add:easy controlnetLoaderADV node

2023-12-14 18:27:29 +08:00
parent 22ce1f9f36
commit 1b7af8ea45
9 changed files with 224 additions and 47 deletions
@@ -14,6 +14,9 @@
  "easy controlnetLoader": {
    "title": "简易Controlnet"
  },
+  "easy controlnetLoaderADV": {
+    "title": "简易Controlnet（高级）"
+  },
  "easy LLLite": {
    "title": "简易LLLite"
  },
@@ -19,15 +19,26 @@ EasyUse is simplified on the basis of [tinyterraNodes](https://github.com/TinyTe

 ### Updated

-**[Updated at 12/13/2023]**
+**2023-12-14**
+
+- `easy a1111Loader` and `easy comfyLoader` added `batch_size` of required input parameters 
+- Added the `easy controlnetLoaderADV` node
+- `easy controlnetLoaderADV` and `easy controlnetLoader` added `control_net ` of optional input parameters
+- `easy preSampling` and `easy preSamplingAdvanced` added 'image_to_latent' optional input parameters
+- Added the `easy imageSizeBySide` node, which can be output as a long side or a short side
+
+<details>
+<summary><b>Updated at 12/13/2023</b></summary>

 -  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.
+</details>

-**[Updated at 12/11/2023]**
-
+<details>
+<summary><b>Updated at 12/11/2023</b></summary>
 -  Added the `showSpentTime` node to display the time spent on image diffusion and the time spent on VAE decoding images
+</details>

 ### Major optimizations

@@ -15,19 +15,31 @@

 <img src="./docs/workflow_node_compare.png">

-EasyUse 在 [tinyterraNodes](https://github.com/TinyTerra/ComfyUI_tinyterraNodes) 的基础上做了简化，在简化的节点中去除了过多的传入和传出参数，建议您配合 [tinyterraNodes](https://github.com/TinyTerra/ComfyUI_tinyterraNodes) 中的 **pipeIn**、**pipeOut**、**pipeEdit** 使用，可参考下方示例里 [图生图的工作流](https://github.com/yolain/ComfyUI-Easy-Use?tab=readme-ov-file#%E5%9B%BE%E7%94%9F%E5%9B%BEcontrolnet)。
+EasyUse 在 [tinyterraNodes](https://github.com/TinyTerra/ComfyUI_tinyterraNodes) 的基础上做了简化，在简化的节点中去除了过多的传入和传出参数，建议您配合 [tinyterraNodes](https://github.com/TinyTerra/ComfyUI_tinyterraNodes) 中的 **pipeIn**、**pipeOut**、**pipeEdit** 使用，可参考下方示例里 [工作流](https://github.com/yolain/ComfyUI-Easy-Use?tab=readme-ov-file#sdturbo%E9%AB%98%E6%B8%85%E4%BF%AE%E5%A4%8Dsvd)。

 ### 更新

-**2023-12-13**
+**2023-12-14**
+
+- `easy a1111Loader` 和 `easy comfyLoader` 新增 `batch_size` 传入参数
+- 新增 `easy controlnetLoaderADV` 节点
+- `easy controlnetLoaderADV` 和 `easy controlnetLoader` 新增 `control_net` 可选传入参数
+- `easy preSampling` 和 `easy preSamplingAdvanced` 新增 `image_to_latent` 可选传入参数
+- 新增 `easy imageSizeBySide` 节点，可选输出为长边或短边
+
+<details>
+<summary><b>2023-12-13</b></summary>

 -  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。
+</details>

-**2023-12-11**
+<details>
+<summary><b>2023-12-11</b></summary>

 -  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。
+</details>

 ### 主要的优化

@@ -28,7 +28,7 @@ from comfy_extras.chainner_models import model_loading
 from typing import Dict, List, Optional, Tuple, Union, Any
 from .adv_encode import advanced_encode, advanced_encode_XL

