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from diffusers import StableDiffusionControlNetPipeline, ControlNetModel |
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from typing import Dict, List, Any |
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from io import BytesIO |
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from PIL import Image |
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import base64 |
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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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if device.type != 'cuda': |
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raise ValueError("need to run on GPU") |
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dtype = torch.bfloat16 if torch.cuda.get_device_capability()[0] == 8 else torch.float16 |
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class EndpointHandler(): |
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def __init__(self, path=""): |
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self.stable_diffusion_id = "stabilityai/stable-diffusion-2-1-base" |
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controlnet = ControlNetModel.from_pretrained("rgres/Seg2Sat-sd-controlnet", torch_dtype=torch.float16) |
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self.pipe = StableDiffusionControlNetPipeline.from_pretrained( |
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self.stable_diffusion_id, controlnet=controlnet, torch_dtype=dtype, safety_checker=None |
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).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("prompt", None) |
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image = data.pop("image", None) |
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steps = data.pop("steps", 30) |
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seed = data.pop("seed", 0) |
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steps = int(steps) |
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seed = int(seed) |
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if prompt is None and image is None: |
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return {"error": "Please provide a prompt and base64 encoded image."} |
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image = self.decode_base64_image(image) |
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self.generator = torch.Generator(device="cpu").manual_seed(seed) |
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image_out = self.pipe( |
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prompt=prompt, |
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image=image, |
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num_inference_steps=steps, |
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generator=self.generator |
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).images[0] |
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return image_out |
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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 |
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