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diff --git a/ComfyUI/custom_nodes/example_node.py.example b/ComfyUI/custom_nodes/example_node.py.example
new file mode 100644
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+++ b/ComfyUI/custom_nodes/example_node.py.example
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+class Example:
+    """
+    A example node
+
+    Class methods
+    -------------
+    INPUT_TYPES (dict):
+        Tell the main program input parameters of nodes.
+    IS_CHANGED:
+        optional method to control when the node is re executed.
+
+    Attributes
+    ----------
+    RETURN_TYPES (`tuple`):
+        The type of each element in the output tuple.
+    RETURN_NAMES (`tuple`):
+        Optional: The name of each output in the output tuple.
+    FUNCTION (`str`):
+        The name of the entry-point method. For example, if `FUNCTION = "execute"` then it will run Example().execute()
+    OUTPUT_NODE ([`bool`]):
+        If this node is an output node that outputs a result/image from the graph. The SaveImage node is an example.
+        The backend iterates on these output nodes and tries to execute all their parents if their parent graph is properly connected.
+        Assumed to be False if not present.
+    CATEGORY (`str`):
+        The category the node should appear in the UI.
+    DEPRECATED (`bool`):
+        Indicates whether the node is deprecated. Deprecated nodes are hidden by default in the UI, but remain
+        functional in existing workflows that use them.
+    EXPERIMENTAL (`bool`):
+        Indicates whether the node is experimental. Experimental nodes are marked as such in the UI and may be subject to
+        significant changes or removal in future versions. Use with caution in production workflows.
+    execute(s) -> tuple || None:
+        The entry point method. The name of this method must be the same as the value of property `FUNCTION`.
+        For example, if `FUNCTION = "execute"` then this method's name must be `execute`, if `FUNCTION = "foo"` then it must be `foo`.
+    """
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(s):
+        """
+            Return a dictionary which contains config for all input fields.
+            Some types (string): "MODEL", "VAE", "CLIP", "CONDITIONING", "LATENT", "IMAGE", "INT", "STRING", "FLOAT".
+            Input types "INT", "STRING" or "FLOAT" are special values for fields on the node.
+            The type can be a list for selection.
+
+            Returns: `dict`:
+                - Key input_fields_group (`string`): Can be either required, hidden or optional. A node class must have property `required`
+                - Value input_fields (`dict`): Contains input fields config:
+                    * Key field_name (`string`): Name of a entry-point method's argument
+                    * Value field_config (`tuple`):
+                        + First value is a string indicate the type of field or a list for selection.
+                        + Second value is a config for type "INT", "STRING" or "FLOAT".
+        """
+        return {
+            "required": {
+                "image": ("IMAGE",),
+                "int_field": ("INT", {
+                    "default": 0, 
+                    "min": 0, #Minimum value
+                    "max": 4096, #Maximum value
+                    "step": 64, #Slider's step
+                    "display": "number", # Cosmetic only: display as "number" or "slider"
+                    "lazy": True # Will only be evaluated if check_lazy_status requires it
+                }),
+                "float_field": ("FLOAT", {
+                    "default": 1.0,
+                    "min": 0.0,
+                    "max": 10.0,
+                    "step": 0.01,
+                    "round": 0.001, #The value representing the precision to round to, will be set to the step value by default. Can be set to False to disable rounding.
+                    "display": "number",
+                    "lazy": True
+                }),
+                "print_to_screen": (["enable", "disable"],),
+                "string_field": ("STRING", {
+                    "multiline": False, #True if you want the field to look like the one on the ClipTextEncode node
+                    "default": "Hello World!",
+                    "lazy": True
+                }),
+            },
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    #RETURN_NAMES = ("image_output_name",)
+
+    FUNCTION = "test"
+
+    #OUTPUT_NODE = False
+
+    CATEGORY = "Example"
+
+    def check_lazy_status(self, image, string_field, int_field, float_field, print_to_screen):
+        """
+            Return a list of input names that need to be evaluated.
