Merge pull request #441 from Creepybits/main

Fix: Correct scheduler input on StyleAligned Sample Reference Latents node

.github/workflows/publish.yml

@@ -7,15 +7,19 @@ on:
     paths:
       - "pyproject.toml"
 
+permissions:
+  issues: write
+
 jobs:
   publish-node:
     name: Publish Custom Node to registry
     runs-on: ubuntu-latest
+    if: ${{ github.repository_owner == 'shadowcz007' }}
     steps:
       - name: Check out code
         uses: actions/checkout@v4
       - name: Publish Custom Node
-        uses: Comfy-Org/publish-node-action@main
+        uses: Comfy-Org/publish-node-action@v1
         with:
           ## Add your own personal access token to your Github Repository secrets and reference it here.
-          personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }}
+          personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }}

README.md

@@ -10,7 +10,9 @@ For business cooperation, please contact email 389570357@qq.com
 
 ##### `最新`：
 
-- 新增 SimulateDevDesignDiscussions，需要安装[swarm](https://github.com/openai/swarm)和[Comfyui-ChatTTS](https://github.com/shadowcz007/Comfyui-ChatTTS)，工作流下载[./workflow/swarm制作的播客节点workflow.json]
+- 新增[fal.ai](https://fal.ai/dashboard)的视频生成:Kling、RunwayGen3、LumaDreamMachine，[工作流下载](./workflow/video-all-in-one-test-workflow.json)
+
+- 新增 SimulateDevDesignDiscussions，需要安装[swarm](https://github.com/openai/swarm)和[Comfyui-ChatTTS](https://github.com/shadowcz007/Comfyui-ChatTTS)，[工作流下载](./workflow/swarm制作的播客节点workflow.json)
 
 - 新增 SenseVoice
 

__init__.py

@@ -1455,4 +1455,23 @@ try:
 except Exception as e:
     logging.info('Whisper.available False' )  
 
+
+try:
+    from .nodes.FalVideo import VideoGenKlingNode,VideoGenLumaDreamMachineNode,VideoGenRunwayGen3Node,LoadVideoFromURL
+    logging.info('FalVideo.available')
+    # Update Node class mappings
+    NODE_CLASS_MAPPINGS['VideoGenKlingNode']=VideoGenKlingNode
+    NODE_CLASS_MAPPINGS['VideoGenRunwayGen3Node']=VideoGenRunwayGen3Node
+    NODE_CLASS_MAPPINGS['VideoGenLumaDreamMachineNode']=VideoGenLumaDreamMachineNode
+    NODE_CLASS_MAPPINGS['LoadVideoFromURL']=LoadVideoFromURL
+    
+    NODE_DISPLAY_NAME_MAPPINGS["VideoGenKlingNode"]=  "Kling Video Generation @fal"
+    NODE_DISPLAY_NAME_MAPPINGS["VideoGenRunwayGen3Node"]= "Runway Gen3 Image-to-Video @fal"
+    NODE_DISPLAY_NAME_MAPPINGS["VideoGenLumaDreamMachineNode"]= "Luma Dream Machine @fal"
+    NODE_DISPLAY_NAME_MAPPINGS["LoadVideoFromURL"]= "Load Video from URL"
+    
+
+except Exception as e:
+    logging.info('FalVideo.available False' )  
+
 logging.info('\033[93m -------------- \033[0m')

nodes/ChatGPT.py

@@ -843,12 +843,7 @@ class JsonRepair:
 
 # 以下为固定提示词的LLM节点示例
 class SimulateDevDesignDiscussions:
-    def __init__(self):
-        # self.__client = OpenAI()
-        self.session_history = []  # 用于存储会话历史的列表
-        # self.seed=0
-        self.system_content="You are ChatGPT, a large language model trained by OpenAI. Answer as concisely as possible."
-
+    
     @classmethod
     def INPUT_TYPES(cls):
 

