-
-
Notifications
You must be signed in to change notification settings - Fork 888
/
txt2vid.py
1013 lines (805 loc) · 47.4 KB
/
txt2vid.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
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
# This file is part of stable-diffusion-webui (https://github.com/sd-webui/stable-diffusion-webui/).
# Copyright 2022 sd-webui team.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
# base webui import and utils.
"""
Implementation of Text to Video based on the
https://github.com/nateraw/stable-diffusion-videos
repo and the original gist script from
https://gist.github.com/karpathy/00103b0037c5aaea32fe1da1af553355
"""
from sd_utils import *
# streamlit imports
from streamlit import StopException
from streamlit.elements import image as STImage
#streamlit components section
from streamlit_server_state import server_state, server_state_lock
#other imports
import os, sys
from PIL import Image
import torch
import numpy as np
import time, inspect, timeit
import torch
from torch import autocast
from io import BytesIO
import imageio
from slugify import slugify
from diffusers import StableDiffusionPipeline, DiffusionPipeline
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, \
PNDMScheduler
# streamlit components
from custom_components import key_phrase_suggestions
# Temp imports
# end of imports
#---------------------------------------------------------------------------------------------------------------
key_phrase_suggestions.init()
try:
# this silences the annoying "Some weights of the model checkpoint were not used when initializing..." message at start.
from transformers import logging
logging.set_verbosity_error()
except:
pass
class plugin_info():
plugname = "txt2vid"
description = "Text to Image"
isTab = True
displayPriority = 1
#
# -----------------------------------------------------------------------------
@torch.no_grad()
def diffuse(
pipe,
cond_embeddings, # text conditioning, should be (1, 77, 768)
cond_latents, # image conditioning, should be (1, 4, 64, 64)
num_inference_steps,
cfg_scale,
eta,
):
torch_device = cond_latents.get_device()
# classifier guidance: add the unconditional embedding
max_length = cond_embeddings.shape[1] # 77
uncond_input = pipe.tokenizer([""], padding="max_length", max_length=max_length, return_tensors="pt")
uncond_embeddings = pipe.text_encoder(uncond_input.input_ids.to(torch_device))[0]
text_embeddings = torch.cat([uncond_embeddings, cond_embeddings])
# if we use LMSDiscreteScheduler, let's make sure latents are mulitplied by sigmas
if isinstance(pipe.scheduler, LMSDiscreteScheduler):
cond_latents = cond_latents * pipe.scheduler.sigmas[0]
# init the scheduler
accepts_offset = "offset" in set(inspect.signature(pipe.scheduler.set_timesteps).parameters.keys())
extra_set_kwargs = {}
if accepts_offset:
extra_set_kwargs["offset"] = 1
pipe.scheduler.set_timesteps(num_inference_steps + st.session_state.sampling_steps, **extra_set_kwargs)
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
# and should be between [0, 1]
accepts_eta = "eta" in set(inspect.signature(pipe.scheduler.step).parameters.keys())
extra_step_kwargs = {}
if accepts_eta:
extra_step_kwargs["eta"] = eta
step_counter = 0
inference_counter = 0
if "current_chunk_speed" not in st.session_state:
st.session_state["current_chunk_speed"] = 0
if "previous_chunk_speed_list" not in st.session_state:
st.session_state["previous_chunk_speed_list"] = [0]
st.session_state["previous_chunk_speed_list"].append(st.session_state["current_chunk_speed"])
if "update_preview_frequency_list" not in st.session_state:
st.session_state["update_preview_frequency_list"] = [0]
st.session_state["update_preview_frequency_list"].append(st.session_state["update_preview_frequency"])
try:
# diffuse!
for i, t in enumerate(pipe.scheduler.timesteps):
start = timeit.default_timer()
#status_text.text(f"Running step: {step_counter}{total_number_steps} {percent} | {duration:.2f}{speed}")
# expand the latents for classifier free guidance
latent_model_input = torch.cat([cond_latents] * 2)
if isinstance(pipe.scheduler, LMSDiscreteScheduler):
sigma = pipe.scheduler.sigmas[i]
latent_model_input = latent_model_input / ((sigma**2 + 1) ** 0.5)
# predict the noise residual
noise_pred = pipe.unet(latent_model_input, t, encoder_hidden_states=text_embeddings)["sample"]
# cfg
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + cfg_scale * (noise_pred_text - noise_pred_uncond)
# compute the previous noisy sample x_t -> x_t-1
if isinstance(pipe.scheduler, LMSDiscreteScheduler):
cond_latents = pipe.scheduler.step(noise_pred, i, cond_latents, **extra_step_kwargs)["prev_sample"]
else:
cond_latents = pipe.scheduler.step(noise_pred, t, cond_latents, **extra_step_kwargs)["prev_sample"]
#update the preview image if it is enabled and the frequency matches the step_counter
if st.session_state["update_preview"]:
step_counter += 1
if st.session_state["update_preview_frequency"] == step_counter or step_counter == st.session_state.sampling_steps:
if st.session_state.dynamic_preview_frequency:
st.session_state["current_chunk_speed"],
st.session_state["previous_chunk_speed_list"],
st.session_state["update_preview_frequency"],
