Contrast-X benchmark and FlowMI model enable synthesis of contrast-enhanced images from arbitrary non-contrast modality inputs using multi-modal flow matching.
High-resolution image synthesis with latent diffu- sion models,
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FreeGraftor performs subject-driven text-to-image generation without training by cross-image feature grafting via semantic matching, position-constrained attention fusion, and a noise initialization strategy that preserves reference geometry.
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Contrast-X: A Multi-Modal Contrast Image Synthesis Benchmark and Universal Modality Flow Matching
Contrast-X benchmark and FlowMI model enable synthesis of contrast-enhanced images from arbitrary non-contrast modality inputs using multi-modal flow matching.
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FreeGraftor: Training-Free Cross-Image Feature Grafting for Subject-Driven Text-to-Image Generation
FreeGraftor performs subject-driven text-to-image generation without training by cross-image feature grafting via semantic matching, position-constrained attention fusion, and a noise initialization strategy that preserves reference geometry.