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2026-02-10 08:33:00 +00:00 · 2026-02-10 00:30:03 -08:00 · 2026-02-10 08:22:05 +00:00 · 2026-02-10 07:58:43 +00:00 · 2026-02-09 17:51:37 -08:00 · 2026-02-09 17:32:57 -08:00
145 changed files with 8837 additions and 2215 deletions
@@ -0,0 +1,42 @@
+# Repository Guidelines
+
+## Project Structure & Module Organization
+- Core Python package: `fastvideo/` (models, pipelines, training, distributed runtime, CLI entrypoints).
+- CUDA/custom kernels: `fastvideo-kernel/` (separate build/test flow).
+- Tests:
+  - `fastvideo/tests/` for package-level tests (dataset, encoders, inference, training, SSIM, workflow).
+  - `tests/local_tests/` for additional local/component checks.
+- Docs and guides: `docs/` (MkDocs source), with contributor docs in `docs/contributing/`.
+- Runnable examples and scripts: `examples/` and `scripts/`.
+- Static assets: `assets/`, `images/`, `videos/`, and `comfyui/assets/`.
+
+## Build, Test, and Development Commands
+- `uv pip install -e .[dev]`: editable install with lint/test extras.
+- `pre-commit install --hook-type pre-commit --hook-type commit-msg`: enable local hooks.
+- `pre-commit run --all-files`: run formatter/lint/type/spelling checks.
+- `pytest tests/`: run top-level test suite.
+- `pytest fastvideo/tests/ -v`: run package tests.
+- `pytest fastvideo/tests/ssim/ -vs`: run SSIM regression tests (GPU-heavy).
+- `cd fastvideo-kernel && ./build.sh`: build kernel extensions.
+
+## Coding Style & Naming Conventions
+- Python 3.10+; 4-space indentation; keep code and imports readable and explicit.
+- Style tools are configured in `pyproject.toml` and `.pre-commit-config.yaml`:
+  - `yapf` (format), `ruff` (lint, auto-fix), `mypy` (typing), `codespell`.
+- Target line length is 80.
+- Naming: `snake_case` for functions/files, `PascalCase` for classes, `UPPER_SNAKE_CASE` for constants.
+
+## Testing Guidelines
+- Use `pytest` and place tests near relevant domains (e.g., `fastvideo/tests/encoders/`).
+- Prefer descriptive names like `test_<feature>_<expected_behavior>.py`.
+- For new pipelines/backends, include at least one regression-oriented test; add SSIM coverage when output quality must be preserved.
+- Document GPU assumptions in tests that require specific hardware.
+
+## Commit & Pull Request Guidelines
+- Follow existing commit style: short subject with optional tag prefix, e.g. `[bugfix]: ...`, `[feat]: ...`, `[misc]: ...`, and include PR reference like `(#1234)` when applicable.
+- Keep commits focused by concern (feature, refactor, fix).
+- PRs should include:
+  - clear problem/solution summary,
+  - test evidence (`pytest`/SSIM outputs or rationale if skipped),
+  - linked issue/PR context,
+  - screenshots or sample outputs for UI/demo/docs changes.
@@ -0,0 +1 @@
+@AGENTS.md
@@ -9,27 +9,26 @@
 **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>
+- `2025/11/19`: Release [CausalWan2.2 I2V A14B Preview](https://huggingface.co/FastVideo/CausalWan2.2-I2V-A14B-Preview-Diffusers) models, [Blog](https://hao-ai-lab.github.io/blogs/fastvideo_causalwan_preview/) and [Inference Code!](https://github.com/hao-ai-lab/FastVideo/blob/main/examples/inference/basic/basic_self_forcing_causal_wan2_2_i2v.py)
+- `2025/08/04`: Release [FastWan](https://hao-ai-lab.github.io/FastVideo/distillation/dmd) models and [Sparse-Distillation](https://hao-ai-lab.github.io/blogs/fastvideo_post_training/).

- ```2025/06/14```: Release finetuning and inference code for [VSA](https://arxiv.org/pdf/2505.13389)
- ```2025/04/24```: [FastVideo V1](https://hao-ai-lab.github.io/blogs/fastvideo/) is released!
- ```2025/02/18```: Release the inference code for [Sliding Tile Attention](https://hao-ai-lab.github.io/blogs/sta/).
+### More News

-</details>
+- `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/).

 ## Key Features

 FastVideo has the following features:
+
 - 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
  - 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
+  - [Sparse distillation](https://hao-ai-lab.github.io/blogs/fastvideo_post_training/) to achieve >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.
@@ -44,6 +43,7 @@ FastVideo has the following features:
  - 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:

 ```bash
@@ -58,18 +58,20 @@ pip install fastvideo
 Please see our [docs](https://hao-ai-lab.github.io/FastVideo/getting_started/installation/) for more detailed installation instructions.

 ## Sparse Distillation
+
 For our sparse distillation techniques, please see our [distillation docs](https://hao-ai-lab.github.io/FastVideo/distillation/dmd/) and check out our [blog](https://hao-ai-lab.github.io/blogs/fastvideo_post_training/).

 See below for recipes and datasets:

-|                                            Model                                              |                                               Sparse Distillation                                                 |                                                  Dataset                                                  |
-|:-------------------------------------------------------------------------------------------:  |:---------------------------------------------------------------------------------------------------------------:  |:--------------------------------------------------------------------------------------------------------: |
-| [FastWan2.1-T2V-1.3B](https://huggingface.co/FastVideo/FastWan2.1-T2V-1.3B-Diffusers)         |    [Recipe](https://github.com/hao-ai-lab/FastVideo/tree/main/examples/distill/Wan2.1-T2V/Wan-Syn-Data-480P)      | [FastVideo Synthetic Wan2.1 480P](https://huggingface.co/datasets/FastVideo/Wan-Syn_77x448x832_600k)      |
-| [FastWan2.1-T2V-14B-Preview](https://huggingface.co/FastVideo/FastWan2.1-T2V-14B-Diffusers)   |                                                   Coming soon!                                                    |   [FastVideo Synthetic Wan2.1 720P](https://huggingface.co/datasets/FastVideo/Wan-Syn_77x768x1280_250k)   |
-| [FastWan2.2-TI2V-5B](https://huggingface.co/FastVideo/FastWan2.2-TI2V-5B-Diffusers)           | [Recipe](https://github.com/hao-ai-lab/FastVideo/tree/main/examples/distill/Wan2.2-TI2V-5B-Diffusers/Data-free)   | [FastVideo Synthetic Wan2.2 720P](https://huggingface.co/datasets/FastVideo/Wan2.2-Syn-121x704x1280_32k)  |
+| Model                                                                                 | Sparse Distillation                                                                                             | Dataset                                                                                                  |
+| ------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------- |
+| [FastWan2.1-T2V-1.3B](https://huggingface.co/FastVideo/FastWan2.1-T2V-1.3B-Diffusers) | [Recipe](https://github.com/hao-ai-lab/FastVideo/tree/main/examples/distill/Wan2.1-T2V/Wan-Syn-Data-480P)       | [FastVideo Synthetic Wan2.1 480P](https://huggingface.co/datasets/FastVideo/Wan-Syn_77x448x832_600k)     |
+| [FastWan2.2-TI2V-5B](https://huggingface.co/FastVideo/FastWan2.2-TI2V-5B-Diffusers)   | [Recipe](https://github.com/hao-ai-lab/FastVideo/tree/main/examples/distill/Wan2.2-TI2V-5B-Diffusers/Data-free) | [FastVideo Synthetic Wan2.2 720P](https://huggingface.co/datasets/FastVideo/Wan2.2-Syn-121x704x1280_32k) |

 ## Inference
+
 ### Generating Your First Video
+
 Here's a minimal example to generate a video using the default settings. Make sure VSA kernels are [installed](https://hao-ai-lab.github.io/FastVideo/video_sparse_attention/installation/). Create a file called `example.py` with the following code:

 ```python
@@ -108,55 +110,36 @@ python example.py

 For a more detailed guide, please see our [inference quick start](https://hao-ai-lab.github.io/FastVideo/inference/inference_quick_start/).

-### Other docs:
+## More Guides

 - [Design Overview](https://hao-ai-lab.github.io/FastVideo/design/overview/)
- [Contribution Guide](https://hao-ai-lab.github.io/FastVideo/getting_started/installation/)
-
-## Distillation and Finetuning
 - [Distillation Guide](https://hao-ai-lab.github.io/FastVideo/distillation/dmd/)
-<!-- - [Finetuning Guide](https://hao-ai-lab.github.io/FastVideo/training/finetune.html) -->
+- [Contribution Guide](https://hao-ai-lab.github.io/FastVideo/contributing/overview/)

 ## Awesome work using FastVideo or our research projects

- [SGLang](https://github.com/sgl-project/sglang/tree/main/python/sglang/multimodal_gen): SGLang's diffusion inference functionality is based  on a fork of FastVideo on Sept. 24, 2025. [![Star](https://img.shields.io/github/stars/sgl-project/sglang.svg?style=social&label=Star)](https://github.com/sgl-project/sglang)
-
- [DanceGRPO](https://github.com/XueZeyue/DanceGRPO): A  unified framework to adapt Group Relative Policy Optimization (GRPO) to visual generation paradigms. Code based on FastVideo. [![Star](https://img.shields.io/github/stars/XueZeyue/DanceGRPO.svg?style=social&label=Star)](https://github.com/XueZeyue/DanceGRPO)
- [SRPO](https://github.com/Tencent-Hunyuan/SRPO): A method to directly align the full diffusion trajectory with fine-grained human preference. Code based on FastVideo. [![Star](https://img.shields.io/github/stars/Tencent-Hunyuan/SRPO.svg?style=social&label=Star)](https://github.com/Tencent-Hunyuan/SRPO)
- [DCM](https://github.com/Vchitect/DCM): Dual-expert consistency model for efficient and high-quality video generation. Code based on FastVideo. [![Star](https://img.shields.io/github/stars/Vchitect/DCM.svg?style=social&label=Star)](https://github.com/Vchitect/DCM)
- [Hunyuan Video 1.5](https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5): A leading lightweight video generation model, where they proposed SSTA based on Sliding Tile Attention. [![Star](https://img.shields.io/github/stars/Tencent-Hunyuan/HunyuanVideo-1.5.svg?style=social&label=Star)](https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5)
- [Kandinsky-5.0](https://github.com/kandinskylab/kandinsky-5): A family of diffusion models for video & image generation, where their NABLA attention includes a Sliding Tile Attention branch. [![Star](https://img.shields.io/github/stars/kandinskylab/kandinsky-5.svg?style=social&label=Star)](https://github.com/kandinskylab/kandinsky-5)
- [LongCat Video](https://github.com/meituan-longcat/LongCat-Video): A foundational video generation model with 13.6B parameters with block-sparse attention similar to Video Sparse Attention. [![Star](https://img.shields.io/github/stars/meituan-longcat/LongCat-Video.svg?style=social&label=Star)](https://github.com/meituan-longcat/LongCat-Video)
+- [SGLang](https://github.com/sgl-project/sglang/tree/main/python/sglang/multimodal_gen): SGLang's diffusion inference functionality is based on a fork of FastVideo on Sept. 24, 2025.
+- [DanceGRPO](https://github.com/XueZeyue/DanceGRPO): A unified framework to adapt Group Relative Policy Optimization (GRPO) to visual generation paradigms. Code based on FastVideo.
+- [SRPO](https://github.com/Tencent-Hunyuan/SRPO): A method to directly align the full diffusion trajectory with fine-grained human preference. Code based on FastVideo.
+- [DCM](https://github.com/Vchitect/DCM): Dual-expert consistency model for efficient and high-quality video generation. Code based on FastVideo.
+- [Hunyuan Video 1.5](https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5): A leading lightweight video generation model, where they proposed SSTA based on Sliding Tile Attention.
+- [Kandinsky-5.0](https://github.com/kandinskylab/kandinsky-5): A family of diffusion models for video & image generation, where their NABLA attention includes a Sliding Tile Attention branch.
+- [LongCat Video](https://github.com/meituan-longcat/LongCat-Video): A foundational video generation model with 13.6B parameters with block-sparse attention similar to Video Sparse Attention.

 ## 🤝 Contributing

 We welcome all contributions. Please check out our guide [here](https://hao-ai-lab.github.io/FastVideo/contributing/overview/).
 See details in [development roadmap](https://github.com/hao-ai-lab/FastVideo/issues/899).
-## Acknowledgement
-We learned and reused code from the following projects:
- [Wan-Video](https://github.com/Wan-Video)
- [ThunderKittens](https://github.com/HazyResearch/ThunderKittens)
- [Triton](https://github.com/triton-lang/triton)
- [DMD2](https://github.com/tianweiy/DMD2)
- [diffusers](https://github.com/huggingface/diffusers)
- [xDiT](https://github.com/xdit-project/xDiT)
- [vLLM](https://github.com/vllm-project/vllm)
- [SGLang](https://github.com/sgl-project/sglang)

-We thank [MBZUAI](https://ifm.mbzuai.ac.ae/), [Anyscale](https://www.anyscale.com/), and [GMI Cloud](https://www.gmicloud.ai/) for their support throughout this project.
+## Acknowledgement
+
+We learned the design and reused code from the following projects: [Wan-Video](https://github.com/Wan-Video), [ThunderKittens](https://github.com/HazyResearch/ThunderKittens), [DMD2](https://github.com/tianweiy/DMD2), [diffusers](https://github.com/huggingface/diffusers), [xDiT](https://github.com/xdit-project/xDiT), [vLLM](https://github.com/vllm-project/vllm), [SGLang](https://github.com/sgl-project/sglang). We thank [MBZUAI](https://ifm.mbzuai.ac.ae/), [Anyscale](https://www.anyscale.com/), and [GMI Cloud](https://www.gmicloud.ai/) for their support throughout this project.

 ## Citation
-If you find FastVideo useful, please considering citing our work:
+
+If you find FastVideo useful, please consider citing our research work:

 ```bibtex
-@software{fastvideo2024,
-  title        = {FastVideo: A Unified Framework for Accelerated Video Generation},
-  author       = {The FastVideo Team},
-  url          = {https://github.com/hao-ai-lab/FastVideo},
-  month        = apr,
-  year         = {2024},
-}
-
@article{zhang2025vsa,
  title={Vsa: Faster video diffusion with trainable sparse attention},
  author={Zhang, Peiyuan and Chen, Yongqi and Huang, Haofeng and Lin, Will and Liu, Zhengzhong and Stoica, Ion and Xing, Eric and Zhang, Hao},
@@ -43,7 +43,7 @@ RUN source $HOME/.local/bin/env && \
    source /opt/venv/bin/activate && \
    uv pip install --no-cache-dir --upgrade pip && \
    uv pip install --no-cache-dir .[dev] && \
-    uv pip install --no-cache-dir https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.5.4/flash_attn-2.8.3%2Bcu128torch2.9-cp310-cp310-linux_x86_64.whl
+    uv pip install --no-cache-dir https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.7.16/flash_attn-2.8.3+cu128torch2.10-cp310-cp310-linux_x86_64.whl

 COPY . .

@@ -43,7 +43,7 @@ RUN source $HOME/.local/bin/env && \
    source /opt/venv/bin/activate && \
    uv pip install --no-cache-dir --upgrade pip && \
    uv pip install --no-cache-dir .[dev] && \
-    uv pip install --no-cache-dir https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.5.4/flash_attn-2.8.3%2Bcu128torch2.9-cp311-cp311-linux_x86_64.whl
+    uv pip install --no-cache-dir https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.7.16/flash_attn-2.8.3+cu128torch2.10-cp311-cp311-linux_x86_64.whl

 COPY . .

@@ -43,7 +43,7 @@ RUN source $HOME/.local/bin/env && \
    source /opt/venv/bin/activate && \
    uv pip install --no-cache-dir --upgrade pip && \
    uv pip install --no-cache-dir .[dev] && \
-    uv pip install --no-cache-dir https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.5.4/flash_attn-2.8.3%2Bcu128torch2.9-cp312-cp312-linux_x86_64.whl
+    uv pip install --no-cache-dir https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.7.16/flash_attn-2.8.3+cu128torch2.10-cp312-cp312-linux_x86_64.whl

 COPY . .

@@ -121,6 +121,15 @@ This page contains the complete API reference for the FastVideo library.
      show_root_toc_entry: true
      heading_level: 4

+## fastvideo.registry
+
+::: fastvideo.registry
+    options:
+      show_source: true
+      show_root_heading: true
+      show_root_toc_entry: true
+      heading_level: 3
+
 ## fastvideo.pipelines

 ::: fastvideo.pipelines
@@ -324,8 +324,8 @@ Purpose:
 Action:

 - Add a new pipeline config + sampling params.
- Register them in `fastvideo/configs/pipelines/registry.py` and
-  `fastvideo/configs/sample/registry.py`.
+- Register them in `fastvideo/registry.py` using explicit
+  `register_configs(...)` blocks (this file is the single source of truth now).

 ### 5) Wire pipeline stages

@@ -17,7 +17,7 @@ You can easily use the FastVideo Pod Template on [RunPod](https://www.runpod.io)
 - Select the "FastVideo" or "fastvideo-dev" Pod Template.
 ![RunPod Pod Template Selection](../../assets/images/runpod_create.png)

- Set the Pod name to "<name>-<FastVideo>-<date>".
+- Set the Pod name to "`<name>-<FastVideo>-<date>`".

 - Finally, once the pod is deployed (will take a few minutes as the image is being pulled), you can SSH into it using "SSH exposed over TCP". You'll need to use the matching private ssh key you provided.
 ![RunPod SSH](../../assets/images/runpod_ssh.png)
@@ -51,8 +51,9 @@ runtime parameters consistent:
 - `fastvideo/configs/pipelines/`: pipeline wiring and required components.
 - `fastvideo/configs/sample/`: default sampling parameters (steps, frames,
  guidance scale, resolution, fps).
- `fastvideo/configs/registry.py`: pipeline registry.
- `fastvideo/configs/sample/registry.py`: sampling param registry.
+- `fastvideo/registry.py`: unified registry for pipeline config + sampling
+  defaults and model metadata resolution, defined via explicit
+  `register_configs(...)` blocks (no separate dict registries).

 `FastVideoArgs` (in `fastvideo/fastvideo_args.py`) provides runtime settings and
 is passed into pipeline construction and stages.
@@ -131,17 +131,41 @@ self.attn = DistributedAttention(

 ### Registering Models

-Register implemented modules in the model registry:
+Register implemented modules for auto‑discovery by adding `EntryClass` in each
+model module (the registry scans for it):

 ```python
-# In fastvideo/models/registry.py
-_TEXT_TO_VIDEO_DIT_MODELS = {
-    "YourTransformerModel": ("dits", "yourmodule", "YourTransformerClass"),
-}
+# In fastvideo/models/dits/your_module.py
+class YourTransformerModel(...):
+    ...

-_VAE_MODELS = {
-    "YourVAEModel": ("vaes", "yourvae", "YourVAEClass"),
-}
+# Entry point for model registry
+EntryClass = YourTransformerModel
+```
+
+```python
+# In fastvideo/models/vaes/your_vae.py
+class YourVAEModel(...):
+    ...
+
+# Entry point for model registry
+EntryClass = YourVAEModel
+```
+
+Register pipeline config + sampling defaults in the unified registry:
+
+```python
+# In fastvideo/registry.py
+register_configs(
+    sampling_param_cls=YourSamplingParam,
+    pipeline_config_cls=YourPipelineConfig,
+    hf_model_paths=[
+        "org/your-model-id",
+    ],
+    model_detectors=[
+        lambda path: "your-model" in path.lower(),
+    ],
+)
 ```

 ## Step 2: Directory Structure
@@ -0,0 +1,51 @@
+# SPDX-License-Identifier: Apache-2.0
+from fastvideo import VideoGenerator
+from fastvideo.configs.sample import SamplingParam
+
+
+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,
+    )
+
+    sampling_param = SamplingParam.from_pretrained(model_path)
+
+    # image2world example from official repo
+    image_path = "images/bus_terminal.jpg"
+
+    prompt = (
+        "A nighttime city bus terminal gradually shifts from stillness to subtle movement. "
+        "At first, multiple double-decker buses are parked under the glow of overhead lights, "
+        "with a central bus labeled '87D' facing forward and stationary. "
+        "As the video progresses, the bus in the middle moves ahead slowly, its headlights brightening the surrounding area "
+        "and casting reflections onto adjacent vehicles. "
+        "The motion creates space in the lineup, signaling activity within the otherwise quiet station. "
+        "It then comes to a smooth stop, resuming its position in line. "
+        "Overhead signage in Chinese characters remains illuminated, enhancing the vibrant, urban night scene."
+    )
+
+    generator.generate_video(
+        prompt,
+        sampling_param=sampling_param,
+        image_path=str(image_path),
+        num_cond_frames=1,
+        output_path="outputs_video/cosmos2_5_i2w.mp4",
+        save_video=True,
+    )
+
+    generator.shutdown()
+
+
+if __name__ == "__main__":
+    main()
+
+
@@ -1,4 +1,5 @@
 from fastvideo import VideoGenerator
+from fastvideo.configs.sample import SamplingParam


 def main():
@@ -15,19 +16,27 @@ def main():
        pin_cpu_memory=True,
    )

+    # Load default sampling parameters (negative_prompt, resolution, steps, etc.)
+    sampling_param = SamplingParam.from_pretrained(model_path)
+
    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."
+        "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(
+    generator.generate_video(
        prompt,
-        negative_prompt="",
-        height=704,
-        width=1280,
-        num_frames=77,
-        num_inference_steps=35,
-        guidance_scale=7.0,
-        fps=24,
+        sampling_param=sampling_param,
        output_path="outputs_video/cosmos2_5_t2w.mp4",
        save_video=True,
    )
@@ -0,0 +1,54 @@
+# SPDX-License-Identifier: Apache-2.0
+from fastvideo import VideoGenerator
+from fastvideo.configs.sample import SamplingParam
+
+
+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,
+    )
+
+    sampling_param = SamplingParam.from_pretrained(model_path)
+
+    # video2world example from official repo
+    video_path = "videos/robot_pouring.mp4"
+
+    prompt = (
+        "A robotic arm, primarily white with black joints and cables, is shown in a clean, modern indoor setting with a white tabletop. "
+        "The arm, equipped with a gripper holding a small, light green pitcher, is positioned above a clear glass containing a reddish-brown liquid and a spoon. "
+        "The robotic arm is in the process of pouring a transparent liquid into the glass. "
+        "To the left of the pitcher, there is an opened jar with a similar reddish-brown substance visible through its transparent body. "
+        "In the background, a vase with white flowers and a brown couch are partially visible, adding to the contemporary ambiance. "
+        "The lighting is bright, casting soft shadows on the table. "
+        "The robotic arm's movements are smooth and controlled, demonstrating precision in its task. "
+        "As the video progresses, the robotic arm completes the pour, leaving the glass half-filled with the reddish-brown liquid. "
+        "The jar remains untouched throughout the sequence, and the spoon inside the glass remains stationary. "
+        "The other robotic arm on the right side also stays stationary throughout the video. "
+        "The final frame captures the robotic arm with the pitcher finishing the pour, with the glass now filled to a higher level, while the pitcher is slightly tilted but still held securely by the gripper."
+    )
+
+    generator.generate_video(
+        prompt,
+        sampling_param=sampling_param,
+        video_path=str(video_path),
+        num_cond_frames=1,
+        output_path="outputs_video/cosmos2_5_v2w.mp4",
+        save_video=True,
+    )
+
+    generator.shutdown()
+
+
+if __name__ == "__main__":
+    main()
+
+
@@ -39,4 +39,4 @@ def main():


 if __name__ == "__main__":
-    main()
+    main()
@@ -0,0 +1,43 @@
+from fastvideo import VideoGenerator
+import json
+# from fastvideo.configs.sample import SamplingParam
+
+OUTPUT_PATH = "video_samples_hy15_1080p"
+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(
+        "weizhou03/HunyuanVideo-1.5-Diffusers-1080p-2SR", # 480p -> 720p -> 1080p
+        # or "weizhou03/HunyuanVideo-1.5-Diffusers-1080p" # 720p -> 1080p
+        # 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,
+        vae_cpu_offload=True,
+        text_encoder_cpu_offload=True,
+        pin_cpu_memory=True, # set to false if low CPU RAM or hit obscure "CUDA error: Invalid argument"
+        # image_encoder_cpu_offload=False,
+    )
+
+    prompt = (
+        "A curious raccoon peers through a vibrant field of yellow sunflowers, its eyes "
+        "wide with interest. The playful yet serene atmosphere is complemented by soft "
+        "natural light filtering through the petals. Mid-shot, warm and cheerful tones."
+    )
+
+    video = generator.generate_video(prompt, output_path=OUTPUT_PATH, save_video=True, negative_prompt="")
+
+    prompt2 = (
+        "A majestic lion strides across the golden savanna, its powerful frame "
+        "glistening under the warm afternoon sun. The tall grass ripples gently in "
+        "the breeze, enhancing the lion's commanding presence. The tone is vibrant, "
+        "embodying the raw energy of the wild. Low angle, steady tracking shot, "
+        "cinematic.")
+
+    video2 = generator.generate_video(prompt2, output_path=OUTPUT_PATH, save_video=True, negative_prompt="")
+
+
+if __name__ == "__main__":
+    main()
@@ -1,165 +1,11 @@
-# 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'

+OUTPUT_PATH = "video_samples_hyworld"
 def main():
    import argparse

@@ -169,7 +15,7 @@ def main():
    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("--output_path", type=str, default=OUTPUT_PATH, 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")
@@ -185,8 +31,7 @@ def main():

    # Initialize generator
    print("\nInitializing VideoGenerator for HYWorld...")
-    
-    generator = HYWorldVideoGenerator.from_pretrained(
+    generator = VideoGenerator.from_pretrained(
        "FastVideo/HY-WorldPlay-Bidirectional-Diffusers",
        num_gpus=1,
        use_fsdp_inference=True,
@@ -198,11 +43,12 @@ def main():
    )

