---
base_model: stabilityai/stable-diffusion-3-medium-diffusers
library_name: diffusers
license: openrail++
tags:
- text-to-image
- diffusers-training
- diffusers
- sd3
- sd3-diffusers
- template:sd-lora
instance_prompt: niul, colorful object, engraved with NiUl
widget:
- text: niul, colorful object, engraved with NiUl
  output:
    url: image_0.png
- text: niul, colorful object, engraved with NiUl
  output:
    url: image_1.png
- text: niul, colorful object, engraved with NiUl
  output:
    url: image_2.png
- text: niul, colorful object, engraved with NiUl
  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. -->


# SD3 DreamBooth - hjvision/models

<Gallery />

## Model description

These are hjvision/models DreamBooth weights for stabilityai/stable-diffusion-3-medium-diffusers.

The weights were trained using [DreamBooth](https://dreambooth.github.io/) with the [SD3 diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_sd3.md).

Was the text encoder fine-tuned? False.

## Trigger words

You should use `niul, colorful object, engraved with NiUl` to trigger the image generation.

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

```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('hjvision/models', torch_dtype=torch.float16).to('cuda')
image = pipeline('niul, colorful object, engraved with NiUl').images[0]
```

## License

Please adhere to the licensing terms as described `[here](https://huggingface.co./stabilityai/stable-diffusion-3-medium/blob/main/LICENSE)`.


## 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]