SSD-1B: Redefining Text-to-Image Generation Efficiency - Step-By-Step Guide

November 2, 2023
By
Kunal Mahatha
Sign up for Free Trial

Table of Contents

Example H2
Example H3
Example H4
Example H5
Example H6

Introduction to Text-to-Image Generative AI Models

Generative AI technology is a type of artificial intelligence that can produce various types of content, including text, imagery, audio, and synthetic data. It focuses on creating new and original content, chat responses, designs, synthetic data. It is proving to be particularly valuable in creative fields and for novel problem-solving, as it can autonomously generate many types of new outputs. 

Generative AI models can be used in product design and development to generate new design ideas, enhance data augmentation in computer vision, generate synthetic data for training other machine learning models, and even in natural language processing, where it powers LLMs (Large Language Models) such as GPT, Llama2, Falcon, Mistral. 

However, similar to LLMs, text-to-image models have become increasingly powerful over time. Midjourney and Adobe Firefly are examples of proprietary AI for this, which have been creating some fascinating art. A recent article in ‘The Guardian’ even writes about how it is redefining the field of architecture. 

There are now several AI text-to-image generative models available, and the top models vary depending on the specific needs and use cases. 

Here are some of the top open-source AI text-to-image generative models:

DeepDream

DeepDream is an open-source AI image generator developed by Google and specifically designed for creating surrealistic artistic effects from existing images.

Stable Diffusion v1-5

The latent text-to-image model Stable Diffusion v1-5 combines an autoencoder with a diffusion model to produce life-like images. The model has been trained on an exhaustive laion-aesthetics v2 5+ dataset and fine-tuned on over 595k steps at a resolution of 512×512 pixels.

DeepFloyd IF

DeepFloyd IF is a text-to-image model that handles text particularly well and is basically an open-source version of Google's Imagen. It is a model that shows the potential of larger UNet architectures in the first stage of cascaded diffusion models, and thus offers a promising future for text-to-image synthesis.

These open-source AI text-to-image generative models use natural language descriptions to create intricate visuals, making creative expression more accessible and efficient than ever before. They can be used in various domains, including art and design, providing inspiration.

However, one of the big challenges with Generative AI has been the associated costs of training models.

Efficiency and Inference Costs

Efficiency is a significant challenge in deploying Generative AI models in production, including text-to-image Generative AI models. 

More realistic models require larger datasets and more complex algorithms to generate accurate results, while simpler models can be trained faster but may not produce as accurate or detailed outputs. This trade-off between realism and computational efficiency is a significant challenge in deploying text-to-image Generative AI models in production.

Also, even as Generative AI models are becoming increasingly popular, deploying them at scale can be challenging due to computational bottlenecks. Large-scale Generative AI deployments require significant computational resources, including high-performance computing clusters and specialized hardware.

To address these efficiency challenges, new models are emerging that are showing similar capabilities at lower compact sizes. 

This is where SSD-1B becomes interesting.

What Is SSD-1B?

SSD-1B is an open-source T2i (Text-to-Image) model developed by Segmind which is a significant advancement in text-to-image technology. It is a 1.3B parameter model that is 50% more compact compared to SDXL, making it easier to deploy and utilize in various systems and platforms without sacrificing performance. SSD-1B is trained on approximately 200 hours of 4x A100 80GB GPU hours, which equips it with enhanced capabilities to generate a wide spectrum of visual content based on textual prompts. 

The model comes with strong generation abilities out of the box, but for the best performance on a specific task, it is recommended to fine-tune the model on private data. SSD-1B is compatible with SDXL 1.0, and it can be used directly with the HuggingFace Diffusers library just like SDXL 1.0. 

Technological Advancements in SSD-1B

SSD-1B represents a leap in AI-driven image synthesis thanks to several innovative technical breakthroughs. It goes beyond being a development in text-to-image (T2I) generation.

Compact Design: SSD-1B is 50% smaller than its predecessor, SDXL, making it easier to deploy and utilize in various systems and platforms without sacrificing performance.

