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Wan Scail Replacement

Community
artokun
wan-scail-replacement

SCAIL-2 in-video character replacement on WAN 2.1. WanSCAILToVideo + SCAIL2ColoredMask + SAM3, the reference-image framing→scale rule, and the tuning/compositing pitfalls

Overview

Publisherartokun
Repositorycomfyui-mcp
Skill namewan-scail-replacement
Stars
811
Forks
137
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by artokun on GitHub. Read the source before you install it.

Installation

Install the Wan Scail Replacement AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/artokun/comfyui-mcp/tree/main/plugin/skills/wan-scail-replacement
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/artokun/comfyui-mcp.git /tmp/comfyui-mcp
mkdir -p .claude/skills
cp -r /tmp/comfyui-mcp/plugin/skills/wan-scail-replacement .claude/skills/wan-scail-replacement
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Wan Scail Replacement in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Wan Scail Replacement on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Wan Scail Replacement is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

SCAIL-2 In-Video Character Replacement (WAN 2.1)

SCAIL-2 (zai-org, on WAN 2.1 14B) replaces the person in a driving video with a character you supply as a reference image, end-to-end, with no pose maps, and with multi-character support. It is the successor to WAN Animate / motion transfer for the "swap the subject, keep the motion" job. The official ComfyUI template is video_wan21_scail2_character_replacement_int8, built around WanSCAILToVideo (+ SCAIL2ColoredMask) with a SAM3 mask driving where the character goes.

This skill documents the non-obvious behaviours that cost a full multi-minute render to discover. It is not a from-scratch graph. Start from the official template and apply the guidance below.

The one rule that costs a re-render: reference framing controls SCALE

In replacement_mode: true, the reference image's FRAMING controls the output character's SIZE as well as its appearance. The SAM3 mask controls where the character is placed; the reference image controls how large.

Measured on a 720x1280 driving clip where the subject occupied ~30% of frame height (subject bbox 363 px):

Reference framingPerson bbox in outputvs driving subject
Full-bleed portrait (person ~93% of frame)621 px1.71x oversized
Reframed (person ~34% of frame)364 px1.003x — correct

After reframing, top/bottom registration matched the driving subject within 1 px (the character stands on the same ground plane at the same height). Pose transfer was correct in both cases. Only the scale was wrong, which makes it easy to misread as "the model works" until you A/B against the source.

Guidance: pad or reframe the reference before you render.

Pad/reframe the reference image onto a canvas at the working resolution so the person occupies roughly the same fraction of frame height as the subject in the driving video. A full-bleed portrait reference against a wide-shot driving clip renders the character oversized in proportion to the framing mismatch.

It is a trap because the template's on-canvas notes do not state it, and it does not show up in the popular Civitai motion-transfer workflows. Those run animation mode, where the reference legitimately fills the frame.

Tuning trade-off: distill LoRA strength / shift leaks driving-subject detail

Running the lightx2v distill LoRA at 0.8 with ModelSamplingSD3 shift 5 (the settings the popular Civitai workflow uses) improves colour and detail versus 1.0 / shift 8. It also increases adherence to the driving video enough that original-subject details bleed onto the replacement character. In one run the original golfer's neon-yellow shoe appeared on a replacement character who wears white shoes in the reference (same seed, same reference, only those two params changed). If you see source details you didn't ask for, raise the LoRA strength / shift back toward 1.0 / shift 8.

Don't "fix" colour with post-hoc compositing — it's a regression

The raw SCAIL-2 output shows a measurable colour error (background ~-5 per channel from the VAE round-trip; the character loses red ~2.4x faster than green, which reads as a slight green cast). It is tempting to composite the generated character back over the original plate through the SAM3 mask to "correct" it. Don't. It measures better but looks worse:

  • The mask boundary produces an obvious halo.
  • Layering the original plate's props (e.g. a golf club) over generated hands severs the grip relationship the model had solved coherently.

SCAIL-2 resolves colour, edges, grip and occlusion jointly; correcting any one of them in isolation breaks the others. Leave the raw output alone.

Models (reference set)

The template's int8 build was exercised with:

  • wan2.1_14B_SCAIL_2_int8_convrot, the SCAIL-2 model
  • wan2.1_SCAIL_2_DPO_lora_bf16, the DPO LoRA
  • lightx2v_I2V_14B_480p_cfg_step_distill_rank64_bf16, the distill LoRA (see tuning note)
  • Wan2_1_VAE_bf16
  • umt5_xxl_fp8_e4m3fn_scaled, the text encoder
  • clip_vision_h
  • sam3.1_multiplex_fp16, the SAM3 mask model

VRAM: ~22.2 GB peak at 576x1024 / 81-frame chunks on a 24 GB card.

See also

  • wan-t2v-video, wan-flf-video for other WAN 2.x video pipelines
  • director for multi-shot scene direction for video pipelines

Sources

  • Official: none found.
  • Empirical: sampler values, wiring, and prompt notes from working graphs in packs/ and observed renders; not a vendor prompting guide.

Frequently asked questions

What does the Wan Scail Replacement AI skill do?

SCAIL-2 in-video character replacement on WAN 2.1. WanSCAILToVideo + SCAIL2ColoredMask + SAM3, the reference-image framing→scale rule, and the tuning/compositing pitfalls

Why use Wan Scail Replacement on TypingMind?

Because you install it once and use it with any model. Wan Scail Replacement is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Wan Scail Replacement in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/artokun/comfyui-mcp/tree/main/plugin/skills/wan-scail-replacement. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Wan Scail Replacement?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Wan Scail Replacement?

As many as you like. As long as a model supports skills, you can use Wan Scail Replacement with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Wan Scail Replacement AI skill free?

Yes. It is published on GitHub by artokun under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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