A Guide to Smaller and Faster SDXL Variants SSD-1B + SDXL Turbo
In this article, we will deep dive into SDXL and its variants (SSD-1B and SDXL Turbo) along with practical applications of these models.
Created on April 24|Last edited on April 28
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Introduction
The ability of AI to generate lifelike images has extensive implications, sparking new possibilities in all kinds of industries ranging from the medical field and entertainment to urban planning and industrial design. Companies can now generate customized visuals tailored to specific audiences, improving the relevance and appeal of their marketing materials. Furthermore, these image-generation models can serve as valuable tools for educational purposes, helping students and researchers visualize complex data or historical scenarios. It is truly fascinating!
AI over the years evolved from just an image enhancement tool, to then becoming an expert at photo generation with the groundbreaking release of Stable Diffusion XL (SDXL) version 1.0 in July 2023, which revolutionized image generation, and then soon after its release, more efficient variants of SDXL 1.0 that further enhanced the model were introduced which are SSD-1B and SDXL Turbo. These variants are considered purified versions of SDXL 1.0, offering much faster text-to-image generation while maintaining high-quality image generation capabilities.
Understanding SDXL 1.0
SDXL version 1.0 is one of the latest AI image generation models capable of creating realistic faces, clear text within images, and improved overall image composition using concise, simple prompts. Like earlier versions, SDXL also supports generating variations of an image through image-to-image prompts, inpainting (modifying selected areas of an image), and outpainting (extending beyond the original image boundaries).
Moreover, SDXL 1.0 is specifically optimized for producing vivid and precise colors, offering enhanced contrast, lighting, and shadow details compared to its predecessor, SDXL 0.8, all while maintaining a native resolution of 1024x1024. Additionally, SDXL 1.0 has the capability to create complex visual concepts that are traditionally challenging for image models, such as hands, text, and compositions with specific spatial arrangements.
SDXL 1.0 is also among the most extensive open-access image models available, featuring a substantial parameter count. It includes a base model with 3.5 billion parameters and a more complex ensemble pipeline that utilizes 6.6 billion parameters. This ensemble approach enhances the final image by using two models to generate and then aggregate the results.
The model employs a mixture-of-experts approach within a latent diffusion framework. Initially, the base model generates preliminary (noisy) latents. These are then refined through a specialized refinement model that performs the final denoising steps. Notably, the base model can function independently as well.
This dual-stage structure ensures robust image generation capabilities without sacrificing speed or requiring extensive computational resources. SDXL 1.0 is designed to be efficient enough to operate on consumer-grade GPUs with at least 8GB of VRAM and is also compatible with commonly used cloud computing instances.
Deep Dive into SSD-1B
As we mentioned earlier, SDXL version 1.0 has later on evolved into smaller and faster variants which are SSD-1B and SDXL Turbo. These models are designed for efficiency with SSD-1B’s main focus likely being enhancing the scale and sophistication of the model, while SDXL Turbo works to enhance the capabilities of its predecessors with a focus on speed and efficiency.
Let’s begin with SSD-1B, which is a highly advanced image generation model that emphasizes depth, complexity, and quality. Built around a massive architecture that includes around one billion parameters as the “1B” in its name suggests, and to be precise, it's actually 1.3 billion which is an immense reduction from the 6.6 billion of the SDXL 1.0. This reduction was achieved by selectively eliminating certain layers while carefully maintaining image quality. It’s also important to mention that SSD-1B has been trained on diverse and extensive datasets that include various styles, contexts, and scenarios to enhance its ability to generate images that meet the specifications of your text prompts.
After removing redundant layers from the SDXL 1.0 blocks, a 50% decrease in the model's size was achieved, along with a remarkable 60% improvement in speed for both inference and fine-tuning processes.
Let us now look at a few images to highlight the differences between SDXL 1.0 and SSD-1B.
Prompt: A man walking his dog.
SDXL 1.0
SSD-1B
As you can see from the images above, the SSD-1B model is more likely to provide close-up shots compared to the SDXL 1.0 model. Besides that, you can see that the SDXL 1.0 model did not put too much effort into trying to make its image detailed because as you can clearly see the man has no face and the dog is just plain black looking like a shadow. On the other hand, the SSD-1B model gave both the dog and the man much more detail.
Let’s explore another prompt to see how they can both handle complex images.
Prompt: Man painting on a canvas mountains and rivers.
SDXL 1.0
SSD-1B
Here, you can see that the SSD-1B model creates images with more vivid colors and higher levels of sophistication, while the SDXL 1.0 model has a different approach which is creating simple and plain images.
Let’s see how the models would handle long and detailed prompts.
Prompt: A 1950s-style diner on Mars, with robots serving space burgers, and red rock landscapes visible through panoramic windows, under a glowing green sky.
SDXL 1.0
SSD-1B
The SSD-1B model gave more focus to the landscape, detailed close-up of the robots, and a better quality image, while SDXL 1.0 focused on the structure of the diner more, but as you can see it struggled a bit with the rest of the prompt.
Prompt: An underwater jazz club, with fish as musicians playing saxophones and drums, surrounded by coral reefs and bubbles, under soft blue lights.
SDXL 1.0
SSD-1B
Prompt: Close-up face of a middle-aged man smiling.
SDXL 1.0
SSD-1B
Exploring SDXL Turbo
SDXL Turbo is also another purified and enhanced version of SDXL 1.0, which employs an innovative distillation method known as Adversarial Diffusion Distillation (ADD), allowing it to produce image outputs in a single step and deliver real-time text-to-image results with excellent visual appeal and sophistication.
