PLORAV7 / logs /0_log.txt
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{
"type": "diffusion_trainer",
"training_folder": "/app/ai-toolkit/output",
"sqlite_db_path": "/app/ai-toolkit/aitk_db.db",
"device": "cuda",
"trigger_word": null,
"performance_log_every": 10,
"network": {
"type": "lora",
"linear": 32,
"linear_alpha": 32,
"conv": 16,
"conv_alpha": 16,
"lokr_full_rank": true,
"lokr_factor": -1,
"network_kwargs": {
"ignore_if_contains": []
}
},
"save": {
"dtype": "bf16",
"save_every": 500,
"max_step_saves_to_keep": 400,
"save_format": "diffusers",
"push_to_hub": false
},
"datasets": [
{
"folder_path": "/app/ai-toolkit/datasets/49",
"mask_path": null,
"mask_min_value": 0.1,
"default_caption": "",
"caption_ext": "txt",
"caption_dropout_rate": 0,
"cache_latents_to_disk": true,
"is_reg": false,
"network_weight": 1,
"resolution": [
512
],
"controls": [],
"shrink_video_to_frames": true,
"num_frames": 49,
"flip_x": false,
"flip_y": false,
"num_repeats": 2,
"do_i2v": false,
"fps": 24
},
{
"folder_path": "/app/ai-toolkit/datasets/73",
"mask_path": null,
"mask_min_value": 0.1,
"default_caption": "",
"caption_ext": "txt",
"caption_dropout_rate": 0,
"cache_latents_to_disk": true,
"is_reg": false,
"network_weight": 1,
"resolution": [
512
],
"controls": [],
"shrink_video_to_frames": true,
"num_frames": 73,
"flip_x": false,
"flip_y": false,
"num_repeats": 2
},
{
"folder_path": "/app/ai-toolkit/datasets/81",
"mask_path": null,
"mask_min_value": 0.1,
"default_caption": "",
"caption_ext": "txt",
"caption_dropout_rate": 0,
"cache_latents_to_disk": true,
"is_reg": false,
"network_weight": 1,
"resolution": [
512
],
"controls": [],
"shrink_video_to_frames": true,
"num_frames": 81,
"flip_x": false,
"flip_y": false,
"num_repeats": 2
},
{
"folder_path": "/app/ai-toolkit/datasets/89",
"mask_path": null,
"mask_min_value": 0.1,
"default_caption": "",
"caption_ext": "txt",
"caption_dropout_rate": 0,
"cache_latents_to_disk": true,
"is_reg": false,
"network_weight": 1,
"resolution": [
512
],
"controls": [],
"shrink_video_to_frames": true,
"num_frames": 89,
"flip_x": false,
"flip_y": false,
"num_repeats": 2
},
{
"folder_path": "/app/ai-toolkit/datasets/97",
"mask_path": null,
"mask_min_value": 0.1,
"default_caption": "",
"caption_ext": "txt",
"caption_dropout_rate": 0,
"cache_latents_to_disk": true,
"is_reg": false,
"network_weight": 1,
"resolution": [
512
],
"controls": [],
"shrink_video_to_frames": true,
"num_frames": 97,
"flip_x": false,
"flip_y": false,
"num_repeats": 2
},
{
"folder_path": "/app/ai-toolkit/datasets/images",
"mask_path": null,
"mask_min_value": 0.1,
"default_caption": "",
"caption_ext": "txt",
"caption_dropout_rate": 0,
"cache_latents_to_disk": true,
"is_reg": false,
"network_weight": 1,
"resolution": [
512,
768
],
"controls": [],
"shrink_video_to_frames": true,
"num_frames": 1,
"flip_x": false,
"flip_y": false,
"num_repeats": 1
}
],
"train": {
"batch_size": 1,
"bypass_guidance_embedding": false,
"steps": 25000,
"gradient_accumulation": 1,
"train_unet": true,
"train_text_encoder": false,
"gradient_checkpointing": true,
"noise_scheduler": "flowmatch",
"optimizer": "adamw8bit",
"timestep_type": "weighted",
"content_or_style": "balanced",
"optimizer_params": {
"weight_decay": 0.0001
},
"unload_text_encoder": false,
"cache_text_embeddings": true,
"lr": 0.0001,
"ema_config": {
"use_ema": false,
"ema_decay": 0.99
},
"skip_first_sample": false,
"force_first_sample": false,
"disable_sampling": true,
"dtype": "bf16",
"diff_output_preservation": false,
"diff_output_preservation_multiplier": 1,
"diff_output_preservation_class": "person",
