Everything in one box — no clutter, no delays. Supports Low VRAM (6–8GB)! 💡

✅ Perfect even for laptops with 6–8 GB of video memory!

🧠 Even if you're just getting started — this bundle will make your projects fast, stable, and beautiful.

This workflow is designed to make working with Wan easier, faster, and more enjoyable. Instead of cluttered, unnecessary nodes — clean, compact blocks where all the logic is hidden "under the hood". You're left to just create 🎨.

💡 Main idea: “The backend stays in the shadows — all the magic happens in your hands.”

Support for Low VRAM (only 6–8 GB!) makes this project accessible even on laptops. Built-in optimizations and normalizations allow running pipelines on weak devices without pain or long waits ⏳.

⚠️ Important Warning

When copying nodes into ComfyUI, parameters can sometimes shift (known bugs).

👉 To avoid issues:

- Use only unpacked versions, as in this project.

- All nodes are separated and thoroughly tested — safely copyable!

- Ensure all components are connected in the correct order (especially Wan Setup).

Node Parameters

- ?input - optional input

- _output - hidden output (often used for debugging)

- [input/output] - input/output within a single iteration

🎨 Color logic for nodes

- Yellow — Useful utilities

- Purple — Pipeline settings and configuration

- Cyan — Conditioning nodes

- Green — Samplers

- Red — Coders/decoders (VAE)

- Purple-blue — Conditional blocks (branching logic)

- Blue — "Everything in one" — powerful compact nodes

- Black — Specific to Wan Animate, but essentially the same utilities ✨

🛠️ Connected Custom Nodes

- ComfyUI-GGUF

- ComfyUI-wanBlockswap

- ComfyUI-MagCache

- rgthree-comfy

- ComfyUI-KJNodes

- ComfyUI-Easy-Use

- comfyui_controlnet_aux

- ComfyUI-VideoHelperSuite

- ComfyUI-Frame-Interpolation

- ComfyUI-segment-anything-2

- ComfyUI-SAM2

📦 Models

1. Wan_2.2_ComfyUI_Repackaged

2. wan2-gguf (Calcuis Repackaged)

3. WanVideo_comfy (Kijai Repackaged)

🔍 Optimization Recommendations

Below is a table of parameters that can reduce VRAM consumption or speed up video generation:

| Node | Parameter | Impact | Performance | Feature |

| -------------------- | ---------------- | ------ | --------------- | ----------------------- |

| Wan Setup->Load Wan | GGUF | strong | enable | reduces VRAM, speeds up |

| Wan Setup->Load Clip | GGUF | medium | enable | reduces VRAM, speeds up |

| Wan Optimizer | Sage Attention | strong | auto | reduces VRAM, speeds up |

| Wan Optimizer | FP16 | low | enable | reduces VRAM |

| Wan Optimizer | MagCache | medium | enable | speeds up |

| Wan Optimizer | Compile | medium | enable | reduces VRAM, speeds up |

| Wan Optimizer | Block swap | strong | higher = better | reduces VRAM |

| Image Normalize | is_scale | strong | enable | reduces VRAM, speeds up |

| Image Normalize | megapixels | strong | lower = better | reduces VRAM, speeds up |

| Decode | VAE Tiled Decode | strong | enable | reduces VRAM |

Best settings for low VRAM (6–8 GB):

- Wan Setup->Load Wan - GGUF - Q4_K_M

- Wan Setup->Load Clip - GGUF - Q4_K_M

- Wan Optimizer - Sage Attention - auto

- Wan Optimizer - FP16

- Wan Optimizer - Block swap - 40, if working with high-resolution videos or Wan Animate.

- Image Normalize - megapixels - 0.21 for Wan 2.2 14B

- Decode - VAE Tiled Decode

💾 Tip: When RAM is insufficient — set up virtual memory or use Mem Reduct.

💻 My Test Configuration

- 🎮 GPU: RTX 3060 Laptop (6GB)

- 🧠 RAM: 24 GB + 32 GB swap

- ⚡ CPU: Intel i5-11300H

- 💻 Laptop: Asus TUF Dash F15

Runs stably even under these conditions 🏆.

📜 License

I don't know why it's here, but use at your own risk ¯\_(ツ)_/¯

👤 Author

Created by NeuroContent ✨

- CivitAI 🧠

- Instagram 🎨

- Pinterest 📌

- Donate ☕

TODO

- [ ] Add nodes for T2I

- [x] Add clip_vision

- [ ] Support S2V

- [ ] Support Lucy Edit

- [ ] Support Vace Fun

- [x] Add EasyCache and LazyCache to Wan Optimizer

- [ ] Add support for generating long videos through the Wan Context Windows node

Changelog

1.1

- Fix for the new version of ComfyUI

- Standard and tile-based encode/decode are now combined into two compact nodes

- Support for clip_vision has been added. You can now enable/disable clip_vision in setup. A new utility node for optional use of clip_vision has been added. It is now connected to each conditioning node, resulting in additional conditioning nodes.

- The reduce_vram parameter has been removed from Optimizer to improve node usage experience

- EasyCache and LazyCache optimizers have been added

- Fix for the negative parameter in Wan Full Setup