Spaces:
Running
Running
File size: 52,799 Bytes
91fb4ef 32b4f0f 91fb4ef 4905a7d 91fb4ef 947f205 32b4f0f 91fb4ef 947f205 91fb4ef 947f205 91fb4ef 32b4f0f 91fb4ef 32b4f0f 91fb4ef f577b1e 91fb4ef f577b1e 91fb4ef f577b1e 91fb4ef 32b4f0f 4905a7d 32b4f0f 91fb4ef 32b4f0f 91fb4ef 32b4f0f 91fb4ef 32b4f0f 91fb4ef 32b4f0f 91fb4ef 32b4f0f 91fb4ef 32b4f0f 91fb4ef 32b4f0f 91fb4ef c90af3c 91fb4ef 947f205 91fb4ef 947f205 91fb4ef 947f205 91fb4ef c90af3c 91fb4ef 947f205 91fb4ef c90af3c 91fb4ef c90af3c 91fb4ef c90af3c 91fb4ef c90af3c 91fb4ef 32b4f0f 91fb4ef df0584b 91fb4ef 32b4f0f 91fb4ef 32b4f0f 91fb4ef e020146 32b4f0f e020146 91fb4ef 32b4f0f 91fb4ef |
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 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 |
import platform
import subprocess
#import sys
#print("python = ", sys.version)
# can be "Linux", "Darwin"
if platform.system() == "Linux":
# for some reason it says "pip not found"
# and also "pip3 not found"
# subprocess.run(
# "pip install flash-attn --no-build-isolation",
#
# # hmm... this should be False, since we are in a CUDA environment, no?
# env={"FLASH_ATTENTION_SKIP_CUDA_BUILD": "TRUE"},
#
# shell=True,
# )
pass
import gradio as gr
from pathlib import Path
import logging
import mimetypes
import shutil
import os
import traceback
import asyncio
import tempfile
import zipfile
from typing import Any, Optional, Dict, List, Union, Tuple
from typing import AsyncGenerator
from training_service import TrainingService
from captioning_service import CaptioningService
from splitting_service import SplittingService
from import_service import ImportService
from config import (
STORAGE_PATH, VIDEOS_TO_SPLIT_PATH, STAGING_PATH,
TRAINING_PATH, LOG_FILE_PATH, TRAINING_VIDEOS_PATH, MODEL_PATH, OUTPUT_PATH, DEFAULT_CAPTIONING_BOT_INSTRUCTIONS,
DEFAULT_PROMPT_PREFIX, HF_API_TOKEN, ASK_USER_TO_DUPLICATE_SPACE, MODEL_TYPES, TRAINING_BUCKETS
)
from utils import make_archive, count_media_files, format_media_title, is_image_file, is_video_file, validate_model_repo, format_time
from finetrainers_utils import copy_files_to_training_dir, prepare_finetrainers_dataset
from training_log_parser import TrainingLogParser
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
httpx_logger = logging.getLogger('httpx')
httpx_logger.setLevel(logging.WARN)
class VideoTrainerUI:
def __init__(self):
self.trainer = TrainingService()
self.splitter = SplittingService()
self.importer = ImportService()
self.captioner = CaptioningService()
self._should_stop_captioning = False
self.log_parser = TrainingLogParser()
def update_training_ui(self, training_state: Dict[str, Any]):
"""Update UI components based on training state"""
updates = {}
print("update_training_ui: training_state = ", training_state)
# Update status box with high-level information
status_text = []
if training_state["status"] != "idle":
status_text.extend([
f"Status: {training_state['status']}",
f"Progress: {training_state['progress']}",
f"Step: {training_state['current_step']}/{training_state['total_steps']}",
# Epoch information
# there is an issue with how epoch is reported because we display:
# Progress: 96.9%, Step: 872/900, Epoch: 12/50
# we should probably just show the steps
#f"Epoch: {training_state['current_epoch']}/{training_state['total_epochs']}",
f"Time elapsed: {training_state['elapsed']}",
f"Estimated remaining: {training_state['remaining']}",
"",
f"Current loss: {training_state['step_loss']}",
f"Learning rate: {training_state['learning_rate']}",
f"Gradient norm: {training_state['grad_norm']}",
f"Memory usage: {training_state['memory']}"
])
if training_state["error_message"]:
status_text.append(f"\nError: {training_state['error_message']}")
updates["status_box"] = "\n".join(status_text)
# Update button states
updates["start_btn"] = gr.Button(
"Start training",
interactive=(training_state["status"] in ["idle", "completed", "error", "stopped"]),
variant="primary" if training_state["status"] == "idle" else "secondary"
)
updates["stop_btn"] = gr.Button(
"Stop training",
interactive=(training_state["status"] in ["training", "initializing"]),
variant="stop"
)
return updates
