MetaPoint represents 2D coordinates as special tokens in visual generative models to enable precise spatial control using existing positional encodings without architectural modifications.
Itercomp: Iterative composition-aware feedback learning from model gallery for text-to-image generation
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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.
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
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MetaPoint: Unlocking Precise Spatial Control in Agentic Visual Generation
MetaPoint represents 2D coordinates as special tokens in visual generative models to enable precise spatial control using existing positional encodings without architectural modifications.
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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.
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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.