Controlnet conditioning updates

otls/mlops.sd_controlnet_conditioning.2.0.hda/INDEX__SECTION

@@ -10,5 +10,5 @@ Inputs:       1 to 1
 Subnet:       true
 Python:       false
 Empty:        false
-Modified:     Sun Jul  9 13:12:03 2023
+Modified:     Tue Sep 12 09:06:21 2023
 

otls/mlops.sd_controlnet_conditioning.2.0.hda/mlops_8_8Sop_1sd__controlnet__conditioning_8_82.0/Contents.dir/Contents.mime

@@ -30,7 +30,7 @@ matchesdef = 0
 Content-Disposition: attachment; filename="hdaroot.def"
 Content-Type: text/plain
 
-sopflags sopflags = 
+sopflags sopflags =
 comment ""
 position -0.851307 -0.411438
 connectornextid 1
@@ -53,7 +53,7 @@ stat
   author Paul@DESKTOP-PAFFS1R
   access 0777
 }
-color UT_Color RGB 0.8 0.8 0.8 
+color UT_Color RGB 0.8 0.8 0.8
 delscript ""
 exprlanguage hscript
 end
@@ -87,7 +87,7 @@ matchesdef = 1
 Content-Disposition: attachment; filename="hdaroot/output0.def"
 Content-Type: text/plain
 
-sopflags sopflags = 
+sopflags sopflags =
 comment ""
 position -4.94997 2.43356
 connectornextid 1
@@ -110,7 +110,7 @@ stat
   author Paul@M16AMTA
   access 0777
 }
-color UT_Color RGB 0.8 0.8 0.8 
+color UT_Color RGB 0.8 0.8 0.8
 delscript ""
 exprlanguage hscript
 end
@@ -146,7 +146,7 @@ matchesdef = 1
 Content-Disposition: attachment; filename="hdaroot/python1.def"
 Content-Type: text/plain
 
-sopflags sopflags = 
+sopflags sopflags =
 comment ""
 position -4.94997 5.51136
 connectornextid 2
@@ -170,7 +170,7 @@ stat
   author Paul@DESKTOP-PAFFS1R
   access 0777
 }
-color UT_Color RGB 0.8 0.8 0.8 
+color UT_Color RGB 0.8 0.8 0.8
 delscript ""
 exprlanguage hscript
 end
@@ -189,7 +189,7 @@ from imagepipeline import controlnet_condition_image
 from imp import reload
 reload(mlops_utils)
 reload(mlops_image_utils)
-reload(controlnet_condition_image) 
+reload(controlnet_condition_image)
 
 # Input Geometry
 node = hou.pwd()
@@ -198,7 +198,7 @@ geo = node.geometry()
 # Parameters
 cache_only = node.parent().parm(\"cache_only\").evalAsInt()
 model_type = node.parent().parm(\"conditioning\").evalAsString()
-model = node.parent().parm(\"model\").evalAsString() 
+model = node.parent().parm(\"model\").evalAsString()
 
 # Attributes
 input_colors = mlops_image_utils.colored_points_to_numpy_array(geo)
@@ -233,7 +233,7 @@ matchesdef = 1
 Content-Disposition: attachment; filename="hdaroot/attribwrangle2.def"
 Content-Type: text/plain
 
-sopflags sopflags = 
+sopflags sopflags =
 comment ""
 position -4.95297 6.54926
 connectornextid 2
@@ -257,7 +257,7 @@ stat
   author Paul@M16AMTA
   access 0777
 }
-color UT_Color RGB 0.8 0.8 0.8 
+color UT_Color RGB 0.8 0.8 0.8
 delscript ""
 exprlanguage hscript
 end
@@ -314,7 +314,7 @@ matchesdef = 1
 Content-Disposition: attachment; filename="hdaroot/attribwrangle3.def"
 Content-Type: text/plain
 
-sopflags sopflags = 
+sopflags sopflags =
 comment ""
 position -4.95297 4.47146
 connectornextid 2
@@ -338,7 +338,7 @@ stat
   author Paul@M16AMTA
   access 0777
 }
-color UT_Color RGB 0.8 0.8 0.8 
+color UT_Color RGB 0.8 0.8 0.8
 delscript ""
 exprlanguage hscript
 end
@@ -393,7 +393,7 @@ matchesdef = 1
 Content-Disposition: attachment; filename="hdaroot/attribdelete1.def"
 Content-Type: text/plain
 
-sopflags sopflags = 
+sopflags sopflags =
 comment ""
 position -4.95342 3.43356
 connectornextid 2
@@ -417,7 +417,7 @@ stat
   author Paul@DESKTOP-PAFFS1R
   access 0777
 }
-color UT_Color RGB 0.8 0.8 0.8 
+color UT_Color RGB 0.8 0.8 0.8
 delscript ""
 exprlanguage hscript
 end
@@ -463,7 +463,7 @@ matchesdef = 1
 Content-Disposition: attachment; filename="hdaroot/resize_image1.def"
 Content-Type: text/plain
 
-sopflags sopflags = 
+sopflags sopflags =
 comment ""
 position -4.94997 7.58916
 connectornextid 2
@@ -487,7 +487,7 @@ stat
   author Paul@DESKTOP-PAFFS1R
   access 0777
 }
-color UT_Color RGB 0.8 0.8 0.8 
+color UT_Color RGB 0.8 0.8 0.8
 delscript ""
 exprlanguage hscript
 end
@@ -559,7 +559,7 @@ matchesdef = 1
 Content-Disposition: attachment; filename="hdaroot/null1.def"
 Content-Type: text/plain
 
