diff --git a/ComfyUI-Easy-Use.json b/ComfyUI-Easy-Use.json index c9017c1..cd90397 100644 --- a/ComfyUI-Easy-Use.json +++ b/ComfyUI-Easy-Use.json @@ -14,12 +14,18 @@ "easy controlnetLoader": { "title": "简易Controlnet" }, + "easy LLLite": { + "title": "简易LLLite" + }, "easy globalSeed": { "title": "全局Seed" }, "easy preSampling": { "title": "预采样参数（基础）" }, + "easy preSamplingAdvanced": { + "title": "预采样参数（高级）" + }, "easy preSamplingSdTurbo": { "title": "预采样参数（SdTurbo）" }, diff --git a/README.en.md b/README.en.md index e827b2e..e8f2fe2 100644 --- a/README.en.md +++ b/README.en.md @@ -19,7 +19,15 @@ EasyUse is simplified on the basis of [tinyterraNodes](https://github.com/TinyTe ### Updated -- **[Updated 12/11/2023]** Added the `showSpentTime` node to display the time spent on image diffusion and the time spent on VAE decoding images +**[Updated at 12/13/2023]** + +- Added the `easy LLLiteLoader` node, if you have pre-installed the kohya-ss/ControlNet-LLLite-ComfyUI package, please move the model files in the models to `ComfyUI\models\controlnet\` (i.e. in the default controlnet path of comfy, please do not change the file name of the model, otherwise it will not be read). +- Modify `easy controlnetLoader` to the bottom of the loader category. +- Added size display for `easy imageSize` and `easy imageSizeByLongerSize` outputs. + +**[Updated at 12/11/2023]** + +- Added the `showSpentTime` node to display the time spent on image diffusion and the time spent on VAE decoding images ### Major optimizations @@ -34,6 +42,7 @@ Disclaimer: Opened source was not easy. I have a lot of respect for the contribu |:---------------------------|:----------------------------------------------------------------------------|:----------------------------------| | easy SetNode | [diffus3/ComfyUI-extensions](https://github.com/diffus3/ComfyUI-extensions) | diffus3.SetNode | | easy GetNode | [diffus3/ComfyUI-extensions](https://github.com/diffus3/ComfyUI-extensions) | diffus3.GetNode | +| easy LLLiteLoader | [kohya-ss/ControlNet-LLLite-ComfyUI](https://github.com/kohya-ss/ControlNet-LLLite-ComfyUI) | LLLiteLoader | | easy GlobalSeed | [ltdrdata/ComfyUI-Inspire-Pack](https://github.com/ltdrdata/ComfyUI-Inspire-Pack) | Global Seed (Inspire) | | easy PreSamplingDynamicCFG | [sd-dynamic-thresholding](https://github.com/mcmonkeyprojects/sd-dynamic-thresholding) | DynamicThresholdingFull | | DynamicThresholdingFull | [sd-dynamic-thresholding](https://github.com/mcmonkeyprojects/sd-dynamic-thresholding) | DynamicThresholdingFull | diff --git a/README.md b/README.md index 530fcc1..43f73f7 100644 --- a/README.md +++ b/README.md @@ -8,7 +8,7 @@ 为了更加方便简单地使用ComfyUI，我对一部分常用的节点做了一些优化与整合。 -[![Bilibili Badge](https://img.shields.io/badge/使用说明视频-00A1D6?style=for-the-badge&logo=bilibili&logoColor=white&link=https://www.bilibili.com/video/BV1vQ4y1G7z7)](https://www.bilibili.com/video/BV1vQ4y1G7z7/) +[![Bilibili Badge](https://img.shields.io/badge/视频介绍-00A1D6?style=for-the-badge&logo=bilibili&logoColor=white&link=https://www.bilibili.com/video/BV1vQ4y1G7z7)](https://www.bilibili.com/video/BV1vQ4y1G7z7/) ## 流程对比 @@ -19,7 +19,15 @@ EasyUse 在 [tinyterraNodes](https://github.com/TinyTerra/ComfyUI_tinyterraNodes ### 更新 -- **[2023-12-11]** 新增 `showSpentTime` 节点用于展示图片推理花费时间与VAE解码花费时间。 +**2023-12-13** + +- 新增 `easy LLLiteLoader` 节点，如果您预先安装过 kohya-ss/ControlNet-LLLite-ComfyUI 包，请将 models 里的模型文件移动至 ComfyUI\models\controlnet\ (即comfy默认的controlnet路径里，请勿修改模型的文件名，不然会读取不到)。 +- 修改 `easy controlnetLoader` 到 loader 分类底下。 +- 新增 `easy imageSize` 和 `easy imageSizeByLongerSize` 输出的尺寸显示。 + +**2023-12-11** + +- 新增 `easy showSpentTime` 节点用于展示图片推理花费时间与VAE解码花费时间。 ### 主要的优化 @@ -30,14 +38,15 @@ EasyUse 在 [tinyterraNodes](https://github.com/TinyTerra/ComfyUI_tinyterraNodes 声明: 非常尊重这些原作者们的付出，开源不易，我仅仅只是做了一些整合与优化。 -| 节点名 | 相关的库 | 库相关的节点 | -|:---------------------------|:----------------------------------------------------------------------------|:----------------------| -| easy SetNode | [diffus3/ComfyUI-extensions](https://github.com/diffus3/ComfyUI-extensions) | diffus3.SetNode | -| easy GetNode | [diffus3/ComfyUI-extensions](https://github.com/diffus3/ComfyUI-extensions) | diffus3.GetNode | -| easy GlobalSeed | [ltdrdata/ComfyUI-Inspire-Pack](https://github.com/ltdrdata/ComfyUI-Inspire-Pack) | Global Seed (Inspire) | +| 节点名 | 相关的库 | 库相关的节点 | +|:---------------------------|:----------------------------------------------------------------------------|:------------------------| +| easy SetNode | [diffus3/ComfyUI-extensions](https://github.com/diffus3/ComfyUI-extensions) | diffus3.SetNode | +| easy GetNode | [diffus3/ComfyUI-extensions](https://github.com/diffus3/ComfyUI-extensions) | diffus3.GetNode | +| easy LLLiteLoader | [kohya-ss/ControlNet-LLLite-ComfyUI](https://github.com/kohya-ss/ControlNet-LLLite-ComfyUI) | LLLiteLoader | +| easy GlobalSeed | [ltdrdata/ComfyUI-Inspire-Pack](https://github.com/ltdrdata/ComfyUI-Inspire-Pack) | Global Seed (Inspire) | | easy PreSamplingDynamicCFG | [sd-dynamic-thresholding](https://github.com/mcmonkeyprojects/sd-dynamic-thresholding) | DynamicThresholdingFull | | DynamicThresholdingFull | [sd-dynamic-thresholding](https://github.com/mcmonkeyprojects/sd-dynamic-thresholding) | DynamicThresholdingFull | -| easy ImageInsetCrop | [rgthree/rgthree-comfy](https://github.com/rgthree/rgthree-comfy) | ImageInsetCrop | +| easy ImageInsetCrop | [rgthree/rgthree-comfy](https://github.com/rgthree/rgthree-comfy) | ImageInsetCrop | ## 示例 diff --git a/__init__.py b/__init__.py index 7d29705..273760a 100644 --- a/__init__.py +++ b/__init__.py @@ -8,6 +8,7 @@ node_list = [ "server", "easyNodes", "image", + "lllite" ] NODE_CLASS_MAPPINGS = {} diff --git a/docs/sdturbo_hiresfix_svd.png b/docs/sdturbo_hiresfix_svd.png index 10ac157..4518481 100644 Binary files a/docs/sdturbo_hiresfix_svd.png and b/docs/sdturbo_hiresfix_svd.png differ diff --git a/py/easyNodes.py b/py/easyNodes.py index 6100047..522bae2 100644 --- a/py/easyNodes.py +++ b/py/easyNodes.py @@ -973,7 +973,7 @@ class controlnetSimple: OUTPUT_NODE = True FUNCTION = "controlnetApply" - CATEGORY = "EasyUse/PreSampling" + CATEGORY = "EasyUse/Loader" def controlnetApply(self, pipe, control_net_name, image, positive=None, negative=None, strength=1): controlnet_path = folder_paths.get_full_path("controlnet", control_net_name) diff --git a/py/image.py b/py/image.py index bb69019..86b42c9 100644 --- a/py/image.py +++ b/py/image.py @@ -114,8 +114,10 @@ class imageSize: def image_width_height(self, image): image = tensor2pil(image) if image.size: - return (image.size[0], image.size[1]) - return (0, 0) + result = (image.size[0], image.size[1]) + else: + result = (0, 0) + return {"ui": {"text": "Width: "+str(result[0])+" , Height: "+str(result[1])}, "result": result} # 图像尺寸 class imageSizeByLongerSide: @@ -140,10 +142,12 @@ class imageSizeByLongerSide: image = tensor2pil(image) if image.size: if image.size[0] > image.size[1]: - return (image.size[0],) + result = (image.size[0],) else: - return (image.size[1],) - return (0,) + result = (image.size[1],) + else: + result = (0,) + return {"ui": {"text": str(result[0])}, "result": result} NODE_CLASS_MAPPINGS = { "easy