setDelay(1000/fps).\n\t * @param fps\n\t * float frame rate (frames per second)\n\t */\n\n\tvar setFrameRate = exports.setFrameRate = function setFrameRate(fps) {\n\t\tif (fps != 0xf) delay = Math.round(100 / fps);\n\t};\n\n\t/**\n\t * Sets quality of color quantization (conversion of images to the maximum 256\n\t * colors allowed by the GIF specification). Lower values (minimum = 1)\n\t * produce better colors, but slow processing significantly. 10 is the\n\t * default, and produces good color mapping at reasonable speeds. Values\n\t * greater than 20 do not yield significant improvements in speed.\n\t * @param quality\n\t * int greater than 0.\n\t * @return\n\t */\n\n\tvar setQuality = exports.setQuality = function setQuality(quality) {\n\t\tif (quality < 1) quality = 1;\n\t\tsample = quality;\n\t};\n\n\t/**\n\t * Sets the GIF frame size. The default size is the size of the first frame\n\t * added if this method is not invoked.\n\t * @param w\n\t * int frame width.\n\t * @param h\n\t * int frame width.\n\t */\n\n\tvar setSize = exports.setSize = function setSize(w, h) {\n\n\t\tif (started && !firstFrame) return;\n\t\twidth = w;\n\t\theight = h;\n\t\tif (width < 1) width = 320;\n\t\tif (height < 1) height = 240;\n\t\tsizeSet = true;\n\t};\n\n\t/**\n\t * Initiates GIF file creation on the given stream.\n\t * @param os\n\t * OutputStream on which GIF images are written.\n\t * @return false if initial write failed.\n\t */\n\n\tvar start = exports.start = function start() {\n\n\t\treset();\n\t\tvar ok = true;\n\t\tcloseStream = false;\n\t\tout = new ByteArray();\n\t\ttry {\n\t\t\tout.writeUTFBytes(\"GIF89a\"); // header\n\t\t} catch (e) {\n\t\t\tok = false;\n\t\t}\n\n\t\treturn started = ok;\n\t};\n\n\tvar cont = exports.cont = function cont() {\n\n\t\treset();\n\t\tvar ok = true;\n\t\tcloseStream = false;\n\t\tout = new ByteArray();\n\n\t\treturn started = ok;\n\t};\n\n\t/**\n\t * Analyzes image colors and creates color map.\n\t */\n\n\tvar analyzePixels = function analyzePixels() {\n\n\t\tvar len = pixels.length;\n\t\tvar nPix = len / 3;\n\t\tindexedPixels = [];\n\t\tvar nq = new NeuQuant(pixels, len, sample);\n\n\t\t// initialize quantizer\n\t\tcolorTab = nq.process(); // create reduced palette\n\n\t\t// map image pixels to new palette\n\t\tvar k = 0;\n\t\tfor (var j = 0; j < nPix; j++) {\n\t\t\tvar index = nq.map(pixels[k++] & 0xff, pixels[k++] & 0xff, pixels[k++] & 0xff);\n\t\t\tusedEntry[index] = true;\n\t\t\tindexedPixels[j] = index;\n\t\t}\n\n\t\tpixels = null;\n\t\tcolorDepth = 8;\n\t\tpalSize = 7;\n\n\t\t// get closest match to transparent color if specified\n\t\tif (transparent !== null) {\n\t\t\ttransIndex = findClosest(transparent);\n\t\t}\n\t};\n\n\t/**\n\t * Returns index of palette color closest to c\n\t */\n\n\tvar findClosest = function findClosest(c) {\n\n\t\tif (colorTab === null) return -1;\n\t\tvar r = (c & 0xFF0000) >> 16;\n\t\tvar g = (c & 0x00FF00) >> 8;\n\t\tvar b = (c & 0x0000FF);\n\t\tvar minpos = 0;\n\t\tvar dmin = 256 * 256 * 256;\n\t\tvar len = colorTab.length;\n\n\t\tfor (var i = 0; i < len;) {\n\t\t\tvar dr = r - (colorTab[i++] & 0xff);\n\t\t\tvar dg = g - (colorTab[i++] & 0xff);\n\t\t\tvar db = b - (colorTab[i] & 0xff);\n\t\t\tvar d = dr * dr + dg * dg + db * db;\n\t\t\tvar index = i / 3;\n\t\t\tif (usedEntry[index] && (d < dmin)) {\n\t\t\t\tdmin = d;\n\t\t\t\tminpos = index;\n\t\t\t}\n\t\t\ti++;\n\t\t}\n\t\treturn minpos;\n\t};\n\n\t/**\n\t * Extracts image pixels into byte array \"pixels\n\t */\n\n\tvar getImagePixels = function getImagePixels() {\n\t\tvar w = width;\n\t\tvar h = height;\n\t\tpixels = [];\n\t\tvar data = image;\n\t\tvar count = 0;\n\n\t\tfor (var i = 0; i < h; i++) {\n\n\t\t\tfor (var j = 0; j < w; j++) {\n\n\t\t\t\tvar b = (i * w * 4) + j * 4;\n\t\t\t\tpixels[count++] = data[b];\n\t\t\t\tpixels[count++] = data[b + 1];\n\t\t\t\tpixels[count++] = data[b + 2];\n\n\t\t\t}\n\n\t\t}\n\t};\n\n\t/**\n\t * Writes Graphic Control Extension\n\t */\n\n\tvar writeGraphicCtrlExt = function writeGraphicCtrlExt() {\n\t\tout.writeByte(0x21); // extension introducer\n\t\tout.writeByte(0xf9); // GCE label\n\t\tout.writeByte(4); // data block size\n\t\tvar transp;\n\t\tvar disp;\n\t\tif (transparent === null) {\n\t\t\ttransp = 0;\n\t\t\tdisp = 0; // dispose = no action\n\t\t} else {\n\t\t\ttransp = 1;\n\t\t\tdisp = 2; // force clear if using transparent color\n\t\t}\n\t\tif (dispose >= 0) {\n\t\t\tdisp = dispose & 7; // user override\n\t\t}\n\t\tdisp <<= 2;\n\t\t// packed fields\n\t\tout.writeByte(0 | // 1:3 reserved\n\t\t\tdisp | // 4:6 disposal\n\t\t\t0 | // 7 user input - 0 = none\n\t\t\ttransp); // 8 transparency flag\n\n\t\tWriteShort(delay); // delay x 1/100 sec\n\t\tout.writeByte(transIndex); // transparent color index\n\t\tout.writeByte(0); // block terminator\n\t};\n\n\t/**\n\t * Writes Comment Extention\n\t */\n\n\tvar writeCommentExt = function writeCommentExt() {\n\t\tout.writeByte(0x21); // extension introducer\n\t\tout.writeByte(0xfe); // comment label\n\t\tout.writeByte(comment.length); // Block Size (s)\n\t\tout.writeUTFBytes(comment);\n\t\tout.writeByte(0); // block terminator\n\t};\n\n\n\t/**\n\t * Writes Image Descriptor\n\t */\n\n\tvar writeImageDesc = function writeImageDesc() {\n\n\t\tout.writeByte(0x2c); // image separator\n\t\tWriteShort(0); // image position x,y = 0,0\n\t\tWriteShort(0);\n\t\tWriteShort(width); // image size\n\t\tWriteShort(height);\n\n\t\t// packed fields\n\t\tif (firstFrame) {\n\t\t\t// no LCT - GCT is used for first (or only) frame\n\t\t\tout.writeByte(0);\n\t\t} else {\n\t\t\t// specify normal LCT\n\t\t\tout.writeByte(0x80 | // 1 local color table 1=yes\n\t\t\t\t0 | // 2 interlace - 0=no\n\t\t\t\t0 | // 3 sorted - 0=no\n\t\t\t\t0 | // 4-5 reserved\n\t\t\t\tpalSize); // 6-8 size of color table\n\t\t}\n\t};\n\n\t/**\n\t * Writes Logical Screen Descriptor\n\t */\n\n\tvar writeLSD = function writeLSD() {\n\n\t\t// logical screen size\n\t\tWriteShort(width);\n\t\tWriteShort(height);\n\t\t// packed fields\n\t\tout.writeByte((0x80 | // 1 : global color table flag = 1 (gct used)\n\t\t\t0x70 | // 2-4 : color resolution = 7\n\t\t\t0x00 | // 5 : gct sort flag = 0\n\t\t\tpalSize)); // 6-8 : gct size\n\n\t\tout.writeByte(0); // background color index\n\t\tout.writeByte(0); // pixel aspect ratio - assume 1:1\n\t};\n\n\t/**\n\t * Writes Netscape application extension to define repeat count.\n\t */\n\n\tvar writeNetscapeExt = function writeNetscapeExt() {\n\t\tout.writeByte(0x21); // extension introducer\n\t\tout.writeByte(0xff); // app extension label\n\t\tout.writeByte(11); // block size\n\t\tout.writeUTFBytes(\"NETSCAPE\" + \"2.0\"); // app id + auth code\n\t\tout.writeByte(3); // sub-block size\n\t\tout.writeByte(1); // loop sub-block id\n\t\tWriteShort(repeat); // loop count (extra iterations, 0=repeat forever)\n\t\tout.writeByte(0); // block terminator\n\t};\n\n\t/**\n\t * Writes color table\n\t */\n\n\tvar writePalette = function writePalette() {\n\t\tout.writeBytes(colorTab);\n\t\tvar n = (3 * 256) - colorTab.length;\n\t\tfor (var i = 0; i < n; i++) out.writeByte(0);\n\t};\n\n\tvar WriteShort = function WriteShort(pValue) {\n\t\tout.writeByte(pValue & 0xFF);\n\t\tout.writeByte((pValue >> 8) & 0xFF);\n\t};\n\n\t/**\n\t * Encodes and writes pixel data\n\t */\n\n\tvar writePixels = function writePixels() {\n\t\tvar myencoder = new LZWEncoder(width, height, indexedPixels, colorDepth);\n\t\tmyencoder.encode(out);\n\t};\n\n\t/**\n\t * Retrieves the GIF stream\n\t */\n\n\tvar stream = exports.stream = function stream() {\n\t\treturn out;\n\t};\n\n\tvar setProperties = exports.setProperties = function setProperties(has_start, is_first) {\n\t\tstarted = has_start;\n\t\tfirstFrame = is_first;\n\t};\n\n\treturn exports;\n\n};\n/**\n * This class handles LZW encoding\n * Adapted from Jef Poskanzer's Java port by way of J. M. G. Elliott.\n * @author Kevin Weiner (original Java version - kweiner@fmsware.com)\n * @author Thibault Imbert (AS3 version - bytearray.org)\n * @author Kevin Kwok (JavaScript version - https://github.com/antimatter15/jsgif)\n * @version 0.1 AS3 implementation\n */\n\nLZWEncoder = function() {\n\n\tvar exports = {};\n\tvar EOF = -1;\n\tvar imgW;\n\tvar imgH;\n\tvar pixAry;\n\tvar initCodeSize;\n\tvar remaining;\n\tvar curPixel;\n\n\t// GIFCOMPR.C - GIF Image compression routines\n\t// Lempel-Ziv compression based on 'compress'. GIF modifications by\n\t// David Rowley (mgardi@watdcsu.waterloo.edu)\n\t// General DEFINEs\n\n\tvar BITS = 12;\n\tvar HSIZE = 5003; // 80% occupancy\n\n\t// GIF Image compression - modified 'compress'\n\t// Based on: compress.c - File compression ala IEEE Computer, June 1984.\n\t// By Authors: Spencer W. Thomas (decvax!harpo!utah-cs!utah-gr!thomas)\n\t// Jim McKie (decvax!mcvax!jim)\n\t// Steve Davies (decvax!vax135!petsd!peora!srd)\n\t// Ken Turkowski (decvax!decwrl!turtlevax!ken)\n\t// James A. Woods (decvax!ihnp4!ames!jaw)\n\t// Joe Orost (decvax!vax135!petsd!joe)\n\n\tvar n_bits; // number of bits/code\n\tvar maxbits = BITS; // user settable max # bits/code\n\tvar maxcode; // maximum code, given n_bits\n\tvar maxmaxcode = 1 << BITS; // should NEVER generate this code\n\tvar htab = [];\n\tvar codetab = [];\n\tvar hsize = HSIZE; // for dynamic table sizing\n\tvar free_ent = 0; // first unused entry\n\n\t// block compression parameters -- after all codes are used up,\n\t// and compression rate changes, start over.\n\n\tvar clear_flg = false;\n\n\t// Algorithm: use open addressing double hashing (no chaining) on the\n\t// prefix code / next character combination. We do a variant of Knuth's\n\t// algorithm D (vol. 3, sec. 6.4) along with G. Knott's relatively-prime\n\t// secondary probe. Here, the modular division first probe is gives way\n\t// to a faster exclusive-or manipulation. Also do block compression with\n\t// an adaptive reset, whereby the code table is cleared when the compression\n\t// ratio decreases, but after the table fills. The variable-length output\n\t// codes are re-sized at this point, and a special CLEAR code is generated\n\t// for the decompressor. Late addition: construct the table according to\n\t// file size for noticeable speed improvement on small files. Please direct\n\t// questions about this implementation to ames!jaw.\n\n\tvar g_init_bits;\n\tvar ClearCode;\n\tvar EOFCode;\n\n\t// output\n\t// Output the given code.\n\t// Inputs:\n\t// code: A n_bits-bit integer. If == -1, then EOF. This assumes\n\t// that n_bits =< wordsize - 1.\n\t// Outputs:\n\t// Outputs code to the file.\n\t// Assumptions:\n\t// Chars are 8 bits long.