mirror of
https://github.com/comfyanonymous/ComfyUI.git
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a38968f098
- Validation errors that occur early in the lifecycle of prompt execution now get propagated to their callers in the EmbeddedComfyClient. This includes error messages about missing node classes. - The execution context now includes the node_id and the prompt_id - Latent previews are now sent with a node_id. This is not backwards compatible with old frontends. - Dependency execution errors are now modeled correctly. - Distributed progress encodes image previews with node and prompt IDs. - Typing for models - The frontend was updated to use node IDs with previews - Improvements to torch.compile experiments - Some controlnet_aux nodes were upstreamed
104 lines
2.9 KiB
Python
104 lines
2.9 KiB
Python
"""
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Copyright 2023 Lvmin Zhang
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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# High Quality Edge Thinning using Pure Python
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# Written by Lvmin Zhang
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# 2023 April
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# Stanford University
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# If you use this, please Cite "High Quality Edge Thinning using Pure Python", Lvmin Zhang, In Mikubill/sd-webui-controlnet.
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import cv2
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import numpy as np
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lvmin_kernels_raw = [
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np.array([
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[-1, -1, -1],
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[0, 1, 0],
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[1, 1, 1]
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], dtype=np.int32),
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np.array([
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[0, -1, -1],
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[1, 1, -1],
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[0, 1, 0]
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], dtype=np.int32)
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]
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lvmin_kernels = []
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lvmin_kernels += [np.rot90(x, k=0, axes=(0, 1)) for x in lvmin_kernels_raw]
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lvmin_kernels += [np.rot90(x, k=1, axes=(0, 1)) for x in lvmin_kernels_raw]
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lvmin_kernels += [np.rot90(x, k=2, axes=(0, 1)) for x in lvmin_kernels_raw]
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lvmin_kernels += [np.rot90(x, k=3, axes=(0, 1)) for x in lvmin_kernels_raw]
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lvmin_prunings_raw = [
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np.array([
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[-1, -1, -1],
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[-1, 1, -1],
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[0, 0, -1]
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], dtype=np.int32),
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np.array([
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[-1, -1, -1],
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[-1, 1, -1],
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[-1, 0, 0]
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], dtype=np.int32)
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]
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lvmin_prunings = []
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lvmin_prunings += [np.rot90(x, k=0, axes=(0, 1)) for x in lvmin_prunings_raw]
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lvmin_prunings += [np.rot90(x, k=1, axes=(0, 1)) for x in lvmin_prunings_raw]
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lvmin_prunings += [np.rot90(x, k=2, axes=(0, 1)) for x in lvmin_prunings_raw]
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lvmin_prunings += [np.rot90(x, k=3, axes=(0, 1)) for x in lvmin_prunings_raw]
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def remove_pattern(x, kernel):
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objects = cv2.morphologyEx(x, cv2.MORPH_HITMISS, kernel)
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objects = np.where(objects > 127)
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x[objects] = 0
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return x, objects[0].shape[0] > 0
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def thin_one_time(x, kernels):
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y = x
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is_done = True
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for k in kernels:
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y, has_update = remove_pattern(y, k)
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if has_update:
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is_done = False
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return y, is_done
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def lvmin_thin(x, prunings=True):
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y = x
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for i in range(32):
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y, is_done = thin_one_time(y, lvmin_kernels)
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if is_done:
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break
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if prunings:
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y, _ = thin_one_time(y, lvmin_prunings)
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return y
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def nake_nms(x):
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f1 = np.array([[0, 0, 0], [1, 1, 1], [0, 0, 0]], dtype=np.uint8)
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f2 = np.array([[0, 1, 0], [0, 1, 0], [0, 1, 0]], dtype=np.uint8)
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f3 = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]], dtype=np.uint8)
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f4 = np.array([[0, 0, 1], [0, 1, 0], [1, 0, 0]], dtype=np.uint8)
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y = np.zeros_like(x)
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for f in [f1, f2, f3, f4]:
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np.putmask(y, cv2.dilate(x, kernel=f) == x, x)
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return y
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