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from typing import Dict, List, Any |
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import torch |
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from diffusers import DPMSolverMultistepScheduler, StableDiffusionInpaintPipeline |
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from PIL import Image |
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import base64 |
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from io import BytesIO |
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') |
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if device.type != 'cuda': |
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raise ValueError("need to run on GPU") |
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class EndpointHandler(): |
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def __init__(self, path=""): |
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self.pipe = StableDiffusionInpaintPipeline.from_pretrained(path, torch_dtype=torch.float16) |
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self.pipe.scheduler = DPMSolverMultistepScheduler.from_config(self.pipe.scheduler.config) |
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self.pipe = self.pipe.to(device) |
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def __call__(self, data: Any) -> List[List[Dict[str, float]]]: |
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""" |
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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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inputs = data.pop("inputs", data) |
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encoded_image = data.pop("image", None) |
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encoded_mask_image = data.pop("mask_image", None) |
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num_inference_steps = data.pop("num_inference_steps", 25) |
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guidance_scale = data.pop("guidance_scale", 7.5) |
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negative_prompt = data.pop("negative_prompt", None) |
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height = data.pop("height", None) |
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width = data.pop("width", None) |
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if encoded_image is not None and encoded_mask_image is not None: |
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image = self.decode_base64_image(encoded_image) |
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mask_image = self.decode_base64_image(encoded_mask_image) |
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else: |
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image = None |
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mask_image = None |
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out = self.pipe(inputs, |
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image=image, |
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mask_image=mask_image, |
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num_inference_steps=num_inference_steps, |
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guidance_scale=guidance_scale, |
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num_images_per_prompt=1, |
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negative_prompt=negative_prompt, |
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height=height, |
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width=width |
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) |
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return out.images[0] |
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def decode_base64_image(self, image_string): |
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base64_image = base64.b64decode(image_string) |
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buffer = BytesIO(base64_image) |
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image = Image.open(buffer) |
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return image |