Faster Generation Times: SSD-1B is designed for speed and efficiency, with a 60% speed-up compared to its predecessor, ensuring rapid text-to-image translations.

Enhanced Effectiveness through Distillation: One of the achievements of SSD-1B lies in its use of model distillation. This method leverages the compression and knowledge transfer capabilities of expert models like JuggernautXL, ZavyChromaXL and SDXL 1.0. By employing comprehensive knowledge distillation techniques, SSD-1B inherits the expertise of its predecessors enabling it to generate high-quality photos with a smaller model size.

Optimized Size without Compromising Image Quality: The design philosophy behind SSD-1B focuses on maximizing efficiency while maintaining image quality. To achieve this, essential components such as transformer blocks, Attention layers, Resnet layers and gradually distilled Unet blocks are meticulously eliminated from the foundation model. As a result, SSD-1B is 50% smaller than SDXL 1.0 while remaining committed to delivering image outputs.

Deploying and Using SSD-1B

There are several processes involved in deploying a machine learning model such as SSD-1B. Here is a condensed example of deploying a model and building a simple text-to-image creation API using Flask, Docker, and Python.

Prerequisites

First, we need a GPU node to try this out. Head to E2E Cloud, and launch a GPU node. 

‍

Once you have launched it, make sure to add your SSH keys. Then ssh into the node, and follow the next steps. 

Visual Studio Workflow (Optional Step) 

You could write your code on your laptop, commit to a repo, and then clone in the remote server. 

However, for quicker experiments, we recommend using Visual Studio Code and the Remote Explorer extension. 

Once you install this extension, you would be able to ssh to the remote machine from your local Visual Studio itself, and then code on it as if it's your local development environment. 

Once this is done, you can create a folder on the remote machine for the experiment below.

Also, don’t forget to create a python virtual environment first in an appropriate folder. This is how: 


python3 -m venv ssd-1b-env
source ssd-1b-env/bin/activate

This would activate your virtual environment.

Create a Flask Application

Next, create a Flask application for your API. Install it using 


pip install flask.

Also install pillow and clarifai by 


pip install pillow
pip install clarifai

Then create app.py, with the following code: 


from flask import Flask, request, jsonify
from PIL import Image
from io import BytesIO
from clarifai.client.model import Model

app = Flask(__name)

# Initialize the SSD-1B model
model = Model("https://clarifai.com/segmind/segmind-stable-diffusion/models/ssd-1b")

@app.route('/generate-image', methods=['POST'])
def generate_image():
	try:
    	# Get the text input from the request
    	text = request.json['text']

    	# Model Predict
    	model_prediction = model.predict_by_bytes(text.encode(), "text")
    	image_bytes = model_prediction.outputs[0].data.image.base64

    	# Convert the image bytes to a PIL image
    	image_stream = BytesIO(image_bytes)
    	image = Image.open(image_stream)

    	# You can save the image or return it as a response
    	# image.save('generated_image.jpeg')
    	return jsonify({"message": "Image generated successfully"})

	except Exception as e:
    	return jsonify({"error": str(e)})

if __name__ == '__main__':
	app.run(host='0.0.0.0', port=5000)

You can now test this using: 


python app.py

Also, you would need to open the port 5000 using the ufw firewall tool (or the one that you prefer). If you are using ufw, here’s how: 


sudo ufw allow 5000

If you don’t know how to use ufw, head over to the Ubuntu documentation. 

Once you open the port, you would be able to access the model using your local browse, and the public IP of the node you had started.

Dockerize Your Application (Optional)

We also recommend containerizing. To containerize your application, create a Dockerfile to specify the environment and dependencies.


# Use an official Python runtime as a parent image
FROM python:3.8-slim

# Set the working directory to /app
WORKDIR /app

# Copy the current directory contents into the container at /app
COPY . /app

# Install any needed packages specified in requirements.txt
RUN pip install -r requirements.txt

# Make port 80 available to the world outside this container
EXPOSE 5000

# Define environment variable
ENV NAME World

# Run app.py when the container launches
CMD ["python", "app.py"]

Build and Run the Docker Container

In your terminal, navigate to the directory containing your Dockerfile and execute the following commands to build and run the Docker container:


# Build the Docker image
docker build -t ssd-1b-api .

# Run the Docker container
docker run -p 5000:5000 ssd-1b-api

Use the API

With the Flask application running inside a Docker container, you can send POST requests to it to generate images. You can use Python's requests library for this purpose.

