End of training
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- README.md +21 -0
- checkpoint-1000/optimizer.bin +3 -0
- checkpoint-1000/pytorch_model.bin +3 -0
- checkpoint-1000/random_states_0.pkl +3 -0
- checkpoint-1000/scaler.pt +3 -0
- checkpoint-1000/scheduler.bin +3 -0
- checkpoint-10000/optimizer.bin +3 -0
- checkpoint-10000/pytorch_model.bin +3 -0
- checkpoint-10000/random_states_0.pkl +3 -0
- checkpoint-10000/scaler.pt +3 -0
- checkpoint-10000/scheduler.bin +3 -0
- checkpoint-10500/optimizer.bin +3 -0
- checkpoint-10500/pytorch_model.bin +3 -0
- checkpoint-10500/random_states_0.pkl +3 -0
- checkpoint-10500/scaler.pt +3 -0
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- checkpoint-11000/optimizer.bin +3 -0
- checkpoint-11000/pytorch_model.bin +3 -0
- checkpoint-11000/random_states_0.pkl +3 -0
- checkpoint-11000/scaler.pt +3 -0
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- checkpoint-11500/scaler.pt +3 -0
- checkpoint-11500/scheduler.bin +3 -0
- checkpoint-12000/optimizer.bin +3 -0
- checkpoint-12000/pytorch_model.bin +3 -0
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- checkpoint-12500/optimizer.bin +3 -0
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- checkpoint-13000/optimizer.bin +3 -0
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- checkpoint-13000/scaler.pt +3 -0
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- checkpoint-13500/optimizer.bin +3 -0
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- checkpoint-13500/scheduler.bin +3 -0
- checkpoint-14000/optimizer.bin +3 -0
- checkpoint-14000/pytorch_model.bin +3 -0
- checkpoint-14000/random_states_0.pkl +3 -0
- checkpoint-14000/scaler.pt +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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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 text2image fine-tuning - jpawan33/kag-sd-100-model
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These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the jpawan33/kag100-image-captioning-dataset 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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checkpoint-13500/scheduler.bin
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checkpoint-14000/optimizer.bin
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checkpoint-14000/pytorch_model.bin
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checkpoint-14000/random_states_0.pkl
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checkpoint-14000/scaler.pt
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