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Running
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Zero
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import os
import torch
import numpy as np
import argparse
from peft import LoraConfig
from pipeline_dedit_sdxl import DEditSDXLPipeline
from pipeline_dedit_sd import DEditSDPipeline
from utils import load_image, load_mask, load_mask_edit
from utils_mask import process_mask_move_torch, process_mask_remove_torch, mask_union_torch, mask_substract_torch, create_outer_edge_mask_torch
from utils_mask import check_mask_overlap_torch, check_cover_all_torch, visualize_mask_list, get_mask_difference_torch, save_mask_list_to_npys
def run_main(
name="example_tmp",
name_2=None,
mask_np_list=None,
mask_label_list=None,
image_gt=None,
dpm="sd",
resolution=512,
seed=42,
embedding_learning_rate=1e-4,
max_emb_train_steps=200,
diffusion_model_learning_rate=5e-5,
max_diffusion_train_steps=200,
train_batch_size=1,
gradient_accumulation_steps=1,
num_tokens=1,
load_trained=False ,
num_sampling_steps=50,
guidance_scale= 3 ,
strength=0.8,
train_full_lora=False ,
lora_rank=4,
lora_alpha=4,
prompt_auxin_list = None,
prompt_auxin_idx_list= None,
load_edited_mask=False,
load_edited_processed_mask=False,
edge_thickness=20,
num_imgs= 1 ,
active_mask_list = None,
tgt_index=None,
recon=False ,
recon_an_item=False,
recon_prompt=None,
text=False,
tgt_prompt=None,
image=False ,
src_index=None,
tgt_name=None,
move_resize=False ,
tgt_indices_list=None,
delta_x_list=None,
delta_y_list=None,
priority_list=None,
force_mask_remain=None,
resize_list=None,
remove=False,
load_edited_removemask=False
):
torch.cuda.manual_seed_all(seed)
torch.manual_seed(seed)
base_input_folder = "."
base_output_folder = "."
input_folder = os.path.join(base_input_folder, name)
mask_list = []
for mask_np in mask_np_list:
mask = torch.from_numpy(mask_np.astype(np.uint8))
mask_list.append(mask)
#mask_list, mask_label_list = load_mask(input_folder)
assert mask_list[0].shape[0] == resolution, "Segmentation should be done on size {}".format(resolution)
#try:
# image_gt = load_image(os.path.join(input_folder, "img_{}.png".format(resolution) ), size = resolution)
#except:
# image_gt = load_image(os.path.join(input_folder, "img_{}.jpg".format(resolution) ), size = resolution)
if image:
input_folder_2 = os.path.join(base_input_folder, name_2)
mask_list_2, mask_label_list_2 = load_mask(input_folder_2)
assert mask_list_2[0].shape[0] == resolution, "Segmentation should be done on size {}".format(resolution)
try:
image_gt_2 = load_image(os.path.join(input_folder_2, "img_{}.png".format(resolution) ), size = resolution)
except:
image_gt_2 = load_image(os.path.join(input_folder_2, "img_{}.jpg".format(resolution) ), size = resolution)
output_dir = os.path.join(base_output_folder, name + "_" + name_2)
os.makedirs(output_dir, exist_ok = True)
else:
output_dir = os.path.join(base_output_folder, name)
os.makedirs(output_dir, exist_ok = True)
if dpm == "sd":
if image:
pipe = DEditSDPipeline(mask_list, mask_label_list, mask_list_2, mask_label_list_2, resolution = resolution, num_tokens = num_tokens)
else:
pipe = DEditSDPipeline(mask_list, mask_label_list, resolution = resolution, num_tokens = num_tokens)
elif dpm == "sdxl":
if image:
pipe = DEditSDXLPipeline(mask_list, mask_label_list, mask_list_2, mask_label_list_2, resolution = resolution, num_tokens = num_tokens)
else:
