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---
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] |