End of training
Browse files- README.md +43 -0
- checkpoint-100/optimizer.bin +3 -0
- checkpoint-100/pytorch_lora_weights.safetensors +3 -0
- checkpoint-100/random_states_0.pkl +3 -0
- checkpoint-100/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/scheduler.bin +3 -0
- checkpoint-300/optimizer.bin +3 -0
- checkpoint-300/pytorch_lora_weights.safetensors +3 -0
- checkpoint-300/random_states_0.pkl +3 -0
- checkpoint-300/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/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/scheduler.bin +3 -0
- logs/dreambooth-lora/1740412001.6763341/events.out.tfevents.1740412001.daintlabH.934955.1 +3 -0
- logs/dreambooth-lora/1740412001.6858206/hparams.yml +56 -0
- logs/dreambooth-lora/events.out.tfevents.1740412001.daintlabH.934955.0 +3 -0
- pytorch_lora_weights.safetensors +3 -0
README.md
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---
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base_model: stable-diffusion-v1-5/stable-diffusion-v1-5
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library_name: diffusers
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license: creativeml-openrail-m
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inference: true
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instance_prompt: a photo of sks backpack
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tags:
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- text-to-image
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- diffusers
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- lora
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- diffusers-training
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- stable-diffusion
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- stable-diffusion-diffusers
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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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# LoRA DreamBooth - BJEon121/trained-sd1.5_backpack
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These are LoRA adaption weights for stable-diffusion-v1-5/stable-diffusion-v1-5. The weights were trained on a photo of sks backpack 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: False.
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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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logs/dreambooth-lora/1740412001.6763341/events.out.tfevents.1740412001.daintlabH.934955.1
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logs/dreambooth-lora/1740412001.6858206/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: 0.01
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allow_tf32: false
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center_crop: false
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checkpointing_steps: 100
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checkpoints_total_limit: null
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class_data_dir: null
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class_labels_conditioning: null
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class_prompt: null
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dataloader_num_workers: 0
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enable_xformers_memory_efficient_attention: false
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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: /nas/home/jj275/Personalization_ICCV_2025/data/backpack
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instance_prompt: a photo of sks backpack
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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: 500
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mixed_precision: null
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num_class_images: 100
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num_train_epochs: 84
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num_validation_images: 4
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output_dir: trained-sd1.5_backpack
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pre_compute_text_embeddings: false
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pretrained_model_name_or_path: stable-diffusion-v1-5/stable-diffusion-v1-5
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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: 512
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resume_from_checkpoint: null
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revision: null
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| 44 |
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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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text_encoder_use_attention_mask: false
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| 48 |
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tokenizer_max_length: null
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tokenizer_name: null
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| 50 |
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train_batch_size: 1
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| 51 |
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train_text_encoder: false
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| 52 |
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use_8bit_adam: false
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| 53 |
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validation_epochs: 50
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| 54 |
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validation_prompt: null
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| 55 |
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variant: null
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| 56 |
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with_prior_preservation: false
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