Wan2.1 I2v 720p 14b Fp16.safetensors -

If you are looking for specific workflows to run this model on a 16GB or 24GB card, I can suggest memory-saving techniques. Wan-AI/Wan2.1-I2V-14B-720P - Hugging Face

Do not write image prompts. Write .

The native output is 720p. If you need 4K, use a post-process video upscaler (e.g., Topaz Video AI or Real-ESRGAN for video). Do not try to generate higher than 720p natively; the model will collapse.

: Unlike models limited to 480P or lower, this model delivers crisp 720P, suitable for professional creative applications. wan2.1 i2v 720p 14b fp16.safetensors

The fp16 model file size is approximately 31 GB for the diffusion model alone. Additionally, the model requires a VAE (~243 MB) and a text encoder (several GB).

These technologies allow the 720p 14B model to push the boundaries of what is possible, making it suitable for professional and cinematic applications.

Note: If you have less than 24GB of VRAM (e.g., an RTX 3080 or 4070 with 12GB/16GB), you will need to utilize quantized versions of the model, such as GGUF or NF4 variants (e.g., Q4 or Q8 precision), which drastically reduce VRAM usage at a minor cost to visual fidelity. How to Set Up and Run the Model If you are looking for specific workflows to

: Reduce the number of frames processed simultaneously to lower peak VRAM spikes. Deployment in ComfyUI

The model’s high VRAM requirement is a significant barrier for many users. For example, generating a 77-frame 720p video on an RTX 4090 using the fp16 model required approximately 33GB of VRAM (24GB VRAM + 9GB CPU memory via block swapping), leading to an inference time of around 30 hours.

Unlike Text-to-Video (T2V) models that generate clips entirely from a text prompt, models require a reference image as the starting anchor. The model takes this static frame and infers realistic motion over time, utilizing text prompts to guide how the image moves, rather than what is in the image. 3. 720p (Target Resolution) The native output is 720p

Running a 14-billion parameter model in FP16 precision demands substantial computational power. Below are the hardware tiers for running this specific weights file. Hardware Specifications

Here is content broken down by your probable use case.

This public link is valid for 7 days and shares a thread, including any personal information you added. This link or copies made by others cannot be deleted. If you share with third parties, their policies apply. Can’t copy the link right now. Try again later.

: The model matches the input image. Use high-resolution, uncompressed 16:9 images with clean lighting. Avoid blurry or AI-artifact-heavy starter images.