End of training
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- README.md +17 -0
- checkpoint-10000/optimizer.bin +3 -0
- checkpoint-10000/optimizer_1.bin +3 -0
- checkpoint-10000/pytorch_model.bin +3 -0
- checkpoint-10000/random_states_0.pkl +3 -0
- checkpoint-10000/scheduler.bin +3 -0
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- checkpoint-15000/optimizer.bin +3 -0
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- checkpoint-15000/pytorch_model.bin +3 -0
- checkpoint-15000/random_states_0.pkl +3 -0
- checkpoint-15000/scheduler.bin +3 -0
- checkpoint-15000/scheduler_1.bin +3 -0
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- checkpoint-20000/optimizer_1.bin +3 -0
- checkpoint-20000/pytorch_model.bin +3 -0
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- checkpoint-20000/scheduler_1.bin +3 -0
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- checkpoint-25000/scheduler.bin +3 -0
- checkpoint-25000/scheduler_1.bin +3 -0
- checkpoint-30000/optimizer.bin +3 -0
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- checkpoint-40000/optimizer_1.bin +3 -0
- checkpoint-40000/pytorch_model.bin +3 -0
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- checkpoint-45000/optimizer_1.bin +3 -0
- checkpoint-45000/pytorch_model.bin +3 -0
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- checkpoint-45000/scheduler.bin +3 -0
- checkpoint-45000/scheduler_1.bin +3 -0
- checkpoint-5000/optimizer.bin +3 -0
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 - erkam/sd-clevr-sg2im-no_cap_6000-e2e
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These are LoRA adaption weights for stabilityai/stable-diffusion-2. The weights were fine-tuned on the erkam/clevr-full-v4 dataset. You can find some example images in the following.
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checkpoint-45000/pytorch_model.bin
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checkpoint-45000/random_states_0.pkl
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checkpoint-45000/scheduler.bin
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checkpoint-45000/scheduler_1.bin
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checkpoint-5000/optimizer.bin
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