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@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+assets/av_example_1.jpg filter=lfs diff=lfs merge=lfs -text
+assets/vila_hd_results.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,121 @@
+---
+license: cc-by-sa-4.0
+language:
+- en
+pipeline_tag: image-text-to-text
+tags:
+- VLM
+- VILA-HD
+- PS3
+---
+<div align="center">
+
+# Scaling Vision Pre-Training to 4K Resolution
+
+[![website](https://img.shields.io/badge/website-76b900?style=for-the-badge&logo=safari&labelColor=555555)](https://nvlabs.github.io/PS3/)
+[![Arxiv](https://img.shields.io/badge/Arxiv-b31b1b?style=for-the-badge&logo=arxiv&labelColor=555555)](https://arxiv.org/abs/2503.19903)
+[![VILA-HD Demo](https://img.shields.io/badge/-VILA--HD_Demo-brightgreen?style=for-the-badge&logo=huggingface&labelColor=555555&color=ff6e00)](https://huggingface.co/spaces/bfshi/VILA-HD-demo)
+[![PS3 Models](https://img.shields.io/badge/PS3%20Models%20-ffd21e?style=for-the-badge&logo=huggingface&labelColor=555555)](https://huggingface.co/collections/nvidia/ps3-scaling-vision-pre-training-to-4k-resolution-682d0535b61c07afd45242e9)
+[![VILA-HD Models](https://img.shields.io/badge/VILA--HD%20Models%20-ffd21e?style=for-the-badge&logo=huggingface&labelColor=555555)](https://huggingface.co/collections/nvidia/ps3-scaling-vision-pre-training-to-4k-resolution-682d0535b61c07afd45242e9)
+[![PS3 Code](https://img.shields.io/badge/PS3%20Code%20-181717?style=for-the-badge&logo=github&labelColor=555555)](https://github.com/NVlabs/PS3)
+
+<div style="font-family: charter;">
+  <a href="https://bfshi.github.io" target="_blank" style="color: #6f6f6f; text-decoration: none;">Baifeng Shi</a><sup style="font-size: 0.6em;">1,2</sup>&nbsp;&nbsp;&nbsp;
+  <a href="https://sites.google.com/site/boyilics/home" target="_blank" style="color: #6f6f6f; text-decoration: none;">Boyi Li</a><sup style="font-size: 0.6em;">1,2</sup>&nbsp;&nbsp;&nbsp;
+  <a href="https://han-cai.github.io/" target="_blank" style="color: #6f6f6f; text-decoration: none;">Han Cai</a><sup style="font-size: 0.6em;">2</sup>&nbsp;&nbsp;&nbsp;
+  <a href="https://scholar.google.com/citations?user=OI7zFmwAAAAJ&hl=en/" target="_blank" style="color: #6f6f6f; text-decoration: none;">Yao Lu</a><sup style="font-size: 0.6em;">2</sup>&nbsp;&nbsp;&nbsp;
+  <a href="https://sifeiliu.net/" target="_blank" style="color: #6f6f6f; text-decoration: none;">Sifei Liu</a><sup style="font-size: 0.6em;">2</sup>&nbsp;&nbsp;&nbsp;
+  <a href="https://research.nvidia.com/person/marco-pavone" target="blank" style="color: #6f6f6f; text-decoration: none;">Marco Pavone</a><sup style="font-size: 0.6em;">2</sup>
+  <br>
+  <a href="https://jankautz.com/" target="_blank" style="color: #6f6f6f; text-decoration: none;">Jan Kautz</a><sup style="font-size: 0.6em;">2</sup>&nbsp;&nbsp;&nbsp;
+  <a href="https://hanlab.mit.edu/songhan/" target="_blank" style="color: #6f6f6f; text-decoration: none;">Song Han</a><sup style="font-size: 0.6em;">2</sup>&nbsp;&nbsp;&nbsp;
+  <a href="https://people.eecs.berkeley.edu/~trevor/" target="_blank" style="color: #6f6f6f; text-decoration: none;">Trevor Darrell</a><sup style="font-size: 0.6em;">1</sup>&nbsp;&nbsp;&nbsp;
+  <a href="https://www.pmolchanov.com/" target="_blank" style="color: #6f6f6f; text-decoration: none;">Pavlo Molchanov</a><sup style="font-size: 0.6em;">2</sup>&nbsp;&nbsp;&nbsp;
+  <a href="https://hongxu-yin.github.io/" target="_blank" style="color: #6f6f6f; text-decoration: none;">Hongxu Yin</a><sup style="font-size: 0.6em;">2</sup>
+  <br>
+  </a><sup style="font-size: 0.6em;">1</sup> UC Berkeley&nbsp;&nbsp;&nbsp;
+  </a><sup style="font-size: 0.6em;">2</sup> NVIDIA&nbsp;&nbsp;&nbsp;
+</div>
+
+</div>
+
+<hr style="border: 2px solid gray;"></hr>
+
+## Demo
+
+Try out the VILA-HD demo [here](https://huggingface.co/spaces/bfshi/VILA-HD-demo)!
