End of training
Browse files- .gitattributes +3 -0
- README.md +47 -0
- image_0.png +3 -0
- image_1.png +3 -0
- image_2.png +3 -0
- image_3.png +0 -0
- logs/dreambooth-lora/1725240551.6990728/events.out.tfevents.1725240551.6fec217efaa8.2593680.1 +3 -0
- logs/dreambooth-lora/1725240551.7004247/hparams.yml +56 -0
- logs/dreambooth-lora/events.out.tfevents.1725240551.6fec217efaa8.2593680.0 +3 -0
- pytorch_lora_weights.safetensors +3 -0
.gitattributes
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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base_model: stabilityai/stable-diffusion-2
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library_name: diffusers
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license: creativeml-openrail-m
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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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inference: true
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instance_prompt: I want to eat delicious meal made of beef or steak
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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 - codeiceman/beaf-model2
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These are LoRA adaption weights for stabilityai/stable-diffusion-2. The weights were trained on I want to eat delicious meal made of beef or steak 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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image_0.png
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Git LFS Details
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image_1.png
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Git LFS Details
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image_2.png
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Git LFS Details
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image_3.png
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logs/dreambooth-lora/1725240551.6990728/events.out.tfevents.1725240551.6fec217efaa8.2593680.1
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version https://git-lfs.github.com/spec/v1
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oid sha256:682668616649fba13ac4851038b3b401c54f87c806a54c025d37aee5ac62d37a
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size 2646
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logs/dreambooth-lora/1725240551.7004247/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: beaf
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instance_prompt: I want to eat delicious meal made of beef or steak
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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: 10
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mixed_precision: null
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num_class_images: 100
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num_train_epochs: 1
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num_validation_images: 4
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output_dir: beaf-model2
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pre_compute_text_embeddings: false
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pretrained_model_name_or_path: stabilityai/stable-diffusion-2
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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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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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tokenizer_max_length: null
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tokenizer_name: null
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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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validation_epochs: 50
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validation_prompt: I want to have some delicious meal with beef or steak
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variant: null
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with_prior_preservation: false
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logs/dreambooth-lora/events.out.tfevents.1725240551.6fec217efaa8.2593680.0
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version https://git-lfs.github.com/spec/v1
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oid sha256:9623e4b20f09afe75247049ebd7d9dee5c16841d133c6586ec5807ce21ecf553
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size 8567929
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pytorch_lora_weights.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:b4e2e754ec147920758a7f1f9ff8d148a19a725e2a883140060323a5086bbad5
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size 3357296
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