1. Introduction
Recently, I participated in the Amazon Web Services 【Cloud Exploration Lab】 event, where I built my first AIGC based on the Stable Diffusion model using Amazon SageMaker , which was very simple and quick. I initially thought it would be very complex and that I wouldn’t understand how to operate it, but in reality, it was very easy to use and not as scary as I imagined. The overall experience was very pleasant. I will first give a brief introduction to Amazon SageMaker , and then summarize how to build an AIGC application based on the Stable Diffusion model.
2. Brief Introduction to Amazon SageMaker
Amazon SageMaker is a comprehensive machine learning (ML) platform from Amazon Web Services (AWS) designed to make it easier for data scientists, developers, and businesses to build, train, and deploy machine learning models. Amazon SageMaker offers a full suite of tools and frameworks, including data labeling, model training, model deployment, and automated modeling, while also supporting various common data science frameworks such as TensorFlow , PyTorch , and Apache MXNet . Amazon SageMaker builds on Amazon’s two decades of experience developing real-world machine learning applications, including product recommendations, personalization, smart shopping, robotics, and voice-assisted devices.
Official website:
3. Building AIGC Applications Based on the Stable Diffusion Model
1. Check Quotas First
We will use ml.g4dn.xlarge , and first, we need to ensure that ml.g4dn.xlarge has quota. Click here: please click Check Quotas and enter ml.g4dn.xlarge for endpoint usage in the search box. If your quota is as shown in the image, and the second column in the ml.g4dn.xlarge for endpoint usage row is 0 , please continue with the following steps.
Select ml.g4dn.xlarge for endpoint usage , then click the orange button in the upper right corner “Request Quota Increase” and follow the steps.
2. Create an Amazon SageMaker Notebook Instance
1. Log in to the console
2. Search for SageMaker in the service search bar and configure the notebook instance.
3. Configure an IAM role.
3. Create Frontend and Backend Web Applications in AWS Cloud9
1. Create an Environment
Here we need to create an AWS Cloud9 environment and install boto3 and other environments. For specific operations, see here
2. Run app.py and preview the frontend page
3. Simple Test Prompt
Input: a siamese cat wearing glasses, working hard at the computer
4. Summary
Building an AIGC application based on the Stable Diffusion model is very simple. Even if we don’t understand much, we can still operate successfully. The functionality is very powerful, and the experience is very comfortable. I sincerely recommend everyone to use it.
4. Introduction to the Stable Diffusion Model and Core Competitiveness
1. Model Structure Diagram
2. Model Principles
3. Model Training
Training Objective: Step by step denoising random Gaussian noise ( denoise )
Advantages: Latent diffusion is called “latent” because the model performs the diffusion process in a low-dimensional latent space rather than in the actual pixel space, which reduces memory consumption and computational complexity (for example, if the input shape is (3,512,512), the downsampling factor is 8, it becomes (3,64,64) in the latent space, saving 8×8=64 times memory). After training, the model can represent an image as a low-dimensional latent feature.
4. Model Inference
As shown in the figure (assuming the batch size is 1), the user input prompt is encoded by the CLIP Text encoder into a latent feature of 77×768, and random noise is represented as a latent feature of 64×64; then U-Net iteratively calculates based on the prompt features. The noise residual output by U-Net is processed by a scheduling algorithm ( scheduler algorithm ) to compute the final denoised image latent representation based on the previous noise representation and the predicted noise residual. The suggested scheduling algorithms for Stable Diffusion are three:
•PNDM scheduler (Pseudo Numerical Methods for Diffusion Models on Manifolds, default)
•DDIM scheduler
•K-LMS scheduler
After obtaining the image latent representation, it can be sent to the VAE decoder to decode into an image.
5. Core Competitiveness of the Stable Diffusion Algorithm Model
•Stable Diffusion provides higher compression rates and clarity surpassing algorithms like JPEG .
• Compared to purely transformer-based methods, the method in this paper is more suitable for high-dimensional data; it can also be efficiently applied to high-resolution synthesis of million-pixel images.
• Significantly reduces computational costs, achieving competitive performance across multiple tasks (unconditional image synthesis, inpainting, super-resolution) and datasets. It significantly lowers inference costs compared to pixel-based diffusion methods.
