Nodes using UNIQUE_ID as input are NOT_IDEMPOTENT (#4793)

As suggested by @ltdrdata, we can automatically consider nodes that take
the UNIQUE_ID hidden input to be NOT_IDEMPOTENT.

.github/workflows/stable-release.yml

@@ -12,7 +12,7 @@ on:
         description: 'CUDA version'
         required: true
         type: string
-        default: "121"
+        default: "124"
       python_minor:
         description: 'Python minor version'
         required: true

README.md

@@ -94,6 +94,8 @@ Workflow examples can be found on the [Examples page](https://comfyanonymous.git
 | Alt + `+`                          | Canvas Zoom in                                                                                                     |
 | Alt + `-`                          | Canvas Zoom out                                                                                                    |
 | Ctrl + Shift + LMB + Vertical drag | Canvas Zoom in/out                                                                                                 |
+| P                                  | Pin/Unpin selected nodes                                                                                           |
+| Ctrl + G                           | Group selected nodes                                                                                               |
 | Q                                  | Toggle visibility of the queue                                                                                     |
 | H                                  | Toggle visibility of history                                                                                       |
 | R                                  | Refresh graph                                                                                                      |

comfy/cli_args.py

@@ -92,6 +92,8 @@ class LatentPreviewMethod(enum.Enum):
 
 parser.add_argument("--preview-method", type=LatentPreviewMethod, default=LatentPreviewMethod.NoPreviews, help="Default preview method for sampler nodes.", action=EnumAction)
 
+parser.add_argument("--preview-size", type=int, default=512, help="Sets the maximum preview size for sampler nodes.")
+
 cache_group = parser.add_mutually_exclusive_group()
 cache_group.add_argument("--cache-classic", action="store_true", help="Use the old style (aggressive) caching.")
 cache_group.add_argument("--cache-lru", type=int, default=0, help="Use LRU caching with a maximum of N node results cached. May use more RAM/VRAM.")

comfy/controlnet.py

@@ -430,9 +430,9 @@ def load_controlnet_hunyuandit(controlnet_data):
     control = ControlNet(control_model, compression_ratio=1, latent_format=latent_format, load_device=load_device, manual_cast_dtype=manual_cast_dtype, extra_conds=extra_conds, strength_type=StrengthType.CONSTANT)
     return control
 
-def load_controlnet_flux_xlabs(sd):
+def load_controlnet_flux_xlabs_mistoline(sd, mistoline=False):
     model_config, operations, load_device, unet_dtype, manual_cast_dtype, offload_device = controlnet_config(sd)
-    control_model = comfy.ldm.flux.controlnet.ControlNetFlux(operations=operations, device=offload_device, dtype=unet_dtype, **model_config.unet_config)
+    control_model = comfy.ldm.flux.controlnet.ControlNetFlux(mistoline=mistoline, operations=operations, device=offload_device, dtype=unet_dtype, **model_config.unet_config)
     control_model = controlnet_load_state_dict(control_model, sd)
     extra_conds = ['y', 'guidance']
     control = ControlNet(control_model, load_device=load_device, manual_cast_dtype=manual_cast_dtype, extra_conds=extra_conds)
@@ -457,6 +457,10 @@ def load_controlnet_flux_instantx(sd):
     control = ControlNet(control_model, compression_ratio=1, latent_format=latent_format, load_device=load_device, manual_cast_dtype=manual_cast_dtype, extra_conds=extra_conds)
     return control
 
+def convert_mistoline(sd):
+    return comfy.utils.state_dict_prefix_replace(sd, {"single_controlnet_blocks.": "controlnet_single_blocks."})
+
+
 def load_controlnet(ckpt_path, model=None):
     controlnet_data = comfy.utils.load_torch_file(ckpt_path, safe_load=True)
     if 'after_proj_list.18.bias' in controlnet_data.keys(): #Hunyuan DiT
@@ -518,13 +522,15 @@ def load_controlnet(ckpt_path, model=None):
         if len(leftover_keys) > 0:
             logging.warning("leftover keys: {}".format(leftover_keys))
         controlnet_data = new_sd
-    elif "controlnet_blocks.0.weight" in controlnet_data: #SD3 diffusers format
+    elif "controlnet_blocks.0.weight" in controlnet_data:
         if "double_blocks.0.img_attn.norm.key_norm.scale" in controlnet_data:
-            return load_controlnet_flux_xlabs(controlnet_data)
+            return load_controlnet_flux_xlabs_mistoline(controlnet_data)
         elif "pos_embed_input.proj.weight" in controlnet_data:
-            return load_controlnet_mmdit(controlnet_data)
+            return load_controlnet_mmdit(controlnet_data) #SD3 diffusers controlnet
         elif "controlnet_x_embedder.weight" in controlnet_data:
             return load_controlnet_flux_instantx(controlnet_data)
+    elif "controlnet_blocks.0.linear.weight" in controlnet_data: #mistoline flux
+        return load_controlnet_flux_xlabs_mistoline(convert_mistoline(controlnet_data), mistoline=True)
 
     pth_key = 'control_model.zero_convs.0.0.weight'
     pth = False

comfy/float.py

@@ -41,9 +41,8 @@ def manual_stochastic_round_to_float8(x, dtype, generator=None):
         (2.0 ** (exponent - EXPONENT_BIAS)) * (1.0 + abs_x),
         (2.0 ** (-EXPONENT_BIAS + 1)) * abs_x
     )
-    del abs_x
 
-    return sign.to(dtype=dtype)
+    return sign
 
 
 
