This repository serves as a curated collection of Chinese-language Stable Diffusion models, applications, datasets, and tutorials. It aims to consolidate resources for the Chinese AI art community, offering a centralized hub for those interested in text-to-image generation with a focus on Chinese language support.

How It Works

The project highlights various approaches to achieving Chinese language understanding in diffusion models. Key strategies include replacing English-centric text encoders (like CLIP) with Chinese-specific models (e.g., Chinese-CLIP, StructBert, GLM-4), training on large-scale Chinese image-text datasets, and fine-tuning existing models with Chinese data. Some models also incorporate multi-stage generation pipelines, cascaded diffusion models for higher resolutions, and specialized modules for better semantic understanding and detail rendering.

Quick Start & Requirements

• Installation and usage vary by individual model. Most models are available via Hugging Face or GitHub repositories, often requiring Python environments and specific deep learning frameworks (PyTorch, MindSpore).
• Dependencies commonly include libraries like diffusers, transformers, accelerate, and potentially CUDA-enabled GPUs for efficient inference.
• Specific model requirements, such as GPU memory, dataset sizes, and framework versions, are detailed within each linked repository.

Highlighted Details

• Comprehensive listing of open-source and closed-source Chinese text-to-image models.
• Detailed technical descriptions of model architectures, training data, and methodologies.
• Focus on models that offer improved Chinese language comprehension and generation capabilities compared to English-only counterparts.
• Includes information on models trained on specific Chinese datasets and optimized for Chinese cultural contexts.

Maintenance & Community

• The project is community-driven, encouraging contributions via Pull Requests for new repositories.
• Links to specific model repositories and their associated communities (e.g., GitHub, Hugging Face) are provided.

Licensing & Compatibility

• Licenses vary significantly across the listed models, ranging from Apache 2.0, MIT, to more restrictive licenses. Users must verify the license of each individual model for compatibility with their intended use, especially for commercial applications.

Limitations & Caveats

• This is a collection, not a single unified model; users must integrate and manage individual models.
• Performance and quality can vary greatly between listed models, with some still in beta or experimental stages.
• The rapid evolution of the field means some listed models may become outdated or superseded.