comfyUI-LongLook

Towards consistent motion and prompt adherence for Wan 2.2 video generation.

TL;DR:

• Chunked generation (best and most reliable use case): Stable motion provides clean anchors AND makes the next chunk far more likely to correctly continue the direction of a given action
• Single generation: Can smooth motion reversal and "ping-pong" in 81+ frame generations.

Works with both i2v (image-to-video) and t2v (text-to-video), though i2v sees the most benefit due to anchor-based continuation.

See Demo Workflows in Folder and demo clip below:

If you find this useful:

What It Does

The Core: FreeLong Spectral Blending

Based on FreeLong (NeurIPS 2024), this implements frequency-aware attention blending that dramatically improves motion consistency:

Without FreeLong	With FreeLong
More prone to motion reversal ("ping-pong")	More likely to maintain stable direction
More prone to subject drift between "scenes"	More likely to keep consistent appearance
More prone to ignoring motion in prompt	More likely to follow prompt intent
More prone to multiple distinct "scenes"	More likely to produce coherent sequence

How it works:

• Global stream: Full-sequence attention captures overall motion direction
• Local stream: Windowed attention preserves sharp details
• Spectral blend: FFT combines low frequencies (motion) from global + high frequencies (detail) from local

This is essentially motion-level prompt enforcement - the model maintains its initial motion intent rather than "forgetting" mid-generation.

The Bonus: Unlimited Length via Chunking

FreeLong's stable motion interpretation is what makes reliable chunk chaining possible:

[Chunk 1: 81 frames] → last frame → [Chunk 2: 81 frames] → last frame → [Chunk 3...] → ∞

Why this works:

Without FreeLong, chunk boundaries can often fail because:

• Motion may have reversed mid-chunk → last frame has wrong direction
• Subject drifted → last frame doesn't match earlier frames
• Ambiguous anchor → next chunk likely misinterprets intended motion

With FreeLong, each chunk has:

• Consistent motion direction throughout → last frame shows correct direction
• Stable subject interpretation → clean visual handoff
• Clear motion cues → next chunk far more likely to correctly continue

Two-sided benefit: FreeLong provides clean anchor frames AND makes the next generation far more likely to correctly interpret the motion direction. Clear input signals lead to better continuation.

Additional chunking benefits:

• Different prompts per chunk (scene evolution)
• Constant VRAM (no scaling with length)
• Fresh anchor resets any accumulated drift

Wan 2.2's attention mechanism struggles with temporal coherence:

• Native context is 81 frames (~5 seconds)
• Motion often reverses or drifts within that window
• Extending beyond 81 frames compounds these issues

Installation

1. Clone into ComfyUI custom_nodes:

cd ComfyUI/custom_nodes
git clone https://github.com/shootthesound/comfyUI-LongLook.git

2. Restart ComfyUI

3. Load example workflow from examples/Car-Racing-Example.json and Single-Shot-Example.json

Quick Start

Single Generation (Improved Motion)

Just add WanFreeLong before your sampler:

Load Model → WanFreeLong → KSampler → VAE Decode

Even without chunking, you get more consistent motion within 81 frames.

Chained Generation (Unlimited Length)

[Chunk 1]
Image → WanImageToVideo → FreeLong → Sampler → VAE Decode
                                                      ↓
                                                [Last Frame]
                                                      ↓
[Chunk 2]
FreeLong → WanContinuationConditioning → Sampler → VAE Decode
                    ↑                                    ↓
              [New Prompt]                         [Last Frame]
                                                        ↓
[Chunk 3...]                                        [Repeat]

Nodes

WanFreeLong (Spectral Blend)

Patches Wan model for FreeLong spectral blending.

Parameter	Default	Description
enabled	true	Toggle for A/B testing
blend_strength	0.7	Spectral blend strength (0=off, 1=full)
low_freq_ratio	0.5	Portion of spectrum from global stream
local_window_frames	33	Video frames for detail window (~40% of total recommended)
blend_start_block	0	First transformer block to apply
blend_end_block	-1	Last block (-1 = all)

Optimal settings: blend_strength=0.8, low_freq_ratio=0.8, local_window_frames=33 for 81-frame videos

WanContinuationConditioning

Creates i2v conditioning from previous chunk's last frame.

Parameter	Default	Description
width	512	Output video width
height	512	Output video height
video_frames	81	Frames to generate

Inputs: positive, negative, anchor_images (from previous VAE decode), vae

Workflows

Chained Generation (Car-Racing-Example.json)

Multi-chunk continuation with different prompts per chunk for scene evolution.

Single Shot (Single-Shot-Example.json)

Single generation with FreeLong for improved motion consistency. This workflow shows you a short clip with and without FeeLong node activation for comparison of 81+ frame gens

Parameter Tuning

Goal	Adjustment
More motion consistency	Increase blend_strength (0.7-0.9)
More detail/sharpness	Decrease blend_strength (0.4-0.6)
Smoother motion	Increase low_freq_ratio (0.5-0.7)
More dynamic motion	Decrease low_freq_ratio (0.3-0.4)
Less morphing	Increase local_window_frames (49-65)

Technical Details

FreeLong Implementation

• Dual-stream processing at transformer block level
• 50% overlapping windows with cosine crossfade
• Complementary FFT filters (low + high = 1.0)
• RoPE embeddings properly sliced per window
• Float32 FFT operations for numerical stability

Continuation Conditioning

• Last frame extracted from decoded video (float32, no 8-bit quantization)
• Re-encoded as i2v conditioning via VAE
• Proper mask format for Wan's conditioning system

Memory Efficiency

• Each chunk processes independently
• Only last frame passed between chunks
• No VRAM accumulation across chains
• Unlimited chunk count
• 8GB+ VRAM compatible

Experimental: Beyond 81 Frames

FreeLong was designed to extend beyond training length. You may be able to generate 97, 113, or more frames in a single pass. Trade-offs:

• VRAM scales with frame count
• Quality may degrade at extreme lengths
• Chunking approach is proven stable

Requirements

• ComfyUI
• Wan 2.2 models (i2v recommended)
• VideoHelperSuite (for video output)

Credits

• FreeLong paper: arxiv.org/abs/2407.19918
• Wan 2.2: Alibaba

License

MIT