Merge branch 'Main' into feature/preview-latent

comfy_extras/nodes_canny.py

@@ -0,0 +1,299 @@
+#From https://github.com/kornia/kornia
+import math
+
+import torch
+import torch.nn.functional as F
+
+
+def get_canny_nms_kernel(device=None, dtype=None):
+    """Utility function that returns 3x3 kernels for the Canny Non-maximal suppression."""
+    return torch.tensor(
+        [
+            [[[0.0, 0.0, 0.0], [0.0, 1.0, -1.0], [0.0, 0.0, 0.0]]],
+            [[[0.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, -1.0]]],
+            [[[0.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, -1.0, 0.0]]],
+            [[[0.0, 0.0, 0.0], [0.0, 1.0, 0.0], [-1.0, 0.0, 0.0]]],
+            [[[0.0, 0.0, 0.0], [-1.0, 1.0, 0.0], [0.0, 0.0, 0.0]]],
+            [[[-1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 0.0]]],
+            [[[0.0, -1.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 0.0]]],
+            [[[0.0, 0.0, -1.0], [0.0, 1.0, 0.0], [0.0, 0.0, 0.0]]],
+        ],
+        device=device,
+        dtype=dtype,
+    )
+
+
+def get_hysteresis_kernel(device=None, dtype=None):
+    """Utility function that returns the 3x3 kernels for the Canny hysteresis."""
+    return torch.tensor(
+        [
+            [[[0.0, 0.0, 0.0], [0.0, 0.0, 1.0], [0.0, 0.0, 0.0]]],
+            [[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 1.0]]],
+            [[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 1.0, 0.0]]],
+            [[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [1.0, 0.0, 0.0]]],
+            [[[0.0, 0.0, 0.0], [1.0, 0.0, 0.0], [0.0, 0.0, 0.0]]],
+            [[[1.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]],
+            [[[0.0, 1.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]],
+            [[[0.0, 0.0, 1.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]],
+        ],
+        device=device,
+        dtype=dtype,
+    )
+
+def gaussian_blur_2d(img, kernel_size, sigma):
+    ksize_half = (kernel_size - 1) * 0.5
+
+    x = torch.linspace(-ksize_half, ksize_half, steps=kernel_size)
+
+    pdf = torch.exp(-0.5 * (x / sigma).pow(2))
+
+    x_kernel = pdf / pdf.sum()
+    x_kernel = x_kernel.to(device=img.device, dtype=img.dtype)
+
+    kernel2d = torch.mm(x_kernel[:, None], x_kernel[None, :])
+    kernel2d = kernel2d.expand(img.shape[-3], 1, kernel2d.shape[0], kernel2d.shape[1])
+
+    padding = [kernel_size // 2, kernel_size // 2, kernel_size // 2, kernel_size // 2]
+
+    img = torch.nn.functional.pad(img, padding, mode="reflect")
+    img = torch.nn.functional.conv2d(img, kernel2d, groups=img.shape[-3])
+
+    return img
+
+def get_sobel_kernel2d(device=None, dtype=None):
+    kernel_x = torch.tensor([[-1.0, 0.0, 1.0], [-2.0, 0.0, 2.0], [-1.0, 0.0, 1.0]], device=device, dtype=dtype)
+    kernel_y = kernel_x.transpose(0, 1)
+    return torch.stack([kernel_x, kernel_y])
+
+def spatial_gradient(input, normalized: bool = True):
+    r"""Compute the first order image derivative in both x and y using a Sobel operator.
+    .. image:: _static/img/spatial_gradient.png
+    Args:
+        input: input image tensor with shape :math:`(B, C, H, W)`.
+        mode: derivatives modality, can be: `sobel` or `diff`.
+        order: the order of the derivatives.
+        normalized: whether the output is normalized.
+    Return:
+        the derivatives of the input feature map. with shape :math:`(B, C, 2, H, W)`.
+    .. note::
+       See a working example `here <https://kornia-tutorials.readthedocs.io/en/latest/
+       filtering_edges.html>`__.
+    Examples:
+        >>> input = torch.rand(1, 3, 4, 4)
+        >>> output = spatial_gradient(input)  # 1x3x2x4x4
+        >>> output.shape
+        torch.Size([1, 3, 2, 4, 4])
+    """
+    # KORNIA_CHECK_IS_TENSOR(input)
+    # KORNIA_CHECK_SHAPE(input, ['B', 'C', 'H', 'W'])
+
+    # allocate kernel
+    kernel = get_sobel_kernel2d(device=input.device, dtype=input.dtype)
+    if normalized:
+        kernel = normalize_kernel2d(kernel)
+
+    # prepare kernel
+    b, c, h, w = input.shape
+    tmp_kernel = kernel[:, None, ...]
+
+    # Pad with "replicate for spatial dims, but with zeros for channel
+    spatial_pad = [kernel.size(1) // 2, kernel.size(1) // 2, kernel.size(2) // 2, kernel.size(2) // 2]
+    out_channels: int = 2
+    padded_inp = torch.nn.functional.pad(input.reshape(b * c, 1, h, w), spatial_pad, 'replicate')
+    out = F.conv2d(padded_inp, tmp_kernel, groups=1, padding=0, stride=1)
+    return out.reshape(b, c, out_channels, h, w)
+
+def rgb_to_grayscale(image, rgb_weights = None):
+    r"""Convert a RGB image to grayscale version of image.
+
+    .. image:: _static/img/rgb_to_grayscale.png
+
+    The image data is assumed to be in the range of (0, 1).
+
+    Args:
+        image: RGB image to be converted to grayscale with shape :math:`(*,3,H,W)`.
+        rgb_weights: Weights that will be applied on each channel (RGB).
+            The sum of the weights should add up to one.
+    Returns:
+        grayscale version of the image with shape :math:`(*,1,H,W)`.
+
+    .. note::
+       See a working example `here <https://kornia-tutorials.readthedocs.io/en/latest/
+       color_conversions.html>`__.
+
+    Example:
+        >>> input = torch.rand(2, 3, 4, 5)
+        >>> gray = rgb_to_grayscale(input) # 2x1x4x5
+    """
+
+    if len(image.shape) < 3 or image.shape[-3] != 3:
+        raise ValueError(f"Input size must have a shape of (*, 3, H, W). Got {image.shape}")
+
+    if rgb_weights is None:
+        # 8 bit images
+        if image.dtype == torch.uint8:
+            rgb_weights = torch.tensor([76, 150, 29], device=image.device, dtype=torch.uint8)
+        # floating point images
+        elif image.dtype in (torch.float16, torch.float32, torch.float64):
+            rgb_weights = torch.tensor([0.299, 0.587, 0.114], device=image.device, dtype=image.dtype)
+        else:
+            raise TypeError(f"Unknown data type: {image.dtype}")
+    else:
+        # is tensor that we make sure is in the same device/dtype
+        rgb_weights = rgb_weights.to(image)
+
+    # unpack the color image channels with RGB order
+    r: Tensor = image[..., 0:1, :, :]
+    g: Tensor = image[..., 1:2, :, :]
+    b: Tensor = image[..., 2:3, :, :]
+
+    w_r, w_g, w_b = rgb_weights.unbind()
+    return w_r * r + w_g * g + w_b * b
+
+def canny(
+    input,
+    low_threshold = 0.1,
+    high_threshold = 0.2,
+    kernel_size  = 5,
+    sigma = 1,
+    hysteresis = True,
+    eps = 1e-6,
+):
+    r"""Find edges of the input image and filters them using the Canny algorithm.
+    .. image:: _static/img/canny.png
+    Args:
+        input: input image tensor with shape :math:`(B,C,H,W)`.
+        low_threshold: lower threshold for the hysteresis procedure.
+        high_threshold: upper threshold for the hysteresis procedure.
+        kernel_size: the size of the kernel for the gaussian blur.
+        sigma: the standard deviation of the kernel for the gaussian blur.
+        hysteresis: if True, applies the hysteresis edge tracking.
+            Otherwise, the edges are divided between weak (0.5) and strong (1) edges.
+        eps: regularization number to avoid NaN during backprop.
+    Returns:
+        - the canny edge magnitudes map, shape of :math:`(B,1,H,W)`.
+        - the canny edge detection filtered by thresholds and hysteresis, shape of :math:`(B,1,H,W)`.
+    .. note::
+       See a working example `here <https://kornia-tutorials.readthedocs.io/en/latest/
+       canny.html>`__.
+    Example:
+        >>> input = torch.rand(5, 3, 4, 4)
+        >>> magnitude, edges = canny(input)  # 5x3x4x4
+        >>> magnitude.shape
+        torch.Size([5, 1, 4, 4])
+        >>> edges.shape
+        torch.Size([5, 1, 4, 4])
+    """
+    # KORNIA_CHECK_IS_TENSOR(input)
+    # KORNIA_CHECK_SHAPE(input, ['B', 'C', 'H', 'W'])
+    # KORNIA_CHECK(
+    #     low_threshold <= high_threshold,
