ConsisID addresses the challenge of maintaining consistent human identity in text-to-video generation. It targets researchers and developers in AI video synthesis, offering a DiT-based controllable model that leverages frequency decomposition for identity preservation without fine-tuning.

How It Works

ConsisID employs a novel frequency decomposition approach inspired by vision and diffusion transformers. This method allows the model to disentangle identity-related features from other visual information, enabling consistent identity representation across generated video frames. The architecture is built upon a diffusion transformer (DiT) backbone, providing a strong foundation for high-quality video synthesis.

Quick Start & Requirements

• Install: pip install git+https://github.com/huggingface/diffusers.git (dev version) or follow environment setup instructions.
• Prerequisites: Python 3.11.0, PyTorch (CUDA 11.8 or 12.1 recommended), requirements.txt.
• Resources: Requires significant GPU memory (44 GB for full resolution, ~22 GB with optimizations like enable_model_cpu_offload and vae.enable_tiling).
• Links: Diffusers API Demo, Jupyter Notebook, Project Page.

Highlighted Details

• CVPR 2025 Highlight.
• Tuning-free identity-preserving text-to-video generation.
• Supports parallel inference via xDiT and cache inference via TeaCache.
• Offers GPU memory optimization techniques for broader accessibility.

Maintenance & Community

The project is actively maintained by PKU-YuanGroup, with contributions noted from Hugging Face developers and community members. Links to community extensions (ComfyUI, Windows Docker) and active development discussions are provided.

Licensing & Compatibility

Licensed under Apache 2.0, with a specific license for the CogVideoX-5B model component. Generally permissive for research and commercial use, but users should verify the CogVideoX license terms.

Limitations & Caveats

High GPU memory requirements can be a barrier for users without high-end hardware, though optimizations are provided. The README notes that results can vary between machines even with identical seeds and prompts.