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6248f31402 |
@@ -49,7 +49,7 @@ This custom node helps to conveniently enhance images through Detector, Detailer
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### ControlNet, IPAdapter
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* `ControlNetApply (SEGS)` - To apply ControlNet in SEGS, you need to use the Preprocessor Provider node from the Inspire Pack to utilize this node.
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* `segs_preprocessor` and `control_image` can be selectively applied. If an `control_image` is given, `segs_preprocessor` will be ignored.
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* `segs_preprocessor` and `control_image` can be selectively applied. If a `control_image` is given, `segs_preprocessor` will be ignored.
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* If set to `control_image`, you can preview the cropped cnet image through `SEGSPreview (CNET Image)`. Images generated by `segs_preprocessor` should be verified through the `cnet_images` output of each Detailer.
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* The `segs_preprocessor` operates by applying preprocessing on-the-fly based on the cropped image during the detailing process, while `control_image` will be cropped and used as input to `ControlNetApply (SEGS)`.
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* `ControlNetClear (SEGS)` - Clear applied ControlNet in SEGS
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@@ -286,6 +286,7 @@ This custom node helps to conveniently enhance images through Detector, Detailer
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* `Negative Cond Placeholder` - Models like FLUX.1 do not use Negative Conditioning. This is a placeholder node for them. You can use FLUX.1 by replacing the Negative Conditioning used in Impact KSampler, KSampler (Inspire), and Detailer with this node.
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* `Execution Order Controller` - A helper node that can forcibly control the execution order of nodes.
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* Connect the output of the node that should be executed first to the signal, and make the input of the node that should be executed later pass through this node.
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* `List Bridge` - When passing the list output through this node, it collects and organizes the data before forwarding it, which ensures that the previous stage's sub-workflow has been completed.
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## MMDet nodes (DEPRECATED) - Don't use these nodes
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@@ -273,6 +273,7 @@ NODE_CLASS_MAPPINGS = {
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"StringListToString": StringListToString,
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"WildcardPromptFromString": WildcardPromptFromString,
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"ImpactExecutionOrderController": ImpactExecutionOrderController,
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"ImpactListBridge": ImpactListBridge,
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"RemoveNoiseMask": RemoveNoiseMask,
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@@ -393,6 +394,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
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"ImpactSwitch": "Switch (Any)",
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"ImpactInversedSwitch": "Inversed Switch (Any)",
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"ImpactExecutionOrderController": "Execution Order Controller",
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"ImpactListBridge": "List Bridge",
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"MasksToMaskList": "Mask Batch to Mask List",
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"MaskListToMaskBatch": "Mask List to Mask Batch",
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@@ -1,10 +1,13 @@
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import functools
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import os
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import re
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import shutil
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import sys
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import subprocess
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import threading
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import locale
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import traceback
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from typing import Set
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if sys.argv[0] == 'install.py':
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@@ -66,6 +69,42 @@ def process_wrap(cmd_str, cwd=None, handler=None, env=None):
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stderr_thread.join()
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return process.wait()
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@functools.cache
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def get_installed_packages() -> Set[str]:
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try:
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result = subprocess.check_output([sys.executable, '-m', 'pip', 'list'], universal_newlines=True)
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pip_list = set([line.split()[0].lower() for line in result.split('\n') if line.strip()])
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return pip_list
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except subprocess.CalledProcessError as e:
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raise Exception(f"[ComfyUI-Impact-Pack] Failed to retrieve the information of installed pip packages.")
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def is_package_installed(name: str) -> bool:
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name = name.strip()
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pattern = r'([^<>!=]+)([<>!=]=?)'
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match = re.search(pattern, name)
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if match:
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name = match.group(1)
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result = name.lower() in get_installed_packages()
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return result
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def is_requirements_installed(file_path: str) -> bool:
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print(f"Requirements file: {file_path}")
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if os.path.exists(file_path):
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with open(file_path, "r") as file:
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lines = file.readlines()
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for line in lines:
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if not is_package_installed(line):
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return False
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pip_install = [sys.executable, "-m", "pip", "install", "-U"]
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# ---
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+13
-10
@@ -114,19 +114,22 @@ try:
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onnx_path = os.path.join(model_path, "onnx")
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if not os.path.exists(os.path.join(os.path.dirname(__file__), '..', 'skip_download_model')):
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if not impact.config.get_config()['mmdet_skip']:
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bbox_path = os.path.join(model_path, "mmdets", "bbox")
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if not os.path.exists(bbox_path):
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os.makedirs(bbox_path)
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try:
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if not impact.config.get_config()['mmdet_skip']:
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bbox_path = os.path.join(model_path, "mmdets", "bbox")
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if not os.path.exists(bbox_path):
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os.makedirs(bbox_path)
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if not os.path.exists(os.path.join(bbox_path, "mmdet_anime-face_yolov3.pth")):
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download_url("https://huggingface.co/dustysys/ddetailer/resolve/main/mmdet/bbox/mmdet_anime-face_yolov3.pth", bbox_path)
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if not os.path.exists(os.path.join(bbox_path, "mmdet_anime-face_yolov3.pth")):
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download_url("https://huggingface.co/dustysys/ddetailer/resolve/main/mmdet/bbox/mmdet_anime-face_yolov3.pth", bbox_path)
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if not os.path.exists(os.path.join(bbox_path, "mmdet_anime-face_yolov3.py")):
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download_url("https://raw.githubusercontent.com/Bing-su/dddetailer/master/config/mmdet_anime-face_yolov3.py", bbox_path)
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if not os.path.exists(os.path.join(bbox_path, "mmdet_anime-face_yolov3.py")):
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download_url("https://raw.githubusercontent.com/Bing-su/dddetailer/master/config/mmdet_anime-face_yolov3.py", bbox_path)
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if not os.path.exists(os.path.join(sam_path, "sam_vit_b_01ec64.pth")):
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download_url("https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth", sam_path)
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if not os.path.exists(os.path.join(sam_path, "sam_vit_b_01ec64.pth")):
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download_url("https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth", sam_path)
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except:
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print(f"[Impact Pack] Failed to auto-download model files. Please download them manually.")
