -1, bwd_l_tile>;
-    using bwd_d_global  = gl<float, -1, -1, -1, -1, bwd_d_tile>;
-
-    using bwd_global_args = bwd_globals<D>;
-
-    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), static_cast<uint32_t>(D)};
-    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), static_cast<uint32_t>(D)};
-    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), static_cast<uint32_t>(D)};
-    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), static_cast<uint32_t>(D)};
-    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), static_cast<uint32_t>(D)};
-    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), static_cast<uint32_t>(D)};
-    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), static_cast<uint32_t>(D)};
-    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_global_args bwd_global{bwd_q_arg, bwd_k_arg, bwd_v_arg, bwd_og_arg, bwd_qg_arg, bwd_kg_arg, bwd_vg_arg, bwd_l_arg, bwd_d_arg, 
-                        static_cast<int>(seq_len), static_cast<int>(hr), static_cast<int>(max_q_blocks_per_kv),
-                        k2q_block_sparse_index_ptr, k2q_block_sparse_num_ptr, block_size_ptr};
-
-    dim3 grid_bwd_main(seq_len/64, qo_heads, batch);
-    int threads_main = 128;
-    // Calibrated shared memory sizes for different head dimensions
-    int bwd_mem_size = (D == 64) ? 72000 : 113000;
-
-    cudaFuncSetAttribute(
-        bwd_attend_ker<D>,
-        cudaFuncAttributeMaxDynamicSharedMemorySize,
-        bwd_mem_size
-    );
-    bwd_attend_ker<D><<<grid_bwd_main, threads_main, bwd_mem_size, stream>>>(bwd_global);
-}
-
 #include "pyutils/torch_helpers.cuh"
 #include <ATen/cuda/CUDAContext.h>
 #include <iostream>
@@ -810,23 +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");
@@ -834,11 +705,9 @@ 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");
     TORCH_CHECK(k.size(3) == head_dim, "K head dimension - idx 3 - must match for all non-vector inputs");
@@ -864,12 +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));
         
@@ -880,32 +749,110 @@ block_sparse_attention_forward(
     float* l_ptr = reinterpret_cast<float*>(l_vec.data_ptr<float>());
     float* d_l   = reinterpret_cast<float*>(l_ptr);
 
+    //cudadevicesynchronize();
     const c10::cuda::OptionalCUDAGuard device_guard(q.device());    
     const cudaStream_t stream = at::cuda::getCurrentCUDAStream().stream(); 
 
-    // Temporated implementation to avoid code duplication between head_dim=64 and 128
     if (head_dim == 64) {
-        block_sparse_attention_forward_impl<64>(
-            d_q, d_k, d_v, d_l, d_o,
-            batch, qo_heads, kv_heads, seq_len, hr,
-            max_kv_blocks_per_q,
-            reinterpret_cast<int32_t*>(q2k_block_sparse_index.data_ptr()),
+        using q_tile    =         st_bf<fwd_attend_ker_tile_dims<64>::qo_height, fwd_attend_ker_tile_dims<64>::tile_width>;
+        using k_tile    =         st_bf<fwd_attend_ker_tile_dims<64>::kv_height, fwd_attend_ker_tile_dims<64>::tile_width>;
+        using v_tile    =         st_bf<fwd_attend_ker_tile_dims<64>::kv_height, fwd_attend_ker_tile_dims<64>::tile_width>;
+        using l_col_vec = col_vec<st_fl<fwd_attend_ker_tile_dims<64>::qo_height, fwd_attend_ker_tile_dims<64>::tile_width>>;
+        using o_tile    =         st_bf<fwd_attend_ker_tile_dims<64>::qo_height, fwd_attend_ker_tile_dims<64>::tile_width>;
+
+        using q_global = gl<bf16,  -1, -1, -1, -1, q_tile>;
+        using k_global = gl<bf16,  -1, -1, -1, -1, k_tile>;
+        using v_global = gl<bf16,  -1, -1, -1, -1, v_tile>;
+        using l_global = gl<float, -1, -1, -1, -1, l_col_vec>;
+        using o_global = gl<bf16,  -1, -1, -1, -1, o_tile>;
+
+        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>(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, 
+            kg_arg, 
+            vg_arg, 
+            lg_arg, 
+            og_arg, 
+            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()),
-            stream
+            reinterpret_cast<int32_t*>(kv_block_size.data_ptr())
+        };
+
+        constexpr int mem_size = 54000;
+
+        dim3 grid(q_seq_len/(BLOCK_M), qo_heads, batch);
+
+        cudaFuncSetAttribute(
+            fwd_attend_ker<64>,
+            cudaFuncAttributeMaxDynamicSharedMemorySize,
+            mem_size
         );
-    } else if (head_dim == 128) {
-        block_sparse_attention_forward_impl<128>(
-            d_q, d_k, d_v, d_l, d_o,
-            batch, qo_heads, kv_heads, seq_len, hr,
-            max_kv_blocks_per_q,
-            reinterpret_cast<int32_t*>(q2k_block_sparse_index.data_ptr()),
+
+        fwd_attend_ker<64><<<grid, (128), mem_size, stream>>>(g);
+
+        CHECK_CUDA_ERROR(cudaGetLastError());
+        // cudaStreamSynchronize(stream);
+    }
+
+    if (head_dim == 128) {
+        using q_tile    =         st_bf<fwd_attend_ker_tile_dims<128>::qo_height, fwd_attend_ker_tile_dims<128>::tile_width>;
+        using k_tile    =         st_bf<fwd_attend_ker_tile_dims<128>::kv_height, fwd_attend_ker_tile_dims<128>::tile_width>;
+        using v_tile    =         st_bf<fwd_attend_ker_tile_dims<128>::kv_height, fwd_attend_ker_tile_dims<128>::tile_width>;
+        using l_col_vec = col_vec<st_fl<fwd_attend_ker_tile_dims<128>::qo_height, fwd_attend_ker_tile_dims<128>::tile_width>>;
+        using o_tile    =         st_bf<fwd_attend_ker_tile_dims<128>::qo_height, fwd_attend_ker_tile_dims<128>::tile_width>;
+
+        using q_global = gl<bf16,  -1, -1, -1, -1, q_tile>;
+        using k_global = gl<bf16,  -1, -1, -1, -1, k_tile>;
+        using v_global = gl<bf16,  -1, -1, -1, -1, v_tile>;
+        using l_global = gl<float, -1, -1, -1, -1, l_col_vec>;
+        using o_global = gl<bf16,  -1, -1, -1, -1, o_tile>;
+
+        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>(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, 
+            kg_arg, 
+            vg_arg, 
+            lg_arg, 
+            og_arg, 
+            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()),
-            stream
+            reinterpret_cast<int32_t*>(kv_block_size.data_ptr())
+        };
+
+        constexpr int mem_size = 54000;
+
+        dim3 grid(q_seq_len/(BLOCK_M), qo_heads, batch);
+
+        cudaFuncSetAttribute(
+            fwd_attend_ker<128>,
+            cudaFuncAttributeMaxDynamicSharedMemorySize,
+            mem_size
         );
-    } else {
-        TORCH_CHECK(false, "Unsupported head_dim: ", head_dim, ". Only 64 and 128 are supported.");
+
+        fwd_attend_ker<128><<<grid, (128), mem_size, stream>>>(g);
+
+        CHECK_CUDA_ERROR(cudaGetLastError());
+        // cudaStreamSynchronize(stream);
     }
 
     return {o, l_vec};
@@ -921,7 +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);
@@ -930,11 +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");
@@ -945,23 +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);
@@ -988,20 +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>();
@@ -1030,7 +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>;
@@ -1043,9 +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};
 
@@ -1082,15 +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, 
@@ -1101,14 +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();
@@ -1147,9 +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};
 
@@ -1186,15 +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, 
@@ -1205,14 +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();
@@ -1233,4 +1187,4 @@ block_sparse_attention_backward(torch::Tensor q,
 
     return {qg, kg, vg};
     //cudadevicesynchronize();
-}
+}

fastvideo-kernel/csrc/turbodiffusion/norm/layernorm.cu

@@ -4,6 +4,7 @@
 #include <torch/all.h>
 #include <torch/python.h>
 #include <cutlass/cutlass.h>
+#include <cutlass/numeric_types.h>
 #include "common/common.hpp"
 #include "norm/layernorm.hpp"
 
@@ -14,10 +15,6 @@ auto layer_norm(
   std::optional<at::Tensor const> const B,
   std::optional<at::Tensor> Output
 ) {
-  using ElementIn = float;
-  using ElementOut = float;
-  using ElementWeight = float;
-
   int64_t const m = Input.size(0);
   int64_t const n = Input.size(1);
   torch::Device const input_device = Input.device();
@@ -26,31 +23,70 @@ auto layer_norm(
     Output.emplace(
       torch::empty(
         {m, n},
-        torch::TensorOptions().device(input_device).dtype(torch::kFloat32)
+        torch::TensorOptions().device(input_device).dtype(Input.scalar_type())
       )
     );
   }
 
+  TORCH_CHECK(Output.value().scalar_type() == Input.scalar_type(),
+              "Output dtype must match Input dtype. Got Output=",
+              Output.value().scalar_type(), ", Input=", Input.scalar_type());
+  if (W.has_value()) {
+    TORCH_CHECK(W.value().scalar_type() == Input.scalar_type(),
+                "W dtype must match Input dtype. Got W=",
+                W.value().scalar_type(), ", Input=", Input.scalar_type());
+  }
+  if (B.has_value()) {
+    TORCH_CHECK(B.value().scalar_type() == Input.scalar_type(),
+                "B dtype must match Input dtype. Got B=",
+                B.value().scalar_type(), ", Input=", Input.scalar_type());
+  }
 
   void *Iptr = Input.data_ptr();
   void *Wptr = W.has_value() ? W.value().data_ptr() : nullptr;
   void *Bptr = B.has_value() ? B.value().data_ptr() : nullptr;
   void *Optr = Output.value().data_ptr();
 
-  BOOL_SWITCH(B.has_value(), BIAS, [&]{
-    BOOL_SWITCH(W.has_value(), AFFINE, [&]{
-    CONFIG_SWITCH(n, [&]{
-    layernorm<
-    ElementIn, ElementOut, ElementWeight,
-    AFFINE, BIAS,
-    MAX_HIDDEN_SIZE, NUM_THR_PER_CTA> (
-      Iptr, Wptr, Bptr,
-      Optr, eps, m, n,
-      at::cuda::getCurrentCUDAStream().stream()
-        );
+  auto stream = at::cuda::getCurrentCUDAStream().stream();
+  if (Input.scalar_type() == at::kHalf) {
+    using ElementIn = cutlass::half_t;
+    using ElementOut = cutlass::half_t;
+    using ElementWeight = cutlass::half_t;
+    BOOL_SWITCH(B.has_value(), BIAS, [&]{
+      BOOL_SWITCH(W.has_value(), AFFINE, [&]{
+        CONFIG_SWITCH(n, [&]{
+          layernorm<ElementIn, ElementOut, ElementWeight, AFFINE, BIAS, MAX_HIDDEN_SIZE, NUM_THR_PER_CTA>(
+              Iptr, Wptr, Bptr, Optr, eps, m, n, stream);
+        });
       });
     });
-  });
+  } else if (Input.scalar_type() == at::kBFloat16) {
+    using ElementIn = cutlass::bfloat16_t;
+    using ElementOut = cutlass::bfloat16_t;
+    using ElementWeight = cutlass::bfloat16_t;
+    BOOL_SWITCH(B.has_value(), BIAS, [&]{
+      BOOL_SWITCH(W.has_value(), AFFINE, [&]{
+        CONFIG_SWITCH(n, [&]{
+          layernorm<ElementIn, ElementOut, ElementWeight, AFFINE, BIAS, MAX_HIDDEN_SIZE, NUM_THR_PER_CTA>(
+              Iptr, Wptr, Bptr, Optr, eps, m, n, stream);
+        });
+      });
+    });
+  } else if (Input.scalar_type() == at::kFloat) {
+    using ElementIn = float;
+    using ElementOut = float;
+    using ElementWeight = float;
+    BOOL_SWITCH(B.has_value(), BIAS, [&]{
+      BOOL_SWITCH(W.has_value(), AFFINE, [&]{
+        CONFIG_SWITCH(n, [&]{
+          layernorm<ElementIn, ElementOut, ElementWeight, AFFINE, BIAS, MAX_HIDDEN_SIZE, NUM_THR_PER_CTA>(
+              Iptr, Wptr, Bptr, Optr, eps, m, n, stream);
+        });
+      });
+    });
+  } else {
+    TORCH_CHECK(false, "Unsupported dtype for layer_norm_cuda: ", Input.scalar_type());
+  }
   
     
 

fastvideo-kernel/csrc/turbodiffusion/norm/layernorm.hpp

@@ -68,9 +68,22 @@ public:
     // mean reduction
     float u = _reduce_sum(x, shared_data) / params.n;
 
+    // IMPORTANT:
+    // Loader pads out-of-range lanes with 0. That is OK for the sum, but after
+    // subtracting mean, those padded lanes become -u and would incorrectly
+    // contribute to the variance. Mask them back to 0 before variance reduction.
+    // We launch exactly NumThrPerCta threads for a 1xMaxHiddenSize tile,
+    // so each thread is responsible for a contiguous chunk in N.
+    int thr_n_offset = tidx * NumElementPerThread;
     CUTLASS_PRAGMA_UNROLL
-    for (int i = 0; i < NumElementPerThread; ++i)
-      x[i] -= u;
+    for (int i = 0; i < NumElementPerThread; ++i) {
+      int idx = thr_n_offset + i;
+      if (idx < params.n) {
+        x[i] -= u;
+      } else {
+        x[i] = 0.f;
+      }
+    }
 
     __syncthreads();
     // var reduction

fastvideo-kernel/csrc/turbodiffusion/norm/rmsnorm.cu

@@ -4,6 +4,7 @@
 #include <torch/all.h>
 #include <torch/python.h>
 #include <cutlass/cutlass.h>
+#include <cutlass/numeric_types.h>
 #include <pybind11/pybind11.h>
 
 #include "common/common.hpp"
@@ -16,10 +17,6 @@ auto rms_norm(
   std::optional<at::Tensor>& Output
 ) {
   
-  using ElementIn = float;
-  using ElementOut = float;
-  using ElementWeight = float;
-  
   int64_t const m = Input.size(0);
   int64_t const n = Input.size(1);
   torch::Device const input_device = Input.device();
@@ -28,27 +25,51 @@ auto rms_norm(
     Output.emplace(
       torch::empty(
         {m, n},
-        torch::TensorOptions().device(input_device).dtype(torch::kFloat32)
+        torch::TensorOptions().device(input_device).dtype(Input.scalar_type())
       )
     );
   }
 
+  TORCH_CHECK(Output.value().scalar_type() == Input.scalar_type(),
+              "Output dtype must match Input dtype. Got Output=",
+              Output.value().scalar_type(), ", Input=", Input.scalar_type());
+  if (Weight.has_value()) {
+    TORCH_CHECK(Weight.value().scalar_type() == Input.scalar_type(),
+                "Weight dtype must match Input dtype. Got Weight=",
+                Weight.value().scalar_type(), ", Input=", Input.scalar_type());
+  }
+
   void *Iptr = Input.data_ptr();
   void *Wptr = Weight.has_value() ? Weight.value().data_ptr() : nullptr;
   void *Optr = Output.value().data_ptr();
 
-
-  CONFIG_SWITCH(n, [&]{
-    rmsnorm<
-    ElementIn, ElementOut, ElementWeight,
-    MAX_HIDDEN_SIZE, NUM_THR_PER_CTA
-    > (
-      Iptr, Wptr,
-      Optr, 
-      eps, m, n, 
-      at::cuda::getCurrentCUDAStream().stream()
-    );
-});
+  if (Input.scalar_type() == at::kHalf) {
+    using ElementIn = cutlass::half_t;
+    using ElementOut = cutlass::half_t;
+    using ElementWeight = cutlass::half_t;
+    CONFIG_SWITCH(n, [&]{
+      rmsnorm<ElementIn, ElementOut, ElementWeight, MAX_HIDDEN_SIZE, NUM_THR_PER_CTA>(
+          Iptr, Wptr, Optr, eps, m, n, at::cuda::getCurrentCUDAStream().stream());
+    });
+  } else if (Input.scalar_type() == at::kBFloat16) {
+    using ElementIn = cutlass::bfloat16_t;
+    using ElementOut = cutlass::bfloat16_t;
+    using ElementWeight = cutlass::bfloat16_t;
+    CONFIG_SWITCH(n, [&]{
+      rmsnorm<ElementIn, ElementOut, ElementWeight, MAX_HIDDEN_SIZE, NUM_THR_PER_CTA>(
+          Iptr, Wptr, Optr, eps, m, n, at::cuda::getCurrentCUDAStream().stream());
+    });
+  } else if (Input.scalar_type() == at::kFloat) {
+    using ElementIn = float;
+    using ElementOut = float;
+    using ElementWeight = float;
+    CONFIG_SWITCH(n, [&]{
+      rmsnorm<ElementIn, ElementOut, ElementWeight, MAX_HIDDEN_SIZE, NUM_THR_PER_CTA>(
+          Iptr, Wptr, Optr, eps, m, n, at::cuda::getCurrentCUDAStream().stream());
+    });
+  } else {
+    TORCH_CHECK(false, "Unsupported dtype for rms_norm_cuda: ", Input.scalar_type());
+  }
   
 
   return Output;

fastvideo-kernel/pyproject.toml

@@ -9,7 +9,7 @@ build-backend = "scikit_build_core.build"
 
