Image and mask batching and JS to handle the node inputs dynamically. Readme updates
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SuperBeastsAI
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This repository contains custom nodes for ComfyUI created and used by SuperBeasts.AI (@SuperBeasts.AI on Instagram)
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##Updates
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- 30/04/24:
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-- Updated readme with documentation
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-- Added Deflicker and PixelDeflicker nodes for reducing flickering artifacts in image sequences.
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-- Introduced CrossFadeImageBatches node for smooth cross-fade transitions between image batches
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-- Enhanced ImageBatchManagement and MaskBatchManagement nodes with resizing, cropping, and reordering capabilities
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- 27/03/24: Released and also pushed an update to resolve batched images for videos etc
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# Image Effects
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## HDR Effects (SuperBeasts.AI)
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The HDR Effects is an image processing application that enhances the dynamic range and visual appeal of input images. It provides a set of adjustable parameters to fine-tune the HDR effect according to user preferences.
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@@ -51,5 +61,61 @@ The application provides the following adjustable parameters:
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- Enhances the color saturation of the image
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- Higher values result in more vibrant colors
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##Updates
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- 27/03/24: Released and also pushed an update to resolve batched images for videos etc
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#Video tools
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## Deflicker
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The Deflicker node is designed to reduce flickering artifacts in a sequence of images. It adjusts the brightness of each frame based on the average brightness of its surrounding context frames.
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### Features
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- Adjusts brightness of each frame based on the context frames
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- Applies noise reduction and gradient smoothing to the adjusted images
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- Blends the adjusted images with the original images using adaptive blending strength
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- Supports batch processing for efficient computation
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## PixelDeflicker
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The PixelDeflicker node reduces flickering artifacts in a sequence of images by applying temporal smoothing at the pixel level. It blends the smoothed frames with the original frames to achieve a more stable output.
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### Features
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- Applies temporal smoothing to reduce flickering
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- Blends the smoothed frames with the original frames
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- Supports batch processing for efficient computation
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# Image & Mask Batch Management
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Keep your image and masks sized, cropped and ordered how ever you like without having to recreate the masks or mess with connections.
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Note the resizing capability doesn't provide any settings, we simply crop and resize to maximise the image size with your provided width/height.
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## ImageBatchManagement
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The ImageBatchManagement node provides functionality to resize, crop, and reorder a batch of images. It ensures that all images in the batch have the same specified dimensions.
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### Features
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- Resizes and crops images to the specified width and height
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- Allows reordering the images in the batch based on a provided order
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- Supports unlimited input images
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## MaskBatchManagement
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The MaskBatchManagement node is similar to the ImageBatchManagement node but works with mask inputs. It resizes, crops, and reorders a batch of masks to match the specified dimensions.
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### Features
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- Resizes and crops masks to the specified width and height
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- Allows reordering the masks in the batch based on a provided order
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- Supports unlimited input masks
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# Deprecated / Removed
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## MakeResizedMaskBatch (Deprecated please use MaskBatchManagement)
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The MakeResizedMaskBatch node creates a batch of masks from multiple individual masks or batches.
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It resizes and crops the input masks to match the specified width and height.
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## Cross Fade Image Batches (SuperBeasts.AI)
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There is another preexisting node that does this and more so I've removed this function.