+
+<hr style="border: 2px solid gray;"></hr>
+
+## Pre-Trained Models
+
+### VILA-HD models
+
+| Vision Model    | Max Resolution | Pre-Trained Weights                                                     |
+|-----------------|----------------|-------------------------------------------------------------------------|
+| VILA-HD-8B-PS3-1.5K-SigLIP | 1512 * 1512    | [nvidia/VILA-HD-8B-PS3-1.5K-SigLIP](https://huggingface.co/nvidia/VILA-HD-8B-PS3-1.5K-SigLIP) |
+| VILA-HD-8B-PS3-4K-SigLIP   | 3780 * 3780    | [nvidia/VILA-HD-8B-PS3-4K-SigLIP](https://huggingface.co/nvidia/VILA-HD-8B-PS3-4K-SigLIP)     |
+
+<hr style="border: 2px solid gray;"></hr>
+
+## Performance
+
+![Performance of VILA-HD models](assets/vila_hd_results.png)
+
+<hr style="border: 2px solid gray;"></hr>
+
+
+## Installation
+
+First install VILA, following the instructions [here](https://github.com/NVlabs/VILA).
+
+Then install PS3, following the instructions in [PS3 repo](https://github.com/NVlabs/PS3).
+
+<hr style="border: 2px solid gray;"></hr>
+
+
+## Inference
+
+VILA-HD inference shares the same API as VILA (see [here](https://github.com/NVlabs/VILA#inference)). Specifically, we provide `vila-infer` as a CLI tool to infer with VILA-HD models. As an example:
+
+```bash
+vila-infer --model-path nvidia/VILA-HD-8B-PS3-4K-SigLIP --conv-mode auto --text "Where does the exit lead to?" --media assets/av_example_1.jpg
+```
+
+VILA-HD has several arguments controlling the total number of high-res patches to process, the number of high-res patches to process at each scale, the mode of patch selection, etc. These can be controlled by setting the following **environment variables** during inference:
+
+- `NUM_LOOK_CLOSE`: How many times to run high-res encoding. Each time PS3 encodes 2560 high-res patches. Can be set between 1 and 6 for 1.5K model and between 1 and 35 for 4K model. Default is 6 for both models.
+- `NUM_TOKEN_LOOK_CLOSE`: How many high-res patches to encode. Provides more fine-grained control of # high-res patches than `NUM_LOOK_CLOSE`. Can be set between 1 and 14580 for 1.5K model and between 1 and 87480 for 4K model. Setting this will override `NUM_LOOK_CLOSE`. Default is `None`.
+- `SELECT_NUM_EACH_SCALE`: The number of high-res patches to encode at each high-res scale. For example, setting `SELECT_NUM_EACH_SCALE=512+2048` for 1.5K model means the number of high-res patches to encode at 756 and 1512 scales are and 512 and 2048 respectively. By default, the number of patches at each scale is proportional to the number of total patches at that scale, i.e., `512+2048` for 1.5K model and `85+340+2125` for 4K model.
+- `LOOK_CLOSE_MODE`: The mode of patch selection. Can be set as `after_prompt` or `after_image`. `after_prompt` means the high-res patches are selected based on the text prompt. `after_image` means the high-res patches are selected based on image saliency. Default is `after_prompt`.
+- `SMOOTH_SELECTION_PROB`: Whether to use smooth selection probability during high-res patch selection. Can be set as 'true' or 'false'. Default is `false`.
+
+For example, if you want to make VILA-HD to run high-res encoding for 12 times for better accuracy, you can set `NUM_LOOK_CLOSE=12` when running inference:
+```bash
+NUM_LOOK_CLOSE=12 vila-infer --model-path nvidia/VILA-HD-8B-PS3-4K-SigLIP --conv-mode auto --text "Where does the exit lead to?" --media assets/av_example_1.jpg
+```
+
+<hr style="border: 2px solid gray;"></hr>
+
+## Evaluation
+
+Same as VILA, we provide `vila-eval` as a CLI tool to evaluate the performance of VILA-HD models. For example, to reproduce the results in Table 3 of the paper, you can run:
+
+```bash
+NUM_LOOK_CLOSE=6 SELECT_NUM_EACH_SCALE=512+2048 vila-eval -m nvidia/VILA-HD-8B-PS3-1.5K-SigLIP -c auto -t textvqa,lmms-chartqa,lmms-docvqa_val,lmms-ocrbench,lmms-realworldqa,mathvista_testmini,lmms-mmbench,lmms-mme,lmms-mmmu_pro,lmms-mmmu_val,lmms-scienceqa_full,lmms-infovqa_val
+```
+
+<hr style="border: 2px solid gray;"></hr>
+
+## Citation
+
+If you find this work useful in your research, please consider citing:
+
+```bibtex
+@article{shi2025scaling,
+  title={Scaling Vision Pre-Training to 4K Resolution},
+  author={Shi, Baifeng and Li, Boyi and Cai, Han and Lu, Yao and Liu, Sifei and Pavone, Marco and Kautz, Jan and Han, Song and Darrell, Trevor and Molchanov, Pavlo and others},
+  journal={arXiv preprint arXiv:2503.19903},
+  year={2025}
+}
+```

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