Controlling image generation
Text prompts are expressive but imprecise. Production image workflows usually add structural controls so the model respects layout, pose, identity, style, masks, or brand constraints.
Control channels
| Control | Use |
|---|---|
| Inpainting | edit a masked region while preserving the rest |
| Image-to-image | transform an existing image while keeping composition |
| ControlNet | condition on edge maps, depth, pose, segmentation, or sketches |
| LoRA/adapters | specialize style, subject, product, or domain |
| Reference image | preserve identity, style, or composition |
| Negative prompt | discourage known artifacts or unwanted content |
Workflow pattern
- Define what must stay fixed: layout, subject, style, brand, or object geometry.
- Choose the control signal that represents that constraint.
- Generate multiple candidates with fixed seed or controlled variation.
- Evaluate prompt adherence, visual quality, artifacts, safety, and rights.
- Keep provenance metadata for source images, adapters, prompts, and edits.
Production concerns
- Consent for likeness, style, and source images.
- Consistency across a campaign or product catalog.
- Safety review for generated people, minors, logos, and protected attributes.
- Asset lineage when generated output enters a design workflow.
Pitfall
More control can reduce creative variation. Treat controls as constraints you add because the product needs them, not because every slider should be maxed out.
Connects to: LoRA and adapters · conditioning and CFG · data documentation