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야무진_까만_기린

추천 0 비추천 0 댓글 16 조회수 872 작성일 2022-11-02 01:25:08

https://arca.live/b/hypernetworks/61989786

전체

C:\ProgramData\Anaconda3\envs\ldm\lib\site-packages\pytorch_lightning\loggers\test_tube.py:104: 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.

rank_zero_deprecation(

Monitoring val/loss_simple_ema as checkpoint metric.

Merged modelckpt-cfg:

{'target': 'pytorch_lightning.callbacks.ModelCheckpoint', 'params': {'dirpath': 'logs\\MyProject2022-11-02T10-19-32_nahida\\checkpoints', 'filename': '{epoch:03}-{global_step:05}', 'verbose': True, 'save_last': True, 'monitor': 'val/loss_simple_ema', 'save_top_k': 3, 'every_n_epochs': 1, 'save_on_train_epoch_end': False}}

GPU available: True, used: True

TPU available: False, using: 0 TPU cores

IPU available: False, using: 0 IPUs

**** Loading data set: data_root: training_samples\MyProject, as set: train

**** Loaded 26 images fromt training_samples\MyProject

**** Loading data set: data_root: training_samples\MyProject, as set: train

**** Loaded 26 images fromt training_samples\MyProject

#### Data #####

train, WrappedDataset, 130

validation, WrappedDataset, 26

accumulate_grad_batches = 1

++++ NOT USING LR SCALING ++++

Setting learning rate to 1.00e-06

C:\ProgramData\Anaconda3\envs\ldm\lib\site-packages\pytorch_lightning\trainer\configuration_validator.py:275: 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.

rank_zero_deprecation(

C:\ProgramData\Anaconda3\envs\ldm\lib\site-packages\pytorch_lightning\trainer\configuration_validator.py:284: LightningDeprecationWarning: Base `LightningModule.on_train_batch_start` hook signature has changed in v1.5. The `dataloader_idx` argument will be removed in v1.7.

rank_zero_deprecation(

C:\ProgramData\Anaconda3\envs\ldm\lib\site-packages\pytorch_lightning\trainer\configuration_validator.py:291: LightningDeprecationWarning: Base `Callback.on_train_batch_end` hook signature has changed in v1.5. The `dataloader_idx` argument will be removed in v1.7.

rank_zero_deprecation(

C:\ProgramData\Anaconda3\envs\ldm\lib\site-packages\pytorch_lightning\core\datamodule.py:469: LightningDeprecationWarning: DataModule.setup has already been called, so it will not be called again. In v1.6 this behavior will change to always call DataModule.setup.

rank_zero_deprecation(

LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]

LatentDiffusion: Also optimizing conditioner params!

Project config

model:

base_learning_rate: 1.0e-06

target: ldm.models.diffusion.ddpm.LatentDiffusion

params:

reg_weight: 1.0

linear_start: 0.00085

linear_end: 0.012

num_timesteps_cond: 1

log_every_t: 200

timesteps: 1000

first_stage_key: image

cond_stage_key: caption

image_size: 64

channels: 4

cond_stage_trainable: true

conditioning_key: crossattn

monitor: val/loss_simple_ema

scale_factor: 0.18215

use_ema: false

embedding_reg_weight: 0.0

unfreeze_model: true

model_lr: 1.0e-06

unet_config:

target: ldm.modules.diffusionmodules.openaimodel.UNetModel

params:

image_size: 32

in_channels: 4

out_channels: 4

model_channels: 320

attention_resolutions:

- 4

- 2

- 1

num_res_blocks: 2

channel_mult:

- 1

- 2

- 4

num_heads: 8

use_spatial_transformer: true

transformer_depth: 1

context_dim: 768

use_checkpoint: true

legacy: false

first_stage_config:

target: ldm.models.autoencoder.AutoencoderKL

params:

embed_dim: 4

monitor: val/rec_loss

ddconfig:

double_z: true

z_channels: 4

resolution: 512

in_channels: 3

out_ch: 3

ch: 128

ch_mult:

