📧 Contact: wuyang.li@epfl.ch

✨ Highlight

Stable Video Infinity (SVI) is able to generate ANY-length videos with high temporal consistency, plausible scene transitions, and controllable streaming storylines in ANY domains.

• OpenSVI: Everything is open-sourced: training & evaluation scripts, datasets, and more.
• Infinite Length: No inherent limit on video duration; generate arbitrarily long stories (see the 10‑minute “Tom and Jerry” demo).
• Versatile: Supports diverse in-the-wild generation tasks: multi-scene short films, single‑scene animations, skeleton-/audio-conditioned generation, cartoons, and more.
• Efficient: Only LoRA adapters are tuned, requiring very little training data: anyone can make their own SVI easily.

🔥 News

[10-13-2025] SVI is now fully open-sourced and online!

PS: If you think this project is useful, we would really appreciate your star ⭐, which encourages us to better develop the open-source community! This repository will be continuously maintained. Thank you!

🔧 Environment Setup

conda create -n svi python=3.10 
conda activate svi

# For svi family
pip install -e .
pip install flash_attn==2.8.0.post2

conda install -c conda-forge ffmpeg
conda install -c conda-forge librosa
conda install -c conda-forge libiconv

📦 Model Preparation

Download Wan 2.1 I2V 14B

huggingface-cli download Wan-AI/Wan2.1-I2V-14B-480P --local-dir ./weights/Wan2.1-I2V-14B-480P

Download SVI Family

Model	Task	Input	Output	Hugging Face Link	Comments
ALL	Infinite possibility	Image + X	X video	🤗 Folder	Family bucket! I want to play with all!
SVI-Shot	Single-scene generation	Image + Text prompt	Long video	🤗 Model	Generate consistent long video with 1 text prompt. (This will never drift)
SVI-Film	Multi-scene generation	Image + Text prompt stream	Film-style video	🤗 Model	Generate creative long video with 1 text prompt stream (5 second per text).
SVI-Film (Transition)	Multi-scene generation	Image + Text prompt stream	Film-style video	🤗 Model	Generate creative long video with 1 text prompt stream. (More scene transitions due to the training data)
SVI-Tom&Jerry	Cartoon animation	Image	Cartoon video	🤗 Model	Generate creative long cartoon videos with 1 text prompt stream (This will never drift in our 20 min test)
SVI-Talk	Talking head	Image + Audio	Talking video	🤗 Model	Generate long videos with audio-conditioned human speaking
SVI-Dance	Dancing animation	Image + Skeleton	Dance video	🤗 Model	Generate long videos with skeleton-conditioned human dancing

Note: If you want to play with T2V, you can directly use SVI with an image generated by any T2I model!

# login with your fine-grained token
huggingface-cli login

# Option 1: Download SVI Family bucket!
huggingface-cli download vita-video-gen/svi-model --local-dir ./weights/Stable-Video-Infinity --include "version-1.0/*"

# Option 2: Download individual models
huggingface-cli download vita-video-gen/svi-model version-1.0/svi-shot.safetensors --local-dir ./weights/Stable-Video-Infinity
huggingface-cli download vita-video-gen/svi-model version-1.0/svi-film.safetensors --local-dir ./weights/Stable-Video-Infinity
huggingface-cli download vita-video-gen/svi-model version-1.0/svi-film-transitions.safetensors --local-dir ./weights/Stable-Video-Infinity
huggingface-cli download vita-video-gen/svi-model version-1.0/svi-film-transitions.safetensors --local-dir ./weights/Stable-Video-Infinity
huggingface-cli download vita-video-gen/svi-model version-1.0/svi-tom.safetensors --local-dir ./weights/Stable-Video-Infinity
huggingface-cli download vita-video-gen/svi-model version-1.0/svi-talk.safetensors --local-dir ./weights/Stable-Video-Infinity
huggingface-cli download vita-video-gen/svi-model version-1.0/svi-dance.safetensors --local-dir ./weights/Stable-Video-Infinity

