program(1.0) [buildInfo = dict, tensor>({{"coremlc-component-MIL", "4.28.4"}, {"coremlc-version", "1429.0.0"}, {"coremltools-component-torch", "1.13.0"}, {"coremltools-version", "6.1"}})] { func main(tensor z) { tensor var_7 = const()[name = tensor("op_7"), val = tensor(1)]; tensor var_10 = const()[name = tensor("op_10"), val = tensor([1, 1])]; tensor var_12 = const()[name = tensor("op_12"), val = tensor([1, 1])]; tensor input_1_pad_type_0 = const()[name = tensor("input_1_pad_type_0"), val = tensor("custom")]; tensor input_1_pad_0 = const()[name = tensor("input_1_pad_0"), val = tensor([0, 0, 0, 0])]; tensor post_quant_conv_weight_to_fp16 = const()[name = tensor("post_quant_conv_weight_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; tensor post_quant_conv_bias_to_fp16 = const()[name = tensor("post_quant_conv_bias_to_fp16"), val = tensor([-0x1.a6p-6, -0x1.9f4p-4, -0x1.b58p-3, 0x1.7fp-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); tensor var_21 = const()[name = tensor("op_21"), val = tensor(-1)]; tensor var_27 = const()[name = tensor("op_27"), val = tensor(1)]; tensor var_45 = const()[name = tensor("op_45"), val = tensor([1, 1])]; tensor var_47 = const()[name = tensor("op_47"), 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_47, groups = var_27, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = var_45, weight = decoder_conv_in_weight_to_fp16, x = input_1_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); 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); tensor sub_0_cast = sub(x = reshape_0_cast, y = reduce_mean_0_cast); tensor square_0_cast = square(x = sub_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); 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); tensor sqrt_0_cast = sqrt(x = add_0_cast); tensor real_div_0_cast = real_div(x = sub_0_cast, y = sqrt_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); 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); tensor input_7_cast = silu(x = add_1_cast); tensor var_66 = const()[name = tensor("op_66"), val = tensor([1, 1])]; tensor var_68 = const()[name = tensor("op_68"), 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_68, groups = var_27, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = var_66, weight = decoder_mid_block_resnets_0_conv1_weight_to_fp16, x = input_7_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); 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); tensor sub_2_cast = sub(x = reshape_4_cast, y = reduce_mean_3_cast); tensor square_1_cast = square(x = sub_2_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); 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); tensor sqrt_1_cast = sqrt(x = add_2_cast); tensor real_div_1_cast = real_div(x = sub_2_cast, y = sqrt_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); tensor add_3_mean_0_to_fp16 = const()[name = tensor("add_3_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4762304)))]; tensor add_3_variance_0_to_fp16 = const()[name = tensor("add_3_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4763392)))]; 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(4764480)))]; 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(4765568)))]; 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_3_mean_0_to_fp16, variance = add_3_variance_0_to_fp16, x = reshape_5_cast); tensor input_13_cast = silu(x = add_3_cast); tensor var_78 = const()[name = tensor("op_78"), val = tensor([1, 1])]; tensor var_80 = const()[name = tensor("op_80"), 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(4766656)))]; 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(9485312)))]; tensor hidden_states_1_cast = conv(bias = decoder_mid_block_resnets_0_conv2_bias_to_fp16, dilations = var_80, groups = var_27, pad = hidden_states_1_pad_0, pad_type = hidden_states_1_pad_type_0, strides = var_78, weight = decoder_mid_block_resnets_0_conv2_weight_to_fp16, x = input_13_cast); tensor var_83_cast = add(x = input_3_cast, y = hidden_states_1_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_83_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); tensor sub_4_cast = sub(x = reshape_8_cast, y = reduce_mean_6_cast); tensor square_2_cast = square(x = sub_4_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); 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); tensor sqrt_2_cast = sqrt(x = add_4_cast); tensor real_div_2_cast = real_div(x = sub_4_cast, y = sqrt_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); tensor add_5_mean_0_to_fp16 = const()[name = tensor("add_5_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9486400)))]; tensor add_5_variance_0_to_fp16 = const()[name = tensor("add_5_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9487488)))]; 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(9488576)))]; 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(9489664)))]; 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_5_mean_0_to_fp16, variance = add_5_variance_0_to_fp16, x = reshape_9_cast); tensor var_102 = const()[name = tensor("op_102"), val = tensor([1, 512, 4096])]; tensor var_103_cast = reshape(shape = var_102, x = add_5_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(9490752)))]; 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(10015104)))]; tensor transpose_2 = transpose(perm = input_17_perm_0, x = var_103_cast); tensor query_proj_cast = linear(bias = decoder_mid_block_attentions_0_query_bias_to_fp16, weight = decoder_mid_block_attentions_0_query_weight_to_fp16, x = transpose_2); 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(10016192)))]; 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(10540544)))]; tensor key_proj_cast = linear(bias = decoder_mid_block_attentions_0_key_bias_to_fp16, weight = decoder_mid_block_attentions_0_key_weight_to_fp16, x = transpose_2); 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(10541632)))]; 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(11065984)))]; tensor value_proj_cast = linear(bias = decoder_mid_block_attentions_0_value_bias_to_fp16, weight = decoder_mid_block_attentions_0_value_weight_to_fp16, x = transpose_2); tensor var_119_perm_0 = const()[name = tensor("op_119_perm_0"), val = tensor([0, -1, -2])]; tensor var_19_to_fp16 = const()[name = tensor("op_19_to_fp16"), val = tensor(0x1.6ap-5)]; tensor query_proj_scaled_cast = mul(x = var_19_to_fp16, y = query_proj_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_1 = transpose(perm = var_119_perm_0, x = key_proj_cast); 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_1); tensor var_122_cast = softmax(axis = var_21, x = attention_scores_1_bmm_cast); tensor input_19_transpose_x_0 = const()[name = tensor("input_19_transpose_x_0"), val = tensor(false)]; tensor input_19_transpose_y_0 = const()[name = tensor("input_19_transpose_y_0"), val = tensor(false)]; tensor input_19_cast = matmul(transpose_x = input_19_transpose_x_0, transpose_y = input_19_transpose_y_0, x = var_122_cast, y = value_proj_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(11067072)))]; 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(11591424)))]; 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); tensor var_128_perm_0 = const()[name = tensor("op_128_perm_0"), val = tensor([0, -1, -2])]; tensor var_129 = const()[name = tensor("op_129"), val = tensor([1, 512, 64, 64])]; tensor transpose_0 = transpose(perm = var_128_perm_0, x = hidden_states_7_cast); tensor hidden_states_9_cast = reshape(shape = var_129, x = transpose_0); tensor var_131_cast = add(x = hidden_states_9_cast, y = var_83_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_131_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); tensor sub_6_cast = sub(x = reshape_12_cast, y = reduce_mean_9_cast); tensor square_3_cast = square(x = sub_6_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); 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); tensor sqrt_3_cast = sqrt(x = add_6_cast); tensor real_div_3_cast = real_div(x = sub_6_cast, y = sqrt_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); tensor add_7_mean_0_to_fp16 = const()[name = tensor("add_7_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11592512)))]; tensor add_7_variance_0_to_fp16 = const()[name = tensor("add_7_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11593600)))]; 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(11594688)))]; 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(11595776)))]; 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_7_mean_0_to_fp16, variance = add_7_variance_0_to_fp16, x = reshape_13_cast); tensor input_25_cast = silu(x = add_7_cast); tensor var_144 = const()[name = tensor("op_144"), val = tensor([1, 1])]; tensor var_146 = const()[name = tensor("op_146"), 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(11596864)))]; 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(16315520)))]; tensor input_27_cast = conv(bias = decoder_mid_block_resnets_1_conv1_bias_to_fp16, dilations = var_146, groups = var_27, pad = input_27_pad_0, pad_type = input_27_pad_type_0, strides = var_144, weight = decoder_mid_block_resnets_1_conv1_weight_to_fp16, x = input_25_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); 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); tensor sub_8_cast = sub(x = reshape_16_cast, y = reduce_mean_12_cast); tensor square_4_cast = square(x = sub_8_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); 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); tensor sqrt_4_cast = sqrt(x = add_8_cast); tensor real_div_4_cast = real_div(x = sub_8_cast, y = sqrt_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); tensor add_9_mean_0_to_fp16 = const()[name = tensor("add_9_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16316608)))]; tensor add_9_variance_0_to_fp16 = const()[name = tensor("add_9_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16317696)))]; 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(16318784)))]; 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(16319872)))]; 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_9_mean_0_to_fp16, variance = add_9_variance_0_to_fp16, x = reshape_17_cast); tensor input_31_cast = silu(x = add_9_cast); tensor var_156 = const()[name = tensor("op_156"), val = tensor([1, 1])]; tensor var_158 = const()[name = tensor("op_158"), 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(16320960)))]; 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(21039616)))]; tensor hidden_states_11_cast = conv(bias = decoder_mid_block_resnets_1_conv2_bias_to_fp16, dilations = var_158, groups = var_27, pad = hidden_states_11_pad_0, pad_type = hidden_states_11_pad_type_0, strides = var_156, weight = decoder_mid_block_resnets_1_conv2_weight_to_fp16, x = input_31_cast); tensor var_161_cast = add(x = var_131_cast, y = hidden_states_11_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_161_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); tensor sub_10_cast = sub(x = reshape_20_cast, y = reduce_mean_15_cast); tensor square_5_cast = square(x = sub_10_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); 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); tensor sqrt_5_cast = sqrt(x = add_10_cast); tensor real_div_5_cast = real_div(x = sub_10_cast, y = sqrt_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); tensor add_11_mean_0_to_fp16 = const()[name = tensor("add_11_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21040704)))]; tensor add_11_variance_0_to_fp16 = const()[name = tensor("add_11_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21041792)))]; 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(21042880)))]; 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(21043968)))]; 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_11_mean_0_to_fp16, variance = add_11_variance_0_to_fp16, x = reshape_21_cast); tensor input_39_cast = silu(x = add_11_cast); tensor var_182 = const()[name = tensor("op_182"), val = tensor([1, 1])]; tensor var_184 = const()[name = tensor("op_184"), 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(21045056)))]; 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(25763712)))]; tensor input_41_cast = conv(bias = decoder_up_blocks_0_resnets_0_conv1_bias_to_fp16, dilations = var_184, groups = var_27, pad = input_41_pad_0, pad_type = input_41_pad_type_0, strides = var_182, weight = decoder_up_blocks_0_resnets_0_conv1_weight_to_fp16, x = input_39_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); 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); tensor sub_12_cast = sub(x = reshape_24_cast, y = reduce_mean_18_cast); tensor square_6_cast = square(x = sub_12_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); 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); tensor sqrt_6_cast = sqrt(x = add_12_cast); tensor real_div_6_cast = real_div(x = sub_12_cast, y = sqrt_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); tensor add_13_mean_0_to_fp16 = const()[name = tensor("add_13_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25764800)))]; tensor add_13_variance_0_to_fp16 = const()[name = tensor("add_13_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(25765888)))]; 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(25766976)))]; 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(25768064)))]; 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_13_mean_0_to_fp16, variance = add_13_variance_0_to_fp16, x = reshape_25_cast); tensor input_45_cast = silu(x = add_13_cast); tensor var_194 = const()[name = tensor("op_194"), val = tensor([1, 1])]; tensor var_196 = const()[name = tensor("op_196"), 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(25769152)))]; 