Merge branch 'kijai:main' into main

nodes/image_nodes.py

@@ -2475,7 +2475,7 @@ class ImageResizeKJv2:
                 "width": ("INT", { "default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1, }),
                 "height": ("INT", { "default": 512, "min": 0, "max": MAX_RESOLUTION, "step": 1, }),
                 "upscale_method": (s.upscale_methods,),
-                "keep_proportion": (["stretch", "resize", "pad", "pad_edge", "pad_edge_pixel", "crop", "pillarbox_blur"], { "default": False }),
+                "keep_proportion": (["stretch", "resize", "pad", "pad_edge", "pad_edge_pixel", "crop", "pillarbox_blur", "total_pixels"], { "default": False }),
                 "pad_color": ("STRING", { "default": "0, 0, 0", "tooltip": "Color to use for padding."}),
                 "crop_position": (["center", "top", "bottom", "left", "right"], { "default": "center" }),
                 "divisible_by": ("INT", { "default": 2, "min": 0, "max": 512, "step": 1, }),
@@ -2512,19 +2512,23 @@ highest dimension.
         else:
             device = torch.device("cpu")
 
-        if width == 0:
-            width = W
-        if height == 0:
-            height = H
-
         pillarbox_blur = keep_proportion == "pillarbox_blur"
         
         # Initialize padding variables
         pad_left = pad_right = pad_top = pad_bottom = 0
 
-        if keep_proportion == "resize" or keep_proportion.startswith("pad") or pillarbox_blur:
+        if keep_proportion in ["resize", "total_pixels"] or keep_proportion.startswith("pad") or pillarbox_blur:
+            if keep_proportion == "total_pixels":
+                total_pixels = width * height
+                aspect_ratio = W / H
+                new_height = int(math.sqrt(total_pixels / aspect_ratio))
+                new_width = int(math.sqrt(total_pixels * aspect_ratio))
+                
             # If one of the dimensions is zero, calculate it to maintain the aspect ratio
-            if width == 0 and height != 0:
+            elif width == 0 and height == 0:
+                new_width = W
+                new_height = H
+            elif width == 0 and height != 0:
                 ratio = height / H
                 new_width = round(W * ratio)
                 new_height = height
@@ -2570,6 +2574,11 @@ highest dimension.
 
             width = new_width
             height = new_height
+        else:
+            if width == 0:
+                width = W
+            if height == 0:
+                height = H
 
         if divisible_by > 1:
             width = width - (width % divisible_by)

nodes/model_optimization_nodes.py

@@ -709,6 +709,7 @@ class TorchCompileModelFluxAdvancedV2:
                 },
                 "optional": {
                     "dynamo_cache_size_limit": ("INT", {"default": 64, "min": 0, "max": 1024, "step": 1, "tooltip": "torch._dynamo.config.cache_size_limit"}),
+                    "force_parameter_static_shapes": ("BOOLEAN", {"default": True, "tooltip": "torch._dynamo.config.force_parameter_static_shapes"}),
                 }
                 }
     RETURN_TYPES = ("MODEL",)
@@ -717,11 +718,12 @@ class TorchCompileModelFluxAdvancedV2:
     CATEGORY = "KJNodes/torchcompile"
     EXPERIMENTAL = True
 
-    def patch(self, model, backend, mode, fullgraph, single_blocks, double_blocks, dynamic, dynamo_cache_size_limit):
+    def patch(self, model, backend, mode, fullgraph, single_blocks, double_blocks, dynamic, dynamo_cache_size_limit=64, force_parameter_static_shapes=True):
         from comfy_api.torch_helpers import set_torch_compile_wrapper
         m = model.clone()
         diffusion_model = m.get_model_object("diffusion_model")
         torch._dynamo.config.cache_size_limit = dynamo_cache_size_limit
+        torch._dynamo.config.force_parameter_static_shapes = force_parameter_static_shapes
 
         compile_key_list = []
         
