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  1. dataset_sample.ipynb +2 -2
  2. samples/unet_1024x1280_0.jpg +2 -2
  3. samples/unet_1088x1280_0.jpg +2 -2
  4. samples/unet_1152x1280_0.jpg +2 -2
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  6. samples/unet_1280x1024_0.jpg +2 -2
  7. samples/unet_1280x1088_0.jpg +2 -2
  8. samples/unet_1280x1152_0.jpg +2 -2
  9. samples/unet_1280x1216_0.jpg +2 -2
  10. samples/unet_1280x1280_0.jpg +2 -2
  11. samples/unet_1280x640_0.jpg +2 -2
  12. samples/unet_1280x704_0.jpg +2 -2
  13. samples/unet_1280x768_0.jpg +2 -2
  14. samples/unet_1280x832_0.jpg +2 -2
  15. samples/unet_1280x896_0.jpg +2 -2
  16. samples/unet_1280x960_0.jpg +2 -2
  17. samples/unet_640x1280_0.jpg +2 -2
  18. samples/unet_704x1280_0.jpg +2 -2
  19. samples/unet_768x1280_0.jpg +2 -2
  20. samples/unet_832x1280_0.jpg +2 -2
  21. samples/unet_896x1280_0.jpg +2 -2
  22. samples/unet_960x1280_0.jpg +2 -2
  23. train.py +5 -4
  24. unet/diffusion_pytorch_model.safetensors +1 -1
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train.py CHANGED
@@ -40,9 +40,9 @@ num_gpus = torch.cuda.device_count()
40
  ## total batch (split // num `GPU)
41
  batch_size = local_bs * num_gpus
42
  print(f"GPUs: {num_gpus}, Local BS: {local_bs}, Global BS: {local_bs * num_gpus}")
43
- base_learning_rate = 3e-5
44
- min_learning_rate = 3e-6
45
- num_epochs = num_gpus * 8
46
  sample_interval_share = 20
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  cfg_dropout = 0.10
48
  max_length = 248
@@ -395,7 +395,8 @@ else:
395
  dataset = load_from_disk(ds_path)
396
 
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  dataset = dataset.filter(
398
- lambda x: [not (path.startswith("//workspace/dataset/animesfw") or path.startswith("//workspace/dataset/d4/animesfw")) for path in x["image_path"]],
 
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  batched=True,
400
  batch_size=10000, # обрабатываем по 10к строк за раз
401
  num_proc=8
 
40
  ## total batch (split // num `GPU)
41
  batch_size = local_bs * num_gpus
42
  print(f"GPUs: {num_gpus}, Local BS: {local_bs}, Global BS: {local_bs * num_gpus}")
43
+ base_learning_rate = 1e-5
44
+ min_learning_rate = 1e-6
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+ num_epochs = 3 #num_gpus * 8
46
  sample_interval_share = 20
47
  cfg_dropout = 0.10
48
  max_length = 248
 
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  dataset = load_from_disk(ds_path)
396
 
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  dataset = dataset.filter(
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+
400
  batched=True,
401
  batch_size=10000, # обрабатываем по 10к строк за раз
402
  num_proc=8
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