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, 2]
+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+            print(f"[Impact Pack] Your ComfyUI is outdated.")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]

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 = "8.1.2"
+version = "8.1.4"
 license = { file = "LICENSE.txt" }
 dependencies = ["segment-anything", "scikit-image", "piexif", "transformers", "opencv-python-headless", "GitPython", "scipy>=1.11.4"]