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Comfyui Video Pipeline

  • 877 installs
  • 85 repo stars
  • Updated March 18, 2026
  • mckruz/comfyui-expert

comfyui-video-pipeline is a Claude Code skill that orchestrates ComfyUI video generation across Wan 2.2, FramePack, and AnimateDiff for text-to-video, image-to-video, and motion-controlled animation.

About

comfyui-video-pipeline is a user-invocable Claude Code skill from mckruz/comfyui-expert that routes video requests to one of three ComfyUI engines based on quality needs and hardware. The readme documents engine selection logic, including film-level quality paths that assume 24GB+ VRAM, and supports text-to-video, image-to-video, talking heads, and motion-controlled animation from character images or text descriptions. Metadata requires curl or wget and uses COMFYUI_URL as the primary environment variable across darwin, linux, and win32. Developers reach for comfyui-video-pipeline when they already run ComfyUI and want an agent to pick Wan 2.2, FramePack, or AnimateDiff instead of hand-building workflows per request. The skill is an orchestration layer over ComfyUI graphs, not a standalone video encoder or cloud API wrapper.

  • Orchestrates three video generation engines: Wan 2.2, FramePack, and AnimateDiff
  • Intelligent engine selection based on quality, length, speed, and motion-control needs
  • Supports image-to-video, text-to-video, talking heads, and motion-controlled animation
  • Handles long videos up to 60 seconds on 6GB VRAM with FramePack
  • Provides first+last frame control exclusively with Wan 2.2 MoE

Comfyui Video Pipeline by the numbers

  • 877 all-time installs (skills.sh)
  • +37 installs in the week ending Aug 4, 2026 (Skillselion tracking)
  • Ranked #305 of 1,335 Generative Media skills by installs in the Skillselion catalog
  • Security screen: LOW risk (skills.sh audit)
  • Data as of Aug 4, 2026 (Skillselion catalog sync)
npx skills add https://github.com/mckruz/comfyui-expert --skill comfyui-video-pipeline

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Listed on Skillselion
Installs877
repo stars85
Security audit3 / 3 scanners passed
Last updatedMarch 18, 2026
Repositorymckruz/comfyui-expert

How do you pick a ComfyUI video engine?

Generate high-quality videos from text prompts or character images using multiple ComfyUI video engines.

Who is it for?

Developers running a local or remote ComfyUI instance who need agent-guided engine selection for varied video generation tasks.

Skip if: Teams without ComfyUI infrastructure, or projects needing a single fixed pipeline with no engine switching logic.

When should I use this skill?

A user requests video from text, character images, talking heads, or motion-controlled animation via ComfyUI.

What you get

Rendered video clips from selected ComfyUI engine workflows with matched quality and VRAM profile.

  • Generated video file
  • Selected engine workflow path

By the numbers

  • Orchestrates 3 ComfyUI video engines: Wan 2.2, FramePack, and AnimateDiff
  • Documents film-level quality path requiring 24GB+ VRAM

Files

SKILL.mdMarkdownGitHub ↗

ComfyUI Video Pipeline

Orchestrates video generation across three engines, selecting the best one based on requirements and available resources.

Engine Selection

VIDEO REQUEST
    |
    |-- Need film-level quality?
    |   |-- Yes + 24GB+ VRAM → Wan 2.2 MoE 14B
    |   |-- Yes + 8GB VRAM → Wan 2.2 1.3B
    |
    |-- Need long video (>10 seconds)?
    |   |-- Yes → FramePack (60 seconds on 6GB)
    |
    |-- Need fast iteration?
    |   |-- Yes → AnimateDiff Lightning (4-8 steps)
    |
    |-- Need camera/motion control?
    |   |-- Yes → AnimateDiff V3 + Motion LoRAs
    |
    |-- Need first+last frame control?
    |   |-- Yes → Wan 2.2 MoE (exclusive feature)
    |
    |-- Default → Wan 2.2 (best general quality)

Pipeline 1: Wan 2.2 MoE (Highest Quality)

Image-to-Video

Prerequisites:

  • wan2.1_i2v_720p_14b_bf16.safetensors in models/diffusion_models/
  • umt5_xxl_fp8_e4m3fn_scaled.safetensors in models/clip/
  • open_clip_vit_h_14.safetensors in models/clip_vision/
  • wan_2.1_vae.safetensors in models/vae/

Settings:

ParameterValueNotes
Resolution1280x720 (landscape) or 720x1280 (portrait)Native training resolution
Frames81 (~5 seconds at 16fps)Multiples of 4 + 1
Steps30-50Higher = better quality
CFG5-7
Sampleruni_pcRecommended for Wan
Schedulernormal

Frame count guide:

DurationFrames (16fps)
1 second17
3 seconds49
5 seconds81
10 seconds161

VRAM optimization:

  • FP8 quantization: halves VRAM with minimal quality loss
  • SageAttention: faster attention computation
  • Reduce frames if OOM

Text-to-Video

Same as I2V but uses wan2.1_t2v_14b_bf16.safetensors and EmptySD3LatentImage instead of image conditioning.

