许可证：apache-2.0
语言：

• 英文
  库名称：transformers
  管道标签：图像文本到文本
  标签：
• 艺术
  基础模型：microsoft/Florence-2-large
  数据集：
• 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)  

# 运行模型示例的函数  
def run_example(task_prompt, text_input, image):  
    prompt = task_prompt + text_input  

    # 确保图像为RGB模式  
    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>", "详细描述这张图片。", image)  

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