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:
- a0fc847cd2bcc0037e26c4100e047cbd02ce2a7c7966f2c7e7d1e5bfa02f782d
- Size of remote file:
- 557 kB
- SHA256:
- d9f3fb0fad334d547f60d97f6bb1d718896dbc0d14621dc778bd407dc065e550
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