-from nodes import MAX_RESOLUTION, VAEEncode, VAEEncodeTiled, VAEDecode, VAEDecodeTiled
+from nodes import MAX_RESOLUTION, RepeatLatentBatch
 from .config import BASE_RESOLUTIONS

 from server import PromptServer
@@ -781,6 +781,7 @@ class a1111Loader:

            "positive": ("STRING", {"default": "Positive", "multiline": True}),
            "negative": ("STRING", {"default": "Negative", "multiline": True}),
+            "batch_size": ("INT", {"default": 1, "min": 1, "max": 64}),
        },
            "optional": {"optional_lora_stack": ("LORA_STACK",)},
            "hidden": {"prompt": "PROMPT", "positive_weight_interpretation": "A1111", "negative_weight_interpretation": "A1111"}, "my_unique_id": "UNIQUE_ID"}
@@ -794,7 +795,7 @@ class a1111Loader:
    def adv_pipeloader(self, ckpt_name, vae_name, clip_skip,
                       lora_name, lora_model_strength, lora_clip_strength,
                       resolution, empty_latent_width, empty_latent_height,
-                       positive, negative, optional_lora_stack=None, prompt=None,
+                       positive, negative, batch_size, optional_lora_stack=None, prompt=None,
                       positive_weight_interpretation='A1111', negative_weight_interpretation='A1111',
                       my_unique_id=None
                       ):
@@ -813,7 +814,7 @@ class a1111Loader:
                raise ValueError("Invalid base_resolution format.")

        # Create Empty Latent
-        latent = torch.zeros([1, 4, empty_latent_height // 8, empty_latent_width // 8]).cpu()
+        latent = torch.zeros([batch_size, 4, empty_latent_height // 8, empty_latent_width // 8]).cpu()
        samples = {"samples": latent}

        # Clean models from loaded_objects
@@ -894,7 +895,7 @@ class a1111Loader:
                                    "negative_balance": None,
                                    "empty_latent_width": empty_latent_width,
                                    "empty_latent_height": empty_latent_height,
-                                    "batch_size": 1,
+                                    "batch_size": batch_size,
                                    "seed": 0,
                                    "empty_samples": samples, }
                }
@@ -921,6 +922,8 @@ class comfyLoader:

            "positive": ("STRING", {"default": "Positive", "multiline": True}),
            "negative": ("STRING", {"default": "Negative", "multiline": True}),
+
+            "batch_size": ("INT", {"default": 1, "min": 1, "max": 64}),
        },
            "optional": {"optional_lora_stack": ("LORA_STACK",)},
            "hidden": {"prompt": "PROMPT", "positive_weight_interpretation": "comfy", "negative_weight_interpretation": "comfy"}, "my_unique_id": "UNIQUE_ID"}
@@ -934,7 +937,7 @@ class comfyLoader:
    def adv_pipeloader(self, ckpt_name, vae_name, clip_skip,
                       lora_name, lora_model_strength, lora_clip_strength,
                       resolution, empty_latent_width, empty_latent_height,
-                       positive, negative, optional_lora_stack=None, prompt=None,
+                       positive, negative, batch_size, optional_lora_stack=None, prompt=None,
                       positive_weight_interpretation='comfy', negative_weight_interpretation='comfy',
                       my_unique_id=None
                       ):
@@ -943,7 +946,7 @@ class comfyLoader:
            ckpt_name, vae_name, clip_skip,
            lora_name, lora_model_strength, lora_clip_strength,
            resolution, empty_latent_width, empty_latent_height,
-            positive, negative, optional_lora_stack, prompt,
+            positive, negative, batch_size, optional_lora_stack, prompt,
            positive_weight_interpretation, negative_weight_interpretation,
            my_unique_id
        )
@@ -958,12 +961,11 @@ class controlnetSimple:
        return {
            "required": {
                "pipe": ("PIPE_LINE",),
-                "control_net_name": (folder_paths.get_filename_list("controlnet"),),
                "image": ("IMAGE",),
+                "control_net_name": (folder_paths.get_filename_list("controlnet"),),
            },
            "optional": {
-                "positive": ("CONDITIONING",),
-                "negative": ("CONDITIONING",),
+                "control_net": ("CONTROL_NET",),
                "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01})
            }
        }
@@ -975,16 +977,17 @@ class controlnetSimple:
    FUNCTION = "controlnetApply"
    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)
-        control_net = comfy.controlnet.load_controlnet(controlnet_path)
+    def controlnetApply(self, pipe, image, control_net_name, control_net=None,strength=1):
+        if control_net is None:
+            controlnet_path = folder_paths.get_full_path("controlnet", control_net_name)
+            control_net = comfy.controlnet.load_controlnet(controlnet_path)
        control_hint = image.movedim(-1, 1)

