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  1. samples/unet_384x768_0.jpg +2 -2
  2. samples/unet_416x768_0.jpg +2 -2
  3. samples/unet_448x768_0.jpg +2 -2
  4. samples/unet_480x768_0.jpg +2 -2
  5. samples/unet_512x768_0.jpg +2 -2
  6. samples/unet_544x768_0.jpg +2 -2
  7. samples/unet_576x768_0.jpg +2 -2
  8. samples/unet_608x768_0.jpg +2 -2
  9. samples/unet_640x768_0.jpg +2 -2
  10. samples/unet_672x768_0.jpg +2 -2
  11. samples/unet_704x768_0.jpg +2 -2
  12. samples/unet_736x768_0.jpg +2 -2
  13. samples/unet_768x384_0.jpg +2 -2
  14. samples/unet_768x416_0.jpg +2 -2
  15. samples/unet_768x448_0.jpg +2 -2
  16. samples/unet_768x480_0.jpg +2 -2
  17. samples/unet_768x512_0.jpg +2 -2
  18. samples/unet_768x544_0.jpg +2 -2
  19. samples/unet_768x576_0.jpg +2 -2
  20. samples/unet_768x608_0.jpg +2 -2
  21. samples/unet_768x640_0.jpg +2 -2
  22. samples/unet_768x672_0.jpg +2 -2
  23. samples/unet_768x704_0.jpg +2 -2
  24. samples/unet_768x736_0.jpg +2 -2
  25. samples/unet_768x768_0.jpg +2 -2
  26. train.py +7 -7
  27. unet/diffusion_pytorch_model.safetensors +1 -1
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train.py CHANGED
@@ -8,7 +8,7 @@ from torch.utils.data import DataLoader, Sampler
8
  from torch.utils.data.distributed import DistributedSampler
9
  from torch.optim.lr_scheduler import LambdaLR
10
  from collections import defaultdict
11
- from diffusers import UNet2DConditionModel, AutoencoderKL,AutoencoderKLFlux2
12
  from accelerate import Accelerator
13
  from datasets import load_from_disk
14
  from tqdm import tqdm
@@ -32,7 +32,7 @@ project = "unet"
32
  batch_size = 36
33
  base_learning_rate = 2.7e-5
34
  min_learning_rate = 1e-5 #2.7e-5
35
- num_epochs = 80
36
  sample_interval_share = 5
37
  max_length = 192
38
  use_wandb = True
@@ -49,11 +49,11 @@ comet_ml_api_key = "Agctp26mbqnoYrrlvQuKSTk6r"
49
  comet_ml_workspace = "recoilme"
50
  torch.backends.cuda.matmul.allow_tf32 = True
51
  torch.backends.cudnn.allow_tf32 = True
52
- torch.backends.cuda.enable_mem_efficient_sdp(False)
53
  dtype = torch.float32
54
  save_barrier = 1.01
55
  warmup_percent = 0.01
56
- percentile_clipping = 96 #97
57
  betta2 = 0.999
58
  eps = 1e-7
59
  clip_grad_norm = 1.0
@@ -71,7 +71,7 @@ device = accelerator.device
71
  # Параметры для диффузии
72
  n_diffusion_steps = 40
73
  samples_to_generate = 12
74
- guidance_scale = 2
75
 
76
  # Папки для сохранения результатов
77
  generated_folder = "samples"
@@ -736,8 +736,8 @@ for epoch in range(start_epoch, start_epoch + num_epochs):
736
 
737
  if global_step % sample_interval == 0:
738
  # Передаем tuple (emb, mask) для негатива
739
- if epoch % 10 == 0:
740
- generate_and_save_samples(fixed_samples, (uncond_emb, uncond_mask), global_step)
741
  last_n = sample_interval
742
 
743
  if save_model:
 
8
  from torch.utils.data.distributed import DistributedSampler
9
  from torch.optim.lr_scheduler import LambdaLR
10
  from collections import defaultdict
11
+ from diffusers import UNet2DConditionModel, AutoencoderKL#,AutoencoderKLFlux2
12
  from accelerate import Accelerator
13
  from datasets import load_from_disk
14
  from tqdm import tqdm
 
32
  batch_size = 36
33
  base_learning_rate = 2.7e-5
34
  min_learning_rate = 1e-5 #2.7e-5
35
+ num_epochs = 20
36
  sample_interval_share = 5
37
  max_length = 192
38
  use_wandb = True
 
49
  comet_ml_workspace = "recoilme"
50
  torch.backends.cuda.matmul.allow_tf32 = True
51
  torch.backends.cudnn.allow_tf32 = True
52
+ #torch.backends.cuda.enable_mem_efficient_sdp(False)
53
  dtype = torch.float32
54
  save_barrier = 1.01
55
  warmup_percent = 0.01
56
+ percentile_clipping = 95 #96 #97
57
  betta2 = 0.999
58
  eps = 1e-7
59
  clip_grad_norm = 1.0
 
71
  # Параметры для диффузии
72
  n_diffusion_steps = 40
73
  samples_to_generate = 12
74
+ guidance_scale = 4
75
 
76
  # Папки для сохранения результатов
77
  generated_folder = "samples"
 
736
 
737
  if global_step % sample_interval == 0:
738
  # Передаем tuple (emb, mask) для негатива
739
+ #if epoch % 10 == 0:
740
+ generate_and_save_samples(fixed_samples, (uncond_emb, uncond_mask), global_step)
741
  last_n = sample_interval
742
 
743
  if save_model:
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