+
+            This function will be called if there are any lazy inputs which have not yet been
+            evaluated. As long as you return at least one field which has not yet been evaluated
+            (and more exist), this function will be called again once the value of the requested
+            field is available.
+
+            Any evaluated inputs will be passed as arguments to this function. Any unevaluated
+            inputs will have the value None.
+        """
+        if print_to_screen == "enable":
+            return ["int_field", "float_field", "string_field"]
+        else:
+            return []
+
+    def test(self, image, string_field, int_field, float_field, print_to_screen):
+        if print_to_screen == "enable":
+            print(f"""Your input contains:
+                string_field aka input text: {string_field}
+                int_field: {int_field}
+                float_field: {float_field}
+            """)
+        #do some processing on the image, in this example I just invert it
+        image = 1.0 - image
+        return (image,)
+
+    """
+        The node will always be re executed if any of the inputs change but
+        this method can be used to force the node to execute again even when the inputs don't change.
+        You can make this node return a number or a string. This value will be compared to the one returned the last time the node was
+        executed, if it is different the node will be executed again.
+        This method is used in the core repo for the LoadImage node where they return the image hash as a string, if the image hash
+        changes between executions the LoadImage node is executed again.
+    """
+    #@classmethod
+    #def IS_CHANGED(s, image, string_field, int_field, float_field, print_to_screen):
+    #    return ""
+
+# Set the web directory, any .js file in that directory will be loaded by the frontend as a frontend extension
+# WEB_DIRECTORY = "./somejs"
+
+
+# Add custom API routes, using router
+from aiohttp import web
+from server import PromptServer
+
+@PromptServer.instance.routes.get("/hello")
+async def get_hello(request):
+    return web.json_response("hello")
+
+
+# A dictionary that contains all nodes you want to export with their names
+# NOTE: names should be globally unique
+NODE_CLASS_MAPPINGS = {
+    "Example": Example
+}
+
+# A dictionary that contains the friendly/humanly readable titles for the nodes
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "Example": "Example Node"
+}
diff --git a/ComfyUI/custom_nodes/runway_text2img/__init__.py b/ComfyUI/custom_nodes/runway_text2img/__init__.py
new file mode 100644
index 00000000..bed0f3ad
--- /dev/null
+++ b/ComfyUI/custom_nodes/runway_text2img/__init__.py
@@ -0,0 +1,108 @@
+import requests
+import os
+import time
+from io import BytesIO
+from PIL import Image
+import numpy as np
+import torch
+from dotenv import load_dotenv
+
+# Load API key
+load_dotenv()
+RUNWAY_API_KEY = os.getenv("RUNWAY_API_KEY")
+RUNWAY_API_URL = "https://api.dev.runwayml.com/v1/text_to_image"
+
+class RunwayTextToImage:
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "prompt": ("STRING", {
+                    "multiline": True,
+                    "default": "a fantasy landscape with mountains and rivers"
+                }),
+                "ratio": ([
+                    "1920:1080", "1080:1920", "1024:1024", "1280:720", "720:1280",
+                    "720:720", "960:720", "720:960", "1360:768", "1168:880",
+                    "1440:1080", "1080:1440", "1808:768", "2112:912", "1680:720"
+                ], {"default": "1024:1024"}),
+                "timeout": ("INT", {"default": 30, "min": 1, "max": 1200}),
+                "seed": ("INT", {"default": 42, "min": 0, "max": 4294967295}),
+                "mock": ("BOOLEAN", {"default": False}),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("images",)
+    FUNCTION = "run"
+    CATEGORY = "Runway"
+
+# ✅ ComfyUI expects this function to exist at module level
+def run(prompt, ratio, timeout, seed, mock):
+    width, height = map(int, ratio.split(":"))
+
+    if mock:
+        print("✅ Mock mode enabled – skipping real API call.")
