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  1. NOTE! Installing ujson may make loading annotations faster.
  2. Global seed set to 23
  3. Running on GPUs 0,
  4. Loading model from model.ckpt
  5. LatentDiffusion: Running in eps-prediction mode
  6. DiffusionWrapper has 859.52 M params.
  7. making attention of type 'vanilla' with 512 in_channels
  8. Working with z of shape (1, 4, 64, 64) = 16384 dimensions.
  9. making attention of type 'vanilla' with 512 in_channels
  10. Some weights of the model checkpoint at openai/clip-vit-large-patch14 were not used when initializing CLIPTextModel: ['vision_model.encoder.layers.14.layer_norm2.weight', 'vision_model.encoder.layers.19.layer_norm2.bias', 'vision_model.encoder.layers.1.mlp.fc1.bias', 'vision_model.encoder.layers.8.self_attn.v_proj.bias', 'vision_model.encoder.layers.22.mlp.fc2.weight', 'vision_model.encoder.layers.12.self_attn.q_proj.weight', 'vision_model.encoder.layers.3.mlp.fc1.bias', 'vision_model.encoder.layers.19.self_attn.out_proj.bias', 'vision_model.encoder.layers.5.mlp.fc1.bias', 'vision_model.encoder.layers.11.mlp.fc1.weight', 'vision_model.encoder.layers.12.self_attn.k_proj.weight', 'vision_model.encoder.layers.2.mlp.fc2.weight', 'vision_model.encoder.layers.7.layer_norm1.weight', 'vision_model.encoder.layers.21.mlp.fc2.bias', 'vision_model.encoder.layers.1.self_attn.v_proj.bias', 'vision_model.encoder.layers.6.self_attn.v_proj.weight', 'vision_model.encoder.layers.9.layer_norm2.weight', 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'vision_model.encoder.layers.0.self_attn.q_proj.weight', 'vision_model.encoder.layers.12.layer_norm1.bias', 'vision_model.encoder.layers.18.self_attn.k_proj.bias', 'vision_model.encoder.layers.2.layer_norm1.bias', 'vision_model.encoder.layers.11.layer_norm1.bias', 'vision_model.encoder.layers.21.layer_norm1.bias', 'vision_model.encoder.layers.1.self_attn.v_proj.weight', 'vision_model.encoder.layers.12.mlp.fc2.weight', 'vision_model.post_layernorm.bias', 'visual_projection.weight']
  11. - This IS expected if you are initializing CLIPTextModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
  12. - This IS NOT expected if you are initializing CLIPTextModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
  13. Restored from model.ckpt with 0 missing and 688 unexpected keys
  14. Unexpected Keys: ['model_ema.decay', 'model_ema.num_updates', 'model_ema.diffusion_modeltime_embed0weight', 'model_ema.diffusion_modeltime_embed0bias', 'model_ema.diffusion_modeltime_embed2weight', 'model_ema.diffusion_modeltime_embed2bias', 'model_ema.diffusion_modelinput_blocks00weight', 'model_ema.diffusion_modelinput_blocks00bias', 'model_ema.diffusion_modelinput_blocks10in_layers0weight', 'model_ema.diffusion_modelinput_blocks10in_layers0bias', 'model_ema.diffusion_modelinput_blocks10in_layers2weight', 'model_ema.diffusion_modelinput_blocks10in_layers2bias', 'model_ema.diffusion_modelinput_blocks10emb_layers1weight', 'model_ema.diffusion_modelinput_blocks10emb_layers1bias', 'model_ema.diffusion_modelinput_blocks10out_layers0weight', 'model_ema.diffusion_modelinput_blocks10out_layers0bias', 'model_ema.diffusion_modelinput_blocks10out_layers3weight', 'model_ema.diffusion_modelinput_blocks10out_layers3bias', 'model_ema.diffusion_modelinput_blocks11normweight', 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'model_ema.diffusion_modeloutput_blocks111transformer_blocks0attn2to_kweight', 