-
Notifications
You must be signed in to change notification settings - Fork 4
/
utils.py
326 lines (293 loc) · 11.8 KB
/
utils.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
from transformers import PretrainedConfig
from PIL import Image
import torch
import numpy as np
import PIL
import os
from tqdm.auto import tqdm
from diffusers.models.attention_processor import (
AttnProcessor2_0,
LoRAAttnProcessor2_0,
LoRAXFormersAttnProcessor,
XFormersAttnProcessor,
)
device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')
def myroll2d(a, delta_x, delta_y):
h, w = a.shape[0], a.shape[1]
delta_x = -delta_x
delta_y = -delta_y
if isinstance(a, np.ndarray):
b = np.zeros ([h,w]).astype(np.uint8)
elif isinstance(a, torch.Tensor):
b = torch.zeros([h,w]).to(torch.uint8)
if delta_x > 0:
left_a = delta_x
right_a = w
left_b = 0
right_b = w - delta_x
else:
left_a = 0
right_a = w + delta_x
left_b = -delta_x
right_b = w
if delta_y > 0:
top_a = delta_y
bot_a = h
top_b = 0
bot_b = h-delta_y
else:
top_a = 0
bot_a = h + delta_y
top_b = -delta_y
bot_b = h
b[left_b: right_b, top_b: bot_b] = a[left_a: right_a, top_a: bot_a]
return b
def import_model_class_from_model_name_or_path(
pretrained_model_name_or_path: str, revision = None, subfolder: str = "text_encoder"
):
text_encoder_config = PretrainedConfig.from_pretrained(
pretrained_model_name_or_path, subfolder=subfolder, revision=revision
)
model_class = text_encoder_config.architectures[0]
if model_class == "CLIPTextModel":
from transformers import CLIPTextModel
return CLIPTextModel
elif model_class == "CLIPTextModelWithProjection":
from transformers import CLIPTextModelWithProjection
return CLIPTextModelWithProjection
else:
raise ValueError(f"{model_class} is not supported.")
@torch.no_grad()
def image2latent(image, vae = None, dtype=None):
with torch.no_grad():
if type(image) is Image or type(image) is PIL.PngImagePlugin.PngImageFile or type(image) is PIL.JpegImagePlugin.JpegImageFile:
image = np.array(image)
if type(image) is torch.Tensor and image.dim() == 4:
latents = image
else:
image = torch.from_numpy(image).float() / 127.5 - 1
image = image.permute(2, 0, 1).unsqueeze(0).to(device, dtype= dtype)
latents = vae.encode(image).latent_dist.sample()
latents = latents * vae.config.scaling_factor
return latents
@torch.no_grad()
def latent2image(latents, return_type = 'np', vae = None):
# needs_upcasting = vae.dtype == torch.float16 and vae.config.force_upcast
needs_upcasting = True
if needs_upcasting:
upcast_vae(vae)
latents = latents.to(next(iter(vae.post_quant_conv.parameters())).dtype)
image = vae.decode(latents /vae.config.scaling_factor, return_dict=False)[0]
if return_type == 'np':
image = (image / 2 + 0.5).clamp(0, 1)
image = image.cpu().permute(0, 2, 3, 1).numpy()#[0]
image = (image * 255).astype(np.uint8)
if needs_upcasting:
vae.to(dtype=torch.float16)
return image
def upcast_vae(vae):
dtype = vae.dtype
vae.to(dtype=torch.float32)
use_torch_2_0_or_xformers = isinstance(
vae.decoder.mid_block.attentions[0].processor,
(
AttnProcessor2_0,
XFormersAttnProcessor,
LoRAXFormersAttnProcessor,
LoRAAttnProcessor2_0,
),
)
# if xformers or torch_2_0 is used attention block does not need
# to be in float32 which can save lots of memory
if use_torch_2_0_or_xformers:
vae.post_quant_conv.to(dtype)
vae.decoder.conv_in.to(dtype)
vae.decoder.mid_block.to(dtype)
def prompt_to_emb_length_sdxl(prompt, tokenizer, text_encoder, length = None):
