add locales partly.

added: more descriptions
added: locales/ko

.gitignore

@@ -7,3 +7,5 @@ subpack
 impact_subpack
 *.txt
 *.yaml
+!requirements.txt
+!LICENSE.txt

install.py

@@ -69,7 +69,6 @@ def process_wrap(cmd_str, cwd=None, handler=None, env=None):
 
 try:
     import platform
-    import folder_paths
     from torchvision.datasets.utils import download_url
     import impact.config
 

js/comboBoolMigration.js

@@ -1,35 +0,0 @@
-import { ComfyApp, app } from "../../scripts/app.js";
-
-let conflict_check = undefined;
-
-app.registerExtension({
-	name: "Comfy.impact.comboBoolMigration",
-
-	nodeCreated(node, app) {
-		for(let i in node.widgets) {
-			let widget = node.widgets[i];
-
-			if(conflict_check == undefined) {
-				conflict_check = !!app.extensions.find((ext) => ext.name === "Comfy.comboBoolMigration");
-			}
-
-			if(conflict_check)
-				return;
-
-			if(widget.type == "toggle") {
-				let value = widget.value;
-
-				var v = Object.getOwnPropertyDescriptor(widget, 'value');
-				if(!v) {
-					Object.defineProperty(widget, "value", {
-						set: (value) => {
-								delete widget.value;
-								widget.value = value == true || value == widget.options.on;
-							},
-						get: () => { return value; }
-					});
-				}
-			}
-		}
-	}
-});

js/impact-pack.js

@@ -93,7 +93,7 @@ const input_dirty = {};
 const output_tracking = {};
 
 function progressExecuteHandler(event) {
-	if(event.detail.output.aux){
+	if(event.detail?.output?.aux){
 		const id = event.detail.node;
 		if(input_tracking.hasOwnProperty(id)) {
 			if(input_tracking.hasOwnProperty(id) && input_tracking[id][0] != event.detail.output.aux[0]) {
@@ -222,6 +222,31 @@ api.addEventListener("executed", progressExecuteHandler);
 
 app.registerExtension({
 	name: "Comfy.Impack",
+
+    commands: [
+      {
+        id: 'refresh-impact-wildcard',
+        label: 'Impact: Refresh Wildcard',
+        function: async () => {
+        	await api.fetchApi('/impact/wildcards/refresh');
+        	await load_wildcards();
+        	app.extensionManager.toast.add({
+				severity: 'info',
+				summary: 'Refreshed!',
+				detail: 'Impact Wildcard List is refreshed!!',
+				life: 3000
+			});
+        }
+      }
+    ],
+
+    menuCommands: [
+      {
+        path: ['Edit'],
+        commands: ['refresh-impact-wildcard']
+      }
+    ],
+
 	loadedGraphNode(node, app) {
 		if (node.comfyClass == "MaskPainter") {
 			input_dirty[node.id + ""] = true;
@@ -248,7 +273,7 @@ app.registerExtension({
 				}
 				else {
 					const node = app.graph.getNodeById(link_info.origin_id);
-					slot_type = node.outputs[link_info.origin_slot].type;
+					slot_type = node.outputs[link_info.origin_slot]?.type;
 				}
 
 				this.inputs[0].type = slot_type;
@@ -340,7 +365,11 @@ app.registerExtension({
 					// connect input
 					if(this.inputs[0].type == '*'){
 						const node = app.graph.getNodeById(link_info.origin_id);
-						let origin_type = node.outputs[link_info.origin_slot].type;
+						let origin_type = node.outputs[link_info.origin_slot]?.type;
+
+						if(origin_type==undefined) {
+							return; // fallback
+						}
 
 						if(origin_type == '*') {
 							this.disconnectInput(link_info.target_slot);
@@ -730,14 +759,20 @@ app.registerExtension({
 			// mode combo
 			Object.defineProperty(mode_widget, "value", {
 				set: (value) => {
-						node._mode_value = value == true || value == "Populate";
-						populated_text_widget.inputEl.disabled = value == true || value == "Populate";
+						if(value == true)
+							node._mode_value = "populate";
+						else if(value == false)
+							node._mode_value = "fixed";
+						else
+							node._mode_value = value; // combo value
+
+						populated_text_widget.inputEl.disabled = node._mode_value != 'populate';
 					},
 				get: () => {
 						if(node._mode_value != undefined)
 							return node._mode_value;
 						else
-							return true;
+							return 'populate';
 					 }
 			});
 		}

js/impact-wildcard.js

@@ -1,20 +0,0 @@
-import { ComfyApp, app } from "../../scripts/app.js";
-import { api } from "../../scripts/api.js";
-
-let refresh_btn = document.getElementById('comfy-refresh-button');
-let refresh_btn2 = document.querySelector('button[title="Refresh widgets in nodes to find new models or files"]');
-
-let orig = refresh_btn.onclick;
-
-if(refresh_btn) {
-    refresh_btn.onclick = function() {
-        orig();
-        api.fetchApi('/impact/wildcards/refresh');
-    };
-}
-
-if(refresh_btn2) {
-    refresh_btn2?.addEventListener('click', function() {
-        api.fetchApi('/impact/wildcards/refresh');
-    });
-}

modules/impact/animatediff_nodes.py

@@ -27,7 +27,7 @@ class SEGSDetailerForAnimateDiff:
                      "sampler_name": (comfy.samplers.KSampler.SAMPLERS,),
                      "scheduler": (core.SCHEDULERS,),
                      "denoise": ("FLOAT", {"default": 0.5, "min": 0.0001, "max": 1.0, "step": 0.01}),
-                     "basic_pipe": ("BASIC_PIPE",),
+                     "basic_pipe": ("BASIC_PIPE", {"tooltip": "If the `ImpactDummyInput` is connected to the model in the basic_pipe, the inference stage is skipped."}),
                      "refiner_ratio": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0}),
                      },
                 "optional": {
@@ -45,6 +45,8 @@ class SEGSDetailerForAnimateDiff:
 
     CATEGORY = "ImpactPack/Detailer"
 
+    DESCRIPTION = "This node enhances details by inpainting each region within the detected area bundle (SEGS) after enlarging them based on the guide size.\nThis node is applied specifically to SEGS rather than the entire image. To apply it to the entire image, use the 'SEGS Paste' node.\nAs a specialized detailer node for improving video details, such as in AnimateDiff, this node can handle cases where the masks contained in SEGS serve as batch masks spanning multiple frames."
+
     @staticmethod
     def do_detail(image_frames, segs, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, scheduler,
                   denoise, basic_pipe, refiner_ratio=None, refiner_basic_pipe_opt=None, noise_mask_feather=0, scheduler_func_opt=None):
@@ -60,7 +62,7 @@ class SEGSDetailerForAnimateDiff:
         new_segs = []
         cnet_image_list = []
 
-        if noise_mask_feather > 0 and 'denoise_mask_function' not in model.model_options:
+        if not (isinstance(model, str) and model == "DUMMY") and noise_mask_feather > 0 and 'denoise_mask_function' not in model.model_options:
             model = nodes_differential_diffusion.DifferentialDiffusion().apply(model)[0]
 
         for seg in segs[1]:
@@ -94,13 +96,18 @@ class SEGSDetailerForAnimateDiff:
                 for condition, details in negative
             ]
 