@@ -57,6 +56,11 @@ def stochastic_rounding(value, dtype, seed=0):
     if dtype == torch.float8_e4m3fn or dtype == torch.float8_e5m2:
         generator = torch.Generator(device=value.device)
         generator.manual_seed(seed)
-        return manual_stochastic_round_to_float8(value, dtype, generator=generator)
+        output = torch.empty_like(value, dtype=dtype)
+        num_slices = max(1, (value.numel() / (4096 * 4096)))
+        slice_size = max(1, round(value.shape[0] / num_slices))
+        for i in range(0, value.shape[0], slice_size):
+            output[i:i+slice_size].copy_(manual_stochastic_round_to_float8(value[i:i+slice_size], dtype, generator=generator))
+        return output
 
     return value.to(dtype=dtype)

comfy/ldm/flux/controlnet.py

@@ -1,4 +1,5 @@
 #Original code can be found on: https://github.com/XLabs-AI/x-flux/blob/main/src/flux/controlnet.py
+#modified to support different types of flux controlnets
 
 import torch
 import math
@@ -12,22 +13,65 @@ from .layers import (DoubleStreamBlock, EmbedND, LastLayer,
 from .model import Flux
 import comfy.ldm.common_dit
 
+class MistolineCondDownsamplBlock(nn.Module):
+    def __init__(self, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.encoder = nn.Sequential(
+            operations.Conv2d(3, 16, 3, padding=1, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Conv2d(16, 16, 1, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Conv2d(16, 16, 3, padding=1, stride=2, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Conv2d(16, 16, 1, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Conv2d(16, 16, 3, padding=1, dtype=dtype, device=device)
+        )
+
+    def forward(self, x):
+        return self.encoder(x)
+
+class MistolineControlnetBlock(nn.Module):
+    def __init__(self, hidden_size, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.linear = operations.Linear(hidden_size, hidden_size, dtype=dtype, device=device)
+        self.act = nn.SiLU()
+
+    def forward(self, x):
+        return self.act(self.linear(x))
+
 
 class ControlNetFlux(Flux):
-    def __init__(self, latent_input=False, num_union_modes=0, image_model=None, dtype=None, device=None, operations=None, **kwargs):
+    def __init__(self, latent_input=False, num_union_modes=0, mistoline=False, image_model=None, dtype=None, device=None, operations=None, **kwargs):
         super().__init__(final_layer=False, dtype=dtype, device=device, operations=operations, **kwargs)
 
         self.main_model_double = 19
         self.main_model_single = 38
+
+        self.mistoline = mistoline
         # add ControlNet blocks
+        if self.mistoline:
+            control_block = lambda : MistolineControlnetBlock(self.hidden_size, dtype=dtype, device=device, operations=operations)
+        else:
+            control_block = lambda : operations.Linear(self.hidden_size, self.hidden_size, dtype=dtype, device=device)
+
         self.controlnet_blocks = nn.ModuleList([])
         for _ in range(self.params.depth):
-            controlnet_block = operations.Linear(self.hidden_size, self.hidden_size, dtype=dtype, device=device)
-            self.controlnet_blocks.append(controlnet_block)
+            self.controlnet_blocks.append(control_block())
 
         self.controlnet_single_blocks = nn.ModuleList([])
         for _ in range(self.params.depth_single_blocks):
-            self.controlnet_single_blocks.append(operations.Linear(self.hidden_size, self.hidden_size, dtype=dtype, device=device))
+            self.controlnet_single_blocks.append(control_block())
 
         self.num_union_modes = num_union_modes
         self.controlnet_mode_embedder = None
@@ -38,6 +82,9 @@ class ControlNetFlux(Flux):
         self.latent_input = latent_input
         self.pos_embed_input = operations.Linear(self.in_channels, self.hidden_size, bias=True, dtype=dtype, device=device)
         if not self.latent_input:
+            if self.mistoline:
+                self.input_cond_block = MistolineCondDownsamplBlock(dtype=dtype, device=device, operations=operations)
+            else:
                 self.input_hint_block = nn.Sequential(
                     operations.Conv2d(3, 16, 3, padding=1, dtype=dtype, device=device),
                     nn.SiLU(),
@@ -73,9 +120,6 @@ class ControlNetFlux(Flux):
 
         # running on sequences img
         img = self.img_in(img)
-        if not self.latent_input:
-            controlnet_cond = self.input_hint_block(controlnet_cond)
-            controlnet_cond = rearrange(controlnet_cond, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=2, pw=2)
 
         controlnet_cond = self.pos_embed_input(controlnet_cond)
         img = img + controlnet_cond
@@ -131,9 +175,14 @@ class ControlNetFlux(Flux):
         patch_size = 2
         if self.latent_input:
             hint = comfy.ldm.common_dit.pad_to_patch_size(hint, (patch_size, patch_size))
-            hint = rearrange(hint, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size)
+        elif self.mistoline:
+            hint = hint * 2.0 - 1.0
+            hint = self.input_cond_block(hint)
         else:
             hint = hint * 2.0 - 1.0
+            hint = self.input_hint_block(hint)
+
+        hint = rearrange(hint, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=patch_size, pw=patch_size)
 
         bs, c, h, w = x.shape
         x = comfy.ldm.common_dit.pad_to_patch_size(x, (patch_size, patch_size))

comfy/ldm/modules/diffusionmodules/openaimodel.py

@@ -842,6 +842,11 @@ class UNetModel(nn.Module):
         t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype)
         emb = self.time_embed(t_emb)
 
