| import torch |
| import numpy as np |
| from diffusers import WanImageToVideoPipeline, AutoencoderKLWan |
| from diffusers.utils import export_to_video, load_image |
|
|
|
|
| model_id = "/mnt/bn/yufan-dev-my/ysh/Ckpts/Wan-AI/Wan2.2-TI2V-5B-Diffusers" |
| dtype = torch.bfloat16 |
| device = "cuda" |
|
|
| vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32) |
| pipe = WanImageToVideoPipeline.from_pretrained(model_id, vae=vae, torch_dtype=dtype) |
| vae.to(device) |
| pipe.to(device) |
|
|
| |
| image = load_image( |
| "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/astronaut.jpg" |
| ) |
| max_area = 480 * 832 |
| aspect_ratio = image.height / image.width |
| mod_value = pipe.vae_scale_factor_spatial * pipe.transformer.config.patch_size[1] |
| height = round(np.sqrt(max_area * aspect_ratio)) // mod_value * mod_value |
| width = round(np.sqrt(max_area / aspect_ratio)) // mod_value * mod_value |
| image = image.resize((width, height)) |
|
|
| height, width = image.height, image.width |
| print(f"height: {height}, width: {width}") |
| num_frames = 121 |
| 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) |
