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Text-to-Image Workspace

Route: /#/image/text2image

Use this workspace when you want to generate images from text only, with no reference image.

First-time rule of thumb

If both are true, this is usually the right page:

  1. your final output is an image, not text
  2. you only have a text prompt, with no input image

Typical use cases

  • poster, illustration, cover, or character-concept prompts
  • comparing how original / workspace / vN changes image output
  • comparing the same prompt on different image models

If you already have an input image, use Image-to-Image Workspace.

When the reference-image actions are useful

Even though the main mode here is “text only,” recent releases also connected reference-image-assisted prompt work into this workspace.

Near the left-side header, the current UI can expose two reference-image actions:

  • Replicate: ignore the current prompt and infer a reusable prompt plus variables from the reference image
  • Style Learn: keep your current subject goal, but learn style, composition, and color language from the image

These actions are especially useful when:

  • you already have a finished or style reference image and want to turn it back into reusable prompt material
  • you already know what subject you want, but want to borrow visual style without switching to image-to-image

What must be configured before using them

Reference-image actions are not normal right-side generation. They depend on a separate image recognition model.

So if you want to use:

  • reference-image replication
  • style learning
  • variable extraction from images

you need to configure an image-recognition-capable model separately in model management.

If that model is not configured, normal text-to-image generation can still work, but the reference-image actions will not be fully available.

If you only want the fastest start

  1. write the image prompt on the left
  2. run one left-side analysis or optimization
  3. keep one image model fixed on the right
  4. compare original / workspace / vN through real images

What the left side edits

The left side edits the image prompt itself.

The left side uses a text model, not an image model.

What the right side tests

The right side tests:

  • one prompt version
  • one image model
  • the real generated image

If you use the reference-image actions, you can think about the workflow as three different steps:

  • reference-image actions: pull prompt clues from the image
  • left-side analysis / optimization: rewrite those clues into a cleaner prompt
  • right-side testing / comparison: check whether the real images now match the goal
  1. write the original image prompt
  2. optimize or analyze it once on the left
  3. keep one image model fixed and compare original / workspace / vN
  4. select the better prompt version
  5. then keep that version fixed and compare image models

If your starting point is a reference image, a better sequence is:

  1. upload the reference image and choose Replicate or Style Learn
  2. apply the generated prompt or extracted variables back into the current prompt
  3. run one left-side analysis or optimization pass
  4. then compare real image results on the right