NoiseTilt introduces NTRK, a reward-guided diffusion sampler that injects reward gradients via the noise term without altering the reverse kernel. By using a whitening operator, NTRK safely biases noise toward high reward, preserving sample quality while maintaining strong guidance. On aesthetic generation, NTRK achieves superior reward performance with 25 NFEs, reducing compute by 20× compared to state-of-the-art baselines.
STAR introduces a spatio-temporal reward allocation method for text-to-image generation, using attention maps to dynamically assign advantages across denoising steps. It improves semantic alignment, text rendering, and preference optimization in Stable Diffusion 3.5 Medium, achieving 0.9759, 0.9757, and 23.60 on GenEval, OCR, and PickScore respectively.
ActiveSAM is a training-free, zero-shot framework that enhances SAM 3 for open-vocabulary semantic segmentation by identifying an image-conditioned active class set. It improves speed-accuracy tradeoff, outperforming SegEarth-OV3 by +1.4 mIoU on average and running up to 5.5x faster on large-vocabulary datasets, with strong robustness under image corruption.
ActiveSAM is a training-free, zero-shot framework that enhances SAM 3 for open-vocabulary semantic segmentation by identifying an image-conditioned active class set. It improves speed-accuracy tradeoff, outperforming SegEarth-OV3 by +1.4 mIoU on average and running up to 5.5x faster on large-vocabulary datasets, with strong robustness to image corruption.
Atomistic Language Models (ALMs) unify language and atomistic structures, enabling natural language-driven crystal generation and optimization. ALMs use a continuous bridge to map language embeddings into atomistic diffusion steering space and employ Text-to-Crystal Feynman-Kac for stoichiometric accuracy. The ALM Bench benchmark evaluates text-conditioned material generation and optimization, with code and weights to be released soon.
A study compares 2D and 3D diffusion models for generating synthetic X-ray images. It shows that 2D diffusion-based synthetic X-rays can train AI models to perform as well as models trained on real X-rays, offering a viable path to large, diverse datasets without relying on real patient data.
Boogu-Image-0.1 is an Apache-2.0 licensed open-source unified image generation and editing model family, including Base, Turbo, and Edit variants. It offers high-quality text-to-image generation, fast generation, image editing, and strong Chinese-English text rendering, with training data scale roughly one order of magnitude smaller than closed-source systems yet achieving competitive performance through improved model understanding and data quality.
NanoMaestro Realtime is a 50MB AI music model with 13M parameters that generates piano music in real time using a 2-layer LSTM. It runs locally in the browser via ONNX and Transformers.js with WASM, requiring no GPU or server backend, and works on older Raspberry Pi models.
The Moebius 0.2B image inpainting model has been successfully ported to run in the browser using WebGPU and ONNX Runtime. The project, initiated with Claude Code, converts the model's weights to ONNX and deploys them via Hugging Face, with a simple web interface available at simonw.github.io/moebius-web/.
PP-OCRv6, a new optical character recognition model, is now available on Hugging Face. It supports 50 languages and scales from 1.5 million to 34.5 million parameters, offering improved accuracy and efficiency across diverse languages.
A pretrained speech classifier is repurposed as a backbone for guided diffusion-based speech generation. By attaching a lightweight subnetwork and training it under denoising score matching, the approach achieves high speech quality with reduced memory and computational cost, using a single model instead of two separately trained components.
FreeStyle proposes a framework that mines community LoRAs to generate large-scale style-content dual-reference image triplets. It employs a two-stage curriculum with disentanglement mechanisms to suppress style leakage and introduces a benchmark with style-invariant and VLM-based scores to evaluate content preservation and leakage rejection.
A study shows diffusion models can achieve global minimizers without explicit timestep embeddings. Ablation studies on CelebA and CIFAR-10 reveal time-agnostic models maintain high fidelity and outperform conditioned ones in FID, precision, and recall.
A pretrained speech classifier is repurposed as a backbone for guided diffusion-based speech generation. By attaching a lightweight subnetwork and training it under denoising score matching, the approach achieves high speech quality with reduced memory and computational cost, using a single model instead of two separately trained components.
A novel variable-length tokenizer uses learnable global merging to enable cross-length representation alignment in diffusion models. This data-independent approach overcomes position-dependent semantics and improves the quality-compute trade-off on ImageNet 256×25-6 generation compared to prior methods.
MakeupMirror, a diffusion-based makeup transfer model, enhances facial feature and skin tone preservation over Stable-Makeup. It achieves +60% improvement in facial recognition similarity and -50% reduction in skin tone difference, with 94% expert acceptance and 0.7s inference latency on diverse datasets.
A hybrid ANN-SNN pipeline uses pretrained EfficientNet encoders and converts their activations to spike trains via rate-coding. The system trains a CoLaNET spiking classifier with local plasticity rules, achieving 99.09% accuracy on ImageNet's 64-class benchmark, matching conventional deep networks.
PU-UNet introduces stable product-unit residual blocks into U-Net for medical image segmentation, enabling explicit multiplicative feature interactions without numerical instability. It achieves high Dice scores on ISIC 2018, Kvasir-SEG, and BUSI, outperforms a Residual U-Net baseline in Dice and IoU, and eliminates false-positive rates on normal BUSI cases.
MakeupMirror, a diffusion-based makeup transfer model, achieves +60% improvement in facial recognition similarity and -50% reduction in skin tone difference compared to Stable-Makeup. It preserves facial features and skin tone with expert acceptance of 94% across identity criteria, operating at 0.7s latency through a Levenberg-Marquardt Langevin sampler.
A new black-box probe distinguishes whether text-to-image models memorize identities or fabricate them, without needing reference photos or training data. The NAMESAKES dataset includes over one thousand public figures' names and faces, along with less famous perturbed names, to benchmark this capability across state-of-the-art models.