FisherKing-WAN2.2-[GGUF-14B]-T2V-ReferenceWorkflow-v1.0 [Low VRAM Compatible]

Workflow Goal

Provide a clean, educational reference implementation for WAN 2.2 Text-to-Video generation.

This workflow focuses on simplicity, reproducibility, and education rather than including every available feature. It provides a tested baseline for generating high-quality cinematic videos directly from text prompts while remaining easy to understand, modify, and extend.

Treat this workflow as a starting point and customize the prompt, LoRA stack, and generation settings for your preferred artistic direction.

Version 1.0

Purpose

✓ Educational

✓ Reference Workflow

✓ Easy to Understand

✓ Easy to Extend

✓ Low VRAM Friendly

Workflow Pipeline

Text Prompt

↓

Prompt Engineering

↓

High Noise Sampling

↓

Low Noise Sampling

↓

VAE Decode

↓

(Optional) RIFE Frame Interpolation

↓

Final Video Output

Verified Settings

The following settings were used to validate this workflow and are recommended as the baseline configuration.

Sampling

✓ CFG : 1.0

✓ Steps : 6 (3 High Noise + 3 Low Noise)

✓ Shift : 10

✓ Sampler : Euler

✓ Scheduler : Simple

Video

✓ Frames : 81

✓ Output FPS : 16 FPS

✓ Final FPS (RIFE Enabled) : 32 FPS

✓ Recommended Resolution : 640 × 360 (16:9)

Required LoRA Configuration

This workflow uses the same Lightx2V LoRA during both sampling stages.

LoRA

lightx2v_t2v_14b_cfg_step_distill_v2_lora_rank32_bf16

High Noise

Strength : 2.0

Low Noise

Strength : 1.0

These strengths were used to validate the workflow and are recommended as the baseline configuration.

Optional Post Processing

The workflow includes an optional RIFE Frame Interpolation stage.

When enabled:

Input : 16 FPS

Output : 32 FPS

Produces smoother motion while preserving the original video duration.

Disable this stage if you prefer faster processing or do not have the required RIFE model installed.

Hardware & Resolution Notes

Validated using:

✓ NVIDIA RTX 2080 (8 GB VRAM)

✓ 64 GB System RAM

✓ ComfyUI v0.27+

Although optimized for 8 GB VRAM, WAN 2.2 Text-to-Video remains computationally intensive.

System RAM is equally important for handling intermediate tensors and memory paging during video generation.

Expected behavior:

8 GB VRAM + 16 GB RAM

Possible out-of-memory errors

Slower generation

8 GB VRAM + 32 GB RAM

Better stability

Performance depends on available system memory

8 GB VRAM + 64 GB RAM (or more)

Recommended configuration

Matches the environment used to validate this workflow

Design Philosophy

This workflow intentionally avoids unnecessary complexity.

The objective is to provide a stable, reproducible reference implementation that users can understand, learn from, and extend for their own creative projects.

Features

✓ Clean reference implementation

✓ Prompt Engineering ready

✓ High / Low Noise sampling pipeline

✓ Modular LoRA configuration

✓ Optional RIFE interpolation (16 → 32 FPS)

✓ Chrono Save integration for CivitAI

✓ Low VRAM focused workflow design