End of training
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- README.md +43 -0
- checkpoint-100/model.safetensors +3 -0
- checkpoint-100/optimizer.bin +3 -0
- checkpoint-100/pytorch_lora_weights.safetensors +3 -0
- checkpoint-100/random_states_0.pkl +3 -0
- checkpoint-100/scheduler.bin +3 -0
- checkpoint-150/model.safetensors +3 -0
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- checkpoint-150/pytorch_lora_weights.safetensors +3 -0
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- checkpoint-200/pytorch_lora_weights.safetensors +3 -0
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- checkpoint-500/pytorch_lora_weights.safetensors +3 -0
- checkpoint-500/random_states_0.pkl +3 -0
README.md
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---
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license: creativeml-openrail-m
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library_name: diffusers
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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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inference: true
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base_model: CompVis/stable-diffusion-v1-4
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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 - amanalok/pokemon-lora
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These are LoRA adaption weights for CompVis/stable-diffusion-v1-4. The weights were fine-tuned on the lambdalabs/pokemon-blip-captions dataset. You can find some example images in the following.
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![img_0](./image_0.png)
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![img_1](./image_1.png)
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![img_2](./image_2.png)
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![img_3](./image_3.png)
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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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