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Converts 200+ hours of expert Figma screen recordings into 3,469 Playwright-MCP action trajectories for training and evaluating vision-language and GUI agents; includes 126 long‑horizon tasks, phase labels, a 10‑skill taxonomy, and is CC‑BY‑4.0 licensed.
Contains ~2 million human pairwise preference judgments comparing images generated from text prompts; each example pairs two images with a preferred/tie label and is formatted for preference learning, reward-model training, and evaluation.
Generates group images that bind up to ten reference identities to distinct people and locations by predicting an explicit identity–layout plan and supervising faces with Layout-Grounded ID Loss. Improves identity fidelity while cutting copy-paste duplication; suited for multi-person image synthesis but requires identity-annotated face regions and paired training data.
Provides 115,293 illustrated page images and a 975,345-row manifest sampled from scanned Encyclopaedia Britannica volumes (1768–1929), with per-page classifier probabilities for illustration — ready for image-classification, OCR-aware vision research, and illustration mining.
Generates L2-normalized multimodal embeddings (default 4,096‑D) for text, images, videos and visual documents, supporting interleaved inputs and flexible dimension truncation (Matryoshka). Designed for cross-modal retrieval, ranking and downstream retrieval systems; audio is not supported.
A natively multimodal model for text and image→text generation, long-context reasoning, and complex coding/agent workloads. Uses 320B total / 18B active params with a hybrid sparse+linear attention and manifold-constrained hyper-connections to reduce long-context serving cost.
Converts image-level rewards into explicit intermediate targets for diffusion-model denoising via an on-policy self-distillation loop. Constructs bounded positive/negative targets around anchors from reward gradients, fits those targets with finite updates, and refreshes a behavior policy by EMA—improving aligned performance across backbones while reducing GPU hours.
Turns a flow-matching image generator's self-exploration into dense, per-step supervision without a pretrained teacher; it branches the student's next-state into stochastic SDE candidates, scores them against a deterministic self-reference, and applies an advantage-weighted pull–push velocity regression with reward-level fusion for multi-objective alignment.
Proposes GenFirst, a generation-before-reconstruction end-to-end training strategy for latent generative models that avoids latent collapse by prioritizing generative objectives and then progressively strengthening reconstruction, validated with strong gFID/GenEval results on ImageNet-256 and text-to-image tasks.
Provides image-level tag correction instructions for a Danbooru anime-image tagging corpus, listing per-post tags to add or remove. Contains 1.74M normalized correction rows (snapshot 2026-08-30); it's a corrections manifest (no images) intended to be applied to existing metadata.
An experimental multimodal model that adds visual understanding to DeepSeek-V4-Flash: accepts text+image inputs and returns text analyses. Improves vision-dependent agent workflows while maintaining comparable text-only performance; released under an MIT license on Hugging Face.
Builds high-fidelity image generators by pairing a 6B Diffusion Transformer with a frozen LLaDA2.0-Mini vision-language module, relying on extensive image-only pretraining and mid-training; model weights, training code, and recipes are released.