Update pyproject.toml

Add crossOrigin='anonymous' to image elements in CanvasState._createLayerFromSrc() method. This prevents canvas tainting when images are restored from IndexedDB after page refresh, ensuring export functions work correctly.

.github/ISSUE_TEMPLATE/docs_request.yml

@@ -3,11 +3,17 @@ description: Suggest improvements or additions to documentation
 title: "[Docs] "
 labels: [documentation]
 body:
+  - type: markdown
+    attributes:
+      value: |
+        > This template is only for suggesting improvements or additions **to existing documentation**.  
+        > If you want to suggest a new feature, functionality, or enhancement for the project itself, please use the **Feature Request** template instead.  
+        > Thank you!
   - type: input
     id: doc_area
     attributes:
       label: Area of documentation
-      placeholder: e.g. Getting started, Node API, Deployment guide
+      placeholder: e.g. Key Features, Installation, Controls & Shortcuts
     validations:
       required: true
   - type: textarea

.github/ISSUE_TEMPLATE/feature-request.yml

@@ -3,6 +3,10 @@ description: Suggest an idea for this project
 title: '[Feature Request]: '
 labels: ['enhancement']
 body:
+  - type: markdown
+    attributes:
+      value: |
+        **Before suggesting a new feature, please make sure you are using the latest version of the project and that this functionality does not already exist.**
   - type: markdown
     attributes:
       value: |

canvas_node.py

@@ -179,6 +179,10 @@ class LayerForgeNode:
                 "trigger": ("INT", {"default": 0, "min": 0, "max": 99999999, "step": 1}),
                 "node_id": ("STRING", {"default": "0"}),
             },
+            "optional": {
+                "input_image": ("IMAGE",),
+                "input_mask": ("MASK",),
+            },
             "hidden": {
                 "prompt": ("PROMPT",),
                 "unique_id": ("UNIQUE_ID",),
@@ -239,7 +243,7 @@ class LayerForgeNode:
 
     _processing_lock = threading.Lock()
 
-    def process_canvas_image(self, fit_on_add, show_preview, auto_refresh_after_generation, trigger, node_id, prompt=None, unique_id=None):
+    def process_canvas_image(self, fit_on_add, show_preview, auto_refresh_after_generation, trigger, node_id, input_image=None, input_mask=None, prompt=None, unique_id=None):
         
         try:
 
@@ -250,6 +254,81 @@ class LayerForgeNode:
 
             log_info(f"Lock acquired. Starting process_canvas_image for node_id: {node_id} (fallback unique_id: {unique_id})")
 
+            # Always store fresh input data, even if None, to clear stale data
+            log_info(f"Storing input data for node {node_id} - Image: {input_image is not None}, Mask: {input_mask is not None}")
+            
+            with self.__class__._storage_lock:
+                input_data = {}
+                
+                if input_image is not None:
+                    # Convert image tensor(s) to base64 - handle batch
+                    if isinstance(input_image, torch.Tensor):
+                        # Ensure correct shape [B, H, W, C]
+                        if input_image.dim() == 3:
+                            input_image = input_image.unsqueeze(0)
+                        
+                        batch_size = input_image.shape[0]
+                        log_info(f"Processing batch of {batch_size} image(s)")
+                        
+                        if batch_size == 1:
+                            # Single image - keep backward compatibility
+                            img_np = (input_image.squeeze(0).cpu().numpy() * 255).astype(np.uint8)
+                            pil_img = Image.fromarray(img_np, 'RGB')
+                            
+                            # Convert to base64
+                            buffered = io.BytesIO()
+                            pil_img.save(buffered, format="PNG")
+                            img_str = base64.b64encode(buffered.getvalue()).decode()
+                            input_data['input_image'] = f"data:image/png;base64,{img_str}"
+                            input_data['input_image_width'] = pil_img.width
+                            input_data['input_image_height'] = pil_img.height
+                            log_debug(f"Stored single input image: {pil_img.width}x{pil_img.height}")
+                        else:
+                            # Multiple images - store as array
+                            images_array = []
+                            for i in range(batch_size):
+                                img_np = (input_image[i].cpu().numpy() * 255).astype(np.uint8)
+                                pil_img = Image.fromarray(img_np, 'RGB')
+                                
+                                # Convert to base64
+                                buffered = io.BytesIO()
+                                pil_img.save(buffered, format="PNG")
+                                img_str = base64.b64encode(buffered.getvalue()).decode()
+                                images_array.append({
+                                    'data': f"data:image/png;base64,{img_str}",
+                                    'width': pil_img.width,
+                                    'height': pil_img.height
+                                })
+                                log_debug(f"Stored batch image {i+1}/{batch_size}: {pil_img.width}x{pil_img.height}")
+                            
+                            input_data['input_images_batch'] = images_array
+                            log_info(f"Stored batch of {batch_size} images")
+                
+                if input_mask is not None:
+                    # Convert mask tensor to base64
+                    if isinstance(input_mask, torch.Tensor):
+                        # Ensure correct shape
+                        if input_mask.dim() == 2:
+                            input_mask = input_mask.unsqueeze(0)
+                        if input_mask.dim() == 3 and input_mask.shape[0] == 1:
+                            input_mask = input_mask.squeeze(0)
+                        
+                        # Convert to numpy and then to PIL
+                        mask_np = (input_mask.cpu().numpy() * 255).astype(np.uint8)
+                        pil_mask = Image.fromarray(mask_np, 'L')
+                        
+                        # Convert to base64
+                        mask_buffered = io.BytesIO()
+                        pil_mask.save(mask_buffered, format="PNG")
+                        mask_str = base64.b64encode(mask_buffered.getvalue()).decode()
+                        input_data['input_mask'] = f"data:image/png;base64,{mask_str}"
+                        log_debug(f"Stored input mask: {pil_mask.width}x{pil_mask.height}")
+                
+                input_data['fit_on_add'] = fit_on_add
+                
+                # Store in a special key for input data (overwrites any previous data)
+                self.__class__._canvas_data_storage[f"{node_id}_input"] = input_data
+
             storage_key = node_id
             
             processed_image = None
@@ -433,6 +512,63 @@ class LayerForgeNode:
             log_info("WebSocket connection closed")
             return ws
 
+        @PromptServer.instance.routes.get("/layerforge/get_input_data/{node_id}")
+        async def get_input_data(request):
+            try:
+                node_id = request.match_info["node_id"]
+                log_debug(f"Checking for input data for node: {node_id}")
+                
+                with cls._storage_lock:
+                    input_key = f"{node_id}_input"
+                    input_data = cls._canvas_data_storage.get(input_key, None)
+                
+                if input_data:
+                    log_info(f"Input data found for node {node_id}, sending to frontend")
+                    return web.json_response({
+                        'success': True,
+                        'has_input': True,
+                        'data': input_data
+                    })
+                else:
+                    log_debug(f"No input data found for node {node_id}")
+                    return web.json_response({
+                        'success': True,
+                        'has_input': False
+                    })
+                    
+            except Exception as e:
+                log_error(f"Error in get_input_data: {str(e)}")
+                return web.json_response({
+                    'success': False,
+                    'error': str(e)
+                }, status=500)
+
+        @PromptServer.instance.routes.post("/layerforge/clear_input_data/{node_id}")
+        async def clear_input_data(request):
+            try:
+                node_id = request.match_info["node_id"]
+                log_info(f"Clearing input data for node: {node_id}")
+                
+                with cls._storage_lock:
+                    input_key = f"{node_id}_input"
+                    if input_key in cls._canvas_data_storage:
+                        del cls._canvas_data_storage[input_key]
+                        log_info(f"Input data cleared for node {node_id}")
+                    else:
+                        log_debug(f"No input data to clear for node {node_id}")
+                
+                return web.json_response({
+                    'success': True,
+                    'message': f'Input data cleared for node {node_id}'
+                })
+                    
+            except Exception as e:
+                log_error(f"Error in clear_input_data: {str(e)}")
+                return web.json_response({
+                    'success': False,
+                    'error': str(e)
+                }, status=500)
+
         @PromptServer.instance.routes.get("/ycnode/get_canvas_data/{node_id}")
         async def get_canvas_data(request):
             try:
@@ -911,4 +1047,3 @@ def convert_tensor_to_base64(tensor, alpha_mask=None, original_alpha=None):
         log_error(f"Error in convert_tensor_to_base64: {str(e)}")
         log_debug(f"Tensor shape: {tensor.shape}, dtype: {tensor.dtype}")
         raise
-

js/Canvas.js

@@ -73,6 +73,8 @@ export class Canvas {
         this.canvasContainer = null;
         this.dataInitialized = false;
         this.pendingDataCheck = null;
+        this.pendingInputDataCheck = null;
+        this.inputDataLoaded = false;
         this.imageCache = new Map();
         this.requestSaveState = () => { };
         this.outputAreaShape = null;
@@ -372,6 +374,10 @@ export class Canvas {
             return widget ? widget.value : false;
         };
         const handleExecutionStart = () => {
+            // Check for input data when execution starts, but don't reset the flag
+            log.debug('Execution started, checking for input data...');
+            // On start, only allow images; mask should load on mask-connect or after execution completes
+            this.canvasIO.checkForInputData({ allowImage: true, allowMask: false, reason: 'execution_start' });
             if (getAutoRefreshValue()) {
                 lastExecutionStartTime = Date.now();
                 // Store a snapshot of the context for the upcoming batch
@@ -394,6 +400,9 @@ export class Canvas {
             }
         };
         const handleExecutionSuccess = async () => {
+            // Always check for input data after execution completes
+            log.debug('Execution success, checking for input data...');
+            await this.canvasIO.checkForInputData({ allowImage: true, allowMask: true, reason: 'execution_success' });
             if (getAutoRefreshValue()) {
                 log.info('Auto-refresh triggered, importing latest images.');
                 if (!this.pendingBatchContext) {

js/CanvasIO.js

@@ -2,6 +2,7 @@ import { createCanvas } from "./utils/CommonUtils.js";
 import { createModuleLogger } from "./utils/LoggerUtils.js";
 import { showErrorNotification } from "./utils/NotificationUtils.js";
 import { webSocketManager } from "./utils/WebSocketManager.js";
+import { scaleImageToFit, createImageFromSource, tensorToImageData, createImageFromImageData } from "./utils/ImageUtils.js";
 const log = createModuleLogger('CanvasIO');
 export class CanvasIO {
     constructor(canvas) {
@@ -247,17 +248,12 @@ export class CanvasIO {
     async addInputToCanvas(inputImage, inputMask) {
         try {
             log.debug("Adding input to canvas:", { inputImage });
-            const { canvas: tempCanvas, ctx: tempCtx } = createCanvas(inputImage.width, inputImage.height);
+            // Use unified tensorToImageData for RGB image
-            if (!tempCtx)
+            const imageData = tensorToImageData(inputImage, 'rgb');
-                throw new Error("Could not create temp context");
+            if (!imageData)
-            const imgData = new ImageData(new Uint8ClampedArray(inputImage.data), inputImage.width, inputImage.height);
+                throw new Error("Failed to convert input image tensor");
-            tempCtx.putImageData(imgData, 0, 0);
+            // Create HTMLImageElement from ImageData
-            const image = new Image();
+            const image = await createImageFromImageData(imageData);
-            await new Promise((resolve, reject) => {
-                image.onload = resolve;
-                image.onerror = reject;
-                image.src = tempCanvas.toDataURL();
-            });
             const bounds = this.canvas.outputAreaBounds;
             const scale = Math.min(bounds.width / inputImage.width * 0.8, bounds.height / inputImage.height * 0.8);
             const layer = await this.canvas.canvasLayers.addLayerWithImage(image, {
@@ -283,17 +279,10 @@ export class CanvasIO {
             if (!tensor || !tensor.data || !tensor.width || !tensor.height) {
                 throw new Error("Invalid tensor data");
             }
-            const { canvas, ctx } = createCanvas(tensor.width, tensor.height, '2d', { willReadFrequently: true });
+            const imageData = tensorToImageData(tensor, 'rgb');
-            if (!ctx)
+            if (!imageData)
-                throw new Error("Could not create canvas context");
+                throw new Error("Failed to convert tensor to image data");
-            const imageData = new ImageData(new Uint8ClampedArray(tensor.data), tensor.width, tensor.height);
+            return await createImageFromImageData(imageData);
-            ctx.putImageData(imageData, 0, 0);
-            return new Promise((resolve, reject) => {
-                const img = new Image();
-                img.onload = () => resolve(img);
-                img.onerror = (e) => reject(new Error("Failed to load image: " + e));
-                img.src = canvas.toDataURL();
-            });
         }
         catch (error) {
             log.error("Error converting tensor to image:", error);
@@ -314,12 +303,26 @@ export class CanvasIO {
     async initNodeData() {
         try {
             log.info("Starting node data initialization...");
+            // First check for input data from the backend (new feature)
+            await this.checkForInputData();
+            // If we've already loaded input data, don't continue with old initialization
+            if (this.canvas.inputDataLoaded) {
+                log.debug("Input data already loaded, skipping old initialization");
+                this.canvas.dataInitialized = true;
+                return;
+            }
             if (!this.canvas.node || !this.canvas.node.inputs) {
                 log.debug("Node or inputs not ready");
                 return this.scheduleDataCheck();
             }
             if (this.canvas.node.inputs[0] && this.canvas.node.inputs[0].link) {
                 const imageLinkId = this.canvas.node.inputs[0].link;
+                // Check if we already loaded this link
+                if (this.canvas.lastLoadedLinkId === imageLinkId) {
+                    log.debug(`Link ${imageLinkId} already loaded via new system, marking as initialized`);
+                    this.canvas.dataInitialized = true;
+                    return;
+                }
                 const imageData = window.app.nodeOutputs[imageLinkId];
                 if (imageData) {
                     log.debug("Found image data:", imageData);
@@ -331,6 +334,10 @@ export class CanvasIO {
                     return this.scheduleDataCheck();
                 }
             }
+            else {
+                // No input connected, mark as initialized to stop repeated checks
+                this.canvas.dataInitialized = true;
+            }
             if (this.canvas.node.inputs[1] && this.canvas.node.inputs[1].link) {
                 const maskLinkId = this.canvas.node.inputs[1].link;
                 const maskData = window.app.nodeOutputs[maskLinkId];
@@ -345,6 +352,390 @@ export class CanvasIO {
             return this.scheduleDataCheck();
         }
     }
+    async checkForInputData(options) {
+        try {
+            const nodeId = this.canvas.node.id;
+            const allowImage = options?.allowImage ?? true;
+            const allowMask = options?.allowMask ?? true;
+            const reason = options?.reason ?? 'unspecified';
+            log.info(`Checking for input data for node ${nodeId}... opts: image=${allowImage}, mask=${allowMask}, reason=${reason}`);
+            // Track loaded links separately for image and mask
+            let imageLoaded = false;
+            let maskLoaded = false;
+            let imageChanged = false;
+            // First, try to get data from connected node's output if available (IMAGES)
+            if (allowImage && this.canvas.node.inputs && this.canvas.node.inputs[0] && this.canvas.node.inputs[0].link) {
+                const linkId = this.canvas.node.inputs[0].link;
+                const graph = this.canvas.node.graph;
+                // Always check if images have changed first
+                if (graph) {
+                    const link = graph.links[linkId];
+                    if (link) {
+                        const sourceNode = graph.getNodeById(link.origin_id);
+                        if (sourceNode && sourceNode.imgs && sourceNode.imgs.length > 0) {
+                            // Create current batch identifier (all image sources combined)
+                            const currentBatchImageSrcs = sourceNode.imgs.map((img) => img.src).join('|');
+                            // Check if this is the same link we loaded before
+                            if (this.canvas.lastLoadedLinkId === linkId) {
+                                // Same link, check if images actually changed
+                                if (this.canvas.lastLoadedImageSrc !== currentBatchImageSrcs) {
+                                    log.info(`Batch images changed for link ${linkId} (${sourceNode.imgs.length} images), will reload...`);
+                                    log.debug(`Previous batch hash: ${this.canvas.lastLoadedImageSrc?.substring(0, 100)}...`);
+                                    log.debug(`Current batch hash: ${currentBatchImageSrcs.substring(0, 100)}...`);
+                                    imageChanged = true;
+                                    // Clear the inputDataLoaded flag to force reload from backend
+                                    this.canvas.inputDataLoaded = false;
+                                    // Clear the lastLoadedImageSrc to force reload
+                                    this.canvas.lastLoadedImageSrc = undefined;
+                                    // Clear backend data to force fresh load
+                                    fetch(`/layerforge/clear_input_data/${nodeId}`, { method: 'POST' })
+                                        .then(() => log.debug("Backend input data cleared due to image change"))
+                                        .catch(err => log.error("Failed to clear backend data:", err));
+                                }
+                                else {
+                                    log.debug(`Batch images for link ${linkId} unchanged (${sourceNode.imgs.length} images)`);
+                                    imageLoaded = true;
+                                }
+                            }
+                            else {
+                                // Different link or first load
+                                log.info(`New link ${linkId} detected, will load ${sourceNode.imgs.length} images`);
+                                imageChanged = false; // It's not a change, it's a new link
+                                imageLoaded = false; // Need to load
+                                // Reset the inputDataLoaded flag for new link
+                                this.canvas.inputDataLoaded = false;
+                            }
+                        }
+                    }
+                }
+                if (!imageLoaded || imageChanged) {
+                    // Reset the inputDataLoaded flag when images change
+                    if (imageChanged) {
+                        this.canvas.inputDataLoaded = false;
+                        log.info("Resetting inputDataLoaded flag due to image change");
+                    }
+                    if (this.canvas.node.graph) {
+                        const graph2 = this.canvas.node.graph;
+                        const link2 = graph2.links[linkId];
+                        if (link2) {
+                            const sourceNode = graph2.getNodeById(link2.origin_id);
+                            if (sourceNode && sourceNode.imgs && sourceNode.imgs.length > 0) {
+                                // The connected node has images in its output - handle multiple images (batch)
+                                log.info(`Found ${sourceNode.imgs.length} image(s) in connected node's output, loading all`);
+                                // Create a combined source identifier for batch detection
+                                const batchImageSrcs = sourceNode.imgs.map((img) => img.src).join('|');
+                                // Mark this link and batch sources as loaded
+                                this.canvas.lastLoadedLinkId = linkId;
+                                this.canvas.lastLoadedImageSrc = batchImageSrcs;
+                                // Don't clear layers - just add new ones
+                                if (imageChanged) {
+                                    log.info("Image change detected, will add new layers");
+                                }
+                                // Determine add mode
+                                const fitOnAddWidget = this.canvas.node.widgets.find((w) => w.name === "fit_on_add");
+                                const addMode = (fitOnAddWidget && fitOnAddWidget.value) ? 'fit' : 'center';
+                                // Add all images from the batch as separate layers
+                                for (let i = 0; i < sourceNode.imgs.length; i++) {
+                                    const img = sourceNode.imgs[i];
+                                    await this.canvas.canvasLayers.addLayerWithImage(img, { name: `Batch Image ${i + 1}` }, // Give each layer a unique name
+                                    addMode, this.canvas.outputAreaBounds);
+                                    log.debug(`Added batch image ${i + 1}/${sourceNode.imgs.length} to canvas`);
+                                }
+                                this.canvas.inputDataLoaded = true;
+                                imageLoaded = true;
+                                log.info(`All ${sourceNode.imgs.length} input images from batch added as separate layers`);
+                                this.canvas.render();
+                                this.canvas.saveState();
+                            }
+                        }
+                    }
+                }
+            }
+            // Check for mask input separately (from nodeOutputs) ONLY when allowed
+            if (allowMask && this.canvas.node.inputs && this.canvas.node.inputs[1] && this.canvas.node.inputs[1].link) {
+                const maskLinkId = this.canvas.node.inputs[1].link;
+                // Check if we already loaded this mask link
+                if (this.canvas.lastLoadedMaskLinkId === maskLinkId) {
+                    log.debug(`Mask link ${maskLinkId} already loaded`);
+                    maskLoaded = true;
+                }
+                else {
+                    // Try to get mask tensor from nodeOutputs using origin_id (not link id)
+                    const graph = this.canvas.node.graph;
+                    let maskOutput = null;
+                    if (graph) {
+                        const link = graph.links[maskLinkId];
+                        if (link && link.origin_id) {
+                            // Use origin_id to get the actual node output
+                            const nodeOutput = window.app?.nodeOutputs?.[link.origin_id];
+                            log.debug(`Looking for mask output from origin node ${link.origin_id}, found:`, !!nodeOutput);
+                            if (nodeOutput) {
+                                log.debug(`Node ${link.origin_id} output structure:`, {
+                                    hasData: !!nodeOutput.data,
+                                    hasShape: !!nodeOutput.shape,
+                                    dataType: typeof nodeOutput.data,
+                                    shapeType: typeof nodeOutput.shape,
+                                    keys: Object.keys(nodeOutput)
+                                });
+                                // Only use if it has actual tensor data
+                                if (nodeOutput.data && nodeOutput.shape) {
+                                    maskOutput = nodeOutput;
+                                }
+                            }
+                        }
+                    }
+                    if (maskOutput && maskOutput.data && maskOutput.shape) {
+                        try {
+                            // Derive dimensions from shape or explicit width/height
+                            let width = maskOutput.width || 0;
+                            let height = maskOutput.height || 0;
+                            const shape = maskOutput.shape; // e.g. [1,H,W] or [1,H,W,1]
+                            if ((!width || !height) && Array.isArray(shape)) {
+                                if (shape.length >= 3) {
+                                    height = shape[1];
+                                    width = shape[2];
+                                }
+                                else if (shape.length === 2) {
+                                    height = shape[0];
+                                    width = shape[1];
+                                }
+                            }
+                            if (!width || !height) {
+                                throw new Error("Cannot determine mask dimensions from nodeOutputs");
+                            }
+                            // Determine channels count
+                            let channels = 1;
+                            if (Array.isArray(shape) && shape.length >= 4) {
+                                channels = shape[3];
+                            }
+                            else if (maskOutput.channels) {
+                                channels = maskOutput.channels;
+                            }
+                            else {
+                                const len = maskOutput.data.length;
+                                channels = Math.max(1, Math.floor(len / (width * height)));
+                            }
+                            // Use unified tensorToImageData for masks
+                            const maskImageData = tensorToImageData(maskOutput, 'grayscale');
+                            if (!maskImageData)
+                                throw new Error("Failed to convert mask tensor to image data");
+                            // Create canvas and put image data
+                            const { canvas: maskCanvas, ctx } = createCanvas(width, height, '2d', { willReadFrequently: true });
+                            if (!ctx)
+                                throw new Error("Could not create mask context");
+                            ctx.putImageData(maskImageData, 0, 0);
+                            // Convert to HTMLImageElement
+                            const maskImg = await createImageFromSource(maskCanvas.toDataURL());
+                            // Respect fit_on_add (scale to output area)
+                            const widgets = this.canvas.node.widgets;
+                            const fitOnAddWidget = widgets ? widgets.find((w) => w.name === "fit_on_add") : null;
+                            const shouldFit = fitOnAddWidget && fitOnAddWidget.value;
+                            let finalMaskImg = maskImg;
+                            if (shouldFit) {
+                                const bounds = this.canvas.outputAreaBounds;
+                                finalMaskImg = await scaleImageToFit(maskImg, bounds.width, bounds.height);
+                            }
+                            // Apply to MaskTool (centers internally)
+                            if (this.canvas.maskTool) {
+                                this.canvas.maskTool.setMask(finalMaskImg, true);
+                                this.canvas.maskAppliedFromInput = true;
+                                this.canvas.canvasState.saveMaskState();
+                                this.canvas.render();
+                                // Mark this mask link as loaded to avoid re-applying
+                                this.canvas.lastLoadedMaskLinkId = maskLinkId;
+                                maskLoaded = true;
+                                log.info("Applied input mask from nodeOutputs immediately on connection" + (shouldFit ? " (fitted to output area)" : ""));
+                            }
+                        }
+                        catch (err) {
+                            log.warn("Failed to apply mask from nodeOutputs immediately; will wait for backend input_mask after execution", err);
+                        }
+                    }
+                    else {
+                        // nodeOutputs exist but don't have tensor data yet (need workflow execution)
+                        log.info(`Mask node ${this.canvas.node.graph?.links[maskLinkId]?.origin_id} found but has no tensor data yet. Mask will be applied automatically after workflow execution.`);
+                        // Don't retry - data won't be available until workflow runs
+                    }
+                }
+            }
+            // Only check backend if we have actual inputs connected
+            const hasImageInput = this.canvas.node.inputs && this.canvas.node.inputs[0] && this.canvas.node.inputs[0].link;
+            const hasMaskInput = this.canvas.node.inputs && this.canvas.node.inputs[1] && this.canvas.node.inputs[1].link;
+            // If mask input is disconnected, clear any currently applied mask to ensure full separation
+            if (!hasMaskInput) {
+                this.canvas.maskAppliedFromInput = false;
+                this.canvas.lastLoadedMaskLinkId = undefined;
+                log.info("Mask input disconnected - cleared mask to enforce separation from input_image");
+            }
+            if (!hasImageInput && !hasMaskInput) {
+                log.debug("No inputs connected, skipping backend check");
+                this.canvas.inputDataLoaded = true;
+                return;
+            }
+            // Skip backend check during mask connection if we didn't get immediate data
+            if (reason === "mask_connect" && !maskLoaded) {
+                log.info("No immediate mask data available during connection, skipping backend check to avoid stale data. Will check after execution.");
+                return;
+            }
+            // Check backend for input data only if we have connected inputs
+            const response = await fetch(`/layerforge/get_input_data/${nodeId}`);
+            const result = await response.json();
+            if (result.success && result.has_input) {
+                // Dedupe: skip only if backend payload matches last loaded batch hash
+                let backendBatchHash;
+                if (result.data?.input_images_batch && Array.isArray(result.data.input_images_batch)) {
+                    backendBatchHash = result.data.input_images_batch.map((i) => i.data).join('|');
+                }
+                else if (result.data?.input_image) {
+                    backendBatchHash = result.data.input_image;
+                }
+                // Check mask separately - don't skip if only images are unchanged AND mask is actually connected AND allowed
+                const shouldCheckMask = hasMaskInput && allowMask;
+                if (backendBatchHash && this.canvas.lastLoadedImageSrc === backendBatchHash && !shouldCheckMask) {
+                    log.debug("Backend input data unchanged and no mask to check, skipping reload");
+                    this.canvas.inputDataLoaded = true;
+                    return;
+                }
+                else if (backendBatchHash && this.canvas.lastLoadedImageSrc === backendBatchHash && shouldCheckMask) {
+                    log.debug("Images unchanged but need to check mask, continuing...");
+                    imageLoaded = true; // Mark images as already loaded to skip reloading them
+                }
+                // Check if we already loaded image data (by checking the current link)
+                if (allowImage && !imageLoaded && this.canvas.node.inputs && this.canvas.node.inputs[0] && this.canvas.node.inputs[0].link) {
+                    const currentLinkId = this.canvas.node.inputs[0].link;
+                    if (this.canvas.lastLoadedLinkId !== currentLinkId) {
+                        // Mark this link as loaded
+                        this.canvas.lastLoadedLinkId = currentLinkId;
+                        imageLoaded = false; // Will load from backend
+                    }
+                }
+                // Check for mask data from backend ONLY when mask input is actually connected AND allowed
+                // Only reset if the mask link actually changed
+                if (allowMask && hasMaskInput && this.canvas.node.inputs && this.canvas.node.inputs[1]) {
+                    const currentMaskLinkId = this.canvas.node.inputs[1].link;
+                    // Only reset if this is a different mask link than what we loaded before
+                    if (this.canvas.lastLoadedMaskLinkId !== currentMaskLinkId) {
+                        maskLoaded = false;
+                        log.debug(`New mask input detected (${currentMaskLinkId}), will check backend for mask data`);
+                    }
+                    else {
+                        log.debug(`Same mask input (${currentMaskLinkId}), mask already loaded`);
+                        maskLoaded = true;
+                    }
+                }
+                else {
+                    // No mask input connected, or mask loading not allowed right now
+                    maskLoaded = true; // Mark as loaded to skip mask processing
+                    if (!allowMask) {
+                        log.debug("Mask loading is currently disabled by caller, skipping mask check");
+                    }
+                    else {
+                        log.debug("No mask input connected, skipping mask check");
+                    }
+                }
+                log.info("Input data found from backend, adding to canvas");
+                const inputData = result.data;
+                // Compute backend batch hash for dedupe and state
+                let backendHashNow;
+                if (inputData?.input_images_batch && Array.isArray(inputData.input_images_batch)) {
+                    backendHashNow = inputData.input_images_batch.map((i) => i.data).join('|');
+                }
+                else if (inputData?.input_image) {
+                    backendHashNow = inputData.input_image;
+                }
+                // Just update the hash without removing any layers
+                if (backendHashNow) {
+                    log.info("New backend input data detected, adding new layers");
+                    this.canvas.lastLoadedImageSrc = backendHashNow;
+                }
+                // Mark that we've loaded input data for this execution
+                this.canvas.inputDataLoaded = true;
+                // Determine add mode based on fit_on_add setting
+                const widgets = this.canvas.node.widgets;
+                const fitOnAddWidget = widgets ? widgets.find((w) => w.name === "fit_on_add") : null;
+                const addMode = (fitOnAddWidget && fitOnAddWidget.value) ? 'fit' : 'center';
+                // Load input image(s) only if image input is actually connected, not already loaded, and allowed
+                if (allowImage && !imageLoaded && hasImageInput) {
+                    if (inputData.input_images_batch) {
+                        // Handle batch of images
+                        const batch = inputData.input_images_batch;
+                        log.info(`Processing batch of ${batch.length} images from backend`);
+                        for (let i = 0; i < batch.length; i++) {
+                            const imgData = batch[i];
+                            const img = await createImageFromSource(imgData.data);
+                            // Add image to canvas with unique name
+                            await this.canvas.canvasLayers.addLayerWithImage(img, { name: `Batch Image ${i + 1}` }, addMode, this.canvas.outputAreaBounds);
+                            log.debug(`Added batch image ${i + 1}/${batch.length} from backend`);
+                        }
+                        log.info(`All ${batch.length} batch images added from backend`);
+                        this.canvas.render();
+                        this.canvas.saveState();
+                    }
+                    else if (inputData.input_image) {
+                        // Handle single image (backward compatibility)
+                        const img = await createImageFromSource(inputData.input_image);
+                        // Add image to canvas at output area position
+                        await this.canvas.canvasLayers.addLayerWithImage(img, {}, addMode, this.canvas.outputAreaBounds);
+                        log.info("Single input image added as new layer to canvas");
+                        this.canvas.render();
+                        this.canvas.saveState();
+                    }
+                    else {
+                        log.debug("No input image data from backend");
+                    }
+                }
+                else if (!hasImageInput && (inputData.input_images_batch || inputData.input_image)) {
+                    log.debug("Backend has image data but no image input connected, skipping image load");
+                }
+                // Handle mask separately only if mask input is actually connected, allowed, and not already loaded
+                if (allowMask && !maskLoaded && hasMaskInput && inputData.input_mask) {
+                    log.info("Processing input mask");
+                    // Load mask image
+                    const maskImg = await createImageFromSource(inputData.input_mask);
+                    // Determine if we should fit the mask or use it at original size
+                    const fitOnAddWidget2 = this.canvas.node.widgets.find((w) => w.name === "fit_on_add");
+                    const shouldFit = fitOnAddWidget2 && fitOnAddWidget2.value;
+                    let finalMaskImg = maskImg;
+                    if (shouldFit && this.canvas.maskTool) {
+                        const bounds = this.canvas.outputAreaBounds;
+                        finalMaskImg = await scaleImageToFit(maskImg, bounds.width, bounds.height);
+                    }
+                    // Apply to MaskTool (centers internally)
+                    if (this.canvas.maskTool) {
+                        this.canvas.maskTool.setMask(finalMaskImg, true);
+                    }
+                    this.canvas.maskAppliedFromInput = true;
+                    // Save the mask state
+                    this.canvas.canvasState.saveMaskState();
+                    log.info("Applied input mask to mask tool" + (shouldFit ? " (fitted to output area)" : " (original size)"));
+                }
+                else if (!hasMaskInput && inputData.input_mask) {
+                    log.debug("Backend has mask data but no mask input connected, skipping mask load");
+                }
+                else if (!allowMask && inputData.input_mask) {
+                    log.debug("Mask input data present in backend but mask loading is disabled by caller; skipping");
+                }
+            }
+            else {
+                log.debug("No input data from backend");
+                // Don't schedule another check - we'll only check when explicitly triggered
+            }
+        }
+        catch (error) {
+            log.error("Error checking for input data:", error);
+            // Don't schedule another check on error
+        }
+    }
+    scheduleInputDataCheck() {
+        // Schedule a retry for mask data check when nodeOutputs are not ready yet
+        if (this.canvas.pendingInputDataCheck) {
+            clearTimeout(this.canvas.pendingInputDataCheck);
+        }
+        this.canvas.pendingInputDataCheck = window.setTimeout(() => {
+            this.canvas.pendingInputDataCheck = null;
+            log.debug("Retrying input data check for mask...");
+        }, 500); // Shorter delay for mask data retry
+    }
     scheduleDataCheck() {
         if (this.canvas.pendingDataCheck) {
             clearTimeout(this.canvas.pendingDataCheck);
@@ -423,51 +814,10 @@ export class CanvasIO {
         }
     }
     convertTensorToImageData(tensor) {
-        try {
+        return tensorToImageData(tensor, 'rgb');
-            const shape = tensor.shape;
-            const height = shape[1];
-            const width = shape[2];
-            const channels = shape[3];
-            log.debug("Converting tensor:", {
-                shape: shape,
-                dataRange: {
-                    min: tensor.min_val,
-                    max: tensor.max_val
-                }
-            });
-            const imageData = new ImageData(width, height);
-            const data = new Uint8ClampedArray(width * height * 4);
-            const flatData = tensor.data;
-            const pixelCount = width * height;
-            for (let i = 0; i < pixelCount; i++) {
-                const pixelIndex = i * 4;
-                const tensorIndex = i * channels;
-                for (let c = 0; c < channels; c++) {
-                    const value = flatData[tensorIndex + c];
-                    const normalizedValue = (value - tensor.min_val) / (tensor.max_val - tensor.min_val);
-                    data[pixelIndex + c] = Math.round(normalizedValue * 255);
-                }
-                data[pixelIndex + 3] = 255;
-            }
-            imageData.data.set(data);
-            return imageData;
-        }
-        catch (error) {
-            log.error("Error converting tensor:", error);
-            return null;
-        }
     }
     async createImageFromData(imageData) {
-        return new Promise((resolve, reject) => {
+        return createImageFromImageData(imageData);
-            const { canvas, ctx } = createCanvas(imageData.width, imageData.height, '2d', { willReadFrequently: true });
-            if (!ctx)
-                throw new Error("Could not create canvas context");
-            ctx.putImageData(imageData, 0, 0);
-            const img = new Image();
-            img.onload = () => resolve(img);
-            img.onerror = reject;
-            img.src = canvas.toDataURL();
-        });
     }
     async processMaskData(maskData) {
         try {
@@ -527,12 +877,7 @@ export class CanvasIO {
                 log.info(`Received ${result.images.length} new images, adding to canvas.`);
                 const newLayers = [];
                 for (const imageData of result.images) {
-                    const img = new Image();
+                    const img = await createImageFromSource(imageData);
-                    await new Promise((resolve, reject) => {
-                        img.onload = resolve;
-                        img.onerror = reject;
-                        img.src = imageData;
-                    });
                     let processedImage = img;
                     // If there's a custom shape, clip the image to that shape
                     if (this.canvas.outputAreaShape && this.canvas.outputAreaShape.isClosed) {
@@ -559,33 +904,27 @@ export class CanvasIO {
         }
     }
     async clipImageToShape(image, shape) {
-        return new Promise((resolve, reject) => {
+        const { canvas, ctx } = createCanvas(image.width, image.height);
-            const { canvas, ctx } = createCanvas(image.width, image.height);
+        if (!ctx) {
-            if (!ctx) {
+            throw new Error("Could not create canvas context for clipping");
-                reject(new Error("Could not create canvas context for clipping"));
+        }
-                return;
+        // Draw the image first
-            }
+        ctx.drawImage(image, 0, 0);
-            // Draw the image first
+        // Calculate custom shape position accounting for extensions
-            ctx.drawImage(image, 0, 0);
+        // Custom shape should maintain its relative position within the original canvas area
-            // Calculate custom shape position accounting for extensions
+        const ext = this.canvas.outputAreaExtensionEnabled ? this.canvas.outputAreaExtensions : { top: 0, bottom: 0, left: 0, right: 0 };
-            // Custom shape should maintain its relative position within the original canvas area
+        const shapeOffsetX = ext.left; // Add left extension to maintain relative position
-            const ext = this.canvas.outputAreaExtensionEnabled ? this.canvas.outputAreaExtensions : { top: 0, bottom: 0, left: 0, right: 0 };
+        const shapeOffsetY = ext.top; // Add top extension to maintain relative position
-            const shapeOffsetX = ext.left; // Add left extension to maintain relative position
+        // Create a clipping mask using the shape with extension offset
-            const shapeOffsetY = ext.top; // Add top extension to maintain relative position
+        ctx.globalCompositeOperation = 'destination-in';
-            // Create a clipping mask using the shape with extension offset
+        ctx.beginPath();
-            ctx.globalCompositeOperation = 'destination-in';
+        ctx.moveTo(shape.points[0].x + shapeOffsetX, shape.points[0].y + shapeOffsetY);
-            ctx.beginPath();
+        for (let i = 1; i < shape.points.length; i++) {
-            ctx.moveTo(shape.points[0].x + shapeOffsetX, shape.points[0].y + shapeOffsetY);
+            ctx.lineTo(shape.points[i].x + shapeOffsetX, shape.points[i].y + shapeOffsetY);
-            for (let i = 1; i < shape.points.length; i++) {
+        }
-                ctx.lineTo(shape.points[i].x + shapeOffsetX, shape.points[i].y + shapeOffsetY);
+        ctx.closePath();
-            }
+        ctx.fill();
-            ctx.closePath();
+        // Create a new image from the clipped canvas
-            ctx.fill();
+        return await createImageFromSource(canvas.toDataURL());
-            // Create a new image from the clipped canvas
-            const clippedImage = new Image();
-            clippedImage.onload = () => resolve(clippedImage);
-            clippedImage.onerror = () => reject(new Error("Failed to create clipped image"));
-            clippedImage.src = canvas.toDataURL();
-        });
     }
 }

