inference.py

from diffusers import StableDiffusionPipeline
import torch
import base64
import numpy as np


def process_data(data: dict) -> dict:
    return {
        "prompt": [data.pop("prompt", data)] * min(data.pop("number", 2), 5),
        "guidance_scale": data.pop("guidance_scale", 7.5),
        "num_inference_steps": min(data.pop("num_inference_steps", 50), 50),
        "height": 512,
        "width": 512,
    }


def model_fn(model_dir: str):
    t2i_pipe = StableDiffusionPipeline.from_pretrained(
        model_dir,
    )
    if torch.cuda.is_available():
        t2i_pipe = t2i_pipe.to("cuda")

    t2i_pipe.enable_attention_slicing()
    return t2i_pipe


def predict_fn(data: dict, hgf_pipe) -> dict:

    with torch.autocast("cuda"):
        images = hgf_pipe(**process_data(data))["images"]

    # return dictionary, which will be json serializable
    return {
        "images": [
            base64.b64encode(np.array(image).astype(np.uint8)).decode("utf-8")
            for image in images
        ]
    }