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professional photo, photorealism, sharp, exposure blend, volumetric, ray tracing, hdr, 4k, 8k, absurd res, realistic, (best quality, good quality:1.2)\nBREAK" ], "color": "#757575", "bgcolor": "#616161" }, { "id": 1202, "type": "ConditioningCombine", "pos": [ 1800, 380 ], "size": [ 216.92578125, 46 ], "flags": { "collapsed": true }, "order": 168, "mode": 0, "inputs": [ { "name": "conditioning_1", "type": "CONDITIONING", "link": 2497 }, { "name": "conditioning_2", "type": "CONDITIONING", "link": 2498 } ], "outputs": [ { "name": "CONDITIONING", "type": "CONDITIONING", "links": [ 2979 ] } ], "properties": { "cnr_id": "comfy-core", "ver": "0.3.48", "Node name for S&R": "ConditioningCombine" }, "widgets_values": [], "color": "#432", "bgcolor": "#653" }, { "id": 1216, "type": "Reroute", "pos": [ 2740, 640 ], "size": [ 75, 26 ], "flags": {}, "order": 88, "mode": 0, "inputs": [ { "name": "", "type": "*", "widget": { "name": "value" }, "link": 2586 } ], "outputs": [ { "name": "", "type": 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"bgcolor": "#653" }, { "id": 199, "type": "CLIPSetLastLayer", "pos": [ -310, 450 ], "size": [ 210, 58 ], "flags": { "collapsed": false }, "order": 72, "mode": 0, "inputs": [ { "name": "clip", "type": "CLIP", "link": 2742 } ], "outputs": [ { "name": "CLIP", "type": "CLIP", "slot_index": 0, "links": [ 2680 ] } ], "properties": { "cnr_id": "comfy-core", "ver": "0.3.15", "Node name for S&R": "CLIPSetLastLayer" }, "widgets_values": [ -2 ], "color": "#432", "bgcolor": "#653" }, { "id": 499, "type": "VAEDecodeTiled", "pos": [ 2480, 510 ], "size": [ 210, 150 ], "flags": { "collapsed": true }, "order": 78, "mode": 0, "inputs": [ { "name": "samples", "type": "LATENT", "link": 988 }, { "name": "vae", "type": "VAE", "link": 1431 } ], "outputs": [ { "name": "IMAGE", "type": "IMAGE", "slot_index": 0, "links": [ 972 ] } ], "title": "VAE Decode", "properties": { "cnr_id": "comfy-core", "ver": "0.3.15", "Node name for S&R": "VAEDecodeTiled" }, "widgets_values": [ 1024, 64, 64, 8 ], "color": "#332922", "bgcolor": "#593930" }, { "id": 510, "type": "GetNode", "pos": [ 2300, 510 ], "size": [ 210, 60 ], "flags": { "collapsed": true }, "order": 9, "mode": 0, "inputs": [], "outputs": [ { "name": "LATENT", "type": "LATENT", "links": [ 988 ] } ], "title": "Get_InpaintLatent", "properties": {}, "widgets_values": [ "InpaintLatent" ], "color": "#323", "bgcolor": "#535" }, { "id": 930, "type": "AIO_Preprocessor", "pos": [ 650, 60 ], "size": [ 320, 82 ], "flags": { "collapsed": false }, "order": 116, "mode": 0, "inputs": [ { "name": "image", "type": "IMAGE", "link": 2651 }, { "name": "resolution", "shape": 7, "type": "INT", "widget": { "name": "resolution" }, "link": 1957 } ], "outputs": [ { "name": "IMAGE", "type": "IMAGE", "links": [ 1946 ] } ], "properties": { "cnr_id": "comfyui_controlnet_aux", "ver": "1.0.7", "Node name for S&R": "AIO_Preprocessor" }, "widgets_values": [ "Manga2Anime_LineArt_Preprocessor", 1344 ], "color": "#2a363b", "bgcolor": "#3f5159" }, { "id": 1245, "type": "Note", "pos": [ -1890, -770 ], "size": [ 400, 170 ], "flags": { "collapsed": true }, "order": 10, "mode": 0, "inputs": [], "outputs": [], "properties": {}, "widgets_values": [ "explicit, size difference, duo, massive muscular anthro tiger male and a small anthro lynx male, in a steamy shower stall, water spraying down their bodies, the tiger is standing with a smug grin, one hand braced against the tiled wall, the lynx is on his knees, eagerly performing fellatio, deepthroating the tiger's huge, erect humanoid penis, tiger's hand on the back of the lynx's head, guiding him, the lynx's eyes are wide with adoration and effort, wet fur, glistening bodies, steam, low angle