Supported Models - HyperGen

Overview

HyperGen works with any diffusers-compatible model from HuggingFace. Below are popular models that have been tested and work well with HyperGen.

HyperGen uses the diffusers library under the hood, so any model with a DiffusionPipeline should work.

Image Generation Models

FLUX.1

Black Forest Labs’ state-of-the-art image generation model.

FLUX.1 Dev

Model ID: black-forest-labs/FLUX.1-devBest for: High-quality, detailed imagesRequirements:

16GB+ VRAM
bfloat16 dtype recommended

Usage:

m = model.load("black-forest-labs/FLUX.1-dev", torch_dtype="bfloat16")

FLUX.1 Schnell

Model ID: black-forest-labs/FLUX.1-schnellBest for: Fast generationRequirements:

12GB+ VRAM
Faster than Dev variant

Usage:

m = model.load("black-forest-labs/FLUX.1-schnell", torch_dtype="bfloat16")

Recommended Settings:

dtype: bfloat16
LoRA rank: 32-64
Training steps: 1500-2500

Stable Diffusion XL (SDXL)

Stability AI’s flagship image generation model.

SDXL Base

Model ID: stabilityai/stable-diffusion-xl-base-1.0Best for: General-purpose, high-quality imagesRequirements:

8GB+ VRAM
Most popular choice

Usage:

m = model.load("stabilityai/stable-diffusion-xl-base-1.0")

SDXL Turbo

Model ID: stabilityai/sdxl-turboBest for: Fast generation (1-4 steps)Requirements:

8GB+ VRAM
Optimized for speed

Usage:

m = model.load("stabilityai/sdxl-turbo")

Recommended Settings:

dtype: float16
LoRA rank: 16-32
Training steps: 1000-2000

Stable Diffusion 3 (SD3)

Latest generation Stability AI model with improved text rendering.

SD3 Medium

Model ID: stabilityai/stable-diffusion-3-medium-diffusersBest for: Text rendering, detailed scenesRequirements:

12GB+ VRAM
Excellent prompt following

Usage:

m = model.load("stabilityai/stable-diffusion-3-medium-diffusers")

Recommended Settings:

dtype: float16 or bfloat16
LoRA rank: 16-32
Training steps: 1000-2000

Stable Diffusion 1.5

Classic Stable Diffusion - lightweight and fast.

SD 1.5

Model ID: runwayml/stable-diffusion-v1-5Best for: Fast generation, low VRAMRequirements:

6GB+ VRAM
Lightweight and fast

Usage:

m = model.load("runwayml/stable-diffusion-v1-5")

SD 1.4

Model ID: CompVis/stable-diffusion-v1-4Best for: Research, experimentationRequirements:

6GB+ VRAM

Usage:

m = model.load("CompVis/stable-diffusion-v1-4")

Recommended Settings:

dtype: float16
LoRA rank: 8-16
Training steps: 800-1500

Stable Diffusion 2.1

Improved version of SD 1.5 with better quality.

SD 2.1

Model ID: stabilityai/stable-diffusion-2-1Best for: Balance of quality and speedRequirements:

8GB+ VRAM

Usage:

m = model.load("stabilityai/stable-diffusion-2-1")

Video Generation Models

CogVideoX

Tsinghua University’s text-to-video generation model.

CogVideoX-2B

Model ID: THUDM/CogVideoX-2bBest for: Short video clips (2-6s)Requirements:

16GB+ VRAM
Smaller, faster variant

Usage:

m = model.load("THUDM/CogVideoX-2b")

CogVideoX-5B

Model ID: THUDM/CogVideoX-5bBest for: Higher quality videosRequirements:

24GB+ VRAM
Better quality than 2B

Usage:

m = model.load("THUDM/CogVideoX-5b")

Video model support requires the video optional dependencies: pip install hypergen[video]

Specialized Models

ControlNet Models

ControlNet-enabled models for conditional generation.

ControlNet support is coming in a future release.

PixArt Models

High-resolution text-to-image models.

m = model.load("PixArt-alpha/PixArt-XL-2-1024-MS")

Kandinsky Models

Bilingual (English/Russian) text-to-image models.

m = model.load("kandinsky-community/kandinsky-2-2-decoder")

Fine-Tuned Community Models

HyperGen works with community fine-tunes on HuggingFace:

# Anime style
m = model.load("stablediffusionapi/anything-v5")

# Realistic photos
m = model.load("SG161222/Realistic_Vision_V5.1_noVAE")

# Artistic style
m = model.load("dreamlike-art/dreamlike-photoreal-2.0")

Browse HuggingFace’s diffusion models for thousands of community models.

