aquiles-image 0.1.8

Aquiles-Image.

Aquiles-Image

Easy, fast and cheap Diffusion Models that work for everyone.

🚀 FastAPI • Diffusers • Compatible with the OpenAI client

🔥 What's New in Aquiles-Image

Feature	Description
⚡ 3x Faster	Advanced inference optimizations
🎨 More Models	Support for FLUX, SD3-3.5, Qwen-Image and more
🔧 Better DevX	Improved CLI and monitoring capabilities
🔌 OpenAI Compatible	Drop-in replacement for OpenAI's image APIs

📋 Prerequisites

• Python 3.8+
• CUDA-compatible GPU with 24GB+ VRAM
• 10GB+ free disk space

⚙️ Installation

From Pypi

uv pip install aquiles-image

From source

git clone https://github.com/Aquiles-ai/Aquiles-Image.git
cd Aquiles-Image
uv pip install .

🚀 Launch your Aquiles-Image server

aquiles-image serve --host "0.0.0.0" --port 5500 --model "stabilityai/stable-diffusion-3.5-medium"

Note on model compatibility

⚠️ Keep in mind that many of these models require a lot of VRAM to run, choose the model that fits your GPU and has about 10GB left to avoid errors when decoding or saving.

The supported models for the imaging endpoint are: stabilityai/stable-diffusion-3-medium, stabilityai/stable-diffusion-3.5-large, stabilityai/stable-diffusion-3.5-large-turbo, stabilityai/stable-diffusion-3.5-medium, black-forest-labs/FLUX.1-dev, black-forest-labs/FLUX.1-schnell, black-forest-labs/FLUX.1-Krea-dev, Qwen/Qwen-Image

For Edit endpoints (Available, but with some errors that do not guarantee full endpoint functionality): black-forest-labs/FLUX.1-Kontext-dev, Qwen/Qwen-Image-Edit

🎉 Generate your first image with Aquiles-Image

from openai import OpenAI
import requests

client = OpenAI(base_url="http://127.0.0.1:5500", api_key="__UNKNOWN__")

result = client.images.generate(
    model="stabilityai/stable-diffusion-3.5-medium",
    prompt="a white siamese cat",
    size="1024x1024"
)

print(f"URL of the generated image: {result.data[0].url}\n")

image_url = result.data[0].url
response = requests.get(image_url)

with open("image.png", "wb") as f:
    f.write(response.content)

print(f"Image downloaded successfully\n")

🎯 Perfect For

Use Case	Description
🚀 AI Startups	Building image generation features
👨‍💻 Developers	Prototyping with Image Generation Models
🏢 Enterprises	Scalable image AI infrastructure
🔬 Researchers	Experimenting with multiple models

Built with ❤️ for the AI community

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