js/CanvasInteractions.js

@@ -39,6 +39,8 @@ export class CanvasInteractions {
             keyMovementInProgress: false,
             canvasResizeRect: null,
             canvasMoveRect: null,
+            outputAreaTransformHandle: null,
+            outputAreaTransformAnchor: { x: 0, y: 0 },
         };
         this.originalLayerPositions = new Map();
     }
@@ -157,6 +159,7 @@ export class CanvasInteractions {
         this.interaction.canvasMoveRect = null;
         this.interaction.hasClonedInDrag = false;
         this.interaction.transformingLayer = null;
+        this.interaction.outputAreaTransformHandle = null;
         this.canvas.canvas.style.cursor = 'default';
     }
     handleMouseDown(e) {
@@ -168,6 +171,18 @@ export class CanvasInteractions {
             // Don't render here - mask tool will handle its own drawing
             return;
         }
+        if (this.interaction.mode === 'transformingOutputArea') {
+            // Check if clicking on output area transform handle
+            const handle = this.getOutputAreaHandle(coords.world);
+            if (handle) {
+                this.startOutputAreaTransform(handle, coords.world);
+                return;
+            }
+            // If clicking outside, exit transform mode
+            this.interaction.mode = 'none';
+            this.canvas.render();
+            return;
+        }
         if (this.canvas.shapeTool.isActive) {
             this.canvas.shapeTool.addPoint(coords.world);
             return;
@@ -258,6 +273,14 @@ export class CanvasInteractions {
             case 'movingCanvas':
                 this.updateCanvasMove(coords.world);
                 break;
+            case 'transformingOutputArea':
+                if (this.interaction.outputAreaTransformHandle) {
+                    this.resizeOutputAreaFromHandle(coords.world, e.shiftKey);
+                }
+                else {
+                    this.updateOutputAreaTransformCursor(coords.world);
+                }
+                break;
             default:
                 this.updateCursor(coords.world);
                 // Update brush cursor on overlay if mask tool is active
@@ -285,6 +308,10 @@ export class CanvasInteractions {
         if (this.interaction.mode === 'movingCanvas') {
             this.finalizeCanvasMove();
         }
+        if (this.interaction.mode === 'transformingOutputArea' && this.interaction.outputAreaTransformHandle) {
+            this.finalizeOutputAreaTransform();
+            return;
+        }
         // Log layer positions when dragging ends
         if (this.interaction.mode === 'dragging' && this.canvas.canvasSelection.selectedLayers.length > 0) {
             this.logDragCompletion(coords);
@@ -1128,4 +1155,168 @@ export class CanvasInteractions {
         }
         await this.canvas.canvasLayers.clipboardManager.handlePaste('mouse', preference);
     }
+    // New methods for output area transformation
+    activateOutputAreaTransform() {
+        // Clear any existing interaction state before starting transform
+        this.resetInteractionState();
+        // Deactivate any active tools that might conflict
+        if (this.canvas.shapeTool.isActive) {
+            this.canvas.shapeTool.deactivate();
+        }
+        if (this.canvas.maskTool.isActive) {
+            this.canvas.maskTool.deactivate();
+        }
+        // Clear selection to avoid confusion
+        this.canvas.canvasSelection.updateSelection([]);
+        // Set transform mode
+        this.interaction.mode = 'transformingOutputArea';
+        this.canvas.render();
+    }
+    getOutputAreaHandle(worldCoords) {
+        const bounds = this.canvas.outputAreaBounds;
+        const threshold = 10 / this.canvas.viewport.zoom;
+        // Define handle positions
+        const handles = {
+            'nw': { x: bounds.x, y: bounds.y },
+            'n': { x: bounds.x + bounds.width / 2, y: bounds.y },
+            'ne': { x: bounds.x + bounds.width, y: bounds.y },
+            'e': { x: bounds.x + bounds.width, y: bounds.y + bounds.height / 2 },
+            'se': { x: bounds.x + bounds.width, y: bounds.y + bounds.height },
+            's': { x: bounds.x + bounds.width / 2, y: bounds.y + bounds.height },
+            'sw': { x: bounds.x, y: bounds.y + bounds.height },
+            'w': { x: bounds.x, y: bounds.y + bounds.height / 2 },
+        };
+        for (const [name, pos] of Object.entries(handles)) {
+            const dx = worldCoords.x - pos.x;
+            const dy = worldCoords.y - pos.y;
+            if (Math.sqrt(dx * dx + dy * dy) < threshold) {
+                return name;
+            }
+        }
+        return null;
+    }
+    startOutputAreaTransform(handle, worldCoords) {
+        this.interaction.outputAreaTransformHandle = handle;
+        this.interaction.dragStart = { ...worldCoords };
+        const bounds = this.canvas.outputAreaBounds;
+        this.interaction.transformOrigin = {
+            x: bounds.x,
+            y: bounds.y,
+            width: bounds.width,
+            height: bounds.height,
+            rotation: 0,
+            centerX: bounds.x + bounds.width / 2,
+            centerY: bounds.y + bounds.height / 2
+        };
+        // Set anchor point (opposite corner for resize)
+        const anchorMap = {
+            'nw': { x: bounds.x + bounds.width, y: bounds.y + bounds.height },
+            'n': { x: bounds.x + bounds.width / 2, y: bounds.y + bounds.height },
+            'ne': { x: bounds.x, y: bounds.y + bounds.height },
+            'e': { x: bounds.x, y: bounds.y + bounds.height / 2 },
+            'se': { x: bounds.x, y: bounds.y },
+            's': { x: bounds.x + bounds.width / 2, y: bounds.y },
+            'sw': { x: bounds.x + bounds.width, y: bounds.y },
+            'w': { x: bounds.x + bounds.width, y: bounds.y + bounds.height / 2 },
+        };
+        this.interaction.outputAreaTransformAnchor = anchorMap[handle];
+    }
+    resizeOutputAreaFromHandle(worldCoords, isShiftPressed) {
+        const o = this.interaction.transformOrigin;
+        if (!o)
+            return;
+        const handle = this.interaction.outputAreaTransformHandle;
+        const anchor = this.interaction.outputAreaTransformAnchor;
+        let newX = o.x;
+        let newY = o.y;
+        let newWidth = o.width;
+        let newHeight = o.height;
+        // Calculate new dimensions based on handle
+        if (handle?.includes('w')) {
+            const deltaX = worldCoords.x - anchor.x;
+            newWidth = Math.abs(deltaX);
+            newX = Math.min(worldCoords.x, anchor.x);
+        }
+        if (handle?.includes('e')) {
+            const deltaX = worldCoords.x - anchor.x;
+            newWidth = Math.abs(deltaX);
+            newX = Math.min(worldCoords.x, anchor.x);
+        }
+        if (handle?.includes('n')) {
+            const deltaY = worldCoords.y - anchor.y;
+            newHeight = Math.abs(deltaY);
+            newY = Math.min(worldCoords.y, anchor.y);
+        }
+        if (handle?.includes('s')) {
+            const deltaY = worldCoords.y - anchor.y;
+            newHeight = Math.abs(deltaY);
+            newY = Math.min(worldCoords.y, anchor.y);
+        }
+        // Maintain aspect ratio if shift is held
+        if (isShiftPressed && o.width > 0 && o.height > 0) {
+            const aspectRatio = o.width / o.height;
+            if (handle === 'n' || handle === 's') {
+                newWidth = newHeight * aspectRatio;
+            }
+            else if (handle === 'e' || handle === 'w') {
+                newHeight = newWidth / aspectRatio;
+            }
+            else {
+                // Corner handles
+                const proposedRatio = newWidth / newHeight;
+                if (proposedRatio > aspectRatio) {
+                    newHeight = newWidth / aspectRatio;
+                }
+                else {
+                    newWidth = newHeight * aspectRatio;
+                }
+            }
+        }
+        // Snap to grid if Ctrl is held
+        if (this.interaction.isCtrlPressed) {
+            newX = snapToGrid(newX);
+            newY = snapToGrid(newY);
+            newWidth = snapToGrid(newWidth);
+            newHeight = snapToGrid(newHeight);
+        }
+        // Apply minimum size
+        if (newWidth < 10)
+            newWidth = 10;
+        if (newHeight < 10)
+            newHeight = 10;
+        // Update output area bounds temporarily for preview
+        this.canvas.outputAreaBounds = {
+            x: newX,
+            y: newY,
+            width: newWidth,
+            height: newHeight
+        };
+        this.canvas.render();
+    }
+    updateOutputAreaTransformCursor(worldCoords) {
+        const handle = this.getOutputAreaHandle(worldCoords);
+        if (handle) {
+            const cursorMap = {
+                'n': 'ns-resize', 's': 'ns-resize',
+                'e': 'ew-resize', 'w': 'ew-resize',
+                'nw': 'nwse-resize', 'se': 'nwse-resize',
+                'ne': 'nesw-resize', 'sw': 'nesw-resize',
+            };
+            this.canvas.canvas.style.cursor = cursorMap[handle] || 'default';
+        }
+        else {
+            this.canvas.canvas.style.cursor = 'default';
+        }
+    }
+    finalizeOutputAreaTransform() {
+        const bounds = this.canvas.outputAreaBounds;
+        // Update canvas size and mask tool
+        this.canvas.updateOutputAreaSize(bounds.width, bounds.height);
+        // Update mask canvas for new output area
+        this.canvas.maskTool.updateMaskCanvasForOutputArea();
+        // Save state
+        this.canvas.saveState();
+        // Reset transform handle but keep transform mode active
+        this.interaction.outputAreaTransformHandle = null;
+    }
 }

js/CanvasLayers.js

@@ -96,6 +96,7 @@ export class CanvasLayers {
                         tempCtx.globalCompositeOperation = 'destination-in';
                         tempCtx.drawImage(maskCanvas, 0, 0);
                         const newImage = new Image();
+                        newImage.crossOrigin = 'anonymous';
                         newImage.src = tempCanvas.toDataURL();
                         layer.image = newImage;
                     }
@@ -158,6 +159,7 @@ export class CanvasLayers {
                     reader.readAsDataURL(blob);
                 });
                 const img = new Image();
+                img.crossOrigin = 'anonymous';
                 img.onload = () => {
                     if (!this.canvas.node.imgs) {
                         this.canvas.node.imgs = [];
@@ -196,6 +198,117 @@ export class CanvasLayers {
             }
         }
     }
+    /**
+     * Automatically adjust output area to fit selected layers
+     * Calculates precise bounding box for all selected layers including rotation and crop mode support
+     */
+    autoAdjustOutputToSelection() {
+        const selectedLayers = this.canvas.canvasSelection.selectedLayers;
+        if (selectedLayers.length === 0) {
+            return false;
+        }
+        // Calculate bounding box of selected layers
+        let minX = Infinity, minY = Infinity, maxX = -Infinity, maxY = -Infinity;
+        selectedLayers.forEach((layer) => {
+            // For crop mode layers, use the visible crop bounds
+            if (layer.cropMode && layer.cropBounds && layer.originalWidth && layer.originalHeight) {
+                const layerScaleX = layer.width / layer.originalWidth;
+                const layerScaleY = layer.height / layer.originalHeight;
+                const cropWidth = layer.cropBounds.width * layerScaleX;
+                const cropHeight = layer.cropBounds.height * layerScaleY;
+                const effectiveCropX = layer.flipH
+                    ? layer.originalWidth - (layer.cropBounds.x + layer.cropBounds.width)
+                    : layer.cropBounds.x;
+                const effectiveCropY = layer.flipV
+                    ? layer.originalHeight - (layer.cropBounds.y + layer.cropBounds.height)
+                    : layer.cropBounds.y;
+                const cropOffsetX = effectiveCropX * layerScaleX;
+                const cropOffsetY = effectiveCropY * layerScaleY;
+                const centerX = layer.x + layer.width / 2;
+                const centerY = layer.y + layer.height / 2;
+                const rad = layer.rotation * Math.PI / 180;
+                const cos = Math.cos(rad);
+                const sin = Math.sin(rad);
+                // Calculate corners of the crop rectangle
+                const corners = [
+                    { x: cropOffsetX, y: cropOffsetY },
+                    { x: cropOffsetX + cropWidth, y: cropOffsetY },
+                    { x: cropOffsetX + cropWidth, y: cropOffsetY + cropHeight },
+                    { x: cropOffsetX, y: cropOffsetY + cropHeight }
+                ];
+                corners.forEach(p => {
+                    // Transform to layer space (centered)
+                    const localX = p.x - layer.width / 2;
+                    const localY = p.y - layer.height / 2;
+                    // Apply rotation
+                    const worldX = centerX + (localX * cos - localY * sin);
+                    const worldY = centerY + (localX * sin + localY * cos);
+                    minX = Math.min(minX, worldX);
+                    minY = Math.min(minY, worldY);
+                    maxX = Math.max(maxX, worldX);
+                    maxY = Math.max(maxY, worldY);
+                });
+            }
+            else {
+                // For normal layers, use the full layer bounds
+                const centerX = layer.x + layer.width / 2;
+                const centerY = layer.y + layer.height / 2;
+                const rad = layer.rotation * Math.PI / 180;
+                const cos = Math.cos(rad);
+                const sin = Math.sin(rad);
+                const halfW = layer.width / 2;
+                const halfH = layer.height / 2;
+                const corners = [
+                    { x: -halfW, y: -halfH },
+                    { x: halfW, y: -halfH },
+                    { x: halfW, y: halfH },
+                    { x: -halfW, y: halfH }
+                ];
+                corners.forEach(p => {
+                    const worldX = centerX + (p.x * cos - p.y * sin);
+                    const worldY = centerY + (p.x * sin + p.y * cos);
+                    minX = Math.min(minX, worldX);
+                    minY = Math.min(minY, worldY);
+                    maxX = Math.max(maxX, worldX);
+                    maxY = Math.max(maxY, worldY);
+                });
+            }
+        });
+        // Calculate new dimensions without padding for precise fit
+        const newWidth = Math.ceil(maxX - minX);
+        const newHeight = Math.ceil(maxY - minY);
+        if (newWidth <= 0 || newHeight <= 0) {
+            log.error("Cannot calculate valid output area dimensions");
+            return false;
+        }
+        // Update output area bounds
+        this.canvas.outputAreaBounds = {
+            x: minX,
+            y: minY,
+            width: newWidth,
+            height: newHeight
+        };
+        // Update canvas dimensions
+        this.canvas.width = newWidth;
+        this.canvas.height = newHeight;
+        this.canvas.maskTool.resize(newWidth, newHeight);
+        this.canvas.canvas.width = newWidth;
+        this.canvas.canvas.height = newHeight;
+        // Reset extensions
+        this.canvas.outputAreaExtensions = { top: 0, bottom: 0, left: 0, right: 0 };
+        this.canvas.outputAreaExtensionEnabled = false;
+        this.canvas.lastOutputAreaExtensions = { top: 0, bottom: 0, left: 0, right: 0 };
+        // Update original canvas size and position
+        this.canvas.originalCanvasSize = { width: newWidth, height: newHeight };
+        this.canvas.originalOutputAreaPosition = { x: minX, y: minY };
+        // Save state and render
+        this.canvas.render();
+        this.canvas.saveState();
+        log.info(`Auto-adjusted output area to fit ${selectedLayers.length} selected layer(s)`, {
+            bounds: { x: minX, y: minY, width: newWidth, height: newHeight }
+        });
+        return true;
+    }
     pasteLayers() {
         if (this.internalClipboard.length === 0)
             return;
@@ -742,6 +855,7 @@ export class CanvasLayers {
         }
         // Convert canvas to image
         const processedImage = new Image();
+        processedImage.crossOrigin = 'anonymous';
         processedImage.src = processedCanvas.toDataURL();
         return processedImage;
     }
@@ -1611,6 +1725,7 @@ export class CanvasLayers {
             tempCtx.translate(-minX, -minY);
             this._drawLayers(tempCtx, this.canvas.canvasSelection.selectedLayers);
             const fusedImage = new Image();
+            fusedImage.crossOrigin = 'anonymous';
             fusedImage.src = tempCanvas.toDataURL();
             await new Promise((resolve, reject) => {
                 fusedImage.onload = resolve;