text_input = tokenizer(
[prompt],
padding="max_length",
max_length=length,
truncation=True,
return_tensors="pt",
)
prompt_embeds = text_encoder(text_input.input_ids.to(device),output_hidden_states=True)
pooled_prompt_embeds = prompt_embeds[0]
prompt_embeds = prompt_embeds.hidden_states[-2]
bs_embed, seq_len, _ = prompt_embeds.shape
prompt_embeds = prompt_embeds.view(bs_embed, seq_len, -1)
pooled_prompt_embeds = pooled_prompt_embeds.view(bs_embed, -1)
return {"prompt_embeds": prompt_embeds, "pooled_prompt_embeds": pooled_prompt_embeds}
def prompt_to_emb_length_sd(prompt, tokenizer, text_encoder, length = None):
text_input = tokenizer(
[prompt],
padding="max_length",
max_length=length,
truncation=True,
return_tensors="pt",
)
emb = text_encoder(text_input.input_ids.to(device))[0]
return emb
def sdxl_prepare_input_decom(
set_string_list,
tokenizer,
tokenizer_2,
text_encoder_1,
text_encoder_2,
length = 20,
bsz = 1,
weight_dtype = torch.float32,
resolution = 1024,
normal_token_id_list = []
):
encoder_hidden_states_list = []
pooled_prompt_embeds = 0
for m_idx in range(len(set_string_list)):
prompt_embeds_list = []
if ("#" in set_string_list[m_idx] or "$" in set_string_list[m_idx]) and m_idx not in normal_token_id_list : ###
out = prompt_to_emb_length_sdxl(
set_string_list[m_idx], tokenizer, text_encoder_1, length = length
)
else:
out = prompt_to_emb_length_sdxl(
set_string_list[m_idx], tokenizer, text_encoder_1, length = 77
)
print(m_idx, set_string_list[m_idx])
prompt_embeds, _ = out["prompt_embeds"].to(dtype=weight_dtype), out["pooled_prompt_embeds"].to(dtype=weight_dtype)
prompt_embeds = prompt_embeds.repeat(bsz, 1, 1)
prompt_embeds_list.append(prompt_embeds)
if ("#" in set_string_list[m_idx] or "$" in set_string_list[m_idx]) and m_idx not in normal_token_id_list:
out = prompt_to_emb_length_sdxl(
set_string_list[m_idx], tokenizer_2, text_encoder_2, length = length
)
else:
out = prompt_to_emb_length_sdxl(
set_string_list[m_idx], tokenizer_2, text_encoder_2, length = 77
)
print(m_idx, set_string_list[m_idx])
prompt_embeds = out["prompt_embeds"].to(dtype=weight_dtype)
pooled_prompt_embeds += out["pooled_prompt_embeds"].to(dtype=weight_dtype)
prompt_embeds = prompt_embeds.repeat(bsz, 1, 1)
prompt_embeds_list.append(prompt_embeds)
encoder_hidden_states_list.append(torch.concat(prompt_embeds_list, dim=-1))
add_text_embeds = pooled_prompt_embeds /len(set_string_list)
target_size, original_size,crops_coords_top_left = (resolution,resolution),(resolution,resolution),(0,0)
add_time_ids = list(original_size + crops_coords_top_left + target_size)
add_time_ids = torch.tensor([add_time_ids], dtype=prompt_embeds.dtype,device = pooled_prompt_embeds.device) #[B,6]
return encoder_hidden_states_list, add_text_embeds, add_time_ids
def sd_prepare_input_decom(
set_string_list,
tokenizer,
text_encoder_1,
length = 20,
bsz = 1,
weight_dtype = torch.float32,
normal_token_id_list = []
):
encoder_hidden_states_list = []
for m_idx in range(len(set_string_list)):
if ("#" in set_string_list[m_idx] or "$" in set_string_list[m_idx]) and m_idx not in normal_token_id_list : ###
encoder_hidden_states = prompt_to_emb_length_sd(
set_string_list[m_idx], tokenizer, text_encoder_1, length = length
)
else:
encoder_hidden_states = prompt_to_emb_length_sd(
set_string_list[m_idx], tokenizer, text_encoder_1, length = 77
)
print(m_idx, set_string_list[m_idx])
encoder_hidden_states = encoder_hidden_states.repeat(bsz, 1, 1)
