| program(1.3) |
| [buildInfo = dict<string, string>({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.12.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})] |
| { |
| func main<ios18>(tensor<fp32, [1, 4, 128, 128]> latent_sample) { |
| tensor<fp32, [4]> vae_post_quant_conv_bias = const()[name = string("vae_post_quant_conv_bias"), val = tensor<fp32, [4]>([-0x1.d7cp-5, 0x1.cf4p-3, -0x1.c7p-4, 0x1.adp-3])]; |
| tensor<fp32, [4, 4, 1, 1]> vae_post_quant_conv_weight = const()[name = string("vae_post_quant_conv_weight"), val = tensor<fp32, [4, 4, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))]; |
| tensor<fp32, [512]> vae_decoder_conv_in_bias = const()[name = string("vae_decoder_conv_in_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(192)))]; |
| tensor<fp32, [512, 4, 3, 3]> vae_decoder_conv_in_weight = const()[name = string("vae_decoder_conv_in_weight"), val = tensor<fp32, [512, 4, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2304)))]; |
| tensor<fp32, [512]> vae_decoder_mid_block_resnets_0_conv1_bias = const()[name = string("vae_decoder_mid_block_resnets_0_conv1_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(76096)))]; |
| tensor<fp32, [512, 512, 3, 3]> vae_decoder_mid_block_resnets_0_conv1_weight = const()[name = string("vae_decoder_mid_block_resnets_0_conv1_weight"), val = tensor<fp32, [512, 512, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(78208)))]; |
| tensor<fp32, [512]> vae_decoder_mid_block_resnets_0_conv2_bias = const()[name = string("vae_decoder_mid_block_resnets_0_conv2_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9515456)))]; |
| tensor<fp32, [512, 512, 3, 3]> vae_decoder_mid_block_resnets_0_conv2_weight = const()[name = string("vae_decoder_mid_block_resnets_0_conv2_weight"), val = tensor<fp32, [512, 512, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9517568)))]; |
| tensor<fp32, [512]> vae_decoder_mid_block_attentions_0_to_q_bias = const()[name = string("vae_decoder_mid_block_attentions_0_to_q_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18954816)))]; |
| tensor<fp32, [512, 512]> vae_decoder_mid_block_attentions_0_to_q_weight = const()[name = string("vae_decoder_mid_block_attentions_0_to_q_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18956928)))]; |
| tensor<fp32, [512]> vae_decoder_mid_block_attentions_0_to_k_bias = const()[name = string("vae_decoder_mid_block_attentions_0_to_k_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20005568)))]; |
| tensor<fp32, [512, 512]> vae_decoder_mid_block_attentions_0_to_k_weight = const()[name = string("vae_decoder_mid_block_attentions_0_to_k_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(20007680)))]; |
| tensor<fp32, [512]> vae_decoder_mid_block_attentions_0_to_v_bias = const()[name = string("vae_decoder_mid_block_attentions_0_to_v_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21056320)))]; |
| tensor<fp32, [512, 512]> vae_decoder_mid_block_attentions_0_to_v_weight = const()[name = string("vae_decoder_mid_block_attentions_0_to_v_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(21058432)))]; |
| tensor<fp32, [512]> vae_decoder_mid_block_attentions_0_to_out_0_bias = const()[name = string("vae_decoder_mid_block_attentions_0_to_out_0_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22107072)))]; |
| tensor<fp32, [512, 512]> vae_decoder_mid_block_attentions_0_to_out_0_weight = const()[name = string("vae_decoder_mid_block_attentions_0_to_out_0_weight"), val = tensor<fp32, [512, 512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(22109184)))]; |
| tensor<fp32, [512]> vae_decoder_mid_block_resnets_1_conv1_bias = const()[name = string("vae_decoder_mid_block_resnets_1_conv1_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23157824)))]; |
| tensor<fp32, [512, 512, 3, 3]> vae_decoder_mid_block_resnets_1_conv1_weight = const()[name = string("vae_decoder_mid_block_resnets_1_conv1_weight"), val = tensor<fp32, [512, 512, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23159936)))]; |
| tensor<fp32, [512]> vae_decoder_mid_block_resnets_1_conv2_bias = const()[name = string("vae_decoder_mid_block_resnets_1_conv2_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32597184)))]; |
| tensor<fp32, [512, 512, 3, 3]> vae_decoder_mid_block_resnets_1_conv2_weight = const()[name = string("vae_decoder_mid_block_resnets_1_conv2_weight"), val = tensor<fp32, [512, 512, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32599296)))]; |
| tensor<fp32, [512]> vae_decoder_up_blocks_0_resnets_0_conv1_bias = const()[name = string("vae_decoder_up_blocks_0_resnets_0_conv1_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42036544)))]; |
| tensor<fp32, [512, 512, 3, 3]> vae_decoder_up_blocks_0_resnets_0_conv1_weight = const()[name = string("vae_decoder_up_blocks_0_resnets_0_conv1_weight"), val = tensor<fp32, [512, 512, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42038656)))]; |
| tensor<fp32, [512]> vae_decoder_up_blocks_0_resnets_0_conv2_bias = const()[name = string("vae_decoder_up_blocks_0_resnets_0_conv2_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51475904)))]; |
| tensor<fp32, [512, 512, 3, 3]> vae_decoder_up_blocks_0_resnets_0_conv2_weight = const()[name = string("vae_decoder_up_blocks_0_resnets_0_conv2_weight"), val = tensor<fp32, [512, 512, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51478016)))]; |
| tensor<fp32, [512]> vae_decoder_up_blocks_0_resnets_1_conv1_bias = const()[name = string("vae_decoder_up_blocks_0_resnets_1_conv1_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60915264)))]; |
| tensor<fp32, [512, 512, 3, 3]> vae_decoder_up_blocks_0_resnets_1_conv1_weight = const()[name = string("vae_decoder_up_blocks_0_resnets_1_conv1_weight"), val = tensor<fp32, [512, 512, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60917376)))]; |
| tensor<fp32, [512]> vae_decoder_up_blocks_0_resnets_1_conv2_bias = const()[name = string("vae_decoder_up_blocks_0_resnets_1_conv2_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(70354624)))]; |
| tensor<fp32, [512, 512, 3, 3]> vae_decoder_up_blocks_0_resnets_1_conv2_weight = const()[name = string("vae_decoder_up_blocks_0_resnets_1_conv2_weight"), val = tensor<fp32, [512, 512, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(70356736)))]; |
| tensor<fp32, [512]> vae_decoder_up_blocks_0_resnets_2_conv1_bias = const()[name = string("vae_decoder_up_blocks_0_resnets_2_conv1_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(79793984)))]; |
| tensor<fp32, [512, 512, 3, 3]> vae_decoder_up_blocks_0_resnets_2_conv1_weight = const()[name = string("vae_decoder_up_blocks_0_resnets_2_conv1_weight"), val = tensor<fp32, [512, 512, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(79796096)))]; |
| tensor<fp32, [512]> vae_decoder_up_blocks_0_resnets_2_conv2_bias = const()[name = string("vae_decoder_up_blocks_0_resnets_2_conv2_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(89233344)))]; |
| tensor<fp32, [512, 512, 3, 3]> vae_decoder_up_blocks_0_resnets_2_conv2_weight = const()[name = string("vae_decoder_up_blocks_0_resnets_2_conv2_weight"), val = tensor<fp32, [512, 512, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(89235456)))]; |
| tensor<fp32, [512]> vae_decoder_up_blocks_0_upsamplers_0_conv_bias = const()[name = string("vae_decoder_up_blocks_0_upsamplers_0_conv_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(98672704)))]; |
| tensor<fp32, [512, 512, 3, 3]> vae_decoder_up_blocks_0_upsamplers_0_conv_weight = const()[name = string("vae_decoder_up_blocks_0_upsamplers_0_conv_weight"), val = tensor<fp32, [512, 512, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(98674816)))]; |
| tensor<fp32, [512]> vae_decoder_up_blocks_1_resnets_0_conv1_bias = const()[name = string("vae_decoder_up_blocks_1_resnets_0_conv1_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(108112064)))]; |
| tensor<fp32, [512, 512, 3, 3]> vae_decoder_up_blocks_1_resnets_0_conv1_weight = const()[name = string("vae_decoder_up_blocks_1_resnets_0_conv1_weight"), val = tensor<fp32, [512, 512, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(108114176)))]; |
| tensor<fp32, [512]> vae_decoder_up_blocks_1_resnets_0_conv2_bias = const()[name = string("vae_decoder_up_blocks_1_resnets_0_conv2_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(117551424)))]; |
| tensor<fp32, [512, 512, 3, 3]> vae_decoder_up_blocks_1_resnets_0_conv2_weight = const()[name = string("vae_decoder_up_blocks_1_resnets_0_conv2_weight"), val = tensor<fp32, [512, 512, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(117553536)))]; |
| tensor<fp32, [512]> vae_decoder_up_blocks_1_resnets_1_conv1_bias = const()[name = string("vae_decoder_up_blocks_1_resnets_1_conv1_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(126990784)))]; |
| tensor<fp32, [512, 512, 3, 3]> vae_decoder_up_blocks_1_resnets_1_conv1_weight = const()[name = string("vae_decoder_up_blocks_1_resnets_1_conv1_weight"), val = tensor<fp32, [512, 512, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(126992896)))]; |
| tensor<fp32, [512]> vae_decoder_up_blocks_1_resnets_1_conv2_bias = const()[name = string("vae_decoder_up_blocks_1_resnets_1_conv2_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(136430144)))]; |
| tensor<fp32, [512, 512, 3, 3]> vae_decoder_up_blocks_1_resnets_1_conv2_weight = const()[name = string("vae_decoder_up_blocks_1_resnets_1_conv2_weight"), val = tensor<fp32, [512, 512, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(136432256)))]; |
| tensor<fp32, [512]> vae_decoder_up_blocks_1_resnets_2_conv1_bias = const()[name = string("vae_decoder_up_blocks_1_resnets_2_conv1_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(145869504)))]; |
| tensor<fp32, [512, 512, 3, 3]> vae_decoder_up_blocks_1_resnets_2_conv1_weight = const()[name = string("vae_decoder_up_blocks_1_resnets_2_conv1_weight"), val = tensor<fp32, [512, 512, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(145871616)))]; |
| tensor<fp32, [512]> vae_decoder_up_blocks_1_resnets_2_conv2_bias = const()[name = string("vae_decoder_up_blocks_1_resnets_2_conv2_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(155308864)))]; |
| tensor<fp32, [512, 512, 3, 3]> vae_decoder_up_blocks_1_resnets_2_conv2_weight = const()[name = string("vae_decoder_up_blocks_1_resnets_2_conv2_weight"), val = tensor<fp32, [512, 512, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(155310976)))]; |
| tensor<fp32, [512]> vae_decoder_up_blocks_1_upsamplers_0_conv_bias = const()[name = string("vae_decoder_up_blocks_1_upsamplers_0_conv_bias"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164748224)))]; |
| tensor<fp32, [512, 512, 3, 3]> vae_decoder_up_blocks_1_upsamplers_0_conv_weight = const()[name = string("vae_decoder_up_blocks_1_upsamplers_0_conv_weight"), val = tensor<fp32, [512, 512, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(164750336)))]; |
| tensor<fp32, [256]> vae_decoder_up_blocks_2_resnets_0_conv1_bias = const()[name = string("vae_decoder_up_blocks_2_resnets_0_conv1_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(174187584)))]; |
| tensor<fp32, [256, 512, 3, 3]> vae_decoder_up_blocks_2_resnets_0_conv1_weight = const()[name = string("vae_decoder_up_blocks_2_resnets_0_conv1_weight"), val = tensor<fp32, [256, 512, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(174188672)))]; |
| tensor<fp32, [256]> vae_decoder_up_blocks_2_resnets_0_conv2_bias = const()[name = string("vae_decoder_up_blocks_2_resnets_0_conv2_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(178907328)))]; |
| tensor<fp32, [256, 256, 3, 3]> vae_decoder_up_blocks_2_resnets_0_conv2_weight = const()[name = string("vae_decoder_up_blocks_2_resnets_0_conv2_weight"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(178908416)))]; |
| tensor<fp32, [256]> vae_decoder_up_blocks_2_resnets_0_conv_shortcut_bias = const()[name = string("vae_decoder_up_blocks_2_resnets_0_conv_shortcut_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(181267776)))]; |
| tensor<fp32, [256, 512, 1, 1]> vae_decoder_up_blocks_2_resnets_0_conv_shortcut_weight = const()[name = string("vae_decoder_up_blocks_2_resnets_0_conv_shortcut_weight"), val = tensor<fp32, [256, 512, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(181268864)))]; |
| tensor<fp32, [256]> vae_decoder_up_blocks_2_resnets_1_conv1_bias = const()[name = string("vae_decoder_up_blocks_2_resnets_1_conv1_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(181793216)))]; |
| tensor<fp32, [256, 256, 3, 3]> vae_decoder_up_blocks_2_resnets_1_conv1_weight = const()[name = string("vae_decoder_up_blocks_2_resnets_1_conv1_weight"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(181794304)))]; |
| tensor<fp32, [256]> vae_decoder_up_blocks_2_resnets_1_conv2_bias = const()[name = string("vae_decoder_up_blocks_2_resnets_1_conv2_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(184153664)))]; |
| tensor<fp32, [256, 256, 3, 3]> vae_decoder_up_blocks_2_resnets_1_conv2_weight = const()[name = string("vae_decoder_up_blocks_2_resnets_1_conv2_weight"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(184154752)))]; |
| tensor<fp32, [256]> vae_decoder_up_blocks_2_resnets_2_conv1_bias = const()[name = string("vae_decoder_up_blocks_2_resnets_2_conv1_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(186514112)))]; |
| tensor<fp32, [256, 256, 3, 3]> vae_decoder_up_blocks_2_resnets_2_conv1_weight = const()[name = string("vae_decoder_up_blocks_2_resnets_2_conv1_weight"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(186515200)))]; |
| tensor<fp32, [256]> vae_decoder_up_blocks_2_resnets_2_conv2_bias = const()[name = string("vae_decoder_up_blocks_2_resnets_2_conv2_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(188874560)))]; |
| tensor<fp32, [256, 256, 3, 3]> vae_decoder_up_blocks_2_resnets_2_conv2_weight = const()[name = string("vae_decoder_up_blocks_2_resnets_2_conv2_weight"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(188875648)))]; |
| tensor<fp32, [256]> vae_decoder_up_blocks_2_upsamplers_0_conv_bias = const()[name = string("vae_decoder_up_blocks_2_upsamplers_0_conv_bias"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(191235008)))]; |
| tensor<fp32, [256, 256, 3, 3]> vae_decoder_up_blocks_2_upsamplers_0_conv_weight = const()[name = string("vae_decoder_up_blocks_2_upsamplers_0_conv_weight"), val = tensor<fp32, [256, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(191236096)))]; |
| tensor<fp32, [128]> vae_decoder_up_blocks_3_resnets_0_conv1_bias = const()[name = string("vae_decoder_up_blocks_3_resnets_0_conv1_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(193595456)))]; |
| tensor<fp32, [128, 256, 3, 3]> vae_decoder_up_blocks_3_resnets_0_conv1_weight = const()[name = string("vae_decoder_up_blocks_3_resnets_0_conv1_weight"), val = tensor<fp32, [128, 256, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(193596032)))]; |
| tensor<fp32, [128]> vae_decoder_up_blocks_3_resnets_0_conv2_bias = const()[name = string("vae_decoder_up_blocks_3_resnets_0_conv2_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(194775744)))]; |
| tensor<fp32, [128, 128, 3, 3]> vae_decoder_up_blocks_3_resnets_0_conv2_weight = const()[name = string("vae_decoder_up_blocks_3_resnets_0_conv2_weight"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(194776320)))]; |
| tensor<fp32, [128]> vae_decoder_up_blocks_3_resnets_0_conv_shortcut_bias = const()[name = string("vae_decoder_up_blocks_3_resnets_0_conv_shortcut_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(195366208)))]; |
| tensor<fp32, [128, 256, 1, 1]> vae_decoder_up_blocks_3_resnets_0_conv_shortcut_weight = const()[name = string("vae_decoder_up_blocks_3_resnets_0_conv_shortcut_weight"), val = tensor<fp32, [128, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(195366784)))]; |
| tensor<fp32, [128]> vae_decoder_up_blocks_3_resnets_1_conv1_bias = const()[name = string("vae_decoder_up_blocks_3_resnets_1_conv1_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(195497920)))]; |
| tensor<fp32, [128, 128, 3, 3]> vae_decoder_up_blocks_3_resnets_1_conv1_weight = const()[name = string("vae_decoder_up_blocks_3_resnets_1_conv1_weight"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(195498496)))]; |
| tensor<fp32, [128]> vae_decoder_up_blocks_3_resnets_1_conv2_bias = const()[name = string("vae_decoder_up_blocks_3_resnets_1_conv2_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(196088384)))]; |
| tensor<fp32, [128, 128, 3, 3]> vae_decoder_up_blocks_3_resnets_1_conv2_weight = const()[name = string("vae_decoder_up_blocks_3_resnets_1_conv2_weight"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(196088960)))]; |
| tensor<fp32, [128]> vae_decoder_up_blocks_3_resnets_2_conv1_bias = const()[name = string("vae_decoder_up_blocks_3_resnets_2_conv1_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(196678848)))]; |
| tensor<fp32, [128, 128, 3, 3]> vae_decoder_up_blocks_3_resnets_2_conv1_weight = const()[name = string("vae_decoder_up_blocks_3_resnets_2_conv1_weight"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(196679424)))]; |
