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"*" }, { "links": null, "localized_name": "any4", "name": "any4", "type": "*" } ], "pos": [ 410, 270 ], "properties": { "Node name for S&R": "Exec Python Code Script", "cnr_id": "python-interpreter-node", "ver": "2.1.0" }, "size": [ 290, 790 ], "type": "Exec Python Code Script", "widgets_values": [ "import numpy as np # Extract image tensor, assuming it's a batched Torch tensor img = image1.data[0] # remove batch dimension # Get shape: height and width height, width = img.shape[:2] # Pick smaller side smaller = min(height, width) # Set output text1.to(str(smaller)) # For debugging print(f\"Fixed image shape: {img.shape}\") print(f\"Smaller side: {smaller}\")", 0, 0, "hello", "world", true, "" ] }, { "flags": { "collapsed": true }, "id": 1175, "inputs": [ { "link": null, "localized_name": "value", "name": "value", "type": "STRING", "widget": { "name": "value" } } ], "mode": 0, "order": 57, "outputs": [ { "links": [ 2666 ], "localized_name": "STRING", "name": "STRING", "type": "STRING" } ], "pos": [ 260, 270 ], "properties": { "Node name for S&R": "PrimitiveStringMultiline", "cnr_id": "comfy-core", "ver": "0.3.48" }, "size": [ 440, 160 ], "title": "Code", "type": "PrimitiveStringMultiline", "widgets_values": [ "# Extract image tensor, assuming it's a batched Torch tensor\nimg = image1.data[0] # remove batch dimension\n\n# Get shape: height and width\nheight, width = img.shape[:2]\n\n# Pick smaller side\nsmaller = min(height, width)\n\n# Bucket to nearest multiple of 64\nsmaller = round(smaller / 64) * 64\n\n# Set output\nnumber1.to(int(smaller))\n\n# For debugging\nprint(f\"Fixed image shape: {img.shape}\")\nprint(f\"Smaller side (bucketed to 64): {smaller}\")\n" ] }, { "bgcolor": "#335", "color": "#223", "flags": { "pinned": true }, "id": 157, "inputs": [ { "link": 2725, "localized_name": "model", "name": "model", "type": "MODEL" }, { "link": null, "localized_name": "text", "name": "text", "type": "STRING", "widget": { "name": "text" } }, { "link": 215, "name": "clip", "shape": 7, "type": "CLIP" }, { "link": null, "name": "lora_stack", "shape": 7, "type": "LORA_STACK" } ], "mode": 0, "order": 124, "outputs": [ { "links": [ 2450 ], "localized_name": "MODEL", "name": "MODEL", "type": "MODEL" }, { "links": [ 218 ], "localized_name": "CLIP", "name": "CLIP", "type": "CLIP" }, { "links": [ 253 ], "localized_name": "trigger_words", "name": "trigger_words", "type": "STRING" }, { "links": [], "localized_name": "loaded_loras", "name": "loaded_loras", "type": "STRING" } ], "pos": [ -1790, -610 ], "properties": { "Node name for S&R": "Lora Loader (LoraManager)", "aux_id": "willmiao/ComfyUI-Lora-Manager", "cnr_id": "comfyui-lora-manager", "ver": "0.8.3" }, "size": [ 440, 510 ], "type": "Lora Loader (LoraManager)", "widgets_values": [ "", [ { "active": true, "clipStrength": 0.6, "expanded": false, "name": "cfg_scale_boost", "strength": 0.6 }, { "active": false, "clipStrength": 1, "expanded": false, "name": "DetailedFur", "strength": 1 }, { "active": true, "clipStrength": "0.60", "expanded": false, "name": "Tapering_Penis_With_Sheath_IL", "strength": "0.60" }, { "active": true, "clipStrength": "0.60", "expanded": false, "name": "feline-penis-pdxl", "strength": "0.60" } ] ] }, { "flags": { "collapsed": true }, "id": 1168, "inputs": [ { "link": null, "localized_name": "value", "name": "value", "type": "STRING", "widget": { "name": "value" } } ], "mode": 0, "order": 58, "outputs": [ { "links": [ 2454 ], "localized_name": "STRING", "name": "STRING", "type": "STRING" } ], "pos": [ 180, -1250 ], "properties": { "Node name for S&R": "PrimitiveStringMultiline", "cnr_id": "comfy-core", "ver": "0.3.48" }, "size": [ 440, 160 ], "title": "Code", "type": "PrimitiveStringMultiline", "widgets_values": [ "text1_extracted = ''\n\nif isinstance(getattr(any1, 'data', None), str):\n data = any1.data\n\n # Remove all text inside *...