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Introduction

HyperGen provides a simple, high-level API for training LoRA (Low-Rank Adaptation) adapters on diffusion models. The framework is designed to be:
  • Dead Simple: Train a LoRA in 5 lines of code
  • Optimized: Built on PEFT, Diffusers, and PyTorch for maximum efficiency
  • Flexible: Simple for beginners, powerful for experts
  • Universal: Works with any diffusers-compatible model

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Training Methods

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LoRA (Low-Rank Adaptation)

LoRA is the primary fine-tuning method in HyperGen. It works by training small adapter layers that can be added to a base model without modifying the original weights. Benefits:
  • Fast training (minutes instead of hours)
  • Low VRAM requirements (8GB+ vs 24GB+ for full fine-tuning)
  • Small file sizes (typically 50-200MB vs 5-10GB for full models)
  • Easily shareable and switchable
Current Status:  Available (training loop implementation in progress)

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Quick Example

from hypergen import model, dataset

# Load model and dataset
m = model.load("stabilityai/stable-diffusion-xl-base-1.0")
m.to("cuda")
ds = dataset.load("./my_images")

# Train LoRA
lora = m.train_lora(ds, steps=1000)

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Development Roadmap

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Phase 1: Core Architecture 

1

Model Loading

 Complete - Load any diffusers-compatible model from HuggingFace
2

Dataset Handling

 Complete - Load images and captions from folders
3

LoRA Training Scaffold

 Complete - PEFT integration and parameter configuration
4

Training Loop

=� In Progress - Implementing noise scheduling and loss calculation

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Phase 2: Optimizations �

Planned optimizations for faster training and lower memory usage:
  • Gradient Checkpointing: Trade compute for memory
  • Mixed Precision Training: Faster training with FP16/BF16
  • Flash Attention: Memory-efficient attention computation
  • Auto-configuration: Automatic batch size and learning rate tuning
  • Memory-efficient Loading: Load models with less VRAM overhead

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Phase 3: Advanced Features =.

Future enhancements for production use:
  • Multi-GPU Training: Distributed training across multiple GPUs
  • Custom Training Loops: Fine-grained control over training
  • Advanced Schedulers: Cosine, polynomial, and custom LR schedules
  • Validation and Metrics: Track training progress with metrics
  • Resume from Checkpoint: Continue interrupted training

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Current Limitations

HyperGen is currently in pre-alpha status. The following limitations apply:
Training:
  • LoRA training loop is not fully implemented yet
  • No validation or metric tracking
  • Single GPU only
  • Basic optimizations only
What Works Now:
  • Model and dataset loading
  • LoRA configuration with PEFT
  • Training scaffold and parameter setup
  • Checkpoint saving
Coming Soon:
  • Complete training loop with loss calculation
  • Gradient checkpointing and mixed precision
  • Automatic optimization based on available VRAM

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Training Performance

Expected performance after Phase 2 optimizations:

SDXL LoRA

GPU: RTX 4090 (24GB)
  • Steps: 1000
  • Time: ~15 minutes
  • Memory: ~12GB VRAM

FLUX.1 LoRA

GPU: RTX 4090 (24GB)
  • Steps: 1000
  • Time: ~25 minutes
  • Memory: ~18GB VRAM

SD 1.5 LoRA

GPU: RTX 3060 (12GB)
  • Steps: 1000
  • Time: ~8 minutes
  • Memory: ~6GB VRAM

CogVideoX LoRA

GPU: A100 (40GB)
  • Steps: 500
  • Time: ~45 minutes
  • Memory: ~28GB VRAM
These are estimated performance targets. Actual performance may vary based on dataset size, image resolution, and configuration.

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Supported Architectures

HyperGen works with any diffusers-compatible model:
  •  Stable Diffusion 1.5
  •  Stable Diffusion XL (SDXL)
  •  Stable Diffusion 3 (SD3)
  •  FLUX.1 (Dev/Schnell)
  •  CogVideoX (video models)
  •  Any other diffusers pipeline

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Next Steps

Dataset Guide

Learn how to prepare your training data

LoRA Training

Complete guide to LoRA parameters and configuration

Supported Models

See all compatible model architectures

Examples

View complete training examples on GitHub