ORIGINAL_README.md

<p align="center">
    <img src="https://github.com/Yaofang-Liu/Pusa-VidGen/blob/f867c49d9570b88e7bbce6e25583a0ad2417cdf7/icon.png" width="70"/>

</p>


# Pusa: Thousands Timesteps Video Diffusion Model
<p align="center">
    <a href="https://yaofang-liu.github.io/Pusa_Web/"><img alt="Project Page" src="https://img.shields.io/badge/Project-Page-blue?style=for-the-badge"></a>

    <a href="https://github.com/Yaofang-Liu/Pusa-VidGen/blob/e99c3dcf866789a2db7fbe2686888ec398076a82/PusaV1/PusaV1.0_Report.pdf"><img alt="Technical Report" src="https://img.shields.io/badge/Technical_Report-📜-B31B1B?style=for-the-badge"></a>

    <a href="https://huggingface.co/RaphaelLiu/PusaV1"><img alt="Model" src="https://img.shields.io/badge/Pusa_V1.0-Model-FFD700?style=for-the-badge&logo=huggingface"></a>

    <a href="https://huggingface.co/datasets/RaphaelLiu/PusaV1_training"><img alt="Dataset" src="https://img.shields.io/badge/Pusa_V1.0-Dataset-6495ED?style=for-the-badge&logo=huggingface"></a>

</p>

<p align="center">

    <a href="https://github.com/Yaofang-Liu/Mochi-Full-Finetuner"><img alt="Code" src="https://img.shields.io/badge/Code-Training%20Scripts-32CD32?logo=github"></a>

    <a href="https://arxiv.org/abs/2410.03160"><img alt="Paper" src="https://img.shields.io/badge/📜-FVDM%20Paper-B31B1B?logo=arxiv"></a>

    <a href="https://x.com/stephenajason"><img alt="Twitter" src="https://img.shields.io/badge/🐦-Twitter-1DA1F2?logo=twitter"></a>

    <a href="https://www.xiaohongshu.com/user/profile/5c6f928f0000000010015ca1?xsec_token=YBEf_x-s5bOBQIMJuNQvJ6H23Anwey1nnDgC9wiLyDHPU=&xsec_source=app_share&xhsshare=CopyLink&appuid=5c6f928f0000000010015ca1&apptime=1752622393&share_id=60f9a8041f974cb7ac5e3f0f161bf748"><img alt="Xiaohongshu" src="https://img.shields.io/badge/📕-Xiaohongshu-FF2442"></a>

</p>



## 🔥🔥🔥🚀 Announcing Pusa V1.0 🚀🔥🔥🔥

We are excited to release **Pusa V1.0**, a groundbreaking paradigm that leverages **vectorized timestep adaptation (VTA)** to enable fine-grained temporal control within a unified video diffusion framework. By finetuning the SOTA **Wan-T2V-14B** model with VTA, Pusa V1.0 achieves unprecedented efficiency --**surpassing the performance of Wan-I2V-14B with ≤ 1/200 of the training cost ($500 vs. ≥ $100,000)** and **≤ 1/2500 of the dataset size (4K vs. ≥ 10M samples)**. The codebase has been integrated into the `PusaV1` directory, based on `DiffSynth-Studio`.

<img width="1000" alt="Image" src="https://github.com/Yaofang-Liu/Pusa-VidGen/blob/d98ef44c1f7c11724a6887b71fe35152493c68b4/PusaV1/pusa_benchmark_figure_dark.png" />

Pusa V1.0 not only sets a new standard for image-to-video generation but also unlocks many other zero-shot multi-task capabilities such as start-end frames and video extension, all without task-specific training while preserving the base model's T2V capabilities.

For detailed usage and examples for Pusa V1.0, please see the **[Pusa V1.0 README](./PusaV1/README.md)**.