-        _positive = pipe["positive"] if positive is None else positive
-        _negative = pipe["negative"] if negative is None else negative
+        positive = pipe["positive"]
+        negative = pipe["negative"]

        if strength != 0:
-            if _negative is None:
+            if negative is None:
                p = []
                for t in positive:
                    n = [t[0], t[1].copy()]
@@ -994,11 +997,11 @@ class controlnetSimple:
                    n[1]['control'] = c_net
                    n[1]['control_apply_to_uncond'] = True
                    p.append(n)
-                _positive = p
+                positive = p
            else:
                cnets = {}
                out = []
-                for conditioning in [_positive, _negative]:
+                for conditioning in [positive, negative]:
                    c = []
                    for t in conditioning:
                        d = t[1].copy()
@@ -1016,12 +1019,94 @@ class controlnetSimple:
                        n = [t[0], d]
                        c.append(n)
                    out.append(c)
-                _positive = out[0]
-                _negative = out[1]
+                positive = out[0]
+                negative = out[1]

-        # 拼接条件
-        positive = _positive if positive is None else _positive + pipe['positive']
-        negative = _negative if negative is None else _negative + pipe['negative']
+        new_pipe = {
+            "model": pipe['model'],
+            "positive": positive,
+            "negative": negative,
+            "vae": pipe['vae'],
+            "clip": pipe['clip'],
+
+            "samples": pipe["samples"],
+            "images": pipe["images"],
+            "seed": 0,
+
+            "loader_settings": pipe["loader_settings"]
+        }
+
+        return (new_pipe,)
+
+# controlnetADV
+class controlnetAdvanced:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "pipe": ("PIPE_LINE",),
+                "image": ("IMAGE",),
+                "control_net_name": (folder_paths.get_filename_list("controlnet"),),
+            },
+            "optional": {
+                "control_net": ("CONTROL_NET",),
+                "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
+                "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001})
+            }
+        }
+
+    RETURN_TYPES = ("PIPE_LINE",)
+    RETURN_NAMES = ("pipe",)
+    OUTPUT_NODE = True
+
+    FUNCTION = "controlnetApply"
+    CATEGORY = "EasyUse/Loader"
+
+    def controlnetApply(self, pipe, image, control_net_name, control_net=None, strength=1, start_percent=0, end_percent=1):
+        if control_net is None:
+            controlnet_path = folder_paths.get_full_path("controlnet", control_net_name)
+            control_net = comfy.controlnet.load_controlnet(controlnet_path)
+        control_hint = image.movedim(-1, 1)
+
+        positive = pipe["positive"]
+        negative = pipe["negative"]
+
+        if strength != 0:
+            if negative is None:
+                p = []
+                for t in positive:
+                    n = [t[0], t[1].copy()]
+                    c_net = control_net.copy().set_cond_hint(control_hint, strength)
+                    if 'control' in t[1]:
+                        c_net.set_previous_controlnet(t[1]['control'])
+                    n[1]['control'] = c_net
+                    n[1]['control_apply_to_uncond'] = True
+                    p.append(n)
+                positive = p
+            else:
+                cnets = {}
+                out = []
+                for conditioning in [positive, negative]:
+                    c = []
+                    for t in conditioning:
+                        d = t[1].copy()
+
+                        prev_cnet = d.get('control', None)
+                        if prev_cnet in cnets:
+                            c_net = cnets[prev_cnet]
+                        else:
+                            c_net = control_net.copy().set_cond_hint(control_hint, strength, (start_percent, end_percent))
+                            c_net.set_previous_controlnet(prev_cnet)
+                            cnets[prev_cnet] = c_net
+
+                        d['control'] = c_net
+                        d['control_apply_to_uncond'] = False
+                        n = [t[0], d]
+                        c.append(n)
+                    out.append(c)
+                positive = out[0]
+                negative = out[1]

