Uni-Edit introduces a data synthesis pipeline turning VQA data into reasoning-intensive editing instructions, enabling single-task tuning that boosts all three capabilities in models like BAGEL and Janus-Pro.
Scaling rectified flow transformers for high-resolution image synthesis
9 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 9representative citing papers
TMPO uses Softmax Trajectory Balance to match policy probabilities over multiple trajectories to a Boltzmann reward distribution, improving diversity by 9.1% in diffusion alignment tasks.
StitchVM stitches clean-image reward models with diffusion backbones to enable efficient value estimation for noisy latents, speeding up diffusion alignment methods like DPS by 3.2x and halving memory.
ACE-LoRA introduces adaptive orthogonal decoupling and rank-invariant compression for continual image editing in diffusion models, plus the CIE-Bench benchmark.
PRISM improves text image super-resolution by rectifying global priors with flow-matching and modeling local structural uncertainty in a single diffusion pass, achieving SOTA results at millisecond inference.
TAVR generates high-fidelity talking avatars from cross-scene video references via token selection and three-stage training (same-scene pretraining, cross-scene fine-tuning, identity RL), outperforming baselines on a new 158-pair benchmark.
A 17B-parameter sparse MoE diffusion transformer activates 2B parameters per pass and reaches competitive quality on image generation benchmarks without post-training.
JoyAI-Image unifies visual understanding and generation via an MLLM-MMDiT architecture with spatial training signals to reach competitive benchmark performance and stronger spatial intelligence.
Show-o2 unifies text, image, and video understanding and generation in a single autoregressive-plus-flow-matching model built on 3D causal VAE representations.
citing papers explorer
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Uni-Edit: Intelligent Editing Is A General Task For Unified Model Tuning
Uni-Edit introduces a data synthesis pipeline turning VQA data into reasoning-intensive editing instructions, enabling single-task tuning that boosts all three capabilities in models like BAGEL and Janus-Pro.
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TMPO: Trajectory Matching Policy Optimization for Diverse and Efficient Diffusion Alignment
TMPO uses Softmax Trajectory Balance to match policy probabilities over multiple trajectories to a Boltzmann reward distribution, improving diversity by 9.1% in diffusion alignment tasks.
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Stitched Value Model for Diffusion Alignment
StitchVM stitches clean-image reward models with diffusion backbones to enable efficient value estimation for noisy latents, speeding up diffusion alignment methods like DPS by 3.2x and halving memory.
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ACE-LoRA: Adaptive Orthogonal Decoupling for Continual Image Editing
ACE-LoRA introduces adaptive orthogonal decoupling and rank-invariant compression for continual image editing in diffusion models, plus the CIE-Bench benchmark.
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PRISM: Prior Rectification and Uncertainty-Aware Structure Modeling for Diffusion-Based Text Image Super-Resolution
PRISM improves text image super-resolution by rectifying global priors with flow-matching and modeling local structural uncertainty in a single diffusion pass, achieving SOTA results at millisecond inference.
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Generate Your Talking Avatar from Video Reference
TAVR generates high-fidelity talking avatars from cross-scene video references via token selection and three-stage training (same-scene pretraining, cross-scene fine-tuning, identity RL), outperforming baselines on a new 158-pair benchmark.
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Nucleus-Image: Sparse MoE for Image Generation
A 17B-parameter sparse MoE diffusion transformer activates 2B parameters per pass and reaches competitive quality on image generation benchmarks without post-training.
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JoyAI-Image: Awaking Spatial Intelligence in Unified Multimodal Understanding and Generation
JoyAI-Image unifies visual understanding and generation via an MLLM-MMDiT architecture with spatial training signals to reach competitive benchmark performance and stronger spatial intelligence.
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Show-o2: Improved Native Unified Multimodal Models
Show-o2 unifies text, image, and video understanding and generation in a single autoregressive-plus-flow-matching model built on 3D causal VAE representations.