+        if "emb_patch" in transformer_patches:
+            patch = transformer_patches["emb_patch"]
+            for p in patch:
+                emb = p(emb, self.model_channels, transformer_options)
+
         if self.num_classes is not None:
             assert y.shape[0] == x.shape[0]
             emb = emb + self.label_emb(y)

comfy/lora.py

@@ -324,6 +324,7 @@ def model_lora_keys_unet(model, key_map={}):
                 to = diffusers_keys[k]
                 key_map["transformer.{}".format(k[:-len(".weight")])] = to #simpletrainer and probably regular diffusers flux lora format
                 key_map["lycoris_{}".format(k[:-len(".weight")].replace(".", "_"))] = to #simpletrainer lycoris
+                key_map["lora_transformer_{}".format(k[:-len(".weight")].replace(".", "_"))] = to #onetrainer
 
     return key_map
 
@@ -527,20 +528,40 @@ def calculate_weight(patches, weight, key, intermediate_dtype=torch.float32):
             except Exception as e:
                 logging.error("ERROR {} {} {}".format(patch_type, key, e))
         elif patch_type == "glora":
-            if v[4] is not None:
-                alpha = v[4] / v[0].shape[0]
-            else:
-                alpha = 1.0
-
             dora_scale = v[5]
 
+            old_glora = False
+            if v[3].shape[1] == v[2].shape[0] == v[0].shape[0] == v[1].shape[1]:
+                rank = v[0].shape[0]
+                old_glora = True
+
+            if v[3].shape[0] == v[2].shape[1] == v[0].shape[1] == v[1].shape[0]:
+                if old_glora and v[1].shape[0] == weight.shape[0] and weight.shape[0] == weight.shape[1]:
+                    pass
+                else:
+                    old_glora = False
+                    rank = v[1].shape[0]
+
             a1 = comfy.model_management.cast_to_device(v[0].flatten(start_dim=1), weight.device, intermediate_dtype)
             a2 = comfy.model_management.cast_to_device(v[1].flatten(start_dim=1), weight.device, intermediate_dtype)
             b1 = comfy.model_management.cast_to_device(v[2].flatten(start_dim=1), weight.device, intermediate_dtype)
             b2 = comfy.model_management.cast_to_device(v[3].flatten(start_dim=1), weight.device, intermediate_dtype)
 
+            if v[4] is not None:
+                alpha = v[4] / rank
+            else:
+                alpha = 1.0
+
             try:
-                lora_diff = (torch.mm(b2, b1) + torch.mm(torch.mm(weight.flatten(start_dim=1).to(dtype=intermediate_dtype), a2), a1)).reshape(weight.shape)
+                if old_glora:
+                    lora_diff = (torch.mm(b2, b1) + torch.mm(torch.mm(weight.flatten(start_dim=1).to(dtype=intermediate_dtype), a2), a1)).reshape(weight.shape) #old lycoris glora
+                else:
+                    if weight.dim() > 2:
+                        lora_diff = torch.einsum("o i ..., i j -> o j ...", torch.einsum("o i ..., i j -> o j ...", weight.to(dtype=intermediate_dtype), a1), a2).reshape(weight.shape)
+                    else:
+                        lora_diff = torch.mm(torch.mm(weight.to(dtype=intermediate_dtype), a1), a2).reshape(weight.shape)
+                    lora_diff += torch.mm(b1, b2).reshape(weight.shape)
+
                 if dora_scale is not None:
                     weight = function(weight_decompose(dora_scale, weight, lora_diff, alpha, strength, intermediate_dtype))
                 else:

comfy/model_management.py

@@ -426,7 +426,7 @@ def free_memory(memory_required, device, keep_loaded=[]):
         shift_model = current_loaded_models[i]
         if shift_model.device == device:
             if shift_model not in keep_loaded:
-                can_unload.append((sys.getrefcount(shift_model.model), shift_model.model_memory(), i))
+                can_unload.append((-shift_model.model_offloaded_memory(), sys.getrefcount(shift_model.model), shift_model.model_memory(), i))
                 shift_model.currently_used = False
 
     for x in sorted(can_unload):

comfy_execution/caching.py

@@ -98,7 +98,7 @@ class CacheKeySetInputSignature(CacheKeySet):
         class_type = node["class_type"]
         class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
         signature = [class_type, self.is_changed_cache.get(node_id)]
-        if self.include_node_id_in_input() or (hasattr(class_def, "NOT_IDEMPOTENT") and class_def.NOT_IDEMPOTENT):
+        if self.include_node_id_in_input() or (hasattr(class_def, "NOT_IDEMPOTENT") and class_def.NOT_IDEMPOTENT) or "UNIQUE_ID" in class_def.INPUT_TYPES().get("hidden", {}).values():
             signature.append(node_id)
         inputs = node["inputs"]
         for key in sorted(inputs.keys()):

