<h2 id="wan-video">Wan Video</h2>Note: There are other Wan Video files hosted on Civitai - these may be duplicates, but this model card is primarily to host the files used by Wan Video in the Civitai Generator.These files are the <a rel="ugc" href="https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/tree/main/split_files/diffusion_models">ComfyUI Repack</a> - the original files can be found in <a rel="ugc" href="https://huggingface.co/Wan-AI">Diffusers/multi-part safetensors format here.</a>Wan2.2, a major upgrade to our visual generative models, which is now open-sourced, offering more powerful capabilities, better performance, and superior visual quality. With Wan2.2, we have focused on incorporating the following technical innovations:👍 MoE Architecture: Wan2.2 introduces a Mixture-of-Experts (MoE) architecture into video diffusion models. By separating the denoising process cross timesteps with specialized powerful expert models, this enlarges the overall model capacity while maintaining the same computational cost.💪🏻 Data Scaling: Compared to Wan2.1, Wan2.2 is trained on a significantly larger data, with +65.6% more images and +83.2% more videos. This expansion notably enhances the model's generalization across multiple dimensions such as motions, semantics, and aesthetics, achieving TOP performance among all open-sourced and closed-sourced models.🎬 Cinematic Aesthetics: Wan2.2 incorporates specially curated aesthetic data with fine-grained labels for lighting, composition, and color. This allows for more precise and controllable cinematic style generation, facilitating the creation of videos with customizable aesthetic preferences.🚀 Efficient High-Definition Hybrid TI2V: Wan2.2 open-sources a 5B model built with our advanced Wan2.2-VAE that achieves a compression ratio of 16×16×4. This model supports both text-to-video and image-to-video generation at 720P resolution with 24fps and can also run on consumer-grade graphics cards like 4090. It one of the fastest 720P@24fps models currently available, capable of serving both the industrial and academic sectors simultaneously.<h2 id="wan2.2-t2v-a14b">Wan2.2-T2V-A14B</h2>The T2V-A14B model, supports generating 5s videos at both 480P and 720P resolutions. Built with a Mixture-of-Experts (MoE) architecture, it delivers outstanding video generation quality. On our new benchmark Wan-Bench 2.0, the model surpasses leading commercial models across most key evaluation dimensions.<h2 id="wan2.2-i2v-a14b">Wan2.2-I2V-A14B</h2>The I2V-A14B model, designed for image-to-video generation, supports both 480P and 720P resolutions. Built with a Mixture-of-Experts (MoE) architecture, it achieves more stable video synthesis with reduced unrealistic camera movements and offers enhanced support for diverse stylized scenes.<h2 id="wan2.2-ti2v-5b">Wan2.2-TI2V-5B</h2>The TI2V-5B model is built with the advanced Wan2.2-VAE that achieves a compression ratio of 16×16×4. This model supports both text-to-video and image-to-video generation at 720P resolution with 24fps and can runs on single consumer-grade GPU such as the 4090. It is one of the fastest 720P@24fps models available, meeting the needs of both industrial applications and academic research.GitHub: <a target="_blank" rel="ugc" href="https://github.com/Wan-Video/Wan2.2">https://github.com/Wan-Video/Wan2.2</a>Originally HuggingFace Repo: <a target="_blank" rel="ugc" href="https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/tree/main/split_files/diffusion_models">https://huggingface.co/Comfy-Org/Wan_2.2_ComfyUI_Repackaged/tree/main/split_files/diffusion_models</a>

Wan Video 2.2

wanVideo22_ti2v5BFp16.safetensors

ti2v_5B_fp16

14B Image-to-Video

14B Text-to-Video

5B Text-Image-to-Video

i2v_high_noise_14B_fp16

i2v_low_noise_14B_fp16

t2v_high_noise_14B_fp16

t2v_low_noise_14B_fp16

i2v_high_noise_14B_fp8_sd

i2v_low_noise_14B_fp8_scd

t2v_high_noise_14B

t2v_low_noise_14B

wan2.2_vae

<a target="_blank" rel="ugc" href="https://arxiv.org/abs/2503.20314">Wan: Open and Advanced Large-Scale Video Generative Models</a>We are excited to introduce Wan2.2, a major upgrade to our foundational video models. With Wan2.2, we have focused on incorporating the following innovations:<ul><li>👍 Effective MoE Architecture: Wan2.2 introduces a Mixture-of-Experts (MoE) architecture into video diffusion models. By separating the denoising process cross timesteps with specialized powerful expert models, this enlarges the overall model capacity while maintaining the same computational cost.</li><li>👍 Cinematic-level Aesthetics: Wan2.2 incorporates meticulously curated aesthetic data, complete with detailed labels for lighting, composition, contrast, color tone, and more. This allows for more precise and controllable cinematic style generation, facilitating the creation of videos with customizable aesthetic preferences.</li><li>👍 Complex Motion Generation: Compared to Wan2.1, Wan2.2 is trained on a significantly larger data, with +65.6% more images and +83.2% more videos. This expansion notably enhances the model's generalization across multiple dimensions such as motions, semantics, and aesthetics, achieving TOP performance among all open-sourced and closed-sourced models.</li><li>👍 Efficient High-Definition Hybrid TI2V: Wan2.2 open-sources a 5B model built with our advanced Wan2.2-VAE that achieves a compression ratio of 16×16×4. This model supports both text-to-video and image-to-video generation at 720P resolution with 24fps and can also run on consumer-grade graphics cards like 4090. It is one of the fastest 720P@24fps models currently available, capable of serving both the industrial and academic sectors simultaneously.</li></ul>This repository contains our TI2V-5B model, built with the advanced Wan2.2-VAE that achieves a compression ratio of 16×16×4. This model supports both text-to-video and image-to-video generation at 720P resolution with 24fps and can runs on single consumer-grade GPU such as the 4090. It is one of the fastest 720P@24fps models available, meeting the needs of both industrial applications and academic research.

wan2.2_ti2v_5B_fp16

wan22Ti2v5BFp16_v10.safetensors

v1.0

Files with SHA256: 456f901338bd9eadbded3828b819109a9b68e8a525ca5cf8d0049a69fcfeca1e