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(30487808)))]; tensor hidden_states_13_cast = conv(bias = decoder_up_blocks_0_resnets_0_conv2_bias_to_fp16, dilations = var_196, groups = var_27, pad = hidden_states_13_pad_0, pad_type = hidden_states_13_pad_type_0, strides = var_194, weight = decoder_up_blocks_0_resnets_0_conv2_weight_to_fp16, x = input_45_cast); tensor var_199_cast = add(x = var_161_cast, y = hidden_states_13_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_199_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); tensor sub_14_cast = sub(x = reshape_28_cast, y = reduce_mean_21_cast); tensor square_7_cast = square(x = sub_14_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); 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); tensor sqrt_7_cast = sqrt(x = add_14_cast); tensor real_div_7_cast = real_div(x = sub_14_cast, y = sqrt_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); tensor add_15_mean_0_to_fp16 = const()[name = tensor("add_15_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30488896)))]; tensor add_15_variance_0_to_fp16 = const()[name = tensor("add_15_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30489984)))]; 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(30491072)))]; 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(30492160)))]; 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_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_29_cast); tensor input_53_cast = silu(x = add_15_cast); tensor var_212 = const()[name = tensor("op_212"), val = tensor([1, 1])]; tensor var_214 = const()[name = tensor("op_214"), 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(30493248)))]; 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(35211904)))]; tensor input_55_cast = conv(bias = decoder_up_blocks_0_resnets_1_conv1_bias_to_fp16, dilations = var_214, groups = var_27, pad = input_55_pad_0, pad_type = input_55_pad_type_0, strides = var_212, weight = decoder_up_blocks_0_resnets_1_conv1_weight_to_fp16, x = input_53_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); 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); tensor sub_16_cast = sub(x = reshape_32_cast, y = reduce_mean_24_cast); tensor square_8_cast = square(x = sub_16_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); 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); tensor sqrt_8_cast = sqrt(x = add_16_cast); tensor real_div_8_cast = real_div(x = sub_16_cast, y = sqrt_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); tensor add_17_mean_0_to_fp16 = const()[name = tensor("add_17_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(35212992)))]; tensor add_17_variance_0_to_fp16 = const()[name = tensor("add_17_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(35214080)))]; 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(35215168)))]; 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(35216256)))]; 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_17_mean_0_to_fp16, variance = add_17_variance_0_to_fp16, x = reshape_33_cast); tensor input_59_cast = silu(x = add_17_cast); tensor var_224 = const()[name = tensor("op_224"), val = tensor([1, 1])]; tensor var_226 = const()[name = tensor("op_226"), 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(35217344)))]; 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(39936000)))]; tensor hidden_states_15_cast = conv(bias = decoder_up_blocks_0_resnets_1_conv2_bias_to_fp16, dilations = var_226, groups = var_27, pad = hidden_states_15_pad_0, pad_type = hidden_states_15_pad_type_0, strides = var_224, weight = decoder_up_blocks_0_resnets_1_conv2_weight_to_fp16, x = input_59_cast); tensor var_229_cast = add(x = var_199_cast, y = hidden_states_15_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_229_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); tensor sub_18_cast = sub(x = reshape_36_cast, y = reduce_mean_27_cast); tensor square_9_cast = square(x = sub_18_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); 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); tensor sqrt_9_cast = sqrt(x = add_18_cast); tensor real_div_9_cast = real_div(x = sub_18_cast, y = sqrt_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); tensor add_19_mean_0_to_fp16 = const()[name = tensor("add_19_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39937088)))]; tensor add_19_variance_0_to_fp16 = const()[name = tensor("add_19_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(39938176)))]; 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(39939264)))]; 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(39940352)))]; 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_19_mean_0_to_fp16, variance = add_19_variance_0_to_fp16, x = reshape_37_cast); tensor input_67_cast = silu(x = add_19_cast); tensor var_242 = const()[name = tensor("op_242"), val = tensor([1, 1])]; tensor var_244 = const()[name = tensor("op_244"), 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(39941440)))]; 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(44660096)))]; tensor input_69_cast = conv(bias = decoder_up_blocks_0_resnets_2_conv1_bias_to_fp16, dilations = var_244, groups = var_27, pad = input_69_pad_0, pad_type = input_69_pad_type_0, strides = var_242, weight = decoder_up_blocks_0_resnets_2_conv1_weight_to_fp16, x = input_67_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); 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); tensor sub_20_cast = sub(x = reshape_40_cast, y = reduce_mean_30_cast); tensor square_10_cast = square(x = sub_20_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); 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); tensor sqrt_10_cast = sqrt(x = add_20_cast); tensor real_div_10_cast = real_div(x = sub_20_cast, y = sqrt_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); tensor add_21_mean_0_to_fp16 = const()[name = tensor("add_21_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44661184)))]; tensor add_21_variance_0_to_fp16 = const()[name = tensor("add_21_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(44662272)))]; 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(44663360)))]; 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(44664448)))]; 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_21_mean_0_to_fp16, variance = add_21_variance_0_to_fp16, x = reshape_41_cast); tensor input_73_cast = silu(x = add_21_cast); tensor var_254 = const()[name = tensor("op_254"), val = tensor([1, 1])]; tensor var_256 = const()[name = tensor("op_256"), 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(44665536)))]; 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(49384192)))]; tensor hidden_states_17_cast = conv(bias = decoder_up_blocks_0_resnets_2_conv2_bias_to_fp16, dilations = var_256, groups = var_27, pad = hidden_states_17_pad_0, pad_type = hidden_states_17_pad_type_0, strides = var_254, weight = decoder_up_blocks_0_resnets_2_conv2_weight_to_fp16, x = input_73_cast); tensor var_259_cast = add(x = var_229_cast, y = hidden_states_17_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_259_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 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(49385280)))]; 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(54103936)))]; tensor input_79_cast = conv(bias = decoder_up_blocks_0_upsamplers_0_conv_bias_to_fp16, dilations = var_269, groups = var_27, pad = input_79_pad_0, pad_type = input_79_pad_type_0, strides = var_267, weight = decoder_up_blocks_0_upsamplers_0_conv_weight_to_fp16, x = input_77_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); 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); tensor sub_22_cast = sub(x = reshape_44_cast, y = reduce_mean_33_cast); tensor square_11_cast = square(x = sub_22_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); 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); tensor sqrt_11_cast = sqrt(x = add_22_cast); tensor real_div_11_cast = real_div(x = sub_22_cast, y = sqrt_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); tensor add_23_mean_0_to_fp16 = const()[name = tensor("add_23_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(54105024)))]; tensor add_23_variance_0_to_fp16 = const()[name = tensor("add_23_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(54106112)))]; 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(54107200)))]; 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(54108288)))]; 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_23_mean_0_to_fp16, variance = add_23_variance_0_to_fp16, x = reshape_45_cast); tensor input_83_cast = silu(x = add_23_cast); tensor var_290 = const()[name = tensor("op_290"), val = tensor([1, 1])]; tensor var_292 = const()[name = tensor("op_292"), 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(54109376)))]; 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(58828032)))]; tensor input_85_cast = conv(bias = decoder_up_blocks_1_resnets_0_conv1_bias_to_fp16, dilations = var_292, groups = var_27, pad = input_85_pad_0, pad_type = input_85_pad_type_0, strides = var_290, weight = decoder_up_blocks_1_resnets_0_conv1_weight_to_fp16, x = input_83_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); 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); tensor sub_24_cast = sub(x = reshape_48_cast, y = reduce_mean_36_cast); tensor square_12_cast = square(x = sub_24_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); 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); tensor sqrt_12_cast = sqrt(x = add_24_cast); tensor real_div_12_cast = real_div(x = sub_24_cast, y = sqrt_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); tensor add_25_mean_0_to_fp16 = const()[name = tensor("add_25_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58829120)))]; tensor add_25_variance_0_to_fp16 = const()[name = tensor("add_25_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58830208)))]; 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(58831296)))]; 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(58832384)))]; 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_25_mean_0_to_fp16, variance = add_25_variance_0_to_fp16, x = reshape_49_cast); tensor input_89_cast = silu(x = add_25_cast); tensor var_302 = const()[name = tensor("op_302"), val = tensor([1, 1])]; tensor var_304 = const()[name = tensor("op_304"), 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(58833472)))]; 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(63552128)))]; tensor hidden_states_21_cast = conv(bias = decoder_up_blocks_1_resnets_0_conv2_bias_to_fp16, dilations = var_304, groups = var_27, pad = hidden_states_21_pad_0, pad_type = hidden_states_21_pad_type_0, strides = var_302, weight = decoder_up_blocks_1_resnets_0_conv2_weight_to_fp16, x = input_89_cast); tensor var_307_cast = add(x = input_79_cast, y = hidden_states_21_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_307_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); tensor sub_26_cast = sub(x = reshape_52_cast, y = reduce_mean_39_cast); tensor square_13_cast = square(x = sub_26_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); 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); tensor sqrt_13_cast = sqrt(x = add_26_cast); tensor real_div_13_cast = real_div(x = sub_26_cast, y = sqrt_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); tensor add_27_mean_0_to_fp16 = const()[name = tensor("add_27_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63553216)))]; tensor add_27_variance_0_to_fp16 = const()[name = tensor("add_27_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63554304)))]; 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(63555392)))]; 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(63556480)))]; 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_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_53_cast); tensor input_97_cast = silu(x = add_27_cast); tensor var_320 = const()[name = tensor("op_320"), val = tensor([1, 1])]; tensor var_322 = const()[name = tensor("op_322"), 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(63557568)))]; 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(68276224)))]; tensor input_99_cast = conv(bias = decoder_up_blocks_1_resnets_1_conv1_bias_to_fp16, dilations = var_322, groups = var_27, pad = input_99_pad_0, pad_type = input_99_pad_type_0, strides = var_320, weight = decoder_up_blocks_1_resnets_1_conv1_weight_to_fp16, x = input_97_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); 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); tensor sub_28_cast = sub(x = reshape_56_cast, y = reduce_mean_42_cast); tensor square_14_cast = square(x = sub_28_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); 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); tensor sqrt_14_cast = sqrt(x = add_28_cast); tensor real_div_14_cast = real_div(x = sub_28_cast, y = sqrt_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); tensor add_29_mean_0_to_fp16 = const()[name = tensor("add_29_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68277312)))]; tensor add_29_variance_0_to_fp16 = const()[name = tensor("add_29_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(68278400)))]; 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(68279488)))]; 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(68280576)))]; 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_29_mean_0_to_fp16, variance = add_29_variance_0_to_fp16, x = reshape_57_cast); tensor input_103_cast = silu(x = add_29_cast); tensor var_332 = const()[name = tensor("op_332"), val = tensor([1, 1])]; tensor var_334 = const()[name = tensor("op_334"), 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(68281664)))]; 