diff --git "a/original/compiled/VAEDecoder.mlmodelc/model.mil" "b/original/compiled/VAEDecoder.mlmodelc/model.mil" new file mode 100644--- /dev/null +++ "b/original/compiled/VAEDecoder.mlmodelc/model.mil" @@ -0,0 +1,965 @@ +program(1.0) +[buildInfo = dict, tensor>({{"coremlc-component-MIL", "3304.5.2"}, {"coremlc-version", "3304.6.2"}, {"coremltools-component-torch", "2.1.2"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "7.1"}})] +{ + func main(tensor z) { + tensor var_7 = const()[name = tensor("op_7"), val = tensor(1)]; + tensor var_10 = const()[name = tensor("op_10"), val = tensor([1, 1])]; + tensor var_12 = const()[name = tensor("op_12"), val = tensor([1, 1])]; + tensor input_1_pad_type_0 = const()[name = tensor("input_1_pad_type_0"), val = tensor("custom")]; + tensor input_1_pad_0 = const()[name = tensor("input_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor post_quant_conv_weight_to_fp16 = const()[name = tensor("post_quant_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; + tensor post_quant_conv_bias_to_fp16 = const()[name = tensor("post_quant_conv_bias_to_fp16"), val = tensor([0x1.06cp-5, -0x1.594p-4, -0x1.f24p-3, 0x1.0d8p-3])]; + tensor input_1_cast_fp16 = conv(bias = post_quant_conv_bias_to_fp16, dilations = var_12, groups = var_7, pad = input_1_pad_0, pad_type = input_1_pad_type_0, strides = var_10, weight = post_quant_conv_weight_to_fp16, x = z)[name = tensor("input_1_cast_fp16")]; + tensor var_26 = const()[name = tensor("op_26"), val = tensor(1)]; + tensor var_44 = const()[name = tensor("op_44"), val = tensor([1, 1])]; + tensor var_46 = const()[name = tensor("op_46"), val = tensor([1, 1])]; + tensor input_3_pad_type_0 = const()[name = tensor("input_3_pad_type_0"), val = tensor("custom")]; + tensor input_3_pad_0 = const()[name = tensor("input_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor decoder_conv_in_weight_to_fp16 = const()[name = tensor("decoder_conv_in_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(192)))]; + tensor decoder_conv_in_bias_to_fp16 = const()[name = tensor("decoder_conv_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(37120)))]; + tensor input_3_cast_fp16 = conv(bias = decoder_conv_in_bias_to_fp16, dilations = var_46, groups = var_26, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = var_44, weight = decoder_conv_in_weight_to_fp16, x = input_1_cast_fp16)[name = tensor("input_3_cast_fp16")]; + tensor reshape_0_shape_0 = const()[name = tensor("reshape_0_shape_0"), val = tensor([1, 32, 16, 48, 80])]; + tensor reshape_0_cast_fp16 = reshape(shape = reshape_0_shape_0, x = input_3_cast_fp16)[name = tensor("reshape_0_cast_fp16")]; + 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_cast_fp16 = reduce_mean(axes = reduce_mean_0_axes_0, keep_dims = reduce_mean_0_keep_dims_0, x = reshape_0_cast_fp16)[name = tensor("reduce_mean_0_cast_fp16")]; + tensor sub_0_cast_fp16 = sub(x = reshape_0_cast_fp16, y = reduce_mean_0_cast_fp16)[name = tensor("sub_0_cast_fp16")]; + tensor square_0_cast_fp16 = square(x = sub_0_cast_fp16)[name = tensor("square_0_cast_fp16")]; + 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_cast_fp16 = reduce_mean(axes = reduce_mean_2_axes_0, keep_dims = reduce_mean_2_keep_dims_0, x = square_0_cast_fp16)[name = tensor("reduce_mean_2_cast_fp16")]; + tensor add_0_y_0_to_fp16 = const()[name = tensor("add_0_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_0_cast_fp16 = add(x = reduce_mean_2_cast_fp16, y = add_0_y_0_to_fp16)[name = tensor("add_0_cast_fp16")]; + tensor sqrt_0_cast_fp16 = sqrt(x = add_0_cast_fp16)[name = tensor("sqrt_0_cast_fp16")]; + tensor real_div_0_cast_fp16 = real_div(x = sub_0_cast_fp16, y = sqrt_0_cast_fp16)[name = tensor("real_div_0_cast_fp16")]; + tensor reshape_1_shape_0 = const()[name = tensor("reshape_1_shape_0"), val = tensor([1, 512, 48, 80])]; + tensor reshape_1_cast_fp16 = reshape(shape = reshape_1_shape_0, x = real_div_0_cast_fp16)[name = tensor("reshape_1_cast_fp16")]; + tensor add_1_mean_0_to_fp16 = const()[name = tensor("add_1_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(38208)))]; + tensor add_1_variance_0_to_fp16 = const()[name = tensor("add_1_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39296)))]; + tensor add_1_gamma_0_to_fp16 = const()[name = tensor("add_1_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(40384)))]; + tensor add_1_beta_0_to_fp16 = const()[name = tensor("add_1_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(41472)))]; + tensor add_1_epsilon_0_to_fp16 = const()[name = tensor("add_1_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_1_cast_fp16 = batch_norm(beta = add_1_beta_0_to_fp16, epsilon = add_1_epsilon_0_to_fp16, gamma = add_1_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_1_cast_fp16)[name = tensor("add_1_cast_fp16")]; + tensor hidden_states_1_cast_fp16 = silu(x = add_1_cast_fp16)[name = tensor("hidden_states_1_cast_fp16")]; + tensor var_65 = const()[name = tensor("op_65"), val = tensor([1, 1])]; + tensor var_67 = const()[name = tensor("op_67"), val = tensor([1, 1])]; + tensor input_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 decoder_mid_block_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("decoder_mid_block_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42560)))]; + tensor decoder_mid_block_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("decoder_mid_block_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4761216)))]; + tensor input_7_cast_fp16 = conv(bias = decoder_mid_block_resnets_0_conv1_bias_to_fp16, dilations = var_67, groups = var_26, pad = input_7_pad_0, pad_type = input_7_pad_type_0, strides = var_65, weight = decoder_mid_block_resnets_0_conv1_weight_to_fp16, x = hidden_states_1_cast_fp16)[name = tensor("input_7_cast_fp16")]; + tensor reshape_4_shape_0 = const()[name = tensor("reshape_4_shape_0"), val = tensor([1, 32, 16, 48, 80])]; + tensor reshape_4_cast_fp16 = reshape(shape = reshape_4_shape_0, x = input_7_cast_fp16)[name = tensor("reshape_4_cast_fp16")]; + 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_cast_fp16 = reduce_mean(axes = reduce_mean_3_axes_0, keep_dims = reduce_mean_3_keep_dims_0, x = reshape_4_cast_fp16)[name = tensor("reduce_mean_3_cast_fp16")]; + tensor sub_2_cast_fp16 = sub(x = reshape_4_cast_fp16, y = reduce_mean_3_cast_fp16)[name = tensor("sub_2_cast_fp16")]; + tensor square_1_cast_fp16 = square(x = sub_2_cast_fp16)[name = tensor("square_1_cast_fp16")]; + 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_cast_fp16 = reduce_mean(axes = reduce_mean_5_axes_0, keep_dims = reduce_mean_5_keep_dims_0, x = square_1_cast_fp16)[name = tensor("reduce_mean_5_cast_fp16")]; + tensor add_2_y_0_to_fp16 = const()[name = tensor("add_2_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_2_cast_fp16 = add(x = reduce_mean_5_cast_fp16, y = add_2_y_0_to_fp16)[name = tensor("add_2_cast_fp16")]; + tensor sqrt_1_cast_fp16 = sqrt(x = add_2_cast_fp16)[name = tensor("sqrt_1_cast_fp16")]; + tensor real_div_1_cast_fp16 = real_div(x = sub_2_cast_fp16, y = sqrt_1_cast_fp16)[name = tensor("real_div_1_cast_fp16")]; + tensor reshape_5_shape_0 = const()[name = tensor("reshape_5_shape_0"), val = tensor([1, 512, 48, 80])]; + tensor reshape_5_cast_fp16 = reshape(shape = reshape_5_shape_0, x = real_div_1_cast_fp16)[name = tensor("reshape_5_cast_fp16")]; + tensor add_3_gamma_0_to_fp16 = const()[name = tensor("add_3_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4762304)))]; + tensor add_3_beta_0_to_fp16 = const()[name = tensor("add_3_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4763392)))]; + tensor add_3_epsilon_0_to_fp16 = const()[name = tensor("add_3_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_3_cast_fp16 = batch_norm(beta = add_3_beta_0_to_fp16, epsilon = add_3_epsilon_0_to_fp16, gamma = add_3_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_5_cast_fp16)[name = tensor("add_3_cast_fp16")]; + tensor input_11_cast_fp16 = silu(x = add_3_cast_fp16)[name = tensor("input_11_cast_fp16")]; + tensor var_77 = const()[name = tensor("op_77"), val = tensor([1, 1])]; + tensor var_79 = const()[name = tensor("op_79"), val = tensor([1, 1])]; + tensor hidden_states_5_pad_type_0 = const()[name = tensor("hidden_states_5_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_5_pad_0 = const()[name = tensor("hidden_states_5_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor decoder_mid_block_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("decoder_mid_block_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4764480)))]; + tensor decoder_mid_block_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("decoder_mid_block_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9483136)))]; + tensor hidden_states_5_cast_fp16 = conv(bias = decoder_mid_block_resnets_0_conv2_bias_to_fp16, dilations = var_79, groups = var_26, pad = hidden_states_5_pad_0, pad_type = hidden_states_5_pad_type_0, strides = var_77, weight = decoder_mid_block_resnets_0_conv2_weight_to_fp16, x = input_11_cast_fp16)[name = tensor("hidden_states_5_cast_fp16")]; + tensor var_82_cast_fp16 = add(x = input_3_cast_fp16, y = hidden_states_5_cast_fp16)[name = tensor("op_82_cast_fp16")]; + tensor reshape_8_shape_0 = const()[name = tensor("reshape_8_shape_0"), val = tensor([1, 32, 16, 3840])]; + tensor reshape_8_cast_fp16 = reshape(shape = reshape_8_shape_0, x = var_82_cast_fp16)[name = tensor("reshape_8_cast_fp16")]; + tensor reduce_mean_6_axes_0 = const()[name = tensor("reduce_mean_6_axes_0"), val = tensor([2, 3])]; + tensor reduce_mean_6_keep_dims_0 = const()[name = tensor("reduce_mean_6_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_6_cast_fp16 = reduce_mean(axes = reduce_mean_6_axes_0, keep_dims = reduce_mean_6_keep_dims_0, x = reshape_8_cast_fp16)[name = tensor("reduce_mean_6_cast_fp16")]; + tensor sub_4_cast_fp16 = sub(x = reshape_8_cast_fp16, y = reduce_mean_6_cast_fp16)[name = tensor("sub_4_cast_fp16")]; + tensor square_2_cast_fp16 = square(x = sub_4_cast_fp16)[name = tensor("square_2_cast_fp16")]; + tensor reduce_mean_8_axes_0 = const()[name = tensor("reduce_mean_8_axes_0"), val = tensor([2, 3])]; + tensor reduce_mean_8_keep_dims_0 = const()[name = tensor("reduce_mean_8_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_8_cast_fp16 = reduce_mean(axes = reduce_mean_8_axes_0, keep_dims = reduce_mean_8_keep_dims_0, x = square_2_cast_fp16)[name = tensor("reduce_mean_8_cast_fp16")]; + tensor add_4_y_0_to_fp16 = const()[name = tensor("add_4_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_4_cast_fp16 = add(x = reduce_mean_8_cast_fp16, y = add_4_y_0_to_fp16)[name = tensor("add_4_cast_fp16")]; + tensor sqrt_2_cast_fp16 = sqrt(x = add_4_cast_fp16)[name = tensor("sqrt_2_cast_fp16")]; + tensor real_div_2_cast_fp16 = real_div(x = sub_4_cast_fp16, y = sqrt_2_cast_fp16)[name = tensor("real_div_2_cast_fp16")]; + tensor reshape_9_shape_0 = const()[name = tensor("reshape_9_shape_0"), val = tensor([1, 512, 3840])]; + tensor reshape_9_cast_fp16 = reshape(shape = reshape_9_shape_0, x = real_div_2_cast_fp16)[name = tensor("reshape_9_cast_fp16")]; + tensor reshape_10_to_fp16 = const()[name = tensor("reshape_10_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9484224)))]; + tensor mul_2_cast_fp16 = mul(x = reshape_9_cast_fp16, y = reshape_10_to_fp16)[name = tensor("mul_2_cast_fp16")]; + tensor reshape_11_to_fp16 = const()[name = tensor("reshape_11_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9485312)))]; + tensor add_5_cast_fp16 = add(x = mul_2_cast_fp16, y = reshape_11_to_fp16)[name = tensor("add_5_cast_fp16")]; + tensor input_15_perm_0 = const()[name = tensor("input_15_perm_0"), val = tensor([0, 2, 1])]; + tensor decoder_mid_block_attentions_0_to_q_weight_to_fp16 = const()[name = tensor("decoder_mid_block_attentions_0_to_q_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9486400)))]; + tensor decoder_mid_block_attentions_0_to_q_bias_to_fp16 = const()[name = tensor("decoder_mid_block_attentions_0_to_q_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10010752)))]; + tensor transpose_11 = transpose(perm = input_15_perm_0, x = add_5_cast_fp16)[name = tensor("transpose_11")]; + tensor linear_0_cast_fp16 = linear(bias = decoder_mid_block_attentions_0_to_q_bias_to_fp16, weight = decoder_mid_block_attentions_0_to_q_weight_to_fp16, x = transpose_11)[name = tensor("linear_0_cast_fp16")]; + tensor decoder_mid_block_attentions_0_to_k_weight_to_fp16 = const()[name = tensor("decoder_mid_block_attentions_0_to_k_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10011840)))]; + tensor decoder_mid_block_attentions_0_to_k_bias_to_fp16 = const()[name = tensor("decoder_mid_block_attentions_0_to_k_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10536192)))]; + tensor linear_1_cast_fp16 = linear(bias = decoder_mid_block_attentions_0_to_k_bias_to_fp16, weight = decoder_mid_block_attentions_0_to_k_weight_to_fp16, x = transpose_11)[name = tensor("linear_1_cast_fp16")]; + tensor decoder_mid_block_attentions_0_to_v_weight_to_fp16 = const()[name = tensor("decoder_mid_block_attentions_0_to_v_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10537280)))]; + tensor decoder_mid_block_attentions_0_to_v_bias_to_fp16 = const()[name = tensor("decoder_mid_block_attentions_0_to_v_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11061632)))]; + tensor linear_2_cast_fp16 = linear(bias = decoder_mid_block_attentions_0_to_v_bias_to_fp16, weight = decoder_mid_block_attentions_0_to_v_weight_to_fp16, x = transpose_11)[name = tensor("linear_2_cast_fp16")]; + tensor var_123 = const()[name = tensor("op_123"), val = tensor([1, -1, 1, 512])]; + tensor var_124_cast_fp16 = reshape(shape = var_123, x = linear_0_cast_fp16)[name = tensor("op_124_cast_fp16")]; + tensor var_126 = const()[name = tensor("op_126"), val = tensor([1, -1, 1, 512])]; + tensor var_127_cast_fp16 = reshape(shape = var_126, x = linear_1_cast_fp16)[name = tensor("op_127_cast_fp16")]; + tensor var_129 = const()[name = tensor("op_129"), val = tensor([1, -1, 1, 512])]; + tensor var_130_cast_fp16 = reshape(shape = var_129, x = linear_2_cast_fp16)[name = tensor("op_130_cast_fp16")]; + tensor value_perm_0 = const()[name = tensor("value_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor mul_3_y_0_to_fp16 = const()[name = tensor("mul_3_y_0_to_fp16"), val = tensor(0x1.6ap-5)]; + tensor mul_3_cast_fp16 = mul(x = var_124_cast_fp16, y = mul_3_y_0_to_fp16)[name = tensor("mul_3_cast_fp16")]; + tensor matmul_0_transpose_y_0 = const()[name = tensor("matmul_0_transpose_y_0"), val = tensor(true)]; + tensor matmul_0_transpose_x_0 = const()[name = tensor("matmul_0_transpose_x_0"), val = tensor(false)]; + tensor transpose_4_perm_0 = const()[name = tensor("transpose_4_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_5_perm_0 = const()[name = tensor("transpose_5_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor transpose_8 = transpose(perm = transpose_5_perm_0, x = var_127_cast_fp16)[name = tensor("transpose_8")]; + tensor transpose_9 = transpose(perm = transpose_4_perm_0, x = mul_3_cast_fp16)[name = tensor("transpose_9")]; + tensor matmul_0_cast_fp16 = matmul(transpose_x = matmul_0_transpose_x_0, transpose_y = matmul_0_transpose_y_0, x = transpose_9, y = transpose_8)[name = tensor("matmul_0_cast_fp16")]; + tensor softmax_0_axis_0 = const()[name = tensor("softmax_0_axis_0"), val = tensor(-1)]; + tensor softmax_0_cast_fp16 = softmax(axis = softmax_0_axis_0, x = matmul_0_cast_fp16)[name = tensor("softmax_0_cast_fp16")]; + tensor hidden_states_11_transpose_x_0 = const()[name = tensor("hidden_states_11_transpose_x_0"), val = tensor(false)]; + tensor hidden_states_11_transpose_y_0 = const()[name = tensor("hidden_states_11_transpose_y_0"), val = tensor(false)]; + tensor transpose_10 = transpose(perm = value_perm_0, x = var_130_cast_fp16)[name = tensor("transpose_10")]; + tensor hidden_states_11_cast_fp16 = matmul(transpose_x = hidden_states_11_transpose_x_0, transpose_y = hidden_states_11_transpose_y_0, x = softmax_0_cast_fp16, y = transpose_10)[name = tensor("hidden_states_11_cast_fp16")]; + tensor var_133_perm_0 = const()[name = tensor("op_133_perm_0"), val = tensor([0, 2, 1, 3])]; + tensor var_137 = const()[name = tensor("op_137"), val = tensor([1, -1, 512])]; + tensor transpose_7 = transpose(perm = var_133_perm_0, x = hidden_states_11_cast_fp16)[name = tensor("transpose_7")]; + tensor hidden_states_13_cast_fp16 = reshape(shape = var_137, x = transpose_7)[name = tensor("hidden_states_13_cast_fp16")]; + tensor decoder_mid_block_attentions_0_to_out_0_weight_to_fp16 = const()[name = tensor("decoder_mid_block_attentions_0_to_out_0_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11062720)))]; + tensor decoder_mid_block_attentions_0_to_out_0_bias_to_fp16 = const()[name = tensor("decoder_mid_block_attentions_0_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11587072)))]; + tensor linear_3_cast_fp16 = linear(bias = decoder_mid_block_attentions_0_to_out_0_bias_to_fp16, weight = decoder_mid_block_attentions_0_to_out_0_weight_to_fp16, x = hidden_states_13_cast_fp16)[name = tensor("linear_3_cast_fp16")]; + tensor var_144_perm_0 = const()[name = tensor("op_144_perm_0"), val = tensor([0, -1, -2])]; + tensor var_145 = const()[name = tensor("op_145"), val = tensor([1, 512, 48, 80])]; + tensor transpose_6 = transpose(perm = var_144_perm_0, x = linear_3_cast_fp16)[name = tensor("transpose_6")]; + tensor hidden_states_17_cast_fp16 = reshape(shape = var_145, x = transpose_6)[name = tensor("hidden_states_17_cast_fp16")]; + tensor hidden_states_19_cast_fp16 = add(x = hidden_states_17_cast_fp16, y = var_82_cast_fp16)[name = tensor("hidden_states_19_cast_fp16")]; + tensor reshape_12_shape_0 = const()[name = tensor("reshape_12_shape_0"), val = tensor([1, 32, 16, 48, 80])]; + tensor reshape_12_cast_fp16 = reshape(shape = reshape_12_shape_0, x = hidden_states_19_cast_fp16)[name = tensor("reshape_12_cast_fp16")]; + 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_cast_fp16 = reduce_mean(axes = reduce_mean_9_axes_0, keep_dims = reduce_mean_9_keep_dims_0, x = reshape_12_cast_fp16)[name = tensor("reduce_mean_9_cast_fp16")]; + tensor sub_6_cast_fp16 = sub(x = reshape_12_cast_fp16, y = reduce_mean_9_cast_fp16)[name = tensor("sub_6_cast_fp16")]; + tensor square_3_cast_fp16 = square(x = sub_6_cast_fp16)[name = tensor("square_3_cast_fp16")]; + 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_cast_fp16 = reduce_mean(axes = reduce_mean_11_axes_0, keep_dims = reduce_mean_11_keep_dims_0, x = square_3_cast_fp16)[name = tensor("reduce_mean_11_cast_fp16")]; + tensor add_6_y_0_to_fp16 = const()[name = tensor("add_6_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_6_cast_fp16 = add(x = reduce_mean_11_cast_fp16, y = add_6_y_0_to_fp16)[name = tensor("add_6_cast_fp16")]; + tensor sqrt_3_cast_fp16 = sqrt(x = add_6_cast_fp16)[name = tensor("sqrt_3_cast_fp16")]; + tensor real_div_3_cast_fp16 = real_div(x = sub_6_cast_fp16, y = sqrt_3_cast_fp16)[name = tensor("real_div_3_cast_fp16")]; + tensor reshape_13_shape_0 = const()[name = tensor("reshape_13_shape_0"), val = tensor([1, 512, 48, 80])]; + tensor reshape_13_cast_fp16 = reshape(shape = reshape_13_shape_0, x = real_div_3_cast_fp16)[name = tensor("reshape_13_cast_fp16")]; + tensor add_7_gamma_0_to_fp16 = const()[name = tensor("add_7_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11588160)))]; + tensor add_7_beta_0_to_fp16 = const()[name = tensor("add_7_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11589248)))]; + tensor add_7_epsilon_0_to_fp16 = const()[name = tensor("add_7_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_7_cast_fp16 = batch_norm(beta = add_7_beta_0_to_fp16, epsilon = add_7_epsilon_0_to_fp16, gamma = add_7_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_13_cast_fp16)[name = tensor("add_7_cast_fp16")]; + tensor hidden_states_21_cast_fp16 = silu(x = add_7_cast_fp16)[name = tensor("hidden_states_21_cast_fp16")]; + tensor var_160 = const()[name = tensor("op_160"), val = tensor([1, 1])]; + tensor var_162 = const()[name = tensor("op_162"), val = tensor([1, 1])]; + tensor input_25_pad_type_0 = const()[name = tensor("input_25_pad_type_0"), val = tensor("custom")]; + tensor input_25_pad_0 = const()[name = tensor("input_25_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor decoder_mid_block_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("decoder_mid_block_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11590336)))]; + tensor decoder_mid_block_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("decoder_mid_block_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16308992)))]; + tensor input_25_cast_fp16 = conv(bias = decoder_mid_block_resnets_1_conv1_bias_to_fp16, dilations = var_162, groups = var_26, pad = input_25_pad_0, pad_type = input_25_pad_type_0, strides = var_160, weight = decoder_mid_block_resnets_1_conv1_weight_to_fp16, x = hidden_states_21_cast_fp16)[name = tensor("input_25_cast_fp16")]; + tensor reshape_16_shape_0 = const()[name = tensor("reshape_16_shape_0"), val = tensor([1, 32, 16, 48, 80])]; + tensor reshape_16_cast_fp16 = reshape(shape = reshape_16_shape_0, x = input_25_cast_fp16)[name = tensor("reshape_16_cast_fp16")]; + 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_cast_fp16 = reduce_mean(axes = reduce_mean_12_axes_0, keep_dims = reduce_mean_12_keep_dims_0, x = reshape_16_cast_fp16)[name = tensor("reduce_mean_12_cast_fp16")]; + tensor sub_8_cast_fp16 = sub(x = reshape_16_cast_fp16, y = reduce_mean_12_cast_fp16)[name = tensor("sub_8_cast_fp16")]; + tensor square_4_cast_fp16 = square(x = sub_8_cast_fp16)[name = tensor("square_4_cast_fp16")]; + 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_cast_fp16 = reduce_mean(axes = reduce_mean_14_axes_0, keep_dims = reduce_mean_14_keep_dims_0, x = square_4_cast_fp16)[name = tensor("reduce_mean_14_cast_fp16")]; + tensor add_8_y_0_to_fp16 = const()[name = tensor("add_8_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_8_cast_fp16 = add(x = reduce_mean_14_cast_fp16, y = add_8_y_0_to_fp16)[name = tensor("add_8_cast_fp16")]; + tensor sqrt_4_cast_fp16 = sqrt(x = add_8_cast_fp16)[name = tensor("sqrt_4_cast_fp16")]; + tensor real_div_4_cast_fp16 = real_div(x = sub_8_cast_fp16, y = sqrt_4_cast_fp16)[name = tensor("real_div_4_cast_fp16")]; + tensor reshape_17_shape_0 = const()[name = tensor("reshape_17_shape_0"), val = tensor([1, 512, 48, 80])]; + tensor reshape_17_cast_fp16 = reshape(shape = reshape_17_shape_0, x = real_div_4_cast_fp16)[name = tensor("reshape_17_cast_fp16")]; + tensor add_9_gamma_0_to_fp16 = const()[name = tensor("add_9_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16310080)))]; + tensor add_9_beta_0_to_fp16 = const()[name = tensor("add_9_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16311168)))]; + tensor add_9_epsilon_0_to_fp16 = const()[name = tensor("add_9_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_9_cast_fp16 = batch_norm(beta = add_9_beta_0_to_fp16, epsilon = add_9_epsilon_0_to_fp16, gamma = add_9_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_17_cast_fp16)[name = tensor("add_9_cast_fp16")]; + tensor input_29_cast_fp16 = silu(x = add_9_cast_fp16)[name = tensor("input_29_cast_fp16")]; + tensor var_172 = const()[name = tensor("op_172"), val = tensor([1, 1])]; + tensor var_174 = const()[name = tensor("op_174"), val = tensor([1, 1])]; + tensor hidden_states_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 decoder_mid_block_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("decoder_mid_block_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16312256)))]; + tensor decoder_mid_block_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("decoder_mid_block_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21030912)))]; + tensor hidden_states_25_cast_fp16 = conv(bias = decoder_mid_block_resnets_1_conv2_bias_to_fp16, dilations = var_174, groups = var_26, pad = hidden_states_25_pad_0, pad_type = hidden_states_25_pad_type_0, strides = var_172, weight = decoder_mid_block_resnets_1_conv2_weight_to_fp16, x = input_29_cast_fp16)[name = tensor("hidden_states_25_cast_fp16")]; + tensor var_177_cast_fp16 = add(x = hidden_states_19_cast_fp16, y = hidden_states_25_cast_fp16)[name = tensor("op_177_cast_fp16")]; + tensor reshape_20_shape_0 = const()[name = tensor("reshape_20_shape_0"), val = tensor([1, 32, 16, 48, 80])]; + tensor reshape_20_cast_fp16 = reshape(shape = reshape_20_shape_0, x = var_177_cast_fp16)[name = tensor("reshape_20_cast_fp16")]; + 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_cast_fp16 = reduce_mean(axes = reduce_mean_15_axes_0, keep_dims = reduce_mean_15_keep_dims_0, x = reshape_20_cast_fp16)[name = tensor("reduce_mean_15_cast_fp16")]; + tensor sub_10_cast_fp16 = sub(x = reshape_20_cast_fp16, y = reduce_mean_15_cast_fp16)[name = tensor("sub_10_cast_fp16")]; + tensor square_5_cast_fp16 = square(x = sub_10_cast_fp16)[name = tensor("square_5_cast_fp16")]; + 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_cast_fp16 = reduce_mean(axes = reduce_mean_17_axes_0, keep_dims = reduce_mean_17_keep_dims_0, x = square_5_cast_fp16)[name = tensor("reduce_mean_17_cast_fp16")]; + tensor add_10_y_0_to_fp16 = const()[name = tensor("add_10_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_10_cast_fp16 = add(x = reduce_mean_17_cast_fp16, y = add_10_y_0_to_fp16)[name = tensor("add_10_cast_fp16")]; + tensor sqrt_5_cast_fp16 = sqrt(x = add_10_cast_fp16)[name = tensor("sqrt_5_cast_fp16")]; + tensor real_div_5_cast_fp16 = real_div(x = sub_10_cast_fp16, y = sqrt_5_cast_fp16)[name = tensor("real_div_5_cast_fp16")]; + tensor reshape_21_shape_0 = const()[name = tensor("reshape_21_shape_0"), val = tensor([1, 512, 48, 80])]; + tensor reshape_21_cast_fp16 = reshape(shape = reshape_21_shape_0, x = real_div_5_cast_fp16)[name = tensor("reshape_21_cast_fp16")]; + tensor add_11_gamma_0_to_fp16 = const()[name = tensor("add_11_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21032000)))]; + tensor add_11_beta_0_to_fp16 = const()[name = tensor("add_11_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21033088)))]; + tensor add_11_epsilon_0_to_fp16 = const()[name = tensor("add_11_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_11_cast_fp16 = batch_norm(beta = add_11_beta_0_to_fp16, epsilon = add_11_epsilon_0_to_fp16, gamma = add_11_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_21_cast_fp16)[name = tensor("add_11_cast_fp16")]; + tensor hidden_states_27_cast_fp16 = silu(x = add_11_cast_fp16)[name = tensor("hidden_states_27_cast_fp16")]; + tensor var_199 = const()[name = tensor("op_199"), val = tensor([1, 1])]; + tensor var_201 = const()[name = tensor("op_201"), val = tensor([1, 1])]; + tensor input_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 decoder_up_blocks_0_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_0_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21034176)))]; + tensor decoder_up_blocks_0_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_0_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25752832)))]; + tensor input_35_cast_fp16 = conv(bias = decoder_up_blocks_0_resnets_0_conv1_bias_to_fp16, dilations = var_201, groups = var_26, pad = input_35_pad_0, pad_type = input_35_pad_type_0, strides = var_199, weight = decoder_up_blocks_0_resnets_0_conv1_weight_to_fp16, x = hidden_states_27_cast_fp16)[name = tensor("input_35_cast_fp16")]; + tensor reshape_24_shape_0 = const()[name = tensor("reshape_24_shape_0"), val = tensor([1, 32, 16, 48, 80])]; + tensor reshape_24_cast_fp16 = reshape(shape = reshape_24_shape_0, x = input_35_cast_fp16)[name = tensor("reshape_24_cast_fp16")]; + 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_cast_fp16 = reduce_mean(axes = reduce_mean_18_axes_0, keep_dims = reduce_mean_18_keep_dims_0, x = reshape_24_cast_fp16)[name = tensor("reduce_mean_18_cast_fp16")]; + tensor sub_12_cast_fp16 = sub(x = reshape_24_cast_fp16, y = reduce_mean_18_cast_fp16)[name = tensor("sub_12_cast_fp16")]; + tensor square_6_cast_fp16 = square(x = sub_12_cast_fp16)[name = tensor("square_6_cast_fp16")]; + 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_cast_fp16 = reduce_mean(axes = reduce_mean_20_axes_0, keep_dims = reduce_mean_20_keep_dims_0, x = square_6_cast_fp16)[name = tensor("reduce_mean_20_cast_fp16")]; + tensor add_12_y_0_to_fp16 = const()[name = tensor("add_12_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_12_cast_fp16 = add(x = reduce_mean_20_cast_fp16, y = add_12_y_0_to_fp16)[name = tensor("add_12_cast_fp16")]; + tensor sqrt_6_cast_fp16 = sqrt(x = add_12_cast_fp16)[name = tensor("sqrt_6_cast_fp16")]; + tensor real_div_6_cast_fp16 = real_div(x = sub_12_cast_fp16, y = sqrt_6_cast_fp16)[name = tensor("real_div_6_cast_fp16")]; + tensor reshape_25_shape_0 = const()[name = tensor("reshape_25_shape_0"), val = tensor([1, 512, 48, 80])]; + tensor reshape_25_cast_fp16 = reshape(shape = reshape_25_shape_0, x = real_div_6_cast_fp16)[name = tensor("reshape_25_cast_fp16")]; + tensor add_13_gamma_0_to_fp16 = const()[name = tensor("add_13_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25753920)))]; + tensor add_13_beta_0_to_fp16 = const()[name = tensor("add_13_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25755008)))]; + tensor add_13_epsilon_0_to_fp16 = const()[name = tensor("add_13_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_13_cast_fp16 = batch_norm(beta = add_13_beta_0_to_fp16, epsilon = add_13_epsilon_0_to_fp16, gamma = add_13_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_25_cast_fp16)[name = tensor("add_13_cast_fp16")]; + tensor input_39_cast_fp16 = silu(x = add_13_cast_fp16)[name = tensor("input_39_cast_fp16")]; + tensor var_211 = const()[name = tensor("op_211"), val = tensor([1, 1])]; + tensor var_213 = const()[name = tensor("op_213"), val = tensor([1, 1])]; + tensor hidden_states_31_pad_type_0 = const()[name = tensor("hidden_states_31_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_31_pad_0 = const()[name = tensor("hidden_states_31_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor decoder_up_blocks_0_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_0_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25756096)))]; + tensor decoder_up_blocks_0_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_0_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30474752)))]; + tensor hidden_states_31_cast_fp16 = conv(bias = decoder_up_blocks_0_resnets_0_conv2_bias_to_fp16, dilations = var_213, groups = var_26, pad = hidden_states_31_pad_0, pad_type = hidden_states_31_pad_type_0, strides = var_211, weight = decoder_up_blocks_0_resnets_0_conv2_weight_to_fp16, x = input_39_cast_fp16)[name = tensor("hidden_states_31_cast_fp16")]; + tensor var_216_cast_fp16 = add(x = var_177_cast_fp16, y = hidden_states_31_cast_fp16)[name = tensor("op_216_cast_fp16")]; + tensor reshape_28_shape_0 = const()[name = tensor("reshape_28_shape_0"), val = tensor([1, 32, 16, 48, 80])]; + tensor reshape_28_cast_fp16 = reshape(shape = reshape_28_shape_0, x = var_216_cast_fp16)[name = tensor("reshape_28_cast_fp16")]; + 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_cast_fp16 = reduce_mean(axes = reduce_mean_21_axes_0, keep_dims = reduce_mean_21_keep_dims_0, x = reshape_28_cast_fp16)[name = tensor("reduce_mean_21_cast_fp16")]; + tensor sub_14_cast_fp16 = sub(x = reshape_28_cast_fp16, y = reduce_mean_21_cast_fp16)[name = tensor("sub_14_cast_fp16")]; + tensor square_7_cast_fp16 = square(x = sub_14_cast_fp16)[name = tensor("square_7_cast_fp16")]; + 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_cast_fp16 = reduce_mean(axes = reduce_mean_23_axes_0, keep_dims = reduce_mean_23_keep_dims_0, x = square_7_cast_fp16)[name = tensor("reduce_mean_23_cast_fp16")]; + tensor add_14_y_0_to_fp16 = const()[name = tensor("add_14_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_14_cast_fp16 = add(x = reduce_mean_23_cast_fp16, y = add_14_y_0_to_fp16)[name = tensor("add_14_cast_fp16")]; + tensor sqrt_7_cast_fp16 = sqrt(x = add_14_cast_fp16)[name = tensor("sqrt_7_cast_fp16")]; + tensor real_div_7_cast_fp16 = real_div(x = sub_14_cast_fp16, y = sqrt_7_cast_fp16)[name = tensor("real_div_7_cast_fp16")]; + tensor reshape_29_shape_0 = const()[name = tensor("reshape_29_shape_0"), val = tensor([1, 512, 48, 80])]; + tensor reshape_29_cast_fp16 = reshape(shape = reshape_29_shape_0, x = real_div_7_cast_fp16)[name = tensor("reshape_29_cast_fp16")]; + tensor add_15_gamma_0_to_fp16 = const()[name = tensor("add_15_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30475840)))]; + tensor add_15_beta_0_to_fp16 = const()[name = tensor("add_15_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30476928)))]; + tensor add_15_epsilon_0_to_fp16 = const()[name = tensor("add_15_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_15_cast_fp16 = batch_norm(beta = add_15_beta_0_to_fp16, epsilon = add_15_epsilon_0_to_fp16, gamma = add_15_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_29_cast_fp16)[name = tensor("add_15_cast_fp16")]; + tensor hidden_states_33_cast_fp16 = silu(x = add_15_cast_fp16)[name = tensor("hidden_states_33_cast_fp16")]; + tensor var_229 = const()[name = tensor("op_229"), val = tensor([1, 1])]; + tensor var_231 = const()[name = tensor("op_231"), val = tensor([1, 1])]; + tensor input_45_pad_type_0 = const()[name = tensor("input_45_pad_type_0"), val = tensor("custom")]; + tensor input_45_pad_0 = const()[name = tensor("input_45_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor decoder_up_blocks_0_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_0_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30478016)))]; + tensor decoder_up_blocks_0_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_0_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(35196672)))]; + tensor input_45_cast_fp16 = conv(bias = decoder_up_blocks_0_resnets_1_conv1_bias_to_fp16, dilations = var_231, groups = var_26, pad = input_45_pad_0, pad_type = input_45_pad_type_0, strides = var_229, weight = decoder_up_blocks_0_resnets_1_conv1_weight_to_fp16, x = hidden_states_33_cast_fp16)[name = tensor("input_45_cast_fp16")]; + tensor reshape_32_shape_0 = const()[name = tensor("reshape_32_shape_0"), val = tensor([1, 32, 16, 48, 80])]; + tensor reshape_32_cast_fp16 = reshape(shape = reshape_32_shape_0, x = input_45_cast_fp16)[name = tensor("reshape_32_cast_fp16")]; + 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_cast_fp16 = reduce_mean(axes = reduce_mean_24_axes_0, keep_dims = reduce_mean_24_keep_dims_0, x = reshape_32_cast_fp16)[name = tensor("reduce_mean_24_cast_fp16")]; + tensor sub_16_cast_fp16 = sub(x = reshape_32_cast_fp16, y = reduce_mean_24_cast_fp16)[name = tensor("sub_16_cast_fp16")]; + tensor square_8_cast_fp16 = square(x = sub_16_cast_fp16)[name = tensor("square_8_cast_fp16")]; + 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_cast_fp16 = reduce_mean(axes = reduce_mean_26_axes_0, keep_dims = reduce_mean_26_keep_dims_0, x = square_8_cast_fp16)[name = tensor("reduce_mean_26_cast_fp16")]; + tensor add_16_y_0_to_fp16 = const()[name = tensor("add_16_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_16_cast_fp16 = add(x = reduce_mean_26_cast_fp16, y = add_16_y_0_to_fp16)[name = tensor("add_16_cast_fp16")]; + tensor sqrt_8_cast_fp16 = sqrt(x = add_16_cast_fp16)[name = tensor("sqrt_8_cast_fp16")]; + tensor real_div_8_cast_fp16 = real_div(x = sub_16_cast_fp16, y = sqrt_8_cast_fp16)[name = tensor("real_div_8_cast_fp16")]; + tensor reshape_33_shape_0 = const()[name = tensor("reshape_33_shape_0"), val = tensor([1, 512, 48, 80])]; + tensor reshape_33_cast_fp16 = reshape(shape = reshape_33_shape_0, x = real_div_8_cast_fp16)[name = tensor("reshape_33_cast_fp16")]; + tensor add_17_gamma_0_to_fp16 = const()[name = tensor("add_17_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(35197760)))]; + tensor add_17_beta_0_to_fp16 = const()[name = tensor("add_17_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(35198848)))]; + tensor add_17_epsilon_0_to_fp16 = const()[name = tensor("add_17_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_17_cast_fp16 = batch_norm(beta = add_17_beta_0_to_fp16, epsilon = add_17_epsilon_0_to_fp16, gamma = add_17_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_33_cast_fp16)[name = tensor("add_17_cast_fp16")]; + tensor input_49_cast_fp16 = silu(x = add_17_cast_fp16)[name = tensor("input_49_cast_fp16")]; + tensor var_241 = const()[name = tensor("op_241"), val = tensor([1, 1])]; + tensor var_243 = const()[name = tensor("op_243"), val = tensor([1, 1])]; + tensor hidden_states_37_pad_type_0 = const()[name = tensor("hidden_states_37_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_37_pad_0 = const()[name = tensor("hidden_states_37_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor decoder_up_blocks_0_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_0_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(35199936)))]; + tensor decoder_up_blocks_0_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_0_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39918592)))]; + tensor hidden_states_37_cast_fp16 = conv(bias = decoder_up_blocks_0_resnets_1_conv2_bias_to_fp16, dilations = var_243, groups = var_26, pad = hidden_states_37_pad_0, pad_type = hidden_states_37_pad_type_0, strides = var_241, weight = decoder_up_blocks_0_resnets_1_conv2_weight_to_fp16, x = input_49_cast_fp16)[name = tensor("hidden_states_37_cast_fp16")]; + tensor var_246_cast_fp16 = add(x = var_216_cast_fp16, y = hidden_states_37_cast_fp16)[name = tensor("op_246_cast_fp16")]; + tensor reshape_36_shape_0 = const()[name = tensor("reshape_36_shape_0"), val = tensor([1, 32, 16, 48, 80])]; + tensor reshape_36_cast_fp16 = reshape(shape = reshape_36_shape_0, x = var_246_cast_fp16)[name = tensor("reshape_36_cast_fp16")]; + 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_cast_fp16 = reduce_mean(axes = reduce_mean_27_axes_0, keep_dims = reduce_mean_27_keep_dims_0, x = reshape_36_cast_fp16)[name = tensor("reduce_mean_27_cast_fp16")]; + tensor sub_18_cast_fp16 = sub(x = reshape_36_cast_fp16, y = reduce_mean_27_cast_fp16)[name = tensor("sub_18_cast_fp16")]; + tensor square_9_cast_fp16 = square(x = sub_18_cast_fp16)[name = tensor("square_9_cast_fp16")]; + 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_cast_fp16 = reduce_mean(axes = reduce_mean_29_axes_0, keep_dims = reduce_mean_29_keep_dims_0, x = square_9_cast_fp16)[name = tensor("reduce_mean_29_cast_fp16")]; + tensor add_18_y_0_to_fp16 = const()[name = tensor("add_18_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_18_cast_fp16 = add(x = reduce_mean_29_cast_fp16, y = add_18_y_0_to_fp16)[name = tensor("add_18_cast_fp16")]; + tensor sqrt_9_cast_fp16 = sqrt(x = add_18_cast_fp16)[name = tensor("sqrt_9_cast_fp16")]; + tensor real_div_9_cast_fp16 = real_div(x = sub_18_cast_fp16, y = sqrt_9_cast_fp16)[name = tensor("real_div_9_cast_fp16")]; + tensor reshape_37_shape_0 = const()[name = tensor("reshape_37_shape_0"), val = tensor([1, 512, 48, 80])]; + tensor reshape_37_cast_fp16 = reshape(shape = reshape_37_shape_0, x = real_div_9_cast_fp16)[name = tensor("reshape_37_cast_fp16")]; + tensor add_19_gamma_0_to_fp16 = const()[name = tensor("add_19_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39919680)))]; + tensor add_19_beta_0_to_fp16 = const()[name = tensor("add_19_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39920768)))]; + tensor add_19_epsilon_0_to_fp16 = const()[name = tensor("add_19_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_19_cast_fp16 = batch_norm(beta = add_19_beta_0_to_fp16, epsilon = add_19_epsilon_0_to_fp16, gamma = add_19_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_37_cast_fp16)[name = tensor("add_19_cast_fp16")]; + tensor hidden_states_39_cast_fp16 = silu(x = add_19_cast_fp16)[name = tensor("hidden_states_39_cast_fp16")]; + tensor var_259 = const()[name = tensor("op_259"), val = tensor([1, 1])]; + tensor var_261 = const()[name = tensor("op_261"), val = tensor([1, 1])]; + tensor input_55_pad_type_0 = const()[name = tensor("input_55_pad_type_0"), val = tensor("custom")]; + tensor input_55_pad_0 = const()[name = tensor("input_55_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor decoder_up_blocks_0_resnets_2_conv1_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_0_resnets_2_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39921856)))]; + tensor decoder_up_blocks_0_resnets_2_conv1_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_0_resnets_2_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44640512)))]; + tensor input_55_cast_fp16 = conv(bias = decoder_up_blocks_0_resnets_2_conv1_bias_to_fp16, dilations = var_261, groups = var_26, pad = input_55_pad_0, pad_type = input_55_pad_type_0, strides = var_259, weight = decoder_up_blocks_0_resnets_2_conv1_weight_to_fp16, x = hidden_states_39_cast_fp16)[name = tensor("input_55_cast_fp16")]; + tensor reshape_40_shape_0 = const()[name = tensor("reshape_40_shape_0"), val = tensor([1, 32, 16, 48, 80])]; + tensor reshape_40_cast_fp16 = reshape(shape = reshape_40_shape_0, x = input_55_cast_fp16)[name = tensor("reshape_40_cast_fp16")]; + 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_cast_fp16 = reduce_mean(axes = reduce_mean_30_axes_0, keep_dims = reduce_mean_30_keep_dims_0, x = reshape_40_cast_fp16)[name = tensor("reduce_mean_30_cast_fp16")]; + tensor sub_20_cast_fp16 = sub(x = reshape_40_cast_fp16, y = reduce_mean_30_cast_fp16)[name = tensor("sub_20_cast_fp16")]; + tensor square_10_cast_fp16 = square(x = sub_20_cast_fp16)[name = tensor("square_10_cast_fp16")]; + 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_cast_fp16 = reduce_mean(axes = reduce_mean_32_axes_0, keep_dims = reduce_mean_32_keep_dims_0, x = square_10_cast_fp16)[name = tensor("reduce_mean_32_cast_fp16")]; + tensor add_20_y_0_to_fp16 = const()[name = tensor("add_20_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_20_cast_fp16 = add(x = reduce_mean_32_cast_fp16, y = add_20_y_0_to_fp16)[name = tensor("add_20_cast_fp16")]; + tensor sqrt_10_cast_fp16 = sqrt(x = add_20_cast_fp16)[name = tensor("sqrt_10_cast_fp16")]; + tensor real_div_10_cast_fp16 = real_div(x = sub_20_cast_fp16, y = sqrt_10_cast_fp16)[name = tensor("real_div_10_cast_fp16")]; + tensor reshape_41_shape_0 = const()[name = tensor("reshape_41_shape_0"), val = tensor([1, 512, 48, 80])]; + tensor reshape_41_cast_fp16 = reshape(shape = reshape_41_shape_0, x = real_div_10_cast_fp16)[name = tensor("reshape_41_cast_fp16")]; + tensor add_21_gamma_0_to_fp16 = const()[name = tensor("add_21_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44641600)))]; + tensor add_21_beta_0_to_fp16 = const()[name = tensor("add_21_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44642688)))]; + tensor add_21_epsilon_0_to_fp16 = const()[name = tensor("add_21_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_21_cast_fp16 = batch_norm(beta = add_21_beta_0_to_fp16, epsilon = add_21_epsilon_0_to_fp16, gamma = add_21_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_41_cast_fp16)[name = tensor("add_21_cast_fp16")]; + tensor input_59_cast_fp16 = silu(x = add_21_cast_fp16)[name = tensor("input_59_cast_fp16")]; + tensor var_271 = const()[name = tensor("op_271"), val = tensor([1, 1])]; + tensor var_273 = const()[name = tensor("op_273"), val = tensor([1, 1])]; + tensor hidden_states_43_pad_type_0 = const()[name = tensor("hidden_states_43_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_43_pad_0 = const()[name = tensor("hidden_states_43_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor decoder_up_blocks_0_resnets_2_conv2_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_0_resnets_2_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44643776)))]; + tensor decoder_up_blocks_0_resnets_2_conv2_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_0_resnets_2_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49362432)))]; + tensor hidden_states_43_cast_fp16 = conv(bias = decoder_up_blocks_0_resnets_2_conv2_bias_to_fp16, dilations = var_273, groups = var_26, pad = hidden_states_43_pad_0, pad_type = hidden_states_43_pad_type_0, strides = var_271, weight = decoder_up_blocks_0_resnets_2_conv2_weight_to_fp16, x = input_59_cast_fp16)[name = tensor("hidden_states_43_cast_fp16")]; + tensor var_276_cast_fp16 = add(x = var_246_cast_fp16, y = hidden_states_43_cast_fp16)[name = tensor("op_276_cast_fp16")]; + tensor hidden_states_47_scale_factor_height_0 = const()[name = tensor("hidden_states_47_scale_factor_height_0"), val = tensor(0x1p+1)]; + tensor hidden_states_47_scale_factor_width_0 = const()[name = tensor("hidden_states_47_scale_factor_width_0"), val = tensor(0x1p+1)]; + tensor hidden_states_47_cast_fp16 = upsample_nearest_neighbor(scale_factor_height = hidden_states_47_scale_factor_height_0, scale_factor_width = hidden_states_47_scale_factor_width_0, x = var_276_cast_fp16)[name = tensor("hidden_states_47_cast_fp16")]; + tensor var_284 = const()[name = tensor("op_284"), val = tensor([1, 1])]; + tensor var_286 = const()[name = tensor("op_286"), val = tensor([1, 1])]; + tensor input_61_pad_type_0 = const()[name = tensor("input_61_pad_type_0"), val = tensor("custom")]; + tensor input_61_pad_0 = const()[name = tensor("input_61_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor decoder_up_blocks_0_upsamplers_0_conv_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_0_upsamplers_0_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(49363520)))]; + tensor decoder_up_blocks_0_upsamplers_0_conv_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_0_upsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(54082176)))]; + tensor input_61_cast_fp16 = conv(bias = decoder_up_blocks_0_upsamplers_0_conv_bias_to_fp16, dilations = var_286, groups = var_26, pad = input_61_pad_0, pad_type = input_61_pad_type_0, strides = var_284, weight = decoder_up_blocks_0_upsamplers_0_conv_weight_to_fp16, x = hidden_states_47_cast_fp16)[name = tensor("input_61_cast_fp16")]; + tensor reshape_44_shape_0 = const()[name = tensor("reshape_44_shape_0"), val = tensor([1, 32, 16, 96, 160])]; + tensor reshape_44_cast_fp16 = reshape(shape = reshape_44_shape_0, x = input_61_cast_fp16)[name = tensor("reshape_44_cast_fp16")]; + 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_cast_fp16 = reduce_mean(axes = reduce_mean_33_axes_0, keep_dims = reduce_mean_33_keep_dims_0, x = reshape_44_cast_fp16)[name = tensor("reduce_mean_33_cast_fp16")]; + tensor sub_22_cast_fp16 = sub(x = reshape_44_cast_fp16, y = reduce_mean_33_cast_fp16)[name = tensor("sub_22_cast_fp16")]; + tensor square_11_cast_fp16 = square(x = sub_22_cast_fp16)[name = tensor("square_11_cast_fp16")]; + 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_cast_fp16 = reduce_mean(axes = reduce_mean_35_axes_0, keep_dims = reduce_mean_35_keep_dims_0, x = square_11_cast_fp16)[name = tensor("reduce_mean_35_cast_fp16")]; + tensor add_22_y_0_to_fp16 = const()[name = tensor("add_22_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_22_cast_fp16 = add(x = reduce_mean_35_cast_fp16, y = add_22_y_0_to_fp16)[name = tensor("add_22_cast_fp16")]; + tensor sqrt_11_cast_fp16 = sqrt(x = add_22_cast_fp16)[name = tensor("sqrt_11_cast_fp16")]; + tensor real_div_11_cast_fp16 = real_div(x = sub_22_cast_fp16, y = sqrt_11_cast_fp16)[name = tensor("real_div_11_cast_fp16")]; + tensor reshape_45_shape_0 = const()[name = tensor("reshape_45_shape_0"), val = tensor([1, 512, 96, 160])]; + tensor reshape_45_cast_fp16 = reshape(shape = reshape_45_shape_0, x = real_div_11_cast_fp16)[name = tensor("reshape_45_cast_fp16")]; + tensor add_23_gamma_0_to_fp16 = const()[name = tensor("add_23_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(54083264)))]; + tensor add_23_beta_0_to_fp16 = const()[name = tensor("add_23_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(54084352)))]; + tensor add_23_epsilon_0_to_fp16 = const()[name = tensor("add_23_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_23_cast_fp16 = batch_norm(beta = add_23_beta_0_to_fp16, epsilon = add_23_epsilon_0_to_fp16, gamma = add_23_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_45_cast_fp16)[name = tensor("add_23_cast_fp16")]; + tensor hidden_states_49_cast_fp16 = silu(x = add_23_cast_fp16)[name = tensor("hidden_states_49_cast_fp16")]; + tensor var_307 = const()[name = tensor("op_307"), val = tensor([1, 1])]; + tensor var_309 = const()[name = tensor("op_309"), val = tensor([1, 1])]; + tensor input_65_pad_type_0 = const()[name = tensor("input_65_pad_type_0"), val = tensor("custom")]; + tensor input_65_pad_0 = const()[name = tensor("input_65_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor decoder_up_blocks_1_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_1_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(54085440)))]; + tensor decoder_up_blocks_1_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_1_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58804096)))]; + tensor input_65_cast_fp16 = conv(bias = decoder_up_blocks_1_resnets_0_conv1_bias_to_fp16, dilations = var_309, groups = var_26, pad = input_65_pad_0, pad_type = input_65_pad_type_0, strides = var_307, weight = decoder_up_blocks_1_resnets_0_conv1_weight_to_fp16, x = hidden_states_49_cast_fp16)[name = tensor("input_65_cast_fp16")]; + tensor reshape_48_shape_0 = const()[name = tensor("reshape_48_shape_0"), val = tensor([1, 32, 16, 96, 160])]; + tensor reshape_48_cast_fp16 = reshape(shape = reshape_48_shape_0, x = input_65_cast_fp16)[name = tensor("reshape_48_cast_fp16")]; + 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_cast_fp16 = reduce_mean(axes = reduce_mean_36_axes_0, keep_dims = reduce_mean_36_keep_dims_0, x = reshape_48_cast_fp16)[name = tensor("reduce_mean_36_cast_fp16")]; + tensor sub_24_cast_fp16 = sub(x = reshape_48_cast_fp16, y = reduce_mean_36_cast_fp16)[name = tensor("sub_24_cast_fp16")]; + tensor square_12_cast_fp16 = square(x = sub_24_cast_fp16)[name = tensor("square_12_cast_fp16")]; + 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_cast_fp16 = reduce_mean(axes = reduce_mean_38_axes_0, keep_dims = reduce_mean_38_keep_dims_0, x = square_12_cast_fp16)[name = tensor("reduce_mean_38_cast_fp16")]; + tensor add_24_y_0_to_fp16 = const()[name = tensor("add_24_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_24_cast_fp16 = add(x = reduce_mean_38_cast_fp16, y = add_24_y_0_to_fp16)[name = tensor("add_24_cast_fp16")]; + tensor sqrt_12_cast_fp16 = sqrt(x = add_24_cast_fp16)[name = tensor("sqrt_12_cast_fp16")]; + tensor real_div_12_cast_fp16 = real_div(x = sub_24_cast_fp16, y = sqrt_12_cast_fp16)[name = tensor("real_div_12_cast_fp16")]; + tensor reshape_49_shape_0 = const()[name = tensor("reshape_49_shape_0"), val = tensor([1, 512, 96, 160])]; + tensor reshape_49_cast_fp16 = reshape(shape = reshape_49_shape_0, x = real_div_12_cast_fp16)[name = tensor("reshape_49_cast_fp16")]; + tensor add_25_gamma_0_to_fp16 = const()[name = tensor("add_25_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58805184)))]; + tensor add_25_beta_0_to_fp16 = const()[name = tensor("add_25_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58806272)))]; + tensor add_25_epsilon_0_to_fp16 = const()[name = tensor("add_25_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_25_cast_fp16 = batch_norm(beta = add_25_beta_0_to_fp16, epsilon = add_25_epsilon_0_to_fp16, gamma = add_25_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_49_cast_fp16)[name = tensor("add_25_cast_fp16")]; + tensor input_69_cast_fp16 = silu(x = add_25_cast_fp16)[name = tensor("input_69_cast_fp16")]; + tensor var_319 = const()[name = tensor("op_319"), val = tensor([1, 1])]; + tensor var_321 = const()[name = tensor("op_321"), val = tensor([1, 1])]; + tensor hidden_states_53_pad_type_0 = const()[name = tensor("hidden_states_53_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_53_pad_0 = const()[name = tensor("hidden_states_53_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor decoder_up_blocks_1_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_1_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58807360)))]; + tensor decoder_up_blocks_1_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_1_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63526016)))]; + tensor hidden_states_53_cast_fp16 = conv(bias = decoder_up_blocks_1_resnets_0_conv2_bias_to_fp16, dilations = var_321, groups = var_26, pad = hidden_states_53_pad_0, pad_type = hidden_states_53_pad_type_0, strides = var_319, weight = decoder_up_blocks_1_resnets_0_conv2_weight_to_fp16, x = input_69_cast_fp16)[name = tensor("hidden_states_53_cast_fp16")]; + tensor var_324_cast_fp16 = add(x = input_61_cast_fp16, y = hidden_states_53_cast_fp16)[name = tensor("op_324_cast_fp16")]; + tensor reshape_52_shape_0 = const()[name = tensor("reshape_52_shape_0"), val = tensor([1, 32, 16, 96, 160])]; + tensor reshape_52_cast_fp16 = reshape(shape = reshape_52_shape_0, x = var_324_cast_fp16)[name = tensor("reshape_52_cast_fp16")]; + 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_cast_fp16 = reduce_mean(axes = reduce_mean_39_axes_0, keep_dims = reduce_mean_39_keep_dims_0, x = reshape_52_cast_fp16)[name = tensor("reduce_mean_39_cast_fp16")]; + tensor sub_26_cast_fp16 = sub(x = reshape_52_cast_fp16, y = reduce_mean_39_cast_fp16)[name = tensor("sub_26_cast_fp16")]; + tensor square_13_cast_fp16 = square(x = sub_26_cast_fp16)[name = tensor("square_13_cast_fp16")]; + 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_cast_fp16 = reduce_mean(axes = reduce_mean_41_axes_0, keep_dims = reduce_mean_41_keep_dims_0, x = square_13_cast_fp16)[name = tensor("reduce_mean_41_cast_fp16")]; + tensor add_26_y_0_to_fp16 = const()[name = tensor("add_26_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_26_cast_fp16 = add(x = reduce_mean_41_cast_fp16, y = add_26_y_0_to_fp16)[name = tensor("add_26_cast_fp16")]; + tensor sqrt_13_cast_fp16 = sqrt(x = add_26_cast_fp16)[name = tensor("sqrt_13_cast_fp16")]; + tensor real_div_13_cast_fp16 = real_div(x = sub_26_cast_fp16, y = sqrt_13_cast_fp16)[name = tensor("real_div_13_cast_fp16")]; + tensor reshape_53_shape_0 = const()[name = tensor("reshape_53_shape_0"), val = tensor([1, 512, 96, 160])]; + tensor reshape_53_cast_fp16 = reshape(shape = reshape_53_shape_0, x = real_div_13_cast_fp16)[name = tensor("reshape_53_cast_fp16")]; + tensor add_27_gamma_0_to_fp16 = const()[name = tensor("add_27_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63527104)))]; + tensor add_27_beta_0_to_fp16 = const()[name = tensor("add_27_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63528192)))]; + tensor add_27_epsilon_0_to_fp16 = const()[name = tensor("add_27_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_27_cast_fp16 = batch_norm(beta = add_27_beta_0_to_fp16, epsilon = add_27_epsilon_0_to_fp16, gamma = add_27_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_53_cast_fp16)[name = tensor("add_27_cast_fp16")]; + tensor hidden_states_55_cast_fp16 = silu(x = add_27_cast_fp16)[name = tensor("hidden_states_55_cast_fp16")]; + tensor var_337 = const()[name = tensor("op_337"), val = tensor([1, 1])]; + tensor var_339 = const()[name = tensor("op_339"), val = tensor([1, 1])]; + tensor input_75_pad_type_0 = const()[name = tensor("input_75_pad_type_0"), val = tensor("custom")]; + tensor input_75_pad_0 = const()[name = tensor("input_75_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor decoder_up_blocks_1_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_1_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63529280)))]; + tensor decoder_up_blocks_1_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_1_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68247936)))]; + tensor input_75_cast_fp16 = conv(bias = decoder_up_blocks_1_resnets_1_conv1_bias_to_fp16, dilations = var_339, groups = var_26, pad = input_75_pad_0, pad_type = input_75_pad_type_0, strides = var_337, weight = decoder_up_blocks_1_resnets_1_conv1_weight_to_fp16, x = hidden_states_55_cast_fp16)[name = tensor("input_75_cast_fp16")]; + tensor reshape_56_shape_0 = const()[name = tensor("reshape_56_shape_0"), val = tensor([1, 32, 16, 96, 160])]; + tensor reshape_56_cast_fp16 = reshape(shape = reshape_56_shape_0, x = input_75_cast_fp16)[name = tensor("reshape_56_cast_fp16")]; + 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_cast_fp16 = reduce_mean(axes = reduce_mean_42_axes_0, keep_dims = reduce_mean_42_keep_dims_0, x = reshape_56_cast_fp16)[name = tensor("reduce_mean_42_cast_fp16")]; + tensor sub_28_cast_fp16 = sub(x = reshape_56_cast_fp16, y = reduce_mean_42_cast_fp16)[name = tensor("sub_28_cast_fp16")]; + tensor square_14_cast_fp16 = square(x = sub_28_cast_fp16)[name = tensor("square_14_cast_fp16")]; + 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_cast_fp16 = reduce_mean(axes = reduce_mean_44_axes_0, keep_dims = reduce_mean_44_keep_dims_0, x = square_14_cast_fp16)[name = tensor("reduce_mean_44_cast_fp16")]; + tensor add_28_y_0_to_fp16 = const()[name = tensor("add_28_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_28_cast_fp16 = add(x = reduce_mean_44_cast_fp16, y = add_28_y_0_to_fp16)[name = tensor("add_28_cast_fp16")]; + tensor sqrt_14_cast_fp16 = sqrt(x = add_28_cast_fp16)[name = tensor("sqrt_14_cast_fp16")]; + tensor real_div_14_cast_fp16 = real_div(x = sub_28_cast_fp16, y = sqrt_14_cast_fp16)[name = tensor("real_div_14_cast_fp16")]; + tensor reshape_57_shape_0 = const()[name = tensor("reshape_57_shape_0"), val = tensor([1, 512, 96, 160])]; + tensor reshape_57_cast_fp16 = reshape(shape = reshape_57_shape_0, x = real_div_14_cast_fp16)[name = tensor("reshape_57_cast_fp16")]; + tensor add_29_gamma_0_to_fp16 = const()[name = tensor("add_29_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68249024)))]; + tensor add_29_beta_0_to_fp16 = const()[name = tensor("add_29_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68250112)))]; + tensor add_29_epsilon_0_to_fp16 = const()[name = tensor("add_29_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_29_cast_fp16 = batch_norm(beta = add_29_beta_0_to_fp16, epsilon = add_29_epsilon_0_to_fp16, gamma = add_29_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_57_cast_fp16)[name = tensor("add_29_cast_fp16")]; + tensor input_79_cast_fp16 = silu(x = add_29_cast_fp16)[name = tensor("input_79_cast_fp16")]; + tensor var_349 = const()[name = tensor("op_349"), val = tensor([1, 1])]; + tensor var_351 = const()[name = tensor("op_351"), val = tensor([1, 1])]; + tensor hidden_states_59_pad_type_0 = const()[name = tensor("hidden_states_59_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_59_pad_0 = const()[name = tensor("hidden_states_59_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor decoder_up_blocks_1_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_1_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68251200)))]; + tensor decoder_up_blocks_1_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_1_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(72969856)))]; + tensor hidden_states_59_cast_fp16 = conv(bias = decoder_up_blocks_1_resnets_1_conv2_bias_to_fp16, dilations = var_351, groups = var_26, pad = hidden_states_59_pad_0, pad_type = hidden_states_59_pad_type_0, strides = var_349, weight = decoder_up_blocks_1_resnets_1_conv2_weight_to_fp16, x = input_79_cast_fp16)[name = tensor("hidden_states_59_cast_fp16")]; + tensor var_354_cast_fp16 = add(x = var_324_cast_fp16, y = hidden_states_59_cast_fp16)[name = tensor("op_354_cast_fp16")]; + tensor reshape_60_shape_0 = const()[name = tensor("reshape_60_shape_0"), val = tensor([1, 32, 16, 96, 160])]; + tensor reshape_60_cast_fp16 = reshape(shape = reshape_60_shape_0, x = var_354_cast_fp16)[name = tensor("reshape_60_cast_fp16")]; + 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_cast_fp16 = reduce_mean(axes = reduce_mean_45_axes_0, keep_dims = reduce_mean_45_keep_dims_0, x = reshape_60_cast_fp16)[name = tensor("reduce_mean_45_cast_fp16")]; + tensor sub_30_cast_fp16 = sub(x = reshape_60_cast_fp16, y = reduce_mean_45_cast_fp16)[name = tensor("sub_30_cast_fp16")]; + tensor square_15_cast_fp16 = square(x = sub_30_cast_fp16)[name = tensor("square_15_cast_fp16")]; + 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_cast_fp16 = reduce_mean(axes = reduce_mean_47_axes_0, keep_dims = reduce_mean_47_keep_dims_0, x = square_15_cast_fp16)[name = tensor("reduce_mean_47_cast_fp16")]; + tensor add_30_y_0_to_fp16 = const()[name = tensor("add_30_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_30_cast_fp16 = add(x = reduce_mean_47_cast_fp16, y = add_30_y_0_to_fp16)[name = tensor("add_30_cast_fp16")]; + tensor sqrt_15_cast_fp16 = sqrt(x = add_30_cast_fp16)[name = tensor("sqrt_15_cast_fp16")]; + tensor real_div_15_cast_fp16 = real_div(x = sub_30_cast_fp16, y = sqrt_15_cast_fp16)[name = tensor("real_div_15_cast_fp16")]; + tensor reshape_61_shape_0 = const()[name = tensor("reshape_61_shape_0"), val = tensor([1, 512, 96, 160])]; + tensor reshape_61_cast_fp16 = reshape(shape = reshape_61_shape_0, x = real_div_15_cast_fp16)[name = tensor("reshape_61_cast_fp16")]; + tensor add_31_gamma_0_to_fp16 = const()[name = tensor("add_31_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(72970944)))]; + tensor add_31_beta_0_to_fp16 = const()[name = tensor("add_31_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(72972032)))]; + tensor add_31_epsilon_0_to_fp16 = const()[name = tensor("add_31_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_31_cast_fp16 = batch_norm(beta = add_31_beta_0_to_fp16, epsilon = add_31_epsilon_0_to_fp16, gamma = add_31_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_61_cast_fp16)[name = tensor("add_31_cast_fp16")]; + tensor hidden_states_61_cast_fp16 = silu(x = add_31_cast_fp16)[name = tensor("hidden_states_61_cast_fp16")]; + tensor var_367 = const()[name = tensor("op_367"), val = tensor([1, 1])]; + tensor var_369 = const()[name = tensor("op_369"), val = tensor([1, 1])]; + tensor input_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([1, 1, 1, 1])]; + tensor decoder_up_blocks_1_resnets_2_conv1_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_1_resnets_2_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(72973120)))]; + tensor decoder_up_blocks_1_resnets_2_conv1_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_1_resnets_2_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(77691776)))]; + tensor input_85_cast_fp16 = conv(bias = decoder_up_blocks_1_resnets_2_conv1_bias_to_fp16, dilations = var_369, groups = var_26, pad = input_85_pad_0, pad_type = input_85_pad_type_0, strides = var_367, weight = decoder_up_blocks_1_resnets_2_conv1_weight_to_fp16, x = hidden_states_61_cast_fp16)[name = tensor("input_85_cast_fp16")]; + tensor reshape_64_shape_0 = const()[name = tensor("reshape_64_shape_0"), val = tensor([1, 32, 16, 96, 160])]; + tensor reshape_64_cast_fp16 = reshape(shape = reshape_64_shape_0, x = input_85_cast_fp16)[name = tensor("reshape_64_cast_fp16")]; + 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_cast_fp16 = reduce_mean(axes = reduce_mean_48_axes_0, keep_dims = reduce_mean_48_keep_dims_0, x = reshape_64_cast_fp16)[name = tensor("reduce_mean_48_cast_fp16")]; + tensor sub_32_cast_fp16 = sub(x = reshape_64_cast_fp16, y = reduce_mean_48_cast_fp16)[name = tensor("sub_32_cast_fp16")]; + tensor square_16_cast_fp16 = square(x = sub_32_cast_fp16)[name = tensor("square_16_cast_fp16")]; + 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_cast_fp16 = reduce_mean(axes = reduce_mean_50_axes_0, keep_dims = reduce_mean_50_keep_dims_0, x = square_16_cast_fp16)[name = tensor("reduce_mean_50_cast_fp16")]; + tensor add_32_y_0_to_fp16 = const()[name = tensor("add_32_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_32_cast_fp16 = add(x = reduce_mean_50_cast_fp16, y = add_32_y_0_to_fp16)[name = tensor("add_32_cast_fp16")]; + tensor sqrt_16_cast_fp16 = sqrt(x = add_32_cast_fp16)[name = tensor("sqrt_16_cast_fp16")]; + tensor real_div_16_cast_fp16 = real_div(x = sub_32_cast_fp16, y = sqrt_16_cast_fp16)[name = tensor("real_div_16_cast_fp16")]; + tensor reshape_65_shape_0 = const()[name = tensor("reshape_65_shape_0"), val = tensor([1, 512, 96, 160])]; + tensor reshape_65_cast_fp16 = reshape(shape = reshape_65_shape_0, x = real_div_16_cast_fp16)[name = tensor("reshape_65_cast_fp16")]; + tensor add_33_gamma_0_to_fp16 = const()[name = tensor("add_33_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(77692864)))]; + tensor add_33_beta_0_to_fp16 = const()[name = tensor("add_33_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(77693952)))]; + tensor add_33_epsilon_0_to_fp16 = const()[name = tensor("add_33_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_33_cast_fp16 = batch_norm(beta = add_33_beta_0_to_fp16, epsilon = add_33_epsilon_0_to_fp16, gamma = add_33_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_65_cast_fp16)[name = tensor("add_33_cast_fp16")]; + tensor input_89_cast_fp16 = silu(x = add_33_cast_fp16)[name = tensor("input_89_cast_fp16")]; + tensor var_379 = const()[name = tensor("op_379"), val = tensor([1, 1])]; + tensor var_381 = const()[name = tensor("op_381"), val = tensor([1, 1])]; + tensor hidden_states_65_pad_type_0 = const()[name = tensor("hidden_states_65_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_65_pad_0 = const()[name = tensor("hidden_states_65_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor decoder_up_blocks_1_resnets_2_conv2_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_1_resnets_2_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(77695040)))]; + tensor decoder_up_blocks_1_resnets_2_conv2_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_1_resnets_2_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(82413696)))]; + tensor hidden_states_65_cast_fp16 = conv(bias = decoder_up_blocks_1_resnets_2_conv2_bias_to_fp16, dilations = var_381, groups = var_26, pad = hidden_states_65_pad_0, pad_type = hidden_states_65_pad_type_0, strides = var_379, weight = decoder_up_blocks_1_resnets_2_conv2_weight_to_fp16, x = input_89_cast_fp16)[name = tensor("hidden_states_65_cast_fp16")]; + tensor var_384_cast_fp16 = add(x = var_354_cast_fp16, y = hidden_states_65_cast_fp16)[name = tensor("op_384_cast_fp16")]; + tensor hidden_states_69_scale_factor_height_0 = const()[name = tensor("hidden_states_69_scale_factor_height_0"), val = tensor(0x1p+1)]; + tensor hidden_states_69_scale_factor_width_0 = const()[name = tensor("hidden_states_69_scale_factor_width_0"), val = tensor(0x1p+1)]; + tensor hidden_states_69_cast_fp16 = upsample_nearest_neighbor(scale_factor_height = hidden_states_69_scale_factor_height_0, scale_factor_width = hidden_states_69_scale_factor_width_0, x = var_384_cast_fp16)[name = tensor("hidden_states_69_cast_fp16")]; + tensor var_392 = const()[name = tensor("op_392"), val = tensor([1, 1])]; + tensor var_394 = const()[name = tensor("op_394"), val = tensor([1, 1])]; + tensor input_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 decoder_up_blocks_1_upsamplers_0_conv_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_1_upsamplers_0_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(82414784)))]; + tensor decoder_up_blocks_1_upsamplers_0_conv_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_1_upsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(87133440)))]; + tensor input_91_cast_fp16 = conv(bias = decoder_up_blocks_1_upsamplers_0_conv_bias_to_fp16, dilations = var_394, groups = var_26, pad = input_91_pad_0, pad_type = input_91_pad_type_0, strides = var_392, weight = decoder_up_blocks_1_upsamplers_0_conv_weight_to_fp16, x = hidden_states_69_cast_fp16)[name = tensor("input_91_cast_fp16")]; + tensor reshape_68_shape_0 = const()[name = tensor("reshape_68_shape_0"), val = tensor([1, 32, 16, 192, 320])]; + tensor reshape_68_cast_fp16 = reshape(shape = reshape_68_shape_0, x = input_91_cast_fp16)[name = tensor("reshape_68_cast_fp16")]; + 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_cast_fp16 = reduce_mean(axes = reduce_mean_51_axes_0, keep_dims = reduce_mean_51_keep_dims_0, x = reshape_68_cast_fp16)[name = tensor("reduce_mean_51_cast_fp16")]; + tensor sub_34_cast_fp16 = sub(x = reshape_68_cast_fp16, y = reduce_mean_51_cast_fp16)[name = tensor("sub_34_cast_fp16")]; + tensor square_17_cast_fp16 = square(x = sub_34_cast_fp16)[name = tensor("square_17_cast_fp16")]; + 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_cast_fp16 = reduce_mean(axes = reduce_mean_53_axes_0, keep_dims = reduce_mean_53_keep_dims_0, x = square_17_cast_fp16)[name = tensor("reduce_mean_53_cast_fp16")]; + tensor add_34_y_0_to_fp16 = const()[name = tensor("add_34_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_34_cast_fp16 = add(x = reduce_mean_53_cast_fp16, y = add_34_y_0_to_fp16)[name = tensor("add_34_cast_fp16")]; + tensor sqrt_17_cast_fp16 = sqrt(x = add_34_cast_fp16)[name = tensor("sqrt_17_cast_fp16")]; + tensor real_div_17_cast_fp16 = real_div(x = sub_34_cast_fp16, y = sqrt_17_cast_fp16)[name = tensor("real_div_17_cast_fp16")]; + tensor reshape_69_shape_0 = const()[name = tensor("reshape_69_shape_0"), val = tensor([1, 512, 192, 320])]; + tensor reshape_69_cast_fp16 = reshape(shape = reshape_69_shape_0, x = real_div_17_cast_fp16)[name = tensor("reshape_69_cast_fp16")]; + tensor add_35_gamma_0_to_fp16 = const()[name = tensor("add_35_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(87134528)))]; + tensor add_35_beta_0_to_fp16 = const()[name = tensor("add_35_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(87135616)))]; + tensor add_35_epsilon_0_to_fp16 = const()[name = tensor("add_35_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_35_cast_fp16 = batch_norm(beta = add_35_beta_0_to_fp16, epsilon = add_35_epsilon_0_to_fp16, gamma = add_35_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_69_cast_fp16)[name = tensor("add_35_cast_fp16")]; + tensor hidden_states_71_cast_fp16 = silu(x = add_35_cast_fp16)[name = tensor("hidden_states_71_cast_fp16")]; + tensor var_416 = const()[name = tensor("op_416"), val = tensor([1, 1])]; + tensor var_418 = const()[name = tensor("op_418"), val = tensor([1, 1])]; + tensor input_95_pad_type_0 = const()[name = tensor("input_95_pad_type_0"), val = tensor("custom")]; + tensor input_95_pad_0 = const()[name = tensor("input_95_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor decoder_up_blocks_2_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_2_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(87136704)))]; + tensor decoder_up_blocks_2_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_2_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(89496064)))]; + tensor input_95_cast_fp16 = conv(bias = decoder_up_blocks_2_resnets_0_conv1_bias_to_fp16, dilations = var_418, groups = var_26, pad = input_95_pad_0, pad_type = input_95_pad_type_0, strides = var_416, weight = decoder_up_blocks_2_resnets_0_conv1_weight_to_fp16, x = hidden_states_71_cast_fp16)[name = tensor("input_95_cast_fp16")]; + tensor reshape_72_shape_0 = const()[name = tensor("reshape_72_shape_0"), val = tensor([1, 32, 8, 192, 320])]; + tensor reshape_72_cast_fp16 = reshape(shape = reshape_72_shape_0, x = input_95_cast_fp16)[name = tensor("reshape_72_cast_fp16")]; + tensor reduce_mean_54_axes_0 = const()[name = tensor("reduce_mean_54_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_54_keep_dims_0 = const()[name = tensor("reduce_mean_54_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_54_cast_fp16 = reduce_mean(axes = reduce_mean_54_axes_0, keep_dims = reduce_mean_54_keep_dims_0, x = reshape_72_cast_fp16)[name = tensor("reduce_mean_54_cast_fp16")]; + tensor sub_36_cast_fp16 = sub(x = reshape_72_cast_fp16, y = reduce_mean_54_cast_fp16)[name = tensor("sub_36_cast_fp16")]; + tensor square_18_cast_fp16 = square(x = sub_36_cast_fp16)[name = tensor("square_18_cast_fp16")]; + tensor reduce_mean_56_axes_0 = const()[name = tensor("reduce_mean_56_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_56_keep_dims_0 = const()[name = tensor("reduce_mean_56_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_56_cast_fp16 = reduce_mean(axes = reduce_mean_56_axes_0, keep_dims = reduce_mean_56_keep_dims_0, x = square_18_cast_fp16)[name = tensor("reduce_mean_56_cast_fp16")]; + tensor add_36_y_0_to_fp16 = const()[name = tensor("add_36_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_36_cast_fp16 = add(x = reduce_mean_56_cast_fp16, y = add_36_y_0_to_fp16)[name = tensor("add_36_cast_fp16")]; + tensor sqrt_18_cast_fp16 = sqrt(x = add_36_cast_fp16)[name = tensor("sqrt_18_cast_fp16")]; + tensor real_div_18_cast_fp16 = real_div(x = sub_36_cast_fp16, y = sqrt_18_cast_fp16)[name = tensor("real_div_18_cast_fp16")]; + tensor reshape_73_shape_0 = const()[name = tensor("reshape_73_shape_0"), val = tensor([1, 256, 192, 320])]; + tensor reshape_73_cast_fp16 = reshape(shape = reshape_73_shape_0, x = real_div_18_cast_fp16)[name = tensor("reshape_73_cast_fp16")]; + tensor add_37_mean_0_to_fp16 = const()[name = tensor("add_37_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(89496640)))]; + tensor add_37_variance_0_to_fp16 = const()[name = tensor("add_37_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(89497216)))]; + tensor add_37_gamma_0_to_fp16 = const()[name = tensor("add_37_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(89497792)))]; + tensor add_37_beta_0_to_fp16 = const()[name = tensor("add_37_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(89498368)))]; + tensor add_37_epsilon_0_to_fp16 = const()[name = tensor("add_37_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_37_cast_fp16 = batch_norm(beta = add_37_beta_0_to_fp16, epsilon = add_37_epsilon_0_to_fp16, gamma = add_37_gamma_0_to_fp16, mean = add_37_mean_0_to_fp16, variance = add_37_variance_0_to_fp16, x = reshape_73_cast_fp16)[name = tensor("add_37_cast_fp16")]; + tensor input_99_cast_fp16 = silu(x = add_37_cast_fp16)[name = tensor("input_99_cast_fp16")]; + tensor var_428 = const()[name = tensor("op_428"), val = tensor([1, 1])]; + tensor var_430 = const()[name = tensor("op_430"), val = tensor([1, 1])]; + tensor hidden_states_75_pad_type_0 = const()[name = tensor("hidden_states_75_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_75_pad_0 = const()[name = tensor("hidden_states_75_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor decoder_up_blocks_2_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_2_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(89498944)))]; + tensor decoder_up_blocks_2_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_2_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90678656)))]; + tensor hidden_states_75_cast_fp16 = conv(bias = decoder_up_blocks_2_resnets_0_conv2_bias_to_fp16, dilations = var_430, groups = var_26, pad = hidden_states_75_pad_0, pad_type = hidden_states_75_pad_type_0, strides = var_428, weight = decoder_up_blocks_2_resnets_0_conv2_weight_to_fp16, x = input_99_cast_fp16)[name = tensor("hidden_states_75_cast_fp16")]; + tensor var_435 = const()[name = tensor("op_435"), val = tensor([1, 