Auraflow V0.2

🚀 AuraFlow v0.2

AuraFlow v0.2 is a fully open - sourced, large - scale flow - based text - to - image generation model. It addresses the challenge of high - quality text - to - image conversion, offering state - of - the - art results in the field.

🚀 Quick Start

AuraFlow v0.2 is the fully open - sourced largest flow - based text - to - image generation model. Compared to the previous version AuraFlow - v0.1, this model was trained with more compute resources.

This model achieves state - of - the - art results on GenEval. For more technical details, read our blog post. 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 crucial. Join fal's Discord to give us feedback and stay updated on the model development.

Credits: We are extremely grateful to @cloneofsimo and @isidentical for bringing this project to life. It's amazing what two talented engineers can achieve in such a short time. We also thank the outstanding researchers whose prior work laid the foundation for our project.

✨ Features

• Fully open - sourced text - to - image generation model.
• Trained with more compute compared to the previous version.
• Achieves state - of - the - art results on GenEval.

📦 Installation

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

💻 Usage Examples

Basic Usage

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")

📄 License

This project is licensed under the Apache 2.0 license.