Stable Diffusion 2.1
| Property | Value |
|---|---|
| License | CreativeML Open RAIL++-M |
| Authors | Robin Rombach, Patrick Esser |
| Framework | Diffusers |
| Paper | High-Resolution Image Synthesis With Latent Diffusion Models |
What is stable-diffusion-2-1?
Stable Diffusion 2.1 is an advanced text-to-image generation model that builds upon the success of Stable Diffusion 2. It's fine-tuned with an additional 55k steps on the original dataset and 155k extra steps with enhanced safety parameters, making it more robust and reliable for image generation tasks.
Implementation Details
The model utilizes a Latent Diffusion architecture with OpenCLIP-ViT/H as its text encoder. It processes images through an autoencoder with a downsampling factor of 8, converting images of shape H x W x 3 to latents of shape H/f x W/f x 4.
- Trained on filtered LAION-5B dataset
- Uses DPMSolverMultistepScheduler for efficient sampling
- Supports 768x768 resolution outputs
- Implements v-objective for improved quality
Core Capabilities
- High-quality text-to-image generation
- Advanced compositional understanding
- Enhanced safety filters
- Efficient latent space processing
- Multiple resolution support
Frequently Asked Questions
Q: What makes this model unique?
This model stands out due to its additional fine-tuning steps and improved safety measures, making it more reliable for general use while maintaining high-quality output. It also features better handling of complex prompts and improved image quality compared to its predecessors.
Q: What are the recommended use cases?
The model is primarily intended for research purposes, including safe deployment studies, artistic applications, educational tools, and research on generative models. It specifically excludes the generation of harmful, offensive, or misrepresentative content.