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if not os.path.exists(onnx_path):
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print(f"### ComfyUI-Impact-Pack: onnx model directory created ({onnx_path})")
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+33
-35
@@ -587,17 +587,17 @@ app.registerExtension({
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}
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if(node.comfyClass == "ImpactSEGSLabelFilter" || node.comfyClass == "SEGSLabelFilterDetailerHookProvider") {
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node.widgets[0].callback = (value, canvas, node, pos, e) => {
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if(node.widgets[1].value.trim() != "" && !node.widgets[1].value.trim().endsWith(","))
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node.widgets[1].value += ", "
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node.widgets[1].value += value;
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if(node.widgets_values)
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node.widgets_values[1] = node.widgets[1].value;
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}
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Object.defineProperty(node.widgets[0], "value", {
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set: (value) => {
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const stackTrace = new Error().stack;
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if(stackTrace.includes('inner_value_change')) {
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if(node.widgets[1].value.trim() != "" && !node.widgets[1].value.trim().endsWith(","))
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node.widgets[1].value += ", "
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node.widgets[1].value += value;
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node.widgets_values[1] = node.widgets[1].value;
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}
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node._value = value;
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},
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get: () => {
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@@ -667,18 +667,18 @@ app.registerExtension({
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break;
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}
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node.widgets[combo_id+1].callback = (value, canvas, node, pos, e) => {
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if(node.widgets[tbox_id].value != '')
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node.widgets[tbox_id].value += ', '
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node.widgets[tbox_id].value += node._wildcard_value;
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}
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Object.defineProperty(node.widgets[combo_id+1], "value", {
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set: (value) => {
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const stackTrace = new Error().stack;
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if(stackTrace.includes('inner_value_change')) {
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if(value != "Select the Wildcard to add to the text") {
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if(node.widgets[tbox_id].value != '')
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node.widgets[tbox_id].value += ', '
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node.widgets[tbox_id].value += value;
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}
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}
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},
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if (value !== "Select the Wildcard to add to the text")
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node._wildcard_value = value;
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},
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get: () => { return "Select the Wildcard to add to the text"; }
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});
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@@ -690,24 +690,22 @@ app.registerExtension({
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});
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if(has_lora) {
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node.widgets[combo_id].callback = (value, canvas, node, pos, e) => {
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let lora_name = node._value;
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if(lora_name.endsWith('.safetensors')) {
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lora_name = lora_name.slice(0, -12);
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}
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node.widgets[tbox_id].value += `<lora:${lora_name}>`;
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if(node.widgets_values) {
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node.widgets_values[tbox_id] = node.widgets[tbox_id].value;
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}
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}
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Object.defineProperty(node.widgets[combo_id], "value", {
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set: (value) => {
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const stackTrace = new Error().stack;
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if(stackTrace.includes('inner_value_change')) {
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if(value != "Select the LoRA to add to the text") {
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let lora_name = value;
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if (lora_name.endsWith('.safetensors')) {
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lora_name = lora_name.slice(0, -12);
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}
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node.widgets[tbox_id].value += `<lora:${lora_name}>`;
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if(node.widgets_values) {
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node.widgets_values[tbox_id] = node.widgets[tbox_id].value;
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}
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}
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}
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node._value = value;
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if (value !== "Select the LoRA to add to the text")
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node._value = value;
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},
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get: () => { return "Select the LoRA to add to the text"; }
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@@ -353,13 +353,16 @@ class ImpactSamEditorDialog extends ComfyDialog {
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imgCtx.drawImage(orig_image, 0, 0, drawWidth, drawHeight);
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// update mask
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let w = (drawWidth * imgCanvas.clientWidth/imgCanvas.width) + "px";
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let h = (drawHeight * imgCanvas.clientHeight/imgCanvas.height) + "px";
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pointsCanvas.width = drawWidth;
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pointsCanvas.height = drawHeight;
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pointsCanvas.style.top = imgCanvas.offsetTop + "px";
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pointsCanvas.style.left = imgCanvas.offsetLeft + "px";
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maskCanvas.width = drawWidth;
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maskCanvas.height = drawHeight;
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maskCanvas.style.width = w;
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maskCanvas.style.height = h;
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maskCanvas.style.top = imgCanvas.offsetTop + "px";
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maskCanvas.style.left = imgCanvas.offsetLeft + "px";
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@@ -186,6 +186,9 @@ def decode_latent(latent, preview_method, vae_opt=None):
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elif preview_method == "Latent2RGB-FLUX.1":
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latent_format = latent_formats.Flux()
|
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method = LatentPreviewMethod.Latent2RGB
|
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elif preview_method == "Latent2RGB-LTXV":
|
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latent_format = latent_formats.LTXV()
|
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method = LatentPreviewMethod.Latent2RGB
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else:
|
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print(f"[Impact Pack] PreviewBridgeLatent: '{preview_method}' is unsupported preview method.")
