| from typing import Dict, List, Any |
| import torch |
| from torch import autocast |
| from diffusers import StableDiffusionPipeline |
| import base64 |
| from io import BytesIO |
|
|
|
|
| device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') |
|
|
| if device.type != 'cuda': |
| raise ValueError("Must run SDXL on a GPU instance.") |
|
|
|
|
| class EndpointHandler(): |
|
|
|
|
| def __init__(self,path=""): |
| self.pipe = StableDiffusionPipeline.from_pretrained(path,torch_dtype=torch.float16) |
| self.pipe = self.pipe.to(device) |
|
|
|
|
| def __call__(self): |
| """ |
| """ |
|
|
| inputs = data.pop("inputs",data) |
|
|
| with autocast(device.type): |
| image = self.pipe(inputs,guidance_scale=9)["sample"][0] |
|
|
| buffer = BytesIO() |
| image.save(buffer, format="JPEG") |
| img_str = base64.b64decode(buffer.getvalue()) |
|
|
| return {"image": img_str.decode} |
