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 +1,977 @@ +program(1.0) +[buildInfo = dict, tensor>({{"coremlc-component-MIL", "5.30.0"}, {"coremlc-version", "1839.0.0"}, {"coremltools-component-torch", "2.0.1"}, {"coremltools-version", "7.0b1"}})] +{ + 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 = 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")]; + tensor var_22 = const()[name = tensor("op_22"), val = tensor(-1)]; + tensor var_28 = const()[name = tensor("op_28"), val = tensor(1)]; + tensor var_46 = const()[name = tensor("op_46"), val = tensor([1, 1])]; + tensor var_48 = const()[name = tensor("op_48"), 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 = conv(bias = decoder_conv_in_bias_to_fp16, dilations = var_48, groups = var_28, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = var_46, weight = decoder_conv_in_weight_to_fp16, x = input_1_cast)[name = tensor("input_3_cast")]; + tensor reshape_0_shape_0 = const()[name = tensor("reshape_0_shape_0"), val = tensor([1, 32, 16, 64, 64])]; + tensor reshape_0_cast = reshape(shape = reshape_0_shape_0, x = input_3_cast)[name = tensor("reshape_0_cast")]; + 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 = reduce_mean(axes = reduce_mean_0_axes_0, keep_dims = reduce_mean_0_keep_dims_0, x = reshape_0_cast)[name = tensor("reduce_mean_0_cast")]; + tensor sub_0_cast = sub(x = reshape_0_cast, y = reduce_mean_0_cast)[name = tensor("sub_0_cast")]; + tensor square_0_cast = square(x = sub_0_cast)[name = tensor("square_0_cast")]; + 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 = reduce_mean(axes = reduce_mean_2_axes_0, keep_dims = reduce_mean_2_keep_dims_0, x = square_0_cast)[name = tensor("reduce_mean_2_cast")]; + 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 = add(x = reduce_mean_2_cast, y = add_0_y_0_to_fp16)[name = tensor("add_0_cast")]; + tensor sqrt_0_cast = sqrt(x = add_0_cast)[name = tensor("sqrt_0_cast")]; + tensor real_div_0_cast = real_div(x = sub_0_cast, y = sqrt_0_cast)[name = tensor("real_div_0_cast")]; + tensor reshape_1_shape_0 = const()[name = tensor("reshape_1_shape_0"), val = tensor([1, 512, 64, 64])]; + tensor reshape_1_cast = reshape(shape = reshape_1_shape_0, x = real_div_0_cast)[name = tensor("reshape_1_cast")]; + 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 = 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)[name = tensor("add_1_cast")]; + tensor input_7_cast = silu(x = add_1_cast)[name = tensor("input_7_cast")]; + tensor var_67 = const()[name = tensor("op_67"), val = tensor([1, 1])]; + tensor var_69 = const()[name = tensor("op_69"), val = tensor([1, 1])]; + tensor input_9_pad_type_0 = const()[name = tensor("input_9_pad_type_0"), val = tensor("custom")]; + tensor input_9_pad_0 = const()[name = tensor("input_9_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor 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_9_cast = conv(bias = decoder_mid_block_resnets_0_conv1_bias_to_fp16, dilations = var_69, groups = var_28, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = var_67, weight = decoder_mid_block_resnets_0_conv1_weight_to_fp16, x = input_7_cast)[name = tensor("input_9_cast")]; + tensor reshape_4_shape_0 = const()[name = tensor("reshape_4_shape_0"), val = tensor([1, 32, 16, 64, 64])]; + tensor reshape_4_cast = reshape(shape = reshape_4_shape_0, x = input_9_cast)[name = tensor("reshape_4_cast")]; + 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 = reduce_mean(axes = reduce_mean_3_axes_0, keep_dims = reduce_mean_3_keep_dims_0, x = reshape_4_cast)[name = tensor("reduce_mean_3_cast")]; + tensor sub_2_cast = sub(x = reshape_4_cast, y = reduce_mean_3_cast)[name = tensor("sub_2_cast")]; + tensor square_1_cast = square(x = sub_2_cast)[name = tensor("square_1_cast")]; + 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 = reduce_mean(axes = reduce_mean_5_axes_0, keep_dims = reduce_mean_5_keep_dims_0, x = square_1_cast)[name = tensor("reduce_mean_5_cast")]; + 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 = add(x = reduce_mean_5_cast, y = add_2_y_0_to_fp16)[name = tensor("add_2_cast")]; + tensor sqrt_1_cast = sqrt(x = add_2_cast)[name = tensor("sqrt_1_cast")]; + tensor real_div_1_cast = real_div(x = sub_2_cast, y = sqrt_1_cast)[name = tensor("real_div_1_cast")]; + tensor reshape_5_shape_0 = const()[name = tensor("reshape_5_shape_0"), val = tensor([1, 512, 64, 64])]; + tensor reshape_5_cast = reshape(shape = reshape_5_shape_0, x = real_div_1_cast)[name = tensor("reshape_5_cast")]; + 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 = 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)[name = tensor("add_3_cast")]; + tensor input_13_cast = silu(x = add_3_cast)[name = tensor("input_13_cast")]; + tensor var_79 = const()[name = tensor("op_79"), val = tensor([1, 1])]; + tensor var_81 = const()[name = tensor("op_81"), val = tensor([1, 1])]; + tensor hidden_states_1_pad_type_0 = const()[name = tensor("hidden_states_1_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_1_pad_0 = const()[name = tensor("hidden_states_1_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor 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_1_cast = conv(bias = decoder_mid_block_resnets_0_conv2_bias_to_fp16, dilations = var_81, groups = var_28, pad = hidden_states_1_pad_0, pad_type = hidden_states_1_pad_type_0, strides = var_79, weight = decoder_mid_block_resnets_0_conv2_weight_to_fp16, x = input_13_cast)[name = tensor("hidden_states_1_cast")]; + tensor var_84_cast = add(x = input_3_cast, y = hidden_states_1_cast)[name = tensor("op_84_cast")]; + tensor reshape_8_shape_0 = const()[name = tensor("reshape_8_shape_0"), val = tensor([1, 32, 16, 64, 64])]; + tensor reshape_8_cast = reshape(shape = reshape_8_shape_0, x = var_84_cast)[name = tensor("reshape_8_cast")]; + tensor reduce_mean_6_axes_0 = const()[name = tensor("reduce_mean_6_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_6_keep_dims_0 = const()[name = tensor("reduce_mean_6_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_6_cast = reduce_mean(axes = reduce_mean_6_axes_0, keep_dims = reduce_mean_6_keep_dims_0, x = reshape_8_cast)[name = tensor("reduce_mean_6_cast")]; + tensor sub_4_cast = sub(x = reshape_8_cast, y = reduce_mean_6_cast)[name = tensor("sub_4_cast")]; + tensor square_2_cast = square(x = sub_4_cast)[name = tensor("square_2_cast")]; + tensor reduce_mean_8_axes_0 = const()[name = tensor("reduce_mean_8_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_8_keep_dims_0 = const()[name = tensor("reduce_mean_8_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_8_cast = reduce_mean(axes = reduce_mean_8_axes_0, keep_dims = reduce_mean_8_keep_dims_0, x = square_2_cast)[name = tensor("reduce_mean_8_cast")]; + 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 = add(x = reduce_mean_8_cast, y = add_4_y_0_to_fp16)[name = tensor("add_4_cast")]; + tensor sqrt_2_cast = sqrt(x = add_4_cast)[name = tensor("sqrt_2_cast")]; + tensor real_div_2_cast = real_div(x = sub_4_cast, y = sqrt_2_cast)[name = tensor("real_div_2_cast")]; + tensor reshape_9_shape_0 = const()[name = tensor("reshape_9_shape_0"), val = tensor([1, 512, 64, 64])]; + tensor reshape_9_cast = reshape(shape = reshape_9_shape_0, x = real_div_2_cast)[name = tensor("reshape_9_cast")]; + tensor add_5_gamma_0_to_fp16 = const()[name = tensor("add_5_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9484224)))]; + tensor add_5_beta_0_to_fp16 = const()[name = tensor("add_5_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9485312)))]; + tensor add_5_epsilon_0_to_fp16 = const()[name = tensor("add_5_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_5_cast = batch_norm(beta = add_5_beta_0_to_fp16, epsilon = add_5_epsilon_0_to_fp16, gamma = add_5_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_9_cast)[name = tensor("add_5_cast")]; + tensor var_103 = const()[name = tensor("op_103"), val = tensor([1, 512, 4096])]; + tensor var_104_cast = reshape(shape = var_103, x = add_5_cast)[name = tensor("op_104_cast")]; + tensor input_17_perm_0 = const()[name = tensor("input_17_perm_0"), val = tensor([0, 2, 1])]; + tensor decoder_mid_block_attentions_0_query_weight_to_fp16 = const()[name = tensor("decoder_mid_block_attentions_0_query_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9486400)))]; + tensor decoder_mid_block_attentions_0_query_bias_to_fp16 = const()[name = tensor("decoder_mid_block_attentions_0_query_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10010752)))]; + tensor transpose_6 = transpose(perm = input_17_perm_0, x = var_104_cast)[name = tensor("transpose_6")]; + tensor tensor_1_cast = linear(bias = decoder_mid_block_attentions_0_query_bias_to_fp16, weight = decoder_mid_block_attentions_0_query_weight_to_fp16, x = transpose_6)[name = tensor("tensor_1_cast")]; + tensor decoder_mid_block_attentions_0_key_weight_to_fp16 = const()[name = tensor("decoder_mid_block_attentions_0_key_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10011840)))]; + tensor decoder_mid_block_attentions_0_key_bias_to_fp16 = const()[name = tensor("decoder_mid_block_attentions_0_key_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10536192)))]; + tensor tensor_7_cast = linear(bias = decoder_mid_block_attentions_0_key_bias_to_fp16, weight = decoder_mid_block_attentions_0_key_weight_to_fp16, x = transpose_6)[name = tensor("tensor_7_cast")]; + tensor decoder_mid_block_attentions_0_value_weight_to_fp16 = const()[name = tensor("decoder_mid_block_attentions_0_value_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10537280)))]; + tensor decoder_mid_block_attentions_0_value_bias_to_fp16 = const()[name = tensor("decoder_mid_block_attentions_0_value_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11061632)))]; + tensor tensor_13_cast = linear(bias = decoder_mid_block_attentions_0_value_bias_to_fp16, weight = decoder_mid_block_attentions_0_value_weight_to_fp16, x = transpose_6)[name = tensor("tensor_13_cast")]; + tensor var_122 = const()[name = tensor("op_122"), val = tensor([1, 4096, 1, 512])]; + tensor tensor_3_cast = reshape(shape = var_122, x = tensor_1_cast)[name = tensor("tensor_3_cast")]; + tensor var_124 = const()[name = tensor("op_124"), val = tensor([0, 