> ## Documentation Index
> Fetch the complete documentation index at: https://hyper.julian.sc/llms.txt
> Use this file to discover all available pages before exploring further.
# 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
Install HyperGen via pip:
```bash theme={null}
pip install hypergen
```
For GPU support, make sure you have PyTorch with CUDA installed:
```bash theme={null}
# If you don't have PyTorch with CUDA
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu121
```
Check that HyperGen is installed correctly:
```bash theme={null}
hypergen --version
```
## Your First LoRA Training
Train a LoRA in just 5 lines of code:
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.
Create a Python file and run:
```python theme={null}
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.
Use your trained model to generate images:
```python theme={null}
# 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:
```bash theme={null}
hypergen serve stabilityai/stable-diffusion-xl-base-1.0 --api-key your-secret-key
```
The server will start on `http://localhost:8000`
Use the OpenAI Python client:
```python theme={null}
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:
```python theme={null}
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
* 8GB VRAM (NVIDIA GPU)
* CUDA 11.8+
* For SDXL/SD 1.5 models
* 16GB+ VRAM
* CUDA 12.1+
* For FLUX.1 and larger models
## Next Steps
Detailed installation guide with all options
Complete LoRA training documentation
Production deployment and API usage
All compatible model architectures