| 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) |
|
|
| |
| 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) |