nodes/FalVideo.py

@@ -0,0 +1,332 @@
+# 修改自 https://github.com/gokayfem/ComfyUI-fal-API/blob/main/nodes/video_node.py
+# image-to-video all in one
+
+import os,sys
+import torch
+from PIL import Image
+import tempfile
+import numpy as np
+import requests
+import cv2
+import subprocess
+import importlib.util
+python = sys.executable
+
+def is_installed(package, package_overwrite=None,auto_install=True):
+    is_has=False
+    try:
+        spec = importlib.util.find_spec(package)
+        is_has=spec is not None
+    except ModuleNotFoundError:
+        pass
+
+    package = package_overwrite or package
+
+    if spec is None:
+        if auto_install==True:
+            print(f"Installing {package}...")
+            # 清华源 -i https://pypi.tuna.tsinghua.edu.cn/simple
+            command = f'"{python}" -m pip install {package}'
+    
+            result = subprocess.run(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True, env=os.environ)
+
+            is_has=True
+
+            if result.returncode != 0:
+                print(f"Couldn't install\nCommand: {command}\nError code: {result.returncode}")
+                is_has=False
+    else:
+        print(package+'## OK')
+
+    return is_has
+
+
+try:
+    if is_installed('fal_client','fal-client')==True:
+        from fal_client import submit, upload_file
+except:
+    print("#install fal-client error")
+
+
+def upload_image(image):
+    try:
+        # Convert the image tensor to a numpy array
+        if isinstance(image, torch.Tensor):
+            image_np = image.cpu().numpy()
+        else:
+            image_np = np.array(image)
+
+        # Ensure the image is in the correct format (H, W, C)
+        if image_np.ndim == 4:
+            image_np = image_np.squeeze(0)  # Remove batch dimension if present
+        if image_np.ndim == 2:
+            image_np = np.stack([image_np] * 3, axis=-1)  # Convert grayscale to RGB
+        elif image_np.shape[0] == 3:
+            image_np = np.transpose(image_np, (1, 2, 0))  # Change from (C, H, W) to (H, W, C)
+
+        # Normalize the image data to 0-255 range
+        if image_np.dtype == np.float32 or image_np.dtype == np.float64:
+            image_np = (image_np * 255).astype(np.uint8)
+
+        # Convert to PIL Image
+        pil_image = Image.fromarray(image_np)
+
+        # Save the image to a temporary file
+        with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as temp_file:
+            pil_image.save(temp_file, format="PNG")
+            temp_file_path = temp_file.name
+
+        # Upload the temporary file
+        image_url = upload_file(temp_file_path)
+        return image_url
+    except Exception as e:
+        print(f"Error uploading image: {str(e)}")
+        return None
+    finally:
+        # Clean up the temporary file
+        if 'temp_file_path' in locals():
+            os.unlink(temp_file_path)
+
+
+class VideoGenKlingNode:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "prompt": ("STRING", {"default": "", "multiline": True}),
+                "duration": (["5", "10"], {"default": "5"}),
+                "aspect_ratio": (["16:9", "9:16", "1:1"], {"default": "16:9"}),
+                "mode": (["standard", "pro"], {"default": "standard"}),
+                "fal_key":("STRING", {"forceInput": True,}),
+            },
+            "optional": {
+                "image": ("IMAGE",),
+            },
+        }
+
+    RETURN_TYPES = ("STRING",)
+    FUNCTION = "generate_video"
+    CATEGORY = "♾️Mixlab/Video"
+
+    def generate_video(self, prompt, duration, aspect_ratio,mode,fal_key, image=None):
+        arguments = {
+            "prompt": prompt,
+            "duration": duration,
+            "aspect_ratio": aspect_ratio,
+        }
+
+        os.environ["FAL_KEY"] = fal_key
+
+        api_url="fal-ai/kling-video/v1/"+mode
+
+        try:
+            if image is not None:
+                image_url = upload_image(image)
+                if image_url:
+                    arguments["image_url"] = image_url
+                    handler = submit(api_url+"/image-to-video", arguments=arguments)
+                else:
+                    return ("Error: Unable to upload image.",)
+            else:
+                handler = submit(api_url+"/text-to-video", arguments=arguments)
+
+            result = handler.get()
+            video_url = result["video"]["url"]
+            return (video_url,)
+        except Exception as e:
+            print(f"Error generating video: {str(e)}")
+            return ("Error: Unable to generate video.",)
+
+
+class VideoGenRunwayGen3Node:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "prompt": ("STRING", {"default": "", "multiline": True}),
+                "image": ("IMAGE",),
+                "duration": (["5", "10"], {"default": "5"}),
+                "aspect_ratio": (["16:9", "9:16"], {"default": "16:9"}),
+                "fal_key":("STRING", {"forceInput": True,}),
+            },
+        }
+
+    RETURN_TYPES = ("STRING",)
+    FUNCTION = "generate_video"
+    CATEGORY = "♾️Mixlab/Video"
+
+    def generate_video(self, prompt, image, duration,aspect_ratio,fal_key):
+        os.environ["FAL_KEY"] = fal_key
+        try:
+            image_url = upload_image(image)
+            if not image_url:
+                return ("Error: Unable to upload image.",)
+
+            arguments = {
+                "prompt": prompt,
+                "image_url": image_url,
+                "duration": duration,
+                "ratio":aspect_ratio
+            }
+
+            handler = submit("fal-ai/runway-gen3/turbo/image-to-video", arguments=arguments)
+            result = handler.get()
+            video_url = result["video"]["url"]
+            return (video_url,)
+        except Exception as e:
+            print(f"Error generating video: {str(e)}")
+            return ("Error: Unable to generate video.",)
+