First+Last Frame Control (Wan 2.2 Exclusive)

Wan 2.2 MoE allows specifying both the first and last frame, enabling precise video planning: 1. Generate two hero images with consistent character 2. Use first as start frame, second as end frame 3. Wan interpolates the motion between them

Pipeline 2: FramePack (Long Videos, Low VRAM)

Key Innovation

VRAM usage is invariant to video length - generates 60-second videos at 30fps on just 6GB VRAM.

How it works:

  • Dynamic context compression: 1536 markers for key frames, 192 for transitions
  • Bidirectional memory with reverse generation prevents drift
  • Frame-by-frame generation with context window

Settings

ParameterValueNotes
Resolution640x384 to 1280x720Depends on VRAM
DurationUp to 60 secondsVRAM-invariant
QualityHigh (comparable to Wan)Uses same base models

When to Use

  • Videos longer than 10 seconds
  • Limited VRAM systems (but RTX 5090 doesn't need this)
  • When VRAM is needed for parallel operations
  • Batch video generation

Pipeline 3: AnimateDiff V3 (Fast, Controllable)

Strengths

  • Motion LoRAs for camera control (pan, zoom, tilt, roll)
  • Effect LoRAs (shatter, smoke, explosion, liquid)
  • Sliding context window for infinite length
  • Very fast with Lightning model (4-8 steps)

Settings

ParameterValue (Standard)Value (Lightning)
Motion Modulev3_sd15_mm.ckptanimatediff_lightning_4step.safetensors
Steps20-254-8
CFG7-81.5-2.0
Samplereuler_ancestrallcm
Resolution512x512512x512
Context Length1616
Context Overlap44

Camera Motion LoRAs

LoRAMotion
v2_lora_ZoomInCamera zooms in
v2_lora_ZoomOutCamera zooms out
v2_lora_PanLeftCamera pans left
v2_lora_PanRightCamera pans right
v2_lora_TiltUpCamera tilts up
v2_lora_TiltDownCamera tilts down
v2_lora_RollingClockwiseCamera rolls clockwise

Post-Processing Pipeline

After any video generation:

1. Frame Interpolation (RIFE)

Doubles or quadruples frame count for smoother motion:

Input (16fps) → RIFE 2x → Output (32fps)
Input (16fps) → RIFE 4x → Output (64fps)

Use rife47 or rife49 model.

2. Face Enhancement (if character video)

Apply FaceDetailer to each frame:

  • denoise: 0.3-0.4 (lower than image - preserves temporal consistency)
  • guide_size: 384 (speed optimization for video)
  • detection_model: face_yolov8m.pt

3. Deflicker (if needed)

Reduces temporal inconsistencies between frames.

4. Color Correction

Maintain consistent color grading across frames.

5. Video Combine

Final output via VHS Video Combine:

frame_rate: 16 (native) or 24/30 (after interpolation)
format: "video/h264-mp4"
crf: 19 (high quality) to 23 (smaller file)

Talking Head Pipeline

Complete pipeline for character dialogue:

1. Generate audio → comfyui-voice-pipeline
2. Generate base video → This skill (Wan I2V or AnimateDiff)
   - Prompt: "{character}, talking naturally, slight head movement"
   - Duration: match audio length
3. Apply lip-sync → Wav2Lip or LatentSync
4. Enhance faces → FaceDetailer + CodeFormer
5. Final output → video-assembly

Quality Checklist

Before marking video as complete:

  • [ ] Character identity consistent across frames
  • [ ] No flickering or temporal artifacts
  • [ ] Motion looks natural (not jerky or frozen)
  • [ ] Face enhancement applied if character video
  • [ ] Frame rate is smooth (24+ fps for delivery)
  • [ ] Audio synced (if talking head)
  • [ ] Resolution matches delivery target

Reference

  • references/workflows.md - Workflow templates for Wan and AnimateDiff
  • references/models.md - Video model download links
  • references/research-log.md - Latest video generation advances
  • state/inventory.json - Available video models

Related skills

How it compares

Pick this when ComfyUI is already deployed and you need multi-engine routing; use a single-engine template when the pipeline never changes.

FAQ

Which ComfyUI engines does comfyui-video-pipeline support?

comfyui-video-pipeline orchestrates Wan 2.2, FramePack, and AnimateDiff, choosing among them based on whether film-level quality is required and whether roughly 24GB+ VRAM is available.

What environment does comfyui-video-pipeline need?

comfyui-video-pipeline expects a reachable ComfyUI server via COMFYUI_URL plus curl or wget on the host, and supports darwin, linux, and win32 according to skill metadata.

Is Comfyui Video Pipeline safe to install?

skills.sh reports 3 of 3 security scanners passed. Review the Security Audits panel on this page before installing in production.

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