 [project]
 name = "fastvideo-kernel"
-version = "0.2.1"
+version = "0.2.5"
 description = "Unified CUDA kernels for FastVideo"
 readme = "README.md"
 requires-python = ">=3.10"

fastvideo-kernel/python/fastvideo_kernel/block_sparse_attn.py

@@ -0,0 +1,295 @@
+from __future__ import annotations
+
+import os
+from typing import Tuple
+
+import torch
+
+
+def _get_sm90_ops():
+    try:
+        from fastvideo_kernel._C import fastvideo_kernel_ops  # type: ignore
+    except Exception:
+        return None, None
+    return (
+        getattr(fastvideo_kernel_ops, "block_sparse_fwd", None),
+        getattr(fastvideo_kernel_ops, "block_sparse_bwd", None),
+    )
+
+
+def _is_sm90() -> bool:
+    if not torch.cuda.is_available():
+        return False
+    major, minor = torch.cuda.get_device_capability(0)
+    return major == 9 and minor == 0
+
+
+def _force_triton() -> bool:
+    # Force Triton even on SM90 and even if the compiled extension is available.
+    # Useful for CI / debugging / parity testing.
+    return os.environ.get("FASTVIDEO_KERNEL_VSA_FORCE_TRITON", "0") == "1"
+
+
+def _map_to_index(block_map: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+    """
+    Preferred map->index conversion used by the wrapper.
+
+    This wrapper **requires** the Triton implementation.
+    If Triton (or the Triton map_to_index module) is not available, it raises.
+    """
+    if block_map.dim() == 3:
+        block_map = block_map.unsqueeze(0)
+    if block_map.dim() != 4:
+        raise ValueError(f"block_map must be [B,H,Q,KV] (or [H,Q,KV]), got shape={tuple(block_map.shape)}")
+    if block_map.dtype != torch.bool:
+        block_map = block_map.to(torch.bool)
+
+    if not block_map.is_cuda:
+        raise RuntimeError("block_map must be a CUDA tensor (Triton map_to_index required).")
+
+    try:
+        from fastvideo_kernel.triton_kernels.index import map_to_index as triton_map_to_index  # local import
+    except Exception as e:
+        raise ImportError(
+            "Triton map_to_index is required but not available. "
+            "Ensure Triton is installed and fastvideo_kernel.triton_kernels.index is importable."
+        ) from e
+    return triton_map_to_index(block_map)
+
+
+@torch.library.custom_op(
+    "fastvideo_kernel::block_sparse_attn_triton",
+    mutates_args=(),
+    device_types="cuda",
+)
+def block_sparse_attn_triton(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    block_map: torch.Tensor,
+    variable_block_sizes: torch.Tensor,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    q = q.contiguous()
+    k = k.contiguous()
+    v = v.contiguous()
+    block_map = block_map.to(torch.bool)
+    q2k_idx, q2k_num = _map_to_index(block_map)
+
+    from fastvideo_kernel.triton_kernels.block_sparse_attn_triton import (  # local import
+        triton_block_sparse_attn_forward,
+    )
+
+    o, M = triton_block_sparse_attn_forward(q, k, v, q2k_idx, q2k_num, variable_block_sizes)
+    return o, M
+
+
+
+@torch.library.register_fake("fastvideo_kernel::block_sparse_attn_triton")
+def _block_sparse_attn_triton_fake(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    block_map: torch.Tensor,
+    variable_block_sizes: torch.Tensor,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    o = torch.empty_like(q)
+    M = torch.empty((q.shape[0], q.shape[1], q.shape[2]), device=q.device, dtype=torch.float32)
+    return o, M
+
+
+@torch.library.custom_op(
+    "fastvideo_kernel::block_sparse_attn_backward_triton",
+    mutates_args=(),
+    device_types="cuda",
+)
+def block_sparse_attn_backward_triton(
+    grad_output: torch.Tensor,
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    o: torch.Tensor,
+    M: torch.Tensor,
+    block_map: torch.Tensor,
+    variable_block_sizes: torch.Tensor,
+) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    grad_output = grad_output.contiguous()
+    block_map = block_map.to(torch.bool)
+    q2k_idx, q2k_num = _map_to_index(block_map)
+    k2q_idx, k2q_num = _map_to_index(block_map.transpose(-1, -2).contiguous())
+
+    from fastvideo_kernel.triton_kernels.block_sparse_attn_triton import (  # local import
+        triton_block_sparse_attn_backward,
+    )
+
+    dq, dk, dv = triton_block_sparse_attn_backward(
+        grad_output, q, k, v, o, M, q2k_idx, q2k_num, k2q_idx, k2q_num, variable_block_sizes
+    )
+    return dq, dk, dv
+
+
+@torch.library.register_fake("fastvideo_kernel::block_sparse_attn_backward_triton")
+def _block_sparse_attn_backward_triton_fake(
+    grad_output: torch.Tensor,
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    o: torch.Tensor,
+    M: torch.Tensor,
+    block_map: torch.Tensor,
+    variable_block_sizes: torch.Tensor,
+) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    dq = torch.empty_like(q)
+    dk = torch.empty_like(k)
+    dv = torch.empty_like(v)
+    return dq, dk, dv
+
+
+def _backward_triton(ctx, grad_o, grad_M):
+    q, k, v, o, M, block_map, variable_block_sizes = ctx.saved_tensors
+    dq, dk, dv = block_sparse_attn_backward_triton(grad_o, q, k, v, o, M, block_map, variable_block_sizes)
+    return dq, dk, dv, None, None
+
+
+def _setup_context_triton(ctx, inputs, output):
+    q, k, v, block_map, variable_block_sizes = inputs
+    o, M = output
+    ctx.save_for_backward(q, k, v, o, M, block_map, variable_block_sizes)
+
+
+block_sparse_attn_triton.register_autograd(_backward_triton, setup_context=_setup_context_triton)
+
+
+@torch.library.custom_op(
+    "fastvideo_kernel::block_sparse_attn_sm90",
+    mutates_args=(),
+    device_types="cuda",
+)
+def block_sparse_attn_sm90(
+    q_padded: torch.Tensor,
+    k_padded: torch.Tensor,
+    v_padded: torch.Tensor,
+    block_map: torch.Tensor,
+    variable_block_sizes: torch.Tensor,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    block_sparse_fwd, _ = _get_sm90_ops()
+    if block_sparse_fwd is None:
+        raise ImportError("fastvideo_kernel_ops.block_sparse_fwd is not available")
+
+    q_padded = q_padded.contiguous()
+    k_padded = k_padded.contiguous()
+    v_padded = v_padded.contiguous()
+    block_map = block_map.to(torch.bool)
+    q2k_idx, q2k_num = _map_to_index(block_map)
+
+    o_padded, lse_padded = block_sparse_fwd(
+        q_padded, k_padded, v_padded, q2k_idx, q2k_num, variable_block_sizes.int()
+    )
+    return o_padded, lse_padded
+
+
+@torch.library.register_fake("fastvideo_kernel::block_sparse_attn_sm90")
+def _block_sparse_attn_sm90_fake(
+    q_padded: torch.Tensor,
+    k_padded: torch.Tensor,
+    v_padded: torch.Tensor,
+    block_map: torch.Tensor,
+    variable_block_sizes: torch.Tensor,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    o = torch.empty_like(q_padded)
+    lse = torch.empty((q_padded.shape[0], q_padded.shape[1], q_padded.shape[2], 1), device=q_padded.device, dtype=torch.float32)
+    return o, lse
+
+
+@torch.library.custom_op(
+    "fastvideo_kernel::block_sparse_attn_backward_sm90",
+    mutates_args=(),
+    device_types="cuda",
+)
+def block_sparse_attn_backward_sm90(
+    grad_output_padded: torch.Tensor,
+    q_padded: torch.Tensor,
+    k_padded: torch.Tensor,
+    v_padded: torch.Tensor,
+    o_padded: torch.Tensor,
+    lse_padded: torch.Tensor,
+    block_map: torch.Tensor,
+    variable_block_sizes: torch.Tensor,
+) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    _, block_sparse_bwd = _get_sm90_ops()
+    if block_sparse_bwd is None:
+        raise ImportError("fastvideo_kernel_ops.block_sparse_bwd is not available")
+
+    grad_output_padded = grad_output_padded.contiguous()
+    block_map = block_map.to(torch.bool)
+    k2q_idx, k2q_num = _map_to_index(block_map.transpose(-1, -2).contiguous())
+
+    dq, dk, dv = block_sparse_bwd(
+        q_padded,
+        k_padded,
+        v_padded,
+        o_padded,
+        lse_padded,
+        grad_output_padded,
+        k2q_idx,
+        k2q_num,
+        variable_block_sizes.int(),
+    )
+    # C++ kernel returns fp32 grads; cast back to match PyTorch convention if needed
+    return dq.to(grad_output_padded.dtype), dk.to(grad_output_padded.dtype), dv.to(grad_output_padded.dtype)
+
+
+@torch.library.register_fake("fastvideo_kernel::block_sparse_attn_backward_sm90")
+def _block_sparse_attn_backward_sm90_fake(
+    grad_output_padded: torch.Tensor,
+    q_padded: torch.Tensor,
+    k_padded: torch.Tensor,
+    v_padded: torch.Tensor,
+    o_padded: torch.Tensor,
+    lse_padded: torch.Tensor,
+    block_map: torch.Tensor,
+    variable_block_sizes: torch.Tensor,
+) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+    dq = torch.empty_like(q_padded)
+    dk = torch.empty_like(k_padded)
+    dv = torch.empty_like(v_padded)
+    return dq, dk, dv
+
+
+def _backward_sm90(ctx, grad_o, grad_lse):
+    q, k, v, o, lse, block_map, variable_block_sizes = ctx.saved_tensors
+    dq, dk, dv = block_sparse_attn_backward_sm90(
+        grad_o, q, k, v, o, lse, block_map, variable_block_sizes
+    )
+    return dq, dk, dv, None, None
+
+
+def _setup_context_sm90(ctx, inputs, output):
+    q, k, v, block_map, variable_block_sizes = inputs
+    o, lse = output
+    ctx.save_for_backward(q, k, v, o, lse, block_map, variable_block_sizes)
+
+
+block_sparse_attn_sm90.register_autograd(_backward_sm90, setup_context=_setup_context_sm90)
+
+
+def block_sparse_attn(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    v: torch.Tensor,
+    block_map: torch.Tensor,
+    variable_block_sizes: torch.Tensor,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    """
+    Unified block-sparse attention op with autograd support.
+    - On SM90 with compiled extension present: uses fastvideo_kernel_ops.block_sparse_fwd/bwd.
+    - Otherwise: uses Triton implementation (requires q/k/v to have same padded length today).
+    """
+    block_sparse_fwd, block_sparse_bwd = _get_sm90_ops()
+    if (not _force_triton()) and _is_sm90() and (block_sparse_fwd is not None) and (block_sparse_bwd is not None):
+        return block_sparse_attn_sm90(q, k, v, block_map, variable_block_sizes)
+    # Triton path: generally assumes q/k/v share the same padded length
+    if q.shape[2] != k.shape[2] or q.shape[2] != v.shape[2]:
+        raise RuntimeError("Triton fallback requires q/k/v to have the same padded length.")
+    return block_sparse_attn_triton(q, k, v, block_map, variable_block_sizes)
+
+

fastvideo-kernel/python/fastvideo_kernel/ops.py

@@ -1,5 +1,6 @@
 import math
 import torch
+from .block_sparse_attn import block_sparse_attn
 from .triton_kernels.block_sparse_attn_triton import triton_block_sparse_attn_forward
 from .triton_kernels.st_attn_triton import sliding_tile_attention_triton
 from .triton_kernels.index import map_to_index
@@ -45,14 +46,22 @@ def sliding_tile_attention(
     flag = shape_map[seq_shape]
 
     for head_idx, (t, h, w) in enumerate(window_size):
+        # Per-head slices are not contiguous in the batch dimension when batch>1
+        # (they keep the original head-stride). The TK kernel assumes contiguous
+        # [B, H, S, D] layout, so we materialize a contiguous [B,1,S,D] view.
+        q_h = q[:, head_idx:head_idx + 1].contiguous()
+        k_h = k[:, head_idx:head_idx + 1].contiguous()
+        v_h = v[:, head_idx:head_idx + 1].contiguous()
+        o_h = torch.empty_like(q_h)
         sta_fwd(
-            q[:, head_idx:head_idx + 1], k[:, head_idx:head_idx + 1],
-            v[:, head_idx:head_idx + 1], output[:, head_idx:head_idx + 1],
+            q_h, k_h,
+            v_h, o_h,
             t, h, w, text_length, False, has_text, flag
         )
+        output[:, head_idx:head_idx + 1] = o_h
 
     if has_text:
-        sta_fwd(q, k, v, output, 3, 3, 3, text_length, True, True, flag)
+        sta_fwd(q.contiguous(), k.contiguous(), v.contiguous(), output, 3, 3, 3, text_length, True, True, flag)
 
     return output[:, :, :seq_length]
 
@@ -62,6 +71,7 @@ def video_sparse_attn(
     k: torch.Tensor,
     v: torch.Tensor,
     variable_block_sizes: torch.Tensor,
+    q_variable_block_sizes: torch.Tensor,
     topk: int,
     block_size: int | tuple = 64,
     compress_attn_weight: torch.Tensor = None,
@@ -70,14 +80,42 @@ def video_sparse_attn(
         block_size = (block_size, block_size, block_size)
 
     block_elements = block_size[0] * block_size[1] * block_size[2]
-    batch, heads, seq_len, dim = q.shape
+    batch, heads, q_seq_len, dim = q.shape
+    kv_seq_len = k.shape[2]
+    if v.shape[2] != kv_seq_len:
+        raise ValueError(
+            f"Expected k and v to have the same sequence length, got "
+            f"k.shape[2]={kv_seq_len}, v.shape[2]={v.shape[2]}"
+        )
+    if k.shape[0] != batch or v.shape[0] != batch or k.shape[1] != heads or v.shape[1] != heads:
+        raise ValueError("Expected q/k/v to have the same batch and head dimensions.")
+
+    if q_seq_len % block_elements != 0 or kv_seq_len % block_elements != 0:
+        raise ValueError(
+            f"q_seq_len and kv_seq_len must be divisible by block_elements={block_elements}, "
+            f"got q_seq_len={q_seq_len}, kv_seq_len={kv_seq_len}"
+        )
+    q_num_blocks = q_seq_len // block_elements
+    kv_num_blocks = kv_seq_len // block_elements
+
+    if variable_block_sizes.numel() != kv_num_blocks:
+        raise ValueError(
+            f"variable_block_sizes must have length kv_num_blocks={kv_num_blocks}, "
+            f"got {variable_block_sizes.numel()}"
+        )
+
+    if q_variable_block_sizes.numel() != q_num_blocks:
+        raise ValueError(
+            f"q_variable_block_sizes must have length q_num_blocks={q_num_blocks}, "
+            f"got {q_variable_block_sizes.numel()}"
+        )
 
     # Compression branch
-    q_c = q.view(batch, heads, seq_len // block_elements, block_elements, dim)
-    k_c = k.view(batch, heads, seq_len // block_elements, block_elements, dim)
-    v_c = v.view(batch, heads, seq_len // block_elements, block_elements, dim)
+    q_c = q.view(batch, heads, q_num_blocks, block_elements, dim)
+    k_c = k.view(batch, heads, kv_num_blocks, block_elements, dim)
+    v_c = v.view(batch, heads, kv_num_blocks, block_elements, dim)
 
-    q_c = (q_c.float().sum(dim=3) / variable_block_sizes.view(1, 1, -1, 1)).to(
+    q_c = (q_c.float().sum(dim=3) / q_variable_block_sizes.view(1, 1, -1, 1)).to(
         q.dtype)
     k_c = (k_c.float().sum(dim=3) / variable_block_sizes.view(1, 1, -1, 1)).to(
         k.dtype)
@@ -88,9 +126,9 @@ def video_sparse_attn(
     attn = torch.softmax(scores, dim=-1)
     out_c = torch.matmul(attn, v_c)
 
-    out_c = out_c.view(batch, heads, seq_len // block_elements, 1, dim)
+    out_c = out_c.view(batch, heads, q_num_blocks, 1, dim)
     out_c = out_c.repeat(1, 1, 1, block_elements,
-                         1).view(batch, heads, seq_len, dim)
+                         1).view(batch, heads, q_seq_len, dim)
 
     # Sparse branch
     topk_idx = torch.topk(scores, topk, dim=-1).indices
@@ -100,12 +138,17 @@ def video_sparse_attn(
     idx, num = map_to_index(mask)
     
     if block_sparse_fwd is not None:
-        out_s = block_sparse_fwd(
-            q, k, v, idx, num, variable_block_sizes.int()
-        )[0] # block_sparse_fwd returns vector<Tensor>
+        # Use autograd-enabled wrapper so backward works (and still uses SM90 kernel when available)
+        out_s = block_sparse_attn(q, k, v, mask, variable_block_sizes)[0]
     else:
-        out_s, _ = triton_block_sparse_attn_forward(q, k, v, idx, num,
-                                                    variable_block_sizes)
+        if q_seq_len != kv_seq_len:
+            raise RuntimeError(
+                "q/k have different lengths, but the compiled CUDA kernel (block_sparse_fwd) "
+                "is not available. The Triton fallback currently requires q and k/v to have "
+                "the same padded length."
+            )
+        # Triton-only forward (kept for environments without the wrapper deps)
+        out_s, _ = triton_block_sparse_attn_forward(q, k, v, idx, num, variable_block_sizes)
 
     if compress_attn_weight is not None:
         return out_c * compress_attn_weight + out_s