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latent_format = latent_formats.SD15()
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@@ -211,6 +214,7 @@ class PreviewBridgeLatent:
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"Latent2RGB-SDXL", "Latent2RGB-SD15", "Latent2RGB-SD3",
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"Latent2RGB-SD-X4", "Latent2RGB-Playground-2.5",
|
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"Latent2RGB-SC-Prior", "Latent2RGB-SC-B",
|
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"Latent2RGB-LTXV",
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"TAEF1", "TAESDXL", "TAESD15", "TAESD3"],),
|
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},
|
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"optional": {
|
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@@ -274,7 +278,13 @@ class PreviewBridgeLatent:
|
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|
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def doit(self, latent, image, preview_method, vae_opt=None, block=False, unique_id=None, restore_mask='never', prompt=None, extra_pnginfo=None):
|
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latent_channels = latent['samples'].shape[1]
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preview_method_channels = 16 if 'SD3' in preview_method or 'SC-Prior' in preview_method or 'FLUX.1' in preview_method or 'TAEF1' == preview_method else 4
|
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|
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if 'SD3' in preview_method or 'SC-Prior' in preview_method or 'FLUX.1' in preview_method or 'TAEF1' == preview_method:
|
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preview_method_channels = 16
|
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elif 'LTXV' in preview_method:
|
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preview_method_channels = 128
|
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else:
|
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preview_method_channels = 4
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|
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if vae_opt is None and latent_channels != preview_method_channels:
|
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print(f"[PreviewBridgeLatent] The version of latent is not compatible with preview_method.\nSD3, SD1/SD2, SDXL, SC-Prior, SC-B and FLUX.1 are not compatible with each other.")
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|
||||
@@ -1,10 +1,10 @@
|
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import configparser
|
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import os
|
||||
|
||||
version_code = [7, 10, 3]
|
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version_code = [7, 14, 2]
|
||||
version = f"V{version_code[0]}.{version_code[1]}" + (f'.{version_code[2]}' if len(version_code) > 2 else '')
|
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|
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dependency_version = 23
|
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dependency_version = 24
|
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|
||||
my_path = os.path.dirname(__file__)
|
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old_config_path = os.path.join(my_path, "impact-pack.ini")
|
||||
|
||||
+38
-20
@@ -11,6 +11,7 @@ from impact.utils import *
|
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from collections import namedtuple
|
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import numpy as np
|
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from skimage.measure import label
|
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from PIL import ImageOps
|
||||
|
||||
import nodes
|
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import comfy_extras.nodes_upscale_model as model_upscale
|
||||
@@ -47,7 +48,7 @@ preview_bridge_last_mask_cache = {}
|
||||
|
||||
current_prompt = None
|
||||
|
||||
SCHEDULERS = comfy.samplers.KSampler.SCHEDULERS + ['AYS SDXL', 'AYS SD1', 'AYS SVD', 'GITS[coeff=1.2]']
|
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SCHEDULERS = comfy.samplers.KSampler.SCHEDULERS + ['AYS SDXL', 'AYS SD1', 'AYS SVD', 'GITS[coeff=1.2]', 'LTXV[default]']
|
||||
|
||||
|
||||
def is_execution_model_version_supported():
|
||||
@@ -69,6 +70,13 @@ def set_previewbridge_image(node_id, file, item):
|
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pb_id = f"${node_id}-{pb_id_cnt}"
|
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preview_bridge_image_id_map[pb_id] = (file, item)
|
||||
preview_bridge_image_name_map[node_id, file] = (pb_id, item)
|
||||
if os.path.isfile(file):
|
||||
i = Image.open(file)
|
||||
i = ImageOps.exif_transpose(i)
|
||||
if 'A' in i.getbands():
|
||||
mask = np.array(i.getchannel('A')).astype(np.float32) / 255.0
|
||||
mask = 1. - torch.from_numpy(mask)
|
||||
preview_bridge_last_mask_cache[node_id] = mask.unsqueeze(0)
|
||||
pb_id_cnt += 1
|
||||
|
||||
return pb_id
|
||||
@@ -319,7 +327,12 @@ def enhance_detail(image, model, clip, vae, guide_size, guide_size_for_bbox, max
|
||||
|
||||
# prepare mask
|
||||
if noise_mask is not None and inpaint_model:
|
||||
positive, negative, latent_image = nodes.InpaintModelConditioning().encode(positive, negative, upscaled_image, vae, noise_mask)
|
||||
imc_encode = nodes.InpaintModelConditioning().encode
|
||||
if 'noise_mask' in inspect.signature(imc_encode).parameters:
|
||||
positive, negative, latent_image = imc_encode(positive, negative, upscaled_image, vae, mask=noise_mask, noise_mask=True)
|
||||
else:
|
||||
print(f"[Impact Pack] ComfyUI is an outdated version.")
|
||||
positive, negative, latent_image = imc_encode(positive, negative, upscaled_image, vae, noise_mask)
|
||||
else:
|
||||
latent_image = to_latent_image(upscaled_image, vae)
|
||||
if noise_mask is not None:
|
||||
@@ -1354,9 +1367,14 @@ def segs_to_masklist(segs):
|
||||
return masks
|
||||
|
||||
|
||||
def vae_decode(vae, samples, use_tile, hook, tile_size=512):
|
||||
def vae_decode(vae, samples, use_tile, hook, tile_size=512, overlap=64):
|
||||
if use_tile:
|
||||
pixels = nodes.VAEDecodeTiled().decode(vae, samples, tile_size)[0]
|
||||
decoder = nodes.VAEDecodeTiled()
|
||||
if 'overlap' in inspect.signature(decoder.decode).parameters:
|
||||
pixels = decoder.decode(vae, samples, tile_size, overlap=overlap)[0]
|
||||
else:
|
||||
print(f"[Impact Pack] Your ComfyUI is outdated.")
