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:
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
This project is licensed under the CC BY-NC 4.0.
The copyright of the images belongs to the original authors.
See LICENSE for more information.
Contact Us
Please feel free to contact us via email infimmbytedance@gmail.com if you have any questions.
Citation
@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}
}