Instructions to use codemichaeld/T5Base_fp8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use codemichaeld/T5Base_fp8 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("codemichaeld/T5Base_fp8", 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:
- d2308ee011807009c45e2aad6824c99a3bae8c8cae5c13c5c8bfa977994c5478
- Size of remote file:
- 26.7 MB
- SHA256:
- e5ec153a2735cba30ebf68270278cb90a87b4b3de39bb5c267f49b0ec24d1a88
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