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Installation

npx skills add https://github.com/mckruz/comfyui-expert --skill comfyui-video-pipeline

Summary

Generate videos using ComfyUI with Wan 2.2, FramePack, or AnimateDiff. Handles image-to-video, text-to-video, talking heads, and motion-controlled animation. Use when creating any video content from character images or text descriptions.

SKILL.md

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: | Parameter | Value | Notes | |-----------|-------|-------| | Resolution | 1280x720 (landscape) or 720x1280 (portrait) | Native training resolution | | Frames | 81 (~5 seconds at 16fps) | Multiples of 4 + 1 | | Steps | 30-50 | Higher = better quality | | CFG | 5-7 | | | Sampler | uni_pc | Recommended for Wan | | Scheduler | normal | |

Frame count guide: | Duration | Frames (16fps) | |----------|----------------| | 1 second | 17 | | 3 seconds | 49 | | 5 seconds | 81 | | 10 seconds | 161 |

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

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