    # Generate video
+    # The pose string is automatically converted to camera matrices by the pipeline
    print("\nGenerating video...")
-    start_time = time.time()
-    video = generator.generate_video(
+    generator.generate_video(
        prompt=args.prompt,
        image_path=args.image,
+        pose=args.pose,  # Camera trajectory control
        output_path=args.output_path,
        save_video=True,
        negative_prompt="",
@@ -211,13 +57,9 @@ def main():
        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")
+    print(f"\nVideo saved to: {args.output_path}")


 if __name__ == "__main__":
@@ -1,5 +1,6 @@
 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 "
@@ -16,16 +17,20 @@ PROMPT = (


 def main() -> None:
+    # Uses FastVideo default sampling settings for LTX2 base.
    generator = VideoGenerator.from_pretrained(
-        "FastVideo/LTX2-Distilled-Diffusers",
+        "Davids048/LTX2-Base-Diffusers",
        num_gpus=1,
    )

-    output_path = "outputs_video/ltx2_basic/output_ltx2_distilled_t2v.mp4"
+    output_path = "outputs_video/ltx2_basic/output_ltx2_base_t2v_1088_1920_1.1.mp4"
    generator.generate_video(
        prompt=PROMPT,
        output_path=output_path,
        save_video=True,
+        num_frames=121,
+        height=1088,
+        width=1920,
    )
    generator.shutdown()

@@ -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()
@@ -4,7 +4,7 @@ export WANDB_BASE_URL="https://api.wandb.ai"
 export WANDB_MODE=online
 export TOKENIZERS_PARALLELISM=false

-MODEL_PATH="Wan-AI/Wan2.1-T2V-1.3B-Diffusers"
+MODEL_PATH="wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers"
 DATA_DIR="data/crush-smol_processed_t2v_1_3b_ode_init/"
 VALIDATION_DATASET_FILE="$(dirname "$0")/validation.json"
 NUM_GPUS=1
@@ -14,7 +14,6 @@ NUM_GPUS=1
 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
  --train_batch_size 1
@@ -34,7 +33,7 @@ parallel_args=(
  --sp_size 1
  --tp_size 1
  --hsdp_replicate_dim 1
-  --hsdp_shard_dim 1
+  --hsdp_shard_dim $NUM_GPUS
 )

 # Model arguments
@@ -51,20 +50,17 @@ dataset_args=(

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

 # 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
+  --weight_decay 0.01
  --max_grad_norm 1.0
 )

@@ -0,0 +1,23 @@
+# LTX-2 Crush-Smol Example
+# TODO: Update this doc.
+
+These are e2e example scripts for finetuning LTX-2 on the crush-smol dataset.
+
+## Execute the following commands from `FastVideo/` to run training:
+
+### Download crush-smol dataset:
+
+`bash examples/training/finetune/ltx2/overfit/download_dataset.sh`
+
+### Preprocess the videos and captions into latents:
+
+`bash examples/training/finetune/ltx2/overfit/preprocess_ltx2_data_t2v_new.sh`
+
+### Edit the following file and run finetuning:
+
+`bash examples/training/finetune/ltx2/overfit/finetune_t2v.sh`
+
+Notes:
+- Update `DATASET_PATH` in the preprocess script to point to your merged dataset root (`videos/` + `videos2caption.json`).
+- `MODEL_PATH` should point to a local LTX-2 diffusers-style directory that contains `model_index.json` and `text_encoder/gemma`.
+
@@ -0,0 +1,3 @@
+# #!/bin/bash
+#
+python scripts/huggingface/download_hf.py --repo_id "wlsaidhi/crush-smol-merged" --local_dir "data/crush-smol" --repo_type "dataset"
@@ -0,0 +1,95 @@
+#!/bin/bash
+
+export WANDB_BASE_URL="https://api.wandb.ai"
+export WANDB_MODE=online
+export TOKENIZERS_PARALLELISM=false
+
+MODEL_PATH="Davids048/LTX2-Base-Diffusers"
+# Also can use simple 1 video for overfitting experiments.
+# DATA_DIR="/home/hal-jundas/codes/FastVideo/data/crush-smol"
+DATA_DIR="<PATH_TO_PROCESSED_DATASET>"
+VALIDATION_DATASET_FILE="$(dirname "$0")/validation.json"
+echo  VALIDATION_DATASET_FILE: $VALIDATION_DATASET_FILE
+NUM_GPUS=4
+OVERFIT_HEIGHT=480
+OVERFIT_WIDTH=832
+OVERFIT_FRAMES=73
+
+training_args=(
+  --tracker_project_name "ltx2_t2v_finetune"
+  --output_dir "checkpoints/ltx2_t2v_finetune"
+  --max_train_steps 5000
+  --train_batch_size 1
+  --train_sp_batch_size 1
+  --gradient_accumulation_steps 1
+  --num_latent_t 10
+  --num_height $OVERFIT_HEIGHT
+  --num_width $OVERFIT_WIDTH
+  --num_frames $OVERFIT_FRAMES
+  --ltx2-first-frame-conditioning-p 0.1
+  --enable_gradient_checkpointing_type "full"
+  --mode "finetuning"
+)
+
+parallel_args=(
+  --num_gpus $NUM_GPUS
+  --sp_size $NUM_GPUS
+  --tp_size 1
+  --hsdp_replicate_dim 1
+  --hsdp_shard_dim $NUM_GPUS
+)
+
+model_args=(
+  --model_path $MODEL_PATH
+  --pretrained_model_name_or_path $MODEL_PATH
+)
+
+dataset_args=(
+  --data_path $DATA_DIR
+  --dataloader_num_workers 1
+)
+
+validation_args=(
+  --log_validation
+  --validation_dataset_file $VALIDATION_DATASET_FILE
+  --validation_steps 50
+  --validation_sampling_steps "50"
+  --validation_guidance_scale "3.0"
+)
+
+optimizer_args=(
+  --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
+  --lr_scheduler "linear"
+)
+
+miscellaneous_args=(
+  --inference_mode False
+  --checkpoints_total_limit 3
+  --dit_precision "fp32"
+  --dit_cpu_offload False
+  --dit_layerwise_offload False
+  --text_encoder_cpu_offload False
+  --image_encoder_cpu_offload False
+  --vae_cpu_offload False
+)
+
+# NOTE: Setting this environment variable to TORCH_SDPA to avoid the issue of stacking that failed in flash attn. 
+export FASTVIDEO_ATTENTION_BACKEND=TORCH_SDPA
+
+
+torchrun \
+  --nnodes 1 \
+  --nproc_per_node $NUM_GPUS \
+    fastvideo/training/ltx2_training_pipeline.py \
+    "${parallel_args[@]}" \
+    "${model_args[@]}" \
+    "${dataset_args[@]}" \
+    "${training_args[@]}" \
+    "${optimizer_args[@]}" \
+    "${validation_args[@]}" \
+    "${miscellaneous_args[@]}"
@@ -0,0 +1,80 @@
+#!/bin/bash
+
+export WANDB_BASE_URL="https://api.wandb.ai"
+export WANDB_MODE=online
+export TOKENIZERS_PARALLELISM=false
+
+MODEL_PATH="/path/to/LTX-2"
+DATA_DIR="data/crush-smol"
+VALIDATION_DATASET_FILE="$(dirname "$0")/validation.json"
+NUM_GPUS=1
+
+training_args=(
+  --tracker_project_name "ltx2_t2v_lora_finetune"
+  --output_dir "checkpoints/ltx2_t2v_lora_finetune"
+  --max_train_steps 2000
+  --train_batch_size 1
+  --train_sp_batch_size 1
+  --gradient_accumulation_steps 8
+  --num_latent_t 10
+  --num_height 480
+  --num_width 832
+  --num_frames 77
+  --ltx2-first-frame-conditioning-p 0.1
+  --enable_gradient_checkpointing_type "full"
+)
+
+parallel_args=(
+  --num_gpus $NUM_GPUS
+  --sp_size $NUM_GPUS
+  --tp_size 1
+  --hsdp_replicate_dim 1
+  --hsdp_shard_dim $NUM_GPUS
+)
+
+model_args=(
+  --model_path $MODEL_PATH
+  --pretrained_model_name_or_path $MODEL_PATH
+)
+
+dataset_args=(
+  --data_path $DATA_DIR
+  --dataloader_num_workers 1
+)
+
+validation_args=(
+  --log_validation
+  --validation_dataset_file $VALIDATION_DATASET_FILE
+  --validation_steps 200
+  --validation_sampling_steps "50"
+  --validation_guidance_scale "3.0"
+)
+
+optimizer_args=(
+  --learning_rate 2e-4
+  --mixed_precision "bf16"
+  --weight_only_checkpointing_steps 1000
+  --training_state_checkpointing_steps 1000
+  --weight_decay 1e-4
+  --max_grad_norm 1.0
+  --lora_training True
+  --lora_rank 16
+  --lora_alpha 16
+)
+
+miscellaneous_args=(
+  --inference_mode False
+  --checkpoints_total_limit 3
+)
+
+torchrun \
+  --nnodes 1 \
+  --nproc_per_node $NUM_GPUS \
+    fastvideo/training/ltx2_training_pipeline.py \
+    "${parallel_args[@]}" \
+    "${model_args[@]}" \
+    "${dataset_args[@]}" \
+    "${training_args[@]}" \
+    "${optimizer_args[@]}" \
+    "${validation_args[@]}" \
+    "${miscellaneous_args[@]}"
@@ -0,0 +1,35 @@
+#!/bin/bash
+
+GPU_NUM=1
+MODEL_PATH="Davids048/LTX2-Base-Diffusers"
+# DATASET_PATH="data/overfit"
+DATASET_PATH="data/crush-smol"
+OUTPUT_DIR="$DATASET_PATH"
+WITH_AUDIO=true
+
+# Convert one-file overfit metadata into merged format if needed.
+if [ ! -f "$DATASET_PATH/videos2caption.json" ] && [ -f "$DATASET_PATH/overfit.json" ]; then
+  python scripts/dataset_preparation/convert_to_merged_dataset.py \
+    --items-json "$DATASET_PATH/overfit.json" \
+    --output-dir "$DATASET_PATH"
+fi
+
+
+torchrun --nproc_per_node=$GPU_NUM \
+    --master_port=29513 \
+    -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.with_audio $WITH_AUDIO \
+    --preprocess.preprocess_video_batch_size 1 \
+    --preprocess.dataloader_num_workers 0 \
+    --preprocess.max_height 480 \
+    --preprocess.max_width 832 \
+    --preprocess.num_frames 73 \
+    --preprocess.train_fps 16 \
+    --preprocess.video_length_tolerance_range 5
@@ -0,0 +1,13 @@
+{
+  "data": [
+    {
+      "caption": "The camera opens in a calm, sunlit frog yoga studio. Warm morning light washes over the wooden floor as incense smoke drifts lazily in the air. The senior frog instructor sits cross-legged at the center, eyes closed, voice deep and calm. “We are one with the pond.” All the frogs answer softly: “Ommm...” “We are one with the mud.” “Ommm...” He smiles faintly. “We are one with the flies.” A quiet pause. The camera slowly pans to the side — one frog twitches, eyes darting. Suddenly — *thwip!* — its tongue snaps out, catching a fly mid-air and pulling it into its mouth. The master exhales slowly, still serene.",
+      "image_path": null,
+      "video_path": null,
+      "num_inference_steps": 50,
+      "height": 1088,
+      "width": 1920,
+      "num_frames": 121
+    }
+  ]
+}
@@ -0,0 +1,31 @@
+{
+  "data": [
+    {
+      "caption": "A large metal cylinder is seen pressing down on a pile of Oreo cookies, flattening them as if they were under a hydraulic press.",
+      "image_path": null,
+      "video_path": null,
+      "num_inference_steps": 50,
+      "height": 480,
+      "width": 832,
+      "num_frames": 77
+    },
+    {
+      "caption": "A large metal cylinder is seen compressing colorful clay into a compact shape, demonstrating the power of a hydraulic press.",
+      "image_path": null,
+      "video_path": null,
+      "num_inference_steps": 50,
+      "height": 480,
+      "width": 832,
+      "num_frames": 77
+    },
+    {
+      "caption": "A large metal cylinder is seen pressing down on a pile of colorful candies, flattening them as if they were under a hydraulic press. The candies are crushed and broken into small pieces, creating a mess on the table.",
+      "image_path": null,
+      "video_path": null,
+      "num_inference_steps": 50,
+      "height": 480,
+      "width": 832,
+      "num_frames": 77
+    }
+  ]
+}
@@ -86,6 +86,7 @@ class PreprocessConfig:

    # Model configuration
    training_cfg_rate: float = 0.0
+    with_audio: bool = False

    # framework configuration
    seed: int = 42
@@ -190,6 +191,10 @@ class PreprocessConfig:
                                     type=float,
                                     default=PreprocessConfig.training_cfg_rate,
                                     help="Training CFG rate")
+        preprocess_args.add_argument(f"--{prefix_with_dot}with-audio",
+                                     action=StoreBoolean,
+                                     default=PreprocessConfig.with_audio,
+                                     help="Whether to extract and encode audio")
        preprocess_args.add_argument(f"--{prefix_with_dot}seed",
                                     type=int,
                                     default=PreprocessConfig.seed,
@@ -5,6 +5,7 @@ from fastvideo.configs.models.vaes.base import VAEConfig
 from fastvideo.configs.models.audio import (LTX2AudioDecoderConfig,
                                            LTX2AudioEncoderConfig,
                                            LTX2VocoderConfig)
+from fastvideo.configs.models.upsamplers.base import UpsamplerConfig

 __all__ = [
    "ModelConfig",
@@ -14,4 +15,5 @@ __all__ = [
    "LTX2AudioEncoderConfig",
    "LTX2AudioDecoderConfig",
    "LTX2VocoderConfig",
+    "UpsamplerConfig",
 ]
@@ -55,6 +55,8 @@ 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",

            # 2. txt_in_2 mapping:
            r"^context_embedder_2\.(.*)$":
@@ -8,12 +8,8 @@ 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_refiner_block(n: str, m) -> bool:
+    return "refiner" in n and str.isdigit(n.split(".")[-1])


 def is_txt_in(n: str, m) -> bool:
@@ -23,10 +19,10 @@ def is_txt_in(n: str, m) -> bool:
@dataclass
 class HYWorldArchConfig(DiTArchConfig):
    _fsdp_shard_conditions: list = field(
-        default_factory=lambda: [is_double_block, is_single_block])
+        default_factory=lambda: [is_double_block, is_refiner_block])

    _compile_conditions: list = field(
-        default_factory=lambda: [is_double_block, is_single_block, is_txt_in])
+        default_factory=lambda: [is_double_block, is_refiner_block, is_txt_in])

    param_names_mapping: dict = field(
        default_factory=lambda: {
@@ -54,8 +50,7 @@ class HYWorldArchConfig(DiTArchConfig):
            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):
+            # 2. time_in mappings:
            r"^time_in\.mlp\.0\.(.*)$":
            r"time_in.timestep_embedder.mlp.fc_in.\1",
            r"^time_in\.mlp\.2\.(.*)$":
@@ -7,10 +7,12 @@ from dataclasses import dataclass, field

 from fastvideo.configs.models.dits.base import DiTArchConfig, DiTConfig

+import re
+

 def is_ltx2_blocks(name: str, _module) -> bool:
-    """FSDP shard condition for LTX-2 transformer blocks."""
-    return "transformer_blocks" in name
+    res = re.search(r"(?:^|\.)transformer_blocks\.\d+$", name) is not None
+    return res


@dataclass
@@ -1,4 +1,6 @@
 from dataclasses import dataclass, field
+
+import torch
 from fastvideo.configs.models.dits.wanvideo import WanVideoArchConfig, WanVideoConfig


@@ -76,8 +78,14 @@ class MatrixGameWanVideoArchConfig(WanVideoArchConfig):
    image_dim: int = 1280


+def _is_transformer_block(param_name: str, module: torch.nn.Module) -> bool:
+    return bool("blocks" in param_name and param_name.split(".")[-1].isdigit())
+
+
@dataclass
 class MatrixGameWanVideoConfig(WanVideoConfig):
    arch_config: MatrixGameWanVideoArchConfig = field(
        default_factory=MatrixGameWanVideoArchConfig)
    prefix: str = "Wan"
+    _compile_conditions: list = field(
+        default_factory=lambda: [_is_transformer_block])
@@ -87,10 +87,10 @@ class CLIPVisionConfig(ImageEncoderConfig):
    arch_config: ImageEncoderArchConfig = field(
        default_factory=CLIPVisionArchConfig)

-    num_hidden_layers_override: int | None = None
+    num_hidden_layers_override: int | None = 31
    require_post_norm: bool | None = None
-    enable_scale: bool = True
-    is_causal: bool = True
+    enable_scale: bool = False
+    is_causal: bool = False
    prefix: str = "clip"


@@ -0,0 +1,6 @@
+from fastvideo.configs.models.upsamplers.hunyuan15 import SRTo720pUpsamplerConfig, SRTo1080pUpsamplerConfig
+from fastvideo.configs.models.upsamplers.base import UpsamplerConfig
+
+__all__ = [
+    "SRTo720pUpsamplerConfig", "SRTo1080pUpsamplerConfig", "UpsamplerConfig"
+]
@@ -0,0 +1,7 @@
+from dataclasses import dataclass
+from fastvideo.configs.models.base import ModelConfig
+
+
+@dataclass
+class UpsamplerConfig(ModelConfig):
+    pass
@@ -0,0 +1,20 @@
+from dataclasses import dataclass
+from fastvideo.configs.models.upsamplers.base import UpsamplerConfig
+
+
+@dataclass
+class SRTo720pUpsamplerConfig(UpsamplerConfig):
+    in_channels: int = 0
+    out_channels: int = 0
+    hidden_channels: int = 64
+    num_blocks: int = 6
+    global_residual: bool = False
+
+
+@dataclass
+class SRTo1080pUpsamplerConfig(UpsamplerConfig):
+    z_channels: int = 0
+    out_channels: int = 0
+    block_out_channels: tuple[int, ...] = (0, 0)
+    num_res_blocks: int = 2
+    is_residual: bool = False
@@ -6,8 +6,7 @@ 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.registry import get_pipeline_config_cls_from_name
 from fastvideo.configs.pipelines.stepvideo import StepVideoT2VConfig
 from fastvideo.configs.pipelines.wan import (SelfForcingWanT2V480PConfig,
                                             WanI2V480PConfig, WanI2V720PConfig,
@@ -8,7 +8,7 @@ from typing import Any, cast
 import torch

 from fastvideo.configs.models import (DiTConfig, EncoderConfig, ModelConfig,
-                                      VAEConfig)
+                                      VAEConfig, UpsamplerConfig)
 from fastvideo.configs.models.encoders import BaseEncoderOutput
 from fastvideo.configs.utils import update_config_from_args
 from fastvideo.logger import init_logger
@@ -44,12 +44,15 @@ class PipelineConfig:
    # Video generation parameters
    embedded_cfg_scale: float = 6.0
    flow_shift: float | None = None
+    flow_shift_sr: float | None = None
    disable_autocast: bool = False
    is_causal: bool = False

    # Model configuration
    dit_config: DiTConfig = field(default_factory=DiTConfig)
    dit_precision: str = "bf16"
+    upsampler_config: UpsamplerConfig = field(default_factory=UpsamplerConfig)
+    upsampler_precision: str = "fp32"

    # VAE configuration
    vae_config: VAEConfig = field(default_factory=VAEConfig)
@@ -216,6 +219,24 @@ class PipelineConfig:
            "Comma-separated list of denoising steps (e.g., '1000,757,522')",
        )

+        # STA (Sliding Tile Attention) parameters
+        parser.add_argument(
+            f"--{prefix_with_dot}STA-mode",
+            type=str,
+            dest=f"{prefix_with_dot.replace('-', '_')}STA_mode",
+            default=PipelineConfig.STA_mode.value,
+            choices=[mode.value for mode in STA_Mode],
+            help=
+            "STA mode: STA_inference, STA_searching, STA_tuning, STA_tuning_cfg, None",
+        )
+        parser.add_argument(
+            f"--{prefix_with_dot}skip-time-steps",
+            type=int,
+            dest=f"{prefix_with_dot.replace('-', '_')}skip_time_steps",
+            default=PipelineConfig.skip_time_steps,
+            help="Number of time steps to warmup (full attention) for STA",
+        )
+
        # Add VAE configuration arguments
        from fastvideo.configs.models.vaes.base import VAEConfig
        VAEConfig.add_cli_args(parser, prefix=f"{prefix_with_dot}vae-config")
@@ -245,8 +266,7 @@ class PipelineConfig:
        """
        use the pipeline class setting from model_path to match the pipeline config
        """
-        from fastvideo.configs.pipelines.registry import (
-            get_pipeline_config_cls_from_name)
+        from fastvideo.registry import get_pipeline_config_cls_from_name
        pipeline_config_cls = get_pipeline_config_cls_from_name(model_path)

        return cast(PipelineConfig, pipeline_config_cls(model_path=model_path))
@@ -260,8 +280,7 @@ class PipelineConfig:
        kwargs: dictionary of kwargs
        config_cli_prefix: prefix of CLI arguments for this PipelineConfig instance
        """
-        from fastvideo.configs.pipelines.registry import (
-            get_pipeline_config_cls_from_name)
+        from fastvideo.registry import get_pipeline_config_cls_from_name

        prefix_with_dot = f"{config_cli_prefix}." if (config_cli_prefix.strip()
                                                      != "") else ""
@@ -298,6 +317,12 @@ class PipelineConfig:
        # 4. Update PipelineConfig from CLI arguments if provided
        kwargs[prefix_with_dot + 'model_path'] = model_path
        pipeline_config.update_config_from_dict(kwargs, config_cli_prefix)
+
+        # Convert STA_mode string to enum if necessary
+        if isinstance(pipeline_config.STA_mode, str) and not isinstance(
+                pipeline_config.STA_mode, STA_Mode):
+            pipeline_config.STA_mode = STA_Mode(pipeline_config.STA_mode)
+
        return pipeline_config

    def check_pipeline_config(self) -> None:
@@ -11,7 +11,8 @@ from fastvideo.configs.models.dits import HunyuanVideo15Config
 from fastvideo.configs.models.encoders import (BaseEncoderOutput,
                                               Qwen2_5_VLConfig, T5Config)
 from fastvideo.configs.models.vaes import Hunyuan15VAEConfig
-from fastvideo.configs.pipelines.base import PipelineConfig
+from fastvideo.configs.models.upsamplers import SRTo720pUpsamplerConfig, SRTo1080pUpsamplerConfig
+from fastvideo.configs.pipelines.base import PipelineConfig, UpsamplerConfig

 PROMPT_TEMPLATE_TOKEN_LENGTH = 108

@@ -131,9 +132,35 @@ class Hunyuan15T2V480PConfig(PipelineConfig):
        self.vae_config.load_decoder = True


