Stable Diffusion 2 Depth
| Property | Value |
|---|---|
| License | OpenRAIL++ |
| Authors | Robin Rombach, Patrick Esser |
| Base Paper | High-Resolution Image Synthesis With Latent Diffusion Models |
| Training Data | LAION-5B filtered subset |
What is stable-diffusion-2-depth?
Stable Diffusion 2 Depth is an advanced variant of the Stable Diffusion 2 model that incorporates depth awareness into image generation. Built upon the stable-diffusion-2-base model, it has been fine-tuned for 200,000 steps with an additional input channel that processes depth predictions from the MiDaS DPT-Hybrid model. This enables the model to better understand and maintain spatial relationships in generated images.
Implementation Details
The model leverages a Latent Diffusion architecture with OpenCLIP-ViT/H as its text encoder. It processes images through an autoencoder with a downsampling factor of 8, mapping images to latent representations. The unique feature is its depth-awareness implementation through MiDaS integration.
- Uses StableDiffusionDepth2ImgPipeline for inference
- Supports both CPU and GPU execution (recommended with CUDA)
- Compatible with xformers for memory-efficient attention
- Includes attention slicing for lower VRAM usage
Core Capabilities
- Depth-aware image generation and modification
- High-quality image synthesis with spatial consistency
- Support for negative prompts
- Adjustable strength parameters for depth influence
Frequently Asked Questions
Q: What makes this model unique?
This model's unique feature is its ability to understand and incorporate depth information during image generation, leading to more spatially coherent results. It's particularly useful for tasks requiring precise control over the spatial arrangement of elements in generated images.
Q: What are the recommended use cases?
The model is ideal for research purposes, artistic applications, educational tools, and design processes where depth awareness is crucial. It excels in scenarios requiring precise control over spatial relationships in generated images.
