Janspiry/Image-Super-Resolution-via-Iterative-Refinement
Image Super Resolution Via Iterative Refinement
这是一个用于 AI 图像超分辨率和面部合成的深度学习框架。它提供了一个扩散模型图像放大器和生成式面部图像合成器，能够使用预训练模型权重将低分辨率图像转换为高分辨率输出。
该系统利用迭代扩散细化和低分辨率引导采样来恢复精细细节和清晰度。它支持无条件图像生成（从零开始创建图像）和用于高保真面部重建的引导分辨率增强。
该仓库包括一个带有多 GPU 分布式训练和预训练权重初始化的扩散模型训练管道。该环境由模型实验追踪、外部指标记录和基于检查点的模型恢复功能支持。
Features
- Resolution Upscaling - Increases image resolution and clarity using iterative latent diffusion processes.
- Diffusion Image Restoration Frameworks - Integrates diffusion models into a framework specifically for reconstructing low-resolution images.
- Diffusion Refinement - Uses a diffusion-based iterative refinement process to progressively increase image resolution and detail.
- Image Super Resolution Models - Provides a deep learning framework to reconstruct fine details and increase resolution of low-quality images.
- High-Resolution Synthesis - Synthesizes high-fidelity facial images using pretrained diffusion models.
- Facial Synthesis - Generates high-fidelity synthetic faces using deep learning encoders and generators.
- Image Resolution Reconstruction - Reconstructs fine details in high-resolution images using low-resolution inputs as structural priors.
- AI Upscaling - Increases the resolution and clarity of low-quality images using AI-driven upscaling.
- Distributed GPU Training - Distributes the computational load of model weight optimization across multiple GPUs.
- Diffusion Model Training - Implements a training workflow for diffusion models featuring U-Net architectures and time embeddings.
- Unconditional Generation - Generates new images from scratch based on learned distributions without external guidance.
- Pretrained Weight Initializers - Accelerates model convergence by initializing training with existing pretrained weights.
- Super Resolution - Iterative refinement for image super-resolution.
- Generation - Listed in the “Generation” section of the Awesome Diffusion Models awesome list.
