---
base_model: Efficient-Large-Model/Sana_1600M_1024px_BF16_diffusers
library_name: diffusers
license: other
instance_prompt: a photo of sks cat
widget:
- text: A photo of sks cat is sitting on the wooden chair with dark studio background
  output:
    url: image_0.png
- text: A photo of sks cat is sitting on the wooden chair with dark studio background
  output:
    url: image_1.png
- text: A photo of sks cat is sitting on the wooden chair with dark studio background
  output:
    url: image_2.png
- text: A photo of sks cat is sitting on the wooden chair with dark studio background
  output:
    url: image_3.png
tags:
- text-to-image
- diffusers-training
- diffusers
- lora
- sana
- sana-diffusers
- template:sd-lora
---

<!-- 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. -->


# Sana DreamBooth LoRA - ainjarts/trained-sana-cat-lora

<Gallery />

## Model description

These are ainjarts/trained-sana-cat-lora DreamBooth LoRA weights for Efficient-Large-Model/Sana_1600M_1024px_BF16_diffusers.

The weights were trained using [DreamBooth](https://dreambooth.github.io/) with the [Sana diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_sana.md).


## Trigger words

You should use `a photo of sks cat` to trigger the image generation.

## Download model

[Download the *.safetensors LoRA](ainjarts/trained-sana-cat-lora/tree/main) in the Files & versions tab.

## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)

```py
TODO
```

For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)

## License

TODO


## 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]