Installable with links to the known models

Author: doctorpangloss

__init__.py

@@ -1,6 +1,2 @@
-from .adv_control.nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
-from .adv_control import documentation
-
 WEB_DIRECTORY = "./web"
-__all__ = ['NODE_CLASS_MAPPINGS', 'NODE_DISPLAY_NAME_MAPPINGS', "WEB_DIRECTORY"]
-documentation.format_descriptions(NODE_CLASS_MAPPINGS)
+

adv_control/__init__.py

@@ -141,7 +141,7 @@ def __init__(self, condhint: Tensor):
 class ReferenceAdvanced(ControlBase, AdvancedControlBase):
     CHANNEL_TO_MULT = {320: 1, 640: 2, 1280: 4}
 
-    def __init__(self, ref_opts: ReferenceOptions, timestep_keyframes: TimestepKeyframeGroup, device=None):
+    def __init__(self, ref_opts: ReferenceOptions, timestep_keyframes: TimestepKeyframeGroup | None, device=None):
         super().__init__(device)
         AdvancedControlBase.__init__(self, super(), timestep_keyframes=timestep_keyframes, weights_default=ControlWeights.controllllite(), allow_condhint_latents=True)
         # TODO: allow vae_optional to be used instead of preprocessor

adv_control/control_reference.py

@@ -1,4 +1,4 @@
-import folder_paths
+from comfy.model_downloader import get_or_download, get_filename_list_with_downloadable
 import comfy.utils
 import comfy.model_detection
 import comfy.model_management
@@ -37,8 +37,8 @@ class ControlNetLoaderWithLoraAdvanced:
     def INPUT_TYPES(s):
         return {
             "required": {
-                "control_net_name": (folder_paths.get_filename_list("controlnet"), ),
-                "cn_lora_name": (folder_paths.get_filename_list("controlnet"), ),
+                "control_net_name": (get_filename_list_with_downloadable("controlnet"), ),
+                "cn_lora_name": (get_filename_list_with_downloadable("controlnet"), ),
                 "cn_lora_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}),
             },
             "optional": {
@@ -54,12 +54,12 @@ def INPUT_TYPES(s):
     def load_controlnet(self, control_net_name, cn_lora_name, cn_lora_strength: float,
                         timestep_keyframe: TimestepKeyframeGroup=None
                         ):
-        controlnet_path = folder_paths.get_full_path("controlnet", control_net_name)
+        controlnet_path = get_or_download("controlnet", control_net_name)
         controlnet: ControlNetAdvanced = load_controlnet(controlnet_path, timestep_keyframe)
         if not isinstance(controlnet, ControlNetAdvanced):
             raise ValueError("Type {} is not compatible with CN LoRA features at this time.")
         # now, try to load CN LoRA
-        lora_path = folder_paths.get_full_path("controlnet", cn_lora_name)
+        lora_path = get_or_download("controlnet", cn_lora_name)
         lora_data = convert_cn_lora_from_diffusers(cn_model=controlnet.control_model_wrapped, lora_path=lora_path)
         # apply patches to wrapped control_model
         controlnet.control_model_wrapped.add_patches(lora_data, strength_patch=cn_lora_strength)

adv_control/nodes.py

@@ -1,8 +1,7 @@
 from torch import Tensor
 import math
 
-import folder_paths
-
+from comfy.model_downloader import get_or_download, get_filename_list_with_downloadable
 from .control_plusplus import load_controlnetplusplus, PlusPlusType, PlusPlusInput, PlusPlusInputGroup, PlusPlusImageWrapper
 from .utils import BIGMAX
 
@@ -13,7 +12,7 @@ def INPUT_TYPES(s):
         return {
             "required": {
                 "plus_input": ("PLUS_INPUT", ),
-                "name": (folder_paths.get_filename_list("controlnet"), ),
+                "name": (get_filename_list_with_downloadable("controlnet"), ),
             }
         }
 
@@ -23,7 +22,7 @@ def INPUT_TYPES(s):
     CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/ControlNet++"
 
     def load_controlnet_plusplus(self, plus_input: PlusPlusInputGroup, name: str):
-        controlnet_path = folder_paths.get_full_path("controlnet", name)
+        controlnet_path = get_or_download("controlnet", name)
         controlnet = load_controlnetplusplus(controlnet_path)
         controlnet.verify_control_type(name, plus_input)
         return (controlnet, PlusPlusImageWrapper(plus_input),)
@@ -34,7 +33,7 @@ class PlusPlusLoaderSingle:
     def INPUT_TYPES(s):
         return {
             "required": {
-                "name": (folder_paths.get_filename_list("controlnet"), ),
+                "name": (get_filename_list_with_downloadable("controlnet"), ),
                 "control_type": (PlusPlusType._LIST_WITH_NONE, {"default": PlusPlusType.NONE}, ),
             }
         }
@@ -45,7 +44,7 @@ def INPUT_TYPES(s):
     CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/ControlNet++"
 
