refactor: use ControlNetApplyAdvanced instead of legacy ControlNetApply

https://github.com/ltdrdata/ComfyUI-Impact-Pack/issues/863

modules/impact/config.py

@@ -1,7 +1,7 @@
 import configparser
 import os
 
-version_code = [8, 1, 1]
+version_code = [8, 1, 4]
 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

@@ -1384,9 +1384,14 @@ def vae_decode(vae, samples, use_tile, hook, tile_size=512, overlap=64):
     return pixels
 
 
-def vae_encode(vae, pixels, use_tile, hook, tile_size=512):
+def vae_encode(vae, pixels, use_tile, hook, tile_size=512, overlap=64):
     if use_tile:
-        samples = nodes.VAEEncodeTiled().encode(vae, pixels, tile_size)[0]
+        encoder = nodes.VAEEncodeTiled()
+        if 'overlap' in inspect.signature(encoder.encode).parameters:
+            samples = encoder.encode(vae, pixels, tile_size, overlap=overlap)[0]
+        else:
+            print(f"[Impact Pack] Your ComfyUI is outdated.")
+            samples = encoder.encode(vae, pixels, tile_size)[0]
     else:
         samples = nodes.VAEEncode().encode(vae, pixels)[0]
 
@@ -1412,7 +1417,7 @@ def latent_upscale_on_pixel_space_shape2(samples, scale_method, w, h, vae, use_t
     if hook is not None:
         pixels = hook.post_upscale(pixels)
 
-    return (vae_encode(vae, pixels, use_tile, hook, tile_size=tile_size), old_pixels)
+    return vae_encode(vae, pixels, use_tile, hook, tile_size=tile_size, overlap=overlap), old_pixels
 
 
 def latent_upscale_on_pixel_space(samples, scale_method, scale_factor, vae, use_tile=False, tile_size=512, save_temp_prefix=None, hook=None, overlap=64):
@@ -1433,7 +1438,7 @@ def latent_upscale_on_pixel_space2(samples, scale_method, scale_factor, vae, use
     if hook is not None:
         pixels = hook.post_upscale(pixels)
 
-    return (vae_encode(vae, pixels, use_tile, hook, tile_size=tile_size), old_pixels)
+    return vae_encode(vae, pixels, use_tile, hook, tile_size=tile_size, overlap=overlap), old_pixels
 
 
 def latent_upscale_on_pixel_space_with_model_shape(samples, scale_method, upscale_model, new_w, new_h, vae, use_tile=False, tile_size=512, save_temp_prefix=None, hook=None, overlap=64):
@@ -1464,7 +1469,7 @@ def latent_upscale_on_pixel_space_with_model_shape2(samples, scale_method, upsca
     if hook is not None:
         pixels = hook.post_upscale(pixels)
 
-    return (vae_encode(vae, pixels, use_tile, hook, tile_size=tile_size), old_pixels)
+    return vae_encode(vae, pixels, use_tile, hook, tile_size=tile_size, overlap=overlap), old_pixels
 
 
 def latent_upscale_on_pixel_space_with_model(samples, scale_method, upscale_model, scale_factor, vae, use_tile=False,
@@ -1500,7 +1505,7 @@ def latent_upscale_on_pixel_space_with_model2(samples, scale_method, upscale_mod
     if hook is not None:
         pixels = hook.post_upscale(pixels)
 
-    return (vae_encode(vae, pixels, use_tile, hook, tile_size=tile_size), old_pixels)
+    return vae_encode(vae, pixels, use_tile, hook, tile_size=tile_size, overlap=overlap), old_pixels
 
 
 class TwoSamplersForMaskUpscaler:
@@ -1670,8 +1675,14 @@ class PixelKSampleUpscaler:
                 preprocessor = nodes.NODE_CLASS_MAPPINGS['TilePreprocessor']()
                 # might add capacity to set pyrUp_iters later, not needed for now though
                 preprocessed = preprocessor.execute(images, pyrUp_iters=3, resolution=min(image_w, image_h))[0]
-                apply_cnet = getattr(nodes.ControlNetApply(), nodes.ControlNetApply.FUNCTION)
-                positive = apply_cnet(positive, self.tile_cnet, preprocessed, strength=self.tile_cnet_strength)[0]
+                positive, negative = nodes.ControlNetApplyAdvanced().apply_controlnet(positive=positive,
+                                                                                      negative=negative,
+                                                                                      control_net=self.tile_cnet,
+                                                                                      image=preprocessed,
+                                                                                      strength=self.tile_cnet_strength,
+                                                                                      start_percent=0,
+                                                                                      end_percent=1.0,
+                                                                                      vae=self.vae)
 
