# Image conditioning

**Source:** https://promtable.com/glossary/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.

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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

- 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.

## Related terms

- [controlnet](https://promtable.com/glossary/controlnet)
- [diffusion-model](https://promtable.com/glossary/diffusion-model)
- [lora](https://promtable.com/glossary/lora)

*Last updated: 2026-06-01*
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Original page: https://promtable.com/glossary/image-conditioning
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