comfy_extras/nodes_lora_extract.py

@@ -0,0 +1,91 @@
+import torch
+import comfy.model_management
+import comfy.utils
+import folder_paths
+import os
+import logging
+
+CLAMP_QUANTILE = 0.99
+
+def extract_lora(diff, rank):
+    conv2d = (len(diff.shape) == 4)
+    kernel_size = None if not conv2d else diff.size()[2:4]
+    conv2d_3x3 = conv2d and kernel_size != (1, 1)
+    out_dim, in_dim = diff.size()[0:2]
+    rank = min(rank, in_dim, out_dim)
+
+    if conv2d:
+        if conv2d_3x3:
+            diff = diff.flatten(start_dim=1)
+        else:
+            diff = diff.squeeze()
+
+
+    U, S, Vh = torch.linalg.svd(diff.float())
+    U = U[:, :rank]
+    S = S[:rank]
+    U = U @ torch.diag(S)
+    Vh = Vh[:rank, :]
+
+    dist = torch.cat([U.flatten(), Vh.flatten()])
+    hi_val = torch.quantile(dist, CLAMP_QUANTILE)
+    low_val = -hi_val
+
+    U = U.clamp(low_val, hi_val)
+    Vh = Vh.clamp(low_val, hi_val)
+    if conv2d:
+        U = U.reshape(out_dim, rank, 1, 1)
+        Vh = Vh.reshape(rank, in_dim, kernel_size[0], kernel_size[1])
+    return (U, Vh)
+
+class LoraSave:
+    def __init__(self):
+        self.output_dir = folder_paths.get_output_directory()
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"filename_prefix": ("STRING", {"default": "loras/ComfyUI_extracted_lora"}),
+                              "rank": ("INT", {"default": 8, "min": 1, "max": 1024, "step": 1}),
+                            },
+                "optional": {"model_diff": ("MODEL",),},
+    }
+    RETURN_TYPES = ()
+    FUNCTION = "save"
+    OUTPUT_NODE = True
+
+    CATEGORY = "_for_testing"
+
+    def save(self, filename_prefix, rank, model_diff=None):
+        if model_diff is None:
+            return {}
+
+        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir)
+
+        output_sd = {}
+        prefix_key = "diffusion_model."
+        stored = set()
+
+        comfy.model_management.load_models_gpu([model_diff], force_patch_weights=True)
+        sd = model_diff.model_state_dict(filter_prefix=prefix_key)
+
+        for k in sd:
+            if k.endswith(".weight"):
+                weight_diff = sd[k]
+                if weight_diff.ndim < 2:
+                    continue
+                try:
+                    out = extract_lora(weight_diff, rank)
+                    output_sd["{}.lora_up.weight".format(k[:-7])] = out[0].contiguous().half().cpu()
+                    output_sd["{}.lora_down.weight".format(k[:-7])] = out[1].contiguous().half().cpu()
+                except:
+                    logging.warning("Could not generate lora weights for key {}, is the weight difference a zero?".format(k))
+
+        output_checkpoint = f"{filename}_{counter:05}_.safetensors"
+        output_checkpoint = os.path.join(full_output_folder, output_checkpoint)
+
+        comfy.utils.save_torch_file(output_sd, output_checkpoint, metadata=None)
+        return {}
+
+NODE_CLASS_MAPPINGS = {
+    "LoraSave": LoraSave
+}

latent_preview.py

@@ -9,7 +9,7 @@ import folder_paths
 import comfy.utils
 import logging
 
-MAX_PREVIEW_RESOLUTION = 512
+MAX_PREVIEW_RESOLUTION = args.preview_size
 
 def preview_to_image(latent_image):
         latents_ubyte = (((latent_image + 1.0) / 2.0).clamp(0, 1)  # change scale from -1..1 to 0..1

main.py

@@ -247,6 +247,7 @@ if __name__ == "__main__":
     folder_paths.add_model_folder_path("clip", os.path.join(folder_paths.get_output_directory(), "clip"))
     folder_paths.add_model_folder_path("vae", os.path.join(folder_paths.get_output_directory(), "vae"))
     folder_paths.add_model_folder_path("diffusion_models", os.path.join(folder_paths.get_output_directory(), "diffusion_models"))
+    folder_paths.add_model_folder_path("loras", os.path.join(folder_paths.get_output_directory(), "loras"))
 
     if args.input_directory:
         input_dir = os.path.abspath(args.input_directory)

nodes.py

@@ -2101,6 +2101,7 @@ def init_builtin_extra_nodes():
         "nodes_controlnet.py",
         "nodes_hunyuan.py",
         "nodes_flux.py",
+        "nodes_lora_extract.py",
     ]
 
     import_failed = []

web/assets/index-CrROdkG4.js

@@ -1,6 +1,6 @@
 var __defProp = Object.defineProperty;
 var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { C as ComfyDialog, $ as $el, a as ComfyApp, b as app, L as LGraphCanvas, c as LiteGraph, d as LGraphNode, e as applyTextReplacements, f as ComfyWidgets, g as addValueControlWidgets, D as DraggableList, h as api, u as useToastStore, i as LGraphGroup } from "./index-CI3N807S.js";
+import { C as ComfyDialog, $ as $el, a as ComfyApp, b as app, L as LGraphCanvas, c as LiteGraph, d as LGraphNode, e as applyTextReplacements, f as ComfyWidgets, g as addValueControlWidgets, D as DraggableList, h as api, i as LGraphGroup, u as useToastStore } from "./index-Dfv2aLsq.js";
 class ClipspaceDialog extends ComfyDialog {
   static {
     __name(this, "ClipspaceDialog");
@@ -3650,7 +3650,7 @@ app.registerExtension({
           content: "Add Group For Selected Nodes",
           disabled: !Object.keys(app.canvas.selected_nodes || {}).length,
           callback: /* @__PURE__ */ __name(() => {
-            var group2 = new LiteGraph.LGraphGroup();
+            const group2 = new LGraphGroup();
             addNodesToGroup(group2, this.selected_nodes);
             app.canvas.graph.add(group2);
             this.graph.change();
@@ -5088,7 +5088,7 @@ app.registerExtension({
             data = JSON.parse(data);
             const nodeIds = Object.keys(app.canvas.selected_nodes);
             for (let i = 0; i < nodeIds.length; i++) {
-              const node = app.graph.getNodeById(Number.parseInt(nodeIds[i]));
+              const node = app.graph.getNodeById(nodeIds[i]);
               const nodeData = node?.constructor.nodeData;
               let groupData = GroupNodeHandler.getGroupData(node);
               if (groupData) {
@@ -5955,7 +5955,7 @@ app.registerExtension({
   },
   onNodeOutputsUpdated(nodeOutputs) {
     for (const [nodeId, output] of Object.entries(nodeOutputs)) {
-      const node = app.graph.getNodeById(Number.parseInt(nodeId));
+      const node = app.graph.getNodeById(nodeId);
       if ("audio" in output) {
         const audioUIWidget = node.widgets.find(
           (w) => w.name === "audioUI"
@@ -6026,4 +6026,4 @@ app.registerExtension({
     };
   }
 });
-//# sourceMappingURL=index-BD-Ia1C4.js.map
+//# sourceMappingURL=index-CrROdkG4.js.map