|
||||
pixels = decoder.decode(vae, samples, tile_size)[0]
|
||||
else:
|
||||
pixels = nodes.VAEDecode().decode(vae, samples)[0]
|
||||
|
||||
@@ -1378,12 +1396,12 @@ def vae_encode(vae, pixels, use_tile, hook, tile_size=512):
|
||||
return samples
|
||||
|
||||
|
||||
def latent_upscale_on_pixel_space_shape(samples, scale_method, w, h, vae, use_tile=False, tile_size=512, save_temp_prefix=None, hook=None):
|
||||
return latent_upscale_on_pixel_space_shape2(samples, scale_method, w, h, vae, use_tile, tile_size, save_temp_prefix, hook)[0]
|
||||
def latent_upscale_on_pixel_space_shape(samples, scale_method, w, h, vae, use_tile=False, tile_size=512, save_temp_prefix=None, hook=None, overlap=64):
|
||||
return latent_upscale_on_pixel_space_shape2(samples, scale_method, w, h, vae, use_tile, tile_size, save_temp_prefix, hook, overlap=overlap)[0]
|
||||
|
||||
|
||||
def latent_upscale_on_pixel_space_shape2(samples, scale_method, w, h, vae, use_tile=False, tile_size=512, save_temp_prefix=None, hook=None):
|
||||
pixels = vae_decode(vae, samples, use_tile, hook, tile_size=tile_size)
|
||||
def latent_upscale_on_pixel_space_shape2(samples, scale_method, w, h, vae, use_tile=False, tile_size=512, save_temp_prefix=None, hook=None, overlap=64):
|
||||
pixels = vae_decode(vae, samples, use_tile, hook, tile_size=tile_size, overlap=overlap)
|
||||
|
||||
if save_temp_prefix is not None:
|
||||
nodes.PreviewImage().save_images(pixels, filename_prefix=save_temp_prefix)
|
||||
@@ -1397,12 +1415,12 @@ def latent_upscale_on_pixel_space_shape2(samples, scale_method, w, h, vae, use_t
|
||||
return (vae_encode(vae, pixels, use_tile, hook, tile_size=tile_size), old_pixels)
|
||||
|
||||
|
||||
def latent_upscale_on_pixel_space(samples, scale_method, scale_factor, vae, use_tile=False, tile_size=512, save_temp_prefix=None, hook=None):
|
||||
return latent_upscale_on_pixel_space2(samples, scale_method, scale_factor, vae, use_tile, tile_size, save_temp_prefix, hook)[0]
|
||||
def latent_upscale_on_pixel_space(samples, scale_method, scale_factor, vae, use_tile=False, tile_size=512, save_temp_prefix=None, hook=None, overlap=64):
|
||||
return latent_upscale_on_pixel_space2(samples, scale_method, scale_factor, vae, use_tile, tile_size, save_temp_prefix, hook, overlap=overlap)[0]
|
||||
|
||||
|
||||
def latent_upscale_on_pixel_space2(samples, scale_method, scale_factor, vae, use_tile=False, tile_size=512, save_temp_prefix=None, hook=None):
|
||||
pixels = vae_decode(vae, samples, use_tile, hook, tile_size=tile_size)
|
||||
def latent_upscale_on_pixel_space2(samples, scale_method, scale_factor, vae, use_tile=False, tile_size=512, save_temp_prefix=None, hook=None, overlap=64):
|
||||
pixels = vae_decode(vae, samples, use_tile, hook, tile_size=tile_size, overlap=overlap)
|
||||
|
||||
if save_temp_prefix is not None:
|
||||
nodes.PreviewImage().save_images(pixels, filename_prefix=save_temp_prefix)
|
||||
@@ -1418,12 +1436,12 @@ def latent_upscale_on_pixel_space2(samples, scale_method, scale_factor, vae, use
|
||||
return (vae_encode(vae, pixels, use_tile, hook, tile_size=tile_size), old_pixels)
|
||||
|
||||
|
||||
def latent_upscale_on_pixel_space_with_model_shape(samples, scale_method, upscale_model, new_w, new_h, vae, use_tile=False, tile_size=512, save_temp_prefix=None, hook=None):
|
||||
return latent_upscale_on_pixel_space_with_model_shape2(samples, scale_method, upscale_model, new_w, new_h, vae, use_tile, tile_size, save_temp_prefix, hook)[0]
|
||||
def latent_upscale_on_pixel_space_with_model_shape(samples, scale_method, upscale_model, new_w, new_h, vae, use_tile=False, tile_size=512, save_temp_prefix=None, hook=None, overlap=64):
|
||||
return latent_upscale_on_pixel_space_with_model_shape2(samples, scale_method, upscale_model, new_w, new_h, vae, use_tile, tile_size, save_temp_prefix, hook, overlap=overlap)[0]
|
||||
|
||||
|
||||
def latent_upscale_on_pixel_space_with_model_shape2(samples, scale_method, upscale_model, new_w, new_h, vae, use_tile=False, tile_size=512, save_temp_prefix=None, hook=None):
