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disable_verbosity()
import sys
import os
import cv2
import einops
import gradio as gr
import numpy as np
import torch
import random
import json
import argparse
file_path = os.path.abspath(__file__)
parent_dir = os.path.abspath(os.path.dirname(file_path) + '/..')
if parent_dir not in sys.path:
sys.path.append(parent_dir)
from PIL import Image
from pytorch_lightning import seed_everything
from oldm.model import create_model, load_state_dict
from oldm.ddim_hacked import DDIMSampler
from oft import inject_trainable_oft, inject_trainable_oft_conv, inject_trainable_oft_extended, inject_trainable_oft_with_norm
from hra import inject_trainable_hra
from lora import inject_trainable_lora
from dataset.utils import return_dataset
def process(input_image, prompt, hint_image, a_prompt, n_prompt, num_samples, image_resolution, ddim_steps, guess_mode, strength, scale, seed, eta, low_threshold, high_threshold):
with torch.no_grad():
# img = resize_image(HWC3(input_image), image_resolution)
H, W, C = input_image.shape
#detected_map = apply_canny(input_image, low_threshold, high_threshold)
#detected_map = HWC3(detected_map)
# control = torch.from_numpy(hint_image.copy()).float().cuda() / 255.0
control = torch.from_numpy(hint_image.copy()).float().cuda()
control = torch.stack([control for _ in range(num_samples)], dim=0)
control = einops.rearrange(control, 'b h w c -> b c h w').clone()
if seed == -1:
seed = random.randint(0, 65535)
seed_everything(seed)
# if config.save_memory:
# model.low_vram_shift(is_diffusing=False)
cond = {"c_concat": [control], "c_crossattn": [model.get_learned_conditioning([prompt + ', ' + a_prompt] * num_samples)]}
un_cond = {"c_concat": None if guess_mode else [control], "c_crossattn": [model.get_learned_conditioning([n_prompt] * num_samples)]}
shape = (4, H // 8, W // 8)
# if config.save_memory:
# model.low_vram_shift(is_diffusing=True)
model.control_scales = [strength * (0.825 ** float(12 - i)) for i in range(13)] if guess_mode else ([strength] * 13) # Magic number. IDK why. Perhaps because 0.825**12<0.01 but 0.826**12>0.01
samples, intermediates = ddim_sampler.sample(ddim_steps, num_samples,
shape, cond, verbose=False, eta=eta,
unconditional_guidance_scale=scale,
unconditional_conditioning=un_cond)
# if config.save_memory:
# model.low_vram_shift(is_diffusing=False)
x_samples = model.decode_first_stage(samples)
x_samples = (einops.rearrange(x_samples, 'b c h w -> b h w c') * 127.5 + 127.5).cpu().numpy().clip(0, 255).astype(np.uint8)
results = [x_samples[i] for i in range(num_samples)]
# return [255 - hint_image] + results
return [input_image] + [hint_image] + results
parser = argparse.ArgumentParser()
parser.add_argument('--d', type=int, help='the index of GPU', default=0)
# HRA
parser.add_argument('--hra_r', type=int, default=8)
parser.add_argument('--hra_apply_GS', action="store_true", default=False)
# OFT
parser.add_argument('--oft_r', type=int, default=4)
parser.add_argument('--oft_eps',
type=float,
choices=[1e-3, 2e-5, 7e-6],
default=7e-6,
)
parser.add_argument('--oft_coft', action="store_true", default=True)
parser.add_argument('--oft_block_share', action="store_true", default=False)
parser.add_argument('--img_ID', type=int, default=1)
parser.add_argument('--num_samples', type=int, default=1)
parser.add_argument('--batch', type=int, default=20)
parser.add_argument('--sd_locked', action="store_true", default=True)
parser.add_argument('--only_mid_control', action="store_true", default=False)
parser.add_argument('--num_gpus', type=int, default=8)
# parser.add_argument('--time_str', type=str, default=datetime.now().strftime("%Y-%m-%d-%H-%M-%S-%f"))
parser.add_argument('--time_str', type=str, default='2024-03-18-10-55-21-089985')
parser.add_argument('--epoch', type=int, default=19)
parser.add_argument('--control',
type=str,
help='control signal. Options are [segm, sketch, densepose, depth, canny, landmark]',
default="segm")
args = parser.parse_args()
if __name__ == '__main__':
# Configs
epoch = args.epoch
control = args.control
_, dataset, data_name, logger_freq, max_epochs = return_dataset(control, full=True)
# specify the experiment name
# experiment = './log/image_log_oft_{}_{}_eps_{}_pe_diff_mlp_r_{}_{}gpu'.format(data_name, control, args.eps, args.r, args.num_gpus)
num_gpus = args.num_gpus
