sangyun884/blur-diffusion
Official PyTorch implementation of the paper Progressive Deblurring of Diffusion Models for Coarse-to-Fine Image Synthesis.
When creating new images from scratch, this tool helps generate high-quality images by focusing on the overall scene before adding fine details. It takes a conceptual idea and produces a clear, detailed image, making it useful for digital artists, designers, or anyone needing realistic image generation.
156 stars. No commits in the last 6 months.
Use this if you need to generate highly realistic and detailed images from a basic concept, especially when visual quality and a smooth refinement process are critical.
Not ideal if your primary need is simple image editing, adding effects to existing photos, or very quick, low-fidelity image generation.
Stars
156
Forks
10
Language
Python
License
MIT
Category
Last pushed
Sep 11, 2022
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/diffusion/sangyun884/blur-diffusion"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
devzhk/InverseBench
InverseBench (ICLR 2025 spotlight)
guyyariv/DyPE
Official implementation for "DyPE: Dynamic Position Extrapolation for Ultra High Resolution Diffusion".
wyhuai/DDNM
[ICLR 2023 Oral] Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model
bahjat-kawar/ddrm
[NeurIPS 2022] Denoising Diffusion Restoration Models -- Official Code Repository
yuanzhi-zhu/DiffPIR
"Denoising Diffusion Models for Plug-and-Play Image Restoration", Yuanzhi Zhu, Kai Zhang,...