| tensor<fp32, [128]> vae_decoder_up_blocks_3_resnets_2_conv2_bias = const()[name = string("vae_decoder_up_blocks_3_resnets_2_conv2_bias"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197269312)))]; |
| tensor<fp32, [128, 128, 3, 3]> vae_decoder_up_blocks_3_resnets_2_conv2_weight = const()[name = string("vae_decoder_up_blocks_3_resnets_2_conv2_weight"), val = tensor<fp32, [128, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197269888)))]; |
| tensor<fp32, [3]> vae_decoder_conv_out_bias = const()[name = string("vae_decoder_conv_out_bias"), val = tensor<fp32, [3]>([0x1.fbp-4, 0x1.4dcp-4, 0x1.944p-5])]; |
| tensor<fp32, [3, 128, 3, 3]> vae_decoder_conv_out_weight = const()[name = string("vae_decoder_conv_out_weight"), val = tensor<fp32, [3, 128, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197859776)))]; |
| fp32 _inversed_input_1_y_0 = const()[name = string("_inversed_input_1_y_0"), val = fp32(0x1.eb5cdcp+2)]; |
| tensor<fp32, [1, 4, 128, 128]> _inversed_input_1 = mul(x = latent_sample, y = _inversed_input_1_y_0)[name = string("_inversed_input_1")]; |
| string input_3_pad_type_0 = const()[name = string("input_3_pad_type_0"), val = string("valid")]; |
| tensor<int32, [2]> input_3_strides_0 = const()[name = string("input_3_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [4]> input_3_pad_0 = const()[name = string("input_3_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])]; |
| tensor<int32, [2]> input_3_dilations_0 = const()[name = string("input_3_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 input_3_groups_0 = const()[name = string("input_3_groups_0"), val = int32(1)]; |
| tensor<fp32, [1, 4, 128, 128]> input_3 = conv(bias = vae_post_quant_conv_bias, dilations = input_3_dilations_0, groups = input_3_groups_0, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = input_3_strides_0, weight = vae_post_quant_conv_weight, x = _inversed_input_1)[name = string("input_3")]; |
| string input_5_pad_type_0 = const()[name = string("input_5_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> input_5_pad_0 = const()[name = string("input_5_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_5_strides_0 = const()[name = string("input_5_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_5_dilations_0 = const()[name = string("input_5_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 input_5_groups_0 = const()[name = string("input_5_groups_0"), val = int32(1)]; |
| tensor<fp32, [1, 512, 128, 128]> input_5 = conv(bias = vae_decoder_conv_in_bias, dilations = input_5_dilations_0, groups = input_5_groups_0, pad = input_5_pad_0, pad_type = input_5_pad_type_0, strides = input_5_strides_0, weight = vae_decoder_conv_in_weight, x = input_3)[name = string("input_5")]; |
| tensor<int32, [5]> reshape_0_shape_0 = const()[name = string("reshape_0_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 128, 128])]; |
| tensor<fp32, [1, 32, 16, 128, 128]> reshape_0 = reshape(shape = reshape_0_shape_0, x = input_5)[name = string("reshape_0")]; |
| tensor<int32, [3]> reduce_mean_0_axes_0 = const()[name = string("reduce_mean_0_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_0_keep_dims_0 = const()[name = string("reduce_mean_0_keep_dims_0"), val = bool(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_0 = reduce_mean(axes = reduce_mean_0_axes_0, keep_dims = reduce_mean_0_keep_dims_0, x = reshape_0)[name = string("reduce_mean_0")]; |
| tensor<fp32, [1, 32, 16, 128, 128]> sub_0 = sub(x = reshape_0, y = reduce_mean_0)[name = string("sub_0")]; |
| tensor<fp32, [1, 32, 16, 128, 128]> square_0 = square(x = sub_0)[name = string("square_0")]; |
| tensor<int32, [3]> reduce_mean_2_axes_0 = const()[name = string("reduce_mean_2_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_2_keep_dims_0 = const()[name = string("reduce_mean_2_keep_dims_0"), val = bool(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_2 = reduce_mean(axes = reduce_mean_2_axes_0, keep_dims = reduce_mean_2_keep_dims_0, x = square_0)[name = string("reduce_mean_2")]; |
| fp32 add_0_y_0 = const()[name = string("add_0_y_0"), val = fp32(0x1.0c6f7ap-20)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> add_0 = add(x = reduce_mean_2, y = add_0_y_0)[name = string("add_0")]; |
| tensor<fp32, [1, 32, 1, 1, 1]> sqrt_0 = sqrt(x = add_0)[name = string("sqrt_0")]; |
| tensor<fp32, [1, 32, 16, 128, 128]> real_div_0 = real_div(x = sub_0, y = sqrt_0)[name = string("real_div_0")]; |
| tensor<int32, [4]> reshape_1_shape_0 = const()[name = string("reshape_1_shape_0"), val = tensor<int32, [4]>([1, 512, 128, 128])]; |
| tensor<fp32, [1, 512, 128, 128]> reshape_1 = reshape(shape = reshape_1_shape_0, x = real_div_0)[name = string("reshape_1")]; |
| tensor<fp32, [512]> add_1_mean_0 = const()[name = string("add_1_mean_0"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197873664)))]; |
| tensor<fp32, [512]> add_1_variance_0 = const()[name = string("add_1_variance_0"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197875776)))]; |
| tensor<fp32, [512]> add_1_gamma_0 = const()[name = string("add_1_gamma_0"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197877888)))]; |
| tensor<fp32, [512]> add_1_beta_0 = const()[name = string("add_1_beta_0"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197880000)))]; |
| fp32 add_1_epsilon_0 = const()[name = string("add_1_epsilon_0"), val = fp32(0x1.4f8b58p-17)]; |
| tensor<fp32, [1, 512, 128, 128]> 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 = string("add_1")]; |
| tensor<fp32, [1, 512, 128, 128]> input_9 = silu(x = add_1)[name = string("input_9")]; |
| string input_11_pad_type_0 = const()[name = string("input_11_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> input_11_pad_0 = const()[name = string("input_11_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_11_strides_0 = const()[name = string("input_11_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_11_dilations_0 = const()[name = string("input_11_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 input_11_groups_0 = const()[name = string("input_11_groups_0"), val = int32(1)]; |
| tensor<fp32, [1, 512, 128, 128]> input_11 = conv(bias = vae_decoder_mid_block_resnets_0_conv1_bias, dilations = input_11_dilations_0, groups = input_11_groups_0, pad = input_11_pad_0, pad_type = input_11_pad_type_0, strides = input_11_strides_0, weight = vae_decoder_mid_block_resnets_0_conv1_weight, x = input_9)[name = string("input_11")]; |
| tensor<int32, [5]> reshape_4_shape_0 = const()[name = string("reshape_4_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 128, 128])]; |
| tensor<fp32, [1, 32, 16, 128, 128]> reshape_4 = reshape(shape = reshape_4_shape_0, x = input_11)[name = string("reshape_4")]; |
| tensor<int32, [3]> reduce_mean_3_axes_0 = const()[name = string("reduce_mean_3_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_3_keep_dims_0 = const()[name = string("reduce_mean_3_keep_dims_0"), val = bool(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_3 = reduce_mean(axes = reduce_mean_3_axes_0, keep_dims = reduce_mean_3_keep_dims_0, x = reshape_4)[name = string("reduce_mean_3")]; |
| tensor<fp32, [1, 32, 16, 128, 128]> sub_2 = sub(x = reshape_4, y = reduce_mean_3)[name = string("sub_2")]; |
| tensor<fp32, [1, 32, 16, 128, 128]> square_1 = square(x = sub_2)[name = string("square_1")]; |
| tensor<int32, [3]> reduce_mean_5_axes_0 = const()[name = string("reduce_mean_5_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_5_keep_dims_0 = const()[name = string("reduce_mean_5_keep_dims_0"), val = bool(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_5 = reduce_mean(axes = reduce_mean_5_axes_0, keep_dims = reduce_mean_5_keep_dims_0, x = square_1)[name = string("reduce_mean_5")]; |
| fp32 add_2_y_0 = const()[name = string("add_2_y_0"), val = fp32(0x1.0c6f7ap-20)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> add_2 = add(x = reduce_mean_5, y = add_2_y_0)[name = string("add_2")]; |
| tensor<fp32, [1, 32, 1, 1, 1]> sqrt_1 = sqrt(x = add_2)[name = string("sqrt_1")]; |
| tensor<fp32, [1, 32, 16, 128, 128]> real_div_1 = real_div(x = sub_2, y = sqrt_1)[name = string("real_div_1")]; |
| tensor<int32, [4]> reshape_5_shape_0 = const()[name = string("reshape_5_shape_0"), val = tensor<int32, [4]>([1, 512, 128, 128])]; |
| tensor<fp32, [1, 512, 128, 128]> reshape_5 = reshape(shape = reshape_5_shape_0, x = real_div_1)[name = string("reshape_5")]; |
| tensor<fp32, [512]> add_3_gamma_0 = const()[name = string("add_3_gamma_0"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197882112)))]; |
| tensor<fp32, [512]> add_3_beta_0 = const()[name = string("add_3_beta_0"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197884224)))]; |
| fp32 add_3_epsilon_0 = const()[name = string("add_3_epsilon_0"), val = fp32(0x1.4f8b58p-17)]; |
| tensor<fp32, [1, 512, 128, 128]> 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 = string("add_3")]; |
| tensor<fp32, [1, 512, 128, 128]> input_15 = silu(x = add_3)[name = string("input_15")]; |
| string hidden_states_1_pad_type_0 = const()[name = string("hidden_states_1_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> hidden_states_1_pad_0 = const()[name = string("hidden_states_1_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> hidden_states_1_strides_0 = const()[name = string("hidden_states_1_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> hidden_states_1_dilations_0 = const()[name = string("hidden_states_1_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 hidden_states_1_groups_0 = const()[name = string("hidden_states_1_groups_0"), val = int32(1)]; |
| tensor<fp32, [1, 512, 128, 128]> hidden_states_1 = conv(bias = vae_decoder_mid_block_resnets_0_conv2_bias, dilations = hidden_states_1_dilations_0, groups = hidden_states_1_groups_0, pad = hidden_states_1_pad_0, pad_type = hidden_states_1_pad_type_0, strides = hidden_states_1_strides_0, weight = vae_decoder_mid_block_resnets_0_conv2_weight, x = input_15)[name = string("hidden_states_1")]; |
| tensor<fp32, [1, 512, 128, 128]> var_86 = add(x = input_5, y = hidden_states_1)[name = string("op_86")]; |
| tensor<int32, [4]> reshape_8_shape_0 = const()[name = string("reshape_8_shape_0"), val = tensor<int32, [4]>([1, 32, 16, 16384])]; |
| tensor<fp32, [1, 32, 16, 16384]> reshape_8 = reshape(shape = reshape_8_shape_0, x = var_86)[name = string("reshape_8")]; |
| tensor<int32, [2]> reduce_mean_6_axes_0 = const()[name = string("reduce_mean_6_axes_0"), val = tensor<int32, [2]>([2, 3])]; |
| bool reduce_mean_6_keep_dims_0 = const()[name = string("reduce_mean_6_keep_dims_0"), val = bool(true)]; |
| tensor<fp32, [1, 32, 1, 1]> reduce_mean_6 = reduce_mean(axes = reduce_mean_6_axes_0, keep_dims = reduce_mean_6_keep_dims_0, x = reshape_8)[name = string("reduce_mean_6")]; |
| tensor<fp32, [1, 32, 16, 16384]> sub_4 = sub(x = reshape_8, y = reduce_mean_6)[name = string("sub_4")]; |
| tensor<fp32, [1, 32, 16, 16384]> square_2 = square(x = sub_4)[name = string("square_2")]; |
| tensor<int32, [2]> reduce_mean_8_axes_0 = const()[name = string("reduce_mean_8_axes_0"), val = tensor<int32, [2]>([2, 3])]; |
| bool reduce_mean_8_keep_dims_0 = const()[name = string("reduce_mean_8_keep_dims_0"), val = bool(true)]; |
| tensor<fp32, [1, 32, 1, 1]> reduce_mean_8 = reduce_mean(axes = reduce_mean_8_axes_0, keep_dims = reduce_mean_8_keep_dims_0, x = square_2)[name = string("reduce_mean_8")]; |
| fp32 add_4_y_0 = const()[name = string("add_4_y_0"), val = fp32(0x1.0c6f7ap-20)]; |
| tensor<fp32, [1, 32, 1, 1]> add_4 = add(x = reduce_mean_8, y = add_4_y_0)[name = string("add_4")]; |
| tensor<fp32, [1, 32, 1, 1]> sqrt_2 = sqrt(x = add_4)[name = string("sqrt_2")]; |
| tensor<fp32, [1, 32, 16, 16384]> real_div_2 = real_div(x = sub_4, y = sqrt_2)[name = string("real_div_2")]; |
| tensor<int32, [3]> reshape_9_shape_0 = const()[name = string("reshape_9_shape_0"), val = tensor<int32, [3]>([1, 512, 16384])]; |
| tensor<fp32, [1, 512, 16384]> reshape_9 = reshape(shape = reshape_9_shape_0, x = real_div_2)[name = string("reshape_9")]; |
| tensor<fp32, [1, 512, 1]> reshape_10 = const()[name = string("reshape_10"), val = tensor<fp32, [1, 512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197886336)))]; |
| tensor<fp32, [1, 512, 16384]> mul_2 = mul(x = reshape_9, y = reshape_10)[name = string("mul_2")]; |
| tensor<fp32, [1, 512, 1]> reshape_11 = const()[name = string("reshape_11"), val = tensor<fp32, [1, 512, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197888448)))]; |
| tensor<fp32, [1, 512, 16384]> add_5 = add(x = mul_2, y = reshape_11)[name = string("add_5")]; |
| tensor<int32, [3]> input_21_perm_0 = const()[name = string("input_21_perm_0"), val = tensor<int32, [3]>([0, 2, 1])]; |
| tensor<fp32, [1, 16384, 512]> input_21 = transpose(perm = input_21_perm_0, x = add_5)[name = string("transpose_14")]; |
| tensor<fp32, [1, 16384, 512]> query_1 = linear(bias = vae_decoder_mid_block_attentions_0_to_q_bias, weight = vae_decoder_mid_block_attentions_0_to_q_weight, x = input_21)[name = string("linear_0")]; |
| tensor<fp32, [1, 16384, 512]> key_1 = linear(bias = vae_decoder_mid_block_attentions_0_to_k_bias, weight = vae_decoder_mid_block_attentions_0_to_k_weight, x = input_21)[name = string("linear_1")]; |
| tensor<fp32, [1, 16384, 512]> value_1 = linear(bias = vae_decoder_mid_block_attentions_0_to_v_bias, weight = vae_decoder_mid_block_attentions_0_to_v_weight, x = input_21)[name = string("linear_2")]; |
| tensor<int32, [4]> concat_1 = const()[name = string("concat_1"), val = tensor<int32, [4]>([1, -1, 1, 512])]; |
| tensor<fp32, [1, 16384, 1, 512]> var_128 = reshape(shape = concat_1, x = query_1)[name = string("op_128")]; |
| tensor<int32, [4]> concat_2 = const()[name = string("concat_2"), val = tensor<int32, [4]>([1, -1, 1, 512])]; |
| tensor<fp32, [1, 16384, 1, 512]> var_131 = reshape(shape = concat_2, x = key_1)[name = string("op_131")]; |
| tensor<int32, [4]> concat_3 = const()[name = string("concat_3"), val = tensor<int32, [4]>([1, -1, 1, 512])]; |
| tensor<fp32, [1, 16384, 1, 512]> var_134 = reshape(shape = concat_3, x = value_1)[name = string("op_134")]; |
| tensor<int32, [4]> transpose_6_perm_0 = const()[name = string("transpose_6_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<int32, [4]> transpose_7_perm_0 = const()[name = string("transpose_7_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<int32, [4]> transpose_8_perm_0 = const()[name = string("transpose_8_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<fp32, [1, 1, 16384, 512]> transpose_8 = transpose(perm = transpose_8_perm_0, x = var_134)[name = string("transpose_11")]; |
| tensor<fp32, [1, 1, 16384, 512]> transpose_7 = transpose(perm = transpose_7_perm_0, x = var_131)[name = string("transpose_12")]; |
| tensor<fp32, [1, 1, 16384, 512]> transpose_6 = transpose(perm = transpose_6_perm_0, x = var_128)[name = string("transpose_13")]; |
| tensor<fp32, [1, 1, 16384, 512]> hidden_states_7 = scaled_dot_product_attention(key = transpose_7, query = transpose_6, value = transpose_8)[name = string("hidden_states_7")]; |
| tensor<int32, [4]> var_137_perm_0 = const()[name = string("op_137_perm_0"), val = tensor<int32, [4]>([0, 2, 1, 3])]; |
| tensor<int32, [3]> concat_4 = const()[name = string("concat_4"), val = tensor<int32, [3]>([1, -1, 512])]; |
| tensor<fp32, [1, 16384, 1, 512]> var_137 = transpose(perm = var_137_perm_0, x = hidden_states_7)[name = string("transpose_10")]; |
| tensor<fp32, [1, 16384, 512]> hidden_states_9 = reshape(shape = concat_4, x = var_137)[name = string("hidden_states_9")]; |
| tensor<fp32, [1, 16384, 512]> input_25 = 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 = string("linear_3")]; |
| tensor<int32, [3]> var_148_perm_0 = const()[name = string("op_148_perm_0"), val = tensor<int32, [3]>([0, -1, -2])]; |
| tensor<int32, [4]> var_149 = const()[name = string("op_149"), val = tensor<int32, [4]>([1, 512, 128, 128])]; |
| tensor<fp32, [1, 512, 16384]> var_148 = transpose(perm = var_148_perm_0, x = input_25)[name = string("transpose_9")]; |
| tensor<fp32, [1, 512, 128, 128]> hidden_states_13 = reshape(shape = var_149, x = var_148)[name = string("hidden_states_13")]; |
| tensor<fp32, [1, 512, 128, 128]> hidden_states_15 = add(x = hidden_states_13, y = var_86)[name = string("hidden_states_15")]; |
| tensor<int32, [5]> reshape_12_shape_0 = const()[name = string("reshape_12_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 128, 128])]; |
| tensor<fp32, [1, 32, 16, 128, 128]> reshape_12 = reshape(shape = reshape_12_shape_0, x = hidden_states_15)[name = string("reshape_12")]; |
| tensor<int32, [3]> reduce_mean_9_axes_0 = const()[name = string("reduce_mean_9_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_9_keep_dims_0 = const()[name = string("reduce_mean_9_keep_dims_0"), val = bool(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_9 = reduce_mean(axes = reduce_mean_9_axes_0, keep_dims = reduce_mean_9_keep_dims_0, x = reshape_12)[name = string("reduce_mean_9")]; |
| tensor<fp32, [1, 32, 16, 128, 128]> sub_6 = sub(x = reshape_12, y = reduce_mean_9)[name = string("sub_6")]; |
| tensor<fp32, [1, 32, 16, 128, 128]> square_3 = square(x = sub_6)[name = string("square_3")]; |
