| | --- |
| | base_model: stabilityai/stable-diffusion-3-medium-diffusers |
| | library_name: diffusers |
| | license: other |
| | instance_prompt: a photo of sks dog |
| | widget: [] |
| | tags: |
| | - text-to-image |
| | - diffusers-training |
| | - diffusers |
| | - template:sd-lora |
| | - sd3 |
| | - sd3-diffusers |
| | --- |
| | |
| | <!-- 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 - MoritzAMLLaura/trained-sd3 |
| |
|
| | <Gallery /> |
| |
|
| | ## Model description |
| |
|
| | These are MoritzAMLLaura/trained-sd3 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 `a photo of sks dog` 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('MoritzAMLLaura/trained-sd3', torch_dtype=torch.float16).to('cuda') |
| | image = pipeline('a photo of sks dog').images[0] |
| | ``` |
| |
|
| | ## License |
| |
|
| | Please adhere to the licensing terms as described `[here](https://huggingface.co/stabilityai/stable-diffusion-3-medium/blob/main/LICENSE.md)`. |
| |
|
| |
|
| | ## 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] |