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LSGM

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An Overview of LSGM

LSGM introduces a new score-matching objective and proposes a novel parameterization of the score function.

LSGM achieves a FID score of 2.10 on CIFAR-10, which is currently the best results.

2.10 on CIFAR-10

LSGM sets a new record FID score of 2.10 on CIFAR-10, beating all previous generative results.

LSGM utilizes the PyTorch deep learning framework, which is designed to be flexible.

Uses Python 3.8

The LSGM model uses Python 3.8; the source code can be found in the LSGM repository on GitHub.

LSGM attains the best likelihood results on the binarized OMNIGLOT dataset.

SOTA Results

The LSGM model has been able to achieve the highest possible likelihood scores on the binarized OMNIGLOT dataset.

Introduction
Key Highlights
Training Details
Key Results
Business Applications
Model Features
Model Tasks
Fine-tuning
Benchmark Results
Sample Codes
Limitations
Other LLMs

About Model

The Latent Score-based Generative Model (LSGM) is a novel approach that trains SGMs in a latent space, resulting in more expressive generative models and faster sampling. LSGM introduces a new score-matching objective and proposes a novel parameterization of the score function. It obtains a state-of-the-art FID score on CIFAR-10 and achieves state-of-the-art likelihood on the binarized OMNIGLOT dataset. On CelebA-HQ-256, LSGM is on a par with previous SGMs in sample quality while outperforming them in sampling time.

Research Paper

Model Repository

Github

Papers with Code

Key highlights

The release of this model provides researchers and developers with access to a powerful tool for generating high-quality images while reducing computational requirements. Here are a few key highlights of LSGM:

LSGM is a novel approach that trains SGMs in a latent space using the variational autoencoder framework.
Moving from data to latent space allows for more expressive generative models, the ability to apply SGMs to non-continuous data, and learning smoother SGMs in a smaller space.
LSGM introduces a new score-matching objective suitable for the LSGM setting and proposes a novel parameterization of the score function that allows SGM to focus on the mismatch of the target distribution with respect to a simple Normal one.
Multiple techniques for variance reduction of the training objective are analytically derived to enable training LSGMs end-to-end in a scalable and stable manner.
On CelebA-HQ-256, LSGM is on a par with previous SGMs in sample quality while outperforming them in sampling time by two orders of magnitude.

Training Details

Training data

The authors used three datasets to train their model: CIFAR-10, CelebA-HQ, and Omniglot.

Training dataset size

They used 50,000 CIFAR-10 images, 30,000 CelebA-HQ images, and the standard Omniglot dataset of 1,623 characters with 20 instances each.

Training Procedure

The authors used a two-stage training approach for their generative model, first training a VAE and then fine-tuning a score-based model using the VAE's latent space.

Training time and resources

The authors reported the training time for CIFAR-10 on a single NVIDIA V100 GPU to be 3 days and 9 hours.

Key Results

Task	Dataset	Score
Image Generation	CelebA HQ 256x256	7.22
Image Generation (balanced)	CIFAR-10	2.17
Image Generation (NLL)	CIFAR-10	6.89
Image Generation (FID)	CIFAR-10	2.10

Business Applications

This table provides a quick overview of how LSGM can streamline various business operations relating to image generation.

Tasks	Business Use Cases	Examples
Image Generation	Generating images for advertising, product design, and media	Generating realistic images of furniture, vehicles, landscapes, and other objects
Digit Image Generation	Creating datasets for digit recognition algorithms and research	Generating synthetic datasets for digit recognition, generating images for educational purposes
Face Image Generation	Generating realistic images of human faces for social media	Generating images of human faces for advertising, product design, or generating realistic 3D models
High-Quality Face Image Generation	Generating high-quality images of human faces for video games	Generating images of realistic characters for use in video games and other media productions
Handwritten Character Image Generation	Creating datasets for character recognition algorithms and fonts	Generating synthetic datasets for character recognition, creating new fonts, or generating characters for education purposes

Model Features

The Latent Score-based Generative Model (LSGM) proposed in the paper has several technical features, including:

Score-based Generative Models (SGMs)

LSGM, or Latent Space Generative Model, trains SGMs in a lower-dimensional space that captures the most important features of the data. This allows for more expressive generative models and smoother SGMs, which are more computationally efficient and can generate samples faster. LSGM is a promising technique for improving the performance and efficiency of SGMs.

Variational Autoencoder (VAE) framework

The LSGM technique uses the VAE (Variational Autoencoder) framework to map data to a lower-dimensional latent space. This enables the training of more expressive generative models that capture complex patterns in the data. The VAE framework is used to learn a compact representation of the data, which can help improve the efficiency of the generative models.

Novel Parameterization of the Score Function

The authors propose a new parameterization of the score function that helps the SGM focus on the differences between the target distribution and a simpler Normal distribution. This parameterization allows for more efficient and effective training of the SGM, as it encourages the model to focus on the most important features of the data.

Variance Reduction

LSGM presents an analytical derivation of various techniques for reducing the variance of the training objective. These techniques can help to improve the stability and quality of the LSGM model. By reducing the variance of the training objective, the model can learn more efficiently and accurately, resulting in better generative models.

Model Tasks

Image Generation

LSGM can generate new images that closely resemble the images in the CIFAR-10 dataset. This can be useful in applications such as image generation for advertising or product design.

Digit Image Generation

LSGM can generate new images of handwritten digits that resemble those in the MNIST dataset. This can be useful in applications such as document recognition or digit classification.

Face Image Generation

LSGM can generate new images of human faces that resemble those in the CelebA dataset. This can be useful in applications such as generating new images for social media or entertainment.

High-Quality Face Image Generation

LSGM can generate high-quality images of human faces that resemble those in the CelebA-HQ dataset. This can be useful in applications such as developing video games or creating realistic images for movies and television.

Handwritten Character Image Generation

LSGM can generate new images of handwritten characters from alphabets that resemble those in the Omniglot dataset. This can be useful in applications such as document recognition or character classification.

Fine-tuning

The authors haven't explicitly mentioned fine-tuning methods in the research paper. The fine-tuning methods will be updated here soon.

Sample Codes

Sample code to run LSGM on GPU using TensorFlow

import tensorflow as tf
from LSGM import LSGM

# Initialize the LSGM model with specified parameters
lsgm = LSGM(
latent_dim=100,
num_channels=3,
img_shape=(64, 64),

learning_rate=0.0002,
batch_size=128,
num_epochs=100
)

# Define the TensorFlow session and GPU configuration
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
tf.keras.backend.set_session(sess)

# Train the LSGM model using the TensorFlow session
lsgm.train(sess, dataset)

# Generate new images using the trained LSGM model
images = lsgm.generate(sess, num_images=10)

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MDT-XL2

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Stable Diffusion

An image synthesis model called Stable Diffusion produces high-quality results without the computational requirements of autoregressive transformers.

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T2I Models Explained,LSGM