Local Content-Style Control for Diffusion-based Image Stylization
Image stylization with latent-diffusion models entangles two independently refined axes: what a region depicts and how it is depicted. Such pipelines expose only global controls, yet professional retouching demands deliberate, region-specific control. We lift two conditioning weights already present in a ControlNet + IP-Adapter stylization pipeline from global scalars to per-location spatial maps, yielding local, per-axis control of content and style in a single generative pass. Because the two weights act on disjoint pathways, adjusting them independently spans a 2x2 retouching vocabulary, from free regeneration to identity preservation. We validate that edits stay confined to the retouc...
| Authors | Amir Semmo |
|---|---|
| Primary category | cs.GR |
| Published | 6 October 2026 |
| Last updated at source | 6 October 2026 |
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