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", 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
- Draw Things
- DiffusionBee
- Xet hash:
- 4bd1c8d561f80095c45e98cf18ad72ebc5de419ced1643b2653a1b9323ed5f79
- Size of remote file:
- 488 kB
- SHA256:
- a88d6fc9d7bc87a371deffdea3038c5c6bd6e990ae0002462b1f81af4389a49d
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