Merge remote-tracking branch 'upstream/master'

# Conflicts:
#	comfy/samplers.py

.ci/nightly/update_windows/update.py

@@ -1,65 +0,0 @@
-import pygit2
-from datetime import datetime
-import sys
-
-def pull(repo, remote_name='origin', branch='master'):
-    for remote in repo.remotes:
-        if remote.name == remote_name:
-            remote.fetch()
-            remote_master_id = repo.lookup_reference('refs/remotes/origin/%s' % (branch)).target
-            merge_result, _ = repo.merge_analysis(remote_master_id)
-            # Up to date, do nothing
-            if merge_result & pygit2.GIT_MERGE_ANALYSIS_UP_TO_DATE:
-                return
-            # We can just fastforward
-            elif merge_result & pygit2.GIT_MERGE_ANALYSIS_FASTFORWARD:
-                repo.checkout_tree(repo.get(remote_master_id))
-                try:
-                    master_ref = repo.lookup_reference('refs/heads/%s' % (branch))
-                    master_ref.set_target(remote_master_id)
-                except KeyError:
-                    repo.create_branch(branch, repo.get(remote_master_id))
-                repo.head.set_target(remote_master_id)
-            elif merge_result & pygit2.GIT_MERGE_ANALYSIS_NORMAL:
-                repo.merge(remote_master_id)
-
-                if repo.index.conflicts is not None:
-                    for conflict in repo.index.conflicts:
-                        print('Conflicts found in:', conflict[0].path)
-                    raise AssertionError('Conflicts, ahhhhh!!')
-
-                user = repo.default_signature
-                tree = repo.index.write_tree()
-                commit = repo.create_commit('HEAD',
-                                            user,
-                                            user,
-                                            'Merge!',
-                                            tree,
-                                            [repo.head.target, remote_master_id])
-                # We need to do this or git CLI will think we are still merging.
-                repo.state_cleanup()
-            else:
-                raise AssertionError('Unknown merge analysis result')
-
-
-repo = pygit2.Repository(str(sys.argv[1]))
-ident = pygit2.Signature('comfyui', 'comfy@ui')
-try:
-    print("stashing current changes")
-    repo.stash(ident)
-except KeyError:
-    print("nothing to stash")
-backup_branch_name = 'backup_branch_{}'.format(datetime.today().strftime('%Y-%m-%d_%H_%M_%S'))
-print("creating backup branch: {}".format(backup_branch_name))
-repo.branches.local.create(backup_branch_name, repo.head.peel())
-
-print("checking out master branch")
-branch = repo.lookup_branch('master')
-ref = repo.lookup_reference(branch.name)
-repo.checkout(ref)
-
-print("pulling latest changes")
-pull(repo)
-
-print("Done!")
-

.ci/nightly/update_windows/update_comfyui.bat

@@ -1,2 +0,0 @@
-..\python_embeded\python.exe .\update.py ..\ComfyUI\
-pause

.ci/nightly/update_windows/update_comfyui_and_python_dependencies.bat

@@ -1,3 +1,3 @@
 ..\python_embeded\python.exe .\update.py ..\ComfyUI\
-..\python_embeded\python.exe -s -m pip install --upgrade --pre torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu118 -r ../ComfyUI/requirements.txt pygit2
+..\python_embeded\python.exe -s -m pip install --upgrade --pre torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/cu118 -r ../ComfyUI/requirements.txt pygit2
 pause

.ci/nightly/windows_base_files/README_VERY_IMPORTANT.txt

@@ -1,27 +0,0 @@
-HOW TO RUN:
-
-if you have a NVIDIA gpu:
-
-run_nvidia_gpu.bat
-
-
-
-To run it in slow CPU mode:
-
-run_cpu.bat
-
-
-
-IF YOU GET A RED ERROR IN THE UI MAKE SURE YOU HAVE A MODEL/CHECKPOINT IN: ComfyUI\models\checkpoints
-
-You can download the stable diffusion 1.5 one from: https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.ckpt
-
-
-
-RECOMMENDED WAY TO UPDATE:
-To update the ComfyUI code: update\update_comfyui.bat
-
-
-
-To update ComfyUI with the python dependencies:
-update\update_comfyui_and_python_dependencies.bat

.ci/nightly/windows_base_files/run_cpu.bat

@@ -1,2 +0,0 @@
-.\python_embeded\python.exe -s ComfyUI\main.py --cpu --windows-standalone-build
-pause

