diff --git "a/original/compiled/VAEEncoder.mlmodelc/model.mil" "b/original/compiled/VAEEncoder.mlmodelc/model.mil" new file mode 100644--- /dev/null +++ "b/original/compiled/VAEEncoder.mlmodelc/model.mil" @@ -0,0 +1,748 @@ +program(1.0) +[buildInfo = dict, tensor>({{"coremlc-component-MIL", "5.33.4"}, {"coremlc-version", "1436.100.10"}, {"coremltools-component-torch", "2.1.0.dev20230718"}, {"coremltools-version", "7.0b1"}})] +{ + func main(tensor x) { + tensor vae_encoder_conv_out_bias = const()[name = tensor("vae_encoder_conv_out_bias"), val = tensor([0x1.c86f7ep-7, -0x1.04c2c4p-4, 0x1.943bdap-3, 0x1.d9b392p-3, 0x1.e77b8cp-3, 0x1.78c68p-5, 0x1.bb6b1ep-5, -0x1.825106p-3])]; + tensor vae_encoder_conv_out_weight = const()[name = tensor("vae_encoder_conv_out_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; + tensor vae_encoder_mid_block_resnets_1_conv2_bias = const()[name = tensor("vae_encoder_mid_block_resnets_1_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(147584)))]; + tensor vae_encoder_mid_block_resnets_1_conv2_weight = const()[name = tensor("vae_encoder_mid_block_resnets_1_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(149696)))]; + tensor vae_encoder_mid_block_resnets_1_conv1_bias = const()[name = tensor("vae_encoder_mid_block_resnets_1_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9586944)))]; + tensor vae_encoder_mid_block_resnets_1_conv1_weight = const()[name = tensor("vae_encoder_mid_block_resnets_1_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9589056)))]; + tensor vae_encoder_mid_block_resnets_0_conv2_bias = const()[name = tensor("vae_encoder_mid_block_resnets_0_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19026304)))]; + tensor vae_encoder_mid_block_resnets_0_conv2_weight = const()[name = tensor("vae_encoder_mid_block_resnets_0_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19028416)))]; + tensor vae_encoder_mid_block_resnets_0_conv1_bias = const()[name = tensor("vae_encoder_mid_block_resnets_0_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28465664)))]; + tensor vae_encoder_mid_block_resnets_0_conv1_weight = const()[name = tensor("vae_encoder_mid_block_resnets_0_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28467776)))]; + tensor vae_encoder_mid_block_attentions_0_to_out_0_bias = const()[name = tensor("vae_encoder_mid_block_attentions_0_to_out_0_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(37905024)))]; + tensor vae_encoder_mid_block_attentions_0_to_out_0_weight = const()[name = tensor("vae_encoder_mid_block_attentions_0_to_out_0_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(37907136)))]; + tensor vae_encoder_mid_block_attentions_0_to_v_bias = const()[name = tensor("vae_encoder_mid_block_attentions_0_to_v_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38955776)))]; + tensor vae_encoder_mid_block_attentions_0_to_v_weight = const()[name = tensor("vae_encoder_mid_block_attentions_0_to_v_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38957888)))]; + tensor vae_encoder_mid_block_attentions_0_to_k_bias = const()[name = tensor("vae_encoder_mid_block_attentions_0_to_k_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40006528)))]; + tensor vae_encoder_mid_block_attentions_0_to_k_weight = const()[name = tensor("vae_encoder_mid_block_attentions_0_to_k_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40008640)))]; + tensor vae_encoder_mid_block_attentions_0_to_q_bias = const()[name = tensor("vae_encoder_mid_block_attentions_0_to_q_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41057280)))]; + tensor vae_encoder_mid_block_attentions_0_to_q_weight = const()[name = tensor("vae_encoder_mid_block_attentions_0_to_q_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41059392)))]; + tensor vae_encoder_down_blocks_3_resnets_1_conv2_bias = const()[name = tensor("vae_encoder_down_blocks_3_resnets_1_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42108032)))]; + tensor vae_encoder_down_blocks_3_resnets_1_conv2_weight = const()[name = tensor("vae_encoder_down_blocks_3_resnets_1_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42110144)))]; + tensor vae_encoder_down_blocks_3_resnets_1_conv1_bias = const()[name = tensor("vae_encoder_down_blocks_3_resnets_1_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51547392)))]; + tensor vae_encoder_down_blocks_3_resnets_1_conv1_weight = const()[name = tensor("vae_encoder_down_blocks_3_resnets_1_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(51549504)))]; + tensor vae_encoder_down_blocks_3_resnets_0_conv2_bias = const()[name = tensor("vae_encoder_down_blocks_3_resnets_0_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(60986752)))]; + tensor vae_encoder_down_blocks_3_resnets_0_conv2_weight = const()[name = tensor("vae_encoder_down_blocks_3_resnets_0_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(60988864)))]; + tensor vae_encoder_down_blocks_3_resnets_0_conv1_bias = const()[name = tensor("vae_encoder_down_blocks_3_resnets_0_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(70426112)))]; + tensor vae_encoder_down_blocks_3_resnets_0_conv1_weight = const()[name = tensor("vae_encoder_down_blocks_3_resnets_0_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(70428224)))]; + tensor vae_encoder_down_blocks_2_downsamplers_0_conv_bias = const()[name = tensor("vae_encoder_down_blocks_2_downsamplers_0_conv_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79865472)))]; + tensor vae_encoder_down_blocks_2_downsamplers_0_conv_weight = const()[name = tensor("vae_encoder_down_blocks_2_downsamplers_0_conv_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(79867584)))]; + tensor vae_encoder_down_blocks_2_resnets_1_conv2_bias = const()[name = tensor("vae_encoder_down_blocks_2_resnets_1_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(89304832)))]; + tensor vae_encoder_down_blocks_2_resnets_1_conv2_weight = const()[name = tensor("vae_encoder_down_blocks_2_resnets_1_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(89306944)))]; + tensor vae_encoder_down_blocks_2_resnets_1_conv1_bias = const()[name = tensor("vae_encoder_down_blocks_2_resnets_1_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98744192)))]; + tensor vae_encoder_down_blocks_2_resnets_1_conv1_weight = const()[name = tensor("vae_encoder_down_blocks_2_resnets_1_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98746304)))]; + tensor vae_encoder_down_blocks_2_resnets_0_conv_shortcut_bias = const()[name = tensor("vae_encoder_down_blocks_2_resnets_0_conv_shortcut_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(108183552)))]; + tensor vae_encoder_down_blocks_2_resnets_0_conv_shortcut_weight = const()[name = tensor("vae_encoder_down_blocks_2_resnets_0_conv_shortcut_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(108185664)))]; + tensor vae_encoder_down_blocks_2_resnets_0_conv2_bias = const()[name = tensor("vae_encoder_down_blocks_2_resnets_0_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(108710016)))]; + tensor vae_encoder_down_blocks_2_resnets_0_conv2_weight = const()[name = tensor("vae_encoder_down_blocks_2_resnets_0_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(108712128)))]; + tensor vae_encoder_down_blocks_2_resnets_0_conv1_bias = const()[name = tensor("vae_encoder_down_blocks_2_resnets_0_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(118149376)))]; + tensor vae_encoder_down_blocks_2_resnets_0_conv1_weight = const()[name = tensor("vae_encoder_down_blocks_2_resnets_0_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(118151488)))]; + tensor vae_encoder_down_blocks_1_downsamplers_0_conv_bias = const()[name = tensor("vae_encoder_down_blocks_1_downsamplers_0_conv_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(122870144)))]; + tensor vae_encoder_down_blocks_1_downsamplers_0_conv_weight = const()[name = tensor("vae_encoder_down_blocks_1_downsamplers_0_conv_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(122871232)))]; + tensor vae_encoder_down_blocks_1_resnets_1_conv2_bias = const()[name = tensor("vae_encoder_down_blocks_1_resnets_1_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(125230592)))]; + tensor vae_encoder_down_blocks_1_resnets_1_conv2_weight = const()[name = tensor("vae_encoder_down_blocks_1_resnets_1_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(125231680)))]; + tensor vae_encoder_down_blocks_1_resnets_1_conv1_bias = const()[name = tensor("vae_encoder_down_blocks_1_resnets_1_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(127591040)))]; + tensor vae_encoder_down_blocks_1_resnets_1_conv1_weight = const()[name = tensor("vae_encoder_down_blocks_1_resnets_1_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(127592128)))]; + tensor vae_encoder_down_blocks_1_resnets_0_conv_shortcut_bias = const()[name = tensor("vae_encoder_down_blocks_1_resnets_0_conv_shortcut_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(129951488)))]; + tensor vae_encoder_down_blocks_1_resnets_0_conv_shortcut_weight = const()[name = tensor("vae_encoder_down_blocks_1_resnets_0_conv_shortcut_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(129952576)))]; + tensor vae_encoder_down_blocks_1_resnets_0_conv2_bias = const()[name = tensor("vae_encoder_down_blocks_1_resnets_0_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(130083712)))]; + tensor vae_encoder_down_blocks_1_resnets_0_conv2_weight = const()[name = tensor("vae_encoder_down_blocks_1_resnets_0_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(130084800)))]; + tensor vae_encoder_down_blocks_1_resnets_0_conv1_bias = const()[name = tensor("vae_encoder_down_blocks_1_resnets_0_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(132444160)))]; + tensor vae_encoder_down_blocks_1_resnets_0_conv1_weight = const()[name = tensor("vae_encoder_down_blocks_1_resnets_0_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(132445248)))]; + tensor vae_encoder_down_blocks_0_downsamplers_0_conv_bias = const()[name = tensor("vae_encoder_down_blocks_0_downsamplers_0_conv_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(133624960)))]; + tensor vae_encoder_down_blocks_0_downsamplers_0_conv_weight = const()[name = tensor("vae_encoder_down_blocks_0_downsamplers_0_conv_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(133625536)))]; + tensor vae_encoder_down_blocks_0_resnets_1_conv2_bias = const()[name = tensor("vae_encoder_down_blocks_0_resnets_1_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(134215424)))]; + tensor vae_encoder_down_blocks_0_resnets_1_conv2_weight = const()[name = tensor("vae_encoder_down_blocks_0_resnets_1_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(134216000)))]; + tensor vae_encoder_down_blocks_0_resnets_1_conv1_bias = const()[name = tensor("vae_encoder_down_blocks_0_resnets_1_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(134805888)))]; + tensor vae_encoder_down_blocks_0_resnets_1_conv1_weight = const()[name = tensor("vae_encoder_down_blocks_0_resnets_1_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(134806464)))]; + tensor vae_encoder_down_blocks_0_resnets_0_conv2_bias = const()[name = tensor("vae_encoder_down_blocks_0_resnets_0_conv2_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(135396352)))]; + tensor vae_encoder_down_blocks_0_resnets_0_conv2_weight = const()[name = tensor("vae_encoder_down_blocks_0_resnets_0_conv2_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(135396928)))]; + tensor vae_encoder_down_blocks_0_resnets_0_conv1_bias = const()[name = tensor("vae_encoder_down_blocks_0_resnets_0_conv1_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(135986816)))]; + tensor vae_encoder_down_blocks_0_resnets_0_conv1_weight = const()[name = tensor("vae_encoder_down_blocks_0_resnets_0_conv1_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(135987392)))]; + tensor vae_encoder_conv_in_bias = const()[name = tensor("vae_encoder_conv_in_bias"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136577280)))]; + tensor vae_encoder_conv_in_weight = const()[name = tensor("vae_encoder_conv_in_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136577856)))]; + tensor quant_conv_bias = const()[name = tensor("quant_conv_bias"), val = tensor([0x1.f47634p-4, 0x1.089c3cp-4, -0x1.e48162p-3, -0x1.bf9626p-2, -0x1.56d7acp+4, -0x1.5983ecp+4, -0x1.621e88p+4, -0x1.66511ep+4])]; + tensor quant_conv_weight = const()[name = tensor("quant_conv_weight"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136591744)))]; + tensor var_807 = const()[name = tensor("op_807"), val = tensor(0x1.6a09e6p-5)]; + tensor var_809 = const()[name = tensor("op_809"), val = tensor(-1)]; + tensor var_819 = const()[name = tensor("op_819"), val = tensor(1)]; + tensor var_823 = const()[name = tensor("op_823"), val = tensor([1, 1])]; + tensor var_825 = const()[name = tensor("op_825"), val = tensor([1, 1])]; + tensor input_1_pad_type_0 = const()[name = tensor("input_1_pad_type_0"), val = tensor("custom")]; + tensor input_1_pad_0 = const()[name = tensor("input_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_1 = conv(bias = vae_encoder_conv_in_bias, dilations = var_825, groups = var_819, pad = input_1_pad_0, pad_type = input_1_pad_type_0, strides = var_823, weight = vae_encoder_conv_in_weight, x = x)[name = tensor("input_1")]; + tensor reshape_0_shape_0 = const()[name = tensor("reshape_0_shape_0"), val = tensor([1, 32, 4, 1024, 1024])]; + tensor reshape_0 = reshape(shape = reshape_0_shape_0, x = input_1)[name = tensor("reshape_0")]; + tensor reduce_mean_0_axes_0 = const()[name = tensor("reduce_mean_0_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_0_keep_dims_0 = const()[name = tensor("reduce_mean_0_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_0 = reduce_mean(axes = reduce_mean_0_axes_0, keep_dims = reduce_mean_0_keep_dims_0, x = reshape_0)[name = tensor("reduce_mean_0")]; + tensor sub_0 = sub(x = reshape_0, y = reduce_mean_0)[name = tensor("sub_0")]; + tensor square_0 = square(x = sub_0)[name = tensor("square_0")]; + tensor reduce_mean_2_axes_0 = const()[name = tensor("reduce_mean_2_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_2_keep_dims_0 = const()[name = tensor("reduce_mean_2_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_2 = reduce_mean(axes = reduce_mean_2_axes_0, keep_dims = reduce_mean_2_keep_dims_0, x = square_0)[name = tensor("reduce_mean_2")]; + tensor add_0_y_0 = const()[name = tensor("add_0_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_0 = add(x = reduce_mean_2, y = add_0_y_0)[name = tensor("add_0")]; + tensor sqrt_0 = sqrt(x = add_0)[name = tensor("sqrt_0")]; + tensor real_div_0 = real_div(x = sub_0, y = sqrt_0)[name = tensor("real_div_0")]; + tensor reshape_1_shape_0 = const()[name = tensor("reshape_1_shape_0"), val = tensor([1, 128, 1024, 1024])]; + tensor reshape_1 = reshape(shape = reshape_1_shape_0, x = real_div_0)[name = tensor("reshape_1")]; + tensor add_1_mean_0 = const()[name = tensor("add_1_mean_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136592064)))]; + tensor add_1_variance_0 = const()[name = tensor("add_1_variance_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136592640)))]; + tensor add_1_gamma_0 = const()[name = tensor("add_1_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136593216)))]; + tensor add_1_beta_0 = const()[name = tensor("add_1_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136593792)))]; + tensor add_1_epsilon_0 = const()[name = tensor("add_1_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_1 = batch_norm(beta = add_1_beta_0, epsilon = add_1_epsilon_0, gamma = add_1_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_1)[name = tensor("add_1")]; + tensor input_5 = silu(x = add_1)[name = tensor("input_5")]; + tensor var_830 = const()[name = tensor("op_830"), val = tensor([1, 1])]; + tensor var_832 = const()[name = tensor("op_832"), val = tensor([1, 1])]; + tensor input_7_pad_type_0 = const()[name = tensor("input_7_pad_type_0"), val = tensor("custom")]; + tensor input_7_pad_0 = const()[name = tensor("input_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_7 = conv(bias = vae_encoder_down_blocks_0_resnets_0_conv1_bias, dilations = var_832, groups = var_819, pad = input_7_pad_0, pad_type = input_7_pad_type_0, strides = var_830, weight = vae_encoder_down_blocks_0_resnets_0_conv1_weight, x = input_5)[name = tensor("input_7")]; + tensor reshape_4_shape_0 = const()[name = tensor("reshape_4_shape_0"), val = tensor([1, 32, 4, 1024, 1024])]; + tensor reshape_4 = reshape(shape = reshape_4_shape_0, x = input_7)[name = tensor("reshape_4")]; + tensor reduce_mean_3_axes_0 = const()[name = tensor("reduce_mean_3_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_3_keep_dims_0 = const()[name = tensor("reduce_mean_3_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_3 = reduce_mean(axes = reduce_mean_3_axes_0, keep_dims = reduce_mean_3_keep_dims_0, x = reshape_4)[name = tensor("reduce_mean_3")]; + tensor sub_2 = sub(x = reshape_4, y = reduce_mean_3)[name = tensor("sub_2")]; + tensor square_1 = square(x = sub_2)[name = tensor("square_1")]; + tensor reduce_mean_5_axes_0 = const()[name = tensor("reduce_mean_5_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_5_keep_dims_0 = const()[name = tensor("reduce_mean_5_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_5 = reduce_mean(axes = reduce_mean_5_axes_0, keep_dims = reduce_mean_5_keep_dims_0, x = square_1)[name = tensor("reduce_mean_5")]; + tensor add_2_y_0 = const()[name = tensor("add_2_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_2 = add(x = reduce_mean_5, y = add_2_y_0)[name = tensor("add_2")]; + tensor sqrt_1 = sqrt(x = add_2)[name = tensor("sqrt_1")]; + tensor real_div_1 = real_div(x = sub_2, y = sqrt_1)[name = tensor("real_div_1")]; + tensor reshape_5_shape_0 = const()[name = tensor("reshape_5_shape_0"), val = tensor([1, 128, 1024, 1024])]; + tensor reshape_5 = reshape(shape = reshape_5_shape_0, x = real_div_1)[name = tensor("reshape_5")]; + tensor add_3_gamma_0 = const()[name = tensor("add_3_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136594368)))]; + tensor add_3_beta_0 = const()[name = tensor("add_3_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136594944)))]; + tensor add_3_epsilon_0 = const()[name = tensor("add_3_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_3 = batch_norm(beta = add_3_beta_0, epsilon = add_3_epsilon_0, gamma = add_3_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_5)[name = tensor("add_3")]; + tensor input_11 = silu(x = add_3)[name = tensor("input_11")]; + tensor var_838 = const()[name = tensor("op_838"), val = tensor([1, 1])]; + tensor var_840 = const()[name = tensor("op_840"), val = tensor([1, 1])]; + tensor hidden_states_1_pad_type_0 = const()[name = tensor("hidden_states_1_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_1_pad_0 = const()[name = tensor("hidden_states_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor hidden_states_1 = conv(bias = vae_encoder_down_blocks_0_resnets_0_conv2_bias, dilations = var_840, groups = var_819, pad = hidden_states_1_pad_0, pad_type = hidden_states_1_pad_type_0, strides = var_838, weight = vae_encoder_down_blocks_0_resnets_0_conv2_weight, x = input_11)[name = tensor("hidden_states_1")]; + tensor var_843 = add(x = input_1, y = hidden_states_1)[name = tensor("op_843")]; + tensor reshape_8_shape_0 = const()[name = tensor("reshape_8_shape_0"), val = tensor([1, 32, 4, 1024, 1024])]; + tensor reshape_8 = reshape(shape = reshape_8_shape_0, x = var_843)[name = tensor("reshape_8")]; + tensor reduce_mean_6_axes_0 = const()[name = tensor("reduce_mean_6_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_6_keep_dims_0 = const()[name = tensor("reduce_mean_6_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_6 = reduce_mean(axes = reduce_mean_6_axes_0, keep_dims = reduce_mean_6_keep_dims_0, x = reshape_8)[name = tensor("reduce_mean_6")]; + tensor sub_4 = sub(x = reshape_8, y = reduce_mean_6)[name = tensor("sub_4")]; + tensor square_2 = square(x = sub_4)[name = tensor("square_2")]; + tensor reduce_mean_8_axes_0 = const()[name = tensor("reduce_mean_8_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_8_keep_dims_0 = const()[name = tensor("reduce_mean_8_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_8 = reduce_mean(axes = reduce_mean_8_axes_0, keep_dims = reduce_mean_8_keep_dims_0, x = square_2)[name = tensor("reduce_mean_8")]; + tensor add_4_y_0 = const()[name = tensor("add_4_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_4 = add(x = reduce_mean_8, y = add_4_y_0)[name = tensor("add_4")]; + tensor sqrt_2 = sqrt(x = add_4)[name = tensor("sqrt_2")]; + tensor real_div_2 = real_div(x = sub_4, y = sqrt_2)[name = tensor("real_div_2")]; + tensor reshape_9_shape_0 = const()[name = tensor("reshape_9_shape_0"), val = tensor([1, 128, 1024, 1024])]; + tensor reshape_9 = reshape(shape = reshape_9_shape_0, x = real_div_2)[name = tensor("reshape_9")]; + tensor add_5_gamma_0 = const()[name = tensor("add_5_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136595520)))]; + tensor add_5_beta_0 = const()[name = tensor("add_5_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136596096)))]; + tensor add_5_epsilon_0 = const()[name = tensor("add_5_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_5 = batch_norm(beta = add_5_beta_0, epsilon = add_5_epsilon_0, gamma = add_5_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_9)[name = tensor("add_5")]; + tensor input_19 = silu(x = add_5)[name = tensor("input_19")]; + tensor var_848 = const()[name = tensor("op_848"), val = tensor([1, 1])]; + tensor var_850 = const()[name = tensor("op_850"), val = tensor([1, 1])]; + tensor input_21_pad_type_0 = const()[name = tensor("input_21_pad_type_0"), val = tensor("custom")]; + tensor input_21_pad_0 = const()[name = tensor("input_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_21 = conv(bias = vae_encoder_down_blocks_0_resnets_1_conv1_bias, dilations = var_850, groups = var_819, pad = input_21_pad_0, pad_type = input_21_pad_type_0, strides = var_848, weight = vae_encoder_down_blocks_0_resnets_1_conv1_weight, x = input_19)[name = tensor("input_21")]; + tensor reshape_12_shape_0 = const()[name = tensor("reshape_12_shape_0"), val = tensor([1, 32, 4, 1024, 1024])]; + tensor reshape_12 = reshape(shape = reshape_12_shape_0, x = input_21)[name = tensor("reshape_12")]; + tensor reduce_mean_9_axes_0 = const()[name = tensor("reduce_mean_9_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_9_keep_dims_0 = const()[name = tensor("reduce_mean_9_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_9 = reduce_mean(axes = reduce_mean_9_axes_0, keep_dims = reduce_mean_9_keep_dims_0, x = reshape_12)[name = tensor("reduce_mean_9")]; + tensor sub_6 = sub(x = reshape_12, y = reduce_mean_9)[name = tensor("sub_6")]; + tensor square_3 = square(x = sub_6)[name = tensor("square_3")]; + tensor reduce_mean_11_axes_0 = const()[name = tensor("reduce_mean_11_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_11_keep_dims_0 = const()[name = tensor("reduce_mean_11_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_11 = reduce_mean(axes = reduce_mean_11_axes_0, keep_dims = reduce_mean_11_keep_dims_0, x = square_3)[name = tensor("reduce_mean_11")]; + tensor add_6_y_0 = const()[name = tensor("add_6_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_6 = add(x = reduce_mean_11, y = add_6_y_0)[name = tensor("add_6")]; + tensor sqrt_3 = sqrt(x = add_6)[name = tensor("sqrt_3")]; + tensor real_div_3 = real_div(x = sub_6, y = sqrt_3)[name = tensor("real_div_3")]; + tensor reshape_13_shape_0 = const()[name = tensor("reshape_13_shape_0"), val = tensor([1, 128, 1024, 1024])]; + tensor reshape_13 = reshape(shape = reshape_13_shape_0, x = real_div_3)[name = tensor("reshape_13")]; + tensor add_7_gamma_0 = const()[name = tensor("add_7_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136596672)))]; + tensor add_7_beta_0 = const()[name = tensor("add_7_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136597248)))]; + tensor add_7_epsilon_0 = const()[name = tensor("add_7_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_7 = batch_norm(beta = add_7_beta_0, epsilon = add_7_epsilon_0, gamma = add_7_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_13)[name = tensor("add_7")]; + tensor input_25 = silu(x = add_7)[name = tensor("input_25")]; + tensor var_856 = const()[name = tensor("op_856"), val = tensor([1, 1])]; + tensor var_858 = const()[name = tensor("op_858"), val = tensor([1, 1])]; + tensor hidden_states_3_pad_type_0 = const()[name = tensor("hidden_states_3_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_3_pad_0 = const()[name = tensor("hidden_states_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor hidden_states_3 = conv(bias = vae_encoder_down_blocks_0_resnets_1_conv2_bias, dilations = var_858, groups = var_819, pad = hidden_states_3_pad_0, pad_type = hidden_states_3_pad_type_0, strides = var_856, weight = vae_encoder_down_blocks_0_resnets_1_conv2_weight, x = input_25)[name = tensor("hidden_states_3")]; + tensor var_861 = add(x = var_843, y = hidden_states_3)[name = tensor("op_861")]; + tensor hidden_states_7_pad_0 = const()[name = tensor("hidden_states_7_pad_0"), val = tensor([0, 0, 0, 0, 0, 1, 0, 1])]; + tensor hidden_states_7_mode_0 = const()[name = tensor("hidden_states_7_mode_0"), val = tensor("constant")]; + tensor hidden_states_7_constant_val_0 = const()[name = tensor("hidden_states_7_constant_val_0"), val = tensor(0x0p+0)]; + tensor hidden_states_7 = pad(constant_val = hidden_states_7_constant_val_0, mode = hidden_states_7_mode_0, pad = hidden_states_7_pad_0, x = var_861)[name = tensor("hidden_states_7")]; + tensor var_866 = const()[name = tensor("op_866"), val = tensor([2, 2])]; + tensor var_868 = const()[name = tensor("op_868"), val = tensor([1, 1])]; + tensor input_29_pad_type_0 = const()[name = tensor("input_29_pad_type_0"), val = tensor("custom")]; + tensor input_29_pad_0 = const()[name = tensor("input_29_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_29 = conv(bias = vae_encoder_down_blocks_0_downsamplers_0_conv_bias, dilations = var_868, groups = var_819, pad = input_29_pad_0, pad_type = input_29_pad_type_0, strides = var_866, weight = vae_encoder_down_blocks_0_downsamplers_0_conv_weight, x = hidden_states_7)[name = tensor("input_29")]; + tensor reshape_16_shape_0 = const()[name = tensor("reshape_16_shape_0"), val = tensor([1, 32, 4, 512, 512])]; + tensor reshape_16 = reshape(shape = reshape_16_shape_0, x = input_29)[name = tensor("reshape_16")]; + tensor reduce_mean_12_axes_0 = const()[name = tensor("reduce_mean_12_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_12_keep_dims_0 = const()[name = tensor("reduce_mean_12_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_12 = reduce_mean(axes = reduce_mean_12_axes_0, keep_dims = reduce_mean_12_keep_dims_0, x = reshape_16)[name = tensor("reduce_mean_12")]; + tensor sub_8 = sub(x = reshape_16, y = reduce_mean_12)[name = tensor("sub_8")]; + tensor square_4 = square(x = sub_8)[name = tensor("square_4")]; + tensor reduce_mean_14_axes_0 = const()[name = tensor("reduce_mean_14_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_14_keep_dims_0 = const()[name = tensor("reduce_mean_14_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_14 = reduce_mean(axes = reduce_mean_14_axes_0, keep_dims = reduce_mean_14_keep_dims_0, x = square_4)[name = tensor("reduce_mean_14")]; + tensor add_8_y_0 = const()[name = tensor("add_8_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_8 = add(x = reduce_mean_14, y = add_8_y_0)[name = tensor("add_8")]; + tensor sqrt_4 = sqrt(x = add_8)[name = tensor("sqrt_4")]; + tensor real_div_4 = real_div(x = sub_8, y = sqrt_4)[name = tensor("real_div_4")]; + tensor reshape_17_shape_0 = const()[name = tensor("reshape_17_shape_0"), val = tensor([1, 128, 512, 512])]; + tensor reshape_17 = reshape(shape = reshape_17_shape_0, x = real_div_4)[name = tensor("reshape_17")]; + tensor add_9_gamma_0 = const()[name = tensor("add_9_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136597824)))]; + tensor add_9_beta_0 = const()[name = tensor("add_9_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136598400)))]; + tensor add_9_epsilon_0 = const()[name = tensor("add_9_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_9 = batch_norm(beta = add_9_beta_0, epsilon = add_9_epsilon_0, gamma = add_9_gamma_0, mean = add_1_mean_0, variance = add_1_variance_0, x = reshape_17)[name = tensor("add_9")]; + tensor input_33 = silu(x = add_9)[name = tensor("input_33")]; + tensor var_873 = const()[name = tensor("op_873"), val = tensor([1, 1])]; + tensor var_875 = const()[name = tensor("op_875"), val = tensor([1, 1])]; + tensor input_35_pad_type_0 = const()[name = tensor("input_35_pad_type_0"), val = tensor("custom")]; + tensor input_35_pad_0 = const()[name = tensor("input_35_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_35 = conv(bias = vae_encoder_down_blocks_1_resnets_0_conv1_bias, dilations = var_875, groups = var_819, pad = input_35_pad_0, pad_type = input_35_pad_type_0, strides = var_873, weight = vae_encoder_down_blocks_1_resnets_0_conv1_weight, x = input_33)[name = tensor("input_35")]; + tensor reshape_20_shape_0 = const()[name = tensor("reshape_20_shape_0"), val = tensor([1, 32, 8, 512, 512])]; + tensor reshape_20 = reshape(shape = reshape_20_shape_0, x = input_35)[name = tensor("reshape_20")]; + tensor reduce_mean_15_axes_0 = const()[name = tensor("reduce_mean_15_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_15_keep_dims_0 = const()[name = tensor("reduce_mean_15_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_15 = reduce_mean(axes = reduce_mean_15_axes_0, keep_dims = reduce_mean_15_keep_dims_0, x = reshape_20)[name = tensor("reduce_mean_15")]; + tensor sub_10 = sub(x = reshape_20, y = reduce_mean_15)[name = tensor("sub_10")]; + tensor square_5 = square(x = sub_10)[name = tensor("square_5")]; + tensor reduce_mean_17_axes_0 = const()[name = tensor("reduce_mean_17_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_17_keep_dims_0 = const()[name = tensor("reduce_mean_17_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_17 = reduce_mean(axes = reduce_mean_17_axes_0, keep_dims = reduce_mean_17_keep_dims_0, x = square_5)[name = tensor("reduce_mean_17")]; + tensor add_10_y_0 = const()[name = tensor("add_10_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_10 = add(x = reduce_mean_17, y = add_10_y_0)[name = tensor("add_10")]; + tensor sqrt_5 = sqrt(x = add_10)[name = tensor("sqrt_5")]; + tensor real_div_5 = real_div(x = sub_10, y = sqrt_5)[name = tensor("real_div_5")]; + tensor reshape_21_shape_0 = const()[name = tensor("reshape_21_shape_0"), val = tensor([1, 256, 512, 512])]; + tensor reshape_21 = reshape(shape = reshape_21_shape_0, x = real_div_5)[name = tensor("reshape_21")]; + tensor add_11_mean_0 = const()[name = tensor("add_11_mean_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136598976)))]; + tensor add_11_variance_0 = const()[name = tensor("add_11_variance_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136600064)))]; + tensor add_11_gamma_0 = const()[name = tensor("add_11_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136601152)))]; + tensor add_11_beta_0 = const()[name = tensor("add_11_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136602240)))]; + tensor add_11_epsilon_0 = const()[name = tensor("add_11_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_11 = batch_norm(beta = add_11_beta_0, epsilon = add_11_epsilon_0, gamma = add_11_gamma_0, mean = add_11_mean_0, variance = add_11_variance_0, x = reshape_21)[name = tensor("add_11")]; + tensor input_39 = silu(x = add_11)[name = tensor("input_39")]; + tensor var_881 = const()[name = tensor("op_881"), val = tensor([1, 1])]; + tensor var_883 = const()[name = tensor("op_883"), val = tensor([1, 1])]; + tensor hidden_states_9_pad_type_0 = const()[name = tensor("hidden_states_9_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_9_pad_0 = const()[name = tensor("hidden_states_9_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor hidden_states_9 = conv(bias = vae_encoder_down_blocks_1_resnets_0_conv2_bias, dilations = var_883, groups = var_819, pad = hidden_states_9_pad_0, pad_type = hidden_states_9_pad_type_0, strides = var_881, weight = vae_encoder_down_blocks_1_resnets_0_conv2_weight, x = input_39)[name = tensor("hidden_states_9")]; + tensor var_886 = const()[name = tensor("op_886"), val = tensor([1, 1])]; + tensor var_888 = const()[name = tensor("op_888"), val = tensor([1, 1])]; + tensor input_tensor_1_pad_type_0 = const()[name = tensor("input_tensor_1_pad_type_0"), val = tensor("custom")]; + tensor input_tensor_1_pad_0 = const()[name = tensor("input_tensor_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_tensor_1 = conv(bias = vae_encoder_down_blocks_1_resnets_0_conv_shortcut_bias, dilations = var_888, groups = var_819, pad = input_tensor_1_pad_0, pad_type = input_tensor_1_pad_type_0, strides = var_886, weight = vae_encoder_down_blocks_1_resnets_0_conv_shortcut_weight, x = input_29)[name = tensor("input_tensor_1")]; + tensor var_891 = add(x = input_tensor_1, y = hidden_states_9)[name = tensor("op_891")]; + tensor reshape_24_shape_0 = const()[name = tensor("reshape_24_shape_0"), val = tensor([1, 32, 8, 512, 