End of training
Browse files- README.md +23 -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-1200/optimizer.bin +3 -0
- checkpoint-1200/pytorch_lora_weights.safetensors +3 -0
- checkpoint-1200/random_states_0.pkl +3 -0
- checkpoint-1200/scaler.pt +3 -0
- checkpoint-1200/scheduler.bin +3 -0
- checkpoint-200/optimizer.bin +3 -0
- checkpoint-200/pytorch_lora_weights.safetensors +3 -0
- checkpoint-200/random_states_0.pkl +3 -0
- checkpoint-200/scaler.pt +3 -0
- checkpoint-200/scheduler.bin +3 -0
- checkpoint-400/optimizer.bin +3 -0
- checkpoint-400/pytorch_lora_weights.safetensors +3 -0
- checkpoint-400/random_states_0.pkl +3 -0
- checkpoint-400/scaler.pt +3 -0
- checkpoint-400/scheduler.bin +3 -0
- checkpoint-600/optimizer.bin +3 -0
- checkpoint-600/pytorch_lora_weights.safetensors +3 -0
- checkpoint-600/random_states_0.pkl +3 -0
- checkpoint-600/scaler.pt +3 -0
- checkpoint-600/scheduler.bin +3 -0
- checkpoint-800/optimizer.bin +3 -0
- checkpoint-800/pytorch_lora_weights.safetensors +3 -0
- checkpoint-800/random_states_0.pkl +3 -0
- checkpoint-800/scaler.pt +3 -0
- checkpoint-800/scheduler.bin +3 -0
- logs/dreambooth-lora-sd-xl/1695475716.1602964/events.out.tfevents.1695475716.9c239552fcde.4324.1 +3 -0
- logs/dreambooth-lora-sd-xl/1695475716.162109/hparams.yml +53 -0
- logs/dreambooth-lora-sd-xl/events.out.tfevents.1695475716.9c239552fcde.4324.0 +3 -0
- pytorch_lora_weights.safetensors +3 -0
README.md
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---
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license: openrail++
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base_model: stabilityai/stable-diffusion-xl-base-1.0
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instance_prompt: a photo of sks person
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tags:
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- stable-diffusion-xl
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- stable-diffusion-xl-diffusers
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- text-to-image
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- diffusers
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- lora
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inference: true
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---
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# LoRA DreamBooth - Doggerz/lora-trained-xl-dadu
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These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on a photo of sks person using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
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LoRA for the text encoder was enabled: True.
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Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
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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: 0.01
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allow_tf32: false
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center_crop: false
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checkpointing_steps: 200
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checkpoints_total_limit: null
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class_data_dir: drive/MyDrive/class_data
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class_prompt: a photo of a person
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crops_coords_top_left_h: 0
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crops_coords_top_left_w: 0
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dataloader_num_workers: 0
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enable_xformers_memory_efficient_attention: true
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gradient_accumulation_steps: 1
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gradient_checkpointing: false
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hub_model_id: null
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hub_token: null
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instance_data_dir: drive/MyDrive/instance_data
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instance_prompt: a photo of sks person
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learning_rate: 0.0001
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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: 1200
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mixed_precision: fp16
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num_class_images: 250
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num_train_epochs: 10
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num_validation_images: 4
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output_dir: lora-trained-xl-dadu
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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: madebyollin/sdxl-vae-fp16-fix
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prior_generation_precision: null
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prior_loss_weight: 1.0
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push_to_hub: true
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rank: 4
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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: 2
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scale_lr: false
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seed: null
|
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train_batch_size: 2
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train_text_encoder: true
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use_8bit_adam: true
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validation_epochs: 50
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validation_prompt: null
|
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with_prior_preservation: true
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logs/dreambooth-lora-sd-xl/events.out.tfevents.1695475716.9c239552fcde.4324.0
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