Phase 2: Image Generation · 55 min · Python · Hugging Face Diffusers · ComfyUI
ControlNet for Structural Control
ControlNet is how you tell a diffusion model not just what to generate, but how to compose it — depth, pose, edges, structure.
What you will learn
- Apply ControlNet for structural control: depth maps, Canny edge detection, OpenPose
- Combine multiple ControlNets (multi-ControlNet) for simultaneous control conditions
- Understand when ControlNet beats prompt engineering for precise composition control
- Integrate ControlNet into a production image generation pipeline
Objective
Produce and compare image candidates against an explicit creative brief. In the paired studio lab, build a pipeline that uses ControlNet to generate images with precise structural control. Test at least 3 ControlNet types (depth, Canny, OpenPose) and combine 2 ControlNets simultaneously (multi-ControlNet). Generate images that maintain specific compositions from reference images.
The Problem
A team generates product images with FLUX. The images look good, but the composition is unpredictable — sometimes the product is centered, sometimes it's off to the side, sometimes the background dominates. The client needs consistent composition: product centered, specific background layout, consistent angle. Prompt engineering can't achieve this — "centered product with simple background" doesn't reliably control composition.
ControlNet solves this. You provide a structural image (depth map, edge map, pose skeleton) and the model follows that structure while generating content. This is precise compositional control that prompt engineering alone can't achieve.
What you'll build
A pipeline that uses ControlNet to generate images with precise structural control. Test depth, Canny, and OpenPose ControlNets, then combine 2 ControlNets simultaneously (multi-ControlNet).
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