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main.py
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main.py
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import os
import torch
from diffusers import StableDiffusionPipeline, DDIMScheduler
import utils
from attentionControl import AttentionReplace
import diff_harmon
from PIL import Image
import numpy as np
import argparse
import glob
from natsort import ns, natsorted
from PIL import ImageFile
from HarmonizationDetect.inference import harmon_detect
import random
import shutil
ImageFile.LOAD_TRUNCATED_IMAGES = True
def run_harmonization_no_evaluator(image, prompts, diffusion_model, diffusion_steps, guidance=7.5, generator=None, device='cpu',
cross_replace_steps=1., self_replace_steps=1., init_guidance=0, mask=None, size=512,
save_dir="./output", args=None):
os.makedirs(save_dir, exist_ok=True)
bg = np.array(image.resize((size, size), resample=Image.LANCZOS))[:, :, :3]
m = (np.array(mask.resize((size, size), resample=Image.LANCZOS)) > 100).astype(np.float32)
if len(m.shape) == 2:
m = m[:, :, None]
elif m.shape[2] != 1:
m = m[:, :, 0:1]
for ind in range(args.harmonize_iterations):
print(f"\n======================================================\n"
f"=============== Iteration:{ind} ================\n"
f"======================================================")
"""Do DDIM inversion. Collect all the intermediate latents in the inverse steps."""
init_image = image
init_prompt = [prompts[0][0]]
x_t, inversion_latents = diff_harmon.ddim_reverse_sample(init_image, init_prompt, diffusion_model,
diffusion_steps,
init_guidance, generator, args=args)
"""Do the Diffusion Harmonization."""
controller = AttentionReplace(prompts[0], diffusion_model.tokenizer, diffusion_steps,
cross_replace_steps=cross_replace_steps, self_replace_steps=self_replace_steps,
device=device)
out_img, _ = diff_harmon.run(diffusion_model, prompts[0], controller, latent=x_t,
num_inference_steps=diffusion_steps,
guidance_scale=guidance, generator=generator,
inversion_latents=inversion_latents[::-1], mask=mask, size=size, args=args,
original_image=image)
"""Visualize the attention maps and the final results."""
# utils.show_cross_attention(prompts, diffusion_model.tokenizer, controller, res=size // 32,
# from_where=("up", "down"),
# save_path="{}/{}_repeat_attentionFG.jpg".format(save_dir, str(ind).rjust(2, '0')))
# utils.show_self_attention_comp(prompts, controller, res=size // 32, from_where=("up", "down"),
# save_path="{}/{}_repeat_selfAttention.jpg".format(save_dir,
# str(ind).rjust(2, '0')))
#
# ori = Image.fromarray(out_img[-1].astype(np.uint8))
# ori.save("{}/{}_repeat_ori.jpg".format(save_dir, str(ind).rjust(2, '0')))
image = m * out_img[-1] + (1 - m) * bg
image = Image.fromarray(image.astype(np.uint8))
image.save("{}/{}_repeat_blend.jpg".format(save_dir, str(ind).rjust(2, '0')))
def run_harmonization(image, prompts, diffusion_model, diffusion_steps, guidance=7.5, generator=None, device='cpu',
cross_replace_steps=1., self_replace_steps=1., init_guidance=0, mask=None, size=512,
save_dir="./output", args=None):
os.makedirs(save_dir, exist_ok=True)
bg = np.array(image.resize((size, size), resample=Image.LANCZOS))[:, :, :3]
m = (np.array(mask.resize((size, size), resample=Image.LANCZOS)) > 100).astype(np.float32)
if len(m.shape) == 2:
m = m[:, :, None]
elif m.shape[2] != 1:
m = m[:, :, 0:1]
"""
Below are the processes of the Performance Evaluation.
