Fix #430, #431 and add new logo

README.md

@@ -1,7 +1,12 @@
 # ComfyUI's ControlNet Auxiliary Preprocessors
-![](./examples/example_mesh_graphormer.png)
 Plug-and-play [ComfyUI](https://github.com/comfyanonymous/ComfyUI) node sets for making [ControlNet](https://github.com/lllyasviel/ControlNet/) hint images
 
+"anime style, a protest in the street, cyberpunk city, a woman with pink hair and golden eyes (looking at the viewer) is holding a sign with the text "ComfyUI ControlNet Aux" in bold, neon pink" on Flux.1 Dev
+[Original image with workflow](./examples/ExecuteAll.png)
+![](./examples/ExecuteAll1.jpg)
+![](./examples/ExecuteAll2.jpg)
+
+
 The code is copy-pasted from the respective folders in https://github.com/lllyasviel/ControlNet/tree/main/annotator and connected to [the 🤗 Hub](https://huggingface.co/lllyasviel/Annotators).
 
 All credit & copyright goes to https://github.com/lllyasviel.

__init__.py

@@ -1,9 +1,7 @@
 import sys, os
 from .utils import here, define_preprocessor_inputs, INPUT
 from pathlib import Path
-import threading
 import traceback
-import warnings
 import importlib
 from .log import log, blue_text, cyan_text, get_summary, get_label
 from .hint_image_enchance import NODE_CLASS_MAPPINGS as HIE_NODE_CLASS_MAPPINGS
@@ -62,8 +60,10 @@ def load_nodes():
 
 AUX_NODE_MAPPINGS, AUX_DISPLAY_NAME_MAPPINGS = load_nodes()
 
-AIO_NOT_SUPPORTED = ["InpaintPreprocessor"]
-#For nodes not mapping image to image
+#For nodes not mapping image to image or has special requirements
+AIO_NOT_SUPPORTED = ["InpaintPreprocessor", "MeshGraphormer+ImpactDetector-DepthMapPreprocessor", "DiffusionEdge_Preprocessor"]
+AIO_NOT_SUPPORTED += ["SavePoseKpsAsJsonFile", "FacialPartColoringFromPoseKps", "UpperBodyTrackingFromPoseKps", "RenderPeopleKps", "RenderAnimalKps"]
+AIO_NOT_SUPPORTED += ["Unimatch_OptFlowPreprocessor", "MaskOptFlow"]
 
 def preprocessor_options():
     auxs = list(AUX_NODE_MAPPINGS.keys())
@@ -119,6 +119,63 @@ def execute(self, preprocessor, image, resolution=512):
 
             return getattr(aux_class(), aux_class.FUNCTION)(**params)
 
+class ControlNetAuxSimpleAddText:
+    @classmethod
+    def INPUT_TYPES(s):
+        return dict(
+            required=dict(image=INPUT.IMAGE(), text=INPUT.STRING())
+        )
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "execute"
+    CATEGORY = "ControlNet Preprocessors"
+    def execute(self, image, text):
+        from PIL import Image, ImageDraw, ImageFont
+        import numpy as np
+        import torch
+
+        font = ImageFont.truetype(str((here / "NotoSans-Regular.ttf").resolve()), 40)
+        img = Image.fromarray(image[0].cpu().numpy().__mul__(255.).astype(np.uint8))
+        ImageDraw.Draw(img).text((0,0), text, fill=(0,255,0), font=font)
+        return (torch.from_numpy(np.array(img)).unsqueeze(0) / 255.,)
+
+class ExecuteAllControlNetPreprocessors:
+    @classmethod
+    def INPUT_TYPES(s):
+        return define_preprocessor_inputs(resolution=INPUT.RESOLUTION())
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "execute"
+
+    CATEGORY = "ControlNet Preprocessors"
+
+    def execute(self, image, resolution=512):
+        try:
+            from comfy_execution.graph_utils import GraphBuilder
+        except:
+            raise RuntimeError("ExecuteAllControlNetPreprocessor requries [Execution Model Inversion](https://github.com/comfyanonymous/ComfyUI/commit/5cfe38). Update ComfyUI/SwarmUI to get this feature")
+
+        graph = GraphBuilder()
+        curr_outputs = []
+        for preprocc in PREPROCESSOR_OPTIONS:
+            preprocc_node = graph.node("AIO_Preprocessor", preprocessor=preprocc, image=image, resolution=resolution)
+            hint_img = preprocc_node.out(0)
+            add_text_node = graph.node("ControlNetAuxSimpleAddText", image=hint_img, text=preprocc)
+            curr_outputs.append(add_text_node.out(0))
+
+        while len(curr_outputs) > 1:
+            _outputs = []
+            for i in range(0, len(curr_outputs), 2):
+                if i+1 < len(curr_outputs):
+                    image_batch = graph.node("ImageBatch", image1=curr_outputs[i], image2=curr_outputs[i+1])
+                    _outputs.append(image_batch.out(0))
+                else:
+                    _outputs.append(curr_outputs[i])
+            curr_outputs = _outputs
+
+        return {
+            "result": (curr_outputs[0],),
+            "expand": graph.finalize(),
+        }
 
