Wan2.1 I2v 720p 14b Fp16.safetensors Jun 2026

: Use the ComfyUI Manager to search for and install the official custom node wrapper supporting Wan2.1 (e.g., Kijai's ComfyUI-WanVideoWrapper ).

: The native target resolution. The model is trained to natively output videos at resolution without requiring immediate external upscaling.

Wan2.1 typically thrives on a lower CFG scale (between 2.5 and 5.0) and requires roughly 30 to 50 sampling steps depending on the sampler used. Conclusion

By comparison, the 480p version of the I2V model ( wan2.1_i2v_480p_14B_fp16.safetensors ) serves as a more accessible alternative for those with hardware constraints. wan2.1 i2v 720p 14b fp16.safetensors

: Enable CPU offloading to shift layers out of VRAM when they are not actively calculating.

: The underlying model architecture family. Wan2.1 introduces optimized spatio-temporal attention mechanisms that significantly outperform older architectures like Sora-style variants or early diffusion models.

The I2V variant relies heavily on text prompts to guide the motion. By combining a 14B parameter text encoder with the visual data of your source image, you can dictate specific actions, camera movements, and environmental changes while preserving the original character and style of the input photo. High-Fidelity Details : Use the ComfyUI Manager to search for

The file is a high-performance image-to-video (I2V) foundation model developed by Alibaba's Wan-AI . This specific variant is optimized for producing 720p high-definition video clips with realistic physics and complex motion dynamics. Core Features & Specifications Wan-AI/Wan2.1-I2V-14B-720P - Hugging Face

Discuss the differences between this 14B model and the .

: Image-to-Video. This denotes the model's primary modality. It takes a static reference image (and an optional text prompt) and animates it, preserving structural consistency while introducing realistic motion. : The underlying model architecture family

Construct a node workflow consisting of: Load Image -> Wan Image-to-Video Sampler -> VAE Decode (Wan) -> AnimateDiff/Video Combine . 2. Diffusers Library (Python Scripting)

This specifies the precision of the model's numerical weights, where numbers are stored in a 16-bit floating-point format.

In contrast, the same test using the community-built fp8_e4m3fn model (the file Wan2_1-I2V-14B-720P_fp8_e4m3fn.safetensors ) required a steadier 23 GB of VRAM and completed the generation in just 25 minutes—a dramatic 98% reduction in runtime.

Achieving photorealistic video requires fine-tuning several generation parameters:

The 720p 14B model is a significant step up in quality, but that leap requires substantial hardware investment. Real-world tests on an provide clear benchmarks. A test generating a 77-frame video at 528x960 resolution took approximately 30 hours to complete using the fp16 model, as it required 33 GB of GPU memory, overflowing the 24 GB VRAM and spilling over into slower system memory.