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conceptVision, Audio & Multimodal AI~1 min readUpdated 2026-06-07#text-to-image#diffusion#conditioning#cfg

Text-to-image conditioning and CFG

Text-to-image generation works by steering a visual denoising process with a text representation. The prompt does not draw the image; it biases each denoising step toward visual features associated with the text.

Conditioning path

  • A tokenizer splits the prompt into text tokens.
  • A text encoder turns tokens into embeddings.
  • The denoising network attends to those embeddings while predicting noise.
  • Optional negative prompts describe features to avoid.
  • Other conditioning can include image, mask, depth, pose, edge map, style, or reference subject.

Classifier-free guidance

Classifier-free guidance compares two denoising predictions:

Prediction Meaning
Conditional what the model predicts given the prompt
Unconditional what the model predicts with no or empty prompt
Guided push away from unconditional and toward prompt-conditioned direction

Higher guidance can improve prompt adherence but may reduce realism, diversity, and color balance.

Prompt control levers

  • Specific subject, medium, composition, lighting, and style.
  • Negative prompt for recurring unwanted artifacts.
  • Seed for reproducibility.
  • Sampling steps and sampler choice.
  • Guidance scale for adherence versus naturalness.
  • Image size and aspect ratio.

Pitfall

Prompting is only one control channel. Precise layout, pose, identity, and product consistency usually need structural conditioning or fine-tuned adapters, not more adjectives.

Connects to: prompt anatomy · controlling image generation · tokenization

Related notes

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