+class VideoGenLumaDreamMachineNode:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "prompt": ("STRING", {"default": "", "multiline": True}), 
+                "aspect_ratio": (["16:9", "9:16", "4:3", "3:4", "21:9", "9:21"], {"default": "16:9"}),
+                "fal_key":("STRING", {"forceInput": True,}),
+            },
+            "optional": {
+                "image": ("IMAGE",),
+                "loop": ("BOOLEAN", {"default": True}),
+            }, 
+        }
+
+    RETURN_TYPES = ("STRING",)
+    FUNCTION = "generate_video"
+    CATEGORY = "♾️Mixlab/Video"
+
+    def generate_video(self, prompt, aspect_ratio,fal_key, image=None, loop=True):
+
+        os.environ["FAL_KEY"] = fal_key
+
+        arguments = {
+            "prompt": prompt,
+            "aspect_ratio": aspect_ratio,
+            "loop": loop,
+        }
+
+        try:
+            if image is not None:
+                image_url = upload_image(image)
+                if not image_url:
+                    return ("Error: Unable to upload image.",)
+                arguments["image_url"] = image_url
+                endpoint = "fal-ai/luma-dream-machine/image-to-video"
+            else:
+                endpoint = "fal-ai/luma-dream-machine"
+
+            handler = submit(endpoint, arguments=arguments)
+            result = handler.get()
+            video_url = result["video"]["url"]
+            return (video_url,)
+        except Exception as e:
+            print(f"Error generating video: {str(e)}")
+            return ("Error: Unable to generate video.",)
+
+class LoadVideoFromURL:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "url": ("STRING", {"default": "https://example.com/video.mp4"}),
+                "force_rate": ("INT", {"default": 0, "min": 0, "max": 60, "step": 1}),
+                "force_size": (["Disabled", "Custom Height", "Custom Width", "Custom", "256x?", "?x256", "256x256", "512x?", "?x512", "512x512"],),
+                "custom_width": ("INT", {"default": 512, "min": 0, "max": 8192, "step": 8}),
+                "custom_height": ("INT", {"default": 512, "min": 0, "max": 8192, "step": 8}),
+                "frame_load_cap": ("INT", {"default": 0, "min": 0, "max": 1000000, "step": 1}),
+                "skip_first_frames": ("INT", {"default": 0, "min": 0, "max": 1000000, "step": 1}),
+                "select_every_nth": ("INT", {"default": 1, "min": 1, "max": 1000000, "step": 1}),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE", "INT", "VHS_VIDEOINFO")
+    RETURN_NAMES = ("frames", "frame_count", "video_info")
+    FUNCTION = "load_video_from_url"
+    CATEGORY = "♾️Mixlab/Video"
+
+    def load_video_from_url(self, url, force_rate, force_size, custom_width, custom_height, frame_load_cap, skip_first_frames, select_every_nth):
+        # Download the video to a temporary file
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".mp4") as temp_file:
+            response = requests.get(url, stream=True)
+            for chunk in response.iter_content(chunk_size=8192):
+                temp_file.write(chunk)
+            temp_file_path = temp_file.name
+
+        # Load the video using OpenCV
+        cap = cv2.VideoCapture(temp_file_path)
+        
+        # Get video properties
+        fps = cap.get(cv2.CAP_PROP_FPS)
+        total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+        width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+        height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+        duration = total_frames / fps
+
+        # Calculate target size
+        if force_size != "Disabled":
+            if force_size == "Custom Width":
+                new_height = int(height * (custom_width / width))
+                new_width = custom_width
+            elif force_size == "Custom Height":
+                new_width = int(width * (custom_height / height))
+                new_height = custom_height
+            elif force_size == "Custom":
+                new_width, new_height = custom_width, custom_height
+            else:
+                target_width, target_height = map(int, force_size.replace("?", "0").split("x"))
+                if target_width == 0:
+                    new_width = int(width * (target_height / height))
+                    new_height = target_height
+                else:
+                    new_height = int(height * (target_width / width))
+                    new_width = target_width
+        else:
+            new_width, new_height = width, height
+
+        frames = []
+        frame_count = 0
+
+        for i in range(total_frames):
+            ret, frame = cap.read()
+            if not ret:
+                break
+
+            if i < skip_first_frames:
+                continue
+
+            if (i - skip_first_frames) % select_every_nth != 0:
+                continue
+
+            if force_size != "Disabled":
+                frame = cv2.resize(frame, (new_width, new_height))
+
+            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+            frame = torch.from_numpy(frame).float() / 255.0
+            frames.append(frame)
+
+            frame_count += 1
+
+            if frame_load_cap > 0 and frame_count >= frame_load_cap:
+                break
+
+        cap.release()
+        os.unlink(temp_file_path)
+
+        frames = torch.stack(frames)
+
+        video_info = {
+            "source_fps": fps,
+            "source_frame_count": total_frames,
+            "source_duration": duration,
+            "source_width": width,
+            "source_height": height,
+            "loaded_fps": fps if force_rate == 0 else force_rate,
+            "loaded_frame_count": frame_count,
+            "loaded_duration": frame_count / (fps if force_rate == 0 else force_rate),
+            "loaded_width": new_width,
+            "loaded_height": new_height,
+        }
+
+        return (frames, frame_count, video_info)
+

