EAM is a DiT-based blind super-resolution model that uses a triple-flow Ψ-DiT block, progressive masked image modeling, and in-context subject-aware prompting to reach state-of-the-art quantitative and visual results on standard datasets.
Lsdir: A large scale dataset for image restoration
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CV 2verdicts
UNVERDICTED 2representative citing papers
SANA-SR uses 32x deep compression autoencoding and linear-attention DiT to deliver competitive real-world image super-resolution at 0.019s inference after pruning.
citing papers explorer
-
EAM: Enhancing Anything with Diffusion Transformers for Blind Super-Resolution
EAM is a DiT-based blind super-resolution model that uses a triple-flow Ψ-DiT block, progressive masked image modeling, and in-context subject-aware prompting to reach state-of-the-art quantitative and visual results on standard datasets.
-
Efficient One-Step Diffusion Restoration Model with Compact Token Compression and Linear Attention
SANA-SR uses 32x deep compression autoencoding and linear-attention DiT to deliver competitive real-world image super-resolution at 0.019s inference after pruning.