"switch_boundary_every": 1,
"loss_type": "mse"
},
"logging": {
"log_every": 1,
"use_ui_logger": true
},
"model": {
"name_or_path": "Lightricks/LTX-2",
"quantize": true,
"qtype": "qfloat8",
"quantize_te": true,
"qtype_te": "uint6",
"arch": "ltx2",
"low_vram": true,
"model_kwargs": {},
"layer_offloading": false,
"layer_offloading_text_encoder_percent": 1,
"layer_offloading_transformer_percent": 1
},
"sample": {
"sampler": "flowmatch",
"sample_every": 250,
"width": 768,
"height": 768,
"samples": [],
"neg": "",
"seed": 42,
"walk_seed": true,
"guidance_scale": 4,
"sample_steps": 25,
"num_frames": 121,
"fps": 24
}
}
Changing sample extention to animated webp
Using SQLite database at /app/ai-toolkit/aitk_db.db
Job ID: "bf530ed1-e796-42a1-9ffa-55399fe92986"
#############################################
# Running job: PLORAV7_LTX
#############################################
Running 1 process
Loading LTX2 model
Loading transformer
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(…)ion_pytorch_model.safetensors.index.json: 0.00B [00:00, ?B/s] (…)ion_pytorch_model.safetensors.index.json: 0.00B [00:00, ?B/s] (…)ion_pytorch_model.safetensors.index.json: 378kB [00:00, 300MB/s] (…)ion_pytorch_model.safetensors.index.json: 378kB [00:00, 300MB/s]
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Loading checkpoint shards: 0%| | 0/8 [00:00<?, ?it/s] Loading checkpoint shards: 0%| | 0/8 [00:00<?, ?it/s] Loading checkpoint shards: 25%|##5 | 2/8 [00:00<00:00, 16.24it/s] Loading checkpoint shards: 25%|##5 | 2/8 [00:00<00:00, 16.24it/s] Loading checkpoint shards: 75%|#######5 | 6/8 [00:00<00:00, 28.15it/s] Loading checkpoint shards: 75%|#######5 | 6/8 [00:00<00:00, 28.15it/s] Loading checkpoint shards: 100%|##########| 8/8 [00:00<00:00, 29.10it/s] Loading checkpoint shards: 100%|##########| 8/8 [00:00<00:00, 29.10it/s]
Quantizing Transformer
- quantizing 48 transformer blocks
0%| | 0/48 [00:00<?, ?it/s] 0%| | 0/48 [00:00<?, ?it/s] 2%|2 | 1/48 [00:00<00:27, 1.70it/s] 2%|2 | 1/48 [00:00<00:27, 1.70it/s] 4%|4 | 2/48 [00:00<00:19, 2.35it/s] 4%|4 | 2/48 [00:00<00:19, 2.35it/s] 6%|6 | 3/48 [00:01<00:18, 2.43it/s] 6%|6 | 3/48 [00:01<00:18, 2.43it/s] 8%|8 | 4/48 [00:01<00:16, 2.66it/s] 8%|8 | 4/48 [00:01<00:16, 2.66it/s] 10%|# | 5/48 [00:01<00:15, 2.82it/s] 10%|# | 5/48 [00:01<00:15, 2.82it/s] 12%|#2 | 6/48 [00:02<00:16, 2.62it/s] 12%|#2 | 6/48 [00:02<00:16, 2.62it/s] 15%|#4 | 7/48 [00:02<00:16, 2.47it/s] 15%|#4 | 7/48 [00:02<00:16, 2.47it/s] 17%|#6 | 8/48 [00:03<00:16, 2.45it/s] 17%|#6 | 8/48 [00:03<00:16, 2.45it/s] 19%|#8 | 9/48 [00:03<00:16, 2.41it/s] 19%|#8 | 9/48 [00:03<00:16, 2.41it/s] 21%|## | 10/48 [00:04<00:14, 2.54it/s] 21%|## | 10/48 [00:04<00:14, 2.54it/s] 23%|##2 | 11/48 [00:04<00:15, 2.38it/s] 23%|##2 | 11/48 [00:04<00:15, 2.38it/s] 25%|##5 | 12/48 [00:04<00:15, 2.29it/s] 25%|##5 | 12/48 [00:04<00:15, 2.29it/s] 27%|##7 | 13/48 [00:05<00:16, 2.19it/s] 27%|##7 | 13/48 [00:05<00:16, 2.19it/s] 29%|##9 | 14/48 [00:05<00:14, 2.40it/s] 29%|##9 | 14/48 [00:05<00:14, 2.40it/s] 31%|###1 | 15/48 [00:06<00:12, 2.55it/s] 31%|###1 | 15/48 [00:06<00:12, 2.55it/s] 33%|###3 | 16/48 [00:06<00:11, 2.68it/s] 33%|###3 | 16/48 [00:06<00:11, 2.68it/s] 35%|###5 | 17/48 [00:06<00:11, 2.80it/s] 35%|###5 | 17/48 [00:06<00:11, 2.80it/s] 38%|###7 | 18/48 [00:07<00:10, 2.87it/s] 38%|###7 | 18/48 [00:07<00:10, 2.87it/s] 40%|###9 | 19/48 [00:07<00:11, 2.56it/s] 40%|###9 | 19/48 [00:07<00:11, 2.56it/s] 42%|####1 | 20/48 [00:07<00:10, 2.70it/s] 42%|####1 | 20/48 [00:07<00:10, 2.70it/s] 44%|####3 | 21/48 [00:08<00:09, 2.79it/s] 44%|####3 | 21/48 [00:08<00:09, 2.79it/s] 46%|####5 | 22/48 [00:08<00:09, 2.86it/s] 46%|####5 | 22/48 [00:08<00:09, 2.86it/s] 48%|####7 | 23/48 [00:08<00:08, 2.92it/s] 48%|####7 | 23/48 [00:08<00:08, 2.92it/s] 50%|##### | 24/48 [00:09<00:08, 2.96it/s] 50%|##### | 24/48 [00:09<00:08, 2.96it/s] 52%|#####2 | 25/48 [00:09<00:08, 2.62it/s] 52%|#####2 | 25/48 [00:09<00:08, 2.62it/s] 54%|#####4 | 26/48 [00:10<00:08, 2.74it/s] 54%|#####4 | 26/48 [00:10<00:08, 2.74it/s] 56%|#####6 | 27/48 [00:10<00:07, 2.84it/s] 56%|#####6 | 27/48 [00:10<00:07, 2.84it/s] 58%|#####8 | 28/48 [00:10<00:06, 2.89it/s] 58%|#####8 | 28/48 [00:10<00:06, 2.89it/s] 60%|###### | 29/48 [00:11<00:06, 2.96it/s] 60%|###### | 29/48 [00:11<00:06, 2.96it/s] 62%|######2 | 30/48 [00:11<00:05, 3.01it/s] 62%|######2 | 30/48 [00:11<00:05, 3.01it/s] 65%|######4 | 31/48 [00:11<00:05, 3.01it/s] 65%|######4 | 31/48 [00:11<00:05, 3.01it/s] 67%|######6 | 32/48 [00:12<00:06, 2.65it/s] 67%|######6 | 32/48 [00:12<00:06, 2.65it/s] 69%|######8 | 33/48 [00:12<00:05, 2.77it/s] 69%|######8 | 33/48 [00:12<00:05, 2.77it/s] 71%|####### | 34/48 [00:12<00:04, 2.85it/s] 71%|####### | 34/48 [00:12<00:04, 2.85it/s] 73%|#######2 | 35/48 [00:13<00:04, 2.90it/s] 73%|#######2 | 35/48 [00:13<00:04, 2.90it/s] 75%|#######5 | 36/48 [00:13<00:04, 2.95it/s] 75%|#######5 | 36/48 [00:13<00:04, 2.95it/s] 77%|#######7 | 37/48 [00:13<00:03, 2.97it/s] 77%|#######7 | 37/48 [00:13<00:03, 2.97it/s] 79%|#######9 | 38/48 [00:14<00:03, 2.61it/s] 79%|#######9 | 38/48 [00:14<00:03, 2.61it/s] 81%|########1 | 39/48 [00:14<00:03, 2.73it/s] 81%|########1 | 39/48 [00:14<00:03, 2.73it/s] 83%|########3 | 40/48 [00:14<00:02, 2.82it/s] 83%|########3 | 40/48 [00:14<00:02, 2.82it/s] 85%|########5 | 41/48 [00:15<00:02, 2.90it/s] 85%|########5 | 41/48 [00:15<00:02, 2.90it/s] 88%|########7 | 42/48 [00:15<00:02, 2.95it/s] 88%|########7 | 42/48 [00:15<00:02, 2.95it/s] 90%|########9 | 43/48 [00:15<00:01, 2.99it/s] 90%|########9 | 43/48 [00:15<00:01, 2.99it/s] 92%|#########1| 44/48 [00:16<00:01, 3.01it/s] 92%|#########1| 44/48 [00:16<00:01, 3.01it/s] 94%|#########3| 45/48 [00:16<00:01, 2.63it/s] 94%|#########3| 45/48 [00:16<00:01, 2.63it/s] 96%|#########5| 46/48 [00:17<00:00, 2.75it/s] 96%|#########5| 46/48 [00:17<00:00, 2.75it/s] 98%|#########7| 47/48 [00:17<00:00, 2.85it/s] 98%|#########7| 47/48 [00:17<00:00, 2.85it/s] 100%|##########| 48/48 [00:17<00:00, 2.91it/s] 100%|##########| 48/48 [00:17<00:00, 2.91it/s] 100%|##########| 48/48 [00:17<00:00, 2.71it/s] 100%|##########| 48/48 [00:17<00:00, 2.71it/s]
- quantizing extras
Moving transformer to CPU
Loading text encoder
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Quantizing Text Encoder
Loading VAEs and other components
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Making pipe
Preparing Model
Model Loaded
create LoRA network. base dim (rank): 32, alpha: 32
neuron dropout: p=None, rank dropout: p=None, module dropout: p=None
apply LoRA to Conv2d with kernel size (3,3). dim (rank): 16, alpha: 16
create LoRA for Text Encoder: 0 modules.
create LoRA for U-Net: 1344 modules.
enable LoRA for U-Net
Dataset: /app/ai-toolkit/datasets/49
- Preprocessing video dimensions
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- Found 140 videos
Bucket sizes for /app/ai-toolkit/datasets/49:
576x416: 78 files
576x448: 44 files
544x416: 16 files
512x384: 2 files
4 buckets made
Caching latents for /app/ai-toolkit/datasets/49
- Saving latents to disk
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Caching text_embeddings for /app/ai-toolkit/datasets/49
- Saving text embeddings to disk
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