Video Generation - Wan2.2 TI2V 5B

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Compact Model for Both Text-to-Video and Image-to-Video

Generate 720p video clips at 24 fps from a text prompt or from a text prompt anchored to a starting image using the same single model.

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What it does

Wan2.2 TI2V 5B is a video generation model from the Wan-AI team. "TI2V" stands for Text-Image-to-Video, meaning the same model can do both text-to-video (give it just a text prompt and it imagines the scene from scratch) and image-to-video (give it a starting image plus a text prompt and it animates the image). Whichever mode you use depends only on whether you pass an image. The model outputs MP4 video at 720p (1280×704 or 704×1280) with 24 fps, and was designed specifically to be small and fast - only 5 billion parameters, runnable on a single consumer-grade GPU. Bilingual prompts (English and Chinese) are supported. The "secret sauce" behind its speed/size at this quality level is a high-compression video VAE (the Wan2.2-VAE) that shrinks video data much more aggressively than typical video diffusion VAEs while still reconstructing it cleanly.

Problem it solves

One model for two modes – Text-to-video and image-to-video in a single unified model, no need to swap checkpoints or pipelines
720p / 24 fps output – Cinematic-class resolution and frame rate at small-model cost
Runs on consumer GPUs – Works on a single RTX 4090 (≥24 GB VRAM) — no datacenter GPU required
Fast – Among the fastest 720p@24fps open-source video generators; 5 seconds of 720p video in <9 minutes on a 4090
Animate a still image – Provide a reference image plus a text description; the model produces video starting from that image, following the prompt
Bilingual prompts – Natively supports both English and Chinese text prompts
Open and permissive – Apache 2.0 license; users own their generated content

Input/Output

Input:
- Text to Video:
  - Plain-language description of the scene you want
  - English or Chinese
- Image to Video:
  - One reference image to use as the first frame
  - A text description of what should happen starting from that image
Options (defaults from the HF model card):
- Resolution (W × H, ratio) — default 1280 × 704 (16:9)
  - Pick the ratio that matches the input image (when using image-to-video)
- Frame count (Wan VAE multiples) — default 121 frames (~5 s at 24 fps)
  - Must be a value the Wan2.2-VAE accepts — pick one of the offered multiples
- Output FPS (24 is the native frame rate) — default 24
- Diffusion steps (20 – 60, default 50)
  - More steps = higher quality, slower generation
- Advanced Options:
  - CFG scale (default 5) — how strictly the output follows the prompt
  - Additional negative prompt (default empty) — extra description of what to avoid in the output. The model already ships with a built-in default negative prompt (Chinese-language list suppressing bad anatomy, extra fingers, JPEG artifacts, etc.); this field is for anything you want to add on top
  - Random seed (-1 = random, default 1) — set a fixed integer to reproduce the exact same video
Output: Generated video
- Format: MP4
- Resolution: 720p (1280×704 or 704×1280)
- Frame rate: 24 fps
- Length: Configurable via the frame count (default 121 frames ≈ 5 s at 24 fps)
Test Wan2.2.mp4

Accuracy & Speed


Model size	5 billion parameters — a dense model (no Mixture-of-Experts)
Speed (single GPU)	A 5-second 720p video in < 9 minutes on a single consumer-grade GPU (e.g., RTX 4090); among the fastest 720p@24fps open-source video generators
VRAM requirement	≥ 24 GB VRAM for single-GPU 720p inference (e.g., RTX 4090)
Multi-GPU scaling	FSDP + DeepSpeed Ulysses for distributed inference; `--ulysses_size 8` etc.
Bigger siblings	Wan2.2 T2V-A14B (text-to-video, MoE, ~27 B total / 14 B active) and Wan2.2 I2V-A14B (image-to-video, MoE) — higher quality, much heavier to run
Languages	English and Chinese prompts
Default video output	1280 × 704 at 24 fps, 121 frames (≈ 5 seconds)
Comparisons	The Wan team reports favorable results vs. leading closed-source commercial video generators on their Wan-Bench 2.0 evaluation

Technical Details


Architecture	Dense video diffusion transformer with the Wan2.2-VAE (a high-compression video VAE)
VAE Compression	4 × 16 × 16 (Time × Height × Width) — overall 64× compression vs. raw pixels
Effective Compression with Patchification	4 × 32 × 32 ≈ 4096× — what enables a 5B dense model to handle 720p video efficiently
Why it's small and fast	The high-compression VAE shrinks the latent space dramatically, so the diffusion transformer can work in a much smaller space — letting a 5B dense model match what would otherwise require a much bigger model
Model family note	The Wan2.2 family's larger 14B variants (T2V-A14B, I2V-A14B) use a Mixture-of-Experts architecture with a high-noise expert (early denoising) and a low-noise expert (later denoising). The 5B TI2V variant is dense — different design choice, optimized for compactness and speed
Parameters	5B (dense)
Precision	bfloat16 (recommended)
Default Inference Hyperparameters	`num_inference_steps=50`, `guidance_scale=5.0`, `num_frames=121`, `height=704`, `width=1280`, `fps=24`
Training Data (vs. Wan2.1)	+65.6% more images, +83.2% more videos; meticulously curated aesthetic data with detailed labels for lighting, composition, contrast, color tone
Supported Frameworks	Hugging Face Diffusers (`WanPipeline`, `AutoencoderKLWan`, `WanTransformer3DModel`, `UniPCMultistepScheduler`), Safetensors, ComfyUI (official integration)
Diffusers Note	Currently requires the `main` branch of diffusers (not the latest PyPI release): `pip install git+https://github.com/huggingface/diffusers`
Release date	July 28, 2025
Paper	"Wan: Open and Advanced Large-Scale Video Generative Models" — arXiv:2503.20314