AuraFlow

The AuraFlow is a state-of-the-art flow-based text-to-image generation model developed by Fal AI. It is fully open-source and excels in creating high-quality images from text prompts, making it a versatile tool for various applications.

Download AuraFlow

Key Features of AuraFlow

Open Source

Fully open-source model licensed under Apache 2.0, promoting community-driven development.

High-Resolution Output

Capable of generating images in various resolutions, including 256×256, 512×512, and 1024×1024.

Efficient Training

Optimized with Torch Dynamo + Inductor for efficient training and inference.

Advanced Fine-Tuning

Allows for advanced fine-tuning to improve performance on specific tasks and datasets.

Download and Install AuraFlow

Step 1: Install the Package

Run the following command to install the required package:

pip install transformers accelerate protobuf sentencepiece

Step 2: Download the Model

Use the following code to download the model files from Hugging Face:


from huggingface_hub import snapshot_download
from pathlib import Pathmodel_path = Path.home().joinpath(‘auraflow_model’)
model_path.mkdir(parents=True, exist_ok=True)
snapshot_download(repo_id=”fal/AuraFlow”, allow_patterns=[“*”], local_dir=model_path)

How to Use AuraFlow?

Using the Model for Image Generation

Initialize the pipeline with:


from diffusers import AuraFlowPipeline
import torchpipeline = AuraFlowPipeline.from_pretrained(
“fal/AuraFlow”,
torch_dtype=torch.float16
).to(“cuda”)image = pipeline(
prompt=”a detailed painting of a futuristic cityscape at sunset, with flying cars and skyscrapers”,
height=1024,
width=1024,
num_inference_steps=50,
guidance_scale=7.5
).images[0]image.save(“output_image.png”)

Additional Tips for AuraFlow

Optimizing Performance

  • Use a lower guidance scale for more creative outputs.
  • Ensure your hardware meets the requirements for running large models efficiently.

Function Calling

Example function call implementation:

from mistral_common.protocol.instruct.tool_calls import Function, Tool
from mistral_inference.transformer import Transformer
from mistral_inference.generate import generate
from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
from mistral_common.protocol.instruct.messages import UserMessage
from mistral_common.protocol.instruct.request import ChatCompletionRequesttokenizer = MistralTokenizer.from_file(f”{model_path}/tekken.json”)
model = Transformer.from_folder(model_path)completion_request = ChatCompletionRequest(
tools=[
Tool(
function=Function(
name=”get_current_weather”,
description=”Get the current weather”,
parameters={…},
),
)
],
messages=[
UserMessage(content=”What’s the weather like today in Paris?”),
],
)tokens = tokenizer.encode_chat_completion(completion_request).tokens
out_tokens, _ = generate([tokens], model, max_tokens=256, temperature=0.35, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id)
result = tokenizer.decode(out_tokens[0])print(result)

AuraFlow is designed to be a robust and flexible model suitable for a wide range of applications. Whether you need high-resolution image generation or efficient training capabilities, this model provides a comprehensive solution. By following the installation and usage guidelines, you can harness the full potential of AuraFlow for your projects.

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