SDXL (AI model): news timeline & CVEs

DMAD: Distribution Matching as Adversarial Distillation for Fast Visual Generation

DMAD distills few-step image and video generators by learning distribution matching with adversarial discriminators.

DMAD turns distribution-matching distillation into classification, training a few-step student from discriminator logit losses without fitting an auxiliary score model. Two heads on a shared backbone separate real and teacher samples from student samples, and gap-based reweighting adapts teacher supervision across noise levels. Reported scores include FID 1.04 for one-step ImageNet-64, FID 14.47 for four-step SDXL on COCO-10K, and VBench 85.15 for four-step Wan2.1-T2V-14B. A four-step MiniMax-H3-33B student wins 79.1% human preference over DMD2 and 84.6% over rCM on joint audio-video, excluding ties.

arXiv cs.AI / cs.LG / cs.CLOct 1, 17:59 UTC · 7d agoAI research

DMAD: Distribution Matching as Adversarial Distillation for Fast Visual Generation

DMAD distills few-step image and video generators via adversarial distribution matching, reporting top compared scores.

DMAD recasts distribution-matching distillation as adversarial classification so a few-step student can be trained without fitting an auxiliary diffusion model to the student distribution. Two discriminator heads estimate the needed log-density ratios, and gap-based reweighting adjusts teacher supervision across noise levels. Reported results include FID 1.04 for one-step ImageNet-64x64 generation, FID 14.47 for four-step SDXL on COCO-10K, and a VBench total of 85.15 for four-step Wan2.1-T2V-14B. On MiniMax-H3-33B, the four-step student wins 79.1% of comparisons against DMD2 and 84.6% against rCM for joint audio-video generation, excluding ties.

Hugging Face daily papersSep 30, 20:00 UTC · 8d agoAI research1

ReGain: Restoring Subject Fidelity in Personalization on Synthetic Images

ReGain downscales inflated guidance so DreamBooth on synthetic images recovers much of real-photo subject fidelity.

DreamBooth personalization on diffusion-generated subject images degrades fidelity, producing oversaturated color and excess high-frequency detail. The authors trace this to classifier-free guidance: the angle and difference norm between conditional and unconditional noise predictions inflate, especially at high frequencies and nearby prompts. ReGain is a training-free sampling correction that downscales frequency bands whose guidance is inflated relative to the base model. On Stable Diffusion v1.5 it closes 51-64% of the gap to a real-photo personalization on DINO, DINOv2, and CLIP-I, and it also helps SDXL and SD 3.5 without hurting text alignment.

Hugging Face daily papersSep 29, 20:00 UTC · 9d agoAI research

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DMAD: Distribution Matching as Adversarial Distillation for Fast Visual Generation

DMAD: Distribution Matching as Adversarial Distillation for Fast Visual Generation

ReGain: Restoring Subject Fidelity in Personalization on Synthetic Images