st.session_state["avg_update_preview_frequency"] = optimize_update_preview_frequency(st.session_state["current_chunk_speed"],
st.session_state["previous_chunk_speed_list"],
st.session_state["update_preview_frequency"],
st.session_state["update_preview_frequency_list"])
#scale and decode the image latents with vae
cond_latents_2 = 1 / 0.18215 * cond_latents
image = pipe.vae.decode(cond_latents_2)
# generate output numpy image as uint8
image = torch.clamp((image["sample"] + 1.0) / 2.0, min=0.0, max=1.0)
image2 = transforms.ToPILImage()(image.squeeze_(0))
st.session_state["preview_image"].image(image2)
step_counter = 0
duration = timeit.default_timer() - start
st.session_state["current_chunk_speed"] = duration
if duration >= 1:
speed = "s/it"
else:
speed = "it/s"
duration = 1 / duration
if i > st.session_state.sampling_steps:
inference_counter += 1
inference_percent = int(100 * float(inference_counter + 1 if inference_counter < num_inference_steps else num_inference_steps)/float(num_inference_steps))
inference_progress = f"{inference_counter + 1 if inference_counter < num_inference_steps else num_inference_steps}/{num_inference_steps} {inference_percent}% "
else:
inference_progress = ""
percent = int(100 * float(i+1 if i+1 < st.session_state.sampling_steps else st.session_state.sampling_steps)/float(st.session_state.sampling_steps))
frames_percent = int(100 * float(st.session_state.current_frame if st.session_state.current_frame < st.session_state.max_duration_in_seconds else st.session_state.max_duration_in_seconds)/float(
st.session_state.max_duration_in_seconds))
if "progress_bar_text" in st.session_state:
st.session_state["progress_bar_text"].text(
f"Running step: {i+1 if i+1 < st.session_state.sampling_steps else st.session_state.sampling_steps}/{st.session_state.sampling_steps} "
f"{percent if percent < 100 else 100}% {inference_progress}{duration:.2f}{speed} | "
f"Frame: {st.session_state.current_frame + 1 if st.session_state.current_frame < st.session_state.max_duration_in_seconds else st.session_state.max_duration_in_seconds}/{st.session_state.max_duration_in_seconds} "
f"{frames_percent if frames_percent < 100 else 100}% {st.session_state.frame_duration:.2f}{st.session_state.frame_speed}"
)
if "progress_bar" in st.session_state:
st.session_state["progress_bar"].progress(percent if percent < 100 else 100)
except KeyError:
raise StopException
#scale and decode the image latents with vae
cond_latents_2 = 1 / 0.18215 * cond_latents
image = pipe.vae.decode(cond_latents_2)
# generate output numpy image as uint8
image = torch.clamp((image["sample"] + 1.0) / 2.0, min=0.0, max=1.0)
image2 = transforms.ToPILImage()(image.squeeze_(0))
return image2
#
def load_diffusers_model(weights_path,torch_device):
with server_state_lock["model"]:
if "model" in server_state:
del server_state["model"]
if "textual_inversion" in st.session_state:
del st.session_state['textual_inversion']
try:
with server_state_lock["pipe"]:
if "pipe" not in server_state:
if "weights_path" in st.session_state and st.session_state["weights_path"] != weights_path:
del st.session_state["weights_path"]
st.session_state["weights_path"] = weights_path
server_state['float16'] = st.session_state['defaults'].general.use_float16
server_state['no_half'] = st.session_state['defaults'].general.no_half
server_state['optimized'] = st.session_state['defaults'].general.optimized
#if folder "models/diffusers/stable-diffusion-v1-4" exists, load the model from there
if weights_path == "CompVis/stable-diffusion-v1-4":
model_path = os.path.join("models", "diffusers", "stable-diffusion-v1-4")
if weights_path == "runwayml/stable-diffusion-v1-5":
model_path = os.path.join("models", "diffusers", "stable-diffusion-v1-5")
if not os.path.exists(model_path + "/model_index.json"):
server_state["pipe"] = DiffusionPipeline.from_pretrained(
weights_path,
use_local_file=True,
use_auth_token=st.session_state["defaults"].general.huggingface_token,
torch_dtype=torch.float16 if st.session_state['defaults'].general.use_float16 else None,
revision="fp16" if not st.session_state['defaults'].general.no_half else None,
safety_checker=None, # Very important for videos...lots of false positives while interpolating
custom_pipeline="interpolate_stable_diffusion",
)
DiffusionPipeline.save_pretrained(server_state["pipe"], model_path)
else:
server_state["pipe"] = DiffusionPipeline.from_pretrained(
model_path,
use_local_file=True,
torch_dtype=torch.float16 if st.session_state['defaults'].general.use_float16 else None,
revision="fp16" if not st.session_state['defaults'].general.no_half else None,
safety_checker=None, # Very important for videos...lots of false positives while interpolating
custom_pipeline="interpolate_stable_diffusion",
)
server_state["pipe"].unet.to(torch_device)
server_state["pipe"].vae.to(torch_device)
server_state["pipe"].text_encoder.to(torch_device)
if st.session_state.defaults.general.enable_attention_slicing:
server_state["pipe"].enable_attention_slicing()
if st.session_state.defaults.general.enable_minimal_memory_usage:
server_state["pipe"].enable_minimal_memory_usage()
logger.info("Tx2Vid Model Loaded")
else:
# if the float16 or no_half options have changed since the last time the model was loaded then we need to reload the model.
