A minimal four-node Wan 2.2 Animate pack: drive a source clip, mask the performer, render it in looped chunks, and pick the model branch. It coexists with the original comfyui_trend_studio pack — class names, wire types and server routes are separate, so both can stay installed.

Nodes

| Node | Purpose | | --- | --- | | Zura Load Video | Local file or direct URL, trimmed to start/duration, decoded at 24 fps. Outputs an ordinary VIDEO — frames plus the original audio, understood by any ComfyUI video node — and the decoded frames. Choose local video uploads into ComfyUI's input folder and selects the file automatically. | | Zura Mask | Takes any VIDEO wire, so it works outside the Wan pipeline too. One dropdown for the replacement area — Whole character, Whole head (hair, forehead and ears, cut at the neck), or Face only — plus mask expansion and a Blockify mask switch with a free block size integer (8–512 px, step 1, KJNodes BlockifyMask parity; it delegates to the installed KJNodes node and reproduces its bbox block-grid algorithm exactly as fallback). Inputs: video. Outputs: mask_preview (red-tinted control video), masked_footage (the payload the sampler consumes) and mask (the replacement mask itself, as a native MASK). | | Zura Wan 2.2 Looped Chunks Sampler | The looped WanAnimateToVideo renderer: native continue_motion continuation between chunks, hard reset at detected or manual cuts. steps and cfg are plain widgets the Turbo Switch node can override. Outputs generated frames and a finished VIDEO with the original audio and exact timing — no separate finish node. | | Zura Wan 2.2 Turbo Switch | Lazy base/accelerated model selector emitting model/steps/cfg. Wan 2.2 specific: turbo runs the accelerated branch at 4–8 steps with cfg 1.0, quality runs the base branch at 40 steps with cfg 5.0. LoRA branches stay visible in the graph (Power LoRA Loaders or similar). |

Every wire is a standard ComfyUI type except one: masked_footage from Zura Mask into the sampler, which has to carry the pose, face and mask tensors a video wire cannot. Load Video and Mask speak plain VIDEO, the mask is a real MASK, and the reference character goes straight into the sampler's reference_image input from any LoadImage node.

Example workflow

workflows/Zura_Wan_2.2_Character_Replacement_Workflow.json is the example graph titled Zura Wan 2.2 Character Replacement Workflow (Face, Head, Body or Full Character Replacement), showing all four nodes wired together, including driving-clip, mask-check and raw-mask preview nodes. The mask leaves on a native MASK slot wired to ComfyUI's own Preview Mask. The graph is a starting point: replace the example_clip.mp4 and example_character.png placeholders with your own input video and reference image, and confirm that the named model and LoRA files exist in your ComfyUI installation before running it.

The example uses Power Lora Loader (rgthree) so the three named Wan LoRA weights remain visible in the graph. Install rgthree-comfy, or replace that node with an equivalent loader while keeping the model and CLIP connections. Load it from ComfyUI's Workflows menu (copy it into user/default/workflows/) or open the file directly.

Recommended LoRA stack

Measured on one 41-frame chunk of real footage, same seed, only the LoRA stack changed. This is the stack the example ships with:

| LoRA | Strength | | --- | --- | | wan2.2_i2v_lightx2v_4steps_lora_v1_high_noise | 0.70 | | lightx2v_I2V_14B_480p_cfg_step_distill_rank64_bf16 | 0.60 | | wan2.2_animate_14B_relight_lora_bf16 | 1.00 |

6 steps, cfg 1.0 at these settings (the Turbo Switch node's default). What the sweep found, in case you want to trade speed for detail:

The official bf16 relight LoRA beats the resized fp16 conversion: brighter, more saturated, no loss of detail, and one less derived file to keep around.
Wan14B_RealismBoost is a taste knob, not a quality knob - it lowers measured edge detail. The example keeps the row wired but switched off.
LoRA wiring order makes no measurable difference.
Step count is the only real speed lever: 4 steps is about a third faster than 6 for roughly 6% less edge detail. 5 steps buys nothing over either neighbour.

How chunk continuation works

Long clips render in 41-frame windows. At every chunk border the renderer uses native continuation plus a cross-fade, which is the same mechanism a hand-built Wan 2.2 Animate loop uses (continue_motion → sampler → trim_image):

The previous chunk's real frames are handed over as continue_motion. The next chunk's sampling window starts on those exact frames, and the source conditioning (pose / face / background / mask) is aligned to the same absolute frames, so the model samples motion it has genuinely seen instead of inventing a fresh take. A graph loop drops the frames the native node reports as trim_image because they only repeat output it already emitted; here they are kept, because they are the cross-fade partner (step 2).
The join is a flat-ended Hann cross-fade. Those shared frames now hold two renditions of the same absolute frames: the predecessor's real pixels and this chunk's rendition of them, produced with those pixels in its context. Because the two agree closely by construction, ramping between them has almost nothing to hide — but whatever difference remains is spread across the whole zone rather than stepping at one frame boundary. The zone's first frame is exactly the predecessor's frame and its last frame is exactly this chunk's own continuation, which the following frame continues in the same pass: neither edge steps.