Model Comparison

Performance Comparison

Based on RTX 4090, 50 inference steps, 1024x1024:

Model	VRAM	Generation Time	Quality	Best For
SD 1.5	~6GB	~2s	Good	Speed, low VRAM
SD 2.1	~8GB	~2.5s	Better	Balanced
SDXL	~12GB	~4s	Excellent	General use
SD3	~14GB	~5s	Excellent+	Text rendering
FLUX.1 Dev	~18GB	~8s	Outstanding	Best quality
FLUX.1 Schnell	~16GB	~3s	Excellent	Fast + quality

Training Comparison

Approximate LoRA training times (1000 steps, 50 images, rank 16):

Model	VRAM	Training Time	File Size
SD 1.5	~6GB	~8 min	~30MB
SDXL	~12GB	~15 min	~50MB
SD3	~14GB	~18 min	~60MB
FLUX.1	~18GB	~25 min	~80MB

Model Selection Guide

Choose Based on Use Case

Speed Priority
Quality Priority
Balanced
Low VRAM

Best Choice: SDXL Turbo or SD 1.5

m = model.load("stabilityai/sdxl-turbo")
# Or
m = model.load("runwayml/stable-diffusion-v1-5")

Fastest generation
Good for prototyping
Lower VRAM requirements

Best Choice: FLUX.1 Dev or SD3

m = model.load("black-forest-labs/FLUX.1-dev", torch_dtype="bfloat16")
# Or
m = model.load("stabilityai/stable-diffusion-3-medium-diffusers")

Best image quality
Excellent prompt following
Requires more VRAM

Best Choice: SDXL Base

m = model.load("stabilityai/stable-diffusion-xl-base-1.0")

Great quality
Reasonable speed
Works on 8GB+ VRAM

Best Choice: SD 1.5

m = model.load("runwayml/stable-diffusion-v1-5")

Works on 6GB VRAM
Fast generation
Proven reliability

Choose Based on VRAM

6-8GB VRAM

SD 1.5
SD 2.1
SDXL (with optimizations) �

8-12GB VRAM

SDXL
SD3 (with optimizations) �
All SD variants

12-16GB VRAM

SDXL
SD3
FLUX.1 Schnell �

16GB+ VRAM

All models
FLUX.1 Dev
CogVideoX-2B

24GB+ VRAM

All models
Large batch sizes
CogVideoX-5B

Loading Custom Models

From Local Path

m = model.load("/path/to/local/model")

From Private HuggingFace Repos

from huggingface_hub import login

login(token="your_hf_token")

m = model.load("your-org/private-model")

With Custom Configuration

m = model.load(
    "stabilityai/stable-diffusion-xl-base-1.0",
    torch_dtype="bfloat16",
    variant="fp16",  # Load fp16 variant
    use_safetensors=True,
)

Model Licensing

Always check the license for any model you use!

Common licenses:

CreativeML OpenRAIL-M - Most Stable Diffusion models (permissive, with restrictions)
Apache 2.0 - FLUX.1 Schnell (permissive)
FLUX.1 Dev License - FLUX.1 Dev (non-commercial unless licensed)
Custom Licenses - Check each model’s HuggingFace page

Testing New Models

To test if a model works with HyperGen:

from hypergen import model

try:
    m = model.load("model/id")
    m.to("cuda")
    image = m.generate("test prompt")
    print("Model works! ")
except Exception as e:
    print(f"Model failed: {e}")

Next Steps

Training

Train LoRAs on any of these models

Serving

Serve models with the API

Quick Start

Get started in 5 minutes

Installation

Install HyperGen with GPU support

​Overview

​Image Generation Models

​FLUX.1

FLUX.1 Dev

FLUX.1 Schnell

​Stable Diffusion XL (SDXL)

SDXL Base

SDXL Turbo

​Stable Diffusion 3 (SD3)

SD3 Medium

​Stable Diffusion 1.5

SD 1.5

SD 1.4

​Stable Diffusion 2.1

SD 2.1

​Video Generation Models

​CogVideoX

CogVideoX-2B

CogVideoX-5B

​Specialized Models

​ControlNet Models

​PixArt Models

​Kandinsky Models

​Fine-Tuned Community Models

​Model Comparison

​Performance Comparison

​Training Comparison

​Model Selection Guide

​Choose Based on Use Case

​Choose Based on VRAM

​Loading Custom Models

​From Local Path

​From Private HuggingFace Repos

​With Custom Configuration

​Model Licensing

​Testing New Models

​Next Steps

Training

Serving

Quick Start

Installation

Overview

Image Generation Models

FLUX.1

Stable Diffusion XL (SDXL)

Stable Diffusion 3 (SD3)

Stable Diffusion 1.5

Stable Diffusion 2.1

Video Generation Models

CogVideoX

Specialized Models

ControlNet Models

PixArt Models

Kandinsky Models

Fine-Tuned Community Models

Model Comparison

Performance Comparison

Training Comparison

Model Selection Guide

Choose Based on Use Case

Choose Based on VRAM

Loading Custom Models

From Local Path

From Private HuggingFace Repos

With Custom Configuration

Model Licensing

Testing New Models

Next Steps