SEAL changes 5% of face pixels to block diffusion identity cloning
On CelebA-HQ and VGGFace2, protected images stay above 41 dB PSNR while identity similarity falls to at most 0.15.
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On CelebA-HQ and VGGFace2, protected images stay above 41 dB PSNR while identity similarity falls to at most 0.15.
The Hugging Face card lists int8 diffusion weights and fp32 Flash VAE files for 2x and 4x upscaling, sourced from kopper/RelayVSR.
Task guidance is embedded in the compressed trajectory without extra decoding complexity.
The identity extends the Gaussian posterior-mean relation to kernel scoring rules and user-chosen noise paths.
NotaryMark aligns watermarks with diffusion latent denoising for robust black-box auditing across datasets and models.
The method recasts FlowGRPO, AWM, DiffusionNFT, DiffusionOPSD and ReFL so rollouts no longer need a many-step sampler.
EDGAR corrects artifacts in Gaussian avatar renderings using a fusion block with Sapiens identity features and a Mixture-of-Experts architecture.
StepX-OPD lets one student learn all capabilities and few-step sampling using its own rollout states, scoring highest among four-step methods on SD3.5-Medium and FLUX.1-dev while using 37% fewer GPU-hours.
RangeSWA reduces perplexity and repetition in long-text generation using sliding-window attention and positional remapping.
Reference-Model Calibration uses an earlier checkpoint; Temporal Self-Calibration uses one model's neighboring diffusion timesteps.
SynthFaces expands Arc2Face diffusion model with flow matching to create diverse synthetic faces, reducing privacy risks and biases in real datasets.
Authors say progressive coupling beats iterative methods and other diffusion samplers when scan overlap is reduced.
Across five benchmarks, the plug-in cuts inference FLOPs 20.6% on average versus the strongest efficient baseline while keeping competitive accuracy.
Tests use only the intrinsic dimension of generated images and assume unknown adaptations and model families.
A diffusion reconstructor and a relation-closure operator are trained separately and calibrated only on normal data.
An 8B LLaCO model uses spherical noising and semantic retention scheduling and is reported to rival comparable discrete diffusion models.
On SDXL-Turbo one-step generation, forgetting success rises 36.34 points over the best baseline.
PC-DAPS prunes candidates by prior consistency without ground truth to reduce prior-inconsistent samples on ambiguous nonlinear problems.
Authors report that jointly varying diffusion effort and acceptance leniency beats single-step diffusion drafting on the speed-quality frontier.
On MP-20, 13.8% of attempts stayed stable or metastable, unique, novel, and in the requested group after relaxation.

Inception's own tests give Mercury 3 eight of 12 wins and 1,068 tokens per second, with no independent verification yet.
On MIMIC-IV, a block size of 8 evaluates about 1.43 times faster, while linear probes nearly match autoregressive models.
Exact two-record sensitivity and an SDP replace loose diameter bounds and isotropic Gaussian noise.
A diffusion planner drafts K parallel reasoning paths; a large autoregressive model selects one and writes the answer.
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