nodes/ImageNode.py

@@ -123,8 +123,13 @@ def composite_images(foreground, background, mask, is_multiply_blend=False, posi
         }
 
     # Resize the foreground image with antialiasing
-    layer_image = layer['image'].resize((layer['width'], layer['height']), Image.ANTIALIAS)
-    layer_mask = layer['mask'].resize((layer['width'], layer['height']), Image.ANTIALIAS)
+    try:
+        resampling_method = Image.Resampling.LANCZOS
+    except AttributeError:
+        resampling_method = Image.ANTIALIAS
+
+    layer_image = layer['image'].resize((layer['width'], layer['height']), resampling_method)
+    layer_mask = layer['mask'].resize((layer['width'], layer['height']), resampling_method)
 
     bg_image.paste(layer_image, (layer['x'], layer['y']), layer_mask)
 
@@ -1041,29 +1046,33 @@ def generate_text_image(text,
 
     if layout == "vertical":
         for line in lines:
-            max_char_width = max(font.getsize(char)[0] for char in line)
+            max_char_width = max(font.getbbox(char)[2] - font.getbbox(char)[0] for char in line)
             for char in line:
-                char_width, char_height = font.getsize(char)
+                left, top, right, bottom = font.getbbox(char)
+                char_width = right - left
+                char_height = bottom - top
                 char_coordinates.append((x, y))
                 y += char_height + spacing
                 max_height = max(max_height, y + padding)
             x += max_char_width + line_spacing
             y = padding
         max_width = x
-        total_line_width = sum(font.getsize(line)[1] for line in lines)
+        total_line_width = sum(font.getbbox(line)[2] - font.getbbox(line)[0] for line in lines)
         total_spacing = line_spacing * (len(lines) - 1)
         max_width = total_line_width + total_spacing + padding * 2
     else:
         for line in lines:
-            line_width, line_height = font.getsize(line)
+            line_width, line_height = font.getbbox(line)[2] - font.getbbox(line)[0], font.getbbox(line)[3] - font.getbbox(line)[1]
             for char in line:
-                char_width, char_height = font.getsize(char)
+                left, top, right, bottom = font.getbbox(char)
+                char_width = right - left
+                char_height = bottom - top
                 char_coordinates.append((x, y))
                 x += char_width + spacing
                 max_width = max(max_width, x + padding)
             y += line_height + line_spacing
             x = padding
-        total_line_heights = sum(font.getsize(line)[1] for line in lines)
+        total_line_heights = sum(font.getbbox(line)[3] - font.getbbox(line)[1] for line in lines)
         total_spacing = line_spacing * (len(lines) - 1)
         max_height = total_line_heights + total_spacing + padding * 2
 
@@ -2951,11 +2960,14 @@ class ResizeImage:
                     im=tensor2pil(im)
 
                     im=im.convert('RGB')
-                    a_im,hex=get_average_color_image(im)
 
+                    a_im,hex=get_average_color_image(im)
+                    
                     if average_color=='on':
                         fill_color=hex
-                        
+
+                    a_im=resize_image(a_im,scale_option,w,h,fill_color)
+
                     im=resize_image(im,scale_option,w,h,fill_color)
 
                     im=pil2tensor(im)

nodes/Style.py

@@ -234,7 +234,7 @@ class StyleAlignedSampleReferenceLatents:
                     "steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
                     "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}),
 
-                    "scheduler": (comfy.samplers.KSampler.SCHEDULERS.reverse(), ), 
+                    "scheduler": (comfy.samplers.KSampler.SCHEDULERS, ), 
                     "denoise": ("FLOAT", {"default": 1, "min": 0.0, "max": 1.0, "step": 0.01}),
                     
