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added single-concept: woody

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Files changed (3) hide show
  1. experiments/single-concept/woody/5468_woody_ortho.yml +98 -0
  2. experiments/single-concept/woody/models/edlora_model-latest.pth +3 -0
  3. experiments/single-concept/woody/train_5468_woody_ortho_20240612_024139.log +193 -0
experiments/single-concept/woody/5468_woody_ortho.yml ADDED
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+ # GENERATE TIME: Wed Jun 12 02:41:39 2024
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+ # CMD:
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+ # train_edlora.py -opt single-concept/train_configs/5468_woody_ortho.yml
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+
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+ name: 5468_woody_ortho
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+ manual_seed: 5468
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+ mixed_precision: fp16
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+ gradient_accumulation_steps: 1
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+ datasets:
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+ train:
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+ name: LoraDataset
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+ concept_list: single-concept/data_configs/woody.json
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+ use_caption: true
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+ use_mask: true
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+ instance_transform:
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+ - type: HumanResizeCropFinalV3
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+ size: 512
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+ crop_p: 0.5
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+ - type: ToTensor
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+ - type: Normalize
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+ mean:
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+ - 0.5
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+ std:
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+ - 0.5
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+ - type: ShuffleCaption
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+ keep_token_num: 1
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+ - type: EnhanceText
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+ enhance_type: human
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+ replace_mapping:
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+ <TOK>: <woody1> <woody2>
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+ batch_size_per_gpu: 2
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+ dataset_enlarge_ratio: 500
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+ val_vis:
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+ name: PromptDataset
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+ prompts: single-concept/validation_prompts/characters/test_man.txt
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+ num_samples_per_prompt: 8
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+ latent_size:
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+ - 4
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+ - 64
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+ - 64
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+ replace_mapping:
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+ <TOK>: <woody1> <woody2>
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+ batch_size_per_gpu: 4
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+ models:
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+ pretrained_path: nitrosocke/mo-di-diffusion
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+ enable_edlora: true
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+ finetune_cfg:
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+ text_embedding:
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+ enable_tuning: true
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+ lr: 0.001
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+ text_encoder:
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+ enable_tuning: true
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+ lora_cfg:
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+ rank: 5
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+ alpha: 1.0
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+ where: CLIPAttention
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+ lr: 1.0e-05
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+ unet:
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+ enable_tuning: true
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+ lora_cfg:
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+ rank: 5
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+ alpha: 1.0
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+ where: Attention
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+ lr: 0.0001
