A ComfyUI custom node that generates SCAIL-2 videos of any length in a single queue. It automatically splits generation into chunks (the model's limit is 81 frames), anchors each chunk on the last frames of the previous one, color-matches to prevent drift, and stitches the result — no manual extension sections, bypassing, or frame math.

📥 Download the example workflow — SCAIL Auto Extend V3.json (right-click → Save As, or drag it into ComfyUI).

SCAIL-2 generates at most 81 frames per pass. Longer videos require extension passes where the first 5 frames are anchored to the last 5 frames of the previous chunk, so each extension only contributes 76 new frames — and the final chunk length depends on the input video. Doing this by hand means duplicated node sections, manual bypassing, and recalculating the last chunk for every video.

The SCAIL Auto Extend Sampler node does the whole loop internally at runtime:

1. Reads the pose video length (controlled as usual by your video loader's force_rate / skip / frame cap), trimmed to the nearest 4n+1 frames.
2. Plans the chunks: 81, then 81 (76 new + 5 overlap) repeating, with an automatically sized final chunk. E.g. 197 frames → 81 + 81 + 45.
3. For each chunk: builds the SCAIL-2 conditioning, samples, decodes, and (optionally) Reinhard-LAB color-matches the new frames to the last frame of the previous chunk.
4. Stitches everything and outputs the full image batch plus a frame count.

It calls ComfyUI's own WanSCAILToVideo, SamplerCustom, and ColorTransfer implementations internally, so output is identical to the equivalent hand-built chain.

• ComfyUI with the SCAIL-2 nodes (merged June 2026 — update to a recent ComfyUI)
• SCAIL-2 models: https://huggingface.co/Comfy-Org/SCAIL-2/tree/main/diffusion_models

The bundled example workflows additionally use:

• ComfyUI-VideoHelperSuite (video load/combine)
• ComfyUI-KJNodes (resize, Set/Get, model loader)
• ComfyUI-SAM3 (person tracking for the colored masks)
• ComfyUI_essentials (GetImageSize+)
• ComfyUI-RMBG (background removal on the reference image — V3 workflow)

RMBG is optional and swappable. Removing the reference image's background and padding it to the video's aspect ratio helps the model produce cleaner replacements, but you can replace the RMBG node with any background-removal node you prefer, feed a reference image whose background is already removed, or skip background removal entirely and see how it goes.

cd ComfyUI/custom_nodes
git clone https://github.com/Brobert-in-aus/scail-auto-extend

Restart ComfyUI. The nodes appear as SCAIL Auto Extend Sampler (sampling/video), plus SCAIL-2 Identity Tracker, SCAIL-2 Identity Seeder and SCAIL-2 Multi-Reference (experimental) (conditioning/video_models/scail) for multi-person work.

Load the included workflow: SCAIL Auto Extend V3.json (direct download — right-click → Save As, or drag the file into ComfyUI) — input video + reference image in, finished video out.

Or wire the node into your own workflow in place of your sampler section:

Outputs: images (stitched batch → VHS_VideoCombine) and frame_count.

• Total length is driven by however many frames reach pose_video — cap or trim at your video loader. Input is trimmed to the nearest 4n+1 frames (loses at most 3).
• Progress is reported per chunk; the console logs the chunk plan, e.g. [SCAIL Auto Extend] 197 pose frames -> 197 output frames, 3 chunk(s): [81, 81, 45].
• Interrupting cancels cleanly between/during chunks.

Replace several people in the driving video, each with a different character from a single composited reference image. Three helper nodes (conditioning/video_models/scail):

• SCAIL-2 Identity Tracker — an interactive canvas that outputs ref_track_data + driving_track_data for Create SCAIL-2 Colored Mask. It's an output node, so it gets a ▶ play button (step 2 below). Per side (Reference / Driving tabs):
  • Box mode (default, most reliable): drag a box per person; boxes are selectable, draggable and resizable.
  • Point mode: click = new identity, Shift+click adds a positive point, Alt+click a negative one. Use 2+ points to capture a whole person — a single click tends to grab only the part under it (a shirt, a face).
  • Right-click removes a box / a single point (or the identity if it was its last point); Delete removes the selected one.
  • Optional text detection: wire a CLIPTextEncode ("person") into reference_conditioning and/or driving_conditioning. With auto_detect on, text adds identities beyond your boxes — up to 6 on the reference, up to the reference box count on the driving side (caps are ceilings, not quotas; it only adds people it actually detects). With no boxes at all, this reproduces the V1 text-only flow.
  • With auto_detect off and fewer reference than driving subjects, a warning appears below the preview (some driving people would have no reference to map to).
• SCAIL-2 Identity Seeder — a headless variant that takes point/box coordinates and outputs per-person masks for SAM3_VideoTrack's initial_mask.
• SCAIL-2 Multi-Reference (experimental) — feeds each identity as a separate reference frame. It binds by colour (position-independent) but the model blends appearances, so it's not recommended for quality — see Findings.

