End of training
Browse files- .gitattributes +3 -0
- README.md +21 -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-15000/optimizer.bin +3 -0
- checkpoint-15000/pytorch_model.bin +3 -0
- checkpoint-15000/random_states_0.pkl +3 -0
- checkpoint-15000/scaler.pt +3 -0
- checkpoint-15000/scheduler.bin +3 -0
- checkpoint-5000/optimizer.bin +3 -0
- checkpoint-5000/pytorch_model.bin +3 -0
- checkpoint-5000/random_states_0.pkl +3 -0
- checkpoint-5000/scaler.pt +3 -0
- checkpoint-5000/scheduler.bin +3 -0
- image_0.png +3 -0
- image_1.png +3 -0
- image_2.png +0 -0
- image_3.png +3 -0
- pytorch_lora_weights.safetensors +3 -0
.gitattributes
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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image_0.png filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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license: creativeml-openrail-m
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base_model: stabilityai/stable-diffusion-2
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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-100-lora
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These are LoRA adaption weights for stabilityai/stable-diffusion-2. 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-10000/optimizer.bin
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checkpoint-10000/scheduler.bin
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image_0.png
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Git LFS Details
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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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Git LFS Details
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pytorch_lora_weights.safetensors
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