exp_code/1_benchmark/Wan/inference_5B_i2v.py

from cv2 import transform
import torch
import numpy as np
from diffusers import AutoencoderKLWan, ModularPipeline
from diffusers.utils import export_to_video
from modulars.pipeline_wan_i2v import WanImageToVideoPipeline
from modulars.transformer_wan import WanTransformer3DModel

model_id = "/mnt/workspace/checkpoints/Wan-AI/Wan2.2-TI2V-5B-Diffusers"
dtype = torch.bfloat16
device = "cuda:0"

transformer = WanTransformer3DModel.from_pretrained(model_id, subfolder="transformer", torch_dtype=torch.bfloat16)
vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
pipe = WanImageToVideoPipeline.from_pretrained(model_id, transformer=transformer, vae=vae, torch_dtype=dtype)
pipe.enable_model_cpu_offload(device=device)

# use default wan image processor to resize and crop the image
image_processor = ModularPipeline.from_pretrained("/mnt/workspace/checkpoints/YiYiXu/WanImageProcessor", trust_remote_code=True)
image = image_processor(
    image="wan_i2v_input.JPG",
    max_area=1280*704, output="processed_image")

height, width = image.height, image.width
print(f"height: {height}, width: {width}")
num_frames = 33
num_inference_steps = 50
guidance_scale = 5.0

prompt = "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside."

negative_prompt = "色调艳丽，过曝，静态，细节模糊不清，字幕，风格，作品，画作，画面，静止，整体发灰，最差质量，低质量，JPEG压缩残留，丑陋的，残缺的，多余的手指，画得不好的手部，画得不好的脸部，畸形的，毁容的，形态畸形的肢体，手指融合，静止不动的画面，杂乱的背景，三条腿，背景人很多，倒着走"

output = pipe(
    image=image,
    prompt=prompt,
    negative_prompt=negative_prompt,
    height=height,
    width=width,
    num_frames=num_frames,
    guidance_scale=guidance_scale,
    num_inference_steps=num_inference_steps,
).frames[0]
export_to_video(output, "yiyi_test_6_ti2v_5b_output.mp4", fps=24)