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/controlaltai-nodes/flux_union_controlnet_node.py

@@ -0,0 +1,98 @@
+import torch
+import comfy
+import folder_paths
+
+class FluxUnionControlNetApply:
+    # Correct UNION_CONTROLNET_TYPES mapping
+    UNION_CONTROLNET_TYPES = {
+        "canny": 0,
+        "tile": 1,
+        "depth": 2,
+        "blur": 3,
+        "pose": 4,
+        "gray": 5,
+        "low quality": 6,
+    }
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "conditioning": ("CONDITIONING", ),
+                "control_net": ("CONTROL_NET", ),
+                "image": ("IMAGE", ),
+                "union_controlnet_type": (list(s.UNION_CONTROLNET_TYPES.keys()), ),
+                "strength": ("FLOAT", {
+                    "default": 1.0,
+                    "min": 0.0,
+                    "max": 10.0,
+                    "step": 0.01
+                }),
+                "start_percent": ("FLOAT", {
+                    "default": 0.0,
+                    "min": 0.0,
+                    "max": 1.0,
+                    "step": 0.001
+                }),
+                "end_percent": ("FLOAT", {
+                    "default": 1.0,
+                    "min": 0.0,
+                    "max": 1.0,
+                    "step": 0.001
+                }),
+                "vae": ("VAE", ),
+            },
+        }
+
+    RETURN_TYPES = ("CONDITIONING", "VAE")
+    FUNCTION = "apply_flux_union_controlnet"
+    CATEGORY = "ControlAltAI Nodes/Flux"
+
+    def apply_flux_union_controlnet(self, conditioning, control_net, image, union_controlnet_type, strength, start_percent, end_percent, vae):
+        if strength == 0:
+            return (conditioning, vae)
+
+        # Map the 'union_controlnet_type' to 'control_type'
+        control_type = self.UNION_CONTROLNET_TYPES[union_controlnet_type]
+        control_type_list = [control_type]
+
+        # Set the 'control_type' using 'set_extra_arg'
+        control_net = control_net.copy()
+        control_net.set_extra_arg("control_type", control_type_list)
+
+        # Process the image to get 'control_hint'
+        control_hint = image.movedim(-1, 1)  # Assuming the image is in HWC format
+
+        # Apply the ControlNet to the positive conditioning
+        cnets = {}
+        c = []
+        for t in conditioning:
+            d = t[1].copy()
+            prev_cnet = d.get('control', None)
+
+            # Create a unique key for caching
+            cache_key = (prev_cnet, tuple(control_net.extra_args.get('control_type', [])))
+
+            if cache_key in cnets:
+                c_net_instance = cnets[cache_key]
+            else:
+                # Create a copy of the 'control_net' and set the conditional hint
+                c_net_instance = control_net.copy().set_cond_hint(control_hint, strength, (start_percent, end_percent), vae=vae)
+                c_net_instance.set_previous_controlnet(prev_cnet)
+                cnets[cache_key] = c_net_instance
+
+            d['control'] = c_net_instance
+            d['control_apply_to_uncond'] = False
+
+            n = [t[0], d]
+            c.append(n)
+
+        return (c, vae)
+
+NODE_CLASS_MAPPINGS = {
+    "FluxUnionControlNetApply": FluxUnionControlNetApply,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "FluxUnionControlNetApply": "Flux Union ControlNet",
+}