fastvideo-kernel/python/fastvideo_kernel/triton_kernels/sla_triton.py

@@ -0,0 +1,311 @@
+# SPDX-License-Identifier: Apache-2.0
+# Adapted from TurboDiffusion SLA implementation
+# Copyright (c) 2025 by SLA team.
+#
+# Citation:
+# @article{zhang2025sla,
+#   title={SLA: Beyond Sparsity in Diffusion Transformers via Fine-Tunable Sparse-Linear Attention},
+#   author={Jintao Zhang and Haoxu Wang and Kai Jiang and Shuo Yang and Kaiwen Zheng and
+#           Haocheng Xi and Ziteng Wang and Hongzhou Zhu and Min Zhao and Ion Stoica and
+#           Joseph E. Gonzalez and Jun Zhu and Jianfei Chen},
+#   journal={arXiv preprint arXiv:2509.24006},
+#   year={2025}
+# }
+
+import torch
+import triton
+import triton.language as tl
+
+
+@triton.jit
+def _attn_fwd(
+    Q, K, V,
+    qk_scale: tl.constexpr,
+    topk: tl.constexpr,
+    LUT, LSE, OS,
+    L: tl.constexpr,
+    M_BLOCKS: tl.constexpr,
+    D: tl.constexpr,
+    BLOCK_M: tl.constexpr,
+    BLOCK_N: tl.constexpr,
+):
+    idx_m = tl.program_id(0).to(tl.int64)
+    idx_bh = tl.program_id(1).to(tl.int64)
+
+    qkv_offset = idx_bh * L * D
+    lut_offset = (idx_bh * M_BLOCKS + idx_m) * topk
+    lse_offset = idx_bh * L
+    offs_m = idx_m * BLOCK_M + tl.arange(0, BLOCK_M)
+    offs_n = tl.arange(0, BLOCK_N)
+    offs_d = tl.arange(0, D)
+
+    Q_ptrs = Q + qkv_offset + offs_m[:, None] * D + offs_d[None, :]
+    K_ptrs = K + qkv_offset + offs_n[None, :] * D + offs_d[:, None]
+    V_ptrs = V + qkv_offset + offs_n[:, None] * D + offs_d[None, :]
+    OS_ptrs = OS + qkv_offset + offs_m[:, None] * D + offs_d[None, :]
+    LUT_ptr = LUT + lut_offset
+    LSE_ptrs = LSE + lse_offset + offs_m
+    
+    m_i = tl.full([BLOCK_M], -float('inf'), dtype=tl.float32)
+    l_i = tl.zeros([BLOCK_M], dtype=tl.float32)
+    o_s = tl.zeros([BLOCK_M, D], dtype=tl.float32)
+
+    q = tl.load(Q_ptrs, mask=offs_m[:, None] < L)
+    for block_idx in tl.range(topk):
+        idx_n = tl.load(LUT_ptr + block_idx)
+        n_mask = offs_n < L - idx_n * BLOCK_N
+        
+        k = tl.load(K_ptrs + idx_n * BLOCK_N * D, mask=n_mask[None, :])
+        qk = tl.dot(q, k) * (qk_scale * 1.4426950408889634)  # = 1 / ln(2)
+        if L - idx_n * BLOCK_N < BLOCK_N:
+            qk = tl.where(n_mask[None, :], qk, float("-inf"))
+
+        v = tl.load(V_ptrs + idx_n * BLOCK_N * D, mask=n_mask[:, None])
+        local_m = tl.max(qk, 1)
+        new_m = tl.maximum(m_i, local_m)
+        qk = qk - new_m[:, None]
+
+        p = tl.math.exp2(qk)
+        l_ij = tl.sum(p, 1)
+        alpha = tl.math.exp2(m_i - new_m)
+        o_s = o_s * alpha[:, None]
+        o_s += tl.dot(p.to(v.dtype), v)
+
+        l_i = l_i * alpha + l_ij
+        m_i = new_m
+
+    o_s = o_s / l_i[:, None]
+    tl.store(OS_ptrs, o_s.to(OS.type.element_ty), mask=offs_m[:, None] < L)
+    
+    m_i += tl.math.log2(l_i)
+    tl.store(LSE_ptrs, m_i, mask=offs_m < L)
+
+
+@triton.jit
+def _attn_bwd_preprocess(
+    OS, DOS, DELTAS,
+    L,
+    D: tl.constexpr,
+    BLOCK_M: tl.constexpr,
+):
+    idx_m = tl.program_id(0).to(tl.int64)
+    idx_bh = tl.program_id(1).to(tl.int64)
+
+    OS += idx_bh * L * D
+    DOS += idx_bh * L * D
+    DELTAS += idx_bh * L
+
+    offs_m = idx_m * BLOCK_M + tl.arange(0, BLOCK_M)
+    offs_d = tl.arange(0, D)
+
+    o_s = tl.load(OS + offs_m[:, None] * D + offs_d[None, :], mask=offs_m[:, None] < L)
+    do_s = tl.load(DOS + offs_m[:, None] * D + offs_d[None, :], mask=offs_m[:, None] < L)
+    
+    delta_s = tl.sum(o_s * do_s, axis=1).to(DELTAS.type.element_ty)
+    tl.store(DELTAS + offs_m, delta_s, mask=offs_m < L)
+
+
+@triton.jit
+def _attn_bwd_dq(
+    Q, K, V, LSE, DELTAS,
+    DOS, DQ, LUT,
+    qk_scale: tl.constexpr,
+    topk: tl.constexpr,
+    L: tl.constexpr,
+    M_BLOCKS: tl.constexpr,
+    D: tl.constexpr,
+    BLOCK_M: tl.constexpr,
+    BLOCK_N: tl.constexpr,
+):
+    idx_m = tl.program_id(0).to(tl.int64)
+    idx_bh = tl.program_id(1).to(tl.int64)
+
+    offs_m = idx_m * BLOCK_M + tl.arange(0, BLOCK_M)
+    offs_n = tl.arange(0, BLOCK_N)
+    offs_d = tl.arange(0, D)
+
+    qkv_offset = idx_bh * L * D
+    lse_offset = idx_bh * L
+    lut_offset = (idx_bh * M_BLOCKS + idx_m) * topk
+
+    Q_ptrs = Q + qkv_offset + offs_m[:, None] * D + offs_d[None, :]
+    K_ptrs = K + qkv_offset + offs_n[:, None] * D + offs_d[None, :]
+    V_ptrs = V + qkv_offset + offs_n[:, None] * D + offs_d[None, :]
+    DQ_ptrs = DQ + qkv_offset + offs_m[:, None] * D + offs_d[None, :]
+    DOS_ptrs = DOS + qkv_offset + offs_m[:, None] * D + offs_d[None, :]
+    LSE_ptrs = LSE + lse_offset + offs_m
+    DELTAS_ptrs = DELTAS + lse_offset + offs_m
+    LUT_ptr = LUT + lut_offset
+
+    q = tl.load(Q_ptrs, mask=offs_m[:, None] < L)
+    do_s = tl.load(DOS_ptrs, mask=offs_m[:, None] < L)
+    delta_s = tl.load(DELTAS_ptrs, mask=offs_m < L)
+    lse = tl.load(LSE_ptrs, mask=offs_m < L, other=float("inf"))
+    
+    dq = tl.zeros([BLOCK_M, D], dtype=tl.float32)
+    for block_idx in tl.range(topk, num_stages=2):
+        idx_n = tl.load(LUT_ptr + block_idx)
+        n_mask = offs_n < L - idx_n * BLOCK_N
+        
+        k = tl.load(K_ptrs + idx_n * BLOCK_N * D, mask=n_mask[:, None])
+        v = tl.load(V_ptrs + idx_n * BLOCK_N * D, mask=n_mask[:, None])
+        qk = tl.dot(q, k.T) * (qk_scale * 1.4426950408889634)
+        p = tl.math.exp2(qk - lse[:, None])
+        p = tl.where(n_mask[None, :], p, 0.0)
+        
+        dp = tl.dot(do_s, v.T).to(tl.float32)
+        ds = p * (dp - delta_s[:, None])
+        dq += tl.dot(ds.to(k.dtype), k)
+    tl.store(DQ_ptrs, dq * qk_scale, mask=offs_m[:, None] < L)
+    
+
+@triton.jit
+def _attn_bwd_dkdv(
+    Q, K, V, DOS, DK, DV,
+    qk_scale, KBID, LSE, DELTAS,
+    L: tl.constexpr,
+    M_BLOCKS: tl.constexpr,
+    N_BLOCKS: tl.constexpr,
+    D: tl.constexpr,
+    BLOCK_M: tl.constexpr,
+    BLOCK_N: tl.constexpr,
+    BLOCK_SLICE_FACTOR: tl.constexpr,
+):
+    BLOCK_M2: tl.constexpr = BLOCK_M // BLOCK_SLICE_FACTOR
+
+    idx_n = tl.program_id(0).to(tl.int64)
+    idx_bh = tl.program_id(1).to(tl.int64)
+
+    offs_n = idx_n * BLOCK_N + tl.arange(0, BLOCK_N)
+    offs_m = tl.arange(0, BLOCK_M2)
+    offs_d = tl.arange(0, D)
+
+    qkv_offset = idx_bh * L * D
+    kbid_offset = idx_bh * M_BLOCKS * N_BLOCKS
+    lse_offset = idx_bh * L
+
+    Q_ptrs = Q + qkv_offset + offs_m[:, None] * D + offs_d[None, :]
+    K_ptrs = K + qkv_offset + offs_n[:, None] * D + offs_d[None, :]
+    V_ptrs = V + qkv_offset + offs_n[:, None] * D + offs_d[None, :]
+    DOS_ptrs = DOS + qkv_offset + offs_m[:, None] * D + offs_d[None, :]
+    DK_ptrs = DK + qkv_offset + offs_n[:, None] * D + offs_d[None, :]
+    DV_ptrs = DV + qkv_offset + offs_n[:, None] * D + offs_d[None, :]
+    LSE_ptrs = LSE + lse_offset + offs_m
+    DELTAS_ptrs = DELTAS + lse_offset + offs_m
+    KBID_ptr = KBID + kbid_offset + idx_n
+
+    k = tl.load(K_ptrs, mask=offs_n[:, None] < L)
+    v = tl.load(V_ptrs, mask=offs_n[:, None] < L)
+        
+    dk = tl.zeros([BLOCK_N, D], dtype=tl.float32)
+    dv = tl.zeros([BLOCK_N, D], dtype=tl.float32)
+    for idx_m in tl.range(0, L, BLOCK_M2):
+        kbid = tl.load(KBID_ptr)
+        if kbid == 1:
+            m_mask = offs_m < L - idx_m
+            q = tl.load(Q_ptrs, mask=m_mask[:, None])
+            lse = tl.load(LSE_ptrs, mask=m_mask, other=float("inf"))
+            qkT = tl.dot(k, q.T) * (qk_scale * 1.4426950408889634)
+            pT = tl.math.exp2(qkT - lse[None, :])
+            pT = tl.where(offs_n[:, None] < L, pT, 0.0)
+
+            do = tl.load(DOS_ptrs, mask=m_mask[:, None])
+            dv += tl.dot(pT.to(do.dtype), do)
+            delta = tl.load(DELTAS_ptrs, mask=m_mask)
+            dpT = tl.dot(v, tl.trans(do))
+            dsT = pT * (dpT - delta[None, :])
+            dk += tl.dot(dsT.to(q.dtype), q)
+        
+        Q_ptrs += BLOCK_M2 * D
+        DOS_ptrs += BLOCK_M2 * D
+        LSE_ptrs += BLOCK_M2
+        DELTAS_ptrs += BLOCK_M2
+        if (idx_m + BLOCK_M2) % BLOCK_M == 0:
+            KBID_ptr += N_BLOCKS
+
+    tl.store(DK_ptrs, dk * qk_scale, mask=offs_n[:, None] < L)
+    tl.store(DV_ptrs, dv, mask=offs_n[:, None] < L)
+    
+
+class _attention(torch.autograd.Function):
+    """Sparse attention forward/backward with autograd support."""
+    
+    @staticmethod
+    def forward(ctx, q, k, v, k_block_id, lut, topk, BLOCK_M, BLOCK_N, qk_scale=None):
+        assert q.is_contiguous() and k.is_contiguous() and v.is_contiguous()
+        assert k_block_id.is_contiguous() and lut.is_contiguous()
+
+        assert BLOCK_M == 64 or BLOCK_M == 128
+        assert BLOCK_N == 64
+
+        B, H, L, D = q.shape
+        if qk_scale is None:
+            qk_scale = D**-0.5
+
+        M_BLOCKS = triton.cdiv(L, BLOCK_M)
+
+        o_s = torch.empty_like(v)
+        lse = torch.empty(q.shape[:-1], device=q.device, dtype=torch.float32)
+
+        grid = (M_BLOCKS, B * H)
+        _attn_fwd[grid](
+            q, k, v, qk_scale, topk,
+            lut, lse, o_s,
+            L, M_BLOCKS,
+            D, BLOCK_M, BLOCK_N,
+            num_warps=4 if q.shape[-1] == 64 else 8,
+            num_stages=3
+        )
+        
+        ctx.save_for_backward(q, k, v, k_block_id, lut, lse, o_s)
+        ctx.qk_scale = qk_scale
+        ctx.topk = topk
+        ctx.BLOCK_M = BLOCK_M
+        ctx.BLOCK_N = BLOCK_N
+        return o_s
+
+    @staticmethod
+    def backward(ctx, do_s):
+        q, k, v, k_block_id, lut, lse, o_s = ctx.saved_tensors
+        do_s = do_s.contiguous()
+
+        BLOCK_M, BLOCK_N = ctx.BLOCK_M, ctx.BLOCK_N
+        B, H, L, D = q.shape
+
+        M_BLOCKS = triton.cdiv(L, BLOCK_M)
+        N_BLOCKS = triton.cdiv(L, BLOCK_N)
+
+        dq = torch.empty_like(q)
+        dk = torch.empty_like(k)
+        dv = torch.empty_like(v)
+        delta_s = torch.empty_like(lse)
+
+        grid = (M_BLOCKS, B * H)
+        _attn_bwd_preprocess[grid](
+            o_s, do_s, delta_s,
+            L, D, BLOCK_M,
+        )
+
+        grid = (M_BLOCKS, B * H)
+        _attn_bwd_dq[grid](
+            q, k, v, lse, delta_s,
+            do_s, dq, lut,
+            ctx.qk_scale, ctx.topk,
+            L, M_BLOCKS,
+            D, BLOCK_M, BLOCK_N,
+            num_warps=4 if q.shape[-1] == 64 else 8,
+            num_stages=4 if q.shape[-1] == 64 else 5
+        )
+
+        grid = (N_BLOCKS, B * H)
+        _attn_bwd_dkdv[grid](
+            q, k, v, do_s, dk, dv,
+            ctx.qk_scale, k_block_id, lse, delta_s,
+            L, M_BLOCKS, N_BLOCKS,
+            D, BLOCK_M, BLOCK_N,
+            BLOCK_SLICE_FACTOR=BLOCK_M // 64,
+            num_warps=4 if q.shape[-1] == 64 else 8,
+            num_stages=4 if q.shape[-1] == 64 else 5
+        )
+
+        return dq, dk, dv, None, None, None, None, None, None

fastvideo-kernel/python/fastvideo_kernel/version.py

@@ -1 +1 @@
-__version__ = "0.2.1"
+__version__ = "0.2.5"

fastvideo-kernel/tests/test_vsa.py

@@ -42,37 +42,13 @@ def block_sparse_kernel_test(Q, K, V, block_sparse_mask, variable_block_sizes, q
     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)
-    # Use raw kernel or triton
-    try:
-        from fastvideo_kernel._C import fastvideo_kernel_ops
-        raw_kernel = getattr(fastvideo_kernel_ops, "block_sparse_fwd", None)
-    except ImportError:
-        raw_kernel = None
+    # Use autograd-enabled wrapper (internally dispatches to SM90 kernel or Triton)
+    from fastvideo_kernel.block_sparse_attn import block_sparse_attn
+    output_padded, _aux = block_sparse_attn(
+        q_padded, k_padded, v_padded, block_sparse_mask, variable_block_sizes
+    )
 
-    from fastvideo_kernel.triton_kernels.index import map_to_index
-
-    
-    # Convert mask to indices
-    # block_sparse_mask is [H, M, N] bool
-    # We need to map it to index.
-    # block_sparse_mask needs to be expanded/reshaped? 
-    # generate_block_sparse_mask_for_function returns [H, NumBlocksQ, NumBlocksKV]
-    
-    # Ops.py logic:
-    # mask = torch.zeros_like(scores, dtype=torch.bool).scatter_(-1, topk_idx, True)
-    # idx, num = map_to_index(mask)
-    
-    idx, num = map_to_index(block_sparse_mask.unsqueeze(0)) # Add batch dim [1, H, M, N]
-    
-    if raw_kernel:
-        out_s = raw_kernel(q_padded, k_padded, v_padded, idx, num, variable_block_sizes.int())
-        output = out_s[0]
-    else:
-        # Fallback to triton testing if C++ not available
-        from fastvideo_kernel.triton_kernels.block_sparse_attn_triton import triton_block_sparse_attn_forward
-        output, _ = triton_block_sparse_attn_forward(q_padded, k_padded, v_padded, idx, num, variable_block_sizes)
-
-    output = output[:, :, q_non_pad_index, :]
+    output = output_padded[:, :, q_non_pad_index, :]
     output.backward(dO)
     return output, Q.grad, K.grad, V.grad
 
@@ -264,7 +240,6 @@ def generate_error_graphs_qkdiff(h, d, error_mode='all'):
 
     print("-" * 150)
 
-@pytest.mark.skip()
 def test_video_sparse_attention_backward():
     if not torch.cuda.is_available():
         return

fastvideo-kernel/tests/test_vsa_forward.py

@@ -3,6 +3,7 @@ import sys
 from typing import Tuple
 
 import torch
+import pytest
 
 from .utils import (
     generate_block_sparse_mask_for_function,
@@ -57,23 +58,14 @@ def block_sparse_forward_test(
     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)
 
-    # Use raw kernel or triton
+    # Use autograd-enabled wrapper (internally dispatches SM90 C++ vs Triton)
+    from fastvideo_kernel.block_sparse_attn import block_sparse_attn
     try:
-        from fastvideo_kernel._C import fastvideo_kernel_ops
-        raw_kernel = getattr(fastvideo_kernel_ops, "block_sparse_fwd", None)
-    except ImportError:
-        raw_kernel = None
-        
-    from fastvideo_kernel.triton_kernels.index import map_to_index
-    idx, num = map_to_index(block_sparse_mask)
-    
-    if raw_kernel:
-        out_padded = raw_kernel(q_padded, k_padded, v_padded, idx, num, variable_block_sizes.int())[0]
-    else:
-        from fastvideo_kernel.triton_kernels.block_sparse_attn_triton import triton_block_sparse_attn_forward
-        out_padded, _ = triton_block_sparse_attn_forward(
-            q_padded, k_padded, v_padded, idx, num, variable_block_sizes
+        out_padded, _aux = block_sparse_attn(
+            q_padded, k_padded, v_padded, block_sparse_mask, variable_block_sizes
         )
+    except RuntimeError as e:
+        pytest.skip(str(e))
 
