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# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
import numpy as np
from PIL import Image
import torch
import torch.nn.functional as F
import torchvision.transforms.functional as TF
class VaceImageProcessor(object):
def __init__(self, downsample=None, seq_len=None):
self.downsample = downsample
self.seq_len = seq_len
def _pillow_convert(self, image, cvt_type='RGB'):
if image.mode != cvt_type:
if image.mode == 'P':
image = image.convert(f'{cvt_type}A')
if image.mode == f'{cvt_type}A':
bg = Image.new(cvt_type,
size=(image.width, image.height),
color=(255, 255, 255))
bg.paste(image, (0, 0), mask=image)
image = bg
else:
image = image.convert(cvt_type)
return image
def _load_image(self, img_path):
if img_path is None or img_path == '':
return None
img = Image.open(img_path)
img = self._pillow_convert(img)
return img
def _resize_crop(self, img, oh, ow, normalize=True):
"""
Resize, center crop, convert to tensor, and normalize.
"""
# resize and crop
iw, ih = img.size
if iw != ow or ih != oh:
# resize
scale = max(ow / iw, oh / ih)
img = img.resize(
(round(scale * iw), round(scale * ih)),
resample=Image.Resampling.LANCZOS
)
assert img.width >= ow and img.height >= oh
# center crop
x1 = (img.width - ow) // 2
y1 = (img.height - oh) // 2
img = img.crop((x1, y1, x1 + ow, y1 + oh))
# normalize
if normalize:
img = TF.to_tensor(img).sub_(0.5).div_(0.5).unsqueeze(1)
return img
def _image_preprocess(self, img, oh, ow, normalize=True, **kwargs):
return self._resize_crop(img, oh, ow, normalize)
def load_image(self, data_key, **kwargs):
return self.load_image_batch(data_key, **kwargs)
def load_image_pair(self, data_key, data_key2, **kwargs):
return self.load_image_batch(data_key, data_key2, **kwargs)
def load_image_batch(self, *data_key_batch, normalize=True, seq_len=None, **kwargs):
seq_len = self.seq_len if seq_len is None else seq_len
imgs = []
for data_key in data_key_batch:
img = self._load_image(data_key)
imgs.append(img)
w, h = imgs[0].size
dh, dw = self.downsample[1:]
# compute output size
scale = min(1., np.sqrt(seq_len / ((h / dh) * (w / dw))))
oh = int(h * scale) // dh * dh
ow = int(w * scale) // dw * dw
assert (oh // dh) * (ow // dw) <= seq_len
imgs = [self._image_preprocess(img, oh, ow, normalize) for img in imgs]
return *imgs, (oh, ow)
class VaceVideoProcessor(object):
def __init__(self, downsample, min_area, max_area, min_fps, max_fps, zero_start, seq_len, keep_last, **kwargs):
self.downsample = downsample
self.min_area = min_area
self.max_area = max_area
self.min_fps = min_fps
self.max_fps = max_fps
self.zero_start = zero_start
self.keep_last = keep_last
self.seq_len = seq_len
assert seq_len >= min_area / (self.downsample[1] * self.downsample[2])
def set_area(self, area):
self.min_area = area
self.max_area = area
def set_seq_len(self, seq_len):
self.seq_len = seq_len
@staticmethod
def resize_crop(video: torch.Tensor, oh: int, ow: int):
"""
Resize, center crop and normalize for decord loaded video (torch.Tensor type)
Parameters:
video - video to process (torch.Tensor): Tensor from `reader.get_batch(frame_ids)`, in shape of (T, H, W, C)
oh - target height (int)
ow - target width (int)
Returns:
The processed video (torch.Tensor): Normalized tensor range [-1, 1], in shape of (C, T, H, W)
Raises:
"""
# permute ([t, h, w, c] -> [t, c, h, w])
video = video.permute(0, 3, 1, 2)
# resize and crop
ih, iw = video.shape[2:]
if ih != oh or iw != ow:
# resize
scale = max(ow / iw, oh / ih)
video = F.interpolate(
video,
size=(round(scale * ih), round(scale * iw)),
mode='bicubic',
antialias=True
)
assert video.size(3) >= ow and video.size(2) >= oh
# center crop
x1 = (video.size(3) - ow) // 2
y1 = (video.size(2) - oh) // 2
video = video[:, :, y1:y1 + oh, x1:x1 + ow]
# permute ([t, c, h, w] -> [c, t, h, w]) and normalize
video = video.transpose(0, 1).float().div_(127.5).sub_(1.)
