Control Inputs: Pose, Depth and Edge Guidance
Control pose, layout and structure precisely with pose, depth and edge conditioning
A taste of a lesson
I used an edge map from a product photo and every variant copies the wood grain from the original table. I only wanted the layout.
Edge detection picked up the grain lines as structure, so the model faithfully reproduces them. Options: switch to depth control, which keeps the layout and volume but ignores surface texture; or raise the edge threshold so only strong outlines survive, then check the map before generating; or lower edge strength and end it at around half the steps so details are free. For layout only, depth is usually the right tool. Want to try depth at moderate weight and compare with the same seed?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Choose between pose, depth, edge and other control types for a task
- Preprocess references and inspect maps before generating
- Tune control strength and step range for balance
- Combine controls and resolve conflicts with the prompt
Lesson plan
- 1 How control works Understand conditioning on structural maps and where it plugs in. Start
- 2 Choosing the control type Match pose, depth, edge, line and segmentation to jobs. Start
- 3 Preprocessing and inspecting maps Produce clean maps and catch errors before generating. Start
- 4 Strength and timing Balance faithfulness and freedom with weight and step range. Start
- 5 Combining controls and fixing conflicts Use multiple controls together without over constraining. Start
- 6 Control in real projects Apply control to illustration, product and architecture workflows. Start
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About this tutor
An advanced tutor for people who need images to match a specific pose, layout or structure, not just a description. You will learn how control inputs work: a reference is turned into a pose skeleton, depth map, edge map, line art or segmentation map, and the model is conditioned to follow it. We cover choosing the right control type for each job, preprocessing well, tuning strength and timing, combining several controls, and debugging conflicts between control and prompt. Examples include product layouts, figure poses for illustration, architecture from massing models and consistent compositions across a series.
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About the teacher
The technical side of image AI: local models, node pipelines, adapters and settings
9 tutors 331 lessons taught Sample
I teach the engineering side of image generation to people who want control rather than a single text box. I started as a hobbyist running open models on my own machine and later built image pipelines for small studios, so I know where the frustrations are: memory errors, inconsistent batches, settings nobody explains. I teach from first principles, then from...
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