-            enhanced_image_tensor, cnet_images = core.enhance_detail_for_animatediff(cropped_image_frames, model, clip, vae, guide_size, guide_size_for, max_size,
-                                                                                     seg.bbox, seed, steps, cfg, sampler_name, scheduler,
-                                                                                     cropped_positive, cropped_negative, denoise, seg.cropped_mask,
-                                                                                     refiner_ratio=refiner_ratio, refiner_model=refiner_model,
-                                                                                     refiner_clip=refiner_clip, refiner_positive=refiner_positive,
-                                                                                     refiner_negative=refiner_negative, control_net_wrapper=seg.control_net_wrapper,
-                                                                                     noise_mask_feather=noise_mask_feather, scheduler_func=scheduler_func_opt)
+            if not (isinstance(model, str) and model == "DUMMY"):
+                enhanced_image_tensor, cnet_images = core.enhance_detail_for_animatediff(cropped_image_frames, model, clip, vae, guide_size, guide_size_for, max_size,
+                                                                                         seg.bbox, seed, steps, cfg, sampler_name, scheduler,
+                                                                                         cropped_positive, cropped_negative, denoise, seg.cropped_mask,
+                                                                                         refiner_ratio=refiner_ratio, refiner_model=refiner_model,
+                                                                                         refiner_clip=refiner_clip, refiner_positive=refiner_positive,
+                                                                                         refiner_negative=refiner_negative, control_net_wrapper=seg.control_net_wrapper,
+                                                                                         noise_mask_feather=noise_mask_feather, scheduler_func=scheduler_func_opt)
+            else:
+                enhanced_image_tensor = cropped_image_frames
+                cnet_images = None
+
             if cnet_images is not None:
                 cnet_image_list.extend(cnet_images)
 
@@ -143,7 +150,7 @@ class DetailerForEachPipeForAnimateDiff:
                       "scheduler": (core.SCHEDULERS,),
                       "denoise": ("FLOAT", {"default": 0.5, "min": 0.0001, "max": 1.0, "step": 0.01}),
                       "feather": ("INT", {"default": 5, "min": 0, "max": 100, "step": 1}),
-                      "basic_pipe": ("BASIC_PIPE", ),
+                      "basic_pipe": ("BASIC_PIPE", {"tooltip": "If the `ImpactDummyInput` is connected to the model in the basic_pipe, the inference stage is skipped."}),
                       "refiner_ratio": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0}),
                       },
                 "optional": {
@@ -161,6 +168,8 @@ class DetailerForEachPipeForAnimateDiff:
 
     CATEGORY = "ImpactPack/Detailer"
 
+    DESCRIPTION = "This node enhances details by inpainting each region within the detected area bundle (SEGS) after enlarging them based on the guide size.\nThis node is a specialized detailer node for enhancing video details, such as in AnimateDiff. It can handle cases where the masks contained in SEGS serve as batch masks spanning multiple frames."
+
     @staticmethod
     def doit(image_frames, segs, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, scheduler,
              denoise, feather, basic_pipe, refiner_ratio=None, detailer_hook=None, refiner_basic_pipe_opt=None,

modules/impact/config.py

@@ -1,7 +1,7 @@
 import configparser
 import os
 
-version_code = [8, 1, 3]
+version_code = [8, 7]
 version = f"V{version_code[0]}.{version_code[1]}" + (f'.{version_code[2]}' if len(version_code) > 2 else '')
 
 dependency_version = 24

modules/impact/core.py

@@ -244,7 +244,8 @@ def enhance_detail(image, model, clip, vae, guide_size, guide_size_for_bbox, max
                    detailer_hook=None,
                    refiner_ratio=None, refiner_model=None, refiner_clip=None, refiner_positive=None,
                    refiner_negative=None, control_net_wrapper=None, cycle=1,
-                   inpaint_model=False, noise_mask_feather=0, scheduler_func=None):
+                   inpaint_model=False, noise_mask_feather=0, scheduler_func=None,
+                   vae_tiled_encode=False, vae_tiled_decode=False):
 
     if noise_mask is not None:
         noise_mask = utils.tensor_gaussian_blur_mask(noise_mask, noise_mask_feather)
@@ -334,7 +335,7 @@ def enhance_detail(image, model, clip, vae, guide_size, guide_size_for_bbox, max
             print(f"[Impact Pack] ComfyUI is an outdated version.")
             positive, negative, latent_image = imc_encode(positive, negative, upscaled_image, vae, noise_mask)
     else:
-        latent_image = to_latent_image(upscaled_image, vae)
+        latent_image = to_latent_image(upscaled_image, vae, vae_tiled_encode=vae_tiled_encode)
         if noise_mask is not None:
             latent_image['noise_mask'] = noise_mask
 
@@ -369,12 +370,18 @@ def enhance_detail(image, model, clip, vae, guide_size, guide_size_for_bbox, max
         refined_latent = detailer_hook.pre_decode(refined_latent)
 
     # non-latent downscale - latent downscale cause bad quality
-    try:
-        # try to decode image normally
-        refined_image = vae.decode(refined_latent['samples'])
-    except Exception as e:
-        #usually an out-of-memory exception from the decode, so try a tiled approach
-        refined_image = vae.decode_tiled(refined_latent["samples"], tile_x=64, tile_y=64, )
+    start = time.time()
+    if vae_tiled_decode:
+        (refined_image,) = nodes.VAEDecodeTiled().decode(vae, refined_latent, 512) # using default settings
+        print(f"[Impact Pack] vae decoded (tiled) in {time.time() - start:.1f}s")
+    else:
+        try:
+            refined_image = vae.decode(refined_latent['samples'])
+        except Exception as e:
+            # usually an out-of-memory exception from the decode, so try a tiled approach
+            print(f"[Impact Pack] failed after {time.time() - start:.1f}s, doing vae.decode_tiled 64...")
+            refined_image = vae.decode_tiled(refined_latent["samples"], tile_x=64, tile_y=64, )
+        print(f"[Impact Pack] vae decoded in {time.time() - start:.1f}s")
 
     if detailer_hook is not None:
         refined_image = detailer_hook.post_decode(refined_image)
@@ -1675,8 +1682,14 @@ class PixelKSampleUpscaler:
                 preprocessor = nodes.NODE_CLASS_MAPPINGS['TilePreprocessor']()
                 # might add capacity to set pyrUp_iters later, not needed for now though
                 preprocessed = preprocessor.execute(images, pyrUp_iters=3, resolution=min(image_w, image_h))[0]
-                apply_cnet = getattr(nodes.ControlNetApply(), nodes.ControlNetApply.FUNCTION)
-                positive = apply_cnet(positive, self.tile_cnet, preprocessed, strength=self.tile_cnet_strength)[0]
+                positive, negative = nodes.ControlNetApplyAdvanced().apply_controlnet(positive=positive,
+                                                                                      negative=negative,
+                                                                                      control_net=self.tile_cnet,
+                                                                                      image=preprocessed,
+                                                                                      strength=self.tile_cnet_strength,
+                                                                                      start_percent=0,
+                                                                                      end_percent=1.0,
+                                                                                      vae=self.vae)
 