web/assets/index-W4jP-SrU.css

@@ -1475,21 +1475,21 @@
   width: 5rem !important;
 }
 
-.info-chip[data-v-25bd5f50] {
+.info-chip[data-v-ffbfdf57] {
   background: transparent;
 }
-.setting-item[data-v-25bd5f50] {
+.setting-item[data-v-ffbfdf57] {
   display: flex;
   justify-content: space-between;
   align-items: center;
   margin-bottom: 1rem;
 }
-.setting-label[data-v-25bd5f50] {
+.setting-label[data-v-ffbfdf57] {
   display: flex;
   align-items: center;
   flex: 1;
 }
-.setting-input[data-v-25bd5f50] {
+.setting-input[data-v-ffbfdf57] {
   flex: 1;
   display: flex;
   justify-content: flex-end;
@@ -1497,19 +1497,19 @@
 }
 
 /* Ensure PrimeVue components take full width of their container */
-.setting-input[data-v-25bd5f50] .p-inputtext,
-.setting-input[data-v-25bd5f50] .input-slider,
-.setting-input[data-v-25bd5f50] .p-select,
-.setting-input[data-v-25bd5f50] .p-togglebutton {
+.setting-input[data-v-ffbfdf57] .p-inputtext,
+.setting-input[data-v-ffbfdf57] .input-slider,
+.setting-input[data-v-ffbfdf57] .p-select,
+.setting-input[data-v-ffbfdf57] .p-togglebutton {
   width: 100%;
   max-width: 200px;
 }
-.setting-input[data-v-25bd5f50] .p-inputtext {
+.setting-input[data-v-ffbfdf57] .p-inputtext {
   max-width: unset;
 }
 
 /* Special case for ToggleSwitch to align it to the right */
-.setting-input[data-v-25bd5f50] .p-toggleswitch {
+.setting-input[data-v-ffbfdf57] .p-toggleswitch {
   margin-left: auto;
 }
 
@@ -1655,21 +1655,21 @@
   margin-left: 0.5rem;
 }
 
-.comfy-error-report[data-v-12539d86] {
+.comfy-error-report[data-v-a103fd62] {
   display: flex;
   flex-direction: column;
   gap: 1rem;
 }
-.action-container[data-v-12539d86] {
+.action-container[data-v-a103fd62] {
   display: flex;
   gap: 1rem;
   justify-content: flex-end;
 }
-.wrapper-pre[data-v-12539d86] {
+.wrapper-pre[data-v-a103fd62] {
   white-space: pre-wrap;
   word-wrap: break-word;
 }
-.no-results-placeholder[data-v-12539d86] {
+.no-results-placeholder[data-v-a103fd62] {
   padding-top: 0;
 }
 .lds-ring {
@@ -3158,7 +3158,7 @@ body {
   overflow: hidden;
   grid-template-columns: auto 1fr auto;
   grid-template-rows: auto 1fr auto;
-  background-color: var(--bg-color);
+  background: var(--bg-color) var(--bg-img);
   color: var(--fg-color);
   min-height: -webkit-fill-available;
   max-height: -webkit-fill-available;
@@ -3833,17 +3833,19 @@ audio.comfy-audio.empty-audio-widget {
   box-sizing: border-box;
 }
 
-.node-title-editor[data-v-77799b26] {
+.group-title-editor.node-title-editor[data-v-f0cbabc5] {
   z-index: 9999;
   padding: 0.25rem;
 }
-[data-v-77799b26] .editable-text {
+[data-v-f0cbabc5] .editable-text {
   width: 100%;
   height: 100%;
 }
-[data-v-77799b26] .editable-text input {
+[data-v-f0cbabc5] .editable-text input {
   width: 100%;
   height: 100%;
+  /* Override the default font size */
+  font-size: inherit;
 }
 
 .side-bar-button-icon {
@@ -4086,26 +4088,26 @@ audio.comfy-audio.empty-audio-widget {
   color: var(--error-text);
 }
 
-.comfy-vue-node-search-container[data-v-077af1a9] {
+.comfy-vue-node-search-container[data-v-d28bffc4] {
   display: flex;
   width: 100%;
   min-width: 24rem;
   align-items: center;
   justify-content: center;
 }
-.comfy-vue-node-search-container[data-v-077af1a9] * {
+.comfy-vue-node-search-container[data-v-d28bffc4] * {
   pointer-events: auto;
 }
-.comfy-vue-node-preview-container[data-v-077af1a9] {
+.comfy-vue-node-preview-container[data-v-d28bffc4] {
   position: absolute;
   left: -350px;
   top: 50px;
 }
-.comfy-vue-node-search-box[data-v-077af1a9] {
+.comfy-vue-node-search-box[data-v-d28bffc4] {
   z-index: 10;
   flex-grow: 1;
 }
-.option-container[data-v-077af1a9] {
+.option-container[data-v-d28bffc4] {
   display: flex;
   width: 100%;
   cursor: pointer;
@@ -4117,12 +4119,12 @@ audio.comfy-audio.empty-audio-widget {
   padding-top: 0px;
   padding-bottom: 0px;
 }
-.option-display-name[data-v-077af1a9] {
+.option-display-name[data-v-d28bffc4] {
   display: flex;
   flex-direction: column;
   font-weight: 600;
 }
-.option-category[data-v-077af1a9] {
+.option-category[data-v-d28bffc4] {
   overflow: hidden;
   text-overflow: ellipsis;
   font-size: 0.875rem;
@@ -4133,7 +4135,7 @@ audio.comfy-audio.empty-audio-widget {
   /* Keeps the text on a single line by default */
   white-space: nowrap;
 }
-[data-v-077af1a9] .highlight {
+[data-v-d28bffc4] .highlight {
   background-color: var(--p-primary-color);
   color: var(--p-primary-contrast-color);
   font-weight: bold;
@@ -4141,10 +4143,10 @@ audio.comfy-audio.empty-audio-widget {
   padding: 0rem 0.125rem;
   margin: -0.125rem 0.125rem;
 }
-._filter-button[data-v-077af1a9] {
+._filter-button[data-v-d28bffc4] {
   z-index: 10;
 }
-._dialog[data-v-077af1a9] {
+._dialog[data-v-d28bffc4] {
   min-width: 24rem;
 }
 
