AI Dataset2026
Collection of 1,000 AI-generated dreamcore aesthetic images (2K JPEGs, numbered 001–1000) intended for creative prototyping and visual research. Images were produced with GPT Image 2 and released under an MIT license.
AI Model2026
W4A4-quantized build of a 25B-parameter multimodal LLM that produces text from image+text inputs and supports conversational tool use. Trades very small quality differences for much lower GPU memory and latency so inference can run on smaller accelerators (vLLM support).
AI Video2026
Generates minute-scale, 720p videos from a single image using a 2.6B image-to-video diffusion transformer with precise 6‑DoF camera control and an optional LTX‑2 refiner; designed for long-context, memory-efficient modeling but requires large refiner checkpoints (~41 GB).
AI Image2026
Node-based infinite-canvas web workstation for iterative visual creation — integrates image/video generation, reference editing, prompt library, multi-agent assistants, and asset management. Runs in-browser with configurable OpenAI-compatible endpoints; suited for local/personal deployment (AGPL-3.0).
AI Dataset2026
Provides 100,000 generated low-quality↔high-quality image pairs created with modern multi-frame/multi-modal models to boost generalization of image restoration methods; includes train/test JSONL lists, baseline training code, and pretrained checkpoints under CC BY‑NC‑ND 4.0.
AI Model2026
A ternary-weight (~1.58-bit) 4B text-to-image diffusion transformer optimized for NVIDIA GPUs using Gemlite INT2 and HQQ; it reduces the transformer to ~1.21 GB (4.55 GB CUDA payload) and targets 1024×1024 generation with a 4-step FlowMatch-Euler sampler.
AI Dataset2026
Creator-centric benchmark for evaluating text-to-image models with 1,000 bilingual prompts and a 3-level, 56-facet taxonomy. Includes a trained Q-Judger judge model and leaderboard-ready evaluation scripts to surface gaps in real-world fidelity and creative generation.
AI Model2026
Performs fast, high-quality vision–language grounding: given an image plus a natural-language prompt it returns bounding boxes or points for referred objects. Uses Parallel Box Decoding for parallel coordinate prediction (higher throughput) and targets research/non-commercial use.
AI Dataset2026
Benchmark for evaluating vision–language models on measurement-grounded inputs vs. RGB, emphasizing low-light, HDR, and visibility-sensitive evidence recovery. Contains 2,183 paired test examples with local image assets for controlled RAW↔RGB comparisons.
AI Model2026
Generates high-fidelity images from text prompts using NVIDIA's 64B Cosmos3-Super multimodal foundation model. Integrates with Hugging Face Diffusers and vLLM‑Omni, is released under OpenMDW1.1 for commercial use, and is optimized for Physical AI workflows (robotics, AV, simulation).
AI Model2026
GGUF quantizations of Step-3.7-Flash: a sparse multimodal Mixture-of-Experts LLM with native image understanding, selectable reasoning levels, and a 256K context window. Ships multiple calibrated Q3/Q4/IQ quant files plus an mmproj vision projector for local llama.cpp inference on high-memory hosts.
Reallocates injected noise energy across frequency bands to match a diffusion model's spectral bias, improving sampling fidelity without retraining. Uses a timestep- and frequency-dependent colored-noise schedule as a plug-and-play inference-time SDE solver; shows sizable FID drops on ImageNet-256.