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+ new_concept_token: <woody1>+<woody2>
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+ initializer_token: <rand-0.013>+man
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+ noise_offset: 0.01
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+ attn_reg_weight: 0.01
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+ reg_full_identity: false
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+ use_mask_loss: true
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+ gradient_checkpoint: false
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+ enable_xformers: true
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+ path:
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+ pretrain_network: null
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+ train:
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+ optim_g:
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+ type: AdamW
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+ lr: 0.0
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+ weight_decay: 0.01
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+ betas:
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+ - 0.9
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+ - 0.999
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+ unet_kv_drop_rate: 0
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+ scheduler: linear
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+ emb_norm_threshold: 0.55
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+ val:
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+ val_during_save: true
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+ compose_visualize: true
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+ alpha_list:
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+ - 0
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+ - 0.7
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+ - 1.0
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+ sample:
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+ num_inference_steps: 50
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+ guidance_scale: 7.5
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+ logger:
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+ print_freq: 10
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+ save_checkpoint_freq: 10000.0
experiments/single-concept/woody/models/edlora_model-latest.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:4e6feadc85448a4f2e38f210cb063a6d04987fabd19cf005f8a093c7539abb45
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+ size 35173046
experiments/single-concept/woody/train_5468_woody_ortho_20240612_024139.log ADDED
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+ 2024-06-12 02:41:39,471 INFO: Distributed environment: MULTI_GPU Backend: nccl
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+ Num processes: 2
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+ Process index: 0
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+ Local process index: 0
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+ Device: cuda:0
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+
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+ Mixed precision type: fp16
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+
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+ 2024-06-12 02:41:39,471 INFO:
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+ name: 5468_woody_ortho
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+ manual_seed: 5468
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+ mixed_precision: fp16
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+ gradient_accumulation_steps: 1
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+ datasets:[
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+ train:[
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+ name: LoraDataset
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+ concept_list: single-concept/data_configs/woody.json
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+ use_caption: True
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+ use_mask: True
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+ instance_transform: [{'type': 'HumanResizeCropFinalV3', 'size': 512, 'crop_p': 0.5}, {'type': 'ToTensor'}, {'type': 'Normalize', 'mean': [0.5], 'std': [0.5]}, {'type': 'ShuffleCaption', 'keep_token_num': 1}, {'type': 'EnhanceText', 'enhance_type': 'human'}]
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+ replace_mapping:[
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+ <TOK>: <woody1> <woody2>
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+ ]
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+ batch_size_per_gpu: 2
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+ dataset_enlarge_ratio: 500
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+ ]
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+ val_vis:[
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+ name: PromptDataset
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+ prompts: single-concept/validation_prompts/characters/test_man.txt