js/CanvasLayersPanel.js

@@ -103,6 +103,7 @@ export class CanvasLayersPanel {
         this.container.tabIndex = 0; // Umożliwia fokus na panelu
         this.container.innerHTML = `
             <div class="layers-panel-header">
+                <div class="master-visibility-toggle" title="Toggle all layers visibility"></div>
                 <span class="layers-panel-title">Layers</span>
                 <div class="layers-panel-controls">
                     <button class="layers-btn" id="delete-layer-btn" title="Delete layer"></button>
@@ -115,6 +116,7 @@ export class CanvasLayersPanel {
         this.layersContainer = this.container.querySelector('#layers-container');
         // Setup event listeners dla przycisków
         this.setupControlButtons();
+        this.setupMasterVisibilityToggle();
         // Dodaj listener dla klawiatury, aby usuwanie działało z panelu
         this.container.addEventListener('keydown', (e) => {
             if (e.key === 'Delete' || e.key === 'Backspace') {
@@ -142,6 +144,67 @@ export class CanvasLayersPanel {
         // Initial button state update
         this.updateButtonStates();
     }
+    setupMasterVisibilityToggle() {
+        if (!this.container)
+            return;
+        const toggleContainer = this.container.querySelector('.master-visibility-toggle');
+        if (!toggleContainer)
+            return;
+        const updateToggleState = () => {
+            const total = this.canvas.layers.length;
+            const visibleCount = this.canvas.layers.filter(l => l.visible).length;
+            toggleContainer.innerHTML = '';
+            const checkboxContainer = document.createElement('div');
+            checkboxContainer.className = 'checkbox-container';
+            const checkbox = document.createElement('input');
+            checkbox.type = 'checkbox';
+            checkbox.id = 'master-visibility-checkbox';
+            const customCheckbox = document.createElement('span');
+            customCheckbox.className = 'custom-checkbox';
+            checkboxContainer.appendChild(checkbox);
+            checkboxContainer.appendChild(customCheckbox);
+            if (visibleCount === 0) {
+                checkbox.checked = false;
+                checkbox.indeterminate = false;
+                customCheckbox.classList.remove('checked', 'indeterminate');
+            }
+            else if (visibleCount === total) {
+                checkbox.checked = true;
+                checkbox.indeterminate = false;
+                customCheckbox.classList.add('checked');
+                customCheckbox.classList.remove('indeterminate');
+            }
+            else {
+                checkbox.checked = false;
+                checkbox.indeterminate = true;
+                customCheckbox.classList.add('indeterminate');
+                customCheckbox.classList.remove('checked');
+            }
+            checkboxContainer.addEventListener('click', (e) => {
+                e.stopPropagation();
+                let newVisible;
+                if (checkbox.indeterminate) {
+                    newVisible = false; // hide all when mixed
+                }
+                else if (checkbox.checked) {
+                    newVisible = false; // toggle to hide all
+                }
+                else {
+                    newVisible = true; // toggle to show all
+                }
+                this.canvas.layers.forEach(layer => {
+                    layer.visible = newVisible;
+                });
+                this.canvas.render();
+                this.canvas.requestSaveState();
+                updateToggleState();
+                this.renderLayers();
+            });
+            toggleContainer.appendChild(checkboxContainer);
+        };
+        updateToggleState();
+        this._updateMasterVisibilityToggle = updateToggleState;
+    }
     renderLayers() {
         if (!this.layersContainer) {
             log.warn('Layers container not initialized');
@@ -158,6 +221,8 @@ export class CanvasLayersPanel {
             if (this.layersContainer)
                 this.layersContainer.appendChild(layerElement);
         });
+        if (this._updateMasterVisibilityToggle)
+            this._updateMasterVisibilityToggle();
         log.debug(`Rendered ${sortedLayers.length} layers`);
     }
     createLayerElement(layer, index) {

js/CanvasRenderer.js

@@ -147,6 +147,7 @@ export class CanvasRenderer {
         this.renderInteractionElements(ctx);
         this.canvas.shapeTool.render(ctx);
         this.drawMaskAreaBounds(ctx); // Draw mask area bounds when mask tool is active
+        this.renderOutputAreaTransformHandles(ctx); // Draw output area transform handles
         this.renderLayerInfo(ctx);
         // Update custom shape menu position and visibility
         if (this.canvas.outputAreaShape) {
@@ -832,4 +833,40 @@ export class CanvasRenderer {
         // Just ensure it's the right size
         this.updateOverlaySize();
     }
+    /**
+     * Draw transform handles for output area when in transform mode
+     */
+    renderOutputAreaTransformHandles(ctx) {
+        if (this.canvas.canvasInteractions.interaction.mode !== 'transformingOutputArea') {
+            return;
+        }
+        const bounds = this.canvas.outputAreaBounds;
+        const handleRadius = 5 / this.canvas.viewport.zoom;
+        // Define handle positions
+        const handles = {
+            'nw': { x: bounds.x, y: bounds.y },
+            'n': { x: bounds.x + bounds.width / 2, y: bounds.y },
+            'ne': { x: bounds.x + bounds.width, y: bounds.y },
+            'e': { x: bounds.x + bounds.width, y: bounds.y + bounds.height / 2 },
+            'se': { x: bounds.x + bounds.width, y: bounds.y + bounds.height },
+            's': { x: bounds.x + bounds.width / 2, y: bounds.y + bounds.height },
+            'sw': { x: bounds.x, y: bounds.y + bounds.height },
+            'w': { x: bounds.x, y: bounds.y + bounds.height / 2 },
+        };
+        // Draw handles
+        ctx.fillStyle = '#ffffff';
+        ctx.strokeStyle = '#000000';
+        ctx.lineWidth = 1 / this.canvas.viewport.zoom;
+        for (const [name, pos] of Object.entries(handles)) {
+            ctx.beginPath();
+            ctx.arc(pos.x, pos.y, handleRadius, 0, Math.PI * 2);
+            ctx.fill();
+            ctx.stroke();
+        }
+        // Draw a highlight around the output area
+        ctx.strokeStyle = 'rgba(0, 150, 255, 0.8)';
+        ctx.lineWidth = 3 / this.canvas.viewport.zoom;
+        ctx.setLineDash([]);
+        ctx.strokeRect(bounds.x, bounds.y, bounds.width, bounds.height);
+    }
 }

js/CanvasState.js

@@ -200,6 +200,7 @@ export class CanvasState {
     _createLayerFromSrc(layerData, imageSrc, index, resolve) {
         if (typeof imageSrc === 'string') {
             const img = new Image();
+            img.crossOrigin = 'anonymous';
             img.onload = () => {
                 log.debug(`Layer ${index}: Image loaded successfully.`);
                 const newLayer = { ...layerData, image: img };
@@ -216,6 +217,7 @@ export class CanvasState {
             if (ctx) {
                 ctx.drawImage(imageSrc, 0, 0);
                 const img = new Image();
+                img.crossOrigin = 'anonymous';
                 img.onload = () => {
                     log.debug(`Layer ${index}: Image loaded successfully from ImageBitmap.`);
                     const newLayer = { ...layerData, image: img };
@@ -404,12 +406,10 @@ If you see dark images or masks in the output, make sure node_id is set to ${cor
         }
         if (this.maskUndoStack.length > 0) {
             const prevState = this.maskUndoStack[this.maskUndoStack.length - 1];
-            const maskCanvas = this.canvas.maskTool.getMask();
+            // Use the new restoreMaskFromSavedState method that properly clears chunks first
-            const maskCtx = maskCanvas.getContext('2d', { willReadFrequently: true });
+            this.canvas.maskTool.restoreMaskFromSavedState(prevState);
-            if (maskCtx) {
+            // Clear stroke overlay to prevent old drawing previews from persisting
-                maskCtx.clearRect(0, 0, maskCanvas.width, maskCanvas.height);
+            this.canvas.canvasRenderer.clearMaskStrokeOverlay();
-                maskCtx.drawImage(prevState, 0, 0);
-            }
             this.canvas.render();
         }
         this.canvas.updateHistoryButtons();
@@ -420,12 +420,10 @@ If you see dark images or masks in the output, make sure node_id is set to ${cor
         const nextState = this.maskRedoStack.pop();
         if (nextState) {
             this.maskUndoStack.push(nextState);
-            const maskCanvas = this.canvas.maskTool.getMask();
+            // Use the new restoreMaskFromSavedState method that properly clears chunks first
-            const maskCtx = maskCanvas.getContext('2d', { willReadFrequently: true });
+            this.canvas.maskTool.restoreMaskFromSavedState(nextState);
-            if (maskCtx) {
+            // Clear stroke overlay to prevent old drawing previews from persisting
-                maskCtx.clearRect(0, 0, maskCanvas.width, maskCanvas.height);
+            this.canvas.canvasRenderer.clearMaskStrokeOverlay();
-                maskCtx.drawImage(nextState, 0, 0);
-            }
             this.canvas.render();
         }
         this.canvas.updateHistoryButtons();

js/CanvasView.js

@@ -8,7 +8,7 @@ import { clearAllCanvasStates } from "./db.js";
 import { ImageCache } from "./ImageCache.js";
 import { createCanvas } from "./utils/CommonUtils.js";
 import { createModuleLogger } from "./utils/LoggerUtils.js";
-import { showErrorNotification, showSuccessNotification, showInfoNotification } from "./utils/NotificationUtils.js";
+import { showErrorNotification, showSuccessNotification, showInfoNotification, showWarningNotification } from "./utils/NotificationUtils.js";
 import { iconLoader, LAYERFORGE_TOOLS } from "./utils/IconLoader.js";
 import { setupSAMDetectorHook } from "./SAMDetectorIntegration.js";
 const log = createModuleLogger('Canvas_view');
@@ -213,88 +213,32 @@ async function createCanvasWidget(node, widget, app) {
             ]),
             $el("div.painter-separator"),
             $el("div.painter-button-group", {}, [
+                $el("button.painter-button.requires-selection", {
+                    textContent: "Auto Adjust Output",
+                    title: "Automatically adjust output area to fit selected layers",
+                    onclick: () => {
+                        const selectedLayers = canvas.canvasSelection.selectedLayers;
+                        if (selectedLayers.length === 0) {
+                            showWarningNotification("Please select one or more layers first");
+                            return;
+                        }
+                        const success = canvas.canvasLayers.autoAdjustOutputToSelection();
+                        if (success) {
+                            const bounds = canvas.outputAreaBounds;
+                            showSuccessNotification(`Output area adjusted to ${bounds.width}x${bounds.height}px`);
+                        }
+                        else {
+                            showErrorNotification("Cannot calculate valid output area dimensions");
+                        }
+                    }
+                }),
                 $el("button.painter-button", {
                     textContent: "Output Area Size",
-                    title: "Set the size of the output area",
+                    title: "Transform output area - drag handles to resize",
                     onclick: () => {
-                        const dialog = $el("div.painter-dialog", {
+                        // Activate output area transform mode
-                            style: {
+                        canvas.canvasInteractions.activateOutputAreaTransform();
-                                position: 'fixed',
+                        showInfoNotification("Click and drag the handles to resize the output area. Click anywhere else to exit.", 3000);
-                                left: '50%',
-                                top: '50%',
-                                transform: 'translate(-50%, -50%)',
-                                zIndex: '9999'
-                            }
-                        }, [
-                            $el("div", {
-                                style: {
-                                    color: "white",
-                                    marginBottom: "10px"
-                                }
-                            }, [
-                                $el("label", {
-                                    style: {
-                                        marginRight: "5px"
-                                    }
-                                }, [
-                                    $el("span", {}, ["Width: "])
-                                ]),
-                                $el("input", {
-                                    type: "number",
-                                    id: "canvas-width",
-                                    value: String(canvas.width),
-                                    min: "1",
-                                    max: "4096"
-                                })
-                            ]),
-                            $el("div", {
-                                style: {
-                                    color: "white",
-                                    marginBottom: "10px"
-                                }
-                            }, [
-                                $el("label", {
-                                    style: {
-                                        marginRight: "5px"
-                                    }
-                                }, [
-                                    $el("span", {}, ["Height: "])
-                                ]),
-                                $el("input", {
-                                    type: "number",
-                                    id: "canvas-height",
-                                    value: String(canvas.height),
-                                    min: "1",
-                                    max: "4096"
-                                })
-                            ]),
-                            $el("div", {
-                                style: {
-                                    textAlign: "right"
-                                }
-                            }, [
-                                $el("button", {
-                                    id: "cancel-size",
-                                    textContent: "Cancel"
-                                }),
-                                $el("button", {
-                                    id: "confirm-size",
-                                    textContent: "OK"
-                                })
-                            ])
-                        ]);
-                        document.body.appendChild(dialog);
-                        document.getElementById('confirm-size').onclick = () => {
-                            const widthInput = document.getElementById('canvas-width');
-                            const heightInput = document.getElementById('canvas-height');
-                            const width = parseInt(widthInput.value) || canvas.width;
-                            const height = parseInt(heightInput.value) || canvas.height;
-                            canvas.setOutputAreaSize(width, height);
-                            document.body.removeChild(dialog);
-                        };
-                        document.getElementById('cancel-size').onclick = () => {
-                            document.body.removeChild(dialog);
-                        };
                     }
                 }),
                 $el("button.painter-button.requires-selection", {
@@ -911,7 +855,9 @@ async function createCanvasWidget(node, widget, app) {
             height: "100%"
         }
     }, [controlPanel, canvasContainer, layersPanelContainer]);
-    node.addDOMWidget("mainContainer", "widget", mainContainer);
+    if (node.addDOMWidget) {
+        node.addDOMWidget("mainContainer", "widget", mainContainer);
+    }
     const openEditorBtn = controlPanel.querySelector(`#open-editor-btn-${node.id}`);
     let backdrop = null;
     let originalParent = null;
@@ -1000,7 +946,11 @@ async function createCanvasWidget(node, widget, app) {
     if (!window.canvasExecutionStates) {
         window.canvasExecutionStates = new Map();
     }
-    node.canvasWidget = canvas;
+    // Store the entire widget object, not just the canvas
+    node.canvasWidget = {
+        canvas: canvas,
+        panel: controlPanel
+    };
     setTimeout(() => {
         canvas.loadInitialState();
         if (canvas.canvasLayersPanel) {
@@ -1017,7 +967,7 @@ async function createCanvasWidget(node, widget, app) {
             if (canvas && canvas.setPreviewVisibility) {
                 canvas.setPreviewVisibility(value);
             }
-            if (node.graph && node.graph.canvas) {
+            if (node.graph && node.graph.canvas && node.setDirtyCanvas) {
                 node.setDirtyCanvas(true, true);
             }
         };
@@ -1096,9 +1046,144 @@ app.registerExtension({
                 const canvasWidget = await createCanvasWidget(this, null, app);
                 canvasNodeInstances.set(this.id, canvasWidget);
                 log.info(`Registered CanvasNode instance for ID: ${this.id}`);
+                // Store the canvas widget on the node
+                this.canvasWidget = canvasWidget;
+                // Check if there are already connected inputs
                 setTimeout(() => {
-                    this.setDirtyCanvas(true, true);
+                    if (this.inputs && this.inputs.length > 0) {
-                }, 100);
+                        // Check if input_image (index 0) is connected
+                        if (this.inputs[0] && this.inputs[0].link) {
+                            log.info("Input image already connected on node creation, checking for data...");
+                            if (canvasWidget.canvas && canvasWidget.canvas.canvasIO) {
+                                canvasWidget.canvas.inputDataLoaded = false;
+                                // Only allow images on init; mask should load only on mask connect or execution
+                                canvasWidget.canvas.canvasIO.checkForInputData({ allowImage: true, allowMask: false, reason: "init_image_connected" });
+                            }
+                        }
+                    }
+                    if (this.setDirtyCanvas) {
+                        this.setDirtyCanvas(true, true);
+                    }
+                }, 500);
+            };
+            // Add onConnectionsChange handler to detect when inputs are connected
+            nodeType.prototype.onConnectionsChange = function (type, index, connected, link_info) {
+                log.info(`onConnectionsChange called: type=${type}, index=${index}, connected=${connected}`, link_info);
+                // Check if this is an input connection (type 1 = INPUT)
+                if (type === 1) {
+                    // Get the canvas widget - it might be in different places
+                    const canvasWidget = this.canvasWidget;
+                    const canvas = canvasWidget?.canvas || canvasWidget;
+                    if (!canvas || !canvas.canvasIO) {
+                        log.warn("Canvas not ready in onConnectionsChange, scheduling retry...");
+                        // Retry multiple times with increasing delays
+                        const retryDelays = [500, 1000, 2000];
+                        let retryCount = 0;
+                        const tryAgain = () => {
+                            const retryCanvas = this.canvasWidget?.canvas || this.canvasWidget;
+                            if (retryCanvas && retryCanvas.canvasIO) {
+                                log.info("Canvas now ready, checking for input data...");
+                                if (connected) {
+                                    retryCanvas.inputDataLoaded = false;
+                                    // Respect which input triggered the connection:
+                                    const opts = (index === 1)
+                                        ? { allowImage: false, allowMask: true, reason: "mask_connect" }
+                                        : { allowImage: true, allowMask: false, reason: "image_connect" };
+                                    retryCanvas.canvasIO.checkForInputData(opts);
+                                }
+                            }
+                            else if (retryCount < retryDelays.length) {
+                                log.warn(`Canvas still not ready, retry ${retryCount + 1}/${retryDelays.length}...`);
+                                setTimeout(tryAgain, retryDelays[retryCount++]);
+                            }
+                            else {
+                                log.error("Canvas failed to initialize after multiple retries");
+                            }
+                        };
+                        setTimeout(tryAgain, retryDelays[retryCount++]);
+                        return;
+                    }
+                    // Handle input_image connection (index 0)
+                    if (index === 0) {
+                        if (connected && link_info) {
+                            log.info("Input image connected, marking for data check...");
+                            // Reset the input data loaded flag to allow loading the new connection
+                            canvas.inputDataLoaded = false;
+                            // Also reset the last loaded image source and link ID to allow the new image
+                            canvas.lastLoadedImageSrc = undefined;
+                            canvas.lastLoadedLinkId = undefined;
+                            // Mark that we have a pending input connection
+                            canvas.hasPendingInputConnection = true;
+                            // If mask input is not connected and a mask was auto-applied from input_mask before, clear it now
+                            if (!(this.inputs && this.inputs[1] && this.inputs[1].link)) {
+                                if (canvas.maskAppliedFromInput && canvas.maskTool) {
+                                    canvas.maskTool.clear();
+                                    canvas.render();
+                                    canvas.maskAppliedFromInput = false;
+                                    canvas.lastLoadedMaskLinkId = undefined;
+                                    log.info("Cleared auto-applied mask because input_image connected without input_mask");
+                                }
+                            }
+                            // Check for data immediately when connected
+                            setTimeout(() => {
+                                log.info("Checking for input data after connection...");
+                                // Only load images here; masks should not auto-load on image connect
+                                canvas.canvasIO.checkForInputData({ allowImage: true, allowMask: false, reason: "image_connect" });
+                            }, 500);
+                        }
+                        else {
+                            log.info("Input image disconnected");
+                            canvas.hasPendingInputConnection = false;
+                            // Reset when disconnected so a new connection can load
+                            canvas.inputDataLoaded = false;
+                            canvas.lastLoadedImageSrc = undefined;
+                            canvas.lastLoadedLinkId = undefined;
+                        }
+                    }
+                    // Handle input_mask connection (index 1)
+                    if (index === 1) {
+                        if (connected && link_info) {
+                            log.info("Input mask connected");
+                            // DON'T clear existing mask when connecting a new input
+                            // Reset the loaded mask link ID to allow loading from the new connection
+                            canvas.lastLoadedMaskLinkId = undefined;
+                            // Mark that we have a pending mask connection
+                            canvas.hasPendingMaskConnection = true;
+                            // Check for data immediately when connected
+                            setTimeout(() => {
+                                log.info("Checking for input data after mask connection...");
+                                // Only load mask here if it's immediately available from the connected node
+                                // Don't load stale masks from backend storage
+                                canvas.canvasIO.checkForInputData({ allowImage: false, allowMask: true, reason: "mask_connect" });
+                            }, 500);
+                        }
+                        else {
+                            log.info("Input mask disconnected");
+                            canvas.hasPendingMaskConnection = false;
+                            // If the current mask came from input_mask, clear it to avoid affecting images when mask is not connected
+                            if (canvas.maskAppliedFromInput && canvas.maskTool) {
+                                canvas.maskAppliedFromInput = false;
+                                canvas.lastLoadedMaskLinkId = undefined;
+                                log.info("Cleared auto-applied mask due to mask input disconnection");
+                            }
+                        }
+                    }
+                }
+            };
+            // Add onExecuted handler to check for input data after workflow execution
+            const originalOnExecuted = nodeType.prototype.onExecuted;
+            nodeType.prototype.onExecuted = function (message) {
+                log.info("Node executed, checking for input data...");
+                const canvas = this.canvasWidget?.canvas || this.canvasWidget;
+                if (canvas && canvas.canvasIO) {
+                    // Don't reset inputDataLoaded - just check for new data
+                    // On execution we allow both image and mask to load
+                    canvas.canvasIO.checkForInputData({ allowImage: true, allowMask: true, reason: "execution" });
+                }
+                // Call original if it exists
+                if (originalOnExecuted) {
+                    originalOnExecuted.apply(this, arguments);
+                }
             };
             const onRemoved = nodeType.prototype.onRemoved;
             nodeType.prototype.onRemoved = function () {
@@ -1211,8 +1296,8 @@ app.registerExtension({
                         callback: async () => {
                             try {
                                 log.info("Opening LayerForge canvas in MaskEditor");
-                                if (self.canvasWidget && self.canvasWidget.startMaskEditor) {
+                                if (self.canvasWidget && self.canvasWidget.canvas) {
-                                    await self.canvasWidget.startMaskEditor(null, true);
+                                    await self.canvasWidget.canvas.startMaskEditor(null, true);
                                 }
                                 else {
                                     log.error("Canvas widget not available");
@@ -1229,9 +1314,9 @@ app.registerExtension({
                         content: "Open Image",
                         callback: async () => {
                             try {
-                                if (!self.canvasWidget)
+                                if (!self.canvasWidget || !self.canvasWidget.canvas)
                                     return;
-                                const blob = await self.canvasWidget.getFlattenedCanvasAsBlob();
+                                const blob = await self.canvasWidget.canvas.canvasLayers.getFlattenedCanvasAsBlob();
                                 if (!blob)
                                     return;
                                 const url = URL.createObjectURL(blob);
@@ -1247,9 +1332,9 @@ app.registerExtension({
                         content: "Open Image with Mask Alpha",
                         callback: async () => {
                             try {
-                                if (!self.canvasWidget)
+                                if (!self.canvasWidget || !self.canvasWidget.canvas)
                                     return;
-                                const blob = await self.canvasWidget.getFlattenedCanvasWithMaskAsBlob();
+                                const blob = await self.canvasWidget.canvas.canvasLayers.getFlattenedCanvasWithMaskAsBlob();
                                 if (!blob)
                                     return;
                                 const url = URL.createObjectURL(blob);
@@ -1265,9 +1350,9 @@ app.registerExtension({
                         content: "Copy Image",
                         callback: async () => {
                             try {
-                                if (!self.canvasWidget)
+                                if (!self.canvasWidget || !self.canvasWidget.canvas)
                                     return;
-                                const blob = await self.canvasWidget.getFlattenedCanvasAsBlob();
+                                const blob = await self.canvasWidget.canvas.canvasLayers.getFlattenedCanvasAsBlob();
                                 if (!blob)
                                     return;
                                 const item = new ClipboardItem({ 'image/png': blob });
@@ -1284,9 +1369,9 @@ app.registerExtension({
                         content: "Copy Image with Mask Alpha",
                         callback: async () => {
                             try {
-                                if (!self.canvasWidget)
+                                if (!self.canvasWidget || !self.canvasWidget.canvas)
                                     return;
-                                const blob = await self.canvasWidget.getFlattenedCanvasWithMaskAsBlob();
+                                const blob = await self.canvasWidget.canvas.canvasLayers.getFlattenedCanvasWithMaskAsBlob();
                                 if (!blob)
                                     return;
                                 const item = new ClipboardItem({ 'image/png': blob });
@@ -1303,9 +1388,9 @@ app.registerExtension({
                         content: "Save Image",
                         callback: async () => {
                             try {
-                                if (!self.canvasWidget)
+                                if (!self.canvasWidget || !self.canvasWidget.canvas)
                                     return;
-                                const blob = await self.canvasWidget.getFlattenedCanvasAsBlob();
+                                const blob = await self.canvasWidget.canvas.canvasLayers.getFlattenedCanvasAsBlob();
                                 if (!blob)
                                     return;
                                 const url = URL.createObjectURL(blob);
@@ -1326,9 +1411,9 @@ app.registerExtension({
                         content: "Save Image with Mask Alpha",
                         callback: async () => {
                             try {
-                                if (!self.canvasWidget)
+                                if (!self.canvasWidget || !self.canvasWidget.canvas)
                                     return;
-                                const blob = await self.canvasWidget.getFlattenedCanvasWithMaskAsBlob();
+                                const blob = await self.canvasWidget.canvas.canvasLayers.getFlattenedCanvasWithMaskAsBlob();
                                 if (!blob)
                                     return;
                                 const url = URL.createObjectURL(blob);

js/MaskEditorIntegration.js

@@ -424,7 +424,6 @@ export class MaskEditorIntegration {
             boundsPos: { x: bounds.x, y: bounds.y },
             maskSize: { width: bounds.width, height: bounds.height }
         });
-        // Use the chunk system instead of direct canvas manipulation
         this.maskTool.setMask(maskAsImage);
         // Update node preview using PreviewUtils
         await updateNodePreview(this.canvas, this.node, true);

