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Rich Text To Image

Rich Text To Image is a Python research project that uses formatting attributes in rich-text prompts to steer text-to-image generation, including token emphasis, object color, localized artistic style and region-specific details. It converts formatted text to JSON and processes that input with a region-based diffusion method.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Rich Text To Image — OpenViST project profile in Image & Video AI.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when a text-to-image prompt needs element-level emphasis, color guidance, localized style or supplementary detail, and you can work with its JSON-based generation workflow.

Who it is for

  • Image creators seeking more control over emphasis, color or local style than plain-text prompts provide.
  • Developers exploring rich-text conditioning in text-to-image workflows.

Documented uses

  • Give selected prompt tokens more weight through font size.
  • Guide the color of a described object using text color.
  • Assign different artistic styles to selected elements.
  • Add supplementary descriptions to selected text with footnotes.
  • Convert formatted prompt text to JSON for the generation pipeline.
Before you choose

Requirements & limits

The repository is public and identifies an MIT license. It links to a Hugging Face demo and an AUTOMATIC1111 WebUI extension; these are distinct access or integration routes from the local Python workflow.

  • The core workflow expects rich-text attributes encoded as JSON before sampling.
  • The documented local setup uses Python 3.8, PyTorch 1.11, Conda and OpenAI CLIP.
  • The README lists Stable Diffusion 1.5, Stable Diffusion XL and ANIMAGINE-XL as supported model options.
  • The project’s controls target specific prompt spans and regions rather than only whole-prompt wording.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Prepare the local generation environment

Set up the documented Python environment for the Rich Text To Image repository.

Before you start

  • Git and Conda
  • Python 3.8 and PyTorch 1.11 are the versions identified in the repository setup instructions

Steps

  1. Clone the repository and change into its directory.

  2. Create the Conda environment using environment.yaml.

  3. Install OpenAI CLIP from its Git repository.

  4. Activate the rich-text Conda environment.

Expected outcome

The documented Conda environment is activated and ready for the next usage stage described by the project.

Guide limitations

  • This covers environment setup only; the repository’s generation commands are a separate stage.
  • The listed versions reflect the README’s tested setup, not a compatibility guarantee.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
16 October 2026
Original website title
songweige/rich-text-to-image · Official homepage title · retained in its source language
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