     # Remove padding on the query side
     out = out_padded[:, :, q_non_pad_index, :]
@@ -156,11 +148,6 @@ def run_forward_qk_diff(
 ) -> 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"
 

fastvideo/attention/backends/sla.py

@@ -0,0 +1,587 @@
+# SPDX-License-Identifier: Apache-2.0
+# SLA (Sparse-Linear Attention) backend for FastVideo
+# Adapted from TurboDiffusion SLA implementation
+#
+# Copyright (c) 2025 by SLA team.
+# Citation:
+# @article{zhang2025sla,
+#   title={SLA: Beyond Sparsity in Diffusion Transformers via Fine-Tunable Sparse-Linear Attention},
+#   author={Jintao Zhang and Haoxu Wang and Kai Jiang and Shuo Yang and Kaiwen Zheng and
+#           Haocheng Xi and Ziteng Wang and Hongzhou Zhu and Min Zhao and Ion Stoica and
+#           Joseph E. Gonzalez and Jun Zhu and Jianfei Chen},
+#   journal={arXiv preprint arXiv:2509.24006},
+#   year={2025}
+# }
+
+from dataclasses import dataclass
+from typing import Any
+from collections.abc import Callable
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import triton
+import triton.language as tl
+
+from fastvideo.attention.backends.abstract import (
+    AttentionBackend,
+    AttentionImpl,
+    AttentionMetadata,
+    AttentionMetadataBuilder,
+)
+from fastvideo_kernel.triton_kernels.sla_triton import _attention
+from fastvideo.logger import init_logger
+
+logger = init_logger(__name__)
+
+# ============================================================================
+# SLA Utility functions (moved from sla_kernels/utils.py)
+# ============================================================================
+
+
+@triton.jit
+def compress_kernel(
+    X,
+    XM,
+    L: tl.constexpr,
+    D: tl.constexpr,
+    BLOCK_L: tl.constexpr,
+):
+    idx_l = tl.program_id(0)
+    idx_bh = tl.program_id(1)
+
+    offs_l = idx_l * BLOCK_L + tl.arange(0, BLOCK_L)
+    offs_d = tl.arange(0, D)
+
+    x_offset = idx_bh * L * D
+    xm_offset = idx_bh * ((L + BLOCK_L - 1) // BLOCK_L) * D
+    x = tl.load(X + x_offset + offs_l[:, None] * D + offs_d[None, :],
+                mask=offs_l[:, None] < L)
+
+    nx = min(BLOCK_L, L - idx_l * BLOCK_L)
+    x_mean = tl.sum(x, axis=0, dtype=tl.float32) / nx
+    tl.store(XM + xm_offset + idx_l * D + offs_d,
+             x_mean.to(XM.dtype.element_ty))
+
+
+def mean_pool(x: torch.Tensor, BLK: int) -> torch.Tensor:
+    """Mean pool tensor along sequence dimension with block size BLK."""
+    assert x.is_contiguous()
+
+    B, H, L, D = x.shape
+    L_BLOCKS = (L + BLK - 1) // BLK
+    x_mean = torch.empty((B, H, L_BLOCKS, D), device=x.device, dtype=x.dtype)
+
+    grid = (L_BLOCKS, B * H)
+    compress_kernel[grid](x, x_mean, L, D, BLK)
+    return x_mean
+
+
+def get_block_map(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    topk_ratio: float,
+    BLKQ: int = 64,
+    BLKK: int = 64,
+) -> tuple[torch.Tensor, torch.Tensor, int]:
+    """Compute sparse block map for attention based on QK similarity.
+    
+    Args:
+        q: Query tensor of shape (B, H, L, D)
+        k: Key tensor of shape (B, H, L, D)
+        topk_ratio: Ratio of key blocks to attend to (0-1)
+        BLKQ: Query block size
+        BLKK: Key block size
+        
+    Returns:
+        sparse_map: Binary mask of shape (B, H, num_q_blocks, num_k_blocks)
+        lut: Top-k indices of shape (B, H, num_q_blocks, topk)
+        topk: Number of key blocks selected
+    """
+    arg_k = k - torch.mean(
+        k, dim=-2, keepdim=True)  # smooth-k technique from SageAttention
+    pooled_qblocks = mean_pool(q, BLKQ)
+    pooled_kblocks = mean_pool(arg_k, BLKK)
+    pooled_score = pooled_qblocks @ pooled_kblocks.transpose(-1, -2)
+
+    K = pooled_score.shape[-1]
+    topk = min(K, int(topk_ratio * K))
+    lut = torch.topk(pooled_score, topk, dim=-1, sorted=False).indices
+
+    sparse_map = torch.zeros_like(pooled_score, dtype=torch.int8)
+    sparse_map.scatter_(-1, lut, 1)
+    return sparse_map, lut, topk
+
+
+# ============================================================================
+# SLA Backend classes
+# ============================================================================
+
+
+class SLAAttentionBackend(AttentionBackend):
+    """Sparse-Linear Attention backend."""
+
+    accept_output_buffer: bool = True
+
+    @staticmethod
+    def get_supported_head_sizes() -> list[int]:
+        return [64, 128]
+
+    @staticmethod
+    def get_name() -> str:
+        return "SLA_ATTN"
+
+    @staticmethod
+    def get_impl_cls() -> type["SLAAttentionImpl"]:
+        return SLAAttentionImpl
+
+    @staticmethod
+    def get_metadata_cls() -> type["SLAAttentionMetadata"]:
+        return SLAAttentionMetadata
+
+    @staticmethod
+    def get_builder_cls() -> type["SLAAttentionMetadataBuilder"]:
+        return SLAAttentionMetadataBuilder
+
+
+@dataclass
+class SLAAttentionMetadata(AttentionMetadata):
+    """Metadata for SLA attention."""
+    current_timestep: int
+    topk_ratio: float = 0.5  # Ratio of key blocks to attend to
+
+
+class SLAAttentionMetadataBuilder(AttentionMetadataBuilder):
+    """Builder for SLA attention metadata."""
+
+    def __init__(self) -> None:
+        pass
+
+    def prepare(self) -> None:
+        pass
+
+    def build(
+        self,
+        current_timestep: int,
+        topk_ratio: float = 0.5,
+        **kwargs: dict[str, Any],
+    ) -> SLAAttentionMetadata:
+        return SLAAttentionMetadata(
+            current_timestep=current_timestep,
+            topk_ratio=topk_ratio,
+        )
+
+
+class SLAAttentionImpl(AttentionImpl, nn.Module):
+    """SLA attention implementation with learnable linear projection.
+    
+    This implementation combines sparse attention with linear attention,
+    using a learnable projection to blend the outputs. The sparse attention
+    uses a block-sparse pattern determined by QK similarity.
+    
+    Args:
+        num_heads: Number of attention heads
+        head_size: Dimension of each head
+        topk_ratio: Ratio of key blocks to attend to (0-1), default 0.5
+        feature_map: Feature map for linear attention ('softmax', 'elu', 'relu')
+        BLKQ: Query block size for sparse attention
+        BLKK: Key block size for sparse attention
+        use_bf16: Whether to use bfloat16 for computation
+    """
+
+    def __init__(
+        self,
+        num_heads: int,
+        head_size: int,
+        causal: bool = False,
+        softmax_scale: float | None = None,
+        num_kv_heads: int | None = None,
+        prefix: str = "",
+        # SLA-specific parameters - matched to TurboDiffusion defaults
+        topk_ratio: float = 0.1,  # TurboDiffusion uses topk=0.1
+        feature_map: str = "softmax",
+        BLKQ: int = 128,  # TurboDiffusion uses BLKQ=128
+        BLKK: int = 64,  # TurboDiffusion uses BLKK=64
+        use_bf16: bool = True,
+        **extra_impl_args,
+    ) -> None:
+        nn.Module.__init__(self)
+
+        self.num_heads = num_heads
+        self.head_size = head_size
+        self.softmax_scale = softmax_scale if softmax_scale else head_size**-0.5
+        self.causal = causal
+        self.prefix = prefix
+
+        # SLA-specific config
+        self.topk_ratio = topk_ratio
+        self.BLKQ = BLKQ
+        self.BLKK = BLKK
+        self.dtype = torch.bfloat16 if use_bf16 else torch.float16
+
+        # Learnable linear projection for combining sparse + linear attention
+        self.proj_l = nn.Linear(head_size, head_size, dtype=torch.float32)
+
+        # Feature map for linear attention
+        # Type annotation for callables
+        self.feature_map_q: Callable[[torch.Tensor], torch.Tensor]
+        self.feature_map_k: Callable[[torch.Tensor], torch.Tensor]
+        if feature_map == "elu":
+            self.feature_map_q = lambda x: F.elu(x) + 1
+            self.feature_map_k = lambda x: F.elu(x) + 1
+        elif feature_map == "relu":
+            self.feature_map_q = F.relu
+            self.feature_map_k = F.relu
+        elif feature_map == "softmax":
+            self.feature_map_q = lambda x: F.softmax(x, dim=-1)
+            self.feature_map_k = lambda x: F.softmax(x, dim=-1)
+        else:
+            raise ValueError(f"Unknown feature map: {feature_map}")
+
+        self._init_weights()
+
+    def _init_weights(self) -> None:
+        """Initialize projection weights to zero for residual-like behavior."""
+        with torch.no_grad():
+            nn.init.zeros_(self.proj_l.weight)
+            nn.init.zeros_(self.proj_l.bias)  # type: ignore[arg-type]
+
+    def _calc_linear_attention(
+        self,
+        q: torch.Tensor,
+        k: torch.Tensor,
+        v: torch.Tensor,
+    ) -> torch.Tensor:
+        """Compute linear attention: (Q @ K^T @ V) / normalizer.
+        
+        Args:
+            q: Query tensor (B, H, L, D) after feature map
+            k: Key tensor (B, H, L, D) after feature map
+            v: Value tensor (B, H, L, D)
+            
+        Returns:
+            Linear attention output (B, H, L, D)
+        """
+        kvsum = k.transpose(-1, -2) @ v  # (B, H, D, D)
+        ksum = torch.sum(k, dim=-2, keepdim=True)  # (B, H, 1, D)
+        return (q @ kvsum) / (1e-5 + (q * ksum).sum(dim=-1, keepdim=True))
+
+    def forward(
+        self,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        attn_metadata: AttentionMetadata,
+    ) -> torch.Tensor:
+        """Forward pass for SLA attention.
+        
+        Input tensors are in FastVideo format: (B, L, H, D)
+        Internally converted to SLA format: (B, H, L, D)
+        
+        Args:
+            query: Query tensor (B, L, H, D)
+            key: Key tensor (B, L, H, D)
+            value: Value tensor (B, L, H, D)
+            attn_metadata: Attention metadata
+            
+        Returns:
+            Output tensor (B, L, H, D)
+        """
+        original_dtype = query.dtype
+
+        # Convert from FastVideo format (B, L, H, D) to SLA format (B, H, L, D)
+        q = query.transpose(1, 2).contiguous()
+        k = key.transpose(1, 2).contiguous()
+        v = value.transpose(1, 2).contiguous()
+
+        # Get topk ratio from metadata if available
+        topk_ratio = self.topk_ratio
+        if hasattr(attn_metadata, 'topk_ratio'):
+            topk_ratio = attn_metadata.topk_ratio  # type: ignore[union-attr]
+
+        # Compute block-sparse attention pattern
+        sparse_map, lut, real_topk = get_block_map(q,
+                                                   k,
+                                                   topk_ratio=topk_ratio,
+                                                   BLKQ=self.BLKQ,
+                                                   BLKK=self.BLKK)
+
+        # Convert to compute dtype
+        q = q.to(self.dtype)
+        k = k.to(self.dtype)
+        v = v.to(self.dtype)
+
+        # Sparse attention
+        o_s = _attention.apply(q, k, v, sparse_map, lut, real_topk, self.BLKQ,
+                               self.BLKK)
+
+        # Linear attention with feature maps
+        q_linear = self.feature_map_q(q).contiguous().to(self.dtype)
+        k_linear = self.feature_map_k(k).contiguous().to(self.dtype)
+        o_l = self._calc_linear_attention(q_linear, k_linear, v)
+
+        # Project linear attention output and combine
+        with torch.amp.autocast('cuda', dtype=self.dtype):
+            o_l = self.proj_l(o_l)
+
+        # Combine sparse and linear outputs
+        output = (o_s + o_l).to(original_dtype)
+
+        # Convert back to FastVideo format (B, L, H, D)
+        output = output.transpose(1, 2)
+
+        return output
+
+
+# Check if spas_sage_attn is available for SageSLA
+SAGESLA_ENABLED = True
+try:
+    import spas_sage_attn._qattn as qattn
+    import spas_sage_attn._fused as fused
+    from spas_sage_attn.utils import get_vanilla_qk_quant, block_map_lut_triton
+except ImportError:
+    SAGESLA_ENABLED = False
+
+SAGE2PP_ENABLED = True
+try:
+    from spas_sage_attn._qattn import qk_int8_sv_f8_accum_f16_block_sparse_attn_inst_buf_fuse_v_scale_with_pv_threshold
+except ImportError:
+    SAGE2PP_ENABLED = False
+
+
+class SageSLAAttentionBackend(AttentionBackend):
+    """Quantized Sparse-Linear Attention backend using SageAttention kernels."""
+
+    accept_output_buffer: bool = True
+
+    @staticmethod
+    def get_supported_head_sizes() -> list[int]:
+        return [64, 128]
+
+    @staticmethod
+    def get_name() -> str:
+        return "SAGE_SLA_ATTN"
+
+    @staticmethod
+    def get_impl_cls() -> type["SageSLAAttentionImpl"]:
+        return SageSLAAttentionImpl
+
+    @staticmethod
+    def get_metadata_cls() -> type["SLAAttentionMetadata"]:
+        return SLAAttentionMetadata
+
+    @staticmethod
+    def get_builder_cls() -> type["SLAAttentionMetadataBuilder"]:
+        return SLAAttentionMetadataBuilder
+
+
+def _get_cuda_arch(device_index: int) -> str:
+    """Get CUDA architecture string for the given device."""
+    major, minor = torch.cuda.get_device_capability(device_index)
+    return f"sm{major}{minor}"
+
+
+class SageSLAAttentionImpl(AttentionImpl, nn.Module):
+    """SageSLA attention implementation using quantized SageAttention kernels.
+    
+    This uses INT8 quantization for Q/K and FP8 for V to achieve better performance
+    while maintaining accuracy. Requires spas_sage_attn package.
+    
+    Args:
+        num_heads: Number of attention heads
+        head_size: Dimension of each head (must be 64 or 128)
+        topk_ratio: Ratio of key blocks to attend to (0-1), default 0.5
+        feature_map: Feature map for linear attention ('softmax', 'elu', 'relu')
+        use_bf16: Whether to use bfloat16 for computation
+    """
+
+    def __init__(
+        self,
+        num_heads: int,
+        head_size: int,
+        causal: bool = False,
+        softmax_scale: float | None = None,
+        num_kv_heads: int | None = None,
+        prefix: str = "",
+        # SageSLA-specific parameters
+        topk_ratio: float = 0.5,
+        feature_map: str = "softmax",
+        use_bf16: bool = True,
+        **extra_impl_args,
+    ) -> None:
+        nn.Module.__init__(self)
+
+        if not SAGESLA_ENABLED:
+            raise ImportError(
+                "SageSLA requires spas_sage_attn. "
+                "Install with: pip install git+https://github.com/thu-ml/SpargeAttn.git"
+            )
+
+        assert head_size in [
+            64, 128
+        ], f"SageSLA requires head_size in [64, 128], got {head_size}"
+
+        self.num_heads = num_heads
+        self.head_size = head_size
+        self.softmax_scale = softmax_scale if softmax_scale else head_size**-0.5
+        self.causal = causal
+        self.prefix = prefix
+
+        # SageSLA-specific config
+        self.topk_ratio = topk_ratio
+        self.dtype = torch.bfloat16 if use_bf16 else torch.float16
+
+        # Learnable linear projection for combining sparse + linear attention
+        self.proj_l = nn.Linear(head_size, head_size, dtype=torch.float32)
+
+        # Feature map for linear attention
+        # Type annotation for callables
+        self.feature_map_q: Callable[[torch.Tensor], torch.Tensor]
+        self.feature_map_k: Callable[[torch.Tensor], torch.Tensor]
+        if feature_map == "elu":
+            self.feature_map_q = lambda x: F.elu(x) + 1
+            self.feature_map_k = lambda x: F.elu(x) + 1
+        elif feature_map == "relu":
+            self.feature_map_q = F.relu
+            self.feature_map_k = F.relu
+        elif feature_map == "softmax":
+            self.feature_map_q = lambda x: F.softmax(x, dim=-1)
+            self.feature_map_k = lambda x: F.softmax(x, dim=-1)
+        else:
+            raise ValueError(f"Unknown feature map: {feature_map}")
+
+        self._init_weights()
+
+    def _init_weights(self) -> None:
+        """Initialize projection weights to zero for residual-like behavior."""
+        with torch.no_grad():
+            nn.init.zeros_(self.proj_l.weight)
+            nn.init.zeros_(self.proj_l.bias)  # type: ignore[arg-type]
+
+    def _calc_linear_attention(
+        self,
+        q: torch.Tensor,
+        k: torch.Tensor,
+        v: torch.Tensor,
+    ) -> torch.Tensor:
+        """Compute linear attention: (Q @ K^T @ V) / normalizer."""
+        kvsum = k.transpose(-1, -2) @ v
+        ksum = torch.sum(k, dim=-2, keepdim=True)
+        return (q @ kvsum) / (1e-5 + (q * ksum).sum(dim=-1, keepdim=True))
+
+    def forward(
+        self,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        attn_metadata: AttentionMetadata,
+    ) -> torch.Tensor:
+        """Forward pass for SageSLA attention with quantized kernels.
+        
+        Input tensors are in FastVideo format: (B, L, H, D)
+        
+        Args:
+            query: Query tensor (B, L, H, D)
+            key: Key tensor (B, L, H, D)
+            value: Value tensor (B, L, H, D)
+            attn_metadata: Attention metadata
+            
+        Returns:
+            Output tensor (B, L, H, D)
+        """
+        original_dtype = query.dtype
+
+        # Convert from FastVideo format (B, L, H, D) to SLA format (B, H, L, D)
+        q = query.transpose(1, 2).contiguous()
+        k = key.transpose(1, 2).contiguous()
+        v = value.transpose(1, 2).contiguous()
+
+        # Get topk ratio from metadata if available
+        topk_ratio = self.topk_ratio
+        if hasattr(attn_metadata, 'topk_ratio'):
+            topk_ratio = attn_metadata.topk_ratio  # type: ignore[union-attr]
+
+        # Determine block sizes based on GPU architecture
+        arch = _get_cuda_arch(q.device.index)
+        if arch == "sm90":
+            BLKQ, BLKK = 64, 128
+        else:
+            BLKQ, BLKK = 128, 64
+
+        # Compute block-sparse attention pattern
+        sparse_map, lut, real_topk = get_block_map(q,
+                                                   k,
+                                                   topk_ratio=topk_ratio,
+                                                   BLKQ=BLKQ,
+                                                   BLKK=BLKK)
+
+        # Convert to compute dtype
+        q = q.to(self.dtype)
+        k = k.to(self.dtype)
+        v = v.to(self.dtype)
+
+        # ========== SPARGE QUANTIZED ATTENTION ==========
+        km = k.mean(dim=-2, keepdim=True)
+        headdim = q.size(-1)
+        scale = 1.0 / (headdim**0.5)
+
+        # Quantize Q, K to INT8
+        q_int8, q_scale, k_int8, k_scale = get_vanilla_qk_quant(
+            q, k, km, BLKQ, BLKK)
+        lut_triton, valid_block_num = block_map_lut_triton(sparse_map)
+
+        # Quantize V to FP8
+        b, h_kv, kv_len, head_dim = v.shape
+        padded_len = (kv_len + 127) // 128 * 128
+        v_transposed_permutted = torch.empty((b, h_kv, head_dim, padded_len),
+                                             dtype=v.dtype,
+                                             device=v.device)
+        fused.transpose_pad_permute_cuda(v, v_transposed_permutted, 1)
+        v_fp8 = torch.empty(v_transposed_permutted.shape,
+                            dtype=torch.float8_e4m3fn,
+                            device=v.device)
+        v_scale = torch.empty((b, h_kv, head_dim),
+                              dtype=torch.float32,
+                              device=v.device)
+        fused.scale_fuse_quant_cuda(v_transposed_permutted, v_fp8, v_scale,
+                                    kv_len, 2.25, 1)
+
+        # Sparse attention with quantized kernels
+        o_s = torch.empty_like(q)
+        if arch == "sm90":
+            qattn.qk_int8_sv_f8_accum_f32_block_sparse_attn_inst_buf_fuse_v_scale_sm90(
+                q_int8, k_int8, v_fp8, o_s, lut_triton, valid_block_num,
+                q_scale, k_scale, v_scale, 1, False, 1, scale)
+        else:
+            pvthreshold = torch.full((q.shape[-3], ),
+                                     1e6,
+                                     dtype=torch.float32,
+                                     device=q.device)
+            if SAGE2PP_ENABLED:
+                qk_int8_sv_f8_accum_f16_block_sparse_attn_inst_buf_fuse_v_scale_with_pv_threshold(
+                    q_int8, k_int8, v_fp8, o_s, lut_triton, valid_block_num,
+                    pvthreshold, q_scale, k_scale, v_scale, 1, False, 1, scale,
+                    0)
+            else:
+                qattn.qk_int8_sv_f8_accum_f32_block_sparse_attn_inst_buf_fuse_v_scale_with_pv_threshold(
+                    q_int8, k_int8, v_fp8, o_s, lut_triton, valid_block_num,
+                    pvthreshold, q_scale, k_scale, v_scale, 1, False, 1, scale,
+                    0)
+        # ========== END SPARGE ==========
+
+        # Linear attention with feature maps
+        q_linear = self.feature_map_q(q).contiguous().to(self.dtype)
+        k_linear = self.feature_map_k(k).contiguous().to(self.dtype)
+        o_l = self._calc_linear_attention(q_linear, k_linear, v)
+
+        # Project linear attention output and combine
+        with torch.amp.autocast('cuda', dtype=self.dtype):
+            o_l = self.proj_l(o_l)
+
+        # Combine sparse and linear outputs
+        output = (o_s + o_l).to(original_dtype)
+
+        # Convert back to FastVideo format (B, L, H, D)
+        output = output.transpose(1, 2)
+
+        return output

fastvideo/attention/backends/video_sparse_attn.py

@@ -276,8 +276,9 @@ class VideoSparseAttentionImpl(AttentionImpl):
             query,
             key,
             value,
-            variable_block_sizes=attn_metadata.variable_block_sizes,
-            topk=cur_topk,
+            attn_metadata.variable_block_sizes,
+            attn_metadata.variable_block_sizes,
+            cur_topk,
             block_size=VSA_TILE_SIZE,
             compress_attn_weight=gate_compress).transpose(1, 2)
 

fastvideo/attention/layer.py

@@ -50,6 +50,10 @@ class DistributedAttention(nn.Module):
                                   num_kv_heads=num_kv_heads,
                                   prefix=f"{prefix}.impl",
                                   **extra_impl_args)
+        # Register attn_impl as submodule if it has learnable parameters (e.g., SLA's proj_l)
+        # This ensures its parameters are included in state_dict() for saving/loading
+        if isinstance(self.attn_impl, nn.Module):
+            self.add_module('attn_impl', self.attn_impl)
         self.num_heads = num_heads
         self.head_size = head_size
         self.num_kv_heads = num_kv_heads

fastvideo/configs/models/__init__.py

@@ -2,5 +2,16 @@ from fastvideo.configs.models.base import ModelConfig
 from fastvideo.configs.models.dits.base import DiTConfig
 from fastvideo.configs.models.encoders.base import EncoderConfig
 from fastvideo.configs.models.vaes.base import VAEConfig
+from fastvideo.configs.models.audio import (LTX2AudioDecoderConfig,
+                                            LTX2AudioEncoderConfig,
+                                            LTX2VocoderConfig)
 