encoder_hidden_states_list.append(encoder_hidden_states.to(dtype=weight_dtype))
return encoder_hidden_states_list
def load_mask (input_folder):
np_mask_dtype = 'uint8'
mask_np_list = []
mask_label_list = []
files = [
file_name for file_name in os.listdir(input_folder) \
if "mask" in file_name and ".npy" in file_name \
and "_" in file_name and "Edited" not in file_name
]
files = sorted(files, key = lambda x: int(x.split("_")[0][4:]))
for idx, file_name in enumerate(files):
if "mask" in file_name and ".npy" in file_name and "_" in file_name \
and "Edited" not in file_name:
mask_np = np.load(os.path.join(input_folder, file_name)).astype(np_mask_dtype)
mask_np_list.append(mask_np)
mask_label = file_name.split("_")[1][:-4]
mask_label_list.append(mask_label)
mask_list = []
for mask_np in mask_np_list:
mask = torch.from_numpy(mask_np)
mask_list.append(mask)
try:
assert torch.all(sum(mask_list)==1)
except:
print("please check mask")
# plt.imsave( "out_mask.png", mask_list_edit[0])
import pdb; pdb.set_trace()
return mask_list, mask_label_list
def load_image(image_path, left=0, right=0, top=0, bottom=0, size = 512):
if type(image_path) is str:
image = np.array(Image.open(image_path))[:, :, :3]
else:
image = image_path
h, w, c = image.shape
left = min(left, w-1)
right = min(right, w - left - 1)
top = min(top, h - left - 1)
bottom = min(bottom, h - top - 1)
image = image[top:h-bottom, left:w-right]
h, w, c = image.shape
if h < w:
offset = (w - h) // 2
image = image[:, offset:offset + h]
elif w < h:
offset = (h - w) // 2
image = image[offset:offset + w]
image = np.array(Image.fromarray(image).resize((size, size)))
return image
def mask_union_torch(*masks):
masks = [m.to(torch.float) for m in masks]
res = sum(masks)>0
return res
def load_mask_edit(input_folder):
np_mask_dtype = 'uint8'
mask_np_list = []
mask_label_list = []
files = [file_name for file_name in os.listdir(input_folder) if "mask" in file_name and ".npy" in file_name and "_" in file_name and "Edited" in file_name and "-1" not in file_name]
files = sorted(files, key = lambda x: int(x.split("_")[0][10:]))
for idx, file_name in enumerate(files):
if "mask" in file_name and ".npy" in file_name and "_" in file_name and "Edited" in file_name and "-1" not in file_name:
mask_np = np.load(os.path.join(input_folder, file_name)).astype(np_mask_dtype)
mask_np_list.append(mask_np)
mask_label = file_name.split("_")[1][:-4]
# mask_label = mask_label.split("-")[0]
mask_label_list.append(mask_label)
mask_list = []
for mask_np in mask_np_list:
mask = torch.from_numpy(mask_np)
mask_list.append(mask)
try:
assert torch.all(sum(mask_list)==1)
except:
print("Make sure maskEdited is in the folder, if not, generate using the UI")
import pdb; pdb.set_trace()
return mask_list, mask_label_list
def save_images(images,filename, num_rows=1, offset_ratio=0.02):
if type(images) is list:
num_empty = len(images) % num_rows
elif images.ndim == 4:
num_empty = images.shape[0] % num_rows
else:
images = [images]
num_empty = 0
empty_images = np.ones(images[0].shape, dtype=np.uint8) * 255
images = [image.astype(np.uint8) for image in images] + [empty_images] * num_empty
num_items = len(images)
folder = os.path.dirname(filename)
for i, image in enumerate(images):
pil_img = Image.fromarray(image)
name = filename.split("/")[-1]
name = name.split(".")[-2]+"_{}".format(i) +"."+filename.split(".")[-1]
pil_img.save(os.path.join(folder, name))
print("saved to ", os.path.join(folder, name))