Flow Matching
flow-matching-30edbf16·3 events·first seen 1mo agoAliases: Flow Matching, flow-matching
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Dynamics-Level Watermarking of Flow Matching Models with Random Codes
This paper proposes embedding watermarks directly into the velocity field (continuous dynamics) of flow matching generative models, rather than into weights or outputs. The method uses key-dependent perturbations added during training, formulated as random coding over a continuous channel, allowing black-box message recovery at detection time. The perturbation is designed to leave the generated distribution unchanged. Experiments on MNIST and CIFAR-10 demonstrate reliable message recovery, preserved generation quality, and chance-level decoding without the secret key.
GPIC: Stanford Releases 28-Trillion-Pixel Permissively Licensed Image Corpus for Visual Generation Research
Stanford Vision Lab introduces GPIC, a Giant Permissive Image Corpus of approximately 28 trillion pixels comprising 100M training, 200K validation, and 1M test images, all permissively licensed for research and commercial use. Images are captioned by a state-of-the-art vision-language model, safety-filtered, deduplicated, and hosted on Hugging Face. The release includes a benchmarking protocol for generative modeling and a reference baseline using pixel-space flow matching. The dataset addresses a key gap in scalable visual generative modeling research by providing a large, stable, and openly licensed resource.
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.