         refined_latent = impact_sampling.impact_sample(model, seed, steps, cfg, sampler_name, scheduler,
                                                        positive, negative, upscaled_latent, denoise, scheduler_func=self.scheduler_func)
@@ -1947,7 +1960,7 @@ class PixelTiledKSampleUpscaler:
     def __init__(self, scale_method, model, vae, seed, steps, cfg, sampler_name, scheduler, positive, negative,
                  denoise,
                  tile_width, tile_height, tiling_strategy,
-                 upscale_model_opt=None, hook_opt=None, tile_cnet_opt=None, tile_size=512, tile_cnet_strength=1.0):
+                 upscale_model_opt=None, hook_opt=None, tile_cnet_opt=None, tile_size=512, tile_cnet_strength=1.0, overlap=64):
         self.params = scale_method, model, vae, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise
         self.vae = vae
         self.tile_params = tile_width, tile_height, tiling_strategy
@@ -1957,6 +1970,7 @@ class PixelTiledKSampleUpscaler:
         self.tile_size = tile_size
         self.is_tiled = True
         self.tile_cnet_strength = tile_cnet_strength
+        self.overlap = overlap
 
     def tiled_ksample(self, latent, images):
         if "BNK_TiledKSampler" in nodes.NODE_CLASS_MAPPINGS:
@@ -1979,8 +1993,14 @@ class PixelTiledKSampleUpscaler:
                 preprocessor = nodes.NODE_CLASS_MAPPINGS['TilePreprocessor']()
                 # might add capacity to set pyrUp_iters later, not needed for now though
                 preprocessed = preprocessor.execute(images, pyrUp_iters=3, resolution=min(image_w, image_h))[0]
-                apply_cnet = getattr(nodes.ControlNetApply(), nodes.ControlNetApply.FUNCTION)
-                positive = apply_cnet(positive, self.tile_cnet, preprocessed, strength=self.tile_cnet_strength)[0]
+
+                positive, negative = nodes.ControlNetApplyAdvanced().apply_controlnet(positive=positive,
+                                                                                      negative=negative,
+                                                                                      control_net=self.tile_cnet,
+                                                                                      image=preprocessed,
+                                                                                      strength=self.tile_cnet_strength,
+                                                                                      start_percent=0, end_percent=1.0,
+                                                                                      vae=self.vae)
 
         return TiledKSampler().sample(model, seed, tile_width, tile_height, tiling_strategy, steps, cfg, sampler_name,
                                       scheduler, positive, negative, latent, denoise)[0]

modules/impact/impact_pack.py

@@ -28,6 +28,7 @@ import base64
 import impact.wildcards as wildcards
 from . import hooks
 from . import utils
+import inspect
 
 
 try:
@@ -191,7 +192,7 @@ class DetailerForEach:
         return {"required": {
                     "image": ("IMAGE", ),
                     "segs": ("SEGS", ),
-                    "model": ("MODEL",),
+                    "model": ("MODEL", {"tooltip": "If the `ImpactDummyInput` is connected to the model, the inference stage is skipped."}),
                     "clip": ("CLIP",),
                     "vae": ("VAE",),
                     "guide_size": ("FLOAT", {"default": 512, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 8}),
@@ -217,6 +218,8 @@ class DetailerForEach:
                     "inpaint_model": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
                     "noise_mask_feather": ("INT", {"default": 20, "min": 0, "max": 100, "step": 1}),
                     "scheduler_func_opt": ("SCHEDULER_FUNC",),
+                    "tiled_encode": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
+                    "tiled_decode": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
                    }
                 }
 
@@ -225,6 +228,8 @@ class DetailerForEach:
 
     CATEGORY = "ImpactPack/Detailer"
 
+    DESCRIPTION = "It enhances details by inpainting each region within the detected area bundle (SEGS) after enlarging them based on the guide size."
+
     @staticmethod
     def get_core_module():
         return core
@@ -233,7 +238,7 @@ class DetailerForEach:
     def do_detail(image, segs, model, clip, vae, guide_size, guide_size_for_bbox, max_size, seed, steps, cfg, sampler_name, scheduler,
                   positive, negative, denoise, feather, noise_mask, force_inpaint, wildcard_opt=None, detailer_hook=None,
                   refiner_ratio=None, refiner_model=None, refiner_clip=None, refiner_positive=None, refiner_negative=None,
-                  cycle=1, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None):
+                  cycle=1, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None, tiled_encode=False, tiled_decode=False):
 
         if len(image) > 1:
             raise Exception('[Impact Pack] ERROR: DetailerForEach does not allow image batches.\nPlease refer to https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/batching-detailer.md for more information.')
@@ -269,7 +274,7 @@ class DetailerForEach:
         else:
             ordered_segs = segs[1]
 
-        if noise_mask_feather > 0 and 'denoise_mask_function' not in model.model_options:
+        if not (isinstance(model, str) and model == "DUMMY") and noise_mask_feather > 0 and 'denoise_mask_function' not in model.model_options:
             model = nodes_differential_diffusion.DifferentialDiffusion().apply(model)[0]
 
         for i, seg in enumerate(ordered_segs):
@@ -297,13 +302,16 @@ class DetailerForEach:
 
             seg_seed = seed + i if seg_seed is None else seg_seed
 
-            cropped_positive = [
-                [condition, {
-                    k: core.crop_condition_mask(v, image, seg.crop_region) if k == "mask" else v
-                    for k, v in details.items()
-                }]
-                for condition, details in positive
-            ]
+            if not isinstance(positive, str):
+                cropped_positive = [
+                    [condition, {
+                        k: core.crop_condition_mask(v, image, seg.crop_region) if k == "mask" else v
+                        for k, v in details.items()
+                    }]
+                    for condition, details in positive
+                ]
+            else:
+                cropped_positive = positive
 
             if not isinstance(negative, str):
                 cropped_negative = [
@@ -324,16 +332,21 @@ class DetailerForEach:
                 break
 
