<h2 id="wan-2.2-i2v-workflow-(optimized-for-12gb-gpus)">WAN 2.2 – I2V Workflow (Optimized for 12GB GPUs)</h2>A fast, clean, and VRAM-efficient Image-to-Video workflow built around WAN 2.2. Fast render times on mid-range GPUs. I tried to keep this simple and easy to use, while maintaining good results. Utilizing well known nodes, and minimizing node bloat. The workflow also has comments everywhere and clear flow.Ver 1.0 - Base workflow, can do 5 second clips in one iteration. (very fast for 12gb)Ver 1.1 - More stability, can run 100 times consecutively in 8hrsVer 1.2 - Renders 20 second videos. Cleanup of wires.Ver 1.3 - MMAudio added.Ver 1.4 - 2x Upscaling, color correction, &amp; sharpening in between passes for quality consistency.<h3></h3><ul><li>WAN 2.2 Model Use either lightweight GGUF models or full <code>.safetensors</code> checkpoints. Lightning LoRAs are baked into the .safetensors model, so LoRA use is optional.</li><li>WAN 2.1 VAE Reduces VRAM load while maintaining strong color, detail structure, and temporal consistency.</li><li>SageAttention + FP16 Accumulation Patch Automatically applied for speed and throughput.</li><li>Dual KSampler passes (HIGH/ LOW) Uses 6 steps, and 101-frame length for smooth animations and solid adherence.</li><li>LoRA Loader + Model Shift Controls Supports stylistic LoRAs with synchronized shift values across both samplers.</li><li>RIFE Frame Interpolation (60 FPS) Creates ultra-fluid motion.</li><li>Upscale + Adaptive Sharpen Pass In between each phase ultrasharp upscaler is used for consistency.</li></ul><h3 id="performance">Performance</h3><ul><li>GPU: 12GB VRAM</li><li>Render Speed: ~6 minutes (v1.0)</li><li>Output FPS: ~60–64FPS</li><li>Resolution: ~1072 × 1616 (post-upscale)</li></ul><h3 id="requirements">Requirements</h3><ul><li>KJNodes</li><li>MMAudio</li><li>Rgthree Nodes</li><li>ComfyUI-Frame-Interpolation</li><li>Cache-Cleaner</li><li>CG-Use-Everywhere</li><li>ComfyUI-Easy-Use</li><li>ComfyUI-Essentials</li><li>ComfyUI-Videohelpersuite</li><li>ComfyUI-Custom_Scripts (<a target="_blank" rel="ugc" href="https://github.com/pythongosssss/ComfyUI-Custom-Scripts">https://github.com/pythongosssss/ComfyUI-Custom-Scripts</a>)</li><li>GGUF Nodes</li><li>AIO Wan2.2 Model: <a target="_blank" rel="ugc" href="https://civarchive.com/models/2053259/wan-22-enhanced-nsfw-or-camera-prompt-adherence-lightning-edition-i2v-and-t2v-fp8-gguf">https://civarchive.com/models/2053259/wan-22-enhanced-nsfw-or-camera-prompt-adherence-lightning-edition-i2v-and-t2v-fp8-gguf</a></li><li>GGUF Model (if not using AIO): <a target="_blank" rel="ugc" href="https://civarchive.com/models/1820829/wan22-i2v-a14b-gguf">https://civarchive.com/models/1820829/wan22-i2v-a14b-gguf</a></li><li>Lightning Lora (If not using AIO): <a target="_blank" rel="ugc" href="https://civarchive.com/models/1585622/lightning-lora-massive-speed-up-for-wan21-wan22-made-by-lightx2v-kijai">https://civarchive.com/models/1585622/lightning-lora-massive-speed-up-for-wan21-wan22-made-by-lightx2v-kijai</a></li><li>CLIP: <a target="_blank" rel="ugc" href="https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/tree/main/split_files/text_encoders">https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/tree/main/split_files/text_encoders</a></li><li>VAE: <a target="_blank" rel="ugc" href="https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/tree/main/split_files/vae">https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged/tree/main/split_files/vae</a></li><li>Ultrasharp: <a target="_blank" rel="ugc" href="https://huggingface.co/Kim2091/UltraSharpV2/tree/main">https://huggingface.co/Kim2091/UltraSharpV2/tree/main</a></li></ul>

Wan2.2 I2V 12GB (20 Seconds, MMAUDIO, 60FPS, LOW VRAM)

wan22I2V12GB20Seconds_v10.zip

Files with SHA256: 181e6e1e8af9faa9dfff8fe66ff573e23e0215107aaeb7f259fb20f6b5b11c88