Instructions to use aorellanat/dog with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use aorellanat/dog with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("aorellanat/dog", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
| license: creativeml-openrail-m | |
| tags: | |
| - text-to-image | |
| ### Dog en Stable Diffusion via Dreambooth | |
| #### Modelo creado por aorellanat | |
| Este es el modelo Stable Diffusion ajustado con el concepto "Dog" mediante Dreambooth. | |
| Puedes usarlo modificando el `instance_prompt`: **<pet> dog** | |
| También puedes entrenar tus propios conceptos y subirlos a la biblioteca usando [este notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb). | |
| Puedes ejecutar tu nuevo concepto con `diffusers`: [Notebook de inferencia en Colab](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces con los conceptos públicos](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts) | |
| Imágenes usadas para entrenar este concepto: | |
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