ldm-celebahq-256
| Property | Value |
|---|---|
| License | Apache 2.0 |
| Paper | High-Resolution Image Synthesis with Latent Diffusion Models |
| Downloads | 25,400 |
| Framework | PyTorch |
What is ldm-celebahq-256?
ldm-celebahq-256 is a Latent Diffusion Model (LDM) specifically designed for generating high-quality 256x256 face images. This model represents a significant advancement in efficient image synthesis by operating in the latent space rather than pixel space, dramatically reducing computational requirements while maintaining high visual fidelity.
Implementation Details
The model utilizes a two-stage approach: a VQ-VAE for encoding images into latent space and a UNet-based diffusion model for generation. It implements the DDIM scheduler for inference and can generate images in 200 steps for optimal quality.
- Processes images in latent space using pretrained autoencoders
- Incorporates cross-attention layers for flexible generation
- Supports both pipeline and unrolled loop implementations
- Optimized for 256x256 resolution face synthesis
Core Capabilities
- High-quality face image generation
- Efficient computational performance
- Flexible integration with diffusers library
- Stable inference with customizable steps
Frequently Asked Questions
Q: What makes this model unique?
This model's uniqueness lies in its efficient latent space operation, allowing it to achieve high-quality face generation with significantly reduced computational resources compared to pixel-space diffusion models.
Q: What are the recommended use cases?
The model is ideal for face image generation tasks, research in generative AI, and applications requiring high-quality synthetic face images. It's particularly suitable for environments with limited computational resources.
