Video Generation on a Laptop
Hello!
This workflow utilizes a few custom nodes from Kijai and other sources to ensure smooth performance on an RTX 3050 Laptop Edition with just 4GB of VRAM. It's optimized to improve generation length, visual quality, and overall functionality.
🧠 Workflow Info
This is several ComfyUI workflow capable of running:
2.0-ALL -- Includes all workflows:
Wan2.1 T2V
Wan2.1 I2V
Wan2.1 Vace
Wan2.1 First Frame Last Frame
Funcontrol (experimental)
Funcameraimage (experimental)
Coming soon: Inpainting experimentals get updated
🚀 Results (Performance)
*to be updated
🎥 Video Explainer (Vace edition):
🎥 Installation Guide (V1.8):
📦 DOWNLOAD SECTION
⚙️ Nodes Used (Install via ComfyUI Manager or links below)
Note: rgthree Only needed for Stack Lora Loader
📦 Model Downloads
*these are conversions from the original models to run on less VRAM.
most versions
Faster/Better quants for i2v
fun,inpainting,T2V,Vace
fun-control
🔗 WAN2.1 Fun-Camera-control 14B GGUF
fun-Camera-Control
All these GGUF conversions are done by:
https://huggingface.co/calcuis
https://huggingface.co/QuantStack
*If you cant find the model you are looking for check out there profiles!
🧩 Additional Required Files (Do not downlaod from Model Downloads)
📥 What to Download & How to Use It
✅ Quantization Tips:
Q_5 – 🔥 Best balance of speed and quality
Q_3_K_M – Fast and fairly accurate
Q_2_K – Usable, but with some quality loss
1.3B models – ⚡ Super fast, lower detail (good for testing)
14B models – 🎯 High quality, slower and VRAM-heavy
Reminder: Lower "Q" = faster and less VRAM, but lower quality
Higher "Q" = better quality, but more VRAM and slower speed
🧩 Model Types & What They Do
Wan Video – Generates video from a text prompt (Text-to-Video)
Wan VACE – Generates video from a single image (Image-to-Video)
Wan2.1 Fun Control – Adds control inputs like depth, pose, or edges for guided video generation
Wan2.1 Fun Camera – Simulates camera movements (zoom, pan, etc.) for dynamic video from static input
Wan2.1 Fun InP – Allows video inpainting (fix or edit specific regions in video frames)
First–Last Frame – Generates a video by interpolating between a start and end image
📂 File Placement Guide
All WAN model
.gguffiles →
Place them in yourComfyUI/models/diffusion_models/folder⚠️ Always check the model's download page for instructions —
Converted models often list exact folder structure or dependencies
🔗 Helpful Sources:
Installing Triton: https://www.patreon.com/posts/easy-guide-sage-124253103
Common Errors: https://civarchive.com/articles/17240
Reddit Threads:
https://www.reddit.com/r/StableDiffusion/comments/1j1r791/wan_21_comfyui_prompting_tips https://civarchive.com/articles/17240
https://www.reddit.com/r/comfyui/comments/1j1ieqd/going_to_do_a_detailed_wan_guide_post_including
🚀 Performance Tips
To improve speed further, use:
✅ Xformer
✅ Sage Attention
✅ Triton
✅ Adjust internal settings for optimization
If you have any questions or need help, feel free to reach out!
Hope this helps you generate realistic AI video with just a laptop 🙌
Description
FAQ
Comments (3)
Hey I have Created a common errors article as a resource I will try to add all the problems from the Comment section!
The trick with all video generation workflows is to try to keep the models out of VRAM entirely. Instead they should stream from RAM across the PICe bus as needed- sadly ComfyUI and the CUDA libraries are very badly written, so the models are always trying to install or cache in VRAM, causing all the OOM and slowdown issues. Of course, if your video generation has too large a resolution and/or too many frames, it won't fit in VRAM, and your render times will be stupidly long regardless!
Models need to be in VRAM to run efficiently. PCIe bandwidth is too limited to stream model weights from system RAM in real time. Doing so would severely bottleneck performance. GPUs are designed to process data that's already in VRAM.
ComfyUI and CUDA aren't badly written. CUDA is optimized for GPU workloads, and ComfyUI gives users a lot of control.
High resolution and long frame sequences will always push memory and performance limits. The solution is proper optimization: reduce resolution when possible, split longer sequences, use more efficient models, or apply tiling and batching.