-__all__ = ["ModelConfig", "VAEConfig", "DiTConfig", "EncoderConfig"]
+__all__ = [
+    "ModelConfig",
+    "VAEConfig",
+    "DiTConfig",
+    "EncoderConfig",
+    "LTX2AudioEncoderConfig",
+    "LTX2AudioDecoderConfig",
+    "LTX2VocoderConfig",
+]

fastvideo/configs/models/audio/__init__.py

@@ -0,0 +1,13 @@
+# SPDX-License-Identifier: Apache-2.0
+
+from fastvideo.configs.models.audio.ltx2_audio_vae import (
+    LTX2AudioDecoderConfig,
+    LTX2AudioEncoderConfig,
+    LTX2VocoderConfig,
+)
+
+__all__ = [
+    "LTX2AudioEncoderConfig",
+    "LTX2AudioDecoderConfig",
+    "LTX2VocoderConfig",
+]

fastvideo/configs/models/audio/ltx2_audio_vae.py

@@ -0,0 +1,31 @@
+# SPDX-License-Identifier: Apache-2.0
+"""
+LTX-2 audio VAE and vocoder configuration.
+"""
+
+from dataclasses import dataclass, field
+
+from fastvideo.configs.models.base import ArchConfig, ModelConfig
+
+
+@dataclass
+class LTX2AudioArchConfig(ArchConfig):
+    architectures: list[str] = field(default_factory=list)
+
+
+@dataclass
+class LTX2AudioEncoderConfig(ModelConfig):
+    arch_config: ArchConfig = field(default_factory=lambda: LTX2AudioArchConfig(
+        architectures=["LTX2AudioEncoder"]))
+
+
+@dataclass
+class LTX2AudioDecoderConfig(ModelConfig):
+    arch_config: ArchConfig = field(default_factory=lambda: LTX2AudioArchConfig(
+        architectures=["LTX2AudioDecoder"]))
+
+
+@dataclass
+class LTX2VocoderConfig(ModelConfig):
+    arch_config: ArchConfig = field(default_factory=lambda: LTX2AudioArchConfig(
+        architectures=["LTX2Vocoder"]))

fastvideo/configs/models/dits/__init__.py

@@ -3,11 +3,13 @@ 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.longcat import LongCatVideoConfig
+from fastvideo.configs.models.dits.ltx2 import LTX2VideoConfig
 from fastvideo.configs.models.dits.stepvideo import StepVideoConfig
 from fastvideo.configs.models.dits.wanvideo import WanVideoConfig
+from fastvideo.configs.models.dits.hyworld import HYWorldConfig
 
 __all__ = [
     "HunyuanVideoConfig", "HunyuanVideo15Config", "WanVideoConfig",
     "StepVideoConfig", "CosmosVideoConfig", "Cosmos25VideoConfig",
-    "LongCatVideoConfig"
+    "LongCatVideoConfig", "LTX2VideoConfig", "HYWorldConfig"
 ]

fastvideo/configs/models/dits/base.py

@@ -18,7 +18,8 @@ class DiTArchConfig(ArchConfig):
         AttentionBackendEnum.SLIDING_TILE_ATTN, AttentionBackendEnum.SAGE_ATTN,
         AttentionBackendEnum.FLASH_ATTN, AttentionBackendEnum.TORCH_SDPA,
         AttentionBackendEnum.VIDEO_SPARSE_ATTN, AttentionBackendEnum.VMOBA_ATTN,
-        AttentionBackendEnum.SAGE_ATTN_THREE)
+        AttentionBackendEnum.SAGE_ATTN_THREE, AttentionBackendEnum.SLA_ATTN,
+        AttentionBackendEnum.SAGE_SLA_ATTN)
 
     hidden_size: int = 0
     num_attention_heads: int = 0

fastvideo/configs/models/dits/hunyuanvideo15.py

@@ -26,6 +26,9 @@ class HunyuanVideo15ArchConfig(DiTArchConfig):
 
     param_names_mapping: dict = field(
         default_factory=lambda: {
+            r"^cond_type_embed\.(.*)$":
+            r"cond_type_embed.\1",
+
             # 1. context_embedder.time_text_embed submodules (specific rules, applied first):
             r"^context_embedder\.time_text_embed\.timestep_embedder\.linear_1\.(.*)$":
             r"txt_in.t_embedder.mlp.fc_in.\1",
@@ -55,6 +58,16 @@ class HunyuanVideo15ArchConfig(DiTArchConfig):
             r"txt_in.refiner_blocks.\1.mlp.fc_out.\2",
             r"^context_embedder\.token_refiner\.refiner_blocks\.(\d+)\.norm_out\.linear\.(.*)$":
             r"txt_in.refiner_blocks.\1.adaLN_modulation.linear.\2",
+            r"^context_embedder\.token_refiner\.refiner_blocks\.(\d+)\.self_attn_qkv\.(.*)$":
+            r"txt_in.refiner_blocks.\1.self_attn_qkv.\2",
+            r"^image_embedder\.linear_1\.(.*)$":
+            r"image_embedder.linear_1.\1",
+            r"^image_embedder\.linear_2\.(.*)$":
+            r"image_embedder.linear_2.\1",
+            r"^image_embedder\.norm_in\.(.*)$":
+            r"image_embedder.norm_in.\1",
+            r"^image_embedder\.norm_out\.(.*)$":
+            r"image_embedder.norm_out.\1",
 
             # 2. txt_in_2 mapping:
             r"^context_embedder_2\.(.*)$":
@@ -144,6 +157,12 @@ class HunyuanVideo15ArchConfig(DiTArchConfig):
     exclude_lora_layers: list[str] = field(
         default_factory=lambda: ["img_in", "txt_in", "time_in", "vector_in"])
 
+    # Causal HunyuanVideo1.5
+    local_attn_size: int = -1  # Window size for temporal local attention (-1 indicates global attention)
+    sink_size: int = 0  # Size of the attention sink, we keep the first `sink_size` frames unchanged when rolling the KV cache
+    num_frames_per_block: int = 3
+    sliding_window_num_frames: int = 31
+
     def __post_init__(self):
         super().__post_init__()
         self.hidden_size: int = self.attention_head_dim * self.num_attention_heads

fastvideo/configs/models/dits/hyworld.py

@@ -0,0 +1,207 @@
+# 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" in n and str.isdigit(n.split(".")[-1])
+
+
+def is_single_block(n: str, m) -> bool:
+    return "single" 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 HYWorldArchConfig(DiTArchConfig):
+    _fsdp_shard_conditions: list = field(
+        default_factory=lambda: [is_double_block, is_single_block])
+
+    _compile_conditions: list = field(
+        default_factory=lambda: [is_double_block, is_single_block, is_txt_in])
+
+    param_names_mapping: dict = field(
+        default_factory=lambda: {
+            # 1. txt_in submodules (text embedder, refiner blocks):
+            r"^txt_in\.t_embedder\.mlp\.0\.(.*)$":
+            r"txt_in.t_embedder.mlp.fc_in.\1",
+            r"^txt_in\.t_embedder\.mlp\.2\.(.*)$":
+            r"txt_in.t_embedder.mlp.fc_out.\1",
+            r"^txt_in\.c_embedder\.linear_1\.(.*)$":
+            r"txt_in.c_embedder.fc_in.\1",
+            r"^txt_in\.c_embedder\.linear_2\.(.*)$":
+            r"txt_in.c_embedder.fc_out.\1",
+            r"^txt_in\.individual_token_refiner\.blocks\.(\d+)\.norm1\.(.*)$":
+            r"txt_in.refiner_blocks.\1.norm1.\2",
+            r"^txt_in\.individual_token_refiner\.blocks\.(\d+)\.norm2\.(.*)$":
+            r"txt_in.refiner_blocks.\1.norm2.\2",
+            r"^txt_in\.individual_token_refiner\.blocks\.(\d+)\.self_attn_qkv\.(.*)$":
+            r"txt_in.refiner_blocks.\1.self_attn_qkv.\2",
+            r"^txt_in\.individual_token_refiner\.blocks\.(\d+)\.self_attn_proj\.(.*)$":
+            r"txt_in.refiner_blocks.\1.self_attn_proj.\2",
+            r"^txt_in\.individual_token_refiner\.blocks\.(\d+)\.mlp\.fc1\.(.*)$":
+            r"txt_in.refiner_blocks.\1.mlp.fc_in.\2",
+            r"^txt_in\.individual_token_refiner\.blocks\.(\d+)\.mlp\.fc2\.(.*)$":
+            r"txt_in.refiner_blocks.\1.mlp.fc_out.\2",
+            r"^txt_in\.individual_token_refiner\.blocks\.(\d+)\.adaLN_modulation\.1\.(.*)$":
+            r"txt_in.refiner_blocks.\1.adaLN_modulation.linear.\2",
+
+            # 2. time_in mappings (HYWorld uses TimestepEmbedder directly,
+            #    but FastVideo model inherits HunyuanVideo15TimeEmbedding with timestep_embedder):
+            r"^time_in\.mlp\.0\.(.*)$":
+            r"time_in.timestep_embedder.mlp.fc_in.\1",
+            r"^time_in\.mlp\.2\.(.*)$":
+            r"time_in.timestep_embedder.mlp.fc_out.\1",
+
+            # 3. action_in mappings:
+            r"^action_in\.mlp\.0\.(.*)$":
+            r"action_in.mlp.fc_in.\1",
+            r"^action_in\.mlp\.2\.(.*)$":
+            r"action_in.mlp.fc_out.\1",
+
+            # 4. byt5_in -> txt_in_2 mappings:
+            r"^byt5_in\.layernorm\.(.*)$":
+            r"txt_in_2.norm.\1",
+            r"^byt5_in\.fc1\.(.*)$":
+            r"txt_in_2.linear_1.\1",
+            r"^byt5_in\.fc2\.(.*)$":
+            r"txt_in_2.linear_2.\1",
+            r"^byt5_in\.fc3\.(.*)$":
+            r"txt_in_2.linear_3.\1",
+
+            # 5. cond_type_embedding -> cond_type_embed:
+            r"^cond_type_embedding\.(.*)$":
+            r"cond_type_embed.\1",
+
+            # 6. vision_in -> image_embedder mappings:
+            r"^vision_in\.proj\.0\.(.*)$":
+            r"image_embedder.norm_in.\1",
+            r"^vision_in\.proj\.1\.(.*)$":
+            r"image_embedder.linear_1.\1",
+            r"^vision_in\.proj\.3\.(.*)$":
+            r"image_embedder.linear_2.\1",
+            r"^vision_in\.proj\.4\.(.*)$":
+            r"image_embedder.norm_out.\1",
+
+            # 7. double_blocks mapping:
+            r"^double_blocks\.(\d+)\.img_attn_q\.(.*)$":
+            (r"double_blocks.\1.img_attn_qkv.\2", 0, 3),
+            r"^double_blocks\.(\d+)\.img_attn_k\.(.*)$":
+            (r"double_blocks.\1.img_attn_qkv.\2", 1, 3),
+            r"^double_blocks\.(\d+)\.img_attn_v\.(.*)$":
+            (r"double_blocks.\1.img_attn_qkv.\2", 2, 3),
+            r"^double_blocks\.(\d+)\.txt_attn_q\.(.*)$":
+            (r"double_blocks.\1.txt_attn_qkv.\2", 0, 3),
+            r"^double_blocks\.(\d+)\.txt_attn_k\.(.*)$":
+            (r"double_blocks.\1.txt_attn_qkv.\2", 1, 3),
+            r"^double_blocks\.(\d+)\.txt_attn_v\.(.*)$":
+            (r"double_blocks.\1.txt_attn_qkv.\2", 2, 3),
+            r"^double_blocks\.(\d+)\.img_mlp\.fc1\.(.*)$":
+            r"double_blocks.\1.img_mlp.fc_in.\2",
+            r"^double_blocks\.(\d+)\.img_mlp\.fc2\.(.*)$":
+            r"double_blocks.\1.img_mlp.fc_out.\2",
+            r"^double_blocks\.(\d+)\.txt_mlp\.fc1\.(.*)$":
+            r"double_blocks.\1.txt_mlp.fc_in.\2",
+            r"^double_blocks\.(\d+)\.txt_mlp\.fc2\.(.*)$":
+            r"double_blocks.\1.txt_mlp.fc_out.\2",
+
+            # 8. Final layer mapping:
+            r"^final_layer\.adaLN_modulation\.1\.(.*)$":
+            r"final_layer.adaLN_modulation.linear.\1",
+        })
+
+    # Reverse mapping for saving checkpoints: custom -> hf
+    reverse_param_names_mapping: dict = field(default_factory=lambda: {})
+
+    # Parameters from HY-WorldPlay config.json (loaded from checkpoint)
+    patch_size: list | tuple | int = field(default_factory=lambda: [1, 1, 1])
+    # Base latent channels - will be expanded in __post_init__ if concat_condition=True
+    in_channels: int = 32
+    concat_condition: bool = True
+    out_channels: int = 32
+    hidden_size: int = 2048
+    heads_num: int = 16
+    mlp_width_ratio: float = 4.0
+    mlp_act_type: str = "gelu_tanh"
+    mm_double_blocks_depth: int = 54
+    mm_single_blocks_depth: int = 0
+    rope_dim_list: list | tuple = field(default_factory=lambda: [16, 56, 56])
+    qkv_bias: bool = True
+    qk_norm: bool | str = True
+    qk_norm_type: str = "rms"
+    guidance_embed: bool = False
+    use_meanflow: bool = False
+    text_projection: str = "single_refiner"
+    use_attention_mask: bool = True
+    text_states_dim: int = 3584
+    text_states_dim_2: int | None = None
+    text_pool_type: str | None = None
+    rope_theta: float = 256.0
+    attn_mode: str = "flash"
+    attn_param: str | None = None
+    glyph_byT5_v2: bool = True
+    vision_projection: str = "linear"
+    vision_states_dim: int = 1152
+    is_reshape_temporal_channels: bool = False
+    use_cond_type_embedding: bool = True
+    ideal_resolution: str = "480p"
+    ideal_task: str = "i2v"
+    task_type: str = "i2v"
+    exclude_lora_layers: list[str] = field(
+        default_factory=lambda: ["img_in", "txt_in", "time_in", "vector_in"])
+
+    def __post_init__(self):
+        super().__post_init__()
+        # Convert HY-WorldPlay naming to FastVideo naming conventions
+        self.num_attention_heads: int = self.heads_num
+        self.attention_head_dim: int = self.hidden_size // self.heads_num
+        self.num_layers: int = self.mm_double_blocks_depth
+        self.num_single_layers: int = self.mm_single_blocks_depth
+        self.num_refiner_layers: int = 2  # Default for HYWorld
+        self.mlp_ratio: float = float(self.mlp_width_ratio)
+        self.text_embed_dim: int = self.text_states_dim
+        self.text_embed_2_dim: int = self.text_states_dim_2 if self.text_states_dim_2 else 1472
+        self.image_embed_dim: int = self.vision_states_dim
+        self.rope_axes_dim: tuple[int, ...] = tuple(self.rope_dim_list)
+        self.num_channels_latents: int = self.out_channels
+        self.target_size: int = 640
+
+        # Handle concat_condition: when True, actual in_channels = base * 2 + 1
+        # (base latent + condition latent + mask channel)
+        # config.json has base in_channels (32), but img_in needs full (65)
+        if self.concat_condition and self.in_channels == 32:
+            if self.is_reshape_temporal_channels:
+                self.in_channels = self.in_channels + self.in_channels // 2 + 1
+            else:
+                self.in_channels = self.in_channels * 2 + 1  # 32 * 2 + 1 = 65
+
+        # Handle patch_size (can be list/tuple or int)
+        if isinstance(self.patch_size, list | tuple):
+            self.patch_size_t: int = self.patch_size[0]
+            # assume square patch size for height and width
+            patch_size_hw: int = self.patch_size[1]
+            object.__setattr__(self, 'patch_size', patch_size_hw)
+        else:
+            self.patch_size_t = 1
+
+        # Convert qk_norm to string format
+        if isinstance(self.qk_norm, bool):
+            if self.qk_norm:
+                self.qk_norm = "rms_norm" if self.qk_norm_type == "rms" else self.qk_norm_type
+            else:
+                self.qk_norm = "none"
+
+
+@dataclass
+class HYWorldConfig(DiTConfig):
+    arch_config: DiTArchConfig = field(default_factory=HYWorldArchConfig)
+
+    prefix: str = "HYWorld"

fastvideo/configs/models/dits/longcat.py

@@ -26,7 +26,6 @@ class LongCatVideoArchConfig(DiTArchConfig):
         default_factory=lambda: [is_longcat_blocks])
 
