Upscaling#
The Upscaling tab (3) enlarges an image in two stages: an upscale model (a super-resolution network) multiplies the pixels, then a tiled generative pass with a tile control layer adds the detail the network cannot invent. The generative pass works with SD 1.5 and SDXL main models.

Steps#
- Drop or pick the source image (JPG, PNG or WebP up to 100 MB), or send one from the gallery or the Edit tab (Send to Enhance).
- Pick the upscale model. The catalogue ships several: RealESRGAN x4 and x2 for photos, ESRGAN SRx4 for general images, SwinIR for fine texture, AnimeSharp for drawings and line art.
- Set the scale (2x or 4x). The output size is the input multiplied by the scale; the tab warns when it exceeds the maximum dimension.
- Pick the main model (SD 1.5 or SDXL) and its tile ControlNet; the tab lists what is missing and links to the model manager.
- Optional prompt: describe the image, not the change. A short, accurate caption helps the detail pass; a style word shifts the look.
- Set creativity and structure, or pick a preset: Conservative, Balanced, Creative, Artistic.
- Press Generate. The result lands in the gallery next to the original.
Creativity and structure#
- Creativity is how much freedom the model has when adding detail. Low stays close to the source; high invents, and with a prompt it increases the prompt's influence.
- Structure is how strictly the layout is kept. High keeps every edge where it was; low allows the composition to shift.
Photos and renders: conservative or balanced. Rough drafts and small generations you want to "finish": creative. Concept art you are willing to let change: artistic.
Tiles#
The generative pass runs on tiles so any size fits in memory. Tile size defaults to 768 for SD 1.5 and 1024 for SDXL; lower it if you run out of memory. Tile overlap (default 128) hides the seams; raise it if you see grid artefacts on flat areas, at the cost of time.
When to upscale#
Upscaling a 1024 generation is the right way to reach print or 4K sizes; generating directly at those sizes repeats subjects, except with Flux models and DyPE. For a single region of a large image, the canvas recipe in Inpainting is more precise than a whole-image pass.