ImIR: Image-Instruction Tuning for All-in-One Image Restoration
ImIR replaces text prompts with image-derived instructions so one Qwen adapter restores many degradations.
ImIR adapts a pretrained Qwen-Image-Edit model to all-in-one image restoration by replacing the text prompt with a continuous instruction derived from the degraded image. Structure comes from the model's VAE, while a lightweight token mapper shifts the vision-language embedding toward a clean-image embedding. One low-rank adapter covers six tasks after about three hours of training on a single GPU. Image instructions beat matched text conditioning and support label-free restoration, including scalable outputs for non-unique targets such as low-light enhancement.
- One Qwen-Image-Edit adapter covers six restoration tasks.
- Training takes about three hours on a single GPU.
- Continuous image instructions outperform matched text prompts.
- No degradation label is required for task-agnostic restoration.
- AI models
- Qwen-Image-Edit
From the original article
1 min read at huggingface.co
Degradations vary widely across images, so a practical restoration system has to handle many degradation types with one model. A recent and effective recipe adapts a large pretrained image-editing model to restoration using a small low-rank adapter with a text prompt. We replace that prompt with an instruction derived from the degraded image itself.
By Süleyman Aslan, Görkay Aydemir, Mısra Yavuz · huggingface.co
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