Instructions to use DankCloth/2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use DankCloth/2 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("DankCloth/2", dtype=torch.bfloat16, device_map="cuda") prompt = "a photo of owen wilson man" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
| 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: a photo of owen wilson man | |
| widget: [] | |
| <!-- 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 LoRA - DankCloth/2 | |
| <Gallery /> | |
| ## Model description | |
| These are DankCloth/2 DreamBooth weights for stabilityai/stable-diffusion-3-medium-diffusers. | |
| The weights were trained using [DreamBooth](https://dreambooth.github.io/). | |
| ## Trigger words | |
| You should use a photo of owen wilson man to trigger the image generation. | |
| ## Download model | |
| [Download](DankCloth/2/tree/main) them in the Files & versions tab. | |
| ## 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] |