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recoilme commited on
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2efafae
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1 Parent(s): 319962a

create unet 2b

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Files changed (24) hide show
  1. 2b/config.json +3 -0
  2. {unet → 2b}/diffusion_pytorch_model.safetensors +2 -2
  3. result_grid.jpg +2 -2
  4. samples/unet_192x384_0.jpg +2 -2
  5. samples/unet_256x384_0.jpg +2 -2
  6. samples/unet_320x384_0.jpg +2 -2
  7. samples/unet_320x576_0.jpg +2 -2
  8. samples/unet_384x192_0.jpg +2 -2
  9. samples/unet_384x256_0.jpg +2 -2
  10. samples/unet_384x320_0.jpg +2 -2
  11. samples/unet_384x384_0.jpg +2 -2
  12. samples/unet_384x576_0.jpg +2 -2
  13. samples/unet_448x576_0.jpg +2 -2
  14. samples/unet_512x576_0.jpg +2 -2
  15. samples/unet_576x320_0.jpg +2 -2
  16. samples/unet_576x384_0.jpg +2 -2
  17. samples/unet_576x448_0.jpg +2 -2
  18. samples/unet_576x512_0.jpg +2 -2
  19. samples/unet_576x576_0.jpg +2 -2
  20. src/sdxs_create_unet-2b.ipynb +3 -0
  21. src/sdxs_create_unet.ipynb +2 -2
  22. test.ipynb +2 -2
  23. train.py +10 -10
  24. vae/config.json +2 -2
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{unet → 2b}/diffusion_pytorch_model.safetensors RENAMED
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src/sdxs_create_unet-2b.ipynb ADDED
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train.py CHANGED
@@ -25,14 +25,14 @@ import bitsandbytes as bnb
25
  import torch.nn.functional as F
26
 
27
  # --------------------------- Параметры ---------------------------
28
- ds_path = "datasets/576"
29
- project = "unet"
30
- batch_size = 30
31
- base_learning_rate = 9.5e-6
32
- min_learning_rate = 8.5e-6
33
- num_epochs = 20
34
  # samples/save per epoch
35
- sample_interval_share = 10
36
  use_wandb = True
37
  save_model = True
38
  use_decay = True
@@ -43,7 +43,7 @@ unet_gradient = True
43
  clip_sample = False #Scheduler
44
  fixed_seed = False
45
  shuffle = True
46
- dispersive_loss_enabled = False
47
  torch.backends.cuda.matmul.allow_tf32 = True
48
  torch.backends.cudnn.allow_tf32 = True
49
  torch.backends.cuda.enable_mem_efficient_sdp(False)
@@ -59,7 +59,7 @@ steps_offset = 1 # Scheduler
59
  limit = 0
60
  checkpoints_folder = ""
61
  mixed_precision = "bf16" #"fp16"
62
- gradient_accumulation_steps = 2
63
  accelerator = Accelerator(
64
  mixed_precision=mixed_precision,
65
  gradient_accumulation_steps=gradient_accumulation_steps
@@ -695,7 +695,7 @@ for epoch in range(start_epoch, start_epoch + num_epochs):
695
  if save_model == False and step == 5 :
696
  used_gb = torch.cuda.max_memory_allocated() / 1024**3
697
  print(f"Шаг {step}: {used_gb:.2f} GB")
698
-
699
  # Forward pass
700
  noise = torch.randn_like(latents, dtype=latents.dtype)
701
 
 
25
  import torch.nn.functional as F
26
 
27
  # --------------------------- Параметры ---------------------------
28
+ ds_path = "datasets/384"
29
+ project = "2b"
30
+ batch_size = 200 #50
31
+ base_learning_rate = 8e-5
32
+ min_learning_rate = 4e-5
33
+ num_epochs = 40
34
  # samples/save per epoch
35
+ sample_interval_share = 5
36
  use_wandb = True
37
  save_model = True
38
  use_decay = True
 
43
  clip_sample = False #Scheduler
44
  fixed_seed = False
45
  shuffle = True
46
+ dispersive_loss_enabled = True
47
  torch.backends.cuda.matmul.allow_tf32 = True
48
  torch.backends.cudnn.allow_tf32 = True
49
  torch.backends.cuda.enable_mem_efficient_sdp(False)
 
59
  limit = 0
60
  checkpoints_folder = ""
61
  mixed_precision = "bf16" #"fp16"
62
+ gradient_accumulation_steps = 1
63
  accelerator = Accelerator(
64
  mixed_precision=mixed_precision,
65
  gradient_accumulation_steps=gradient_accumulation_steps
 
695
  if save_model == False and step == 5 :
696
  used_gb = torch.cuda.max_memory_allocated() / 1024**3
697
  print(f"Шаг {step}: {used_gb:.2f} GB")
698
+
699
  # Forward pass
700
  noise = torch.randn_like(latents, dtype=latents.dtype)
701
 
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