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(sdiff) root@193df87d3047:/workspace/matfuse-sd/logs/2024-06-07T19-26-44_multi-vq_f8# python src/main.py --base src/configs/diffusion/matfuse-ldm-vq_f8.yaml --train --gpus 0,
python: can't open file '/workspace/matfuse-sd/logs/2024-06-07T19-26-44_multi-vq_f8/src/main.py': [Errno 2] No such file or directory
(sdiff) root@193df87d3047:/workspace/matfuse-sd/logs/2024-06-07T19-26-44_multi-vq_f8# cd ..
(sdiff) root@193df87d3047:/workspace/matfuse-sd/logs# cd ..
(sdiff) root@193df87d3047:/workspace/matfuse-sd# python src/main.py --base src/configs/diffusion/matfuse-ldm-vq_f8.yaml --train --gpus 0,
Global seed set to 23
Running on GPUs 0,
LatentDiffusion: Running in eps-prediction mode
DiffusionWrapper has 395.03 M params.
Keeping EMAs of 628.
making attention of type 'vanilla' with 512 in_channels
Working with z of shape (1, 256, 32, 32) = 262144 dimensions.
making attention of type 'vanilla' with 512 in_channels
making attention of type 'vanilla' with 512 in_channels
making attention of type 'vanilla' with 512 in_channels
making attention of type 'vanilla' with 512 in_channels
making attention of type 'vanilla' with 512 in_channels
Working with z of shape (1, 256, 32, 32) = 262144 dimensions.
making attention of type 'vanilla' with 512 in_channels
Restored from /workspace/matfuse-sd/logs/2024-06-07T19-26-44_multi-vq_f8/checkpoints/epoch=000003.ckpt with 576 missing and 7 unexpected keys out of a total of 7
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'quant_conv_2.bias', 'quant_conv_3.weight', 'quant_conv_3.bias']
Unexpected Keys: ['epoch', 'global_step', 'pytorch-lightning_version', 'state_dict', 'callbacks', 'optimizer_states', 'lr_schedulers']
Conditional model: Multiconditional
Monitoring val/loss_simple_ema as checkpoint metric.
Merged modelckpt-cfg: 
{'target': 'pytorch_lightning.callbacks.ModelCheckpoint', 'params': {'dirpath': 'logs/2024-06-10T17-26-39_matfuse-ldm-vq_f8/checkpoints', 'filename': '{epoch:06}', 'verbose': True, 'save_last': True, 'monitor': 'val/loss_simple_ema', 'save_top_k': 3}}
GPU available: True, used: True
TPU available: False, using: 0 TPU cores
IPU available: False, using: 0 IPUs
#### Data #####
train, MatFuseDataset, 173319
validation, MatFuseDataset, 63
accumulate_grad_batches = 1
Setting learning rate to 8.00e-06 = 1 (accumulate_grad_batches) * 1 (num_gpus) * 8 (batchsize) * 1.00e-06 (base_lr)
/root/anaconda3/envs/sdiff/lib/python3.10/site-packages/pytorch_lightning/core/datamodule.py:423: 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:
    linear_start: 0.0015
    linear_end: 0.0195
    num_timesteps_cond: 1
    log_every_t: 200
    timesteps: 1000
    first_stage_key: packed
    cond_stage_key:
    - image_embed
    - sketch
    - palette
    - text
    image_size: 32
    channels: 12
    cond_stage_trainable: true
    conditioning_key: hybrid
    monitor: val/loss_simple_ema
    ucg_training:
      image_embed:
        p: 0.5
        val: 0.0
      palette:
        p: 0.5
        val: 0.0
      sketch:
        p: 0.5
        val: 0.0
      text:
        p: 0.5
        val: ''
    unet_config:
      target: ldm.modules.diffusionmodules.openaimodel.UNetModel
      params:
        image_size: 32
        in_channels: 16
        out_channels: 12
        model_channels: 256
        attention_resolutions:
        - 4
        - 2
        - 1
        num_res_blocks: 2
        channel_mult:
        - 1
        - 2
        - 4
        num_head_channels: 32
        use_spatial_transformer: true
        transformer_depth: 1
        context_dim: 512
        use_checkpoint: true
        legacy: false
    first_stage_config:
      target: ldm.models.autoencoder.VQModelMulti
      params:
        embed_dim: 3
        n_embed: 4096
        ckpt_path: /workspace/matfuse-sd/logs/2024-06-07T19-26-44_multi-vq_f8/checkpoints/epoch=000003.ckpt
        ddconfig:
          double_z: false
