FIXED: Anomaly where cropped becomes identical to cropped_refined

https://github.com/ltdrdata/ComfyUI-extension-tutorials/issues/28

modules/impact/config.py

@@ -1,7 +1,7 @@
 import configparser
 import os
 
-version_code = [7, 6]
+version_code = [7, 6, 1]
 version = f"V{version_code[0]}.{version_code[1]}" + (f'.{version_code[2]}' if len(version_code) > 2 else '')
 
 dependency_version = 22

modules/impact/impact_pack.py

@@ -319,6 +319,7 @@ class DetailerForEach:
             if wildcard_item and wildcard_item.strip() == '[STOP]':
                 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,
@@ -338,7 +339,7 @@ class DetailerForEach:
                 # use image paste
                 image = image.cpu()
                 enhanced_image = enhanced_image.cpu()
-                tensor_paste(image, enhanced_image, (seg.crop_region[0], seg.crop_region[1]), mask)
+                tensor_paste(image, enhanced_image, (seg.crop_region[0], seg.crop_region[1]), mask)  # this code affecting to `cropped_image`.
                 enhanced_list.append(enhanced_image)
 
                 if detailer_hook is not None:
@@ -356,7 +357,7 @@ class DetailerForEach:
             else:
                 new_seg_image = None
 
-            cropped_list.append(cropped_image)
+            cropped_list.append(orig_cropped_image) # NOTE: Don't use `cropped_image`
 
             new_seg = SEG(new_seg_image, seg.cropped_mask, seg.confidence, seg.crop_region, seg.bbox, seg.label, seg.control_net_wrapper)
             new_segs.append(new_seg)

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "comfyui-impact-pack"
 description = "This extension offers various detector nodes and detailer nodes that allow you to configure a workflow that automatically enhances facial details. And provide iterative upscaler."
-version = "7.6"
+version = "7.6.1"
 license = { file = "LICENSE.txt" }
 dependencies = ["segment-anything", "scikit-image", "piexif", "transformers", "opencv-python-headless", "GitPython", "scipy>=1.11.4"]