| --- |
| library_name: diffusers |
| pipeline_tag: text-to-video |
| tags: |
| - physisserve |
| - wan |
| - autoregressive |
| - video-generation |
| - same-step-kv |
| - safetensors |
| --- |
| |
| <p align="center"> |
| <a href="https://github.com/Physis-AI/PhysisServe"> |
| <img src="./assets/logo.png" alt="PhysisServe" width="180"> |
| </a> |
| </p> |
| |
| # PhysisServe — Wan2.2 5B |
|
|
| A development release of **Wan2.2 5B** with autoregressive video weights trained by **Physis-AI** for same-step noisy-KV inference. The `ls-dev` suffix identifies Physis-AI-trained weights. |
|
|
| [Code](https://github.com/Physis-AI/PhysisServe) · [Technical blog](https://physis-ai.com/blog/physisserve/en) · [Model collection](https://huggingface.co/collections/Physis-AI/physisserve-dev-model) |
|
|
| These weights are intended for **PhysisServe's four-step autoregressive inference**. The Diffusers component layout does not make a stock Diffusers pipeline equivalent to this inference procedure: same-step KV caching, the attention sink, and the denoising schedule are implemented by PhysisServe. |
|
|
| ## Inference profile |
|
|
| The following defaults are defined by the PhysisServe model profile. |
|
|
| | Setting | Default | |
| | --- | --- | |
| | Resolution (width × height) | 1280 × 704 | |
| | Output FPS | 24 | |
| | Denoising steps per chunk | 4 | |
| | First chunk | 3 latent frames | |
| | Each subsequent chunk | 1 latent frame | |
| | Chunks per generation, including the first | 29 | |
| | Attention sink | Clean bootstrap, σ = 0 | |
| | KV history | Up to 18 recent chunks, plus the first-chunk sink without duplication | |
|
|
| At denoising step `s`, a chunk reads noisy KV entries recorded at the same step `s` by preceding chunks. The first chunk is generated before the rest and receives an additional forward pass to build the persistent sink. A latent frame is not a decoded video frame; the VAE determines the temporal expansion. FPS is fixed by the model profile. |
|
|
| ## Quick start |
|
|
| Use Python 3.10+ with a CUDA-enabled PyTorch installation, then install PhysisServe: |
|
|
| ```bash |
| pip install "git+https://github.com/Physis-AI/PhysisServe.git" |
| ``` |
|
|
| Save the following as `generate.py` and run `python generate.py`. Adjust `pipeline_parallel_size` to your available GPUs and memory before running; `1` below is an API example, not a single-GPU memory guarantee. |
|
|
| ```python |
| import imageio.v3 as iio |
| from physisserve import PhysisServe, SamplingParams |
| |
| |
| def main(): |
| model = PhysisServe( |
| "Physis-AI/physisserve-wan2p2_5b-same_step-ls-dev", |
| pipeline_parallel_size=1, |
| ) |
| sampling_params = SamplingParams(mode="offline_decode", seed=0) |
| prompts = ["A corgi running along the beach at sunset."] |
| outputs = model.generate(prompts, sampling_params) |
| |
| for index, output in enumerate(outputs): |
| iio.imwrite( |
| f"video-{index}.mp4", |
| output["video"].numpy(), |
| fps=output["fps"], |
| codec="libx264", |
| pixelformat="yuv420p", |
| macro_block_size=None, |
| output_params=["-movflags", "+faststart"], |
| ) |
| |
| |
| if __name__ == "__main__": |
| main() |
| ``` |
|
|
| `offline_decode` saves latents under a separate directory in `./latents` for each call, waits for all streams to finish denoising, and then decodes them. Set `SamplingParams(latent_dir="your-directory", mode="offline_decode")` to change the save location. The example writes playable MP4 files; `generate()` itself does not write MP4s. |
|
|
| A single prompt string or a list of prompts uses the same API. Multiple prompts create separate streams and increase memory demand. |
|
|
| ## Acknowledgments |
|
|
| We thank the [Wan](https://github.com/Wan-Video/Wan2.2) team for the base model and the [Diffusers](https://github.com/huggingface/diffusers) contributors for the model components. |
|
|