Download Multitalk Cross-Attention for SVI-Talk Training/Test

# Download audio encoder
huggingface-cli download TencentGameMate/chinese-wav2vec2-base --local-dir ./weights/chinese-wav2vec2-base 
huggingface-cli download TencentGameMate/chinese-wav2vec2-base model.safetensors --revision refs/pr/1 --local-dir ./weights/chinese-wav2vec2-base

# Download multitalk weight
huggingface-cli download MeiGen-AI/MeiGen-MultiTalk --local-dir ./weights/MeiGen-MultiTalk

# Link Multitalk
ln -s $PWD/weights/MeiGen-MultiTalk/multitalk.safetensors weights/Wan2.1-I2V-14B-480P/

Download UniAnimate-DiT LoRA for SVI-Dance Training

huggingface-cli download ZheWang123/UniAnimate-DiT --local-dir ./weights/UniAnimate-DiT

Check Model

After downloading all the models, your weights/ directory structure should look like this:

weights/
├── Wan2.1-I2V-14B-480P/
│   ├── diffusion_pytorch_model-00001-of-00007.safetensors
│   ├── diffusion_pytorch_model-00002-of-00007.safetensors
│   ├── diffusion_pytorch_model-00003-of-00007.safetensors
│   ├── diffusion_pytorch_model-00004-of-00007.safetensors
│   ├── diffusion_pytorch_model-00005-of-00007.safetensors
│   ├── diffusion_pytorch_model-00006-of-00007.safetensors
│   ├── diffusion_pytorch_model-00007-of-00007.safetensors
│   ├── diffusion_pytorch_model.safetensors.index.json
│   ├── models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth
│   ├── models_t5_umt5-xxl-enc-bf16.pth
│   ├── Wan2.1_VAE.pth
│   ├── multitalk.safetensors (symlink)
│   └── README.md
├── Stable-Video-Infinity/
│   └── version-1.0/
│       ├── svi-shot.safetensors
│       ├── svi-film.safetensors
│       ├── svi-film-transitions.safetensors
│       ├── svi-tom.safetensors
│       ├── svi-talk.safetensors
│       └── svi-dance.safetensors
├── chinese-wav2vec2-base/ (for SVI-Talk)
│   ├── config.json
│   ├── model.safetensors
│   ├── preprocessor_config.json
│   └── README.md
├── MeiGen-MultiTalk/ (for SVI-Talk)
│   ├── diffusion_pytorch_model.safetensors.index.json
│   ├── multitalk.safetensors
│   └── README.md
└── UniAnimate-DiT/ (for SVI-Dance)
    ├── unet/
    └── README.md

🎮 Play with Official SVI

Inference Scripts

The following scripts will use data in data/demo for inference. You can also use custom data to inference by simply changing the data path.

# SVI-Shot
bash scripts/test/svi_shot.sh 

# SVI-Film
bash scripts/test/svi_film.sh 

# SVI-Talk
bash scripts/test/svi_talk.sh 

# SVI-Dance
bash scripts/test/svi_dance.sh 

# SVI-Tom&Jerry
bash scripts/test/svi_tom.sh 

Gradio Demo

Currently, gradio demo only supports SVI-Shot and SVI-Film.

bash gradio_demo.sh

🔥 Train Your Own SVI

We have prepared the toy training data data/toy_train/. You can simply follow the data format to train SVI with your custom data.
Please modify --num_nodes if you use more nodes for training. We have tested both 8 and 64 GPUs for training, where larger batch-size gave a better performance.

SVI-Shot

# (Optionally) Use scripts/data_preprocess/process_mixkit.py from CausVid to pre-process data
# start training
bash scripts/train/svi_shot.sh 

SVI-Film

# (Optionally) Use scripts/data_preprocess/process_mixkit.py from CausVid to pre-process data
# start training
bash scripts/train/svi_film.sh 

SVI-Talk

# Preprocess the toy training data
python scripts/data_preprocess/prepare_video_audio.py 

# Start training
bash scripts/train/svi_talk.sh 

SVI-Dance

# Preprocess the toy training data
python scripts/data_preprocess/prepare_video_audio.py 

# Start training
bash scripts/train/svi_dance.sh 

📝 Test Your Trained SVI

Model Post-processing

# Change .pt files to .safetensors files
# zero_to_fp32.py will be automatically generated in your model dir, change $DIR_WITH_SAFETENSORS into your desired DIR
python zero_to_fp32.py . $DIR_WITH_SAFETENSORS --safe_serialization