+
+        dummy_np = (np.random.rand(height, width, 3) * 255).astype(np.uint8)  # [H, W, 3]
+        dummy_tensor = torch.from_numpy(dummy_np).permute(2, 0, 1).float() / 255.0  # [3, H, W]
+        print(f"✅ Dummy tensor created with shape: {dummy_tensor.shape}, dtype: {dummy_tensor.dtype}")
+
+        return (dummy_tensor,)
+
+
+    api_key = os.getenv("RUNWAY_API_KEY")
+    if not api_key:
+        raise RuntimeError("❌ Missing RUNWAY_API_KEY environment variable.")
+
+    headers = {
+        "Authorization": f"Bearer {api_key}",
+        "Content-Type": "application/json",
+        "X-Runway-Version": "2024-11-06"
+    }
+
+    payload = {
+        "model": "gen4_image",
+        "promptText": prompt,
+        "ratio": ratio,
+        "seed": seed
+    }
+
+    print("got prompt")
+    print("=== Runway Payload ===")
+    print(payload)
+
+    response = requests.post(RUNWAY_API_URL, json=payload, headers=headers)
+    print("=== Response Code ===", response.status_code)
+    print("=== Response Body ===", response.text)
+    response.raise_for_status()
+
+    job_id = response.json()["id"]
+
+    # Polling for result
+    image_url = None
+    for _ in range(timeout):
+        poll = requests.get(f"{RUNWAY_API_URL}/{job_id}", headers=headers)
+        poll_data = poll.json()
+        if poll_data.get("status") == "succeeded":
+            image_url = poll_data["outputs"][0]["uri"]
+            break
+        elif poll_data.get("status") == "failed":
+            raise RuntimeError("❌ Runway generation failed.")
+        time.sleep(1)
+
+    if not image_url:
+        raise RuntimeError("⏰ Timed out waiting for Runway result.")
+
+    image_bytes = requests.get(image_url).content
+    image = Image.open(BytesIO(image_bytes)).convert("RGB")
+    image_tensor = torch.from_numpy(np.array(image)).float() / 255.0
+    image_tensor = image_tensor.permute(2, 0, 1)  # [3, H, W]
+    return (image_tensor,)  # no batch dim
+
+RunwayTextToImage.run = staticmethod(run)
+
+NODE_CLASS_MAPPINGS = {
+    "RunwayTextToImage": RunwayTextToImage
+}
diff --git a/ComfyUI/custom_nodes/runway_text2img/__pycache__/__init__.cpython-311.pyc b/ComfyUI/custom_nodes/runway_text2img/__pycache__/__init__.cpython-311.pyc
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diff --git a/ComfyUI/custom_nodes/websocket_image_save.py b/ComfyUI/custom_nodes/websocket_image_save.py
new file mode 100644
index 00000000..ca56ebc8
--- /dev/null
+++ b/ComfyUI/custom_nodes/websocket_image_save.py
@@ -0,0 +1,44 @@
+from PIL import Image
+import numpy as np
+import comfy.utils
+import time
+
+#You can use this node to save full size images through the websocket, the
+#images will be sent in exactly the same format as the image previews: as
+#binary images on the websocket with a 8 byte header indicating the type
+#of binary message (first 4 bytes) and the image format (next 4 bytes).
+
+#Note that no metadata will be put in the images saved with this node.
+
+class SaveImageWebsocket:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"images": ("IMAGE", ),}
+                }
+
+    RETURN_TYPES = ()
+    FUNCTION = "save_images"
+
+    OUTPUT_NODE = True
+
+    CATEGORY = "api/image"
+
+    def save_images(self, images):
+        pbar = comfy.utils.ProgressBar(images.shape[0])
+        step = 0
+        for image in images:
+            i = 255. * image.cpu().numpy()
+            img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
+            pbar.update_absolute(step, images.shape[0], ("PNG", img, None))
+            step += 1
+
+        return {}
+
+    @classmethod
+    def IS_CHANGED(s, images):
+        return time.time()
+
+NODE_CLASS_MAPPINGS = {
+    "SaveImageWebsocket": SaveImageWebsocket,
+}