\n\t// Algorithm:\n\t// Maintain a BITS character long buffer (so that 8 codes will\n\t// fit in it exactly). Use the VAX insv instruction to insert each\n\t// code in turn. When the buffer fills up empty it and start over.\n\n\tvar cur_accum = 0;\n\tvar cur_bits = 0;\n\tvar masks = [0x0000, 0x0001, 0x0003, 0x0007, 0x000F, 0x001F, 0x003F, 0x007F, 0x00FF, 0x01FF, 0x03FF, 0x07FF, 0x0FFF, 0x1FFF, 0x3FFF, 0x7FFF, 0xFFFF];\n\n\t// Number of characters so far in this 'packet'\n\tvar a_count;\n\n\t// Define the storage for the packet accumulator\n\tvar accum = [];\n\n\tvar LZWEncoder = exports.LZWEncoder = function LZWEncoder(width, height, pixels, color_depth) {\n\t\timgW = width;\n\t\timgH = height;\n\t\tpixAry = pixels;\n\t\tinitCodeSize = Math.max(2, color_depth);\n\t};\n\n\t// Add a character to the end of the current packet, and if it is 254\n\t// characters, flush the packet to disk.\n\tvar char_out = function char_out(c, outs) {\n\t\taccum[a_count++] = c;\n\t\tif (a_count >= 254) flush_char(outs);\n\t};\n\n\t// Clear out the hash table\n\t// table clear for block compress\n\n\tvar cl_block = function cl_block(outs) {\n\t\tcl_hash(hsize);\n\t\tfree_ent = ClearCode + 2;\n\t\tclear_flg = true;\n\t\toutput(ClearCode, outs);\n\t};\n\n\t// reset code table\n\tvar cl_hash = function cl_hash(hsize) {\n\t\tfor (var i = 0; i < hsize; ++i) htab[i] = -1;\n\t};\n\n\tvar compress = exports.compress = function compress(init_bits, outs) {\n\n\t\tvar fcode;\n\t\tvar i; /* = 0 */\n\t\tvar c;\n\t\tvar ent;\n\t\tvar disp;\n\t\tvar hsize_reg;\n\t\tvar hshift;\n\n\t\t// Set up the globals: g_init_bits - initial number of bits\n\t\tg_init_bits = init_bits;\n\n\t\t// Set up the necessary values\n\t\tclear_flg = false;\n\t\tn_bits = g_init_bits;\n\t\tmaxcode = MAXCODE(n_bits);\n\n\t\tClearCode = 1 << (init_bits - 1);\n\t\tEOFCode = ClearCode + 1;\n\t\tfree_ent = ClearCode + 2;\n\n\t\ta_count = 0; // clear packet\n\n\t\tent = nextPixel();\n\n\t\thshift = 0;\n\t\tfor (fcode = hsize; fcode < 65536; fcode *= 2)\n\t\t\t++hshift;\n\t\thshift = 8 - hshift; // set hash code range bound\n\n\t\thsize_reg = hsize;\n\t\tcl_hash(hsize_reg); // clear hash table\n\n\t\toutput(ClearCode, outs);\n\n\t\touter_loop: while ((c = nextPixel()) != EOF) {\n\t\t\tfcode = (c << maxbits) + ent;\n\t\t\ti = (c << hshift) ^ ent; // xor hashing\n\n\t\t\tif (htab[i] == fcode) {\n\t\t\t\tent = codetab[i];\n\t\t\t\tcontinue;\n\t\t\t}\n\n\t\t\telse if (htab[i] >= 0) { // non-empty slot\n\n\t\t\t\tdisp = hsize_reg - i; // secondary hash (after G. Knott)\n\t\t\t\tif (i === 0) disp = 1;\n\n\t\t\t\tdo {\n\t\t\t\t\tif ((i -= disp) < 0)\n\t\t\t\t\t\ti += hsize_reg;\n\n\t\t\t\t\tif (htab[i] == fcode) {\n\t\t\t\t\t\tent = codetab[i];\n\t\t\t\t\t\tcontinue outer_loop;\n\t\t\t\t\t}\n\t\t\t\t} while (htab[i] >= 0);\n\t\t\t}\n\n\t\t\toutput(ent, outs);\n\t\t\tent = c;\n\t\t\tif (free_ent < maxmaxcode) {\n\t\t\t\tcodetab[i] = free_ent++; // code -> hashtable\n\t\t\t\thtab[i] = fcode;\n\t\t\t}\n\t\t\telse cl_block(outs);\n\t\t}\n\n\t\t// Put out the final code.\n\t\toutput(ent, outs);\n\t\toutput(EOFCode, outs);\n\t};\n\n\t// ----------------------------------------------------------------------------\n\tvar encode = exports.encode = function encode(os) {\n\t\tos.writeByte(initCodeSize); // write \"initial code size\" byte\n\t\tremaining = imgW * imgH; // reset navigation variables\n\t\tcurPixel = 0;\n\t\tcompress(initCodeSize + 1, os); // compress and write the pixel data\n\t\tos.writeByte(0); // write block terminator\n\t};\n\n\t// Flush the packet to disk, and reset the accumulator\n\tvar flush_char = function flush_char(outs) {\n\t\tif (a_count > 0) {\n\t\t\touts.writeByte(a_count);\n\t\t\touts.writeBytes(accum, 0, a_count);\n\t\t\ta_count = 0;\n\t\t}\n\t};\n\n\tvar MAXCODE = function MAXCODE(n_bits) {\n\t\treturn (1 << n_bits) - 1;\n\t};\n\n\t// ----------------------------------------------------------------------------\n\t// Return the next pixel from the image\n\t// ----------------------------------------------------------------------------\n\n\tvar nextPixel = function nextPixel() {\n\t\tif (remaining === 0) return EOF;\n\t\t--remaining;\n\t\tvar pix = pixAry[curPixel++];\n\t\treturn pix & 0xff;\n\t};\n\n\tvar output = function output(code, outs) {\n\n\t\tcur_accum &= masks[cur_bits];\n\n\t\tif (cur_bits > 0) cur_accum |= (code << cur_bits);\n\t\telse cur_accum = code;\n\n\t\tcur_bits += n_bits;\n\n\t\twhile (cur_bits >= 8) {\n\t\t\tchar_out((cur_accum & 0xff), outs);\n\t\t\tcur_accum >>= 8;\n\t\t\tcur_bits -= 8;\n\t\t}\n\n\t\t// If the next entry is going to be too big for the code size,\n\t\t// then increase it, if possible.\n\n\t\tif (free_ent > maxcode || clear_flg) {\n\n\t\t\tif (clear_flg) {\n\n\t\t\t\tmaxcode = MAXCODE(n_bits = g_init_bits);\n\t\t\t\tclear_flg = false;\n\n\t\t\t} else {\n\n\t\t\t\t++n_bits;\n\t\t\t\tif (n_bits == maxbits) maxcode = maxmaxcode;\n\t\t\t\telse maxcode = MAXCODE(n_bits);\n\t\t\t}\n\t\t}\n\n\t\tif (code == EOFCode) {\n\n\t\t\t// At EOF, write the rest of the buffer.\n\t\t\twhile (cur_bits > 0) {\n\t\t\t\tchar_out((cur_accum & 0xff), outs);\n\t\t\t\tcur_accum >>= 8;\n\t\t\t\tcur_bits -= 8;\n\t\t\t}\n\n\t\t\tflush_char(outs);\n\t\t}\n\t};\n\n\tLZWEncoder.apply(this, arguments);\n\treturn exports;\n};\n/*\n * NeuQuant Neural-Net Quantization Algorithm\n * ------------------------------------------\n *\n * Copyright (c) 1994 Anthony Dekker\n *\n * NEUQUANT Neural-Net quantization algorithm by Anthony Dekker, 1994. See\n * \"Kohonen neural networks for optimal colour quantization\" in \"Network:\n * Computation in Neural Systems\" Vol. 5 (1994) pp 351-367. for a discussion of\n * the algorithm.\n *\n * Any party obtaining a copy of these files from the author, directly or\n * indirectly, is granted, free of charge, a full and unrestricted irrevocable,\n * world-wide, paid up, royalty-free, nonexclusive right and license to deal in\n * this software and documentation files (the \"Software\"), including without\n * limitation the rights to use, copy, modify, merge, publish, distribute,\n * sublicense, and/or sell copies of the Software, and to permit persons who\n * receive copies from any such party to do so, with the only requirement being\n * that this copyright notice remain intact.\n */\n\n/*\n * This class handles Neural-Net quantization algorithm\n * @author Kevin Weiner (original Java version - kweiner@fmsware.com)\n * @author Thibault Imbert (AS3 version - bytearray.org)\n * @author Kevin Kwok (JavaScript version - https://github.com/antimatter15/jsgif)\n * @version 0.1 AS3 implementation\n */\n\nNeuQuant = function() {\n\n\tvar exports = {};\n\tvar netsize = 256; /* number of colours used */\n\n\t/* four primes near 500 - assume no image has a length so large */\n\t/* that it is divisible by all four primes */\n\n\tvar prime1 = 499;\n\tvar prime2 = 491;\n\tvar prime3 = 487;\n\tvar prime4 = 503;\n\tvar minpicturebytes = (3 * prime4); /* minimum size for input image */\n\n\t/*\n\t * Program Skeleton ---------------- [select samplefac in range 1..30] [read\n\t * image from input file] pic = (unsigned char*) malloc(3*width*height);\n\t * initnet(pic,3*width*height,samplefac); learn(); unbiasnet(); [write output\n\t * image header, using writecolourmap(f)] inxbuild(); write output image using\n\t * inxsearch(b,g,r)\n\t */\n\n\t/*\n\t * Network Definitions -------------------\n\t */\n\n\tvar maxnetpos = (netsize - 1);\n\tvar netbiasshift = 4; /* bias for colour values */\n\tvar ncycles = 100; /* no. of learning cycles */\n\n\t/* defs for freq and bias */\n\tvar intbiasshift = 16; /* bias for fractions */\n\tvar intbias = (1 << intbiasshift);\n\tvar gammashift = 10; /* gamma = 1024 */\n\tvar gamma = (1 << gammashift);\n\tvar betashift = 10;\n\tvar beta = (intbias >> betashift); /* beta = 1/1024 */\n\tvar betagamma = (intbias << (gammashift - betashift));\n\n\t/* defs for decreasing radius factor */\n\tvar initrad = (netsize >> 3); /* for 256 cols, radius starts */\n\tvar radiusbiasshift = 6; /* at 32.0 biased by 6 bits */\n\tvar radiusbias = (1 << radiusbiasshift);\n\tvar initradius = (initrad * radiusbias); /* and decreases by a */\n\tvar radiusdec = 30; /* factor of 1/30 each cycle */\n\n\t/* defs for decreasing alpha factor */\n\tvar alphabiasshift = 10; /* alpha starts at 1.0 */\n\tvar initalpha = (1 << alphabiasshift);\n\tvar alphadec; /* biased by 10 bits */\n\n\t/* radbias and alpharadbias used for radpower calculation */\n\tvar radbiasshift = 8;\n\tvar radbias = (1 << radbiasshift);\n\tvar alpharadbshift = (alphabiasshift + radbiasshift);\n\tvar alpharadbias = (1 << alpharadbshift);\n\n\t/*\n\t * Types and Global Variables --------------------------\n\t */\n\n\tvar thepicture; /* the input image itself */\n\tvar lengthcount; /* lengthcount = H*W*3 */\n\tvar samplefac; /* sampling factor 1..30 */\n\n\t// typedef int pixel[4]; /* BGRc */\n\tvar network; /* the network itself - [netsize][4] */\n\tvar netindex = [];\n\n\t/* for network lookup - really 256 */\n\tvar bias = [];\n\n\t/* bias and freq arrays for learning */\n\tvar freq = [];\n\tvar radpower = [];\n\n\tvar NeuQuant = exports.NeuQuant = function NeuQuant(thepic, len, sample) {\n\n\t\tvar i;\n\t\tvar p;\n\n\t\tthepicture = thepic;\n\t\tlengthcount = len;\n\t\tsamplefac = sample;\n\n\t\tnetwork = new Array(netsize);\n\n\t\tfor (i = 0; i < netsize; i++) {\n\n\t\t\tnetwork[i] = new Array(4);\n\t\t\tp = network[i];\n\t\t\tp[0] = p[1] = p[2] = (i << (netbiasshift + 8)) / netsize;\n\t\t\tfreq[i] = intbias / netsize; /* 1/netsize */\n\t\t\tbias[i] = 0;\n\t\t}\n\t};\n\n\tvar colorMap = function colorMap() {\n\n\t\tvar map = [];\n\t\tvar index = new Array(netsize);\n\n\t\tfor (var i = 0; i < netsize; i++)\n\t\t\tindex[network[i][3]] = i;\n\n\t\tvar k = 0;\n\t\tfor (var l = 0; l < netsize; l++) {\n\t\t\tvar j = index[l];\n\t\t\tmap[k++] = (network[j][0]);\n\t\t\tmap[k++] = (network[j][1]);\n\t\t\tmap[k++] = (network[j][2]);\n\t\t}\n\n\t\treturn map;\n\t};\n\n\t/*\n\t * Insertion sort of network and building of netindex[0..255] (to do after\n\t * unbias)\n\t * -------------------------------------------------------------------------------\n\t */\n\n\tvar inxbuild = function inxbuild() {\n\n\t\tvar i;\n\t\tvar j;\n\t\tvar smallpos;\n\t\tvar smallval;\n\t\tvar p;\n\t\tvar q;\n\t\tvar previouscol;\n\t\tvar startpos;\n\n\t\tpreviouscol = 0;\n\t\tstartpos = 0;\n\t\tfor (i = 0; i < netsize; i++) {\n\n\t\t\tp = network[i];\n\t\t\tsmallpos = i;\n\t\t\tsmallval = p[1]; /* index on g */\n\n\t\t\t/* find smallest in i..netsize-1 */\n\t\t\tfor (j = i + 1; j < netsize; j++) {\n\n\t\t\t\tq = network[j];\n\t\t\t\tif (q[1] < smallval) { /* index on g */\n\t\t\t\t\tsmallpos = j;\n\t\t\t\t\tsmallval = q[1]; /* index on g */\n\t\t\t\t}\n\t\t\t}\n\t\t\tq = network[smallpos];\n\n\t\t\t/* swap p (i) and q (smallpos) entries */\n\t\t\tif (i != smallpos) {\n\t\t\t\tj = q[0];\n\t\t\t\tq[0] = p[0];\n\t\t\t\tp[0] = j;\n\t\t\t\tj = q[1];\n\t\t\t\tq[1] = p[1];\n\t\t\t\tp[1] = j;\n\t\t\t\tj = q[2];\n\t\t\t\tq[2] = p[2];\n\t\t\t\tp[2] = j;\n\t\t\t\tj = q[3];\n\t\t\t\tq[3] = p[3];\n\t\t\t\tp[3] = j;\n\t\t\t}\n\n\t\t\t/* smallval entry is now in position i */\n\n\t\t\tif (smallval != previouscol) {\n\n\t\t\t\tnetindex[previouscol] = (startpos + i) >> 1;\n\n\t\t\t\tfor (j = previouscol + 1; j < smallval; j++) netindex[j] = i;\n\n\t\t\t\tpreviouscol = smallval;\n\t\t\t\tstartpos = i;\n\t\t\t}\n\t\t}\n\n\t\tnetindex[previouscol] = (startpos + maxnetpos) >> 1;\n\t\tfor (j = previouscol + 1; j < 256; j++) netindex[j] = maxnetpos; /* really 256 */\n\t};\n\n\t/*\n\t * Main Learning Loop ------------------\n\t */\n\n\tvar learn = function learn() {\n\n\t\tvar i;\n\t\tvar j;\n\t\tvar b;\n\t\tvar g;\n\t\tvar r;\n\t\tvar radius;\n\t\tvar rad;\n\t\tvar alpha;\n\t\tvar step;\n\t\tvar delta;\n\t\tvar samplepixels;\n\t\tvar p;\n\t\tvar pix;\n\t\tvar lim;\n\n\t\tif (lengthcount < minpicturebytes) samplefac = 1;\n\n\t\talphadec = 30 + ((samplefac - 1) / 3);\n\t\tp = thepicture;\n\t\tpix = 0;\n\t\tlim = lengthcount;\n\t\tsamplepixels = lengthcount / (3 * samplefac);\n\t\tdelta = (samplepixels / ncycles) | 0;\n\t\talpha = initalpha;\n\t\tradius = initradius;\n\n\t\trad = radius >> radiusbiasshift;\n\t\tif (rad <= 1) rad = 0;\n\n\t\tfor (i = 0; i < rad; i++) radpower[i] = alpha * (((rad * rad - i * i) * radbias) / (rad * rad));\n\n\t\tif (lengthcount < minpicturebytes) step = 3;\n\n\t\telse if ((lengthcount % prime1) !== 0) step = 3 * prime1;\n\n\t\telse {\n\n\t\t\tif ((lengthcount % prime2) !== 0) step = 3 * prime2;\n\t\t\telse {\n\t\t\t\tif ((lengthcount % prime3) !== 0) step = 3 * prime3;\n\t\t\t\telse step = 3 * prime4;\n\t\t\t}\n\t\t}\n\n\t\ti = 0;\n\t\twhile (i < samplepixels) {\n\n\t\t\tb = (p[pix + 0] & 0xff) << netbiasshift;\n\t\t\tg = (p[pix + 1] & 0xff) << netbiasshift;\n\t\t\tr = (p[pix + 2] & 0xff) << netbiasshift;\n\t\t\tj = contest(b, g, r);\n\n\t\t\taltersingle(alpha, j, b, g, r);\n\t\t\tif (rad !== 0) alterneigh(rad, j, b, g, r); /* alter neighbours */\n\n\t\t\tpix += step;\n\t\t\tif (pix >= lim) pix -= lengthcount;\n\n\t\t\ti++;\n\n\t\t\tif (delta === 0) delta = 1;\n\n\t\t\tif (i % delta === 0) {\n\t\t\t\talpha -= alpha / alphadec;\n\t\t\t\tradius -= radius / radiusdec;\n\t\t\t\trad = radius >> radiusbiasshift;\n\n\t\t\t\tif (rad <= 1) rad = 0;\n\n\t\t\t\tfor (j = 0; j < rad; j++) radpower[j] = alpha * (((rad * rad - j * j) * radbias) / (rad * rad));\n\t\t\t}\n\t\t}\n\t};\n\n\t/*\n\t ** Search for BGR values 0..255 (after net is unbiased) and return colour\n\t * index\n\t * ----------------------------------------------------------------------------\n\t */\n\n\tvar map = exports.map = function map(b, g, r) {\n\n\t\tvar i;\n\t\tvar j;\n\t\tvar dist;\n\t\tvar a;\n\t\tvar bestd;\n\t\tvar p;\n\t\tvar best;\n\n\t\tbestd = 1000; /* biggest possible dist is 256*3 */\n\t\tbest = -1;\n\t\ti = netindex[g]; /* index on g */\n\t\tj = i - 1; /* start at netindex[g] and work outwards */\n\n\t\twhile ((i < netsize) || (j >= 0)) {\n\n\t\t\tif (i < netsize) {\n\t\t\t\tp = network[i];\n\t\t\t\tdist = p[1] - g; /* inx key */\n\n\t\t\t\tif (dist >= bestd) i = netsize; /* stop iter */\n\n\t\t\t\telse {\n\n\t\t\t\t\ti++;\n\t\t\t\t\tif (dist < 0) dist = -dist;\n\t\t\t\t\ta = p[0] - b;\n\t\t\t\t\tif (a < 0) a = -a;\n\t\t\t\t\tdist += a;\n\n\t\t\t\t\tif (dist < bestd) {\n\t\t\t\t\t\ta = p[2] - r;\n\t\t\t\t\t\tif (a < 0) a = -a;\n\t\t\t\t\t\tdist += a;\n\n\t\t\t\t\t\tif (dist < bestd) {\n\t\t\t\t\t\t\tbestd = dist;\n\t\t\t\t\t\t\tbest = p[3];\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\n\t\t\tif (j >= 0) {\n\n\t\t\t\tp = network[j];\n\t\t\t\tdist = g - p[1]; /* inx key - reverse dif */\n\n\t\t\t\tif (dist >= bestd) j = -1; /* stop iter */\n\n\t\t\t\telse {\n\n\t\t\t\t\tj--;\n\t\t\t\t\tif (dist < 0) dist = -dist;\n\t\t\t\t\ta = p[0] - b;\n\t\t\t\t\tif (a < 0) a = -a;\n\t\t\t\t\tdist += a;\n\n\t\t\t\t\tif (dist < bestd) {\n\t\t\t\t\t\ta = p[2] - r;\n\t\t\t\t\t\tif (a < 0) a = -a;\n\t\t\t\t\t\tdist += a;\n\t\t\t\t\t\tif (dist < bestd) {\n\t\t\t\t\t\t\tbestd = dist;\n\t\t\t\t\t\t\tbest = p[3];\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\treturn (best);\n\t};\n\n\tvar process = exports.process = function process() {\n\t\tlearn();\n\t\tunbiasnet();\n\t\tinxbuild();\n\t\treturn colorMap();\n\t};\n\n\t/*\n\t * Unbias network to give byte values 0..255 and record position i to prepare\n\t * for sort\n\t * -----------------------------------------------------------------------------------\n\t */\n\n\tvar unbiasnet = function unbiasnet() {\n\n\t\tvar i;\n\t\tvar j;\n\n\t\tfor (i = 0; i < netsize; i++) {\n\t\t\tnetwork[i][0] >>= netbiasshift;\n\t\t\tnetwork[i][1] >>= netbiasshift;\n\t\t\tnetwork[i][2] >>= netbiasshift;\n\t\t\tnetwork[i][3] = i; /* record colour no */\n\t\t}\n\t};\n\n\t/*\n\t * Move adjacent neurons by precomputed alpha*(1-((i-j)^2/[r]^2)) in\n\t * radpower[|i-j|]\n\t * ---------------------------------------------------------------------------------\n\t */\n\n\tvar alterneigh = function alterneigh(rad, i, b, g, r) {\n\n\t\tvar j;\n\t\tvar k;\n\t\tvar lo;\n\t\tvar hi;\n\t\tvar a;\n\t\tvar m;\n\t\tvar p;\n\n\t\tlo = i - rad;\n\t\tif (lo < -1) lo = -1;\n\n\t\thi = i + rad;\n\t\tif (hi > netsize) hi = netsize;\n\n\t\tj = i + 1;\n\t\tk = i - 1;\n\t\tm = 1;\n\n\t\twhile ((j < hi) || (k > lo)) {\n\t\t\ta = radpower[m++];\n\n\t\t\tif (j < hi) {\n\t\t\t\tp = network[j++];\n\n\t\t\t\ttry {\n\t\t\t\t\tp[0] -= (a * (p[0] - b)) / alpharadbias;\n\t\t\t\t\tp[1] -= (a * (p[1] - g)) / alpharadbias;\n\t\t\t\t\tp[2] -= (a * (p[2] - r)) / alpharadbias;\n\t\t\t\t} catch (e) {} // prevents 1.3 miscompilation\n\t\t\t}\n\n\t\t\tif (k > lo) {\n\t\t\t\tp = network[k--];\n\n\t\t\t\ttry {\n\t\t\t\t\tp[0] -= (a * (p[0] - b)) / alpharadbias;\n\t\t\t\t\tp[1] -= (a * (p[1] - g)) / alpharadbias;\n\t\t\t\t\tp[2] -= (a * (p[2] - r)) / alpharadbias;\n\t\t\t\t} catch (e) {}\n\t\t\t}\n\t\t}\n\t};\n\n\t/*\n\t * Move neuron i towards biased (b,g,r) by factor alpha\n\t * ----------------------------------------------------\n\t */\n\n\tvar altersingle = function altersingle(alpha, i, b, g, r) {\n\n\t\t/* alter hit neuron */\n\t\tvar n = network[i];\n\t\tn[0] -= (alpha * (n[0] - b)) / initalpha;\n\t\tn[1] -= (alpha * (n[1] - g)) / initalpha;\n\t\tn[2] -= (alpha * (n[2] - r)) / initalpha;\n\t};\n\n\t/*\n\t * Search for biased BGR values ----------------------------\n\t */\n\n\tvar contest = function contest(b, g, r) {\n\n\t\t/* finds closest neuron (min dist) and updates freq */\n\t\t/* finds best neuron (min dist-bias) and returns position */\n\t\t/* for frequently chosen neurons, freq[i] is high and bias[i] is negative */\n\t\t/* bias[i] = gamma*((1/netsize)-freq[i]) */\n\n\t\tvar i;\n\t\tvar dist;\n\t\tvar a;\n\t\tvar biasdist;\n\t\tvar betafreq;\n\t\tvar bestpos;\n\t\tvar bestbiaspos;\n\t\tvar bestd;\n\t\tvar bestbiasd;\n\t\tvar n;\n\n\t\tbestd = ~ (1 << 31);\n\t\tbestbiasd = bestd;\n\t\tbestpos = -1;\n\t\tbestbiaspos = bestpos;\n\n\t\tfor (i = 0; i < netsize; i++) {\n\t\t\tn = network[i];\n\t\t\tdist = n[0] - b;\n\t\t\tif (dist < 0) dist = -dist;\n\t\t\ta = n[1] - g;\n\t\t\tif (a < 0) a = -a;\n\t\t\tdist += a;\n\t\t\ta = n[2] - r;\n\t\t\tif (a < 0) a = -a;\n\t\t\tdist += a;\n\n\t\t\tif (dist < bestd) {\n\t\t\t\tbestd = dist;\n\t\t\t\tbestpos = i;\n\t\t\t}\n\n\t\t\tbiasdist = dist - ((bias[i]) >> (intbiasshift - netbiasshift));\n\n\t\t\tif (biasdist < bestbiasd) {\n\t\t\t\tbestbiasd = biasdist;\n\t\t\t\tbestbiaspos = i;\n\t\t\t}\n\n\t\t\tbetafreq = (freq[i] >> betashift);\n\t\t\tfreq[i] -= betafreq;\n\t\t\tbias[i] += (betafreq << gammashift);\n\t\t}\n\n\t\tfreq[bestpos] += beta;\n\t\tbias[bestpos] -= betagamma;\n\t\treturn (bestbiaspos);\n\t};\n\n\tNeuQuant.apply(this, arguments);\n\treturn exports;\n};\nfunction encode64(input) {\n\tvar output = \"\", i = 0, l = input.length,\n\tkey = \"ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/=\", \n\tchr1, chr2, chr3, enc1, enc2, enc3, enc4;\n\twhile (i < l) {\n\t\tchr1 = input.charCodeAt(i++);\n\t\tchr2 = input.charCodeAt(i++);\n\t\tchr3 = input.charCodeAt(i++);\n\t\tenc1 = chr1 >> 2;\n\t\tenc2 = ((chr1 & 3) << 4) | (chr2 >> 4);\n\t\tenc3 = ((chr2 & 15) << 2) | (chr3 >> 6);\n\t\tenc4 = chr3 & 63;\n\t\tif (isNaN(chr2)) enc3 = enc4 = 64;\n\t\telse if (isNaN(chr3)) enc4 = 64;\n\t\toutput = output + key.charAt(enc1) + key.charAt(enc2) + key.charAt(enc3) + key.charAt(enc4);\n\t}\n\treturn output;\n}\n"
},
{
"path": "assets/page.html",
"content": "\n\n \n \n \n \n \n \n
\n \n
\n \n {TITLE}
\n{DESCRIPTION}
\n \n\n \n
\n
\n
\n
\n \n
\n \n \n
\n \n \n \n \n \n \n\n" }, { "path": "assets/proc.js", "content": "/*\n * This file is part of Infinity Grid Generator, view the README.md at https://github.com/mcmonkeyprojects/sd-infinity-grid-generator-script for more information.\n*/\n\nlet suppressUpdate = true;\nlet file_extensions_alt = {};\n\nfunction loadData() {\n let rawHash = window.location.hash;\n document.getElementById('x_' + rawData.axes[0].id).click();\n document.getElementById('x2_none').click();\n document.getElementById('y2_none').click();\n let makegif_axis = document.getElementById('makegif_axis');\n // rawData.ext/title/description\n for (var axis of rawData.axes) {\n // axis.id/title/description\n for (var val of axis.values) {\n // val.key/title/description/show\n let clicktab = getNavValTab(axis.id, val.key);\n clicktab.addEventListener('click', fillTable);\n if (!val.show) {\n setShowVal(axis.id, val.key, false);\n }\n }\n for (var prefix of ['x_', 'y_', 'x2_', 'y2_']) {\n document.getElementById(prefix + axis.id).addEventListener('click', fillTable);\n }\n for (var label of ['x2_none', 'y2_none']) {\n document.getElementById(label).addEventListener('click', fillTable);\n }\n makegif_axis.appendChild(new Option(axis.title, axis.id));\n }\n console.log(`Loaded data for '${rawData.title}'`);\n document.getElementById('autoScaleImages').addEventListener('change', updateScaling);\n document.getElementById('stickyNavigation').addEventListener('change', toggleTopSticky);\n document.getElementById('stickyLabels').addEventListener('change', toggleLabelSticky);\n document.getElementById('toggle_nav_button').addEventListener('click', updateTitleSticky);\n document.getElementById('toggle_adv_button').addEventListener('click', updateTitleSticky);\n document.getElementById('showDescriptions').checked = rawData.defaults.show_descriptions;\n document.getElementById('autoScaleImages').checked = rawData.defaults.autoscale;\n document.getElementById('stickyNavigation').checked = rawData.defaults.sticky;\n document.getElementById('stickyLabels').checked = rawData.defaults.sticky_labels;\n document.getElementById('score_display').addEventListener('click', fillTable);\n document.getElementById('score_setting').style.display = typeof getScoreFor == 'undefined' ? 