SliderEdit: Continuous Image Editing with Fine-Grained Instruction Control
Arman Zarei, Samyadeep Basu, Mobina Pournemat, Sayan Nag, Ryan Rossi, Soheil Feizi

TL;DR
SliderEdit introduces a novel framework for continuous, fine-grained control in instruction-based image editing, enabling smooth adjustments of individual edits with a single trained model, improving precision and user steerability.
Contribution
It proposes a unified, slider-based control mechanism for image editing instructions that generalizes across diverse edits without requiring separate training for each attribute.
Findings
Enhanced control over individual edit strengths.
Preservation of spatial locality and semantic consistency.
Improved edit controllability and visual coherence.
Abstract
Instruction-based image editing models have recently achieved impressive performance, enabling complex edits to an input image from a multi-instruction prompt. However, these models apply each instruction in the prompt with a fixed strength, limiting the user's ability to precisely and continuously control the intensity of individual edits. We introduce SliderEdit, a framework for continuous image editing with fine-grained, interpretable instruction control. Given a multi-part edit instruction, SliderEdit disentangles the individual instructions and exposes each as a globally trained slider, allowing smooth adjustment of its strength. Unlike prior works that introduced slider-based attribute controls in text-to-image generation, typically requiring separate training or fine-tuning for each attribute or concept, our method learns a single set of low-rank adaptation matrices that…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Multimodal Machine Learning Applications · Advanced Image and Video Retrieval Techniques
