feat: ControlNetApply (SEGS) - support control_image

feat: SEGSPreviewCnet

README.md

@@ -41,6 +41,8 @@ This custom node helps to conveniently enhance images through Detector, Detailer
 
 * ControlNet
   * ControlNetApply (SEGS) - To apply ControlNet in SEGS, you need to use the Preprocessor Provider node from the Inspire Pack to utilize this node.
+    * `SEGSPreprocessor` and `Image` can be selectively applied. If an `Image` is given, `SEGSPreprocessor` will be ignored.
+    * If set to `Image`, you can preview the cropped cnet image through `SEGSPreview (CNET Image)`. Images generated by `SEGSPreprocessor` should be verified through the `cnet_pil` output of each Detailer.
   * ControlNetClear (SEGS) - Clear applied ControlNet in SEGS 
 
 * Bitwise(SEGS & SEGS) - Performs a 'bitwise and' operation between two SEGS.
@@ -81,6 +83,7 @@ This custom node helps to conveniently enhance images through Detector, Detailer
   * SEGSPreview - Provides a preview of SEGS.
      * This option is used to preview the improved image through `SEGSDetailer` before merging it into the original. Prior to going through ```SEGSDetailer```, SEGS only contains mask information without image information. If fallback_image_opt is connected to the original image, SEGS without image information will generate a preview using the original image. However, if SEGS already contains image information, fallback_image_opt will be ignored.
      * This node can be used in conjunction with the processing results of AnimateDiff.
+  * SEGSPreview (CNET Image) - Show images configured with `ControlNetApply (SEGS)` for debugging purposes.
   * SEGSToImageList - Convert SEGS To Image List
   * SEGSToMaskList - Convert SEGS To Mask List
   * SEGS Filter (label) - This node filters SEGS based on the label of the detected areas. 

__init__.py

@@ -253,6 +253,7 @@ NODE_CLASS_MAPPINGS = {
     "SEGSDetailer": SEGSDetailer,
     "SEGSPaste": SEGSPaste,
     "SEGSPreview": SEGSPreview,
+    "SEGSPreviewCNet": SEGSPreviewCNet,
     "SEGSToImageList": SEGSToImageList,
     "ImpactSEGSToMaskList": SEGSToMaskList,
     "ImpactSEGSToMaskBatch": SEGSToMaskBatch,
@@ -430,7 +431,9 @@ NODE_DISPLAY_NAME_MAPPINGS = {
     "ImpactSEGSClassify": "SEGS Classify",
 
     "LatentSwitch": "Switch (latent/legacy)",
-    "SEGSSwitch": "Switch (SEGS/legacy)"
+    "SEGSSwitch": "Switch (SEGS/legacy)",
+
+    "SEGSPreviewCNet": "SEGSPreview (CNET Image)"
 }
 
 if not impact.config.get_config()['mmdet_skip']:

modules/impact/config.py

@@ -2,7 +2,7 @@ import configparser
 import os
 
 
-version_code = [4, 62]
+version_code = [4, 63]
 version = f"V{version_code[0]}.{version_code[1]}" + (f'.{version_code[2]}' if len(version_code) > 2 else '')
 
 dependency_version = 20

modules/impact/core.py

@@ -1592,13 +1592,19 @@ class PixelKSampleUpscaler:
 
 
 class ControlNetWrapper:
-    def __init__(self, control_net, strength, preprocessor, prev_control_net=None):
+    def __init__(self, control_net, strength, preprocessor, prev_control_net=None,
+                 original_size=None, crop_region=None, control_image=None):
         self.control_net = control_net
         self.strength = strength
         self.preprocessor = preprocessor
-        self.image = None
         self.prev_control_net = prev_control_net
 
+        if original_size is not None and crop_region is not None and control_image is not None:
+            self.control_image = utils.tensor_resize(control_image, original_size[1], original_size[0])
+            self.control_image = torch.tensor(utils.tensor_crop(self.control_image, crop_region))
+        else:
+            self.control_image = None
+
     def apply(self, conditioning, image, mask=None):
         cnet_pils = []
         prev_cnet_pils = []
@@ -1606,7 +1612,9 @@ class ControlNetWrapper:
         if self.prev_control_net is not None:
             conditioning, prev_cnet_pils = self.prev_control_net.apply(conditioning, image, mask)
 
-        if self.preprocessor is not None:
+        if self.control_image is not None:
+            cnet_pil = self.control_image
+        elif self.preprocessor is not None:
             cnet_pil = self.preprocessor.apply(image, mask)
         else:
             cnet_pil = image

modules/impact/segs_nodes.py

@@ -270,6 +270,53 @@ class SEGSPaste:
         return (result, )
 
 
+class SEGSPreviewCNet:
+    def __init__(self):
+        self.output_dir = folder_paths.get_temp_directory()
+        self.type = "temp"
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"segs": ("SEGS", ),}, }
+
+    RETURN_TYPES = ("IMAGE", )
+    OUTPUT_IS_LIST = (True, )
+    FUNCTION = "doit"
+
+    CATEGORY = "ImpactPack/Util"
+
+    OUTPUT_NODE = True
+
+    def doit(self, segs):
+        full_output_folder, filename, counter, subfolder, filename_prefix = \
+            folder_paths.get_save_image_path("impact_seg_preview", self.output_dir, segs[0][1], segs[0][0])
+
+        results = list()
+        result_image_list = []
+
+        for seg in segs[1]:
+            file = f"{filename}_{counter:05}_.webp"
+
+            if seg.control_net_wrapper is not None and seg.control_net_wrapper.control_image is not None:
+                cnet_image = seg.control_net_wrapper.control_image
+                result_image_list.append(cnet_image)
+            else:
+                cnet_image = empty_pil_tensor(64, 64)
+
+            cnet_pil = utils.tensor2pil(cnet_image)
+            cnet_pil.save(os.path.join(full_output_folder, file))
+
+            results.append({
+                "filename": file,
+                "subfolder": subfolder,
+                "type": self.type
+            })
+
+            counter += 1
+
+        return {"ui": {"images": results}, "result": (result_image_list,)}
+
+
 class SEGSPreview:
     def __init__(self):
         self.output_dir = folder_paths.get_temp_directory()
@@ -1128,6 +1175,7 @@ class ControlNetApplySEGS:
                     },
                 "optional": {
                     "segs_preprocessor": ("SEGS_PREPROCESSOR",),
+                    "control_image": ("IMAGE",)
                     }
                 }
 
@@ -1136,11 +1184,12 @@ class ControlNetApplySEGS:
 
     CATEGORY = "ImpactPack/Util"
 
-    def doit(self, segs, control_net, strength, segs_preprocessor=None):
+    def doit(self, segs, control_net, strength, segs_preprocessor=None, control_image=None):
         new_segs = []
 
         for seg in segs[1]:
-            control_net_wrapper = core.ControlNetWrapper(control_net, strength, segs_preprocessor, seg.control_net_wrapper)
+            control_net_wrapper = core.ControlNetWrapper(control_net, strength, segs_preprocessor, seg.control_net_wrapper,
+                                                         original_size=segs[0], crop_region=seg.crop_region, control_image=control_image)
             new_seg = SEG(seg.cropped_image, seg.cropped_mask, seg.confidence, seg.crop_region, seg.bbox, seg.label, control_net_wrapper)
             new_segs.append(new_seg)