                      }

nodes/edit_mask.py

@@ -7,6 +7,7 @@ import os
 import folder_paths 
 import node_helpers
 import hashlib
+from uuid import uuid4
 
 # Tensor to PIL
 def tensor2pil(image):
@@ -26,7 +27,7 @@ def tensor_to_hash(tensor):
     return hash_value
 
 
-def create_temp_file(image):
+def create_temp_file(image, uuid):
     output_dir = folder_paths.get_temp_directory()
 
     (
@@ -35,7 +36,7 @@ def create_temp_file(image):
             counter,
             subfolder,
             _,
-        ) = folder_paths.get_save_image_path('material', output_dir)
+        ) = folder_paths.get_save_image_path(f'material_{uuid}', output_dir)
 
     
     image=tensor2pil(image)
@@ -59,6 +60,7 @@ class EditMask:
 
     def __init__(self):
         self.image_id = None
+        self.uuid = str(uuid4())
 
     @classmethod
     def INPUT_TYPES(s):
@@ -117,13 +119,13 @@ class EditMask:
                 image_path = os.path.join(base_dir,subfolder, name)
         
         if image_path==None:
-            image_path,images=create_temp_file(image)
+            image_path,images=create_temp_file(image, self.uuid)
 
         print('#image_path',os.path.exists(image_path),image_path)
         # image_path = folder_paths.get_annotated_filepath(image) #文件名
         
         if not os.path.exists(image_path):
-            image_path,images=create_temp_file(image)
+            image_path,images=create_temp_file(image, self.uuid)
 
 
         img = node_helpers.pillow(Image.open, image_path)

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "comfyui-mixlab-nodes"
 description = "3D, ScreenShareNode & FloatingVideoNode, SpeechRecognition & SpeechSynthesis, GPT, LoadImagesFromLocal, Layers, Other Nodes, ..."
-version = "0.45.0"
+version = "0.46.0"
 license = "MIT"
 dependencies = ["numpy", "pyOpenSSL", "watchdog", "opencv-python-headless", "matplotlib", "openai", "simple-lama-inpainting", "clip-interrogator==0.6.0", "transformers>=4.36.0", "lark-parser", "imageio-ffmpeg", "rembg[gpu]", "omegaconf==2.3.0", "Pillow>=9.5.0", "einops==0.7.0", "trimesh>=4.0.5", "huggingface-hub", "scikit-image"]
 

web/javascript/app_mixlab.js

@@ -267,7 +267,13 @@ function downloadJsonFile (jsonData, fileName = 'mix_app.json') {
 }
 
 async function save (json, download = false, showInfo = true) {
-  let nodesAll = window._nodesAll || (await getObjectInfo())
+  if (!window._nodesAll) {
+    window._nodesAll = await getObjectInfo();
+  }
+
+  let nodesAll = window._nodesAll;
+  
+  // let nodesAll = window._nodesAll || (await getObjectInfo())
 
   console.log('####SAVE', nodesAll, json)
 
@@ -417,9 +423,9 @@ function getInputsAndOutputs () {
 app.registerExtension({
   name: 'Mixlab.utils.AppInfo',
   init () {
-    if (!window._nodesAll) {
-      getObjectInfo().then(r => (window._nodesAll = r))
-    }
+    // if (!window._nodesAll) {
+    //   getObjectInfo().then(r => (window._nodesAll = r))
+    // }
   },
   async beforeRegisterNodeDef (nodeType, nodeData, app) {
     if (nodeType.comfyClass == 'AppInfo') {

web/javascript/checkVersion_mixlab.js

@@ -3,7 +3,7 @@ import { app } from '../../../scripts/app.js'
 const repoOwner = 'shadowcz007' // 替换为仓库的所有者
 const repoName = 'comfyui-mixlab-nodes' // 替换为仓库的名称
 
-const version = 'v0.45.0'
+const version = 'v0.46.0'
 
 fetch(`https://api.github.com/repos/${repoOwner}/${repoName}/releases/latest`)
   .then(response => response.json())

web/javascript/sound-lab.js

@@ -3,7 +3,7 @@ import { api } from '../../../scripts/api.js'
 import { ComfyWidgets } from '../../../scripts/widgets.js'
 import { $el } from '../../../scripts/ui.js'
 
