Extensions/Wan-video-extender

ComfyUI Extension

Wan-video-extender

Wan Video Extender extends a short video or a single image into a longer, temporally consistent clip using a VACE based control latent. It runs in multiple extension…

By Granddyser·Created 9 months ago·Updated 8 months ago· 33

Granddyser/wan-video-extender

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CategoryWanCustom

Stars33

Updated8 months ago

Nodes (2)

Wan 2.2 Video Extender PRO high+low-noise

WanCustom

Wan 2.2 Video Extender PRO

WanCustom

Readme

WAN Video Extender PRO

Demo

⚠️ Yes, I know this node looks like a high-rise building on your canvas. It's a skyscraper. But I built it this way because I'd rather give you a luxury high-rise of functionality than a sprawling slum of spaghetti wires.

[Update v2.0]: Added Memory-Optimized disk caching (infinite length support), VACE Endframe guidance, per-loop Image/Reference injection, and a new High/Low Noise Extender node.

🚀 Latest Update: Memory Optimization & Endframe Control

True High/Low LoRA Support: The High/Low Noise node now features dedicated, independent LoRA slots for both model stages, giving you granular control over the diffusion process.
Enhanced Loop Controls: Per-loop support for Hard Cuts (Image Injection), Reference Images (Character Consistency), and distinct LoRA patching.

v1.3.3

fix for high-low-noise-node
optional endframe für Extender-native-node

v1.3.2 - Logic Fixes & Memory Optimization

Memory Fix: Resolved high RAM usage during final video assembly in both nodes - segments now merge incrementally instead of loading all at once.
Reference Image Logic Fix (Native): Fixed use_reference_loop_X flags not being evaluated correctly - global reference image now properly respects per-loop toggles.
Workflow Updates: Updated example workflows for both native and high/low-noise nodes.

v1.3.0 - High/Low Noise Support (Beta)

New Node: Added WanVideoExtenderLowHigh support. You can now use separate High/Low noise diffusion models and LoRAs for more precise control.
New Workflow: Included wan-video-extender-high-low.json to get started with the split model setup.
Fix: Resolved issues with VACE module integration.
⚠️ Beta Warning: The High/Low node is currently in Beta. Please monitor your System RAM (CPU RAM, not VRAM). Usage may increase significantly after Loop 5 depending on your hardware configuration.

v1.2.1: VACE Fixes & Per-Loop Control

🐛 VACE Logic Fix: Fixed a critical bug where the Reference Image would overwrite the Input/Start frames. The start context is now correctly injected and protected.
🔄 Per-Loop Control: You can now set a unique Reference Image and toggle Overlap individually for every iteration loop.
💡 Transition Tip: Supports using the target image of the upcoming loop as the Reference Image for the current loop to create smoother transitions.
📂 New Workflow: Added an updated workflow example demonstrating these new looping features.
**(Note: Support for "High/Low Noise Models" is coming in a future update.)

Extend your WAN 2.2 videos with advanced features: per-loop prompts, LoRA switching, reference images, and smart overlap for maximum character consistency.

NEW: Memory-optimized architecture with disk-based segment storage - efficient RAM usage even for long videos!

📦 Recommended Model

Download: WAN2.2-14B-Rapid-AllInOne

Recommended Settings:

Steps: 4 (optimized for rapid generation)
Sampler: euler_a
Scheduler: beta
CFG: 1.0
Overlap Frames: 8-24 (more = better consistency)

🚀 Features

✅ Core Features

Flexible Input Modes - Video input (extend existing), Image input (animate static image), or No input (pure T2V generation)
Multi-Loop Video Extension - Extend videos through multiple generation cycles
Memory Optimized - Segment-based storage keeps RAM usage efficient
Smart Overlap System - Use last N frames as context for seamless transitions (4-64 frames)
Custom Resolution - Set any resolution when starting without input (256-2048px)

