Instructions to use amd/FLUX.1-dev-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use amd/FLUX.1-dev-onnx 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/FLUX.1-dev-onnx", torch_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:
- 39d9f4839bfe3054961b44b3decc1955f1ec6e5df49c6266a51d54819f3090e5
- Size of remote file:
- 23.8 GB
- SHA256:
- 5b7d3b6abcb449e70e829659c42a381c9af509c7e2cefd99baa43a4f0b015915
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