Merge branch 'comfyanonymous:master' into master

.ci/windows_base_files/README_VERY_IMPORTANT.txt

@@ -25,3 +25,7 @@ To update the ComfyUI code: update\update_comfyui.bat
 To update ComfyUI with the python dependencies, note that you should ONLY run this if you have issues with python dependencies.
 update\update_comfyui_and_python_dependencies.bat
 
+
+TO SHARE MODELS BETWEEN COMFYUI AND ANOTHER UI:
+In the ComfyUI directory you will find a file: extra_model_paths.yaml.example
+Rename this file to: extra_model_paths.yaml and edit it with your favorite text editor.

README.md

@@ -30,6 +30,11 @@ This ui will let you design and execute advanced stable diffusion pipelines usin
 
 Workflow examples can be found on the [Examples page](https://comfyanonymous.github.io/ComfyUI_examples/)
 
+## Shortcuts
+- **Ctrl + A** select all nodes
+- **Ctrl + M** mute/unmute selected nodes
+- **Delete** or **Backspace** delete selected nodes
+
 # Installing
 
 ## Windows
@@ -40,6 +45,10 @@ There is a portable standalone build for Windows that should work for running on
 
 Just download, extract and run. Make sure you put your Stable Diffusion checkpoints/models (the huge ckpt/safetensors files) in: ComfyUI\models\checkpoints
 
+#### How do I share models between another UI and ComfyUI?
+
+See the [Config file](extra_model_paths.yaml.example) to set the search paths for models. In the standalone windows build you can find this file in the ComfyUI directory. Rename this file to extra_model_paths.yaml and edit it with your favorite text editor.
+
 ## Colab Notebook
 
 To run it on colab or paperspace you can use my [Colab Notebook](notebooks/comfyui_colab.ipynb) here: [Link to open with google colab](https://colab.research.google.com/github/comfyanonymous/ComfyUI/blob/master/notebooks/comfyui_colab.ipynb)
@@ -64,7 +73,7 @@ AMD users can install rocm and pytorch with pip if you don't have it already ins
 
 Nvidia users should install torch and xformers using this command:
 
-```pip install torch==1.13.1 torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu117 xformers```
+```pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu118 xformers```
 
 #### Troubleshooting
 
@@ -97,7 +106,6 @@ With cmd.exe: ```"path_to_other_sd_gui\venv\Scripts\activate.bat"```
 
 And then you can use that terminal to run Comfyui without installing any dependencies. Note that the venv folder might be called something else depending on the SD UI.
 
