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_tiling_modes.py

@@ -0,0 +1,134 @@
+"""
+Tests for different upscaling modes and seam fix modes.
+"""
+
+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 image_name_format(prefix: str, mode: str) -> str:
+    """Helper for the image name format for the tests below."""
+    return f"{prefix}_{mode.lower().replace(' ', '_')}{EXT}"
+
+
+class TestTilingModes:
+    def _test_upscale_variant(
+        self,
+        base_image,
+        loaded_checkpoint,
+        node_classes,
+        seed,
+        test_dirs: DirectoryConfig,
+        mode_type,
+        seam_fix_mode,
+        seam_fix_denoise,
+        filename_prefix,
+    ):
+        """Helper method to test upscale variants with different parameters."""
+        logger = logging.getLogger(f"test_{filename_prefix}")
+        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=2.0,
+                seed=seed,
+                steps=3,
+                cfg=8,
+                sampler_name="euler",
+                scheduler="normal",
+                denoise=0.2,
+                upscale_model=None,
+                mode_type=mode_type,
+                tile_width=512,
+                tile_height=512,
+                mask_blur=8,
+                tile_padding=32,
+                seam_fix_mode=seam_fix_mode,
+                seam_fix_denoise=seam_fix_denoise,
+                seam_fix_width=64,
+                seam_fix_mask_blur=8,
+                seam_fix_padding=16,
+                force_uniform_tiles=True,
+                tiled_decode=False,
+            )
+
+        # Save and reload sample image
+        sample_dir = test_dirs.sample_images
+        filename = CATEGORY / filename_prefix
+        save_image(upscaled[0], sample_dir / filename)
+        upscaled = load_image(sample_dir / filename)
+
+        # Compare with reference
+        test_image_dir = test_dirs.test_images
+        test_image = load_image(test_image_dir / filename)
+        diff = img_tensor_mae(blur(upscaled), blur(test_image))
+        logger.info(f"{filename_prefix} MAE: {diff}")
+        assert diff < 0.05, f"{filename_prefix} output doesn't match reference"
+
+    # "Chess" is tested in the main workflow test
+    @pytest.mark.parametrize("mode_type", ["Linear", "None"])
+    def test_mode_types(
+        self,
+        base_image,
+        loaded_checkpoint,
+        node_classes,
+        seed,
+        mode_type,
+        test_dirs: DirectoryConfig,
+    ):
+        """Test different tiling mode types."""
+        filename = image_name_format("mode", mode_type)
+        self._test_upscale_variant(
+            base_image,
+            loaded_checkpoint,
+            node_classes,
+            seed,
+            test_dirs,
+            mode_type=mode_type,
+            seam_fix_mode="None",
+            seam_fix_denoise=1.0,
+            filename_prefix=filename,
+        )
+
+    @pytest.mark.parametrize(
+        "seam_fix_mode", ["None", "Band Pass", "Half Tile", "Half Tile + Intersections"]
+    )
+    def test_seam_fix_modes(
+        self,
+        base_image,
+        loaded_checkpoint,
+        node_classes,
+        seed,
+        seam_fix_mode,
+        test_dirs: DirectoryConfig,
+    ):
+        """Test different seam fix modes."""
+        filename = image_name_format("seamfix", seam_fix_mode)
+        self._test_upscale_variant(
+            base_image,
+            loaded_checkpoint,
+            node_classes,
+            seed,
+            test_dirs,
+            mode_type="None",
+            seam_fix_mode=seam_fix_mode,
+            seam_fix_denoise=0.5,
+            filename_prefix=filename,
+        )