feat: MaskToSEGS for AnimateDiff allows batch mask

refactor: change some doit functions to staticmethod
update README.md

https://github.com/ltdrdata/ComfyUI-extension-tutorials/issues/44

README.md

@@ -72,7 +72,9 @@ This custom node helps to conveniently enhance images through Detector, Detailer
   * `DetailerDebug (SEGS)` - Refines the image based on SEGS. Additionally, it provides the ability to monitor the cropped image and the refined image of the cropped image.
     * To prevent regeneration caused by the seed that does not change every time when using 'external_seed', please disable the 'seed random generate' option in the 'Detailer...' node.
   * `MASK to SEGS` - Generates SEGS based on the mask.
-    * `MASK to SEGS For AnimateDiff` - Generates SEGS based on the mask for AnimateDiff.
+  * `MASK to SEGS For AnimateDiff` - Generates SEGS based on the mask for AnimateDiff.
+    * When using a single mask, convert it to SEGS to apply it to the entire frame.
+    * When using a batch mask, the contour fill feature is disabled.
   * `MediaPipe FaceMesh to SEGS` - Separate each landmark from the mediapipe facemesh image to create labeled SEGS.
     * Usually, the size of images created through the MediaPipe facemesh preprocessor is downscaled. It resizes the MediaPipe facemesh image to the original size given as reference_image_opt for matching sizes during processing. 
   * `ToBinaryMask` - Separates the mask generated with alpha values between 0 and 255 into 0 and 255. The non-zero parts are always set to 255.

modules/impact/config.py

@@ -2,7 +2,7 @@ import configparser
 import os
 
 
-version_code = [5, 6]
+version_code = [5, 7]
 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

@@ -1013,6 +1013,21 @@ class ONNXDetector:
         pass
 
 
+def batch_mask_to_segs(mask, combined, crop_factor, bbox_fill, drop_size=1, label='A', crop_min_size=None, detailer_hook=None):
+    combined_mask = mask.max(dim=0).values
+
+    segs = mask_to_segs(combined_mask, combined, crop_factor, bbox_fill, drop_size, label, crop_min_size, detailer_hook)
+
+    new_segs = []
+    for seg in segs[1]:
+        x1, y1, x2, y2 = seg.crop_region
+        cropped_mask = mask[:, y1:y2, x1:x2]
+        item = SEG(None, cropped_mask, 1.0, seg.crop_region, seg.bbox, label, None)
+        new_segs.append(item)
+
+    return segs[0], new_segs
+
+
 def mask_to_segs(mask, combined, crop_factor, bbox_fill, drop_size=1, label='A', crop_min_size=None, detailer_hook=None, is_contour=True):
     drop_size = max(drop_size, 1)
     if mask is None:

modules/impact/detectors.py

@@ -410,7 +410,7 @@ class SimpleDetectorForAnimateDiff:
                 return segs_by_frames[0][1]
             else:
                 merged_mask = get_whole_merged_mask()
-                return segs_nodes.MaskToSEGS().doit(merged_mask, False, crop_factor, False, drop_size, contour_fill=True)[0]
+                return segs_nodes.MaskToSEGS.doit(merged_mask, False, crop_factor, False, drop_size, contour_fill=True)[0]
 
         def get_merged_neighboring_segs():
             pivot_segs = get_pivot_segs()

modules/impact/segs_nodes.py

@@ -1141,10 +1141,11 @@ class MaskToSEGS:
 
     CATEGORY = "ImpactPack/Operation"
 
-    def doit(self, mask, combined, crop_factor, bbox_fill, drop_size, contour_fill=False):
+    @staticmethod
+    def doit(mask, combined, crop_factor, bbox_fill, drop_size, contour_fill=False):
         mask = make_2d_mask(mask)
-
         result = core.mask_to_segs(mask, combined, crop_factor, bbox_fill, drop_size, is_contour=contour_fill)
+
         return (result, )
 
 
@@ -1166,11 +1167,17 @@ class MaskToSEGS_for_AnimateDiff:
 
     CATEGORY = "ImpactPack/Operation"
 
-    def doit(self, mask, combined, crop_factor, bbox_fill, drop_size, contour_fill=False):
+    @staticmethod
+    def doit(mask, combined, crop_factor, bbox_fill, drop_size, contour_fill=False):
+        if (len(mask.shape) == 4 and mask.shape[1] > 1) or (len(mask.shape) == 3 and mask.shape[0] > 1):
+            mask = make_3d_mask(mask)
+            if contour_fill:
+                print(f"[Impact Pack] MaskToSEGS_for_AnimateDiff: 'contour_fill' is ignored because batch mask 'contour_fill' is not supported.")
+            result = core.batch_mask_to_segs(mask, combined, crop_factor, bbox_fill, drop_size)
+            return (result, )
+
         mask = make_2d_mask(mask)
-
         segs = core.mask_to_segs(mask, combined, crop_factor, bbox_fill, drop_size, is_contour=contour_fill)
-
         all_masks = SEGSToMaskList().doit(segs)[0]
 
         result_mask = (all_masks[0] * 255).to(torch.uint8)
@@ -1180,7 +1187,7 @@ class MaskToSEGS_for_AnimateDiff:
         result_mask = (result_mask/255.0).to(torch.float32)
         result_mask = utils.to_binary_mask(result_mask, 0.1)[0]
 