In your local machine, you can create a simple script for this: image_generator.py.


import requests

# Define the API endpoint
api_url = 'http://:5000/generate-image'

# Define the input text
input_text = {
	'text': 'with smoke, half ice and half fire and ultra realistic in detail.wolf, typography, dark fantasy, wildlife photography, vibrant, cinematic and on a black background'
}

# Send a POST request to generate the image
response = requests.post(api_url, json=input_text)

# Check the response
if response.status_code == 200:
	print('Image generated successfully!')
else:
	print('Image generation failed. Error:', response.text)

Using the above, you would be able to vary the prompt, and generate images on your local machine. Think of it as your personal Midjourney! 

Applications of SSD-1B Text-to-Image AI

SSD-1B is a text-to-image Generative AI model that has various uses in different domains. Here are some of the different use cases of SSD-1B:

Art and Design 

SSD-1B can be used to generate images based on textual prompts, providing inspiration for artists and designers. Increasingly designers and artists are moving towards using Generative AI to augment their workflow, and this offers a next step towards that.

Product Design and Development 

SSD-1B can be used to generate new design ideas and enhance data augmentation in computer vision. Many vision models require data to train. Text-to-image models can be used to generate synthetic images that can then train vision models. 

Synthetic Data Generation 

SSD-1B can be used to generate synthetic data for training other machine learning models.

Natural language processing: SSD-1B can be used in natural language processing, where it powers LLMs (Large Language Models) such as GPT. This is especially the case in multi-modal AI, which is now blending text, images and other metaphors in AI pipelines.

Research

SSD-1B can be used in research to generate new images and explore the capabilities of Generative AI models. Consider fine-tuning the SSD-1B in a domain-specific set of images, and then using it to generate more such images for research purposes.

Summary

There has been an advancement in the field of high-quality text-to-image conversion with the introduction of SSD 1B. This open-source model, a part of Segmind’s distillation series, offers a remarkable combination of speed and compactness while maintaining excellent image quality. With a 60% increase in speed and a 50% reduction in size compared to its predecessor, SSD-1B stands out as a choice for real-time image generation and other applications that require resource efficient solutions.

For companies and developers seeking high-performing solutions for text-to-image production, the SSD-1B opens up exciting possibilities by bridging the gap between performance and picture quality.

Sign up for Free Trial
Latest Blogs
June 24, 2024

Build with E2E: Enhancing Python Code Generation with Updated Documentation Using Llama 3

June 24, 2024

E2E Networks: Your MeitY Empanelled Cloud Service Provider

July 1, 2024

Building a Healthcare Knowledge Graph RAG with Neo4j, LangChain, and Llama 3

July 1, 2024

Implementing Advanced RAG Techniques with Elasticsearch Vector DB

June 24, 2024

Build with E2E Cloud: Step-by-Step Guide to Build Advanced Multistage AI Agents Using DSPy

June 14, 2024

Build with E2E Cloud: Step-by-Step Guide to Use DSPy to Build Multi-Hop Optimized RAG

This is a decorative image for: A Complete Guide To Customer Acquisition For Startups
October 18, 2022

A Complete Guide To Customer Acquisition For Startups

Any business is enlivened by its customers. Therefore, a strategy to constantly bring in new clients is an ongoing requirement. In this regard, having a proper customer acquisition strategy can be of great importance.

So, if you are just starting your business, or planning to expand it, read on to learn more about this concept.

The problem with customer acquisition

As an organization, when working in a diverse and competitive market like India, you need to have a well-defined customer acquisition strategy to attain success. However, this is where most startups struggle. Now, you may have a great product or service, but if you are not in the right place targeting the right demographic, you are not likely to get the results you want.