.github/workflows/windows_release_cu118_dependencies_2.yml

@@ -17,7 +17,7 @@ jobs:
 
         - shell: bash
           run: |
-            python -m pip wheel --no-cache-dir torch torchvision torchaudio xformers==0.0.19.dev516 --extra-index-url https://download.pytorch.org/whl/cu118 -r requirements.txt pygit2 -w ./temp_wheel_dir
+            python -m pip wheel --no-cache-dir torch torchvision torchaudio xformers --extra-index-url https://download.pytorch.org/whl/cu118 -r requirements.txt pygit2 -w ./temp_wheel_dir
             python -m pip install --no-cache-dir ./temp_wheel_dir/*
             echo installed basic
             ls -lah temp_wheel_dir

.github/workflows/windows_release_nightly_pytorch.yml

@@ -46,6 +46,8 @@ jobs:
             mkdir update
             cp -r ComfyUI/.ci/update_windows/* ./update/
             cp -r ComfyUI/.ci/windows_base_files/* ./
+            cp -r ComfyUI/.ci/nightly/update_windows/* ./update/
+            cp -r ComfyUI/.ci/nightly/windows_base_files/* ./
 
             cd ..
 

README.md

@@ -7,6 +7,8 @@ A powerful and modular stable diffusion GUI and backend.
 This ui will let you design and execute advanced stable diffusion pipelines using a graph/nodes/flowchart based interface. For some workflow examples and see what ComfyUI can do you can check out:
 ### [ComfyUI Examples](https://comfyanonymous.github.io/ComfyUI_examples/)
 
+### [Installing ComfyUI](#installing)
+
 ## Features
 - Nodes/graph/flowchart interface to experiment and create complex Stable Diffusion workflows without needing to code anything.
 - Fully supports SD1.x and SD2.x

comfy/cli_args.py

@@ -10,6 +10,7 @@ parser.add_argument("--output-directory", type=str, default=None, help="Set the
 parser.add_argument("--cuda-device", type=int, default=None, metavar="DEVICE_ID", help="Set the id of the cuda device this instance will use.")
 parser.add_argument("--dont-upcast-attention", action="store_true", help="Disable upcasting of attention. Can boost speed but increase the chances of black images.")
 parser.add_argument("--force-fp32", action="store_true", help="Force fp32 (If this makes your GPU work better please report it).")
+parser.add_argument("--directml", type=int, nargs="?", metavar="DIRECTML_DEVICE", const=-1, help="Use torch-directml.")
 
 attn_group = parser.add_mutually_exclusive_group()
 attn_group.add_argument("--use-split-cross-attention", action="store_true", help="Use the split cross attention optimization instead of the sub-quadratic one. Ignored when xformers is used.")

comfy/model_management.py

@@ -20,15 +20,30 @@ total_vram_available_mb = -1
 accelerate_enabled = False
 xpu_available = False
 
+directml_enabled = False
+if args.directml is not None:
+    import torch_directml
+    directml_enabled = True
+    device_index = args.directml
+    if device_index < 0:
+        directml_device = torch_directml.device()
+    else:
+        directml_device = torch_directml.device(device_index)
+    print("Using directml with device:", torch_directml.device_name(device_index))
+    # torch_directml.disable_tiled_resources(True)
+
 try:
     import torch
-    try:
-        import intel_extension_for_pytorch as ipex
-        if torch.xpu.is_available():
-            xpu_available = True
-            total_vram = torch.xpu.get_device_properties(torch.xpu.current_device()).total_memory / (1024 * 1024)
-    except:
-        total_vram = torch.cuda.mem_get_info(torch.cuda.current_device())[1] / (1024 * 1024)
+    if directml_enabled:
+        total_vram = 4097 #TODO
+    else:
+        try:
+            import intel_extension_for_pytorch as ipex
+            if torch.xpu.is_available():
+                xpu_available = True
+                total_vram = torch.xpu.get_device_properties(torch.xpu.current_device()).total_memory / (1024 * 1024)
+        except:
+            total_vram = torch.cuda.mem_get_info(torch.cuda.current_device())[1] / (1024 * 1024)
     total_ram = psutil.virtual_memory().total / (1024 * 1024)
     if not args.normalvram and not args.cpu:
         if total_vram <= 4096:
@@ -217,6 +232,10 @@ def unload_if_low_vram(model):
 
 def get_torch_device():
     global xpu_available
+    global directml_enabled
+    if directml_enabled:
+        global directml_device
+        return directml_device
     if vram_state == VRAMState.MPS:
         return torch.device("mps")
     if vram_state == VRAMState.CPU:
@@ -234,8 +253,14 @@ def get_autocast_device(dev):
 
 
 def xformers_enabled():
+    global xpu_available
+    global directml_enabled
     if vram_state == VRAMState.CPU:
         return False
+    if xpu_available:
+        return False
+    if directml_enabled:
+        return False
     return XFORMERS_IS_AVAILABLE
 