pipe = DEditSDXLPipeline(mask_list, mask_label_list, resolution = resolution, num_tokens = num_tokens)
else:
raise NotImplementedError
set_string_list = pipe.set_string_list
if prompt_auxin_list is not None:
for auxin_idx, auxin_prompt in zip(prompt_auxin_idx_list, prompt_auxin_list):
set_string_list[auxin_idx] = auxin_prompt.replace("*", set_string_list[auxin_idx] )
print(set_string_list)
if image:
set_string_list_2 = pipe.set_string_list_2
print(set_string_list_2)
if load_trained:
unet_save_path = os.path.join(output_dir, "unet.pt")
unet_state_dict = torch.load(unet_save_path)
text_encoder1_save_path = os.path.join(output_dir, "text_encoder1.pt")
text_encoder1_state_dict = torch.load(text_encoder1_save_path)
if dpm == "sdxl":
text_encoder2_save_path = os.path.join(output_dir, "text_encoder2.pt")
text_encoder2_state_dict = torch.load(text_encoder2_save_path)
if 'lora' in ''.join(unet_state_dict.keys()):
unet_lora_config = LoraConfig(
r=lora_rank,
lora_alpha=lora_alpha,
init_lora_weights="gaussian",
target_modules=["to_k", "to_q", "to_v", "to_out.0"],
)
pipe.unet.add_adapter(unet_lora_config)
pipe.unet.load_state_dict(unet_state_dict)
pipe.text_encoder.load_state_dict(text_encoder1_state_dict)
if dpm == "sdxl":
pipe.text_encoder_2.load_state_dict(text_encoder2_state_dict)
else:
if image:
pipe.mask_list = [m.cuda() for m in pipe.mask_list]
pipe.mask_list_2 = [m.cuda() for m in pipe.mask_list_2]
pipe.train_emb_2imgs(
image_gt,
image_gt_2,
set_string_list,
set_string_list_2,
gradient_accumulation_steps = gradient_accumulation_steps,
embedding_learning_rate = embedding_learning_rate,
max_emb_train_steps = max_emb_train_steps,
train_batch_size = train_batch_size,
)
pipe.train_model_2imgs(
image_gt,
image_gt_2,
set_string_list,
set_string_list_2,
gradient_accumulation_steps = gradient_accumulation_steps,
max_diffusion_train_steps = max_diffusion_train_steps,
diffusion_model_learning_rate = diffusion_model_learning_rate ,
train_batch_size =train_batch_size,
train_full_lora = train_full_lora,
lora_rank = lora_rank,
lora_alpha = lora_alpha
)
else:
pipe.mask_list = [m.cuda() for m in pipe.mask_list]
pipe.train_emb(
image_gt,
set_string_list,
gradient_accumulation_steps = gradient_accumulation_steps,
embedding_learning_rate = embedding_learning_rate,
max_emb_train_steps = max_emb_train_steps,
train_batch_size = train_batch_size,
)
pipe.train_model(
image_gt,
set_string_list,
gradient_accumulation_steps = gradient_accumulation_steps,
max_diffusion_train_steps = max_diffusion_train_steps,
diffusion_model_learning_rate = diffusion_model_learning_rate ,
train_batch_size = train_batch_size,
train_full_lora = train_full_lora,
lora_rank = lora_rank,
lora_alpha = lora_alpha
)
unet_save_path = os.path.join(output_dir, "unet.pt")
torch.save(pipe.unet.state_dict(),unet_save_path )
text_encoder1_save_path = os.path.join(output_dir, "text_encoder1.pt")
torch.save(pipe.text_encoder.state_dict(), text_encoder1_save_path)
if dpm == "sdxl":
text_encoder2_save_path = os.path.join(output_dir, "text_encoder2.pt")
torch.save(pipe.text_encoder_2.state_dict(), text_encoder2_save_path )
if recon:
output_dir = os.path.join(output_dir, "recon")
os.makedirs(output_dir, exist_ok = True)
if recon_an_item:
mask_list = [torch.from_numpy(np.ones_like(mask_list[0].numpy()))]
tgt_string = set_string_list[tgt_index]
tgt_string = recon_prompt.replace("*", tgt_string)
set_string_list = [tgt_string]
print(set_string_list)
save_path = os.path.join(output_dir, "out_recon.png")
