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gdivya-nvidia
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sdxl_1k_finetune

Text-to-Image
Diffusers
TensorBoard
diffusers-training
lora
template:sd-lora
stable-diffusion-xl
stable-diffusion-xl-diffusers
Model card Files Metrics Community
sdxl_1k_finetune
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---
base_model: stabilityai/stable-diffusion-xl-base-1.0
library_name: diffusers
license: openrail++
tags:
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
instance_prompt: infrared sensor face image
widget:
- text: infrared sensor face image of woman with makeup, eyemakeup, mascara
  output:
    url: image_0.png
- text: infrared sensor face image of woman with makeup, eyemakeup, mascara
  output:
    url: image_1.png
- text: infrared sensor face image of woman with makeup, eyemakeup, mascara
  output:
    url: image_2.png
- text: infrared sensor face image of woman with makeup, eyemakeup, mascara
  output:
    url: image_3.png
---

<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->


# SDXL LoRA DreamBooth - gdivya-nvidia/sdxl_1k_finetune

<Gallery />

## Model description

These are gdivya-nvidia/sdxl_1k_finetune LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.

The weights were trained  using [DreamBooth](https://dreambooth.github.io/).

LoRA for the text encoder was enabled: False.

Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.

## Trigger words

You should use infrared sensor face image to trigger the image generation.

## Download model

Weights for this model are available in Safetensors format.

[Download](gdivya-nvidia/sdxl_1k_finetune/tree/main) them in the Files & versions tab.



## Intended uses & limitations

#### How to use

```python
# TODO: add an example code snippet for running this diffusion pipeline
```

#### Limitations and bias

[TODO: provide examples of latent issues and potential remediations]

## Training details

[TODO: describe the data used to train the model]