Model Detail
omni-dreams-models
—omni-dreams-models is an image generation model with 2B parameters released by NVIDIA. The model is registered under the image-to-video pipeline tag on Hugging Face, distributed under a other license.
omni-dreams-models ships with 2B parameters. Access is gated on Hugging Face under the other license, which means a manual approval step before weights can be downloaded.
omni-dreams-models is best fit for text-to-image generation and creative iteration. It is a less obvious choice for production photography pipelines that need exact reproducibility. Treat this as a starting matrix rather than a benchmark verdict — the right deployment usually depends on the specific evaluation suite that mirrors your workload.
OmniCam: Omni-Camera Trajectory Generation via Geometry-Grounded Pose Token Learning
arXiv:2610.09513v2 Announce Type: replace-cross Abstract: Camera trajectories control viewpoint changes in video generation, scene reconstruction, and robotic perception. Generating them from language requires both scene geometry and target-aware framing. We introduce OmniCam, an autoregressive mode
Omni-Modal LLMs Know More Than They Say
arXiv:2610.16135v1 Announce Type: cross Abstract: Omni-modal large language models (Omni-LLMs) extend LLMs to process heterogeneous modalities (e.g., vision, audio, and IMU signals) alongside language, enabling a broader range of multimodal tasks. Yet we find that these models often fail to express
Omni-Embed-Audio: Leveraging Multimodal LLMs for Robust Audio-Text Retrieval
arXiv:2604.18360v4 Announce Type: replace-cross Abstract: Audio-text retrieval systems based on Contrastive Language-Audio Pretraining (CLAP) achieve strong performance on traditional benchmarks; however, these benchmarks rely on caption-style queries that differ substantially from real-world search
Omni-Diffusion-Distill: Few-Step Distillation of Unified Multimodal Diffusion Large Language Models
arXiv:2610.10990v1 Announce Type: cross Abstract: Unified multimodal diffusion large language models (dLLMs) offer a single architecture for both image generation and multimodal understanding, but their iterative decoding requires tens to hundreds of forward passes. Existing few-step distillation me
Syn-Omni: Structured Specialization and Progressive Collaboration for Omnimodal Embeddings
arXiv:2610.12256v1 Announce Type: cross Abstract: Omnimodal embeddings naturally involve both shared representations and modality-specific features across heterogeneous inputs. However, existing omnimodal embedding methods often rely on a single shared parameter space over mixed-modality data, limit
PersonaMem-v3: Toward Omni-Platform Personal Intelligence for Holistic User Understanding, Recommendation, and Agentic Tasks
arXiv:2608.21381v2 Announce Type: replace-cross Abstract: Personal intelligence is becoming a central frontier for user-facing AI agents. To be helpful in everyday life, agents must understand users across the digital contexts where their preferences, intents, habits, social relationships, and needs