'none' : 'inline-block';\n updateStylesToMatchInputs();\n for (var axis of ['x', 'y', 'x2', 'y2']) {\n if (rawData.defaults[axis] != '') {\n document.getElementById(axis + '_' + rawData.defaults[axis]).click();\n }\n }\n applyHash(rawHash);\n suppressUpdate = false;\n fillTable();\n startAutoScroll();\n if (rawData.will_run) {\n setTimeout(checkForUpdates, 5000);\n }\n}\n\nfunction getExtension(filePath) {\n if (filePath in file_extensions_alt) {\n return file_extensions_alt[filePath];\n }\n return rawData.ext;\n}\n\n/** External callable. */\nfunction fix_video(path) {\n let ext = getExtension(path);\n let matches = document.querySelectorAll(`img[data-img_path=\"${path}\"]`);\n for (let match of matches) {\n if (ext != 'webm' && ext != 'mp4') {\n if (!match.src.endsWith(ext)) {\n match.src = `${path}.${ext}`;\n }\n continue;\n }\n if (match.tagName == 'VIDEO') {\n continue;\n }\n let video = document.createElement('video');\n video.loop = true;\n video.muted = true;\n video.autoplay = true;\n video.classList = match.classList;\n video.dataset.img_path = match.dataset.img_path;\n video.onclick = \"doPopupFor(this)\";\n video.onerror = \"setImgPlaceholder(this)\";\n let source = document.createElement('source');\n source.src = `${path}.${ext}`;\n source.type = `video/${ext}`;\n video.appendChild(source);\n match.parentElement.replaceChild(video, match);\n }\n}\n\nfunction updateStylesToMatchInputs() {\n toggleTopSticky();\n toggleLabelSticky();\n}\n\nfunction getAxisById(id) {\n return rawData.axes.find(axis => axis.id == id);\n}\n\nfunction getNextAxis(axes, startId) {\n var next = false;\n for (var subAxis of axes) {\n if (subAxis.id == startId) {\n next = true;\n }\n else if (next) {\n return subAxis;\n }\n }\n return null;\n}\n\nfunction getSelectedValKey(axis) {\n for (var subVal of axis.values) {\n if (window.getComputedStyle(document.getElementById('tab_' + axis.id + '__' + subVal.key)).display != 'none') {\n return subVal.path;\n }\n }\n return null;\n}\n\nvar popoverLastImg = null;\n\nfunction clickRowImage(rows, x, y) {\n $('#image_info_modal').modal('hide');\n var columns = rows[y].getElementsByTagName('td');\n columns[x].getElementsByTagName('img')[0].click();\n}\n\nwindow.addEventListener('keydown', function(kbevent) {\n if ($('#image_info_modal').is(':visible')) {\n if (kbevent.key == 'Escape') {\n $('#image_info_modal').modal('toggle');\n kbevent.preventDefault();\n kbevent.stopPropagation();\n return false;\n }\n var tableElem = document.getElementById('image_table');\n var rows = tableElem.getElementsByTagName('tr');\n var matchedRow = null;\n var x = 0, y = 0;\n for (var row of rows) {\n var columns = row.getElementsByTagName('td');\n for (var column of columns) {\n var images = column.getElementsByTagName('img');\n if (images.length == 1 && images[0] == popoverLastImg) {\n matchedRow = row;\n break;\n }\n x++;\n }\n if (matchedRow != null) {\n break;\n }\n x = 0;\n y++;\n }\n if (matchedRow == null) {\n return;\n }\n if (kbevent.key == 'ArrowLeft') {\n if (x > 1) {\n x--;\n clickRowImage(rows, x, y);\n }\n }\n else if (kbevent.key == 'ArrowRight') {\n x++;\n var columns = matchedRow.getElementsByTagName('td');\n if (columns.length > x) {\n clickRowImage(rows, x, y);\n }\n }\n else if (kbevent.key == 'ArrowUp') {\n if (y > 1) {\n y--;\n clickRowImage(rows, x, y);\n }\n }\n else if (kbevent.key == 'ArrowDown') {\n y++;\n if (rows.length > y) {\n clickRowImage(rows, x, y);\n }\n }\n else {\n return;\n }\n kbevent.preventDefault();\n kbevent.stopPropagation();\n return false;\n }\n var elem = document.activeElement;\n if (!elem.id.startsWith('clicktab_')) {\n return;\n }\n var axisId = elem.id.substring('clicktab_'.length);\n var splitIndex = axisId.lastIndexOf('__');\n axisId = axisId.substring(0, splitIndex);\n var axis = getAxisById(axisId);\n if (kbevent.key == 'ArrowLeft') {\n var tabPage = document.getElementById('tablist_' + axis.id);\n var tabs = tabPage.getElementsByClassName('nav-link');\n var newTab = clickTabAfterActiveTab(Array.from(tabs).reverse());\n newTab.focus({ preventScroll: true });\n }\n else if (kbevent.key == 'ArrowRight') {\n var tabPage = document.getElementById('tablist_' + axis.id);\n var tabs = tabPage.getElementsByClassName('nav-link');\n var newTab = clickTabAfterActiveTab(tabs);\n newTab.focus({ preventScroll: true });\n }\n else if (kbevent.key == 'ArrowUp') {\n var next = getNextAxis(Array.from(rawData.axes).reverse(), axisId);\n if (next != null) {\n var selectedKey = getSelectedValKey(next);\n var swapToTab = getNavValTab(next.id, selectedKey);\n swapToTab.focus({ preventScroll: true });\n }\n }\n else if (kbevent.key == 'ArrowDown') {\n var next = getNextAxis(rawData.axes, axisId);\n if (next != null) {\n var selectedKey = getSelectedValKey(next);\n var swapToTab = getNavValTab(next.id, selectedKey);\n swapToTab.focus({ preventScroll: true });\n }\n }\n else {\n return;\n }\n kbevent.preventDefault();\n kbevent.stopPropagation();\n return false;\n}, true);\n\nfunction escapeHtml(text) {\n if (typeof text != 'string') {\n return text;\n }\n return text.replaceAll('&', '&').replaceAll('<', '<').replaceAll('>', '>').replaceAll('\"', '"').replaceAll(\"'\", ''');\n}\n\nfunction unescapeHtml(text) {\n return text.replaceAll('<', '<').replaceAll('>', '>').replaceAll('"', '\"').replaceAll(''', \"'\").replaceAll('&', '&');\n}\n\nfunction canShowVal(axis, val) {\n return document.getElementById(`showval_${axis}__${val}`).checked;\n}\n\nfunction setShowVal(axis, val, show) {\n document.getElementById(`showval_${axis}__${val}`).checked = show;\n getNavValTab(axis, val).classList.toggle('tab_hidden', !show);\n}\n\nfunction getNavValTab(axis, val) {\n return document.getElementById(`clicktab_${axis}__${val}`);\n}\n\nfunction percentToRedGreen(percent) {\n return `color-mix(in srgb, red, green ${percent}%)`;\n}\n\nlet scoreTrackCounter = 0;\nlet scoreUpdates = [];\nlet lastScoreBump = Date.now();\nlet scoreBumpTracker = null;\nlet scoreMin = 0, scoreMax = 1;\n\nfunction getXAxisContent(x, y, xAxis, yval, x2Axis, x2val, y2Axis, y2val) {\n let scriptDump = document.getElementById('image_script_dump');\n let imgPath = [];\n let index = 0;\n for (let subAxis of rawData.axes) {\n if (subAxis.id == x) {\n index = imgPath.length;\n imgPath.push(null);\n }\n else if (subAxis.id == y) {\n imgPath.push(yval.path);\n }\n else if (x2Axis != null && subAxis.id == x2Axis.id) {\n imgPath.push(x2val.path);\n }\n else if (y2Axis != null && subAxis.id == y2Axis.id) {\n imgPath.push(y2val.path);\n }\n else {\n imgPath.push(getSelectedValKey(subAxis));\n }\n }\n let newContent = '';\n let subInd = 0;\n let scoreDisplay = document.getElementById('score_display').value;\n for (let xVal of xAxis.values) {\n subInd++;\n if (!canShowVal(xAxis.id, xVal.key)) {\n continue;\n }\n imgPath[index] = xVal.path;\n let slashed = imgPath.join('/');\n let ext = getExtension(slashed);\n let actualUrl = slashed + '.' + ext;\n let id = scoreTrackCounter++;\n newContent += `
${Math.round(score * 100)}%
' : (val != null ? '
' : '');\n}\n\nfunction fillTable() {\n if (suppressUpdate) {\n return;\n }\n var x = getCurrentSelectedAxis('x');\n var y = getCurrentSelectedAxis('y');\n var x2 = getCurrentSelectedAxis('x2');\n var y2 = getCurrentSelectedAxis('y2');\n console.log('Do fill table, x=' + x + ', y=' + y + ', x2=' + x2 + ', y2=' + y2);\n var xAxis = getAxisById(x);\n var yAxis = getAxisById(y);\n var x2Axis = x2 == 'None' || x2 == x || x2 == y ? null : getAxisById(x2);\n var y2Axis = y2 == 'None' || y2 == x2 || y2 == x || y2 == y ? null : getAxisById(y2);\n var table = document.getElementById('image_table');\n var newContent = '
');\n let text = 'Image: ' + img.alt + (params.length > 1 ? ', parameters:
' + params : '
(parameters hidden)');\n modalElem.innerHTML = `
${text}
`;\n $('#image_info_modal').modal('toggle');\n}\n\nfunction updateTitleStickyDirect() {\n var height = Math.round(document.getElementById('top_nav_bar').getBoundingClientRect().height);\n var header = document.getElementById('image_table_header');\n if (header.style.top != height + 'px') { // This check is to reduce the odds of the browser yelling at us\n header.style.top = height + 'px';\n }\n}\n\nfunction updateTitleSticky() {\n var header = document.getElementById('image_table_header');\n if (!header) {\n return;\n }\n updateHash();\n var topBar = document.getElementById('top_nav_bar');\n if (!topBar.classList.contains('sticky_top')) {\n header.style.top = '0';\n return;\n }\n // client rect is dynamically animated, so, uh, just hack it for now.\n // TODO: Actually smooth attachment.\n var rate = 50;\n for (var time = 0; time <= 500; time += rate) {\n setTimeout(updateTitleStickyDirect, time);\n }\n}\n\nfunction toggleTopSticky() {\n var topBar = document.getElementById('top_nav_bar');\n topBar.classList.remove('sticky_top');\n if (document.getElementById('stickyNavigation').checked) {\n topBar.classList.add('sticky_top');\n }\n updateTitleSticky();\n}\n\nfunction toggleLabelSticky() {\n updateHash();\n var table = document.getElementById('image_table');\n table.classList.remove('nostickytable');\n if (!document.getElementById('stickyLabels').checked) {\n table.classList.add('nostickytable');\n }\n}\n\nfunction removeGeneratedImages() {\n document.getElementById('save_image_output').innerHTML = '';\n}\n\nfunction makeImage(minRow = 0, doClear = true) {\n // Preprocess data\n var imageTable = document.getElementById('image_table');\n var rows = Array.from(imageTable.getElementsByTagName('tr')).filter(e => e.getElementsByTagName('img').length > 0);\n var header = document.getElementById('image_table_header');\n var headers = Array.from(header.getElementsByTagName('th')).slice(1);\n var widest_width = 0;\n var total_height = 0;\n var columns = 0;\n var rowData = [];\n var pad_x = 64, pad_y = 64;\n let count = 0;\n let sizeMult = parseFloat(document.getElementById('makeimage_size').value.replaceAll('x', ''));\n for (var row of rows) {\n count++;\n if (count < minRow) {\n continue;\n }\n var images = Array.from(row.getElementsByTagName('img'));\n var real_images = images.filter(i => i.src != 'placeholder.png');\n widest_width = Math.max(widest_width, ...real_images.map(i => i.naturalWidth * sizeMult));\n var height = Math.max(...real_images.map(i => i.naturalHeight * sizeMult));\n var y = pad_y + total_height;\n if (total_height + height > 30000) { // 32,767 is max canvas size\n setTimeout(() => makeImage(count, false), 100);\n break;\n }\n total_height += height + 1;\n columns = Math.max(columns, images.length);\n var label = row.getElementsByClassName('axis_label_td')[0];\n rowData.push({ row, images, real_images, height, label, y });\n }\n var holder = document.getElementById('save_image_output');\n if (doClear) {\n removeGeneratedImages();\n }\n // Temporary canvas to measure what padding we need\n var canvas = new OffscreenCanvas(256, 256);\n var ctx = canvas.getContext('2d');\n ctx.beginPath();\n ctx.rect(0, 0, canvas.width, canvas.height);\n ctx.font = '16px sans';\n ctx.textBaseline = 'top';\n for (var row of rowData) {\n var blocks = row.label.getElementsByTagName('b');\n pad_x = Math.max(pad_x, ctx.measureText(blocks[0].textContent).width);\n if (blocks.length == 2) {\n pad_x = Math.max(pad_x, ctx.measureText(blocks[1].textContent).width);\n }\n }\n pad_x = Math.min(pad_x, widest_width / 2);\n pad_x += 5;\n canvas = document.createElement('canvas');\n canvas.width = (widest_width + 1) * columns + pad_x;\n canvas.height = total_height + pad_y;\n ctx = canvas.getContext('2d');\n // Background\n ctx.beginPath();\n ctx.rect(0, 0, canvas.width, canvas.height);\n ctx.fillStyle = '#202020';\n ctx.fill();\n // Secondary color toggling\n var doColor = false;\n ctx.fillStyle = '#303030';\n var grid_x = pad_x;\n for (var part of headers) {\n if (part.getElementsByTagName('b').length == 2) {\n doColor = !doColor;\n }\n if (doColor) {\n ctx.beginPath();\n ctx.rect(grid_x, 0, widest_width, pad_y);\n ctx.fill();\n }\n grid_x += widest_width + 1;\n }\n doColor = false;\n for (var row of rowData) {\n if (row.label.getElementsByTagName('b').length == 2) {\n doColor = !doColor;\n }\n if (doColor) {\n ctx.beginPath();\n ctx.rect(0, row.y, pad_x, row.height);\n ctx.fill();\n }\n }\n // Grid lines\n ctx.fillStyle = '#000000';\n for (var row of rowData) {\n ctx.beginPath();\n ctx.rect(0, row.y, canvas.width, 1);\n ctx.fill();\n }\n grid_x = pad_x - 1;\n for (var i = 0; i < columns; i++) {\n ctx.beginPath();\n ctx.rect(grid_x, 0, 1, canvas.height);\n ctx.fill();\n grid_x += widest_width + 1;\n }\n // Text Labels\n ctx.font = '16px sans';\n ctx.textBaseline = 'top';\n ctx.fillStyle = '#ffffff';\n ctx.beginPath();\n ctx.rect(0, 0, canvas.width, canvas.height);\n grid_x = pad_x + 5;\n for (var part of headers) {\n var blocks = part.getElementsByTagName('b');\n if (blocks.length == 2) {\n ctx.fillText(blocks[0].textContent, grid_x, 5, widest_width);\n ctx.fillText(blocks[1].textContent, grid_x, 25, widest_width);\n }\n else {\n ctx.fillText(blocks[0].textContent, grid_x, 25, widest_width);\n }\n grid_x += widest_width + 1;\n }\n function wrap(text, width) {\n var words = text.split(' ');\n var lines = [];\n var line = '';\n for (var word of words) {\n var newLine = line + word + ' ';\n if (ctx.measureText(newLine).width > width) {\n lines.push(line);\n line = word + ' ';\n }\n else {\n line = newLine;\n }\n }\n lines.push(line);\n return lines.join('\\n');\n }\n function writeMultiline(ctx, text, x, y) {\n for (var line of text.split('\\n')) {\n ctx.fillText(line, x, y);\n y += 16;\n }\n }\n for (var row of rowData) {\n var blocks = row.label.getElementsByTagName('b');\n if (blocks.length == 2) {\n writeMultiline(ctx, wrap(blocks[0].textContent + \"\\n\" + blocks[1].textContent, widest_width / 2), 5, row.y + 4);\n }\n else {\n writeMultiline(ctx, wrap(blocks[0].textContent, widest_width / 2), 5, row.y + 25);\n }\n }\n // Images\n for (var row of rowData) {\n var x = pad_x;\n for (var image of row.images) {\n if (image.src != 'placeholder.png') {\n ctx.drawImage(image, x, row.y, image.naturalWidth * sizeMult, image.naturalHeight * sizeMult);\n x += widest_width + 1;\n }\n }\n }\n var imageType = document.getElementById('makeimage_type').value;\n try {\n var data = canvas.toDataURL(`image/${imageType}`);\n canvas.remove();\n var img = new Image();\n img.className = 'save_image_output_img';\n img.src = data;\n holder.appendChild(img);\n }\n catch (e) {\n holder.appendChild(canvas);\n canvas.className = 'save_image_output_img';\n canvas.style.width = \"200px\";\n canvas.style.height = \"200px\";\n }\n $('#save_image_output_modal').modal('show');\n}\n\nfunction makeGif() {\n let holder = document.getElementById('save_image_output');\n removeGeneratedImages();\n let axisId = document.getElementById('makegif_axis').value;\n if (axisId == 'x-axis') {\n axisId = getCurrentSelectedAxis('x');\n }\n let sizeMult = parseFloat(document.getElementById('makegif_size').value.replaceAll('x', ''));\n let speed = parseFloat(document.getElementById('makegif_speed').value.replaceAll('/s', ''));\n let axis = getAxisById(axisId);\n let images = [];\n let imgPath = [];\n let index = 0;\n for (let subAxis of rawData.axes) {\n if (subAxis.id == axisId) {\n index = imgPath.length;\n imgPath.push(null);\n }\n else {\n imgPath.push(getSelectedValKey(subAxis));\n }\n }\n for (let val of axis.values) {\n if (!canShowVal(axis.id, val.key)) {\n continue;\n }\n imgPath[index] = val.path;\n let slashed = imgPath.join('/');\n let actualUrl = slashed + '.' + getExtension(slashed);\n images.push(actualUrl);\n }\n if (document.getElementById('makegif_direction').value == 'Backwards') {\n images.reverse();\n }\n let encoder = new GIFEncoder();\n encoder.setRepeat(0);\n encoder.setDelay(1000 / speed);\n encoder.start();\n let image1 = new Image();\n image1.src = images[0];\n image1.decode().then(() => {\n let canvas = document.createElement('canvas');\n canvas.width = image1.naturalWidth * sizeMult;\n canvas.height = image1.naturalHeight * sizeMult;\n ctx = canvas.getContext('2d');\n ctx.beginPath();\n let id = 1;\n let image2 = new Image();\n let callback = () => {\n ctx.drawImage(image2, 0, 0, canvas.width, canvas.height);\n encoder.addFrame(ctx);\n if (id >= images.length) {\n encoder.finish();\n let binary_gif = encoder.stream().getData();\n let data_url = 'data:image/gif;base64,' + encode64(binary_gif);\n let animatedImage = document.createElement('img');\n animatedImage.className = 'save_image_output_img';\n animatedImage.src = data_url;\n image1.remove();\n image2.remove();\n holder.appendChild(animatedImage);\n $('#save_image_output_modal').modal('show');\n }\n else {\n image2 = new Image();\n image2.src = images[id];\n id++;\n image2.decode().then(callback);\n }\n };\n image2.src = images[0];\n image2.decode().then(callback);\n });\n\n}\n\nfunction updateHash() {\n var hash = `#auto-loc`;\n for (let elem of ['showDescriptions', 'autoScaleImages', 'stickyNavigation', 'stickyLabels']) {\n hash += `,${document.getElementById(elem).checked}`;\n }\n for (let val of ['x', 'y', 'x2', 'y2']) {\n hash += `,${encodeURIComponent(getCurrentSelectedAxis(val))}`;\n }\n for (let subAxis of rawData.axes) {\n hash += `,${encodeURIComponent(getSelectedValKey(subAxis))}`;\n }\n for (let axis of rawData.axes) {\n for (let value of axis.values) {\n if (!canShowVal(axis.id, value.key)) {\n hash += `&hide=${encodeURIComponent(axis.id)},${encodeURIComponent(value.key)}`;\n }\n }\n }\n history.pushState(null, null, hash);\n}\n\nfunction applyHash(hash) {\n if (!hash) {\n return;\n }\n let params = hash.substring(1).split('&');\n for (let hidden of params.slice(1)) {\n let [action, value] = hidden.split('=');\n if (action == 'hide') {\n let [axis, val] = value.split(',');\n setShowVal(axis, val, false);\n }\n }\n let hashInputs = params[0].split(',');\n let expectedLen = 1 + 4 + 4 + rawData.axes.length;\n if (hashInputs.length != expectedLen) {\n console.log(`Hash length mismatch: ${hashInputs.length} != ${expectedLen}, skipping value reload.`);\n return;\n }\n if (hashInputs[0] != 'auto-loc') {\n console.log(`Hash prefix mismatch: ${hashInputs[0]} != auto-loc, skipping value reload.`);\n return;\n }\n let index = 1;\n for (let elem of ['showDescriptions', 'autoScaleImages', 'stickyNavigation', 'stickyLabels']) {\n document.getElementById(elem).checked = hashInputs[index++] == 'true';\n }\n for (let axis of ['x', 'y', 'x2', 'y2']) {\n let id = axis + '_' + decodeURIComponent(hashInputs[index++]);\n let target = document.getElementById(id);\n if (!target) {\n console.log(`Axis element not found: ${id}, skipping value reload.`);\n return;\n }\n target.click();\n }\n for (let subAxis of rawData.axes) {\n let val = decodeURIComponent(hashInputs[index++]);\n let target = getNavValTab(subAxis.id, val);\n if (!target) {\n console.log(`Axis-value element not found: ${id}, skipping value reload.`);\n return;\n }\n target.click();\n }\n updateStylesToMatchInputs();\n}\n\nlet lastUpdateObj = null;\nlet updateCheckCount = 0;\nlet updatesWithoutData = 0;\n\nfunction tryReloadImg(img) {\n let target = img.dataset.errored_src;\n delete img.dataset.errored_src;\n img.removeAttribute('width');\n img.removeAttribute('height');\n img.addEventListener('error', function() {\n setImgPlaceholder(img);\n });\n img.src = target;\n if (typeof getMetadataScriptFor != 'undefined') {\n let newScr = document.createElement('script');\n newScr.src = getMetadataScriptFor(img.dataset.img_path);\n document.getElementById('image_script_dump').appendChild(newScr);\n }\n}\n\nfunction checkForUpdates() {\n if (!window.lastUpdated) {\n if (updatesWithoutData++ > 2) {\n console.log('Update-checker has no more updates.');\n for (let img of document.querySelectorAll(`img[data-errored_src]`)) {\n tryReloadImg(img);\n }\n return;\n }\n }\n else {\n console.log(`Update-checker found ${window.lastUpdated.length} updates.`);\n for (let url of window.lastUpdated) {\n for (let img of document.querySelectorAll(`img[data-errored_src]`)) {\n if (img.dataset.errored_src.endsWith(url)) {\n tryReloadImg(img);\n }\n }\n }\n updateScaling();\n window.lastUpdated = null;\n }\n if (lastUpdateObj != null) {\n lastUpdateObj.remove();\n }\n lastUpdateObj = document.createElement('script');\n lastUpdateObj.src = `last.js?vary=${updateCheckCount++}`;\n document.body.appendChild(lastUpdateObj);\n setTimeout(checkForUpdates, 5 * 1000);\n}\n\nloadData();\n"
},
{
"path": "assets/styles.css",
