Instructions to use pcuenq/debug-training-output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use pcuenq/debug-training-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("pcuenq/debug-training-output", 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
- Xet hash:
- a1507dd2e82b9130bf77f07ee562d51d83cfc73364cab800c5e0265d1bd23d76
- Size of remote file:
- 455 MB
- SHA256:
- abc9b5582c221de6c85e338e2111800f3beecdceb112b8dc8a20abe10006d0c8
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