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(73000320)))]; tensor hidden_states_23_cast = conv(bias = decoder_up_blocks_1_resnets_1_conv2_bias_to_fp16, dilations = var_334, groups = var_27, pad = hidden_states_23_pad_0, pad_type = hidden_states_23_pad_type_0, strides = var_332, weight = decoder_up_blocks_1_resnets_1_conv2_weight_to_fp16, x = input_103_cast); tensor var_337_cast = add(x = var_307_cast, y = hidden_states_23_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_337_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); tensor sub_30_cast = sub(x = reshape_60_cast, y = reduce_mean_45_cast); tensor square_15_cast = square(x = sub_30_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); 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); tensor sqrt_15_cast = sqrt(x = add_30_cast); tensor real_div_15_cast = real_div(x = sub_30_cast, y = sqrt_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); tensor add_31_mean_0_to_fp16 = const()[name = tensor("add_31_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73001408)))]; tensor add_31_variance_0_to_fp16 = const()[name = tensor("add_31_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(73002496)))]; 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(73003584)))]; 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(73004672)))]; 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_31_mean_0_to_fp16, variance = add_31_variance_0_to_fp16, x = reshape_61_cast); tensor input_111_cast = silu(x = add_31_cast); tensor var_350 = const()[name = tensor("op_350"), val = tensor([1, 1])]; tensor var_352 = const()[name = tensor("op_352"), 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(73005760)))]; 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(77724416)))]; tensor input_113_cast = conv(bias = decoder_up_blocks_1_resnets_2_conv1_bias_to_fp16, dilations = var_352, groups = var_27, pad = input_113_pad_0, pad_type = input_113_pad_type_0, strides = var_350, weight = decoder_up_blocks_1_resnets_2_conv1_weight_to_fp16, x = input_111_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); 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); tensor sub_32_cast = sub(x = reshape_64_cast, y = reduce_mean_48_cast); tensor square_16_cast = square(x = sub_32_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); 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); tensor sqrt_16_cast = sqrt(x = add_32_cast); tensor real_div_16_cast = real_div(x = sub_32_cast, y = sqrt_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); tensor add_33_mean_0_to_fp16 = const()[name = tensor("add_33_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(77725504)))]; tensor add_33_variance_0_to_fp16 = const()[name = tensor("add_33_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(77726592)))]; 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(77727680)))]; 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(77728768)))]; 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_33_mean_0_to_fp16, variance = add_33_variance_0_to_fp16, x = reshape_65_cast); tensor input_117_cast = silu(x = add_33_cast); tensor var_362 = const()[name = tensor("op_362"), val = tensor([1, 1])]; tensor var_364 = const()[name = tensor("op_364"), 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(77729856)))]; 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(82448512)))]; tensor hidden_states_25_cast = conv(bias = decoder_up_blocks_1_resnets_2_conv2_bias_to_fp16, dilations = var_364, groups = var_27, pad = hidden_states_25_pad_0, pad_type = hidden_states_25_pad_type_0, strides = var_362, weight = decoder_up_blocks_1_resnets_2_conv2_weight_to_fp16, x = input_117_cast); tensor var_367_cast = add(x = var_337_cast, y = hidden_states_25_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_367_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 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(82449600)))]; 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(87168256)))]; tensor input_123_cast = conv(bias = decoder_up_blocks_1_upsamplers_0_conv_bias_to_fp16, dilations = var_377, groups = var_27, pad = input_123_pad_0, pad_type = input_123_pad_type_0, strides = var_375, weight = decoder_up_blocks_1_upsamplers_0_conv_weight_to_fp16, x = input_121_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); 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); tensor sub_34_cast = sub(x = reshape_68_cast, y = reduce_mean_51_cast); tensor square_17_cast = square(x = sub_34_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); 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); tensor sqrt_17_cast = sqrt(x = add_34_cast); tensor real_div_17_cast = real_div(x = sub_34_cast, y = sqrt_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); tensor add_35_mean_0_to_fp16 = const()[name = tensor("add_35_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(87169344)))]; tensor add_35_variance_0_to_fp16 = const()[name = tensor("add_35_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(87170432)))]; 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(87171520)))]; 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(87172608)))]; 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_35_mean_0_to_fp16, variance = add_35_variance_0_to_fp16, x = reshape_69_cast); tensor input_127_cast = silu(x = add_35_cast); tensor var_399 = const()[name = tensor("op_399"), val = tensor([1, 1])]; tensor var_401 = const()[name = tensor("op_401"), 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(87173696)))]; 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(89533056)))]; tensor input_129_cast = conv(bias = decoder_up_blocks_2_resnets_0_conv1_bias_to_fp16, dilations = var_401, groups = var_27, pad = input_129_pad_0, pad_type = input_129_pad_type_0, strides = var_399, weight = decoder_up_blocks_2_resnets_0_conv1_weight_to_fp16, x = input_127_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); 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); tensor sub_36_cast = sub(x = reshape_72_cast, y = reduce_mean_54_cast); tensor square_18_cast = square(x = sub_36_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); 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); tensor sqrt_18_cast = sqrt(x = add_36_cast); tensor real_div_18_cast = real_div(x = sub_36_cast, y = sqrt_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); 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(89533632)))]; 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(89534208)))]; 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(89534784)))]; 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(89535360)))]; 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); tensor input_133_cast = silu(x = add_37_cast); tensor var_411 = const()[name = tensor("op_411"), val = tensor([1, 1])]; tensor