- 1

- 2

- 4

num_res_blocks: 2

attn_resolutions: []

dropout: 0.0

lossconfig:

target: torch.nn.Identity

cond_stage_config:

target: ldm.modules.encoders.modules.FrozenCLIPEmbedder

ckpt_path: animefull-final-pruned.ckpt

data:

target: main.DataModuleFromConfig

params:

batch_size: 1

num_workers: 6

wrap: falsegit

train:

target: ldm.data.every_dream.EveryDreamBatch

params:

size: 512

set: train

repeats: 5

validation:

target: ldm.data.personalized.PersonalizedBase

params:

size: 512

set: val

repeats: 1

Lightning config

modelcheckpoint:

params:

every_n_epochs: 1

save_on_train_epoch_end: false

callbacks:

image_logger:

target: main.ImageLogger

params:

batch_frequency: 200

max_images: 16

increase_log_steps: false

trainer:

benchmark: true

max_epochs: 300

max_steps: 20000

gpus: 0,

| Name | Type | Params

---------------------------------------------------------

0 | model | DiffusionWrapper | 859 M

1 | first_stage_model | AutoencoderKL | 83.7 M

2 | cond_stage_model | FrozenCLIPEmbedder | 123 M

---------------------------------------------------------

982 M Trainable params

83.7 M Non-trainable params

1.1 B Total params

4,264.941 Total estimated model params size (MB)

Validation sanity check: 0%| | 0/2 [00:00<?, ?it/s]C:\ProgramData\Anaconda3\envs\ldm\lib\site-packages\pytorch_lightning\utilities\data.py:59: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 19. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`.

warning_cache.warn(

Global seed set to 23

Epoch 0: 0%| | 0/156 [00:00<?, ?it/s]C:\ProgramData\Anaconda3\envs\ldm\lib\site-packages\pytorch_lightning\utilities\data.py:59: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 1. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`.

warning_cache.warn(

C:\ProgramData\Anaconda3\envs\ldm\lib\site-packages\pytorch_lightning\trainer\connectors\logger_connector\result.py:227: UserWarning: You called `self.log('global_step', ...)` in your `training_step` but the value needs to be floating point. Converting it to torch.float32.

warning_cache.warn(

Summoning checkpoint.

Training complete. max_steps or max_epochs, reached or we blew up.

Traceback (most recent call last):

File "main.py", line 740, in <module>

trainer.fit(model, data)

File "C:\ProgramData\Anaconda3\envs\ldm\lib\site-packages\pytorch_lightning\trainer\trainer.py", line 740, in fit

self._call_and_handle_interrupt(

File "C:\ProgramData\Anaconda3\envs\ldm\lib\site-packages\pytorch_lightning\trainer\trainer.py", line 685, in _call_and_handle_interrupt

return trainer_fn(*args, **kwargs)

File "C:\ProgramData\Anaconda3\envs\ldm\lib\site-packages\pytorch_lightning\trainer\trainer.py", line 777, in _fit_impl

self._run(model, ckpt_path=ckpt_path)

File "C:\ProgramData\Anaconda3\envs\ldm\lib\site-packages\pytorch_lightning\trainer\trainer.py", line 1199, in _run

self._dispatch()

File "C:\ProgramData\Anaconda3\envs\ldm\lib\site-packages\pytorch_lightning\trainer\trainer.py", line 1279, in _dispatch

self.training_type_plugin.start_training(self)

File "C:\ProgramData\Anaconda3\envs\ldm\lib\site-packages\pytorch_lightning\plugins\training_type\training_type_plugin.py", line 202, in start_training

self._results = trainer.run_stage()

File "C:\ProgramData\Anaconda3\envs\ldm\lib\site-packages\pytorch_lightning\trainer\trainer.py", line 1289, in run_stage

return self._run_train()

File "C:\ProgramData\Anaconda3\envs\ldm\lib\site-packages\pytorch_lightning\trainer\trainer.py", line 1319, in _run_train

self.fit_loop.run()

File "C:\ProgramData\Anaconda3\envs\ldm\lib\site-packages\pytorch_lightning\loops\base.py", line 145, in run

self.advance(*args, **kwargs)

File "C:\ProgramData\Anaconda3\envs\ldm\lib\site-packages\pytorch_lightning\loops\fit_loop.py", line 234, in advance

self.epoch_loop.run(data_fetcher)

File "C:\ProgramData\Anaconda3\envs\ldm\lib\site-packages\pytorch_lightning\loops\base.py", line 145, in run

self.advance(*args, **kwargs)

File "C:\ProgramData\Anaconda3\envs\ldm\lib\site-packages\pytorch_lightning\loops\epoch\training_epoch_loop.py", line 193, in advance

batch_output = self.batch_loop.run(batch, batch_idx)

File "C:\ProgramData\Anaconda3\envs\ldm\lib\site-packages\pytorch_lightning\loops\base.py", line 145, in run

self.advance(*args, **kwargs)

File "C:\ProgramData\Anaconda3\envs\ldm\lib\site-packages\pytorch_lightning\loops\batch\training_batch_loop.py", line 88, in advance

outputs = self.optimizer_loop.run(split_batch, optimizers, batch_idx)