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+ num_samples_per_prompt: 8
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+ latent_size: [4, 64, 64]
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+ replace_mapping:[
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+ <TOK>: <woody1> <woody2>
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+ ]
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+ batch_size_per_gpu: 4
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+ ]
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+ ]
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+ models:[
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+ pretrained_path: nitrosocke/mo-di-diffusion
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+ enable_edlora: True
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+ finetune_cfg:[
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+ text_embedding:[
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+ enable_tuning: True
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+ lr: 0.001
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+ ]
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+ text_encoder:[
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+ enable_tuning: True
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+ lora_cfg:[
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+ rank: 5
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+ alpha: 1.0
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+ where: CLIPAttention
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+ ]
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+ lr: 1e-05
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+ ]
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+ unet:[
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+ enable_tuning: True
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+ lora_cfg:[
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+ rank: 5
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+ alpha: 1.0
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+ where: Attention
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+ ]
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+ lr: 0.0001
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+ ]
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+ ]
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+ new_concept_token: <woody1>+<woody2>
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+ initializer_token: <rand-0.013>+man
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+ noise_offset: 0.01
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+ attn_reg_weight: 0.01
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+ reg_full_identity: False
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+ use_mask_loss: True
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+ gradient_checkpoint: False
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+ enable_xformers: True
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+ ]
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+ path:[
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+ pretrain_network: None
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+ experiments_root: /home/ujinsong/workspace/ortha/experiments/5468_woody_ortho
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+ models: /home/ujinsong/workspace/ortha/experiments/5468_woody_ortho/models
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+ log: /home/ujinsong/workspace/ortha/experiments/5468_woody_ortho
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+ visualization: /home/ujinsong/workspace/ortha/experiments/5468_woody_ortho/visualization
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+ ]
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+ train:[
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+ optim_g:[
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+ type: AdamW
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+ lr: 0.0
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+ weight_decay: 0.01
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+ betas: [0.9, 0.999]
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+ ]
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+ unet_kv_drop_rate: 0
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+ scheduler: linear
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+ emb_norm_threshold: 0.55
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+ ]
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+ val:[
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+ val_during_save: True
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+ compose_visualize: True
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+ alpha_list: [0, 0.7, 1.0]
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+ sample:[
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+ num_inference_steps: 50
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+ guidance_scale: 7.5
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+ ]