         refined_latent = impact_sampling.impact_sample(model, seed, steps, cfg, sampler_name, scheduler,
                                                        positive, negative, upscaled_latent, denoise, scheduler_func=self.scheduler_func)
@@ -1974,8 +1985,14 @@ class PixelTiledKSampleUpscaler:
                 preprocessor = nodes.NODE_CLASS_MAPPINGS['TilePreprocessor']()
                 # might add capacity to set pyrUp_iters later, not needed for now though
                 preprocessed = preprocessor.execute(images, pyrUp_iters=3, resolution=min(image_w, image_h))[0]
-                apply_cnet = getattr(nodes.ControlNetApply(), nodes.ControlNetApply.FUNCTION)
-                positive = apply_cnet(positive, self.tile_cnet, preprocessed, strength=self.tile_cnet_strength)[0]
+
+                positive, negative = nodes.ControlNetApplyAdvanced().apply_controlnet(positive=positive,
+                                                                                      negative=negative,
+                                                                                      control_net=self.tile_cnet,
+                                                                                      image=preprocessed,
+                                                                                      strength=self.tile_cnet_strength,
+                                                                                      start_percent=0, end_percent=1.0,
+                                                                                      vae=self.vae)
 
         return TiledKSampler().sample(model, seed, tile_width, tile_height, tiling_strategy, steps, cfg, sampler_name,
                                       scheduler, positive, negative, latent, denoise)[0]

modules/impact/impact_server.py

@@ -22,6 +22,7 @@ import comfy
 from io import BytesIO
 import random
 from server import PromptServer
+import logging
 
 
 sam_predictor = None
@@ -79,7 +80,7 @@ async def sam_prepare(request):
 
         model_name = os.path.join(impact_pack.model_path, "sams", model_name)
 
-        print(f"[INFO] ComfyUI-Impact-Pack: Loading SAM model '{impact_pack.model_path}'")
+        logging.info(f"[Impact Pack] Loading SAM model '{impact_pack.model_path}'")
 
         filename, image_dir = folder_paths.annotated_filepath(data["filename"])
 
@@ -95,7 +96,7 @@ async def sam_prepare(request):
         thread = threading.Thread(target=async_prepare_sam, args=(image_dir, model_name, filename,))
         thread.start()
 
-        print(f"[INFO] ComfyUI-Impact-Pack: SAM model loaded. ")
+        logging.info("[Impact Pack] SAM model loaded. ")
     return web.Response(status=200)
 
 
@@ -107,7 +108,7 @@ async def release_sam(request):
         del sam_predictor
         sam_predictor = None
 
-    print(f"[INFO] ComfyUI-Impact-Pack: unloading SAM model")
+    logging.info("[Impact Pack]: unloading SAM model")
 
 
 @PromptServer.instance.routes.post("/sam/detect")
@@ -324,20 +325,24 @@ def onprompt_for_switch(json_data):
 
         cls = v['class_type']
         if cls == 'ImpactInversedSwitch':
+            # if 'sel_mode' is 'select_on_prompt'
             if 'sel_mode' in v['inputs'] and v['inputs']['sel_mode'] and 'select' in v['inputs']:
                 select_input = v['inputs']['select']
+                # if 'select' is converted input
                 if isinstance(select_input, list) and len(select_input) == 2:
                     input_node = json_data['prompt'][select_input[0]]
                     if input_node['class_type'] == 'ImpactInt' and 'inputs' in input_node and 'value' in input_node['inputs']:
                         inversed_switch_info[k] = input_node['inputs']['value']
                     else:
-                        print(f"\n##### ##### #####\n[WARN] {cls}: For the 'select' operation, only 'select_index' of the 'ImpactInversedSwitch', which is not an input, or 'ImpactInt' and 'Primitive' are allowed as inputs if 'select_on_prompt' is selected.\n##### ##### #####\n")
+                        logging.warning(f"\n##### ##### #####\n[Impact Pack] {cls}: For the 'select' operation, only 'select_index' of the 'ImpactInversedSwitch', which is not an input, or 'ImpactInt' and 'Primitive' are allowed as inputs if 'select_on_prompt' is selected.\n##### ##### #####\n")
                 else:
                     inversed_switch_info[k] = select_input
 
