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Running on Zero
Running on Zero
| title: Omni-Diffusion | |
| emoji: 🌀 | |
| colorFrom: yellow | |
| colorTo: purple | |
| sdk: gradio | |
| sdk_version: 5.49.1 | |
| app_file: app.py | |
| short_description: Any-to-any multimodal masked discrete diffusion model | |
| python_version: "3.10" | |
| startup_duration_timeout: 1h | |
| pinned: false | |
| # Omni-Diffusion | |
| Interactive demo for **Omni-Diffusion: Unified Multimodal Understanding and | |
| Generation with Masked Discrete Diffusion** | |
| ([paper](https://huggingface.co/papers/2603.06577) · | |
| [model](https://huggingface.co/lijiang/Omni-Diffusion) · | |
| [code](https://github.com/VITA-MLLM/Omni-Diffusion)). | |
| Omni-Diffusion is an any-to-any multimodal language model built on a mask-based | |
| discrete diffusion model. It jointly models discrete tokens of text and images. | |
| This Space exposes three of the model's capabilities: | |
| - **Text → Image**: generate an image from a text prompt. | |
| - **Image → Text (VQA)**: answer questions / describe an uploaded image. | |
| - **Text → Text**: instruction following / chat. | |
| Image (de)tokenization uses [`showlab/magvitv2`](https://huggingface.co/showlab/magvitv2). | |