def stop_all_and_clear(self) -> Dict[str, str]:
"""Stop all running processes and clear data
Returns:
Dict with status messages for different components
"""
status_messages = {}
try:
# Stop training if running
if self.trainer.is_training_running():
training_result = self.trainer.stop_training()
status_messages["training"] = training_result["status"]
# Stop captioning if running
if self.captioner:
self.captioner.stop_captioning()
status_messages["captioning"] = "Captioning stopped"
# Stop scene detection if running
if self.splitter.is_processing():
self.splitter.processing = False
status_messages["splitting"] = "Scene detection stopped"
# Properly close logging before clearing log file
if self.trainer.file_handler:
self.trainer.file_handler.close()
logger.removeHandler(self.trainer.file_handler)
self.trainer.file_handler = None
if LOG_FILE_PATH.exists():
LOG_FILE_PATH.unlink()
# Clear all data directories
for path in [VIDEOS_TO_SPLIT_PATH, STAGING_PATH, TRAINING_VIDEOS_PATH, TRAINING_PATH,
MODEL_PATH, OUTPUT_PATH]:
if path.exists():
try:
shutil.rmtree(path)
path.mkdir(parents=True, exist_ok=True)
except Exception as e:
status_messages[f"clear_{path.name}"] = f"Error clearing {path.name}: {str(e)}"
else:
status_messages[f"clear_{path.name}"] = f"Cleared {path.name}"
# Reset any persistent state
self._should_stop_captioning = True
self.splitter.processing = False
# Recreate logging setup
self.trainer.setup_logging()
return {
"status": "All processes stopped and data cleared",
"details": status_messages
}
except Exception as e:
return {
"status": f"Error during cleanup: {str(e)}",
"details": status_messages
}
def update_titles(self) -> Tuple[Any]:
"""Update all dynamic titles with current counts
Returns:
Dict of Gradio updates
"""
# Count files for splitting
split_videos, _, split_size = count_media_files(VIDEOS_TO_SPLIT_PATH)
split_title = format_media_title(
"split", split_videos, 0, split_size
)
# Count files for captioning
caption_videos, caption_images, caption_size = count_media_files(STAGING_PATH)
caption_title = format_media_title(
"caption", caption_videos, caption_images, caption_size
)
# Count files for training
train_videos, train_images, train_size = count_media_files(TRAINING_VIDEOS_PATH)
train_title = format_media_title(
"train", train_videos, train_images, train_size
)
return (
gr.Markdown(value=split_title),
gr.Markdown(value=caption_title),
gr.Markdown(value=f"{train_title} available for training")
)
def copy_files_to_training_dir(self, prompt_prefix: str):
"""Run auto-captioning process"""
# Initialize captioner if not already done
self._should_stop_captioning = False
try:
copy_files_to_training_dir(prompt_prefix)
except Exception as e:
traceback.print_exc()
raise gr.Error(f"Error copying assets to training dir: {str(e)}")
async def start_caption_generation(self, captioning_bot_instructions: str, prompt_prefix: str) -> AsyncGenerator[gr.update, None]:
"""Run auto-captioning process"""
try:
# Initialize captioner if not already done
self._should_stop_captioning = False
async for rows in self.captioner.start_caption_generation(captioning_bot_instructions, prompt_prefix):
# Yield UI update
yield gr.update(
value=rows,
headers=["name", "status"]
)
# Final update after completion
yield gr.update(
value=self.list_training_files_to_caption(),
headers=["name", "status"]
)
except Exception as e:
yield gr.update(
value=[[str(e), "error"]],
headers=["name", "status"]
)
def list_training_files_to_caption(self) -> List[List[str]]:
"""List all clips and images - both pending and captioned"""
files = []
already_listed: Dict[str, bool] = {}
# Check files in STAGING_PATH
for file in STAGING_PATH.glob("*.*"):
if is_video_file(file) or is_image_file(file):
txt_file = file.with_suffix('.txt')
status = "captioned" if txt_file.exists() else "no caption"
file_type = "video" if is_video_file(file) else "image"
files.append([file.name, f"{status} ({file_type})", str(file)])
already_listed[str(file.name)] = True
# Check files in TRAINING_VIDEOS_PATH
for file in TRAINING_VIDEOS_PATH.glob("*.*"):
if not str(file.name) in already_listed:
if is_video_file(file) or is_image_file(file):
