jaidaken
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Includes 30 custom nodes committed directly, 7 Civitai-exclusive loras stored via Git LFS, and a setup script that installs all dependencies and downloads HuggingFace-hosted models on vast.ai. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
104 lines
3.3 KiB
Python
104 lines
3.3 KiB
Python
from typing import Optional, Sequence, Tuple
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import torch
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import torch.nn as nn
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import torchvision.transforms.functional as F
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from torchvision.transforms import Normalize, Compose, RandomResizedCrop, InterpolationMode, ToTensor, Resize, \
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CenterCrop
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from .constants import OPENAI_DATASET_MEAN, OPENAI_DATASET_STD
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class ResizeMaxSize(nn.Module):
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def __init__(self, max_size, interpolation=InterpolationMode.BICUBIC, fn='max', fill=0):
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super().__init__()
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if not isinstance(max_size, int):
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raise TypeError(f"Size should be int. Got {type(max_size)}")
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self.max_size = max_size
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self.interpolation = interpolation
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self.fn = min if fn == 'min' else min
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self.fill = fill
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def forward(self, img):
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if isinstance(img, torch.Tensor):
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height, width = img.shape[:2]
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else:
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width, height = img.size
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scale = self.max_size / float(max(height, width))
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if scale != 1.0:
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new_size = tuple(round(dim * scale) for dim in (height, width))
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img = F.resize(img, new_size, self.interpolation)
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pad_h = self.max_size - new_size[0]
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pad_w = self.max_size - new_size[1]
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img = F.pad(img, padding=[pad_w//2, pad_h//2, pad_w - pad_w//2, pad_h - pad_h//2], fill=self.fill)
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return img
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def _convert_to_rgb(image):
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return image.convert('RGB')
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# class CatGen(nn.Module):
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# def __init__(self, num=4):
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# self.num = num
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# def mixgen_batch(image, text):
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# batch_size = image.shape[0]
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# index = np.random.permutation(batch_size)
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# cat_images = []
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# for i in range(batch_size):
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# # image mixup
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# image[i,:] = lam * image[i,:] + (1 - lam) * image[index[i],:]
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# # text concat
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# text[i] = tokenizer((str(text[i]) + " " + str(text[index[i]])))[0]
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# text = torch.stack(text)
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# return image, text
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def image_transform(
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image_size: int,
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is_train: bool,
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mean: Optional[Tuple[float, ...]] = None,
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std: Optional[Tuple[float, ...]] = None,
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resize_longest_max: bool = False,
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fill_color: int = 0,
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):
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mean = mean or OPENAI_DATASET_MEAN
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if not isinstance(mean, (list, tuple)):
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mean = (mean,) * 3
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std = std or OPENAI_DATASET_STD
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if not isinstance(std, (list, tuple)):
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std = (std,) * 3
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if isinstance(image_size, (list, tuple)) and image_size[0] == image_size[1]:
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# for square size, pass size as int so that Resize() uses aspect preserving shortest edge
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image_size = image_size[0]
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normalize = Normalize(mean=mean, std=std)
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if is_train:
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return Compose([
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RandomResizedCrop(image_size, scale=(0.9, 1.0), interpolation=InterpolationMode.BICUBIC),
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_convert_to_rgb,
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ToTensor(),
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normalize,
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])
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else:
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if resize_longest_max:
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transforms = [
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ResizeMaxSize(image_size, fill=fill_color)
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]
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else:
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transforms = [
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Resize(image_size, interpolation=InterpolationMode.BICUBIC),
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CenterCrop(image_size),
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]
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transforms.extend([
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_convert_to_rgb,
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ToTensor(),
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normalize,
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])
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return Compose(transforms)
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