script to generate 100 frames transition from init image towards the promp with Stablediffusion

i2is.py

from torch import autocast
import torch
import requests
from PIL import Image
import sys
from image_to_image import StableDiffusionImg2ImgPipeline, preprocess
import random

# script to generate 100 frames transition from init image towards the prompt

# usage:
#
# python i2is.py prompt init-image output_file [start_step seed]
#

# needs image_to_image.py from https://github.com/huggingface/diffusers/blob/main/examples/inference/image_to_image.py

# load the pipeline
device = "cuda"
pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
    "CompVis/stable-diffusion-v1-4",
    revision="fp16", 
    torch_dtype=torch.float16,
    use_auth_token=True
).to(device)


fn = sys.argv[2]
ofn = sys.argv[3]
if len(sys.argv) > 4:
    start = int(sys.argv[4])
else:
    start = 0
if len(sys.argv) > 5:
    seed = int(sys.argv[5])
else:
    seed = random.randint(0,63444)
    
steps = 100

print(start, seed)

init_image = Image.open(fn).convert("RGB")
init_image = init_image.resize((768, 768))
init_image = preprocess(init_image)

prompt =  sys.argv[1]

for n in range(start,steps):
    beta = n/100

    with autocast("cuda"):
      generator = torch.Generator("cuda").manual_seed(seed)    
      images = pipe(prompt=prompt, init_image=init_image, strength=beta, guidance_scale=7.5, generator=generator)["sample"]

    images[0].save(ofn+"-"+str(n)+".png")