End of training
Browse files- README.md +62 -0
- checkpoint-1000/optimizer.bin +3 -0
- checkpoint-1000/pytorch_lora_weights.safetensors +3 -0
- checkpoint-1000/random_states_0.pkl +3 -0
- checkpoint-1000/scaler.pt +3 -0
- checkpoint-1000/scheduler.bin +3 -0
- checkpoint-1500/optimizer.bin +3 -0
- checkpoint-1500/pytorch_lora_weights.safetensors +3 -0
- checkpoint-1500/random_states_0.pkl +3 -0
- checkpoint-1500/scaler.pt +3 -0
- checkpoint-1500/scheduler.bin +3 -0
- checkpoint-500/optimizer.bin +3 -0
- checkpoint-500/pytorch_lora_weights.safetensors +3 -0
- checkpoint-500/random_states_0.pkl +3 -0
- checkpoint-500/scaler.pt +3 -0
- checkpoint-500/scheduler.bin +3 -0
- pytorch_lora_weights.safetensors +3 -0
README.md
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---
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base_model: stabilityai/stable-diffusion-xl-base-1.0
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library_name: diffusers
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license: openrail++
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tags:
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- text-to-image
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- text-to-image
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- diffusers-training
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- diffusers
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- lora
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- template:sd-lora
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- stable-diffusion-xl
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- stable-diffusion-xl-diffusers
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instance_prompt: a portrait of lmlm man
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widget: []
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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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# SDXL LoRA DreamBooth - Shamima/lora-sdxl-abeer-face
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<Gallery />
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## Model description
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These are Shamima/lora-sdxl-abeer-face LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
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The weights were trained using [DreamBooth](https://dreambooth.github.io/).
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LoRA for the text encoder was enabled: True.
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Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
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## Trigger words
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You should use a portrait of lmlm man to trigger the image generation.
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## Download model
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Weights for this model are available in Safetensors format.
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[Download](Shamima/lora-sdxl-abeer-face/tree/main) them in the Files & versions tab.
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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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checkpoint-1000/optimizer.bin
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checkpoint-1000/pytorch_lora_weights.safetensors
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checkpoint-1000/random_states_0.pkl
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checkpoint-1000/scaler.pt
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checkpoint-1000/scheduler.bin
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checkpoint-1500/optimizer.bin
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checkpoint-1500/pytorch_lora_weights.safetensors
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checkpoint-1500/random_states_0.pkl
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checkpoint-1500/scaler.pt
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checkpoint-1500/scheduler.bin
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checkpoint-500/optimizer.bin
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checkpoint-500/pytorch_lora_weights.safetensors
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checkpoint-500/random_states_0.pkl
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checkpoint-500/scaler.pt
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pytorch_lora_weights.safetensors
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