Image conditioning

Image conditioning is the diffusion-model technique where input images (reference, pose, depth, edge, sketch) steer the output — ControlNet, IP-Adapter, Flux Redux, Image-to-Image are 2026 implementations.

Pure text-to-image leaves too much to chance: same prompt, ten outputs, ten different layouts. Image conditioning anchors generation to reference inputs. Common modes: image-to-image (use the input as a starting point + add noise), reference image (preserve subject identity / style — IP-Adapter, Flux Redux), pose / depth / edge conditioning (ControlNet — input pose skeleton, depth map, edge map controls geometry), sketch-to-image (rough sketch becomes finished render). Production patterns: editorial workflows (consistent character across N panels), product photography (transfer pose / lighting), branding (preserve logo placement). Trade-offs: stronger conditioning = less creative variance; pick conditioning weight per use case.

When to use image conditioning

• Consistent characters / products across images.
• Pose / composition control.
• Image-to-image editing.

Common mistakes

• Over-conditioning — output looks identical to input, defeats the point.
• Under-conditioning — output ignores the reference, defeats the point.

FAQ

Related terms

• ControlNet — ControlNet is a neural-network architecture that conditions a diffusion image model on extra spatial inputs — edges, depth, pose, segmentation — for precise control over output structure.
• Diffusion model — A diffusion model is a generative neural network that creates images, video, or audio by iteratively denoising random noise toward a learned target distribution.
• LoRA (Low-Rank Adaptation) — LoRA is a fine-tuning method that trains a small set of low-rank adapter weights on top of a frozen base model — cheaper to train and store than full fine-tuning.

Last updated: 2026-06-01. Raw markdown: https://promtable.com/glossary/image-conditioning.md.