|
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("need to run on GPU") |
|
|
|
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, data: Any) -> List[List[Dict[str, float]]]: |
|
""" |
|
Args: |
|
data (:obj:): |
|
includes the input data and the parameters for the inference. |
|
Return: |
|
A :obj:`dict`:. base64 encoded image |
|
""" |
|
inputs = data.pop("inputs", data) |
|
|
|
|
|
with autocast(device.type): |
|
image = self.pipe(inputs, guidance_scale=7.5)["sample"][0] |
|
|
|
|
|
buffered = BytesIO() |
|
image.save(buffered, format="JPEG") |
|
img_str = base64.b64encode(buffered.getvalue()) |
|
|
|
|
|
return {"image": img_str.decode()} |
|
|
