End of training
Browse files- README.md +43 -0
- checkpoint-10000/model.safetensors +3 -0
- checkpoint-10000/optimizer.bin +3 -0
- checkpoint-10000/pytorch_lora_weights.safetensors +3 -0
- checkpoint-10000/random_states_0.pkl +3 -0
- checkpoint-10000/scaler.pt +3 -0
- checkpoint-10000/scheduler.bin +3 -0
- checkpoint-8000/model.safetensors +3 -0
- checkpoint-8000/optimizer.bin +3 -0
- checkpoint-8000/pytorch_lora_weights.safetensors +3 -0
- checkpoint-8000/random_states_0.pkl +3 -0
- checkpoint-8000/scaler.pt +3 -0
- checkpoint-8000/scheduler.bin +3 -0
- checkpoint-8500/model.safetensors +3 -0
- checkpoint-8500/optimizer.bin +3 -0
- checkpoint-8500/pytorch_lora_weights.safetensors +3 -0
- checkpoint-8500/random_states_0.pkl +3 -0
- checkpoint-8500/scaler.pt +3 -0
- checkpoint-8500/scheduler.bin +3 -0
- checkpoint-9000/model.safetensors +3 -0
- checkpoint-9000/optimizer.bin +3 -0
- checkpoint-9000/pytorch_lora_weights.safetensors +3 -0
- checkpoint-9000/random_states_0.pkl +3 -0
- checkpoint-9000/scaler.pt +3 -0
- checkpoint-9000/scheduler.bin +3 -0
- checkpoint-9500/model.safetensors +3 -0
- checkpoint-9500/optimizer.bin +3 -0
- checkpoint-9500/pytorch_lora_weights.safetensors +3 -0
- checkpoint-9500/random_states_0.pkl +3 -0
- checkpoint-9500/scaler.pt +3 -0
- checkpoint-9500/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.safetensors +3 -0
README.md
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---
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base_model: runwayml/stable-diffusion-v1-5
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library_name: diffusers
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license: creativeml-openrail-m
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inference: true
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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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- diffusers-training
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- lora
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---
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<!-- This model card has been generated automatically according to the information the training script had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# LoRA text2image fine-tuning - sageofai/sageofai-lora-kvasir-trained-improved
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These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the waitwhoami/vqa_caption.dataset-test dataset. You can find some example images in the following.
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## Intended uses & limitations
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#### How to use
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```python
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# TODO: add an example code snippet for running this diffusion pipeline
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```
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#### Limitations and bias
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[TODO: provide examples of latent issues and potential remediations]
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## Training details
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[TODO: describe the data used to train the model]
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