+@dataclass
+class Hunyuan15I2V480PStepDistilledConfig(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 Hunyuan15I2V720PConfig(Hunyuan15T2V720PConfig):
+    """Base configuration for HunYuan pipeline architecture."""
+
+    # HunyuanConfig-specific parameters with defaults
+    flow_shift: int = 7
+
+
+@dataclass
+class Hunyuan15SR1080PConfig(Hunyuan15T2V720PConfig):
+    """Base configuration for HunYuan pipeline architecture."""
+
+    # HunyuanConfig-specific parameters with defaults
+    flow_shift: int = 7
+    flow_shift_sr: int = 2
+    upsampler_config: tuple[UpsamplerConfig, ...] = field(
+        default_factory=lambda:
+        (SRTo720pUpsamplerConfig(), SRTo1080pUpsamplerConfig()))
+    upsampler_precision: str = "fp32"
@@ -1,214 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""Registry for pipeline weight-specific configurations."""
-
-import os
-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.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 (
-    FastWan2_1_T2V_480P_Config, FastWan2_2_TI2V_5B_Config,
-    Wan2_2_I2V_A14B_Config, Wan2_2_T2V_A14B_Config, Wan2_2_TI2V_5B_Config,
-    WanI2V480PConfig, WanI2V720PConfig, WanT2V480PConfig, WanT2V720PConfig,
-    SelfForcingWanT2V480PConfig, WANV2VConfig, SelfForcingWan2_2_T2V480PConfig,
-    MatrixGameI2V480PConfig)
-# isort: on
-from fastvideo.logger import init_logger
-from fastvideo.utils import (maybe_download_model_index,
-                             verify_model_config_and_directory)
-
-logger = init_logger(__name__)
-
-# Registry maps specific model weights to their config classes
-PIPE_NAME_TO_CONFIG: dict[str, type[PipelineConfig]] = {
-    "FastVideo/FastHunyuan-diffusers": FastHunyuanConfig,
-    "hunyuanvideo-community/HunyuanVideo": HunyuanConfig,
-    "hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-480p_t2v":
-    Hunyuan15T2V480PConfig,
-    "hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_t2v":
-    Hunyuan15T2V720PConfig,
-    "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,
-    "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers": WanI2V480PConfig,
-    "Wan-AI/Wan2.1-I2V-14B-720P-Diffusers": WanI2V720PConfig,
-    "Wan-AI/Wan2.1-T2V-14B-Diffusers": WanT2V720PConfig,
-    "FastVideo/FastWan2.1-T2V-1.3B-Diffusers": FastWan2_1_T2V_480P_Config,
-    "FastVideo/FastWan2.1-T2V-14B-480P-Diffusers": FastWan2_1_T2V_480P_Config,
-    "FastVideo/FastWan2.2-TI2V-5B-Diffusers": FastWan2_2_TI2V_5B_Config,
-    "FastVideo/stepvideo-t2v-diffusers": StepVideoT2VConfig,
-    "FastVideo/Wan2.1-VSA-T2V-14B-720P-Diffusers": WanT2V720PConfig,
-    "wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers": SelfForcingWanT2V480PConfig,
-    "rand0nmr/SFWan2.2-T2V-A14B-Diffusers": SelfForcingWan2_2_T2V480PConfig,
-    "FastVideo/SFWan2.2-I2V-A14B-Preview-Diffusers":
-    SelfForcingWan2_2_T2V480PConfig,
-    "Wan-AI/Wan2.2-TI2V-5B-Diffusers": Wan2_2_TI2V_5B_Config,
-    "Wan-AI/Wan2.2-T2V-A14B-Diffusers": Wan2_2_T2V_A14B_Config,
-    "Wan-AI/Wan2.2-I2V-A14B-Diffusers": Wan2_2_I2V_A14B_Config,
-    "nvidia/Cosmos-Predict2-2B-Video2World": CosmosConfig,
-    "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":
-    lambda id: "wanpipeline" in id.lower(),
-    "wanimagetovideo":
-    lambda id: "wanimagetovideo" in id.lower(),
-    "wandmdpipeline":
-    lambda id: "wandmdpipeline" in id.lower(),
-    "wancausaldmdpipeline":
-    lambda id: "wancausaldmdpipeline" in id.lower(),
-    "stepvideo":
-    lambda id: "stepvideo" in id.lower(),
-    "cosmos":
-    lambda id: "cosmos" in id.lower() 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,
-    "turbodiffusion": TurboDiffusionT2V_1_3B_Config,
-    "ltx2": LTX2T2VConfig,
-    # Other fallbacks by architecture
-}
-
-
-def get_pipeline_config_cls_from_name(
-        pipeline_name_or_path: str) -> type[PipelineConfig]:
-    """Get the appropriate configuration class for a given pipeline name or path.
-
-    This function implements a multi-step lookup process to find the most suitable
-    configuration class for a given pipeline. It follows this order:
-    1. Exact match in the PIPE_NAME_TO_CONFIG
-    2. Partial match in the PIPE_NAME_TO_CONFIG
-    3. Fallback to class name in the model_index.json
-    4. else raise an error
-
-    Args:
-        pipeline_name_or_path (str): The name or path of the pipeline. This can be:
-            - A registered model ID (e.g., "FastVideo/FastHunyuan-diffusers")
-            - A local path to a model directory
-            - A model ID that will be downloaded
-
-    Returns:
-        Type[PipelineConfig]: The configuration class that best matches the pipeline.
-            This will be one of:
-            - A specific weight configuration class if an exact match is found
-            - A fallback configuration class based on the pipeline architecture
-            - The base PipelineConfig class if no matches are found
-
-    Note:
-        - For local paths, the function will verify the model configuration
-        - For remote models, it will attempt to download the model index
-        - Warning messages are logged when falling back to less specific configurations
-    """
-
-    pipeline_config_cls: type[PipelineConfig] | None = None
-
-    # First try exact match for specific weights
-    if pipeline_name_or_path in PIPE_NAME_TO_CONFIG:
-        pipeline_config_cls = PIPE_NAME_TO_CONFIG[pipeline_name_or_path]
-        return pipeline_config_cls
-
-    # Try partial matches (for local paths that might include the weight ID)
-    for registered_id, config_class in PIPE_NAME_TO_CONFIG.items():
-        if registered_id in pipeline_name_or_path:
-            pipeline_config_cls = config_class
-            break
-
-    # If no match, try to use the fallback config
-    if pipeline_config_cls is None:
-        if os.path.exists(pipeline_name_or_path):
-            config = verify_model_config_and_directory(pipeline_name_or_path)
-        else:
-            config = maybe_download_model_index(pipeline_name_or_path)
-        logger.warning(
-            "Trying to use the config from the model_index.json. FastVideo may not correctly identify the optimal config for this model in this situation."
-        )
-
-        pipeline_name = config["_class_name"]
-        # Try to determine pipeline architecture for fallback
-        for pipeline_type, detector in PIPELINE_DETECTOR.items():
-            if detector(pipeline_name.lower()):
-                pipeline_config_cls = PIPELINE_FALLBACK_CONFIG.get(
-                    pipeline_type)
-                break
-
-        if pipeline_config_cls is not None:
-            logger.warning(
-                "No match found for pipeline %s, using fallback config %s.",
-                pipeline_name_or_path, pipeline_config_cls)
-
-    if pipeline_config_cls is None:
-        raise ValueError(
-            f"No match found for pipeline {pipeline_name_or_path}, please check the pipeline name or path."
-        )
-
-    return pipeline_config_cls
@@ -28,6 +28,9 @@ class SamplingParam:
    keyboard_cond: Any | None = None  # Shape: (B, T, K)
    grid_sizes: Any | None = None  # Shape: (3,) [F,H,W]

+    # Camera control inputs (HYWorld)
+    pose: str | None = None  # Camera trajectory: pose string (e.g., 'w-31') or JSON file path
+
    # Refine inputs (LongCat 480p->720p upscaling)
    # Path-based refine (load stage1 video from disk, e.g. MP4)
    refine_from: str | None = None  # Path to stage1 video (480p output from distill)
@@ -55,10 +58,13 @@ class SamplingParam:
    num_frames_round_down: bool = False  # Whether to round down num_frames if it's not divisible by num_gpus
    height: int = 720
    width: int = 1280
+    height_sr: int = 1072
+    width_sr: int = 1920
    fps: int = 24

    # Denoising parameters
    num_inference_steps: int = 50
+    num_inference_steps_sr: int = 50
    guidance_scale: float = 1.0
    guidance_rescale: float = 0.0
    boundary_ratio: float | None = None
@@ -92,8 +98,7 @@ class SamplingParam:

    @classmethod
    def from_pretrained(cls, model_path: str) -> "SamplingParam":
-        from fastvideo.configs.sample.registry import (
-            get_sampling_param_cls_for_name)
+        from fastvideo.registry import get_sampling_param_cls_for_name
        sampling_cls = get_sampling_param_cls_for_name(model_path)
        if sampling_cls is not None:
            sampling_param: SamplingParam = sampling_cls()
@@ -5,15 +5,19 @@ 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
+class Cosmos25SamplingParamBase(SamplingParam):
+    height: int = 704
+    width: int = 1280
+    num_frames: int = 77
    fps: int = 24
+    seed: int = 0

    guidance_scale: float = 7.0
-    # Official Cosmos2.5 sampling uses empty string as unconditional.
-    negative_prompt: str = ""
+    negative_prompt: str = (
+        "The video captures a series of frames showing ugly scenes, static with no motion, motion blur, "
+        "over-saturation, shaky footage, low resolution, grainy texture, pixelated images, poorly lit areas, "
+        "underexposed and overexposed scenes, poor color balance, washed out colors, choppy sequences, jerky movements, "
+        "low frame rate, artifacting, color banding, unnatural transitions, outdated special effects, fake elements, "
+        "unconvincing visuals, poorly edited content, jump cuts, visual noise, and flickering. "
+        "Overall, the video is of poor quality.")
    num_inference_steps: int = 35
@@ -28,7 +28,33 @@ class Hunyuan15_480P_SamplingParam(SamplingParam):
            np.linspace(1.0, 0.0, self.num_inference_steps + 1)[:-1])


+@dataclass
+class Hunyuan15_480P_StepDistilled_I2V_SamplingParam(
+        Hunyuan15_480P_SamplingParam):
+    num_inference_steps: int = 12
+
+    height: int = 720
+    width: int = 1280
+    guidance_scale: float = 1.0
+
+
@dataclass
 class Hunyuan15_720P_SamplingParam(Hunyuan15_480P_SamplingParam):
    height: int = 720
    width: int = 1280
+
+
+@dataclass
+class Hunyuan15_720P_Distilled_I2V_SamplingParam(Hunyuan15_720P_SamplingParam):
+    guidance_scale: float = 1.0
+
+
+@dataclass
+class Hunyuan15_SR_1080P_SamplingParam(Hunyuan15_480P_SamplingParam):
+    height_sr: int = 1072
+    width_sr: int = 1920
+
+    num_inference_steps: int = 12
+    num_inference_steps_sr: int = 8
+
+    guidance_scale: float = 1.0
@@ -5,10 +5,44 @@ from fastvideo.configs.sample.base import SamplingParam


@dataclass
-class LTX2SamplingParam(SamplingParam):
-    """Default sampling parameters for LTX-2 distilled T2V.
+class LTX2BaseSamplingParam(SamplingParam):
+    """Default sampling parameters for LTX-2 base one-stage T2V.
+
+    Values follow the official LTX-2 one-stage defaults.
    """

+    seed: int = 10
+    num_frames: int = 121
+    height: int = 512
+    width: int = 768
+    fps: int = 24
+    num_inference_steps: int = 40
+    guidance_scale: float = 3.0
+    # Copied/following official LTX-2 DEFAULT_NEGATIVE_PROMPT.
+    negative_prompt: str = (
+        "blurry, out of focus, overexposed, underexposed, low contrast, "
+        "washed out colors, excessive noise, grainy texture, poor lighting, "
+        "flickering, motion blur, distorted proportions, unnatural skin "
+        "tones, deformed facial features, asymmetrical face, missing facial "
+        "features, extra limbs, disfigured hands, wrong hand count, "
+        "artifacts around text, inconsistent perspective, camera shake, "
+        "incorrect depth of field, background too sharp, background clutter, "
+        "distracting reflections, harsh shadows, inconsistent lighting "
+        "direction, color banding, cartoonish rendering, 3D CGI look, "
+        "unrealistic materials, uncanny valley effect, incorrect ethnicity, "
+        "wrong gender, exaggerated expressions, wrong gaze direction, "
+        "mismatched lip sync, silent or muted audio, distorted voice, "
+        "robotic voice, echo, background noise, off-sync audio, incorrect "
+        "dialogue, added dialogue, repetitive speech, jittery movement, "
+        "awkward pauses, incorrect timing, unnatural transitions, "
+        "inconsistent framing, tilted camera, flat lighting, inconsistent "
+        "tone, cinematic oversaturation, stylized filters, or AI artifacts.")
+
+
+@dataclass
+class LTX2DistilledSamplingParam(SamplingParam):
+    """Default sampling parameters for LTX-2 distilled one-stage T2V."""
+
    seed: int = 10
    num_frames: int = 121
    height: int = 1024
@@ -18,3 +52,7 @@ class LTX2SamplingParam(SamplingParam):
    guidance_scale: float = 1.0
    # No default negative_prompt for distilled models
    negative_prompt: str = ""
+
+
+# Backward compatibility alias.
+LTX2SamplingParam = LTX2DistilledSamplingParam
@@ -1,204 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-import os
-from collections.abc import Callable
-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.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 (
-    FastWanT2V480P_SamplingParam,
-    Wan2_1_Fun_1_3B_InP_SamplingParam,
-    Wan2_2_I2V_A14B_SamplingParam,
-    Wan2_2_T2V_A14B_SamplingParam,
-    Wan2_2_TI2V_5B_SamplingParam,
-    WanI2V_14B_480P_SamplingParam,
-    WanI2V_14B_720P_SamplingParam,
-    WanT2V_1_3B_SamplingParam,
-    WanT2V_14B_SamplingParam,
-    Wan2_1_Fun_1_3B_Control_SamplingParam,
-    SelfForcingWan2_1_T2V_1_3B_480P_SamplingParam,
-    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,
-                             verify_model_config_and_directory)
-
-logger = init_logger(__name__)
-# Registry maps specific model weights to their config classes
-SAMPLING_PARAM_REGISTRY: dict[str, Any] = {
-    "FastVideo/FastHunyuan-diffusers": FastHunyuanSamplingParam,
-    "hunyuanvideo-community/HunyuanVideo": HunyuanSamplingParam,
-    "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
-    "Wan-AI/Wan2.1-T2V-1.3B-Diffusers": WanT2V_1_3B_SamplingParam,
-    "Wan-AI/Wan2.1-T2V-14B-Diffusers": WanT2V_14B_SamplingParam,
-    "Wan-AI/Wan2.1-I2V-14B-480P-Diffusers": WanI2V_14B_480P_SamplingParam,
-    "Wan-AI/Wan2.1-I2V-14B-720P-Diffusers": WanI2V_14B_720P_SamplingParam,
-    "weizhou03/Wan2.1-Fun-1.3B-InP-Diffusers":
-    Wan2_1_Fun_1_3B_InP_SamplingParam,
-    "IRMChen/Wan2.1-Fun-1.3B-Control-Diffusers":
-    Wan2_1_Fun_1_3B_Control_SamplingParam,
-
-    # Wan2.2
-    "Wan-AI/Wan2.2-TI2V-5B-Diffusers": Wan2_2_TI2V_5B_SamplingParam,
-    "FastVideo/FastWan2.2-TI2V-5B-FullAttn-Diffusers":
-    Wan2_2_TI2V_5B_SamplingParam,
-    "Wan-AI/Wan2.2-T2V-A14B-Diffusers": Wan2_2_T2V_A14B_SamplingParam,
-    "Wan-AI/Wan2.2-I2V-A14B-Diffusers": Wan2_2_I2V_A14B_SamplingParam,
-
-    # FastWan2.1
-    "FastVideo/FastWan2.1-T2V-1.3B-Diffusers": FastWanT2V480P_SamplingParam,
-
-    # FastWan2.2
-    "FastVideo/FastWan2.2-TI2V-5B-Diffusers": Wan2_2_TI2V_5B_SamplingParam,
-
-    # Causal Self-Forcing Wan2.1
-    "wlsaidhi/SFWan2.1-T2V-1.3B-Diffusers":
-    SelfForcingWan2_1_T2V_1_3B_480P_SamplingParam,
-
-    # Causal Self-Forcing Wan2.2
-    "rand0nmr/SFWan2.2-T2V-A14B-Diffusers":
-    SelfForcingWan2_2_T2V_A14B_480P_SamplingParam,
-    "FastVideo/SFWan2.2-I2V-A14B-Preview-Diffusers":
-    SelfForcingWan2_2_T2V_A14B_480P_SamplingParam,
-
-    # Cosmos2
-    "nvidia/Cosmos-Predict2-2B-Video2World":
-    Cosmos_Predict2_2B_Video2World_SamplingParam,
-
-    # 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
-}
-
-# For determining pipeline type from model ID
-SAMPLING_PARAM_DETECTOR: dict[str, Callable[[str], bool]] = {
-    "hunyuan":
-    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":
-    lambda id: "wanimagetovideo" in id.lower(),
-    "stepvideo":
-    lambda id: "stepvideo" in id.lower(),
-    "wandmdpipeline":
-    lambda id: "wandmdpipeline" in id.lower(),
-    "wancausaldmdpipeline":
-    lambda id: "wancausaldmdpipeline" in id.lower(),
-    "matrixgame":
-    lambda id: "matrixgame" in id.lower() or "matrix-game" in id.lower(),
-    "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
-}
-
-# Fallback configs when exact match isn't found but architecture is detected
-SAMPLING_FALLBACK_PARAM: dict[str, Any] = {
-    "hunyuan":
-    HunyuanSamplingParam,  # Base Hunyuan config as fallback for any Hunyuan variant
-    "hunyuan15":
-    Hunyuan15_480P_SamplingParam,  # Base Hunyuan15 config as fallback for any Hunyuan15 variant
-    "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,
-    "wandmdpipeline": FastWanT2V480P_SamplingParam,
-    "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]
-
-    # Try partial matches (for local paths that might include the weight ID)
-    for registered_id, config_class in SAMPLING_PARAM_REGISTRY.items():
-        if registered_id in pipeline_name_or_path:
-            return config_class
-
-    matrixgame_patterns = ["Matrix-Game", "Skywork--Matrix-Game", "matrixgame"]
-    for pattern in matrixgame_patterns:
-        if pattern.lower() in pipeline_name_or_path.lower():
-            return MatrixGame2_SamplingParam
-
-    if os.path.exists(pipeline_name_or_path):
-        config = verify_model_config_and_directory(pipeline_name_or_path)
-    else:
-        config = maybe_download_model_index(pipeline_name_or_path)
-
-    pipeline_name = config["_class_name"]
-
-    # If no match, try to use the fallback config
-    fallback_config = None
-    # Try to determine pipeline architecture for fallback
-    for pipeline_type, detector in SAMPLING_PARAM_DETECTOR.items():
-        if detector(pipeline_name.lower()):
-            fallback_config = SAMPLING_FALLBACK_PARAM.get(pipeline_type)
-            break
-
-    logger.warning(
-        "No match found for pipeline %s, using fallback sampling param %s.",
-        pipeline_name_or_path, fallback_config)
-    return fallback_config
@@ -4,6 +4,8 @@ from torchvision.transforms import Lambda

 from fastvideo.dataset.parquet_dataset_map_style import (
    build_parquet_map_style_dataloader)
+from fastvideo.dataset.ltx2_precomputed_dataset import (
+    build_ltx2_precomputed_dataloader, LTX2PrecomputedDataset)
 from fastvideo.dataset.preprocessing_datasets import VideoCaptionMergedDataset, TextDataset
 from fastvideo.dataset.transform import (CenterCropResizeVideo, Normalize255,
                                         TemporalRandomCrop)
@@ -46,6 +48,10 @@ def gettextdataset(args) -> TextDataset:


 __all__ = [
-    "build_parquet_map_style_dataloader", "ValidationDataset",
-    "VideoCaptionMergedDataset", "TextDataset"
+    "build_parquet_map_style_dataloader",
+    "build_ltx2_precomputed_dataloader",
+    "LTX2PrecomputedDataset",
+    "ValidationDataset",
+    "VideoCaptionMergedDataset",
+    "TextDataset",
 ]
@@ -0,0 +1,210 @@
+# SPDX-License-Identifier: Apache-2.0
+# Dataset utilities for loading LTX2 precomputed training artifacts.
+#
+# Usage:
+# - Input root can be either `<data_root>/` or `<data_root>/.precomputed/`.
+# - Required sources are `latents/` and `conditions/` with matching `.pt` files.
+# - Optional source `audio_latents/` is loaded when provided in `data_sources`.
+# - `build_ltx2_precomputed_dataloader(...)` is the intended entrypoint used by
+#   `fastvideo/training/ltx2_training_pipeline.py`.
+
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Any
+
+import torch
+from einops import rearrange
+from torch.utils.data import Dataset
+from torchdata.stateful_dataloader import StatefulDataLoader
+
+from fastvideo.dataset.parquet_dataset_map_style import DP_SP_BatchSampler
+from fastvideo.distributed import get_sp_world_size, get_world_rank, get_world_size
+from fastvideo.logger import init_logger
+
+logger = init_logger(__name__)
+
+PRECOMPUTED_DIR_NAME = ".precomputed"
+
+
+class LTX2PrecomputedDataset(Dataset):
+    """Dataset for LTX-2 precomputed latents and conditions.
+
+    Expected directory structure (data_root):
+      .precomputed/
+        latents/*.pt
+        conditions/*.pt
+        audio_latents/*.pt (optional)
+    """
+
+    def __init__(
+        self,
+        data_root: str,
+        data_sources: dict[str, str] | list[str] | None = None,
+    ) -> None:
+        super().__init__()
+        self.data_root = self._setup_data_root(data_root)
+        self.data_sources = self._normalize_data_sources(data_sources)
+        self.source_paths = self._setup_source_paths()
+        self.sample_files = self._discover_samples()
+        self._validate_setup()
+
+    @staticmethod
+    def _setup_data_root(data_root: str) -> Path:
+        data_root_path = Path(data_root).expanduser().resolve()
+        if not data_root_path.exists():
+            raise FileNotFoundError(
+                f"Data root directory does not exist: {data_root_path}")
+        if (data_root_path / PRECOMPUTED_DIR_NAME).exists():
+            data_root_path = data_root_path / PRECOMPUTED_DIR_NAME
+        return data_root_path
+
+    @staticmethod
+    def _normalize_data_sources(
+        data_sources: dict[str, str] | list[str] | None,
+    ) -> dict[str, str]:
+        if data_sources is None:
+            return {"latents": "latents", "conditions": "conditions"}
+        if isinstance(data_sources, list):
+            return {source: source for source in data_sources}
+        if isinstance(data_sources, dict):
+            return data_sources.copy()
+        raise TypeError(
+            f"data_sources must be dict, list, or None, got {type(data_sources)}")
+
+    def _setup_source_paths(self) -> dict[str, Path]:
+        source_paths: dict[str, Path] = {}
+        for dir_name in self.data_sources:
+            source_path = self.data_root / dir_name
+            if not source_path.exists():
+                raise FileNotFoundError(
+                    f"Required {dir_name} directory does not exist: {source_path}")
+            source_paths[dir_name] = source_path
+        return source_paths
+
+    def _discover_samples(self) -> dict[str, list[Path]]:
+        data_key = ("latents"
+                    if "latents" in self.data_sources else next(iter(
+                        self.data_sources.keys())))
+        data_path = self.source_paths[data_key]
+        data_files = list(data_path.glob("**/*.pt"))
+        if not data_files:
+            raise ValueError(f"No data files found in {data_path}")
+
+        sample_files = {output_key: [] for output_key in self.data_sources.values()}
+        for data_file in data_files:
+            rel_path = data_file.relative_to(data_path)
+            if self._all_source_files_exist(data_file, rel_path):
+                self._fill_sample_data_files(data_file, rel_path, sample_files)
+        return sample_files
+
+    def _all_source_files_exist(self, data_file: Path, rel_path: Path) -> bool:
+        for dir_name in self.data_sources:
+            expected_path = self._get_expected_file_path(dir_name, data_file,
+                                                         rel_path)
+            if not expected_path.exists():
+                logger.warning(
+                    "No matching %s file found for: %s (expected in: %s)",
+                    dir_name,
+                    data_file.name,
+                    expected_path,
+                )
+                return False
+        return True
+
+    def _get_expected_file_path(self, dir_name: str, data_file: Path,
+                                rel_path: Path) -> Path:
+        source_path = self.source_paths[dir_name]
+        if dir_name == "conditions" and data_file.name.startswith("latent_"):
+            return source_path / f"condition_{data_file.stem[7:]}.pt"
+        return source_path / rel_path
+
+    def _fill_sample_data_files(self, data_file: Path, rel_path: Path,
+                                sample_files: dict[str, list[Path]]) -> None:
+        for dir_name, output_key in self.data_sources.items():
+            expected_path = self._get_expected_file_path(dir_name, data_file,
+                                                         rel_path)
+            sample_files[output_key].append(
+                expected_path.relative_to(self.source_paths[dir_name]))
+
+    def _validate_setup(self) -> None:
+        if not self.sample_files:
+            raise ValueError(
+                "No valid samples found - all data sources must have matching files"
+            )
+        sample_counts = {
+            key: len(files)
+            for key, files in self.sample_files.items()
+        }
+        if len(set(sample_counts.values())) > 1:
+            raise ValueError(
+                f"Mismatched sample counts across sources: {sample_counts}")
+
+    def __len__(self) -> int:
+        first_key = next(iter(self.sample_files.keys()))
+        return len(self.sample_files[first_key])
+
+    def __getitem__(self, index: int) -> dict[str, torch.Tensor]:
+        result: dict[str, Any] = {}
+        for dir_name, output_key in self.data_sources.items():
+            source_path = self.source_paths[dir_name]
+            file_rel_path = self.sample_files[output_key][index]
+            file_path = source_path / file_rel_path
+            try:
+                data = torch.load(file_path, map_location="cpu", weights_only=True)
+                if "latent" in dir_name.lower():
+                    data = self._normalize_video_latents(data)
+                result[output_key] = data
+            except Exception as e:
+                raise RuntimeError(
+                    f"Failed to load {output_key} from {file_path}: {e}") from e
+        result["idx"] = index
+        return result
+
+    @staticmethod
+    def _normalize_video_latents(data: dict) -> dict:
+        latents = data["latents"]
+        if latents.dim() == 2:
+            num_frames = data["num_frames"]
+            height = data["height"]
+            width = data["width"]
+            latents = rearrange(
+                latents,
+                "(f h w) c -> c f h w",
+                f=num_frames,
+                h=height,
+                w=width,
+            )
+            data = data.copy()
+            data["latents"] = latents
+        return data
+
+
+def build_ltx2_precomputed_dataloader(
+    path: str,
+    batch_size: int,
+    num_data_workers: int,
+    data_sources: dict[str, str] | list[str] | None = None,
+    drop_last: bool = True,
+    seed: int = 42,
+) -> tuple[LTX2PrecomputedDataset, StatefulDataLoader]:
+    dataset = LTX2PrecomputedDataset(path, data_sources=data_sources)
+    sampler = DP_SP_BatchSampler(
+        batch_size=batch_size,
+        dataset_size=len(dataset),
+        num_sp_groups=get_world_size() // get_sp_world_size(),
+        sp_world_size=get_sp_world_size(),
+        global_rank=get_world_rank(),
+        drop_last=drop_last,
+        drop_first_row=False,
+        seed=seed,
+    )
+    loader = StatefulDataLoader(
+        dataset,
+        batch_sampler=sampler,
+        collate_fn=None,
+        num_workers=num_data_workers,
+        pin_memory=True,
+        persistent_workers=num_data_workers > 0,
+    )
+    return dataset, loader
@@ -148,62 +148,6 @@ class DataValidationStage(DatasetFilterStage):
        return batch


-class ResolutionFilterStage(DatasetFilterStage):
-    """Stage for filtering data items based on resolution constraints."""
-
-    def __init__(self,
-                 max_h_div_w_ratio: float = 17 / 16,
-                 min_h_div_w_ratio: float = 8 / 16,
-                 max_height: int = 1024,
-                 max_width: int = 1024):
-        self.max_h_div_w_ratio = max_h_div_w_ratio
-        self.min_h_div_w_ratio = min_h_div_w_ratio
-        self.max_height = max_height
-        self.max_width = max_width
-
-    def should_keep(self, batch: PreprocessBatch, **kwargs) -> bool:
-        """
-        Check if data item passes resolution filtering.
-        
-        Args:
-            batch: Dataset batch with resolution information
-            
-        Returns:
-            True if passes filter, False otherwise
-        """
-        # Only apply to videos
-        if not batch.is_video:
-            return True
-
-        if batch.resolution is None:
-            return False
-
-        height = batch.resolution.get("height", None)
-        width = batch.resolution.get("width", None)
-        if height is None or width is None:
-            return False
-
-        # Check aspect ratio
-        aspect = self.max_height / self.max_width
-        hw_aspect_thr = 1.5
-
-        return self.filter_resolution(
-            height,
-            width,
-            max_h_div_w_ratio=hw_aspect_thr * aspect,
-            min_h_div_w_ratio=1 / hw_aspect_thr * aspect,
-        )
-
-    def process(self, batch: PreprocessBatch, **kwargs) -> PreprocessBatch:
-        """Process does nothing for resolution filtering - filtering is handled by should_keep."""
-        return batch
-
-    def filter_resolution(self, h: int, w: int, max_h_div_w_ratio: float,
-                          min_h_div_w_ratio: float) -> bool:
-        """Filter based on height/width ratio."""
-        return h / w <= max_h_div_w_ratio and h / w >= min_h_div_w_ratio
-
-
 class FrameSamplingStage(DatasetFilterStage):
    """Stage for temporal frame sampling and indexing."""

