WAN2.2 note:
The WAN2.2 integration is designed as a practical, VRAM-safe workflow.
Direct WAN2.2 First-Last Frame generation remains the main continuity path, while LRW geodesic keyframes are softly blended into the video latent before sampling.
This allows the geodesic structure to influence intermediate motion without sacrificing first-to-last continuity.

Demo

Direct WAN2.2 FLF vs LRW-Guided Latent Blend

In both comparison videos:

Left: Direct WAN2.2 First-Last Frame baseline
Right: WAN2.2 with LRW geodesic-guided latent blend

The right-side result uses LRW geodesic keyframes as a latent guide before KSampler.

4-step comparison

https://github.com/user-attachments/assets/07416cb8-5d9d-4c26-b306-48ea34323533

KSampler steps: 4
Left: Direct WAN2.2 FLF
Right: LRW-guided latent blend

6-step comparison

https://github.com/user-attachments/assets/43b13522-367e-4974-8920-d5dbab3dc597

KSampler steps: 6
Left: Direct WAN2.2 FLF
Right: LRW-guided latent blend

Recommended demo workflow:

examples/workflows/wan22_practical_direct_flf_plus_lrw_guided_blend.json

What the WAN2.2 LRW workflow does

The practical WAN2.2 workflow uses a two-branch structure.

Main continuity branch:

first image + last image
→ WanFirstLastFrameToVideo
→ base video latent

LRW guide branch:

first latent + last latent
→ LRW_WanTemporalMetric
→ LRW_WanCurvatureGuide
→ LRW_WanGeodesicKeyframes
→ geodesic guide latent

Final sampling branch:

base video latent + geodesic guide latent
→ LRW_WanLatentGuideBlend
→ KSampler
→ VAEDecode
→ VHS_VideoCombine

This keeps the target last frame active while allowing the LRW geodesic branch to influence the intermediate latent motion.

Node List

WAN2.2 Video Nodes (`lrw/wan`) ★ Core Feature

Designed for WAN2.2 First-Last Frame video generation.

Node	Description
LRW WAN Temporal Metric	Creates a lightweight LRW-compatible metric descriptor for WAN latent-space analysis. Avoids WAN VAE decoder Jacobian paths for VRAM safety.
LRW WAN Curvature Guide	Estimates latent distance / curvature-like complexity between first and last latent states. Useful for choosing steps, frame count, and keyframe strategy.
LRW WAN Geodesic Keyframes	Generates LRW geodesic-style keyframe latents between first and last latent states. Supports WAN-safe 5D latent output.
LRW Latent Keyframe Picker	Selects one geodesic keyframe from a stacked keyframe latent batch. Can output WAN-compatible 5D latent format.
LRW WAN Latent Guide Blend	Blends a selected LRW geodesic guide into the actual WAN video latent before KSampler. This is the main practical node for making LRW affect video generation.

Core Bridge Nodes (`lrw/core`)

Node	Description
VAE Decoder Bridge	Wraps a decoder/VAE-style function for LRW metric experiments.
Latent Blend / Frame Select	Selects or blends latent frames from a latent path.
Latent Vector from Diff	Creates a latent direction vector from two latents.
Apply Transported Vector	Applies a transported style/vector direction to a target latent.

Metric Nodes (`lrw/metric`)

Node	Description
Pullback Metric	Computes pullback metric experiments for compatible decoders.
Latent Curvature Map	Visualizes local metric/volume behavior where supported.

Geodesic Nodes (`lrw/geodesic`)

Node	Description
Geodesic Interpolate	Interpolates between latents using LRW geodesic tools where supported.
Geodesic Distance	Estimates geodesic distance between latent states.
SLERP Interpolate	Baseline spherical interpolation for comparison.

Transport Nodes (`lrw/transport`)

Node	Description
Parallel Transport	Transports a latent/vector direction along a path using LRW transport tools.

World Model Nodes (`lrw/world`)

Node	Description
Latent Trajectory	Generates latent-space trajectories for experimental world-model workflows.

Example Workflows

1. WAN2.2 Direct FLF + LRW-Guided Latent Blend

examples/workflows/wan22_practical_direct_flf_plus_lrw_guided_blend.json

Recommended practical WAN2.2 workflow.

It keeps WAN2.2 First-Last Frame as the main continuity path, then injects LRW geodesic guidance into the video latent before KSampler.

