LeapAlign fine-tunes flow matching models by constructing two consecutive leaps that skip multiple ODE steps with randomized timesteps and consistency weighting, enabling stable updates at any generation step.
Itercomp: Iterative composition-aware feedback learning from model gallery for text-to-image generation
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.CV 2years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
Presents MRT, a 20B-parameter masked region diffusion model unifying text-to-layers, image-to-layers, and layers-to-layers tasks with an overflow-aware canvas layer for complete editable outputs.
citing papers explorer
-
LeapAlign: Post-Training Flow Matching Models at Any Generation Step by Building Two-Step Trajectories
LeapAlign fine-tunes flow matching models by constructing two consecutive leaps that skip multiple ODE steps with randomized timesteps and consistency weighting, enabling stable updates at any generation step.
-
MRT: Masked Region Transformer for Layered Image Generation and Editing at Scale
Presents MRT, a 20B-parameter masked region diffusion model unifying text-to-layers, image-to-layers, and layers-to-layers tasks with an overflow-aware canvas layer for complete editable outputs.