@@ -4353,28 +4355,60 @@ img.galleria-image {
   gap: 0.5rem;
 }
 
-.node-tree-leaf[data-v-adf5f221] {
+.tree-node[data-v-d4b7b060] {
   width: 100%;
   display: flex;
   align-items: center;
   justify-content: space-between;
 }
-.node-content[data-v-adf5f221] {
+.leaf-count-badge[data-v-d4b7b060] {
+  margin-left: 0.5rem;
+}
+.node-content[data-v-d4b7b060] {
   display: flex;
   align-items: center;
   flex-grow: 1;
 }
-.node-label[data-v-adf5f221] {
+.leaf-label[data-v-d4b7b060] {
   margin-left: 0.5rem;
 }
-.bookmark-button[data-v-adf5f221] {
-  width: unset;
-  padding: 0.25rem;
+[data-v-d4b7b060] .editable-text span {
+  word-break: break-all;
 }
 
-.node-tree-folder[data-v-f2d72e9b] {
+[data-v-9d3310b9] .tree-explorer-node-label {
+  width: 100%;
   display: flex;
   align-items: center;
+  margin-left: var(--p-tree-node-gap);
+  flex-grow: 1;
+}
+/*
+ * The following styles are necessary to avoid layout shift when dragging nodes over folders.
+ * By setting the position to relative on the parent and using an absolutely positioned pseudo-element,
+ * we can create a visual indicator for the drop target without affecting the layout of other elements.
+ */
+[data-v-9d3310b9] .p-tree-node-content:has(.tree-folder) {
+  position: relative;
+}
+[data-v-9d3310b9] .p-tree-node-content:has(.tree-folder.can-drop)::after {
+  content: '';
+  position: absolute;
+  top: 0;
+  left: 0;
+  right: 0;
+  bottom: 0;
+  border: 1px solid var(--p-content-color);
+  pointer-events: none;
+}
+
+.node-lib-node-container[data-v-3238e135] {
+  height: 100%;
+  width: 100%;
+}
+.bookmark-button[data-v-3238e135] {
+  width: unset;
+  padding: 0.25rem;
 }
 
 .p-selectbutton .p-button[data-v-91077f2a] {
@@ -4394,45 +4428,33 @@ img.galleria-image {
   gap: 0.5rem;
 }
 
-.node-lib-tree-node-label {
-  display: flex;
-  align-items: center;
-  margin-left: var(--p-tree-node-gap);
-  flex-grow: 1;
-}
 .node-lib-filter-popup {
   margin-left: -13px;
 }
 
-[data-v-87967891] .node-lib-search-box {
+[data-v-85688f44] .node-lib-search-box {
   margin-left: 1rem;
   margin-right: 1rem;
   margin-top: 1rem;
 }
-[data-v-87967891] .comfy-vue-side-bar-body {
+[data-v-85688f44] .comfy-vue-side-bar-body {
   background: var(--p-tree-background);
 }
-
-/*
- * The following styles are necessary to avoid layout shift when dragging nodes over folders.
- * By setting the position to relative on the parent and using an absolutely positioned pseudo-element,
- * we can create a visual indicator for the drop target without affecting the layout of other elements.
- */
-[data-v-87967891] .p-tree-node-content:has(.node-tree-folder) {
-  position: relative;
+[data-v-85688f44] .node-lib-bookmark-tree-explorer {
+  padding-bottom: 2px;
 }
-[data-v-87967891] .p-tree-node-content:has(.node-tree-folder.can-drop)::after {
-  content: '';
-  position: absolute;
-  top: 0;
-  left: 0;
-  right: 0;
-  bottom: 0;
-  border: 1px solid var(--p-content-color);
-  pointer-events: none;
+[data-v-85688f44] .node-lib-tree-explorer {
+  padding-top: 2px;
+}
+[data-v-85688f44] .p-divider {
+  margin: var(--comfy-tree-explorer-item-padding) 0px;
 }
 
-.spinner[data-v-8616e7a1] {
+.p-tree-node-content {
+  padding: var(--comfy-tree-explorer-item-padding) !important;
+}
+
+.spinner[data-v-75e4840f] {
     position: absolute;
     inset: 0px;
     display: flex;

web/assets/userSelection-DSpF-zVD.js

@@ -1,6 +1,6 @@
 var __defProp = Object.defineProperty;
 var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { j as createSpinner, h as api, $ as $el } from "./index-CI3N807S.js";
+import { j as createSpinner, h as api, $ as $el } from "./index-Dfv2aLsq.js";
 class UserSelectionScreen {
   static {
     __name(this, "UserSelectionScreen");
@@ -117,4 +117,4 @@ window.comfyAPI.userSelection.UserSelectionScreen = UserSelectionScreen;
 export {
   UserSelectionScreen
 };
-//# sourceMappingURL=userSelection-CyXKCVy3.js.map
+//# sourceMappingURL=userSelection-DSpF-zVD.js.map

web/extensions/core/clipspace.js

@@ -1,2 +1,2 @@
-// Shim for extensions\core\clipspace.ts
+// Shim for extensions/core/clipspace.ts
 export const ClipspaceDialog = window.comfyAPI.clipspace.ClipspaceDialog;

