PureCC introduces a decoupled learning objective, dual-branch training pipeline with frozen extractor, and adaptive guidance scale λ* for high-fidelity concept customization while preserving original model behavior in text-to-image generation.
For each personalized concept, both the represen- tation extractorv θ1 t and the trainable modelv θ2 t are trained in 400 steps
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PureCC: Pure Learning for Text-to-Image Concept Customization
PureCC introduces a decoupled learning objective, dual-branch training pipeline with frozen extractor, and adaptive guidance scale λ* for high-fidelity concept customization while preserving original model behavior in text-to-image generation.