js/MaskTool.js

@@ -1352,6 +1352,23 @@ export class MaskTool {
         this.canvasInstance.render();
         log.info("Cleared all mask data from all chunks");
     }
+    /**
+     * Clears all chunks and restores mask from saved state
+     * This is used during undo/redo operations to ensure clean state restoration
+     */
+    restoreMaskFromSavedState(savedMaskCanvas) {
+        // First, clear ALL chunks to ensure no leftover data
+        this.clearAllMaskChunks();
+        // Now apply the saved mask state to chunks
+        if (savedMaskCanvas.width > 0 && savedMaskCanvas.height > 0) {
+            // Apply the saved mask to the chunk system at the correct position
+            const bounds = this.canvasInstance.outputAreaBounds;
+            this.applyMaskCanvasToChunks(savedMaskCanvas, this.x, this.y);
+        }
+        // Update the active mask canvas to show the restored state
+        this.updateActiveMaskCanvas(true);
+        log.debug("Restored mask from saved state with clean chunk system");
+    }
     getMask() {
         // Return the current active mask canvas which shows all chunks
         // Only update if there are pending changes to avoid unnecessary redraws
@@ -1445,13 +1462,44 @@ export class MaskTool {
         this.isOverlayVisible = !this.isOverlayVisible;
         log.info(`Mask overlay visibility toggled to: ${this.isOverlayVisible}`);
     }
-    setMask(image) {
+    setMask(image, isFromInputMask = false) {
-        // Clear existing mask chunks in the output area first
         const bounds = this.canvasInstance.outputAreaBounds;
-        this.clearMaskInArea(bounds.x, bounds.y, image.width, image.height);
+        if (isFromInputMask) {
-        // Add the new mask using the chunk system
+            // For INPUT MASK - process black background to transparent using luminance
-        this.addMask(image);
+            // Center like input images
-        log.info(`MaskTool set new mask using chunk system at bounds (${bounds.x}, ${bounds.y})`);
+            const centerX = bounds.x + (bounds.width - image.width) / 2;
+            const centerY = bounds.y + (bounds.height - image.height) / 2;
+            // Prepare mask where alpha = luminance (white = applied, black = transparent)
+            const { canvas: maskCanvas, ctx } = createCanvas(image.width, image.height, '2d', { willReadFrequently: true });
+            if (!ctx)
+                throw new Error("Could not create mask processing context");
+            ctx.drawImage(image, 0, 0);
+            const imgData = ctx.getImageData(0, 0, image.width, image.height);
+            const data = imgData.data;
+            for (let i = 0; i < data.length; i += 4) {
+                const r = data[i], g = data[i + 1], b = data[i + 2];
+                const lum = Math.round(0.299 * r + 0.587 * g + 0.114 * b);
+                data[i] = 255; // force white color (color channels ignored downstream)
+                data[i + 1] = 255;
+                data[i + 2] = 255;
+                data[i + 3] = lum; // alpha encodes mask strength: white -> strong, black -> 0
+            }
+            ctx.putImageData(imgData, 0, 0);
+            // Clear target area and apply to chunked system at centered position
+            this.clearMaskInArea(centerX, centerY, image.width, image.height);
+            this.applyMaskCanvasToChunks(maskCanvas, centerX, centerY);
+            // Refresh state and UI
+            this.updateActiveMaskCanvas(true);
+            this.canvasInstance.canvasState.saveMaskState();
+            this.canvasInstance.render();
+            log.info(`MaskTool set INPUT MASK at centered position (${centerX}, ${centerY}) using luminance as alpha`);
+        }
+        else {
+            // For SAM Detector and other sources - just clear and add without processing
+            this.clearMaskInArea(bounds.x, bounds.y, bounds.width, bounds.height);
+            this.addMask(image);
+            log.info(`MaskTool set mask using chunk system at bounds (${bounds.x}, ${bounds.y})`);
+        }
     }
     /**
      * Clears mask data in a specific area by clearing affected chunks

js/SAMDetectorIntegration.js

@@ -242,35 +242,61 @@ async function handleSAMDetectorResult(node, resultImage) {
                 // Try to reload the image with a fresh request
                 log.debug("Attempting to reload SAM result image");
                 const originalSrc = resultImage.src;
-                // Add cache-busting parameter to force fresh load
+                // Check if it's a data URL (base64) - don't add parameters to data URLs
-                const url = new URL(originalSrc);
+                if (originalSrc.startsWith('data:')) {
-                url.searchParams.set('_t', Date.now().toString());
+                    log.debug("Image is a data URL, skipping reload with parameters");
-                await new Promise((resolve, reject) => {
+                    // For data URLs, just ensure the image is loaded
-                    const img = new Image();
+                    if (!resultImage.complete || resultImage.naturalWidth === 0) {
-                    img.crossOrigin = "anonymous";
+                        await new Promise((resolve, reject) => {
-                    img.onload = () => {
+                            const img = new Image();
-                        // Copy the loaded image data to the original image
+                            img.onload = () => {
-                        resultImage.src = img.src;
+                                resultImage.width = img.width;
-                        resultImage.width = img.width;
+                                resultImage.height = img.height;
-                        resultImage.height = img.height;
+                                log.debug("Data URL image loaded successfully", {
-                        log.debug("SAM result image reloaded successfully", {
+                                    width: img.width,
-                            width: img.width,
+                                    height: img.height
-                            height: img.height,
+                                });
-                            originalSrc: originalSrc,
+                                resolve(img);
-                            newSrc: img.src
+                            };
+                            img.onerror = (error) => {
+                                log.error("Failed to load data URL image", error);
+                                reject(error);
+                            };
+                            img.src = originalSrc; // Use original src without modifications
                         });
-                        resolve(img);
+                    }
-                    };
+                }
-                    img.onerror = (error) => {
+                else {
-                        log.error("Failed to reload SAM result image", {
+                    // For regular URLs, add cache-busting parameter
-                            originalSrc: originalSrc,
+                    const url = new URL(originalSrc);
-                            newSrc: url.toString(),
+                    url.searchParams.set('_t', Date.now().toString());
-                            error: error
+                    await new Promise((resolve, reject) => {
-                        });
+                        const img = new Image();
-                        reject(error);
+                        img.crossOrigin = "anonymous";
-                    };
+                        img.onload = () => {
-                    img.src = url.toString();
+                            // Copy the loaded image data to the original image
-                });
+                            resultImage.src = img.src;
+                            resultImage.width = img.width;
+                            resultImage.height = img.height;
+                            log.debug("SAM result image reloaded successfully", {
+                                width: img.width,
+                                height: img.height,
+                                originalSrc: originalSrc,
+                                newSrc: img.src
+                            });
+                            resolve(img);
+                        };
+                        img.onerror = (error) => {
+                            log.error("Failed to reload SAM result image", {
+                                originalSrc: originalSrc,
+                                newSrc: url.toString(),
+                                error: error
+                            });
+                            reject(error);
+                        };
+                        img.src = url.toString();
+                    });
+                }
             }
         }
         catch (error) {
@@ -290,27 +316,37 @@ async function handleSAMDetectorResult(node, resultImage) {
         // Apply mask to LayerForge canvas using MaskTool.setMask method
         log.debug("Checking canvas and maskTool availability", {
             hasCanvas: !!canvas,
+            hasCanvasProperty: !!canvas.canvas,
+            canvasCanvasKeys: canvas.canvas ? Object.keys(canvas.canvas) : [],
             hasMaskTool: !!canvas.maskTool,
+            hasCanvasMaskTool: !!(canvas.canvas && canvas.canvas.maskTool),
             maskToolType: typeof canvas.maskTool,
+            canvasMaskToolType: canvas.canvas ? typeof canvas.canvas.maskTool : 'undefined',
             canvasKeys: Object.keys(canvas)
         });
-        if (!canvas.maskTool) {
+        // Get the actual Canvas object and its maskTool
+        const actualCanvas = canvas.canvas || canvas;
+        const maskTool = actualCanvas.maskTool;
+        if (!maskTool) {
             log.error("MaskTool is not available. Canvas state:", {
                 hasCanvas: !!canvas,
+                hasActualCanvas: !!actualCanvas,
                 canvasConstructor: canvas.constructor.name,
+                actualCanvasConstructor: actualCanvas ? actualCanvas.constructor.name : 'undefined',
                 canvasKeys: Object.keys(canvas),
-                maskToolValue: canvas.maskTool
+                actualCanvasKeys: actualCanvas ? Object.keys(actualCanvas) : [],
+                maskToolValue: maskTool
             });
             throw new Error("Mask tool not available or not initialized");
         }
-        log.debug("Applying SAM mask to canvas using addMask method");
+        log.debug("Applying SAM mask to canvas using setMask method");
-        // Use the addMask method which overlays on existing mask without clearing it
+        // Use the setMask method which clears existing mask and sets new one
-        canvas.maskTool.addMask(maskAsImage);
+        maskTool.setMask(maskAsImage);
         // Update canvas and save state (same as MaskEditorIntegration)
-        canvas.render();
+        actualCanvas.render();
-        canvas.saveState();
+        actualCanvas.saveState();
         // Update node preview using PreviewUtils
-        await updateNodePreview(canvas, node, true);
+        await updateNodePreview(actualCanvas, node, true);
         log.info("SAM Detector mask applied successfully to LayerForge canvas");
         // Show success notification
         showSuccessNotification("SAM Detector mask applied to LayerForge!");
@@ -340,13 +376,20 @@ export function setupSAMDetectorHook(node, options) {
                 try {
                     log.info("Intercepted 'Open in SAM Detector' - automatically sending to clipspace and starting monitoring");
                     // Automatically send canvas to clipspace and start monitoring
-                    if (node.canvasWidget && node.canvasWidget.canvas) {
+                    if (node.canvasWidget) {
-                        const canvas = node.canvasWidget; // canvasWidget IS the Canvas object
+                        const canvasWidget = node.canvasWidget;
-                        // Use ImageUploadUtils to upload canvas
+                        const canvas = canvasWidget.canvas || canvasWidget; // Get actual Canvas object
+                        // Use ImageUploadUtils to upload canvas and get server URL (Impact Pack compatibility)
                         const uploadResult = await uploadCanvasAsImage(canvas, {
                             filenamePrefix: 'layerforge-sam',
                             nodeId: node.id
                         });
+                        log.debug("Uploaded canvas for SAM Detector", {
+                            filename: uploadResult.filename,
+                            imageUrl: uploadResult.imageUrl,
+                            width: uploadResult.imageElement.width,
+                            height: uploadResult.imageElement.height
+                        });
                         // Set the image to the node for clipspace
                         node.imgs = [uploadResult.imageElement];
                         node.clipspaceImg = uploadResult.imageElement;

js/css/canvas_view.css

@@ -638,7 +638,7 @@
     width: 100vw;
     height: 100vh;
     background-color: rgba(0, 0, 0, 0.8);
-    z-index: 111;
+    z-index: 999999;
     display: flex;
     align-items: center;
     justify-content: center;

js/css/layers_panel.css

@@ -23,6 +23,85 @@
     margin-bottom: 8px;
 }
 
+.checkbox-container {
+    display: flex;
+    align-items: center;
+    gap: 8px;
+    padding: 5px 0;
+    border-radius: 5px;
+    cursor: pointer;
+    transition: background-color 0.2s;
+    position: relative;
+}
+
+.checkbox-container:hover {
+    background-color: #4a4a4a;
+}
+
+.checkbox-container input[type="checkbox"] {
+    position: absolute;
+    opacity: 0;
+    cursor: pointer;
+    height: 0;
+    width: 0;
+}
+
+.checkbox-container .custom-checkbox {
+    height: 16px;
+    width: 16px;
+    background-color: #2a2a2a;
+    border: 1px solid #666;
+    border-radius: 3px;
+    transition: all 0.2s;
+    position: relative;
+    flex-shrink: 0;
+}
+
+.checkbox-container input:checked ~ .custom-checkbox {
+    background-color: #3a76d6;
+    border-color: #3a76d6;
+}
+
+.checkbox-container .custom-checkbox::after {
+    content: "";
+    position: absolute;
+    display: none;
+    left: 5px;
+    top: 1px;
+    width: 4px;
+    height: 9px;
+    border: solid white;
+    border-width: 0 2px 2px 0;
+    transform: rotate(45deg);
+}
+
+.checkbox-container input:checked ~ .custom-checkbox::after {
+    display: block;
+}
+
+.checkbox-container input:indeterminate ~ .custom-checkbox {
+    background-color: #3a76d6;
+    border-color: #3a76d6;
+}
+
+.checkbox-container input:indeterminate ~ .custom-checkbox::after {
+    display: block;
+    content: "";
+    position: absolute;
+    top: 7px;
+    left: 3px;
+    width: 8px;
+    height: 2px;
+    background-color: white;
+    border: none;
+    transform: none;
+    box-shadow: none;
+}
+
+.checkbox-container:hover {
+    background-color: #4a4a4a;
+}
+
 .layers-panel-title {
     font-weight: bold;
     color: #ffffff;

js/utils/ClipboardManager.js

@@ -1,5 +1,5 @@
 import { createModuleLogger } from "./LoggerUtils.js";
-import { showNotification, showInfoNotification } from "./NotificationUtils.js";
+import { showNotification, showInfoNotification, showErrorNotification, showWarningNotification } from "./NotificationUtils.js";
 import { withErrorHandling, createValidationError, createNetworkError, createFileError } from "../ErrorHandler.js";
 import { safeClipspacePaste } from "./ClipspaceUtils.js";
 // @ts-ignore
@@ -18,6 +18,7 @@ export class ClipboardManager {
             if (this.canvas.canvasLayers.internalClipboard.length > 0) {
                 log.info("Found layers in internal clipboard, pasting layers");
                 this.canvas.canvasLayers.pasteLayers();
+                showInfoNotification("Layers pasted from internal clipboard");
                 return true;
             }
             if (preference === 'clipspace') {
@@ -27,9 +28,20 @@ export class ClipboardManager {
                     return true;
                 }
                 log.info("No image found in ComfyUI Clipspace");
+                // Don't show error here, will try system clipboard next
             }
             log.info("Attempting paste from system clipboard");
-            return await this.trySystemClipboardPaste(addMode);
+            const systemSuccess = await this.trySystemClipboardPaste(addMode);
+            if (!systemSuccess) {
+                // No valid image found in any clipboard
+                if (preference === 'clipspace') {
+                    showWarningNotification("No valid image found in Clipspace or system clipboard");
+                }
+                else {
+                    showWarningNotification("No valid image found in clipboard");
+                }
+            }
+            return systemSuccess;
         }, 'ClipboardManager.handlePaste');
         /**
          * Attempts to paste from ComfyUI Clipspace
@@ -51,6 +63,7 @@ export class ClipboardManager {
                     const img = new Image();
                     img.onload = async () => {
                         await this.canvas.canvasLayers.addLayerWithImage(img, {}, addMode);
+                        showInfoNotification("Image pasted from Clipspace");
                     };
                     img.src = clipspaceImage.src;
                     return true;
@@ -96,6 +109,7 @@ export class ClipboardManager {
                 img.onload = async () => {
                     log.info("Successfully loaded image from backend response");
                     await this.canvas.canvasLayers.addLayerWithImage(img, {}, addMode);
+                    showInfoNotification("Image loaded from file path");
                     resolve(true);
                 };
                 img.onerror = () => {
@@ -131,6 +145,7 @@ export class ClipboardManager {
                                 img.onload = async () => {
                                     log.info("Successfully loaded image from system clipboard");
                                     await this.canvas.canvasLayers.addLayerWithImage(img, {}, addMode);
+                                    showInfoNotification("Image pasted from system clipboard");
                                 };
                                 if (event.target?.result) {
                                     img.src = event.target.result;
@@ -173,11 +188,22 @@ export class ClipboardManager {
             try {
                 const text = await navigator.clipboard.readText();
                 log.debug("Found text in clipboard:", text);
-                if (text && this.isValidImagePath(text)) {
+                if (text) {
-                    log.info("Found valid image path in clipboard:", text);
+                    // Check if it's a data URI (base64 encoded image)
-                    const success = await this.loadImageFromPath(text, addMode);
+                    if (this.isDataURI(text)) {
-                    if (success) {
+                        log.info("Found data URI in clipboard");
-                        return true;
+                        const success = await this.loadImageFromDataURI(text, addMode);
+                        if (success) {
+                            return true;
+                        }
+                    }
+                    // Check if it's a regular file path or URL
+                    else if (this.isValidImagePath(text)) {
+                        log.info("Found valid image path in clipboard:", text);
+                        const success = await this.loadImageFromPath(text, addMode);
+                        if (success) {
+                            return true;
+                        }
                     }
                 }
             }
@@ -188,6 +214,48 @@ export class ClipboardManager {
         log.debug("No images or valid image paths found in system clipboard");
         return false;
     }
+    /**
+     * Checks if a text string is a data URI (base64 encoded image)
+     * @param {string} text - The text to check
+     * @returns {boolean} - True if the text is a data URI
+     */
+    isDataURI(text) {
+        if (!text || typeof text !== 'string') {
+            return false;
+        }
+        // Check if it starts with data:image
+        return text.trim().startsWith('data:image/');
+    }
+    /**
+     * Loads an image from a data URI (base64 encoded image)
+     * @param {string} dataURI - The data URI to load
+     * @param {AddMode} addMode - The mode for adding the layer
+     * @returns {Promise<boolean>} - True if successful, false otherwise
+     */
+    async loadImageFromDataURI(dataURI, addMode) {
+        return new Promise((resolve) => {
+            try {
+                const img = new Image();
+                img.onload = async () => {
+                    log.info("Successfully loaded image from data URI");
+                    await this.canvas.canvasLayers.addLayerWithImage(img, {}, addMode);
+                    showInfoNotification("Image pasted from clipboard (base64)");
+                    resolve(true);
+                };
+                img.onerror = () => {
+                    log.warn("Failed to load image from data URI");
+                    showErrorNotification("Failed to load base64 image from clipboard", 5000, true);
+                    resolve(false);
+                };
+                img.src = dataURI;
+            }
+            catch (error) {
+                log.error("Error loading data URI:", error);
+                showErrorNotification("Error processing base64 image from clipboard", 5000, true);
+                resolve(false);
+            }
+        });
+    }
     /**
      * Validates if a text string is a valid image file path or URL
      * @param {string} text - The text to validate
@@ -252,10 +320,12 @@ export class ClipboardManager {
                     img.onload = async () => {
                         log.info("Successfully loaded image from URL");
                         await this.canvas.canvasLayers.addLayerWithImage(img, {}, addMode);
+                        showInfoNotification("Image loaded from URL");
                         resolve(true);
                     };
                     img.onerror = () => {
                         log.warn("Failed to load image from URL:", filePath);
+                        showErrorNotification(`Failed to load image from URL\nThe link might be incorrect or may not point to an image file.: ${filePath}`, 5000, true);
                         resolve(false);
                     };
                     img.src = filePath;
@@ -313,6 +383,7 @@ export class ClipboardManager {
                             img.onload = async () => {
                                 log.info("Successfully loaded image from file picker");
                                 await this.canvas.canvasLayers.addLayerWithImage(img, {}, addMode);
+                                showInfoNotification("Image loaded from selected file");
                                 resolve(true);
                             };
                             img.onerror = () => {

js/utils/ImageUtils.js

@@ -314,3 +314,102 @@ export function canvasToMaskImage(canvas) {
         img.src = canvas.toDataURL();
     });
 }
+/**
+ * Scales an image to fit within specified bounds while maintaining aspect ratio
+ * @param image - Image to scale
+ * @param targetWidth - Target width to fit within
+ * @param targetHeight - Target height to fit within
+ * @returns Promise with scaled Image element
+ */
+export async function scaleImageToFit(image, targetWidth, targetHeight) {
+    const scale = Math.min(targetWidth / image.width, targetHeight / image.height);
+    const scaledWidth = Math.max(1, Math.round(image.width * scale));
+    const scaledHeight = Math.max(1, Math.round(image.height * scale));
+    const { canvas, ctx } = createCanvas(scaledWidth, scaledHeight, '2d', { willReadFrequently: true });
+    if (!ctx)
+        throw new Error("Could not create scaled image context");
+    ctx.drawImage(image, 0, 0, scaledWidth, scaledHeight);
+    return new Promise((resolve, reject) => {
+        const scaledImg = new Image();
+        scaledImg.onload = () => resolve(scaledImg);
+        scaledImg.onerror = reject;
+        scaledImg.src = canvas.toDataURL();
+    });
+}
+/**
+ * Unified tensor to image data conversion
+ * Handles both RGB images and grayscale masks
+ * @param tensor - Input tensor data
+ * @param mode - 'rgb' for images or 'grayscale' for masks
+ * @returns ImageData object
+ */
+export function tensorToImageData(tensor, mode = 'rgb') {
+    try {
+        const shape = tensor.shape;
+        const height = shape[1];
+        const width = shape[2];
+        const channels = shape[3] || 1; // Default to 1 for masks
+        log.debug("Converting tensor:", { shape, channels, mode });
+        const imageData = new ImageData(width, height);
+        const data = new Uint8ClampedArray(width * height * 4);
+        const flatData = tensor.data;
+        const pixelCount = width * height;
+        const min = tensor.min_val ?? 0;
+        const max = tensor.max_val ?? 1;
+        const denom = (max - min) || 1;
+        for (let i = 0; i < pixelCount; i++) {
+            const pixelIndex = i * 4;
+            const tensorIndex = i * channels;
+            let lum;
+            if (mode === 'grayscale' || channels === 1) {
+                lum = flatData[tensorIndex];
+            }
+            else {
+                // Compute luminance for RGB
+                const r = flatData[tensorIndex + 0] ?? 0;
+                const g = flatData[tensorIndex + 1] ?? 0;
+                const b = flatData[tensorIndex + 2] ?? 0;
+                lum = 0.299 * r + 0.587 * g + 0.114 * b;
+            }
+            let norm = (lum - min) / denom;
+            if (!isFinite(norm))
+                norm = 0;
+            norm = Math.max(0, Math.min(1, norm));
+            const value = Math.round(norm * 255);
+            if (mode === 'grayscale') {
+                // For masks: RGB = value, A = 255 (MaskTool reads luminance)
+                data[pixelIndex] = value;
+                data[pixelIndex + 1] = value;
+                data[pixelIndex + 2] = value;
+                data[pixelIndex + 3] = 255;
+            }
+            else {
+                // For images: RGB from channels, A = 255
+                for (let c = 0; c < Math.min(3, channels); c++) {
+                    const channelValue = flatData[tensorIndex + c];
+                    const channelNorm = (channelValue - min) / denom;
+                    data[pixelIndex + c] = Math.round(channelNorm * 255);
+                }
+                data[pixelIndex + 3] = 255;
+            }
+        }
+        imageData.data.set(data);
+        return imageData;
+    }
+    catch (error) {
+        log.error("Error converting tensor:", error);
+        return null;
+    }
+}
+/**
+ * Creates an HTMLImageElement from ImageData
+ * @param imageData - Input ImageData
+ * @returns Promise with HTMLImageElement
+ */
+export async function createImageFromImageData(imageData) {
+    const { canvas, ctx } = createCanvas(imageData.width, imageData.height, '2d', { willReadFrequently: true });
+    if (!ctx)
+        throw new Error("Could not create canvas context");
+    ctx.putImageData(imageData, 0, 0);
+    return await createImageFromSource(canvas.toDataURL());
+}

js/utils/NotificationUtils.js

@@ -1,5 +1,7 @@
 import { createModuleLogger } from "./LoggerUtils.js";
 const log = createModuleLogger('NotificationUtils');
+// Store active notifications for deduplication
+const activeNotifications = new Map();
 /**
  * Utility functions for showing notifications to the user
  */
@@ -8,10 +10,50 @@ const log = createModuleLogger('NotificationUtils');
  * @param message - The message to show
  * @param backgroundColor - Background color (default: #4a6cd4)
  * @param duration - Duration in milliseconds (default: 3000)
+ * @param type - Type of notification
+ * @param deduplicate - If true, will not show duplicate messages and will refresh existing ones (default: false)
  */
-export function showNotification(message, backgroundColor = "#4a6cd4", duration = 3000, type = "info") {
+export function showNotification(message, backgroundColor = "#4a6cd4", duration = 3000, type = "info", deduplicate = false) {
     // Remove any existing prefix to avoid double prefixing
     message = message.replace(/^\[Layer Forge\]\s*/, "");
+    // If deduplication is enabled, check if this message already exists
+    if (deduplicate) {
+        const existingNotification = activeNotifications.get(message);
+        if (existingNotification) {
+            log.debug(`Notification already exists, refreshing timer: ${message}`);
+            // Clear existing timeout
+            if (existingNotification.timeout !== null) {
+                clearTimeout(existingNotification.timeout);
+            }
+            // Find the progress bar and restart its animation
+            const progressBar = existingNotification.element.querySelector('div[style*="animation"]');
+            if (progressBar) {
+                // Reset animation
+                progressBar.style.animation = 'none';
+                // Force reflow
+                void progressBar.offsetHeight;
+                // Restart animation
+                progressBar.style.animation = `lf-progress ${duration / 1000}s linear`;
+            }
+            // Set new timeout
+            const newTimeout = window.setTimeout(() => {
+                const notification = existingNotification.element;
+                notification.style.animation = 'lf-fadeout 0.3s ease-out forwards';
+                notification.addEventListener('animationend', () => {
+                    if (notification.parentNode) {
+                        notification.parentNode.removeChild(notification);
+                        activeNotifications.delete(message);
+                        const container = document.getElementById('lf-notification-container');
+                        if (container && container.children.length === 0) {
+                            container.remove();
+                        }
+                    }
+                });
+            }, duration);
+            existingNotification.timeout = newTimeout;
+            return; // Don't create a new notification
+        }
+    }
     // Type-specific config
     const config = {
         success: { icon: "✔️", title: "Success", bg: "#1fd18b" },
@@ -148,6 +190,10 @@ export function showNotification(message, backgroundColor = "#4a6cd4", duration
     body.classList.add('notification-scrollbar');
     let dismissTimeout = null;
     const closeNotification = () => {
+        // Remove from active notifications map if deduplicate is enabled
+        if (deduplicate) {
+            activeNotifications.delete(message);
+        }
         notification.style.animation = 'lf-fadeout 0.3s ease-out forwards';
         notification.addEventListener('animationend', () => {
             if (notification.parentNode) {
@@ -171,40 +217,77 @@ export function showNotification(message, backgroundColor = "#4a6cd4", duration
         progressBar.style.transform = computedStyle.transform;
         progressBar.style.animation = 'lf-progress-rewind 0.5s ease-out forwards';
     };
-    notification.addEventListener('mouseenter', pauseAndRewindTimer);
+    notification.addEventListener('mouseenter', () => {
-    notification.addEventListener('mouseleave', startDismissTimer);
+        pauseAndRewindTimer();
+        // Update stored timeout if deduplicate is enabled
+        if (deduplicate) {
+            const stored = activeNotifications.get(message);
+            if (stored) {
+                stored.timeout = null;
+            }
+        }
+    });
+    notification.addEventListener('mouseleave', () => {
+        startDismissTimer();
+        // Update stored timeout if deduplicate is enabled
+        if (deduplicate) {
+            const stored = activeNotifications.get(message);
+            if (stored) {
+                stored.timeout = dismissTimeout;
+            }
+        }
+    });
     startDismissTimer();
+    // Store notification if deduplicate is enabled
+    if (deduplicate) {
+        activeNotifications.set(message, { element: notification, timeout: dismissTimeout });
+    }
     log.debug(`Notification shown: [Layer Forge] ${message}`);
 }
 /**
  * Shows a success notification
+ * @param message - The message to show
+ * @param duration - Duration in milliseconds (default: 3000)
+ * @param deduplicate - If true, will not show duplicate messages (default: false)
  */
-export function showSuccessNotification(message, duration = 3000) {
+export function showSuccessNotification(message, duration = 3000, deduplicate = false) {
-    showNotification(message, undefined, duration, "success");
+    showNotification(message, undefined, duration, "success", deduplicate);
 }
 /**
  * Shows an error notification
+ * @param message - The message to show
+ * @param duration - Duration in milliseconds (default: 5000)
+ * @param deduplicate - If true, will not show duplicate messages (default: false)
  */
-export function showErrorNotification(message, duration = 5000) {
+export function showErrorNotification(message, duration = 5000, deduplicate = false) {
-    showNotification(message, undefined, duration, "error");
+    showNotification(message, undefined, duration, "error", deduplicate);
 }
 /**
  * Shows an info notification
+ * @param message - The message to show
+ * @param duration - Duration in milliseconds (default: 3000)
+ * @param deduplicate - If true, will not show duplicate messages (default: false)
  */
-export function showInfoNotification(message, duration = 3000) {
+export function showInfoNotification(message, duration = 3000, deduplicate = false) {
-    showNotification(message, undefined, duration, "info");
+    showNotification(message, undefined, duration, "info", deduplicate);
 }
 /**
  * Shows a warning notification
+ * @param message - The message to show
+ * @param duration - Duration in milliseconds (default: 3000)
+ * @param deduplicate - If true, will not show duplicate messages (default: false)
  */
-export function showWarningNotification(message, duration = 3000) {
+export function showWarningNotification(message, duration = 3000, deduplicate = false) {
-    showNotification(message, undefined, duration, "warning");
+    showNotification(message, undefined, duration, "warning", deduplicate);
 }
 /**
  * Shows an alert notification
+ * @param message - The message to show
+ * @param duration - Duration in milliseconds (default: 3000)
+ * @param deduplicate - If true, will not show duplicate messages (default: false)
  */
-export function showAlertNotification(message, duration = 3000) {
+export function showAlertNotification(message, duration = 3000, deduplicate = false) {
-    showNotification(message, undefined, duration, "alert");
+    showNotification(message, undefined, duration, "alert", deduplicate);
 }
 /**
  * Shows a sequence of all notification types for debugging purposes.
@@ -214,7 +297,7 @@ export function showAllNotificationTypes(message) {
     types.forEach((type, index) => {
         const notificationMessage = message || `This is a '${type}' notification.`;
         setTimeout(() => {
-            showNotification(notificationMessage, undefined, 3000, type);
+            showNotification(notificationMessage, undefined, 3000, type, false);
         }, index * 400); // Stagger the notifications
     });
 }

pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "layerforge"
 description = "Photoshop-like layered canvas editor to your ComfyUI workflow. This node is perfect for complex compositing, inpainting, and outpainting, featuring multi-layer support, masking, blend modes, and precise transformations. Includes optional AI-powered background removal for streamlined image editing."
-version = "1.5.5"
+version = "1.5.9"
 license = { text = "MIT License" }
 dependencies = ["torch", "torchvision", "transformers", "aiohttp", "numpy", "tqdm", "Pillow"]
 

src/Canvas.ts

@@ -91,6 +91,11 @@ export class Canvas {
     onStateChange: (() => void) | undefined;
     pendingBatchContext: any;
     pendingDataCheck: number | null;
+    pendingInputDataCheck: number | null;
+    inputDataLoaded: boolean;
+    lastLoadedImageSrc?: string;
+    lastLoadedLinkId?: number;
+    lastLoadedMaskLinkId?: number;
     previewVisible: boolean;
     requestSaveState: () => void;
     viewport: Viewport;
@@ -138,6 +143,8 @@ export class Canvas {
 
         this.dataInitialized = false;
         this.pendingDataCheck = null;
+        this.pendingInputDataCheck = null;
+        this.inputDataLoaded = false;
         this.imageCache = new Map();
 
         this.requestSaveState = () => {};
@@ -483,6 +490,11 @@ export class Canvas {
         };
 
         const handleExecutionStart = () => {
+            // Check for input data when execution starts, but don't reset the flag
+            log.debug('Execution started, checking for input data...');
+            // On start, only allow images; mask should load on mask-connect or after execution completes
+            this.canvasIO.checkForInputData({ allowImage: true, allowMask: false, reason: 'execution_start' });
+            
             if (getAutoRefreshValue()) {
                 lastExecutionStartTime = Date.now();
                 // Store a snapshot of the context for the upcoming batch
@@ -506,6 +518,10 @@ export class Canvas {
         };
 
         const handleExecutionSuccess = async () => {
+            // Always check for input data after execution completes
+            log.debug('Execution success, checking for input data...');
+            await this.canvasIO.checkForInputData({ allowImage: true, allowMask: true, reason: 'execution_success' });
+            
             if (getAutoRefreshValue()) {
                 log.info('Auto-refresh triggered, importing latest images.');
 

src/CanvasIO.ts

@@ -2,6 +2,7 @@ import { createCanvas } from "./utils/CommonUtils.js";
 import { createModuleLogger } from "./utils/LoggerUtils.js";
 import { showErrorNotification } from "./utils/NotificationUtils.js";
 import { webSocketManager } from "./utils/WebSocketManager.js";
+import { scaleImageToFit, createImageFromSource, tensorToImageData, createImageFromImageData } from "./utils/ImageUtils.js";
 import type { Canvas } from './Canvas';
 import type { Layer, Shape } from './types';
 
