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--- |
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license: apache-2.0 |
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tags: |
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- text-to-image |
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--- |
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# AuraFlow v0.2 |
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AuraFlow v0.2 is the fully open-sourced largest flow-based text-to-image generation model. The model was trained with more compute compared to the previous version, [AuraFlow-v0.1](https://huggingface.co/fal/AuraFlow) |
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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. You can also check out the comparison with other models on this gallery [page](https://cloneofsimo.github.io/compare_aura_sd3/). |
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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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Credits: A huge thank you to [@cloneofsimo](https://twitter.com/cloneofsimo) and [@isidentical](https://twitter.com/isidentical) for bringing this project to life. It's incredible what two cracked engineers can achieve in |
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such a short period of time. We also extend our gratitude to the incredible researchers whose prior work laid the foundation for our efforts. |
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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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from diffusers import AuraFlowPipeline |
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import torch |
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pipeline = AuraFlowPipeline.from_pretrained( |
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"fal/AuraFlow-v0.2", |
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torch_dtype=torch.float16, |
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variant="fp16", |
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).to("cuda") |
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image = pipeline( |
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prompt="close-up portrait of a majestic iguana with vibrant blue-green scales, piercing amber eyes, and orange spiky crest. Intricate textures and details visible on scaly skin. Wrapped in dark hood, giving regal appearance. Dramatic lighting against black background. Hyper-realistic, high-resolution image showcasing the reptile's expressive features and coloration.", |
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height=1024, |
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width=1024, |
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num_inference_steps=50, |
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generator=torch.Generator().manual_seed(666), |
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guidance_scale=3.5, |
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).images[0] |
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image.save("output.png") |
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``` |