txt_file = file.with_suffix('.txt')
if txt_file.exists():
file_type = "video" if is_video_file(file) else "image"
files.append([file.name, f"captioned ({file_type})", str(file)])
# Sort by filename
files.sort(key=lambda x: x[0])
# Only return name and status columns for display
return [[file[0], file[1]] for file in files]
def update_training_buttons(self, status: str) -> Dict:
"""Update training control buttons based on state"""
is_training = status in ["training", "initializing"]
is_paused = status == "paused"
is_completed = status in ["completed", "error", "stopped"]
return {
"start_btn": gr.Button(
interactive=not is_training and not is_paused,
variant="primary" if not is_training else "secondary",
),
"stop_btn": gr.Button(
interactive=is_training or is_paused,
variant="stop",
),
"pause_resume_btn": gr.Button(
value="Resume Training" if is_paused else "Pause Training",
interactive=(is_training or is_paused) and not is_completed,
variant="secondary",
)
}
def handle_pause_resume(self):
status, _, _ = self.get_latest_status_message_and_logs()
if status == "paused":
self.trainer.resume_training()
else:
self.trainer.pause_training()
return self.get_latest_status_message_logs_and_button_labels()
def handle_stop(self):
self.trainer.stop_training()
return self.get_latest_status_message_logs_and_button_labels()
def handle_training_dataset_select(self, evt: gr.SelectData) -> Tuple[Optional[str], Optional[str], Optional[str]]:
"""Handle selection of both video clips and images"""
try:
if not evt:
return [
gr.Image(
interactive=False,
visible=False
),
gr.Video(
interactive=False,
visible=False
),
gr.Textbox(
visible=False
),
"No file selected"
]
file_name = evt.value
if not file_name:
return [
gr.Image(
interactive=False,
visible=False
),
gr.Video(
interactive=False,
visible=False
),
gr.Textbox(
visible=False
),
"No file selected"
]
# Check both possible locations for the file
possible_paths = [
STAGING_PATH / file_name,
# note: we use to look into this dir for already-captioned clips,
# but we don't do this anymore
#TRAINING_VIDEOS_PATH / file_name
]
# Find the first existing file path
file_path = None
for path in possible_paths:
if path.exists():
file_path = path
break
if not file_path:
return [
gr.Image(
interactive=False,
visible=False
),
gr.Video(
interactive=False,
visible=False
),
gr.Textbox(
visible=False
),
f"File not found: {file_name}"
]
txt_path = file_path.with_suffix('.txt')
caption = txt_path.read_text() if txt_path.exists() else ""
# Handle video files
if is_video_file(file_path):
return [
gr.Image(
interactive=False,
visible=False
),
gr.Video(
label="Video Preview",
interactive=False,
visible=True,
value=str(file_path)
),
gr.Textbox(
label="Caption",
lines=6,
interactive=True,
visible=True,
value=str(caption)
),
None
]
# Handle image files
elif is_image_file(file_path):
return [
gr.Image(
label="Image Preview",
interactive=False,
visible=True,
value=str(file_path)
),
gr.Video(
interactive=False,
visible=False
),
gr.Textbox(
label="Caption",
lines=6,
interactive=True,
visible=True,
value=str(caption)
),
None
]
else:
return [
gr.Image(
interactive=False,
visible=False
),
gr.Video(
interactive=False,
visible=False
),
gr.Textbox(
interactive=False,
visible=False
),
f"Unsupported file type: {file_path.suffix}"
]
except Exception as e:
logger.error(f"Error handling selection: {str(e)}")
return [
gr.Image(
interactive=False,
visible=False
),
gr.Video(
interactive=False,
visible=False
),
gr.Textbox(
interactive=False,
visible=False
),
f"Error handling selection: {str(e)}"
]
def save_caption_changes(self, preview_caption: str, preview_image: str, preview_video: str, prompt_prefix: str):
"""Save changes to caption"""
try:
# Add prefix if not already present
if prompt_prefix and not preview_caption.startswith(prompt_prefix):
full_caption = f"{prompt_prefix}{preview_caption}"
else:
full_caption = preview_caption
path = Path(preview_video if preview_video else preview_image)
if path.suffix == '.txt':
self.trainer.update_file_caption(path.with_suffix(''), full_caption)
else:
self.trainer.update_file_caption(path, full_caption)
return gr.update(value="Caption saved successfully!")
