ermongroup/SDEdit

Health v2 · maintenance only

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Adoption not part of the score

1168 stars · 92 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

SDEdit is a PyTorch implementation of the ICLR 2022 paper 'Guided Image Synthesis and Editing with Stochastic Differential Equations'. It performs stroke-based image synthesis, editing, and compositing by adding noise to an input image and denoising it with a pre-trained score-based/diffusion model, without task-specific training.

Use cases

• edit images with hand-drawn colored strokes
• generate realistic images from rough stroke paintings
• composite and manipulate photos using a diffusion prior
• run guided image synthesis with pretrained SDE models on CelebA-HQ and LSUN
• apply img2img-style noise-and-denoise editing to diffusion models
• reproduce the SDEdit paper results in PyTorch

When to choose

• you want stroke-based or guide-based image editing without task-specific training
• you need a research reference implementation of SDE-based image editing
• you want to plug editing into off-the-shelf pretrained score-based or diffusion models

When to avoid

• you need a production-ready image editor with a polished UI
• you want text-guided editing out of the box (use Stable Diffusion img2img instead)
• you lack a GPU or cannot download large pretrained checkpoints

Facets

library · maturity maintenance

image-processing machine-learning deep-learning image-processing machine-learning computer-vision python diffusion-models score-based-generative-models image-editing image-synthesis pytorch research-code iclr-2022 gpu

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markdown · JSON · MCP: product_card(name="ermongroup/SDEdit")