x_np = pipe.inference_with_mask(
save_path,
guidance_scale = guidance_scale,
num_sampling_steps = num_sampling_steps,
seed = seed,
num_imgs = num_imgs,
set_string_list = set_string_list,
mask_list = mask_list
)
if text:
print("*** Text-guided editing ")
output_dir = os.path.join(output_dir, "text")
os.makedirs(output_dir, exist_ok = True)
save_path = os.path.join(output_dir, "out_text.png")
set_string_list[tgt_index] = tgt_prompt
mask_active = torch.zeros_like(mask_list[0])
mask_active = mask_union_torch(mask_active, mask_list[tgt_index])
if active_mask_list is not None:
for midx in active_mask_list:
mask_active = mask_union_torch(mask_active, mask_list[midx])
if load_edited_mask:
mask_list_edited, mask_label_list_edited = load_mask_edit(input_folder)
mask_diff = get_mask_difference_torch(mask_list_edited, mask_list)
mask_active = mask_union_torch(mask_active, mask_diff)
mask_list = mask_list_edited
save_path = os.path.join(output_dir, "out_textEdited.png")
mask_hard = mask_substract_torch(torch.ones_like(mask_list[0]), mask_active)
mask_soft = create_outer_edge_mask_torch(mask_active, edge_thickness = edge_thickness)
mask_hard = mask_substract_torch(mask_hard, mask_soft)
pipe.inference_with_mask(
save_path,
orig_image = image_gt,
set_string_list = set_string_list,
guidance_scale = guidance_scale,
strength = strength,
num_imgs = num_imgs,
mask_hard= mask_hard,
mask_soft = mask_soft,
mask_list = mask_list,
seed = seed,
num_sampling_steps = num_sampling_steps
)
if remove:
output_dir = os.path.join(output_dir, "remove")
save_path = os.path.join(output_dir, "out_remove.png")
os.makedirs(output_dir, exist_ok = True)
mask_active = torch.zeros_like(mask_list[0])
if load_edited_mask:
mask_list_edited, _ = load_mask_edit(input_folder)
mask_diff = get_mask_difference_torch(mask_list_edited, mask_list)
mask_active = mask_union_torch(mask_active, mask_diff)
mask_list = mask_list_edited
if load_edited_processed_mask:
# manually edit or draw masks after removing one index, then load
mask_list_processed, _ = load_mask_edit(output_dir)
mask_remain = get_mask_difference_torch(mask_list_processed, mask_list)
else:
# generate masks after removing one index, using nearest neighbor algorithm
mask_list_processed, mask_remain = process_mask_remove_torch(mask_list, tgt_index)
save_mask_list_to_npys(output_dir, mask_list_processed, mask_label_list, name = "mask")
visualize_mask_list(mask_list_processed, os.path.join(output_dir, "seg_removed.png"))
check_cover_all_torch(*mask_list_processed)
mask_active = mask_union_torch(mask_active, mask_remain)
if active_mask_list is not None:
for midx in active_mask_list:
mask_active = mask_union_torch(mask_active, mask_list[midx])
mask_hard = 1 - mask_active
mask_soft = create_outer_edge_mask_torch(mask_remain, edge_thickness = edge_thickness)
mask_hard = mask_substract_torch(mask_hard, mask_soft)
pipe.inference_with_mask(
save_path,
orig_image = image_gt,
guidance_scale = guidance_scale,
strength = strength,
num_imgs = num_imgs,
mask_hard= mask_hard,
mask_soft = mask_soft,
mask_list = mask_list_processed,
seed = seed,
num_sampling_steps = num_sampling_steps
)
if image:
output_dir = os.path.join(output_dir, "image")
save_path = os.path.join(output_dir, "out_image.png")
os.makedirs(output_dir, exist_ok = True)
mask_active = torch.zeros_like(mask_list[0])
if None not in (tgt_name, src_index, tgt_index):
if tgt_name == name:
set_string_list_tgt = set_string_list
set_string_list_src = set_string_list_2