• Compared to previous works (simultaneously learning encoder/decoder architectures and score-based priors), the method does not require precise trade-offs between reconstruction and generation capabilities. This ensures reasonable reconstruction effects with very little latent space regularization.
• For tasks that are conditionally intensive in input, such as super-resolution, image inpainting, and semantic synthesis, the model can be applied in a convolutional manner and output images up to 1024 resolution.
• A general conditional mechanism based on cross-attention enables multimodal training. It can be used to train conditional models, text-to-image models, and layout-to-image models.
• Stable Diffusion creates images very quickly.
In summary, just train a universal auto-encoding phase once, and it can be used for multiple DM training or explore completely different tasks.
5. Powerful Features of Amazon SageMaker
1. Model Training Functionality
Amazon SageMaker provides a complete model training process, supporting various common deep learning and machine learning algorithms, including linear regression, logistic regression, k-means clustering, random forests, etc. Users can select appropriate algorithms in SageMaker and train models through an easy-to-use interface or API. Additionally, users can train using pre-trained models or their own model files.
Of course, we can also use Amazon’s training model SageMaker , as illustrated in the following image:
2. Model Deployment Functionality
Amazon SageMaker offers various model deployment options, including real-time endpoints, batch inference, and containerized deployment. Users can choose the appropriate deployment method according to their needs and deploy through an easy-to-use interface or API. Moreover, users can optimize deployment based on their needs, such as accelerating inference speed by using GPU instances; the following image illustrates the working principle.
We can click here to deploy models for inference to learn more.
3. Data Labeling Functionality
Amazon SageMaker provides a complete set of data labeling tools, including text classification, image classification, object detection, etc. Users can select suitable data labeling tasks and perform labeling through an easy-to-use interface or API. Additionally, users can use services like Amazon Mechanical Turk to obtain more labeled data.
4. Automated Modeling Functionality
Amazon SageMaker offers automated modeling capabilities that can automatically generate models based on user-provided data. Users only need to upload datasets, and SageMaker will automatically generate the best model architecture and parameters, train, and optimize it. This feature greatly simplifies the model-building process and improves model accuracy and efficiency.
5. Modeling Capability, Speed, and Usability
Amazon SageMaker provides a complete set of excellent modeling capabilities to help users quickly build high-quality models. At the same time, SageMaker has a very fast training speed, which can significantly shorten model training time. Additionally, SageMaker is very user-friendly, allowing users to operate through an easy-to-use interface or API without requiring professional knowledge.
6. Framework Support Functionality
Amazon SageMaker supports various common data science frameworks, including TensorFlow , PyTorch , and Apache MXNet , etc. Users can choose the appropriate framework for model training and deployment while enjoying the various excellent features provided by SageMaker .
7. Other Features
In addition to the features mentioned above, Amazon SageMaker also offers many other useful features, such as model tuning, model monitoring, model interpretation, etc. These features can help users better understand and manage their machine learning models.
6. Technical Principles of Amazon SageMaker
1. Machine Learning Principles and Performance
Amazon SageMaker is based on common machine learning frameworks such as TensorFlow , PyTorch , and MXNet , utilizing efficient distributed computing, automated hyperparameter tuning, and automated feature engineering techniques to improve the training speed and efficiency of machine learning models. At the same time, SageMaker also provides a variety of excellent algorithm libraries to help users better build and optimize their machine learning models.
1. Supervised Learning
Amazon SageMaker provides a variety of built-in general algorithms for classification or regression problems.
•AutoGluon-Tables
•CatBoost
•Factorization Machine Algorithm
•K-Nearest Neighbors (k-NN) Algorithm
•LightGBM
•Linear Learner Algorithm
•TabTransformer
•XGBoost Algorithm
•Object2Vec Algorithm
•DeepAR Prediction Algorithm
2. Unsupervised Learning
Amazon SageMaker provides various built-in algorithms for various unsupervised learning tasks, such as clustering, dimensionality reduction, pattern recognition, and anomaly detection.
•Principal Component Analysis (PCA) Algorithm
•K-Means Algorithm
•IP Insights
•Random Cut Forest (RCF) Algorithm
3. Text Analysis
SageMaker provides algorithms tailored for analyzing natural language processing, document classification or summarization, topic modeling or classification, and language transcription or translation.