To resolve this, typically, companies invest, but if that is not channelized properly, it will be futile.

So, the best way out of this dilemma is to have a clear customer acquisition strategy in place.

How can you create the ideal customer acquisition strategy for your business?

  • Define what your goals are

You need to define your goals so that you can meet the revenue expectations you have for the current fiscal year. You need to find a value for the metrics –

  • MRR – Monthly recurring revenue, which tells you all the income that can be generated from all your income channels.
  • CLV – Customer lifetime value tells you how much a customer is willing to spend on your business during your mutual relationship duration.  
  • CAC – Customer acquisition costs, which tells how much your organization needs to spend to acquire customers constantly.
  • Churn rate – It tells you the rate at which customers stop doing business.

All these metrics tell you how well you will be able to grow your business and revenue.

  • Identify your ideal customers

You need to understand who your current customers are and who your target customers are. Once you are aware of your customer base, you can focus your energies in that direction and get the maximum sale of your products or services. You can also understand what your customers require through various analytics and markers and address them to leverage your products/services towards them.

  • Choose your channels for customer acquisition

How will you acquire customers who will eventually tell at what scale and at what rate you need to expand your business? You could market and sell your products on social media channels like Instagram, Facebook and YouTube, or invest in paid marketing like Google Ads. You need to develop a unique strategy for each of these channels. 

  • Communicate with your customers

If you know exactly what your customers have in mind, then you will be able to develop your customer strategy with a clear perspective in mind. You can do it through surveys or customer opinion forms, email contact forms, blog posts and social media posts. After that, you just need to measure the analytics, clearly understand the insights, and improve your strategy accordingly.

Combining these strategies with your long-term business plan will bring results. However, there will be challenges on the way, where you need to adapt as per the requirements to make the most of it. At the same time, introducing new technologies like AI and ML can also solve such issues easily. To learn more about the use of AI and ML and how they are transforming businesses, keep referring to the blog section of E2E Networks.

‍

Reference Links

https://www.helpscout.com/customer-acquisition/

https://www.cloudways.com/blog/customer-acquisition-strategy-for-startups/

https://blog.hubspot.com/service/customer-acquisition

This is a decorative image for: Constructing 3D objects through Deep Learning
October 18, 2022

Image-based 3D Object Reconstruction State-of-the-Art and trends in the Deep Learning Era

3D reconstruction is one of the most complex issues of deep learning systems. There have been multiple types of research in this field, and almost everything has been tried on it — computer vision, computer graphics and machine learning, but to no avail. However, that has resulted in CNN or convolutional neural networks foraying into this field, which has yielded some success.

The Main Objective of the 3D Object Reconstruction

Developing this deep learning technology aims to infer the shape of 3D objects from 2D images. So, to conduct the experiment, you need the following:

  • Highly calibrated cameras that take a photograph of the image from various angles.
  • Large training datasets can predict the geometry of the object whose 3D image reconstruction needs to be done. These datasets can be collected from a database of images, or they can be collected and sampled from a video.

By using the apparatus and datasets, you will be able to proceed with the 3D reconstruction from 2D datasets.

State-of-the-art Technology Used by the Datasets for the Reconstruction of 3D Objects

The technology used for this purpose needs to stick to the following parameters:

  • Input

Training with the help of one or multiple RGB images, where the segmentation of the 3D ground truth needs to be done. It could be one image, multiple images or even a video stream.

The testing will also be done on the same parameters, which will also help to create a uniform, cluttered background, or both.

  • Output

The volumetric output will be done in both high and low resolution, and the surface output will be generated through parameterisation, template deformation and point cloud. Moreover, the direct and intermediate outputs will be calculated this way.

  • Network architecture used

The architecture used in training is 3D-VAE-GAN, which has an encoder and a decoder, with TL-Net and conditional GAN. At the same time, the testing architecture is 3D-VAE, which has an encoder and a decoder.

  • Training used

The degree of supervision used in 2D vs 3D supervision, weak supervision along with loss functions have to be included in this system. The training procedure is adversarial training with joint 2D and 3D embeddings. Also, the network architecture is extremely important for the speed and processing quality of the output images.

