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/image_resize.py

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+import torch
+import comfy.utils
+import nodes
+
+from .constants import get_category, get_name
+
+
+class RgthreeImageResize:
+  """Image Resize."""
+
+  NAME = get_name("Image Resize")
+  CATEGORY = get_category()
+
+  @classmethod
+  def INPUT_TYPES(cls):  # pylint: disable = invalid-name, missing-function-docstring
+    return {
+      "required": {
+        "image": ("IMAGE",),
+        "measurement": (["pixels", "percentage"],),
+        "width": (
+          "INT", {
+            "default": 0,
+            "min": 0,
+            "max": nodes.MAX_RESOLUTION,
+            "step": 1,
+            "tooltip": (
+              "The width of the desired resize. A pixel value if measurement is 'pixels' or a"
+              " 100% scale percentage value if measurement is 'percentage'. Passing '0' will"
+              " calculate the dimension based on the height."
+            ),
+          },
+        ),
+        "height": ("INT", {
+          "default": 0,
+          "min": 0,
+          "max": nodes.MAX_RESOLUTION,
+          "step": 1
+        }),
+        "fit": (["crop", "pad", "contain"], {
+          "tooltip": (
+            "'crop' resizes so the image covers the desired width and height, and center-crops the"
+            " excess, returning exactly the desired width and height."
+            "\n'pad' resizes so the image fits inside the desired width and height, and fills the"
+            " empty space returning exactly the desired width and height."
+            "\n'contain' resizes so the image fits inside the desired width and height, and"
+            " returns the image with it's new size, with one side liekly smaller than the desired."
+            "\n\nNote, if either width or height is '0', the effective fit is 'contain'."
+          )
+        },
+               ),
+        "method": (nodes.ImageScale.upscale_methods,),
+      },
+    }
+
+  RETURN_TYPES = ("IMAGE", "INT", "INT",)
+  RETURN_NAMES = ("IMAGE", "WIDTH", "HEIGHT",)
+  FUNCTION = "main"
+  DESCRIPTION = """Resize the image."""
+
+  def main(self, image, measurement, width, height, method, fit):
+    """Resizes the image."""
+    _, H, W, _ = image.shape
+
+    if measurement == "percentage":
+      width = round(width * W / 100)
+      height = round(height * H / 100)
+
+    if (width == 0 and height == 0) or (width == W and height == H):
+      return (image, W, H)
+
+    # If one dimension is 0, then calculate the desired value from the ratio of the set dimension.
+    # This also implies a 'contain' fit since the width and height will be scaled with a locked
+    # aspect ratio.
+    if width == 0 or height == 0:
+      width = round(height / H * W) if width == 0 else width
+      height = round(width / W * H) if height == 0 else height
+      fit = "contain"
+
+    # At this point, width and height are our output height, but our resize sizes will be different.
+    resized_width = width
+    resized_height = height
+    if fit == "crop":
+      # If we resize against the opposite ratio, then choose the ratio that has the overhang.
+      if (height / H * W) > width:
+        resized_width = round(height / H * W)
+      elif (width / W * H) > height:
+        resized_height = round(width / W * H)
+    elif fit == "contain" or fit == "pad":
+      # If we resize against the opposite ratio, then choose the ratio that has the overhang.
+      if (height / H * W) > width:
+        resized_height = round(width / W * H)
+      elif (width / W * H) > height:
+        resized_width = round(height / H * W)
+
+    out_image = comfy.utils.common_upscale(
+      image.clone().movedim(-1, 1), resized_width, resized_height, method, crop="disabled"
+    ).movedim(1, -1)
+    OB, OH, OW, OC = out_image.shape
+
+    if fit != "contain":
+      # First, we crop, then we pad; no need to check fit (other than not 'contain') since the size
+      # should already be correct.
+      if OW > width:
+        out_image = out_image.narrow(-2, (OW - width) // 2, width)
+      if OH > height:
+        out_image = out_image.narrow(-3, (OH - height) // 2, height)
+
+      OB, OH, OW, OC = out_image.shape
+      if width != OW or height != OH:
+        padded_image = torch.zeros((OB, height, width, OC), dtype=image.dtype, device=image.device)
+        x = (width - OW) // 2
+        y = (height - OH) // 2
+        for b in range(OB):
+          padded_image[b, y:y + OH, x:x + OW, :] = out_image[b]
+        out_image = padded_image
+
+    return (out_image, out_image.shape[2], out_image.shape[1])