@@ -282,22 +283,12 @@ export class CanvasIO {
         try {
             log.debug("Adding input to canvas:", { inputImage });
 
-            const { canvas: tempCanvas, ctx: tempCtx } = createCanvas(inputImage.width, inputImage.height);
+            // Use unified tensorToImageData for RGB image
-            if (!tempCtx) throw new Error("Could not create temp context");
+            const imageData = tensorToImageData(inputImage, 'rgb');
+            if (!imageData) throw new Error("Failed to convert input image tensor");
 
-            const imgData = new ImageData(
+            // Create HTMLImageElement from ImageData
-                new Uint8ClampedArray(inputImage.data),
+            const image = await createImageFromImageData(imageData);
-                inputImage.width,
-                inputImage.height
-            );
-            tempCtx.putImageData(imgData, 0, 0);
-
-            const image = new Image();
-            await new Promise((resolve, reject) => {
-                image.onload = resolve;
-                image.onerror = reject;
-                image.src = tempCanvas.toDataURL();
-            });
 
             const bounds = this.canvas.outputAreaBounds;
             const scale = Math.min(
@@ -333,23 +324,10 @@ export class CanvasIO {
                 throw new Error("Invalid tensor data");
             }
 
-            const { canvas, ctx } = createCanvas(tensor.width, tensor.height, '2d', { willReadFrequently: true });
+            const imageData = tensorToImageData(tensor, 'rgb');
-            if (!ctx) throw new Error("Could not create canvas context");
+            if (!imageData) throw new Error("Failed to convert tensor to image data");
 
-            const imageData = new ImageData(
+            return await createImageFromImageData(imageData);
-                new Uint8ClampedArray(tensor.data),
-                tensor.width,
-                tensor.height
-            );
-
-            ctx.putImageData(imageData, 0, 0);
-
-            return new Promise((resolve, reject) => {
-                const img = new Image();
-                img.onload = () => resolve(img);
-                img.onerror = (e) => reject(new Error("Failed to load image: " + e));
-                img.src = canvas.toDataURL();
-            });
         } catch (error) {
             log.error("Error converting tensor to image:", error);
             throw error;
@@ -372,6 +350,16 @@ export class CanvasIO {
         try {
             log.info("Starting node data initialization...");
 
+            // First check for input data from the backend (new feature)
+            await this.checkForInputData();
+
+            // If we've already loaded input data, don't continue with old initialization
+            if (this.canvas.inputDataLoaded) {
+                log.debug("Input data already loaded, skipping old initialization");
+                this.canvas.dataInitialized = true;
+                return;
+            }
+
             if (!this.canvas.node || !(this.canvas.node as any).inputs) {
                 log.debug("Node or inputs not ready");
                 return this.scheduleDataCheck();
@@ -379,6 +367,14 @@ export class CanvasIO {
 
             if ((this.canvas.node as any).inputs[0] && (this.canvas.node as any).inputs[0].link) {
                 const imageLinkId = (this.canvas.node as any).inputs[0].link;
+                
+                // Check if we already loaded this link
+                if (this.canvas.lastLoadedLinkId === imageLinkId) {
+                    log.debug(`Link ${imageLinkId} already loaded via new system, marking as initialized`);
+                    this.canvas.dataInitialized = true;
+                    return;
+                }
+                
                 const imageData = (window as any).app.nodeOutputs[imageLinkId];
 
                 if (imageData) {
@@ -389,6 +385,9 @@ export class CanvasIO {
                     log.debug("Image data not available yet");
                     return this.scheduleDataCheck();
                 }
+            } else {
+                // No input connected, mark as initialized to stop repeated checks
+                this.canvas.dataInitialized = true;
             }
 
             if ((this.canvas.node as any).inputs[1] && (this.canvas.node as any).inputs[1].link) {
@@ -407,6 +406,439 @@ export class CanvasIO {
         }
     }
 
+    async checkForInputData(options?: { allowImage?: boolean; allowMask?: boolean; reason?: string }): Promise<void> {
+        try {
+            const nodeId = this.canvas.node.id;
+            const allowImage = options?.allowImage ?? true;
+            const allowMask = options?.allowMask ?? true;
+            const reason = options?.reason ?? 'unspecified';
+            log.info(`Checking for input data for node ${nodeId}... opts: image=${allowImage}, mask=${allowMask}, reason=${reason}`);
+            
+            // Track loaded links separately for image and mask
+            let imageLoaded = false;
+            let maskLoaded = false;
+            let imageChanged = false;
+            
+            // First, try to get data from connected node's output if available (IMAGES)
+            if (allowImage && this.canvas.node.inputs && this.canvas.node.inputs[0] && this.canvas.node.inputs[0].link) {
+                const linkId = this.canvas.node.inputs[0].link;
+                const graph = (this.canvas.node as any).graph;
+                
+                // Always check if images have changed first
+                if (graph) {
+                    const link = graph.links[linkId];
+                    if (link) {
+                        const sourceNode = graph.getNodeById(link.origin_id);
+                        if (sourceNode && sourceNode.imgs && sourceNode.imgs.length > 0) {
+                            // Create current batch identifier (all image sources combined)
+                            const currentBatchImageSrcs = sourceNode.imgs.map((img: HTMLImageElement) => img.src).join('|');
+                            
+                            // Check if this is the same link we loaded before
+                            if (this.canvas.lastLoadedLinkId === linkId) {
+                                // Same link, check if images actually changed
+                                if (this.canvas.lastLoadedImageSrc !== currentBatchImageSrcs) {
+                                    log.info(`Batch images changed for link ${linkId} (${sourceNode.imgs.length} images), will reload...`);
+                                    log.debug(`Previous batch hash: ${this.canvas.lastLoadedImageSrc?.substring(0, 100)}...`);
+                                    log.debug(`Current batch hash: ${currentBatchImageSrcs.substring(0, 100)}...`);
+                                    imageChanged = true;
+                                    // Clear the inputDataLoaded flag to force reload from backend
+                                    this.canvas.inputDataLoaded = false;
+                                    // Clear the lastLoadedImageSrc to force reload
+                                    this.canvas.lastLoadedImageSrc = undefined;
+                                    // Clear backend data to force fresh load
+                                    fetch(`/layerforge/clear_input_data/${nodeId}`, { method: 'POST' })
+                                        .then(() => log.debug("Backend input data cleared due to image change"))
+                                        .catch(err => log.error("Failed to clear backend data:", err));
+                                } else {
+                                    log.debug(`Batch images for link ${linkId} unchanged (${sourceNode.imgs.length} images)`);
+                                    imageLoaded = true;
+                                }
+                            } else {
+                                // Different link or first load
+                                log.info(`New link ${linkId} detected, will load ${sourceNode.imgs.length} images`);
+                                imageChanged = false; // It's not a change, it's a new link
+                                imageLoaded = false; // Need to load
+                                // Reset the inputDataLoaded flag for new link
+                                this.canvas.inputDataLoaded = false;
+                            }
+                        }
+                    }
+                }
+                
+                if (!imageLoaded || imageChanged) {
+                    // Reset the inputDataLoaded flag when images change
+                    if (imageChanged) {
+                        this.canvas.inputDataLoaded = false;
+                        log.info("Resetting inputDataLoaded flag due to image change");
+                    }
+                
+                    if ((this.canvas.node as any).graph) {
+                        const graph2 = (this.canvas.node as any).graph;
+                        const link2 = graph2.links[linkId];
+                        if (link2) {
+                            const sourceNode = graph2.getNodeById(link2.origin_id);
+                            if (sourceNode && sourceNode.imgs && sourceNode.imgs.length > 0) {
+                                // The connected node has images in its output - handle multiple images (batch)
+                                log.info(`Found ${sourceNode.imgs.length} image(s) in connected node's output, loading all`);
+                                
+                                // Create a combined source identifier for batch detection
+                                const batchImageSrcs = sourceNode.imgs.map((img: HTMLImageElement) => img.src).join('|');
+                                
+                                // Mark this link and batch sources as loaded
+                                this.canvas.lastLoadedLinkId = linkId;
+                                this.canvas.lastLoadedImageSrc = batchImageSrcs;
+                                    
+                                // Don't clear layers - just add new ones
+                                if (imageChanged) {
+                                    log.info("Image change detected, will add new layers");
+                                }
+                                
+                                // Determine add mode
+                                const fitOnAddWidget = this.canvas.node.widgets.find((w) => w.name === "fit_on_add");
+                                const addMode = (fitOnAddWidget && fitOnAddWidget.value) ? 'fit' : 'center';
+                                
+                                // Add all images from the batch as separate layers
+                                for (let i = 0; i < sourceNode.imgs.length; i++) {
+                                    const img = sourceNode.imgs[i];
+                                    await this.canvas.canvasLayers.addLayerWithImage(
+                                        img, 
+                                        { name: `Batch Image ${i + 1}` }, // Give each layer a unique name
+                                        addMode,
+                                        this.canvas.outputAreaBounds
+                                    );
+                                    log.debug(`Added batch image ${i + 1}/${sourceNode.imgs.length} to canvas`);
+                                }
+                                
+                                this.canvas.inputDataLoaded = true;
+                                imageLoaded = true;
+                                log.info(`All ${sourceNode.imgs.length} input images from batch added as separate layers`);
+                                this.canvas.render();
+                                this.canvas.saveState();
+                            }
+                        }
+                    }
+                }
+            }
+            
+            // Check for mask input separately (from nodeOutputs) ONLY when allowed
+            if (allowMask && this.canvas.node.inputs && this.canvas.node.inputs[1] && this.canvas.node.inputs[1].link) {
+                const maskLinkId = this.canvas.node.inputs[1].link;
+                
+                // Check if we already loaded this mask link
+                if (this.canvas.lastLoadedMaskLinkId === maskLinkId) {
+                    log.debug(`Mask link ${maskLinkId} already loaded`);
+                    maskLoaded = true;
+                } else {
+                    // Try to get mask tensor from nodeOutputs using origin_id (not link id)
+                    const graph = (this.canvas.node as any).graph;
+                    let maskOutput = null;
+                    
+                    if (graph) {
+                        const link = graph.links[maskLinkId];
+                        if (link && link.origin_id) {
+                            // Use origin_id to get the actual node output
+                            const nodeOutput = (window as any).app?.nodeOutputs?.[link.origin_id];
+                            log.debug(`Looking for mask output from origin node ${link.origin_id}, found:`, !!nodeOutput);
+                            
+                            if (nodeOutput) {
+                                log.debug(`Node ${link.origin_id} output structure:`, {
+                                    hasData: !!nodeOutput.data,
+                                    hasShape: !!nodeOutput.shape,
+                                    dataType: typeof nodeOutput.data,
+                                    shapeType: typeof nodeOutput.shape,
+                                    keys: Object.keys(nodeOutput)
+                                });
+                                
+                                // Only use if it has actual tensor data
+                                if (nodeOutput.data && nodeOutput.shape) {
+                                    maskOutput = nodeOutput;
+                                }
+                            }
+                        }
+                    }
+                    
+                    if (maskOutput && maskOutput.data && maskOutput.shape) {
+                        try {
+                            // Derive dimensions from shape or explicit width/height
+                            let width = (maskOutput.width as number) || 0;
+                            let height = (maskOutput.height as number) || 0;
+                            const shape = maskOutput.shape as number[]; // e.g. [1,H,W] or [1,H,W,1]
+                            if ((!width || !height) && Array.isArray(shape)) {
+                                if (shape.length >= 3) {
+                                    height = shape[1];
+                                    width = shape[2];
+                                } else if (shape.length === 2) {
+                                    height = shape[0];
+                                    width = shape[1];
+                                }
+                            }
+                            if (!width || !height) {
+                                throw new Error("Cannot determine mask dimensions from nodeOutputs");
+                            }
+                            
+                            // Determine channels count
+                            let channels = 1;
+                            if (Array.isArray(shape) && shape.length >= 4) {
+                                channels = shape[3];
+                            } else if ((maskOutput as any).channels) {
+                                channels = (maskOutput as any).channels;
+                            } else {
+                                const len = (maskOutput.data as any).length;
+                                channels = Math.max(1, Math.floor(len / (width * height)));
+                            }
+                            
+                            // Use unified tensorToImageData for masks
+                            const maskImageData = tensorToImageData(maskOutput, 'grayscale');
+                            if (!maskImageData) throw new Error("Failed to convert mask tensor to image data");
+                            
+                            // Create canvas and put image data
+                            const { canvas: maskCanvas, ctx } = createCanvas(width, height, '2d', { willReadFrequently: true });
+                            if (!ctx) throw new Error("Could not create mask context");
+                            ctx.putImageData(maskImageData, 0, 0);
+                            
+                            // Convert to HTMLImageElement
+                            const maskImg = await createImageFromSource(maskCanvas.toDataURL());
+                            
+                            // Respect fit_on_add (scale to output area)
+                            const widgets = this.canvas.node.widgets;
+                            const fitOnAddWidget = widgets ? widgets.find((w: any) => w.name === "fit_on_add") : null;
+                            const shouldFit = fitOnAddWidget && fitOnAddWidget.value;
+                            
+                            let finalMaskImg: HTMLImageElement = maskImg;
+                            if (shouldFit) {
+                                const bounds = this.canvas.outputAreaBounds;
+                                finalMaskImg = await scaleImageToFit(maskImg, bounds.width, bounds.height);
+                            }
+                            
+                            // Apply to MaskTool (centers internally)
+                            if (this.canvas.maskTool) {
+                                this.canvas.maskTool.setMask(finalMaskImg, true);
+                                (this.canvas as any).maskAppliedFromInput = true;
+                                this.canvas.canvasState.saveMaskState();
+                                this.canvas.render();
+                                // Mark this mask link as loaded to avoid re-applying
+                                this.canvas.lastLoadedMaskLinkId = maskLinkId;
+                                maskLoaded = true;
+                                log.info("Applied input mask from nodeOutputs immediately on connection" + (shouldFit ? " (fitted to output area)" : ""));
+                            }
+                        } catch (err) {
+                            log.warn("Failed to apply mask from nodeOutputs immediately; will wait for backend input_mask after execution", err);
+                        }
+                    } else {
+                        // nodeOutputs exist but don't have tensor data yet (need workflow execution)
+                        log.info(`Mask node ${(this.canvas.node as any).graph?.links[maskLinkId]?.origin_id} found but has no tensor data yet. Mask will be applied automatically after workflow execution.`);
+                        // Don't retry - data won't be available until workflow runs
+                    }
+                }
+            }
+            
+            // Only check backend if we have actual inputs connected
+            const hasImageInput = this.canvas.node.inputs && this.canvas.node.inputs[0] && this.canvas.node.inputs[0].link;
+            const hasMaskInput = this.canvas.node.inputs && this.canvas.node.inputs[1] && this.canvas.node.inputs[1].link;
+
+            // If mask input is disconnected, clear any currently applied mask to ensure full separation
+            if (!hasMaskInput) {
+                (this.canvas as any).maskAppliedFromInput = false;
+                this.canvas.lastLoadedMaskLinkId = undefined;
+                log.info("Mask input disconnected - cleared mask to enforce separation from input_image");
+            }
+            
+            if (!hasImageInput && !hasMaskInput) {
+                log.debug("No inputs connected, skipping backend check");
+                this.canvas.inputDataLoaded = true;
+                return;
+            }
+            
+            // Skip backend check during mask connection if we didn't get immediate data
+            if (reason === "mask_connect" && !maskLoaded) {
+                log.info("No immediate mask data available during connection, skipping backend check to avoid stale data. Will check after execution.");
+                return;
+            }
+            
+            // Check backend for input data only if we have connected inputs
+            const response = await fetch(`/layerforge/get_input_data/${nodeId}`);
+            const result = await response.json();
+
+            if (result.success && result.has_input) {
+                // Dedupe: skip only if backend payload matches last loaded batch hash
+                let backendBatchHash: string | undefined;
+                if (result.data?.input_images_batch && Array.isArray(result.data.input_images_batch)) {
+                    backendBatchHash = result.data.input_images_batch.map((i: any) => i.data).join('|');
+                } else if (result.data?.input_image) {
+                    backendBatchHash = result.data.input_image;
+                }
+                // Check mask separately - don't skip if only images are unchanged AND mask is actually connected AND allowed
+                const shouldCheckMask = hasMaskInput && allowMask;
+                
+                if (backendBatchHash && this.canvas.lastLoadedImageSrc === backendBatchHash && !shouldCheckMask) {
+                    log.debug("Backend input data unchanged and no mask to check, skipping reload");
+                    this.canvas.inputDataLoaded = true;
+                    return;
+                } else if (backendBatchHash && this.canvas.lastLoadedImageSrc === backendBatchHash && shouldCheckMask) {
+                    log.debug("Images unchanged but need to check mask, continuing...");
+                    imageLoaded = true; // Mark images as already loaded to skip reloading them
+                }
+                
+                // Check if we already loaded image data (by checking the current link)
+                if (allowImage && !imageLoaded && this.canvas.node.inputs && this.canvas.node.inputs[0] && this.canvas.node.inputs[0].link) {
+                    const currentLinkId = this.canvas.node.inputs[0].link;
+                    if (this.canvas.lastLoadedLinkId !== currentLinkId) {
+                        // Mark this link as loaded
+                        this.canvas.lastLoadedLinkId = currentLinkId;
+                        imageLoaded = false; // Will load from backend
+                    }
+                }
+                
+                // Check for mask data from backend ONLY when mask input is actually connected AND allowed
+                // Only reset if the mask link actually changed
+                if (allowMask && hasMaskInput && this.canvas.node.inputs && this.canvas.node.inputs[1]) {
+                    const currentMaskLinkId = this.canvas.node.inputs[1].link;
+                    // Only reset if this is a different mask link than what we loaded before
+                    if (this.canvas.lastLoadedMaskLinkId !== currentMaskLinkId) {
+                        maskLoaded = false;
+                        log.debug(`New mask input detected (${currentMaskLinkId}), will check backend for mask data`);
+                    } else {
+                        log.debug(`Same mask input (${currentMaskLinkId}), mask already loaded`);
+                        maskLoaded = true;
+                    }
+                } else {
+                    // No mask input connected, or mask loading not allowed right now
+                    maskLoaded = true; // Mark as loaded to skip mask processing
+                    if (!allowMask) {
+                        log.debug("Mask loading is currently disabled by caller, skipping mask check");
+                    } else {
+                        log.debug("No mask input connected, skipping mask check");
+                    }
+                }
+                
+                log.info("Input data found from backend, adding to canvas");
+                const inputData = result.data;
+                
+                // Compute backend batch hash for dedupe and state
+                let backendHashNow: string | undefined;
+                if (inputData?.input_images_batch && Array.isArray(inputData.input_images_batch)) {
+                    backendHashNow = inputData.input_images_batch.map((i: any) => i.data).join('|');
+                } else if (inputData?.input_image) {
+                    backendHashNow = inputData.input_image;
+                }
+                
+                // Just update the hash without removing any layers
+                if (backendHashNow) {
+                    log.info("New backend input data detected, adding new layers");
+                    this.canvas.lastLoadedImageSrc = backendHashNow;
+                }
+                
+                // Mark that we've loaded input data for this execution
+                this.canvas.inputDataLoaded = true;
+                
+                // Determine add mode based on fit_on_add setting
+                const widgets = this.canvas.node.widgets;
+                const fitOnAddWidget = widgets ? widgets.find((w: any) => w.name === "fit_on_add") : null;
+                const addMode = (fitOnAddWidget && fitOnAddWidget.value) ? 'fit' : 'center';
+                
+                // Load input image(s) only if image input is actually connected, not already loaded, and allowed
+                if (allowImage && !imageLoaded && hasImageInput) {
+                    if (inputData.input_images_batch) {
+                        // Handle batch of images
+                        const batch = inputData.input_images_batch;
+                        log.info(`Processing batch of ${batch.length} images from backend`);
+                        
+                                for (let i = 0; i < batch.length; i++) {
+                                    const imgData = batch[i];
+                                    const img = await createImageFromSource(imgData.data);
+                                    
+                                    // Add image to canvas with unique name
+                                    await this.canvas.canvasLayers.addLayerWithImage(
+                                        img, 
+                                        { name: `Batch Image ${i + 1}` },
+                                        addMode,
+                                        this.canvas.outputAreaBounds
+                                    );
+                                    
+                                    log.debug(`Added batch image ${i + 1}/${batch.length} from backend`);
+                                }
+                        
+                        log.info(`All ${batch.length} batch images added from backend`);
+                        this.canvas.render();
+                        this.canvas.saveState();
+                        
+                    } else if (inputData.input_image) {
+                        // Handle single image (backward compatibility)
+                        const img = await createImageFromSource(inputData.input_image);
+                        
+                        // Add image to canvas at output area position
+                        await this.canvas.canvasLayers.addLayerWithImage(
+                            img, 
+                            {},
+                            addMode,
+                            this.canvas.outputAreaBounds
+                        );
+                        
+                        log.info("Single input image added as new layer to canvas");
+                        this.canvas.render();
+                        this.canvas.saveState();
+                    } else {
+                        log.debug("No input image data from backend");
+                    }
+                } else if (!hasImageInput && (inputData.input_images_batch || inputData.input_image)) {
+                    log.debug("Backend has image data but no image input connected, skipping image load");
+                }
+                
+                // Handle mask separately only if mask input is actually connected, allowed, and not already loaded
+                if (allowMask && !maskLoaded && hasMaskInput && inputData.input_mask) {
+                    log.info("Processing input mask");
+                    
+                    // Load mask image
+                    const maskImg = await createImageFromSource(inputData.input_mask);
+                    
+                    // Determine if we should fit the mask or use it at original size
+                    const fitOnAddWidget2 = this.canvas.node.widgets.find((w) => w.name === "fit_on_add");
+                    const shouldFit = fitOnAddWidget2 && fitOnAddWidget2.value;
+                    
+                    let finalMaskImg: HTMLImageElement = maskImg;
+                    if (shouldFit && this.canvas.maskTool) {
+                        const bounds = this.canvas.outputAreaBounds;
+                        finalMaskImg = await scaleImageToFit(maskImg, bounds.width, bounds.height);
+                    }
+                    
+                    // Apply to MaskTool (centers internally)
+                    if (this.canvas.maskTool) {
+                        this.canvas.maskTool.setMask(finalMaskImg, true);
+                    }
+                    
+                    (this.canvas as any).maskAppliedFromInput = true;
+                    // Save the mask state
+                    this.canvas.canvasState.saveMaskState()
+                    
+                    log.info("Applied input mask to mask tool" + (shouldFit ? " (fitted to output area)" : " (original size)"));
+                } else if (!hasMaskInput && inputData.input_mask) {
+                    log.debug("Backend has mask data but no mask input connected, skipping mask load");
+                } else if (!allowMask && inputData.input_mask) {
+                    log.debug("Mask input data present in backend but mask loading is disabled by caller; skipping");
+                }
+            } else {
+                log.debug("No input data from backend");
+                // Don't schedule another check - we'll only check when explicitly triggered
+            }
+        } catch (error) {
+            log.error("Error checking for input data:", error);
+            // Don't schedule another check on error
+        }
+    }
+
+    scheduleInputDataCheck(): void {
+        // Schedule a retry for mask data check when nodeOutputs are not ready yet
+        if (this.canvas.pendingInputDataCheck) {
+            clearTimeout(this.canvas.pendingInputDataCheck);
+        }
+
+        this.canvas.pendingInputDataCheck = window.setTimeout(() => {
+            this.canvas.pendingInputDataCheck = null;
+            log.debug("Retrying input data check for mask...");
+            
+        }, 500); // Shorter delay for mask data retry
+    }
+
     scheduleDataCheck(): void {
         if (this.canvas.pendingDataCheck) {
             clearTimeout(this.canvas.pendingDataCheck);
@@ -499,59 +931,11 @@ export class CanvasIO {
     }
 
     convertTensorToImageData(tensor: any): ImageData | null {
-        try {
+        return tensorToImageData(tensor, 'rgb');
-            const shape = tensor.shape;
-            const height = shape[1];
-            const width = shape[2];
-            const channels = shape[3];
-
-            log.debug("Converting tensor:", {
-                shape: shape,
-                dataRange: {
-                    min: tensor.min_val,
-                    max: tensor.max_val
-                }
-            });
-
-            const imageData = new ImageData(width, height);
-            const data = new Uint8ClampedArray(width * height * 4);
-
-            const flatData = tensor.data;
-            const pixelCount = width * height;
-
-            for (let i = 0; i < pixelCount; i++) {
-                const pixelIndex = i * 4;
-                const tensorIndex = i * channels;
-
-                for (let c = 0; c < channels; c++) {
-                    const value = flatData[tensorIndex + c];
-
-                    const normalizedValue = (value - tensor.min_val) / (tensor.max_val - tensor.min_val);
-                    data[pixelIndex + c] = Math.round(normalizedValue * 255);
-                }
-
-                data[pixelIndex + 3] = 255;
-            }
-
-            imageData.data.set(data);
-            return imageData;
-        } catch (error) {
-            log.error("Error converting tensor:", error);
-            return null;
-        }
     }
 
     async createImageFromData(imageData: ImageData): Promise<HTMLImageElement> {
-        return new Promise((resolve, reject) => {
+        return createImageFromImageData(imageData);
-            const { canvas, ctx } = createCanvas(imageData.width, imageData.height, '2d', { willReadFrequently: true });
-            if (!ctx) throw new Error("Could not create canvas context");
-            ctx.putImageData(imageData, 0, 0);
-
-            const img = new Image();
-            img.onload = () => resolve(img);
-            img.onerror = reject;
-            img.src = canvas.toDataURL();
-        });
     }
 
     async processMaskData(maskData: any): Promise<void> {
@@ -618,12 +1002,7 @@ export class CanvasIO {
                 const newLayers: (Layer | null)[] = [];
 
                 for (const imageData of result.images) {
-                    const img = new Image();
+                    const img = await createImageFromSource(imageData);
-                    await new Promise((resolve, reject) => {
-                        img.onload = resolve;
-                        img.onerror = reject;
-                        img.src = imageData;
-                    });
                     
                     let processedImage = img;
                     