             orig_cropped_image = cropped_image.clone()
-            enhanced_image, cnet_pils = core.enhance_detail(cropped_image, model, clip, vae, guide_size, guide_size_for_bbox, max_size,
-                                                            seg.bbox, seg_seed, steps, cfg, sampler_name, scheduler,
-                                                            cropped_positive, cropped_negative, denoise, cropped_mask, force_inpaint,
-                                                            wildcard_opt=wildcard_item, wildcard_opt_concat_mode=wildcard_concat_mode,
-                                                            detailer_hook=detailer_hook,
-                                                            refiner_ratio=refiner_ratio, refiner_model=refiner_model,
-                                                            refiner_clip=refiner_clip, refiner_positive=refiner_positive,
-                                                            refiner_negative=refiner_negative, control_net_wrapper=seg.control_net_wrapper,
-                                                            cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather,
-                                                            scheduler_func=scheduler_func_opt)
+            if not (isinstance(model, str) and model == "DUMMY"):
+                enhanced_image, cnet_pils = core.enhance_detail(cropped_image, model, clip, vae, guide_size, guide_size_for_bbox, max_size,
+                                                                seg.bbox, seg_seed, steps, cfg, sampler_name, scheduler,
+                                                                cropped_positive, cropped_negative, denoise, cropped_mask, force_inpaint,
+                                                                wildcard_opt=wildcard_item, wildcard_opt_concat_mode=wildcard_concat_mode,
+                                                                detailer_hook=detailer_hook,
+                                                                refiner_ratio=refiner_ratio, refiner_model=refiner_model,
+                                                                refiner_clip=refiner_clip, refiner_positive=refiner_positive,
+                                                                refiner_negative=refiner_negative, control_net_wrapper=seg.control_net_wrapper,
+                                                                cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather,
+                                                                scheduler_func=scheduler_func_opt, vae_tiled_encode=tiled_encode,
+                                                                vae_tiled_decode=tiled_decode)
+            else:
+                enhanced_image = cropped_image
+                cnet_pils = None
 
             if cnet_pils is not None:
                 cnet_pil_list.extend(cnet_pils)
@@ -376,13 +389,15 @@ class DetailerForEach:
 
     def doit(self, image, segs, model, clip, vae, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name,
              scheduler, positive, negative, denoise, feather, noise_mask, force_inpaint, wildcard, cycle=1,
-             detailer_hook=None, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None):
+             detailer_hook=None, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None,
+             tiled_encode=False, tiled_decode=False):
 
         enhanced_img, *_ = \
             DetailerForEach.do_detail(image, segs, model, clip, vae, guide_size, guide_size_for, max_size, seed, steps,
                                       cfg, sampler_name, scheduler, positive, negative, denoise, feather, noise_mask,
                                       force_inpaint, wildcard, detailer_hook,
-                                      cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt)
+                                      cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, 
+                                      scheduler_func_opt=scheduler_func_opt, tiled_encode=tiled_encode, tiled_decode=tiled_decode)
 
         return (enhanced_img, )
 
@@ -405,7 +420,7 @@ class DetailerForEachPipe:
                       "feather": ("INT", {"default": 5, "min": 0, "max": 100, "step": 1}),
                       "noise_mask": ("BOOLEAN", {"default": True, "label_on": "enabled", "label_off": "disabled"}),
                       "force_inpaint": ("BOOLEAN", {"default": True, "label_on": "enabled", "label_off": "disabled"}),
-                      "basic_pipe": ("BASIC_PIPE", ),
+                      "basic_pipe": ("BASIC_PIPE", {"tooltip": "If the `ImpactDummyInput` is connected to the model in the basic_pipe, the inference stage is skipped."}),
                       "wildcard": ("STRING", {"multiline": True, "dynamicPrompts": False}),
                       "refiner_ratio": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0}),
 
@@ -417,6 +432,8 @@ class DetailerForEachPipe:
                       "inpaint_model": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
                       "noise_mask_feather": ("INT", {"default": 20, "min": 0, "max": 100, "step": 1}),
                       "scheduler_func_opt": ("SCHEDULER_FUNC",),
+                      "tiled_encode": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
+                      "tiled_decode": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
                      }
                 }
 
@@ -427,10 +444,13 @@ class DetailerForEachPipe:
 
     CATEGORY = "ImpactPack/Detailer"
 
+    DESCRIPTION = DetailerForEach.DESCRIPTION
+
     def doit(self, image, segs, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, scheduler,
              denoise, feather, noise_mask, force_inpaint, basic_pipe, wildcard,
              refiner_ratio=None, detailer_hook=None, refiner_basic_pipe_opt=None,
-             cycle=1, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None):
+             cycle=1, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None,
+             tiled_encode=False, tiled_decode=False):
 
         if len(image) > 1:
             raise Exception('[Impact Pack] ERROR: DetailerForEach does not allow image batches.\nPlease refer to https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/batching-detailer.md for more information.')
@@ -448,7 +468,8 @@ class DetailerForEachPipe:
                                       force_inpaint, wildcard, detailer_hook,
                                       refiner_ratio=refiner_ratio, refiner_model=refiner_model,
                                       refiner_clip=refiner_clip, refiner_positive=refiner_positive, refiner_negative=refiner_negative,
-                                      cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt)
+                                      cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt,
+                                      tiled_encode=tiled_encode, tiled_decode=tiled_decode)
 
         # set fallback image
         if len(cnet_pil_list) == 0:
@@ -462,7 +483,7 @@ class FaceDetailer:
     def INPUT_TYPES(s):
         return {"required": {
                      "image": ("IMAGE", ),
-                     "model": ("MODEL",),
+                     "model": ("MODEL", {"tooltip": "If the `ImpactDummyInput` is connected to the model, the inference stage is skipped."}),
                      "clip": ("CLIP",),
                      "vae": ("VAE",),
                      "guide_size": ("FLOAT", {"default": 512, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 8}),
@@ -505,6 +526,8 @@ class FaceDetailer:
                     "inpaint_model": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
                     "noise_mask_feather": ("INT", {"default": 20, "min": 0, "max": 100, "step": 1}),
                     "scheduler_func_opt": ("SCHEDULER_FUNC",),
+                    "tiled_encode": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
+                    "tiled_decode": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
                 }}
 
     RETURN_TYPES = ("IMAGE", "IMAGE", "IMAGE", "MASK", "DETAILER_PIPE", "IMAGE")
@@ -514,6 +537,8 @@ class FaceDetailer:
 
     CATEGORY = "ImpactPack/Simple"
 
+    DESCRIPTION = "This node enhances details by automatically detecting specific objects in the input image using detection models (bbox, segm, sam) and regenerating the image by enlarging the detected area based on the guide size.\nAlthough this node is specialized to simplify the commonly used facial detail enhancement workflow, it can also be used for various automatic inpainting purposes depending on the detection model."
+
     @staticmethod
     def enhance_face(image, model, clip, vae, guide_size, guide_size_for_bbox, max_size, seed, steps, cfg, sampler_name, scheduler,
                      positive, negative, denoise, feather, noise_mask, force_inpaint,
@@ -522,7 +547,7 @@ class FaceDetailer:
                      sam_mask_hint_use_negative, drop_size,
                      bbox_detector, segm_detector=None, sam_model_opt=None, wildcard_opt=None, detailer_hook=None,
                      refiner_ratio=None, refiner_model=None, refiner_clip=None, refiner_positive=None, refiner_negative=None, cycle=1,
-                     inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None):
+                     inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None, tiled_encode=False, tiled_decode=False):
 
