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path-to-save-model

Text-to-Image
Diffusers
TensorBoard
lora
diffusers-training
stable-diffusion
stable-diffusion-diffusers
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  1. README.md +47 -0
  2. image_0.png +0 -0
  3. image_1.png +0 -0
  4. image_2.png +0 -0
  5. image_3.png +0 -0
  6. logs/dreambooth-lora/1722411394.0251274/events.out.tfevents.1722411394.6fec217efaa8.41121.1 +3 -0
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  41. logs/dreambooth-lora/events.out.tfevents.1722417794.6fec217efaa8.4262.0 +3 -0
  42. pytorch_lora_weights.safetensors +3 -0
README.md ADDED
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+ ---
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+ base_model: runwayml/stable-diffusion-v1-5
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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: a photo of sks dog
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+ ---
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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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+
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+
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+ # LoRA DreamBooth - codeiceman/path-to-save-model
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+
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+ These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
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+
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+ ![img_0](./image_0.png)
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+ ![img_1](./image_1.png)
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+ ![img_2](./image_2.png)
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+ ![img_3](./image_3.png)
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+
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+
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+ LoRA for the text encoder was enabled: False.
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+
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+
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+ ## Intended uses & limitations
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+
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+ #### How to use
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+
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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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+
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+ #### Limitations and bias
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+
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+ [TODO: provide examples of latent issues and potential remediations]
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+
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+ ## Training details
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+
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+ [TODO: describe the data used to train the model]
image_0.png ADDED
image_1.png ADDED
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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: dog
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+ instance_prompt: a photo of sks dog
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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: path-to-save-model
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+ pre_compute_text_embeddings: false
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+ pretrained_model_name_or_path: runwayml/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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+ sample_batch_size: 4
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+ scale_lr: false
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+ seed: 0
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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: A photo of sks dog in a bucket
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+ variant: null
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+ with_prior_preservation: false
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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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+ instance_prompt: a photo of sks dog
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+ learning_rate: 0.0001
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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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+ num_validation_images: 4
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+ pretrained_model_name_or_path: runwayml/stable-diffusion-v1-5
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+ variant: null
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+ adam_beta2: 0.999
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+ lr_num_cycles: 1
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+ lr_num_cycles: 1
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