 
@@ -251,6 +276,7 @@ def pytorch_attention_enabled():
 
 def get_free_memory(dev=None, torch_free_too=False):
     global xpu_available
+    global directml_enabled
     if dev is None:
         dev = get_torch_device()
 
@@ -258,7 +284,10 @@ def get_free_memory(dev=None, torch_free_too=False):
         mem_free_total = psutil.virtual_memory().available
         mem_free_torch = mem_free_total
     else:
-        if xpu_available:
+        if directml_enabled:
+            mem_free_total = 1024 * 1024 * 1024 #TODO
+            mem_free_torch = mem_free_total
+        elif xpu_available:
             mem_free_total = torch.xpu.get_device_properties(dev).total_memory - torch.xpu.memory_allocated(dev)
             mem_free_torch = mem_free_total
         else:
@@ -293,9 +322,14 @@ def mps_mode():
 
 def should_use_fp16():
     global xpu_available
+    global directml_enabled
+
     if FORCE_FP32:
         return False
 
+    if directml_enabled:
+        return False
+
     if cpu_mode() or mps_mode() or xpu_available:
         return False #TODO ?
 

comfy/samplers.py

@@ -10,7 +10,6 @@ from .ldm.modules.diffusionmodules.util import make_ddim_timesteps
 #The main sampling function shared by all the samplers
 #Returns predicted noise
 def sampling_function(model_function, x, timestep, uncond, cond, cond_scale, cond_concat=None, model_options={}):
-
         def get_area_and_mult(cond, x_in, cond_concat_in, timestep_in):
             area = (x_in.shape[2], x_in.shape[3], 0, 0)
             strength = 1.0
@@ -24,8 +23,38 @@ def sampling_function(model_function, x, timestep, uncond, cond, cond_scale, con
                 adm_cond = cond[1]['adm_encoded']
 
             input_x = x_in[:,:,area[2]:area[0] + area[2],area[3]:area[1] + area[3]]
-            mult = torch.ones_like(input_x) * strength
+            if 'mask' in cond[1]:
+                # Scale the mask to the size of the input
+                # The mask should have been resized as we began the sampling process
+                mask_strength = 1.0
+                if "mask_strength" in cond[1]:
+                    mask_strength = cond[1]["mask_strength"]
+                mask = cond[1]['mask']
+                assert(mask.shape[1] == x_in.shape[2])
+                assert(mask.shape[2] == x_in.shape[3])
+                mask = mask[:,area[2]:area[0] + area[2],area[3]:area[1] + area[3]] * mask_strength
+                mask = mask.unsqueeze(1).repeat(input_x.shape[0] // mask.shape[0], input_x.shape[1], 1, 1)
+            else:
+                mask = torch.ones_like(input_x)
+            mult = mask * strength
 
+            if 'mask' not in cond[1]:
+                rr = 8
+                if area[2] != 0:
+                    for t in range(rr):
+                        mult[:,:,t:1+t,:] *= ((1.0/rr) * (t + 1))
+                if (area[0] + area[2]) < x_in.shape[2]:
+                    for t in range(rr):
+                        mult[:,:,area[0] - 1 - t:area[0] - t,:] *= ((1.0/rr) * (t + 1))
+                if area[3] != 0:
+                    for t in range(rr):
+                        mult[:,:,:,t:1+t] *= ((1.0/rr) * (t + 1))
+                if (area[1] + area[3]) < x_in.shape[3]:
+                    for t in range(rr):
+                        mult[:,:,:,area[1] - 1 - t:area[1] - t] *= ((1.0/rr) * (t + 1))
+
+            conditioning = {}
+            conditioning['c_crossattn'] = cond[0]
             rr = 8
             if area[2] != 0:
                 for t in range(rr):
@@ -198,12 +227,6 @@ def sampling_function(model_function, x, timestep, uncond, cond, cond_scale, con
                 if control is not None:
                     c['control'] = control.get_control(input_x, timestep_, c['c_crossattn'], len(cond_or_uncond))
 