+    #     "Invalid input thresholds. low_threshold should be smaller than the high_threshold. Got: "
+    #     f"{low_threshold}>{high_threshold}",
+    # )
+    # KORNIA_CHECK(0 < low_threshold < 1, f'Invalid low threshold. Should be in range (0, 1). Got: {low_threshold}')
+    # KORNIA_CHECK(0 < high_threshold < 1, f'Invalid high threshold. Should be in range (0, 1). Got: {high_threshold}')
+
+    device = input.device
+    dtype = input.dtype
+
+    # To Grayscale
+    if input.shape[1] == 3:
+        input = rgb_to_grayscale(input)
+
+    # Gaussian filter
+    blurred: Tensor = gaussian_blur_2d(input, kernel_size, sigma)
+
+    # Compute the gradients
+    gradients: Tensor = spatial_gradient(blurred, normalized=False)
+
+    # Unpack the edges
+    gx: Tensor = gradients[:, :, 0]
+    gy: Tensor = gradients[:, :, 1]
+
+    # Compute gradient magnitude and angle
+    magnitude: Tensor = torch.sqrt(gx * gx + gy * gy + eps)
+    angle: Tensor = torch.atan2(gy, gx)
+
+    # Radians to Degrees
+    angle = 180.0 * angle / math.pi
+
+    # Round angle to the nearest 45 degree
+    angle = torch.round(angle / 45) * 45
+
+    # Non-maximal suppression
+    nms_kernels: Tensor = get_canny_nms_kernel(device, dtype)
+    nms_magnitude: Tensor = F.conv2d(magnitude, nms_kernels, padding=nms_kernels.shape[-1] // 2)
+
+    # Get the indices for both directions
+    positive_idx: Tensor = (angle / 45) % 8
+    positive_idx = positive_idx.long()
+
+    negative_idx: Tensor = ((angle / 45) + 4) % 8
+    negative_idx = negative_idx.long()
+
+    # Apply the non-maximum suppression to the different directions
+    channel_select_filtered_positive: Tensor = torch.gather(nms_magnitude, 1, positive_idx)
+    channel_select_filtered_negative: Tensor = torch.gather(nms_magnitude, 1, negative_idx)
+
+    channel_select_filtered: Tensor = torch.stack(
+        [channel_select_filtered_positive, channel_select_filtered_negative], 1
+    )
+
+    is_max: Tensor = channel_select_filtered.min(dim=1)[0] > 0.0
+
+    magnitude = magnitude * is_max
+
+    # Threshold
+    edges: Tensor = F.threshold(magnitude, low_threshold, 0.0)
+
+    low: Tensor = magnitude > low_threshold
+    high: Tensor = magnitude > high_threshold
+
+    edges = low * 0.5 + high * 0.5
+    edges = edges.to(dtype)
+
+    # Hysteresis
+    if hysteresis:
+        edges_old: Tensor = -torch.ones(edges.shape, device=edges.device, dtype=dtype)
+        hysteresis_kernels: Tensor = get_hysteresis_kernel(device, dtype)
+
+        while ((edges_old - edges).abs() != 0).any():
+            weak: Tensor = (edges == 0.5).float()
+            strong: Tensor = (edges == 1).float()
+
+            hysteresis_magnitude: Tensor = F.conv2d(
+                edges, hysteresis_kernels, padding=hysteresis_kernels.shape[-1] // 2
+            )
+            hysteresis_magnitude = (hysteresis_magnitude == 1).any(1, keepdim=True).to(dtype)
+            hysteresis_magnitude = hysteresis_magnitude * weak + strong
+
+            edges_old = edges.clone()
+            edges = hysteresis_magnitude + (hysteresis_magnitude == 0) * weak * 0.5
+
+        edges = hysteresis_magnitude
+
+    return magnitude, edges
+
+
+class Canny:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"image": ("IMAGE",),
+                                "low_threshold": ("FLOAT", {"default": 0.4, "min": 0.01, "max": 0.99, "step": 0.01}),
+                                "high_threshold": ("FLOAT", {"default": 0.8, "min": 0.01, "max": 0.99, "step": 0.01})
+                                }}
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "detect_edge"
+
+    CATEGORY = "image/preprocessors"
+
+    def detect_edge(self, image, low_threshold, high_threshold):
+        output = canny(image.movedim(-1, 1), low_threshold, high_threshold)
+        img_out = output[1].repeat(1, 3, 1, 1).movedim(1, -1)
+        return (img_out,)
+
+NODE_CLASS_MAPPINGS = {
+    "Canny": Canny,
+}

