Like the default HF StableDiffusion img2img sample, only it uses the sd2-simpsons-blip and DPMSolverMultistepScheduler

sd-simpsons-model-img2img.py

import requests
import torch
from PIL import Image
from io import BytesIO

from diffusers import StableDiffusionImg2ImgPipeline, DPMSolverMultistepScheduler

def main():
#////////////////////////////////////////////
    seed = 42
    num_inference_steps = 10
    model_id_or_path = "Norod78/sd2-simpsons-blip"
#////////////////////////////////////////////   

    scheduler = DPMSolverMultistepScheduler(
        beta_start=0.00085,
        beta_end=0.012,
        beta_schedule="scaled_linear",
        num_train_timesteps=1000,
        trained_betas=None,
        predict_epsilon=True,
        thresholding=False,
        algorithm_type="dpmsolver++",
        solver_type="midpoint",
        lower_order_final=True,
)

    # load the pipeline
    device = "cuda" if torch.cuda.is_available() else "cpu"
    dtype = torch.float16 if device == "cuda" else torch.float32
    torch.manual_seed(seed)
    generator = torch.Generator()
    generator.manual_seed(seed)

    pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
        model_id_or_path,
        torch_dtype=dtype,
        use_auth_token=True
    )
    pipe.scheduler = scheduler
    pipe = pipe.to(device)    

    # let's download an initial image
    url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg"

    response = requests.get(url)
    init_image = Image.open(BytesIO(response.content)).convert("RGB")
    #init_image = Image.open('Me_segmented.png').convert('RGB')
    init_image = init_image.resize((768, 512))

    prompt = "A fantasy landscape, trending on artstation"

    images = pipe(prompt=prompt, init_image=init_image, num_inference_steps= num_inference_steps, strength=0.75, guidance_scale=7.5, generator=generator).images

    images[0].save("fantasy_landscape.png")

if __name__ == '__main__':
    main()