-        return MaskToSEGS().doit(result_mask, False, crop_factor, False, drop_size, contour_fill)
+        return MaskToSEGS.doit(result_mask, False, crop_factor, False, drop_size, contour_fill)
 
 
 class IPAdapterApplySEGS:
@@ -1211,7 +1218,8 @@ class IPAdapterApplySEGS:
 
     CATEGORY = "ImpactPack/Util"
 
-    def doit(self, segs, ipadapter_pipe, weight, noise, weight_type, start_at, end_at, unfold_batch, faceid_v2, weight_v2, context_crop_factor, reference_image, combine_embeds="concat", neg_image=None):
+    @staticmethod
+    def doit(segs, ipadapter_pipe, weight, noise, weight_type, start_at, end_at, unfold_batch, faceid_v2, weight_v2, context_crop_factor, reference_image, combine_embeds="concat", neg_image=None):
 
         if len(ipadapter_pipe) == 4:
             print(f"[Impact Pack] IPAdapterApplySEGS: Installed Inspire Pack is outdated.")
@@ -1255,7 +1263,8 @@ class ControlNetApplySEGS:
 
     CATEGORY = "ImpactPack/Util"
 
-    def doit(self, segs, control_net, strength, segs_preprocessor=None, control_image=None):
+    @staticmethod
+    def doit(segs, control_net, strength, segs_preprocessor=None, control_image=None):
         new_segs = []
 
         for seg in segs[1]:
@@ -1288,7 +1297,8 @@ class ControlNetApplyAdvancedSEGS:
 
     CATEGORY = "ImpactPack/Util"
 
-    def doit(self, segs, control_net, strength, start_percent, end_percent, segs_preprocessor=None, control_image=None):
+    @staticmethod
+    def doit(segs, control_net, strength, start_percent, end_percent, segs_preprocessor=None, control_image=None):
         new_segs = []
 
         for seg in segs[1]:
@@ -1311,7 +1321,8 @@ class ControlNetClearSEGS:
 
     CATEGORY = "ImpactPack/Util"
 
-    def doit(self, segs):
+    @staticmethod
+    def doit(segs):
         new_segs = []
 
         for seg in segs[1]:
@@ -1369,7 +1380,8 @@ class SEGSPicker:
 
     CATEGORY = "ImpactPack/Util"
 
-    def doit(self, picks, segs, fallback_image_opt=None, unique_id=None):
+    @staticmethod
+    def doit(picks, segs, fallback_image_opt=None, unique_id=None):
         if fallback_image_opt is not None:
             segs = core.segs_scale_match(segs, fallback_image_opt.shape)
 
@@ -1424,7 +1436,8 @@ class DefaultImageForSEGS:
 
     CATEGORY = "ImpactPack/Util"
 
-    def doit(self, segs, image, override):
+    @staticmethod
+    def doit(segs, image, override):
         results = []
 
         segs = core.segs_scale_match(segs, image.shape)
@@ -1468,7 +1481,8 @@ class RemoveImageFromSEGS:
 
     CATEGORY = "ImpactPack/Util"
 
-    def doit(self, segs):
+    @staticmethod
+    def doit(segs):
         results = []
 
         if len(segs[1]) > 0:
@@ -1505,7 +1519,8 @@ class MakeTileSEGS:
 
     CATEGORY = "ImpactPack/__for_testing"
 
-    def doit(self, images, bbox_size, crop_factor, min_overlap, filter_segs_dilation, mask_irregularity=0, irregular_mask_mode="Reuse fast", filter_in_segs_opt=None, filter_out_segs_opt=None):
+    @staticmethod
+    def doit(images, bbox_size, crop_factor, min_overlap, filter_segs_dilation, mask_irregularity=0, irregular_mask_mode="Reuse fast", filter_in_segs_opt=None, filter_out_segs_opt=None):
         if bbox_size <= 2*min_overlap:
             new_min_overlap = bbox_size / 2
             print(f"[MakeTileSEGS] min_overlap should be greater than bbox_size. (value changed: {min_overlap} => {new_min_overlap})")

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 = "5.6"
+version = "5.7"
 license = "LICENSE"
 dependencies = ["segment-anything", "scikit-image", "piexif", "transformers", "opencv-python-headless", "GitPython", "scipy>=1.11.4"]
 
@@ -11,5 +11,5 @@ Repository = "https://github.com/ltdrdata/ComfyUI-Impact-Pack"
 
 [tool.comfy]
 PublisherId = "drltdata"
-DisplayName = "ComfyUI-Impact-Pack"
+DisplayName = "ComfyUI Impact Pack"
 Icon = ""