Control AOV — Multi-Control Fusion (C2C)

ControlNet changed everything about local generation, and the modern version of the game is stacking conditions - depth for spatial layout, canny for edges, pose for structure, normal for surface, motion for video, an ID matte for identity. That's normally a chain of separate preprocessor packs. ControlAOVC2C collapses it into one node that emits every pass at once, plus a channel-packed image (depth=R, canny=G, pose=B) designed for union ControlNets, plus a preview blend. It's the "all the AOVs, in one stop" node of the C2C Control suite.

The mental model is VFX render passes. In a 3D pipeline you render depth, normals, and ID as separate "arbitrary output variables" and recombine them in comp. This node does the same for ControlNet conditioning: each pass comes out of its own output, so you can feed them to separate ControlNets and stack them for maximum spatial lock, or use the convenience outputs to feed a single union control.

How it works

The node is a fusion hub with two philosophies of preprocessing. Canny and optical-flow motion run internally - OpenCV Canny (no model) and optical flow on your frame batch. Everything else is either vendored (runs inside the pack: DepthAnything v1/v2/v3, MiDaS, Depth Pro, DepthCrafter, NormalCrafter, ViTPose) or delegated to comfyui_controlnet_aux (the depth_anything_*, dwpose, openpose, hed, lineart options). You pick per-pass: leave a pass off and wire in an external map (from your own depth node, a SAM matte, whatever) - the node treats those as passthrough AOVs. Weights per pass (0–2) let you tune how hard each condition bites.

The inputs that matter

• image - source frames; preprocessors run on these.
• depth_model / normal_model / pose_model / id_matte_model / edge_model - per-pass backends, mostly off by default so you wire in what you need.
• run_canny + canny_low / canny_high (100/200) - the internal edge pass.
• run_motion - optical-flow pass, needs ≥2 frames.
• preproc_resolution (512) - resolution handed to delegated preprocessors.
• depth_invert - flip near/far when your source convention doesn't match the ControlNet's.
• The weight inputs (depth_weight, canny_weight, pose_weight, normal_weight).

Outputs: blended, combined (side-by-side/grid preview), channel_packed, and the separate depth, canny, pose, normal, motion, id_matte passes, plus an info string describing per-pass status.

Installing it

Part of Code2Collapse/ComfyUI-CustomNodePacks. ComfyUI Manager → search "CustomNodePacks", or:

cd ComfyUI/custom_nodes
git clone https://github.com/Code2Collapse/ComfyUI-CustomNodePacks.git

Be ready for model downloads: the vendored depth/normal/pose backends pull weights from HuggingFace on first use, and the delegated options need comfyui_controlnet_aux installed. This is the heaviest node in the pack.

Gotchas

First: the delegated options quietly depend on another pack (comfyui_controlnet_aux) - if you pick dwpose or lineart without it, expect a missing-module error. Prefer the vendored backends to stay self-contained. Second, run_motion needs at least two frames or it has nothing to compute - wire a video batch, not a single image. Third, depth_invert matters more than people think: a "far=bright" depth source fed to a ControlNet expecting "near=bright" gives you inverted spatial cues and garbage structure. And finally, the combined preview is for your eyes, not the model - for actual conditioning use the separate passes or channel_packed, not the pretty blend.