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from diffusers import StableDiffusionImg2ImgPipeline, DDIMScheduler
from PIL import Image
import torch
def stable_diffusion_img2img(
model_path:str,
image_path:str,
prompt:str,
negative_prompt:str,
num_samples:int,
guidance_scale:int,
num_inference_step:int,
):
image = Image.open(image_path)
pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
model_path,
safety_checker=None,
torch_dtype=torch.float16
)
pipe.to("cuda")
pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
pipe.enable_xformers_memory_efficient_attention()
output = pipe(
prompt = prompt,
image = image,
negative_prompt = negative_prompt,
num_images_per_prompt = num_samples,
num_inference_steps = num_inference_step,
guidance_scale = guidance_scale,
).images
return output
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