-
 # Running
 
 ```python main.py```

comfy/samplers.py

@@ -221,7 +221,7 @@ class KSamplerX0Inpaint(torch.nn.Module):
     def forward(self, x, sigma, uncond, cond, cond_scale, denoise_mask, cond_concat=None):
         if denoise_mask is not None:
             latent_mask = 1. - denoise_mask
-            x = x * denoise_mask + (self.latent_image + self.noise * sigma) * latent_mask
+            x = x * denoise_mask + (self.latent_image + self.noise * sigma.reshape([sigma.shape[0]] + [1] * (len(self.noise.shape) - 1))) * latent_mask
         out = self.inner_model(x, sigma, cond=cond, uncond=uncond, cond_scale=cond_scale, cond_concat=cond_concat)
         if denoise_mask is not None:
             out *= denoise_mask

comfy/sd.py

@@ -439,9 +439,14 @@ class VAE:
         model_management.unload_model()
         self.first_stage_model = self.first_stage_model.to(self.device)
         try:
-            samples = samples_in.to(self.device)
-            pixel_samples = self.first_stage_model.decode(1. / self.scale_factor * samples)
-            pixel_samples = torch.clamp((pixel_samples + 1.0) / 2.0, min=0.0, max=1.0)
+            free_memory = model_management.get_free_memory(self.device)
+            batch_number = int((free_memory * 0.7) / (2562 * samples_in.shape[2] * samples_in.shape[3] * 64))
+            batch_number = max(1, batch_number)
+
+            pixel_samples = torch.empty((samples_in.shape[0], 3, round(samples_in.shape[2] * 8), round(samples_in.shape[3] * 8)), device="cpu")
+            for x in range(0, samples_in.shape[0], batch_number):
+                samples = samples_in[x:x+batch_number].to(self.device)
+                pixel_samples[x:x+batch_number] = torch.clamp((self.first_stage_model.decode(1. / self.scale_factor * samples) + 1.0) / 2.0, min=0.0, max=1.0).cpu()
         except model_management.OOM_EXCEPTION as e:
             print("Warning: Ran out of memory when regular VAE decoding, retrying with tiled VAE decoding.")
             pixel_samples = self.decode_tiled_(samples_in)

execution.py

@@ -65,8 +65,11 @@ def recursive_execute(server, prompt, outputs, current_item, extra_data={}):
 
     nodes.before_node_execution()
     outputs[unique_id] = getattr(obj, obj.FUNCTION)(**input_data_all)
-    if "ui" in outputs[unique_id] and server.client_id is not None:
-        server.send_sync("executed", { "node": unique_id, "output": outputs[unique_id]["ui"] }, server.client_id)
+    if "ui" in outputs[unique_id]:
+        if server.client_id is not None:
+            server.send_sync("executed", { "node": unique_id, "output": outputs[unique_id]["ui"] }, server.client_id)
+        if "result" in outputs[unique_id]:
+            outputs[unique_id] = outputs[unique_id]["result"]
     return executed + [unique_id]
 
 def recursive_will_execute(prompt, outputs, current_item):

main.py

@@ -18,6 +18,7 @@ if __name__ == "__main__":
         print("\t--use-split-cross-attention\tUse the split cross attention optimization instead of the sub-quadratic one.\n\t\t\t\t\tIgnored when xformers is used.")
         print("\t--use-pytorch-cross-attention\tUse the new pytorch 2.0 cross attention function.")
         print("\t--disable-xformers\t\tdisables xformers")
+        print("\t--cuda-device 1\t\tSet the id of the cuda device this instance will use.")
         print()
         print("\t--highvram\t\t\tBy default models will be unloaded to CPU memory after being used.\n\t\t\t\t\tThis option keeps them in GPU memory.\n")
         print("\t--normalvram\t\t\tUsed to force normal vram use if lowvram gets automatically enabled.")
@@ -31,6 +32,14 @@ if __name__ == "__main__":
         print("disabling upcasting of attention")
         os.environ['ATTN_PRECISION'] = "fp16"
 
+    try:
+        index = sys.argv.index('--cuda-device')
+        device = sys.argv[index + 1]
+        os.environ['CUDA_VISIBLE_DEVICES'] = device
+        print("Set cuda device to:", device)
+    except:
+        pass
+
 import execution
 import server
 import folder_paths

notebooks/comfyui_colab.ipynb

@@ -47,7 +47,7 @@
         "  !git pull\n",
         "\n",
         "!echo -= Install dependencies =-\n",
-        "!pip -q install xformers -r requirements.txt"
+        "!pip install xformers==0.0.16 -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cu117"
       ]
     },
     {

requirements.txt

@@ -3,7 +3,7 @@ torchdiffeq
 torchsde
 einops
 open-clip-torch
-transformers
+transformers>=4.25.1
 safetensors
 pytorch_lightning
 aiohttp

web/extensions/core/saveImageExtraOutput.js

@@ -0,0 +1,100 @@
+import { app } from "/scripts/app.js";
+
+// Use widget values and dates in output filenames
+
+app.registerExtension({
+	name: "Comfy.SaveImageExtraOutput",
+	async beforeRegisterNodeDef(nodeType, nodeData, app) {