@@ -328,11 +272,6 @@ class VideoTransformStage(DatasetStage):
        video = rearrange(video, "t c h w -> c t h w")
        video = video.to(torch.uint8)

-        h, w = video.shape[-2:]
-        assert (
-            h / w <= 17 / 16 and h / w >= 8 / 16
-        ), f"Only videos with a ratio (h/w) less than 17/16 and more than 8/16 are supported. But video ({batch.path}) found ratio is {round(h / w, 2)} with the shape of {video.shape}"
-
        video = video.float() / 127.5 - 1.0
        batch.pixel_values = video
        return batch
@@ -489,8 +428,6 @@ class VideoCaptionMergedDataset(torch.utils.data.IterableDataset,
                     tokenizer) -> None:
        """Initialize all processing stages."""
        self.validation_stage = DataValidationStage()
-        self.resolution_filter_stage = ResolutionFilterStage(
-            max_height=args.max_height, max_width=args.max_width)
        self.frame_sampling_stage = FrameSamplingStage(
            num_frames=args.num_frames,
            train_fps=args.train_fps,
@@ -543,7 +480,6 @@ class VideoCaptionMergedDataset(torch.utils.data.IterableDataset,
        # Initialize counters
        filter_counts = {
            "validation_failed": 0,
-            "resolution_failed": 0,
            "frame_sampling_failed": 0
        }
        sample_num_frames: list[int] = []
@@ -579,10 +515,6 @@ class VideoCaptionMergedDataset(torch.utils.data.IterableDataset,
            filter_counts["validation_failed"] += 1
            return False

-        if not self.resolution_filter_stage.should_keep(batch):
-            filter_counts["resolution_failed"] += 1
-            return False
-
        if not self.frame_sampling_stage.should_keep(batch):
            filter_counts["frame_sampling_failed"] += 1
            return False
@@ -594,10 +526,9 @@ class VideoCaptionMergedDataset(torch.utils.data.IterableDataset,
                             after_count: int):
        """Log filtering statistics."""
        logger.info(
-            "validation_failed: %d, resolution_failed: %d, frame_sampling_failed: %d, "
+            "validation_failed: %d, frame_sampling_failed: %d, "
            "Counter(sample_num_frames): %s, before filter: %d, after filter: %d",
            filter_counts['validation_failed'],
-            filter_counts['resolution_failed'],
            filter_counts['frame_sampling_failed'], Counter(sample_num_frames),
            before_count, after_count)

@@ -18,6 +18,7 @@ __all__ = [
    "cleanup_dist_env_and_memory",
    "model_parallel_is_initialized",
    "maybe_init_distributed_environment_and_model_parallel",
+    "warmup_sequence_parallel_communication",

    # World group
    "get_world_group",
@@ -7,10 +7,17 @@ import torch.distributed
 from fastvideo.distributed.parallel_state import (get_sp_group,
                                                  get_sp_parallel_rank,
                                                  get_sp_world_size,
-                                                  get_tp_group)
+                                                  get_tp_group,
+                                                  model_parallel_is_initialized)
 from fastvideo.distributed.utils import (unpad_sequence_tensor,
                                         compute_padding_for_sp,
                                         pad_sequence_tensor)
+from fastvideo.logger import init_logger
+
+logger = init_logger(__name__)
+
+# Track if SP communication has been warmed up
+_sp_warmup_done = False


 def tensor_model_parallel_all_reduce(input_: torch.Tensor) -> torch.Tensor:
@@ -101,3 +108,86 @@ def sequence_model_parallel_shard(input_: torch.Tensor,
    input_ = input_.movedim(0, dim)

    return input_, original_seq_len
+
+
+def warmup_sequence_parallel_communication(
+        device: torch.device | None = None) -> None:
+    """Warmup NCCL communicators for sequence parallel all-to-all operations.
+    
+    The first NCCL collective operation is slow due to lazy communicator
+    initialization. This function runs dummy all-to-all operations to
+    trigger the initialization upfront, before the first real forward pass.
+    
+    Args:
+        device: Device to use for warmup tensors. If None, uses CUDA device 0.
+    """
+    global _sp_warmup_done
+
+    if _sp_warmup_done:
+        return
+
+    if not model_parallel_is_initialized():
+        return
+
+    sp_world_size = get_sp_world_size()
+    if sp_world_size <= 1:
+        _sp_warmup_done = True
+        return
+
+    if device is None:
+        device = torch.device("cuda")
+
+    logger.info("Warming up sequence parallel communication (SP=%d)...",
+                sp_world_size)
+
+    # Use small but representative tensor shapes for warmup
+    # Shape: [batch, seq_len, num_heads, head_dim]
+    # The all-to-all patterns used in attention:
+    #   1. scatter_dim=2 (heads), gather_dim=1 (seq) - before attention
+    #   2. scatter_dim=1 (seq), gather_dim=2 (heads) - after attention
+    batch_size = 1
+    seq_len_per_rank = 16  # Small sequence per rank
+    num_heads = sp_world_size * 4  # Must be divisible by sp_world_size
+    head_dim = 64
+
+    # Create dummy tensor for warmup
+    dummy = torch.zeros(batch_size,
+                        seq_len_per_rank,
+                        num_heads,
+                        head_dim,
+                        device=device,
+                        dtype=torch.bfloat16)
+
+    # Warmup pattern 1: scatter heads, gather sequence (before attention)
+    _ = sequence_model_parallel_all_to_all_4D(dummy,
+                                              scatter_dim=2,
+                                              gather_dim=1)
+
+    # Warmup pattern 2: scatter sequence, gather heads (after attention)
+    dummy2 = torch.zeros(batch_size,
+                         seq_len_per_rank * sp_world_size,
+                         num_heads // sp_world_size,
+                         head_dim,
+                         device=device,
+                         dtype=torch.bfloat16)
+    _ = sequence_model_parallel_all_to_all_4D(dummy2,
+                                              scatter_dim=1,
+                                              gather_dim=2)
+
+    # Warmup all-gather (used for replicated tokens)
+    dummy3 = torch.zeros(batch_size,
+                         8,
+                         num_heads // sp_world_size,
+                         head_dim,
+                         device=device,
+                         dtype=torch.bfloat16)
+    _ = sequence_model_parallel_all_gather(dummy3, dim=2)
+
+    # Synchronize to ensure warmup completes
+    torch.cuda.synchronize(device)
+
+    # Clean up
+    del dummy, dummy2, dummy3
+
+    _sp_warmup_done = True
+    logger.info("Sequence parallel communication warmup complete.")
@@ -9,6 +9,7 @@ diffusion models.
 import math
 import os
 import re
+import threading
 import time
 from copy import deepcopy
 from typing import Any
@@ -31,6 +32,21 @@ from fastvideo.worker.executor import Executor
 logger = init_logger(__name__)


+def _infer_latent_batch_size(batch: ForwardBatch) -> int:
+    if isinstance(batch.prompt, list):
+        latent_batch_size = len(batch.prompt)
+    elif batch.prompt is not None:
+        latent_batch_size = 1
+    elif batch.prompt_embeds is not None and len(batch.prompt_embeds) > 0:
+        latent_batch_size = batch.prompt_embeds[0].shape[0]
+    else:
+        raise ValueError(
+            "Cannot infer batch size from batch; no prompt or prompt_embeds found"
+        )
+    latent_batch_size *= batch.num_videos_per_prompt
+    return latent_batch_size
+
+
 class VideoGenerator:
    """
    A unified class for generating videos using diffusion models.
@@ -372,8 +388,31 @@ class VideoGenerator:

        # Run inference
        start_time = time.perf_counter()
-        output_batch = self.executor.execute_forward(batch, fastvideo_args)
-        samples = output_batch.output
+
+        # Execute forward pass in a new thread for non-blocking tensor allocation
+        result_container = {}
+
+        def execute_forward_thread():
+            result_container['output_batch'] = self.executor.execute_forward(
+                batch, fastvideo_args)
+
+        thread = threading.Thread(target=execute_forward_thread)
+        thread.start()
+        latent_batch_size = _infer_latent_batch_size(batch)
+        samples = torch.empty((latent_batch_size, 3, sampling_param.num_frames,
+                               sampling_param.height, sampling_param.width),
+                              device='cpu',
+                              pin_memory=fastvideo_args.pin_cpu_memory)
+        thread.join()
+
+        output_batch = result_container['output_batch']
+        if output_batch.output.shape == samples.shape:
+            samples.copy_(output_batch.output)
+        else:
+            logger.warning(
+                "Output shape %s does not match expected shape %s; use slow path",
+                output_batch.output.shape, samples.shape)
+            samples = output_batch.output.cpu()
        logging_info = output_batch.logging_info

        gen_time = time.perf_counter() - start_time
@@ -10,7 +10,7 @@ from enum import Enum
 from typing import Any, TYPE_CHECKING

 from fastvideo.configs.configs import PreprocessConfig
-from fastvideo.configs.pipelines.base import PipelineConfig, STA_Mode
+from fastvideo.configs.pipelines.base import PipelineConfig
 from fastvideo.configs.utils import clean_cli_args
 from fastvideo.layers.quantization import QUANTIZATION_METHODS, QuantizationMethods
 from fastvideo.logger import init_logger
@@ -141,8 +141,6 @@ class FastVideoArgs:

    # STA (Sliding Tile Attention) parameters
    mask_strategy_file_path: str | None = None
-    STA_mode: STA_Mode = STA_Mode.STA_INFERENCE
-    skip_time_steps: int = 15

    # Compilation
    enable_torch_compile: bool = False
@@ -179,6 +177,7 @@ class FastVideoArgs:
    model_loaded: dict[str, bool] = field(default_factory=lambda: {
        "transformer": True,
        "vae": True,
+        "upsampler": True,
    })

    override_text_encoder_safetensors: str | None = None  # path to safetensors file for text encoder override
@@ -464,20 +463,6 @@ class FastVideoArgs:
        )

        # STA (Sliding Tile Attention) parameters
-        parser.add_argument(
-            "--STA-mode",
-            type=str,
-            default=FastVideoArgs.STA_mode.value,
-            choices=[mode.value for mode in STA_Mode],
-            help=
-            "STA mode contains STA_inference, STA_searching, STA_tuning, STA_tuning_cfg, None",
-        )
-        parser.add_argument(
-            "--skip-time-steps",
-            type=int,
-            default=FastVideoArgs.skip_time_steps,
-            help="Number of time steps to warmup (full attention) for STA",
-        )
        parser.add_argument(
            "--mask-strategy-file-path",
            type=str,
@@ -918,6 +903,7 @@ class TrainingArgs(FastVideoArgs):
    lora_rank: int | None = None
    lora_alpha: int | None = None
    lora_training: bool = False
+    ltx2_first_frame_conditioning_p: float = 0.1

    # distillation args
    generator_update_interval: int = 5
@@ -931,6 +917,7 @@ class TrainingArgs(FastVideoArgs):
    training_state_checkpointing_steps: int = 0  # for resuming training
    weight_only_checkpointing_steps: int = 0  # for inference
    log_visualization: bool = False
+    visualization_steps: int = 0
    # simulate generator forward to match inference
    simulate_generator_forward: bool = False
    warp_denoising_step: bool = False
@@ -1094,6 +1081,9 @@ class TrainingArgs(FastVideoArgs):
        parser.add_argument("--log-validation",
                            action=StoreBoolean,
                            help="Whether to log validation results")
+        parser.add_argument("--visualization-steps",
+                            type=int,
+                            help="Number of visualization steps")
        parser.add_argument("--tracker-project-name",
                            type=str,
                            help="Project name for tracking")
@@ -1268,6 +1258,13 @@ class TrainingArgs(FastVideoArgs):
                            help="Whether to use LoRA training")
        parser.add_argument("--lora-rank", type=int, help="LoRA rank")
        parser.add_argument("--lora-alpha", type=int, help="LoRA alpha")
+        parser.add_argument(
+            "--ltx2-first-frame-conditioning-p",
+            type=float,
+            default=TrainingArgs.ltx2_first_frame_conditioning_p,
+            help=
+            "Probability of conditioning on the first frame during LTX-2 training",
+        )

        # V-MoBA parameters
        parser.add_argument(
@@ -193,3 +193,46 @@ class ImageProcessor:
        tensor = 2.0 * tensor - 1.0

        return tensor
+
+    def _preprocess_cosmos25(
+        self,
+        image: PIL.Image.Image | np.ndarray | torch.Tensor,
+        height: int,
+        width: int,
+    ) -> torch.Tensor:
+        """
+        Cosmos-Predict2.5-style preprocessing for image2world:
+        - aspect-preserving resize (scale so both dims >= target)
+        - center crop to (height, width)
+        - normalize to [-1, 1]
+
+        Returns:
+            torch.Tensor: (1, 3, height, width) in [-1, 1]
+        """
+        if not isinstance(image, PIL.Image.Image):
+            # Reuse the generic preprocess for non-PIL inputs.
+            return self.preprocess(image, height=height, width=width)
+
+        # Ensure RGB (official uses torchvision.to_tensor on PIL, which yields C=3 for RGB inputs)
+        image = image.convert("RGB")
+
+        # Make sure target dims are multiples of VAE scale factor (FastVideo convention)
+        height = height - (height % self.vae_scale_factor)
+        width = width - (width % self.vae_scale_factor)
+
+        orig_w, orig_h = image.size
+        # Scale so that resized image fully covers the target box (like official resize_input()).
+        scale = max(width / float(orig_w), height / float(orig_h))
+        resized_w = int(np.ceil(scale * orig_w))
+        resized_h = int(np.ceil(scale * orig_h))
+
+        image = image.resize((resized_w, resized_h),
+                             resample=PIL.Image.Resampling.BILINEAR)
+
+        # Center crop
+        left = max(0, (resized_w - width) // 2)
+        top = max(0, (resized_h - height) // 2)
+        image = image.crop((left, top, left + width, top + height))
+
+        image_np = np.array(image, dtype=np.float32) / 255.0  # [0,1]
+        return self._normalize_to_tensor(image_np)  # -> [-1,1], (1,3,H,W)
@@ -27,7 +27,7 @@ RESET = '\033[0;0m'
 _warned_local_main_process = False
 _warned_main_process = False

-_FORMAT = (f"{FASTVIDEO_LOGGING_PREFIX}%(levelname)s %(asctime)s "
+_FORMAT = (f"{FASTVIDEO_LOGGING_PREFIX}%(levelname)s %(asctime)s.%(msecs)03d "
           "[%(filename)s:%(lineno)d] %(message)s")
 _DATE_FORMAT = "%m-%d %H:%M:%S"

@@ -102,6 +102,7 @@ def _info(logger: Logger,
        - When both are False, the message will be logged from all processes
        - By default, only logs from processes with LOCAL_RANK=0
    """
+    is_distributed = int(os.environ.get("WORLD_SIZE", 1)) > 1
    try:
        local_rank = int(os.environ["LOCAL_RANK"])
        rank = int(os.environ["RANK"])
@@ -118,20 +119,22 @@ def _info(logger: Logger,

    global _warned_local_main_process, _warned_main_process

-    if not _warned_local_main_process and local_main_process_only:
-        logger.warning(
-            '%s By default, logger.info(..) will only log from the local main process. Set logger.info(..., is_local_main_process=False) to log from all processes.%s',
-            GREEN,
-            RESET,
-        )
-        _warned_local_main_process = True
-    if not _warned_main_process and main_process_only:
-        logger.warning(
-            '%s is_main_process_only is set to True, logging only from the main (RANK==0) process.%s',
-            GREEN,
-            RESET,
-        )
-        _warned_main_process = True
+    # Only show process-awareness warnings when actually running distributed
+    if is_distributed:
+        if not _warned_local_main_process and local_main_process_only:
+            logger.warning(
+                '%s By default, logger.info(..) will only log from the local main process. Set logger.info(..., is_local_main_process=False) to log from all processes.%s',
+                GREEN,
+                RESET,
+            )
+            _warned_local_main_process = True
+        if not _warned_main_process and main_process_only:
+            logger.warning(
+                '%s is_main_process_only is set to True, logging only from the main (RANK==0) process.%s',
+                GREEN,
+                RESET,
+            )
+            _warned_main_process = True

    if not main_process_only and not local_main_process_only:
        logger.log(logging.INFO, msg, *args, stacklevel=2, **kwargs)
@@ -0,0 +1,61 @@
+# SPDX-License-Identifier: Apache-2.0
+"""Audio preprocessing helpers for LTX-2 training."""
+
+from __future__ import annotations
+
+import torch
+import torchaudio
+from torch import nn
+
+
+class AudioProcessor(nn.Module):
+    """Converts audio waveforms to log-mel spectrograms with resampling."""
+
+    def __init__(
+        self,
+        sample_rate: int,
+        mel_bins: int,
+        mel_hop_length: int,
+        n_fft: int,
+    ) -> None:
+        super().__init__()
+        self.sample_rate = sample_rate
+        self.mel_transform = torchaudio.transforms.MelSpectrogram(
+            sample_rate=sample_rate,
+            n_fft=n_fft,
+            win_length=n_fft,
+            hop_length=mel_hop_length,
+            f_min=0.0,
+            f_max=sample_rate / 2.0,
+            n_mels=mel_bins,
+            window_fn=torch.hann_window,
+            center=True,
+            pad_mode="reflect",
+            power=1.0,
+            mel_scale="slaney",
+            norm="slaney",
+        )
+
+    def resample_waveform(
+        self,
+        waveform: torch.Tensor,
+        source_rate: int,
+        target_rate: int,
+    ) -> torch.Tensor:
+        if source_rate == target_rate:
+            return waveform
+        resampled = torchaudio.functional.resample(
+            waveform, source_rate, target_rate)
+        return resampled.to(device=waveform.device, dtype=waveform.dtype)
+
+    def waveform_to_mel(
+        self,
+        waveform: torch.Tensor,
+        waveform_sample_rate: int,
+    ) -> torch.Tensor:
+        waveform = self.resample_waveform(
+            waveform, waveform_sample_rate, self.sample_rate)
+        mel = self.mel_transform(waveform)
+        mel = torch.log(torch.clamp(mel, min=1e-5))
+        mel = mel.to(device=waveform.device, dtype=waveform.dtype)
+        return mel.permute(0, 1, 3, 2).contiguous()
@@ -1325,3 +1325,6 @@ def decode_audio(
    decoded_audio = audio_decoder(latent)
    decoded_audio = vocoder(decoded_audio).squeeze(0).float()
    return decoded_audio
+
+# Entry point for model registry
+EntryClass = [LTX2AudioEncoder, LTX2AudioDecoder, LTX2Vocoder]
@@ -33,7 +33,7 @@ from fastvideo.layers.visual_embedding import (PatchEmbed)
 from fastvideo.logger import init_logger
 from fastvideo.models.dits.base import BaseDiT
 from fastvideo.models.dits.wanvideo import WanT2VCrossAttention, WanTimeTextImageEmbedding
-from fastvideo.platforms import AttentionBackendEnum
+from fastvideo.platforms import AttentionBackendEnum, current_platform

 logger = init_logger(__name__)
 class CausalWanSelfAttention(nn.Module):
@@ -286,6 +286,8 @@ class CausalWanTransformerBlock(nn.Module):
        null_shift = null_scale = torch.tensor([0], device=hidden_states.device)
        norm_hidden_states, hidden_states = self.self_attn_residual_norm(
            hidden_states, attn_output, gate_msa, null_shift, null_scale)
+        norm_hidden_states, hidden_states = norm_hidden_states.to(
+            orig_dtype), hidden_states.to(orig_dtype)

        # 2. Cross-attention
        attn_output = self.attn2(norm_hidden_states,
@@ -452,7 +454,6 @@ class CausalWanTransformer3DModel(BaseDiT):
        This function will be run for num_frame times.
        Process the latent frames one by one (1560 tokens each)
        """
-        from fastvideo.platforms import current_platform

        orig_dtype = hidden_states.dtype
        if not isinstance(encoder_hidden_states, torch.Tensor):
@@ -676,4 +677,7 @@ class CausalWanTransformer3DModel(BaseDiT):
            # u = torch.einsum('fhwpqrc->cfphqwr', u.contiguous())
            u = u.reshape(c, *[i * j for i, j in zip(v, self.patch_size)])
            out.append(u)
-        return out
+        return out
+
+# Entry point for model registry
+EntryClass = CausalWanTransformer3DModel
@@ -723,4 +723,7 @@ class CosmosTransformer3DModel(BaseDiT):
        hidden_states = hidden_states.permute(0, 7, 1, 6, 2, 4, 3, 5)
        hidden_states = hidden_states.flatten(6, 7).flatten(4, 5).flatten(2, 3)

-        return hidden_states
+        return hidden_states
+
+# Entry point for model registry
+EntryClass = CosmosTransformer3DModel
@@ -959,3 +959,5 @@ class Cosmos25Transformer3DModel(BaseDiT):

        return hidden_states

+# Entry point for model registry
+EntryClass = Cosmos25Transformer3DModel
@@ -940,3 +940,6 @@ class FinalLayer(nn.Module):
        x = self.norm_final(x) * (1.0 + scale.unsqueeze(1)) + shift.unsqueeze(1)
        x, _ = self.linear(x)
        return x
+
+# Entry point for model registry
+EntryClass = HunyuanVideoTransformer3DModel
@@ -852,4 +852,7 @@ class FinalLayer(nn.Module):
        scale, shift = self.adaLN_modulation(c).chunk(2, dim=-1)
        x = self.norm_final(x) * (1.0 + scale.unsqueeze(1)) + shift.unsqueeze(1)
        x, _ = self.linear(x)
-        return x
+        return x
+
+# Entry point for model registry
+EntryClass = HunyuanVideo15Transformer3DModel
@@ -22,3 +22,6 @@ __all__ = [
    # Inference utilities (used by examples)
    "get_resolution_from_image",
 ]
+
+# Entry point for model registry
+EntryClass = HYWorldTransformer3DModel
@@ -536,8 +536,8 @@ class HYWorldTransformer3DModel(HunyuanVideo15Transformer3DModel):
                    temb_txt,
                    freqs_cis,
                    seq_attention_mask,
-                    viewmats_seq, # hyworld
-                    Ks_seq, # hyworld
+                    viewmats_seq,
+                    Ks_seq,
                )
        else:
            for block in self.double_blocks:
@@ -549,8 +549,8 @@ class HYWorldTransformer3DModel(HunyuanVideo15Transformer3DModel):
                    temb_txt,
                    freqs_cis,
                    seq_attention_mask,
-                    viewmats=viewmats_seq, # hyworld
-                    Ks=Ks_seq, # hyworld
+                    viewmats_seq,
+                    Ks_seq,
                )


@@ -1134,3 +1134,5 @@ class LongCatTransformer3DModel(CachableDiT):
        )
        return x

+# Entry point for model registry
+EntryClass = LongCatTransformer3DModel
@@ -9,3 +9,6 @@ __all__ = [
    "CausalMatrixGameTransformerBlock",
    "ActionModule",
 ]
+
+# Entry point for model registry
+EntryClass = [MatrixGameWanModel, CausalMatrixGameWanModel]
@@ -9,20 +9,32 @@ import torch.nn as nn
 from fastvideo.attention import DistributedAttention
 from fastvideo.configs.models.dits.matrixgame import MatrixGameWanVideoConfig
 from fastvideo.distributed.parallel_state import get_sp_world_size
-from fastvideo.layers.layernorm import (FP32LayerNorm, LayerNormScaleShift,
-                                        RMSNorm, ScaleResidual,
-                                        ScaleResidualLayerNormScaleShift)
+from fastvideo.layers.layernorm import (
+    FP32LayerNorm,
+    LayerNormScaleShift,
+    RMSNorm,
+    ScaleResidual,
+    ScaleResidualLayerNormScaleShift,
+)
 from fastvideo.layers.linear import ReplicatedLinear
 from fastvideo.layers.mlp import MLP
-from fastvideo.layers.rotary_embedding import (_apply_rotary_emb,
-                                               get_rotary_pos_embed)
-from fastvideo.layers.visual_embedding import PatchEmbed, TimestepEmbedder, ModulateProjection
+from fastvideo.layers.rotary_embedding import (
+    _apply_rotary_emb,
+    get_rotary_pos_embed,
+)
+from fastvideo.layers.visual_embedding import (
+    PatchEmbed,
+    TimestepEmbedder,
+    ModulateProjection,
+)
 from fastvideo.logger import init_logger
 from fastvideo.models.dits.base import BaseDiT
-from fastvideo.models.dits.wanvideo import (WanSelfAttention,
-                                           WanI2VCrossAttention,
-                                           WanT2VCrossAttention,
-                                           WanImageEmbedding)
+from fastvideo.models.dits.wanvideo import (
+    WanSelfAttention,
+    WanI2VCrossAttention,
+    WanT2VCrossAttention,
+    WanImageEmbedding,
+)
 from fastvideo.platforms import AttentionBackendEnum, current_platform

 # Import ActionModule
@@ -41,11 +53,12 @@ class MatrixGameTimeImageEmbedding(nn.Module):
        super().__init__()

        self.time_embedder = TimestepEmbedder(
-            dim, frequency_embedding_size=time_freq_dim, act_layer="silu")
-        self.time_modulation = ModulateProjection(dim,
-                                                  factor=6,
-                                                  act_layer="silu")
-        
+            dim, frequency_embedding_size=time_freq_dim, act_layer="silu"
+        )
+        self.time_modulation = ModulateProjection(
+            dim, factor=6, act_layer="silu"
+        )
+
        self.image_embedder = None
        if image_embed_dim is not None:
            self.image_embedder = WanImageEmbedding(image_embed_dim, dim)
@@ -53,7 +66,7 @@ class MatrixGameTimeImageEmbedding(nn.Module):
    def forward(
        self,
        timestep: torch.Tensor,
-        encoder_hidden_states: torch.Tensor, # Kept for interface compatibility
+        encoder_hidden_states: torch.Tensor,  # Kept for interface compatibility
        encoder_hidden_states_image: torch.Tensor | None = None,
        timestep_seq_len: int | None = None,
    ):
@@ -61,17 +74,25 @@ class MatrixGameTimeImageEmbedding(nn.Module):
        timestep_proj = self.time_modulation(temb)