## News 
#### 🔥🔥🔥 2025.07:  Pusa V1.0 (Pusa-Wan) Code, Technical Report, and Dataset, all released!!! Check our [project page](https://yaofang-liu.github.io/Pusa_Web/) and [paper](https://github.com/Yaofang-Liu/Pusa-VidGen/blob/e99c3dcf866789a2db7fbe2686888ec398076a82/PusaV1/PusaV1.0_Report.pdf) for more info.
#### 🔥🔥🔥 2025.04:  Pusa V0.5 (Pusa-Mochi) released.

 

<p align="center">
    <img src="https://github.com/Yaofang-Liu/Pusa-VidGen/blob/55de93a198427525e23a509e0f0d04616b10d71f/assets/demo0.gif" width="1000" autoplay loop muted/>

    <br>

    <em>Pusa V0.5 showcases </em>

</p>


<p align="center">
    <img src="https://github.com/Yaofang-Liu/Pusa-VidGen/blob/8d2af9cad78859361cb1bc6b8df56d06b2c2fbb8/assets/demo_T2V.gif" width="1000" autoplay loop muted/>

    <br>

    <em>Pusa V0.5 still can do text-to-video generation like base model Mochi </em>

</p>


**Pusa can do many more other things, you may check details below.**

 

## Table of Contents
- [Overview](#overview)
- [Changelog](#changelog)
- [Pusa V1.0 (Based on Wan)](#pusa-v10-based-on-wan)
- [Pusa V0.5 (Based on Mochi)](#pusa-v05-based-on-mochi)
- [Training](#training)
- [Limitations](#limitations)
- [Current Status and Roadmap](#current-status-and-roadmap)
- [Related Work](#related-work)
- [BibTeX](#bibtex)

## Overview

Pusa (*pu: 'sA:*, from "Thousand-Hand Guanyin" in Chinese) introduces a paradigm shift in video diffusion modeling through frame-level noise control with vectorized timesteps, departing from conventional scalar timestep approaches. This shift was first presented in our [FVDM](https://arxiv.org/abs/2410.03160) paper. 

**Pusa V1.0** is based on the SOTA **Wan-T2V-14B** model and enhances it with our unique vectorized timestep adaptations (VTA), a non-destructive adaptation that fully preserves the capabilities of the base model.

**Pusa V0.5** leverages this architecture, and it is based on [Mochi1-Preview](https://huggingface.co/genmo/mochi-1-preview). We are open-sourcing this work to foster community collaboration, enhance methodologies, and expand capabilities.


Pusa's novel frame-level noise architecture with vectorized timesteps compared with conventional video diffusion models with a scalar timestep

https://github.com/user-attachments/assets/7d751fd8-9a14-42e6-bcde-6db940df6537


### ✨ Key Features

- **Comprehensive Multi-task Support**:
  - Text-to-Video 
  - Image-to-Video 
  - Start-End Frames
  - Video completion/transitions
  - Video Extension
  - And more...

- **Unprecedented Efficiency**:
  - Surpasses Wan-I2V-14B with **≤ 1/200 of the training cost** (\$500 vs. ≥ \$100,000)
  - Trained on a dataset **≤ 1/2500 of the size** (4K vs. ≥ 10M samples)
  - Achieves a **VBench-I2V score of 87.32%** (vs. 86.86% for Wan-I2V-14B)

- **Complete Open-Source Release**:
  - Full codebase and training/inference scripts
  - LoRA model weights and dataset for Pusa V1.0
  - Detailed architecture specifications
  - Comprehensive training methodology

### 🔍 Unique Architecture

- **Novel Diffusion Paradigm**: Implements frame-level noise control with vectorized timesteps, originally introduced in the [FVDM paper](https://arxiv.org/abs/2410.03160), enabling unprecedented flexibility and scalability.

- **Non-destructive Modification**: Our adaptations to the base model preserve its original Text-to-Video generation capabilities. After this adaptation, we only need a slight fine-tuning.

