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:

Generate two hero images with consistent character
Use first as start frame, second as end frame
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

Parameter	Value	Notes
Resolution	640x384 to 1280x720	Depends on VRAM
Duration	Up to 60 seconds	VRAM-invariant
Quality	High (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

Parameter	Value (Standard)	Value (Lightning)
Motion Module	`v3_sd15_mm.ckpt`	`animatediff_lightning_4step.safetensors`
Steps	20-25	4-8
CFG	7-8	1.5-2.0
Sampler	euler_ancestral	lcm
Resolution	512x512	512x512
Context Length	16	16
Context Overlap	4	4

Camera Motion LoRAs

LoRA	Motion
v2_lora_ZoomIn	Camera zooms in
v2_lora_ZoomOut	Camera zooms out
v2_lora_PanLeft	Camera pans left
v2_lora_PanRight	Camera pans right
v2_lora_TiltUp	Camera tilts up
v2_lora_TiltDown	Camera tilts down
v2_lora_RollingClockwise	Camera 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-2025.md - Latest video generation advances
state/inventory.json - Available video models

comfyui-video-pipeline

ComfyUI Video Pipeline

Engine Selection