| tensor<int32, [3]> reduce_mean_11_axes_0 = const()[name = string("reduce_mean_11_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_11_keep_dims_0 = const()[name = string("reduce_mean_11_keep_dims_0"), val = bool(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_11 = reduce_mean(axes = reduce_mean_11_axes_0, keep_dims = reduce_mean_11_keep_dims_0, x = square_3)[name = string("reduce_mean_11")]; |
| fp32 add_6_y_0 = const()[name = string("add_6_y_0"), val = fp32(0x1.0c6f7ap-20)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> add_6 = add(x = reduce_mean_11, y = add_6_y_0)[name = string("add_6")]; |
| tensor<fp32, [1, 32, 1, 1, 1]> sqrt_3 = sqrt(x = add_6)[name = string("sqrt_3")]; |
| tensor<fp32, [1, 32, 16, 128, 128]> real_div_3 = real_div(x = sub_6, y = sqrt_3)[name = string("real_div_3")]; |
| tensor<int32, [4]> reshape_13_shape_0 = const()[name = string("reshape_13_shape_0"), val = tensor<int32, [4]>([1, 512, 128, 128])]; |
| tensor<fp32, [1, 512, 128, 128]> reshape_13 = reshape(shape = reshape_13_shape_0, x = real_div_3)[name = string("reshape_13")]; |
| tensor<fp32, [512]> add_7_gamma_0 = const()[name = string("add_7_gamma_0"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197890560)))]; |
| tensor<fp32, [512]> add_7_beta_0 = const()[name = string("add_7_beta_0"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197892672)))]; |
| fp32 add_7_epsilon_0 = const()[name = string("add_7_epsilon_0"), val = fp32(0x1.4f8b58p-17)]; |
| tensor<fp32, [1, 512, 128, 128]> 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 = string("add_7")]; |
| tensor<fp32, [1, 512, 128, 128]> input_31 = silu(x = add_7)[name = string("input_31")]; |
| string input_33_pad_type_0 = const()[name = string("input_33_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> input_33_pad_0 = const()[name = string("input_33_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_33_strides_0 = const()[name = string("input_33_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_33_dilations_0 = const()[name = string("input_33_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 input_33_groups_0 = const()[name = string("input_33_groups_0"), val = int32(1)]; |
| tensor<fp32, [1, 512, 128, 128]> input_33 = conv(bias = vae_decoder_mid_block_resnets_1_conv1_bias, dilations = input_33_dilations_0, groups = input_33_groups_0, pad = input_33_pad_0, pad_type = input_33_pad_type_0, strides = input_33_strides_0, weight = vae_decoder_mid_block_resnets_1_conv1_weight, x = input_31)[name = string("input_33")]; |
| tensor<int32, [5]> reshape_16_shape_0 = const()[name = string("reshape_16_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 128, 128])]; |
| tensor<fp32, [1, 32, 16, 128, 128]> reshape_16 = reshape(shape = reshape_16_shape_0, x = input_33)[name = string("reshape_16")]; |
| tensor<int32, [3]> reduce_mean_12_axes_0 = const()[name = string("reduce_mean_12_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_12_keep_dims_0 = const()[name = string("reduce_mean_12_keep_dims_0"), val = bool(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_12 = reduce_mean(axes = reduce_mean_12_axes_0, keep_dims = reduce_mean_12_keep_dims_0, x = reshape_16)[name = string("reduce_mean_12")]; |
| tensor<fp32, [1, 32, 16, 128, 128]> sub_8 = sub(x = reshape_16, y = reduce_mean_12)[name = string("sub_8")]; |
| tensor<fp32, [1, 32, 16, 128, 128]> square_4 = square(x = sub_8)[name = string("square_4")]; |
| tensor<int32, [3]> reduce_mean_14_axes_0 = const()[name = string("reduce_mean_14_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_14_keep_dims_0 = const()[name = string("reduce_mean_14_keep_dims_0"), val = bool(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_14 = reduce_mean(axes = reduce_mean_14_axes_0, keep_dims = reduce_mean_14_keep_dims_0, x = square_4)[name = string("reduce_mean_14")]; |
| fp32 add_8_y_0 = const()[name = string("add_8_y_0"), val = fp32(0x1.0c6f7ap-20)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> add_8 = add(x = reduce_mean_14, y = add_8_y_0)[name = string("add_8")]; |
| tensor<fp32, [1, 32, 1, 1, 1]> sqrt_4 = sqrt(x = add_8)[name = string("sqrt_4")]; |
| tensor<fp32, [1, 32, 16, 128, 128]> real_div_4 = real_div(x = sub_8, y = sqrt_4)[name = string("real_div_4")]; |
| tensor<int32, [4]> reshape_17_shape_0 = const()[name = string("reshape_17_shape_0"), val = tensor<int32, [4]>([1, 512, 128, 128])]; |
| tensor<fp32, [1, 512, 128, 128]> reshape_17 = reshape(shape = reshape_17_shape_0, x = real_div_4)[name = string("reshape_17")]; |
| tensor<fp32, [512]> add_9_gamma_0 = const()[name = string("add_9_gamma_0"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197894784)))]; |
| tensor<fp32, [512]> add_9_beta_0 = const()[name = string("add_9_beta_0"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197896896)))]; |
| fp32 add_9_epsilon_0 = const()[name = string("add_9_epsilon_0"), val = fp32(0x1.4f8b58p-17)]; |
| tensor<fp32, [1, 512, 128, 128]> 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 = string("add_9")]; |
| tensor<fp32, [1, 512, 128, 128]> input_37 = silu(x = add_9)[name = string("input_37")]; |
| string hidden_states_17_pad_type_0 = const()[name = string("hidden_states_17_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> hidden_states_17_pad_0 = const()[name = string("hidden_states_17_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> hidden_states_17_strides_0 = const()[name = string("hidden_states_17_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> hidden_states_17_dilations_0 = const()[name = string("hidden_states_17_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 hidden_states_17_groups_0 = const()[name = string("hidden_states_17_groups_0"), val = int32(1)]; |
| tensor<fp32, [1, 512, 128, 128]> hidden_states_17 = conv(bias = vae_decoder_mid_block_resnets_1_conv2_bias, dilations = hidden_states_17_dilations_0, groups = hidden_states_17_groups_0, pad = hidden_states_17_pad_0, pad_type = hidden_states_17_pad_type_0, strides = hidden_states_17_strides_0, weight = vae_decoder_mid_block_resnets_1_conv2_weight, x = input_37)[name = string("hidden_states_17")]; |
| tensor<fp32, [1, 512, 128, 128]> var_181 = add(x = hidden_states_15, y = hidden_states_17)[name = string("op_181")]; |
| tensor<int32, [5]> reshape_20_shape_0 = const()[name = string("reshape_20_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 128, 128])]; |
| tensor<fp32, [1, 32, 16, 128, 128]> reshape_20 = reshape(shape = reshape_20_shape_0, x = var_181)[name = string("reshape_20")]; |
| tensor<int32, [3]> reduce_mean_15_axes_0 = const()[name = string("reduce_mean_15_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_15_keep_dims_0 = const()[name = string("reduce_mean_15_keep_dims_0"), val = bool(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_15 = reduce_mean(axes = reduce_mean_15_axes_0, keep_dims = reduce_mean_15_keep_dims_0, x = reshape_20)[name = string("reduce_mean_15")]; |
| tensor<fp32, [1, 32, 16, 128, 128]> sub_10 = sub(x = reshape_20, y = reduce_mean_15)[name = string("sub_10")]; |
| tensor<fp32, [1, 32, 16, 128, 128]> square_5 = square(x = sub_10)[name = string("square_5")]; |
| tensor<int32, [3]> reduce_mean_17_axes_0 = const()[name = string("reduce_mean_17_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_17_keep_dims_0 = const()[name = string("reduce_mean_17_keep_dims_0"), val = bool(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_17 = reduce_mean(axes = reduce_mean_17_axes_0, keep_dims = reduce_mean_17_keep_dims_0, x = square_5)[name = string("reduce_mean_17")]; |
| fp32 add_10_y_0 = const()[name = string("add_10_y_0"), val = fp32(0x1.0c6f7ap-20)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> add_10 = add(x = reduce_mean_17, y = add_10_y_0)[name = string("add_10")]; |
| tensor<fp32, [1, 32, 1, 1, 1]> sqrt_5 = sqrt(x = add_10)[name = string("sqrt_5")]; |
| tensor<fp32, [1, 32, 16, 128, 128]> real_div_5 = real_div(x = sub_10, y = sqrt_5)[name = string("real_div_5")]; |
| tensor<int32, [4]> reshape_21_shape_0 = const()[name = string("reshape_21_shape_0"), val = tensor<int32, [4]>([1, 512, 128, 128])]; |
| tensor<fp32, [1, 512, 128, 128]> reshape_21 = reshape(shape = reshape_21_shape_0, x = real_div_5)[name = string("reshape_21")]; |
| tensor<fp32, [512]> add_11_gamma_0 = const()[name = string("add_11_gamma_0"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197899008)))]; |
| tensor<fp32, [512]> add_11_beta_0 = const()[name = string("add_11_beta_0"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197901120)))]; |
| fp32 add_11_epsilon_0 = const()[name = string("add_11_epsilon_0"), val = fp32(0x1.4f8b58p-17)]; |
| tensor<fp32, [1, 512, 128, 128]> 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 = string("add_11")]; |
| tensor<fp32, [1, 512, 128, 128]> input_45 = silu(x = add_11)[name = string("input_45")]; |
| string input_47_pad_type_0 = const()[name = string("input_47_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> input_47_pad_0 = const()[name = string("input_47_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_47_strides_0 = const()[name = string("input_47_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_47_dilations_0 = const()[name = string("input_47_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 input_47_groups_0 = const()[name = string("input_47_groups_0"), val = int32(1)]; |
| tensor<fp32, [1, 512, 128, 128]> input_47 = conv(bias = vae_decoder_up_blocks_0_resnets_0_conv1_bias, dilations = input_47_dilations_0, groups = input_47_groups_0, pad = input_47_pad_0, pad_type = input_47_pad_type_0, strides = input_47_strides_0, weight = vae_decoder_up_blocks_0_resnets_0_conv1_weight, x = input_45)[name = string("input_47")]; |
| tensor<int32, [5]> reshape_24_shape_0 = const()[name = string("reshape_24_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 128, 128])]; |
| tensor<fp32, [1, 32, 16, 128, 128]> reshape_24 = reshape(shape = reshape_24_shape_0, x = input_47)[name = string("reshape_24")]; |
| tensor<int32, [3]> reduce_mean_18_axes_0 = const()[name = string("reduce_mean_18_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_18_keep_dims_0 = const()[name = string("reduce_mean_18_keep_dims_0"), val = bool(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_18 = reduce_mean(axes = reduce_mean_18_axes_0, keep_dims = reduce_mean_18_keep_dims_0, x = reshape_24)[name = string("reduce_mean_18")]; |
| tensor<fp32, [1, 32, 16, 128, 128]> sub_12 = sub(x = reshape_24, y = reduce_mean_18)[name = string("sub_12")]; |
| tensor<fp32, [1, 32, 16, 128, 128]> square_6 = square(x = sub_12)[name = string("square_6")]; |
| tensor<int32, [3]> reduce_mean_20_axes_0 = const()[name = string("reduce_mean_20_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_20_keep_dims_0 = const()[name = string("reduce_mean_20_keep_dims_0"), val = bool(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_20 = reduce_mean(axes = reduce_mean_20_axes_0, keep_dims = reduce_mean_20_keep_dims_0, x = square_6)[name = string("reduce_mean_20")]; |
| fp32 add_12_y_0 = const()[name = string("add_12_y_0"), val = fp32(0x1.0c6f7ap-20)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> add_12 = add(x = reduce_mean_20, y = add_12_y_0)[name = string("add_12")]; |
| tensor<fp32, [1, 32, 1, 1, 1]> sqrt_6 = sqrt(x = add_12)[name = string("sqrt_6")]; |
| tensor<fp32, [1, 32, 16, 128, 128]> real_div_6 = real_div(x = sub_12, y = sqrt_6)[name = string("real_div_6")]; |
| tensor<int32, [4]> reshape_25_shape_0 = const()[name = string("reshape_25_shape_0"), val = tensor<int32, [4]>([1, 512, 128, 128])]; |
| tensor<fp32, [1, 512, 128, 128]> reshape_25 = reshape(shape = reshape_25_shape_0, x = real_div_6)[name = string("reshape_25")]; |
| tensor<fp32, [512]> add_13_gamma_0 = const()[name = string("add_13_gamma_0"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197903232)))]; |
| tensor<fp32, [512]> add_13_beta_0 = const()[name = string("add_13_beta_0"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197905344)))]; |
| fp32 add_13_epsilon_0 = const()[name = string("add_13_epsilon_0"), val = fp32(0x1.4f8b58p-17)]; |
| tensor<fp32, [1, 512, 128, 128]> 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 = string("add_13")]; |
| tensor<fp32, [1, 512, 128, 128]> input_51 = silu(x = add_13)[name = string("input_51")]; |
| string hidden_states_19_pad_type_0 = const()[name = string("hidden_states_19_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> hidden_states_19_pad_0 = const()[name = string("hidden_states_19_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> hidden_states_19_strides_0 = const()[name = string("hidden_states_19_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> hidden_states_19_dilations_0 = const()[name = string("hidden_states_19_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 hidden_states_19_groups_0 = const()[name = string("hidden_states_19_groups_0"), val = int32(1)]; |
| tensor<fp32, [1, 512, 128, 128]> hidden_states_19 = conv(bias = vae_decoder_up_blocks_0_resnets_0_conv2_bias, dilations = hidden_states_19_dilations_0, groups = hidden_states_19_groups_0, pad = hidden_states_19_pad_0, pad_type = hidden_states_19_pad_type_0, strides = hidden_states_19_strides_0, weight = vae_decoder_up_blocks_0_resnets_0_conv2_weight, x = input_51)[name = string("hidden_states_19")]; |
| tensor<fp32, [1, 512, 128, 128]> var_219 = add(x = var_181, y = hidden_states_19)[name = string("op_219")]; |
| tensor<int32, [5]> reshape_28_shape_0 = const()[name = string("reshape_28_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 128, 128])]; |
| tensor<fp32, [1, 32, 16, 128, 128]> reshape_28 = reshape(shape = reshape_28_shape_0, x = var_219)[name = string("reshape_28")]; |
| tensor<int32, [3]> reduce_mean_21_axes_0 = const()[name = string("reduce_mean_21_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_21_keep_dims_0 = const()[name = string("reduce_mean_21_keep_dims_0"), val = bool(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_21 = reduce_mean(axes = reduce_mean_21_axes_0, keep_dims = reduce_mean_21_keep_dims_0, x = reshape_28)[name = string("reduce_mean_21")]; |
| tensor<fp32, [1, 32, 16, 128, 128]> sub_14 = sub(x = reshape_28, y = reduce_mean_21)[name = string("sub_14")]; |
| tensor<fp32, [1, 32, 16, 128, 128]> square_7 = square(x = sub_14)[name = string("square_7")]; |
| tensor<int32, [3]> reduce_mean_23_axes_0 = const()[name = string("reduce_mean_23_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_23_keep_dims_0 = const()[name = string("reduce_mean_23_keep_dims_0"), val = bool(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_23 = reduce_mean(axes = reduce_mean_23_axes_0, keep_dims = reduce_mean_23_keep_dims_0, x = square_7)[name = string("reduce_mean_23")]; |
| fp32 add_14_y_0 = const()[name = string("add_14_y_0"), val = fp32(0x1.0c6f7ap-20)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> add_14 = add(x = reduce_mean_23, y = add_14_y_0)[name = string("add_14")]; |
| tensor<fp32, [1, 32, 1, 1, 1]> sqrt_7 = sqrt(x = add_14)[name = string("sqrt_7")]; |
| tensor<fp32, [1, 32, 16, 128, 128]> real_div_7 = real_div(x = sub_14, y = sqrt_7)[name = string("real_div_7")]; |
| tensor<int32, [4]> reshape_29_shape_0 = const()[name = string("reshape_29_shape_0"), val = tensor<int32, [4]>([1, 512, 128, 128])]; |
| tensor<fp32, [1, 512, 128, 128]> reshape_29 = reshape(shape = reshape_29_shape_0, x = real_div_7)[name = string("reshape_29")]; |
| tensor<fp32, [512]> add_15_gamma_0 = const()[name = string("add_15_gamma_0"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197907456)))]; |
| tensor<fp32, [512]> add_15_beta_0 = const()[name = string("add_15_beta_0"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197909568)))]; |
| fp32 add_15_epsilon_0 = const()[name = string("add_15_epsilon_0"), val = fp32(0x1.4f8b58p-17)]; |
| tensor<fp32, [1, 512, 128, 128]> 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 = string("add_15")]; |
| tensor<fp32, [1, 512, 128, 128]> input_59 = silu(x = add_15)[name = string("input_59")]; |
| string input_61_pad_type_0 = const()[name = string("input_61_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> input_61_pad_0 = const()[name = string("input_61_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_61_strides_0 = const()[name = string("input_61_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_61_dilations_0 = const()[name = string("input_61_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 input_61_groups_0 = const()[name = string("input_61_groups_0"), val = int32(1)]; |
| tensor<fp32, [1, 512, 128, 128]> input_61 = conv(bias = vae_decoder_up_blocks_0_resnets_1_conv1_bias, dilations = input_61_dilations_0, groups = input_61_groups_0, pad = input_61_pad_0, pad_type = input_61_pad_type_0, strides = input_61_strides_0, weight = vae_decoder_up_blocks_0_resnets_1_conv1_weight, x = input_59)[name = string("input_61")]; |
| tensor<int32, [5]> reshape_32_shape_0 = const()[name = string("reshape_32_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 128, 128])]; |
| tensor<fp32, [1, 32, 16, 128, 128]> reshape_32 = reshape(shape = reshape_32_shape_0, x = input_61)[name = string("reshape_32")]; |
