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:
- aa45d56f8aa0f70033ad4547cbf1e2e962c44e739d6267308a4f5fa6922386f2
- Size of remote file:
- 28.3 MB
- SHA256:
- 102be5ed7914f46cef0a4f89f7c5d79274a0db4a4ef1a0c2621a0a0d05ec4054
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