except Exception as e:
return gr.update(value=f"Error saving caption: {str(e)}")
def get_model_info(self, model_type: str) -> str:
"""Get information about the selected model type"""
if model_type == "hunyuan_video":
return """### HunyuanVideo (LoRA)
- Best for learning complex video generation patterns
- Required VRAM: ~47GB minimum
- Recommended batch size: 1-2
- Typical training time: 2-4 hours
- Default resolution: 49x512x768
- Default LoRA rank: 128"""
elif model_type == "ltx_video":
return """### LTX-Video (LoRA)
- Lightweight video model
- Required VRAM: ~18GB minimum
- Recommended batch size: 1-4
- Typical training time: 1-3 hours
- Default resolution: 49x512x768
- Default LoRA rank: 128"""
return ""
def get_default_params(self, model_type: str) -> Dict[str, Any]:
"""Get default training parameters for model type"""
if model_type == "hunyuan_video":
return {
"num_epochs": 70,
"batch_size": 1,
"learning_rate": 2e-5,
"save_iterations": 500,
"video_resolution_buckets": TRAINING_BUCKETS,
"video_reshape_mode": "center",
"caption_dropout_p": 0.05,
"gradient_accumulation_steps": 1,
"rank": 128,
"lora_alpha": 128
}
else: # ltx_video
return {
"num_epochs": 70,
"batch_size": 1,
"learning_rate": 3e-5,
"save_iterations": 500,
"video_resolution_buckets": TRAINING_BUCKETS,
"video_reshape_mode": "center",
"caption_dropout_p": 0.05,
"gradient_accumulation_steps": 4,
"rank": 128,
"lora_alpha": 128
}
def preview_file(self, selected_text: str) -> Dict:
"""Generate preview based on selected file
Args:
selected_text: Text of the selected item containing filename
Returns:
Dict with preview content for each preview component
"""
if not selected_text or "Caption:" in selected_text:
return {
"video": None,
"image": None,
"text": None
}
# Extract filename from the preview text (remove size info)
filename = selected_text.split(" (")[0].strip()
file_path = TRAINING_VIDEOS_PATH / filename
if not file_path.exists():
return {
"video": None,
"image": None,
"text": f"File not found: {filename}"
}
# Detect file type
mime_type, _ = mimetypes.guess_type(str(file_path))
if not mime_type:
return {
"video": None,
"image": None,
"text": f"Unknown file type: {filename}"
}
# Return appropriate preview
if mime_type.startswith('video/'):
return {
"video": str(file_path),
"image": None,
"text": None
}
elif mime_type.startswith('image/'):
return {
"video": None,
"image": str(file_path),
"text": None
}
elif mime_type.startswith('text/'):
try:
text_content = file_path.read_text()
return {
"video": None,
"image": None,
"text": text_content
}
except Exception as e:
return {
"video": None,
"image": None,
"text": f"Error reading file: {str(e)}"
}
else:
return {
"video": None,
"image": None,
"text": f"Unsupported file type: {mime_type}"
}
def list_unprocessed_videos(self) -> gr.Dataframe:
"""Update list of unprocessed videos"""
videos = self.splitter.list_unprocessed_videos()
# videos is already in [[name, status]] format from splitting_service
return gr.Dataframe(
headers=["name", "status"],
value=videos,
interactive=False
)
async def start_scene_detection(self, enable_splitting: bool) -> str:
"""Start background scene detection process
Args:
enable_splitting: Whether to split videos into scenes
"""
if self.splitter.is_processing():
return "Scene detection already running"
try:
await self.splitter.start_processing(enable_splitting)
return "Scene detection completed"
except Exception as e:
return f"Error during scene detection: {str(e)}"
def get_latest_status_message_and_logs(self) -> Tuple[str, str, str]:
state = self.trainer.get_status()
logs = self.trainer.get_logs()