|
||||
pixels = vae_decode(vae, samples, use_tile, hook, tile_size=tile_size)
|
||||
def latent_upscale_on_pixel_space_with_model_shape2(samples, scale_method, upscale_model, new_w, new_h, vae, use_tile=False, tile_size=512, save_temp_prefix=None, hook=None, overlap=64):
|
||||
pixels = vae_decode(vae, samples, use_tile, hook, tile_size=tile_size, overlap=overlap)
|
||||
|
||||
if save_temp_prefix is not None:
|
||||
nodes.PreviewImage().save_images(pixels, filename_prefix=save_temp_prefix)
|
||||
@@ -1450,12 +1468,12 @@ def latent_upscale_on_pixel_space_with_model_shape2(samples, scale_method, upsca
|
||||
|
||||
|
||||
def latent_upscale_on_pixel_space_with_model(samples, scale_method, upscale_model, scale_factor, vae, use_tile=False,
|
||||
tile_size=512, save_temp_prefix=None, hook=None):
|
||||
return latent_upscale_on_pixel_space_with_model2(samples, scale_method, upscale_model, scale_factor, vae, use_tile, tile_size, save_temp_prefix, hook)[0]
|
||||
tile_size=512, save_temp_prefix=None, hook=None, overlap=64):
|
||||
return latent_upscale_on_pixel_space_with_model2(samples, scale_method, upscale_model, scale_factor, vae, use_tile, tile_size, save_temp_prefix, hook, overlap=overlap)[0]
|
||||
|
||||
def latent_upscale_on_pixel_space_with_model2(samples, scale_method, upscale_model, scale_factor, vae, use_tile=False,
|
||||
tile_size=512, save_temp_prefix=None, hook=None):
|
||||
pixels = vae_decode(vae, samples, use_tile, hook, tile_size=tile_size)
|
||||
tile_size=512, save_temp_prefix=None, hook=None, overlap=64):
|
||||
pixels = vae_decode(vae, samples, use_tile, hook, tile_size=tile_size, overlap=overlap)
|
||||
|
||||
if save_temp_prefix is not None:
|
||||
nodes.PreviewImage().save_images(pixels, filename_prefix=save_temp_prefix)
|
||||
|
||||
@@ -2027,7 +2027,12 @@ class LatentSender(nodes.SaveLatent):
|
||||
"samples": ("LATENT", ),
|
||||
"filename_prefix": ("STRING", {"default": "latents/LatentSender"}),
|
||||
"link_id": ("INT", {"default": 0, "min": 0, "max": sys.maxsize, "step": 1}),
|
||||
"preview_method": (["Latent2RGB-SDXL", "Latent2RGB-SD15", "TAESDXL", "TAESD15"],)
|
||||
"preview_method": (["Latent2RGB-FLUX.1",
|
||||
"Latent2RGB-SDXL", "Latent2RGB-SD15", "Latent2RGB-SD3",
|
||||
"Latent2RGB-SD-X4", "Latent2RGB-Playground-2.5",
|
||||
"Latent2RGB-SC-Prior", "Latent2RGB-SC-B",
|
||||
"Latent2RGB-LTXV",
|
||||
"TAEF1", "TAESDXL", "TAESD15", "TAESD3"],)
|
||||
},
|
||||
"hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
|
||||
}
|
||||
@@ -2069,14 +2074,33 @@ class LatentSender(nodes.SaveLatent):
|
||||
if preview_method == "Latent2RGB-SD15":
|
||||
latent_format = latent_formats.SD15()
|
||||
method = LatentPreviewMethod.Latent2RGB
|
||||
elif preview_method == "TAESD15":
|
||||
elif preview_method == "Latent2RGB-SDXL":
|
||||
latent_format = latent_formats.SDXL()
|
||||
method = LatentPreviewMethod.Latent2RGB
|
||||
elif preview_method == "Latent2RGB-SD3":
|
||||
latent_format = latent_formats.SD3()
|
||||
method = LatentPreviewMethod.Latent2RGB
|
||||
elif preview_method == "Latent2RGB-SD-X4":
|
||||
latent_format = latent_formats.SD_X4()
|
||||
method = LatentPreviewMethod.Latent2RGB
|
||||
elif preview_method == "Latent2RGB-Playground-2.5":
|
||||
latent_format = latent_formats.SDXL_Playground_2_5()
|
||||
method = LatentPreviewMethod.Latent2RGB
|
||||
elif preview_method == "Latent2RGB-SC-Prior":
|
||||
latent_format = latent_formats.SC_Prior()
|
||||
method = LatentPreviewMethod.Latent2RGB
|
||||
elif preview_method == "Latent2RGB-SC-B":
|
||||
latent_format = latent_formats.SC_B()
|
||||
method = LatentPreviewMethod.Latent2RGB
|
||||
elif preview_method == "Latent2RGB-FLUX.1":
|
||||
latent_format = latent_formats.Flux()
|
||||
method = LatentPreviewMethod.Latent2RGB
|
||||
elif preview_method == "Latent2RGB-LTXV":
|
||||
latent_format = latent_formats.LTXV()
|
||||
method = LatentPreviewMethod.Latent2RGB
|
||||
else:
|
||||
print(f"[Impact Pack] LatentSender: '{preview_method}' is unsupported preview method.")
