ip2p_ipynb

if not os.path.exists('/content'):
    os.mkdir('/content')
from google.colab import drive
drive.mount('content/gdrive')
!pip install transformers accelerate torch
!pip install git+https://github.com/huggingface/diffusers.git

#############################################################

import os
import glob
import torch
from diffusers import StableDiffusionInstructPix2PixPipeline, EulerAncestralDiscreteScheduler
from tqdm import tqdm
from IPython.display import display
from PIL import Image

model_id = "timbrooks/instruct-pix2pix"
pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16, safety_checker=None)
pipe.to("cuda")
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)

#############################################################

torch.cuda.empty_cache()
import gc
gc.collect()

img_folder = "/content/content/gdrive/MyDrive/ip2p_input_directory"
out_folder = "/content/content/gdrive/MyDrive/ip2p_output_directory"
prompt = "turn them into flowers"

if not os.path.exists(out_folder):
    os.mkdir(out_folder)

for img_path in tqdm(glob.glob(os.path.join(img_folder, "*.png"))):
    img = Image.open(img_path)

    #if image has alpha channel, remove it
    if img.mode == "RGBA":
        img = img.convert("RGB")
    img = pipe(prompt, image=img).images[0]
    img.save(os.path.join(out_folder, os.path.basename(img_path)))
    display(img)