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from typing import Dict, List, Any |
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import torch |
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from diffusers import DPMSolverMultistepScheduler, StableDiffusionXLPipeline |
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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 = StableDiffusionXLPipeline.from_pretrained( |
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path, torch_dtype=torch.float16, variant="fp16", use_safetensors=True |
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) |
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self.pipe.scheduler = DPMSolverMultistepScheduler.from_config( |
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self.pipe.scheduler.config |
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) |
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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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prompt = data.pop("inputs", data) |
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num_inference_steps = data.pop("num_inference_steps", 30) |
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guidance_scale = data.pop("guidance_scale", 8) |
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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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out = self.pipe( |
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prompt, |
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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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