512])]; + tensor reshape_24 = reshape(shape = reshape_24_shape_0, x = var_891)[name = tensor("reshape_24")]; + tensor reduce_mean_18_axes_0 = const()[name = tensor("reduce_mean_18_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_18_keep_dims_0 = const()[name = tensor("reduce_mean_18_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_18 = reduce_mean(axes = reduce_mean_18_axes_0, keep_dims = reduce_mean_18_keep_dims_0, x = reshape_24)[name = tensor("reduce_mean_18")]; + tensor sub_12 = sub(x = reshape_24, y = reduce_mean_18)[name = tensor("sub_12")]; + tensor square_6 = square(x = sub_12)[name = tensor("square_6")]; + tensor reduce_mean_20_axes_0 = const()[name = tensor("reduce_mean_20_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_20_keep_dims_0 = const()[name = tensor("reduce_mean_20_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_20 = reduce_mean(axes = reduce_mean_20_axes_0, keep_dims = reduce_mean_20_keep_dims_0, x = square_6)[name = tensor("reduce_mean_20")]; + tensor add_12_y_0 = const()[name = tensor("add_12_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_12 = add(x = reduce_mean_20, y = add_12_y_0)[name = tensor("add_12")]; + tensor sqrt_6 = sqrt(x = add_12)[name = tensor("sqrt_6")]; + tensor real_div_6 = real_div(x = sub_12, y = sqrt_6)[name = tensor("real_div_6")]; + tensor reshape_25_shape_0 = const()[name = tensor("reshape_25_shape_0"), val = tensor([1, 256, 512, 512])]; + tensor reshape_25 = reshape(shape = reshape_25_shape_0, x = real_div_6)[name = tensor("reshape_25")]; + tensor add_13_gamma_0 = const()[name = tensor("add_13_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136603328)))]; + tensor add_13_beta_0 = const()[name = tensor("add_13_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136604416)))]; + tensor add_13_epsilon_0 = const()[name = tensor("add_13_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_13 = batch_norm(beta = add_13_beta_0, epsilon = add_13_epsilon_0, gamma = add_13_gamma_0, mean = add_11_mean_0, variance = add_11_variance_0, x = reshape_25)[name = tensor("add_13")]; + tensor input_47 = silu(x = add_13)[name = tensor("input_47")]; + tensor var_896 = const()[name = tensor("op_896"), val = tensor([1, 1])]; + tensor var_898 = const()[name = tensor("op_898"), val = tensor([1, 1])]; + tensor input_49_pad_type_0 = const()[name = tensor("input_49_pad_type_0"), val = tensor("custom")]; + tensor input_49_pad_0 = const()[name = tensor("input_49_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_49 = conv(bias = vae_encoder_down_blocks_1_resnets_1_conv1_bias, dilations = var_898, groups = var_819, pad = input_49_pad_0, pad_type = input_49_pad_type_0, strides = var_896, weight = vae_encoder_down_blocks_1_resnets_1_conv1_weight, x = input_47)[name = tensor("input_49")]; + tensor reshape_28_shape_0 = const()[name = tensor("reshape_28_shape_0"), val = tensor([1, 32, 8, 512, 512])]; + tensor reshape_28 = reshape(shape = reshape_28_shape_0, x = input_49)[name = tensor("reshape_28")]; + tensor reduce_mean_21_axes_0 = const()[name = tensor("reduce_mean_21_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_21_keep_dims_0 = const()[name = tensor("reduce_mean_21_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_21 = reduce_mean(axes = reduce_mean_21_axes_0, keep_dims = reduce_mean_21_keep_dims_0, x = reshape_28)[name = tensor("reduce_mean_21")]; + tensor sub_14 = sub(x = reshape_28, y = reduce_mean_21)[name = tensor("sub_14")]; + tensor square_7 = square(x = sub_14)[name = tensor("square_7")]; + tensor reduce_mean_23_axes_0 = const()[name = tensor("reduce_mean_23_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_23_keep_dims_0 = const()[name = tensor("reduce_mean_23_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_23 = reduce_mean(axes = reduce_mean_23_axes_0, keep_dims = reduce_mean_23_keep_dims_0, x = square_7)[name = tensor("reduce_mean_23")]; + tensor add_14_y_0 = const()[name = tensor("add_14_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_14 = add(x = reduce_mean_23, y = add_14_y_0)[name = tensor("add_14")]; + tensor sqrt_7 = sqrt(x = add_14)[name = tensor("sqrt_7")]; + tensor real_div_7 = real_div(x = sub_14, y = sqrt_7)[name = tensor("real_div_7")]; + tensor reshape_29_shape_0 = const()[name = tensor("reshape_29_shape_0"), val = tensor([1, 256, 512, 512])]; + tensor reshape_29 = reshape(shape = reshape_29_shape_0, x = real_div_7)[name = tensor("reshape_29")]; + tensor add_15_gamma_0 = const()[name = tensor("add_15_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136605504)))]; + tensor add_15_beta_0 = const()[name = tensor("add_15_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136606592)))]; + tensor add_15_epsilon_0 = const()[name = tensor("add_15_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_15 = batch_norm(beta = add_15_beta_0, epsilon = add_15_epsilon_0, gamma = add_15_gamma_0, mean = add_11_mean_0, variance = add_11_variance_0, x = reshape_29)[name = tensor("add_15")]; + tensor input_53 = silu(x = add_15)[name = tensor("input_53")]; + tensor var_904 = const()[name = tensor("op_904"), val = tensor([1, 1])]; + tensor var_906 = const()[name = tensor("op_906"), val = tensor([1, 1])]; + tensor hidden_states_11_pad_type_0 = const()[name = tensor("hidden_states_11_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_11_pad_0 = const()[name = tensor("hidden_states_11_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor hidden_states_11 = conv(bias = vae_encoder_down_blocks_1_resnets_1_conv2_bias, dilations = var_906, groups = var_819, pad = hidden_states_11_pad_0, pad_type = hidden_states_11_pad_type_0, strides = var_904, weight = vae_encoder_down_blocks_1_resnets_1_conv2_weight, x = input_53)[name = tensor("hidden_states_11")]; + tensor var_909 = add(x = var_891, y = hidden_states_11)[name = tensor("op_909")]; + tensor hidden_states_15_pad_0 = const()[name = tensor("hidden_states_15_pad_0"), val = tensor([0, 0, 0, 0, 0, 1, 0, 1])]; + tensor hidden_states_15_mode_0 = const()[name = tensor("hidden_states_15_mode_0"), val = tensor("constant")]; + tensor hidden_states_15_constant_val_0 = const()[name = tensor("hidden_states_15_constant_val_0"), val = tensor(0x0p+0)]; + tensor hidden_states_15 = pad(constant_val = hidden_states_15_constant_val_0, mode = hidden_states_15_mode_0, pad = hidden_states_15_pad_0, x = var_909)[name = tensor("hidden_states_15")]; + tensor var_914 = const()[name = tensor("op_914"), val = tensor([2, 2])]; + tensor var_916 = const()[name = tensor("op_916"), val = tensor([1, 1])]; + tensor input_57_pad_type_0 = const()[name = tensor("input_57_pad_type_0"), val = tensor("custom")]; + tensor input_57_pad_0 = const()[name = tensor("input_57_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_57 = conv(bias = vae_encoder_down_blocks_1_downsamplers_0_conv_bias, dilations = var_916, groups = var_819, pad = input_57_pad_0, pad_type = input_57_pad_type_0, strides = var_914, weight = vae_encoder_down_blocks_1_downsamplers_0_conv_weight, x = hidden_states_15)[name = tensor("input_57")]; + tensor reshape_32_shape_0 = const()[name = tensor("reshape_32_shape_0"), val = tensor([1, 32, 8, 256, 256])]; + tensor reshape_32 = reshape(shape = reshape_32_shape_0, x = input_57)[name = tensor("reshape_32")]; + tensor reduce_mean_24_axes_0 = const()[name = tensor("reduce_mean_24_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_24_keep_dims_0 = const()[name = tensor("reduce_mean_24_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_24 = reduce_mean(axes = reduce_mean_24_axes_0, keep_dims = reduce_mean_24_keep_dims_0, x = reshape_32)[name = tensor("reduce_mean_24")]; + tensor sub_16 = sub(x = reshape_32, y = reduce_mean_24)[name = tensor("sub_16")]; + tensor square_8 = square(x = sub_16)[name = tensor("square_8")]; + tensor reduce_mean_26_axes_0 = const()[name = tensor("reduce_mean_26_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_26_keep_dims_0 = const()[name = tensor("reduce_mean_26_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_26 = reduce_mean(axes = reduce_mean_26_axes_0, keep_dims = reduce_mean_26_keep_dims_0, x = square_8)[name = tensor("reduce_mean_26")]; + tensor add_16_y_0 = const()[name = tensor("add_16_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_16 = add(x = reduce_mean_26, y = add_16_y_0)[name = tensor("add_16")]; + tensor sqrt_8 = sqrt(x = add_16)[name = tensor("sqrt_8")]; + tensor real_div_8 = real_div(x = sub_16, y = sqrt_8)[name = tensor("real_div_8")]; + tensor reshape_33_shape_0 = const()[name = tensor("reshape_33_shape_0"), val = tensor([1, 256, 256, 256])]; + tensor reshape_33 = reshape(shape = reshape_33_shape_0, x = real_div_8)[name = tensor("reshape_33")]; + tensor add_17_gamma_0 = const()[name = tensor("add_17_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136607680)))]; + tensor add_17_beta_0 = const()[name = tensor("add_17_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136608768)))]; + tensor add_17_epsilon_0 = const()[name = tensor("add_17_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_17 = batch_norm(beta = add_17_beta_0, epsilon = add_17_epsilon_0, gamma = add_17_gamma_0, mean = add_11_mean_0, variance = add_11_variance_0, x = reshape_33)[name = tensor("add_17")]; + tensor input_61 = silu(x = add_17)[name = tensor("input_61")]; + tensor var_921 = const()[name = tensor("op_921"), val = tensor([1, 1])]; + tensor var_923 = const()[name = tensor("op_923"), val = tensor([1, 1])]; + tensor input_63_pad_type_0 = const()[name = tensor("input_63_pad_type_0"), val = tensor("custom")]; + tensor input_63_pad_0 = const()[name = tensor("input_63_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_63 = conv(bias = vae_encoder_down_blocks_2_resnets_0_conv1_bias, dilations = var_923, groups = var_819, pad = input_63_pad_0, pad_type = input_63_pad_type_0, strides = var_921, weight = vae_encoder_down_blocks_2_resnets_0_conv1_weight, x = input_61)[name = tensor("input_63")]; + tensor reshape_36_shape_0 = const()[name = tensor("reshape_36_shape_0"), val = tensor([1, 32, 16, 256, 256])]; + tensor reshape_36 = reshape(shape = reshape_36_shape_0, x = input_63)[name = tensor("reshape_36")]; + tensor reduce_mean_27_axes_0 = const()[name = tensor("reduce_mean_27_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_27_keep_dims_0 = const()[name = tensor("reduce_mean_27_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_27 = reduce_mean(axes = reduce_mean_27_axes_0, keep_dims = reduce_mean_27_keep_dims_0, x = reshape_36)[name = tensor("reduce_mean_27")]; + tensor sub_18 = sub(x = reshape_36, y = reduce_mean_27)[name = tensor("sub_18")]; + tensor square_9 = square(x = sub_18)[name = tensor("square_9")]; + tensor reduce_mean_29_axes_0 = const()[name = tensor("reduce_mean_29_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_29_keep_dims_0 = const()[name = tensor("reduce_mean_29_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_29 = reduce_mean(axes = reduce_mean_29_axes_0, keep_dims = reduce_mean_29_keep_dims_0, x = square_9)[name = tensor("reduce_mean_29")]; + tensor add_18_y_0 = const()[name = tensor("add_18_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_18 = add(x = reduce_mean_29, y = add_18_y_0)[name = tensor("add_18")]; + tensor sqrt_9 = sqrt(x = add_18)[name = tensor("sqrt_9")]; + tensor real_div_9 = real_div(x = sub_18, y = sqrt_9)[name = tensor("real_div_9")]; + tensor reshape_37_shape_0 = const()[name = tensor("reshape_37_shape_0"), val = tensor([1, 512, 256, 256])]; + tensor reshape_37 = reshape(shape = reshape_37_shape_0, x = real_div_9)[name = tensor("reshape_37")]; + tensor add_19_mean_0 = const()[name = tensor("add_19_mean_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136609856)))]; + tensor add_19_variance_0 = const()[name = tensor("add_19_variance_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136611968)))]; + tensor add_19_gamma_0 = const()[name = tensor("add_19_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136614080)))]; + tensor add_19_beta_0 = const()[name = tensor("add_19_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136616192)))]; + tensor add_19_epsilon_0 = const()[name = tensor("add_19_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_19 = batch_norm(beta = add_19_beta_0, epsilon = add_19_epsilon_0, gamma = add_19_gamma_0, mean = add_19_mean_0, variance = add_19_variance_0, x = reshape_37)[name = tensor("add_19")]; + tensor input_67 = silu(x = add_19)[name = tensor("input_67")]; + tensor var_929 = const()[name = tensor("op_929"), val = tensor([1, 1])]; + tensor var_931 = const()[name = tensor("op_931"), val = tensor([1, 1])]; + tensor hidden_states_17_pad_type_0 = const()[name = tensor("hidden_states_17_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_17_pad_0 = const()[name = tensor("hidden_states_17_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor hidden_states_17 = conv(bias = vae_encoder_down_blocks_2_resnets_0_conv2_bias, dilations = var_931, groups = var_819, pad = hidden_states_17_pad_0, pad_type = hidden_states_17_pad_type_0, strides = var_929, weight = vae_encoder_down_blocks_2_resnets_0_conv2_weight, x = input_67)[name = tensor("hidden_states_17")]; + tensor var_934 = const()[name = tensor("op_934"), val = tensor([1, 1])]; + tensor var_936 = const()[name = tensor("op_936"), val = tensor([1, 1])]; + tensor input_tensor_pad_type_0 = const()[name = tensor("input_tensor_pad_type_0"), val = tensor("custom")]; + tensor input_tensor_pad_0 = const()[name = tensor("input_tensor_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_tensor = conv(bias = vae_encoder_down_blocks_2_resnets_0_conv_shortcut_bias, dilations = var_936, groups = var_819, pad = input_tensor_pad_0, pad_type = input_tensor_pad_type_0, strides = var_934, weight = vae_encoder_down_blocks_2_resnets_0_conv_shortcut_weight, x = input_57)[name = tensor("input_tensor")]; + tensor var_939 = add(x = input_tensor, y = hidden_states_17)[name = tensor("op_939")]; + tensor reshape_40_shape_0 = const()[name = tensor("reshape_40_shape_0"), val = tensor([1, 32, 16, 256, 256])]; + tensor reshape_40 = reshape(shape = reshape_40_shape_0, x = var_939)[name = tensor("reshape_40")]; + tensor reduce_mean_30_axes_0 = const()[name = tensor("reduce_mean_30_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_30_keep_dims_0 = const()[name = tensor("reduce_mean_30_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_30 = reduce_mean(axes = reduce_mean_30_axes_0, keep_dims = reduce_mean_30_keep_dims_0, x = reshape_40)[name = tensor("reduce_mean_30")]; + tensor sub_20 = sub(x = reshape_40, y = reduce_mean_30)[name = tensor("sub_20")]; + tensor square_10 = square(x = sub_20)[name = tensor("square_10")]; + tensor reduce_mean_32_axes_0 = const()[name = tensor("reduce_mean_32_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_32_keep_dims_0 = const()[name = tensor("reduce_mean_32_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_32 = reduce_mean(axes = reduce_mean_32_axes_0, keep_dims = reduce_mean_32_keep_dims_0, x = square_10)[name = tensor("reduce_mean_32")]; + tensor add_20_y_0 = const()[name = tensor("add_20_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_20 = add(x = reduce_mean_32, y = add_20_y_0)[name = tensor("add_20")]; + tensor sqrt_10 = sqrt(x = add_20)[name = tensor("sqrt_10")]; + tensor real_div_10 = real_div(x = sub_20, y = sqrt_10)[name = tensor("real_div_10")]; + tensor reshape_41_shape_0 = const()[name = tensor("reshape_41_shape_0"), val = tensor([1, 512, 256, 256])]; + tensor reshape_41 = reshape(shape = reshape_41_shape_0, x = real_div_10)[name = tensor("reshape_41")]; + tensor add_21_gamma_0 = const()[name = tensor("add_21_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136618304)))]; + tensor add_21_beta_0 = const()[name = tensor("add_21_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136620416)))]; + tensor add_21_epsilon_0 = const()[name = tensor("add_21_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_21 = batch_norm(beta = add_21_beta_0, epsilon = add_21_epsilon_0, gamma = add_21_gamma_0, mean = add_19_mean_0, variance = add_19_variance_0, x = reshape_41)[name = tensor("add_21")]; + tensor input_75 = silu(x = add_21)[name = tensor("input_75")]; + tensor var_944 = const()[name = tensor("op_944"), val = tensor([1, 1])]; + tensor var_946 = const()[name = tensor("op_946"), val = tensor([1, 1])]; + tensor input_77_pad_type_0 = const()[name = tensor("input_77_pad_type_0"), val = tensor("custom")]; + tensor input_77_pad_0 = const()[name = tensor("input_77_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_77 = conv(bias = vae_encoder_down_blocks_2_resnets_1_conv1_bias, dilations = var_946, groups = var_819, pad = input_77_pad_0, pad_type = input_77_pad_type_0, strides = var_944, weight = vae_encoder_down_blocks_2_resnets_1_conv1_weight, x = input_75)[name = tensor("input_77")]; + tensor reshape_44_shape_0 = const()[name = tensor("reshape_44_shape_0"), val = tensor([1, 32, 16, 256, 256])]; + tensor reshape_44 = reshape(shape = reshape_44_shape_0, x = input_77)[name = tensor("reshape_44")]; + tensor reduce_mean_33_axes_0 = const()[name = tensor("reduce_mean_33_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_33_keep_dims_0 = const()[name = tensor("reduce_mean_33_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_33 = reduce_mean(axes = reduce_mean_33_axes_0, keep_dims = reduce_mean_33_keep_dims_0, x = reshape_44)[name = tensor("reduce_mean_33")]; + tensor sub_22 = sub(x = reshape_44, y = reduce_mean_33)[name = tensor("sub_22")]; + tensor square_11 = square(x = sub_22)[name = tensor("square_11")]; + tensor reduce_mean_35_axes_0 = const()[name = tensor("reduce_mean_35_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_35_keep_dims_0 = const()[name = tensor("reduce_mean_35_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_35 = reduce_mean(axes = reduce_mean_35_axes_0, keep_dims = reduce_mean_35_keep_dims_0, x = square_11)[name = tensor("reduce_mean_35")]; + tensor add_22_y_0 = const()[name = tensor("add_22_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_22 = add(x = reduce_mean_35, y = add_22_y_0)[name = tensor("add_22")]; + tensor sqrt_11 = sqrt(x = add_22)[name = tensor("sqrt_11")]; + tensor real_div_11 = real_div(x = sub_22, y = sqrt_11)[name = tensor("real_div_11")]; + tensor reshape_45_shape_0 = const()[name = tensor("reshape_45_shape_0"), val = tensor([1, 512, 256, 256])]; + tensor reshape_45 = reshape(shape = reshape_45_shape_0, x = real_div_11)[name = tensor("reshape_45")]; + tensor add_23_gamma_0 = const()[name = tensor("add_23_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136622528)))]; + tensor add_23_beta_0 = const()[name = tensor("add_23_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136624640)))]; + tensor add_23_epsilon_0 = const()[name = tensor("add_23_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_23 = batch_norm(beta = add_23_beta_0, epsilon = add_23_epsilon_0, gamma = add_23_gamma_0, mean = add_19_mean_0, variance = add_19_variance_0, x = reshape_45)[name = tensor("add_23")]; + tensor input_81 = silu(x = add_23)[name = tensor("input_81")]; + tensor var_952 = const()[name = tensor("op_952"), val = tensor([1, 1])]; + tensor var_954 = const()[name = tensor("op_954"), val = tensor([1, 1])]; + tensor hidden_states_19_pad_type_0 = const()[name = tensor("hidden_states_19_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_19_pad_0 = const()[name = tensor("hidden_states_19_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor hidden_states_19 = conv(bias = vae_encoder_down_blocks_2_resnets_1_conv2_bias, dilations = var_954, groups = var_819, pad = hidden_states_19_pad_0, pad_type = hidden_states_19_pad_type_0, strides = var_952, weight = vae_encoder_down_blocks_2_resnets_1_conv2_weight, x = input_81)[name = tensor("hidden_states_19")]; + tensor var_957 = add(x = var_939, y = hidden_states_19)[name = tensor("op_957")]; + tensor hidden_states_23_pad_0 = const()[name = tensor("hidden_states_23_pad_0"), val = tensor([0, 0, 0, 0, 0, 1, 0, 1])]; + tensor hidden_states_23_mode_0 = const()[name = tensor("hidden_states_23_mode_0"), val = tensor("constant")]; + tensor hidden_states_23_constant_val_0 = const()[name = tensor("hidden_states_23_constant_val_0"), val = tensor(0x0p+0)]; + tensor hidden_states_23 = pad(constant_val = hidden_states_23_constant_val_0, mode = hidden_states_23_mode_0, pad = hidden_states_23_pad_0, x = var_957)[name = tensor("hidden_states_23")]; + tensor var_962 = const()[name = tensor("op_962"), val = tensor([2, 2])]; + tensor var_964 = const()[name = tensor("op_964"), val = tensor([1, 1])]; + tensor input_85_pad_type_0 = const()[name = tensor("input_85_pad_type_0"), val = tensor("custom")]; + tensor input_85_pad_0 = const()[name = tensor("input_85_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor input_85 = conv(bias = vae_encoder_down_blocks_2_downsamplers_0_conv_bias, dilations = var_964, groups = var_819, pad = input_85_pad_0, pad_type = input_85_pad_type_0, strides = var_962, weight = vae_encoder_down_blocks_2_downsamplers_0_conv_weight, x = hidden_states_23)[name = tensor("input_85")]; + tensor reshape_48_shape_0 = const()[name = tensor("reshape_48_shape_0"), val = tensor([1, 32, 16, 128, 128])]; + tensor reshape_48 = reshape(shape = reshape_48_shape_0, x = input_85)[name = tensor("reshape_48")]; + tensor reduce_mean_36_axes_0 = const()[name = tensor("reduce_mean_36_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_36_keep_dims_0 = const()[name = tensor("reduce_mean_36_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_36 = reduce_mean(axes = reduce_mean_36_axes_0, keep_dims = reduce_mean_36_keep_dims_0, x = reshape_48)[name = tensor("reduce_mean_36")]; + tensor sub_24 = sub(x = reshape_48, y = reduce_mean_36)[name = tensor("sub_24")]; + tensor square_12 = square(x = sub_24)[name = tensor("square_12")]; + tensor reduce_mean_38_axes_0 = const()[name = tensor("reduce_mean_38_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_38_keep_dims_0 = const()[name = tensor("reduce_mean_38_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_38 = reduce_mean(axes = reduce_mean_38_axes_0, keep_dims = reduce_mean_38_keep_dims_0, x = square_12)[name = tensor("reduce_mean_38")]; + tensor add_24_y_0 = const()[name = tensor("add_24_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_24 = add(x = reduce_mean_38, y = add_24_y_0)[name = tensor("add_24")]; + tensor sqrt_12 = sqrt(x = add_24)[name = tensor("sqrt_12")]; + tensor real_div_12 = real_div(x = sub_24, y = sqrt_12)[name = tensor("real_div_12")]; + tensor reshape_49_shape_0 = const()[name = tensor("reshape_49_shape_0"), val = tensor([1, 512, 128, 128])]; + tensor reshape_49 = reshape(shape = reshape_49_shape_0, x = real_div_12)[name = tensor("reshape_49")]; + tensor add_25_gamma_0 = const()[name = tensor("add_25_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136626752)))]; + tensor add_25_beta_0 = const()[name = tensor("add_25_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136628864)))]; + tensor add_25_epsilon_0 = const()[name = tensor("add_25_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_25 = batch_norm(beta = add_25_beta_0, epsilon = add_25_epsilon_0, gamma = add_25_gamma_0, mean = add_19_mean_0, variance = add_19_variance_0, x = reshape_49)[name = tensor("add_25")]; + tensor input_89 = silu(x = add_25)[name = tensor("input_89")]; + tensor var_969 = const()[name = tensor("op_969"), val = tensor([1, 1])]; + tensor var_971 = const()[name = tensor("op_971"), val = tensor([1, 1])]; + tensor input_91_pad_type_0 = const()[name = tensor("input_91_pad_type_0"), val = tensor("custom")]; + tensor input_91_pad_0 = const()[name = tensor("input_91_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_91 = conv(bias = vae_encoder_down_blocks_3_resnets_0_conv1_bias, dilations = var_971, groups = var_819, pad = input_91_pad_0, pad_type = input_91_pad_type_0, strides = var_969, weight = vae_encoder_down_blocks_3_resnets_0_conv1_weight, x = input_89)[name = tensor("input_91")]; + tensor reshape_52_shape_0 = const()[name = tensor("reshape_52_shape_0"), val = tensor([1, 32, 16, 128, 128])]; + tensor reshape_52 = reshape(shape = reshape_52_shape_0, x = input_91)[name = tensor("reshape_52")]; + tensor reduce_mean_39_axes_0 = const()[name = tensor("reduce_mean_39_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_39_keep_dims_0 = const()[name = tensor("reduce_mean_39_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_39 = reduce_mean(axes = reduce_mean_39_axes_0, keep_dims = reduce_mean_39_keep_dims_0, x = reshape_52)[name = tensor("reduce_mean_39")]; + tensor sub_26 = sub(x = reshape_52, y = reduce_mean_39)[name = tensor("sub_26")]; + tensor square_13 = square(x = sub_26)[name = tensor("square_13")]; + tensor reduce_mean_41_axes_0 = const()[name = tensor("reduce_mean_41_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_41_keep_dims_0 = const()[name = tensor("reduce_mean_41_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_41 = reduce_mean(axes = reduce_mean_41_axes_0, keep_dims = reduce_mean_41_keep_dims_0, x = square_13)[name = tensor("reduce_mean_41")]; + tensor add_26_y_0 = const()[name = tensor("add_26_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_26 = add(x = reduce_mean_41, y = add_26_y_0)[name = tensor("add_26")]; + tensor sqrt_13 = sqrt(x = add_26)[name = tensor("sqrt_13")]; + tensor real_div_13 = real_div(x = sub_26, y = sqrt_13)[name = tensor("real_div_13")]; + tensor reshape_53_shape_0 = const()[name = tensor("reshape_53_shape_0"), val = tensor([1, 512, 128, 128])]; + tensor reshape_53 = reshape(shape = reshape_53_shape_0, x = real_div_13)[name = tensor("reshape_53")]; + tensor add_27_gamma_0 = const()[name = tensor("add_27_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136630976)))]; + tensor add_27_beta_0 = const()[name = tensor("add_27_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136633088)))]; + tensor add_27_epsilon_0 = const()[name = tensor("add_27_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_27 = batch_norm(beta = add_27_beta_0, epsilon = add_27_epsilon_0, gamma = add_27_gamma_0, mean = add_19_mean_0, variance = add_19_variance_0, x = reshape_53)[name = tensor("add_27")]; + tensor input_95 = silu(x = add_27)[name = tensor("input_95")]; + tensor var_977 = const()[name = tensor("op_977"), val = tensor([1, 1])]; + tensor var_979 = const()[name = tensor("op_979"), val = tensor([1, 1])]; + tensor hidden_states_25_pad_type_0 = const()[name = tensor("hidden_states_25_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_25_pad_0 = const()[name = tensor("hidden_states_25_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor hidden_states_25 = conv(bias = vae_encoder_down_blocks_3_resnets_0_conv2_bias, dilations = var_979, groups = var_819, pad = hidden_states_25_pad_0, pad_type = hidden_states_25_pad_type_0, strides = var_977, weight = vae_encoder_down_blocks_3_resnets_0_conv2_weight, x = input_95)[name = tensor("hidden_states_25")]; + tensor var_982 = add(x = input_85, y = hidden_states_25)[name = tensor("op_982")]; + tensor reshape_56_shape_0 = const()[name = tensor("reshape_56_shape_0"), val = tensor([1, 32, 16, 128, 128])]; + tensor reshape_56 = reshape(shape = reshape_56_shape_0, x = var_982)[name = tensor("reshape_56")]; + tensor reduce_mean_42_axes_0 = const()[name = tensor("reduce_mean_42_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_42_keep_dims_0 = const()[name = tensor("reduce_mean_42_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_42 = reduce_mean(axes = reduce_mean_42_axes_0, keep_dims = reduce_mean_42_keep_dims_0, x = reshape_56)[name = tensor("reduce_mean_42")]; + tensor sub_28 = sub(x = reshape_56, y = reduce_mean_42)[name = tensor("sub_28")]; + tensor square_14 = square(x = sub_28)[name = tensor("square_14")]; + tensor reduce_mean_44_axes_0 = const()[name = tensor("reduce_mean_44_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_44_keep_dims_0 = const()[name = tensor("reduce_mean_44_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_44 = reduce_mean(axes = reduce_mean_44_axes_0, keep_dims = reduce_mean_44_keep_dims_0, x = square_14)[name = tensor("reduce_mean_44")]; + tensor add_28_y_0 = const()[name = tensor("add_28_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_28 = add(x = reduce_mean_44, y = add_28_y_0)[name = tensor("add_28")]; + tensor sqrt_14 = sqrt(x = add_28)[name = tensor("sqrt_14")]; + tensor real_div_14 = real_div(x = sub_28, y = sqrt_14)[name = tensor("real_div_14")]; + tensor reshape_57_shape_0 = const()[name = tensor("reshape_57_shape_0"), val = tensor([1, 512, 128, 128])]; + tensor reshape_57 = reshape(shape = reshape_57_shape_0, x = real_div_14)[name = tensor("reshape_57")]; + tensor add_29_gamma_0 = const()[name = tensor("add_29_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136635200)))]; + tensor add_29_beta_0 = const()[name = tensor("add_29_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136637312)))]; + tensor add_29_epsilon_0 = const()[name = tensor("add_29_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_29 = batch_norm(beta = add_29_beta_0, epsilon = add_29_epsilon_0, gamma = add_29_gamma_0, mean = add_19_mean_0, variance = add_19_variance_0, x = reshape_57)[name = tensor("add_29")]; + tensor input_103 = silu(x = add_29)[name = tensor("input_103")]; + tensor var_987 = const()[name = tensor("op_987"), val = tensor([1, 1])]; + tensor var_989 = const()[name = tensor("op_989"), val = tensor([1, 1])]; + tensor input_105_pad_type_0 = const()[name = tensor("input_105_pad_type_0"), val = tensor("custom")]; + tensor input_105_pad_0 = const()[name = tensor("input_105_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_105 = conv(bias = vae_encoder_down_blocks_3_resnets_1_conv1_bias, dilations = var_989, groups = var_819, pad = input_105_pad_0, pad_type = input_105_pad_type_0, strides = var_987, weight = vae_encoder_down_blocks_3_resnets_1_conv1_weight, x = input_103)[name = tensor("input_105")]; + tensor reshape_60_shape_0 = const()[name = tensor("reshape_60_shape_0"), val = tensor([1, 32, 16, 128, 128])]; + tensor reshape_60 = reshape(shape = reshape_60_shape_0, x = input_105)[name = tensor("reshape_60")]; + tensor reduce_mean_45_axes_0 = const()[name = tensor("reduce_mean_45_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_45_keep_dims_0 = const()[name = tensor("reduce_mean_45_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_45 = reduce_mean(axes = reduce_mean_45_axes_0, keep_dims = reduce_mean_45_keep_dims_0, x = reshape_60)[name = tensor("reduce_mean_45")]; + tensor sub_30 = sub(x = reshape_60, y = reduce_mean_45)[name = tensor("sub_30")]; + tensor square_15 = square(x = sub_30)[name = tensor("square_15")]; + tensor reduce_mean_47_axes_0 = const()[name = tensor("reduce_mean_47_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_47_keep_dims_0 = const()[name = tensor("reduce_mean_47_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_47 = reduce_mean(axes = reduce_mean_47_axes_0, keep_dims = reduce_mean_47_keep_dims_0, x = square_15)[name = tensor("reduce_mean_47")]; + tensor add_30_y_0 = const()[name = tensor("add_30_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_30 = add(x = reduce_mean_47, y = add_30_y_0)[name = tensor("add_30")]; + tensor sqrt_15 = sqrt(x = add_30)[name = tensor("sqrt_15")]; + tensor real_div_15 = real_div(x = sub_30, y = sqrt_15)[name = tensor("real_div_15")]; + tensor reshape_61_shape_0 = const()[name = tensor("reshape_61_shape_0"), val = tensor([1, 512, 128, 128])]; + tensor reshape_61 = reshape(shape = reshape_61_shape_0, x = real_div_15)[name = tensor("reshape_61")]; + tensor add_31_gamma_0 = const()[name = tensor("add_31_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136639424)))]; + tensor add_31_beta_0 = const()[name = tensor("add_31_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136641536)))]; + tensor add_31_epsilon_0 = const()[name = tensor("add_31_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_31 = batch_norm(beta = add_31_beta_0, epsilon = add_31_epsilon_0, gamma = add_31_gamma_0, mean = add_19_mean_0, variance = add_19_variance_0, x = reshape_61)[name = tensor("add_31")]; + tensor input_109 = silu(x = add_31)[name = tensor("input_109")]; + tensor var_995 = const()[name = tensor("op_995"), val = tensor([1, 1])]; + tensor var_997 = const()[name = tensor("op_997"), val = tensor([1, 1])]; + tensor hidden_states_27_pad_type_0 = const()[name = tensor("hidden_states_27_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_27_pad_0 = const()[name = tensor("hidden_states_27_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor hidden_states_27 = conv(bias = vae_encoder_down_blocks_3_resnets_1_conv2_bias, dilations = var_997, groups = var_819, pad = hidden_states_27_pad_0, pad_type = hidden_states_27_pad_type_0, strides = var_995, weight = vae_encoder_down_blocks_3_resnets_1_conv2_weight, x = input_109)[name = tensor("hidden_states_27")]; + tensor var_1000 = add(x = var_982, y = hidden_states_27)[name = tensor("op_1000")]; + tensor reshape_64_shape_0 = const()[name = tensor("reshape_64_shape_0"), val = tensor([1, 32, 16, 128, 128])]; + tensor reshape_64 = reshape(shape = reshape_64_shape_0, x = var_1000)[name = tensor("reshape_64")]; + tensor