Workflow Goal:

Provide a clean reference implementation for WAN based image-to-video generation.

This workflow focuses on simplicity, reproducibility, and education rather than including every available feature. It reflects a tested baseline for producing high-quality videos from a starting frame. Treat it as a starting point and adapt the prompt and LoRA stack for your preferred artistic direction.

IMPORTANT NOTE:

Validated using an NVIDIA RTX 2080 (8 GB VRAM) with 64 GB System RAM.

Low VRAM does not mean low hardware requirements.

WAN 2.2 Image-to-Video is computationally intensive. Even when GPU VRAM is managed efficiently, higher resolutions and longer videos can require substantial system RAM for memory paging and intermediate data.

⚠️ Hardware & Resolution Notes

While the workflow is designed to be 8 GB VRAM friendly, the available 64 GB of system RAM also helped accommodate memory-intensive operations.

If your system has:

8 GB VRAM + 16 GB RAM, expect slower generation speeds or possible out-of-memory errors.

8 GB VRAM + 32 GB RAM, results should be closer but still depend on your operating system and background memory usage.

8 GB VRAM + 64 GB RAM (or more), you are more likely to reproduce the results achieved during

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Version 1.0

Created as a simple reference workflow for users getting started with WAN Video generation using quantized models (GGUF).

Purpose

✓ Educational

✓ Minimal

✓ Easy to extend

Verified

✓ Image-to-Video

Verified Settings

✓ CFG : 1.0

✓ Steps : 4 (2 High + 2 Low)

✓ 81 Frames (5 seconds @ 16 fps)

✓ Sampler : Euler

✓ Scheduler : sgm_uniform

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

Verified Resolution:

✓ 480p

Hardware Tested:

✓ RTX 2080 (8 GB)

✓ 64 GB System RAM

✓ ComfyUI 0.27.0