handler.py

from typing import  Dict, List, Any
import base64
from PIL import Image
from io import BytesIO
from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker

import torch
from diffusers import StableCascadeDecoderPipeline, StableCascadePriorPipeline


# # set device
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
if device.type != 'cuda':
    raise ValueError("need to run on GPU")
# set mixed precision dtype
dtype = torch.bfloat16 if torch.cuda.get_device_capability()[0] == 8 else torch.float16

class EndpointHandler():
    def __init__(self, path=""):
        #  self.stable_diffusion_id = "Lykon/dreamshaper-8"

        #  self.prior_pipeline = StableCascadePriorPipeline.from_pretrained("stabilityai/stable-cascade-prior", torch_dtype=dtype)#.to(device)
        #  self.decoder_pipeline = StableCascadeDecoderPipeline.from_pretrained("stabilityai/stable-cascade",  torch_dtype=dtype)#.to(device) 


        self.generator = torch.Generator(device=device.type).manual_seed(3)

    def __call__(self, data: Any) -> List[List[Dict[str, float]]]:
#      import torch

        device = "cuda"
        num_images_per_prompt = 2

        prior = StableCascadePriorPipeline.from_pretrained("stabilityai/stable-cascade-prior", torch_dtype=torch.bfloat16).to(device)
        decoder = StableCascadeDecoderPipeline.from_pretrained("stabilityai/stable-cascade",  torch_dtype=torch.float16).to(device)

        prompt = "Anthropomorphic cat dressed as a pilot"
        negative_prompt = ""

        prior_output = prior(
            prompt=prompt,
            height=1024,
            width=1024,
            negative_prompt=negative_prompt,
            guidance_scale=4.0,
            num_images_per_prompt=num_images_per_prompt,
            num_inference_steps=20
        )
        decoder_output = decoder(
            image_embeddings=prior_output.image_embeddings.half(),
            prompt=prompt,
            negative_prompt=negative_prompt,
            guidance_scale=0.0,
            output_type="pil",
            num_inference_steps=10
        ).images
        return decoder_output