| tensor<int32, [3]> reduce_mean_24_axes_0 = const()[name = string("reduce_mean_24_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_24_keep_dims_0 = const()[name = string("reduce_mean_24_keep_dims_0"), val = bool(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_24 = reduce_mean(axes = reduce_mean_24_axes_0, keep_dims = reduce_mean_24_keep_dims_0, x = reshape_32)[name = string("reduce_mean_24")]; |
| tensor<fp32, [1, 32, 16, 128, 128]> sub_16 = sub(x = reshape_32, y = reduce_mean_24)[name = string("sub_16")]; |
| tensor<fp32, [1, 32, 16, 128, 128]> square_8 = square(x = sub_16)[name = string("square_8")]; |
| tensor<int32, [3]> reduce_mean_26_axes_0 = const()[name = string("reduce_mean_26_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_26_keep_dims_0 = const()[name = string("reduce_mean_26_keep_dims_0"), val = bool(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_26 = reduce_mean(axes = reduce_mean_26_axes_0, keep_dims = reduce_mean_26_keep_dims_0, x = square_8)[name = string("reduce_mean_26")]; |
| fp32 add_16_y_0 = const()[name = string("add_16_y_0"), val = fp32(0x1.0c6f7ap-20)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> add_16 = add(x = reduce_mean_26, y = add_16_y_0)[name = string("add_16")]; |
| tensor<fp32, [1, 32, 1, 1, 1]> sqrt_8 = sqrt(x = add_16)[name = string("sqrt_8")]; |
| tensor<fp32, [1, 32, 16, 128, 128]> real_div_8 = real_div(x = sub_16, y = sqrt_8)[name = string("real_div_8")]; |
| tensor<int32, [4]> reshape_33_shape_0 = const()[name = string("reshape_33_shape_0"), val = tensor<int32, [4]>([1, 512, 128, 128])]; |
| tensor<fp32, [1, 512, 128, 128]> reshape_33 = reshape(shape = reshape_33_shape_0, x = real_div_8)[name = string("reshape_33")]; |
| tensor<fp32, [512]> add_17_gamma_0 = const()[name = string("add_17_gamma_0"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197911680)))]; |
| tensor<fp32, [512]> add_17_beta_0 = const()[name = string("add_17_beta_0"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197913792)))]; |
| fp32 add_17_epsilon_0 = const()[name = string("add_17_epsilon_0"), val = fp32(0x1.4f8b58p-17)]; |
| tensor<fp32, [1, 512, 128, 128]> 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 = string("add_17")]; |
| tensor<fp32, [1, 512, 128, 128]> input_65 = silu(x = add_17)[name = string("input_65")]; |
| string hidden_states_21_pad_type_0 = const()[name = string("hidden_states_21_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> hidden_states_21_pad_0 = const()[name = string("hidden_states_21_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> hidden_states_21_strides_0 = const()[name = string("hidden_states_21_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> hidden_states_21_dilations_0 = const()[name = string("hidden_states_21_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 hidden_states_21_groups_0 = const()[name = string("hidden_states_21_groups_0"), val = int32(1)]; |
| tensor<fp32, [1, 512, 128, 128]> hidden_states_21 = conv(bias = vae_decoder_up_blocks_0_resnets_1_conv2_bias, dilations = hidden_states_21_dilations_0, groups = hidden_states_21_groups_0, pad = hidden_states_21_pad_0, pad_type = hidden_states_21_pad_type_0, strides = hidden_states_21_strides_0, weight = vae_decoder_up_blocks_0_resnets_1_conv2_weight, x = input_65)[name = string("hidden_states_21")]; |
| tensor<fp32, [1, 512, 128, 128]> var_249 = add(x = var_219, y = hidden_states_21)[name = string("op_249")]; |
| tensor<int32, [5]> reshape_36_shape_0 = const()[name = string("reshape_36_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 128, 128])]; |
| tensor<fp32, [1, 32, 16, 128, 128]> reshape_36 = reshape(shape = reshape_36_shape_0, x = var_249)[name = string("reshape_36")]; |
| tensor<int32, [3]> reduce_mean_27_axes_0 = const()[name = string("reduce_mean_27_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_27_keep_dims_0 = const()[name = string("reduce_mean_27_keep_dims_0"), val = bool(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_27 = reduce_mean(axes = reduce_mean_27_axes_0, keep_dims = reduce_mean_27_keep_dims_0, x = reshape_36)[name = string("reduce_mean_27")]; |
| tensor<fp32, [1, 32, 16, 128, 128]> sub_18 = sub(x = reshape_36, y = reduce_mean_27)[name = string("sub_18")]; |
| tensor<fp32, [1, 32, 16, 128, 128]> square_9 = square(x = sub_18)[name = string("square_9")]; |
| tensor<int32, [3]> reduce_mean_29_axes_0 = const()[name = string("reduce_mean_29_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_29_keep_dims_0 = const()[name = string("reduce_mean_29_keep_dims_0"), val = bool(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_29 = reduce_mean(axes = reduce_mean_29_axes_0, keep_dims = reduce_mean_29_keep_dims_0, x = square_9)[name = string("reduce_mean_29")]; |
| fp32 add_18_y_0 = const()[name = string("add_18_y_0"), val = fp32(0x1.0c6f7ap-20)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> add_18 = add(x = reduce_mean_29, y = add_18_y_0)[name = string("add_18")]; |
| tensor<fp32, [1, 32, 1, 1, 1]> sqrt_9 = sqrt(x = add_18)[name = string("sqrt_9")]; |
| tensor<fp32, [1, 32, 16, 128, 128]> real_div_9 = real_div(x = sub_18, y = sqrt_9)[name = string("real_div_9")]; |
| tensor<int32, [4]> reshape_37_shape_0 = const()[name = string("reshape_37_shape_0"), val = tensor<int32, [4]>([1, 512, 128, 128])]; |
| tensor<fp32, [1, 512, 128, 128]> reshape_37 = reshape(shape = reshape_37_shape_0, x = real_div_9)[name = string("reshape_37")]; |
| tensor<fp32, [512]> add_19_gamma_0 = const()[name = string("add_19_gamma_0"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197915904)))]; |
| tensor<fp32, [512]> add_19_beta_0 = const()[name = string("add_19_beta_0"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197918016)))]; |
| fp32 add_19_epsilon_0 = const()[name = string("add_19_epsilon_0"), val = fp32(0x1.4f8b58p-17)]; |
| tensor<fp32, [1, 512, 128, 128]> 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 = string("add_19")]; |
| tensor<fp32, [1, 512, 128, 128]> input_73 = silu(x = add_19)[name = string("input_73")]; |
| string input_75_pad_type_0 = const()[name = string("input_75_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> input_75_pad_0 = const()[name = string("input_75_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_75_strides_0 = const()[name = string("input_75_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_75_dilations_0 = const()[name = string("input_75_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 input_75_groups_0 = const()[name = string("input_75_groups_0"), val = int32(1)]; |
| tensor<fp32, [1, 512, 128, 128]> input_75 = conv(bias = vae_decoder_up_blocks_0_resnets_2_conv1_bias, dilations = input_75_dilations_0, groups = input_75_groups_0, pad = input_75_pad_0, pad_type = input_75_pad_type_0, strides = input_75_strides_0, weight = vae_decoder_up_blocks_0_resnets_2_conv1_weight, x = input_73)[name = string("input_75")]; |
| tensor<int32, [5]> reshape_40_shape_0 = const()[name = string("reshape_40_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 128, 128])]; |
| tensor<fp32, [1, 32, 16, 128, 128]> reshape_40 = reshape(shape = reshape_40_shape_0, x = input_75)[name = string("reshape_40")]; |
| tensor<int32, [3]> reduce_mean_30_axes_0 = const()[name = string("reduce_mean_30_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_30_keep_dims_0 = const()[name = string("reduce_mean_30_keep_dims_0"), val = bool(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_30 = reduce_mean(axes = reduce_mean_30_axes_0, keep_dims = reduce_mean_30_keep_dims_0, x = reshape_40)[name = string("reduce_mean_30")]; |
| tensor<fp32, [1, 32, 16, 128, 128]> sub_20 = sub(x = reshape_40, y = reduce_mean_30)[name = string("sub_20")]; |
| tensor<fp32, [1, 32, 16, 128, 128]> square_10 = square(x = sub_20)[name = string("square_10")]; |
| tensor<int32, [3]> reduce_mean_32_axes_0 = const()[name = string("reduce_mean_32_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_32_keep_dims_0 = const()[name = string("reduce_mean_32_keep_dims_0"), val = bool(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_32 = reduce_mean(axes = reduce_mean_32_axes_0, keep_dims = reduce_mean_32_keep_dims_0, x = square_10)[name = string("reduce_mean_32")]; |
| fp32 add_20_y_0 = const()[name = string("add_20_y_0"), val = fp32(0x1.0c6f7ap-20)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> add_20 = add(x = reduce_mean_32, y = add_20_y_0)[name = string("add_20")]; |
| tensor<fp32, [1, 32, 1, 1, 1]> sqrt_10 = sqrt(x = add_20)[name = string("sqrt_10")]; |
| tensor<fp32, [1, 32, 16, 128, 128]> real_div_10 = real_div(x = sub_20, y = sqrt_10)[name = string("real_div_10")]; |
| tensor<int32, [4]> reshape_41_shape_0 = const()[name = string("reshape_41_shape_0"), val = tensor<int32, [4]>([1, 512, 128, 128])]; |
| tensor<fp32, [1, 512, 128, 128]> reshape_41 = reshape(shape = reshape_41_shape_0, x = real_div_10)[name = string("reshape_41")]; |
| tensor<fp32, [512]> add_21_gamma_0 = const()[name = string("add_21_gamma_0"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197920128)))]; |
| tensor<fp32, [512]> add_21_beta_0 = const()[name = string("add_21_beta_0"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197922240)))]; |
| fp32 add_21_epsilon_0 = const()[name = string("add_21_epsilon_0"), val = fp32(0x1.4f8b58p-17)]; |
| tensor<fp32, [1, 512, 128, 128]> 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 = string("add_21")]; |
| tensor<fp32, [1, 512, 128, 128]> input_79 = silu(x = add_21)[name = string("input_79")]; |
| string hidden_states_23_pad_type_0 = const()[name = string("hidden_states_23_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> hidden_states_23_pad_0 = const()[name = string("hidden_states_23_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> hidden_states_23_strides_0 = const()[name = string("hidden_states_23_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> hidden_states_23_dilations_0 = const()[name = string("hidden_states_23_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 hidden_states_23_groups_0 = const()[name = string("hidden_states_23_groups_0"), val = int32(1)]; |
| tensor<fp32, [1, 512, 128, 128]> hidden_states_23 = conv(bias = vae_decoder_up_blocks_0_resnets_2_conv2_bias, dilations = hidden_states_23_dilations_0, groups = hidden_states_23_groups_0, pad = hidden_states_23_pad_0, pad_type = hidden_states_23_pad_type_0, strides = hidden_states_23_strides_0, weight = vae_decoder_up_blocks_0_resnets_2_conv2_weight, x = input_79)[name = string("hidden_states_23")]; |
| tensor<fp32, [1, 512, 128, 128]> var_279 = add(x = var_249, y = hidden_states_23)[name = string("op_279")]; |
| fp32 input_83_scale_factor_height_0 = const()[name = string("input_83_scale_factor_height_0"), val = fp32(0x1p+1)]; |
| fp32 input_83_scale_factor_width_0 = const()[name = string("input_83_scale_factor_width_0"), val = fp32(0x1p+1)]; |
| tensor<fp32, [1, 512, 256, 256]> input_83 = upsample_nearest_neighbor(scale_factor_height = input_83_scale_factor_height_0, scale_factor_width = input_83_scale_factor_width_0, x = var_279)[name = string("input_83")]; |
| string input_85_pad_type_0 = const()[name = string("input_85_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> input_85_pad_0 = const()[name = string("input_85_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_85_strides_0 = const()[name = string("input_85_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_85_dilations_0 = const()[name = string("input_85_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 input_85_groups_0 = const()[name = string("input_85_groups_0"), val = int32(1)]; |
| tensor<fp32, [1, 512, 256, 256]> input_85 = conv(bias = vae_decoder_up_blocks_0_upsamplers_0_conv_bias, dilations = input_85_dilations_0, groups = input_85_groups_0, pad = input_85_pad_0, pad_type = input_85_pad_type_0, strides = input_85_strides_0, weight = vae_decoder_up_blocks_0_upsamplers_0_conv_weight, x = input_83)[name = string("input_85")]; |
| tensor<int32, [5]> reshape_44_shape_0 = const()[name = string("reshape_44_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 256, 256])]; |
| tensor<fp32, [1, 32, 16, 256, 256]> reshape_44 = reshape(shape = reshape_44_shape_0, x = input_85)[name = string("reshape_44")]; |
| tensor<int32, [3]> reduce_mean_33_axes_0 = const()[name = string("reduce_mean_33_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_33_keep_dims_0 = const()[name = string("reduce_mean_33_keep_dims_0"), val = bool(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_33 = reduce_mean(axes = reduce_mean_33_axes_0, keep_dims = reduce_mean_33_keep_dims_0, x = reshape_44)[name = string("reduce_mean_33")]; |
| tensor<fp32, [1, 32, 16, 256, 256]> sub_22 = sub(x = reshape_44, y = reduce_mean_33)[name = string("sub_22")]; |
| tensor<fp32, [1, 32, 16, 256, 256]> square_11 = square(x = sub_22)[name = string("square_11")]; |
| tensor<int32, [3]> reduce_mean_35_axes_0 = const()[name = string("reduce_mean_35_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_35_keep_dims_0 = const()[name = string("reduce_mean_35_keep_dims_0"), val = bool(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_35 = reduce_mean(axes = reduce_mean_35_axes_0, keep_dims = reduce_mean_35_keep_dims_0, x = square_11)[name = string("reduce_mean_35")]; |
| fp32 add_22_y_0 = const()[name = string("add_22_y_0"), val = fp32(0x1.0c6f7ap-20)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> add_22 = add(x = reduce_mean_35, y = add_22_y_0)[name = string("add_22")]; |
| tensor<fp32, [1, 32, 1, 1, 1]> sqrt_11 = sqrt(x = add_22)[name = string("sqrt_11")]; |
| tensor<fp32, [1, 32, 16, 256, 256]> real_div_11 = real_div(x = sub_22, y = sqrt_11)[name = string("real_div_11")]; |
| tensor<int32, [4]> reshape_45_shape_0 = const()[name = string("reshape_45_shape_0"), val = tensor<int32, [4]>([1, 512, 256, 256])]; |
| tensor<fp32, [1, 512, 256, 256]> reshape_45 = reshape(shape = reshape_45_shape_0, x = real_div_11)[name = string("reshape_45")]; |
| tensor<fp32, [512]> add_23_gamma_0 = const()[name = string("add_23_gamma_0"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197924352)))]; |
| tensor<fp32, [512]> add_23_beta_0 = const()[name = string("add_23_beta_0"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197926464)))]; |
| fp32 add_23_epsilon_0 = const()[name = string("add_23_epsilon_0"), val = fp32(0x1.4f8b58p-17)]; |
| tensor<fp32, [1, 512, 256, 256]> 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 = string("add_23")]; |
| tensor<fp32, [1, 512, 256, 256]> input_89 = silu(x = add_23)[name = string("input_89")]; |
| string input_91_pad_type_0 = const()[name = string("input_91_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> input_91_pad_0 = const()[name = string("input_91_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_91_strides_0 = const()[name = string("input_91_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_91_dilations_0 = const()[name = string("input_91_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 input_91_groups_0 = const()[name = string("input_91_groups_0"), val = int32(1)]; |
| tensor<fp32, [1, 512, 256, 256]> input_91 = conv(bias = vae_decoder_up_blocks_1_resnets_0_conv1_bias, dilations = input_91_dilations_0, groups = input_91_groups_0, pad = input_91_pad_0, pad_type = input_91_pad_type_0, strides = input_91_strides_0, weight = vae_decoder_up_blocks_1_resnets_0_conv1_weight, x = input_89)[name = string("input_91")]; |
| tensor<int32, [5]> reshape_48_shape_0 = const()[name = string("reshape_48_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 256, 256])]; |
| tensor<fp32, [1, 32, 16, 256, 256]> reshape_48 = reshape(shape = reshape_48_shape_0, x = input_91)[name = string("reshape_48")]; |
| tensor<int32, [3]> reduce_mean_36_axes_0 = const()[name = string("reduce_mean_36_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_36_keep_dims_0 = const()[name = string("reduce_mean_36_keep_dims_0"), val = bool(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_36 = reduce_mean(axes = reduce_mean_36_axes_0, keep_dims = reduce_mean_36_keep_dims_0, x = reshape_48)[name = string("reduce_mean_36")]; |
| tensor<fp32, [1, 32, 16, 256, 256]> sub_24 = sub(x = reshape_48, y = reduce_mean_36)[name = string("sub_24")]; |
| tensor<fp32, [1, 32, 16, 256, 256]> square_12 = square(x = sub_24)[name = string("square_12")]; |
| tensor<int32, [3]> reduce_mean_38_axes_0 = const()[name = string("reduce_mean_38_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_38_keep_dims_0 = const()[name = string("reduce_mean_38_keep_dims_0"), val = bool(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_38 = reduce_mean(axes = reduce_mean_38_axes_0, keep_dims = reduce_mean_38_keep_dims_0, x = square_12)[name = string("reduce_mean_38")]; |
| fp32 add_24_y_0 = const()[name = string("add_24_y_0"), val = fp32(0x1.0c6f7ap-20)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> add_24 = add(x = reduce_mean_38, y = add_24_y_0)[name = string("add_24")]; |
| tensor<fp32, [1, 32, 1, 1, 1]> sqrt_12 = sqrt(x = add_24)[name = string("sqrt_12")]; |
| tensor<fp32, [1, 32, 16, 256, 256]> real_div_12 = real_div(x = sub_24, y = sqrt_12)[name = string("real_div_12")]; |
| tensor<int32, [4]> reshape_49_shape_0 = const()[name = string("reshape_49_shape_0"), val = tensor<int32, [4]>([1, 512, 256, 256])]; |