         elif cls in ['ImpactSwitch', 'LatentSwitch', 'SEGSSwitch', 'ImpactMakeImageList']:
+            # if 'sel_mode' is 'select_on_prompt'
             if 'sel_mode' in v['inputs'] and v['inputs']['sel_mode'] and 'select' in v['inputs']:
                 select_input = v['inputs']['select']
+                # if 'select' is converted input
                 if isinstance(select_input, list) and len(select_input) == 2:
                     input_node = json_data['prompt'][select_input[0]]
                     if input_node['class_type'] == 'ImpactInt' and 'inputs' in input_node and 'value' in input_node['inputs']:
@@ -346,7 +351,7 @@ def onprompt_for_switch(json_data):
                         if isinstance(input_node['inputs']['select'], int):
                             onprompt_switch_info[k] = input_node['inputs']['select']
                         else:
-                            print(f"\n##### ##### #####\n[WARN] {cls}: For the 'select' operation, only 'select_index' of the 'ImpactSwitch', which is not an input, or 'ImpactInt' and 'Primitive' are allowed as inputs if 'select_on_prompt' is selected.\n##### ##### #####\n")
+                            logging.warning(f"\n##### ##### #####\n[Impact Pack] {cls}: For the 'select' operation, only 'select_index' of the 'ImpactSwitch', which is not an input, or 'ImpactInt' and 'Primitive' are allowed as inputs if 'select_on_prompt' is selected.\n##### ##### #####\n")
                 else:
                     onprompt_switch_info[k] = select_input
 
@@ -377,6 +382,8 @@ def onprompt_for_switch(json_data):
                 if vv[0] in inversed_switch_info:
                     if vv[1] + 1 != inversed_switch_info[vv[0]]:
                         disable_targets.add(kk)
+                    else:
+                        del inversed_switch_info[k]
 
                 if vv[0] in disabled_switch:
                     disable_targets.add(kk)
@@ -396,6 +403,11 @@ def onprompt_for_switch(json_data):
         for kk in disable_targets:
             del v['inputs'][kk]
 
+    # inversed_switch - select out of range
+    for target in inversed_switch_info.keys():
+        del json_data['prompt'][target]['inputs']['input']
+
+
 def onprompt_for_pickers(json_data):
     detected_pickers = set()
 
@@ -479,7 +491,7 @@ def onprompt_populate_wildcards(json_data):
                             if not isinstance(input_seed, int):
                                 continue
                         else:
-                            print(f"[Impact Pack] Only `ImpactInt`, `Seed (rgthree)` and `Primitive` Node are allowed as the seed for '{v['class_type']}'. It will be ignored. ")
+                            logging.info(f"[Impact Pack] Only `ImpactInt`, `Seed (rgthree)` and `Primitive` Node are allowed as the seed for '{v['class_type']}'. It will be ignored. ")
                             continue
                     except:
                         continue
@@ -542,7 +554,7 @@ def onprompt(json_data):
         regional_sampler_seed_update(json_data)
         core.current_prompt = json_data
     except Exception as e:
-        print(f"[WARN] ComfyUI-Impact-Pack: Error on prompt - several features will not work.\n{e}")
+        logging.warning(f"[Impact Pack] ComfyUI-Impact-Pack: Error on prompt - several features will not work.\n{e}")
 
     return json_data
 

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 = "8.1.1"
+version = "8.1.4"
 license = { file = "LICENSE.txt" }
 dependencies = ["segment-anything", "scikit-image", "piexif", "transformers", "opencv-python-headless", "GitPython", "scipy>=1.11.4"]