-                # if attention is not None:
-                #     generated_attention = c['c_crossattn'][0]
-                #     mixed_attention = attention_weight * torch.cat(attention) + (1 - attention_weight) * generated_attention
-                #     c['c_crossattn'] = [mixed_attention]
-
-
                 if patches is not None:
                     if "patches" in transformer_options:
                         cur_patches = transformer_options["patches"].copy()
@@ -215,7 +238,6 @@ def sampling_function(model_function, x, timestep, uncond, cond, cond_scale, con
                     else:
                         transformer_options["patches"] = patches
 
-                # transformer_options['return_attention'] = True
                 c['transformer_options'] = transformer_options
 
                 if transformer_options.get("return_attention", False):
@@ -325,6 +347,71 @@ def blank_inpaint_image_like(latent_image):
     blank_image[:,3] *= 0.1380
     return blank_image
 
+def get_mask_aabb(masks):
+    if masks.numel() == 0:
+        return torch.zeros((0, 4), device=masks.device, dtype=torch.int)
+
+    b = masks.shape[0]
+
+    bounding_boxes = torch.zeros((b, 4), device=masks.device, dtype=torch.int)
+    is_empty = torch.zeros((b), device=masks.device, dtype=torch.bool)
+    for i in range(b):
+        mask = masks[i]
+        if mask.numel() == 0:
+            continue
+        if torch.max(mask != 0) == False:
+            is_empty[i] = True
+            continue
+        y, x = torch.where(mask)
+        bounding_boxes[i, 0] = torch.min(x)
+        bounding_boxes[i, 1] = torch.min(y)
+        bounding_boxes[i, 2] = torch.max(x)
+        bounding_boxes[i, 3] = torch.max(y)
+
+    return bounding_boxes, is_empty
+
+def resolve_cond_masks(conditions, h, w, device):
+    # We need to decide on an area outside the sampling loop in order to properly generate opposite areas of equal sizes.
+    # While we're doing this, we can also resolve the mask device and scaling for performance reasons
+    for i in range(len(conditions)):
+        c = conditions[i]
+        if 'mask' in c[1]:
+            mask = c[1]['mask']
+            mask = mask.to(device=device)
+            modified = c[1].copy()
+            if len(mask.shape) == 2:
+                mask = mask.unsqueeze(0)
+            if mask.shape[2] != h or mask.shape[3] != w:
+                mask = torch.nn.functional.interpolate(mask.unsqueeze(1), size=(h, w), mode='bilinear', align_corners=False).squeeze(1)
+
+            if modified.get("set_area_to_bounds", False):
+                bounds = torch.max(torch.abs(mask),dim=0).values.unsqueeze(0)
+                boxes, is_empty = get_mask_aabb(bounds)
+                if is_empty[0]:
+                    # Use the minimum possible size for efficiency reasons. (Since the mask is all-0, this becomes a noop anyway)
+                    modified['area'] = (8, 8, 0, 0)
+                else:
+                    box = boxes[0]
+                    H, W, Y, X = (box[3] - box[1] + 1, box[2] - box[0] + 1, box[1], box[0])
+                    # Make sure the height and width are divisible by 8
+                    if X % 8 != 0:
+                        newx = X // 8 * 8
+                        W = W + (X - newx)
+                        X = newx
+                    if Y % 8 != 0:
+                        newy = Y // 8 * 8
+                        H = H + (Y - newy)
+                        Y = newy
+                    if H % 8 != 0:
+                        H = H + (8 - (H % 8))
+                    if W % 8 != 0:
+                        W = W + (8 - (W % 8))
+                    area = (int(H), int(W), int(Y), int(X))
+                    modified['area'] = area
+
+            modified['mask'] = mask
+            conditions[i] = [c[0], modified]
+
 def create_cond_with_same_area_if_none(conds, c):
     if 'area' not in c[1]:
         return
@@ -509,6 +596,10 @@ class KSampler:
 
         positive = positive[:]
         negative = negative[:]
+
+        resolve_cond_masks(positive, noise.shape[2], noise.shape[3], self.device)
+        resolve_cond_masks(negative, noise.shape[2], noise.shape[3], self.device)
+
         #make sure each cond area has an opposite one with the same area
         for c in positive:
             create_cond_with_same_area_if_none(negative, c)

nodes.py

@@ -65,6 +65,36 @@ class ConditioningCombine:
     def combine(self, conditioning_1, conditioning_2):
         return (conditioning_1 + conditioning_2, )
 