 class ControlNetPreprocessorSelector:
     @classmethod
@@ -144,11 +201,14 @@ def get_preprocessor(self, preprocessor: str):
     "AIO_Preprocessor": AIO_Preprocessor,
     "ControlNetPreprocessorSelector": ControlNetPreprocessorSelector,
     **HIE_NODE_CLASS_MAPPINGS,
+    "ExecuteAllControlNetPreprocessors": ExecuteAllControlNetPreprocessors,
+    "ControlNetAuxSimpleAddText": ControlNetAuxSimpleAddText
 }
 
 NODE_DISPLAY_NAME_MAPPINGS = {
     **AUX_DISPLAY_NAME_MAPPINGS,
     "AIO_Preprocessor": "AIO Aux Preprocessor",
     "ControlNetPreprocessorSelector": "Preprocessor Selector",
     **HIE_NODE_DISPLAY_NAME_MAPPINGS,
+    "ExecuteAllControlNetPreprocessors": "Execute All ControlNet Preprocessors"
 }

node_wrappers/depth_anything.py

@@ -6,9 +6,9 @@ class Depth_Anything_Preprocessor:
     def INPUT_TYPES(s):
         return define_preprocessor_inputs(
             ckpt_name=INPUT.COMBO(
-                ["depth_anything_vitl14.pth", "depth_anything_vitb14.pth", "depth_anything_vits14.pth"],
-                resolution=INPUT.RESOLUTION()
-            )
+                ["depth_anything_vitl14.pth", "depth_anything_vitb14.pth", "depth_anything_vits14.pth"]
+            ),
+            resolution=INPUT.RESOLUTION()
         )
 
     RETURN_TYPES = ("IMAGE",)

node_wrappers/recolor.py

@@ -22,7 +22,7 @@ class ImageIntensityDetector:
     @classmethod
     def INPUT_TYPES(s):
         #https://github.com/Mikubill/sd-webui-controlnet/blob/416c345072c9c2066101e225964e3986abe6945e/scripts/processor.py#L1229
-        define_preprocessor_inputs(
+        return define_preprocessor_inputs(
             gamma_correction=INPUT.FLOAT(default=1.0, min=0.1, max=2.0),
             resolution=INPUT.RESOLUTION()
         )

node_wrappers/unimatch.py

@@ -27,7 +27,7 @@ def INPUT_TYPES(s):
     CATEGORY = "ControlNet Preprocessors/Optical Flow"
 
     def estimate(self, image, ckpt_name, backward_flow=False, bidirectional_flow=False):
-        assert len(image) > 1, "[Unimatch] Requiring as least two frames as a optical flow estimator. Only use this node on video input."    
+        assert len(image) > 1, "[Unimatch] Requiring as least two frames as an optical flow estimator. Only use this node on video input."    
         from controlnet_aux.unimatch import UnimatchDetector
         tensor_images = image
         model = UnimatchDetector.from_pretrained(filename=ckpt_name).to(model_management.get_torch_device())

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "comfyui_controlnet_aux"
 description = "Plug-and-play ComfyUI node sets for making ControlNet hint images"
-version = "1.0.4-alpha.1"
+version = "1.0.4-alpha.2"
 license = "LICENSE"
 dependencies = ["torch", "importlib_metadata", "huggingface_hub", "scipy", "opencv-python>=4.7.0.72", "filelock", "numpy", "Pillow", "einops", "torchvision", "pyyaml", "scikit-image", "python-dateutil", "mediapipe", "svglib", "fvcore", "yapf", "omegaconf", "ftfy", "addict", "yacs", "trimesh[easy]", "albumentations", "scikit-learn"]
 

src/controlnet_aux/anime_face_segment/__init__.py

@@ -22,7 +22,7 @@ def from_pretrained(cls, pretrained_model_or_path=BDS_MODEL_NAME, filename="UNet
         seg_model_path = custom_hf_download("skytnt/anime-seg", seg_filename)
 
         model = UNet()
-        ckpt = torch.load(model_path)
+        ckpt = torch.load(model_path, map_location="cpu")
         model.load_state_dict(ckpt)
         model.eval()