@@ -872,6 +874,10 @@ class TorchCompileModelWanVideoV2:
                 "dynamic": ("BOOLEAN", {"default": False, "tooltip": "Enable dynamic mode"}),
                 "compile_transformer_blocks_only": ("BOOLEAN", {"default": True, "tooltip": "Compile only transformer blocks, faster compile and less error prone"}),
                 "dynamo_cache_size_limit": ("INT", {"default": 64, "min": 0, "max": 1024, "step": 1, "tooltip": "torch._dynamo.config.cache_size_limit"}),
+                
+            },
+            "optional": {
+                "force_parameter_static_shapes": ("BOOLEAN", {"default": True, "tooltip": "torch._dynamo.config.force_parameter_static_shapes"}),
             },
         }
     RETURN_TYPES = ("MODEL",)
@@ -880,11 +886,12 @@ class TorchCompileModelWanVideoV2:
     CATEGORY = "KJNodes/torchcompile"
     EXPERIMENTAL = True
 
-    def patch(self, model, backend, fullgraph, mode, dynamic, dynamo_cache_size_limit, compile_transformer_blocks_only):
+    def patch(self, model, backend, fullgraph, mode, dynamic, dynamo_cache_size_limit, compile_transformer_blocks_only, force_parameter_static_shapes=True):
         from comfy_api.torch_helpers import set_torch_compile_wrapper
         m = model.clone()
         diffusion_model = m.get_model_object("diffusion_model")
         torch._dynamo.config.cache_size_limit = dynamo_cache_size_limit
+        torch._dynamo.config.force_parameter_static_shapes = force_parameter_static_shapes
         try:
             if compile_transformer_blocks_only:
                 compile_key_list = []

web/js/setgetnodes.js

@@ -103,11 +103,9 @@ app.registerExtension({
 					}
 					//On Connect
 					if (link_info && node.graph && slotType == 1 && isChangeConnect) {
-						const fromNode = node.graph._nodes.find((otherNode) => otherNode.id == link_info.origin_id);
+						const resolve = link_info.resolve(node.graph)
-						
+						const type = (resolve?.subgraphInput ?? resolve?.output)?.type
-						if (fromNode && fromNode.outputs && fromNode.outputs[link_info.origin_slot]) {
+						if (type) {
-							const type = fromNode.outputs[link_info.origin_slot].type;
-						
 							if (this.title === "Set"){
 								this.title = (!disablePrefix ? "Set_" : "") + type;
 							}
@@ -470,9 +468,8 @@ app.registerExtension({
 				};
 
 				this.goToSetter = function() {
-					const setter = this.findSetter(this.graph);	
+					this.canvas.centerOnNode(this.currentSetter);
-					this.canvas.centerOnNode(setter);
+					this.canvas.selectNode(this.currentSetter, false);
-					this.canvas.selectNode(setter, false);
 				};
 				
 				// This node is purely frontend and does not impact the resulting prompt so should not be serialized
@@ -496,7 +493,8 @@ app.registerExtension({
 			}
 			getExtraMenuOptions(_, options) {
 				let menuEntry = this.drawConnection ? "Hide connections" : "Show connections";
-				
+				this.currentSetter = this.findSetter(this.graph)
+				if (!this.currentSetter) return
 				options.unshift(
 					{
 						content: "Go to setter",
@@ -507,12 +505,9 @@ app.registerExtension({
 					{
 						content: menuEntry,
 						callback: () => {
-							this.currentSetter = this.findSetter(this.graph);
-							if (this.currentSetter.length == 0) return;
 							let linkType = (this.currentSetter.inputs[0].type);
 							this.drawConnection = !this.drawConnection;
 							this.slotColor = this.canvas.default_connection_color_byType[linkType]
-							menuEntry = this.drawConnection ? "Hide connections" : "Show connections";
 							this.canvas.setDirty(true, true);
 						},
 					},