Local Models Overview

OpenTryOn provides adapters for local inference models that run directly on your hardware. Unlike the cloud API adapters in tryon.api, these models require local GPU resources but offer several advantages:

• No API costs: Run unlimited inferences without per-request charges
• Privacy: Your data never leaves your machine
• Customization: Fine-tune models for your specific use case
• Offline capability: Work without internet connectivity

Available Models

Model	Type	VRAM Required	Speed	Use Case
FLUX.2-dev Turbo	Image Generation	12GB+	6x faster	Fast text-to-image, image-to-image

Requirements

All local models require:

• CUDA-capable GPU (NVIDIA recommended)
• PyTorch 2.1+ with CUDA support
• diffusers >= 0.29.0
• transformers
• accelerate

VRAM Considerations

Local models are memory-intensive. FLUX.2-dev Turbo supports automatic model selection based on available VRAM:

Available VRAM	Model Selection
≥64GB	Full precision model
≥48GB	8-bit quantized
≥38GB	4-bit quantized
<38GB	4-bit quantized (with warnings)

Quick Start

from tryon.models import Flux2TurboAdapter

# Initialize (auto-selects model based on VRAM)
adapter = Flux2TurboAdapter()

# Generate image
images = adapter.generate_text_to_image(
    prompt="A fashion model wearing an elegant dress",
    width=1024,
    height=1024
)
images[0].save("output.png")

Installation

Install the required dependencies:

pip install diffusers>=0.29.0 transformers accelerate torch

# For quantized models (lower VRAM requirements)
pip install bitsandbytes

Memory Optimization Tips

1. Enable CPU Offloading: For GPUs with limited VRAM

   adapter = Flux2TurboAdapter(enable_cpu_offload=True)

2. Use Attention Slicing: Reduces peak memory at slight speed cost

   adapter = Flux2TurboAdapter(enable_attention_slicing=True)

3. Lower Resolution: Start with smaller images for testing

   images = adapter.generate_text_to_image(prompt="...", width=512, height=512)

4. Clear CUDA Cache: Between generations

   import torch
   torch.cuda.empty_cache()