| --- |
| license: apache-2.0 |
| language: |
| - en |
| base_model: MiniMaxAI/MiniMax-H3 |
| base_model_relation: adapter |
| datasets: |
| - iapple1/CoCode-World-Data |
| pipeline_tag: image-to-video |
| tags: |
| - lora |
| - minimax-h3 |
| - video-generation |
| - multi-view |
| - world-model |
| --- |
| |
| # CoCode-World |
|
|
| **Shared 3D Proxy Representation for Multi-Player World Models** |
|
|
| [Project Page](https://iapple233.github.io/CCW/) 路 [Code](https://github.com/IApple233/CoCode-World) 路 [Dataset](https://huggingface.co/datasets/iapple1/CoCode-World-Data) 路 [Examples](https://github.com/IApple233/CoCode-World/tree/main/examples/evaluation) |
|
|
| ## Model overview |
|
|
| CoCode-World generates synchronized video from two player perspectives using a shared 3D proxy representation. A reference image defines the visual appearance, a proxy video provides scene structure and motion, and a text prompt describes the scene. |
|
|
| This repository provides the official CoCode-World LoRA checkpoint for **MiniMax-H3 Ref2VA**. The recommended EMA weights are used in the released evaluation examples. |
|
|
| | Property | Description | |
| |---|---| |
| | Base model | MiniMax-H3 Ref2VA | |
| | Adapter | Rank-64 LoRA | |
| | Checkpoint | Step 4000 | |
| | Inputs | Reference image, synchronized proxy video, and text prompt | |
| | Output | Two player views arranged side by side | |
| | Resolution | 2560 脳 720 at 24 FPS | |
|
|
| ## Download |
|
|
| ```bash |
| pip install -U huggingface_hub |
| hf download iapple1/CoCode-World-Model --local-dir checkpoints/step-004000 |
| ``` |
|
|
| The repository includes `ema.safetensors` for inference, `lora.safetensors` for the task adapter, and training state and configuration files. Use the checkpoint loader in the official code repository. Download the MiniMax-H3 base model separately. |
|
|
| ## Usage |
|
|
| Follow the [installation guide](https://github.com/IApple233/CoCode-World/blob/main/docs/SETUP.md), then download the required models from the code repository: |
|
|
| ```bash |
| python scripts/download_models.py base |
| python scripts/download_models.py cocode |
| ``` |
|
|
| The [evaluation guide](https://github.com/IApple233/CoCode-World/blob/main/docs/EVALUATION.md) explains input preparation and generation. Eight ready-to-use examples include reference images, prompts, proxy videos, and generated results. |
|
|
| ## Training |
|
|
| Training starts from the official [MiniMax-H3 weights](https://huggingface.co/MiniMaxAI/MiniMax-H3) and [DiffSynth-Studio's Ref2VA DeCFG LoRA](https://www.modelscope.cn/models/DiffSynth-Studio/MiniMax-H3-TrainingAdapter). The DeCFG adapter is used as a frozen training preset, followed by training a separate CoCode-World task LoRA. Inference uses the original MiniMax-H3 base and the CoCode-World EMA LoRA. |
|
|
| Training code and instructions are available in the [training guide](https://github.com/IApple233/CoCode-World/blob/main/docs/TRAINING.md). The public dataset is a selected subset of the original training collection. |
|
|
| ## Intended use and limitations |
|
|
| The model supports research on multi-player world models, synchronized multi-view generation, and proxy-conditioned video synthesis. The first-frame reference guides appearance. Generated videos may contain visual artifacts or temporal inconsistencies, and the selected examples do not constitute a quantitative benchmark. |
|
|
| ## License and acknowledgments |
|
|
| The task adapter is released under Apache 2.0. The MiniMax-H3 base model is governed by the [MiniMax-H3 Community License Agreement](https://huggingface.co/MiniMaxAI/MiniMax-H3/blob/main/LICENSE). The DeCFG training adapter retains its upstream terms. |
|
|
| CoCode-World builds on [MiniMax-H3](https://huggingface.co/MiniMaxAI/MiniMax-H3), [DiffSynth-Studio](https://github.com/modelscope/DiffSynth-Studio), [Diffusers](https://github.com/huggingface/diffusers), and [Code World Model](https://github.com/buaacyw/code-world-model). |
|
|