main.py

@@ -1,22 +1,24 @@
 import os
 import importlib.util
 import folder_paths
-
+import time
 
 def execute_prestartup_script():
     def execute_script(script_path):
-        if os.path.exists(script_path):
         module_name = os.path.splitext(script_path)[0]
         try:
             spec = importlib.util.spec_from_file_location(module_name, script_path)
             module = importlib.util.module_from_spec(spec)
             spec.loader.exec_module(module)
+            return True
         except Exception as e:
             print(f"Failed to execute startup-script: {script_path} / {e}")
+        return False
 
     node_paths = folder_paths.get_folder_paths("custom_nodes")
     for custom_node_path in node_paths:
         possible_modules = os.listdir(custom_node_path)
+        node_prestartup_times = []
 
         for possible_module in possible_modules:
             module_path = os.path.join(custom_node_path, possible_module)
@@ -24,8 +26,19 @@ def execute_prestartup_script():
                 continue
 
             script_path = os.path.join(module_path, "prestartup_script.py")
-            execute_script(script_path)
+            if os.path.exists(script_path):
-
+                time_before = time.perf_counter()
+                success = execute_script(script_path)
+                node_prestartup_times.append((time.perf_counter() - time_before, module_path, success))
+    if len(node_prestartup_times) > 0:
+        print("\nPrestartup times for custom nodes:")
+        for n in sorted(node_prestartup_times):
+            if n[2]:
+                import_message = ""
+            else:
+                import_message = " (PRESTARTUP FAILED)"
+            print("{:6.1f} seconds{}:".format(n[0], import_message), n[1])
+        print()
 
 execute_prestartup_script()
 
@@ -36,7 +49,6 @@ import itertools
 import shutil
 import threading
 import gc
-import time
 
 from comfy.cli_args import args
 import comfy.utils

nodes.py

@@ -9,7 +9,7 @@ import math
 import time
 import random
 
-from PIL import Image, ImageDraw, ImageOps
+from PIL import Image, ImageOps
 from PIL.PngImagePlugin import PngInfo
 import numpy as np
 import safetensors.torch
@@ -116,7 +116,7 @@ class ConditioningConcat:
     RETURN_TYPES = ("CONDITIONING",)
     FUNCTION = "concat"
 
