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:
- 199016b2e6534c287e505843598b592dc001f0a158bb0c83089ee7a95e10a4ee
- Size of remote file:
- 130 MB
- SHA256:
- 655295c02d6a42072ec3e764dba72bd7d3d248f768a47282991d3ed044dde94d
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