•BlazingText Algorithm
•Sequence-to-Sequence Algorithm
•Latent Dirichlet Allocation (LDA) Algorithm
•Neural Topic Model (NTM) Algorithm
•Text Classification – TensorFlow
4. Image Processing
SageMaker also provides image processing algorithms for image classification, object detection, and computer vision.
•Image Classification – MXNet
•Image Classification – TensorFlow
•Semantic Segmentation Algorithm
•Object Detection – MXNet
•Object Detection – TensorFlow
2. Summary of Major Functional Algorithms
Amazon SageMaker provides a variety of common machine learning and deep learning algorithms, including linear regression, logistic regression, k-means clustering, random forests, etc. At the same time, SageMaker also supports custom algorithms, allowing users to extend and optimize according to their needs.
7. Applicable Scenarios and Experience of Amazon SageMaker
1. Applicable Scenarios
Amazon SageMaker is suitable for various types and scales of machine learning projects, including computer vision, natural language processing, recommendation systems, etc. It can help users more easily build, train, and deploy machine learning models, improving model accuracy and efficiency.
2. Experience Advantages
1. Easy Access
Amazon SageMaker can seamlessly integrate with other AWS services, such as Amazon S3 , Amazon Redshift , AWS Lambda , etc. This allows users to easily integrate their data and applications with SageMaker without worrying about data migration and management issues. Click here to get started.
2. Rich Features
Amazon SageMaker provides a complete set of machine learning tools and frameworks, including model training, model deployment, data labeling, automated modeling, etc. Users can choose the appropriate features according to their needs and operate through an easy-to-use interface or API.
3. Abundant Documentation
Amazon SageMaker provides detailed help documentation and examples to help users better understand and use SageMaker . Additionally, AWS also offers rich support services, allowing users to contact the AWS support team for assistance at any time.
Common questions can be resolved.
3. Customer Business Cases
Amazon SageMaker has been widely used in various machine learning projects, such as:
Spam Filtering: Using SageMaker to train models to identify spam, improving the efficiency and accuracy of email filtering.
Image Classification: Using SageMaker to train models to recognize different categories of images, such as vehicles, people, animals, etc.
Speech Recognition: Using SageMaker to train models to recognize speech, such as voice search and speech recognition.
Recommendation Systems: Using SageMaker to train models to predict user purchasing behavior, improving the accuracy and efficiency of recommendation systems.
Some customers are shown in the image below:
8. Summary of Amazon SageMaker Products
1. Technical Summary
Amazon SageMaker is based on common machine learning frameworks such as TensorFlow , PyTorch , and MXNet , utilizing efficient distributed computing, automated hyperparameter tuning, and automated feature engineering techniques to improve the training speed and efficiency of machine learning models. At the same time, SageMaker also provides various excellent algorithm libraries and tools to help users better build and optimize their machine learning models.
2. Performance Summary
Amazon SageMaker provides efficient data labeling, model training, and model deployment functionalities, helping users more easily build, train, and deploy machine learning models. At the same time, SageMaker also offers various excellent algorithm libraries and tools to improve model accuracy and efficiency.
3. Core Competitiveness Summary
Amazon SageMaker provides a complete set of excellent machine learning tools and frameworks, including model training, model deployment, data labeling, automated modeling, etc. Users can select appropriate features according to their needs and operate through an easy-to-use interface or API. Additionally, SageMaker also offers efficient distributed computing, automated hyperparameter tuning, and automated feature engineering techniques, greatly improving the training speed and efficiency of machine learning models.
4. Summary of Meeting Public Demand
Amazon SageMaker has been widely applied in various machine learning projects and has received widespread recognition and praise. It provides a complete set of excellent machine learning tools and frameworks to help users more easily build, train, and deploy machine learning models, improving model accuracy and efficiency.
9. Friendly Reminder
Currently, the Cloud Exploration Lab is ongoing, and everyone is welcome to participate.
Event introduction and link: https://dev.amazoncloud.cn/experience?trk=cndc-detail&sc_medium=corecontent&sc_campaign=product&sc_channel=csdnEvent positioning: Through the Cloud Exploration Lab, developers can learn and practice cloud technology while sharing their technical insights with other developer peers. Create, share, inspire each other, and play with cloud technology together. The Cloud Exploration Lab is not only a space for experience but also a platform for sharing.