metadata
license: apache-2.0
language:
- en
library_name: transformers
pipeline_tag: image-text-to-text
tags:
- art
base_model: microsoft/Florence-2-large
datasets:
- kadirnar/fluxdev_controlnet_16k
pip install -q 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-Large", trust_remote_code=True).to(device).eval()
processor = AutoProcessor.from_pretrained("gokaygokay/Florence-2-Flux-Large", 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)