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Video Generation on a Laptop
Perfil de ejecución
Descripción de la fuente
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
Comentario generado por IA
Explicación generada por IA basada en los detalles de la fuente y la configuración. Las sugerencias están claramente identificadas.
This ComfyUI workflow is for generating video on a laptop; it supports Wan2.1 text-to-video, image-to-video, Vace, first/last-frame, Funcontrol, and Funcameraimage modes using text prompts, images, or control inputs.
The 2.0-ALL option lists Wan2.1 T2V, I2V, Vace, First Frame Last Frame, Funcontrol, and Funcameraimage workflows.
For text-to-video, use a text prompt; for image-to-video or Vace, use a single image; for first/last-frame generation, use a start image and an end image. These modes produce video.
For guided video, use depth, pose, or edge controls; for camera-control video, use a static input with movements such as zoom or pan.
Place WAN model .gguf files in ComfyUI/models/diffusion_models/ and check each model’s download page for its exact folder structure and required files.
Sugerencia · sin verificar
Install the listed nodes through ComfyUI Manager or their linked pages; rgthree-comfy is only needed for Stack Lora Loader.
Sugerencia · sin verificar
The listed node packs are comfyui-frame-interpolation, comfyui-kjnodes, comfyui-videohelpersuite, gguf, rgthree-comfy, and tiled_ksampler.
The listed model files are clip_vision_h_fp8_e4m3fn.safetensors, RealESRGAN_x4.pth, t5xxl_um_fp8_e4m3fn_scaled.safetensors, wan_2.1_vae.safetensors, and wan2.1-flf2v-720p-14b-q2_k.gguf.
The guidance describes Q_5 as the best balance of speed and quality, Q_3_K_M as faster and fairly accurate, and Q_2_K as usable with some quality loss.
The guidance describes 1.3B models as faster with lower detail and 14B models as higher quality, slower, and VRAM-heavy; it lists Xformer, Sage Attention, and Triton as speed options.
Funcontrol and Funcameraimage are labeled experimental, and inpainting experiments are described as coming soon.
Verify the listed node packs and model files in ComfyUI before using the workflow.
Sugerencia · sin verificar
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Fuentes
1 fuenteExtractos de fuentes
2 extractosSource context: 28264 downloads · Type Workflows · Base model Wan Video
Wan2.1 First Frame Last Frame
Funcontrol (experimental)
Funcameraimage (experimental)
Coming soon: Inpainting experimentals get updated
🚀 Results (Performance)
Article
*to be updated
🎥 Video Explainer (Vace edition):
🎥 Installation Guide (V1.8):
📦 DOWNLOAD SECTION
⚙️ Nodes Used (Install via ComfyUI Manager or links below)
🔗 GGUF
🔗 WanVideoWrapper
🔗 Tiled KSampler
🔗 KJNodes
🔗 Video Helper Suite
🔗 rgthree-comfy
Note: rgthree Only needed for Stack Lora Loader
📦 Model Downloads
*these are conversions from the original models to run on less VRAM.
🔗 WAN GGUF Models
most versions
🔗 Alternative for Image2Video
Faster/Better quants for i2v
🔗 WAN2.1 1.3B GGUF
fun,inpainting,T2V,Vace
🔗 WAN2.1 Fun-control 14B GGUF
fun-control
🔗 WAN2.1 Fun-Camera-control 14B GGUF
fun-Camera-Control
🔗 Alternative GGUF Conversions
All these GGUF conversions are done by:
*If you cant find the model you are looking for check out there profiles!
🧩 Additional Required Files (Do not downlaod from Model Downloads)
🔗 VAE, CLIP, CLIP Vision, Text Encoder
📥 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 .gguf files → Place them in your ComfyUI/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://civitai.com/articles/17240
Reddit Threads:
https://www.reddit.com/r/StableDiffusion/comments/1j1r791/wan_21_comfyui_prompting_tips https://civitai.com/articles/17240
🚀 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 🙌
Requisito de VRAM estimado
Estimación no disponible
Los tamaños de los modelos aún no están lo bastante completos para calcular este requisito.
Requisitos
11 requisitosclip_vision_h_fp8_e4m3fn.safetensors
No resueltoVision model · Unknown
RealESRGAN_x4.pth
No resueltoUpscaler · Unknown
t5xxl_um_fp8_e4m3fn_scaled.safetensors
No resueltoText encoder · Unknown
wan_2.1_vae.safetensors
No resueltoVAE · Unknown
wan2.1-flf2v-720p-14b-q2_k.gguf
No resueltoCheckpoint · Unknown
Paquete de nodos · Registry
Paquete de nodos · Registry
Source context: 28202 downloads · Type Workflows · Base model Wan Video
Paquete de nodos · Registry
Paquete de nodos · Registry
Paquete de nodos · Registry
Paquete de nodos · Registry
