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Diffusers - PyTorch/FLAX扩散模型库
Unlock the power of state-of-the-art diffusion models with 🤗 Diffusers – a user-friendly library for image, video, and audio generation in PyTorch and FLAX.
Python
Added on 2025年6月29日29,537
Stars6,069
ForksPython
LanguageProject Introduction
Summary
🤗 Diffusers is a library designed to make the exploration, training, and inference of diffusion models accessible to everyone. It offers state-of-the-art pre-trained models and building blocks for generative AI applications.
Problem Solved
Working with complex diffusion models often involves deep technical knowledge and intricate code setups. Diffusers simplifies this by providing standardized pipelines and components.
Core Features
Model Hub Integration
Access a wide range of pre-trained diffusion models (Stable Diffusion, DALL-E 2, etc.) directly from the Hugging Face Hub.
Flexible Schedulers
Utilize various noise schedulers (DDPM, DDIM, Karras, etc.) to control the generation process and output quality.
Training Utilities
Provides scripts and tools for fine-tuning diffusion models on custom datasets.
Tech Stack
PyTorch
FLAX
Hugging Face Transformers
Accelerate
Safetensors
使用场景
Diffusers can be applied in various domains requiring creative content generation, data augmentation, or complex conditional generation tasks.
场景一：文本到图像生成 (Text-to-Image)
Details
Generate high-quality images from textual descriptions using models like Stable Diffusion for digital art or content creation.
User Value
Enables rapid generation of diverse visual content based on natural language prompts.
场景二：图像编辑与转换 (Image-to-Image / Inpainting)
Details
Modify existing images based on text prompts or reference images, useful for photo editing and stylization.
User Value
Provides powerful tools for non-destructive image manipulation and creative transformations.
场景三：模型训练与微调 (Training & Fine-tuning)
Details
Train custom diffusion models on specific datasets (e.g., medical images, specific art styles) or fine-tune existing ones.
User Value
Allows researchers and developers to adapt models for niche applications or improve performance on specific tasks.
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