        new_pipe = {
            "model": pipe['model'],
@@ -1095,6 +1180,9 @@ class samplerSettings:
                     "seed_num": ("INT", {"default": 0, "min": 0, "max": 1125899906842624}),
                     "control_before_generate": (["fixed", "increment", "decrement", "randomize"], {"default": "randomize"}),
                     },
+                "optional": {
+                    "image_to_latent": ("IMAGE",),
+                },
                "hidden":
                    {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO", "my_unique_id": "UNIQUE_ID"},
                }
@@ -1106,7 +1194,7 @@ class samplerSettings:
    FUNCTION = "settings"
    CATEGORY = "EasyUse/PreSampling"

-    def settings(self, pipe, steps, cfg, sampler_name, scheduler, denoise, seed_num, control_before_generate, prompt=None, extra_pnginfo=None, my_unique_id=None):
+    def settings(self, pipe, steps, cfg, sampler_name, scheduler, denoise, seed_num, control_before_generate, image_to_latent=None, prompt=None, extra_pnginfo=None, my_unique_id=None):

        # seed生成
        seed_num = control_seed(control_before_generate, seed_num)
@@ -1117,6 +1205,17 @@ class samplerSettings:
                length = len(node["widgets_values"])
                node["widgets_values"][length-2] = seed_num

+        vae = pipe["vae"]
+        # 图生图转换
+        if image_to_latent is not None:
+            batch_size = pipe["loader_settings"]["batch_size"] if "batch_size" in pipe["loader_settings"] else 1
+            samples = {"samples": vae.encode(image_to_latent)}
+            samples = RepeatLatentBatch().repeat(samples, batch_size)[0]
+            images = image_to_latent
+        else:
+            samples = pipe["samples"]
+            images = pipe["images"]
+        print(samples)
        new_pipe = {
            "model": pipe['model'],
            "positive": pipe['positive'],
@@ -1124,8 +1223,8 @@ class samplerSettings:
            "vae": pipe['vae'],
            "clip": pipe['clip'],

-            "samples": pipe["samples"],
-            "images": pipe["images"],
+            "samples": samples,
+            "images": images,
            "seed": seed_num,

            "loader_settings": {
@@ -1163,6 +1262,9 @@ class samplerSettingsAdvanced:
                     "seed_num": ("INT", {"default": 0, "min": 0, "max": 1125899906842624}),
                     "control_before_generate": (["fixed", "increment", "decrement", "randomize"], {"default": "randomize"}),
                     },
+                "optional": {
+                    "image_to_latent": ("Image",)
+                },
                "hidden":
                    {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO", "my_unique_id": "UNIQUE_ID"},
                }
@@ -1174,7 +1276,7 @@ class samplerSettingsAdvanced:
    FUNCTION = "settings"
    CATEGORY = "EasyUse/PreSampling"

-    def settings(self, pipe, steps, cfg, sampler_name, scheduler, start_at_step, end_at_step, add_noise, seed_num, control_before_generate, prompt=None, extra_pnginfo=None, my_unique_id=None):
+    def settings(self, pipe, steps, cfg, sampler_name, scheduler, start_at_step, end_at_step, add_noise, seed_num, control_before_generate, image_to_latent=None, prompt=None, extra_pnginfo=None, my_unique_id=None):

