GenEval
geneval-0b75c699·3 events·first seen 22d agoAliases: GenEval
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Meta Research Improves Image Generation via Staged Planning and Self-Revision Fine-Tuning
Researchers from Meta and collaborating universities propose a fine-tuning method that teaches image generators to compose images through discrete plan-sketch-inspect-refine cycles rather than generating all at once. Starting from BAGEL-7B, they construct ~62,000 training examples using GPT-4o and FLUX.1 Kontext to supervise each stage, achieving 83% on GenEval versus 77% for the base model and a competing method (PARM) that required 11x more training data and ~8x more inference steps. The approach improves spatial relationship accuracy, object attribute fidelity, and real-world knowledge grounding in generated images.
Channel-wise Vector Quantization (CVQ): A New Image Tokenization Paradigm with Next-Channel Prediction
Researchers introduce Channel-wise Vector Quantization (CVQ), which replaces conventional patch-wise discrete tokens with channel-wise tokens that represent an image as discrete levels of visual detail. Built on CVQ, the Channel-wise Autoregressive (CAR) model uses a 'next-channel prediction' objective, generating images by progressively refining from global structure to fine-grained attributes. CVQ achieves 100% codebook utilization with a 16K+ codebook and the CAR model scores 86.7 on DPG and 0.79 on GenEval for text-to-image generation. The approach offers a structural alternative to raster-order patch-based autoregressive image generation.
Drifting Preference Optimization (DrPO) for One-Step Text-to-Image Generators
DrPO is a new online preference fine-tuning method designed specifically for deterministic one-step text-to-image generators like SD-Turbo and SDXL-Turbo, which are difficult to align with standard RLHF methods that require policy likelihoods or differentiable reward gradients. The method samples candidates per prompt, ranks them with a target reward, and synthesizes a feature-space update direction via a non-parametric dipole preference field plus a reference drift from the frozen base model. Because the reward is used only for ranking, DrPO supports black-box and non-differentiable reward functions while keeping inference as a single forward pass. Evaluations on HPSv3 and GenEval show improved alignment over reward-gradient-free baselines and a 3.51× reduction in training compute by eliminating reward-model backpropagation.