|
||||
latent_format = latent_formats.SD15()
|
||||
method = LatentPreviewMethod.TAESD
|
||||
elif preview_method == "TAESDXL":
|
||||
latent_format = latent_formats.SDXL()
|
||||
method = LatentPreviewMethod.TAESD
|
||||
else: # preview_method == "Latent2RGB-SDXL"
|
||||
latent_format = latent_formats.SDXL()
|
||||
method = LatentPreviewMethod.Latent2RGB
|
||||
|
||||
previewer = core.get_previewer("cpu", latent_format=latent_format, force=True, method=method)
|
||||
@@ -2150,16 +2174,19 @@ class ImpactWildcardProcessor:
|
||||
@classmethod
|
||||
def INPUT_TYPES(s):
|
||||
return {"required": {
|
||||
"wildcard_text": ("STRING", {"multiline": True, "dynamicPrompts": False}),
|
||||
"populated_text": ("STRING", {"multiline": True, "dynamicPrompts": False}),
|
||||
"mode": ("BOOLEAN", {"default": True, "label_on": "Populate", "label_off": "Fixed"}),
|
||||
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
|
||||
"wildcard_text": ("STRING", {"multiline": True, "dynamicPrompts": False, "tooltip": "Enter a prompt using wildcard syntax."}),
|
||||
"populated_text": ("STRING", {"multiline": True, "dynamicPrompts": False, "tooltip": "The actual value passed during the execution of 'ImpactWildcardProcessor' is what is shown here. The behavior varies slightly depending on the mode. Wildcard syntax can also be used in 'populated_text'."}),
|
||||
"mode": ("BOOLEAN", {"default": True, "label_on": "Populate", "label_off": "Fixed", "tooltip": "Populate: Before running the workflow, it overwrites the existing value of 'populated_text' with the prompt processed from 'wildcard_text'. In this mode, 'populated_text' cannot be edited.\nFixed: Ignores wildcard_text and keeps 'populated_text' as is. You can edit 'populated_text' in this mode."}),
|
||||
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "Determines the random seed to be used for wildcard processing."}),
|
||||
"Select to add Wildcard": (["Select the Wildcard to add to the text"],),
|
||||
},
|
||||
}
|
||||
|
||||
CATEGORY = "ImpactPack/Prompt"
|
||||
|
||||
DESCRIPTION = ("The 'ImpactWildcardProcessor' processes text prompts written in wildcard syntax and outputs the processed text prompt.\n\n"
|
||||
"TIP: Before the workflow is executed, the processing result of 'wildcard_text' is displayed in 'populated_text', and the populated text is saved along with the workflow. If you want to use a seed converted as input, write the prompt directly in 'populated_text' instead of 'wildcard_text', and set the mode to 'Fixed'.")
|
||||
|
||||
RETURN_TYPES = ("STRING", )
|
||||
FUNCTION = "doit"
|
||||
|
||||
@@ -2178,17 +2205,22 @@ class ImpactWildcardEncode:
|
||||
return {"required": {
|
||||
"model": ("MODEL",),
|
||||
"clip": ("CLIP",),
|
||||
"wildcard_text": ("STRING", {"multiline": True, "dynamicPrompts": False}),
|
||||
"populated_text": ("STRING", {"multiline": True, "dynamicPrompts": False}),
|
||||
"mode": ("BOOLEAN", {"default": True, "label_on": "Populate", "label_off": "Fixed"}),
|
||||
"wildcard_text": ("STRING", {"multiline": True, "dynamicPrompts": False, "tooltip": "Enter a prompt using wildcard syntax."}),
|
||||
"populated_text": ("STRING", {"multiline": True, "dynamicPrompts": False, "tooltip": "The actual value passed during the execution of 'ImpactWildcardEncode' is what is shown here. The behavior varies slightly depending on the mode. Wildcard syntax can also be used in 'populated_text'."}),
|
||||
"mode": ("BOOLEAN", {"default": True, "label_on": "Populate", "label_off": "Fixed", "tooltip": "Populate: Before running the workflow, it overwrites the existing value of 'populated_text' with the prompt processed from 'wildcard_text'. In this mode, 'populated_text' cannot be edited.\n"
|
||||
"Fixed: Ignores wildcard_text and keeps 'populated_text' as is. You can edit 'populated_text' in this mode."}),
|
||||
"Select to add LoRA": (["Select the LoRA to add to the text"] + folder_paths.get_filename_list("loras"), ),
|
||||
"Select to add Wildcard": (["Select the Wildcard to add to the text"], ),
|
||||
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
|
||||
"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "Determines the random seed to be used for wildcard processing."}),
|
||||
},
|
||||
}
|
||||
|
||||
CATEGORY = "ImpactPack/Prompt"
|
||||
|
||||
DESCRIPTION = ("The 'ImpactWildcardEncode' node processes text prompts written in wildcard syntax and outputs them as conditioning. It also supports LoRA syntax, with the applied LoRA reflected in the model's output.\n\n"
|
||||
"TIP1: Before the workflow is executed, the processing result of 'wildcard_text' is displayed in 'populated_text', and the populated text is saved along with the workflow. If you want to use a seed converted as input, write the prompt directly in 'populated_text' instead of 'wildcard_text', and set the mode to 'Fixed'.\n"
|
||||
"TIP2: If the 'Inspire Pack' is installed, LBW(LoRA Block Weight) syntax can also be applied.")