LoadImage(first) ─┐
                  ├─ WanFirstLastFrameToVideo ─┐
LoadImage(last)  ─┘                             │
                                                ├─ LRW_WanLatentGuideBlend → KSampler → VAEDecode → VHS_VideoCombine
VAEEncode(first) ─┐                             │
                  ├─ LRW_WanGeodesicKeyframes ─┘
VAEEncode(last)  ─┘

2. WAN2.2 Direct FLF Baseline

examples/workflows/wan22_direct_flf_baseline.json

Baseline workflow for comparison.

3. Geodesic Interpolation for Images

examples/workflows/geodesic_interpolation_workflow.json

Image-to-image latent geodesic interpolation for compatible image models.

4. Style Transfer via Parallel Transport

examples/workflows/style_transfer_workflow.json

Transport style direction vectors along latent paths.

Recommended repository layout

comfyui-lrw-nodes/
├─ examples/
│  ├─ workflows/
│  │  ├─ wan22_direct_flf_baseline.json
│  │  └─ wan22_practical_direct_flf_plus_lrw_guided_blend.json
│  │
│  └─ media/
│     ├─ output_compare_side_by_side.mp4
│     └─ output_compare_side_by_side01.mp4
│
├─ nodes/
│  ├─ wan_nodes.py
│  ├─ latent_keyframe_picker.py
│  └─ lrw_wan_latent_guide_blend.py
│
├─ README.md
└─ requirements.txt

Installation

Via ComfyUI Manager

Search for:

comfyui-lrw-nodes

Manual installation

cd ComfyUI/custom_nodes
git clone https://github.com/lajjadred/comfyui-lrw-nodes
cd comfyui-lrw-nodes
pip install -r requirements.txt

Restart ComfyUI after installation.

Required custom nodes for WAN2.2 workflows

The WAN2.2 demo workflows may require:

ComfyUI-GGUF — UnetLoaderGGUF
WAN2.2 First-Last Frame support — WanFirstLastFrameToVideo
ComfyUI-VideoHelperSuite — VHS_VideoCombine
Optional: ComfyUI-KJNodes for image resizing and utility nodes

VRAM Guide

VRAM	Recommended WAN2.2 Setting
12GB	GGUF Q3/Q4, `blend_strength=0.05–0.10`, lower frame count
16GB	GGUF Q3/Q4, `blend_strength=0.10–0.20`, 81 frames practical
24GB+	Larger models or higher steps, stronger experimentation possible

For the practical LRW-guided WAN workflow:

LRW_WanLatentGuideBlend
blend_strength: 0.10–0.20
time_schedule: middle_focus
keyframe_index: middle keyframe

Suggested starting point:

blend_strength = 0.15
time_schedule = middle_focus
normalize_guide = true

Why use LRW guidance with WAN2.2?

Direct WAN2.2 First-Last Frame generation is usually the best source of first-to-last continuity.
However, with large changes in pose, gaze, framing, or camera distance, the intermediate motion can become abrupt or unstable.

LRW-guided latent blending adds a soft latent-space guide:

Method	Role	Expected effect
Direct FLF	Main continuity path	Strong first-to-last arrival
LRW Geodesic Keyframes	Latent-space guide	Provides intermediate direction
LRW Latent Guide Blend	Practical injection point	Lets the geodesic guide influence KSampler input
Strong midpoint replacement	Not recommended as default	Can weaken arrival at the real last frame

The recommended workflow does not replace the last frame with a midpoint.
Instead, it keeps the actual last frame in WAN FLF and blends LRW guidance into the video latent before sampling.

Mathematical note

For general compatible decoders, LRW can be used to explore pullback metrics, geodesic interpolation, transport, and latent-space trajectories.

For WAN2.2, the integration is intentionally practical and VRAM-safe:

WAN VAE decoder Jacobian / dense pullback metric paths can be too expensive or incompatible with some vmap/in-place operations.
The WAN workflow therefore uses LRW geodesic keyframes as a latent guide.
The guide is softly blended into the WAN video latent rather than replacing WAN's first-last conditioning.

This keeps the workflow usable on consumer GPUs while still allowing LRW geometry to influence video generation.

Requirements

ComfyUI
latent-riemannian-world >= 0.3.0
torch >= 2.4
Python compatible with your ComfyUI installation

README.md Unescape Escape

comfyui-lrw-nodes