Instructions to use Vargol/auraflow0.2-fp16-diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Vargol/auraflow0.2-fp16-diffusers with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Vargol/auraflow0.2-fp16-diffusers", 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 Settings
- Draw Things
- DiffusionBee
| license: apache-2.0 | |
| tags: | |
| - text-to-image | |
| # AuraFlow v0.2 | |
| This is a copy of `fal/AuraFlow-v0.2` but with the transformer converted to float16 in order to save disk space, | |
| allow faster loading, and download if using something without static storage, like Colab. | |
| You may get black images, if so use madebyollin's fp16 fixed SDXL VAE, I don't seem to need it when using MPS | |
| but I did have some issues on Colab. | |
|  | |
| 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) | |
| 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/). | |
| The model is currently in beta. We are working on improving it and the community's feedback is important. | |
| Join [fal's Discord](https://discord.gg/fal-ai) to give us feedback and stay in touch with the model development. | |
| 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 | |
| such a short period of time. We also extend our gratitude to the incredible researchers whose prior work laid the foundation for our efforts. | |
| ## Usage | |
| ```bash | |
| $ pip install transformers accelerate protobuf sentencepiece | |
| $ pip install git+https://github.com/huggingface/diffusers.git | |
| ``` | |
| ```python | |
| from diffusers import AuraFlowPipeline | |
| import torch | |
| pipeline = AuraFlowPipeline.from_pretrained( | |
| "Vargol/auraflow0.2-fp16-diffusers", | |
| torch_dtype=torch.float16, | |
| variant="fp16", | |
| ).to("cuda") | |
| image = pipeline( | |
| 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.", | |
| height=1024, | |
| width=1024, | |
| num_inference_steps=50, | |
| generator=torch.Generator().manual_seed(666), | |
| guidance_scale=3.5, | |
| ).images[0] | |
| image.save("output.png") | |
| ``` |