HunyuanCaptioner

Hunyuan-Captioner

Hunyuan-Captioner meets the need of text-to-image techniques by maintaining a high degree of image-text consistency. It can generate high-quality image descriptions from a variety of angles, including object description, objects relationships, background information, image style, etc. Our code is based on LLaVA implementation.

Instructions

a. Install dependencies

The dependencies and installation are basically the same as the base model.

b. Data download

cd HunyuanDiT
wget -O ./dataset/data_demo.zip https://dit.hunyuan.tencent.com/download/HunyuanDiT/data_demo.zip
unzip ./dataset/data_demo.zip -d ./dataset
mkdir ./dataset/porcelain/arrows ./dataset/porcelain/jsons

c. Model download

# Use the huggingface-cli tool to download the model.
huggingface-cli download Tencent-Hunyuan/HunyuanCaptioner --local-dir ./ckpts/captioner

Inference

Current supported prompt templates:

a. Single picture inference in Chinese

python mllm/caption_demo.py --mode "caption_zh" --image_file "mllm/images/demo1.png" --model_path "./ckpts/captioner"

b. Insert specific knowledge into caption

python mllm/caption_demo.py --mode "insert_content" --content "宫保鸡丁" --image_file "mllm/images/demo2.png" --model_path "./ckpts/captioner"

c. Single picture inference in English

python mllm/caption_demo.py --mode "caption_en" --image_file "mllm/images/demo3.png" --model_path "./ckpts/captioner"

d. Multiple pictures inference in Chinese

### Convert multiple pictures to csv file. 
python mllm/make_csv.py --img_dir "mllm/images" --input_file "mllm/images/demo.csv"

### Multiple pictures inference
python mllm/caption_demo.py --mode "caption_zh" --input_file "mllm/images/demo.csv" --output_file "mllm/images/demo_res.csv" --model_path "./ckpts/captioner"

(Optional) To convert the output csv file to Arrow format, please refer to Data Preparation #3 for detailed instructions.

Gradio

To launch a Gradio demo locally, please execute the following commands sequentially. Ensure each command is running in the background. For more detailed instructions, please refer to LLaVA.

cd mllm
python -m llava.serve.controller --host 0.0.0.0 --port 10000
python -m llava.serve.gradio_web_server --controller http://0.0.0.0:10000 --model-list-mode reload --port 443
python -m llava.serve.model_worker --host 0.0.0.0 --controller http://0.0.0.0:10000 --port 40000 --worker http://0.0.0.0:40000 --model-path "../ckpts/captioner" --model-name LlavaMistral

Then the demo can be accessed through http://0.0.0.0:443. It should be noted that the 0.0.0.0 here needs to be X.X.X.X with your server IP.