Added initial modules

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintainted in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
+*.pyc
+
+.idea
+/node_modules

__init__.py

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+from .nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
+
+__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS']

nodes.py

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+from diffusers.pipelines.controlnet.multicontrolnet import MultiControlNetModel
+import numpy as np
+from PIL import Image
+import torch
+
+
+class AnyType(str):
+    """A special class that is always equal in not equal comparisons. Credit to pythongosssss"""
+
+    def __eq__(self, _) -> bool:
+        return True
+
+    def __ne__(self, __value: object) -> bool:
+        return False
+
+any = AnyType("*")
+
+
+class MultiControlNetConverter:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "controlnets": (any,),
+            },
+        }
+
+    RETURN_TYPES = ("MODEL",)
+    RETURN_NAMES = ("multicontrolnet",)
+    CATEGORY = "ControlNetUtils"
+    FUNCTION = "execute"
+
+    def execute(self, controlnets):
+        model = MultiControlNetModel(controlnets)
+        return [model]
+    
+    
+class NHWC2NCHWTensor:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+            },
+            "optional": {
+            }
+        }
+
+    CATEGORY = "ControlNetUtils"
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("image",)
+
+    FUNCTION = "execute"
+    def execute(self, image):
+        print(image.shape)
+        image.permute(0, 3, 1, 2)
+        print(image.shape)
+        return [image]
+    
+class NHWCTensor2PIL:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+            },
+            "optional": {
+            }
+        }
+
+    CATEGORY = "ControlNetUtils"
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("image",)
+
+    FUNCTION = "execute"
+    
+    def execute(self, image):
+        # convert NHWC Tensor to PIL Image
+        img_array = image.squeeze(0).cpu().numpy() * 255.0
+        img_pil = Image.fromarray(np.clip(img_array, 0, 255).astype(np.uint8))
+        return [img_pil]
+    
+    
+class PIL2NHWCTensor:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "image": ("IMAGE",),
+            },
+            "optional": {
+            }
+        }
+
+    CATEGORY = "ControlNetUtils"
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("image",)
+
+    FUNCTION = "execute"
+    def execute(self, image):
+        # 将 PIL.Image 转换为 numpy.ndarray
+        img_array = np.array(image)
+        # 转换 numpy.ndarray 为 torch.Tensor
+        img_tensor = torch.from_numpy(img_array).float() / 255.
+        # 转换图像格式为 CHW (如果需要)
+        if img_tensor.ndim == 3 and img_tensor.shape[-1] == 3:
+            img_tensor = img_tensor.permute(2, 0, 1)
+        # 添加批次维度并转换为 NHWC
+        img_tensor = img_tensor.unsqueeze(0).permute(0, 2, 3, 1)
+        return [img_tensor]
+    
+    
+class ListOfImages:
+    def __init__(self):
+        pass
+
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+            },
+            "optional": {
+                "any_1": ("IMAGE",),
+                "any_2": ("IMAGE",),
+                "any_3": ("IMAGE",),
+                "any_4": ("IMAGE",),
+                "any_5": ("IMAGE",),
+                "any_6": ("IMAGE",),
+                "any_7": ("IMAGE",),
+                "any_8": ("IMAGE",),
+            }
+        }
+
+    CATEGORY = "ControlNetUtils"
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("images",)
+
+    FUNCTION = "execute"
+
+    def execute(self,
+                any_1=None,
+                any_2=None,
+                any_3=None,
+                any_4=None,
+                any_5=None,
+                any_6=None,
+                any_7=None,
+                any_8=None):
+
+        list_any = []
+
+        if any_1 is not None:
+            try:
+                list_any.append(any_1)
+            except Exception as e:
+                raise e
+        if any_2 is not None:
+            try:
+                list_any.append(any_2)
+            except Exception as e:
+                raise e
+        if any_3 is not None:
+            try:
+                list_any.append(any_3)
+            except Exception as e:
+                raise e
+        if any_4 is not None:
+            try:
+                list_any.append(any_4)
+            except Exception as e:
+                raise e
+        if any_5 is not None:
+            try:
+                list_any.append(any_5)
+            except Exception as e:
+                raise e
+        if any_6 is not None:
+            try:
+                list_any.append(any_6)
+            except Exception as e:
+                raise e
+        if any_7 is not None:
+            try:
+                list_any.append(any_7)
+            except Exception as e:
+                raise e
+        if any_8 is not None:
+            try:
+                list_any.append(any_8)
+            except Exception as e:
+                raise e
+
+        # yes, double brackets are needed because of the OUTPUT_IS_LIST... ¯\_(ツ)_/¯
+        return [list_any]
+
+NODE_CLASS_MAPPINGS = {
+    "MultiControlNetConverter": MultiControlNetConverter,
+    "ListOfImages": ListOfImages,
+    "PIL2NHWCTensor": PIL2NHWCTensor,
+    "NHWC2NCHWTensor": NHWC2NCHWTensor,
+    "NHWCTensor2PIL": NHWCTensor2PIL
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "MultiControlNetConverter": "Multi-ControlNet Converter",
+    "ListOfImages": "List of Images",
+    "PIL2NHWCTensor": "Convert PIL to Tensor (NHWC)",
+    "NHWC2NCHWTensor": "Convert Tensor (NHWC) to (NCHW)",
+    "NHWCTensor2PIL": "Convert Tensor (NHWC) to PIL"
+}

requirements.txt

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+diffusers