if ("float16" in server_state and server_state['float16'] != st.session_state['defaults'].general.use_float16) \
or ("no_half" in server_state and server_state['no_half'] != st.session_state['defaults'].general.no_half) \
or ("optimized" in server_state and server_state['optimized'] != st.session_state['defaults'].general.optimized):
del server_state['float16']
del server_state['no_half']
with server_state_lock["pipe"]:
del server_state["pipe"]
torch_gc()
del server_state['optimized']
server_state['float16'] = st.session_state['defaults'].general.use_float16
server_state['no_half'] = st.session_state['defaults'].general.no_half
server_state['optimized'] = st.session_state['defaults'].general.optimized
load_diffusers_model(weights_path, torch_device)
else:
logger.info("Tx2Vid Model already Loaded")
except (EnvironmentError, OSError) as e:
if "huggingface_token" not in st.session_state or st.session_state["defaults"].general.huggingface_token == "None":
if "progress_bar_text" in st.session_state:
st.session_state["progress_bar_text"].error(
"You need a huggingface token in order to use the Text to Video tab. Use the Settings page from the sidebar on the left to add your token."
)
raise OSError("You need a huggingface token in order to use the Text to Video tab. Use the Settings page from the sidebar on the left to add your token.")
else:
if "progress_bar_text" in st.session_state:
st.session_state["progress_bar_text"].error(e)
#
def save_video_to_disk(frames, seeds, sanitized_prompt, fps=6,save_video=True, outdir='outputs'):
if save_video:
# write video to memory
#output = io.BytesIO()
#writer = imageio.get_writer(os.path.join(os.getcwd(), st.session_state['defaults'].general.outdir, "txt2vid"), im, extension=".mp4", fps=30)
#try:
video_path = os.path.join(os.getcwd(), outdir, "txt2vid",f"{seeds}_{sanitized_prompt}.mp4")
writer = imageio.get_writer(video_path, fps=fps)
for frame in frames:
writer.append_data(frame)
writer.close()
#except:
# print("Can't save video, skipping.")
return video_path
#
def txt2vid(
# --------------------------------------
# args you probably want to change
prompts = ["blueberry spaghetti", "strawberry spaghetti"], # prompt to dream about
gpu:int = st.session_state['defaults'].general.gpu, # id of the gpu to run on
#name:str = 'test', # name of this project, for the output directory
#rootdir:str = st.session_state['defaults'].general.outdir,
num_steps:int = 200, # number of steps between each pair of sampled points
max_duration_in_seconds:int = 30, # number of frames to write and then exit the script
num_inference_steps:int = 50, # more (e.g. 100, 200 etc) can create slightly better images
cfg_scale:float = 5.0, # can depend on the prompt. usually somewhere between 3-10 is good
save_video = True,
save_video_on_stop = False,
outdir='outputs',
do_loop = False,
use_lerp_for_text = False,
seeds = None,
quality:int = 100, # for jpeg compression of the output images
eta:float = 0.0,
width:int = 256,
height:int = 256,
weights_path = "runwayml/stable-diffusion-v1-5",
scheduler="klms", # choices: default, ddim, klms
disable_tqdm = False,
#-----------------------------------------------
beta_start = 0.0001,
beta_end = 0.00012,
beta_schedule = "scaled_linear",
starting_image=None
):
"""
prompt = ["blueberry spaghetti", "strawberry spaghetti"], # prompt to dream about
gpu:int = st.session_state['defaults'].general.gpu, # id of the gpu to run on
#name:str = 'test', # name of this project, for the output directory
#rootdir:str = st.session_state['defaults'].general.outdir,
num_steps:int = 200, # number of steps between each pair of sampled points
max_duration_in_seconds:int = 10000, # number of frames to write and then exit the script
num_inference_steps:int = 50, # more (e.g. 100, 200 etc) can create slightly better images
cfg_scale:float = 5.0, # can depend on the prompt. usually somewhere between 3-10 is good
do_loop = False,
use_lerp_for_text = False,
seed = None,
quality:int = 100, # for jpeg compression of the output images
eta:float = 0.0,
width:int = 256,
height:int = 256,
weights_path = "runwayml/stable-diffusion-v1-5",
scheduler="klms", # choices: default, ddim, klms
disable_tqdm = False,
beta_start = 0.0001,
beta_end = 0.00012,
beta_schedule = "scaled_linear"
"""
mem_mon = MemUsageMonitor('MemMon')
mem_mon.start()
seeds = seed_to_int(seeds)