web/extensions/core/groupNode.js

@@ -1,3 +1,3 @@
-// Shim for extensions\core\groupNode.ts
+// Shim for extensions/core/groupNode.ts
 export const GroupNodeConfig = window.comfyAPI.groupNode.GroupNodeConfig;
 export const GroupNodeHandler = window.comfyAPI.groupNode.GroupNodeHandler;

web/extensions/core/groupNodeManage.js

@@ -1,2 +1,2 @@
-// Shim for extensions\core\groupNodeManage.ts
+// Shim for extensions/core/groupNodeManage.ts
 export const ManageGroupDialog = window.comfyAPI.groupNodeManage.ManageGroupDialog;

web/extensions/core/widgetInputs.js

@@ -1,4 +1,4 @@
-// Shim for extensions\core\widgetInputs.ts
+// Shim for extensions/core/widgetInputs.ts
 export const getWidgetConfig = window.comfyAPI.widgetInputs.getWidgetConfig;
 export const setWidgetConfig = window.comfyAPI.widgetInputs.setWidgetConfig;
 export const mergeIfValid = window.comfyAPI.widgetInputs.mergeIfValid;

web/index.html

@@ -14,8 +14,8 @@
 		</style> -->
 		<link rel="stylesheet" type="text/css" href="user.css" />
 		<link rel="stylesheet" type="text/css" href="materialdesignicons.min.css" />
-
-		<link rel="stylesheet" type="text/css" href="user.css" />
+		<script type="module" crossorigin src="./assets/index-Dfv2aLsq.js"></script>
+		<link rel="stylesheet" crossorigin href="./assets/index-W4jP-SrU.css">
 	</head>
 	<body class="litegraph">
 		<div id="vue-app"></div>

web/scripts/api.js

@@ -1,2 +1,2 @@
-// Shim for scripts\api.ts
+// Shim for scripts/api.ts
 export const api = window.comfyAPI.api.api;

web/scripts/app.js

@@ -1,4 +1,4 @@
-// Shim for scripts\app.ts
+// Shim for scripts/app.ts
 export const ANIM_PREVIEW_WIDGET = window.comfyAPI.app.ANIM_PREVIEW_WIDGET;
 export const ComfyApp = window.comfyAPI.app.ComfyApp;
 export const app = window.comfyAPI.app.app;

web/scripts/changeTracker.js

@@ -1,2 +1,2 @@
-// Shim for scripts\changeTracker.ts
+// Shim for scripts/changeTracker.ts
 export const ChangeTracker = window.comfyAPI.changeTracker.ChangeTracker;

web/scripts/defaultGraph.js

@@ -1,2 +1,2 @@
-// Shim for scripts\defaultGraph.ts
+// Shim for scripts/defaultGraph.ts
 export const defaultGraph = window.comfyAPI.defaultGraph.defaultGraph;

web/scripts/domWidget.js

@@ -1,2 +1,2 @@
-// Shim for scripts\domWidget.ts
+// Shim for scripts/domWidget.ts
 export const addDomClippingSetting = window.comfyAPI.domWidget.addDomClippingSetting;

web/scripts/logging.js

@@ -1,2 +1,2 @@
-// Shim for scripts\logging.ts
+// Shim for scripts/logging.ts
 export const ComfyLogging = window.comfyAPI.logging.ComfyLogging;

web/scripts/metadata/flac.js

@@ -1,3 +1,3 @@
-// Shim for scripts\metadata\flac.ts
+// Shim for scripts/metadata/flac.ts
 export const getFromFlacBuffer = window.comfyAPI.flac.getFromFlacBuffer;
 export const getFromFlacFile = window.comfyAPI.flac.getFromFlacFile;

web/scripts/metadata/png.js

@@ -1,3 +1,3 @@
-// Shim for scripts\metadata\png.ts
+// Shim for scripts/metadata/png.ts
 export const getFromPngBuffer = window.comfyAPI.png.getFromPngBuffer;
 export const getFromPngFile = window.comfyAPI.png.getFromPngFile;

web/scripts/pnginfo.js

@@ -1,4 +1,4 @@
-// Shim for scripts\pnginfo.ts
+// Shim for scripts/pnginfo.ts
 export const getPngMetadata = window.comfyAPI.pnginfo.getPngMetadata;
 export const getFlacMetadata = window.comfyAPI.pnginfo.getFlacMetadata;
 export const getWebpMetadata = window.comfyAPI.pnginfo.getWebpMetadata;

web/scripts/ui.js

@@ -1,4 +1,4 @@
-// Shim for scripts\ui.ts
+// Shim for scripts/ui.ts
 export const ComfyDialog = window.comfyAPI.ui.ComfyDialog;
 export const $el = window.comfyAPI.ui.$el;
 export const ComfyUI = window.comfyAPI.ui.ComfyUI;

web/scripts/ui/components/asyncDialog.js

@@ -1,2 +1,2 @@
-// Shim for scripts\ui\components\asyncDialog.ts
+// Shim for scripts/ui/components/asyncDialog.ts
 export const ComfyAsyncDialog = window.comfyAPI.asyncDialog.ComfyAsyncDialog;

web/scripts/ui/components/button.js

@@ -1,2 +1,2 @@
-// Shim for scripts\ui\components\button.ts
+// Shim for scripts/ui/components/button.ts
 export const ComfyButton = window.comfyAPI.button.ComfyButton;

web/scripts/ui/components/buttonGroup.js

@@ -1,2 +1,2 @@
-// Shim for scripts\ui\components\buttonGroup.ts
+// Shim for scripts/ui/components/buttonGroup.ts
 export const ComfyButtonGroup = window.comfyAPI.buttonGroup.ComfyButtonGroup;