(Details can be found in Section 3.3 in our paper-v2)
"""
args.prompt_num = len(prompts)
args.prompt_change_flag = None
harmonization_scores = dict((i, []) for i in range(args.prompt_num))
for ind in range(args.harmonize_iterations):
print(f"\n======================================================\n"
f"=============== Iteration:{ind} ================\n"
f"======================================================")
init_image = image
if ind == 0:
# At first, calculate harmonization score for each prompt
for idx in range(args.prompt_num):
init_prompt = [prompts[idx][0]]
"""Do DDIM inversion. Collect all the intermediate latents in the inverse steps."""
x_t, inversion_latents = diff_harmon.ddim_reverse_sample(init_image, init_prompt, diffusion_model,
diffusion_steps,
init_guidance, generator, args=args)
"""Do the Diffusion Harmonization."""
controller = AttentionReplace(prompts[idx], diffusion_model.tokenizer, diffusion_steps,
cross_replace_steps=cross_replace_steps, self_replace_steps=self_replace_steps,
device=device)
out_img, _ = diff_harmon.run(diffusion_model, prompts[idx], controller, latent=x_t,
num_inference_steps=diffusion_steps,
guidance_scale=guidance, generator=generator,
inversion_latents=inversion_latents[::-1], mask=mask, size=size, args=args,
original_image=image)
"""Visualize the attention maps and the final results."""
# utils.show_cross_attention(prompts[idx], diffusion_model.tokenizer, controller, res=size // 32,
# from_where=("up", "down"),
# save_path="{}/{}_repeat_attentionFG_prompt{}.jpg".format(save_dir,
# str(ind).rjust(2, '0'),str(idx).rjust(2, '0')))
# utils.show_self_attention_comp(prompts[idx], controller, res=size // 32, from_where=("up", "down"),
# save_path="{}/{}_repeat_selfAttention_prompt{}.jpg".format(save_dir,
# str(ind).rjust(2, '0'),str(idx).rjust(2, '0')))
#
# ori = Image.fromarray(out_img[-1].astype(np.uint8))
# ori.save("{}/{}_repeat_ori_prompt{}.jpg".format(save_dir, str(ind).rjust(2, '0'),str(idx).rjust(2, '0')))
image = m * out_img[-1] + (1 - m) * bg
image = Image.fromarray(image.astype(np.uint8))
image.save("{}/{}_repeat_blend_prompt{}.jpg".format(save_dir, str(ind).rjust(2, '0'),str(idx).rjust(2, '0')))
for idx in range(args.prompt_num):
score = harmon_detect("{}/{}_repeat_blend_prompt{}.jpg".format(save_dir, str(ind).rjust(2, '0'),
str(idx).rjust(2, '0')),args.mask_path)
harmonization_scores[idx].append(score)
print("The harmonization scores of each prompt in the first iteration are: {}".format(harmonization_scores))
# Among the initially generated several prompts, choose the prompt with the highest score.
args.max_score = max(harmonization_scores.values())
print("The prompt with the highest score is: {}".format(args.max_score))
args.max_score_prompt = max(harmonization_scores, key=harmonization_scores.get)
print("The prompt with the highest score is: {}".format(prompts[args.max_score_prompt]))
image = Image.open("{}/{}_repeat_blend_prompt{}.jpg".format(save_dir, str(ind).rjust(2, '0'),str(args.max_score_prompt).rjust(2, '0')))
else:
if args.prompt_change_flag is not None:
prompt_used_idx = args.prompt_change_flag
else:
prompt_used_idx = args.max_score_prompt
init_prompt = [prompts[prompt_used_idx][0]]
"""Do DDIM inversion. Collect all the intermediate latents in the inverse steps."""
x_t, inversion_latents = diff_harmon.ddim_reverse_sample(init_image, init_prompt, diffusion_model,
diffusion_steps,
init_guidance, generator, args=args)
"""Do the Diffusion Harmonization."""
controller = AttentionReplace(prompts[prompt_used_idx], diffusion_model.tokenizer, diffusion_steps,
cross_replace_steps=cross_replace_steps,
self_replace_steps=self_replace_steps,
device=device)
out_img, _ = diff_harmon.run(diffusion_model, prompts[prompt_used_idx], controller, latent=x_t,
num_inference_steps=diffusion_steps,
guidance_scale=guidance, generator=generator,
inversion_latents=inversion_latents[::-1], mask=mask, size=size, args=args,
original_image=image)
"""Visualize the attention maps and the final results."""
