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| title: PerceptionDLM Region Captioning | |
| emoji: 🎯 | |
| colorFrom: indigo | |
| colorTo: yellow | |
| sdk: gradio | |
| sdk_version: 5.42.0 | |
| app_file: app.py | |
| short_description: Parallel region captioning with multimodal diffusion LLM | |
| python_version: "3.12" | |
| startup_duration_timeout: 1h | |
| # PerceptionDLM Region Captioning | |
| A Gradio demo for [MSALab/PerceptionDLM](https://huggingface.co/MSALab/PerceptionDLM), a 9.2B parameter multimodal diffusion language model for parallel region captioning. | |
| ## How it works | |
| Upload an image, then choose regions to caption. By default you **generate masks with SAM 3** by clicking point(s) on the image; SAM 3 turns each click set into a binary mask. You can also switch to the secondary mode and **upload pre-existing binary mask files**. The model then generates descriptions for all provided regions **simultaneously** in a single denoising process — avoiding the linear latency growth of autoregressive region captioners. | |
| The decoding animation replays each diffusion step so you can watch captions emerge token by token. | |
| SAM 3 ([facebook/sam3](https://huggingface.co/facebook/sam3)) is used only as a mask-generation tool; the region captioning itself is always done by PerceptionDLM. | |
| ## Model details | |
| - **Base:** LLaDA-8B (diffusion language model) + SigLIP2 vision encoder | |
| - **Precision:** bfloat16 | |
| - **Region prompts:** up to 6 per image | |
| - **Default inference:** 32 diffusion steps, generation length 32 per mask | |
| - **Paper:** [arXiv:2606.19534](https://arxiv.org/abs/2606.19534) | |
| - **Code:** [GitHub](https://github.com/MSALab-PKU/PerceptionDLM) |