AuraFlow v0.3 is the fully open-sourced flow-based text-to-image generation model. The model was trained with more compute compared to the previous version,
AuraFlow-v0.2.
Compared to AuraFlow-v0.2, the model is fine-tuned on more aesthetic datasets and now supports various aspect ratio, (now width and height up to 1536 pixels).
This model achieves state-of-the-art results on GenEval. Read our
blog post for more technical details. You can also check out the comparison with other models on this gallery
page.
The model is currently in beta. We are working on improving it and the community's feedback is important.
Join
fal's Discord to give us feedback and stay in touch with the model development.
Credits: A huge thank you to
@cloneofsimo and
@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.
1$ pip install transformers accelerate protobuf sentencepiece
2$ pip install git+https://github.com/huggingface/diffusers.git
1from diffusers import AuraFlowPipeline
2import torch
3
4pipeline = AuraFlowPipeline.from_pretrained(
5 "fal/AuraFlow-v0.3",
6 torch_dtype=torch.float16,
7 variant="fp16",
8).to("cuda")
9
10image = pipeline(
11 prompt="rempage of the iguana character riding F1, fast and furious, cinematic movie poster",
12 width=1536,
13 height=768,
14 num_inference_steps=50,
15 generator=torch.Generator().manual_seed(1),
16 guidance_scale=3.5,
17).images[0]
18
19image.save("output.png")
