End of training
Browse files- .gitattributes +4 -0
- README.md +43 -0
- checkpoint-2000/model.safetensors +3 -0
- checkpoint-2000/optimizer.bin +3 -0
- checkpoint-2000/pytorch_lora_weights.safetensors +3 -0
- checkpoint-2000/random_states_0.pkl +3 -0
- checkpoint-2000/scheduler.bin +3 -0
- checkpoint-4000/model.safetensors +3 -0
- checkpoint-4000/optimizer.bin +3 -0
- checkpoint-4000/pytorch_lora_weights.safetensors +3 -0
- checkpoint-4000/random_states_0.pkl +3 -0
- checkpoint-4000/scheduler.bin +3 -0
- checkpoint-6000/model.safetensors +3 -0
- checkpoint-6000/optimizer.bin +3 -0
- checkpoint-6000/pytorch_lora_weights.safetensors +3 -0
- checkpoint-6000/random_states_0.pkl +3 -0
- checkpoint-6000/scheduler.bin +3 -0
- image_0.png +3 -0
- image_1.png +3 -0
- image_2.png +3 -0
- image_3.png +3 -0
- logs/text2image-fine-tune/1741722665.2000098/events.out.tfevents.1741722665.b6ae8f1329d5.15187.1 +3 -0
- logs/text2image-fine-tune/1741722665.209074/hparams.yml +50 -0
- logs/text2image-fine-tune/events.out.tfevents.1741722665.b6ae8f1329d5.15187.0 +3 -0
- pytorch_lora_weights.safetensors +3 -0
.gitattributes
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@@ -33,3 +33,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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base_model: CompVis/stable-diffusion-v1-4
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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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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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---
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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 - Akirashindo39/kanji-diffusion-v1-4-kanjidic2
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These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were fine-tuned on the Akirashindo39/KANJIDIC2 dataset. You can find some example images in the following.
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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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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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cache_dir: null
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caption_column: text
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center_crop: false
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checkpointing_steps: 2000
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checkpoints_total_limit: null
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dataloader_num_workers: 0
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dataset_config_name: null
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dataset_name: Akirashindo39/KANJIDIC2
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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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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: 0
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max_grad_norm: 1.0
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max_train_samples: null
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max_train_steps: 6413
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mixed_precision: null
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noise_offset: 0
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num_train_epochs: 1
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num_validation_images: 4
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output_dir: Akirashindo39/kanji-diffusion-v1-4-kanjidic2
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prediction_type: null
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pretrained_model_name_or_path: CompVis/stable-diffusion-v1-4
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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: 42
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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: 1
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validation_prompt: A kanji meaning Elon Musk
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variant: null
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