# We add an extra frame because most
# of the time the first frame is just the noise.
#max_duration_in_seconds +=1
assert torch.cuda.is_available()
assert height % 8 == 0 and width % 8 == 0
torch.manual_seed(seeds)
torch_device = f"cuda:{gpu}"
# init the output dir
sanitized_prompt = slugify(prompts)
full_path = os.path.join(os.getcwd(), st.session_state['defaults'].general.outdir, "txt2vid", "samples", sanitized_prompt)
if len(full_path) > 220:
sanitized_prompt = sanitized_prompt[:220-len(full_path)]
full_path = os.path.join(os.getcwd(), st.session_state['defaults'].general.outdir, "txt2vid", "samples", sanitized_prompt)
os.makedirs(full_path, exist_ok=True)
# Write prompt info to file in output dir so we can keep track of what we did
if st.session_state.write_info_files:
with open(os.path.join(full_path , f'{slugify(str(seeds))}_config.json' if len(prompts) > 1 else "prompts_config.json"), "w") as outfile:
outfile.write(json.dumps(
dict(
prompts = prompts,
gpu = gpu,
num_steps = num_steps,
max_duration_in_seconds = max_duration_in_seconds,
num_inference_steps = num_inference_steps,
cfg_scale = cfg_scale,
do_loop = do_loop,
use_lerp_for_text = use_lerp_for_text,
seeds = seeds,
quality = quality,
eta = eta,
width = width,
height = height,
weights_path = weights_path,
scheduler=scheduler,
disable_tqdm = disable_tqdm,
beta_start = beta_start,
beta_end = beta_end,
beta_schedule = beta_schedule
),
indent=2,
sort_keys=False,
))
#print(scheduler)
default_scheduler = PNDMScheduler(
beta_start=beta_start, beta_end=beta_end, beta_schedule=beta_schedule
)
# ------------------------------------------------------------------------------
#Schedulers
ddim_scheduler = DDIMScheduler(
beta_start=beta_start,
beta_end=beta_end,
beta_schedule=beta_schedule,
clip_sample=False,
set_alpha_to_one=False,
)
klms_scheduler = LMSDiscreteScheduler(
beta_start=beta_start, beta_end=beta_end, beta_schedule=beta_schedule
)
SCHEDULERS = dict(default=default_scheduler, ddim=ddim_scheduler, klms=klms_scheduler)
with st.session_state["progress_bar_text"].container():
with hc.HyLoader('Loading Models...', hc.Loaders.standard_loaders,index=[0]):
load_diffusers_model(weights_path, torch_device)
if "pipe" not in server_state:
logger.error('wtf')
server_state["pipe"].scheduler = SCHEDULERS[scheduler]
server_state["pipe"].use_multiprocessing_for_evaluation = False
server_state["pipe"].use_multiprocessed_decoding = False
if do_loop:
prompts = str([prompts, prompts])
seeds = [seeds, seeds]
#first_seed, *seeds = seeds
#prompts.append(prompts)
#seeds.append(first_seed)
with torch.autocast('cuda'):
# get the conditional text embeddings based on the prompt
text_input = server_state["pipe"].tokenizer(prompts, padding="max_length", max_length=server_state["pipe"].tokenizer.model_max_length, truncation=True, return_tensors="pt")
cond_embeddings = server_state["pipe"].text_encoder(text_input.input_ids.to(torch_device) )[0]
#
if st.session_state.defaults.general.use_sd_concepts_library:
prompt_tokens = re.findall('<([a-zA-Z0-9-]+)>', prompts)
if prompt_tokens:
# compviz
#tokenizer = (st.session_state["model"] if not st.session_state['defaults'].general.optimized else st.session_state.modelCS).cond_stage_model.tokenizer
#text_encoder = (st.session_state["model"] if not st.session_state['defaults'].general.optimized else st.session_state.modelCS).cond_stage_model.transformer
# diffusers
tokenizer = st.session_state.pipe.tokenizer
text_encoder = st.session_state.pipe.text_encoder
ext = ('pt', 'bin')
#print (prompt_tokens)
if len(prompt_tokens) > 1:
for token_name in prompt_tokens:
embedding_path = os.path.join(st.session_state['defaults'].general.sd_concepts_library_folder, token_name)
if os.path.exists(embedding_path):
for files in os.listdir(embedding_path):
if files.endswith(ext):
load_learned_embed_in_clip(f"{os.path.join(embedding_path, files)}", text_encoder, tokenizer, f"<{token_name}>")
else:
embedding_path = os.path.join(st.session_state['defaults'].general.sd_concepts_library_folder, prompt_tokens[0])
if os.path.exists(embedding_path):
for files in os.listdir(embedding_path):
if files.endswith(ext):
load_learned_embed_in_clip(f"{os.path.join(embedding_path, files)}", text_encoder, tokenizer, f"<{prompt_tokens[0]}>")
# sample a source
init1 = torch.randn((1, server_state["pipe"].unet.in_channels, height // 8, width // 8), device=torch_device)
# iterate the loop
frames = []
frame_index = 0
second_count = 1
st.session_state["total_frames_avg_duration"] = []
st.session_state["total_frames_avg_speed"] = []
try:
while second_count < max_duration_in_seconds:
st.session_state["frame_duration"] = 0
st.session_state["frame_speed"] = 0
st.session_state["current_frame"] = frame_index
#print(f"Second: {second_count+1}/{max_duration_in_seconds}")
# sample the destination
init2 = torch.randn((1, server_state["pipe"].unet.in_channels, height // 8, width // 8), device=torch_device)
for i, t in enumerate(np.linspace(0, 1, num_steps)):
start = timeit.default_timer()
logger.info(f"COUNT: {frame_index+1}/{max_duration_in_seconds}")
if use_lerp_for_text:
init = torch.lerp(init1, init2, float(t))
else:
init = slerp(gpu, float(t), init1, init2)
#init = slerp(gpu, float(t), init1, init2)
with autocast("cuda"):
image = diffuse(server_state["pipe"], cond_embeddings, init, num_inference_steps, cfg_scale, eta)
if st.session_state["save_individual_images"] and not st.session_state["use_GFPGAN"] and not st.session_state["use_RealESRGAN"]:
#im = Image.fromarray(image)
outpath = os.path.join(full_path, 'frame%06d.png' % frame_index)
image.save(outpath, quality=quality)
# send the image to the UI to update it
#st.session_state["preview_image"].image(im)
#append the frames to the frames list so we can use them later.
frames.append(np.asarray(image))
#
#try:
#if st.session_state["use_GFPGAN"] and server_state["GFPGAN"] is not None and not st.session_state["use_RealESRGAN"]:
if st.session_state["use_GFPGAN"] and server_state["GFPGAN"] is not None:
#print("Running GFPGAN on image ...")
if "progress_bar_text" in st.session_state:
st.session_state["progress_bar_text"].text("Running GFPGAN on image ...")