🎨 Advanced Character Consistency

Reference Image Support - Use a character portrait for consistent appearance across all loops
Intermediate Image Injection - Insert new images between loops to guide the generation
Per-Loop Prompts - Different prompt for each loop (up to 10 loops)
LoRA Support - Load different LoRAs for specific loops
Flexible Overlap - Higher overlap = better consistency (recommended: 16-24 frames)

💾 Memory Optimization

Disk-Based Segments - Each loop saves to disk, only overlap kept in RAM
Aggressive Cleanup - Unloads all models between loops to prevent VRAM leaks
Smart Input Handling - Efficient video processing
Final Assembly - Combines all segments from disk at the end

🔧 Installation

Available directly via ComfyUI Manager! Just search for 'Wan Video Extender' or copy the node file to your ComfyUI custom nodes:

git clone https://github.com/Granddyser/wan-video-extender.git

pip install requirements.txt

🎯 Quick Start

Input Modes

1. Video Input (V2V) - Extend existing video

Input: Video → WAN Video Extender PRO
Result: Original video + extended frames

2. Image Input (I2V) - Animate static image

Input: Image → WAN Video Extender PRO
Result: Animated video from still image

3. No Input (T2V) - Pure text-to-video generation

No input connected
Settings: default_width=832, default_height=480
Result: Video generated from prompt only

Basic Settings

Settings:
  - extension_loops: 2-3
  - generate_frames: 81
  - overlap_frames: 16
  - steps: 4
  - cfg: 1.0
  - sampler: euler_a
  - scheduler: beta
  - positive_prompt: "your scene description"

⚠️ Resolution Handling

Important: The default_width and default_height parameters are only used for T2V (Text-to-Video) generation when no input image is provided.

When using I2V (Image-to-Video) or providing any image input:

The node automatically uses the resolution of the input image
default_width and default_height are ignored
Make sure your input images are already at the desired resolution before feeding them to the node

🎓 Understanding Overlap

Overlap determines how many frames from your existing video are used as context for the next generation.

How it works:

Input: Your video frames

Loop 1:
  - Takes last N frames as context (overlap_frames)
  - Generates new frames
  - Adds only the new frames (skips overlap duplicates)
  
Loop 2:
  - Takes last N frames as context from previous output
  - Generates new frames
  - Adds new frames
  
Result: Original + all generated frames combined

Understanding Image Inputs

The node has two types of image inputs that serve different purposes:

1. Global `image` Input (Top of Node)

Purpose: Initial starting image for the entire workflow
Used once: Loaded at the beginning and provides context for Loop 1 (if image_loop_1 is empty)
Automatic context: Last N frames are kept as overlap context for subsequent loops

2. Loop-Specific `image_loop_X` Inputs (Per Loop)

Purpose: Optional override for each individual loop
Hard cut: When provided, creates a scene cut using these frames as new context
Fallback behavior: When empty, automatically uses context from previous loop

Behavior Matrix

| Scenario | Global image | image_loop_1 | Result | |----------|---------------|----------------|---------| | Normal start | ✅ Provided | ⬜ Empty | Loop 1 uses global image | | Hard cut Loop 1 | ⬜ Empty | ✅ Provided | Loop 1 uses loop image | | Both provided | ✅ Provided | ✅ Provided | Loop 1 uses loop image (overrides global) | | Auto-extend | ✅ Provided | ⬜ Empty (Loop 2) | Loop 2 extends from Loop 1's last frames |

Typical Workflow Example

Global image: Initial scene
  ↓
Loop 1: image_loop_1 = empty → Uses global image
  ↓
Loop 2: image_loop_2 = empty → Auto-extends from Loop 1
  ↓
Loop 3: image_loop_3 = Next-Scene LoRA output → Hard cut to new scene
  ↓
Loop 4: image_loop_4 = empty → Auto-extends from Loop 3

Key Point: Set the global image input once at the start. Use image_loop_X only when you want to inject a new scene at a specific loop.