- **Universal Applicability**: The methodology can be readily applied to other leading video diffusion models including Hunyuan Video, Wan2.1, and others. *Collaborations enthusiastically welcomed!*


## Changelog

**v1.0 (July 15, 2025)**
- Released Pusa V1.0, based on the Wan-Video models.
- Released Technical Report, V1.0 model weights and dataset.
- Integrated codebase as `/PusaV1`.
- Added new examples and training scripts for Pusa V1.0 in `PusaV1/`.
- Updated documentation for the V1.0 release.

**v0.5 (June 3, 2025)**
- Released inference scripts for Start&End Frames Generation, Multi-Frames Generation, Video Transition, and Video Extension.

**v0.5 (April 10, 2025)**
- Released our training codes and details [here](https://github.com/Yaofang-Liu/Mochi-Full-Finetuner)
- Support multi-nodes/single-node full finetuning code for both Pusa and Mochi
- Released our training dataset [dataset](https://huggingface.co/datasets/RaphaelLiu/PusaV0.5_Training)

## Pusa V1.0 (Based on Wan)

Pusa V1.0 leverages the powerful Wan-Video models and enhances them with our custom LoRA models and training scripts. For detailed instructions on installation, model preparation, usage examples, and training, please refer to the **[Pusa V1.0 README](./PusaV1/README.md)**.

## Pusa V0.5 (Based on Mochi)

<details>
<summary>Click to expand for Pusa V0.5 details</summary>

### Installation 

You may install using [uv](https://github.com/astral-sh/uv):

```bash

git clone https://github.com/genmoai/models

cd models 

pip install uv

uv venv .venv

source .venv/bin/activate

uv pip install setuptools

uv pip install -e . --no-build-isolation

```

If you want to install flash attention, you can use:
```

uv pip install -e .[flash] --no-build-isolation

```

### Download Weights

**Option 1**: Use the Hugging Face CLI:
```bash

pip install huggingface_hub

huggingface-cli download RaphaelLiu/Pusa-V0.5 --local-dir <path_to_downloaded_directory>

```

**Option 2**: Download directly from [Hugging Face](https://huggingface.co/RaphaelLiu/Pusa-V0.5) to your local machine.


## Usage

### Image-to-Video Generation

```bash

python ./demos/cli_test_ti2v_release.py \

  --model_dir "/path/to/Pusa-V0.5" \

  --dit_path "/path/to/Pusa-V0.5/pusa_v0_dit.safetensors" \

  --prompt "Your_prompt_here" \

  --image_dir "/path/to/input/image.jpg" \

  --cond_position 0 \

  --num_steps 30 \

  --noise_multiplier 0

```
Note: We suggest you to try different `con_position` here, and you may also modify the level of noise added to the condition image. You'd be likely to get some surprises.

Take `./demos/example.jpg` as an example and run with 4 GPUs:
```bash

CUDA_VISIBLE_DEVICES=0,1,2,3 python ./demos/cli_test_ti2v_release.py \

  --model_dir "/path/to/Pusa-V0.5" \

  --dit_path "/path/to/Pusa-V0.5/pusa_v0_dit.safetensors" \

  --prompt "The camera remains still, the man is surfing on a wave with his surfboard." \

  --image_dir "./demos/example.jpg" \

  --cond_position 0 \

  --num_steps 30 \

  --noise_multiplier 0.4

```
You can get this result:

<p align="center">
    <img src="https://github.com/Yaofang-Liu/Pusa-VidGen/blob/62526737953d9dc757414f2a368b94a0492ca6da/assets/example.gif" width="300" autoplay loop muted/>

    <br>

</p>


You may ref to the baselines' results from the [VideoGen-Eval](https://github.com/AILab-CVC/VideoGen-Eval) benchmark for comparison:

<p align="center">
    <img src="https://github.com/Yaofang-Liu/Pusa-VidGen/blob/62526737953d9dc757414f2a368b94a0492ca6da/assets/example_baseline.gif" width="1000" autoplay loop muted/>

    <br>

</p>


#### Processing A Group of Images
```bash

python ./demos/cli_test_ti2v_release.py \

  --model_dir "/path/to/Pusa-V0.5" \

  --dit_path "/path/to/Pusa-V0.5/pusa_v0_dit.safetensors" \

  --image_dir "/path/to/image/directory" \

  --prompt_dir "/path/to/prompt/directory" \

  --cond_position 1 \

  --num_steps 30

```

For group processing, each image should have a corresponding text file with the same name in the prompt directory.

