From the first look gallery of images produced by Imagen, its strength lies in creating photorealistic outputs. You can head to the Imagen research page to see the images for yourself.
Imagen: Photorealistic Image Generation with AI
The most important detail here is that such an encoder ensures that the text encoding understands how the words within the caption relate to one another. the model can then be "split in half", and we can start from randomly sampled Gaussian noise which we use the Diffusion Model to gradually denoise in order to generate an image. Google's research into AI text-to-image systems has found that using larger language models is the key to creating higher-quality images. Plus images that more closely align with the text description.These models must therefore overcome challenges like capturing spatial relationships, understanding cardinality, and properly interpreting how words in the description relate to one another. the image-generation model receives the text encoding as an input, which has the effect of telling the model what is in the caption so it can create a corresponding image. The output is a small image that reflects visually the caption we input to the text encoder.
Imagen: Conditional Image Generation with Upsampling
The overall process is basically the same as the base model; except, instead of conditioning on just the caption encoding, we also condition on the smaller image which we are upsampling. It gives Google a much-needed chance to get feedback from users and fix any problems with the model before it goes mainstream.The architecture of the neural model itself has yet to be discussed or established.
The only relevant restriction on the neural model is that its input and output dimensionalities must be the same. the central question being addressed by this choice is whether or not a massive language model trained on a massive dataset independent of the task of image generation is a worthwhile trade-off for a non-specialized text encoder. he output vectors from the T5 text encoder are pooled and added into the timestep embedding from above. This process is visualized in the below image.we'll look at the overarching architecture of Imagen with a high-level explanation of how it works, and then inspect each component more thoroughly in the subsections below.
The text encodings are then passed into an image generation Diffusion Model, which starts with Gaussian noise and then gradually removes noise to generate a novel image which reflects the semantic information within the caption. The output of this model is a 64x64 pixel image.The text-encoder generates a useful representation of the caption input to Imagen, but we still need to devise a method to generate an image that uses this representation. To do this, Imagen uses a Diffusion Model.At different resolutions in the U-Net, this vector is projected to having c components.
It spits out a random image that looks like it could belong to the training dataset. Recall that our goal is to create images that encapsulate the semantic information of the caption we input into Imagen. The central intuition in using T5 is that extremely large language models, by virtue of their sheer size alone, may still learn useful representations despite the fact that they are not explicitly trained with any text/image task in mind. In addition to the extremely large size of some language models. There's an interesting thread on Parti by Jason Baldridge here, and a short overview here by Google. I wonder how well the 20B model will do on text characters inside images compared to the other diffusion-based approaches like Imagen and DALL-E 2.
Creating Ethical Images: Imagen's AI Vision
The contrast between the images these systems create and the thorny ethical issues is stark for Julie Carpenter, a research scientist and fellow in the Ethics and Emerging Sciences Group at California Polytechnic State University, San Luis Obispo. City Dreamer empowers users to bring their architectural visions to life by utilizing the AI capabilities of the tool. It presents an extraordinary opportunity to create virtual worlds reminiscent of popular games such as Minecraft or SimCity.
Google has released Imagen through an app called AI Test Kitchen, and if you haven't heard of it before, it's worth checking out. This is where Google likes to test different AI projects before they are released to the public. We'll cover how to get access to Imagen later on.The result is 1024 x 1024 pixel image that visually reflects the semantics within our caption. At the highest level.
Imagen is all there is to it! For a slightly more detailed look at each of Imagen's big components Artists in particular have criticized AI companies for not gaining consent from the owner of an image or artwork before using it to train its AI model.A text-to-image model takes in a short textual description of a scene and then generates an image which reflects the described scene. Google’s research into AI text-to-image systems has found that utilizing larger language models is the key to creating higher-quality images that closely align with the given textual description.