Instructions to use brendanm1234/output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use brendanm1234/output with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("brendanm1234/output", dtype=torch.bfloat16, device_map="cuda") prompt = "photo of a <new1> cat" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 1f39fdbf4b3f6597c791b698aade9225e40ddffc12ff8c51e6782e3443110ad4
- Size of remote file:
- 76.7 MB
- SHA256:
- e4a58c91d99da76d749d243dee60eccb13fcf44bccdae88d1c97bb5effd1ff0f
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