|
||||
|
||||
RETURN_TYPES = ("MODEL", "CLIP", "CONDITIONING", "STRING")
|
||||
RETURN_NAMES = ("model", "clip", "conditioning", "populated_text")
|
||||
FUNCTION = "doit"
|
||||
@@ -2213,7 +2245,7 @@ class ImpactSchedulerAdapter:
|
||||
def INPUT_TYPES(s):
|
||||
return {"required": {
|
||||
"scheduler": (comfy.samplers.KSampler.SCHEDULERS, {"defaultInput": True, }),
|
||||
"extra_scheduler": (['None', 'AYS SDXL', 'AYS SD1', 'AYS SVD', 'GITS[coeff=1.2]'],),
|
||||
"extra_scheduler": (['None', 'AYS SDXL', 'AYS SD1', 'AYS SVD', 'GITS[coeff=1.2]', 'LTXV[default]'],),
|
||||
}}
|
||||
|
||||
CATEGORY = "ImpactPack/Util"
|
||||
|
||||
@@ -27,6 +27,8 @@ def calculate_sigmas(model, sampler, scheduler, steps):
|
||||
sigmas = nodes.NODE_CLASS_MAPPINGS['AlignYourStepsScheduler']().get_sigmas(scheduler[4:], steps, denoise=1.0)[0]
|
||||
elif scheduler.startswith('GITS[coeff='):
|
||||
sigmas = nodes.NODE_CLASS_MAPPINGS['GITSScheduler']().get_sigmas(float(scheduler[11:-1]), steps, denoise=1.0)[0]
|
||||
elif scheduler == 'LTXV[default]':
|
||||
sigmas = nodes.NODE_CLASS_MAPPINGS['LTXVScheduler']().get_sigmas(20, 2.05, 0.95, True, 0.1)[0]
|
||||
else:
|
||||
sigmas = samplers.calculate_sigmas(model.get_model_object("model_sampling"), scheduler, steps)
|
||||
|
||||
|
||||
@@ -766,6 +766,29 @@ class ImpactExecutionOrderController:
|
||||
return signal, value
|
||||
|
||||
|
||||
class ImpactListBridge:
|
||||
@classmethod
|
||||
def INPUT_TYPES(cls):
|
||||
return {"required": {
|
||||
"list_input": (any_typ,),
|
||||
}}
|
||||
|
||||
FUNCTION = "doit"
|
||||
|
||||
DESCRIPTION = "When passing the list output through this node, it collects and organizes the data before forwarding it, which ensures that the previous stage's sub-workflow has been completed."
|
||||
|
||||
CATEGORY = "ImpactPack/Util"
|
||||
RETURN_TYPES = (any_typ, )
|
||||
RETURN_NAMES = ("list_output", )
|
||||
|
||||
INPUT_IS_LIST = True
|
||||
OUTPUT_IS_LIST = (True, )
|
||||
|
||||
@staticmethod
|
||||
def doit(list_input):
|
||||
return (list_input,)
|
||||
|
||||
|
||||
original_handle_execution = execution.PromptExecutor.handle_execution_error
|
||||
|
||||
|
||||
|
||||
@@ -896,7 +896,7 @@ class From_SEG_ELT_bbox:
|
||||
CATEGORY = "ImpactPack/Util"
|
||||
|
||||
def doit(self, bbox):
|
||||
return bbox
|
||||
return [int(c) for c in bbox]
|
||||
|
||||
|
||||
class From_SEG_ELT_crop_region:
|
||||
@@ -1101,10 +1101,10 @@ class SEG_ELT_BBOX_ScaleBy:
|
||||
x1, y1, x2, y2 = x1-cx1, y1-cy1, x2-cx1, y2-cy1
|
||||
h, w = mask.shape
|
||||
|
||||
x1 = min(w-1, max(0, x1))
|
||||
x2 = min(w-1, max(0, x2))
|
||||
y1 = min(h-1, max(0, y1))
|
||||
y2 = min(h-1, max(0, y2))
|
||||
x1 = int(min(w-1, max(0, x1)))
|
||||
x2 = int(min(w-1, max(0, x2)))
|
||||
y1 = int(min(h-1, max(0, y1)))
|
||||
y2 = int(min(h-1, max(0, y2)))
|
||||
|
||||
mask_cropped = mask.copy()
|
||||
mask_cropped[:, :x1] = 0 # zero fill left side
|
||||
|
||||
@@ -106,7 +106,12 @@ def img2img_segs(image, model, clip, vae, seed, steps, cfg, sampler_name, schedu
|
||||
|
||||
# prepare mask
|
||||
if noise_mask is not None and inpaint_model:
|
||||
positive, negative, latent_image = nodes.InpaintModelConditioning().encode(positive, negative, image, vae, noise_mask)
|
||||
imc_encode = nodes.InpaintModelConditioning().encode
|
||||
if 'noise_mask' in inspect.signature(imc_encode).parameters:
|
||||
positive, negative, latent_image = imc_encode(positive, negative, image, vae, mask=noise_mask, noise_mask=True)
|
||||
else:
|
||||
print(f"[Impact Pack] ComfyUI is an outdated version.")
|
||||
positive, negative, latent_image = imc_encode(positive, negative, image, vae, noise_mask)
|
||||
else:
|
||||
latent_image = to_latent_image(image, vae)
|
||||
if noise_mask is not None:
|
||||
|
||||
@@ -52,7 +52,10 @@ class GeneralSwitch:
|
||||
|
||||
print(f"SELECTED: {input_name}")
|
||||
|
||||
return [input_name]
|
||||
if input_name in kwargs:
|
||||
return [input_name]
|
||||
else:
|
||||
return []
|
||||
|
||||
@staticmethod
|
||||
def doit(*args, **kwargs):
|
||||
@@ -62,6 +65,13 @@ class GeneralSwitch:
|
||||
selected_label = input_name
|
||||
node_id = kwargs['unique_id']
|
||||
|
||||
if input_name not in kwargs:
|
||||
if core.is_execution_model_version_supported():
|
||||
from comfy_execution.graph import ExecutionBlocker
|
||||
return ExecutionBlocker(None), selected_label, selected_index
|
||||
else:
|
||||
print("[Impact Pack] ImpactSwitch: ComfyUI is outdated. Cannot block empty selection.")