         # make default prompt as 'face' if empty prompt for CLIPSeg
         bbox_detector.setAux('face')
@@ -554,7 +579,8 @@ class FaceDetailer:
                                           refiner_ratio=refiner_ratio, refiner_model=refiner_model,
                                           refiner_clip=refiner_clip, refiner_positive=refiner_positive,
                                           refiner_negative=refiner_negative,
-                                          cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt)
+                                          cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather,
+                                          scheduler_func_opt=scheduler_func_opt, tiled_encode=tiled_encode, tiled_decode=tiled_decode)
         else:
             enhanced_img = image
             cropped_enhanced = []
@@ -580,7 +606,8 @@ class FaceDetailer:
              bbox_threshold, bbox_dilation, bbox_crop_factor,
              sam_detection_hint, sam_dilation, sam_threshold, sam_bbox_expansion, sam_mask_hint_threshold,
              sam_mask_hint_use_negative, drop_size, bbox_detector, wildcard, cycle=1,
-             sam_model_opt=None, segm_detector_opt=None, detailer_hook=None, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None):
+             sam_model_opt=None, segm_detector_opt=None, detailer_hook=None, inpaint_model=False, noise_mask_feather=0,
+             scheduler_func_opt=None, tiled_encode=False, tiled_decode=False):
 
         result_img = None
         result_mask = None
@@ -598,7 +625,8 @@ class FaceDetailer:
                 bbox_threshold, bbox_dilation, bbox_crop_factor,
                 sam_detection_hint, sam_dilation, sam_threshold, sam_bbox_expansion, sam_mask_hint_threshold,
                 sam_mask_hint_use_negative, drop_size, bbox_detector, segm_detector_opt, sam_model_opt, wildcard, detailer_hook,
-                cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt)
+                cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt,
+                tiled_encode=tiled_encode, tiled_decode=tiled_decode)
 
             result_img = torch.cat((result_img, enhanced_img), dim=0) if result_img is not None else enhanced_img
             result_mask = torch.cat((result_mask, mask), dim=0) if result_mask is not None else mask
@@ -986,6 +1014,7 @@ class PixelTiledKSampleUpscalerProvider:
                         "pk_hook_opt": ("PK_HOOK", ),
                         "tile_cnet_opt": ("CONTROL_NET", ),
                         "tile_cnet_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+                        "overlap": ("INT", {"default": 64, "min": 0, "max": 4096, "step": 32}),
                     }
                 }
 
@@ -995,11 +1024,11 @@ class PixelTiledKSampleUpscalerProvider:
     CATEGORY = "ImpactPack/Upscale"
 
     def doit(self, scale_method, model, vae, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise, tile_width, tile_height, tiling_strategy, upscale_model_opt=None,
-             pk_hook_opt=None, tile_cnet_opt=None, tile_cnet_strength=1.0):
+             pk_hook_opt=None, tile_cnet_opt=None, tile_cnet_strength=1.0, overlap=64):
         if "BNK_TiledKSampler" in nodes.NODE_CLASS_MAPPINGS:
             upscaler = core.PixelTiledKSampleUpscaler(scale_method, model, vae, seed, steps, cfg, sampler_name, scheduler, positive, negative, denoise,
                                                       tile_width, tile_height, tiling_strategy, upscale_model_opt, pk_hook_opt, tile_cnet_opt,
-                                                      tile_size=max(tile_width, tile_height), tile_cnet_strength=tile_cnet_strength)
+                                                      tile_size=max(tile_width, tile_height), tile_cnet_strength=tile_cnet_strength, overlap=overlap)
             return (upscaler, )
         else:
             utils.try_install_custom_node('https://github.com/BlenderNeko/ComfyUI_TiledKSampler',
@@ -1312,7 +1341,11 @@ class IterativeImageUpscale:
 
         core.update_node_status(unique_id, "VAEEncode (first)", 0)
         if upscaler.is_tiled:
-            latent = nodes.VAEEncodeTiled().encode(vae, pixels, upscaler.tile_size)[0]
+            encoder = nodes.VAEEncodeTiled()
+            if 'overlap' in inspect.signature(encoder.encode).parameters:
+                latent = encoder.encode(vae, pixels, upscaler.tile_size, overlap=upscaler.overlap)[0]
+            else:
+                latent = encoder.encode(vae, pixels, upscaler.tile_size)[0]
         else:
             latent = nodes.VAEEncode().encode(vae, pixels)[0]
 
@@ -1334,7 +1367,7 @@ class FaceDetailerPipe:
     def INPUT_TYPES(s):
         return {"required": {
                     "image": ("IMAGE", ),
-                    "detailer_pipe": ("DETAILER_PIPE",),
+                    "detailer_pipe": ("DETAILER_PIPE", {"tooltip": "If the `ImpactDummyInput` is connected to the model in the detailer_pipe, the inference stage is skipped."}),
                     "guide_size": ("FLOAT", {"default": 512, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 8}),
                     "guide_size_for": ("BOOLEAN", {"default": True, "label_on": "bbox", "label_off": "crop_region"}),
                     "max_size": ("FLOAT", {"default": 1024, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 8}),
@@ -1368,6 +1401,8 @@ class FaceDetailerPipe:
                     "inpaint_model": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
                     "noise_mask_feather": ("INT", {"default": 20, "min": 0, "max": 100, "step": 1}),
                     "scheduler_func_opt": ("SCHEDULER_FUNC",),
+                    "tiled_encode": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
+                    "tiled_decode": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
                    }
                 }
 
@@ -1378,11 +1413,14 @@ class FaceDetailerPipe:
 
     CATEGORY = "ImpactPack/Simple"
 
+    DESCRIPTION = FaceDetailer.DESCRIPTION
+
     def doit(self, image, detailer_pipe, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, scheduler,
              denoise, feather, noise_mask, force_inpaint, bbox_threshold, bbox_dilation, bbox_crop_factor,
              sam_detection_hint, sam_dilation, sam_threshold, sam_bbox_expansion,
              sam_mask_hint_threshold, sam_mask_hint_use_negative, drop_size, refiner_ratio=None,
-             cycle=1, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None):
+             cycle=1, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None,
+             tiled_encode=False, tiled_decode=False):
 
         result_img = None
         result_mask = None
@@ -1405,7 +1443,8 @@ class FaceDetailerPipe:
                 sam_mask_hint_use_negative, drop_size, bbox_detector, segm_detector, sam_model_opt, wildcard, detailer_hook,
                 refiner_ratio=refiner_ratio, refiner_model=refiner_model,
                 refiner_clip=refiner_clip, refiner_positive=refiner_positive, refiner_negative=refiner_negative,
-                cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt)
+                cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt,
+                tiled_encode=tiled_encode, tiled_decode=tiled_decode)
 
             result_img = torch.cat((result_img, enhanced_img), dim=0) if result_img is not None else enhanced_img
             result_mask = torch.cat((result_mask, mask), dim=0) if result_mask is not None else mask
@@ -1471,6 +1510,8 @@ class MaskDetailerPipe:
 
     CATEGORY = "ImpactPack/Detailer"
 
