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-KJNodes/nodes/audioscheduler_nodes.py

@@ -0,0 +1,251 @@
+# to be used with https://github.com/a1lazydog/ComfyUI-AudioScheduler 
+import torch
+from torchvision.transforms import functional as TF
+from PIL import Image, ImageDraw
+import numpy as np
+from ..utility.utility import pil2tensor
+from nodes import MAX_RESOLUTION
+
+class NormalizedAmplitudeToMask:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                    "normalized_amp": ("NORMALIZED_AMPLITUDE",),
+                    "width": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
+                    "height": ("INT", {"default": 512,"min": 16, "max": 4096, "step": 1}),
+                    "frame_offset": ("INT", {"default": 0,"min": -255, "max": 255, "step": 1}),
+                    "location_x": ("INT", {"default": 256,"min": 0, "max": 4096, "step": 1}),
+                    "location_y": ("INT", {"default": 256,"min": 0, "max": 4096, "step": 1}),
+                    "size": ("INT", {"default": 128,"min": 8, "max": 4096, "step": 1}),
+                    "shape": (
+                        [   
+                            'none',
+                            'circle',
+                            'square',
+                            'triangle',
+                        ],
+                        {
+                        "default": 'none'
+                        }),
+                    "color": (
+                        [   
+                            'white',
+                            'amplitude',
+                        ],
+                        {
+                        "default": 'amplitude'
+                        }),
+                     },}
+
+    CATEGORY = "KJNodes/audio"
+    RETURN_TYPES = ("MASK",)
+    FUNCTION = "convert"
+    DESCRIPTION = """
+Works as a bridge to the AudioScheduler -nodes:  
+https://github.com/a1lazydog/ComfyUI-AudioScheduler  
+Creates masks based on the normalized amplitude.
+"""
+
+    def convert(self, normalized_amp, width, height, frame_offset, shape, location_x, location_y, size, color):
+        # Ensure normalized_amp is an array and within the range [0, 1]
+        normalized_amp = np.clip(normalized_amp, 0.0, 1.0)
+
+        # Offset the amplitude values by rolling the array
+        normalized_amp = np.roll(normalized_amp, frame_offset)
+        
+        # Initialize an empty list to hold the image tensors
+        out = []
+        # Iterate over each amplitude value to create an image
+        for amp in normalized_amp:
+            # Scale the amplitude value to cover the full range of grayscale values
+            if color == 'amplitude':
+                grayscale_value = int(amp * 255)
+            elif color == 'white':
+                grayscale_value = 255
+            # Convert the grayscale value to an RGB format
+            gray_color = (grayscale_value, grayscale_value, grayscale_value)
+            finalsize = size * amp
+            
+            if shape == 'none':
+                shapeimage = Image.new("RGB", (width, height), gray_color)
+            else:
+                shapeimage = Image.new("RGB", (width, height), "black")
+
+            draw = ImageDraw.Draw(shapeimage)
+            if shape == 'circle' or shape == 'square':
+                # Define the bounding box for the shape
+                left_up_point = (location_x - finalsize, location_y - finalsize)
+                right_down_point = (location_x + finalsize,location_y + finalsize)
+                two_points = [left_up_point, right_down_point]
+
+                if shape == 'circle':
+                    draw.ellipse(two_points, fill=gray_color)
+                elif shape == 'square':
+                    draw.rectangle(two_points, fill=gray_color)
+                    
+            elif shape == 'triangle':
+                # Define the points for the triangle
+                left_up_point = (location_x - finalsize, location_y + finalsize) # bottom left
+                right_down_point = (location_x + finalsize, location_y + finalsize) # bottom right
+                top_point = (location_x, location_y) # top point
+                draw.polygon([top_point, left_up_point, right_down_point], fill=gray_color)
+            
+            shapeimage = pil2tensor(shapeimage)
+            mask = shapeimage[:, :, :, 0]
+            out.append(mask)
+        
+        return (torch.cat(out, dim=0),)
+    
+class NormalizedAmplitudeToFloatList:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+                    "normalized_amp": ("NORMALIZED_AMPLITUDE",),
+                     },}
+
+    CATEGORY = "KJNodes/audio"
+    RETURN_TYPES = ("FLOAT",)
+    FUNCTION = "convert"
+    DESCRIPTION = """
+Works as a bridge to the AudioScheduler -nodes:  
+https://github.com/a1lazydog/ComfyUI-AudioScheduler  
+Creates a list of floats from the normalized amplitude.
+"""
+
+    def convert(self, normalized_amp):
+        # Ensure normalized_amp is an array and within the range [0, 1]
+        normalized_amp = np.clip(normalized_amp, 0.0, 1.0)
+        return (normalized_amp.tolist(),)
+
+class OffsetMaskByNormalizedAmplitude:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "normalized_amp": ("NORMALIZED_AMPLITUDE",),
+                "mask": ("MASK",),
+                "x": ("INT", { "default": 0, "min": -4096, "max": MAX_RESOLUTION, "step": 1, "display": "number" }),
+                "y": ("INT", { "default": 0, "min": -4096, "max": MAX_RESOLUTION, "step": 1, "display": "number" }),
+                "rotate": ("BOOLEAN", { "default": False }),
+                "angle_multiplier": ("FLOAT", { "default": 0.0, "min": -1.0, "max": 1.0, "step": 0.001, "display": "number" }),
+            }
+        }
+
+    RETURN_TYPES = ("MASK",)
+    RETURN_NAMES = ("mask",)
+    FUNCTION = "offset"
+    CATEGORY = "KJNodes/audio"
+    DESCRIPTION = """
+Works as a bridge to the AudioScheduler -nodes:  
+https://github.com/a1lazydog/ComfyUI-AudioScheduler  
+Offsets masks based on the normalized amplitude.