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+ ]
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+ logger:[
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+ print_freq: 10
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+ save_checkpoint_freq: 10000.0
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+ ]
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+ is_train: True
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+
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+ 2024-06-12 02:43:20,128 INFO: <woody1> (49408-49423) is random initialized by: <rand-0.013>
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+ 2024-06-12 02:43:20,868 INFO: <woody2> (49424-49439) is random initialized by existing token (man): 786
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+ 2024-06-12 02:43:20,873 INFO: optimizing embedding using lr: 0.001
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+ 2024-06-12 02:43:21,420 INFO: optimizing text_encoder (48 LoRAs), using lr: 1e-05
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+ 2024-06-12 02:43:22,674 INFO: optimizing unet (128 LoRAs), using lr: 0.0001
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+ 2024-06-12 02:43:26,045 INFO: ***** Running training *****
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+ 2024-06-12 02:43:26,045 INFO: Num examples = 3000
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+ 2024-06-12 02:43:26,045 INFO: Instantaneous batch size per device = 2
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+ 2024-06-12 02:43:26,046 INFO: Total train batch size (w. parallel, distributed & accumulation) = 4
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+ 2024-06-12 02:43:26,046 INFO: Total optimization steps = 750.0
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+ 2024-06-12 02:44:06,961 INFO: [5468_..][Iter: 10, lr:(9.867e-04,9.867e-06,9.867e-05,)] [eta: 0:45:48] loss: 5.7099e-01 Norm_mean: 3.6939e-01
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+ 2024-06-12 02:44:18,766 INFO: [5468_..][Iter: 20, lr:(9.733e-04,9.733e-06,9.733e-05,)] [eta: 0:30:30] loss: 6.7207e-01 Norm_mean: 3.8671e-01
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+ 2024-06-12 02:44:30,974 INFO: [5468_..][Iter: 30, lr:(9.600e-04,9.600e-06,9.600e-05,)] [eta: 0:25:05] loss: 1.1701e+00 Norm_mean: 3.9964e-01
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+ 2024-06-12 02:44:42,911 INFO: [5468_..][Iter: 40, lr:(9.467e-04,9.467e-06,9.467e-05,)] [eta: 0:22:09] loss: 1.2839e+00 Norm_mean: 4.1098e-01
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+ 2024-06-12 02:44:54,917 INFO: [5468_..][Iter: 50, lr:(9.333e-04,9.333e-06,9.333e-05,)] [eta: 0:20:18] loss: 6.2330e-01 Norm_mean: 4.2076e-01
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+ 2024-06-12 02:45:07,042 INFO: [5468_..][Iter: 60, lr:(9.200e-04,9.200e-06,9.200e-05,)] [eta: 0:19:00] loss: 9.9929e-01 Norm_mean: 4.2928e-01
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+ 2024-06-12 02:45:19,059 INFO: [5468_..][Iter: 70, lr:(9.067e-04,9.067e-06,9.067e-05,)] [eta: 0:18:00] loss: 2.5575e-01 Norm_mean: 4.3723e-01
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+ 2024-06-12 02:45:31,197 INFO: [5468_..][Iter: 80, lr:(8.933e-04,8.933e-06,8.933e-05,)] [eta: 0:17:13] loss: 1.2155e+00 Norm_mean: 4.4369e-01
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+ 2024-06-12 02:45:43,729 INFO: [5468_..][Iter: 90, lr:(8.800e-04,8.800e-06,8.800e-05,)] [eta: 0:16:37] loss: 2.6324e-01 Norm_mean: 4.4957e-01
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+ 2024-06-12 02:45:56,110 INFO: [5468_..][Iter: 100, lr:(8.667e-04,8.667e-06,8.667e-05,)] [eta: 0:16:04] loss: 1.6536e+00 Norm_mean: 4.5521e-01
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+ 2024-06-12 02:46:08,328 INFO: [5468_..][Iter: 110, lr:(8.533e-04,8.533e-06,8.533e-05,)] [eta: 0:15:34] loss: 5.7876e-01 Norm_mean: 4.6003e-01
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+ 2024-06-12 02:46:20,526 INFO: [5468_..][Iter: 120, lr:(8.400e-04,8.400e-06,8.400e-05,)] [eta: 0:15:07] loss: 3.8680e-01 Norm_mean: 4.6427e-01
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+ 2024-06-12 02:46:32,565 INFO: [5468_..][Iter: 130, lr:(8.267e-04,8.267e-06,8.267e-05,)] [eta: 0:14:41] loss: 9.8066e-02 Norm_mean: 4.6867e-01
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+ 2024-06-12 02:46:44,843 INFO: [5468_..][Iter: 140, lr:(8.133e-04,8.133e-06,8.133e-05,)] [eta: 0:14:18] loss: 5.0228e-01 Norm_mean: 4.7362e-01
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+ 2024-06-12 02:46:57,031 INFO: [5468_..][Iter: 150, lr:(8.000e-04,8.000e-06,8.000e-05,)] [eta: 0:13:56] loss: 4.5112e-01 Norm_mean: 4.7771e-01
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+ 2024-06-12 02:47:09,303 INFO: [5468_..][Iter: 160, lr:(7.867e-04,7.867e-06,7.867e-05,)] [eta: 0:13:36] loss: 8.8030e-01 Norm_mean: 4.8141e-01
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+ 2024-06-12 02:47:21,632 INFO: [5468_..][Iter: 170, lr:(7.733e-04,7.733e-06,7.733e-05,)] [eta: 0:13:17] loss: 1.0749e+00 Norm_mean: 4.8524e-01