+    DESCRIPTION = ""
+
     def doit(self, image, mask, basic_pipe, guide_size, guide_size_for, max_size, mask_mode,
              seed, steps, cfg, sampler_name, scheduler, denoise,
              feather, crop_factor, drop_size, refiner_ratio, batch_size, cycle=1,
@@ -1539,7 +1580,7 @@ class DetailerForEachTest(DetailerForEach):
 
     def doit(self, image, segs, model, clip, vae, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name,
              scheduler, positive, negative, denoise, feather, noise_mask, force_inpaint, wildcard, detailer_hook=None,
-             cycle=1, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None):
+             cycle=1, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None, tiled_encode=False, tiled_decode=False):
 
         if len(image) > 1:
             raise Exception('[Impact Pack] ERROR: DetailerForEach does not allow image batches.\nPlease refer to https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/batching-detailer.md for more information.')
@@ -1548,7 +1589,8 @@ class DetailerForEachTest(DetailerForEach):
             DetailerForEach.do_detail(image, segs, model, clip, vae, guide_size, guide_size_for, max_size, seed, steps,
                                       cfg, sampler_name, scheduler, positive, negative, denoise, feather, noise_mask,
                                       force_inpaint, wildcard, detailer_hook,
-                                      cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt)
+                                      cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, 
+                                      scheduler_func_opt=scheduler_func_opt, tiled_encode=tiled_encode, tiled_decode=tiled_decode)
 
         # set fallback image
         if len(cropped) == 0:
@@ -1575,9 +1617,12 @@ class DetailerForEachTestPipe(DetailerForEachPipe):
 
     CATEGORY = "ImpactPack/Detailer"
 
+    DESCRIPTION = DetailerForEach.DESCRIPTION
+
     def doit(self, image, segs, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, scheduler,
              denoise, feather, noise_mask, force_inpaint, basic_pipe, wildcard, cycle=1,
-             refiner_ratio=None, detailer_hook=None, refiner_basic_pipe_opt=None, inpaint_model=False, noise_mask_feather=0, scheduler_func_opt=None):
+             refiner_ratio=None, detailer_hook=None, refiner_basic_pipe_opt=None, inpaint_model=False, noise_mask_feather=0,
+             scheduler_func_opt=None, tiled_encode=False, tiled_decode=False):
 
         if len(image) > 1:
             raise Exception('[Impact Pack] ERROR: DetailerForEach does not allow image batches.\nPlease refer to https://github.com/ltdrdata/ComfyUI-extension-tutorials/blob/Main/ComfyUI-Impact-Pack/tutorial/batching-detailer.md for more information.')
@@ -1596,7 +1641,8 @@ class DetailerForEachTestPipe(DetailerForEachPipe):
                                       refiner_ratio=refiner_ratio, refiner_model=refiner_model,
                                       refiner_clip=refiner_clip, refiner_positive=refiner_positive,
                                       refiner_negative=refiner_negative,
-                                      cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func_opt=scheduler_func_opt)
+                                      cycle=cycle, inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather,
+                                      scheduler_func_opt=scheduler_func_opt, tiled_encode=tiled_encode, tiled_decode=tiled_decode)
 
         # set fallback image
         if len(cropped) == 0:
@@ -2309,7 +2355,11 @@ class ImpactWildcardProcessor:
         return {"required": {
                         "wildcard_text": ("STRING", {"multiline": True, "dynamicPrompts": False, "tooltip": "Enter a prompt using wildcard syntax."}),
                         "populated_text": ("STRING", {"multiline": True, "dynamicPrompts": False, "tooltip": "The actual value passed during the execution of 'ImpactWildcardProcessor' is what is shown here. The behavior varies slightly depending on the mode. Wildcard syntax can also be used in 'populated_text'."}),
-                        "mode": ("BOOLEAN", {"default": True, "label_on": "Populate", "label_off": "Fixed", "tooltip": "Populate: Before running the workflow, it overwrites the existing value of 'populated_text' with the prompt processed from 'wildcard_text'. In this mode, 'populated_text' cannot be edited.\nFixed: Ignores wildcard_text and keeps 'populated_text' as is. You can edit 'populated_text' in this mode."}),
+                        "mode": (["populate", "fixed", "reproduce"], {"default": "populate", "tooltip":
+                            "populate: Before running the workflow, it overwrites the existing value of 'populated_text' with the prompt processed from 'wildcard_text'. In this mode, 'populated_text' cannot be edited.\n"
+                            "fixed: Ignores wildcard_text and keeps 'populated_text' as is. You can edit 'populated_text' in this mode.\n"
+                            "reproduce: This mode operates as 'fixed' mode only once for reproduction, and then it switches to 'populate' mode."
+                            }),
                         "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "Determines the random seed to be used for wildcard processing."}),
                         "Select to add Wildcard": (["Select the Wildcard to add to the text"],),
                     },
@@ -2318,9 +2368,10 @@ class ImpactWildcardProcessor:
     CATEGORY = "ImpactPack/Prompt"
 
     DESCRIPTION = ("The 'ImpactWildcardProcessor' processes text prompts written in wildcard syntax and outputs the processed text prompt.\n\n"
-                   "TIP: Before the workflow is executed, the processing result of 'wildcard_text' is displayed in 'populated_text', and the populated text is saved along with the workflow. If you want to use a seed converted as input, write the prompt directly in 'populated_text' instead of 'wildcard_text', and set the mode to 'Fixed'.")
+                   "TIP: Before the workflow is executed, the processing result of 'wildcard_text' is displayed in 'populated_text', and the populated text is saved along with the workflow. If you want to use a seed converted as input, write the prompt directly in 'populated_text' instead of 'wildcard_text', and set the mode to 'fixed'.")
 
     RETURN_TYPES = ("STRING", )
+    RETURN_NAMES = ("processed text",)
     FUNCTION = "doit"
 
     @staticmethod
@@ -2340,8 +2391,10 @@ class ImpactWildcardEncode:
                         "clip": ("CLIP",),
                         "wildcard_text": ("STRING", {"multiline": True, "dynamicPrompts": False, "tooltip": "Enter a prompt using wildcard syntax."}),
                         "populated_text": ("STRING", {"multiline": True, "dynamicPrompts": False, "tooltip": "The actual value passed during the execution of 'ImpactWildcardEncode' is what is shown here. The behavior varies slightly depending on the mode. Wildcard syntax can also be used in 'populated_text'."}),
-                        "mode": ("BOOLEAN", {"default": True, "label_on": "Populate", "label_off": "Fixed", "tooltip": "Populate: Before running the workflow, it overwrites the existing value of 'populated_text' with the prompt processed from 'wildcard_text'. In this mode, 'populated_text' cannot be edited.\n"
-                                                                                                                       "Fixed: Ignores wildcard_text and keeps 'populated_text' as is. You can edit 'populated_text' in this mode."}),
+                        "mode": (["populate", "fixed", "reproduce"], {"tooltip": 
+                            "populate: Before running the workflow, it overwrites the existing value of 'populated_text' with the prompt processed from 'wildcard_text'. In this mode, 'populated_text' cannot be edited.\n"
+                            "fixed: Ignores wildcard_text and keeps 'populated_text' as is. You can edit 'populated_text' in this mode\n."
+                            "reproduce: This mode operates as 'fixed' mode only once for reproduction, and then it switches to 'populate' mode."}),
                         "Select to add LoRA": (["Select the LoRA to add to the text"] + folder_paths.get_filename_list("loras"), ),
                         "Select to add Wildcard": (["Select the Wildcard to add to the text"], ),
                         "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "tooltip": "Determines the random seed to be used for wildcard processing."}),
@@ -2351,7 +2404,7 @@ class ImpactWildcardEncode:
     CATEGORY = "ImpactPack/Prompt"
 
