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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

57,200 subscribers

56,368 次观看 • 2 年前 •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

4 条评论

Gradio 的头像

Gradio2 年前

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

Rainmaker 的头像

Rainmaker2 年前

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 Roberts 的头像

Bob Roberts2 年前

@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 Amacker 的头像

Bryce Amacker2 年前

@JasonGUTU @xinntao @memdotai mem it

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