End of training
Browse files- README.md +43 -0
- checkpoint-22400/model.safetensors +3 -0
- checkpoint-22400/optimizer.bin +3 -0
- checkpoint-22400/pytorch_lora_weights.safetensors +3 -0
- checkpoint-22400/random_states_0.pkl +3 -0
- checkpoint-22400/scaler.pt +3 -0
- checkpoint-22400/scheduler.bin +3 -0
- checkpoint-22600/model.safetensors +3 -0
- checkpoint-22600/optimizer.bin +3 -0
- checkpoint-22600/pytorch_lora_weights.safetensors +3 -0
- checkpoint-22600/random_states_0.pkl +3 -0
- checkpoint-22600/scaler.pt +3 -0
- checkpoint-22600/scheduler.bin +3 -0
- checkpoint-22800/model.safetensors +3 -0
- checkpoint-22800/optimizer.bin +3 -0
- checkpoint-22800/pytorch_lora_weights.safetensors +3 -0
- checkpoint-22800/random_states_0.pkl +3 -0
- checkpoint-22800/scaler.pt +3 -0
- checkpoint-22800/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/text2image-fine-tune/1711252443.5940113/events.out.tfevents.1711252443.SILVRDartmouth.15924.1 +3 -0
- logs/text2image-fine-tune/1711252443.5970123/hparams.yml +50 -0
- logs/text2image-fine-tune/events.out.tfevents.1711252443.SILVRDartmouth.15924.0 +3 -0
- pytorch_lora_weights.safetensors +3 -0
README.md
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---
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license: creativeml-openrail-m
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library_name: diffusers
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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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- diffusers-training
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- lora
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inference: true
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base_model: runwayml/stable-diffusion-v1-5
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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 text2image fine-tuning - iamkaikai/BASQUIAT-LORA
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These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the iamkaikai/BASQUIAT-ART dataset. You can find some example images in the following.
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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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## 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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image_1.png
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image_2.png
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image_3.png
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logs/text2image-fine-tune/1711252443.5940113/events.out.tfevents.1711252443.SILVRDartmouth.15924.1
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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: true
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cache_dir: null
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caption_column: text
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center_crop: true
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checkpointing_steps: 200
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checkpoints_total_limit: 3
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dataloader_num_workers: 0
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dataset_config_name: null
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dataset_name: iamkaikai/BASQUIAT-ART
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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: BASQUIAT-LORA
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hub_token: null
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image_column: image
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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_scheduler: constant
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lr_warmup_steps: 400
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max_grad_norm: 1.0
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max_train_samples: null
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max_train_steps: 22800
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mixed_precision: fp16
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noise_offset: 0
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num_train_epochs: 100
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num_validation_images: 4
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output_dir: BASQUIAT-LORA
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prediction_type: null
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pretrained_model_name_or_path: runwayml/stable-diffusion-v1-5
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push_to_hub: true
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random_flip: 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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scale_lr: false
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seed: 1337
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snr_gamma: null
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train_batch_size: 1
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train_data_dir: null
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use_8bit_adam: false
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validation_epochs: 10
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validation_prompt: BASQUIAT-ART, black, silver, gray
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variant: null
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logs/text2image-fine-tune/events.out.tfevents.1711252443.SILVRDartmouth.15924.0
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