Update README.md
Browse files# Florence-2-base-PromptGen v1.5 (with config and code updates)
This is [MiaoshouAI/Florence-2-base-PromptGen-v1.5](https://huggingface.co/MiaoshouAI/Florence-2-base-PromptGen-v1.5) which retains its existing features, but with changes to supporting configuration and code to ensure drop-in replacement for [Microsoft Florence-2 Model Base](https://huggingface.co/microsoft/Florence-2-base) the when using Transformers library in Python.
1) The config.json has been updated with an auto_map property and key-values added matching the Florence-2-base [resolving this issue](https://huggingface.co/MiaoshouAI/Florence-2-base-PromptGen-v1.5/discussions/4).
2) Python code that is located in the root of the repo in Florence-2-base but in florence2_base_ft in [MiaoshouAI/Florence-2-base-PromptGen-v1.5](https://huggingface.co/MiaoshouAI/Florence-2-base-PromptGen-v1.5) has been moved to the root of the repo as this prevented trust_remove_code=True in the AutoProcessor.from_pretrained from loading the code.
3) Changes to Florence2-base's modeling_florence2.py to ensure that the class Florence2LanguageForConditionalGeneration inherits from GenerationMixin, secondary to PreTrainedModel to ensure compatibility with transformers from v4.50 onwards .
## About PromptGen
Florence-2-base-PromptGen is a model trained by [MiaoshouAI](https://huggingface.co/MiaoshouAI) that specializes in generating highly descriptive prompts and tags that assist with training image generation models like [FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev) and creating descriptive prompts for image generation.
Supported prompts include standard prompts from Florence2-base such as <0D> for identifying object locations and enhanced prompts by MiaoshouAI including <CAPTION>, <DETAILED_CAPTION>, <MORE_DETAILED_CAPTION> and additional prompts included <GENERATE_TAGS> and <MIXED_CAPTION>. See the original repo for more details.
## How to use:
To use this model, you can load it directly from the Hugging Face Model Hub.
First, install dependancies (in a virtual environent if you prefer), for example:
```
pip3 install torch torchvision --index-url https://download.pytorch.org/whl/cu124 -U
pip3 install transformers pillow einops timm
```
The following code is based on the microsoft/Florence2-base example but with updated prompt and model, and correct imports.
```python
import requests
import torch
from PIL import Image
from transformers import AutoProcessor, AutoModelForCausalLM
device = "cuda:0" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
model = AutoModelForCausalLM.from_pretrained("/Florence-2-base-PromptGen-v1.5", torch_dtype=torch_dtype, trust_remote_code=True).to(device)
processor = AutoProcessor.from_pretrained("/Florence-2-base-PromptGen-v1.5", trust_remote_code=True)
# Examples include CAPTION>, <DETAILED_CAPTION>, <MORE_DETAILED_CAPTION>,<GENERATE_TAGS>, <MIXED_CAPTION>, <0D>
prompt = "<CAPTION>"
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, torch_dtype)
generated_ids = model.generate(
input_ids=inputs["input_ids"],
pixel_values=inputs["pixel_values"],
max_new_tokens=1024,
num_beams=3,
do_sample=False
)
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[prompt])
```