#skip_save = True # #287 >_>
torch_gc()
cropped_faces, restored_faces, restored_img = server_state["GFPGAN"].enhance(np.array(image)[:,:,::-1], has_aligned=False, only_center_face=False, paste_back=True)
gfpgan_sample = restored_img[:,:,::-1]
gfpgan_image = Image.fromarray(gfpgan_sample)
outpath = os.path.join(full_path, 'frame%06d.png' % frame_index)
gfpgan_image.save(outpath, quality=quality)
#append the frames to the frames list so we can use them later.
frames.append(np.asarray(gfpgan_image))
try:
st.session_state["preview_image"].image(gfpgan_image)
except KeyError:
logger.error ("Cant get session_state, skipping image preview.")
#except (AttributeError, KeyError):
#print("Cant perform GFPGAN, skipping.")
#increase frame_index counter.
frame_index += 1
st.session_state["current_frame"] = frame_index
duration = timeit.default_timer() - start
if duration >= 1:
speed = "s/it"
else:
speed = "it/s"
duration = 1 / duration
st.session_state["frame_duration"] = duration
st.session_state["frame_speed"] = speed
init1 = init2
# save the video after the generation is done.
video_path = save_video_to_disk(frames, seeds, sanitized_prompt, save_video=save_video, outdir=outdir)
except StopException:
if save_video_on_stop:
logger.info("Streamlit Stop Exception Received. Saving video")
video_path = save_video_to_disk(frames, seeds, sanitized_prompt, save_video=save_video, outdir=outdir)
else:
video_path = None
if video_path and "preview_video" in st.session_state:
# show video preview on the UI
st.session_state["preview_video"].video(open(video_path, 'rb').read())
mem_max_used, mem_total = mem_mon.read_and_stop()
time_diff = time.time()- start
info = f"""
{prompts}
Sampling Steps: {num_steps}, Sampler: {scheduler}, CFG scale: {cfg_scale}, Seed: {seeds}, Max Frames: {max_duration_in_seconds}""".strip()
stats = f'''
Took { round(time_diff, 2) }s total ({ round(time_diff/(max_duration_in_seconds),2) }s per image)
Peak memory usage: { -(mem_max_used // -1_048_576) } MiB / { -(mem_total // -1_048_576) } MiB / { round(mem_max_used/mem_total*100, 3) }%'''
return video_path, seeds, info, stats
#
def layout():
with st.form("txt2vid-inputs"):
st.session_state["generation_mode"] = "txt2vid"
input_col1, generate_col1 = st.columns([10,1])
with input_col1:
#prompt = st.text_area("Input Text","")
placeholder = "A corgi wearing a top hat as an oil painting."
prompt = st.text_area("Input Text","", placeholder=placeholder, height=54)
key_phrase_suggestions.suggestion_area(placeholder)
# Every form must have a submit button, the extra blank spaces is a temp way to align it with the input field. Needs to be done in CSS or some other way.
generate_col1.write("")
generate_col1.write("")
generate_button = generate_col1.form_submit_button("Generate")
# creating the page layout using columns
col1, col2, col3 = st.columns([1,2,1], gap="large")
with col1:
width = st.slider("Width:", min_value=st.session_state['defaults'].txt2vid.width.min_value, max_value=st.session_state['defaults'].txt2vid.width.max_value,
value=st.session_state['defaults'].txt2vid.width.value, step=st.session_state['defaults'].txt2vid.width.step)
height = st.slider("Height:", min_value=st.session_state['defaults'].txt2vid.height.min_value, max_value=st.session_state['defaults'].txt2vid.height.max_value,
value=st.session_state['defaults'].txt2vid.height.value, step=st.session_state['defaults'].txt2vid.height.step)
cfg_scale = st.number_input("CFG (Classifier Free Guidance Scale):", min_value=st.session_state['defaults'].txt2vid.cfg_scale.min_value,
value=st.session_state['defaults'].txt2vid.cfg_scale.value,
step=st.session_state['defaults'].txt2vid.cfg_scale.step,
help="How strongly the image should follow the prompt.")
#uploaded_images = st.file_uploader("Upload Image", accept_multiple_files=False, type=["png", "jpg", "jpeg", "webp"],
#help="Upload an image which will be used for the image to image generation.")
seed = st.text_input("Seed:", value=st.session_state['defaults'].txt2vid.seed, help=" The seed to use, if left blank a random seed will be generated.")