+class ConditioningAverage :
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"conditioning_to": ("CONDITIONING", ), "conditioning_from": ("CONDITIONING", ),
+                              "conditioning_to_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01})
+                             }}
+    RETURN_TYPES = ("CONDITIONING",)
+    FUNCTION = "addWeighted"
+
+    CATEGORY = "conditioning"
+
+    def addWeighted(self, conditioning_to, conditioning_from, conditioning_to_strength):
+        out = []
+
+        if len(conditioning_from) > 1:
+            print("Warning: ConditioningAverage conditioning_from contains more than 1 cond, only the first one will actually be applied to conditioning_to.")
+
+        cond_from = conditioning_from[0][0]
+
+        for i in range(len(conditioning_to)):
+            t1 = conditioning_to[i][0]
+            t0 = cond_from[:,:t1.shape[1]]
+            if t0.shape[1] < t1.shape[1]:
+                t0 = torch.cat([t0] + [torch.zeros((1, (t1.shape[1] - t0.shape[1]), t1.shape[2]))], dim=1)
+
+            tw = torch.mul(t1, conditioning_to_strength) + torch.mul(t0, (1.0 - conditioning_to_strength))
+            n = [tw, conditioning_to[i][1].copy()]
+            out.append(n)
+        return (out, )
+
 class ConditioningSetArea:
     def __init__(self, event_dispatcher):
         self.event_dispatcher = event_dispatcher
@@ -88,11 +118,41 @@ class ConditioningSetArea:
             n = [t[0], t[1].copy()]
             n[1]['area'] = (height // 8, width // 8, y // 8, x // 8)
             n[1]['strength'] = strength
+            n[1]['set_area_to_bounds'] = False
             n[1]['min_sigma'] = min_sigma
             n[1]['max_sigma'] = max_sigma
             c.append(n)
         return (c, )
 
+class ConditioningSetMask:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"conditioning": ("CONDITIONING", ),
+                              "mask": ("MASK", ),
+                              "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                              "set_cond_area": (["default", "mask bounds"],),
+                             }}
+    RETURN_TYPES = ("CONDITIONING",)
+    FUNCTION = "append"
+
+    CATEGORY = "conditioning"
+
+    def append(self, conditioning, mask, set_cond_area, strength):
+        c = []
+        set_area_to_bounds = False
+        if set_cond_area != "default":
+            set_area_to_bounds = True
+        if len(mask.shape) < 3:
+            mask = mask.unsqueeze(0)
+        for t in conditioning:
+            n = [t[0], t[1].copy()]
+            _, h, w = mask.shape
+            n[1]['mask'] = mask
+            n[1]['set_area_to_bounds'] = set_area_to_bounds
+            n[1]['mask_strength'] = strength
+            c.append(n)
+        return (c, )
+
 class VAEDecode:
     def __init__(self, device="cpu", event_dispatcher=None):
         self.device = device
@@ -1217,8 +1277,10 @@ NODE_CLASS_MAPPINGS = {
     "ImageScale": ImageScale,
     "ImageInvert": ImageInvert,
     "ImagePadForOutpaint": ImagePadForOutpaint,
+    "ConditioningAverage ": ConditioningAverage ,
     "ConditioningCombine": ConditioningCombine,
     "ConditioningSetArea": ConditioningSetArea,
+    "ConditioningSetMask": ConditioningSetMask,
     "KSamplerAdvanced": KSamplerAdvanced,
     "SetLatentNoiseMask": SetLatentNoiseMask,
     "LatentComposite": LatentComposite,
@@ -1267,7 +1329,9 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "CLIPTextEncode": "CLIP Text Encode (Prompt)",
     "CLIPSetLastLayer": "CLIP Set Last Layer",
     "ConditioningCombine": "Conditioning (Combine)",
+    "ConditioningAverage ": "Conditioning (Average)",
     "ConditioningSetArea": "Conditioning (Set Area)",
+    "ConditioningSetMask": "Conditioning (Set Mask)",
     "ControlNetApply": "Apply ControlNet",
     # Latent
     "VAEEncodeForInpaint": "VAE Encode (for Inpainting)",