# Parse new log lines
if logs:
last_state = None
for line in logs.splitlines():
state_update = self.log_parser.parse_line(line)
if state_update:
last_state = state_update
if last_state:
ui_updates = self.update_training_ui(last_state)
state["message"] = ui_updates.get("status_box", state["message"])
# Parse status for training state
if "completed" in state["message"].lower():
state["status"] = "completed"
return (state["status"], state["message"], logs)
def get_latest_status_message_logs_and_button_labels(self) -> Tuple[str, str, Any, Any, Any]:
status, message, logs = self.get_latest_status_message_and_logs()
return (
message,
logs,
*self.update_training_buttons(status).values()
)
def get_latest_button_labels(self) -> Tuple[Any, Any, Any]:
status, message, logs = self.get_latest_status_message_and_logs()
return self.update_training_buttons(status).values()
def refresh_dataset(self):
"""Refresh all dynamic lists and training state"""
video_list = self.splitter.list_unprocessed_videos()
training_dataset = self.list_training_files_to_caption()
return (
video_list,
training_dataset
)
def update_training_params(self, preset_name: str) -> Dict:
"""Update UI components based on selected preset"""
preset = TRAINING_PRESETS[preset_name]
# Get preset description for display
description = preset.get("description", "")
bucket_info = f"\nBucket configuration: {len(preset['training_buckets'])} buckets"
info_text = f"{description}{bucket_info}"
return {
"model_type": gr.Dropdown(value=MODEL_TYPES[preset["model_type"]]),
"lora_rank": gr.Dropdown(value=preset["lora_rank"]),
"lora_alpha": gr.Dropdown(value=preset["lora_alpha"]),
"num_epochs": gr.Number(value=preset["num_epochs"]),
"batch_size": gr.Number(value=preset["batch_size"]),
"learning_rate": gr.Number(value=preset["learning_rate"]),
"save_iterations": gr.Number(value=preset["save_iterations"]),
"preset_info": gr.Markdown(value=info_text)
}
def create_ui(self):
"""Create Gradio interface"""
with gr.Blocks(title="🎥 Video Model Studio") as app:
gr.Markdown("# 🎥 Video Model Studio")
with gr.Tabs() as tabs:
with gr.TabItem("1️⃣ Import", id="import_tab"):
with gr.Row():
gr.Markdown("## Automatic splitting and captioning")
with gr.Row():
enable_automatic_video_split = gr.Checkbox(
label="Automatically split videos into smaller clips",
info="Note: a clip is a single camera shot, usually a few seconds",
value=True,
visible=True
)
enable_automatic_content_captioning = gr.Checkbox(
label="Automatically caption photos and videos",
info="Note: this uses LlaVA and takes some extra time to load and process",
value=False,
visible=True,
)
with gr.Row():
with gr.Column(scale=3):
with gr.Row():
with gr.Column():
gr.Markdown("## Import video files")
gr.Markdown("You can upload either:")
gr.Markdown("- A single MP4 video file")
gr.Markdown("- A ZIP archive containing multiple videos and optional caption files")
gr.Markdown("For ZIP files: Create a folder containing videos (name is not important) and optional caption files with the same name (eg. `some_video.txt` for `some_video.mp4`)")
with gr.Row():
files = gr.Files(
label="Upload Images, Videos or ZIP",
#file_count="multiple",
file_types=[".jpg", ".jpeg", ".png", ".webp", ".webp", ".avif", ".heic", ".mp4", ".zip"],
type="filepath"
)
with gr.Column(scale=3):
with gr.Row():
with gr.Column():
gr.Markdown("## Import a YouTube video")
gr.Markdown("You can also use a YouTube video as reference, by pasting its URL here:")
with gr.Row():
youtube_url = gr.Textbox(
label="Import YouTube Video",
placeholder="https://www.youtube.com/watch?v=..."
