End of training
Browse files- README.md +1 -1
- checkpoint-100/optimizer.bin +3 -0
- checkpoint-100/pytorch_model.bin +3 -0
- checkpoint-100/random_states_0.pkl +3 -0
- checkpoint-100/scaler.pt +3 -0
- checkpoint-100/scheduler.bin +3 -0
- checkpoint-150/optimizer.bin +3 -0
- checkpoint-150/pytorch_model.bin +3 -0
- checkpoint-150/random_states_0.pkl +3 -0
- checkpoint-150/scaler.pt +3 -0
- checkpoint-150/scheduler.bin +3 -0
- checkpoint-200/optimizer.bin +3 -0
- checkpoint-200/pytorch_model.bin +3 -0
- checkpoint-200/random_states_0.pkl +3 -0
- checkpoint-200/scaler.pt +3 -0
- checkpoint-200/scheduler.bin +3 -0
- checkpoint-50/optimizer.bin +3 -0
- checkpoint-50/pytorch_model.bin +3 -0
- checkpoint-50/random_states_0.pkl +3 -0
- checkpoint-50/scaler.pt +3 -0
- checkpoint-50/scheduler.bin +3 -0
- image_0.png +2 -2
- image_1.png +2 -2
- image_2.png +2 -2
- image_3.png +2 -2
- pytorch_lora_weights.bin +1 -1
README.md
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---
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# LoRA text2image fine-tuning - https://huggingface.co/dzbao/SKINCON-LoRA
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These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the
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![img_0](./image_0.png)
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![img_1](./image_1.png)
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---
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# LoRA text2image fine-tuning - https://huggingface.co/dzbao/SKINCON-LoRA
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These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the None 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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