Instructions to use amd/Nitro-E with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use amd/Nitro-E with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("amd/Nitro-E", 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
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 0567916fea81f7dbee827d82b5921b3b1c39341ddda1c6cd4d0f098410d836a1
- Size of remote file:
- 1.24 GB
- SHA256:
- 06398fb646dbe2d9830902fab7086da3c66fe267211a36a7bc26ee4fd12d88d4
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