)
with gr.Row():
youtube_download_btn = gr.Button("Download YouTube Video", variant="secondary")
with gr.Row():
import_status = gr.Textbox(label="Status", interactive=False)
with gr.TabItem("2️⃣ Split", id="split_tab"):
with gr.Row():
split_title = gr.Markdown("## Splitting of 0 videos (0 bytes)")
with gr.Row():
with gr.Column():
detect_btn = gr.Button("Split videos into single-camera shots", variant="primary")
detect_status = gr.Textbox(label="Status", interactive=False)
with gr.Column():
video_list = gr.Dataframe(
headers=["name", "status"],
label="Videos to split",
interactive=False,
wrap=True,
#selection_mode="cell" # Enable cell selection
)
with gr.TabItem("3️⃣ Caption"):
with gr.Row():
caption_title = gr.Markdown("## Captioning of 0 files (0 bytes)")
with gr.Row():
with gr.Column():
with gr.Row():
custom_prompt_prefix = gr.Textbox(
scale=3,
label='Prefix to add to ALL captions (eg. "In the style of TOK, ")',
placeholder="In the style of TOK, ",
lines=2,
value=DEFAULT_PROMPT_PREFIX
)
captioning_bot_instructions = gr.Textbox(
scale=6,
label="System instructions for the automatic captioning model",
placeholder="Please generate a full description of...",
lines=5,
value=DEFAULT_CAPTIONING_BOT_INSTRUCTIONS
)
with gr.Row():
run_autocaption_btn = gr.Button(
"Automatically fill missing captions",
variant="primary" # Makes it green by default
)
copy_files_to_training_dir_btn = gr.Button(
"Copy assets to training directory",
variant="primary" # Makes it green by default
)
stop_autocaption_btn = gr.Button(
"Stop Captioning",
variant="stop", # Red when enabled
interactive=False # Disabled by default
)
with gr.Row():
with gr.Column():
training_dataset = gr.Dataframe(
headers=["name", "status"],
interactive=False,
wrap=True,
value=self.list_training_files_to_caption(),
row_count=10, # Optional: set a reasonable row count
#selection_mode="cell"
)
with gr.Column():
preview_video = gr.Video(
label="Video Preview",
interactive=False,
visible=False
)
preview_image = gr.Image(
label="Image Preview",
interactive=False,
visible=False
)
preview_caption = gr.Textbox(
label="Caption",
lines=6,
interactive=True
)
save_caption_btn = gr.Button("Save Caption")
preview_status = gr.Textbox(
label="Status",
interactive=False,
visible=True
)
with gr.TabItem("4️⃣ Train"):
with gr.Row():
with gr.Column():
with gr.Row():
train_title = gr.Markdown("## 0 files available for training (0 bytes)")
with gr.Row():
with gr.Column():
training_preset = gr.Dropdown(
choices=list(TRAINING_PRESETS.keys()),
label="Training Preset",
value=list(TRAINING_PRESETS.keys())[0]
)
preset_info = gr.Markdown()
with gr.Row():
with gr.Column():
model_type = gr.Dropdown(
choices=list(MODEL_TYPES.keys()),
label="Model Type",
value=list(MODEL_TYPES.keys())[0]
)
model_info = gr.Markdown(
value=self.get_model_info(list(MODEL_TYPES.keys())[0])
)
with gr.Row():
lora_rank = gr.Dropdown(
label="LoRA Rank",
choices=["16", "32", "64", "128", "256"],
value="128",
type="value"
)
lora_alpha = gr.Dropdown(
label="LoRA Alpha",
choices=["16", "32", "64", "128", "256"],
value="128",
type="value"
)
with gr.Row():
num_epochs = gr.Number(
label="Number of Epochs",
value=70,
minimum=1,
precision=0
)
batch_size = gr.Number(
label="Batch Size",
value=1,
minimum=1,
precision=0
)
with gr.Row():
learning_rate = gr.Number(
label="Learning Rate",
value=2e-5,
minimum=1e-7
)
save_iterations = gr.Number(
label="Save checkpoint every N iterations",
value=500,
minimum=50,
precision=0,
info="Model will be saved periodically after these many steps"
)
with gr.Column():
with gr.Row():
start_btn = gr.Button(
"Start Training",
variant="primary",
interactive=not ASK_USER_TO_DUPLICATE_SPACE
)
pause_resume_btn = gr.Button(
"Resume Training",
variant="secondary",
interactive=False
)
stop_btn = gr.Button(
"Stop Training",
variant="stop",
interactive=False
)
with gr.Row():
with gr.Column():
status_box = gr.Textbox(
label="Training Status",
interactive=False,
lines=4
)
with gr.Accordion("See training logs"):
log_box = gr.TextArea(
label="Finetrainers output (see HF Space logs for more details)",
interactive=False,
lines=40,
max_lines=200,
autoscroll=True
)
with gr.TabItem("5️⃣ Manage"):
with gr.Column():
