GramSR uses DINOv3 visual features instead of text captions to condition a one-step diffusion model for super-resolution via sequential pixel, semantic, and texture LoRA modules.
IEEE Trans
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Dress-ED is the first large-scale benchmark unifying virtual try-on, try-off, and text-guided garment editing with 146k verified samples plus a multimodal diffusion baseline.
citing papers explorer
-
GramSR: Visual Feature Conditioning for Diffusion-Based Super-Resolution
GramSR uses DINOv3 visual features instead of text captions to condition a one-step diffusion model for super-resolution via sequential pixel, semantic, and texture LoRA modules.
-
Dress-ED: Instruction-Guided Editing for Virtual Try-On and Try-Off
Dress-ED is the first large-scale benchmark unifying virtual try-on, try-off, and text-guided garment editing with 146k verified samples plus a multimodal diffusion baseline.