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  1. 2b/diffusion_pytorch_model.safetensors +1 -1
  2. samples/2b_192x384_0.jpg +2 -2
  3. samples/2b_256x384_0.jpg +2 -2
  4. samples/2b_320x384_0.jpg +2 -2
  5. samples/2b_384x192_0.jpg +2 -2
  6. samples/2b_384x256_0.jpg +2 -2
  7. samples/2b_384x320_0.jpg +2 -2
  8. samples/2b_384x384_0.jpg +2 -2
  9. train.py +4 -4
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train.py CHANGED
@@ -27,10 +27,10 @@ import torch.nn.functional as F
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  # --------------------------- Параметры ---------------------------
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  ds_path = "datasets/384"
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  project = "2b"
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- batch_size = 16 #50
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  base_learning_rate = 8e-5
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  min_learning_rate = 4e-5
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- num_epochs = 10
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  # samples/save per epoch
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  sample_interval_share = 10
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  use_wandb = True
@@ -43,7 +43,7 @@ unet_gradient = False
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  clip_sample = False #Scheduler
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  fixed_seed = False
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  shuffle = True
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- dispersive_loss_enabled = True
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  torch.backends.cuda.matmul.allow_tf32 = True
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  torch.backends.cudnn.allow_tf32 = True
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  torch.backends.cuda.enable_mem_efficient_sdp(False)
@@ -59,7 +59,7 @@ steps_offset = 1 # Scheduler
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  limit = 0
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  checkpoints_folder = ""
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  mixed_precision = "bf16" #"fp16"
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- gradient_accumulation_steps = 1
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  accelerator = Accelerator(
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  mixed_precision=mixed_precision,
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  gradient_accumulation_steps=gradient_accumulation_steps
 
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  # --------------------------- Параметры ---------------------------
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  ds_path = "datasets/384"
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  project = "2b"
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+ batch_size = 8 #50
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  base_learning_rate = 8e-5
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  min_learning_rate = 4e-5
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+ num_epochs = 6
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  # samples/save per epoch
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  sample_interval_share = 10
36
  use_wandb = True
 
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  clip_sample = False #Scheduler
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  fixed_seed = False
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  shuffle = True
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+ dispersive_loss_enabled = False
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  torch.backends.cuda.matmul.allow_tf32 = True
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  torch.backends.cudnn.allow_tf32 = True
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  torch.backends.cuda.enable_mem_efficient_sdp(False)
 
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  limit = 0
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  checkpoints_folder = ""
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  mixed_precision = "bf16" #"fp16"
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+ gradient_accumulation_steps = 2
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  accelerator = Accelerator(
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  mixed_precision=mixed_precision,
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  gradient_accumulation_steps=gradient_accumulation_steps