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+2
-2
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WEB_DIRECTORY = "js"
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from .superbeastsai_nodes import NODE_CLASS_MAPPINGS, NODE_DISPLAY_NAME_MAPPINGS
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__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]
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__all__ = ["NODE_CLASS_MAPPINGS", "NODE_DISPLAY_NAME_MAPPINGS"]
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import { ComfyApp, app } from "../../scripts/app.js";
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import { api } from "../../scripts/api.js";
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app.registerExtension({
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name: "Comfy.superbeastsai_nodes",
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async beforeRegisterNodeDef(nodeType, nodeData, app) {
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if (nodeData.name == "Image Batch Manager (SuperBeasts.AI)" ||
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nodeData.name == "Mask Batch Manager (SuperBeasts.AI)") {
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var input_name = "input";
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switch(nodeData.name) {
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case 'Image Batch Manager (SuperBeasts.AI)':
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input_name = "image";
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break;
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case 'Mask Batch Manager (SuperBeasts.AI)':
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input_name = "mask";
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break;
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}
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const onConnectionsChange = nodeType.prototype.onConnectionsChange;
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nodeType.prototype.onConnectionsChange = function (type, index, connected, link_info) {
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if (!link_info) return;
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const node = app.graph.getNodeById(link_info.origin_id);
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if (!node) {
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return;
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}
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let slot_type = node.outputs[link_info.origin_slot].type;
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let select_slot = this.inputs.find(x => x.name == "select");
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let mode_slot = this.inputs.find(x => x.name == "sel_mode");
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let converted_count = 0;
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converted_count += select_slot?1:0;
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converted_count += mode_slot?1:0;
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if (!connected && (this.inputs.length > 1+converted_count)) {
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const stackTrace = new Error().stack;
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if(
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!stackTrace.includes('LGraphNode.prototype.connect') && // for touch device
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!stackTrace.includes('LGraphNode.connect') && // for mouse device
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!stackTrace.includes('loadGraphData') &&
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this.inputs[index].name != 'select') {
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this.removeInput(index);
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}
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}
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let slot_i = 1;
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for (let i = 0; i < this.inputs.length; i++) {
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let input_i = this.inputs[i];
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if(input_i.name != 'select'&& input_i.name != 'sel_mode') {
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input_i.name = `${input_name}${slot_i}`
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slot_i++;
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}
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}
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let last_slot = this.inputs[this.inputs.length - 1];
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if (
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(last_slot.name == 'select' && last_slot.name != 'sel_mode' && this.inputs[this.inputs.length - 2].link != undefined)
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|| (last_slot.name != 'select' && last_slot.name != 'sel_mode' && last_slot.link != undefined)) {
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this.addInput(`${input_name}${slot_i}`, this.outputs[0].type);
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}
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}
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}
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}
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});
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+10
-94
@@ -410,57 +410,6 @@ class PixelDeflicker:
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return (blended_tensor,)
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class CrossFadeImageBatches:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"batch1": ("IMAGE",),
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"batch2": ("IMAGE",),
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"fade_frames": ("INT", {"default": 10, "min": 1, "max": 100, "step": 1}),
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"fade_strength": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
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"ease_type": (["linear", "ease_in", "ease_out", "ease_in_out"], {"default": "linear"}),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "crossfade"
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CATEGORY = "SuperBeastsAI/Animation"
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def crossfade(self, batch1, batch2, fade_frames=10, fade_strength=0.5, ease_type="linear"):
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num_frames_batch1 = batch1.shape[0]
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num_frames_batch2 = batch2.shape[0]
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if num_frames_batch1 < fade_frames or num_frames_batch2 < fade_frames:
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raise ValueError("The number of fade frames must be less than or equal to the number of frames in each batch.")
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if batch1.shape[2:] != batch2.shape[2:]:
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raise ValueError("The spatial dimensions of batch1 and batch2 must match. Please resize the batches to the same size before using this node.")
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# Extract the frames to be faded from each batch
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fade_frames_batch1 = batch1[-fade_frames:]
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fade_frames_batch2 = batch2[:fade_frames]
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# Generate the interpolation weights based on the selected easing function
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if ease_type == "linear":
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weights = torch.linspace(0, 1, fade_frames)
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elif ease_type == "ease_in":
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weights = torch.pow(torch.linspace(0, 1, fade_frames), 2)
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elif ease_type == "ease_out":
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weights = 1 - torch.pow(torch.linspace(1, 0, fade_frames), 2)
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elif ease_type == "ease_in_out":
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weights = 0.5 * (1 - torch.cos(torch.linspace(0, torch.pi, fade_frames)))
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weights = weights.view(-1, 1, 1, 1) # Reshape weights to match the dimensions of the frames
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# Perform the cross-fade between the fade frames of each batch
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faded_frames = fade_frames_batch1 * (1 - weights * fade_strength) + fade_frames_batch2 * (weights * fade_strength)
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# Combine the non-faded frames from batch1, the faded frames, and the remaining frames from batch2
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output_batch = torch.cat((batch1[:-fade_frames], faded_frames, batch2[fade_frames:]), dim=0)
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return (output_batch,)
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def resize_and_crop(pil_img, target_width, target_height):
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"""Resize and crop an image to fit exactly the specified dimensions."""