*\n import re\n text1_extracted = re.sub(r'\\*.*?\\*', '', data).strip()\n\ntext1.to(text1_extracted)\n" ] }, { "flags": { "collapsed": true }, "id": 1171, "inputs": [ { "link": null, "localized_name": "value", "name": "value", "type": "STRING", "widget": { "name": "value" } } ], "mode": 0, "order": 59, "outputs": [ { "links": [ 2457 ], "localized_name": "STRING", "name": "STRING", "type": "STRING" } ], "pos": [ 730, -960 ], "properties": { "Node name for S&R": "PrimitiveStringMultiline", "cnr_id": "comfy-core", "ver": "0.3.48" }, "size": [ 440, 160 ], "title": "Code", "type": "PrimitiveStringMultiline", "widgets_values": [ "import os\nfrom PIL import Image\nimport torchvision.transforms as T\n\ntemp_dir = os.path.join('.', 'ComfyUI', 'temp')\nos.makedirs(temp_dir, exist_ok=True)\n\nsave_path = os.path.join(temp_dir, 'tempimage1.png')\nlog_path = os.path.join('.', 'ComfyUI', 'input', 'log.txt') # changed from temp to input\n\nif any1:\n # Wipe tempimage1.png if exists\n if os.path.exists(save_path):\n os.remove(save_path)\n # Create or empty log.txt in 'input' folder\n with open(log_path, 'w') as f:\n pass # just open in write mode to truncate file or create it\nelse:\n # Extract tensor from wrapper\n tensor = image1.tensor if hasattr(image1, 'tensor') else image1.image if hasattr(image1, 'image') else image1.data\n\n # Assume tensor shape is [1, H, W, C], take first frame and first 3 channels, permute to [C, H, W]\n tensor = tensor[0, :, :, :3].permute(2, 0, 1)\n\n # Normalize if needed\n if tensor.max() > 1.0:\n tensor = tensor / 255.0\n\n # Convert and save\n T.ToPILImage()(tensor).save(save_path)\n" ] }, { "flags": { "collapsed": true }, "id": 1081, "inputs": [ { "link": 2334, "localized_name": "image1", "name": "image1", "shape": 7, "type": "IMAGE" }, { "link": 2348, "localized_name": "image2", "name": "image2", "shape": 7, "type": "IMAGE" }, { "link": null, "localized_name": "mask1", "name": "mask1", "shape": 7, "type": "MASK" }, { "link": null, "localized_name": "mask2", "name": "mask2", "shape": 7, "type": "MASK" }, { "link": null, "localized_name": "list1", "name": "list1", "shape": 7, "type": "*" }, { "link": null, "localized_name": "dict1", "name": "dict1", "shape": 7, "type": "*" }, { "link": 2401, "localized_name": "any1", "name": "any1", "shape": 7, "type": "*" }, { "link": null, "localized_name": "any2", "name": "any2", "shape": 7, "type": "*" }, { "link": null, "localized_name": "any3", "name": "any3", "shape": 7, "type": "*" }, { "link": null, "localized_name": "any4", "name": "any4", "shape": 7, "type": "*" }, { "link": 2457, "localized_name": "raw_code", "name": "raw_code", "type": "STRING", "widget": { "name": "raw_code" } }, { "link": null, "localized_name": "number1", "name": "number1", "shape": 7, "type": "FLOAT", "widget": { "name": "number1" } }, { "link": null, "localized_name": "number2", "name": "number2", "shape": 7, "type": "INT", "widget": { "name": "number2" } }, { "link": null, "localized_name": "text1", "name": "text1", "shape": 7, "type": "STRING", "widget": { "name": "text1" } }, { "link": null, "localized_name": "text2", "name": "text2", "shape": 7, "type": "STRING", "widget": { "name": "text2" } }, { "link": null, "localized_name": "verbose", "name": "verbose", "shape": 7, "type": "BOOLEAN", "widget": { "name": "verbose" } } ], "mode": 0, "order": 120, "outputs": [ { "links": [], "localized_name": "image1", "name": "image1", "type": "*" }, { "links": [], "localized_name": "image2", "name": "image2", "type": "*" }, { "links": null, "localized_name": "mask1", "name": "mask1", "type": "*" }, { "links": null, "localized_name": "mask2", "name": "mask2", "type": "*" }, { "links": [], "localized_name": "number1", "name": "number1", "slot_index": 4, "type": "*" }, { "links": null, "localized_name": "number2", "name": "number2", "type": "*" }, { "links": [], "localized_name": "text1", "name": "text1", "slot_index": 6, "type": "*" }, { "links": [], "localized_name": "text2", "name": "text2", "slot_index": 7, "type": "*" }, { "links": null, "localized_name": "list1", "name": "list1", "type": "*" }, { "links": null, "localized_name": "dict1", "name": "dict1", "type": "*" }, { "links": null, "localized_name": "any1", "name": "any1", "type": "*" }, { "links": [], "localized_name": "any2", "name": "any2", "slot_index": 11, "type": "*" }, { "links": null, "localized_name": "any3", "name": "any3", "type": "*" }, { "links": null, "localized_name": "any4", "name": "any4", "type": "*" } ], "pos": [ 870, -1000 ], "properties": { "Node name for S&R": "Exec Python Code Script", "cnr_id": "python-interpreter-node", "ver": "1493d77cb9a05f065afe53381b69ada867231f8b" }, "size": [ 310, 546 ], "title": "Clean", "type": "Exec Python Code Script", "widgets_values": [ "# Default text1 and text2 to empty strings\r\ntext1_extracted = ''\r\ntext2_extracted = ''\r\n\r\n# Ensure any1.data exists and is a string\r\nif hasattr(any1, 'data') and isinstance(any1.data, str):\r\n # Extract text between the last \"ASSISTANT: \" and \"=== End History ===\"\r\n assistant_index = any1.data.rfind(\"ASSISTANT: \")\r\n end_history_index = any1.data.rfind(\"=== End History ===\")\r\n\r\n if assistant_index != -1 and end_history_index != -1 and assistant_index < end_history_index:\r\n text2_extracted = any1.data[assistant_index + len(\"ASSISTANT: \"):end_history_index].strip()\r\n\r\n # Extract text between the last `` closing tag\r\n last_clip_end = any1.data.rfind('>') # Find the last closing tag\r\n if last_clip_end != -1:\r\n last_clip_start = any1.data.rfind(' 1.0:\n tensor = tensor / 255.0\n\n# --- Convert to PIL for saving / hashing ---\nimg_pil = T.ToPILImage()(tensor)\n\n# --- Compute hash of current input image ---\ncurr_bytes = img_pil.tobytes()\ncurr_hash = hashlib.sha256(curr_bytes).hexdigest()\n\n# --- Compare with saved image ---\nif os.path.exists(save_path):\n # Load previous image\n prev_img = Image.open(save_path)\n prev_hash = hashlib.sha256(prev_img.tobytes()).hexdigest()\nelse:\n prev_hash = None\n\n# --- Decide output ---\nif prev_hash == curr_hash:\n text1.data = \"always\" # <--- assign to .data\nelse:\n text1.data = \"never\"\n\n# --- Save current image to buffer ---\nimg_pil.save(save_path)\n" ] }, { "bgcolor": "#653", "color": "#432", "flags": { "collapsed": false }, "id": 1242, "inputs": [ { "link": null, "localized_name": "value", "name": "value", "type": "BOOLEAN", "widget": { "name": "value" } } ], "mode": 0, "order": 75, "outputs": [ { "links": [ 2665 ], "localized_name": "BOOLEAN", "name": "BOOLEAN", "type": "BOOLEAN" } ], "pos": [ 1010, -870 ], "properties": { "Node name for S&R": "PrimitiveBoolean", "cnr_id": "comfy-core", "ver": "0.3.44" }, "size": [ 210, 58 ], "title": "Stop if mask empty", "type": "PrimitiveBoolean", "widgets_values": [ true ] }, { "bgcolor": "#593930", "color": "#332922", "flags": { "collapsed": true }, "id": 967, "inputs": [ { "link": 2245, "localized_name": "samples", "name": "samples", "type": "LATENT" }, { "link": 2026, "localized_name": "vae", "name": "vae", "type": "VAE" } ], "mode": 0, "order": 