        # MatrixGame does not use text embeddings, so we ignore encoder_hidden_states
-        
+
        if encoder_hidden_states_image is not None:
            assert self.image_embedder is not None
            encoder_hidden_states_image = self.image_embedder(
-                encoder_hidden_states_image)
+                encoder_hidden_states_image
+            )

-        encoder_hidden_states = torch.zeros((timestep.shape[0], 0, temb.shape[-1]),
-                                            device=temb.device,
-                                            dtype=temb.dtype)
+        encoder_hidden_states = torch.zeros(
+            (timestep.shape[0], 0, temb.shape[-1]),
+            device=temb.device,
+            dtype=temb.dtype,
+        )

-        return temb, timestep_proj, encoder_hidden_states, encoder_hidden_states_image
+        return (
+            temb,
+            timestep_proj,
+            encoder_hidden_states,
+            encoder_hidden_states_image,
+        )


 class MatrixGameCrossAttention(WanSelfAttention):
@@ -87,7 +108,7 @@ class MatrixGameCrossAttention(WanSelfAttention):

        # compute query, key, value
        q = self.norm_q(self.to_q(x)[0]).view(b, -1, n, d)
-        
+
        if crossattn_cache is not None:
            if not crossattn_cache["is_init"]:
                crossattn_cache["is_init"] = True
@@ -101,7 +122,7 @@ class MatrixGameCrossAttention(WanSelfAttention):
        else:
            k = self.norm_k(self.to_k(context)[0]).view(b, -1, n, d)
            v = self.to_v(context)[0].view(b, -1, n, d)
-        
+
        # compute attention
        x = self.attn(q, k, v)

@@ -112,19 +133,20 @@ class MatrixGameCrossAttention(WanSelfAttention):


 class MatrixGameTransformerBlock(nn.Module):
-    
-    def __init__(self,
-                 dim: int,
-                 ffn_dim: int,
-                 num_heads: int,
-                 qk_norm: str = "rms_norm_across_heads",
-                 cross_attn_norm: bool = False,
-                 eps: float = 1e-6,
-                 added_kv_proj_dim: int | None = None,
-                 supported_attention_backends: tuple[AttentionBackendEnum, ...]
-                 | None = None,
-                 prefix: str = "",
-                 action_config: dict | None = None):
+    def __init__(
+        self,
+        dim: int,
+        ffn_dim: int,
+        num_heads: int,
+        qk_norm: str = "rms_norm_across_heads",
+        cross_attn_norm: bool = False,
+        eps: float = 1e-6,
+        added_kv_proj_dim: int | None = None,
+        supported_attention_backends: tuple[AttentionBackendEnum, ...]
+        | None = None,
+        prefix: str = "",
+        action_config: dict | None = None,
+    ):
        super().__init__()
        action_config = action_config or {}

@@ -140,7 +162,8 @@ class MatrixGameTransformerBlock(nn.Module):
            head_size=dim // num_heads,
            causal=False,
            supported_attention_backends=supported_attention_backends,
-            prefix=f"{prefix}.attn1")
+            prefix=f"{prefix}.attn1",
+        )
        self.hidden_dim = dim
        self.num_attention_heads = num_heads
        dim_head = dim // num_heads
@@ -161,28 +184,28 @@ class MatrixGameTransformerBlock(nn.Module):
            eps=eps,
            elementwise_affine=True,
            dtype=torch.float32,
-            compute_dtype=torch.float32)
+            compute_dtype=torch.float32,
+        )

        # 2. Cross-attention
        if added_kv_proj_dim is not None:
            # I2V
-            self.attn2 = WanI2VCrossAttention(dim,
-                                              num_heads,
-                                              qk_norm=qk_norm,
-                                              eps=eps)
+            self.attn2 = WanI2VCrossAttention(
+                dim, num_heads, qk_norm=qk_norm, eps=eps
+            )
        else:
            # T2V
-            self.attn2 = WanT2VCrossAttention(dim,
-                                              num_heads,
-                                              qk_norm=qk_norm,
-                                              eps=eps)
+            self.attn2 = WanT2VCrossAttention(
+                dim, num_heads, qk_norm=qk_norm, eps=eps
+            )
        self.cross_attn_residual_norm = ScaleResidualLayerNormScaleShift(
            dim,
            norm_type="layer",
            eps=eps,
            elementwise_affine=False,
            dtype=torch.float32,
-            compute_dtype=torch.float32)
+            compute_dtype=torch.float32,
+        )

        # 2.1. Action Module Integration
        self.use_action_module = len(action_config) > 0
@@ -204,7 +227,7 @@ class MatrixGameTransformerBlock(nn.Module):
        temb: torch.Tensor,
        freqs_cis: tuple[torch.Tensor, torch.Tensor],
        # Action Module specific args
-        grid_sizes: torch.Tensor | None = None,
+        grid_sizes: torch.Tensor,
        mouse_cond: torch.Tensor | None = None,
        keyboard_cond: torch.Tensor | None = None,
    ) -> torch.Tensor:
@@ -214,9 +237,16 @@ class MatrixGameTransformerBlock(nn.Module):

        if temb.dim() == 4:
            # temb: batch_size, seq_len, 6, inner_dim (wan2.2 ti2v)
-            shift_msa, scale_msa, gate_msa, c_shift_msa, c_scale_msa, c_gate_msa = (
-                self.scale_shift_table.unsqueeze(0) + temb.float()
-            ).chunk(6, dim=2)
+            (
+                shift_msa,
+                scale_msa,
+                gate_msa,
+                c_shift_msa,
+                c_scale_msa,
+                c_gate_msa,
+            ) = (self.scale_shift_table.unsqueeze(0) + temb.float()).chunk(
+                6, dim=2
+            )
            # batch_size, seq_len, 1, inner_dim
            shift_msa = shift_msa.squeeze(2)
            scale_msa = scale_msa.squeeze(2)
@@ -227,13 +257,20 @@ class MatrixGameTransformerBlock(nn.Module):
        else:
            # temb: batch_size, 6, inner_dim (wan2.1/wan2.2 14B)
            e = self.scale_shift_table + temb.float()
-            shift_msa, scale_msa, gate_msa, c_shift_msa, c_scale_msa, c_gate_msa = e.chunk(
-                6, dim=1)
+            (
+                shift_msa,
+                scale_msa,
+                gate_msa,
+                c_shift_msa,
+                c_scale_msa,
+                c_gate_msa,
+            ) = e.chunk(6, dim=1)
        assert shift_msa.dtype == torch.float32

        # 1. Self-attention
-        norm_hidden_states = (self.norm1(hidden_states.float()) *
-                              (1 + scale_msa) + shift_msa).to(orig_dtype)
+        norm_hidden_states = (
+            self.norm1(hidden_states.float()) * (1 + scale_msa) + shift_msa
+        ).to(orig_dtype)
        query, _ = self.to_q(norm_hidden_states)
        key, _ = self.to_k(norm_hidden_states)
        value, _ = self.to_v(norm_hidden_states)
@@ -249,9 +286,10 @@ class MatrixGameTransformerBlock(nn.Module):

        # Apply rotary embeddings
        cos, sin = freqs_cis
-        query, key = _apply_rotary_emb(query, cos, sin,
-                                       is_neox_style=False), _apply_rotary_emb(
-                                           key, cos, sin, is_neox_style=False)
+        query, key = (
+            _apply_rotary_emb(query, cos, sin, is_neox_style=False),
+            _apply_rotary_emb(key, cos, sin, is_neox_style=False),
+        )

        attn_output, _ = self.attn1(query, key, value)
        attn_output = attn_output.flatten(2)
@@ -260,18 +298,24 @@ class MatrixGameTransformerBlock(nn.Module):

        null_shift = null_scale = torch.tensor([0], device=hidden_states.device)
        norm_hidden_states, hidden_states = self.self_attn_residual_norm(
-            hidden_states, attn_output, gate_msa, null_shift, null_scale)
-        norm_hidden_states, hidden_states = norm_hidden_states.to(
-            orig_dtype), hidden_states.to(orig_dtype)
+            hidden_states, attn_output, gate_msa, null_shift, null_scale
+        )
+        norm_hidden_states, hidden_states = (
+            norm_hidden_states.to(orig_dtype),
+            hidden_states.to(orig_dtype),
+        )

        # 2. Cross-attention
-        attn_output = self.attn2(norm_hidden_states,
-                                 context=encoder_hidden_states,
-                                 context_lens=None)
+        attn_output = self.attn2(
+            norm_hidden_states, context=encoder_hidden_states, context_lens=None
+        )
        norm_hidden_states, hidden_states = self.cross_attn_residual_norm(
-            hidden_states, attn_output, 1, c_shift_msa, c_scale_msa)
-        norm_hidden_states, hidden_states = norm_hidden_states.to(
-            orig_dtype), hidden_states.to(orig_dtype)
+            hidden_states, attn_output, 1, c_shift_msa, c_scale_msa
+        )
+        norm_hidden_states, hidden_states = (
+            norm_hidden_states.to(orig_dtype),
+            hidden_states.to(orig_dtype),
+        )

        # ================= Action Module =================
        if self.action_model is not None:
@@ -280,9 +324,12 @@ class MatrixGameTransformerBlock(nn.Module):
                # ActionModule implementation takes hidden_states directly
                hidden_states = self.action_model(
                    hidden_states,
-                    grid_sizes[0], grid_sizes[1], grid_sizes[2],
-                    mouse_cond, keyboard_cond,
-                    num_frame_per_block=grid_sizes[0],
+                    int(grid_sizes[0]),
+                    int(grid_sizes[1]),
+                    int(grid_sizes[2]),
+                    mouse_cond,
+                    keyboard_cond,
+                    num_frame_per_block=int(grid_sizes[0]),
                )
        # =================================================

@@ -293,6 +340,7 @@ class MatrixGameTransformerBlock(nn.Module):

        return hidden_states

+
 _DEFAULT_MATRIXGAME_CONFIG = MatrixGameWanVideoConfig()


@@ -302,15 +350,23 @@ class MatrixGameWanModel(BaseDiT):

    _fsdp_shard_conditions = _DEFAULT_MATRIXGAME_CONFIG._fsdp_shard_conditions
    _compile_conditions = _DEFAULT_MATRIXGAME_CONFIG._compile_conditions
-    _supported_attention_backends = _DEFAULT_MATRIXGAME_CONFIG._supported_attention_backends
+    _supported_attention_backends = (
+        _DEFAULT_MATRIXGAME_CONFIG._supported_attention_backends
+    )
    param_names_mapping = _DEFAULT_MATRIXGAME_CONFIG.param_names_mapping
-    reverse_param_names_mapping = _DEFAULT_MATRIXGAME_CONFIG.reverse_param_names_mapping
-    lora_param_names_mapping = _DEFAULT_MATRIXGAME_CONFIG.lora_param_names_mapping
+    reverse_param_names_mapping = (
+        _DEFAULT_MATRIXGAME_CONFIG.reverse_param_names_mapping
+    )
+    lora_param_names_mapping = (
+        _DEFAULT_MATRIXGAME_CONFIG.lora_param_names_mapping
+    )

-    def __init__(self,
-                 config: MatrixGameWanVideoConfig,
-                 hf_config: dict[str, Any],
-                 **kwargs) -> None:
+    def __init__(
+        self,
+        config: MatrixGameWanVideoConfig,
+        hf_config: dict[str, Any],
+        **kwargs,
+    ) -> None:
        super().__init__(config=config, hf_config=hf_config)

        inner_dim = config.num_attention_heads * config.attention_head_dim
@@ -320,10 +376,12 @@ class MatrixGameWanModel(BaseDiT):
        self.patch_size = config.patch_size

        # 1. Patch & position embedding
-        self.patch_embedding = PatchEmbed(in_chans=config.in_channels,
-                                          embed_dim=inner_dim,
-                                          patch_size=config.patch_size,
-                                          flatten=False)
+        self.patch_embedding = PatchEmbed(
+            in_chans=config.in_channels,
+            embed_dim=inner_dim,
+            patch_size=config.patch_size,
+            flatten=False,
+        )

        # 2. Condition embeddings
        self.condition_embedder = MatrixGameTimeImageEmbedding(
@@ -333,57 +391,73 @@ class MatrixGameWanModel(BaseDiT):
        )

        # 2.1. Get action config
-        self.action_config = getattr(config, 'action_config', {})
+        self.action_config = getattr(config, "action_config", {})

        # 3. Transformer blocks
-        self.blocks = nn.ModuleList([
-            MatrixGameTransformerBlock(
-                inner_dim,
-                config.ffn_dim,
-                config.num_attention_heads,
-                config.qk_norm,
-                config.cross_attn_norm,
-                config.eps,
-                config.added_kv_proj_dim,
-                self._supported_attention_backends,
-                prefix=f"{getattr(config, 'prefix', 'Wan')}.blocks.{i}",
-                action_config=self.action_config)
-            for i in range(config.num_layers)
-        ])
+        self.blocks = nn.ModuleList(
+            [
+                MatrixGameTransformerBlock(
+                    inner_dim,
+                    config.ffn_dim,
+                    config.num_attention_heads,
+                    config.qk_norm,
+                    config.cross_attn_norm,
+                    config.eps,
+                    config.added_kv_proj_dim,
+                    self._supported_attention_backends,
+                    prefix=f"{getattr(config, 'prefix', 'Wan')}.blocks.{i}",
+                    action_config=self.action_config,
+                )
+                for i in range(config.num_layers)
+            ]
+        )

        # 4. Output norm & projection
-        self.norm_out = LayerNormScaleShift(inner_dim,
-                                            norm_type="layer",
-                                            eps=config.eps,
-                                            elementwise_affine=False,
-                                            dtype=torch.float32,
-                                            compute_dtype=torch.float32)
+        self.norm_out = LayerNormScaleShift(
+            inner_dim,
+            norm_type="layer",
+            eps=config.eps,
+            elementwise_affine=False,
+            dtype=torch.float32,
+            compute_dtype=torch.float32,
+        )
        self.proj_out = nn.Linear(
-            inner_dim, config.out_channels * math.prod(config.patch_size))
+            inner_dim, config.out_channels * math.prod(config.patch_size)
+        )
        self.scale_shift_table = nn.Parameter(
-            torch.randn(1, 2, inner_dim) / inner_dim**0.5)
- 
+            torch.randn(1, 2, inner_dim) / inner_dim**0.5
+        )
+
        self.gradient_checkpointing = False

-    def forward(self,
-                hidden_states: torch.Tensor,
-                encoder_hidden_states: torch.Tensor | list[torch.Tensor],
-                timestep: torch.LongTensor,
-                encoder_hidden_states_image: torch.Tensor
-                | list[torch.Tensor] | None = None,
-                # Action inputs
-                mouse_cond: torch.Tensor | None = None,
-                keyboard_cond: torch.Tensor | None = None,
-                **kwargs) -> torch.Tensor:
-        if encoder_hidden_states is not None and not isinstance(encoder_hidden_states, torch.Tensor):
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        encoder_hidden_states: torch.Tensor | list[torch.Tensor],
+        timestep: torch.LongTensor,
+        encoder_hidden_states_image: torch.Tensor
+        | list[torch.Tensor]
+        | None = None,
+        # Action inputs
+        mouse_cond: torch.Tensor | None = None,
+        keyboard_cond: torch.Tensor | None = None,
+        **kwargs,
+    ) -> torch.Tensor:
+        if encoder_hidden_states is not None and not isinstance(
+            encoder_hidden_states, torch.Tensor
+        ):
            encoder_hidden_states = encoder_hidden_states[0]
-        if isinstance(encoder_hidden_states_image,
-                      list) and len(encoder_hidden_states_image) > 0:
+        if (
+            isinstance(encoder_hidden_states_image, list)
+            and len(encoder_hidden_states_image) > 0
+        ):
            encoder_hidden_states_image = encoder_hidden_states_image[0]
        else:
            encoder_hidden_states_image = None

-        batch_size, num_channels, num_frames, height, width = hidden_states.shape
+        batch_size, num_channels, num_frames, height, width = (
+            hidden_states.shape
+        )
        p_t, p_h, p_w = self.patch_size
        post_patch_num_frames = num_frames // p_t
        post_patch_height = height // p_h
@@ -393,18 +467,25 @@ class MatrixGameWanModel(BaseDiT):
        d = self.hidden_size // self.num_attention_heads
        rope_dim_list = [d - 4 * (d // 6), 2 * (d // 6), 2 * (d // 6)]
        freqs_cos, freqs_sin = get_rotary_pos_embed(
-            (post_patch_num_frames * get_sp_world_size(), post_patch_height,
-             post_patch_width),
+            (
+                post_patch_num_frames * get_sp_world_size(),
+                post_patch_height,
+                post_patch_width,
+            ),
            self.hidden_size,
            self.num_attention_heads,
            rope_dim_list,
            dtype=torch.float32 if current_platform.is_mps() else torch.float64,
            rope_theta=10000,
-            do_sp_sharding=True)
+            do_sp_sharding=True,
+        )
        freqs_cos = freqs_cos.to(hidden_states.device)
        freqs_sin = freqs_sin.to(hidden_states.device)
-        freqs_cis = (freqs_cos.float(),
-                     freqs_sin.float()) if freqs_cos is not None else None
+        freqs_cis = (
+            (freqs_cos.float(), freqs_sin.float())
+            if freqs_cos is not None
+            else None
+        )

        hidden_states = self.patch_embedding(hidden_states)
        hidden_states = hidden_states.flatten(2).transpose(1, 2)
@@ -412,8 +493,14 @@ class MatrixGameWanModel(BaseDiT):
        if timestep.dim() == 2:
            timestep = timestep.flatten()

-        temb, timestep_proj, encoder_hidden_states, encoder_hidden_states_image = self.condition_embedder(
-            timestep, encoder_hidden_states, encoder_hidden_states_image)
+        (
+            temb,
+            timestep_proj,
+            encoder_hidden_states,
+            encoder_hidden_states_image,
+        ) = self.condition_embedder(
+            timestep, encoder_hidden_states, encoder_hidden_states_image
+        )
        timestep_proj = timestep_proj.unflatten(1, (6, -1))

        if encoder_hidden_states is not None:
@@ -429,25 +516,30 @@ class MatrixGameWanModel(BaseDiT):
        if encoder_hidden_states_image is not None:
            if encoder_hidden_states is not None:
                encoder_hidden_states = torch.concat(
-                    [encoder_hidden_states_image, encoder_hidden_states], dim=1)
+                    [encoder_hidden_states_image, encoder_hidden_states], dim=1
+                )
            else:
                encoder_hidden_states = encoder_hidden_states_image

        # This is [F, H, W] for the ActionModule
-        grid_sizes = torch.tensor([
-            post_patch_num_frames, post_patch_height, post_patch_width
-        ],
-                                 device=hidden_states.device)
+        grid_sizes = torch.tensor(
+            [post_patch_num_frames, post_patch_height, post_patch_width],
+            device=hidden_states.device,
+        )

        # Blocks
        for block in self.blocks:
            if torch.is_grad_enabled() and self.gradient_checkpointing:
                hidden_states = self._gradient_checkpointing_func(
-                    block, hidden_states, encoder_hidden_states, timestep_proj,
+                    block,
+                    hidden_states,
+                    encoder_hidden_states,
+                    timestep_proj,
                    freqs_cis,
                    grid_sizes=grid_sizes,
                    mouse_cond=mouse_cond,
-                    keyboard_cond=keyboard_cond)
+                    keyboard_cond=keyboard_cond,
+                )
            else:
                hidden_states = block(
                    hidden_states,
@@ -456,19 +548,27 @@ class MatrixGameWanModel(BaseDiT):
                    freqs_cis,
                    grid_sizes=grid_sizes,
                    mouse_cond=mouse_cond,
-                    keyboard_cond=keyboard_cond)
+                    keyboard_cond=keyboard_cond,
+                )

        # Output
-        shift, scale = (self.scale_shift_table + temb.unsqueeze(1)).chunk(2,
-                                                                          dim=1)
+        shift, scale = (self.scale_shift_table + temb.unsqueeze(1)).chunk(
+            2, dim=1
+        )
        hidden_states = self.norm_out(hidden_states, shift, scale)
        hidden_states = self.proj_out(hidden_states)

-        hidden_states = hidden_states.reshape(batch_size, post_patch_num_frames,
-                                              post_patch_height,
-                                              post_patch_width, p_t, p_h, p_w,
-                                              -1)
+        hidden_states = hidden_states.reshape(
+            batch_size,
+            post_patch_num_frames,
+            post_patch_height,
+            post_patch_width,
+            p_t,
+            p_h,
+            p_w,
+            -1,
+        )
        hidden_states = hidden_states.permute(0, 7, 1, 4, 2, 5, 3, 6)
        output = hidden_states.flatten(6, 7).flatten(4, 5).flatten(2, 3)

-        return output
+        return output
@@ -685,3 +685,6 @@ class StepVideoModel(BaseDiT):

        output = rearrange(output, '(b f) c h w -> b c f h w', f=frame)
        return output
+
+# Entry point for model registry
+EntryClass = StepVideoModel
@@ -871,4 +871,6 @@ class WanTransformer3DModel(CachableDiT):
            return hidden_states + self.previous_residual_even
        else:
            return hidden_states + self.previous_residual_odd
-        
+
+# Entry point for model registry
+EntryClass = WanTransformer3DModel
@@ -631,3 +631,6 @@ class CLIPVisionModel(ImageEncoder):
                weight_loader(param, loaded_weight)
            loaded_params.add(name)
        return loaded_params
+
+# Entry point for model registry
+EntryClass = [CLIPTextModel, CLIPVisionModel]
@@ -3,11 +3,11 @@ from __future__ import annotations

 from dataclasses import dataclass
 import os
-from typing import Iterable
+from typing import Any, Iterable

 import torch
 from torch import nn
-from transformers import Gemma3ForConditionalGeneration
+from transformers import AutoTokenizer, Gemma3ForConditionalGeneration

 from fastvideo.configs.models.encoders import BaseEncoderOutput, TextEncoderConfig
 from fastvideo.models.encoders.base import TextEncoder
@@ -447,6 +447,60 @@ class LTX2GemmaTextEncoderModel(TextEncoder):

        return encoded, encoded_for_audio, attention_mask.squeeze(-1)

+    @torch.no_grad()
+    def preprocess_text_embeddings(
+        self,
+        prompts: str | list[str],
+        tokenizer: AutoTokenizer,
+        tokenizer_kwargs: dict[str, Any] | None = None,
+        padding_side: str | None = None,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        """Compute pre-connector text embeddings for LTX-2 training preprocessing."""
+        if isinstance(prompts, str):
+            prompts = [prompts]
+
+        model = self.gemma_model
+        kwargs: dict[str, Any] = {
+            "padding": "max_length",
+            "truncation": True,
+            "return_tensors": "pt",
+        }
+        if tokenizer_kwargs is not None:
+            kwargs.update(tokenizer_kwargs)
+        if "max_length" not in kwargs:
+            kwargs["max_length"] = self.config.arch_config.text_len
+
+        original_padding_side = tokenizer.padding_side
+        target_padding_side = padding_side or self.padding_side
+        tokenizer.padding_side = target_padding_side
+        try:
+            text_inputs = tokenizer(prompts, **kwargs)
+        finally:
+            tokenizer.padding_side = original_padding_side
+
+        input_ids = text_inputs["input_ids"].to(device=model.device)
+        attention_mask = text_inputs["attention_mask"].to(device=model.device)
+        outputs = model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            output_hidden_states=True,
+            return_dict=True,
+        )
+        prompt_embeds = self._run_feature_extractor(
+            outputs.hidden_states,
+            attention_mask,
+            padding_side=target_padding_side,
+        )
+        return prompt_embeds, attention_mask
+
+    def run_connectors(
+        self,
+        encoded_input: torch.Tensor,
+        attention_mask: torch.Tensor,
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        """Apply embedding connectors to precomputed Gemma features."""
+        return self._run_connectors(encoded_input, attention_mask)
+
    def forward(
        self,
        input_ids: torch.Tensor | None,
@@ -561,3 +615,6 @@ def _norm_and_concat_padded_batch(
    mask_flattened = mask.reshape(b, t, 1).expand(-1, -1, d * l)
    normed = normed.masked_fill(~mask_flattened, 0.0)
    return normed
+
+# Entry point for model registry
+EntryClass = LTX2GemmaTextEncoderModel
@@ -429,3 +429,6 @@ class LlamaModel(TextEncoder):
                weight_loader(param, loaded_weight)
            loaded_params.add(name)
        return loaded_params
+
+# Entry point for model registry
+EntryClass = LlamaModel
@@ -385,3 +385,6 @@ class Qwen2_5_VLTextModel(TextEncoder):
                weight_loader(param, loaded_weight)
            loaded_params.add(name)
        return loaded_params
+
+# Entry point for model registry
+EntryClass = Qwen2_5_VLTextModel
@@ -351,3 +351,5 @@ class Reason1TextEncoder(TextEncoder):
            tensor.std(dim=-1, keepdim=True) + 1e-8
        )

+# Entry point for model registry
+EntryClass = Reason1TextEncoder
@@ -417,3 +417,6 @@ class SiglipVisionModel(ImageEncoder):
            loaded_params.add(name)
            
        return loaded_params
+
+# Entry point for model registry
+EntryClass = SiglipVisionModel
@@ -583,3 +583,6 @@ class STEP1TextEncoder(torch.nn.Module):
                                      self.device) if with_mask else None)
            y_mask = txt_tokens.attention_mask
        return y.transpose(0, 1), y_mask
+
+# Entry point for model registry
+EntryClass = STEP1TextEncoder
@@ -719,3 +719,6 @@ class UMT5EncoderModel(TextEncoder):
                weight_loader(param, loaded_weight)
            loaded_params.add(name)
        return loaded_params
+
+# Entry point for model registry
+EntryClass = [UMT5EncoderModel, T5EncoderModel]
@@ -79,6 +79,7 @@ class ComponentLoader(ABC):
            "scheduler": (SchedulerLoader, "diffusers"),
            "transformer": (TransformerLoader, "diffusers"),
            "transformer_2": (TransformerLoader, "diffusers"),
+            "transformer_3": (TransformerLoader, "diffusers"),
            "vae": (VAELoader, "diffusers"),
            "audio_vae": (AudioDecoderLoader, "diffusers"),
            "audio_decoder": (AudioDecoderLoader, "diffusers"),
@@ -90,6 +91,8 @@ class ComponentLoader(ABC):
            "image_processor": (ImageProcessorLoader, "transformers"),
            "feature_extractor": (ImageProcessorLoader, "transformers"),
            "image_encoder": (ImageEncoderLoader, "transformers"),
+            "upsampler": (UpsamplerLoader, "diffusers"),
+            "upsampler_2": (UpsamplerLoader, "diffusers"),
        }

        if module_type in module_loaders:
@@ -558,7 +561,8 @@ class VAELoader(ComponentLoader):
    def load(self, model_path: str, fastvideo_args: FastVideoArgs):
        """Load the VAE based on the model path, and inference args."""
        config = get_diffusers_config(model=model_path)
-        class_name = config.get("_class_name")
+        class_name = config.pop("_class_name")
+        config.pop("_name_or_path", None)
        assert class_name is not None, (
            "Model config does not contain a _class_name attribute. Only diffusers format is supported."
        )
@@ -731,6 +735,7 @@ class TransformerLoader(ComponentLoader):
        config = get_diffusers_config(model=model_path)
        hf_config = deepcopy(config)
        cls_name = config.pop("_class_name")
+        config.pop("_name_or_path", None)
        if cls_name is None:
            raise ValueError(
                "Model config does not contain a _class_name attribute. "
@@ -745,7 +750,7 @@ class TransformerLoader(ComponentLoader):
        fastvideo_args.model_paths["transformer"] = model_path

        # Config from Diffusers supersedes fastvideo's model config
-        dit_config = fastvideo_args.pipeline_config.dit_config
+        dit_config = deepcopy(fastvideo_args.pipeline_config.dit_config)
        dit_config.update_model_arch(config)

        model_cls, _ = ModelRegistry.resolve_model_cls(cls_name)
@@ -877,6 +882,50 @@ class SchedulerLoader(ComponentLoader):
        return scheduler


+class UpsamplerLoader(ComponentLoader):
+    """Loader for upsamplers."""
+
+    def load(self, model_path: str, fastvideo_args: FastVideoArgs):
+        """Load the upsampler based on the model path, and inference args."""
+        config_dict = get_diffusers_config(model=model_path)
+        class_name = config_dict.pop("_class_name", None)
+
+        if class_name is None:
+            raise ValueError(
+                "Model config does not contain a _class_name attribute. "
+                "Only diffusers format is supported."
+            )
+
+        try:
+            upsampler_cfg = deepcopy(fastvideo_args.pipeline_config.upsampler_config[0])
+            upsampler_cfg.update_model_config(config_dict)
+        except Exception as e:
+            upsampler_cfg = deepcopy(fastvideo_args.pipeline_config.upsampler_config[1])
+            upsampler_cfg.update_model_config(config_dict)
+
+        model_cls, _ = ModelRegistry.resolve_model_cls(class_name)
+        model = model_cls(upsampler_cfg)
+
+        target_device = get_local_torch_device()
+        model = model.to(target_device, dtype=PRECISION_TO_TYPE[fastvideo_args.pipeline_config.upsampler_precision])
+
+        # Find all safetensors files
+        safetensors_list = glob.glob(
+            os.path.join(str(model_path), "*.safetensors"))
+        if not safetensors_list:
+            raise ValueError(f"No safetensors files found in {model_path}")
+        
+        if len(safetensors_list) == 1:
+            loaded = safetensors_load_file(safetensors_list[0])
+        else:
+            loaded = {}
+            for sf_file in safetensors_list:
+                loaded.update(safetensors_load_file(sf_file))
+        
+        model.load_state_dict(loaded, strict=True)
+
+        return model.eval()
+
 class GenericComponentLoader(ComponentLoader):
    """Generic loader for components that don't have a specific loader."""