-import WaveSurfer from 'https://cdn.jsdelivr.net/npm/wavesurfer.js@7/dist/wavesurfer.esm.js'
+import WaveSurfer from './wavesurfer.esm.js'
 
 function get_position_style (ctx, widget_width, y, node_height) {
   const MARGIN = 4 // the margin around the html element

web/javascript/ui_mixlab.js

@@ -1735,14 +1735,16 @@ app.registerExtension({
 
       // 把json往里 拖
       document.addEventListener('drop', async event => {
-        event.preventDefault()
-        event.stopPropagation()
-
-        // Dragging from Chrome->Firefox there is a file but its a bmp, so ignore that
+        // Only intercept the JSON files handled here. Calling preventDefault()
+        // unconditionally swallowed every drop, so ComfyUI's native drag&drop
+        // (guarded by event.defaultPrevented) never loaded dropped workflows
+        // (PNG / JSON / etc.). Keep preventDefault scoped to the handled case.
         if (
           event.dataTransfer.files.length &&
           event.dataTransfer.files[0].type == 'application/json'
         ) {
+          event.preventDefault()
+          event.stopPropagation()
           const reader = new FileReader()
           reader.onload = async () => {
             loadAppJson(reader.result)
@@ -2171,6 +2173,10 @@ app.registerExtension({
 
     fetch('manager/badge_mode').then(r => {
       if (r.status === 404) {
+        // 已有ComfyUI自带的badge
+        if(node.badges?.[0]?.()){
+          return
+        }
         // 右上角的badge是否已经绘制
         if (!node.badge_enabled) {
           if (!node.getNickname) {