-    CATEGORY = "advanced/conditioning"
+    CATEGORY = "conditioning"
 
     def concat(self, conditioning_to, conditioning_from):
         out = []
@@ -1531,6 +1531,7 @@ NODE_CLASS_MAPPINGS = {
     "ImagePadForOutpaint": ImagePadForOutpaint,
     "ConditioningAverage ": ConditioningAverage ,
     "ConditioningCombine": ConditioningCombine,
+    "ConditioningConcat": ConditioningConcat,
     "ConditioningSetArea": ConditioningSetArea,
     "ConditioningSetMask": ConditioningSetMask,
     "KSamplerAdvanced": KSamplerAdvanced,
@@ -1564,7 +1565,6 @@ NODE_CLASS_MAPPINGS = {
     "SaveLatent": SaveLatent,
 
     "ConditioningZeroOut": ConditioningZeroOut,
-    "ConditioningConcat": ConditioningConcat,
     "SavePreviewLatent": SavePreviewLatent,
 }
 
@@ -1590,6 +1590,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "CLIPSetLastLayer": "CLIP Set Last Layer",
     "ConditioningCombine": "Conditioning (Combine)",
     "ConditioningAverage ": "Conditioning (Average)",
+    "ConditioningConcat": "Conditioning (Concat)",
     "ConditioningSetArea": "Conditioning (Set Area)",
     "ConditioningSetMask": "Conditioning (Set Mask)",
     "ControlNetApply": "Apply ControlNet",
@@ -1622,7 +1623,7 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "VAEEncodeTiled": "VAE Encode (Tiled)",
 }
 
-def load_custom_node(module_path):
+def load_custom_node(module_path, ignore=set()):
     module_name = os.path.basename(module_path)
     if os.path.isfile(module_path):
         sp = os.path.splitext(module_path)
@@ -1636,7 +1637,9 @@ def load_custom_node(module_path):
         sys.modules[module_name] = module
         module_spec.loader.exec_module(module)
         if hasattr(module, "NODE_CLASS_MAPPINGS") and getattr(module, "NODE_CLASS_MAPPINGS") is not None:
-            NODE_CLASS_MAPPINGS.update(module.NODE_CLASS_MAPPINGS)
+            for name in module.NODE_CLASS_MAPPINGS:
+                if name not in ignore:
+                    NODE_CLASS_MAPPINGS[name] = module.NODE_CLASS_MAPPINGS[name]
             if hasattr(module, "NODE_DISPLAY_NAME_MAPPINGS") and getattr(module, "NODE_DISPLAY_NAME_MAPPINGS") is not None:
                 NODE_DISPLAY_NAME_MAPPINGS.update(module.NODE_DISPLAY_NAME_MAPPINGS)
             return True
@@ -1649,6 +1652,7 @@ def load_custom_node(module_path):
         return False
 
 def load_custom_nodes():
+    base_node_names = set(NODE_CLASS_MAPPINGS.keys())
     node_paths = folder_paths.get_folder_paths("custom_nodes")
     node_import_times = []
     for custom_node_path in node_paths:
@@ -1661,7 +1665,7 @@ def load_custom_nodes():
             if os.path.isfile(module_path) and os.path.splitext(module_path)[1] != ".py": continue
             if module_path.endswith(".disabled"): continue
             time_before = time.perf_counter()
-            success = load_custom_node(module_path)
+            success = load_custom_node(module_path, base_node_names)
             node_import_times.append((time.perf_counter() - time_before, module_path, success))
 
     if len(node_import_times) > 0:
@@ -1683,4 +1687,5 @@ def init_custom_nodes():
     load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_model_merging.py"))
     load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_tomesd.py"))
     load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_clip_sdxl.py"))
+    load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_canny.py"))
     load_custom_nodes()

server.py

@@ -444,7 +444,8 @@ class PromptServer():
                     prompt_id = str(uuid.uuid4())
                     outputs_to_execute = valid[2]
                     self.prompt_queue.put((number, prompt_id, prompt, extra_data, outputs_to_execute))
-                    return web.json_response({"prompt_id": prompt_id, "number": number})
+                    response = {"prompt_id": prompt_id, "number": number, "node_errors": valid[3]}
+                    return web.json_response(response)
                 else:
                     print("invalid prompt:", valid[1])
                     return web.json_response({"error": valid[1], "node_errors": valid[3]}, status=400)

web/scripts/api.js

@@ -202,6 +202,8 @@ class ComfyApi extends EventTarget {
 				response: await res.json(),
 			};
 		}
+
+		return await res.json();
 	}
 