The SCAIL Auto Extend V3.json workflow wires the Identity Tracker end to end.

1. Feed the processed reference image (background removed + padded to the video's aspect ratio) into reference_image, the resized pose video into pose_video, a SAM3 model into sam3_model. Optionally wire CLIPTextEncode into reference_conditioning / driving_conditioning for text detection.
2. Prepare the canvas (partial execution). Press the node's ▶ play button — Queue Selected Output Nodes runs the graph only up to this node, rendering the reference and driving frames onto the canvas without running the sampler. (Background removal / padding must sit upstream so the canvas shows the exact pixels the model will see, and your masks line up.)
3. Mark each person on the Reference tab, then the Driving tab — boxes (recommended) or multi-point identities. Marker order = colour order.
4. On Create SCAIL-2 Colored Mask, set sort_by = left_to_right so both sides colour by position (see Findings for why).
5. Queue the workflow normally.

See Findings for how identity mapping actually behaves and where it breaks.

MIT

Notes from working out how SCAIL-2 handles multi-identity replacement, recorded so they aren't re-discovered the hard way.

• Routing is position-first, not colour-first. With a single composited reference frame, the model assigns reference characters to driving people by spatial position; the colour mask's real job is temporal consistency (pinning identities frame to frame), not initial assignment. So control who-becomes-whom by ordering the reference composite left-to-right to match the driving people, and set SCAIL2ColoredMask sort_by = left_to_right so both sides colour by position and the two signals agree. (Mechanism: the reference is concatenated as one extra frame, the colour mask is an additive signal that loses to RoPE positional encoding, and only one reference latent is ever used.)
• You can't force colour over position. Rearranging the reference to break the spatial correspondence (e.g. stacking characters vertically instead of in a row) does not override position-first routing — tested, no effect.
• Multi-reference binds by colour but blends appearance. Feeding each identity as its own reference frame (the experimental Multi-Reference node) does make binding colour-driven and position-independent — but the model can't keep the appearances apart and blends them across characters. Correct routing, degraded fidelity; matches the model card's "not optimized for multi-reference." Kept as a documented dead end.
• Max 6 identities. The model was trained on a fixed 6-colour palette; a 7th wraps and collides.
• Constant subject count. The model expects the driving people present to correspond to the reference. People entering or leaving mid-shot produce artifacts — it tries to realise all reference identities from the start, cramming/hallucinating. Split the clip at the entrance/exit and render each segment with a reference of only the people present, then concatenate.
• Crossings & occlusion are the weak point. Identity is held over time only by the per-frame tracked colour mask (the reference is static), so when people cross or one passes in front of another it can break in two places: the model's position-first routing momentarily points each person at the other's reference region (swap / bleed / flicker), and SAM3's tracker can swap the colour assignment as the masks overlap. Footage where people stay in their lanes is far more reliable. To tell which layer failed, preview pose_video_mask through the crossing — swapped colours there mean the tracker; a clean mask but a swapped output means the model.
• Resolution must be divisible by 32. Width and height should be multiples of 32, not just 16. The pose/mask conditioning runs at half resolution, so its latent is dim/16; with the model's patch size of 2 that has to be even → dim divisible by 32. Out-of-spec dimensions get circular-padded (common_dit.pad_to_patch_size), which wraps the top edge of the frame onto the bottom — the symptom is the bottom ~8–16 px echoing the top. The trap is a multiple of 16-but-not-32: the main latent is clean but the half-res pose latent is odd, so it only shows up at some resolutions. Set your resize node's divisible_by to 32 (the bundled workflow does).
• Framerate. SCAIL-2 is a Wan-2.1 model trained at 16 fps. For smoother output, generate at 16 and interpolate (e.g. FILM VFI), setting Video Combine's frame_rate to the interpolated rate — cheaper than, and truer to the model's cadence than, raising force_rate in the loader (which generates proportionally more frames and pushes the model off 16 fps).