workflow/video-all-in-one-test-workflow.json

@@ -0,0 +1,775 @@
+{
+  "last_node_id": 18,
+  "last_link_id": 15,
+  "nodes": [
+    {
+      "id": 6,
+      "type": "LoadImage",
+      "pos": [
+        -32,
+        79
+      ],
+      "size": {
+        "0": 315,
+        "1": 314
+      },
+      "flags": {},
+      "order": 0,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "IMAGE",
+          "type": "IMAGE",
+          "links": [
+            1,
+            2,
+            3
+          ],
+          "shape": 3,
+          "slot_index": 0
+        },
+        {
+          "name": "MASK",
+          "type": "MASK",
+          "links": null,
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LoadImage"
+      },
+      "widgets_values": [
+        "1.jpg",
+        "image"
+      ]
+    },
+    {
+      "id": 7,
+      "type": "KeyInput",
+      "pos": [
+        -34,
+        585
+      ],
+      "size": {
+        "0": 315,
+        "1": 94
+      },
+      "flags": {},
+      "order": 1,
+      "mode": 0,
+      "outputs": [
+        {
+          "name": "key",
+          "type": "STRING",
+          "links": [
+            4,
+            5,
+            6
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "KeyInput"
+      },
+      "widgets_values": [
+        null,
+        null
+      ]
+    },
+    {
+      "id": 10,
+      "type": "LoadVideoFromURL",
+      "pos": [
+        1038,
+        21
+      ],
+      "size": [
+        315,
+        266
+      ],
+      "flags": {},
+      "order": 5,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "url",
+          "type": "STRING",
+          "link": 7,
+          "widget": {
+            "name": "url"
+          }
+        }
+      ],
+      "outputs": [
+        {
+          "name": "frames",
+          "type": "IMAGE",
+          "links": [
+            8
+          ],
+          "shape": 3,
+          "slot_index": 0
+        },
+        {
+          "name": "frame_count",
+          "type": "INT",
+          "links": null,
+          "shape": 3
+        },
+        {
+          "name": "video_info",
+          "type": "VHS_VIDEOINFO",
+          "links": null,
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LoadVideoFromURL"
+      },
+      "widgets_values": [
+        "https://example.com/video.mp4",
+        0,
+        "Disabled",
+        512,
+        512,
+        0,
+        0,
+        1
+      ]
+    },
+    {
+      "id": 13,
+      "type": "LoadVideoFromURL",
+      "pos": [
+        1032,
+        715
+      ],
+      "size": {
+        "0": 315,
+        "1": 266
+      },
+      "flags": {},
+      "order": 9,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "url",
+          "type": "STRING",
+          "link": 10,
+          "widget": {
+            "name": "url"
+          }
+        }
+      ],
+      "outputs": [
+        {
+          "name": "frames",
+          "type": "IMAGE",
+          "links": [
+            12
+          ],
+          "shape": 3,
+          "slot_index": 0
+        },
+        {
+          "name": "frame_count",
+          "type": "INT",
+          "links": null,
+          "shape": 3
+        },
+        {
+          "name": "video_info",
+          "type": "VHS_VIDEOINFO",
+          "links": null,
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LoadVideoFromURL"
+      },
+      "widgets_values": [
+        "https://example.com/video.mp4",
+        0,
+        "Disabled",
+        512,
+        512,
+        0,
+        0,
+        1
+      ]
+    },
+    {
+      "id": 11,
+      "type": "PreviewImage",
+      "pos": [
+        1436,
+        6
+      ],
+      "size": [
+        210,
+        246
+      ],
+      "flags": {},
+      "order": 11,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 8
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "PreviewImage"
+      }
+    },
+    {
+      "id": 3,
+      "type": "VideoGenKlingNode",
+      "pos": [
+        527.4816383216086,
+        19.100152539671797
+      ],
+      "size": {
+        "0": 400,
+        "1": 200
+      },
+      "flags": {},
+      "order": 2,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "link": 1
+        },
+        {
+          "name": "fal_key",
+          "type": "STRING",
+          "link": 4,
+          "widget": {
+            "name": "fal_key"
+          }
+        }
+      ],
+      "outputs": [
+        {
+          "name": "STRING",
+          "type": "STRING",
+          "links": [
+            7,
+            13
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VideoGenKlingNode"
+      },
+      "widgets_values": [
+        "The man is shaking his head with a wry smile.\n\n",
+        "5",
+        "16:9",
+        "standard",
+        ""
+      ]
+    },
+    {
+      "id": 16,
+      "type": "ShowTextForGPT",
+      "pos": [
+        1702,
+        -85
+      ],
+      "size": [
+        400,
+        200
+      ],
+      "flags": {},
+      "order": 6,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "text",
+          "type": "STRING",
+          "link": 13,
+          "widget": {
+            "name": "text"
+          }
+        },
+        {
+          "name": "output_dir",
+          "type": "STRING",
+          "link": null,
+          "widget": {
+            "name": "output_dir"
+          }
+        }
+      ],
+      "outputs": [
+        {
+          "name": "STRING",
+          "type": "STRING",
+          "links": null,
+          "shape": 6
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "ShowTextForGPT"
+      },
+      "widgets_values": [
+        "",
+        "",
+        "https://v2.fal.media/files/0f9f44093c7442d5b0819616880bca65_output.mp4"
+      ]
+    },
+    {
+      "id": 4,
+      "type": "VideoGenRunwayGen3Node",
+      "pos": [
+        529,
+        297
+      ],
+      "size": {
+        "0": 400,
+        "1": 200
+      },
+      "flags": {},
+      "order": 3,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "link": 2
+        },
+        {
+          "name": "fal_key",
+          "type": "STRING",
+          "link": 5,
+          "widget": {
+            "name": "fal_key"
+          }
+        }
+      ],
+      "outputs": [
+        {
+          "name": "STRING",
+          "type": "STRING",
+          "links": [
+            9,
+            14
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VideoGenRunwayGen3Node"