@@ -652,37 +1031,31 @@ export class CanvasIO {
     }
 
     async clipImageToShape(image: HTMLImageElement, shape: Shape): Promise<HTMLImageElement> {
-        return new Promise((resolve, reject) => {
+        const { canvas, ctx } = createCanvas(image.width, image.height);
-            const { canvas, ctx } = createCanvas(image.width, image.height);
+        if (!ctx) {
-            if (!ctx) {
+            throw new Error("Could not create canvas context for clipping");
-                reject(new Error("Could not create canvas context for clipping"));
+        }
-                return;
-            }
 
-            // Draw the image first
+        // Draw the image first
-            ctx.drawImage(image, 0, 0);
+        ctx.drawImage(image, 0, 0);
 
-            // Calculate custom shape position accounting for extensions
+        // Calculate custom shape position accounting for extensions
-            // Custom shape should maintain its relative position within the original canvas area
+        // Custom shape should maintain its relative position within the original canvas area
-            const ext = this.canvas.outputAreaExtensionEnabled ? this.canvas.outputAreaExtensions : { top: 0, bottom: 0, left: 0, right: 0 };
+        const ext = this.canvas.outputAreaExtensionEnabled ? this.canvas.outputAreaExtensions : { top: 0, bottom: 0, left: 0, right: 0 };
-            const shapeOffsetX = ext.left;  // Add left extension to maintain relative position
+        const shapeOffsetX = ext.left;  // Add left extension to maintain relative position
-            const shapeOffsetY = ext.top;   // Add top extension to maintain relative position
+        const shapeOffsetY = ext.top;   // Add top extension to maintain relative position
 
-            // Create a clipping mask using the shape with extension offset
+        // Create a clipping mask using the shape with extension offset
-            ctx.globalCompositeOperation = 'destination-in';
+        ctx.globalCompositeOperation = 'destination-in';
-            ctx.beginPath();
+        ctx.beginPath();
-            ctx.moveTo(shape.points[0].x + shapeOffsetX, shape.points[0].y + shapeOffsetY);
+        ctx.moveTo(shape.points[0].x + shapeOffsetX, shape.points[0].y + shapeOffsetY);
-            for (let i = 1; i < shape.points.length; i++) {
+        for (let i = 1; i < shape.points.length; i++) {
-                ctx.lineTo(shape.points[i].x + shapeOffsetX, shape.points[i].y + shapeOffsetY);
+            ctx.lineTo(shape.points[i].x + shapeOffsetX, shape.points[i].y + shapeOffsetY);
-            }
+        }
-            ctx.closePath();
+        ctx.closePath();
-            ctx.fill();
+        ctx.fill();
 
-            // Create a new image from the clipped canvas
+        // Create a new image from the clipped canvas
-            const clippedImage = new Image();
+        return await createImageFromSource(canvas.toDataURL());
-            clippedImage.onload = () => resolve(clippedImage);
-            clippedImage.onerror = () => reject(new Error("Failed to create clipped image"));
-            clippedImage.src = canvas.toDataURL();
-        });
     }
 }

src/CanvasInteractions.ts

@@ -31,7 +31,7 @@ interface TransformOrigin {
 }
 
 interface InteractionState {
-    mode: 'none' | 'panning' | 'dragging' | 'resizing' | 'rotating' | 'drawingMask' | 'resizingCanvas' | 'movingCanvas' | 'potential-drag' | 'drawingShape';
+    mode: 'none' | 'panning' | 'dragging' | 'resizing' | 'rotating' | 'drawingMask' | 'resizingCanvas' | 'movingCanvas' | 'potential-drag' | 'drawingShape' | 'transformingOutputArea';
     panStart: Point;
     dragStart: Point;
     transformOrigin: TransformOrigin | null;
@@ -49,6 +49,8 @@ interface InteractionState {
     keyMovementInProgress: boolean;
     canvasResizeRect: { x: number, y: number, width: number, height: number } | null;
     canvasMoveRect: { x: number, y: number, width: number, height: number } | null;
+    outputAreaTransformHandle: string | null;
+    outputAreaTransformAnchor: Point;
 }
 
 export class CanvasInteractions {
@@ -94,6 +96,8 @@ export class CanvasInteractions {
             keyMovementInProgress: false,
             canvasResizeRect: null,
             canvasMoveRect: null,
+            outputAreaTransformHandle: null,
+            outputAreaTransformAnchor: { x: 0, y: 0 },
         };
         this.originalLayerPositions = new Map();
     }
@@ -238,6 +242,7 @@ export class CanvasInteractions {
         this.interaction.canvasMoveRect = null;
         this.interaction.hasClonedInDrag = false;
         this.interaction.transformingLayer = null;
+        this.interaction.outputAreaTransformHandle = null;
         this.canvas.canvas.style.cursor = 'default';
     }
 
@@ -252,6 +257,19 @@ export class CanvasInteractions {
             return;
         }
 
+        if (this.interaction.mode === 'transformingOutputArea') {
+            // Check if clicking on output area transform handle
+            const handle = this.getOutputAreaHandle(coords.world);
+            if (handle) {
+                this.startOutputAreaTransform(handle, coords.world);
+                return;
+            }
+            // If clicking outside, exit transform mode
+            this.interaction.mode = 'none';
+            this.canvas.render();
+            return;
+        }
+
         if (this.canvas.shapeTool.isActive) {
             this.canvas.shapeTool.addPoint(coords.world);
             return;
@@ -352,6 +370,13 @@ export class CanvasInteractions {
             case 'movingCanvas':
                 this.updateCanvasMove(coords.world);
                 break;
+            case 'transformingOutputArea':
+                if (this.interaction.outputAreaTransformHandle) {
+                    this.resizeOutputAreaFromHandle(coords.world, e.shiftKey);
+                } else {
+                    this.updateOutputAreaTransformCursor(coords.world);
+                }
+                break;
             default:
                 this.updateCursor(coords.world);
                 // Update brush cursor on overlay if mask tool is active
@@ -384,6 +409,11 @@ export class CanvasInteractions {
             this.finalizeCanvasMove();
         }
 
+        if (this.interaction.mode === 'transformingOutputArea' && this.interaction.outputAreaTransformHandle) {
+            this.finalizeOutputAreaTransform();
+            return;
+        }
+
         // Log layer positions when dragging ends
         if (this.interaction.mode === 'dragging' && this.canvas.canvasSelection.selectedLayers.length > 0) {
             this.logDragCompletion(coords);
@@ -1313,4 +1343,189 @@ export class CanvasInteractions {
         await this.canvas.canvasLayers.clipboardManager.handlePaste('mouse', preference);
     }
 
+    // New methods for output area transformation
+    public activateOutputAreaTransform(): void {
+        // Clear any existing interaction state before starting transform
+        this.resetInteractionState();
+        
+        // Deactivate any active tools that might conflict
+        if (this.canvas.shapeTool.isActive) {
+            this.canvas.shapeTool.deactivate();
+        }
+        if (this.canvas.maskTool.isActive) {
+            this.canvas.maskTool.deactivate();
+        }
+        
+        // Clear selection to avoid confusion
+        this.canvas.canvasSelection.updateSelection([]);
+        
+        // Set transform mode
+        this.interaction.mode = 'transformingOutputArea';
+        this.canvas.render();
+    }
+
+    private getOutputAreaHandle(worldCoords: Point): string | null {
+        const bounds = this.canvas.outputAreaBounds;
+        const threshold = 10 / this.canvas.viewport.zoom;
+        
+        // Define handle positions
+        const handles = {
+            'nw': { x: bounds.x, y: bounds.y },
+            'n': { x: bounds.x + bounds.width / 2, y: bounds.y },
+            'ne': { x: bounds.x + bounds.width, y: bounds.y },
+            'e': { x: bounds.x + bounds.width, y: bounds.y + bounds.height / 2 },
+            'se': { x: bounds.x + bounds.width, y: bounds.y + bounds.height },
+            's': { x: bounds.x + bounds.width / 2, y: bounds.y + bounds.height },
+            'sw': { x: bounds.x, y: bounds.y + bounds.height },
+            'w': { x: bounds.x, y: bounds.y + bounds.height / 2 },
+        };
+
+        for (const [name, pos] of Object.entries(handles)) {
+            const dx = worldCoords.x - pos.x;
+            const dy = worldCoords.y - pos.y;
+            if (Math.sqrt(dx * dx + dy * dy) < threshold) {
+                return name;
+            }
+        }
+
+        return null;
+    }
+
+    private startOutputAreaTransform(handle: string, worldCoords: Point): void {
+        this.interaction.outputAreaTransformHandle = handle;
+        this.interaction.dragStart = { ...worldCoords };
+        
+        const bounds = this.canvas.outputAreaBounds;
+        this.interaction.transformOrigin = {
+            x: bounds.x,
+            y: bounds.y,
+            width: bounds.width,
+            height: bounds.height,
+            rotation: 0,
+            centerX: bounds.x + bounds.width / 2,
+            centerY: bounds.y + bounds.height / 2
+        };
+
+        // Set anchor point (opposite corner for resize)
+        const anchorMap: { [key: string]: Point } = {
+            'nw': { x: bounds.x + bounds.width, y: bounds.y + bounds.height },
+            'n': { x: bounds.x + bounds.width / 2, y: bounds.y + bounds.height },
+            'ne': { x: bounds.x, y: bounds.y + bounds.height },
+            'e': { x: bounds.x, y: bounds.y + bounds.height / 2 },
+            'se': { x: bounds.x, y: bounds.y },
+            's': { x: bounds.x + bounds.width / 2, y: bounds.y },
+            'sw': { x: bounds.x + bounds.width, y: bounds.y },
+            'w': { x: bounds.x + bounds.width, y: bounds.y + bounds.height / 2 },
+        };
+        
+        this.interaction.outputAreaTransformAnchor = anchorMap[handle];
+    }
+
+    private resizeOutputAreaFromHandle(worldCoords: Point, isShiftPressed: boolean): void {
+        const o = this.interaction.transformOrigin;
+        if (!o) return;
+        
+        const handle = this.interaction.outputAreaTransformHandle;
+        const anchor = this.interaction.outputAreaTransformAnchor;
+        
+        let newX = o.x;
+        let newY = o.y;
+        let newWidth = o.width;
+        let newHeight = o.height;
+
+        // Calculate new dimensions based on handle
+        if (handle?.includes('w')) {
+            const deltaX = worldCoords.x - anchor.x;
+            newWidth = Math.abs(deltaX);
+            newX = Math.min(worldCoords.x, anchor.x);
+        }
+        if (handle?.includes('e')) {
+            const deltaX = worldCoords.x - anchor.x;
+            newWidth = Math.abs(deltaX);
+            newX = Math.min(worldCoords.x, anchor.x);
+        }
+        if (handle?.includes('n')) {
+            const deltaY = worldCoords.y - anchor.y;
+            newHeight = Math.abs(deltaY);
+            newY = Math.min(worldCoords.y, anchor.y);
+        }
+        if (handle?.includes('s')) {
+            const deltaY = worldCoords.y - anchor.y;
+            newHeight = Math.abs(deltaY);
+            newY = Math.min(worldCoords.y, anchor.y);
+        }
+
+        // Maintain aspect ratio if shift is held
+        if (isShiftPressed && o.width > 0 && o.height > 0) {
+            const aspectRatio = o.width / o.height;
+            if (handle === 'n' || handle === 's') {
+                newWidth = newHeight * aspectRatio;
+            } else if (handle === 'e' || handle === 'w') {
+                newHeight = newWidth / aspectRatio;
+            } else {
+                // Corner handles
+                const proposedRatio = newWidth / newHeight;
+                if (proposedRatio > aspectRatio) {
+                    newHeight = newWidth / aspectRatio;
+                } else {
+                    newWidth = newHeight * aspectRatio;
+                }
+            }
+        }
+
+        // Snap to grid if Ctrl is held
+        if (this.interaction.isCtrlPressed) {
+            newX = snapToGrid(newX);
+            newY = snapToGrid(newY);
+            newWidth = snapToGrid(newWidth);
+            newHeight = snapToGrid(newHeight);
+        }
+
+        // Apply minimum size
+        if (newWidth < 10) newWidth = 10;
+        if (newHeight < 10) newHeight = 10;
+
+        // Update output area bounds temporarily for preview
+        this.canvas.outputAreaBounds = {
+            x: newX,
+            y: newY,
+            width: newWidth,
+            height: newHeight
+        };
+
+        this.canvas.render();
+    }
+
+    private updateOutputAreaTransformCursor(worldCoords: Point): void {
+        const handle = this.getOutputAreaHandle(worldCoords);
+        
+        if (handle) {
+            const cursorMap: { [key: string]: string } = {
+                'n': 'ns-resize', 's': 'ns-resize',
+                'e': 'ew-resize', 'w': 'ew-resize',
+                'nw': 'nwse-resize', 'se': 'nwse-resize',
+                'ne': 'nesw-resize', 'sw': 'nesw-resize',
+            };
+            this.canvas.canvas.style.cursor = cursorMap[handle] || 'default';
+        } else {
+            this.canvas.canvas.style.cursor = 'default';
+        }
+    }
+
+    private finalizeOutputAreaTransform(): void {
+        const bounds = this.canvas.outputAreaBounds;
+        
+        // Update canvas size and mask tool
+        this.canvas.updateOutputAreaSize(bounds.width, bounds.height);
+        
+        // Update mask canvas for new output area
+        this.canvas.maskTool.updateMaskCanvasForOutputArea();
+        
+        // Save state
+        this.canvas.saveState();
+        
+        // Reset transform handle but keep transform mode active
+        this.interaction.outputAreaTransformHandle = null;
+    }
+
 }

src/CanvasLayers.ts

@@ -100,6 +100,7 @@ export class CanvasLayers {
                 });
 
                 const img = new Image();
+                img.crossOrigin = 'anonymous';
                 img.onload = () => {
                     if (!this.canvas.node.imgs) {
                         this.canvas.node.imgs = [];
@@ -135,6 +136,142 @@ export class CanvasLayers {
         }
     }
 
+    /**
+     * Automatically adjust output area to fit selected layers
+     * Calculates precise bounding box for all selected layers including rotation and crop mode support
+     */
+    autoAdjustOutputToSelection(): boolean {
+        const selectedLayers = this.canvas.canvasSelection.selectedLayers;
+        if (selectedLayers.length === 0) {
+            return false;
+        }
+        
+        // Calculate bounding box of selected layers
+        let minX = Infinity, minY = Infinity, maxX = -Infinity, maxY = -Infinity;
+        
+        selectedLayers.forEach((layer: Layer) => {
+            // For crop mode layers, use the visible crop bounds
+            if (layer.cropMode && layer.cropBounds && layer.originalWidth && layer.originalHeight) {
+                const layerScaleX = layer.width / layer.originalWidth;
+                const layerScaleY = layer.height / layer.originalHeight;
+                
+                const cropWidth = layer.cropBounds.width * layerScaleX;
+                const cropHeight = layer.cropBounds.height * layerScaleY;
+                
+                const effectiveCropX = layer.flipH 
+                    ? layer.originalWidth - (layer.cropBounds.x + layer.cropBounds.width) 
+                    : layer.cropBounds.x;
+                const effectiveCropY = layer.flipV 
+                    ? layer.originalHeight - (layer.cropBounds.y + layer.cropBounds.height) 
+                    : layer.cropBounds.y;
+                
+                const cropOffsetX = effectiveCropX * layerScaleX;
+                const cropOffsetY = effectiveCropY * layerScaleY;
+                
+                const centerX = layer.x + layer.width / 2;
+                const centerY = layer.y + layer.height / 2;
+                
+                const rad = layer.rotation * Math.PI / 180;
+                const cos = Math.cos(rad);
+                const sin = Math.sin(rad);
+                
+                // Calculate corners of the crop rectangle
+                const corners = [
+                    { x: cropOffsetX, y: cropOffsetY },
+                    { x: cropOffsetX + cropWidth, y: cropOffsetY },
+                    { x: cropOffsetX + cropWidth, y: cropOffsetY + cropHeight },
+                    { x: cropOffsetX, y: cropOffsetY + cropHeight }
+                ];
+                
+                corners.forEach(p => {
+                    // Transform to layer space (centered)
+                    const localX = p.x - layer.width / 2;
+                    const localY = p.y - layer.height / 2;
+                    
+                    // Apply rotation
+                    const worldX = centerX + (localX * cos - localY * sin);
+                    const worldY = centerY + (localX * sin + localY * cos);
+                    
+                    minX = Math.min(minX, worldX);
+                    minY = Math.min(minY, worldY);
+                    maxX = Math.max(maxX, worldX);
+                    maxY = Math.max(maxY, worldY);
+                });
+            } else {
+                // For normal layers, use the full layer bounds
+                const centerX = layer.x + layer.width / 2;
+                const centerY = layer.y + layer.height / 2;
+                
+                const rad = layer.rotation * Math.PI / 180;
+                const cos = Math.cos(rad);
+                const sin = Math.sin(rad);
+                
+                const halfW = layer.width / 2;
+                const halfH = layer.height / 2;
+                
+                const corners = [
+                    { x: -halfW, y: -halfH },
+                    { x: halfW, y: -halfH },
+                    { x: halfW, y: halfH },
+                    { x: -halfW, y: halfH }
+                ];
+                
+                corners.forEach(p => {
+                    const worldX = centerX + (p.x * cos - p.y * sin);
+                    const worldY = centerY + (p.x * sin + p.y * cos);
+                    
+                    minX = Math.min(minX, worldX);
+                    minY = Math.min(minY, worldY);
+                    maxX = Math.max(maxX, worldX);
+                    maxY = Math.max(maxY, worldY);
+                });
+            }
+        });
+        
+        // Calculate new dimensions without padding for precise fit
+        const newWidth = Math.ceil(maxX - minX);
+        const newHeight = Math.ceil(maxY - minY);
+        
+        if (newWidth <= 0 || newHeight <= 0) {
+            log.error("Cannot calculate valid output area dimensions");
+            return false;
+        }
+        
+        // Update output area bounds
+        this.canvas.outputAreaBounds = {
+            x: minX,
+            y: minY,
+            width: newWidth,
+            height: newHeight
+        };
+        
+        // Update canvas dimensions
+        this.canvas.width = newWidth;
+        this.canvas.height = newHeight;
+        this.canvas.maskTool.resize(newWidth, newHeight);
+        this.canvas.canvas.width = newWidth;
+        this.canvas.canvas.height = newHeight;
+        
+        // Reset extensions
+        this.canvas.outputAreaExtensions = { top: 0, bottom: 0, left: 0, right: 0 };
+        this.canvas.outputAreaExtensionEnabled = false;
+        this.canvas.lastOutputAreaExtensions = { top: 0, bottom: 0, left: 0, right: 0 };
+        
+        // Update original canvas size and position
+        this.canvas.originalCanvasSize = { width: newWidth, height: newHeight };
+        this.canvas.originalOutputAreaPosition = { x: minX, y: minY };
+        
+        // Save state and render
+        this.canvas.render();
+        this.canvas.saveState();
+        
+        log.info(`Auto-adjusted output area to fit ${selectedLayers.length} selected layer(s)`, {
+            bounds: { x: minX, y: minY, width: newWidth, height: newHeight }
+        });
+        
+        return true;
+    }
+
     pasteLayers(): void {
         if (this.internalClipboard.length === 0) return;
         this.canvas.saveState();
@@ -266,6 +403,7 @@ export class CanvasLayers {
                     tempCtx.drawImage(maskCanvas, 0, 0);
     
                     const newImage = new Image();
+                    newImage.crossOrigin = 'anonymous';
                     newImage.src = tempCanvas.toDataURL();
                     layer.image = newImage;
                 }
@@ -864,6 +1002,7 @@ export class CanvasLayers {
 
         // Convert canvas to image
         const processedImage = new Image();
+        processedImage.crossOrigin = 'anonymous';
         processedImage.src = processedCanvas.toDataURL();
         return processedImage;
     }
@@ -1884,6 +2023,7 @@ export class CanvasLayers {
             this._drawLayers(tempCtx, this.canvas.canvasSelection.selectedLayers);
 
             const fusedImage = new Image();
+            fusedImage.crossOrigin = 'anonymous';
             fusedImage.src = tempCanvas.toDataURL();
             await new Promise((resolve, reject) => {
                 fusedImage.onload = resolve;

src/CanvasLayersPanel.ts

@@ -121,6 +121,7 @@ export class CanvasLayersPanel {
         this.container.tabIndex = 0; // Umożliwia fokus na panelu
         this.container.innerHTML = `
             <div class="layers-panel-header">
+                <div class="master-visibility-toggle" title="Toggle all layers visibility"></div>
                 <span class="layers-panel-title">Layers</span>
                 <div class="layers-panel-controls">
                     <button class="layers-btn" id="delete-layer-btn" title="Delete layer"></button>
@@ -135,6 +136,7 @@ export class CanvasLayersPanel {
         
         // Setup event listeners dla przycisków
         this.setupControlButtons();
+        this.setupMasterVisibilityToggle();
 
         // Dodaj listener dla klawiatury, aby usuwanie działało z panelu
         this.container.addEventListener('keydown', (e: KeyboardEvent) => {
@@ -169,6 +171,74 @@ export class CanvasLayersPanel {
         this.updateButtonStates();
     }
 
+    setupMasterVisibilityToggle(): void {
+        if (!this.container) return;
+        const toggleContainer = this.container.querySelector('.master-visibility-toggle') as HTMLElement;
+        if (!toggleContainer) return;
+
+        const updateToggleState = () => {
+            const total = this.canvas.layers.length;
+            const visibleCount = this.canvas.layers.filter(l => l.visible).length;
+            toggleContainer.innerHTML = '';
+
+            const checkboxContainer = document.createElement('div');
+            checkboxContainer.className = 'checkbox-container';
+
+            const checkbox = document.createElement('input');
+            checkbox.type = 'checkbox';
+            checkbox.id = 'master-visibility-checkbox';
+
+            const customCheckbox = document.createElement('span');
+            customCheckbox.className = 'custom-checkbox';
+
+            checkboxContainer.appendChild(checkbox);
+            checkboxContainer.appendChild(customCheckbox);
+
+            if (visibleCount === 0) {
+                checkbox.checked = false;
+                checkbox.indeterminate = false;
+                customCheckbox.classList.remove('checked', 'indeterminate');
+            } else if (visibleCount === total) {
+                checkbox.checked = true;
+                checkbox.indeterminate = false;
+                customCheckbox.classList.add('checked');
+                customCheckbox.classList.remove('indeterminate');
+            } else {
+                checkbox.checked = false;
+                checkbox.indeterminate = true;
+                customCheckbox.classList.add('indeterminate');
+                customCheckbox.classList.remove('checked');
+            }
+
+            checkboxContainer.addEventListener('click', (e) => {
+                e.stopPropagation();
+                let newVisible: boolean;
+                if (checkbox.indeterminate) {
+                    newVisible = false; // hide all when mixed
+                } else if (checkbox.checked) {
+                    newVisible = false; // toggle to hide all
+                } else {
+                    newVisible = true; // toggle to show all
+                }
+
+                this.canvas.layers.forEach(layer => {
+                    layer.visible = newVisible;
+                });
+                this.canvas.render();
+                this.canvas.requestSaveState();
+                updateToggleState();
+                this.renderLayers();
+            });
+
+            toggleContainer.appendChild(checkboxContainer);
+        };
+
+        updateToggleState();
+        this._updateMasterVisibilityToggle = updateToggleState;
+    }
+
+    private _updateMasterVisibilityToggle?: () => void;
+
     renderLayers(): void {
         if (!this.layersContainer) {
             log.warn('Layers container not initialized');
@@ -186,10 +256,11 @@ export class CanvasLayersPanel {
 
         sortedLayers.forEach((layer: Layer, index: number) => {
             const layerElement = this.createLayerElement(layer, index);
-            if(this.layersContainer)
+            if (this.layersContainer)
                 this.layersContainer.appendChild(layerElement);
         });
 
+        if (this._updateMasterVisibilityToggle) this._updateMasterVisibilityToggle();
         log.debug(`Rendered ${sortedLayers.length} layers`);
     }
 

src/CanvasRenderer.ts

@@ -195,6 +195,7 @@ export class CanvasRenderer {
         this.renderInteractionElements(ctx);
         this.canvas.shapeTool.render(ctx);
         this.drawMaskAreaBounds(ctx); // Draw mask area bounds when mask tool is active
+        this.renderOutputAreaTransformHandles(ctx); // Draw output area transform handles
         this.renderLayerInfo(ctx);
         