2, 1, 3])]; + tensor var_131 = const()[name = tensor("op_131"), val = tensor([1, 4096, 512])]; + tensor transpose_5 = transpose(perm = var_124, x = tensor_3_cast)[name = tensor("transpose_5")]; + tensor query_proj_cast = reshape(shape = var_131, x = transpose_5)[name = tensor("query_proj_cast")]; + tensor var_140 = const()[name = tensor("op_140"), val = tensor([1, 4096, 1, 512])]; + tensor tensor_9_cast = reshape(shape = var_140, x = tensor_7_cast)[name = tensor("tensor_9_cast")]; + tensor var_142 = const()[name = tensor("op_142"), val = tensor([0, 2, 1, 3])]; + tensor var_149 = const()[name = tensor("op_149"), val = tensor([1, 4096, 512])]; + tensor transpose_4 = transpose(perm = var_142, x = tensor_9_cast)[name = tensor("transpose_4")]; + tensor key_proj_cast = reshape(shape = var_149, x = transpose_4)[name = tensor("key_proj_cast")]; + tensor var_158 = const()[name = tensor("op_158"), val = tensor([1, 4096, 1, 512])]; + tensor tensor_15_cast = reshape(shape = var_158, x = tensor_13_cast)[name = tensor("tensor_15_cast")]; + tensor var_160 = const()[name = tensor("op_160"), val = tensor([0, 2, 1, 3])]; + tensor var_167 = const()[name = tensor("op_167"), val = tensor([1, 4096, 512])]; + tensor transpose_3 = transpose(perm = var_160, x = tensor_15_cast)[name = tensor("transpose_3")]; + tensor value_proj_cast = reshape(shape = var_167, x = transpose_3)[name = tensor("value_proj_cast")]; + tensor var_174_perm_0 = const()[name = tensor("op_174_perm_0"), val = tensor([0, -1, -2])]; + tensor var_20_to_fp16 = const()[name = tensor("op_20_to_fp16"), val = tensor(0x1.6ap-5)]; + tensor query_proj_scaled_cast = mul(x = var_20_to_fp16, y = query_proj_cast)[name = tensor("query_proj_scaled_cast")]; + tensor attention_scores_1_bmm_transpose_x_0 = const()[name = tensor("attention_scores_1_bmm_transpose_x_0"), val = tensor(false)]; + tensor attention_scores_1_bmm_transpose_y_0 = const()[name = tensor("attention_scores_1_bmm_transpose_y_0"), val = tensor(false)]; + tensor transpose_2 = transpose(perm = var_174_perm_0, x = key_proj_cast)[name = tensor("transpose_2")]; + tensor attention_scores_1_bmm_cast = matmul(transpose_x = attention_scores_1_bmm_transpose_x_0, transpose_y = attention_scores_1_bmm_transpose_y_0, x = query_proj_scaled_cast, y = transpose_2)[name = tensor("attention_scores_1_bmm_cast")]; + tensor var_177_cast = softmax(axis = var_22, x = attention_scores_1_bmm_cast)[name = tensor("op_177_cast")]; + tensor tensor_19_transpose_x_0 = const()[name = tensor("tensor_19_transpose_x_0"), val = tensor(false)]; + tensor tensor_19_transpose_y_0 = const()[name = tensor("tensor_19_transpose_y_0"), val = tensor(false)]; + tensor tensor_19_cast = matmul(transpose_x = tensor_19_transpose_x_0, transpose_y = tensor_19_transpose_y_0, x = var_177_cast, y = value_proj_cast)[name = tensor("tensor_19_cast")]; + tensor var_188 = const()[name = tensor("op_188"), val = tensor([1, 1, 4096, 512])]; + tensor tensor_cast = reshape(shape = var_188, x = tensor_19_cast)[name = tensor("tensor_cast")]; + tensor var_190 = const()[name = tensor("op_190"), val = tensor([0, 2, 1, 3])]; + tensor var_195 = const()[name = tensor("op_195"), val = tensor([1, 4096, 512])]; + tensor transpose_1 = transpose(perm = var_190, x = tensor_cast)[name = tensor("transpose_1")]; + tensor input_19_cast = reshape(shape = var_195, x = transpose_1)[name = tensor("input_19_cast")]; + tensor decoder_mid_block_attentions_0_proj_attn_weight_to_fp16 = const()[name = tensor("decoder_mid_block_attentions_0_proj_attn_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11062720)))]; + tensor decoder_mid_block_attentions_0_proj_attn_bias_to_fp16 = const()[name = tensor("decoder_mid_block_attentions_0_proj_attn_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11587072)))]; + tensor hidden_states_7_cast = linear(bias = decoder_mid_block_attentions_0_proj_attn_bias_to_fp16, weight = decoder_mid_block_attentions_0_proj_attn_weight_to_fp16, x = input_19_cast)[name = tensor("hidden_states_7_cast")]; + tensor var_200_perm_0 = const()[name = tensor("op_200_perm_0"), val = tensor([0, -1, -2])]; + tensor var_201 = const()[name = tensor("op_201"), val = tensor([1, 512, 64, 64])]; + tensor transpose_0 = transpose(perm = var_200_perm_0, x = hidden_states_7_cast)[name = tensor("transpose_0")]; + tensor hidden_states_9_cast = reshape(shape = var_201, x = transpose_0)[name = tensor("hidden_states_9_cast")]; + tensor var_203_cast = add(x = hidden_states_9_cast, y = var_84_cast)[name = tensor("op_203_cast")]; + tensor reshape_12_shape_0 = const()[name = tensor("reshape_12_shape_0"), val = tensor([1, 32, 16, 64, 64])]; + tensor reshape_12_cast = reshape(shape = reshape_12_shape_0, x = var_203_cast)[name = tensor("reshape_12_cast")]; + 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 = reduce_mean(axes = reduce_mean_9_axes_0, keep_dims = reduce_mean_9_keep_dims_0, x = reshape_12_cast)[name = tensor("reduce_mean_9_cast")]; + tensor sub_6_cast = sub(x = reshape_12_cast, y = reduce_mean_9_cast)[name = tensor("sub_6_cast")]; + tensor square_3_cast = square(x = sub_6_cast)[name = tensor("square_3_cast")]; + 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 = reduce_mean(axes = reduce_mean_11_axes_0, keep_dims = reduce_mean_11_keep_dims_0, x = square_3_cast)[name = tensor("reduce_mean_11_cast")]; + 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 = add(x = reduce_mean_11_cast, y = add_6_y_0_to_fp16)[name = tensor("add_6_cast")]; + tensor sqrt_3_cast = sqrt(x = add_6_cast)[name = tensor("sqrt_3_cast")]; + tensor real_div_3_cast = real_div(x = sub_6_cast, y = sqrt_3_cast)[name = tensor("real_div_3_cast")]; + tensor reshape_13_shape_0 = const()[name = tensor("reshape_13_shape_0"), val = tensor([1, 512, 64, 64])]; + tensor reshape_13_cast = reshape(shape = reshape_13_shape_0, x = real_div_3_cast)[name = tensor("reshape_13_cast")]; + 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 = 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)[name = tensor("add_7_cast")]; + tensor input_25_cast = silu(x = add_7_cast)[name = tensor("input_25_cast")]; + tensor var_216 = const()[name = tensor("op_216"), val = tensor([1, 1])]; + tensor var_218 = const()[name = tensor("op_218"), val = tensor([1, 1])]; + tensor input_27_pad_type_0 = const()[name = tensor("input_27_pad_type_0"), val = tensor("custom")]; + tensor input_27_pad_0 = const()[name = tensor("input_27_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_27_cast = conv(bias = decoder_mid_block_resnets_1_conv1_bias_to_fp16, dilations = var_218, groups = var_28, pad = input_27_pad_0, pad_type = input_27_pad_type_0, strides = var_216, weight = decoder_mid_block_resnets_1_conv1_weight_to_fp16, x = input_25_cast)[name = tensor("input_27_cast")]; + tensor reshape_16_shape_0 = const()[name = tensor("reshape_16_shape_0"), val = tensor([1, 32, 16, 64, 64])]; + tensor reshape_16_cast = reshape(shape = reshape_16_shape_0, x = input_27_cast)[name = tensor("reshape_16_cast")]; + 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 = reduce_mean(axes = reduce_mean_12_axes_0, keep_dims = reduce_mean_12_keep_dims_0, x = reshape_16_cast)[name = tensor("reduce_mean_12_cast")]; + tensor sub_8_cast = sub(x = reshape_16_cast, y = reduce_mean_12_cast)[name = tensor("sub_8_cast")]; + tensor square_4_cast = square(x = sub_8_cast)[name = tensor("square_4_cast")]; + 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 = reduce_mean(axes = reduce_mean_14_axes_0, keep_dims = reduce_mean_14_keep_dims_0, x = square_4_cast)[name = tensor("reduce_mean_14_cast")]; + 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 = add(x = reduce_mean_14_cast, y = add_8_y_0_to_fp16)[name = tensor("add_8_cast")]; + tensor sqrt_4_cast = sqrt(x = add_8_cast)[name = tensor("sqrt_4_cast")]; + tensor real_div_4_cast = real_div(x = sub_8_cast, y = sqrt_4_cast)[name = tensor("real_div_4_cast")]; + tensor reshape_17_shape_0 = const()[name = tensor("reshape_17_shape_0"), val = tensor([1, 512, 64, 64])]; + tensor reshape_17_cast = reshape(shape = reshape_17_shape_0, x = real_div_4_cast)[name = tensor("reshape_17_cast")]; + 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 = 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)[name = tensor("add_9_cast")]; + tensor input_31_cast = silu(x = add_9_cast)[name = tensor("input_31_cast")]; + tensor var_228 = const()[name = tensor("op_228"), val = tensor([1, 1])]; + tensor var_230 = const()[name = tensor("op_230"), val = tensor([1, 1])]; + tensor hidden_states_11_pad_type_0 = const()[name = tensor("hidden_states_11_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_11_pad_0 = const()[name = tensor("hidden_states_11_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor 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_11_cast = conv(bias = decoder_mid_block_resnets_1_conv2_bias_to_fp16, dilations = var_230, groups = var_28, pad = hidden_states_11_pad_0, pad_type = hidden_states_11_pad_type_0, strides = var_228, weight = decoder_mid_block_resnets_1_conv2_weight_to_fp16, x = input_31_cast)[name = tensor("hidden_states_11_cast")]; + tensor var_233_cast = add(x = var_203_cast, y = hidden_states_11_cast)[name = tensor("op_233_cast")]; + tensor reshape_20_shape_0 = const()[name = tensor("reshape_20_shape_0"), val = tensor([1, 32, 16, 64, 64])]; + tensor reshape_20_cast = reshape(shape = reshape_20_shape_0, x = var_233_cast)[name = tensor("reshape_20_cast")]; + 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 = reduce_mean(axes = reduce_mean_15_axes_0, keep_dims = reduce_mean_15_keep_dims_0, x = reshape_20_cast)[name = tensor("reduce_mean_15_cast")]; + tensor sub_10_cast = sub(x = reshape_20_cast, y = reduce_mean_15_cast)[name = tensor("sub_10_cast")]; + tensor square_5_cast = square(x = sub_10_cast)[name = tensor("square_5_cast")]; + 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 = reduce_mean(axes = reduce_mean_17_axes_0, keep_dims = reduce_mean_17_keep_dims_0, x = square_5_cast)[name = tensor("reduce_mean_17_cast")]; + 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 = add(x = reduce_mean_17_cast, y = add_10_y_0_to_fp16)[name = tensor("add_10_cast")]; + tensor sqrt_5_cast = sqrt(x = add_10_cast)[name = tensor("sqrt_5_cast")]; + tensor real_div_5_cast = real_div(x = sub_10_cast, y = sqrt_5_cast)[name = tensor("real_div_5_cast")]; + tensor reshape_21_shape_0 = const()[name = tensor("reshape_21_shape_0"), val = tensor([1, 512, 64, 64])]; + tensor reshape_21_cast = reshape(shape = reshape_21_shape_0, x = real_div_5_cast)[name = tensor("reshape_21_cast")]; + 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 = 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)[name = tensor("add_11_cast")]; + tensor input_39_cast = silu(x = add_11_cast)[name = tensor("input_39_cast")]; + tensor var_255 = const()[name = tensor("op_255"), val = tensor([1, 1])]; + tensor var_257 = const()[name = tensor("op_257"), val = tensor([1, 1])]; + tensor input_41_pad_type_0 = const()[name = tensor("input_41_pad_type_0"), val = tensor("custom")]; + tensor input_41_pad_0 = const()[name = tensor("input_41_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_41_cast = conv(bias = decoder_up_blocks_0_resnets_0_conv1_bias_to_fp16, dilations = var_257, groups = var_28, pad = input_41_pad_0, pad_type = input_41_pad_type_0, strides = var_255, weight = decoder_up_blocks_0_resnets_0_conv1_weight_to_fp16, x = input_39_cast)[name = tensor("input_41_cast")]; + tensor reshape_24_shape_0 = const()[name = tensor("reshape_24_shape_0"), val = tensor([1, 32, 16, 64, 64])]; + tensor reshape_24_cast = reshape(shape = reshape_24_shape_0, x = input_41_cast)[name = tensor("reshape_24_cast")]; + 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 = reduce_mean(axes = reduce_mean_18_axes_0, keep_dims = reduce_mean_18_keep_dims_0, x = reshape_24_cast)[name = tensor("reduce_mean_18_cast")]; + tensor sub_12_cast = sub(x = reshape_24_cast, y = reduce_mean_18_cast)[name = tensor("sub_12_cast")]; + tensor square_6_cast = square(x = sub_12_cast)[name = tensor("square_6_cast")]; + 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 = reduce_mean(axes = reduce_mean_20_axes_0, keep_dims = reduce_mean_20_keep_dims_0, x = square_6_cast)[name = tensor("reduce_mean_20_cast")]; + 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 = add(x = reduce_mean_20_cast, y = add_12_y_0_to_fp16)[name = tensor("add_12_cast")]; + tensor sqrt_6_cast = sqrt(x = add_12_cast)[name = tensor("sqrt_6_cast")]; + tensor real_div_6_cast = real_div(x = sub_12_cast, y = sqrt_6_cast)[name = tensor("real_div_6_cast")]; + tensor reshape_25_shape_0 = const()[name = tensor("reshape_25_shape_0"), val = tensor([1, 512, 64, 64])]; + tensor reshape_25_cast = reshape(shape = reshape_25_shape_0, x = real_div_6_cast)[name = tensor("reshape_25_cast")]; + 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 = 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)[name = tensor("add_13_cast")]; + tensor input_45_cast = silu(x = add_13_cast)[name = tensor("input_45_cast")]; + tensor var_267 = const()[name = tensor("op_267"), val = tensor([1, 1])]; + tensor var_269 = const()[name = tensor("op_269"), val = tensor([1, 1])]; + tensor hidden_states_13_pad_type_0 = const()[name = tensor("hidden_states_13_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_13_pad_0 = const()[name = tensor("hidden_states_13_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_13_cast = conv(bias = decoder_up_blocks_0_resnets_0_conv2_bias_to_fp16, dilations = var_269, groups = var_28, pad = hidden_states_13_pad_0, pad_type = hidden_states_13_pad_type_0, strides = var_267, weight = decoder_up_blocks_0_resnets_0_conv2_weight_to_fp16, x = input_45_cast)[name = tensor("hidden_states_13_cast")]; + tensor var_272_cast = add(x = var_233_cast, y = hidden_states_13_cast)[name = tensor("op_272_cast")]; + tensor reshape_28_shape_0 = const()[name = tensor("reshape_28_shape_0"), val = tensor([1, 32, 16, 64, 64])]; + tensor reshape_28_cast = reshape(shape = reshape_28_shape_0, x = var_272_cast)[name = tensor("reshape_28_cast")]; + 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 = reduce_mean(axes = reduce_mean_21_axes_0, keep_dims = reduce_mean_21_keep_dims_0, x = reshape_28_cast)[name = tensor("reduce_mean_21_cast")]; + tensor sub_14_cast = sub(x = reshape_28_cast, y = reduce_mean_21_cast)[name = tensor("sub_14_cast")]; + tensor square_7_cast = square(x = sub_14_cast)[name = tensor("square_7_cast")]; + 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 = reduce_mean(axes = reduce_mean_23_axes_0, keep_dims = reduce_mean_23_keep_dims_0, x = square_7_cast)[name = tensor("reduce_mean_23_cast")]; + 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 = add(x = reduce_mean_23_cast, y = add_14_y_0_to_fp16)[name = tensor("add_14_cast")]; + tensor sqrt_7_cast = sqrt(x = add_14_cast)[name = tensor("sqrt_7_cast")]; + tensor real_div_7_cast = real_div(x = sub_14_cast, y = sqrt_7_cast)[name = tensor("real_div_7_cast")]; + tensor reshape_29_shape_0 = const()[name = tensor("reshape_29_shape_0"), val = tensor([1, 512, 64, 64])]; + tensor reshape_29_cast = reshape(shape = reshape_29_shape_0, x = real_div_7_cast)[name = tensor("reshape_29_cast")]; + 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 = 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)[name = tensor("add_15_cast")]; + tensor input_53_cast = silu(x = add_15_cast)[name = tensor("input_53_cast")]; + tensor var_285 = const()[name = tensor("op_285"), val = tensor([1, 1])]; + tensor var_287 = const()[name = tensor("op_287"), 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_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_55_cast = conv(bias = decoder_up_blocks_0_resnets_1_conv1_bias_to_fp16, dilations = var_287, groups = var_28, pad = input_55_pad_0, pad_type = input_55_pad_type_0, strides = var_285, weight = decoder_up_blocks_0_resnets_1_conv1_weight_to_fp16, x = input_53_cast)[name = tensor("input_55_cast")]; + tensor reshape_32_shape_0 = const()[name = tensor("reshape_32_shape_0"), val = tensor([1, 32, 16, 64, 64])]; + tensor reshape_32_cast = reshape(shape = reshape_32_shape_0, x = input_55_cast)[name = tensor("reshape_32_cast")]; + 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 = reduce_mean(axes = reduce_mean_24_axes_0, keep_dims = reduce_mean_24_keep_dims_0, x = reshape_32_cast)[name = tensor("reduce_mean_24_cast")]; + tensor sub_16_cast = sub(x = reshape_32_cast, y = reduce_mean_24_cast)[name = tensor("sub_16_cast")]; + tensor square_8_cast = square(x = sub_16_cast)[name = tensor("square_8_cast")]; + 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 = reduce_mean(axes = reduce_mean_26_axes_0, keep_dims = reduce_mean_26_keep_dims_0, x = square_8_cast)[name = tensor("reduce_mean_26_cast")]; + 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 = add(x = reduce_mean_26_cast, y = add_16_y_0_to_fp16)[name = tensor("add_16_cast")]; + tensor sqrt_8_cast = sqrt(x = add_16_cast)[name = tensor("sqrt_8_cast")]; + tensor real_div_8_cast = real_div(x = sub_16_cast, y = sqrt_8_cast)[name = tensor("real_div_8_cast")]; + tensor reshape_33_shape_0 = const()[name = tensor("reshape_33_shape_0"), val = tensor([1, 512, 64, 64])]; + tensor reshape_33_cast = reshape(shape = reshape_33_shape_0, x = real_div_8_cast)[name = tensor("reshape_33_cast")]; + 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 = 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)[name = tensor("add_17_cast")]; + tensor input_59_cast = silu(x = add_17_cast)[name = tensor("input_59_cast")]; + tensor var_297 = const()[name = tensor("op_297"), val = tensor([1, 1])]; + tensor var_299 = const()[name = tensor("op_299"), val = tensor([1, 1])]; + tensor hidden_states_15_pad_type_0 = const()[name = tensor("hidden_states_15_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_15_pad_0 = const()[name = tensor("hidden_states_15_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_15_cast = conv(bias = decoder_up_blocks_0_resnets_1_conv2_bias_to_fp16, dilations = var_299, groups = var_28, pad = hidden_states_15_pad_0, pad_type = hidden_states_15_pad_type_0, strides = var_297, weight = decoder_up_blocks_0_resnets_1_conv2_weight_to_fp16, x = input_59_cast)[name = tensor("hidden_states_15_cast")]; + tensor var_302_cast = add(x = var_272_cast, y = hidden_states_15_cast)[name = tensor("op_302_cast")]; + tensor reshape_36_shape_0 = const()[name = tensor("reshape_36_shape_0"), val = tensor([1, 32, 16, 64, 64])]; + tensor reshape_36_cast = reshape(shape = reshape_36_shape_0, x = var_302_cast)[name = tensor("reshape_36_cast")]; + 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 = reduce_mean(axes = reduce_mean_27_axes_0, keep_dims = reduce_mean_27_keep_dims_0, x = reshape_36_cast)[name = tensor("reduce_mean_27_cast")]; + tensor sub_18_cast = sub(x = reshape_36_cast, y = reduce_mean_27_cast)[name = tensor("sub_18_cast")]; + tensor square_9_cast = square(x = sub_18_cast)[name = tensor("square_9_cast")]; + 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 = reduce_mean(axes = reduce_mean_29_axes_0, keep_dims = reduce_mean_29_keep_dims_0, x = square_9_cast)[name = tensor("reduce_mean_29_cast")]; + 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 = add(x = reduce_mean_29_cast, y = add_18_y_0_to_fp16)[name = tensor("add_18_cast")]; + tensor sqrt_9_cast = sqrt(x = add_18_cast)[name = tensor("sqrt_9_cast")]; + tensor real_div_9_cast = real_div(x = sub_18_cast, y = sqrt_9_cast)[name = tensor("real_div_9_cast")]; + tensor reshape_37_shape_0 = const()[name = tensor("reshape_37_shape_0"), val = tensor([1, 512, 64, 64])]; + tensor reshape_37_cast = reshape(shape = reshape_37_shape_0, x = real_div_9_cast)[name = tensor("reshape_37_cast")]; + 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 = 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)[name = tensor("add_19_cast")]; + tensor input_67_cast = silu(x = add_19_cast)[name = tensor("input_67_cast")]; + tensor var_315 = const()[name = tensor("op_315"), val = tensor([1, 1])]; + tensor var_317 = const()[name = tensor("op_317"), val = tensor([1, 1])]; + tensor input_69_pad_type_0 = const()[name = tensor("input_69_pad_type_0"), val = tensor("custom")]; + tensor input_69_pad_0 = const()[name = tensor("input_69_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_69_cast = conv(bias = decoder_up_blocks_0_resnets_2_conv1_bias_to_fp16, dilations = var_317, groups = var_28, pad = input_69_pad_0, pad_type = input_69_pad_type_0, strides = var_315, weight = decoder_up_blocks_0_resnets_2_conv1_weight_to_fp16, x = input_67_cast)[name = tensor("input_69_cast")]; + tensor reshape_40_shape_0 = const()[name = tensor("reshape_40_shape_0"), val = tensor([1, 32, 16, 64, 64])]; + tensor reshape_40_cast = reshape(shape = reshape_40_shape_0, x = input_69_cast)[name = tensor("reshape_40_cast")]; + 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 = reduce_mean(axes = reduce_mean_30_axes_0, keep_dims = reduce_mean_30_keep_dims_0, x = reshape_40_cast)[name = tensor("reduce_mean_30_cast")]; + tensor sub_20_cast = sub(x = reshape_40_cast, y = reduce_mean_30_cast)[name = tensor("sub_20_cast")]; + tensor square_10_cast = square(x = sub_20_cast)[name = tensor("square_10_cast")]; + 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 = reduce_mean(axes = reduce_mean_32_axes_0, keep_dims = reduce_mean_32_keep_dims_0, x = square_10_cast)[name = tensor("reduce_mean_32_cast")]; + 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 = add(x = reduce_mean_32_cast, y = add_20_y_0_to_fp16)[name = tensor("add_20_cast")]; + tensor sqrt_10_cast = sqrt(x = add_20_cast)[name = tensor("sqrt_10_cast")]; + tensor real_div_10_cast = real_div(x = sub_20_cast, y = sqrt_10_cast)[name = tensor("real_div_10_cast")]; + tensor reshape_41_shape_0 = const()[name = tensor("reshape_41_shape_0"), val = tensor([1, 512, 64, 64])]; + tensor reshape_41_cast = reshape(shape = reshape_41_shape_0, x = real_div_10_cast)[name = tensor("reshape_41_cast")]; + 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 = 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)[name = tensor("add_21_cast")]; + tensor input_73_cast = silu(x = add_21_cast)[name = tensor("input_73_cast")]; + tensor var_327 = const()[name = tensor("op_327"), val = tensor([1, 1])]; + tensor var_329 = const()[name = tensor("op_329"), val = tensor([1, 1])]; + tensor hidden_states_17_pad_type_0 = const()[name = tensor("hidden_states_17_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_17_pad_0 = const()[name = tensor("hidden_states_17_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor 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_17_cast = conv(bias = decoder_up_blocks_0_resnets_2_conv2_bias_to_fp16, dilations = var_329, groups = var_28, pad = hidden_states_17_pad_0, pad_type = hidden_states_17_pad_type_0, strides = var_327, weight = decoder_up_blocks_0_resnets_2_conv2_weight_to_fp16, x = input_73_cast)[name = tensor("hidden_states_17_cast")]; + tensor var_332_cast = add(x = var_302_cast, y = hidden_states_17_cast)[name = tensor("op_332_cast")]; + tensor input_77_scale_factor_height_0 = const()[name = tensor("input_77_scale_factor_height_0"), val = tensor(0x1p+1)]; + tensor input_77_scale_factor_width_0 = const()[name = tensor("input_77_scale_factor_width_0"), val = tensor(0x1p+1)]; + tensor input_77_cast = upsample_nearest_neighbor(scale_factor_height = input_77_scale_factor_height_0, scale_factor_width = input_77_scale_factor_width_0, x = var_332_cast)[name = tensor("input_77_cast")]; + tensor var_340 = const()[name = tensor("op_340"), val = tensor([1, 1])]; + tensor var_342 = const()[name = tensor("op_342"), val = tensor([1, 1])]; + tensor input_79_pad_type_0 = const()[name = tensor("input_79_pad_type_0"), val = tensor("custom")]; + tensor input_79_pad_0 = const()[name = tensor("input_79_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_79_cast = conv(bias = decoder_up_blocks_0_upsamplers_0_conv_bias_to_fp16, dilations = var_342, groups = var_28, pad = input_79_pad_0, pad_type = input_79_pad_type_0, strides = var_340, weight = decoder_up_blocks_0_upsamplers_0_conv_weight_to_fp16, x = input_77_cast)[name = tensor("input_79_cast")]; + tensor reshape_44_shape_0 = const()[name = tensor("reshape_44_shape_0"), val = tensor([1, 32, 16, 128, 128])]; + tensor reshape_44_cast = reshape(shape = reshape_44_shape_0, x = input_79_cast)[name = tensor("reshape_44_cast")]; + 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 = reduce_mean(axes = reduce_mean_33_axes_0, keep_dims = reduce_mean_33_keep_dims_0, x = reshape_44_cast)[name = tensor("reduce_mean_33_cast")]; + tensor sub_22_cast = sub(x = reshape_44_cast, y = reduce_mean_33_cast)[name = tensor("sub_22_cast")]; + tensor square_11_cast = square(x = sub_22_cast)[name = tensor("square_11_cast")]; + 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 = reduce_mean(axes = reduce_mean_35_axes_0, keep_dims = reduce_mean_35_keep_dims_0, x = square_11_cast)[name = tensor("reduce_mean_35_cast")]; + 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 = add(x = reduce_mean_35_cast, y = add_22_y_0_to_fp16)[name = tensor("add_22_cast")]; + tensor sqrt_11_cast = sqrt(x = add_22_cast)[name = tensor("sqrt_11_cast")]; + tensor real_div_11_cast = real_div(x = sub_22_cast, y = sqrt_11_cast)[name = tensor("real_div_11_cast")]; + tensor reshape_45_shape_0 = const()[name = tensor("reshape_45_shape_0"), val = tensor([1, 512, 128, 128])]; + tensor reshape_45_cast = reshape(shape = reshape_45_shape_0, x = real_div_11_cast)[name = tensor("reshape_45_cast")]; + 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 = 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)[name = tensor("add_23_cast")]; + tensor input_83_cast = silu(x = add_23_cast)[name = tensor("input_83_cast")]; + tensor var_363 = const()[name = tensor("op_363"), val = tensor([1, 1])]; + tensor var_365 = const()[name = tensor("op_365"), 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_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_85_cast = conv(bias = decoder_up_blocks_1_resnets_0_conv1_bias_to_fp16, dilations = var_365, groups = var_28, pad = input_85_pad_0, pad_type = input_85_pad_type_0, strides = var_363, weight = decoder_up_blocks_1_resnets_0_conv1_weight_to_fp16, x = input_83_cast)[name = tensor("input_85_cast")]; + tensor reshape_48_shape_0 = const()[name = tensor("reshape_48_shape_0"), val = tensor([1, 32, 16, 128, 128])]; + tensor reshape_48_cast = reshape(shape = reshape_48_shape_0, x = input_85_cast)[name = tensor("reshape_48_cast")]; + 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 = reduce_mean(axes = reduce_mean_36_axes_0, keep_dims = reduce_mean_36_keep_dims_0, x = reshape_48_cast)[name = tensor("reduce_mean_36_cast")]; + tensor sub_24_cast = sub(x = reshape_48_cast, y = reduce_mean_36_cast)[name = tensor("sub_24_cast")]; + tensor square_12_cast = square(x = sub_24_cast)[name = tensor("square_12_cast")]; + 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 = reduce_mean(axes = reduce_mean_38_axes_0, keep_dims = reduce_mean_38_keep_dims_0, x = square_12_cast)[name = tensor("reduce_mean_38_cast")]; + 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 = add(x = reduce_mean_38_cast, y = add_24_y_0_to_fp16)[name = tensor("add_24_cast")]; + tensor sqrt_12_cast = sqrt(x = add_24_cast)[name = tensor("sqrt_12_cast")]; + tensor real_div_12_cast = real_div(x = sub_24_cast, y = sqrt_12_cast)[name = tensor("real_div_12_cast")]; + tensor reshape_49_shape_0 = const()[name = tensor("reshape_49_shape_0"), val = tensor([1, 512, 128, 128])]; + tensor reshape_49_cast = reshape(shape = reshape_49_shape_0, x = real_div_12_cast)[name = tensor("reshape_49_cast")]; + 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 = 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)[name = tensor("add_25_cast")]; + tensor input_89_cast = silu(x = add_25_cast)[name = tensor("input_89_cast")]; + tensor var_375 = const()[name = tensor("op_375"), val = tensor([1, 1])]; + tensor var_377 = const()[name = tensor("op_377"), val = tensor([1, 1])]; + tensor hidden_states_21_pad_type_0 = const()[name = tensor("hidden_states_21_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_21_pad_0 = const()[name = tensor("hidden_states_21_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor 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_21_cast = conv(bias = decoder_up_blocks_1_resnets_0_conv2_bias_to_fp16, dilations = var_377, groups = var_28, pad = hidden_states_21_pad_0, pad_type = hidden_states_21_pad_type_0, strides = var_375, weight = decoder_up_blocks_1_resnets_0_conv2_weight_to_fp16, x = input_89_cast)[name = tensor("hidden_states_21_cast")]; + tensor var_380_cast = add(x = input_79_cast, y = hidden_states_21_cast)[name = tensor("op_380_cast")]; + tensor reshape_52_shape_0 = const()[name = tensor("reshape_52_shape_0"), val = tensor([1, 32, 16, 128, 128])]; + tensor reshape_52_cast = reshape(shape = reshape_52_shape_0, x = var_380_cast)[name = tensor("reshape_52_cast")]; + 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 = reduce_mean(axes = reduce_mean_39_axes_0, keep_dims = reduce_mean_39_keep_dims_0, x = reshape_52_cast)[name = tensor("reduce_mean_39_cast")]; + tensor sub_26_cast = sub(x = reshape_52_cast, y = reduce_mean_39_cast)[name = tensor("sub_26_cast")]; + tensor square_13_cast = square(x = sub_26_cast)[name = tensor("square_13_cast")]; + 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 = reduce_mean(axes = reduce_mean_41_axes_0, keep_dims = reduce_mean_41_keep_dims_0, x = square_13_cast)[name = tensor("reduce_mean_41_cast")]; + 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 = add(x = reduce_mean_41_cast, y = add_26_y_0_to_fp16)[name = tensor("add_26_cast")]; + tensor sqrt_13_cast = sqrt(x = add_26_cast)[name = tensor("sqrt_13_cast")]; + tensor real_div_13_cast = real_div(x = sub_26_cast, y = sqrt_13_cast)[name = tensor("real_div_13_cast")]; + tensor reshape_53_shape_0 = const()[name = tensor("reshape_53_shape_0"), val = tensor([1, 512, 128, 128])]; + tensor reshape_53_cast = reshape(shape = reshape_53_shape_0, x = real_div_13_cast)[name = tensor("reshape_53_cast")]; + 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 = 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)[name = tensor("add_27_cast")]; + tensor input_97_cast = silu(x = add_27_cast)[name = tensor("input_97_cast")]; + tensor var_393 = const()[name = tensor("op_393"), val = tensor([1, 1])]; + tensor var_395 = const()[name = tensor("op_395"), val = tensor([1, 1])]; + tensor input_99_pad_type_0 = const()[name = tensor("input_99_pad_type_0"), val = tensor("custom")]; + tensor input_99_pad_0 = const()[name = tensor("input_99_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_99_cast = conv(bias = decoder_up_blocks_1_resnets_1_conv1_bias_to_fp16, dilations = var_395, groups = var_28, pad = input_99_pad_0, pad_type = input_99_pad_type_0, strides = var_393, weight = decoder_up_blocks_1_resnets_1_conv1_weight_to_fp16, x = input_97_cast)[name = tensor("input_99_cast")]; + tensor reshape_56_shape_0 = const()[name = tensor("reshape_56_shape_0"), val = tensor([1, 32, 16, 128, 128])]; + tensor reshape_56_cast = reshape(shape = reshape_56_shape_0, x = input_99_cast)[name = tensor("reshape_56_cast")]; + 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 = reduce_mean(axes = reduce_mean_42_axes_0, keep_dims = reduce_mean_42_keep_dims_0, x = reshape_56_cast)[name = tensor("reduce_mean_42_cast")]; + tensor sub_28_cast = sub(x = reshape_56_cast, y = reduce_mean_42_cast)[name = tensor("sub_28_cast")]; + tensor square_14_cast = square(x = sub_28_cast)[name = tensor("square_14_cast")]; + 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 = reduce_mean(axes = reduce_mean_44_axes_0, keep_dims = reduce_mean_44_keep_dims_0, x = square_14_cast)[name = tensor("reduce_mean_44_cast")]; + 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 = add(x = reduce_mean_44_cast, y = add_28_y_0_to_fp16)[name = tensor("add_28_cast")]; + tensor sqrt_14_cast = sqrt(x = add_28_cast)[name = tensor("sqrt_14_cast")]; + tensor real_div_14_cast = real_div(x = sub_28_cast, y = sqrt_14_cast)[name = tensor("real_div_14_cast")]; + tensor reshape_57_shape_0 = const()[name = tensor("reshape_57_shape_0"), val = tensor([1, 512, 128, 128])]; + tensor reshape_57_cast = reshape(shape = reshape_57_shape_0, x = real_div_14_cast)[name = tensor("reshape_57_cast")]; + 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 = 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)[name = tensor("add_29_cast")]; + tensor input_103_cast = silu(x = add_29_cast)[name = tensor("input_103_cast")]; + tensor var_405 = const()[name = tensor("op_405"), val = tensor([1, 1])]; + tensor var_407 = const()[name = tensor("op_407"), val = tensor([1, 1])]; + tensor hidden_states_23_pad_type_0 = const()[name = tensor("hidden_states_23_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_23_pad_0 = const()[name = tensor("hidden_states_23_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor 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_23_cast = conv(bias = decoder_up_blocks_1_resnets_1_conv2_bias_to_fp16, dilations = var_407, groups = var_28, pad = hidden_states_23_pad_0, pad_type = hidden_states_23_pad_type_0, strides = var_405, weight = decoder_up_blocks_1_resnets_1_conv2_weight_to_fp16, x = input_103_cast)[name = tensor("hidden_states_23_cast")]; + tensor var_410_cast = add(x = var_380_cast, y = hidden_states_23_cast)[name = tensor("op_410_cast")]; + tensor reshape_60_shape_0 = const()[name = tensor("reshape_60_shape_0"), val = tensor([1, 32, 16, 128, 128])]; + tensor reshape_60_cast = reshape(shape = reshape_60_shape_0, x = var_410_cast)[name = tensor("reshape_60_cast")]; + 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 = reduce_mean(axes = reduce_mean_45_axes_0, keep_dims = reduce_mean_45_keep_dims_0, x = reshape_60_cast)[name = tensor("reduce_mean_45_cast")]; + tensor sub_30_cast = sub(x = reshape_60_cast, y = reduce_mean_45_cast)[name = tensor("sub_30_cast")]; + tensor square_15_cast = square(x = sub_30_cast)[name = tensor("square_15_cast")]; + 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 = reduce_mean(axes = reduce_mean_47_axes_0, keep_dims = reduce_mean_47_keep_dims_0, x = square_15_cast)[name = tensor("reduce_mean_47_cast")]; + 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 = add(x = reduce_mean_47_cast, y = add_30_y_0_to_fp16)[name = tensor("add_30_cast")]; + tensor sqrt_15_cast = sqrt(x = add_30_cast)[name = tensor("sqrt_15_cast")]; + tensor real_div_15_cast = real_div(x = sub_30_cast, y = sqrt_15_cast)[name = tensor("real_div_15_cast")]; + tensor reshape_61_shape_0 = const()[name = tensor("reshape_61_shape_0"), val = tensor([1, 512, 128, 128])]; + tensor reshape_61_cast = reshape(shape = reshape_61_shape_0, x = real_div_15_cast)[name = tensor("reshape_61_cast")]; + 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 = 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)[name = tensor("add_31_cast")]; + tensor input_111_cast = silu(x = add_31_cast)[name = tensor("input_111_cast")]; + tensor var_423 = const()[name = tensor("op_423"), val = tensor([1, 1])]; + tensor var_425 = const()[name = tensor("op_425"), val = tensor([1, 1])]; + tensor input_113_pad_type_0 = const()[name = tensor("input_113_pad_type_0"), val = tensor("custom")]; + tensor input_113_pad_0 = const()[name = tensor("input_113_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_113_cast = conv(bias = decoder_up_blocks_1_resnets_2_conv1_bias_to_fp16, dilations = var_425, groups = var_28, pad = input_113_pad_0, pad_type = input_113_pad_type_0, strides = var_423, weight = decoder_up_blocks_1_resnets_2_conv1_weight_to_fp16, x = input_111_cast)[name = tensor("input_113_cast")]; + tensor reshape_64_shape_0 = const()[name = tensor("reshape_64_shape_0"), val = tensor([1, 32, 16, 128, 128])]; + tensor reshape_64_cast = reshape(shape = reshape_64_shape_0, x = input_113_cast)[name = tensor("reshape_64_cast")]; + 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 = reduce_mean(axes = reduce_mean_48_axes_0, keep_dims = reduce_mean_48_keep_dims_0, x = reshape_64_cast)[name = tensor("reduce_mean_48_cast")]; + tensor sub_32_cast = sub(x = reshape_64_cast, y = reduce_mean_48_cast)[name = tensor("sub_32_cast")]; + tensor square_16_cast = square(x = sub_32_cast)[name = tensor("square_16_cast")]; + 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 = reduce_mean(axes = reduce_mean_50_axes_0, keep_dims = reduce_mean_50_keep_dims_0, x = square_16_cast)[name = tensor("reduce_mean_50_cast")]; + 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 = add(x = reduce_mean_50_cast, y = add_32_y_0_to_fp16)[name = tensor("add_32_cast")]; + tensor sqrt_16_cast = sqrt(x = add_32_cast)[name = tensor("sqrt_16_cast")]; + tensor real_div_16_cast = real_div(x = sub_32_cast, y = sqrt_16_cast)[name = tensor("real_div_16_cast")]; + tensor reshape_65_shape_0 = const()[name = tensor("reshape_65_shape_0"), val = tensor([1, 512, 128, 128])]; + tensor reshape_65_cast = reshape(shape = reshape_65_shape_0, x = real_div_16_cast)[name = tensor("reshape_65_cast")]; + 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 = 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)[name = tensor("add_33_cast")]; + tensor input_117_cast = silu(x = add_33_cast)[name = tensor("input_117_cast")]; + 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 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_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_25_cast = conv(bias = decoder_up_blocks_1_resnets_2_conv2_bias_to_fp16, dilations = var_437, groups = var_28, pad = hidden_states_25_pad_0, pad_type = hidden_states_25_pad_type_0, strides = var_435, weight = decoder_up_blocks_1_resnets_2_conv2_weight_to_fp16, x = input_117_cast)[name = tensor("hidden_states_25_cast")]; + tensor var_440_cast = add(x = var_410_cast, y = hidden_states_25_cast)[name = tensor("op_440_cast")]; + tensor input_121_scale_factor_height_0 = const()[name = tensor("input_121_scale_factor_height_0"), val = tensor(0x1p+1)]; + tensor input_121_scale_factor_width_0 = const()[name = tensor("input_121_scale_factor_width_0"), val = tensor(0x1p+1)]; + tensor input_121_cast = upsample_nearest_neighbor(scale_factor_height = input_121_scale_factor_height_0, scale_factor_width = input_121_scale_factor_width_0, x = var_440_cast)[name = tensor("input_121_cast")]; + tensor var_448 = const()[name = tensor("op_448"), val = tensor([1, 1])]; + tensor var_450 = const()[name = tensor("op_450"), val = tensor([1, 1])]; + tensor input_123_pad_type_0 = const()[name = tensor("input_123_pad_type_0"), val = tensor("custom")]; + tensor input_123_pad_0 = const()[name = tensor("input_123_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_123_cast = conv(bias = decoder_up_blocks_1_upsamplers_0_conv_bias_to_fp16, dilations = var_450, groups = var_28, pad = input_123_pad_0, pad_type = input_123_pad_type_0, strides = var_448, weight = decoder_up_blocks_1_upsamplers_0_conv_weight_to_fp16, x = input_121_cast)[name = tensor("input_123_cast")]; + tensor reshape_68_shape_0 = const()[name = tensor("reshape_68_shape_0"), val = tensor([1, 32, 16, 256, 256])]; + tensor reshape_68_cast = reshape(shape = reshape_68_shape_0, x = input_123_cast)[name = tensor("reshape_68_cast")]; + 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 = reduce_mean(axes = reduce_mean_51_axes_0, keep_dims = reduce_mean_51_keep_dims_0, x = reshape_68_cast)[name = tensor("reduce_mean_51_cast")]; + tensor sub_34_cast = sub(x = reshape_68_cast, y = reduce_mean_51_cast)[name = tensor("sub_34_cast")]; + tensor square_17_cast = square(x = sub_34_cast)[name = tensor("square_17_cast")]; + 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 = reduce_mean(axes = reduce_mean_53_axes_0, keep_dims = reduce_mean_53_keep_dims_0, x = square_17_cast)[name = tensor("reduce_mean_53_cast")]; + 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 = add(x = reduce_mean_53_cast, y = add_34_y_0_to_fp16)[name = tensor("add_34_cast")]; + tensor sqrt_17_cast = sqrt(x = add_34_cast)[name = tensor("sqrt_17_cast")]; + tensor real_div_17_cast = real_div(x = sub_34_cast, y = sqrt_17_cast)[name = tensor("real_div_17_cast")]; + tensor reshape_69_shape_0 = const()[name = tensor("reshape_69_shape_0"), val = tensor([1, 512, 256, 256])]; + tensor reshape_69_cast = reshape(shape = reshape_69_shape_0, x = real_div_17_cast)[name = tensor("reshape_69_cast")]; + 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 = 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)[name = tensor("add_35_cast")]; + tensor input_127_cast = silu(x = add_35_cast)[name = tensor("input_127_cast")]; + tensor var_472 = const()[name = tensor("op_472"), val = tensor([1, 