⬡ Polyhedron Power Upscale

Power Upscale is the heavy lifter of this pack's upscaling pair, and it's built around one specific fact: the model you're most likely upscaling with is Wan 2.2, and Wan 2.2 doesn't come as one model. It ships as a high-noise and a low-noise expert, and most upscalers make you pretend otherwise. This one doesn't.

The core recipe is the classic tiled diffusion upscale - cut the image into overlapping tiles, refine each with a diffusion pass, blend them back - but with the details that make the pattern reliable. The tiles stitch with smoothstep-feathered weights whose accumulated weight is exactly 1 everywhere, which is why the node carries no "seam fix" at all: there's nothing to fix. Tile size is chosen against free VRAM rather than a fixed guess, and tiles process in batches (per_batch) so a long video doesn't have to fit in VRAM at once. The seed is worth a mention: every tile derives its noise from (seed, tile index), deliberately not from the frame number, so a video batch keeps the same noise structure per tile over time - less flicker.

The inputs that matter

• dual_moe - the headline. Off, it's a single stage: ESRGAN up to upscale_by, then a tiled refine on model. On, it becomes a stage chain: stage H (the high-noise expert) shapes structure at upscale_by/denoise/steps/cfg, then stage L (model_low) polishes detail at the _low versions. Each stage gets its own upscale model. This is the correct MoE translation for refine-strength denoises - a sigma-boundary split like the Sampler's Handoff would hand the high expert zero steps below the boundary.
• denoise - the refine strength, default ~0.19. The upscaling playbook says it plainly: ~0.2 keeps the input and adds detail; higher re-imagines. With distilled/Lightning experts you can afford low steps (steps defaults to 3) and CFG near 1.
• sigma_shift - the flow-matching shift. Wan 2.2 wants its native shift (typically 8.0) or the tile schedules are mis-scaled. sigma_shift_low (default -1 = same as high) gives the low stage its own; set it 0 to leave stage L untouched.
• pixel_stage - where the ESRGAN pass sits. The measured insight: a pixel model in front of a VAE round trip is nearly free of effect, because the /8 compression can't carry its fine detail. So model final runs the pixel model once behind the final decode, where its detail goes straight to the file - and final_upscale_by lets you keep the canvas size you dialed while the model's imprint still reaches it.
• tile_size / tile_overlap - uniform tiles are what diffusion models like; overlap targets ~64 and the planner narrows it when the canvas can't host it safely.
• vae_tiling - spatial VAE tiling for big canvases, with one rule the tooltip hammers: never temporal. A Wan VAE compresses time 4:1, so time-tiling would stutter.

Outputs are image and video; a wired VIDEO carries its audio and frame rate through, and a Wan VAE turns a tile's whole frame stack into one 5D video latent, so the model stays temporally consistent inside the tile.

Installing it

Same pack, same story:

cd ComfyUI/custom_nodes
git clone https://github.com/PolyhedronAI/ComfyUI-PolyhedronLoRAStack.git
# restart ComfyUI

Or ComfyUI Manager, search "Polyhedron Suite". No hard dependencies. If you're missing video features, pip install "polyhedron-lora-stack[media]" adds PyAV.

Where people get burned

Don't forget per_batch. There's no 0 any more, because 0 meant "whole batch" - and 129 frames through a 4× ESRGAN model in one go is a ~15GB tensor. Set 256 if you genuinely want the old behavior. And if sigma_shift_low is wired away from -1, re-check your proven denoise_low values: the shift moves where on the noise scale a given denoise fraction lands.