+		if (nodeData.name === "SaveImage") {
+			const onNodeCreated = nodeType.prototype.onNodeCreated;
+
+         // Simple date formatter
+			const parts = {
+				d: (d) => d.getDate(),
+				M: (d) => d.getMonth() + 1,
+				h: (d) => d.getHours(),
+				m: (d) => d.getMinutes(),
+				s: (d) => d.getSeconds(),
+			};
+			const format =
+				Object.keys(parts)
+					.map((k) => k + k + "?")
+					.join("|") + "|yyy?y?";
+
+			function formatDate(text, date) {
+				return text.replace(new RegExp(format, "g"), function (text) {
+					if (text === "yy") return (date.getFullYear() + "").substring(2);
+					if (text === "yyyy") return date.getFullYear();
+					if (text[0] in parts) {
+						const p = parts[text[0]](date);
+						return (p + "").padStart(text.length, "0");
+					}
+					return text;
+				});
+			}
+
+         // When the SaveImage node is created we want to override the serialization of the output name widget to run our S&R
+			nodeType.prototype.onNodeCreated = function () {
+				const r = onNodeCreated ? onNodeCreated.apply(this, arguments) : undefined;
+
+				const widget = this.widgets.find((w) => w.name === "filename_prefix");
+				widget.serializeValue = () => {
+					return widget.value.replace(/%([^%]+)%/g, function (match, text) {
+						const split = text.split(".");
+						if (split.length !== 2) {
+                     // Special handling for dates
+							if (split[0].startsWith("date:")) {
+								return formatDate(split[0].substring(5), new Date());
+							}
+
+							if (text !== "width" && text !== "height") {
+								// Dont warn on standard replacements
+								console.warn("Invalid replacement pattern", text);
+							}
+							return match;
+						}
+
+						// Find node with matching S&R property name
+						let nodes = app.graph._nodes.filter((n) => n.properties?.["Node name for S&R"] === split[0]);
+						// If we cant, see if there is a node with that title
+						if (!nodes.length) {
+							nodes = app.graph._nodes.filter((n) => n.title === split[0]);
+						}
+						if (!nodes.length) {
+							console.warn("Unable to find node", split[0]);
+							return match;
+						}
+
+						if (nodes.length > 1) {
+							console.warn("Multiple nodes matched", split[0], "using first match");
+						}
+
+						const node = nodes[0];
+
+						const widget = node.widgets?.find((w) => w.name === split[1]);
+						if (!widget) {
+							console.warn("Unable to find widget", split[1], "on node", split[0], node);
+							return match;
+						}
+
+						return ((widget.value ?? "") + "").replaceAll(/\/|\\/g, "_");
+					});
+				};
+
+				return r;
+			};
+		} else {
+         // When any other node is created add a property to alias the node
+			const onNodeCreated = nodeType.prototype.onNodeCreated;
+			nodeType.prototype.onNodeCreated = function () {
+				const r = onNodeCreated ? onNodeCreated.apply(this, arguments) : undefined;
+
+				if (!this.properties || !("Node name for S&R" in this.properties)) {
+					this.addProperty("Node name for S&R", this.title, "string");
+				}
+
+				return r;
+			};
+		}
+	},
+});

web/scripts/app.js

@@ -417,6 +417,59 @@ class ComfyApp {
 		};
 	}
 
+	/**
+	 * Handle keypress
+	 *
+	 * Ctrl + M mute/unmute selected nodes
+	 */
+	#addProcessKeyHandler() {
+		const self = this;
+		const origProcessKey = LGraphCanvas.prototype.processKey;
+		LGraphCanvas.prototype.processKey = function(e) {
+			const res = origProcessKey.apply(this, arguments);
+
+			if (res === false) {
+				return res;
+			}
+
+			if (!this.graph) {
+				return;
+			}
+
+			var block_default = false;
+
+			if (e.target.localName == "input") {
+				return;
+			}
+
+			if (e.type == "keydown") {
+				// Ctrl + M mute/unmute
+				if (e.keyCode == 77 && e.ctrlKey) {
+					if (this.selected_nodes) {
+						for (var i in this.selected_nodes) {
+							if (this.selected_nodes[i].mode === 2) { // never
+								this.selected_nodes[i].mode = 0; // always
+							} else {
+								this.selected_nodes[i].mode = 2; // never
+							}
+						}
+					}
+					block_default = true;
+				}
+			}
+
+			this.graph.change();
+
+			if (block_default) {
+				e.preventDefault();
+				e.stopImmediatePropagation();
+				return false;
+			}
+
+			return res;
+		};
+	}
+
 	/**
 	 * Draws group header bar
 	 */
@@ -465,10 +518,11 @@ class ComfyApp {
 	 * Draws node highlights (executing, drag drop) and progress bar
 	 */
 	#addDrawNodeHandler() {
-		const orig = LGraphCanvas.prototype.drawNodeShape;
+		const origDrawNodeShape = LGraphCanvas.prototype.drawNodeShape;
 		const self = this;
+
 		LGraphCanvas.prototype.drawNodeShape = function (node, ctx, size, fgcolor, bgcolor, selected, mouse_over) {
-			const res = orig.apply(this, arguments);
+			const res = origDrawNodeShape.apply(this, arguments);
 