File "C:\ProgramData\Anaconda3\envs\ldm\lib\site-packages\pytorch_lightning\loops\base.py", line 145, in run

self.advance(*args, **kwargs)

File "C:\ProgramData\Anaconda3\envs\ldm\lib\site-packages\pytorch_lightning\loops\optimization\optimizer_loop.py", line 215, in advance

result = self._run_optimization(

File "C:\ProgramData\Anaconda3\envs\ldm\lib\site-packages\pytorch_lightning\loops\optimization\optimizer_loop.py", line 266, in _run_optimization

self._optimizer_step(optimizer, opt_idx, batch_idx, closure)

File "C:\ProgramData\Anaconda3\envs\ldm\lib\site-packages\pytorch_lightning\loops\optimization\optimizer_loop.py", line 378, in _optimizer_step

lightning_module.optimizer_step(

File "C:\ProgramData\Anaconda3\envs\ldm\lib\site-packages\pytorch_lightning\core\lightning.py", line 1652, in optimizer_step

optimizer.step(closure=optimizer_closure)

File "C:\ProgramData\Anaconda3\envs\ldm\lib\site-packages\pytorch_lightning\core\optimizer.py", line 164, in step

trainer.accelerator.optimizer_step(self._optimizer, self._optimizer_idx, closure, **kwargs)

File "C:\ProgramData\Anaconda3\envs\ldm\lib\site-packages\pytorch_lightning\accelerators\accelerator.py", line 336, in optimizer_step

self.precision_plugin.optimizer_step(model, optimizer, opt_idx, closure, **kwargs)

File "C:\ProgramData\Anaconda3\envs\ldm\lib\site-packages\pytorch_lightning\plugins\precision\precision_plugin.py", line 163, in optimizer_step

optimizer.step(closure=closure, **kwargs)

File "C:\ProgramData\Anaconda3\envs\ldm\lib\site-packages\torch\optim\optimizer.py", line 88, in wrapper

return func(*args, **kwargs)

File "C:\ProgramData\Anaconda3\envs\ldm\lib\site-packages\torch\autograd\grad_mode.py", line 28, in decorate_context

return func(*args, **kwargs)

File "C:\ProgramData\Anaconda3\envs\ldm\lib\site-packages\torch\optim\adamw.py", line 92, in step

loss = closure()

File "C:\ProgramData\Anaconda3\envs\ldm\lib\site-packages\pytorch_lightning\plugins\precision\precision_plugin.py", line 148, in _wrap_closure

closure_result = closure()

File "C:\ProgramData\Anaconda3\envs\ldm\lib\site-packages\pytorch_lightning\loops\optimization\optimizer_loop.py", line 160, in __call__

self._result = self.closure(*args, **kwargs)

File "C:\ProgramData\Anaconda3\envs\ldm\lib\site-packages\pytorch_lightning\loops\optimization\optimizer_loop.py", line 155, in closure

self._backward_fn(step_output.closure_loss)

File "C:\ProgramData\Anaconda3\envs\ldm\lib\site-packages\pytorch_lightning\loops\optimization\optimizer_loop.py", line 327, in backward_fn

self.trainer.accelerator.backward(loss, optimizer, opt_idx)

File "C:\ProgramData\Anaconda3\envs\ldm\lib\site-packages\pytorch_lightning\accelerators\accelerator.py", line 311, in backward

self.precision_plugin.backward(self.lightning_module, closure_loss, *args, **kwargs)

File "C:\ProgramData\Anaconda3\envs\ldm\lib\site-packages\pytorch_lightning\plugins\precision\precision_plugin.py", line 91, in backward

model.backward(closure_loss, optimizer, *args, **kwargs)

File "C:\ProgramData\Anaconda3\envs\ldm\lib\site-packages\pytorch_lightning\core\lightning.py", line 1434, in backward

loss.backward(*args, **kwargs)

File "C:\ProgramData\Anaconda3\envs\ldm\lib\site-packages\torch\_tensor.py", line 307, in backward

torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs)

File "C:\ProgramData\Anaconda3\envs\ldm\lib\site-packages\torch\autograd\__init__.py", line 154, in backward

Variable._execution_engine.run_backward(

RuntimeError: CUDA out of memory. Tried to allocate 3.23 GiB (GPU 0; 23.99 GiB total capacity; 4.33 GiB already allocated; 16.53 GiB free; 4.44 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF

이러고 떨어지는데 뭐가 문제인지 알 수 있는 사람 있으면 도움 바람

댓글 [16]

야무진_까만_기린

2022-11-02 02:38:53

컴퓨터 키고 바로 함..