| tensor<fp32, [1, 512, 256, 256]> reshape_49 = reshape(shape = reshape_49_shape_0, x = real_div_12)[name = string("reshape_49")]; |
| tensor<fp32, [512]> add_25_gamma_0 = const()[name = string("add_25_gamma_0"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197928576)))]; |
| tensor<fp32, [512]> add_25_beta_0 = const()[name = string("add_25_beta_0"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197930688)))]; |
| fp32 add_25_epsilon_0 = const()[name = string("add_25_epsilon_0"), val = fp32(0x1.4f8b58p-17)]; |
| tensor<fp32, [1, 512, 256, 256]> 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 = string("add_25")]; |
| tensor<fp32, [1, 512, 256, 256]> input_95 = silu(x = add_25)[name = string("input_95")]; |
| string hidden_states_27_pad_type_0 = const()[name = string("hidden_states_27_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> hidden_states_27_pad_0 = const()[name = string("hidden_states_27_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> hidden_states_27_strides_0 = const()[name = string("hidden_states_27_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> hidden_states_27_dilations_0 = const()[name = string("hidden_states_27_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 hidden_states_27_groups_0 = const()[name = string("hidden_states_27_groups_0"), val = int32(1)]; |
| tensor<fp32, [1, 512, 256, 256]> hidden_states_27 = conv(bias = vae_decoder_up_blocks_1_resnets_0_conv2_bias, dilations = hidden_states_27_dilations_0, groups = hidden_states_27_groups_0, pad = hidden_states_27_pad_0, pad_type = hidden_states_27_pad_type_0, strides = hidden_states_27_strides_0, weight = vae_decoder_up_blocks_1_resnets_0_conv2_weight, x = input_95)[name = string("hidden_states_27")]; |
| tensor<fp32, [1, 512, 256, 256]> var_327 = add(x = input_85, y = hidden_states_27)[name = string("op_327")]; |
| tensor<int32, [5]> reshape_52_shape_0 = const()[name = string("reshape_52_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 256, 256])]; |
| tensor<fp32, [1, 32, 16, 256, 256]> reshape_52 = reshape(shape = reshape_52_shape_0, x = var_327)[name = string("reshape_52")]; |
| tensor<int32, [3]> reduce_mean_39_axes_0 = const()[name = string("reduce_mean_39_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_39_keep_dims_0 = const()[name = string("reduce_mean_39_keep_dims_0"), val = bool(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_39 = reduce_mean(axes = reduce_mean_39_axes_0, keep_dims = reduce_mean_39_keep_dims_0, x = reshape_52)[name = string("reduce_mean_39")]; |
| tensor<fp32, [1, 32, 16, 256, 256]> sub_26 = sub(x = reshape_52, y = reduce_mean_39)[name = string("sub_26")]; |
| tensor<fp32, [1, 32, 16, 256, 256]> square_13 = square(x = sub_26)[name = string("square_13")]; |
| tensor<int32, [3]> reduce_mean_41_axes_0 = const()[name = string("reduce_mean_41_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_41_keep_dims_0 = const()[name = string("reduce_mean_41_keep_dims_0"), val = bool(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_41 = reduce_mean(axes = reduce_mean_41_axes_0, keep_dims = reduce_mean_41_keep_dims_0, x = square_13)[name = string("reduce_mean_41")]; |
| fp32 add_26_y_0 = const()[name = string("add_26_y_0"), val = fp32(0x1.0c6f7ap-20)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> add_26 = add(x = reduce_mean_41, y = add_26_y_0)[name = string("add_26")]; |
| tensor<fp32, [1, 32, 1, 1, 1]> sqrt_13 = sqrt(x = add_26)[name = string("sqrt_13")]; |
| tensor<fp32, [1, 32, 16, 256, 256]> real_div_13 = real_div(x = sub_26, y = sqrt_13)[name = string("real_div_13")]; |
| tensor<int32, [4]> reshape_53_shape_0 = const()[name = string("reshape_53_shape_0"), val = tensor<int32, [4]>([1, 512, 256, 256])]; |
| tensor<fp32, [1, 512, 256, 256]> reshape_53 = reshape(shape = reshape_53_shape_0, x = real_div_13)[name = string("reshape_53")]; |
| tensor<fp32, [512]> add_27_gamma_0 = const()[name = string("add_27_gamma_0"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197932800)))]; |
| tensor<fp32, [512]> add_27_beta_0 = const()[name = string("add_27_beta_0"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197934912)))]; |
| fp32 add_27_epsilon_0 = const()[name = string("add_27_epsilon_0"), val = fp32(0x1.4f8b58p-17)]; |
| tensor<fp32, [1, 512, 256, 256]> 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 = string("add_27")]; |
| tensor<fp32, [1, 512, 256, 256]> input_103 = silu(x = add_27)[name = string("input_103")]; |
| string input_105_pad_type_0 = const()[name = string("input_105_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> input_105_pad_0 = const()[name = string("input_105_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_105_strides_0 = const()[name = string("input_105_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_105_dilations_0 = const()[name = string("input_105_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 input_105_groups_0 = const()[name = string("input_105_groups_0"), val = int32(1)]; |
| tensor<fp32, [1, 512, 256, 256]> input_105 = conv(bias = vae_decoder_up_blocks_1_resnets_1_conv1_bias, dilations = input_105_dilations_0, groups = input_105_groups_0, pad = input_105_pad_0, pad_type = input_105_pad_type_0, strides = input_105_strides_0, weight = vae_decoder_up_blocks_1_resnets_1_conv1_weight, x = input_103)[name = string("input_105")]; |
| tensor<int32, [5]> reshape_56_shape_0 = const()[name = string("reshape_56_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 256, 256])]; |
| tensor<fp32, [1, 32, 16, 256, 256]> reshape_56 = reshape(shape = reshape_56_shape_0, x = input_105)[name = string("reshape_56")]; |
| tensor<int32, [3]> reduce_mean_42_axes_0 = const()[name = string("reduce_mean_42_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_42_keep_dims_0 = const()[name = string("reduce_mean_42_keep_dims_0"), val = bool(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_42 = reduce_mean(axes = reduce_mean_42_axes_0, keep_dims = reduce_mean_42_keep_dims_0, x = reshape_56)[name = string("reduce_mean_42")]; |
| tensor<fp32, [1, 32, 16, 256, 256]> sub_28 = sub(x = reshape_56, y = reduce_mean_42)[name = string("sub_28")]; |
| tensor<fp32, [1, 32, 16, 256, 256]> square_14 = square(x = sub_28)[name = string("square_14")]; |
| tensor<int32, [3]> reduce_mean_44_axes_0 = const()[name = string("reduce_mean_44_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_44_keep_dims_0 = const()[name = string("reduce_mean_44_keep_dims_0"), val = bool(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_44 = reduce_mean(axes = reduce_mean_44_axes_0, keep_dims = reduce_mean_44_keep_dims_0, x = square_14)[name = string("reduce_mean_44")]; |
| fp32 add_28_y_0 = const()[name = string("add_28_y_0"), val = fp32(0x1.0c6f7ap-20)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> add_28 = add(x = reduce_mean_44, y = add_28_y_0)[name = string("add_28")]; |
| tensor<fp32, [1, 32, 1, 1, 1]> sqrt_14 = sqrt(x = add_28)[name = string("sqrt_14")]; |
| tensor<fp32, [1, 32, 16, 256, 256]> real_div_14 = real_div(x = sub_28, y = sqrt_14)[name = string("real_div_14")]; |
| tensor<int32, [4]> reshape_57_shape_0 = const()[name = string("reshape_57_shape_0"), val = tensor<int32, [4]>([1, 512, 256, 256])]; |
| tensor<fp32, [1, 512, 256, 256]> reshape_57 = reshape(shape = reshape_57_shape_0, x = real_div_14)[name = string("reshape_57")]; |
| tensor<fp32, [512]> add_29_gamma_0 = const()[name = string("add_29_gamma_0"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197937024)))]; |
| tensor<fp32, [512]> add_29_beta_0 = const()[name = string("add_29_beta_0"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197939136)))]; |
| fp32 add_29_epsilon_0 = const()[name = string("add_29_epsilon_0"), val = fp32(0x1.4f8b58p-17)]; |
| tensor<fp32, [1, 512, 256, 256]> 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 = string("add_29")]; |
| tensor<fp32, [1, 512, 256, 256]> input_109 = silu(x = add_29)[name = string("input_109")]; |
| string hidden_states_29_pad_type_0 = const()[name = string("hidden_states_29_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> hidden_states_29_pad_0 = const()[name = string("hidden_states_29_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> hidden_states_29_strides_0 = const()[name = string("hidden_states_29_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> hidden_states_29_dilations_0 = const()[name = string("hidden_states_29_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 hidden_states_29_groups_0 = const()[name = string("hidden_states_29_groups_0"), val = int32(1)]; |
| tensor<fp32, [1, 512, 256, 256]> hidden_states_29 = conv(bias = vae_decoder_up_blocks_1_resnets_1_conv2_bias, dilations = hidden_states_29_dilations_0, groups = hidden_states_29_groups_0, pad = hidden_states_29_pad_0, pad_type = hidden_states_29_pad_type_0, strides = hidden_states_29_strides_0, weight = vae_decoder_up_blocks_1_resnets_1_conv2_weight, x = input_109)[name = string("hidden_states_29")]; |
| tensor<fp32, [1, 512, 256, 256]> var_357 = add(x = var_327, y = hidden_states_29)[name = string("op_357")]; |
| tensor<int32, [5]> reshape_60_shape_0 = const()[name = string("reshape_60_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 256, 256])]; |
| tensor<fp32, [1, 32, 16, 256, 256]> reshape_60 = reshape(shape = reshape_60_shape_0, x = var_357)[name = string("reshape_60")]; |
| tensor<int32, [3]> reduce_mean_45_axes_0 = const()[name = string("reduce_mean_45_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_45_keep_dims_0 = const()[name = string("reduce_mean_45_keep_dims_0"), val = bool(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_45 = reduce_mean(axes = reduce_mean_45_axes_0, keep_dims = reduce_mean_45_keep_dims_0, x = reshape_60)[name = string("reduce_mean_45")]; |
| tensor<fp32, [1, 32, 16, 256, 256]> sub_30 = sub(x = reshape_60, y = reduce_mean_45)[name = string("sub_30")]; |
| tensor<fp32, [1, 32, 16, 256, 256]> square_15 = square(x = sub_30)[name = string("square_15")]; |
| tensor<int32, [3]> reduce_mean_47_axes_0 = const()[name = string("reduce_mean_47_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_47_keep_dims_0 = const()[name = string("reduce_mean_47_keep_dims_0"), val = bool(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_47 = reduce_mean(axes = reduce_mean_47_axes_0, keep_dims = reduce_mean_47_keep_dims_0, x = square_15)[name = string("reduce_mean_47")]; |
| fp32 add_30_y_0 = const()[name = string("add_30_y_0"), val = fp32(0x1.0c6f7ap-20)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> add_30 = add(x = reduce_mean_47, y = add_30_y_0)[name = string("add_30")]; |
| tensor<fp32, [1, 32, 1, 1, 1]> sqrt_15 = sqrt(x = add_30)[name = string("sqrt_15")]; |
| tensor<fp32, [1, 32, 16, 256, 256]> real_div_15 = real_div(x = sub_30, y = sqrt_15)[name = string("real_div_15")]; |
| tensor<int32, [4]> reshape_61_shape_0 = const()[name = string("reshape_61_shape_0"), val = tensor<int32, [4]>([1, 512, 256, 256])]; |
| tensor<fp32, [1, 512, 256, 256]> reshape_61 = reshape(shape = reshape_61_shape_0, x = real_div_15)[name = string("reshape_61")]; |
| tensor<fp32, [512]> add_31_gamma_0 = const()[name = string("add_31_gamma_0"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197941248)))]; |
| tensor<fp32, [512]> add_31_beta_0 = const()[name = string("add_31_beta_0"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197943360)))]; |
| fp32 add_31_epsilon_0 = const()[name = string("add_31_epsilon_0"), val = fp32(0x1.4f8b58p-17)]; |
| tensor<fp32, [1, 512, 256, 256]> 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 = string("add_31")]; |
| tensor<fp32, [1, 512, 256, 256]> input_117 = silu(x = add_31)[name = string("input_117")]; |
| string input_119_pad_type_0 = const()[name = string("input_119_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> input_119_pad_0 = const()[name = string("input_119_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_119_strides_0 = const()[name = string("input_119_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_119_dilations_0 = const()[name = string("input_119_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 input_119_groups_0 = const()[name = string("input_119_groups_0"), val = int32(1)]; |
| tensor<fp32, [1, 512, 256, 256]> input_119 = conv(bias = vae_decoder_up_blocks_1_resnets_2_conv1_bias, dilations = input_119_dilations_0, groups = input_119_groups_0, pad = input_119_pad_0, pad_type = input_119_pad_type_0, strides = input_119_strides_0, weight = vae_decoder_up_blocks_1_resnets_2_conv1_weight, x = input_117)[name = string("input_119")]; |
| tensor<int32, [5]> reshape_64_shape_0 = const()[name = string("reshape_64_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 256, 256])]; |
| tensor<fp32, [1, 32, 16, 256, 256]> reshape_64 = reshape(shape = reshape_64_shape_0, x = input_119)[name = string("reshape_64")]; |
| tensor<int32, [3]> reduce_mean_48_axes_0 = const()[name = string("reduce_mean_48_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_48_keep_dims_0 = const()[name = string("reduce_mean_48_keep_dims_0"), val = bool(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_48 = reduce_mean(axes = reduce_mean_48_axes_0, keep_dims = reduce_mean_48_keep_dims_0, x = reshape_64)[name = string("reduce_mean_48")]; |
| tensor<fp32, [1, 32, 16, 256, 256]> sub_32 = sub(x = reshape_64, y = reduce_mean_48)[name = string("sub_32")]; |
| tensor<fp32, [1, 32, 16, 256, 256]> square_16 = square(x = sub_32)[name = string("square_16")]; |
| tensor<int32, [3]> reduce_mean_50_axes_0 = const()[name = string("reduce_mean_50_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_50_keep_dims_0 = const()[name = string("reduce_mean_50_keep_dims_0"), val = bool(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_50 = reduce_mean(axes = reduce_mean_50_axes_0, keep_dims = reduce_mean_50_keep_dims_0, x = square_16)[name = string("reduce_mean_50")]; |
| fp32 add_32_y_0 = const()[name = string("add_32_y_0"), val = fp32(0x1.0c6f7ap-20)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> add_32 = add(x = reduce_mean_50, y = add_32_y_0)[name = string("add_32")]; |
| tensor<fp32, [1, 32, 1, 1, 1]> sqrt_16 = sqrt(x = add_32)[name = string("sqrt_16")]; |
| tensor<fp32, [1, 32, 16, 256, 256]> real_div_16 = real_div(x = sub_32, y = sqrt_16)[name = string("real_div_16")]; |
| tensor<int32, [4]> reshape_65_shape_0 = const()[name = string("reshape_65_shape_0"), val = tensor<int32, [4]>([1, 512, 256, 256])]; |
| tensor<fp32, [1, 512, 256, 256]> reshape_65 = reshape(shape = reshape_65_shape_0, x = real_div_16)[name = string("reshape_65")]; |
| tensor<fp32, [512]> add_33_gamma_0 = const()[name = string("add_33_gamma_0"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197945472)))]; |
| tensor<fp32, [512]> add_33_beta_0 = const()[name = string("add_33_beta_0"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197947584)))]; |
| fp32 add_33_epsilon_0 = const()[name = string("add_33_epsilon_0"), val = fp32(0x1.4f8b58p-17)]; |
| tensor<fp32, [1, 512, 256, 256]> 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 = string("add_33")]; |
| tensor<fp32, [1, 512, 256, 256]> input_123 = silu(x = add_33)[name = string("input_123")]; |
| string hidden_states_31_pad_type_0 = const()[name = string("hidden_states_31_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> hidden_states_31_pad_0 = const()[name = string("hidden_states_31_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> hidden_states_31_strides_0 = const()[name = string("hidden_states_31_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> hidden_states_31_dilations_0 = const()[name = string("hidden_states_31_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 hidden_states_31_groups_0 = const()[name = string("hidden_states_31_groups_0"), val = int32(1)]; |
| tensor<fp32, [1, 512, 256, 256]> hidden_states_31 = conv(bias = vae_decoder_up_blocks_1_resnets_2_conv2_bias, dilations = hidden_states_31_dilations_0, groups = hidden_states_31_groups_0, pad = hidden_states_31_pad_0, pad_type = hidden_states_31_pad_type_0, strides = hidden_states_31_strides_0, weight = vae_decoder_up_blocks_1_resnets_2_conv2_weight, x = input_123)[name = string("hidden_states_31")]; |
| tensor<fp32, [1, 512, 256, 256]> var_387 = add(x = var_357, y = hidden_states_31)[name = string("op_387")]; |
| fp32 input_127_scale_factor_height_0 = const()[name = string("input_127_scale_factor_height_0"), val = fp32(0x1p+1)]; |
| fp32 input_127_scale_factor_width_0 = const()[name = string("input_127_scale_factor_width_0"), val = fp32(0x1p+1)]; |
| tensor<fp32, [1, 512, 512, 512]> input_127 = upsample_nearest_neighbor(scale_factor_height = input_127_scale_factor_height_0, scale_factor_width = input_127_scale_factor_width_0, x = var_387)[name = string("input_127")]; |
| string input_129_pad_type_0 = const()[name = string("input_129_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> input_129_pad_0 = const()[name = string("input_129_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_129_strides_0 = const()[name = string("input_129_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_129_dilations_0 = const()[name = string("input_129_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 input_129_groups_0 = const()[name = string("input_129_groups_0"), val = int32(1)]; |