'model_ema.diffusion_modeloutput_blocks111transformer_blocks0attn2to_vweight', 'model_ema.diffusion_modeloutput_blocks111transformer_blocks0attn2to_out0weight', 'model_ema.diffusion_modeloutput_blocks111transformer_blocks0attn2to_out0bias', 'model_ema.diffusion_modeloutput_blocks111transformer_blocks0norm1weight', 'model_ema.diffusion_modeloutput_blocks111transformer_blocks0norm1bias', 'model_ema.diffusion_modeloutput_blocks111transformer_blocks0norm2weight', 'model_ema.diffusion_modeloutput_blocks111transformer_blocks0norm2bias', 'model_ema.diffusion_modeloutput_blocks111transformer_blocks0norm3weight', 'model_ema.diffusion_modeloutput_blocks111transformer_blocks0norm3bias', 'model_ema.diffusion_modeloutput_blocks111proj_outweight', 'model_ema.diffusion_modeloutput_blocks111proj_outbias', 'model_ema.diffusion_modelout0weight', 'model_ema.diffusion_modelout0bias', 'model_ema.diffusion_modelout2weight', 'model_ema.diffusion_modelout2bias']
  15. /home/twsjysd655/.local/lib/python3.8/site-packages/pytorch_lightning/loggers/test_tube.py:105: LightningDeprecationWarning: The TestTubeLogger is deprecated since v1.5 and will be removed in v1.7. We recommend switching to the `pytorch_lightning.loggers.TensorBoardLogger` as an alternative.
  16. rank_zero_deprecation(
  17. Monitoring val/loss_simple_ema as checkpoint metric.
  18. Merged modelckpt-cfg:
  19. {'target': 'pytorch_lightning.callbacks.ModelCheckpoint', 'params': {'dirpath': 'logs/training_samples2022-10-03T05-28-06_dada0/checkpoints', 'filename': '{epoch:06}', 'verbose': True, 'save_last': True, 'monitor': 'val/loss_simple_ema', 'save_top_k': 1, 'every_n_train_steps': 500}}
  20. GPU available: True, used: True
  21. TPU available: False, using: 0 TPU cores
  22. IPU available: False, using: 0 IPUs
  23. HPU available: False, using: 0 HPUs
  24. #### Data #####
  25. train, PersonalizedBase, 18400
  26. reg, PersonalizedBase, 2010
  27. validation, PersonalizedBase, 184
  28. accumulate_grad_batches = 1
  29. ++++ NOT USING LR SCALING ++++
  30. Setting learning rate to 1.00e-06
  31. /home/twsjysd655/.local/lib/python3.8/site-packages/pytorch_lightning/trainer/configuration_validator.py:326: LightningDeprecationWarning: Base `LightningModule.on_train_batch_start` hook signature has changed in v1.5. The `dataloader_idx` argument will be removed in v1.7.
  32. rank_zero_deprecation(
  33. /home/twsjysd655/.local/lib/python3.8/site-packages/pytorch_lightning/trainer/configuration_validator.py:335: LightningDeprecationWarning: The `on_keyboard_interrupt` callback hook was deprecated in v1.5 and will be removed in v1.7. Please use the `on_exception` callback hook instead.
  34. rank_zero_deprecation(
  35. /home/twsjysd655/.local/lib/python3.8/site-packages/pytorch_lightning/trainer/configuration_validator.py:391: LightningDeprecationWarning: The `Callback.on_pretrain_routine_start` hook has been deprecated in v1.6 and will be removed in v1.8. Please use `Callback.on_fit_start` instead.
  36. rank_zero_deprecation(
  37. /home/twsjysd655/.local/lib/python3.8/site-packages/pytorch_lightning/trainer/configuration_validator.py:342: LightningDeprecationWarning: Base `Callback.on_train_batch_end` hook signature has changed in v1.5. The `dataloader_idx` argument will be removed in v1.7.
  38. rank_zero_deprecation(
  39. LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]
  40. LatentDiffusion: Also optimizing conditioner params!
  41.  