     # Parameter name mapping for weight conversion
-    # Maps original LongCat third_party names -> native FastVideo names
     param_names_mapping: dict = field(
         default_factory=lambda: {
             # Embedders

fastvideo/configs/models/dits/ltx2.py

@@ -0,0 +1,84 @@
+# SPDX-License-Identifier: Apache-2.0
+"""
+LTX-2 Transformer configuration for native FastVideo integration.
+"""
+
+from dataclasses import dataclass, field
+
+from fastvideo.configs.models.dits.base import DiTArchConfig, DiTConfig
+
+
+def is_ltx2_blocks(name: str, _module) -> bool:
+    """FSDP shard condition for LTX-2 transformer blocks."""
+    return "transformer_blocks" in name
+
+
+@dataclass
+class LTX2VideoArchConfig(DiTArchConfig):
+    """Architecture configuration for LTX-2 video transformer."""
+
+    _fsdp_shard_conditions: list = field(
+        default_factory=lambda: [is_ltx2_blocks])
+    _compile_conditions: list = field(default_factory=lambda: [is_ltx2_blocks])
+
+    # Parameter name mapping for weight conversion (hf/comfy -> FastVideo)
+    param_names_mapping: dict = field(
+        default_factory=lambda: {
+            r"^model\.diffusion_model\.(.*)$": r"model.\1",
+            r"^diffusion_model\.(.*)$": r"model.\1",
+            r"^model\.(.*)$": r"model.\1",
+            r"^(.*)$": r"model.\1",
+        })
+
+    reverse_param_names_mapping: dict = field(default_factory=lambda: {})
+    lora_param_names_mapping: dict = field(default_factory=lambda: {})
+
+    # Core transformer settings (defaults from LTX-2 metadata)
+    num_attention_heads: int = 32
+    attention_head_dim: int = 128
+    num_layers: int = 48
+    cross_attention_dim: int = 4096
+    caption_channels: int = 3840
+    norm_eps: float = 1e-6
+    attention_type: str = "default"
+    rope_type: str = "split"
+    double_precision_rope: bool = True
+
+    positional_embedding_theta: float = 10000.0
+    positional_embedding_max_pos: list[int] = field(
+        default_factory=lambda: [20, 2048, 2048])
+    timestep_scale_multiplier: int = 1000
+    use_middle_indices_grid: bool = True
+
+    # Patchification (video-only path)
+    patch_size: tuple[int, int, int] = (1, 1, 1)
+    num_channels_latents: int = 128
+    in_channels: int | None = None
+    out_channels: int | None = None
+
+    # Audio defaults (reserved for joint AV ports)
+    audio_num_attention_heads: int = 32
+    audio_attention_head_dim: int = 64
+    audio_in_channels: int = 128
+    audio_out_channels: int = 128
+    audio_cross_attention_dim: int = 2048
+    audio_positional_embedding_max_pos: list[int] = field(
+        default_factory=lambda: [20])
+    av_ca_timestep_scale_multiplier: int = 1
+
+    def __post_init__(self):
+        super().__post_init__()
+        patch_volume = self.patch_size[0] * self.patch_size[
+            1] * self.patch_size[2]
+        if self.in_channels is None:
+            self.in_channels = self.num_channels_latents * patch_volume
+        if self.out_channels is None:
+            self.out_channels = self.in_channels
+
+
+@dataclass
+class LTX2VideoConfig(DiTConfig):
+    """Main configuration for LTX-2 transformer."""
+
+    arch_config: DiTArchConfig = field(default_factory=LTX2VideoArchConfig)
+    prefix: str = "ltx2"

fastvideo/configs/models/encoders/__init__.py

@@ -7,10 +7,14 @@ from fastvideo.configs.models.encoders.clip import (
 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
+from fastvideo.configs.models.encoders.siglip import SiglipVisionConfig
+from fastvideo.configs.models.encoders.reason1 import Reason1ArchConfig, Reason1Config
+from fastvideo.configs.models.encoders.gemma import LTX2GemmaConfig
 
 __all__ = [
     "EncoderConfig", "TextEncoderConfig", "ImageEncoderConfig",
     "BaseEncoderOutput", "CLIPTextConfig", "CLIPVisionConfig",
     "WAN2_1ControlCLIPVisionConfig", "LlamaConfig", "T5Config", "T5LargeConfig",
-    "Qwen2_5_VLConfig"
+    "Qwen2_5_VLConfig", "Reason1ArchConfig", "Reason1Config", "LTX2GemmaConfig",
+    "SiglipVisionConfig"
 ]

fastvideo/configs/models/encoders/gemma.py

@@ -0,0 +1,48 @@
+# SPDX-License-Identifier: Apache-2.0
+from dataclasses import dataclass, field
+
+from fastvideo.configs.models.encoders.base import (
+    TextEncoderArchConfig,
+    TextEncoderConfig,
+)
+
+
+@dataclass
+class LTX2GemmaArchConfig(TextEncoderArchConfig):
+    architectures: list[str] = field(
+        default_factory=lambda: ["LTX2GemmaTextEncoderModel"])
+    hidden_size: int = 3840
+    num_hidden_layers: int = 48
+    num_attention_heads: int = 30
+    text_len: int = 1024
+    pad_token_id: int = 0
+    eos_token_id: int = 2
+
+    gemma_model_path: str = ""
+    gemma_dtype: str = "bfloat16"
+    padding_side: str = "left"
+
+    feature_extractor_in_features: int = 3840 * 49
+    feature_extractor_out_features: int = 3840
+
+    connector_num_attention_heads: int = 30
+    connector_attention_head_dim: int = 128
+    connector_num_layers: int = 2
+    connector_positional_embedding_theta: float = 10000.0
+    connector_positional_embedding_max_pos: list[int] = field(
+        default_factory=lambda: [4096])
+    connector_rope_type: str = "split"
+    connector_double_precision_rope: bool = False
+    connector_num_learnable_registers: int | None = 128
+
+    def __post_init__(self) -> None:
+        super().__post_init__()
+        self.tokenizer_kwargs["padding"] = "max_length"
+
+
+@dataclass
+class LTX2GemmaConfig(TextEncoderConfig):
+    arch_config: TextEncoderArchConfig = field(
+        default_factory=LTX2GemmaArchConfig)
+
+    prefix: str = "ltx2_gemma"

fastvideo/configs/models/encoders/reason1.py

@@ -0,0 +1,72 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Config for Reason1 (Qwen2.5-VL) text encoder."""
+
+from dataclasses import dataclass, field
+from typing import Any
+
+from fastvideo.configs.models.encoders.base import TextEncoderArchConfig, TextEncoderConfig
+
+
+@dataclass
+class Reason1ArchConfig(TextEncoderArchConfig):
+    """Architecture settings (defaults match Qwen2.5-VL-7B-Instruct)."""
+
+    architectures: list[str] = field(
+        default_factory=lambda: ["Qwen2_5_VLForConditionalGeneration"])
+    model_type: str = "qwen2_5_vl"
+
+    vocab_size: int = 152064
+    hidden_size: int = 3584
+    num_hidden_layers: int = 28
+    num_attention_heads: int = 28
+    num_key_value_heads: int = 4
+    intermediate_size: int = 18944
+
+    text_len: int = 512
+    hidden_state_skip_layer: int = 0
+    bos_token_id: int = 151643
+    pad_token_id: int = 151643
+    eos_token_id: int = 151645
+
+    image_token_id: int = 151655
+    video_token_id: int = 151656
+    vision_token_id: int = 151654
+    vision_start_token_id: int = 151652
+    vision_end_token_id: int = 151653
+
+    vision_config: dict[str, Any] | None = None
+
+    rope_theta: float = 1000000.0
+    rope_scaling: dict[str, Any] | None = field(default_factory=lambda: {
+        "type": "mrope",
+        "mrope_section": [16, 24, 24]
+    })
+    max_position_embeddings: int = 128000
+    max_window_layers: int = 28
+
+    embedding_concat_strategy: str = "mean_pooling"
+    n_layers_per_group: int = 5
+    num_embedding_padding_tokens: int = 512
+
+    attention_dropout: float = 0.0
+    hidden_act: str = "silu"
+    initializer_range: float = 0.02
+    rms_norm_eps: float = 1e-6
+
+    use_sliding_window: bool = False
+    sliding_window: int = 32768
+
+    tie_word_embeddings: bool = False
+    use_cache: bool = False
+    output_hidden_states: bool = True
+
+    torch_dtype: str = "bfloat16"
+    _attn_implementation: str = "flash_attention_2"
+
+
+@dataclass
+class Reason1Config(TextEncoderConfig):
+    """Reason1 text encoder config."""
+
+    arch_config: Reason1ArchConfig = field(default_factory=Reason1ArchConfig)
+    tokenizer_type: str = "Qwen/Qwen2.5-VL-7B-Instruct"

fastvideo/configs/models/encoders/siglip.py

@@ -0,0 +1,53 @@
+# SPDX-License-Identifier: Apache-2.0
+"""SigLIP vision encoder configuration for FastVideo."""
+
+from dataclasses import dataclass, field
+
+from fastvideo.configs.models.encoders.base import (ImageEncoderArchConfig,
+                                                    ImageEncoderConfig)
+
+
+@dataclass
+class SiglipVisionArchConfig(ImageEncoderArchConfig):
+    """Architecture configuration for SigLIP vision encoder.
+    
+    Fields match the config.json from HuggingFace SigLIP checkpoints.
+    """
+
+    # From config.json
+    architectures: list[str] = field(
+        default_factory=lambda: ["SiglipVisionModel"])
+    attention_dropout: float = 0.0
+    dtype: str | None = None
+    hidden_act: str = "gelu_pytorch_tanh"
+    hidden_size: int = 1152
+    image_size: int = 384
+    intermediate_size: int = 4304
+    layer_norm_eps: float = 1e-6
+    model_type: str = "siglip_vision_model"
+    num_attention_heads: int = 16
+    num_channels: int = 3
+    num_hidden_layers: int = 27
+    patch_size: int = 14
+
+    # FastVideo specific - QKV fusion mapping
+    stacked_params_mapping: list = field(default_factory=lambda: [
+        ("qkv_proj", "q_proj", "q"),
+        ("qkv_proj", "k_proj", "k"),
+        ("qkv_proj", "v_proj", "v"),
+    ])
+
+
+@dataclass
+class SiglipVisionConfig(ImageEncoderConfig):
+    """Configuration for SigLIP vision encoder."""
+
+    arch_config: ImageEncoderArchConfig = field(
+        default_factory=SiglipVisionArchConfig)
+
+    # FastVideo specific
+    num_hidden_layers_override: int | None = None
+    require_post_norm: bool | None = None
+    enable_scale: bool = True
+    is_causal: bool = False
+    prefix: str = "siglip"

fastvideo/configs/models/vaes/__init__.py

@@ -1,6 +1,8 @@
 from fastvideo.configs.models.vaes.cosmosvae import CosmosVAEConfig
+from fastvideo.configs.models.vaes.cosmos2_5vae import Cosmos25VAEConfig
 from fastvideo.configs.models.vaes.hunyuanvae import HunyuanVAEConfig
 from fastvideo.configs.models.vaes.hunyuan15vae import Hunyuan15VAEConfig
+from fastvideo.configs.models.vaes.ltx2vae import LTX2VAEConfig
 from fastvideo.configs.models.vaes.stepvideovae import StepVideoVAEConfig
 from fastvideo.configs.models.vaes.wanvae import WanVAEConfig
 
@@ -9,5 +11,7 @@ __all__ = [
     "WanVAEConfig",
     "StepVideoVAEConfig",
     "CosmosVAEConfig",
+    "Cosmos25VAEConfig",
     "Hunyuan15VAEConfig",
+    "LTX2VAEConfig",
 ]

fastvideo/configs/models/vaes/cosmos2_5vae.py

@@ -0,0 +1,223 @@
+"""Cosmos 2.5 (Wan2.1-style) VAE config and checkpoint-key mapping."""
+
+from __future__ import annotations
+
+import re
+from dataclasses import dataclass, field
+
+import torch
+
+from fastvideo.configs.models.vaes.base import VAEArchConfig, VAEConfig
+
+
+@dataclass
+class Cosmos25VAEArchConfig(VAEArchConfig):
+    _name_or_path: str = ""
+    base_dim: int = 96
+    decoder_base_dim: int | None = None
+    z_dim: int = 16
+    dim_mult: tuple[int, ...] = (1, 2, 4, 4)
+    num_res_blocks: int = 2
+    attn_scales: tuple[float, ...] = ()
+    temperal_downsample: tuple[bool, ...] = (False, True, True)
+    dropout: float = 0.0
+    is_residual: bool = False
+    in_channels: int = 3
+    out_channels: int = 3
+    patch_size: int | None = None
+    scale_factor_temporal: int = 4
+    scale_factor_spatial: int = 8
+    clip_output: bool = True
+
+    latents_mean: tuple[float, ...] = (
+        -0.7571,
+        -0.7089,
+        -0.9113,
+        0.1075,
+        -0.1745,
+        0.9653,
+        -0.1517,
+        1.5508,
+        0.4134,
+        -0.0715,
+        0.5517,
+        -0.3632,
+        -0.1922,
+        -0.9497,
+        0.2503,
+        -0.2921,
+    )
+    latents_std: tuple[float, ...] = (
+        2.8184,
+        1.4541,
+        2.3275,
+        2.6558,
+        1.2196,
+        1.7708,
+        2.6052,
+        2.0743,
+        3.2687,
+        2.1526,
+        2.8652,
+        1.5579,
+        1.6382,
+        1.1253,
+        2.8251,
+        1.9160,
+    )
+
+    # Simple 1:1 renames. More complex decoder remapping is handled by
+    # `map_official_key()`.
+    param_names_mapping: dict[str, str] = field(
+        default_factory=lambda: {
+            r"^conv1\.(.*)$": r"quant_conv.\1",
+            r"^conv2\.(.*)$": r"post_quant_conv.\1",
+            r"^encoder\.conv1\.(.*)$": r"encoder.conv_in.\1",
+            r"^decoder\.conv1\.(.*)$": r"decoder.conv_in.\1",
+            r"^encoder\.head\.0\.gamma$": r"encoder.norm_out.gamma",
+            r"^encoder\.head\.2\.(.*)$": r"encoder.conv_out.\1",
+            r"^decoder\.head\.0\.gamma$": r"decoder.norm_out.gamma",
+            r"^decoder\.head\.2\.(.*)$": r"decoder.conv_out.\1",
+        })
+
+    @staticmethod
+    def map_official_key(key: str) -> str | None:
+        """Map a single official checkpoint key into FastVideo key space."""
+
+        def map_residual_subkey(prefix: str, sub: str) -> str | None:
+            if re.match(r"^residual\.0\.gamma$", sub):
+                return f"{prefix}.norm1.gamma"
+            m = re.match(r"^residual\.2\.(weight|bias)$", sub)
+            if m:
+                return f"{prefix}.conv1.{m.group(1)}"
+            if re.match(r"^residual\.3\.gamma$", sub):
+                return f"{prefix}.norm2.gamma"
+            m = re.match(r"^residual\.6\.(weight|bias)$", sub)
+            if m:
+                return f"{prefix}.conv2.{m.group(1)}"
+            m = re.match(r"^shortcut\.(weight|bias)$", sub)
+            if m:
+                return f"{prefix}.conv_shortcut.{m.group(1)}"
+            return None
+
+        def map_attn_subkey(prefix: str, sub: str) -> str | None:
+            if re.match(r"^norm\.gamma$", sub):
+                return f"{prefix}.norm.gamma"
+            m = re.match(r"^to_qkv\.(weight|bias)$", sub)
+            if m:
+                return f"{prefix}.to_qkv.{m.group(1)}"
+            m = re.match(r"^proj\.(weight|bias)$", sub)
+            if m:
+                return f"{prefix}.proj.{m.group(1)}"
+            return None
+
+        def map_resample_subkey(prefix: str, sub: str) -> str | None:
+            m = re.match(r"^resample\.1\.(weight|bias)$", sub)
+            if m:
+                return f"{prefix}.resample.1.{m.group(1)}"
+            m = re.match(r"^time_conv\.(weight|bias)$", sub)
+            if m:
+                return f"{prefix}.time_conv.{m.group(1)}"
+            return None
+
+        m = re.match(r"^conv1\.(weight|bias)$", key)
+        if m:
+            return f"quant_conv.{m.group(1)}"
+        m = re.match(r"^conv2\.(weight|bias)$", key)
+        if m:
+            return f"post_quant_conv.{m.group(1)}"
+        m = re.match(r"^(encoder|decoder)\.conv1\.(weight|bias)$", key)
+        if m:
+            return f"{m.group(1)}.conv_in.{m.group(2)}"
+        m = re.match(r"^(encoder|decoder)\.head\.0\.gamma$", key)
+        if m:
+            return f"{m.group(1)}.norm_out.gamma"
+        m = re.match(r"^(encoder|decoder)\.head\.2\.(weight|bias)$", key)
+        if m:
+            return f"{m.group(1)}.conv_out.{m.group(2)}"
+
+        m = re.match(r"^(encoder|decoder)\.middle\.0\.(.*)$", key)
+        if m:
+            return map_residual_subkey(f"{m.group(1)}.mid_block.resnets.0",
+                                       m.group(2))
+        m = re.match(r"^(encoder|decoder)\.middle\.1\.(.*)$", key)
+        if m:
+            return map_attn_subkey(f"{m.group(1)}.mid_block.attentions.0",
+                                   m.group(2))
+        m = re.match(r"^(encoder|decoder)\.middle\.2\.(.*)$", key)
+        if m:
+            return map_residual_subkey(f"{m.group(1)}.mid_block.resnets.1",
+                                       m.group(2))
+
+        m = re.match(r"^encoder\.downsamples\.(\d+)\.(.*)$", key)
+        if m:
+            idx = int(m.group(1))
+            sub = m.group(2)
+            if sub.startswith("residual.") or sub.startswith("shortcut."):
+                return map_residual_subkey(f"encoder.down_blocks.{idx}", sub)
+            if sub.startswith("resample.") or sub.startswith("time_conv."):
+                return map_resample_subkey(f"encoder.down_blocks.{idx}", sub)
+            return None
+
+        m = re.match(r"^decoder\.upsamples\.(\d+)\.(.*)$", key)
+        if m:
+            uidx = int(m.group(1))
+            sub = m.group(2)
+
+            if uidx in (0, 1, 2):
+                block_i, res_i = 0, uidx
+            elif uidx == 3:
+                block_i, res_i = 0, None
+            elif uidx in (4, 5, 6):
+                block_i, res_i = 1, uidx - 4
+            elif uidx == 7:
+                block_i, res_i = 1, None
+            elif uidx in (8, 9, 10):
+                block_i, res_i = 2, uidx - 8
+            elif uidx == 11:
+                block_i, res_i = 2, None
+            elif uidx in (12, 13, 14):
+                block_i, res_i = 3, uidx - 12
+            else:
+                return None
+
+            if res_i is None:
+                return map_resample_subkey(
+                    f"decoder.up_blocks.{block_i}.upsamplers.0",
+                    sub,
+                )
+
+            return map_residual_subkey(
+                f"decoder.up_blocks.{block_i}.resnets.{res_i}",
+                sub,
+            )
+
+        return None
+
+    temporal_compression_ratio: int = 4
+    spatial_compression_ratio: int = 8
+
+    def __post_init__(self):
+        self.scaling_factor: torch.Tensor = 1.0 / torch.tensor(
+            self.latents_std).view(1, self.z_dim, 1, 1, 1)
+        self.shift_factor: torch.Tensor = torch.tensor(self.latents_mean).view(
+            1, self.z_dim, 1, 1, 1)
+        self.temporal_compression_ratio = self.scale_factor_temporal
+        self.spatial_compression_ratio = self.scale_factor_spatial
+
+
+@dataclass
+class Cosmos25VAEConfig(VAEConfig):
+    """Cosmos2.5 VAE config."""
+
+    arch_config: Cosmos25VAEArchConfig = field(
+        default_factory=Cosmos25VAEArchConfig)
+
+    use_feature_cache: bool = True
+    use_tiling: bool = False
+    use_temporal_tiling: bool = False
+    use_parallel_tiling: bool = False
+
+    def __post_init__(self):
+        self.blend_num_frames = (self.tile_sample_min_num_frames -
+                                 self.tile_sample_stride_num_frames) * 2

