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
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]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: dog
También puedes entrenar tus propios conceptos y subirlos a la biblioteca usando este notebook.
Puedes ejecutar tu nuevo concepto con diffusers: Notebook de inferencia en Colab, Spaces con los conceptos públicos
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