Controlnet for Wan2.2 (tile)
This repo contains the code for controlnet module for Wan2.2. See Github code.
Same approach as controlnet for Wan2.1.
For ComfyUI
Use the cool ComfyUI-WanVideoWrapper.
Inference examples
Simple inference with cli
python -m inference.cli_demo \
--video_path "resources/bubble.mp4" \
--prompt "Close-up shot with soft lighting, focusing sharply on the lower half of a young woman's face. Her lips are slightly parted as she blows an enormous bubblegum bubble. The bubble is semi-transparent, shimmering gently under the light, and surprisingly contains a miniature aquarium inside, where two orange-and-white goldfish slowly swim, their fins delicately fluttering as if in an aquatic universe. The background is a pure light blue color." \
--controlnet_type "tile" \
--base_model_path Wan-AI/Wan2.2-TI2V-5B-Diffusers \
--controlnet_model_path TheDenk/wan2.2-ti2v-5b-controlnet-tile-v1
Minimal code example
import os
os.environ['CUDA_VISIBLE_DEVICES'] = "0"
os.environ["TOKENIZERS_PARALLELISM"] = "false"
import cv2
from PIL import Image
import torch
from diffusers.utils import load_video, export_to_video
from diffusers import AutoencoderKLWan, UniPCMultistepScheduler
from wan_controlnet import WanControlnet
from wan_transformer import CustomWanTransformer3DModel
from wan_t2v_controlnet_pipeline import WanTextToVideoControlnetPipeline
base_model_path = "Wan-AI/Wan2.2-TI2V-5B-Diffusers"
controlnet_model_path = "TheDenk/wan2.2-ti2v-5b-controlnet-tile-v1"
vae = AutoencoderKLWan.from_pretrained(base_model_path, subfolder="vae", torch_dtype=torch.float32)
transformer = CustomWanTransformer3DModel.from_pretrained(base_model_path, subfolder="transformer", torch_dtype=torch.bfloat16)
controlnet = WanControlnet.from_pretrained(controlnet_model_path, torch_dtype=torch.bfloat16)
pipe = WanTextToVideoControlnetPipeline.from_pretrained(
pretrained_model_name_or_path=base_model_path,
controlnet=controlnet,
transformer=transformer,
vae=vae,
torch_dtype=torch.bfloat16
)
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config, flow_shift=5.0)
pipe.enable_model_cpu_offload()
img_h = 704 # 704 480
img_w = 1280 # 1280 832
num_frames = 121 # 121 81 49
def apply_gaussian_blur(image, ksize=5, sigmaX=1.0):
image_np = np.array(image)
if ksize % 2 == 0:
ksize += 1
blurred_image = cv2.GaussianBlur(image_np, (ksize, ksize), sigmaX=sigmaX)
return Image.fromarray(blurred_image)
video_path = 'bubble.mp4'
video_frames = load_video(video_path)[:num_frames]
ksize = 5
downscale_coef =4
controlnet_frames = [x.resize((img_w // downscale_coef, img_h // downscale_coef)) for x in video_frames]
controlnet_frames = [apply_gaussian_blur(x, ksize=ksize, sigmaX=ksize // 2) for x in controlnet_frames]
controlnet_frames = [x.resize((img_w, img_h)) for x in controlnet_frames]
prompt = "Close-up shot with soft lighting, focusing sharply on the lower half of a young woman's face. Her lips are slightly parted as she blows an enormous bubblegum bubble. The bubble is semi-transparent, shimmering gently under the light, and surprisingly contains a miniature aquarium inside, where two orange-and-white goldfish slowly swim, their fins delicately fluttering as if in an aquatic universe. The background is a pure light blue color."
negative_prompt = "bad quality, worst quality"
output = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
height=img_h,
width=img_w,
num_frames=num_frames,
guidance_scale=5,
generator=torch.Generator(device="cuda").manual_seed(42),
output_type="pil",
controlnet_frames=controlnet_frames,
controlnet_guidance_start=0.0,
controlnet_guidance_end=0.8,
controlnet_weight=0.8,
teacache_treshold=0.6,
).frames[0]
export_to_video(output, "output.mp4", fps=16)
Acknowledgements
Original code and models Wan2.2.
Citations
@misc{TheDenk,
title={Wan2.2 Controlnet},
author={Karachev Denis},
url={https://github.com/TheDenk/wan2.2-controlnet},
publisher={Github},
year={2025}
}