End of training
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- README.md +18 -0
- checkpoint-10000/optimizer.bin +3 -0
- checkpoint-10000/optimizer_1.bin +3 -0
- checkpoint-10000/pytorch_lora_weights.bin +3 -0
- checkpoint-10000/pytorch_model.bin +3 -0
- checkpoint-10000/pytorch_model_1.bin +3 -0
- checkpoint-10000/random_states_0.pkl +3 -0
- checkpoint-10000/scaler.pt +3 -0
- checkpoint-10000/scheduler.bin +3 -0
- checkpoint-10000/scheduler_1.bin +3 -0
- checkpoint-10000/sg_encoder.pt +3 -0
- checkpoint-100000/optimizer.bin +3 -0
- checkpoint-100000/optimizer_1.bin +3 -0
- checkpoint-100000/pytorch_lora_weights.bin +3 -0
- checkpoint-100000/pytorch_model.bin +3 -0
- checkpoint-100000/pytorch_model_1.bin +3 -0
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- checkpoint-100000/scheduler.bin +3 -0
- checkpoint-100000/scheduler_1.bin +3 -0
- checkpoint-100000/sg_encoder.pt +3 -0
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- checkpoint-15000/pytorch_lora_weights.bin +3 -0
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- checkpoint-15000/pytorch_model_1.bin +3 -0
- checkpoint-15000/random_states_0.pkl +3 -0
- checkpoint-15000/scaler.pt +3 -0
- checkpoint-15000/scheduler.bin +3 -0
- checkpoint-15000/scheduler_1.bin +3 -0
- checkpoint-15000/sg_encoder.pt +3 -0
- checkpoint-20000/optimizer.bin +3 -0
- checkpoint-20000/optimizer_1.bin +3 -0
- checkpoint-20000/pytorch_lora_weights.bin +3 -0
- checkpoint-20000/pytorch_model.bin +3 -0
- checkpoint-20000/pytorch_model_1.bin +3 -0
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- checkpoint-20000/scaler.pt +3 -0
- checkpoint-20000/scheduler.bin +3 -0
- checkpoint-20000/scheduler_1.bin +3 -0
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- checkpoint-25000/optimizer_1.bin +3 -0
- checkpoint-25000/pytorch_lora_weights.bin +3 -0
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- checkpoint-25000/random_states_0.pkl +3 -0
- checkpoint-25000/scaler.pt +3 -0
- checkpoint-25000/scheduler.bin +3 -0
- checkpoint-25000/scheduler_1.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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- sg-to-image
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- scene-graph
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- stable-diffusion
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- stable-diffusion-diffusers
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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/sg2im-256-bs-16x2-lr1e4-depth-snr-12k
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These are LoRA adaption weights for stabilityai/stable-diffusion-2. The weights were fine-tuned on the vg dataset. You can find some example images in the following.
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checkpoint-25000/scaler.pt
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checkpoint-25000/scheduler_1.bin
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