This is a modified implementation of impact-pack's iterative upscaler. It leans in on the idea that giving too much attention to computation at high resolutions isn't a good idea.

Node settings

upscale_by: Final resolution multiplier (e.g., 2.0 = double width and height)
upscale_curve: Progression curve for all parameters (resolution, CFG, steps, denoise). 1.0 = linear progression, >1.0 = exponential progression
iterations: Number of complete sampling cycles to perform (4 iterations would mean doing four sets of Steps)
steps_start: Number of sampling steps for the first iteration
steps_end: Number of sampling steps for the last iteration
cfg_start: CFG scale for the first iteration
cfg_end: CFG scale for the last iteration
denoise_start: Denoise strength for the first iteration
denoise_end: Denoise strength for the last iteration
resize_filter: Image resizing filter algorithm - "lanczos", "nearest-exact", "bilinear", "area", or "bicubic"
tile_size: Tile size for VAE operations to manage memory usage (if you get lag due to low VRAM then set this lower)

How It Works

Each iteration upscales the image to a progressively larger resolution. Steps, CFG, and denoise values evolve from start to end values across iterations. The upscale_curve determines how linear or non-linear this progression is.

Upscale Curve Examples (4 iterations)

upscale_curve = 1.0: Linear progression → 1.25x → 1.50x → 1.75x → 2.00x
upscale_curve = 2.0: More exponential → 1.13x → 1.42x → 1.69x → 2.00x
Higher upscale_curve values:
- Faster, spends more time sampling at lower resolutions
- Smarter, reduces body horror
- Less detail, resembles base image more
Lower upscale_curve values:
- Slower, spends more time sampling at higher resolutions
- Dumber, more body horror
- More detail, different to base image

So try to strike a balance. And if you change upscale_by then you'll definitely want to change upscale_curve as well.

Tips

Reducing Artifacts: Either decrease CFG, increase steps, or connect the model input to a Skimmed CFG node.
Too Many Fingers/Body Horror: Either reduce denoise, reduce the base resolution of the image you're feeding the upscaler, or increase the upscale_curve.
Better Images: If you connect the model input to a Skimmed CFG node and set cfg_start really high, it'll usually result in better images.

Known Issues

Cancelling doesn't instantly stop the generation process. You have to wait for the current iteration to finish before the process will terminate. Please share if you have the solution.