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
Update README.md
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README.md
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@@ -25,7 +25,7 @@ https://github.com/TensorStack-AI/OnnxStack
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```
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// csharp example
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// Create Pipeline
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var pipeline = FluxPipeline.CreatePipeline("D:\\Models\\
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// Prompt
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var promptOptions = new PromptOptions
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{
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```
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// csharp example
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// Create Pipeline
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var pipeline = FluxPipeline.CreatePipeline("D:\\Models\\FLUX.1-dev-onnx");
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// Prompt
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var promptOptions = new PromptOptions
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{
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