web/scripts/ui/components/popup.js

@@ -1,2 +1,2 @@
-// Shim for scripts\ui\components\popup.ts
+// Shim for scripts/ui/components/popup.ts
 export const ComfyPopup = window.comfyAPI.popup.ComfyPopup;

web/scripts/ui/components/splitButton.js

@@ -1,2 +1,2 @@
-// Shim for scripts\ui\components\splitButton.ts
+// Shim for scripts/ui/components/splitButton.ts
 export const ComfySplitButton = window.comfyAPI.splitButton.ComfySplitButton;

web/scripts/ui/dialog.js

@@ -1,2 +1,2 @@
-// Shim for scripts\ui\dialog.ts
+// Shim for scripts/ui/dialog.ts
 export const ComfyDialog = window.comfyAPI.dialog.ComfyDialog;

web/scripts/ui/draggableList.js

@@ -1,2 +1,2 @@
-// Shim for scripts\ui\draggableList.ts
+// Shim for scripts/ui/draggableList.ts
 export const DraggableList = window.comfyAPI.draggableList.DraggableList;

web/scripts/ui/imagePreview.js

@@ -1,3 +1,3 @@
-// Shim for scripts\ui\imagePreview.ts
+// Shim for scripts/ui/imagePreview.ts
 export const calculateImageGrid = window.comfyAPI.imagePreview.calculateImageGrid;
 export const createImageHost = window.comfyAPI.imagePreview.createImageHost;

web/scripts/ui/menu/index.js

@@ -1,2 +1,2 @@
-// Shim for scripts\ui\menu\index.ts
+// Shim for scripts/ui/menu/index.ts
 export const ComfyAppMenu = window.comfyAPI.index.ComfyAppMenu;

web/scripts/ui/menu/interruptButton.js

@@ -1,2 +1,2 @@
-// Shim for scripts\ui\menu\interruptButton.ts
+// Shim for scripts/ui/menu/interruptButton.ts
 export const getInterruptButton = window.comfyAPI.interruptButton.getInterruptButton;

web/scripts/ui/menu/queueButton.js

@@ -1,2 +1,2 @@
-// Shim for scripts\ui\menu\queueButton.ts
+// Shim for scripts/ui/menu/queueButton.ts
 export const ComfyQueueButton = window.comfyAPI.queueButton.ComfyQueueButton;

web/scripts/ui/menu/queueOptions.js

@@ -1,2 +1,2 @@
-// Shim for scripts\ui\menu\queueOptions.ts
+// Shim for scripts/ui/menu/queueOptions.ts
 export const ComfyQueueOptions = window.comfyAPI.queueOptions.ComfyQueueOptions;

web/scripts/ui/menu/workflows.js

@@ -1,3 +1,3 @@
-// Shim for scripts\ui\menu\workflows.ts
+// Shim for scripts/ui/menu/workflows.ts
 export const ComfyWorkflowsMenu = window.comfyAPI.workflows.ComfyWorkflowsMenu;
 export const ComfyWorkflowsContent = window.comfyAPI.workflows.ComfyWorkflowsContent;

web/scripts/ui/settings.js

@@ -1,2 +1,2 @@
-// Shim for scripts\ui\settings.ts
+// Shim for scripts/ui/settings.ts
 export const ComfySettingsDialog = window.comfyAPI.settings.ComfySettingsDialog;

web/scripts/ui/spinner.js

@@ -1,2 +1,2 @@
-// Shim for scripts\ui\spinner.ts
+// Shim for scripts/ui/spinner.ts
 export const createSpinner = window.comfyAPI.spinner.createSpinner;

web/scripts/ui/toggleSwitch.js

@@ -1,2 +1,2 @@
-// Shim for scripts\ui\toggleSwitch.ts
+// Shim for scripts/ui/toggleSwitch.ts
 export const toggleSwitch = window.comfyAPI.toggleSwitch.toggleSwitch;

web/scripts/ui/userSelection.js

@@ -1,2 +1,2 @@
-// Shim for scripts\ui\userSelection.ts
+// Shim for scripts/ui/userSelection.ts
 export const UserSelectionScreen = window.comfyAPI.userSelection.UserSelectionScreen;

web/scripts/ui/utils.js

@@ -1,3 +1,3 @@
-// Shim for scripts\ui\utils.ts
+// Shim for scripts/ui/utils.ts
 export const applyClasses = window.comfyAPI.utils.applyClasses;
 export const toggleElement = window.comfyAPI.utils.toggleElement;

web/scripts/utils.js

@@ -1,4 +1,4 @@
-// Shim for scripts\utils.ts
+// Shim for scripts/utils.ts
 export const clone = window.comfyAPI.utils.clone;
 export const applyTextReplacements = window.comfyAPI.utils.applyTextReplacements;
 export const addStylesheet = window.comfyAPI.utils.addStylesheet;

web/scripts/widgets.js

@@ -1,4 +1,4 @@
-// Shim for scripts\widgets.ts
+// Shim for scripts/widgets.ts
 export const updateControlWidgetLabel = window.comfyAPI.widgets.updateControlWidgetLabel;
 export const addValueControlWidget = window.comfyAPI.widgets.addValueControlWidget;
 export const addValueControlWidgets = window.comfyAPI.widgets.addValueControlWidgets;

web/scripts/workflows.js

@@ -1,4 +1,4 @@
-// Shim for scripts\workflows.ts
+// Shim for scripts/workflows.ts
 export const trimJsonExt = window.comfyAPI.workflows.trimJsonExt;
 export const ComfyWorkflowManager = window.comfyAPI.workflows.ComfyWorkflowManager;
 export const ComfyWorkflow = window.comfyAPI.workflows.ComfyWorkflow;