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Ldm Celebahq 256

Developed by CompVis
The Latent Diffusion Model (LDM) is an efficient image generation method that applies diffusion models in latent space, significantly reducing computational requirements while maintaining high-quality generation results.
Downloads 268
Release Time : 7/15/2022

Model Overview

By applying diffusion models in the latent space of a pre-trained autoencoder, LDM achieves a balance between reduced complexity and detail preservation, supporting tasks such as unconditional image generation, semantic scene synthesis, and super-resolution.

Model Features

Latent Space Diffusion
Applies diffusion models in the latent space of a pre-trained autoencoder, significantly reducing computational requirements while maintaining high-quality generation results.
Efficient Inference
Compared to pixel-based diffusion models, LDM significantly reduces computational resource consumption during inference.
Flexible Conditional Control
Supports general conditional inputs such as text or bounding boxes through cross-attention layers, enabling controllable image generation.

Model Capabilities

Unconditional image generation
High-resolution image synthesis
Latent space image processing

Use Cases

Creative content generation
Facial image generation
Generates high-quality facial images using a model trained on the CelebA-HQ dataset
Generates 256x256 resolution facial images
Image processing
Image super-resolution
Converts low-resolution images into high-resolution versions
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