+      },
+      "widgets_values": [
+        "The man is shaking his head with a wry smile.\n\n",
+        "5",
+        "16:9",
+        ""
+      ]
+    },
+    {
+      "id": 5,
+      "type": "VideoGenLumaDreamMachineNode",
+      "pos": [
+        534,
+        570
+      ],
+      "size": {
+        "0": 400,
+        "1": 200
+      },
+      "flags": {},
+      "order": 4,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "image",
+          "type": "IMAGE",
+          "link": 3
+        },
+        {
+          "name": "fal_key",
+          "type": "STRING",
+          "link": 6,
+          "widget": {
+            "name": "fal_key"
+          }
+        }
+      ],
+      "outputs": [
+        {
+          "name": "STRING",
+          "type": "STRING",
+          "links": [
+            10,
+            15
+          ],
+          "shape": 3,
+          "slot_index": 0
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "VideoGenLumaDreamMachineNode"
+      },
+      "widgets_values": [
+        "The man is shaking his head with a wry smile.\n\n",
+        "16:9",
+        "",
+        true
+      ]
+    },
+    {
+      "id": 18,
+      "type": "ShowTextForGPT",
+      "pos": [
+        1719,
+        665
+      ],
+      "size": [
+        400,
+        200
+      ],
+      "flags": {},
+      "order": 10,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "text",
+          "type": "STRING",
+          "link": 15,
+          "widget": {
+            "name": "text"
+          }
+        },
+        {
+          "name": "output_dir",
+          "type": "STRING",
+          "link": null,
+          "widget": {
+            "name": "output_dir"
+          }
+        }
+      ],
+      "outputs": [
+        {
+          "name": "STRING",
+          "type": "STRING",
+          "links": null,
+          "shape": 6
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "ShowTextForGPT"
+      },
+      "widgets_values": [
+        "",
+        "",
+        "https://v2.fal.media/files/a622a5aac002452ba0e75f7d8871389d_output.mp4"
+      ]
+    },
+    {
+      "id": 12,
+      "type": "LoadVideoFromURL",
+      "pos": [
+        1049,
+        349
+      ],
+      "size": {
+        "0": 315,
+        "1": 266
+      },
+      "flags": {},
+      "order": 7,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "url",
+          "type": "STRING",
+          "link": 9,
+          "widget": {
+            "name": "url"
+          }
+        }
+      ],
+      "outputs": [
+        {
+          "name": "frames",
+          "type": "IMAGE",
+          "links": [
+            11
+          ],
+          "shape": 3,
+          "slot_index": 0
+        },
+        {
+          "name": "frame_count",
+          "type": "INT",
+          "links": null,
+          "shape": 3
+        },
+        {
+          "name": "video_info",
+          "type": "VHS_VIDEOINFO",
+          "links": null,
+          "shape": 3
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "LoadVideoFromURL"
+      },
+      "widgets_values": [
+        "https://example.com/video.mp4",
+        0,
+        "Disabled",
+        512,
+        512,
+        0,
+        0,
+        1
+      ]
+    },
+    {
+      "id": 17,
+      "type": "ShowTextForGPT",
+      "pos": [
+        1711,
+        318
+      ],
+      "size": [
+        400,
+        200
+      ],
+      "flags": {},
+      "order": 8,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "text",
+          "type": "STRING",
+          "link": 14,
+          "widget": {
+            "name": "text"
+          }
+        },
+        {
+          "name": "output_dir",
+          "type": "STRING",
+          "link": null,
+          "widget": {
+            "name": "output_dir"
+          }
+        }
+      ],
+      "outputs": [
+        {
+          "name": "STRING",
+          "type": "STRING",
+          "links": null,
+          "shape": 6
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "ShowTextForGPT"
+      },
+      "widgets_values": [
+        "",
+        "",
+        "https://v2.fal.media/files/755d51c8984a4445a852268a209b07e4_output.mp4"
+      ]
+    },
+    {
+      "id": 15,
+      "type": "PreviewImage",
+      "pos": [
+        1444,
+        650
+      ],
+      "size": [
+        210,
+        246
+      ],
+      "flags": {},
+      "order": 13,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 12
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "PreviewImage"
+      }
+    },
+    {
+      "id": 14,
+      "type": "PreviewImage",
+      "pos": [
+        1416,
+        310
+      ],
+      "size": [
+        210,
+        246
+      ],
+      "flags": {},
+      "order": 12,
+      "mode": 0,
+      "inputs": [
+        {
+          "name": "images",
+          "type": "IMAGE",
+          "link": 11
+        }
+      ],
+      "properties": {
+        "Node name for S&R": "PreviewImage"
+      }
+    }
+  ],
+  "links": [
+    [
+      1,
+      6,
+      0,
+      3,
+      0,
+      "IMAGE"
+    ],
+    [
+      2,
+      6,
+      0,
+      4,
+      0,
+      "IMAGE"
+    ],
+    [
+      3,
+      6,
+      0,
+      5,
+      0,
+      "IMAGE"
+    ],
+    [
+      4,
+      7,
+      0,
+      3,
+      1,
+      "STRING"
+    ],
+    [
+      5,
+      7,
+      0,
+      4,
+      1,
+      "STRING"
+    ],
+    [
+      6,
+      7,
+      0,
+      5,
+      1,
+      "STRING"
+    ],
+    [
+      7,
+      3,
+      0,
+      10,
+      0,
+      "STRING"
+    ],
+    [
+      8,
+      10,
+      0,
+      11,
+      0,
+      "IMAGE"
+    ],
+    [
+      9,
+      4,
+      0,
+      12,
+      0,
+      "STRING"
+    ],
+    [
+      10,
+      5,
+      0,
+      13,
+      0,
+      "STRING"
+    ],
+    [
+      11,
+      12,
+      0,
+      14,
+      0,
+      "IMAGE"
+    ],
+    [
+      12,
+      13,
+      0,
+      15,
+      0,
+      "IMAGE"
+    ],
+    [
+      13,
+      3,
+      0,
+      16,
+      0,
+      "STRING"
+    ],
+    [
+      14,
+      4,
+      0,
+      17,
+      0,
+      "STRING"
+    ],
+    [
+      15,
+      5,
+      0,
+      18,
+      0,
+      "STRING"
+    ]
+  ],
+  "groups": [],
+  "config": {},
+  "extra": {
+    "ds": {
+      "scale": 0.7247295000000012,
+      "offset": [
+        -59.526177135875514,
+        197.10624904779425
+      ]
+    }
+  },
+  "version": 0.4
+}