200, "outputs": [ { "links": [ 2070 ], "localized_name": "IMAGE", "name": "IMAGE", "slot_index": 0, "type": "IMAGE" } ], "pos": [ 30, -610 ], "properties": { "Node name for S&R": "VAEDecode", "cnr_id": "comfy-core", "ver": "0.3.15" }, "size": [ 140, 50 ], "title": "VAE", "type": "VAEDecode", "widgets_values": [] }, { "flags": { "collapsed": true }, "id": 1026, "inputs": [ { "link": 2246, "localized_name": "on_true", "name": "on_true", "type": "LATENT" }, { "link": 2247, "localized_name": "on_false", "name": "on_false", "type": "LATENT" }, { "link": 2248, "localized_name": "boolean", "name": "boolean", "type": "BOOLEAN", "widget": { "name": "boolean" } } ], "mode": 0, "order": 192, "outputs": [ { "links": [ 2245 ], "localized_name": "latent", "name": "latent", "type": "LATENT" } ], "pos": [ -100, -610 ], "properties": { "Node name for S&R": "Switch latent [Crystools]", "cnr_id": "comfyui-crystools", "ver": "1.25.2" }, "size": [ 210, 78 ], "title": "Switch", "type": "Switch latent [Crystools]", "widgets_values": [ true ] }, { "bgcolor": "#533", "color": "#322", "flags": { "collapsed": true }, "id": 504, "inputs": [], "mode": 0, "order": 76, "outputs": [ { "links": [ 2026 ], "name": "VAE", "type": "VAE" } ], "pos": [ -100, -570 ], "properties": {}, "size": [ 210, 60 ], "title": "Get_VAE", "type": "GetNode", "widgets_values": [ "VAE" ] }, { "bgcolor": "#3f5159", "color": "#2a363b", "flags": { "collapsed": true }, "id": 932, "inputs": [], "mode": 0, "order": 77, "outputs": [ { "links": [ 2483 ], "name": "IMAGE", "type": "IMAGE" } ], "pos": [ -100, -770 ], "properties": {}, "size": [ 210, 60 ], "title": "Get_lineart", "type": "GetNode", "widgets_values": [ "lineart" ] }, { "flags": { "collapsed": true }, "id": 1246, "inputs": [ { "link": 2787, "localized_name": "value", "name": "value", "type": "*" }, { "link": 2669, "localized_name": "mode", "name": "mode", "type": "BOOLEAN", "widget": { "name": "mode" } }, { "link": null, "localized_name": "behavior", "name": "behavior", "type": "COMBO", "widget": { "name": "behavior" } } ], "mode": 0, "order": 115, "outputs": [ { "label": "*", "links": [ 2668 ], "localized_name": "value", "name": "value", "type": "*" } ], "pos": [ 40, 230 ], "properties": { "Node name for S&R": "ImpactControlBridge", "cnr_id": "comfyui-impact-pack", "ver": "8.25.1" }, "size": [ 270, 82 ], "type": "ImpactControlBridge", "widgets_values": [ true, "Stop" ] }, { "bgcolor": "#3f5159", "color": "#2a363b", "flags": { "collapsed": true }, "id": 1300, "inputs": [], "mode": 0, "order": 78, "outputs": [ { "links": [ 2787 ], "name": "IMAGE", "type": "IMAGE" } ], "pos": [ -120, 270 ], "properties": {}, "size": [ 210, 60 ], "title": "Get_image1", "type": "GetNode", "widgets_values": [ "image1" ] }, { "bgcolor": "#3f5159", "color": "#2a363b", "flags": { "collapsed": false }, "id": 466, "inputs": [ { "link": 2647, "localized_name": "image", "name": "image", "type": "IMAGE" }, { "link": null, "localized_name": "preprocessor", "name": "preprocessor", "shape": 7, "type": "COMBO", "widget": { "name": "preprocessor" } }, { "link": 1089, "localized_name": "resolution", "name": "resolution", "shape": 7, "type": "INT", "widget": { "name": "resolution" } } ], "mode": 0, "order": 153, "outputs": [ { "links": [ 878 ], "localized_name": "IMAGE", "name": "IMAGE", "type": "IMAGE" } ], "pos": [ 650, -130 ], "properties": { "Node name for S&R": "AIO_Preprocessor", "cnr_id": "comfyui_controlnet_aux", "ver": "1.0.7" }, "size": [ 320, 82 ], "type": "AIO_Preprocessor", "widgets_values": [ "DepthAnythingV2Preprocessor", 1344 ] }, { "flags": { "collapsed": true }, "id": 1275, "inputs": [ { "link": 