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import base64 |
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from io import BytesIO |
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from typing import Dict, Any |
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import torch |
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from PIL import Image |
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from diffusers import StableDiffusionPipeline |
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def decode_base64_image(image_string): |
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base64_image = base64.b64decode(image_string) |
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buffer = BytesIO(base64_image) |
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return Image.open(buffer) |
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class EndpointHandler: |
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def __init__(self, path=""): |
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self.pipe = StableDiffusionPipeline.from_pretrained("/repository/stable-diffusion-v1-5", |
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torch_dtype=torch.float16, revision="fp16") |
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self.pipe = self.pipe.to("cuda") |
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def __call__(self, data: Any) -> Dict[str, str]: |
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""" |
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Return predict value. |
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:param data: A dictionary contains `inputs` and optional `image` field. |
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:return: A dictionary with `image` field contains image in base64. |
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""" |
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prompts = data.pop("inputs", None) |
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encoded_image = data.pop("image", None) |
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init_image = None |
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if encoded_image: |
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init_image = decode_base64_image(encoded_image) |
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init_image.thumbnail((768, 768)) |
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image = self.pipe(prompts, init_image=init_image).images[0] |
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buffered = BytesIO() |
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image.save(buffered, format="png") |
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img_str = base64.b64encode(buffered.getvalue()) |
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return {"image": img_str.decode()} |
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