Adversarial Diffusion Distillation (ADD) is a sophisticated technique that combines elements from diffusion models and adversarial training. Starting with the forward diffusion process, diffusion models begin with generating images by reversing a controlled process that transforms data into random noise. Once the model has learned how to add noise, it also learns to reverse this noise addition in a process called the reverse diffusion process, essentially learning to generate data from noise by predicting and subtracting out the noise at each step.
In ADD, alongside the diffusion process, an adversarial component is integrated, usually involving a discriminator model. This discriminator is trained to distinguish between real images and the images generated by the diffusion model at various stages of the noise reduction process.
Finally, The diffusion model then acts as a generator that improves over time as it tries to produce images that the discriminator cannot distinguish from real images. The discriminator provides feedback to the generator about the realism of its outputs, helping to refine the quality continuously.
Adversarial Diffusion Distillation (ADD) offers several compelling advantages for real-time image synthesis, including rapid generation process as ADD significantly reduces the number of diffusion steps required to generate an image, all without a loss in output quality. In addition to that, The inclusion of an adversarial component ensures that the images are not only generated quickly but also maintain a high degree of realism, as well as high levels of detail and clearness, matching the quality expected from more traditional, slower generation methods.
Furthermore, The model’s efficiency and the reduced computational demand allow it to be deployed across various platforms, from high-end servers to more constrained devices like mobile phones or embedded systems, along with reducing the computational resources needed for generating images. This leads to lower energy consumption and less strain on hardware, which is essential for sustainability and cost-effectiveness.
To sum it all up, Adversarial Diffusion Distillation (ADD) is an advanced training technique that allows large foundational image diffusion models to efficiently generate samples in just 1-4 steps, while still preserving high image quality.
Practical Applications
Image-generating models like SDXL, SSD-1B, and SDXL Turbo have broad and impactful practical applications across various industries and creative domains. Starting with healthcare, these models revolutionized the medical field by improving medical training as it made generating detailed anatomical images or simulating medical scenarios possible, which can now help in training healthcare professionals without the need for real-life dissections or scenarios.
In the film industry, realistic backgrounds, special effects, or even concept art, can all be generated, significantly reducing the time and cost associated with physical sets or traditional CGI techniques. In fashion design, designers can now create visual representations of their envisioned clothing designs and also showcase them in various styles or on different body types.
Here’s an image of the anatomy of the human heart generated by the SSD-B1 model.
Here’s an image of a creative concept design for a t-shirt generated on a fit adult male by the SSD-1B model.
Here’s an image of a lake with a realistic mountain landscape in the background demonstrating the high level of realism that can be achieved with image generation models making it suitable for use in the film industry.
Now, let’s dive deep into the code implementation of SDXL 1.0 and how to log your results on Weights & Biases. We’ll use the prompt: A hidden library built within an ancient, sprawling tree, books glowing with magic, surrounded by fireflies at twilight.
Step 1: Installation
Installing the necessary Python packages
pip install wandbpip install diffusers --upgradepip install invisible_watermark transformers accelerate safetensors
“pip install wandb” adds Weights & Biases to your environment. “diffusers” is the main library for running diffusion models. The upgrade ensures you have the latest version with all the features and bug fixes. “invisible_watermark”, “transformers”, “accelerate”, “safetensors” are additional libraries that enhance the functionality of diffusers. They support features like model optimization, watermarking, safe tensor operations, and efficient data handling.
Step 2: Importing libraries
Importing necessary libraries
from diffusers import DiffusionPipelineimport torchimport wandb
“DiffusionPipeline” class from the “diffusers” library handles the loading and running of diffusion models. “torch” is PyTorch, the deep learning framework that “diffusers” utilize to operate on tensors and execute model computations. “wandb” is the Weights & Biases library.
Step 3: Model Setup
Logging in and initializing a new Weights & Biases run
wandb.login()wandb.init(project="image_generation", entity="your_wandb_username")
Setting up the model
pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16, use_safetensors=True, variant="fp16")pipe.to("cuda")
This code initializes and configures an image generation model called "stable-diffusion-xl-base-1.0". It sets the model to use 16-bit floating point precision for more efficient computation and enables safe handling of tensors. Then, it moves the model's operations to a GPU (cuda) for faster processing.
Step 4: Generating an Image
Generating the image based on the prompt
prompt = "A hidden library built within an ancient, sprawling tree, books glowing with magic, surrounded by fireflies at twilight."images = pipe(prompt=prompt).images[0]
“prompt” This string defines what the model will generate an image of. Here, it's set on the prompt we mentioned above. “pipe(prompt=prompt).images[0]” line triggers the model to generate an image based on the provided prompt.
Step 5: Logging results to W&B
Log the generated image to Weights & Biases
wandb.log({"generated_images": [wandb.Image(images, caption=prompt)]})
Finish the W&B run
wandb.finish()
Here’s the generated image along with the given prompt logged on Weights & Biases.
Below is the same image generated on SSD-1B and SDXL Turbo.
SDXL Turbo
SSD-1B
As you can see in the images above, even though SDXL Turbo aims to maintain high sampling fidelity, the emphasis on speed might lead to some compromise in the finer details of image quality when compared to slower, more detail-focused models.
Conclusion
Let’s now recap the key points about SSD-1B and SDXL Turbo. SSD-1B, with its streamlined architecture, offers a balance between efficiency and quality, making it ideal for applications requiring faster processing with good image fidelity. Conversely, SDXL Turbo focuses on speed, utilizing Adversarial Diffusion Distillation to achieve real-time image synthesis, suitable for interactive environments.
These innovations highlight the rapid evolution in AI capabilities, enhancing creative processes across various industries such as gaming, media, and digital arts. As AI continues to evolve, there's immense potential for further exploration and adaptation of these models. I would encourage all developers and creators around the world to experiment with these tools to unlock new possibilities in content creation and beyond, pushing the boundaries of what AI can achieve in numerous sectors.
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