| tensor<fp32, [1, 512, 512, 512]> input_129 = conv(bias = vae_decoder_up_blocks_1_upsamplers_0_conv_bias, dilations = input_129_dilations_0, groups = input_129_groups_0, pad = input_129_pad_0, pad_type = input_129_pad_type_0, strides = input_129_strides_0, weight = vae_decoder_up_blocks_1_upsamplers_0_conv_weight, x = input_127)[name = string("input_129")]; |
| tensor<int32, [5]> reshape_68_shape_0 = const()[name = string("reshape_68_shape_0"), val = tensor<int32, [5]>([1, 32, 16, 512, 512])]; |
| tensor<fp32, [1, 32, 16, 512, 512]> reshape_68 = reshape(shape = reshape_68_shape_0, x = input_129)[name = string("reshape_68")]; |
| tensor<int32, [3]> reduce_mean_51_axes_0 = const()[name = string("reduce_mean_51_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_51_keep_dims_0 = const()[name = string("reduce_mean_51_keep_dims_0"), val = bool(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_51 = reduce_mean(axes = reduce_mean_51_axes_0, keep_dims = reduce_mean_51_keep_dims_0, x = reshape_68)[name = string("reduce_mean_51")]; |
| tensor<fp32, [1, 32, 16, 512, 512]> sub_34 = sub(x = reshape_68, y = reduce_mean_51)[name = string("sub_34")]; |
| tensor<fp32, [1, 32, 16, 512, 512]> square_17 = square(x = sub_34)[name = string("square_17")]; |
| tensor<int32, [3]> reduce_mean_53_axes_0 = const()[name = string("reduce_mean_53_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_53_keep_dims_0 = const()[name = string("reduce_mean_53_keep_dims_0"), val = bool(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_53 = reduce_mean(axes = reduce_mean_53_axes_0, keep_dims = reduce_mean_53_keep_dims_0, x = square_17)[name = string("reduce_mean_53")]; |
| fp32 add_34_y_0 = const()[name = string("add_34_y_0"), val = fp32(0x1.0c6f7ap-20)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> add_34 = add(x = reduce_mean_53, y = add_34_y_0)[name = string("add_34")]; |
| tensor<fp32, [1, 32, 1, 1, 1]> sqrt_17 = sqrt(x = add_34)[name = string("sqrt_17")]; |
| tensor<fp32, [1, 32, 16, 512, 512]> real_div_17 = real_div(x = sub_34, y = sqrt_17)[name = string("real_div_17")]; |
| tensor<int32, [4]> reshape_69_shape_0 = const()[name = string("reshape_69_shape_0"), val = tensor<int32, [4]>([1, 512, 512, 512])]; |
| tensor<fp32, [1, 512, 512, 512]> reshape_69 = reshape(shape = reshape_69_shape_0, x = real_div_17)[name = string("reshape_69")]; |
| tensor<fp32, [512]> add_35_gamma_0 = const()[name = string("add_35_gamma_0"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197949696)))]; |
| tensor<fp32, [512]> add_35_beta_0 = const()[name = string("add_35_beta_0"), val = tensor<fp32, [512]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197951808)))]; |
| fp32 add_35_epsilon_0 = const()[name = string("add_35_epsilon_0"), val = fp32(0x1.4f8b58p-17)]; |
| tensor<fp32, [1, 512, 512, 512]> 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 = string("add_35")]; |
| tensor<fp32, [1, 512, 512, 512]> input_133 = silu(x = add_35)[name = string("input_133")]; |
| string input_135_pad_type_0 = const()[name = string("input_135_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> input_135_pad_0 = const()[name = string("input_135_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_135_strides_0 = const()[name = string("input_135_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_135_dilations_0 = const()[name = string("input_135_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 input_135_groups_0 = const()[name = string("input_135_groups_0"), val = int32(1)]; |
| tensor<fp32, [1, 256, 512, 512]> input_135 = conv(bias = vae_decoder_up_blocks_2_resnets_0_conv1_bias, dilations = input_135_dilations_0, groups = input_135_groups_0, pad = input_135_pad_0, pad_type = input_135_pad_type_0, strides = input_135_strides_0, weight = vae_decoder_up_blocks_2_resnets_0_conv1_weight, x = input_133)[name = string("input_135")]; |
| tensor<int32, [5]> reshape_72_shape_0 = const()[name = string("reshape_72_shape_0"), val = tensor<int32, [5]>([1, 32, 8, 512, 512])]; |
| tensor<fp32, [1, 32, 8, 512, 512]> reshape_72 = reshape(shape = reshape_72_shape_0, x = input_135)[name = string("reshape_72")]; |
| tensor<int32, [3]> reduce_mean_54_axes_0 = const()[name = string("reduce_mean_54_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_54_keep_dims_0 = const()[name = string("reduce_mean_54_keep_dims_0"), val = bool(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_54 = reduce_mean(axes = reduce_mean_54_axes_0, keep_dims = reduce_mean_54_keep_dims_0, x = reshape_72)[name = string("reduce_mean_54")]; |
| tensor<fp32, [1, 32, 8, 512, 512]> sub_36 = sub(x = reshape_72, y = reduce_mean_54)[name = string("sub_36")]; |
| tensor<fp32, [1, 32, 8, 512, 512]> square_18 = square(x = sub_36)[name = string("square_18")]; |
| tensor<int32, [3]> reduce_mean_56_axes_0 = const()[name = string("reduce_mean_56_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_56_keep_dims_0 = const()[name = string("reduce_mean_56_keep_dims_0"), val = bool(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_56 = reduce_mean(axes = reduce_mean_56_axes_0, keep_dims = reduce_mean_56_keep_dims_0, x = square_18)[name = string("reduce_mean_56")]; |
| fp32 add_36_y_0 = const()[name = string("add_36_y_0"), val = fp32(0x1.0c6f7ap-20)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> add_36 = add(x = reduce_mean_56, y = add_36_y_0)[name = string("add_36")]; |
| tensor<fp32, [1, 32, 1, 1, 1]> sqrt_18 = sqrt(x = add_36)[name = string("sqrt_18")]; |
| tensor<fp32, [1, 32, 8, 512, 512]> real_div_18 = real_div(x = sub_36, y = sqrt_18)[name = string("real_div_18")]; |
| tensor<int32, [4]> reshape_73_shape_0 = const()[name = string("reshape_73_shape_0"), val = tensor<int32, [4]>([1, 256, 512, 512])]; |
| tensor<fp32, [1, 256, 512, 512]> reshape_73 = reshape(shape = reshape_73_shape_0, x = real_div_18)[name = string("reshape_73")]; |
| tensor<fp32, [256]> add_37_mean_0 = const()[name = string("add_37_mean_0"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197953920)))]; |
| tensor<fp32, [256]> add_37_variance_0 = const()[name = string("add_37_variance_0"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197955008)))]; |
| tensor<fp32, [256]> add_37_gamma_0 = const()[name = string("add_37_gamma_0"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197956096)))]; |
| tensor<fp32, [256]> add_37_beta_0 = const()[name = string("add_37_beta_0"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197957184)))]; |
| fp32 add_37_epsilon_0 = const()[name = string("add_37_epsilon_0"), val = fp32(0x1.4f8b58p-17)]; |
| tensor<fp32, [1, 256, 512, 512]> 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 = string("add_37")]; |
| tensor<fp32, [1, 256, 512, 512]> input_139 = silu(x = add_37)[name = string("input_139")]; |
| string hidden_states_35_pad_type_0 = const()[name = string("hidden_states_35_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> hidden_states_35_pad_0 = const()[name = string("hidden_states_35_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> hidden_states_35_strides_0 = const()[name = string("hidden_states_35_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> hidden_states_35_dilations_0 = const()[name = string("hidden_states_35_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 hidden_states_35_groups_0 = const()[name = string("hidden_states_35_groups_0"), val = int32(1)]; |
| tensor<fp32, [1, 256, 512, 512]> hidden_states_35 = conv(bias = vae_decoder_up_blocks_2_resnets_0_conv2_bias, dilations = hidden_states_35_dilations_0, groups = hidden_states_35_groups_0, pad = hidden_states_35_pad_0, pad_type = hidden_states_35_pad_type_0, strides = hidden_states_35_strides_0, weight = vae_decoder_up_blocks_2_resnets_0_conv2_weight, x = input_139)[name = string("hidden_states_35")]; |
| string input_tensor_1_pad_type_0 = const()[name = string("input_tensor_1_pad_type_0"), val = string("valid")]; |
| tensor<int32, [2]> input_tensor_1_strides_0 = const()[name = string("input_tensor_1_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [4]> input_tensor_1_pad_0 = const()[name = string("input_tensor_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])]; |
| tensor<int32, [2]> input_tensor_1_dilations_0 = const()[name = string("input_tensor_1_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 input_tensor_1_groups_0 = const()[name = string("input_tensor_1_groups_0"), val = int32(1)]; |
| tensor<fp32, [1, 256, 512, 512]> input_tensor_1 = conv(bias = vae_decoder_up_blocks_2_resnets_0_conv_shortcut_bias, dilations = input_tensor_1_dilations_0, groups = input_tensor_1_groups_0, pad = input_tensor_1_pad_0, pad_type = input_tensor_1_pad_type_0, strides = input_tensor_1_strides_0, weight = vae_decoder_up_blocks_2_resnets_0_conv_shortcut_weight, x = input_129)[name = string("input_tensor_1")]; |
| tensor<fp32, [1, 256, 512, 512]> var_443 = add(x = input_tensor_1, y = hidden_states_35)[name = string("op_443")]; |
| tensor<int32, [5]> reshape_76_shape_0 = const()[name = string("reshape_76_shape_0"), val = tensor<int32, [5]>([1, 32, 8, 512, 512])]; |
| tensor<fp32, [1, 32, 8, 512, 512]> reshape_76 = reshape(shape = reshape_76_shape_0, x = var_443)[name = string("reshape_76")]; |
| tensor<int32, [3]> reduce_mean_57_axes_0 = const()[name = string("reduce_mean_57_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_57_keep_dims_0 = const()[name = string("reduce_mean_57_keep_dims_0"), val = bool(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_57 = reduce_mean(axes = reduce_mean_57_axes_0, keep_dims = reduce_mean_57_keep_dims_0, x = reshape_76)[name = string("reduce_mean_57")]; |
| tensor<fp32, [1, 32, 8, 512, 512]> sub_38 = sub(x = reshape_76, y = reduce_mean_57)[name = string("sub_38")]; |
| tensor<fp32, [1, 32, 8, 512, 512]> square_19 = square(x = sub_38)[name = string("square_19")]; |
| tensor<int32, [3]> reduce_mean_59_axes_0 = const()[name = string("reduce_mean_59_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_59_keep_dims_0 = const()[name = string("reduce_mean_59_keep_dims_0"), val = bool(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_59 = reduce_mean(axes = reduce_mean_59_axes_0, keep_dims = reduce_mean_59_keep_dims_0, x = square_19)[name = string("reduce_mean_59")]; |
| fp32 add_38_y_0 = const()[name = string("add_38_y_0"), val = fp32(0x1.0c6f7ap-20)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> add_38 = add(x = reduce_mean_59, y = add_38_y_0)[name = string("add_38")]; |
| tensor<fp32, [1, 32, 1, 1, 1]> sqrt_19 = sqrt(x = add_38)[name = string("sqrt_19")]; |
| tensor<fp32, [1, 32, 8, 512, 512]> real_div_19 = real_div(x = sub_38, y = sqrt_19)[name = string("real_div_19")]; |
| tensor<int32, [4]> reshape_77_shape_0 = const()[name = string("reshape_77_shape_0"), val = tensor<int32, [4]>([1, 256, 512, 512])]; |
| tensor<fp32, [1, 256, 512, 512]> reshape_77 = reshape(shape = reshape_77_shape_0, x = real_div_19)[name = string("reshape_77")]; |
| tensor<fp32, [256]> add_39_gamma_0 = const()[name = string("add_39_gamma_0"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197958272)))]; |
| tensor<fp32, [256]> add_39_beta_0 = const()[name = string("add_39_beta_0"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197959360)))]; |
| fp32 add_39_epsilon_0 = const()[name = string("add_39_epsilon_0"), val = fp32(0x1.4f8b58p-17)]; |
| tensor<fp32, [1, 256, 512, 512]> 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 = string("add_39")]; |
| tensor<fp32, [1, 256, 512, 512]> input_147 = silu(x = add_39)[name = string("input_147")]; |
| string input_149_pad_type_0 = const()[name = string("input_149_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> input_149_pad_0 = const()[name = string("input_149_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_149_strides_0 = const()[name = string("input_149_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_149_dilations_0 = const()[name = string("input_149_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 input_149_groups_0 = const()[name = string("input_149_groups_0"), val = int32(1)]; |
| tensor<fp32, [1, 256, 512, 512]> input_149 = conv(bias = vae_decoder_up_blocks_2_resnets_1_conv1_bias, dilations = input_149_dilations_0, groups = input_149_groups_0, pad = input_149_pad_0, pad_type = input_149_pad_type_0, strides = input_149_strides_0, weight = vae_decoder_up_blocks_2_resnets_1_conv1_weight, x = input_147)[name = string("input_149")]; |
| tensor<int32, [5]> reshape_80_shape_0 = const()[name = string("reshape_80_shape_0"), val = tensor<int32, [5]>([1, 32, 8, 512, 512])]; |
| tensor<fp32, [1, 32, 8, 512, 512]> reshape_80 = reshape(shape = reshape_80_shape_0, x = input_149)[name = string("reshape_80")]; |
| tensor<int32, [3]> reduce_mean_60_axes_0 = const()[name = string("reduce_mean_60_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_60_keep_dims_0 = const()[name = string("reduce_mean_60_keep_dims_0"), val = bool(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_60 = reduce_mean(axes = reduce_mean_60_axes_0, keep_dims = reduce_mean_60_keep_dims_0, x = reshape_80)[name = string("reduce_mean_60")]; |
| tensor<fp32, [1, 32, 8, 512, 512]> sub_40 = sub(x = reshape_80, y = reduce_mean_60)[name = string("sub_40")]; |
| tensor<fp32, [1, 32, 8, 512, 512]> square_20 = square(x = sub_40)[name = string("square_20")]; |
| tensor<int32, [3]> reduce_mean_62_axes_0 = const()[name = string("reduce_mean_62_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_62_keep_dims_0 = const()[name = string("reduce_mean_62_keep_dims_0"), val = bool(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_62 = reduce_mean(axes = reduce_mean_62_axes_0, keep_dims = reduce_mean_62_keep_dims_0, x = square_20)[name = string("reduce_mean_62")]; |
| fp32 add_40_y_0 = const()[name = string("add_40_y_0"), val = fp32(0x1.0c6f7ap-20)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> add_40 = add(x = reduce_mean_62, y = add_40_y_0)[name = string("add_40")]; |
| tensor<fp32, [1, 32, 1, 1, 1]> sqrt_20 = sqrt(x = add_40)[name = string("sqrt_20")]; |
| tensor<fp32, [1, 32, 8, 512, 512]> real_div_20 = real_div(x = sub_40, y = sqrt_20)[name = string("real_div_20")]; |
| tensor<int32, [4]> reshape_81_shape_0 = const()[name = string("reshape_81_shape_0"), val = tensor<int32, [4]>([1, 256, 512, 512])]; |
| tensor<fp32, [1, 256, 512, 512]> reshape_81 = reshape(shape = reshape_81_shape_0, x = real_div_20)[name = string("reshape_81")]; |
| tensor<fp32, [256]> add_41_gamma_0 = const()[name = string("add_41_gamma_0"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197960448)))]; |
| tensor<fp32, [256]> add_41_beta_0 = const()[name = string("add_41_beta_0"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197961536)))]; |
| fp32 add_41_epsilon_0 = const()[name = string("add_41_epsilon_0"), val = fp32(0x1.4f8b58p-17)]; |
| tensor<fp32, [1, 256, 512, 512]> 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 = string("add_41")]; |
| tensor<fp32, [1, 256, 512, 512]> input_153 = silu(x = add_41)[name = string("input_153")]; |
| string hidden_states_37_pad_type_0 = const()[name = string("hidden_states_37_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> hidden_states_37_pad_0 = const()[name = string("hidden_states_37_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> hidden_states_37_strides_0 = const()[name = string("hidden_states_37_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> hidden_states_37_dilations_0 = const()[name = string("hidden_states_37_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 hidden_states_37_groups_0 = const()[name = string("hidden_states_37_groups_0"), val = int32(1)]; |
| tensor<fp32, [1, 256, 512, 512]> hidden_states_37 = conv(bias = vae_decoder_up_blocks_2_resnets_1_conv2_bias, dilations = hidden_states_37_dilations_0, groups = hidden_states_37_groups_0, pad = hidden_states_37_pad_0, pad_type = hidden_states_37_pad_type_0, strides = hidden_states_37_strides_0, weight = vae_decoder_up_blocks_2_resnets_1_conv2_weight, x = input_153)[name = string("hidden_states_37")]; |
| tensor<fp32, [1, 256, 512, 512]> var_473 = add(x = var_443, y = hidden_states_37)[name = string("op_473")]; |
| tensor<int32, [5]> reshape_84_shape_0 = const()[name = string("reshape_84_shape_0"), val = tensor<int32, [5]>([1, 32, 8, 512, 512])]; |
| tensor<fp32, [1, 32, 8, 512, 512]> reshape_84 = reshape(shape = reshape_84_shape_0, x = var_473)[name = string("reshape_84")]; |
| tensor<int32, [3]> reduce_mean_63_axes_0 = const()[name = string("reduce_mean_63_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_63_keep_dims_0 = const()[name = string("reduce_mean_63_keep_dims_0"), val = bool(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_63 = reduce_mean(axes = reduce_mean_63_axes_0, keep_dims = reduce_mean_63_keep_dims_0, x = reshape_84)[name = string("reduce_mean_63")]; |
| tensor<fp32, [1, 32, 8, 512, 512]> sub_42 = sub(x = reshape_84, y = reduce_mean_63)[name = string("sub_42")]; |
