SD3.5-Turbo-Realism-2.0-LoRA
| Property | Value |
|---|---|
| Author | prithivMLmods |
| Network Dimensions | 64 |
| Network Alpha | 32 |
| Training Images | 27 |
| Epochs | 13 |
| Base Model | SD3.5-Turbo |
What is SD3.5-Turbo-Realism-2.0-LoRA?
SD3.5-Turbo-Realism-2.0-LoRA is a specialized fine-tuning model designed to enhance the photorealistic capabilities of Stable Diffusion 3.5 Turbo. Built using LoRA (Low-Rank Adaptation) technology, this model focuses on generating highly realistic images with particular emphasis on portrait photography and human subjects.
Implementation Details
The model employs a constant learning rate scheduler with AdamW optimizer, incorporating advanced noise handling with a 0.03 noise offset and multi-resolution noise processing. Training was conducted over 13 epochs with 2350 steps per repeat, using carefully curated dataset of 27 images.
- Utilizes bfloat16 precision on CUDA-enabled devices
- Implements florence2-en labeling for natural language processing
- Features integrated noise discount of 0.1 with 10 iterations
- Supports various aspect ratios including 1:2, 85:128, and 128:85
Core Capabilities
- High-resolution photorealistic image generation
- Enhanced portrait photography output
- Specialized in human subject rendering
- Supports various lighting and environmental conditions
- Customizable through trigger word "Turbo Realism"
Frequently Asked Questions
Q: What makes this model unique?
This model combines the speed of SD3.5-Turbo with enhanced realism capabilities, specifically optimized for portrait and human subject photography. The careful training process with 27 curated images ensures consistent, high-quality output while maintaining the base model's efficiency.
Q: What are the recommended use cases?
The model excels at generating realistic portraits, headshots, and full-body photographs. It's particularly well-suited for creating professional-looking images with controlled lighting and environmental conditions, making it ideal for portfolio-style photos and professional photography simulations.
