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System Requirements

Python Version

Python 3.10 or higher

GPU (Recommended)

NVIDIA GPU with CUDA 11.8+

VRAM Requirements

Minimum 8GB, Recommended 16GB+

Operating System

Linux, macOS, or Windows

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Installation Methods

The simplest way to install HyperGen: 
pip install hypergen
This will install HyperGen and all required dependencies.

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Install from Source

For the latest development version or to contribute:
1

Clone the repository

git clone https://github.com/ntegrals/hypergen.git
cd hypergen
2

Install in editable mode

pip install -e .
The -e flag installs in editable mode, so changes to the source code are reflected immediately.

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Install with uv (Faster)

For faster installation using uv: 
uv pip install hypergen
Or from source: 
git clone https://github.com/ntegrals/hypergen.git
cd hypergen
uv pip install -e .

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GPU Setup

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CUDA Installation

HyperGen requires PyTorch with CUDA support for GPU acceleration.
  • CUDA 12.1
  • CUDA 11.8
  • CPU Only
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu121

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Verify GPU Setup

Check that PyTorch can access your GPU: 
import torch

print(f"CUDA available: {torch.cuda.is_available()}")
print(f"CUDA version: {torch.version.cuda}")
print(f"GPU count: {torch.cuda.device_count()}")
if torch.cuda.is_available():
    print(f"GPU name: {torch.cuda.get_device_name(0)}")
Expected output: 
CUDA available: True
CUDA version: 12.1
GPU count: 1
GPU name: NVIDIA GeForce RTX 4090

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Dependencies

HyperGen automatically installs the following core dependencies:
PackageVersionPurpose
torch>=2.0.0Deep learning framework
diffusers>=0.30.0Diffusion model pipelines
transformers>=4.40.0Text encoders and tokenizers
peft>=0.8.0LoRA and parameter-efficient fine-tuning
accelerate>=0.30.0Training acceleration
safetensors>=0.4.0Safe model serialization
pillow>=10.0.0Image processing
numpy>=1.24.0Numerical operations
fastapi>=0.104.0API server framework
uvicorn>=0.24.0ASGI server
pydantic>=2.0.0Data validation

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Optional Dependencies

For faster training and inference with attention optimization: 
pip install hypergen[flash]
Or manually: 
pip install flash-attn>=2.5.0
Flash Attention requires CUDA and may take several minutes to compile on first installation.

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xFormers (Alternative Optimization)

Alternative memory-efficient attention implementation: 
pip install hypergen[xformers]
Or manually: 
pip install xformers>=0.0.25

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DeepSpeed (Multi-GPU Training)

For distributed training across multiple GPUs: 
pip install hypergen[deepspeed]
Or manually: 
pip install deepspeed>=0.14.0

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Video Models Support

For CogVideoX and other video diffusion models: 
pip install hypergen[video]
Or manually: 
pip install av>=12.0.0 opencv-python>=4.9.0

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All Optional Dependencies

Install everything at once: 
pip install hypergen[all]
This will install all optional dependencies, which may take significant time and disk space.

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Development Installation

For contributing to HyperGen: 
git clone https://github.com/ntegrals/hypergen.git
cd hypergen

# Install with dev dependencies
pip install -e ".[dev]"
This includes:
  • pytest - Testing framework
  • pytest-cov - Coverage reporting
  • ruff - Linting and formatting

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Troubleshooting

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CUDA Out of Memory

If you encounter CUDA out of memory errors:
  1. Reduce batch size: 
    lora = m.train_lora(ds, batch_size=1)
  2. Enable gradient checkpointing (coming in Phase 2)
  3. Use a smaller model or lower precision: 
    m = model.load("model_id", torch_dtype="float16")

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Installation Fails on Flash Attention

Flash Attention requires specific CUDA versions and compilation:
  1. Ensure you have CUDA 11.8 or 12.1 installed
  2. Install build tools: 
    # Ubuntu/Debian
sudo apt-get install build-essential

# macOS
xcode-select --install
  3. Skip Flash Attention for now: 
    pip install hypergen
# Don't install hypergen[flash]

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Import Errors

If you get import errors after installation:
  1. Verify installation: 
    pip show hypergen
  2. Reinstall in a clean environment: 
    python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install hypergen

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macOS Metal (MPS) Support

For Apple Silicon Macs: 
# Use MPS backend instead of CUDA
m = model.load("stabilityai/stable-diffusion-xl-base-1.0")
m.to("mps")
MPS support is experimental. Some features may not work as expected.

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Verifying Installation

Test your installation with a quick script: 
from hypergen import model, dataset
import torch

print(f"HyperGen installed successfully!")
print(f"PyTorch version: {torch.__version__}")
print(f"CUDA available: {torch.cuda.is_available()}")

# Try loading a small model (optional)
# m = model.load("runwayml/stable-diffusion-v1-5")
# print("Model loaded successfully!")

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Next Steps

Quick Start

Train your first LoRA in 5 minutes

Training Guide

Learn about LoRA training parameters

Serving Guide

Deploy a model with the API server

Supported Models

See which models are compatible