End of training - counterfeit 2.5
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- README.md +6 -2
- checkpoint-1000/optimizer.bin +1 -1
- checkpoint-1000/pytorch_model.bin +1 -1
- checkpoint-1000/random_states_0.pkl +1 -1
- checkpoint-10000/optimizer.bin +1 -1
- checkpoint-10000/pytorch_model.bin +1 -1
- checkpoint-10000/random_states_0.pkl +1 -1
- checkpoint-10500/optimizer.bin +1 -1
- checkpoint-10500/pytorch_model.bin +1 -1
- checkpoint-10500/random_states_0.pkl +1 -1
- checkpoint-11000/optimizer.bin +1 -1
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- checkpoint-11000/random_states_0.pkl +1 -1
- checkpoint-11500/optimizer.bin +1 -1
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- checkpoint-11500/random_states_0.pkl +1 -1
- checkpoint-12000/optimizer.bin +1 -1
- checkpoint-12000/pytorch_model.bin +1 -1
- checkpoint-12000/random_states_0.pkl +1 -1
- checkpoint-12500/optimizer.bin +1 -1
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- checkpoint-12500/random_states_0.pkl +1 -1
- checkpoint-13000/optimizer.bin +1 -1
- checkpoint-13000/pytorch_model.bin +1 -1
- checkpoint-13000/random_states_0.pkl +1 -1
- checkpoint-13500/optimizer.bin +1 -1
- checkpoint-13500/pytorch_model.bin +1 -1
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- checkpoint-1500/optimizer.bin +1 -1
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- checkpoint-15000/optimizer.bin +1 -1
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- checkpoint-3000/optimizer.bin +1 -1
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- checkpoint-3500/optimizer.bin +1 -1
README.md
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---
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license: creativeml-openrail-m
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base_model:
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tags:
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- stable-diffusion
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---
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# LoRA text2image fine-tuning - dotta/mtg
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These are LoRA adaption weights for
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---
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license: creativeml-openrail-m
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base_model: gsdf/Counterfeit-V2.5
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tags:
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- stable-diffusion
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- stable-diffusion-diffusers
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---
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# LoRA text2image fine-tuning - dotta/mtg
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These are LoRA adaption weights for gsdf/Counterfeit-V2.5. The weights were fine-tuned on the dotta/mtg dataset. You can find some example images in the following.
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