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CARE: Condition-Aware Representation Regularization for Diffusion Models
Not specified in the provided content
regularizationdiffusion modelsrepresentation learningimage generation
2609.28561
Builder Relevance
1h ago80%
Abstract
CARE introduces a lightweight regularization framework that improves sample quality and training efficiency in diffusion models by leveraging built-in conditioning signals.
Reality Card
Core Claim
CARE achieves a 19.08% reduction in FID on ImageNet in 400k training steps, leading to a 3.5x speed-up.
Method / Result
16.61% reduction in FID for text-to-image generation in 200k iterations.
Limitations
The paper does not specify potential limitations or reproducibility concerns.
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