End of training
Browse files- README.md +62 -0
- checkpoint-1000/optimizer.bin +3 -0
- checkpoint-1000/pytorch_lora_weights.safetensors +3 -0
- checkpoint-1000/random_states_0.pkl +3 -0
- checkpoint-1000/scaler.pt +3 -0
- checkpoint-1000/scheduler.bin +3 -0
- checkpoint-500/optimizer.bin +3 -0
- checkpoint-500/pytorch_lora_weights.safetensors +3 -0
- checkpoint-500/random_states_0.pkl +3 -0
- checkpoint-500/scaler.pt +3 -0
- checkpoint-500/scheduler.bin +3 -0
- logs/dreambooth-lora-sd-xl/1712763197.0518105/events.out.tfevents.1712763197.150-136-220-21.1289442.1 +3 -0
- logs/dreambooth-lora-sd-xl/1712763197.0537565/hparams.yml +69 -0
- logs/dreambooth-lora-sd-xl/1712770776.9011922/events.out.tfevents.1712770776.150-136-220-21.1290869.1 +3 -0
- logs/dreambooth-lora-sd-xl/1712770776.9030526/hparams.yml +69 -0
- logs/dreambooth-lora-sd-xl/events.out.tfevents.1712763197.150-136-220-21.1289442.0 +3 -0
- logs/dreambooth-lora-sd-xl/events.out.tfevents.1712770776.150-136-220-21.1290869.0 +3 -0
- pytorch_lora_weights.safetensors +3 -0
README.md
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---
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license: openrail++
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library_name: diffusers
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tags:
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- text-to-image
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- text-to-image
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- diffusers-training
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- diffusers
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- lora
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- template:sd-lora
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- stable-diffusion-xl
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- stable-diffusion-xl-diffusers
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base_model: stabilityai/stable-diffusion-xl-base-1.0
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instance_prompt: A [v]
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widget: []
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---
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<!-- This model card has been generated automatically according to the information the training script had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# SDXL LoRA DreamBooth - multimodalart/B-LoRA_style
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<Gallery />
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## Model description
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These are multimodalart/B-LoRA_style LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
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The weights were trained using [DreamBooth](https://dreambooth.github.io/).
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LoRA for the text encoder was enabled: False.
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Special VAE used for training: None.
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## Trigger words
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You should use A [v] to trigger the image generation.
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## Download model
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Weights for this model are available in Safetensors format.
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[Download](multimodalart/B-LoRA_style/tree/main) them in the Files & versions tab.
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## Intended uses & limitations
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#### How to use
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```python
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# TODO: add an example code snippet for running this diffusion pipeline
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```
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#### Limitations and bias
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[TODO: provide examples of latent issues and potential remediations]
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## Training details
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[TODO: describe the data used to train the model]
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checkpoint-1000/optimizer.bin
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checkpoint-1000/pytorch_lora_weights.safetensors
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checkpoint-1000/random_states_0.pkl
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checkpoint-500/optimizer.bin
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checkpoint-500/pytorch_lora_weights.safetensors
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checkpoint-500/random_states_0.pkl
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checkpoint-500/scaler.pt
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checkpoint-500/scheduler.bin
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logs/dreambooth-lora-sd-xl/1712763197.0518105/events.out.tfevents.1712763197.150-136-220-21.1289442.1
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logs/dreambooth-lora-sd-xl/1712763197.0537565/hparams.yml
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adam_beta1: 0.9
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adam_beta2: 0.999
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adam_epsilon: 1.0e-08
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adam_weight_decay: 0.0001
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adam_weight_decay_text_encoder: 0.001
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allow_tf32: false
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cache_dir: null
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caption_column: null
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center_crop: false
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checkpointing_steps: 500
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checkpoints_total_limit: null
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class_data_dir: null
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class_prompt: null
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dataloader_num_workers: 0
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dataset_config_name: null
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dataset_name: null
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do_edm_style_training: false
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enable_xformers_memory_efficient_attention: false
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gradient_accumulation_steps: 1
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gradient_checkpointing: true
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hub_model_id: null
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hub_token: null
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image_column: image
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instance_data_dir: ./style
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instance_prompt: A [v]
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learning_rate: 5.0e-05
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local_rank: -1
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logging_dir: logs
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lr_num_cycles: 1
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lr_power: 1.0
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lr_scheduler: constant
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lr_warmup_steps: 0
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max_grad_norm: 1.0
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max_train_steps: 1000
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mixed_precision: fp16
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num_class_images: 100
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num_train_epochs: 1000
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num_validation_images: 4
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optimizer: AdamW
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output_dir: B-LoRA_style
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pretrained_model_name_or_path: stabilityai/stable-diffusion-xl-base-1.0
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pretrained_vae_model_name_or_path: null
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prior_generation_precision: null
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prior_loss_weight: 1.0
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prodigy_beta3: null
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prodigy_decouple: true
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prodigy_safeguard_warmup: true
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prodigy_use_bias_correction: true
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push_to_hub: false
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random_flip: false
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rank: 64
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repeats: 1
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report_to: tensorboard
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resolution: 1024
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resume_from_checkpoint: null
|
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revision: null
|
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sample_batch_size: 4
|
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scale_lr: false
|
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seed: 0
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snr_gamma: null
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text_encoder_lr: 5.0e-06
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train_batch_size: 1
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train_text_encoder: false
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use_8bit_adam: false
|
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use_dora: false
|
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validation_epochs: 50
|
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validation_prompt: null
|
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variant: null
|
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with_prior_preservation: false
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logs/dreambooth-lora-sd-xl/1712770776.9011922/events.out.tfevents.1712770776.150-136-220-21.1290869.1
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logs/dreambooth-lora-sd-xl/1712770776.9030526/hparams.yml
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adam_beta1: 0.9
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adam_beta2: 0.999
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adam_epsilon: 1.0e-08
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adam_weight_decay: 0.0001
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adam_weight_decay_text_encoder: 0.001
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allow_tf32: false
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cache_dir: null
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gradient_checkpointing: true
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hub_token: null
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image_column: image
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instance_data_dir: ./style
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instance_prompt: A [v]
|
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learning_rate: 5.0e-05
|
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local_rank: -1
|
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logging_dir: logs
|
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lr_num_cycles: 1
|
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lr_power: 1.0
|
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lr_scheduler: constant
|
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lr_warmup_steps: 0
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max_grad_norm: 1.0
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max_train_steps: 1000
|
35 |
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mixed_precision: fp16
|
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num_class_images: 100
|
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num_train_epochs: 1000
|
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num_validation_images: 4
|
39 |
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optimizer: AdamW
|
40 |
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output_dir: B-LoRA_style
|
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|
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pretrained_vae_model_name_or_path: null
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prior_loss_weight: 1.0
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prodigy_beta3: null
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prodigy_decouple: true
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prodigy_use_bias_correction: true
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repeats: 1
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use_8bit_adam: false
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validation_epochs: 50
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variant: null
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