@@ -1,6 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # Adapted from vllm: https://github.com/vllm-project/vllm/blob/v0.7.3/vllm/model_executor/models/registry.py

+import ast
 import importlib
 import os
 import pickle
@@ -98,7 +99,12 @@ _SCHEDULERS = {
    ("schedulers", "scheduling_rcm", "RCMScheduler"),
 }

-_FAST_VIDEO_MODELS = {
+_UPSAMPLERS = {
+    "SRTo720pUpsampler": ("upsamplers", "hunyuan15", "SRTo720pUpsampler"),
+    "SRTo1080pUpsampler": ("upsamplers", "hunyuan15", "SRTo1080pUpsampler"),
+}
+
+_LEGACY_FAST_VIDEO_MODELS = {
    **_TEXT_TO_VIDEO_DIT_MODELS,
    **_IMAGE_TO_VIDEO_DIT_MODELS,
    **_TEXT_ENCODER_MODELS,
@@ -106,8 +112,102 @@ _FAST_VIDEO_MODELS = {
    **_VAE_MODELS,
    **_AUDIO_MODELS,
    **_SCHEDULERS,
+    **_UPSAMPLERS,
 }

+MODELS_PATH = os.path.dirname(__file__)
+
+
+@lru_cache(maxsize=None)
+def _discover_and_register_models() -> dict[str, tuple[str, str, str]]:
+    discovered_models: dict[str, tuple[str, str, str]] = {}
+    for root, dirs, files in os.walk(MODELS_PATH):
+        dirs[:] = [
+            d for d in dirs
+            if not d.startswith(".") and d != "__pycache__"
+        ]
+
+        for filename in files:
+            if not filename.endswith(".py"):
+                continue
+
+            filepath = os.path.join(root, filename)
+            try:
+                with open(filepath, "r", encoding="utf-8") as f:
+                    source = f.read()
+                tree = ast.parse(source, filename=filename)
+
+                entry_class_node = None
+                first_class_def = None
+
+                for node in ast.walk(tree):
+                    if isinstance(node, ast.Assign):
+                        for target in node.targets:
+                            if isinstance(target, ast.Name) and target.id == "EntryClass":
+                                entry_class_node = node
+                                break
+                    if first_class_def is None and isinstance(node, ast.ClassDef):
+                        first_class_def = node
+
+                if not entry_class_node or not first_class_def:
+                    continue
+
+                model_cls_name_list: list[str] = []
+                value_node = entry_class_node.value
+
+                if isinstance(value_node, ast.Name):
+                    model_cls_name_list.append(value_node.id)
+                elif isinstance(value_node, (ast.List, ast.Tuple)):
+                    for elt in value_node.elts:
+                        if isinstance(elt, ast.Constant) and isinstance(
+                                elt.value, str):
+                            model_cls_name_list.append(elt.value)
+                        elif isinstance(elt, ast.Name):
+                            model_cls_name_list.append(elt.id)
+
+                if not model_cls_name_list:
+                    continue
+
+                rel_dir = os.path.relpath(root, MODELS_PATH)
+                if rel_dir == ".":
+                    continue
+
+                rel_parts = rel_dir.split(os.sep)
+                component_name = rel_parts[0]
+                sub_parts = rel_parts[1:]
+
+                if filename == "__init__.py":
+                    mod_relname = ".".join(sub_parts)
+                else:
+                    mod_base = filename[:-3]
+                    mod_relname = ".".join(sub_parts +
+                                           [mod_base]) if sub_parts else mod_base
+
+                for model_cls_str in model_cls_name_list:
+                    if model_cls_str in discovered_models:
+                        logger.warning(
+                            "Duplicate architecture found: %s. Overwriting.",
+                            model_cls_str)
+                    discovered_models[model_cls_str] = (
+                        component_name,
+                        mod_relname,
+                        model_cls_str,
+                    )
+
+            except Exception as e:
+                logger.warning("Could not parse %s to find models: %s",
+                               filepath, e)
+
+    return discovered_models
+
+
+_DISCOVERED_MODELS = _discover_and_register_models()
+_FAST_VIDEO_MODELS = dict(_DISCOVERED_MODELS)
+for model_arch, spec in _LEGACY_FAST_VIDEO_MODELS.items():
+    if model_arch in _FAST_VIDEO_MODELS:
+        continue
+    _FAST_VIDEO_MODELS[model_arch] = spec
+
 _SUBPROCESS_COMMAND = [sys.executable, "-m", "fastvideo.models.dits.registry"]

 _T = TypeVar("_T")
@@ -339,7 +439,8 @@ class _ModelRegistry:
 ModelRegistry = _ModelRegistry({
    model_arch:
    _LazyRegisteredModel(
-        module_name=f"fastvideo.models.{component_name}.{mod_relname}",
+        module_name=(f"fastvideo.models.{component_name}.{mod_relname}"
+                     if mod_relname else f"fastvideo.models.{component_name}"),
        component_name=component_name,
        class_name=cls_name,
    )
@@ -685,3 +685,6 @@ class FlowMatchEulerDiscreteScheduler(SchedulerMixin, ConfigMixin,

    def __len__(self) -> int:
        return self.config.num_train_timesteps
+
+# Entry point for model registry
+EntryClass = FlowMatchEulerDiscreteScheduler
@@ -853,3 +853,6 @@ class FlowUniPCMultistepScheduler(SchedulerMixin, ConfigMixin, BaseScheduler):

    def __len__(self):
        return self.config.num_train_timesteps
+
+# Entry point for model registry
+EntryClass = FlowUniPCMultistepScheduler
@@ -321,3 +321,6 @@ class RCMScheduler(SchedulerMixin, ConfigMixin, BaseScheduler):

    def __len__(self) -> int:
        return self.config.num_train_timesteps
+
+# Entry point for model registry
+EntryClass = RCMScheduler
@@ -160,4 +160,6 @@ class SelfForcingFlowMatchScheduler(BaseScheduler, ConfigMixin, SchedulerMixin):

    def set_shift(self, shift: float) -> None:
        self.shift = shift
-    
+
+# Entry point for model registry
+EntryClass = SelfForcingFlowMatchScheduler
@@ -1094,4 +1094,7 @@ class UniPCMultistepScheduler(SchedulerMixin, ConfigMixin, BaseScheduler):
        return noisy_samples

    def __len__(self):
-        return self.config.num_train_timesteps
+        return self.config.num_train_timesteps
+
+# Entry point for model registry
+EntryClass = UniPCMultistepScheduler
@@ -0,0 +1,170 @@
+# Licensed under the TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5/blob/main/LICENSE
+#
+# Unless and only to the extent required by applicable law, the Tencent Hunyuan works and any
+# output and results therefrom are provided "AS IS" without any express or implied warranties of
+# any kind including any warranties of title, merchantability, noninfringement, course of dealing,
+# usage of trade, or fitness for a particular purpose. You are solely responsible for determining the
+# appropriateness of using, reproducing, modifying, performing, displaying or distributing any of
+# the Tencent Hunyuan works or outputs and assume any and all risks associated with your or a
+# third party's use or distribution of any of the Tencent Hunyuan works or outputs and your exercise
+# of rights and permissions under this agreement.
+# See the License for the specific language governing permissions and limitations under the License.
+
+from collections.abc import Sequence
+from dataclasses import dataclass
+from enum import Enum
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange
+from torch import Tensor
+
+from fastvideo.models.vaes.hunyuan15vae import (
+    HunyuanVideo15CausalConv3d,
+    HunyuanVideo15RMS_norm,
+)
+
+from fastvideo.layers.activation import get_act_fn
+from fastvideo.configs.models.upsamplers import SRTo720pUpsamplerConfig, SRTo1080pUpsamplerConfig
+
+class HunyuanVideo15ResnetBlock(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int | None = None,
+        non_linearity: str = "swish",
+    ) -> None:
+        super().__init__()
+        out_channels = out_channels or in_channels
+
+        self.nonlinearity = get_act_fn(non_linearity)
+
+        self.norm1 = HunyuanVideo15RMS_norm(in_channels, images=False)
+        self.conv1 = HunyuanVideo15CausalConv3d(in_channels, out_channels, kernel_size=3)
+
+        self.norm2 = HunyuanVideo15RMS_norm(out_channels, images=False)
+        self.conv2 = HunyuanVideo15CausalConv3d(out_channels, out_channels, kernel_size=3)
+
+        self.nin_shortcut = None
+        if in_channels != out_channels:
+            self.nin_shortcut = nn.Conv3d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
+
+    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
+        residual = hidden_states
+
+        hidden_states = self.norm1(hidden_states)
+        hidden_states = self.nonlinearity(hidden_states)
+        hidden_states = self.conv1(hidden_states)
+
+        hidden_states = self.norm2(hidden_states)
+        hidden_states = self.nonlinearity(hidden_states)
+        hidden_states = self.conv2(hidden_states)
+
+        if self.nin_shortcut is not None:
+            residual = self.nin_shortcut(residual)
+
+        return hidden_states + residual
+
+class SRResidualCausalBlock3D(nn.Module):
+    def __init__(self, channels: int):
+        super().__init__()
+        self.block = nn.Sequential(
+            HunyuanVideo15CausalConv3d(channels, channels, kernel_size=3),
+            nn.SiLU(inplace=True),
+            HunyuanVideo15CausalConv3d(channels, channels, kernel_size=3),
+            nn.SiLU(inplace=True),
+            HunyuanVideo15CausalConv3d(channels, channels, kernel_size=3),
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return x + self.block(x)
+
+
+class SRTo720pUpsampler(nn.Module):
+
+    def __init__(
+        self,
+        config: SRTo720pUpsamplerConfig,
+    ):
+        super().__init__()
+        self.in_conv = HunyuanVideo15CausalConv3d(config.in_channels, config.hidden_channels, kernel_size=3)
+        self.blocks = nn.ModuleList([SRResidualCausalBlock3D(config.hidden_channels) for _ in range(config.num_blocks)])
+        self.out_conv = HunyuanVideo15CausalConv3d(config.hidden_channels, config.out_channels, kernel_size=3)
+        self.global_residual = bool(config.global_residual)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        residual = x
+        y = self.in_conv(x)
+        for blk in self.blocks:
+            y = blk(y)
+        y = self.out_conv(y)
+        if self.global_residual and (y.shape == residual.shape):
+            y = y + residual
+        return y
+
+
+class SRTo1080pUpsampler(nn.Module):
+
+    def __init__(
+        self,
+        config: SRTo1080pUpsamplerConfig,
+    ):
+        super().__init__()
+        self.num_res_blocks = config.num_res_blocks
+        self.block_out_channels = config.block_out_channels
+        self.z_channels = config.z_channels
+
+        block_in = config.block_out_channels[0]
+        self.conv_in = HunyuanVideo15CausalConv3d(config.z_channels, block_in, kernel_size=3)
+
+        self.up = nn.ModuleList()
+        for i_level, ch in enumerate(config.block_out_channels):
+            block = nn.ModuleList()
+            block_out = ch
+            for _ in range(self.num_res_blocks + 1):
+                block.append(HunyuanVideo15ResnetBlock(in_channels=block_in, out_channels=block_out))
+                block_in = block_out
+            up = nn.Module()
+            up.block = block
+
+            self.up.append(up)
+
+        self.norm_out = HunyuanVideo15RMS_norm(block_in, images=False)
+        self.conv_out = HunyuanVideo15CausalConv3d(block_in, config.out_channels, kernel_size=3)
+
+        self.gradient_checkpointing = False
+        self.is_residual = config.is_residual
+
+    def forward(self, z: Tensor, target_shape: Sequence[int] = None) -> Tensor:
+        """
+        Args:
+            z: (B, C, T, H, W)
+            target_shape: (H, W)
+        """
+        if target_shape is not None and z.shape[-2:] != target_shape:
+            bsz = z.shape[0]
+            z = rearrange(z, "b c f h w -> (b f) c h w")
+            z = F.interpolate(z, size=target_shape, mode="bilinear", align_corners=False)
+            z = rearrange(z, "(b f) c h w -> b c f h w", b=bsz)
+
+        # z to block_in
+        repeats = self.block_out_channels[0] // (self.z_channels)
+        h = self.conv_in(z) + z.repeat_interleave(repeats=repeats, dim=1)
+
+        # upsampling
+        for i_level in range(len(self.block_out_channels)):
+            for i_block in range(self.num_res_blocks + 1):
+                h = self.up[i_level].block[i_block](h)
+            if hasattr(self.up[i_level], "upsample"):
+                h = self.up[i_level].upsample(h)
+
+        # end
+        h = self.norm_out(h)
+        h = get_act_fn("swish")(h)
+        h = self.conv_out(h)
+        return h
@@ -700,4 +700,7 @@ class AutoencoderKLHunyuanVideo15(nn.Module, ParallelTiledVAE):
        else:
            z = posterior.mode()
        dec = self.decode(z)
-        return dec
+        return dec
+
+# Entry point for model registry
+EntryClass = AutoencoderKLHunyuanVideo15
@@ -850,3 +850,6 @@ class AutoencoderKLHunyuanVideo(nn.Module, ParallelTiledVAE):
            z = posterior.mode()
        dec = self.decode(z)
        return dec
+
+# Entry point for model registry
+EntryClass = AutoencoderKLHunyuanVideo
@@ -15,15 +15,977 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.

-from fastvideo.models.vaes.hunyuan15vae import AutoencoderKLHunyuanVideo15
-from fastvideo.configs.models.vaes import Hunyuan15VAEConfig
+from typing import List, Optional, Tuple, Union