#### Using the Provided Shell Script
We also provide a shell script for convenience:

```bash

# Edit cli_test_ti2v_release.sh to set your paths

# Then run:

bash ./demos/cli_test_ti2v_release.sh

```

### Multi-frame Condition

Pusa supports generating videos from multiple keyframes (2 or more) placed at specific positions in the sequence. This is useful for both start-end frame generation and multi-keyframe interpolation.

#### Start & End Frame Generation

```bash

python ./demos/cli_test_multi_frames_release.py \

  --model_dir "/path/to/Pusa-V0.5" \

  --dit_path "/path/to/Pusa-V0.5/pusa_v0_dit.safetensors" \

  --prompt "Drone view of waves crashing against the rugged cliffs along Big Sur’s garay point beach. The crashing blue waters create white-tipped waves, while the golden light of the setting sun illuminates the rocky shore. A small island with a lighthouse sits in the distance, and green shrubbery covers the cliff’s edge. The steep drop from the road down to the beach is a dramatic feat, with the cliff’s edges jutting out over the sea. This is a view that captures the raw beauty of the coast and the rugged landscape of the Pacific Coast Highway." \

  --multi_cond '{"0": ["./demos/example3.jpg", 0.3], "20": ["./demos/example5.jpg", 0.7]}' \

  --num_steps 30

```

The `multi_cond` parameter specifies frame condition positions and their corresponding image paths and noise multipliers. In this example, the first frame (position 0) uses `./demos/example3.jpg` with noise multiplier 0.3, and frame 20 uses `./demos/example5.jpg` with noise multiplier 0.5.

Alternatively, use the provided shell script:
```bash

# Edit parameters in cli_test_multi_frames_release.sh first

bash ./demos/cli_test_multi_frames_release.sh

```

#### Multi-keyframe Interpolation

To generate videos with more than two keyframes (e.g., start, middle, and end):

```bash

python ./demos/cli_test_multi_frames_release.py \

  --model_dir "/path/to/Pusa-V0.5" \

  --dit_path "/path/to/Pusa-V0.5/pusa_v0_dit.safetensors" \

  --prompt "Drone view of waves crashing against the rugged cliffs along Big Sur’s garay point beach. The crashing blue waters create white-tipped waves, while the golden light of the setting sun illuminates the rocky shore. A small island with a lighthouse sits in the distance, and green shrubbery covers the cliff’s edge. The steep drop from the road down to the beach is a dramatic feat, with the cliff’s edges jutting out over the sea. This is a view that captures the raw beauty of the coast and the rugged landscape of the Pacific Coast Highway." \

  --multi_cond '{"0": ["./demos/example3.jpg", 0.3], "13": ["./demos/example4.jpg", 0.7], "27": ["./demos/example5.jpg", 0.7]}' \

  --num_steps 30

```

### Video Transition

Create smooth transitions between two videos:

```bash

python ./demos/cli_test_transition_release.py \

  --model_dir "/path/to/Pusa-V0.5" \

  --dit_path "/path/to/Pusa-V0.5/pusa_v0_dit.safetensors" \

  --prompt "A fluffy Cockapoo, perched atop a vibrant pink flamingo jumps into a crystal-clear pool." \

  --video_start_dir "./demos/example1.mp4" \

  --video_end_dir "./demos/example2.mp4" \

  --cond_position_start "[0]" \

  --cond_position_end "[-3,-2,-1]" \

  --noise_multiplier "[0.3,0.8,0.8,0.8]" \

  --num_steps 30

```

Parameters:
- `cond_position_start`: Frame indices from the start video to use as conditioning
- `cond_position_end`: Frame indices from the end video to use as conditioning
- `noise_multiplier`: Noise level multipliers for each conditioning frame