time_str = args.time_str
# experiment = 'log/image_log_oft_{}_{}_eps_{}_pe_diff_mlp_r_{}_{}gpu_{}'.format(data_name, control, args.eps, args.r, num_gpus, time_str)
experiment = './log/image_log_hra_0.0_ADE20K_segm_pe_diff_mlp_r_8_8gpu_2024-06-27-19-57-34-979197'
# experiment = './log/image_log_oft_ADE20K_segm_eps_0.001_pe_diff_mlp_r_4_8gpu_2024-03-25-21-04-17-549433/train_with_norm'
assert args.control in experiment
if 'train_with_norm' in experiment:
epoch = 4
else:
if 'COCO' in experiment:
epoch = 9
else:
epoch = 19
resume_path = os.path.join(experiment, f'model-epoch={epoch:02d}.ckpt')
sd_locked = args.sd_locked
only_mid_control = args.only_mid_control
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
# Result directory
result_dir = os.path.join(experiment, 'results', str(epoch))
os.makedirs(result_dir, exist_ok=True)
source_dir = os.path.join(experiment, 'source', str(epoch))
os.makedirs(source_dir, exist_ok=True)
hint_dir = os.path.join(experiment, 'hints', str(epoch))
os.makedirs(hint_dir, exist_ok=True)
model = create_model('./configs/oft_ldm_v15.yaml').cpu()
model.model.requires_grad_(False)
if 'hra' in experiment:
unet_lora_params, train_names = inject_trainable_hra(model.model, r=args.hra_r, apply_GS=args.hra_apply_GS)
elif 'lora' in experiment:
unet_lora_params, train_names = inject_trainable_lora(model.model, rank=args.r, network_alpha=None)
else:
if 'train_with_norm' in experiment:
unet_opt_params, train_names = inject_trainable_oft_with_norm(model.model, r=args.oft_r, eps=args.oft_eps, is_coft=args.oft_coft, block_share=args.oft_block_share)
else:
unet_lora_params, train_names = inject_trainable_oft(model.model, r=args.oft_r, eps=args.oft_eps, is_coft=args.oft_coft, block_share=args.oft_block_share)
# unet_lora_params, train_names = inject_trainable_oft_conv(model.model, r=args.r, eps=args.eps, is_coft=args.coft, block_share=args.block_share)
# unet_lora_params, train_names = inject_trainable_oft_extended(model.model, r=args.r, eps=args.eps, is_coft=args.coft, block_share=args.block_share)
model.load_state_dict(load_state_dict(resume_path, location='cuda'))
model = model.cuda()
ddim_sampler = DDIMSampler(model)
# pack = range(0, len(dataset), args.batch)
# formatted_data = {}
# for index in range(args.batch):
# # import ipdb; ipdb.set_trace()
# start_point = pack[args.img_ID]
# idx = start_point + index
# canny
# img_list = [378, 441, 0, 31, 115, 182, 59, 60, 66, 269, ]
# landmark
# img_list = [139, 179, 197, 144, 54, 71, 76, 98, 100, 277, ]
# segm
# img_list = [14, 667, 576, 1387, 1603, 1697, 987, 1830, 1232, 1881, ]
# for idx in img_list:
num_pack = len(dataset) // args.num_gpus
start_idx = args.d * num_pack
end_idx = (args.d + 1) * num_pack if args.d < args.num_gpus - 1 else len(dataset)
for idx in range(start_idx, end_idx):
data = dataset[idx]
input_image, prompt, hint = data['jpg'], data['txt'], data['hint']
# input_image, hint = input_image.to(device), hint.to(device)
if not os.path.exists(os.path.join(result_dir, f'result_{idx}_0.png')):
result_images = process(
input_image=input_image,
prompt=prompt,
hint_image=hint,
a_prompt="",
n_prompt="",
num_samples=args.num_samples,
image_resolution=512,
ddim_steps=50,
guess_mode=False,
strength=1,
scale=9.0,
seed=-1,
eta=0.0,
low_threshold=100,
high_threshold=200,
)
for i, image in enumerate(result_images):
if i == 0:
image = ((image + 1) * 127.5).clip(0, 255).astype(np.uint8)
pil_image = Image.fromarray(image)
output_path = os.path.join(source_dir, f'image_{idx}.png')
pil_image.save(output_path)
elif i == 1:
image = (image * 255).clip(0, 255).astype(np.uint8)
# Convert numpy array to PIL Image
pil_image = Image.fromarray(image)
# Save PIL Image to file
output_path = os.path.join(hint_dir, f'hint_{idx}.png')
pil_image.save(output_path)
else:
n = i - 2
# Convert numpy array to PIL Image
pil_image = Image.fromarray(image)
# Save PIL Image to file
output_path = os.path.join(result_dir, f'result_{idx}_{n}.png')
pil_image.save(output_path)
# formatted_data[f"item{idx}"] = {
# "image_name": f'result_{idx}.png',
# "prompt": prompt
# }
# with open(os.path.join(experiment, 'results_{}.json'.format(img_ID)), 'w') as f:
# json.dump(formatted_data, f)
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