-class AutoencoderKLHYWorld(AutoencoderKLHunyuanVideo15):
-    # TODO(mingjia): add temporal caching support for HYWorld VAE
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from einops import rearrange
+
+from fastvideo.layers.activation import get_act_fn
+from fastvideo.configs.models.vaes import Hunyuan15VAEConfig
+from fastvideo.models.vaes.common import ParallelTiledVAE
+from fastvideo.models.vaes.hunyuan15vae import (
+    HunyuanVideo15RMS_norm as HYWorldRMS_norm,
+    HunyuanVideo15AttnBlock as HYWorldAttnBlock,
+)
+
+# Cache size for temporal feature caching (number of frames to cache)
+CACHE_T = 2
+
+class HYWorldCausalConv3d(nn.Module):
+    """Causal Conv3d with optional cache support for temporal feature caching."""
+    
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: Union[int, Tuple[int, int, int]] = 3,
+        stride: Union[int, Tuple[int, int, int]] = 1,
+        padding: Union[int, Tuple[int, int, int]] = 0,
+        dilation: Union[int, Tuple[int, int, int]] = 1,
+        bias: bool = True,
+        pad_mode: str = "replicate",
+    ) -> None:
+        super().__init__()
+
+        kernel_size = (kernel_size, kernel_size, kernel_size) if isinstance(kernel_size, int) else kernel_size
+
+        self.pad_mode = pad_mode
+        # Padding format: (W_left, W_right, H_left, H_right, T_left, T_right)
+        self.time_causal_padding = (
+            kernel_size[0] // 2,  # W_left (spatial)
+            kernel_size[0] // 2,  # W_right (spatial)
+            kernel_size[1] // 2,  # H_left (spatial)
+            kernel_size[1] // 2,  # H_right (spatial)
+            kernel_size[2] - 1,   # T_left (temporal causal padding)
+            0,                    # T_right (no future padding for causal)
+        )
+
+        self.conv = nn.Conv3d(in_channels, out_channels, kernel_size, stride, padding, dilation, bias=bias)
+
+    def forward(self, hidden_states: torch.Tensor, cache_x: Optional[torch.Tensor] = None) -> torch.Tensor:
+        """
+        Forward pass with optional temporal caching.
+        
+        Args:
+            hidden_states: Input tensor of shape (B, C, T, H, W)
+            cache_x: Optional cached frames from previous chunk, shape (B, C, CACHE_T, H, W)
+                    When provided, uses cached frames instead of padding for temporal dimension.
+        """
+        padding = list(self.time_causal_padding)
+        
+        if cache_x is not None and self.time_causal_padding[4] > 0:  # Has temporal padding and cache
+            cache_x = cache_x.to(hidden_states.device)
+            # Concatenate cached frames with current input on temporal dimension
+            hidden_states = torch.cat([cache_x, hidden_states], dim=2)
+            # Reduce temporal padding since we have cached frames
+            padding[4] -= cache_x.shape[2]
+        
+        hidden_states = F.pad(hidden_states, padding, mode=self.pad_mode)
+        return self.conv(hidden_states)
+
+
+class HYWorldUpsample(nn.Module):
+    """Hierarchical upsampling with temporal/spatial support and optional caching."""
+    
+    def __init__(self, in_channels: int, out_channels: int, add_temporal_upsample: bool = True):
+        super().__init__()
+        factor = 2 * 2 * 2 if add_temporal_upsample else 1 * 2 * 2
+        self.conv = HYWorldCausalConv3d(in_channels, out_channels * factor, kernel_size=3)
+        self.add_temporal_upsample = add_temporal_upsample
+        self.repeats = factor * out_channels // in_channels
+
+    def forward(
+        self, 
+        x: torch.Tensor,
+        feat_cache: Optional[List[Optional[torch.Tensor]]] = None,
+        feat_idx: Optional[List[int]] = None,
+        first_chunk: bool = False,
+    ):
+        """
+        Forward pass with optional temporal caching.
+        
+        Args:
+            x: Input tensor of shape (B, C, T, H, W)
+            feat_cache: List of cached features for each conv layer
+            feat_idx: List containing current cache index [idx]
+            first_chunk: Whether this is the first chunk (affects temporal upsample behavior)
+        """
+        r1 = 2 if self.add_temporal_upsample else 1
+        if feat_cache is not None and feat_idx is not None:
+            idx = feat_idx[0]
+            cache_x = x[:, :, -CACHE_T:, :, :].clone()
+            if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
+                cache_x = torch.cat(
+                    [feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x],
+                    dim=2,
+                )
+            h = self.conv(x, feat_cache[idx])
+            feat_cache[idx] = cache_x
+            feat_idx[0] += 1
+        else:
+            h = self.conv(x)
+
+        if self.add_temporal_upsample:
+            if first_chunk:
+                # First chunk: only spatial upsample
+                h = rearrange(h, "b (r2 r3 c) f h w -> b c f (h r2) (w r3)", r2=2, r3=2)
+                h = h[:, : h.shape[1] // 2]
+                # Compute the shortcut part
+                shortcut = rearrange(x, "b (r2 r3 c) f h w -> b c f (h r2) (w r3)", r2=2, r3=2)
+                shortcut = shortcut.repeat_interleave(repeats=self.repeats // 2, dim=1)
+            elif feat_cache is None and x.shape[2] > 1:
+                # No cache and multiple frames: separate first frame and rest
+                h_first = h[:, :, :1, :, :]
+                h_rest = h[:, :, 1:, :, :]
+                x_first = x[:, :, :1, :, :]
+                x_rest = x[:, :, 1:, :, :]
+
+                # First frame: only spatial upsample
+                h_first = rearrange(h_first, "b (r2 r3 c) f h w -> b c f (h r2) (w r3)", r2=2, r3=2)
+                h_first = h_first[:, : h_first.shape[1] // 2]
+                shortcut_first = rearrange(x_first, "b (r2 r3 c) f h w -> b c f (h r2) (w r3)", r2=2, r3=2)
+                shortcut_first = shortcut_first.repeat_interleave(repeats=self.repeats // 2, dim=1)
+                out_first = h_first + shortcut_first
+
+                # Remaining frames: spatio-temporal upsample
+                h_rest = rearrange(h_rest, "b (r1 r2 r3 c) f h w -> b c (f r1) (h r2) (w r3)", r1=r1, r2=2, r3=2)
+                shortcut_rest = rearrange(x_rest, "b (r1 r2 r3 c) f h w -> b c (f r1) (h r2) (w r3)", r1=r1, r2=2, r3=2)
+                shortcut_rest = shortcut_rest.repeat_interleave(repeats=self.repeats, dim=1)
+                out_rest = h_rest + shortcut_rest
+
+                return torch.cat([out_first, out_rest], dim=2)
+            else:
+                # Subsequent chunks with cache: spatio-temporal upsample
+                h = rearrange(h, "b (r1 r2 r3 c) f h w -> b c (f r1) (h r2) (w r3)", r1=r1, r2=2, r3=2)
+                shortcut = rearrange(x, "b (r1 r2 r3 c) f h w -> b c (f r1) (h r2) (w r3)", r1=r1, r2=2, r3=2)
+                shortcut = shortcut.repeat_interleave(repeats=self.repeats, dim=1)
+        else:
+            h = rearrange(h, "b (r1 r2 r3 c) f h w -> b c (f r1) (h r2) (w r3)", r1=r1, r2=2, r3=2)
+            shortcut = x.repeat_interleave(repeats=self.repeats, dim=1)
+            shortcut = rearrange(shortcut, "b (r1 r2 r3 c) f h w -> b c (f r1) (h r2) (w r3)", r1=r1, r2=2, r3=2)
+        
+        return h + shortcut
+
+
+class HYWorldDownsample(nn.Module):
+    """Hierarchical downsampling with temporal/spatial support and optional caching."""
+    
+    def __init__(self, in_channels: int, out_channels: int, add_temporal_downsample: bool = True):
+        super().__init__()
+        factor = 2 * 2 * 2 if add_temporal_downsample else 1 * 2 * 2
+        self.conv = HYWorldCausalConv3d(in_channels, out_channels // factor, kernel_size=3)
+
+        self.add_temporal_downsample = add_temporal_downsample
+        self.group_size = factor * in_channels // out_channels
+
+    def forward(
+        self, 
+        x: torch.Tensor,
+        feat_cache: Optional[List[Optional[torch.Tensor]]] = None,
+        feat_idx: Optional[List[int]] = None,
+    ):
+        """
+        Forward pass with optional temporal caching.
+        
+        Args:
+            x: Input tensor of shape (B, C, T, H, W)
+            feat_cache: List of cached features for each conv layer
+            feat_idx: List containing current cache index [idx]
+        """
+        r1 = 2 if self.add_temporal_downsample else 1
+        
+        # Apply conv with caching
+        if feat_cache is not None and feat_idx is not None:
+            idx = feat_idx[0]
+            cache_x = x[:, :, -CACHE_T:, :, :].clone()
+            if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
+                cache_x = torch.cat(
+                    [feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x],
+                    dim=2,
+                )
+            h = self.conv(x, feat_cache[idx])
+            feat_cache[idx] = cache_x
+            feat_idx[0] += 1
+        else:
+            h = self.conv(x) # Change the channel, ready for spatial or temporal downsample
+        
+        if self.add_temporal_downsample:
+            if x.shape[2] == 1:
+                # Single frame: only spatial downsample
+                h = rearrange(h, "b c f (h r2) (w r3) -> b (r2 r3 c) f h w", r2=2, r3=2)
+                h = torch.cat([h, h], dim=1)
+                # Compute the shortcut part
+                shortcut = rearrange(x, "b c f (h r2) (w r3) -> b (r2 r3 c) f h w", r2=2, r3=2)
+                B, C, T, H, W = shortcut.shape
+                shortcut = shortcut.view(B, h.shape[1], self.group_size // 2, T, H, W).mean(dim=2)
+            else:
+                # Multiple frames: full spatio-temporal downsample
+                h = rearrange(h, "b c (f r1) (h r2) (w r3) -> b (r1 r2 r3 c) f h w", r1=r1, r2=2, r3=2)
+                # Shortcut computation
+                shortcut = rearrange(x, "b c (f r1) (h r2) (w r3) -> b (r1 r2 r3 c) f h w", r1=r1, r2=2, r3=2)
+                B, C, T, H, W = shortcut.shape
+                shortcut = shortcut.view(B, h.shape[1], self.group_size, T, H, W).mean(dim=2)
+        else:
+            h = rearrange(h, "b c (f r1) (h r2) (w r3) -> b (r1 r2 r3 c) f h w", r1=r1, r2=2, r3=2)
+            shortcut = rearrange(x, "b c (f r1) (h r2) (w r3) -> b (r1 r2 r3 c) f h w", r1=r1, r2=2, r3=2)
+            B, C, T, H, W = shortcut.shape
+            shortcut = shortcut.view(B, h.shape[1], self.group_size, T, H, W).mean(dim=2)
+
+        return h + shortcut
+
+
+class HYWorldResnetBlock(nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: Optional[int] = None,
+        non_linearity: str = "swish",
+    ) -> None:
+        super().__init__()
+        self.in_channels = in_channels
+        out_channels = in_channels if out_channels is None else out_channels
+        self.out_channels = out_channels
+
+        self.nonlinearity = get_act_fn(non_linearity)
+
+        self.norm1 = HYWorldRMS_norm(in_channels, images=False)
+        self.conv1 = HYWorldCausalConv3d(in_channels, out_channels, kernel_size=3)
+
+        self.norm2 = HYWorldRMS_norm(out_channels, images=False)
+        self.conv2 = HYWorldCausalConv3d(out_channels, out_channels, kernel_size=3)
+        if in_channels != out_channels:
+            self.nin_shortcut = nn.Conv3d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
+    
+    def forward(
+        self, 
+        hidden_states: torch.Tensor,
+        feat_cache: Optional[List[Optional[torch.Tensor]]] = None,
+        feat_idx: Optional[List[int]] = None,
+    ) -> torch.Tensor:
+        """
+        Forward pass with optional temporal feature caching.
+        
+        Args:
+            hidden_states: Input tensor of shape (B, C, T, H, W)
+            feat_cache: List of cached features for each conv layer
+            feat_idx: List containing current cache index [idx]
+        """
+        residual = hidden_states
+
+        hidden_states = self.norm1(hidden_states)
+        hidden_states = self.nonlinearity(hidden_states)
+        
+        # apply the feature cacheing mechanism
+        if feat_cache is not None and feat_idx is not None:
+            # Retrieve the current layer index.
+            idx = feat_idx[0]
+
+            # Clone the last CACHE_T frames from the current input to store for the next step.
+            cache_x = hidden_states[:, :, -CACHE_T:, :, :].clone()
+            
+            # Handle boundary conditions: if the current temporal chunk is too short (< 2 frames)
+            # and we have a previous cache, prepend the last frame of the previous cache.
+            # This ensures sufficient temporal context for the convolution kernel.
+            if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
+                # actually this means chunk 1 after chunk 0
+                cache_x = torch.cat(
+                    [feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x],
+                    dim=2,
+                )
+            
+            # Apply the convolution layer using `hidden_states`and the *previous* cached state.
+            hidden_states = self.conv1(hidden_states, feat_cache[idx])
+            
+            # Update the cache for this layer with the newly prepared context ('cache_x').
+            feat_cache[idx] = cache_x
+
+            # Increment the global layer index.
+            feat_idx[0] += 1
+        else:
+            hidden_states = self.conv1(hidden_states)
+
+        hidden_states = self.norm2(hidden_states)
+        hidden_states = self.nonlinearity(hidden_states)
+
+        # Second conv with caching
+        if feat_cache is not None and feat_idx is not None:
+            idx = feat_idx[0]
+            cache_x = hidden_states[:, :, -CACHE_T:, :, :].clone()
+            if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
+                cache_x = torch.cat(
+                    [feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x],
+                    dim=2,
+                )
+            hidden_states = self.conv2(hidden_states, feat_cache[idx])
+            feat_cache[idx] = cache_x
+            feat_idx[0] += 1
+        else:
+            hidden_states = self.conv2(hidden_states)
+
+        if self.in_channels != self.out_channels:
+            residual = self.nin_shortcut(residual)
+
+        return hidden_states + residual
+
+
+class HYWorldMidBlock(nn.Module):
+    """Mid block with attention and resnet blocks, with optional caching support."""
+    
+    def __init__(
+        self,
+        in_channels: int,
+        num_layers: int = 1,
+        add_attention: bool = True,
+    ) -> None:
+        super().__init__()
+        self.add_attention = add_attention
+
+        # There is always at least one resnet
+        resnets = [
+            HYWorldResnetBlock(
+                in_channels=in_channels,
+                out_channels=in_channels,
+            )
+        ]
+        attentions = []
+
+        for _ in range(num_layers):
+            if self.add_attention:
+                attentions.append(HYWorldAttnBlock(in_channels))
+            else:
+                attentions.append(None)
+
+            resnets.append(
+                HYWorldResnetBlock(
+                    in_channels=in_channels,
+                    out_channels=in_channels,
+                )
+            )
+
+        self.attentions = nn.ModuleList(attentions)
+        self.resnets = nn.ModuleList(resnets)
+
+        self.gradient_checkpointing = False
+
+    def forward(
+        self, 
+        hidden_states: torch.Tensor,
+        feat_cache: Optional[List[Optional[torch.Tensor]]] = None,
+        feat_idx: Optional[List[int]] = None,
+    ) -> torch.Tensor:
+        """
+        Forward pass with optional temporal caching.
+        
+        Args:
+            hidden_states: Input tensor of shape (B, C, T, H, W)
+            feat_cache: List of cached features for each conv layer
+            feat_idx: List containing current cache index [idx]
+        """
+        hidden_states = self.resnets[0](hidden_states, feat_cache, feat_idx)
+
+        for attn, resnet in zip(self.attentions, self.resnets[1:]):
+            if attn is not None:
+                hidden_states = attn(hidden_states)
+            hidden_states = resnet(hidden_states, feat_cache, feat_idx)
+
+        return hidden_states
+
+
+class HYWorldDownBlock3D(nn.Module):
+    """Down block with resnet blocks and optional downsampling, with caching support."""
+    
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        num_layers: int = 1,
+        downsample_out_channels: Optional[int] = None,
+        add_temporal_downsample: int = True,
+    ) -> None:
+        super().__init__()
+        resnets = []
+
+        for i in range(num_layers):
+            in_channels = in_channels if i == 0 else out_channels
+            resnets.append(
+                HYWorldResnetBlock(
+                    in_channels=in_channels,
+                    out_channels=out_channels,
+                )
+            )
+
+        self.resnets = nn.ModuleList(resnets)
+
+        if downsample_out_channels is not None:
+            self.downsamplers = nn.ModuleList(
+                [
+                    HYWorldDownsample(
+                        out_channels,
+                        out_channels=downsample_out_channels,
+                        add_temporal_downsample=add_temporal_downsample,
+                    )
+                ]
+            )
+        else:
+            self.downsamplers = None
+
+        self.gradient_checkpointing = False
+
+    def forward(
+        self, 
+        hidden_states: torch.Tensor,
+        feat_cache: Optional[List[Optional[torch.Tensor]]] = None,
+        feat_idx: Optional[List[int]] = None,
+    ) -> torch.Tensor:
+        """
+        Forward pass with optional temporal caching.
+        
+        Args:
+            hidden_states: Input tensor of shape (B, C, T, H, W)
+            feat_cache: List of cached features for each conv layer
+            feat_idx: List containing current cache index [idx]
+        """
+        for resnet in self.resnets:
+            hidden_states = resnet(hidden_states, feat_cache, feat_idx)
+
+        if self.downsamplers is not None:
+            for downsampler in self.downsamplers:
+                hidden_states = downsampler(hidden_states, feat_cache, feat_idx)
+
+        return hidden_states
+
+
+class HYWorldUpBlock3D(nn.Module):
+    """Up block with resnet blocks and optional upsampling, with caching support."""
+    
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        num_layers: int = 1,
+        upsample_out_channels: Optional[int] = None,
+        add_temporal_upsample: bool = True,
+    ) -> None:
+        super().__init__()
+        resnets = []
+
+        for i in range(num_layers):
+            input_channels = in_channels if i == 0 else out_channels
+
+            resnets.append(
+                HYWorldResnetBlock(
+                    in_channels=input_channels,
+                    out_channels=out_channels,
+                )
+            )
+
+        self.resnets = nn.ModuleList(resnets)
+
+        if upsample_out_channels is not None:
+            self.upsamplers = nn.ModuleList(
+                [
+                    HYWorldUpsample(
+                        out_channels,
+                        out_channels=upsample_out_channels,
+                        add_temporal_upsample=add_temporal_upsample,
+                    )
+                ]
+            )
+        else:
+            self.upsamplers = None
+
+        self.gradient_checkpointing = False
+
+    def forward(
+        self, 
+        hidden_states: torch.Tensor,
+        feat_cache: Optional[List[Optional[torch.Tensor]]] = None,
+        feat_idx: Optional[List[int]] = None,
+        first_chunk: bool = False,
+    ) -> torch.Tensor:
+        """
+        Forward pass with optional temporal caching.
+        
+        Args:
+            hidden_states: Input tensor of shape (B, C, T, H, W)
+            feat_cache: List of cached features for each conv layer
+            feat_idx: List containing current cache index [idx]
+            first_chunk: Whether this is the first chunk (for upsampling behavior)
+        """
+        if torch.is_grad_enabled() and self.gradient_checkpointing:
+            for resnet in self.resnets:
+                hidden_states = self._gradient_checkpointing_func(resnet, hidden_states)
+        else:
+            for resnet in self.resnets:
+                hidden_states = resnet(hidden_states, feat_cache, feat_idx)
+
+        if self.upsamplers is not None:
+            for upsampler in self.upsamplers:
+                hidden_states = upsampler(hidden_states, feat_cache, feat_idx, first_chunk)
+
+        return hidden_states
+
+
+class HYWorldEncoder3D(nn.Module):
+    r"""
+    3D vae encoder for HunyuanImageRefiner with optional temporal caching.
+    """

    def __init__(
        self,
-        config: Hunyuan15VAEConfig,
+        in_channels: int = 3,
+        out_channels: int = 64,
+        block_out_channels: Tuple[int, ...] = (128, 256, 512, 1024, 1024),
+        layers_per_block: int = 2,
+        temporal_compression_ratio: int = 4,
+        spatial_compression_ratio: int = 16,
+        downsample_match_channel: bool = True,
    ) -> None:
-        AutoencoderKLHunyuanVideo15.__init__(self, config)
+        super().__init__()

+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.group_size = block_out_channels[-1] // self.out_channels
+        self.block_out_channels = block_out_channels
+
+        self.conv_in = HYWorldCausalConv3d(in_channels, block_out_channels[0], kernel_size=3)
+        self.mid_block = None
+        self.down_blocks = nn.ModuleList([])
+
+        input_channel = block_out_channels[0]
+        for i in range(len(block_out_channels)):
+            add_spatial_downsample = i < np.log2(spatial_compression_ratio)
+            output_channel = block_out_channels[i]
+            if not add_spatial_downsample:
+                down_block = HYWorldDownBlock3D(
+                    num_layers=layers_per_block,
+                    in_channels=input_channel,
+                    out_channels=output_channel,
+                    downsample_out_channels=None,
+                    add_temporal_downsample=False,
+                )
+                input_channel = output_channel
+            else:
+                add_temporal_downsample = i >= np.log2(spatial_compression_ratio // temporal_compression_ratio)
+                downsample_out_channels = block_out_channels[i + 1] if downsample_match_channel else output_channel
+                down_block = HYWorldDownBlock3D(
+                    num_layers=layers_per_block,
+                    in_channels=input_channel,
+                    out_channels=output_channel,
+                    downsample_out_channels=downsample_out_channels,
+                    add_temporal_downsample=add_temporal_downsample,
+                )
+                input_channel = downsample_out_channels
+
+            self.down_blocks.append(down_block)
+
+        self.mid_block = HYWorldMidBlock(in_channels=block_out_channels[-1])
+
+        self.norm_out = HYWorldRMS_norm(block_out_channels[-1], images=False)
+        self.conv_act = nn.SiLU()
+        self.conv_out = HYWorldCausalConv3d(block_out_channels[-1], out_channels, kernel_size=3)
+
+        self.gradient_checkpointing = False
+
+    def forward(
+        self, 
+        hidden_states: torch.Tensor,
+        feat_cache: Optional[List[Optional[torch.Tensor]]] = None,
+        feat_idx: Optional[List[int]] = None,
+    ) -> torch.Tensor:
+        """
+        Forward pass with optional temporal caching.
+        
+        Args:
+            hidden_states: Input tensor of shape (B, C, T, H, W)
+            feat_cache: List of cached features for each conv layer
+            feat_idx: List containing current cache index [idx]
+        """
+        # conv_in with caching
+        if feat_cache is not None and feat_idx is not None:
+            idx = feat_idx[0]
+            cache_x = hidden_states[:, :, -CACHE_T:, :, :].clone()
+            if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
+                cache_x = torch.cat(
+                    [feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x],
+                    dim=2,
+                )
+            hidden_states = self.conv_in(hidden_states, feat_cache[idx])
+            feat_cache[idx] = cache_x
+            feat_idx[0] += 1
+        else:
+            hidden_states = self.conv_in(hidden_states)
+
+        if torch.is_grad_enabled() and self.gradient_checkpointing:
+            for down_block in self.down_blocks:
+                hidden_states = self._gradient_checkpointing_func(down_block, hidden_states)
+            hidden_states = self._gradient_checkpointing_func(self.mid_block, hidden_states)
+        else:
+            for down_block in self.down_blocks:
+                hidden_states = down_block(hidden_states, feat_cache, feat_idx)
+            hidden_states = self.mid_block(hidden_states, feat_cache, feat_idx)
+
+        batch_size, _, frame, height, width = hidden_states.shape
+        short_cut = hidden_states.view(batch_size, -1, self.group_size, frame, height, width).mean(dim=2)
+
+        hidden_states = self.norm_out(hidden_states)
+        hidden_states = self.conv_act(hidden_states)
+
+        # conv_out with caching
+        if feat_cache is not None and feat_idx is not None:
+            idx = feat_idx[0]
+            cache_x = hidden_states[:, :, -CACHE_T:, :, :].clone()
+            if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
+                cache_x = torch.cat(
+                    [feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x],
+                    dim=2,
+                )
+            hidden_states = self.conv_out(hidden_states, feat_cache[idx])
+            feat_cache[idx] = cache_x
+            feat_idx[0] += 1
+        else:
+            hidden_states = self.conv_out(hidden_states)
+
+        hidden_states += short_cut
+
+        return hidden_states
+
+
+class HYWorldDecoder3D(nn.Module):
+    r"""
+    Causal decoder for 3D video-like data used for HunyuanImage-1.5 Refiner with optional temporal caching.
+    """
+
+    def __init__(
+        self,
+        in_channels: int = 32,
+        out_channels: int = 3,
+        block_out_channels: Tuple[int, ...] = (1024, 1024, 512, 256, 128),
+        layers_per_block: int = 2,
+        spatial_compression_ratio: int = 16,
+        temporal_compression_ratio: int = 4,
+        upsample_match_channel: bool = True,
+    ):
+        super().__init__()
+        self.layers_per_block = layers_per_block
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.block_out_channels = block_out_channels
+        self.repeat = block_out_channels[0] // self.in_channels
+
+        self.conv_in = HYWorldCausalConv3d(self.in_channels, block_out_channels[0], kernel_size=3)
+        self.up_blocks = nn.ModuleList([])
+
+        # mid
+        self.mid_block = HYWorldMidBlock(in_channels=block_out_channels[0])
+
+        # up
+        input_channel = block_out_channels[0]
+        for i in range(len(block_out_channels)):
+            output_channel = block_out_channels[i]
+
+            add_spatial_upsample = i < np.log2(spatial_compression_ratio)
+            add_temporal_upsample = i < np.log2(temporal_compression_ratio)
+            if add_spatial_upsample or add_temporal_upsample:
+                upsample_out_channels = block_out_channels[i + 1] if upsample_match_channel else output_channel
+                up_block = HYWorldUpBlock3D(
+                    num_layers=self.layers_per_block + 1,
+                    in_channels=input_channel,
+                    out_channels=output_channel,
+                    upsample_out_channels=upsample_out_channels,
+                    add_temporal_upsample=add_temporal_upsample,
+                )
+                input_channel = upsample_out_channels
+            else:
+                up_block = HYWorldUpBlock3D(
+                    num_layers=self.layers_per_block + 1,
+                    in_channels=input_channel,
+                    out_channels=output_channel,
+                    upsample_out_channels=None,
+                    add_temporal_upsample=False,
+                )
+                input_channel = output_channel
+
+            self.up_blocks.append(up_block)
+
+        # out
+        self.norm_out = HYWorldRMS_norm(block_out_channels[-1], images=False)
+        self.conv_act = nn.SiLU()
+        self.conv_out = HYWorldCausalConv3d(block_out_channels[-1], out_channels, kernel_size=3)
+
+        self.gradient_checkpointing = False
+
+    def forward(
+        self, 
+        hidden_states: torch.Tensor,
+        feat_cache: Optional[List[Optional[torch.Tensor]]] = None,
+        feat_idx: Optional[List[int]] = None,
+        first_chunk: bool = False,
+    ) -> torch.Tensor:
+        """
+        Forward pass with optional temporal caching.
+        
+        Args:
+            hidden_states: Input tensor of shape (B, C, T, H, W)
+            feat_cache: List of cached features for each conv layer
+            feat_idx: List containing current cache index [idx]
+            first_chunk: Whether this is the first chunk (for upsampling behavior)
+        """
+        # conv_in with caching
+        if feat_cache is not None and feat_idx is not None:
+            idx = feat_idx[0]
+            cache_x = hidden_states[:, :, -CACHE_T:, :, :].clone()
+            if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
+                cache_x = torch.cat(
+                    [feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x],
+                    dim=2,
+                )
+            hidden_states = self.conv_in(hidden_states, feat_cache[idx]) + hidden_states.repeat_interleave(
+                repeats=self.repeat, dim=1
+            )
+            feat_cache[idx] = cache_x
+            feat_idx[0] += 1
+        else:
+            hidden_states = self.conv_in(hidden_states) + hidden_states.repeat_interleave(repeats=self.repeat, dim=1)
+
+        if torch.is_grad_enabled() and self.gradient_checkpointing:
+            hidden_states = self._gradient_checkpointing_func(self.mid_block, hidden_states)
+            for up_block in self.up_blocks:
+                hidden_states = self._gradient_checkpointing_func(up_block, hidden_states)
+        else:
+            hidden_states = self.mid_block(hidden_states, feat_cache, feat_idx)
+            for up_block in self.up_blocks:
+                hidden_states = up_block(hidden_states, feat_cache, feat_idx, first_chunk)
+
+        # post-process
+        hidden_states = self.norm_out(hidden_states)
+        hidden_states = self.conv_act(hidden_states)
+
+        # conv_out with caching
+        if feat_cache is not None and feat_idx is not None:
+            idx = feat_idx[0]
+            cache_x = hidden_states[:, :, -CACHE_T:, :, :].clone()
+            if cache_x.shape[2] < 2 and feat_cache[idx] is not None:
+                cache_x = torch.cat(
+                    [feat_cache[idx][:, :, -1, :, :].unsqueeze(2).to(cache_x.device), cache_x],
+                    dim=2,
+                )
+            hidden_states = self.conv_out(hidden_states, feat_cache[idx])
+            feat_cache[idx] = cache_x
+            feat_idx[0] += 1
+        else:
+            hidden_states = self.conv_out(hidden_states)
+        
+        return hidden_states
+
+
+class AutoencoderKLHYWorld(nn.Module, ParallelTiledVAE):
+    r"""
+    A VAE model with KL loss for encoding videos into latents and decoding latent representations into videos.
+    Revised from HunyuanVideo-1.5 with temporal caching support for HY-WorldPlay integration.
+    """
+
+    _supports_gradient_checkpointing = True
+
+    def __init__(
+        self,
+        config: Hunyuan15VAEConfig, # HYWorld use the same VAE architecture as HunyuanVideo-1.5
+    ) -> None:
+        nn.Module.__init__(self)
+        ParallelTiledVAE.__init__(self, config)
+
+        self.encoder: Optional[HYWorldEncoder3D] = None
+        self.decoder: Optional[HYWorldDecoder3D] = None
+
+        if config.load_encoder:
+            self.encoder = HYWorldEncoder3D(
+                in_channels=config.in_channels,
+                out_channels=config.latent_channels * 2,
+                block_out_channels=config.block_out_channels,
+                layers_per_block=config.layers_per_block,
+                temporal_compression_ratio=config.temporal_compression_ratio,
+                spatial_compression_ratio=config.spatial_compression_ratio,
+                downsample_match_channel=config.downsample_match_channel,
+            )
+
+        if config.load_decoder:
+            self.decoder = HYWorldDecoder3D(
+                in_channels=config.latent_channels,
+                out_channels=config.out_channels,
+                block_out_channels=list(reversed(config.block_out_channels)),
+                layers_per_block=config.layers_per_block,
+                temporal_compression_ratio=config.temporal_compression_ratio,
+                spatial_compression_ratio=config.spatial_compression_ratio,
+                upsample_match_channel=config.upsample_match_channel,
+            )
+
+        # TODO: Add spatial tiling.
+        self.use_tiling = False
+
+        # The minimal tile height and width for spatial tiling to be used
+        self.tile_sample_min_height = 256
+        self.tile_sample_min_width = 256
+        self.tile_sample_min_num_frames = 2000 # Fill in a random large number, as hy1.5 vae does not use temporal tiling
+
+        # Cache-related attributes (initialized in clear_cache)
+        self._conv_num: int = 0
+        self._conv_idx: List[int] = [0]
+        self._feat_map: List[Optional[torch.Tensor]] = []
+        self._enc_conv_num: int = 0
+        self._enc_conv_idx: List[int] = [0]
+        self._enc_feat_map: List[Optional[torch.Tensor]] = []
+
+        # Precompute and cache conv counts for encoder and decoder for clear_cache speedup
+        self._cached_conv_counts = {
+            "decoder": (
+                sum(1 for m in self.decoder.modules() if isinstance(m, HYWorldCausalConv3d))
+                if self.decoder is not None
+                else 0
+            ),
+            "encoder": (
+                sum(1 for m in self.encoder.modules() if isinstance(m, HYWorldCausalConv3d))
+                if self.encoder is not None
+                else 0
+            ),
+        }
+
+    def clear_cache(self) -> None:
+        """
+        Initialize/clear the feature cache for chunk-based encoding/decoding.
+        
+        This should be called before starting a new encode/decode sequence to ensure
+        the cache is properly initialized.
+        """
+        # Cache for decoder
+        self._conv_num = self._cached_conv_counts["decoder"]
+        self._conv_idx = [0]
+        self._feat_map: List[Optional[torch.Tensor]] = [None] * self._conv_num
+        
+        # Cache for encoder
+        self._enc_conv_num = self._cached_conv_counts["encoder"]
+        self._enc_conv_idx = [0]
+        self._enc_feat_map: List[Optional[torch.Tensor]] = [None] * self._enc_conv_num
+
+    def _encode(self, x: torch.Tensor) -> torch.Tensor:
+        """
+        Encode video with temporal caching for chunk-based processing.
+        
+        This processes video in chunks (first frame separately, then 4 frames at a time)
+        while maintaining temporal context through caching. This matches the HY-WorldPlay
+        behavior for memory-efficient long video encoding.
+        
+        Args:
+            x: Input video tensor of shape (B, C, T, H, W)
+            
+        Returns:
+            Encoded latent tensor
+        """
+        assert self.encoder is not None, "Encoder not loaded"
+        _, _, num_frame, _, _ = x.shape
+
+        self.clear_cache()
+        
+        # Process in chunks: first frame alone, then groups of 4 frames
+        iter_ = 1 + (num_frame - 1) // 4
+        for i in range(iter_):
+            self._enc_conv_idx = [0]
+            if i == 0:
+                # First frame
+                out = self.encoder(
+                    x[:, :, :1, :, :],
+                    feat_cache=self._enc_feat_map,
+                    feat_idx=self._enc_conv_idx,
+                )
+            else:
+                # Subsequent frames in groups of 4
+                out_ = self.encoder(
+                    x[:, :, 1 + 4 * (i - 1) : 1 + 4 * i, :, :],
+                    feat_cache=self._enc_feat_map,
+                    feat_idx=self._enc_conv_idx,
+                )
+                out = torch.cat([out, out_], dim=2)
+
+        self.clear_cache()
+        return out
+
+    def _decode(self, z: torch.Tensor) -> torch.Tensor:
+        """
+        Decode latents with temporal caching for chunk-based processing.
+        
+        This processes latents one frame at a time while maintaining temporal context
+        through caching. This matches the HY-WorldPlay behavior for memory-efficient
+        long video decoding.
+        
+        Args:
+            z: Latent tensor of shape (B, C, T, H, W)
+            
+        Returns:
+            Decoded video tensor
+        """
+        assert self.decoder is not None, "Decoder not loaded"
+        _, _, num_frame, _, _ = z.shape
+
+        self.clear_cache()
+        
+        # Process one frame at a time with caching
+        for i in range(num_frame):
+            self._conv_idx = [0]
+            if i == 0:
+                # First frame with first_chunk=True
+                out = self.decoder(
+                    z[:, :, i : i + 1, :, :],
+                    feat_cache=self._feat_map,
+                    feat_idx=self._conv_idx,
+                    first_chunk=True,
+                )
+            else:
+                # Subsequent frames
+                out_ = self.decoder(
+                    z[:, :, i : i + 1, :, :],
+                    feat_cache=self._feat_map,
+                    feat_idx=self._conv_idx,
+                    first_chunk=False,
+                )
+                out = torch.cat([out, out_], dim=2)
+
+        self.clear_cache()
+        return out
+
+    def forward(
+        self,
+        sample: torch.Tensor,
+        sample_posterior: bool = False,
+        return_dict: bool = True,
+        generator: Optional[torch.Generator] = None,
+    ) -> torch.Tensor:
+        r"""
+        Args:
+            sample (`torch.Tensor`): Input sample.
+            sample_posterior (`bool`, *optional*, defaults to `False`):
+                Whether to sample from the posterior.
+            return_dict (`bool`, *optional*, defaults to `True`):
+                Whether or not to return a [`DecoderOutput`] instead of a plain tuple.
+            generator (`torch.Generator`, *optional*):
+                Generator for sampling.
+        """
+        x = sample
+
+        # encode() uses temporal caching by default (via _encode)
+        posterior = self.encode(x)
+
+        if sample_posterior:
+            z = posterior.sample(generator=generator)
+        else:
+            z = posterior.mode()
+
+        # decode() uses temporal caching by default (via _decode)
+        dec = self.decode(z)
+
+        return dec
+
+# Entry point for model registry
+EntryClass = AutoencoderKLHYWorld
@@ -1802,3 +1802,6 @@ class LTX2CausalVideoAutoencoder(nn.Module):
        if previous_chunk is not None:
            previous_weights = previous_weights.clamp(min=1e-8)
            yield previous_chunk / previous_weights
+
+# Entry point for model registry
+EntryClass = LTX2CausalVideoAutoencoder
@@ -1138,3 +1138,6 @@ class AutoencoderKLStepvideo(nn.Module, ParallelTiledVAE):
            z = posterior.mode()
        dec = self.decode(z)
        return dec
+
+# Entry point for model registry
+EntryClass = AutoencoderKLStepvideo
@@ -1319,3 +1319,5 @@ class AutoencoderKLWan(nn.Module, ParallelTiledVAE):
        dec = self.decode(z)
        return dec