with gr.Row():
with gr.Column():
gr.Markdown("## Publishing")
gr.Markdown("You model can be pushed to Hugging Face (this will use HF_API_TOKEN)")
with gr.Row():
with gr.Column():
repo_id = gr.Textbox(
label="HuggingFace Model Repository",
placeholder="username/model-name",
info="The repository will be created if it doesn't exist"
)
gr.Checkbox(label="Check this to make your model public (ie. visible and downloadable by anyone)", info="You model is private by default"),
global_stop_btn = gr.Button(
"Push my model",
#variant="stop"
)
with gr.Row():
with gr.Column():
with gr.Row():
with gr.Column():
gr.Markdown("## Storage management")
with gr.Row():
download_dataset_btn = gr.DownloadButton(
"Download dataset",
variant="secondary",
size="lg"
)
download_model_btn = gr.DownloadButton(
"Download model",
variant="secondary",
size="lg"
)
with gr.Row():
global_stop_btn = gr.Button(
"Stop everything and delete my data",
variant="stop"
)
global_status = gr.Textbox(
label="Global Status",
interactive=False,
visible=False
)
# Event handlers
def update_model_info(model):
params = self.get_default_params(MODEL_TYPES[model])
info = self.get_model_info(MODEL_TYPES[model])
return {
model_info: info,
num_epochs: params["num_epochs"],
batch_size: params["batch_size"],
learning_rate: params["learning_rate"],
save_iterations: params["save_iterations"]
}
def validate_repo(repo_id: str) -> dict:
validation = validate_model_repo(repo_id)
if validation["error"]:
return gr.update(value=repo_id, error=validation["error"])
return gr.update(value=repo_id, error=None)
# Connect events
model_type.change(
fn=update_model_info,
inputs=[model_type],
outputs=[model_info, num_epochs, batch_size, learning_rate, save_iterations]
)
async def on_import_success(enable_splitting, enable_automatic_content_captioning, prompt_prefix):
videos = self.list_unprocessed_videos()
# If scene detection isn't already running and there are videos to process,
# and auto-splitting is enabled, start the detection
if videos and not self.splitter.is_processing() and enable_splitting:
await self.start_scene_detection(enable_splitting)
msg = "Starting automatic scene detection..."
else:
# Just copy files without splitting if auto-split disabled
for video_file in VIDEOS_TO_SPLIT_PATH.glob("*.mp4"):
await self.splitter.process_video(video_file, enable_splitting=False)
msg = "Copying videos without splitting..."
copy_files_to_training_dir(prompt_prefix)
# Start auto-captioning if enabled
if enable_automatic_content_captioning:
await self.start_caption_generation(
DEFAULT_CAPTIONING_BOT_INSTRUCTIONS,
prompt_prefix
)
return {
tabs: gr.Tabs(selected="split_tab"),
video_list: videos,
detect_status: msg
}
async def update_titles_after_import(enable_splitting, enable_automatic_content_captioning, prompt_prefix):
"""Handle post-import updates including titles"""
import_result = await on_import_success(enable_splitting, enable_automatic_content_captioning, prompt_prefix)
titles = self.update_titles()
return (*import_result, *titles)
files.upload(
fn=lambda x: self.importer.process_uploaded_files(x),
inputs=[files],
outputs=[import_status]
).success(
fn=update_titles_after_import,
inputs=[enable_automatic_video_split, enable_automatic_content_captioning, custom_prompt_prefix],
outputs=[
tabs, video_list, detect_status,
split_title, caption_title, train_title
]
)
youtube_download_btn.click(
fn=self.importer.download_youtube_video,
inputs=[youtube_url],
outputs=[import_status]
).success(
fn=on_import_success,
inputs=[enable_automatic_video_split, enable_automatic_content_captioning, custom_prompt_prefix],
outputs=[tabs, video_list, detect_status]
)
# Scene detection events
detect_btn.click(
fn=self.start_scene_detection,
inputs=[enable_automatic_video_split],
outputs=[detect_status]
)
# Update button states based on captioning status
def update_button_states(is_running):
return {
run_autocaption_btn: gr.Button(
interactive=not is_running,
variant="secondary" if is_running else "primary",
),
stop_autocaption_btn: gr.Button(
interactive=is_running,
variant="secondary",
),
}
run_autocaption_btn.click(
fn=self.start_caption_generation,
inputs=[captioning_bot_instructions, custom_prompt_prefix],