  42. | Name | Type | Params
  43. ---------------------------------------------------------
  44. 0 | model | DiffusionWrapper | 859 M
  45. 1 | first_stage_model | AutoencoderKL | 83.7 M
  46. 2 | cond_stage_model | FrozenCLIPEmbedder | 123 M
  47. ---------------------------------------------------------
  48. 982 M Trainable params
  49. 83.7 M Non-trainable params
  50. 1.1 B Total params
  51. 4,264.941 Total estimated model params size (MB)
  52. Project config
  53. model:
  54. base_learning_rate: 1.0e-06
  55. target: ldm.models.diffusion.ddpm.LatentDiffusion
  56. params:
  57. reg_weight: 1.0
  58. linear_start: 0.00085
  59. linear_end: 0.012
  60. num_timesteps_cond: 1
  61. log_every_t: 200
  62. timesteps: 1000
  63. first_stage_key: image
  64. cond_stage_key: caption
  65. image_size: 64
  66. channels: 4
  67. cond_stage_trainable: true
  68. conditioning_key: crossattn
  69. monitor: val/loss_simple_ema
  70. scale_factor: 0.18215
  71. use_ema: false
  72. embedding_reg_weight: 0.0
  73. unfreeze_model: true
  74. model_lr: 1.0e-06
  75. personalization_config:
  76. target: ldm.modules.embedding_manager.EmbeddingManager
  77. params:
  78. placeholder_strings:
  79. - '*'
  80. initializer_words:
  81. - sculpture
  82. per_image_tokens: false
  83. num_vectors_per_token: 1
  84. progressive_words: false
  85. unet_config:
  86. target: ldm.modules.diffusionmodules.openaimodel.UNetModel
  87. params:
  88. image_size: 32
  89. in_channels: 4
  90. out_channels: 4
  91. model_channels: 320
  92. attention_resolutions:
  93. - 4
  94. - 2
  95. - 1
  96. num_res_blocks: 2
  97. channel_mult:
  98. - 1
  99. - 2
  100. - 4
  101. - 4
  102. num_heads: 8
  103. use_spatial_transformer: true
  104. transformer_depth: 1
  105. context_dim: 768
  106. use_checkpoint: true
  107. legacy: false
  108. first_stage_config:
  109. target: ldm.models.autoencoder.AutoencoderKL
  110. params:
  111. embed_dim: 4
  112. monitor: val/rec_loss
  113. ddconfig:
  114. double_z: true
  115. z_channels: 4
  116. resolution: 512
  117. in_channels: 3
  118. out_ch: 3
  119. ch: 128
  120. ch_mult:
  121. - 1
  122. - 2
  123. - 4
  124. - 4
  125. num_res_blocks: 2
  126. attn_resolutions: []
  127. dropout: 0.0
  128. lossconfig:
  129. target: torch.nn.Identity
  130. cond_stage_config:
  131. target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
  132. ckpt_path: model.ckpt
  133. data:
  134. target: main.DataModuleFromConfig
  135. params:
  136. batch_size: 1
  137. num_workers: 1
  138. wrap: false
  139. train:
  140. target: ldm.data.personalized.PersonalizedBase
  141. params:
  142. size: 512
  143. set: train
  144. per_image_tokens: false
  145. repeats: 100
  146. placeholder_token: anime
  147. reg:
  148. target: ldm.data.personalized.PersonalizedBase
  149. params:
  150. size: 512
  151. set: train
  152. reg: true
  153. per_image_tokens: false
  154. repeats: 10
  155. placeholder_token: anime
  156. validation:
  157. target: ldm.data.personalized.PersonalizedBase
  158. params:
  159. size: 512
  160. set: val
  161. per_image_tokens: false
  162. repeats: 10
  163. placeholder_token: anime
  164. girl: null
  165.  
  166. Lightning config
  167. modelcheckpoint:
  168. params:
  169. every_n_train_steps: 500
  170. callbacks:
  171. image_logger:
  172. target: main.ImageLogger
  173. params:
  174. batch_frequency: 500
  175. max_images: 8
  176. increase_log_steps: false
  177. trainer:
  178. benchmark: true
  179. max_steps: 2020
  180. gpus: 0,
  181.  
