--- license: apache-2.0 language: - en library_name: transformers pipeline_tag: image-text-to-text tags: - art base_model: microsoft/Florence-2-base datasets: - kadirnar/fluxdev_controlnet_16k --- ``` pip install -q torch==2.4.0 datasets flash_attn timm einops ``` ```python 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(" ", "Describe this image in great detail.", image) final_answer = answer[" "] print(final_answer) ```