var_413 = const()[name = tensor("op_413"), 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(89535936)))]; 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(90715648)))]; tensor hidden_states_29_cast = conv(bias = decoder_up_blocks_2_resnets_0_conv2_bias_to_fp16, dilations = var_413, groups = var_27, pad = hidden_states_29_pad_0, pad_type = hidden_states_29_pad_type_0, strides = var_411, weight = decoder_up_blocks_2_resnets_0_conv2_weight_to_fp16, x = input_133_cast); tensor var_418 = const()[name = tensor("op_418"), val = tensor([1, 1])]; tensor var_420 = const()[name = tensor("op_420"), 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(90716224)))]; 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(90978432)))]; tensor input_tensor_1_cast = conv(bias = decoder_up_blocks_2_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_420, groups = var_27, pad = input_tensor_1_pad_0, pad_type = input_tensor_1_pad_type_0, strides = var_418, weight = decoder_up_blocks_2_resnets_0_conv_shortcut_weight_to_fp16, x = input_123_cast); tensor var_423_cast = add(x = input_tensor_1_cast, y = hidden_states_29_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_423_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); tensor sub_38_cast = sub(x = reshape_76_cast, y = reduce_mean_57_cast); tensor square_19_cast = square(x = sub_38_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); 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); tensor sqrt_19_cast = sqrt(x = add_38_cast); tensor real_div_19_cast = real_div(x = sub_38_cast, y = sqrt_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); tensor add_39_mean_0_to_fp16 = const()[name = tensor("add_39_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90979008)))]; tensor add_39_variance_0_to_fp16 = const()[name = tensor("add_39_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(90979584)))]; 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(90980160)))]; 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(90980736)))]; 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_39_mean_0_to_fp16, variance = add_39_variance_0_to_fp16, x = reshape_77_cast); tensor input_141_cast = silu(x = add_39_cast); tensor var_436 = const()[name = tensor("op_436"), val = tensor([1, 1])]; tensor var_438 = const()[name = tensor("op_438"), 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(90981312)))]; 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(92161024)))]; tensor input_143_cast = conv(bias = decoder_up_blocks_2_resnets_1_conv1_bias_to_fp16, dilations = var_438, groups = var_27, pad = input_143_pad_0, pad_type = input_143_pad_type_0, strides = var_436, weight = decoder_up_blocks_2_resnets_1_conv1_weight_to_fp16, x = input_141_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); 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); tensor sub_40_cast = sub(x = reshape_80_cast, y = reduce_mean_60_cast); tensor square_20_cast = square(x = sub_40_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); 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); tensor sqrt_20_cast = sqrt(x = add_40_cast); tensor real_div_20_cast = real_div(x = sub_40_cast, y = sqrt_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); tensor add_41_mean_0_to_fp16 = const()[name = tensor("add_41_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(92161600)))]; tensor add_41_variance_0_to_fp16 = const()[name = tensor("add_41_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(92162176)))]; 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(92162752)))]; 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(92163328)))]; 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_41_mean_0_to_fp16, variance = add_41_variance_0_to_fp16, x = reshape_81_cast); tensor input_147_cast = silu(x = add_41_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 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(92163904)))]; 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(93343616)))]; tensor hidden_states_31_cast = conv(bias = decoder_up_blocks_2_resnets_1_conv2_bias_to_fp16, dilations = var_450, groups = var_27, pad = hidden_states_31_pad_0, pad_type = hidden_states_31_pad_type_0, strides = var_448, weight = decoder_up_blocks_2_resnets_1_conv2_weight_to_fp16, x = input_147_cast); tensor var_453_cast = add(x = var_423_cast, y = hidden_states_31_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_453_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); tensor sub_42_cast = sub(x = reshape_84_cast, y = reduce_mean_63_cast); tensor square_21_cast = square(x = sub_42_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); 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); tensor sqrt_21_cast = sqrt(x = add_42_cast); tensor real_div_21_cast = real_div(x = sub_42_cast, y = sqrt_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); tensor add_43_mean_0_to_fp16 = const()[name = tensor("add_43_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(93344192)))]; tensor add_43_variance_0_to_fp16 = const()[name = tensor("add_43_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(93344768)))]; 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(93345344)))]; 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(93345920)))]; 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_43_mean_0_to_fp16, variance = add_43_variance_0_to_fp16, x = reshape_85_cast); tensor input_155_cast = silu(x = add_43_cast); tensor var_466 = const()[name = tensor("op_466"), val = tensor([1, 1])]; tensor var_468 = const()[name = tensor("op_468"), 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(93346496)))]; 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(94526208)))]; tensor input_157_cast = conv(bias = decoder_up_blocks_2_resnets_2_conv1_bias_to_fp16, dilations = var_468, groups = var_27, pad = input_157_pad_0, pad_type = input_157_pad_type_0, strides = var_466, weight = decoder_up_blocks_2_resnets_2_conv1_weight_to_fp16, x = input_155_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); 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); tensor sub_44_cast = sub(x = reshape_88_cast, y = reduce_mean_66_cast); tensor square_22_cast = square(x = sub_44_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); 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); tensor sqrt_22_cast = sqrt(x = add_44_cast); tensor real_div_22_cast = real_div(x = sub_44_cast, y = sqrt_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); tensor add_45_mean_0_to_fp16 = const()[name = tensor("add_45_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(94526784)))]; tensor add_45_variance_0_to_fp16 = const()[name = tensor("add_45_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(94527360)))]; 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(94527936)))]; 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(94528512)))]; 