shot, deep throat, tears" ], "color": "#432", "bgcolor": "#653" }, { "id": 1175, "type": "PrimitiveStringMultiline", "pos": [ 260, 270 ], "size": [ 440, 160 ], "flags": { "collapsed": true }, "order": 11, "mode": 0, "inputs": [], "outputs": [ { "name": "STRING", "type": "STRING", "links": [ 2666 ] } ], "title": "Code", "properties": { "cnr_id": "comfy-core", "ver": "0.3.48", "Node name for S&R": "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" ] }, { "id": 410, "type": "GetNode", "pos": [ -870, -1260 ], "size": [ 210, 60 ], "flags": { "collapsed": true }, "order": 12, "mode": 0, "inputs": [], "outputs": [ { "name": "IMAGE", "type": "IMAGE", "links": [ 3007 ] } ], "title": "Get_image1", "properties": {}, "widgets_values": [ "image1" ], "color": "#2a363b", "bgcolor": "#3f5159" }, { "id": 1238, "type": "VAEDecode", "pos": [ -1020, -20 ], "size": [ 140, 46 ], "flags": { "collapsed": true }, "order": 67, "mode": 0, "inputs": [ { "name": "samples", "type": "LATENT", "link": 2658 }, { "name": "vae", "type": "VAE", "link": 2659 } ], "outputs": [ { "name": "IMAGE", "type": "IMAGE", "links": [ 2771 ] } ], "properties": { "cnr_id": "comfy-core", "ver": "0.3.59", "Node name for S&R": "VAEDecode" }, "widgets_values": [] }, { "id": 1288, "type": "Exec Python Code Script", "pos": [ -860, -20 ], "size": [ 290, 790 ], "flags": { "collapsed": true }, "order": 90, "mode": 0, "inputs": [ { "name": "image1", "shape": 7, "type": "IMAGE", "link": 2771 }, { "name": "image2", "shape": 7, "type": "IMAGE", "link": null }, { "name": "mask1", "shape": 7, "type": "MASK", "link": null }, { "name": "mask2", "shape": 7, "type": "MASK", "link": null }, { "name": "list1", "shape": 7, "type": "*", "link": null }, { "name": "dict1", "shape": 7, "type": "*", "link": null }, { "name": "any1", "shape": 7, "type": "*", "link": 2770 }, { "name": "any2", "shape": 7, "type": "*", "link": null }, { "name": "any3", "shape": 7, "type": "*", "link": null }, { "name": "any4", "shape": 7, "type": "*", "link": null }, { "name": "raw_code", "type": "STRING", "widget": { "name": "raw_code" }, "link": 2768 } ], "outputs": [ { "name": "image1", "type": "*", "links": [ 2772 ] }, { "name": "image2", "type": "*", "links": null }, { "name": "mask1", "type": "*", "links": null }, { "name": "mask2", "type": "*", "links": null }, { "name": "number1", "type": "*", "links": [] }, { "name": "number2", "type": "*", "links": [] }, { "name": "text1", "type": "*", "links": [] }, { "name": "text2", "type": "*", "links": null }, { "name": "list1", "type": "*", "links": null }, { "name": "dict1", "type": "*", "links": null }, { "name": "any1", "type": "*", "links": null }, { "name": "any2", "type": "*", "links": null }, { "name": "any3", "type": "*", "links": null }, { "name": "any4", "type": "*", "links": null } ], "title": "Upscale to a bucket", "properties": { "cnr_id": 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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 = 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"collapsed": true }, "order": 39, "mode": 0, "inputs": [], "outputs": [ { "name": "STRING", "type": "STRING", "links": [ 2774 ] } ], "title": "Code", "properties": { "cnr_id": "comfy-core", "ver": "0.3.48", "Node name for S&R": "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" ] }, { "id": 1360, "type": "Reroute", "pos": [ -710, -740 ], "size": [ 75, 26 ], "flags": {}, "order": 123, "mode": 0, "inputs": [ { "name": "", "type": "*", "link": 2938 } ], "outputs": [ { "name": "", "type": "CLIP", "links": [ 2941 ] } ], "properties": { "showOutputText": false, "horizontal": false } }, { "id": 1361, "type": "Reroute", "pos": [ 40, -740 ], "size": [ 75, 26 ], "flags": {}, "order": 140, "mode": 0, "inputs": [ { "name": "", "type": "*", "link": 