Text-to-image model

As explained in Deep Learning with R, Third Edition

A text-to-image model is a generative model that produces images corresponding to textual descriptions.

A text-to-image model generates an image matching a written description. Diffusion models begin with pure noise and repeatedly denoise it while using a text representation to guide the result. section 17.1.1 section 17.4

Figure 17.2 Language-guided image generation

Follow the mapping from natural-language prompts into a visual latent space, then back into generated images. section 17.1.1

A typical system combines a pretrained text encoder with a diffusion model trained on prompt–image pairs. The denoiser receives both noisy images and text embeddings, allowing an unseen prompt to guide generation from pure noise. section 17.3

Generating an image with Stable Diffusion

In R with KerasHub, create a TextToImage task from the stable_diffusion_3_medium preset and generate an image from “A NASA astronaut riding an origami elephant in New York City.” Negative prompts can steer away from features such as blue color, while num_steps controls the diffusion process. section 17.3 section 17.3

How Deep Learning with R, Third Edition covers it

How deep it goes

Explains latent spaces, text conditioning, denoising inputs, and pretrained use, but does not train a capable model from scratch.

Examples

Demonstrates generated flowers, an astronaut riding an origami elephant, negative prompts, and varying diffusion-step counts.

What it assumes

Assumes basic familiarity with diffusion denoising and text encoders while introducing prompt conditioning through a high-level generation task.

What you can do after

Operate/configure a pretrained Stable Diffusion model to generate and steer images with prompts, negative prompts, and diffusion steps.

How it connects

Connects diffusion models with text encoders, text embeddings, latent spaces, VAEs, GANs, and Stable Diffusion.

liveBookmanning.com

Read Deep Learning with R, Third Edition on liveBook Get the book at manning.com

Also explained in

• 

  liveBookmanning.com
  Build a Text-to-Image Generator (from Scratch)A text-to-image model is a machine-learning model that generates images from textual descriptions.
• 

  liveBookmanning.com
  Learn Generative AI with PyTorchThe generative task of producing an image conditioned on a text prompt.
• 

  liveBookmanning.com
  Introduction to Generative AI, Second EditionA text-to-image model is a generative model that creates images from textual descriptions.
• 

  liveBookmanning.com
  The AI Pocket BookA generative AI technique that produces images conditioned on textual descriptions.
• 

  liveBookmanning.com
  Grokking AI Algorithms, Second EditionText-to-image generation is the process of synthesizing an image from a textual description.

Compare how 6 books explain text-to-image model →

Related concepts

• diffusion modeluses · A diffusion model is a generative model that learns to produce data by reversing a process that progressively adds noise to data.
• sequence predictionkind of · Sequence prediction is the task of predicting or generating elements of an ordered sequence from preceding elements or contextual input.
• multimodalkind of · A multimodal model is a model that processes, generates, or relates information represented in two or more data modalities, such as text, images, audio, or video.
• Stable Diffusionincludes · Stable Diffusion is a family of latent diffusion models that generates and edits images from text and other conditioning inputs.
• conversational aikind of · Conversational AI is artificial intelligence that enables software systems to interact with users through natural-language dialogue.

This explainer was generated by Manning's knowledge graph from the text of Deep Learning with R, Third Edition. The section links point to the parts of the book it draws on.