How does Dall-e-2 make variations?

Not let us put together all the components to understand how dalle-2 works

1. The CLIP text encoder encodes the text embedding by analyzing the text description the user provides
2. The Diffusion prior uses the text embedding of the CLIP encoder and converts it to CLIP image encoding. This image encoding captures the key ideas from the user prompt.
3. The image decoder generates the image based on CLIP image embedding. It utilizes CLIP text embedding for conditional generation. The decoder introduces some randomness during the creation process. This randomness is what allows DALL-E 2 to generate multiple variations that all relate to your original prompt but have slight differences

Another feature that highlights DALL·E 2’s awareness of image content and style is its capacity to create variations of a given image. With the use of this feature, users can request variations for an original image

The model can create changes in response to instructions from the user by preserving the original’s basic features and at the same time making precise changes. This is how DALL·E 2 does it:

1. Decoding the original Image’s Contents: Identify key elements of the image using the image encoder
2. Understanding image context: It then aims to find the best textual description of the image. It utilizes CLIP and the chat got the model to do so.
3. Combining textual Prompts: Since it now has the textual as well as visual understanding of the image it aims to incorporate the changes proposed by the user in the prompt. It first understands the difference between the current textual description and the changes provided by the user. It then uses this difference vector to modify specific elements of the image embeddings. It does so by aiming to keep the essence and context of the original image intact. This means that while certain aspects may change, the overall theme is preserved.
4. Re-synthesis of the Image: The model then synthesizes new images that reflect the original’s context and any new directions provided by the user’s prompts.

Recent advancements in text-based image generation models have captured the collective imagination, seamlessly blending linguistic expression with visual creativity. Models such as Midjourney, Stable Diffusion, and DALL-E have gained popularity, with DALL-E 2 emerging as a notable successor to its predecessor. This open-source model, developed by OpenAI, leverages the diffusion model for image generation, departing from the limitations of DALL-E 1. In this tutorial, we will deep dive into the DALL-E 2’s architecture, and how does it works?