Instructions to use qkrwnstj/output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use qkrwnstj/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("qkrwnstj/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:
- 5c6dfb801cf3d1c0bbc94e06d7976b83b23f6b5eebb8b68d7fb35f5879e1bac8
- Size of remote file:
- 76.7 MB
- SHA256:
- 23e95f1d8c15d6f5b2c41db75d809a7f33752cfeac9d411d1dbd4d786f5f516a
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