diff --git "a/original/compiled/VAEDecoder.mlmodelc/model.mil" "b/original/compiled/VAEDecoder.mlmodelc/model.mil" --- "a/original/compiled/VAEDecoder.mlmodelc/model.mil" +++ "b/original/compiled/VAEDecoder.mlmodelc/model.mil" @@ -2,98 +2,98 @@ program(1.0) [buildInfo = dict, tensor>({{"coremlc-component-MIL", "5.33.4"}, {"coremlc-version", "1436.100.10"}, {"coremltools-component-torch", "2.1.0.dev20230718"}, {"coremltools-version", "7.0b1"}})] { func main(tensor z) { - tensor vae_decoder_conv_out_bias = const()[name = tensor("vae_decoder_conv_out_bias"), val = tensor([0x1.fb1412p-4, 0x1.4da0b8p-4, 0x1.943aa8p-5])]; - tensor vae_decoder_conv_out_weight = const()[name = tensor("vae_decoder_conv_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; - tensor vae_decoder_mid_block_resnets_1_conv2_bias = const()[name = tensor("vae_decoder_mid_block_resnets_1_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(13952)))]; - tensor vae_decoder_mid_block_resnets_1_conv2_weight = const()[name = tensor("vae_decoder_mid_block_resnets_1_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16064)))]; - tensor vae_decoder_mid_block_resnets_1_conv1_bias = const()[name = tensor("vae_decoder_mid_block_resnets_1_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9453312)))]; - tensor vae_decoder_mid_block_resnets_1_conv1_weight = const()[name = tensor("vae_decoder_mid_block_resnets_1_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9455424)))]; - tensor vae_decoder_mid_block_resnets_0_conv2_bias = const()[name = tensor("vae_decoder_mid_block_resnets_0_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18892672)))]; - tensor vae_decoder_mid_block_resnets_0_conv2_weight = const()[name = tensor("vae_decoder_mid_block_resnets_0_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18894784)))]; - tensor vae_decoder_mid_block_resnets_0_conv1_bias = const()[name = tensor("vae_decoder_mid_block_resnets_0_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28332032)))]; - tensor vae_decoder_mid_block_resnets_0_conv1_weight = const()[name = tensor("vae_decoder_mid_block_resnets_0_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28334144)))]; - tensor vae_decoder_mid_block_attentions_0_to_out_0_bias = const()[name = tensor("vae_decoder_mid_block_attentions_0_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(37771392)))]; - tensor vae_decoder_mid_block_attentions_0_to_out_0_weight = const()[name = tensor("vae_decoder_mid_block_attentions_0_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(37773504)))]; - tensor vae_decoder_mid_block_attentions_0_to_v_bias = const()[name = tensor("vae_decoder_mid_block_attentions_0_to_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38822144)))]; - tensor vae_decoder_mid_block_attentions_0_to_v_weight = const()[name = tensor("vae_decoder_mid_block_attentions_0_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38824256)))]; - tensor vae_decoder_mid_block_attentions_0_to_k_bias = const()[name = tensor("vae_decoder_mid_block_attentions_0_to_k_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39872896)))]; - tensor vae_decoder_mid_block_attentions_0_to_k_weight = const()[name = tensor("vae_decoder_mid_block_attentions_0_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39875008)))]; - tensor vae_decoder_mid_block_attentions_0_to_q_bias = const()[name = tensor("vae_decoder_mid_block_attentions_0_to_q_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40923648)))]; - tensor vae_decoder_mid_block_attentions_0_to_q_weight = const()[name = tensor("vae_decoder_mid_block_attentions_0_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40925760)))]; - tensor vae_decoder_up_blocks_3_resnets_2_conv2_bias = const()[name = tensor("vae_decoder_up_blocks_3_resnets_2_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41974400)))]; - tensor vae_decoder_up_blocks_3_resnets_2_conv2_weight = const()[name = tensor("vae_decoder_up_blocks_3_resnets_2_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41974976)))]; - tensor vae_decoder_up_blocks_3_resnets_2_conv1_bias = const()[name = tensor("vae_decoder_up_blocks_3_resnets_2_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42564864)))]; - tensor vae_decoder_up_blocks_3_resnets_2_conv1_weight = const()[name = tensor("vae_decoder_up_blocks_3_resnets_2_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42565440)))]; - tensor vae_decoder_up_blocks_3_resnets_1_conv2_bias = const()[name = tensor("vae_decoder_up_blocks_3_resnets_1_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(43155328)))]; - tensor vae_decoder_up_blocks_3_resnets_1_conv2_weight = const()[name = tensor("vae_decoder_up_blocks_3_resnets_1_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(43155904)))]; - tensor vae_decoder_up_blocks_3_resnets_1_conv1_bias = const()[name = tensor("vae_decoder_up_blocks_3_resnets_1_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(43745792)))]; - tensor vae_decoder_up_blocks_3_resnets_1_conv1_weight = const()[name = tensor("vae_decoder_up_blocks_3_resnets_1_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(43746368)))]; - tensor vae_decoder_up_blocks_3_resnets_0_conv_shortcut_bias = const()[name = tensor("vae_decoder_up_blocks_3_resnets_0_conv_shortcut_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44336256)))]; - tensor vae_decoder_up_blocks_3_resnets_0_conv_shortcut_weight = const()[name = tensor("vae_decoder_up_blocks_3_resnets_0_conv_shortcut_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44336832)))]; - tensor vae_decoder_up_blocks_3_resnets_0_conv2_bias = const()[name = tensor("vae_decoder_up_blocks_3_resnets_0_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44467968)))]; - tensor vae_decoder_up_blocks_3_resnets_0_conv2_weight = const()[name = tensor("vae_decoder_up_blocks_3_resnets_0_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44468544)))]; - tensor vae_decoder_up_blocks_3_resnets_0_conv1_bias = const()[name = tensor("vae_decoder_up_blocks_3_resnets_0_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45058432)))]; - tensor vae_decoder_up_blocks_3_resnets_0_conv1_weight = const()[name = tensor("vae_decoder_up_blocks_3_resnets_0_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(45059008)))]; - tensor vae_decoder_up_blocks_2_upsamplers_0_conv_bias = const()[name = tensor("vae_decoder_up_blocks_2_upsamplers_0_conv_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46238720)))]; - tensor vae_decoder_up_blocks_2_upsamplers_0_conv_weight = const()[name = tensor("vae_decoder_up_blocks_2_upsamplers_0_conv_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(46239808)))]; - tensor vae_decoder_up_blocks_2_resnets_2_conv2_bias = const()[name = tensor("vae_decoder_up_blocks_2_resnets_2_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48599168)))]; - tensor vae_decoder_up_blocks_2_resnets_2_conv2_weight = const()[name = tensor("vae_decoder_up_blocks_2_resnets_2_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(48600256)))]; - tensor vae_decoder_up_blocks_2_resnets_2_conv1_bias = const()[name = tensor("vae_decoder_up_blocks_2_resnets_2_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50959616)))]; - tensor vae_decoder_up_blocks_2_resnets_2_conv1_weight = const()[name = tensor("vae_decoder_up_blocks_2_resnets_2_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(50960704)))]; - tensor vae_decoder_up_blocks_2_resnets_1_conv2_bias = const()[name = tensor("vae_decoder_up_blocks_2_resnets_1_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(53320064)))]; - tensor vae_decoder_up_blocks_2_resnets_1_conv2_weight = const()[name = tensor("vae_decoder_up_blocks_2_resnets_1_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(53321152)))]; - tensor vae_decoder_up_blocks_2_resnets_1_conv1_bias = const()[name = tensor("vae_decoder_up_blocks_2_resnets_1_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(55680512)))]; - tensor vae_decoder_up_blocks_2_resnets_1_conv1_weight = const()[name = tensor("vae_decoder_up_blocks_2_resnets_1_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(55681600)))]; - tensor vae_decoder_up_blocks_2_resnets_0_conv_shortcut_bias = const()[name = tensor("vae_decoder_up_blocks_2_resnets_0_conv_shortcut_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58040960)))]; - tensor vae_decoder_up_blocks_2_resnets_0_conv_shortcut_weight = const()[name = tensor("vae_decoder_up_blocks_2_resnets_0_conv_shortcut_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58042048)))]; - tensor vae_decoder_up_blocks_2_resnets_0_conv2_bias = const()[name = tensor("vae_decoder_up_blocks_2_resnets_0_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58566400)))]; - tensor vae_decoder_up_blocks_2_resnets_0_conv2_weight = const()[name = tensor("vae_decoder_up_blocks_2_resnets_0_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58567488)))]; - tensor vae_decoder_up_blocks_2_resnets_0_conv1_bias = const()[name = tensor("vae_decoder_up_blocks_2_resnets_0_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(60926848)))]; - tensor vae_decoder_up_blocks_2_resnets_0_conv1_weight = const()[name = tensor("vae_decoder_up_blocks_2_resnets_0_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(60927936)))]; - tensor vae_decoder_up_blocks_1_upsamplers_0_conv_bias = const()[name = tensor("vae_decoder_up_blocks_1_upsamplers_0_conv_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65646592)))]; - tensor vae_decoder_up_blocks_1_upsamplers_0_conv_weight = const()[name = tensor("vae_decoder_up_blocks_1_upsamplers_0_conv_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65648704)))]; - tensor vae_decoder_up_blocks_1_resnets_2_conv2_bias = const()[name = tensor("vae_decoder_up_blocks_1_resnets_2_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(75085952)))]; - tensor vae_decoder_up_blocks_1_resnets_2_conv2_weight = const()[name = tensor("vae_decoder_up_blocks_1_resnets_2_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(75088064)))]; - tensor vae_decoder_up_blocks_1_resnets_2_conv1_bias = const()[name = tensor("vae_decoder_up_blocks_1_resnets_2_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(84525312)))]; - tensor vae_decoder_up_blocks_1_resnets_2_conv1_weight = const()[name = tensor("vae_decoder_up_blocks_1_resnets_2_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(84527424)))]; - tensor vae_decoder_up_blocks_1_resnets_1_conv2_bias = const()[name = tensor("vae_decoder_up_blocks_1_resnets_1_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(93964672)))]; - tensor vae_decoder_up_blocks_1_resnets_1_conv2_weight = const()[name = tensor("vae_decoder_up_blocks_1_resnets_1_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(93966784)))]; - tensor vae_decoder_up_blocks_1_resnets_1_conv1_bias = const()[name = tensor("vae_decoder_up_blocks_1_resnets_1_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(103404032)))]; - tensor vae_decoder_up_blocks_1_resnets_1_conv1_weight = const()[name = tensor("vae_decoder_up_blocks_1_resnets_1_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(103406144)))]; - tensor vae_decoder_up_blocks_1_resnets_0_conv2_bias = const()[name = tensor("vae_decoder_up_blocks_1_resnets_0_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(112843392)))]; - tensor vae_decoder_up_blocks_1_resnets_0_conv2_weight = const()[name = tensor("vae_decoder_up_blocks_1_resnets_0_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(112845504)))]; - tensor vae_decoder_up_blocks_1_resnets_0_conv1_bias = const()[name = tensor("vae_decoder_up_blocks_1_resnets_0_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(122282752)))]; - tensor vae_decoder_up_blocks_1_resnets_0_conv1_weight = const()[name = tensor("vae_decoder_up_blocks_1_resnets_0_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(122284864)))]; - tensor vae_decoder_up_blocks_0_upsamplers_0_conv_bias = const()[name = tensor("vae_decoder_up_blocks_0_upsamplers_0_conv_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(131722112)))]; - tensor vae_decoder_up_blocks_0_upsamplers_0_conv_weight = const()[name = tensor("vae_decoder_up_blocks_0_upsamplers_0_conv_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(131724224)))]; - tensor vae_decoder_up_blocks_0_resnets_2_conv2_bias = const()[name = tensor("vae_decoder_up_blocks_0_resnets_2_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(141161472)))]; - tensor vae_decoder_up_blocks_0_resnets_2_conv2_weight = const()[name = tensor("vae_decoder_up_blocks_0_resnets_2_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(141163584)))]; - tensor vae_decoder_up_blocks_0_resnets_2_conv1_bias = const()[name = tensor("vae_decoder_up_blocks_0_resnets_2_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(150600832)))]; - tensor vae_decoder_up_blocks_0_resnets_2_conv1_weight = const()[name = tensor("vae_decoder_up_blocks_0_resnets_2_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(150602944)))]; - tensor vae_decoder_up_blocks_0_resnets_1_conv2_bias = const()[name = tensor("vae_decoder_up_blocks_0_resnets_1_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(160040192)))]; - tensor vae_decoder_up_blocks_0_resnets_1_conv2_weight = const()[name = tensor("vae_decoder_up_blocks_0_resnets_1_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(160042304)))]; - tensor vae_decoder_up_blocks_0_resnets_1_conv1_bias = const()[name = tensor("vae_decoder_up_blocks_0_resnets_1_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(169479552)))]; - tensor vae_decoder_up_blocks_0_resnets_1_conv1_weight = const()[name = tensor("vae_decoder_up_blocks_0_resnets_1_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(169481664)))]; - tensor vae_decoder_up_blocks_0_resnets_0_conv2_bias = const()[name = tensor("vae_decoder_up_blocks_0_resnets_0_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(178918912)))]; - tensor vae_decoder_up_blocks_0_resnets_0_conv2_weight = const()[name = tensor("vae_decoder_up_blocks_0_resnets_0_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(178921024)))]; - tensor vae_decoder_up_blocks_0_resnets_0_conv1_bias = const()[name = tensor("vae_decoder_up_blocks_0_resnets_0_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(188358272)))]; - tensor vae_decoder_up_blocks_0_resnets_0_conv1_weight = const()[name = tensor("vae_decoder_up_blocks_0_resnets_0_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(188360384)))]; - tensor vae_decoder_conv_in_bias = const()[name = tensor("vae_decoder_conv_in_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197797632)))]; - tensor vae_decoder_conv_in_weight = const()[name = tensor("vae_decoder_conv_in_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197799744)))]; tensor post_quant_conv_bias = const()[name = tensor("post_quant_conv_bias"), val = tensor([-0x1.d7af42p-5, 0x1.cf585cp-3, -0x1.c6f7fcp-4, 0x1.acf664p-3])]; - tensor post_quant_conv_weight = const()[name = tensor("post_quant_conv_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197873536)))]; - tensor var_1065 = const()[name = tensor("op_1065"), val = tensor(1)]; - tensor var_1066 = const()[name = tensor("op_1066"), val = tensor([1, 1])]; - tensor var_1068 = const()[name = tensor("op_1068"), val = tensor([1, 1])]; + tensor post_quant_conv_weight = const()[name = tensor("post_quant_conv_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; + tensor decoder_conv_in_bias = const()[name = tensor("decoder_conv_in_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(192)))]; + tensor decoder_conv_in_weight = const()[name = tensor("decoder_conv_in_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2304)))]; + tensor decoder_mid_block_resnets_0_conv1_bias = const()[name = tensor("decoder_mid_block_resnets_0_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(76096)))]; + tensor decoder_mid_block_resnets_0_conv1_weight = const()[name = tensor("decoder_mid_block_resnets_0_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(78208)))]; + tensor decoder_mid_block_resnets_0_conv2_bias = const()[name = tensor("decoder_mid_block_resnets_0_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9515456)))]; + tensor decoder_mid_block_resnets_0_conv2_weight = const()[name = tensor("decoder_mid_block_resnets_0_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9517568)))]; + tensor decoder_mid_block_attentions_0_to_q_bias = const()[name = tensor("decoder_mid_block_attentions_0_to_q_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18954816)))]; + tensor decoder_mid_block_attentions_0_to_q_weight = const()[name = tensor("decoder_mid_block_attentions_0_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(18956928)))]; + tensor decoder_mid_block_attentions_0_to_k_bias = const()[name = tensor("decoder_mid_block_attentions_0_to_k_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20005568)))]; + tensor decoder_mid_block_attentions_0_to_k_weight = const()[name = tensor("decoder_mid_block_attentions_0_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20007680)))]; + tensor decoder_mid_block_attentions_0_to_v_bias = const()[name = tensor("decoder_mid_block_attentions_0_to_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21056320)))]; + tensor decoder_mid_block_attentions_0_to_v_weight = const()[name = tensor("decoder_mid_block_attentions_0_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21058432)))]; + tensor decoder_mid_block_attentions_0_to_out_0_bias = const()[name = tensor("decoder_mid_block_attentions_0_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22107072)))]; + tensor decoder_mid_block_attentions_0_to_out_0_weight = const()[name = tensor("decoder_mid_block_attentions_0_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(22109184)))]; + tensor decoder_mid_block_resnets_1_conv1_bias = const()[name = tensor("decoder_mid_block_resnets_1_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23157824)))]; + tensor decoder_mid_block_resnets_1_conv1_weight = const()[name = tensor("decoder_mid_block_resnets_1_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(23159936)))]; + tensor decoder_mid_block_resnets_1_conv2_bias = const()[name = tensor("decoder_mid_block_resnets_1_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32597184)))]; + tensor decoder_mid_block_resnets_1_conv2_weight = const()[name = tensor("decoder_mid_block_resnets_1_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32599296)))]; + tensor decoder_up_blocks_0_resnets_0_conv1_bias = const()[name = tensor("decoder_up_blocks_0_resnets_0_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42036544)))]; + tensor decoder_up_blocks_0_resnets_0_conv1_weight = const()[name = tensor("decoder_up_blocks_0_resnets_0_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42038656)))]; + tensor decoder_up_blocks_0_resnets_0_conv2_bias = const()[name = tensor("decoder_up_blocks_0_resnets_0_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51475904)))]; + tensor decoder_up_blocks_0_resnets_0_conv2_weight = const()[name = tensor("decoder_up_blocks_0_resnets_0_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51478016)))]; + tensor decoder_up_blocks_0_resnets_1_conv1_bias = const()[name = tensor("decoder_up_blocks_0_resnets_1_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(60915264)))]; + tensor decoder_up_blocks_0_resnets_1_conv1_weight = const()[name = tensor("decoder_up_blocks_0_resnets_1_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(60917376)))]; + tensor decoder_up_blocks_0_resnets_1_conv2_bias = const()[name = tensor("decoder_up_blocks_0_resnets_1_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(70354624)))]; + tensor decoder_up_blocks_0_resnets_1_conv2_weight = const()[name = tensor("decoder_up_blocks_0_resnets_1_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(70356736)))]; + tensor decoder_up_blocks_0_resnets_2_conv1_bias = const()[name = tensor("decoder_up_blocks_0_resnets_2_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79793984)))]; + tensor decoder_up_blocks_0_resnets_2_conv1_weight = const()[name = tensor("decoder_up_blocks_0_resnets_2_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79796096)))]; + tensor decoder_up_blocks_0_resnets_2_conv2_bias = const()[name = tensor("decoder_up_blocks_0_resnets_2_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(89233344)))]; + tensor decoder_up_blocks_0_resnets_2_conv2_weight = const()[name = tensor("decoder_up_blocks_0_resnets_2_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(89235456)))]; + tensor decoder_up_blocks_0_upsamplers_0_conv_bias = const()[name = tensor("decoder_up_blocks_0_upsamplers_0_conv_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98672704)))]; + tensor decoder_up_blocks_0_upsamplers_0_conv_weight = const()[name = tensor("decoder_up_blocks_0_upsamplers_0_conv_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98674816)))]; + tensor decoder_up_blocks_1_resnets_0_conv1_bias = const()[name = tensor("decoder_up_blocks_1_resnets_0_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(108112064)))]; + tensor decoder_up_blocks_1_resnets_0_conv1_weight = const()[name = tensor("decoder_up_blocks_1_resnets_0_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(108114176)))]; + tensor decoder_up_blocks_1_resnets_0_conv2_bias = const()[name = tensor("decoder_up_blocks_1_resnets_0_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(117551424)))]; + tensor decoder_up_blocks_1_resnets_0_conv2_weight = const()[name = tensor("decoder_up_blocks_1_resnets_0_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(117553536)))]; + tensor decoder_up_blocks_1_resnets_1_conv1_bias = const()[name = tensor("decoder_up_blocks_1_resnets_1_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(126990784)))]; + tensor decoder_up_blocks_1_resnets_1_conv1_weight = const()[name = tensor("decoder_up_blocks_1_resnets_1_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(126992896)))]; + tensor decoder_up_blocks_1_resnets_1_conv2_bias = const()[name = tensor("decoder_up_blocks_1_resnets_1_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136430144)))]; + tensor decoder_up_blocks_1_resnets_1_conv2_weight = const()[name = tensor("decoder_up_blocks_1_resnets_1_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136432256)))]; + tensor decoder_up_blocks_1_resnets_2_conv1_bias = const()[name = tensor("decoder_up_blocks_1_resnets_2_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(145869504)))]; + tensor decoder_up_blocks_1_resnets_2_conv1_weight = const()[name = tensor("decoder_up_blocks_1_resnets_2_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(145871616)))]; + tensor decoder_up_blocks_1_resnets_2_conv2_bias = const()[name = tensor("decoder_up_blocks_1_resnets_2_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(155308864)))]; + tensor decoder_up_blocks_1_resnets_2_conv2_weight = const()[name = tensor("decoder_up_blocks_1_resnets_2_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(155310976)))]; + tensor decoder_up_blocks_1_upsamplers_0_conv_bias = const()[name = tensor("decoder_up_blocks_1_upsamplers_0_conv_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(164748224)))]; + tensor decoder_up_blocks_1_upsamplers_0_conv_weight = const()[name = tensor("decoder_up_blocks_1_upsamplers_0_conv_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(164750336)))]; + tensor decoder_up_blocks_2_resnets_0_conv1_bias = const()[name = tensor("decoder_up_blocks_2_resnets_0_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(174187584)))]; + tensor decoder_up_blocks_2_resnets_0_conv1_weight = const()[name = tensor("decoder_up_blocks_2_resnets_0_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(174188672)))]; + tensor decoder_up_blocks_2_resnets_0_conv2_bias = const()[name = tensor("decoder_up_blocks_2_resnets_0_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(178907328)))]; + tensor decoder_up_blocks_2_resnets_0_conv2_weight = const()[name = tensor("decoder_up_blocks_2_resnets_0_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(178908416)))]; + tensor decoder_up_blocks_2_resnets_0_conv_shortcut_bias = const()[name = tensor("decoder_up_blocks_2_resnets_0_conv_shortcut_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(181267776)))]; + tensor decoder_up_blocks_2_resnets_0_conv_shortcut_weight = const()[name = tensor("decoder_up_blocks_2_resnets_0_conv_shortcut_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(181268864)))]; + tensor decoder_up_blocks_2_resnets_1_conv1_bias = const()[name = tensor("decoder_up_blocks_2_resnets_1_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(181793216)))]; + tensor decoder_up_blocks_2_resnets_1_conv1_weight = const()[name = tensor("decoder_up_blocks_2_resnets_1_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(181794304)))]; + tensor decoder_up_blocks_2_resnets_1_conv2_bias = const()[name = tensor("decoder_up_blocks_2_resnets_1_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(184153664)))]; + tensor decoder_up_blocks_2_resnets_1_conv2_weight = const()[name = tensor("decoder_up_blocks_2_resnets_1_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(184154752)))]; + tensor decoder_up_blocks_2_resnets_2_conv1_bias = const()[name = tensor("decoder_up_blocks_2_resnets_2_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(186514112)))]; + tensor decoder_up_blocks_2_resnets_2_conv1_weight = const()[name = tensor("decoder_up_blocks_2_resnets_2_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(186515200)))]; + tensor decoder_up_blocks_2_resnets_2_conv2_bias = const()[name = tensor("decoder_up_blocks_2_resnets_2_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(188874560)))]; + tensor decoder_up_blocks_2_resnets_2_conv2_weight = const()[name = tensor("decoder_up_blocks_2_resnets_2_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(188875648)))]; + tensor decoder_up_blocks_2_upsamplers_0_conv_bias = const()[name = tensor("decoder_up_blocks_2_upsamplers_0_conv_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(191235008)))]; + tensor decoder_up_blocks_2_upsamplers_0_conv_weight = const()[name = tensor("decoder_up_blocks_2_upsamplers_0_conv_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(191236096)))]; + tensor decoder_up_blocks_3_resnets_0_conv1_bias = const()[name = tensor("decoder_up_blocks_3_resnets_0_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(193595456)))]; + tensor