What is Wan 2.2?

Wan 2.2 is a family of video generation models that are the legitimate successors to Wan 2.1. It consists largely of two models:

  • 14B: A two-stage architecture that switches between high_noise and low_noise models.
  • 5B: A TI2V model that handles text2video and image2video with a single model + Wan 2.2 VAE.

Wan 2.2 14B Model

Wan 2.2-A14B has a two-stage pipeline where the high_noise model handles the first half of sampling and the low_noise model handles the second half.

By dividing it into two models, the model size is doubled to improve performance without increasing VRAM usage compared to Wan 2.1.

Recommended Settings

  • Recommended Resolution
    • 480p (854×480) - 720p (1280×720)
  • Maximum Number of Frames
    • 81 frames
  • FPS
    • Often output around 16fps
    • However, since 16fps often results in slow-motion video, adjust it by saving at 24fps or dropping frames.

Model Download

📂ComfyUI/
└── 📂models/
    ├── 📂diffusion_models/
    │   ├── wan2.2_t2v_high_noise_14B_fp8_scaled.safetensors
    │   ├── wan2.2_t2v_low_noise_14B_fp8_scaled.safetensors
    │   ├── wan2.2_i2v_high_noise_14B_fp8_scaled.safetensors
    │   └── wan2.2_i2v_low_noise_14B_fp8_scaled.safetensors
    ├── 📂text_encoders/
    │   └── umt5_xxl_fp8_e4m3fn_scaled.safetensors
    ├── 📂unet/
    │   ├── wan2.2_t2v_high_noise_14B-XXXX.gguf    ← Only when using gguf
    │   ├── wan2.2_t2v_low_noise_14B-XXXX.gguf     ← Only when using gguf
    │   ├── wan2.2_i2v_high_noise_14B-XXXX.gguf    ← Only when using gguf
    │   └── wan2.2_i2v_low_noise_14B-XXXX.gguf     ← Only when using gguf
    └── 📂vae/
        └── wan_2.1_vae.safetensors

text2video (14B)

Use KSampler Advanced to process the first half with the high_noise model and the second half with the low_noise model.

wan2_2_text2video_14b.json
  • 🟩 Specify at which step out of the total 20 steps to switch from high_noise -> low_noise.
    • As for the timing of this switch, it is recommended that the ratio of "non-noise parts" to "noise parts" be 1 : 1.
    • Although it is possible to calculate this, it is difficult because Sampler / Scheduler / sigma_shift / number of steps are all intertwined.
    • Also, perfectly matching this does not necessarily mean it is optimal.
    • In this workflow, we switch at 4 steps, but please try experimenting with this as a baseline.
  • 🟨🟥 The text encoder and VAE are the same as Wan 2.1.

image2video (14B)

wan2_2_image2video_14b.json
  • 🟦 Input the start image into the WanImageToVideo node.
  • Unlike Wan 2.1, Wan 2.2 does not use clip_vision.

FLF2V (14B / First–Last Frame to Video)

Wan 2.1 had a dedicated model for FLF2V, but Wan 2.2's image2video model also supports FLF2V.

In ComfyUI, you can generate a video that interpolates between two images simply by inputting the Start / End images into the WanFirstLastFrameToVideo node.

wan2_2_image2video_flf2v_14b.json
  • 🟦 As with Wan 2.1, input the Start / End images into the WanFirstLastFrameToVideo node.

Wan 2.2 5B Model (TI2V-5B)

Wan 2.2-TI2V-5B is a TI2V model that handles both text2video and image2video with a single model. By combining a higher compression VAE and patchification processing, it can generate 720p, 24fps, ~5 second videos with lighter computational cost than 14B.

Rather than being a 1.3B-like scaled-down version of 14B, it is better to think of it as a line with a fundamentally different design.

The design is interesting, but unfortunately, it cannot beat 14B in performance, and in reality, it is rarely used.

Recommended Settings

  • Recommended Resolution
    • 720p (1280×720)
  • Maximum Number of Frames
    • 121 frames
  • FPS
    • 24fps

Model Download

Placement example:

📂ComfyUI/
└── 📂models/
    ├── 📂diffusion_models/
    │   └── wan2.2_ti2v_5B_fp16.safetensors
    ├── 📂unet/
    │   └── Wan2.2-TI2V-5B-XXXX.gguf     ← Only when using gguf
    └── 📂vae/
        └── wan2.2_vae.safetensors

text2video (5B)

wan2_2_text2video_5b.json

In 5B text2video, the video is generated internally via the "first frame latent".

  • 🟥 Use wan2.2_vae for VAE. Since the compression structure is different from Wan 2.1 VAE, failing to replace it will cause significant degradation in image quality and movement.
  • 🟩 Even for text2video, insert a latent node for TI2V (e.g., Wan22ImageToVideoLatent). Since 5B is designed based on the "1 frame latent -> video" pipeline, a configuration that skips this step is not envisaged.

If you understand it as "text2video, but essentially a special case of image2video", it will be easier to organize along with other TI2V models.

image2video (5B)

wan2_2_image2video_5b.json

image2video also uses the same TI2V model as text2video. Only the input increases, and the subsequent steps from KSampler onwards are almost common.

  • 🟦 Input the start image into the latent node for TI2V to create a compressed latent.
  • 🟩 Since both text2video / image2video can be done with the same model, it is easy to solidify the workflow with 5B first and then add 14B as needed.

Reference Links