image_tgt = image_gt
if load_edited_mask:
mask_list_edited, _ = load_mask_edit(input_folder)
mask_diff = get_mask_difference_torch(mask_list_edited, mask_list)
mask_active = mask_union_torch(mask_active, mask_diff)
mask_list = mask_list_edited
save_path = os.path.join(output_dir, "out_imageEdited.png")
mask_list_tgt = mask_list
elif tgt_name == name_2:
set_string_list_tgt = set_string_list_2
set_string_list_src = set_string_list
image_tgt = image_gt_2
if load_edited_mask:
mask_list_2_edited, _ = load_mask_edit(input_folder_2)
mask_diff = get_mask_difference_torch(mask_list_2_edited, mask_list_2)
mask_active = mask_union_torch(mask_active, mask_diff)
mask_list_2 = mask_list_2_edited
save_path = os.path.join(output_dir, "out_imageEdited.png")
mask_list_tgt = mask_list_2
else:
exit("tgt_name should be either name or name_2")
set_string_list_tgt[tgt_index] = set_string_list_src[src_index]
mask_active = mask_list_tgt[tgt_index]
mask_frozen = (1-mask_active.float()).to(mask_active.device)
mask_soft = create_outer_edge_mask_torch(mask_active.cpu(), edge_thickness = edge_thickness)
mask_hard = mask_substract_torch(mask_frozen.cpu(), mask_soft.cpu())
mask_list_tgt = [m.cuda() for m in mask_list_tgt]
pipe.inference_with_mask(
save_path,
set_string_list = set_string_list_tgt,
mask_list = mask_list_tgt,
guidance_scale = guidance_scale,
num_sampling_steps = num_sampling_steps,
mask_hard = mask_hard.cuda(),
mask_soft = mask_soft.cuda(),
num_imgs = num_imgs,
orig_image = image_tgt,
strength = strength,
)
if move_resize:
output_dir = os.path.join(output_dir, "move_resize")
os.makedirs(output_dir, exist_ok = True)
save_path = os.path.join(output_dir, "out_moveresize.png")
mask_active = torch.zeros_like(mask_list[0])
if load_edited_mask:
mask_list_edited, _ = load_mask_edit(input_folder)
mask_diff = get_mask_difference_torch(mask_list_edited, mask_list)
mask_active = mask_union_torch(mask_active, mask_diff)
mask_list = mask_list_edited
# save_path = os.path.join(output_dir, "out_moveresizeEdited.png")
if load_edited_processed_mask:
mask_list_processed, _ = load_mask_edit(output_dir)
mask_remain = get_mask_difference_torch(mask_list_processed, mask_list)
else:
mask_list_processed, mask_remain = process_mask_move_torch(
mask_list,
tgt_indices_list,
delta_x_list,
delta_y_list, priority_list,
force_mask_remain = force_mask_remain,
resize_list = resize_list
)
save_mask_list_to_npys(output_dir, mask_list_processed, mask_label_list, name = "mask")
visualize_mask_list(mask_list_processed, os.path.join(output_dir, "seg_move_resize.png"))
active_idxs = tgt_indices_list
mask_active = mask_union_torch(mask_active, *[m for midx, m in enumerate(mask_list_processed) if midx in active_idxs])
mask_active = mask_union_torch(mask_remain, mask_active)
if active_mask_list is not None:
for midx in active_mask_list:
mask_active = mask_union_torch(mask_active, mask_list_processed[midx])
mask_frozen =(1 - mask_active.float())
mask_soft = create_outer_edge_mask_torch(mask_active, edge_thickness = edge_thickness)
mask_hard = mask_substract_torch(mask_frozen, mask_soft)
check_mask_overlap_torch(mask_hard, mask_soft)
pipe.inference_with_mask(
save_path,
strength = strength,
orig_image = image_gt,
guidance_scale = guidance_scale,
num_sampling_steps = num_sampling_steps,
num_imgs = num_imgs,
mask_hard= mask_hard,
mask_soft = mask_soft,
mask_list = mask_list_processed,
seed = seed
)
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