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 &amp; Resolution NotesWhile 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----------------------------------------------------Version 1.0Created as a simple reference workflow for users getting started with WAN Video generation using quantized models (GGUF).Purpose✓ Educational✓ Minimal✓ Easy to extendVerified✓ Image-to-VideoVerified Settings✓ CFG : 1.0✓ Steps : 4 (2 High + 2 Low)✓ 81 Frames (5 seconds @ 16 fps)✓ Sampler : Euler✓ Scheduler : sgm_uniformThese settings were used to validate this workflow and are recommended as the baseline configuration.Verified Resolution:✓ 480pHardware Tested:✓ RTX 2080 (8 GB)✓ 64 GB System RAM✓ ComfyUI 0.27.0

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