# (Optionally) Extract and only save LoRA parameters to reduce disk space
python utils/extract_lora.py --checkpoint_dir $DIR_WITH_SAFETENSORS --output_dir $XXX

Inference

Please modify the inference scripts in ./scripts/test/ accordingly by changing the inference samples and your new weight

🗃️ Datasets

You can also use our benchmark datasets made by our Automatic Prompt Stream Engine (see Appendix. A for more details), where you can find images and associated prompt streams according to specific storylines.

Data	Use	HuggingFace Link	Comment
Consistent Video Generation	Test	🤗 Dataset	Generate 1 long video using 1 text prompt
Creative Video Generation	Test	🤗 Dataset	Generate 1 long video using 1 text prompt stream according to storyline (1 prompt for 5 sec clip)
Creative Video Generation (More prompts)	Test	🤗 Dataset	Generate 1 long video using 1 text prompt stream according to storyline (1 prompt for 5 sec clip)

The following is the training data we used for SVI family.

Data	Use	HuggingFace Link	Comment
Customized Datasets	Train	🤗 Dataset	You can make your customized datasets using this format
Consistent/Creative Video Generation	Train	🤗 Dataset	MixKit Dataset
Consistent/Creative Video Generation	Train	🤗 Dataset	UltraVideo Dataset
Human Talking	Train	🤗 Dataset	Hallo v3
Human Dancing	Train	🤗 Dataset	TikTok

huggingface-cli download --repo-type dataset vita-video-gen/svi-benchmark --local-dir ./data/svi-benchmark

📋 TODO List

• Release everything about SVI
• Wan 2.2 based SVI
• Streaming generation model
• [Call for TODO] Write down your idea in the Issue

🙏 Acknowledgement

We greatly appreciate the tremendous effort for the following fantastic projects!

[1] Wan: Open and Advanced Large-Scale Video Generative Models
[2] UniAnimate-DiT: Human Image Animation with Large-Scale Video Diffusion Transformer
[3] Let Them Talk: Audio-Driven Multi-Person Conversational Video Generation

❤️ Citation

If you find our work helpful for your research, please consider citing our paper. Thank you so much!

@article{li2025stable,
      title={Stable Video Infinity: Infinite-Length Video Generation with Error Recycling}, 
      author={Wuyang Li and Wentao Pan and Po-Chien Luan and Yang Gao and Alexandre Alahi},
      journal={arXiv preprint arXiv: arXiv:2510.09212},
      year={2025},
}

📌 Abstract

We propose Stable Video Infinity (SVI) that is able to generate infinite-length videos with high temporal consistency, plausible scene transitions, and controllable streaming storylines. While existing long-video methods attempt to mitigate accumulated errors via handcrafted anti-drifting (e.g., modified noise scheduler, frame anchoring), they remain limited to single-prompt extrapolation, producing homogeneous scenes with repetitive motions. We identify that the fundamental challenge extends beyond error accumulation to a critical discrepancy between the training assumption (seeing clean data) and the test-time autoregressive reality (conditioning on self-generated, error-prone outputs). To bridge this hypothesis gap, SVI incorporates Error-Recycling Fine-Tuning, a new type of efficient training that recycles the Diffusion Transformer (DiT)'s self-generated errors into supervisory prompts, thereby encouraging DiT to actively identify and correct its own errors. This is achieved by injecting, collecting, and banking errors through closed-loop recycling, autoregressively learning from error-injected feedback. Specifically, we (i) inject historical errors made by DiT to intervene on clean inputs, simulating error-accumulated trajectories in flow matching; (ii) efficiently approximate predictions with one-step bidirectional integration and calculate errors with residuals; (iii) dynamically bank errors into replay memory across discretized timesteps, which are resampled for new input. SVI is able to scale videos from seconds to infinite durations with no additional inference cost, while remaining compatible with diverse conditions (e.g., audio, skeleton, and text streams). We evaluate SVI on three benchmarks, including consistent, creative, and conditional settings, thoroughly verifying its versatility and state-of-the-art role.