return video
def _video_preprocess(self, video, oh, ow):
return self.resize_crop(video, oh, ow)
def _get_frameid_bbox_default(self, fps, frame_timestamps, h, w, crop_box, rng):
target_fps = min(fps, self.max_fps)
duration = frame_timestamps[-1].mean()
x1, x2, y1, y2 = [0, w, 0, h] if crop_box is None else crop_box
h, w = y2 - y1, x2 - x1
ratio = h / w
df, dh, dw = self.downsample
area_z = min(self.seq_len, self.max_area / (dh * dw), (h // dh) * (w // dw))
of = min(
(int(duration * target_fps) - 1) // df + 1,
int(self.seq_len / area_z)
)
# deduce target shape of the [latent video]
target_area_z = min(area_z, int(self.seq_len / of))
oh = round(np.sqrt(target_area_z * ratio))
ow = int(target_area_z / oh)
of = (of - 1) * df + 1
oh *= dh
ow *= dw
# sample frame ids
target_duration = of / target_fps
begin = 0. if self.zero_start else rng.uniform(0, duration - target_duration)
timestamps = np.linspace(begin, begin + target_duration, of)
frame_ids = np.argmax(np.logical_and(
timestamps[:, None] >= frame_timestamps[None, :, 0],
timestamps[:, None] < frame_timestamps[None, :, 1]
), axis=1).tolist()
return frame_ids, (x1, x2, y1, y2), (oh, ow), target_fps
def _get_frameid_bbox_adjust_last(self, fps, frame_timestamps, h, w, crop_box, rng):
duration = frame_timestamps[-1].mean()
x1, x2, y1, y2 = [0, w, 0, h] if crop_box is None else crop_box
h, w = y2 - y1, x2 - x1
ratio = h / w
df, dh, dw = self.downsample
area_z = min(self.seq_len, self.max_area / (dh * dw), (h // dh) * (w // dw))
of = min(
(len(frame_timestamps) - 1) // df + 1,
int(self.seq_len / area_z)
)
# deduce target shape of the [latent video]
target_area_z = min(area_z, int(self.seq_len / of))
oh = round(np.sqrt(target_area_z * ratio))
ow = int(target_area_z / oh)
of = (of - 1) * df + 1
oh *= dh
ow *= dw
# sample frame ids
target_duration = duration
target_fps = of / target_duration
timestamps = np.linspace(0., target_duration, of)
frame_ids = np.argmax(np.logical_and(
timestamps[:, None] >= frame_timestamps[None, :, 0],
timestamps[:, None] <= frame_timestamps[None, :, 1]
), axis=1).tolist()
# print(oh, ow, of, target_duration, target_fps, len(frame_timestamps), len(frame_ids))
return frame_ids, (x1, x2, y1, y2), (oh, ow), target_fps
def _get_frameid_bbox(self, fps, frame_timestamps, h, w, crop_box, rng):
if self.keep_last:
return self._get_frameid_bbox_adjust_last(fps, frame_timestamps, h, w, crop_box, rng)
else:
return self._get_frameid_bbox_default(fps, frame_timestamps, h, w, crop_box, rng)
def load_video(self, data_key, crop_box=None, seed=2024, **kwargs):
return self.load_video_batch(data_key, crop_box=crop_box, seed=seed, **kwargs)
def load_video_pair(self, data_key, data_key2, crop_box=None, seed=2024, **kwargs):
return self.load_video_batch(data_key, data_key2, crop_box=crop_box, seed=seed, **kwargs)
def load_video_batch(self, *data_key_batch, crop_box=None, seed=2024, **kwargs):
rng = np.random.default_rng(seed + hash(data_key_batch[0]) % 10000)
# read video
import decord
decord.bridge.set_bridge('torch')
readers = []
for data_k in data_key_batch:
reader = decord.VideoReader(data_k)
readers.append(reader)
fps = readers[0].get_avg_fps()
length = min([len(r) for r in readers])
frame_timestamps = [readers[0].get_frame_timestamp(i) for i in range(length)]
frame_timestamps = np.array(frame_timestamps, dtype=np.float32)
h, w = readers[0].next().shape[:2]
frame_ids, (x1, x2, y1, y2), (oh, ow), fps = self._get_frameid_bbox(fps, frame_timestamps, h, w, crop_box, rng)
# preprocess video
videos = [reader.get_batch(frame_ids)[:, y1:y2, x1:x2, :] for reader in readers]
videos = [self._video_preprocess(video, oh, ow) for video in videos]
return *videos, frame_ids, (oh, ow), fps
# return videos if len(videos) > 1 else videos[0]
def prepare_source(src_video, src_mask, src_ref_images, num_frames, image_size, device):
for i, (sub_src_video, sub_src_mask) in enumerate(zip(src_video, src_mask)):
if sub_src_video is None and sub_src_mask is None:
src_video[i] = torch.zeros((3, num_frames, image_size[0], image_size[1]), device=device)
src_mask[i] = torch.ones((1, num_frames, image_size[0], image_size[1]), device=device)
for i, ref_images in enumerate(src_ref_images):
if ref_images is not None:
for j, ref_img in enumerate(ref_images):
if ref_img is not None and ref_img.shape[-2:] != image_size:
canvas_height, canvas_width = image_size
ref_height, ref_width = ref_img.shape[-2:]
white_canvas = torch.ones((3, 1, canvas_height, canvas_width), device=device) # [-1, 1]
scale = min(canvas_height / ref_height, canvas_width / ref_width)
new_height = int(ref_height * scale)
new_width = int(ref_width * scale)
resized_image = F.interpolate(ref_img.squeeze(1).unsqueeze(0), size=(new_height, new_width), mode='bilinear', align_corners=False).squeeze(0).unsqueeze(1)
top = (canvas_height - new_height) // 2
left = (canvas_width - new_width) // 2
white_canvas[:, :, top:top + new_height, left:left + new_width] = resized_image
src_ref_images[i][j] = white_canvas
return src_video, src_mask, src_ref_images
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