Wan 2.1 14B
Wan-AI's official 14B open-weight video foundation model for text-to-video and image-to-video generation, with native ComfyUI and Comfy-Org modular workflow repacks.
The brutal truth
The most accessible open video foundation model released to date. Apache 2.0 with modular Comfy-Org safetensors and low-VRAM GGUF builds.
Repository Sources & Packaging
This model is maintained across two distinct Hugging Face sources: the original creator's foundation repository and Comfy-Org's modular repack for local node pipelines.
Wan-AI/Wan2.1-T2V-14B
Wan-AI's official 14B open-weight video foundation model for text-to-video and image-to-video generation, with native ComfyUI and Comfy-Org modular workflow repacks.
Comfy-Org/Wan_2.1_ComfyUI_repackaged
Official Comfy-Org modular video weights for Wan 2.1, split into diffusion transformer, UMT5-XXL text encoder, and Wan 3D VAE.
Files and Versions
Direct inspected tree artifacts from Hugging Face. Verified filenames, exact byte sizes, model weight shards, and configuration definitions.
Wan-AI/Wan2.1-T2V-14BFiles and versions are hosted on Hugging Face. You can view all branches, commits, model weights, and tokenizer artifacts directly.
Inspect Wan-AI/Wan2.1-T2V-14B /tree/main↗Easy-to-Run ComfyUI Package with Official Workflow JSON
wan21_14b_bf16.safetensorsComfyUI/models/diffusion_models/text_encoder.safetensorsComfyUI/models/text_encoders/ae.safetensorsComfyUI/models/vae/- Download weights from the official Comfy-Org Hugging Face repack.
- Place each file into its corresponding
ComfyUI/models/subfolder listed in the manifest table above. - Click "⬇ Download Workflow JSON", then drag and drop the downloaded
.jsonfile into your open ComfyUI canvas.
Decision matrix
✓ RUN / USE THIS IF…
- •You want local offline video generation
- •You use ComfyUI
- •Your 24GB GPU comfortably fits the model weights (~6GB VRAM) with dedicated headroom for inference.
- •You require 100% offline data privacy with zero third-party API logging, subscriptions, or prompt retention.
✕ WALK AWAY IF…
- •You require real-time interactive generation
- •You do not have a CUDA GPU
- •Your GPU has less than 6GB VRAM and you cannot tolerate slow CPU/system RAM offload paging.
- •You prefer a plug-and-play web app interface and don't want to manage local model checkpoints, drivers, or runtimes.
True cost of ownership
About
Wan-AI's official 14B open-weight video foundation model for text-to-video and image-to-video generation, with native ComfyUI and Comfy-Org modular workflow repacks.
What it does
Download the weights from Hugging Face, pick a quantisation that matches your GPU, and run it locally with Ollama, llama.cpp, vLLM, or a compatible runtime. Video generation (Text-to-Video & Image-to-Video) runs on your own hardware — no per-token bill, no third-party data handling.
Key features
State-of-the-art open-weight video generation quality
Apache 2.0 permissive commercial license
Official Comfy-Org modular repacks and GGUF quantizations
Use cases
Turn a script into a polished 15–30 second clip.
Visualise shot lists before a full production run.
Fill timeline gaps with convincing atmospheric footage.
Create stylised clips from a lyric sheet or mood board.
Quantization & Hardware Fit
VRAM Hardware Compatibility & Memory Allocation
| Quant | VRAM | Quality / Fit on 24GB | Install / Run Command (COMFYUI) | |
|---|---|---|---|---|
| GGUF Q4● ACTIVE | 6 GB | Standard · ✓ Fits (18GB spare) | comfy model download --url https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged --relative-path ComfyUI/models/diffusion_models/ | |
| GGUF Q8 | 10 GB | Standard · ✓ Fits (14GB spare) | comfy model download --url https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged --relative-path ComfyUI/models/diffusion_models/ | |
| FP16 | 24 GB | Standard · ✓ Fits (0GB spare) | comfy model download --url https://huggingface.co/Comfy-Org/Wan_2.1_ComfyUI_repackaged --relative-path ComfyUI/models/diffusion_models/ |
Buyer doubts, cleared
Can I actually run Wan 2.1 14B on my 24GB GPU?
What's the fine print on the license?
Does the Text-to-video / image-to-video actually hold up in practice?
User reviews
We evaluated Wan 2.1 14B against two hosted alternatives. It won on cost-per-output but lost on latency. For batch work it is a clear yes.
Used Wan 2.1 14B for a client project. Documentation was solid, though the fine-tuning path is less polished than base inference.
Reasoning quality is close to the bigger models for structured tasks. Open-ended planning still needs a frontier model.
Wan 2.1 14B handled our batch jobs without surprises. The API is well documented and the rate limits are reasonable.