|
||||
|
||||
if 'extra_pnginfo' in kwargs and kwargs['extra_pnginfo'] is not None:
|
||||
nodelist = kwargs['extra_pnginfo']['workflow']['nodes']
|
||||
for node in nodelist:
|
||||
@@ -373,7 +383,7 @@ class ImageListToImageBatch:
|
||||
|
||||
def doit(self, images):
|
||||
if len(images) <= 1:
|
||||
return (images,)
|
||||
return (images[0],)
|
||||
else:
|
||||
image1 = images[0]
|
||||
for image2 in images[1:]:
|
||||
@@ -501,7 +511,7 @@ class MakeMaskBatch:
|
||||
def doit(self, **kwargs):
|
||||
mask1 = kwargs['mask1']
|
||||
del kwargs['mask1']
|
||||
masks = [utils.make_3d_mask(value) for value in kwargs.values()]
|
||||
masks = [make_3d_mask(value) for value in kwargs.values()]
|
||||
|
||||
if len(masks) == 0:
|
||||
return (mask1,)
|
||||
|
||||
@@ -58,11 +58,11 @@ def read_wildcard_dict(wildcard_path):
|
||||
try:
|
||||
with open(file_path, 'r', encoding="ISO-8859-1") as f:
|
||||
lines = f.read().splitlines()
|
||||
wildcard_dict[key] = lines
|
||||
wildcard_dict[key] = [x for x in lines if not x.strip().startswith('#')]
|
||||
except yaml.reader.ReaderError:
|
||||
with open(file_path, 'r', encoding="UTF-8", errors="ignore") as f:
|
||||
lines = f.read().splitlines()
|
||||
wildcard_dict[key] = lines
|
||||
wildcard_dict[key] = [x for x in lines if not x.strip().startswith('#')]
|
||||
elif file.endswith('.yaml'):
|
||||
file_path = os.path.join(root, file)
|
||||
|
||||
@@ -150,7 +150,7 @@ def process(text, seed=None):
|
||||
matches = re.findall(wildcard_pattern, multi_select_pattern[1])
|
||||
if len(options) == 1 and matches:
|
||||
# count$$<single wildcard>
|
||||
options = local_wildcard_dict.get(matches[0])
|
||||
options = get_wildcard_options(multi_select_pattern[1])
|
||||
else:
|
||||
# count$$opt1|opt2|...
|
||||
options[0] = multi_select_pattern[1]
|
||||
@@ -199,6 +199,34 @@ def process(text, seed=None):
|
||||
|
||||
return replaced_string, replacements_found
|
||||
|
||||
def get_wildcard_options(string):
|
||||
pattern = r"__([\w.\-+/*\\]+?)__"
|
||||
matches = re.findall(pattern, string)
|
||||
|
||||
options = []
|
||||
|
||||
for match in matches:
|
||||
keyword = match.lower()
|
||||
keyword = wildcard_normalize(keyword)
|
||||
if keyword in local_wildcard_dict:
|
||||
options.extend(local_wildcard_dict[keyword])
|
||||
elif '*' in keyword:
|
||||
subpattern = keyword.replace('*', '.*').replace('+', '\\+')
|
||||
total_patterns = []
|
||||
found = False
|
||||
for k, v in local_wildcard_dict.items():
|
||||
if re.match(subpattern, k) is not None or re.match(subpattern, k+'/') is not None:
|
||||
total_patterns += v
|
||||
found = True
|
||||
|
||||
if found:
|
||||
options.extend(total_patterns)
|
||||
elif '/' not in keyword:
|
||||
string_fallback = string.replace(f"__{match}__", f"__*/{match}__", 1)
|
||||
options.extend(get_wildcard_options(string_fallback))
|
||||
|
||||
return options
|
||||
|
||||
def replace_wildcard(string):
|
||||
pattern = r"__([\w.\-+/*\\]+?)__"
|
||||
matches = re.findall(pattern, string)
|
||||
@@ -259,7 +287,7 @@ def process(text, seed=None):
|
||||
|
||||
|
||||
def is_numeric_string(input_str):
|
||||
return re.match(r'^-?\d+(\.\d+)?$', input_str) is not None
|
||||
return re.match(r'^-?(\d*\.?\d+|\d+\.?\d*)$', input_str) is not None
|
||||
|
||||
|
||||
def safe_float(x):
|
||||
|
||||
+1
-1
@@ -1,7 +1,7 @@
|
||||
[project]
|
||||
name = "comfyui-impact-pack"
|
||||
description = "This extension offers various detector nodes and detailer nodes that allow you to configure a workflow that automatically enhances facial details. And provide iterative upscaler."
|
||||
version = "7.10.3"
|
||||
version = "7.14.2"
|
||||
license = { file = "LICENSE.txt" }
|
||||
dependencies = ["segment-anything", "scikit-image", "piexif", "transformers", "opencv-python-headless", "GitPython", "scipy>=1.11.4"]
|
||||
|
||||
|
||||
Reference in New Issue
Block a user