River-Zhang/ICEdit

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

2102 stars · 111 forks observed · 2026-08-28

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

ICEdit (In-Context Edit) is a research framework for instruction-based image editing built on large-scale Diffusion Transformers, using a LoRA-MoE hybrid tuning strategy with only ~1% trainable parameters and 0.1% of the training data of prior methods. It includes inference and training code, ComfyUI nodes, and a Gradio demo, and can run with as little as 4GB VRAM.

Use cases

• edit images with natural language instructions
• change objects or attributes in a photo via text prompts
• run instruction-based image editing on a low-VRAM GPU
• train a custom image editing LoRA
• multi-turn precise photo editing
• open-source alternative to GPT-4o image editing

When to choose

• you need instruction-following image editing with strong identity persistence
• you have limited GPU memory (as low as 4GB with quantization)
• you want to train your own editing LoRA cheaply
• you want a ComfyUI-integrated editing workflow

When to avoid

• you need text-to-image generation from scratch rather than editing existing images
• you need a fully permissive license (license is custom/unspecified)
• you need commercial-grade support or guaranteed stability
• you work outside diffusion/DiT-based pipelines

Facets

library · maturity active

image-processing machine-learning deep-learning llm-inference image-processing artificial-intelligence deep-learning python diffusion-transformer lora instruction-based-image-editing comfyui moe gradio-demo research-code linux gpu docker

Member repositories

For agents

markdown · JSON · MCP: product_card(name="River-Zhang/ICEdit")