Conditioning

As explained in Build a Text-to-Image Generator (from Scratch)

Conditioning information is auxiliary input supplied to a generative process to influence the characteristics of its output.

Conditioning information is auxiliary input supplied to a generative process to influence its output. In diffusion models, it can be a class label or text prompt that steers image generation toward a desired concept. section 6.1.1

Classifier-free guidance uses conditioning in three stages:

• Train the diffusion model both with conditioning and without it, allowing conditional and unconstrained generation. section 6.1.1
• During inference, combine the model’s conditional and unconditional predictions to steer partially denoised samples. section 6.1.1
• Adjust the CFG scale to trade adherence to the condition against output diversity: higher values strengthen alignment, while lower values preserve creativity. section 6.1.1

Prompt-guided image generation

A text prompt supplies conditioning information to a diffusion model. Class labels work similarly, while the model’s unconditional prediction provides a reference for controlling how strongly the requested concept shapes the image. section 6.1.1

Figure 1.10 The eight steps to building a text-to-image generator from scratch

Use the eight-step roadmap to locate conditioning after basic diffusion and before CLIP-guided text-to-image generation. section 1.4

How Build a Text-to-Image Generator (from Scratch) covers it

How deep it goes

Explains conditional and unconditional training, prediction combination, CFG scale, and the resulting quality-diversity trade-off.

Examples

Uses class labels, text prompts, partially denoised samples, and conditional-versus-unconditional predictions as concrete examples.

What it assumes

Assumes basic forward and reverse diffusion before introducing conditioning as a subsequent generation-control step.

What you can do after

Apply classifier-free guidance to control how closely generated images match conditioning information.

How it connects

Connects conditioning information to diffusion models, classifier-free guidance, class labels, text prompts, and CLIP guidance.

liveBookmanning.com

Read Build a Text-to-Image Generator (from Scratch) on liveBook Get the book at manning.com

Also explained in

• 

  liveBookmanning.com
  Bayesian Optimization in ActionConditioning is the mathematical operation of updating a probability distribution after fixing or observing the value of one or more variables.

Compare how 2 books explain conditioning →

Related concepts

• conditional distributionproduces · A conditional distribution is the probability distribution of a random quantity given specified information about other quantities or conditions.
• independenceused by · Two events or random quantities are independent when knowing the outcome or value of one does not change the probability or distribution of the other.
• collideruses · A collider is a variable in a causal graph that receives directed edges from at least two other variables, potentially inducing associations when conditioned on.
• path blockinguses · Path blocking is the technique of conditioning on variables so that a graph path cannot transmit statistical association between its endpoints.
• confounderaddresses · A confounder is a variable associated with both an explanatory or treatment variable and an outcome, whose influence can distort their apparent relationship or estimated causal effect.
• backdoor pathaddresses · A backdoor path is a path between a putative cause and an outcome that begins with an arrow into the cause and can create a noncausal association.

This explainer was generated by Manning's knowledge graph from the text of Build a Text-to-Image Generator (from Scratch). The section links point to the parts of the book it draws on.