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Ncsnpp Ffhq 256

Developed by google
A generative model based on stochastic differential equations, capable of producing high-quality images by gradually removing noise from a prior distribution
Downloads 46
Release Time : 7/19/2022

Model Overview

This model proposes a Stochastic Differential Equation (SDE) framework that transforms data distribution into a prior distribution by gradually injecting noise and generates data through reverse-time SDE. It combines the advantages of score-based generative modeling and diffusion probabilistic modeling, supporting high-resolution image generation.

Model Features

Stochastic Differential Equation framework
Smoothly transforms data distribution into a prior distribution via SDE and generates data through reverse-time SDE
Predictor-corrector framework
Corrects errors in discretized reverse-time SDE evolution to improve generation quality
High-resolution image generation
Capable of generating high-fidelity images at 1024×1024 resolution
Versatile applications
Supports various tasks such as class-conditional generation, image inpainting, and colorization

Model Capabilities

Unconditional image generation
High-resolution image synthesis
Image inpainting
Image colorization

Use Cases

Image generation
Facial image generation
Generate high-quality facial images
Generates 256x256 resolution facial images on the FFHQ dataset
Image processing
Image inpainting
Repair images with damaged or missing parts
Image colorization
Add color to black-and-white images
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