| tensor<fp32, [1, 32, 8, 512, 512]> square_21 = square(x = sub_42)[name = string("square_21")]; |
| tensor<int32, [3]> reduce_mean_65_axes_0 = const()[name = string("reduce_mean_65_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_65_keep_dims_0 = const()[name = string("reduce_mean_65_keep_dims_0"), val = bool(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_65 = reduce_mean(axes = reduce_mean_65_axes_0, keep_dims = reduce_mean_65_keep_dims_0, x = square_21)[name = string("reduce_mean_65")]; |
| fp32 add_42_y_0 = const()[name = string("add_42_y_0"), val = fp32(0x1.0c6f7ap-20)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> add_42 = add(x = reduce_mean_65, y = add_42_y_0)[name = string("add_42")]; |
| tensor<fp32, [1, 32, 1, 1, 1]> sqrt_21 = sqrt(x = add_42)[name = string("sqrt_21")]; |
| tensor<fp32, [1, 32, 8, 512, 512]> real_div_21 = real_div(x = sub_42, y = sqrt_21)[name = string("real_div_21")]; |
| tensor<int32, [4]> reshape_85_shape_0 = const()[name = string("reshape_85_shape_0"), val = tensor<int32, [4]>([1, 256, 512, 512])]; |
| tensor<fp32, [1, 256, 512, 512]> reshape_85 = reshape(shape = reshape_85_shape_0, x = real_div_21)[name = string("reshape_85")]; |
| tensor<fp32, [256]> add_43_gamma_0 = const()[name = string("add_43_gamma_0"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197962624)))]; |
| tensor<fp32, [256]> add_43_beta_0 = const()[name = string("add_43_beta_0"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197963712)))]; |
| fp32 add_43_epsilon_0 = const()[name = string("add_43_epsilon_0"), val = fp32(0x1.4f8b58p-17)]; |
| tensor<fp32, [1, 256, 512, 512]> 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 = string("add_43")]; |
| tensor<fp32, [1, 256, 512, 512]> input_161 = silu(x = add_43)[name = string("input_161")]; |
| string input_163_pad_type_0 = const()[name = string("input_163_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> input_163_pad_0 = const()[name = string("input_163_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_163_strides_0 = const()[name = string("input_163_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_163_dilations_0 = const()[name = string("input_163_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 input_163_groups_0 = const()[name = string("input_163_groups_0"), val = int32(1)]; |
| tensor<fp32, [1, 256, 512, 512]> input_163 = conv(bias = vae_decoder_up_blocks_2_resnets_2_conv1_bias, dilations = input_163_dilations_0, groups = input_163_groups_0, pad = input_163_pad_0, pad_type = input_163_pad_type_0, strides = input_163_strides_0, weight = vae_decoder_up_blocks_2_resnets_2_conv1_weight, x = input_161)[name = string("input_163")]; |
| tensor<int32, [5]> reshape_88_shape_0 = const()[name = string("reshape_88_shape_0"), val = tensor<int32, [5]>([1, 32, 8, 512, 512])]; |
| tensor<fp32, [1, 32, 8, 512, 512]> reshape_88 = reshape(shape = reshape_88_shape_0, x = input_163)[name = string("reshape_88")]; |
| tensor<int32, [3]> reduce_mean_66_axes_0 = const()[name = string("reduce_mean_66_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_66_keep_dims_0 = const()[name = string("reduce_mean_66_keep_dims_0"), val = bool(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_66 = reduce_mean(axes = reduce_mean_66_axes_0, keep_dims = reduce_mean_66_keep_dims_0, x = reshape_88)[name = string("reduce_mean_66")]; |
| tensor<fp32, [1, 32, 8, 512, 512]> sub_44 = sub(x = reshape_88, y = reduce_mean_66)[name = string("sub_44")]; |
| tensor<fp32, [1, 32, 8, 512, 512]> square_22 = square(x = sub_44)[name = string("square_22")]; |
| tensor<int32, [3]> reduce_mean_68_axes_0 = const()[name = string("reduce_mean_68_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_68_keep_dims_0 = const()[name = string("reduce_mean_68_keep_dims_0"), val = bool(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_68 = reduce_mean(axes = reduce_mean_68_axes_0, keep_dims = reduce_mean_68_keep_dims_0, x = square_22)[name = string("reduce_mean_68")]; |
| fp32 add_44_y_0 = const()[name = string("add_44_y_0"), val = fp32(0x1.0c6f7ap-20)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> add_44 = add(x = reduce_mean_68, y = add_44_y_0)[name = string("add_44")]; |
| tensor<fp32, [1, 32, 1, 1, 1]> sqrt_22 = sqrt(x = add_44)[name = string("sqrt_22")]; |
| tensor<fp32, [1, 32, 8, 512, 512]> real_div_22 = real_div(x = sub_44, y = sqrt_22)[name = string("real_div_22")]; |
| tensor<int32, [4]> reshape_89_shape_0 = const()[name = string("reshape_89_shape_0"), val = tensor<int32, [4]>([1, 256, 512, 512])]; |
| tensor<fp32, [1, 256, 512, 512]> reshape_89 = reshape(shape = reshape_89_shape_0, x = real_div_22)[name = string("reshape_89")]; |
| tensor<fp32, [256]> add_45_gamma_0 = const()[name = string("add_45_gamma_0"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197964800)))]; |
| tensor<fp32, [256]> add_45_beta_0 = const()[name = string("add_45_beta_0"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197965888)))]; |
| fp32 add_45_epsilon_0 = const()[name = string("add_45_epsilon_0"), val = fp32(0x1.4f8b58p-17)]; |
| tensor<fp32, [1, 256, 512, 512]> 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 = string("add_45")]; |
| tensor<fp32, [1, 256, 512, 512]> input_167 = silu(x = add_45)[name = string("input_167")]; |
| string hidden_states_39_pad_type_0 = const()[name = string("hidden_states_39_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> hidden_states_39_pad_0 = const()[name = string("hidden_states_39_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> hidden_states_39_strides_0 = const()[name = string("hidden_states_39_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> hidden_states_39_dilations_0 = const()[name = string("hidden_states_39_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 hidden_states_39_groups_0 = const()[name = string("hidden_states_39_groups_0"), val = int32(1)]; |
| tensor<fp32, [1, 256, 512, 512]> hidden_states_39 = conv(bias = vae_decoder_up_blocks_2_resnets_2_conv2_bias, dilations = hidden_states_39_dilations_0, groups = hidden_states_39_groups_0, pad = hidden_states_39_pad_0, pad_type = hidden_states_39_pad_type_0, strides = hidden_states_39_strides_0, weight = vae_decoder_up_blocks_2_resnets_2_conv2_weight, x = input_167)[name = string("hidden_states_39")]; |
| tensor<fp32, [1, 256, 512, 512]> var_503 = add(x = var_473, y = hidden_states_39)[name = string("op_503")]; |
| fp32 input_171_scale_factor_height_0 = const()[name = string("input_171_scale_factor_height_0"), val = fp32(0x1p+1)]; |
| fp32 input_171_scale_factor_width_0 = const()[name = string("input_171_scale_factor_width_0"), val = fp32(0x1p+1)]; |
| tensor<fp32, [1, 256, 1024, 1024]> input_171 = upsample_nearest_neighbor(scale_factor_height = input_171_scale_factor_height_0, scale_factor_width = input_171_scale_factor_width_0, x = var_503)[name = string("input_171")]; |
| string input_173_pad_type_0 = const()[name = string("input_173_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> input_173_pad_0 = const()[name = string("input_173_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_173_strides_0 = const()[name = string("input_173_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_173_dilations_0 = const()[name = string("input_173_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 input_173_groups_0 = const()[name = string("input_173_groups_0"), val = int32(1)]; |
| tensor<fp32, [1, 256, 1024, 1024]> input_173 = conv(bias = vae_decoder_up_blocks_2_upsamplers_0_conv_bias, dilations = input_173_dilations_0, groups = input_173_groups_0, pad = input_173_pad_0, pad_type = input_173_pad_type_0, strides = input_173_strides_0, weight = vae_decoder_up_blocks_2_upsamplers_0_conv_weight, x = input_171)[name = string("input_173")]; |
| tensor<int32, [5]> reshape_92_shape_0 = const()[name = string("reshape_92_shape_0"), val = tensor<int32, [5]>([1, 32, 8, 1024, 1024])]; |
| tensor<fp32, [1, 32, 8, 1024, 1024]> reshape_92 = reshape(shape = reshape_92_shape_0, x = input_173)[name = string("reshape_92")]; |
| tensor<int32, [3]> reduce_mean_69_axes_0 = const()[name = string("reduce_mean_69_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_69_keep_dims_0 = const()[name = string("reduce_mean_69_keep_dims_0"), val = bool(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_69 = reduce_mean(axes = reduce_mean_69_axes_0, keep_dims = reduce_mean_69_keep_dims_0, x = reshape_92)[name = string("reduce_mean_69")]; |
| tensor<fp32, [1, 32, 8, 1024, 1024]> sub_46 = sub(x = reshape_92, y = reduce_mean_69)[name = string("sub_46")]; |
| tensor<fp32, [1, 32, 8, 1024, 1024]> square_23 = square(x = sub_46)[name = string("square_23")]; |
| tensor<int32, [3]> reduce_mean_71_axes_0 = const()[name = string("reduce_mean_71_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_71_keep_dims_0 = const()[name = string("reduce_mean_71_keep_dims_0"), val = bool(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_71 = reduce_mean(axes = reduce_mean_71_axes_0, keep_dims = reduce_mean_71_keep_dims_0, x = square_23)[name = string("reduce_mean_71")]; |
| fp32 add_46_y_0 = const()[name = string("add_46_y_0"), val = fp32(0x1.0c6f7ap-20)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> add_46 = add(x = reduce_mean_71, y = add_46_y_0)[name = string("add_46")]; |
| tensor<fp32, [1, 32, 1, 1, 1]> sqrt_23 = sqrt(x = add_46)[name = string("sqrt_23")]; |
| tensor<fp32, [1, 32, 8, 1024, 1024]> real_div_23 = real_div(x = sub_46, y = sqrt_23)[name = string("real_div_23")]; |
| tensor<int32, [4]> reshape_93_shape_0 = const()[name = string("reshape_93_shape_0"), val = tensor<int32, [4]>([1, 256, 1024, 1024])]; |
| tensor<fp32, [1, 256, 1024, 1024]> reshape_93 = reshape(shape = reshape_93_shape_0, x = real_div_23)[name = string("reshape_93")]; |
| tensor<fp32, [256]> add_47_gamma_0 = const()[name = string("add_47_gamma_0"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197966976)))]; |
| tensor<fp32, [256]> add_47_beta_0 = const()[name = string("add_47_beta_0"), val = tensor<fp32, [256]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197968064)))]; |
| fp32 add_47_epsilon_0 = const()[name = string("add_47_epsilon_0"), val = fp32(0x1.4f8b58p-17)]; |
| tensor<fp32, [1, 256, 1024, 1024]> 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 = string("add_47")]; |
| tensor<fp32, [1, 256, 1024, 1024]> input_177 = silu(x = add_47)[name = string("input_177")]; |
| string input_179_pad_type_0 = const()[name = string("input_179_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> input_179_pad_0 = const()[name = string("input_179_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_179_strides_0 = const()[name = string("input_179_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_179_dilations_0 = const()[name = string("input_179_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 input_179_groups_0 = const()[name = string("input_179_groups_0"), val = int32(1)]; |
| tensor<fp32, [1, 128, 1024, 1024]> input_179 = conv(bias = vae_decoder_up_blocks_3_resnets_0_conv1_bias, dilations = input_179_dilations_0, groups = input_179_groups_0, pad = input_179_pad_0, pad_type = input_179_pad_type_0, strides = input_179_strides_0, weight = vae_decoder_up_blocks_3_resnets_0_conv1_weight, x = input_177)[name = string("input_179")]; |
| tensor<int32, [5]> reshape_96_shape_0 = const()[name = string("reshape_96_shape_0"), val = tensor<int32, [5]>([1, 32, 4, 1024, 1024])]; |
| tensor<fp32, [1, 32, 4, 1024, 1024]> reshape_96 = reshape(shape = reshape_96_shape_0, x = input_179)[name = string("reshape_96")]; |
| tensor<int32, [3]> reduce_mean_72_axes_0 = const()[name = string("reduce_mean_72_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_72_keep_dims_0 = const()[name = string("reduce_mean_72_keep_dims_0"), val = bool(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_72 = reduce_mean(axes = reduce_mean_72_axes_0, keep_dims = reduce_mean_72_keep_dims_0, x = reshape_96)[name = string("reduce_mean_72")]; |
| tensor<fp32, [1, 32, 4, 1024, 1024]> sub_48 = sub(x = reshape_96, y = reduce_mean_72)[name = string("sub_48")]; |
| tensor<fp32, [1, 32, 4, 1024, 1024]> square_24 = square(x = sub_48)[name = string("square_24")]; |
| tensor<int32, [3]> reduce_mean_74_axes_0 = const()[name = string("reduce_mean_74_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_74_keep_dims_0 = const()[name = string("reduce_mean_74_keep_dims_0"), val = bool(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_74 = reduce_mean(axes = reduce_mean_74_axes_0, keep_dims = reduce_mean_74_keep_dims_0, x = square_24)[name = string("reduce_mean_74")]; |
| fp32 add_48_y_0 = const()[name = string("add_48_y_0"), val = fp32(0x1.0c6f7ap-20)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> add_48 = add(x = reduce_mean_74, y = add_48_y_0)[name = string("add_48")]; |
| tensor<fp32, [1, 32, 1, 1, 1]> sqrt_24 = sqrt(x = add_48)[name = string("sqrt_24")]; |
| tensor<fp32, [1, 32, 4, 1024, 1024]> real_div_24 = real_div(x = sub_48, y = sqrt_24)[name = string("real_div_24")]; |
| tensor<int32, [4]> reshape_97_shape_0 = const()[name = string("reshape_97_shape_0"), val = tensor<int32, [4]>([1, 128, 1024, 1024])]; |
| tensor<fp32, [1, 128, 1024, 1024]> reshape_97 = reshape(shape = reshape_97_shape_0, x = real_div_24)[name = string("reshape_97")]; |
| tensor<fp32, [128]> add_49_mean_0 = const()[name = string("add_49_mean_0"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197969152)))]; |
| tensor<fp32, [128]> add_49_variance_0 = const()[name = string("add_49_variance_0"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197969728)))]; |
| tensor<fp32, [128]> add_49_gamma_0 = const()[name = string("add_49_gamma_0"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197970304)))]; |
| tensor<fp32, [128]> add_49_beta_0 = const()[name = string("add_49_beta_0"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197970880)))]; |
| fp32 add_49_epsilon_0 = const()[name = string("add_49_epsilon_0"), val = fp32(0x1.4f8b58p-17)]; |
| tensor<fp32, [1, 128, 1024, 1024]> 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 = string("add_49")]; |
| tensor<fp32, [1, 128, 1024, 1024]> input_183 = silu(x = add_49)[name = string("input_183")]; |
| string hidden_states_43_pad_type_0 = const()[name = string("hidden_states_43_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> hidden_states_43_pad_0 = const()[name = string("hidden_states_43_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> hidden_states_43_strides_0 = const()[name = string("hidden_states_43_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> hidden_states_43_dilations_0 = const()[name = string("hidden_states_43_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 hidden_states_43_groups_0 = const()[name = string("hidden_states_43_groups_0"), val = int32(1)]; |
| tensor<fp32, [1, 128, 1024, 1024]> hidden_states_43 = conv(bias = vae_decoder_up_blocks_3_resnets_0_conv2_bias, dilations = hidden_states_43_dilations_0, groups = hidden_states_43_groups_0, pad = hidden_states_43_pad_0, pad_type = hidden_states_43_pad_type_0, strides = hidden_states_43_strides_0, weight = vae_decoder_up_blocks_3_resnets_0_conv2_weight, x = input_183)[name = string("hidden_states_43")]; |
| string input_tensor_pad_type_0 = const()[name = string("input_tensor_pad_type_0"), val = string("valid")]; |
| tensor<int32, [2]> input_tensor_strides_0 = const()[name = string("input_tensor_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [4]> input_tensor_pad_0 = const()[name = string("input_tensor_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])]; |
| tensor<int32, [2]> input_tensor_dilations_0 = const()[name = string("input_tensor_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 input_tensor_groups_0 = const()[name = string("input_tensor_groups_0"), val = int32(1)]; |
| tensor<fp32, [1, 128, 1024, 1024]> input_tensor = conv(bias = vae_decoder_up_blocks_3_resnets_0_conv_shortcut_bias, dilations = input_tensor_dilations_0, groups = input_tensor_groups_0, pad = input_tensor_pad_0, pad_type = input_tensor_pad_type_0, strides = input_tensor_strides_0, weight = vae_decoder_up_blocks_3_resnets_0_conv_shortcut_weight, x = input_173)[name = string("input_tensor")]; |
| tensor<fp32, [1, 128, 1024, 1024]> var_557 = add(x = input_tensor, y = hidden_states_43)[name = string("op_557")]; |
| tensor<int32, [5]> reshape_100_shape_0 = const()[name = string("reshape_100_shape_0"), val = tensor<int32, [5]>([1, 32, 4, 1024, 1024])]; |
| tensor<fp32, [1, 32, 4, 1024, 1024]> reshape_100 = reshape(shape = reshape_100_shape_0, x = var_557)[name = string("reshape_100")]; |
| tensor<int32, [3]> reduce_mean_75_axes_0 = const()[name = string("reduce_mean_75_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_75_keep_dims_0 = const()[name = string("reduce_mean_75_keep_dims_0"), val = bool(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_75 = reduce_mean(axes = reduce_mean_75_axes_0, keep_dims = reduce_mean_75_keep_dims_0, x = reshape_100)[name = string("reduce_mean_75")]; |
| tensor<fp32, [1, 32, 4, 1024, 1024]> sub_50 = sub(x = reshape_100, y = reduce_mean_75)[name = string("sub_50")]; |
| tensor<fp32, [1, 32, 4, 1024, 1024]> square_25 = square(x = sub_50)[name = string("square_25")]; |