     DESCRIPTION = ("The 'ImpactWildcardEncode' node processes text prompts written in wildcard syntax and outputs them as conditioning. It also supports LoRA syntax, with the applied LoRA reflected in the model's output.\n\n"
-                   "TIP1: Before the workflow is executed, the processing result of 'wildcard_text' is displayed in 'populated_text', and the populated text is saved along with the workflow. If you want to use a seed converted as input, write the prompt directly in 'populated_text' instead of 'wildcard_text', and set the mode to 'Fixed'.\n"
+                   "TIP1: Before the workflow is executed, the processing result of 'wildcard_text' is displayed in 'populated_text', and the populated text is saved along with the workflow. If you want to use a seed converted as input, write the prompt directly in 'populated_text' instead of 'wildcard_text', and set the mode to 'fixed'.\n"
                    "TIP2: If the 'Inspire Pack' is installed, LBW(LoRA Block Weight) syntax can also be applied.")
 
     RETURN_TYPES = ("MODEL", "CLIP", "CONDITIONING", "STRING")

modules/impact/impact_server.py

@@ -78,9 +78,13 @@ async def sam_prepare(request):
         if data['sam_model_name'] == 'auto':
             model_name = impact.config.get_config()['sam_editor_model']
 
-        model_name = os.path.join(impact_pack.model_path, "sams", model_name)
+        model_path = folder_paths.get_full_path("sams", model_name)
 
-        logging.info(f"[Impact Pack] Loading SAM model '{impact_pack.model_path}'")
+        if model_path is None:
+            logging.error(f"[Impact Pack] The '{model_name}' model file cannot be found in any sams model path.")
+            return web.Response(status=400)
+
+        logging.info(f"[Impact Pack] Loading SAM model '{model_path}'")
 
         filename, image_dir = folder_paths.annotated_filepath(data["filename"])
 
@@ -93,7 +97,7 @@ async def sam_prepare(request):
         if image_dir is None:
             return web.Response(status=400)
 
-        thread = threading.Thread(target=async_prepare_sam, args=(image_dir, model_name, filename,))
+        thread = threading.Thread(target=async_prepare_sam, args=(image_dir, model_path, filename,))
         thread.start()
 
         logging.info("[Impact Pack] SAM model loaded. ")
@@ -478,7 +482,17 @@ def onprompt_populate_wildcards(json_data):
     for k, v in prompt.items():
         if 'class_type' in v and (v['class_type'] == 'ImpactWildcardEncode' or v['class_type'] == 'ImpactWildcardProcessor'):
             inputs = v['inputs']
-            if inputs['mode'] and isinstance(inputs['populated_text'], str):
+
+            # legacy adapter
+            if isinstance(inputs['mode'], bool):
+                if inputs['mode']:
+                    new_mode = 'populate'
+                else:
+                    new_mode = 'fixed'
+
+                inputs['mode'] = new_mode
+
+            if inputs['mode'] == 'populate' and isinstance(inputs['populated_text'], str):
                 if isinstance(inputs['seed'], list):
                     try:
                         input_node = prompt[inputs['seed'][0]]
@@ -499,17 +513,22 @@ def onprompt_populate_wildcards(json_data):
                     input_seed = int(inputs['seed'])
 
                 inputs['populated_text'] = wildcards.process(inputs['wildcard_text'], input_seed)
-                inputs['mode'] = False
+                inputs['mode'] = 'reproduce'
 
                 PromptServer.instance.send_sync("impact-node-feedback", {"node_id": k, "widget_name": "populated_text", "type": "STRING", "value": inputs['populated_text']})
                 updated_widget_values[k] = inputs['populated_text']
+            
+            if inputs['mode'] == 'reproduce':
+                PromptServer.instance.send_sync("impact-node-feedback", {"node_id": k, "widget_name": "mode", "type": "STRING", "value": 'populate'})
+
+
 
     if 'extra_data' in json_data and 'extra_pnginfo' in json_data['extra_data']:
         for node in json_data['extra_data']['extra_pnginfo']['workflow']['nodes']:
             key = str(node['id'])
             if key in updated_widget_values:
                 node['widgets_values'][1] = updated_widget_values[key]
-                node['widgets_values'][2] = False
+                node['widgets_values'][2] = 'reproduce'
 
 
 def onprompt_for_remote(json_data):

modules/impact/segs_nodes.py

@@ -38,7 +38,7 @@ class SEGSDetailer:
                      "denoise": ("FLOAT", {"default": 0.5, "min": 0.0001, "max": 1.0, "step": 0.01}),
                      "noise_mask": ("BOOLEAN", {"default": True, "label_on": "enabled", "label_off": "disabled"}),
                      "force_inpaint": ("BOOLEAN", {"default": True, "label_on": "enabled", "label_off": "disabled"}),
-                     "basic_pipe": ("BASIC_PIPE",),
+                     "basic_pipe": ("BASIC_PIPE", {"tooltip": "If the `ImpactDummyInput` is connected to the model in the basic_pipe, the inference stage is skipped."}),
                      "refiner_ratio": ("FLOAT", {"default": 0.2, "min": 0.0, "max": 1.0}),
                      "batch_size": ("INT", {"default": 1, "min": 1, "max": 100}),
 
@@ -60,6 +60,8 @@ class SEGSDetailer:
 
     CATEGORY = "ImpactPack/Detailer"
 
+    DESCRIPTION = "This node enhances details by inpainting each region within the detected area bundle (SEGS) after enlarging them based on the guide size.\nThis node is applied specifically to SEGS rather than the entire image. To apply it to the entire image, use the 'SEGS Paste' node."
+
     @staticmethod
     def do_detail(image, segs, guide_size, guide_size_for, max_size, seed, steps, cfg, sampler_name, scheduler,
                   denoise, noise_mask, force_inpaint, basic_pipe, refiner_ratio=None, batch_size=1, cycle=1,
@@ -76,7 +78,7 @@ class SEGSDetailer:
         new_segs = []
         cnet_pil_list = []
 
-        if noise_mask_feather > 0 and 'denoise_mask_function' not in model.model_options:
+        if not (isinstance(model, str) and model == "DUMMY") and noise_mask_feather > 0 and 'denoise_mask_function' not in model.model_options:
             model = nodes_differential_diffusion.DifferentialDiffusion().apply(model)[0]
 
         for i in range(batch_size):
@@ -113,13 +115,17 @@ class SEGSDetailer:
                     for condition, details in negative
                 ]
 
