Instructions to use fal/AuraFlow with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use fal/AuraFlow with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("fal/AuraFlow", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
Install from main instruction for diffusers until a release is made
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by multimodalart HF Staff - opened
README.md
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AuraFlow v0.1 is the fully open-sourced largest flow-based text-to-image generation model.
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This model achieves state-of-the-art results on GenEval. Read blog post for more technical details.
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The model is currently in beta. We are working on improving it and the community's feedback is important.
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Join [fal's Discord](https://discord.gg/fal-ai) to give us feedback and stay in touch with the model development.
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## Usage
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```bash
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$ pip install
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```
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```python
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AuraFlow v0.1 is the fully open-sourced largest flow-based text-to-image generation model.
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This model achieves state-of-the-art results on GenEval. Read our [blog post](https://blog.fal.ai/auraflow/) for more technical details.
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The model is currently in beta. We are working on improving it and the community's feedback is important.
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Join [fal's Discord](https://discord.gg/fal-ai) to give us feedback and stay in touch with the model development.
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## Usage
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```bash
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$ pip install transformers accelerate protobuf sentencepiece
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$ pip install git+https://github.com/huggingface/diffusers.git
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```
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```python
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