




from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available

@dataclass
class        snake_case__    (A__		):







						"""simple docstring"""




						__lowerCAmelCase					:Union[List[PIL.Image.Image], np.ndarray]
						__lowerCAmelCase					:Optional[List[bool]]


if is_transformers_available() and is_torch_available():
		from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline









import torch

from diffusers import StableDiffusionPipeline


_lowercase		:				Optional[int]      		="path-to-your-trained-model"
_lowercase		:				Union[str, Any]      		=StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("cuda")

_lowercase		:				Any      		="A photo of sks dog in a bucket"
_lowercase		:				Any      		=pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]

image.save("dog-bucket.png")




