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| import os, pdb | |
| import argparse | |
| import numpy as np | |
| import torch | |
| import requests | |
| from PIL import Image | |
| from lavis.models import load_model_and_preprocess | |
| from utils.ddim_inv import DDIMInversion | |
| from utils.scheduler import DDIMInverseScheduler | |
| if __name__=="__main__": | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument('--input_image', type=str, default='assets/test_images/cat_a.png') | |
| parser.add_argument('--results_folder', type=str, default='output/test_cat') | |
| parser.add_argument('--num_ddim_steps', type=int, default=50) | |
| parser.add_argument('--model_path', type=str, default='CompVis/stable-diffusion-v1-4') | |
| parser.add_argument('--use_float_16', action='store_true') | |
| args = parser.parse_args() | |
| # make the output folders | |
| os.makedirs(os.path.join(args.results_folder, "inversion"), exist_ok=True) | |
| os.makedirs(os.path.join(args.results_folder, "prompt"), exist_ok=True) | |
| if args.use_float_16: | |
| torch_dtype = torch.float16 | |
| else: | |
| torch_dtype = torch.float32 | |
| # load the BLIP model | |
| model_blip, vis_processors, _ = load_model_and_preprocess(name="blip_caption", model_type="base_coco", is_eval=True, device=torch.device("cuda")) | |
| # make the DDIM inversion pipeline | |
| pipe = DDIMInversion.from_pretrained(args.model_path, torch_dtype=torch_dtype).to("cuda") | |
| pipe.scheduler = DDIMInverseScheduler.from_config(pipe.scheduler.config) | |
| # if the input is a folder, collect all the images as a list | |
| if os.path.isdir(args.input_image): | |
| l_img_paths = sorted(glob(os.path.join(args.input_image, "*.png"))) | |
| else: | |
| l_img_paths = [args.input_image] | |
| for img_path in l_img_paths: | |
| bname = os.path.basename(args.input_image).split(".")[0] | |
| img = Image.open(args.input_image).resize((512,512), Image.Resampling.LANCZOS) | |
| # generate the caption | |
| _image = vis_processors["eval"](img).unsqueeze(0).cuda() | |
| prompt_str = model_blip.generate({"image": _image})[0] | |
| x_inv, x_inv_image, x_dec_img = pipe( | |
| prompt_str, | |
| guidance_scale=1, | |
| num_inversion_steps=args.num_ddim_steps, | |
| img=img, | |
| torch_dtype=torch_dtype | |
| ) | |
| # save the inversion | |
| print("Inside inversion >> save the inversion >>>") | |
| print(os.path.join(args.results_folder, f"inversion/{bname}.pt")) | |
| torch.save(x_inv[0], os.path.join(args.results_folder, f"inversion/{bname}.pt")) | |
| # save the prompt string | |
| print("Inside inversion >> save the prompt string >>>") | |
| print(os.path.join(args.results_folder, f"prompt/{bname}.txt")) | |
| with open(os.path.join(args.results_folder, f"prompt/{bname}.txt"), "w") as f: | |
| f.write(prompt_str) | |