Overlap Settings Guide

| Overlap | New/Loop | Speed | Consistency | Best For | |---------|----------|-------|-------------|----------| | 8-12 | 69-73 | ⚡ Fast | ⭐⭐ | Quick tests | | 16-20 | 61-65 | 🚀 Good | ⭐⭐⭐ | Recommended | | 24-32 | 49-57 | ⏱️ OK | ⭐⭐⭐⭐ | High quality | | 40-48 | 33-41 | 🐌 Slow | ⭐⭐⭐⭐⭐ | Maximum consistency |

Rule of thumb: More overlap = Better consistency, but slower generation.

🎨 Using Reference Images

Reference images drastically improve character consistency.

Setup:

LoadImage (Character portrait)
  ↓
WAN Video Extender PRO
  └─ reference_image input

Tips:

Alternative: First frame from your input video
Increase overlap up to 20 for higher consistence, but the final video will be shorter

🔄 Per-Loop Prompts

Each loop can have its own prompt for storytelling.

Example:

extension_loops: 4

positive_prompt: "woman in forest"  (fallback)

prompt_loop_1: "woman walking through sunlit forest"
prompt_loop_2: "woman discovers ancient ruins"
prompt_loop_3: "woman examining mysterious artifact"
prompt_loop_4: "woman holding glowing artifact at sunset"

Result: A video with narrative progression!

🎭 Per-Loop LoRAs & Prompts

Custom Prompts: Each loop can use a different prompt for scene changes.

LoRA Support: Load different LoRAs for specific loops (without .safetensors extension).

💾 How Memory Optimization Works

Our efficient approach:

Input: Extract only overlap frames needed → Delete rest
Generation: Each loop generates segment
Storage: Save segment to disk (/tmp/wan_segments_XXX/)
Memory: Only overlap + current segment in RAM
Final: Load all segments from disk and combine

Result: Generate long videos without running out of RAM!

Intermediate Images: You can also inject new images between loops by connecting different images to the image input for each workflow run, allowing you to guide the generation at specific points.

🎯 Advanced Workflows

Maximum Character Consistency

- Input: Portrait image
- reference_image: Same portrait
- extension_loops: 5
- overlap_frames: 24
- lora_loop_1: "your_character_lora" @ 0.8
- steps: 4, sampler: euler_a, scheduler: beta

Long Video Generation

- Input: 81 frames
- extension_loops: 8
- overlap_frames: 20
- generate_frames: 81
- Result: Extended long-form video

Style Evolution

- extension_loops: 4
- overlap_frames: 32
- Different LoRAs per loop for smooth style transitions

📝 Parameter Reference

Key Parameters

| Parameter | Default | Range | Description | |-----------|---------|-------|-------------| | extension_loops | 1 | 1-10 | How many times to extend | | generate_frames | 81 | 16-200 | Frames per loop | | overlap_frames | 16 | 4-64 | Context frames | | steps | 20 | 1-100 | Sampling steps (try 4 for rapid) | | cfg | 1.0 | 0-100 | CFG scale (1.0 recommended for WAN 2.2 Rapid) | | sampler_name | - | - | euler_a recommended | | scheduler | - | - | beta recommended | | strength | 1.0 | 0-10 | VACE strength | | seed | 0 | 0-∞ | Random seed |

Optional Inputs

| Input | Description | |-------|-------------| | image | Input image to extend | | video | Input video to extend | | reference_image | Character reference for consistency | | prompt_loop_1..10 | Per-loop custom prompts | | lora_loop_1..10 | Per-loop LoRA files | | lora_strength_1..10 | LoRA strengths |

📄 License

This node is provided as-is for use with ComfyUI and WAN 2.2.

Special thanks to phr00t for the complete model.

☕ Support the Project

Building optimized tools requires deep dives and long hours. I build this because I believe in pushing the boundaries of what's possible locally. If you share that vision, your support helps bridge the gap between a rough experiment and a polished tool for everyone.