Instructions to use hhf1/output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use hhf1/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("hhf1/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:
- 09d2806f7cad507ae3d6019b192bf8bc3ddac50a3b20d9d2d136baae141f6114
- Size of remote file:
- 76.7 MB
- SHA256:
- b5ab9e99582b62f66a78206d08a6866f5b99977ecbb15b2da1f1dd9f8442cc93
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