Diffusion Model: The Brain Behind Multimodal LLMs

Hey AI,

Create an underwater scene with glowing animals and plants, surrounded by old shipwrecks, fish, and coral.

🤖 (Diffusion Models): For sure! Here you go:

Fig: AI-Generated Image

Such instantaneous magic is now a reality and is in working – all thanks to the various kinds of Generative AI models!

In 2024, organizations are increasingly harnessing the capabilities of GenAI models such as Generative Adversarial Networks (GANs), Transformer Models, and Variational Autoencoders to elevate their productivity across multiple use cases while streamlining resources, time, and expenses.

However, the phenomenon you observed above is because of the Diffusion Model. What’s that?

Answer: In this blog, I’ll help you get into the depths of the Diffusion Model, its benefits, and intricacies. You will also gain insights into various Diffusion Models, empowering you to apply them in solving real-world challenges.

So, let’s get started!

What is a Diffusion Model?

The diffusion model is a technique that stands at the forefront of the latest advancements in Multimodal Large Language Models (MLLM). It is utilized to produce lifelike images from random noise, mimicking real-world visuals with remarkable accuracy.

Generating high-quality images from text used to be hard, needing deep understanding and skill. But diffusion models have changed this, making the process simpler with minimal text input.

Fact: Did you know?

GANs use a generator and discriminator to create images, whereas the diffusion model uses random 
noise as input to achieve similar results.

Using diffusion models, you can generate images in two ways:

• Unconditional image generation: Here, the model transforms noise into random data samples without any specific direction or control. The model has the freedom to generate images of any kind without being guided by external information. This method is typically used to generate artistic and creative images.
• Conditional image generation: This method guides the model with text or labels to create specific types of images, often used for generating realistic and high-resolution images based on given text prompts.

Let me clarify by providing a better understanding of its potential.

Benefits of Diffusion Models

Diffusion models have numerous advantages compared to traditional generative models like GANs and VAEs. Basically, these benefits arise from the unique data generation and reverse diffusion process of the diffusion model. Here are some of them:

1. Good Image Quality: Diffusion models create highly detailed and realistic images by understanding data patterns and using reverse diffusion, resulting in fewer errors and more organized structures compared to conventional GANs.

2. Stable Training: They are more reliable than GANs in training because they use stable likelihood-based methods, avoiding issues like mode collapse that GANs face due to complex learning rate adjustments.

3. Handling Data Privacy Issue: They are a good choice to handle data privacy issues, as they use special techniques that allow us to generate synthetic data without revealing the real information from the original data.

4. Handling Missing Data: They can easily handle the missing data concerns by generating new images that fill gaps through reverse diffusion. This allows the model to produce complete output images even if parts of the input images are missing.

5. Robustness to Overfitting: They are better at avoiding overfitting than GANs. They achieve this by using likelihood-based training and reverse diffusion, which stops them from memorizing specific examples. Instead, they generate a wider range of realistic images.

6. Interpretable Latent Space: They offer more latent spaces compared to GANs. Adding latent variables helps to capture variation and sample diversity. Reverse diffusion simplifies data distribution, enabling meaningful representation, insights, and precise control over generated images.

7. Scalability to High-Dimensional Data: They can easily handle high-resolution images as they use a step-by-step diffusion process, which helps them to efficiently generate complex, detailed images without struggling with the large size of the data.

Curious about how all of this happens?

Well, next, you’ll dive into the workings of diffusion models and how they generate high-quality images.

Working of Diffusion Based Image Generation Model

Here’s the core procedure of how every diffusion model works:

• First, we start by disrupting the structure of the original image by adding noise.
• Then, a neural network helps us restore the original image by eliminating the noise at each step.
• Through multiple iterations with high-quality data, the neural network learns the underlying patterns of the original data.
• Eventually, we can generate new images by starting with just noise and relying on the trained network to recreate images similar to those in the original dataset.

Now, let’s understand each step without diving into the mathematical details:

Fig: Forward and Backward/Reverse Diffusion process

1.Forward Diffusion Process (Right to Left):

• Input: Represented by X₀ that is Original Image.
• Noise Schedule: We start with an original image (X₀) and gradually introduce noise to it in a systematic way. This noise is added in a series of steps, each time using scaled Gaussian noise.
• Noising Function: Shown as a gray box labeled Noising Function with an arrow entering from the Original Data and exiting as Noised Data. This process continues for a specified number of time steps, denoted as T, resulting in a final image X_T. Each image at a particular time step (X_t) is created by adding noise to the previous image (X_t-1).
• During this process no specific model is used. Instead, the focus is on gradually transforming the original image into a noisy version.
• Output: At the end of the forward diffusion stage, the final image X_T represents a noisy version of the original image. This noise follows an Isotropic Gaussian distribution, meaning it has a standard normal distribution with consistent variance across all dimensions. Essentially, the data distribution is transformed into a Gaussian distribution.

