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
File size: 1,518 Bytes
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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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