     def load_controlnet_plusplus(self, name: str, control_type: str):
-        controlnet_path = folder_paths.get_full_path("controlnet", name)
+        controlnet_path = get_or_download("controlnet", name)
         controlnet = load_controlnetplusplus(controlnet_path)
         controlnet.single_control_type = control_type
         controlnet.verify_control_type(name)

adv_control/nodes_loosecontrol.py

@@ -1,6 +1,6 @@
 from torch import Tensor
 
-from nodes import VAEEncode
+from comfy.nodes.base_nodes import VAEEncode
 import comfy.utils
 from comfy.sd import VAE
 
@@ -82,9 +82,6 @@ def preprocess_images(self, vae: VAE, image: Tensor, latent_size: Tensor):
         image = comfy.utils.common_upscale(image, latent_size["samples"].shape[3] * 8, latent_size["samples"].shape[2] * 8, 'nearest-exact', "center")
         image = image.movedim(1,-1)
         # then, vae encode
-        try:
-            image = vae.vae_encode_crop_pixels(image)
-        except Exception:
-            image = VAEEncode.vae_encode_crop_pixels(image)
+        image = vae.vae_encode_crop_pixels(image)
         encoded = vae.encode(image[:,:,:,:3])
         return (ReferencePreprocWrapper(condhint=encoded),)

adv_control/nodes_plusplus.py

@@ -1,7 +1,6 @@
 from torch import Tensor
 
-import folder_paths
-from nodes import VAEEncode
+from comfy.model_downloader import get_or_download, get_filename_list_with_downloadable
 import comfy.utils
 from comfy.sd import VAE
 
@@ -16,7 +15,7 @@ class SparseCtrlLoaderAdvanced:
     def INPUT_TYPES(s):
         return {
             "required": {
-                "sparsectrl_name": (folder_paths.get_filename_list("controlnet"), ),
+                "sparsectrl_name": (get_filename_list_with_downloadable("controlnet"), ),
                 "use_motion": ("BOOLEAN", {"default": True}, ),
                 "motion_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                 "motion_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
@@ -30,15 +29,15 @@ def INPUT_TYPES(s):
                 "sparse_mask_mult": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
             }
         }
-    
+
     RETURN_TYPES = ("CONTROL_NET", )
     FUNCTION = "load_controlnet"
 
     CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/SparseCtrl"
 
     def load_controlnet(self, sparsectrl_name: str, use_motion: bool, motion_strength: float, motion_scale: float, sparse_method: SparseMethod=SparseSpreadMethod(), tk_optional: TimestepKeyframeGroup=None,
                         context_aware=SparseContextAware.NEAREST_HINT, sparse_hint_mult=1.0, sparse_nonhint_mult=1.0, sparse_mask_mult=1.0):
-        sparsectrl_path = folder_paths.get_full_path("controlnet", sparsectrl_name)
+        sparsectrl_path = get_or_download("controlnet", sparsectrl_name)
         sparse_settings = SparseSettings(sparse_method=sparse_method, use_motion=use_motion, motion_strength=motion_strength, motion_scale=motion_scale,
                                          context_aware=context_aware,
                                          sparse_mask_mult=sparse_mask_mult, sparse_hint_mult=sparse_hint_mult, sparse_nonhint_mult=sparse_nonhint_mult)
@@ -51,8 +50,8 @@ class SparseCtrlMergedLoaderAdvanced:
     def INPUT_TYPES(s):
         return {
             "required": {
-                "sparsectrl_name": (folder_paths.get_filename_list("controlnet"), ),
-                "control_net_name": (folder_paths.get_filename_list("controlnet"), ),
+                "sparsectrl_name": (get_filename_list_with_downloadable("controlnet"), ),
+                "control_net_name": (get_filename_list_with_downloadable("controlnet"), ),
                 "use_motion": ("BOOLEAN", {"default": True}, ),
                 "motion_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
                 "motion_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.001}, ),
@@ -62,15 +61,15 @@ def INPUT_TYPES(s):
                 "tk_optional": ("TIMESTEP_KEYFRAME", ),
             }
         }
-    
+
     RETURN_TYPES = ("CONTROL_NET", )
     FUNCTION = "load_controlnet"
 
     CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/SparseCtrl/experimental"
 
     def load_controlnet(self, sparsectrl_name: str, control_net_name: str, use_motion: bool, motion_strength: float, motion_scale: float, sparse_method: SparseMethod=SparseSpreadMethod(), tk_optional: TimestepKeyframeGroup=None):
-        sparsectrl_path = folder_paths.get_full_path("controlnet", sparsectrl_name)
-        controlnet_path = folder_paths.get_full_path("controlnet", control_net_name)
+        sparsectrl_path = get_or_download("controlnet", sparsectrl_name)
+        controlnet_path = get_or_download("controlnet", control_net_name)
         sparse_settings = SparseSettings(sparse_method=sparse_method, use_motion=use_motion, motion_strength=motion_strength, motion_scale=motion_scale, merged=True)
         # first, load normal controlnet
         controlnet = load_controlnet(controlnet_path, timestep_keyframe=tk_optional)
@@ -96,7 +95,7 @@ def INPUT_TYPES(s):
                 "indexes": ("STRING", {"default": "0"}),
             }
         }
-    
+
     RETURN_TYPES = ("SPARSE_METHOD",)
     FUNCTION = "get_method"
 
@@ -115,7 +114,7 @@ def INPUT_TYPES(s):
                 "spread": (SparseSpreadMethod.LIST,),
             }
         }
-    
+
     RETURN_TYPES = ("SPARSE_METHOD",)
     FUNCTION = "get_method"
 
@@ -151,10 +150,7 @@ def preprocess_images(self, vae: VAE, image: Tensor, latent_size: Tensor):
         image = comfy.utils.common_upscale(image, latent_size["samples"].shape[3] * 8, latent_size["samples"].shape[2] * 8, 'nearest-exact', "center")
         image = image.movedim(1,-1)
         # then, vae encode
-        try:
-            image = vae.vae_encode_crop_pixels(image)
-        except Exception:
-            image = VAEEncode.vae_encode_crop_pixels(image)
+        image = vae.vae_encode_crop_pixels(image)
         encoded = vae.encode(image[:,:,:,:3])
         return (PreprocSparseRGBWrapper(condhint=encoded),)
 
@@ -171,14 +167,16 @@ def INPUT_TYPES(s):
                 "autosize": ("ACNAUTOSIZE", {"padding": 50}),
             }
         }
-    
+
     RETURN_TYPES = ("CN_WEIGHTS_EXTRAS", )
     RETURN_NAMES = ("cn_extras", )
     FUNCTION = "create_weight_extras"
 
     CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/SparseCtrl/extras"
 
-    def create_weight_extras(self, cn_extras: dict[str]={}, sparse_hint_mult=1.0, sparse_nonhint_mult=1.0, sparse_mask_mult=1.0):
+    def create_weight_extras(self, cn_extras=None, sparse_hint_mult=1.0, sparse_nonhint_mult=1.0, sparse_mask_mult=1.0):
+        if cn_extras is None:
+            cn_extras = {}
         cn_extras = cn_extras.copy()
         cn_extras[SparseConst.HINT_MULT] = sparse_hint_mult
         cn_extras[SparseConst.NONHINT_MULT] = sparse_nonhint_mult

adv_control/nodes_reference.py

@@ -23,7 +23,9 @@ def INPUT_TYPES(s):
 
     CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights"
 
-    def load_weights(self, cn_extras: dict[str]={}):
+    def load_weights(self, cn_extras=None):
+        if cn_extras is None:
+            cn_extras = {}
         weights = ControlWeights.default(extras=cn_extras)
         return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights))) 
 
@@ -53,8 +55,10 @@ def INPUT_TYPES(s):
     CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights"
 
     def load_weights(self, mask: Tensor, min_base_multiplier: float, max_base_multiplier: float, lock_min=False, lock_max=False,
-                     uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
+                     uncond_multiplier: float=1.0, cn_extras=None):
         # normalize mask
+        if cn_extras is None:
+            cn_extras = {}
         mask = mask.clone()
         x_min = 0.0 if lock_min else mask.min()
         x_max = 1.0 if lock_max else mask.max()
@@ -86,7 +90,9 @@ def INPUT_TYPES(s):
 
     CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights"
 
-    def load_weights(self, base_multiplier, uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
+    def load_weights(self, base_multiplier, uncond_multiplier: float=1.0, cn_extras=None):
+        if cn_extras is None:
+            cn_extras = {}
         weights = ControlWeights.universal(base_multiplier=base_multiplier, uncond_multiplier=uncond_multiplier, extras=cn_extras)
         return (weights, TimestepKeyframeGroup.default(TimestepKeyframe(control_weights=weights)))
 