#batch_count = st.slider("Batch count.", min_value=1, max_value=100, value=st.session_state['defaults'].txt2vid.batch_count,
# step=1, help="How many iterations or batches of images to generate in total.")
#batch_size = st.slider("Batch size", min_value=1, max_value=250, value=st.session_state['defaults'].txt2vid.batch_size, step=1,
#help="How many images are at once in a batch.\
#It increases the VRAM usage a lot but if you have enough VRAM it can reduce the time it takes to finish generation as more images are generated at once.\
#Default: 1")
st.session_state["max_duration_in_seconds"] = st.number_input("Max Duration In Seconds:", value=st.session_state['defaults'].txt2vid.max_duration_in_seconds,
help="Specify the max duration in seconds you want your video to be.")
with st.expander("Preview Settings"):
#st.session_state["update_preview"] = st.checkbox("Update Image Preview", value=st.session_state['defaults'].txt2vid.update_preview,
#help="If enabled the image preview will be updated during the generation instead of at the end. \
#You can use the Update Preview \Frequency option bellow to customize how frequent it's updated. \
#By default this is enabled and the frequency is set to 1 step.")
st.session_state["update_preview"] = st.session_state["defaults"].general.update_preview
st.session_state["update_preview_frequency"] = st.number_input("Update Image Preview Frequency",
min_value=1,
value=st.session_state['defaults'].txt2vid.update_preview_frequency,
help="Frequency in steps at which the the preview image is updated. By default the frequency \
is set to 1 step.")
st.session_state["dynamic_preview_frequency"] = st.checkbox("Dynamic Preview Frequency", value=st.session_state['defaults'].txt2vid.dynamic_preview_frequency,
help="This option tries to find the best value at which we can update \
the preview image during generation while minimizing the impact it has in performance. Default: True")
#
with col2:
preview_tab, gallery_tab = st.tabs(["Preview", "Gallery"])
with preview_tab:
#st.write("Image")
#Image for testing
#image = Image.open(requests.get("https://icon-library.com/images/image-placeholder-icon/image-placeholder-icon-13.jpg", stream=True).raw).convert('RGB')
#new_image = image.resize((175, 240))
#preview_image = st.image(image)
# create an empty container for the image, progress bar, etc so we can update it later and use session_state to hold them globally.
st.session_state["preview_image"] = st.empty()
st.session_state["loading"] = st.empty()
st.session_state["progress_bar_text"] = st.empty()
st.session_state["progress_bar"] = st.empty()
#generate_video = st.empty()
st.session_state["preview_video"] = st.empty()
preview_video = st.session_state["preview_video"]
message = st.empty()
with gallery_tab:
st.write('Here should be the image gallery, if I could make a grid in streamlit.')
with col3:
# If we have custom models available on the "models/custom"
#folder then we show a menu to select which model we want to use, otherwise we use the main model for SD
custom_models_available()
if server_state["CustomModel_available"]:
custom_model = st.selectbox("Custom Model:", st.session_state["defaults"].txt2vid.custom_models_list,
index=st.session_state["defaults"].txt2vid.custom_models_list.index(st.session_state["defaults"].txt2vid.default_model),
help="Select the model you want to use. This option is only available if you have custom models \
on your 'models/custom' folder. The model name that will be shown here is the same as the name\
the file for the model has on said folder, it is recommended to give the .ckpt file a name that \
will make it easier for you to distinguish it from other models. Default: Stable Diffusion v1.5")
else:
custom_model = "runwayml/stable-diffusion-v1-5"
#st.session_state["weights_path"] = custom_model
#else:
#custom_model = "runwayml/stable-diffusion-v1-5"
#st.session_state["weights_path"] = f"CompVis/{slugify(custom_model.lower())}"
st.session_state.sampling_steps = st.number_input("Sampling Steps", value=st.session_state['defaults'].txt2vid.sampling_steps.value,
min_value=st.session_state['defaults'].txt2vid.sampling_steps.min_value,
step=st.session_state['defaults'].txt2vid.sampling_steps.step, help="Number of steps between each pair of sampled points")
st.session_state.num_inference_steps = st.number_input("Inference Steps:", value=st.session_state['defaults'].txt2vid.num_inference_steps.value,
min_value=st.session_state['defaults'].txt2vid.num_inference_steps.min_value,
step=st.session_state['defaults'].txt2vid.num_inference_steps.step,
help="Higher values (e.g. 100, 200 etc) can create better images.")
#sampler_name_list = ["k_lms", "k_euler", "k_euler_a", "k_dpm_2", "k_dpm_2_a", "k_heun", "PLMS", "DDIM"]
#sampler_name = st.selectbox("Sampling method", sampler_name_list,
#index=sampler_name_list.index(st.session_state['defaults'].txt2vid.default_sampler), help="Sampling method to use. Default: k_euler")
scheduler_name_list = ["klms", "ddim"]
scheduler_name = st.selectbox("Scheduler:", scheduler_name_list,
index=scheduler_name_list.index(st.session_state['defaults'].txt2vid.scheduler_name), help="Scheduler to use. Default: klms")
beta_scheduler_type_list = ["scaled_linear", "linear"]
beta_scheduler_type = st.selectbox("Beta Schedule Type:", beta_scheduler_type_list,
index=beta_scheduler_type_list.index(st.session_state['defaults'].txt2vid.beta_scheduler_type), help="Schedule Type to use. Default: linear")
#basic_tab, advanced_tab = st.tabs(["Basic", "Advanced"])
#with basic_tab:
#summit_on_enter = st.radio("Submit on enter?", ("Yes", "No"), horizontal=True,
#help="Press the Enter key to summit, when 'No' is selected you can use the Enter key to write multiple lines.")
with st.expander("Advanced"):
with st.expander("Output Settings"):
st.session_state["separate_prompts"] = st.checkbox("Create Prompt Matrix.", value=st.session_state['defaults'].txt2vid.separate_prompts,
help="Separate multiple prompts using the `|` character, and get all combinations of them.")