2941 } ], "outputs": [ { "name": "", "type": "CLIP", "links": [ 2942 ] } ], "properties": { "showOutputText": false, "horizontal": false } }, { "id": 1025, "type": "GetNode", "pos": [ -280, -740 ], "size": [ 210, 60 ], "flags": { "collapsed": true }, "order": 40, "mode": 0, "inputs": [], "outputs": [ { "name": "BOOLEAN", "type": "BOOLEAN", "links": [ 2248 ] } ], "title": "Get_Hires", "properties": {}, "widgets_values": [ "Hires" ] }, { "id": 1391, "type": "PreviewImage", "pos": [ 2410, 1040 ], "size": [ 210, 246.0000000000001 ], "flags": {}, "order": 96, "mode": 0, "inputs": [ { "name": "images", "type": "IMAGE", "link": 3012 } ], "outputs": [], "properties": { "cnr_id": "comfy-core", "ver": "0.6.0", "Node name for S&R": "PreviewImage" }, "widgets_values": [] }, { "id": 1292, "type": "PrimitiveFloat", "pos": [ 1400, -180 ], "size": [ 210, 58 ], "flags": {}, "order": 41, "mode": 0, "inputs": [], "outputs": [ { "name": "FLOAT", "type": "FLOAT", "links": [ 2773 ] } ], "title": "Upscale by", "properties": { "cnr_id": "comfy-core", "ver": "0.3.68", "Node name for S&R": "PrimitiveFloat" }, "widgets_values": [ 1.3 ] }, { "id": 1294, "type": "Exec Python Code Script", "pos": [ 1660, -210 ], "size": [ 290, 790 ], "flags": { "collapsed": true }, "order": 117, "mode": 0, "inputs": [ { "name": "image1", "shape": 7, "type": "IMAGE", "link": 2775 }, { "name": "image2", "shape": 7, "type": "IMAGE", "link": null }, { "name": "mask1", "shape": 7, "type": "MASK", "link": null }, { "name": "mask2", "shape": 7, "type": "MASK", "link": null }, { "name": "list1", "shape": 7, "type": "*", "link": null }, { "name": "dict1", "shape": 7, "type": "*", "link": null }, { "name": "any1", "shape": 7, "type": "*", "link": 2773 }, { "name": "any2", "shape": 7, "type": "*", "link": null }, { "name": "any3", "shape": 7, "type": "*", "link": null }, { "name": "any4", "shape": 7, "type": "*", "link": null }, { "name": "raw_code", "type": "STRING", "widget": { "name": "raw_code" }, "link": 2774 } ], "outputs": [ { "name": "image1", "type": "*", "links": [ 2776 ] }, { "name": "image2", "type": "*", "links": null }, { "name": "mask1", "type": "*", "links": null }, { "name": "mask2", "type": "*", "links": null }, { "name": "number1", "type": "*", "links": [] }, { "name": "number2", "type": "*", "links": [] }, { "name": "text1", "type": "*", "links": [] }, { "name": "text2", "type": "*", "links": null }, { "name": "list1", "type": "*", "links": null }, { "name": "dict1", "type": "*", "links": null }, { "name": "any1", "type": "*", "links": null }, { "name": "any2", "type": "*", "links": null }, { "name": "any3", "type": "*", "links": null }, { "name": "any4", "type": "*", "links": null } ], "title": "Upscale to a bucket", "properties": { "cnr_id": "python-interpreter-node", "ver": "2.1.0", "Node name for S&R": "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, "" ] }, { "id": 82, "type": "UltralyticsDetectorProvider", "pos": [ -50, -860 ], "size": [ 370, 80 ], "flags": { "collapsed": true }, "order": 42, "mode": 0, "inputs": [], "outputs": [ { "name": "BBOX_DETECTOR", "type": "BBOX_DETECTOR", "links": [ 2894 ] }, { "name": "SEGM_DETECTOR", "type": "SEGM_DETECTOR", "slot_index": 1, "links": [ 2675, 2893 ] } ], "title": "UltralyticsDetector", "properties": { "cnr_id": "comfyui-impact-subpack", "ver": "1.2.9", "Node name for S&R": "UltralyticsDetectorProvider" }, "widgets_values": [ "segm/fdetailerAdetailerFor_v11.pt" ] }, { "id": 919, "type": "SegmDetectorCombined_v2", "pos": [ 400, -860 ], "size": [ 271.8443298339844, 102 ], "flags": { "collapsed": true }, "order": 104, "mode": 0, "inputs": [ { "name": "segm_detector", "type": "SEGM_DETECTOR", "link": 2675 }, { "name": "image", "type": "IMAGE", "link": 2783 } ], "outputs": [ { "name": "MASK", "type": "MASK", "links": [ 2759 ] } ], "title": "SEGM Detector", "properties": { "cnr_id": "comfyui-impact-pack", "ver": "8.17.0", "Node name for S&R": "SegmDetectorCombined_v2" }, "widgets_values": [ 0.6000000000000001, 10 ] }, { "id": 1296, "type": "PrimitiveStringMultiline", "pos": [ 610, -690 ], "size": [ 440, 160 ], "flags": { "collapsed": true }, "order": 43, "mode": 0, "inputs": [], "outputs": [ { "name": "STRING", "type": "STRING", "links": [ 2777 ] } ], "title": "Code", "properties": { "cnr_id": "comfy-core", "ver": "0.3.48", "Node name for S&R": "PrimitiveStringMultiline" }, "widgets_values": [ "import os\nfrom PIL import Image\nimport torchvision.transforms as T\nimport torch\n\n# --- Paths ---\ntemp_dir = os.path.join('.', 'ComfyUI', 'temp')\nos.makedirs(temp_dir, exist_ok=True)\nsave_path = os.path.join(temp_dir, 'imagebuffer.png') # persistent buffer\n\n# --- Extract input tensor ---\ntensor = image1.tensor if hasattr(image1, 'tensor') else image1.image if hasattr(image1, 'image') else image1.data\n\n# Take first frame and first 3 channels, permute to [C,H,W]\ntensor = tensor[0, :, :, :3].permute(2, 0, 1)\n\n# Normalize if needed\nif tensor.max() > 1.0:\n tensor = tensor / 255.0\n\n# Convert to PIL and tensor for comparison\nimg_pil = T.ToPILImage()(tensor)\ncurr_tensor = T.ToTensor()(img_pil) # [C,H,W]\n\n# --- Compare with saved image ---\nif os.path.exists(save_path):\n prev_img = Image.open(save_path).convert(\"RGB\")\n prev_tensor = T.ToTensor()(prev_img)\n\n # Resize if dimensions differ\n if prev_tensor.shape != curr_tensor.shape:\n prev_tensor = torch.nn.functional.interpolate(prev_tensor.unsqueeze(0), size=curr_tensor.shape[1:], mode='bilinear', align_corners=False).squeeze(0)\n\n # Compute MSE\n mse = torch.mean((curr_tensor - prev_tensor) ** 2).item()\nelse:\n mse = 1.0 # force \"never\" on first run\n\n# --- Decide output with tolerance ---\nthreshold = 1e-3 # adjust tolerance: higher = more tolerant\nif mse < threshold:\n text1.data = \"always\"\nelse:\n text1.data = \"never\"\n\n# --- Save current image to buffer ---\nimg_pil.save(save_path)\n" ] }, { "id": 1295, "type": "Exec Python Code Script", "pos": [ 790, -690 ], "size": [ 290, 790 ], "flags": { "collapsed": true }, "order": 179, "mode": 0, "inputs": [ { "name": "image1", "shape": 7, "type": "IMAGE", "link": 2782 }, { "name": "image2", "shape": 7, "type": "IMAGE", "link": null }, { "name": "mask1", "shape": 7, "type": "MASK", "link": null }, { "name": "mask2", "shape": 7, "type": "MASK", "link": null }, { "name": "list1", "shape": 7, "type": "*", "link": null }, { "name": "dict1", "shape": 7, "type": "*", "link": null }, { "name": "any1", "shape": 7, "type": "*", "link": null }, { "name": "any2", "shape": 7, "type": "*", "link": null }, { "name": "any3", "shape": 7, "type": "*", "link": null }, { "name": "any4", "shape": 7, "type": "*", "link": null }, { "name": "raw_code", "type": 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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, "" ] }, { "id": 139, "type": "KSampler", "pos": [ -430, -690 ], "size": [ 270, 486 ], "flags": {}, "order": 151, "mode": 0, "inputs": [ { "name": "model", "type": "MODEL", "link": 3021 }, { "name": "positive", "type": "CONDITIONING", "link": 2293 }, { "name": "negative", "type": "CONDITIONING", "link": 2294 }, { "name": "latent_image", "type": "LATENT", "link": 2295 }, { "name": "seed", "type": "INT", "widget": { "name": "seed" }, "link": 795 }, { "name": "steps", "type": "INT", "widget": { "name": "steps" }, "link": 2078 }, { "name": "cfg", "type": "FLOAT", "widget": { "name": "cfg" }, "link": 2079 } ], "outputs": [ { "name": "LATENT", "type": "LATENT", 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