 			let color = null;
 			if (node.id === +self.runningNodeId) {
@@ -517,6 +571,21 @@ class ComfyApp {
 
 			return res;
 		};
+
+		const origDrawNode = LGraphCanvas.prototype.drawNode;
+		LGraphCanvas.prototype.drawNode = function (node, ctx) {
+			var editor_alpha = this.editor_alpha;
+
+			if (node.mode === 2) { // never
+				this.editor_alpha = 0.4;
+			}
+
+			const res = origDrawNode.apply(this, arguments);
+
+			this.editor_alpha = editor_alpha;
+
+			return res;
+		};
 	}
 
 	/**
@@ -548,6 +617,10 @@ class ComfyApp {
 
 		api.addEventListener("executed", ({ detail }) => {
 			this.nodeOutputs[detail.node] = detail.output;
+			const node = this.graph.getNodeById(detail.node);
+			if (node?.onExecuted) {
+				node.onExecuted(detail.output);
+			}
 		});
 
 		api.init();
@@ -588,6 +661,7 @@ class ComfyApp {
 		document.body.prepend(canvasEl);
 
 		this.#addProcessMouseHandler();
+		this.#addProcessKeyHandler();
 
 		this.graph = new LGraph();
 		const canvas = (this.canvas = new LGraphCanvas(canvasEl, this.graph));
@@ -669,18 +743,22 @@ class ComfyApp {
 						const inputData = inputs[inputName];
 						const type = inputData[0];
 
-						if (Array.isArray(type)) {
-							// Enums
-							Object.assign(config, widgets.COMBO(this, inputName, inputData, app) || {});
-						} else if (`${type}:${inputName}` in widgets) {
-							// Support custom widgets by Type:Name
-							Object.assign(config, widgets[`${type}:${inputName}`](this, inputName, inputData, app) || {});
-						} else if (type in widgets) {
-							// Standard type widgets
-							Object.assign(config, widgets[type](this, inputName, inputData, app) || {});
-						} else {
-							// Node connection inputs
+						if(inputData[1]?.forceInput) {
 							this.addInput(inputName, type);
+						} else {
+							if (Array.isArray(type)) {
+								// Enums
+								Object.assign(config, widgets.COMBO(this, inputName, inputData, app) || {});
+							} else if (`${type}:${inputName}` in widgets) {
+								// Support custom widgets by Type:Name
+								Object.assign(config, widgets[`${type}:${inputName}`](this, inputName, inputData, app) || {});
+							} else if (type in widgets) {
+								// Standard type widgets
+								Object.assign(config, widgets[type](this, inputName, inputData, app) || {});
+							} else {
+								// Node connection inputs
+								this.addInput(inputName, type);
+							}
 						}
 					}
 
@@ -777,6 +855,11 @@ class ComfyApp {
 				continue;
 			}
 
+			if (node.mode === 2) {
+				// Don't serialize muted nodes
+				continue;
+			}
+
 			const inputs = {};
 			const widgets = node.widgets;
 
@@ -816,6 +899,18 @@ class ComfyApp {
 			};
 		}
 
+		// Remove inputs connected to removed nodes
+
+		for (const o in output) {
+			for (const i in output[o].inputs) {
+				if (Array.isArray(output[o].inputs[i])
+					&& output[o].inputs[i].length === 2
+					&& !output[output[o].inputs[i][0]]) {
+					delete output[o].inputs[i];
+				}
+			}
+		}
+
 		return { workflow, output };
 	}
 

web/style.css

@@ -140,6 +140,12 @@ body {
 	display: flex;
 	flex-direction: column;
 	align-items: center;
+	color: #999;
+	background-color: #353535;
+	font-family: sans-serif;
+	padding: 10px;
+	border-radius: 0 8px 8px 8px;
+	box-shadow: 3px 3px 8px rgba(0, 0, 0, 0.4);
 }
 
 .comfy-menu button {
@@ -154,6 +160,22 @@ body {
 .comfy-menu-btns button {
 	font-size: 10px;
 	width: 50%;
+	color: #999 !important;
+}
+
+.comfy-menu > button {
+	width: 100%;
+}
+
+.comfy-menu > button,
+.comfy-menu-btns button,
+.comfy-menu .comfy-list button {
+	color: #ddd;
+	background-color: #222;
+	border-radius: 8px;
+	border-color: #4e4e4e;
+	border-style: solid;
+	margin-top: 2px;
 }
 
 .comfy-menu span.drag-handle {
@@ -186,14 +208,18 @@ body {
 }
 
 .comfy-list {
-	background-color: rgb(225, 225, 225);
+	color: #999;
+	background-color: #333;
 	margin-bottom: 10px;
+	border-color: #4e4e4e;
+	border-style: solid;
 }
 
 .comfy-list-items {
 	overflow-y: scroll;
 	max-height: 100px;
-	background-color: #d0d0d0;
+	min-height: 25px;
+	background-color: #222;
 	padding: 5px;
 }
 
@@ -220,6 +246,7 @@ body {
 }
 
 button.comfy-settings-btn {
+	background-color: rgba(0, 0, 0, 0);
 	font-size: 12px;
 	padding: 0;
 	position: absolute;
@@ -227,6 +254,10 @@ button.comfy-settings-btn {
 	border: none;
 }
 
+button.comfy-queue-btn {
+	margin: 6px 0 !important;
+}
+
 .comfy-modal.comfy-settings {
 	background-color: var(--bg-color);
 	color: var(--fg-color);
@@ -235,6 +266,13 @@ button.comfy-settings-btn {
 
 @media only screen and (max-height: 850px) {
 	.comfy-menu {
-		margin-top: -70px;
+		top: 0 !important;
+		bottom: 0 !important;
+		left: auto !important;
+		right: 0 !important;
+		border-radius: 0px;
+	}
+	.comfy-menu span.drag-handle {
+		visibility:hidden
 	}
 }