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upload Elastigirl weights

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  1. experiments/single-concept/elastigirl/.DS_Store +0 -0
  2. experiments/single-concept/elastigirl/3101_elastigirl_ortho.yml +98 -0
  3. experiments/single-concept/elastigirl/models/edlora_model-latest.pth +3 -0
  4. experiments/single-concept/elastigirl/train_3101_elastigirl_ortho_20240612_143054.log +192 -0
experiments/single-concept/elastigirl/.DS_Store ADDED
Binary file (6.15 kB). View file
 
experiments/single-concept/elastigirl/3101_elastigirl_ortho.yml ADDED
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+ # GENERATE TIME: Wed Jun 12 14:30:54 2024
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+ # CMD:
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+ # train_edlora.py -opt single-concept/train_configs/3101_elastigirl_ortho.yml
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+
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+ name: 3101_elastigirl_ortho
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+ manual_seed: 3101
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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/elastigirl.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>: <elastigirl1> <elastigirl2>
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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_woman.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>: <elastigirl1> <elastigirl2>
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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: <elastigirl1>+<elastigirl2>
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+ initializer_token: <rand-0.013>+woman
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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: false
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+ compose_visualize: false
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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/elastigirl/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 sha256:574ae8359510362e6d93664c2478e574fa1c3ae25d3e4d86f986d60093589fea
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+ size 35173046
experiments/single-concept/elastigirl/train_3101_elastigirl_ortho_20240612_143054.log ADDED
@@ -0,0 +1,192 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ 2024-06-12 14:30:54,522 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 14:30:54,523 INFO:
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+ name: 3101_elastigirl_ortho
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+ manual_seed: 3101
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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/elastigirl.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>: <elastigirl1> <elastigirl2>
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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_woman.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>: <elastigirl1> <elastigirl2>
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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: <elastigirl1>+<elastigirl2>
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+ initializer_token: <rand-0.013>+woman
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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/3101_elastigirl_ortho
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+ models: /home/ujinsong/workspace/ortha/experiments/3101_elastigirl_ortho/models
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+ log: /home/ujinsong/workspace/ortha/experiments/3101_elastigirl_ortho
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+ visualization: /home/ujinsong/workspace/ortha/experiments/3101_elastigirl_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: False