st.session_state["normalize_prompt_weights"] = st.checkbox("Normalize Prompt Weights.",
value=st.session_state['defaults'].txt2vid.normalize_prompt_weights, help="Ensure the sum of all weights add up to 1.0")
st.session_state["save_individual_images"] = st.checkbox("Save individual images.",
value=st.session_state['defaults'].txt2vid.save_individual_images,
help="Save each image generated before any filter or enhancement is applied.")
st.session_state["save_video"] = st.checkbox("Save video",value=st.session_state['defaults'].txt2vid.save_video,
help="Save a video with all the images generated as frames at the end of the generation.")
save_video_on_stop = st.checkbox("Save video on Stop",value=st.session_state['defaults'].txt2vid.save_video_on_stop,
help="Save a video with all the images generated as frames when we hit the stop button during a generation.")
st.session_state["group_by_prompt"] = st.checkbox("Group results by prompt", value=st.session_state['defaults'].txt2vid.group_by_prompt,
help="Saves all the images with the same prompt into the same folder. When using a prompt \
matrix each prompt combination will have its own folder.")
st.session_state["write_info_files"] = st.checkbox("Write Info file", value=st.session_state['defaults'].txt2vid.write_info_files,
help="Save a file next to the image with informartion about the generation.")
st.session_state["do_loop"] = st.checkbox("Do Loop", value=st.session_state['defaults'].txt2vid.do_loop,
help="Loop the prompt making two prompts from a single one.")
st.session_state["use_lerp_for_text"] = st.checkbox("Use Lerp Instead of Slerp", value=st.session_state['defaults'].txt2vid.use_lerp_for_text,
help="Uses torch.lerp() instead of slerp. When interpolating between related prompts. \
e.g. 'a lion in a grassy meadow' -> 'a bear in a grassy meadow' tends to keep the meadow \
the whole way through when lerped, but slerping will often find a path where the meadow \
disappears in the middle")
st.session_state["save_as_jpg"] = st.checkbox("Save samples as jpg", value=st.session_state['defaults'].txt2vid.save_as_jpg, help="Saves the images as jpg instead of png.")
#
if "GFPGAN_available" not in st.session_state:
GFPGAN_available()
if "RealESRGAN_available" not in st.session_state:
RealESRGAN_available()
if "LDSR_available" not in st.session_state:
LDSR_available()
if st.session_state["GFPGAN_available"] or st.session_state["RealESRGAN_available"] or st.session_state["LDSR_available"]:
with st.expander("Post-Processing"):
face_restoration_tab, upscaling_tab = st.tabs(["Face Restoration", "Upscaling"])
with face_restoration_tab:
# GFPGAN used for face restoration
if st.session_state["GFPGAN_available"]:
#with st.expander("Face Restoration"):
#if st.session_state["GFPGAN_available"]:
#with st.expander("GFPGAN"):
st.session_state["use_GFPGAN"] = st.checkbox("Use GFPGAN", value=st.session_state['defaults'].txt2vid.use_GFPGAN,
help="Uses the GFPGAN model to improve faces after the generation.\
This greatly improve the quality and consistency of faces but uses\
extra VRAM. Disable if you need the extra VRAM.")
st.session_state["GFPGAN_model"] = st.selectbox("GFPGAN model", st.session_state["GFPGAN_models"],
index=st.session_state["GFPGAN_models"].index(st.session_state['defaults'].general.GFPGAN_model))
#st.session_state["GFPGAN_strenght"] = st.slider("Effect Strenght", min_value=1, max_value=100, value=1, step=1, help='')
else:
st.session_state["use_GFPGAN"] = False
with upscaling_tab:
st.session_state['us_upscaling'] = st.checkbox("Use Upscaling", value=st.session_state['defaults'].txt2vid.use_upscaling)
# RealESRGAN and LDSR used for upscaling.
if st.session_state["RealESRGAN_available"] or st.session_state["LDSR_available"]:
upscaling_method_list = []
if st.session_state["RealESRGAN_available"]:
upscaling_method_list.append("RealESRGAN")
if st.session_state["LDSR_available"]:
upscaling_method_list.append("LDSR")
st.session_state["upscaling_method"] = st.selectbox("Upscaling Method", upscaling_method_list,
index=upscaling_method_list.index(st.session_state['defaults'].general.upscaling_method))
if st.session_state["RealESRGAN_available"]:
with st.expander("RealESRGAN"):
if st.session_state["upscaling_method"] == "RealESRGAN" and st.session_state['us_upscaling']:
st.session_state["use_RealESRGAN"] = True
else:
st.session_state["use_RealESRGAN"] = False
st.session_state["RealESRGAN_model"] = st.selectbox("RealESRGAN model", st.session_state["RealESRGAN_models"],
index=st.session_state["RealESRGAN_models"].index(st.session_state['defaults'].general.RealESRGAN_model))
else:
st.session_state["use_RealESRGAN"] = False
st.session_state["RealESRGAN_model"] = "RealESRGAN_x4plus"
#
if st.session_state["LDSR_available"]:
with st.expander("LDSR"):
if st.session_state["upscaling_method"] == "LDSR" and st.session_state['us_upscaling']:
st.session_state["use_LDSR"] = True
else:
st.session_state["use_LDSR"] = False
st.session_state["LDSR_model"] = st.selectbox("LDSR model", st.session_state["LDSR_models"],
index=st.session_state["LDSR_models"].index(st.session_state['defaults'].general.LDSR_model))
st.session_state["ldsr_sampling_steps"] = st.number_input("Sampling Steps", value=st.session_state['defaults'].txt2vid.LDSR_config.sampling_steps,
help="")
st.session_state["preDownScale"] = st.number_input("PreDownScale", value=st.session_state['defaults'].txt2vid.LDSR_config.preDownScale,
help="")
st.session_state["postDownScale"] = st.number_input("postDownScale", value=st.session_state['defaults'].txt2vid.LDSR_config.postDownScale,
help="")
downsample_method_list = ['Nearest', 'Lanczos']
st.session_state["downsample_method"] = st.selectbox("Downsample Method", downsample_method_list,
index=downsample_method_list.index(st.session_state['defaults'].txt2vid.LDSR_config.downsample_method))
else:
st.session_state["use_LDSR"] = False
st.session_state["LDSR_model"] = "model"
with st.expander("Variant"):
st.session_state["variant_amount"] = st.number_input("Variant Amount:", value=st.session_state['defaults'].txt2vid.variant_amount.value,
min_value=st.session_state['defaults'].txt2vid.variant_amount.min_value,
max_value=st.session_state['defaults'].txt2vid.variant_amount.max_value,
step=st.session_state['defaults'].txt2vid.variant_amount.step)
st.session_state["variant_seed"] = st.text_input("Variant Seed:", value=st.session_state['defaults'].txt2vid.seed,
help="The seed to use when generating a variant, if left blank a random seed will be generated.")
