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lieding1994/promptgen-base
promptgen-base is a machine learning model from lieding1994. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
Florence-2-base-PromptGen is a model trained for MiaoshouAI Tagger for ComfyUI. It is an advanced image captioning tool based on the Microsoft Florence-2 Model and fine-tuned to perfection.
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From the Hugging Face model README
Florence-2-base-PromptGen is a model trained for MiaoshouAI Tagger for ComfyUI. It is an advanced image captioning tool based on the Microsoft Florence-2 Model and fine-tuned to perfection.
Most vision models today are trained mainly for general vision recognition purposes, but when doing prompting and image tagging for model training, the format and details of the captions is quite different.
Florence-2-base-PromptGen is trained on such a purpose as aiming to improve the tagging experience and accuracy of the prompt and tagging job. The model is trained based on images and cleaned tags from Civitai so that the end result for tagging the images are the prompts you use to generate these images.
A new instruction prompt <GENERATE_PROMPT> is created for this purpose in addition to <DETAILED_CAPTION> and <MORE_DETAILED_CAPTION>. It will respond back in danbooru tagging style with much better accuracy and proper level of details.
v0.8 New Instruction trained for <GENERATE_PROMPT>
v0.9 Improved vision ability for uncensored data for <DETAILED_CAPTION> and <MORE_DETAILED_CAPTION>
To use this model, you can load it directly from the Hugging Face Model Hub:
model = AutoModelForCausalLM.from_pretrained("MiaoshouAI/Florence-2-base-PromptGen", trust_remote_code=True)
processor = AutoProcessor.from_pretrained("MiaoshouAI/Florence-2-base-PromptGen", trust_remote_code=True)
prompt = "<GENERATE_PROMPT>"
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg?download=true"
image = Image.open(requests.get(url, stream=True).raw)
inputs = processor(text=prompt, images=image, return_tensors="pt").to(device)
generated_ids = model.generate(
input_ids=inputs["input_ids"],
pixel_values=inputs["pixel_values"],
max_new_tokens=1024,
do_sample=False,
num_beams=3
)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
parsed_answer = processor.post_process_generation(generated_text, task=prompt, image_size=(image.width, image.height))
print(parsed_answer)
If you just want to use this model, you can use it under ComfyUI-Miaoshouai-Tagger
https://github.com/miaoshouai/ComfyUI-Miaoshouai-Tagger
A detailed use and install instruction is already there.