End of training
Browse files- .gitattributes +4 -0
- .gitignore +2 -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-20000/optimizer.bin +3 -0
- checkpoint-20000/pytorch_model.bin +3 -0
- checkpoint-20000/random_states_0.pkl +3 -0
- checkpoint-20000/scaler.pt +3 -0
- checkpoint-20000/scheduler.bin +3 -0
- checkpoint-25000/optimizer.bin +3 -0
- checkpoint-25000/pytorch_model.bin +3 -0
- checkpoint-25000/random_states_0.pkl +3 -0
- checkpoint-25000/scaler.pt +3 -0
- checkpoint-25000/scheduler.bin +3 -0
- checkpoint-30000/optimizer.bin +3 -0
- checkpoint-30000/pytorch_model.bin +3 -0
- checkpoint-30000/random_states_0.pkl +3 -0
- checkpoint-30000/scaler.pt +3 -0
- checkpoint-30000/scheduler.bin +3 -0
- checkpoint-35000/optimizer.bin +3 -0
- checkpoint-35000/pytorch_model.bin +3 -0
- checkpoint-35000/random_states_0.pkl +3 -0
- checkpoint-35000/scaler.pt +3 -0
- checkpoint-35000/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 +3 -0
- image_3.png +3 -0
- pytorch_lora_weights.bin +3 -0
.gitattributes
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@@ -32,3 +32,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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step_*
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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-depth
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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 - https://huggingface.co/erkam/sd-clevr-source-to-target-noinit
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These are LoRA adaption weights for https://huggingface.co/erkam/sd-clevr-source-to-target-noinit. The weights were fine-tuned on the erkam/clevr-with-depth-full dataset. You can find some example images in the following.
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