outputs=[training_dataset],
).then(
fn=lambda: update_button_states(True),
outputs=[run_autocaption_btn, stop_autocaption_btn]
)
copy_files_to_training_dir_btn.click(
fn=self.copy_files_to_training_dir,
inputs=[custom_prompt_prefix]
)
stop_autocaption_btn.click(
fn=lambda: (self.captioner.stop_captioning() if self.captioner else None, update_button_states(False)),
outputs=[run_autocaption_btn, stop_autocaption_btn]
)
training_dataset.select(
fn=self.handle_training_dataset_select,
outputs=[preview_image, preview_video, preview_caption, preview_status]
)
save_caption_btn.click(
fn=self.save_caption_changes,
inputs=[preview_caption, preview_image, preview_video, custom_prompt_prefix],
outputs=[preview_status]
).success(
fn=self.list_training_files_to_caption,
outputs=[training_dataset]
)
training_preset.change(
fn=self.update_training_params,
inputs=[training_preset],
outputs=[
model_type, lora_rank, lora_alpha,
num_epochs, batch_size, learning_rate,
save_iterations, preset_info
]
)
# Training control events
start_btn.click(
fn=lambda preset, model_type, *args: (
self.log_parser.reset(),
self.trainer.start_training(
MODEL_TYPES[model_type],
*args,
preset_name=preset
)
),
inputs=[
training_preset,
model_type,
lora_rank,
lora_alpha,
num_epochs,
batch_size,
learning_rate,
save_iterations,
repo_id
],
outputs=[status_box, log_box]
).success(
fn=self.get_latest_status_message_logs_and_button_labels,
outputs=[status_box, log_box, start_btn, stop_btn, pause_resume_btn]
)
pause_resume_btn.click(
fn=self.handle_pause_resume,
outputs=[status_box, log_box, start_btn, stop_btn, pause_resume_btn]
)
stop_btn.click(
fn=self.handle_stop,
outputs=[status_box, log_box, start_btn, stop_btn, pause_resume_btn]
)
def handle_global_stop():
result = self.stop_all_and_clear()
# Update all relevant UI components
status = result["status"]
details = "\n".join(f"{k}: {v}" for k, v in result["details"].items())
full_status = f"{status}\n\nDetails:\n{details}"
# Get fresh lists after cleanup
videos = self.splitter.list_unprocessed_videos()
clips = self.list_training_files_to_caption()
return {
global_status: gr.update(value=full_status, visible=True),
video_list: videos,
training_dataset: clips,
status_box: "Training stopped and data cleared",
log_box: "",
detect_status: "Scene detection stopped",
import_status: "All data cleared",
preview_status: "Captioning stopped"
}
download_dataset_btn.click(
fn=self.trainer.create_training_dataset_zip,
outputs=[download_dataset_btn]
)
download_model_btn.click(
fn=self.trainer.get_model_output_safetensors,
outputs=[download_model_btn]
)
global_stop_btn.click(
fn=handle_global_stop,
outputs=[
global_status,
video_list,
training_dataset,
status_box,
log_box,
detect_status,
import_status,
preview_status
]
)
# Auto-refresh timers
app.load(
fn=lambda: (
self.refresh_dataset()
),
outputs=[
video_list, training_dataset
]
)
timer = gr.Timer(value=1)
timer.tick(
fn=lambda: (
self.get_latest_status_message_logs_and_button_labels()
),
outputs=[
status_box,
log_box,
start_btn,
stop_btn,
pause_resume_btn
]
)
timer = gr.Timer(value=5)
timer.tick(
fn=lambda: (
self.refresh_dataset()
),
outputs=[
video_list, training_dataset
]
)
timer = gr.Timer(value=6)
timer.tick(
fn=lambda: self.update_titles(),
outputs=[
split_title, caption_title, train_title
]
)
return app
def create_app():
if ASK_USER_TO_DUPLICATE_SPACE:
with gr.Blocks() as app:
gr.Markdown("""# Finetrainers UI
This Hugging Face space needs to be duplicated to your own billing account to work.
Click the 'Duplicate Space' button at the top of the page to create your own copy.
It is recommended to use a Nvidia L40S and a persistent storage space.
To avoid overpaying for your space, you can configure the auto-sleep settings to fit your personal budget.""")
return app
ui = VideoTrainerUI()
return ui.create_ui()
if __name__ == "__main__":
app = create_app()
allowed_paths = [
str(STORAGE_PATH), # Base storage
str(VIDEOS_TO_SPLIT_PATH),
str(STAGING_PATH),
str(TRAINING_PATH),
str(TRAINING_VIDEOS_PATH),
str(MODEL_PATH),
str(OUTPUT_PATH)
]
app.queue(default_concurrency_limit=1).launch(
server_name="0.0.0.0",
allowed_paths=allowed_paths
) |