@@ -123,9 +129,11 @@ def INPUT_TYPES(s):
 
     CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights/ControlNet"
 
-    def load_weights(self, output_0, output_1, output_2, output_3, output_4, output_5, output_6, 
+    def load_weights(self, output_0, output_1, output_2, output_3, output_4, output_5, output_6,
                      output_7, output_8, output_9, output_10, output_11, middle_0,
-                     uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
+                     uncond_multiplier: float=1.0, cn_extras=None):
+        if cn_extras is None:
+            cn_extras = {}
         return CustomControlNetWeightsSD15.load_weights(self,
                                                         output_0=output_0, output_1=output_1, output_2=output_2, output_3=output_3,
                                                         output_4=output_4, output_5=output_5, output_6=output_6, output_7=output_7,
@@ -166,9 +174,11 @@ def INPUT_TYPES(s):
 
     CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights/ControlNet"
 
-    def load_weights(self, output_0, output_1, output_2, output_3, output_4, output_5, output_6, 
+    def load_weights(self, output_0, output_1, output_2, output_3, output_4, output_5, output_6,
                      output_7, output_8, output_9, output_10, output_11, middle_0,
-                     uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
+                     uncond_multiplier: float=1.0, cn_extras=None):
+        if cn_extras is None:
+            cn_extras = {}
         weights_output = [output_0, output_1, output_2, output_3, output_4, output_5, output_6,
                           output_7, output_8, output_9, output_10, output_11]
         weights_middle = [middle_0]
@@ -214,10 +224,12 @@ def INPUT_TYPES(s):
 
     CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights/ControlNet"
 
-    def load_weights(self, input_0, input_1, input_2, input_3, input_4, input_5, input_6, 
+    def load_weights(self, input_0, input_1, input_2, input_3, input_4, input_5, input_6,
                      input_7, input_8, input_9, input_10, input_11, input_12, input_13,
                      input_14, input_15, input_16, input_17, input_18,
-                     uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
+                     uncond_multiplier: float=1.0, cn_extras=None):
+        if cn_extras is None:
+            cn_extras = {}
         weights_input = [input_0, input_1, input_2, input_3, input_4, input_5,
                          input_6, input_7, input_8, input_9, input_10, input_11,
                          input_12, input_13, input_14, input_15, input_16, input_17, input_18]
@@ -249,7 +261,9 @@ def INPUT_TYPES(s):
     CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights/T2IAdapter"
 
     def load_weights(self, input_0, input_1, input_2, input_3,
-                     uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
+                     uncond_multiplier: float=1.0, cn_extras=None):
+        if cn_extras is None:
+            cn_extras = {}
         return CustomT2IAdapterWeights.load_weights(self, input_0=input_0, input_1=input_1, input_2=input_2, input_3=input_3,
                                                     uncond_multiplier=uncond_multiplier, cn_extras=cn_extras)
 
@@ -278,7 +292,9 @@ def INPUT_TYPES(s):
     CATEGORY = "Adv-ControlNet 🛂🅐🅒🅝/weights/T2IAdapter"
 
     def load_weights(self, input_0, input_1, input_2, input_3,
-                     uncond_multiplier: float=1.0, cn_extras: dict[str]={}):
+                     uncond_multiplier: float=1.0, cn_extras=None):
+        if cn_extras is None:
+            cn_extras = {}
         weights = [input_0, input_1, input_2, input_3]
         weights = get_properly_arranged_t2i_weights(weights)
         weights = ControlWeights.t2iadapter(weights_input=weights, uncond_multiplier=uncond_multiplier, extras=cn_extras)

adv_control/nodes_sparsectrl.py

@@ -13,3 +13,24 @@ Repository = "https://github.com/Kosinkadink/ComfyUI-Advanced-ControlNet"
 PublisherId = "kosinkadink"
 DisplayName = "ComfyUI-Advanced-ControlNet"
 Icon = ""
+
+[build-system]
+requires = ["setuptools", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[tool.setuptools]
+packages = [
+  "comfyui_advanced_controlnet",
+  "comfyui_advanced_controlnet.adv_control",
+]
+
+[tool.setuptools.package-dir]
+"comfyui_advanced_controlnet" = "."
+"comfyui_advanced_controlnet.adv_control" = "adv_control"
+
+[project.entry-points."comfyui.custom_nodes"]
+comfyui_advanced_controlnet = "comfyui_advanced_controlnet.adv_control.nodes"
+comfyui_advanced_controlnet_web_directory = "comfyui_advanced_controlnet"
+
+[tool.setuptools.package-data]
+"*" = ["**/*.js", "**/*.json"]