reduce_mean_48_axes_0 = const()[name = tensor("reduce_mean_48_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_48_keep_dims_0 = const()[name = tensor("reduce_mean_48_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_48 = reduce_mean(axes = reduce_mean_48_axes_0, keep_dims = reduce_mean_48_keep_dims_0, x = reshape_64)[name = tensor("reduce_mean_48")]; + tensor sub_32 = sub(x = reshape_64, y = reduce_mean_48)[name = tensor("sub_32")]; + tensor square_16 = square(x = sub_32)[name = tensor("square_16")]; + tensor reduce_mean_50_axes_0 = const()[name = tensor("reduce_mean_50_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_50_keep_dims_0 = const()[name = tensor("reduce_mean_50_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_50 = reduce_mean(axes = reduce_mean_50_axes_0, keep_dims = reduce_mean_50_keep_dims_0, x = square_16)[name = tensor("reduce_mean_50")]; + tensor add_32_y_0 = const()[name = tensor("add_32_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_32 = add(x = reduce_mean_50, y = add_32_y_0)[name = tensor("add_32")]; + tensor sqrt_16 = sqrt(x = add_32)[name = tensor("sqrt_16")]; + tensor real_div_16 = real_div(x = sub_32, y = sqrt_16)[name = tensor("real_div_16")]; + tensor reshape_65_shape_0 = const()[name = tensor("reshape_65_shape_0"), val = tensor([1, 512, 128, 128])]; + tensor reshape_65 = reshape(shape = reshape_65_shape_0, x = real_div_16)[name = tensor("reshape_65")]; + tensor add_33_gamma_0 = const()[name = tensor("add_33_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136643648)))]; + tensor add_33_beta_0 = const()[name = tensor("add_33_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136645760)))]; + tensor add_33_epsilon_0 = const()[name = tensor("add_33_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_33 = batch_norm(beta = add_33_beta_0, epsilon = add_33_epsilon_0, gamma = add_33_gamma_0, mean = add_19_mean_0, variance = add_19_variance_0, x = reshape_65)[name = tensor("add_33")]; + tensor input_117 = silu(x = add_33)[name = tensor("input_117")]; + tensor var_1005 = const()[name = tensor("op_1005"), val = tensor([1, 1])]; + tensor var_1007 = const()[name = tensor("op_1007"), val = tensor([1, 1])]; + tensor input_119_pad_type_0 = const()[name = tensor("input_119_pad_type_0"), val = tensor("custom")]; + tensor input_119_pad_0 = const()[name = tensor("input_119_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_119 = conv(bias = vae_encoder_mid_block_resnets_0_conv1_bias, dilations = var_1007, groups = var_819, pad = input_119_pad_0, pad_type = input_119_pad_type_0, strides = var_1005, weight = vae_encoder_mid_block_resnets_0_conv1_weight, x = input_117)[name = tensor("input_119")]; + tensor reshape_68_shape_0 = const()[name = tensor("reshape_68_shape_0"), val = tensor([1, 32, 16, 128, 128])]; + tensor reshape_68 = reshape(shape = reshape_68_shape_0, x = input_119)[name = tensor("reshape_68")]; + tensor reduce_mean_51_axes_0 = const()[name = tensor("reduce_mean_51_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_51_keep_dims_0 = const()[name = tensor("reduce_mean_51_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_51 = reduce_mean(axes = reduce_mean_51_axes_0, keep_dims = reduce_mean_51_keep_dims_0, x = reshape_68)[name = tensor("reduce_mean_51")]; + tensor sub_34 = sub(x = reshape_68, y = reduce_mean_51)[name = tensor("sub_34")]; + tensor square_17 = square(x = sub_34)[name = tensor("square_17")]; + tensor reduce_mean_53_axes_0 = const()[name = tensor("reduce_mean_53_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_53_keep_dims_0 = const()[name = tensor("reduce_mean_53_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_53 = reduce_mean(axes = reduce_mean_53_axes_0, keep_dims = reduce_mean_53_keep_dims_0, x = square_17)[name = tensor("reduce_mean_53")]; + tensor add_34_y_0 = const()[name = tensor("add_34_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_34 = add(x = reduce_mean_53, y = add_34_y_0)[name = tensor("add_34")]; + tensor sqrt_17 = sqrt(x = add_34)[name = tensor("sqrt_17")]; + tensor real_div_17 = real_div(x = sub_34, y = sqrt_17)[name = tensor("real_div_17")]; + tensor reshape_69_shape_0 = const()[name = tensor("reshape_69_shape_0"), val = tensor([1, 512, 128, 128])]; + tensor reshape_69 = reshape(shape = reshape_69_shape_0, x = real_div_17)[name = tensor("reshape_69")]; + tensor add_35_gamma_0 = const()[name = tensor("add_35_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136647872)))]; + tensor add_35_beta_0 = const()[name = tensor("add_35_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136649984)))]; + tensor add_35_epsilon_0 = const()[name = tensor("add_35_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_35 = batch_norm(beta = add_35_beta_0, epsilon = add_35_epsilon_0, gamma = add_35_gamma_0, mean = add_19_mean_0, variance = add_19_variance_0, x = reshape_69)[name = tensor("add_35")]; + tensor input_123 = silu(x = add_35)[name = tensor("input_123")]; + tensor var_1013 = const()[name = tensor("op_1013"), val = tensor([1, 1])]; + tensor var_1015 = const()[name = tensor("op_1015"), val = tensor([1, 1])]; + tensor hidden_states_29_pad_type_0 = const()[name = tensor("hidden_states_29_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_29_pad_0 = const()[name = tensor("hidden_states_29_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor hidden_states_29 = conv(bias = vae_encoder_mid_block_resnets_0_conv2_bias, dilations = var_1015, groups = var_819, pad = hidden_states_29_pad_0, pad_type = hidden_states_29_pad_type_0, strides = var_1013, weight = vae_encoder_mid_block_resnets_0_conv2_weight, x = input_123)[name = tensor("hidden_states_29")]; + tensor var_1018 = add(x = var_1000, y = hidden_states_29)[name = tensor("op_1018")]; + tensor reshape_72_shape_0 = const()[name = tensor("reshape_72_shape_0"), val = tensor([1, 32, 16, 16384])]; + tensor reshape_72 = reshape(shape = reshape_72_shape_0, x = var_1018)[name = tensor("reshape_72")]; + tensor reduce_mean_54_axes_0 = const()[name = tensor("reduce_mean_54_axes_0"), val = tensor([2, 3])]; + tensor reduce_mean_54_keep_dims_0 = const()[name = tensor("reduce_mean_54_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_54 = reduce_mean(axes = reduce_mean_54_axes_0, keep_dims = reduce_mean_54_keep_dims_0, x = reshape_72)[name = tensor("reduce_mean_54")]; + tensor sub_36 = sub(x = reshape_72, y = reduce_mean_54)[name = tensor("sub_36")]; + tensor square_18 = square(x = sub_36)[name = tensor("square_18")]; + tensor reduce_mean_56_axes_0 = const()[name = tensor("reduce_mean_56_axes_0"), val = tensor([2, 3])]; + tensor reduce_mean_56_keep_dims_0 = const()[name = tensor("reduce_mean_56_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_56 = reduce_mean(axes = reduce_mean_56_axes_0, keep_dims = reduce_mean_56_keep_dims_0, x = square_18)[name = tensor("reduce_mean_56")]; + tensor add_36_y_0 = const()[name = tensor("add_36_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_36 = add(x = reduce_mean_56, y = add_36_y_0)[name = tensor("add_36")]; + tensor sqrt_18 = sqrt(x = add_36)[name = tensor("sqrt_18")]; + tensor real_div_18 = real_div(x = sub_36, y = sqrt_18)[name = tensor("real_div_18")]; + tensor reshape_73_shape_0 = const()[name = tensor("reshape_73_shape_0"), val = tensor([1, 512, 16384])]; + tensor reshape_73 = reshape(shape = reshape_73_shape_0, x = real_div_18)[name = tensor("reshape_73")]; + tensor reshape_74 = const()[name = tensor("reshape_74"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136652096)))]; + tensor mul_18 = mul(x = reshape_73, y = reshape_74)[name = tensor("mul_18")]; + tensor reshape_75 = const()[name = tensor("reshape_75"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136654208)))]; + tensor add_37 = add(x = mul_18, y = reshape_75)[name = tensor("add_37")]; + tensor input_129_perm_0 = const()[name = tensor("input_129_perm_0"), val = tensor([0, 2, 1])]; + tensor transpose_6 = transpose(perm = input_129_perm_0, x = add_37)[name = tensor("transpose_6")]; + tensor tensor_1 = linear(bias = vae_encoder_mid_block_attentions_0_to_q_bias, weight = vae_encoder_mid_block_attentions_0_to_q_weight, x = transpose_6)[name = tensor("tensor_1")]; + tensor tensor_7 = linear(bias = vae_encoder_mid_block_attentions_0_to_k_bias, weight = vae_encoder_mid_block_attentions_0_to_k_weight, x = transpose_6)[name = tensor("tensor_7")]; + tensor tensor_13 = linear(bias = vae_encoder_mid_block_attentions_0_to_v_bias, weight = vae_encoder_mid_block_attentions_0_to_v_weight, x = transpose_6)[name = tensor("tensor_13")]; + tensor var_1046 = const()[name = tensor("op_1046"), val = tensor([1, 16384, 1, 512])]; + tensor tensor_3 = reshape(shape = var_1046, x = tensor_1)[name = tensor("tensor_3")]; + tensor var_1048 = const()[name = tensor("op_1048"), val = tensor([0, 2, 1, 3])]; + tensor var_1055 = const()[name = tensor("op_1055"), val = tensor([1, 16384, 512])]; + tensor transpose_5 = transpose(perm = var_1048, x = tensor_3)[name = tensor("transpose_5")]; + tensor query = reshape(shape = var_1055, x = transpose_5)[name = tensor("query")]; + tensor var_1064 = const()[name = tensor("op_1064"), val = tensor([1, 16384, 1, 512])]; + tensor tensor_9 = reshape(shape = var_1064, x = tensor_7)[name = tensor("tensor_9")]; + tensor var_1066 = const()[name = tensor("op_1066"), val = tensor([0, 2, 1, 3])]; + tensor var_1073 = const()[name = tensor("op_1073"), val = tensor([1, 16384, 512])]; + tensor transpose_4 = transpose(perm = var_1066, x = tensor_9)[name = tensor("transpose_4")]; + tensor key = reshape(shape = var_1073, x = transpose_4)[name = tensor("key")]; + tensor var_1082 = const()[name = tensor("op_1082"), val = tensor([1, 16384, 1, 512])]; + tensor tensor_15 = reshape(shape = var_1082, x = tensor_13)[name = tensor("tensor_15")]; + tensor var_1084 = const()[name = tensor("op_1084"), val = tensor([0, 2, 1, 3])]; + tensor var_1091 = const()[name = tensor("op_1091"), val = tensor([1, 16384, 512])]; + tensor transpose_3 = transpose(perm = var_1084, x = tensor_15)[name = tensor("transpose_3")]; + tensor value = reshape(shape = var_1091, x = transpose_3)[name = tensor("value")]; + tensor var_1098_perm_0 = const()[name = tensor("op_1098_perm_0"), val = tensor([0, -1, -2])]; + tensor query_scaled = mul(x = var_807, y = query)[name = tensor("query_scaled")]; + tensor attention_scores_1_bmm_transpose_x_0 = const()[name = tensor("attention_scores_1_bmm_transpose_x_0"), val = tensor(false)]; + tensor attention_scores_1_bmm_transpose_y_0 = const()[name = tensor("attention_scores_1_bmm_transpose_y_0"), val = tensor(false)]; + tensor transpose_2 = transpose(perm = var_1098_perm_0, x = key)[name = tensor("transpose_2")]; + tensor attention_scores_1_bmm = matmul(transpose_x = attention_scores_1_bmm_transpose_x_0, transpose_y = attention_scores_1_bmm_transpose_y_0, x = query_scaled, y = transpose_2)[name = tensor("attention_scores_1_bmm")]; + tensor attention_probs_1 = softmax(axis = var_809, x = attention_scores_1_bmm)[name = tensor("attention_probs_1")]; + tensor tensor_19_transpose_x_0 = const()[name = tensor("tensor_19_transpose_x_0"), val = tensor(false)]; + tensor tensor_19_transpose_y_0 = const()[name = tensor("tensor_19_transpose_y_0"), val = tensor(false)]; + tensor tensor_19 = matmul(transpose_x = tensor_19_transpose_x_0, transpose_y = tensor_19_transpose_y_0, x = attention_probs_1, y = value)[name = tensor("tensor_19")]; + tensor var_1111 = const()[name = tensor("op_1111"), val = tensor([1, 1, 16384, 512])]; + tensor tensor_workaround = reshape(shape = var_1111, x = tensor_19)[name = tensor("tensor_workaround")]; + tensor var_1113 = const()[name = tensor("op_1113"), val = tensor([0, 2, 1, 3])]; + tensor var_1120 = const()[name = tensor("op_1120"), val = tensor([1, 16384, 512])]; + tensor transpose_1 = transpose(perm = var_1113, x = tensor_workaround)[name = tensor("transpose_1")]; + tensor input_131 = reshape(shape = var_1120, x = transpose_1)[name = tensor("input_131")]; + tensor input_133 = linear(bias = vae_encoder_mid_block_attentions_0_to_out_0_bias, weight = vae_encoder_mid_block_attentions_0_to_out_0_weight, x = input_131)[name = tensor("input_133")]; + tensor var_1124_perm_0 = const()[name = tensor("op_1124_perm_0"), val = tensor([0, -1, -2])]; + tensor var_1125 = const()[name = tensor("op_1125"), val = tensor([1, 512, 128, 128])]; + tensor transpose_0 = transpose(perm = var_1124_perm_0, x = input_133)[name = tensor("transpose_0")]; + tensor hidden_states_37 = reshape(shape = var_1125, x = transpose_0)[name = tensor("hidden_states_37")]; + tensor hidden_states_39 = add(x = hidden_states_37, y = var_1018)[name = tensor("hidden_states_39")]; + tensor reshape_76_shape_0 = const()[name = tensor("reshape_76_shape_0"), val = tensor([1, 32, 16, 128, 128])]; + tensor reshape_76 = reshape(shape = reshape_76_shape_0, x = hidden_states_39)[name = tensor("reshape_76")]; + tensor reduce_mean_57_axes_0 = const()[name = tensor("reduce_mean_57_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_57_keep_dims_0 = const()[name = tensor("reduce_mean_57_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_57 = reduce_mean(axes = reduce_mean_57_axes_0, keep_dims = reduce_mean_57_keep_dims_0, x = reshape_76)[name = tensor("reduce_mean_57")]; + tensor sub_38 = sub(x = reshape_76, y = reduce_mean_57)[name = tensor("sub_38")]; + tensor square_19 = square(x = sub_38)[name = tensor("square_19")]; + tensor reduce_mean_59_axes_0 = const()[name = tensor("reduce_mean_59_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_59_keep_dims_0 = const()[name = tensor("reduce_mean_59_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_59 = reduce_mean(axes = reduce_mean_59_axes_0, keep_dims = reduce_mean_59_keep_dims_0, x = square_19)[name = tensor("reduce_mean_59")]; + tensor add_38_y_0 = const()[name = tensor("add_38_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_38 = add(x = reduce_mean_59, y = add_38_y_0)[name = tensor("add_38")]; + tensor sqrt_19 = sqrt(x = add_38)[name = tensor("sqrt_19")]; + tensor real_div_19 = real_div(x = sub_38, y = sqrt_19)[name = tensor("real_div_19")]; + tensor reshape_77_shape_0 = const()[name = tensor("reshape_77_shape_0"), val = tensor([1, 512, 128, 128])]; + tensor reshape_77 = reshape(shape = reshape_77_shape_0, x = real_div_19)[name = tensor("reshape_77")]; + tensor add_39_gamma_0 = const()[name = tensor("add_39_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136656320)))]; + tensor add_39_beta_0 = const()[name = tensor("add_39_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136658432)))]; + tensor add_39_epsilon_0 = const()[name = tensor("add_39_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_39 = batch_norm(beta = add_39_beta_0, epsilon = add_39_epsilon_0, gamma = add_39_gamma_0, mean = add_19_mean_0, variance = add_19_variance_0, x = reshape_77)[name = tensor("add_39")]; + tensor input_139 = silu(x = add_39)[name = tensor("input_139")]; + tensor var_1132 = const()[name = tensor("op_1132"), val = tensor([1, 1])]; + tensor var_1134 = const()[name = tensor("op_1134"), val = tensor([1, 1])]; + tensor input_141_pad_type_0 = const()[name = tensor("input_141_pad_type_0"), val = tensor("custom")]; + tensor input_141_pad_0 = const()[name = tensor("input_141_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input_141 = conv(bias = vae_encoder_mid_block_resnets_1_conv1_bias, dilations = var_1134, groups = var_819, pad = input_141_pad_0, pad_type = input_141_pad_type_0, strides = var_1132, weight = vae_encoder_mid_block_resnets_1_conv1_weight, x = input_139)[name = tensor("input_141")]; + tensor reshape_80_shape_0 = const()[name = tensor("reshape_80_shape_0"), val = tensor([1, 32, 16, 128, 128])]; + tensor reshape_80 = reshape(shape = reshape_80_shape_0, x = input_141)[name = tensor("reshape_80")]; + tensor reduce_mean_60_axes_0 = const()[name = tensor("reduce_mean_60_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_60_keep_dims_0 = const()[name = tensor("reduce_mean_60_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_60 = reduce_mean(axes = reduce_mean_60_axes_0, keep_dims = reduce_mean_60_keep_dims_0, x = reshape_80)[name = tensor("reduce_mean_60")]; + tensor sub_40 = sub(x = reshape_80, y = reduce_mean_60)[name = tensor("sub_40")]; + tensor square_20 = square(x = sub_40)[name = tensor("square_20")]; + tensor reduce_mean_62_axes_0 = const()[name = tensor("reduce_mean_62_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_62_keep_dims_0 = const()[name = tensor("reduce_mean_62_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_62 = reduce_mean(axes = reduce_mean_62_axes_0, keep_dims = reduce_mean_62_keep_dims_0, x = square_20)[name = tensor("reduce_mean_62")]; + tensor add_40_y_0 = const()[name = tensor("add_40_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_40 = add(x = reduce_mean_62, y = add_40_y_0)[name = tensor("add_40")]; + tensor sqrt_20 = sqrt(x = add_40)[name = tensor("sqrt_20")]; + tensor real_div_20 = real_div(x = sub_40, y = sqrt_20)[name = tensor("real_div_20")]; + tensor reshape_81_shape_0 = const()[name = tensor("reshape_81_shape_0"), val = tensor([1, 512, 128, 128])]; + tensor reshape_81 = reshape(shape = reshape_81_shape_0, x = real_div_20)[name = tensor("reshape_81")]; + tensor add_41_gamma_0 = const()[name = tensor("add_41_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136660544)))]; + tensor add_41_beta_0 = const()[name = tensor("add_41_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136662656)))]; + tensor add_41_epsilon_0 = const()[name = tensor("add_41_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_41 = batch_norm(beta = add_41_beta_0, epsilon = add_41_epsilon_0, gamma = add_41_gamma_0, mean = add_19_mean_0, variance = add_19_variance_0, x = reshape_81)[name = tensor("add_41")]; + tensor input_145 = silu(x = add_41)[name = tensor("input_145")]; + tensor var_1140 = const()[name = tensor("op_1140"), val = tensor([1, 1])]; + tensor var_1142 = const()[name = tensor("op_1142"), val = tensor([1, 1])]; + tensor hidden_states_pad_type_0 = const()[name = tensor("hidden_states_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_pad_0 = const()[name = tensor("hidden_states_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor hidden_states = conv(bias = vae_encoder_mid_block_resnets_1_conv2_bias, dilations = var_1142, groups = var_819, pad = hidden_states_pad_0, pad_type = hidden_states_pad_type_0, strides = var_1140, weight = vae_encoder_mid_block_resnets_1_conv2_weight, x = input_145)[name = tensor("hidden_states")]; + tensor var_1145 = add(x = hidden_states_39, y = hidden_states)[name = tensor("op_1145")]; + tensor reshape_84_shape_0 = const()[name = tensor("reshape_84_shape_0"), val = tensor([1, 32, 16, 128, 128])]; + tensor reshape_84 = reshape(shape = reshape_84_shape_0, x = var_1145)[name = tensor("reshape_84")]; + tensor reduce_mean_63_axes_0 = const()[name = tensor("reduce_mean_63_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_63_keep_dims_0 = const()[name = tensor("reduce_mean_63_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_63 = reduce_mean(axes = reduce_mean_63_axes_0, keep_dims = reduce_mean_63_keep_dims_0, x = reshape_84)[name = tensor("reduce_mean_63")]; + tensor sub_42 = sub(x = reshape_84, y = reduce_mean_63)[name = tensor("sub_42")]; + tensor square_21 = square(x = sub_42)[name = tensor("square_21")]; + tensor reduce_mean_65_axes_0 = const()[name = tensor("reduce_mean_65_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_65_keep_dims_0 = const()[name = tensor("reduce_mean_65_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_65 = reduce_mean(axes = reduce_mean_65_axes_0, keep_dims = reduce_mean_65_keep_dims_0, x = square_21)[name = tensor("reduce_mean_65")]; + tensor add_42_y_0 = const()[name = tensor("add_42_y_0"), val = tensor(0x1.0c6f7ap-20)]; + tensor add_42 = add(x = reduce_mean_65, y = add_42_y_0)[name = tensor("add_42")]; + tensor sqrt_21 = sqrt(x = add_42)[name = tensor("sqrt_21")]; + tensor real_div_21 = real_div(x = sub_42, y = sqrt_21)[name = tensor("real_div_21")]; + tensor reshape_85_shape_0 = const()[name = tensor("reshape_85_shape_0"), val = tensor([1, 512, 128, 128])]; + tensor reshape_85 = reshape(shape = reshape_85_shape_0, x = real_div_21)[name = tensor("reshape_85")]; + tensor add_43_gamma_0 = const()[name = tensor("add_43_gamma_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136664768)))]; + tensor add_43_beta_0 = const()[name = tensor("add_43_beta_0"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(136666880)))]; + tensor add_43_epsilon_0 = const()[name = tensor("add_43_epsilon_0"), val = tensor(0x1.4f8b58p-17)]; + tensor add_43 = batch_norm(beta = add_43_beta_0, epsilon = add_43_epsilon_0, gamma = add_43_gamma_0, mean = add_19_mean_0, variance = add_19_variance_0, x = reshape_85)[name = tensor("add_43")]; + tensor input_153 = silu(x = add_43)[name = tensor("input_153")]; + tensor var_1150 = const()[name = tensor("op_1150"), val = tensor([1, 1])]; + tensor var_1152 = const()[name = tensor("op_1152"), val = tensor([1, 1])]; + tensor input_pad_type_0 = const()[name = tensor("input_pad_type_0"), val = tensor("custom")]; + tensor input_pad_0 = const()[name = tensor("input_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor input = conv(bias = vae_encoder_conv_out_bias, dilations = var_1152, groups = var_819, pad = input_pad_0, pad_type = input_pad_type_0, strides = var_1150, weight = vae_encoder_conv_out_weight, x = input_153)[name = tensor("input")]; + tensor var_1158 = const()[name = tensor("op_1158"), val = tensor(1)]; + tensor var_1159 = const()[name = tensor("op_1159"), val = tensor([1, 1])]; + tensor var_1161 = const()[name = tensor("op_1161"), val = tensor([1, 1])]; + tensor var_1163_pad_type_0 = const()[name = tensor("op_1163_pad_type_0"), val = tensor("custom")]; + tensor var_1163_pad_0 = const()[name = tensor("op_1163_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor latent = conv(bias = quant_conv_bias, dilations = var_1161, groups = var_1158, pad = var_1163_pad_0, pad_type = var_1163_pad_type_0, strides = var_1159, weight = quant_conv_weight, x = input)[name = tensor("op_1163")]; + } -> (latent); +} \ No newline at end of file