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_ultimatesdupscale/test/test_settings.py

@@ -0,0 +1,69 @@
+"""
+Test for other settings included in the upscaling nodes.
+"""
+
+import logging
+import pathlib
+import pytest
+import torch
+
+from tensor_utils import img_tensor_mae, blur
+from io_utils import save_image, load_image
+from configs import DirectoryConfig
+from fixtures_images import EXT
+
+# Image file names
+CATEGORY = pathlib.Path(pathlib.Path(__file__).stem.removeprefix("test_"))
+
+
+def test_minimal_tile_sizes(
+    base_image, loaded_checkpoint, node_classes, seed, test_dirs: DirectoryConfig
+):
+    """Test upscaling with minimal tile sizes."""
+    filename = "non_uniform_tiles"
+    image, positive, negative = base_image
+    model, clip, vae = loaded_checkpoint
+
+    with torch.inference_mode():
+        usdu = node_classes["UltimateSDUpscale"]
+        (upscaled,) = usdu().upscale(
+            image=image[0:1],
+            model=model,
+            positive=positive,
+            negative=negative,
+            vae=vae,
+            upscale_by=1.5,
+            seed=seed,
+            steps=5,
+            cfg=8,
+            sampler_name="euler",
+            scheduler="normal",
+            denoise=0.15,
+            upscale_model=None,
+            mode_type="Chess",
+            tile_width=512,
+            tile_height=512,
+            mask_blur=8,
+            tile_padding=8,
+            seam_fix_mode="None",
+            seam_fix_denoise=1.0,
+            seam_fix_width=16,
+            seam_fix_mask_blur=8,
+            seam_fix_padding=4,
+            force_uniform_tiles=False,
+            tiled_decode=False,
+        )
+
+    # Save and reload sample image
+    sample_dir = test_dirs.sample_images
+    filename_path = CATEGORY / (filename + EXT)
+    save_image(upscaled[0], sample_dir / filename_path)
+    upscaled = load_image(sample_dir / filename_path)
+
+    # Compare with reference
+    test_image_dir = test_dirs.test_images
+    test_image = load_image(test_image_dir / filename_path)
+    diff = img_tensor_mae(blur(upscaled), blur(test_image))
+    logger = logging.getLogger(__name__)
+    logger.info(f"{filename} MAE: {diff}")
+    assert diff < 0.05, f"{filename} output doesn't match reference"