mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2026-01-11 14:50:49 +08:00
b0be335d59
- ComfyUI can now load EXR files. - There are new arithmetic nodes for floats and integers. - EXR nodes can load depth maps and be remapped with ImageApplyColormap. This allows end users to use ground truth depth data from video game engines or 3D graphics tools and recolor it to the format expected by depth ControlNets: grayscale inverse depth maps and "inferno" colored inverse depth maps. - Fixed license notes. - Added an additional known ControlNet model. - Because CV2 is now used to read OpenEXR files, an environment variable must be set early on in the application, before CV2 is imported. This file, main_pre, is now imported early on in more places.
87 lines
3.1 KiB
Python
87 lines
3.1 KiB
Python
"""
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Portions of this code are adapted from the repository
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https://github.com/spacepxl/ComfyUI-HQ-Image-Save
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MIT License
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Copyright (c) 2023 spacepxl
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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"""
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import copy
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from typing import Sequence, Tuple
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import cv2 as cv
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import numpy as np
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import torch
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from torch import Tensor
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from .component_model.images_types import RgbMaskTuple
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def mut_srgb_to_linear(np_array) -> None:
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less = np_array <= 0.0404482362771082
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np_array[less] = np_array[less] / 12.92
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np_array[~less] = np.power((np_array[~less] + 0.055) / 1.055, 2.4)
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def mut_linear_to_srgb(np_array) -> None:
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less = np_array <= 0.0031308
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np_array[less] = np_array[less] * 12.92
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np_array[~less] = np.power(np_array[~less], 1 / 2.4) * 1.055 - 0.055
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def load_exr(file_path: str, srgb: bool) -> RgbMaskTuple:
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image = cv.imread(file_path, cv.IMREAD_UNCHANGED).astype(np.float32)
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rgb = np.flip(image[:, :, :3], 2).copy()
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if srgb:
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mut_linear_to_srgb(rgb)
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rgb = np.clip(rgb, 0, 1)
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rgb = torch.unsqueeze(torch.from_numpy(rgb), 0)
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mask = torch.zeros((1, image.shape[0], image.shape[1]), dtype=torch.float32)
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if image.shape[2] > 3:
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mask[0] = torch.from_numpy(np.clip(image[:, :, 3], 0, 1))
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return RgbMaskTuple(rgb, mask)
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def load_exr_latent(file_path: str) -> Tuple[Tensor]:
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image = cv.imread(file_path, cv.IMREAD_UNCHANGED).astype(np.float32)
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image = image[:, :, np.array([2, 1, 0, 3])]
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image = torch.unsqueeze(torch.from_numpy(image), 0)
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image = torch.movedim(image, -1, 1)
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return image,
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def save_exr(images: Tensor, filepaths_batched: Sequence[str], colorspace="linear"):
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linear = images.detach().clone().cpu().numpy().astype(np.float32)
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if colorspace == "linear":
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mut_srgb_to_linear(linear[:, :, :, :3]) # only convert RGB, not Alpha
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bgr = copy.deepcopy(linear)
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bgr[:, :, :, 0] = linear[:, :, :, 2] # flip RGB to BGR for opencv
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bgr[:, :, :, 2] = linear[:, :, :, 0]
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if bgr.shape[-1] > 3:
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bgr[:, :, :, 3] = np.clip(1 - linear[:, :, :, 3], 0, 1) # invert alpha
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for i in range(len(linear.shape[0])):
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cv.imwrite(filepaths_batched[i], bgr[i])
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