Instructions to use amd/FLUX.1-dev_io16_amdgpu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use amd/FLUX.1-dev_io16_amdgpu 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_io16_amdgpu", 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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_io16: model input is fp16, perform ops in fp16 and write the final result in fp16
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https://github.com/TensorStack-AI/OnnxStack
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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_amdgpu");
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// Save Image Result
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await result.SaveAsync("Result.png");
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```
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## Inference Result
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_io16: model input is fp16, perform ops in fp16 and write the final result in fp16
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## Running
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### 1. Using Amuse GUI Application
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Use Amuse GUI application to run it: https://www.amuse-ai.com/
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### 2. C# Inference Demo
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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\\Flux.1-dev_amdgpu");
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// Save Image Result
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await result.SaveAsync("Result.png");
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```
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## Inference Result
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