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섬세한_은색_개복치

2022-11-02 03:40:06

자세히보면 로그 희안하네. 16기가 여유가 있는데 3기가를 못 넣는다..?

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야무진_까만_기린

2022-11-02 03:51:13

미취겠소요

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섬세한_은색_개복치

2022-11-02 03:52:34

왠지 학습 시킬 이미지의 크기가 잘못된거 같네. 일단 학습시킬 이미지들을 다른데 옮겨둔 다음에, 학습할 이미지를 512 x 512 의 이미지 한개만 넣고 실행해보시게.

펼쳐보기▼

야무진_까만_기린

2022-11-02 03:54:39

섬세한_은색_개복치

2022-11-02 03:56:30

됨?

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야무진_까만_기린

2022-11-02 03:58:54

여태까진 10초면 저 오류로 떨어졌었는데 뭔가 계속 대기중임

펼쳐보기▼

야무진_까만_기린

2022-11-02 03:59:40

| Name              | Type               | Params
---------------------------------------------------------
0 | model             | DiffusionWrapper   | 859 M
1 | first_stage_model | AutoencoderKL      | 83.7 M
2 | cond_stage_model  | FrozenCLIPEmbedder | 123 M
---------------------------------------------------------
982 M     Trainable params
83.7 M    Non-trainable params
1.1 B     Total params
4,264.941 Total estimated model params size (MB)
Validation sanity check:   0%|                                                                   | 0/1 [00:00<?, ?it/s]C:\ProgramData\Anaconda3\envs\ldm\lib\site-packages\pytorch_lightning\utilities\data.py:59: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 10. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`.
  warning_cache.warn(
Global seed set to 23
C:\ProgramData\Anaconda3\envs\ldm\lib\site-packages\pytorch_lightning\trainer\data_loading.py:432: UserWarning: The number of training samples (5) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.
  rank_zero_warn(
Epoch 0:   0%|                                                                                   | 0/6 [00:00<?, ?it/s]C:\ProgramData\Anaconda3\envs\ldm\lib\site-packages\pytorch_lightning\utilities\data.py:59: UserWarning: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 1. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`.
  warning_cache.warn(
C:\ProgramData\Anaconda3\envs\ldm\lib\site-packages\pytorch_lightning\trainer\connectors\logger_connector\result.py:227: UserWarning: You called `self.log('global_step', ...)` in your `training_step` but the value needs to be floating point. Converting it to torch.float32.
  warning_cache.warn(
Epoch 0: 100%|█| 6/6 [00:15<00:00,  2.64s/it, loss=0.0324, v_num=0, train/loss_simple_step=0.00137, train/loss_vlb_stepEpoch 0, global step 4: val/loss_simple_ema reached 0.02045 (best 0.02045), saving model to "D:\Dreambooth\EveryDream-trainer\logs\MyProject2022-11-02T12-55-16_mahiro\checkpoints\epoch=000-global_step=004.0.ckpt" as top 3


근데 뭐가 오류가 뜨긴 뜨는거 보니 문제가 있는 듯

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섬세한_은색_개복치

2022-11-02 04:04:17

ㄴㄴ 중간에 뜨는건 경고라서 상관없. 학습되네.

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야무진_까만_기린

2022-11-02 04:05:19

아 되는거임? 뭔가 checkpoints 폴더에 11기가짜리 파일을 네개씩 생성하기 시작했는데

펼쳐보기▼

섬세한_은색_개복치

2022-11-02 04:05:31

ㅇㅇ 그럼 된거. 축하

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야무진_까만_기린

2022-11-02 04:05:45

이미지로 문제가 발생할 수도 있구나.. 정말 고마워요

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섬세한_은색_개복치

2022-11-02 04:06:22

그럼 이제, 원래 학습시키려던 파일 중에서, 잘못된게 있나 확인해 보고 다시 시도해보시구랴. 
1. 사이즈가 가로세로가 안맞는다. > 맞춰준다
2. 사이즈가 너모 크다 > 일단 512 X 512 로 맞춰주자

펼쳐보기▼

야무진_까만_기린

2022-11-02 04:07:57

이미지 준비는 많이 해봐서 괜찮을 줄 알았는데..
가로세로 512x512 맞음 ㅇㅇ;

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섬세한_은색_개복치

2022-11-02 04:20:07

학습 시킬 이미지 잘못되는건 드물다보니, 나도 잘 몰라서 조언하기 힘들 듯. 어떤 그림이 범인인지 색출해봅세.

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야무진_까만_기린

2022-11-02 04:22:01

본 게시물에 댓글을 작성하실 권한이 없습니다. 로그인 하신 후 댓글을 다실 수 있습니다. 아카라이브 로그인

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