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---
license: apache-2.0
---
```
from diffusers.utils import load_image, export_to_video
from transformers import CLIPVisionModel, CLIPImageProcessor, UMT5EncoderModel
from diffusers import WanI2VPipeline, WanTransformer3DModel
import torch
pretrained_model_name_or_path = "./wan_i2v" # TODO replace with our hf id
image_encoder = CLIPVisionModel.from_pretrained(pretrained_model_name_or_path, subfolder='image_encoder',
torch_dtype=torch.float16)
transformer_i2v = WanTransformer3DModel.from_pretrained(pretrained_model_name_or_path, subfolder='transformer_i2v_480p')
# for 720p
# transformer_i2v = WanTransformer3DModel.from_pretrained(pretrained_model_name_or_path, subfolder='transformer_i2v_720p',
# torch_dtype=torch.bfloat16)
image_processor = CLIPImageProcessor.from_pretrained(pretrained_model_name_or_path, subfolder='image_processor')
text_encoder = UMT5EncoderModel.from_pretrained(pretrained_model_name_or_path, subfolder='text_encoder',
torch_dtype=torch.bfloat16)
pipe = WanI2VPipeline.from_pretrained(
pretrained_model_name_or_path,
transformer=transformer_i2v,
text_encoder=text_encoder,
image_encoder=image_encoder,
image_processor=image_processor,
)
image = load_image(
"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/astronaut.jpg"
)
device = "cuda"
seed = 0
prompt = ("An astronaut hatching from an egg, on the surface of the moon, the darkness and depth of space realised in "
"the background. High quality, ultrarealistic detail and breath-taking movie-like camera shot.")
generator = torch.Generator(device=device).manual_seed(seed)
# pipe.to(device)
pipe.enable_model_cpu_offload()
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'
inputs = {
'image': image,
"prompt": prompt,
# 'max_area': 720 * 1280, # for 720p
"negative_prompt": negative_prompt,
'max_area': 480 * 832,
"generator": generator,
"num_inference_steps": 40,
"guidance_scale": 5.0,
"num_frames": 81,
"max_sequence_length": 512,
"output_type": "np",
# 'flow_shift': 5.0, # for 720p
'flow_shift': 3.0
}
output = pipe(**inputs).frames[0]
export_to_video(output, "output.mp4", fps=16)
``` |