---
language: en
tags:
- multimodal
- text
- image
- image-to-text
datasets:
- HuggingFaceM4/OBELICS
- laion/laion2B-en
- coyo-700m
- mmc4
pipeline_tag: text-generation
inference: true
---
## Paper

More detailes can be found in our paper at https://arxiv.org/abs/2403.01487. We have released the pretraining model and the pyotrch code at https://github.com/InfiMM/infimm-hd/. Feel free to build your model from our pretrained model.

## Quickstart

Use the code below to get started with the base model:
```python
import torch
from transformers import AutoModelForCausalLM, AutoProcessor

processor = AutoProcessor.from_pretrained("Infi-MM/infimm-hd", trust_remote_code=True)

prompts = [
    {
        "role": "user",
        "content": [
            {"image": "/xxx/test.jpg"}, # change it with you image
            "Please describe the image in detail.",
        ],
    }
]
inputs = processor(prompts)
# use bf16 and gpu 0
model = AutoModelForCausalLM.from_pretrained(
    "Infi-MM/infimm-hd",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
).to(0).eval()

inputs = inputs

inputs["batch_images"] = inputs["batch_images"].to(torch.bfloat16)
for k in inputs:
    inputs[k] = inputs[k].to(model.device)

generated_ids = model.generate(
    **inputs,
    min_new_tokens=0,
    max_new_tokens=256,
)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)
print(generated_text)
```
## License

<a href="https://creativecommons.org/licenses/by-nc/4.0/deed.en">
	<img src="https://upload.wikimedia.org/wikipedia/commons/thumb/d/d3/Cc_by-nc_icon.svg/600px-Cc_by-nc_icon.svg.png" width="160">
</a>

This project is licensed under the **CC BY-NC 4.0**.

The copyright of the images belongs to the original authors.

See [LICENSE](LICENSE) for more information.

## Contact Us

Please feel free to contact us via email [infimmbytedance@gmail.com](infimmbytedance@gmail.com) if you have any questions.

## Citation

```latex
@misc{liu2024infimmhd,
      title={InfiMM-HD: A Leap Forward in High-Resolution Multimodal Understanding}, 
      author={Haogeng Liu and Quanzeng You and Xiaotian Han and Yiqi Wang and Bohan Zhai and Yongfei Liu and Yunzhe Tao and Huaibo Huang and Ran He and Hongxia Yang},
      year={2024},
      eprint={2403.01487},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
```