"content": "/*\n If you want to customize your stylesheet, please edit 'styles-user.css' instead of this.\n*/\n\n.sel_table tr td {\n border: 1px solid black;\n padding-right: 16px;\n padding-left: 16px;\n}\n.sel_table {\n min-width: 60em;\n width: 100%;\n}\n.secondary {\n background-color: #202020;\n}\n.emptytab {\n width: 0px;\n}\n.primary {\n background-color: #303030;\n}\n.axis_table_cell {\n width: calc(min(40em, max(10em, 100vw - 40em)));\n max-height: 8em;\n overflow: auto;\n}\n.tabval_subdiv {\n min-width: calc(100vw - 5em - min(40em, max(10em, 100vw - 40em)));\n max-height: 8em;\n overflow: auto;\n}\ntable tr td,\ntable tr th {\n vertical-align: top;\n border: 1px solid black;\n word-break: break-all;\n}\nth {\n position: sticky;\n top: -1px;\n background-color: #202020;\n background-clip: padding-box;\n}\ntable tr td:first-child {\n position: sticky;\n left: -1px;\n}\n.nostickytable tr:first-child {\n position: static !important;\n}\n.nostickytable tr th {\n position: static !important;\n}\n.nostickytable tr td:first-child {\n position: static !important;\n}\n.popup_modal_background {\n font-family: monospace, monospace;\n background-color: rgb(50, 50, 50, 0.7);\n text-align: center;\n}\n.popup_modal_img {\n height: 100%;\n width: 100%;\n max-height: calc(min(100vw, 100vh - 8em));\n max-width: 90vw;\n position: relative;\n margin: auto;\n object-fit: contain;\n}\n.popup_modal_undertext {\n background-color: #404060;\n overflow-wrap: break-word;\n}\n.modal_inner_div {\n max-width: 90vw;\n margin: auto;\n}\n.advanced_settings_section {\n max-width: calc(max(1500px, 90em));\n text-align: left;\n margin: auto;\n}\n.timer_range {\n vertical-align: bottom;\n}\n.tab_hidden {\n display: none;\n}\n.nav-link:focus {\n background-color: #909050 !important;\n}\n.axis_label_td {\n left: 0px;\n background-color: #202020;\n background-clip: padding-box;\n word-break: break-all;\n min-width: 10em;\n}\n.superaxis_second {\n background-color: #303030;\n}\n.axis_selectors {\n text-align: right;\n margin: auto;\n width: fit-content;\n}\n.sticky_top {\n position: sticky;\n top: 0;\n}\n.accordion-button {\n padding: 0.5rem;\n}\n.save_image_area {\n text-align: center;\n}\n.save_image_area img {\n margin-right: 1rem;\n margin-left: 1rem;\n border: 2px solid orange;\n border-radius: 0.5rem;\n}\n.content_box {\n display: inline-block;\n}\n.top_nav_bar {\n position: sticky;\n left: 2.5vw;\n width: 95vw;\n}\n.image_table_box {\n display: inline-block;\n min-width: 100%;\n}\n.image_table {\n margin: auto;\n} \n.save_image_output {\n text-align: center;\n}\n.save_image_output_img {\n max-width: 100%;\n max-height: 70vh;\n}\n.advanced-checkbox {\n display: inline-block;\n border-radius: 0.2rem;\n border: 1px dashed rgba(255, 255, 255, 0.25);\n margin: 0.1rem;\n}\nlabel {\n display: inline;\n}\n"
},
{
"path": "gridgencore.py",
"content": "# This file is part of Infinity Grid Generator, view the README.md at https://github.com/mcmonkeyprojects/sd-infinity-grid-generator-script for more information.\n\nimport os, glob, yaml, json, shutil, math, re, time\nfrom copy import copy\nfrom PIL import Image\nfrom git import Repo\n\n######################### Core Variables #########################\n\nASSET_DIR = os.path.dirname(__file__) + \"/assets\"\nEXTRA_FOOTER = \"...\"\nEXTRA_ASSETS = []\nVERSION = None\nvalid_modes = {}\nIMAGES_CACHE = None\n\n######################### Hooks #########################\n\n# hook(SingleGridCall)\ngrid_call_init_hook: callable = None\n# hook(SingleGridCall, param_name: str, value: any) -> bool\ngrid_call_param_add_hook: callable = None\n# hook(SingleGridCall, param_name: str, dry: bool)\ngrid_call_apply_hook: callable = None\n# hook(GridRunner)\ngrid_runner_pre_run_hook: callable = None\n# hook(GridRunner)\ngrid_runner_pre_dry_hook: callable = None\n# hook(GridRunner, PassThroughObject, set: SingleGridCall) -> ResultObject\ngrid_runner_post_dry_hook: callable = None\n# hook(GridRunner, SingleGridCall) -> int\ngrid_runner_count_steps: callable = None\n# hook(PassThroughObject) -> dict\nwebdata_get_base_param_data: callable = None\n\n######################### Utilities #########################\n\ndef escape_html(text: str):\n return str(text).replace('&', '&').replace('<', '<').replace('>', '>').replace('\"', '"')\n\ndef clean_file_path(fn: str):\n fn = fn.replace('\\\\', '/')\n while '//' in fn:\n fn = fn.replace('//', '/')\n return fn\n\ndef get_version():\n global VERSION\n if VERSION is not None:\n return VERSION\n try:\n repo = Repo(os.path.dirname(__file__))\n VERSION = repo.head.commit.hexsha[:8]\n except Exception:\n VERSION = \"Unknown\"\n return VERSION\n\ndef list_image_files():\n global IMAGES_CACHE\n if IMAGES_CACHE is not None:\n return IMAGES_CACHE\n image_dir = clean_file_path(ASSET_DIR + \"/images\")\n IMAGES_CACHE = list()\n for path, _, files in os.walk(image_dir):\n for name in files:\n _, ext = os.path.splitext(name)\n if ext in [\".jpg\", \".png\", \".webp\"]:\n fn = path + \"/\" + name\n fn = clean_file_path(fn).replace(image_dir, '')\n while fn.startswith('/'):\n fn = fn[1:]\n IMAGES_CACHE.append(fn)\n return IMAGES_CACHE\n\ndef clear_caches():\n global IMAGES_CACHE\n IMAGES_CACHE = None\n\ndef get_name_list():\n file_list = glob.glob(ASSET_DIR + \"/*.yml\")\n just_file_names = sorted(list(map(lambda f: os.path.relpath(f, ASSET_DIR), file_list)))\n return just_file_names\n\ndef fix_dict(d: dict):\n if d is None:\n return None\n if type(d) is not dict:\n raise RuntimeError(f\"Value '{d}' is supposed to be submapping but isn't (it's plaintext, a list, or some other incorrect format). Did you typo the formatting?\")\n return {str(k).lower(): v for k, v in d.items()}\n\ndef clean_for_web(text: str):\n if text is None:\n return None\n if type(text) is not str:\n raise RuntimeError(f\"Value '{text}' is supposed to be text but isn't (it's a datamapping, list, or some other incorrect format). Did you typo the formatting?\")\n return text.replace('\"', '"')\n\ndef clean_id(id: str):\n return re.sub(\"[^a-z0-9]\", \"_\", id.lower().strip())\n\ndef clean_mode(id: str):\n return re.sub(\"[^a-z]\", \"\", id.lower().strip())\n\ndef clean_name(name: str):\n return str(name).lower().replace(' ', '').replace('[', '').replace(']', '').strip()\n\ndef get_best_in_list(name: str, list: list):\n backup = None\n best_len = 999\n name = clean_name(name)\n for list_val in list:\n list_val_clean = clean_name(list_val)\n if list_val_clean == name:\n return list_val\n if name in list_val_clean:\n if len(list_val_clean) < best_len:\n backup = list_val\n best_len = len(list_val_clean)\n return backup\n\ndef choose_better_file_name(raw_name: str, full_name: str):\n partial_name = os.path.splitext(os.path.basename(full_name))[0]\n if '/' in raw_name or '\\\\' in raw_name or '.' in raw_name or len(raw_name) >= len(partial_name):\n return raw_name\n return partial_name\n\ndef fix_num(num):\n if num is None or math.isinf(num) or math.isnan(num):\n return None\n return num\n\ndef expand_numeric_list_ranges(in_list, num_type):\n out_list = list()\n for i in range(0, len(in_list)):\n raw_val = str(in_list[i]).strip()\n if raw_val in [\"..\", \"...\", \"....\"]:\n if i < 2 or i + 1 >= len(in_list):\n raise RuntimeError(f\"Cannot use ellipses notation at index {i}/{len(in_list)} - must have at least 2 values before and 1 after.\")\n prior = out_list[-1]\n double_prior = out_list[-2]\n after = num_type(in_list[i + 1])\n step = prior - double_prior\n if (step < 0) != ((after - prior) < 0):\n raise RuntimeError(f\"Ellipses notation failed for step {step} between {prior} and {after} - steps backwards.\")\n count = int((after - prior) / step)\n for x in range(1, count):\n out_list.append(prior + x * step)\n else:\n out_list.append(num_type(raw_val))\n return out_list\n\n######################### Value Modes #########################\n\nclass GridSettingMode:\n \"\"\"\n Defines a custom parameter input mode for an Infinity Grid Generator.\n 'dry' is True if the mode should be processed in dry runs, or False if it should be skipped.\n 'type' is 'text', 'integer', 'decimal', or 'boolean'\n 'apply' is a function to call taking (passthroughObject, value)\n 'min' is for integer/decimal type, optional minimum value\n 'max' is for integer/decimal type, optional maximum value\n 'valid_list' is for text type, an optional lambda that returns a list of valid values\n 'clean' is an optional function to call that takes (passthroughObject, value) and returns a cleaned copy of the value, or raises an error if invalid\n 'parse_list' is an optional function to call that takes a List and returns a List, to apply any special pre-processing for list-format inputs.\n \"\"\"\n def __init__(self, dry: bool, type: str, apply: callable, min: float = None, max: float = None, valid_list: callable = None, clean: callable = None, parse_list: callable = None):\n self.dry = dry\n self.type = type\n self.apply = apply\n self.min = min\n self.max = max\n self.clean = clean\n self.valid_list = valid_list\n self.parse_list = parse_list\n\ndef registerMode(name: str, mode: GridSettingMode):\n mode.name = name\n valid_modes[clean_name(name)] = mode\n\n######################### Validation #########################\n\ndef validate_params(grid, params: dict):\n for p,v in params.items():\n params[p] = validate_single_param(p, grid.proc_variables(v))\n\ndef apply_field(name: str):\n def applier(p, v):\n setattr(p, name, v)\n return applier\n\ndef apply_field_as_image_data(name: str):\n def applier(p, v):\n file_name = get_best_in_list(v, list_image_files())\n if file_name is None:\n raise RuntimeError(\"Invalid parameter '{p}' as '{v}': image file does not exist\")\n path = ASSET_DIR + \"/images/\" + file_name\n image = Image.open(path)\n setattr(p, name, image)\n return applier\n\ndef validate_single_param(p: str, v):\n orig_v = v\n p = clean_mode(p)\n mode = valid_modes.get(p)\n if mode is None:\n raise RuntimeError(f\"Invalid grid parameter '{p}': unknown mode\")\n mode_type = mode.type\n if mode_type == \"integer\":\n v_int = int(v)\n if v_int is None:\n raise RuntimeError(f\"Invalid parameter '{p}' as '{orig_v}': must be an integer number\")\n min = mode.min\n max = mode.max\n if min is not None and v_int < min:\n raise RuntimeError(f\"Invalid parameter '{p}' as '{orig_v}': must be at least {min}\")\n if max is not None and v_int > max:\n raise RuntimeError(f\"Invalid parameter '{p}' as '{orig_v}': must not exceed {max}\")\n v = v_int\n elif mode_type == \"decimal\":\n v_float = float(v)\n if v_float is None:\n raise RuntimeError(f\"Invalid parameter '{p}' as '{orig_v}': must be a decimal number\")\n min = mode.min\n max = mode.max\n if min is not None and v_float < min:\n raise RuntimeError(f\"Invalid parameter '{p}' as '{orig_v}': must be at least {min}\")\n if max is not None and v_float > max:\n raise RuntimeError(f\"Invalid parameter '{p}' as '{orig_v}': must not exceed {max}\")\n v = v_float\n elif mode_type == \"boolean\":\n v_clean = str(v).lower().strip()\n if v_clean == \"true\":\n v = True\n elif v_clean == \"false\":\n v = False\n else:\n raise RuntimeError(f\"Invalid parameter '{p}' as '{orig_v}': must be either 'true' or 'false'\")\n elif mode_type == \"text\" and mode.valid_list is not None:\n valid_list = mode.valid_list()\n v = get_best_in_list(clean_name(v), valid_list)\n if v is None:\n raise RuntimeError(f\"Invalid parameter '{p}' as '{orig_v}': not matched to any entry in list {list(valid_list)}\")\n if mode.clean is not None:\n return mode.clean(p, v)\n return v\n\n######################### YAML Parsing and Processing #########################\n\nclass GridYamlLoader(yaml.SafeLoader):\n pass\n\ntry:\n from yamlinclude import YamlIncludeConstructor\n YamlIncludeConstructor.add_to_loader_class(loader_class=GridYamlLoader, base_dir=ASSET_DIR)\nexcept:\n from yaml_include import Constructor\n yaml.add_constructor(\"!include\", Constructor(base_dir=ASSET_DIR), GridYamlLoader)\n\nclass AxisValue:\n def __init__(self, axis, grid, key: str, val):\n self.axis = axis\n self.key = clean_id(str(key))\n if any(x.key == self.key for x in axis.values):\n self.key += f\"__{len(axis.values)}\"\n self.params = list()\n if isinstance(val, str):\n halves = val.split('=', maxsplit=1)\n if len(halves) != 2:\n raise RuntimeError(f\"Invalid value '{key}': '{val}': not expected format\")\n self.skip = False\n halves[0] = grid.proc_variables(halves[0])\n halves[1] = grid.proc_variables(halves[1])\n try:\n halves[1] = validate_single_param(halves[0], halves[1])\n except RuntimeError:\n if grid.skip_invalid:\n self.skip = True\n else:\n raise\n self.title = halves[1]\n self.params = { clean_mode(halves[0]): halves[1] }\n self.description = None\n self.show = True\n self.path = clean_name(self.key)\n else:\n self.title = grid.proc_variables(val.get(\"title\"))\n self.description = grid.proc_variables(val.get(\"description\"))\n self.skip = (str(grid.proc_variables(val.get(\"skip\")))).lower() == \"true\"\n self.params = fix_dict(val.get(\"params\"))\n self.show = (str(grid.proc_variables(val.get(\"show\")))).lower() != \"false\"\n self.path = str(val.get(\"path\") or clean_name(self.key))\n if self.title is None or self.params is None:\n raise RuntimeError(f\"Invalid value '{key}': '{val}': missing title or params\")\n if not self.skip:\n try:\n validate_params(grid, self.params)\n except RuntimeError:\n if grid.skip_invalid:\n self.skip = True\n else:\n raise\n \n def __str__(self):\n return f\"(title={self.title}, description={self.description}, params={self.params})\"\n def __unicode__(self):\n return self.__str__()\n\nclass Axis:\n def build_from_list_str(self, id, grid, list_str):\n is_split_by_double_pipe = \"||\" in list_str\n values_list = list_str.split(\"||\" if is_split_by_double_pipe else \",\")\n self.mode_name = clean_name(str(id))\n self.mode = valid_modes.get(clean_mode(self.mode_name))\n if self.mode is None:\n raise RuntimeError(f\"Invalid axis mode '{self.mode}' from '{id}': unknown mode\")\n if self.mode.type == \"integer\":\n values_list = expand_numeric_list_ranges(values_list, int)\n elif self.mode.type == \"decimal\":\n values_list = expand_numeric_list_ranges(values_list, float)\n index = 0\n if self.mode.parse_list is not None:\n values_list = self.mode.parse_list(values_list)\n for val in values_list:\n try:\n if isinstance(val, dict):\n self.values.append(AxisValue(self, grid, str(index), val))\n continue\n val = str(val).strip()\n index += 1\n if is_split_by_double_pipe and val == \"\" and index == len(values_list):\n continue\n self.values.append(AxisValue(self, grid, str(index), f\"{id}={val}\"))\n except Exception as e:\n raise RuntimeError(f\"value '{val}' errored: {e}\")\n\n def __init__(self, grid, id: str, obj):\n self.raw_id = id\n self.values = list()\n self.id = clean_id(str(id))\n if any(x.id == self.id for x in grid.axes):\n self.id += f\"__{len(grid.axes)}\"\n if isinstance(obj, str):\n self.title = id\n self.default = None\n self.description = \"\"\n self.build_from_list_str(id, grid, obj)\n else:\n self.title = grid.proc_variables(obj.get(\"title\"))\n self.default = grid.proc_variables(obj.get(\"default\"))\n if self.title is None:\n raise RuntimeError(\"missing title\")\n self.description = grid.proc_variables(obj.get(\"description\"))\n values_obj = obj.get(\"values\")\n if values_obj is None:\n raise RuntimeError(\"missing values\")\n elif isinstance(values_obj, str):\n self.build_from_list_str(id, grid, values_obj)\n else:\n for key, val in values_obj.items():\n try:\n self.values.append(AxisValue(self, grid, key, val))\n except Exception as e:\n raise RuntimeError(f\"value '{key}' errored: {e}\")\n\nclass GridFileHelper:\n def proc_variables(self, text):\n if text is None:\n return None\n text = str(text)\n for key, val in self.variables.items():\n text = text.replace(key, val)\n return text\n \n def read_grid_direct(self, key: str):\n return self.grid_obj.get(key)\n \n def read_str_from_grid(self, key: str):\n return self.proc_variables(self.read_grid_direct(key))\n\n def parse_yaml(self, yaml_content: dict, grid_file: str):\n self.variables = dict()\n self.axes = list()\n yaml_content = fix_dict(yaml_content)\n vars_obj = fix_dict(yaml_content.get(\"variables\"))\n if vars_obj is not None:\n for key, val in vars_obj.items():\n self.variables[str(key).lower()] = str(val)\n self.grid_obj = fix_dict(yaml_content.get(\"grid\"))\n if self.grid_obj is None:\n raise RuntimeError(f\"Invalid file {grid_file}: missing basic 'grid' root key\")\n self.title = self.read_str_from_grid(\"title\")\n self.description = self.read_str_from_grid(\"description\")\n self.stylesheet = self.read_str_from_grid(\"stylesheet\") or ''\n self.author = self.read_str_from_grid(\"author\")\n self.format = self.read_str_from_grid(\"format\")\n self.out_path = self.grid_obj.get(\"outpath\")\n self.skip_invalid = self.read_grid_direct(\"skip_invalid\") or getattr(self, 'skip_invalid', False)\n if self.title is None or self.description is None or self.author is None or self.format is None:\n raise RuntimeError(f\"Invalid file {grid_file}: missing grid title, author, format, or description in grid obj {self.grid_obj}\")\n self.params = fix_dict(self.grid_obj.get(\"params\"))\n if self.params is not None:\n validate_params(self, self.params)\n axes_obj = fix_dict(yaml_content.get(\"axes\"))\n if axes_obj is None:\n raise RuntimeError(f\"Invalid file {grid_file}: missing basic 'axes' root key\")\n for id, axis_obj in axes_obj.items():\n try:\n self.axes.append(Axis(self, id, axis_obj if isinstance(axis_obj, str) else fix_dict(axis_obj)))\n except Exception as e:\n raise RuntimeError(f\"Invalid axis '{id}': errored: {e}\")\n total_count = 1\n for axis in self.axes:\n total_count *= len(axis.values)\n if total_count <= 0:\n raise RuntimeError(f\"Invalid file {grid_file}: something went wrong ... is an axis empty? total count is {total_count} for {len(self.axes)} axes\")\n clean_desc = self.description.replace('\\n', ' ')\n print(f\"Loaded grid file, title '{self.title}', description '{clean_desc}', with {len(self.axes)} axes... combines to {total_count} total images\")\n return self\n\n######################### Actual Execution Logic #########################\n\nclass SingleGridCall:\n def __init__(self, values: list):\n self.values = values\n self.skip = False\n for val in values:\n if val.skip:\n self.skip = True\n if grid_call_init_hook is not None:\n grid_call_init_hook(self)\n\n def flatten_params(self, grid: GridFileHelper):\n self.grid = grid\n self.params = grid.params.copy() if grid.params is not None else dict()\n for val in self.values:\n for p, v in val.params.items():\n if grid_call_param_add_hook is None or not grid_call_param_add_hook(self, p, v):\n self.params[p] = v\n\n def apply_to(self, p, dry: bool):\n for name, val in self.params.items():\n mode = valid_modes[clean_mode(name)]\n if not dry or mode.dry:\n mode.apply(p, val)\n if grid_call_apply_hook is not None:\n grid_call_apply_hook(self, p, dry)\n\nclass GridRunner:\n def __init__(self, grid: GridFileHelper, do_overwrite: bool, base_path: str, p, fast_skip: bool):\n self.grid = grid\n self.total_run = 0\n self.total_skip = 0\n self.total_steps = 0\n self.do_overwrite = do_overwrite\n self.base_path = base_path\n self.fast_skip = fast_skip\n self.p = p\n grid.min_width = None\n grid.min_height = None\n grid.initial_p = p\n self.last_update = []\n\n def update_live_file(self, new_file: str):\n t_now = time.time()\n self.last_update = [x for x in self.last_update if t_now - x['t'] < 20]\n self.last_update.append({'f': new_file, 't': t_now})\n with open(self.base_path + '/last.js', 'w', encoding=\"utf-8\") as f:\n update_str = '\", \"'.join([x['f'] for x in self.last_update])\n f.write(f'window.lastUpdated = [\"{update_str}\"]')\n\n def build_value_set_list(self, axis_list: list):\n result = list()\n if len(axis_list) == 0:\n return result\n cur_axis = axis_list[0]\n if len(axis_list) == 1:\n for val in cur_axis.values:\n if not val.skip or not self.fast_skip:\n new_list = list()\n new_list.append(val)\n result.append(SingleGridCall(new_list))\n return result\n next_axis_list = axis_list[1::]\n for obj in self.build_value_set_list(next_axis_list):\n for val in cur_axis.values:\n if not val.skip or not self.fast_skip:\n new_list = obj.values.copy()\n new_list.append(val)\n result.append(SingleGridCall(new_list))\n return result\n\n def preprocess(self):\n self.value_sets = self.build_value_set_list(list(reversed(self.grid.axes)))\n print(f'Have {len(self.value_sets)} unique value sets, will go into {self.base_path}')\n for set in self.value_sets:\n set.filepath = self.base_path + '/' + '/'.join(list(map(lambda v: v.path, set.values)))\n set.data = ', '.join(list(map(lambda v: f\"{v.axis.title}={v.title}\", set.values)))\n set.flatten_params(self.grid)\n set.do_skip = set.skip or (not self.do_overwrite and os.path.exists(set.filepath + \".\" + self.grid.format))\n if set.do_skip:\n self.total_skip += 1\n else:\n self.total_run += 1\n self.total_steps += grid_runner_count_steps(self, set) if grid_runner_count_steps is not None else 1\n print(f\"Skipped {self.total_skip} files, will run {self.total_run} files, for {self.total_steps} total steps\")\n\n def run(self, dry: bool):\n if grid_runner_pre_run_hook is not None:\n grid_runner_pre_run_hook(self)\n iteration = 0\n last = None\n for set in self.value_sets:\n if set.do_skip:\n continue\n iteration += 1\n if not dry:\n print(f'On {iteration}/{self.total_run} ... Set: {set.data}, file {set.filepath}')\n p = copy(self.p)\n if grid_runner_pre_dry_hook is not None:\n grid_runner_pre_dry_hook(self)\n set.apply_to(p, dry)\n if dry:\n continue\n try:\n last = grid_runner_post_dry_hook(self, p, set)\n except FileNotFoundError as e:\n if e.strerror == 'The filename or extension is too long' and hasattr(e, 'winerror') and e.winerror == 206:\n print(f\"\\n\\n\\nOS Error: {e.strerror} - see this article to fix that: https://www.autodesk.com/support/technical/article/caas/sfdcarticles/sfdcarticles/The-Windows-10-default-path-length-limitation-MAX-PATH-is-256-characters.html \\n\\n\\n\")\n raise e\n self.update_live_file(set.filepath + \".