| tensor<int32, [3]> reduce_mean_77_axes_0 = const()[name = string("reduce_mean_77_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_77_keep_dims_0 = const()[name = string("reduce_mean_77_keep_dims_0"), val = bool(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_77 = reduce_mean(axes = reduce_mean_77_axes_0, keep_dims = reduce_mean_77_keep_dims_0, x = square_25)[name = string("reduce_mean_77")]; |
| fp32 add_50_y_0 = const()[name = string("add_50_y_0"), val = fp32(0x1.0c6f7ap-20)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> add_50 = add(x = reduce_mean_77, y = add_50_y_0)[name = string("add_50")]; |
| tensor<fp32, [1, 32, 1, 1, 1]> sqrt_25 = sqrt(x = add_50)[name = string("sqrt_25")]; |
| tensor<fp32, [1, 32, 4, 1024, 1024]> real_div_25 = real_div(x = sub_50, y = sqrt_25)[name = string("real_div_25")]; |
| tensor<int32, [4]> reshape_101_shape_0 = const()[name = string("reshape_101_shape_0"), val = tensor<int32, [4]>([1, 128, 1024, 1024])]; |
| tensor<fp32, [1, 128, 1024, 1024]> reshape_101 = reshape(shape = reshape_101_shape_0, x = real_div_25)[name = string("reshape_101")]; |
| tensor<fp32, [128]> add_51_gamma_0 = const()[name = string("add_51_gamma_0"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197971456)))]; |
| tensor<fp32, [128]> add_51_beta_0 = const()[name = string("add_51_beta_0"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197972032)))]; |
| fp32 add_51_epsilon_0 = const()[name = string("add_51_epsilon_0"), val = fp32(0x1.4f8b58p-17)]; |
| tensor<fp32, [1, 128, 1024, 1024]> 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 = string("add_51")]; |
| tensor<fp32, [1, 128, 1024, 1024]> input_191 = silu(x = add_51)[name = string("input_191")]; |
| string input_193_pad_type_0 = const()[name = string("input_193_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> input_193_pad_0 = const()[name = string("input_193_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_193_strides_0 = const()[name = string("input_193_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_193_dilations_0 = const()[name = string("input_193_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 input_193_groups_0 = const()[name = string("input_193_groups_0"), val = int32(1)]; |
| tensor<fp32, [1, 128, 1024, 1024]> input_193 = conv(bias = vae_decoder_up_blocks_3_resnets_1_conv1_bias, dilations = input_193_dilations_0, groups = input_193_groups_0, pad = input_193_pad_0, pad_type = input_193_pad_type_0, strides = input_193_strides_0, weight = vae_decoder_up_blocks_3_resnets_1_conv1_weight, x = input_191)[name = string("input_193")]; |
| tensor<int32, [5]> reshape_104_shape_0 = const()[name = string("reshape_104_shape_0"), val = tensor<int32, [5]>([1, 32, 4, 1024, 1024])]; |
| tensor<fp32, [1, 32, 4, 1024, 1024]> reshape_104 = reshape(shape = reshape_104_shape_0, x = input_193)[name = string("reshape_104")]; |
| tensor<int32, [3]> reduce_mean_78_axes_0 = const()[name = string("reduce_mean_78_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_78_keep_dims_0 = const()[name = string("reduce_mean_78_keep_dims_0"), val = bool(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_78 = reduce_mean(axes = reduce_mean_78_axes_0, keep_dims = reduce_mean_78_keep_dims_0, x = reshape_104)[name = string("reduce_mean_78")]; |
| tensor<fp32, [1, 32, 4, 1024, 1024]> sub_52 = sub(x = reshape_104, y = reduce_mean_78)[name = string("sub_52")]; |
| tensor<fp32, [1, 32, 4, 1024, 1024]> square_26 = square(x = sub_52)[name = string("square_26")]; |
| tensor<int32, [3]> reduce_mean_80_axes_0 = const()[name = string("reduce_mean_80_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_80_keep_dims_0 = const()[name = string("reduce_mean_80_keep_dims_0"), val = bool(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_80 = reduce_mean(axes = reduce_mean_80_axes_0, keep_dims = reduce_mean_80_keep_dims_0, x = square_26)[name = string("reduce_mean_80")]; |
| fp32 add_52_y_0 = const()[name = string("add_52_y_0"), val = fp32(0x1.0c6f7ap-20)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> add_52 = add(x = reduce_mean_80, y = add_52_y_0)[name = string("add_52")]; |
| tensor<fp32, [1, 32, 1, 1, 1]> sqrt_26 = sqrt(x = add_52)[name = string("sqrt_26")]; |
| tensor<fp32, [1, 32, 4, 1024, 1024]> real_div_26 = real_div(x = sub_52, y = sqrt_26)[name = string("real_div_26")]; |
| tensor<int32, [4]> reshape_105_shape_0 = const()[name = string("reshape_105_shape_0"), val = tensor<int32, [4]>([1, 128, 1024, 1024])]; |
| tensor<fp32, [1, 128, 1024, 1024]> reshape_105 = reshape(shape = reshape_105_shape_0, x = real_div_26)[name = string("reshape_105")]; |
| tensor<fp32, [128]> add_53_gamma_0 = const()[name = string("add_53_gamma_0"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197972608)))]; |
| tensor<fp32, [128]> add_53_beta_0 = const()[name = string("add_53_beta_0"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197973184)))]; |
| fp32 add_53_epsilon_0 = const()[name = string("add_53_epsilon_0"), val = fp32(0x1.4f8b58p-17)]; |
| tensor<fp32, [1, 128, 1024, 1024]> 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 = string("add_53")]; |
| tensor<fp32, [1, 128, 1024, 1024]> input_197 = silu(x = add_53)[name = string("input_197")]; |
| string hidden_states_45_pad_type_0 = const()[name = string("hidden_states_45_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> hidden_states_45_pad_0 = const()[name = string("hidden_states_45_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> hidden_states_45_strides_0 = const()[name = string("hidden_states_45_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> hidden_states_45_dilations_0 = const()[name = string("hidden_states_45_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 hidden_states_45_groups_0 = const()[name = string("hidden_states_45_groups_0"), val = int32(1)]; |
| tensor<fp32, [1, 128, 1024, 1024]> hidden_states_45 = conv(bias = vae_decoder_up_blocks_3_resnets_1_conv2_bias, dilations = hidden_states_45_dilations_0, groups = hidden_states_45_groups_0, pad = hidden_states_45_pad_0, pad_type = hidden_states_45_pad_type_0, strides = hidden_states_45_strides_0, weight = vae_decoder_up_blocks_3_resnets_1_conv2_weight, x = input_197)[name = string("hidden_states_45")]; |
| tensor<fp32, [1, 128, 1024, 1024]> var_587 = add(x = var_557, y = hidden_states_45)[name = string("op_587")]; |
| tensor<int32, [5]> reshape_108_shape_0 = const()[name = string("reshape_108_shape_0"), val = tensor<int32, [5]>([1, 32, 4, 1024, 1024])]; |
| tensor<fp32, [1, 32, 4, 1024, 1024]> reshape_108 = reshape(shape = reshape_108_shape_0, x = var_587)[name = string("reshape_108")]; |
| tensor<int32, [3]> reduce_mean_81_axes_0 = const()[name = string("reduce_mean_81_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_81_keep_dims_0 = const()[name = string("reduce_mean_81_keep_dims_0"), val = bool(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_81 = reduce_mean(axes = reduce_mean_81_axes_0, keep_dims = reduce_mean_81_keep_dims_0, x = reshape_108)[name = string("reduce_mean_81")]; |
| tensor<fp32, [1, 32, 4, 1024, 1024]> sub_54 = sub(x = reshape_108, y = reduce_mean_81)[name = string("sub_54")]; |
| tensor<fp32, [1, 32, 4, 1024, 1024]> square_27 = square(x = sub_54)[name = string("square_27")]; |
| tensor<int32, [3]> reduce_mean_83_axes_0 = const()[name = string("reduce_mean_83_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_83_keep_dims_0 = const()[name = string("reduce_mean_83_keep_dims_0"), val = bool(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_83 = reduce_mean(axes = reduce_mean_83_axes_0, keep_dims = reduce_mean_83_keep_dims_0, x = square_27)[name = string("reduce_mean_83")]; |
| fp32 add_54_y_0 = const()[name = string("add_54_y_0"), val = fp32(0x1.0c6f7ap-20)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> add_54 = add(x = reduce_mean_83, y = add_54_y_0)[name = string("add_54")]; |
| tensor<fp32, [1, 32, 1, 1, 1]> sqrt_27 = sqrt(x = add_54)[name = string("sqrt_27")]; |
| tensor<fp32, [1, 32, 4, 1024, 1024]> real_div_27 = real_div(x = sub_54, y = sqrt_27)[name = string("real_div_27")]; |
| tensor<int32, [4]> reshape_109_shape_0 = const()[name = string("reshape_109_shape_0"), val = tensor<int32, [4]>([1, 128, 1024, 1024])]; |
| tensor<fp32, [1, 128, 1024, 1024]> reshape_109 = reshape(shape = reshape_109_shape_0, x = real_div_27)[name = string("reshape_109")]; |
| tensor<fp32, [128]> add_55_gamma_0 = const()[name = string("add_55_gamma_0"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197973760)))]; |
| tensor<fp32, [128]> add_55_beta_0 = const()[name = string("add_55_beta_0"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197974336)))]; |
| fp32 add_55_epsilon_0 = const()[name = string("add_55_epsilon_0"), val = fp32(0x1.4f8b58p-17)]; |
| tensor<fp32, [1, 128, 1024, 1024]> 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 = string("add_55")]; |
| tensor<fp32, [1, 128, 1024, 1024]> input_205 = silu(x = add_55)[name = string("input_205")]; |
| string input_207_pad_type_0 = const()[name = string("input_207_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> input_207_pad_0 = const()[name = string("input_207_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> input_207_strides_0 = const()[name = string("input_207_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> input_207_dilations_0 = const()[name = string("input_207_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 input_207_groups_0 = const()[name = string("input_207_groups_0"), val = int32(1)]; |
| tensor<fp32, [1, 128, 1024, 1024]> input_207 = conv(bias = vae_decoder_up_blocks_3_resnets_2_conv1_bias, dilations = input_207_dilations_0, groups = input_207_groups_0, pad = input_207_pad_0, pad_type = input_207_pad_type_0, strides = input_207_strides_0, weight = vae_decoder_up_blocks_3_resnets_2_conv1_weight, x = input_205)[name = string("input_207")]; |
| tensor<int32, [5]> reshape_112_shape_0 = const()[name = string("reshape_112_shape_0"), val = tensor<int32, [5]>([1, 32, 4, 1024, 1024])]; |
| tensor<fp32, [1, 32, 4, 1024, 1024]> reshape_112 = reshape(shape = reshape_112_shape_0, x = input_207)[name = string("reshape_112")]; |
| tensor<int32, [3]> reduce_mean_84_axes_0 = const()[name = string("reduce_mean_84_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_84_keep_dims_0 = const()[name = string("reduce_mean_84_keep_dims_0"), val = bool(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_84 = reduce_mean(axes = reduce_mean_84_axes_0, keep_dims = reduce_mean_84_keep_dims_0, x = reshape_112)[name = string("reduce_mean_84")]; |
| tensor<fp32, [1, 32, 4, 1024, 1024]> sub_56 = sub(x = reshape_112, y = reduce_mean_84)[name = string("sub_56")]; |
| tensor<fp32, [1, 32, 4, 1024, 1024]> square_28 = square(x = sub_56)[name = string("square_28")]; |
| tensor<int32, [3]> reduce_mean_86_axes_0 = const()[name = string("reduce_mean_86_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_86_keep_dims_0 = const()[name = string("reduce_mean_86_keep_dims_0"), val = bool(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_86 = reduce_mean(axes = reduce_mean_86_axes_0, keep_dims = reduce_mean_86_keep_dims_0, x = square_28)[name = string("reduce_mean_86")]; |
| fp32 add_56_y_0 = const()[name = string("add_56_y_0"), val = fp32(0x1.0c6f7ap-20)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> add_56 = add(x = reduce_mean_86, y = add_56_y_0)[name = string("add_56")]; |
| tensor<fp32, [1, 32, 1, 1, 1]> sqrt_28 = sqrt(x = add_56)[name = string("sqrt_28")]; |
| tensor<fp32, [1, 32, 4, 1024, 1024]> real_div_28 = real_div(x = sub_56, y = sqrt_28)[name = string("real_div_28")]; |
| tensor<int32, [4]> reshape_113_shape_0 = const()[name = string("reshape_113_shape_0"), val = tensor<int32, [4]>([1, 128, 1024, 1024])]; |
| tensor<fp32, [1, 128, 1024, 1024]> reshape_113 = reshape(shape = reshape_113_shape_0, x = real_div_28)[name = string("reshape_113")]; |
| tensor<fp32, [128]> add_57_gamma_0 = const()[name = string("add_57_gamma_0"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197974912)))]; |
| tensor<fp32, [128]> add_57_beta_0 = const()[name = string("add_57_beta_0"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197975488)))]; |
| fp32 add_57_epsilon_0 = const()[name = string("add_57_epsilon_0"), val = fp32(0x1.4f8b58p-17)]; |
| tensor<fp32, [1, 128, 1024, 1024]> 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 = string("add_57")]; |
| tensor<fp32, [1, 128, 1024, 1024]> input_211 = silu(x = add_57)[name = string("input_211")]; |
| string hidden_states_pad_type_0 = const()[name = string("hidden_states_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> hidden_states_pad_0 = const()[name = string("hidden_states_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> hidden_states_strides_0 = const()[name = string("hidden_states_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> hidden_states_dilations_0 = const()[name = string("hidden_states_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 hidden_states_groups_0 = const()[name = string("hidden_states_groups_0"), val = int32(1)]; |
| tensor<fp32, [1, 128, 1024, 1024]> hidden_states = conv(bias = vae_decoder_up_blocks_3_resnets_2_conv2_bias, dilations = hidden_states_dilations_0, groups = hidden_states_groups_0, pad = hidden_states_pad_0, pad_type = hidden_states_pad_type_0, strides = hidden_states_strides_0, weight = vae_decoder_up_blocks_3_resnets_2_conv2_weight, x = input_211)[name = string("hidden_states")]; |
| tensor<fp32, [1, 128, 1024, 1024]> var_617 = add(x = var_587, y = hidden_states)[name = string("op_617")]; |
| tensor<int32, [5]> reshape_116_shape_0 = const()[name = string("reshape_116_shape_0"), val = tensor<int32, [5]>([1, 32, 4, 1024, 1024])]; |
| tensor<fp32, [1, 32, 4, 1024, 1024]> reshape_116 = reshape(shape = reshape_116_shape_0, x = var_617)[name = string("reshape_116")]; |
| tensor<int32, [3]> reduce_mean_87_axes_0 = const()[name = string("reduce_mean_87_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_87_keep_dims_0 = const()[name = string("reduce_mean_87_keep_dims_0"), val = bool(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_87 = reduce_mean(axes = reduce_mean_87_axes_0, keep_dims = reduce_mean_87_keep_dims_0, x = reshape_116)[name = string("reduce_mean_87")]; |
| tensor<fp32, [1, 32, 4, 1024, 1024]> sub_58 = sub(x = reshape_116, y = reduce_mean_87)[name = string("sub_58")]; |
| tensor<fp32, [1, 32, 4, 1024, 1024]> square_29 = square(x = sub_58)[name = string("square_29")]; |
| tensor<int32, [3]> reduce_mean_89_axes_0 = const()[name = string("reduce_mean_89_axes_0"), val = tensor<int32, [3]>([2, 3, 4])]; |
| bool reduce_mean_89_keep_dims_0 = const()[name = string("reduce_mean_89_keep_dims_0"), val = bool(true)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> reduce_mean_89 = reduce_mean(axes = reduce_mean_89_axes_0, keep_dims = reduce_mean_89_keep_dims_0, x = square_29)[name = string("reduce_mean_89")]; |
| fp32 add_58_y_0 = const()[name = string("add_58_y_0"), val = fp32(0x1.0c6f7ap-20)]; |
| tensor<fp32, [1, 32, 1, 1, 1]> add_58 = add(x = reduce_mean_89, y = add_58_y_0)[name = string("add_58")]; |
| tensor<fp32, [1, 32, 1, 1, 1]> sqrt_29 = sqrt(x = add_58)[name = string("sqrt_29")]; |
| tensor<fp32, [1, 32, 4, 1024, 1024]> real_div_29 = real_div(x = sub_58, y = sqrt_29)[name = string("real_div_29")]; |
| tensor<int32, [4]> reshape_117_shape_0 = const()[name = string("reshape_117_shape_0"), val = tensor<int32, [4]>([1, 128, 1024, 1024])]; |
| tensor<fp32, [1, 128, 1024, 1024]> reshape_117 = reshape(shape = reshape_117_shape_0, x = real_div_29)[name = string("reshape_117")]; |
| tensor<fp32, [128]> add_59_gamma_0 = const()[name = string("add_59_gamma_0"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197976064)))]; |
| tensor<fp32, [128]> add_59_beta_0 = const()[name = string("add_59_beta_0"), val = tensor<fp32, [128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(197976640)))]; |
| fp32 add_59_epsilon_0 = const()[name = string("add_59_epsilon_0"), val = fp32(0x1.4f8b58p-17)]; |
| tensor<fp32, [1, 128, 1024, 1024]> 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 = string("add_59")]; |
| tensor<fp32, [1, 128, 1024, 1024]> input = silu(x = add_59)[name = string("input")]; |
| string var_630_pad_type_0 = const()[name = string("op_630_pad_type_0"), val = string("custom")]; |
| tensor<int32, [4]> var_630_pad_0 = const()[name = string("op_630_pad_0"), val = tensor<int32, [4]>([1, 1, 1, 1])]; |
| tensor<int32, [2]> var_630_strides_0 = const()[name = string("op_630_strides_0"), val = tensor<int32, [2]>([1, 1])]; |
| tensor<int32, [2]> var_630_dilations_0 = const()[name = string("op_630_dilations_0"), val = tensor<int32, [2]>([1, 1])]; |
| int32 var_630_groups_0 = const()[name = string("op_630_groups_0"), val = int32(1)]; |
| tensor<fp32, [1, 3, 1024, 1024]> var_630 = conv(bias = vae_decoder_conv_out_bias, dilations = var_630_dilations_0, groups = var_630_groups_0, pad = var_630_pad_0, pad_type = var_630_pad_type_0, strides = var_630_strides_0, weight = vae_decoder_conv_out_weight, x = input)[name = string("op_630")]; |
| } -> (var_630); |
| } |