        # seed生成
        seed_num = control_seed(control_before_generate, seed_num)
@@ -1185,6 +1287,17 @@ class samplerSettingsAdvanced:
                length = len(node["widgets_values"])
                node["widgets_values"][length-2] = seed_num

+        # 图生图转换
+        vae = pipe["vae"]
+        if image_to_latent is not None:
+            batch_size = pipe["loader_settings"]["batch_size"] if "batch_size" in pipe["loader_settings"] else 1
+            samples = {"samples": vae.encode(image_to_latent)}
+            samples = RepeatLatentBatch().repeat(samples, batch_size)[0]
+            images = image_to_latent
+        else:
+            samples = pipe["samples"]
+            images = pipe["images"]
+
        new_pipe = {
            "model": pipe['model'],
            "positive": pipe['positive'],
@@ -1192,8 +1305,8 @@ class samplerSettingsAdvanced:
            "vae": pipe['vae'],
            "clip": pipe['clip'],

-            "samples": pipe["samples"],
-            "images": pipe["images"],
+            "samples": samples,
+            "images": images,
            "seed": seed_num,

            "loader_settings": {
@@ -1516,9 +1629,6 @@ class samplerSimple:
        if add_noise == "disable":
            disable_noise = True

-        def vae_decode_latent(vae, samples, tile_size):
-            return VAEDecodeTiled().decode(vae, samples, tile_size)[0] if tile_size is not None else VAEDecode().decode(vae, samples)[0]
-
        def process_sample_state(pipe, samp_model, samp_clip, samp_samples, samp_vae, samp_seed, samp_positive,
                                 samp_negative,
                                 steps, start_step, last_step, cfg, sampler_name, scheduler, denoise,
@@ -1533,7 +1643,13 @@ class samplerSimple:
            # 推理结束时间
            end_time = int(time.time() * 1000)
            # 解码图片
-            samp_images = vae_decode_latent(samp_vae, samp_samples, tile_size)
+            latent = samp_samples["samples"]
+
+            # 解码图片
+            if tile_size is not None:
+                samp_images = samp_vae.decode_tiled(latent, tile_x=tile_size // 8, tile_y=tile_size // 8, )
+            else:
+                samp_images = samp_vae.decode(latent).cpu()

            # 推理总耗时（包含解码）
            end_decode_time = int(time.time() * 1000)
@@ -1686,8 +1802,8 @@ class samplerSDTurbo:
        latent = samp_samples['samples']

        # 解码图片
-        if tile_size:
-            samp_images = (samp_vae.decode_tiled(latent, tile_x=tile_size // 8, tile_y=tile_size // 8, ),)
+        if tile_size is not None:
+            samp_images = samp_vae.decode_tiled(latent, tile_x=tile_size // 8, tile_y=tile_size // 8, )
        else:
            samp_images = samp_vae.decode(latent).cpu()

@@ -1770,6 +1886,7 @@ NODE_CLASS_MAPPINGS = {
    "easy a1111Loader": a1111Loader,
    "easy comfyLoader": comfyLoader,
    "easy controlnetLoader": controlnetSimple,
+    "easy controlnetLoaderADV": controlnetAdvanced,
    "easy globalSeed": globalSeed,
    "easy preSampling": samplerSettings,
    "easy preSamplingAdvanced": samplerSettingsAdvanced,
@@ -1785,6 +1902,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
    "easy a1111Loader": "EasyLoader (A1111)",
    "easy comfyLoader": "EasyLoader (comfy)",
    "easy controlnetLoader": "EasyControlnet",
+    "easy controlnetLoaderADV": "EasyControlnet (Advanced)",
    "easy globalSeed": "GlobalSeed",
    "easy preSampling": "PreSampling",
    "easy preSamplingAdvanced": "PreSampling (Advanced)",
@@ -119,7 +119,38 @@ class imageSize:
      result = (0, 0)
    return {"ui": {"text": "Width: "+str(result[0])+" , Height: "+str(result[1])}, "result": result}

