Add custom nodes, Civitai loras (LFS), and vast.ai setup script

Includes 30 custom nodes committed directly, 7 Civitai-exclusive
loras stored via Git LFS, and a setup script that installs all
dependencies and downloads HuggingFace-hosted models on vast.ai.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

custom_nodes/comfyui_controlnet_aux/node_wrappers/anyline.py

@@ -0,0 +1,87 @@
+import torch
+import numpy as np
+import comfy.model_management as model_management
+import comfy.utils
+
+# Requires comfyui_controlnet_aux funcsions and classes
+from ..utils import common_annotator_call, INPUT, define_preprocessor_inputs
+
+def get_intensity_mask(image_array, lower_bound, upper_bound):
+    mask = image_array[:, :, 0]
+    mask = np.where((mask >= lower_bound) & (mask <= upper_bound), mask, 0)
+    mask = np.expand_dims(mask, 2).repeat(3, axis=2)
+    return mask
+
+def combine_layers(base_layer, top_layer):
+    mask = top_layer.astype(bool)
+    temp = 1 - (1 - top_layer) * (1 - base_layer)
+    result = base_layer * (~mask) + temp * mask
+    return result
+
+class AnyLinePreprocessor:
+    @classmethod
+    def INPUT_TYPES(s):
+        return define_preprocessor_inputs(
+            merge_with_lineart=INPUT.COMBO(["lineart_standard", "lineart_realisitic", "lineart_anime", "manga_line"], default="lineart_standard"),
+            resolution=INPUT.RESOLUTION(default=1280, step=8),
+            lineart_lower_bound=INPUT.FLOAT(default=0),
+            lineart_upper_bound=INPUT.FLOAT(default=1),
+            object_min_size=INPUT.INT(default=36, min=1),
+            object_connectivity=INPUT.INT(default=1, min=1)
+        )
+
+    RETURN_TYPES = ("IMAGE",)
+    RETURN_NAMES = ("image",)
+
+    FUNCTION = "get_anyline"
+    CATEGORY = "ControlNet Preprocessors/Line Extractors"
+
+    def __init__(self):
+        self.device = model_management.get_torch_device()
+
+    def get_anyline(self, image, merge_with_lineart="lineart_standard", resolution=512, lineart_lower_bound=0, lineart_upper_bound=1, object_min_size=36, object_connectivity=1):
+        from custom_controlnet_aux.teed import TEDDetector
+        from skimage import morphology
+        pbar = comfy.utils.ProgressBar(3)
+
+        # Process the image with MTEED model
+        mteed_model = TEDDetector.from_pretrained("TheMistoAI/MistoLine", "MTEED.pth", subfolder="Anyline").to(self.device)
+        mteed_result = common_annotator_call(mteed_model, image, resolution=resolution, show_pbar=False)
+        mteed_result = mteed_result.numpy()
+        del mteed_model
+        pbar.update(1)
+
+        # Process the image with the lineart standard preprocessor
+        if merge_with_lineart == "lineart_standard":
+            from custom_controlnet_aux.lineart_standard import LineartStandardDetector
+            lineart_standard_detector = LineartStandardDetector()
+            lineart_result = common_annotator_call(lineart_standard_detector, image, guassian_sigma=2, intensity_threshold=3, resolution=resolution, show_pbar=False).numpy()
+            del lineart_standard_detector
+        else:
+            from custom_controlnet_aux.lineart import LineartDetector
+            from custom_controlnet_aux.lineart_anime import LineartAnimeDetector
+            from custom_controlnet_aux.manga_line import LineartMangaDetector
+            lineart_detector = dict(lineart_realisitic=LineartDetector, lineart_anime=LineartAnimeDetector, manga_line=LineartMangaDetector)[merge_with_lineart]
+            lineart_detector = lineart_detector.from_pretrained().to(self.device)
+            lineart_result = common_annotator_call(lineart_detector, image, resolution=resolution, show_pbar=False).numpy()
+            del lineart_detector
+        pbar.update(1)
+        
+        final_result = []
+        for i in range(len(image)):
+            _lineart_result  = get_intensity_mask(lineart_result[i], lower_bound=lineart_lower_bound, upper_bound=lineart_upper_bound)
+            _cleaned = morphology.remove_small_objects(_lineart_result.astype(bool), min_size=object_min_size, connectivity=object_connectivity)
+            _lineart_result = _lineart_result * _cleaned
+            _mteed_result = mteed_result[i]
+
+            # Combine the results
+            final_result.append(torch.from_numpy(combine_layers(_mteed_result, _lineart_result)))
+        pbar.update(1)
+        return (torch.stack(final_result),)
+
+NODE_CLASS_MAPPINGS = {
+    "AnyLineArtPreprocessor_aux": AnyLinePreprocessor
+}
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "AnyLineArtPreprocessor_aux": "AnyLine Lineart"
+}