1])]; + tensor var_437 = const()[name = tensor("op_437"), val = tensor([1, 1])]; + tensor input_tensor_1_pad_type_0 = const()[name = tensor("input_tensor_1_pad_type_0"), val = tensor("custom")]; + tensor input_tensor_1_pad_0 = const()[name = tensor("input_tensor_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor decoder_up_blocks_2_resnets_0_conv_shortcut_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_2_resnets_0_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90679232)))]; + tensor decoder_up_blocks_2_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_2_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90941440)))]; + tensor input_tensor_1_cast_fp16 = conv(bias = decoder_up_blocks_2_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_437, groups = var_26, pad = input_tensor_1_pad_0, pad_type = input_tensor_1_pad_type_0, strides = var_435, weight = decoder_up_blocks_2_resnets_0_conv_shortcut_weight_to_fp16, x = input_91_cast_fp16)[name = tensor("input_tensor_1_cast_fp16")]; + tensor var_440_cast_fp16 = add(x = input_tensor_1_cast_fp16, y = hidden_states_75_cast_fp16)[name = tensor("op_440_cast_fp16")]; + tensor reshape_76_shape_0 = const()[name = tensor("reshape_76_shape_0"), val = tensor([1, 32, 8, 192, 320])]; + tensor reshape_76_cast_fp16 = reshape(shape = reshape_76_shape_0, x = var_440_cast_fp16)[name = tensor("reshape_76_cast_fp16")]; + 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_cast_fp16 = reduce_mean(axes = reduce_mean_57_axes_0, keep_dims = reduce_mean_57_keep_dims_0, x = reshape_76_cast_fp16)[name = tensor("reduce_mean_57_cast_fp16")]; + tensor sub_38_cast_fp16 = sub(x = reshape_76_cast_fp16, y = reduce_mean_57_cast_fp16)[name = tensor("sub_38_cast_fp16")]; + tensor square_19_cast_fp16 = square(x = sub_38_cast_fp16)[name = tensor("square_19_cast_fp16")]; + 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_cast_fp16 = reduce_mean(axes = reduce_mean_59_axes_0, keep_dims = reduce_mean_59_keep_dims_0, x = square_19_cast_fp16)[name = tensor("reduce_mean_59_cast_fp16")]; + tensor add_38_y_0_to_fp16 = const()[name = tensor("add_38_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_38_cast_fp16 = add(x = reduce_mean_59_cast_fp16, y = add_38_y_0_to_fp16)[name = tensor("add_38_cast_fp16")]; + tensor sqrt_19_cast_fp16 = sqrt(x = add_38_cast_fp16)[name = tensor("sqrt_19_cast_fp16")]; + tensor real_div_19_cast_fp16 = real_div(x = sub_38_cast_fp16, y = sqrt_19_cast_fp16)[name = tensor("real_div_19_cast_fp16")]; + tensor reshape_77_shape_0 = const()[name = tensor("reshape_77_shape_0"), val = tensor([1, 256, 192, 320])]; + tensor reshape_77_cast_fp16 = reshape(shape = reshape_77_shape_0, x = real_div_19_cast_fp16)[name = tensor("reshape_77_cast_fp16")]; + tensor add_39_gamma_0_to_fp16 = const()[name = tensor("add_39_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90942016)))]; + tensor add_39_beta_0_to_fp16 = const()[name = tensor("add_39_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90942592)))]; + tensor add_39_epsilon_0_to_fp16 = const()[name = tensor("add_39_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_39_cast_fp16 = batch_norm(beta = add_39_beta_0_to_fp16, epsilon = add_39_epsilon_0_to_fp16, gamma = add_39_gamma_0_to_fp16, mean = add_37_mean_0_to_fp16, variance = add_37_variance_0_to_fp16, x = reshape_77_cast_fp16)[name = tensor("add_39_cast_fp16")]; + tensor hidden_states_77_cast_fp16 = silu(x = add_39_cast_fp16)[name = tensor("hidden_states_77_cast_fp16")]; + tensor var_453 = const()[name = tensor("op_453"), val = tensor([1, 1])]; + tensor var_455 = const()[name = tensor("op_455"), val = tensor([1, 1])]; + tensor input_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 decoder_up_blocks_2_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_2_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90943168)))]; + tensor decoder_up_blocks_2_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_2_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(92122880)))]; + tensor input_105_cast_fp16 = conv(bias = decoder_up_blocks_2_resnets_1_conv1_bias_to_fp16, dilations = var_455, groups = var_26, pad = input_105_pad_0, pad_type = input_105_pad_type_0, strides = var_453, weight = decoder_up_blocks_2_resnets_1_conv1_weight_to_fp16, x = hidden_states_77_cast_fp16)[name = tensor("input_105_cast_fp16")]; + tensor reshape_80_shape_0 = const()[name = tensor("reshape_80_shape_0"), val = tensor([1, 32, 8, 192, 320])]; + tensor reshape_80_cast_fp16 = reshape(shape = reshape_80_shape_0, x = input_105_cast_fp16)[name = tensor("reshape_80_cast_fp16")]; + 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_cast_fp16 = reduce_mean(axes = reduce_mean_60_axes_0, keep_dims = reduce_mean_60_keep_dims_0, x = reshape_80_cast_fp16)[name = tensor("reduce_mean_60_cast_fp16")]; + tensor sub_40_cast_fp16 = sub(x = reshape_80_cast_fp16, y = reduce_mean_60_cast_fp16)[name = tensor("sub_40_cast_fp16")]; + tensor square_20_cast_fp16 = square(x = sub_40_cast_fp16)[name = tensor("square_20_cast_fp16")]; + 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_cast_fp16 = reduce_mean(axes = reduce_mean_62_axes_0, keep_dims = reduce_mean_62_keep_dims_0, x = square_20_cast_fp16)[name = tensor("reduce_mean_62_cast_fp16")]; + tensor add_40_y_0_to_fp16 = const()[name = tensor("add_40_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_40_cast_fp16 = add(x = reduce_mean_62_cast_fp16, y = add_40_y_0_to_fp16)[name = tensor("add_40_cast_fp16")]; + tensor sqrt_20_cast_fp16 = sqrt(x = add_40_cast_fp16)[name = tensor("sqrt_20_cast_fp16")]; + tensor real_div_20_cast_fp16 = real_div(x = sub_40_cast_fp16, y = sqrt_20_cast_fp16)[name = tensor("real_div_20_cast_fp16")]; + tensor reshape_81_shape_0 = const()[name = tensor("reshape_81_shape_0"), val = tensor([1, 256, 192, 320])]; + tensor reshape_81_cast_fp16 = reshape(shape = reshape_81_shape_0, x = real_div_20_cast_fp16)[name = tensor("reshape_81_cast_fp16")]; + tensor add_41_gamma_0_to_fp16 = const()[name = tensor("add_41_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(92123456)))]; + tensor add_41_beta_0_to_fp16 = const()[name = tensor("add_41_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(92124032)))]; + tensor add_41_epsilon_0_to_fp16 = const()[name = tensor("add_41_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_41_cast_fp16 = batch_norm(beta = add_41_beta_0_to_fp16, epsilon = add_41_epsilon_0_to_fp16, gamma = add_41_gamma_0_to_fp16, mean = add_37_mean_0_to_fp16, variance = add_37_variance_0_to_fp16, x = reshape_81_cast_fp16)[name = tensor("add_41_cast_fp16")]; + tensor input_109_cast_fp16 = silu(x = add_41_cast_fp16)[name = tensor("input_109_cast_fp16")]; + tensor var_465 = const()[name = tensor("op_465"), val = tensor([1, 1])]; + tensor var_467 = const()[name = tensor("op_467"), val = tensor([1, 1])]; + tensor hidden_states_81_pad_type_0 = const()[name = tensor("hidden_states_81_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_81_pad_0 = const()[name = tensor("hidden_states_81_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor decoder_up_blocks_2_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_2_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(92124608)))]; + tensor decoder_up_blocks_2_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_2_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(93304320)))]; + tensor hidden_states_81_cast_fp16 = conv(bias = decoder_up_blocks_2_resnets_1_conv2_bias_to_fp16, dilations = var_467, groups = var_26, pad = hidden_states_81_pad_0, pad_type = hidden_states_81_pad_type_0, strides = var_465, weight = decoder_up_blocks_2_resnets_1_conv2_weight_to_fp16, x = input_109_cast_fp16)[name = tensor("hidden_states_81_cast_fp16")]; + tensor var_470_cast_fp16 = add(x = var_440_cast_fp16, y = hidden_states_81_cast_fp16)[name = tensor("op_470_cast_fp16")]; + tensor reshape_84_shape_0 = const()[name = tensor("reshape_84_shape_0"), val = tensor([1, 32, 8, 192, 320])]; + tensor reshape_84_cast_fp16 = reshape(shape = reshape_84_shape_0, x = var_470_cast_fp16)[name = tensor("reshape_84_cast_fp16")]; + 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_cast_fp16 = reduce_mean(axes = reduce_mean_63_axes_0, keep_dims = reduce_mean_63_keep_dims_0, x = reshape_84_cast_fp16)[name = tensor("reduce_mean_63_cast_fp16")]; + tensor sub_42_cast_fp16 = sub(x = reshape_84_cast_fp16, y = reduce_mean_63_cast_fp16)[name = tensor("sub_42_cast_fp16")]; + tensor square_21_cast_fp16 = square(x = sub_42_cast_fp16)[name = tensor("square_21_cast_fp16")]; + 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_cast_fp16 = reduce_mean(axes = reduce_mean_65_axes_0, keep_dims = reduce_mean_65_keep_dims_0, x = square_21_cast_fp16)[name = tensor("reduce_mean_65_cast_fp16")]; + tensor add_42_y_0_to_fp16 = const()[name = tensor("add_42_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_42_cast_fp16 = add(x = reduce_mean_65_cast_fp16, y = add_42_y_0_to_fp16)[name = tensor("add_42_cast_fp16")]; + tensor sqrt_21_cast_fp16 = sqrt(x = add_42_cast_fp16)[name = tensor("sqrt_21_cast_fp16")]; + tensor real_div_21_cast_fp16 = real_div(x = sub_42_cast_fp16, y = sqrt_21_cast_fp16)[name = tensor("real_div_21_cast_fp16")]; + tensor reshape_85_shape_0 = const()[name = tensor("reshape_85_shape_0"), val = tensor([1, 256, 192, 320])]; + tensor reshape_85_cast_fp16 = reshape(shape = reshape_85_shape_0, x = real_div_21_cast_fp16)[name = tensor("reshape_85_cast_fp16")]; + tensor add_43_gamma_0_to_fp16 = const()[name = tensor("add_43_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(93304896)))]; + tensor add_43_beta_0_to_fp16 = const()[name = tensor("add_43_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(93305472)))]; + tensor add_43_epsilon_0_to_fp16 = const()[name = tensor("add_43_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_43_cast_fp16 = batch_norm(beta = add_43_beta_0_to_fp16, epsilon = add_43_epsilon_0_to_fp16, gamma = add_43_gamma_0_to_fp16, mean = add_37_mean_0_to_fp16, variance = add_37_variance_0_to_fp16, x = reshape_85_cast_fp16)[name = tensor("add_43_cast_fp16")]; + tensor hidden_states_83_cast_fp16 = silu(x = add_43_cast_fp16)[name = tensor("hidden_states_83_cast_fp16")]; + tensor var_483 = const()[name = tensor("op_483"), val = tensor([1, 1])]; + tensor var_485 = const()[name = tensor("op_485"), val = tensor([1, 1])]; + tensor input_115_pad_type_0 = const()[name = tensor("input_115_pad_type_0"), val = tensor("custom")]; + tensor input_115_pad_0 = const()[name = tensor("input_115_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor decoder_up_blocks_2_resnets_2_conv1_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_2_resnets_2_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(93306048)))]; + tensor decoder_up_blocks_2_resnets_2_conv1_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_2_resnets_2_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(94485760)))]; + tensor input_115_cast_fp16 = conv(bias = decoder_up_blocks_2_resnets_2_conv1_bias_to_fp16, dilations = var_485, groups = var_26, pad = input_115_pad_0, pad_type = input_115_pad_type_0, strides = var_483, weight = decoder_up_blocks_2_resnets_2_conv1_weight_to_fp16, x = hidden_states_83_cast_fp16)[name = tensor("input_115_cast_fp16")]; + tensor reshape_88_shape_0 = const()[name = tensor("reshape_88_shape_0"), val = tensor([1, 32, 8, 192, 320])]; + tensor reshape_88_cast_fp16 = reshape(shape = reshape_88_shape_0, x = input_115_cast_fp16)[name = tensor("reshape_88_cast_fp16")]; + tensor reduce_mean_66_axes_0 = const()[name = tensor("reduce_mean_66_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_66_keep_dims_0 = const()[name = tensor("reduce_mean_66_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_66_cast_fp16 = reduce_mean(axes = reduce_mean_66_axes_0, keep_dims = reduce_mean_66_keep_dims_0, x = reshape_88_cast_fp16)[name = tensor("reduce_mean_66_cast_fp16")]; + tensor sub_44_cast_fp16 = sub(x = reshape_88_cast_fp16, y = reduce_mean_66_cast_fp16)[name = tensor("sub_44_cast_fp16")]; + tensor square_22_cast_fp16 = square(x = sub_44_cast_fp16)[name = tensor("square_22_cast_fp16")]; + tensor reduce_mean_68_axes_0 = const()[name = tensor("reduce_mean_68_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_68_keep_dims_0 = const()[name = tensor("reduce_mean_68_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_68_cast_fp16 = reduce_mean(axes = reduce_mean_68_axes_0, keep_dims = reduce_mean_68_keep_dims_0, x = square_22_cast_fp16)[name = tensor("reduce_mean_68_cast_fp16")]; + tensor add_44_y_0_to_fp16 = const()[name = tensor("add_44_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_44_cast_fp16 = add(x = reduce_mean_68_cast_fp16, y = add_44_y_0_to_fp16)[name = tensor("add_44_cast_fp16")]; + tensor sqrt_22_cast_fp16 = sqrt(x = add_44_cast_fp16)[name = tensor("sqrt_22_cast_fp16")]; + tensor real_div_22_cast_fp16 = real_div(x = sub_44_cast_fp16, y = sqrt_22_cast_fp16)[name = tensor("real_div_22_cast_fp16")]; + tensor reshape_89_shape_0 = const()[name = tensor("reshape_89_shape_0"), val = tensor([1, 256, 192, 320])]; + tensor reshape_89_cast_fp16 = reshape(shape = reshape_89_shape_0, x = real_div_22_cast_fp16)[name = tensor("reshape_89_cast_fp16")]; + tensor add_45_gamma_0_to_fp16 = const()[name = tensor("add_45_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(94486336)))]; + tensor add_45_beta_0_to_fp16 = const()[name = tensor("add_45_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(94486912)))]; + tensor add_45_epsilon_0_to_fp16 = const()[name = tensor("add_45_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_45_cast_fp16 = batch_norm(beta = add_45_beta_0_to_fp16, epsilon = add_45_epsilon_0_to_fp16, gamma = add_45_gamma_0_to_fp16, mean = add_37_mean_0_to_fp16, variance = add_37_variance_0_to_fp16, x = reshape_89_cast_fp16)[name = tensor("add_45_cast_fp16")]; + tensor input_119_cast_fp16 = silu(x = add_45_cast_fp16)[name = tensor("input_119_cast_fp16")]; + tensor var_495 = const()[name = tensor("op_495"), val = tensor([1, 1])]; + tensor var_497 = const()[name = tensor("op_497"), val = tensor([1, 1])]; + tensor hidden_states_87_pad_type_0 = const()[name = tensor("hidden_states_87_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_87_pad_0 = const()[name = tensor("hidden_states_87_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor decoder_up_blocks_2_resnets_2_conv2_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_2_resnets_2_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(94487488)))]; + tensor decoder_up_blocks_2_resnets_2_conv2_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_2_resnets_2_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(95667200)))]; + tensor hidden_states_87_cast_fp16 = conv(bias = decoder_up_blocks_2_resnets_2_conv2_bias_to_fp16, dilations = var_497, groups = var_26, pad = hidden_states_87_pad_0, pad_type = hidden_states_87_pad_type_0, strides = var_495, weight = decoder_up_blocks_2_resnets_2_conv2_weight_to_fp16, x = input_119_cast_fp16)[name = tensor("hidden_states_87_cast_fp16")]; + tensor var_500_cast_fp16 = add(x = var_470_cast_fp16, y = hidden_states_87_cast_fp16)[name = tensor("op_500_cast_fp16")]; + tensor hidden_states_91_scale_factor_height_0 = const()[name = tensor("hidden_states_91_scale_factor_height_0"), val = tensor(0x1p+1)]; + tensor hidden_states_91_scale_factor_width_0 = const()[name = tensor("hidden_states_91_scale_factor_width_0"), val = tensor(0x1p+1)]; + tensor hidden_states_91_cast_fp16 = upsample_nearest_neighbor(scale_factor_height = hidden_states_91_scale_factor_height_0, scale_factor_width = hidden_states_91_scale_factor_width_0, x = var_500_cast_fp16)[name = tensor("hidden_states_91_cast_fp16")]; + tensor var_508 = const()[name = tensor("op_508"), val = tensor([1, 1])]; + tensor var_510 = const()[name = tensor("op_510"), val = tensor([1, 1])]; + tensor input_121_pad_type_0 = const()[name = tensor("input_121_pad_type_0"), val = tensor("custom")]; + tensor input_121_pad_0 = const()[name = tensor("input_121_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor decoder_up_blocks_2_upsamplers_0_conv_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_2_upsamplers_0_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(95667776)))]; + tensor decoder_up_blocks_2_upsamplers_0_conv_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_2_upsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96847488)))]; + tensor input_121_cast_fp16 = conv(bias = decoder_up_blocks_2_upsamplers_0_conv_bias_to_fp16, dilations = var_510, groups = var_26, pad = input_121_pad_0, pad_type = input_121_pad_type_0, strides = var_508, weight = decoder_up_blocks_2_upsamplers_0_conv_weight_to_fp16, x = hidden_states_91_cast_fp16)[name = tensor("input_121_cast_fp16")]; + tensor reshape_92_shape_0 = const()[name = tensor("reshape_92_shape_0"), val = tensor([1, 32, 8, 384, 640])]; + tensor reshape_92_cast_fp16 = reshape(shape = reshape_92_shape_0, x = input_121_cast_fp16)[name = tensor("reshape_92_cast_fp16")]; + tensor reduce_mean_69_axes_0 = const()[name = tensor("reduce_mean_69_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_69_keep_dims_0 = const()[name = tensor("reduce_mean_69_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_69_cast_fp16 = reduce_mean(axes = reduce_mean_69_axes_0, keep_dims = reduce_mean_69_keep_dims_0, x = reshape_92_cast_fp16)[name = tensor("reduce_mean_69_cast_fp16")]; + tensor sub_46_cast_fp16 = sub(x = reshape_92_cast_fp16, y = reduce_mean_69_cast_fp16)[name = tensor("sub_46_cast_fp16")]; + tensor square_23_cast_fp16 = square(x = sub_46_cast_fp16)[name = tensor("square_23_cast_fp16")]; + tensor reduce_mean_71_axes_0 = const()[name = tensor("reduce_mean_71_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_71_keep_dims_0 = const()[name = tensor("reduce_mean_71_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_71_cast_fp16 = reduce_mean(axes = reduce_mean_71_axes_0, keep_dims = reduce_mean_71_keep_dims_0, x = square_23_cast_fp16)[name = tensor("reduce_mean_71_cast_fp16")]; + tensor add_46_y_0_to_fp16 = const()[name = tensor("add_46_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_46_cast_fp16 = add(x = reduce_mean_71_cast_fp16, y = add_46_y_0_to_fp16)[name = tensor("add_46_cast_fp16")]; + tensor sqrt_23_cast_fp16 = sqrt(x = add_46_cast_fp16)[name = tensor("sqrt_23_cast_fp16")]; + tensor real_div_23_cast_fp16 = real_div(x = sub_46_cast_fp16, y = sqrt_23_cast_fp16)[name = tensor("real_div_23_cast_fp16")]; + tensor reshape_93_shape_0 = const()[name = tensor("reshape_93_shape_0"), val = tensor([1, 256, 384, 640])]; + tensor reshape_93_cast_fp16 = reshape(shape = reshape_93_shape_0, x = real_div_23_cast_fp16)[name = tensor("reshape_93_cast_fp16")]; + tensor add_47_gamma_0_to_fp16 = const()[name = tensor("add_47_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96848064)))]; + tensor add_47_beta_0_to_fp16 = const()[name = tensor("add_47_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96848640)))]; + tensor add_47_epsilon_0_to_fp16 = const()[name = tensor("add_47_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_47_cast_fp16 = batch_norm(beta = add_47_beta_0_to_fp16, epsilon = add_47_epsilon_0_to_fp16, gamma = add_47_gamma_0_to_fp16, mean = add_37_mean_0_to_fp16, variance = add_37_variance_0_to_fp16, x = reshape_93_cast_fp16)[name = tensor("add_47_cast_fp16")]; + tensor hidden_states_93_cast_fp16 = silu(x = add_47_cast_fp16)[name = tensor("hidden_states_93_cast_fp16")]; + tensor var_530 = const()[name = tensor("op_530"), val = tensor([1, 1])]; + tensor var_532 = const()[name = tensor("op_532"), val = tensor([1, 1])]; + tensor input_125_pad_type_0 = const()[name = tensor("input_125_pad_type_0"), val = tensor("custom")]; + tensor input_125_pad_0 = const()[name = tensor("input_125_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor decoder_up_blocks_3_resnets_0_conv1_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_3_resnets_0_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96849216)))]; + tensor decoder_up_blocks_3_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_3_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97439104)))]; + tensor input_125_cast_fp16 = conv(bias = decoder_up_blocks_3_resnets_0_conv1_bias_to_fp16, dilations = var_532, groups = var_26, pad = input_125_pad_0, pad_type = input_125_pad_type_0, strides = var_530, weight = decoder_up_blocks_3_resnets_0_conv1_weight_to_fp16, x = hidden_states_93_cast_fp16)[name = tensor("input_125_cast_fp16")]; + tensor reshape_96_shape_0 = const()[name = tensor("reshape_96_shape_0"), val = tensor([1, 32, 4, 384, 640])]; + tensor reshape_96_cast_fp16 = reshape(shape = reshape_96_shape_0, x = input_125_cast_fp16)[name = tensor("reshape_96_cast_fp16")]; + tensor reduce_mean_72_axes_0 = const()[name = tensor("reduce_mean_72_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_72_keep_dims_0 = const()[name = tensor("reduce_mean_72_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_72_cast_fp16 = reduce_mean(axes = reduce_mean_72_axes_0, keep_dims = reduce_mean_72_keep_dims_0, x = reshape_96_cast_fp16)[name = tensor("reduce_mean_72_cast_fp16")]; + tensor sub_48_cast_fp16 = sub(x = reshape_96_cast_fp16, y = reduce_mean_72_cast_fp16)[name = tensor("sub_48_cast_fp16")]; + tensor square_24_cast_fp16 = square(x = sub_48_cast_fp16)[name = tensor("square_24_cast_fp16")]; + tensor reduce_mean_74_axes_0 = const()[name = tensor("reduce_mean_74_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_74_keep_dims_0 = const()[name = tensor("reduce_mean_74_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_74_cast_fp16 = reduce_mean(axes = reduce_mean_74_axes_0, keep_dims = reduce_mean_74_keep_dims_0, x = square_24_cast_fp16)[name = tensor("reduce_mean_74_cast_fp16")]; + tensor add_48_y_0_to_fp16 = const()[name = tensor("add_48_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_48_cast_fp16 = add(x = reduce_mean_74_cast_fp16, y = add_48_y_0_to_fp16)[name = tensor("add_48_cast_fp16")]; + tensor sqrt_24_cast_fp16 = sqrt(x = add_48_cast_fp16)[name = tensor("sqrt_24_cast_fp16")]; + tensor real_div_24_cast_fp16 = real_div(x = sub_48_cast_fp16, y = sqrt_24_cast_fp16)[name = tensor("real_div_24_cast_fp16")]; + tensor reshape_97_shape_0 = const()[name = tensor("reshape_97_shape_0"), val = tensor([1, 128, 384, 640])]; + tensor reshape_97_cast_fp16 = reshape(shape = reshape_97_shape_0, x = real_div_24_cast_fp16)[name = tensor("reshape_97_cast_fp16")]; + tensor add_49_mean_0_to_fp16 = const()[name = tensor("add_49_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97439424)))]; + tensor add_49_variance_0_to_fp16 = const()[name = tensor("add_49_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97439744)))]; + tensor add_49_gamma_0_to_fp16 = const()[name = tensor("add_49_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97440064)))]; + tensor add_49_beta_0_to_fp16 = const()[name = tensor("add_49_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97440384)))]; + tensor add_49_epsilon_0_to_fp16 = const()[name = tensor("add_49_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_49_cast_fp16 = batch_norm(beta = add_49_beta_0_to_fp16, epsilon = add_49_epsilon_0_to_fp16, gamma = add_49_gamma_0_to_fp16, mean = add_49_mean_0_to_fp16, variance = add_49_variance_0_to_fp16, x = reshape_97_cast_fp16)[name = tensor("add_49_cast_fp16")]; + tensor input_129_cast_fp16 = silu(x = add_49_cast_fp16)[name = tensor("input_129_cast_fp16")]; + tensor var_542 = const()[name = tensor("op_542"), val = tensor([1, 1])]; + tensor var_544 = const()[name = tensor("op_544"), val = tensor([1, 1])]; + tensor hidden_states_97_pad_type_0 = const()[name = tensor("hidden_states_97_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_97_pad_0 = const()[name = tensor("hidden_states_97_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor decoder_up_blocks_3_resnets_0_conv2_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_3_resnets_0_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97440704)))]; + tensor decoder_up_blocks_3_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_3_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97735680)))]; + tensor hidden_states_97_cast_fp16 = conv(bias = decoder_up_blocks_3_resnets_0_conv2_bias_to_fp16, dilations = var_544, groups = var_26, pad = hidden_states_97_pad_0, pad_type = hidden_states_97_pad_type_0, strides = var_542, weight = decoder_up_blocks_3_resnets_0_conv2_weight_to_fp16, x = input_129_cast_fp16)[name = tensor("hidden_states_97_cast_fp16")]; + tensor var_549 = const()[name = tensor("op_549"), val = tensor([1, 1])]; + tensor var_551 = const()[name = tensor("op_551"), val = tensor([1, 1])]; + tensor input_tensor_pad_type_0 = const()[name = tensor("input_tensor_pad_type_0"), val = tensor("custom")]; + tensor input_tensor_pad_0 = const()[name = tensor("input_tensor_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor decoder_up_blocks_3_resnets_0_conv_shortcut_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_3_resnets_0_conv_shortcut_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97736000)))]; + tensor decoder_up_blocks_3_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_3_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97801600)))]; + tensor input_tensor_cast_fp16 = conv(bias = decoder_up_blocks_3_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_551, groups = var_26, pad = input_tensor_pad_0, pad_type = input_tensor_pad_type_0, strides = var_549, weight = decoder_up_blocks_3_resnets_0_conv_shortcut_weight_to_fp16, x = input_121_cast_fp16)[name = tensor("input_tensor_cast_fp16")]; + tensor var_554_cast_fp16 = add(x = input_tensor_cast_fp16, y = hidden_states_97_cast_fp16)[name = tensor("op_554_cast_fp16")]; + tensor reshape_100_shape_0 = const()[name = tensor("reshape_100_shape_0"), val = tensor([1, 32, 4, 384, 640])]; + tensor reshape_100_cast_fp16 = reshape(shape = reshape_100_shape_0, x = var_554_cast_fp16)[name = tensor("reshape_100_cast_fp16")]; + tensor reduce_mean_75_axes_0 = const()[name = tensor("reduce_mean_75_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_75_keep_dims_0 = const()[name = tensor("reduce_mean_75_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_75_cast_fp16 = reduce_mean(axes = reduce_mean_75_axes_0, keep_dims = reduce_mean_75_keep_dims_0, x = reshape_100_cast_fp16)[name = tensor("reduce_mean_75_cast_fp16")]; + tensor sub_50_cast_fp16 = sub(x = reshape_100_cast_fp16, y = reduce_mean_75_cast_fp16)[name = tensor("sub_50_cast_fp16")]; + tensor square_25_cast_fp16 = square(x = sub_50_cast_fp16)[name = tensor("square_25_cast_fp16")]; + tensor reduce_mean_77_axes_0 = const()[name = tensor("reduce_mean_77_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_77_keep_dims_0 = const()[name = tensor("reduce_mean_77_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_77_cast_fp16 = reduce_mean(axes = reduce_mean_77_axes_0, keep_dims = reduce_mean_77_keep_dims_0, x = square_25_cast_fp16)[name = tensor("reduce_mean_77_cast_fp16")]; + tensor add_50_y_0_to_fp16 = const()[name = tensor("add_50_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_50_cast_fp16 = add(x = reduce_mean_77_cast_fp16, y = add_50_y_0_to_fp16)[name = tensor("add_50_cast_fp16")]; + tensor sqrt_25_cast_fp16 = sqrt(x = add_50_cast_fp16)[name = tensor("sqrt_25_cast_fp16")]; + tensor real_div_25_cast_fp16 = real_div(x = sub_50_cast_fp16, y = sqrt_25_cast_fp16)[name = tensor("real_div_25_cast_fp16")]; + tensor reshape_101_shape_0 = const()[name = tensor("reshape_101_shape_0"), val = tensor([1, 128, 384, 640])]; + tensor reshape_101_cast_fp16 = reshape(shape = reshape_101_shape_0, x = real_div_25_cast_fp16)[name = tensor("reshape_101_cast_fp16")]; + tensor add_51_gamma_0_to_fp16 = const()[name = tensor("add_51_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97801920)))]; + tensor add_51_beta_0_to_fp16 = const()[name = tensor("add_51_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97802240)))]; + tensor add_51_epsilon_0_to_fp16 = const()[name = tensor("add_51_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_51_cast_fp16 = batch_norm(beta = add_51_beta_0_to_fp16, epsilon = add_51_epsilon_0_to_fp16, gamma = add_51_gamma_0_to_fp16, mean = add_49_mean_0_to_fp16, variance = add_49_variance_0_to_fp16, x = reshape_101_cast_fp16)[name = tensor("add_51_cast_fp16")]; + tensor hidden_states_99_cast_fp16 = silu(x = add_51_cast_fp16)[name = tensor("hidden_states_99_cast_fp16")]; + tensor var_567 = const()[name = tensor("op_567"), val = tensor([1, 1])]; + tensor var_569 = const()[name = tensor("op_569"), val = tensor([1, 1])]; + tensor input_135_pad_type_0 = const()[name = tensor("input_135_pad_type_0"), val = tensor("custom")]; + tensor input_135_pad_0 = const()[name = tensor("input_135_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor decoder_up_blocks_3_resnets_1_conv1_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_3_resnets_1_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97802560)))]; + tensor decoder_up_blocks_3_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_3_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98097536)))]; + tensor input_135_cast_fp16 = conv(bias = decoder_up_blocks_3_resnets_1_conv1_bias_to_fp16, dilations = var_569, groups = var_26, pad = input_135_pad_0, pad_type = input_135_pad_type_0, strides = var_567, weight = decoder_up_blocks_3_resnets_1_conv1_weight_to_fp16, x = hidden_states_99_cast_fp16)[name = tensor("input_135_cast_fp16")]; + tensor reshape_104_shape_0 = const()[name = tensor("reshape_104_shape_0"), val = tensor([1, 32, 4, 384, 640])]; + tensor reshape_104_cast_fp16 = reshape(shape = reshape_104_shape_0, x = input_135_cast_fp16)[name = tensor("reshape_104_cast_fp16")]; + tensor reduce_mean_78_axes_0 = const()[name = tensor("reduce_mean_78_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_78_keep_dims_0 = const()[name = tensor("reduce_mean_78_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_78_cast_fp16 = reduce_mean(axes = reduce_mean_78_axes_0, keep_dims = reduce_mean_78_keep_dims_0, x = reshape_104_cast_fp16)[name = tensor("reduce_mean_78_cast_fp16")]; + tensor sub_52_cast_fp16 = sub(x = reshape_104_cast_fp16, y = reduce_mean_78_cast_fp16)[name = tensor("sub_52_cast_fp16")]; + tensor square_26_cast_fp16 = square(x = sub_52_cast_fp16)[name = tensor("square_26_cast_fp16")]; + tensor reduce_mean_80_axes_0 = const()[name = tensor("reduce_mean_80_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_80_keep_dims_0 = const()[name = tensor("reduce_mean_80_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_80_cast_fp16 = reduce_mean(axes = reduce_mean_80_axes_0, keep_dims = reduce_mean_80_keep_dims_0, x = square_26_cast_fp16)[name = tensor("reduce_mean_80_cast_fp16")]; + tensor add_52_y_0_to_fp16 = const()[name = tensor("add_52_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_52_cast_fp16 = add(x = reduce_mean_80_cast_fp16, y = add_52_y_0_to_fp16)[name = tensor("add_52_cast_fp16")]; + tensor sqrt_26_cast_fp16 = sqrt(x = add_52_cast_fp16)[name = tensor("sqrt_26_cast_fp16")]; + tensor real_div_26_cast_fp16 = real_div(x = sub_52_cast_fp16, y = sqrt_26_cast_fp16)[name = tensor("real_div_26_cast_fp16")]; + tensor reshape_105_shape_0 = const()[name = tensor("reshape_105_shape_0"), val = tensor([1, 128, 384, 640])]; + tensor reshape_105_cast_fp16 = reshape(shape = reshape_105_shape_0, x = real_div_26_cast_fp16)[name = tensor("reshape_105_cast_fp16")]; + tensor add_53_gamma_0_to_fp16 = const()[name = tensor("add_53_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98097856)))]; + tensor add_53_beta_0_to_fp16 = const()[name = tensor("add_53_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98098176)))]; + tensor add_53_epsilon_0_to_fp16 = const()[name = tensor("add_53_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_53_cast_fp16 = batch_norm(beta = add_53_beta_0_to_fp16, epsilon = add_53_epsilon_0_to_fp16, gamma = add_53_gamma_0_to_fp16, mean = add_49_mean_0_to_fp16, variance = add_49_variance_0_to_fp16, x = reshape_105_cast_fp16)[name = tensor("add_53_cast_fp16")]; + tensor input_139_cast_fp16 = silu(x = add_53_cast_fp16)[name = tensor("input_139_cast_fp16")]; + tensor var_579 = const()[name = tensor("op_579"), val = tensor([1, 1])]; + tensor var_581 = const()[name = tensor("op_581"), val = tensor([1, 1])]; + tensor hidden_states_103_pad_type_0 = const()[name = tensor("hidden_states_103_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_103_pad_0 = const()[name = tensor("hidden_states_103_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor decoder_up_blocks_3_resnets_1_conv2_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_3_resnets_1_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98098496)))]; + tensor decoder_up_blocks_3_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_3_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98393472)))]; + tensor hidden_states_103_cast_fp16 = conv(bias = decoder_up_blocks_3_resnets_1_conv2_bias_to_fp16, dilations = var_581, groups = var_26, pad = hidden_states_103_pad_0, pad_type = hidden_states_103_pad_type_0, strides = var_579, weight = decoder_up_blocks_3_resnets_1_conv2_weight_to_fp16, x = input_139_cast_fp16)[name = tensor("hidden_states_103_cast_fp16")]; + tensor var_584_cast_fp16 = add(x = var_554_cast_fp16, y = hidden_states_103_cast_fp16)[name = tensor("op_584_cast_fp16")]; + tensor reshape_108_shape_0 = const()[name = tensor("reshape_108_shape_0"), val = tensor([1, 32, 4, 384, 640])]; + tensor reshape_108_cast_fp16 = reshape(shape = reshape_108_shape_0, x = var_584_cast_fp16)[name = tensor("reshape_108_cast_fp16")]; + tensor reduce_mean_81_axes_0 = const()[name = tensor("reduce_mean_81_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_81_keep_dims_0 = const()[name = tensor("reduce_mean_81_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_81_cast_fp16 = reduce_mean(axes = reduce_mean_81_axes_0, keep_dims = reduce_mean_81_keep_dims_0, x = reshape_108_cast_fp16)[name = tensor("reduce_mean_81_cast_fp16")]; + tensor sub_54_cast_fp16 = sub(x = reshape_108_cast_fp16, y = reduce_mean_81_cast_fp16)[name = tensor("sub_54_cast_fp16")]; + tensor square_27_cast_fp16 = square(x = sub_54_cast_fp16)[name = tensor("square_27_cast_fp16")]; + tensor reduce_mean_83_axes_0 = const()[name = tensor("reduce_mean_83_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_83_keep_dims_0 = const()[name = tensor("reduce_mean_83_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_83_cast_fp16 = reduce_mean(axes = reduce_mean_83_axes_0, keep_dims = reduce_mean_83_keep_dims_0, x = square_27_cast_fp16)[name = tensor("reduce_mean_83_cast_fp16")]; + tensor add_54_y_0_to_fp16 = const()[name = tensor("add_54_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_54_cast_fp16 = add(x = reduce_mean_83_cast_fp16, y = add_54_y_0_to_fp16)[name = tensor("add_54_cast_fp16")]; + tensor sqrt_27_cast_fp16 = sqrt(x = add_54_cast_fp16)[name = tensor("sqrt_27_cast_fp16")]; + tensor real_div_27_cast_fp16 = real_div(x = sub_54_cast_fp16, y = sqrt_27_cast_fp16)[name = tensor("real_div_27_cast_fp16")]; + tensor reshape_109_shape_0 = const()[name = tensor("reshape_109_shape_0"), val = tensor([1, 128, 384, 640])]; + tensor reshape_109_cast_fp16 = reshape(shape = reshape_109_shape_0, x = real_div_27_cast_fp16)[name = tensor("reshape_109_cast_fp16")]; + tensor add_55_gamma_0_to_fp16 = const()[name = tensor("add_55_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98393792)))]; + tensor add_55_beta_0_to_fp16 = const()[name = tensor("add_55_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98394112)))]; + tensor add_55_epsilon_0_to_fp16 = const()[name = tensor("add_55_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_55_cast_fp16 = batch_norm(beta = add_55_beta_0_to_fp16, epsilon = add_55_epsilon_0_to_fp16, gamma = add_55_gamma_0_to_fp16, mean = add_49_mean_0_to_fp16, variance = add_49_variance_0_to_fp16, x = reshape_109_cast_fp16)[name = tensor("add_55_cast_fp16")]; + tensor hidden_states_105_cast_fp16 = silu(x = add_55_cast_fp16)[name = tensor("hidden_states_105_cast_fp16")]; + tensor var_597 = const()[name = tensor("op_597"), val = tensor([1, 1])]; + tensor var_599 = const()[name = tensor("op_599"), val = tensor([1, 1])]; + tensor input_145_pad_type_0 = const()[name = tensor("input_145_pad_type_0"), val = tensor("custom")]; + tensor input_145_pad_0 = const()[name = tensor("input_145_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor decoder_up_blocks_3_resnets_2_conv1_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_3_resnets_2_conv1_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98394432)))]; + tensor decoder_up_blocks_3_resnets_2_conv1_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_3_resnets_2_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98689408)))]; + tensor input_145_cast_fp16 = conv(bias = decoder_up_blocks_3_resnets_2_conv1_bias_to_fp16, dilations = var_599, groups = var_26, pad = input_145_pad_0, pad_type = input_145_pad_type_0, strides = var_597, weight = decoder_up_blocks_3_resnets_2_conv1_weight_to_fp16, x = hidden_states_105_cast_fp16)[name = tensor("input_145_cast_fp16")]; + tensor reshape_112_shape_0 = const()[name = tensor("reshape_112_shape_0"), val = tensor([1, 32, 4, 384, 640])]; + tensor reshape_112_cast_fp16 = reshape(shape = reshape_112_shape_0, x = input_145_cast_fp16)[name = tensor("reshape_112_cast_fp16")]; + tensor reduce_mean_84_axes_0 = const()[name = tensor("reduce_mean_84_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_84_keep_dims_0 = const()[name = tensor("reduce_mean_84_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_84_cast_fp16 = reduce_mean(axes = reduce_mean_84_axes_0, keep_dims = reduce_mean_84_keep_dims_0, x = reshape_112_cast_fp16)[name = tensor("reduce_mean_84_cast_fp16")]; + tensor sub_56_cast_fp16 = sub(x = reshape_112_cast_fp16, y = reduce_mean_84_cast_fp16)[name = tensor("sub_56_cast_fp16")]; + tensor square_28_cast_fp16 = square(x = sub_56_cast_fp16)[name = tensor("square_28_cast_fp16")]; + tensor reduce_mean_86_axes_0 = const()[name = tensor("reduce_mean_86_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_86_keep_dims_0 = const()[name = tensor("reduce_mean_86_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_86_cast_fp16 = reduce_mean(axes = reduce_mean_86_axes_0, keep_dims = reduce_mean_86_keep_dims_0, x = square_28_cast_fp16)[name = tensor("reduce_mean_86_cast_fp16")]; + tensor add_56_y_0_to_fp16 = const()[name = tensor("add_56_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_56_cast_fp16 = add(x = reduce_mean_86_cast_fp16, y = add_56_y_0_to_fp16)[name = tensor("add_56_cast_fp16")]; + tensor sqrt_28_cast_fp16 = sqrt(x = add_56_cast_fp16)[name = tensor("sqrt_28_cast_fp16")]; + tensor real_div_28_cast_fp16 = real_div(x = sub_56_cast_fp16, y = sqrt_28_cast_fp16)[name = tensor("real_div_28_cast_fp16")]; + tensor reshape_113_shape_0 = const()[name = tensor("reshape_113_shape_0"), val = tensor([1, 128, 384, 640])]; + tensor reshape_113_cast_fp16 = reshape(shape = reshape_113_shape_0, x = real_div_28_cast_fp16)[name = tensor("reshape_113_cast_fp16")]; + tensor add_57_gamma_0_to_fp16 = const()[name = tensor("add_57_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98689728)))]; + tensor add_57_beta_0_to_fp16 = const()[name = tensor("add_57_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98690048)))]; + tensor add_57_epsilon_0_to_fp16 = const()[name = tensor("add_57_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_57_cast_fp16 = batch_norm(beta = add_57_beta_0_to_fp16, epsilon = add_57_epsilon_0_to_fp16, gamma = add_57_gamma_0_to_fp16, mean = add_49_mean_0_to_fp16, variance = add_49_variance_0_to_fp16, x = reshape_113_cast_fp16)[name = tensor("add_57_cast_fp16")]; + tensor input_149_cast_fp16 = silu(x = add_57_cast_fp16)[name = tensor("input_149_cast_fp16")]; + tensor var_609 = const()[name = tensor("op_609"), val = tensor([1, 1])]; + tensor var_611 = const()[name = tensor("op_611"), val = tensor([1, 1])]; + tensor hidden_states_pad_type_0 = const()[name = tensor("hidden_states_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_pad_0 = const()[name = tensor("hidden_states_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor decoder_up_blocks_3_resnets_2_conv2_weight_to_fp16 = const()[name = tensor("decoder_up_blocks_3_resnets_2_conv2_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98690368)))]; + tensor decoder_up_blocks_3_resnets_2_conv2_bias_to_fp16 = const()[name = tensor("decoder_up_blocks_3_resnets_2_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98985344)))]; + tensor hidden_states_cast_fp16 = conv(bias = decoder_up_blocks_3_resnets_2_conv2_bias_to_fp16, dilations = var_611, groups = var_26, pad = hidden_states_pad_0, pad_type = hidden_states_pad_type_0, strides = var_609, weight = decoder_up_blocks_3_resnets_2_conv2_weight_to_fp16, x = input_149_cast_fp16)[name = tensor("hidden_states_cast_fp16")]; + tensor var_614_cast_fp16 = add(x = var_584_cast_fp16, y = hidden_states_cast_fp16)[name = tensor("op_614_cast_fp16")]; + tensor reshape_116_shape_0 = const()[name = tensor("reshape_116_shape_0"), val = tensor([1, 32, 4, 384, 640])]; + tensor reshape_116_cast_fp16 = reshape(shape = reshape_116_shape_0, x = var_614_cast_fp16)[name = tensor("reshape_116_cast_fp16")]; + tensor reduce_mean_87_axes_0 = const()[name = tensor("reduce_mean_87_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_87_keep_dims_0 = const()[name = tensor("reduce_mean_87_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_87_cast_fp16 = reduce_mean(axes = reduce_mean_87_axes_0, keep_dims = reduce_mean_87_keep_dims_0, x = reshape_116_cast_fp16)[name = tensor("reduce_mean_87_cast_fp16")]; + tensor sub_58_cast_fp16 = sub(x = reshape_116_cast_fp16, y = reduce_mean_87_cast_fp16)[name = tensor("sub_58_cast_fp16")]; + tensor square_29_cast_fp16 = square(x = sub_58_cast_fp16)[name = tensor("square_29_cast_fp16")]; + tensor reduce_mean_89_axes_0 = const()[name = tensor("reduce_mean_89_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_89_keep_dims_0 = const()[name = tensor("reduce_mean_89_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_89_cast_fp16 = reduce_mean(axes = reduce_mean_89_axes_0, keep_dims = reduce_mean_89_keep_dims_0, x = square_29_cast_fp16)[name = tensor("reduce_mean_89_cast_fp16")]; + tensor add_58_y_0_to_fp16 = const()[name = tensor("add_58_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_58_cast_fp16 = add(x = reduce_mean_89_cast_fp16, y = add_58_y_0_to_fp16)[name = tensor("add_58_cast_fp16")]; + tensor sqrt_29_cast_fp16 = sqrt(x = add_58_cast_fp16)[name = tensor("sqrt_29_cast_fp16")]; + tensor real_div_29_cast_fp16 = real_div(x = sub_58_cast_fp16, y = sqrt_29_cast_fp16)[name = tensor("real_div_29_cast_fp16")]; + tensor reshape_117_shape_0 = const()[name = tensor("reshape_117_shape_0"), val = tensor([1, 128, 384, 640])]; + tensor reshape_117_cast_fp16 = reshape(shape = reshape_117_shape_0, x = real_div_29_cast_fp16)[name = tensor("reshape_117_cast_fp16")]; + tensor add_59_gamma_0_to_fp16 = const()[name = tensor("add_59_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98985664)))]; + tensor add_59_beta_0_to_fp16 = const()[name = tensor("add_59_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98985984)))]; + tensor add_59_epsilon_0_to_fp16 = const()[name = tensor("add_59_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_59_cast_fp16 = batch_norm(beta = add_59_beta_0_to_fp16, epsilon = add_59_epsilon_0_to_fp16, gamma = add_59_gamma_0_to_fp16, mean = add_49_mean_0_to_fp16, variance = add_49_variance_0_to_fp16, x = reshape_117_cast_fp16)[name = tensor("add_59_cast_fp16")]; + tensor input_cast_fp16 = silu(x = add_59_cast_fp16)[name = tensor("input_cast_fp16")]; + tensor var_623 = const()[name = tensor("op_623"), val = tensor([1, 1])]; + tensor var_625 = const()[name = tensor("op_625"), val = tensor([1, 1])]; + tensor var_627_pad_type_0 = const()[name = tensor("op_627_pad_type_0"), val = tensor("custom")]; + tensor var_627_pad_0 = const()[name = tensor("op_627_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor decoder_conv_out_weight_to_fp16 = const()[name = tensor("decoder_conv_out_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98986304)))]; + tensor decoder_conv_out_bias_to_fp16 = const()[name = tensor("decoder_conv_out_bias_to_fp16"), val = tensor([0x1.02p-6, -0x1.4ccp-6, -0x1.7bcp-5])]; + tensor var_627_cast_fp16 = conv(bias = decoder_conv_out_bias_to_fp16, dilations = var_625, groups = var_26, pad = var_627_pad_0, pad_type = var_627_pad_type_0, strides = var_623, weight = decoder_conv_out_weight_to_fp16, x = input_cast_fp16)[name = tensor("op_627_cast_fp16")]; + tensor var_627_cast_fp16_to_fp32_dtype_0 = const()[name = tensor("op_627_cast_fp16_to_fp32_dtype_0"), val = tensor("fp32")]; + tensor image = cast(dtype = var_627_cast_fp16_to_fp32_dtype_0, x = var_627_cast_fp16)[name = tensor("cast_37")]; + } -> (image); +} \ No newline at end of file