  182. Sanity Checking: 0it [00:00, ?it/s]/home/twsjysd655/.local/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:240: PossibleUserWarning: The dataloader, val_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 36 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.
  183. rank_zero_warn(
  184. Sanity Checking DataLoader 0: 0%| | 0/2 [00:00<?, ?it/s]Here comes the checkpoint...
  185. Training complete. max_training_steps reached or we blew up.
  186. Traceback (most recent call last):
  187. File "main.py", line 848, in <module>
  188. trainer.fit(model, data)
  189. File "/home/twsjysd655/.local/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 770, in fit
  190. self._call_and_handle_interrupt(
  191. File "/home/twsjysd655/.local/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 723, in _call_and_handle_interrupt
  192. return trainer_fn(*args, **kwargs)
  193. File "/home/twsjysd655/.local/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 811, in _fit_impl
  194. results = self._run(model, ckpt_path=self.ckpt_path)
  195. File "/home/twsjysd655/.local/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1236, in _run
  196. results = self._run_stage()
  197. File "/home/twsjysd655/.local/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1323, in _run_stage
  198. return self._run_train()
  199. File "/home/twsjysd655/.local/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1345, in _run_train
  200. self._run_sanity_check()
  201. File "/home/twsjysd655/.local/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1413, in _run_sanity_check
  202. val_loop.run()
  203. File "/home/twsjysd655/.local/lib/python3.8/site-packages/pytorch_lightning/loops/base.py", line 204, in run
  204. self.advance(*args, **kwargs)
  205. File "/home/twsjysd655/.local/lib/python3.8/site-packages/pytorch_lightning/loops/dataloader/evaluation_loop.py", line 155, in advance
  206. dl_outputs = self.epoch_loop.run(self._data_fetcher, dl_max_batches, kwargs)
  207. File "/home/twsjysd655/.local/lib/python3.8/site-packages/pytorch_lightning/loops/base.py", line 204, in run
  208. self.advance(*args, **kwargs)
  209. File "/home/twsjysd655/.local/lib/python3.8/site-packages/pytorch_lightning/loops/epoch/evaluation_epoch_loop.py", line 128, in advance
  210. output = self._evaluation_step(**kwargs)
  211. File "/home/twsjysd655/.local/lib/python3.8/site-packages/pytorch_lightning/loops/epoch/evaluation_epoch_loop.py", line 226, in _evaluation_step
  212. output = self.trainer._call_strategy_hook("validation_step", *kwargs.values())
  213. File "/home/twsjysd655/.local/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1765, in _call_strategy_hook
  214. output = fn(*args, **kwargs)
  215. File "/home/twsjysd655/.local/lib/python3.8/site-packages/pytorch_lightning/strategies/strategy.py", line 344, in validation_step
  216. return self.model.validation_step(*args, **kwargs)
  217. File "/opt/conda/lib/python3.8/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
  218. return func(*args, **kwargs)
  219. File "/home/twsjysd655/Dreambooth-Stable-Diffusion/ldm/models/diffusion/ddpm.py", line 368, in validation_step
  220. _, loss_dict_no_ema = self.shared_step(batch)
  221. File "/home/twsjysd655/Dreambooth-Stable-Diffusion/ldm/models/diffusion/ddpm.py", line 908, in shared_step
  222. loss = self(x, c)
  223. File "/opt/conda/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1186, in _call_impl
  224. return forward_call(*input, **kwargs)
  225. File "/home/twsjysd655/Dreambooth-Stable-Diffusion/ldm/models/diffusion/ddpm.py", line 942, in forward
  226. return self.p_losses(x, c, t, *args, **kwargs)
  227. File "/home/twsjysd655/Dreambooth-Stable-Diffusion/ldm/models/diffusion/ddpm.py", line 1093, in p_losses
  228. logvar_t = self.logvar[t].to(self.device)
  229. RuntimeError: indices should be either on cpu or on the same device as the indexed tensor (cpu)
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