AuraFlow VAE

AuraFlow v0.1 是一个完全开源的基于流的文本到图像生成模型。

AuraFlow v0.2 是一个完全开源的基于流的文本到图像生成模型。该模型相比前一版本使用了更多的计算资源进行训练。

AuraFlow v0.3 是一个完全开源的基于流的文本到图像生成模型。该模型相比前一版本使用了更多的计算资源进行训练。

该模型在 GenEval 上取得了最先进的结果。欲了解更详细的技术信息，请阅读我们的博客文章。您还可以在此画廊页面查看与其他模型的对比。

目前该模型处于测试阶段。我们正在努力改进它，社区的反馈至关重要。请加入fal 的 Discord 提供反馈并关注模型开发进展。

致谢：衷心感谢@cloneofsimo 和 @isidentical 将这一项目变为现实。两位杰出的工程师在如此短的时间内取得的成就令人惊叹。我们也要感谢那些为本项目奠定基础的杰出研究者们。

使用方法（v0.1）

$ pip install transformers accelerate protobuf sentencepiece
$ pip install git+https://github.com/huggingface/diffusers.git

from diffusers import AuraFlowPipeline
import torch

pipeline = AuraFlowPipeline.from_pretrained(
    "fal/AuraFlow",
    torch_dtype=torch.float16
).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]

使用方法（v0.2）

$ pip install transformers accelerate protobuf sentencepiece
$ pip install git+https://github.com/huggingface/diffusers.git

from diffusers import AuraFlowPipeline
import torch

pipeline = AuraFlowPipeline.from_pretrained(
    "fal/AuraFlow-v0.2",
    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")