web/extensions/core/slotDefaults.js

@@ -6,6 +6,7 @@ app.registerExtension({
 	name: "Comfy.SlotDefaults",
 	suggestionsNumber: null,
 	init() {
+		LiteGraph.search_filter_enabled = true;
 		LiteGraph.middle_click_slot_add_default_node = true;
 		this.suggestionsNumber = app.ui.settings.addSetting({
 			id: "Comfy.NodeSuggestions.number",
@@ -43,6 +44,14 @@ app.registerExtension({
 			}
 			if (this.slot_types_default_out[type].includes(nodeId)) continue;
 			this.slot_types_default_out[type].push(nodeId);
+
+			// Input types have to be stored as lower case
+			// Store each node that can handle this input type
+			const lowerType = type.toLocaleLowerCase();
+			if (!(lowerType in LiteGraph.registered_slot_in_types)) {
+				LiteGraph.registered_slot_in_types[lowerType] = { nodes: [] };
+			}
+			LiteGraph.registered_slot_in_types[lowerType].nodes.push(nodeType.comfyClass);
 		} 
 
 		var outputs = nodeData["output"];
@@ -53,6 +62,16 @@ app.registerExtension({
 			}
 
 			this.slot_types_default_in[type].push(nodeId);
+
+			// Store each node that can handle this output type
+			if (!(type in LiteGraph.registered_slot_out_types)) {
+				LiteGraph.registered_slot_out_types[type] = { nodes: [] };
+			}
+			LiteGraph.registered_slot_out_types[type].nodes.push(nodeType.comfyClass);
+
+			if(!LiteGraph.slot_types_out.includes(type)) {
+				LiteGraph.slot_types_out.push(type);
+			}
 		}
 		var maxNum = this.suggestionsNumber.value;
 		this.setDefaults(maxNum);

web/lib/litegraph.core.js

@@ -3628,6 +3628,18 @@
         return size;
     };
 
+    LGraphNode.prototype.inResizeCorner = function(canvasX, canvasY) {
+        var rows = this.outputs ? this.outputs.length : 1;
+        var outputs_offset = (this.constructor.slot_start_y || 0) + rows * LiteGraph.NODE_SLOT_HEIGHT;
+        return isInsideRectangle(canvasX,
+            canvasY,
+            this.pos[0] + this.size[0] - 15,
+            this.pos[1] + Math.max(this.size[1] - 15, outputs_offset),
+            20,
+            20
+        );
+    }
+
     /**
      * returns all the info available about a property of this node.
      *
@@ -5877,14 +5889,7 @@ LGraphNode.prototype.executeAction = function(action)
                 if ( !this.connecting_node && !node.flags.collapsed && !this.live_mode ) {
                     //Search for corner for resize
                     if ( !skip_action &&
-                        node.resizable !== false &&
-                        isInsideRectangle( e.canvasX,
-                            e.canvasY,
-                            node.pos[0] + node.size[0] - 5,
-                            node.pos[1] + node.size[1] - 5,
-                            10,
-                            10
-                        )
+                        node.resizable !== false && node.inResizeCorner(e.canvasX, e.canvasY)
                     ) {
 						this.graph.beforeChange();
                         this.resizing_node = node;
@@ -6424,16 +6429,7 @@ LGraphNode.prototype.executeAction = function(action)
 
                 //Search for corner
                 if (this.canvas) {
-                    if (
-                        isInsideRectangle(
-                            e.canvasX,
-                            e.canvasY,
-                            node.pos[0] + node.size[0] - 5,
-                            node.pos[1] + node.size[1] - 5,
-                            5,
-                            5
-                        )
-                    ) {
+                    if (node.inResizeCorner(e.canvasX, e.canvasY)) {
                         this.canvas.style.cursor = "se-resize";
                     } else {
                         this.canvas.style.cursor = "crosshair";

web/style.css

@@ -120,7 +120,7 @@ body {
 .comfy-menu > button,
 .comfy-menu-btns button,
 .comfy-menu .comfy-list button,
-.comfy-modal button{
+.comfy-modal button {
 	color: var(--input-text);
 	background-color: var(--comfy-input-bg);
 	border-radius: 8px;
@@ -129,6 +129,15 @@ body {
 	margin-top: 2px;
 }
 
+.comfy-menu > button:hover,
+.comfy-menu-btns button:hover,
+.comfy-menu .comfy-list button:hover,
+.comfy-modal button:hover,
+.comfy-settings-btn:hover {
+	filter: brightness(1.2);
+	cursor: pointer;
+}
+
 .comfy-menu span.drag-handle {
 	width: 10px;
 	height: 20px;
@@ -284,4 +293,7 @@ button.comfy-queue-btn {
 	top: 0;
 	right: 2px;
  }
- 
+ 
+ .litecontextmenu {
+	z-index: 9999 !important;
+}