Table of Contents
1. Introduction to Retekx.com
- 1.1 Overview
- 1.2 Core Features
- 1.3 System Requirements
2. Getting Started
- 2.1 Installation and Setup
- 2.2 Supported Platforms
3. Stable Diffusion Integration
- 3.1 NVIDIA Optimizations
- 3.2 Model Architecture
4. API Reference
- 4.1 Authentication
- 4.2 Image Generation Endpoints
- 4.3 Python SDK Examples
5. Advanced Features
- 5.1 Model Fine-tuning
- 5.2 Inference Optimization
6. Performance Guidelines
- 6.1 Recommended Batch Sizes
- 6.2 Resolution vs Performance
7. Rate Limits and Quotas
- 7.1 API Limits
Get Setup
1. Introduction to Retekx.com
1.1 Overview
Retekx.com is a specialized generative AI platform that leverages NVIDIA's Stable Diffusion SDK to deliver state-of-the-art image synthesis capabilities. Our platform provides enterprise- grade image generation solutions optimized for both cloud and on-premises deployments.
1.2 Core Features
- Advanced text-to-image generation with Stable Diffusion
- Multi-style image synthesis with customizable parameters
- High-resolution output support up to 4096x4096
- Batch processing capabilities for enterprise workloads
- Fine-tuning support for custom image generation models
1.3 System Requirements
-
Hardware
- NVIDIA GPU with CUDA support (Minimum 8GB VRAM)
- Recommended: NVIDIA A100, V100, or RTX 4090
- System Memory: 16GB RAM minimum, 32GB recommended
-
Software
- Ubuntu 20.04+ or Windows 10/11
- NVIDIA CUDA Toolkit (v11.8 or later)
- NVIDIA TensorRT (v8.5 or later)
2. Getting Started
2.1 Installation and Setup
1. Install the NVIDIA CUDA Toolkit:
sudo apt update
sudo apt install cuda
2. Configure NVIDIA TensorRT:
Follow the steps below to download and configure NVIDIA TensorRT:
# Download TensorRT
wget https://developer.nvidia.com/compute/machine-learning/tensorrt/secure/8.5.0/local_repos/
nv-tensorrt-local-repo-ubuntu2004-8.5.0-cuda-11.8_1.0-1_amd64.deb
# Install TensorRT
sudo dpkg -i nv-tensorrt-local-repo-ubuntu2004-8.5.0-cuda-11.8_1.0-1_amd64.deb
sudo apt update
sudo apt install tensorrt
3. Install Retekx SDK:
pip install retekx-sdk
2.2 Supported Platforms
- Cloud: Google Cloud Platform (GCP), AWS, Azure
- On-premises: NVIDIA DGX Systems, custom GPU clusters
- Edge: Compatible with NVIDIA Jetson devices
3. Stable Diffusion Integration
3.1 NVIDIA Optimizations
Retekx leverages NVIDIA's optimized Stable Diffusion pipeline featuring:
- FP16 precision support for faster inference
- Attention block optimization
- Memory-efficient cross-attention
- Dynamic batch processing
- Automated mixed precision (AMP)
3.2 Model Architecture
Our implementation uses NVIDIA's optimized UNet architecture with:
- Enhanced attention mechanisms
- Optimized memory usage
- Cross-frame attention for consistency
- Custom CLIP text encoder integration
- Neural network training and inference
- Parallel processing for faster outputs
4. API Reference
4.1 Authentication
http
Authorization: Bearer <your_api_key>
4.2 Image Generation Endpoints
Text-to-Image
http
POST /api/v1/generate-image
Content-Type: application/json
{
"prompt": "<description>",
"negative_prompt": "<avoidance>",
"width": 1024,
"height": 1024,
"num_inference_steps": 50,
"guidance_scale": 7.5,
"seed": 12345
}
Image-to-Image
http
POST /api/v1/image-to-image
Content-Type: multipart/form-data
{
"image": "<base64_encoded_image>",
"prompt": "<description>",
"strength": 0.8,
"guidance_scale": 7.5
}
4.3 Python SDK Examples
Basic Image Generation:
python
from retekx import Client
client = Client(api_key="your_api_key")
response = client.generate_image(
prompt="A photorealistic landscape of a mountain lake at sunset",
width=1024,
height=1024,
num_inference_steps=50
)
Batch Generation:
responses = client.generate_image_batch(
prompts=["landscape 1", "landscape 2"],
batch_size=2,
width=1024,
height=1024
)
5. Modules and Capabilities
Retekx.com offers modular AI capabilities that cater to diverse generative needs. Each module is optimized using NVIDIA SDKs for performance and scalability.
5.1 Text Generation
Description: The text generation module enables the creation of natural language content based on user-defined prompts. It is ideal for applications such as chatbots, content creation, and code generation.
Key Features:
- Context-aware text creation.
- Adjustable creativity parameters (temperature, top_p).
- Multi-language support.
5.2 Image Synthesis
Description: Generate high-quality images based on text descriptions or reference inputs. This module is ideal for design, entertainment, and marketing.
Key Features:
- Style customization using GANs (Generative Adversarial Networks).
- Support for resolution upscaling with NVIDIA's DLSS (Deep Learning Super Sampling).
- Text-to-image pipeline with fine-grained control.
5. Advanced Features
5.1 Model Fine-tuning
from retekx import Client
client = Client(api_key="your_api_key")
# Initialize fine-tuning job
job = client.create_finetune_job(
training_data="path/to/images",
prompt_data="path/to/prompts.json",
epochs=1000,
learning_rate=1e-5
)
# Monitor progress
status = client.get_finetune_status(job_id=job.id)
5.2 Inference Optimization
- Dynamic batch size adjustment
- Automatic memory management
- Pipeline parallelization
- Attention optimization
6. Performance Guidelines
6.1 Recommended Batch Sizes
- A100 80GB: Up to 32 images per batch
- V100 32GB: Up to 16 images per batch
- RTX 4090 24GB: Up to 8 images per batch
6.2 Resolution vs Performance
Resolution Impact on VRAM:
- 512x512: Base memory usage
- 1024x1024: 4x base memory
- 2048x2048: 16x base memory
7. Rate Limits and Quotas
7.1 API Limits
- Free tier: 100 images/day
- Pro tier: 1000 images/day
- Enterprise tier: Custom limits
7.2 Concurrent Requests
- Maximum concurrent requests: 10
- Batch size limits: Based on subscription tier
- Rate limiting: 100 requests per minute
