DreamSR uses a dual-branch MM-ControlNet with patch-level and global prompts plus a receptive-field enhancement training strategy in a diffusion transformer to reduce over-generation and improve local texture details in ultra-high-resolution super-resolution.
Denoising dif- fusion probabilistic models
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A training-free method modifies diffusion model sampling with differentiable Sliced 1-Wasserstein distance for color-conditional image generation.
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DreamSR: Towards Ultra-High-Resolution Image Super-Resolution via a Receptive-Field Enhanced Diffusion Transformer
DreamSR uses a dual-branch MM-ControlNet with patch-level and global prompts plus a receptive-field enhancement training strategy in a diffusion transformer to reduce over-generation and improve local texture details in ultra-high-resolution super-resolution.
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Color Conditional Generation with Sliced Wasserstein Guidance
A training-free method modifies diffusion model sampling with differentiable Sliced 1-Wasserstein distance for color-conditional image generation.