#st.session_state["beta_start"] = st.slider("Beta Start:", value=st.session_state['defaults'].txt2vid.beta_start.value,
#min_value=st.session_state['defaults'].txt2vid.beta_start.min_value,
#max_value=st.session_state['defaults'].txt2vid.beta_start.max_value,
#step=st.session_state['defaults'].txt2vid.beta_start.step, format=st.session_state['defaults'].txt2vid.beta_start.format)
#st.session_state["beta_end"] = st.slider("Beta End:", value=st.session_state['defaults'].txt2vid.beta_end.value,
#min_value=st.session_state['defaults'].txt2vid.beta_end.min_value, max_value=st.session_state['defaults'].txt2vid.beta_end.max_value,
#step=st.session_state['defaults'].txt2vid.beta_end.step, format=st.session_state['defaults'].txt2vid.beta_end.format)
if generate_button:
#print("Loading models")
# load the models when we hit the generate button for the first time, it wont be loaded after that so dont worry.
#load_models(False, st.session_state["use_GFPGAN"], True, st.session_state["RealESRGAN_model"])
if st.session_state["use_GFPGAN"]:
if "GFPGAN" in server_state:
logger.info("GFPGAN already loaded")
else:
with col2:
with hc.HyLoader('Loading Models...', hc.Loaders.standard_loaders,index=[0]):
# Load GFPGAN
if os.path.exists(st.session_state["defaults"].general.GFPGAN_dir):
try:
load_GFPGAN()
logger.info("Loaded GFPGAN")
except Exception:
import traceback
logger.error("Error loading GFPGAN:", file=sys.stderr)
logger.error(traceback.format_exc(), file=sys.stderr)
else:
if "GFPGAN" in server_state:
del server_state["GFPGAN"]
#try:
# run video generation
video, seed, info, stats = txt2vid(prompts=prompt, gpu=st.session_state["defaults"].general.gpu,
num_steps=st.session_state.sampling_steps, max_duration_in_seconds=st.session_state.max_duration_in_seconds,
num_inference_steps=st.session_state.num_inference_steps,
cfg_scale=cfg_scale, save_video_on_stop=save_video_on_stop,
outdir=st.session_state["defaults"].general.outdir,
do_loop=st.session_state["do_loop"],
use_lerp_for_text=st.session_state["use_lerp_for_text"],
seeds=seed, quality=100, eta=0.0, width=width,
height=height, weights_path=custom_model, scheduler=scheduler_name,
disable_tqdm=False, beta_start=st.session_state['defaults'].txt2vid.beta_start.value,
beta_end=st.session_state['defaults'].txt2vid.beta_end.value,
beta_schedule=beta_scheduler_type, starting_image=None)
if video and save_video_on_stop:
# show video preview on the UI after we hit the stop button
# currently not working as session_state is cleared on StopException
preview_video.video(open(video, 'rb').read())
#message.success('Done!', icon="✅")
message.success('Render Complete: ' + info + '; Stats: ' + stats, icon="✅")
#history_tab,col1,col2,col3,PlaceHolder,col1_cont,col2_cont,col3_cont = st.session_state['historyTab']
#if 'latestVideos' in st.session_state:
#for i in video:
##push the new image to the list of latest images and remove the oldest one
##remove the last index from the list\
#st.session_state['latestVideos'].pop()
##add the new image to the start of the list
#st.session_state['latestVideos'].insert(0, i)
#PlaceHolder.empty()
#with PlaceHolder.container():
#col1, col2, col3 = st.columns(3)
#col1_cont = st.container()
#col2_cont = st.container()
#col3_cont = st.container()
#with col1_cont:
#with col1:
#st.image(st.session_state['latestVideos'][0])
#st.image(st.session_state['latestVideos'][3])
#st.image(st.session_state['latestVideos'][6])
#with col2_cont:
#with col2:
#st.image(st.session_state['latestVideos'][1])
#st.image(st.session_state['latestVideos'][4])
#st.image(st.session_state['latestVideos'][7])
#with col3_cont:
#with col3:
#st.image(st.session_state['latestVideos'][2])
#st.image(st.session_state['latestVideos'][5])
#st.image(st.session_state['latestVideos'][8])