feat: wildcards nodes - reproduce mode is added.

https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues/882

fixed: js crash
https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues/886#issuecomment-2613922083

removed: combo bool migration script

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]) {
@@ -273,7 +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;
@@ -365,7 +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);
@@ -755,14 +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';
 					 }
 			});
 		}

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": {
@@ -60,7 +60,7 @@ 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 +94,18 @@ 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 +148,7 @@ 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": {

modules/impact/config.py

@@ -1,7 +1,7 @@
 import configparser
 import os
 
-version_code = [8, 3]
+version_code = [8, 5]
 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)

modules/impact/impact_pack.py

@@ -192,7 +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}),
@@ -218,6 +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"}),
                    }
                 }
 
@@ -234,7 +236,7 @@ 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.')
@@ -338,7 +340,8 @@ class DetailerForEach:
                                                                 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)
+                                                                scheduler_func=scheduler_func_opt, vae_tiled_encode=tiled_encode,
+                                                                vae_tiled_decode=tiled_decode)
             else:
                 enhanced_image = cropped_image
                 cnet_pils = None
@@ -384,13 +387,15 @@ 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, )
 
@@ -413,7 +418,7 @@ 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}),
 
@@ -425,6 +430,8 @@ 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"}),
                      }
                 }
 
@@ -438,7 +445,8 @@ class DetailerForEachPipe:
     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.')
@@ -456,7 +464,8 @@ 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:
@@ -470,7 +479,7 @@ 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}),
@@ -513,6 +522,8 @@ 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")
@@ -530,7 +541,7 @@ 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')
@@ -562,7 +573,8 @@ 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 = []
@@ -588,7 +600,8 @@ 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
@@ -606,7 +619,8 @@ 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
@@ -1347,7 +1361,7 @@ 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}),
@@ -1381,6 +1395,8 @@ 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"}),
                    }
                 }
 
@@ -1395,7 +1411,8 @@ class FaceDetailerPipe:
              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
@@ -1418,7 +1435,8 @@ 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
@@ -1552,7 +1570,7 @@ 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.')
@@ -1561,7 +1579,8 @@ 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:
@@ -1590,7 +1609,8 @@ class DetailerForEachTestPipe(DetailerForEachPipe):
 
     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.')
@@ -1609,7 +1629,8 @@ 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:
@@ -2322,7 +2343,11 @@ 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"],),
                     },
@@ -2331,7 +2356,7 @@ 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", )
     FUNCTION = "doit"
@@ -2353,8 +2378,10 @@ 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."}),
@@ -2364,7 +2391,7 @@ 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

@@ -478,7 +478,17 @@ 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]]
@@ -499,10 +509,15 @@ 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']:

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}),
 
@@ -76,7 +76,7 @@ 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 +113,17 @@ 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)

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):

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 = "8.3"
+version = "8.5"
 license = { file = "LICENSE.txt" }
 dependencies = ["segment-anything", "scikit-image", "piexif", "transformers", "opencv-python-headless", "GitPython", "scipy>=1.11.4"]