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_45_mean_0_to_fp16, variance = add_45_variance_0_to_fp16, x = reshape_89_cast); tensor input_161_cast = silu(x = add_45_cast); tensor var_478 = const()[name = tensor("op_478"), val = tensor([1, 1])]; tensor var_480 = const()[name = tensor("op_480"), 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(94529088)))]; 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(95708800)))]; tensor hidden_states_33_cast = conv(bias = decoder_up_blocks_2_resnets_2_conv2_bias_to_fp16, dilations = var_480, groups = var_27, pad = hidden_states_33_pad_0, pad_type = hidden_states_33_pad_type_0, strides = var_478, weight = decoder_up_blocks_2_resnets_2_conv2_weight_to_fp16, x = input_161_cast); tensor var_483_cast = add(x = var_453_cast, y = hidden_states_33_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_483_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_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(95709376)))]; 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(96889088)))]; tensor input_167_cast = conv(bias = decoder_up_blocks_2_upsamplers_0_conv_bias_to_fp16, dilations = var_493, groups = var_27, pad = input_167_pad_0, pad_type = input_167_pad_type_0, strides = var_491, weight = decoder_up_blocks_2_upsamplers_0_conv_weight_to_fp16, x = input_165_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); 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); tensor sub_46_cast = sub(x = reshape_92_cast, y = reduce_mean_69_cast); tensor square_23_cast = square(x = sub_46_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); 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); tensor sqrt_23_cast = sqrt(x = add_46_cast); tensor real_div_23_cast = real_div(x = sub_46_cast, y = sqrt_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); tensor add_47_mean_0_to_fp16 = const()[name = tensor("add_47_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96889664)))]; tensor add_47_variance_0_to_fp16 = const()[name = tensor("add_47_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(96890240)))]; 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(96890816)))]; 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(96891392)))]; 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_47_mean_0_to_fp16, variance = add_47_variance_0_to_fp16, x = reshape_93_cast); tensor input_171_cast = silu(x = add_47_cast); tensor var_513 = const()[name = tensor("op_513"), val = tensor([1, 1])]; tensor var_515 = const()[name = tensor("op_515"), 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(96891968)))]; 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(97481856)))]; tensor input_173_cast = conv(bias = decoder_up_blocks_3_resnets_0_conv1_bias_to_fp16, dilations = var_515, groups = var_27, pad = input_173_pad_0, pad_type = input_173_pad_type_0, strides = var_513, weight = decoder_up_blocks_3_resnets_0_conv1_weight_to_fp16, x = input_171_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); 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); tensor sub_48_cast = sub(x = reshape_96_cast, y = reduce_mean_72_cast); tensor square_24_cast = square(x = sub_48_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); 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); tensor sqrt_24_cast = sqrt(x = add_48_cast); tensor real_div_24_cast = real_div(x = sub_48_cast, y = sqrt_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); 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(97482176)))]; 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(97482496)))]; 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(97482816)))]; 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(97483136)))]; 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); tensor input_177_cast = silu(x = add_49_cast); tensor var_525 = const()[name = tensor("op_525"), val = tensor([1, 1])]; tensor var_527 = const()[name = tensor("op_527"), 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(97483456)))]; 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(97778432)))]; tensor hidden_states_37_cast = conv(bias = decoder_up_blocks_3_resnets_0_conv2_bias_to_fp16, dilations = var_527, groups = var_27, pad = hidden_states_37_pad_0, pad_type = hidden_states_37_pad_type_0, strides = var_525, weight = decoder_up_blocks_3_resnets_0_conv2_weight_to_fp16, x = input_177_cast); tensor var_532 = const()[name = tensor("op_532"), val = tensor([1, 1])]; tensor var_534 = const()[name = tensor("op_534"), 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(97778752)))]; 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(97844352)))]; tensor input_tensor_cast = conv(bias = decoder_up_blocks_3_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_534, groups = var_27, pad = input_tensor_pad_0, pad_type = input_tensor_pad_type_0, strides = var_532, weight = decoder_up_blocks_3_resnets_0_conv_shortcut_weight_to_fp16, x = input_167_cast); tensor var_537_cast = add(x = input_tensor_cast, y = hidden_states_37_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_537_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); tensor sub_50_cast = sub(x = reshape_100_cast, y = reduce_mean_75_cast); tensor square_25_cast = square(x = sub_50_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); 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); tensor sqrt_25_cast = sqrt(x = add_50_cast); tensor real_div_25_cast = real_div(x = sub_50_cast, y = sqrt_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); tensor add_51_mean_0_to_fp16 = const()[name = tensor("add_51_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97844672)))]; tensor add_51_variance_0_to_fp16 = const()[name = tensor("add_51_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(97844992)))]; 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(97845312)))]; 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(97845632)))]; 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_51_mean_0_to_fp16, variance = add_51_variance_0_to_fp16, x = reshape_101_cast); tensor input_185_cast = silu(x = add_51_cast); tensor var_550 = const()[name = tensor("op_550"), val = tensor([1, 1])]; tensor var_552 = const()[name = tensor("op_552"), 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(97845952)))]; 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(98140928)))]; tensor input_187_cast = conv(bias = decoder_up_blocks_3_resnets_1_conv1_bias_to_fp16, dilations = var_552, groups = var_27, pad = input_187_pad_0, pad_type = input_187_pad_type_0, strides = var_550, weight = decoder_up_blocks_3_resnets_1_conv1_weight_to_fp16, x = input_185_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); 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); tensor sub_52_cast = sub(x = reshape_104_cast, y = reduce_mean_78_cast); tensor square_26_cast = square(x = sub_52_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); 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); tensor sqrt_26_cast = sqrt(x = add_52_cast); tensor real_div_26_cast = real_div(x = sub_52_cast, y = sqrt_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); tensor add_53_mean_0_to_fp16 = const()[name = tensor("add_53_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98141248)))]; tensor add_53_variance_0_to_fp16 = const()[name = tensor("add_53_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98141568)))]; 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(98141888)))]; 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(98142208)))]; 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_53_mean_0_to_fp16, variance = add_53_variance_0_to_fp16, x = reshape_105_cast); tensor input_191_cast = silu(x = add_53_cast); tensor var_562 = const()[name = tensor("op_562"), val = tensor([1, 1])]; tensor var_564 = const()[name = tensor("op_564"), 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(98142528)))]; 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(98437504)))]; tensor hidden_states_39_cast = conv(bias = decoder_up_blocks_3_resnets_1_conv2_bias_to_fp16, dilations = var_564, groups = var_27, pad = hidden_states_39_pad_0, pad_type = hidden_states_39_pad_type_0, strides = var_562, weight = decoder_up_blocks_3_resnets_1_conv2_weight_to_fp16, x = input_191_cast); tensor var_567_cast = add(x = var_537_cast, y = hidden_states_39_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_567_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); tensor sub_54_cast = sub(x = reshape_108_cast, y = reduce_mean_81_cast); tensor square_27_cast = square(x = sub_54_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); 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); tensor sqrt_27_cast = sqrt(x = add_54_cast); tensor real_div_27_cast = real_div(x = sub_54_cast, y = sqrt_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); tensor add_55_mean_0_to_fp16 = const()[name = tensor("add_55_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98437824)))]; tensor add_55_variance_0_to_fp16 = const()[name = tensor("add_55_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98438144)))]; 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(98438464)))]; 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(98438784)))]; 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_55_mean_0_to_fp16, variance = add_55_variance_0_to_fp16, x = reshape_109_cast); tensor input_199_cast = silu(x = add_55_cast); tensor var_580 = const()[name = tensor("op_580"), val = tensor([1, 1])]; tensor var_582 = const()[name = tensor("op_582"), 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(98439104)))]; 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(98734080)))]; tensor input_201_cast = conv(bias = decoder_up_blocks_3_resnets_2_conv1_bias_to_fp16, dilations = var_582, groups = var_27, pad = input_201_pad_0, pad_type = input_201_pad_type_0, strides = var_580, weight = decoder_up_blocks_3_resnets_2_conv1_weight_to_fp16, x = input_199_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); 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); tensor sub_56_cast = sub(x = reshape_112_cast, y = reduce_mean_84_cast); tensor square_28_cast = square(x = sub_56_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); 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); tensor sqrt_28_cast = sqrt(x = add_56_cast); tensor real_div_28_cast = real_div(x = sub_56_cast, y = sqrt_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); tensor add_57_mean_0_to_fp16 = const()[name = tensor("add_57_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98734400)))]; tensor add_57_variance_0_to_fp16 = const()[name = tensor("add_57_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(98734720)))]; 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(98735040)))]; 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(98735360)))]; 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_57_mean_0_to_fp16, variance = add_57_variance_0_to_fp16, x = reshape_113_cast); tensor input_205_cast = silu(x = add_57_cast); tensor var_592 = const()[name = tensor("op_592"), val = tensor([1, 1])]; tensor var_594 = const()[name = tensor("op_594"), 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(98735680)))]; 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(99030656)))]; tensor hidden_states_cast = conv(bias = decoder_up_blocks_3_resnets_2_conv2_bias_to_fp16, dilations = var_594, groups = var_27, pad = hidden_states_pad_0, pad_type = hidden_states_pad_type_0, strides = var_592, weight = decoder_up_blocks_3_resnets_2_conv2_weight_to_fp16, x = input_205_cast); tensor var_597_cast = add(x = var_567_cast, y = hidden_states_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_597_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); tensor sub_58_cast = sub(x = reshape_116_cast, y = reduce_mean_87_cast); tensor square_29_cast = square(x = sub_58_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); 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); tensor sqrt_29_cast = sqrt(x = add_58_cast); tensor real_div_29_cast = real_div(x = sub_58_cast, y = sqrt_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); tensor add_59_mean_0_to_fp16 = const()[name = tensor("add_59_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(99030976)))]; tensor add_59_variance_0_to_fp16 = const()[name = tensor("add_59_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(99031296)))]; 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(99031616)))]; 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(99031936)))]; 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_59_mean_0_to_fp16, variance = add_59_variance_0_to_fp16, x = reshape_117_cast); tensor input_cast = silu(x = add_59_cast); tensor var_606 = const()[name = tensor("op_606"), val = tensor([1, 1])]; tensor var_608 = const()[name = tensor("op_608"), val = tensor([1, 1])]; tensor var_610_pad_type_0 = const()[name = tensor("op_610_pad_type_0"), val = tensor("custom")]; tensor var_610_pad_0 = const()[name = tensor("op_610_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(99032256)))]; tensor decoder_conv_out_bias_to_fp16 = const()[name = tensor("decoder_conv_out_bias_to_fp16"), val = tensor([0x1.514p-8, -0x1.c4cp-6, -0x1.67p-5])]; tensor var_610_cast = conv(bias = decoder_conv_out_bias_to_fp16, dilations = var_608, groups = var_27, pad = var_610_pad_0, pad_type = var_610_pad_type_0, strides = var_606, weight = decoder_conv_out_weight_to_fp16, x = input_cast); tensor var_610_cast_to_fp32_dtype_0 = const()[name = tensor("op_610_cast_to_fp32_dtype_0"), val = tensor("fp32")]; tensor image = cast(dtype = var_610_cast_to_fp32_dtype_0, x = var_610_cast); } -> (image); }