pip install -q torch==2.4.0 datasets flash_attn timm einops

from transformers import AutoModelForCausalLM, AutoProcessor, AutoConfig
import torch

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

model = AutoModelForCausalLM.from_pretrained("gokaygokay/Florence-2-Flux", trust_remote_code=True).to(device).eval()
processor = AutoProcessor.from_pretrained("gokaygokay/Florence-2-Flux", trust_remote_code=True)

# Function to run the model on an example
def run_example(task_prompt, text_input, image):
    prompt = task_prompt + text_input

    # Ensure the image is in RGB mode
    if image.mode != "RGB":
        image = image.convert("RGB")

    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,
        num_beams=3,
        repetition_penalty=1.10,
    )
    generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
    parsed_answer = processor.post_process_generation(generated_text, task=task_prompt, image_size=(image.width, image.height))
    return parsed_answer

from PIL import Image
import requests
import copy

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)
answer = run_example("<DESCRIPTION>", "Describe this image in great detail.", image)

final_answer = answer["<DESCRIPTION>"]
print(final_answer)
  

Citation and attribution

This model release is maintained by Gökay Aydoğan. If you reference this repository in academic work, please cite it as follows and also cite the upstream models, datasets, or projects it builds upon.

@software{aydogan2024florence_2_flux,
  author = {Aydoğan, Gökay},
  title = {{Florence-2-Flux}},
  year = {2024},
  publisher = {Hugging Face},
  url = {https://huggingface.co/gokaygokay/Florence-2-Flux},
  note = {Model repository; cite the base model and upstream datasets as required.}
}