1])]; + tensor var_474 = const()[name = tensor("op_474"), val = tensor([1, 1])]; + tensor input_129_pad_type_0 = const()[name = tensor("input_129_pad_type_0"), val = tensor("custom")]; + tensor input_129_pad_0 = const()[name = tensor("input_129_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_129_cast = conv(bias = decoder_up_blocks_2_resnets_0_conv1_bias_to_fp16, dilations = var_474, groups = var_28, pad = input_129_pad_0, pad_type = input_129_pad_type_0, strides = var_472, weight = decoder_up_blocks_2_resnets_0_conv1_weight_to_fp16, x = input_127_cast)[name = tensor("input_129_cast")]; + tensor reshape_72_shape_0 = const()[name = tensor("reshape_72_shape_0"), val = tensor([1, 32, 8, 256, 256])]; + tensor reshape_72_cast = reshape(shape = reshape_72_shape_0, x = input_129_cast)[name = tensor("reshape_72_cast")]; + 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 = reduce_mean(axes = reduce_mean_54_axes_0, keep_dims = reduce_mean_54_keep_dims_0, x = reshape_72_cast)[name = tensor("reduce_mean_54_cast")]; + tensor sub_36_cast = sub(x = reshape_72_cast, y = reduce_mean_54_cast)[name = tensor("sub_36_cast")]; + tensor square_18_cast = square(x = sub_36_cast)[name = tensor("square_18_cast")]; + 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 = reduce_mean(axes = reduce_mean_56_axes_0, keep_dims = reduce_mean_56_keep_dims_0, x = square_18_cast)[name = tensor("reduce_mean_56_cast")]; + 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 = add(x = reduce_mean_56_cast, y = add_36_y_0_to_fp16)[name = tensor("add_36_cast")]; + tensor sqrt_18_cast = sqrt(x = add_36_cast)[name = tensor("sqrt_18_cast")]; + tensor real_div_18_cast = real_div(x = sub_36_cast, y = sqrt_18_cast)[name = tensor("real_div_18_cast")]; + tensor reshape_73_shape_0 = const()[name = tensor("reshape_73_shape_0"), val = tensor([1, 256, 256, 256])]; + tensor reshape_73_cast = reshape(shape = reshape_73_shape_0, x = real_div_18_cast)[name = tensor("reshape_73_cast")]; + 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 = 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)[name = tensor("add_37_cast")]; + tensor input_133_cast = silu(x = add_37_cast)[name = tensor("input_133_cast")]; + tensor var_484 = const()[name = tensor("op_484"), val = tensor([1, 1])]; + tensor var_486 = const()[name = tensor("op_486"), val = tensor([1, 1])]; + tensor hidden_states_29_pad_type_0 = const()[name = tensor("hidden_states_29_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_29_pad_0 = const()[name = tensor("hidden_states_29_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor 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_29_cast = conv(bias = decoder_up_blocks_2_resnets_0_conv2_bias_to_fp16, dilations = var_486, groups = var_28, pad = hidden_states_29_pad_0, pad_type = hidden_states_29_pad_type_0, strides = var_484, weight = decoder_up_blocks_2_resnets_0_conv2_weight_to_fp16, x = input_133_cast)[name = tensor("hidden_states_29_cast")]; + tensor var_491 = const()[name = tensor("op_491"), val = tensor([1, 1])]; + tensor var_493 = const()[name = tensor("op_493"), 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 = conv(bias = decoder_up_blocks_2_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_493, groups = var_28, pad = input_tensor_1_pad_0, pad_type = input_tensor_1_pad_type_0, strides = var_491, weight = decoder_up_blocks_2_resnets_0_conv_shortcut_weight_to_fp16, x = input_123_cast)[name = tensor("input_tensor_1_cast")]; + tensor var_496_cast = add(x = input_tensor_1_cast, y = hidden_states_29_cast)[name = tensor("op_496_cast")]; + tensor reshape_76_shape_0 = const()[name = tensor("reshape_76_shape_0"), val = tensor([1, 32, 8, 256, 256])]; + tensor reshape_76_cast = reshape(shape = reshape_76_shape_0, x = var_496_cast)[name = tensor("reshape_76_cast")]; + 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 = reduce_mean(axes = reduce_mean_57_axes_0, keep_dims = reduce_mean_57_keep_dims_0, x = reshape_76_cast)[name = tensor("reduce_mean_57_cast")]; + tensor sub_38_cast = sub(x = reshape_76_cast, y = reduce_mean_57_cast)[name = tensor("sub_38_cast")]; + tensor square_19_cast = square(x = sub_38_cast)[name = tensor("square_19_cast")]; + 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 = reduce_mean(axes = reduce_mean_59_axes_0, keep_dims = reduce_mean_59_keep_dims_0, x = square_19_cast)[name = tensor("reduce_mean_59_cast")]; + 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 = add(x = reduce_mean_59_cast, y = add_38_y_0_to_fp16)[name = tensor("add_38_cast")]; + tensor sqrt_19_cast = sqrt(x = add_38_cast)[name = tensor("sqrt_19_cast")]; + tensor real_div_19_cast = real_div(x = sub_38_cast, y = sqrt_19_cast)[name = tensor("real_div_19_cast")]; + tensor reshape_77_shape_0 = const()[name = tensor("reshape_77_shape_0"), val = tensor([1, 256, 256, 256])]; + tensor reshape_77_cast = reshape(shape = reshape_77_shape_0, x = real_div_19_cast)[name = tensor("reshape_77_cast")]; + 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 = 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)[name = tensor("add_39_cast")]; + tensor input_141_cast = silu(x = add_39_cast)[name = tensor("input_141_cast")]; + tensor var_509 = const()[name = tensor("op_509"), val = tensor([1, 1])]; + tensor var_511 = const()[name = tensor("op_511"), val = tensor([1, 1])]; + tensor input_143_pad_type_0 = const()[name = tensor("input_143_pad_type_0"), val = tensor("custom")]; + tensor input_143_pad_0 = const()[name = tensor("input_143_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_143_cast = conv(bias = decoder_up_blocks_2_resnets_1_conv1_bias_to_fp16, dilations = var_511, groups = var_28, pad = input_143_pad_0, pad_type = input_143_pad_type_0, strides = var_509, weight = decoder_up_blocks_2_resnets_1_conv1_weight_to_fp16, x = input_141_cast)[name = tensor("input_143_cast")]; + tensor reshape_80_shape_0 = const()[name = tensor("reshape_80_shape_0"), val = tensor([1, 32, 8, 256, 256])]; + tensor reshape_80_cast = reshape(shape = reshape_80_shape_0, x = input_143_cast)[name = tensor("reshape_80_cast")]; + 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 = reduce_mean(axes = reduce_mean_60_axes_0, keep_dims = reduce_mean_60_keep_dims_0, x = reshape_80_cast)[name = tensor("reduce_mean_60_cast")]; + tensor sub_40_cast = sub(x = reshape_80_cast, y = reduce_mean_60_cast)[name = tensor("sub_40_cast")]; + tensor square_20_cast = square(x = sub_40_cast)[name = tensor("square_20_cast")]; + 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 = reduce_mean(axes = reduce_mean_62_axes_0, keep_dims = reduce_mean_62_keep_dims_0, x = square_20_cast)[name = tensor("reduce_mean_62_cast")]; + 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 = add(x = reduce_mean_62_cast, y = add_40_y_0_to_fp16)[name = tensor("add_40_cast")]; + tensor sqrt_20_cast = sqrt(x = add_40_cast)[name = tensor("sqrt_20_cast")]; + tensor real_div_20_cast = real_div(x = sub_40_cast, y = sqrt_20_cast)[name = tensor("real_div_20_cast")]; + tensor reshape_81_shape_0 = const()[name = tensor("reshape_81_shape_0"), val = tensor([1, 256, 256, 256])]; + tensor reshape_81_cast = reshape(shape = reshape_81_shape_0, x = real_div_20_cast)[name = tensor("reshape_81_cast")]; + 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 = 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)[name = tensor("add_41_cast")]; + tensor input_147_cast = silu(x = add_41_cast)[name = tensor("input_147_cast")]; + tensor var_521 = const()[name = tensor("op_521"), val = tensor([1, 1])]; + tensor var_523 = const()[name = tensor("op_523"), 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_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_31_cast = conv(bias = decoder_up_blocks_2_resnets_1_conv2_bias_to_fp16, dilations = var_523, groups = var_28, pad = hidden_states_31_pad_0, pad_type = hidden_states_31_pad_type_0, strides = var_521, weight = decoder_up_blocks_2_resnets_1_conv2_weight_to_fp16, x = input_147_cast)[name = tensor("hidden_states_31_cast")]; + tensor var_526_cast = add(x = var_496_cast, y = hidden_states_31_cast)[name = tensor("op_526_cast")]; + tensor reshape_84_shape_0 = const()[name = tensor("reshape_84_shape_0"), val = tensor([1, 32, 8, 256, 256])]; + tensor reshape_84_cast = reshape(shape = reshape_84_shape_0, x = var_526_cast)[name = tensor("reshape_84_cast")]; + 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 = reduce_mean(axes = reduce_mean_63_axes_0, keep_dims = reduce_mean_63_keep_dims_0, x = reshape_84_cast)[name = tensor("reduce_mean_63_cast")]; + tensor sub_42_cast = sub(x = reshape_84_cast, y = reduce_mean_63_cast)[name = tensor("sub_42_cast")]; + tensor square_21_cast = square(x = sub_42_cast)[name = tensor("square_21_cast")]; + 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 = reduce_mean(axes = reduce_mean_65_axes_0, keep_dims = reduce_mean_65_keep_dims_0, x = square_21_cast)[name = tensor("reduce_mean_65_cast")]; + 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 = add(x = reduce_mean_65_cast, y = add_42_y_0_to_fp16)[name = tensor("add_42_cast")]; + tensor sqrt_21_cast = sqrt(x = add_42_cast)[name = tensor("sqrt_21_cast")]; + tensor real_div_21_cast = real_div(x = sub_42_cast, y = sqrt_21_cast)[name = tensor("real_div_21_cast")]; + tensor reshape_85_shape_0 = const()[name = tensor("reshape_85_shape_0"), val = tensor([1, 256, 256, 256])]; + tensor reshape_85_cast = reshape(shape = reshape_85_shape_0, x = real_div_21_cast)[name = tensor("reshape_85_cast")]; + 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 = 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)[name = tensor("add_43_cast")]; + tensor input_155_cast = silu(x = add_43_cast)[name = tensor("input_155_cast")]; + tensor var_539 = const()[name = tensor("op_539"), val = tensor([1, 1])]; + tensor var_541 = const()[name = tensor("op_541"), val = tensor([1, 1])]; + tensor input_157_pad_type_0 = const()[name = tensor("input_157_pad_type_0"), val = tensor("custom")]; + tensor input_157_pad_0 = const()[name = tensor("input_157_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_157_cast = conv(bias = decoder_up_blocks_2_resnets_2_conv1_bias_to_fp16, dilations = var_541, groups = var_28, pad = input_157_pad_0, pad_type = input_157_pad_type_0, strides = var_539, weight = decoder_up_blocks_2_resnets_2_conv1_weight_to_fp16, x = input_155_cast)[name = tensor("input_157_cast")]; + tensor reshape_88_shape_0 = const()[name = tensor("reshape_88_shape_0"), val = tensor([1, 32, 8, 256, 256])]; + tensor reshape_88_cast = reshape(shape = reshape_88_shape_0, x = input_157_cast)[name = tensor("reshape_88_cast")]; + 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 = reduce_mean(axes = reduce_mean_66_axes_0, keep_dims = reduce_mean_66_keep_dims_0, x = reshape_88_cast)[name = tensor("reduce_mean_66_cast")]; + tensor sub_44_cast = sub(x = reshape_88_cast, y = reduce_mean_66_cast)[name = tensor("sub_44_cast")]; + tensor square_22_cast = square(x = sub_44_cast)[name = tensor("square_22_cast")]; + 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 = reduce_mean(axes = reduce_mean_68_axes_0, keep_dims = reduce_mean_68_keep_dims_0, x = square_22_cast)[name = tensor("reduce_mean_68_cast")]; + 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 = add(x = reduce_mean_68_cast, y = add_44_y_0_to_fp16)[name = tensor("add_44_cast")]; + tensor sqrt_22_cast = sqrt(x = add_44_cast)[name = tensor("sqrt_22_cast")]; + tensor real_div_22_cast = real_div(x = sub_44_cast, y = sqrt_22_cast)[name = tensor("real_div_22_cast")]; + tensor reshape_89_shape_0 = const()[name = tensor("reshape_89_shape_0"), val = tensor([1, 256, 256, 256])]; + tensor reshape_89_cast = reshape(shape = reshape_89_shape_0, x = real_div_22_cast)[name = tensor("reshape_89_cast")]; + 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 = 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)[name = tensor("add_45_cast")]; + tensor input_161_cast = silu(x = add_45_cast)[name = tensor("input_161_cast")]; + tensor var_551 = const()[name = tensor("op_551"), val = tensor([1, 1])]; + tensor var_553 = const()[name = tensor("op_553"), val = tensor([1, 1])]; + tensor hidden_states_33_pad_type_0 = const()[name = tensor("hidden_states_33_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_33_pad_0 = const()[name = tensor("hidden_states_33_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_33_cast = conv(bias = decoder_up_blocks_2_resnets_2_conv2_bias_to_fp16, dilations = var_553, groups = var_28, pad = hidden_states_33_pad_0, pad_type = hidden_states_33_pad_type_0, strides = var_551, weight = decoder_up_blocks_2_resnets_2_conv2_weight_to_fp16, x = input_161_cast)[name = tensor("hidden_states_33_cast")]; + tensor var_556_cast = add(x = var_526_cast, y = hidden_states_33_cast)[name = tensor("op_556_cast")]; + tensor input_165_scale_factor_height_0 = const()[name = tensor("input_165_scale_factor_height_0"), val = tensor(0x1p+1)]; + tensor input_165_scale_factor_width_0 = const()[name = tensor("input_165_scale_factor_width_0"), val = tensor(0x1p+1)]; + tensor input_165_cast = upsample_nearest_neighbor(scale_factor_height = input_165_scale_factor_height_0, scale_factor_width = input_165_scale_factor_width_0, x = var_556_cast)[name = tensor("input_165_cast")]; + tensor var_564 = const()[name = tensor("op_564"), val = tensor([1, 1])]; + tensor var_566 = const()[name = tensor("op_566"), val = tensor([1, 1])]; + tensor input_167_pad_type_0 = const()[name = tensor("input_167_pad_type_0"), val = tensor("custom")]; + tensor input_167_pad_0 = const()[name = tensor("input_167_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_167_cast = conv(bias = decoder_up_blocks_2_upsamplers_0_conv_bias_to_fp16, dilations = var_566, groups = var_28, pad = input_167_pad_0, pad_type = input_167_pad_type_0, strides = var_564, weight = decoder_up_blocks_2_upsamplers_0_conv_weight_to_fp16, x = input_165_cast)[name = tensor("input_167_cast")]; + tensor reshape_92_shape_0 = const()[name = tensor("reshape_92_shape_0"), val = tensor([1, 32, 8, 512, 512])]; + tensor reshape_92_cast = reshape(shape = reshape_92_shape_0, x = input_167_cast)[name = tensor("reshape_92_cast")]; + 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 = reduce_mean(axes = reduce_mean_69_axes_0, keep_dims = reduce_mean_69_keep_dims_0, x = reshape_92_cast)[name = tensor("reduce_mean_69_cast")]; + tensor sub_46_cast = sub(x = reshape_92_cast, y = reduce_mean_69_cast)[name = tensor("sub_46_cast")]; + tensor square_23_cast = square(x = sub_46_cast)[name = tensor("square_23_cast")]; + 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 = reduce_mean(axes = reduce_mean_71_axes_0, keep_dims = reduce_mean_71_keep_dims_0, x = square_23_cast)[name = tensor("reduce_mean_71_cast")]; + 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 = add(x = reduce_mean_71_cast, y = add_46_y_0_to_fp16)[name = tensor("add_46_cast")]; + tensor sqrt_23_cast = sqrt(x = add_46_cast)[name = tensor("sqrt_23_cast")]; + tensor real_div_23_cast = real_div(x = sub_46_cast, y = sqrt_23_cast)[name = tensor("real_div_23_cast")]; + tensor reshape_93_shape_0 = const()[name = tensor("reshape_93_shape_0"), val = tensor([1, 256, 512, 512])]; + tensor reshape_93_cast = reshape(shape = reshape_93_shape_0, x = real_div_23_cast)[name = tensor("reshape_93_cast")]; + 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 = 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)[name = tensor("add_47_cast")]; + tensor input_171_cast = silu(x = add_47_cast)[name = tensor("input_171_cast")]; + tensor var_586 = const()[name = tensor("op_586"), val = tensor([1, 1])]; + tensor var_588 = const()[name = tensor("op_588"), val = tensor([1, 1])]; + tensor input_173_pad_type_0 = const()[name = tensor("input_173_pad_type_0"), val = tensor("custom")]; + tensor input_173_pad_0 = const()[name = tensor("input_173_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_173_cast = conv(bias = decoder_up_blocks_3_resnets_0_conv1_bias_to_fp16, dilations = var_588, groups = var_28, pad = input_173_pad_0, pad_type = input_173_pad_type_0, strides = var_586, weight = decoder_up_blocks_3_resnets_0_conv1_weight_to_fp16, x = input_171_cast)[name = tensor("input_173_cast")]; + tensor reshape_96_shape_0 = const()[name = tensor("reshape_96_shape_0"), val = tensor([1, 32, 4, 512, 512])]; + tensor reshape_96_cast = reshape(shape = reshape_96_shape_0, x = input_173_cast)[name = tensor("reshape_96_cast")]; + 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 = reduce_mean(axes = reduce_mean_72_axes_0, keep_dims = reduce_mean_72_keep_dims_0, x = reshape_96_cast)[name = tensor("reduce_mean_72_cast")]; + tensor sub_48_cast = sub(x = reshape_96_cast, y = reduce_mean_72_cast)[name = tensor("sub_48_cast")]; + tensor square_24_cast = square(x = sub_48_cast)[name = tensor("square_24_cast")]; + 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 = reduce_mean(axes = reduce_mean_74_axes_0, keep_dims = reduce_mean_74_keep_dims_0, x = square_24_cast)[name = tensor("reduce_mean_74_cast")]; + 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 = add(x = reduce_mean_74_cast, y = add_48_y_0_to_fp16)[name = tensor("add_48_cast")]; + tensor sqrt_24_cast = sqrt(x = add_48_cast)[name = tensor("sqrt_24_cast")]; + tensor real_div_24_cast = real_div(x = sub_48_cast, y = sqrt_24_cast)[name = tensor("real_div_24_cast")]; + tensor reshape_97_shape_0 = const()[name = tensor("reshape_97_shape_0"), val = tensor([1, 128, 512, 512])]; + tensor reshape_97_cast = reshape(shape = reshape_97_shape_0, x = real_div_24_cast)[name = tensor("reshape_97_cast")]; + 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 = 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)[name = tensor("add_49_cast")]; + tensor input_177_cast = silu(x = add_49_cast)[name = tensor("input_177_cast")]; + tensor var_598 = const()[name = tensor("op_598"), val = tensor([1, 1])]; + tensor var_600 = const()[name = tensor("op_600"), 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_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_37_cast = conv(bias = decoder_up_blocks_3_resnets_0_conv2_bias_to_fp16, dilations = var_600, groups = var_28, pad = hidden_states_37_pad_0, pad_type = hidden_states_37_pad_type_0, strides = var_598, weight = decoder_up_blocks_3_resnets_0_conv2_weight_to_fp16, x = input_177_cast)[name = tensor("hidden_states_37_cast")]; + tensor var_605 = const()[name = tensor("op_605"), val = tensor([1, 1])]; + tensor var_607 = const()[name = tensor("op_607"), 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 = conv(bias = decoder_up_blocks_3_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_607, groups = var_28, pad = input_tensor_pad_0, pad_type = input_tensor_pad_type_0, strides = var_605, weight = decoder_up_blocks_3_resnets_0_conv_shortcut_weight_to_fp16, x = input_167_cast)[name = tensor("input_tensor_cast")]; + tensor var_610_cast = add(x = input_tensor_cast, y = hidden_states_37_cast)[name = tensor("op_610_cast")]; + tensor reshape_100_shape_0 = const()[name = tensor("reshape_100_shape_0"), val = tensor([1, 32, 4, 512, 512])]; + tensor reshape_100_cast = reshape(shape = reshape_100_shape_0, x = var_610_cast)[name = tensor("reshape_100_cast")]; + 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 = reduce_mean(axes = reduce_mean_75_axes_0, keep_dims = reduce_mean_75_keep_dims_0, x = reshape_100_cast)[name = tensor("reduce_mean_75_cast")]; + tensor sub_50_cast = sub(x = reshape_100_cast, y = reduce_mean_75_cast)[name = tensor("sub_50_cast")]; + tensor square_25_cast = square(x = sub_50_cast)[name = tensor("square_25_cast")]; + 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 = reduce_mean(axes = reduce_mean_77_axes_0, keep_dims = reduce_mean_77_keep_dims_0, x = square_25_cast)[name = tensor("reduce_mean_77_cast")]; + 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 = add(x = reduce_mean_77_cast, y = add_50_y_0_to_fp16)[name = tensor("add_50_cast")]; + tensor sqrt_25_cast = sqrt(x = add_50_cast)[name = tensor("sqrt_25_cast")]; + tensor real_div_25_cast = real_div(x = sub_50_cast, y = sqrt_25_cast)[name = tensor("real_div_25_cast")]; + tensor reshape_101_shape_0 = const()[name = tensor("reshape_101_shape_0"), val = tensor([1, 128, 512, 512])]; + tensor reshape_101_cast = reshape(shape = reshape_101_shape_0, x = real_div_25_cast)[name = tensor("reshape_101_cast")]; + 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 = 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)[name = tensor("add_51_cast")]; + tensor input_185_cast = silu(x = add_51_cast)[name = tensor("input_185_cast")]; + 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 input_187_pad_type_0 = const()[name = tensor("input_187_pad_type_0"), val = tensor("custom")]; + tensor input_187_pad_0 = const()[name = tensor("input_187_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_187_cast = conv(bias = decoder_up_blocks_3_resnets_1_conv1_bias_to_fp16, dilations = var_625, groups = var_28, pad = input_187_pad_0, pad_type = input_187_pad_type_0, strides = var_623, weight = decoder_up_blocks_3_resnets_1_conv1_weight_to_fp16, x = input_185_cast)[name = tensor("input_187_cast")]; + tensor reshape_104_shape_0 = const()[name = tensor("reshape_104_shape_0"), val = tensor([1, 32, 4, 512, 512])]; + tensor reshape_104_cast = reshape(shape = reshape_104_shape_0, x = input_187_cast)[name = tensor("reshape_104_cast")]; + 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 = reduce_mean(axes = reduce_mean_78_axes_0, keep_dims = reduce_mean_78_keep_dims_0, x = reshape_104_cast)[name = tensor("reduce_mean_78_cast")]; + tensor sub_52_cast = sub(x = reshape_104_cast, y = reduce_mean_78_cast)[name = tensor("sub_52_cast")]; + tensor square_26_cast = square(x = sub_52_cast)[name = tensor("square_26_cast")]; + 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 = reduce_mean(axes = reduce_mean_80_axes_0, keep_dims = reduce_mean_80_keep_dims_0, x = square_26_cast)[name = tensor("reduce_mean_80_cast")]; + 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 = add(x = reduce_mean_80_cast, y = add_52_y_0_to_fp16)[name = tensor("add_52_cast")]; + tensor sqrt_26_cast = sqrt(x = add_52_cast)[name = tensor("sqrt_26_cast")]; + tensor real_div_26_cast = real_div(x = sub_52_cast, y = sqrt_26_cast)[name = tensor("real_div_26_cast")]; + tensor reshape_105_shape_0 = const()[name = tensor("reshape_105_shape_0"), val = tensor([1, 128, 512, 512])]; + tensor reshape_105_cast = reshape(shape = reshape_105_shape_0, x = real_div_26_cast)[name = tensor("reshape_105_cast")]; + 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 = 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)[name = tensor("add_53_cast")]; + tensor input_191_cast = silu(x = add_53_cast)[name = tensor("input_191_cast")]; + tensor var_635 = const()[name = tensor("op_635"), val = tensor([1, 1])]; + tensor var_637 = const()[name = tensor("op_637"), val = tensor([1, 1])]; + tensor hidden_states_39_pad_type_0 = const()[name = tensor("hidden_states_39_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_39_pad_0 = const()[name = tensor("hidden_states_39_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor 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_39_cast = conv(bias = decoder_up_blocks_3_resnets_1_conv2_bias_to_fp16, dilations = var_637, groups = var_28, pad = hidden_states_39_pad_0, pad_type = hidden_states_39_pad_type_0, strides = var_635, weight = decoder_up_blocks_3_resnets_1_conv2_weight_to_fp16, x = input_191_cast)[name = tensor("hidden_states_39_cast")]; + tensor var_640_cast = add(x = var_610_cast, y = hidden_states_39_cast)[name = tensor("op_640_cast")]; + tensor reshape_108_shape_0 = const()[name = tensor("reshape_108_shape_0"), val = tensor([1, 32, 4, 512, 512])]; + tensor reshape_108_cast = reshape(shape = reshape_108_shape_0, x = var_640_cast)[name = tensor("reshape_108_cast")]; + 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 = reduce_mean(axes = reduce_mean_81_axes_0, keep_dims = reduce_mean_81_keep_dims_0, x = reshape_108_cast)[name = tensor("reduce_mean_81_cast")]; + tensor sub_54_cast = sub(x = reshape_108_cast, y = reduce_mean_81_cast)[name = tensor("sub_54_cast")]; + tensor square_27_cast = square(x = sub_54_cast)[name = tensor("square_27_cast")]; + 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 = reduce_mean(axes = reduce_mean_83_axes_0, keep_dims = reduce_mean_83_keep_dims_0, x = square_27_cast)[name = tensor("reduce_mean_83_cast")]; + 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 = add(x = reduce_mean_83_cast, y = add_54_y_0_to_fp16)[name = tensor("add_54_cast")]; + tensor sqrt_27_cast = sqrt(x = add_54_cast)[name = tensor("sqrt_27_cast")]; + tensor real_div_27_cast = real_div(x = sub_54_cast, y = sqrt_27_cast)[name = tensor("real_div_27_cast")]; + tensor reshape_109_shape_0 = const()[name = tensor("reshape_109_shape_0"), val = tensor([1, 128, 512, 512])]; + tensor reshape_109_cast = reshape(shape = reshape_109_shape_0, x = real_div_27_cast)[name = tensor("reshape_109_cast")]; + 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 = 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)[name = tensor("add_55_cast")]; + tensor input_199_cast = silu(x = add_55_cast)[name = tensor("input_199_cast")]; + tensor var_653 = const()[name = tensor("op_653"), val = tensor([1, 1])]; + tensor var_655 = const()[name = tensor("op_655"), val = tensor([1, 1])]; + tensor input_201_pad_type_0 = const()[name = tensor("input_201_pad_type_0"), val = tensor("custom")]; + tensor input_201_pad_0 = const()[name = tensor("input_201_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_201_cast = conv(bias = decoder_up_blocks_3_resnets_2_conv1_bias_to_fp16, dilations = var_655, groups = var_28, pad = input_201_pad_0, pad_type = input_201_pad_type_0, strides = var_653, weight = decoder_up_blocks_3_resnets_2_conv1_weight_to_fp16, x = input_199_cast)[name = tensor("input_201_cast")]; + tensor reshape_112_shape_0 = const()[name = tensor("reshape_112_shape_0"), val = tensor([1, 32, 4, 512, 512])]; + tensor reshape_112_cast = reshape(shape = reshape_112_shape_0, x = input_201_cast)[name = tensor("reshape_112_cast")]; + 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 = reduce_mean(axes = reduce_mean_84_axes_0, keep_dims = reduce_mean_84_keep_dims_0, x = reshape_112_cast)[name = tensor("reduce_mean_84_cast")]; + tensor sub_56_cast = sub(x = reshape_112_cast, y = reduce_mean_84_cast)[name = tensor("sub_56_cast")]; + tensor square_28_cast = square(x = sub_56_cast)[name = tensor("square_28_cast")]; + 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 = reduce_mean(axes = reduce_mean_86_axes_0, keep_dims = reduce_mean_86_keep_dims_0, x = square_28_cast)[name = tensor("reduce_mean_86_cast")]; + 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 = add(x = reduce_mean_86_cast, y = add_56_y_0_to_fp16)[name = tensor("add_56_cast")]; + tensor sqrt_28_cast = sqrt(x = add_56_cast)[name = tensor("sqrt_28_cast")]; + tensor real_div_28_cast = real_div(x = sub_56_cast, y = sqrt_28_cast)[name = tensor("real_div_28_cast")]; + tensor reshape_113_shape_0 = const()[name = tensor("reshape_113_shape_0"), val = tensor([1, 128, 512, 512])]; + tensor reshape_113_cast = reshape(shape = reshape_113_shape_0, x = real_div_28_cast)[name = tensor("reshape_113_cast")]; + 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 = 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)[name = tensor("add_57_cast")]; + tensor input_205_cast = silu(x = add_57_cast)[name = tensor("input_205_cast")]; + tensor var_665 = const()[name = tensor("op_665"), val = tensor([1, 1])]; + tensor var_667 = const()[name = tensor("op_667"), 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 = conv(bias = decoder_up_blocks_3_resnets_2_conv2_bias_to_fp16, dilations = var_667, groups = var_28, pad = hidden_states_pad_0, pad_type = hidden_states_pad_type_0, strides = var_665, weight = decoder_up_blocks_3_resnets_2_conv2_weight_to_fp16, x = input_205_cast)[name = tensor("hidden_states_cast")]; + tensor var_670_cast = add(x = var_640_cast, y = hidden_states_cast)[name = tensor("op_670_cast")]; + tensor reshape_116_shape_0 = const()[name = tensor("reshape_116_shape_0"), val = tensor([1, 32, 4, 512, 512])]; + tensor reshape_116_cast = reshape(shape = reshape_116_shape_0, x = var_670_cast)[name = tensor("reshape_116_cast")]; + 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 = reduce_mean(axes = reduce_mean_87_axes_0, keep_dims = reduce_mean_87_keep_dims_0, x = reshape_116_cast)[name = tensor("reduce_mean_87_cast")]; + tensor sub_58_cast = sub(x = reshape_116_cast, y = reduce_mean_87_cast)[name = tensor("sub_58_cast")]; + tensor square_29_cast = square(x = sub_58_cast)[name = tensor("square_29_cast")]; + 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 = reduce_mean(axes = reduce_mean_89_axes_0, keep_dims = reduce_mean_89_keep_dims_0, x = square_29_cast)[name = tensor("reduce_mean_89_cast")]; + 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 = add(x = reduce_mean_89_cast, y = add_58_y_0_to_fp16)[name = tensor("add_58_cast")]; + tensor sqrt_29_cast = sqrt(x = add_58_cast)[name = tensor("sqrt_29_cast")]; + tensor real_div_29_cast = real_div(x = sub_58_cast, y = sqrt_29_cast)[name = tensor("real_div_29_cast")]; + tensor reshape_117_shape_0 = const()[name = tensor("reshape_117_shape_0"), val = tensor([1, 128, 512, 512])]; + tensor reshape_117_cast = reshape(shape = reshape_117_shape_0, x = real_div_29_cast)[name = tensor("reshape_117_cast")]; + 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 = 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)[name = tensor("add_59_cast")]; + tensor input_cast = silu(x = add_59_cast)[name = tensor("input_cast")]; + tensor var_679 = const()[name = tensor("op_679"), val = tensor([1, 1])]; + tensor var_681 = const()[name = tensor("op_681"), val = tensor([1, 1])]; + tensor var_683_pad_type_0 = const()[name = tensor("op_683_pad_type_0"), val = tensor("custom")]; + tensor var_683_pad_0 = const()[name = tensor("op_683_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_683_cast = conv(bias = decoder_conv_out_bias_to_fp16, dilations = var_681, groups = var_28, pad = var_683_pad_0, pad_type = var_683_pad_type_0, strides = var_679, weight = decoder_conv_out_weight_to_fp16, x = input_cast)[name = tensor("op_683_cast")]; + tensor var_683_cast_to_fp32_dtype_0 = const()[name = tensor("op_683_cast_to_fp32_dtype_0"), val = tensor("fp32")]; + tensor image = cast(dtype = var_683_cast_to_fp32_dtype_0, x = var_683_cast)[name = tensor("cast_42")]; + } -> (image); +} \ No newline at end of file