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End of training
Browse files- README.md +21 -0
- checkpoint-2000/optimizer.bin +3 -0
- checkpoint-2000/pytorch_model.bin +3 -0
- checkpoint-2000/random_states_0.pkl +3 -0
- checkpoint-2000/scaler.pt +3 -0
- checkpoint-2000/scheduler.bin +3 -0
- checkpoint-4000/optimizer.bin +3 -0
- checkpoint-4000/pytorch_model.bin +3 -0
- checkpoint-4000/random_states_0.pkl +3 -0
- checkpoint-4000/scaler.pt +3 -0
- checkpoint-4000/scheduler.bin +3 -0
- checkpoint-6000/optimizer.bin +3 -0
- checkpoint-6000/pytorch_model.bin +3 -0
- checkpoint-6000/random_states_0.pkl +3 -0
- checkpoint-6000/scaler.pt +3 -0
- checkpoint-6000/scheduler.bin +3 -0
- image_0.png +0 -0
- image_1.png +0 -0
- image_2.png +0 -0
- image_3.png +0 -0
- pytorch_lora_weights.bin +3 -0
README.md
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---
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license: creativeml-openrail-m
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base_model: CompVis/stable-diffusion-v1-4
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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 - Albe-njupt/sd-gesang_flowers-lora
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These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were fine-tuned on the Albe-njupt/gesang_flowers dataset. You can find some example images in the following.
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checkpoint-2000/optimizer.bin
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checkpoint-2000/pytorch_model.bin
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checkpoint-2000/random_states_0.pkl
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checkpoint-2000/scaler.pt
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checkpoint-2000/scheduler.bin
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checkpoint-4000/optimizer.bin
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checkpoint-4000/pytorch_model.bin
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checkpoint-4000/random_states_0.pkl
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checkpoint-4000/scaler.pt
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checkpoint-4000/scheduler.bin
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checkpoint-6000/optimizer.bin
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checkpoint-6000/pytorch_model.bin
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checkpoint-6000/random_states_0.pkl
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checkpoint-6000/scaler.pt
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checkpoint-6000/scheduler.bin
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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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pytorch_lora_weights.bin
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