  • Practical applications and use cases

Volumetric representations and surface representations can do the reconstruction. Powerful computer systems need to be used for reconstruction.

Given below are some of the places where 3D Object Reconstruction Deep Learning Systems are used:

  • 3D reconstruction technology can be used in the Police Department for drawing the faces of criminals whose images have been procured from a crime site where their faces are not completely revealed.
  • It can be used for re-modelling ruins at ancient architectural sites. The rubble or the debris stubs of structures can be used to recreate the entire building structure and get an idea of how it looked in the past.
  • They can be used in plastic surgery where the organs, face, limbs or any other portion of the body has been damaged and needs to be rebuilt.
  • It can be used in airport security, where concealed shapes can be used for guessing whether a person is armed or is carrying explosives or not.
  • It can also help in completing DNA sequences.

So, if you are planning to implement this technology, then you can rent the required infrastructure from E2E Networks and avoid investing in it. And if you plan to learn more about such topics, then keep a tab on the blog section of the website. 

‍

Reference Links

https://tongtianta.site/paper/68922

https://github.com/natowi/3D-Reconstruction-with-Deep-Learning-Methods

This is a decorative image for: Comprehensive Guide to Deep Q-Learning for Data Science Enthusiasts
October 18, 2022

A Comprehensive Guide To Deep Q-Learning For Data Science Enthusiasts

For all data science enthusiasts who would love to dig deep, we have composed a write-up about Q-Learning specifically for you all. Deep Q-Learning and Reinforcement learning (RL) are extremely popular these days. These two data science methodologies use Python libraries like TensorFlow 2 and openAI’s Gym environment.

So, read on to know more.

What is Deep Q-Learning?

Deep Q-Learning utilizes the principles of Q-learning, but instead of using the Q-table, it uses the neural network. The algorithm of deep Q-Learning uses the states as input and the optimal Q-value of every action possible as the output. The agent gathers and stores all the previous experiences in the memory of the trained tuple in the following order:

State> Next state> Action> Reward

The neural network training stability increases using a random batch of previous data by using the experience replay. Experience replay also means the previous experiences stocking, and the target network uses it for training and calculation of the Q-network and the predicted Q-Value. This neural network uses openAI Gym, which is provided by taxi-v3 environments.

Now, any understanding of Deep Q-Learning   is incomplete without talking about Reinforcement Learning.

What is Reinforcement Learning?

Reinforcement is a subsection of ML. This part of ML is related to the action in which an environmental agent participates in a reward-based system and uses Reinforcement Learning to maximize the rewards. Reinforcement Learning is a different technique from unsupervised learning or supervised learning because it does not require a supervised input/output pair. The number of corrections is also less, so it is a highly efficient technique.

Now, the understanding of reinforcement learning is incomplete without knowing about Markov Decision Process (MDP). MDP is involved with each state that has been presented in the results of the environment, derived from the state previously there. The information which composes both states is gathered and transferred to the decision process. The task of the chosen agent is to maximize the awards. The MDP optimizes the actions and helps construct the optimal policy.

For developing the MDP, you need to follow the Q-Learning Algorithm, which is an extremely important part of data science and machine learning.

What is Q-Learning Algorithm?

The process of Q-Learning is important for understanding the data from scratch. It involves defining the parameters, choosing the actions from the current state and also choosing the actions from the previous state and then developing a Q-table for maximizing the results or output rewards.

The 4 steps that are involved in Q-Learning:

  1. Initializing parameters – The RL (reinforcement learning) model learns the set of actions that the agent requires in the state, environment and time.
  2. Identifying current state – The model stores the prior records for optimal action definition for maximizing the results. For acting in the present state, the state needs to be identified and perform an action combination for it.
  3. Choosing the optimal action set and gaining the relevant experience – A Q-table is generated from the data with a set of specific states and actions, and the weight of this data is calculated for updating the Q-Table to the following step.
  4. Updating Q-table rewards and next state determination – After the relevant experience is gained and agents start getting environmental records. The reward amplitude helps to present the subsequent step.  

