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/rgthree-comfy/py/sdxl_empty_latent_image.py

@@ -0,0 +1,63 @@
+from nodes import EmptyLatentImage
+from .constants import get_category, get_name
+
+
+class RgthreeSDXLEmptyLatentImage:
+
+  NAME = get_name('SDXL Empty Latent Image')
+  CATEGORY = get_category()
+
+  @classmethod
+  def INPUT_TYPES(cls):  # pylint: disable = invalid-name, missing-function-docstring
+    return {
+      "required": {
+        "dimensions": (
+          [
+            # 'Custom',
+            '1536 x 640   (landscape)',
+            '1344 x 768   (landscape)',
+            '1216 x 832   (landscape)',
+            '1152 x 896   (landscape)',
+            '1024 x 1024  (square)',
+            ' 896 x 1152  (portrait)',
+            ' 832 x 1216  (portrait)',
+            ' 768 x 1344  (portrait)',
+            ' 640 x 1536  (portrait)',
+          ],
+          {
+            "default": '1024 x 1024  (square)'
+          }),
+        "clip_scale": ("FLOAT", {
+          "default": 2.0,
+          "min": 1.0,
+          "max": 10.0,
+          "step": .5
+        }),
+        "batch_size": ("INT", {
+          "default": 1,
+          "min": 1,
+          "max": 64
+        }),
+      },
+      # "optional": {
+      #   "custom_width": ("INT", {"min": 1, "max": MAX_RESOLUTION, "step": 64}),
+      #   "custom_height": ("INT", {"min": 1, "max": MAX_RESOLUTION, "step": 64}),
+      # }
+    }
+
+  RETURN_TYPES = ("LATENT", "INT", "INT")
+  RETURN_NAMES = ("LATENT", "CLIP_WIDTH", "CLIP_HEIGHT")
+  FUNCTION = "generate"
+
+  def generate(self, dimensions, clip_scale, batch_size):
+    """Generates the latent and exposes the clip_width and clip_height"""
+    if True:
+      result = [x.strip() for x in dimensions.split('x')]
+      width = int(result[0])
+      height = int(result[1].split(' ')[0])
+    latent = EmptyLatentImage().generate(width, height, batch_size)[0]
+    return (
+      latent,
+      int(width * clip_scale),
+      int(height * clip_scale),
+    )