Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add sandyup/comfyui-mcp --skill wan-flf-videogit clone --depth 1 https://github.com/sandyup/comfyui-mcpWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/sandyup/comfyui-mcp/wan-flf-video)<a href="https://agentmods.dev/skills/sandyup/comfyui-mcp/wan-flf-video"><img src="https://agentmods.dev/badge/skills/sandyup/comfyui-mcp/wan-flf-video/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/sandyup/comfyui-mcp/wan-flf-video"><img src="https://agentmods.dev/badge/skills/sandyup/comfyui-mcp/wan-flf-video.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00033 | $0.07922 |
| Opus 5 | $0.00016 | $0.03961 |
| Sonnet 5 | $0.00007 | $0.01584 |
| Haiku 4.5 | $0.00003 | $0.00792 |
Grade A, and why
wan-flf-video scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
88% identical to wan-flf-video — 192 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 540 lines — stays where its author put it; the contents beside it link to each section on GitHub.
WAN 2.2 First-Last-Frame (FLF) Video Workflows
Overview
First-Last-Frame (FLF) video generation takes a start image and an end image and generates a smooth video transition between them. WAN 2.2 I2V (Image-to-Video) 14B model excels at this.
CRITICAL: Dual Hi-Lo Architecture (REQUIRED)
WAN 2.2 I2V uses a split-noise architecture. Unlike WAN 2.1, the 2.2 model was trained with separate HighNoise and LowNoise components that handle different denoising ranges. You MUST use both models in a two-pass KSamplerAdvanced setup. Using a single model produces low-quality, broken output.
- HighNoise model (pass 1, steps 0→N/2): Establishes structure, motion, and composition
- LowNoise model (pass 2, steps N/2→N): Refines details and ensures fidelity to input frames
- Both passes share the same conditioning from
WanFirstLastFrameToVideo - Pass 1 returns noisy latent → Pass 2 continues from there
NEVER use a single KSampler with only one model for WAN 2.2 I2V.
Two native approaches are available:
- Native Dual Hi-Lo (Default) —
WanFirstLastFrameToVideo+ dualKSamplerAdvancedtwo-pass - WanVideoWrapper —
WanVideoVACEStartToEndFrame+WanVideoVACEEncode+WanVideoSampler(VACE, caching, context windows)
Models
UNET Pairs (Always load BOTH Hi and Lo)
Remix NSFW (Recommended — built-in lightning, fp16):
| Model | Loader | Notes |
|---|---|---|
Wan2.2_Remix_NSFW_i2v_14b_high_lighting_fp16_v2.1.safetensors |
UNETLoader |
HighNoise, built-in lightning acceleration |
Wan2.2_Remix_NSFW_i2v_14b_low_lighting_fp16_v2.1.safetensors |
UNETLoader |
LowNoise, built-in lightning acceleration |
GGUF Q8 (Alternative — needs external lightning LoRAs):
| Model | Loader | Notes |
|---|---|---|
Wan2.2-I2V-A14B-HighNoise-Q8_0.gguf |
UnetLoaderGGUF |
HighNoise, quantized |
Wan2.2-I2V-A14B-LowNoise-Q8_0.gguf |
UnetLoaderGGUF |
LowNoise, quantized |
Official fp8:
| Model | Loader | Notes |
|---|---|---|
wan2.2_i2v_high_noise_14B_fp8_scaled.safetensors |
UNETLoader |
HighNoise, needs lightning LoRA |
wan2.2_i2v_low_noise_14B_fp8_scaled.safetensors |
UNETLoader |
LowNoise, needs lightning LoRA |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 540 lines · 33 tokens per session scan A 0b55840bd6c5
wan-flf-video is a skill published in the GitHub repository sandyup/comfyui-mcp (1 stars, last pushed 2mo ago), licensed MIT. It adds 33 tokens to every session and 7,922 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 88% identical to wan-flf-video, differing in 192 lines, and is treated as a copy.
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