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Multi-Image Workspace

mkdocs/docs/en/image/multiimage-workspace.md

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Multi-Image Workspace

Route: /#/image/multiimage

Use this workspace when you want to generate one new image from multiple input images plus a prompt.

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 need multiple input images to participate in the same generation

Typical use cases

  • combining character, outfit, and scene references into one generated result
  • using multiple references to constrain composition, subject relationships, or style
  • comparing prompt versions against the same ordered set of input images
  • comparing image models against the same ordered set of input images

If you only have text, use Text-to-Image Workspace. If you only have one input image, use Image-to-Image Workspace.

If you only want the fastest start

  1. upload at least two input images
  2. drag the cards to define the meaning of image 1 / image 2 / image 3
  3. remove the wrong image with the top-right X
  4. write the prompt so it explicitly refers to the ordered images
  5. run one left-side analysis or optimization
  6. keep one multi-image-capable model fixed on the right and compare original / workspace / vN

What the left side edits

The left side edits the multi-image prompt itself.

You can think about the page like this:

  • upper-left: the original multi-image prompt
  • middle-left: the input image card area
  • lower-left: the editable workspace and version chain
  • left side uses a text model, not an image model

The goal of left-side analysis, optimization, and iteration is to make the relationship between the images clearer, not just to restate what each image looks like.

What matters most in the input image area

This workspace is not just “image-to-image, but more files.”

The important extra rule is: order carries meaning.

In the current UI:

  • you need at least two images before multi-image generation can start
  • each image appears as a card
  • the footer drag handle changes order
  • the top-right X removes a mistaken image
  • your prompt should usually refer to image 1 / image 2 / image 3

If you change the order, update the prompt references too.

What the right side tests

The right side tests:

  • one prompt version
  • the same ordered set of input images
  • one multi-image-capable image model
  • the real generated image

So the most important baseline in this workspace is:

  • keep the image set fixed
  • keep the image order fixed
  • then compare prompt versions or model differences
  1. upload at least two images
  2. drag them into a clear semantic order
  3. write the prompt using image 1 / image 2 / image 3
  4. optimize or analyze it once on the left
  5. keep one image model fixed and compare original / workspace / vN
  6. select the more reliable prompt version
  7. then keep that version fixed and compare image models

Common mistakes

The biggest problem is usually not model quality. It is a broken comparison baseline.

If you change all of these at once:

  • image order
  • prompt version
  • image model

you will have a hard time telling what actually caused the result change.

A more reliable method is:

  • first keep the same ordered image set and compare prompt versions
  • then keep that prompt version and image set fixed while comparing image models

What the result area usually shows

The current result cards usually show:

  • the generated image
  • any extra text returned by the model
  • output image size and MIME type
  • token metadata
  • inference timing