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from __future__ import annotations


from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler
from diffusers import DPMSolverMultistepScheduler
import torch
import PIL.Image
import numpy as np
import datetime

# Check environment
print(f"Is CUDA available: {torch.cuda.is_available()}")
print(f"CUDA device: {torch.cuda.get_device_name(torch.cuda.current_device())}")

device = "cuda"

class Model:
    def __init__(self, modelID):

        self.modelID = modelID
        self.pipe = StableDiffusionPipeline.from_pretrained(modelID, torch_dtype=torch.float16)
        self.pipe = self.pipe.to(device)
        self.pipe.scheduler = DPMSolverMultistepScheduler.from_config(self.pipe.scheduler.config)
        self.pipe.enable_xformers_memory_efficient_attention()

    def process(self, 
                prompt: str, 
                negative_prompt: str,
                guidance_scale:int = 7,
                num_images:int = 1,
                num_steps:int = 20,
                ):
        seed = np.random.randint(0, np.iinfo(np.int32).max)
        generator = torch.Generator(device).manual_seed(seed)
        now = datetime.datetime.now()
        print(now)
        print(self.modelID)
        print(prompt)
        print(negative_prompt)
        with torch.inference_mode():
            images = self.pipe(prompt=prompt,
                         negative_prompt=negative_prompt,
                         guidance_scale=guidance_scale,
                         num_images_per_prompt=num_images,
                         num_inference_steps=num_steps,
                         generator=generator).images

        return images