2. Reverse Diffusion Process (Left to Right):

• Input: Represented by X_T labeled as Noised Data.
• In this stage we reverse the forward process by removing the noise added earlier. We do this iteratively using a Markov chain approach, employing a neural network model.
• Timestep Embedding: The model’s task is to predict the noise (ϵ) added to the image at step t−1, given the timestep t and the noisy image X_t.
• Conditioning Input (Optional): It is text prompt-like thing that can optional as per requirement.
• Denoising Function: We feed the noisy image X_t to the model, which then predicts the noise (ϵ) that was added to the image at previous step X_t−1 during the forward pass.
• Output: Represented by a X₀ and labeled as Output (closer to the original data).

Another advantage of the iterative nature of this diffusion process is that we can conduct supervised training at each step. In diffusion models the popular architecture used is the UNet (with attention) trained with the Mean Squared Error (MSE) loss function in a supervised manner.

During each step, the amount of noise added to an image is regulated by a variance scheduler or simply a scheduler. The scheduler’s role is to determine the appropriate level of noise addition, ensuring that the image at the end of the forward process denoted as X_T, conforms to an isotropic Gaussian distribution.

We can also use a linear scheduler, meaning that the noise added at each step increases linearly. The number of steps denoted as T was set to T = 1000. Therefore, with Gaussian noise, the model required 1000 iterations to produce the result reflecting the low sampling rate issue. However, recently new techniques and different schedulers have enabled researchers to generate artistic images in as few as T = 4 steps.

Try this code to generate an image based on a given text prompt using the Stable Diffusion XL model:

pipe = StableDiffusionXLPipeline.from_pretrained("segmind/SSD-1B", torch_dtype=torch.float16,
use_safetensors=True, variant="fp16")

pipe.to("cuda")

prompt = " A lonely women standing on a deserted beach at sunrise, silhouette against 
the orange sky, waves crashing at her feet " # Your prompt here

neg_prompt = ""chaotic, unclear, poor quality" # Negative prompt here

image = pipe(prompt=prompt, negative_prompt=neg_prompt).images[0]

Got the gist and flavor of its functionality? Great!

On we move to know about its applications across various sectors. 😀

Applications of Diffusion Models

Diffusion models can be used in multimodal large language models in various ways directly or indirectly. Some of its applications across industries are listed below:

1. Text to Video: By expanding on the idea behind text-to-image conversion, we can apply diffusion models to produce videos based on narratives, songs, poems, and other textual content.

Industry use case: Software companies can use diffusion models to create tutorial videos for their products, improving user onboarding and satisfaction.

2. Text to 3D: By applying concepts like those used in text-to-image conversion, we can create three-dimensional representations based on textual descriptions.

Industry Use Case: In the banking sector, diffusion techniques can be applied to generate 3D visualizations of financial data and trends. Thus, helping analysts for informed decision-making.

3. Text to Motion: This is a new approach using diffusion models to create realistic human movements in computer animation. It aims to accurately capture the nuances of human motion, which is challenging due to the complexity of human movements. Efficient training of this model can lead to high-quality results, surpassing other methods for translating text or actions into movement.

Industry Use Case: Healthcare organizations can use this for rehabilitation programs, where realistic movements are essential for faster patient recovery.

4. Image 2 Image: This is a method to change images based on text prompts, creating new images with different styles or appearances. It converts an existing image into a new one by applying specified changes described in the text. Here’s an example:

Fig: Finished the unfinished image

Industry use case: Quality control departments in the manufacturing sector can use this to enhance images of defective parts for better analysis and identification.

We’re nearing the conclusion of this blog, but before we wrap up, I encourage you to explore some diffusion models and leverage their capabilities to enhance your operations.

Popular Diffusion models used in Multimodal LLMs

Here are some of the popular Diffusion-based Image Generation models that have gained attention in recent months:

1. Sora by OpenAI: This revolutionizes video production, generating media from text input with exceptional precision. It operates on diffusion principles, refining images from noise over multiple iterations, offering single or extended video production.

2. DALL-E 3 by OpenAI: This enhances image generation from text, utilizing CLIP for robust image learning. Its two-stage model preserves semantics and style while modifying non-essential details, enabling precise image generation.

3. GLIDE by OpenAI: This is a text-conditional image synthesis model, producing high-quality images from textual prompts. It employs a 3.5 billion parameter diffusion model, offering editing capabilities for complex prompts.

4. Imagen by Google: This is a text-to-image diffusion model, using large pretrained frozen text encoders and diffusion models for image synthesis. It enhances guidance weights without classifiers, improving photorealism and text alignment.

5. Gemini Pro Vision: This is a leading multimodal model that helps generate text, images, and videos. It can help with things like understanding and summarizing content from infographics, documents, and photos.

6. Stable Diffusion by StabilityAI: This uses diffusion models and latent space efficiency for image generation. It employs Latent Diffusion Models (LDMs) trained in a compressed latent space, offering state-of-the-art results with reduced computational demands.

7. Midjourney: This is an AI-driven text-to-image diffusion model known for its accurate and imaginative outputs. It allows extensive control over image generation, enabling adjustments via prompt parameters for creative manipulation.

Try using these prompts to generate images using different diffusion models:

Hence, keeping abreast of the latest diffusion model advancements is essential for organizational success. By maximizing the potential of these models, productivity can be enhanced, paving the way for innovative opportunities in any kind of creative development process.

For deeper insights into how Generative AI can help you build robust products and scale your business, reach out to us at Nitor Infotech.

Until next time, happy prompting!