decoder_up_blocks_3_resnets_0_conv1_weight = const()[name = tensor("decoder_up_blocks_3_resnets_0_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(193596032)))]; + tensor decoder_up_blocks_3_resnets_0_conv2_bias = const()[name = tensor("decoder_up_blocks_3_resnets_0_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(194775744)))]; + tensor decoder_up_blocks_3_resnets_0_conv2_weight = const()[name = tensor("decoder_up_blocks_3_resnets_0_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(194776320)))]; + tensor decoder_up_blocks_3_resnets_0_conv_shortcut_bias = const()[name = tensor("decoder_up_blocks_3_resnets_0_conv_shortcut_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(195366208)))]; + tensor decoder_up_blocks_3_resnets_0_conv_shortcut_weight = const()[name = tensor("decoder_up_blocks_3_resnets_0_conv_shortcut_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(195366784)))]; + tensor decoder_up_blocks_3_resnets_1_conv1_bias = const()[name = tensor("decoder_up_blocks_3_resnets_1_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(195497920)))]; + tensor decoder_up_blocks_3_resnets_1_conv1_weight = const()[name = tensor("decoder_up_blocks_3_resnets_1_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(195498496)))]; + tensor decoder_up_blocks_3_resnets_1_conv2_bias = const()[name = tensor("decoder_up_blocks_3_resnets_1_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(196088384)))]; + tensor decoder_up_blocks_3_resnets_1_conv2_weight = const()[name = tensor("decoder_up_blocks_3_resnets_1_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(196088960)))]; + tensor decoder_up_blocks_3_resnets_2_conv1_bias = const()[name = tensor("decoder_up_blocks_3_resnets_2_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(196678848)))]; + tensor decoder_up_blocks_3_resnets_2_conv1_weight = const()[name = tensor("decoder_up_blocks_3_resnets_2_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(196679424)))]; + tensor decoder_up_blocks_3_resnets_2_conv2_bias = const()[name = tensor("decoder_up_blocks_3_resnets_2_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197269312)))]; + tensor decoder_up_blocks_3_resnets_2_conv2_weight = const()[name = tensor("decoder_up_blocks_3_resnets_2_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197269888)))]; + tensor decoder_conv_out_bias = const()[name = tensor("decoder_conv_out_bias"), val = tensor([0x1.fb1412p-4, 0x1.4da0b8p-4, 0x1.943aa8p-5])]; + tensor decoder_conv_out_weight = const()[name = tensor("decoder_conv_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(197859776)))]; + tensor var_7 = const()[name = tensor("op_7"), val = tensor(1)]; + tensor var_10 = const()[name = tensor("op_10"), val = tensor([1, 1])]; + tensor var_12 = const()[name = tensor("op_12"), val = tensor([1, 1])]; tensor input_1_pad_type_0 = const()[name = tensor("input_1_pad_type_0"), val = tensor("custom")]; tensor input_1_pad_0 = const()[name = tensor("input_1_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor input_1 = conv(bias = post_quant_conv_bias, dilations = var_1068, groups = var_1065, pad = input_1_pad_0, pad_type = input_1_pad_type_0, strides = var_1066, weight = post_quant_conv_weight, x = z)[name = tensor("input_1")]; - tensor var_1082 = const()[name = tensor("op_1082"), val = tensor(1)]; - tensor var_1086 = const()[name = tensor("op_1086"), val = tensor([1, 1])]; - tensor var_1088 = const()[name = tensor("op_1088"), val = tensor([1, 1])]; + tensor input_1 = conv(bias = post_quant_conv_bias, dilations = var_12, groups = var_7, pad = input_1_pad_0, pad_type = input_1_pad_type_0, strides = var_10, weight = post_quant_conv_weight, x = z)[name = tensor("input_1")]; + tensor var_26 = const()[name = tensor("op_26"), val = tensor(1)]; + tensor var_44 = const()[name = tensor("op_44"), val = tensor([1, 1])]; + tensor var_46 = const()[name = tensor("op_46"), val = tensor([1, 1])]; tensor input_3_pad_type_0 = const()[name = tensor("input_3_pad_type_0"), val = tensor("custom")]; tensor input_3_pad_0 = const()[name = tensor("input_3_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor input_3 = conv(bias = vae_decoder_conv_in_bias, dilations = var_1088, groups = var_1082, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = var_1086, weight = vae_decoder_conv_in_weight, x = input_1)[name = tensor("input_3")]; + tensor input_3 = conv(bias = decoder_conv_in_bias, dilations = var_46, groups = var_26, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = var_44, weight = decoder_conv_in_weight, x = input_1)[name = tensor("input_3")]; tensor reshape_0_shape_0 = const()[name = tensor("reshape_0_shape_0"), val = tensor([1, 32, 16, 128, 128])]; tensor reshape_0 = reshape(shape = reshape_0_shape_0, x = input_3)[name = tensor("reshape_0")]; tensor reduce_mean_0_axes_0 = const()[name = tensor("reduce_mean_0_axes_0"), val = tensor([2, 3, 4])]; @@ -117,11 +117,11 @@ program(1.0) tensor add_1_epsilon_0 = const()[name = tensor("add_1_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; tensor add_1 = batch_norm(beta = add_1_beta_0, epsilon = add_1_epsilon_0, gamma = add_1_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_1)[name = tensor("add_1")]; tensor input_7 = silu(x = add_1)[name = tensor("input_7")]; - tensor var_1093 = const()[name = tensor("op_1093"), val = tensor([1, 1])]; - tensor var_1095 = const()[name = tensor("op_1095"), val = tensor([1, 1])]; + tensor var_65 = const()[name = tensor("op_65"), val = tensor([1, 1])]; + tensor var_67 = const()[name = tensor("op_67"), val = tensor([1, 1])]; tensor input_9_pad_type_0 = const()[name = tensor("input_9_pad_type_0"), val = tensor("custom")]; tensor input_9_pad_0 = const()[name = tensor("input_9_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor input_9 = conv(bias = vae_decoder_mid_block_resnets_0_conv1_bias, dilations = var_1095, groups = var_1082, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = var_1093, weight = vae_decoder_mid_block_resnets_0_conv1_weight, x = input_7)[name = tensor("input_9")]; + tensor input_9 = conv(bias = decoder_mid_block_resnets_0_conv1_bias, dilations = var_67, groups = var_26, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = var_65, weight = decoder_mid_block_resnets_0_conv1_weight, x = input_7)[name = tensor("input_9")]; tensor reshape_4_shape_0 = const()[name = tensor("reshape_4_shape_0"), val = tensor([1, 32, 16, 128, 128])]; tensor reshape_4 = reshape(shape = reshape_4_shape_0, x = input_9)[name = tensor("reshape_4")]; tensor reduce_mean_3_axes_0 = const()[name = tensor("reduce_mean_3_axes_0"), val = tensor([2, 3, 4])]; @@ -143,14 +143,14 @@ program(1.0) tensor add_3_epsilon_0 = const()[name = tensor("add_3_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; tensor add_3 = batch_norm(beta = add_3_beta_0, epsilon = add_3_epsilon_0, gamma = add_3_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_5)[name = tensor("add_3")]; tensor input_13 = silu(x = add_3)[name = tensor("input_13")]; - tensor var_1101 = const()[name = tensor("op_1101"), val = tensor([1, 1])]; - tensor var_1103 = const()[name = tensor("op_1103"), val = tensor([1, 1])]; + tensor var_77 = const()[name = tensor("op_77"), val = tensor([1, 1])]; + tensor var_79 = const()[name = tensor("op_79"), val = tensor([1, 1])]; tensor hidden_states_1_pad_type_0 = const()[name = tensor("hidden_states_1_pad_type_0"), val = tensor("custom")]; tensor hidden_states_1_pad_0 = const()[name = tensor("hidden_states_1_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor hidden_states_1 = conv(bias = vae_decoder_mid_block_resnets_0_conv2_bias, dilations = var_1103, groups = var_1082, pad = hidden_states_1_pad_0, pad_type = hidden_states_1_pad_type_0, strides = var_1101, weight = vae_decoder_mid_block_resnets_0_conv2_weight, x = input_13)[name = tensor("hidden_states_1")]; - tensor var_1106 = add(x = input_3, y = hidden_states_1)[name = tensor("op_1106")]; + tensor hidden_states_1 = conv(bias = decoder_mid_block_resnets_0_conv2_bias, dilations = var_79, groups = var_26, pad = hidden_states_1_pad_0, pad_type = hidden_states_1_pad_type_0, strides = var_77, weight = decoder_mid_block_resnets_0_conv2_weight, x = input_13)[name = tensor("hidden_states_1")]; + tensor var_82 = add(x = input_3, y = hidden_states_1)[name = tensor("op_82")]; tensor reshape_8_shape_0 = const()[name = tensor("reshape_8_shape_0"), val = tensor([1, 32, 16, 16384])]; - tensor reshape_8 = reshape(shape = reshape_8_shape_0, x = var_1106)[name = tensor("reshape_8")]; + tensor reshape_8 = reshape(shape = reshape_8_shape_0, x = var_82)[name = tensor("reshape_8")]; tensor reduce_mean_6_axes_0 = const()[name = tensor("reduce_mean_6_axes_0"), val = tensor([2, 3])]; tensor reduce_mean_6_keep_dims_0 = const()[name = tensor("reduce_mean_6_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_6 = reduce_mean(axes = reduce_mean_6_axes_0, keep_dims = reduce_mean_6_keep_dims_0, x = reshape_8)[name = tensor("reduce_mean_6")]; @@ -171,41 +171,41 @@ program(1.0) tensor add_5 = add(x = mul_2, y = reshape_11)[name = tensor("add_5")]; tensor input_19_perm_0 = const()[name = tensor("input_19_perm_0"), val = tensor([0, 2, 1])]; tensor transpose_11 = transpose(perm = input_19_perm_0, x = add_5)[name = tensor("transpose_11")]; - tensor query_1 = linear(bias = vae_decoder_mid_block_attentions_0_to_q_bias, weight = vae_decoder_mid_block_attentions_0_to_q_weight, x = transpose_11)[name = tensor("query_1")]; - tensor key_1 = linear(bias = vae_decoder_mid_block_attentions_0_to_k_bias, weight = vae_decoder_mid_block_attentions_0_to_k_weight, x = transpose_11)[name = tensor("key_1")]; - tensor value_1 = linear(bias = vae_decoder_mid_block_attentions_0_to_v_bias, weight = vae_decoder_mid_block_attentions_0_to_v_weight, x = transpose_11)[name = tensor("value_1")]; - tensor var_1133 = const()[name = tensor("op_1133"), val = tensor([1, -1, 1, 512])]; - tensor var_1134 = reshape(shape = var_1133, x = query_1)[name = tensor("op_1134")]; - tensor var_1136 = const()[name = tensor("op_1136"), val = tensor([1, -1, 1, 512])]; - tensor var_1137 = reshape(shape = var_1136, x = key_1)[name = tensor("op_1137")]; - tensor var_1139 = const()[name = tensor("op_1139"), val = tensor([1, -1, 1, 512])]; - tensor var_1140 = reshape(shape = var_1139, x = value_1)[name = tensor("op_1140")]; + tensor query_1 = linear(bias = decoder_mid_block_attentions_0_to_q_bias, weight = decoder_mid_block_attentions_0_to_q_weight, x = transpose_11)[name = tensor("query_1")]; + tensor key_1 = linear(bias = decoder_mid_block_attentions_0_to_k_bias, weight = decoder_mid_block_attentions_0_to_k_weight, x = transpose_11)[name = tensor("key_1")]; + tensor value_1 = linear(bias = decoder_mid_block_attentions_0_to_v_bias, weight = decoder_mid_block_attentions_0_to_v_weight, x = transpose_11)[name = tensor("value_1")]; + tensor var_123 = const()[name = tensor("op_123"), val = tensor([1, -1, 1, 512])]; + tensor var_124 = reshape(shape = var_123, x = query_1)[name = tensor("op_124")]; + tensor var_126 = const()[name = tensor("op_126"), val = tensor([1, -1, 1, 512])]; + tensor var_127 = reshape(shape = var_126, x = key_1)[name = tensor("op_127")]; + tensor var_129 = const()[name = tensor("op_129"), val = tensor([1, -1, 1, 512])]; + tensor var_130 = reshape(shape = var_129, x = value_1)[name = tensor("op_130")]; tensor value_perm_0 = const()[name = tensor("value_perm_0"), val = tensor([0, 2, 1, 3])]; tensor mul_3_y_0 = const()[name = tensor("mul_3_y_0"), val = tensor(0x1.6a09e6p-5)]; - tensor mul_3 = mul(x = var_1134, y = mul_3_y_0)[name = tensor("mul_3")]; + tensor mul_3 = mul(x = var_124, y = mul_3_y_0)[name = tensor("mul_3")]; tensor matmul_0_transpose_y_0 = const()[name = tensor("matmul_0_transpose_y_0"), val = tensor(true)]; tensor matmul_0_transpose_x_0 = const()[name = tensor("matmul_0_transpose_x_0"), val = tensor(false)]; tensor transpose_4_perm_0 = const()[name = tensor("transpose_4_perm_0"), val = tensor([0, 2, 1, 3])]; tensor transpose_5_perm_0 = const()[name = tensor("transpose_5_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor transpose_8 = transpose(perm = transpose_5_perm_0, x = var_1137)[name = tensor("transpose_8")]; + tensor transpose_8 = transpose(perm = transpose_5_perm_0, x = var_127)[name = tensor("transpose_8")]; tensor transpose_9 = transpose(perm = transpose_4_perm_0, x = mul_3)[name = tensor("transpose_9")]; tensor matmul_0 = matmul(transpose_x = matmul_0_transpose_x_0, transpose_y = matmul_0_transpose_y_0, x = transpose_9, y = transpose_8)[name = tensor("matmul_0")]; tensor softmax_0_axis_0 = const()[name = tensor("softmax_0_axis_0"), val = tensor(-1)]; tensor softmax_0 = softmax(axis = softmax_0_axis_0, x = matmul_0)[name = tensor("softmax_0")]; tensor hidden_states_7_transpose_x_0 = const()[name = tensor("hidden_states_7_transpose_x_0"), val = tensor(false)]; tensor hidden_states_7_transpose_y_0 = const()[name = tensor("hidden_states_7_transpose_y_0"), val = tensor(false)]; - tensor transpose_10 = transpose(perm = value_perm_0, x = var_1140)[name = tensor("transpose_10")]; + tensor transpose_10 = transpose(perm = value_perm_0, x = var_130)[name = tensor("transpose_10")]; tensor hidden_states_7 = matmul(transpose_x = hidden_states_7_transpose_x_0, transpose_y = hidden_states_7_transpose_y_0, x = softmax_0, y = transpose_10)[name = tensor("hidden_states_7")]; - tensor var_1143_perm_0 = const()[name = tensor("op_1143_perm_0"), val = tensor([0, 2, 1, 3])]; - tensor var_1147 = const()[name = tensor("op_1147"), val = tensor([1, -1, 512])]; - tensor transpose_7 = transpose(perm = var_1143_perm_0, x = hidden_states_7)[name = tensor("transpose_7")]; - tensor hidden_states_9 = reshape(shape = var_1147, x = transpose_7)[name = tensor("hidden_states_9")]; - tensor input_23 = linear(bias = vae_decoder_mid_block_attentions_0_to_out_0_bias, weight = vae_decoder_mid_block_attentions_0_to_out_0_weight, x = hidden_states_9)[name = tensor("input_23")]; - tensor var_1152_perm_0 = const()[name = tensor("op_1152_perm_0"), val = tensor([0, -1, -2])]; - tensor var_1153 = const()[name = tensor("op_1153"), val = tensor([1, 512, 128, 128])]; - tensor transpose_6 = transpose(perm = var_1152_perm_0, x = input_23)[name = tensor("transpose_6")]; - tensor hidden_states_13 = reshape(shape = var_1153, x = transpose_6)[name = tensor("hidden_states_13")]; - tensor hidden_states_15 = add(x = hidden_states_13, y = var_1106)[name = tensor("hidden_states_15")]; + tensor var_133_perm_0 = const()[name = tensor("op_133_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_137 = const()[name = tensor("op_137"), val = tensor([1, -1, 512])]; + tensor transpose_7 = transpose(perm = var_133_perm_0, x = hidden_states_7)[name = tensor("transpose_7")]; + tensor hidden_states_9 = reshape(shape = var_137, x = transpose_7)[name = tensor("hidden_states_9")]; + tensor input_23 = linear(bias = decoder_mid_block_attentions_0_to_out_0_bias, weight = decoder_mid_block_attentions_0_to_out_0_weight, x = hidden_states_9)[name = tensor("input_23")]; + tensor var_144_perm_0 = const()[name = tensor("op_144_perm_0"), val = tensor([0, -1, -2])]; + tensor var_145 = const()[name = tensor("op_145"), val = tensor([1, 512, 128, 128])]; + tensor transpose_6 = transpose(perm = var_144_perm_0, x = input_23)[name = tensor("transpose_6")]; + tensor hidden_states_13 = reshape(shape = var_145, x = transpose_6)[name = tensor("hidden_states_13")]; + tensor hidden_states_15 = add(x = hidden_states_13, y = var_82)[name = tensor("hidden_states_15")]; tensor reshape_12_shape_0 = const()[name = tensor("reshape_12_shape_0"), val = tensor([1, 32, 16, 128, 128])]; tensor reshape_12 = reshape(shape = reshape_12_shape_0, x = hidden_states_15)[name = tensor("reshape_12")]; tensor reduce_mean_9_axes_0 = const()[name = tensor("reduce_mean_9_axes_0"), val = tensor([2, 3, 4])]; @@ -227,11 +227,11 @@ program(1.0) tensor add_7_epsilon_0 = const()[name = tensor("add_7_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; tensor add_7 = batch_norm(beta = add_7_beta_0, epsilon = add_7_epsilon_0, gamma = add_7_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_13)[name = tensor("add_7")]; tensor input_29 = silu(x = add_7)[name = tensor("input_29")]; - tensor var_1160 = const()[name = tensor("op_1160"), val = tensor([1, 1])]; - tensor var_1162 = const()[name = tensor("op_1162"), val = tensor([1, 1])]; + tensor var_160 = const()[name = tensor("op_160"), val = tensor([1, 1])]; + tensor var_162 = const()[name = tensor("op_162"), val = tensor([1, 1])]; tensor input_31_pad_type_0 = const()[name = tensor("input_31_pad_type_0"), val = tensor("custom")]; tensor input_31_pad_0 = const()[name = tensor("input_31_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor input_31 = conv(bias = vae_decoder_mid_block_resnets_1_conv1_bias, dilations = var_1162, groups = var_1082, pad = input_31_pad_0, pad_type = input_31_pad_type_0, strides = var_1160, weight = vae_decoder_mid_block_resnets_1_conv1_weight, x = input_29)[name = tensor("input_31")]; + tensor input_31 = conv(bias = decoder_mid_block_resnets_1_conv1_bias, dilations = var_162, groups = var_26, pad = input_31_pad_0, pad_type = input_31_pad_type_0, strides = var_160, weight = decoder_mid_block_resnets_1_conv1_weight, x = input_29)[name = tensor("input_31")]; tensor reshape_16_shape_0 = const()[name = tensor("reshape_16_shape_0"), val = tensor([1, 32, 16, 128, 128])]; tensor reshape_16 = reshape(shape = reshape_16_shape_0, x = input_31)[name = tensor("reshape_16")]; tensor reduce_mean_12_axes_0 = const()[name = tensor("reduce_mean_12_axes_0"), val = tensor([2, 3, 4])]; @@ -253,14 +253,14 @@ program(1.0) tensor add_9_epsilon_0 = const()[name = tensor("add_9_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; tensor add_9 = batch_norm(beta = add_9_beta_0, epsilon = add_9_epsilon_0, gamma = add_9_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_17)[name = tensor("add_9")]; tensor input_35 = silu(x = add_9)[name = tensor("input_35")]; - tensor var_1168 = const()[name = tensor("op_1168"), val = tensor([1, 1])]; - tensor var_1170 = const()[name = tensor("op_1170"), val = tensor([1, 1])]; + tensor var_172 = const()[name = tensor("op_172"), val = tensor([1, 1])]; + tensor var_174 = const()[name = tensor("op_174"), val = tensor([1, 1])]; tensor hidden_states_17_pad_type_0 = const()[name = tensor("hidden_states_17_pad_type_0"), val = tensor("custom")]; tensor hidden_states_17_pad_0 = const()[name = tensor("hidden_states_17_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor hidden_states_17 = conv(bias = vae_decoder_mid_block_resnets_1_conv2_bias, dilations = var_1170, groups = var_1082, pad = hidden_states_17_pad_0, pad_type = hidden_states_17_pad_type_0, strides = var_1168, weight = vae_decoder_mid_block_resnets_1_conv2_weight, x = input_35)[name = tensor("hidden_states_17")]; - tensor var_1173 = add(x = hidden_states_15, y = hidden_states_17)[name = tensor("op_1173")]; + tensor hidden_states_17 = conv(bias = decoder_mid_block_resnets_1_conv2_bias, dilations = var_174, groups = var_26, pad = hidden_states_17_pad_0, pad_type = hidden_states_17_pad_type_0, strides = var_172, weight = decoder_mid_block_resnets_1_conv2_weight, x = input_35)[name = tensor("hidden_states_17")]; + tensor var_177 = add(x = hidden_states_15, y = hidden_states_17)[name = tensor("op_177")]; tensor reshape_20_shape_0 = const()[name = tensor("reshape_20_shape_0"), val = tensor([1, 32, 16, 128, 128])]; - tensor reshape_20 = reshape(shape = reshape_20_shape_0, x = var_1173)[name = tensor("reshape_20")]; + tensor reshape_20 = reshape(shape = reshape_20_shape_0, x = var_177)[name = tensor("reshape_20")]; tensor reduce_mean_15_axes_0 = const()[name = tensor("reduce_mean_15_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_15_keep_dims_0 = const()[name = tensor("reduce_mean_15_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_15 = reduce_mean(axes = reduce_mean_15_axes_0, keep_dims = reduce_mean_15_keep_dims_0, x = reshape_20)[name = tensor("reduce_mean_15")]; @@ -280,11 +280,11 @@ program(1.0) tensor add_11_epsilon_0 = const()[name = tensor("add_11_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; tensor add_11 = batch_norm(beta = add_11_beta_0, epsilon = add_11_epsilon_0, gamma = add_11_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_21)[name = tensor("add_11")]; tensor input_43 = silu(x = add_11)[name = tensor("input_43")]; - tensor var_1179 = const()[name = tensor("op_1179"), val = tensor([1, 1])]; - tensor var_1181 = const()[name = tensor("op_1181"), val = tensor([1, 1])]; + tensor var_199 = const()[name = tensor("op_199"), val = tensor([1, 1])]; + tensor var_201 = const()[name = tensor("op_201"), val = tensor([1, 1])]; tensor input_45_pad_type_0 = const()[name = tensor("input_45_pad_type_0"), val = tensor("custom")]; tensor input_45_pad_0 = const()[name = tensor("input_45_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor input_45 = conv(bias = vae_decoder_up_blocks_0_resnets_0_conv1_bias, dilations = var_1181, groups = var_1082, pad = input_45_pad_0, pad_type = input_45_pad_type_0, strides = var_1179, weight = vae_decoder_up_blocks_0_resnets_0_conv1_weight, x = input_43)[name = tensor("input_45")]; + tensor input_45 = conv(bias = decoder_up_blocks_0_resnets_0_conv1_bias, dilations = var_201, groups = var_26, pad = input_45_pad_0, pad_type = input_45_pad_type_0, strides = var_199, weight = decoder_up_blocks_0_resnets_0_conv1_weight, x = input_43)[name = tensor("input_45")]; tensor reshape_24_shape_0 = const()[name = tensor("reshape_24_shape_0"), val = tensor([1, 32, 16, 128, 128])]; tensor reshape_24 = reshape(shape = reshape_24_shape_0, x = input_45)[name = tensor("reshape_24")]; tensor reduce_mean_18_axes_0 = const()[name = tensor("reduce_mean_18_axes_0"), val = tensor([2, 3, 4])]; @@ -306,14 +306,14 @@ program(1.0) tensor add_13_epsilon_0 = const()[name = tensor("add_13_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; tensor add_13 = batch_norm(beta = add_13_beta_0, epsilon = add_13_epsilon_0, gamma = add_13_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_25)[name = tensor("add_13")]; tensor input_49 = silu(x = add_13)[name = tensor("input_49")]; - tensor var_1187 = const()[name = tensor("op_1187"), val = tensor([1, 1])]; - tensor var_1189 = const()[name = tensor("op_1189"), val = tensor([1, 1])]; + tensor var_211 = const()[name = tensor("op_211"), val = tensor([1, 1])]; + tensor var_213 = const()[name = tensor("op_213"), val = tensor([1, 1])]; tensor hidden_states_19_pad_type_0 = const()[name = tensor("hidden_states_19_pad_type_0"), val = tensor("custom")]; tensor hidden_states_19_pad_0 = const()[name = tensor("hidden_states_19_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor hidden_states_19 = conv(bias = vae_decoder_up_blocks_0_resnets_0_conv2_bias, dilations = var_1189, groups = var_1082, pad = hidden_states_19_pad_0, pad_type = hidden_states_19_pad_type_0, strides = var_1187, weight = vae_decoder_up_blocks_0_resnets_0_conv2_weight, x = input_49)[name = tensor("hidden_states_19")]; - tensor var_1192 = add(x = var_1173, y = hidden_states_19)[name = tensor("op_1192")]; + tensor hidden_states_19 = conv(bias = decoder_up_blocks_0_resnets_0_conv2_bias, dilations = var_213, groups = var_26, pad = hidden_states_19_pad_0, pad_type = hidden_states_19_pad_type_0, strides = var_211, weight = decoder_up_blocks_0_resnets_0_conv2_weight, x = input_49)[name = tensor("hidden_states_19")]; + tensor var_216 = add(x = var_177, y = hidden_states_19)[name = tensor("op_216")]; tensor reshape_28_shape_0 = const()[name = tensor("reshape_28_shape_0"), val = tensor([1, 32, 16, 128, 128])]; - tensor reshape_28 = reshape(shape = reshape_28_shape_0, x = var_1192)[name = tensor("reshape_28")]; + tensor reshape_28 = reshape(shape = reshape_28_shape_0, x = var_216)[name = tensor("reshape_28")]; tensor reduce_mean_21_axes_0 = const()[name = tensor("reduce_mean_21_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_21_keep_dims_0 = const()[name = tensor("reduce_mean_21_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_21 = reduce_mean(axes = reduce_mean_21_axes_0, keep_dims = reduce_mean_21_keep_dims_0, x = reshape_28)[name = tensor("reduce_mean_21")]; @@ -333,11 +333,11 @@ program(1.0) tensor add_15_epsilon_0 = const()[name = tensor("add_15_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; tensor add_15 = batch_norm(beta = add_15_beta_0, epsilon = add_15_epsilon_0, gamma = add_15_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_29)[name = tensor("add_15")]; tensor input_57 = silu(x = add_15)[name = tensor("input_57")]; - tensor var_1197 = const()[name = tensor("op_1197"), val = tensor([1, 1])]; - tensor var_1199 = const()[name = tensor("op_1199"), val = tensor([1, 1])]; + tensor var_229 = const()[name = tensor("op_229"), val = tensor([1, 1])]; + tensor var_231 = const()[name = tensor("op_231"), val = tensor([1, 1])]; tensor input_59_pad_type_0 = const()[name = tensor("input_59_pad_type_0"), val = tensor("custom")]; tensor input_59_pad_0 = const()[name = tensor("input_59_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor input_59 = conv(bias = vae_decoder_up_blocks_0_resnets_1_conv1_bias, dilations = var_1199, groups = var_1082, pad = input_59_pad_0, pad_type = input_59_pad_type_0, strides = var_1197, weight = vae_decoder_up_blocks_0_resnets_1_conv1_weight, x = input_57)[name = tensor("input_59")]; + tensor input_59 = conv(bias = decoder_up_blocks_0_resnets_1_conv1_bias, dilations = var_231, groups = var_26, pad = input_59_pad_0, pad_type = input_59_pad_type_0, strides = var_229, weight = decoder_up_blocks_0_resnets_1_conv1_weight, x = input_57)[name = tensor("input_59")]; tensor reshape_32_shape_0 = const()[name = tensor("reshape_32_shape_0"), val = tensor([1, 32, 16, 128, 128])]; tensor reshape_32 = reshape(shape = reshape_32_shape_0, x = input_59)[name = tensor("reshape_32")]; tensor reduce_mean_24_axes_0 = const()[name = tensor("reduce_mean_24_axes_0"), val = tensor([2, 3, 4])]; @@ -359,14 +359,14 @@ program(1.0) tensor add_17_epsilon_0 = const()[name = tensor("add_17_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; tensor add_17 = batch_norm(beta = add_17_beta_0, epsilon = add_17_epsilon_0, gamma = add_17_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_33)[name = tensor("add_17")]; tensor input_63 = silu(x = add_17)[name = tensor("input_63")]; - tensor var_1205 = const()[name = tensor("op_1205"), val = tensor([1, 1])]; - tensor var_1207 = const()[name = tensor("op_1207"), val = tensor([1, 1])]; + tensor var_241 = const()[name = tensor("op_241"), val = tensor([1, 1])]; + tensor var_243 = const()[name = tensor("op_243"), val = tensor([1, 1])]; tensor hidden_states_21_pad_type_0 = const()[name = tensor("hidden_states_21_pad_type_0"), val = tensor("custom")]; tensor hidden_states_21_pad_0 = const()[name = tensor("hidden_states_21_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor hidden_states_21 = conv(bias = vae_decoder_up_blocks_0_resnets_1_conv2_bias, dilations = var_1207, groups = var_1082, pad = hidden_states_21_pad_0, pad_type = hidden_states_21_pad_type_0, strides = var_1205, weight = vae_decoder_up_blocks_0_resnets_1_conv2_weight, x = input_63)[name = tensor("hidden_states_21")]; - tensor var_1210 = add(x = var_1192, y = hidden_states_21)[name = tensor("op_1210")]; + tensor hidden_states_21 = conv(bias = decoder_up_blocks_0_resnets_1_conv2_bias, dilations = var_243, groups = var_26, pad = hidden_states_21_pad_0, pad_type = hidden_states_21_pad_type_0, strides = var_241, weight = decoder_up_blocks_0_resnets_1_conv2_weight, x = input_63)[name = tensor("hidden_states_21")]; + tensor var_246 = add(x = var_216, y = hidden_states_21)[name = tensor("op_246")]; tensor reshape_36_shape_0 = const()[name = tensor("reshape_36_shape_0"), val = tensor([1, 32, 16, 128, 128])]; - tensor reshape_36 = reshape(shape = reshape_36_shape_0, x = var_1210)[name = tensor("reshape_36")]; + tensor reshape_36 = reshape(shape = reshape_36_shape_0, x = var_246)[name = tensor("reshape_36")]; tensor reduce_mean_27_axes_0 = const()[name = tensor("reduce_mean_27_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_27_keep_dims_0 = const()[name = tensor("reduce_mean_27_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_27 = reduce_mean(axes = reduce_mean_27_axes_0, keep_dims = reduce_mean_27_keep_dims_0, x = reshape_36)[name = tensor("reduce_mean_27")]; @@ -386,11 +386,11 @@ program(1.0) tensor add_19_epsilon_0 = const()[name = tensor("add_19_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; tensor add_19 = batch_norm(beta = add_19_beta_0, epsilon = add_19_epsilon_0, gamma = add_19_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_37)[name = tensor("add_19")]; tensor input_71 = silu(x = add_19)[name = tensor("input_71")]; - tensor var_1215 = const()[name = tensor("op_1215"), val = tensor([1, 1])]; - tensor var_1217 = const()[name = tensor("op_1217"), val = tensor([1, 1])]; + tensor var_259 = const()[name = tensor("op_259"), val = tensor([1, 1])]; + tensor var_261 = const()[name = tensor("op_261"), val = tensor([1, 1])]; tensor input_73_pad_type_0 = const()[name = tensor("input_73_pad_type_0"), val = tensor("custom")]; tensor input_73_pad_0 = const()[name = tensor("input_73_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor input_73 = conv(bias = vae_decoder_up_blocks_0_resnets_2_conv1_bias, dilations = var_1217, groups = var_1082, pad = input_73_pad_0, pad_type = input_73_pad_type_0, strides = var_1215, weight = vae_decoder_up_blocks_0_resnets_2_conv1_weight, x = input_71)[name = tensor("input_73")]; + tensor input_73 = conv(bias = decoder_up_blocks_0_resnets_2_conv1_bias, dilations = var_261, groups = var_26, pad = input_73_pad_0, pad_type = input_73_pad_type_0, strides = var_259, weight = decoder_up_blocks_0_resnets_2_conv1_weight, x = input_71)[name = tensor("input_73")]; tensor reshape_40_shape_0 = const()[name = tensor("reshape_40_shape_0"), val = tensor([1, 32, 16, 128, 128])]; tensor reshape_40 = reshape(shape = reshape_40_shape_0, x = input_73)[name = tensor("reshape_40")]; tensor reduce_mean_30_axes_0 = const()[name = tensor("reduce_mean_30_axes_0"), val = tensor([2, 3, 4])]; @@ -412,20 +412,20 @@ program(1.0) tensor add_21_epsilon_0 = const()[name = tensor("add_21_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; tensor add_21 = batch_norm(beta = add_21_beta_0, epsilon = add_21_epsilon_0, gamma = add_21_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_41)[name = tensor("add_21")]; tensor input_77 = silu(x = add_21)[name = tensor("input_77")]; - tensor var_1223 = const()[name = tensor("op_1223"), val = tensor([1, 1])]; - tensor var_1225 = const()[name = tensor("op_1225"), val = tensor([1, 1])]; + tensor var_271 = const()[name = tensor("op_271"), val = tensor([1, 1])]; + tensor var_273 = const()[name = tensor("op_273"), val = tensor([1, 1])]; tensor hidden_states_23_pad_type_0 = const()[name = tensor("hidden_states_23_pad_type_0"), val = tensor("custom")]; tensor hidden_states_23_pad_0 = const()[name = tensor("hidden_states_23_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor hidden_states_23 = conv(bias = vae_decoder_up_blocks_0_resnets_2_conv2_bias, dilations = var_1225, groups = var_1082, pad = hidden_states_23_pad_0, pad_type = hidden_states_23_pad_type_0, strides = var_1223, weight = vae_decoder_up_blocks_0_resnets_2_conv2_weight, x = input_77)[name = tensor("hidden_states_23")]; - tensor var_1228 = add(x = var_1210, y = hidden_states_23)[name = tensor("op_1228")]; + tensor hidden_states_23 = conv(bias = decoder_up_blocks_0_resnets_2_conv2_bias, dilations = var_273, groups = var_26, pad = hidden_states_23_pad_0, pad_type = hidden_states_23_pad_type_0, strides = var_271, weight = decoder_up_blocks_0_resnets_2_conv2_weight, x = input_77)[name = tensor("hidden_states_23")]; + tensor var_276 = add(x = var_246, y = hidden_states_23)[name = tensor("op_276")]; tensor input_81_scale_factor_height_0 = const()[name = tensor("input_81_scale_factor_height_0"), val = tensor(0x1p+1)]; tensor input_81_scale_factor_width_0 = const()[name = tensor("input_81_scale_factor_width_0"), val = tensor(0x1p+1)]; - tensor input_81 = upsample_nearest_neighbor(scale_factor_height = input_81_scale_factor_height_0, scale_factor_width = input_81_scale_factor_width_0, x = var_1228)[name = tensor("input_81")]; - tensor var_1233 = const()[name = tensor("op_1233"), val = tensor([1, 1])]; - tensor var_1235 = const()[name = tensor("op_1235"), val = tensor([1, 1])]; + tensor input_81 = upsample_nearest_neighbor(scale_factor_height = input_81_scale_factor_height_0, scale_factor_width = input_81_scale_factor_width_0, x = var_276)[name = tensor("input_81")]; + tensor var_284 = const()[name = tensor("op_284"), val = tensor([1, 1])]; + tensor var_286 = const()[name = tensor("op_286"), val = tensor([1, 1])]; tensor input_83_pad_type_0 = const()[name = tensor("input_83_pad_type_0"), val = tensor("custom")]; tensor input_83_pad_0 = const()[name = tensor("input_83_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor input_83 = conv(bias = vae_decoder_up_blocks_0_upsamplers_0_conv_bias, dilations = var_1235, groups = var_1082, pad = input_83_pad_0, pad_type = input_83_pad_type_0, strides = var_1233, weight = vae_decoder_up_blocks_0_upsamplers_0_conv_weight, x = input_81)[name = tensor("input_83")]; + tensor input_83 = conv(bias = decoder_up_blocks_0_upsamplers_0_conv_bias, dilations = var_286, groups = var_26, pad = input_83_pad_0, pad_type = input_83_pad_type_0, strides = var_284, weight = decoder_up_blocks_0_upsamplers_0_conv_weight, x = input_81)[name = tensor("input_83")]; tensor reshape_44_shape_0 = const()[name = tensor("reshape_44_shape_0"), val = tensor([1, 32, 16, 256, 256])]; tensor reshape_44 = reshape(shape = reshape_44_shape_0, x = input_83)[name = tensor("reshape_44")]; tensor reduce_mean_33_axes_0 = const()[name = tensor("reduce_mean_33_axes_0"), val = tensor([2, 3, 4])]; @@ -447,11 +447,11 @@ program(1.0) tensor add_23_epsilon_0 = const()[name = tensor("add_23_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; tensor add_23 = batch_norm(beta = add_23_beta_0, epsilon = add_23_epsilon_0, gamma = add_23_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_45)[name = tensor("add_23")]; tensor input_87 = silu(x = add_23)[name = tensor("input_87")]; - tensor var_1240 = const()[name = tensor("op_1240"), val = tensor([1, 1])]; - tensor var_1242 = const()[name = tensor("op_1242"), val = tensor([1, 1])]; + tensor var_307 = const()[name = tensor("op_307"), val = tensor([1, 1])]; + tensor var_309 = const()[name = tensor("op_309"), val = tensor([1, 1])]; tensor input_89_pad_type_0 = const()[name = tensor("input_89_pad_type_0"), val = tensor("custom")]; tensor input_89_pad_0 = const()[name = tensor("input_89_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor input_89 = conv(bias = vae_decoder_up_blocks_1_resnets_0_conv1_bias, dilations = var_1242, groups = var_1082, pad = input_89_pad_0, pad_type = input_89_pad_type_0, strides = var_1240, weight = vae_decoder_up_blocks_1_resnets_0_conv1_weight, x = input_87)[name = tensor("input_89")]; + tensor input_89 = conv(bias = decoder_up_blocks_1_resnets_0_conv1_bias, dilations = var_309, groups = var_26, pad = input_89_pad_0, pad_type = input_89_pad_type_0, strides = var_307, weight = decoder_up_blocks_1_resnets_0_conv1_weight, x = input_87)[name = tensor("input_89")]; tensor reshape_48_shape_0 = const()[name = tensor("reshape_48_shape_0"), val = tensor([1, 32, 16, 256, 256])]; tensor reshape_48 = reshape(shape = reshape_48_shape_0, x = input_89)[name = tensor("reshape_48")]; tensor reduce_mean_36_axes_0 = const()[name = tensor("reduce_mean_36_axes_0"), val = tensor([2, 3, 4])]; @@ -473,14 +473,14 @@ program(1.0) tensor add_25_epsilon_0 = const()[name = tensor("add_25_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; tensor add_25 = batch_norm(beta = add_25_beta_0, epsilon = add_25_epsilon_0, gamma = add_25_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_49)[name = tensor("add_25")]; tensor input_93 = silu(x = add_25)[name = tensor("input_93")]; - tensor var_1248 = const()[name = tensor("op_1248"), val = tensor([1, 1])]; - tensor var_1250 = const()[name = tensor("op_1250"), val = tensor([1, 1])]; + tensor var_319 = const()[name = tensor("op_319"), val = tensor([1, 1])]; + tensor var_321 = const()[name = tensor("op_321"), val = tensor([1, 1])]; tensor hidden_states_27_pad_type_0 = const()[name = tensor("hidden_states_27_pad_type_0"), val = tensor("custom")]; tensor hidden_states_27_pad_0 = const()[name = tensor("hidden_states_27_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor hidden_states_27 = conv(bias = vae_decoder_up_blocks_1_resnets_0_conv2_bias, dilations = var_1250, groups = var_1082, pad = hidden_states_27_pad_0, pad_type = hidden_states_27_pad_type_0, strides = var_1248, weight = vae_decoder_up_blocks_1_resnets_0_conv2_weight, x = input_93)[name = tensor("hidden_states_27")]; - tensor var_1253 = add(x = input_83, y = hidden_states_27)[name = tensor("op_1253")]; + tensor hidden_states_27 = conv(bias = decoder_up_blocks_1_resnets_0_conv2_bias, dilations = var_321, groups = var_26, pad = hidden_states_27_pad_0, pad_type = hidden_states_27_pad_type_0, strides = var_319, weight = decoder_up_blocks_1_resnets_0_conv2_weight, x = input_93)[name = tensor("hidden_states_27")]; + tensor var_324 = add(x = input_83, y = hidden_states_27)[name = tensor("op_324")]; tensor reshape_52_shape_0 = const()[name = tensor("reshape_52_shape_0"), val = tensor([1, 32, 16, 256, 256])]; - tensor reshape_52 = reshape(shape = reshape_52_shape_0, x = var_1253)[name = tensor("reshape_52")]; + tensor reshape_52 = reshape(shape = reshape_52_shape_0, x = var_324)[name = tensor("reshape_52")]; tensor reduce_mean_39_axes_0 = const()[name = tensor("reduce_mean_39_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_39_keep_dims_0 = const()[name = tensor("reduce_mean_39_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_39 = reduce_mean(axes = reduce_mean_39_axes_0, keep_dims = reduce_mean_39_keep_dims_0, x = reshape_52)[name = tensor("reduce_mean_39")]; @@ -500,11 +500,11 @@ program(1.0) tensor add_27_epsilon_0 = const()[name = tensor("add_27_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; tensor add_27 = batch_norm(beta = add_27_beta_0, epsilon = add_27_epsilon_0, gamma = add_27_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_53)[name = tensor("add_27")]; tensor input_101 = silu(x = add_27)[name = tensor("input_101")]; - tensor var_1258 = const()[name = tensor("op_1258"), val = tensor([1, 1])]; - tensor var_1260 = const()[name = tensor("op_1260"), val = tensor([1, 1])]; + tensor var_337 = const()[name = tensor("op_337"), val = tensor([1, 1])]; + tensor var_339 = const()[name = tensor("op_339"), val = tensor([1, 1])]; tensor input_103_pad_type_0 = const()[name = tensor("input_103_pad_type_0"), val = tensor("custom")]; tensor input_103_pad_0 = const()[name = tensor("input_103_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor input_103 = conv(bias = vae_decoder_up_blocks_1_resnets_1_conv1_bias, dilations = var_1260, groups = var_1082, pad = input_103_pad_0, pad_type = input_103_pad_type_0, strides = var_1258, weight = vae_decoder_up_blocks_1_resnets_1_conv1_weight, x = input_101)[name = tensor("input_103")]; + tensor input_103 = conv(bias = decoder_up_blocks_1_resnets_1_conv1_bias, dilations = var_339, groups = var_26, pad = input_103_pad_0, pad_type = input_103_pad_type_0, strides = var_337, weight = decoder_up_blocks_1_resnets_1_conv1_weight, x = input_101)[name = tensor("input_103")]; tensor reshape_56_shape_0 = const()[name = tensor("reshape_56_shape_0"), val = tensor([1, 32, 16, 256, 256])]; tensor reshape_56 = reshape(shape = reshape_56_shape_0, x = input_103)[name = tensor("reshape_56")]; tensor reduce_mean_42_axes_0 = const()[name = tensor("reduce_mean_42_axes_0"), val = tensor([2, 3, 4])]; @@ -526,14 +526,14 @@ program(1.0) tensor add_29_epsilon_0 = const()[name = tensor("add_29_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; tensor add_29 = batch_norm(beta = add_29_beta_0, epsilon = add_29_epsilon_0, gamma = add_29_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_57)[name = tensor("add_29")]; tensor input_107 = silu(x = add_29)[name = tensor("input_107")]; - tensor var_1266 = const()[name = tensor("op_1266"), val = tensor([1, 1])]; - tensor var_1268 = const()[name = tensor("op_1268"), val = tensor([1, 1])]; + tensor var_349 = const()[name = tensor("op_349"), val = tensor([1, 1])]; + tensor var_351 = const()[name = tensor("op_351"), val = tensor([1, 1])]; tensor hidden_states_29_pad_type_0 = const()[name = tensor("hidden_states_29_pad_type_0"), val = tensor("custom")]; tensor hidden_states_29_pad_0 = const()[name = tensor("hidden_states_29_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor hidden_states_29 = conv(bias = vae_decoder_up_blocks_1_resnets_1_conv2_bias, dilations = var_1268, groups = var_1082, pad = hidden_states_29_pad_0, pad_type = hidden_states_29_pad_type_0, strides = var_1266, weight = vae_decoder_up_blocks_1_resnets_1_conv2_weight, x = input_107)[name = tensor("hidden_states_29")]; - tensor var_1271 = add(x = var_1253, y = hidden_states_29)[name = tensor("op_1271")]; + tensor hidden_states_29 = conv(bias = decoder_up_blocks_1_resnets_1_conv2_bias, dilations = var_351, groups = var_26, pad = hidden_states_29_pad_0, pad_type = hidden_states_29_pad_type_0, strides = var_349, weight = decoder_up_blocks_1_resnets_1_conv2_weight, x = input_107)[name = tensor("hidden_states_29")]; + tensor var_354 = add(x = var_324, y = hidden_states_29)[name = tensor("op_354")]; tensor reshape_60_shape_0 = const()[name = tensor("reshape_60_shape_0"), val = tensor([1, 32, 16, 256, 256])]; - tensor reshape_60 = reshape(shape = reshape_60_shape_0, x = var_1271)[name = tensor("reshape_60")]; + tensor reshape_60 = reshape(shape = reshape_60_shape_0, x = var_354)[name = tensor("reshape_60")]; tensor reduce_mean_45_axes_0 = const()[name = tensor("reduce_mean_45_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_45_keep_dims_0 = const()[name = tensor("reduce_mean_45_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_45 = reduce_mean(axes = reduce_mean_45_axes_0, keep_dims = reduce_mean_45_keep_dims_0, x = reshape_60)[name = tensor("reduce_mean_45")]; @@ -553,11 +553,11 @@ program(1.0) tensor add_31_epsilon_0 = const()[name = tensor("add_31_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; tensor add_31 = batch_norm(beta = add_31_beta_0, epsilon = add_31_epsilon_0, gamma = add_31_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_61)[name = tensor("add_31")]; tensor input_115 = silu(x = add_31)[name = tensor("input_115")]; - tensor var_1276 = const()[name = tensor("op_1276"), val = tensor([1, 1])]; - tensor var_1278 = const()[name = tensor("op_1278"), val = tensor([1, 1])]; + tensor var_367 = const()[name = tensor("op_367"), val = tensor([1, 1])]; + tensor var_369 = const()[name = tensor("op_369"), val = tensor([1, 1])]; tensor input_117_pad_type_0 = const()[name = tensor("input_117_pad_type_0"), val = tensor("custom")]; tensor input_117_pad_0 = const()[name = tensor("input_117_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor input_117 = conv(bias = vae_decoder_up_blocks_1_resnets_2_conv1_bias, dilations = var_1278, groups = var_1082, pad = input_117_pad_0, pad_type = input_117_pad_type_0, strides = var_1276, weight = vae_decoder_up_blocks_1_resnets_2_conv1_weight, x = input_115)[name = tensor("input_117")]; + tensor input_117 = conv(bias = decoder_up_blocks_1_resnets_2_conv1_bias, dilations = var_369, groups = var_26, pad = input_117_pad_0, pad_type = input_117_pad_type_0, strides = var_367, weight = decoder_up_blocks_1_resnets_2_conv1_weight, x = input_115)[name = tensor("input_117")]; tensor reshape_64_shape_0 = const()[name = tensor("reshape_64_shape_0"), val = tensor([1, 32, 16, 256, 256])]; tensor reshape_64 = reshape(shape = reshape_64_shape_0, x = input_117)[name = tensor("reshape_64")]; tensor reduce_mean_48_axes_0 = const()[name = tensor("reduce_mean_48_axes_0"), val = tensor([2, 3, 4])]; @@ -579,20 +579,20 @@ program(1.0) tensor add_33_epsilon_0 = const()[name = tensor("add_33_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; tensor add_33 = batch_norm(beta = add_33_beta_0, epsilon = add_33_epsilon_0, gamma = add_33_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_65)[name = tensor("add_33")]; tensor input_121 = silu(x = add_33)[name = tensor("input_121")]; - tensor var_1284 = const()[name = tensor("op_1284"), val = tensor([1, 1])]; - tensor var_1286 = const()[name = tensor("op_1286"), val = tensor([1, 1])]; + tensor var_379 = const()[name = tensor("op_379"), val = tensor([1, 1])]; + tensor var_381 = const()[name = tensor("op_381"), val = tensor([1, 1])]; tensor hidden_states_31_pad_type_0 = const()[name = tensor("hidden_states_31_pad_type_0"), val = tensor("custom")]; tensor hidden_states_31_pad_0 = const()[name = tensor("hidden_states_31_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor hidden_states_31 = conv(bias = vae_decoder_up_blocks_1_resnets_2_conv2_bias, dilations = var_1286, groups = var_1082, pad = hidden_states_31_pad_0, pad_type = hidden_states_31_pad_type_0, strides = var_1284, weight = vae_decoder_up_blocks_1_resnets_2_conv2_weight, x = input_121)[name = tensor("hidden_states_31")]; - tensor var_1289 = add(x = var_1271, y = hidden_states_31)[name = tensor("op_1289")]; + tensor hidden_states_31 = conv(bias = decoder_up_blocks_1_resnets_2_conv2_bias, dilations = var_381, groups = var_26, pad = hidden_states_31_pad_0, pad_type = hidden_states_31_pad_type_0, strides = var_379, weight = decoder_up_blocks_1_resnets_2_conv2_weight, x = input_121)[name = tensor("hidden_states_31")]; + tensor var_384 = add(x = var_354, y = hidden_states_31)[name = tensor("op_384")]; tensor input_125_scale_factor_height_0 = const()[name = tensor("input_125_scale_factor_height_0"), val = tensor(0x1p+1)]; tensor input_125_scale_factor_width_0 = const()[name = tensor("input_125_scale_factor_width_0"), val = tensor(0x1p+1)]; - tensor input_125 = upsample_nearest_neighbor(scale_factor_height = input_125_scale_factor_height_0, scale_factor_width = input_125_scale_factor_width_0, x = var_1289)[name = tensor("input_125")]; - tensor var_1294 = const()[name = tensor("op_1294"), val = tensor([1, 1])]; - tensor var_1296 = const()[name = tensor("op_1296"), val = tensor([1, 1])]; + tensor input_125 = upsample_nearest_neighbor(scale_factor_height = input_125_scale_factor_height_0, scale_factor_width = input_125_scale_factor_width_0, x = var_384)[name = tensor("input_125")]; + tensor var_392 = const()[name = tensor("op_392"), val = tensor([1, 1])]; + tensor var_394 = const()[name = tensor("op_394"), val = tensor([1, 1])]; tensor input_127_pad_type_0 = const()[name = tensor("input_127_pad_type_0"), val = tensor("custom")]; tensor input_127_pad_0 = const()[name = tensor("input_127_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor input_127 = conv(bias = vae_decoder_up_blocks_1_upsamplers_0_conv_bias, dilations = var_1296, groups = var_1082, pad = input_127_pad_0, pad_type = input_127_pad_type_0, strides = var_1294, weight = vae_decoder_up_blocks_1_upsamplers_0_conv_weight, x = input_125)[name = tensor("input_127")]; + tensor input_127 = conv(bias = decoder_up_blocks_1_upsamplers_0_conv_bias, dilations = var_394, groups = var_26, pad = input_127_pad_0, pad_type = input_127_pad_type_0, strides = var_392, weight = decoder_up_blocks_1_upsamplers_0_conv_weight, x = input_125)[name = tensor("input_127")]; tensor reshape_68_shape_0 = const()[name = tensor("reshape_68_shape_0"), val = tensor([1, 32, 16, 512, 512])]; tensor reshape_68 = reshape(shape = reshape_68_shape_0, x = input_127)[name = tensor("reshape_68")]; tensor reduce_mean_51_axes_0 = const()[name = tensor("reduce_mean_51_axes_0"), val = tensor([2, 3, 4])]; @@ -614,11 +614,11 @@ program(1.0) tensor add_35_epsilon_0 = const()[name = tensor("add_35_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; tensor add_35 = batch_norm(beta = add_35_beta_0, epsilon = add_35_epsilon_0, gamma = add_35_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_69)[name = tensor("add_35")]; tensor input_131 = silu(x = add_35)[name = tensor("input_131")]; - tensor var_1301 = const()[name = tensor("op_1301"), val = tensor([1, 1])]; - tensor var_1303 = const()[name = tensor("op_1303"), val = tensor([1, 1])]; + tensor var_416 = const()[name = tensor("op_416"), val = tensor([1, 1])]; + tensor var_418 = const()[name = tensor("op_418"), val = tensor([1, 1])]; tensor input_133_pad_type_0 = const()[name = tensor("input_133_pad_type_0"), val = tensor("custom")]; tensor input_133_pad_0 = const()[name = tensor("input_133_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor input_133 = conv(bias = vae_decoder_up_blocks_2_resnets_0_conv1_bias, dilations = var_1303, groups = var_1082, pad = input_133_pad_0, pad_type = input_133_pad_type_0, strides = var_1301, weight = vae_decoder_up_blocks_2_resnets_0_conv1_weight, x = input_131)[name = tensor("input_133")]; + tensor input_133 = conv(bias = decoder_up_blocks_2_resnets_0_conv1_bias, dilations = var_418, groups = var_26, pad = input_133_pad_0, pad_type = input_133_pad_type_0, strides = var_416, weight = decoder_up_blocks_2_resnets_0_conv1_weight, x = input_131)[name = tensor("input_133")]; tensor reshape_72_shape_0 = const()[name = tensor("reshape_72_shape_0"), val = tensor([1, 32, 8, 512, 512])]; tensor reshape_72 = reshape(shape = reshape_72_shape_0, x = input_133)[name = tensor("reshape_72")]; tensor reduce_mean_54_axes_0 = const()[name = tensor("reduce_mean_54_axes_0"), val = tensor([2, 3, 4])]; @@ -642,19 +642,19 @@ program(1.0) tensor add_37_epsilon_0 = const()[name = tensor("add_37_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; tensor add_37 = batch_norm(beta = add_37_beta_0, epsilon = add_37_epsilon_0, gamma = add_37_gamma_0, mean = add_37_mean_0, variance = add_37_variance_0, x = reshape_73)[name = tensor("add_37")]; tensor input_137 = silu(x = add_37)[name = tensor("input_137")]; - tensor var_1309 = const()[name = tensor("op_1309"), val = tensor([1, 1])]; - tensor var_1311 = const()[name = tensor("op_1311"), val = tensor([1, 1])]; + tensor var_428 = const()[name = tensor("op_428"), val = tensor([1, 1])]; + tensor var_430 = const()[name = tensor("op_430"), val = tensor([1, 1])]; tensor hidden_states_35_pad_type_0 = const()[name = tensor("hidden_states_35_pad_type_0"), val = tensor("custom")]; tensor hidden_states_35_pad_0 = const()[name = tensor("hidden_states_35_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor hidden_states_35 = conv(bias = vae_decoder_up_blocks_2_resnets_0_conv2_bias, dilations = var_1311, groups = var_1082, pad = hidden_states_35_pad_0, pad_type = hidden_states_35_pad_type_0, strides = var_1309, weight = vae_decoder_up_blocks_2_resnets_0_conv2_weight, x = input_137)[name = tensor("hidden_states_35")]; - tensor var_1314 = const()[name = tensor("op_1314"), val = tensor([1, 1])]; - tensor var_1316 = const()[name = tensor("op_1316"), val = tensor([1, 1])]; + tensor hidden_states_35 = conv(bias = decoder_up_blocks_2_resnets_0_conv2_bias, dilations = var_430, groups = var_26, pad = hidden_states_35_pad_0, pad_type = hidden_states_35_pad_type_0, strides = var_428, weight = decoder_up_blocks_2_resnets_0_conv2_weight, x = input_137)[name = tensor("hidden_states_35")]; + tensor var_435 = const()[name = tensor("op_435"), val = tensor([1, 1])]; + tensor var_437 = const()[name = tensor("op_437"), val = tensor([1, 1])]; tensor input_tensor_1_pad_type_0 = const()[name = tensor("input_tensor_1_pad_type_0"), val = tensor("custom")]; tensor input_tensor_1_pad_0 = const()[name = tensor("input_tensor_1_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor input_tensor_1 = conv(bias = vae_decoder_up_blocks_2_resnets_0_conv_shortcut_bias, dilations = var_1316, groups = var_1082, pad = input_tensor_1_pad_0, pad_type = input_tensor_1_pad_type_0, strides = var_1314, weight = vae_decoder_up_blocks_2_resnets_0_conv_shortcut_weight, x = input_127)[name = tensor("input_tensor_1")]; - tensor var_1319 = add(x = input_tensor_1, y = hidden_states_35)[name = tensor("op_1319")]; + tensor input_tensor_1 = conv(bias = decoder_up_blocks_2_resnets_0_conv_shortcut_bias, dilations = var_437, groups = var_26, pad = input_tensor_1_pad_0, pad_type = input_tensor_1_pad_type_0, strides = var_435, weight = decoder_up_blocks_2_resnets_0_conv_shortcut_weight, x = input_127)[name = tensor("input_tensor_1")]; + tensor var_440 = add(x = input_tensor_1, y = hidden_states_35)[name = tensor("op_440")]; tensor reshape_76_shape_0 = const()[name = tensor("reshape_76_shape_0"), val = tensor([1, 32, 8, 512, 512])]; - tensor reshape_76 = reshape(shape = reshape_76_shape_0, x = var_1319)[name = tensor("reshape_76")]; + tensor reshape_76 = reshape(shape = reshape_76_shape_0, x = var_440)[name = tensor("reshape_76")]; tensor reduce_mean_57_axes_0 = const()[name = tensor("reduce_mean_57_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_57_keep_dims_0 = const()[name = tensor("reduce_mean_57_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_57 = reduce_mean(axes = reduce_mean_57_axes_0, keep_dims = reduce_mean_57_keep_dims_0, x = reshape_76)[name = tensor("reduce_mean_57")]; @@ -674,11 +674,11 @@ program(1.0) tensor add_39_epsilon_0 = const()[name = tensor("add_39_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; tensor add_39 = batch_norm(beta = add_39_beta_0, epsilon = add_39_epsilon_0, gamma = add_39_gamma_0, mean = add_37_mean_0, variance = add_37_variance_0, x = reshape_77)[name = tensor("add_39")]; tensor input_145 = silu(x = add_39)[name = tensor("input_145")]; - tensor var_1324 = const()[name = tensor("op_1324"), val = tensor([1, 1])]; - tensor var_1326 = const()[name = tensor("op_1326"), val = tensor([1, 1])]; + tensor var_453 = const()[name = tensor("op_453"), val = tensor([1, 1])]; + tensor var_455 = const()[name = tensor("op_455"), val = tensor([1, 1])]; tensor input_147_pad_type_0 = const()[name = tensor("input_147_pad_type_0"), val = tensor("custom")]; tensor input_147_pad_0 = const()[name = tensor("input_147_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor input_147 = conv(bias = vae_decoder_up_blocks_2_resnets_1_conv1_bias, dilations = var_1326, groups = var_1082, pad = input_147_pad_0, pad_type = input_147_pad_type_0, strides = var_1324, weight = vae_decoder_up_blocks_2_resnets_1_conv1_weight, x = input_145)[name = tensor("input_147")]; + tensor input_147 = conv(bias = decoder_up_blocks_2_resnets_1_conv1_bias, dilations = var_455, groups = var_26, pad = input_147_pad_0, pad_type = input_147_pad_type_0, strides = var_453, weight = decoder_up_blocks_2_resnets_1_conv1_weight, x = input_145)[name = tensor("input_147")]; tensor reshape_80_shape_0 = const()[name = tensor("reshape_80_shape_0"), val = tensor([1, 32, 8, 512, 512])]; tensor reshape_80 = reshape(shape = reshape_80_shape_0, x = input_147)[name = tensor("reshape_80")]; tensor reduce_mean_60_axes_0 = const()[name = tensor("reduce_mean_60_axes_0"), val = tensor([2, 3, 4])]; @@ -700,14 +700,14 @@ program(1.0) tensor add_41_epsilon_0 = const()[name = tensor("add_41_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; tensor add_41 = batch_norm(beta = add_41_beta_0, epsilon = add_41_epsilon_0, gamma = add_41_gamma_0, mean = add_37_mean_0, variance = add_37_variance_0, x = reshape_81)[name = tensor("add_41")]; tensor input_151 = silu(x = add_41)[name = tensor("input_151")]; - tensor var_1332 = const()[name = tensor("op_1332"), val = tensor([1, 1])]; - tensor var_1334 = const()[name = tensor("op_1334"), val = tensor([1, 1])]; + tensor var_465 = const()[name = tensor("op_465"), val = tensor([1, 1])]; + tensor var_467 = const()[name = tensor("op_467"), val = tensor([1, 1])]; tensor hidden_states_37_pad_type_0 = const()[name = tensor("hidden_states_37_pad_type_0"), val = tensor("custom")]; tensor hidden_states_37_pad_0 = const()[name = tensor("hidden_states_37_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor hidden_states_37 = conv(bias = vae_decoder_up_blocks_2_resnets_1_conv2_bias, dilations = var_1334, groups = var_1082, pad = hidden_states_37_pad_0, pad_type = hidden_states_37_pad_type_0, strides = var_1332, weight = vae_decoder_up_blocks_2_resnets_1_conv2_weight, x = input_151)[name = tensor("hidden_states_37")]; - tensor var_1337 = add(x = var_1319, y = hidden_states_37)[name = tensor("op_1337")]; + tensor hidden_states_37 = conv(bias = decoder_up_blocks_2_resnets_1_conv2_bias, dilations = var_467, groups = var_26, pad = hidden_states_37_pad_0, pad_type = hidden_states_37_pad_type_0, strides = var_465, weight = decoder_up_blocks_2_resnets_1_conv2_weight, x = input_151)[name = tensor("hidden_states_37")]; + tensor var_470 = add(x = var_440, y = hidden_states_37)[name = tensor("op_470")]; tensor reshape_84_shape_0 = const()[name = tensor("reshape_84_shape_0"), val = tensor([1, 32, 8, 512, 512])]; - tensor reshape_84 = reshape(shape = reshape_84_shape_0, x = var_1337)[name = tensor("reshape_84")]; + tensor reshape_84 = reshape(shape = reshape_84_shape_0, x = var_470)[name = tensor("reshape_84")]; tensor reduce_mean_63_axes_0 = const()[name = tensor("reduce_mean_63_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_63_keep_dims_0 = const()[name = tensor("reduce_mean_63_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_63 = reduce_mean(axes = reduce_mean_63_axes_0, keep_dims = reduce_mean_63_keep_dims_0, x = reshape_84)[name = tensor("reduce_mean_63")]; @@ -727,11 +727,11 @@ program(1.0) tensor add_43_epsilon_0 = const()[name = tensor("add_43_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; tensor add_43 = batch_norm(beta = add_43_beta_0, epsilon = add_43_epsilon_0, gamma = add_43_gamma_0, mean = add_37_mean_0, variance = add_37_variance_0, x = reshape_85)[name = tensor("add_43")]; tensor input_159 = silu(x = add_43)[name = tensor("input_159")]; - tensor var_1342 = const()[name = tensor("op_1342"), val = tensor([1, 1])]; - tensor var_1344 = const()[name = tensor("op_1344"), val = tensor([1, 1])]; + tensor var_483 = const()[name = tensor("op_483"), val = tensor([1, 1])]; + tensor var_485 = const()[name = tensor("op_485"), val = tensor([1, 1])]; tensor input_161_pad_type_0 = const()[name = tensor("input_161_pad_type_0"), val = tensor("custom")]; tensor input_161_pad_0 = const()[name = tensor("input_161_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor input_161 = conv(bias = vae_decoder_up_blocks_2_resnets_2_conv1_bias, dilations = var_1344, groups = var_1082, pad = input_161_pad_0, pad_type = input_161_pad_type_0, strides = var_1342, weight = vae_decoder_up_blocks_2_resnets_2_conv1_weight, x = input_159)[name = tensor("input_161")]; + tensor input_161 = conv(bias = decoder_up_blocks_2_resnets_2_conv1_bias, dilations = var_485, groups = var_26, pad = input_161_pad_0, pad_type = input_161_pad_type_0, strides = var_483, weight = decoder_up_blocks_2_resnets_2_conv1_weight, x = input_159)[name = tensor("input_161")]; tensor reshape_88_shape_0 = const()[name = tensor("reshape_88_shape_0"), val = tensor([1, 32, 8, 512, 512])]; tensor reshape_88 = reshape(shape = reshape_88_shape_0, x = input_161)[name = tensor("reshape_88")]; tensor reduce_mean_66_axes_0 = const()[name = tensor("reduce_mean_66_axes_0"), val = tensor([2, 3, 4])]; @@ -753,20 +753,20 @@ program(1.0) tensor add_45_epsilon_0 = const()[name = tensor("add_45_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; tensor add_45 = batch_norm(beta = add_45_beta_0, epsilon = add_45_epsilon_0, gamma = add_45_gamma_0, mean = add_37_mean_0, variance = add_37_variance_0, x = reshape_89)[name = tensor("add_45")]; tensor input_165 = silu(x = add_45)[name = tensor("input_165")]; - tensor var_1350 = const()[name = tensor("op_1350"), val = tensor([1, 1])]; - tensor var_1352 = const()[name = tensor("op_1352"), val = tensor([1, 1])]; + tensor var_495 = const()[name = tensor("op_495"), val = tensor([1, 1])]; + tensor var_497 = const()[name = tensor("op_497"), val = tensor([1, 1])]; tensor hidden_states_39_pad_type_0 = const()[name = tensor("hidden_states_39_pad_type_0"), val = tensor("custom")]; tensor hidden_states_39_pad_0 = const()[name = tensor("hidden_states_39_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor hidden_states_39 = conv(bias = vae_decoder_up_blocks_2_resnets_2_conv2_bias, dilations = var_1352, groups = var_1082, pad = hidden_states_39_pad_0, pad_type = hidden_states_39_pad_type_0, strides = var_1350, weight = vae_decoder_up_blocks_2_resnets_2_conv2_weight, x = input_165)[name = tensor("hidden_states_39")]; - tensor var_1355 = add(x = var_1337, y = hidden_states_39)[name = tensor("op_1355")]; + tensor hidden_states_39 = conv(bias = decoder_up_blocks_2_resnets_2_conv2_bias, dilations = var_497, groups = var_26, pad = hidden_states_39_pad_0, pad_type = hidden_states_39_pad_type_0, strides = var_495, weight = decoder_up_blocks_2_resnets_2_conv2_weight, x = input_165)[name = tensor("hidden_states_39")]; + tensor var_500 = add(x = var_470, y = hidden_states_39)[name = tensor("op_500")]; tensor input_169_scale_factor_height_0 = const()[name = tensor("input_169_scale_factor_height_0"), val = tensor(0x1p+1)]; tensor input_169_scale_factor_width_0 = const()[name = tensor("input_169_scale_factor_width_0"), val = tensor(0x1p+1)]; - tensor input_169 = upsample_nearest_neighbor(scale_factor_height = input_169_scale_factor_height_0, scale_factor_width = input_169_scale_factor_width_0, x = var_1355)[name = tensor("input_169")]; - tensor var_1360 = const()[name = tensor("op_1360"), val = tensor([1, 1])]; - tensor var_1362 = const()[name = tensor("op_1362"), val = tensor([1, 1])]; + tensor input_169 = upsample_nearest_neighbor(scale_factor_height = input_169_scale_factor_height_0, scale_factor_width = input_169_scale_factor_width_0, x = var_500)[name = tensor("input_169")]; + tensor var_508 = const()[name = tensor("op_508"), val = tensor([1, 1])]; + tensor var_510 = const()[name = tensor("op_510"), val = tensor([1, 1])]; tensor input_171_pad_type_0 = const()[name = tensor("input_171_pad_type_0"), val = tensor("custom")]; tensor input_171_pad_0 = const()[name = tensor("input_171_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor input_171 = conv(bias = vae_decoder_up_blocks_2_upsamplers_0_conv_bias, dilations = var_1362, groups = var_1082, pad = input_171_pad_0, pad_type = input_171_pad_type_0, strides = var_1360, weight = vae_decoder_up_blocks_2_upsamplers_0_conv_weight, x = input_169)[name = tensor("input_171")]; + tensor input_171 = conv(bias = decoder_up_blocks_2_upsamplers_0_conv_bias, dilations = var_510, groups = var_26, pad = input_171_pad_0, pad_type = input_171_pad_type_0, strides = var_508, weight = decoder_up_blocks_2_upsamplers_0_conv_weight, x = input_169)[name = tensor("input_171")]; tensor reshape_92_shape_0 = const()[name = tensor("reshape_92_shape_0"), val = tensor([1, 32, 8, 1024, 1024])]; tensor reshape_92 = reshape(shape = reshape_92_shape_0, x = input_171)[name = tensor("reshape_92")]; tensor reduce_mean_69_axes_0 = const()[name = tensor("reduce_mean_69_axes_0"), val = tensor([2, 3, 4])]; @@ -788,11 +788,11 @@ program(1.0) tensor add_47_epsilon_0 = const()[name = tensor("add_47_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; tensor add_47 = batch_norm(beta = add_47_beta_0, epsilon = add_47_epsilon_0, gamma = add_47_gamma_0, mean = add_37_mean_0, variance = add_37_variance_0, x = reshape_93)[name = tensor("add_47")]; tensor input_175 = silu(x = add_47)[name = tensor("input_175")]; - tensor var_1367 = const()[name = tensor("op_1367"), val = tensor([1, 1])]; - tensor var_1369 = const()[name = tensor("op_1369"), val = tensor([1, 1])]; + tensor var_530 = const()[name = tensor("op_530"), val = tensor([1, 1])]; + tensor var_532 = const()[name = tensor("op_532"), val = tensor([1, 1])]; tensor input_177_pad_type_0 = const()[name = tensor("input_177_pad_type_0"), val = tensor("custom")]; tensor input_177_pad_0 = const()[name = tensor("input_177_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor input_177 = conv(bias = vae_decoder_up_blocks_3_resnets_0_conv1_bias, dilations = var_1369, groups = var_1082, pad = input_177_pad_0, pad_type = input_177_pad_type_0, strides = var_1367, weight = vae_decoder_up_blocks_3_resnets_0_conv1_weight, x = input_175)[name = tensor("input_177")]; + tensor input_177 = conv(bias = decoder_up_blocks_3_resnets_0_conv1_bias, dilations = var_532, groups = var_26, pad = input_177_pad_0, pad_type = input_177_pad_type_0, strides = var_530, weight = decoder_up_blocks_3_resnets_0_conv1_weight, x = input_175)[name = tensor("input_177")]; tensor reshape_96_shape_0 = const()[name = tensor("reshape_96_shape_0"), val = tensor([1, 32, 4, 1024, 1024])]; tensor reshape_96 = reshape(shape = reshape_96_shape_0, x = input_177)[name = tensor("reshape_96")]; tensor reduce_mean_72_axes_0 = const()[name = tensor("reduce_mean_72_axes_0"), val = tensor([2, 3, 4])]; @@ -816,19 +816,19 @@ program(1.0) tensor add_49_epsilon_0 = const()[name = tensor("add_49_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; tensor add_49 = batch_norm(beta = add_49_beta_0, epsilon = add_49_epsilon_0, gamma = add_49_gamma_0, mean = add_49_mean_0, variance = add_49_variance_0, x = reshape_97)[name = tensor("add_49")]; tensor input_181 = silu(x = add_49)[name = tensor("input_181")]; - tensor var_1375 = const()[name = tensor("op_1375"), val = tensor([1, 1])]; - tensor var_1377 = const()[name = tensor("op_1377"), val = tensor([1, 1])]; + tensor var_542 = const()[name = tensor("op_542"), val = tensor([1, 1])]; + tensor var_544 = const()[name = tensor("op_544"), val = tensor([1, 1])]; tensor hidden_states_43_pad_type_0 = const()[name = tensor("hidden_states_43_pad_type_0"), val = tensor("custom")]; tensor hidden_states_43_pad_0 = const()[name = tensor("hidden_states_43_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor hidden_states_43 = conv(bias = vae_decoder_up_blocks_3_resnets_0_conv2_bias, dilations = var_1377, groups = var_1082, pad = hidden_states_43_pad_0, pad_type = hidden_states_43_pad_type_0, strides = var_1375, weight = vae_decoder_up_blocks_3_resnets_0_conv2_weight, x = input_181)[name = tensor("hidden_states_43")]; - tensor var_1380 = const()[name = tensor("op_1380"), val = tensor([1, 1])]; - tensor var_1382 = const()[name = tensor("op_1382"), val = tensor([1, 1])]; + tensor hidden_states_43 = conv(bias = decoder_up_blocks_3_resnets_0_conv2_bias, dilations = var_544, groups = var_26, pad = hidden_states_43_pad_0, pad_type = hidden_states_43_pad_type_0, strides = var_542, weight = decoder_up_blocks_3_resnets_0_conv2_weight, x = input_181)[name = tensor("hidden_states_43")]; + tensor var_549 = const()[name = tensor("op_549"), val = tensor([1, 1])]; + tensor var_551 = const()[name = tensor("op_551"), val = tensor([1, 1])]; tensor input_tensor_pad_type_0 = const()[name = tensor("input_tensor_pad_type_0"), val = tensor("custom")]; tensor input_tensor_pad_0 = const()[name = tensor("input_tensor_pad_0"), val = tensor([0, 0, 0, 0])]; - tensor input_tensor = conv(bias = vae_decoder_up_blocks_3_resnets_0_conv_shortcut_bias, dilations = var_1382, groups = var_1082, pad = input_tensor_pad_0, pad_type = input_tensor_pad_type_0, strides = var_1380, weight = vae_decoder_up_blocks_3_resnets_0_conv_shortcut_weight, x = input_171)[name = tensor("input_tensor")]; - tensor var_1385 = add(x = input_tensor, y = hidden_states_43)[name = tensor("op_1385")]; + tensor input_tensor = conv(bias = decoder_up_blocks_3_resnets_0_conv_shortcut_bias, dilations = var_551, groups = var_26, pad = input_tensor_pad_0, pad_type = input_tensor_pad_type_0, strides = var_549, weight = decoder_up_blocks_3_resnets_0_conv_shortcut_weight, x = input_171)[name = tensor("input_tensor")]; + tensor var_554 = add(x = input_tensor, y = hidden_states_43)[name = tensor("op_554")]; tensor reshape_100_shape_0 = const()[name = tensor("reshape_100_shape_0"), val = tensor([1, 32, 4, 1024, 1024])]; - tensor reshape_100 = reshape(shape = reshape_100_shape_0, x = var_1385)[name = tensor("reshape_100")]; + tensor reshape_100 = reshape(shape = reshape_100_shape_0, x = var_554)[name = tensor("reshape_100")]; tensor reduce_mean_75_axes_0 = const()[name = tensor("reduce_mean_75_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_75_keep_dims_0 = const()[name = tensor("reduce_mean_75_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_75 = reduce_mean(axes = reduce_mean_75_axes_0, keep_dims = reduce_mean_75_keep_dims_0, x = reshape_100)[name = tensor("reduce_mean_75")]; @@ -848,11 +848,11 @@ program(1.0) tensor add_51_epsilon_0 = const()[name = tensor("add_51_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; tensor add_51 = batch_norm(beta = add_51_beta_0, epsilon = add_51_epsilon_0, gamma = add_51_gamma_0, mean = add_49_mean_0, variance = add_49_variance_0, x = reshape_101)[name = tensor("add_51")]; tensor input_189 = silu(x = add_51)[name = tensor("input_189")]; - tensor var_1390 = const()[name = tensor("op_1390"), val = tensor([1, 1])]; - tensor var_1392 = const()[name = tensor("op_1392"), val = tensor([1, 1])]; + tensor var_567 = const()[name = tensor("op_567"), val = tensor([1, 1])]; + tensor var_569 = const()[name = tensor("op_569"), val = tensor([1, 1])]; tensor input_191_pad_type_0 = const()[name = tensor("input_191_pad_type_0"), val = tensor("custom")]; tensor input_191_pad_0 = const()[name = tensor("input_191_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor input_191 = conv(bias = vae_decoder_up_blocks_3_resnets_1_conv1_bias, dilations = var_1392, groups = var_1082, pad = input_191_pad_0, pad_type = input_191_pad_type_0, strides = var_1390, weight = vae_decoder_up_blocks_3_resnets_1_conv1_weight, x = input_189)[name = tensor("input_191")]; + tensor input_191 = conv(bias = decoder_up_blocks_3_resnets_1_conv1_bias, dilations = var_569, groups = var_26, pad = input_191_pad_0, pad_type = input_191_pad_type_0, strides = var_567, weight = decoder_up_blocks_3_resnets_1_conv1_weight, x = input_189)[name = tensor("input_191")]; tensor reshape_104_shape_0 = const()[name = tensor("reshape_104_shape_0"), val = tensor([1, 32, 4, 1024, 1024])]; tensor reshape_104 = reshape(shape = reshape_104_shape_0, x = input_191)[name = tensor("reshape_104")]; tensor reduce_mean_78_axes_0 = const()[name = tensor("reduce_mean_78_axes_0"), val = tensor([2, 3, 4])]; @@ -874,14 +874,14 @@ program(1.0) tensor add_53_epsilon_0 = const()[name = tensor("add_53_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; tensor add_53 = batch_norm(beta = add_53_beta_0, epsilon = add_53_epsilon_0, gamma = add_53_gamma_0, mean = add_49_mean_0, variance = add_49_variance_0, x = reshape_105)[name = tensor("add_53")]; tensor input_195 = silu(x = add_53)[name = tensor("input_195")]; - tensor var_1398 = const()[name = tensor("op_1398"), val = tensor([1, 1])]; - tensor var_1400 = const()[name = tensor("op_1400"), val = tensor([1, 1])]; + tensor var_579 = const()[name = tensor("op_579"), val = tensor([1, 1])]; + tensor var_581 = const()[name = tensor("op_581"), val = tensor([1, 1])]; tensor hidden_states_45_pad_type_0 = const()[name = tensor("hidden_states_45_pad_type_0"), val = tensor("custom")]; tensor hidden_states_45_pad_0 = const()[name = tensor("hidden_states_45_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor hidden_states_45 = conv(bias = vae_decoder_up_blocks_3_resnets_1_conv2_bias, dilations = var_1400, groups = var_1082, pad = hidden_states_45_pad_0, pad_type = hidden_states_45_pad_type_0, strides = var_1398, weight = vae_decoder_up_blocks_3_resnets_1_conv2_weight, x = input_195)[name = tensor("hidden_states_45")]; - tensor var_1403 = add(x = var_1385, y = hidden_states_45)[name = tensor("op_1403")]; + tensor hidden_states_45 = conv(bias = decoder_up_blocks_3_resnets_1_conv2_bias, dilations = var_581, groups = var_26, pad = hidden_states_45_pad_0, pad_type = hidden_states_45_pad_type_0, strides = var_579, weight = decoder_up_blocks_3_resnets_1_conv2_weight, x = input_195)[name = tensor("hidden_states_45")]; + tensor var_584 = add(x = var_554, y = hidden_states_45)[name = tensor("op_584")]; tensor reshape_108_shape_0 = const()[name = tensor("reshape_108_shape_0"), val = tensor([1, 32, 4, 1024, 1024])]; - tensor reshape_108 = reshape(shape = reshape_108_shape_0, x = var_1403)[name = tensor("reshape_108")]; + tensor reshape_108 = reshape(shape = reshape_108_shape_0, x = var_584)[name = tensor("reshape_108")]; tensor reduce_mean_81_axes_0 = const()[name = tensor("reduce_mean_81_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_81_keep_dims_0 = const()[name = tensor("reduce_mean_81_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_81 = reduce_mean(axes = reduce_mean_81_axes_0, keep_dims = reduce_mean_81_keep_dims_0, x = reshape_108)[name = tensor("reduce_mean_81")]; @@ -901,11 +901,11 @@ program(1.0) tensor add_55_epsilon_0 = const()[name = tensor("add_55_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; tensor add_55 = batch_norm(beta = add_55_beta_0, epsilon = add_55_epsilon_0, gamma = add_55_gamma_0, mean = add_49_mean_0, variance = add_49_variance_0, x = reshape_109)[name = tensor("add_55")]; tensor input_203 = silu(x = add_55)[name = tensor("input_203")]; - tensor var_1408 = const()[name = tensor("op_1408"), val = tensor([1, 1])]; - tensor var_1410 = const()[name = tensor("op_1410"), val = tensor([1, 1])]; + tensor var_597 = const()[name = tensor("op_597"), val = tensor([1, 1])]; + tensor var_599 = const()[name = tensor("op_599"), val = tensor([1, 1])]; tensor input_205_pad_type_0 = const()[name = tensor("input_205_pad_type_0"), val = tensor("custom")]; tensor input_205_pad_0 = const()[name = tensor("input_205_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor input_205 = conv(bias = vae_decoder_up_blocks_3_resnets_2_conv1_bias, dilations = var_1410, groups = var_1082, pad = input_205_pad_0, pad_type = input_205_pad_type_0, strides = var_1408, weight = vae_decoder_up_blocks_3_resnets_2_conv1_weight, x = input_203)[name = tensor("input_205")]; + tensor input_205 = conv(bias = decoder_up_blocks_3_resnets_2_conv1_bias, dilations = var_599, groups = var_26, pad = input_205_pad_0, pad_type = input_205_pad_type_0, strides = var_597, weight = decoder_up_blocks_3_resnets_2_conv1_weight, x = input_203)[name = tensor("input_205")]; tensor reshape_112_shape_0 = const()[name = tensor("reshape_112_shape_0"), val = tensor([1, 32, 4, 1024, 1024])]; tensor reshape_112 = reshape(shape = reshape_112_shape_0, x = input_205)[name = tensor("reshape_112")]; tensor reduce_mean_84_axes_0 = const()[name = tensor("reduce_mean_84_axes_0"), val = tensor([2, 3, 4])]; @@ -927,14 +927,14 @@ program(1.0) tensor add_57_epsilon_0 = const()[name = tensor("add_57_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; tensor add_57 = batch_norm(beta = add_57_beta_0, epsilon = add_57_epsilon_0, gamma = add_57_gamma_0, mean = add_49_mean_0, variance = add_49_variance_0, x = reshape_113)[name = tensor("add_57")]; tensor input_209 = silu(x = add_57)[name = tensor("input_209")]; - tensor var_1416 = const()[name = tensor("op_1416"), val = tensor([1, 1])]; - tensor var_1418 = const()[name = tensor("op_1418"), val = tensor([1, 1])]; + tensor var_609 = const()[name = tensor("op_609"), val = tensor([1, 1])]; + tensor var_611 = const()[name = tensor("op_611"), val = tensor([1, 1])]; tensor hidden_states_pad_type_0 = const()[name = tensor("hidden_states_pad_type_0"), val = tensor("custom")]; tensor hidden_states_pad_0 = const()[name = tensor("hidden_states_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor hidden_states = conv(bias = vae_decoder_up_blocks_3_resnets_2_conv2_bias, dilations = var_1418, groups = var_1082, pad = hidden_states_pad_0, pad_type = hidden_states_pad_type_0, strides = var_1416, weight = vae_decoder_up_blocks_3_resnets_2_conv2_weight, x = input_209)[name = tensor("hidden_states")]; - tensor var_1421 = add(x = var_1403, y = hidden_states)[name = tensor("op_1421")]; + tensor hidden_states = conv(bias = decoder_up_blocks_3_resnets_2_conv2_bias, dilations = var_611, groups = var_26, pad = hidden_states_pad_0, pad_type = hidden_states_pad_type_0, strides = var_609, weight = decoder_up_blocks_3_resnets_2_conv2_weight, x = input_209)[name = tensor("hidden_states")]; + tensor var_614 = add(x = var_584, y = hidden_states)[name = tensor("op_614")]; tensor reshape_116_shape_0 = const()[name = tensor("reshape_116_shape_0"), val = tensor([1, 32, 4, 1024, 1024])]; - tensor reshape_116 = reshape(shape = reshape_116_shape_0, x = var_1421)[name = tensor("reshape_116")]; + tensor reshape_116 = reshape(shape = reshape_116_shape_0, x = var_614)[name = tensor("reshape_116")]; tensor reduce_mean_87_axes_0 = const()[name = tensor("reduce_mean_87_axes_0"), val = tensor([2, 3, 4])]; tensor reduce_mean_87_keep_dims_0 = const()[name = tensor("reduce_mean_87_keep_dims_0"), val = tensor(true)]; tensor reduce_mean_87 = reduce_mean(axes = reduce_mean_87_axes_0, keep_dims = reduce_mean_87_keep_dims_0, x = reshape_116)[name = tensor("reduce_mean_87")]; @@ -954,10 +954,10 @@ program(1.0) tensor add_59_epsilon_0 = const()[name = tensor("add_59_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; tensor add_59 = batch_norm(beta = add_59_beta_0, epsilon = add_59_epsilon_0, gamma = add_59_gamma_0, mean = add_49_mean_0, variance = add_49_variance_0, x = reshape_117)[name = tensor("add_59")]; tensor input = silu(x = add_59)[name = tensor("input")]; - tensor var_1426 = const()[name = tensor("op_1426"), val = tensor([1, 1])]; - tensor var_1428 = const()[name = tensor("op_1428"), val = tensor([1, 1])]; - tensor var_1430_pad_type_0 = const()[name = tensor("op_1430_pad_type_0"), val = tensor("custom")]; - tensor var_1430_pad_0 = const()[name = tensor("op_1430_pad_0"), val = tensor([1, 1, 1, 1])]; - tensor image = conv(bias = vae_decoder_conv_out_bias, dilations = var_1428, groups = var_1082, pad = var_1430_pad_0, pad_type = var_1430_pad_type_0, strides = var_1426, weight = vae_decoder_conv_out_weight, x = input)[name = tensor("op_1430")]; + tensor var_623 = const()[name = tensor("op_623"), val = tensor([1, 1])]; + tensor var_625 = const()[name = tensor("op_625"), val = tensor([1, 1])]; + tensor var_627_pad_type_0 = const()[name = tensor("op_627_pad_type_0"), val = tensor("custom")]; + tensor var_627_pad_0 = const()[name = tensor("op_627_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor image = conv(bias = decoder_conv_out_bias, dilations = var_625, groups = var_26, pad = var_627_pad_0, pad_type = var_627_pad_type_0, strides = var_623, weight = decoder_conv_out_weight, x = input)[name = tensor("op_627")]; } -> (image); } \ No newline at end of file