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+ compose_visualize: False
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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 14:31:02,374 INFO: <elastigirl1> (49408-49423) is random initialized by: <rand-0.013>
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+ 2024-06-12 14:31:02,851 INFO: <elastigirl2> (49424-49439) is random initialized by existing token (woman): 2308
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+ 2024-06-12 14:31:02,857 INFO: optimizing embedding using lr: 0.001
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+ 2024-06-12 14:31:03,298 INFO: optimizing text_encoder (48 LoRAs), using lr: 1e-05
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+ 2024-06-12 14:31:04,187 INFO: optimizing unet (128 LoRAs), using lr: 0.0001
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+ 2024-06-12 14:31:05,906 INFO: ***** Running training *****
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+ 2024-06-12 14:31:05,907 INFO: Num examples = 3000
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+ 2024-06-12 14:31:05,907 INFO: Instantaneous batch size per device = 2
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+ 2024-06-12 14:31:05,907 INFO: Total train batch size (w. parallel, distributed & accumulation) = 4
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+ 2024-06-12 14:31:05,907 INFO: Total optimization steps = 750.0
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+ 2024-06-12 14:31:19,553 INFO: [3101_..][Iter: 10, lr:(9.867e-04,9.867e-06,9.867e-05,)] [eta: 0:15:16] loss: 1.7955e+00 Norm_mean: 3.9338e-01
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+ 2024-06-12 14:31:30,549 INFO: [3101_..][Iter: 20, lr:(9.733e-04,9.733e-06,9.733e-05,)] [eta: 0:14:15] loss: 3.2856e-01 Norm_mean: 4.0980e-01
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+ 2024-06-12 14:31:41,738 INFO: [3101_..][Iter: 30, lr:(9.600e-04,9.600e-06,9.600e-05,)] [eta: 0:13:51] loss: 1.8840e+00 Norm_mean: 4.2221e-01
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+ 2024-06-12 14:31:52,945 INFO: [3101_..][Iter: 40, lr:(9.467e-04,9.467e-06,9.467e-05,)] [eta: 0:13:33] loss: 4.7508e-02 Norm_mean: 4.3213e-01
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+ 2024-06-12 14:32:04,246 INFO: [3101_..][Iter: 50, lr:(9.333e-04,9.333e-06,9.333e-05,)] [eta: 0:13:19] loss: 6.2937e-01 Norm_mean: 4.4096e-01
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+ 2024-06-12 14:32:15,557 INFO: [3101_..][Iter: 60, lr:(9.200e-04,9.200e-06,9.200e-05,)] [eta: 0:13:06] loss: 1.7376e-01 Norm_mean: 4.4910e-01
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+ 2024-06-12 14:32:26,654 INFO: [3101_..][Iter: 70, lr:(9.067e-04,9.067e-06,9.067e-05,)] [eta: 0:12:52] loss: 2.0559e-01 Norm_mean: 4.5643e-01
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+ 2024-06-12 14:32:37,983 INFO: [3101_..][Iter: 80, lr:(8.933e-04,8.933e-06,8.933e-05,)] [eta: 0:12:40] loss: 2.0426e+00 Norm_mean: 4.6205e-01
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+ 2024-06-12 14:32:49,171 INFO: [3101_..][Iter: 90, lr:(8.800e-04,8.800e-06,8.800e-05,)] [eta: 0:12:27] loss: 3.2982e-02 Norm_mean: 4.6693e-01
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+ 2024-06-12 14:33:00,609 INFO: [3101_..][Iter: 100, lr:(8.667e-04,8.667e-06,8.667e-05,)] [eta: 0:12:17] loss: 9.1559e-01 Norm_mean: 4.7130e-01
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+ 2024-06-12 14:33:12,029 INFO: [3101_..][Iter: 110, lr:(8.533e-04,8.533e-06,8.533e-05,)] [eta: 0:12:06] loss: 3.6256e-01 Norm_mean: 4.7552e-01
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+ 2024-06-12 14:33:23,515 INFO: [3101_..][Iter: 120, lr:(8.400e-04,8.400e-06,8.400e-05,)] [eta: 0:11:55] loss: 4.2777e-01 Norm_mean: 4.8019e-01
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+ 2024-06-12 14:33:34,913 INFO: [3101_..][Iter: 130, lr:(8.267e-04,8.267e-06,8.267e-05,)] [eta: 0:11:44] loss: 9.1723e-01 Norm_mean: 4.8464e-01
