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

November 2, 2023

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 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.


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, 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("")

@app.route('/generate-image', methods=['POST'])
def generate_image():
    	# 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 =

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

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

if __name__ == '__main__':'', port=5000)

You can now test this using: 


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

# 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

# Define environment variable

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

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:

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 =, json=input_text)

# Check the response
if response.status_code == 200:
	print('Image generated successfully!')
	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.


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.


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.

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