![]() GLIDE leverages its own implementation of the ADM (ADM-G for "guided") to perform its image generation. As the name implies, this model helps upscale the sample to raise the interpretability for machine and human actors. This text-encoding transformer uses 24 residual blocks of width 2048, and has roughly 1.2 billion parameters.įinally, the upsampler diffusion model also has roughly 1.5 billion parameters, and differs from the base model in that its text encoder is smaller, having a width of 1024 and 384 base channels. In practice, this allows for the ADM model to use its learned understanding of the inputted words and their associated images to generate an image from novel combinations of similar text tokens in a unique and photorealistic manner. Second, the last layer of the token embeddings - a sequence of feature vectors - is projected separately to the dimensions for every attention layer in the ADM model and concatenated to the attention context of each. First, the final token embedding is used in place of the class embedding for the ADM model. ![]() The transformer's output then has two uses. ![]() These tokens are then fed into a transformer model. To condition on the text, the text is first encoded into a sequence of K tokens. This is done by training the transformer model on a sufficiently large dataset (the same used in the DALL-E project) consisting of images and their corresponding captions. Unlike Dhariwal and Nichol, however, the GLIDE team wanted to be able to influence the image generation process more directly, so they paired the visual model with an attention-enabled transformer.īy processing the text input prompts, GLIDE enables some measure of control over the output of the image generation process. This results in around 2.3 billion parameters for the ImageNet model. The GLIDE authors used the same ImageNet 64 × 64 model as Dhariwal and Nichol for the ADM, but opted to increase the model width to 512 channels. The roots of the GLIDE project lie in another paper released in 2021, which showed that ADM approaches could outperform contemporarily popular, state-of-the-art generative models in terms of image sample quality. The first two components interact with one another to guide the image generation process to accurately reflect the text prompt, and the latter is necessary to make the images we produce more easy to interpret. The GLIDE architecture can be boiled down to three parts: an Ablated Diffusion Model (ADM) trained to generate a 64 x 64 image, a text model (transformer) that influences that image generation through a text prompt, and an upsampling model that takes our small 64 x 64 images to a more interpretable 256 x 256 pixels. We will first break down how GLIDE's diffusion model based framework runs under the hood, then walk through a code demo for running GLIDE on a Gradient Notebook. In this article, we will examine OpenAI's GLIDE, one of the many exciting projects working towards generating and editing photorealistic images using text-guided diffusion models. This is largely why text to visual models like CLIP tend to pop up so frequently in research on this subject. Interpreting the meaning of a string and translating that message to an image is a task that even humans can struggle with. Thus, the goal of generating pieces so photorealistic that they can fool both AI and human actors is an interesting challenge, and doing so accurately and generally to meet the potential scope of a random text prompt is even more difficult. The problem with this being the uncanny valley: the images generated with these models are accurate to the text prompt in terms of meaning, but they often do not resemble reality. ![]() PixRay can also be used to create colorful and detailed, albeit slightly cartoonish and surreal, art. Earlier this year, we showcased the PixRay project's PixelDrawer to demonstrate how, with nothing but a text prompt, we can generate a unique and creative piece that is accurate to the provided description.
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