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original_width, original_height = pil_img.size
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@@ -503,11 +452,11 @@ class ImageBatchManagement:
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"required": {
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"width": ("INT", {"default": 512}),
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"height": ("INT", {"default": 768}),
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"ordering_enabled": (["disabled", "enabled"], {"default": "disabled"})
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"ordering_enabled": (["disabled", "enabled"], {"default": "disabled"}),
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"image1": ("IMAGE",) # Ensure at least one image is required
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},
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"optional": {
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"new_order": ("STRING", {"default": ""}),
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**{f"image{i}": ("IMAGE",) for i in range(1, 13)}
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},
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}
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CATEGORY = "SuperBeastsAI/Image"
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def reorder(self, width, height, ordering_enabled, new_order, **kwargs):
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image_keys = [f'image{i}' for i in range(1, 13)]
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images = [kwargs.get(key) for key in image_keys if kwargs.get(key) is not None]
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images = [kwargs["image1"]] # Start with the required image1 input
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i = 2
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while f"image{i}" in kwargs:
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images.append(kwargs[f"image{i}"])
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i += 1
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if ordering_enabled == "enabled" and new_order:
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order_indices = [int(idx) - 1 for idx in new_order.split(',') if idx.strip()]
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@@ -578,49 +531,15 @@ class MaskBatchManagement:
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return (result,)
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class ConcatConditionings2:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"conditioning1": ("CONDITIONING", ),
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},
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}
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RETURN_TYPES = ("CONDITIONING", )
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FUNCTION = "doit"
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CATEGORY = "ImpactPack/Util"
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def doit(self, **kwargs):
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conditioning_to = list(kwargs.values())[0]
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for k, conditioning_from in list(kwargs.items())[1:]:
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out = []
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if len(conditioning_from) > 1:
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print("Warning: ConcatConditionings {k} contains more than 1 cond, only the first one will actually be applied to conditioning1.")
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cond_from = conditioning_from[0][0]
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for i in range(len(conditioning_to)):
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t1 = conditioning_to[i][0]
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tw = torch.cat((t1, cond_from), 1)
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n = [tw, conditioning_to[i][1].copy()]
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out.append(n)
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conditioning_to = out
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return (out, )
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NODE_CLASS_MAPPINGS = {
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'HDR Effects (SuperBeasts.AI)': HDREffects,
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'Make Resized Mask Batch (SuperBeasts.AI)': MakeResizedMaskBatch,
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'Deflicker (SuperBeasts.AI)': Deflicker,
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'Pixel Deflicker (SuperBeasts.AI)': PixelDeflicker,
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'Cross Fade Image Batches (SuperBeasts.AI)': CrossFadeImageBatches,
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'Mask Batch Manager (SuperBeasts.AI)': MaskBatchManagement,
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'Image Batch Manager (SuperBeasts.AI)': ImageBatchManagement,
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'Batch Manager Test (SuperBeasts.AI)': ConcatConditionings2
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'Image Batch Manager (SuperBeasts.AI)': ImageBatchManagement
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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@@ -628,9 +547,6 @@ NODE_DISPLAY_NAME_MAPPINGS = {
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'MakeResizedMaskBatch': 'Make Resized Mask Batch (SuperBeasts.AI)',
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'Deflicker': 'Deflicker (SuperBeasts.AI)',
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'PixelDeflicker': 'Pixel Deflicker (SuperBeasts.AI)',
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'CrossFadeImageBatches': 'Cross Fade Image Batches (SuperBeasts.AI)',
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'MaskBatchManagement':'Mask Batch Manager (SuperBeasts.AI)',
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'ImageBatchManagement':'Image Batch Manager (SuperBeasts.AI)',
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'ConcatConditionings2':'Batch Manager Test (SuperBeasts.AI)'
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'ImageBatchManagement':'Image Batch Manager (SuperBeasts.AI)'
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}
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