imageInsetCrop": imageInsetCrop, diff --git a/py/lllite.py b/py/lllite.py new file mode 100644 index 0000000..e0694c2 --- /dev/null +++ b/py/lllite.py @@ -0,0 +1,286 @@ +import math +import torch +import os +import folder_paths +import comfy + +def get_file_list(path): + return [file for file in os.listdir(path) if file != "put_models_here.txt" and "lllite" in file] + + +def extra_options_to_module_prefix(extra_options): + # extra_options = {'transformer_index': 2, 'block_index': 8, 'original_shape': [2, 4, 128, 128], 'block': ('input', 7), 'n_heads': 20, 'dim_head': 64} + + # block is: [('input', 4), ('input', 5), ('input', 7), ('input', 8), ('middle', 0), + # ('output', 0), ('output', 1), ('output', 2), ('output', 3), ('output', 4), ('output', 5)] + # transformer_index is: [0, 1, 2, 3, 4, 5, 6, 7, 8], for each block + # block_index is: 0-1 or 0-9, depends on the block + # input 7 and 8, middle has 10 blocks + + # make module name from extra_options + block = extra_options["block"] + block_index = extra_options["block_index"] + if block[0] == "input": + module_pfx = f"lllite_unet_input_blocks_{block[1]}_1_transformer_blocks_{block_index}" + elif block[0] == "middle": + module_pfx = f"lllite_unet_middle_block_1_transformer_blocks_{block_index}" + elif block[0] == "output": + module_pfx = f"lllite_unet_output_blocks_{block[1]}_1_transformer_blocks_{block_index}" + else: + raise Exception("invalid block name") + return module_pfx + + +def load_control_net_lllite_patch(path, cond_image, multiplier, num_steps, start_percent, end_percent): + # calculate start and end step + start_step = math.floor(num_steps * start_percent * 0.01) if start_percent > 0 else 0 + end_step = math.floor(num_steps * end_percent * 0.01) if end_percent > 0 else num_steps + + # load weights + ctrl_sd = comfy.utils.load_torch_file(path, safe_load=True) + + # split each weights for each module + module_weights = {} + for key, value in ctrl_sd.items(): + fragments = key.split(".") + module_name = fragments[0] + weight_name = ".".join(fragments[1:]) + + if module_name not in module_weights: + module_weights[module_name] = {} + module_weights[module_name][weight_name] = value + + # load each module + modules = {} + for module_name, weights in module_weights.items(): + # ここの自動判定を何とかしたい + if "conditioning1.4.weight" in weights: + depth = 3 + elif weights["conditioning1.2.weight"].shape[-1] == 4: + depth = 2 + else: + depth = 1 + + module = LLLiteModule( + name=module_name, + is_conv2d=weights["down.0.weight"].ndim == 4, + in_dim=weights["down.0.weight"].shape[1], + depth=depth, + cond_emb_dim=weights["conditioning1.0.weight"].shape[0] * 2, + mlp_dim=weights["down.0.weight"].shape[0], + multiplier=multiplier, + num_steps=num_steps, + start_step=start_step, + end_step=end_step, + ) + info = module.load_state_dict(weights) + modules[module_name] = module + if len(modules) == 1: + module.is_first = True + + print(f"loaded {path} successfully, {len(modules)} modules") + + # cond imageをセットする + cond_image = cond_image.permute(0, 3, 1, 2) # b,h,w,3 -> b,3,h,w + cond_image = cond_image * 2.0 - 1.0 # 0-1 -> -1-+1 + + for module in modules.values(): + module.set_cond_image(cond_image) + + class control_net_lllite_patch: + def __init__(self, modules): + self.modules = modules + + def __call__(self, q, k, v, extra_options): + module_pfx = extra_options_to_module_prefix(extra_options) + + is_attn1 = q.shape[-1] == k.shape[-1] # self attention + if is_attn1: + module_pfx = module_pfx + "_attn1" + else: + module_pfx = module_pfx + "_attn2" + + module_pfx_to_q = module_pfx + "_to_q" + module_pfx_to_k = module_pfx + "_to_k" + module_pfx_to_v = module_pfx + "_to_v" + + if module_pfx_to_q in self.modules: + q = q + self.modules[module_pfx_to_q](q) + if module_pfx_to_k in self.modules: + k = k + self.modules[module_pfx_to_k](k) + if module_pfx_to_v in self.modules: + v = v + self.modules[module_pfx_to_v](v) + + return q, k, v + + def to(self, device): + for d in self.modules.keys(): + self.modules[d] = self.modules[d].to(device) + return self + + return control_net_lllite_patch(modules) + + +class LLLiteModule(torch.nn.Module): + def __init__( + self, + name: str, + is_conv2d: bool, + in_dim: int, + depth: int, + cond_emb_dim: int, + mlp_dim: int, + multiplier: int, + num_steps: int, + start_step: int, + end_step: int, + ): + super().__init__() + self.name = name + self.is_conv2d = is_conv2d + self.multiplier = multiplier + self.num_steps = num_steps + self.start_step = start_step + self.end_step = end_step + self.is_first = False + + modules = [] + modules.append(torch.nn.Conv2d(3, cond_emb_dim // 2, kernel_size=4, stride=4, padding=0)) # to latent (from VAE) size*2 + if depth == 1: + modules.append(torch.nn.ReLU(inplace=True)) + modules.append(torch.nn.Conv2d(cond_emb_dim // 2, cond_emb_dim, kernel_size=2, stride=2, padding=0)) + elif depth == 2: + modules.append(torch.nn.ReLU(inplace=True)) + modules.append(torch.nn.Conv2d(cond_emb_dim // 2, cond_emb_dim, kernel_size=4, stride=4, padding=0)) + elif depth == 3: + # kernel size 8は大きすぎるので、4にする / kernel size 8 is too large, so set it to 4 + modules.append(torch.nn.ReLU(inplace=True)) + modules.append(torch.nn.Conv2d(cond_emb_dim // 2, cond_emb_dim // 2, kernel_size=4, stride=4, padding=0)) + modules.append(torch.nn.ReLU(inplace=True)) + modules.append(torch.nn.Conv2d(cond_emb_dim // 2, cond_emb_dim, kernel_size=2, stride=2, padding=0)) + + self.conditioning1 = torch.nn.Sequential(*modules) + + if self.is_conv2d: + self.down = torch.nn.Sequential( + torch.nn.Conv2d(in_dim, mlp_dim, kernel_size=1, stride=1, padding=0), + torch.nn.ReLU(inplace=True), + ) + self.mid = torch.nn.Sequential( + torch.nn.Conv2d(mlp_dim + cond_emb_dim, mlp_dim, kernel_size=1, stride=1, padding=0), + torch.nn.ReLU(inplace=True), + ) + self.up = torch.nn.Sequential( + torch.nn.Conv2d(mlp_dim, in_dim, kernel_size=1, stride=1, padding=0), + ) + else: + self.down = torch.nn.Sequential( + torch.nn.Linear(in_dim, mlp_dim), + torch.nn.ReLU(inplace=True), + ) + self.mid = torch.nn.Sequential( + torch.nn.Linear(mlp_dim + cond_emb_dim, mlp_dim), + torch.nn.ReLU(inplace=True), + ) + self.up = torch.nn.Sequential( + torch.nn.Linear(mlp_dim, in_dim), + ) + + self.depth = depth + self.cond_image = None + self.cond_emb = None + self.current_step = 0 + + # @torch.inference_mode() + def set_cond_image(self, cond_image): + # print("set_cond_image", self.name) + self.cond_image = cond_image + self.cond_emb = None + self.current_step = 0 + + def forward(self, x): + if self.num_steps > 0: + if self.current_step < self.start_step: + self.current_step += 1 + return torch.zeros_like(x) + elif self.current_step >= self.end_step: + if self.is_first and self.current_step == self.end_step: + print(f"end LLLite: step {self.current_step}") + self.current_step += 1 + if self.current_step >= self.num_steps: + self.current_step = 0 # reset + return torch.zeros_like(x) + else: + if self.is_first and self.current_step == self.start_step: + print(f"start LLLite: step {self.current_step}") + self.current_step += 1 + if self.current_step >= self.num_steps: + self.current_step = 0 # reset + + if self.cond_emb is None: + # print(f"cond_emb is None, {self.name}") + cx = self.conditioning1(self.cond_image.to(x.device, dtype=x.dtype)) + if not self.is_conv2d: + # reshape / b,c,h,w -> b,h*w,c + n, c, h, w = cx.shape + cx = cx.view(n, c, h * w).permute(0, 2, 1) + self.cond_emb = cx + + cx = self.cond_emb + # print(f"forward {self.name}, {cx.shape}, {x.shape}") + + # uncond/condでxはバッチサイズが2倍 + if x.shape[0] != cx.shape[0]: + if self.is_conv2d: + cx = cx.repeat(x.shape[0] // cx.shape[0], 1, 1, 1) + else: + # print("x.shape[0] != cx.shape[0]", x.shape[0], cx.shape[0]) + cx = cx.repeat(x.shape[0] // cx.shape[0], 1, 1) + + cx = torch.cat([cx, self.down(x)], dim=1 if self.is_conv2d else 2) + cx = self.mid(cx) + cx = self.up(cx) + return cx * self.multiplier + + +class LLLiteLoader: + def __init__(self): + pass + + @classmethod + def INPUT_TYPES(s): + return { + "required": { + "model": ("MODEL",), + "model_name": (get_file_list(folder_paths.get_folder_paths("controlnet")[0]),), + "cond_image": ("IMAGE",), + "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}), + "steps": ("INT", {"default": 0, "min": 0, "max": 200, "step": 1}), + "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step": 0.1}), + "end_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step": 0.1}), + } + } + + RETURN_TYPES = ("MODEL",) + FUNCTION = "load_lllite" + CATEGORY = "EasyUse/Loader" + + def load_lllite(self, model, model_name, cond_image, strength, steps, start_percent, end_percent): + # cond_image is b,h,w,3, 0-1 + + model_path = os.path.join(folder_paths.get_folder_paths("controlnet")[0], model_name) + + model_lllite = model.clone() + patch = load_control_net_lllite_patch(model_path, cond_image, strength, steps, start_percent, end_percent) + if patch is not None: + model_lllite.set_model_attn1_patch(patch) + model_lllite.set_model_attn2_patch(patch) + + return (model_lllite,) + + +NODE_CLASS_MAPPINGS = {"easy LLLiteLoader": LLLiteLoader} + +NODE_DISPLAY_NAME_MAPPINGS = { + "easy LLLiteLoader": "EasyLLLite", +} diff --git a/web/js/getset.js b/web/js/getset.js index 6cc3c2e..acf130a 100644 --- a/web/js/getset.js +++ b/web/js/getset.js @@ -42,7 +42,7 @@ app.registerExtension({ link_info, output ) { - console.log("onConnectionsChange"); + // console.log("onConnectionsChange"); //On Disconnect if (slotType == 1 && !isChangeConnect) { this.inputs[slot].type = '*'; @@ -90,7 +90,7 @@ app.registerExtension({ } this.clone = function () { - console.log("CLONE"); + // console.log("CLONE"); const cloned = SetNode.prototype.clone.apply(this); //cloned.inputs = []; cloned.inputs[0].name = '*'; @@ -106,8 +106,8 @@ app.registerExtension({ this.update = function() { - console.log("SetNode.update()"); - console.log(this.widgets[0].value); + // console.log("SetNode.update()"); + // console.log(this.widgets[0].value); if (node.graph) { this.findGetters(node.graph).forEach((getter) => { getter.setType(this.inputs[0].type); @@ -146,9 +146,9 @@ app.registerExtension({ } onRemoved() { - console.log("onRemove"); - console.log(this); - console.log(this.flags); + // console.log("onRemove"); + // console.log(this); + // console.log(this.flags); const allGetters = this.graph._nodes.filter((otherNode) => otherNode.type == "easy getNode"); allGetters.forEach((otherNode) => { if (otherNode.setComboValues) { @@ -221,8 +221,8 @@ app.registerExtension({ this.setName = function(name) { - console.log("renaming getter: "); - console.log(node.widgets[0].value + " -> " + name); + // console.log("renaming getter: "); + // console.log(node.widgets[0].value + " -> " + name); node.widgets[0].value = name; node.onRename(); node.serialize(); @@ -230,7 +230,7 @@ app.registerExtension({ this.onRename = function() { - console.log("onRename"); + // console.log("onRename"); const setter = this.findSetter(node.graph); if (setter) { @@ -248,13 +248,13 @@ app.registerExtension({ }; this.validateLinks = function() { - console.log("validating links"); + // console.log("validating links"); if (this.outputs[0].type != '*' && this.outputs[0].links) { - console.log("in"); + // console.log("in"); this.outputs[0].links.forEach((linkId) => { const link = node.graph.links[linkId]; if (link && link.type != this.outputs[0].type && link.type != '*') { - console.log("removing link"); + // console.log("removing link"); node.graph.removeLink(linkId) } }) @@ -286,10 +286,10 @@ app.registerExtension({ getInputLink(slot) { - console.log("get.getInputLink(): " + slot); + // console.log("get.getInputLink(): " + slot); const setter = this.findSetter(this.graph); - console.log("setter:"); - console.log(setter); + // console.log("setter:"); + // console.log(setter); // const setters = app.graph._nodes.filter((otherNode) => { @@ -311,16 +311,16 @@ app.registerExtension({ if (setter) { const slot_info = setter.inputs[slot]; - console.log("slot info"); - console.log(slot_info); - console.log(this.graph.links); + // console.log("slot info"); + // console.log(slot_info); + // console.log(this.graph.links); const link = this.graph.links[ slot_info.link ]; - console.log("link:"); - console.log(link); + // console.log("link:"); + // console.log(link); return link; } else { - console.log(this.widgets[0]); - console.log(this.widgets[0].value); + // console.log(this.widgets[0]); + // console.log(this.widgets[0].value); throw new Error("No setter found for " + this.widgets[0].value + "(" + this.type + ")"); } diff --git a/web/js/image.js b/web/js/image.js new file mode 100644 index 0000000..9f38cac --- /dev/null +++ b/web/js/image.js @@ -0,0 +1,66 @@ +import { app } from "../../../scripts/app.js"; + + +app.registerExtension({ + name: "comfy.easyUse.imageWidgets", + + nodeCreated(node) { + if (["easy imageSize","easy imageSizeByLongerSide"].includes(node.comfyClass)) { + + const inputEl = document.createElement("textarea"); + inputEl.className = "comfy-multiline-input"; + inputEl.readOnly = true + + const widget = node.addDOMWidget("info", "customtext", inputEl, { + getValue() { + return inputEl.value; + }, + setValue(v) { + inputEl.value = v; + }, + serialize: false + }); + widget.inputEl = inputEl; + + inputEl.addEventListener("input", () => { + widget.callback?.(widget.value); + }); + } + }, + + beforeRegisterNodeDef(nodeType, nodeData, app) { + if (["easy imageSize","easy imageSizeByLongerSide"].includes(nodeData.name)) { + function populate(arr_text) { + var text = ''; + for (let i = 0; i < arr_text.length; i++){ + text += arr_text[i]; + } + if (this.widgets) { + const pos = this.widgets.findIndex((w) => w.name === "info"); + if (pos !== -1 && this.widgets[pos]) { + const w = this.widgets[pos] + w.value = text; + } + } + requestAnimationFrame(() => { + const sz = this.computeSize(); + if (sz[0] < this.size[0]) { + sz[0] = this.size[0]; + } + if (sz[1] < this.size[1]) { + sz[1] = this.size[1]; + } + this.onResize?.(sz); + app.graph.setDirtyCanvas(true, false); + }); + } + + // When the node is executed we will be sent the input text, display this in the widget + const onExecuted = nodeType.prototype.onExecuted; + nodeType.prototype.onExecuted = function (message) { + onExecuted?.apply(this, arguments); + populate.call(this, message.text); + }; + } + } +}) \ No newline at end of file