pip install torch numpy diffusers

import os
import torch as th
import numpy as np
from diffusers import DiffusionPipeline

# Ensure the save directory exists
save_dir = './seed_resize/'
os.makedirs(save_dir, exist_ok=True)

has_cuda = th.cuda.is_available()
device = th.device('cpu' if not has_cuda else 'cuda')

pipe = DiffusionPipeline.from_pretrained(
    "CompVis/stable-diffusion-v1-4",
    custom_pipeline="seed_resize_stable_diffusion"
).to(device)

def dummy(images, **kwargs):
    return images, False

pipe.safety_checker = dummy

images = []
th.manual_seed(0)
generator = th.Generator("cuda").manual_seed(0)

seed = 0
prompt = "A painting of a futuristic cop"

width = 512
height = 512

res = pipe(
    prompt,
    guidance_scale=7.5,
    num_inference_steps=50,
    height=height,
    width=width,
    generator=generator)
image = res.images[0]
image.save(os.path.join(save_dir, 'seed_resize_{w}_{h}_image.png'.format(w=width, h=height)))

th.manual_seed(0)
generator = th.Generator("cuda").manual_seed(0)

pipe = DiffusionPipeline.from_pretrained(
    "CompVis/stable-diffusion-v1-4",
    custom_pipeline="seed_resize_stable_diffusion"
).to(device)

width = 512
height = 592

res = pipe(
    prompt,
    guidance_scale=7.5,
    num_inference_steps=50,
    height=height,
    width=width,
    generator=generator)
image = res.images[0]
image.save(os.path.join(save_dir, 'seed_resize_{w}_{h}_image.png'.format(w=width, h=height)))

pipe_compare = DiffusionPipeline.from_pretrained(
    "CompVis/stable-diffusion-v1-4",
    custom_pipeline="seed_resize_stable_diffusion"
).to(device)

res = pipe_compare(
    prompt,
    guidance_scale=7.5,
    num_inference_steps=50,
    height=height,
    width=width,
    generator=generator
)

image = res.images[0]
image.save(os.path.join(save_dir, 'seed_resize_{w}_{h}_image_compare.png'.format(w=width, h=height)))