vslinx-ComfyUI-vslinx-nodes/web/docs/vsLinx_UpscaleByFactorWithModel.md

This node upscales an image using a selected upscale model and then resizes the result to a target scale factor. Upscale models typically operate at a fixed scale (e.g. 2× or 4×). This node first runs the model at its native scale, then applies a final resize step to match your requested factor.

This node does the following:

• Upscales the image using the selected upscale_model via tiled processing (to reduce VRAM usage).
• Computes the target size from your input image dimensions and the provided factor.
• Resizes the model output to exactly match that target size using the chosen upscale_method.
• Returns the final image.

Parameters:

Parameter	Type	Description
upscale_model	UPSCALE_MODEL	The upscaling model to use
image	IMAGE	The input image to upscale.
upscale_method	nearest-exact / bilinear / area / lanczos	The resampling method used for the final resize step to match your target factor.
factor	FLOAT	Target scaling factor relative to the original image size (min: 0.1, max 8.0).

Outputs:

Parameter	Type	Description
image	IMAGE	The final upscaled + resized image.

Notes:

• The upscaling model is always applied at its native scale (e.g. 2×/4×). The factor is achieved by resizing the model output to the target dimensions afterward.
• For factor values smaller than the model scale, this results in “upscale then downscale” (often still looks good).
• For very large factors (e.g. 8× with a 2× model), the additional scaling beyond the model’s native scale is performed by the final resize step (interpolation), which can look softer depending on upscale_method.
• area generally works best for downscaling; nearest-exact preserves hard edges but can look blocky; bilinear is smoother but may soften details; lanczos gives the sharpest, highest-quality resize but runs on the CPU (via PIL), so it is somewhat slower than the other methods.