-sopflags sopflags = 
+sopflags sopflags =
 comment ""
 position -4.94997 8.68186
 connectornextid 2
@@ -583,7 +583,7 @@ stat
   author Mo@MO-GPU
   access 0777
 }
-color UT_Color RGB 0.6 0.7 0.77 
+color UT_Color RGB 0.6 0.7 0.77
 delscript ""
 exprlanguage hscript
 end
@@ -620,7 +620,7 @@ matchesdef = 1
 Content-Disposition: attachment; filename="hdaroot/python2.def"
 Content-Type: text/plain
 
-sopflags sopflags = 
+sopflags sopflags =
 comment ""
 position -8.47403 4.9679
 connectornextid 0
@@ -641,7 +641,7 @@ stat
   author Paul@DESKTOP-PAFFS1R
   access 0777
 }
-color UT_Color RGB 0.8 0.8 0.8 
+color UT_Color RGB 0.8 0.8 0.8
 delscript ""
 exprlanguage hscript
 end
@@ -704,3 +704,4 @@ Content-Type: text/plain
 1
 
 --HOUDINIMIMEBOUNDARY0xD3ADD339-0x00000F49-0x56B122C9-0x00000001HOUDINIMIMEBOUNDARY--
+

otls/mlops.sd_controlnet_conditioning.2.0.hda/mlops_8_8Sop_1sd__controlnet__conditioning_8_82.0/DialogScript

@@ -56,6 +56,34 @@
             }
             parmtag { "script_callback_language" "python" }
         }
+        parm {
+            name    "sepparm"
+            label   "Separator"
+            type    separator
+            default { "" }
+        }
+        groupcollapsible {
+            name    "xn__DetectorParameters_cra"
+            label   "Detector Parameters"
+
+            parm {
+                name    "low_threshold"
+                label   "Low Threshold"
+                type    integer
+                default { "100" }
+                range   { 0 255 }
+                parmtag { "script_callback_language" "python" }
+            }
+            parm {
+                name    "high_threshold"
+                label   "High Threshold"
+                type    integer
+                default { "200" }
+                range   { 0 255 }
+                parmtag { "script_callback_language" "python" }
+            }
+        }
+
     }
 
 }

scripts/python/imagepipeline/controlnet_condition_image.py

@@ -20,39 +20,46 @@
 from transformers import AutoImageProcessor, UperNetForSemanticSegmentation
 
 
-def run(model, mode, input_colors, cache_only):
+def run(model, mode, input_colors, cache_only,low_threshold=None, high_threshold=None):
     img = mlops_image_utils.colors_numpy_array_to_pil(input_colors)
-
+    width, height = img.width, img.height
+    args = dict()
+    args['detect_resolution'] = width
+    args['image_resolution'] = width
     if mode == "hed":
         hed = HEDdetector.from_pretrained(model)
-        processed_image = hed(img)
+        processed_image = hed(img, **args)
     elif mode == "midas":
         midas = MidasDetector.from_pretrained(model)
-        processed_image = midas(img)
+        processed_image = midas(img, **args)
     elif mode == "mlsd":
         mlsd = MLSDdetector.from_pretrained(model)
-        processed_image = mlsd(img)
+        processed_image = mlsd(img, **args)
     elif mode == "openpose":
         open_pose = OpenposeDetector.from_pretrained(model)
-        processed_image = open_pose(img, hand_and_face=True)
+        processed_image = open_pose(img, hand_and_face=True, **args)
     elif mode == "pidi":
         pidi = PidiNetDetector.from_pretrained(model)
-        processed_image = pidi(img, safe=True)
+        processed_image = pidi(img, safe=True, **args)
     elif mode == "bae":
         normal_bae = NormalBaeDetector.from_pretrained(model)
-        processed_image = normal_bae(img)
+        processed_image = normal_bae(img, **args)
     elif mode == "lineart":
         lineart = LineartDetector.from_pretrained(model)
-        processed_image = lineart(img, coarse=True)
+        processed_image = lineart(img, coarse=True, **args)
     elif mode == "lineartanime":
         lineart_anime = LineartAnimeDetector.from_pretrained(model)
-        processed_image = lineart_anime(img)
+        processed_image = lineart_anime(img, **args)
     elif mode == "zoe":
         zoe = ZoeDetector.from_pretrained(model)
-        processed_image = zoe(img)
+        processed_image = zoe(img, **args)
     elif mode == "canny":
+        if low_threshold is not None:
+            args['low_threshold'] = low_threshold
+        if high_threshold is not None:
+            args['high_threshold'] = high_threshold
         canny = CannyDetector()
-        processed_image = canny(img)
+        processed_image = canny(img, **args)
     elif mode == "content":
         content = ContentShuffleDetector()
         processed_image = content(img)
@@ -61,7 +68,7 @@ def run(model, mode, input_colors, cache_only):
         processed_image = face_detector(img)
     elif mode == "leres":
         leres_detector = LeresDetector.from_pretrained(model)
-        processed_image = leres_detector(img)
+        processed_image = leres_detector(img, **args)
     elif mode == "segment":
         image_processor = AutoImageProcessor.from_pretrained(
             "openmmlab/upernet-convnext-small", local_files_only=cache_only