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_controlnet.py

@@ -0,0 +1,96 @@
+"""
+Test using controlnet in the upscaling workflow.
+"""
+
+import logging
+import pathlib
+import pytest
+import torch
+
+from setup_utils import execute
+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
+
+CATEGORY = pathlib.Path(pathlib.Path(__file__).stem.removeprefix("test_"))
+CONTROLNET_TILE_OUTPUT_IMAGE = "controlnet_tile" + EXT
+TEST_CONTROLNET_TILE_MODEL = "control_v11f1e_sd15_tile.pth"
+
+
+class TestControlNet:
+    """Integration tests for the upscaling workflow with ControlNet."""
+
+    @pytest.fixture(scope="class")
+    def controlnet_upscaled_image(
+        self,
+        base_image,
+        loaded_checkpoint,
+        upscale_model,
+        node_classes,
+        seed,
+        test_dirs,
+    ):
+        """Generate upscaled images using ControlNet."""
+        image, positive, negative = base_image
+        model, clip, vae = loaded_checkpoint
+        image = image[0:1]
+
+        (controlnet_tile_model,) = execute(
+            node_classes["ControlNetLoader"], TEST_CONTROLNET_TILE_MODEL
+        )
+        (positive,) = execute(
+            node_classes["ControlNetApply"], positive, controlnet_tile_model, image, 1.0
+        )
+
+        with torch.inference_mode():
+            # Run upscale with ControlNet
+            usdu = node_classes["UltimateSDUpscale"]
+            (upscaled,) = usdu().upscale(
+                image=image,
+                model=model,
+                positive=positive,
+                negative=negative,
+                vae=vae,
+                upscale_by=2.0,
+                seed=seed,
+                steps=5,
+                cfg=8,
+                sampler_name="euler",
+                scheduler="normal",
+                denoise=1.0,
+                upscale_model=None,
+                mode_type="Chess",
+                tile_width=512,
+                tile_height=512,
+                mask_blur=8,
+                tile_padding=32,
+                seam_fix_mode="None",
+                seam_fix_denoise=1.0,
+                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 / CONTROLNET_TILE_OUTPUT_IMAGE
+        save_image(upscaled[0], sample_dir / filename)
+        upscaled = load_image(sample_dir / filename)
+        return upscaled
+
+    def test_controlnet_upscaled_image_matches_reference(
+        self, controlnet_upscaled_image, test_dirs: DirectoryConfig
+    ):
+        """
+        Verify ControlNet upscaled images match reference images.
+        """
+        logger = logging.getLogger("test_controlnet_upscaled_image_matches_reference")
+        test_img_dir = test_dirs.test_images
+        test_img = load_image(test_img_dir / CATEGORY / CONTROLNET_TILE_OUTPUT_IMAGE)
+
+        # Reduce high-frequency noise differences with gaussian blur
+        diff = img_tensor_mae(blur(controlnet_upscaled_image), blur(test_img))
+        logger.info(f"ControlNet Upscaled Image Diff: {diff}")
+        assert diff < 0.05, "ControlNet upscaled image does not match its test image."