          z_channels: 256
          resolution: 256
          in_channels: 3
          out_ch: 12
          ch: 128
          ch_mult:
          - 1
          - 1
          - 2
          - 4
          num_res_blocks: 2
          attn_resolutions:
          - null
          dropout: 0.0
        lossconfig:
          target: torch.nn.Identity
    cond_stage_config:
      target: ldm.modules.encoders.multicondition.MultiConditionEncoder
      params:
        image_embed_config:
          target: ldm.modules.encoders.modules.FrozenCLIPImageEmbedder
          params:
            model: ViT-B/16
        text_embed_config:
          target: ldm.modules.encoders.modules.FrozenCLIPSentenceEmbedder
          params:
            version: sentence-transformers/clip-ViT-B-16
        binary_encoder_config:
          target: ldm.modules.encoders.modules.SimpleEncoder
          params:
            in_channels: 1
            out_channels: 4
        palette_proj_config:
          target: ldm.modules.encoders.multicondition.PaletteEncoder
          params:
            in_ch: 3
            hid_ch: 64
            out_ch: 512
data:
  target: main.DataModuleFromConfig
  params:
    batch_size: 8
    num_workers: 0
    wrap: false
    train:
      target: ldm.data.matfuse.MatFuseDataset
      params:
        data_root: data/train
        size: 256
        output_names:
        - diffuse
        - normal
        - roughness
        - specular
    validation:
      target: ldm.data.matfuse.MatFuseDataset
      params:
        data_root: data/test
        size: 256
        output_names:
        - diffuse
        - normal
        - roughness
        - specular

Lightning config
callbacks:
  image_logger:
    target: main.ImageLogger
    params:
      batch_frequency: 6
      max_images: 4
      increase_log_steps: false
      log_images_kwargs:
        ddim_steps: 50
trainer:
  strategy: ddp
  replace_sampler_ddp: false
  gpus: 0,


  | Name              | Type                  | Params
------------------------------------------------------------
0 | model             | DiffusionWrapper      | 395 M 
1 | model_ema         | LitEma                | 0     
2 | first_stage_model | VQModelMulti          | 132 M 
3 | cond_stage_model  | MultiConditionEncoder | 299 M 
------------------------------------------------------------
545 M     Trainable params
281 M     Non-trainable params
827 M     Total params
3,308.097 Total estimated model params size (MB)
Validation sanity check: 0it [00:00, ?it/s]/root/anaconda3/envs/sdiff/lib/python3.10/site-packages/pytorch_lightning/trainer/data_loading.py:105: UserWarning: 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 64 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.
  rank_zero_warn(
Global seed set to 23                                                                                                                                                                                                                                                                   
/root/anaconda3/envs/sdiff/lib/python3.10/site-packages/pytorch_lightning/trainer/data_loading.py:105: UserWarning: The dataloader, train dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 64 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.
  rank_zero_warn(
/root/anaconda3/envs/sdiff/lib/python3.10/site-packages/pytorch_lightning/callbacks/lr_monitor.py:112: RuntimeWarning: You are using `LearningRateMonitor` callback with models that have no learning rate schedulers. Please see documentation for `configure_optimizers` method.
  rank_zero_warn(
Epoch 0:   0%|▏                                                                                                        | 14/6258 [00:13<1:30:16,  1.15it/s, loss=0.992, v_num=fdaz, train/loss_simple_step=0.985, train/loss_vlb_step=0.00564, train/loss_step=0.985, global_step=13.00]
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