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What are Diffusion Models?

Generative models are a class of machine learning models that can generate new data based on training data. Other generative models include Generative adversarial networks (GANs), Variational Autoencoders (VAEs), and Flow-based models. Each can produce high-quality images, but they all have limitations that make them inferior to diffusion models.
At a high level, Diffusion models work by destroying training data by adding noise and then learn to recover the data by reversing this noising process. In Other words, Diffusion models can generate coherent images from noise.
Diffusion models train by adding noise to images, which the model then learns how to remove. The model then applies this denoising process to random seeds to generate realistic images.
Combined with text-to-image guidance, these models can be used to create a near-infinite variety of images from text alone by conditioning the image generation process. Inputs from embeddings like CLIP can guide the seeds to provide powerful text-to-image capabilities.
Diffusion models can complete various tasks, including image generation, image denoising, inpainting, outpainting, and bit diffusion.
Popular diffusion models include Open AI’s Dall-E 2, Google’s Imagen, and Stability AI's Stable Diffusion.
Dall-E 2: Dall-E 2 revealed in April 2022, generated even more realistic images at higher resolutions than the original Dall-E. As of September 28, 2022 Dall-E 2 is open to the public on the OpenAI website, with a limited number of free images and additional images available for purchase.
Imagen : is Google’s May 2022, version of a text-to-image diffusion model, which is not available to the public.
Stable Diffusion :  In August 2022, Stability AI released Stable Diffusion, an open-source Diffusion model similar to Dall-E 2 and Imagen. Stability AI’s released open source code and model weights, opening up the models to the entire AI community. Stable Diffusion was trained on an open dataset, using the 2 billion English label subset of the CLIP-filtered image-text pairs open dataset LAION 5b, a general crawl of the internet created by the German charity LAION.
Midjourney : is another diffusion model released in July 2022 and available via API and a discord bot.
Simply put, Diffusion models are generative tools that enable users to create almost any image they can imagine.

Diffusion Models: Why Are They Important?

Diffusion models represent that zenith of generative capabilities today. However, these models stand on the shoulders of giants, owing their success to over a decade of advancements in machine learning techniques, the widespread availability of massive amounts of image data, and improved hardware.
For some context, below is a brief outline of significant machine learning developments.
In 2009 at CVPR, the seminal Imagenet paper and dataset were released, which contained over 14 million hand-annotated images. This dataset was massive at the time, and it remains relevant to researchers and businesses building models today.
In 2014, GANs were introduced by Ian Goodfellow, establishing powerful generative capabilities for machine learning models.
In 2018 LLM’s hit the scene with the original GPT release, followed shortly by its successors GPT-2, and the current GPT-3, which have text generation capabilities.
In 2020, NeRFs allowed the world to produce 3D objects from a series of images, and known camera poses.
Over the past few years, Diffusion models have continued this evolution, giving us even more powerful generative capabilities.
What about diffusion models makes them so strikingly different from their predecessors? The most apparent answer is their ability to generate highly realistic imagery and match the distribution of real images better than GANs. Also, diffusion models are more stable than GANs, which are subject to mode collapse, where they only represent a few modes of the true distribution of data after training. This mode collapse means that in the extreme case, only a single image would be returned for any prompt, though the issue is not quite as extreme in practice. Diffusion models avoid the problem as the diffusion process smooths out the distribution, resulting in diffusion models having more diversity in imagery than GANs.
Diffusion models also can be conditioned on a wide variety of inputs, such as text for text-to-image generation, bounding boxes for layout-to-image generation, masked images for inpainting, and lower-resolution images for super-resolution.
The applications for diffusion models are vast, and the practical uses of these models are still evolving. These models will greatly impact Retail and eCommerce, Entertainment, Social Media, AR/VR, Marketing, and more.

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What are Diffusion Models?

Generative models are a class of machine learning models that can generate new data based on training data. Other generative models include Generative adversarial networks (GANs), Variational Autoencoders (VAEs), and Flow-based models. Each can produce high-quality images, but they all have limitations that make them inferior to diffusion models.
At a high level, Diffusion models work by destroying training data by adding noise and then learn to recover the data by reversing this noising process. In Other words, Diffusion models can generate coherent images from noise.
Diffusion models train by adding noise to images, which the model then learns how to remove. The model then applies this denoising process to random seeds to generate realistic images.
Combined with text-to-image guidance, these models can be used to create a near-infinite variety of images from text alone by conditioning the image generation process. Inputs from embeddings like CLIP can guide the seeds to provide powerful text-to-image capabilities.
Diffusion models can complete various tasks, including image generation, image denoising, inpainting, outpainting, and bit diffusion.
Popular diffusion models include Open AI’s Dall-E 2, Google’s Imagen, and Stability AI's Stable Diffusion.
Dall-E 2: Dall-E 2 revealed in April 2022, generated even more realistic images at higher resolutions than the original Dall-E. As of September 28, 2022 Dall-E 2 is open to the public on the OpenAI website, with a limited number of free images and additional images available for purchase.
Imagen : is Google’s May 2022, version of a text-to-image diffusion model, which is not available to the public.
Stable Diffusion :  In August 2022, Stability AI released Stable Diffusion, an open-source Diffusion model similar to Dall-E 2 and Imagen. Stability AI’s released open source code and model weights, opening up the models to the entire AI community. Stable Diffusion was trained on an open dataset, using the 2 billion English label subset of the CLIP-filtered image-text pairs open dataset LAION 5b, a general crawl of the internet created by the German charity LAION.
Midjourney : is another diffusion model released in July 2022 and available via API and a discord bot.
Simply put, Diffusion models are generative tools that enable users to create almost any image they can imagine.

Diffusion Models: Why Are They Important?

Diffusion models represent that zenith of generative capabilities today. However, these models stand on the shoulders of giants, owing their success to over a decade of advancements in machine learning techniques, the widespread availability of massive amounts of image data, and improved hardware.
For some context, below is a brief outline of significant machine learning developments.
In 2009 at CVPR, the seminal Imagenet paper and dataset were released, which contained over 14 million hand-annotated images. This dataset was massive at the time, and it remains relevant to researchers and businesses building models today.
In 2014, GANs were introduced by Ian Goodfellow, establishing powerful generative capabilities for machine learning models.
In 2018 LLM’s hit the scene with the original GPT release, followed shortly by its successors GPT-2, and the current GPT-3, which have text generation capabilities.
In 2020, NeRFs allowed the world to produce 3D objects from a series of images, and known camera poses.
Over the past few years, Diffusion models have continued this evolution, giving us even more powerful generative capabilities.
What about diffusion models makes them so strikingly different from their predecessors? The most apparent answer is their ability to generate highly realistic imagery and match the distribution of real images better than GANs. Also, diffusion models are more stable than GANs, which are subject to mode collapse, where they only represent a few modes of the true distribution of data after training. This mode collapse means that in the extreme case, only a single image would be returned for any prompt, though the issue is not quite as extreme in practice. Diffusion models avoid the problem as the diffusion process smooths out the distribution, resulting in diffusion models having more diversity in imagery than GANs.
Diffusion models also can be conditioned on a wide variety of inputs, such as text for text-to-image generation, bounding boxes for layout-to-image generation, masked images for inpainting, and lower-resolution images for super-resolution.
The applications for diffusion models are vast, and the practical uses of these models are still evolving. These models will greatly impact Retail and eCommerce, Entertainment, Social Media, AR/VR, Marketing, and more.