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import re |
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import torch |
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import requests |
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from PIL import Image, ImageDraw |
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from transformers import AutoProcessor, Kosmos2_5ForConditionalGeneration |
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repo = "microsoft/kosmos-2.5" |
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device = "cuda:0" |
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dtype = torch.bfloat16 |
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model = Kosmos2_5ForConditionalGeneration.from_pretrained(repo, device_map=device, torch_dtype=dtype) |
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processor = AutoProcessor.from_pretrained(repo) |
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url = "https://huggingface.co/microsoft/kosmos-2.5/blob/main/receipt_00008.png" |
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image = Image.open(requests.get(url, stream=True).raw) |
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prompt = "<md>" |
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inputs = processor(text=prompt, images=image, return_tensors="pt") |
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height, width = inputs.pop("height"), inputs.pop("width") |
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raw_width, raw_height = image.size |
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scale_height = raw_height / height |
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scale_width = raw_width / width |
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inputs = {k: v.to(device) if v is not None else None for k, v in inputs.items()} |
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inputs["flattened_patches"] = inputs["flattened_patches"].to(dtype) |
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generated_ids = model.generate( |
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**inputs, |
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max_new_tokens=1024, |
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) |
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generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True) |
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print(generated_text[0]) |
