| --- |
| license: other |
| license_name: edenlive-mixed |
| license_link: https://huggingface.co/VAST-AI/EdenLive-14B-Causal-Diffusers#license |
| library_name: diffusers |
| pipeline_tag: image-to-video |
| base_model: VAST-AI/EdenLive |
| tags: |
| - video-generation |
| - diffusers |
| - EdenLive |
| --- |
| |
| # EdenLive 14B Causal — Diffusers |
|
|
| [EdenLive](https://github.com/VAST-AI-Research/EdenLive) generates video |
| from an RGB proxy video, a reference image, and a text prompt. |
| The reference image provides appearance, while the proxy video provides |
| motion and scene structure. |
|
|
| This repository contains the 14B causal checkpoint in Diffusers format. |
| The model generates video in causal chunks, with four denoising steps per chunk by default. |
| A pipeline call returns the complete video. |
|
|
| [Project](https://vast-eden.ai/?p=edenlive) · |
| [Code](https://github.com/VAST-AI-Research/EdenLive) · |
| [Original checkpoints](https://huggingface.co/VAST-AI/EdenLive) |
|
|
| ## Installation |
|
|
| Install a CUDA-compatible version of PyTorch and torchvision, then install |
| Diffusers from the official repository and the required dependencies: |
|
|
| ```bash |
| pip install --upgrade "git+https://github.com/huggingface/diffusers.git" |
| pip install "transformers>=4.56.0" accelerate sentencepiece ftfy \ |
| av imageio imageio-ffmpeg |
| ``` |
|
|
| ## Quick start |
|
|
| Prepare a reference image and an RGB proxy video sampled at 16 fps. |
| The example uses the first 49 frames of the proxy video. |
|
|
| Replace the input paths and prompt with your own. Resample your video |
| before loading it if its frame rate differs from 16 fps. |
|
|
| ```python |
| import torch |
| from diffusers import EdenLiveCausalPipeline |
| from diffusers.utils import export_to_video, load_image, load_video |
| |
| pipe = EdenLiveCausalPipeline.from_pretrained( |
| "VAST-AI/EdenLive-14B-Causal-Diffusers", |
| torch_dtype={ |
| "default": torch.bfloat16, |
| "proxy_encoder": torch.float32, |
| }, |
| ) |
| pipe.enable_model_cpu_offload() |
| |
| frames = pipe( |
| image=load_image("reference.png"), |
| video=load_video("proxy-16fps.mp4"), |
| prompt="A character explores a snowy landscape.", |
| height=480, |
| width=832, |
| num_frames=49, |
| num_inference_steps=4, |
| guidance_scale=1.0, |
| generator=torch.Generator(device="cpu").manual_seed(42), |
| ).frames[0] |
| |
| export_to_video(frames, "edenlive-14b-causal.mp4", fps=16) |
| ``` |
|
|
| ## Usage notes |
|
|
| - The pipeline requires a reference image and a proxy video in addition |
| to the text prompt. |
| - The output frame count must satisfy $N = 4k + 1$ and must not exceed |
| the number of input video frames. Here, $k$ is a nonnegative integer. |
| - Custom output height and width must be multiples of 16. |
| - Keep the proxy encoder in FP32 when loading the other components in BF16. |
| The transformer preserves its internal proxy projection in FP32. |
| - The example uses CPU offload to reduce GPU memory usage. If the model |
| fits in GPU memory, replace `pipe.enable_model_cpu_offload()` with |
| `pipe.to("cuda")`. |
| - The standard Wan VAE is used by default. |
| - To change the prompt between chunks, pass `chunk_prompts` with one prompt |
| per chunk instead of `prompt`. With the default chunk size, a 49-frame |
| video uses five prompts. Each pipeline call starts a new video. |
|
|
| ## License |
|
|
| EdenLive components use [Apache 2.0](LICENSE). |
| DINOv3 components are covered by the |
| [DINOv3 License](proxy_encoder/LICENSE.md). |
| CLIP components use the [MIT license](image_encoder/LICENSE.md). |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{edenlive2026, |
| title = {EdenLive: Real-Time Generative Rendering from Any Visual Reference}, |
| author = {Zehuan Huang and Tianxing Xu and Chunli Peng and Cheng Zhang and Zeyi Zheng and Yifei Song and Zekai Gu and Zhipeng Yu and Peng Wang and Yanpei Cao}, |
| year = {2026}, |
| url = {https://vast-eden.ai/?p=edenlive} |
| } |
| ``` |
|
|