+# Entry point for model registry
+EntryClass = AutoencoderKLWan
@@ -277,7 +277,7 @@ def load_video(
    if convert_method is not None:
        pil_images = convert_method(pil_images)

-    return pil_images, original_fps if return_fps else pil_images
+    return (pil_images, original_fps) if return_fps else pil_images


 def get_default_height_width(
@@ -12,10 +12,9 @@ from fastvideo.logger import init_logger
 from fastvideo.pipelines.composed_pipeline_base import ComposedPipelineBase
 from fastvideo.pipelines.lora_pipeline import LoRAPipeline
 from fastvideo.pipelines.pipeline_batch_info import ForwardBatch, TrainingBatch
-from fastvideo.pipelines.pipeline_registry import (PipelineType,
-                                                   get_pipeline_registry)
-from fastvideo.utils import (maybe_download_model,
-                             verify_model_config_and_directory)
+from fastvideo.pipelines.pipeline_registry import PipelineType
+from fastvideo.registry import get_model_info
+from fastvideo.utils import maybe_download_model

 logger = init_logger(__name__)

@@ -42,30 +41,17 @@ def build_pipeline(
    # fastvideo_args.downloaded_model_path = model_path
    logger.info("Model path: %s", model_path)

-    config = verify_model_config_and_directory(model_path)
-    pipeline_name = config.get("_class_name")
-
-    if fastvideo_args.override_pipeline_cls_name:
-        logger.info("Overriding pipeline class name from %s to %s",
-                    pipeline_name, fastvideo_args.override_pipeline_cls_name)
-        pipeline_name = fastvideo_args.override_pipeline_cls_name
-
-    if pipeline_name is None:
-        raise ValueError(
-            "Model config does not contain a _class_name attribute. "
-            "Only diffusers format is supported.")
-
-    # Get the appropriate pipeline registry based on pipeline_type
    logger.info(
        "Building pipeline of type: %s", pipeline_type.value if isinstance(
            pipeline_type, PipelineType) else pipeline_type)
-    pipeline_registry = get_pipeline_registry(pipeline_type)

-    if isinstance(pipeline_type, str):
-        pipeline_type = PipelineType.from_string(pipeline_type)
-
-    pipeline_cls = pipeline_registry.resolve_pipeline_cls(
-        pipeline_name, pipeline_type, fastvideo_args.workload_type)
+    model_info = get_model_info(
+        model_path=model_path,
+        pipeline_type=pipeline_type,
+        workload_type=fastvideo_args.workload_type,
+        override_pipeline_cls_name=fastvideo_args.override_pipeline_cls_name,
+    )
+    pipeline_cls = model_info.pipeline_cls

    # instantiate the pipelines
    pipeline = pipeline_cls(model_path, fastvideo_args)
@@ -5,8 +5,8 @@ from fastvideo.fastvideo_args import FastVideoArgs
 from fastvideo.logger import init_logger
 from fastvideo.pipelines.composed_pipeline_base import ComposedPipelineBase
 from fastvideo.pipelines.stages import (ConditioningStage,
-                                        Cosmos25DenoisingStage,
-                                        Cosmos25LatentPreparationStage,
+                                        Cosmos25AutoDenoisingStage,
+                                        Cosmos25AutoLatentPreparationStage,
                                        DecodingStage, InputValidationStage,
                                        Cosmos25TextEncodingStage,
                                        Cosmos25TimestepPreparationStage)
@@ -42,13 +42,13 @@ class Cosmos2_5Pipeline(ComposedPipelineBase):
                           scheduler=self.get_module("scheduler")))

        self.add_stage(stage_name="latent_preparation_stage",
-                       stage=Cosmos25LatentPreparationStage(
+                       stage=Cosmos25AutoLatentPreparationStage(
                           scheduler=self.get_module("scheduler"),
                           transformer=self.get_module("transformer"),
                           vae=self.get_module("vae")))

        self.add_stage(stage_name="denoising_stage",
-                       stage=Cosmos25DenoisingStage(
+                       stage=Cosmos25AutoDenoisingStage(
                           transformer=self.get_module("transformer"),
                           scheduler=self.get_module("scheduler")))

@@ -0,0 +1,162 @@
+# SPDX-License-Identifier: Apache-2.0
+"""
+Hunyuan video diffusion pipeline implementation.
+
+This module contains an implementation of the Hunyuan video diffusion pipeline
+using the modular pipeline architecture.
+"""
+import torch
+import time
+from fastvideo.fastvideo_args import FastVideoArgs
+from fastvideo.pipelines.pipeline_batch_info import ForwardBatch
+from fastvideo.logger import init_logger
+from fastvideo.pipelines.composed_pipeline_base import ComposedPipelineBase
+from fastvideo.pipelines.stages import (
+    ConditioningStage, DecodingStage, DenoisingStage, InputValidationStage,
+    LatentPreparationStage, TextEncodingStage, TimestepPreparationStage,
+    Hy15ImageEncodingStage, SRDenoisingStage)
+from fastvideo.distributed.parallel_state import get_local_torch_device
+
+# TODO(will): move PRECISION_TO_TYPE to better place
+
+logger = init_logger(__name__)
+
+
+class HunyuanVideo152SRPipeline(ComposedPipelineBase):
+
+    _required_config_modules = [
+        "text_encoder", "text_encoder_2", "tokenizer", "tokenizer_2", "vae",
+        "transformer", "transformer_2", "transformer_3", "scheduler",
+        "upsampler", "upsampler_2"
+    ]
+
+    def create_pipeline_stages(self, fastvideo_args: FastVideoArgs):
+        """Set up pipeline stages with proper dependency injection."""
+
+        self.add_stage(stage_name="input_validation_stage",
+                       stage=InputValidationStage())
+
+        self.add_stage(stage_name="prompt_encoding_stage_primary",
+                       stage=TextEncodingStage(
+                           text_encoders=[
+                               self.get_module("text_encoder"),
+                               self.get_module("text_encoder_2")
+                           ],
+                           tokenizers=[
+                               self.get_module("tokenizer"),
+                               self.get_module("tokenizer_2")
+                           ],
+                       ))
+
+        self.add_stage(stage_name="conditioning_stage",
+                       stage=ConditioningStage())
+
+        self.add_stage(stage_name="timestep_preparation_stage",
+                       stage=TimestepPreparationStage(
+                           scheduler=self.get_module("scheduler")))
+
+        self.add_stage(stage_name="latent_preparation_stage",
+                       stage=LatentPreparationStage(
+                           scheduler=self.get_module("scheduler"),
+                           transformer=self.get_module("transformer")))
+
+        self.add_stage(stage_name="image_encoding_stage",
+                       stage=Hy15ImageEncodingStage(image_encoder=None,
+                                                    image_processor=None))
+
+        self.add_stage(stage_name="denoising_stage",
+                       stage=DenoisingStage(
+                           transformer=self.get_module("transformer"),
+                           scheduler=self.get_module("scheduler")))
+
+        self.add_stage(stage_name="sr_720p_latent_preparation_stage",
+                       stage=LatentPreparationStage(
+                           scheduler=self.get_module("scheduler"),
+                           transformer=self.get_module("transformer_2")))
+
+        self.add_stage(stage_name="sr_720p_denoising_stage",
+                       stage=SRDenoisingStage(
+                           transformer=self.get_module("transformer_2"),
+                           scheduler=self.get_module("scheduler"),
+                           upsampler=self.get_module("upsampler")))
+
+        self.add_stage(stage_name="sr_1080p_latent_preparation_stage",
+                       stage=LatentPreparationStage(
+                           scheduler=self.get_module("scheduler"),
+                           transformer=self.get_module("transformer_3")))
+
+        self.add_stage(stage_name="sr_1080p_denoising_stage",
+                       stage=SRDenoisingStage(
+                           transformer=self.get_module("transformer_3"),
+                           scheduler=self.get_module("scheduler"),
+                           upsampler=self.get_module("upsampler_2")))
+
+        self.add_stage(stage_name="decoding_stage",
+                       stage=DecodingStage(vae=self.get_module("vae")))
+
+    @torch.no_grad()
+    def forward(
+        self,
+        batch: ForwardBatch,
+        fastvideo_args: FastVideoArgs,
+    ) -> ForwardBatch:
+        """
+        Generate a video or image using the pipeline.
+        
+        Args:
+            batch: The batch to generate from.
+            fastvideo_args: The inference arguments.
+        Returns:
+            ForwardBatch: The batch with the generated video or image.
+        """
+        if not self.post_init_called:
+            self.post_init()
+
+        self.get_module("transformer").to(get_local_torch_device())
+        # Execute each stage
+        logger.info("Running pipeline stages: %s",
+                    self._stage_name_mapping.keys())
+        # logger.info("Batch: %s", batch)
+        batch = self.input_validation_stage(batch, fastvideo_args)
+        batch = self.prompt_encoding_stage_primary(batch, fastvideo_args)
+        batch = self.conditioning_stage(batch, fastvideo_args)
+        batch = self.timestep_preparation_stage(batch, fastvideo_args)
+        batch = self.latent_preparation_stage(batch, fastvideo_args)
+        batch = self.image_encoding_stage(batch, fastvideo_args)
+        batch = self.denoising_stage(batch, fastvideo_args)
+        self.get_module("transformer").to("cpu")
+
+        # 720p SR
+        self.get_module("transformer_2").to(get_local_torch_device())
+        batch.lq_latents = batch.latents
+        batch.latents = None
+        batch.height = 720
+        batch.width = 1280
+        batch.num_inference_steps_sr = 6
+        batch = self.sr_720p_latent_preparation_stage(batch, fastvideo_args)
+        batch = self.image_encoding_stage(batch, fastvideo_args)
+        batch = self.sr_720p_denoising_stage(batch, fastvideo_args)
+        self.get_module("transformer_2").to("cpu")
+
+        # 1080p SR
+        self.get_module("transformer_3").to(get_local_torch_device())
+        batch.lq_latents = batch.latents
+        batch.latents = None
+        batch.height = 1072
+        batch.width = 1920
+        batch.num_inference_steps_sr = 8
+        batch = self.sr_1080p_latent_preparation_stage(batch, fastvideo_args)
+        batch = self.image_encoding_stage(batch, fastvideo_args)
+        batch = self.sr_1080p_denoising_stage(batch, fastvideo_args)
+        self.get_module("transformer_3").to("cpu")
+
+        start_time = time.time()
+        batch = self.decoding_stage(batch, fastvideo_args)
+        end_time = time.time()
+        logger.info("Decoding time: %s seconds", end_time - start_time)
+
+        # Return the output
+        return batch
+
+
+EntryClass = HunyuanVideo152SRPipeline
@@ -0,0 +1,74 @@
+# SPDX-License-Identifier: Apache-2.0
+"""
+Hunyuan video diffusion pipeline implementation.
+
+This module contains an implementation of the Hunyuan video diffusion pipeline
+using the modular pipeline architecture.
+"""
+
+from fastvideo.fastvideo_args import FastVideoArgs
+from fastvideo.logger import init_logger
+from fastvideo.pipelines.composed_pipeline_base import ComposedPipelineBase
+from fastvideo.pipelines.stages import (ConditioningStage, DecodingStage,
+                                        DenoisingStage, InputValidationStage,
+                                        LatentPreparationStage,
+                                        TextEncodingStage,
+                                        TimestepPreparationStage,
+                                        Hy15ImageEncodingStage)
+
+# TODO(will): move PRECISION_TO_TYPE to better place
+
+logger = init_logger(__name__)
+
+
+class HunyuanVideo15ImageToVideoPipeline(ComposedPipelineBase):
+
+    _required_config_modules = [
+        "text_encoder", "text_encoder_2", "tokenizer", "tokenizer_2", "vae",
+        "transformer", "scheduler"
+    ]
+
+    def create_pipeline_stages(self, fastvideo_args: FastVideoArgs):
+        """Set up pipeline stages with proper dependency injection."""
+
+        self.add_stage(stage_name="input_validation_stage",
+                       stage=InputValidationStage())
+
+        self.add_stage(stage_name="prompt_encoding_stage_primary",
+                       stage=TextEncodingStage(
+                           text_encoders=[
+                               self.get_module("text_encoder"),
+                               self.get_module("text_encoder_2")
+                           ],
+                           tokenizers=[
+                               self.get_module("tokenizer"),
+                               self.get_module("tokenizer_2")
+                           ],
+                       ))
+
+        self.add_stage(stage_name="conditioning_stage",
+                       stage=ConditioningStage())
+
+        self.add_stage(stage_name="timestep_preparation_stage",
+                       stage=TimestepPreparationStage(
+                           scheduler=self.get_module("scheduler")))
+
+        self.add_stage(stage_name="latent_preparation_stage",
+                       stage=LatentPreparationStage(
+                           scheduler=self.get_module("scheduler"),
+                           transformer=self.get_module("transformer")))
+
+        self.add_stage(stage_name="image_encoding_stage",
+                       stage=Hy15ImageEncodingStage(image_encoder=None,
+                                                    image_processor=None))
+
+        self.add_stage(stage_name="denoising_stage",
+                       stage=DenoisingStage(
+                           transformer=self.get_module("transformer"),
+                           scheduler=self.get_module("scheduler")))
+
+        self.add_stage(stage_name="decoding_stage",
+                       stage=DecodingStage(vae=self.get_module("vae")))
+
+
+EntryClass = HunyuanVideo15ImageToVideoPipeline
@@ -0,0 +1,136 @@
+# SPDX-License-Identifier: Apache-2.0
+"""
+Hunyuan video diffusion pipeline implementation.
+
+This module contains an implementation of the Hunyuan video diffusion pipeline
+using the modular pipeline architecture.
+"""
+import torch
+import time
+from fastvideo.fastvideo_args import FastVideoArgs
+from fastvideo.pipelines.pipeline_batch_info import ForwardBatch
+from fastvideo.logger import init_logger
+from fastvideo.pipelines.composed_pipeline_base import ComposedPipelineBase
+from fastvideo.pipelines.stages import (
+    ConditioningStage, DecodingStage, DenoisingStage, InputValidationStage,
+    LatentPreparationStage, TextEncodingStage, TimestepPreparationStage,
+    Hy15ImageEncodingStage, SRDenoisingStage)
+from fastvideo.distributed.parallel_state import get_local_torch_device
+
+# TODO(will): move PRECISION_TO_TYPE to better place
+
+logger = init_logger(__name__)
+
+
+class HunyuanVideo15SRPipeline(ComposedPipelineBase):
+
+    _required_config_modules = [
+        "text_encoder", "text_encoder_2", "tokenizer", "tokenizer_2", "vae",
+        "transformer", "transformer_2", "scheduler", "upsampler"
+    ]
+
+    def create_pipeline_stages(self, fastvideo_args: FastVideoArgs):
+        """Set up pipeline stages with proper dependency injection."""
+
+        self.add_stage(stage_name="input_validation_stage",
+                       stage=InputValidationStage())
+
+        self.add_stage(stage_name="prompt_encoding_stage_primary",
+                       stage=TextEncodingStage(
+                           text_encoders=[
+                               self.get_module("text_encoder"),
+                               self.get_module("text_encoder_2")
+                           ],
+                           tokenizers=[
+                               self.get_module("tokenizer"),
+                               self.get_module("tokenizer_2")
+                           ],
+                       ))
+
+        self.add_stage(stage_name="conditioning_stage",
+                       stage=ConditioningStage())
+
+        self.add_stage(stage_name="timestep_preparation_stage",
+                       stage=TimestepPreparationStage(
+                           scheduler=self.get_module("scheduler")))
+
+        self.add_stage(stage_name="latent_preparation_stage",
+                       stage=LatentPreparationStage(
+                           scheduler=self.get_module("scheduler"),
+                           transformer=self.get_module("transformer")))
+
+        self.add_stage(stage_name="image_encoding_stage",
+                       stage=Hy15ImageEncodingStage(image_encoder=None,
+                                                    image_processor=None))
+
+        self.add_stage(stage_name="denoising_stage",
+                       stage=DenoisingStage(
+                           transformer=self.get_module("transformer"),
+                           scheduler=self.get_module("scheduler")))
+
+        self.add_stage(stage_name="sr_latent_preparation_stage",
+                       stage=LatentPreparationStage(
+                           scheduler=self.get_module("scheduler"),
+                           transformer=self.get_module("transformer_2")))
+
+        self.add_stage(stage_name="sr_denoising_stage",
+                       stage=SRDenoisingStage(
+                           transformer=self.get_module("transformer_2"),
+                           scheduler=self.get_module("scheduler"),
+                           upsampler=self.get_module("upsampler")))
+
+        self.add_stage(stage_name="decoding_stage",
+                       stage=DecodingStage(vae=self.get_module("vae")))
+
+    @torch.no_grad()
+    def forward(
+        self,
+        batch: ForwardBatch,
+        fastvideo_args: FastVideoArgs,
+    ) -> ForwardBatch:
+        """
+        Generate a video or image using the pipeline.
+        
+        Args:
+            batch: The batch to generate from.
+            fastvideo_args: The inference arguments.
+        Returns:
+            ForwardBatch: The batch with the generated video or image.
+        """
+        if not self.post_init_called:
+            self.post_init()
+
+        # Execute each stage
+        logger.info("Running pipeline stages: %s",
+                    self._stage_name_mapping.keys())
+        # logger.info("Batch: %s", batch)
+        self.get_module("transformer").to(get_local_torch_device())
+        batch = self.input_validation_stage(batch, fastvideo_args)
+        batch = self.prompt_encoding_stage_primary(batch, fastvideo_args)
+        batch = self.conditioning_stage(batch, fastvideo_args)
+        batch = self.timestep_preparation_stage(batch, fastvideo_args)
+        batch = self.latent_preparation_stage(batch, fastvideo_args)
+        batch = self.image_encoding_stage(batch, fastvideo_args)
+        batch = self.denoising_stage(batch, fastvideo_args)
+        self.get_module("transformer").to("cpu")
+
+        self.get_module("transformer_2").to(get_local_torch_device())
+        batch.lq_latents = batch.latents
+        batch.latents = None
+        batch.height = batch.height_sr
+        batch.width = batch.width_sr
+        batch = self.sr_latent_preparation_stage(batch, fastvideo_args)
+        batch = self.image_encoding_stage(batch, fastvideo_args)
+        batch = self.sr_denoising_stage(batch, fastvideo_args)
+        self.get_module("transformer_2").to("cpu")
+
+        start_time = time.time()
+        batch = self.decoding_stage(batch, fastvideo_args)
+        end_time = time.time()
+        logger.info("Decoding time: %s seconds", end_time - start_time)
+
+        # Return the output
+        return batch
+
+
+EntryClass = HunyuanVideo15SRPipeline
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
SolitaryThinker	a8dddfaa16	missing file	2026-02-10 08:33:00 +00:00
Will Lin	7210c68f1b	lint	2026-02-10 00:30:03 -08:00
SolitaryThinker	5602dc1bad	revert	2026-02-10 08:22:05 +00:00
SolitaryThinker	ac4bc4ab84	uipdate	2026-02-10 07:58:43 +00:00
Matthew Noto	bee27f9f74	Merge branch 'main' into ltx-base	2026-02-09 17:51:37 -08:00
Junda (David) Su Davids048 Matthew Noto	ad58f802f3	[Feat] Port LTX2 trainer (#1074 ) Co-authored-by: Davids048 <jundasu@ucsd.edu> Co-authored-by: Matthew Noto <99706358+RandNMR73@users.noreply.github.com>	2026-02-09 17:32:57 -08:00
Wei Zhou	04fa356ee3	[Misc] [Training] Fixed a bunch of bugs in current training pipeline (#1084 )	2026-02-09 16:01:05 -08:00
Matthew Notoandgemini-code-assist[bot]	f9c076fe2b	[misc] add AGENTS.md file (#1085 ) Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>	2026-02-09 01:03:49 -08:00
Will Lin	dff0ea401a	update	2026-02-08 00:22:21 -08:00
XOR-op	f76efe798e	[bugfix]: `_compile_conditions` regression (#1077 )	2026-02-06 18:57:43 -08:00
Zhang Peiyuan	09f455233e	[misc] readme small fix (#1076 )	2026-02-06 16:57:48 -08:00
XOR-op	aea300f690	[perf]: use CUDA IPC in multiproc executor to avoid serialization overhead (#1061 )	2026-02-06 19:43:18 -05:00
Hao Zhang	b92219f6a6	more fix and relocate STA arguments to pipeline config (#1073 )	2026-02-06 13:38:35 -08:00
Jinzhe Pan	a321b95a8a	[Fix] remove video ratio limitation (#1069 )	2026-02-05 20:33:08 -08:00
Davids048	becd379f58	Split LTX2 mappings and add registry coverage. Detail: - Merged LTX2 sampling behavior into the global registry and removed the ambiguous local converted-path default. - Replaced single LTX2 sampling mapping with explicit model-ID mappings: - Lightricks/LTX-2 -> LTX2BaseSamplingParam - FastVideo/LTX2-base -> LTX2BaseSamplingParam - FastVideo/LTX2-Distilled-Diffusers -> LTX2DistilledSamplingParam - Kept LTX2T2VConfig as the pipeline config for all explicitly mapped LTX2 IDs. - Removed implicit mapping for converted/ltx2_diffusers to avoid guessing base vs distilled for user-local conversions. - Added focused local tests at tests/local_tests/test_ltx2_registry.py for: - exact base/distilled sampling resolution, - pipeline config resolution, - no fallback behavior for ambiguous local converted paths. Assumptions: - Canonical rename/ID intent: - “base” names map to LTX2BaseSamplingParam. - “Distilled” names map to LTX2DistilledSamplingParam. - converted/ltx2_diffusers is intentionally ambiguous across users and must not be auto-assigned. - Unknown/non-canonical names containing “LTX”/“LTX2” (but not matching explicit registered IDs) should not auto- resolve to base or distilled. - Sampling resolver returns None (caller falls back to generic defaults/user overrides). - Pipeline config lookup raises a “No match found” error. Notes: - This change prioritizes explicitness over convenience: only predetermined, canonical model IDs get LTX2-specific defaults; everything else requires user intent.	2026-02-05 20:28:46 -08:00
Davids048	1ed7d7e1b0	Add gemma tokenizer to LTX2 conversion script. - Also clean up for PR.	2026-02-05 18:53:22 -08:00
Davids048	d9fabcc5ef	Add some annotations.	2026-02-05 18:53:22 -08:00
Davids048	9db48498de	Update quality test script.	2026-02-05 18:53:22 -08:00
Davids048	1cd7038315	Add LTX2 base model.	2026-02-05 18:53:12 -08:00
Wei Zhou	98308db7e0	[Feature] [Hy1.5] Support HY1.5 super-resolution pipeline for 1080p videos (#1046 )	2026-02-05 16:39:20 -08:00
Hao Zhang	c1e18f6722	Some minor fixes (#1068 )	2026-02-05 16:28:00 -08:00
William Lin	75e193a2c9	[core] Refactor and centralize our registry for models, pipelines, and sampling params (#1066 )	2026-02-05 14:40:30 -08:00
XOR-op	d6e0a7d0dd	[refactor] Action module (#1065 )	2026-02-05 13:56:35 -08:00
William Linandgemini-code-assist[bot]	7fc5f241da	[misc] Fix naming instruction in runpod.md (#1067 ) Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>	2026-02-05 13:45:49 -08:00
Mingjia Huo	aae48a7e90	[feat] HYworld VAE with cache (#1057 )	2026-02-05 04:18:17 -08:00
William Lin	88f38eb0f4	[misc] upgrade torch to 2.10 (#1048 )	2026-02-05 04:15:22 -08:00
Shao Duan	d750b463dc	Added Sequence Parallelism for LTX-2 Distilled (#1036 )	2026-02-04 15:25:30 -08:00
KyleShaoandWill Lin	e10b26a3d8	[feat] Add Cosmos 2.5 I2W/V2W support (staged pipeline + examples) (#1021 ) Co-authored-by: Will Lin <wlsaidhi@gmail.com>	2026-02-04 14:19:30 -08:00
XOR-op	74636ba246	[chore]: use higher precision timestamp in logging (#1062 )	2026-02-04 13:22:59 -08:00
Wei Zhou	7e2f3f14e7	[Bugfix] [Wan I2V] Fix CLIP Image encoder config (#1063 )	2026-02-04 13:20:19 -08:00