         // Update custom shape menu position and visibility
@@ -1011,4 +1012,46 @@ export class CanvasRenderer {
         // Just ensure it's the right size
         this.updateOverlaySize();
     }
+
+    /**
+     * Draw transform handles for output area when in transform mode
+     */
+    renderOutputAreaTransformHandles(ctx: any): void {
+        if (this.canvas.canvasInteractions.interaction.mode !== 'transformingOutputArea') {
+            return;
+        }
+
+        const bounds = this.canvas.outputAreaBounds;
+        const handleRadius = 5 / this.canvas.viewport.zoom;
+        
+        // Define handle positions
+        const handles = {
+            'nw': { x: bounds.x, y: bounds.y },
+            'n': { x: bounds.x + bounds.width / 2, y: bounds.y },
+            'ne': { x: bounds.x + bounds.width, y: bounds.y },
+            'e': { x: bounds.x + bounds.width, y: bounds.y + bounds.height / 2 },
+            'se': { x: bounds.x + bounds.width, y: bounds.y + bounds.height },
+            's': { x: bounds.x + bounds.width / 2, y: bounds.y + bounds.height },
+            'sw': { x: bounds.x, y: bounds.y + bounds.height },
+            'w': { x: bounds.x, y: bounds.y + bounds.height / 2 },
+        };
+
+        // Draw handles
+        ctx.fillStyle = '#ffffff';
+        ctx.strokeStyle = '#000000';
+        ctx.lineWidth = 1 / this.canvas.viewport.zoom;
+
+        for (const [name, pos] of Object.entries(handles)) {
+            ctx.beginPath();
+            ctx.arc(pos.x, pos.y, handleRadius, 0, Math.PI * 2);
+            ctx.fill();
+            ctx.stroke();
+        }
+
+        // Draw a highlight around the output area
+        ctx.strokeStyle = 'rgba(0, 150, 255, 0.8)';
+        ctx.lineWidth = 3 / this.canvas.viewport.zoom;
+        ctx.setLineDash([]);
+        ctx.strokeRect(bounds.x, bounds.y, bounds.width, bounds.height);
+    }
 }

src/CanvasState.ts

@@ -235,6 +235,7 @@ export class CanvasState {
     _createLayerFromSrc(layerData: Layer, imageSrc: string | ImageBitmap, index: number, resolve: (value: Layer | null) => void): void {
         if (typeof imageSrc === 'string') {
             const img = new Image();
+            img.crossOrigin = 'anonymous';
             img.onload = () => {
                 log.debug(`Layer ${index}: Image loaded successfully.`);
                 const newLayer: Layer = {...layerData, image: img};
@@ -250,6 +251,7 @@ export class CanvasState {
             if (ctx) {
                 ctx.drawImage(imageSrc, 0, 0);
                 const img = new Image();
+                img.crossOrigin = 'anonymous';
                 img.onload = () => {
                     log.debug(`Layer ${index}: Image loaded successfully from ImageBitmap.`);
                     const newLayer: Layer = {...layerData, image: img};
@@ -456,12 +458,13 @@ If you see dark images or masks in the output, make sure node_id is set to ${cor
 
         if (this.maskUndoStack.length > 0) {
             const prevState = this.maskUndoStack[this.maskUndoStack.length - 1];
-            const maskCanvas = this.canvas.maskTool.getMask();
+            
-            const maskCtx = maskCanvas.getContext('2d', { willReadFrequently: true });
+            // Use the new restoreMaskFromSavedState method that properly clears chunks first
-            if (maskCtx) {
+            this.canvas.maskTool.restoreMaskFromSavedState(prevState);
-                maskCtx.clearRect(0, 0, maskCanvas.width, maskCanvas.height);
+            
-                maskCtx.drawImage(prevState, 0, 0);
+            // Clear stroke overlay to prevent old drawing previews from persisting
-            }
+            this.canvas.canvasRenderer.clearMaskStrokeOverlay();
+            
             this.canvas.render();
         }
 
@@ -474,12 +477,13 @@ If you see dark images or masks in the output, make sure node_id is set to ${cor
         const nextState = this.maskRedoStack.pop();
         if (nextState) {
             this.maskUndoStack.push(nextState);
-            const maskCanvas = this.canvas.maskTool.getMask();
+            
-            const maskCtx = maskCanvas.getContext('2d', { willReadFrequently: true });
+            // Use the new restoreMaskFromSavedState method that properly clears chunks first
-            if (maskCtx) {
+            this.canvas.maskTool.restoreMaskFromSavedState(nextState);
-                maskCtx.clearRect(0, 0, maskCanvas.width, maskCanvas.height);
+            
-                maskCtx.drawImage(nextState, 0, 0);
+            // Clear stroke overlay to prevent old drawing previews from persisting
-            }
+            this.canvas.canvasRenderer.clearMaskStrokeOverlay();
+            
             this.canvas.render();
         }
         this.canvas.updateHistoryButtons();

src/CanvasView.ts

@@ -268,90 +268,32 @@ async function createCanvasWidget(node: ComfyNode, widget: any, app: ComfyApp):
 
             $el("div.painter-separator"),
             $el("div.painter-button-group", {}, [
+                $el("button.painter-button.requires-selection", {
+                    textContent: "Auto Adjust Output",
+                    title: "Automatically adjust output area to fit selected layers",
+                    onclick: () => {
+                        const selectedLayers = canvas.canvasSelection.selectedLayers;
+                        if (selectedLayers.length === 0) {
+                            showWarningNotification("Please select one or more layers first");
+                            return;
+                        }
+                        
+                        const success = canvas.canvasLayers.autoAdjustOutputToSelection();
+                        if (success) {
+                            const bounds = canvas.outputAreaBounds;
+                            showSuccessNotification(`Output area adjusted to ${bounds.width}x${bounds.height}px`);
+                        } else {
+                            showErrorNotification("Cannot calculate valid output area dimensions");
+                        }
+                    }
+                }),
                 $el("button.painter-button", {
                     textContent: "Output Area Size",
-                    title: "Set the size of the output area",
+                    title: "Transform output area - drag handles to resize",
                     onclick: () => {
-                        const dialog = $el("div.painter-dialog", {
+                        // Activate output area transform mode
-                            style: {
+                        canvas.canvasInteractions.activateOutputAreaTransform();
-                                position: 'fixed',
+                        showInfoNotification("Click and drag the handles to resize the output area. Click anywhere else to exit.", 3000);
-                                left: '50%',
-                                top: '50%',
-                                transform: 'translate(-50%, -50%)',
-                                zIndex: '9999'
-                            }
-                        }, [
-                            $el("div", {
-                                style: {
-                                    color: "white",
-                                    marginBottom: "10px"
-                                }
-                            }, [
-                                $el("label", {
-                                    style: {
-                                        marginRight: "5px"
-                                    }
-                                }, [
-                                    $el("span", {}, ["Width: "])
-                                ]),
-                                $el("input", {
-                                    type: "number",
-                                    id: "canvas-width",
-                                    value: String(canvas.width),
-                                    min: "1",
-                                    max: "4096"
-                                })
-                            ]),
-                            $el("div", {
-                                style: {
-                                    color: "white",
-                                    marginBottom: "10px"
-                                }
-                            }, [
-                                $el("label", {
-                                    style: {
-                                        marginRight: "5px"
-                                    }
-                                }, [
-                                    $el("span", {}, ["Height: "])
-                                ]),
-                                $el("input", {
-                                    type: "number",
-                                    id: "canvas-height",
-                                    value: String(canvas.height),
-                                    min: "1",
-                                    max: "4096"
-                                })
-                            ]),
-                            $el("div", {
-                                style: {
-                                    textAlign: "right"
-                                }
-                            }, [
-                                $el("button", {
-                                    id: "cancel-size",
-                                    textContent: "Cancel"
-                                }),
-                                $el("button", {
-                                    id: "confirm-size",
-                                    textContent: "OK"
-                                })
-                            ])
-                        ]);
-                        document.body.appendChild(dialog);
-
-                        (document.getElementById('confirm-size') as HTMLButtonElement).onclick = () => {
-                            const widthInput = document.getElementById('canvas-width') as HTMLInputElement;
-                            const heightInput = document.getElementById('canvas-height') as HTMLInputElement;
-                            const width = parseInt(widthInput.value) || canvas.width;
-                            const height = parseInt(heightInput.value) || canvas.height;
-                            canvas.setOutputAreaSize(width, height);
-                            document.body.removeChild(dialog);
-                        };
-
-                        (document.getElementById('cancel-size') as HTMLButtonElement).onclick = () => {
-                            document.body.removeChild(dialog);
-                        };
                     }
                 }),
                 $el("button.painter-button.requires-selection", {
@@ -1029,7 +971,9 @@ $el("label.clipboard-switch.mask-switch", {
         }
     }, [controlPanel, canvasContainer, layersPanelContainer]) as HTMLDivElement;
 
-    node.addDOMWidget("mainContainer", "widget", mainContainer);
+    if (node.addDOMWidget) {
+        node.addDOMWidget("mainContainer", "widget", mainContainer);
+    }
 
     const openEditorBtn = controlPanel.querySelector(`#open-editor-btn-${node.id}`) as HTMLButtonElement;
     let backdrop: HTMLDivElement | null = null;
@@ -1141,7 +1085,12 @@ $el("label.clipboard-switch.mask-switch", {
     if (!(window as any).canvasExecutionStates) {
         (window as any).canvasExecutionStates = new Map<string, any>();
     }
-    (node as any).canvasWidget = canvas;
+    
+    // Store the entire widget object, not just the canvas
+    (node as any).canvasWidget = {
+        canvas: canvas,
+        panel: controlPanel
+    };
 
     setTimeout(() => {
         canvas.loadInitialState();
@@ -1163,7 +1112,7 @@ $el("label.clipboard-switch.mask-switch", {
                 canvas.setPreviewVisibility(value);
             }
 
-            if ((node as any).graph && (node as any).graph.canvas) {
+            if ((node as any).graph && (node as any).graph.canvas && node.setDirtyCanvas) {
                 node.setDirtyCanvas(true, true);
             }
         };
@@ -1255,10 +1204,156 @@ app.registerExtension({
                 const canvasWidget = await createCanvasWidget(this, null, app);
                 canvasNodeInstances.set(this.id, canvasWidget);
                 log.info(`Registered CanvasNode instance for ID: ${this.id}`);
+                
+                // Store the canvas widget on the node
+                (this as any).canvasWidget = canvasWidget;
 
+                // Check if there are already connected inputs
                 setTimeout(() => {
-                    this.setDirtyCanvas(true, true);
+                        if (this.inputs && this.inputs.length > 0) {
-                }, 100);
+                            // Check if input_image (index 0) is connected
+                            if (this.inputs[0] && this.inputs[0].link) {
+                                log.info("Input image already connected on node creation, checking for data...");
+                                if (canvasWidget.canvas && canvasWidget.canvas.canvasIO) {
+                                    canvasWidget.canvas.inputDataLoaded = false;
+                                    // Only allow images on init; mask should load only on mask connect or execution
+                                    canvasWidget.canvas.canvasIO.checkForInputData({ allowImage: true, allowMask: false, reason: "init_image_connected" });
+                                }
+                            }
+                        }
+                    if (this.setDirtyCanvas) {
+                        this.setDirtyCanvas(true, true);
+                    }
+                }, 500);
+            };
+
+            // Add onConnectionsChange handler to detect when inputs are connected
+            nodeType.prototype.onConnectionsChange = function (this: ComfyNode, type: number, index: number, connected: boolean, link_info: any) {
+                log.info(`onConnectionsChange called: type=${type}, index=${index}, connected=${connected}`, link_info);
+                
+                // Check if this is an input connection (type 1 = INPUT)
+                if (type === 1) {
+                    // Get the canvas widget - it might be in different places
+                    const canvasWidget = (this as any).canvasWidget;
+                    const canvas = canvasWidget?.canvas || canvasWidget;
+                    
+                    if (!canvas || !canvas.canvasIO) {
+                        log.warn("Canvas not ready in onConnectionsChange, scheduling retry...");
+                        // Retry multiple times with increasing delays
+                        const retryDelays = [500, 1000, 2000];
+                        let retryCount = 0;
+                        
+                        const tryAgain = () => {
+                            const retryCanvas = (this as any).canvasWidget?.canvas || (this as any).canvasWidget;
+                            if (retryCanvas && retryCanvas.canvasIO) {
+                                log.info("Canvas now ready, checking for input data...");
+                                if (connected) {
+                                    retryCanvas.inputDataLoaded = false;
+                                    // Respect which input triggered the connection:
+                                    const opts = (index === 1)
+                                        ? { allowImage: false, allowMask: true, reason: "mask_connect" }
+                                        : { allowImage: true, allowMask: false, reason: "image_connect" };
+                                    retryCanvas.canvasIO.checkForInputData(opts);
+                                }
+                            } else if (retryCount < retryDelays.length) {
+                                log.warn(`Canvas still not ready, retry ${retryCount + 1}/${retryDelays.length}...`);
+                                setTimeout(tryAgain, retryDelays[retryCount++]);
+                            } else {
+                                log.error("Canvas failed to initialize after multiple retries");
+                            }
+                        };
+                        
+                        setTimeout(tryAgain, retryDelays[retryCount++]);
+                        return;
+                    }
+
+                    // Handle input_image connection (index 0)
+                    if (index === 0) {
+                        if (connected && link_info) {
+                            log.info("Input image connected, marking for data check...");
+                            // Reset the input data loaded flag to allow loading the new connection
+                            canvas.inputDataLoaded = false;
+                            // Also reset the last loaded image source and link ID to allow the new image
+                            canvas.lastLoadedImageSrc = undefined;
+                            canvas.lastLoadedLinkId = undefined;
+                            // Mark that we have a pending input connection
+                            canvas.hasPendingInputConnection = true;
+
+                            // If mask input is not connected and a mask was auto-applied from input_mask before, clear it now
+                            if (!(this.inputs && this.inputs[1] && this.inputs[1].link)) {
+                                if ((canvas as any).maskAppliedFromInput && canvas.maskTool) {
+                                    canvas.maskTool.clear();
+                                    canvas.render();
+                                    (canvas as any).maskAppliedFromInput = false;
+                                    canvas.lastLoadedMaskLinkId = undefined;
+                                    log.info("Cleared auto-applied mask because input_image connected without input_mask");
+                                }
+                            }
+
+                            // Check for data immediately when connected
+                            setTimeout(() => {
+                                log.info("Checking for input data after connection...");
+                                // Only load images here; masks should not auto-load on image connect
+                                canvas.canvasIO.checkForInputData({ allowImage: true, allowMask: false, reason: "image_connect" });
+                            }, 500);
+                        } else {
+                            log.info("Input image disconnected");
+                            canvas.hasPendingInputConnection = false;
+                            // Reset when disconnected so a new connection can load
+                            canvas.inputDataLoaded = false;
+                            canvas.lastLoadedImageSrc = undefined;
+                            canvas.lastLoadedLinkId = undefined;
+                        }
+                    }
+                    
+                    // Handle input_mask connection (index 1)
+                    if (index === 1) {
+                        if (connected && link_info) {
+                            log.info("Input mask connected");
+                            
+                            // DON'T clear existing mask when connecting a new input
+                            // Reset the loaded mask link ID to allow loading from the new connection
+                            canvas.lastLoadedMaskLinkId = undefined;
+                            
+                            // Mark that we have a pending mask connection
+                            canvas.hasPendingMaskConnection = true;
+                            // Check for data immediately when connected
+                            setTimeout(() => {
+                                log.info("Checking for input data after mask connection...");
+                                // Only load mask here if it's immediately available from the connected node
+                                // Don't load stale masks from backend storage
+                                canvas.canvasIO.checkForInputData({ allowImage: false, allowMask: true, reason: "mask_connect" });
+                            }, 500);
+                        } else {
+                            log.info("Input mask disconnected");
+                            canvas.hasPendingMaskConnection = false;
+                            // If the current mask came from input_mask, clear it to avoid affecting images when mask is not connected
+                            if ((canvas as any).maskAppliedFromInput && canvas.maskTool) {
+                                (canvas as any).maskAppliedFromInput = false;
+                                canvas.lastLoadedMaskLinkId = undefined;
+                                log.info("Cleared auto-applied mask due to mask input disconnection");
+                            }
+                        }
+                    }
+                }
+            };
+
+            // Add onExecuted handler to check for input data after workflow execution
+            const originalOnExecuted = nodeType.prototype.onExecuted;
+            nodeType.prototype.onExecuted = function (this: ComfyNode, message: any) {
+                log.info("Node executed, checking for input data...");
+                
+                const canvas = (this as any).canvasWidget?.canvas || (this as any).canvasWidget;
+                if (canvas && canvas.canvasIO) {
+                    // Don't reset inputDataLoaded - just check for new data
+                    // On execution we allow both image and mask to load
+                    canvas.canvasIO.checkForInputData({ allowImage: true, allowMask: true, reason: "execution" });
+                }
+                
+                // Call original if it exists
+                if (originalOnExecuted) {
+                    originalOnExecuted.apply(this, arguments as any);
+                }
             };
 
             const onRemoved = nodeType.prototype.onRemoved;
@@ -1395,8 +1490,8 @@ app.registerExtension({
                         callback: async () => {
                             try {
                                 log.info("Opening LayerForge canvas in MaskEditor");
-                                if ((self as any).canvasWidget && (self as any).canvasWidget.startMaskEditor) {
+                                if ((self as any).canvasWidget && (self as any).canvasWidget.canvas) {
-                                    await (self as any).canvasWidget.startMaskEditor(null, true);
+                                    await (self as any).canvasWidget.canvas.startMaskEditor(null, true);
                                 } else {
                                     log.error("Canvas widget not available");
                                     showErrorNotification("Canvas not ready. Please try again.");
@@ -1411,8 +1506,8 @@ app.registerExtension({
                         content: "Open Image",
                         callback: async () => {
                             try {
-                                if (!(self as any).canvasWidget) return;
+                                if (!(self as any).canvasWidget || !(self as any).canvasWidget.canvas) return;
-                                const blob = await (self as any).canvasWidget.getFlattenedCanvasAsBlob();
+                                const blob = await (self as any).canvasWidget.canvas.canvasLayers.getFlattenedCanvasAsBlob();
                                 if (!blob) return;
                                 const url = URL.createObjectURL(blob);
                                 window.open(url, '_blank');
@@ -1426,8 +1521,8 @@ app.registerExtension({
                         content: "Open Image with Mask Alpha",
                         callback: async () => {
                             try {
-                                if (!(self as any).canvasWidget) return;
+                                if (!(self as any).canvasWidget || !(self as any).canvasWidget.canvas) return;
-                                const blob = await (self as any).canvasWidget.getFlattenedCanvasWithMaskAsBlob();
+                                const blob = await (self as any).canvasWidget.canvas.canvasLayers.getFlattenedCanvasWithMaskAsBlob();
                                 if (!blob) return;
                                 const url = URL.createObjectURL(blob);
                                 window.open(url, '_blank');
@@ -1441,8 +1536,8 @@ app.registerExtension({
                         content: "Copy Image",
                         callback: async () => {
                             try {
-                                if (!(self as any).canvasWidget) return;
+                                if (!(self as any).canvasWidget || !(self as any).canvasWidget.canvas) return;
-                                const blob = await (self as any).canvasWidget.getFlattenedCanvasAsBlob();
+                                const blob = await (self as any).canvasWidget.canvas.canvasLayers.getFlattenedCanvasAsBlob();
                                 if (!blob) return;
                                 const item = new ClipboardItem({'image/png': blob});
                                 await navigator.clipboard.write([item]);
@@ -1457,8 +1552,8 @@ app.registerExtension({
                         content: "Copy Image with Mask Alpha",
                         callback: async () => {
                             try {
-                                if (!(self as any).canvasWidget) return;
+                                if (!(self as any).canvasWidget || !(self as any).canvasWidget.canvas) return;
-                                const blob = await (self as any).canvasWidget.getFlattenedCanvasWithMaskAsBlob();
+                                const blob = await (self as any).canvasWidget.canvas.canvasLayers.getFlattenedCanvasWithMaskAsBlob();
                                 if (!blob) return;
                                 const item = new ClipboardItem({'image/png': blob});
                                 await navigator.clipboard.write([item]);
@@ -1473,8 +1568,8 @@ app.registerExtension({
                         content: "Save Image",
                         callback: async () => {
                             try {
-                                if (!(self as any).canvasWidget) return;
+                                if (!(self as any).canvasWidget || !(self as any).canvasWidget.canvas) return;
-                                const blob = await (self as any).canvasWidget.getFlattenedCanvasAsBlob();
+                                const blob = await (self as any).canvasWidget.canvas.canvasLayers.getFlattenedCanvasAsBlob();
                                 if (!blob) return;
                                 const url = URL.createObjectURL(blob);
                                 const a = document.createElement('a');
@@ -1493,8 +1588,8 @@ app.registerExtension({
                         content: "Save Image with Mask Alpha",
                         callback: async () => {
                             try {
-                                if (!(self as any).canvasWidget) return;
+                                if (!(self as any).canvasWidget || !(self as any).canvasWidget.canvas) return;
-                                const blob = await (self as any).canvasWidget.getFlattenedCanvasWithMaskAsBlob();
+                                const blob = await (self as any).canvasWidget.canvas.canvasLayers.getFlattenedCanvasWithMaskAsBlob();
                                 if (!blob) return;
                                 const url = URL.createObjectURL(blob);
                                 const a = document.createElement('a');

src/MaskEditorIntegration.ts

@@ -507,7 +507,6 @@ export class MaskEditorIntegration {
             maskSize: {width: bounds.width, height: bounds.height}
         });
 
-        // Use the chunk system instead of direct canvas manipulation
         this.maskTool.setMask(maskAsImage);
 
         // Update node preview using PreviewUtils

src/MaskTool.ts

@@ -1674,6 +1674,27 @@ export class MaskTool {
         log.info("Cleared all mask data from all chunks");
     }
 
+    /**
+     * Clears all chunks and restores mask from saved state
+     * This is used during undo/redo operations to ensure clean state restoration
+     */
+    restoreMaskFromSavedState(savedMaskCanvas: HTMLCanvasElement): void {
+        // First, clear ALL chunks to ensure no leftover data
+        this.clearAllMaskChunks();
+        
+        // Now apply the saved mask state to chunks
+        if (savedMaskCanvas.width > 0 && savedMaskCanvas.height > 0) {
+            // Apply the saved mask to the chunk system at the correct position
+            const bounds = this.canvasInstance.outputAreaBounds;
+            this.applyMaskCanvasToChunks(savedMaskCanvas, this.x, this.y);
+        }
+        
+        // Update the active mask canvas to show the restored state
+        this.updateActiveMaskCanvas(true);
+        
+        log.debug("Restored mask from saved state with clean chunk system");
+    }
+
     getMask(): HTMLCanvasElement {
         // Return the current active mask canvas which shows all chunks
         // Only update if there are pending changes to avoid unnecessary redraws
@@ -1793,15 +1814,47 @@ export class MaskTool {
         log.info(`Mask overlay visibility toggled to: ${this.isOverlayVisible}`);
     }
 
-    setMask(image: HTMLImageElement): void {
+    setMask(image: HTMLImageElement, isFromInputMask: boolean = false): void {
-        // Clear existing mask chunks in the output area first
         const bounds = this.canvasInstance.outputAreaBounds;
-        this.clearMaskInArea(bounds.x, bounds.y, image.width, image.height);
         
-        // Add the new mask using the chunk system
+        if (isFromInputMask) {
-        this.addMask(image);
+            // For INPUT MASK - process black background to transparent using luminance
-        
+            // Center like input images
-        log.info(`MaskTool set new mask using chunk system at bounds (${bounds.x}, ${bounds.y})`);
+            const centerX = bounds.x + (bounds.width - image.width) / 2;
+            const centerY = bounds.y + (bounds.height - image.height) / 2;
+            
+            // Prepare mask where alpha = luminance (white = applied, black = transparent)
+            const { canvas: maskCanvas, ctx } = createCanvas(image.width, image.height, '2d', { willReadFrequently: true });
+            if (!ctx) throw new Error("Could not create mask processing context");
+            ctx.drawImage(image, 0, 0);
+            
+            const imgData = ctx.getImageData(0, 0, image.width, image.height);
+            const data = imgData.data;
+            for (let i = 0; i < data.length; i += 4) {
+                const r = data[i], g = data[i + 1], b = data[i + 2];
+                const lum = Math.round(0.299 * r + 0.587 * g + 0.114 * b);
+                data[i] = 255;        // force white color (color channels ignored downstream)
+                data[i + 1] = 255;
+                data[i + 2] = 255;
+                data[i + 3] = lum;    // alpha encodes mask strength: white -> strong, black -> 0
+            }
+            ctx.putImageData(imgData, 0, 0);
+            
+            // Clear target area and apply to chunked system at centered position
+            this.clearMaskInArea(centerX, centerY, image.width, image.height);
+            this.applyMaskCanvasToChunks(maskCanvas, centerX, centerY);
+            
+            // Refresh state and UI
+            this.updateActiveMaskCanvas(true);
+            this.canvasInstance.canvasState.saveMaskState();
+            this.canvasInstance.render();
+            log.info(`MaskTool set INPUT MASK at centered position (${centerX}, ${centerY}) using luminance as alpha`);
+        } else {
+            // For SAM Detector and other sources - just clear and add without processing
+            this.clearMaskInArea(bounds.x, bounds.y, bounds.width, bounds.height);
+            this.addMask(image);
+            log.info(`MaskTool set mask using chunk system at bounds (${bounds.x}, ${bounds.y})`);
+        }
     }
 
     /**

src/SAMDetectorIntegration.ts

@@ -282,36 +282,61 @@ async function handleSAMDetectorResult(node: ComfyNode, resultImage: HTMLImageEl
                 log.debug("Attempting to reload SAM result image");
                 const originalSrc = resultImage.src;
                 
