stable-diffusion-2-1-realistic
| Property | Value |
|---|---|
| License | OpenRAIL++ |
| Base Model | Stable Diffusion 2.1 |
| Training Data | PhotoChat_120_square_HQ |
| Paper | Latent Diffusion Model |
What is stable-diffusion-2-1-realistic?
stable-diffusion-2-1-realistic is a specialized fine-tuned version of Stable Diffusion 2.1, optimized for generating highly realistic images. The model was trained on a carefully curated dataset of 120 high-quality image-text pairs from PhotoChat, specifically processed for optimal quality using Gigapixel enhancement and BLIP-2 captioning.
Implementation Details
The model utilizes a Latent Diffusion architecture with a fixed, pretrained OpenCLIP-ViT/H text encoder. It's designed to work with the Hugging Face Diffusers library and can be easily implemented using StableDiffusionPipeline.
- Supports customizable inference parameters including guidance scale and number of steps
- Optimized for 768x768 pixel output resolution
- Includes specialized prompt templates for both human and non-human subjects
- Implements negative prompting for quality enhancement
Core Capabilities
- High-quality realistic image generation from text descriptions
- Specialized performance for portrait and human-centric images
- Support for detailed prompt engineering with templates
- Integration with modern AI frameworks and tools
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its focus on realistic image generation, particularly for human subjects, trained on a highly curated dataset of 120 high-quality images. It includes specialized prompt templates and negative prompting strategies for optimal results.
Q: What are the recommended use cases?
The model excels at generating realistic photographs, particularly for human subjects and real-world scenes. It's especially effective when used with the provided prompt templates for either human or non-human subjects, making it suitable for professional photography-style image generation.