fastvideo/configs/models/vaes/ltx2vae.py

@@ -0,0 +1,45 @@
+# SPDX-License-Identifier: Apache-2.0
+"""
+LTX-2 VAE configuration.
+"""
+
+from dataclasses import dataclass, field
+
+from fastvideo.configs.models.vaes.base import VAEArchConfig, VAEConfig
+
+
+@dataclass
+class LTX2VAEArchConfig(VAEArchConfig):
+    # Mirrors LTX-2 safetensors metadata config under "vae"
+    _class_name: str = "CausalVideoAutoencoder"
+    dims: int = 3
+    in_channels: int = 3
+    out_channels: int = 3
+    latent_channels: int = 128
+    encoder_blocks: list = field(default_factory=list)
+    decoder_blocks: list = field(default_factory=list)
+    patch_size: int = 4
+    norm_layer: str = "pixel_norm"
+    latent_log_var: str = "uniform"
+    encoder_spatial_padding_mode: str = "zeros"
+    decoder_spatial_padding_mode: str = "reflect"
+    causal_decoder: bool = False
+    timestep_conditioning: bool = True
+    use_quant_conv: bool = False
+    scaling_factor: float = 1.0
+    normalize_latent_channels: bool = False
+
+    # Match FastVideo naming for compression ratios (LTX-2 default)
+    temporal_compression_ratio: int = 8
+    spatial_compression_ratio: int = 32
+
+
+@dataclass
+class LTX2VAEConfig(VAEConfig):
+    arch_config: VAEArchConfig = field(default_factory=LTX2VAEArchConfig)
+
+    # LTX-2 tiling defaults (match ltx_core.video_vae.TilingConfig.default()).
+    ltx2_spatial_tile_size_in_pixels: int = 512
+    ltx2_spatial_tile_overlap_in_pixels: int = 64
+    ltx2_temporal_tile_size_in_frames: int = 64
+    ltx2_temporal_tile_overlap_in_frames: int = 24

fastvideo/configs/pipelines/__init__.py

@@ -1,8 +1,11 @@
 from fastvideo.configs.pipelines.base import (PipelineConfig,
                                               SlidingTileAttnConfig)
 from fastvideo.configs.pipelines.cosmos import CosmosConfig
+from fastvideo.configs.pipelines.cosmos2_5 import Cosmos25Config
 from fastvideo.configs.pipelines.hunyuan import FastHunyuanConfig, HunyuanConfig
 from fastvideo.configs.pipelines.hunyuan15 import Hunyuan15T2V480PConfig, Hunyuan15T2V720PConfig
+from fastvideo.configs.pipelines.hyworld import HYWorldConfig
+from fastvideo.configs.pipelines.ltx2 import LTX2T2VConfig
 from fastvideo.configs.pipelines.registry import (
     get_pipeline_config_cls_from_name)
 from fastvideo.configs.pipelines.stepvideo import StepVideoT2VConfig
@@ -15,5 +18,6 @@ __all__ = [
     "Hunyuan15T2V480PConfig", "Hunyuan15T2V720PConfig", "SlidingTileAttnConfig",
     "WanT2V480PConfig", "WanI2V480PConfig", "WanT2V720PConfig",
     "WanI2V720PConfig", "StepVideoT2VConfig", "SelfForcingWanT2V480PConfig",
-    "CosmosConfig", "get_pipeline_config_cls_from_name"
+    "CosmosConfig", "Cosmos25Config", "LTX2T2VConfig", "HYWorldConfig",
+    "get_pipeline_config_cls_from_name"
 ]

fastvideo/configs/pipelines/base.py

@@ -12,8 +12,7 @@ from fastvideo.configs.models import (DiTConfig, EncoderConfig, ModelConfig,
 from fastvideo.configs.models.encoders import BaseEncoderOutput
 from fastvideo.configs.utils import update_config_from_args
 from fastvideo.logger import init_logger
-from fastvideo.utils import (FlexibleArgumentParser, StoreBoolean,
-                             PRECISION_TO_TYPE, shallow_asdict)
+from fastvideo.utils import FlexibleArgumentParser, StoreBoolean, shallow_asdict
 
 logger = init_logger(__name__)
 
@@ -312,21 +311,6 @@ class PipelineConfig:
                 f"Length of text encoder configs ({len(self.text_encoder_configs)}) must be equal to length of text encoder precisions ({len(self.text_encoder_precisions)})"
             )
 
-        unsupported_precisions = [
-            precision for precision in self.text_encoder_precisions
-            if precision not in PRECISION_TO_TYPE
-            and not precision.startswith("fp8")
-        ]
-        if unsupported_precisions:
-            supported = ", ".join(PRECISION_TO_TYPE.keys())
-            logger.warning(
-                "Unsupported text encoder precision(s) detected in config: %s. "
-                "FastVideo will attempt to load them with transformers AutoModel when possible. "
-                "Supported fast paths: %s.",
-                unsupported_precisions,
-                supported,
-            )
-
         if len(self.text_encoder_configs) != len(self.preprocess_text_funcs):
             raise ValueError(
                 f"Length of text encoder configs ({len(self.text_encoder_configs)}) must be equal to length of text preprocessing functions ({len(self.preprocess_text_funcs)})"

fastvideo/configs/pipelines/cosmos2_5.py

@@ -0,0 +1,89 @@
+# SPDX-License-Identifier: Apache-2.0
+from collections.abc import Callable
+from dataclasses import dataclass, field
+
+import torch
+
+from fastvideo.configs.models import DiTConfig, EncoderConfig, VAEConfig
+from fastvideo.configs.models.dits import Cosmos25VideoConfig
+from fastvideo.configs.models.dits.cosmos2_5 import Cosmos25ArchConfig
+from fastvideo.configs.models.encoders import BaseEncoderOutput
+from fastvideo.configs.models.encoders.reason1 import Reason1Config, Reason1ArchConfig
+from fastvideo.configs.models.vaes import Cosmos25VAEConfig
+from fastvideo.configs.pipelines.base import PipelineConfig, STA_Mode
+
+
+def _identity_preprocess_text(prompt: str) -> str:
+    return prompt
+
+
+def reason1_postprocess_text(outputs: BaseEncoderOutput) -> torch.Tensor:
+    hidden_states = getattr(outputs, "hidden_states", None)
+    if hidden_states is None:
+        raise ValueError("Reason1 postprocess requires outputs.hidden_states")
+
+    hs = list(hidden_states)[1:]
+    normed = []
+    for h in hs:
+        h = h.float()
+        h = (h - h.mean(dim=-1, keepdim=True)) / (h.std(dim=-1, keepdim=True) +
+                                                  1e-8)
+        normed.append(h)
+    return torch.cat(normed, dim=-1).to(hidden_states[0].dtype)
+
+
+@dataclass
+class Cosmos25Config(PipelineConfig):
+    """Configuration for Cosmos 2.5 (Predict2.5) video generation pipeline."""
+
+    dit_config: DiTConfig = field(default_factory=lambda: Cosmos25VideoConfig(
+        arch_config=Cosmos25ArchConfig(
+            num_attention_heads=16,
+            attention_head_dim=128,
+            in_channels=16,
+            out_channels=16,
+            num_layers=28,
+            patch_size=[1, 2, 2],
+            max_size=[128, 240, 240],
+            rope_scale=[1.0, 3.0, 3.0],
+            text_embed_dim=1024,
+            mlp_ratio=4.0,
+            adaln_lora_dim=256,
+            use_adaln_lora=True,
+            concat_padding_mask=True,
+            extra_pos_embed_type=None,
+            use_crossattn_projection=True,
+            rope_enable_fps_modulation=False,
+            qk_norm="rms_norm",
+        )))
+
+    vae_config: VAEConfig = field(default_factory=Cosmos25VAEConfig)
+
+    text_encoder_configs: tuple[EncoderConfig, ...] = field(
+        default_factory=lambda: (Reason1Config(arch_config=Reason1ArchConfig(
+            embedding_concat_strategy="full_concat")), ))
+
+    preprocess_text_funcs: tuple[Callable[[str], str], ...] = field(
+        default_factory=lambda: (_identity_preprocess_text, ))
+    postprocess_text_funcs: tuple[Callable[[BaseEncoderOutput], torch.Tensor],
+                                  ...] = field(default_factory=lambda:
+                                               (reason1_postprocess_text, ))
+
+    dit_precision: str = "bf16"
+    vae_precision: str = "bf16"
+    text_encoder_precisions: tuple[str, ...] = field(
+        default_factory=lambda: ("bf16", ))
+
+    embedded_cfg_scale: float = 0.0
+    flow_shift: float = 5.0
+
+    vae_tiling: bool = False
+    vae_sp: bool = False
+
+    STA_mode: STA_Mode = STA_Mode.NONE
+    skip_time_steps: int = 0
+
+    def __post_init__(self):
+        self.vae_config.load_encoder = True
+        self.vae_config.load_decoder = True
+        self._vae_latent_dim = 16

fastvideo/configs/pipelines/hunyuan15.py

@@ -127,13 +127,29 @@ class Hunyuan15T2V480PConfig(PipelineConfig):
     vae_tiling: bool = True
 
     def __post_init__(self):
-        self.vae_config.load_encoder = False
+        self.vae_config.load_encoder = True
         self.vae_config.load_decoder = True
 
 
+@dataclass
+class Hunyuan15DistilledI2V480PConfig(Hunyuan15T2V480PConfig):
+    flow_shift: int = 7
+
+
 @dataclass
 class Hunyuan15T2V720PConfig(Hunyuan15T2V480PConfig):
     """Base configuration for HunYuan pipeline architecture."""
 
     # HunyuanConfig-specific parameters with defaults
     flow_shift: int = 9
+
+
+@dataclass
+class SelfForcingHunyuan15T2V480PConfig(Hunyuan15T2V480PConfig):
+    flow_shift: int = 5
+    is_causal: bool = True
+    # dmd_denoising_steps: list[int] | None = field(
+    #     default_factory=lambda: [1000, 750, 500, 250])
+    dmd_denoising_steps: list[int] | None = field(
+        default_factory=lambda: [1000, 875, 750, 625, 500, 375, 250, 125])
+    warp_denoising_step: bool = True

fastvideo/configs/pipelines/hyworld.py

@@ -0,0 +1,29 @@
+# SPDX-License-Identifier: Apache-2.0
+from dataclasses import dataclass, field
+
+from fastvideo.configs.models import DiTConfig, EncoderConfig
+from fastvideo.configs.models.dits import HYWorldConfig as HYWorldDiTConfig
+from fastvideo.configs.models.encoders import SiglipVisionConfig
+from fastvideo.configs.pipelines.hunyuan15 import Hunyuan15T2V480PConfig
+
+
+@dataclass
+class HYWorldConfig(Hunyuan15T2V480PConfig):
+    """Base configuration for HYWorld pipeline architecture."""
+
+    # HYWorldConfig-specific parameters with defaults
+    dit_config: DiTConfig = field(default_factory=HYWorldDiTConfig)
+
+    # SigLIP image encoder for I2V
+    image_encoder_config: EncoderConfig = field(
+        default_factory=SiglipVisionConfig)
+    image_encoder_precision: str = "fp16"
+    # vae_precision: str = "fp32"
+
+    # Text encoding
+    text_encoder_precisions: tuple[str, ...] = field(
+        default_factory=lambda: ("fp16", "fp32"))
+
+    def __post_init__(self):
+        self.vae_config.load_encoder = True
+        self.vae_config.load_decoder = True

fastvideo/configs/pipelines/longcat.py

@@ -17,11 +17,7 @@ from fastvideo.configs.pipelines.base import PipelineConfig
 
 @dataclass
 class LongCatDiTArchConfig(DiTArchConfig):
-    """Extended DiTArchConfig with LongCat-specific fields.
-    
-    NOTE: This is for Phase 1 wrapper compatibility. For native model (Phase 2),
-    use LongCatVideoConfig from fastvideo.configs.models.dits.longcat instead.
-    """
+    """Extended DiTArchConfig with LongCat-specific fields."""
     # LongCat-specific architecture parameters
     adaln_tembed_dim: int = 512
     caption_channels: int = 4096
@@ -88,20 +84,16 @@ def umt5_postprocess_text(outputs: BaseEncoderOutput) -> torch.Tensor:
 
 @dataclass
 class LongCatT2V480PConfig(PipelineConfig):
-    """Configuration for LongCat pipeline (480p) aligned to LongCat-Video modules.
+    """Configuration for LongCat pipeline (480p).
 
     Components expected by loaders:
       - tokenizer: AutoTokenizer
       - text_encoder: UMT5EncoderModel
-      - transformer: LongCatVideoTransformer3DModel (Phase 1 wrapper)
-                  OR LongCatTransformer3DModel (Phase 2 native)
+      - transformer: LongCatTransformer3DModel
       - vae: AutoencoderKLWan (Wan VAE, 4x8 compression)
       - scheduler: FlowMatchEulerDiscreteScheduler
     """
 
-    # DiT config with LongCat-specific arch_config
-    # NOTE: For Phase 1 wrapper, uses LongCatDiTArchConfig
-    # For Phase 2 native model, can use LongCatVideoConfig directly
     dit_config: DiTConfig = field(
         default_factory=lambda: DiTConfig(arch_config=LongCatDiTArchConfig()))
 

fastvideo/configs/pipelines/ltx2.py

@@ -0,0 +1,50 @@
+# SPDX-License-Identifier: Apache-2.0
+from collections.abc import Callable
+from dataclasses import dataclass, field
+
+import torch
+
+from fastvideo.configs.models import (DiTConfig, EncoderConfig, ModelConfig,
+                                      LTX2AudioDecoderConfig, LTX2VocoderConfig,
+                                      VAEConfig)
+from fastvideo.configs.models.dits import LTX2VideoConfig
+from fastvideo.configs.models.encoders import BaseEncoderOutput, LTX2GemmaConfig
+from fastvideo.configs.models.vaes import LTX2VAEConfig
+from fastvideo.configs.pipelines.base import PipelineConfig, preprocess_text
+
+
+def ltx2_postprocess_text(outputs: BaseEncoderOutput) -> torch.Tensor:
+    return outputs.last_hidden_state
+
+
+@dataclass
+class LTX2T2VConfig(PipelineConfig):
+    """Configuration for LTX-2 T2V pipeline."""
+
+    dit_config: DiTConfig = field(default_factory=LTX2VideoConfig)
+    vae_config: VAEConfig = field(default_factory=LTX2VAEConfig)
+    vae_tiling: bool = True
+    vae_sp: bool = False
+
+    text_encoder_configs: tuple[EncoderConfig, ...] = field(
+        default_factory=lambda: (LTX2GemmaConfig(), ))
+    preprocess_text_funcs: tuple[Callable[[str], str], ...] = field(
+        default_factory=lambda: (preprocess_text, ))
+    postprocess_text_funcs: tuple[Callable[[BaseEncoderOutput], torch.Tensor],
+                                  ...] = field(default_factory=lambda:
+                                               (ltx2_postprocess_text, ))
+
+    dit_precision: str = "bf16"
+    vae_precision: str = "bf16"
+    text_encoder_precisions: tuple[str, ...] = field(
+        default_factory=lambda: ("bf16", ))
+
+    audio_decoder_config: ModelConfig = field(
+        default_factory=LTX2AudioDecoderConfig)
+    vocoder_config: ModelConfig = field(default_factory=LTX2VocoderConfig)
+    audio_decoder_precision: str = "bf16"
+    vocoder_precision: str = "bf16"
+
+    def __post_init__(self) -> None:
+        self.vae_config.load_encoder = False
+        self.vae_config.load_decoder = True

fastvideo/configs/pipelines/registry.py

@@ -6,10 +6,16 @@ from collections.abc import Callable
 
 from fastvideo.configs.pipelines.base import PipelineConfig
 from fastvideo.configs.pipelines.cosmos import CosmosConfig
+from fastvideo.configs.pipelines.cosmos2_5 import Cosmos25Config
 from fastvideo.configs.pipelines.hunyuan import FastHunyuanConfig, HunyuanConfig
-from fastvideo.configs.pipelines.hunyuan15 import Hunyuan15T2V480PConfig, Hunyuan15T2V720PConfig
+from fastvideo.configs.pipelines.hyworld import HYWorldConfig
+from fastvideo.configs.pipelines.ltx2 import LTX2T2VConfig
+from fastvideo.configs.pipelines.hunyuan15 import Hunyuan15T2V480PConfig, Hunyuan15T2V720PConfig, SelfForcingHunyuan15T2V480PConfig, Hunyuan15DistilledI2V480PConfig
 from fastvideo.configs.pipelines.stepvideo import StepVideoT2VConfig
 from fastvideo.configs.pipelines.longcat import LongCatT2V480PConfig
+from fastvideo.configs.pipelines.turbodiffusion import (
+    TurboDiffusionT2V_1_3B_Config, TurboDiffusionT2V_14B_Config,
+    TurboDiffusionI2V_A14B_Config)
 
