Quick Start

Get started with HyperGen in 5 minutes

Welcome to HyperGen

HyperGen is an optimized inference and fine-tuning framework for diffusion models. Train LoRAs 3x faster with 80% less VRAM, or serve models with an OpenAI-compatible API.

Installation

1. Install HyperGen

   Install HyperGen via pip:

   pip install hypergen
   

   For GPU support, make sure you have PyTorch with CUDA installed:

   # If you don't have PyTorch with CUDA
   pip install torch torchvision --index-url https://download.pytorch.org/whl/cu121
   
2. Verify Installation

   Check that HyperGen is installed correctly:

   hypergen --version
   

Your First LoRA Training

Train a LoRA in just 5 lines of code:

1. Prepare Your Dataset

   Create a folder with your training images:

   my_images/
     photo1.jpg
     photo1.txt  (optional caption)
     photo2.jpg
     photo2.txt  (optional caption)
     ...
   

   Caption files (.txt) are optional but recommended for better results. Just place a text file with the same name as each image.

2. Train the LoRA

   Create a Python file and run:

   from hypergen import model, dataset
   
   # Load model
   m = model.load("stabilityai/stable-diffusion-xl-base-1.0")
   m.to("cuda")
   
   # Load dataset
   ds = dataset.load("./my_images")
   
   # Train LoRA - that's it!
   lora = m.train_lora(ds, steps=1000)
   

   The first run will download the model from HuggingFace, which may take a few minutes depending on your internet connection.

3. Generate Images

   Use your trained model to generate images:

   # Generate with the base model
   image = m.generate("A beautiful sunset over mountains")
   image[0].save("output.png")
   

Serving a Model

Serve any diffusion model with an OpenAI-compatible API:

1. Start the Server

   hypergen serve stabilityai/stable-diffusion-xl-base-1.0 --api-key your-secret-key
   

   The server will start on http://localhost:8000

2. Generate Images via API

   Use the OpenAI Python client:

   from openai import OpenAI
   import base64
   
   client = OpenAI(
       api_key="your-secret-key",
       base_url="http://localhost:8000/v1"
   )
   
   response = client.images.generate(
       model="sdxl",
       prompt="A cat holding a sign that says hello world",
       n=2,
       size="1024x1024"
   )
   
   # Save images
   for i, img in enumerate(response.data):
       with open(f"image_{i}.png", "wb") as f:
           f.write(base64.b64decode(img.b64_json))
   

Advanced Configuration

Customize your training with additional parameters:

lora = m.train_lora(
    ds,
    steps=2000,
    learning_rate=5e-5,
    rank=32,                    # LoRA rank (higher = more capacity)
    alpha=64,                   # LoRA alpha scaling factor
    batch_size=2,               # Or "auto" for automatic
    save_steps=500,             # Save checkpoints
    output_dir="./checkpoints"
)

GPU Requirements

Minimum

• 8GB VRAM (NVIDIA GPU)
• CUDA 11.8+
• For SDXL/SD 1.5 models

Recommended

• 16GB+ VRAM
• CUDA 12.1+
• For FLUX.1 and larger models

Next Steps

Installation

Detailed installation guide with all options

Training Guide

Complete LoRA training documentation

Serving Guide

Production deployment and API usage

Supported Models

All compatible model architectures