Alternatively, use the provided shell script:
```bash

# Edit parameters in cli_test_transition_release.sh first

bash ./demos/cli_test_transition_release.sh

```

### Video Extension

Extend existing videos with generated content:

```bash

python ./demos/cli_test_extension_release.py \

  --model_dir "/path/to/Pusa-V0.5" \

  --dit_path "/path/to/Pusa-V0.5/pusa_v0_dit.safetensors" \

  --prompt "A cinematic shot captures a fluffy Cockapoo, perched atop a vibrant pink flamingo float, in a sun-drenched Los Angeles swimming pool. The crystal-clear water sparkles under the bright California sun, reflecting the playful scene." \

  --video_dir "./demos/example1.mp4" \

  --cond_position "[0,1,2,3]" \

  --noise_multiplier "[0.1,0.2,0.3,0.4]" \

  --num_steps 30

```

Parameters:
- `cond_position`: Frame indices from the input video to use as conditioning
- `noise_multiplier`: Noise level multipliers for each conditioning frame

Alternatively, use the provided shell script:
```bash

# Edit parameters in cli_test_v2v_release.sh first

bash ./demos/cli_test_v2v_release.sh

```

### Text-to-Video Generation
```bash

python ./demos/cli_test_ti2v_release.py \

  --model_dir "/path/to/Pusa-V0.5" \

  --dit_path "/path/to/Pusa-V0.5/pusa_v0_dit.safetensors" \

  --prompt "A man is playing basketball" \

  --num_steps 30

```

</details>

## Training

For Pusa V1.0, please find the training details in the **[Pusa V1.0 README](./PusaV1/README.md#training)**.

For Pusa V0.5, you can find our training code and details [here](https://github.com/Yaofang-Liu/Mochi-Full-Finetuner), which also supports training for the original Mochi model.

## Limitations

Pusa currently has several known limitations:
- Video generation quality is dependent on the base model (e.g., Wan-T2V-14B for V1.0).
- We anticipate significant quality improvements when applying our methodology to more advanced models.
- We welcome community contributions to enhance model performance and extend its capabilities.

### Currently Available
- ✅ Model weights for Pusa V1.0 and V0.5
- ✅ Inference code for Text-to-Video generation
- ✅ Inference code for Image-to-Video generation
- ✅ Inference scripts for start & end frames, multi-frames, video transition, video extension
- ✅ Training code and details
- ✅ Model full fine-tuning guide (for Pusa V0.5)
- ✅ Training datasets
- ✅ Technical Report for Pusa V1.0
  
### TODO List
- 🔄 Release more advanced versions with SOTA models
- 🔄 More capabilities like long video generation
- 🔄 ....

## Related Work

- [FVDM](https://arxiv.org/abs/2410.03160): Introduces the groundbreaking frame-level noise control with vectorized timestep approach that inspired Pusa.
- [Wan-Video](https://github.com/modelscope/DiffSynth-Studio): The foundation model for Pusa V1.0.
- [Mochi](https://huggingface.co/genmo/mochi-1-preview): The foundation model for Pusa V0.5, recognized as a leading open-source video generation system on the Artificial Analysis Leaderboard.

## BibTeX
If you use this work in your project, please cite the following references.
```

@misc{Liu2025pusa,

  title={Pusa: Thousands Timesteps Video Diffusion Model},

  author={Yaofang Liu and Rui Liu},

  year={2025},

  url={https://github.com/Yaofang-Liu/Pusa-VidGen},

}

```

```

@article{liu2024redefining,

  title={Redefining Temporal Modeling in Video Diffusion: The Vectorized Timestep Approach},

  author={Liu, Yaofang and Ren, Yumeng and Cun, Xiaodong and Artola, Aitor and Liu, Yang and Zeng, Tieyong and Chan, Raymond H and Morel, Jean-michel},

  journal={arXiv preprint arXiv:2410.03160},

  year={2024}

}

```