Stable Diffusion, running on CPU, uses hugging-face diffusers library

stable-cpu.py

#### pip install diffusers==0.2.4 transformers scipy ftfy
####

from diffusers import StableDiffusionPipeline, LMSDiscreteScheduler
import torch

def main():
    seed = 1000 #1000, 42, 420
    torch.manual_seed(seed)
    generator = torch.Generator()
    generator.manual_seed(seed)
    # this will substitute the default PNDM scheduler for K-LMS    
    lms = LMSDiscreteScheduler(
        beta_start=0.00085, 
        beta_end=0.012, 
        beta_schedule="scaled_linear",
        tensor_format = 'pt'
        )

    pipe = StableDiffusionPipeline.from_pretrained(
        "CompVis/stable-diffusion-v1-4", 
        scheduler=lms,
        generator=generator,
        use_auth_token=True,
    ).to("cpu")

    num_inference_steps = 10
    width=448
    height=512    

    prompt = "a photo of an astronaut riding a horse on mars"
    result = pipe(prompt, num_inference_steps=num_inference_steps, height=height, width=width)
    nsfw_content_detected = result["nsfw_content_detected"][0]
    print("nsfw_content_detected: " + str(nsfw_content_detected))
    if nsfw_content_detected:
        prompt = "NSFW-" + prompt
    image = result["sample"][0]
    output_file = prompt.replace(" ", "_") + "-" + str(width) + "x" +str(height)+ "_" + str(num_inference_steps) + "steps" + "_seed" + str(seed) + ".jpg"
    image.save(output_file)
    print("Saved: " + str(output_file))

if __name__ == '__main__':
    main()