 # isort: off
 from fastvideo.configs.pipelines.wan import (
@@ -29,10 +35,15 @@ logger = init_logger(__name__)
 PIPE_NAME_TO_CONFIG: dict[str, type[PipelineConfig]] = {
     "FastVideo/FastHunyuan-diffusers": FastHunyuanConfig,
     "hunyuanvideo-community/HunyuanVideo": HunyuanConfig,
+    "weizhou03/SFHunyuanVideo-1.5-Diffusers-480p_t2v":
+    SelfForcingHunyuan15T2V480PConfig,
+    "hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_i2v_step_distilled":
+    Hunyuan15DistilledI2V480PConfig,
     "hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v":
     Hunyuan15T2V480PConfig,
     "hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_t2v":
     Hunyuan15T2V720PConfig,
+    "FastVideo/HY-WorldPlay-Bidirectional-Diffusers": HYWorldConfig,
     "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,
@@ -52,18 +63,38 @@ PIPE_NAME_TO_CONFIG: dict[str, type[PipelineConfig]] = {
     "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,
+    "KyleShao/Cosmos-Predict2.5-2B-Diffusers": Cosmos25Config,
     "FastVideo/Matrix-Game-2.0-Base-Diffusers": MatrixGameI2V480PConfig,
     "FastVideo/Matrix-Game-2.0-GTA-Diffusers": MatrixGameI2V480PConfig,
     "FastVideo/Matrix-Game-2.0-TempleRun-Diffusers": MatrixGameI2V480PConfig,
+    # LongCat Video models
+    "FastVideo/LongCat-Video-T2V-Diffusers": LongCatT2V480PConfig,
+    "FastVideo/LongCat-Video-I2V-Diffusers": LongCatT2V480PConfig,
+    "FastVideo/LongCat-Video-VC-Diffusers": LongCatT2V480PConfig,
+    # LTX-2 models
+    "Lightricks/LTX-2": LTX2T2VConfig,
+    "converted/ltx2_diffusers": LTX2T2VConfig,
+    # TurboDiffusion models
+    "loayrashid/TurboWan2.1-T2V-1.3B-Diffusers": TurboDiffusionT2V_1_3B_Config,
+    "loayrashid/TurboWan2.1-T2V-14B-Diffusers": TurboDiffusionT2V_14B_Config,
+    "loayrashid/TurboWan2.2-I2V-A14B-Diffusers": TurboDiffusionI2V_A14B_Config,
     # Add other specific weight variants
 }
 
 # For determining pipeline type from model ID
 PIPELINE_DETECTOR: dict[str, Callable[[str], bool]] = {
+    "longcatimagetovideo":
+    lambda id: "longcatimagetovideo" in id.lower(),
+    "longcatvideocontinuation":
+    lambda id: "longcatvideocontinuation" in id.lower(),
+    "longcat":
+    lambda id: "longcat" in id.lower(),
     "hunyuan":
     lambda id: "hunyuan" in id.lower(),
     "hunyuan15":
     lambda id: "hunyuan15" in id.lower(),
+    "hyworld":
+    lambda id: "hyworld" in id.lower(),
     "matrixgame":
     lambda id: "matrix-game" in id.lower() or "matrixgame" in id.lower(),
     "wanpipeline":
@@ -77,26 +108,38 @@ PIPELINE_DETECTOR: dict[str, Callable[[str], bool]] = {
     "stepvideo":
     lambda id: "stepvideo" in id.lower(),
     "cosmos":
-    lambda id: "cosmos" in id.lower(),
-    "longcat":
-    lambda id: "longcat" in id.lower(),
+    lambda id: "cosmos" in id.lower() and ("2.5" not in id.lower(
+    ) and "2_5" not in id.lower() and "25" not in id.lower()),
+    "cosmos25":
+    lambda id: "cosmos25" in id.lower(),
+    "turbodiffusion":
+    lambda id: "turbodiffusion" in id.lower() or "turbowan" in id.lower(),
+    "ltx2":
+    lambda id: "ltx2" in id.lower() or "ltx-2" in id.lower(),
     # Add other pipeline architecture detectors
 }
 
 # Fallback configs when exact match isn't found but architecture is detected
 PIPELINE_FALLBACK_CONFIG: dict[str, type[PipelineConfig]] = {
+    "longcatimagetovideo": LongCatT2V480PConfig,
+    "longcatvideocontinuation": LongCatT2V480PConfig,
     "longcat": LongCatT2V480PConfig,
+    "cosmos25": Cosmos25Config,
     "hunyuan":
     HunyuanConfig,  # Base Hunyuan config as fallback for any Hunyuan variant
     "matrixgame": MatrixGameI2V480PConfig,
     "hunyuan15":
     Hunyuan15T2V480PConfig,  # Base Hunyuan15 config as fallback for any Hunyuan15 variant
+    "hyworld":
+    HYWorldConfig,  # HYWorld-specific config as fallback for any HYWorld variant
     "wanpipeline":
     WanT2V480PConfig,  # Base Wan config as fallback for any Wan variant
     "wanimagetovideo": WanI2V480PConfig,
     "wandmdpipeline": FastWan2_1_T2V_480P_Config,
     "wancausaldmdpipeline": SelfForcingWanT2V480PConfig,
-    "stepvideo": StepVideoT2VConfig
+    "stepvideo": StepVideoT2VConfig,
+    "turbodiffusion": TurboDiffusionT2V_1_3B_Config,
+    "ltx2": LTX2T2VConfig,
     # Other fallbacks by architecture
 }
 

fastvideo/configs/pipelines/turbodiffusion.py

@@ -0,0 +1,128 @@
+# SPDX-License-Identifier: Apache-2.0
+"""
+TurboDiffusion pipeline configurations.
+
+TurboDiffusion uses RCM (recurrent Consistency Model) scheduler with
+SLA (Sparse-Linear Attention) for fast 1-4 step video generation.
+"""
+from dataclasses import dataclass, field
+
+from fastvideo.configs.models import DiTConfig, EncoderConfig, VAEConfig
+from fastvideo.configs.models.dits import WanVideoConfig
+from fastvideo.configs.models.encoders import CLIPVisionConfig
+from fastvideo.configs.models.vaes import WanVAEConfig
+from fastvideo.configs.pipelines.base import PipelineConfig
+from fastvideo.configs.pipelines.wan import t5_postprocess_text, T5Config, BaseEncoderOutput
+
+import torch
+from collections.abc import Callable
+
+
+@dataclass
+class TurboDiffusionT2VConfig(PipelineConfig):
+    """Base configuration for TurboDiffusion T2V pipeline.
+    
+    Uses RCM scheduler with sigma_max=80 for 1-4 step generation.
+    No boundary_ratio (single model, no switching).
+    """
+    # DiT
+    dit_config: DiTConfig = field(default_factory=WanVideoConfig)
+    # VAE
+    vae_config: VAEConfig = field(default_factory=WanVAEConfig)
+    vae_tiling: bool = False
+    vae_sp: bool = False
+
+    # Denoising stage
+    flow_shift: float | None = 3.0
+
+    # No boundary_ratio for T2V (single model)
+    boundary_ratio: float | None = None
+
+    # Text encoding stage
+    text_encoder_configs: tuple[EncoderConfig, ...] = field(
+        default_factory=lambda: (T5Config(), ))
+    postprocess_text_funcs: tuple[Callable[[BaseEncoderOutput], torch.Tensor],
+                                  ...] = field(default_factory=lambda:
+                                               (t5_postprocess_text, ))
+
+    # Precision for each component
+    precision: str = "bf16"
+    vae_precision: str = "fp32"
+    text_encoder_precisions: tuple[str, ...] = field(
+        default_factory=lambda: ("fp32", ))
+
+    # self-forcing params
+    warp_denoising_step: bool = True
+
+    def __post_init__(self):
+        self.vae_config.load_encoder = False
+        self.vae_config.load_decoder = True
+        # Ensure no boundary_ratio is set in dit_config
+        self.dit_config.boundary_ratio = None
+
+
+@dataclass
+class TurboDiffusionT2V_1_3B_Config(TurboDiffusionT2VConfig):
+    """Configuration for TurboDiffusion T2V 1.3B model."""
+    pass
+
+
+@dataclass
+class TurboDiffusionT2V_14B_Config(TurboDiffusionT2VConfig):
+    """Configuration for TurboDiffusion T2V 14B model.
+    
+    Uses same config as 1.3B but with higher flow_shift for 14B model.
+    """
+    flow_shift: float | None = 5.0
+
+
+@dataclass
+class TurboDiffusionI2VConfig(PipelineConfig):
+    """Base configuration for TurboDiffusion I2V pipeline.
+    
+    Uses RCM scheduler with sigma_max=200 for 1-4 step generation.
+    Uses boundary_ratio=0.9 for high-noise to low-noise model switching.
+    """
+    # DiT
+    dit_config: DiTConfig = field(default_factory=WanVideoConfig)
+    # VAE
+    vae_config: VAEConfig = field(default_factory=WanVAEConfig)
+    vae_tiling: bool = False
+    vae_sp: bool = False
+
+    # Denoising stage
+    flow_shift: float | None = 5.0
+
+    boundary_ratio: float | None = 0.9
+
+    # Text encoding stage
+    text_encoder_configs: tuple[EncoderConfig, ...] = field(
+        default_factory=lambda: (T5Config(), ))
+    postprocess_text_funcs: tuple[Callable[[BaseEncoderOutput], torch.Tensor],
+                                  ...] = field(default_factory=lambda:
+                                               (t5_postprocess_text, ))
+
+    # Image encoder for I2V
+    image_encoder_config: EncoderConfig = field(
+        default_factory=CLIPVisionConfig)
+    image_encoder_precision: str = "fp32"
+
+    # Precision for each component
+    precision: str = "bf16"
+    vae_precision: str = "fp32"
+    text_encoder_precisions: tuple[str, ...] = field(
+        default_factory=lambda: ("fp32", ))
+
+    # self-forcing params
+    warp_denoising_step: bool = True
+
+    def __post_init__(self):
+        self.vae_config.load_encoder = True
+        self.vae_config.load_decoder = True
+        self.dit_config.boundary_ratio = self.boundary_ratio
+
+
+@dataclass
+class TurboDiffusionI2V_A14B_Config(TurboDiffusionI2VConfig):
+    """Configuration for TurboDiffusion I2V A14B model."""
+    pass

fastvideo/configs/sample/base.py

@@ -223,7 +223,7 @@ class SamplingParam:
             help="Path to input image for image-to-video generation",
         )
         parser.add_argument(
-            "--video_path",
+            "--video-path",
             type=str,
             default=SamplingParam.video_path,
             help="Path to input video for video-to-video generation",

fastvideo/configs/sample/cosmos2_5.py

@@ -0,0 +1,19 @@
+# SPDX-License-Identifier: Apache-2.0
+from dataclasses import dataclass
+
+from fastvideo.configs.sample.base import SamplingParam
+
+
+@dataclass
+class Cosmos_Predict2_5_2B_Diffusers_SamplingParam(SamplingParam):
+    """Defaults for Cosmos 2.5 (Predict2.5) text-to-video diffusers-format model."""
+
+    height: int = 480
+    width: int = 832
+    num_frames: int = 121
+    fps: int = 24
+
+    guidance_scale: float = 7.0
+    # Official Cosmos2.5 sampling uses empty string as unconditional.
+    negative_prompt: str = ""
+    num_inference_steps: int = 35

fastvideo/configs/sample/hunyuan15.py

@@ -17,11 +17,24 @@ class Hunyuan15_480P_SamplingParam(SamplingParam):
     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]))
+    # sigmas: list[float] | None = field(
+    #     default_factory=lambda: list(np.linspace(1.0, 0.0, 50 + 1)[:-1]))
 
     negative_prompt: str = ""
 
+    def __post_init__(self):
+        super().__post_init__()
+        self.sigmas = list(
+            np.linspace(1.0, 0.0, self.num_inference_steps + 1)[:-1])
+
+
+@dataclass
+class Hunyuan15_480P_Distilled_SamplingParam(Hunyuan15_480P_SamplingParam):
+    num_inference_steps: int = 8
+    fps: int = 24
+
+    guidance_scale: float = 1.0
+
 
 @dataclass
 class Hunyuan15_720P_SamplingParam(Hunyuan15_480P_SamplingParam):

fastvideo/configs/sample/hyworld.py

@@ -0,0 +1,26 @@
+# SPDX-License-Identifier: Apache-2.0
+from dataclasses import dataclass, field
+
+from fastvideo.configs.sample.base import SamplingParam
+import numpy as np
+
+
+@dataclass
+class HYWorld_SamplingParam(SamplingParam):
+    num_inference_steps: int = 50
+
+    num_frames: int = 125
+    height: int = 480
+    width: int = 832
+    fps: int = 24
+
+    # Camera trajectory: pose string (e.g., 'w-31' means generating [1 + 31] latents) or JSON file path
+    pose: str = 'w-31'
+
+    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 = ""

fastvideo/configs/sample/ltx2.py

@@ -0,0 +1,20 @@
+# SPDX-License-Identifier: Apache-2.0
+from dataclasses import dataclass
+
+from fastvideo.configs.sample.base import SamplingParam
+
+
+@dataclass
+class LTX2SamplingParam(SamplingParam):
+    """Default sampling parameters for LTX-2 distilled T2V.
+    """
+
+    seed: int = 10
+    num_frames: int = 121
+    height: int = 1024
+    width: int = 1536
+    fps: int = 24
+    num_inference_steps: int = 8
+    guidance_scale: float = 1.0
+    # No default negative_prompt for distilled models
+    negative_prompt: str = ""

fastvideo/configs/sample/registry.py

@@ -5,10 +5,13 @@ 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.hyworld import HYWorld_SamplingParam
+from fastvideo.configs.sample.hunyuan15 import Hunyuan15_480P_SamplingParam, Hunyuan15_720P_SamplingParam, Hunyuan15_480P_Distilled_SamplingParam
 from fastvideo.configs.sample.stepvideo import StepVideoT2VSamplingParam
 
 from fastvideo.configs.sample.cosmos import Cosmos_Predict2_2B_Video2World_SamplingParam
+from fastvideo.configs.sample.cosmos2_5 import Cosmos_Predict2_5_2B_Diffusers_SamplingParam
+from fastvideo.configs.sample.ltx2 import LTX2SamplingParam
 
 # isort: off
 from fastvideo.configs.sample.wan import (
@@ -26,6 +29,11 @@ from fastvideo.configs.sample.wan import (
     SelfForcingWan2_2_T2V_A14B_480P_SamplingParam,
     MatrixGame2_SamplingParam,
 )
+from fastvideo.configs.sample.turbodiffusion import (
+    TurboDiffusionT2V_1_3B_SamplingParam,
+    TurboDiffusionT2V_14B_SamplingParam,
+    TurboDiffusionI2V_A14B_SamplingParam,
+)
 # isort: on
 from fastvideo.logger import init_logger
 from fastvideo.utils import (maybe_download_model_index,
@@ -36,10 +44,15 @@ logger = init_logger(__name__)
 SAMPLING_PARAM_REGISTRY: dict[str, Any] = {
     "FastVideo/FastHunyuan-diffusers": FastHunyuanSamplingParam,
     "hunyuanvideo-community/HunyuanVideo": HunyuanSamplingParam,
+    "weizhou03/SFHunyuanVideo-1.5-Diffusers-480p_t2v":
+    Hunyuan15_480P_SamplingParam,
+    "hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_i2v_step_distilled":
+    Hunyuan15_480P_Distilled_SamplingParam,
     "hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v":
     Hunyuan15_480P_SamplingParam,
     "hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_t2v":
     Hunyuan15_720P_SamplingParam,
+    "FastVideo/HY-WorldPlay-Bidirectional-Diffusers": HYWorld_SamplingParam,
     "FastVideo/stepvideo-t2v-diffusers": StepVideoT2VSamplingParam,
 
     # Wan2.1
@@ -79,11 +92,27 @@ SAMPLING_PARAM_REGISTRY: dict[str, Any] = {
     "nvidia/Cosmos-Predict2-2B-Video2World":
     Cosmos_Predict2_2B_Video2World_SamplingParam,
 
+    # Cosmos2.5
+    "KyleShao/Cosmos-Predict2.5-2B-Diffusers":
+    Cosmos_Predict2_5_2B_Diffusers_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,
 
+    # TurboDiffusion models
+    "loayrashid/TurboWan2.1-T2V-1.3B-Diffusers":
+    TurboDiffusionT2V_1_3B_SamplingParam,
+    "loayrashid/TurboWan2.1-T2V-14B-Diffusers":
+    TurboDiffusionT2V_14B_SamplingParam,
+    "loayrashid/TurboWan2.2-I2V-A14B-Diffusers":
+    TurboDiffusionI2V_A14B_SamplingParam,
+
+    # LTX-2 models
+    "Lightricks/LTX-2": LTX2SamplingParam,
+    "FastVideo/LTX2-Distilled-Diffusers": LTX2SamplingParam,
+
     # Add other specific weight variants
 }
 
@@ -93,6 +122,8 @@ SAMPLING_PARAM_DETECTOR: dict[str, Callable[[str], bool]] = {
     lambda id: "hunyuan" in id.lower(),
     "hunyuan15":
     lambda id: "hunyuan15" in id.lower(),
+    "hyworld":
+    lambda id: "hyworld" in id.lower(),
     "wanpipeline":
     lambda id: "wanpipeline" in id.lower(),
     "wanimagetovideo":
@@ -105,6 +136,14 @@ SAMPLING_PARAM_DETECTOR: dict[str, Callable[[str], bool]] = {
     lambda id: "wancausaldmdpipeline" in id.lower(),
     "matrixgame":
     lambda id: "matrixgame" in id.lower() or "matrix-game" in id.lower(),
+    "turbodiffusion":
+    lambda id: "turbodiffusion" in id.lower() or "turbowan" in id.lower(),
+    "cosmos25":
+    lambda id: "cosmos2_5" in id.lower(),
+    "cosmos":
+    lambda id: "cosmos" in id.lower() and "2_5" not in id.lower(),
+    "ltx2":
+    lambda id: "ltx2" in id.lower() or "ltx-2" in id.lower(),
     # Add other pipeline architecture detectors
 }
 
@@ -114,6 +153,8 @@ SAMPLING_FALLBACK_PARAM: dict[str, Any] = {
     HunyuanSamplingParam,  # Base Hunyuan config as fallback for any Hunyuan variant
     "hunyuan15":
     Hunyuan15_480P_SamplingParam,  # Base Hunyuan15 config as fallback for any Hunyuan15 variant
+    "hyworld":
+    HYWorld_SamplingParam,  # HYWorld-specific config as fallback for any HYWorld variant
     "wanpipeline":
     WanT2V_1_3B_SamplingParam,  # Base Wan config as fallback for any Wan variant
     "wanimagetovideo": WanI2V_14B_480P_SamplingParam,
@@ -121,13 +162,17 @@ SAMPLING_FALLBACK_PARAM: dict[str, Any] = {
     "wancausaldmdpipeline": SelfForcingWan2_1_T2V_1_3B_480P_SamplingParam,
     "stepvideo": StepVideoT2VSamplingParam,
     "matrixgame": MatrixGame2_SamplingParam,
+    "turbodiffusion":
+    TurboDiffusionT2V_1_3B_SamplingParam,  # Default to T2V for fallback
+    "cosmos25": Cosmos_Predict2_5_2B_Diffusers_SamplingParam,
+    "cosmos": Cosmos_Predict2_2B_Video2World_SamplingParam,
+    "ltx2": LTX2SamplingParam,
     # Other fallbacks by architecture
 }
 
 
 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]
@@ -144,9 +189,6 @@ def get_sampling_param_cls_for_name(pipeline_name_or_path: str) -> Any | None:
 
     if os.path.exists(pipeline_name_or_path):
         config = verify_model_config_and_directory(pipeline_name_or_path)
-        logger.warning(
-            "FastVideo may not correctly identify the optimal sampling param for this model, as the local directory may have been renamed."
-        )
     else:
         config = maybe_download_model_index(pipeline_name_or_path)