Wan22-T2V-I2V-LORA-4GB – AI Model by The-frizzy1 | AlphaNeural AI

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Wan22-T2V-I2V-LORA-4GB

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Wan2.2 T2V / I2V + LoRA — 4GB VRAM GGUF

By: The_frizzy1 Hardware target: RTX 3050 Laptop (4 GB VRAM) CivitAI: https://civitai.com/models/1817858/wan22-workflowlora-i2vt2v-4gb-vram-gguf YouTube: https://www.youtube.com/@the_frizzy1

🎥 Video Explainer: https://www.youtube.com/watch?v=C7ZttV320qk

What This Is

Wan2.2 T2V, I2V, and hybrid workflows on 4 GB VRAM using GGUF-quantised models.

Use 14B models. Lightx2v LoRA strongly recommended. Without LoRA: use CFG 6, 30–60 steps. Second sampler denoise: 0.3–0.5 for 14B models.

Included Workflows

Wan2.2 T2V 14B — Text to Video
Wan2.2 I2V 14B — Image to Video
Wan2.2 TI2V 5B — Hybrid Text + Image to Video

Wan2.2 Highlights

MoE architecture — high-noise + low-noise experts for better quality
Cinematic control — lighting, color, composition
Smooth motion modeling — complex camera and object movement
TI2V-5B — runs on 8 GB VRAM with offloading

Required Custom Nodes

Node	Link
GGUF	https://github.com/calcuis/gguf
WanVideoWrapper	https://github.com/kijai/ComfyUI-WanVideoWrapper
Tiled KSampler	https://github.com/FlyingFireCo/tiled_ksampler
KJNodes	https://github.com/kijai/ComfyUI-KJNodes
Video Helper Suite	https://github.com/Kosinkadink/ComfyUI-VideoHelperSuite
rgthree-comfy (LoRA stacking only)	https://github.com/rgthree/rgthree-comfy

Model Downloads

Model	Link
TI2V 5B	https://huggingface.co/QuantStack/Wan2.2-TI2V-5B-GGUF
T2V 14B	https://huggingface.co/QuantStack/Wan2.2-T2V-A14B-GGUF/
I2V 14B	https://huggingface.co/bullerwins/Wan2.2-I2V-A14B-GGUF

CLIP / Text Encoder: https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/tree/main/split_files

Changelog

Version	Notes
v1.0	Initial release