--- license: apache-2.0 pipeline_tag: image-text-to-video ---
Bernini

Latent Semantic Planning for Video Diffusion

**Chenchen Liu\*, Junyi Chen\*, Lei Li\*, Lu Chi\*,ยง, Mingzhen Sun\*, Zhuoying Li\*, Yi Fu, Ruoyu Guo, Yiheng Wu, Ge Bai, Zehuan Yuanโœ‰** \* Equal contribution  โœ‰ Corresponding author  ยง Project lead [![arXiv](https://img.shields.io/badge/arXiv-2605.22344-b31b1b.svg)](https://arxiv.org/abs/2605.22344) [![Project Page](https://img.shields.io/badge/Project-Page-blue.svg)](https://bernini-ai.github.io/) [![HuggingFace](https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace-Models-yellow)](https://huggingface.co/ByteDance/Bernini)
## ๐ŸŽ‰ News - **[2026-06-01]** We open-sourced the inference code and model weights of the Bernini Renderer (**Bernini-R**). - **[2026-05-22]** We released our paper [Bernini: Latent Semantic Planning for Video Diffusion](https://arxiv.org/abs/2605.22344). ## โœจ Highlights Bernini is a unified framework for video generation and editing that combines an MLLM-based semantic planner with a DiT-based renderer. On video editing, Bernini reaches the first tier among leading closed-source commercial models. The leaderboard below comes from our self-built arena platform, where human annotators blindly vote on paired edits and the votes are aggregated into a Bradley-Terry score and a pairwise win-rate matrix. Video editing arena: Bradley-Terry leaderboard and pairwise win-rate matrix ## ๐Ÿ“ฆ Installation ### Requirements - **Python** 3.11.2. - **CUDA GPU** โ€” a Hopper GPU (H100/H800/H200) is recommended so FlashAttention-3 can be used; other CUDA GPUs fall back to FlashAttention-2 or PyTorch SDPA. - **CUDA toolkit** 12.4 (matches the pinned `torch==2.5.1+cu124`; 12.3+ is the minimum if you build FlashAttention-3). - Pinned in `requirements.txt`: `torch==2.5.1+cu124`, `diffusers==0.35.2`, `accelerate==0.34.2`, `transformers==4.57.3`. Reference environment (Bernini-R is developed and tested on this setup): | Component | Version | |-----------|--------------| | GPU | NVIDIA H100 | | CUDA | 12.4 | | Python | 3.11.2 | | PyTorch | 2.5.1+cu124 | ### Install ```bash git clone https://github.com/bytedance/Bernini.git bernini && cd bernini pip install -r requirements.txt ``` Optional extras: - **Multi-GPU sequence parallel** needs [Open-VeOmni](https://github.com/ByteDance-Seed/VeOmni) (Apache-2.0, Python 3.11). Use `--no-deps` so VeOmni does not pull in a different torch build and override the pinned `torch==2.5.1+cu124`: `pip install --no-deps git+https://github.com/ByteDance-Seed/VeOmni.git@v0.1.10`. Single-GPU inference does not need it. - **Faster attention** (auto-detected if installed; otherwise PyTorch SDPA is used): - FlashAttention-2 โ€” general CUDA GPUs (incl. A100/A800): `pip install flash-attn==2.8.3`. - FlashAttention-3 โ€” Hopper only (H100/H800/H200, CUDA โ‰ฅ 12.3, PyTorch โ‰ฅ 2.4). `flash_attn_interface` is not on PyPI; build it from the [flash-attention](https://github.com/Dao-AILab/flash-attention) repo's `hopper/` directory at tag `v2.8.3`: ```bash git clone https://github.com/Dao-AILab/flash-attention.git cd flash-attention && git checkout v2.8.3 cd hopper && MAX_JOBS=$(nproc) python3 setup.py install --user ``` ### Weights Bernini-R provides two ways to obtain the renderer weights. The **diffusers format is recommended** โ€” it is a self-contained diffusers-format directory whose `transformer` / `transformer_2` already hold the Bernini-R weights, so you point `--config` at it and the weights load directly, with **no** `--high_noise_ckpt` / `--low_noise_ckpt` needed. #### Option A โ€” diffusers format (recommended) A single ready-to-use diffusers-format model from [`ByteDance/Bernini-R-Diffusers`](https://huggingface.co/ByteDance/Bernini-R-Diffusers). It bundles the Wan2.2 base components (VAE, UMT5 text encoder, tokenizer) together with the Bernini-R transformer weights, so nothing else is downloaded at runtime. ```bash pip install -U "huggingface_hub" hf download ByteDance/Bernini-R-Diffusers --local-dir Bernini-R-Diffusers ``` Then pass it via `--config` and omit the checkpoint flags, e.g.: ```bash python infer_single_gpu.py --config Bernini-R-Diffusers \ --case assets/testcases/t2i/t2i.json --num_frames 1 ``` #### Option B โ€” separate checkpoints The original layout, where Bernini-R uses two sets of weights loaded separately: 1. **Wan2.2 base** โ€” [`Wan-AI/Wan2.2-T2V-A14B-Diffusers`](https://huggingface.co/Wan-AI/Wan2.2-T2V-A14B-Diffusers) on Hugging Face. Supplies the VAE, UMT5 text encoder, tokenizer, and the transformer architecture/base weights. It is downloaded automatically on first run (configured by `wan22_base` in `configs/bernini_renderer_wan22/config.json`). 2. **Bernini-R checkpoint** โ€” the trained high-noise / low-noise transformer weights (safetensors) from [ByteDance/Bernini-R](https://huggingface.co/ByteDance/Bernini-R), passed with `--high_noise_ckpt` / `--low_noise_ckpt`. Both a local directory and a Hugging Face repo id are accepted. Download models using huggingface-cli: ```bash pip install -U "huggingface_hub" hf download Wan-AI/Wan2.2-T2V-A14B-Diffusers --local-dir Wan2.2-T2V-A14B-Diffusers hf download ByteDance/Bernini-R --local-dir Bernini-R ``` ## ๐Ÿš€ Usage A run is described by a **case file** โ€” a small JSON under [`assets/testcases/`](assets/testcases/) that bundles one task's routing and inputs (`task_type`, `guidance_mode`, `prompt`, source media, `output`). This keeps long prompts out of the command line. Each task has a directory under `assets/testcases/` holding one or more case files; see [`assets/testcases/`](assets/testcases/) for the format and the bundled `t2i` / `i2i` / `t2v` / `v2v` / `rv2v` /`r2v` examples. ### Prompt enhancer (highly recommended) `--use_pe` enhances the prompt through an OpenAI-compatible endpoint and is recommended for best generation quality. The `openai` SDK is installed by `requirements.txt`; configure the endpoint with environment variables: ```bash export BERNINI_PE_API_KEY=... # or OPENAI_API_KEY export BERNINI_PE_BASE_URL=... # or OPENAI_BASE_URL export BERNINI_PE_MODEL=... # vision-capable chat model ``` ### Examples by task type Unless an example specifies otherwise, inference outputs **480p / 16fps** (the defaults โ€” `--max_image_size 848`, `--fps 16`). Each example runs a bundled case in [`assets/testcases/`](assets/testcases/) โ€” replace `` / `` with your high-/low-noise checkpoint paths. The image tasks (`t2i`, `i2i`) are shown on a single GPU; the video tasks on 8 GPUs via `torchrun`, where `--ulysses N` gives N-way Ulysses sequence parallel per sample and the remaining `world_size / N` ranks run data parallel over the task list. The two scripts take the same inputs, so any example can be run either way. Inputs can also be passed directly as flags instead of `--case` (`--prompt`, `--task_type`, `--guidance_mode`, `--video`, `--image`, `--images`, `--output`); generation parameters (`--seed`, `--num_frames`, ...) are always command-line flags. **Text-to-image** (`t2i`) โ€” single GPU; generates one frame, so pass `--num_frames 1` ```bash python infer_single_gpu.py --high_noise_ckpt --low_noise_ckpt \ --case assets/testcases/t2i/t2i.json --num_frames 1 ``` **Image editing** (`i2i`) โ€” single GPU; generates one frame, so pass `--num_frames 1` ```bash python infer_single_gpu.py --high_noise_ckpt --low_noise_ckpt \ --case assets/testcases/i2i/i2i.json --num_frames 1 ``` **Text-to-video** (`t2v`) ```bash torchrun --nproc-per-node 8 infer_multi_gpu.py \ --high_noise_ckpt --low_noise_ckpt --ulysses 8 \ --case assets/testcases/t2v/t2v.json ``` **Video editing** (`v2v` / `mv2v`) โ€” two cases are provided. For edits where the main subject keeps its ordinary motion (case 1 adds a snowman to the scene), the `v2v` task type is enough: ```bash torchrun --nproc-per-node 8 infer_multi_gpu.py \ --high_noise_ckpt --low_noise_ckpt --ulysses 8 \ --case assets/testcases/v2v/v2v_case1.json ``` For edits that need to change the subject's motion (case 2 makes the person crouch down), the `mv2v` task type gives better results: ```bash torchrun --nproc-per-node 8 infer_multi_gpu.py \ --high_noise_ckpt --low_noise_ckpt --ulysses 8 \ --case assets/testcases/v2v/v2v_case2.json ``` **Reference + video editing** (`rv2v`) โ€” two cases are provided. Case 1 is reference-image-guided video editing โ€” replacing a garment in the source video with one from a reference image: ```bash torchrun --nproc-per-node 8 infer_multi_gpu.py \ --high_noise_ckpt --low_noise_ckpt --ulysses 8 \ --case assets/testcases/rv2v/rv2v_case1.json ``` Case 2 is a video-insertion example โ€” inserting content into the source video. It is run at 720p / 24fps to show the insertion result more clearly: ```bash torchrun --nproc-per-node 8 infer_multi_gpu.py \ --high_noise_ckpt --low_noise_ckpt --ulysses 8 \ --case assets/testcases/rv2v/rv2v_case2.json \ --num_frames 121 --fps 24 --max_image_size 1280 ``` **Reference-to-video** (`r2v`) โ€” drives a video from one or more reference images ```bash torchrun --nproc-per-node 8 infer_multi_gpu.py \ --high_noise_ckpt --low_noise_ckpt --ulysses 8 \ --case assets/testcases/r2v/r2v.json ``` See `python infer_single_gpu.py --help` for the full argument list. ### Gradio demo `gradio_demo.py` exposes the same pipeline through a Gradio UI: the task-type dropdown auto-fills `guidance_mode` (still user-editable), uploaded media is routed to the matching slot, and the result is rendered inline. ```bash # Single GPU python gradio_demo.py --high_noise_ckpt --low_noise_ckpt --port 7860 # 8 GPUs, 8-way Ulysses sequence parallel torchrun --nproc-per-node 8 gradio_demo.py --ulysses 8 \ --high_noise_ckpt --low_noise_ckpt --port 7860 --share ``` Add `--use_pe` (and `export OPENAI_API_KEY=...` / `BERNINI_PE_API_KEY=...`) to enable GPT prompt enhancement; the in-UI checkbox is a per-request switch on top of this flag. ## ๐Ÿ“‘ Citation If you use Bernini in your research, please cite: ```bibtex @article{bernini, title = {Bernini: Latent Semantic Planning for Video Diffusion}, author = {Chenchen Liu and Junyi Chen and Lei Li and Lu Chi and Mingzhen Sun and Zhuoying Li and Yi Fu and Ruoyu Guo and Yiheng Wu and Ge Bai and Zehuan Yuan}, journal = {arXiv preprint arXiv:2605.22344}, year = {2026} } ``` ## ๐Ÿ™ Acknowledgements Bernini builds on several outstanding open-source projects: - [Wan2.2-T2V-A14B](https://huggingface.co/Wan-AI/Wan2.2-T2V-A14B) - [Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) - [VeOmni](https://github.com/ByteDance-Seed/VeOmni) We thank the authors and communities of these projects for their contributions. ## ๐Ÿ“„ License Apache License 2.0. See [LICENSE](LICENSE).