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𝐒𝐔𝐏𝐈𝐑: An advanced image restoration method that combines generative prior and model scaling,using a 20-million image dataset, and can manipulate restorations with textual prompts. Kudos to Fanghua Yu Jinjin Gu Xintao Wang et al🚀 SUPIR supports Gradio demo in repo [links👇]

Gradio

57,743 subscribers

56,374 views • 2 years ago •via X (Twitter)

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4 Comments

Gradio2 years ago

Local Gradio demo: Project: Authors planning to release a hosted demo soon.

Rainmaker2 years ago

Here I share an XGBoost model that delivers a 25% CAGR with minimal drawdown on Visa stock. In this free Substack post I share code and commentary for a powerful Machine Learning strategy that delivers powerful returns.

Bob Roberts2 years ago

@JasonGUTU @xinntao Worth comparing the output with the original high-res image to take "Remarkable" "High-fidelity Image Restoration" with a grain of salt. Upscaled details (e.g. brand, plate number) remain hallucinated, not magically "restored". Fidelity doesn't refer to reality or original image.

Bryce Amacker2 years ago

@JasonGUTU @xinntao @memdotai mem it

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