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Janspiry avatar

Janspiry/Image-Super-Resolution-via-Iterative-Refinement

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3,920 stars·483 forks·Python·Apache-2.0·14 views

Image Super Resolution Via Iterative Refinement

This project is a deep learning framework for AI image super-resolution and facial synthesis. It provides a diffusion model image upscaler and a generative facial image synthesizer capable of transforming low-resolution images into high-resolution outputs using pretrained model weights.

The system utilizes iterative diffusion refinement and low-resolution guided sampling to restore fine details and sharpness. It supports both unconditional image generation, where images are created from scratch, and guided resolution enhancement for high-fidelity facial reconstruction.

The repository includes a diffusion model training pipeline with multi-GPU distributed training and pretrained weight initialization. This environment is supported by model experiment tracking, external metric logging, and checkpoint-based model resumption.

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.

Star history

Star history chart for janspiry/image-super-resolution-via-iterative-refinementStar history chart for janspiry/image-super-resolution-via-iterative-refinement

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

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Projects sharing features with Image Super Resolution Via Iterative Refinement

These projects share indexed features with Image Super Resolution Via Iterative Refinement. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
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    This is a PyTorch implementation of a text-to-image model designed for synthesizing high-fidelity images from natural language descriptions. It utilizes a diffusion image generator to transform latent embeddings into visual data through an iterative denoising process. The system employs a two-stage latent mapping process, using a CLIP-based latent prior to map text embeddings to image embeddings before decoding them into pixels. It features a cascading diffusion decoder that produces high-resolution imagery by passing low-resolution outputs through a sequence of models at increasing scales.

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  • openai/guided-diffusionopenai avatar

    openai/guided-diffusion

    7,395View on GitHub↗

    This is a classifier-guided diffusion framework for high-fidelity image generation. It implements a cascaded diffusion pipeline that chains a base diffusion model with a dedicated upsampler to progressively increase image resolution in stages, and uses classifier-guided diffusion sampling to steer the reverse diffusion process toward higher-quality outputs. The framework provides tools for training diffusion models from scratch using distributed processes with gradient accumulation, as well as training classifier models that provide gradient-based guidance during sampling. It supports both un

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  • sanster/iopaintSanster avatar

    Sanster/IOPaint

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    IOPaint is an AI image editor and Stable Diffusion inpainting tool providing a web interface for removing objects and replacing image content. It utilizes latent diffusion image processing to synthesize high-resolution replacements for erased sections of an image. The project features a specialized AI background remover for isolating subjects and an AI image upscaler that employs super-resolution models for general photos and anime artwork. The software covers a broad range of capabilities including image segmentation for object isolation, face restoration for improving facial details, and t

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Frequently asked questions

What does janspiry/image-super-resolution-via-iterative-refinement do?

This project is a deep learning framework for AI image super-resolution and facial synthesis. It provides a diffusion model image upscaler and a generative facial image synthesizer capable of transforming low-resolution images into high-resolution outputs using pretrained model weights.

What are the main features of janspiry/image-super-resolution-via-iterative-refinement?

The main features of janspiry/image-super-resolution-via-iterative-refinement are: Resolution Upscaling, Diffusion Image Restoration Frameworks, Diffusion Refinement, Image Super Resolution Models, High-Resolution Synthesis, Facial Synthesis, Image Resolution Reconstruction, AI Upscaling.

Which projects share features with janspiry/image-super-resolution-via-iterative-refinement?

Projects with overlapping indexed features include: spipm/depixelization_poc — This project is an AI upscaling framework and deep learning image restorer designed to estimate original source pixels… lucidrains/dalle2-pytorch — This is a PyTorch implementation of a text-to-image model designed for synthesizing high-fidelity images from natural… openai/guided-diffusion — This is a classifier-guided diffusion framework for high-fidelity image generation. It implements a cascaded diffusion… sanster/iopaint — IOPaint is an AI image editor and Stable Diffusion inpainting tool providing a web interface for removing objects and… xpixelgroup/diffbir — DiffBIR is a diffusion-based image restoration framework designed for blind image reconstruction. It utilizes… xinntao/esrgan — ESRGAN is a deep learning image restoration framework designed for image super-resolution. It uses a generative…