# utils.show_cross_attention(prompts[prompt_used_idx], diffusion_model.tokenizer, controller, res=size // 32,
# from_where=("up", "down"),
# save_path="{}/{}_repeat_attentionFG_prompt{}.jpg".format(save_dir,
# str(ind).rjust(2,'0'),
# str(prompt_used_idx).rjust(2,'0')))
# utils.show_self_attention_comp(prompts[prompt_used_idx], controller, res=size // 32, from_where=("up", "down"),
# save_path="{}/{}_repeat_selfAttention_prompt{}.jpg".format(save_dir,str(ind).rjust(2, '0'),
# str(prompt_used_idx).rjust(2, '0')))
# ori = Image.fromarray(out_img[-1].astype(np.uint8))
# ori.save("{}/{}_repeat_ori_prompt{}.jpg".format(save_dir, str(ind).rjust(2, '0'), str(prompt_used_idx).rjust(2, '0')))
image = m * out_img[-1] + (1 - m) * bg
image = Image.fromarray(image.astype(np.uint8))
image.save(
"{}/{}_repeat_blend_prompt{}.jpg".format(save_dir, str(ind).rjust(2, '0'), str(prompt_used_idx).rjust(2, '0')))
# Leverage the evaluator (a lightweight classifier) to calculate the harmonization score.
score = harmon_detect("{}/{}_repeat_blend_prompt{}.jpg".format(save_dir, str(ind).rjust(2, '0'),
str(prompt_used_idx).rjust(2, '0')), args.mask_path)
harmonization_scores[prompt_used_idx].append(score)
for i in range(args.prompt_num):
if i != prompt_used_idx:
harmonization_scores[i].append(0)
# If decrease three times, then regenerate (change) the prompt, or stop.
if len(harmonization_scores[prompt_used_idx]) > 2:
if harmonization_scores[prompt_used_idx][-1] < harmonization_scores[prompt_used_idx][-2] < \
harmonization_scores[prompt_used_idx][-3]:
if args.prompt_change_flag is not None:
scores = [item for sublist in list(harmonization_scores.values()) for item in sublist]
final_max_score = max(scores)
print("The prompt with the highest score is: {}".format(final_max_score))
for key, value in harmonization_scores.items():
if final_max_score in harmonization_scores[key]:
final_max_score_prompt = key
final_ind = harmonization_scores[key].index(final_max_score)
break
print("The prompt is: {}".format(prompts[final_max_score_prompt]))
# copy the image and rename it
shutil.copy("{}/{}_repeat_blend_prompt{}.jpg".format(save_dir, str(final_ind).rjust(2, '0'),
str(final_max_score_prompt).rjust(2, '0')),
"{}/final_output.jpg".format(save_dir))
break
else:
"""
Regenerate (change) prompt idx. In case potential network connection error,
here we provide an offline way to directly use the pre-generated mutliple prompts.
You can easily adapt it back to the online way (as dicted in our paper-v2) by referring
to our `gemini_mini_vision.py`.
"""
prompts_id_list = list(harmonization_scores.keys())
prompts_id_list.remove(args.max_score_prompt)
if len(prompts_id_list) == 0:
break
args.prompt_change_flag = random.choice(prompts_id_list)
print("\n ==== Change prompt to: ", args.prompt_change_flag, prompts[args.prompt_change_flag])
"Back to the prev-best status."
image = Image.open(
"{}/{}_repeat_blend_prompt{}.jpg".format(save_dir, str(ind - 2).rjust(2, '0'),
str(prompt_used_idx).rjust(2, '0')))
# get the max score prompt
scores = [item for sublist in list(harmonization_scores.values()) for item in sublist]
final_max_score = max(scores)
print("The prompt with the highest score is: {}".format(final_max_score))
for key,value in harmonization_scores.items():
if final_max_score in harmonization_scores[key]:
final_max_score_prompt = key
final_ind = harmonization_scores[key].index(final_max_score)
break
print("The prompt is: {}".format(prompts[final_max_score_prompt]))
shutil.copy(
"{}/{}_repeat_blend_prompt{}.jpg".format(save_dir, str(final_ind).rjust(2, '0'),
str(final_max_score_prompt).rjust(2, '0')),
"{}/final_output.jpg".format(save_dir))
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--save_dir', default="./output", type=str,
help='Where to save the results')
parser.add_argument('--pretrained_diffusion_path',
default="stabilityai/stable-diffusion-2-base",
type=str,
help='Set the path to `stabilityai/stable-diffusion-2-base`.')