-# 图像尺寸
+# 图像尺寸（最长边）
+class imageSizeBySide:
+  def __init__(self):
+    pass
+
+  @classmethod
+  def INPUT_TYPES(cls):
+    return {
+      "required": {
+        "image": ("IMAGE",),
+        "side": (["Longest", "Shortest"],)
+      }
+    }
+
+  RETURN_TYPES = ("INT",)
+  RETURN_NAMES = ("resolution",)
+  FUNCTION = "image_side"
+
+  CATEGORY = "EasyUse/Image"
+
+  def image_side(self, image, side):
+    image = tensor2pil(image)
+    if image.size:
+      if side == "Longest":
+         result = (image.size[0],) if image.size[0] > image.size[1] else (image.size[1],)
+      elif side == 'Shortest':
+         result = (image.size[0],) if image.size[0] < image.size[1] else (image.size[1],)
+    else:
+      result = (0,)
+    return {"ui": {"text": str(result[0])}, "result": result}
+
+# 图像尺寸（最长边）
 class imageSizeByLongerSide:
  def __init__(self):
    pass
@@ -152,11 +183,13 @@ class imageSizeByLongerSide:
 NODE_CLASS_MAPPINGS = {
  "easy imageInsetCrop": imageInsetCrop,
  "easy imageSize": imageSize,
+  "easy imageSizeBySide": imageSizeBySide,
  "easy imageSizeByLongerSide": imageSizeByLongerSide
 }

 NODE_DISPLAY_NAME_MAPPINGS = {
  "easy imageInsetCrop": "ImageInsetCrop",
  "easy imageSize": "ImageSize",
+  "easy imageSizeBySide": "ImageSize (Side)",
  "easy imageSizeByLongerSide": "ImageSize (LongerSide)"
 }
@@ -4,8 +4,8 @@ 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 get_file_list(filenames):
+    return [file for file in filenames if file != "put_models_here.txt" and "lllite" in file]


 def extra_options_to_module_prefix(extra_options):
@@ -252,7 +252,7 @@ class LLLiteLoader:
        return {
            "required": {
                "model": ("MODEL",),
-                "model_name": (get_file_list(folder_paths.get_folder_paths("controlnet")[0]),),
+                "model_name": (get_file_list(folder_paths.get_filename_list("controlnet")),),
                "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}),
@@ -268,7 +268,7 @@ class LLLiteLoader:
    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_path = os.path.join(folder_paths.get_full_path("controlnet", model_name))

        model_lllite = model.clone()
        patch = load_control_net_lllite_patch(model_path, cond_image, strength, steps, start_percent, end_percent)
@@ -5,7 +5,7 @@ app.registerExtension({
    name: "comfy.easyUse.imageWidgets",

    nodeCreated(node) {
-        if (["easy imageSize","easy imageSizeByLongerSide"].includes(node.comfyClass)) {
+        if (["easy imageSize","easy imageSizeBySide","easy imageSizeByLongerSide"].includes(node.comfyClass)) {

 			const inputEl = document.createElement("textarea");
 			inputEl.className = "comfy-multiline-input";
@@ -29,7 +29,7 @@ app.registerExtension({
    },

    beforeRegisterNodeDef(nodeType, nodeData, app) {
-        if (["easy imageSize","easy imageSizeByLongerSide"].includes(nodeData.name)) {
+        if (["easy imageSize","easy imageSizeBySide","easy imageSizeByLongerSide"].includes(nodeData.name)) {
 			function populate(arr_text) {
 				var text = '';
 				for (let i = 0; i < arr_text.length; i++){