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+ 2024-06-12 14:33:46,306 INFO: [3101_..][Iter: 140, lr:(8.133e-04,8.133e-06,8.133e-05,)] [eta: 0:11:32] loss: 1.8349e-01 Norm_mean: 4.8828e-01
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+ 2024-06-12 14:33:57,753 INFO: [3101_..][Iter: 150, lr:(8.000e-04,8.000e-06,8.000e-05,)] [eta: 0:11:21] loss: 1.7093e+00 Norm_mean: 4.9239e-01
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+ 2024-06-12 14:34:09,097 INFO: [3101_..][Iter: 160, lr:(7.867e-04,7.867e-06,7.867e-05,)] [eta: 0:11:10] loss: 1.0194e-01 Norm_mean: 4.9714e-01
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+ 2024-06-12 14:34:20,639 INFO: [3101_..][Iter: 170, lr:(7.733e-04,7.733e-06,7.733e-05,)] [eta: 0:10:59] loss: 6.3093e-01 Norm_mean: 5.0115e-01
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+ 2024-06-12 14:34:32,182 INFO: [3101_..][Iter: 180, lr:(7.600e-04,7.600e-06,7.600e-05,)] [eta: 0:10:48] loss: 3.2305e-01 Norm_mean: 5.0517e-01
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+ 2024-06-12 14:34:43,540 INFO: [3101_..][Iter: 190, lr:(7.467e-04,7.467e-06,7.467e-05,)] [eta: 0:10:36] loss: 6.1072e-02 Norm_mean: 5.0899e-01
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+ 2024-06-12 14:34:54,989 INFO: [3101_..][Iter: 200, lr:(7.333e-04,7.333e-06,7.333e-05,)] [eta: 0:10:25] loss: 1.6823e+00 Norm_mean: 5.1253e-01
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+ 2024-06-12 14:35:06,479 INFO: [3101_..][Iter: 210, lr:(7.200e-04,7.200e-06,7.200e-05,)] [eta: 0:10:14] loss: 1.1665e+00 Norm_mean: 5.1562e-01
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+ 2024-06-12 14:35:17,990 INFO: [3101_..][Iter: 220, lr:(7.067e-04,7.067e-06,7.067e-05,)] [eta: 0:10:03] loss: 1.6114e-01 Norm_mean: 5.1809e-01
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+ 2024-06-12 14:35:29,365 INFO: [3101_..][Iter: 230, lr:(6.933e-04,6.933e-06,6.933e-05,)] [eta: 0:09:51] loss: 6.7085e-02 Norm_mean: 5.2108e-01
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+ 2024-06-12 14:35:40,883 INFO: [3101_..][Iter: 240, lr:(6.800e-04,6.800e-06,6.800e-05,)] [eta: 0:09:40] loss: 4.5825e-01 Norm_mean: 5.2453e-01
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+ 2024-06-12 14:35:52,476 INFO: [3101_..][Iter: 250, lr:(6.667e-04,6.667e-06,6.667e-05,)] [eta: 0:09:29] loss: 2.4489e-01 Norm_mean: 5.2843e-01
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+ 2024-06-12 14:36:04,118 INFO: [3101_..][Iter: 260, lr:(6.533e-04,6.533e-06,6.533e-05,)] [eta: 0:09:18] loss: 2.5160e-02 Norm_mean: 5.3223e-01
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+ 2024-06-12 14:36:15,767 INFO: [3101_..][Iter: 270, lr:(6.400e-04,6.400e-06,6.400e-05,)] [eta: 0:09:07] loss: 3.6775e-01 Norm_mean: 5.3573e-01
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+ 2024-06-12 14:36:27,380 INFO: [3101_..][Iter: 280, lr:(6.267e-04,6.267e-06,6.267e-05,)] [eta: 0:08:56] loss: 4.3587e-01 Norm_mean: 5.3877e-01
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+ 2024-06-12 14:36:38,987 INFO: [3101_..][Iter: 290, lr:(6.133e-04,6.133e-06,6.133e-05,)] [eta: 0:08:45] loss: 3.9278e-01 Norm_mean: 5.4123e-01
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+ 2024-06-12 14:36:50,508 INFO: [3101_..][Iter: 300, lr:(6.000e-04,6.000e-06,6.000e-05,)] [eta: 0:08:34] loss: 2.6086e-01 Norm_mean: 5.4371e-01
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+ 2024-06-12 14:37:01,994 INFO: [3101_..][Iter: 310, lr:(5.867e-04,5.867e-06,5.867e-05,)] [eta: 0:08:22] loss: 1.0341e+00 Norm_mean: 5.4617e-01
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+ 2024-06-12 14:37:13,704 INFO: [3101_..][Iter: 320, lr:(5.733e-04,5.733e-06,5.733e-05,)] [eta: 0:08:11] loss: 1.1104e+00 Norm_mean: 5.4801e-01