2745, "localized_name": "segm_detector", "name": "segm_detector", "type": "SEGM_DETECTOR" }, { "link": 2784, "localized_name": "image", "name": "image", "type": "IMAGE" }, { "link": null, "localized_name": "threshold", "name": "threshold", "type": "FLOAT", "widget": { "name": "threshold" } }, { "link": null, "localized_name": "dilation", "name": "dilation", "type": "INT", "widget": { "name": "dilation" } } ], "mode": 0, "order": 142, "outputs": [ { "links": [ 2766 ], "localized_name": "MASK", "name": "MASK", "type": "MASK" } ], "pos": [ 350, -720 ], "properties": { "Node name for S&R": "SegmDetectorCombined_v2", "cnr_id": "comfyui-impact-pack", "ver": "8.17.0" }, "size": [ 271.8443298339844, 102 ], "title": "SEGM Detector", "type": "SegmDetectorCombined_v2", "widgets_values": [ 0.6000000000000001, 10 ] }, { "flags": { "collapsed": true }, "id": 1283, "inputs": [ { "link": 2759, "localized_name": "mask", "name": "mask", "type": "MASK" }, { "link": null, "localized_name": "min", "name": "min", "type": "FLOAT", "widget": { "name": "min" } }, { "link": null, "localized_name": "max", "name": "max", "type": "FLOAT", "widget": { "name": "max" } } ], "mode": 4, "order": 158, "outputs": [ { "links": [ 2761 ], "localized_name": "mask", "name": "mask", "type": "MASK" } ], "pos": [ 510, -820 ], "properties": { "Node name for S&R": "RemapMaskRange", "cnr_id": "comfyui-kjnodes", "ver": "1.1.8" }, "size": [ 210, 82 ], "type": "RemapMaskRange", "widgets_values": [ 0, 0.8 ] }, { "flags": { "collapsed": true }, "id": 919, "inputs": [ { "link": 2675, "localized_name": "segm_detector", "name": "segm_detector", "type": "SEGM_DETECTOR" }, { "link": 2783, "localized_name": "image", "name": "image", "type": "IMAGE" }, { "link": null, "localized_name": "threshold", "name": "threshold", "type": "FLOAT", "widget": { "name": "threshold" } }, { "link": null, "localized_name": "dilation", "name": "dilation", "type": "INT", "widget": { "name": "dilation" } } ], "mode": 0, "order": 141, "outputs": [ { "links": [ 2759 ], "localized_name": "MASK", "name": "MASK", "type": "MASK" } ], "pos": [ 180, -820 ], "properties": { "Node name for S&R": "SegmDetectorCombined_v2", "cnr_id": "comfyui-impact-pack", "ver": "8.17.0" }, "size": [ 271.8443298339844, 102 ], "title": "SEGM Detector", "type": "SegmDetectorCombined_v2", "widgets_values": [ 0.6000000000000001, 10 ] }, { "flags": { "collapsed": true }, "id": 1197, "inputs": [ { "link": 2483, "localized_name": "image", "name": "image", "type": "IMAGE" }, { "link": null, "localized_name": "mask", "name": "mask", "shape": 7, "type": "MASK" }, { "link": 2672, "localized_name": "width", "name": "width", "type": "INT", "widget": { "name": "width" } }, { "link": 2671, "localized_name": "height", "name": "height", "type": "INT", "widget": { "name": "height" } }, { "link": null, "localized_name": "upscale_method", "name": "upscale_method", "type": "COMBO", "widget": { "name": "upscale_method" } }, { "link": null, "localized_name": "keep_proportion", "name": "keep_proportion", "type": "COMBO", "widget": { "name": "keep_proportion" } }, { "link": null, "localized_name": "pad_color", "name": "pad_color", "type": "STRING", "widget": { "name": "pad_color" } }, { "link": null, "localized_name": "crop_position", "name": "crop_position", "type": "COMBO", "widget": { "name": "crop_position" } }, { "link": null, "localized_name": "divisible_by", "name": "divisible_by", "type": "INT", "widget": { "name": "divisible_by" } }, { "link": null, "localized_name": "device", "name": "device", "shape": 7, "type": "COMBO", "widget": { "name": "device" } } ], "mode": 0, "order": 157, "outputs": [ { "links": [ 2480 ], "localized_name": "IMAGE", "name": "IMAGE", "type": "IMAGE" }, { "links": null, "localized_name": "width", "name": "width", "type": "INT" }, { "links": null, "localized_name": "height", "name": "height", "type": "INT" }, { "links": null, "localized_name": "mask", "name": "mask", "type": "MASK" } ], "pos": [ 180, -770 ], "properties": { "Node name for S&R": "ImageResizeKJv2", "cnr_id": "comfyui-kjnodes", "ver": "1.1.3" }, "size": [ 270, 336 ], "type": "ImageResizeKJv2", "widgets_values": [ 512, 512, "nearest-exact", "resize", "0, 0, 0", "center", 2, "cpu" ] }, { "flags": { "collapsed": true }, "id": 1247, "inputs": [ { "link": 2670, "localized_name": "value", "name": "value", "type": "IMAGE" } ], "mode": 0, "order": 140, "outputs": [ { "links": null, "localized_name": "batch", "name": "batch", "type": "INT" }, { "links": [ 2671 ], "localized_name": "height", "name": "height", "type": "INT" }, { "links": [ 2672 ], "localized_name": "width", "name": "width", "type": "INT" }, { "links": null, "localized_name": "channel", "name": "channel", "type": "INT" } ], "pos": [ -30, -510 ], "properties": { "Node name for S&R": "ImpactImageInfo", "cnr_id": "comfyui-impact-pack", "ver": "8.25.1" }, "size": [ 150.901953125, 86 ], "type": "ImpactImageInfo", "widgets_values": [] }, { "flags": {}, "id": 1292, "inputs": [ { "link": null, "localized_name": "value", "name": "value", "type": "FLOAT", "widget": { "name": "value" } } ], "mode": 0, "order": 79, "outputs": [ { "links": [ 2773 ], "localized_name": "FLOAT", "name": "FLOAT", "type": "FLOAT" } ], "pos": [ 1410, -150 ], "properties": { "Node name for S&R": "PrimitiveFloat", "cnr_id": "comfy-core", "ver": "0.3.68" }, "size": [ 210, 58 ], "title": "Upscale by", "type": "PrimitiveFloat", "widgets_values": [ 1.3 ] }, { "flags": { "collapsed": true }, "id": 1293, "inputs": [ { "link": null, "localized_name": "value", "name": "value", "type": "STRING", "widget": { "name": "value" } } ], "mode": 0, "order": 80, "outputs": [ { "links": [ 2774 ], "localized_name": "STRING", "name": "STRING", "type": "STRING" } ], "pos": [ 1530, -50 ], "properties": { "Node name for S&R": "PrimitiveStringMultiline", "cnr_id": "comfy-core", "ver": "0.3.48" }, "size": [ 440, 160 ], "title": "Code", "type": "PrimitiveStringMultiline", "widgets_values": [ "import torch\nimport torch.nn.functional as F\n\nimg = image1.data # [B,H,W,C] NHWC\nscale = float(any1.data)\n\n# Convert to float if needed\nif not torch.is_floating_point(img):\n img = img.float() / 255.0\n\nB, H, W, C = img.shape\n\n# Determine smaller side\nif H < W:\n smaller, larger = H, W\n smaller_is_height = True\nelse:\n smaller, larger = W, H\n smaller_is_height = False\n\n# Scale smaller side and bucket to nearest multiple of 64\nsmaller_scaled = smaller * scale\nsmaller_bucket = max(64, round(smaller_scaled / 64) * 64)\n\n# Compute larger side to preserve aspect ratio\nlarger_scaled = larger * (smaller_bucket / smaller)\n# Bucket larger side to nearest multiple of 64\nlarger_bucket = max(64, round(larger_scaled / 64) * 64)\n\n# Assign final H and W preserving aspect ratio\nif smaller_is_height:\n new_H, new_W = smaller_bucket, larger_bucket\nelse:\n new_W, new_H = smaller_bucket, larger_bucket\n\n# Resize with interpolate\nupscaled = F.interpolate(img.permute(0,3,1,2), size=(new_H,new_W), mode='bilinear', align_corners=False)\nupscaled = upscaled.permute(0,2,3,1)\n\n# Output\nimage1.to(upscaled)\n" ] }, { "flags": {}, "id": 998, "inputs": [ { "link": 2169, "name": "", "type": "*" } ], "mode": 0, "order": 181, "outputs": [ { "links": [ 2170 ], "name": "IMAGE", "type": "IMAGE" } ], "pos": [ 1400, 130 ], "properties": { 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