Because continuation is driven by real pixels, the cross-fade is between two versions of the same motion and should reduce visible boundary artefacts. The result still depends on the source clip, mask, conditioning, model, and sampling settings; inspect the preview and joins for each render.

overlap_frames (default 5, must be 4n+1 and at most half of chunk_frames) is the length of both the continuation anchor and the cross-fade zone; raise it to 9 for a tighter hold on fast movement. A full continuation chunk's window is still exactly chunk_frames frames, so the continuation costs no extra sampling. Every chunk logs mean |real - rendition| for the frames it was given (continuation_delta, also in the receipt) — near zero means a true continuation; a large value means raise overlap_frames.

Shots

shot_mode decides where continuation restarts:

Continuous — the whole clip is one shot; every border is a continuation.
Detect cuts — automatic structural cut detection; within a shot chunks continue from each other, and at a cut the new shot starts with unconditioned frames, so the cut stays a hard cut.
Manual cuts — the same, with frame numbers you type (comma separated).

A sampling window never spans a cut and continuation never reaches back past the start of its own shot, so a detected or manual cut is always reproduced exactly. The receipt lists the resolved shot ranges (shots) next to the detected cuts, and the progress panel shows which shot and frame range is rendering.

Files

driving_clip.py, mask_performer.py, render.py, speed.py — the four nodes (Zura Load Video, Zura Mask, Zura Wan 2.2 Looped Chunks Sampler, Zura Wan 2.2 Turbo Switch).
media_source.py — local/URL/download, 24 fps decode, YouTube discovery + preview (routes /zura/video_candidates, /zura/preview_video).
planning.py, cut_detection.py — frame planner and adaptive cut detector (unchanged from v1).
segmentation.py — CPU YOLO person mask.
video.py — native VideoFromComponents assembly with a test fallback.
web/ — picker UI and the render progress panel.

Naming

Class IDs (TrendStudioV2DrivingClip, TrendStudioV2MaskPerformer, TrendStudioV2Render, TrendStudioV2Speed) are the workflow contract, so they are kept from the pack's pre-Zura name: graphs saved earlier still load and run. Everything user-visible — display names, node category, wire type, routes and docs — is Zura.

Install

Search for Zura Nodes in ComfyUI Manager, or clone the Zura Nodes repository into ComfyUI/custom_nodes/ComfyUI_zura_nodes/ and restart ComfyUI. Install the Python extras from requirements.txt into the same Python environment that runs ComfyUI (requests, yt-dlp, and ultralytics).

Zura Mask also requires the WanAnimate preprocessing custom node: https://github.com/kijai/ComfyUI-WanAnimatePreprocess. It supplies the OnnxDetectionModelLoader, PoseAndFaceDetection, and DrawViTPose nodes used for pose and face conditioning. Install it through ComfyUI Manager or clone it into the same custom_nodes directory, then restart ComfyUI.

Put ffmpeg and ffprobe on the system PATH. They are used to decode and assemble video, including local clips. Node.js must also be available on PATH when using YouTube discovery or downloads, because yt-dlp uses it as a JavaScript runtime for YouTube extraction. Local clips do not need YouTube or Node.js.

Place the model files in these standard ComfyUI model directories:

ComfyUI/models/ultralytics/segm/person_yolov8m-seg.pt for the CPU person segmentation pass. ComfyUI Impact Subpack can download this file through its model installer; the upstream model file is also available from Bingsu's ADetailer repository.
ComfyUI/models/detection/vitpose-l-wholebody.onnx for ViTPose whole-body pose estimation.
ComfyUI/models/detection/yolov10m.onnx for the WanAnimate detector.

The latter two files are the names selected by Zura's preprocessing call and are loaded from the detection directory registered by ComfyUI-WanAnimatePreprocess. The YOLO file is in the Wan 2.2 Animate process checkpoint, and the Large whole-body ViTPose release is linked by the preprocessing project. Follow that project's current model instructions if its filenames or locations change.

The example workflow names the Wan 2.2 Animate UNet, UMT5 text encoder, Wan VAE, CLIP Vision encoder, and three LoRAs explicitly. Replace those widget values with the filenames installed in your own model folders. The workflow also contains user input placeholders as described above.

Updates

Install available releases through ComfyUI Manager and restart ComfyUI. For contributors and maintainers, RELEASING.md explains how GitHub releases are published automatically to the Comfy Registry.

Licence

Zura Nodes' original code is released under AGPL-3.0-only. See LICENSE and THIRD_PARTY_NOTICES.md for the KJNodes-derived component and other upstream dependencies. Model weights are downloaded separately and retain their own licences.

Additional workflow

workflows/Zura_Gemma_4_Prompt_Enhancer.json is a compact Gemma 4 prompt enhancement workflow for everyday use. Its optional image input is the placeholder example_reference.png; no reference image is included in the node pack. It expects the matching Gemma model and the ComfyUI installation that provides the prompt-enhancer subgraph used by the workflow.

Additional workflow examples

The pack also includes three reference workflows with their original graph layouts preserved:

workflows/Zura_SAM3_Text_Prompt_Masking_Roto.json — SAM 3 text-prompt masking and roto preview.
workflows/Zura_MiniMax_H3_Head_Face_Swap.json — MiniMax H3 head and face replacement with SAM 3 masking.
workflows/Zura_MiniMax_H3_Ref2Vid_Speedboosts.json — MiniMax H3 reference to video with the supplied speed-boost and LoRA branches.

These files contain placeholder media names only. The source images and videos used to create them are not bundled or shared. They depend on the ComfyUI and custom-node packs represented by their node classes, plus the model files named inside each graph.