Instructions to use RoundtTble/dreambooth-dog-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use RoundtTble/dreambooth-dog-lora with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("RoundtTble/dreambooth-dog-lora") prompt = "a photo of sks dog" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Commit ·
3ad93c0
1
Parent(s): 9079edc
End of training
Browse files- README.md +25 -0
- checkpoint-100/optimizer.bin +3 -0
- checkpoint-100/pytorch_lora_weights.bin +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.bin +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.bin +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.bin +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.bin +3 -0
- checkpoint-500/random_states_0.pkl +3 -0
- checkpoint-500/scheduler.bin +3 -0
- image_0.png +0 -0
- image_1.png +0 -0
- image_2.png +0 -0
- image_3.png +0 -0
- logs/dreambooth-lora/1690964983.120034/events.out.tfevents.1690964983.ds.3502528.1 +3 -0
- logs/dreambooth-lora/1690964983.1226704/hparams.yml +55 -0
- logs/dreambooth-lora/events.out.tfevents.1690964983.ds.3502528.0 +3 -0
- pytorch_lora_weights.bin +3 -0
README.md
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---
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license: creativeml-openrail-m
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base_model: runwayml/stable-diffusion-v1-5
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instance_prompt: a photo of sks dog
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tags:
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- stable-diffusion
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- stable-diffusion-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 - RoundtTble/dreambooth-dog-lora
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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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LoRA for the text encoder was enabled: False.
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image_0.png
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image_1.png
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image_3.png
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logs/dreambooth-lora/1690964983.120034/events.out.tfevents.1690964983.ds.3502528.1
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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: /home/hpds/Repositories/dreambooth/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: 100
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num_validation_images: 4
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output_dir: dreambooth-dog-lora
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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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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: A photo of sks dog in a bucket
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with_prior_preservation: false
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size 4809982
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