Image-Super-Resolution-Via-Iterative-Refinement

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Image-Super-Resolution-via-Iterative-Refinement is an open-source photo editing tool that provides an unofficial PyTorch implementation of the SR3 model for image super-resolution. It enhances image resolution through iterative refinement, supporting various upscaling tasks.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is Image-Super-Resolution-via-Iterative-Refinement?

Image-Super-Resolution-via-Iterative-Refinement offers an unofficial PyTorch implementation of the SR3 (Image Super-Resolution via Iterative Refinement) model. This tool focuses on enhancing image resolution through an iterative refinement process, utilizing ResNet blocks and channel concatenation similar to vanilla DDPM. It supports conditional generation tasks like upscaling 16x16 to 128x128 and 64x64 to 512x512 on datasets like FFHQ-CelebaHQ, as well as unconditional generation for face generation. The project provides pre-trained models and scripts for training, evaluation, and inference, making it suitable for researchers and developers working with diffusion models and image super-resolution.

Best used for

Ideal for researchers and developers who need to implement and experiment with advanced image super-resolution techniques, train custom models on specific datasets, and evaluate performance metrics. Especially valuable for those working with diffusion probabilistic models and seeking to enhance image quality.

Common actions

upscale images

refine image resolution

implement diffusion models

research image generation

automated workflowlow-code/no-codedeepfakeopen-sourcecollaborationface swappinggithub copilotworkflows"AI Agents"

Capabilities

Key features

Image super-resolution
Iterative refinement
PyTorch implementation
Conditional generation
Unconditional generation
Pre-trained models
Weights and Biases logging

Target Audience

professor

Integrations

wandb

Pricing & Plans

Open Source

Free

FAQs

What are the key differences between this implementation and the official SR3 paper?

This unofficial implementation uses ResNet blocks and channel concatenation like vanilla DDPM, incorporates attention mechanisms in low-resolution features, and encodes gamma as FilM structure. The posterior variance definition also varies slightly from the paper's description.

What image upscaling ratios does this model support?

The model supports various upscaling tasks, including 16x16 to 128x128 and 64x64 to 512x512 for conditional generation on datasets like FFHQ-CelebaHQ. It also supports unconditional generation for faces up to 1024x1024.

How can I use my own image data with this tool?

You can prepare your own image data by organizing it into high-resolution, low-resolution, and super-resolution directories. The provided `prepare_data.py` script can assist with resizing and structuring your dataset for training and inference.

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Research & Education › Academic Research Coding & Development › Open Source & Models

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