 	/**

web/scripts/app.js

@@ -836,7 +836,7 @@ export class ComfyApp {
 		LGraphCanvas.prototype.drawNodeShape = function (node, ctx, size, fgcolor, bgcolor, selected, mouse_over) {
 			const res = origDrawNodeShape.apply(this, arguments);
 
-			const nodeErrors = self.lastPromptError?.node_errors[node.id];
+			const nodeErrors = self.lastNodeErrors?.[node.id];
 
 			let color = null;
 			let lineWidth = 1;
@@ -845,7 +845,7 @@ export class ComfyApp {
 			} else if (self.dragOverNode && node.id === self.dragOverNode.id) {
 				color = "dodgerblue";
 			}
-			else if (self.lastPromptError != null && nodeErrors?.errors) {
+			else if (nodeErrors?.errors) {
 				color = "red";
 				lineWidth = 2;
 			}
@@ -1413,7 +1413,7 @@ export class ComfyApp {
 		}
 
 		this.#processingQueue = true;
-		this.lastPromptError = null;
+		this.lastNodeErrors = null;
 
 		try {
 			while (this.#queueItems.length) {
@@ -1423,12 +1423,16 @@ export class ComfyApp {
 					const p = await this.graphToPrompt();
 
 					try {
-						await api.queuePrompt(number, p);
+						const res = await api.queuePrompt(number, p);
+						this.lastNodeErrors = res.node_errors;
+						if (this.lastNodeErrors.length > 0) {
+							this.canvas.draw(true, true);
+						}
 					} catch (error) {
 						const formattedError = this.#formatPromptError(error)
 						this.ui.dialog.show(formattedError);
 						if (error.response) {
-							this.lastPromptError = error.response;
+							this.lastNodeErrors = error.response.node_errors;
 							this.canvas.draw(true, true);
 						}
 						break;
@@ -1534,7 +1538,7 @@ export class ComfyApp {
 	clean() {
 		this.nodeOutputs = {};
 		this.nodePreviewImages = {}
-		this.lastPromptError = null;
+		this.lastNodeErrors = null;
 		this.lastExecutionError = null;
 		this.runningNodeId = null;
 	}

web/scripts/ui.js

@@ -670,6 +670,37 @@ export class ComfyUI {
 					}, 0);
 				},
 			}),
+			$el("button", {
+				id: "comfy-dev-save-api-button",
+				textContent: "Save (API Format)",
+				style: {width: "100%", display: "none"},
+				onclick: () => {
+					let filename = "workflow_api.json";
+					if (promptFilename.value) {
+						filename = prompt("Save workflow (API) as:", filename);
+						if (!filename) return;
+						if (!filename.toLowerCase().endsWith(".json")) {
+							filename += ".json";
+						}
+					}
+					app.graphToPrompt().then(p=>{
+						const json = JSON.stringify(p.output, null, 2); // convert the data to a JSON string
+						const blob = new Blob([json], {type: "application/json"});
+						const url = URL.createObjectURL(blob);
+						const a = $el("a", {
+							href: url,
+							download: filename,
+							style: {display: "none"},
+							parent: document.body,
+						});
+						a.click();
+						setTimeout(function () {
+							a.remove();
+							window.URL.revokeObjectURL(url);
+						}, 0);
+					});
+				},
+			}),
 			$el("button", {id: "comfy-load-button", textContent: "Load", onclick: () => fileInput.click()}),
 			$el("button", {
 				id: "comfy-refresh-button",
@@ -694,6 +725,14 @@ export class ComfyUI {
 			}),
 		]);
 
+		const devMode = this.settings.addSetting({
+			id: "Comfy.DevMode",
+			name: "Enable Dev mode Options",
+			type: "boolean",
+			defaultValue: false,
+			onChange: function(value) { document.getElementById("comfy-dev-save-api-button").style.display = value ? "block" : "none"},
+		});
+
 		dragElement(this.menuContainer, this.settings);
 
 		this.setStatus({exec_info: {queue_remaining: "X"}});