What is Refining Rough Collages?
Refining a rough collage is just what it sounds like: you make a quick, rough collage and hand it to an instruction-based image editing model to turn it into one natural-looking image.
You place the objects yourself first, then leave the finishing to the model with something like "clean it up" or "make it a natural photo." That way you get the power of AI while keeping control of the layout in your own hands.
Why "Image Editing" Instead of Subject Transfer?
Flux Kontext, the go-to instruction-based image editing model, was originally built to take "one image + a text prompt" and edit that image. In other words, it simply had no feature for "look at a reference image and put the same subject into a different image."
On the other hand, it was extremely good at editing and redrawing the input image, and could do far more complex things than just changing colors or removing objects.
So I thought:
What if I make a rough collage and tell it "make it a natural photo" or "make it look like one seamless picture"? Couldn't I handle subject transfer as an image editing task?
And it actually worked. People in the community have been having fun with it too.
Advantages of Refining Rough Collages
The latest instruction-based image editing models accept multiple input images, but refining a rough collage still has a few advantages.
Positions Stay Exactly Where You Put Them
Instead of writing "put image 1 here" in text, you feed in an image where image 1 is already sitting there. The model takes that placement as given and only has to make it look natural.
Describing a layout in text is pretty hard, so this is a clear strength.
No Limit on the Number of References
Subject transfer methods often limit how many reference images you can input, because of how the architecture works. And even when you can input many, inference takes longer.
With a rough collage, taken to the extreme, you can paste in as many objects as fit on the canvas. It all becomes a single editing task: "tidy all of this up at once."
About Dedicated LoRAs
Several LoRAs have been made specifically to make collage refining more reliable. I'll just list them here.