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.
Taming diffusion prior for image super-resolution with domain shift sdes
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
SATB-VR trains few-step video restoration diffusion models via SNR-aware trajectory blending of predictor outputs with ground-truth and a denoiser-driven consistency loss to achieve favorable performance on benchmarks.
citing papers explorer
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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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SATB-VR: Training Few-Step Video Restoration Diffusion Model using SNR-Aware Trajectory Blending
SATB-VR trains few-step video restoration diffusion models via SNR-aware trajectory blending of predictor outputs with ground-truth and a denoiser-driven consistency loss to achieve favorable performance on benchmarks.