-                // Add cache-busting parameter to force fresh load
+                // Check if it's a data URL (base64) - don't add parameters to data URLs
-                const url = new URL(originalSrc);
+                if (originalSrc.startsWith('data:')) {
-                url.searchParams.set('_t', Date.now().toString());
+                    log.debug("Image is a data URL, skipping reload with parameters");
-                
+                    // For data URLs, just ensure the image is loaded
-                await new Promise((resolve, reject) => {
+                    if (!resultImage.complete || resultImage.naturalWidth === 0) {
-                    const img = new Image();
+                        await new Promise((resolve, reject) => {
-                    img.crossOrigin = "anonymous";
+                            const img = new Image();
-                    img.onload = () => {
+                            img.onload = () => {
-                        // Copy the loaded image data to the original image
+                                resultImage.width = img.width;
-                        resultImage.src = img.src;
+                                resultImage.height = img.height;
-                        resultImage.width = img.width;
+                                log.debug("Data URL image loaded successfully", {
-                        resultImage.height = img.height;
+                                    width: img.width,
-                        log.debug("SAM result image reloaded successfully", {
+                                    height: img.height
-                            width: img.width,
+                                });
-                            height: img.height,
+                                resolve(img);
-                            originalSrc: originalSrc,
+                            };
-                            newSrc: img.src
+                            img.onerror = (error) => {
+                                log.error("Failed to load data URL image", error);
+                                reject(error);
+                            };
+                            img.src = originalSrc; // Use original src without modifications
                         });
-                        resolve(img);
+                    }
-                    };
+                } else {
-                    img.onerror = (error) => {
+                    // For regular URLs, add cache-busting parameter
-                        log.error("Failed to reload SAM result image", {
+                    const url = new URL(originalSrc);
-                            originalSrc: originalSrc,
+                    url.searchParams.set('_t', Date.now().toString());
-                            newSrc: url.toString(),
+                    
-                            error: error
+                    await new Promise((resolve, reject) => {
-                        });
+                        const img = new Image();
-                        reject(error);
+                        img.crossOrigin = "anonymous";
-                    };
+                        img.onload = () => {
-                    img.src = url.toString();
+                            // Copy the loaded image data to the original image
-                });
+                            resultImage.src = img.src;
+                            resultImage.width = img.width;
+                            resultImage.height = img.height;
+                            log.debug("SAM result image reloaded successfully", {
+                                width: img.width,
+                                height: img.height,
+                                originalSrc: originalSrc,
+                                newSrc: img.src
+                            });
+                            resolve(img);
+                        };
+                        img.onerror = (error) => {
+                            log.error("Failed to reload SAM result image", {
+                                originalSrc: originalSrc,
+                                newSrc: url.toString(),
+                                error: error
+                            });
+                            reject(error);
+                        };
+                        img.src = url.toString();
+                    });
+                }
             }
         } catch (error) {
             log.error("Failed to load image from SAM Detector.", error);
@@ -333,32 +358,43 @@ async function handleSAMDetectorResult(node: ComfyNode, resultImage: HTMLImageEl
         // Apply mask to LayerForge canvas using MaskTool.setMask method
         log.debug("Checking canvas and maskTool availability", {
             hasCanvas: !!canvas,
+            hasCanvasProperty: !!canvas.canvas,
+            canvasCanvasKeys: canvas.canvas ? Object.keys(canvas.canvas) : [],
             hasMaskTool: !!canvas.maskTool,
+            hasCanvasMaskTool: !!(canvas.canvas && canvas.canvas.maskTool),
             maskToolType: typeof canvas.maskTool,
+            canvasMaskToolType: canvas.canvas ? typeof canvas.canvas.maskTool : 'undefined',
             canvasKeys: Object.keys(canvas)
         });
 
-        if (!canvas.maskTool) {
+        // Get the actual Canvas object and its maskTool
+        const actualCanvas = canvas.canvas || canvas;
+        const maskTool = actualCanvas.maskTool;
+
+        if (!maskTool) {
             log.error("MaskTool is not available. Canvas state:", {
                 hasCanvas: !!canvas,
+                hasActualCanvas: !!actualCanvas,
                 canvasConstructor: canvas.constructor.name,
+                actualCanvasConstructor: actualCanvas ? actualCanvas.constructor.name : 'undefined',
                 canvasKeys: Object.keys(canvas),
-                maskToolValue: canvas.maskTool
+                actualCanvasKeys: actualCanvas ? Object.keys(actualCanvas) : [],
+                maskToolValue: maskTool
             });
             throw new Error("Mask tool not available or not initialized");
         }
 
-        log.debug("Applying SAM mask to canvas using addMask method");
+        log.debug("Applying SAM mask to canvas using setMask method");
 
-        // Use the addMask method which overlays on existing mask without clearing it
+        // Use the setMask method which clears existing mask and sets new one
-        canvas.maskTool.addMask(maskAsImage);
+        maskTool.setMask(maskAsImage);
 
         // Update canvas and save state (same as MaskEditorIntegration)
-        canvas.render();
+        actualCanvas.render();
-        canvas.saveState();
+        actualCanvas.saveState();
 
         // Update node preview using PreviewUtils
-        await updateNodePreview(canvas, node, true);
+        await updateNodePreview(actualCanvas, node, true);
 
         log.info("SAM Detector mask applied successfully to LayerForge canvas");
 
@@ -399,15 +435,23 @@ export function setupSAMDetectorHook(node: ComfyNode, options: any[]) {
                     log.info("Intercepted 'Open in SAM Detector' - automatically sending to clipspace and starting monitoring");
                     
                     // Automatically send canvas to clipspace and start monitoring
-                    if ((node as any).canvasWidget && (node as any).canvasWidget.canvas) {
+                    if ((node as any).canvasWidget) {
-                        const canvas = (node as any).canvasWidget; // canvasWidget IS the Canvas object
+                        const canvasWidget = (node as any).canvasWidget;
+                        const canvas = canvasWidget.canvas || canvasWidget; // Get actual Canvas object
                         
-                        // Use ImageUploadUtils to upload canvas
+                        // Use ImageUploadUtils to upload canvas and get server URL (Impact Pack compatibility)
                         const uploadResult = await uploadCanvasAsImage(canvas, {
                             filenamePrefix: 'layerforge-sam',
                             nodeId: node.id
                         });
 
+                        log.debug("Uploaded canvas for SAM Detector", {
+                            filename: uploadResult.filename,
+                            imageUrl: uploadResult.imageUrl,
+                            width: uploadResult.imageElement.width,
+                            height: uploadResult.imageElement.height
+                        });
+
                         // Set the image to the node for clipspace
                         node.imgs = [uploadResult.imageElement];
                         (node as any).clipspaceImg = uploadResult.imageElement;

src/css/canvas_view.css

@@ -638,7 +638,7 @@
     width: 100vw;
     height: 100vh;
     background-color: rgba(0, 0, 0, 0.8);
-    z-index: 111;
+    z-index: 999999;
     display: flex;
     align-items: center;
     justify-content: center;

src/css/layers_panel.css

@@ -23,6 +23,85 @@
     margin-bottom: 8px;
 }
 
+.checkbox-container {
+    display: flex;
+    align-items: center;
+    gap: 8px;
+    padding: 5px 0;
+    border-radius: 5px;
+    cursor: pointer;
+    transition: background-color 0.2s;
+    position: relative;
+}
+
+.checkbox-container:hover {
+    background-color: #4a4a4a;
+}
+
+.checkbox-container input[type="checkbox"] {
+    position: absolute;
+    opacity: 0;
+    cursor: pointer;
+    height: 0;
+    width: 0;
+}
+
+.checkbox-container .custom-checkbox {
+    height: 16px;
+    width: 16px;
+    background-color: #2a2a2a;
+    border: 1px solid #666;
+    border-radius: 3px;
+    transition: all 0.2s;
+    position: relative;
+    flex-shrink: 0;
+}
+
+.checkbox-container input:checked ~ .custom-checkbox {
+    background-color: #3a76d6;
+    border-color: #3a76d6;
+}
+
+.checkbox-container .custom-checkbox::after {
+    content: "";
+    position: absolute;
+    display: none;
+    left: 5px;
+    top: 1px;
+    width: 4px;
+    height: 9px;
+    border: solid white;
+    border-width: 0 2px 2px 0;
+    transform: rotate(45deg);
+}
+
+.checkbox-container input:checked ~ .custom-checkbox::after {
+    display: block;
+}
+
+.checkbox-container input:indeterminate ~ .custom-checkbox {
+    background-color: #3a76d6;
+    border-color: #3a76d6;
+}
+
+.checkbox-container input:indeterminate ~ .custom-checkbox::after {
+    display: block;
+    content: "";
+    position: absolute;
+    top: 7px;
+    left: 3px;
+    width: 8px;
+    height: 2px;
+    background-color: white;
+    border: none;
+    transform: none;
+    box-shadow: none;
+}
+
+.checkbox-container:hover {
+    background-color: #4a4a4a;
+}
+
 .layers-panel-title {
     font-weight: bold;
     color: #ffffff;

src/types.ts

@@ -1,6 +1,14 @@
 import type { Canvas as CanvasClass } from './Canvas';
 import type { CanvasLayers } from './CanvasLayers';
 
+export interface ComfyWidget {
+    name: string;
+    type: string;
+    value: any;
+    callback?: (value: any) => void;
+    options?: any;
+}
+
 export interface Layer {
     id: string;
     image: HTMLImageElement;
@@ -32,15 +40,16 @@ export interface Layer {
 
 export interface ComfyNode {
     id: number;
+    type: string;
+    widgets: ComfyWidget[];
     imgs?: HTMLImageElement[];
-    widgets: any[];
+    size?: [number, number];
-    size: [number, number];
-    graph: any;
-    canvasWidget?: any;
     onResize?: () => void;
-    addDOMWidget: (name: string, type: string, element: HTMLElement, options?: any) => any;
+    setDirtyCanvas?: (dirty: boolean, propagate: boolean) => void;
-    addWidget: (type: string, name: string, value: any, callback?: (value: any) => void, options?: any) => any;
+    graph?: any;
-    setDirtyCanvas: (force: boolean, dirty: boolean) => void;
+    onRemoved?: () => void;
+    addDOMWidget?: (name: string, type: string, element: HTMLElement) => void;
+    inputs?: Array<{ link: any }>;
 }
 
 declare global {
@@ -79,8 +88,14 @@ export interface Canvas {
     imageCache: any;
     dataInitialized: boolean;
     pendingDataCheck: number | null;
+    pendingInputDataCheck: number | null;
     pendingBatchContext: any;
     canvasLayers: any;
+    inputDataLoaded: boolean;
+    lastLoadedLinkId: any;
+    lastLoadedMaskLinkId: any;
+    lastLoadedImageSrc?: string;
+    outputAreaBounds: OutputAreaBounds;
     saveState: () => void;
     render: () => void;
     updateSelection: (layers: Layer[]) => void;

src/utils/ClipboardManager.ts

@@ -1,5 +1,5 @@
 import {createModuleLogger} from "./LoggerUtils.js";
-import { showNotification, showInfoNotification } from "./NotificationUtils.js";
+import { showNotification, showInfoNotification, showErrorNotification, showWarningNotification } from "./NotificationUtils.js";
 import { withErrorHandling, createValidationError, createNetworkError, createFileError } from "../ErrorHandler.js";
 import { safeClipspacePaste } from "./ClipspaceUtils.js";
 
@@ -34,6 +34,7 @@ export class ClipboardManager {
         if (this.canvas.canvasLayers.internalClipboard.length > 0) {
             log.info("Found layers in internal clipboard, pasting layers");
             this.canvas.canvasLayers.pasteLayers();
+            showInfoNotification("Layers pasted from internal clipboard");
             return true;
         }
 
@@ -44,10 +45,22 @@ export class ClipboardManager {
                 return true;
             }
             log.info("No image found in ComfyUI Clipspace");
+            // Don't show error here, will try system clipboard next
         }
 
         log.info("Attempting paste from system clipboard");
-        return await this.trySystemClipboardPaste(addMode);
+        const systemSuccess = await this.trySystemClipboardPaste(addMode);
+        
+        if (!systemSuccess) {
+            // No valid image found in any clipboard
+            if (preference === 'clipspace') {
+                showWarningNotification("No valid image found in Clipspace or system clipboard");
+            } else {
+                showWarningNotification("No valid image found in clipboard");
+            }
+        }
+        
+        return systemSuccess;
     }, 'ClipboardManager.handlePaste');
 
     /**
@@ -72,6 +85,7 @@ export class ClipboardManager {
                 const img = new Image();
                 img.onload = async () => {
                     await this.canvas.canvasLayers.addLayerWithImage(img, {}, addMode);
+                    showInfoNotification("Image pasted from Clipspace");
                 };
                 img.src = clipspaceImage.src;
                 return true;
@@ -105,6 +119,7 @@ export class ClipboardManager {
                                 img.onload = async () => {
                                     log.info("Successfully loaded image from system clipboard");
                                     await this.canvas.canvasLayers.addLayerWithImage(img, {}, addMode);
+                                    showInfoNotification("Image pasted from system clipboard");
                                 };
                                 if (event.target?.result) {
                                     img.src = event.target.result as string;
@@ -148,11 +163,22 @@ export class ClipboardManager {
                 const text = await navigator.clipboard.readText();
                 log.debug("Found text in clipboard:", text);
                 
-                if (text && this.isValidImagePath(text)) {
+                if (text) {
-                    log.info("Found valid image path in clipboard:", text);
+                    // Check if it's a data URI (base64 encoded image)
-                    const success = await this.loadImageFromPath(text, addMode);
+                    if (this.isDataURI(text)) {
-                    if (success) {
+                        log.info("Found data URI in clipboard");
-                        return true;
+                        const success = await this.loadImageFromDataURI(text, addMode);
+                        if (success) {
+                            return true;
+                        }
+                    }
+                    // Check if it's a regular file path or URL
+                    else if (this.isValidImagePath(text)) {
+                        log.info("Found valid image path in clipboard:", text);
+                        const success = await this.loadImageFromPath(text, addMode);
+                        if (success) {
+                            return true;
+                        }
                     }
                 }
             } catch (error) {
@@ -165,6 +191,50 @@ export class ClipboardManager {
     }
 
 
+    /**
+     * Checks if a text string is a data URI (base64 encoded image)
+     * @param {string} text - The text to check
+     * @returns {boolean} - True if the text is a data URI
+     */
+    isDataURI(text: string): boolean {
+        if (!text || typeof text !== 'string') {
+            return false;
+        }
+        
+        // Check if it starts with data:image
+        return text.trim().startsWith('data:image/');
+    }
+
+    /**
+     * Loads an image from a data URI (base64 encoded image)
+     * @param {string} dataURI - The data URI to load
+     * @param {AddMode} addMode - The mode for adding the layer
+     * @returns {Promise<boolean>} - True if successful, false otherwise
+     */
+    async loadImageFromDataURI(dataURI: string, addMode: AddMode): Promise<boolean> {
+        return new Promise((resolve) => {
+            try {
+                const img = new Image();
+                img.onload = async () => {
+                    log.info("Successfully loaded image from data URI");
+                    await this.canvas.canvasLayers.addLayerWithImage(img, {}, addMode);
+                    showInfoNotification("Image pasted from clipboard (base64)");
+                    resolve(true);
+                };
+                img.onerror = () => {
+                    log.warn("Failed to load image from data URI");
+                    showErrorNotification("Failed to load base64 image from clipboard", 5000, true);
+                    resolve(false);
+                };
+                img.src = dataURI;
+            } catch (error) {
+                log.error("Error loading data URI:", error);
+                showErrorNotification("Error processing base64 image from clipboard", 5000, true);
+                resolve(false);
+            }
+        });
+    }
+
     /**
      * Validates if a text string is a valid image file path or URL
      * @param {string} text - The text to validate
@@ -240,15 +310,17 @@ export class ClipboardManager {
                 const img = new Image();
                 img.crossOrigin = 'anonymous';
                 return new Promise((resolve) => {
-                    img.onload = async () => {
+            img.onload = async () => {
-                        log.info("Successfully loaded image from URL");
+                log.info("Successfully loaded image from URL");
-                        await this.canvas.canvasLayers.addLayerWithImage(img, {}, addMode);
+                await this.canvas.canvasLayers.addLayerWithImage(img, {}, addMode);
-                        resolve(true);
+                showInfoNotification("Image loaded from URL");
-                    };
+                resolve(true);
-                    img.onerror = () => {
+            };
-                        log.warn("Failed to load image from URL:", filePath);
+            img.onerror = () => {
-                        resolve(false);
+                log.warn("Failed to load image from URL:", filePath);
-                    };
+                showErrorNotification(`Failed to load image from URL\nThe link might be incorrect or may not point to an image file.: ${filePath}`, 5000, true);
+                resolve(false);
+            };
                     img.src = filePath;
                 });
             } catch (error) {
@@ -326,6 +398,7 @@ export class ClipboardManager {
             img.onload = async () => {
                 log.info("Successfully loaded image from backend response");
                 await this.canvas.canvasLayers.addLayerWithImage(img, {}, addMode);
+                showInfoNotification("Image loaded from file path");
                 resolve(true);
             };
             img.onerror = () => {
@@ -366,6 +439,7 @@ export class ClipboardManager {
                             img.onload = async () => {
                                 log.info("Successfully loaded image from file picker");
                                 await this.canvas.canvasLayers.addLayerWithImage(img, {}, addMode);
+                                showInfoNotification("Image loaded from selected file");
                                 resolve(true);
                             };
                             img.onerror = () => {

src/utils/ImageUtils.ts

@@ -386,3 +386,111 @@ export function canvasToMaskImage(canvas: HTMLCanvasElement): Promise<HTMLImageE
         img.src = canvas.toDataURL();
     });
 }
+
+/**
+ * Scales an image to fit within specified bounds while maintaining aspect ratio
+ * @param image - Image to scale
+ * @param targetWidth - Target width to fit within
+ * @param targetHeight - Target height to fit within
+ * @returns Promise with scaled Image element
+ */
+export async function scaleImageToFit(image: HTMLImageElement, targetWidth: number, targetHeight: number): Promise<HTMLImageElement> {
+    const scale = Math.min(targetWidth / image.width, targetHeight / image.height);
+    const scaledWidth = Math.max(1, Math.round(image.width * scale));
+    const scaledHeight = Math.max(1, Math.round(image.height * scale));
+    
+    const { canvas, ctx } = createCanvas(scaledWidth, scaledHeight, '2d', { willReadFrequently: true });
+    if (!ctx) throw new Error("Could not create scaled image context");
+    
+    ctx.drawImage(image, 0, 0, scaledWidth, scaledHeight);
+    
+    return new Promise((resolve, reject) => {
+        const scaledImg = new Image();
+        scaledImg.onload = () => resolve(scaledImg);
+        scaledImg.onerror = reject;
+        scaledImg.src = canvas.toDataURL();
+    });
+}
+
+/**
+ * Unified tensor to image data conversion
+ * Handles both RGB images and grayscale masks
+ * @param tensor - Input tensor data
+ * @param mode - 'rgb' for images or 'grayscale' for masks
+ * @returns ImageData object
+ */
+export function tensorToImageData(tensor: any, mode: 'rgb' | 'grayscale' = 'rgb'): ImageData | null {
+    try {
+        const shape = tensor.shape;
+        const height = shape[1];
+        const width = shape[2];
+        const channels = shape[3] || 1; // Default to 1 for masks
+
+        log.debug("Converting tensor:", { shape, channels, mode });
+
+        const imageData = new ImageData(width, height);
+        const data = new Uint8ClampedArray(width * height * 4);
+
+        const flatData = tensor.data;
+        const pixelCount = width * height;
+
+        const min = tensor.min_val ?? 0;
+        const max = tensor.max_val ?? 1;
+        const denom = (max - min) || 1;
+
+        for (let i = 0; i < pixelCount; i++) {
+            const pixelIndex = i * 4;
+            const tensorIndex = i * channels;
+
+            let lum: number;
+            if (mode === 'grayscale' || channels === 1) {
+                lum = flatData[tensorIndex];
+            } else {
+                // Compute luminance for RGB
+                const r = flatData[tensorIndex + 0] ?? 0;
+                const g = flatData[tensorIndex + 1] ?? 0;
+                const b = flatData[tensorIndex + 2] ?? 0;
+                lum = 0.299 * r + 0.587 * g + 0.114 * b;
+            }
+
+            let norm = (lum - min) / denom;
+            if (!isFinite(norm)) norm = 0;
+            norm = Math.max(0, Math.min(1, norm));
+            const value = Math.round(norm * 255);
+
+            if (mode === 'grayscale') {
+                // For masks: RGB = value, A = 255 (MaskTool reads luminance)
+                data[pixelIndex] = value;
+                data[pixelIndex + 1] = value;
+                data[pixelIndex + 2] = value;
+                data[pixelIndex + 3] = 255;
+            } else {
+                // For images: RGB from channels, A = 255
+                for (let c = 0; c < Math.min(3, channels); c++) {
+                    const channelValue = flatData[tensorIndex + c];
+                    const channelNorm = (channelValue - min) / denom;
+                    data[pixelIndex + c] = Math.round(channelNorm * 255);
+                }
+                data[pixelIndex + 3] = 255;
+            }
+        }
+
+        imageData.data.set(data);
+        return imageData;
+    } catch (error) {
+        log.error("Error converting tensor:", error);
+        return null;
+    }
+}
+
+/**
+ * Creates an HTMLImageElement from ImageData
+ * @param imageData - Input ImageData
+ * @returns Promise with HTMLImageElement
+ */
+export async function createImageFromImageData(imageData: ImageData): Promise<HTMLImageElement> {
+    const { canvas, ctx } = createCanvas(imageData.width, imageData.height, '2d', { willReadFrequently: true });
+    if (!ctx) throw new Error("Could not create canvas context");
+    ctx.putImageData(imageData, 0, 0);
+    return await createImageFromSource(canvas.toDataURL());
+}

src/utils/NotificationUtils.ts

@@ -2,6 +2,9 @@ import { createModuleLogger } from "./LoggerUtils.js";
 
 const log = createModuleLogger('NotificationUtils');
 
+// Store active notifications for deduplication
+const activeNotifications = new Map<string, { element: HTMLDivElement, timeout: number | null }>();
+
 /**
  * Utility functions for showing notifications to the user
  */
@@ -11,16 +14,62 @@ const log = createModuleLogger('NotificationUtils');
  * @param message - The message to show
  * @param backgroundColor - Background color (default: #4a6cd4)
  * @param duration - Duration in milliseconds (default: 3000)
+ * @param type - Type of notification
+ * @param deduplicate - If true, will not show duplicate messages and will refresh existing ones (default: false)
  */
 export function showNotification(
     message: string,
     backgroundColor: string = "#4a6cd4",
     duration: number = 3000,
-    type: "success" | "error" | "info" | "warning" | "alert" = "info"
+    type: "success" | "error" | "info" | "warning" | "alert" = "info",
+    deduplicate: boolean = false
 ): void {
     // Remove any existing prefix to avoid double prefixing
     message = message.replace(/^\[Layer Forge\]\s*/, "");
 
+    // If deduplication is enabled, check if this message already exists
+    if (deduplicate) {
+        const existingNotification = activeNotifications.get(message);
+        if (existingNotification) {
+            log.debug(`Notification already exists, refreshing timer: ${message}`);
+            
+            // Clear existing timeout
+            if (existingNotification.timeout !== null) {
+                clearTimeout(existingNotification.timeout);
+            }
+            
+            // Find the progress bar and restart its animation
+            const progressBar = existingNotification.element.querySelector('div[style*="animation"]') as HTMLDivElement;
+            if (progressBar) {
+                // Reset animation
+                progressBar.style.animation = 'none';
+                // Force reflow
+                void progressBar.offsetHeight;
+                // Restart animation
+                progressBar.style.animation = `lf-progress ${duration / 1000}s linear`;
+            }
+            
+            // Set new timeout
+            const newTimeout = window.setTimeout(() => {
+                const notification = existingNotification.element;
+                notification.style.animation = 'lf-fadeout 0.3s ease-out forwards';
+                notification.addEventListener('animationend', () => {
+                    if (notification.parentNode) {
+                        notification.parentNode.removeChild(notification);
+                        activeNotifications.delete(message);
+                        const container = document.getElementById('lf-notification-container');
+                        if (container && container.children.length === 0) {
+                            container.remove();
+                        }
+                    }
+                });
+            }, duration);
+            
+            existingNotification.timeout = newTimeout;
+            return; // Don't create a new notification
+        }
+    }
+
     // Type-specific config
     const config = {
         success: { icon: "✔️", title: "Success", bg: "#1fd18b" },
@@ -172,6 +221,11 @@ export function showNotification(
 
     let dismissTimeout: number | null = null;
     const closeNotification = () => {
+        // Remove from active notifications map if deduplicate is enabled
+        if (deduplicate) {
+            activeNotifications.delete(message);
+        }
+        
         notification.style.animation = 'lf-fadeout 0.3s ease-out forwards';
         notification.addEventListener('animationend', () => {
             if (notification.parentNode) {
@@ -198,46 +252,86 @@ export function showNotification(
         progressBar.style.animation = 'lf-progress-rewind 0.5s ease-out forwards';
     };
 
-    notification.addEventListener('mouseenter', pauseAndRewindTimer);
+    notification.addEventListener('mouseenter', () => {
-    notification.addEventListener('mouseleave', startDismissTimer);
+        pauseAndRewindTimer();
+        // Update stored timeout if deduplicate is enabled
+        if (deduplicate) {
+            const stored = activeNotifications.get(message);
+            if (stored) {
+                stored.timeout = null;
+            }
+        }
+    });
+    
+    notification.addEventListener('mouseleave', () => {
+        startDismissTimer();
+        // Update stored timeout if deduplicate is enabled
+        if (deduplicate) {
+            const stored = activeNotifications.get(message);
+            if (stored) {
+                stored.timeout = dismissTimeout;
+            }
+        }
+    });
     
     startDismissTimer();
+    
+    // Store notification if deduplicate is enabled
+    if (deduplicate) {
+        activeNotifications.set(message, { element: notification, timeout: dismissTimeout });
+    }
+    
     log.debug(`Notification shown: [Layer Forge] ${message}`);
 }
 
 /**
  * Shows a success notification
+ * @param message - The message to show
+ * @param duration - Duration in milliseconds (default: 3000)
+ * @param deduplicate - If true, will not show duplicate messages (default: false)
  */
-export function showSuccessNotification(message: string, duration: number = 3000): void {
+export function showSuccessNotification(message: string, duration: number = 3000, deduplicate: boolean = false): void {
-    showNotification(message, undefined, duration, "success");
+    showNotification(message, undefined, duration, "success", deduplicate);
 }
 
 /**
  * Shows an error notification
+ * @param message - The message to show
+ * @param duration - Duration in milliseconds (default: 5000)
+ * @param deduplicate - If true, will not show duplicate messages (default: false)
  */
-export function showErrorNotification(message: string, duration: number = 5000): void {
+export function showErrorNotification(message: string, duration: number = 5000, deduplicate: boolean = false): void {
-    showNotification(message, undefined, duration, "error");
+    showNotification(message, undefined, duration, "error", deduplicate);
 }
 
 /**
  * Shows an info notification
+ * @param message - The message to show
+ * @param duration - Duration in milliseconds (default: 3000)
+ * @param deduplicate - If true, will not show duplicate messages (default: false)
  */
-export function showInfoNotification(message: string, duration: number = 3000): void {
+export function showInfoNotification(message: string, duration: number = 3000, deduplicate: boolean = false): void {
-    showNotification(message, undefined, duration, "info");
+    showNotification(message, undefined, duration, "info", deduplicate);
 }
 
 /**
  * Shows a warning notification
+ * @param message - The message to show
+ * @param duration - Duration in milliseconds (default: 3000)
+ * @param deduplicate - If true, will not show duplicate messages (default: false)
  */
-export function showWarningNotification(message: string, duration: number = 3000): void {
+export function showWarningNotification(message: string, duration: number = 3000, deduplicate: boolean = false): void {
-    showNotification(message, undefined, duration, "warning");
+    showNotification(message, undefined, duration, "warning", deduplicate);
 }
 
 /**
  * Shows an alert notification
+ * @param message - The message to show
+ * @param duration - Duration in milliseconds (default: 3000)
+ * @param deduplicate - If true, will not show duplicate messages (default: false)
  */
-export function showAlertNotification(message: string, duration: number = 3000): void {
+export function showAlertNotification(message: string, duration: number = 3000, deduplicate: boolean = false): void {
-    showNotification(message, undefined, duration, "alert");
+    showNotification(message, undefined, duration, "alert", deduplicate);
 }
 
 /**
@@ -248,7 +342,7 @@ export function showAllNotificationTypes(message?: string): void {
     types.forEach((type, index) => {
         const notificationMessage = message || `This is a '${type}' notification.`;
         setTimeout(() => {
-            showNotification(notificationMessage, undefined, 3000, type);
+            showNotification(notificationMessage, undefined, 3000, type, false);
         }, index * 400); // Stagger the notifications
     });
 }