Intel/sd-1.5-square-quantized
43.4k
1<?xml version="1.0" ?>2<net name="torch_jit" version="11">3 <layers>4 <layer id="0" name="init_image" type="Parameter" version="opset1">5 <data shape="1,3,512,512" element_type="f32"/>6 <rt_info>7 <attribute name="fused_names" version="0" value="init_image"/>8 <attribute name="old_api_map_element_type" version="0" value="f16"/>9 </rt_info>10 <output>11 <port id="0" precision="FP32" names="init_image">12 <dim>1</dim>13 <dim>3</dim>14 <dim>512</dim>15 <dim>512</dim>16 </port>17 </output>18 </layer>19 <layer id="1" name="Constant_19226_compressed" type="Const" version="opset1">20 <data element_type="f16" shape="1, 1, 512" offset="0" size="1024"/>21 <output>22 <port id="0" precision="FP16">23 <dim>1</dim>24 <dim>1</dim>25 <dim>512</dim>26 </port>27 </output>28 </layer>29 <layer id="2" name="Constant_19226" type="Convert" version="opset1">30 <data destination_type="f32"/>31 <rt_info>32 <attribute name="decompression" version="0"/>33 </rt_info>34 <input>35 <port id="0" precision="FP16">36 <dim>1</dim>37 <dim>1</dim>38 <dim>512</dim>39 </port>40 </input>41 <output>42 <port id="1" precision="FP32">43 <dim>1</dim>44 <dim>1</dim>45 <dim>512</dim>46 </port>47 </output>48 </layer>49 <layer id="3" name="Constant_19221_compressed" type="Const" version="opset1">50 <data element_type="f16" shape="1, 1, 512" offset="1024" size="1024"/>51 <output>52 <port id="0" precision="FP16">53 <dim>1</dim>54 <dim>1</dim>55 <dim>512</dim>56 </port>57 </output>58 </layer>59 <layer id="4" name="Constant_19221" type="Convert" version="opset1">60 <data destination_type="f32"/>61 <rt_info>62 <attribute name="decompression" version="0"/>63 </rt_info>64 <input>65 <port id="0" precision="FP16">66 <dim>1</dim>67 <dim>1</dim>68 <dim>512</dim>69 </port>70 </input>71 <output>72 <port id="1" precision="FP32">73 <dim>1</dim>74 <dim>1</dim>75 <dim>512</dim>76 </port>77 </output>78 </layer>79 <layer id="5" name="vae.encoder.conv_in.weight_compressed" type="Const" version="opset1">80 <data element_type="f16" shape="128, 3, 3, 3" offset="2048" size="6912"/>81 <output>82 <port id="0" precision="FP16">83 <dim>128</dim>84 <dim>3</dim>85 <dim>3</dim>86 <dim>3</dim>87 </port>88 </output>89 </layer>90 <layer id="6" name="vae.encoder.conv_in.weight" type="Convert" version="opset1">91 <data destination_type="f32"/>92 <rt_info>93 <attribute name="decompression" version="0"/>94 <attribute name="fused_names" version="0" value="vae.encoder.conv_in.weight"/>95 </rt_info>96 <input>97 <port id="0" precision="FP16">98 <dim>128</dim>99 <dim>3</dim>100 <dim>3</dim>101 <dim>3</dim>102 </port>103 </input>104 <output>105 <port id="1" precision="FP32" names="vae.encoder.conv_in.weight">106 <dim>128</dim>107 <dim>3</dim>108 <dim>3</dim>109 <dim>3</dim>110 </port>111 </output>112 </layer>113 <layer id="7" name="/encoder/conv_in/Conv/WithoutBiases" type="Convolution" version="opset1">114 <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit"/>115 <rt_info>116 <attribute name="fused_names" version="0" value="/encoder/conv_in/Conv/WithoutBiases"/>117 </rt_info>118 <input>119 <port id="0" precision="FP32">120 <dim>1</dim>121 <dim>3</dim>122 <dim>512</dim>123 <dim>512</dim>124 </port>125 <port id="1" precision="FP32">126 <dim>128</dim>127 <dim>3</dim>128 <dim>3</dim>129 <dim>3</dim>130 </port>131 </input>132 <output>133 <port id="2" precision="FP32">134 <dim>1</dim>135 <dim>128</dim>136 <dim>512</dim>137 <dim>512</dim>138 </port>139 </output>140 </layer>141 <layer id="8" name="Reshape_129_compressed" type="Const" version="opset1">142 <data element_type="f16" shape="1, 128, 1, 1" offset="8960" size="256"/>143 <output>144 <port id="0" precision="FP16">145 <dim>1</dim>146 <dim>128</dim>147 <dim>1</dim>148 <dim>1</dim>149 </port>150 </output>151 </layer>152 <layer id="9" name="Reshape_129" type="Convert" version="opset1">153 <data destination_type="f32"/>154 <rt_info>155 <attribute name="decompression" version="0"/>156 </rt_info>157 <input>158 <port id="0" precision="FP16">159 <dim>1</dim>160 <dim>128</dim>161 <dim>1</dim>162 <dim>1</dim>163 </port>164 </input>165 <output>166 <port id="1" precision="FP32">167 <dim>1</dim>168 <dim>128</dim>169 <dim>1</dim>170 <dim>1</dim>171 </port>172 </output>173 </layer>174 <layer id="10" name="/encoder/conv_in/Conv" type="Add" version="opset1">175 <data auto_broadcast="numpy"/>176 <rt_info>177 <attribute name="fused_names" version="0" value="/encoder/conv_in/Conv, Concat_128, Reshape_129"/>178 </rt_info>179 <input>180 <port id="0" precision="FP32">181 <dim>1</dim>182 <dim>128</dim>183 <dim>512</dim>184 <dim>512</dim>185 </port>186 <port id="1" precision="FP32">187 <dim>1</dim>188 <dim>128</dim>189 <dim>1</dim>190 <dim>1</dim>191 </port>192 </input>193 <output>194 <port id="2" precision="FP32" names="/encoder/conv_in/Conv_output_0">195 <dim>1</dim>196 <dim>128</dim>197 <dim>512</dim>198 <dim>512</dim>199 </port>200 </output>201 </layer>202 <layer id="11" name="/encoder/down_blocks.0/resnets.0/norm1/Constant" type="Const" version="opset1">203 <data element_type="i64" shape="3" offset="9216" size="24"/>204 <rt_info>205 <attribute name="fused_names" version="0" value="/encoder/down_blocks.0/resnets.0/norm1/Constant"/>206 </rt_info>207 <output>208 <port id="0" precision="I64" names="/encoder/down_blocks.0/resnets.0/norm1/Constant_output_0">209 <dim>3</dim>210 </port>211 </output>212 </layer>213 <layer id="12" name="/encoder/down_blocks.0/resnets.0/norm1/Reshape" type="Reshape" version="opset1">214 <data special_zero="true"/>215 <rt_info>216 <attribute name="fused_names" version="0" value="/encoder/down_blocks.0/resnets.0/norm1/Reshape"/>217 </rt_info>218 <input>219 <port id="0" precision="FP32">220 <dim>1</dim>221 <dim>128</dim>222 <dim>512</dim>223 <dim>512</dim>224 </port>225 <port id="1" precision="I64">226 <dim>3</dim>227 </port>228 </input>229 <output>230 <port id="2" precision="FP32" names="/encoder/down_blocks.0/resnets.0/norm1/Reshape_output_0">231 <dim>1</dim>232 <dim>32</dim>233 <dim>1048576</dim>234 </port>235 </output>236 </layer>237 <layer id="13" name="Constant_166" type="Const" version="opset1">238 <data element_type="i64" shape="1" offset="9240" size="8"/>239 <rt_info>240 <attribute name="fused_names" version="0" value="Constant_166"/>241 </rt_info>242 <output>243 <port id="0" precision="I64">244 <dim>1</dim>245 </port>246 </output>247 </layer>248 <layer id="14" name="MVN_167" type="MVN" version="opset6">249 <data eps="9.9999999747524271e-07" normalize_variance="true" eps_mode="INSIDE_SQRT"/>250 <rt_info>251 <attribute name="fused_names" version="0" value="/encoder/down_blocks.0/resnets.0/norm1/InstanceNormalization, Concat_186, Concat_231, MVN_167, Multiply_214, Reshape_187, Reshape_232"/>252 </rt_info>253 <input>254 <port id="0" precision="FP32">255 <dim>1</dim>256 <dim>32</dim>257 <dim>1048576</dim>258 </port>259 <port id="1" precision="I64">260 <dim>1</dim>261 </port>262 </input>263 <output>264 <port id="2" precision="FP32" names="/encoder/down_blocks.0/resnets.0/norm1/InstanceNormalization_output_0">265 <dim>1</dim>266 <dim>32</dim>267 <dim>1048576</dim>268 </port>269 </output>270 </layer>271 <layer id="15" name="/encoder/down_blocks.0/resnets.0/norm1/Shape" type="ShapeOf" version="opset3">272 <data output_type="i64"/>273 <rt_info>274 <attribute name="fused_names" version="0" value="/encoder/down_blocks.0/resnets.0/norm1/Shape"/>275 </rt_info>276 <input>277 <port id="0" precision="FP32">278 <dim>1</dim>279 <dim>128</dim>280 <dim>512</dim>281 <dim>512</dim>282 </port>283 </input>284 <output>285 <port id="1" precision="I64" names="/encoder/down_blocks.0/resnets.0/norm1/Shape_output_0">286 <dim>4</dim>287 </port>288 </output>289 </layer>290 <layer id="16" name="/encoder/down_blocks.0/resnets.0/norm1/Reshape_1" type="Reshape" version="opset1">291 <data special_zero="true"/>292 <rt_info>293 <attribute name="fused_names" version="0" value="/encoder/down_blocks.0/resnets.0/norm1/Reshape_1"/>294 </rt_info>295 <input>296 <port id="0" precision="FP32">297 <dim>1</dim>298 <dim>32</dim>299 <dim>1048576</dim>300 </port>301 <port id="1" precision="I64">302 <dim>4</dim>303 </port>304 </input>305 <output>306 <port id="2" precision="FP32" names="/encoder/down_blocks.0/resnets.0/norm1/Reshape_1_output_0">307 <dim>1</dim>308 <dim>128</dim>309 <dim>512</dim>310 <dim>512</dim>311 </port>312 </output>313 </layer>314 <layer id="17" name="Constant_19183_compressed" type="Const" version="opset1">315 <data element_type="f16" shape="1, 128, 1, 1" offset="9248" size="256"/>316 <output>317 <port id="0" precision="FP16">318 <dim>1</dim>319 <dim>128</dim>320 <dim>1</dim>321 <dim>1</dim>322 </port>323 </output>324 </layer>325 <layer id="18" name="Constant_19183" type="Convert" version="opset1">326 <data destination_type="f32"/>327 <rt_info>328 <attribute name="decompression" version="0"/>329 </rt_info>330 <input>331 <port id="0" precision="FP16">332 <dim>1</dim>333 <dim>128</dim>334 <dim>1</dim>335 <dim>1</dim>336 </port>337 </input>338 <output>339 <port id="1" precision="FP32">340 <dim>1</dim>341 <dim>128</dim>342 <dim>1</dim>343 <dim>1</dim>344 </port>345 </output>346 </layer>347 <layer id="19" name="/encoder/down_blocks.0/resnets.0/norm1/Mul" type="Multiply" version="opset1">348 <data auto_broadcast="numpy"/>349 <rt_info>350 <attribute name="fused_names" version="0" value="/encoder/down_blocks.0/resnets.0/norm1/Mul"/>351 </rt_info>352 <input>353 <port id="0" precision="FP32">354 <dim>1</dim>355 <dim>128</dim>356 <dim>512</dim>357 <dim>512</dim>358 </port>359 <port id="1" precision="FP32">360 <dim>1</dim>361 <dim>128</dim>362 <dim>1</dim>363 <dim>1</dim>364 </port>365 </input>366 <output>367 <port id="2" precision="FP32" names="/encoder/down_blocks.0/resnets.0/norm1/Mul_output_0">368 <dim>1</dim>369 <dim>128</dim>370 <dim>512</dim>371 <dim>512</dim>372 </port>373 </output>374 </layer>375 <layer id="20" name="Constant_19184_compressed" type="Const" version="opset1">376 <data element_type="f16" shape="1, 128, 1, 1" offset="9504" size="256"/>377 <output>378 <port id="0" precision="FP16">379 <dim>1</dim>380 <dim>128</dim>381 <dim>1</dim>382 <dim>1</dim>383 </port>384 </output>385 </layer>386 <layer id="21" name="Constant_19184" type="Convert" version="opset1">387 <data destination_type="f32"/>388 <rt_info>389 <attribute name="decompression" version="0"/>390 </rt_info>391 <input>392 <port id="0" precision="FP16">393 <dim>1</dim>394 <dim>128</dim>395 <dim>1</dim>396 <dim>1</dim>397 </port>398 </input>399 <output>400 <port id="1" precision="FP32">401 <dim>1</dim>402 <dim>128</dim>403 <dim>1</dim>404 <dim>1</dim>405 </port>406 </output>407 </layer>408 <layer id="22" name="/encoder/down_blocks.0/resnets.0/norm1/Add" type="Add" version="opset1">409 <data auto_broadcast="numpy"/>410 <rt_info>411 <attribute name="fused_names" version="0" value="/encoder/down_blocks.0/resnets.0/norm1/Add"/>412 </rt_info>413 <input>414 <port id="0" precision="FP32">415 <dim>1</dim>416 <dim>128</dim>417 <dim>512</dim>418 <dim>512</dim>419 </port>420 <port id="1" precision="FP32">421 <dim>1</dim>422 <dim>128</dim>423 <dim>1</dim>424 <dim>1</dim>425 </port>426 </input>427 <output>428 <port id="2" precision="FP32" names="/encoder/down_blocks.0/resnets.0/norm1/Add_output_0">429 <dim>1</dim>430 <dim>128</dim>431 <dim>512</dim>432 <dim>512</dim>433 </port>434 </output>435 </layer>436 <layer id="23" name="/encoder/down_blocks.0/resnets.0/nonlinearity/Mul" type="Swish" version="opset4">437 <rt_info>438 <attribute name="fused_names" version="0" value="/encoder/down_blocks.0/resnets.0/nonlinearity/Mul, /encoder/down_blocks.0/resnets.0/nonlinearity/Sigmoid"/>439 </rt_info>440 <input>441 <port id="0" precision="FP32">442 <dim>1</dim>443 <dim>128</dim>444 <dim>512</dim>445 <dim>512</dim>446 </port>447 </input>448 <output>449 <port id="1" precision="FP32" names="/encoder/down_blocks.0/resnets.0/nonlinearity/Mul_output_0">450 <dim>1</dim>451 <dim>128</dim>452 <dim>512</dim>453 <dim>512</dim>454 </port>455 </output>456 </layer>457 <layer id="24" name="vae.encoder.down_blocks.0.resnets.0.conv1.weight_compressed" type="Const" version="opset1">458 <data element_type="f16" shape="128, 128, 3, 3" offset="9760" size="294912"/>459 <output>460 <port id="0" precision="FP16">461 <dim>128</dim>462 <dim>128</dim>463 <dim>3</dim>464 <dim>3</dim>465 </port>466 </output>467 </layer>468 <layer id="25" name="vae.encoder.down_blocks.0.resnets.0.conv1.weight" type="Convert" version="opset1">469 <data destination_type="f32"/>470 <rt_info>471 <attribute name="decompression" version="0"/>472 <attribute name="fused_names" version="0" value="vae.encoder.down_blocks.0.resnets.0.conv1.weight"/>473 </rt_info>474 <input>475 <port id="0" precision="FP16">476 <dim>128</dim>477 <dim>128</dim>478 <dim>3</dim>479 <dim>3</dim>480 </port>481 </input>482 <output>483 <port id="1" precision="FP32" names="vae.encoder.down_blocks.0.resnets.0.conv1.weight">484 <dim>128</dim>485 <dim>128</dim>486 <dim>3</dim>487 <dim>3</dim>488 </port>489 </output>490 </layer>491 <layer id="26" name="/encoder/down_blocks.0/resnets.0/conv1/Conv/WithoutBiases" type="Convolution" version="opset1">492 <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit"/>493 <rt_info>494 <attribute name="fused_names" version="0" value="/encoder/down_blocks.0/resnets.0/conv1/Conv/WithoutBiases"/>495 </rt_info>496 <input>497 <port id="0" precision="FP32">498 <dim>1</dim>499 <dim>128</dim>500 <dim>512</dim>501 <dim>512</dim>502 </port>503 <port id="1" precision="FP32">504 <dim>128</dim>505 <dim>128</dim>506 <dim>3</dim>507 <dim>3</dim>508 </port>509 </input>510 <output>511 <port id="2" precision="FP32">512 <dim>1</dim>513 <dim>128</dim>514 <dim>512</dim>515 <dim>512</dim>516 </port>517 </output>518 </layer>519 <layer id="27" name="Reshape_291_compressed" type="Const" version="opset1">520 <data element_type="f16" shape="1, 128, 1, 1" offset="304672" size="256"/>521 <output>522 <port id="0" precision="FP16">523 <dim>1</dim>524 <dim>128</dim>525 <dim>1</dim>526 <dim>1</dim>527 </port>528 </output>529 </layer>530 <layer id="28" name="Reshape_291" type="Convert" version="opset1">531 <data destination_type="f32"/>532 <rt_info>533 <attribute name="decompression" version="0"/>534 </rt_info>535 <input>536 <port id="0" precision="FP16">537 <dim>1</dim>538 <dim>128</dim>539 <dim>1</dim>540 <dim>1</dim>541 </port>542 </input>543 <output>544 <port id="1" precision="FP32">545 <dim>1</dim>546 <dim>128</dim>547 <dim>1</dim>548 <dim>1</dim>549 </port>550 </output>551 </layer>552 <layer id="29" name="/encoder/down_blocks.0/resnets.0/conv1/Conv" type="Add" version="opset1">553 <data auto_broadcast="numpy"/>554 <rt_info>555 <attribute name="fused_names" version="0" value="/encoder/down_blocks.0/resnets.0/conv1/Conv, Concat_290, Reshape_291"/>556 </rt_info>557 <input>558 <port id="0" precision="FP32">559 <dim>1</dim>560 <dim>128</dim>561 <dim>512</dim>562 <dim>512</dim>563 </port>564 <port id="1" precision="FP32">565 <dim>1</dim>566 <dim>128</dim>567 <dim>1</dim>568 <dim>1</dim>569 </port>570 </input>571 <output>572 <port id="2" precision="FP32" names="/encoder/down_blocks.0/resnets.0/conv1/Conv_output_0">573 <dim>1</dim>574 <dim>128</dim>575 <dim>512</dim>576 <dim>512</dim>577 </port>578 </output>579 </layer>580 <layer id="30" name="/encoder/down_blocks.0/resnets.0/norm2/Constant" type="Const" version="opset1">581 <data element_type="i64" shape="3" offset="9216" size="24"/>582 <rt_info>583 <attribute name="fused_names" version="0" value="/encoder/down_blocks.0/resnets.0/norm2/Constant"/>584 </rt_info>585 <output>586 <port id="0" precision="I64" names="/encoder/down_blocks.0/resnets.0/norm2/Constant_output_0">587 <dim>3</dim>588 </port>589 </output>590 </layer>591 <layer id="31" name="/encoder/down_blocks.0/resnets.0/norm2/Reshape" type="Reshape" version="opset1">592 <data special_zero="true"/>593 <rt_info>594 <attribute name="fused_names" version="0" value="/encoder/down_blocks.0/resnets.0/norm2/Reshape"/>595 </rt_info>596 <input>597 <port id="0" precision="FP32">598 <dim>1</dim>599 <dim>128</dim>600 <dim>512</dim>601 <dim>512</dim>602 </port>603 <port id="1" precision="I64">604 <dim>3</dim>605 </port>606 </input>607 <output>608 <port id="2" precision="FP32" names="/encoder/down_blocks.0/resnets.0/norm2/Reshape_output_0">609 <dim>1</dim>610 <dim>32</dim>611 <dim>1048576</dim>612 </port>613 </output>614 </layer>615 <layer id="32" name="Constant_328" type="Const" version="opset1">616 <data element_type="i64" shape="1" offset="9240" size="8"/>617 <rt_info>618 <attribute name="fused_names" version="0" value="Constant_328"/>619 </rt_info>620 <output>621 <port id="0" precision="I64">622 <dim>1</dim>623 </port>624 </output>625 </layer>626 <layer id="33" name="MVN_329" type="MVN" version="opset6">627 <data eps="9.9999999747524271e-07" normalize_variance="true" eps_mode="INSIDE_SQRT"/>628 <rt_info>629 <attribute name="fused_names" version="0" value="/encoder/down_blocks.0/resnets.0/norm2/InstanceNormalization, Concat_348, Concat_393, MVN_329, Multiply_376, Reshape_349, Reshape_394"/>630 </rt_info>631 <input>632 <port id="0" precision="FP32">633 <dim>1</dim>634 <dim>32</dim>635 <dim>1048576</dim>636 </port>637 <port id="1" precision="I64">638 <dim>1</dim>639 </port>640 </input>641 <output>642 <port id="2" precision="FP32" names="/encoder/down_blocks.0/resnets.0/norm2/InstanceNormalization_output_0">643 <dim>1</dim>644 <dim>32</dim>645 <dim>1048576</dim>646 </port>647 </output>648 </layer>649 <layer id="34" name="/encoder/down_blocks.0/resnets.0/norm2/Shape" type="ShapeOf" version="opset3">650 <data output_type="i64"/>651 <rt_info>652 <attribute name="fused_names" version="0" value="/encoder/down_blocks.0/resnets.0/norm2/Shape"/>653 </rt_info>654 <input>655 <port id="0" precision="FP32">656 <dim>1</dim>657 <dim>128</dim>658 <dim>512</dim>659 <dim>512</dim>660 </port>661 </input>662 <output>663 <port id="1" precision="I64" names="/encoder/down_blocks.0/resnets.0/norm2/Shape_output_0">664 <dim>4</dim>665 </port>666 </output>667 </layer>668 <layer id="35" name="/encoder/down_blocks.0/resnets.0/norm2/Reshape_1" type="Reshape" version="opset1">669 <data special_zero="true"/>670 <rt_info>671 <attribute name="fused_names" version="0" value="/encoder/down_blocks.0/resnets.0/norm2/Reshape_1"/>672 </rt_info>673 <input>674 <port id="0" precision="FP32">675 <dim>1</dim>676 <dim>32</dim>677 <dim>1048576</dim>678 </port>679 <port id="1" precision="I64">680 <dim>4</dim>681 </port>682 </input>683 <output>684 <port id="2" precision="FP32" names="/encoder/down_blocks.0/resnets.0/norm2/Reshape_1_output_0">685 <dim>1</dim>686 <dim>128</dim>687 <dim>512</dim>688 <dim>512</dim>689 </port>690 </output>691 </layer>692 <layer id="36" name="Constant_19185_compressed" type="Const" version="opset1">693 <data element_type="f16" shape="1, 128, 1, 1" offset="304928" size="256"/>694 <output>695 <port id="0" precision="FP16">696 <dim>1</dim>697 <dim>128</dim>698 <dim>1</dim>699 <dim>1</dim>700 </port>701 </output>702 </layer>703 <layer id="37" name="Constant_19185" type="Convert" version="opset1">704 <data destination_type="f32"/>705 <rt_info>706 <attribute name="decompression" version="0"/>707 </rt_info>708 <input>709 <port id="0" precision="FP16">710 <dim>1</dim>711 <dim>128</dim>712 <dim>1</dim>713 <dim>1</dim>714 </port>715 </input>716 <output>717 <port id="1" precision="FP32">718 <dim>1</dim>719 <dim>128</dim>720 <dim>1</dim>721 <dim>1</dim>722 </port>723 </output>724 </layer>725 <layer id="38" name="/encoder/down_blocks.0/resnets.0/norm2/Mul" type="Multiply" version="opset1">726 <data auto_broadcast="numpy"/>727 <rt_info>728 <attribute name="fused_names" version="0" value="/encoder/down_blocks.0/resnets.0/norm2/Mul"/>729 </rt_info>730 <input>731 <port id="0" precision="FP32">732 <dim>1</dim>733 <dim>128</dim>734 <dim>512</dim>735 <dim>512</dim>736 </port>737 <port id="1" precision="FP32">738 <dim>1</dim>739 <dim>128</dim>740 <dim>1</dim>741 <dim>1</dim>742 </port>743 </input>744 <output>745 <port id="2" precision="FP32" names="/encoder/down_blocks.0/resnets.0/norm2/Mul_output_0">746 <dim>1</dim>747 <dim>128</dim>748 <dim>512</dim>749 <dim>512</dim>750 </port>751 </output>752 </layer>753 <layer id="39" name="Constant_19186_compressed" type="Const" version="opset1">754 <data element_type="f16" shape="1, 128, 1, 1" offset="305184" size="256"/>755 <output>756 <port id="0" precision="FP16">757 <dim>1</dim>758 <dim>128</dim>759 <dim>1</dim>760 <dim>1</dim>761 </port>762 </output>763 </layer>764 <layer id="40" name="Constant_19186" type="Convert" version="opset1">765 <data destination_type="f32"/>766 <rt_info>767 <attribute name="decompression" version="0"/>768 </rt_info>769 <input>770 <port id="0" precision="FP16">771 <dim>1</dim>772 <dim>128</dim>773 <dim>1</dim>774 <dim>1</dim>775 </port>776 </input>777 <output>778 <port id="1" precision="FP32">779 <dim>1</dim>780 <dim>128</dim>781 <dim>1</dim>782 <dim>1</dim>783 </port>784 </output>785 </layer>786 <layer id="41" name="/encoder/down_blocks.0/resnets.0/norm2/Add" type="Add" version="opset1">787 <data auto_broadcast="numpy"/>788 <rt_info>789 <attribute name="fused_names" version="0" value="/encoder/down_blocks.0/resnets.0/norm2/Add"/>790 </rt_info>791 <input>792 <port id="0" precision="FP32">793 <dim>1</dim>794 <dim>128</dim>795 <dim>512</dim>796 <dim>512</dim>797 </port>798 <port id="1" precision="FP32">799 <dim>1</dim>800 <dim>128</dim>801 <dim>1</dim>802 <dim>1</dim>803 </port>804 </input>805 <output>806 <port id="2" precision="FP32" names="/encoder/down_blocks.0/resnets.0/norm2/Add_output_0">807 <dim>1</dim>808 <dim>128</dim>809 <dim>512</dim>810 <dim>512</dim>811 </port>812 </output>813 </layer>814 <layer id="42" name="/encoder/down_blocks.0/resnets.0/nonlinearity_1/Mul" type="Swish" version="opset4">815 <rt_info>816 <attribute name="fused_names" version="0" value="/encoder/down_blocks.0/resnets.0/nonlinearity_1/Mul, /encoder/down_blocks.0/resnets.0/nonlinearity_1/Sigmoid"/>817 </rt_info>818 <input>819 <port id="0" precision="FP32">820 <dim>1</dim>821 <dim>128</dim>822 <dim>512</dim>823 <dim>512</dim>824 </port>825 </input>826 <output>827 <port id="1" precision="FP32" names="/encoder/down_blocks.0/resnets.0/nonlinearity_1/Mul_output_0">828 <dim>1</dim>829 <dim>128</dim>830 <dim>512</dim>831 <dim>512</dim>832 </port>833 </output>834 </layer>835 <layer id="43" name="vae.encoder.down_blocks.0.resnets.0.conv2.weight_compressed" type="Const" version="opset1">836 <data element_type="f16" shape="128, 128, 3, 3" offset="305440" size="294912"/>837 <output>838 <port id="0" precision="FP16">839 <dim>128</dim>840 <dim>128</dim>841 <dim>3</dim>842 <dim>3</dim>843 </port>844 </output>845 </layer>846 <layer id="44" name="vae.encoder.down_blocks.0.resnets.0.conv2.weight" type="Convert" version="opset1">847 <data destination_type="f32"/>848 <rt_info>849 <attribute name="decompression" version="0"/>850 <attribute name="fused_names" version="0" value="vae.encoder.down_blocks.0.resnets.0.conv2.weight"/>851 </rt_info>852 <input>853 <port id="0" precision="FP16">854 <dim>128</dim>855 <dim>128</dim>856 <dim>3</dim>857 <dim>3</dim>858 </port>859 </input>860 <output>861 <port id="1" precision="FP32" names="vae.encoder.down_blocks.0.resnets.0.conv2.weight">862 <dim>128</dim>863 <dim>128</dim>864 <dim>3</dim>865 <dim>3</dim>866 </port>867 </output>868 </layer>869 <layer id="45" name="/encoder/down_blocks.0/resnets.0/conv2/Conv/WithoutBiases" type="Convolution" version="opset1">870 <data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit"/>871 <rt_info>872 <attribute name="fused_names" version="0" value="/encoder/down_blocks.0/resnets.0/conv2/Conv/WithoutBiases"/>873 </rt_info>874 <input>875 <port id="0" precision="FP32">876 <dim>1</dim>877 <dim>128</dim>878 <dim>512</dim>879 <dim>512</dim>880 </port>881 <port id="1" precision="FP32">882 <dim>128</dim>883 <dim>128</dim>884 <dim>3</dim>885 <dim>3</dim>886 </port>887 </input>888 <output>889 <port id="2" precision="FP32">890 <dim>1</dim>891 <dim>128</dim>892 <dim>512</dim>893 <dim>512</dim>894 </port>895 </output>896 </layer>897 <layer id="46" name="Reshape_453_compressed" type="Const" version="opset1">898 <data element_type="f16" shape="1, 128, 1, 1" offset="600352" size="256"/>899 <output>900 <port id="0" precision="FP16">901 <dim>1</dim>902 <dim>128</dim>903 <dim>1</dim>904 <dim>1</dim>905 </port>906 </output>907 </layer>908 <layer id="47" name="Reshape_453" type="Convert" version="opset1">909 <data