OmniPrism proposes a disentanglement method using a new paired dataset (PCD-200K), COD contrastive training, and block embeddings to inject separated concepts into diffusion models for multi-aspect image generation.
Dead- iff: An efficient stylization diffusion model with disentan- gled representations
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A scalable pipeline generates an intra-consistent, inter-diverse 1.4M style image dataset from text-to-image models and uses it to train a style encoder and generalizable style transfer model.
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OmniPrism: Learning Disentangled Visual Concept for Image Generation
OmniPrism proposes a disentanglement method using a new paired dataset (PCD-200K), COD contrastive training, and block embeddings to inject separated concepts into diffusion models for multi-aspect image generation.
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MegaStyle: Constructing Diverse and Scalable Style Dataset via Consistent Text-to-Image Style Mapping
A scalable pipeline generates an intra-consistent, inter-diverse 1.4M style image dataset from text-to-image models and uses it to train a style encoder and generalizable style transfer model.