parser.add_argument('--harmonize_iterations', default=10, type=int, help='How many times to harmonize the images')
parser.add_argument('--is_single_image', action='store_true', help='Whether to test on a single image or images')
parser.add_argument('--use_edge_map', action='store_true', help='Whether to use edge maps')
parser.add_argument('--use_evaluator', action='store_true', help='Whether to automatically pick results')
# For single image
parser.add_argument('--image_path', default="./demo/girl_comp.jpg", type=str)
parser.add_argument('--mask_path', default="./demo/girl_mask.jpg", type=str)
parser.add_argument('--foreground_prompt', default="girl golden autumn", type=str,
help='Text describes the environment of foreground.')
parser.add_argument('--background_prompt', default="girl winter", type=str,
help='Text describes the environment of background.')
# For multiple images
parser.add_argument('--images_root', default="./demo/composite", type=str,
help='The composite images root directory')
parser.add_argument('--masks_root', default="./demo/mask", type=str, )
parser.add_argument('--caption_txt', default="./demo/caption.txt", type=str, help='The caption txt file')
# Hyperparameters
parser.add_argument('--seed', default=8888, type=int, help='Random seed')
parser.add_argument('--diffusion_steps', default=50, type=int, help='Total DDIM sampling steps')
parser.add_argument('--guidance', default=2.5, type=float, help='guidance scale of diffusion models')
parser.add_argument('--size', default=512, type=int, help='The input image resized size')
parser.add_argument('--uncond_optimized_lr', default=1e-1, type=float,
help='Learning rate for optimizing unconditional embeddings.')
parser.add_argument('--regulation_weight', default=1000, type=int, help='Regulation loss weight.')
# Hyperparameters for text embedding optimizing style
parser.add_argument('--op_style_lr', default=5e-4, type=float,
help='Learning rate for optimization style of text embedding.')
parser.add_argument('--op_style_iters', default=2, type=int, help='Optimization iterations.')
args = parser.parse_args()
generator = torch.Generator().manual_seed(args.seed)
diffusion_steps = args.diffusion_steps
guidance = args.guidance
device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
ldm_stable = StableDiffusionPipeline.from_pretrained(args.pretrained_diffusion_path).to(device)
ldm_stable.scheduler = DDIMScheduler.from_config(ldm_stable.scheduler.config)
harmon_fun = run_harmonization if args.use_evaluator else run_harmonization_no_evaluator
if args.is_single_image:
"Test on a single image"
composite_image = Image.open(args.image_path)
mask = Image.open(args.mask_path)
prompts = [[args.foreground_prompt, args.background_prompt]]
harmon_fun(composite_image, prompts, ldm_stable, diffusion_steps, guidance=guidance, generator=generator,
device=device, mask=mask, size=args.size, save_dir=args.save_dir, args=args)
else:
"Test on multiple images"
composite_images = []
mask_images = []
for i in glob.glob(os.path.join(args.images_root, "*")):
composite_images.append(i)
for i in glob.glob(os.path.join(args.masks_root, "*")):
mask_images.append(i)
composite_images = natsorted(composite_images, alg=ns.PATH)
mask_images = natsorted(mask_images, alg=ns.PATH)
with open(args.caption_txt, "r") as f:
data = f.readlines()
captions = []
for i in data:
cap = i.rstrip().split(";")
c_list = []
for i in range(len(cap)):
c = cap[i].split(",")
c_list.append(c)
captions.append(c_list)
for ind, img in enumerate(composite_images):
prefix = img.split("/")[-1][:-4]
composite_image = Image.open(img)
mask = Image.open(mask_images[ind])
args.mask_path = mask_images[ind]
prompts = captions[ind]
harmon_fun(composite_image, prompts, ldm_stable, diffusion_steps, guidance=guidance,
generator=generator, device=device, mask=mask,
save_dir=os.path.join(args.save_dir, prefix), size=args.size, args=args)