In case the Q-table size is huge, then the generation of the model is a time-consuming process. This situation requires Deep Q-learning.

Hopefully, this write-up has provided an outline of Deep Q-Learning and its related concepts. If you wish to learn more about such topics, then keep a tab on the blog section of the E2E Networks website.

Reference Links

https://analyticsindiamag.com/comprehensive-guide-to-deep-q-learning-for-data-science-enthusiasts/

https://medium.com/@jereminuerofficial/a-comprehensive-guide-to-deep-q-learning-8aeed632f52f

This is a decorative image for: GAUDI: A Neural Architect for Immersive 3D Scene Generation
October 13, 2022

GAUDI: A Neural Architect for Immersive 3D Scene Generation

The evolution of artificial intelligence in the past decade has been staggering, and now the focus is shifting towards AI and ML systems to understand and generate 3D spaces. As a result, there has been extensive research on manipulating 3D generative models. In this regard, Apple’s AI and ML scientists have developed GAUDI, a method specifically for this job.

An introduction to GAUDI

The GAUDI 3D immersive technique founders named it after the famous architect Antoni Gaudi. This AI model takes the help of a camera pose decoder, which enables it to guess the possible camera angles of a scene. Hence, the decoder then makes it possible to predict the 3D canvas from almost every angle.

What does GAUDI do?

GAUDI can perform multiple functions –

  • The extensions of these generative models have a tremendous effect on ML and computer vision. Pragmatically, such models are highly useful. They are applied in model-based reinforcement learning and planning world models, SLAM is s, or 3D content creation.
  • Generative modelling for 3D objects has been used for generating scenes using graf, pigan, and gsn, which incorporate a GAN (Generative Adversarial Network). The generator codes radiance fields exclusively. Using the 3D space in the scene along with the camera pose generates the 3D image from that point. This point has a density scalar and RGB value for that specific point in 3D space. This can be done from a 2D camera view. It does this by imposing 3D datasets on those 2D shots. It isolates various objects and scenes and combines them to render a new scene altogether.
  • GAUDI also removes GANs pathologies like mode collapse and improved GAN.
  • GAUDI also uses this to train data on a canonical coordinate system. You can compare it by looking at the trajectory of the scenes.

How is GAUDI applied to the content?

The steps of application for GAUDI have been given below:

  • Each trajectory is created, which consists of a sequence of posed images (These images are from a 3D scene) encoded into a latent representation. This representation which has a radiance field or what we refer to as the 3D scene and the camera path is created in a disentangled way. The results are interpreted as free parameters. The problem is optimized by and formulation of a reconstruction objective.
  • This simple training process is then scaled to trajectories, thousands of them creating a large number of views. The model samples the radiance fields totally from the previous distribution that the model has learned.
  • The scenes are thus synthesized by interpolation within the hidden space.
  • The scaling of 3D scenes generates many scenes that contain thousands of images. During training, there is no issue related to canonical orientation or mode collapse.
  • A novel de-noising optimization technique is used to find hidden representations that collaborate in modelling the camera poses and the radiance field to create multiple datasets with state-of-the-art performance in generating 3D scenes by building a setup that uses images and text.

To conclude, GAUDI has more capabilities and can also be used for sampling various images and video datasets. Furthermore, this will make a foray into AR (augmented reality) and VR (virtual reality). With GAUDI in hand, the sky is only the limit in the field of media creation. So, if you enjoy reading about the latest development in the field of AI and ML, then keep a tab on the blog section of the E2E Networks website.

‍

Reference Links

https://www.researchgate.net/publication/362323995_GAUDI_A_Neural_Architect_for_Immersive_3D_Scene_Generation

https://www.technology.org/2022/07/31/gaudi-a-neural-architect-for-immersive-3d-scene-generation/ 

https://www.patentlyapple.com/2022/08/apple-has-unveiled-gaudi-a-neural-architect-for-immersive-3d-scene-generation.html

Build on the most powerful infrastructure cloud

Get Started for FreeContact Sales
A vector illustration of a tech city using latest cloud technologies & infrastructure