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+ 2024-06-12 14:37:25,242 INFO: [3101_..][Iter: 330, lr:(5.600e-04,5.600e-06,5.600e-05,)] [eta: 0:08:00] loss: 4.4960e-01 Norm_mean: 5.4974e-01
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+ 2024-06-12 14:37:36,784 INFO: [3101_..][Iter: 340, lr:(5.467e-04,5.467e-06,5.467e-05,)] [eta: 0:07:48] loss: 5.2360e-01 Norm_mean: 5.5004e-01
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+ 2024-06-12 14:37:48,603 INFO: [3101_..][Iter: 350, lr:(5.333e-04,5.333e-06,5.333e-05,)] [eta: 0:07:37] loss: 8.7654e-01 Norm_mean: 5.5004e-01
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+ 2024-06-12 14:38:00,590 INFO: [3101_..][Iter: 360, lr:(5.200e-04,5.200e-06,5.200e-05,)] [eta: 0:07:26] loss: 2.5864e-01 Norm_mean: 5.5004e-01
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+ 2024-06-12 14:38:12,451 INFO: [3101_..][Iter: 370, lr:(5.067e-04,5.067e-06,5.067e-05,)] [eta: 0:07:15] loss: 1.0777e+00 Norm_mean: 5.5004e-01
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+ 2024-06-12 14:38:24,111 INFO: [3101_..][Iter: 380, lr:(4.933e-04,4.933e-06,4.933e-05,)] [eta: 0:07:04] loss: 3.4526e-01 Norm_mean: 5.5004e-01
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+ 2024-06-12 14:38:35,688 INFO: [3101_..][Iter: 390, lr:(4.800e-04,4.800e-06,4.800e-05,)] [eta: 0:06:52] loss: 5.8209e-01 Norm_mean: 5.5004e-01
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+ 2024-06-12 14:38:47,263 INFO: [3101_..][Iter: 400, lr:(4.667e-04,4.667e-06,4.667e-05,)] [eta: 0:06:41] loss: 1.0728e+00 Norm_mean: 5.5004e-01
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+ 2024-06-12 14:38:58,981 INFO: [3101_..][Iter: 410, lr:(4.533e-04,4.533e-06,4.533e-05,)] [eta: 0:06:30] loss: 2.2969e-01 Norm_mean: 5.5004e-01
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+ 2024-06-12 14:39:10,708 INFO: [3101_..][Iter: 420, lr:(4.400e-04,4.400e-06,4.400e-05,)] [eta: 0:06:18] loss: 2.2180e-01 Norm_mean: 5.5004e-01
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+ 2024-06-12 14:39:22,502 INFO: [3101_..][Iter: 430, lr:(4.267e-04,4.267e-06,4.267e-05,)] [eta: 0:06:07] loss: 5.8771e-01 Norm_mean: 5.5004e-01
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+ 2024-06-12 14:39:34,180 INFO: [3101_..][Iter: 440, lr:(4.133e-04,4.133e-06,4.133e-05,)] [eta: 0:05:56] loss: 1.2351e+00 Norm_mean: 5.5004e-01
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+ 2024-06-12 14:39:45,659 INFO: [3101_..][Iter: 450, lr:(4.000e-04,4.000e-06,4.000e-05,)] [eta: 0:05:44] loss: 2.2661e-01 Norm_mean: 5.5004e-01
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+ 2024-06-12 14:39:57,067 INFO: [3101_..][Iter: 460, lr:(3.867e-04,3.867e-06,3.867e-05,)] [eta: 0:05:32] loss: 7.9299e-01 Norm_mean: 5.5004e-01
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+ 2024-06-12 14:40:08,507 INFO: [3101_..][Iter: 470, lr:(3.733e-04,3.733e-06,3.733e-05,)] [eta: 0:05:21] loss: 1.9985e+00 Norm_mean: 5.5004e-01
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+ 2024-06-12 14:40:19,914 INFO: [3101_..][Iter: 480, lr:(3.600e-04,3.600e-06,3.600e-05,)] [eta: 0:05:09] loss: 2.2811e-01 Norm_mean: 5.5004e-01
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+ 2024-06-12 14:40:31,475 INFO: [3101_..][Iter: 490, lr:(3.467e-04,3.467e-06,3.467e-05,)] [eta: 0:04:58] loss: 3.1418e-01 Norm_mean: 5.5004e-01
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+ 2024-06-12 14:40:42,928 INFO: [3101_..][Iter: 500, lr:(3.333e-04,3.333e-06,3.333e-05,)] [eta: 0:04:46] loss: 8.7115e-02 Norm_mean: 5.5004e-01
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+ 2024-06-12 14:40:54,443 INFO: [3101_..][Iter: 510, lr:(3.200e-04,3.200e-06,3.200e-05,)] [eta: 0:04:35] loss: 1.6109e+00 Norm_mean: 5.5004e-01
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+ 2024-06-12 14:45:32,274 INFO: Save state to /home/ujinsong/workspace/ortha/experiments/3101_elastigirl_ortho/models/edlora_model-latest.pth