Embodied AI2024
Provides an open platform of omnimodal world models, datasets, and tools to build Physical AI — joint perception, generation, and action reasoning for robots, autonomous vehicles, and smart infrastructure. Supports images, video, audio, and action-conditioned workflows.
AI Dataset2026
Provides curated short video clips (49- and 81-frame) with layered ground truth—edit layers, alpha mattes, and composite targets—for training and evaluating content-preserving layered diffusion video editing. Contains background-replace and object-add edits; Apache-2.0 licensed.
AI Model2026
Generates text by iteratively denoising blocks of tokens with a two-tower design: a frozen autoregressive context tower and a trainable diffusion denoiser tower, trading minimal quality loss for higher wall-clock throughput.
AI Dataset2026
Provides the full caption corpus used to train and ablate the i1 text-to-image model: 12 curated subsets with multiple caption variants (long/short, VLM-generated, rendered text) to enable reproducible training and captioning experiments.
AI Model2026
Generates temporally coherent MP4 videos from a single input image plus text instructions, with configurable resolution, frame count, and optional AAC audio. Optimized for NVIDIA GPU stacks and integrates with vLLM‑Omni and Hugging Face Diffusers for production inference and research workflows.
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
Text-to-image model packaged for Diffusers that uses fp8 quantization to lower memory and speed up inference. Delivered as a safetensors checkpoint on Hugging Face with an Ideogram pipeline; created May 30, 2026 — license unspecified.
AI Model2026
NF4-quantized text-to-image diffusion model released as safetensors and compatible with the Diffusers Ideogram4Pipeline — optimized for lower-memory local inference and faster deployments while preserving the original model's text-to-image capabilities.
AI Model2026
Generates and reasons about multimodal physical-world content—text, images, video, audio, and robot/action trajectories—conditioned on combinations of text, image, video and action inputs. The 64B “Super” variant targets Physical AI use cases and supports vLLM‑Omni, Diffusers, and action prediction.
AI Video2026
Provides the renderer weights and inference code for Bernini’s video renderer, enabling text→video, image→video and video editing inference. Offers a ready diffusers-format bundle or safetensors checkpoints under Apache‑2.0; intended for multi‑GPU/Hopper inference and reproducible research.
AI Video2026
Generates minute-level, multi-shot synchronized audio+video from a single text prompt, using a paired cross-modal memory to preserve character appearance and voice across shots. Uses DMD-distilled few-step inference for ~7.5× speedup; requires high-GPU memory and is released under the LTX-2 community license.
AI Dataset2026
Generates synthetic coding-agent session traces by pairing remotely hosted open agent models with local llama.cpp user models across real open-source codebases. Each trace records read/write/edit/bash actions and tool use; the dataset is a reproducible cartesian product (20×3×20×20 = 24,000 sessions) under an MIT license.