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+ 2024-06-12 02:47:33,899 INFO: [5468_..][Iter: 180, lr:(7.600e-04,7.600e-06,7.600e-05,)] [eta: 0:12:59] loss: 4.8346e-01 Norm_mean: 4.8971e-01
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+ 2024-06-12 02:47:46,179 INFO: [5468_..][Iter: 190, lr:(7.467e-04,7.467e-06,7.467e-05,)] [eta: 0:12:41] loss: 3.2656e-01 Norm_mean: 4.9335e-01
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+ 2024-06-12 02:47:58,373 INFO: [5468_..][Iter: 200, lr:(7.333e-04,7.333e-06,7.333e-05,)] [eta: 0:12:23] loss: 1.1152e+00 Norm_mean: 4.9630e-01
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+ 2024-06-12 02:48:10,567 INFO: [5468_..][Iter: 210, lr:(7.200e-04,7.200e-06,7.200e-05,)] [eta: 0:12:06] loss: 2.1954e-01 Norm_mean: 4.9981e-01
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+ 2024-06-12 02:48:22,717 INFO: [5468_..][Iter: 220, lr:(7.067e-04,7.067e-06,7.067e-05,)] [eta: 0:11:50] loss: 1.0311e+00 Norm_mean: 5.0368e-01
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+ 2024-06-12 02:48:34,814 INFO: [5468_..][Iter: 230, lr:(6.933e-04,6.933e-06,6.933e-05,)] [eta: 0:11:33] loss: 3.1768e-01 Norm_mean: 5.0728e-01
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+ 2024-06-12 02:48:46,979 INFO: [5468_..][Iter: 240, lr:(6.800e-04,6.800e-06,6.800e-05,)] [eta: 0:11:17] loss: 2.7627e+00 Norm_mean: 5.1148e-01
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+ 2024-06-12 02:48:58,988 INFO: [5468_..][Iter: 250, lr:(6.667e-04,6.667e-06,6.667e-05,)] [eta: 0:11:01] loss: 9.6659e-01 Norm_mean: 5.1515e-01
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+ 2024-06-12 02:49:11,312 INFO: [5468_..][Iter: 260, lr:(6.533e-04,6.533e-06,6.533e-05,)] [eta: 0:10:46] loss: 5.9158e-01 Norm_mean: 5.1849e-01
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+ 2024-06-12 02:49:23,229 INFO: [5468_..][Iter: 270, lr:(6.400e-04,6.400e-06,6.400e-05,)] [eta: 0:10:31] loss: 8.9412e-01 Norm_mean: 5.2158e-01
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+ 2024-06-12 02:49:35,194 INFO: [5468_..][Iter: 280, lr:(6.267e-04,6.267e-06,6.267e-05,)] [eta: 0:10:16] loss: 7.5468e-01 Norm_mean: 5.2417e-01
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+ 2024-06-12 02:49:47,086 INFO: [5468_..][Iter: 290, lr:(6.133e-04,6.133e-06,6.133e-05,)] [eta: 0:10:01] loss: 3.6878e-01 Norm_mean: 5.2664e-01
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+ 2024-06-12 02:49:59,200 INFO: [5468_..][Iter: 300, lr:(6.000e-04,6.000e-06,6.000e-05,)] [eta: 0:09:46] loss: 7.5434e-02 Norm_mean: 5.2912e-01
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+ 2024-06-12 02:50:11,234 INFO: [5468_..][Iter: 310, lr:(5.867e-04,5.867e-06,5.867e-05,)] [eta: 0:09:31] loss: 7.0255e-02 Norm_mean: 5.3135e-01
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+ 2024-06-12 02:50:23,281 INFO: [5468_..][Iter: 320, lr:(5.733e-04,5.733e-06,5.733e-05,)] [eta: 0:09:17] loss: 7.6986e-01 Norm_mean: 5.3378e-01
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+ 2024-06-12 02:50:35,246 INFO: [5468_..][Iter: 330, lr:(5.600e-04,5.600e-06,5.600e-05,)] [eta: 0:09:03] loss: 7.8151e-01 Norm_mean: 5.3608e-01
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+ 2024-06-12 02:50:47,352 INFO: [5468_..][Iter: 340, lr:(5.467e-04,5.467e-06,5.467e-05,)] [eta: 0:08:49] loss: 1.9506e-01 Norm_mean: 5.3830e-01
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+ 2024-06-12 02:50:59,349 INFO: [5468_..][Iter: 350, lr:(5.333e-04,5.333e-06,5.333e-05,)] [eta: 0:08:35] loss: 2.5245e-01 Norm_mean: 5.4062e-01
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+ 2024-06-12 02:51:11,361 INFO: [5468_..][Iter: 360, lr:(5.200e-04,5.200e-06,5.200e-05,)] [eta: 0:08:21] loss: 6.4811e-01 Norm_mean: 5.4256e-01
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+ 2024-06-12 02:51:23,561 INFO: [5468_..][Iter: 370, lr:(5.067e-04,5.067e-06,5.067e-05,)] [eta: 0:08:07] loss: 1.5239e+00 Norm_mean: 5.4466e-01
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+ 2024-06-12 02:51:35,924 INFO: [5468_..][Iter: 380, lr:(4.933e-04,4.933e-06,4.933e-05,)] [eta: 0:07:54] loss: 4.0211e-02 Norm_mean: 5.4676e-01
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+ 2024-06-12 02:51:48,339 INFO: [5468_..][Iter: 390, lr:(4.800e-04,4.800e-06,4.800e-05,)] [eta: 0:07:41] loss: 9.7101e-01 Norm_mean: 5.4847e-01
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+ 2024-06-12 02:52:00,363 INFO: [5468_..][Iter: 400, lr:(4.667e-04,4.667e-06,4.667e-05,)] [eta: 0:07:27] loss: 2.0671e-01 Norm_mean: 5.5004e-01
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+ 2024-06-12 02:52:12,307 INFO: [5468_..][Iter: 410, lr:(4.533e-04,4.533e-06,4.533e-05,)] [eta: 0:07:14] loss: 1.5877e-01 Norm_mean: 5.5004e-01
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+ 2024-06-12 02:52:24,574 INFO: [5468_..][Iter: 420, lr:(4.400e-04,4.400e-06,4.400e-05,)] [eta: 0:07:00] loss: 2.5391e-01 Norm_mean: 5.5004e-01
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+ 2024-06-12 02:52:36,622 INFO: [5468_..][Iter: 430, lr:(4.267e-04,4.267e-06,4.267e-05,)] [eta: 0:06:47] loss: 1.1532e-01 Norm_mean: 5.5004e-01
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+ 2024-06-12 02:52:48,741 INFO: [5468_..][Iter: 440, lr:(4.133e-04,4.133e-06,4.133e-05,)] [eta: 0:06:34] loss: 3.0851e-01 Norm_mean: 5.5004e-01