+"""
+
+    def offset(self, mask, x, y, angle_multiplier, rotate, normalized_amp):
+
+         # Ensure normalized_amp is an array and within the range [0, 1]
+        offsetmask = mask.clone()
+        normalized_amp = np.clip(normalized_amp, 0.0, 1.0)
+       
+        batch_size, height, width = mask.shape
+
+        if rotate:
+            for i in range(batch_size):
+                rotation_amp = int(normalized_amp[i] * (360 * angle_multiplier))
+                rotation_angle = rotation_amp
+                offsetmask[i] = TF.rotate(offsetmask[i].unsqueeze(0), rotation_angle).squeeze(0)
+        if x != 0 or y != 0:
+            for i in range(batch_size):
+                offset_amp = normalized_amp[i] * 10
+                shift_x = min(x*offset_amp, width-1)
+                shift_y = min(y*offset_amp, height-1)
+                if shift_x != 0:
+                    offsetmask[i] = torch.roll(offsetmask[i], shifts=int(shift_x), dims=1)
+                if shift_y != 0:
+                    offsetmask[i] = torch.roll(offsetmask[i], shifts=int(shift_y), dims=0)
+        
+        return offsetmask,
+
+class ImageTransformByNormalizedAmplitude:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "normalized_amp": ("NORMALIZED_AMPLITUDE",),
+            "zoom_scale": ("FLOAT", { "default": 0.0, "min": -1.0, "max": 1.0, "step": 0.001, "display": "number" }),
+            "x_offset": ("INT", { "default": 0, "min": (1 -MAX_RESOLUTION), "max": MAX_RESOLUTION, "step": 1, "display": "number" }),
+            "y_offset": ("INT", { "default": 0, "min": (1 -MAX_RESOLUTION), "max": MAX_RESOLUTION, "step": 1, "display": "number" }),
+            "cumulative": ("BOOLEAN", { "default": False }),
+            "image": ("IMAGE",),
+        }}
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "amptransform"
+    CATEGORY = "KJNodes/audio"
+    DESCRIPTION = """
+Works as a bridge to the AudioScheduler -nodes:  
+https://github.com/a1lazydog/ComfyUI-AudioScheduler  
+Transforms image based on the normalized amplitude.
+"""
+
+    def amptransform(self, image, normalized_amp, zoom_scale, cumulative, x_offset, y_offset):
+        # Ensure normalized_amp is an array and within the range [0, 1]
+        normalized_amp = np.clip(normalized_amp, 0.0, 1.0)
+        transformed_images = []
+
+        # Initialize the cumulative zoom factor
+        prev_amp = 0.0
+
+        for i in range(image.shape[0]):
+            img = image[i]  # Get the i-th image in the batch
+            amp = normalized_amp[i]  # Get the corresponding amplitude value
+
+            # Incrementally increase the cumulative zoom factor
+            if cumulative:
+                prev_amp += amp
+                amp += prev_amp
+
+            # Convert the image tensor from BxHxWxC to CxHxW format expected by torchvision
+            img = img.permute(2, 0, 1)
+            
+            # Convert PyTorch tensor to PIL Image for processing
+            pil_img = TF.to_pil_image(img)
+            
+            # Calculate the crop size based on the amplitude
+            width, height = pil_img.size
+            crop_size = int(min(width, height) * (1 - amp * zoom_scale))
+            crop_size = max(crop_size, 1)
+            
+            # Calculate the crop box coordinates (centered crop)
+            left = (width - crop_size) // 2
+            top = (height - crop_size) // 2
+            right = (width + crop_size) // 2
+            bottom = (height + crop_size) // 2
+            
+            # Crop and resize back to original size
+            cropped_img = TF.crop(pil_img, top, left, crop_size, crop_size)
+            resized_img = TF.resize(cropped_img, (height, width))
+            
+            # Convert back to tensor in CxHxW format
+            tensor_img = TF.to_tensor(resized_img)
+            
+            # Convert the tensor back to BxHxWxC format
+            tensor_img = tensor_img.permute(1, 2, 0)
+            
+            # Offset the image based on the amplitude
+            offset_amp = amp * 10  # Calculate the offset magnitude based on the amplitude
+            shift_x = min(x_offset * offset_amp, img.shape[1] - 1)  # Calculate the shift in x direction
+            shift_y = min(y_offset * offset_amp, img.shape[0] - 1)  # Calculate the shift in y direction
+
+            # Apply the offset to the image tensor
+            if shift_x != 0:
+                tensor_img = torch.roll(tensor_img, shifts=int(shift_x), dims=1)
+            if shift_y != 0:
+                tensor_img = torch.roll(tensor_img, shifts=int(shift_y), dims=0)
+
+            # Add to the list
+            transformed_images.append(tensor_img)
+        
+        # Stack all transformed images into a batch
+        transformed_batch = torch.stack(transformed_images)
+        
+        return (transformed_batch,)