Instructions to use SidXXD/dog7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use SidXXD/dog7 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("SidXXD/dog7", dtype=torch.bfloat16, device_map="cuda") prompt = "photo of a <new1> dog" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 473ba6de4410ee0c6ebc2f33baa5dda5252822864d8311ba706c1f748361400e
- Size of remote file:
- 76.7 MB
- SHA256:
- f6bcaac78ed0df9f56f08edadc668cc49d301169c8f38ef5cbd5e138b097b9ba
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