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+ 2024-06-12 02:53:01,070 INFO: [5468_..][Iter: 450, lr:(4.000e-04,4.000e-06,4.000e-05,)] [eta: 0:06:21] loss: 7.8738e-01 Norm_mean: 5.5004e-01
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+ 2024-06-12 02:53:13,317 INFO: [5468_..][Iter: 460, lr:(3.867e-04,3.867e-06,3.867e-05,)] [eta: 0:06:08] loss: 5.9795e-01 Norm_mean: 5.5004e-01
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+ 2024-06-12 02:53:25,576 INFO: [5468_..][Iter: 470, lr:(3.733e-04,3.733e-06,3.733e-05,)] [eta: 0:05:55] loss: 4.9539e-01 Norm_mean: 5.5004e-01
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+ 2024-06-12 02:53:37,715 INFO: [5468_..][Iter: 480, lr:(3.600e-04,3.600e-06,3.600e-05,)] [eta: 0:05:42] loss: 3.8771e-01 Norm_mean: 5.5004e-01
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+ 2024-06-12 02:53:49,877 INFO: [5468_..][Iter: 490, lr:(3.467e-04,3.467e-06,3.467e-05,)] [eta: 0:05:29] loss: 1.5134e+00 Norm_mean: 5.5004e-01
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+ 2024-06-12 02:54:02,035 INFO: [5468_..][Iter: 500, lr:(3.333e-04,3.333e-06,3.333e-05,)] [eta: 0:05:16] loss: 3.3040e-01 Norm_mean: 5.5004e-01
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+ 2024-06-12 02:54:14,330 INFO: [5468_..][Iter: 510, lr:(3.200e-04,3.200e-06,3.200e-05,)] [eta: 0:05:03] loss: 1.4163e+00 Norm_mean: 5.5004e-01
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+ 2024-06-12 02:54:26,506 INFO: [5468_..][Iter: 520, lr:(3.067e-04,3.067e-06,3.067e-05,)] [eta: 0:04:50] loss: 9.3483e-01 Norm_mean: 5.5004e-01
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+ 2024-06-12 02:54:51,002 INFO: [5468_..][Iter: 540, lr:(2.800e-04,2.800e-06,2.800e-05,)] [eta: 0:04:24] loss: 1.2699e+00 Norm_mean: 5.5004e-01
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+ 2024-06-12 02:55:27,784 INFO: [5468_..][Iter: 570, lr:(2.400e-04,2.400e-06,2.400e-05,)] [eta: 0:03:46] loss: 5.7829e-01 Norm_mean: 5.5004e-01
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+ 2024-06-12 02:56:04,727 INFO: [5468_..][Iter: 600, lr:(2.000e-04,2.000e-06,2.000e-05,)] [eta: 0:03:08] loss: 7.2001e-02 Norm_mean: 5.5004e-01
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+ 2024-06-12 02:56:17,283 INFO: [5468_..][Iter: 610, lr:(1.867e-04,1.867e-06,1.867e-05,)] [eta: 0:02:55] loss: 3.5551e-01 Norm_mean: 5.5004e-01
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+ 2024-06-12 02:56:29,458 INFO: [5468_..][Iter: 620, lr:(1.733e-04,1.733e-06,1.733e-05,)] [eta: 0:02:42] loss: 7.2537e-01 Norm_mean: 5.5004e-01
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+ 2024-06-12 02:57:06,395 INFO: [5468_..][Iter: 650, lr:(1.333e-04,1.333e-06,1.333e-05,)] [eta: 0:02:04] loss: 1.3476e+00 Norm_mean: 5.5004e-01
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+ 2024-06-12 02:57:18,388 INFO: [5468_..][Iter: 660, lr:(1.200e-04,1.200e-06,1.200e-05,)] [eta: 0:01:52] loss: 1.6601e+00 Norm_mean: 5.5004e-01
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+ 2024-06-12 02:57:30,607 INFO: [5468_..][Iter: 670, lr:(1.067e-04,1.067e-06,1.067e-05,)] [eta: 0:01:39] loss: 1.1768e-01 Norm_mean: 5.5004e-01
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+ 2024-06-12 02:57:42,804 INFO: [5468_..][Iter: 680, lr:(9.333e-05,9.333e-07,9.333e-06,)] [eta: 0:01:26] loss: 1.9577e-01 Norm_mean: 5.5004e-01
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+ 2024-06-12 02:57:55,018 INFO: [5468_..][Iter: 690, lr:(8.000e-05,8.000e-07,8.000e-06,)] [eta: 0:01:14] loss: 1.7908e+00 Norm_mean: 5.5004e-01
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+ 2024-06-12 02:58:07,173 INFO: [5468_..][Iter: 700, lr:(6.667e-05,6.667e-07,6.667e-06,)] [eta: 0:01:01] loss: 7.1926e-01 Norm_mean: 5.5004e-01
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+ 2024-06-12 02:58:19,291 INFO: [5468_..][Iter: 710, lr:(5.333e-05,5.333e-07,5.333e-06,)] [eta: 0:00:48] loss: 7.9050e-01 Norm_mean: 5.5004e-01
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+ 2024-06-12 02:58:31,159 INFO: [5468_..][Iter: 720, lr:(4.000e-05,4.000e-07,4.000e-06,)] [eta: 0:00:36] loss: 2.6545e-01 Norm_mean: 5.5004e-01
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+ 2024-06-12 02:58:43,107 INFO: [5468_..][Iter: 730, lr:(2.667e-05,2.667e-07,2.667e-06,)] [eta: 0:00:23] loss: 1.2637e+00 Norm_mean: 5.5004e-01
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+ 2024-06-12 02:58:55,208 INFO: [5468_..][Iter: 740, lr:(1.333e-05,1.333e-07,1.333e-06,)] [eta: 0:00:11] loss: 4.1773e-01 Norm_mean: 5.5004e-01
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+ 2024-06-12 02:59:07,338 INFO: [5468_..][Iter: 750, lr:(0.000e+00,0.000e+00,0.000e+00,)] [eta: -1 day, 23:59:59] loss: 4.7732e-01 Norm_mean: 5.5004e-01
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+ 2024-06-12 02:59:07,491 INFO: Save state to /home/ujinsong/workspace/ortha/experiments/5468_woody_ortho/models/edlora_model-latest.pth
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+ 2024-06-12 02:59:07,491 INFO: Start validation /home/ujinsong/workspace/ortha/experiments/5468_woody_ortho/models/edlora_model-latest.pth: