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--- |
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license: apache-2.0 |
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language: |
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- en |
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library_name: transformers |
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pipeline_tag: image-text-to-text |
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tags: |
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- art |
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base_model: microsoft/Florence-2-large |
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datasets: |
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- kadirnar/fluxdev_controlnet_16k |
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--- |
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``` |
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pip install -q datasets flash_attn timm einops |
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``` |
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```python |
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from transformers import AutoModelForCausalLM, AutoProcessor, AutoConfig |
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import torch |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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model = AutoModelForCausalLM.from_pretrained("gokaygokay/Florence-2-Flux-Large", trust_remote_code=True).to(device).eval() |
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processor = AutoProcessor.from_pretrained("gokaygokay/Florence-2-Flux-Large", trust_remote_code=True) |
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# Function to run the model on an example |
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def run_example(task_prompt, text_input, image): |
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prompt = task_prompt + text_input |
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# Ensure the image is in RGB mode |
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if image.mode != "RGB": |
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image = image.convert("RGB") |
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inputs = processor(text=prompt, images=image, return_tensors="pt").to(device) |
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generated_ids = model.generate( |
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input_ids=inputs["input_ids"], |
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pixel_values=inputs["pixel_values"], |
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max_new_tokens=1024, |
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num_beams=3, |
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repetition_penalty=1.10, |
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) |
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generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0] |
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parsed_answer = processor.post_process_generation(generated_text, task=task_prompt, image_size=(image.width, image.height)) |
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return parsed_answer |
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from PIL import Image |
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import requests |
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import copy |
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url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg?download=true" |
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image = Image.open(requests.get(url, stream=True).raw) |
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answer = run_example("<DESCRIPTION>", "Describe this image in great detail.", image) |
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final_answer = answer["<DESCRIPTION>"] |
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print(final_answer) |
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``` |
