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--- |
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license: openrail++ |
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library_name: diffusers |
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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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base_model: SG161222/RealVisXL_V4.0 |
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instance_prompt: a headshot of a sks person |
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widget: |
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- text: closeup portrait photo of a sks person in dark clothes, city street, neon, fog, volumetric |
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wearing business outfit, grey background |
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output: |
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url: image_0.png |
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- text: closeup portrait photo of a sks person in dark clothes, city street, neon, fog, volumetric |
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wearing business outfit, grey background |
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output: |
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url: image_1.png |
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- text: closeup portrait photo of a sks person in dark clothes, city street, neon, fog, volumetric |
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wearing business outfit, grey background |
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output: |
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url: image_2.png |
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- text: closeup portrait photo of a sks person in dark clothes, city street, neon, fog, volumetric |
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wearing business outfit, grey background |
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output: |
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url: image_3.png |
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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 - yehiaa/lora-trained-xl-drew |
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<Gallery /> |
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## Model description |
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These are yehiaa/lora-trained-xl-drew 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: False. |
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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 headshot of a sks person 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](yehiaa/lora-trained-xl-drew/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] |
