mirror of
https://git.datalinker.icu/kijai/ComfyUI-CogVideoXWrapper.git
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171 lines
8.2 KiB
Python
171 lines
8.2 KiB
Python
import os
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import gc
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import numpy as np
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import torch
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from PIL import Image
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# Copyright (c) OpenMMLab. All rights reserved.
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def tensor2pil(image):
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return Image.fromarray(np.clip(255. * image.cpu().numpy(), 0, 255).astype(np.uint8))
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def numpy2pil(image):
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return Image.fromarray(np.clip(255. * image, 0, 255).astype(np.uint8))
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def to_pil(image):
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if isinstance(image, Image.Image):
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return image
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if isinstance(image, torch.Tensor):
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return tensor2pil(image)
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if isinstance(image, np.ndarray):
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return numpy2pil(image)
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raise ValueError(f"Cannot convert {type(image)} to PIL.Image")
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ASPECT_RATIO_512 = {
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'0.25': [256.0, 1024.0], '0.26': [256.0, 992.0], '0.27': [256.0, 960.0], '0.28': [256.0, 928.0],
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'0.32': [288.0, 896.0], '0.33': [288.0, 864.0], '0.35': [288.0, 832.0], '0.4': [320.0, 800.0],
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'0.42': [320.0, 768.0], '0.48': [352.0, 736.0], '0.5': [352.0, 704.0], '0.52': [352.0, 672.0],
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'0.57': [384.0, 672.0], '0.6': [384.0, 640.0], '0.68': [416.0, 608.0], '0.72': [416.0, 576.0],
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'0.78': [448.0, 576.0], '0.82': [448.0, 544.0], '0.88': [480.0, 544.0], '0.94': [480.0, 512.0],
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'1.0': [512.0, 512.0], '1.07': [512.0, 480.0], '1.13': [544.0, 480.0], '1.21': [544.0, 448.0],
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'1.29': [576.0, 448.0], '1.38': [576.0, 416.0], '1.46': [608.0, 416.0], '1.67': [640.0, 384.0],
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'1.75': [672.0, 384.0], '2.0': [704.0, 352.0], '2.09': [736.0, 352.0], '2.4': [768.0, 320.0],
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'2.5': [800.0, 320.0], '2.89': [832.0, 288.0], '3.0': [864.0, 288.0], '3.11': [896.0, 288.0],
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'3.62': [928.0, 256.0], '3.75': [960.0, 256.0], '3.88': [992.0, 256.0], '4.0': [1024.0, 256.0]
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}
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ASPECT_RATIO_RANDOM_CROP_512 = {
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'0.42': [320.0, 768.0], '0.5': [352.0, 704.0],
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'0.57': [384.0, 672.0], '0.68': [416.0, 608.0], '0.78': [448.0, 576.0], '0.88': [480.0, 544.0],
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'0.94': [480.0, 512.0], '1.0': [512.0, 512.0], '1.07': [512.0, 480.0],
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'1.13': [544.0, 480.0], '1.29': [576.0, 448.0], '1.46': [608.0, 416.0], '1.75': [672.0, 384.0],
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'2.0': [704.0, 352.0], '2.4': [768.0, 320.0]
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}
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ASPECT_RATIO_RANDOM_CROP_PROB = [
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1, 2,
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4, 4, 4, 4,
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8, 8, 8,
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4, 4, 4, 4,
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2, 1
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]
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ASPECT_RATIO_RANDOM_CROP_PROB = np.array(ASPECT_RATIO_RANDOM_CROP_PROB) / sum(ASPECT_RATIO_RANDOM_CROP_PROB)
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def get_closest_ratio(height: float, width: float, ratios: dict = ASPECT_RATIO_512):
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aspect_ratio = height / width
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closest_ratio = min(ratios.keys(), key=lambda ratio: abs(float(ratio) - aspect_ratio))
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return ratios[closest_ratio], float(closest_ratio)
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def get_width_and_height_from_image_and_base_resolution(image, base_resolution):
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target_pixels = int(base_resolution) * int(base_resolution)
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original_width, original_height = Image.open(image).size
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ratio = (target_pixels / (original_width * original_height)) ** 0.5
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width_slider = round(original_width * ratio)
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height_slider = round(original_height * ratio)
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return height_slider, width_slider
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def get_image_to_video_latent(validation_image_start, validation_image_end, video_length, sample_size):
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if validation_image_start is not None and validation_image_end is not None:
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if type(validation_image_start) is str and os.path.isfile(validation_image_start):
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image_start = clip_image = Image.open(validation_image_start).convert("RGB")
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image_start = image_start.resize([sample_size[1], sample_size[0]])
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clip_image = clip_image.resize([sample_size[1], sample_size[0]])
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else:
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image_start = clip_image = validation_image_start
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image_start = [_image_start.resize([sample_size[1], sample_size[0]]) for _image_start in image_start]
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clip_image = [_clip_image.resize([sample_size[1], sample_size[0]]) for _clip_image in clip_image]
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if type(validation_image_end) is str and os.path.isfile(validation_image_end):
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image_end = Image.open(validation_image_end).convert("RGB")
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image_end = image_end.resize([sample_size[1], sample_size[0]])
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else:
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image_end = validation_image_end
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image_end = [_image_end.resize([sample_size[1], sample_size[0]]) for _image_end in image_end]
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if type(image_start) is list:
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clip_image = clip_image[0]
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start_video = torch.cat(
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[torch.from_numpy(np.array(_image_start)).permute(2, 0, 1).unsqueeze(1).unsqueeze(0) for _image_start in image_start],
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dim=2
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)
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input_video = torch.tile(start_video[:, :, :1], [1, 1, video_length, 1, 1])
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input_video[:, :, :len(image_start)] = start_video
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input_video_mask = torch.zeros_like(input_video[:, :1])
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input_video_mask[:, :, len(image_start):] = 255
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else:
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input_video = torch.tile(
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torch.from_numpy(np.array(image_start)).permute(2, 0, 1).unsqueeze(1).unsqueeze(0),
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[1, 1, video_length, 1, 1]
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)
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input_video_mask = torch.zeros_like(input_video[:, :1])
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input_video_mask[:, :, 1:] = 255
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if type(image_end) is list:
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image_end = [_image_end.resize(image_start[0].size if type(image_start) is list else image_start.size) for _image_end in image_end]
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end_video = torch.cat(
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[torch.from_numpy(np.array(_image_end)).permute(2, 0, 1).unsqueeze(1).unsqueeze(0) for _image_end in image_end],
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dim=2
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)
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input_video[:, :, -len(end_video):] = end_video
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input_video_mask[:, :, -len(image_end):] = 0
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else:
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image_end = image_end.resize(image_start[0].size if type(image_start) is list else image_start.size)
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input_video[:, :, -1:] = torch.from_numpy(np.array(image_end)).permute(2, 0, 1).unsqueeze(1).unsqueeze(0)
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input_video_mask[:, :, -1:] = 0
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input_video = input_video / 255
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elif validation_image_start is not None:
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if type(validation_image_start) is str and os.path.isfile(validation_image_start):
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image_start = clip_image = Image.open(validation_image_start).convert("RGB")
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image_start = image_start.resize([sample_size[1], sample_size[0]])
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clip_image = clip_image.resize([sample_size[1], sample_size[0]])
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else:
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image_start = clip_image = validation_image_start
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image_start = [_image_start.resize([sample_size[1], sample_size[0]]) for _image_start in image_start]
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clip_image = [_clip_image.resize([sample_size[1], sample_size[0]]) for _clip_image in clip_image]
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image_end = None
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if type(image_start) is list:
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clip_image = clip_image[0]
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start_video = torch.cat(
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[torch.from_numpy(np.array(_image_start)).permute(2, 0, 1).unsqueeze(1).unsqueeze(0) for _image_start in image_start],
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dim=2
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)
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input_video = torch.tile(start_video[:, :, :1], [1, 1, video_length, 1, 1])
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input_video[:, :, :len(image_start)] = start_video
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input_video = input_video / 255
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input_video_mask = torch.zeros_like(input_video[:, :1])
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input_video_mask[:, :, len(image_start):] = 255
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else:
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input_video = torch.tile(
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torch.from_numpy(np.array(image_start)).permute(2, 0, 1).unsqueeze(1).unsqueeze(0),
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[1, 1, video_length, 1, 1]
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) / 255
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input_video_mask = torch.zeros_like(input_video[:, :1])
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input_video_mask[:, :, 1:, ] = 255
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else:
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image_start = None
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image_end = None
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input_video = torch.zeros([1, 3, video_length, sample_size[0], sample_size[1]])
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input_video_mask = torch.ones([1, 1, video_length, sample_size[0], sample_size[1]]) * 255
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clip_image = None
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del image_start
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del image_end
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gc.collect()
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return input_video, input_video_mask, clip_image
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def get_video_to_video_latent(input_video_path, video_length, sample_size):
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input_video = input_video_path
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input_video = torch.from_numpy(np.array(input_video))[:video_length]
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input_video = input_video.permute([3, 0, 1, 2]).unsqueeze(0) / 255
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input_video_mask = torch.zeros_like(input_video[:, :1])
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input_video_mask[:, :, :] = 255
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return input_video, input_video_mask, None |