Wan2.2 TI2V 5B
Frontierby Wan-AI
5B text+image-to-video model from the Wan 2.2 family. Runs on consumer GPUs with 8GB+ VRAM. Takes text and reference image as input to generate coherent video clips.
- 5B params — runs on 8GB+ VRAM
- Text + image to video generation
- Apache 2.0 — fully open for commercial use
- Part of the Wan 2.2 family
Your hardware
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Image Quality Benchmarks
Measured quality metrics for Wan2.2 TI2V 5B outputs.
How often humans prefer this model's output (0-100%)
Visual quality and composition rating (5-9 scale)
VRAM by Scenario
VRAM estimates at FP16 and FP8 precision. FP8 uses ~40% less memory with minimal quality loss. Grade shows how well each GPU handles the generation workload.
FP16 (full precision)
| Scenario | VRAM | RTX 4090 24GB | RTX 3060 12GB | RTX 4060 8GB | MacBook Pro M4 Pro 24GB |
|---|---|---|---|---|---|
| 512×512 · 25 frames | 25.3 GB | B | F | F | F |
| 768×512 · 25 frames | 27.4 GB | B | F | F | F |
| 768×512 · 100 frames | 33.7 GB | F | F | F | F |
| 1280×720 · 25 frames | 35.9 GB | F | F | F | F |
FP8 (quantized — ~40% less VRAM)
| Scenario | VRAM | RTX 4090 24GB | RTX 3060 12GB | RTX 4060 8GB | MacBook Pro M4 Pro 24GB |
|---|---|---|---|---|---|
| 512×512 · 25 frames | 15.1 GB | S | D | F | A |
| 768×512 · 25 frames | 17.2 GB | S | F | F | B |
| 768×512 · 100 frames | 23.5 GB | B | F | F | D |
| 1280×720 · 25 frames | 25.7 GB | B | F | F | F |
Optimization Tips
Turbo / LCM distillation
Use distilled scheduler at 4-8 steps for faster iteration
Run with Python
from diffusers import WanPipeline
import torch
pipe = WanPipeline.from_pretrained(
"Wan-AI/Wan2.2-TI2V-5B-Diffusers",
torch_dtype=torch.float16
)
pipe.to("cuda")
frames = pipe(
prompt="your prompt here",
num_inference_steps=50,
guidance_scale=5.0,
num_frames=81,
).frames[0]
# Save frames or export as videoGet started
Setup instructions for running Wan2.2 TI2V 5B locally
1. Download the model
Get the checkpoint from HuggingFace
2. Place in:
ComfyUI/models/checkpoints/3. Launch ComfyUI
python main.pyMemory Breakdown
VRAM allocation for 25 frames at 768×512 on RTX 4090 24GB
Estimated Generation Time
25 frames at 768×512, 30 steps, FP16.
Sample Outputs
Available Formats & Downloads
Download Wan2.2 TI2V 5B in different precisions. Lower precision = less VRAM but slight quality loss.
| Formato | Precisão | Tamanho | Provedor | |
|---|---|---|---|---|
| safetensorsRecomendado | BF16 | 10.5 GB | official | Baixar |
LoRA Ecosystem
LimitedEarly LoRA ecosystem. Compatible with some Wan 2.1 LoRAs.
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Frequently asked questions
FAQ — Wan2.2 TI2V 5B
How much VRAM does Wan2.2 TI2V 5B need for video?
Wan2.2 TI2V 5B (5B parameters) requires approximately 27.4 GB of VRAM at FP16 precision for generating 25 frames at 768×512. Video generation typically requires more VRAM than image generation due to temporal attention layers.
Can I run Wan2.2 TI2V 5B on RTX 4090?
Wan2.2 TI2V 5B can run on the RTX 4090 with sequential offloading, though video generation will be significantly slower than native fit.
How long does it take to generate a video with Wan2.2 TI2V 5B?
On a reference GPU (RTX 4090 24GB), Wan2.2 TI2V 5B generates a 25-frame video at 768×512 in approximately ~4m 23s at FP16 with 30 inference steps. Faster GPUs with higher memory bandwidth will reduce generation time.
What resolution and frame count does Wan2.2 TI2V 5B support?
Wan2.2 TI2V 5B supports up to 832×480 resolution and 81 frames per generation at 16 FPS. Higher resolutions and frame counts require proportionally more VRAM.
About Wan2.2 TI2V 5B
See also