destination_type="f32"/>910 <rt_info>911 <attribute name="decompression" version="0"/>912 </rt_info>913 <input>914 <port id="0" precision="FP16">915 <dim>1</dim>916 <dim>128</dim>917 <dim>1</dim>918 <dim>1</dim>919 </port>920 </input>921 <output>922 <port id="1" precision="FP32">923 <dim>1</dim>924 <dim>128</dim>925 <dim>1</dim>926 <dim>1</dim>927 </port>928 </output>929 </layer>930 <layer id="48" name="/encoder/down_blocks.0/resnets.0/conv2/Conv" type="Add" version="opset1">931 <data auto_broadcast="numpy"/>932 <rt_info>933 <attribute name="fused_names" version="0" value="/encoder/down_blocks.0/resnets.0/conv2/Conv, Concat_452, Reshape_453"/>934 </rt_info>935 <input>936 <port id="0" precision="FP32">937 <dim>1</dim>938 <dim>128</dim>939 <dim>512</dim>940 <dim>512</dim>941 </port>942 <port id="1" precision="FP32">943 <dim>1</dim>944 <dim>128</dim>945 <dim>1</dim>946 <dim>1</dim>947 </port>948 </input>949 <output>950 <port id="2" precision="FP32" names="/encoder/down_blocks.0/resnets.0/conv2/Conv_output_0">951 <dim>1</dim>952 <dim>128</dim>953 <dim>512</dim>954 <dim>512</dim>955 </port>956 </output>957 </layer>958 <layer id="49" name="/encoder/down_blocks.0/resnets.0/Add" type="Add" version="opset1">959 <data auto_broadcast="numpy"/>960 <rt_info>961 <attribute name="fused_names" version="0" value="/encoder/down_blocks.0/resnets.0/Add, /encoder/down_blocks.0/resnets.0/Div"/>962 </rt_info>963 <input>964 <port id="0" precision="FP32">965 <dim>1</dim>966 <dim>128</dim>967 <dim>512</dim>968 <dim>512</dim>969 </port>970 <port id="1" precision="FP32">971 <dim>1</dim>972 <dim>128</dim>973 <dim>512</dim>974 <dim>512</dim>975 </port>976 </input>977 <output>978 <port id="2" precision="FP32" names="/encoder/down_blocks.0/resnets.0/Add_output_0,/encoder/down_blocks.0/resnets.0/Div_output_0">979 <dim>1</dim>980 <dim>128</dim>981 <dim>512</dim>982 <dim>512</dim>983 </port>984 </output>985 </layer>986 <layer id="50" name="/encoder/down_blocks.0/resnets.1/norm1/Constant" type="Const" version="opset1">987 <data element_type="i64" shape="3" offset="9216" size="24"/>988 <rt_info>989 <attribute name="fused_names" version="0" value="/encoder/down_blocks.0/resnets.1/norm1/Constant"/>990 </rt_info>991 <output>992 <port id="0" precision="I64" names="/encoder/down_blocks.0/resnets.1/norm1/Constant_output_0">993 <dim>3</dim>994 </port>995 </output>996 </layer>997 <layer id="51" name="/encoder/down_blocks.0/resnets.1/norm1/Reshape" type="Reshape" version="opset1">998 <data special_zero="true"/>999 <rt_info>1000 <attribute name="fused_names" version="0" value="/encoder/down_blocks.0/resnets.1/norm1/Reshape"/>1001 </rt_info>1002 <input>1003 <port id="0" precision="FP32">1004 <dim>1</dim>1005 <dim>128</dim>1006 <dim>512</dim>1007 <dim>512</dim>1008 </port>1009 <port id="1" precision="I64">1010 <dim>3</dim>1011 </port>1012 </input>1013 <output>1014 <port id="2" precision="FP32" names="/encoder/down_blocks.0/resnets.1/norm1/Reshape_output_0">1015 <dim>1</dim>1016 <dim>32</dim>1017 <dim>1048576</dim>1018 </port>1019 </output>1020 </layer>1021 <layer id="52" name="Constant_493" type="Const" version="opset1">1022 <data element_type="i64" shape="1" offset="9240" size="8"/>1023 <rt_info>1024 <attribute name="fused_names" version="0" value="Constant_493"/>1025 </rt_info>1026 <output>1027 <port id="0" precision="I64">1028 <dim>1</dim>1029 </port>1030 </output>1031 </layer>1032 <layer id="53" name="MVN_494" type="MVN" version="opset6">1033 <data eps="9.9999999747524271e-07" normalize_variance="true" eps_mode="INSIDE_SQRT"/>1034 <rt_info>1035 <attribute name="fused_names" version="0" value="/encoder/down_blocks.0/resnets.1/norm1/InstanceNormalization, Concat_513, Concat_558, MVN_494, Multiply_541, Reshape_514, Reshape_559"/>1036 </rt_info>1037 <input>1038 <port id="0" precision="FP32">1039 <dim>1</dim>1040 <dim>32</dim>1041 <dim>1048576</dim>1042 </port>1043 <port id="1" precision="I64">1044 <dim>1</dim>1045 </port>1046 </input>1047 <output>1048 <port id="2" precision="FP32" names="/encoder/down_blocks.0/resnets.1/norm1/InstanceNormalization_output_0">1049 <dim>1</dim>1050 <dim>32</dim>1051 <dim>1048576</dim>1052 </port>1053 </output>1054 </layer>1055 <layer id="54" name="/encoder/down_blocks.0/resnets.1/norm1/Shape" type="ShapeOf" version="opset3">1056 <data output_type="i64"/>1057 <rt_info>1058 <attribute name="fused_names" version="0" value="/encoder/down_blocks.0/resnets.1/norm1/Shape"/>1059 </rt_info>1060 <input>1061 <port id="0" precision="FP32">1062 <dim>1</dim>1063 <dim>128</dim>1064 <dim>512</dim>1065 <dim>512</dim>1066 </port>1067 </input>1068 <output>1069 <port id="1" precision="I64" names="/encoder/down_blocks.0/resnets.1/norm1/Shape_output_0">1070 <dim>4</dim>1071 </port>1072 </output>1073 </layer>1074 <layer id="55" name="/encoder/down_blocks.0/resnets.1/norm1/Reshape_1" type="Reshape" version="opset1">1075 <data special_zero="true"/>1076 <rt_info>1077 <attribute name="fused_names" version="0" value="/encoder/down_blocks.0/resnets.1/norm1/Reshape_1"/>1078 </rt_info>1079 <input>1080 <port id="0" precision="FP32">1081 <dim>1</dim>1082 <dim>32</dim>1083 <dim>1048576</dim>1084 </port>1085 <port id="1" precision="I64">1086 <dim>4</dim>1087 </port>1088 </input>1089 <output>1090 <port id="2" precision="FP32" names="/encoder/down_blocks.0/resnets.1/norm1/Reshape_1_output_0">1091 <dim>1</dim>1092 <dim>128</dim>1093 <dim>512</dim>1094 <dim>512</dim>1095 </port>1096 </output>1097 </layer>1098 <layer id="56" name="Constant_19187_compressed" type="Const" version="opset1">1099 <data element_type="f16" shape="1, 128, 1, 1" offset="600608" size="256"/>1100 <output>1101 <port id="0" precision="FP16">1102 <dim>1</dim>1103 <dim>128</dim>1104 <dim>1</dim>1105 <dim>1</dim>1106 </port>1107 </output>1108 </layer>1109 <layer id="57" name="Constant_19187" type="Convert" version="opset1">1110 <data destination_type="f32"/>1111 <rt_info>1112 <attribute name="decompression" version="0"/>1113 </rt_info>1114 <input>1115 <port id="0" precision="FP16">1116 <dim>1</dim>1117 <dim>128</dim>1118 <dim>1</dim>1119 <dim>1</dim>1120 </port>1121 </input>1122 <output>1123 <port id="1" precision="FP32">1124 <dim>1</dim>1125 <dim>128</dim>1126 <dim>1</dim>1127 <dim>1</dim>1128 </port>1129 </output>1130 </layer>1131 <layer id="58" name="/encoder/down_blocks.0/resnets.1/norm1/Mul" type="Multiply" version="opset1">1132 <data auto_broadcast="numpy"/>1133 <rt_info>1134 <attribute name="fused_names" version="0" value="/encoder/down_blocks.0/resnets.1/norm1/Mul"/>1135 </rt_info>1136 <input>1137 <port id="0" precision="FP32">1138 <dim>1</dim>1139 <dim>128</dim>1140 <dim>512</dim>1141 <dim>512</dim>1142 </port>1143 <port id="1" precision="FP32">1144 <dim>1</dim>1145 <dim>128</dim>1146 <dim>1</dim>1147 <dim>1</dim>1148 </port>1149 </input>1150 <output>1151 <port id="2" precision="FP32" names="/encoder/down_blocks.0/resnets.1/norm1/Mul_output_0">1152 <dim>1</dim>1153 <dim>128</dim>1154 <dim>512</dim>1155 <dim>512</dim>1156 </port>1157 </output>1158 </layer>1159 <layer id="59" name="Constant_19188_compressed" type="Const" version="opset1">1160 <data element_type="f16" shape="1, 128, 1, 1" offset="600864" size="256"/>1161 <output>1162 <port id="0" precision="FP16">1163 <dim>1</dim>1164 <dim>128</dim>1165 <dim>1</dim>1166 <dim>1</dim>1167 </port>1168 </output>1169 </layer>1170 <layer id="60" name="Constant_19188" type="Convert" version="opset1">1171 <data destination_type="f32"/>1172 <rt_info>1173 <attribute name="decompression" version="0"/>1174 </rt_info>1175 <input>1176 <port id="0" precision="FP16">1177 <dim>1</dim>1178 <dim>128</dim>1179 <dim>1</dim>1180 <dim>1</dim>1181 </port>1182 </input>1183 <output>1184 <port id="1" precision="FP32">1185 <dim>1</dim>1186 <dim>128</dim>1187 <dim>1</dim>1188 <dim>1</dim>1189 </port>1190 </output>1191 </layer>1192 <layer id="61" name="/encoder/down_blocks.0/resnets.1/norm1/Add" type="Add" version="opset1">1193 <data auto_broadcast="numpy"/>1194 <rt_info>1195 <attribute name="fused_names" version="0" value="/encoder/down_blocks.0/resnets.1/norm1/Add"/>1196 </rt_info>1197 <input>1198 <port id="0" precision="FP32">1199 <dim>1</dim>1200 <dim>128</dim>