-                enhanced_image, cnet_pils = core.enhance_detail(cropped_image, model, clip, vae, guide_size, guide_size_for, max_size,
-                                                                seg.bbox, seed, steps, cfg, sampler_name, scheduler,
-                                                                cropped_positive, cropped_negative, denoise, cropped_mask, force_inpaint,
-                                                                refiner_ratio=refiner_ratio, refiner_model=refiner_model,
-                                                                refiner_clip=refiner_clip, refiner_positive=refiner_positive, refiner_negative=refiner_negative,
-                                                                control_net_wrapper=seg.control_net_wrapper, cycle=cycle,
-                                                                inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func=scheduler_func_opt)
+                if not (isinstance(model, str) and model == "DUMMY"):
+                    enhanced_image, cnet_pils = core.enhance_detail(cropped_image, model, clip, vae, guide_size, guide_size_for, max_size,
+                                                                    seg.bbox, seed, steps, cfg, sampler_name, scheduler,
+                                                                    cropped_positive, cropped_negative, denoise, cropped_mask, force_inpaint,
+                                                                    refiner_ratio=refiner_ratio, refiner_model=refiner_model,
+                                                                    refiner_clip=refiner_clip, refiner_positive=refiner_positive, refiner_negative=refiner_negative,
+                                                                    control_net_wrapper=seg.control_net_wrapper, cycle=cycle,
+                                                                    inpaint_model=inpaint_model, noise_mask_feather=noise_mask_feather, scheduler_func=scheduler_func_opt)
+                else:
+                    enhanced_image = cropped_image
+                    cnet_pils = None
 
                 if cnet_pils is not None:
                     cnet_pil_list.extend(cnet_pils)
@@ -170,6 +176,8 @@ class SEGSPaste:
 
     CATEGORY = "ImpactPack/Detailer"
 
+    DESCRIPTION = "This node provides a function to paste the enhanced SEGS, improved through the SEGS detailer, back onto the original image."
+
     @staticmethod
     def doit(image, segs, feather, alpha=255, ref_image_opt=None):
 
@@ -1482,6 +1490,8 @@ class SEGSPicker:
 
     CATEGORY = "ImpactPack/Util"
 
+    DESCRIPTION = "This node provides a function to select only the chosen SEGS from the input SEGS."
+
     @staticmethod
     def doit(picks, segs, fallback_image_opt=None, unique_id=None):
         if fallback_image_opt is not None:
@@ -1538,6 +1548,8 @@ class DefaultImageForSEGS:
 
     CATEGORY = "ImpactPack/Util"
 
+    DESCRIPTION = "If the SEGS have not passed through the detailer, they contain only detection area information without an image. This node sets a default image for the SEGS."
+
     @staticmethod
     def doit(segs, image, override):
         results = []

modules/impact/util_nodes.py

@@ -526,6 +526,9 @@ class ReencodeLatent:
                         "output_vae": ("VAE", ),
                         "tile_size": ("INT", {"default": 512, "min": 320, "max": 4096, "step": 64}),
                     },
+                "optional": {
+                    "overlap": ("INT", {"default": 64, "min": 0, "max": 4096, "step": 32, "tooltip": "This setting applies when 'tile_mode' is enabled."}),
+                    }
                 }
 
     CATEGORY = "ImpactPack/Util"
@@ -533,14 +536,22 @@ class ReencodeLatent:
     RETURN_TYPES = ("LATENT", )
     FUNCTION = "doit"
 
-    def doit(self, samples, tile_mode, input_vae, output_vae, tile_size=512):
+    def doit(self, samples, tile_mode, input_vae, output_vae, tile_size=512, overlap=64):
         if tile_mode in ["Both", "Decode(input) only"]:
-            pixels = nodes.VAEDecodeTiled().decode(input_vae, samples, tile_size)[0]
+            decoder = nodes.VAEDecodeTiled()
+            if 'overlap' in inspect.signature(decoder.decode).parameters:
+                pixels = decoder.decode(input_vae, samples, tile_size, overlap=overlap)[0]
+            else:
+                pixels = decoder.decode(input_vae, samples, tile_size, overlap=overlap)[0]
         else:
             pixels = nodes.VAEDecode().decode(input_vae, samples)[0]
 
         if tile_mode in ["Both", "Encode(output) only"]:
-            return nodes.VAEEncodeTiled().encode(output_vae, pixels, tile_size)
+            encoder = nodes.VAEEncodeTiled()
+            if 'overlap' in inspect.signature(encoder.encode).parameters:
+                return encoder.encode(output_vae, pixels, tile_size, overlap=overlap)
+            else:
+                return encoder.encode(output_vae, pixels, tile_size)
         else:
             return nodes.VAEEncode().encode(output_vae, pixels)
 

modules/impact/utils.py

@@ -7,6 +7,7 @@ import nodes
 from . import config
 from PIL import Image
 import comfy
+import time
 
 
 class TensorBatchBuilder:
@@ -501,15 +502,21 @@ def crop_image(image, crop_region):
     return crop_tensor4(image, crop_region)
 
 
-def to_latent_image(pixels, vae):
+def to_latent_image(pixels, vae, vae_tiled_encode=False):
     x = pixels.shape[1]
     y = pixels.shape[2]
     if pixels.shape[1] != x or pixels.shape[2] != y:
         pixels = pixels[:, :x, :y, :]
 
-    vae_encode = nodes.VAEEncode()
+    start = time.time()
+    if vae_tiled_encode:
+        encoded = nodes.VAEEncodeTiled().encode(vae, pixels, 512, overlap=64)[0] # using default settings
+        print(f"[Impact Pack] vae encoded (tiled) in {time.time() - start:.1f}s")
+    else:
+        encoded = nodes.VAEEncode().encode(vae, pixels)[0]
+        print(f"[Impact Pack] vae encoded in {time.time() - start:.1f}s")
 
-    return vae_encode.encode(vae, pixels)[0]
+    return encoded
 
 
 def empty_pil_tensor(w=64, h=64):

modules/impact/wildcards.py

@@ -237,7 +237,23 @@ def process(text, seed=None):
             keyword = match.lower()
             keyword = wildcard_normalize(keyword)
             if keyword in local_wildcard_dict:
-                replacement = random_gen.choice(local_wildcard_dict[keyword])
+                # look for adjusted probability
+                adjusted_probabilities = []
+                total_prob = 0
+                options=local_wildcard_dict[keyword]
+                for option in options:
+                    parts = option.split('::', 1)
+                    if len(parts) == 2 and is_numeric_string(parts[0].strip()):
+                        config_value = float(parts[0].strip())
+                    else:
+                        config_value = 1  # Default value if no configuration is provided
+
+                    adjusted_probabilities.append(config_value)
+                    total_prob += config_value
+
+                normalized_probabilities = [prob / total_prob for prob in adjusted_probabilities]
+                selected_item = random_gen.choice(options, p=normalized_probabilities, replace=False)
+                replacement = re.sub(r'^\s*[0-9.]+::', '', selected_item, 1)
                 replacements_found = True
                 string = string.replace(f"__{match}__", replacement, 1)
             elif '*' in keyword:

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "comfyui-impact-pack"
 description = "This node pack offers various detector nodes and detailer nodes that allow you to configure a workflow that automatically enhances facial details. And provide iterative upscaler."
-version = "8.1.3"
+version = "8.7"
 license = { file = "LICENSE.txt" }
 dependencies = ["segment-anything", "scikit-image", "piexif", "transformers", "opencv-python-headless", "GitPython", "scipy>=1.11.4"]