\" + self.grid.format)\n return last\n\n######################### Web Data Builders #########################\n\nclass WebDataBuilder():\n def build_json(grid: GridFileHelper, publish_gen_metadata: bool, p, dry_run: bool):\n def get_axis(axis: str):\n id = grid.read_str_from_grid(axis)\n if id is None:\n return ''\n id = str(id).lower()\n if id == 'none':\n return 'none'\n possible = [x.id for x in grid.axes if x.raw_id == id]\n if len(possible) == 0:\n raise RuntimeError(f\"Cannot find axis '{id}' for axis default '{axis}'... valid: {[x.raw_id for x in grid.axes]}\")\n return possible[0]\n show_descrip = grid.read_grid_direct('show descriptions')\n result = {\n 'title': grid.title,\n 'description': grid.description,\n 'ext': grid.format,\n 'min_width': grid.min_width,\n 'min_height': grid.min_height,\n 'defaults': {\n 'show_descriptions': True if show_descrip is None else show_descrip,\n 'autoscale': grid.read_grid_direct('autoscale') or False,\n 'sticky': grid.read_grid_direct('sticky') or False,\n 'sticky_labels': grid.read_grid_direct('sticky labels') or False,\n 'x': get_axis('x axis'),\n 'y': get_axis('y axis'),\n 'x2': get_axis('x super axis'),\n 'y2': get_axis('y super axis')\n }\n }\n if not dry_run:\n result['will_run'] = True\n if publish_gen_metadata:\n result['metadata'] = None if webdata_get_base_param_data is None else webdata_get_base_param_data(p)\n axes = list()\n for axis in grid.axes:\n j_axis = {\n 'id': str(axis.id).lower(),\n 'title': axis.title,\n 'description': axis.description or \"\"\n }\n exported_paths = {}\n values = list()\n for val in axis.values:\n if val.path in exported_paths:\n continue\n exported_paths[val.path] = val\n j_val = {\n 'key': str(val.key).lower(),\n 'path': str(val.path),\n 'title': val.title,\n 'description': val.description or \"\",\n 'show': val.show\n }\n if publish_gen_metadata:\n j_val['params'] = val.params\n values.append(j_val)\n j_axis['values'] = values\n axes.append(j_axis)\n result['axes'] = axes\n return json.dumps(result)\n\n def radio_button_html(name, id, descrip, label):\n return f'\\n'\n\n def axis_bar(label, content):\n return f'{label}: \\n{content}
\\n'\n\n def build_html(grid):\n with open(ASSET_DIR + \"/page.html\", 'r', encoding=\"utf-8\") as reference_html:\n html = reference_html.read()\n x_select = \"\"\n y_select = \"\"\n x2Select = WebDataBuilder.radio_button_html('x2_axis_selector', f'x2_none', 'None', 'None')\n y2Select = WebDataBuilder.radio_button_html('y2_axis_selector', f'y2_none', 'None', 'None')\n content = '\\n{escape_html(axis.title)}\\n'\n advanced_settings += f'\\n{axis.title}Auto cycle every \\nShow value: '\n axis_class = \"axis_table_cell\"\n if len(axis_descrip.strip()) == 0:\n axis_class += \" emptytab\"\n content += f'{axis_descrip} | \\n
\\n'\n is_first = axis.default is None\n for val in axis.values:\n if axis.default is not None:\n is_first = str(axis.default) == str(val.key)\n active = \" active show\" if is_first else \"\"\n is_first = False\n descrip = clean_for_web(val.description or '')\n if len(descrip.strip()) == 0:\n active += \" emptytab\"\n content += f' {descrip} |
'\n content += WebDataBuilder.axis_bar('X Axis', x_select)\n content += WebDataBuilder.axis_bar('Y Axis', y_select)\n content += WebDataBuilder.axis_bar('X Super-Axis', x2Select)\n content += WebDataBuilder.axis_bar('Y Super-Axis', y2Select)\n content += '
Confused/new? View the README for usage instructions.
\")\n with gr.Row():\n grid_file = gr.Dropdown(value=\"Create in UI\",label=\"Select grid definition file\", choices=[\"Create in UI\"] + core.get_name_list())\n def refresh():\n new_choices = [\"Create in UI\"] + core.get_name_list()\n grid_file.choices = new_choices\n return gr.update(choices=new_choices)\n refresh_button = ui_components.ToolButton(value=refresh_symbol, elem_id=\"infinity_grid_refresh_button\")\n refresh_button.click(fn=refresh, inputs=[], outputs=[grid_file])\n output_file_path = gr.Textbox(value=\"\", label=\"Output folder name (if blank uses yaml's 'outpath' parameter, filename, or current date)\")\n page_will_be = gr.HTML(value=\"(...)
\")\n manual_group = gr.Group(visible=True)\n manual_axes = list()\n sets = list()\n def get_page_url_text(file):\n if file is None:\n return \"(...)\"\n notice = \"\"\n if not os.path.isabs(file):\n out_path = opts.outdir_grids or (opts.outdir_img2img_grids if is_img2img else opts.outdir_txt2img_grids)\n full_out_path = out_path + \"/\" + file\n url = \"/file=\" + full_out_path\n else:\n full_out_path = file\n url = \"file://\" + (\"\" if file.startswith(\"/\") else \"/\") + file\n notice = \"
This is a raw file path, not within the WebUI output directory. You may need to open the output file manually.\"\n if os.path.exists(full_out_path):\n notice += \"
NOTICE: There is already something saved there! This will overwrite prior data.\"\n return f\"Page will be at (Click me)
{full_out_path}{notice}\"\n def update_page_url(file_path, selected_file):\n out_file_update = gr.Textbox.update()\n if file_path == \"\" and selected_file == \"Create in UI\":\n file_path = f\"autonamed_inf_grid_{datetime.now().strftime('%Y_%m_%d_%H_%M_%S')}\"\n out_file_update = gr.Textbox.update(value=file_path)\n info_update = gr.update(value=get_page_url_text(file_path or (selected_file.replace(\".yml\", \"\") if selected_file is not None else None)))\n return [out_file_update, info_update]\n def update_page_url_single(file_path, selected_file):\n (_, info_update) = update_page_url(file_path, selected_file)\n return info_update\n with manual_group:\n with gr.Row():\n with gr.Column():\n axis_count = 0\n for group in range(0, 4):\n group_obj = gr.Group(visible=group == 0)\n with group_obj:\n rows = list()\n for i in range(0, 4):\n with gr.Row():\n axis_count += 1\n row_mode = gr.Dropdown(value=\"\", label=f\"Axis {axis_count} Mode\", choices=[\" \"] + [x.name for x in core.valid_modes.values()])\n row_value = gr.Textbox(label=f\"Axis {axis_count} Value\", lines=1)\n fill_row_button = ui_components.ToolButton(value=fill_values_symbol, visible=False)\n def fill_axis(mode_name):\n core.clear_caches()\n mode = core.valid_modes.get(clean_mode(mode_name))\n if mode is None:\n return gr.update()\n elif mode.type == \"boolean\":\n return \"true, false\"\n elif mode.valid_list is not None:\n return \", \".join(list(mode.valid_list()))\n raise RuntimeError(f\"Can't fill axis for {mode_name}\")\n fill_row_button.click(fn=fill_axis, inputs=[row_mode], outputs=[row_value])\n def on_axis_change(mode_name, out_file):\n mode = core.valid_modes.get(clean_mode(mode_name))\n button_update = gr.Button.update(visible=mode is not None and (mode.valid_list is not None or mode.type == \"boolean\"))\n (out_file_update, info_update) = update_page_url(out_file, \"Create in UI\")\n return [button_update, out_file_update, info_update]\n row_mode.change(fn=on_axis_change, inputs=[row_mode, output_file_path], outputs=[fill_row_button, output_file_path, page_will_be])\n manual_axes += list([row_mode, row_value])\n rows.append(row_mode)\n sets.append([group_obj, rows])\n for group in range(0, 3):\n row_mode = sets[group][1][3]\n group_obj = sets[group + 1][0]\n next_rows = sets[group + 1][1]\n def make_vis(prior, r1, r2, r3, r4):\n return gr.Group.update(visible=(prior+r1+r2+r3+r4).strip() != \"\")\n row_mode.change(fn=make_vis, inputs=[row_mode] + next_rows, outputs=[group_obj])\n gr.HTML('(More input rows will be automatically added after you select modes above.)')\n grid_file.change(\n fn=lambda x: {\"visible\": x == \"Create in UI\", \"__type__\": \"update\"},\n inputs=[grid_file],\n outputs=[manual_group],\n show_progress = False)\n output_file_path.change(fn=update_page_url_single, inputs=[output_file_path, grid_file], outputs=[page_will_be])\n grid_file.change(fn=update_page_url, inputs=[output_file_path, grid_file], outputs=[output_file_path, page_will_be])\n with gr.Row():\n do_overwrite = gr.Checkbox(value=False, label=\"Overwrite existing images (for updating grids)\")\n dry_run = gr.Checkbox(value=False, label=\"Do a dry run to validate your grid file\")\n fast_skip = gr.Checkbox(value=False, label=\"Use more-performant skipping\")\n skip_invalid = gr.Checkbox(value=False, label=\"Skip invalid entries\")\n with gr.Row():\n generate_page = gr.Checkbox(value=True, label=\"Generate infinite-grid webviewer page\")\n validate_replace = gr.Checkbox(value=True, label=\"Validate PromptReplace input\")\n publish_gen_metadata = gr.Checkbox(value=True, label=\"Publish full generation metadata for viewing on-page\")\n return [do_overwrite, generate_page, dry_run, validate_replace, publish_gen_metadata, grid_file, fast_skip, output_file_path, skip_invalid] + manual_axes\n\n def run(self, p, do_overwrite, generate_page, dry_run, validate_replace, publish_gen_metadata, grid_file, fast_skip, output_file_path, skip_invalid, *manual_axes):\n core.clear_caches()\n try_init()\n # Clean up default params\n p = copy(p)\n p.n_iter = 1\n p.batch_size = 1\n p.do_not_save_samples = True\n p.do_not_save_grid = True\n p.seed = processing.get_fixed_seed(p.seed)\n # Store extra variable\n Script.VALIDATE_REPLACE = validate_replace\n # Validate to avoid abuse\n if '..' in grid_file or grid_file == \"\":\n raise RuntimeError(f\"Unacceptable filename '{grid_file}'\")\n if '..' in output_file_path:\n raise RuntimeError(f\"Unacceptable alt file path '{output_file_path}'\")\n if grid_file == \"Create in UI\":\n if output_file_path is None or output_file_path == \"\":\n raise RuntimeError(f\"Must specify the output file path\")\n manual_axes = list(manual_axes)\n else:\n manual_axes = None\n with SettingsFixer():\n result = core.run_grid_gen(p, grid_file, p.outpath_grids, output_file_path, do_overwrite, fast_skip, generate_page, publish_gen_metadata, dry_run, manual_axes, skip_invalid=skip_invalid)\n if result is None:\n return Processed(p, list())\n return result\n" }, { "path": "style.css", "content": "/**\n * This file is part of Stable Diffusion Infinity Grid Generator, view the README.md for more information.\n */\n\n#infinity_grid_refresh_button {\n max-width: 2.5em;\n min-width: 2.5em;\n height: 2.4em;\n}\n" } ]