diff --git "a/original/compiled/Unet.mlmodelc/model.mil" "b/original/compiled/Unet.mlmodelc/model.mil" new file mode 100644--- /dev/null +++ "b/original/compiled/Unet.mlmodelc/model.mil" @@ -0,0 +1,4754 @@ +program(1.0) +[buildInfo = dict, tensor>({{"coremlc-component-MIL", "5.30.0"}, {"coremlc-version", "1839.0.0"}})] +{ + func main(tensor encoder_hidden_states, tensor sample, tensor timestep) { + tensor var_25 = const()[name = tensor("op_25"), val = tensor(-1)]; + tensor var_42_axes_0 = const()[name = tensor("op_42_axes_0"), val = tensor([1])]; + tensor var_42_cast = expand_dims(axes = var_42_axes_0, x = timestep)[name = tensor("op_42_cast")]; + tensor var_44_to_fp16 = const()[name = tensor("op_44_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64)))]; + tensor emb_3_cast = mul(x = var_42_cast, y = var_44_to_fp16)[name = tensor("emb_3_cast")]; + tensor var_49_cast = sin(x = emb_3_cast)[name = tensor("op_49_cast")]; + tensor var_50_cast = cos(x = emb_3_cast)[name = tensor("op_50_cast")]; + tensor emb_interleave_0 = const()[name = tensor("emb_interleave_0"), val = tensor(false)]; + tensor emb_cast = concat(axis = var_25, interleave = emb_interleave_0, values = (var_49_cast, var_50_cast))[name = tensor("emb_cast")]; + tensor var_54_begin_0 = const()[name = tensor("op_54_begin_0"), val = tensor([0, 160])]; + tensor var_54_end_0 = const()[name = tensor("op_54_end_0"), val = tensor([2, 320])]; + tensor var_54_end_mask_0 = const()[name = tensor("op_54_end_mask_0"), val = tensor([true, true])]; + tensor var_54_cast = slice_by_index(begin = var_54_begin_0, end = var_54_end_0, end_mask = var_54_end_mask_0, x = emb_cast)[name = tensor("op_54_cast")]; + tensor var_56_begin_0 = const()[name = tensor("op_56_begin_0"), val = tensor([0, 0])]; + tensor var_56_end_0 = const()[name = tensor("op_56_end_0"), val = tensor([2, 160])]; + tensor var_56_end_mask_0 = const()[name = tensor("op_56_end_mask_0"), val = tensor([true, false])]; + tensor var_56_cast = slice_by_index(begin = var_56_begin_0, end = var_56_end_0, end_mask = var_56_end_mask_0, x = emb_cast)[name = tensor("op_56_cast")]; + tensor sample_interleave_0 = const()[name = tensor("sample_interleave_0"), val = tensor(false)]; + tensor sample_cast = concat(axis = var_25, interleave = sample_interleave_0, values = (var_54_cast, var_56_cast))[name = tensor("sample_cast")]; + tensor var_59 = const()[name = tensor("op_59"), val = tensor(1)]; + tensor var_66_axes_0 = const()[name = tensor("op_66_axes_0"), val = tensor([-1])]; + tensor var_66_cast = expand_dims(axes = var_66_axes_0, x = sample_cast)[name = tensor("op_66_cast")]; + tensor input_1_axes_0 = const()[name = tensor("input_1_axes_0"), val = tensor([-1])]; + tensor input_1_cast = expand_dims(axes = input_1_axes_0, x = var_66_cast)[name = tensor("input_1_cast")]; + tensor var_70 = const()[name = tensor("op_70"), val = tensor([1, 1])]; + tensor var_72 = const()[name = tensor("op_72"), 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([0, 0, 0, 0])]; + tensor time_embedding_linear_1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(448))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(307712))), name = tensor("time_embedding_linear_1_weight_to_fp16_palettized"), shape = tensor([1280, 320, 1, 1])]; + tensor time_embedding_linear_1_bias_to_fp16 = const()[name = tensor("time_embedding_linear_1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(307904)))]; + tensor input_3_cast = conv(bias = time_embedding_linear_1_bias_to_fp16, dilations = var_72, groups = var_59, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = var_70, weight = time_embedding_linear_1_weight_to_fp16_palettized, x = input_1_cast)[name = tensor("input_3_cast")]; + tensor input_5_cast = silu(x = input_3_cast)[name = tensor("input_5_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 input_13_pad_type_0 = const()[name = tensor("input_13_pad_type_0"), val = tensor("custom")]; + tensor input_13_pad_0 = const()[name = tensor("input_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor time_embedding_linear_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(310528))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1539392))), name = tensor("time_embedding_linear_2_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor time_embedding_linear_2_bias_to_fp16 = const()[name = tensor("time_embedding_linear_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1539584)))]; + tensor input_13_cast = conv(bias = time_embedding_linear_2_bias_to_fp16, dilations = var_80, groups = var_59, pad = input_13_pad_0, pad_type = input_13_pad_type_0, strides = var_78, weight = time_embedding_linear_2_weight_to_fp16_palettized, x = input_5_cast)[name = tensor("input_13_cast")]; + tensor var_86 = const()[name = tensor("op_86"), val = tensor(1)]; + tensor var_89 = const()[name = tensor("op_89"), val = tensor([1, 1])]; + tensor var_91 = const()[name = tensor("op_91"), val = tensor([1, 1])]; + tensor input_7_pad_type_0 = const()[name = tensor("input_7_pad_type_0"), val = tensor("custom")]; + tensor input_7_pad_0 = const()[name = tensor("input_7_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1542208))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1550912))), name = tensor("conv_in_weight_to_fp16_palettized"), shape = tensor([320, 4, 3, 3])]; + tensor conv_in_bias_to_fp16 = const()[name = tensor("conv_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1551104)))]; + tensor input_7_cast = conv(bias = conv_in_bias_to_fp16, dilations = var_91, groups = var_86, pad = input_7_pad_0, pad_type = input_7_pad_type_0, strides = var_89, weight = conv_in_weight_to_fp16_palettized, x = sample)[name = tensor("input_7_cast")]; + tensor var_95 = const()[name = tensor("op_95"), val = tensor(3)]; + tensor var_106 = const()[name = tensor("op_106"), val = tensor(true)]; + tensor var_111 = const()[name = tensor("op_111"), val = tensor(1)]; + tensor reshape_0_shape_0 = const()[name = tensor("reshape_0_shape_0"), val = tensor([2, 32, 10, 64, 64])]; + tensor reshape_0_cast = reshape(shape = reshape_0_shape_0, x = input_7_cast)[name = tensor("reshape_0_cast")]; + tensor reduce_mean_0_axes_0 = const()[name = tensor("reduce_mean_0_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_0_keep_dims_0 = const()[name = tensor("reduce_mean_0_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_0_cast = reduce_mean(axes = reduce_mean_0_axes_0, keep_dims = reduce_mean_0_keep_dims_0, x = reshape_0_cast)[name = tensor("reduce_mean_0_cast")]; + tensor sub_0_cast = sub(x = reshape_0_cast, y = reduce_mean_0_cast)[name = tensor("sub_0_cast")]; + tensor square_0_cast = square(x = sub_0_cast)[name = tensor("square_0_cast")]; + tensor reduce_mean_2_axes_0 = const()[name = tensor("reduce_mean_2_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_2_keep_dims_0 = const()[name = tensor("reduce_mean_2_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_2_cast = reduce_mean(axes = reduce_mean_2_axes_0, keep_dims = reduce_mean_2_keep_dims_0, x = square_0_cast)[name = tensor("reduce_mean_2_cast")]; + tensor add_0_y_0_to_fp16 = const()[name = tensor("add_0_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_0_cast = add(x = reduce_mean_2_cast, y = add_0_y_0_to_fp16)[name = tensor("add_0_cast")]; + tensor sqrt_0_cast = sqrt(x = add_0_cast)[name = tensor("sqrt_0_cast")]; + tensor real_div_0_cast = real_div(x = sub_0_cast, y = sqrt_0_cast)[name = tensor("real_div_0_cast")]; + tensor reshape_1_shape_0 = const()[name = tensor("reshape_1_shape_0"), val = tensor([2, 320, 64, 64])]; + tensor reshape_1_cast = reshape(shape = reshape_1_shape_0, x = real_div_0_cast)[name = tensor("reshape_1_cast")]; + tensor add_1_mean_0_to_fp16 = const()[name = tensor("add_1_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1551808)))]; + 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(1552512)))]; + 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(1553216)))]; + 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(1553920)))]; + tensor add_1_epsilon_0_to_fp16 = const()[name = tensor("add_1_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_1_cast = batch_norm(beta = add_1_beta_0_to_fp16, epsilon = add_1_epsilon_0_to_fp16, gamma = add_1_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_1_cast)[name = tensor("add_1_cast")]; + tensor input_11_cast = silu(x = add_1_cast)[name = tensor("input_11_cast")]; + tensor var_133 = const()[name = tensor("op_133"), val = tensor([1, 1])]; + tensor var_135 = const()[name = tensor("op_135"), 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 down_blocks_0_resnets_0_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(1554624))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2245888))), name = tensor("down_blocks_0_resnets_0_conv1_weight_to_fp16_palettized"), shape = tensor([320, 320, 3, 3])]; + tensor down_blocks_0_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("down_blocks_0_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2246080)))]; + tensor hidden_states_1_cast = conv(bias = down_blocks_0_resnets_0_conv1_bias_to_fp16, dilations = var_135, groups = var_111, pad = hidden_states_1_pad_0, pad_type = hidden_states_1_pad_type_0, strides = var_133, weight = down_blocks_0_resnets_0_conv1_weight_to_fp16_palettized, x = input_11_cast)[name = tensor("hidden_states_1_cast")]; + tensor input_15_cast = silu(x = input_13_cast)[name = tensor("input_15_cast")]; + tensor var_141 = const()[name = tensor("op_141"), val = tensor([1, 1])]; + tensor var_143 = const()[name = tensor("op_143"), val = tensor([1, 1])]; + tensor temb_1_pad_type_0 = const()[name = tensor("temb_1_pad_type_0"), val = tensor("custom")]; + tensor temb_1_pad_0 = const()[name = tensor("temb_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_resnets_0_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2246784))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2554048))), name = tensor("down_blocks_0_resnets_0_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([320, 1280, 1, 1])]; + tensor down_blocks_0_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("down_blocks_0_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2554240)))]; + tensor temb_1_cast = conv(bias = down_blocks_0_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_143, groups = var_111, pad = temb_1_pad_0, pad_type = temb_1_pad_type_0, strides = var_141, weight = down_blocks_0_resnets_0_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor("temb_1_cast")]; + tensor input_17_cast = add(x = hidden_states_1_cast, y = temb_1_cast)[name = tensor("input_17_cast")]; + tensor reshape_4_shape_0 = const()[name = tensor("reshape_4_shape_0"), val = tensor([2, 32, 10, 64, 64])]; + tensor reshape_4_cast = reshape(shape = reshape_4_shape_0, x = input_17_cast)[name = tensor("reshape_4_cast")]; + tensor reduce_mean_3_axes_0 = const()[name = tensor("reduce_mean_3_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_3_keep_dims_0 = const()[name = tensor("reduce_mean_3_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_3_cast = reduce_mean(axes = reduce_mean_3_axes_0, keep_dims = reduce_mean_3_keep_dims_0, x = reshape_4_cast)[name = tensor("reduce_mean_3_cast")]; + tensor sub_2_cast = sub(x = reshape_4_cast, y = reduce_mean_3_cast)[name = tensor("sub_2_cast")]; + tensor square_1_cast = square(x = sub_2_cast)[name = tensor("square_1_cast")]; + tensor reduce_mean_5_axes_0 = const()[name = tensor("reduce_mean_5_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_5_keep_dims_0 = const()[name = tensor("reduce_mean_5_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_5_cast = reduce_mean(axes = reduce_mean_5_axes_0, keep_dims = reduce_mean_5_keep_dims_0, x = square_1_cast)[name = tensor("reduce_mean_5_cast")]; + tensor add_2_y_0_to_fp16 = const()[name = tensor("add_2_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_2_cast = add(x = reduce_mean_5_cast, y = add_2_y_0_to_fp16)[name = tensor("add_2_cast")]; + tensor sqrt_1_cast = sqrt(x = add_2_cast)[name = tensor("sqrt_1_cast")]; + tensor real_div_1_cast = real_div(x = sub_2_cast, y = sqrt_1_cast)[name = tensor("real_div_1_cast")]; + tensor reshape_5_shape_0 = const()[name = tensor("reshape_5_shape_0"), val = tensor([2, 320, 64, 64])]; + tensor reshape_5_cast = reshape(shape = reshape_5_shape_0, x = real_div_1_cast)[name = tensor("reshape_5_cast")]; + tensor add_3_gamma_0_to_fp16 = const()[name = tensor("add_3_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2554944)))]; + 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(2555648)))]; + tensor add_3_epsilon_0_to_fp16 = const()[name = tensor("add_3_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_3_cast = batch_norm(beta = add_3_beta_0_to_fp16, epsilon = add_3_epsilon_0_to_fp16, gamma = add_3_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_5_cast)[name = tensor("add_3_cast")]; + tensor input_21_cast = silu(x = add_3_cast)[name = tensor("input_21_cast")]; + tensor var_153 = const()[name = tensor("op_153"), val = tensor([1, 1])]; + tensor var_155 = const()[name = tensor("op_155"), val = tensor([1, 1])]; + tensor hidden_states_3_pad_type_0 = const()[name = tensor("hidden_states_3_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_3_pad_0 = const()[name = tensor("hidden_states_3_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_0_resnets_0_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(2556352))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3247616))), name = tensor("down_blocks_0_resnets_0_conv2_weight_to_fp16_palettized"), shape = tensor([320, 320, 3, 3])]; + tensor down_blocks_0_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("down_blocks_0_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3247808)))]; + tensor hidden_states_3_cast = conv(bias = down_blocks_0_resnets_0_conv2_bias_to_fp16, dilations = var_155, groups = var_111, pad = hidden_states_3_pad_0, pad_type = hidden_states_3_pad_type_0, strides = var_153, weight = down_blocks_0_resnets_0_conv2_weight_to_fp16_palettized, x = input_21_cast)[name = tensor("hidden_states_3_cast")]; + tensor hidden_states_5_cast = add(x = input_7_cast, y = hidden_states_3_cast)[name = tensor("hidden_states_5_cast")]; + tensor reshape_8_shape_0 = const()[name = tensor("reshape_8_shape_0"), val = tensor([2, 32, 10, 64, 64])]; + tensor reshape_8_cast = reshape(shape = reshape_8_shape_0, x = hidden_states_5_cast)[name = tensor("reshape_8_cast")]; + tensor reduce_mean_6_axes_0 = const()[name = tensor("reduce_mean_6_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_6_keep_dims_0 = const()[name = tensor("reduce_mean_6_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_6_cast = reduce_mean(axes = reduce_mean_6_axes_0, keep_dims = reduce_mean_6_keep_dims_0, x = reshape_8_cast)[name = tensor("reduce_mean_6_cast")]; + tensor sub_4_cast = sub(x = reshape_8_cast, y = reduce_mean_6_cast)[name = tensor("sub_4_cast")]; + tensor square_2_cast = square(x = sub_4_cast)[name = tensor("square_2_cast")]; + tensor reduce_mean_8_axes_0 = const()[name = tensor("reduce_mean_8_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_8_keep_dims_0 = const()[name = tensor("reduce_mean_8_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_8_cast = reduce_mean(axes = reduce_mean_8_axes_0, keep_dims = reduce_mean_8_keep_dims_0, x = square_2_cast)[name = tensor("reduce_mean_8_cast")]; + tensor add_4_y_0_to_fp16 = const()[name = tensor("add_4_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_4_cast = add(x = reduce_mean_8_cast, y = add_4_y_0_to_fp16)[name = tensor("add_4_cast")]; + tensor sqrt_2_cast = sqrt(x = add_4_cast)[name = tensor("sqrt_2_cast")]; + tensor real_div_2_cast = real_div(x = sub_4_cast, y = sqrt_2_cast)[name = tensor("real_div_2_cast")]; + tensor reshape_9_shape_0 = const()[name = tensor("reshape_9_shape_0"), val = tensor([2, 320, 64, 64])]; + tensor reshape_9_cast = reshape(shape = reshape_9_shape_0, x = real_div_2_cast)[name = tensor("reshape_9_cast")]; + tensor add_5_gamma_0_to_fp16 = const()[name = tensor("add_5_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3248512)))]; + 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(3249216)))]; + tensor add_5_epsilon_0_to_fp16 = const()[name = tensor("add_5_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_5_cast = batch_norm(beta = add_5_beta_0_to_fp16, epsilon = add_5_epsilon_0_to_fp16, gamma = add_5_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_9_cast)[name = tensor("add_5_cast")]; + tensor var_175 = const()[name = tensor("op_175"), val = tensor([1, 1])]; + tensor var_177 = const()[name = tensor("op_177"), val = tensor([1, 1])]; + tensor hidden_states_7_pad_type_0 = const()[name = tensor("hidden_states_7_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_7_pad_0 = const()[name = tensor("hidden_states_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_0_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3249920))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3326784))), name = tensor("down_blocks_0_attentions_0_proj_in_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor down_blocks_0_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3326976)))]; + tensor hidden_states_7_cast = conv(bias = down_blocks_0_attentions_0_proj_in_bias_to_fp16, dilations = var_177, groups = var_111, pad = hidden_states_7_pad_0, pad_type = hidden_states_7_pad_type_0, strides = var_175, weight = down_blocks_0_attentions_0_proj_in_weight_to_fp16_palettized, x = add_5_cast)[name = tensor("hidden_states_7_cast")]; + tensor var_182 = const()[name = tensor("op_182"), val = tensor([2, 320, 1, 4096])]; + tensor inputs_1_cast = reshape(shape = var_182, x = hidden_states_7_cast)[name = tensor("inputs_1_cast")]; + tensor var_192 = const()[name = tensor("op_192"), val = tensor([1])]; + tensor channels_mean_1_cast = reduce_mean(axes = var_192, keep_dims = var_106, x = inputs_1_cast)[name = tensor("channels_mean_1_cast")]; + tensor zero_mean_1_cast = sub(x = inputs_1_cast, y = channels_mean_1_cast)[name = tensor("zero_mean_1_cast")]; + tensor zero_mean_sq_1_cast = mul(x = zero_mean_1_cast, y = zero_mean_1_cast)[name = tensor("zero_mean_sq_1_cast")]; + tensor var_196 = const()[name = tensor("op_196"), val = tensor([1])]; + tensor var_197_cast = reduce_mean(axes = var_196, keep_dims = var_106, x = zero_mean_sq_1_cast)[name = tensor("op_197_cast")]; + tensor var_198_to_fp16 = const()[name = tensor("op_198_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_199_cast = add(x = var_197_cast, y = var_198_to_fp16)[name = tensor("op_199_cast")]; + tensor denom_1_epsilon_0_to_fp16 = const()[name = tensor("denom_1_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_1_cast = rsqrt(epsilon = denom_1_epsilon_0_to_fp16, x = var_199_cast)[name = tensor("denom_1_cast")]; + tensor out_1_cast = mul(x = zero_mean_1_cast, y = denom_1_cast)[name = tensor("out_1_cast")]; + tensor var_203_to_fp16 = const()[name = tensor("op_203_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3327680)))]; + tensor var_204_cast = add(x = out_1_cast, y = var_203_to_fp16)[name = tensor("op_204_cast")]; + tensor var_206_to_fp16 = const()[name = tensor("op_206_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3328384)))]; + tensor hidden_states_9_cast = mul(x = var_204_cast, y = var_206_to_fp16)[name = tensor("hidden_states_9_cast")]; + tensor var_213 = const()[name = tensor("op_213"), val = tensor([1, 1])]; + tensor var_215 = const()[name = tensor("op_215"), val = tensor([1, 1])]; + tensor q_1_pad_type_0 = const()[name = tensor("q_1_pad_type_0"), val = tensor("custom")]; + tensor q_1_pad_0 = const()[name = tensor("q_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3329088))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3405952))), name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor q_1_cast = conv(dilations = var_215, groups = var_111, pad = q_1_pad_0, pad_type = q_1_pad_type_0, strides = var_213, weight = down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_9_cast)[name = tensor("q_1_cast")]; + tensor var_219 = const()[name = tensor("op_219"), val = tensor([1, 1])]; + tensor var_221 = const()[name = tensor("op_221"), val = tensor([1, 1])]; + tensor k_1_pad_type_0 = const()[name = tensor("k_1_pad_type_0"), val = tensor("custom")]; + tensor k_1_pad_0 = const()[name = tensor("k_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3406144))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3483008))), name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor k_1_cast = conv(dilations = var_221, groups = var_111, pad = k_1_pad_0, pad_type = k_1_pad_type_0, strides = var_219, weight = down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_9_cast)[name = tensor("k_1_cast")]; + tensor var_225 = const()[name = tensor("op_225"), val = tensor([1, 1])]; + tensor var_227 = const()[name = tensor("op_227"), val = tensor([1, 1])]; + tensor v_1_pad_type_0 = const()[name = tensor("v_1_pad_type_0"), val = tensor("custom")]; + tensor v_1_pad_0 = const()[name = tensor("v_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3483200))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3560064))), name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor v_1_cast = conv(dilations = var_227, groups = var_111, pad = v_1_pad_0, pad_type = v_1_pad_type_0, strides = var_225, weight = down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_9_cast)[name = tensor("v_1_cast")]; + tensor var_231 = const()[name = tensor("op_231"), val = tensor([2, 8, 40, -1])]; + tensor var_232_cast = reshape(shape = var_231, x = q_1_cast)[name = tensor("op_232_cast")]; + tensor var_233 = const()[name = tensor("op_233"), val = tensor([2, 8, 40, -1])]; + tensor var_234_cast = reshape(shape = var_233, x = k_1_cast)[name = tensor("op_234_cast")]; + tensor var_235 = const()[name = tensor("op_235"), val = tensor([2, 8, 40, -1])]; + tensor var_236_cast = reshape(shape = var_235, x = v_1_cast)[name = tensor("op_236_cast")]; + tensor attn_weights_1_transpose_x_0 = const()[name = tensor("attn_weights_1_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_1_transpose_y_0 = const()[name = tensor("attn_weights_1_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_1_cast = matmul(transpose_x = attn_weights_1_transpose_x_0, transpose_y = attn_weights_1_transpose_y_0, x = var_232_cast, y = var_234_cast)[name = tensor("attn_weights_1_cast")]; + tensor var_102_to_fp16 = const()[name = tensor("op_102_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor attn_weights_3_cast = mul(x = attn_weights_1_cast, y = var_102_to_fp16)[name = tensor("attn_weights_3_cast")]; + tensor var_240_cast = softmax(axis = var_95, x = attn_weights_3_cast)[name = tensor("op_240_cast")]; + tensor attn_1_transpose_x_0 = const()[name = tensor("attn_1_transpose_x_0"), val = tensor(false)]; + tensor attn_1_transpose_y_0 = const()[name = tensor("attn_1_transpose_y_0"), val = tensor(true)]; + tensor attn_1_cast = matmul(transpose_x = attn_1_transpose_x_0, transpose_y = attn_1_transpose_y_0, x = var_236_cast, y = var_240_cast)[name = tensor("attn_1_cast")]; + tensor var_244 = const()[name = tensor("op_244"), val = tensor([2, 320, 1, -1])]; + tensor input_25_cast = reshape(shape = var_244, x = attn_1_cast)[name = tensor("input_25_cast")]; + tensor var_249 = const()[name = tensor("op_249"), val = tensor([1, 1])]; + tensor var_251 = const()[name = tensor("op_251"), val = tensor([1, 1])]; + tensor var_253_pad_type_0 = const()[name = tensor("op_253_pad_type_0"), val = tensor("custom")]; + tensor var_253_pad_0 = const()[name = tensor("op_253_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3560256))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3637120))), name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3637312)))]; + tensor var_253_cast = conv(bias = down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_251, groups = var_111, pad = var_253_pad_0, pad_type = var_253_pad_type_0, strides = var_249, weight = down_blocks_0_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_25_cast)[name = tensor("op_253_cast")]; + tensor inputs_3_cast = add(x = var_253_cast, y = inputs_1_cast)[name = tensor("inputs_3_cast")]; + tensor var_257 = const()[name = tensor("op_257"), val = tensor([1])]; + tensor channels_mean_3_cast = reduce_mean(axes = var_257, keep_dims = var_106, x = inputs_3_cast)[name = tensor("channels_mean_3_cast")]; + tensor zero_mean_3_cast = sub(x = inputs_3_cast, y = channels_mean_3_cast)[name = tensor("zero_mean_3_cast")]; + tensor zero_mean_sq_3_cast = mul(x = zero_mean_3_cast, y = zero_mean_3_cast)[name = tensor("zero_mean_sq_3_cast")]; + tensor var_261 = const()[name = tensor("op_261"), val = tensor([1])]; + tensor var_262_cast = reduce_mean(axes = var_261, keep_dims = var_106, x = zero_mean_sq_3_cast)[name = tensor("op_262_cast")]; + tensor var_263_to_fp16 = const()[name = tensor("op_263_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_264_cast = add(x = var_262_cast, y = var_263_to_fp16)[name = tensor("op_264_cast")]; + tensor denom_3_epsilon_0_to_fp16 = const()[name = tensor("denom_3_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_3_cast = rsqrt(epsilon = denom_3_epsilon_0_to_fp16, x = var_264_cast)[name = tensor("denom_3_cast")]; + tensor out_3_cast = mul(x = zero_mean_3_cast, y = denom_3_cast)[name = tensor("out_3_cast")]; + tensor var_268_to_fp16 = const()[name = tensor("op_268_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3638016)))]; + tensor var_269_cast = add(x = out_3_cast, y = var_268_to_fp16)[name = tensor("op_269_cast")]; + tensor var_271_to_fp16 = const()[name = tensor("op_271_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3638720)))]; + tensor hidden_states_11_cast = mul(x = var_269_cast, y = var_271_to_fp16)[name = tensor("hidden_states_11_cast")]; + tensor var_278 = const()[name = tensor("op_278"), val = tensor([1, 1])]; + tensor var_280 = const()[name = tensor("op_280"), val = tensor([1, 1])]; + tensor q_3_pad_type_0 = const()[name = tensor("q_3_pad_type_0"), val = tensor("custom")]; + tensor q_3_pad_0 = const()[name = tensor("q_3_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3639424))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3716288))), name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor q_3_cast = conv(dilations = var_280, groups = var_111, pad = q_3_pad_0, pad_type = q_3_pad_type_0, strides = var_278, weight = down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_11_cast)[name = tensor("q_3_cast")]; + tensor var_284 = const()[name = tensor("op_284"), val = tensor([1, 1])]; + tensor var_286 = const()[name = tensor("op_286"), val = tensor([1, 1])]; + tensor k_3_pad_type_0 = const()[name = tensor("k_3_pad_type_0"), val = tensor("custom")]; + tensor k_3_pad_0 = const()[name = tensor("k_3_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3716480))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3900864))), name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([320, 768, 1, 1])]; + tensor k_3_cast = conv(dilations = var_286, groups = var_111, pad = k_3_pad_0, pad_type = k_3_pad_type_0, strides = var_284, weight = down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_3_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 v_3_pad_type_0 = const()[name = tensor("v_3_pad_type_0"), val = tensor("custom")]; + tensor v_3_pad_0 = const()[name = tensor("v_3_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(3901056))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4085440))), name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([320, 768, 1, 1])]; + tensor v_3_cast = conv(dilations = var_292, groups = var_111, pad = v_3_pad_0, pad_type = v_3_pad_type_0, strides = var_290, weight = down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_3_cast")]; + tensor var_296 = const()[name = tensor("op_296"), val = tensor([2, 8, 40, -1])]; + tensor var_297_cast = reshape(shape = var_296, x = q_3_cast)[name = tensor("op_297_cast")]; + tensor var_298 = const()[name = tensor("op_298"), val = tensor([2, 8, 40, -1])]; + tensor var_299_cast = reshape(shape = var_298, x = k_3_cast)[name = tensor("op_299_cast")]; + tensor var_300 = const()[name = tensor("op_300"), val = tensor([2, 8, 40, -1])]; + tensor var_301_cast = reshape(shape = var_300, x = v_3_cast)[name = tensor("op_301_cast")]; + tensor attn_weights_5_transpose_x_0 = const()[name = tensor("attn_weights_5_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_5_transpose_y_0 = const()[name = tensor("attn_weights_5_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_5_cast = matmul(transpose_x = attn_weights_5_transpose_x_0, transpose_y = attn_weights_5_transpose_y_0, x = var_297_cast, y = var_299_cast)[name = tensor("attn_weights_5_cast")]; + tensor attn_weights_7_cast = mul(x = attn_weights_5_cast, y = var_102_to_fp16)[name = tensor("attn_weights_7_cast")]; + tensor var_305_cast = softmax(axis = var_95, x = attn_weights_7_cast)[name = tensor("op_305_cast")]; + tensor attn_3_transpose_x_0 = const()[name = tensor("attn_3_transpose_x_0"), val = tensor(false)]; + tensor attn_3_transpose_y_0 = const()[name = tensor("attn_3_transpose_y_0"), val = tensor(true)]; + tensor attn_3_cast = matmul(transpose_x = attn_3_transpose_x_0, transpose_y = attn_3_transpose_y_0, x = var_301_cast, y = var_305_cast)[name = tensor("attn_3_cast")]; + tensor var_309 = const()[name = tensor("op_309"), val = tensor([2, 320, 1, -1])]; + tensor input_27_cast = reshape(shape = var_309, x = attn_3_cast)[name = tensor("input_27_cast")]; + tensor var_314 = const()[name = tensor("op_314"), val = tensor([1, 1])]; + tensor var_316 = const()[name = tensor("op_316"), val = tensor([1, 1])]; + tensor var_318_pad_type_0 = const()[name = tensor("op_318_pad_type_0"), val = tensor("custom")]; + tensor var_318_pad_0 = const()[name = tensor("op_318_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4085632))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4162496))), name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4162688)))]; + tensor var_318_cast = conv(bias = down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_316, groups = var_111, pad = var_318_pad_0, pad_type = var_318_pad_type_0, strides = var_314, weight = down_blocks_0_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_27_cast)[name = tensor("op_318_cast")]; + tensor inputs_5_cast = add(x = var_318_cast, y = inputs_3_cast)[name = tensor("inputs_5_cast")]; + tensor var_322 = const()[name = tensor("op_322"), val = tensor([1])]; + tensor channels_mean_5_cast = reduce_mean(axes = var_322, keep_dims = var_106, x = inputs_5_cast)[name = tensor("channels_mean_5_cast")]; + tensor zero_mean_5_cast = sub(x = inputs_5_cast, y = channels_mean_5_cast)[name = tensor("zero_mean_5_cast")]; + tensor zero_mean_sq_5_cast = mul(x = zero_mean_5_cast, y = zero_mean_5_cast)[name = tensor("zero_mean_sq_5_cast")]; + tensor var_326 = const()[name = tensor("op_326"), val = tensor([1])]; + tensor var_327_cast = reduce_mean(axes = var_326, keep_dims = var_106, x = zero_mean_sq_5_cast)[name = tensor("op_327_cast")]; + tensor var_328_to_fp16 = const()[name = tensor("op_328_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_329_cast = add(x = var_327_cast, y = var_328_to_fp16)[name = tensor("op_329_cast")]; + tensor denom_5_epsilon_0_to_fp16 = const()[name = tensor("denom_5_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_5_cast = rsqrt(epsilon = denom_5_epsilon_0_to_fp16, x = var_329_cast)[name = tensor("denom_5_cast")]; + tensor out_5_cast = mul(x = zero_mean_5_cast, y = denom_5_cast)[name = tensor("out_5_cast")]; + tensor var_333_to_fp16 = const()[name = tensor("op_333_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4163392)))]; + tensor var_334_cast = add(x = out_5_cast, y = var_333_to_fp16)[name = tensor("op_334_cast")]; + tensor var_336_to_fp16 = const()[name = tensor("op_336_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4164096)))]; + tensor input_29_cast = mul(x = var_334_cast, y = var_336_to_fp16)[name = tensor("input_29_cast")]; + tensor var_344 = const()[name = tensor("op_344"), val = tensor([1, 1])]; + tensor var_346 = const()[name = tensor("op_346"), val = tensor([1, 1])]; + tensor var_348_pad_type_0 = const()[name = tensor("op_348_pad_type_0"), val = tensor("custom")]; + tensor var_348_pad_0 = const()[name = tensor("op_348_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4164800))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4779264))), name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([2560, 320, 1, 1])]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4779456))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4781440))), name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([2560])]; + tensor var_348_cast = conv(bias = down_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_346, groups = var_111, pad = var_348_pad_0, pad_type = var_348_pad_type_0, strides = var_344, weight = down_blocks_0_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_29_cast)[name = tensor("op_348_cast")]; + tensor var_349_split_sizes_0 = const()[name = tensor("op_349_split_sizes_0"), val = tensor([1280, 1280])]; + tensor var_349_axis_0 = const()[name = tensor("op_349_axis_0"), val = tensor(1)]; + tensor var_349_cast_0, tensor var_349_cast_1 = split(axis = var_349_axis_0, split_sizes = var_349_split_sizes_0, x = var_348_cast)[name = tensor("op_349_cast")]; + tensor var_351_mode_0 = const()[name = tensor("op_351_mode_0"), val = tensor("EXACT")]; + tensor var_351_cast = gelu(mode = var_351_mode_0, x = var_349_cast_1)[name = tensor("op_351_cast")]; + tensor input_31_cast = mul(x = var_349_cast_0, y = var_351_cast)[name = tensor("input_31_cast")]; + tensor var_355 = const()[name = tensor("op_355"), val = tensor([1, 1])]; + tensor var_357 = const()[name = tensor("op_357"), val = tensor([1, 1])]; + tensor var_359_pad_type_0 = const()[name = tensor("op_359_pad_type_0"), val = tensor("custom")]; + tensor var_359_pad_0 = const()[name = tensor("op_359_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(4781632))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5088896))), name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([320, 1280, 1, 1])]; + tensor down_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5089088)))]; + tensor var_359_cast = conv(bias = down_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_357, groups = var_111, pad = var_359_pad_0, pad_type = var_359_pad_type_0, strides = var_355, weight = down_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_31_cast)[name = tensor("op_359_cast")]; + tensor hidden_states_15_cast = add(x = var_359_cast, y = inputs_5_cast)[name = tensor("hidden_states_15_cast")]; + tensor var_361 = const()[name = tensor("op_361"), val = tensor([2, 320, 64, 64])]; + tensor input_33_cast = reshape(shape = var_361, x = hidden_states_15_cast)[name = tensor("input_33_cast")]; + tensor var_365 = const()[name = tensor("op_365"), val = tensor([1, 1])]; + tensor var_367 = const()[name = tensor("op_367"), 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([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_0_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5089792))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5166656))), name = tensor("down_blocks_0_attentions_0_proj_out_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor down_blocks_0_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_0_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5166848)))]; + tensor hidden_states_17_cast = conv(bias = down_blocks_0_attentions_0_proj_out_bias_to_fp16, dilations = var_367, groups = var_111, pad = hidden_states_17_pad_0, pad_type = hidden_states_17_pad_type_0, strides = var_365, weight = down_blocks_0_attentions_0_proj_out_weight_to_fp16_palettized, x = input_33_cast)[name = tensor("hidden_states_17_cast")]; + tensor input_35_cast = add(x = hidden_states_17_cast, y = hidden_states_5_cast)[name = tensor("input_35_cast")]; + tensor reshape_12_shape_0 = const()[name = tensor("reshape_12_shape_0"), val = tensor([2, 32, 10, 64, 64])]; + tensor reshape_12_cast = reshape(shape = reshape_12_shape_0, x = input_35_cast)[name = tensor("reshape_12_cast")]; + tensor reduce_mean_9_axes_0 = const()[name = tensor("reduce_mean_9_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_9_keep_dims_0 = const()[name = tensor("reduce_mean_9_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_9_cast = reduce_mean(axes = reduce_mean_9_axes_0, keep_dims = reduce_mean_9_keep_dims_0, x = reshape_12_cast)[name = tensor("reduce_mean_9_cast")]; + tensor sub_6_cast = sub(x = reshape_12_cast, y = reduce_mean_9_cast)[name = tensor("sub_6_cast")]; + tensor square_3_cast = square(x = sub_6_cast)[name = tensor("square_3_cast")]; + tensor reduce_mean_11_axes_0 = const()[name = tensor("reduce_mean_11_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_11_keep_dims_0 = const()[name = tensor("reduce_mean_11_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_11_cast = reduce_mean(axes = reduce_mean_11_axes_0, keep_dims = reduce_mean_11_keep_dims_0, x = square_3_cast)[name = tensor("reduce_mean_11_cast")]; + tensor add_6_y_0_to_fp16 = const()[name = tensor("add_6_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_6_cast = add(x = reduce_mean_11_cast, y = add_6_y_0_to_fp16)[name = tensor("add_6_cast")]; + tensor sqrt_3_cast = sqrt(x = add_6_cast)[name = tensor("sqrt_3_cast")]; + tensor real_div_3_cast = real_div(x = sub_6_cast, y = sqrt_3_cast)[name = tensor("real_div_3_cast")]; + tensor reshape_13_shape_0 = const()[name = tensor("reshape_13_shape_0"), val = tensor([2, 320, 64, 64])]; + tensor reshape_13_cast = reshape(shape = reshape_13_shape_0, x = real_div_3_cast)[name = tensor("reshape_13_cast")]; + tensor add_7_gamma_0_to_fp16 = const()[name = tensor("add_7_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5167552)))]; + 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(5168256)))]; + tensor add_7_epsilon_0_to_fp16 = const()[name = tensor("add_7_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_7_cast = batch_norm(beta = add_7_beta_0_to_fp16, epsilon = add_7_epsilon_0_to_fp16, gamma = add_7_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_13_cast)[name = tensor("add_7_cast")]; + tensor input_39_cast = silu(x = add_7_cast)[name = tensor("input_39_cast")]; + tensor var_382 = const()[name = tensor("op_382"), val = tensor([1, 1])]; + tensor var_384 = const()[name = tensor("op_384"), val = tensor([1, 1])]; + tensor hidden_states_19_pad_type_0 = const()[name = tensor("hidden_states_19_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_19_pad_0 = const()[name = tensor("hidden_states_19_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_0_resnets_1_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5168960))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5860224))), name = tensor("down_blocks_0_resnets_1_conv1_weight_to_fp16_palettized"), shape = tensor([320, 320, 3, 3])]; + tensor down_blocks_0_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("down_blocks_0_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5860416)))]; + tensor hidden_states_19_cast = conv(bias = down_blocks_0_resnets_1_conv1_bias_to_fp16, dilations = var_384, groups = var_111, pad = hidden_states_19_pad_0, pad_type = hidden_states_19_pad_type_0, strides = var_382, weight = down_blocks_0_resnets_1_conv1_weight_to_fp16_palettized, x = input_39_cast)[name = tensor("hidden_states_19_cast")]; + tensor var_390 = const()[name = tensor("op_390"), val = tensor([1, 1])]; + tensor var_392 = const()[name = tensor("op_392"), val = tensor([1, 1])]; + tensor temb_3_pad_type_0 = const()[name = tensor("temb_3_pad_type_0"), val = tensor("custom")]; + tensor temb_3_pad_0 = const()[name = tensor("temb_3_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_resnets_1_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(5861120))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6168384))), name = tensor("down_blocks_0_resnets_1_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([320, 1280, 1, 1])]; + tensor down_blocks_0_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("down_blocks_0_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6168576)))]; + tensor temb_3_cast = conv(bias = down_blocks_0_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_392, groups = var_111, pad = temb_3_pad_0, pad_type = temb_3_pad_type_0, strides = var_390, weight = down_blocks_0_resnets_1_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor("temb_3_cast")]; + tensor input_43_cast = add(x = hidden_states_19_cast, y = temb_3_cast)[name = tensor("input_43_cast")]; + tensor reshape_16_shape_0 = const()[name = tensor("reshape_16_shape_0"), val = tensor([2, 32, 10, 64, 64])]; + tensor reshape_16_cast = reshape(shape = reshape_16_shape_0, x = input_43_cast)[name = tensor("reshape_16_cast")]; + tensor reduce_mean_12_axes_0 = const()[name = tensor("reduce_mean_12_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_12_keep_dims_0 = const()[name = tensor("reduce_mean_12_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_12_cast = reduce_mean(axes = reduce_mean_12_axes_0, keep_dims = reduce_mean_12_keep_dims_0, x = reshape_16_cast)[name = tensor("reduce_mean_12_cast")]; + tensor sub_8_cast = sub(x = reshape_16_cast, y = reduce_mean_12_cast)[name = tensor("sub_8_cast")]; + tensor square_4_cast = square(x = sub_8_cast)[name = tensor("square_4_cast")]; + tensor reduce_mean_14_axes_0 = const()[name = tensor("reduce_mean_14_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_14_keep_dims_0 = const()[name = tensor("reduce_mean_14_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_14_cast = reduce_mean(axes = reduce_mean_14_axes_0, keep_dims = reduce_mean_14_keep_dims_0, x = square_4_cast)[name = tensor("reduce_mean_14_cast")]; + tensor add_8_y_0_to_fp16 = const()[name = tensor("add_8_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_8_cast = add(x = reduce_mean_14_cast, y = add_8_y_0_to_fp16)[name = tensor("add_8_cast")]; + tensor sqrt_4_cast = sqrt(x = add_8_cast)[name = tensor("sqrt_4_cast")]; + tensor real_div_4_cast = real_div(x = sub_8_cast, y = sqrt_4_cast)[name = tensor("real_div_4_cast")]; + tensor reshape_17_shape_0 = const()[name = tensor("reshape_17_shape_0"), val = tensor([2, 320, 64, 64])]; + tensor reshape_17_cast = reshape(shape = reshape_17_shape_0, x = real_div_4_cast)[name = tensor("reshape_17_cast")]; + tensor add_9_gamma_0_to_fp16 = const()[name = tensor("add_9_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6169280)))]; + 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(6169984)))]; + tensor add_9_epsilon_0_to_fp16 = const()[name = tensor("add_9_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_9_cast = batch_norm(beta = add_9_beta_0_to_fp16, epsilon = add_9_epsilon_0_to_fp16, gamma = add_9_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_17_cast)[name = tensor("add_9_cast")]; + tensor input_47_cast = silu(x = add_9_cast)[name = tensor("input_47_cast")]; + tensor var_402 = const()[name = tensor("op_402"), val = tensor([1, 1])]; + tensor var_404 = const()[name = tensor("op_404"), 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 down_blocks_0_resnets_1_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6170688))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6861952))), name = tensor("down_blocks_0_resnets_1_conv2_weight_to_fp16_palettized"), shape = tensor([320, 320, 3, 3])]; + tensor down_blocks_0_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("down_blocks_0_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6862144)))]; + tensor hidden_states_21_cast = conv(bias = down_blocks_0_resnets_1_conv2_bias_to_fp16, dilations = var_404, groups = var_111, pad = hidden_states_21_pad_0, pad_type = hidden_states_21_pad_type_0, strides = var_402, weight = down_blocks_0_resnets_1_conv2_weight_to_fp16_palettized, x = input_47_cast)[name = tensor("hidden_states_21_cast")]; + tensor hidden_states_23_cast = add(x = input_35_cast, y = hidden_states_21_cast)[name = tensor("hidden_states_23_cast")]; + tensor reshape_20_shape_0 = const()[name = tensor("reshape_20_shape_0"), val = tensor([2, 32, 10, 64, 64])]; + tensor reshape_20_cast = reshape(shape = reshape_20_shape_0, x = hidden_states_23_cast)[name = tensor("reshape_20_cast")]; + tensor reduce_mean_15_axes_0 = const()[name = tensor("reduce_mean_15_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_15_keep_dims_0 = const()[name = tensor("reduce_mean_15_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_15_cast = reduce_mean(axes = reduce_mean_15_axes_0, keep_dims = reduce_mean_15_keep_dims_0, x = reshape_20_cast)[name = tensor("reduce_mean_15_cast")]; + tensor sub_10_cast = sub(x = reshape_20_cast, y = reduce_mean_15_cast)[name = tensor("sub_10_cast")]; + tensor square_5_cast = square(x = sub_10_cast)[name = tensor("square_5_cast")]; + tensor reduce_mean_17_axes_0 = const()[name = tensor("reduce_mean_17_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_17_keep_dims_0 = const()[name = tensor("reduce_mean_17_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_17_cast = reduce_mean(axes = reduce_mean_17_axes_0, keep_dims = reduce_mean_17_keep_dims_0, x = square_5_cast)[name = tensor("reduce_mean_17_cast")]; + tensor add_10_y_0_to_fp16 = const()[name = tensor("add_10_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_10_cast = add(x = reduce_mean_17_cast, y = add_10_y_0_to_fp16)[name = tensor("add_10_cast")]; + tensor sqrt_5_cast = sqrt(x = add_10_cast)[name = tensor("sqrt_5_cast")]; + tensor real_div_5_cast = real_div(x = sub_10_cast, y = sqrt_5_cast)[name = tensor("real_div_5_cast")]; + tensor reshape_21_shape_0 = const()[name = tensor("reshape_21_shape_0"), val = tensor([2, 320, 64, 64])]; + tensor reshape_21_cast = reshape(shape = reshape_21_shape_0, x = real_div_5_cast)[name = tensor("reshape_21_cast")]; + tensor add_11_gamma_0_to_fp16 = const()[name = tensor("add_11_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6862848)))]; + 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(6863552)))]; + tensor add_11_epsilon_0_to_fp16 = const()[name = tensor("add_11_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_11_cast = batch_norm(beta = add_11_beta_0_to_fp16, epsilon = add_11_epsilon_0_to_fp16, gamma = add_11_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_21_cast)[name = tensor("add_11_cast")]; + tensor var_424 = const()[name = tensor("op_424"), val = tensor([1, 1])]; + tensor var_426 = const()[name = tensor("op_426"), 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([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_1_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6864256))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6941120))), name = tensor("down_blocks_0_attentions_1_proj_in_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor down_blocks_0_attentions_1_proj_in_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6941312)))]; + tensor hidden_states_25_cast = conv(bias = down_blocks_0_attentions_1_proj_in_bias_to_fp16, dilations = var_426, groups = var_111, pad = hidden_states_25_pad_0, pad_type = hidden_states_25_pad_type_0, strides = var_424, weight = down_blocks_0_attentions_1_proj_in_weight_to_fp16_palettized, x = add_11_cast)[name = tensor("hidden_states_25_cast")]; + tensor var_431 = const()[name = tensor("op_431"), val = tensor([2, 320, 1, 4096])]; + tensor inputs_7_cast = reshape(shape = var_431, x = hidden_states_25_cast)[name = tensor("inputs_7_cast")]; + tensor var_441 = const()[name = tensor("op_441"), val = tensor([1])]; + tensor channels_mean_7_cast = reduce_mean(axes = var_441, keep_dims = var_106, x = inputs_7_cast)[name = tensor("channels_mean_7_cast")]; + tensor zero_mean_7_cast = sub(x = inputs_7_cast, y = channels_mean_7_cast)[name = tensor("zero_mean_7_cast")]; + tensor zero_mean_sq_7_cast = mul(x = zero_mean_7_cast, y = zero_mean_7_cast)[name = tensor("zero_mean_sq_7_cast")]; + tensor var_445 = const()[name = tensor("op_445"), val = tensor([1])]; + tensor var_446_cast = reduce_mean(axes = var_445, keep_dims = var_106, x = zero_mean_sq_7_cast)[name = tensor("op_446_cast")]; + tensor var_447_to_fp16 = const()[name = tensor("op_447_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_448_cast = add(x = var_446_cast, y = var_447_to_fp16)[name = tensor("op_448_cast")]; + tensor denom_7_epsilon_0_to_fp16 = const()[name = tensor("denom_7_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_7_cast = rsqrt(epsilon = denom_7_epsilon_0_to_fp16, x = var_448_cast)[name = tensor("denom_7_cast")]; + tensor out_7_cast = mul(x = zero_mean_7_cast, y = denom_7_cast)[name = tensor("out_7_cast")]; + tensor var_452_to_fp16 = const()[name = tensor("op_452_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6942016)))]; + tensor var_453_cast = add(x = out_7_cast, y = var_452_to_fp16)[name = tensor("op_453_cast")]; + tensor var_455_to_fp16 = const()[name = tensor("op_455_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6942720)))]; + tensor hidden_states_27_cast = mul(x = var_453_cast, y = var_455_to_fp16)[name = tensor("hidden_states_27_cast")]; + tensor var_462 = const()[name = tensor("op_462"), val = tensor([1, 1])]; + tensor var_464 = const()[name = tensor("op_464"), val = tensor([1, 1])]; + tensor q_5_pad_type_0 = const()[name = tensor("q_5_pad_type_0"), val = tensor("custom")]; + tensor q_5_pad_0 = const()[name = tensor("q_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(6943424))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7020288))), name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor q_5_cast = conv(dilations = var_464, groups = var_111, pad = q_5_pad_0, pad_type = q_5_pad_type_0, strides = var_462, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_27_cast)[name = tensor("q_5_cast")]; + tensor var_468 = const()[name = tensor("op_468"), val = tensor([1, 1])]; + tensor var_470 = const()[name = tensor("op_470"), val = tensor([1, 1])]; + tensor k_5_pad_type_0 = const()[name = tensor("k_5_pad_type_0"), val = tensor("custom")]; + tensor k_5_pad_0 = const()[name = tensor("k_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7020480))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7097344))), name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor k_5_cast = conv(dilations = var_470, groups = var_111, pad = k_5_pad_0, pad_type = k_5_pad_type_0, strides = var_468, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_27_cast)[name = tensor("k_5_cast")]; + tensor var_474 = const()[name = tensor("op_474"), val = tensor([1, 1])]; + tensor var_476 = const()[name = tensor("op_476"), val = tensor([1, 1])]; + tensor v_5_pad_type_0 = const()[name = tensor("v_5_pad_type_0"), val = tensor("custom")]; + tensor v_5_pad_0 = const()[name = tensor("v_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7097536))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7174400))), name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor v_5_cast = conv(dilations = var_476, groups = var_111, pad = v_5_pad_0, pad_type = v_5_pad_type_0, strides = var_474, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_27_cast)[name = tensor("v_5_cast")]; + tensor var_480 = const()[name = tensor("op_480"), val = tensor([2, 8, 40, -1])]; + tensor var_481_cast = reshape(shape = var_480, x = q_5_cast)[name = tensor("op_481_cast")]; + tensor var_482 = const()[name = tensor("op_482"), val = tensor([2, 8, 40, -1])]; + tensor var_483_cast = reshape(shape = var_482, x = k_5_cast)[name = tensor("op_483_cast")]; + tensor var_484 = const()[name = tensor("op_484"), val = tensor([2, 8, 40, -1])]; + tensor var_485_cast = reshape(shape = var_484, x = v_5_cast)[name = tensor("op_485_cast")]; + tensor attn_weights_9_transpose_x_0 = const()[name = tensor("attn_weights_9_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_9_transpose_y_0 = const()[name = tensor("attn_weights_9_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_9_cast = matmul(transpose_x = attn_weights_9_transpose_x_0, transpose_y = attn_weights_9_transpose_y_0, x = var_481_cast, y = var_483_cast)[name = tensor("attn_weights_9_cast")]; + tensor attn_weights_11_cast = mul(x = attn_weights_9_cast, y = var_102_to_fp16)[name = tensor("attn_weights_11_cast")]; + tensor var_489_cast = softmax(axis = var_95, x = attn_weights_11_cast)[name = tensor("op_489_cast")]; + tensor attn_5_transpose_x_0 = const()[name = tensor("attn_5_transpose_x_0"), val = tensor(false)]; + tensor attn_5_transpose_y_0 = const()[name = tensor("attn_5_transpose_y_0"), val = tensor(true)]; + tensor attn_5_cast = matmul(transpose_x = attn_5_transpose_x_0, transpose_y = attn_5_transpose_y_0, x = var_485_cast, y = var_489_cast)[name = tensor("attn_5_cast")]; + tensor var_493 = const()[name = tensor("op_493"), val = tensor([2, 320, 1, -1])]; + tensor input_51_cast = reshape(shape = var_493, x = attn_5_cast)[name = tensor("input_51_cast")]; + tensor var_498 = const()[name = tensor("op_498"), val = tensor([1, 1])]; + tensor var_500 = const()[name = tensor("op_500"), val = tensor([1, 1])]; + tensor var_502_pad_type_0 = const()[name = tensor("op_502_pad_type_0"), val = tensor("custom")]; + tensor var_502_pad_0 = const()[name = tensor("op_502_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7174592))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7251456))), name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7251648)))]; + tensor var_502_cast = conv(bias = down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_500, groups = var_111, pad = var_502_pad_0, pad_type = var_502_pad_type_0, strides = var_498, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_51_cast)[name = tensor("op_502_cast")]; + tensor inputs_9_cast = add(x = var_502_cast, y = inputs_7_cast)[name = tensor("inputs_9_cast")]; + tensor var_506 = const()[name = tensor("op_506"), val = tensor([1])]; + tensor channels_mean_9_cast = reduce_mean(axes = var_506, keep_dims = var_106, x = inputs_9_cast)[name = tensor("channels_mean_9_cast")]; + tensor zero_mean_9_cast = sub(x = inputs_9_cast, y = channels_mean_9_cast)[name = tensor("zero_mean_9_cast")]; + tensor zero_mean_sq_9_cast = mul(x = zero_mean_9_cast, y = zero_mean_9_cast)[name = tensor("zero_mean_sq_9_cast")]; + tensor var_510 = const()[name = tensor("op_510"), val = tensor([1])]; + tensor var_511_cast = reduce_mean(axes = var_510, keep_dims = var_106, x = zero_mean_sq_9_cast)[name = tensor("op_511_cast")]; + tensor var_512_to_fp16 = const()[name = tensor("op_512_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_513_cast = add(x = var_511_cast, y = var_512_to_fp16)[name = tensor("op_513_cast")]; + tensor denom_9_epsilon_0_to_fp16 = const()[name = tensor("denom_9_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_9_cast = rsqrt(epsilon = denom_9_epsilon_0_to_fp16, x = var_513_cast)[name = tensor("denom_9_cast")]; + tensor out_9_cast = mul(x = zero_mean_9_cast, y = denom_9_cast)[name = tensor("out_9_cast")]; + tensor var_517_to_fp16 = const()[name = tensor("op_517_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7252352)))]; + tensor var_518_cast = add(x = out_9_cast, y = var_517_to_fp16)[name = tensor("op_518_cast")]; + tensor var_520_to_fp16 = const()[name = tensor("op_520_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7253056)))]; + tensor hidden_states_29_cast = mul(x = var_518_cast, y = var_520_to_fp16)[name = tensor("hidden_states_29_cast")]; + tensor var_527 = const()[name = tensor("op_527"), val = tensor([1, 1])]; + tensor var_529 = const()[name = tensor("op_529"), val = tensor([1, 1])]; + tensor q_7_pad_type_0 = const()[name = tensor("q_7_pad_type_0"), val = tensor("custom")]; + tensor q_7_pad_0 = const()[name = tensor("q_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7253760))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7330624))), name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor q_7_cast = conv(dilations = var_529, groups = var_111, pad = q_7_pad_0, pad_type = q_7_pad_type_0, strides = var_527, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_29_cast)[name = tensor("q_7_cast")]; + tensor var_533 = const()[name = tensor("op_533"), val = tensor([1, 1])]; + tensor var_535 = const()[name = tensor("op_535"), val = tensor([1, 1])]; + tensor k_7_pad_type_0 = const()[name = tensor("k_7_pad_type_0"), val = tensor("custom")]; + tensor k_7_pad_0 = const()[name = tensor("k_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7330816))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7515200))), name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([320, 768, 1, 1])]; + tensor k_7_cast = conv(dilations = var_535, groups = var_111, pad = k_7_pad_0, pad_type = k_7_pad_type_0, strides = var_533, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_7_cast")]; + tensor var_539 = const()[name = tensor("op_539"), val = tensor([1, 1])]; + tensor var_541 = const()[name = tensor("op_541"), val = tensor([1, 1])]; + tensor v_7_pad_type_0 = const()[name = tensor("v_7_pad_type_0"), val = tensor("custom")]; + tensor v_7_pad_0 = const()[name = tensor("v_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7515392))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7699776))), name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([320, 768, 1, 1])]; + tensor v_7_cast = conv(dilations = var_541, groups = var_111, pad = v_7_pad_0, pad_type = v_7_pad_type_0, strides = var_539, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_7_cast")]; + tensor var_545 = const()[name = tensor("op_545"), val = tensor([2, 8, 40, -1])]; + tensor var_546_cast = reshape(shape = var_545, x = q_7_cast)[name = tensor("op_546_cast")]; + tensor var_547 = const()[name = tensor("op_547"), val = tensor([2, 8, 40, -1])]; + tensor var_548_cast = reshape(shape = var_547, x = k_7_cast)[name = tensor("op_548_cast")]; + tensor var_549 = const()[name = tensor("op_549"), val = tensor([2, 8, 40, -1])]; + tensor var_550_cast = reshape(shape = var_549, x = v_7_cast)[name = tensor("op_550_cast")]; + tensor attn_weights_13_transpose_x_0 = const()[name = tensor("attn_weights_13_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_13_transpose_y_0 = const()[name = tensor("attn_weights_13_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_13_cast = matmul(transpose_x = attn_weights_13_transpose_x_0, transpose_y = attn_weights_13_transpose_y_0, x = var_546_cast, y = var_548_cast)[name = tensor("attn_weights_13_cast")]; + tensor attn_weights_15_cast = mul(x = attn_weights_13_cast, y = var_102_to_fp16)[name = tensor("attn_weights_15_cast")]; + tensor var_554_cast = softmax(axis = var_95, x = attn_weights_15_cast)[name = tensor("op_554_cast")]; + tensor attn_7_transpose_x_0 = const()[name = tensor("attn_7_transpose_x_0"), val = tensor(false)]; + tensor attn_7_transpose_y_0 = const()[name = tensor("attn_7_transpose_y_0"), val = tensor(true)]; + tensor attn_7_cast = matmul(transpose_x = attn_7_transpose_x_0, transpose_y = attn_7_transpose_y_0, x = var_550_cast, y = var_554_cast)[name = tensor("attn_7_cast")]; + tensor var_558 = const()[name = tensor("op_558"), val = tensor([2, 320, 1, -1])]; + tensor input_53_cast = reshape(shape = var_558, x = attn_7_cast)[name = tensor("input_53_cast")]; + tensor var_563 = const()[name = tensor("op_563"), val = tensor([1, 1])]; + tensor var_565 = const()[name = tensor("op_565"), val = tensor([1, 1])]; + tensor var_567_pad_type_0 = const()[name = tensor("op_567_pad_type_0"), val = tensor("custom")]; + tensor var_567_pad_0 = const()[name = tensor("op_567_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7699968))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7776832))), name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7777024)))]; + tensor var_567_cast = conv(bias = down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_565, groups = var_111, pad = var_567_pad_0, pad_type = var_567_pad_type_0, strides = var_563, weight = down_blocks_0_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_53_cast)[name = tensor("op_567_cast")]; + tensor inputs_11_cast = add(x = var_567_cast, y = inputs_9_cast)[name = tensor("inputs_11_cast")]; + tensor var_571 = const()[name = tensor("op_571"), val = tensor([1])]; + tensor channels_mean_11_cast = reduce_mean(axes = var_571, keep_dims = var_106, x = inputs_11_cast)[name = tensor("channels_mean_11_cast")]; + tensor zero_mean_11_cast = sub(x = inputs_11_cast, y = channels_mean_11_cast)[name = tensor("zero_mean_11_cast")]; + tensor zero_mean_sq_11_cast = mul(x = zero_mean_11_cast, y = zero_mean_11_cast)[name = tensor("zero_mean_sq_11_cast")]; + tensor var_575 = const()[name = tensor("op_575"), val = tensor([1])]; + tensor var_576_cast = reduce_mean(axes = var_575, keep_dims = var_106, x = zero_mean_sq_11_cast)[name = tensor("op_576_cast")]; + tensor var_577_to_fp16 = const()[name = tensor("op_577_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_578_cast = add(x = var_576_cast, y = var_577_to_fp16)[name = tensor("op_578_cast")]; + tensor denom_11_epsilon_0_to_fp16 = const()[name = tensor("denom_11_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_11_cast = rsqrt(epsilon = denom_11_epsilon_0_to_fp16, x = var_578_cast)[name = tensor("denom_11_cast")]; + tensor out_11_cast = mul(x = zero_mean_11_cast, y = denom_11_cast)[name = tensor("out_11_cast")]; + tensor var_582_to_fp16 = const()[name = tensor("op_582_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7777728)))]; + tensor var_583_cast = add(x = out_11_cast, y = var_582_to_fp16)[name = tensor("op_583_cast")]; + tensor var_585_to_fp16 = const()[name = tensor("op_585_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7778432)))]; + tensor input_55_cast = mul(x = var_583_cast, y = var_585_to_fp16)[name = tensor("input_55_cast")]; + tensor var_593 = const()[name = tensor("op_593"), val = tensor([1, 1])]; + tensor var_595 = const()[name = tensor("op_595"), val = tensor([1, 1])]; + tensor var_597_pad_type_0 = const()[name = tensor("op_597_pad_type_0"), val = tensor("custom")]; + tensor var_597_pad_0 = const()[name = tensor("op_597_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(7779136))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8393600))), name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([2560, 320, 1, 1])]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8393792))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8395776))), name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([2560])]; + tensor var_597_cast = conv(bias = down_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_595, groups = var_111, pad = var_597_pad_0, pad_type = var_597_pad_type_0, strides = var_593, weight = down_blocks_0_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_55_cast)[name = tensor("op_597_cast")]; + tensor var_598_split_sizes_0 = const()[name = tensor("op_598_split_sizes_0"), val = tensor([1280, 1280])]; + tensor var_598_axis_0 = const()[name = tensor("op_598_axis_0"), val = tensor(1)]; + tensor var_598_cast_0, tensor var_598_cast_1 = split(axis = var_598_axis_0, split_sizes = var_598_split_sizes_0, x = var_597_cast)[name = tensor("op_598_cast")]; + tensor var_600_mode_0 = const()[name = tensor("op_600_mode_0"), val = tensor("EXACT")]; + tensor var_600_cast = gelu(mode = var_600_mode_0, x = var_598_cast_1)[name = tensor("op_600_cast")]; + tensor input_57_cast = mul(x = var_598_cast_0, y = var_600_cast)[name = tensor("input_57_cast")]; + tensor var_604 = const()[name = tensor("op_604"), val = tensor([1, 1])]; + tensor var_606 = const()[name = tensor("op_606"), val = tensor([1, 1])]; + tensor var_608_pad_type_0 = const()[name = tensor("op_608_pad_type_0"), val = tensor("custom")]; + tensor var_608_pad_0 = const()[name = tensor("op_608_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8395968))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8703232))), name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([320, 1280, 1, 1])]; + tensor down_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8703424)))]; + tensor var_608_cast = conv(bias = down_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_606, groups = var_111, pad = var_608_pad_0, pad_type = var_608_pad_type_0, strides = var_604, weight = down_blocks_0_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_57_cast)[name = tensor("op_608_cast")]; + tensor hidden_states_33_cast = add(x = var_608_cast, y = inputs_11_cast)[name = tensor("hidden_states_33_cast")]; + tensor var_610 = const()[name = tensor("op_610"), val = tensor([2, 320, 64, 64])]; + tensor input_59_cast = reshape(shape = var_610, x = hidden_states_33_cast)[name = tensor("input_59_cast")]; + tensor var_614 = const()[name = tensor("op_614"), val = tensor([1, 1])]; + tensor var_616 = const()[name = tensor("op_616"), val = tensor([1, 1])]; + tensor hidden_states_35_pad_type_0 = const()[name = tensor("hidden_states_35_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_35_pad_0 = const()[name = tensor("hidden_states_35_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_0_attentions_1_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8704128))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8780992))), name = tensor("down_blocks_0_attentions_1_proj_out_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor down_blocks_0_attentions_1_proj_out_bias_to_fp16 = const()[name = tensor("down_blocks_0_attentions_1_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8781184)))]; + tensor hidden_states_35_cast = conv(bias = down_blocks_0_attentions_1_proj_out_bias_to_fp16, dilations = var_616, groups = var_111, pad = hidden_states_35_pad_0, pad_type = hidden_states_35_pad_type_0, strides = var_614, weight = down_blocks_0_attentions_1_proj_out_weight_to_fp16_palettized, x = input_59_cast)[name = tensor("hidden_states_35_cast")]; + tensor input_61_cast = add(x = hidden_states_35_cast, y = hidden_states_23_cast)[name = tensor("input_61_cast")]; + tensor var_623 = const()[name = tensor("op_623"), val = tensor([2, 2])]; + tensor var_625 = const()[name = tensor("op_625"), val = tensor([1, 1])]; + tensor input_63_pad_type_0 = const()[name = tensor("input_63_pad_type_0"), val = tensor("custom")]; + tensor input_63_pad_0 = const()[name = tensor("input_63_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_0_downsamplers_0_conv_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(8781888))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9473152))), name = tensor("down_blocks_0_downsamplers_0_conv_weight_to_fp16_palettized"), shape = tensor([320, 320, 3, 3])]; + tensor down_blocks_0_downsamplers_0_conv_bias_to_fp16 = const()[name = tensor("down_blocks_0_downsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9473344)))]; + tensor input_63_cast = conv(bias = down_blocks_0_downsamplers_0_conv_bias_to_fp16, dilations = var_625, groups = var_111, pad = input_63_pad_0, pad_type = input_63_pad_type_0, strides = var_623, weight = down_blocks_0_downsamplers_0_conv_weight_to_fp16_palettized, x = input_61_cast)[name = tensor("input_63_cast")]; + tensor var_633 = const()[name = tensor("op_633"), val = tensor(3)]; + tensor var_644 = const()[name = tensor("op_644"), val = tensor(true)]; + tensor var_649 = const()[name = tensor("op_649"), val = tensor(1)]; + tensor reshape_24_shape_0 = const()[name = tensor("reshape_24_shape_0"), val = tensor([2, 32, 10, 32, 32])]; + tensor reshape_24_cast = reshape(shape = reshape_24_shape_0, x = input_63_cast)[name = tensor("reshape_24_cast")]; + tensor reduce_mean_18_axes_0 = const()[name = tensor("reduce_mean_18_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_18_keep_dims_0 = const()[name = tensor("reduce_mean_18_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_18_cast = reduce_mean(axes = reduce_mean_18_axes_0, keep_dims = reduce_mean_18_keep_dims_0, x = reshape_24_cast)[name = tensor("reduce_mean_18_cast")]; + tensor sub_12_cast = sub(x = reshape_24_cast, y = reduce_mean_18_cast)[name = tensor("sub_12_cast")]; + tensor square_6_cast = square(x = sub_12_cast)[name = tensor("square_6_cast")]; + tensor reduce_mean_20_axes_0 = const()[name = tensor("reduce_mean_20_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_20_keep_dims_0 = const()[name = tensor("reduce_mean_20_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_20_cast = reduce_mean(axes = reduce_mean_20_axes_0, keep_dims = reduce_mean_20_keep_dims_0, x = square_6_cast)[name = tensor("reduce_mean_20_cast")]; + tensor add_12_y_0_to_fp16 = const()[name = tensor("add_12_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_12_cast = add(x = reduce_mean_20_cast, y = add_12_y_0_to_fp16)[name = tensor("add_12_cast")]; + tensor sqrt_6_cast = sqrt(x = add_12_cast)[name = tensor("sqrt_6_cast")]; + tensor real_div_6_cast = real_div(x = sub_12_cast, y = sqrt_6_cast)[name = tensor("real_div_6_cast")]; + tensor reshape_25_shape_0 = const()[name = tensor("reshape_25_shape_0"), val = tensor([2, 320, 32, 32])]; + tensor reshape_25_cast = reshape(shape = reshape_25_shape_0, x = real_div_6_cast)[name = tensor("reshape_25_cast")]; + tensor add_13_gamma_0_to_fp16 = const()[name = tensor("add_13_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9474048)))]; + 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(9474752)))]; + tensor add_13_epsilon_0_to_fp16 = const()[name = tensor("add_13_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_13_cast = batch_norm(beta = add_13_beta_0_to_fp16, epsilon = add_13_epsilon_0_to_fp16, gamma = add_13_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_25_cast)[name = tensor("add_13_cast")]; + tensor input_67_cast = silu(x = add_13_cast)[name = tensor("input_67_cast")]; + tensor var_672 = const()[name = tensor("op_672"), val = tensor([1, 1])]; + tensor var_674 = const()[name = tensor("op_674"), 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 down_blocks_1_resnets_0_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(9475456))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10857920))), name = tensor("down_blocks_1_resnets_0_conv1_weight_to_fp16_palettized"), shape = tensor([640, 320, 3, 3])]; + tensor down_blocks_1_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("down_blocks_1_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10858112)))]; + tensor hidden_states_37_cast = conv(bias = down_blocks_1_resnets_0_conv1_bias_to_fp16, dilations = var_674, groups = var_649, pad = hidden_states_37_pad_0, pad_type = hidden_states_37_pad_type_0, strides = var_672, weight = down_blocks_1_resnets_0_conv1_weight_to_fp16_palettized, x = input_67_cast)[name = tensor("hidden_states_37_cast")]; + tensor var_680 = const()[name = tensor("op_680"), val = tensor([1, 1])]; + tensor var_682 = const()[name = tensor("op_682"), val = tensor([1, 1])]; + tensor temb_5_pad_type_0 = const()[name = tensor("temb_5_pad_type_0"), val = tensor("custom")]; + tensor temb_5_pad_0 = const()[name = tensor("temb_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_resnets_0_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(10859456))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11473920))), name = tensor("down_blocks_1_resnets_0_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([640, 1280, 1, 1])]; + tensor down_blocks_1_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("down_blocks_1_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11474112)))]; + tensor temb_5_cast = conv(bias = down_blocks_1_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_682, groups = var_649, pad = temb_5_pad_0, pad_type = temb_5_pad_type_0, strides = var_680, weight = down_blocks_1_resnets_0_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor("temb_5_cast")]; + tensor input_71_cast = add(x = hidden_states_37_cast, y = temb_5_cast)[name = tensor("input_71_cast")]; + tensor reshape_28_shape_0 = const()[name = tensor("reshape_28_shape_0"), val = tensor([2, 32, 20, 32, 32])]; + tensor reshape_28_cast = reshape(shape = reshape_28_shape_0, x = input_71_cast)[name = tensor("reshape_28_cast")]; + tensor reduce_mean_21_axes_0 = const()[name = tensor("reduce_mean_21_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_21_keep_dims_0 = const()[name = tensor("reduce_mean_21_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_21_cast = reduce_mean(axes = reduce_mean_21_axes_0, keep_dims = reduce_mean_21_keep_dims_0, x = reshape_28_cast)[name = tensor("reduce_mean_21_cast")]; + tensor sub_14_cast = sub(x = reshape_28_cast, y = reduce_mean_21_cast)[name = tensor("sub_14_cast")]; + tensor square_7_cast = square(x = sub_14_cast)[name = tensor("square_7_cast")]; + tensor reduce_mean_23_axes_0 = const()[name = tensor("reduce_mean_23_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_23_keep_dims_0 = const()[name = tensor("reduce_mean_23_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_23_cast = reduce_mean(axes = reduce_mean_23_axes_0, keep_dims = reduce_mean_23_keep_dims_0, x = square_7_cast)[name = tensor("reduce_mean_23_cast")]; + tensor add_14_y_0_to_fp16 = const()[name = tensor("add_14_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_14_cast = add(x = reduce_mean_23_cast, y = add_14_y_0_to_fp16)[name = tensor("add_14_cast")]; + tensor sqrt_7_cast = sqrt(x = add_14_cast)[name = tensor("sqrt_7_cast")]; + tensor real_div_7_cast = real_div(x = sub_14_cast, y = sqrt_7_cast)[name = tensor("real_div_7_cast")]; + tensor reshape_29_shape_0 = const()[name = tensor("reshape_29_shape_0"), val = tensor([2, 640, 32, 32])]; + tensor reshape_29_cast = reshape(shape = reshape_29_shape_0, x = real_div_7_cast)[name = tensor("reshape_29_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(11475456)))]; + 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(11476800)))]; + 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(11478144)))]; + 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(11479488)))]; + 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)[name = tensor("add_15_cast")]; + tensor input_75_cast = silu(x = add_15_cast)[name = tensor("input_75_cast")]; + tensor var_692 = const()[name = tensor("op_692"), val = tensor([1, 1])]; + tensor var_694 = const()[name = tensor("op_694"), 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 down_blocks_1_resnets_0_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(11480832))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14245696))), name = tensor("down_blocks_1_resnets_0_conv2_weight_to_fp16_palettized"), shape = tensor([640, 640, 3, 3])]; + tensor down_blocks_1_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("down_blocks_1_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14245888)))]; + tensor hidden_states_39_cast = conv(bias = down_blocks_1_resnets_0_conv2_bias_to_fp16, dilations = var_694, groups = var_649, pad = hidden_states_39_pad_0, pad_type = hidden_states_39_pad_type_0, strides = var_692, weight = down_blocks_1_resnets_0_conv2_weight_to_fp16_palettized, x = input_75_cast)[name = tensor("hidden_states_39_cast")]; + tensor var_699 = const()[name = tensor("op_699"), val = tensor([1, 1])]; + tensor var_701 = const()[name = tensor("op_701"), val = tensor([1, 1])]; + tensor x_1_pad_type_0 = const()[name = tensor("x_1_pad_type_0"), val = tensor("custom")]; + tensor x_1_pad_0 = const()[name = tensor("x_1_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_resnets_0_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14247232))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14400896))), name = tensor("down_blocks_1_resnets_0_conv_shortcut_weight_to_fp16_palettized"), shape = tensor([640, 320, 1, 1])]; + tensor down_blocks_1_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("down_blocks_1_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14401088)))]; + tensor x_1_cast = conv(bias = down_blocks_1_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_701, groups = var_649, pad = x_1_pad_0, pad_type = x_1_pad_type_0, strides = var_699, weight = down_blocks_1_resnets_0_conv_shortcut_weight_to_fp16_palettized, x = input_63_cast)[name = tensor("x_1_cast")]; + tensor hidden_states_41_cast = add(x = x_1_cast, y = hidden_states_39_cast)[name = tensor("hidden_states_41_cast")]; + tensor reshape_32_shape_0 = const()[name = tensor("reshape_32_shape_0"), val = tensor([2, 32, 20, 32, 32])]; + tensor reshape_32_cast = reshape(shape = reshape_32_shape_0, x = hidden_states_41_cast)[name = tensor("reshape_32_cast")]; + tensor reduce_mean_24_axes_0 = const()[name = tensor("reduce_mean_24_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_24_keep_dims_0 = const()[name = tensor("reduce_mean_24_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_24_cast = reduce_mean(axes = reduce_mean_24_axes_0, keep_dims = reduce_mean_24_keep_dims_0, x = reshape_32_cast)[name = tensor("reduce_mean_24_cast")]; + tensor sub_16_cast = sub(x = reshape_32_cast, y = reduce_mean_24_cast)[name = tensor("sub_16_cast")]; + tensor square_8_cast = square(x = sub_16_cast)[name = tensor("square_8_cast")]; + tensor reduce_mean_26_axes_0 = const()[name = tensor("reduce_mean_26_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_26_keep_dims_0 = const()[name = tensor("reduce_mean_26_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_26_cast = reduce_mean(axes = reduce_mean_26_axes_0, keep_dims = reduce_mean_26_keep_dims_0, x = square_8_cast)[name = tensor("reduce_mean_26_cast")]; + tensor add_16_y_0_to_fp16 = const()[name = tensor("add_16_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_16_cast = add(x = reduce_mean_26_cast, y = add_16_y_0_to_fp16)[name = tensor("add_16_cast")]; + tensor sqrt_8_cast = sqrt(x = add_16_cast)[name = tensor("sqrt_8_cast")]; + tensor real_div_8_cast = real_div(x = sub_16_cast, y = sqrt_8_cast)[name = tensor("real_div_8_cast")]; + tensor reshape_33_shape_0 = const()[name = tensor("reshape_33_shape_0"), val = tensor([2, 640, 32, 32])]; + tensor reshape_33_cast = reshape(shape = reshape_33_shape_0, x = real_div_8_cast)[name = tensor("reshape_33_cast")]; + tensor add_17_gamma_0_to_fp16 = const()[name = tensor("add_17_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14402432)))]; + 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(14403776)))]; + 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_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_33_cast)[name = tensor("add_17_cast")]; + tensor var_721 = const()[name = tensor("op_721"), val = tensor([1, 1])]; + tensor var_723 = const()[name = tensor("op_723"), val = tensor([1, 1])]; + tensor hidden_states_43_pad_type_0 = const()[name = tensor("hidden_states_43_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_43_pad_0 = const()[name = tensor("hidden_states_43_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14405120))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14712384))), name = tensor("down_blocks_1_attentions_0_proj_in_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor down_blocks_1_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14712576)))]; + tensor hidden_states_43_cast = conv(bias = down_blocks_1_attentions_0_proj_in_bias_to_fp16, dilations = var_723, groups = var_649, pad = hidden_states_43_pad_0, pad_type = hidden_states_43_pad_type_0, strides = var_721, weight = down_blocks_1_attentions_0_proj_in_weight_to_fp16_palettized, x = add_17_cast)[name = tensor("hidden_states_43_cast")]; + tensor var_728 = const()[name = tensor("op_728"), val = tensor([2, 640, 1, 1024])]; + tensor inputs_13_cast = reshape(shape = var_728, x = hidden_states_43_cast)[name = tensor("inputs_13_cast")]; + tensor var_738 = const()[name = tensor("op_738"), val = tensor([1])]; + tensor channels_mean_13_cast = reduce_mean(axes = var_738, keep_dims = var_644, x = inputs_13_cast)[name = tensor("channels_mean_13_cast")]; + tensor zero_mean_13_cast = sub(x = inputs_13_cast, y = channels_mean_13_cast)[name = tensor("zero_mean_13_cast")]; + tensor zero_mean_sq_13_cast = mul(x = zero_mean_13_cast, y = zero_mean_13_cast)[name = tensor("zero_mean_sq_13_cast")]; + tensor var_742 = const()[name = tensor("op_742"), val = tensor([1])]; + tensor var_743_cast = reduce_mean(axes = var_742, keep_dims = var_644, x = zero_mean_sq_13_cast)[name = tensor("op_743_cast")]; + tensor var_744_to_fp16 = const()[name = tensor("op_744_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_745_cast = add(x = var_743_cast, y = var_744_to_fp16)[name = tensor("op_745_cast")]; + tensor denom_13_epsilon_0_to_fp16 = const()[name = tensor("denom_13_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_13_cast = rsqrt(epsilon = denom_13_epsilon_0_to_fp16, x = var_745_cast)[name = tensor("denom_13_cast")]; + tensor out_13_cast = mul(x = zero_mean_13_cast, y = denom_13_cast)[name = tensor("out_13_cast")]; + tensor var_749_to_fp16 = const()[name = tensor("op_749_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14713920)))]; + tensor var_750_cast = add(x = out_13_cast, y = var_749_to_fp16)[name = tensor("op_750_cast")]; + tensor var_752_to_fp16 = const()[name = tensor("op_752_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14715264)))]; + tensor hidden_states_45_cast = mul(x = var_750_cast, y = var_752_to_fp16)[name = tensor("hidden_states_45_cast")]; + tensor var_759 = const()[name = tensor("op_759"), val = tensor([1, 1])]; + tensor var_761 = const()[name = tensor("op_761"), val = tensor([1, 1])]; + tensor q_9_pad_type_0 = const()[name = tensor("q_9_pad_type_0"), val = tensor("custom")]; + tensor q_9_pad_0 = const()[name = tensor("q_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(14716608))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15023872))), name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_9_cast = conv(dilations = var_761, groups = var_649, pad = q_9_pad_0, pad_type = q_9_pad_type_0, strides = var_759, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_45_cast)[name = tensor("q_9_cast")]; + tensor var_765 = const()[name = tensor("op_765"), val = tensor([1, 1])]; + tensor var_767 = const()[name = tensor("op_767"), val = tensor([1, 1])]; + tensor k_9_pad_type_0 = const()[name = tensor("k_9_pad_type_0"), val = tensor("custom")]; + tensor k_9_pad_0 = const()[name = tensor("k_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15024064))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15331328))), name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor k_9_cast = conv(dilations = var_767, groups = var_649, pad = k_9_pad_0, pad_type = k_9_pad_type_0, strides = var_765, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_45_cast)[name = tensor("k_9_cast")]; + tensor var_771 = const()[name = tensor("op_771"), val = tensor([1, 1])]; + tensor var_773 = const()[name = tensor("op_773"), val = tensor([1, 1])]; + tensor v_9_pad_type_0 = const()[name = tensor("v_9_pad_type_0"), val = tensor("custom")]; + tensor v_9_pad_0 = const()[name = tensor("v_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15331520))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15638784))), name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor v_9_cast = conv(dilations = var_773, groups = var_649, pad = v_9_pad_0, pad_type = v_9_pad_type_0, strides = var_771, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_45_cast)[name = tensor("v_9_cast")]; + tensor var_777 = const()[name = tensor("op_777"), val = tensor([2, 8, 80, -1])]; + tensor var_778_cast = reshape(shape = var_777, x = q_9_cast)[name = tensor("op_778_cast")]; + tensor var_779 = const()[name = tensor("op_779"), val = tensor([2, 8, 80, -1])]; + tensor var_780_cast = reshape(shape = var_779, x = k_9_cast)[name = tensor("op_780_cast")]; + tensor var_781 = const()[name = tensor("op_781"), val = tensor([2, 8, 80, -1])]; + tensor var_782_cast = reshape(shape = var_781, x = v_9_cast)[name = tensor("op_782_cast")]; + tensor attn_weights_17_transpose_x_0 = const()[name = tensor("attn_weights_17_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_17_transpose_y_0 = const()[name = tensor("attn_weights_17_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_17_cast = matmul(transpose_x = attn_weights_17_transpose_x_0, transpose_y = attn_weights_17_transpose_y_0, x = var_778_cast, y = var_780_cast)[name = tensor("attn_weights_17_cast")]; + tensor var_640_to_fp16 = const()[name = tensor("op_640_to_fp16"), val = tensor(0x1.cap-4)]; + tensor attn_weights_19_cast = mul(x = attn_weights_17_cast, y = var_640_to_fp16)[name = tensor("attn_weights_19_cast")]; + tensor var_786_cast = softmax(axis = var_633, x = attn_weights_19_cast)[name = tensor("op_786_cast")]; + tensor attn_9_transpose_x_0 = const()[name = tensor("attn_9_transpose_x_0"), val = tensor(false)]; + tensor attn_9_transpose_y_0 = const()[name = tensor("attn_9_transpose_y_0"), val = tensor(true)]; + tensor attn_9_cast = matmul(transpose_x = attn_9_transpose_x_0, transpose_y = attn_9_transpose_y_0, x = var_782_cast, y = var_786_cast)[name = tensor("attn_9_cast")]; + tensor var_790 = const()[name = tensor("op_790"), val = tensor([2, 640, 1, -1])]; + tensor input_79_cast = reshape(shape = var_790, x = attn_9_cast)[name = tensor("input_79_cast")]; + tensor var_795 = const()[name = tensor("op_795"), val = tensor([1, 1])]; + tensor var_797 = const()[name = tensor("op_797"), val = tensor([1, 1])]; + tensor var_799_pad_type_0 = const()[name = tensor("op_799_pad_type_0"), val = tensor("custom")]; + tensor var_799_pad_0 = const()[name = tensor("op_799_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15638976))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15946240))), name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15946432)))]; + tensor var_799_cast = conv(bias = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_797, groups = var_649, pad = var_799_pad_0, pad_type = var_799_pad_type_0, strides = var_795, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_79_cast)[name = tensor("op_799_cast")]; + tensor inputs_15_cast = add(x = var_799_cast, y = inputs_13_cast)[name = tensor("inputs_15_cast")]; + tensor var_803 = const()[name = tensor("op_803"), val = tensor([1])]; + tensor channels_mean_15_cast = reduce_mean(axes = var_803, keep_dims = var_644, x = inputs_15_cast)[name = tensor("channels_mean_15_cast")]; + tensor zero_mean_15_cast = sub(x = inputs_15_cast, y = channels_mean_15_cast)[name = tensor("zero_mean_15_cast")]; + tensor zero_mean_sq_15_cast = mul(x = zero_mean_15_cast, y = zero_mean_15_cast)[name = tensor("zero_mean_sq_15_cast")]; + tensor var_807 = const()[name = tensor("op_807"), val = tensor([1])]; + tensor var_808_cast = reduce_mean(axes = var_807, keep_dims = var_644, x = zero_mean_sq_15_cast)[name = tensor("op_808_cast")]; + tensor var_809_to_fp16 = const()[name = tensor("op_809_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_810_cast = add(x = var_808_cast, y = var_809_to_fp16)[name = tensor("op_810_cast")]; + tensor denom_15_epsilon_0_to_fp16 = const()[name = tensor("denom_15_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_15_cast = rsqrt(epsilon = denom_15_epsilon_0_to_fp16, x = var_810_cast)[name = tensor("denom_15_cast")]; + tensor out_15_cast = mul(x = zero_mean_15_cast, y = denom_15_cast)[name = tensor("out_15_cast")]; + tensor var_814_to_fp16 = const()[name = tensor("op_814_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15947776)))]; + tensor var_815_cast = add(x = out_15_cast, y = var_814_to_fp16)[name = tensor("op_815_cast")]; + tensor var_817_to_fp16 = const()[name = tensor("op_817_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15949120)))]; + tensor hidden_states_47_cast = mul(x = var_815_cast, y = var_817_to_fp16)[name = tensor("hidden_states_47_cast")]; + tensor var_824 = const()[name = tensor("op_824"), val = tensor([1, 1])]; + tensor var_826 = const()[name = tensor("op_826"), val = tensor([1, 1])]; + tensor q_11_pad_type_0 = const()[name = tensor("q_11_pad_type_0"), val = tensor("custom")]; + tensor q_11_pad_0 = const()[name = tensor("q_11_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(15950464))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16257728))), name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_11_cast = conv(dilations = var_826, groups = var_649, pad = q_11_pad_0, pad_type = q_11_pad_type_0, strides = var_824, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_47_cast)[name = tensor("q_11_cast")]; + tensor var_830 = const()[name = tensor("op_830"), val = tensor([1, 1])]; + tensor var_832 = const()[name = tensor("op_832"), val = tensor([1, 1])]; + tensor k_11_pad_type_0 = const()[name = tensor("k_11_pad_type_0"), val = tensor("custom")]; + tensor k_11_pad_0 = const()[name = tensor("k_11_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16257920))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16626624))), name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([640, 768, 1, 1])]; + tensor k_11_cast = conv(dilations = var_832, groups = var_649, pad = k_11_pad_0, pad_type = k_11_pad_type_0, strides = var_830, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_11_cast")]; + tensor var_836 = const()[name = tensor("op_836"), val = tensor([1, 1])]; + tensor var_838 = const()[name = tensor("op_838"), val = tensor([1, 1])]; + tensor v_11_pad_type_0 = const()[name = tensor("v_11_pad_type_0"), val = tensor("custom")]; + tensor v_11_pad_0 = const()[name = tensor("v_11_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16626816))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16995520))), name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([640, 768, 1, 1])]; + tensor v_11_cast = conv(dilations = var_838, groups = var_649, pad = v_11_pad_0, pad_type = v_11_pad_type_0, strides = var_836, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_11_cast")]; + tensor var_842 = const()[name = tensor("op_842"), val = tensor([2, 8, 80, -1])]; + tensor var_843_cast = reshape(shape = var_842, x = q_11_cast)[name = tensor("op_843_cast")]; + tensor var_844 = const()[name = tensor("op_844"), val = tensor([2, 8, 80, -1])]; + tensor var_845_cast = reshape(shape = var_844, x = k_11_cast)[name = tensor("op_845_cast")]; + tensor var_846 = const()[name = tensor("op_846"), val = tensor([2, 8, 80, -1])]; + tensor var_847_cast = reshape(shape = var_846, x = v_11_cast)[name = tensor("op_847_cast")]; + tensor attn_weights_21_transpose_x_0 = const()[name = tensor("attn_weights_21_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_21_transpose_y_0 = const()[name = tensor("attn_weights_21_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_21_cast = matmul(transpose_x = attn_weights_21_transpose_x_0, transpose_y = attn_weights_21_transpose_y_0, x = var_843_cast, y = var_845_cast)[name = tensor("attn_weights_21_cast")]; + tensor attn_weights_23_cast = mul(x = attn_weights_21_cast, y = var_640_to_fp16)[name = tensor("attn_weights_23_cast")]; + tensor var_851_cast = softmax(axis = var_633, x = attn_weights_23_cast)[name = tensor("op_851_cast")]; + tensor attn_11_transpose_x_0 = const()[name = tensor("attn_11_transpose_x_0"), val = tensor(false)]; + tensor attn_11_transpose_y_0 = const()[name = tensor("attn_11_transpose_y_0"), val = tensor(true)]; + tensor attn_11_cast = matmul(transpose_x = attn_11_transpose_x_0, transpose_y = attn_11_transpose_y_0, x = var_847_cast, y = var_851_cast)[name = tensor("attn_11_cast")]; + tensor var_855 = const()[name = tensor("op_855"), val = tensor([2, 640, 1, -1])]; + tensor input_81_cast = reshape(shape = var_855, x = attn_11_cast)[name = tensor("input_81_cast")]; + tensor var_860 = const()[name = tensor("op_860"), val = tensor([1, 1])]; + tensor var_862 = const()[name = tensor("op_862"), val = tensor([1, 1])]; + tensor var_864_pad_type_0 = const()[name = tensor("op_864_pad_type_0"), val = tensor("custom")]; + tensor var_864_pad_0 = const()[name = tensor("op_864_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(16995712))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(17302976))), name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(17303168)))]; + tensor var_864_cast = conv(bias = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_862, groups = var_649, pad = var_864_pad_0, pad_type = var_864_pad_type_0, strides = var_860, weight = down_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_81_cast)[name = tensor("op_864_cast")]; + tensor inputs_17_cast = add(x = var_864_cast, y = inputs_15_cast)[name = tensor("inputs_17_cast")]; + tensor var_868 = const()[name = tensor("op_868"), val = tensor([1])]; + tensor channels_mean_17_cast = reduce_mean(axes = var_868, keep_dims = var_644, x = inputs_17_cast)[name = tensor("channels_mean_17_cast")]; + tensor zero_mean_17_cast = sub(x = inputs_17_cast, y = channels_mean_17_cast)[name = tensor("zero_mean_17_cast")]; + tensor zero_mean_sq_17_cast = mul(x = zero_mean_17_cast, y = zero_mean_17_cast)[name = tensor("zero_mean_sq_17_cast")]; + tensor var_872 = const()[name = tensor("op_872"), val = tensor([1])]; + tensor var_873_cast = reduce_mean(axes = var_872, keep_dims = var_644, x = zero_mean_sq_17_cast)[name = tensor("op_873_cast")]; + tensor var_874_to_fp16 = const()[name = tensor("op_874_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_875_cast = add(x = var_873_cast, y = var_874_to_fp16)[name = tensor("op_875_cast")]; + tensor denom_17_epsilon_0_to_fp16 = const()[name = tensor("denom_17_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_17_cast = rsqrt(epsilon = denom_17_epsilon_0_to_fp16, x = var_875_cast)[name = tensor("denom_17_cast")]; + tensor out_17_cast = mul(x = zero_mean_17_cast, y = denom_17_cast)[name = tensor("out_17_cast")]; + tensor var_879_to_fp16 = const()[name = tensor("op_879_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(17304512)))]; + tensor var_880_cast = add(x = out_17_cast, y = var_879_to_fp16)[name = tensor("op_880_cast")]; + tensor var_882_to_fp16 = const()[name = tensor("op_882_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(17305856)))]; + tensor input_83_cast = mul(x = var_880_cast, y = var_882_to_fp16)[name = tensor("input_83_cast")]; + tensor var_890 = const()[name = tensor("op_890"), val = tensor([1, 1])]; + tensor var_892 = const()[name = tensor("op_892"), val = tensor([1, 1])]; + tensor var_894_pad_type_0 = const()[name = tensor("op_894_pad_type_0"), val = tensor("custom")]; + tensor var_894_pad_0 = const()[name = tensor("op_894_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(17307200))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19764864))), name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([5120, 640, 1, 1])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19765056))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19768960))), name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([5120])]; + tensor var_894_cast = conv(bias = down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_892, groups = var_649, pad = var_894_pad_0, pad_type = var_894_pad_type_0, strides = var_890, weight = down_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_83_cast)[name = tensor("op_894_cast")]; + tensor var_895_split_sizes_0 = const()[name = tensor("op_895_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_895_axis_0 = const()[name = tensor("op_895_axis_0"), val = tensor(1)]; + tensor var_895_cast_0, tensor var_895_cast_1 = split(axis = var_895_axis_0, split_sizes = var_895_split_sizes_0, x = var_894_cast)[name = tensor("op_895_cast")]; + tensor var_897_mode_0 = const()[name = tensor("op_897_mode_0"), val = tensor("EXACT")]; + tensor var_897_cast = gelu(mode = var_897_mode_0, x = var_895_cast_1)[name = tensor("op_897_cast")]; + tensor input_85_cast = mul(x = var_895_cast_0, y = var_897_cast)[name = tensor("input_85_cast")]; + tensor var_901 = const()[name = tensor("op_901"), val = tensor([1, 1])]; + tensor var_903 = const()[name = tensor("op_903"), val = tensor([1, 1])]; + tensor var_905_pad_type_0 = const()[name = tensor("op_905_pad_type_0"), val = tensor("custom")]; + tensor var_905_pad_0 = const()[name = tensor("op_905_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(19769152))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20998016))), name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([640, 2560, 1, 1])]; + tensor down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20998208)))]; + tensor var_905_cast = conv(bias = down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_903, groups = var_649, pad = var_905_pad_0, pad_type = var_905_pad_type_0, strides = var_901, weight = down_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_85_cast)[name = tensor("op_905_cast")]; + tensor hidden_states_51_cast = add(x = var_905_cast, y = inputs_17_cast)[name = tensor("hidden_states_51_cast")]; + tensor var_907 = const()[name = tensor("op_907"), val = tensor([2, 640, 32, 32])]; + tensor input_87_cast = reshape(shape = var_907, x = hidden_states_51_cast)[name = tensor("input_87_cast")]; + tensor var_911 = const()[name = tensor("op_911"), val = tensor([1, 1])]; + tensor var_913 = const()[name = tensor("op_913"), val = tensor([1, 1])]; + tensor hidden_states_53_pad_type_0 = const()[name = tensor("hidden_states_53_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_53_pad_0 = const()[name = tensor("hidden_states_53_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_0_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(20999552))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21306816))), name = tensor("down_blocks_1_attentions_0_proj_out_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor down_blocks_1_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_0_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21307008)))]; + tensor hidden_states_53_cast = conv(bias = down_blocks_1_attentions_0_proj_out_bias_to_fp16, dilations = var_913, groups = var_649, pad = hidden_states_53_pad_0, pad_type = hidden_states_53_pad_type_0, strides = var_911, weight = down_blocks_1_attentions_0_proj_out_weight_to_fp16_palettized, x = input_87_cast)[name = tensor("hidden_states_53_cast")]; + tensor input_89_cast = add(x = hidden_states_53_cast, y = hidden_states_41_cast)[name = tensor("input_89_cast")]; + tensor reshape_36_shape_0 = const()[name = tensor("reshape_36_shape_0"), val = tensor([2, 32, 20, 32, 32])]; + tensor reshape_36_cast = reshape(shape = reshape_36_shape_0, x = input_89_cast)[name = tensor("reshape_36_cast")]; + tensor reduce_mean_27_axes_0 = const()[name = tensor("reduce_mean_27_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_27_keep_dims_0 = const()[name = tensor("reduce_mean_27_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_27_cast = reduce_mean(axes = reduce_mean_27_axes_0, keep_dims = reduce_mean_27_keep_dims_0, x = reshape_36_cast)[name = tensor("reduce_mean_27_cast")]; + tensor sub_18_cast = sub(x = reshape_36_cast, y = reduce_mean_27_cast)[name = tensor("sub_18_cast")]; + tensor square_9_cast = square(x = sub_18_cast)[name = tensor("square_9_cast")]; + tensor reduce_mean_29_axes_0 = const()[name = tensor("reduce_mean_29_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_29_keep_dims_0 = const()[name = tensor("reduce_mean_29_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_29_cast = reduce_mean(axes = reduce_mean_29_axes_0, keep_dims = reduce_mean_29_keep_dims_0, x = square_9_cast)[name = tensor("reduce_mean_29_cast")]; + tensor add_18_y_0_to_fp16 = const()[name = tensor("add_18_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_18_cast = add(x = reduce_mean_29_cast, y = add_18_y_0_to_fp16)[name = tensor("add_18_cast")]; + tensor sqrt_9_cast = sqrt(x = add_18_cast)[name = tensor("sqrt_9_cast")]; + tensor real_div_9_cast = real_div(x = sub_18_cast, y = sqrt_9_cast)[name = tensor("real_div_9_cast")]; + tensor reshape_37_shape_0 = const()[name = tensor("reshape_37_shape_0"), val = tensor([2, 640, 32, 32])]; + tensor reshape_37_cast = reshape(shape = reshape_37_shape_0, x = real_div_9_cast)[name = tensor("reshape_37_cast")]; + tensor add_19_gamma_0_to_fp16 = const()[name = tensor("add_19_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21308352)))]; + 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(21309696)))]; + 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_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_37_cast)[name = tensor("add_19_cast")]; + tensor input_93_cast = silu(x = add_19_cast)[name = tensor("input_93_cast")]; + tensor var_928 = const()[name = tensor("op_928"), val = tensor([1, 1])]; + tensor var_930 = const()[name = tensor("op_930"), val = tensor([1, 1])]; + tensor hidden_states_55_pad_type_0 = const()[name = tensor("hidden_states_55_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_55_pad_0 = const()[name = tensor("hidden_states_55_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_1_resnets_1_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(21311040))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(24075904))), name = tensor("down_blocks_1_resnets_1_conv1_weight_to_fp16_palettized"), shape = tensor([640, 640, 3, 3])]; + tensor down_blocks_1_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("down_blocks_1_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(24076096)))]; + tensor hidden_states_55_cast = conv(bias = down_blocks_1_resnets_1_conv1_bias_to_fp16, dilations = var_930, groups = var_649, pad = hidden_states_55_pad_0, pad_type = hidden_states_55_pad_type_0, strides = var_928, weight = down_blocks_1_resnets_1_conv1_weight_to_fp16_palettized, x = input_93_cast)[name = tensor("hidden_states_55_cast")]; + tensor var_936 = const()[name = tensor("op_936"), val = tensor([1, 1])]; + tensor var_938 = const()[name = tensor("op_938"), val = tensor([1, 1])]; + tensor temb_7_pad_type_0 = const()[name = tensor("temb_7_pad_type_0"), val = tensor("custom")]; + tensor temb_7_pad_0 = const()[name = tensor("temb_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_resnets_1_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(24077440))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(24691904))), name = tensor("down_blocks_1_resnets_1_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([640, 1280, 1, 1])]; + tensor down_blocks_1_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("down_blocks_1_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(24692096)))]; + tensor temb_7_cast = conv(bias = down_blocks_1_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_938, groups = var_649, pad = temb_7_pad_0, pad_type = temb_7_pad_type_0, strides = var_936, weight = down_blocks_1_resnets_1_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor("temb_7_cast")]; + tensor input_97_cast = add(x = hidden_states_55_cast, y = temb_7_cast)[name = tensor("input_97_cast")]; + tensor reshape_40_shape_0 = const()[name = tensor("reshape_40_shape_0"), val = tensor([2, 32, 20, 32, 32])]; + tensor reshape_40_cast = reshape(shape = reshape_40_shape_0, x = input_97_cast)[name = tensor("reshape_40_cast")]; + tensor reduce_mean_30_axes_0 = const()[name = tensor("reduce_mean_30_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_30_keep_dims_0 = const()[name = tensor("reduce_mean_30_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_30_cast = reduce_mean(axes = reduce_mean_30_axes_0, keep_dims = reduce_mean_30_keep_dims_0, x = reshape_40_cast)[name = tensor("reduce_mean_30_cast")]; + tensor sub_20_cast = sub(x = reshape_40_cast, y = reduce_mean_30_cast)[name = tensor("sub_20_cast")]; + tensor square_10_cast = square(x = sub_20_cast)[name = tensor("square_10_cast")]; + tensor reduce_mean_32_axes_0 = const()[name = tensor("reduce_mean_32_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_32_keep_dims_0 = const()[name = tensor("reduce_mean_32_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_32_cast = reduce_mean(axes = reduce_mean_32_axes_0, keep_dims = reduce_mean_32_keep_dims_0, x = square_10_cast)[name = tensor("reduce_mean_32_cast")]; + tensor add_20_y_0_to_fp16 = const()[name = tensor("add_20_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_20_cast = add(x = reduce_mean_32_cast, y = add_20_y_0_to_fp16)[name = tensor("add_20_cast")]; + tensor sqrt_10_cast = sqrt(x = add_20_cast)[name = tensor("sqrt_10_cast")]; + tensor real_div_10_cast = real_div(x = sub_20_cast, y = sqrt_10_cast)[name = tensor("real_div_10_cast")]; + tensor reshape_41_shape_0 = const()[name = tensor("reshape_41_shape_0"), val = tensor([2, 640, 32, 32])]; + tensor reshape_41_cast = reshape(shape = reshape_41_shape_0, x = real_div_10_cast)[name = tensor("reshape_41_cast")]; + tensor add_21_gamma_0_to_fp16 = const()[name = tensor("add_21_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(24693440)))]; + 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(24694784)))]; + 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_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_41_cast)[name = tensor("add_21_cast")]; + tensor input_101_cast = silu(x = add_21_cast)[name = tensor("input_101_cast")]; + tensor var_948 = const()[name = tensor("op_948"), val = tensor([1, 1])]; + tensor var_950 = const()[name = tensor("op_950"), val = tensor([1, 1])]; + tensor hidden_states_57_pad_type_0 = const()[name = tensor("hidden_states_57_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_57_pad_0 = const()[name = tensor("hidden_states_57_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_1_resnets_1_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(24696128))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27460992))), name = tensor("down_blocks_1_resnets_1_conv2_weight_to_fp16_palettized"), shape = tensor([640, 640, 3, 3])]; + tensor down_blocks_1_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("down_blocks_1_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27461184)))]; + tensor hidden_states_57_cast = conv(bias = down_blocks_1_resnets_1_conv2_bias_to_fp16, dilations = var_950, groups = var_649, pad = hidden_states_57_pad_0, pad_type = hidden_states_57_pad_type_0, strides = var_948, weight = down_blocks_1_resnets_1_conv2_weight_to_fp16_palettized, x = input_101_cast)[name = tensor("hidden_states_57_cast")]; + tensor hidden_states_59_cast = add(x = input_89_cast, y = hidden_states_57_cast)[name = tensor("hidden_states_59_cast")]; + tensor reshape_44_shape_0 = const()[name = tensor("reshape_44_shape_0"), val = tensor([2, 32, 20, 32, 32])]; + tensor reshape_44_cast = reshape(shape = reshape_44_shape_0, x = hidden_states_59_cast)[name = tensor("reshape_44_cast")]; + tensor reduce_mean_33_axes_0 = const()[name = tensor("reduce_mean_33_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_33_keep_dims_0 = const()[name = tensor("reduce_mean_33_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_33_cast = reduce_mean(axes = reduce_mean_33_axes_0, keep_dims = reduce_mean_33_keep_dims_0, x = reshape_44_cast)[name = tensor("reduce_mean_33_cast")]; + tensor sub_22_cast = sub(x = reshape_44_cast, y = reduce_mean_33_cast)[name = tensor("sub_22_cast")]; + tensor square_11_cast = square(x = sub_22_cast)[name = tensor("square_11_cast")]; + tensor reduce_mean_35_axes_0 = const()[name = tensor("reduce_mean_35_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_35_keep_dims_0 = const()[name = tensor("reduce_mean_35_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_35_cast = reduce_mean(axes = reduce_mean_35_axes_0, keep_dims = reduce_mean_35_keep_dims_0, x = square_11_cast)[name = tensor("reduce_mean_35_cast")]; + tensor add_22_y_0_to_fp16 = const()[name = tensor("add_22_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_22_cast = add(x = reduce_mean_35_cast, y = add_22_y_0_to_fp16)[name = tensor("add_22_cast")]; + tensor sqrt_11_cast = sqrt(x = add_22_cast)[name = tensor("sqrt_11_cast")]; + tensor real_div_11_cast = real_div(x = sub_22_cast, y = sqrt_11_cast)[name = tensor("real_div_11_cast")]; + tensor reshape_45_shape_0 = const()[name = tensor("reshape_45_shape_0"), val = tensor([2, 640, 32, 32])]; + tensor reshape_45_cast = reshape(shape = reshape_45_shape_0, x = real_div_11_cast)[name = tensor("reshape_45_cast")]; + tensor add_23_gamma_0_to_fp16 = const()[name = tensor("add_23_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27462528)))]; + 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(27463872)))]; + 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_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_45_cast)[name = tensor("add_23_cast")]; + tensor var_970 = const()[name = tensor("op_970"), val = tensor([1, 1])]; + tensor var_972 = const()[name = tensor("op_972"), val = tensor([1, 1])]; + tensor hidden_states_61_pad_type_0 = const()[name = tensor("hidden_states_61_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_61_pad_0 = const()[name = tensor("hidden_states_61_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27465216))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27772480))), name = tensor("down_blocks_1_attentions_1_proj_in_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor down_blocks_1_attentions_1_proj_in_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27772672)))]; + tensor hidden_states_61_cast = conv(bias = down_blocks_1_attentions_1_proj_in_bias_to_fp16, dilations = var_972, groups = var_649, pad = hidden_states_61_pad_0, pad_type = hidden_states_61_pad_type_0, strides = var_970, weight = down_blocks_1_attentions_1_proj_in_weight_to_fp16_palettized, x = add_23_cast)[name = tensor("hidden_states_61_cast")]; + tensor var_977 = const()[name = tensor("op_977"), val = tensor([2, 640, 1, 1024])]; + tensor inputs_19_cast = reshape(shape = var_977, x = hidden_states_61_cast)[name = tensor("inputs_19_cast")]; + tensor var_987 = const()[name = tensor("op_987"), val = tensor([1])]; + tensor channels_mean_19_cast = reduce_mean(axes = var_987, keep_dims = var_644, x = inputs_19_cast)[name = tensor("channels_mean_19_cast")]; + tensor zero_mean_19_cast = sub(x = inputs_19_cast, y = channels_mean_19_cast)[name = tensor("zero_mean_19_cast")]; + tensor zero_mean_sq_19_cast = mul(x = zero_mean_19_cast, y = zero_mean_19_cast)[name = tensor("zero_mean_sq_19_cast")]; + tensor var_991 = const()[name = tensor("op_991"), val = tensor([1])]; + tensor var_992_cast = reduce_mean(axes = var_991, keep_dims = var_644, x = zero_mean_sq_19_cast)[name = tensor("op_992_cast")]; + tensor var_993_to_fp16 = const()[name = tensor("op_993_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_994_cast = add(x = var_992_cast, y = var_993_to_fp16)[name = tensor("op_994_cast")]; + tensor denom_19_epsilon_0_to_fp16 = const()[name = tensor("denom_19_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_19_cast = rsqrt(epsilon = denom_19_epsilon_0_to_fp16, x = var_994_cast)[name = tensor("denom_19_cast")]; + tensor out_19_cast = mul(x = zero_mean_19_cast, y = denom_19_cast)[name = tensor("out_19_cast")]; + tensor var_998_to_fp16 = const()[name = tensor("op_998_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27774016)))]; + tensor var_999_cast = add(x = out_19_cast, y = var_998_to_fp16)[name = tensor("op_999_cast")]; + tensor var_1001_to_fp16 = const()[name = tensor("op_1001_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27775360)))]; + tensor hidden_states_63_cast = mul(x = var_999_cast, y = var_1001_to_fp16)[name = tensor("hidden_states_63_cast")]; + tensor var_1008 = const()[name = tensor("op_1008"), val = tensor([1, 1])]; + tensor var_1010 = const()[name = tensor("op_1010"), val = tensor([1, 1])]; + tensor q_13_pad_type_0 = const()[name = tensor("q_13_pad_type_0"), val = tensor("custom")]; + tensor q_13_pad_0 = const()[name = tensor("q_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(27776704))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28083968))), name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_13_cast = conv(dilations = var_1010, groups = var_649, pad = q_13_pad_0, pad_type = q_13_pad_type_0, strides = var_1008, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_63_cast)[name = tensor("q_13_cast")]; + tensor var_1014 = const()[name = tensor("op_1014"), val = tensor([1, 1])]; + tensor var_1016 = const()[name = tensor("op_1016"), val = tensor([1, 1])]; + tensor k_13_pad_type_0 = const()[name = tensor("k_13_pad_type_0"), val = tensor("custom")]; + tensor k_13_pad_0 = const()[name = tensor("k_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28084160))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28391424))), name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor k_13_cast = conv(dilations = var_1016, groups = var_649, pad = k_13_pad_0, pad_type = k_13_pad_type_0, strides = var_1014, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_63_cast)[name = tensor("k_13_cast")]; + tensor var_1020 = const()[name = tensor("op_1020"), val = tensor([1, 1])]; + tensor var_1022 = const()[name = tensor("op_1022"), val = tensor([1, 1])]; + tensor v_13_pad_type_0 = const()[name = tensor("v_13_pad_type_0"), val = tensor("custom")]; + tensor v_13_pad_0 = const()[name = tensor("v_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28391616))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28698880))), name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor v_13_cast = conv(dilations = var_1022, groups = var_649, pad = v_13_pad_0, pad_type = v_13_pad_type_0, strides = var_1020, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_63_cast)[name = tensor("v_13_cast")]; + tensor var_1026 = const()[name = tensor("op_1026"), val = tensor([2, 8, 80, -1])]; + tensor var_1027_cast = reshape(shape = var_1026, x = q_13_cast)[name = tensor("op_1027_cast")]; + tensor var_1028 = const()[name = tensor("op_1028"), val = tensor([2, 8, 80, -1])]; + tensor var_1029_cast = reshape(shape = var_1028, x = k_13_cast)[name = tensor("op_1029_cast")]; + tensor var_1030 = const()[name = tensor("op_1030"), val = tensor([2, 8, 80, -1])]; + tensor var_1031_cast = reshape(shape = var_1030, x = v_13_cast)[name = tensor("op_1031_cast")]; + tensor attn_weights_25_transpose_x_0 = const()[name = tensor("attn_weights_25_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_25_transpose_y_0 = const()[name = tensor("attn_weights_25_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_25_cast = matmul(transpose_x = attn_weights_25_transpose_x_0, transpose_y = attn_weights_25_transpose_y_0, x = var_1027_cast, y = var_1029_cast)[name = tensor("attn_weights_25_cast")]; + tensor attn_weights_27_cast = mul(x = attn_weights_25_cast, y = var_640_to_fp16)[name = tensor("attn_weights_27_cast")]; + tensor var_1035_cast = softmax(axis = var_633, x = attn_weights_27_cast)[name = tensor("op_1035_cast")]; + tensor attn_13_transpose_x_0 = const()[name = tensor("attn_13_transpose_x_0"), val = tensor(false)]; + tensor attn_13_transpose_y_0 = const()[name = tensor("attn_13_transpose_y_0"), val = tensor(true)]; + tensor attn_13_cast = matmul(transpose_x = attn_13_transpose_x_0, transpose_y = attn_13_transpose_y_0, x = var_1031_cast, y = var_1035_cast)[name = tensor("attn_13_cast")]; + tensor var_1039 = const()[name = tensor("op_1039"), val = tensor([2, 640, 1, -1])]; + tensor input_105_cast = reshape(shape = var_1039, x = attn_13_cast)[name = tensor("input_105_cast")]; + tensor var_1044 = const()[name = tensor("op_1044"), val = tensor([1, 1])]; + tensor var_1046 = const()[name = tensor("op_1046"), val = tensor([1, 1])]; + tensor var_1048_pad_type_0 = const()[name = tensor("op_1048_pad_type_0"), val = tensor("custom")]; + tensor var_1048_pad_0 = const()[name = tensor("op_1048_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(28699072))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29006336))), name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29006528)))]; + tensor var_1048_cast = conv(bias = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_1046, groups = var_649, pad = var_1048_pad_0, pad_type = var_1048_pad_type_0, strides = var_1044, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_105_cast)[name = tensor("op_1048_cast")]; + tensor inputs_21_cast = add(x = var_1048_cast, y = inputs_19_cast)[name = tensor("inputs_21_cast")]; + tensor var_1052 = const()[name = tensor("op_1052"), val = tensor([1])]; + tensor channels_mean_21_cast = reduce_mean(axes = var_1052, keep_dims = var_644, x = inputs_21_cast)[name = tensor("channels_mean_21_cast")]; + tensor zero_mean_21_cast = sub(x = inputs_21_cast, y = channels_mean_21_cast)[name = tensor("zero_mean_21_cast")]; + tensor zero_mean_sq_21_cast = mul(x = zero_mean_21_cast, y = zero_mean_21_cast)[name = tensor("zero_mean_sq_21_cast")]; + tensor var_1056 = const()[name = tensor("op_1056"), val = tensor([1])]; + tensor var_1057_cast = reduce_mean(axes = var_1056, keep_dims = var_644, x = zero_mean_sq_21_cast)[name = tensor("op_1057_cast")]; + tensor var_1058_to_fp16 = const()[name = tensor("op_1058_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1059_cast = add(x = var_1057_cast, y = var_1058_to_fp16)[name = tensor("op_1059_cast")]; + tensor denom_21_epsilon_0_to_fp16 = const()[name = tensor("denom_21_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_21_cast = rsqrt(epsilon = denom_21_epsilon_0_to_fp16, x = var_1059_cast)[name = tensor("denom_21_cast")]; + tensor out_21_cast = mul(x = zero_mean_21_cast, y = denom_21_cast)[name = tensor("out_21_cast")]; + tensor var_1063_to_fp16 = const()[name = tensor("op_1063_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29007872)))]; + tensor var_1064_cast = add(x = out_21_cast, y = var_1063_to_fp16)[name = tensor("op_1064_cast")]; + tensor var_1066_to_fp16 = const()[name = tensor("op_1066_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29009216)))]; + tensor hidden_states_65_cast = mul(x = var_1064_cast, y = var_1066_to_fp16)[name = tensor("hidden_states_65_cast")]; + tensor var_1073 = const()[name = tensor("op_1073"), val = tensor([1, 1])]; + tensor var_1075 = const()[name = tensor("op_1075"), val = tensor([1, 1])]; + tensor q_15_pad_type_0 = const()[name = tensor("q_15_pad_type_0"), val = tensor("custom")]; + tensor q_15_pad_0 = const()[name = tensor("q_15_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29010560))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29317824))), name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_15_cast = conv(dilations = var_1075, groups = var_649, pad = q_15_pad_0, pad_type = q_15_pad_type_0, strides = var_1073, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_65_cast)[name = tensor("q_15_cast")]; + tensor var_1079 = const()[name = tensor("op_1079"), val = tensor([1, 1])]; + tensor var_1081 = const()[name = tensor("op_1081"), val = tensor([1, 1])]; + tensor k_15_pad_type_0 = const()[name = tensor("k_15_pad_type_0"), val = tensor("custom")]; + tensor k_15_pad_0 = const()[name = tensor("k_15_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29318016))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29686720))), name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([640, 768, 1, 1])]; + tensor k_15_cast = conv(dilations = var_1081, groups = var_649, pad = k_15_pad_0, pad_type = k_15_pad_type_0, strides = var_1079, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_15_cast")]; + tensor var_1085 = const()[name = tensor("op_1085"), val = tensor([1, 1])]; + tensor var_1087 = const()[name = tensor("op_1087"), val = tensor([1, 1])]; + tensor v_15_pad_type_0 = const()[name = tensor("v_15_pad_type_0"), val = tensor("custom")]; + tensor v_15_pad_0 = const()[name = tensor("v_15_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(29686912))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30055616))), name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([640, 768, 1, 1])]; + tensor v_15_cast = conv(dilations = var_1087, groups = var_649, pad = v_15_pad_0, pad_type = v_15_pad_type_0, strides = var_1085, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_15_cast")]; + tensor var_1091 = const()[name = tensor("op_1091"), val = tensor([2, 8, 80, -1])]; + tensor var_1092_cast = reshape(shape = var_1091, x = q_15_cast)[name = tensor("op_1092_cast")]; + tensor var_1093 = const()[name = tensor("op_1093"), val = tensor([2, 8, 80, -1])]; + tensor var_1094_cast = reshape(shape = var_1093, x = k_15_cast)[name = tensor("op_1094_cast")]; + tensor var_1095 = const()[name = tensor("op_1095"), val = tensor([2, 8, 80, -1])]; + tensor var_1096_cast = reshape(shape = var_1095, x = v_15_cast)[name = tensor("op_1096_cast")]; + tensor attn_weights_29_transpose_x_0 = const()[name = tensor("attn_weights_29_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_29_transpose_y_0 = const()[name = tensor("attn_weights_29_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_29_cast = matmul(transpose_x = attn_weights_29_transpose_x_0, transpose_y = attn_weights_29_transpose_y_0, x = var_1092_cast, y = var_1094_cast)[name = tensor("attn_weights_29_cast")]; + tensor attn_weights_31_cast = mul(x = attn_weights_29_cast, y = var_640_to_fp16)[name = tensor("attn_weights_31_cast")]; + tensor var_1100_cast = softmax(axis = var_633, x = attn_weights_31_cast)[name = tensor("op_1100_cast")]; + tensor attn_15_transpose_x_0 = const()[name = tensor("attn_15_transpose_x_0"), val = tensor(false)]; + tensor attn_15_transpose_y_0 = const()[name = tensor("attn_15_transpose_y_0"), val = tensor(true)]; + tensor attn_15_cast = matmul(transpose_x = attn_15_transpose_x_0, transpose_y = attn_15_transpose_y_0, x = var_1096_cast, y = var_1100_cast)[name = tensor("attn_15_cast")]; + tensor var_1104 = const()[name = tensor("op_1104"), val = tensor([2, 640, 1, -1])]; + tensor input_107_cast = reshape(shape = var_1104, x = attn_15_cast)[name = tensor("input_107_cast")]; + tensor var_1109 = const()[name = tensor("op_1109"), val = tensor([1, 1])]; + tensor var_1111 = const()[name = tensor("op_1111"), val = tensor([1, 1])]; + tensor var_1113_pad_type_0 = const()[name = tensor("op_1113_pad_type_0"), val = tensor("custom")]; + tensor var_1113_pad_0 = const()[name = tensor("op_1113_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30055808))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30363072))), name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30363264)))]; + tensor var_1113_cast = conv(bias = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_1111, groups = var_649, pad = var_1113_pad_0, pad_type = var_1113_pad_type_0, strides = var_1109, weight = down_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_107_cast)[name = tensor("op_1113_cast")]; + tensor inputs_23_cast = add(x = var_1113_cast, y = inputs_21_cast)[name = tensor("inputs_23_cast")]; + tensor var_1117 = const()[name = tensor("op_1117"), val = tensor([1])]; + tensor channels_mean_23_cast = reduce_mean(axes = var_1117, keep_dims = var_644, x = inputs_23_cast)[name = tensor("channels_mean_23_cast")]; + tensor zero_mean_23_cast = sub(x = inputs_23_cast, y = channels_mean_23_cast)[name = tensor("zero_mean_23_cast")]; + tensor zero_mean_sq_23_cast = mul(x = zero_mean_23_cast, y = zero_mean_23_cast)[name = tensor("zero_mean_sq_23_cast")]; + tensor var_1121 = const()[name = tensor("op_1121"), val = tensor([1])]; + tensor var_1122_cast = reduce_mean(axes = var_1121, keep_dims = var_644, x = zero_mean_sq_23_cast)[name = tensor("op_1122_cast")]; + tensor var_1123_to_fp16 = const()[name = tensor("op_1123_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1124_cast = add(x = var_1122_cast, y = var_1123_to_fp16)[name = tensor("op_1124_cast")]; + tensor denom_23_epsilon_0_to_fp16 = const()[name = tensor("denom_23_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_23_cast = rsqrt(epsilon = denom_23_epsilon_0_to_fp16, x = var_1124_cast)[name = tensor("denom_23_cast")]; + tensor out_23_cast = mul(x = zero_mean_23_cast, y = denom_23_cast)[name = tensor("out_23_cast")]; + tensor var_1128_to_fp16 = const()[name = tensor("op_1128_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30364608)))]; + tensor var_1129_cast = add(x = out_23_cast, y = var_1128_to_fp16)[name = tensor("op_1129_cast")]; + tensor var_1131_to_fp16 = const()[name = tensor("op_1131_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30365952)))]; + tensor input_109_cast = mul(x = var_1129_cast, y = var_1131_to_fp16)[name = tensor("input_109_cast")]; + tensor var_1139 = const()[name = tensor("op_1139"), val = tensor([1, 1])]; + tensor var_1141 = const()[name = tensor("op_1141"), val = tensor([1, 1])]; + tensor var_1143_pad_type_0 = const()[name = tensor("op_1143_pad_type_0"), val = tensor("custom")]; + tensor var_1143_pad_0 = const()[name = tensor("op_1143_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(30367296))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32824960))), name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([5120, 640, 1, 1])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32825152))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32829056))), name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([5120])]; + tensor var_1143_cast = conv(bias = down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_1141, groups = var_649, pad = var_1143_pad_0, pad_type = var_1143_pad_type_0, strides = var_1139, weight = down_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_109_cast)[name = tensor("op_1143_cast")]; + tensor var_1144_split_sizes_0 = const()[name = tensor("op_1144_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_1144_axis_0 = const()[name = tensor("op_1144_axis_0"), val = tensor(1)]; + tensor var_1144_cast_0, tensor var_1144_cast_1 = split(axis = var_1144_axis_0, split_sizes = var_1144_split_sizes_0, x = var_1143_cast)[name = tensor("op_1144_cast")]; + tensor var_1146_mode_0 = const()[name = tensor("op_1146_mode_0"), val = tensor("EXACT")]; + tensor var_1146_cast = gelu(mode = var_1146_mode_0, x = var_1144_cast_1)[name = tensor("op_1146_cast")]; + tensor input_111_cast = mul(x = var_1144_cast_0, y = var_1146_cast)[name = tensor("input_111_cast")]; + tensor var_1150 = const()[name = tensor("op_1150"), val = tensor([1, 1])]; + tensor var_1152 = const()[name = tensor("op_1152"), val = tensor([1, 1])]; + tensor var_1154_pad_type_0 = const()[name = tensor("op_1154_pad_type_0"), val = tensor("custom")]; + tensor var_1154_pad_0 = const()[name = tensor("op_1154_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(32829248))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34058112))), name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([640, 2560, 1, 1])]; + tensor down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34058304)))]; + tensor var_1154_cast = conv(bias = down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_1152, groups = var_649, pad = var_1154_pad_0, pad_type = var_1154_pad_type_0, strides = var_1150, weight = down_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_111_cast)[name = tensor("op_1154_cast")]; + tensor hidden_states_69_cast = add(x = var_1154_cast, y = inputs_23_cast)[name = tensor("hidden_states_69_cast")]; + tensor var_1156 = const()[name = tensor("op_1156"), val = tensor([2, 640, 32, 32])]; + tensor input_113_cast = reshape(shape = var_1156, x = hidden_states_69_cast)[name = tensor("input_113_cast")]; + tensor var_1160 = const()[name = tensor("op_1160"), val = tensor([1, 1])]; + tensor var_1162 = const()[name = tensor("op_1162"), val = tensor([1, 1])]; + tensor hidden_states_71_pad_type_0 = const()[name = tensor("hidden_states_71_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_71_pad_0 = const()[name = tensor("hidden_states_71_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_1_attentions_1_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34059648))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34366912))), name = tensor("down_blocks_1_attentions_1_proj_out_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor down_blocks_1_attentions_1_proj_out_bias_to_fp16 = const()[name = tensor("down_blocks_1_attentions_1_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34367104)))]; + tensor hidden_states_71_cast = conv(bias = down_blocks_1_attentions_1_proj_out_bias_to_fp16, dilations = var_1162, groups = var_649, pad = hidden_states_71_pad_0, pad_type = hidden_states_71_pad_type_0, strides = var_1160, weight = down_blocks_1_attentions_1_proj_out_weight_to_fp16_palettized, x = input_113_cast)[name = tensor("hidden_states_71_cast")]; + tensor input_115_cast = add(x = hidden_states_71_cast, y = hidden_states_59_cast)[name = tensor("input_115_cast")]; + tensor var_1169 = const()[name = tensor("op_1169"), val = tensor([2, 2])]; + tensor var_1171 = const()[name = tensor("op_1171"), val = tensor([1, 1])]; + tensor input_117_pad_type_0 = const()[name = tensor("input_117_pad_type_0"), val = tensor("custom")]; + tensor input_117_pad_0 = const()[name = tensor("input_117_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_1_downsamplers_0_conv_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(34368448))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(37133312))), name = tensor("down_blocks_1_downsamplers_0_conv_weight_to_fp16_palettized"), shape = tensor([640, 640, 3, 3])]; + tensor down_blocks_1_downsamplers_0_conv_bias_to_fp16 = const()[name = tensor("down_blocks_1_downsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(37133504)))]; + tensor input_117_cast = conv(bias = down_blocks_1_downsamplers_0_conv_bias_to_fp16, dilations = var_1171, groups = var_649, pad = input_117_pad_0, pad_type = input_117_pad_type_0, strides = var_1169, weight = down_blocks_1_downsamplers_0_conv_weight_to_fp16_palettized, x = input_115_cast)[name = tensor("input_117_cast")]; + tensor var_1179 = const()[name = tensor("op_1179"), val = tensor(3)]; + tensor var_1190 = const()[name = tensor("op_1190"), val = tensor(true)]; + tensor var_1195 = const()[name = tensor("op_1195"), val = tensor(1)]; + tensor reshape_48_shape_0 = const()[name = tensor("reshape_48_shape_0"), val = tensor([2, 32, 20, 16, 16])]; + tensor reshape_48_cast = reshape(shape = reshape_48_shape_0, x = input_117_cast)[name = tensor("reshape_48_cast")]; + tensor reduce_mean_36_axes_0 = const()[name = tensor("reduce_mean_36_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_36_keep_dims_0 = const()[name = tensor("reduce_mean_36_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_36_cast = reduce_mean(axes = reduce_mean_36_axes_0, keep_dims = reduce_mean_36_keep_dims_0, x = reshape_48_cast)[name = tensor("reduce_mean_36_cast")]; + tensor sub_24_cast = sub(x = reshape_48_cast, y = reduce_mean_36_cast)[name = tensor("sub_24_cast")]; + tensor square_12_cast = square(x = sub_24_cast)[name = tensor("square_12_cast")]; + tensor reduce_mean_38_axes_0 = const()[name = tensor("reduce_mean_38_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_38_keep_dims_0 = const()[name = tensor("reduce_mean_38_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_38_cast = reduce_mean(axes = reduce_mean_38_axes_0, keep_dims = reduce_mean_38_keep_dims_0, x = square_12_cast)[name = tensor("reduce_mean_38_cast")]; + tensor add_24_y_0_to_fp16 = const()[name = tensor("add_24_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_24_cast = add(x = reduce_mean_38_cast, y = add_24_y_0_to_fp16)[name = tensor("add_24_cast")]; + tensor sqrt_12_cast = sqrt(x = add_24_cast)[name = tensor("sqrt_12_cast")]; + tensor real_div_12_cast = real_div(x = sub_24_cast, y = sqrt_12_cast)[name = tensor("real_div_12_cast")]; + tensor reshape_49_shape_0 = const()[name = tensor("reshape_49_shape_0"), val = tensor([2, 640, 16, 16])]; + tensor reshape_49_cast = reshape(shape = reshape_49_shape_0, x = real_div_12_cast)[name = tensor("reshape_49_cast")]; + tensor add_25_gamma_0_to_fp16 = const()[name = tensor("add_25_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(37134848)))]; + 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(37136192)))]; + 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_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_49_cast)[name = tensor("add_25_cast")]; + tensor input_121_cast = silu(x = add_25_cast)[name = tensor("input_121_cast")]; + tensor var_1218 = const()[name = tensor("op_1218"), val = tensor([1, 1])]; + tensor var_1220 = const()[name = tensor("op_1220"), val = tensor([1, 1])]; + tensor hidden_states_73_pad_type_0 = const()[name = tensor("hidden_states_73_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_73_pad_0 = const()[name = tensor("hidden_states_73_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_2_resnets_0_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(37137536))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42667200))), name = tensor("down_blocks_2_resnets_0_conv1_weight_to_fp16_palettized"), shape = tensor([1280, 640, 3, 3])]; + tensor down_blocks_2_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("down_blocks_2_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42667392)))]; + tensor hidden_states_73_cast = conv(bias = down_blocks_2_resnets_0_conv1_bias_to_fp16, dilations = var_1220, groups = var_1195, pad = hidden_states_73_pad_0, pad_type = hidden_states_73_pad_type_0, strides = var_1218, weight = down_blocks_2_resnets_0_conv1_weight_to_fp16_palettized, x = input_121_cast)[name = tensor("hidden_states_73_cast")]; + tensor var_1226 = const()[name = tensor("op_1226"), val = tensor([1, 1])]; + tensor var_1228 = const()[name = tensor("op_1228"), val = tensor([1, 1])]; + tensor temb_9_pad_type_0 = const()[name = tensor("temb_9_pad_type_0"), val = tensor("custom")]; + tensor temb_9_pad_0 = const()[name = tensor("temb_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_resnets_0_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(42670016))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(43898880))), name = tensor("down_blocks_2_resnets_0_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("down_blocks_2_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(43899072)))]; + tensor temb_9_cast = conv(bias = down_blocks_2_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_1228, groups = var_1195, pad = temb_9_pad_0, pad_type = temb_9_pad_type_0, strides = var_1226, weight = down_blocks_2_resnets_0_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor("temb_9_cast")]; + tensor input_125_cast = add(x = hidden_states_73_cast, y = temb_9_cast)[name = tensor("input_125_cast")]; + tensor reshape_52_shape_0 = const()[name = tensor("reshape_52_shape_0"), val = tensor([2, 32, 40, 16, 16])]; + tensor reshape_52_cast = reshape(shape = reshape_52_shape_0, x = input_125_cast)[name = tensor("reshape_52_cast")]; + tensor reduce_mean_39_axes_0 = const()[name = tensor("reduce_mean_39_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_39_keep_dims_0 = const()[name = tensor("reduce_mean_39_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_39_cast = reduce_mean(axes = reduce_mean_39_axes_0, keep_dims = reduce_mean_39_keep_dims_0, x = reshape_52_cast)[name = tensor("reduce_mean_39_cast")]; + tensor sub_26_cast = sub(x = reshape_52_cast, y = reduce_mean_39_cast)[name = tensor("sub_26_cast")]; + tensor square_13_cast = square(x = sub_26_cast)[name = tensor("square_13_cast")]; + tensor reduce_mean_41_axes_0 = const()[name = tensor("reduce_mean_41_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_41_keep_dims_0 = const()[name = tensor("reduce_mean_41_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_41_cast = reduce_mean(axes = reduce_mean_41_axes_0, keep_dims = reduce_mean_41_keep_dims_0, x = square_13_cast)[name = tensor("reduce_mean_41_cast")]; + tensor add_26_y_0_to_fp16 = const()[name = tensor("add_26_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_26_cast = add(x = reduce_mean_41_cast, y = add_26_y_0_to_fp16)[name = tensor("add_26_cast")]; + tensor sqrt_13_cast = sqrt(x = add_26_cast)[name = tensor("sqrt_13_cast")]; + tensor real_div_13_cast = real_div(x = sub_26_cast, y = sqrt_13_cast)[name = tensor("real_div_13_cast")]; + tensor reshape_53_shape_0 = const()[name = tensor("reshape_53_shape_0"), val = tensor([2, 1280, 16, 16])]; + tensor reshape_53_cast = reshape(shape = reshape_53_shape_0, x = real_div_13_cast)[name = tensor("reshape_53_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(43901696)))]; + 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(43904320)))]; + 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(43906944)))]; + 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(43909568)))]; + 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)[name = tensor("add_27_cast")]; + tensor input_129_cast = silu(x = add_27_cast)[name = tensor("input_129_cast")]; + tensor var_1238 = const()[name = tensor("op_1238"), val = tensor([1, 1])]; + tensor var_1240 = const()[name = tensor("op_1240"), val = tensor([1, 1])]; + tensor hidden_states_75_pad_type_0 = const()[name = tensor("hidden_states_75_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_75_pad_0 = const()[name = tensor("hidden_states_75_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_2_resnets_0_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(43912192))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(54971456))), name = tensor("down_blocks_2_resnets_0_conv2_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor down_blocks_2_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("down_blocks_2_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(54971648)))]; + tensor hidden_states_75_cast = conv(bias = down_blocks_2_resnets_0_conv2_bias_to_fp16, dilations = var_1240, groups = var_1195, pad = hidden_states_75_pad_0, pad_type = hidden_states_75_pad_type_0, strides = var_1238, weight = down_blocks_2_resnets_0_conv2_weight_to_fp16_palettized, x = input_129_cast)[name = tensor("hidden_states_75_cast")]; + tensor var_1245 = const()[name = tensor("op_1245"), val = tensor([1, 1])]; + tensor var_1247 = const()[name = tensor("op_1247"), val = tensor([1, 1])]; + tensor x_3_pad_type_0 = const()[name = tensor("x_3_pad_type_0"), val = tensor("custom")]; + tensor x_3_pad_0 = const()[name = tensor("x_3_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_resnets_0_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(54974272))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(55588736))), name = tensor("down_blocks_2_resnets_0_conv_shortcut_weight_to_fp16_palettized"), shape = tensor([1280, 640, 1, 1])]; + tensor down_blocks_2_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("down_blocks_2_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(55588928)))]; + tensor x_3_cast = conv(bias = down_blocks_2_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_1247, groups = var_1195, pad = x_3_pad_0, pad_type = x_3_pad_type_0, strides = var_1245, weight = down_blocks_2_resnets_0_conv_shortcut_weight_to_fp16_palettized, x = input_117_cast)[name = tensor("x_3_cast")]; + tensor hidden_states_77_cast = add(x = x_3_cast, y = hidden_states_75_cast)[name = tensor("hidden_states_77_cast")]; + tensor reshape_56_shape_0 = const()[name = tensor("reshape_56_shape_0"), val = tensor([2, 32, 40, 16, 16])]; + tensor reshape_56_cast = reshape(shape = reshape_56_shape_0, x = hidden_states_77_cast)[name = tensor("reshape_56_cast")]; + tensor reduce_mean_42_axes_0 = const()[name = tensor("reduce_mean_42_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_42_keep_dims_0 = const()[name = tensor("reduce_mean_42_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_42_cast = reduce_mean(axes = reduce_mean_42_axes_0, keep_dims = reduce_mean_42_keep_dims_0, x = reshape_56_cast)[name = tensor("reduce_mean_42_cast")]; + tensor sub_28_cast = sub(x = reshape_56_cast, y = reduce_mean_42_cast)[name = tensor("sub_28_cast")]; + tensor square_14_cast = square(x = sub_28_cast)[name = tensor("square_14_cast")]; + tensor reduce_mean_44_axes_0 = const()[name = tensor("reduce_mean_44_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_44_keep_dims_0 = const()[name = tensor("reduce_mean_44_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_44_cast = reduce_mean(axes = reduce_mean_44_axes_0, keep_dims = reduce_mean_44_keep_dims_0, x = square_14_cast)[name = tensor("reduce_mean_44_cast")]; + tensor add_28_y_0_to_fp16 = const()[name = tensor("add_28_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_28_cast = add(x = reduce_mean_44_cast, y = add_28_y_0_to_fp16)[name = tensor("add_28_cast")]; + tensor sqrt_14_cast = sqrt(x = add_28_cast)[name = tensor("sqrt_14_cast")]; + tensor real_div_14_cast = real_div(x = sub_28_cast, y = sqrt_14_cast)[name = tensor("real_div_14_cast")]; + tensor reshape_57_shape_0 = const()[name = tensor("reshape_57_shape_0"), val = tensor([2, 1280, 16, 16])]; + tensor reshape_57_cast = reshape(shape = reshape_57_shape_0, x = real_div_14_cast)[name = tensor("reshape_57_cast")]; + tensor add_29_gamma_0_to_fp16 = const()[name = tensor("add_29_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(55591552)))]; + 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(55594176)))]; + 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_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_57_cast)[name = tensor("add_29_cast")]; + tensor var_1267 = const()[name = tensor("op_1267"), val = tensor([1, 1])]; + tensor var_1269 = const()[name = tensor("op_1269"), val = tensor([1, 1])]; + tensor hidden_states_79_pad_type_0 = const()[name = tensor("hidden_states_79_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_79_pad_0 = const()[name = tensor("hidden_states_79_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(55596800))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(56825664))), name = tensor("down_blocks_2_attentions_0_proj_in_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(56825856)))]; + tensor hidden_states_79_cast = conv(bias = down_blocks_2_attentions_0_proj_in_bias_to_fp16, dilations = var_1269, groups = var_1195, pad = hidden_states_79_pad_0, pad_type = hidden_states_79_pad_type_0, strides = var_1267, weight = down_blocks_2_attentions_0_proj_in_weight_to_fp16_palettized, x = add_29_cast)[name = tensor("hidden_states_79_cast")]; + tensor var_1274 = const()[name = tensor("op_1274"), val = tensor([2, 1280, 1, 256])]; + tensor inputs_25_cast = reshape(shape = var_1274, x = hidden_states_79_cast)[name = tensor("inputs_25_cast")]; + tensor var_1284 = const()[name = tensor("op_1284"), val = tensor([1])]; + tensor channels_mean_25_cast = reduce_mean(axes = var_1284, keep_dims = var_1190, x = inputs_25_cast)[name = tensor("channels_mean_25_cast")]; + tensor zero_mean_25_cast = sub(x = inputs_25_cast, y = channels_mean_25_cast)[name = tensor("zero_mean_25_cast")]; + tensor zero_mean_sq_25_cast = mul(x = zero_mean_25_cast, y = zero_mean_25_cast)[name = tensor("zero_mean_sq_25_cast")]; + tensor var_1288 = const()[name = tensor("op_1288"), val = tensor([1])]; + tensor var_1289_cast = reduce_mean(axes = var_1288, keep_dims = var_1190, x = zero_mean_sq_25_cast)[name = tensor("op_1289_cast")]; + tensor var_1290_to_fp16 = const()[name = tensor("op_1290_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1291_cast = add(x = var_1289_cast, y = var_1290_to_fp16)[name = tensor("op_1291_cast")]; + tensor denom_25_epsilon_0_to_fp16 = const()[name = tensor("denom_25_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_25_cast = rsqrt(epsilon = denom_25_epsilon_0_to_fp16, x = var_1291_cast)[name = tensor("denom_25_cast")]; + tensor out_25_cast = mul(x = zero_mean_25_cast, y = denom_25_cast)[name = tensor("out_25_cast")]; + tensor var_1295_to_fp16 = const()[name = tensor("op_1295_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(56828480)))]; + tensor var_1296_cast = add(x = out_25_cast, y = var_1295_to_fp16)[name = tensor("op_1296_cast")]; + tensor var_1298_to_fp16 = const()[name = tensor("op_1298_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(56831104)))]; + tensor hidden_states_81_cast = mul(x = var_1296_cast, y = var_1298_to_fp16)[name = tensor("hidden_states_81_cast")]; + tensor var_1305 = const()[name = tensor("op_1305"), val = tensor([1, 1])]; + tensor var_1307 = const()[name = tensor("op_1307"), val = tensor([1, 1])]; + tensor q_17_pad_type_0 = const()[name = tensor("q_17_pad_type_0"), val = tensor("custom")]; + tensor q_17_pad_0 = const()[name = tensor("q_17_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(56833728))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58062592))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_17_cast = conv(dilations = var_1307, groups = var_1195, pad = q_17_pad_0, pad_type = q_17_pad_type_0, strides = var_1305, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_81_cast)[name = tensor("q_17_cast")]; + tensor var_1311 = const()[name = tensor("op_1311"), val = tensor([1, 1])]; + tensor var_1313 = const()[name = tensor("op_1313"), val = tensor([1, 1])]; + tensor k_17_pad_type_0 = const()[name = tensor("k_17_pad_type_0"), val = tensor("custom")]; + tensor k_17_pad_0 = const()[name = tensor("k_17_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(58062784))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(59291648))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_17_cast = conv(dilations = var_1313, groups = var_1195, pad = k_17_pad_0, pad_type = k_17_pad_type_0, strides = var_1311, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_81_cast)[name = tensor("k_17_cast")]; + tensor var_1317 = const()[name = tensor("op_1317"), val = tensor([1, 1])]; + tensor var_1319 = const()[name = tensor("op_1319"), val = tensor([1, 1])]; + tensor v_17_pad_type_0 = const()[name = tensor("v_17_pad_type_0"), val = tensor("custom")]; + tensor v_17_pad_0 = const()[name = tensor("v_17_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(59291840))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(60520704))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_17_cast = conv(dilations = var_1319, groups = var_1195, pad = v_17_pad_0, pad_type = v_17_pad_type_0, strides = var_1317, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_81_cast)[name = tensor("v_17_cast")]; + tensor var_1323 = const()[name = tensor("op_1323"), val = tensor([2, 8, 160, -1])]; + tensor var_1324_cast = reshape(shape = var_1323, x = q_17_cast)[name = tensor("op_1324_cast")]; + tensor var_1325 = const()[name = tensor("op_1325"), val = tensor([2, 8, 160, -1])]; + tensor var_1326_cast = reshape(shape = var_1325, x = k_17_cast)[name = tensor("op_1326_cast")]; + tensor var_1327 = const()[name = tensor("op_1327"), val = tensor([2, 8, 160, -1])]; + tensor var_1328_cast = reshape(shape = var_1327, x = v_17_cast)[name = tensor("op_1328_cast")]; + tensor attn_weights_33_transpose_x_0 = const()[name = tensor("attn_weights_33_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_33_transpose_y_0 = const()[name = tensor("attn_weights_33_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_33_cast = matmul(transpose_x = attn_weights_33_transpose_x_0, transpose_y = attn_weights_33_transpose_y_0, x = var_1324_cast, y = var_1326_cast)[name = tensor("attn_weights_33_cast")]; + tensor var_1186_to_fp16 = const()[name = tensor("op_1186_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor attn_weights_35_cast = mul(x = attn_weights_33_cast, y = var_1186_to_fp16)[name = tensor("attn_weights_35_cast")]; + tensor var_1332_cast = softmax(axis = var_1179, x = attn_weights_35_cast)[name = tensor("op_1332_cast")]; + tensor attn_17_transpose_x_0 = const()[name = tensor("attn_17_transpose_x_0"), val = tensor(false)]; + tensor attn_17_transpose_y_0 = const()[name = tensor("attn_17_transpose_y_0"), val = tensor(true)]; + tensor attn_17_cast = matmul(transpose_x = attn_17_transpose_x_0, transpose_y = attn_17_transpose_y_0, x = var_1328_cast, y = var_1332_cast)[name = tensor("attn_17_cast")]; + tensor var_1336 = const()[name = tensor("op_1336"), val = tensor([2, 1280, 1, -1])]; + tensor input_133_cast = reshape(shape = var_1336, x = attn_17_cast)[name = tensor("input_133_cast")]; + tensor var_1341 = const()[name = tensor("op_1341"), val = tensor([1, 1])]; + tensor var_1343 = const()[name = tensor("op_1343"), val = tensor([1, 1])]; + tensor var_1345_pad_type_0 = const()[name = tensor("op_1345_pad_type_0"), val = tensor("custom")]; + tensor var_1345_pad_0 = const()[name = tensor("op_1345_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(60520896))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61749760))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61749952)))]; + tensor var_1345_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_1343, groups = var_1195, pad = var_1345_pad_0, pad_type = var_1345_pad_type_0, strides = var_1341, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_133_cast)[name = tensor("op_1345_cast")]; + tensor inputs_27_cast = add(x = var_1345_cast, y = inputs_25_cast)[name = tensor("inputs_27_cast")]; + tensor var_1349 = const()[name = tensor("op_1349"), val = tensor([1])]; + tensor channels_mean_27_cast = reduce_mean(axes = var_1349, keep_dims = var_1190, x = inputs_27_cast)[name = tensor("channels_mean_27_cast")]; + tensor zero_mean_27_cast = sub(x = inputs_27_cast, y = channels_mean_27_cast)[name = tensor("zero_mean_27_cast")]; + tensor zero_mean_sq_27_cast = mul(x = zero_mean_27_cast, y = zero_mean_27_cast)[name = tensor("zero_mean_sq_27_cast")]; + tensor var_1353 = const()[name = tensor("op_1353"), val = tensor([1])]; + tensor var_1354_cast = reduce_mean(axes = var_1353, keep_dims = var_1190, x = zero_mean_sq_27_cast)[name = tensor("op_1354_cast")]; + tensor var_1355_to_fp16 = const()[name = tensor("op_1355_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1356_cast = add(x = var_1354_cast, y = var_1355_to_fp16)[name = tensor("op_1356_cast")]; + tensor denom_27_epsilon_0_to_fp16 = const()[name = tensor("denom_27_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_27_cast = rsqrt(epsilon = denom_27_epsilon_0_to_fp16, x = var_1356_cast)[name = tensor("denom_27_cast")]; + tensor out_27_cast = mul(x = zero_mean_27_cast, y = denom_27_cast)[name = tensor("out_27_cast")]; + tensor var_1360_to_fp16 = const()[name = tensor("op_1360_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61752576)))]; + tensor var_1361_cast = add(x = out_27_cast, y = var_1360_to_fp16)[name = tensor("op_1361_cast")]; + tensor var_1363_to_fp16 = const()[name = tensor("op_1363_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61755200)))]; + tensor hidden_states_83_cast = mul(x = var_1361_cast, y = var_1363_to_fp16)[name = tensor("hidden_states_83_cast")]; + tensor var_1370 = const()[name = tensor("op_1370"), val = tensor([1, 1])]; + tensor var_1372 = const()[name = tensor("op_1372"), val = tensor([1, 1])]; + tensor q_19_pad_type_0 = const()[name = tensor("q_19_pad_type_0"), val = tensor("custom")]; + tensor q_19_pad_0 = const()[name = tensor("q_19_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(61757824))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(62986688))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_19_cast = conv(dilations = var_1372, groups = var_1195, pad = q_19_pad_0, pad_type = q_19_pad_type_0, strides = var_1370, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_83_cast)[name = tensor("q_19_cast")]; + tensor var_1376 = const()[name = tensor("op_1376"), val = tensor([1, 1])]; + tensor var_1378 = const()[name = tensor("op_1378"), val = tensor([1, 1])]; + tensor k_19_pad_type_0 = const()[name = tensor("k_19_pad_type_0"), val = tensor("custom")]; + tensor k_19_pad_0 = const()[name = tensor("k_19_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(62986880))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63724224))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 768, 1, 1])]; + tensor k_19_cast = conv(dilations = var_1378, groups = var_1195, pad = k_19_pad_0, pad_type = k_19_pad_type_0, strides = var_1376, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_19_cast")]; + tensor var_1382 = const()[name = tensor("op_1382"), val = tensor([1, 1])]; + tensor var_1384 = const()[name = tensor("op_1384"), val = tensor([1, 1])]; + tensor v_19_pad_type_0 = const()[name = tensor("v_19_pad_type_0"), val = tensor("custom")]; + tensor v_19_pad_0 = const()[name = tensor("v_19_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(63724416))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64461760))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 768, 1, 1])]; + tensor v_19_cast = conv(dilations = var_1384, groups = var_1195, pad = v_19_pad_0, pad_type = v_19_pad_type_0, strides = var_1382, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_19_cast")]; + tensor var_1388 = const()[name = tensor("op_1388"), val = tensor([2, 8, 160, -1])]; + tensor var_1389_cast = reshape(shape = var_1388, x = q_19_cast)[name = tensor("op_1389_cast")]; + tensor var_1390 = const()[name = tensor("op_1390"), val = tensor([2, 8, 160, -1])]; + tensor var_1391_cast = reshape(shape = var_1390, x = k_19_cast)[name = tensor("op_1391_cast")]; + tensor var_1392 = const()[name = tensor("op_1392"), val = tensor([2, 8, 160, -1])]; + tensor var_1393_cast = reshape(shape = var_1392, x = v_19_cast)[name = tensor("op_1393_cast")]; + tensor attn_weights_37_transpose_x_0 = const()[name = tensor("attn_weights_37_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_37_transpose_y_0 = const()[name = tensor("attn_weights_37_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_37_cast = matmul(transpose_x = attn_weights_37_transpose_x_0, transpose_y = attn_weights_37_transpose_y_0, x = var_1389_cast, y = var_1391_cast)[name = tensor("attn_weights_37_cast")]; + tensor attn_weights_39_cast = mul(x = attn_weights_37_cast, y = var_1186_to_fp16)[name = tensor("attn_weights_39_cast")]; + tensor var_1397_cast = softmax(axis = var_1179, x = attn_weights_39_cast)[name = tensor("op_1397_cast")]; + tensor attn_19_transpose_x_0 = const()[name = tensor("attn_19_transpose_x_0"), val = tensor(false)]; + tensor attn_19_transpose_y_0 = const()[name = tensor("attn_19_transpose_y_0"), val = tensor(true)]; + tensor attn_19_cast = matmul(transpose_x = attn_19_transpose_x_0, transpose_y = attn_19_transpose_y_0, x = var_1393_cast, y = var_1397_cast)[name = tensor("attn_19_cast")]; + tensor var_1401 = const()[name = tensor("op_1401"), val = tensor([2, 1280, 1, -1])]; + tensor input_135_cast = reshape(shape = var_1401, x = attn_19_cast)[name = tensor("input_135_cast")]; + tensor var_1406 = const()[name = tensor("op_1406"), val = tensor([1, 1])]; + tensor var_1408 = const()[name = tensor("op_1408"), val = tensor([1, 1])]; + tensor var_1410_pad_type_0 = const()[name = tensor("op_1410_pad_type_0"), val = tensor("custom")]; + tensor var_1410_pad_0 = const()[name = tensor("op_1410_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(64461952))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65690816))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65691008)))]; + tensor var_1410_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_1408, groups = var_1195, pad = var_1410_pad_0, pad_type = var_1410_pad_type_0, strides = var_1406, weight = down_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_135_cast)[name = tensor("op_1410_cast")]; + tensor inputs_29_cast = add(x = var_1410_cast, y = inputs_27_cast)[name = tensor("inputs_29_cast")]; + tensor var_1414 = const()[name = tensor("op_1414"), val = tensor([1])]; + tensor channels_mean_29_cast = reduce_mean(axes = var_1414, keep_dims = var_1190, x = inputs_29_cast)[name = tensor("channels_mean_29_cast")]; + tensor zero_mean_29_cast = sub(x = inputs_29_cast, y = channels_mean_29_cast)[name = tensor("zero_mean_29_cast")]; + tensor zero_mean_sq_29_cast = mul(x = zero_mean_29_cast, y = zero_mean_29_cast)[name = tensor("zero_mean_sq_29_cast")]; + tensor var_1418 = const()[name = tensor("op_1418"), val = tensor([1])]; + tensor var_1419_cast = reduce_mean(axes = var_1418, keep_dims = var_1190, x = zero_mean_sq_29_cast)[name = tensor("op_1419_cast")]; + tensor var_1420_to_fp16 = const()[name = tensor("op_1420_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1421_cast = add(x = var_1419_cast, y = var_1420_to_fp16)[name = tensor("op_1421_cast")]; + tensor denom_29_epsilon_0_to_fp16 = const()[name = tensor("denom_29_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_29_cast = rsqrt(epsilon = denom_29_epsilon_0_to_fp16, x = var_1421_cast)[name = tensor("denom_29_cast")]; + tensor out_29_cast = mul(x = zero_mean_29_cast, y = denom_29_cast)[name = tensor("out_29_cast")]; + tensor var_1425_to_fp16 = const()[name = tensor("op_1425_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65693632)))]; + tensor var_1426_cast = add(x = out_29_cast, y = var_1425_to_fp16)[name = tensor("op_1426_cast")]; + tensor var_1428_to_fp16 = const()[name = tensor("op_1428_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65696256)))]; + tensor input_137_cast = mul(x = var_1426_cast, y = var_1428_to_fp16)[name = tensor("input_137_cast")]; + tensor var_1436 = const()[name = tensor("op_1436"), val = tensor([1, 1])]; + tensor var_1438 = const()[name = tensor("op_1438"), val = tensor([1, 1])]; + tensor var_1440_pad_type_0 = const()[name = tensor("op_1440_pad_type_0"), val = tensor("custom")]; + tensor var_1440_pad_0 = const()[name = tensor("op_1440_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(65698880))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(75529344))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(75529536))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(75537280))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_1440_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_1438, groups = var_1195, pad = var_1440_pad_0, pad_type = var_1440_pad_type_0, strides = var_1436, weight = down_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_137_cast)[name = tensor("op_1440_cast")]; + tensor var_1441_split_sizes_0 = const()[name = tensor("op_1441_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_1441_axis_0 = const()[name = tensor("op_1441_axis_0"), val = tensor(1)]; + tensor var_1441_cast_0, tensor var_1441_cast_1 = split(axis = var_1441_axis_0, split_sizes = var_1441_split_sizes_0, x = var_1440_cast)[name = tensor("op_1441_cast")]; + tensor var_1443_mode_0 = const()[name = tensor("op_1443_mode_0"), val = tensor("EXACT")]; + tensor var_1443_cast = gelu(mode = var_1443_mode_0, x = var_1441_cast_1)[name = tensor("op_1443_cast")]; + tensor input_139_cast = mul(x = var_1441_cast_0, y = var_1443_cast)[name = tensor("input_139_cast")]; + tensor var_1447 = const()[name = tensor("op_1447"), val = tensor([1, 1])]; + tensor var_1449 = const()[name = tensor("op_1449"), val = tensor([1, 1])]; + tensor var_1451_pad_type_0 = const()[name = tensor("op_1451_pad_type_0"), val = tensor("custom")]; + tensor var_1451_pad_0 = const()[name = tensor("op_1451_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(75537472))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80452736))), name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80452928)))]; + tensor var_1451_cast = conv(bias = down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_1449, groups = var_1195, pad = var_1451_pad_0, pad_type = var_1451_pad_type_0, strides = var_1447, weight = down_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_139_cast)[name = tensor("op_1451_cast")]; + tensor hidden_states_87_cast = add(x = var_1451_cast, y = inputs_29_cast)[name = tensor("hidden_states_87_cast")]; + tensor var_1453 = const()[name = tensor("op_1453"), val = tensor([2, 1280, 16, 16])]; + tensor input_141_cast = reshape(shape = var_1453, x = hidden_states_87_cast)[name = tensor("input_141_cast")]; + tensor var_1457 = const()[name = tensor("op_1457"), val = tensor([1, 1])]; + tensor var_1459 = const()[name = tensor("op_1459"), val = tensor([1, 1])]; + tensor hidden_states_89_pad_type_0 = const()[name = tensor("hidden_states_89_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_89_pad_0 = const()[name = tensor("hidden_states_89_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_0_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(80455552))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(81684416))), name = tensor("down_blocks_2_attentions_0_proj_out_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_0_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(81684608)))]; + tensor hidden_states_89_cast = conv(bias = down_blocks_2_attentions_0_proj_out_bias_to_fp16, dilations = var_1459, groups = var_1195, pad = hidden_states_89_pad_0, pad_type = hidden_states_89_pad_type_0, strides = var_1457, weight = down_blocks_2_attentions_0_proj_out_weight_to_fp16_palettized, x = input_141_cast)[name = tensor("hidden_states_89_cast")]; + tensor input_143_cast = add(x = hidden_states_89_cast, y = hidden_states_77_cast)[name = tensor("input_143_cast")]; + tensor reshape_60_shape_0 = const()[name = tensor("reshape_60_shape_0"), val = tensor([2, 32, 40, 16, 16])]; + tensor reshape_60_cast = reshape(shape = reshape_60_shape_0, x = input_143_cast)[name = tensor("reshape_60_cast")]; + tensor reduce_mean_45_axes_0 = const()[name = tensor("reduce_mean_45_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_45_keep_dims_0 = const()[name = tensor("reduce_mean_45_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_45_cast = reduce_mean(axes = reduce_mean_45_axes_0, keep_dims = reduce_mean_45_keep_dims_0, x = reshape_60_cast)[name = tensor("reduce_mean_45_cast")]; + tensor sub_30_cast = sub(x = reshape_60_cast, y = reduce_mean_45_cast)[name = tensor("sub_30_cast")]; + tensor square_15_cast = square(x = sub_30_cast)[name = tensor("square_15_cast")]; + tensor reduce_mean_47_axes_0 = const()[name = tensor("reduce_mean_47_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_47_keep_dims_0 = const()[name = tensor("reduce_mean_47_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_47_cast = reduce_mean(axes = reduce_mean_47_axes_0, keep_dims = reduce_mean_47_keep_dims_0, x = square_15_cast)[name = tensor("reduce_mean_47_cast")]; + tensor add_30_y_0_to_fp16 = const()[name = tensor("add_30_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_30_cast = add(x = reduce_mean_47_cast, y = add_30_y_0_to_fp16)[name = tensor("add_30_cast")]; + tensor sqrt_15_cast = sqrt(x = add_30_cast)[name = tensor("sqrt_15_cast")]; + tensor real_div_15_cast = real_div(x = sub_30_cast, y = sqrt_15_cast)[name = tensor("real_div_15_cast")]; + tensor reshape_61_shape_0 = const()[name = tensor("reshape_61_shape_0"), val = tensor([2, 1280, 16, 16])]; + tensor reshape_61_cast = reshape(shape = reshape_61_shape_0, x = real_div_15_cast)[name = tensor("reshape_61_cast")]; + tensor add_31_gamma_0_to_fp16 = const()[name = tensor("add_31_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(81687232)))]; + 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(81689856)))]; + 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_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_61_cast)[name = tensor("add_31_cast")]; + tensor input_147_cast = silu(x = add_31_cast)[name = tensor("input_147_cast")]; + tensor var_1474 = const()[name = tensor("op_1474"), val = tensor([1, 1])]; + tensor var_1476 = const()[name = tensor("op_1476"), val = tensor([1, 1])]; + tensor hidden_states_91_pad_type_0 = const()[name = tensor("hidden_states_91_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_91_pad_0 = const()[name = tensor("hidden_states_91_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_2_resnets_1_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(81692480))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(92751744))), name = tensor("down_blocks_2_resnets_1_conv1_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor down_blocks_2_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("down_blocks_2_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(92751936)))]; + tensor hidden_states_91_cast = conv(bias = down_blocks_2_resnets_1_conv1_bias_to_fp16, dilations = var_1476, groups = var_1195, pad = hidden_states_91_pad_0, pad_type = hidden_states_91_pad_type_0, strides = var_1474, weight = down_blocks_2_resnets_1_conv1_weight_to_fp16_palettized, x = input_147_cast)[name = tensor("hidden_states_91_cast")]; + tensor var_1482 = const()[name = tensor("op_1482"), val = tensor([1, 1])]; + tensor var_1484 = const()[name = tensor("op_1484"), val = tensor([1, 1])]; + tensor temb_11_pad_type_0 = const()[name = tensor("temb_11_pad_type_0"), val = tensor("custom")]; + tensor temb_11_pad_0 = const()[name = tensor("temb_11_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_resnets_1_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(92754560))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(93983424))), name = tensor("down_blocks_2_resnets_1_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("down_blocks_2_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(93983616)))]; + tensor temb_11_cast = conv(bias = down_blocks_2_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_1484, groups = var_1195, pad = temb_11_pad_0, pad_type = temb_11_pad_type_0, strides = var_1482, weight = down_blocks_2_resnets_1_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor("temb_11_cast")]; + tensor input_151_cast = add(x = hidden_states_91_cast, y = temb_11_cast)[name = tensor("input_151_cast")]; + tensor reshape_64_shape_0 = const()[name = tensor("reshape_64_shape_0"), val = tensor([2, 32, 40, 16, 16])]; + tensor reshape_64_cast = reshape(shape = reshape_64_shape_0, x = input_151_cast)[name = tensor("reshape_64_cast")]; + tensor reduce_mean_48_axes_0 = const()[name = tensor("reduce_mean_48_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_48_keep_dims_0 = const()[name = tensor("reduce_mean_48_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_48_cast = reduce_mean(axes = reduce_mean_48_axes_0, keep_dims = reduce_mean_48_keep_dims_0, x = reshape_64_cast)[name = tensor("reduce_mean_48_cast")]; + tensor sub_32_cast = sub(x = reshape_64_cast, y = reduce_mean_48_cast)[name = tensor("sub_32_cast")]; + tensor square_16_cast = square(x = sub_32_cast)[name = tensor("square_16_cast")]; + tensor reduce_mean_50_axes_0 = const()[name = tensor("reduce_mean_50_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_50_keep_dims_0 = const()[name = tensor("reduce_mean_50_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_50_cast = reduce_mean(axes = reduce_mean_50_axes_0, keep_dims = reduce_mean_50_keep_dims_0, x = square_16_cast)[name = tensor("reduce_mean_50_cast")]; + tensor add_32_y_0_to_fp16 = const()[name = tensor("add_32_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_32_cast = add(x = reduce_mean_50_cast, y = add_32_y_0_to_fp16)[name = tensor("add_32_cast")]; + tensor sqrt_16_cast = sqrt(x = add_32_cast)[name = tensor("sqrt_16_cast")]; + tensor real_div_16_cast = real_div(x = sub_32_cast, y = sqrt_16_cast)[name = tensor("real_div_16_cast")]; + tensor reshape_65_shape_0 = const()[name = tensor("reshape_65_shape_0"), val = tensor([2, 1280, 16, 16])]; + tensor reshape_65_cast = reshape(shape = reshape_65_shape_0, x = real_div_16_cast)[name = tensor("reshape_65_cast")]; + tensor add_33_gamma_0_to_fp16 = const()[name = tensor("add_33_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(93986240)))]; + 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(93988864)))]; + 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_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_65_cast)[name = tensor("add_33_cast")]; + tensor input_155_cast = silu(x = add_33_cast)[name = tensor("input_155_cast")]; + tensor var_1494 = const()[name = tensor("op_1494"), val = tensor([1, 1])]; + tensor var_1496 = const()[name = tensor("op_1496"), val = tensor([1, 1])]; + tensor hidden_states_93_pad_type_0 = const()[name = tensor("hidden_states_93_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_93_pad_0 = const()[name = tensor("hidden_states_93_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_2_resnets_1_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(93991488))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(105050752))), name = tensor("down_blocks_2_resnets_1_conv2_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor down_blocks_2_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("down_blocks_2_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(105050944)))]; + tensor hidden_states_93_cast = conv(bias = down_blocks_2_resnets_1_conv2_bias_to_fp16, dilations = var_1496, groups = var_1195, pad = hidden_states_93_pad_0, pad_type = hidden_states_93_pad_type_0, strides = var_1494, weight = down_blocks_2_resnets_1_conv2_weight_to_fp16_palettized, x = input_155_cast)[name = tensor("hidden_states_93_cast")]; + tensor hidden_states_95_cast = add(x = input_143_cast, y = hidden_states_93_cast)[name = tensor("hidden_states_95_cast")]; + tensor reshape_68_shape_0 = const()[name = tensor("reshape_68_shape_0"), val = tensor([2, 32, 40, 16, 16])]; + tensor reshape_68_cast = reshape(shape = reshape_68_shape_0, x = hidden_states_95_cast)[name = tensor("reshape_68_cast")]; + tensor reduce_mean_51_axes_0 = const()[name = tensor("reduce_mean_51_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_51_keep_dims_0 = const()[name = tensor("reduce_mean_51_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_51_cast = reduce_mean(axes = reduce_mean_51_axes_0, keep_dims = reduce_mean_51_keep_dims_0, x = reshape_68_cast)[name = tensor("reduce_mean_51_cast")]; + tensor sub_34_cast = sub(x = reshape_68_cast, y = reduce_mean_51_cast)[name = tensor("sub_34_cast")]; + tensor square_17_cast = square(x = sub_34_cast)[name = tensor("square_17_cast")]; + tensor reduce_mean_53_axes_0 = const()[name = tensor("reduce_mean_53_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_53_keep_dims_0 = const()[name = tensor("reduce_mean_53_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_53_cast = reduce_mean(axes = reduce_mean_53_axes_0, keep_dims = reduce_mean_53_keep_dims_0, x = square_17_cast)[name = tensor("reduce_mean_53_cast")]; + tensor add_34_y_0_to_fp16 = const()[name = tensor("add_34_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_34_cast = add(x = reduce_mean_53_cast, y = add_34_y_0_to_fp16)[name = tensor("add_34_cast")]; + tensor sqrt_17_cast = sqrt(x = add_34_cast)[name = tensor("sqrt_17_cast")]; + tensor real_div_17_cast = real_div(x = sub_34_cast, y = sqrt_17_cast)[name = tensor("real_div_17_cast")]; + tensor reshape_69_shape_0 = const()[name = tensor("reshape_69_shape_0"), val = tensor([2, 1280, 16, 16])]; + tensor reshape_69_cast = reshape(shape = reshape_69_shape_0, x = real_div_17_cast)[name = tensor("reshape_69_cast")]; + tensor add_35_gamma_0_to_fp16 = const()[name = tensor("add_35_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(105053568)))]; + 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(105056192)))]; + 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_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_69_cast)[name = tensor("add_35_cast")]; + tensor var_1516 = const()[name = tensor("op_1516"), val = tensor([1, 1])]; + tensor var_1518 = const()[name = tensor("op_1518"), val = tensor([1, 1])]; + tensor hidden_states_97_pad_type_0 = const()[name = tensor("hidden_states_97_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_97_pad_0 = const()[name = tensor("hidden_states_97_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(105058816))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(106287680))), name = tensor("down_blocks_2_attentions_1_proj_in_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_proj_in_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(106287872)))]; + tensor hidden_states_97_cast = conv(bias = down_blocks_2_attentions_1_proj_in_bias_to_fp16, dilations = var_1518, groups = var_1195, pad = hidden_states_97_pad_0, pad_type = hidden_states_97_pad_type_0, strides = var_1516, weight = down_blocks_2_attentions_1_proj_in_weight_to_fp16_palettized, x = add_35_cast)[name = tensor("hidden_states_97_cast")]; + tensor var_1523 = const()[name = tensor("op_1523"), val = tensor([2, 1280, 1, 256])]; + tensor inputs_31_cast = reshape(shape = var_1523, x = hidden_states_97_cast)[name = tensor("inputs_31_cast")]; + tensor var_1533 = const()[name = tensor("op_1533"), val = tensor([1])]; + tensor channels_mean_31_cast = reduce_mean(axes = var_1533, keep_dims = var_1190, x = inputs_31_cast)[name = tensor("channels_mean_31_cast")]; + tensor zero_mean_31_cast = sub(x = inputs_31_cast, y = channels_mean_31_cast)[name = tensor("zero_mean_31_cast")]; + tensor zero_mean_sq_31_cast = mul(x = zero_mean_31_cast, y = zero_mean_31_cast)[name = tensor("zero_mean_sq_31_cast")]; + tensor var_1537 = const()[name = tensor("op_1537"), val = tensor([1])]; + tensor var_1538_cast = reduce_mean(axes = var_1537, keep_dims = var_1190, x = zero_mean_sq_31_cast)[name = tensor("op_1538_cast")]; + tensor var_1539_to_fp16 = const()[name = tensor("op_1539_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1540_cast = add(x = var_1538_cast, y = var_1539_to_fp16)[name = tensor("op_1540_cast")]; + tensor denom_31_epsilon_0_to_fp16 = const()[name = tensor("denom_31_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_31_cast = rsqrt(epsilon = denom_31_epsilon_0_to_fp16, x = var_1540_cast)[name = tensor("denom_31_cast")]; + tensor out_31_cast = mul(x = zero_mean_31_cast, y = denom_31_cast)[name = tensor("out_31_cast")]; + tensor var_1544_to_fp16 = const()[name = tensor("op_1544_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(106290496)))]; + tensor var_1545_cast = add(x = out_31_cast, y = var_1544_to_fp16)[name = tensor("op_1545_cast")]; + tensor var_1547_to_fp16 = const()[name = tensor("op_1547_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(106293120)))]; + tensor hidden_states_99_cast = mul(x = var_1545_cast, y = var_1547_to_fp16)[name = tensor("hidden_states_99_cast")]; + tensor var_1554 = const()[name = tensor("op_1554"), val = tensor([1, 1])]; + tensor var_1556 = const()[name = tensor("op_1556"), val = tensor([1, 1])]; + tensor q_21_pad_type_0 = const()[name = tensor("q_21_pad_type_0"), val = tensor("custom")]; + tensor q_21_pad_0 = const()[name = tensor("q_21_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(106295744))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(107524608))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_21_cast = conv(dilations = var_1556, groups = var_1195, pad = q_21_pad_0, pad_type = q_21_pad_type_0, strides = var_1554, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_99_cast)[name = tensor("q_21_cast")]; + tensor var_1560 = const()[name = tensor("op_1560"), val = tensor([1, 1])]; + tensor var_1562 = const()[name = tensor("op_1562"), val = tensor([1, 1])]; + tensor k_21_pad_type_0 = const()[name = tensor("k_21_pad_type_0"), val = tensor("custom")]; + tensor k_21_pad_0 = const()[name = tensor("k_21_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(107524800))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(108753664))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_21_cast = conv(dilations = var_1562, groups = var_1195, pad = k_21_pad_0, pad_type = k_21_pad_type_0, strides = var_1560, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_99_cast)[name = tensor("k_21_cast")]; + tensor var_1566 = const()[name = tensor("op_1566"), val = tensor([1, 1])]; + tensor var_1568 = const()[name = tensor("op_1568"), val = tensor([1, 1])]; + tensor v_21_pad_type_0 = const()[name = tensor("v_21_pad_type_0"), val = tensor("custom")]; + tensor v_21_pad_0 = const()[name = tensor("v_21_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(108753856))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(109982720))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_21_cast = conv(dilations = var_1568, groups = var_1195, pad = v_21_pad_0, pad_type = v_21_pad_type_0, strides = var_1566, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_99_cast)[name = tensor("v_21_cast")]; + tensor var_1572 = const()[name = tensor("op_1572"), val = tensor([2, 8, 160, -1])]; + tensor var_1573_cast = reshape(shape = var_1572, x = q_21_cast)[name = tensor("op_1573_cast")]; + tensor var_1574 = const()[name = tensor("op_1574"), val = tensor([2, 8, 160, -1])]; + tensor var_1575_cast = reshape(shape = var_1574, x = k_21_cast)[name = tensor("op_1575_cast")]; + tensor var_1576 = const()[name = tensor("op_1576"), val = tensor([2, 8, 160, -1])]; + tensor var_1577_cast = reshape(shape = var_1576, x = v_21_cast)[name = tensor("op_1577_cast")]; + tensor attn_weights_41_transpose_x_0 = const()[name = tensor("attn_weights_41_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_41_transpose_y_0 = const()[name = tensor("attn_weights_41_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_41_cast = matmul(transpose_x = attn_weights_41_transpose_x_0, transpose_y = attn_weights_41_transpose_y_0, x = var_1573_cast, y = var_1575_cast)[name = tensor("attn_weights_41_cast")]; + tensor attn_weights_43_cast = mul(x = attn_weights_41_cast, y = var_1186_to_fp16)[name = tensor("attn_weights_43_cast")]; + tensor var_1581_cast = softmax(axis = var_1179, x = attn_weights_43_cast)[name = tensor("op_1581_cast")]; + tensor attn_21_transpose_x_0 = const()[name = tensor("attn_21_transpose_x_0"), val = tensor(false)]; + tensor attn_21_transpose_y_0 = const()[name = tensor("attn_21_transpose_y_0"), val = tensor(true)]; + tensor attn_21_cast = matmul(transpose_x = attn_21_transpose_x_0, transpose_y = attn_21_transpose_y_0, x = var_1577_cast, y = var_1581_cast)[name = tensor("attn_21_cast")]; + tensor var_1585 = const()[name = tensor("op_1585"), val = tensor([2, 1280, 1, -1])]; + tensor input_159_cast = reshape(shape = var_1585, x = attn_21_cast)[name = tensor("input_159_cast")]; + tensor var_1590 = const()[name = tensor("op_1590"), val = tensor([1, 1])]; + tensor var_1592 = const()[name = tensor("op_1592"), val = tensor([1, 1])]; + tensor var_1594_pad_type_0 = const()[name = tensor("op_1594_pad_type_0"), val = tensor("custom")]; + tensor var_1594_pad_0 = const()[name = tensor("op_1594_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(109982912))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(111211776))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(111211968)))]; + tensor var_1594_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_1592, groups = var_1195, pad = var_1594_pad_0, pad_type = var_1594_pad_type_0, strides = var_1590, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_159_cast)[name = tensor("op_1594_cast")]; + tensor inputs_33_cast = add(x = var_1594_cast, y = inputs_31_cast)[name = tensor("inputs_33_cast")]; + tensor var_1598 = const()[name = tensor("op_1598"), val = tensor([1])]; + tensor channels_mean_33_cast = reduce_mean(axes = var_1598, keep_dims = var_1190, x = inputs_33_cast)[name = tensor("channels_mean_33_cast")]; + tensor zero_mean_33_cast = sub(x = inputs_33_cast, y = channels_mean_33_cast)[name = tensor("zero_mean_33_cast")]; + tensor zero_mean_sq_33_cast = mul(x = zero_mean_33_cast, y = zero_mean_33_cast)[name = tensor("zero_mean_sq_33_cast")]; + tensor var_1602 = const()[name = tensor("op_1602"), val = tensor([1])]; + tensor var_1603_cast = reduce_mean(axes = var_1602, keep_dims = var_1190, x = zero_mean_sq_33_cast)[name = tensor("op_1603_cast")]; + tensor var_1604_to_fp16 = const()[name = tensor("op_1604_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1605_cast = add(x = var_1603_cast, y = var_1604_to_fp16)[name = tensor("op_1605_cast")]; + tensor denom_33_epsilon_0_to_fp16 = const()[name = tensor("denom_33_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_33_cast = rsqrt(epsilon = denom_33_epsilon_0_to_fp16, x = var_1605_cast)[name = tensor("denom_33_cast")]; + tensor out_33_cast = mul(x = zero_mean_33_cast, y = denom_33_cast)[name = tensor("out_33_cast")]; + tensor var_1609_to_fp16 = const()[name = tensor("op_1609_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(111214592)))]; + tensor var_1610_cast = add(x = out_33_cast, y = var_1609_to_fp16)[name = tensor("op_1610_cast")]; + tensor var_1612_to_fp16 = const()[name = tensor("op_1612_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(111217216)))]; + tensor hidden_states_101_cast = mul(x = var_1610_cast, y = var_1612_to_fp16)[name = tensor("hidden_states_101_cast")]; + tensor var_1619 = const()[name = tensor("op_1619"), val = tensor([1, 1])]; + tensor var_1621 = const()[name = tensor("op_1621"), val = tensor([1, 1])]; + tensor q_23_pad_type_0 = const()[name = tensor("q_23_pad_type_0"), val = tensor("custom")]; + tensor q_23_pad_0 = const()[name = tensor("q_23_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(111219840))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(112448704))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_23_cast = conv(dilations = var_1621, groups = var_1195, pad = q_23_pad_0, pad_type = q_23_pad_type_0, strides = var_1619, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_101_cast)[name = tensor("q_23_cast")]; + tensor var_1625 = const()[name = tensor("op_1625"), val = tensor([1, 1])]; + tensor var_1627 = const()[name = tensor("op_1627"), val = tensor([1, 1])]; + tensor k_23_pad_type_0 = const()[name = tensor("k_23_pad_type_0"), val = tensor("custom")]; + tensor k_23_pad_0 = const()[name = tensor("k_23_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(112448896))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(113186240))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 768, 1, 1])]; + tensor k_23_cast = conv(dilations = var_1627, groups = var_1195, pad = k_23_pad_0, pad_type = k_23_pad_type_0, strides = var_1625, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_23_cast")]; + tensor var_1631 = const()[name = tensor("op_1631"), val = tensor([1, 1])]; + tensor var_1633 = const()[name = tensor("op_1633"), val = tensor([1, 1])]; + tensor v_23_pad_type_0 = const()[name = tensor("v_23_pad_type_0"), val = tensor("custom")]; + tensor v_23_pad_0 = const()[name = tensor("v_23_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(113186432))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(113923776))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 768, 1, 1])]; + tensor v_23_cast = conv(dilations = var_1633, groups = var_1195, pad = v_23_pad_0, pad_type = v_23_pad_type_0, strides = var_1631, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_23_cast")]; + tensor var_1637 = const()[name = tensor("op_1637"), val = tensor([2, 8, 160, -1])]; + tensor var_1638_cast = reshape(shape = var_1637, x = q_23_cast)[name = tensor("op_1638_cast")]; + tensor var_1639 = const()[name = tensor("op_1639"), val = tensor([2, 8, 160, -1])]; + tensor var_1640_cast = reshape(shape = var_1639, x = k_23_cast)[name = tensor("op_1640_cast")]; + tensor var_1641 = const()[name = tensor("op_1641"), val = tensor([2, 8, 160, -1])]; + tensor var_1642_cast = reshape(shape = var_1641, x = v_23_cast)[name = tensor("op_1642_cast")]; + tensor attn_weights_45_transpose_x_0 = const()[name = tensor("attn_weights_45_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_45_transpose_y_0 = const()[name = tensor("attn_weights_45_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_45_cast = matmul(transpose_x = attn_weights_45_transpose_x_0, transpose_y = attn_weights_45_transpose_y_0, x = var_1638_cast, y = var_1640_cast)[name = tensor("attn_weights_45_cast")]; + tensor attn_weights_47_cast = mul(x = attn_weights_45_cast, y = var_1186_to_fp16)[name = tensor("attn_weights_47_cast")]; + tensor var_1646_cast = softmax(axis = var_1179, x = attn_weights_47_cast)[name = tensor("op_1646_cast")]; + tensor attn_23_transpose_x_0 = const()[name = tensor("attn_23_transpose_x_0"), val = tensor(false)]; + tensor attn_23_transpose_y_0 = const()[name = tensor("attn_23_transpose_y_0"), val = tensor(true)]; + tensor attn_23_cast = matmul(transpose_x = attn_23_transpose_x_0, transpose_y = attn_23_transpose_y_0, x = var_1642_cast, y = var_1646_cast)[name = tensor("attn_23_cast")]; + tensor var_1650 = const()[name = tensor("op_1650"), val = tensor([2, 1280, 1, -1])]; + tensor input_161_cast = reshape(shape = var_1650, x = attn_23_cast)[name = tensor("input_161_cast")]; + tensor var_1655 = const()[name = tensor("op_1655"), val = tensor([1, 1])]; + tensor var_1657 = const()[name = tensor("op_1657"), val = tensor([1, 1])]; + tensor var_1659_pad_type_0 = const()[name = tensor("op_1659_pad_type_0"), val = tensor("custom")]; + tensor var_1659_pad_0 = const()[name = tensor("op_1659_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(113923968))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(115152832))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(115153024)))]; + tensor var_1659_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_1657, groups = var_1195, pad = var_1659_pad_0, pad_type = var_1659_pad_type_0, strides = var_1655, weight = down_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_161_cast)[name = tensor("op_1659_cast")]; + tensor inputs_35_cast = add(x = var_1659_cast, y = inputs_33_cast)[name = tensor("inputs_35_cast")]; + tensor var_1663 = const()[name = tensor("op_1663"), val = tensor([1])]; + tensor channels_mean_35_cast = reduce_mean(axes = var_1663, keep_dims = var_1190, x = inputs_35_cast)[name = tensor("channels_mean_35_cast")]; + tensor zero_mean_35_cast = sub(x = inputs_35_cast, y = channels_mean_35_cast)[name = tensor("zero_mean_35_cast")]; + tensor zero_mean_sq_35_cast = mul(x = zero_mean_35_cast, y = zero_mean_35_cast)[name = tensor("zero_mean_sq_35_cast")]; + tensor var_1667 = const()[name = tensor("op_1667"), val = tensor([1])]; + tensor var_1668_cast = reduce_mean(axes = var_1667, keep_dims = var_1190, x = zero_mean_sq_35_cast)[name = tensor("op_1668_cast")]; + tensor var_1669_to_fp16 = const()[name = tensor("op_1669_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1670_cast = add(x = var_1668_cast, y = var_1669_to_fp16)[name = tensor("op_1670_cast")]; + tensor denom_35_epsilon_0_to_fp16 = const()[name = tensor("denom_35_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_35_cast = rsqrt(epsilon = denom_35_epsilon_0_to_fp16, x = var_1670_cast)[name = tensor("denom_35_cast")]; + tensor out_35_cast = mul(x = zero_mean_35_cast, y = denom_35_cast)[name = tensor("out_35_cast")]; + tensor var_1674_to_fp16 = const()[name = tensor("op_1674_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(115155648)))]; + tensor var_1675_cast = add(x = out_35_cast, y = var_1674_to_fp16)[name = tensor("op_1675_cast")]; + tensor var_1677_to_fp16 = const()[name = tensor("op_1677_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(115158272)))]; + tensor input_163_cast = mul(x = var_1675_cast, y = var_1677_to_fp16)[name = tensor("input_163_cast")]; + tensor var_1685 = const()[name = tensor("op_1685"), val = tensor([1, 1])]; + tensor var_1687 = const()[name = tensor("op_1687"), val = tensor([1, 1])]; + tensor var_1689_pad_type_0 = const()[name = tensor("op_1689_pad_type_0"), val = tensor("custom")]; + tensor var_1689_pad_0 = const()[name = tensor("op_1689_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(115160896))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(124991360))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(124991552))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(124999296))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_1689_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_1687, groups = var_1195, pad = var_1689_pad_0, pad_type = var_1689_pad_type_0, strides = var_1685, weight = down_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_163_cast)[name = tensor("op_1689_cast")]; + tensor var_1690_split_sizes_0 = const()[name = tensor("op_1690_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_1690_axis_0 = const()[name = tensor("op_1690_axis_0"), val = tensor(1)]; + tensor var_1690_cast_0, tensor var_1690_cast_1 = split(axis = var_1690_axis_0, split_sizes = var_1690_split_sizes_0, x = var_1689_cast)[name = tensor("op_1690_cast")]; + tensor var_1692_mode_0 = const()[name = tensor("op_1692_mode_0"), val = tensor("EXACT")]; + tensor var_1692_cast = gelu(mode = var_1692_mode_0, x = var_1690_cast_1)[name = tensor("op_1692_cast")]; + tensor input_165_cast = mul(x = var_1690_cast_0, y = var_1692_cast)[name = tensor("input_165_cast")]; + tensor var_1696 = const()[name = tensor("op_1696"), val = tensor([1, 1])]; + tensor var_1698 = const()[name = tensor("op_1698"), val = tensor([1, 1])]; + tensor var_1700_pad_type_0 = const()[name = tensor("op_1700_pad_type_0"), val = tensor("custom")]; + tensor var_1700_pad_0 = const()[name = tensor("op_1700_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(124999488))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(129914752))), name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(129914944)))]; + tensor var_1700_cast = conv(bias = down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_1698, groups = var_1195, pad = var_1700_pad_0, pad_type = var_1700_pad_type_0, strides = var_1696, weight = down_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_165_cast)[name = tensor("op_1700_cast")]; + tensor hidden_states_105_cast = add(x = var_1700_cast, y = inputs_35_cast)[name = tensor("hidden_states_105_cast")]; + tensor var_1702 = const()[name = tensor("op_1702"), val = tensor([2, 1280, 16, 16])]; + tensor input_167_cast = reshape(shape = var_1702, x = hidden_states_105_cast)[name = tensor("input_167_cast")]; + tensor var_1706 = const()[name = tensor("op_1706"), val = tensor([1, 1])]; + tensor var_1708 = const()[name = tensor("op_1708"), val = tensor([1, 1])]; + tensor hidden_states_107_pad_type_0 = const()[name = tensor("hidden_states_107_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_107_pad_0 = const()[name = tensor("hidden_states_107_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_2_attentions_1_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(129917568))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(131146432))), name = tensor("down_blocks_2_attentions_1_proj_out_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_2_attentions_1_proj_out_bias_to_fp16 = const()[name = tensor("down_blocks_2_attentions_1_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(131146624)))]; + tensor hidden_states_107_cast = conv(bias = down_blocks_2_attentions_1_proj_out_bias_to_fp16, dilations = var_1708, groups = var_1195, pad = hidden_states_107_pad_0, pad_type = hidden_states_107_pad_type_0, strides = var_1706, weight = down_blocks_2_attentions_1_proj_out_weight_to_fp16_palettized, x = input_167_cast)[name = tensor("hidden_states_107_cast")]; + tensor input_169_cast = add(x = hidden_states_107_cast, y = hidden_states_95_cast)[name = tensor("input_169_cast")]; + tensor var_1715 = const()[name = tensor("op_1715"), val = tensor([2, 2])]; + tensor var_1717 = const()[name = tensor("op_1717"), val = tensor([1, 1])]; + tensor input_171_pad_type_0 = const()[name = tensor("input_171_pad_type_0"), val = tensor("custom")]; + tensor input_171_pad_0 = const()[name = tensor("input_171_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_2_downsamplers_0_conv_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(131149248))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(142208512))), name = tensor("down_blocks_2_downsamplers_0_conv_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor down_blocks_2_downsamplers_0_conv_bias_to_fp16 = const()[name = tensor("down_blocks_2_downsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(142208704)))]; + tensor input_171_cast = conv(bias = down_blocks_2_downsamplers_0_conv_bias_to_fp16, dilations = var_1717, groups = var_1195, pad = input_171_pad_0, pad_type = input_171_pad_type_0, strides = var_1715, weight = down_blocks_2_downsamplers_0_conv_weight_to_fp16_palettized, x = input_169_cast)[name = tensor("input_171_cast")]; + tensor var_1729 = const()[name = tensor("op_1729"), val = tensor(1)]; + tensor reshape_72_shape_0 = const()[name = tensor("reshape_72_shape_0"), val = tensor([2, 32, 40, 8, 8])]; + tensor reshape_72_cast = reshape(shape = reshape_72_shape_0, x = input_171_cast)[name = tensor("reshape_72_cast")]; + tensor reduce_mean_54_axes_0 = const()[name = tensor("reduce_mean_54_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_54_keep_dims_0 = const()[name = tensor("reduce_mean_54_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_54_cast = reduce_mean(axes = reduce_mean_54_axes_0, keep_dims = reduce_mean_54_keep_dims_0, x = reshape_72_cast)[name = tensor("reduce_mean_54_cast")]; + tensor sub_36_cast = sub(x = reshape_72_cast, y = reduce_mean_54_cast)[name = tensor("sub_36_cast")]; + tensor square_18_cast = square(x = sub_36_cast)[name = tensor("square_18_cast")]; + tensor reduce_mean_56_axes_0 = const()[name = tensor("reduce_mean_56_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_56_keep_dims_0 = const()[name = tensor("reduce_mean_56_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_56_cast = reduce_mean(axes = reduce_mean_56_axes_0, keep_dims = reduce_mean_56_keep_dims_0, x = square_18_cast)[name = tensor("reduce_mean_56_cast")]; + tensor add_36_y_0_to_fp16 = const()[name = tensor("add_36_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_36_cast = add(x = reduce_mean_56_cast, y = add_36_y_0_to_fp16)[name = tensor("add_36_cast")]; + tensor sqrt_18_cast = sqrt(x = add_36_cast)[name = tensor("sqrt_18_cast")]; + tensor real_div_18_cast = real_div(x = sub_36_cast, y = sqrt_18_cast)[name = tensor("real_div_18_cast")]; + tensor reshape_73_shape_0 = const()[name = tensor("reshape_73_shape_0"), val = tensor([2, 1280, 8, 8])]; + tensor reshape_73_cast = reshape(shape = reshape_73_shape_0, x = real_div_18_cast)[name = tensor("reshape_73_cast")]; + 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(142211328)))]; + 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(142213952)))]; + 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_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_73_cast)[name = tensor("add_37_cast")]; + tensor input_175_cast = silu(x = add_37_cast)[name = tensor("input_175_cast")]; + tensor var_1745 = const()[name = tensor("op_1745"), val = tensor([1, 1])]; + tensor var_1747 = const()[name = tensor("op_1747"), val = tensor([1, 1])]; + tensor hidden_states_109_pad_type_0 = const()[name = tensor("hidden_states_109_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_109_pad_0 = const()[name = tensor("hidden_states_109_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_3_resnets_0_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(142216576))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(153275840))), name = tensor("down_blocks_3_resnets_0_conv1_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor down_blocks_3_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("down_blocks_3_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(153276032)))]; + tensor hidden_states_109_cast = conv(bias = down_blocks_3_resnets_0_conv1_bias_to_fp16, dilations = var_1747, groups = var_1729, pad = hidden_states_109_pad_0, pad_type = hidden_states_109_pad_type_0, strides = var_1745, weight = down_blocks_3_resnets_0_conv1_weight_to_fp16_palettized, x = input_175_cast)[name = tensor("hidden_states_109_cast")]; + tensor var_1753 = const()[name = tensor("op_1753"), val = tensor([1, 1])]; + tensor var_1755 = const()[name = tensor("op_1755"), val = tensor([1, 1])]; + tensor temb_13_pad_type_0 = const()[name = tensor("temb_13_pad_type_0"), val = tensor("custom")]; + tensor temb_13_pad_0 = const()[name = tensor("temb_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_3_resnets_0_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(153278656))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(154507520))), name = tensor("down_blocks_3_resnets_0_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_3_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("down_blocks_3_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(154507712)))]; + tensor temb_13_cast = conv(bias = down_blocks_3_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_1755, groups = var_1729, pad = temb_13_pad_0, pad_type = temb_13_pad_type_0, strides = var_1753, weight = down_blocks_3_resnets_0_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor("temb_13_cast")]; + tensor input_179_cast = add(x = hidden_states_109_cast, y = temb_13_cast)[name = tensor("input_179_cast")]; + tensor reshape_76_shape_0 = const()[name = tensor("reshape_76_shape_0"), val = tensor([2, 32, 40, 8, 8])]; + tensor reshape_76_cast = reshape(shape = reshape_76_shape_0, x = input_179_cast)[name = tensor("reshape_76_cast")]; + tensor reduce_mean_57_axes_0 = const()[name = tensor("reduce_mean_57_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_57_keep_dims_0 = const()[name = tensor("reduce_mean_57_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_57_cast = reduce_mean(axes = reduce_mean_57_axes_0, keep_dims = reduce_mean_57_keep_dims_0, x = reshape_76_cast)[name = tensor("reduce_mean_57_cast")]; + tensor sub_38_cast = sub(x = reshape_76_cast, y = reduce_mean_57_cast)[name = tensor("sub_38_cast")]; + tensor square_19_cast = square(x = sub_38_cast)[name = tensor("square_19_cast")]; + tensor reduce_mean_59_axes_0 = const()[name = tensor("reduce_mean_59_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_59_keep_dims_0 = const()[name = tensor("reduce_mean_59_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_59_cast = reduce_mean(axes = reduce_mean_59_axes_0, keep_dims = reduce_mean_59_keep_dims_0, x = square_19_cast)[name = tensor("reduce_mean_59_cast")]; + tensor add_38_y_0_to_fp16 = const()[name = tensor("add_38_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_38_cast = add(x = reduce_mean_59_cast, y = add_38_y_0_to_fp16)[name = tensor("add_38_cast")]; + tensor sqrt_19_cast = sqrt(x = add_38_cast)[name = tensor("sqrt_19_cast")]; + tensor real_div_19_cast = real_div(x = sub_38_cast, y = sqrt_19_cast)[name = tensor("real_div_19_cast")]; + tensor reshape_77_shape_0 = const()[name = tensor("reshape_77_shape_0"), val = tensor([2, 1280, 8, 8])]; + tensor reshape_77_cast = reshape(shape = reshape_77_shape_0, x = real_div_19_cast)[name = tensor("reshape_77_cast")]; + tensor add_39_gamma_0_to_fp16 = const()[name = tensor("add_39_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(154510336)))]; + 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(154512960)))]; + 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_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_77_cast)[name = tensor("add_39_cast")]; + tensor input_183_cast = silu(x = add_39_cast)[name = tensor("input_183_cast")]; + tensor var_1765 = const()[name = tensor("op_1765"), val = tensor([1, 1])]; + tensor var_1767 = const()[name = tensor("op_1767"), val = tensor([1, 1])]; + tensor hidden_states_111_pad_type_0 = const()[name = tensor("hidden_states_111_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_111_pad_0 = const()[name = tensor("hidden_states_111_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_3_resnets_0_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(154515584))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(165574848))), name = tensor("down_blocks_3_resnets_0_conv2_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor down_blocks_3_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("down_blocks_3_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(165575040)))]; + tensor hidden_states_111_cast = conv(bias = down_blocks_3_resnets_0_conv2_bias_to_fp16, dilations = var_1767, groups = var_1729, pad = hidden_states_111_pad_0, pad_type = hidden_states_111_pad_type_0, strides = var_1765, weight = down_blocks_3_resnets_0_conv2_weight_to_fp16_palettized, x = input_183_cast)[name = tensor("hidden_states_111_cast")]; + tensor input_185_cast = add(x = input_171_cast, y = hidden_states_111_cast)[name = tensor("input_185_cast")]; + tensor reshape_80_shape_0 = const()[name = tensor("reshape_80_shape_0"), val = tensor([2, 32, 40, 8, 8])]; + tensor reshape_80_cast = reshape(shape = reshape_80_shape_0, x = input_185_cast)[name = tensor("reshape_80_cast")]; + tensor reduce_mean_60_axes_0 = const()[name = tensor("reduce_mean_60_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_60_keep_dims_0 = const()[name = tensor("reduce_mean_60_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_60_cast = reduce_mean(axes = reduce_mean_60_axes_0, keep_dims = reduce_mean_60_keep_dims_0, x = reshape_80_cast)[name = tensor("reduce_mean_60_cast")]; + tensor sub_40_cast = sub(x = reshape_80_cast, y = reduce_mean_60_cast)[name = tensor("sub_40_cast")]; + tensor square_20_cast = square(x = sub_40_cast)[name = tensor("square_20_cast")]; + tensor reduce_mean_62_axes_0 = const()[name = tensor("reduce_mean_62_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_62_keep_dims_0 = const()[name = tensor("reduce_mean_62_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_62_cast = reduce_mean(axes = reduce_mean_62_axes_0, keep_dims = reduce_mean_62_keep_dims_0, x = square_20_cast)[name = tensor("reduce_mean_62_cast")]; + tensor add_40_y_0_to_fp16 = const()[name = tensor("add_40_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_40_cast = add(x = reduce_mean_62_cast, y = add_40_y_0_to_fp16)[name = tensor("add_40_cast")]; + tensor sqrt_20_cast = sqrt(x = add_40_cast)[name = tensor("sqrt_20_cast")]; + tensor real_div_20_cast = real_div(x = sub_40_cast, y = sqrt_20_cast)[name = tensor("real_div_20_cast")]; + tensor reshape_81_shape_0 = const()[name = tensor("reshape_81_shape_0"), val = tensor([2, 1280, 8, 8])]; + tensor reshape_81_cast = reshape(shape = reshape_81_shape_0, x = real_div_20_cast)[name = tensor("reshape_81_cast")]; + tensor add_41_gamma_0_to_fp16 = const()[name = tensor("add_41_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(165577664)))]; + 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(165580288)))]; + 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_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_81_cast)[name = tensor("add_41_cast")]; + tensor input_189_cast = silu(x = add_41_cast)[name = tensor("input_189_cast")]; + tensor var_1782 = const()[name = tensor("op_1782"), val = tensor([1, 1])]; + tensor var_1784 = const()[name = tensor("op_1784"), val = tensor([1, 1])]; + tensor hidden_states_113_pad_type_0 = const()[name = tensor("hidden_states_113_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_113_pad_0 = const()[name = tensor("hidden_states_113_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_3_resnets_1_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(165582912))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(176642176))), name = tensor("down_blocks_3_resnets_1_conv1_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor down_blocks_3_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("down_blocks_3_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(176642368)))]; + tensor hidden_states_113_cast = conv(bias = down_blocks_3_resnets_1_conv1_bias_to_fp16, dilations = var_1784, groups = var_1729, pad = hidden_states_113_pad_0, pad_type = hidden_states_113_pad_type_0, strides = var_1782, weight = down_blocks_3_resnets_1_conv1_weight_to_fp16_palettized, x = input_189_cast)[name = tensor("hidden_states_113_cast")]; + tensor var_1790 = const()[name = tensor("op_1790"), val = tensor([1, 1])]; + tensor var_1792 = const()[name = tensor("op_1792"), val = tensor([1, 1])]; + tensor temb_15_pad_type_0 = const()[name = tensor("temb_15_pad_type_0"), val = tensor("custom")]; + tensor temb_15_pad_0 = const()[name = tensor("temb_15_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor down_blocks_3_resnets_1_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(176644992))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(177873856))), name = tensor("down_blocks_3_resnets_1_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor down_blocks_3_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("down_blocks_3_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(177874048)))]; + tensor temb_15_cast = conv(bias = down_blocks_3_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_1792, groups = var_1729, pad = temb_15_pad_0, pad_type = temb_15_pad_type_0, strides = var_1790, weight = down_blocks_3_resnets_1_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor("temb_15_cast")]; + tensor input_193_cast = add(x = hidden_states_113_cast, y = temb_15_cast)[name = tensor("input_193_cast")]; + tensor reshape_84_shape_0 = const()[name = tensor("reshape_84_shape_0"), val = tensor([2, 32, 40, 8, 8])]; + tensor reshape_84_cast = reshape(shape = reshape_84_shape_0, x = input_193_cast)[name = tensor("reshape_84_cast")]; + tensor reduce_mean_63_axes_0 = const()[name = tensor("reduce_mean_63_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_63_keep_dims_0 = const()[name = tensor("reduce_mean_63_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_63_cast = reduce_mean(axes = reduce_mean_63_axes_0, keep_dims = reduce_mean_63_keep_dims_0, x = reshape_84_cast)[name = tensor("reduce_mean_63_cast")]; + tensor sub_42_cast = sub(x = reshape_84_cast, y = reduce_mean_63_cast)[name = tensor("sub_42_cast")]; + tensor square_21_cast = square(x = sub_42_cast)[name = tensor("square_21_cast")]; + tensor reduce_mean_65_axes_0 = const()[name = tensor("reduce_mean_65_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_65_keep_dims_0 = const()[name = tensor("reduce_mean_65_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_65_cast = reduce_mean(axes = reduce_mean_65_axes_0, keep_dims = reduce_mean_65_keep_dims_0, x = square_21_cast)[name = tensor("reduce_mean_65_cast")]; + tensor add_42_y_0_to_fp16 = const()[name = tensor("add_42_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_42_cast = add(x = reduce_mean_65_cast, y = add_42_y_0_to_fp16)[name = tensor("add_42_cast")]; + tensor sqrt_21_cast = sqrt(x = add_42_cast)[name = tensor("sqrt_21_cast")]; + tensor real_div_21_cast = real_div(x = sub_42_cast, y = sqrt_21_cast)[name = tensor("real_div_21_cast")]; + tensor reshape_85_shape_0 = const()[name = tensor("reshape_85_shape_0"), val = tensor([2, 1280, 8, 8])]; + tensor reshape_85_cast = reshape(shape = reshape_85_shape_0, x = real_div_21_cast)[name = tensor("reshape_85_cast")]; + tensor add_43_gamma_0_to_fp16 = const()[name = tensor("add_43_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(177876672)))]; + 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(177879296)))]; + 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_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_85_cast)[name = tensor("add_43_cast")]; + tensor input_197_cast = silu(x = add_43_cast)[name = tensor("input_197_cast")]; + tensor var_1802 = const()[name = tensor("op_1802"), val = tensor([1, 1])]; + tensor var_1804 = const()[name = tensor("op_1804"), val = tensor([1, 1])]; + tensor hidden_states_115_pad_type_0 = const()[name = tensor("hidden_states_115_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_115_pad_0 = const()[name = tensor("hidden_states_115_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor down_blocks_3_resnets_1_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(177881920))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(188941184))), name = tensor("down_blocks_3_resnets_1_conv2_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor down_blocks_3_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("down_blocks_3_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(188941376)))]; + tensor hidden_states_115_cast = conv(bias = down_blocks_3_resnets_1_conv2_bias_to_fp16, dilations = var_1804, groups = var_1729, pad = hidden_states_115_pad_0, pad_type = hidden_states_115_pad_type_0, strides = var_1802, weight = down_blocks_3_resnets_1_conv2_weight_to_fp16_palettized, x = input_197_cast)[name = tensor("hidden_states_115_cast")]; + tensor input_199_cast = add(x = input_185_cast, y = hidden_states_115_cast)[name = tensor("input_199_cast")]; + tensor var_1812 = const()[name = tensor("op_1812"), val = tensor(3)]; + tensor var_1823 = const()[name = tensor("op_1823"), val = tensor(true)]; + tensor var_1828 = const()[name = tensor("op_1828"), val = tensor(1)]; + tensor reshape_88_shape_0 = const()[name = tensor("reshape_88_shape_0"), val = tensor([2, 32, 40, 8, 8])]; + tensor reshape_88_cast = reshape(shape = reshape_88_shape_0, x = input_199_cast)[name = tensor("reshape_88_cast")]; + tensor reduce_mean_66_axes_0 = const()[name = tensor("reduce_mean_66_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_66_keep_dims_0 = const()[name = tensor("reduce_mean_66_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_66_cast = reduce_mean(axes = reduce_mean_66_axes_0, keep_dims = reduce_mean_66_keep_dims_0, x = reshape_88_cast)[name = tensor("reduce_mean_66_cast")]; + tensor sub_44_cast = sub(x = reshape_88_cast, y = reduce_mean_66_cast)[name = tensor("sub_44_cast")]; + tensor square_22_cast = square(x = sub_44_cast)[name = tensor("square_22_cast")]; + tensor reduce_mean_68_axes_0 = const()[name = tensor("reduce_mean_68_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_68_keep_dims_0 = const()[name = tensor("reduce_mean_68_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_68_cast = reduce_mean(axes = reduce_mean_68_axes_0, keep_dims = reduce_mean_68_keep_dims_0, x = square_22_cast)[name = tensor("reduce_mean_68_cast")]; + tensor add_44_y_0_to_fp16 = const()[name = tensor("add_44_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_44_cast = add(x = reduce_mean_68_cast, y = add_44_y_0_to_fp16)[name = tensor("add_44_cast")]; + tensor sqrt_22_cast = sqrt(x = add_44_cast)[name = tensor("sqrt_22_cast")]; + tensor real_div_22_cast = real_div(x = sub_44_cast, y = sqrt_22_cast)[name = tensor("real_div_22_cast")]; + tensor reshape_89_shape_0 = const()[name = tensor("reshape_89_shape_0"), val = tensor([2, 1280, 8, 8])]; + tensor reshape_89_cast = reshape(shape = reshape_89_shape_0, x = real_div_22_cast)[name = tensor("reshape_89_cast")]; + tensor add_45_gamma_0_to_fp16 = const()[name = tensor("add_45_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(188944000)))]; + 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(188946624)))]; + 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_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_89_cast)[name = tensor("add_45_cast")]; + tensor input_203_cast = silu(x = add_45_cast)[name = tensor("input_203_cast")]; + tensor var_1846 = const()[name = tensor("op_1846"), val = tensor([1, 1])]; + tensor var_1848 = const()[name = tensor("op_1848"), val = tensor([1, 1])]; + tensor hidden_states_117_pad_type_0 = const()[name = tensor("hidden_states_117_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_117_pad_0 = const()[name = tensor("hidden_states_117_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor mid_block_resnets_0_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(188949248))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(200008512))), name = tensor("mid_block_resnets_0_conv1_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor mid_block_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("mid_block_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(200008704)))]; + tensor hidden_states_117_cast = conv(bias = mid_block_resnets_0_conv1_bias_to_fp16, dilations = var_1848, groups = var_1828, pad = hidden_states_117_pad_0, pad_type = hidden_states_117_pad_type_0, strides = var_1846, weight = mid_block_resnets_0_conv1_weight_to_fp16_palettized, x = input_203_cast)[name = tensor("hidden_states_117_cast")]; + tensor var_1854 = const()[name = tensor("op_1854"), val = tensor([1, 1])]; + tensor var_1856 = const()[name = tensor("op_1856"), val = tensor([1, 1])]; + tensor temb_17_pad_type_0 = const()[name = tensor("temb_17_pad_type_0"), val = tensor("custom")]; + tensor temb_17_pad_0 = const()[name = tensor("temb_17_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_resnets_0_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(200011328))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(201240192))), name = tensor("mid_block_resnets_0_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor mid_block_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("mid_block_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(201240384)))]; + tensor temb_17_cast = conv(bias = mid_block_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_1856, groups = var_1828, pad = temb_17_pad_0, pad_type = temb_17_pad_type_0, strides = var_1854, weight = mid_block_resnets_0_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor("temb_17_cast")]; + tensor input_207_cast = add(x = hidden_states_117_cast, y = temb_17_cast)[name = tensor("input_207_cast")]; + tensor reshape_92_shape_0 = const()[name = tensor("reshape_92_shape_0"), val = tensor([2, 32, 40, 8, 8])]; + tensor reshape_92_cast = reshape(shape = reshape_92_shape_0, x = input_207_cast)[name = tensor("reshape_92_cast")]; + tensor reduce_mean_69_axes_0 = const()[name = tensor("reduce_mean_69_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_69_keep_dims_0 = const()[name = tensor("reduce_mean_69_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_69_cast = reduce_mean(axes = reduce_mean_69_axes_0, keep_dims = reduce_mean_69_keep_dims_0, x = reshape_92_cast)[name = tensor("reduce_mean_69_cast")]; + tensor sub_46_cast = sub(x = reshape_92_cast, y = reduce_mean_69_cast)[name = tensor("sub_46_cast")]; + tensor square_23_cast = square(x = sub_46_cast)[name = tensor("square_23_cast")]; + tensor reduce_mean_71_axes_0 = const()[name = tensor("reduce_mean_71_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_71_keep_dims_0 = const()[name = tensor("reduce_mean_71_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_71_cast = reduce_mean(axes = reduce_mean_71_axes_0, keep_dims = reduce_mean_71_keep_dims_0, x = square_23_cast)[name = tensor("reduce_mean_71_cast")]; + tensor add_46_y_0_to_fp16 = const()[name = tensor("add_46_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_46_cast = add(x = reduce_mean_71_cast, y = add_46_y_0_to_fp16)[name = tensor("add_46_cast")]; + tensor sqrt_23_cast = sqrt(x = add_46_cast)[name = tensor("sqrt_23_cast")]; + tensor real_div_23_cast = real_div(x = sub_46_cast, y = sqrt_23_cast)[name = tensor("real_div_23_cast")]; + tensor reshape_93_shape_0 = const()[name = tensor("reshape_93_shape_0"), val = tensor([2, 1280, 8, 8])]; + tensor reshape_93_cast = reshape(shape = reshape_93_shape_0, x = real_div_23_cast)[name = tensor("reshape_93_cast")]; + tensor add_47_gamma_0_to_fp16 = const()[name = tensor("add_47_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(201243008)))]; + 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(201245632)))]; + 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_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_93_cast)[name = tensor("add_47_cast")]; + tensor input_211_cast = silu(x = add_47_cast)[name = tensor("input_211_cast")]; + tensor var_1866 = const()[name = tensor("op_1866"), val = tensor([1, 1])]; + tensor var_1868 = const()[name = tensor("op_1868"), val = tensor([1, 1])]; + tensor hidden_states_119_pad_type_0 = const()[name = tensor("hidden_states_119_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_119_pad_0 = const()[name = tensor("hidden_states_119_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor mid_block_resnets_0_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(201248256))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(212307520))), name = tensor("mid_block_resnets_0_conv2_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor mid_block_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("mid_block_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(212307712)))]; + tensor hidden_states_119_cast = conv(bias = mid_block_resnets_0_conv2_bias_to_fp16, dilations = var_1868, groups = var_1828, pad = hidden_states_119_pad_0, pad_type = hidden_states_119_pad_type_0, strides = var_1866, weight = mid_block_resnets_0_conv2_weight_to_fp16_palettized, x = input_211_cast)[name = tensor("hidden_states_119_cast")]; + tensor hidden_states_121_cast = add(x = input_199_cast, y = hidden_states_119_cast)[name = tensor("hidden_states_121_cast")]; + tensor reshape_96_shape_0 = const()[name = tensor("reshape_96_shape_0"), val = tensor([2, 32, 40, 8, 8])]; + tensor reshape_96_cast = reshape(shape = reshape_96_shape_0, x = hidden_states_121_cast)[name = tensor("reshape_96_cast")]; + tensor reduce_mean_72_axes_0 = const()[name = tensor("reduce_mean_72_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_72_keep_dims_0 = const()[name = tensor("reduce_mean_72_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_72_cast = reduce_mean(axes = reduce_mean_72_axes_0, keep_dims = reduce_mean_72_keep_dims_0, x = reshape_96_cast)[name = tensor("reduce_mean_72_cast")]; + tensor sub_48_cast = sub(x = reshape_96_cast, y = reduce_mean_72_cast)[name = tensor("sub_48_cast")]; + tensor square_24_cast = square(x = sub_48_cast)[name = tensor("square_24_cast")]; + tensor reduce_mean_74_axes_0 = const()[name = tensor("reduce_mean_74_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_74_keep_dims_0 = const()[name = tensor("reduce_mean_74_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_74_cast = reduce_mean(axes = reduce_mean_74_axes_0, keep_dims = reduce_mean_74_keep_dims_0, x = square_24_cast)[name = tensor("reduce_mean_74_cast")]; + tensor add_48_y_0_to_fp16 = const()[name = tensor("add_48_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_48_cast = add(x = reduce_mean_74_cast, y = add_48_y_0_to_fp16)[name = tensor("add_48_cast")]; + tensor sqrt_24_cast = sqrt(x = add_48_cast)[name = tensor("sqrt_24_cast")]; + tensor real_div_24_cast = real_div(x = sub_48_cast, y = sqrt_24_cast)[name = tensor("real_div_24_cast")]; + tensor reshape_97_shape_0 = const()[name = tensor("reshape_97_shape_0"), val = tensor([2, 1280, 8, 8])]; + tensor reshape_97_cast = reshape(shape = reshape_97_shape_0, x = real_div_24_cast)[name = tensor("reshape_97_cast")]; + 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(212310336)))]; + 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(212312960)))]; + 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_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_97_cast)[name = tensor("add_49_cast")]; + tensor var_1888 = const()[name = tensor("op_1888"), val = tensor([1, 1])]; + tensor var_1890 = const()[name = tensor("op_1890"), val = tensor([1, 1])]; + tensor hidden_states_123_pad_type_0 = const()[name = tensor("hidden_states_123_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_123_pad_0 = const()[name = tensor("hidden_states_123_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(212315584))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(213544448))), name = tensor("mid_block_attentions_0_proj_in_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor mid_block_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(213544640)))]; + tensor hidden_states_123_cast = conv(bias = mid_block_attentions_0_proj_in_bias_to_fp16, dilations = var_1890, groups = var_1828, pad = hidden_states_123_pad_0, pad_type = hidden_states_123_pad_type_0, strides = var_1888, weight = mid_block_attentions_0_proj_in_weight_to_fp16_palettized, x = add_49_cast)[name = tensor("hidden_states_123_cast")]; + tensor var_1895 = const()[name = tensor("op_1895"), val = tensor([2, 1280, 1, 64])]; + tensor inputs_37_cast = reshape(shape = var_1895, x = hidden_states_123_cast)[name = tensor("inputs_37_cast")]; + tensor var_1905 = const()[name = tensor("op_1905"), val = tensor([1])]; + tensor channels_mean_37_cast = reduce_mean(axes = var_1905, keep_dims = var_1823, x = inputs_37_cast)[name = tensor("channels_mean_37_cast")]; + tensor zero_mean_37_cast = sub(x = inputs_37_cast, y = channels_mean_37_cast)[name = tensor("zero_mean_37_cast")]; + tensor zero_mean_sq_37_cast = mul(x = zero_mean_37_cast, y = zero_mean_37_cast)[name = tensor("zero_mean_sq_37_cast")]; + tensor var_1909 = const()[name = tensor("op_1909"), val = tensor([1])]; + tensor var_1910_cast = reduce_mean(axes = var_1909, keep_dims = var_1823, x = zero_mean_sq_37_cast)[name = tensor("op_1910_cast")]; + tensor var_1911_to_fp16 = const()[name = tensor("op_1911_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1912_cast = add(x = var_1910_cast, y = var_1911_to_fp16)[name = tensor("op_1912_cast")]; + tensor denom_37_epsilon_0_to_fp16 = const()[name = tensor("denom_37_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_37_cast = rsqrt(epsilon = denom_37_epsilon_0_to_fp16, x = var_1912_cast)[name = tensor("denom_37_cast")]; + tensor out_37_cast = mul(x = zero_mean_37_cast, y = denom_37_cast)[name = tensor("out_37_cast")]; + tensor var_1916_to_fp16 = const()[name = tensor("op_1916_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(213547264)))]; + tensor var_1917_cast = add(x = out_37_cast, y = var_1916_to_fp16)[name = tensor("op_1917_cast")]; + tensor var_1919_to_fp16 = const()[name = tensor("op_1919_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(213549888)))]; + tensor hidden_states_125_cast = mul(x = var_1917_cast, y = var_1919_to_fp16)[name = tensor("hidden_states_125_cast")]; + tensor var_1926 = const()[name = tensor("op_1926"), val = tensor([1, 1])]; + tensor var_1928 = const()[name = tensor("op_1928"), val = tensor([1, 1])]; + tensor q_25_pad_type_0 = const()[name = tensor("q_25_pad_type_0"), val = tensor("custom")]; + tensor q_25_pad_0 = const()[name = tensor("q_25_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(213552512))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(214781376))), name = tensor("mid_block_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_25_cast = conv(dilations = var_1928, groups = var_1828, pad = q_25_pad_0, pad_type = q_25_pad_type_0, strides = var_1926, weight = mid_block_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_125_cast)[name = tensor("q_25_cast")]; + tensor var_1932 = const()[name = tensor("op_1932"), val = tensor([1, 1])]; + tensor var_1934 = const()[name = tensor("op_1934"), val = tensor([1, 1])]; + tensor k_25_pad_type_0 = const()[name = tensor("k_25_pad_type_0"), val = tensor("custom")]; + tensor k_25_pad_0 = const()[name = tensor("k_25_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(214781568))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(216010432))), name = tensor("mid_block_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_25_cast = conv(dilations = var_1934, groups = var_1828, pad = k_25_pad_0, pad_type = k_25_pad_type_0, strides = var_1932, weight = mid_block_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_125_cast)[name = tensor("k_25_cast")]; + tensor var_1938 = const()[name = tensor("op_1938"), val = tensor([1, 1])]; + tensor var_1940 = const()[name = tensor("op_1940"), val = tensor([1, 1])]; + tensor v_25_pad_type_0 = const()[name = tensor("v_25_pad_type_0"), val = tensor("custom")]; + tensor v_25_pad_0 = const()[name = tensor("v_25_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(216010624))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(217239488))), name = tensor("mid_block_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_25_cast = conv(dilations = var_1940, groups = var_1828, pad = v_25_pad_0, pad_type = v_25_pad_type_0, strides = var_1938, weight = mid_block_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_125_cast)[name = tensor("v_25_cast")]; + tensor var_1944 = const()[name = tensor("op_1944"), val = tensor([2, 8, 160, -1])]; + tensor var_1945_cast = reshape(shape = var_1944, x = q_25_cast)[name = tensor("op_1945_cast")]; + tensor var_1946 = const()[name = tensor("op_1946"), val = tensor([2, 8, 160, -1])]; + tensor var_1947_cast = reshape(shape = var_1946, x = k_25_cast)[name = tensor("op_1947_cast")]; + tensor var_1948 = const()[name = tensor("op_1948"), val = tensor([2, 8, 160, -1])]; + tensor var_1949_cast = reshape(shape = var_1948, x = v_25_cast)[name = tensor("op_1949_cast")]; + tensor attn_weights_49_transpose_x_0 = const()[name = tensor("attn_weights_49_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_49_transpose_y_0 = const()[name = tensor("attn_weights_49_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_49_cast = matmul(transpose_x = attn_weights_49_transpose_x_0, transpose_y = attn_weights_49_transpose_y_0, x = var_1945_cast, y = var_1947_cast)[name = tensor("attn_weights_49_cast")]; + tensor var_1819_to_fp16 = const()[name = tensor("op_1819_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor attn_weights_51_cast = mul(x = attn_weights_49_cast, y = var_1819_to_fp16)[name = tensor("attn_weights_51_cast")]; + tensor var_1953_cast = softmax(axis = var_1812, x = attn_weights_51_cast)[name = tensor("op_1953_cast")]; + tensor attn_25_transpose_x_0 = const()[name = tensor("attn_25_transpose_x_0"), val = tensor(false)]; + tensor attn_25_transpose_y_0 = const()[name = tensor("attn_25_transpose_y_0"), val = tensor(true)]; + tensor attn_25_cast = matmul(transpose_x = attn_25_transpose_x_0, transpose_y = attn_25_transpose_y_0, x = var_1949_cast, y = var_1953_cast)[name = tensor("attn_25_cast")]; + tensor var_1957 = const()[name = tensor("op_1957"), val = tensor([2, 1280, 1, -1])]; + tensor input_215_cast = reshape(shape = var_1957, x = attn_25_cast)[name = tensor("input_215_cast")]; + tensor var_1962 = const()[name = tensor("op_1962"), val = tensor([1, 1])]; + tensor var_1964 = const()[name = tensor("op_1964"), val = tensor([1, 1])]; + tensor var_1966_pad_type_0 = const()[name = tensor("op_1966_pad_type_0"), val = tensor("custom")]; + tensor var_1966_pad_0 = const()[name = tensor("op_1966_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(217239680))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(218468544))), name = tensor("mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(218468736)))]; + tensor var_1966_cast = conv(bias = mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_1964, groups = var_1828, pad = var_1966_pad_0, pad_type = var_1966_pad_type_0, strides = var_1962, weight = mid_block_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_215_cast)[name = tensor("op_1966_cast")]; + tensor inputs_39_cast = add(x = var_1966_cast, y = inputs_37_cast)[name = tensor("inputs_39_cast")]; + tensor var_1970 = const()[name = tensor("op_1970"), val = tensor([1])]; + tensor channels_mean_39_cast = reduce_mean(axes = var_1970, keep_dims = var_1823, x = inputs_39_cast)[name = tensor("channels_mean_39_cast")]; + tensor zero_mean_39_cast = sub(x = inputs_39_cast, y = channels_mean_39_cast)[name = tensor("zero_mean_39_cast")]; + tensor zero_mean_sq_39_cast = mul(x = zero_mean_39_cast, y = zero_mean_39_cast)[name = tensor("zero_mean_sq_39_cast")]; + tensor var_1974 = const()[name = tensor("op_1974"), val = tensor([1])]; + tensor var_1975_cast = reduce_mean(axes = var_1974, keep_dims = var_1823, x = zero_mean_sq_39_cast)[name = tensor("op_1975_cast")]; + tensor var_1976_to_fp16 = const()[name = tensor("op_1976_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_1977_cast = add(x = var_1975_cast, y = var_1976_to_fp16)[name = tensor("op_1977_cast")]; + tensor denom_39_epsilon_0_to_fp16 = const()[name = tensor("denom_39_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_39_cast = rsqrt(epsilon = denom_39_epsilon_0_to_fp16, x = var_1977_cast)[name = tensor("denom_39_cast")]; + tensor out_39_cast = mul(x = zero_mean_39_cast, y = denom_39_cast)[name = tensor("out_39_cast")]; + tensor var_1981_to_fp16 = const()[name = tensor("op_1981_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(218471360)))]; + tensor var_1982_cast = add(x = out_39_cast, y = var_1981_to_fp16)[name = tensor("op_1982_cast")]; + tensor var_1984_to_fp16 = const()[name = tensor("op_1984_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(218473984)))]; + tensor hidden_states_127_cast = mul(x = var_1982_cast, y = var_1984_to_fp16)[name = tensor("hidden_states_127_cast")]; + tensor var_1991 = const()[name = tensor("op_1991"), val = tensor([1, 1])]; + tensor var_1993 = const()[name = tensor("op_1993"), val = tensor([1, 1])]; + tensor q_27_pad_type_0 = const()[name = tensor("q_27_pad_type_0"), val = tensor("custom")]; + tensor q_27_pad_0 = const()[name = tensor("q_27_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(218476608))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(219705472))), name = tensor("mid_block_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_27_cast = conv(dilations = var_1993, groups = var_1828, pad = q_27_pad_0, pad_type = q_27_pad_type_0, strides = var_1991, weight = mid_block_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_127_cast)[name = tensor("q_27_cast")]; + tensor var_1997 = const()[name = tensor("op_1997"), val = tensor([1, 1])]; + tensor var_1999 = const()[name = tensor("op_1999"), val = tensor([1, 1])]; + tensor k_27_pad_type_0 = const()[name = tensor("k_27_pad_type_0"), val = tensor("custom")]; + tensor k_27_pad_0 = const()[name = tensor("k_27_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(219705664))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(220443008))), name = tensor("mid_block_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 768, 1, 1])]; + tensor k_27_cast = conv(dilations = var_1999, groups = var_1828, pad = k_27_pad_0, pad_type = k_27_pad_type_0, strides = var_1997, weight = mid_block_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_27_cast")]; + tensor var_2003 = const()[name = tensor("op_2003"), val = tensor([1, 1])]; + tensor var_2005 = const()[name = tensor("op_2005"), val = tensor([1, 1])]; + tensor v_27_pad_type_0 = const()[name = tensor("v_27_pad_type_0"), val = tensor("custom")]; + tensor v_27_pad_0 = const()[name = tensor("v_27_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(220443200))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(221180544))), name = tensor("mid_block_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 768, 1, 1])]; + tensor v_27_cast = conv(dilations = var_2005, groups = var_1828, pad = v_27_pad_0, pad_type = v_27_pad_type_0, strides = var_2003, weight = mid_block_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_27_cast")]; + tensor var_2009 = const()[name = tensor("op_2009"), val = tensor([2, 8, 160, -1])]; + tensor var_2010_cast = reshape(shape = var_2009, x = q_27_cast)[name = tensor("op_2010_cast")]; + tensor var_2011 = const()[name = tensor("op_2011"), val = tensor([2, 8, 160, -1])]; + tensor var_2012_cast = reshape(shape = var_2011, x = k_27_cast)[name = tensor("op_2012_cast")]; + tensor var_2013 = const()[name = tensor("op_2013"), val = tensor([2, 8, 160, -1])]; + tensor var_2014_cast = reshape(shape = var_2013, x = v_27_cast)[name = tensor("op_2014_cast")]; + tensor attn_weights_53_transpose_x_0 = const()[name = tensor("attn_weights_53_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_53_transpose_y_0 = const()[name = tensor("attn_weights_53_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_53_cast = matmul(transpose_x = attn_weights_53_transpose_x_0, transpose_y = attn_weights_53_transpose_y_0, x = var_2010_cast, y = var_2012_cast)[name = tensor("attn_weights_53_cast")]; + tensor attn_weights_55_cast = mul(x = attn_weights_53_cast, y = var_1819_to_fp16)[name = tensor("attn_weights_55_cast")]; + tensor var_2018_cast = softmax(axis = var_1812, x = attn_weights_55_cast)[name = tensor("op_2018_cast")]; + tensor attn_27_transpose_x_0 = const()[name = tensor("attn_27_transpose_x_0"), val = tensor(false)]; + tensor attn_27_transpose_y_0 = const()[name = tensor("attn_27_transpose_y_0"), val = tensor(true)]; + tensor attn_27_cast = matmul(transpose_x = attn_27_transpose_x_0, transpose_y = attn_27_transpose_y_0, x = var_2014_cast, y = var_2018_cast)[name = tensor("attn_27_cast")]; + tensor var_2022 = const()[name = tensor("op_2022"), val = tensor([2, 1280, 1, -1])]; + tensor input_217_cast = reshape(shape = var_2022, x = attn_27_cast)[name = tensor("input_217_cast")]; + tensor var_2027 = const()[name = tensor("op_2027"), val = tensor([1, 1])]; + tensor var_2029 = const()[name = tensor("op_2029"), val = tensor([1, 1])]; + tensor var_2031_pad_type_0 = const()[name = tensor("op_2031_pad_type_0"), val = tensor("custom")]; + tensor var_2031_pad_0 = const()[name = tensor("op_2031_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(221180736))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(222409600))), name = tensor("mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(222409792)))]; + tensor var_2031_cast = conv(bias = mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_2029, groups = var_1828, pad = var_2031_pad_0, pad_type = var_2031_pad_type_0, strides = var_2027, weight = mid_block_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_217_cast)[name = tensor("op_2031_cast")]; + tensor inputs_41_cast = add(x = var_2031_cast, y = inputs_39_cast)[name = tensor("inputs_41_cast")]; + tensor var_2035 = const()[name = tensor("op_2035"), val = tensor([1])]; + tensor channels_mean_41_cast = reduce_mean(axes = var_2035, keep_dims = var_1823, x = inputs_41_cast)[name = tensor("channels_mean_41_cast")]; + tensor zero_mean_41_cast = sub(x = inputs_41_cast, y = channels_mean_41_cast)[name = tensor("zero_mean_41_cast")]; + tensor zero_mean_sq_41_cast = mul(x = zero_mean_41_cast, y = zero_mean_41_cast)[name = tensor("zero_mean_sq_41_cast")]; + tensor var_2039 = const()[name = tensor("op_2039"), val = tensor([1])]; + tensor var_2040_cast = reduce_mean(axes = var_2039, keep_dims = var_1823, x = zero_mean_sq_41_cast)[name = tensor("op_2040_cast")]; + tensor var_2041_to_fp16 = const()[name = tensor("op_2041_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2042_cast = add(x = var_2040_cast, y = var_2041_to_fp16)[name = tensor("op_2042_cast")]; + tensor denom_41_epsilon_0_to_fp16 = const()[name = tensor("denom_41_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_41_cast = rsqrt(epsilon = denom_41_epsilon_0_to_fp16, x = var_2042_cast)[name = tensor("denom_41_cast")]; + tensor out_41_cast = mul(x = zero_mean_41_cast, y = denom_41_cast)[name = tensor("out_41_cast")]; + tensor var_2046_to_fp16 = const()[name = tensor("op_2046_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(222412416)))]; + tensor var_2047_cast = add(x = out_41_cast, y = var_2046_to_fp16)[name = tensor("op_2047_cast")]; + tensor var_2049_to_fp16 = const()[name = tensor("op_2049_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(222415040)))]; + tensor input_219_cast = mul(x = var_2047_cast, y = var_2049_to_fp16)[name = tensor("input_219_cast")]; + tensor var_2057 = const()[name = tensor("op_2057"), val = tensor([1, 1])]; + tensor var_2059 = const()[name = tensor("op_2059"), val = tensor([1, 1])]; + tensor var_2061_pad_type_0 = const()[name = tensor("op_2061_pad_type_0"), val = tensor("custom")]; + tensor var_2061_pad_0 = const()[name = tensor("op_2061_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(222417664))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(232248128))), name = tensor("mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(232248320))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(232256064))), name = tensor("mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_2061_cast = conv(bias = mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_2059, groups = var_1828, pad = var_2061_pad_0, pad_type = var_2061_pad_type_0, strides = var_2057, weight = mid_block_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_219_cast)[name = tensor("op_2061_cast")]; + tensor var_2062_split_sizes_0 = const()[name = tensor("op_2062_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_2062_axis_0 = const()[name = tensor("op_2062_axis_0"), val = tensor(1)]; + tensor var_2062_cast_0, tensor var_2062_cast_1 = split(axis = var_2062_axis_0, split_sizes = var_2062_split_sizes_0, x = var_2061_cast)[name = tensor("op_2062_cast")]; + tensor var_2064_mode_0 = const()[name = tensor("op_2064_mode_0"), val = tensor("EXACT")]; + tensor var_2064_cast = gelu(mode = var_2064_mode_0, x = var_2062_cast_1)[name = tensor("op_2064_cast")]; + tensor input_221_cast = mul(x = var_2062_cast_0, y = var_2064_cast)[name = tensor("input_221_cast")]; + tensor var_2068 = const()[name = tensor("op_2068"), val = tensor([1, 1])]; + tensor var_2070 = const()[name = tensor("op_2070"), val = tensor([1, 1])]; + tensor var_2072_pad_type_0 = const()[name = tensor("op_2072_pad_type_0"), val = tensor("custom")]; + tensor var_2072_pad_0 = const()[name = tensor("op_2072_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(232256256))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(237171520))), name = tensor("mid_block_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor mid_block_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(237171712)))]; + tensor var_2072_cast = conv(bias = mid_block_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_2070, groups = var_1828, pad = var_2072_pad_0, pad_type = var_2072_pad_type_0, strides = var_2068, weight = mid_block_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_221_cast)[name = tensor("op_2072_cast")]; + tensor hidden_states_131_cast = add(x = var_2072_cast, y = inputs_41_cast)[name = tensor("hidden_states_131_cast")]; + tensor var_2074 = const()[name = tensor("op_2074"), val = tensor([2, 1280, 8, 8])]; + tensor input_223_cast = reshape(shape = var_2074, x = hidden_states_131_cast)[name = tensor("input_223_cast")]; + tensor var_2078 = const()[name = tensor("op_2078"), val = tensor([1, 1])]; + tensor var_2080 = const()[name = tensor("op_2080"), val = tensor([1, 1])]; + tensor hidden_states_133_pad_type_0 = const()[name = tensor("hidden_states_133_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_133_pad_0 = const()[name = tensor("hidden_states_133_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_attentions_0_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(237174336))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(238403200))), name = tensor("mid_block_attentions_0_proj_out_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor mid_block_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor("mid_block_attentions_0_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(238403392)))]; + tensor hidden_states_133_cast = conv(bias = mid_block_attentions_0_proj_out_bias_to_fp16, dilations = var_2080, groups = var_1828, pad = hidden_states_133_pad_0, pad_type = hidden_states_133_pad_type_0, strides = var_2078, weight = mid_block_attentions_0_proj_out_weight_to_fp16_palettized, x = input_223_cast)[name = tensor("hidden_states_133_cast")]; + tensor input_225_cast = add(x = hidden_states_133_cast, y = hidden_states_121_cast)[name = tensor("input_225_cast")]; + tensor reshape_100_shape_0 = const()[name = tensor("reshape_100_shape_0"), val = tensor([2, 32, 40, 8, 8])]; + tensor reshape_100_cast = reshape(shape = reshape_100_shape_0, x = input_225_cast)[name = tensor("reshape_100_cast")]; + tensor reduce_mean_75_axes_0 = const()[name = tensor("reduce_mean_75_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_75_keep_dims_0 = const()[name = tensor("reduce_mean_75_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_75_cast = reduce_mean(axes = reduce_mean_75_axes_0, keep_dims = reduce_mean_75_keep_dims_0, x = reshape_100_cast)[name = tensor("reduce_mean_75_cast")]; + tensor sub_50_cast = sub(x = reshape_100_cast, y = reduce_mean_75_cast)[name = tensor("sub_50_cast")]; + tensor square_25_cast = square(x = sub_50_cast)[name = tensor("square_25_cast")]; + tensor reduce_mean_77_axes_0 = const()[name = tensor("reduce_mean_77_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_77_keep_dims_0 = const()[name = tensor("reduce_mean_77_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_77_cast = reduce_mean(axes = reduce_mean_77_axes_0, keep_dims = reduce_mean_77_keep_dims_0, x = square_25_cast)[name = tensor("reduce_mean_77_cast")]; + tensor add_50_y_0_to_fp16 = const()[name = tensor("add_50_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_50_cast = add(x = reduce_mean_77_cast, y = add_50_y_0_to_fp16)[name = tensor("add_50_cast")]; + tensor sqrt_25_cast = sqrt(x = add_50_cast)[name = tensor("sqrt_25_cast")]; + tensor real_div_25_cast = real_div(x = sub_50_cast, y = sqrt_25_cast)[name = tensor("real_div_25_cast")]; + tensor reshape_101_shape_0 = const()[name = tensor("reshape_101_shape_0"), val = tensor([2, 1280, 8, 8])]; + tensor reshape_101_cast = reshape(shape = reshape_101_shape_0, x = real_div_25_cast)[name = tensor("reshape_101_cast")]; + tensor add_51_gamma_0_to_fp16 = const()[name = tensor("add_51_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(238406016)))]; + 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(238408640)))]; + 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_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_101_cast)[name = tensor("add_51_cast")]; + tensor input_229_cast = silu(x = add_51_cast)[name = tensor("input_229_cast")]; + tensor var_2095 = const()[name = tensor("op_2095"), val = tensor([1, 1])]; + tensor var_2097 = const()[name = tensor("op_2097"), val = tensor([1, 1])]; + tensor hidden_states_135_pad_type_0 = const()[name = tensor("hidden_states_135_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_135_pad_0 = const()[name = tensor("hidden_states_135_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor mid_block_resnets_1_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(238411264))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(249470528))), name = tensor("mid_block_resnets_1_conv1_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor mid_block_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("mid_block_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(249470720)))]; + tensor hidden_states_135_cast = conv(bias = mid_block_resnets_1_conv1_bias_to_fp16, dilations = var_2097, groups = var_1828, pad = hidden_states_135_pad_0, pad_type = hidden_states_135_pad_type_0, strides = var_2095, weight = mid_block_resnets_1_conv1_weight_to_fp16_palettized, x = input_229_cast)[name = tensor("hidden_states_135_cast")]; + tensor var_2103 = const()[name = tensor("op_2103"), val = tensor([1, 1])]; + tensor var_2105 = const()[name = tensor("op_2105"), val = tensor([1, 1])]; + tensor temb_19_pad_type_0 = const()[name = tensor("temb_19_pad_type_0"), val = tensor("custom")]; + tensor temb_19_pad_0 = const()[name = tensor("temb_19_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor mid_block_resnets_1_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(249473344))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(250702208))), name = tensor("mid_block_resnets_1_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor mid_block_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("mid_block_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(250702400)))]; + tensor temb_19_cast = conv(bias = mid_block_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_2105, groups = var_1828, pad = temb_19_pad_0, pad_type = temb_19_pad_type_0, strides = var_2103, weight = mid_block_resnets_1_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor("temb_19_cast")]; + tensor input_233_cast = add(x = hidden_states_135_cast, y = temb_19_cast)[name = tensor("input_233_cast")]; + tensor reshape_104_shape_0 = const()[name = tensor("reshape_104_shape_0"), val = tensor([2, 32, 40, 8, 8])]; + tensor reshape_104_cast = reshape(shape = reshape_104_shape_0, x = input_233_cast)[name = tensor("reshape_104_cast")]; + tensor reduce_mean_78_axes_0 = const()[name = tensor("reduce_mean_78_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_78_keep_dims_0 = const()[name = tensor("reduce_mean_78_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_78_cast = reduce_mean(axes = reduce_mean_78_axes_0, keep_dims = reduce_mean_78_keep_dims_0, x = reshape_104_cast)[name = tensor("reduce_mean_78_cast")]; + tensor sub_52_cast = sub(x = reshape_104_cast, y = reduce_mean_78_cast)[name = tensor("sub_52_cast")]; + tensor square_26_cast = square(x = sub_52_cast)[name = tensor("square_26_cast")]; + tensor reduce_mean_80_axes_0 = const()[name = tensor("reduce_mean_80_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_80_keep_dims_0 = const()[name = tensor("reduce_mean_80_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_80_cast = reduce_mean(axes = reduce_mean_80_axes_0, keep_dims = reduce_mean_80_keep_dims_0, x = square_26_cast)[name = tensor("reduce_mean_80_cast")]; + tensor add_52_y_0_to_fp16 = const()[name = tensor("add_52_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_52_cast = add(x = reduce_mean_80_cast, y = add_52_y_0_to_fp16)[name = tensor("add_52_cast")]; + tensor sqrt_26_cast = sqrt(x = add_52_cast)[name = tensor("sqrt_26_cast")]; + tensor real_div_26_cast = real_div(x = sub_52_cast, y = sqrt_26_cast)[name = tensor("real_div_26_cast")]; + tensor reshape_105_shape_0 = const()[name = tensor("reshape_105_shape_0"), val = tensor([2, 1280, 8, 8])]; + tensor reshape_105_cast = reshape(shape = reshape_105_shape_0, x = real_div_26_cast)[name = tensor("reshape_105_cast")]; + tensor add_53_gamma_0_to_fp16 = const()[name = tensor("add_53_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(250705024)))]; + 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(250707648)))]; + 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_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_105_cast)[name = tensor("add_53_cast")]; + tensor input_237_cast = silu(x = add_53_cast)[name = tensor("input_237_cast")]; + tensor var_2115 = const()[name = tensor("op_2115"), val = tensor([1, 1])]; + tensor var_2117 = const()[name = tensor("op_2117"), val = tensor([1, 1])]; + tensor hidden_states_137_pad_type_0 = const()[name = tensor("hidden_states_137_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_137_pad_0 = const()[name = tensor("hidden_states_137_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor mid_block_resnets_1_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(250710272))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(261769536))), name = tensor("mid_block_resnets_1_conv2_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor mid_block_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("mid_block_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(261769728)))]; + tensor hidden_states_137_cast = conv(bias = mid_block_resnets_1_conv2_bias_to_fp16, dilations = var_2117, groups = var_1828, pad = hidden_states_137_pad_0, pad_type = hidden_states_137_pad_type_0, strides = var_2115, weight = mid_block_resnets_1_conv2_weight_to_fp16_palettized, x = input_237_cast)[name = tensor("hidden_states_137_cast")]; + tensor hidden_states_139_cast = add(x = input_225_cast, y = hidden_states_137_cast)[name = tensor("hidden_states_139_cast")]; + tensor var_2128 = const()[name = tensor("op_2128"), val = tensor(1)]; + tensor input_239_interleave_0 = const()[name = tensor("input_239_interleave_0"), val = tensor(false)]; + tensor input_239_cast = concat(axis = var_2128, interleave = input_239_interleave_0, values = (hidden_states_139_cast, input_199_cast))[name = tensor("input_239_cast")]; + tensor reshape_108_shape_0 = const()[name = tensor("reshape_108_shape_0"), val = tensor([2, 32, 80, 8, 8])]; + tensor reshape_108_cast = reshape(shape = reshape_108_shape_0, x = input_239_cast)[name = tensor("reshape_108_cast")]; + tensor reduce_mean_81_axes_0 = const()[name = tensor("reduce_mean_81_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_81_keep_dims_0 = const()[name = tensor("reduce_mean_81_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_81_cast = reduce_mean(axes = reduce_mean_81_axes_0, keep_dims = reduce_mean_81_keep_dims_0, x = reshape_108_cast)[name = tensor("reduce_mean_81_cast")]; + tensor sub_54_cast = sub(x = reshape_108_cast, y = reduce_mean_81_cast)[name = tensor("sub_54_cast")]; + tensor square_27_cast = square(x = sub_54_cast)[name = tensor("square_27_cast")]; + tensor reduce_mean_83_axes_0 = const()[name = tensor("reduce_mean_83_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_83_keep_dims_0 = const()[name = tensor("reduce_mean_83_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_83_cast = reduce_mean(axes = reduce_mean_83_axes_0, keep_dims = reduce_mean_83_keep_dims_0, x = square_27_cast)[name = tensor("reduce_mean_83_cast")]; + tensor add_54_y_0_to_fp16 = const()[name = tensor("add_54_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_54_cast = add(x = reduce_mean_83_cast, y = add_54_y_0_to_fp16)[name = tensor("add_54_cast")]; + tensor sqrt_27_cast = sqrt(x = add_54_cast)[name = tensor("sqrt_27_cast")]; + tensor real_div_27_cast = real_div(x = sub_54_cast, y = sqrt_27_cast)[name = tensor("real_div_27_cast")]; + tensor reshape_109_shape_0 = const()[name = tensor("reshape_109_shape_0"), val = tensor([2, 2560, 8, 8])]; + tensor reshape_109_cast = reshape(shape = reshape_109_shape_0, x = real_div_27_cast)[name = tensor("reshape_109_cast")]; + tensor add_55_mean_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(261772352))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(261774336))), name = tensor("add_55_mean_0_to_fp16_palettized"), shape = tensor([2560])]; + tensor add_55_variance_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(261774528))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(261776512))), name = tensor("add_55_variance_0_to_fp16_palettized"), shape = tensor([2560])]; + tensor add_55_gamma_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(261776704))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(261778688))), name = tensor("add_55_gamma_0_to_fp16_palettized"), shape = tensor([2560])]; + tensor add_55_beta_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(261778880))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(261780864))), name = tensor("add_55_beta_0_to_fp16_palettized"), shape = tensor([2560])]; + 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_palettized, epsilon = add_55_epsilon_0_to_fp16, gamma = add_55_gamma_0_to_fp16_palettized, mean = add_55_mean_0_to_fp16_palettized, variance = add_55_variance_0_to_fp16_palettized, x = reshape_109_cast)[name = tensor("add_55_cast")]; + tensor input_243_cast = silu(x = add_55_cast)[name = tensor("input_243_cast")]; + tensor var_2151 = const()[name = tensor("op_2151"), val = tensor([1, 1])]; + tensor var_2153 = const()[name = tensor("op_2153"), val = tensor([1, 1])]; + tensor hidden_states_141_pad_type_0 = const()[name = tensor("hidden_states_141_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_141_pad_0 = const()[name = tensor("hidden_states_141_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_0_resnets_0_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(261781056))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(283899520))), name = tensor("up_blocks_0_resnets_0_conv1_weight_to_fp16_palettized"), shape = tensor([1280, 2560, 3, 3])]; + tensor up_blocks_0_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(283899712)))]; + tensor hidden_states_141_cast = conv(bias = up_blocks_0_resnets_0_conv1_bias_to_fp16, dilations = var_2153, groups = var_2128, pad = hidden_states_141_pad_0, pad_type = hidden_states_141_pad_type_0, strides = var_2151, weight = up_blocks_0_resnets_0_conv1_weight_to_fp16_palettized, x = input_243_cast)[name = tensor("hidden_states_141_cast")]; + tensor var_2159 = const()[name = tensor("op_2159"), val = tensor([1, 1])]; + tensor var_2161 = const()[name = tensor("op_2161"), val = tensor([1, 1])]; + tensor temb_21_pad_type_0 = const()[name = tensor("temb_21_pad_type_0"), val = tensor("custom")]; + tensor temb_21_pad_0 = const()[name = tensor("temb_21_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_resnets_0_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(283902336))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(285131200))), name = tensor("up_blocks_0_resnets_0_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(285131392)))]; + tensor temb_21_cast = conv(bias = up_blocks_0_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_2161, groups = var_2128, pad = temb_21_pad_0, pad_type = temb_21_pad_type_0, strides = var_2159, weight = up_blocks_0_resnets_0_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor("temb_21_cast")]; + tensor input_247_cast = add(x = hidden_states_141_cast, y = temb_21_cast)[name = tensor("input_247_cast")]; + tensor reshape_112_shape_0 = const()[name = tensor("reshape_112_shape_0"), val = tensor([2, 32, 40, 8, 8])]; + tensor reshape_112_cast = reshape(shape = reshape_112_shape_0, x = input_247_cast)[name = tensor("reshape_112_cast")]; + tensor reduce_mean_84_axes_0 = const()[name = tensor("reduce_mean_84_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_84_keep_dims_0 = const()[name = tensor("reduce_mean_84_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_84_cast = reduce_mean(axes = reduce_mean_84_axes_0, keep_dims = reduce_mean_84_keep_dims_0, x = reshape_112_cast)[name = tensor("reduce_mean_84_cast")]; + tensor sub_56_cast = sub(x = reshape_112_cast, y = reduce_mean_84_cast)[name = tensor("sub_56_cast")]; + tensor square_28_cast = square(x = sub_56_cast)[name = tensor("square_28_cast")]; + tensor reduce_mean_86_axes_0 = const()[name = tensor("reduce_mean_86_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_86_keep_dims_0 = const()[name = tensor("reduce_mean_86_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_86_cast = reduce_mean(axes = reduce_mean_86_axes_0, keep_dims = reduce_mean_86_keep_dims_0, x = square_28_cast)[name = tensor("reduce_mean_86_cast")]; + tensor add_56_y_0_to_fp16 = const()[name = tensor("add_56_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_56_cast = add(x = reduce_mean_86_cast, y = add_56_y_0_to_fp16)[name = tensor("add_56_cast")]; + tensor sqrt_28_cast = sqrt(x = add_56_cast)[name = tensor("sqrt_28_cast")]; + tensor real_div_28_cast = real_div(x = sub_56_cast, y = sqrt_28_cast)[name = tensor("real_div_28_cast")]; + tensor reshape_113_shape_0 = const()[name = tensor("reshape_113_shape_0"), val = tensor([2, 1280, 8, 8])]; + tensor reshape_113_cast = reshape(shape = reshape_113_shape_0, x = real_div_28_cast)[name = tensor("reshape_113_cast")]; + tensor add_57_gamma_0_to_fp16 = const()[name = tensor("add_57_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(285134016)))]; + 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(285136640)))]; + 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_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_113_cast)[name = tensor("add_57_cast")]; + tensor input_251_cast = silu(x = add_57_cast)[name = tensor("input_251_cast")]; + tensor var_2171 = const()[name = tensor("op_2171"), val = tensor([1, 1])]; + tensor var_2173 = const()[name = tensor("op_2173"), val = tensor([1, 1])]; + tensor hidden_states_143_pad_type_0 = const()[name = tensor("hidden_states_143_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_143_pad_0 = const()[name = tensor("hidden_states_143_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_0_resnets_0_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(285139264))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(296198528))), name = tensor("up_blocks_0_resnets_0_conv2_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor up_blocks_0_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(296198720)))]; + tensor hidden_states_143_cast = conv(bias = up_blocks_0_resnets_0_conv2_bias_to_fp16, dilations = var_2173, groups = var_2128, pad = hidden_states_143_pad_0, pad_type = hidden_states_143_pad_type_0, strides = var_2171, weight = up_blocks_0_resnets_0_conv2_weight_to_fp16_palettized, x = input_251_cast)[name = tensor("hidden_states_143_cast")]; + tensor var_2178 = const()[name = tensor("op_2178"), val = tensor([1, 1])]; + tensor var_2180 = const()[name = tensor("op_2180"), val = tensor([1, 1])]; + tensor x_5_pad_type_0 = const()[name = tensor("x_5_pad_type_0"), val = tensor("custom")]; + tensor x_5_pad_0 = const()[name = tensor("x_5_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_resnets_0_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(296201344))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(298659008))), name = tensor("up_blocks_0_resnets_0_conv_shortcut_weight_to_fp16_palettized"), shape = tensor([1280, 2560, 1, 1])]; + tensor up_blocks_0_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(298659200)))]; + tensor x_5_cast = conv(bias = up_blocks_0_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_2180, groups = var_2128, pad = x_5_pad_0, pad_type = x_5_pad_type_0, strides = var_2178, weight = up_blocks_0_resnets_0_conv_shortcut_weight_to_fp16_palettized, x = input_239_cast)[name = tensor("x_5_cast")]; + tensor hidden_states_145_cast = add(x = x_5_cast, y = hidden_states_143_cast)[name = tensor("hidden_states_145_cast")]; + tensor input_253_interleave_0 = const()[name = tensor("input_253_interleave_0"), val = tensor(false)]; + tensor input_253_cast = concat(axis = var_2128, interleave = input_253_interleave_0, values = (hidden_states_145_cast, input_185_cast))[name = tensor("input_253_cast")]; + tensor reshape_116_shape_0 = const()[name = tensor("reshape_116_shape_0"), val = tensor([2, 32, 80, 8, 8])]; + tensor reshape_116_cast = reshape(shape = reshape_116_shape_0, x = input_253_cast)[name = tensor("reshape_116_cast")]; + tensor reduce_mean_87_axes_0 = const()[name = tensor("reduce_mean_87_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_87_keep_dims_0 = const()[name = tensor("reduce_mean_87_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_87_cast = reduce_mean(axes = reduce_mean_87_axes_0, keep_dims = reduce_mean_87_keep_dims_0, x = reshape_116_cast)[name = tensor("reduce_mean_87_cast")]; + tensor sub_58_cast = sub(x = reshape_116_cast, y = reduce_mean_87_cast)[name = tensor("sub_58_cast")]; + tensor square_29_cast = square(x = sub_58_cast)[name = tensor("square_29_cast")]; + tensor reduce_mean_89_axes_0 = const()[name = tensor("reduce_mean_89_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_89_keep_dims_0 = const()[name = tensor("reduce_mean_89_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_89_cast = reduce_mean(axes = reduce_mean_89_axes_0, keep_dims = reduce_mean_89_keep_dims_0, x = square_29_cast)[name = tensor("reduce_mean_89_cast")]; + tensor add_58_y_0_to_fp16 = const()[name = tensor("add_58_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_58_cast = add(x = reduce_mean_89_cast, y = add_58_y_0_to_fp16)[name = tensor("add_58_cast")]; + tensor sqrt_29_cast = sqrt(x = add_58_cast)[name = tensor("sqrt_29_cast")]; + tensor real_div_29_cast = real_div(x = sub_58_cast, y = sqrt_29_cast)[name = tensor("real_div_29_cast")]; + tensor reshape_117_shape_0 = const()[name = tensor("reshape_117_shape_0"), val = tensor([2, 2560, 8, 8])]; + tensor reshape_117_cast = reshape(shape = reshape_117_shape_0, x = real_div_29_cast)[name = tensor("reshape_117_cast")]; + tensor add_59_gamma_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(298661824))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(298663808))), name = tensor("add_59_gamma_0_to_fp16_palettized"), shape = tensor([2560])]; + tensor add_59_beta_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(298664000))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(298665984))), name = tensor("add_59_beta_0_to_fp16_palettized"), shape = tensor([2560])]; + 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_palettized, epsilon = add_59_epsilon_0_to_fp16, gamma = add_59_gamma_0_to_fp16_palettized, mean = add_55_mean_0_to_fp16_palettized, variance = add_55_variance_0_to_fp16_palettized, x = reshape_117_cast)[name = tensor("add_59_cast")]; + tensor input_257_cast = silu(x = add_59_cast)[name = tensor("input_257_cast")]; + tensor var_2198 = const()[name = tensor("op_2198"), val = tensor([1, 1])]; + tensor var_2200 = const()[name = tensor("op_2200"), val = tensor([1, 1])]; + tensor hidden_states_147_pad_type_0 = const()[name = tensor("hidden_states_147_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_147_pad_0 = const()[name = tensor("hidden_states_147_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_0_resnets_1_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(298666176))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(320784640))), name = tensor("up_blocks_0_resnets_1_conv1_weight_to_fp16_palettized"), shape = tensor([1280, 2560, 3, 3])]; + tensor up_blocks_0_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(320784832)))]; + tensor hidden_states_147_cast = conv(bias = up_blocks_0_resnets_1_conv1_bias_to_fp16, dilations = var_2200, groups = var_2128, pad = hidden_states_147_pad_0, pad_type = hidden_states_147_pad_type_0, strides = var_2198, weight = up_blocks_0_resnets_1_conv1_weight_to_fp16_palettized, x = input_257_cast)[name = tensor("hidden_states_147_cast")]; + tensor var_2206 = const()[name = tensor("op_2206"), val = tensor([1, 1])]; + tensor var_2208 = const()[name = tensor("op_2208"), val = tensor([1, 1])]; + tensor temb_23_pad_type_0 = const()[name = tensor("temb_23_pad_type_0"), val = tensor("custom")]; + tensor temb_23_pad_0 = const()[name = tensor("temb_23_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_resnets_1_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(320787456))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(322016320))), name = tensor("up_blocks_0_resnets_1_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(322016512)))]; + tensor temb_23_cast = conv(bias = up_blocks_0_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_2208, groups = var_2128, pad = temb_23_pad_0, pad_type = temb_23_pad_type_0, strides = var_2206, weight = up_blocks_0_resnets_1_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor("temb_23_cast")]; + tensor input_261_cast = add(x = hidden_states_147_cast, y = temb_23_cast)[name = tensor("input_261_cast")]; + tensor reshape_120_shape_0 = const()[name = tensor("reshape_120_shape_0"), val = tensor([2, 32, 40, 8, 8])]; + tensor reshape_120_cast = reshape(shape = reshape_120_shape_0, x = input_261_cast)[name = tensor("reshape_120_cast")]; + tensor reduce_mean_90_axes_0 = const()[name = tensor("reduce_mean_90_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_90_keep_dims_0 = const()[name = tensor("reduce_mean_90_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_90_cast = reduce_mean(axes = reduce_mean_90_axes_0, keep_dims = reduce_mean_90_keep_dims_0, x = reshape_120_cast)[name = tensor("reduce_mean_90_cast")]; + tensor sub_60_cast = sub(x = reshape_120_cast, y = reduce_mean_90_cast)[name = tensor("sub_60_cast")]; + tensor square_30_cast = square(x = sub_60_cast)[name = tensor("square_30_cast")]; + tensor reduce_mean_92_axes_0 = const()[name = tensor("reduce_mean_92_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_92_keep_dims_0 = const()[name = tensor("reduce_mean_92_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_92_cast = reduce_mean(axes = reduce_mean_92_axes_0, keep_dims = reduce_mean_92_keep_dims_0, x = square_30_cast)[name = tensor("reduce_mean_92_cast")]; + tensor add_60_y_0_to_fp16 = const()[name = tensor("add_60_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_60_cast = add(x = reduce_mean_92_cast, y = add_60_y_0_to_fp16)[name = tensor("add_60_cast")]; + tensor sqrt_30_cast = sqrt(x = add_60_cast)[name = tensor("sqrt_30_cast")]; + tensor real_div_30_cast = real_div(x = sub_60_cast, y = sqrt_30_cast)[name = tensor("real_div_30_cast")]; + tensor reshape_121_shape_0 = const()[name = tensor("reshape_121_shape_0"), val = tensor([2, 1280, 8, 8])]; + tensor reshape_121_cast = reshape(shape = reshape_121_shape_0, x = real_div_30_cast)[name = tensor("reshape_121_cast")]; + tensor add_61_gamma_0_to_fp16 = const()[name = tensor("add_61_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(322019136)))]; + tensor add_61_beta_0_to_fp16 = const()[name = tensor("add_61_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(322021760)))]; + tensor add_61_epsilon_0_to_fp16 = const()[name = tensor("add_61_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_61_cast = batch_norm(beta = add_61_beta_0_to_fp16, epsilon = add_61_epsilon_0_to_fp16, gamma = add_61_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_121_cast)[name = tensor("add_61_cast")]; + tensor input_265_cast = silu(x = add_61_cast)[name = tensor("input_265_cast")]; + tensor var_2218 = const()[name = tensor("op_2218"), val = tensor([1, 1])]; + tensor var_2220 = const()[name = tensor("op_2220"), val = tensor([1, 1])]; + tensor hidden_states_149_pad_type_0 = const()[name = tensor("hidden_states_149_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_149_pad_0 = const()[name = tensor("hidden_states_149_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_0_resnets_1_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(322024384))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(333083648))), name = tensor("up_blocks_0_resnets_1_conv2_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor up_blocks_0_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(333083840)))]; + tensor hidden_states_149_cast = conv(bias = up_blocks_0_resnets_1_conv2_bias_to_fp16, dilations = var_2220, groups = var_2128, pad = hidden_states_149_pad_0, pad_type = hidden_states_149_pad_type_0, strides = var_2218, weight = up_blocks_0_resnets_1_conv2_weight_to_fp16_palettized, x = input_265_cast)[name = tensor("hidden_states_149_cast")]; + tensor var_2225 = const()[name = tensor("op_2225"), val = tensor([1, 1])]; + tensor var_2227 = const()[name = tensor("op_2227"), val = tensor([1, 1])]; + tensor x_7_pad_type_0 = const()[name = tensor("x_7_pad_type_0"), val = tensor("custom")]; + tensor x_7_pad_0 = const()[name = tensor("x_7_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_resnets_1_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(333086464))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(335544128))), name = tensor("up_blocks_0_resnets_1_conv_shortcut_weight_to_fp16_palettized"), shape = tensor([1280, 2560, 1, 1])]; + tensor up_blocks_0_resnets_1_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_1_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(335544320)))]; + tensor x_7_cast = conv(bias = up_blocks_0_resnets_1_conv_shortcut_bias_to_fp16, dilations = var_2227, groups = var_2128, pad = x_7_pad_0, pad_type = x_7_pad_type_0, strides = var_2225, weight = up_blocks_0_resnets_1_conv_shortcut_weight_to_fp16_palettized, x = input_253_cast)[name = tensor("x_7_cast")]; + tensor hidden_states_151_cast = add(x = x_7_cast, y = hidden_states_149_cast)[name = tensor("hidden_states_151_cast")]; + tensor input_267_interleave_0 = const()[name = tensor("input_267_interleave_0"), val = tensor(false)]; + tensor input_267_cast = concat(axis = var_2128, interleave = input_267_interleave_0, values = (hidden_states_151_cast, input_171_cast))[name = tensor("input_267_cast")]; + tensor reshape_124_shape_0 = const()[name = tensor("reshape_124_shape_0"), val = tensor([2, 32, 80, 8, 8])]; + tensor reshape_124_cast = reshape(shape = reshape_124_shape_0, x = input_267_cast)[name = tensor("reshape_124_cast")]; + tensor reduce_mean_93_axes_0 = const()[name = tensor("reduce_mean_93_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_93_keep_dims_0 = const()[name = tensor("reduce_mean_93_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_93_cast = reduce_mean(axes = reduce_mean_93_axes_0, keep_dims = reduce_mean_93_keep_dims_0, x = reshape_124_cast)[name = tensor("reduce_mean_93_cast")]; + tensor sub_62_cast = sub(x = reshape_124_cast, y = reduce_mean_93_cast)[name = tensor("sub_62_cast")]; + tensor square_31_cast = square(x = sub_62_cast)[name = tensor("square_31_cast")]; + tensor reduce_mean_95_axes_0 = const()[name = tensor("reduce_mean_95_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_95_keep_dims_0 = const()[name = tensor("reduce_mean_95_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_95_cast = reduce_mean(axes = reduce_mean_95_axes_0, keep_dims = reduce_mean_95_keep_dims_0, x = square_31_cast)[name = tensor("reduce_mean_95_cast")]; + tensor add_62_y_0_to_fp16 = const()[name = tensor("add_62_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_62_cast = add(x = reduce_mean_95_cast, y = add_62_y_0_to_fp16)[name = tensor("add_62_cast")]; + tensor sqrt_31_cast = sqrt(x = add_62_cast)[name = tensor("sqrt_31_cast")]; + tensor real_div_31_cast = real_div(x = sub_62_cast, y = sqrt_31_cast)[name = tensor("real_div_31_cast")]; + tensor reshape_125_shape_0 = const()[name = tensor("reshape_125_shape_0"), val = tensor([2, 2560, 8, 8])]; + tensor reshape_125_cast = reshape(shape = reshape_125_shape_0, x = real_div_31_cast)[name = tensor("reshape_125_cast")]; + tensor add_63_gamma_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(335546944))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(335548928))), name = tensor("add_63_gamma_0_to_fp16_palettized"), shape = tensor([2560])]; + tensor add_63_beta_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(335549120))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(335551104))), name = tensor("add_63_beta_0_to_fp16_palettized"), shape = tensor([2560])]; + tensor add_63_epsilon_0_to_fp16 = const()[name = tensor("add_63_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_63_cast = batch_norm(beta = add_63_beta_0_to_fp16_palettized, epsilon = add_63_epsilon_0_to_fp16, gamma = add_63_gamma_0_to_fp16_palettized, mean = add_55_mean_0_to_fp16_palettized, variance = add_55_variance_0_to_fp16_palettized, x = reshape_125_cast)[name = tensor("add_63_cast")]; + tensor input_271_cast = silu(x = add_63_cast)[name = tensor("input_271_cast")]; + tensor var_2245 = const()[name = tensor("op_2245"), val = tensor([1, 1])]; + tensor var_2247 = const()[name = tensor("op_2247"), val = tensor([1, 1])]; + tensor hidden_states_153_pad_type_0 = const()[name = tensor("hidden_states_153_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_153_pad_0 = const()[name = tensor("hidden_states_153_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_0_resnets_2_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(335551296))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(357669760))), name = tensor("up_blocks_0_resnets_2_conv1_weight_to_fp16_palettized"), shape = tensor([1280, 2560, 3, 3])]; + tensor up_blocks_0_resnets_2_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_2_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(357669952)))]; + tensor hidden_states_153_cast = conv(bias = up_blocks_0_resnets_2_conv1_bias_to_fp16, dilations = var_2247, groups = var_2128, pad = hidden_states_153_pad_0, pad_type = hidden_states_153_pad_type_0, strides = var_2245, weight = up_blocks_0_resnets_2_conv1_weight_to_fp16_palettized, x = input_271_cast)[name = tensor("hidden_states_153_cast")]; + tensor var_2253 = const()[name = tensor("op_2253"), val = tensor([1, 1])]; + tensor var_2255 = const()[name = tensor("op_2255"), val = tensor([1, 1])]; + tensor temb_25_pad_type_0 = const()[name = tensor("temb_25_pad_type_0"), val = tensor("custom")]; + tensor temb_25_pad_0 = const()[name = tensor("temb_25_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_resnets_2_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(357672576))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(358901440))), name = tensor("up_blocks_0_resnets_2_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_0_resnets_2_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_2_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(358901632)))]; + tensor temb_25_cast = conv(bias = up_blocks_0_resnets_2_time_emb_proj_bias_to_fp16, dilations = var_2255, groups = var_2128, pad = temb_25_pad_0, pad_type = temb_25_pad_type_0, strides = var_2253, weight = up_blocks_0_resnets_2_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor("temb_25_cast")]; + tensor input_275_cast = add(x = hidden_states_153_cast, y = temb_25_cast)[name = tensor("input_275_cast")]; + tensor reshape_128_shape_0 = const()[name = tensor("reshape_128_shape_0"), val = tensor([2, 32, 40, 8, 8])]; + tensor reshape_128_cast = reshape(shape = reshape_128_shape_0, x = input_275_cast)[name = tensor("reshape_128_cast")]; + tensor reduce_mean_96_axes_0 = const()[name = tensor("reduce_mean_96_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_96_keep_dims_0 = const()[name = tensor("reduce_mean_96_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_96_cast = reduce_mean(axes = reduce_mean_96_axes_0, keep_dims = reduce_mean_96_keep_dims_0, x = reshape_128_cast)[name = tensor("reduce_mean_96_cast")]; + tensor sub_64_cast = sub(x = reshape_128_cast, y = reduce_mean_96_cast)[name = tensor("sub_64_cast")]; + tensor square_32_cast = square(x = sub_64_cast)[name = tensor("square_32_cast")]; + tensor reduce_mean_98_axes_0 = const()[name = tensor("reduce_mean_98_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_98_keep_dims_0 = const()[name = tensor("reduce_mean_98_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_98_cast = reduce_mean(axes = reduce_mean_98_axes_0, keep_dims = reduce_mean_98_keep_dims_0, x = square_32_cast)[name = tensor("reduce_mean_98_cast")]; + tensor add_64_y_0_to_fp16 = const()[name = tensor("add_64_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_64_cast = add(x = reduce_mean_98_cast, y = add_64_y_0_to_fp16)[name = tensor("add_64_cast")]; + tensor sqrt_32_cast = sqrt(x = add_64_cast)[name = tensor("sqrt_32_cast")]; + tensor real_div_32_cast = real_div(x = sub_64_cast, y = sqrt_32_cast)[name = tensor("real_div_32_cast")]; + tensor reshape_129_shape_0 = const()[name = tensor("reshape_129_shape_0"), val = tensor([2, 1280, 8, 8])]; + tensor reshape_129_cast = reshape(shape = reshape_129_shape_0, x = real_div_32_cast)[name = tensor("reshape_129_cast")]; + tensor add_65_gamma_0_to_fp16 = const()[name = tensor("add_65_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(358904256)))]; + tensor add_65_beta_0_to_fp16 = const()[name = tensor("add_65_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(358906880)))]; + tensor add_65_epsilon_0_to_fp16 = const()[name = tensor("add_65_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_65_cast = batch_norm(beta = add_65_beta_0_to_fp16, epsilon = add_65_epsilon_0_to_fp16, gamma = add_65_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_129_cast)[name = tensor("add_65_cast")]; + tensor input_279_cast = silu(x = add_65_cast)[name = tensor("input_279_cast")]; + tensor var_2265 = const()[name = tensor("op_2265"), val = tensor([1, 1])]; + tensor var_2267 = const()[name = tensor("op_2267"), val = tensor([1, 1])]; + tensor hidden_states_155_pad_type_0 = const()[name = tensor("hidden_states_155_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_155_pad_0 = const()[name = tensor("hidden_states_155_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_0_resnets_2_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(358909504))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(369968768))), name = tensor("up_blocks_0_resnets_2_conv2_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor up_blocks_0_resnets_2_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_2_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(369968960)))]; + tensor hidden_states_155_cast = conv(bias = up_blocks_0_resnets_2_conv2_bias_to_fp16, dilations = var_2267, groups = var_2128, pad = hidden_states_155_pad_0, pad_type = hidden_states_155_pad_type_0, strides = var_2265, weight = up_blocks_0_resnets_2_conv2_weight_to_fp16_palettized, x = input_279_cast)[name = tensor("hidden_states_155_cast")]; + tensor var_2272 = const()[name = tensor("op_2272"), val = tensor([1, 1])]; + tensor var_2274 = const()[name = tensor("op_2274"), val = tensor([1, 1])]; + tensor x_9_pad_type_0 = const()[name = tensor("x_9_pad_type_0"), val = tensor("custom")]; + tensor x_9_pad_0 = const()[name = tensor("x_9_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_0_resnets_2_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(369971584))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(372429248))), name = tensor("up_blocks_0_resnets_2_conv_shortcut_weight_to_fp16_palettized"), shape = tensor([1280, 2560, 1, 1])]; + tensor up_blocks_0_resnets_2_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_0_resnets_2_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(372429440)))]; + tensor x_9_cast = conv(bias = up_blocks_0_resnets_2_conv_shortcut_bias_to_fp16, dilations = var_2274, groups = var_2128, pad = x_9_pad_0, pad_type = x_9_pad_type_0, strides = var_2272, weight = up_blocks_0_resnets_2_conv_shortcut_weight_to_fp16_palettized, x = input_267_cast)[name = tensor("x_9_cast")]; + tensor input_281_cast = add(x = x_9_cast, y = hidden_states_155_cast)[name = tensor("input_281_cast")]; + tensor input_283_scale_factor_height_0 = const()[name = tensor("input_283_scale_factor_height_0"), val = tensor(0x1p+1)]; + tensor input_283_scale_factor_width_0 = const()[name = tensor("input_283_scale_factor_width_0"), val = tensor(0x1p+1)]; + tensor input_283_cast = upsample_nearest_neighbor(scale_factor_height = input_283_scale_factor_height_0, scale_factor_width = input_283_scale_factor_width_0, x = input_281_cast)[name = tensor("input_283_cast")]; + tensor var_2283 = const()[name = tensor("op_2283"), val = tensor([1, 1])]; + tensor var_2285 = const()[name = tensor("op_2285"), val = tensor([1, 1])]; + tensor hidden_states_157_pad_type_0 = const()[name = tensor("hidden_states_157_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_157_pad_0 = const()[name = tensor("hidden_states_157_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_0_upsamplers_0_conv_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(372432064))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(383491328))), name = tensor("up_blocks_0_upsamplers_0_conv_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor up_blocks_0_upsamplers_0_conv_bias_to_fp16 = const()[name = tensor("up_blocks_0_upsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(383491520)))]; + tensor hidden_states_157_cast = conv(bias = up_blocks_0_upsamplers_0_conv_bias_to_fp16, dilations = var_2285, groups = var_2128, pad = hidden_states_157_pad_0, pad_type = hidden_states_157_pad_type_0, strides = var_2283, weight = up_blocks_0_upsamplers_0_conv_weight_to_fp16_palettized, x = input_283_cast)[name = tensor("hidden_states_157_cast")]; + tensor var_2290 = const()[name = tensor("op_2290"), val = tensor(3)]; + tensor var_2301 = const()[name = tensor("op_2301"), val = tensor(true)]; + tensor var_2306 = const()[name = tensor("op_2306"), val = tensor(1)]; + tensor input_285_interleave_0 = const()[name = tensor("input_285_interleave_0"), val = tensor(false)]; + tensor input_285_cast = concat(axis = var_2306, interleave = input_285_interleave_0, values = (hidden_states_157_cast, input_169_cast))[name = tensor("input_285_cast")]; + tensor reshape_132_shape_0 = const()[name = tensor("reshape_132_shape_0"), val = tensor([2, 32, 80, 16, 16])]; + tensor reshape_132_cast = reshape(shape = reshape_132_shape_0, x = input_285_cast)[name = tensor("reshape_132_cast")]; + tensor reduce_mean_99_axes_0 = const()[name = tensor("reduce_mean_99_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_99_keep_dims_0 = const()[name = tensor("reduce_mean_99_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_99_cast = reduce_mean(axes = reduce_mean_99_axes_0, keep_dims = reduce_mean_99_keep_dims_0, x = reshape_132_cast)[name = tensor("reduce_mean_99_cast")]; + tensor sub_66_cast = sub(x = reshape_132_cast, y = reduce_mean_99_cast)[name = tensor("sub_66_cast")]; + tensor square_33_cast = square(x = sub_66_cast)[name = tensor("square_33_cast")]; + tensor reduce_mean_101_axes_0 = const()[name = tensor("reduce_mean_101_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_101_keep_dims_0 = const()[name = tensor("reduce_mean_101_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_101_cast = reduce_mean(axes = reduce_mean_101_axes_0, keep_dims = reduce_mean_101_keep_dims_0, x = square_33_cast)[name = tensor("reduce_mean_101_cast")]; + tensor add_66_y_0_to_fp16 = const()[name = tensor("add_66_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_66_cast = add(x = reduce_mean_101_cast, y = add_66_y_0_to_fp16)[name = tensor("add_66_cast")]; + tensor sqrt_33_cast = sqrt(x = add_66_cast)[name = tensor("sqrt_33_cast")]; + tensor real_div_33_cast = real_div(x = sub_66_cast, y = sqrt_33_cast)[name = tensor("real_div_33_cast")]; + tensor reshape_133_shape_0 = const()[name = tensor("reshape_133_shape_0"), val = tensor([2, 2560, 16, 16])]; + tensor reshape_133_cast = reshape(shape = reshape_133_shape_0, x = real_div_33_cast)[name = tensor("reshape_133_cast")]; + tensor add_67_gamma_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(383494144))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(383496128))), name = tensor("add_67_gamma_0_to_fp16_palettized"), shape = tensor([2560])]; + tensor add_67_beta_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(383496320))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(383498304))), name = tensor("add_67_beta_0_to_fp16_palettized"), shape = tensor([2560])]; + tensor add_67_epsilon_0_to_fp16 = const()[name = tensor("add_67_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_67_cast = batch_norm(beta = add_67_beta_0_to_fp16_palettized, epsilon = add_67_epsilon_0_to_fp16, gamma = add_67_gamma_0_to_fp16_palettized, mean = add_55_mean_0_to_fp16_palettized, variance = add_55_variance_0_to_fp16_palettized, x = reshape_133_cast)[name = tensor("add_67_cast")]; + tensor input_289_cast = silu(x = add_67_cast)[name = tensor("input_289_cast")]; + tensor var_2335 = const()[name = tensor("op_2335"), val = tensor([1, 1])]; + tensor var_2337 = const()[name = tensor("op_2337"), val = tensor([1, 1])]; + tensor hidden_states_159_pad_type_0 = const()[name = tensor("hidden_states_159_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_159_pad_0 = const()[name = tensor("hidden_states_159_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_resnets_0_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(383498496))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(405616960))), name = tensor("up_blocks_1_resnets_0_conv1_weight_to_fp16_palettized"), shape = tensor([1280, 2560, 3, 3])]; + tensor up_blocks_1_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(405617152)))]; + tensor hidden_states_159_cast = conv(bias = up_blocks_1_resnets_0_conv1_bias_to_fp16, dilations = var_2337, groups = var_2306, pad = hidden_states_159_pad_0, pad_type = hidden_states_159_pad_type_0, strides = var_2335, weight = up_blocks_1_resnets_0_conv1_weight_to_fp16_palettized, x = input_289_cast)[name = tensor("hidden_states_159_cast")]; + tensor var_2343 = const()[name = tensor("op_2343"), val = tensor([1, 1])]; + tensor var_2345 = const()[name = tensor("op_2345"), val = tensor([1, 1])]; + tensor temb_27_pad_type_0 = const()[name = tensor("temb_27_pad_type_0"), val = tensor("custom")]; + tensor temb_27_pad_0 = const()[name = tensor("temb_27_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_resnets_0_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(405619776))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(406848640))), name = tensor("up_blocks_1_resnets_0_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_1_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(406848832)))]; + tensor temb_27_cast = conv(bias = up_blocks_1_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_2345, groups = var_2306, pad = temb_27_pad_0, pad_type = temb_27_pad_type_0, strides = var_2343, weight = up_blocks_1_resnets_0_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor("temb_27_cast")]; + tensor input_293_cast = add(x = hidden_states_159_cast, y = temb_27_cast)[name = tensor("input_293_cast")]; + tensor reshape_136_shape_0 = const()[name = tensor("reshape_136_shape_0"), val = tensor([2, 32, 40, 16, 16])]; + tensor reshape_136_cast = reshape(shape = reshape_136_shape_0, x = input_293_cast)[name = tensor("reshape_136_cast")]; + tensor reduce_mean_102_axes_0 = const()[name = tensor("reduce_mean_102_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_102_keep_dims_0 = const()[name = tensor("reduce_mean_102_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_102_cast = reduce_mean(axes = reduce_mean_102_axes_0, keep_dims = reduce_mean_102_keep_dims_0, x = reshape_136_cast)[name = tensor("reduce_mean_102_cast")]; + tensor sub_68_cast = sub(x = reshape_136_cast, y = reduce_mean_102_cast)[name = tensor("sub_68_cast")]; + tensor square_34_cast = square(x = sub_68_cast)[name = tensor("square_34_cast")]; + tensor reduce_mean_104_axes_0 = const()[name = tensor("reduce_mean_104_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_104_keep_dims_0 = const()[name = tensor("reduce_mean_104_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_104_cast = reduce_mean(axes = reduce_mean_104_axes_0, keep_dims = reduce_mean_104_keep_dims_0, x = square_34_cast)[name = tensor("reduce_mean_104_cast")]; + tensor add_68_y_0_to_fp16 = const()[name = tensor("add_68_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_68_cast = add(x = reduce_mean_104_cast, y = add_68_y_0_to_fp16)[name = tensor("add_68_cast")]; + tensor sqrt_34_cast = sqrt(x = add_68_cast)[name = tensor("sqrt_34_cast")]; + tensor real_div_34_cast = real_div(x = sub_68_cast, y = sqrt_34_cast)[name = tensor("real_div_34_cast")]; + tensor reshape_137_shape_0 = const()[name = tensor("reshape_137_shape_0"), val = tensor([2, 1280, 16, 16])]; + tensor reshape_137_cast = reshape(shape = reshape_137_shape_0, x = real_div_34_cast)[name = tensor("reshape_137_cast")]; + tensor add_69_gamma_0_to_fp16 = const()[name = tensor("add_69_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(406851456)))]; + tensor add_69_beta_0_to_fp16 = const()[name = tensor("add_69_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(406854080)))]; + tensor add_69_epsilon_0_to_fp16 = const()[name = tensor("add_69_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_69_cast = batch_norm(beta = add_69_beta_0_to_fp16, epsilon = add_69_epsilon_0_to_fp16, gamma = add_69_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_137_cast)[name = tensor("add_69_cast")]; + tensor input_297_cast = silu(x = add_69_cast)[name = tensor("input_297_cast")]; + tensor var_2355 = const()[name = tensor("op_2355"), val = tensor([1, 1])]; + tensor var_2357 = const()[name = tensor("op_2357"), val = tensor([1, 1])]; + tensor hidden_states_161_pad_type_0 = const()[name = tensor("hidden_states_161_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_161_pad_0 = const()[name = tensor("hidden_states_161_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_resnets_0_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(406856704))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(417915968))), name = tensor("up_blocks_1_resnets_0_conv2_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor up_blocks_1_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(417916160)))]; + tensor hidden_states_161_cast = conv(bias = up_blocks_1_resnets_0_conv2_bias_to_fp16, dilations = var_2357, groups = var_2306, pad = hidden_states_161_pad_0, pad_type = hidden_states_161_pad_type_0, strides = var_2355, weight = up_blocks_1_resnets_0_conv2_weight_to_fp16_palettized, x = input_297_cast)[name = tensor("hidden_states_161_cast")]; + tensor var_2362 = const()[name = tensor("op_2362"), val = tensor([1, 1])]; + tensor var_2364 = const()[name = tensor("op_2364"), val = tensor([1, 1])]; + tensor x_11_pad_type_0 = const()[name = tensor("x_11_pad_type_0"), val = tensor("custom")]; + tensor x_11_pad_0 = const()[name = tensor("x_11_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_resnets_0_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(417918784))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(420376448))), name = tensor("up_blocks_1_resnets_0_conv_shortcut_weight_to_fp16_palettized"), shape = tensor([1280, 2560, 1, 1])]; + tensor up_blocks_1_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(420376640)))]; + tensor x_11_cast = conv(bias = up_blocks_1_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_2364, groups = var_2306, pad = x_11_pad_0, pad_type = x_11_pad_type_0, strides = var_2362, weight = up_blocks_1_resnets_0_conv_shortcut_weight_to_fp16_palettized, x = input_285_cast)[name = tensor("x_11_cast")]; + tensor hidden_states_163_cast = add(x = x_11_cast, y = hidden_states_161_cast)[name = tensor("hidden_states_163_cast")]; + tensor reshape_140_shape_0 = const()[name = tensor("reshape_140_shape_0"), val = tensor([2, 32, 40, 16, 16])]; + tensor reshape_140_cast = reshape(shape = reshape_140_shape_0, x = hidden_states_163_cast)[name = tensor("reshape_140_cast")]; + tensor reduce_mean_105_axes_0 = const()[name = tensor("reduce_mean_105_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_105_keep_dims_0 = const()[name = tensor("reduce_mean_105_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_105_cast = reduce_mean(axes = reduce_mean_105_axes_0, keep_dims = reduce_mean_105_keep_dims_0, x = reshape_140_cast)[name = tensor("reduce_mean_105_cast")]; + tensor sub_70_cast = sub(x = reshape_140_cast, y = reduce_mean_105_cast)[name = tensor("sub_70_cast")]; + tensor square_35_cast = square(x = sub_70_cast)[name = tensor("square_35_cast")]; + tensor reduce_mean_107_axes_0 = const()[name = tensor("reduce_mean_107_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_107_keep_dims_0 = const()[name = tensor("reduce_mean_107_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_107_cast = reduce_mean(axes = reduce_mean_107_axes_0, keep_dims = reduce_mean_107_keep_dims_0, x = square_35_cast)[name = tensor("reduce_mean_107_cast")]; + tensor add_70_y_0_to_fp16 = const()[name = tensor("add_70_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_70_cast = add(x = reduce_mean_107_cast, y = add_70_y_0_to_fp16)[name = tensor("add_70_cast")]; + tensor sqrt_35_cast = sqrt(x = add_70_cast)[name = tensor("sqrt_35_cast")]; + tensor real_div_35_cast = real_div(x = sub_70_cast, y = sqrt_35_cast)[name = tensor("real_div_35_cast")]; + tensor reshape_141_shape_0 = const()[name = tensor("reshape_141_shape_0"), val = tensor([2, 1280, 16, 16])]; + tensor reshape_141_cast = reshape(shape = reshape_141_shape_0, x = real_div_35_cast)[name = tensor("reshape_141_cast")]; + tensor add_71_gamma_0_to_fp16 = const()[name = tensor("add_71_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(420379264)))]; + tensor add_71_beta_0_to_fp16 = const()[name = tensor("add_71_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(420381888)))]; + tensor add_71_epsilon_0_to_fp16 = const()[name = tensor("add_71_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_71_cast = batch_norm(beta = add_71_beta_0_to_fp16, epsilon = add_71_epsilon_0_to_fp16, gamma = add_71_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_141_cast)[name = tensor("add_71_cast")]; + tensor var_2384 = const()[name = tensor("op_2384"), val = tensor([1, 1])]; + tensor var_2386 = const()[name = tensor("op_2386"), val = tensor([1, 1])]; + tensor hidden_states_165_pad_type_0 = const()[name = tensor("hidden_states_165_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_165_pad_0 = const()[name = tensor("hidden_states_165_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(420384512))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(421613376))), name = tensor("up_blocks_1_attentions_0_proj_in_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_1_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(421613568)))]; + tensor hidden_states_165_cast = conv(bias = up_blocks_1_attentions_0_proj_in_bias_to_fp16, dilations = var_2386, groups = var_2306, pad = hidden_states_165_pad_0, pad_type = hidden_states_165_pad_type_0, strides = var_2384, weight = up_blocks_1_attentions_0_proj_in_weight_to_fp16_palettized, x = add_71_cast)[name = tensor("hidden_states_165_cast")]; + tensor var_2391 = const()[name = tensor("op_2391"), val = tensor([2, 1280, 1, 256])]; + tensor inputs_43_cast = reshape(shape = var_2391, x = hidden_states_165_cast)[name = tensor("inputs_43_cast")]; + tensor var_2401 = const()[name = tensor("op_2401"), val = tensor([1])]; + tensor channels_mean_43_cast = reduce_mean(axes = var_2401, keep_dims = var_2301, x = inputs_43_cast)[name = tensor("channels_mean_43_cast")]; + tensor zero_mean_43_cast = sub(x = inputs_43_cast, y = channels_mean_43_cast)[name = tensor("zero_mean_43_cast")]; + tensor zero_mean_sq_43_cast = mul(x = zero_mean_43_cast, y = zero_mean_43_cast)[name = tensor("zero_mean_sq_43_cast")]; + tensor var_2405 = const()[name = tensor("op_2405"), val = tensor([1])]; + tensor var_2406_cast = reduce_mean(axes = var_2405, keep_dims = var_2301, x = zero_mean_sq_43_cast)[name = tensor("op_2406_cast")]; + tensor var_2407_to_fp16 = const()[name = tensor("op_2407_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2408_cast = add(x = var_2406_cast, y = var_2407_to_fp16)[name = tensor("op_2408_cast")]; + tensor denom_43_epsilon_0_to_fp16 = const()[name = tensor("denom_43_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_43_cast = rsqrt(epsilon = denom_43_epsilon_0_to_fp16, x = var_2408_cast)[name = tensor("denom_43_cast")]; + tensor out_43_cast = mul(x = zero_mean_43_cast, y = denom_43_cast)[name = tensor("out_43_cast")]; + tensor var_2412_to_fp16 = const()[name = tensor("op_2412_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(421616192)))]; + tensor var_2413_cast = add(x = out_43_cast, y = var_2412_to_fp16)[name = tensor("op_2413_cast")]; + tensor var_2415_to_fp16 = const()[name = tensor("op_2415_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(421618816)))]; + tensor hidden_states_167_cast = mul(x = var_2413_cast, y = var_2415_to_fp16)[name = tensor("hidden_states_167_cast")]; + tensor var_2422 = const()[name = tensor("op_2422"), val = tensor([1, 1])]; + tensor var_2424 = const()[name = tensor("op_2424"), val = tensor([1, 1])]; + tensor q_29_pad_type_0 = const()[name = tensor("q_29_pad_type_0"), val = tensor("custom")]; + tensor q_29_pad_0 = const()[name = tensor("q_29_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(421621440))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(422850304))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_29_cast = conv(dilations = var_2424, groups = var_2306, pad = q_29_pad_0, pad_type = q_29_pad_type_0, strides = var_2422, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_167_cast)[name = tensor("q_29_cast")]; + tensor var_2428 = const()[name = tensor("op_2428"), val = tensor([1, 1])]; + tensor var_2430 = const()[name = tensor("op_2430"), val = tensor([1, 1])]; + tensor k_29_pad_type_0 = const()[name = tensor("k_29_pad_type_0"), val = tensor("custom")]; + tensor k_29_pad_0 = const()[name = tensor("k_29_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(422850496))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(424079360))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_29_cast = conv(dilations = var_2430, groups = var_2306, pad = k_29_pad_0, pad_type = k_29_pad_type_0, strides = var_2428, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_167_cast)[name = tensor("k_29_cast")]; + tensor var_2434 = const()[name = tensor("op_2434"), val = tensor([1, 1])]; + tensor var_2436 = const()[name = tensor("op_2436"), val = tensor([1, 1])]; + tensor v_29_pad_type_0 = const()[name = tensor("v_29_pad_type_0"), val = tensor("custom")]; + tensor v_29_pad_0 = const()[name = tensor("v_29_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(424079552))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(425308416))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_29_cast = conv(dilations = var_2436, groups = var_2306, pad = v_29_pad_0, pad_type = v_29_pad_type_0, strides = var_2434, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_167_cast)[name = tensor("v_29_cast")]; + tensor var_2440 = const()[name = tensor("op_2440"), val = tensor([2, 8, 160, -1])]; + tensor var_2441_cast = reshape(shape = var_2440, x = q_29_cast)[name = tensor("op_2441_cast")]; + tensor var_2442 = const()[name = tensor("op_2442"), val = tensor([2, 8, 160, -1])]; + tensor var_2443_cast = reshape(shape = var_2442, x = k_29_cast)[name = tensor("op_2443_cast")]; + tensor var_2444 = const()[name = tensor("op_2444"), val = tensor([2, 8, 160, -1])]; + tensor var_2445_cast = reshape(shape = var_2444, x = v_29_cast)[name = tensor("op_2445_cast")]; + tensor attn_weights_57_transpose_x_0 = const()[name = tensor("attn_weights_57_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_57_transpose_y_0 = const()[name = tensor("attn_weights_57_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_57_cast = matmul(transpose_x = attn_weights_57_transpose_x_0, transpose_y = attn_weights_57_transpose_y_0, x = var_2441_cast, y = var_2443_cast)[name = tensor("attn_weights_57_cast")]; + tensor var_2297_to_fp16 = const()[name = tensor("op_2297_to_fp16"), val = tensor(0x1.43cp-4)]; + tensor attn_weights_59_cast = mul(x = attn_weights_57_cast, y = var_2297_to_fp16)[name = tensor("attn_weights_59_cast")]; + tensor var_2449_cast = softmax(axis = var_2290, x = attn_weights_59_cast)[name = tensor("op_2449_cast")]; + tensor attn_29_transpose_x_0 = const()[name = tensor("attn_29_transpose_x_0"), val = tensor(false)]; + tensor attn_29_transpose_y_0 = const()[name = tensor("attn_29_transpose_y_0"), val = tensor(true)]; + tensor attn_29_cast = matmul(transpose_x = attn_29_transpose_x_0, transpose_y = attn_29_transpose_y_0, x = var_2445_cast, y = var_2449_cast)[name = tensor("attn_29_cast")]; + tensor var_2453 = const()[name = tensor("op_2453"), val = tensor([2, 1280, 1, -1])]; + tensor input_301_cast = reshape(shape = var_2453, x = attn_29_cast)[name = tensor("input_301_cast")]; + tensor var_2458 = const()[name = tensor("op_2458"), val = tensor([1, 1])]; + tensor var_2460 = const()[name = tensor("op_2460"), val = tensor([1, 1])]; + tensor var_2462_pad_type_0 = const()[name = tensor("op_2462_pad_type_0"), val = tensor("custom")]; + tensor var_2462_pad_0 = const()[name = tensor("op_2462_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(425308608))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(426537472))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(426537664)))]; + tensor var_2462_cast = conv(bias = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_2460, groups = var_2306, pad = var_2462_pad_0, pad_type = var_2462_pad_type_0, strides = var_2458, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_301_cast)[name = tensor("op_2462_cast")]; + tensor inputs_45_cast = add(x = var_2462_cast, y = inputs_43_cast)[name = tensor("inputs_45_cast")]; + tensor var_2466 = const()[name = tensor("op_2466"), val = tensor([1])]; + tensor channels_mean_45_cast = reduce_mean(axes = var_2466, keep_dims = var_2301, x = inputs_45_cast)[name = tensor("channels_mean_45_cast")]; + tensor zero_mean_45_cast = sub(x = inputs_45_cast, y = channels_mean_45_cast)[name = tensor("zero_mean_45_cast")]; + tensor zero_mean_sq_45_cast = mul(x = zero_mean_45_cast, y = zero_mean_45_cast)[name = tensor("zero_mean_sq_45_cast")]; + tensor var_2470 = const()[name = tensor("op_2470"), val = tensor([1])]; + tensor var_2471_cast = reduce_mean(axes = var_2470, keep_dims = var_2301, x = zero_mean_sq_45_cast)[name = tensor("op_2471_cast")]; + tensor var_2472_to_fp16 = const()[name = tensor("op_2472_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2473_cast = add(x = var_2471_cast, y = var_2472_to_fp16)[name = tensor("op_2473_cast")]; + tensor denom_45_epsilon_0_to_fp16 = const()[name = tensor("denom_45_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_45_cast = rsqrt(epsilon = denom_45_epsilon_0_to_fp16, x = var_2473_cast)[name = tensor("denom_45_cast")]; + tensor out_45_cast = mul(x = zero_mean_45_cast, y = denom_45_cast)[name = tensor("out_45_cast")]; + tensor var_2477_to_fp16 = const()[name = tensor("op_2477_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(426540288)))]; + tensor var_2478_cast = add(x = out_45_cast, y = var_2477_to_fp16)[name = tensor("op_2478_cast")]; + tensor var_2480_to_fp16 = const()[name = tensor("op_2480_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(426542912)))]; + tensor hidden_states_169_cast = mul(x = var_2478_cast, y = var_2480_to_fp16)[name = tensor("hidden_states_169_cast")]; + tensor var_2487 = const()[name = tensor("op_2487"), val = tensor([1, 1])]; + tensor var_2489 = const()[name = tensor("op_2489"), val = tensor([1, 1])]; + tensor q_31_pad_type_0 = const()[name = tensor("q_31_pad_type_0"), val = tensor("custom")]; + tensor q_31_pad_0 = const()[name = tensor("q_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(426545536))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(427774400))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_31_cast = conv(dilations = var_2489, groups = var_2306, pad = q_31_pad_0, pad_type = q_31_pad_type_0, strides = var_2487, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_169_cast)[name = tensor("q_31_cast")]; + tensor var_2493 = const()[name = tensor("op_2493"), val = tensor([1, 1])]; + tensor var_2495 = const()[name = tensor("op_2495"), val = tensor([1, 1])]; + tensor k_31_pad_type_0 = const()[name = tensor("k_31_pad_type_0"), val = tensor("custom")]; + tensor k_31_pad_0 = const()[name = tensor("k_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(427774592))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(428511936))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 768, 1, 1])]; + tensor k_31_cast = conv(dilations = var_2495, groups = var_2306, pad = k_31_pad_0, pad_type = k_31_pad_type_0, strides = var_2493, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_31_cast")]; + tensor var_2499 = const()[name = tensor("op_2499"), val = tensor([1, 1])]; + tensor var_2501 = const()[name = tensor("op_2501"), val = tensor([1, 1])]; + tensor v_31_pad_type_0 = const()[name = tensor("v_31_pad_type_0"), val = tensor("custom")]; + tensor v_31_pad_0 = const()[name = tensor("v_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(428512128))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(429249472))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 768, 1, 1])]; + tensor v_31_cast = conv(dilations = var_2501, groups = var_2306, pad = v_31_pad_0, pad_type = v_31_pad_type_0, strides = var_2499, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_31_cast")]; + tensor var_2505 = const()[name = tensor("op_2505"), val = tensor([2, 8, 160, -1])]; + tensor var_2506_cast = reshape(shape = var_2505, x = q_31_cast)[name = tensor("op_2506_cast")]; + tensor var_2507 = const()[name = tensor("op_2507"), val = tensor([2, 8, 160, -1])]; + tensor var_2508_cast = reshape(shape = var_2507, x = k_31_cast)[name = tensor("op_2508_cast")]; + tensor var_2509 = const()[name = tensor("op_2509"), val = tensor([2, 8, 160, -1])]; + tensor var_2510_cast = reshape(shape = var_2509, x = v_31_cast)[name = tensor("op_2510_cast")]; + tensor attn_weights_61_transpose_x_0 = const()[name = tensor("attn_weights_61_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_61_transpose_y_0 = const()[name = tensor("attn_weights_61_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_61_cast = matmul(transpose_x = attn_weights_61_transpose_x_0, transpose_y = attn_weights_61_transpose_y_0, x = var_2506_cast, y = var_2508_cast)[name = tensor("attn_weights_61_cast")]; + tensor attn_weights_63_cast = mul(x = attn_weights_61_cast, y = var_2297_to_fp16)[name = tensor("attn_weights_63_cast")]; + tensor var_2514_cast = softmax(axis = var_2290, x = attn_weights_63_cast)[name = tensor("op_2514_cast")]; + tensor attn_31_transpose_x_0 = const()[name = tensor("attn_31_transpose_x_0"), val = tensor(false)]; + tensor attn_31_transpose_y_0 = const()[name = tensor("attn_31_transpose_y_0"), val = tensor(true)]; + tensor attn_31_cast = matmul(transpose_x = attn_31_transpose_x_0, transpose_y = attn_31_transpose_y_0, x = var_2510_cast, y = var_2514_cast)[name = tensor("attn_31_cast")]; + tensor var_2518 = const()[name = tensor("op_2518"), val = tensor([2, 1280, 1, -1])]; + tensor input_303_cast = reshape(shape = var_2518, x = attn_31_cast)[name = tensor("input_303_cast")]; + tensor var_2523 = const()[name = tensor("op_2523"), val = tensor([1, 1])]; + tensor var_2525 = const()[name = tensor("op_2525"), val = tensor([1, 1])]; + tensor var_2527_pad_type_0 = const()[name = tensor("op_2527_pad_type_0"), val = tensor("custom")]; + tensor var_2527_pad_0 = const()[name = tensor("op_2527_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(429249664))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(430478528))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(430478720)))]; + tensor var_2527_cast = conv(bias = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_2525, groups = var_2306, pad = var_2527_pad_0, pad_type = var_2527_pad_type_0, strides = var_2523, weight = up_blocks_1_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_303_cast)[name = tensor("op_2527_cast")]; + tensor inputs_47_cast = add(x = var_2527_cast, y = inputs_45_cast)[name = tensor("inputs_47_cast")]; + tensor var_2531 = const()[name = tensor("op_2531"), val = tensor([1])]; + tensor channels_mean_47_cast = reduce_mean(axes = var_2531, keep_dims = var_2301, x = inputs_47_cast)[name = tensor("channels_mean_47_cast")]; + tensor zero_mean_47_cast = sub(x = inputs_47_cast, y = channels_mean_47_cast)[name = tensor("zero_mean_47_cast")]; + tensor zero_mean_sq_47_cast = mul(x = zero_mean_47_cast, y = zero_mean_47_cast)[name = tensor("zero_mean_sq_47_cast")]; + tensor var_2535 = const()[name = tensor("op_2535"), val = tensor([1])]; + tensor var_2536_cast = reduce_mean(axes = var_2535, keep_dims = var_2301, x = zero_mean_sq_47_cast)[name = tensor("op_2536_cast")]; + tensor var_2537_to_fp16 = const()[name = tensor("op_2537_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2538_cast = add(x = var_2536_cast, y = var_2537_to_fp16)[name = tensor("op_2538_cast")]; + tensor denom_47_epsilon_0_to_fp16 = const()[name = tensor("denom_47_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_47_cast = rsqrt(epsilon = denom_47_epsilon_0_to_fp16, x = var_2538_cast)[name = tensor("denom_47_cast")]; + tensor out_47_cast = mul(x = zero_mean_47_cast, y = denom_47_cast)[name = tensor("out_47_cast")]; + tensor var_2542_to_fp16 = const()[name = tensor("op_2542_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(430481344)))]; + tensor var_2543_cast = add(x = out_47_cast, y = var_2542_to_fp16)[name = tensor("op_2543_cast")]; + tensor var_2545_to_fp16 = const()[name = tensor("op_2545_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(430483968)))]; + tensor input_305_cast = mul(x = var_2543_cast, y = var_2545_to_fp16)[name = tensor("input_305_cast")]; + tensor var_2553 = const()[name = tensor("op_2553"), val = tensor([1, 1])]; + tensor var_2555 = const()[name = tensor("op_2555"), val = tensor([1, 1])]; + tensor var_2557_pad_type_0 = const()[name = tensor("op_2557_pad_type_0"), val = tensor("custom")]; + tensor var_2557_pad_0 = const()[name = tensor("op_2557_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(430486592))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(440317056))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(440317248))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(440324992))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_2557_cast = conv(bias = up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_2555, groups = var_2306, pad = var_2557_pad_0, pad_type = var_2557_pad_type_0, strides = var_2553, weight = up_blocks_1_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_305_cast)[name = tensor("op_2557_cast")]; + tensor var_2558_split_sizes_0 = const()[name = tensor("op_2558_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_2558_axis_0 = const()[name = tensor("op_2558_axis_0"), val = tensor(1)]; + tensor var_2558_cast_0, tensor var_2558_cast_1 = split(axis = var_2558_axis_0, split_sizes = var_2558_split_sizes_0, x = var_2557_cast)[name = tensor("op_2558_cast")]; + tensor var_2560_mode_0 = const()[name = tensor("op_2560_mode_0"), val = tensor("EXACT")]; + tensor var_2560_cast = gelu(mode = var_2560_mode_0, x = var_2558_cast_1)[name = tensor("op_2560_cast")]; + tensor input_307_cast = mul(x = var_2558_cast_0, y = var_2560_cast)[name = tensor("input_307_cast")]; + tensor var_2564 = const()[name = tensor("op_2564"), val = tensor([1, 1])]; + tensor var_2566 = const()[name = tensor("op_2566"), val = tensor([1, 1])]; + tensor var_2568_pad_type_0 = const()[name = tensor("op_2568_pad_type_0"), val = tensor("custom")]; + tensor var_2568_pad_0 = const()[name = tensor("op_2568_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(440325184))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(445240448))), name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(445240640)))]; + tensor var_2568_cast = conv(bias = up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_2566, groups = var_2306, pad = var_2568_pad_0, pad_type = var_2568_pad_type_0, strides = var_2564, weight = up_blocks_1_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_307_cast)[name = tensor("op_2568_cast")]; + tensor hidden_states_173_cast = add(x = var_2568_cast, y = inputs_47_cast)[name = tensor("hidden_states_173_cast")]; + tensor var_2570 = const()[name = tensor("op_2570"), val = tensor([2, 1280, 16, 16])]; + tensor input_309_cast = reshape(shape = var_2570, x = hidden_states_173_cast)[name = tensor("input_309_cast")]; + tensor var_2574 = const()[name = tensor("op_2574"), val = tensor([1, 1])]; + tensor var_2576 = const()[name = tensor("op_2576"), val = tensor([1, 1])]; + tensor hidden_states_175_pad_type_0 = const()[name = tensor("hidden_states_175_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_175_pad_0 = const()[name = tensor("hidden_states_175_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_0_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(445243264))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(446472128))), name = tensor("up_blocks_1_attentions_0_proj_out_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_1_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_0_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(446472320)))]; + tensor hidden_states_175_cast = conv(bias = up_blocks_1_attentions_0_proj_out_bias_to_fp16, dilations = var_2576, groups = var_2306, pad = hidden_states_175_pad_0, pad_type = hidden_states_175_pad_type_0, strides = var_2574, weight = up_blocks_1_attentions_0_proj_out_weight_to_fp16_palettized, x = input_309_cast)[name = tensor("hidden_states_175_cast")]; + tensor hidden_states_177_cast = add(x = hidden_states_175_cast, y = hidden_states_163_cast)[name = tensor("hidden_states_177_cast")]; + tensor input_311_interleave_0 = const()[name = tensor("input_311_interleave_0"), val = tensor(false)]; + tensor input_311_cast = concat(axis = var_2306, interleave = input_311_interleave_0, values = (hidden_states_177_cast, input_143_cast))[name = tensor("input_311_cast")]; + tensor reshape_144_shape_0 = const()[name = tensor("reshape_144_shape_0"), val = tensor([2, 32, 80, 16, 16])]; + tensor reshape_144_cast = reshape(shape = reshape_144_shape_0, x = input_311_cast)[name = tensor("reshape_144_cast")]; + tensor reduce_mean_108_axes_0 = const()[name = tensor("reduce_mean_108_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_108_keep_dims_0 = const()[name = tensor("reduce_mean_108_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_108_cast = reduce_mean(axes = reduce_mean_108_axes_0, keep_dims = reduce_mean_108_keep_dims_0, x = reshape_144_cast)[name = tensor("reduce_mean_108_cast")]; + tensor sub_72_cast = sub(x = reshape_144_cast, y = reduce_mean_108_cast)[name = tensor("sub_72_cast")]; + tensor square_36_cast = square(x = sub_72_cast)[name = tensor("square_36_cast")]; + tensor reduce_mean_110_axes_0 = const()[name = tensor("reduce_mean_110_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_110_keep_dims_0 = const()[name = tensor("reduce_mean_110_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_110_cast = reduce_mean(axes = reduce_mean_110_axes_0, keep_dims = reduce_mean_110_keep_dims_0, x = square_36_cast)[name = tensor("reduce_mean_110_cast")]; + tensor add_72_y_0_to_fp16 = const()[name = tensor("add_72_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_72_cast = add(x = reduce_mean_110_cast, y = add_72_y_0_to_fp16)[name = tensor("add_72_cast")]; + tensor sqrt_36_cast = sqrt(x = add_72_cast)[name = tensor("sqrt_36_cast")]; + tensor real_div_36_cast = real_div(x = sub_72_cast, y = sqrt_36_cast)[name = tensor("real_div_36_cast")]; + tensor reshape_145_shape_0 = const()[name = tensor("reshape_145_shape_0"), val = tensor([2, 2560, 16, 16])]; + tensor reshape_145_cast = reshape(shape = reshape_145_shape_0, x = real_div_36_cast)[name = tensor("reshape_145_cast")]; + tensor add_73_gamma_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(446474944))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(446476928))), name = tensor("add_73_gamma_0_to_fp16_palettized"), shape = tensor([2560])]; + tensor add_73_beta_0_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(446477120))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(446479104))), name = tensor("add_73_beta_0_to_fp16_palettized"), shape = tensor([2560])]; + tensor add_73_epsilon_0_to_fp16 = const()[name = tensor("add_73_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_73_cast = batch_norm(beta = add_73_beta_0_to_fp16_palettized, epsilon = add_73_epsilon_0_to_fp16, gamma = add_73_gamma_0_to_fp16_palettized, mean = add_55_mean_0_to_fp16_palettized, variance = add_55_variance_0_to_fp16_palettized, x = reshape_145_cast)[name = tensor("add_73_cast")]; + tensor input_315_cast = silu(x = add_73_cast)[name = tensor("input_315_cast")]; + tensor var_2594 = const()[name = tensor("op_2594"), val = tensor([1, 1])]; + tensor var_2596 = const()[name = tensor("op_2596"), val = tensor([1, 1])]; + tensor hidden_states_179_pad_type_0 = const()[name = tensor("hidden_states_179_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_179_pad_0 = const()[name = tensor("hidden_states_179_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_resnets_1_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(446479296))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(468597760))), name = tensor("up_blocks_1_resnets_1_conv1_weight_to_fp16_palettized"), shape = tensor([1280, 2560, 3, 3])]; + tensor up_blocks_1_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(468597952)))]; + tensor hidden_states_179_cast = conv(bias = up_blocks_1_resnets_1_conv1_bias_to_fp16, dilations = var_2596, groups = var_2306, pad = hidden_states_179_pad_0, pad_type = hidden_states_179_pad_type_0, strides = var_2594, weight = up_blocks_1_resnets_1_conv1_weight_to_fp16_palettized, x = input_315_cast)[name = tensor("hidden_states_179_cast")]; + tensor var_2602 = const()[name = tensor("op_2602"), val = tensor([1, 1])]; + tensor var_2604 = const()[name = tensor("op_2604"), val = tensor([1, 1])]; + tensor temb_29_pad_type_0 = const()[name = tensor("temb_29_pad_type_0"), val = tensor("custom")]; + tensor temb_29_pad_0 = const()[name = tensor("temb_29_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_resnets_1_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(468600576))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(469829440))), name = tensor("up_blocks_1_resnets_1_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_1_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(469829632)))]; + tensor temb_29_cast = conv(bias = up_blocks_1_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_2604, groups = var_2306, pad = temb_29_pad_0, pad_type = temb_29_pad_type_0, strides = var_2602, weight = up_blocks_1_resnets_1_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor("temb_29_cast")]; + tensor input_319_cast = add(x = hidden_states_179_cast, y = temb_29_cast)[name = tensor("input_319_cast")]; + tensor reshape_148_shape_0 = const()[name = tensor("reshape_148_shape_0"), val = tensor([2, 32, 40, 16, 16])]; + tensor reshape_148_cast = reshape(shape = reshape_148_shape_0, x = input_319_cast)[name = tensor("reshape_148_cast")]; + tensor reduce_mean_111_axes_0 = const()[name = tensor("reduce_mean_111_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_111_keep_dims_0 = const()[name = tensor("reduce_mean_111_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_111_cast = reduce_mean(axes = reduce_mean_111_axes_0, keep_dims = reduce_mean_111_keep_dims_0, x = reshape_148_cast)[name = tensor("reduce_mean_111_cast")]; + tensor sub_74_cast = sub(x = reshape_148_cast, y = reduce_mean_111_cast)[name = tensor("sub_74_cast")]; + tensor square_37_cast = square(x = sub_74_cast)[name = tensor("square_37_cast")]; + tensor reduce_mean_113_axes_0 = const()[name = tensor("reduce_mean_113_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_113_keep_dims_0 = const()[name = tensor("reduce_mean_113_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_113_cast = reduce_mean(axes = reduce_mean_113_axes_0, keep_dims = reduce_mean_113_keep_dims_0, x = square_37_cast)[name = tensor("reduce_mean_113_cast")]; + tensor add_74_y_0_to_fp16 = const()[name = tensor("add_74_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_74_cast = add(x = reduce_mean_113_cast, y = add_74_y_0_to_fp16)[name = tensor("add_74_cast")]; + tensor sqrt_37_cast = sqrt(x = add_74_cast)[name = tensor("sqrt_37_cast")]; + tensor real_div_37_cast = real_div(x = sub_74_cast, y = sqrt_37_cast)[name = tensor("real_div_37_cast")]; + tensor reshape_149_shape_0 = const()[name = tensor("reshape_149_shape_0"), val = tensor([2, 1280, 16, 16])]; + tensor reshape_149_cast = reshape(shape = reshape_149_shape_0, x = real_div_37_cast)[name = tensor("reshape_149_cast")]; + tensor add_75_gamma_0_to_fp16 = const()[name = tensor("add_75_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(469832256)))]; + tensor add_75_beta_0_to_fp16 = const()[name = tensor("add_75_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(469834880)))]; + tensor add_75_epsilon_0_to_fp16 = const()[name = tensor("add_75_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_75_cast = batch_norm(beta = add_75_beta_0_to_fp16, epsilon = add_75_epsilon_0_to_fp16, gamma = add_75_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_149_cast)[name = tensor("add_75_cast")]; + tensor input_323_cast = silu(x = add_75_cast)[name = tensor("input_323_cast")]; + tensor var_2614 = const()[name = tensor("op_2614"), val = tensor([1, 1])]; + tensor var_2616 = const()[name = tensor("op_2616"), val = tensor([1, 1])]; + tensor hidden_states_181_pad_type_0 = const()[name = tensor("hidden_states_181_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_181_pad_0 = const()[name = tensor("hidden_states_181_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_resnets_1_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(469837504))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(480896768))), name = tensor("up_blocks_1_resnets_1_conv2_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor up_blocks_1_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(480896960)))]; + tensor hidden_states_181_cast = conv(bias = up_blocks_1_resnets_1_conv2_bias_to_fp16, dilations = var_2616, groups = var_2306, pad = hidden_states_181_pad_0, pad_type = hidden_states_181_pad_type_0, strides = var_2614, weight = up_blocks_1_resnets_1_conv2_weight_to_fp16_palettized, x = input_323_cast)[name = tensor("hidden_states_181_cast")]; + tensor var_2621 = const()[name = tensor("op_2621"), val = tensor([1, 1])]; + tensor var_2623 = const()[name = tensor("op_2623"), val = tensor([1, 1])]; + tensor x_13_pad_type_0 = const()[name = tensor("x_13_pad_type_0"), val = tensor("custom")]; + tensor x_13_pad_0 = const()[name = tensor("x_13_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_resnets_1_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(480899584))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(483357248))), name = tensor("up_blocks_1_resnets_1_conv_shortcut_weight_to_fp16_palettized"), shape = tensor([1280, 2560, 1, 1])]; + tensor up_blocks_1_resnets_1_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_1_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(483357440)))]; + tensor x_13_cast = conv(bias = up_blocks_1_resnets_1_conv_shortcut_bias_to_fp16, dilations = var_2623, groups = var_2306, pad = x_13_pad_0, pad_type = x_13_pad_type_0, strides = var_2621, weight = up_blocks_1_resnets_1_conv_shortcut_weight_to_fp16_palettized, x = input_311_cast)[name = tensor("x_13_cast")]; + tensor hidden_states_183_cast = add(x = x_13_cast, y = hidden_states_181_cast)[name = tensor("hidden_states_183_cast")]; + tensor reshape_152_shape_0 = const()[name = tensor("reshape_152_shape_0"), val = tensor([2, 32, 40, 16, 16])]; + tensor reshape_152_cast = reshape(shape = reshape_152_shape_0, x = hidden_states_183_cast)[name = tensor("reshape_152_cast")]; + tensor reduce_mean_114_axes_0 = const()[name = tensor("reduce_mean_114_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_114_keep_dims_0 = const()[name = tensor("reduce_mean_114_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_114_cast = reduce_mean(axes = reduce_mean_114_axes_0, keep_dims = reduce_mean_114_keep_dims_0, x = reshape_152_cast)[name = tensor("reduce_mean_114_cast")]; + tensor sub_76_cast = sub(x = reshape_152_cast, y = reduce_mean_114_cast)[name = tensor("sub_76_cast")]; + tensor square_38_cast = square(x = sub_76_cast)[name = tensor("square_38_cast")]; + tensor reduce_mean_116_axes_0 = const()[name = tensor("reduce_mean_116_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_116_keep_dims_0 = const()[name = tensor("reduce_mean_116_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_116_cast = reduce_mean(axes = reduce_mean_116_axes_0, keep_dims = reduce_mean_116_keep_dims_0, x = square_38_cast)[name = tensor("reduce_mean_116_cast")]; + tensor add_76_y_0_to_fp16 = const()[name = tensor("add_76_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_76_cast = add(x = reduce_mean_116_cast, y = add_76_y_0_to_fp16)[name = tensor("add_76_cast")]; + tensor sqrt_38_cast = sqrt(x = add_76_cast)[name = tensor("sqrt_38_cast")]; + tensor real_div_38_cast = real_div(x = sub_76_cast, y = sqrt_38_cast)[name = tensor("real_div_38_cast")]; + tensor reshape_153_shape_0 = const()[name = tensor("reshape_153_shape_0"), val = tensor([2, 1280, 16, 16])]; + tensor reshape_153_cast = reshape(shape = reshape_153_shape_0, x = real_div_38_cast)[name = tensor("reshape_153_cast")]; + tensor add_77_gamma_0_to_fp16 = const()[name = tensor("add_77_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(483360064)))]; + tensor add_77_beta_0_to_fp16 = const()[name = tensor("add_77_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(483362688)))]; + tensor add_77_epsilon_0_to_fp16 = const()[name = tensor("add_77_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_77_cast = batch_norm(beta = add_77_beta_0_to_fp16, epsilon = add_77_epsilon_0_to_fp16, gamma = add_77_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_153_cast)[name = tensor("add_77_cast")]; + tensor var_2643 = const()[name = tensor("op_2643"), val = tensor([1, 1])]; + tensor var_2645 = const()[name = tensor("op_2645"), val = tensor([1, 1])]; + tensor hidden_states_185_pad_type_0 = const()[name = tensor("hidden_states_185_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_185_pad_0 = const()[name = tensor("hidden_states_185_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(483365312))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(484594176))), name = tensor("up_blocks_1_attentions_1_proj_in_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_1_attentions_1_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(484594368)))]; + tensor hidden_states_185_cast = conv(bias = up_blocks_1_attentions_1_proj_in_bias_to_fp16, dilations = var_2645, groups = var_2306, pad = hidden_states_185_pad_0, pad_type = hidden_states_185_pad_type_0, strides = var_2643, weight = up_blocks_1_attentions_1_proj_in_weight_to_fp16_palettized, x = add_77_cast)[name = tensor("hidden_states_185_cast")]; + tensor var_2650 = const()[name = tensor("op_2650"), val = tensor([2, 1280, 1, 256])]; + tensor inputs_49_cast = reshape(shape = var_2650, x = hidden_states_185_cast)[name = tensor("inputs_49_cast")]; + tensor var_2660 = const()[name = tensor("op_2660"), val = tensor([1])]; + tensor channels_mean_49_cast = reduce_mean(axes = var_2660, keep_dims = var_2301, x = inputs_49_cast)[name = tensor("channels_mean_49_cast")]; + tensor zero_mean_49_cast = sub(x = inputs_49_cast, y = channels_mean_49_cast)[name = tensor("zero_mean_49_cast")]; + tensor zero_mean_sq_49_cast = mul(x = zero_mean_49_cast, y = zero_mean_49_cast)[name = tensor("zero_mean_sq_49_cast")]; + tensor var_2664 = const()[name = tensor("op_2664"), val = tensor([1])]; + tensor var_2665_cast = reduce_mean(axes = var_2664, keep_dims = var_2301, x = zero_mean_sq_49_cast)[name = tensor("op_2665_cast")]; + tensor var_2666_to_fp16 = const()[name = tensor("op_2666_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2667_cast = add(x = var_2665_cast, y = var_2666_to_fp16)[name = tensor("op_2667_cast")]; + tensor denom_49_epsilon_0_to_fp16 = const()[name = tensor("denom_49_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_49_cast = rsqrt(epsilon = denom_49_epsilon_0_to_fp16, x = var_2667_cast)[name = tensor("denom_49_cast")]; + tensor out_49_cast = mul(x = zero_mean_49_cast, y = denom_49_cast)[name = tensor("out_49_cast")]; + tensor var_2671_to_fp16 = const()[name = tensor("op_2671_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(484596992)))]; + tensor var_2672_cast = add(x = out_49_cast, y = var_2671_to_fp16)[name = tensor("op_2672_cast")]; + tensor var_2674_to_fp16 = const()[name = tensor("op_2674_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(484599616)))]; + tensor hidden_states_187_cast = mul(x = var_2672_cast, y = var_2674_to_fp16)[name = tensor("hidden_states_187_cast")]; + tensor var_2681 = const()[name = tensor("op_2681"), val = tensor([1, 1])]; + tensor var_2683 = const()[name = tensor("op_2683"), val = tensor([1, 1])]; + tensor q_33_pad_type_0 = const()[name = tensor("q_33_pad_type_0"), val = tensor("custom")]; + tensor q_33_pad_0 = const()[name = tensor("q_33_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(484602240))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(485831104))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_33_cast = conv(dilations = var_2683, groups = var_2306, pad = q_33_pad_0, pad_type = q_33_pad_type_0, strides = var_2681, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_187_cast)[name = tensor("q_33_cast")]; + tensor var_2687 = const()[name = tensor("op_2687"), val = tensor([1, 1])]; + tensor var_2689 = const()[name = tensor("op_2689"), val = tensor([1, 1])]; + tensor k_33_pad_type_0 = const()[name = tensor("k_33_pad_type_0"), val = tensor("custom")]; + tensor k_33_pad_0 = const()[name = tensor("k_33_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(485831296))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(487060160))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_33_cast = conv(dilations = var_2689, groups = var_2306, pad = k_33_pad_0, pad_type = k_33_pad_type_0, strides = var_2687, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_187_cast)[name = tensor("k_33_cast")]; + tensor var_2693 = const()[name = tensor("op_2693"), val = tensor([1, 1])]; + tensor var_2695 = const()[name = tensor("op_2695"), val = tensor([1, 1])]; + tensor v_33_pad_type_0 = const()[name = tensor("v_33_pad_type_0"), val = tensor("custom")]; + tensor v_33_pad_0 = const()[name = tensor("v_33_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(487060352))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(488289216))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_33_cast = conv(dilations = var_2695, groups = var_2306, pad = v_33_pad_0, pad_type = v_33_pad_type_0, strides = var_2693, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_187_cast)[name = tensor("v_33_cast")]; + tensor var_2699 = const()[name = tensor("op_2699"), val = tensor([2, 8, 160, -1])]; + tensor var_2700_cast = reshape(shape = var_2699, x = q_33_cast)[name = tensor("op_2700_cast")]; + tensor var_2701 = const()[name = tensor("op_2701"), val = tensor([2, 8, 160, -1])]; + tensor var_2702_cast = reshape(shape = var_2701, x = k_33_cast)[name = tensor("op_2702_cast")]; + tensor var_2703 = const()[name = tensor("op_2703"), val = tensor([2, 8, 160, -1])]; + tensor var_2704_cast = reshape(shape = var_2703, x = v_33_cast)[name = tensor("op_2704_cast")]; + tensor attn_weights_65_transpose_x_0 = const()[name = tensor("attn_weights_65_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_65_transpose_y_0 = const()[name = tensor("attn_weights_65_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_65_cast = matmul(transpose_x = attn_weights_65_transpose_x_0, transpose_y = attn_weights_65_transpose_y_0, x = var_2700_cast, y = var_2702_cast)[name = tensor("attn_weights_65_cast")]; + tensor attn_weights_67_cast = mul(x = attn_weights_65_cast, y = var_2297_to_fp16)[name = tensor("attn_weights_67_cast")]; + tensor var_2708_cast = softmax(axis = var_2290, x = attn_weights_67_cast)[name = tensor("op_2708_cast")]; + tensor attn_33_transpose_x_0 = const()[name = tensor("attn_33_transpose_x_0"), val = tensor(false)]; + tensor attn_33_transpose_y_0 = const()[name = tensor("attn_33_transpose_y_0"), val = tensor(true)]; + tensor attn_33_cast = matmul(transpose_x = attn_33_transpose_x_0, transpose_y = attn_33_transpose_y_0, x = var_2704_cast, y = var_2708_cast)[name = tensor("attn_33_cast")]; + tensor var_2712 = const()[name = tensor("op_2712"), val = tensor([2, 1280, 1, -1])]; + tensor input_327_cast = reshape(shape = var_2712, x = attn_33_cast)[name = tensor("input_327_cast")]; + tensor var_2717 = const()[name = tensor("op_2717"), val = tensor([1, 1])]; + tensor var_2719 = const()[name = tensor("op_2719"), val = tensor([1, 1])]; + tensor var_2721_pad_type_0 = const()[name = tensor("op_2721_pad_type_0"), val = tensor("custom")]; + tensor var_2721_pad_0 = const()[name = tensor("op_2721_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(488289408))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(489518272))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(489518464)))]; + tensor var_2721_cast = conv(bias = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_2719, groups = var_2306, pad = var_2721_pad_0, pad_type = var_2721_pad_type_0, strides = var_2717, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_327_cast)[name = tensor("op_2721_cast")]; + tensor inputs_51_cast = add(x = var_2721_cast, y = inputs_49_cast)[name = tensor("inputs_51_cast")]; + tensor var_2725 = const()[name = tensor("op_2725"), val = tensor([1])]; + tensor channels_mean_51_cast = reduce_mean(axes = var_2725, keep_dims = var_2301, x = inputs_51_cast)[name = tensor("channels_mean_51_cast")]; + tensor zero_mean_51_cast = sub(x = inputs_51_cast, y = channels_mean_51_cast)[name = tensor("zero_mean_51_cast")]; + tensor zero_mean_sq_51_cast = mul(x = zero_mean_51_cast, y = zero_mean_51_cast)[name = tensor("zero_mean_sq_51_cast")]; + tensor var_2729 = const()[name = tensor("op_2729"), val = tensor([1])]; + tensor var_2730_cast = reduce_mean(axes = var_2729, keep_dims = var_2301, x = zero_mean_sq_51_cast)[name = tensor("op_2730_cast")]; + tensor var_2731_to_fp16 = const()[name = tensor("op_2731_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2732_cast = add(x = var_2730_cast, y = var_2731_to_fp16)[name = tensor("op_2732_cast")]; + tensor denom_51_epsilon_0_to_fp16 = const()[name = tensor("denom_51_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_51_cast = rsqrt(epsilon = denom_51_epsilon_0_to_fp16, x = var_2732_cast)[name = tensor("denom_51_cast")]; + tensor out_51_cast = mul(x = zero_mean_51_cast, y = denom_51_cast)[name = tensor("out_51_cast")]; + tensor var_2736_to_fp16 = const()[name = tensor("op_2736_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(489521088)))]; + tensor var_2737_cast = add(x = out_51_cast, y = var_2736_to_fp16)[name = tensor("op_2737_cast")]; + tensor var_2739_to_fp16 = const()[name = tensor("op_2739_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(489523712)))]; + tensor hidden_states_189_cast = mul(x = var_2737_cast, y = var_2739_to_fp16)[name = tensor("hidden_states_189_cast")]; + tensor var_2746 = const()[name = tensor("op_2746"), val = tensor([1, 1])]; + tensor var_2748 = const()[name = tensor("op_2748"), val = tensor([1, 1])]; + tensor q_35_pad_type_0 = const()[name = tensor("q_35_pad_type_0"), val = tensor("custom")]; + tensor q_35_pad_0 = const()[name = tensor("q_35_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(489526336))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(490755200))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_35_cast = conv(dilations = var_2748, groups = var_2306, pad = q_35_pad_0, pad_type = q_35_pad_type_0, strides = var_2746, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_189_cast)[name = tensor("q_35_cast")]; + tensor var_2752 = const()[name = tensor("op_2752"), val = tensor([1, 1])]; + tensor var_2754 = const()[name = tensor("op_2754"), val = tensor([1, 1])]; + tensor k_35_pad_type_0 = const()[name = tensor("k_35_pad_type_0"), val = tensor("custom")]; + tensor k_35_pad_0 = const()[name = tensor("k_35_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(490755392))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(491492736))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 768, 1, 1])]; + tensor k_35_cast = conv(dilations = var_2754, groups = var_2306, pad = k_35_pad_0, pad_type = k_35_pad_type_0, strides = var_2752, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_35_cast")]; + tensor var_2758 = const()[name = tensor("op_2758"), val = tensor([1, 1])]; + tensor var_2760 = const()[name = tensor("op_2760"), val = tensor([1, 1])]; + tensor v_35_pad_type_0 = const()[name = tensor("v_35_pad_type_0"), val = tensor("custom")]; + tensor v_35_pad_0 = const()[name = tensor("v_35_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(491492928))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(492230272))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 768, 1, 1])]; + tensor v_35_cast = conv(dilations = var_2760, groups = var_2306, pad = v_35_pad_0, pad_type = v_35_pad_type_0, strides = var_2758, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_35_cast")]; + tensor var_2764 = const()[name = tensor("op_2764"), val = tensor([2, 8, 160, -1])]; + tensor var_2765_cast = reshape(shape = var_2764, x = q_35_cast)[name = tensor("op_2765_cast")]; + tensor var_2766 = const()[name = tensor("op_2766"), val = tensor([2, 8, 160, -1])]; + tensor var_2767_cast = reshape(shape = var_2766, x = k_35_cast)[name = tensor("op_2767_cast")]; + tensor var_2768 = const()[name = tensor("op_2768"), val = tensor([2, 8, 160, -1])]; + tensor var_2769_cast = reshape(shape = var_2768, x = v_35_cast)[name = tensor("op_2769_cast")]; + tensor attn_weights_69_transpose_x_0 = const()[name = tensor("attn_weights_69_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_69_transpose_y_0 = const()[name = tensor("attn_weights_69_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_69_cast = matmul(transpose_x = attn_weights_69_transpose_x_0, transpose_y = attn_weights_69_transpose_y_0, x = var_2765_cast, y = var_2767_cast)[name = tensor("attn_weights_69_cast")]; + tensor attn_weights_71_cast = mul(x = attn_weights_69_cast, y = var_2297_to_fp16)[name = tensor("attn_weights_71_cast")]; + tensor var_2773_cast = softmax(axis = var_2290, x = attn_weights_71_cast)[name = tensor("op_2773_cast")]; + tensor attn_35_transpose_x_0 = const()[name = tensor("attn_35_transpose_x_0"), val = tensor(false)]; + tensor attn_35_transpose_y_0 = const()[name = tensor("attn_35_transpose_y_0"), val = tensor(true)]; + tensor attn_35_cast = matmul(transpose_x = attn_35_transpose_x_0, transpose_y = attn_35_transpose_y_0, x = var_2769_cast, y = var_2773_cast)[name = tensor("attn_35_cast")]; + tensor var_2777 = const()[name = tensor("op_2777"), val = tensor([2, 1280, 1, -1])]; + tensor input_329_cast = reshape(shape = var_2777, x = attn_35_cast)[name = tensor("input_329_cast")]; + tensor var_2782 = const()[name = tensor("op_2782"), val = tensor([1, 1])]; + tensor var_2784 = const()[name = tensor("op_2784"), val = tensor([1, 1])]; + tensor var_2786_pad_type_0 = const()[name = tensor("op_2786_pad_type_0"), val = tensor("custom")]; + tensor var_2786_pad_0 = const()[name = tensor("op_2786_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(492230464))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(493459328))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(493459520)))]; + tensor var_2786_cast = conv(bias = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_2784, groups = var_2306, pad = var_2786_pad_0, pad_type = var_2786_pad_type_0, strides = var_2782, weight = up_blocks_1_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_329_cast)[name = tensor("op_2786_cast")]; + tensor inputs_53_cast = add(x = var_2786_cast, y = inputs_51_cast)[name = tensor("inputs_53_cast")]; + tensor var_2790 = const()[name = tensor("op_2790"), val = tensor([1])]; + tensor channels_mean_53_cast = reduce_mean(axes = var_2790, keep_dims = var_2301, x = inputs_53_cast)[name = tensor("channels_mean_53_cast")]; + tensor zero_mean_53_cast = sub(x = inputs_53_cast, y = channels_mean_53_cast)[name = tensor("zero_mean_53_cast")]; + tensor zero_mean_sq_53_cast = mul(x = zero_mean_53_cast, y = zero_mean_53_cast)[name = tensor("zero_mean_sq_53_cast")]; + tensor var_2794 = const()[name = tensor("op_2794"), val = tensor([1])]; + tensor var_2795_cast = reduce_mean(axes = var_2794, keep_dims = var_2301, x = zero_mean_sq_53_cast)[name = tensor("op_2795_cast")]; + tensor var_2796_to_fp16 = const()[name = tensor("op_2796_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2797_cast = add(x = var_2795_cast, y = var_2796_to_fp16)[name = tensor("op_2797_cast")]; + tensor denom_53_epsilon_0_to_fp16 = const()[name = tensor("denom_53_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_53_cast = rsqrt(epsilon = denom_53_epsilon_0_to_fp16, x = var_2797_cast)[name = tensor("denom_53_cast")]; + tensor out_53_cast = mul(x = zero_mean_53_cast, y = denom_53_cast)[name = tensor("out_53_cast")]; + tensor var_2801_to_fp16 = const()[name = tensor("op_2801_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(493462144)))]; + tensor var_2802_cast = add(x = out_53_cast, y = var_2801_to_fp16)[name = tensor("op_2802_cast")]; + tensor var_2804_to_fp16 = const()[name = tensor("op_2804_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(493464768)))]; + tensor input_331_cast = mul(x = var_2802_cast, y = var_2804_to_fp16)[name = tensor("input_331_cast")]; + tensor var_2812 = const()[name = tensor("op_2812"), val = tensor([1, 1])]; + tensor var_2814 = const()[name = tensor("op_2814"), val = tensor([1, 1])]; + tensor var_2816_pad_type_0 = const()[name = tensor("op_2816_pad_type_0"), val = tensor("custom")]; + tensor var_2816_pad_0 = const()[name = tensor("op_2816_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(493467392))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(503297856))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(503298048))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(503305792))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_2816_cast = conv(bias = up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_2814, groups = var_2306, pad = var_2816_pad_0, pad_type = var_2816_pad_type_0, strides = var_2812, weight = up_blocks_1_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_331_cast)[name = tensor("op_2816_cast")]; + tensor var_2817_split_sizes_0 = const()[name = tensor("op_2817_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_2817_axis_0 = const()[name = tensor("op_2817_axis_0"), val = tensor(1)]; + tensor var_2817_cast_0, tensor var_2817_cast_1 = split(axis = var_2817_axis_0, split_sizes = var_2817_split_sizes_0, x = var_2816_cast)[name = tensor("op_2817_cast")]; + tensor var_2819_mode_0 = const()[name = tensor("op_2819_mode_0"), val = tensor("EXACT")]; + tensor var_2819_cast = gelu(mode = var_2819_mode_0, x = var_2817_cast_1)[name = tensor("op_2819_cast")]; + tensor input_333_cast = mul(x = var_2817_cast_0, y = var_2819_cast)[name = tensor("input_333_cast")]; + tensor var_2823 = const()[name = tensor("op_2823"), val = tensor([1, 1])]; + tensor var_2825 = const()[name = tensor("op_2825"), val = tensor([1, 1])]; + tensor var_2827_pad_type_0 = const()[name = tensor("op_2827_pad_type_0"), val = tensor("custom")]; + tensor var_2827_pad_0 = const()[name = tensor("op_2827_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(503305984))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(508221248))), name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(508221440)))]; + tensor var_2827_cast = conv(bias = up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_2825, groups = var_2306, pad = var_2827_pad_0, pad_type = var_2827_pad_type_0, strides = var_2823, weight = up_blocks_1_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_333_cast)[name = tensor("op_2827_cast")]; + tensor hidden_states_193_cast = add(x = var_2827_cast, y = inputs_53_cast)[name = tensor("hidden_states_193_cast")]; + tensor var_2829 = const()[name = tensor("op_2829"), val = tensor([2, 1280, 16, 16])]; + tensor input_335_cast = reshape(shape = var_2829, x = hidden_states_193_cast)[name = tensor("input_335_cast")]; + tensor var_2833 = const()[name = tensor("op_2833"), val = tensor([1, 1])]; + tensor var_2835 = const()[name = tensor("op_2835"), val = tensor([1, 1])]; + tensor hidden_states_195_pad_type_0 = const()[name = tensor("hidden_states_195_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_195_pad_0 = const()[name = tensor("hidden_states_195_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_1_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(508224064))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(509452928))), name = tensor("up_blocks_1_attentions_1_proj_out_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_1_attentions_1_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_1_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(509453120)))]; + tensor hidden_states_195_cast = conv(bias = up_blocks_1_attentions_1_proj_out_bias_to_fp16, dilations = var_2835, groups = var_2306, pad = hidden_states_195_pad_0, pad_type = hidden_states_195_pad_type_0, strides = var_2833, weight = up_blocks_1_attentions_1_proj_out_weight_to_fp16_palettized, x = input_335_cast)[name = tensor("hidden_states_195_cast")]; + tensor hidden_states_197_cast = add(x = hidden_states_195_cast, y = hidden_states_183_cast)[name = tensor("hidden_states_197_cast")]; + tensor input_337_interleave_0 = const()[name = tensor("input_337_interleave_0"), val = tensor(false)]; + tensor input_337_cast = concat(axis = var_2306, interleave = input_337_interleave_0, values = (hidden_states_197_cast, input_117_cast))[name = tensor("input_337_cast")]; + tensor reshape_156_shape_0 = const()[name = tensor("reshape_156_shape_0"), val = tensor([2, 32, 60, 16, 16])]; + tensor reshape_156_cast = reshape(shape = reshape_156_shape_0, x = input_337_cast)[name = tensor("reshape_156_cast")]; + tensor reduce_mean_117_axes_0 = const()[name = tensor("reduce_mean_117_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_117_keep_dims_0 = const()[name = tensor("reduce_mean_117_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_117_cast = reduce_mean(axes = reduce_mean_117_axes_0, keep_dims = reduce_mean_117_keep_dims_0, x = reshape_156_cast)[name = tensor("reduce_mean_117_cast")]; + tensor sub_78_cast = sub(x = reshape_156_cast, y = reduce_mean_117_cast)[name = tensor("sub_78_cast")]; + tensor square_39_cast = square(x = sub_78_cast)[name = tensor("square_39_cast")]; + tensor reduce_mean_119_axes_0 = const()[name = tensor("reduce_mean_119_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_119_keep_dims_0 = const()[name = tensor("reduce_mean_119_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_119_cast = reduce_mean(axes = reduce_mean_119_axes_0, keep_dims = reduce_mean_119_keep_dims_0, x = square_39_cast)[name = tensor("reduce_mean_119_cast")]; + tensor add_78_y_0_to_fp16 = const()[name = tensor("add_78_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_78_cast = add(x = reduce_mean_119_cast, y = add_78_y_0_to_fp16)[name = tensor("add_78_cast")]; + tensor sqrt_39_cast = sqrt(x = add_78_cast)[name = tensor("sqrt_39_cast")]; + tensor real_div_39_cast = real_div(x = sub_78_cast, y = sqrt_39_cast)[name = tensor("real_div_39_cast")]; + tensor reshape_157_shape_0 = const()[name = tensor("reshape_157_shape_0"), val = tensor([2, 1920, 16, 16])]; + tensor reshape_157_cast = reshape(shape = reshape_157_shape_0, x = real_div_39_cast)[name = tensor("reshape_157_cast")]; + tensor add_79_mean_0_to_fp16 = const()[name = tensor("add_79_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(509455744)))]; + tensor add_79_variance_0_to_fp16 = const()[name = tensor("add_79_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(509459648)))]; + tensor add_79_gamma_0_to_fp16 = const()[name = tensor("add_79_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(509463552)))]; + tensor add_79_beta_0_to_fp16 = const()[name = tensor("add_79_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(509467456)))]; + tensor add_79_epsilon_0_to_fp16 = const()[name = tensor("add_79_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_79_cast = batch_norm(beta = add_79_beta_0_to_fp16, epsilon = add_79_epsilon_0_to_fp16, gamma = add_79_gamma_0_to_fp16, mean = add_79_mean_0_to_fp16, variance = add_79_variance_0_to_fp16, x = reshape_157_cast)[name = tensor("add_79_cast")]; + tensor input_341_cast = silu(x = add_79_cast)[name = tensor("input_341_cast")]; + tensor var_2853 = const()[name = tensor("op_2853"), val = tensor([1, 1])]; + tensor var_2855 = const()[name = tensor("op_2855"), val = tensor([1, 1])]; + tensor hidden_states_199_pad_type_0 = const()[name = tensor("hidden_states_199_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_199_pad_0 = const()[name = tensor("hidden_states_199_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_resnets_2_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(509471360))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(526060224))), name = tensor("up_blocks_1_resnets_2_conv1_weight_to_fp16_palettized"), shape = tensor([1280, 1920, 3, 3])]; + tensor up_blocks_1_resnets_2_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_2_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(526060416)))]; + tensor hidden_states_199_cast = conv(bias = up_blocks_1_resnets_2_conv1_bias_to_fp16, dilations = var_2855, groups = var_2306, pad = hidden_states_199_pad_0, pad_type = hidden_states_199_pad_type_0, strides = var_2853, weight = up_blocks_1_resnets_2_conv1_weight_to_fp16_palettized, x = input_341_cast)[name = tensor("hidden_states_199_cast")]; + tensor var_2861 = const()[name = tensor("op_2861"), val = tensor([1, 1])]; + tensor var_2863 = const()[name = tensor("op_2863"), val = tensor([1, 1])]; + tensor temb_31_pad_type_0 = const()[name = tensor("temb_31_pad_type_0"), val = tensor("custom")]; + tensor temb_31_pad_0 = const()[name = tensor("temb_31_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_resnets_2_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(526063040))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(527291904))), name = tensor("up_blocks_1_resnets_2_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_1_resnets_2_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_2_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(527292096)))]; + tensor temb_31_cast = conv(bias = up_blocks_1_resnets_2_time_emb_proj_bias_to_fp16, dilations = var_2863, groups = var_2306, pad = temb_31_pad_0, pad_type = temb_31_pad_type_0, strides = var_2861, weight = up_blocks_1_resnets_2_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor("temb_31_cast")]; + tensor input_345_cast = add(x = hidden_states_199_cast, y = temb_31_cast)[name = tensor("input_345_cast")]; + tensor reshape_160_shape_0 = const()[name = tensor("reshape_160_shape_0"), val = tensor([2, 32, 40, 16, 16])]; + tensor reshape_160_cast = reshape(shape = reshape_160_shape_0, x = input_345_cast)[name = tensor("reshape_160_cast")]; + tensor reduce_mean_120_axes_0 = const()[name = tensor("reduce_mean_120_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_120_keep_dims_0 = const()[name = tensor("reduce_mean_120_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_120_cast = reduce_mean(axes = reduce_mean_120_axes_0, keep_dims = reduce_mean_120_keep_dims_0, x = reshape_160_cast)[name = tensor("reduce_mean_120_cast")]; + tensor sub_80_cast = sub(x = reshape_160_cast, y = reduce_mean_120_cast)[name = tensor("sub_80_cast")]; + tensor square_40_cast = square(x = sub_80_cast)[name = tensor("square_40_cast")]; + tensor reduce_mean_122_axes_0 = const()[name = tensor("reduce_mean_122_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_122_keep_dims_0 = const()[name = tensor("reduce_mean_122_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_122_cast = reduce_mean(axes = reduce_mean_122_axes_0, keep_dims = reduce_mean_122_keep_dims_0, x = square_40_cast)[name = tensor("reduce_mean_122_cast")]; + tensor add_80_y_0_to_fp16 = const()[name = tensor("add_80_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_80_cast = add(x = reduce_mean_122_cast, y = add_80_y_0_to_fp16)[name = tensor("add_80_cast")]; + tensor sqrt_40_cast = sqrt(x = add_80_cast)[name = tensor("sqrt_40_cast")]; + tensor real_div_40_cast = real_div(x = sub_80_cast, y = sqrt_40_cast)[name = tensor("real_div_40_cast")]; + tensor reshape_161_shape_0 = const()[name = tensor("reshape_161_shape_0"), val = tensor([2, 1280, 16, 16])]; + tensor reshape_161_cast = reshape(shape = reshape_161_shape_0, x = real_div_40_cast)[name = tensor("reshape_161_cast")]; + tensor add_81_gamma_0_to_fp16 = const()[name = tensor("add_81_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(527294720)))]; + tensor add_81_beta_0_to_fp16 = const()[name = tensor("add_81_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(527297344)))]; + tensor add_81_epsilon_0_to_fp16 = const()[name = tensor("add_81_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_81_cast = batch_norm(beta = add_81_beta_0_to_fp16, epsilon = add_81_epsilon_0_to_fp16, gamma = add_81_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_161_cast)[name = tensor("add_81_cast")]; + tensor input_349_cast = silu(x = add_81_cast)[name = tensor("input_349_cast")]; + tensor var_2873 = const()[name = tensor("op_2873"), val = tensor([1, 1])]; + tensor var_2875 = const()[name = tensor("op_2875"), val = tensor([1, 1])]; + tensor hidden_states_201_pad_type_0 = const()[name = tensor("hidden_states_201_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_201_pad_0 = const()[name = tensor("hidden_states_201_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_resnets_2_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(527299968))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(538359232))), name = tensor("up_blocks_1_resnets_2_conv2_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor up_blocks_1_resnets_2_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_2_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(538359424)))]; + tensor hidden_states_201_cast = conv(bias = up_blocks_1_resnets_2_conv2_bias_to_fp16, dilations = var_2875, groups = var_2306, pad = hidden_states_201_pad_0, pad_type = hidden_states_201_pad_type_0, strides = var_2873, weight = up_blocks_1_resnets_2_conv2_weight_to_fp16_palettized, x = input_349_cast)[name = tensor("hidden_states_201_cast")]; + tensor var_2880 = const()[name = tensor("op_2880"), val = tensor([1, 1])]; + tensor var_2882 = const()[name = tensor("op_2882"), val = tensor([1, 1])]; + tensor x_15_pad_type_0 = const()[name = tensor("x_15_pad_type_0"), val = tensor("custom")]; + tensor x_15_pad_0 = const()[name = tensor("x_15_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_resnets_2_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(538362048))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(540205312))), name = tensor("up_blocks_1_resnets_2_conv_shortcut_weight_to_fp16_palettized"), shape = tensor([1280, 1920, 1, 1])]; + tensor up_blocks_1_resnets_2_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_1_resnets_2_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(540205504)))]; + tensor x_15_cast = conv(bias = up_blocks_1_resnets_2_conv_shortcut_bias_to_fp16, dilations = var_2882, groups = var_2306, pad = x_15_pad_0, pad_type = x_15_pad_type_0, strides = var_2880, weight = up_blocks_1_resnets_2_conv_shortcut_weight_to_fp16_palettized, x = input_337_cast)[name = tensor("x_15_cast")]; + tensor hidden_states_203_cast = add(x = x_15_cast, y = hidden_states_201_cast)[name = tensor("hidden_states_203_cast")]; + tensor reshape_164_shape_0 = const()[name = tensor("reshape_164_shape_0"), val = tensor([2, 32, 40, 16, 16])]; + tensor reshape_164_cast = reshape(shape = reshape_164_shape_0, x = hidden_states_203_cast)[name = tensor("reshape_164_cast")]; + tensor reduce_mean_123_axes_0 = const()[name = tensor("reduce_mean_123_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_123_keep_dims_0 = const()[name = tensor("reduce_mean_123_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_123_cast = reduce_mean(axes = reduce_mean_123_axes_0, keep_dims = reduce_mean_123_keep_dims_0, x = reshape_164_cast)[name = tensor("reduce_mean_123_cast")]; + tensor sub_82_cast = sub(x = reshape_164_cast, y = reduce_mean_123_cast)[name = tensor("sub_82_cast")]; + tensor square_41_cast = square(x = sub_82_cast)[name = tensor("square_41_cast")]; + tensor reduce_mean_125_axes_0 = const()[name = tensor("reduce_mean_125_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_125_keep_dims_0 = const()[name = tensor("reduce_mean_125_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_125_cast = reduce_mean(axes = reduce_mean_125_axes_0, keep_dims = reduce_mean_125_keep_dims_0, x = square_41_cast)[name = tensor("reduce_mean_125_cast")]; + tensor add_82_y_0_to_fp16 = const()[name = tensor("add_82_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_82_cast = add(x = reduce_mean_125_cast, y = add_82_y_0_to_fp16)[name = tensor("add_82_cast")]; + tensor sqrt_41_cast = sqrt(x = add_82_cast)[name = tensor("sqrt_41_cast")]; + tensor real_div_41_cast = real_div(x = sub_82_cast, y = sqrt_41_cast)[name = tensor("real_div_41_cast")]; + tensor reshape_165_shape_0 = const()[name = tensor("reshape_165_shape_0"), val = tensor([2, 1280, 16, 16])]; + tensor reshape_165_cast = reshape(shape = reshape_165_shape_0, x = real_div_41_cast)[name = tensor("reshape_165_cast")]; + tensor add_83_gamma_0_to_fp16 = const()[name = tensor("add_83_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(540208128)))]; + tensor add_83_beta_0_to_fp16 = const()[name = tensor("add_83_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(540210752)))]; + tensor add_83_epsilon_0_to_fp16 = const()[name = tensor("add_83_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_83_cast = batch_norm(beta = add_83_beta_0_to_fp16, epsilon = add_83_epsilon_0_to_fp16, gamma = add_83_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_165_cast)[name = tensor("add_83_cast")]; + tensor var_2902 = const()[name = tensor("op_2902"), val = tensor([1, 1])]; + tensor var_2904 = const()[name = tensor("op_2904"), val = tensor([1, 1])]; + tensor hidden_states_205_pad_type_0 = const()[name = tensor("hidden_states_205_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_205_pad_0 = const()[name = tensor("hidden_states_205_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(540213376))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(541442240))), name = tensor("up_blocks_1_attentions_2_proj_in_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_1_attentions_2_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(541442432)))]; + tensor hidden_states_205_cast = conv(bias = up_blocks_1_attentions_2_proj_in_bias_to_fp16, dilations = var_2904, groups = var_2306, pad = hidden_states_205_pad_0, pad_type = hidden_states_205_pad_type_0, strides = var_2902, weight = up_blocks_1_attentions_2_proj_in_weight_to_fp16_palettized, x = add_83_cast)[name = tensor("hidden_states_205_cast")]; + tensor var_2909 = const()[name = tensor("op_2909"), val = tensor([2, 1280, 1, 256])]; + tensor inputs_55_cast = reshape(shape = var_2909, x = hidden_states_205_cast)[name = tensor("inputs_55_cast")]; + tensor var_2919 = const()[name = tensor("op_2919"), val = tensor([1])]; + tensor channels_mean_55_cast = reduce_mean(axes = var_2919, keep_dims = var_2301, x = inputs_55_cast)[name = tensor("channels_mean_55_cast")]; + tensor zero_mean_55_cast = sub(x = inputs_55_cast, y = channels_mean_55_cast)[name = tensor("zero_mean_55_cast")]; + tensor zero_mean_sq_55_cast = mul(x = zero_mean_55_cast, y = zero_mean_55_cast)[name = tensor("zero_mean_sq_55_cast")]; + tensor var_2923 = const()[name = tensor("op_2923"), val = tensor([1])]; + tensor var_2924_cast = reduce_mean(axes = var_2923, keep_dims = var_2301, x = zero_mean_sq_55_cast)[name = tensor("op_2924_cast")]; + tensor var_2925_to_fp16 = const()[name = tensor("op_2925_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2926_cast = add(x = var_2924_cast, y = var_2925_to_fp16)[name = tensor("op_2926_cast")]; + tensor denom_55_epsilon_0_to_fp16 = const()[name = tensor("denom_55_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_55_cast = rsqrt(epsilon = denom_55_epsilon_0_to_fp16, x = var_2926_cast)[name = tensor("denom_55_cast")]; + tensor out_55_cast = mul(x = zero_mean_55_cast, y = denom_55_cast)[name = tensor("out_55_cast")]; + tensor var_2930_to_fp16 = const()[name = tensor("op_2930_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(541445056)))]; + tensor var_2931_cast = add(x = out_55_cast, y = var_2930_to_fp16)[name = tensor("op_2931_cast")]; + tensor var_2933_to_fp16 = const()[name = tensor("op_2933_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(541447680)))]; + tensor hidden_states_207_cast = mul(x = var_2931_cast, y = var_2933_to_fp16)[name = tensor("hidden_states_207_cast")]; + tensor var_2940 = const()[name = tensor("op_2940"), val = tensor([1, 1])]; + tensor var_2942 = const()[name = tensor("op_2942"), val = tensor([1, 1])]; + tensor q_37_pad_type_0 = const()[name = tensor("q_37_pad_type_0"), val = tensor("custom")]; + tensor q_37_pad_0 = const()[name = tensor("q_37_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(541450304))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(542679168))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_37_cast = conv(dilations = var_2942, groups = var_2306, pad = q_37_pad_0, pad_type = q_37_pad_type_0, strides = var_2940, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_207_cast)[name = tensor("q_37_cast")]; + tensor var_2946 = const()[name = tensor("op_2946"), val = tensor([1, 1])]; + tensor var_2948 = const()[name = tensor("op_2948"), val = tensor([1, 1])]; + tensor k_37_pad_type_0 = const()[name = tensor("k_37_pad_type_0"), val = tensor("custom")]; + tensor k_37_pad_0 = const()[name = tensor("k_37_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(542679360))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(543908224))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor k_37_cast = conv(dilations = var_2948, groups = var_2306, pad = k_37_pad_0, pad_type = k_37_pad_type_0, strides = var_2946, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_207_cast)[name = tensor("k_37_cast")]; + tensor var_2952 = const()[name = tensor("op_2952"), val = tensor([1, 1])]; + tensor var_2954 = const()[name = tensor("op_2954"), val = tensor([1, 1])]; + tensor v_37_pad_type_0 = const()[name = tensor("v_37_pad_type_0"), val = tensor("custom")]; + tensor v_37_pad_0 = const()[name = tensor("v_37_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(543908416))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(545137280))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor v_37_cast = conv(dilations = var_2954, groups = var_2306, pad = v_37_pad_0, pad_type = v_37_pad_type_0, strides = var_2952, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_207_cast)[name = tensor("v_37_cast")]; + tensor var_2958 = const()[name = tensor("op_2958"), val = tensor([2, 8, 160, -1])]; + tensor var_2959_cast = reshape(shape = var_2958, x = q_37_cast)[name = tensor("op_2959_cast")]; + tensor var_2960 = const()[name = tensor("op_2960"), val = tensor([2, 8, 160, -1])]; + tensor var_2961_cast = reshape(shape = var_2960, x = k_37_cast)[name = tensor("op_2961_cast")]; + tensor var_2962 = const()[name = tensor("op_2962"), val = tensor([2, 8, 160, -1])]; + tensor var_2963_cast = reshape(shape = var_2962, x = v_37_cast)[name = tensor("op_2963_cast")]; + tensor attn_weights_73_transpose_x_0 = const()[name = tensor("attn_weights_73_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_73_transpose_y_0 = const()[name = tensor("attn_weights_73_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_73_cast = matmul(transpose_x = attn_weights_73_transpose_x_0, transpose_y = attn_weights_73_transpose_y_0, x = var_2959_cast, y = var_2961_cast)[name = tensor("attn_weights_73_cast")]; + tensor attn_weights_75_cast = mul(x = attn_weights_73_cast, y = var_2297_to_fp16)[name = tensor("attn_weights_75_cast")]; + tensor var_2967_cast = softmax(axis = var_2290, x = attn_weights_75_cast)[name = tensor("op_2967_cast")]; + tensor attn_37_transpose_x_0 = const()[name = tensor("attn_37_transpose_x_0"), val = tensor(false)]; + tensor attn_37_transpose_y_0 = const()[name = tensor("attn_37_transpose_y_0"), val = tensor(true)]; + tensor attn_37_cast = matmul(transpose_x = attn_37_transpose_x_0, transpose_y = attn_37_transpose_y_0, x = var_2963_cast, y = var_2967_cast)[name = tensor("attn_37_cast")]; + tensor var_2971 = const()[name = tensor("op_2971"), val = tensor([2, 1280, 1, -1])]; + tensor input_353_cast = reshape(shape = var_2971, x = attn_37_cast)[name = tensor("input_353_cast")]; + tensor var_2976 = const()[name = tensor("op_2976"), val = tensor([1, 1])]; + tensor var_2978 = const()[name = tensor("op_2978"), val = tensor([1, 1])]; + tensor var_2980_pad_type_0 = const()[name = tensor("op_2980_pad_type_0"), val = tensor("custom")]; + tensor var_2980_pad_0 = const()[name = tensor("op_2980_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(545137472))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(546366336))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(546366528)))]; + tensor var_2980_cast = conv(bias = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_2978, groups = var_2306, pad = var_2980_pad_0, pad_type = var_2980_pad_type_0, strides = var_2976, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_353_cast)[name = tensor("op_2980_cast")]; + tensor inputs_57_cast = add(x = var_2980_cast, y = inputs_55_cast)[name = tensor("inputs_57_cast")]; + tensor var_2984 = const()[name = tensor("op_2984"), val = tensor([1])]; + tensor channels_mean_57_cast = reduce_mean(axes = var_2984, keep_dims = var_2301, x = inputs_57_cast)[name = tensor("channels_mean_57_cast")]; + tensor zero_mean_57_cast = sub(x = inputs_57_cast, y = channels_mean_57_cast)[name = tensor("zero_mean_57_cast")]; + tensor zero_mean_sq_57_cast = mul(x = zero_mean_57_cast, y = zero_mean_57_cast)[name = tensor("zero_mean_sq_57_cast")]; + tensor var_2988 = const()[name = tensor("op_2988"), val = tensor([1])]; + tensor var_2989_cast = reduce_mean(axes = var_2988, keep_dims = var_2301, x = zero_mean_sq_57_cast)[name = tensor("op_2989_cast")]; + tensor var_2990_to_fp16 = const()[name = tensor("op_2990_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_2991_cast = add(x = var_2989_cast, y = var_2990_to_fp16)[name = tensor("op_2991_cast")]; + tensor denom_57_epsilon_0_to_fp16 = const()[name = tensor("denom_57_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_57_cast = rsqrt(epsilon = denom_57_epsilon_0_to_fp16, x = var_2991_cast)[name = tensor("denom_57_cast")]; + tensor out_57_cast = mul(x = zero_mean_57_cast, y = denom_57_cast)[name = tensor("out_57_cast")]; + tensor var_2995_to_fp16 = const()[name = tensor("op_2995_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(546369152)))]; + tensor var_2996_cast = add(x = out_57_cast, y = var_2995_to_fp16)[name = tensor("op_2996_cast")]; + tensor var_2998_to_fp16 = const()[name = tensor("op_2998_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(546371776)))]; + tensor hidden_states_209_cast = mul(x = var_2996_cast, y = var_2998_to_fp16)[name = tensor("hidden_states_209_cast")]; + tensor var_3005 = const()[name = tensor("op_3005"), val = tensor([1, 1])]; + tensor var_3007 = const()[name = tensor("op_3007"), val = tensor([1, 1])]; + tensor q_39_pad_type_0 = const()[name = tensor("q_39_pad_type_0"), val = tensor("custom")]; + tensor q_39_pad_0 = const()[name = tensor("q_39_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(546374400))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(547603264))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor q_39_cast = conv(dilations = var_3007, groups = var_2306, pad = q_39_pad_0, pad_type = q_39_pad_type_0, strides = var_3005, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_209_cast)[name = tensor("q_39_cast")]; + tensor var_3011 = const()[name = tensor("op_3011"), val = tensor([1, 1])]; + tensor var_3013 = const()[name = tensor("op_3013"), val = tensor([1, 1])]; + tensor k_39_pad_type_0 = const()[name = tensor("k_39_pad_type_0"), val = tensor("custom")]; + tensor k_39_pad_0 = const()[name = tensor("k_39_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(547603456))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(548340800))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([1280, 768, 1, 1])]; + tensor k_39_cast = conv(dilations = var_3013, groups = var_2306, pad = k_39_pad_0, pad_type = k_39_pad_type_0, strides = var_3011, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_39_cast")]; + tensor var_3017 = const()[name = tensor("op_3017"), val = tensor([1, 1])]; + tensor var_3019 = const()[name = tensor("op_3019"), val = tensor([1, 1])]; + tensor v_39_pad_type_0 = const()[name = tensor("v_39_pad_type_0"), val = tensor("custom")]; + tensor v_39_pad_0 = const()[name = tensor("v_39_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(548340992))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(549078336))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([1280, 768, 1, 1])]; + tensor v_39_cast = conv(dilations = var_3019, groups = var_2306, pad = v_39_pad_0, pad_type = v_39_pad_type_0, strides = var_3017, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_39_cast")]; + tensor var_3023 = const()[name = tensor("op_3023"), val = tensor([2, 8, 160, -1])]; + tensor var_3024_cast = reshape(shape = var_3023, x = q_39_cast)[name = tensor("op_3024_cast")]; + tensor var_3025 = const()[name = tensor("op_3025"), val = tensor([2, 8, 160, -1])]; + tensor var_3026_cast = reshape(shape = var_3025, x = k_39_cast)[name = tensor("op_3026_cast")]; + tensor var_3027 = const()[name = tensor("op_3027"), val = tensor([2, 8, 160, -1])]; + tensor var_3028_cast = reshape(shape = var_3027, x = v_39_cast)[name = tensor("op_3028_cast")]; + tensor attn_weights_77_transpose_x_0 = const()[name = tensor("attn_weights_77_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_77_transpose_y_0 = const()[name = tensor("attn_weights_77_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_77_cast = matmul(transpose_x = attn_weights_77_transpose_x_0, transpose_y = attn_weights_77_transpose_y_0, x = var_3024_cast, y = var_3026_cast)[name = tensor("attn_weights_77_cast")]; + tensor attn_weights_79_cast = mul(x = attn_weights_77_cast, y = var_2297_to_fp16)[name = tensor("attn_weights_79_cast")]; + tensor var_3032_cast = softmax(axis = var_2290, x = attn_weights_79_cast)[name = tensor("op_3032_cast")]; + tensor attn_39_transpose_x_0 = const()[name = tensor("attn_39_transpose_x_0"), val = tensor(false)]; + tensor attn_39_transpose_y_0 = const()[name = tensor("attn_39_transpose_y_0"), val = tensor(true)]; + tensor attn_39_cast = matmul(transpose_x = attn_39_transpose_x_0, transpose_y = attn_39_transpose_y_0, x = var_3028_cast, y = var_3032_cast)[name = tensor("attn_39_cast")]; + tensor var_3036 = const()[name = tensor("op_3036"), val = tensor([2, 1280, 1, -1])]; + tensor input_355_cast = reshape(shape = var_3036, x = attn_39_cast)[name = tensor("input_355_cast")]; + tensor var_3041 = const()[name = tensor("op_3041"), val = tensor([1, 1])]; + tensor var_3043 = const()[name = tensor("op_3043"), val = tensor([1, 1])]; + tensor var_3045_pad_type_0 = const()[name = tensor("op_3045_pad_type_0"), val = tensor("custom")]; + tensor var_3045_pad_0 = const()[name = tensor("op_3045_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(549078528))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(550307392))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(550307584)))]; + tensor var_3045_cast = conv(bias = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_3043, groups = var_2306, pad = var_3045_pad_0, pad_type = var_3045_pad_type_0, strides = var_3041, weight = up_blocks_1_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_355_cast)[name = tensor("op_3045_cast")]; + tensor inputs_59_cast = add(x = var_3045_cast, y = inputs_57_cast)[name = tensor("inputs_59_cast")]; + tensor var_3049 = const()[name = tensor("op_3049"), val = tensor([1])]; + tensor channels_mean_59_cast = reduce_mean(axes = var_3049, keep_dims = var_2301, x = inputs_59_cast)[name = tensor("channels_mean_59_cast")]; + tensor zero_mean_59_cast = sub(x = inputs_59_cast, y = channels_mean_59_cast)[name = tensor("zero_mean_59_cast")]; + tensor zero_mean_sq_59_cast = mul(x = zero_mean_59_cast, y = zero_mean_59_cast)[name = tensor("zero_mean_sq_59_cast")]; + tensor var_3053 = const()[name = tensor("op_3053"), val = tensor([1])]; + tensor var_3054_cast = reduce_mean(axes = var_3053, keep_dims = var_2301, x = zero_mean_sq_59_cast)[name = tensor("op_3054_cast")]; + tensor var_3055_to_fp16 = const()[name = tensor("op_3055_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3056_cast = add(x = var_3054_cast, y = var_3055_to_fp16)[name = tensor("op_3056_cast")]; + tensor denom_59_epsilon_0_to_fp16 = const()[name = tensor("denom_59_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_59_cast = rsqrt(epsilon = denom_59_epsilon_0_to_fp16, x = var_3056_cast)[name = tensor("denom_59_cast")]; + tensor out_59_cast = mul(x = zero_mean_59_cast, y = denom_59_cast)[name = tensor("out_59_cast")]; + tensor var_3060_to_fp16 = const()[name = tensor("op_3060_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(550310208)))]; + tensor var_3061_cast = add(x = out_59_cast, y = var_3060_to_fp16)[name = tensor("op_3061_cast")]; + tensor var_3063_to_fp16 = const()[name = tensor("op_3063_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(550312832)))]; + tensor input_357_cast = mul(x = var_3061_cast, y = var_3063_to_fp16)[name = tensor("input_357_cast")]; + tensor var_3071 = const()[name = tensor("op_3071"), val = tensor([1, 1])]; + tensor var_3073 = const()[name = tensor("op_3073"), val = tensor([1, 1])]; + tensor var_3075_pad_type_0 = const()[name = tensor("op_3075_pad_type_0"), val = tensor("custom")]; + tensor var_3075_pad_0 = const()[name = tensor("op_3075_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(550315456))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(560145920))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([10240, 1280, 1, 1])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(560146112))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(560153856))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([10240])]; + tensor var_3075_cast = conv(bias = up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_3073, groups = var_2306, pad = var_3075_pad_0, pad_type = var_3075_pad_type_0, strides = var_3071, weight = up_blocks_1_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_357_cast)[name = tensor("op_3075_cast")]; + tensor var_3076_split_sizes_0 = const()[name = tensor("op_3076_split_sizes_0"), val = tensor([5120, 5120])]; + tensor var_3076_axis_0 = const()[name = tensor("op_3076_axis_0"), val = tensor(1)]; + tensor var_3076_cast_0, tensor var_3076_cast_1 = split(axis = var_3076_axis_0, split_sizes = var_3076_split_sizes_0, x = var_3075_cast)[name = tensor("op_3076_cast")]; + tensor var_3078_mode_0 = const()[name = tensor("op_3078_mode_0"), val = tensor("EXACT")]; + tensor var_3078_cast = gelu(mode = var_3078_mode_0, x = var_3076_cast_1)[name = tensor("op_3078_cast")]; + tensor input_359_cast = mul(x = var_3076_cast_0, y = var_3078_cast)[name = tensor("input_359_cast")]; + tensor var_3082 = const()[name = tensor("op_3082"), val = tensor([1, 1])]; + tensor var_3084 = const()[name = tensor("op_3084"), val = tensor([1, 1])]; + tensor var_3086_pad_type_0 = const()[name = tensor("op_3086_pad_type_0"), val = tensor("custom")]; + tensor var_3086_pad_0 = const()[name = tensor("op_3086_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(560154048))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(565069312))), name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([1280, 5120, 1, 1])]; + tensor up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(565069504)))]; + tensor var_3086_cast = conv(bias = up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_3084, groups = var_2306, pad = var_3086_pad_0, pad_type = var_3086_pad_type_0, strides = var_3082, weight = up_blocks_1_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_359_cast)[name = tensor("op_3086_cast")]; + tensor hidden_states_213_cast = add(x = var_3086_cast, y = inputs_59_cast)[name = tensor("hidden_states_213_cast")]; + tensor var_3088 = const()[name = tensor("op_3088"), val = tensor([2, 1280, 16, 16])]; + tensor input_361_cast = reshape(shape = var_3088, x = hidden_states_213_cast)[name = tensor("input_361_cast")]; + tensor var_3092 = const()[name = tensor("op_3092"), val = tensor([1, 1])]; + tensor var_3094 = const()[name = tensor("op_3094"), val = tensor([1, 1])]; + tensor hidden_states_215_pad_type_0 = const()[name = tensor("hidden_states_215_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_215_pad_0 = const()[name = tensor("hidden_states_215_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_1_attentions_2_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(565072128))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(566300992))), name = tensor("up_blocks_1_attentions_2_proj_out_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 1, 1])]; + tensor up_blocks_1_attentions_2_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_1_attentions_2_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(566301184)))]; + tensor hidden_states_215_cast = conv(bias = up_blocks_1_attentions_2_proj_out_bias_to_fp16, dilations = var_3094, groups = var_2306, pad = hidden_states_215_pad_0, pad_type = hidden_states_215_pad_type_0, strides = var_3092, weight = up_blocks_1_attentions_2_proj_out_weight_to_fp16_palettized, x = input_361_cast)[name = tensor("hidden_states_215_cast")]; + tensor input_363_cast = add(x = hidden_states_215_cast, y = hidden_states_203_cast)[name = tensor("input_363_cast")]; + tensor input_365_scale_factor_height_0 = const()[name = tensor("input_365_scale_factor_height_0"), val = tensor(0x1p+1)]; + tensor input_365_scale_factor_width_0 = const()[name = tensor("input_365_scale_factor_width_0"), val = tensor(0x1p+1)]; + tensor input_365_cast = upsample_nearest_neighbor(scale_factor_height = input_365_scale_factor_height_0, scale_factor_width = input_365_scale_factor_width_0, x = input_363_cast)[name = tensor("input_365_cast")]; + tensor var_3103 = const()[name = tensor("op_3103"), val = tensor([1, 1])]; + tensor var_3105 = const()[name = tensor("op_3105"), val = tensor([1, 1])]; + tensor hidden_states_217_pad_type_0 = const()[name = tensor("hidden_states_217_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_217_pad_0 = const()[name = tensor("hidden_states_217_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_1_upsamplers_0_conv_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(566303808))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(577363072))), name = tensor("up_blocks_1_upsamplers_0_conv_weight_to_fp16_palettized"), shape = tensor([1280, 1280, 3, 3])]; + tensor up_blocks_1_upsamplers_0_conv_bias_to_fp16 = const()[name = tensor("up_blocks_1_upsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(577363264)))]; + tensor hidden_states_217_cast = conv(bias = up_blocks_1_upsamplers_0_conv_bias_to_fp16, dilations = var_3105, groups = var_2306, pad = hidden_states_217_pad_0, pad_type = hidden_states_217_pad_type_0, strides = var_3103, weight = up_blocks_1_upsamplers_0_conv_weight_to_fp16_palettized, x = input_365_cast)[name = tensor("hidden_states_217_cast")]; + tensor var_3110 = const()[name = tensor("op_3110"), val = tensor(3)]; + tensor var_3121 = const()[name = tensor("op_3121"), val = tensor(true)]; + tensor var_3126 = const()[name = tensor("op_3126"), val = tensor(1)]; + tensor input_367_interleave_0 = const()[name = tensor("input_367_interleave_0"), val = tensor(false)]; + tensor input_367_cast = concat(axis = var_3126, interleave = input_367_interleave_0, values = (hidden_states_217_cast, input_115_cast))[name = tensor("input_367_cast")]; + tensor reshape_168_shape_0 = const()[name = tensor("reshape_168_shape_0"), val = tensor([2, 32, 60, 32, 32])]; + tensor reshape_168_cast = reshape(shape = reshape_168_shape_0, x = input_367_cast)[name = tensor("reshape_168_cast")]; + tensor reduce_mean_126_axes_0 = const()[name = tensor("reduce_mean_126_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_126_keep_dims_0 = const()[name = tensor("reduce_mean_126_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_126_cast = reduce_mean(axes = reduce_mean_126_axes_0, keep_dims = reduce_mean_126_keep_dims_0, x = reshape_168_cast)[name = tensor("reduce_mean_126_cast")]; + tensor sub_84_cast = sub(x = reshape_168_cast, y = reduce_mean_126_cast)[name = tensor("sub_84_cast")]; + tensor square_42_cast = square(x = sub_84_cast)[name = tensor("square_42_cast")]; + tensor reduce_mean_128_axes_0 = const()[name = tensor("reduce_mean_128_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_128_keep_dims_0 = const()[name = tensor("reduce_mean_128_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_128_cast = reduce_mean(axes = reduce_mean_128_axes_0, keep_dims = reduce_mean_128_keep_dims_0, x = square_42_cast)[name = tensor("reduce_mean_128_cast")]; + tensor add_84_y_0_to_fp16 = const()[name = tensor("add_84_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_84_cast = add(x = reduce_mean_128_cast, y = add_84_y_0_to_fp16)[name = tensor("add_84_cast")]; + tensor sqrt_42_cast = sqrt(x = add_84_cast)[name = tensor("sqrt_42_cast")]; + tensor real_div_42_cast = real_div(x = sub_84_cast, y = sqrt_42_cast)[name = tensor("real_div_42_cast")]; + tensor reshape_169_shape_0 = const()[name = tensor("reshape_169_shape_0"), val = tensor([2, 1920, 32, 32])]; + tensor reshape_169_cast = reshape(shape = reshape_169_shape_0, x = real_div_42_cast)[name = tensor("reshape_169_cast")]; + tensor add_85_gamma_0_to_fp16 = const()[name = tensor("add_85_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(577365888)))]; + tensor add_85_beta_0_to_fp16 = const()[name = tensor("add_85_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(577369792)))]; + tensor add_85_epsilon_0_to_fp16 = const()[name = tensor("add_85_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_85_cast = batch_norm(beta = add_85_beta_0_to_fp16, epsilon = add_85_epsilon_0_to_fp16, gamma = add_85_gamma_0_to_fp16, mean = add_79_mean_0_to_fp16, variance = add_79_variance_0_to_fp16, x = reshape_169_cast)[name = tensor("add_85_cast")]; + tensor input_371_cast = silu(x = add_85_cast)[name = tensor("input_371_cast")]; + tensor var_3155 = const()[name = tensor("op_3155"), val = tensor([1, 1])]; + tensor var_3157 = const()[name = tensor("op_3157"), val = tensor([1, 1])]; + tensor hidden_states_219_pad_type_0 = const()[name = tensor("hidden_states_219_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_219_pad_0 = const()[name = tensor("hidden_states_219_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_2_resnets_0_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(577373696))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(585668160))), name = tensor("up_blocks_2_resnets_0_conv1_weight_to_fp16_palettized"), shape = tensor([640, 1920, 3, 3])]; + tensor up_blocks_2_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(585668352)))]; + tensor hidden_states_219_cast = conv(bias = up_blocks_2_resnets_0_conv1_bias_to_fp16, dilations = var_3157, groups = var_3126, pad = hidden_states_219_pad_0, pad_type = hidden_states_219_pad_type_0, strides = var_3155, weight = up_blocks_2_resnets_0_conv1_weight_to_fp16_palettized, x = input_371_cast)[name = tensor("hidden_states_219_cast")]; + tensor var_3163 = const()[name = tensor("op_3163"), val = tensor([1, 1])]; + tensor var_3165 = const()[name = tensor("op_3165"), val = tensor([1, 1])]; + tensor temb_33_pad_type_0 = const()[name = tensor("temb_33_pad_type_0"), val = tensor("custom")]; + tensor temb_33_pad_0 = const()[name = tensor("temb_33_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_resnets_0_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(585669696))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(586284160))), name = tensor("up_blocks_2_resnets_0_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([640, 1280, 1, 1])]; + tensor up_blocks_2_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(586284352)))]; + tensor temb_33_cast = conv(bias = up_blocks_2_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_3165, groups = var_3126, pad = temb_33_pad_0, pad_type = temb_33_pad_type_0, strides = var_3163, weight = up_blocks_2_resnets_0_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor("temb_33_cast")]; + tensor input_375_cast = add(x = hidden_states_219_cast, y = temb_33_cast)[name = tensor("input_375_cast")]; + tensor reshape_172_shape_0 = const()[name = tensor("reshape_172_shape_0"), val = tensor([2, 32, 20, 32, 32])]; + tensor reshape_172_cast = reshape(shape = reshape_172_shape_0, x = input_375_cast)[name = tensor("reshape_172_cast")]; + tensor reduce_mean_129_axes_0 = const()[name = tensor("reduce_mean_129_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_129_keep_dims_0 = const()[name = tensor("reduce_mean_129_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_129_cast = reduce_mean(axes = reduce_mean_129_axes_0, keep_dims = reduce_mean_129_keep_dims_0, x = reshape_172_cast)[name = tensor("reduce_mean_129_cast")]; + tensor sub_86_cast = sub(x = reshape_172_cast, y = reduce_mean_129_cast)[name = tensor("sub_86_cast")]; + tensor square_43_cast = square(x = sub_86_cast)[name = tensor("square_43_cast")]; + tensor reduce_mean_131_axes_0 = const()[name = tensor("reduce_mean_131_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_131_keep_dims_0 = const()[name = tensor("reduce_mean_131_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_131_cast = reduce_mean(axes = reduce_mean_131_axes_0, keep_dims = reduce_mean_131_keep_dims_0, x = square_43_cast)[name = tensor("reduce_mean_131_cast")]; + tensor add_86_y_0_to_fp16 = const()[name = tensor("add_86_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_86_cast = add(x = reduce_mean_131_cast, y = add_86_y_0_to_fp16)[name = tensor("add_86_cast")]; + tensor sqrt_43_cast = sqrt(x = add_86_cast)[name = tensor("sqrt_43_cast")]; + tensor real_div_43_cast = real_div(x = sub_86_cast, y = sqrt_43_cast)[name = tensor("real_div_43_cast")]; + tensor reshape_173_shape_0 = const()[name = tensor("reshape_173_shape_0"), val = tensor([2, 640, 32, 32])]; + tensor reshape_173_cast = reshape(shape = reshape_173_shape_0, x = real_div_43_cast)[name = tensor("reshape_173_cast")]; + tensor add_87_gamma_0_to_fp16 = const()[name = tensor("add_87_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(586285696)))]; + tensor add_87_beta_0_to_fp16 = const()[name = tensor("add_87_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(586287040)))]; + tensor add_87_epsilon_0_to_fp16 = const()[name = tensor("add_87_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_87_cast = batch_norm(beta = add_87_beta_0_to_fp16, epsilon = add_87_epsilon_0_to_fp16, gamma = add_87_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_173_cast)[name = tensor("add_87_cast")]; + tensor input_379_cast = silu(x = add_87_cast)[name = tensor("input_379_cast")]; + tensor var_3175 = const()[name = tensor("op_3175"), val = tensor([1, 1])]; + tensor var_3177 = const()[name = tensor("op_3177"), val = tensor([1, 1])]; + tensor hidden_states_221_pad_type_0 = const()[name = tensor("hidden_states_221_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_221_pad_0 = const()[name = tensor("hidden_states_221_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_2_resnets_0_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(586288384))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(589053248))), name = tensor("up_blocks_2_resnets_0_conv2_weight_to_fp16_palettized"), shape = tensor([640, 640, 3, 3])]; + tensor up_blocks_2_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(589053440)))]; + tensor hidden_states_221_cast = conv(bias = up_blocks_2_resnets_0_conv2_bias_to_fp16, dilations = var_3177, groups = var_3126, pad = hidden_states_221_pad_0, pad_type = hidden_states_221_pad_type_0, strides = var_3175, weight = up_blocks_2_resnets_0_conv2_weight_to_fp16_palettized, x = input_379_cast)[name = tensor("hidden_states_221_cast")]; + tensor var_3182 = const()[name = tensor("op_3182"), val = tensor([1, 1])]; + tensor var_3184 = const()[name = tensor("op_3184"), val = tensor([1, 1])]; + tensor x_17_pad_type_0 = const()[name = tensor("x_17_pad_type_0"), val = tensor("custom")]; + tensor x_17_pad_0 = const()[name = tensor("x_17_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_resnets_0_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(589054784))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(589976448))), name = tensor("up_blocks_2_resnets_0_conv_shortcut_weight_to_fp16_palettized"), shape = tensor([640, 1920, 1, 1])]; + tensor up_blocks_2_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(589976640)))]; + tensor x_17_cast = conv(bias = up_blocks_2_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_3184, groups = var_3126, pad = x_17_pad_0, pad_type = x_17_pad_type_0, strides = var_3182, weight = up_blocks_2_resnets_0_conv_shortcut_weight_to_fp16_palettized, x = input_367_cast)[name = tensor("x_17_cast")]; + tensor hidden_states_223_cast = add(x = x_17_cast, y = hidden_states_221_cast)[name = tensor("hidden_states_223_cast")]; + tensor reshape_176_shape_0 = const()[name = tensor("reshape_176_shape_0"), val = tensor([2, 32, 20, 32, 32])]; + tensor reshape_176_cast = reshape(shape = reshape_176_shape_0, x = hidden_states_223_cast)[name = tensor("reshape_176_cast")]; + tensor reduce_mean_132_axes_0 = const()[name = tensor("reduce_mean_132_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_132_keep_dims_0 = const()[name = tensor("reduce_mean_132_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_132_cast = reduce_mean(axes = reduce_mean_132_axes_0, keep_dims = reduce_mean_132_keep_dims_0, x = reshape_176_cast)[name = tensor("reduce_mean_132_cast")]; + tensor sub_88_cast = sub(x = reshape_176_cast, y = reduce_mean_132_cast)[name = tensor("sub_88_cast")]; + tensor square_44_cast = square(x = sub_88_cast)[name = tensor("square_44_cast")]; + tensor reduce_mean_134_axes_0 = const()[name = tensor("reduce_mean_134_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_134_keep_dims_0 = const()[name = tensor("reduce_mean_134_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_134_cast = reduce_mean(axes = reduce_mean_134_axes_0, keep_dims = reduce_mean_134_keep_dims_0, x = square_44_cast)[name = tensor("reduce_mean_134_cast")]; + tensor add_88_y_0_to_fp16 = const()[name = tensor("add_88_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_88_cast = add(x = reduce_mean_134_cast, y = add_88_y_0_to_fp16)[name = tensor("add_88_cast")]; + tensor sqrt_44_cast = sqrt(x = add_88_cast)[name = tensor("sqrt_44_cast")]; + tensor real_div_44_cast = real_div(x = sub_88_cast, y = sqrt_44_cast)[name = tensor("real_div_44_cast")]; + tensor reshape_177_shape_0 = const()[name = tensor("reshape_177_shape_0"), val = tensor([2, 640, 32, 32])]; + tensor reshape_177_cast = reshape(shape = reshape_177_shape_0, x = real_div_44_cast)[name = tensor("reshape_177_cast")]; + tensor add_89_gamma_0_to_fp16 = const()[name = tensor("add_89_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(589977984)))]; + tensor add_89_beta_0_to_fp16 = const()[name = tensor("add_89_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(589979328)))]; + tensor add_89_epsilon_0_to_fp16 = const()[name = tensor("add_89_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_89_cast = batch_norm(beta = add_89_beta_0_to_fp16, epsilon = add_89_epsilon_0_to_fp16, gamma = add_89_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_177_cast)[name = tensor("add_89_cast")]; + tensor var_3204 = const()[name = tensor("op_3204"), val = tensor([1, 1])]; + tensor var_3206 = const()[name = tensor("op_3206"), val = tensor([1, 1])]; + tensor hidden_states_225_pad_type_0 = const()[name = tensor("hidden_states_225_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_225_pad_0 = const()[name = tensor("hidden_states_225_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(589980672))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(590287936))), name = tensor("up_blocks_2_attentions_0_proj_in_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor up_blocks_2_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(590288128)))]; + tensor hidden_states_225_cast = conv(bias = up_blocks_2_attentions_0_proj_in_bias_to_fp16, dilations = var_3206, groups = var_3126, pad = hidden_states_225_pad_0, pad_type = hidden_states_225_pad_type_0, strides = var_3204, weight = up_blocks_2_attentions_0_proj_in_weight_to_fp16_palettized, x = add_89_cast)[name = tensor("hidden_states_225_cast")]; + tensor var_3211 = const()[name = tensor("op_3211"), val = tensor([2, 640, 1, 1024])]; + tensor inputs_61_cast = reshape(shape = var_3211, x = hidden_states_225_cast)[name = tensor("inputs_61_cast")]; + tensor var_3221 = const()[name = tensor("op_3221"), val = tensor([1])]; + tensor channels_mean_61_cast = reduce_mean(axes = var_3221, keep_dims = var_3121, x = inputs_61_cast)[name = tensor("channels_mean_61_cast")]; + tensor zero_mean_61_cast = sub(x = inputs_61_cast, y = channels_mean_61_cast)[name = tensor("zero_mean_61_cast")]; + tensor zero_mean_sq_61_cast = mul(x = zero_mean_61_cast, y = zero_mean_61_cast)[name = tensor("zero_mean_sq_61_cast")]; + tensor var_3225 = const()[name = tensor("op_3225"), val = tensor([1])]; + tensor var_3226_cast = reduce_mean(axes = var_3225, keep_dims = var_3121, x = zero_mean_sq_61_cast)[name = tensor("op_3226_cast")]; + tensor var_3227_to_fp16 = const()[name = tensor("op_3227_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3228_cast = add(x = var_3226_cast, y = var_3227_to_fp16)[name = tensor("op_3228_cast")]; + tensor denom_61_epsilon_0_to_fp16 = const()[name = tensor("denom_61_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_61_cast = rsqrt(epsilon = denom_61_epsilon_0_to_fp16, x = var_3228_cast)[name = tensor("denom_61_cast")]; + tensor out_61_cast = mul(x = zero_mean_61_cast, y = denom_61_cast)[name = tensor("out_61_cast")]; + tensor var_3232_to_fp16 = const()[name = tensor("op_3232_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(590289472)))]; + tensor var_3233_cast = add(x = out_61_cast, y = var_3232_to_fp16)[name = tensor("op_3233_cast")]; + tensor var_3235_to_fp16 = const()[name = tensor("op_3235_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(590290816)))]; + tensor hidden_states_227_cast = mul(x = var_3233_cast, y = var_3235_to_fp16)[name = tensor("hidden_states_227_cast")]; + tensor var_3242 = const()[name = tensor("op_3242"), val = tensor([1, 1])]; + tensor var_3244 = const()[name = tensor("op_3244"), val = tensor([1, 1])]; + tensor q_41_pad_type_0 = const()[name = tensor("q_41_pad_type_0"), val = tensor("custom")]; + tensor q_41_pad_0 = const()[name = tensor("q_41_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(590292160))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(590599424))), name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_41_cast = conv(dilations = var_3244, groups = var_3126, pad = q_41_pad_0, pad_type = q_41_pad_type_0, strides = var_3242, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_227_cast)[name = tensor("q_41_cast")]; + tensor var_3248 = const()[name = tensor("op_3248"), val = tensor([1, 1])]; + tensor var_3250 = const()[name = tensor("op_3250"), val = tensor([1, 1])]; + tensor k_41_pad_type_0 = const()[name = tensor("k_41_pad_type_0"), val = tensor("custom")]; + tensor k_41_pad_0 = const()[name = tensor("k_41_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(590599616))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(590906880))), name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor k_41_cast = conv(dilations = var_3250, groups = var_3126, pad = k_41_pad_0, pad_type = k_41_pad_type_0, strides = var_3248, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_227_cast)[name = tensor("k_41_cast")]; + tensor var_3254 = const()[name = tensor("op_3254"), val = tensor([1, 1])]; + tensor var_3256 = const()[name = tensor("op_3256"), val = tensor([1, 1])]; + tensor v_41_pad_type_0 = const()[name = tensor("v_41_pad_type_0"), val = tensor("custom")]; + tensor v_41_pad_0 = const()[name = tensor("v_41_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(590907072))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(591214336))), name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor v_41_cast = conv(dilations = var_3256, groups = var_3126, pad = v_41_pad_0, pad_type = v_41_pad_type_0, strides = var_3254, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_227_cast)[name = tensor("v_41_cast")]; + tensor var_3260 = const()[name = tensor("op_3260"), val = tensor([2, 8, 80, -1])]; + tensor var_3261_cast = reshape(shape = var_3260, x = q_41_cast)[name = tensor("op_3261_cast")]; + tensor var_3262 = const()[name = tensor("op_3262"), val = tensor([2, 8, 80, -1])]; + tensor var_3263_cast = reshape(shape = var_3262, x = k_41_cast)[name = tensor("op_3263_cast")]; + tensor var_3264 = const()[name = tensor("op_3264"), val = tensor([2, 8, 80, -1])]; + tensor var_3265_cast = reshape(shape = var_3264, x = v_41_cast)[name = tensor("op_3265_cast")]; + tensor attn_weights_81_transpose_x_0 = const()[name = tensor("attn_weights_81_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_81_transpose_y_0 = const()[name = tensor("attn_weights_81_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_81_cast = matmul(transpose_x = attn_weights_81_transpose_x_0, transpose_y = attn_weights_81_transpose_y_0, x = var_3261_cast, y = var_3263_cast)[name = tensor("attn_weights_81_cast")]; + tensor var_3117_to_fp16 = const()[name = tensor("op_3117_to_fp16"), val = tensor(0x1.cap-4)]; + tensor attn_weights_83_cast = mul(x = attn_weights_81_cast, y = var_3117_to_fp16)[name = tensor("attn_weights_83_cast")]; + tensor var_3269_cast = softmax(axis = var_3110, x = attn_weights_83_cast)[name = tensor("op_3269_cast")]; + tensor attn_41_transpose_x_0 = const()[name = tensor("attn_41_transpose_x_0"), val = tensor(false)]; + tensor attn_41_transpose_y_0 = const()[name = tensor("attn_41_transpose_y_0"), val = tensor(true)]; + tensor attn_41_cast = matmul(transpose_x = attn_41_transpose_x_0, transpose_y = attn_41_transpose_y_0, x = var_3265_cast, y = var_3269_cast)[name = tensor("attn_41_cast")]; + tensor var_3273 = const()[name = tensor("op_3273"), val = tensor([2, 640, 1, -1])]; + tensor input_383_cast = reshape(shape = var_3273, x = attn_41_cast)[name = tensor("input_383_cast")]; + tensor var_3278 = const()[name = tensor("op_3278"), val = tensor([1, 1])]; + tensor var_3280 = const()[name = tensor("op_3280"), val = tensor([1, 1])]; + tensor var_3282_pad_type_0 = const()[name = tensor("op_3282_pad_type_0"), val = tensor("custom")]; + tensor var_3282_pad_0 = const()[name = tensor("op_3282_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(591214528))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(591521792))), name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(591521984)))]; + tensor var_3282_cast = conv(bias = up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_3280, groups = var_3126, pad = var_3282_pad_0, pad_type = var_3282_pad_type_0, strides = var_3278, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_383_cast)[name = tensor("op_3282_cast")]; + tensor inputs_63_cast = add(x = var_3282_cast, y = inputs_61_cast)[name = tensor("inputs_63_cast")]; + tensor var_3286 = const()[name = tensor("op_3286"), val = tensor([1])]; + tensor channels_mean_63_cast = reduce_mean(axes = var_3286, keep_dims = var_3121, x = inputs_63_cast)[name = tensor("channels_mean_63_cast")]; + tensor zero_mean_63_cast = sub(x = inputs_63_cast, y = channels_mean_63_cast)[name = tensor("zero_mean_63_cast")]; + tensor zero_mean_sq_63_cast = mul(x = zero_mean_63_cast, y = zero_mean_63_cast)[name = tensor("zero_mean_sq_63_cast")]; + tensor var_3290 = const()[name = tensor("op_3290"), val = tensor([1])]; + tensor var_3291_cast = reduce_mean(axes = var_3290, keep_dims = var_3121, x = zero_mean_sq_63_cast)[name = tensor("op_3291_cast")]; + tensor var_3292_to_fp16 = const()[name = tensor("op_3292_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3293_cast = add(x = var_3291_cast, y = var_3292_to_fp16)[name = tensor("op_3293_cast")]; + tensor denom_63_epsilon_0_to_fp16 = const()[name = tensor("denom_63_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_63_cast = rsqrt(epsilon = denom_63_epsilon_0_to_fp16, x = var_3293_cast)[name = tensor("denom_63_cast")]; + tensor out_63_cast = mul(x = zero_mean_63_cast, y = denom_63_cast)[name = tensor("out_63_cast")]; + tensor var_3297_to_fp16 = const()[name = tensor("op_3297_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(591523328)))]; + tensor var_3298_cast = add(x = out_63_cast, y = var_3297_to_fp16)[name = tensor("op_3298_cast")]; + tensor var_3300_to_fp16 = const()[name = tensor("op_3300_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(591524672)))]; + tensor hidden_states_229_cast = mul(x = var_3298_cast, y = var_3300_to_fp16)[name = tensor("hidden_states_229_cast")]; + tensor var_3307 = const()[name = tensor("op_3307"), val = tensor([1, 1])]; + tensor var_3309 = const()[name = tensor("op_3309"), val = tensor([1, 1])]; + tensor q_43_pad_type_0 = const()[name = tensor("q_43_pad_type_0"), val = tensor("custom")]; + tensor q_43_pad_0 = const()[name = tensor("q_43_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(591526016))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(591833280))), name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_43_cast = conv(dilations = var_3309, groups = var_3126, pad = q_43_pad_0, pad_type = q_43_pad_type_0, strides = var_3307, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_229_cast)[name = tensor("q_43_cast")]; + tensor var_3313 = const()[name = tensor("op_3313"), val = tensor([1, 1])]; + tensor var_3315 = const()[name = tensor("op_3315"), val = tensor([1, 1])]; + tensor k_43_pad_type_0 = const()[name = tensor("k_43_pad_type_0"), val = tensor("custom")]; + tensor k_43_pad_0 = const()[name = tensor("k_43_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(591833472))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(592202176))), name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([640, 768, 1, 1])]; + tensor k_43_cast = conv(dilations = var_3315, groups = var_3126, pad = k_43_pad_0, pad_type = k_43_pad_type_0, strides = var_3313, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_43_cast")]; + tensor var_3319 = const()[name = tensor("op_3319"), val = tensor([1, 1])]; + tensor var_3321 = const()[name = tensor("op_3321"), val = tensor([1, 1])]; + tensor v_43_pad_type_0 = const()[name = tensor("v_43_pad_type_0"), val = tensor("custom")]; + tensor v_43_pad_0 = const()[name = tensor("v_43_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(592202368))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(592571072))), name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([640, 768, 1, 1])]; + tensor v_43_cast = conv(dilations = var_3321, groups = var_3126, pad = v_43_pad_0, pad_type = v_43_pad_type_0, strides = var_3319, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_43_cast")]; + tensor var_3325 = const()[name = tensor("op_3325"), val = tensor([2, 8, 80, -1])]; + tensor var_3326_cast = reshape(shape = var_3325, x = q_43_cast)[name = tensor("op_3326_cast")]; + tensor var_3327 = const()[name = tensor("op_3327"), val = tensor([2, 8, 80, -1])]; + tensor var_3328_cast = reshape(shape = var_3327, x = k_43_cast)[name = tensor("op_3328_cast")]; + tensor var_3329 = const()[name = tensor("op_3329"), val = tensor([2, 8, 80, -1])]; + tensor var_3330_cast = reshape(shape = var_3329, x = v_43_cast)[name = tensor("op_3330_cast")]; + tensor attn_weights_85_transpose_x_0 = const()[name = tensor("attn_weights_85_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_85_transpose_y_0 = const()[name = tensor("attn_weights_85_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_85_cast = matmul(transpose_x = attn_weights_85_transpose_x_0, transpose_y = attn_weights_85_transpose_y_0, x = var_3326_cast, y = var_3328_cast)[name = tensor("attn_weights_85_cast")]; + tensor attn_weights_87_cast = mul(x = attn_weights_85_cast, y = var_3117_to_fp16)[name = tensor("attn_weights_87_cast")]; + tensor var_3334_cast = softmax(axis = var_3110, x = attn_weights_87_cast)[name = tensor("op_3334_cast")]; + tensor attn_43_transpose_x_0 = const()[name = tensor("attn_43_transpose_x_0"), val = tensor(false)]; + tensor attn_43_transpose_y_0 = const()[name = tensor("attn_43_transpose_y_0"), val = tensor(true)]; + tensor attn_43_cast = matmul(transpose_x = attn_43_transpose_x_0, transpose_y = attn_43_transpose_y_0, x = var_3330_cast, y = var_3334_cast)[name = tensor("attn_43_cast")]; + tensor var_3338 = const()[name = tensor("op_3338"), val = tensor([2, 640, 1, -1])]; + tensor input_385_cast = reshape(shape = var_3338, x = attn_43_cast)[name = tensor("input_385_cast")]; + tensor var_3343 = const()[name = tensor("op_3343"), val = tensor([1, 1])]; + tensor var_3345 = const()[name = tensor("op_3345"), val = tensor([1, 1])]; + tensor var_3347_pad_type_0 = const()[name = tensor("op_3347_pad_type_0"), val = tensor("custom")]; + tensor var_3347_pad_0 = const()[name = tensor("op_3347_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(592571264))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(592878528))), name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(592878720)))]; + tensor var_3347_cast = conv(bias = up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_3345, groups = var_3126, pad = var_3347_pad_0, pad_type = var_3347_pad_type_0, strides = var_3343, weight = up_blocks_2_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_385_cast)[name = tensor("op_3347_cast")]; + tensor inputs_65_cast = add(x = var_3347_cast, y = inputs_63_cast)[name = tensor("inputs_65_cast")]; + tensor var_3351 = const()[name = tensor("op_3351"), val = tensor([1])]; + tensor channels_mean_65_cast = reduce_mean(axes = var_3351, keep_dims = var_3121, x = inputs_65_cast)[name = tensor("channels_mean_65_cast")]; + tensor zero_mean_65_cast = sub(x = inputs_65_cast, y = channels_mean_65_cast)[name = tensor("zero_mean_65_cast")]; + tensor zero_mean_sq_65_cast = mul(x = zero_mean_65_cast, y = zero_mean_65_cast)[name = tensor("zero_mean_sq_65_cast")]; + tensor var_3355 = const()[name = tensor("op_3355"), val = tensor([1])]; + tensor var_3356_cast = reduce_mean(axes = var_3355, keep_dims = var_3121, x = zero_mean_sq_65_cast)[name = tensor("op_3356_cast")]; + tensor var_3357_to_fp16 = const()[name = tensor("op_3357_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3358_cast = add(x = var_3356_cast, y = var_3357_to_fp16)[name = tensor("op_3358_cast")]; + tensor denom_65_epsilon_0_to_fp16 = const()[name = tensor("denom_65_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_65_cast = rsqrt(epsilon = denom_65_epsilon_0_to_fp16, x = var_3358_cast)[name = tensor("denom_65_cast")]; + tensor out_65_cast = mul(x = zero_mean_65_cast, y = denom_65_cast)[name = tensor("out_65_cast")]; + tensor var_3362_to_fp16 = const()[name = tensor("op_3362_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(592880064)))]; + tensor var_3363_cast = add(x = out_65_cast, y = var_3362_to_fp16)[name = tensor("op_3363_cast")]; + tensor var_3365_to_fp16 = const()[name = tensor("op_3365_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(592881408)))]; + tensor input_387_cast = mul(x = var_3363_cast, y = var_3365_to_fp16)[name = tensor("input_387_cast")]; + tensor var_3373 = const()[name = tensor("op_3373"), val = tensor([1, 1])]; + tensor var_3375 = const()[name = tensor("op_3375"), val = tensor([1, 1])]; + tensor var_3377_pad_type_0 = const()[name = tensor("op_3377_pad_type_0"), val = tensor("custom")]; + tensor var_3377_pad_0 = const()[name = tensor("op_3377_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(592882752))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(595340416))), name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([5120, 640, 1, 1])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(595340608))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(595344512))), name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([5120])]; + tensor var_3377_cast = conv(bias = up_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_3375, groups = var_3126, pad = var_3377_pad_0, pad_type = var_3377_pad_type_0, strides = var_3373, weight = up_blocks_2_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_387_cast)[name = tensor("op_3377_cast")]; + tensor var_3378_split_sizes_0 = const()[name = tensor("op_3378_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_3378_axis_0 = const()[name = tensor("op_3378_axis_0"), val = tensor(1)]; + tensor var_3378_cast_0, tensor var_3378_cast_1 = split(axis = var_3378_axis_0, split_sizes = var_3378_split_sizes_0, x = var_3377_cast)[name = tensor("op_3378_cast")]; + tensor var_3380_mode_0 = const()[name = tensor("op_3380_mode_0"), val = tensor("EXACT")]; + tensor var_3380_cast = gelu(mode = var_3380_mode_0, x = var_3378_cast_1)[name = tensor("op_3380_cast")]; + tensor input_389_cast = mul(x = var_3378_cast_0, y = var_3380_cast)[name = tensor("input_389_cast")]; + tensor var_3384 = const()[name = tensor("op_3384"), val = tensor([1, 1])]; + tensor var_3386 = const()[name = tensor("op_3386"), val = tensor([1, 1])]; + tensor var_3388_pad_type_0 = const()[name = tensor("op_3388_pad_type_0"), val = tensor("custom")]; + tensor var_3388_pad_0 = const()[name = tensor("op_3388_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(595344704))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(596573568))), name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([640, 2560, 1, 1])]; + tensor up_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(596573760)))]; + tensor var_3388_cast = conv(bias = up_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_3386, groups = var_3126, pad = var_3388_pad_0, pad_type = var_3388_pad_type_0, strides = var_3384, weight = up_blocks_2_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_389_cast)[name = tensor("op_3388_cast")]; + tensor hidden_states_233_cast = add(x = var_3388_cast, y = inputs_65_cast)[name = tensor("hidden_states_233_cast")]; + tensor var_3390 = const()[name = tensor("op_3390"), val = tensor([2, 640, 32, 32])]; + tensor input_391_cast = reshape(shape = var_3390, x = hidden_states_233_cast)[name = tensor("input_391_cast")]; + tensor var_3394 = const()[name = tensor("op_3394"), val = tensor([1, 1])]; + tensor var_3396 = const()[name = tensor("op_3396"), val = tensor([1, 1])]; + tensor hidden_states_235_pad_type_0 = const()[name = tensor("hidden_states_235_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_235_pad_0 = const()[name = tensor("hidden_states_235_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_0_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(596575104))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(596882368))), name = tensor("up_blocks_2_attentions_0_proj_out_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor up_blocks_2_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_0_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(596882560)))]; + tensor hidden_states_235_cast = conv(bias = up_blocks_2_attentions_0_proj_out_bias_to_fp16, dilations = var_3396, groups = var_3126, pad = hidden_states_235_pad_0, pad_type = hidden_states_235_pad_type_0, strides = var_3394, weight = up_blocks_2_attentions_0_proj_out_weight_to_fp16_palettized, x = input_391_cast)[name = tensor("hidden_states_235_cast")]; + tensor hidden_states_237_cast = add(x = hidden_states_235_cast, y = hidden_states_223_cast)[name = tensor("hidden_states_237_cast")]; + tensor input_393_interleave_0 = const()[name = tensor("input_393_interleave_0"), val = tensor(false)]; + tensor input_393_cast = concat(axis = var_3126, interleave = input_393_interleave_0, values = (hidden_states_237_cast, input_89_cast))[name = tensor("input_393_cast")]; + tensor reshape_180_shape_0 = const()[name = tensor("reshape_180_shape_0"), val = tensor([2, 32, 40, 32, 32])]; + tensor reshape_180_cast = reshape(shape = reshape_180_shape_0, x = input_393_cast)[name = tensor("reshape_180_cast")]; + tensor reduce_mean_135_axes_0 = const()[name = tensor("reduce_mean_135_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_135_keep_dims_0 = const()[name = tensor("reduce_mean_135_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_135_cast = reduce_mean(axes = reduce_mean_135_axes_0, keep_dims = reduce_mean_135_keep_dims_0, x = reshape_180_cast)[name = tensor("reduce_mean_135_cast")]; + tensor sub_90_cast = sub(x = reshape_180_cast, y = reduce_mean_135_cast)[name = tensor("sub_90_cast")]; + tensor square_45_cast = square(x = sub_90_cast)[name = tensor("square_45_cast")]; + tensor reduce_mean_137_axes_0 = const()[name = tensor("reduce_mean_137_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_137_keep_dims_0 = const()[name = tensor("reduce_mean_137_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_137_cast = reduce_mean(axes = reduce_mean_137_axes_0, keep_dims = reduce_mean_137_keep_dims_0, x = square_45_cast)[name = tensor("reduce_mean_137_cast")]; + tensor add_90_y_0_to_fp16 = const()[name = tensor("add_90_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_90_cast = add(x = reduce_mean_137_cast, y = add_90_y_0_to_fp16)[name = tensor("add_90_cast")]; + tensor sqrt_45_cast = sqrt(x = add_90_cast)[name = tensor("sqrt_45_cast")]; + tensor real_div_45_cast = real_div(x = sub_90_cast, y = sqrt_45_cast)[name = tensor("real_div_45_cast")]; + tensor reshape_181_shape_0 = const()[name = tensor("reshape_181_shape_0"), val = tensor([2, 1280, 32, 32])]; + tensor reshape_181_cast = reshape(shape = reshape_181_shape_0, x = real_div_45_cast)[name = tensor("reshape_181_cast")]; + tensor add_91_gamma_0_to_fp16 = const()[name = tensor("add_91_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(596883904)))]; + tensor add_91_beta_0_to_fp16 = const()[name = tensor("add_91_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(596886528)))]; + tensor add_91_epsilon_0_to_fp16 = const()[name = tensor("add_91_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_91_cast = batch_norm(beta = add_91_beta_0_to_fp16, epsilon = add_91_epsilon_0_to_fp16, gamma = add_91_gamma_0_to_fp16, mean = add_27_mean_0_to_fp16, variance = add_27_variance_0_to_fp16, x = reshape_181_cast)[name = tensor("add_91_cast")]; + tensor input_397_cast = silu(x = add_91_cast)[name = tensor("input_397_cast")]; + tensor var_3414 = const()[name = tensor("op_3414"), val = tensor([1, 1])]; + tensor var_3416 = const()[name = tensor("op_3416"), val = tensor([1, 1])]; + tensor hidden_states_239_pad_type_0 = const()[name = tensor("hidden_states_239_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_239_pad_0 = const()[name = tensor("hidden_states_239_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_2_resnets_1_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(596889152))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(602418816))), name = tensor("up_blocks_2_resnets_1_conv1_weight_to_fp16_palettized"), shape = tensor([640, 1280, 3, 3])]; + tensor up_blocks_2_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(602419008)))]; + tensor hidden_states_239_cast = conv(bias = up_blocks_2_resnets_1_conv1_bias_to_fp16, dilations = var_3416, groups = var_3126, pad = hidden_states_239_pad_0, pad_type = hidden_states_239_pad_type_0, strides = var_3414, weight = up_blocks_2_resnets_1_conv1_weight_to_fp16_palettized, x = input_397_cast)[name = tensor("hidden_states_239_cast")]; + tensor var_3422 = const()[name = tensor("op_3422"), val = tensor([1, 1])]; + tensor var_3424 = const()[name = tensor("op_3424"), val = tensor([1, 1])]; + tensor temb_35_pad_type_0 = const()[name = tensor("temb_35_pad_type_0"), val = tensor("custom")]; + tensor temb_35_pad_0 = const()[name = tensor("temb_35_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_resnets_1_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(602420352))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(603034816))), name = tensor("up_blocks_2_resnets_1_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([640, 1280, 1, 1])]; + tensor up_blocks_2_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(603035008)))]; + tensor temb_35_cast = conv(bias = up_blocks_2_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_3424, groups = var_3126, pad = temb_35_pad_0, pad_type = temb_35_pad_type_0, strides = var_3422, weight = up_blocks_2_resnets_1_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor("temb_35_cast")]; + tensor input_401_cast = add(x = hidden_states_239_cast, y = temb_35_cast)[name = tensor("input_401_cast")]; + tensor reshape_184_shape_0 = const()[name = tensor("reshape_184_shape_0"), val = tensor([2, 32, 20, 32, 32])]; + tensor reshape_184_cast = reshape(shape = reshape_184_shape_0, x = input_401_cast)[name = tensor("reshape_184_cast")]; + tensor reduce_mean_138_axes_0 = const()[name = tensor("reduce_mean_138_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_138_keep_dims_0 = const()[name = tensor("reduce_mean_138_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_138_cast = reduce_mean(axes = reduce_mean_138_axes_0, keep_dims = reduce_mean_138_keep_dims_0, x = reshape_184_cast)[name = tensor("reduce_mean_138_cast")]; + tensor sub_92_cast = sub(x = reshape_184_cast, y = reduce_mean_138_cast)[name = tensor("sub_92_cast")]; + tensor square_46_cast = square(x = sub_92_cast)[name = tensor("square_46_cast")]; + tensor reduce_mean_140_axes_0 = const()[name = tensor("reduce_mean_140_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_140_keep_dims_0 = const()[name = tensor("reduce_mean_140_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_140_cast = reduce_mean(axes = reduce_mean_140_axes_0, keep_dims = reduce_mean_140_keep_dims_0, x = square_46_cast)[name = tensor("reduce_mean_140_cast")]; + tensor add_92_y_0_to_fp16 = const()[name = tensor("add_92_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_92_cast = add(x = reduce_mean_140_cast, y = add_92_y_0_to_fp16)[name = tensor("add_92_cast")]; + tensor sqrt_46_cast = sqrt(x = add_92_cast)[name = tensor("sqrt_46_cast")]; + tensor real_div_46_cast = real_div(x = sub_92_cast, y = sqrt_46_cast)[name = tensor("real_div_46_cast")]; + tensor reshape_185_shape_0 = const()[name = tensor("reshape_185_shape_0"), val = tensor([2, 640, 32, 32])]; + tensor reshape_185_cast = reshape(shape = reshape_185_shape_0, x = real_div_46_cast)[name = tensor("reshape_185_cast")]; + tensor add_93_gamma_0_to_fp16 = const()[name = tensor("add_93_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(603036352)))]; + tensor add_93_beta_0_to_fp16 = const()[name = tensor("add_93_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(603037696)))]; + tensor add_93_epsilon_0_to_fp16 = const()[name = tensor("add_93_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_93_cast = batch_norm(beta = add_93_beta_0_to_fp16, epsilon = add_93_epsilon_0_to_fp16, gamma = add_93_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_185_cast)[name = tensor("add_93_cast")]; + tensor input_405_cast = silu(x = add_93_cast)[name = tensor("input_405_cast")]; + tensor var_3434 = const()[name = tensor("op_3434"), val = tensor([1, 1])]; + tensor var_3436 = const()[name = tensor("op_3436"), val = tensor([1, 1])]; + tensor hidden_states_241_pad_type_0 = const()[name = tensor("hidden_states_241_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_241_pad_0 = const()[name = tensor("hidden_states_241_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_2_resnets_1_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(603039040))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(605803904))), name = tensor("up_blocks_2_resnets_1_conv2_weight_to_fp16_palettized"), shape = tensor([640, 640, 3, 3])]; + tensor up_blocks_2_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(605804096)))]; + tensor hidden_states_241_cast = conv(bias = up_blocks_2_resnets_1_conv2_bias_to_fp16, dilations = var_3436, groups = var_3126, pad = hidden_states_241_pad_0, pad_type = hidden_states_241_pad_type_0, strides = var_3434, weight = up_blocks_2_resnets_1_conv2_weight_to_fp16_palettized, x = input_405_cast)[name = tensor("hidden_states_241_cast")]; + tensor var_3441 = const()[name = tensor("op_3441"), val = tensor([1, 1])]; + tensor var_3443 = const()[name = tensor("op_3443"), val = tensor([1, 1])]; + tensor x_19_pad_type_0 = const()[name = tensor("x_19_pad_type_0"), val = tensor("custom")]; + tensor x_19_pad_0 = const()[name = tensor("x_19_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_resnets_1_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(605805440))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(606419904))), name = tensor("up_blocks_2_resnets_1_conv_shortcut_weight_to_fp16_palettized"), shape = tensor([640, 1280, 1, 1])]; + tensor up_blocks_2_resnets_1_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_1_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(606420096)))]; + tensor x_19_cast = conv(bias = up_blocks_2_resnets_1_conv_shortcut_bias_to_fp16, dilations = var_3443, groups = var_3126, pad = x_19_pad_0, pad_type = x_19_pad_type_0, strides = var_3441, weight = up_blocks_2_resnets_1_conv_shortcut_weight_to_fp16_palettized, x = input_393_cast)[name = tensor("x_19_cast")]; + tensor hidden_states_243_cast = add(x = x_19_cast, y = hidden_states_241_cast)[name = tensor("hidden_states_243_cast")]; + tensor reshape_188_shape_0 = const()[name = tensor("reshape_188_shape_0"), val = tensor([2, 32, 20, 32, 32])]; + tensor reshape_188_cast = reshape(shape = reshape_188_shape_0, x = hidden_states_243_cast)[name = tensor("reshape_188_cast")]; + tensor reduce_mean_141_axes_0 = const()[name = tensor("reduce_mean_141_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_141_keep_dims_0 = const()[name = tensor("reduce_mean_141_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_141_cast = reduce_mean(axes = reduce_mean_141_axes_0, keep_dims = reduce_mean_141_keep_dims_0, x = reshape_188_cast)[name = tensor("reduce_mean_141_cast")]; + tensor sub_94_cast = sub(x = reshape_188_cast, y = reduce_mean_141_cast)[name = tensor("sub_94_cast")]; + tensor square_47_cast = square(x = sub_94_cast)[name = tensor("square_47_cast")]; + tensor reduce_mean_143_axes_0 = const()[name = tensor("reduce_mean_143_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_143_keep_dims_0 = const()[name = tensor("reduce_mean_143_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_143_cast = reduce_mean(axes = reduce_mean_143_axes_0, keep_dims = reduce_mean_143_keep_dims_0, x = square_47_cast)[name = tensor("reduce_mean_143_cast")]; + tensor add_94_y_0_to_fp16 = const()[name = tensor("add_94_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_94_cast = add(x = reduce_mean_143_cast, y = add_94_y_0_to_fp16)[name = tensor("add_94_cast")]; + tensor sqrt_47_cast = sqrt(x = add_94_cast)[name = tensor("sqrt_47_cast")]; + tensor real_div_47_cast = real_div(x = sub_94_cast, y = sqrt_47_cast)[name = tensor("real_div_47_cast")]; + tensor reshape_189_shape_0 = const()[name = tensor("reshape_189_shape_0"), val = tensor([2, 640, 32, 32])]; + tensor reshape_189_cast = reshape(shape = reshape_189_shape_0, x = real_div_47_cast)[name = tensor("reshape_189_cast")]; + tensor add_95_gamma_0_to_fp16 = const()[name = tensor("add_95_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(606421440)))]; + tensor add_95_beta_0_to_fp16 = const()[name = tensor("add_95_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(606422784)))]; + tensor add_95_epsilon_0_to_fp16 = const()[name = tensor("add_95_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_95_cast = batch_norm(beta = add_95_beta_0_to_fp16, epsilon = add_95_epsilon_0_to_fp16, gamma = add_95_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_189_cast)[name = tensor("add_95_cast")]; + tensor var_3463 = const()[name = tensor("op_3463"), val = tensor([1, 1])]; + tensor var_3465 = const()[name = tensor("op_3465"), val = tensor([1, 1])]; + tensor hidden_states_245_pad_type_0 = const()[name = tensor("hidden_states_245_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_245_pad_0 = const()[name = tensor("hidden_states_245_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(606424128))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(606731392))), name = tensor("up_blocks_2_attentions_1_proj_in_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor up_blocks_2_attentions_1_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(606731584)))]; + tensor hidden_states_245_cast = conv(bias = up_blocks_2_attentions_1_proj_in_bias_to_fp16, dilations = var_3465, groups = var_3126, pad = hidden_states_245_pad_0, pad_type = hidden_states_245_pad_type_0, strides = var_3463, weight = up_blocks_2_attentions_1_proj_in_weight_to_fp16_palettized, x = add_95_cast)[name = tensor("hidden_states_245_cast")]; + tensor var_3470 = const()[name = tensor("op_3470"), val = tensor([2, 640, 1, 1024])]; + tensor inputs_67_cast = reshape(shape = var_3470, x = hidden_states_245_cast)[name = tensor("inputs_67_cast")]; + tensor var_3480 = const()[name = tensor("op_3480"), val = tensor([1])]; + tensor channels_mean_67_cast = reduce_mean(axes = var_3480, keep_dims = var_3121, x = inputs_67_cast)[name = tensor("channels_mean_67_cast")]; + tensor zero_mean_67_cast = sub(x = inputs_67_cast, y = channels_mean_67_cast)[name = tensor("zero_mean_67_cast")]; + tensor zero_mean_sq_67_cast = mul(x = zero_mean_67_cast, y = zero_mean_67_cast)[name = tensor("zero_mean_sq_67_cast")]; + tensor var_3484 = const()[name = tensor("op_3484"), val = tensor([1])]; + tensor var_3485_cast = reduce_mean(axes = var_3484, keep_dims = var_3121, x = zero_mean_sq_67_cast)[name = tensor("op_3485_cast")]; + tensor var_3486_to_fp16 = const()[name = tensor("op_3486_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3487_cast = add(x = var_3485_cast, y = var_3486_to_fp16)[name = tensor("op_3487_cast")]; + tensor denom_67_epsilon_0_to_fp16 = const()[name = tensor("denom_67_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_67_cast = rsqrt(epsilon = denom_67_epsilon_0_to_fp16, x = var_3487_cast)[name = tensor("denom_67_cast")]; + tensor out_67_cast = mul(x = zero_mean_67_cast, y = denom_67_cast)[name = tensor("out_67_cast")]; + tensor var_3491_to_fp16 = const()[name = tensor("op_3491_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(606732928)))]; + tensor var_3492_cast = add(x = out_67_cast, y = var_3491_to_fp16)[name = tensor("op_3492_cast")]; + tensor var_3494_to_fp16 = const()[name = tensor("op_3494_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(606734272)))]; + tensor hidden_states_247_cast = mul(x = var_3492_cast, y = var_3494_to_fp16)[name = tensor("hidden_states_247_cast")]; + tensor var_3501 = const()[name = tensor("op_3501"), val = tensor([1, 1])]; + tensor var_3503 = const()[name = tensor("op_3503"), val = tensor([1, 1])]; + tensor q_45_pad_type_0 = const()[name = tensor("q_45_pad_type_0"), val = tensor("custom")]; + tensor q_45_pad_0 = const()[name = tensor("q_45_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(606735616))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(607042880))), name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_45_cast = conv(dilations = var_3503, groups = var_3126, pad = q_45_pad_0, pad_type = q_45_pad_type_0, strides = var_3501, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_247_cast)[name = tensor("q_45_cast")]; + tensor var_3507 = const()[name = tensor("op_3507"), val = tensor([1, 1])]; + tensor var_3509 = const()[name = tensor("op_3509"), val = tensor([1, 1])]; + tensor k_45_pad_type_0 = const()[name = tensor("k_45_pad_type_0"), val = tensor("custom")]; + tensor k_45_pad_0 = const()[name = tensor("k_45_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(607043072))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(607350336))), name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor k_45_cast = conv(dilations = var_3509, groups = var_3126, pad = k_45_pad_0, pad_type = k_45_pad_type_0, strides = var_3507, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_247_cast)[name = tensor("k_45_cast")]; + tensor var_3513 = const()[name = tensor("op_3513"), val = tensor([1, 1])]; + tensor var_3515 = const()[name = tensor("op_3515"), val = tensor([1, 1])]; + tensor v_45_pad_type_0 = const()[name = tensor("v_45_pad_type_0"), val = tensor("custom")]; + tensor v_45_pad_0 = const()[name = tensor("v_45_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(607350528))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(607657792))), name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor v_45_cast = conv(dilations = var_3515, groups = var_3126, pad = v_45_pad_0, pad_type = v_45_pad_type_0, strides = var_3513, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_247_cast)[name = tensor("v_45_cast")]; + tensor var_3519 = const()[name = tensor("op_3519"), val = tensor([2, 8, 80, -1])]; + tensor var_3520_cast = reshape(shape = var_3519, x = q_45_cast)[name = tensor("op_3520_cast")]; + tensor var_3521 = const()[name = tensor("op_3521"), val = tensor([2, 8, 80, -1])]; + tensor var_3522_cast = reshape(shape = var_3521, x = k_45_cast)[name = tensor("op_3522_cast")]; + tensor var_3523 = const()[name = tensor("op_3523"), val = tensor([2, 8, 80, -1])]; + tensor var_3524_cast = reshape(shape = var_3523, x = v_45_cast)[name = tensor("op_3524_cast")]; + tensor attn_weights_89_transpose_x_0 = const()[name = tensor("attn_weights_89_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_89_transpose_y_0 = const()[name = tensor("attn_weights_89_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_89_cast = matmul(transpose_x = attn_weights_89_transpose_x_0, transpose_y = attn_weights_89_transpose_y_0, x = var_3520_cast, y = var_3522_cast)[name = tensor("attn_weights_89_cast")]; + tensor attn_weights_91_cast = mul(x = attn_weights_89_cast, y = var_3117_to_fp16)[name = tensor("attn_weights_91_cast")]; + tensor var_3528_cast = softmax(axis = var_3110, x = attn_weights_91_cast)[name = tensor("op_3528_cast")]; + tensor attn_45_transpose_x_0 = const()[name = tensor("attn_45_transpose_x_0"), val = tensor(false)]; + tensor attn_45_transpose_y_0 = const()[name = tensor("attn_45_transpose_y_0"), val = tensor(true)]; + tensor attn_45_cast = matmul(transpose_x = attn_45_transpose_x_0, transpose_y = attn_45_transpose_y_0, x = var_3524_cast, y = var_3528_cast)[name = tensor("attn_45_cast")]; + tensor var_3532 = const()[name = tensor("op_3532"), val = tensor([2, 640, 1, -1])]; + tensor input_409_cast = reshape(shape = var_3532, x = attn_45_cast)[name = tensor("input_409_cast")]; + tensor var_3537 = const()[name = tensor("op_3537"), val = tensor([1, 1])]; + tensor var_3539 = const()[name = tensor("op_3539"), val = tensor([1, 1])]; + tensor var_3541_pad_type_0 = const()[name = tensor("op_3541_pad_type_0"), val = tensor("custom")]; + tensor var_3541_pad_0 = const()[name = tensor("op_3541_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(607657984))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(607965248))), name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(607965440)))]; + tensor var_3541_cast = conv(bias = up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_3539, groups = var_3126, pad = var_3541_pad_0, pad_type = var_3541_pad_type_0, strides = var_3537, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_409_cast)[name = tensor("op_3541_cast")]; + tensor inputs_69_cast = add(x = var_3541_cast, y = inputs_67_cast)[name = tensor("inputs_69_cast")]; + tensor var_3545 = const()[name = tensor("op_3545"), val = tensor([1])]; + tensor channels_mean_69_cast = reduce_mean(axes = var_3545, keep_dims = var_3121, x = inputs_69_cast)[name = tensor("channels_mean_69_cast")]; + tensor zero_mean_69_cast = sub(x = inputs_69_cast, y = channels_mean_69_cast)[name = tensor("zero_mean_69_cast")]; + tensor zero_mean_sq_69_cast = mul(x = zero_mean_69_cast, y = zero_mean_69_cast)[name = tensor("zero_mean_sq_69_cast")]; + tensor var_3549 = const()[name = tensor("op_3549"), val = tensor([1])]; + tensor var_3550_cast = reduce_mean(axes = var_3549, keep_dims = var_3121, x = zero_mean_sq_69_cast)[name = tensor("op_3550_cast")]; + tensor var_3551_to_fp16 = const()[name = tensor("op_3551_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3552_cast = add(x = var_3550_cast, y = var_3551_to_fp16)[name = tensor("op_3552_cast")]; + tensor denom_69_epsilon_0_to_fp16 = const()[name = tensor("denom_69_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_69_cast = rsqrt(epsilon = denom_69_epsilon_0_to_fp16, x = var_3552_cast)[name = tensor("denom_69_cast")]; + tensor out_69_cast = mul(x = zero_mean_69_cast, y = denom_69_cast)[name = tensor("out_69_cast")]; + tensor var_3556_to_fp16 = const()[name = tensor("op_3556_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(607966784)))]; + tensor var_3557_cast = add(x = out_69_cast, y = var_3556_to_fp16)[name = tensor("op_3557_cast")]; + tensor var_3559_to_fp16 = const()[name = tensor("op_3559_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(607968128)))]; + tensor hidden_states_249_cast = mul(x = var_3557_cast, y = var_3559_to_fp16)[name = tensor("hidden_states_249_cast")]; + tensor var_3566 = const()[name = tensor("op_3566"), val = tensor([1, 1])]; + tensor var_3568 = const()[name = tensor("op_3568"), val = tensor([1, 1])]; + tensor q_47_pad_type_0 = const()[name = tensor("q_47_pad_type_0"), val = tensor("custom")]; + tensor q_47_pad_0 = const()[name = tensor("q_47_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(607969472))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(608276736))), name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_47_cast = conv(dilations = var_3568, groups = var_3126, pad = q_47_pad_0, pad_type = q_47_pad_type_0, strides = var_3566, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_249_cast)[name = tensor("q_47_cast")]; + tensor var_3572 = const()[name = tensor("op_3572"), val = tensor([1, 1])]; + tensor var_3574 = const()[name = tensor("op_3574"), val = tensor([1, 1])]; + tensor k_47_pad_type_0 = const()[name = tensor("k_47_pad_type_0"), val = tensor("custom")]; + tensor k_47_pad_0 = const()[name = tensor("k_47_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(608276928))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(608645632))), name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([640, 768, 1, 1])]; + tensor k_47_cast = conv(dilations = var_3574, groups = var_3126, pad = k_47_pad_0, pad_type = k_47_pad_type_0, strides = var_3572, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_47_cast")]; + tensor var_3578 = const()[name = tensor("op_3578"), val = tensor([1, 1])]; + tensor var_3580 = const()[name = tensor("op_3580"), val = tensor([1, 1])]; + tensor v_47_pad_type_0 = const()[name = tensor("v_47_pad_type_0"), val = tensor("custom")]; + tensor v_47_pad_0 = const()[name = tensor("v_47_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(608645824))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(609014528))), name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([640, 768, 1, 1])]; + tensor v_47_cast = conv(dilations = var_3580, groups = var_3126, pad = v_47_pad_0, pad_type = v_47_pad_type_0, strides = var_3578, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_47_cast")]; + tensor var_3584 = const()[name = tensor("op_3584"), val = tensor([2, 8, 80, -1])]; + tensor var_3585_cast = reshape(shape = var_3584, x = q_47_cast)[name = tensor("op_3585_cast")]; + tensor var_3586 = const()[name = tensor("op_3586"), val = tensor([2, 8, 80, -1])]; + tensor var_3587_cast = reshape(shape = var_3586, x = k_47_cast)[name = tensor("op_3587_cast")]; + tensor var_3588 = const()[name = tensor("op_3588"), val = tensor([2, 8, 80, -1])]; + tensor var_3589_cast = reshape(shape = var_3588, x = v_47_cast)[name = tensor("op_3589_cast")]; + tensor attn_weights_93_transpose_x_0 = const()[name = tensor("attn_weights_93_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_93_transpose_y_0 = const()[name = tensor("attn_weights_93_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_93_cast = matmul(transpose_x = attn_weights_93_transpose_x_0, transpose_y = attn_weights_93_transpose_y_0, x = var_3585_cast, y = var_3587_cast)[name = tensor("attn_weights_93_cast")]; + tensor attn_weights_95_cast = mul(x = attn_weights_93_cast, y = var_3117_to_fp16)[name = tensor("attn_weights_95_cast")]; + tensor var_3593_cast = softmax(axis = var_3110, x = attn_weights_95_cast)[name = tensor("op_3593_cast")]; + tensor attn_47_transpose_x_0 = const()[name = tensor("attn_47_transpose_x_0"), val = tensor(false)]; + tensor attn_47_transpose_y_0 = const()[name = tensor("attn_47_transpose_y_0"), val = tensor(true)]; + tensor attn_47_cast = matmul(transpose_x = attn_47_transpose_x_0, transpose_y = attn_47_transpose_y_0, x = var_3589_cast, y = var_3593_cast)[name = tensor("attn_47_cast")]; + tensor var_3597 = const()[name = tensor("op_3597"), val = tensor([2, 640, 1, -1])]; + tensor input_411_cast = reshape(shape = var_3597, x = attn_47_cast)[name = tensor("input_411_cast")]; + tensor var_3602 = const()[name = tensor("op_3602"), val = tensor([1, 1])]; + tensor var_3604 = const()[name = tensor("op_3604"), val = tensor([1, 1])]; + tensor var_3606_pad_type_0 = const()[name = tensor("op_3606_pad_type_0"), val = tensor("custom")]; + tensor var_3606_pad_0 = const()[name = tensor("op_3606_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(609014720))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(609321984))), name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(609322176)))]; + tensor var_3606_cast = conv(bias = up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_3604, groups = var_3126, pad = var_3606_pad_0, pad_type = var_3606_pad_type_0, strides = var_3602, weight = up_blocks_2_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_411_cast)[name = tensor("op_3606_cast")]; + tensor inputs_71_cast = add(x = var_3606_cast, y = inputs_69_cast)[name = tensor("inputs_71_cast")]; + tensor var_3610 = const()[name = tensor("op_3610"), val = tensor([1])]; + tensor channels_mean_71_cast = reduce_mean(axes = var_3610, keep_dims = var_3121, x = inputs_71_cast)[name = tensor("channels_mean_71_cast")]; + tensor zero_mean_71_cast = sub(x = inputs_71_cast, y = channels_mean_71_cast)[name = tensor("zero_mean_71_cast")]; + tensor zero_mean_sq_71_cast = mul(x = zero_mean_71_cast, y = zero_mean_71_cast)[name = tensor("zero_mean_sq_71_cast")]; + tensor var_3614 = const()[name = tensor("op_3614"), val = tensor([1])]; + tensor var_3615_cast = reduce_mean(axes = var_3614, keep_dims = var_3121, x = zero_mean_sq_71_cast)[name = tensor("op_3615_cast")]; + tensor var_3616_to_fp16 = const()[name = tensor("op_3616_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3617_cast = add(x = var_3615_cast, y = var_3616_to_fp16)[name = tensor("op_3617_cast")]; + tensor denom_71_epsilon_0_to_fp16 = const()[name = tensor("denom_71_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_71_cast = rsqrt(epsilon = denom_71_epsilon_0_to_fp16, x = var_3617_cast)[name = tensor("denom_71_cast")]; + tensor out_71_cast = mul(x = zero_mean_71_cast, y = denom_71_cast)[name = tensor("out_71_cast")]; + tensor var_3621_to_fp16 = const()[name = tensor("op_3621_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(609323520)))]; + tensor var_3622_cast = add(x = out_71_cast, y = var_3621_to_fp16)[name = tensor("op_3622_cast")]; + tensor var_3624_to_fp16 = const()[name = tensor("op_3624_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(609324864)))]; + tensor input_413_cast = mul(x = var_3622_cast, y = var_3624_to_fp16)[name = tensor("input_413_cast")]; + tensor var_3632 = const()[name = tensor("op_3632"), val = tensor([1, 1])]; + tensor var_3634 = const()[name = tensor("op_3634"), val = tensor([1, 1])]; + tensor var_3636_pad_type_0 = const()[name = tensor("op_3636_pad_type_0"), val = tensor("custom")]; + tensor var_3636_pad_0 = const()[name = tensor("op_3636_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(609326208))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(611783872))), name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([5120, 640, 1, 1])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(611784064))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(611787968))), name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([5120])]; + tensor var_3636_cast = conv(bias = up_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_3634, groups = var_3126, pad = var_3636_pad_0, pad_type = var_3636_pad_type_0, strides = var_3632, weight = up_blocks_2_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_413_cast)[name = tensor("op_3636_cast")]; + tensor var_3637_split_sizes_0 = const()[name = tensor("op_3637_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_3637_axis_0 = const()[name = tensor("op_3637_axis_0"), val = tensor(1)]; + tensor var_3637_cast_0, tensor var_3637_cast_1 = split(axis = var_3637_axis_0, split_sizes = var_3637_split_sizes_0, x = var_3636_cast)[name = tensor("op_3637_cast")]; + tensor var_3639_mode_0 = const()[name = tensor("op_3639_mode_0"), val = tensor("EXACT")]; + tensor var_3639_cast = gelu(mode = var_3639_mode_0, x = var_3637_cast_1)[name = tensor("op_3639_cast")]; + tensor input_415_cast = mul(x = var_3637_cast_0, y = var_3639_cast)[name = tensor("input_415_cast")]; + tensor var_3643 = const()[name = tensor("op_3643"), val = tensor([1, 1])]; + tensor var_3645 = const()[name = tensor("op_3645"), val = tensor([1, 1])]; + tensor var_3647_pad_type_0 = const()[name = tensor("op_3647_pad_type_0"), val = tensor("custom")]; + tensor var_3647_pad_0 = const()[name = tensor("op_3647_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(611788160))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(613017024))), name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([640, 2560, 1, 1])]; + tensor up_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(613017216)))]; + tensor var_3647_cast = conv(bias = up_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_3645, groups = var_3126, pad = var_3647_pad_0, pad_type = var_3647_pad_type_0, strides = var_3643, weight = up_blocks_2_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_415_cast)[name = tensor("op_3647_cast")]; + tensor hidden_states_253_cast = add(x = var_3647_cast, y = inputs_71_cast)[name = tensor("hidden_states_253_cast")]; + tensor var_3649 = const()[name = tensor("op_3649"), val = tensor([2, 640, 32, 32])]; + tensor input_417_cast = reshape(shape = var_3649, x = hidden_states_253_cast)[name = tensor("input_417_cast")]; + tensor var_3653 = const()[name = tensor("op_3653"), val = tensor([1, 1])]; + tensor var_3655 = const()[name = tensor("op_3655"), val = tensor([1, 1])]; + tensor hidden_states_255_pad_type_0 = const()[name = tensor("hidden_states_255_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_255_pad_0 = const()[name = tensor("hidden_states_255_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_1_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(613018560))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(613325824))), name = tensor("up_blocks_2_attentions_1_proj_out_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor up_blocks_2_attentions_1_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_1_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(613326016)))]; + tensor hidden_states_255_cast = conv(bias = up_blocks_2_attentions_1_proj_out_bias_to_fp16, dilations = var_3655, groups = var_3126, pad = hidden_states_255_pad_0, pad_type = hidden_states_255_pad_type_0, strides = var_3653, weight = up_blocks_2_attentions_1_proj_out_weight_to_fp16_palettized, x = input_417_cast)[name = tensor("hidden_states_255_cast")]; + tensor hidden_states_257_cast = add(x = hidden_states_255_cast, y = hidden_states_243_cast)[name = tensor("hidden_states_257_cast")]; + tensor input_419_interleave_0 = const()[name = tensor("input_419_interleave_0"), val = tensor(false)]; + tensor input_419_cast = concat(axis = var_3126, interleave = input_419_interleave_0, values = (hidden_states_257_cast, input_63_cast))[name = tensor("input_419_cast")]; + tensor reshape_192_shape_0 = const()[name = tensor("reshape_192_shape_0"), val = tensor([2, 32, 30, 32, 32])]; + tensor reshape_192_cast = reshape(shape = reshape_192_shape_0, x = input_419_cast)[name = tensor("reshape_192_cast")]; + tensor reduce_mean_144_axes_0 = const()[name = tensor("reduce_mean_144_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_144_keep_dims_0 = const()[name = tensor("reduce_mean_144_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_144_cast = reduce_mean(axes = reduce_mean_144_axes_0, keep_dims = reduce_mean_144_keep_dims_0, x = reshape_192_cast)[name = tensor("reduce_mean_144_cast")]; + tensor sub_96_cast = sub(x = reshape_192_cast, y = reduce_mean_144_cast)[name = tensor("sub_96_cast")]; + tensor square_48_cast = square(x = sub_96_cast)[name = tensor("square_48_cast")]; + tensor reduce_mean_146_axes_0 = const()[name = tensor("reduce_mean_146_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_146_keep_dims_0 = const()[name = tensor("reduce_mean_146_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_146_cast = reduce_mean(axes = reduce_mean_146_axes_0, keep_dims = reduce_mean_146_keep_dims_0, x = square_48_cast)[name = tensor("reduce_mean_146_cast")]; + tensor add_96_y_0_to_fp16 = const()[name = tensor("add_96_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_96_cast = add(x = reduce_mean_146_cast, y = add_96_y_0_to_fp16)[name = tensor("add_96_cast")]; + tensor sqrt_48_cast = sqrt(x = add_96_cast)[name = tensor("sqrt_48_cast")]; + tensor real_div_48_cast = real_div(x = sub_96_cast, y = sqrt_48_cast)[name = tensor("real_div_48_cast")]; + tensor reshape_193_shape_0 = const()[name = tensor("reshape_193_shape_0"), val = tensor([2, 960, 32, 32])]; + tensor reshape_193_cast = reshape(shape = reshape_193_shape_0, x = real_div_48_cast)[name = tensor("reshape_193_cast")]; + tensor add_97_mean_0_to_fp16 = const()[name = tensor("add_97_mean_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(613327360)))]; + tensor add_97_variance_0_to_fp16 = const()[name = tensor("add_97_variance_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(613329344)))]; + tensor add_97_gamma_0_to_fp16 = const()[name = tensor("add_97_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(613331328)))]; + tensor add_97_beta_0_to_fp16 = const()[name = tensor("add_97_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(613333312)))]; + tensor add_97_epsilon_0_to_fp16 = const()[name = tensor("add_97_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_97_cast = batch_norm(beta = add_97_beta_0_to_fp16, epsilon = add_97_epsilon_0_to_fp16, gamma = add_97_gamma_0_to_fp16, mean = add_97_mean_0_to_fp16, variance = add_97_variance_0_to_fp16, x = reshape_193_cast)[name = tensor("add_97_cast")]; + tensor input_423_cast = silu(x = add_97_cast)[name = tensor("input_423_cast")]; + tensor var_3673 = const()[name = tensor("op_3673"), val = tensor([1, 1])]; + tensor var_3675 = const()[name = tensor("op_3675"), val = tensor([1, 1])]; + tensor hidden_states_259_pad_type_0 = const()[name = tensor("hidden_states_259_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_259_pad_0 = const()[name = tensor("hidden_states_259_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_2_resnets_2_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(613335296))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(617482560))), name = tensor("up_blocks_2_resnets_2_conv1_weight_to_fp16_palettized"), shape = tensor([640, 960, 3, 3])]; + tensor up_blocks_2_resnets_2_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_2_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(617482752)))]; + tensor hidden_states_259_cast = conv(bias = up_blocks_2_resnets_2_conv1_bias_to_fp16, dilations = var_3675, groups = var_3126, pad = hidden_states_259_pad_0, pad_type = hidden_states_259_pad_type_0, strides = var_3673, weight = up_blocks_2_resnets_2_conv1_weight_to_fp16_palettized, x = input_423_cast)[name = tensor("hidden_states_259_cast")]; + tensor var_3681 = const()[name = tensor("op_3681"), val = tensor([1, 1])]; + tensor var_3683 = const()[name = tensor("op_3683"), val = tensor([1, 1])]; + tensor temb_37_pad_type_0 = const()[name = tensor("temb_37_pad_type_0"), val = tensor("custom")]; + tensor temb_37_pad_0 = const()[name = tensor("temb_37_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_resnets_2_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(617484096))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(618098560))), name = tensor("up_blocks_2_resnets_2_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([640, 1280, 1, 1])]; + tensor up_blocks_2_resnets_2_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_2_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(618098752)))]; + tensor temb_37_cast = conv(bias = up_blocks_2_resnets_2_time_emb_proj_bias_to_fp16, dilations = var_3683, groups = var_3126, pad = temb_37_pad_0, pad_type = temb_37_pad_type_0, strides = var_3681, weight = up_blocks_2_resnets_2_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor("temb_37_cast")]; + tensor input_427_cast = add(x = hidden_states_259_cast, y = temb_37_cast)[name = tensor("input_427_cast")]; + tensor reshape_196_shape_0 = const()[name = tensor("reshape_196_shape_0"), val = tensor([2, 32, 20, 32, 32])]; + tensor reshape_196_cast = reshape(shape = reshape_196_shape_0, x = input_427_cast)[name = tensor("reshape_196_cast")]; + tensor reduce_mean_147_axes_0 = const()[name = tensor("reduce_mean_147_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_147_keep_dims_0 = const()[name = tensor("reduce_mean_147_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_147_cast = reduce_mean(axes = reduce_mean_147_axes_0, keep_dims = reduce_mean_147_keep_dims_0, x = reshape_196_cast)[name = tensor("reduce_mean_147_cast")]; + tensor sub_98_cast = sub(x = reshape_196_cast, y = reduce_mean_147_cast)[name = tensor("sub_98_cast")]; + tensor square_49_cast = square(x = sub_98_cast)[name = tensor("square_49_cast")]; + tensor reduce_mean_149_axes_0 = const()[name = tensor("reduce_mean_149_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_149_keep_dims_0 = const()[name = tensor("reduce_mean_149_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_149_cast = reduce_mean(axes = reduce_mean_149_axes_0, keep_dims = reduce_mean_149_keep_dims_0, x = square_49_cast)[name = tensor("reduce_mean_149_cast")]; + tensor add_98_y_0_to_fp16 = const()[name = tensor("add_98_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_98_cast = add(x = reduce_mean_149_cast, y = add_98_y_0_to_fp16)[name = tensor("add_98_cast")]; + tensor sqrt_49_cast = sqrt(x = add_98_cast)[name = tensor("sqrt_49_cast")]; + tensor real_div_49_cast = real_div(x = sub_98_cast, y = sqrt_49_cast)[name = tensor("real_div_49_cast")]; + tensor reshape_197_shape_0 = const()[name = tensor("reshape_197_shape_0"), val = tensor([2, 640, 32, 32])]; + tensor reshape_197_cast = reshape(shape = reshape_197_shape_0, x = real_div_49_cast)[name = tensor("reshape_197_cast")]; + tensor add_99_gamma_0_to_fp16 = const()[name = tensor("add_99_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(618100096)))]; + tensor add_99_beta_0_to_fp16 = const()[name = tensor("add_99_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(618101440)))]; + tensor add_99_epsilon_0_to_fp16 = const()[name = tensor("add_99_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_99_cast = batch_norm(beta = add_99_beta_0_to_fp16, epsilon = add_99_epsilon_0_to_fp16, gamma = add_99_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_197_cast)[name = tensor("add_99_cast")]; + tensor input_431_cast = silu(x = add_99_cast)[name = tensor("input_431_cast")]; + tensor var_3693 = const()[name = tensor("op_3693"), val = tensor([1, 1])]; + tensor var_3695 = const()[name = tensor("op_3695"), val = tensor([1, 1])]; + tensor hidden_states_261_pad_type_0 = const()[name = tensor("hidden_states_261_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_261_pad_0 = const()[name = tensor("hidden_states_261_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_2_resnets_2_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(618102784))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(620867648))), name = tensor("up_blocks_2_resnets_2_conv2_weight_to_fp16_palettized"), shape = tensor([640, 640, 3, 3])]; + tensor up_blocks_2_resnets_2_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_2_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(620867840)))]; + tensor hidden_states_261_cast = conv(bias = up_blocks_2_resnets_2_conv2_bias_to_fp16, dilations = var_3695, groups = var_3126, pad = hidden_states_261_pad_0, pad_type = hidden_states_261_pad_type_0, strides = var_3693, weight = up_blocks_2_resnets_2_conv2_weight_to_fp16_palettized, x = input_431_cast)[name = tensor("hidden_states_261_cast")]; + tensor var_3700 = const()[name = tensor("op_3700"), val = tensor([1, 1])]; + tensor var_3702 = const()[name = tensor("op_3702"), val = tensor([1, 1])]; + tensor x_21_pad_type_0 = const()[name = tensor("x_21_pad_type_0"), val = tensor("custom")]; + tensor x_21_pad_0 = const()[name = tensor("x_21_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_resnets_2_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(620869184))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(621330048))), name = tensor("up_blocks_2_resnets_2_conv_shortcut_weight_to_fp16_palettized"), shape = tensor([640, 960, 1, 1])]; + tensor up_blocks_2_resnets_2_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_2_resnets_2_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(621330240)))]; + tensor x_21_cast = conv(bias = up_blocks_2_resnets_2_conv_shortcut_bias_to_fp16, dilations = var_3702, groups = var_3126, pad = x_21_pad_0, pad_type = x_21_pad_type_0, strides = var_3700, weight = up_blocks_2_resnets_2_conv_shortcut_weight_to_fp16_palettized, x = input_419_cast)[name = tensor("x_21_cast")]; + tensor hidden_states_263_cast = add(x = x_21_cast, y = hidden_states_261_cast)[name = tensor("hidden_states_263_cast")]; + tensor reshape_200_shape_0 = const()[name = tensor("reshape_200_shape_0"), val = tensor([2, 32, 20, 32, 32])]; + tensor reshape_200_cast = reshape(shape = reshape_200_shape_0, x = hidden_states_263_cast)[name = tensor("reshape_200_cast")]; + tensor reduce_mean_150_axes_0 = const()[name = tensor("reduce_mean_150_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_150_keep_dims_0 = const()[name = tensor("reduce_mean_150_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_150_cast = reduce_mean(axes = reduce_mean_150_axes_0, keep_dims = reduce_mean_150_keep_dims_0, x = reshape_200_cast)[name = tensor("reduce_mean_150_cast")]; + tensor sub_100_cast = sub(x = reshape_200_cast, y = reduce_mean_150_cast)[name = tensor("sub_100_cast")]; + tensor square_50_cast = square(x = sub_100_cast)[name = tensor("square_50_cast")]; + tensor reduce_mean_152_axes_0 = const()[name = tensor("reduce_mean_152_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_152_keep_dims_0 = const()[name = tensor("reduce_mean_152_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_152_cast = reduce_mean(axes = reduce_mean_152_axes_0, keep_dims = reduce_mean_152_keep_dims_0, x = square_50_cast)[name = tensor("reduce_mean_152_cast")]; + tensor add_100_y_0_to_fp16 = const()[name = tensor("add_100_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_100_cast = add(x = reduce_mean_152_cast, y = add_100_y_0_to_fp16)[name = tensor("add_100_cast")]; + tensor sqrt_50_cast = sqrt(x = add_100_cast)[name = tensor("sqrt_50_cast")]; + tensor real_div_50_cast = real_div(x = sub_100_cast, y = sqrt_50_cast)[name = tensor("real_div_50_cast")]; + tensor reshape_201_shape_0 = const()[name = tensor("reshape_201_shape_0"), val = tensor([2, 640, 32, 32])]; + tensor reshape_201_cast = reshape(shape = reshape_201_shape_0, x = real_div_50_cast)[name = tensor("reshape_201_cast")]; + tensor add_101_gamma_0_to_fp16 = const()[name = tensor("add_101_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(621331584)))]; + tensor add_101_beta_0_to_fp16 = const()[name = tensor("add_101_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(621332928)))]; + tensor add_101_epsilon_0_to_fp16 = const()[name = tensor("add_101_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_101_cast = batch_norm(beta = add_101_beta_0_to_fp16, epsilon = add_101_epsilon_0_to_fp16, gamma = add_101_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_201_cast)[name = tensor("add_101_cast")]; + tensor var_3722 = const()[name = tensor("op_3722"), val = tensor([1, 1])]; + tensor var_3724 = const()[name = tensor("op_3724"), val = tensor([1, 1])]; + tensor hidden_states_265_pad_type_0 = const()[name = tensor("hidden_states_265_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_265_pad_0 = const()[name = tensor("hidden_states_265_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(621334272))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(621641536))), name = tensor("up_blocks_2_attentions_2_proj_in_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor up_blocks_2_attentions_2_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(621641728)))]; + tensor hidden_states_265_cast = conv(bias = up_blocks_2_attentions_2_proj_in_bias_to_fp16, dilations = var_3724, groups = var_3126, pad = hidden_states_265_pad_0, pad_type = hidden_states_265_pad_type_0, strides = var_3722, weight = up_blocks_2_attentions_2_proj_in_weight_to_fp16_palettized, x = add_101_cast)[name = tensor("hidden_states_265_cast")]; + tensor var_3729 = const()[name = tensor("op_3729"), val = tensor([2, 640, 1, 1024])]; + tensor inputs_73_cast = reshape(shape = var_3729, x = hidden_states_265_cast)[name = tensor("inputs_73_cast")]; + tensor var_3739 = const()[name = tensor("op_3739"), val = tensor([1])]; + tensor channels_mean_73_cast = reduce_mean(axes = var_3739, keep_dims = var_3121, x = inputs_73_cast)[name = tensor("channels_mean_73_cast")]; + tensor zero_mean_73_cast = sub(x = inputs_73_cast, y = channels_mean_73_cast)[name = tensor("zero_mean_73_cast")]; + tensor zero_mean_sq_73_cast = mul(x = zero_mean_73_cast, y = zero_mean_73_cast)[name = tensor("zero_mean_sq_73_cast")]; + tensor var_3743 = const()[name = tensor("op_3743"), val = tensor([1])]; + tensor var_3744_cast = reduce_mean(axes = var_3743, keep_dims = var_3121, x = zero_mean_sq_73_cast)[name = tensor("op_3744_cast")]; + tensor var_3745_to_fp16 = const()[name = tensor("op_3745_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3746_cast = add(x = var_3744_cast, y = var_3745_to_fp16)[name = tensor("op_3746_cast")]; + tensor denom_73_epsilon_0_to_fp16 = const()[name = tensor("denom_73_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_73_cast = rsqrt(epsilon = denom_73_epsilon_0_to_fp16, x = var_3746_cast)[name = tensor("denom_73_cast")]; + tensor out_73_cast = mul(x = zero_mean_73_cast, y = denom_73_cast)[name = tensor("out_73_cast")]; + tensor var_3750_to_fp16 = const()[name = tensor("op_3750_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(621643072)))]; + tensor var_3751_cast = add(x = out_73_cast, y = var_3750_to_fp16)[name = tensor("op_3751_cast")]; + tensor var_3753_to_fp16 = const()[name = tensor("op_3753_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(621644416)))]; + tensor hidden_states_267_cast = mul(x = var_3751_cast, y = var_3753_to_fp16)[name = tensor("hidden_states_267_cast")]; + tensor var_3760 = const()[name = tensor("op_3760"), val = tensor([1, 1])]; + tensor var_3762 = const()[name = tensor("op_3762"), val = tensor([1, 1])]; + tensor q_49_pad_type_0 = const()[name = tensor("q_49_pad_type_0"), val = tensor("custom")]; + tensor q_49_pad_0 = const()[name = tensor("q_49_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(621645760))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(621953024))), name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_49_cast = conv(dilations = var_3762, groups = var_3126, pad = q_49_pad_0, pad_type = q_49_pad_type_0, strides = var_3760, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_267_cast)[name = tensor("q_49_cast")]; + tensor var_3766 = const()[name = tensor("op_3766"), val = tensor([1, 1])]; + tensor var_3768 = const()[name = tensor("op_3768"), val = tensor([1, 1])]; + tensor k_49_pad_type_0 = const()[name = tensor("k_49_pad_type_0"), val = tensor("custom")]; + tensor k_49_pad_0 = const()[name = tensor("k_49_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(621953216))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(622260480))), name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor k_49_cast = conv(dilations = var_3768, groups = var_3126, pad = k_49_pad_0, pad_type = k_49_pad_type_0, strides = var_3766, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_267_cast)[name = tensor("k_49_cast")]; + tensor var_3772 = const()[name = tensor("op_3772"), val = tensor([1, 1])]; + tensor var_3774 = const()[name = tensor("op_3774"), val = tensor([1, 1])]; + tensor v_49_pad_type_0 = const()[name = tensor("v_49_pad_type_0"), val = tensor("custom")]; + tensor v_49_pad_0 = const()[name = tensor("v_49_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(622260672))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(622567936))), name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor v_49_cast = conv(dilations = var_3774, groups = var_3126, pad = v_49_pad_0, pad_type = v_49_pad_type_0, strides = var_3772, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_267_cast)[name = tensor("v_49_cast")]; + tensor var_3778 = const()[name = tensor("op_3778"), val = tensor([2, 8, 80, -1])]; + tensor var_3779_cast = reshape(shape = var_3778, x = q_49_cast)[name = tensor("op_3779_cast")]; + tensor var_3780 = const()[name = tensor("op_3780"), val = tensor([2, 8, 80, -1])]; + tensor var_3781_cast = reshape(shape = var_3780, x = k_49_cast)[name = tensor("op_3781_cast")]; + tensor var_3782 = const()[name = tensor("op_3782"), val = tensor([2, 8, 80, -1])]; + tensor var_3783_cast = reshape(shape = var_3782, x = v_49_cast)[name = tensor("op_3783_cast")]; + tensor attn_weights_97_transpose_x_0 = const()[name = tensor("attn_weights_97_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_97_transpose_y_0 = const()[name = tensor("attn_weights_97_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_97_cast = matmul(transpose_x = attn_weights_97_transpose_x_0, transpose_y = attn_weights_97_transpose_y_0, x = var_3779_cast, y = var_3781_cast)[name = tensor("attn_weights_97_cast")]; + tensor attn_weights_99_cast = mul(x = attn_weights_97_cast, y = var_3117_to_fp16)[name = tensor("attn_weights_99_cast")]; + tensor var_3787_cast = softmax(axis = var_3110, x = attn_weights_99_cast)[name = tensor("op_3787_cast")]; + tensor attn_49_transpose_x_0 = const()[name = tensor("attn_49_transpose_x_0"), val = tensor(false)]; + tensor attn_49_transpose_y_0 = const()[name = tensor("attn_49_transpose_y_0"), val = tensor(true)]; + tensor attn_49_cast = matmul(transpose_x = attn_49_transpose_x_0, transpose_y = attn_49_transpose_y_0, x = var_3783_cast, y = var_3787_cast)[name = tensor("attn_49_cast")]; + tensor var_3791 = const()[name = tensor("op_3791"), val = tensor([2, 640, 1, -1])]; + tensor input_435_cast = reshape(shape = var_3791, x = attn_49_cast)[name = tensor("input_435_cast")]; + tensor var_3796 = const()[name = tensor("op_3796"), val = tensor([1, 1])]; + tensor var_3798 = const()[name = tensor("op_3798"), val = tensor([1, 1])]; + tensor var_3800_pad_type_0 = const()[name = tensor("op_3800_pad_type_0"), val = tensor("custom")]; + tensor var_3800_pad_0 = const()[name = tensor("op_3800_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(622568128))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(622875392))), name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(622875584)))]; + tensor var_3800_cast = conv(bias = up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_3798, groups = var_3126, pad = var_3800_pad_0, pad_type = var_3800_pad_type_0, strides = var_3796, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_435_cast)[name = tensor("op_3800_cast")]; + tensor inputs_75_cast = add(x = var_3800_cast, y = inputs_73_cast)[name = tensor("inputs_75_cast")]; + tensor var_3804 = const()[name = tensor("op_3804"), val = tensor([1])]; + tensor channels_mean_75_cast = reduce_mean(axes = var_3804, keep_dims = var_3121, x = inputs_75_cast)[name = tensor("channels_mean_75_cast")]; + tensor zero_mean_75_cast = sub(x = inputs_75_cast, y = channels_mean_75_cast)[name = tensor("zero_mean_75_cast")]; + tensor zero_mean_sq_75_cast = mul(x = zero_mean_75_cast, y = zero_mean_75_cast)[name = tensor("zero_mean_sq_75_cast")]; + tensor var_3808 = const()[name = tensor("op_3808"), val = tensor([1])]; + tensor var_3809_cast = reduce_mean(axes = var_3808, keep_dims = var_3121, x = zero_mean_sq_75_cast)[name = tensor("op_3809_cast")]; + tensor var_3810_to_fp16 = const()[name = tensor("op_3810_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3811_cast = add(x = var_3809_cast, y = var_3810_to_fp16)[name = tensor("op_3811_cast")]; + tensor denom_75_epsilon_0_to_fp16 = const()[name = tensor("denom_75_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_75_cast = rsqrt(epsilon = denom_75_epsilon_0_to_fp16, x = var_3811_cast)[name = tensor("denom_75_cast")]; + tensor out_75_cast = mul(x = zero_mean_75_cast, y = denom_75_cast)[name = tensor("out_75_cast")]; + tensor var_3815_to_fp16 = const()[name = tensor("op_3815_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(622876928)))]; + tensor var_3816_cast = add(x = out_75_cast, y = var_3815_to_fp16)[name = tensor("op_3816_cast")]; + tensor var_3818_to_fp16 = const()[name = tensor("op_3818_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(622878272)))]; + tensor hidden_states_269_cast = mul(x = var_3816_cast, y = var_3818_to_fp16)[name = tensor("hidden_states_269_cast")]; + tensor var_3825 = const()[name = tensor("op_3825"), val = tensor([1, 1])]; + tensor var_3827 = const()[name = tensor("op_3827"), val = tensor([1, 1])]; + tensor q_51_pad_type_0 = const()[name = tensor("q_51_pad_type_0"), val = tensor("custom")]; + tensor q_51_pad_0 = const()[name = tensor("q_51_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(622879616))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(623186880))), name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor q_51_cast = conv(dilations = var_3827, groups = var_3126, pad = q_51_pad_0, pad_type = q_51_pad_type_0, strides = var_3825, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_269_cast)[name = tensor("q_51_cast")]; + tensor var_3831 = const()[name = tensor("op_3831"), val = tensor([1, 1])]; + tensor var_3833 = const()[name = tensor("op_3833"), val = tensor([1, 1])]; + tensor k_51_pad_type_0 = const()[name = tensor("k_51_pad_type_0"), val = tensor("custom")]; + tensor k_51_pad_0 = const()[name = tensor("k_51_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(623187072))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(623555776))), name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([640, 768, 1, 1])]; + tensor k_51_cast = conv(dilations = var_3833, groups = var_3126, pad = k_51_pad_0, pad_type = k_51_pad_type_0, strides = var_3831, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_51_cast")]; + tensor var_3837 = const()[name = tensor("op_3837"), val = tensor([1, 1])]; + tensor var_3839 = const()[name = tensor("op_3839"), val = tensor([1, 1])]; + tensor v_51_pad_type_0 = const()[name = tensor("v_51_pad_type_0"), val = tensor("custom")]; + tensor v_51_pad_0 = const()[name = tensor("v_51_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(623555968))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(623924672))), name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([640, 768, 1, 1])]; + tensor v_51_cast = conv(dilations = var_3839, groups = var_3126, pad = v_51_pad_0, pad_type = v_51_pad_type_0, strides = var_3837, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_51_cast")]; + tensor var_3843 = const()[name = tensor("op_3843"), val = tensor([2, 8, 80, -1])]; + tensor var_3844_cast = reshape(shape = var_3843, x = q_51_cast)[name = tensor("op_3844_cast")]; + tensor var_3845 = const()[name = tensor("op_3845"), val = tensor([2, 8, 80, -1])]; + tensor var_3846_cast = reshape(shape = var_3845, x = k_51_cast)[name = tensor("op_3846_cast")]; + tensor var_3847 = const()[name = tensor("op_3847"), val = tensor([2, 8, 80, -1])]; + tensor var_3848_cast = reshape(shape = var_3847, x = v_51_cast)[name = tensor("op_3848_cast")]; + tensor attn_weights_101_transpose_x_0 = const()[name = tensor("attn_weights_101_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_101_transpose_y_0 = const()[name = tensor("attn_weights_101_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_101_cast = matmul(transpose_x = attn_weights_101_transpose_x_0, transpose_y = attn_weights_101_transpose_y_0, x = var_3844_cast, y = var_3846_cast)[name = tensor("attn_weights_101_cast")]; + tensor attn_weights_103_cast = mul(x = attn_weights_101_cast, y = var_3117_to_fp16)[name = tensor("attn_weights_103_cast")]; + tensor var_3852_cast = softmax(axis = var_3110, x = attn_weights_103_cast)[name = tensor("op_3852_cast")]; + tensor attn_51_transpose_x_0 = const()[name = tensor("attn_51_transpose_x_0"), val = tensor(false)]; + tensor attn_51_transpose_y_0 = const()[name = tensor("attn_51_transpose_y_0"), val = tensor(true)]; + tensor attn_51_cast = matmul(transpose_x = attn_51_transpose_x_0, transpose_y = attn_51_transpose_y_0, x = var_3848_cast, y = var_3852_cast)[name = tensor("attn_51_cast")]; + tensor var_3856 = const()[name = tensor("op_3856"), val = tensor([2, 640, 1, -1])]; + tensor input_437_cast = reshape(shape = var_3856, x = attn_51_cast)[name = tensor("input_437_cast")]; + tensor var_3861 = const()[name = tensor("op_3861"), val = tensor([1, 1])]; + tensor var_3863 = const()[name = tensor("op_3863"), val = tensor([1, 1])]; + tensor var_3865_pad_type_0 = const()[name = tensor("op_3865_pad_type_0"), val = tensor("custom")]; + tensor var_3865_pad_0 = const()[name = tensor("op_3865_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(623924864))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(624232128))), name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(624232320)))]; + tensor var_3865_cast = conv(bias = up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_3863, groups = var_3126, pad = var_3865_pad_0, pad_type = var_3865_pad_type_0, strides = var_3861, weight = up_blocks_2_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_437_cast)[name = tensor("op_3865_cast")]; + tensor inputs_77_cast = add(x = var_3865_cast, y = inputs_75_cast)[name = tensor("inputs_77_cast")]; + tensor var_3869 = const()[name = tensor("op_3869"), val = tensor([1])]; + tensor channels_mean_77_cast = reduce_mean(axes = var_3869, keep_dims = var_3121, x = inputs_77_cast)[name = tensor("channels_mean_77_cast")]; + tensor zero_mean_77_cast = sub(x = inputs_77_cast, y = channels_mean_77_cast)[name = tensor("zero_mean_77_cast")]; + tensor zero_mean_sq_77_cast = mul(x = zero_mean_77_cast, y = zero_mean_77_cast)[name = tensor("zero_mean_sq_77_cast")]; + tensor var_3873 = const()[name = tensor("op_3873"), val = tensor([1])]; + tensor var_3874_cast = reduce_mean(axes = var_3873, keep_dims = var_3121, x = zero_mean_sq_77_cast)[name = tensor("op_3874_cast")]; + tensor var_3875_to_fp16 = const()[name = tensor("op_3875_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_3876_cast = add(x = var_3874_cast, y = var_3875_to_fp16)[name = tensor("op_3876_cast")]; + tensor denom_77_epsilon_0_to_fp16 = const()[name = tensor("denom_77_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_77_cast = rsqrt(epsilon = denom_77_epsilon_0_to_fp16, x = var_3876_cast)[name = tensor("denom_77_cast")]; + tensor out_77_cast = mul(x = zero_mean_77_cast, y = denom_77_cast)[name = tensor("out_77_cast")]; + tensor var_3880_to_fp16 = const()[name = tensor("op_3880_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(624233664)))]; + tensor var_3881_cast = add(x = out_77_cast, y = var_3880_to_fp16)[name = tensor("op_3881_cast")]; + tensor var_3883_to_fp16 = const()[name = tensor("op_3883_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(624235008)))]; + tensor input_439_cast = mul(x = var_3881_cast, y = var_3883_to_fp16)[name = tensor("input_439_cast")]; + tensor var_3891 = const()[name = tensor("op_3891"), val = tensor([1, 1])]; + tensor var_3893 = const()[name = tensor("op_3893"), val = tensor([1, 1])]; + tensor var_3895_pad_type_0 = const()[name = tensor("op_3895_pad_type_0"), val = tensor("custom")]; + tensor var_3895_pad_0 = const()[name = tensor("op_3895_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(624236352))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(626694016))), name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([5120, 640, 1, 1])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(626694208))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(626698112))), name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([5120])]; + tensor var_3895_cast = conv(bias = up_blocks_2_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_3893, groups = var_3126, pad = var_3895_pad_0, pad_type = var_3895_pad_type_0, strides = var_3891, weight = up_blocks_2_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_439_cast)[name = tensor("op_3895_cast")]; + tensor var_3896_split_sizes_0 = const()[name = tensor("op_3896_split_sizes_0"), val = tensor([2560, 2560])]; + tensor var_3896_axis_0 = const()[name = tensor("op_3896_axis_0"), val = tensor(1)]; + tensor var_3896_cast_0, tensor var_3896_cast_1 = split(axis = var_3896_axis_0, split_sizes = var_3896_split_sizes_0, x = var_3895_cast)[name = tensor("op_3896_cast")]; + tensor var_3898_mode_0 = const()[name = tensor("op_3898_mode_0"), val = tensor("EXACT")]; + tensor var_3898_cast = gelu(mode = var_3898_mode_0, x = var_3896_cast_1)[name = tensor("op_3898_cast")]; + tensor input_441_cast = mul(x = var_3896_cast_0, y = var_3898_cast)[name = tensor("input_441_cast")]; + tensor var_3902 = const()[name = tensor("op_3902"), val = tensor([1, 1])]; + tensor var_3904 = const()[name = tensor("op_3904"), val = tensor([1, 1])]; + tensor var_3906_pad_type_0 = const()[name = tensor("op_3906_pad_type_0"), val = tensor("custom")]; + tensor var_3906_pad_0 = const()[name = tensor("op_3906_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(626698304))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(627927168))), name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([640, 2560, 1, 1])]; + tensor up_blocks_2_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(627927360)))]; + tensor var_3906_cast = conv(bias = up_blocks_2_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_3904, groups = var_3126, pad = var_3906_pad_0, pad_type = var_3906_pad_type_0, strides = var_3902, weight = up_blocks_2_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_441_cast)[name = tensor("op_3906_cast")]; + tensor hidden_states_273_cast = add(x = var_3906_cast, y = inputs_77_cast)[name = tensor("hidden_states_273_cast")]; + tensor var_3908 = const()[name = tensor("op_3908"), val = tensor([2, 640, 32, 32])]; + tensor input_443_cast = reshape(shape = var_3908, x = hidden_states_273_cast)[name = tensor("input_443_cast")]; + tensor var_3912 = const()[name = tensor("op_3912"), val = tensor([1, 1])]; + tensor var_3914 = const()[name = tensor("op_3914"), val = tensor([1, 1])]; + tensor hidden_states_275_pad_type_0 = const()[name = tensor("hidden_states_275_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_275_pad_0 = const()[name = tensor("hidden_states_275_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_2_attentions_2_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(627928704))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(628235968))), name = tensor("up_blocks_2_attentions_2_proj_out_weight_to_fp16_palettized"), shape = tensor([640, 640, 1, 1])]; + tensor up_blocks_2_attentions_2_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_2_attentions_2_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(628236160)))]; + tensor hidden_states_275_cast = conv(bias = up_blocks_2_attentions_2_proj_out_bias_to_fp16, dilations = var_3914, groups = var_3126, pad = hidden_states_275_pad_0, pad_type = hidden_states_275_pad_type_0, strides = var_3912, weight = up_blocks_2_attentions_2_proj_out_weight_to_fp16_palettized, x = input_443_cast)[name = tensor("hidden_states_275_cast")]; + tensor input_445_cast = add(x = hidden_states_275_cast, y = hidden_states_263_cast)[name = tensor("input_445_cast")]; + tensor input_447_scale_factor_height_0 = const()[name = tensor("input_447_scale_factor_height_0"), val = tensor(0x1p+1)]; + tensor input_447_scale_factor_width_0 = const()[name = tensor("input_447_scale_factor_width_0"), val = tensor(0x1p+1)]; + tensor input_447_cast = upsample_nearest_neighbor(scale_factor_height = input_447_scale_factor_height_0, scale_factor_width = input_447_scale_factor_width_0, x = input_445_cast)[name = tensor("input_447_cast")]; + tensor var_3923 = const()[name = tensor("op_3923"), val = tensor([1, 1])]; + tensor var_3925 = const()[name = tensor("op_3925"), val = tensor([1, 1])]; + tensor hidden_states_277_pad_type_0 = const()[name = tensor("hidden_states_277_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_277_pad_0 = const()[name = tensor("hidden_states_277_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_2_upsamplers_0_conv_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(628237504))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(631002368))), name = tensor("up_blocks_2_upsamplers_0_conv_weight_to_fp16_palettized"), shape = tensor([640, 640, 3, 3])]; + tensor up_blocks_2_upsamplers_0_conv_bias_to_fp16 = const()[name = tensor("up_blocks_2_upsamplers_0_conv_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(631002560)))]; + tensor hidden_states_277_cast = conv(bias = up_blocks_2_upsamplers_0_conv_bias_to_fp16, dilations = var_3925, groups = var_3126, pad = hidden_states_277_pad_0, pad_type = hidden_states_277_pad_type_0, strides = var_3923, weight = up_blocks_2_upsamplers_0_conv_weight_to_fp16_palettized, x = input_447_cast)[name = tensor("hidden_states_277_cast")]; + tensor var_3929 = const()[name = tensor("op_3929"), val = tensor(3)]; + tensor var_3940 = const()[name = tensor("op_3940"), val = tensor(true)]; + tensor var_3945 = const()[name = tensor("op_3945"), val = tensor(1)]; + tensor input_449_interleave_0 = const()[name = tensor("input_449_interleave_0"), val = tensor(false)]; + tensor input_449_cast = concat(axis = var_3945, interleave = input_449_interleave_0, values = (hidden_states_277_cast, input_61_cast))[name = tensor("input_449_cast")]; + tensor reshape_204_shape_0 = const()[name = tensor("reshape_204_shape_0"), val = tensor([2, 32, 30, 64, 64])]; + tensor reshape_204_cast = reshape(shape = reshape_204_shape_0, x = input_449_cast)[name = tensor("reshape_204_cast")]; + tensor reduce_mean_153_axes_0 = const()[name = tensor("reduce_mean_153_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_153_keep_dims_0 = const()[name = tensor("reduce_mean_153_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_153_cast = reduce_mean(axes = reduce_mean_153_axes_0, keep_dims = reduce_mean_153_keep_dims_0, x = reshape_204_cast)[name = tensor("reduce_mean_153_cast")]; + tensor sub_102_cast = sub(x = reshape_204_cast, y = reduce_mean_153_cast)[name = tensor("sub_102_cast")]; + tensor square_51_cast = square(x = sub_102_cast)[name = tensor("square_51_cast")]; + tensor reduce_mean_155_axes_0 = const()[name = tensor("reduce_mean_155_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_155_keep_dims_0 = const()[name = tensor("reduce_mean_155_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_155_cast = reduce_mean(axes = reduce_mean_155_axes_0, keep_dims = reduce_mean_155_keep_dims_0, x = square_51_cast)[name = tensor("reduce_mean_155_cast")]; + tensor add_102_y_0_to_fp16 = const()[name = tensor("add_102_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_102_cast = add(x = reduce_mean_155_cast, y = add_102_y_0_to_fp16)[name = tensor("add_102_cast")]; + tensor sqrt_51_cast = sqrt(x = add_102_cast)[name = tensor("sqrt_51_cast")]; + tensor real_div_51_cast = real_div(x = sub_102_cast, y = sqrt_51_cast)[name = tensor("real_div_51_cast")]; + tensor reshape_205_shape_0 = const()[name = tensor("reshape_205_shape_0"), val = tensor([2, 960, 64, 64])]; + tensor reshape_205_cast = reshape(shape = reshape_205_shape_0, x = real_div_51_cast)[name = tensor("reshape_205_cast")]; + tensor add_103_gamma_0_to_fp16 = const()[name = tensor("add_103_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(631003904)))]; + tensor add_103_beta_0_to_fp16 = const()[name = tensor("add_103_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(631005888)))]; + tensor add_103_epsilon_0_to_fp16 = const()[name = tensor("add_103_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_103_cast = batch_norm(beta = add_103_beta_0_to_fp16, epsilon = add_103_epsilon_0_to_fp16, gamma = add_103_gamma_0_to_fp16, mean = add_97_mean_0_to_fp16, variance = add_97_variance_0_to_fp16, x = reshape_205_cast)[name = tensor("add_103_cast")]; + tensor input_453_cast = silu(x = add_103_cast)[name = tensor("input_453_cast")]; + tensor var_3972 = const()[name = tensor("op_3972"), val = tensor([1, 1])]; + tensor var_3974 = const()[name = tensor("op_3974"), val = tensor([1, 1])]; + tensor hidden_states_279_pad_type_0 = const()[name = tensor("hidden_states_279_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_279_pad_0 = const()[name = tensor("hidden_states_279_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_3_resnets_0_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(631007872))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(633081536))), name = tensor("up_blocks_3_resnets_0_conv1_weight_to_fp16_palettized"), shape = tensor([320, 960, 3, 3])]; + tensor up_blocks_3_resnets_0_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_0_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(633081728)))]; + tensor hidden_states_279_cast = conv(bias = up_blocks_3_resnets_0_conv1_bias_to_fp16, dilations = var_3974, groups = var_3945, pad = hidden_states_279_pad_0, pad_type = hidden_states_279_pad_type_0, strides = var_3972, weight = up_blocks_3_resnets_0_conv1_weight_to_fp16_palettized, x = input_453_cast)[name = tensor("hidden_states_279_cast")]; + tensor var_3980 = const()[name = tensor("op_3980"), val = tensor([1, 1])]; + tensor var_3982 = const()[name = tensor("op_3982"), val = tensor([1, 1])]; + tensor temb_39_pad_type_0 = const()[name = tensor("temb_39_pad_type_0"), val = tensor("custom")]; + tensor temb_39_pad_0 = const()[name = tensor("temb_39_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_resnets_0_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(633082432))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(633389696))), name = tensor("up_blocks_3_resnets_0_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([320, 1280, 1, 1])]; + tensor up_blocks_3_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(633389888)))]; + tensor temb_39_cast = conv(bias = up_blocks_3_resnets_0_time_emb_proj_bias_to_fp16, dilations = var_3982, groups = var_3945, pad = temb_39_pad_0, pad_type = temb_39_pad_type_0, strides = var_3980, weight = up_blocks_3_resnets_0_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor("temb_39_cast")]; + tensor input_457_cast = add(x = hidden_states_279_cast, y = temb_39_cast)[name = tensor("input_457_cast")]; + tensor reshape_208_shape_0 = const()[name = tensor("reshape_208_shape_0"), val = tensor([2, 32, 10, 64, 64])]; + tensor reshape_208_cast = reshape(shape = reshape_208_shape_0, x = input_457_cast)[name = tensor("reshape_208_cast")]; + tensor reduce_mean_156_axes_0 = const()[name = tensor("reduce_mean_156_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_156_keep_dims_0 = const()[name = tensor("reduce_mean_156_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_156_cast = reduce_mean(axes = reduce_mean_156_axes_0, keep_dims = reduce_mean_156_keep_dims_0, x = reshape_208_cast)[name = tensor("reduce_mean_156_cast")]; + tensor sub_104_cast = sub(x = reshape_208_cast, y = reduce_mean_156_cast)[name = tensor("sub_104_cast")]; + tensor square_52_cast = square(x = sub_104_cast)[name = tensor("square_52_cast")]; + tensor reduce_mean_158_axes_0 = const()[name = tensor("reduce_mean_158_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_158_keep_dims_0 = const()[name = tensor("reduce_mean_158_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_158_cast = reduce_mean(axes = reduce_mean_158_axes_0, keep_dims = reduce_mean_158_keep_dims_0, x = square_52_cast)[name = tensor("reduce_mean_158_cast")]; + tensor add_104_y_0_to_fp16 = const()[name = tensor("add_104_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_104_cast = add(x = reduce_mean_158_cast, y = add_104_y_0_to_fp16)[name = tensor("add_104_cast")]; + tensor sqrt_52_cast = sqrt(x = add_104_cast)[name = tensor("sqrt_52_cast")]; + tensor real_div_52_cast = real_div(x = sub_104_cast, y = sqrt_52_cast)[name = tensor("real_div_52_cast")]; + tensor reshape_209_shape_0 = const()[name = tensor("reshape_209_shape_0"), val = tensor([2, 320, 64, 64])]; + tensor reshape_209_cast = reshape(shape = reshape_209_shape_0, x = real_div_52_cast)[name = tensor("reshape_209_cast")]; + tensor add_105_gamma_0_to_fp16 = const()[name = tensor("add_105_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(633390592)))]; + tensor add_105_beta_0_to_fp16 = const()[name = tensor("add_105_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(633391296)))]; + tensor add_105_epsilon_0_to_fp16 = const()[name = tensor("add_105_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_105_cast = batch_norm(beta = add_105_beta_0_to_fp16, epsilon = add_105_epsilon_0_to_fp16, gamma = add_105_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_209_cast)[name = tensor("add_105_cast")]; + tensor input_461_cast = silu(x = add_105_cast)[name = tensor("input_461_cast")]; + tensor var_3992 = const()[name = tensor("op_3992"), val = tensor([1, 1])]; + tensor var_3994 = const()[name = tensor("op_3994"), val = tensor([1, 1])]; + tensor hidden_states_281_pad_type_0 = const()[name = tensor("hidden_states_281_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_281_pad_0 = const()[name = tensor("hidden_states_281_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_3_resnets_0_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(633392000))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634083264))), name = tensor("up_blocks_3_resnets_0_conv2_weight_to_fp16_palettized"), shape = tensor([320, 320, 3, 3])]; + tensor up_blocks_3_resnets_0_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_0_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634083456)))]; + tensor hidden_states_281_cast = conv(bias = up_blocks_3_resnets_0_conv2_bias_to_fp16, dilations = var_3994, groups = var_3945, pad = hidden_states_281_pad_0, pad_type = hidden_states_281_pad_type_0, strides = var_3992, weight = up_blocks_3_resnets_0_conv2_weight_to_fp16_palettized, x = input_461_cast)[name = tensor("hidden_states_281_cast")]; + tensor var_3999 = const()[name = tensor("op_3999"), val = tensor([1, 1])]; + tensor var_4001 = const()[name = tensor("op_4001"), val = tensor([1, 1])]; + tensor x_23_pad_type_0 = const()[name = tensor("x_23_pad_type_0"), val = tensor("custom")]; + tensor x_23_pad_0 = const()[name = tensor("x_23_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_resnets_0_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634084160))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634314624))), name = tensor("up_blocks_3_resnets_0_conv_shortcut_weight_to_fp16_palettized"), shape = tensor([320, 960, 1, 1])]; + tensor up_blocks_3_resnets_0_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_0_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634314816)))]; + tensor x_23_cast = conv(bias = up_blocks_3_resnets_0_conv_shortcut_bias_to_fp16, dilations = var_4001, groups = var_3945, pad = x_23_pad_0, pad_type = x_23_pad_type_0, strides = var_3999, weight = up_blocks_3_resnets_0_conv_shortcut_weight_to_fp16_palettized, x = input_449_cast)[name = tensor("x_23_cast")]; + tensor hidden_states_283_cast = add(x = x_23_cast, y = hidden_states_281_cast)[name = tensor("hidden_states_283_cast")]; + tensor reshape_212_shape_0 = const()[name = tensor("reshape_212_shape_0"), val = tensor([2, 32, 10, 64, 64])]; + tensor reshape_212_cast = reshape(shape = reshape_212_shape_0, x = hidden_states_283_cast)[name = tensor("reshape_212_cast")]; + tensor reduce_mean_159_axes_0 = const()[name = tensor("reduce_mean_159_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_159_keep_dims_0 = const()[name = tensor("reduce_mean_159_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_159_cast = reduce_mean(axes = reduce_mean_159_axes_0, keep_dims = reduce_mean_159_keep_dims_0, x = reshape_212_cast)[name = tensor("reduce_mean_159_cast")]; + tensor sub_106_cast = sub(x = reshape_212_cast, y = reduce_mean_159_cast)[name = tensor("sub_106_cast")]; + tensor square_53_cast = square(x = sub_106_cast)[name = tensor("square_53_cast")]; + tensor reduce_mean_161_axes_0 = const()[name = tensor("reduce_mean_161_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_161_keep_dims_0 = const()[name = tensor("reduce_mean_161_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_161_cast = reduce_mean(axes = reduce_mean_161_axes_0, keep_dims = reduce_mean_161_keep_dims_0, x = square_53_cast)[name = tensor("reduce_mean_161_cast")]; + tensor add_106_y_0_to_fp16 = const()[name = tensor("add_106_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_106_cast = add(x = reduce_mean_161_cast, y = add_106_y_0_to_fp16)[name = tensor("add_106_cast")]; + tensor sqrt_53_cast = sqrt(x = add_106_cast)[name = tensor("sqrt_53_cast")]; + tensor real_div_53_cast = real_div(x = sub_106_cast, y = sqrt_53_cast)[name = tensor("real_div_53_cast")]; + tensor reshape_213_shape_0 = const()[name = tensor("reshape_213_shape_0"), val = tensor([2, 320, 64, 64])]; + tensor reshape_213_cast = reshape(shape = reshape_213_shape_0, x = real_div_53_cast)[name = tensor("reshape_213_cast")]; + tensor add_107_gamma_0_to_fp16 = const()[name = tensor("add_107_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634315520)))]; + tensor add_107_beta_0_to_fp16 = const()[name = tensor("add_107_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634316224)))]; + tensor add_107_epsilon_0_to_fp16 = const()[name = tensor("add_107_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_107_cast = batch_norm(beta = add_107_beta_0_to_fp16, epsilon = add_107_epsilon_0_to_fp16, gamma = add_107_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_213_cast)[name = tensor("add_107_cast")]; + tensor var_4021 = const()[name = tensor("op_4021"), val = tensor([1, 1])]; + tensor var_4023 = const()[name = tensor("op_4023"), val = tensor([1, 1])]; + tensor hidden_states_285_pad_type_0 = const()[name = tensor("hidden_states_285_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_285_pad_0 = const()[name = tensor("hidden_states_285_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634316928))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634393792))), name = tensor("up_blocks_3_attentions_0_proj_in_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor up_blocks_3_attentions_0_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634393984)))]; + tensor hidden_states_285_cast = conv(bias = up_blocks_3_attentions_0_proj_in_bias_to_fp16, dilations = var_4023, groups = var_3945, pad = hidden_states_285_pad_0, pad_type = hidden_states_285_pad_type_0, strides = var_4021, weight = up_blocks_3_attentions_0_proj_in_weight_to_fp16_palettized, x = add_107_cast)[name = tensor("hidden_states_285_cast")]; + tensor var_4028 = const()[name = tensor("op_4028"), val = tensor([2, 320, 1, 4096])]; + tensor inputs_79_cast = reshape(shape = var_4028, x = hidden_states_285_cast)[name = tensor("inputs_79_cast")]; + tensor var_4038 = const()[name = tensor("op_4038"), val = tensor([1])]; + tensor channels_mean_79_cast = reduce_mean(axes = var_4038, keep_dims = var_3940, x = inputs_79_cast)[name = tensor("channels_mean_79_cast")]; + tensor zero_mean_79_cast = sub(x = inputs_79_cast, y = channels_mean_79_cast)[name = tensor("zero_mean_79_cast")]; + tensor zero_mean_sq_79_cast = mul(x = zero_mean_79_cast, y = zero_mean_79_cast)[name = tensor("zero_mean_sq_79_cast")]; + tensor var_4042 = const()[name = tensor("op_4042"), val = tensor([1])]; + tensor var_4043_cast = reduce_mean(axes = var_4042, keep_dims = var_3940, x = zero_mean_sq_79_cast)[name = tensor("op_4043_cast")]; + tensor var_4044_to_fp16 = const()[name = tensor("op_4044_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4045_cast = add(x = var_4043_cast, y = var_4044_to_fp16)[name = tensor("op_4045_cast")]; + tensor denom_79_epsilon_0_to_fp16 = const()[name = tensor("denom_79_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_79_cast = rsqrt(epsilon = denom_79_epsilon_0_to_fp16, x = var_4045_cast)[name = tensor("denom_79_cast")]; + tensor out_79_cast = mul(x = zero_mean_79_cast, y = denom_79_cast)[name = tensor("out_79_cast")]; + tensor var_4049_to_fp16 = const()[name = tensor("op_4049_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634394688)))]; + tensor var_4050_cast = add(x = out_79_cast, y = var_4049_to_fp16)[name = tensor("op_4050_cast")]; + tensor var_4052_to_fp16 = const()[name = tensor("op_4052_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634395392)))]; + tensor hidden_states_287_cast = mul(x = var_4050_cast, y = var_4052_to_fp16)[name = tensor("hidden_states_287_cast")]; + tensor var_4059 = const()[name = tensor("op_4059"), val = tensor([1, 1])]; + tensor var_4061 = const()[name = tensor("op_4061"), val = tensor([1, 1])]; + tensor q_53_pad_type_0 = const()[name = tensor("q_53_pad_type_0"), val = tensor("custom")]; + tensor q_53_pad_0 = const()[name = tensor("q_53_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634396096))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634472960))), name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor q_53_cast = conv(dilations = var_4061, groups = var_3945, pad = q_53_pad_0, pad_type = q_53_pad_type_0, strides = var_4059, weight = up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_287_cast)[name = tensor("q_53_cast")]; + tensor var_4065 = const()[name = tensor("op_4065"), val = tensor([1, 1])]; + tensor var_4067 = const()[name = tensor("op_4067"), val = tensor([1, 1])]; + tensor k_53_pad_type_0 = const()[name = tensor("k_53_pad_type_0"), val = tensor("custom")]; + tensor k_53_pad_0 = const()[name = tensor("k_53_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634473152))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634550016))), name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor k_53_cast = conv(dilations = var_4067, groups = var_3945, pad = k_53_pad_0, pad_type = k_53_pad_type_0, strides = var_4065, weight = up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_287_cast)[name = tensor("k_53_cast")]; + tensor var_4071 = const()[name = tensor("op_4071"), val = tensor([1, 1])]; + tensor var_4073 = const()[name = tensor("op_4073"), val = tensor([1, 1])]; + tensor v_53_pad_type_0 = const()[name = tensor("v_53_pad_type_0"), val = tensor("custom")]; + tensor v_53_pad_0 = const()[name = tensor("v_53_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634550208))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634627072))), name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor v_53_cast = conv(dilations = var_4073, groups = var_3945, pad = v_53_pad_0, pad_type = v_53_pad_type_0, strides = var_4071, weight = up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_287_cast)[name = tensor("v_53_cast")]; + tensor var_4077 = const()[name = tensor("op_4077"), val = tensor([2, 8, 40, -1])]; + tensor var_4078_cast = reshape(shape = var_4077, x = q_53_cast)[name = tensor("op_4078_cast")]; + tensor var_4079 = const()[name = tensor("op_4079"), val = tensor([2, 8, 40, -1])]; + tensor var_4080_cast = reshape(shape = var_4079, x = k_53_cast)[name = tensor("op_4080_cast")]; + tensor var_4081 = const()[name = tensor("op_4081"), val = tensor([2, 8, 40, -1])]; + tensor var_4082_cast = reshape(shape = var_4081, x = v_53_cast)[name = tensor("op_4082_cast")]; + tensor attn_weights_105_transpose_x_0 = const()[name = tensor("attn_weights_105_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_105_transpose_y_0 = const()[name = tensor("attn_weights_105_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_105_cast = matmul(transpose_x = attn_weights_105_transpose_x_0, transpose_y = attn_weights_105_transpose_y_0, x = var_4078_cast, y = var_4080_cast)[name = tensor("attn_weights_105_cast")]; + tensor var_3936_to_fp16 = const()[name = tensor("op_3936_to_fp16"), val = tensor(0x1.43cp-3)]; + tensor attn_weights_107_cast = mul(x = attn_weights_105_cast, y = var_3936_to_fp16)[name = tensor("attn_weights_107_cast")]; + tensor var_4086_cast = softmax(axis = var_3929, x = attn_weights_107_cast)[name = tensor("op_4086_cast")]; + tensor attn_53_transpose_x_0 = const()[name = tensor("attn_53_transpose_x_0"), val = tensor(false)]; + tensor attn_53_transpose_y_0 = const()[name = tensor("attn_53_transpose_y_0"), val = tensor(true)]; + tensor attn_53_cast = matmul(transpose_x = attn_53_transpose_x_0, transpose_y = attn_53_transpose_y_0, x = var_4082_cast, y = var_4086_cast)[name = tensor("attn_53_cast")]; + tensor var_4090 = const()[name = tensor("op_4090"), val = tensor([2, 320, 1, -1])]; + tensor input_465_cast = reshape(shape = var_4090, x = attn_53_cast)[name = tensor("input_465_cast")]; + tensor var_4095 = const()[name = tensor("op_4095"), val = tensor([1, 1])]; + tensor var_4097 = const()[name = tensor("op_4097"), val = tensor([1, 1])]; + tensor var_4099_pad_type_0 = const()[name = tensor("op_4099_pad_type_0"), val = tensor("custom")]; + tensor var_4099_pad_0 = const()[name = tensor("op_4099_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634627264))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634704128))), name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634704320)))]; + tensor var_4099_cast = conv(bias = up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_4097, groups = var_3945, pad = var_4099_pad_0, pad_type = var_4099_pad_type_0, strides = var_4095, weight = up_blocks_3_attentions_0_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_465_cast)[name = tensor("op_4099_cast")]; + tensor inputs_81_cast = add(x = var_4099_cast, y = inputs_79_cast)[name = tensor("inputs_81_cast")]; + tensor var_4103 = const()[name = tensor("op_4103"), val = tensor([1])]; + tensor channels_mean_81_cast = reduce_mean(axes = var_4103, keep_dims = var_3940, x = inputs_81_cast)[name = tensor("channels_mean_81_cast")]; + tensor zero_mean_81_cast = sub(x = inputs_81_cast, y = channels_mean_81_cast)[name = tensor("zero_mean_81_cast")]; + tensor zero_mean_sq_81_cast = mul(x = zero_mean_81_cast, y = zero_mean_81_cast)[name = tensor("zero_mean_sq_81_cast")]; + tensor var_4107 = const()[name = tensor("op_4107"), val = tensor([1])]; + tensor var_4108_cast = reduce_mean(axes = var_4107, keep_dims = var_3940, x = zero_mean_sq_81_cast)[name = tensor("op_4108_cast")]; + tensor var_4109_to_fp16 = const()[name = tensor("op_4109_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4110_cast = add(x = var_4108_cast, y = var_4109_to_fp16)[name = tensor("op_4110_cast")]; + tensor denom_81_epsilon_0_to_fp16 = const()[name = tensor("denom_81_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_81_cast = rsqrt(epsilon = denom_81_epsilon_0_to_fp16, x = var_4110_cast)[name = tensor("denom_81_cast")]; + tensor out_81_cast = mul(x = zero_mean_81_cast, y = denom_81_cast)[name = tensor("out_81_cast")]; + tensor var_4114_to_fp16 = const()[name = tensor("op_4114_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634705024)))]; + tensor var_4115_cast = add(x = out_81_cast, y = var_4114_to_fp16)[name = tensor("op_4115_cast")]; + tensor var_4117_to_fp16 = const()[name = tensor("op_4117_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634705728)))]; + tensor hidden_states_289_cast = mul(x = var_4115_cast, y = var_4117_to_fp16)[name = tensor("hidden_states_289_cast")]; + tensor var_4124 = const()[name = tensor("op_4124"), val = tensor([1, 1])]; + tensor var_4126 = const()[name = tensor("op_4126"), val = tensor([1, 1])]; + tensor q_55_pad_type_0 = const()[name = tensor("q_55_pad_type_0"), val = tensor("custom")]; + tensor q_55_pad_0 = const()[name = tensor("q_55_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634706432))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634783296))), name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor q_55_cast = conv(dilations = var_4126, groups = var_3945, pad = q_55_pad_0, pad_type = q_55_pad_type_0, strides = var_4124, weight = up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_289_cast)[name = tensor("q_55_cast")]; + tensor var_4130 = const()[name = tensor("op_4130"), val = tensor([1, 1])]; + tensor var_4132 = const()[name = tensor("op_4132"), val = tensor([1, 1])]; + tensor k_55_pad_type_0 = const()[name = tensor("k_55_pad_type_0"), val = tensor("custom")]; + tensor k_55_pad_0 = const()[name = tensor("k_55_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634783488))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634967872))), name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([320, 768, 1, 1])]; + tensor k_55_cast = conv(dilations = var_4132, groups = var_3945, pad = k_55_pad_0, pad_type = k_55_pad_type_0, strides = var_4130, weight = up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_55_cast")]; + tensor var_4136 = const()[name = tensor("op_4136"), val = tensor([1, 1])]; + tensor var_4138 = const()[name = tensor("op_4138"), val = tensor([1, 1])]; + tensor v_55_pad_type_0 = const()[name = tensor("v_55_pad_type_0"), val = tensor("custom")]; + tensor v_55_pad_0 = const()[name = tensor("v_55_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(634968064))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(635152448))), name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([320, 768, 1, 1])]; + tensor v_55_cast = conv(dilations = var_4138, groups = var_3945, pad = v_55_pad_0, pad_type = v_55_pad_type_0, strides = var_4136, weight = up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_55_cast")]; + tensor var_4142 = const()[name = tensor("op_4142"), val = tensor([2, 8, 40, -1])]; + tensor var_4143_cast = reshape(shape = var_4142, x = q_55_cast)[name = tensor("op_4143_cast")]; + tensor var_4144 = const()[name = tensor("op_4144"), val = tensor([2, 8, 40, -1])]; + tensor var_4145_cast = reshape(shape = var_4144, x = k_55_cast)[name = tensor("op_4145_cast")]; + tensor var_4146 = const()[name = tensor("op_4146"), val = tensor([2, 8, 40, -1])]; + tensor var_4147_cast = reshape(shape = var_4146, x = v_55_cast)[name = tensor("op_4147_cast")]; + tensor attn_weights_109_transpose_x_0 = const()[name = tensor("attn_weights_109_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_109_transpose_y_0 = const()[name = tensor("attn_weights_109_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_109_cast = matmul(transpose_x = attn_weights_109_transpose_x_0, transpose_y = attn_weights_109_transpose_y_0, x = var_4143_cast, y = var_4145_cast)[name = tensor("attn_weights_109_cast")]; + tensor attn_weights_111_cast = mul(x = attn_weights_109_cast, y = var_3936_to_fp16)[name = tensor("attn_weights_111_cast")]; + tensor var_4151_cast = softmax(axis = var_3929, x = attn_weights_111_cast)[name = tensor("op_4151_cast")]; + tensor attn_55_transpose_x_0 = const()[name = tensor("attn_55_transpose_x_0"), val = tensor(false)]; + tensor attn_55_transpose_y_0 = const()[name = tensor("attn_55_transpose_y_0"), val = tensor(true)]; + tensor attn_55_cast = matmul(transpose_x = attn_55_transpose_x_0, transpose_y = attn_55_transpose_y_0, x = var_4147_cast, y = var_4151_cast)[name = tensor("attn_55_cast")]; + tensor var_4155 = const()[name = tensor("op_4155"), val = tensor([2, 320, 1, -1])]; + tensor input_467_cast = reshape(shape = var_4155, x = attn_55_cast)[name = tensor("input_467_cast")]; + tensor var_4160 = const()[name = tensor("op_4160"), val = tensor([1, 1])]; + tensor var_4162 = const()[name = tensor("op_4162"), val = tensor([1, 1])]; + tensor var_4164_pad_type_0 = const()[name = tensor("op_4164_pad_type_0"), val = tensor("custom")]; + tensor var_4164_pad_0 = const()[name = tensor("op_4164_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(635152640))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(635229504))), name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(635229696)))]; + tensor var_4164_cast = conv(bias = up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_4162, groups = var_3945, pad = var_4164_pad_0, pad_type = var_4164_pad_type_0, strides = var_4160, weight = up_blocks_3_attentions_0_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_467_cast)[name = tensor("op_4164_cast")]; + tensor inputs_83_cast = add(x = var_4164_cast, y = inputs_81_cast)[name = tensor("inputs_83_cast")]; + tensor var_4168 = const()[name = tensor("op_4168"), val = tensor([1])]; + tensor channels_mean_83_cast = reduce_mean(axes = var_4168, keep_dims = var_3940, x = inputs_83_cast)[name = tensor("channels_mean_83_cast")]; + tensor zero_mean_83_cast = sub(x = inputs_83_cast, y = channels_mean_83_cast)[name = tensor("zero_mean_83_cast")]; + tensor zero_mean_sq_83_cast = mul(x = zero_mean_83_cast, y = zero_mean_83_cast)[name = tensor("zero_mean_sq_83_cast")]; + tensor var_4172 = const()[name = tensor("op_4172"), val = tensor([1])]; + tensor var_4173_cast = reduce_mean(axes = var_4172, keep_dims = var_3940, x = zero_mean_sq_83_cast)[name = tensor("op_4173_cast")]; + tensor var_4174_to_fp16 = const()[name = tensor("op_4174_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4175_cast = add(x = var_4173_cast, y = var_4174_to_fp16)[name = tensor("op_4175_cast")]; + tensor denom_83_epsilon_0_to_fp16 = const()[name = tensor("denom_83_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_83_cast = rsqrt(epsilon = denom_83_epsilon_0_to_fp16, x = var_4175_cast)[name = tensor("denom_83_cast")]; + tensor out_83_cast = mul(x = zero_mean_83_cast, y = denom_83_cast)[name = tensor("out_83_cast")]; + tensor var_4179_to_fp16 = const()[name = tensor("op_4179_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(635230400)))]; + tensor var_4180_cast = add(x = out_83_cast, y = var_4179_to_fp16)[name = tensor("op_4180_cast")]; + tensor var_4182_to_fp16 = const()[name = tensor("op_4182_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(635231104)))]; + tensor input_469_cast = mul(x = var_4180_cast, y = var_4182_to_fp16)[name = tensor("input_469_cast")]; + tensor var_4190 = const()[name = tensor("op_4190"), val = tensor([1, 1])]; + tensor var_4192 = const()[name = tensor("op_4192"), val = tensor([1, 1])]; + tensor var_4194_pad_type_0 = const()[name = tensor("op_4194_pad_type_0"), val = tensor("custom")]; + tensor var_4194_pad_0 = const()[name = tensor("op_4194_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(635231808))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(635846272))), name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([2560, 320, 1, 1])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(635846464))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(635848448))), name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([2560])]; + tensor var_4194_cast = conv(bias = up_blocks_3_attentions_0_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_4192, groups = var_3945, pad = var_4194_pad_0, pad_type = var_4194_pad_type_0, strides = var_4190, weight = up_blocks_3_attentions_0_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_469_cast)[name = tensor("op_4194_cast")]; + tensor var_4195_split_sizes_0 = const()[name = tensor("op_4195_split_sizes_0"), val = tensor([1280, 1280])]; + tensor var_4195_axis_0 = const()[name = tensor("op_4195_axis_0"), val = tensor(1)]; + tensor var_4195_cast_0, tensor var_4195_cast_1 = split(axis = var_4195_axis_0, split_sizes = var_4195_split_sizes_0, x = var_4194_cast)[name = tensor("op_4195_cast")]; + tensor var_4197_mode_0 = const()[name = tensor("op_4197_mode_0"), val = tensor("EXACT")]; + tensor var_4197_cast = gelu(mode = var_4197_mode_0, x = var_4195_cast_1)[name = tensor("op_4197_cast")]; + tensor input_471_cast = mul(x = var_4195_cast_0, y = var_4197_cast)[name = tensor("input_471_cast")]; + tensor var_4201 = const()[name = tensor("op_4201"), val = tensor([1, 1])]; + tensor var_4203 = const()[name = tensor("op_4203"), val = tensor([1, 1])]; + tensor var_4205_pad_type_0 = const()[name = tensor("op_4205_pad_type_0"), val = tensor("custom")]; + tensor var_4205_pad_0 = const()[name = tensor("op_4205_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(635848640))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(636155904))), name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([320, 1280, 1, 1])]; + tensor up_blocks_3_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(636156096)))]; + tensor var_4205_cast = conv(bias = up_blocks_3_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_4203, groups = var_3945, pad = var_4205_pad_0, pad_type = var_4205_pad_type_0, strides = var_4201, weight = up_blocks_3_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_471_cast)[name = tensor("op_4205_cast")]; + tensor hidden_states_293_cast = add(x = var_4205_cast, y = inputs_83_cast)[name = tensor("hidden_states_293_cast")]; + tensor var_4207 = const()[name = tensor("op_4207"), val = tensor([2, 320, 64, 64])]; + tensor input_473_cast = reshape(shape = var_4207, x = hidden_states_293_cast)[name = tensor("input_473_cast")]; + tensor var_4211 = const()[name = tensor("op_4211"), val = tensor([1, 1])]; + tensor var_4213 = const()[name = tensor("op_4213"), val = tensor([1, 1])]; + tensor hidden_states_295_pad_type_0 = const()[name = tensor("hidden_states_295_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_295_pad_0 = const()[name = tensor("hidden_states_295_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_0_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(636156800))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(636233664))), name = tensor("up_blocks_3_attentions_0_proj_out_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor up_blocks_3_attentions_0_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_0_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(636233856)))]; + tensor hidden_states_295_cast = conv(bias = up_blocks_3_attentions_0_proj_out_bias_to_fp16, dilations = var_4213, groups = var_3945, pad = hidden_states_295_pad_0, pad_type = hidden_states_295_pad_type_0, strides = var_4211, weight = up_blocks_3_attentions_0_proj_out_weight_to_fp16_palettized, x = input_473_cast)[name = tensor("hidden_states_295_cast")]; + tensor hidden_states_297_cast = add(x = hidden_states_295_cast, y = hidden_states_283_cast)[name = tensor("hidden_states_297_cast")]; + tensor input_475_interleave_0 = const()[name = tensor("input_475_interleave_0"), val = tensor(false)]; + tensor input_475_cast = concat(axis = var_3945, interleave = input_475_interleave_0, values = (hidden_states_297_cast, input_35_cast))[name = tensor("input_475_cast")]; + tensor reshape_216_shape_0 = const()[name = tensor("reshape_216_shape_0"), val = tensor([2, 32, 20, 64, 64])]; + tensor reshape_216_cast = reshape(shape = reshape_216_shape_0, x = input_475_cast)[name = tensor("reshape_216_cast")]; + tensor reduce_mean_162_axes_0 = const()[name = tensor("reduce_mean_162_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_162_keep_dims_0 = const()[name = tensor("reduce_mean_162_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_162_cast = reduce_mean(axes = reduce_mean_162_axes_0, keep_dims = reduce_mean_162_keep_dims_0, x = reshape_216_cast)[name = tensor("reduce_mean_162_cast")]; + tensor sub_108_cast = sub(x = reshape_216_cast, y = reduce_mean_162_cast)[name = tensor("sub_108_cast")]; + tensor square_54_cast = square(x = sub_108_cast)[name = tensor("square_54_cast")]; + tensor reduce_mean_164_axes_0 = const()[name = tensor("reduce_mean_164_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_164_keep_dims_0 = const()[name = tensor("reduce_mean_164_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_164_cast = reduce_mean(axes = reduce_mean_164_axes_0, keep_dims = reduce_mean_164_keep_dims_0, x = square_54_cast)[name = tensor("reduce_mean_164_cast")]; + tensor add_108_y_0_to_fp16 = const()[name = tensor("add_108_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_108_cast = add(x = reduce_mean_164_cast, y = add_108_y_0_to_fp16)[name = tensor("add_108_cast")]; + tensor sqrt_54_cast = sqrt(x = add_108_cast)[name = tensor("sqrt_54_cast")]; + tensor real_div_54_cast = real_div(x = sub_108_cast, y = sqrt_54_cast)[name = tensor("real_div_54_cast")]; + tensor reshape_217_shape_0 = const()[name = tensor("reshape_217_shape_0"), val = tensor([2, 640, 64, 64])]; + tensor reshape_217_cast = reshape(shape = reshape_217_shape_0, x = real_div_54_cast)[name = tensor("reshape_217_cast")]; + tensor add_109_gamma_0_to_fp16 = const()[name = tensor("add_109_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(636234560)))]; + tensor add_109_beta_0_to_fp16 = const()[name = tensor("add_109_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(636235904)))]; + tensor add_109_epsilon_0_to_fp16 = const()[name = tensor("add_109_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_109_cast = batch_norm(beta = add_109_beta_0_to_fp16, epsilon = add_109_epsilon_0_to_fp16, gamma = add_109_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_217_cast)[name = tensor("add_109_cast")]; + tensor input_479_cast = silu(x = add_109_cast)[name = tensor("input_479_cast")]; + tensor var_4231 = const()[name = tensor("op_4231"), val = tensor([1, 1])]; + tensor var_4233 = const()[name = tensor("op_4233"), val = tensor([1, 1])]; + tensor hidden_states_299_pad_type_0 = const()[name = tensor("hidden_states_299_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_299_pad_0 = const()[name = tensor("hidden_states_299_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_3_resnets_1_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(636237248))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(637619712))), name = tensor("up_blocks_3_resnets_1_conv1_weight_to_fp16_palettized"), shape = tensor([320, 640, 3, 3])]; + tensor up_blocks_3_resnets_1_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_1_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(637619904)))]; + tensor hidden_states_299_cast = conv(bias = up_blocks_3_resnets_1_conv1_bias_to_fp16, dilations = var_4233, groups = var_3945, pad = hidden_states_299_pad_0, pad_type = hidden_states_299_pad_type_0, strides = var_4231, weight = up_blocks_3_resnets_1_conv1_weight_to_fp16_palettized, x = input_479_cast)[name = tensor("hidden_states_299_cast")]; + tensor var_4239 = const()[name = tensor("op_4239"), val = tensor([1, 1])]; + tensor var_4241 = const()[name = tensor("op_4241"), val = tensor([1, 1])]; + tensor temb_41_pad_type_0 = const()[name = tensor("temb_41_pad_type_0"), val = tensor("custom")]; + tensor temb_41_pad_0 = const()[name = tensor("temb_41_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_resnets_1_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(637620608))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(637927872))), name = tensor("up_blocks_3_resnets_1_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([320, 1280, 1, 1])]; + tensor up_blocks_3_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(637928064)))]; + tensor temb_41_cast = conv(bias = up_blocks_3_resnets_1_time_emb_proj_bias_to_fp16, dilations = var_4241, groups = var_3945, pad = temb_41_pad_0, pad_type = temb_41_pad_type_0, strides = var_4239, weight = up_blocks_3_resnets_1_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor("temb_41_cast")]; + tensor input_483_cast = add(x = hidden_states_299_cast, y = temb_41_cast)[name = tensor("input_483_cast")]; + tensor reshape_220_shape_0 = const()[name = tensor("reshape_220_shape_0"), val = tensor([2, 32, 10, 64, 64])]; + tensor reshape_220_cast = reshape(shape = reshape_220_shape_0, x = input_483_cast)[name = tensor("reshape_220_cast")]; + tensor reduce_mean_165_axes_0 = const()[name = tensor("reduce_mean_165_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_165_keep_dims_0 = const()[name = tensor("reduce_mean_165_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_165_cast = reduce_mean(axes = reduce_mean_165_axes_0, keep_dims = reduce_mean_165_keep_dims_0, x = reshape_220_cast)[name = tensor("reduce_mean_165_cast")]; + tensor sub_110_cast = sub(x = reshape_220_cast, y = reduce_mean_165_cast)[name = tensor("sub_110_cast")]; + tensor square_55_cast = square(x = sub_110_cast)[name = tensor("square_55_cast")]; + tensor reduce_mean_167_axes_0 = const()[name = tensor("reduce_mean_167_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_167_keep_dims_0 = const()[name = tensor("reduce_mean_167_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_167_cast = reduce_mean(axes = reduce_mean_167_axes_0, keep_dims = reduce_mean_167_keep_dims_0, x = square_55_cast)[name = tensor("reduce_mean_167_cast")]; + tensor add_110_y_0_to_fp16 = const()[name = tensor("add_110_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_110_cast = add(x = reduce_mean_167_cast, y = add_110_y_0_to_fp16)[name = tensor("add_110_cast")]; + tensor sqrt_55_cast = sqrt(x = add_110_cast)[name = tensor("sqrt_55_cast")]; + tensor real_div_55_cast = real_div(x = sub_110_cast, y = sqrt_55_cast)[name = tensor("real_div_55_cast")]; + tensor reshape_221_shape_0 = const()[name = tensor("reshape_221_shape_0"), val = tensor([2, 320, 64, 64])]; + tensor reshape_221_cast = reshape(shape = reshape_221_shape_0, x = real_div_55_cast)[name = tensor("reshape_221_cast")]; + tensor add_111_gamma_0_to_fp16 = const()[name = tensor("add_111_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(637928768)))]; + tensor add_111_beta_0_to_fp16 = const()[name = tensor("add_111_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(637929472)))]; + tensor add_111_epsilon_0_to_fp16 = const()[name = tensor("add_111_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_111_cast = batch_norm(beta = add_111_beta_0_to_fp16, epsilon = add_111_epsilon_0_to_fp16, gamma = add_111_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_221_cast)[name = tensor("add_111_cast")]; + tensor input_487_cast = silu(x = add_111_cast)[name = tensor("input_487_cast")]; + tensor var_4251 = const()[name = tensor("op_4251"), val = tensor([1, 1])]; + tensor var_4253 = const()[name = tensor("op_4253"), val = tensor([1, 1])]; + tensor hidden_states_301_pad_type_0 = const()[name = tensor("hidden_states_301_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_301_pad_0 = const()[name = tensor("hidden_states_301_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_3_resnets_1_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(637930176))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(638621440))), name = tensor("up_blocks_3_resnets_1_conv2_weight_to_fp16_palettized"), shape = tensor([320, 320, 3, 3])]; + tensor up_blocks_3_resnets_1_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_1_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(638621632)))]; + tensor hidden_states_301_cast = conv(bias = up_blocks_3_resnets_1_conv2_bias_to_fp16, dilations = var_4253, groups = var_3945, pad = hidden_states_301_pad_0, pad_type = hidden_states_301_pad_type_0, strides = var_4251, weight = up_blocks_3_resnets_1_conv2_weight_to_fp16_palettized, x = input_487_cast)[name = tensor("hidden_states_301_cast")]; + tensor var_4258 = const()[name = tensor("op_4258"), val = tensor([1, 1])]; + tensor var_4260 = const()[name = tensor("op_4260"), val = tensor([1, 1])]; + tensor x_25_pad_type_0 = const()[name = tensor("x_25_pad_type_0"), val = tensor("custom")]; + tensor x_25_pad_0 = const()[name = tensor("x_25_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_resnets_1_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(638622336))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(638776000))), name = tensor("up_blocks_3_resnets_1_conv_shortcut_weight_to_fp16_palettized"), shape = tensor([320, 640, 1, 1])]; + tensor up_blocks_3_resnets_1_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_1_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(638776192)))]; + tensor x_25_cast = conv(bias = up_blocks_3_resnets_1_conv_shortcut_bias_to_fp16, dilations = var_4260, groups = var_3945, pad = x_25_pad_0, pad_type = x_25_pad_type_0, strides = var_4258, weight = up_blocks_3_resnets_1_conv_shortcut_weight_to_fp16_palettized, x = input_475_cast)[name = tensor("x_25_cast")]; + tensor hidden_states_303_cast = add(x = x_25_cast, y = hidden_states_301_cast)[name = tensor("hidden_states_303_cast")]; + tensor reshape_224_shape_0 = const()[name = tensor("reshape_224_shape_0"), val = tensor([2, 32, 10, 64, 64])]; + tensor reshape_224_cast = reshape(shape = reshape_224_shape_0, x = hidden_states_303_cast)[name = tensor("reshape_224_cast")]; + tensor reduce_mean_168_axes_0 = const()[name = tensor("reduce_mean_168_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_168_keep_dims_0 = const()[name = tensor("reduce_mean_168_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_168_cast = reduce_mean(axes = reduce_mean_168_axes_0, keep_dims = reduce_mean_168_keep_dims_0, x = reshape_224_cast)[name = tensor("reduce_mean_168_cast")]; + tensor sub_112_cast = sub(x = reshape_224_cast, y = reduce_mean_168_cast)[name = tensor("sub_112_cast")]; + tensor square_56_cast = square(x = sub_112_cast)[name = tensor("square_56_cast")]; + tensor reduce_mean_170_axes_0 = const()[name = tensor("reduce_mean_170_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_170_keep_dims_0 = const()[name = tensor("reduce_mean_170_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_170_cast = reduce_mean(axes = reduce_mean_170_axes_0, keep_dims = reduce_mean_170_keep_dims_0, x = square_56_cast)[name = tensor("reduce_mean_170_cast")]; + tensor add_112_y_0_to_fp16 = const()[name = tensor("add_112_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_112_cast = add(x = reduce_mean_170_cast, y = add_112_y_0_to_fp16)[name = tensor("add_112_cast")]; + tensor sqrt_56_cast = sqrt(x = add_112_cast)[name = tensor("sqrt_56_cast")]; + tensor real_div_56_cast = real_div(x = sub_112_cast, y = sqrt_56_cast)[name = tensor("real_div_56_cast")]; + tensor reshape_225_shape_0 = const()[name = tensor("reshape_225_shape_0"), val = tensor([2, 320, 64, 64])]; + tensor reshape_225_cast = reshape(shape = reshape_225_shape_0, x = real_div_56_cast)[name = tensor("reshape_225_cast")]; + tensor add_113_gamma_0_to_fp16 = const()[name = tensor("add_113_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(638776896)))]; + tensor add_113_beta_0_to_fp16 = const()[name = tensor("add_113_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(638777600)))]; + tensor add_113_epsilon_0_to_fp16 = const()[name = tensor("add_113_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_113_cast = batch_norm(beta = add_113_beta_0_to_fp16, epsilon = add_113_epsilon_0_to_fp16, gamma = add_113_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_225_cast)[name = tensor("add_113_cast")]; + tensor var_4280 = const()[name = tensor("op_4280"), val = tensor([1, 1])]; + tensor var_4282 = const()[name = tensor("op_4282"), val = tensor([1, 1])]; + tensor hidden_states_305_pad_type_0 = const()[name = tensor("hidden_states_305_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_305_pad_0 = const()[name = tensor("hidden_states_305_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(638778304))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(638855168))), name = tensor("up_blocks_3_attentions_1_proj_in_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor up_blocks_3_attentions_1_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(638855360)))]; + tensor hidden_states_305_cast = conv(bias = up_blocks_3_attentions_1_proj_in_bias_to_fp16, dilations = var_4282, groups = var_3945, pad = hidden_states_305_pad_0, pad_type = hidden_states_305_pad_type_0, strides = var_4280, weight = up_blocks_3_attentions_1_proj_in_weight_to_fp16_palettized, x = add_113_cast)[name = tensor("hidden_states_305_cast")]; + tensor var_4287 = const()[name = tensor("op_4287"), val = tensor([2, 320, 1, 4096])]; + tensor inputs_85_cast = reshape(shape = var_4287, x = hidden_states_305_cast)[name = tensor("inputs_85_cast")]; + tensor var_4297 = const()[name = tensor("op_4297"), val = tensor([1])]; + tensor channels_mean_85_cast = reduce_mean(axes = var_4297, keep_dims = var_3940, x = inputs_85_cast)[name = tensor("channels_mean_85_cast")]; + tensor zero_mean_85_cast = sub(x = inputs_85_cast, y = channels_mean_85_cast)[name = tensor("zero_mean_85_cast")]; + tensor zero_mean_sq_85_cast = mul(x = zero_mean_85_cast, y = zero_mean_85_cast)[name = tensor("zero_mean_sq_85_cast")]; + tensor var_4301 = const()[name = tensor("op_4301"), val = tensor([1])]; + tensor var_4302_cast = reduce_mean(axes = var_4301, keep_dims = var_3940, x = zero_mean_sq_85_cast)[name = tensor("op_4302_cast")]; + tensor var_4303_to_fp16 = const()[name = tensor("op_4303_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4304_cast = add(x = var_4302_cast, y = var_4303_to_fp16)[name = tensor("op_4304_cast")]; + tensor denom_85_epsilon_0_to_fp16 = const()[name = tensor("denom_85_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_85_cast = rsqrt(epsilon = denom_85_epsilon_0_to_fp16, x = var_4304_cast)[name = tensor("denom_85_cast")]; + tensor out_85_cast = mul(x = zero_mean_85_cast, y = denom_85_cast)[name = tensor("out_85_cast")]; + tensor var_4308_to_fp16 = const()[name = tensor("op_4308_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(638856064)))]; + tensor var_4309_cast = add(x = out_85_cast, y = var_4308_to_fp16)[name = tensor("op_4309_cast")]; + tensor var_4311_to_fp16 = const()[name = tensor("op_4311_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(638856768)))]; + tensor hidden_states_307_cast = mul(x = var_4309_cast, y = var_4311_to_fp16)[name = tensor("hidden_states_307_cast")]; + tensor var_4318 = const()[name = tensor("op_4318"), val = tensor([1, 1])]; + tensor var_4320 = const()[name = tensor("op_4320"), val = tensor([1, 1])]; + tensor q_57_pad_type_0 = const()[name = tensor("q_57_pad_type_0"), val = tensor("custom")]; + tensor q_57_pad_0 = const()[name = tensor("q_57_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(638857472))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(638934336))), name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor q_57_cast = conv(dilations = var_4320, groups = var_3945, pad = q_57_pad_0, pad_type = q_57_pad_type_0, strides = var_4318, weight = up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_307_cast)[name = tensor("q_57_cast")]; + tensor var_4324 = const()[name = tensor("op_4324"), val = tensor([1, 1])]; + tensor var_4326 = const()[name = tensor("op_4326"), val = tensor([1, 1])]; + tensor k_57_pad_type_0 = const()[name = tensor("k_57_pad_type_0"), val = tensor("custom")]; + tensor k_57_pad_0 = const()[name = tensor("k_57_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(638934528))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(639011392))), name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor k_57_cast = conv(dilations = var_4326, groups = var_3945, pad = k_57_pad_0, pad_type = k_57_pad_type_0, strides = var_4324, weight = up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_307_cast)[name = tensor("k_57_cast")]; + tensor var_4330 = const()[name = tensor("op_4330"), val = tensor([1, 1])]; + tensor var_4332 = const()[name = tensor("op_4332"), val = tensor([1, 1])]; + tensor v_57_pad_type_0 = const()[name = tensor("v_57_pad_type_0"), val = tensor("custom")]; + tensor v_57_pad_0 = const()[name = tensor("v_57_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(639011584))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(639088448))), name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor v_57_cast = conv(dilations = var_4332, groups = var_3945, pad = v_57_pad_0, pad_type = v_57_pad_type_0, strides = var_4330, weight = up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_307_cast)[name = tensor("v_57_cast")]; + tensor var_4336 = const()[name = tensor("op_4336"), val = tensor([2, 8, 40, -1])]; + tensor var_4337_cast = reshape(shape = var_4336, x = q_57_cast)[name = tensor("op_4337_cast")]; + tensor var_4338 = const()[name = tensor("op_4338"), val = tensor([2, 8, 40, -1])]; + tensor var_4339_cast = reshape(shape = var_4338, x = k_57_cast)[name = tensor("op_4339_cast")]; + tensor var_4340 = const()[name = tensor("op_4340"), val = tensor([2, 8, 40, -1])]; + tensor var_4341_cast = reshape(shape = var_4340, x = v_57_cast)[name = tensor("op_4341_cast")]; + tensor attn_weights_113_transpose_x_0 = const()[name = tensor("attn_weights_113_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_113_transpose_y_0 = const()[name = tensor("attn_weights_113_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_113_cast = matmul(transpose_x = attn_weights_113_transpose_x_0, transpose_y = attn_weights_113_transpose_y_0, x = var_4337_cast, y = var_4339_cast)[name = tensor("attn_weights_113_cast")]; + tensor attn_weights_115_cast = mul(x = attn_weights_113_cast, y = var_3936_to_fp16)[name = tensor("attn_weights_115_cast")]; + tensor var_4345_cast = softmax(axis = var_3929, x = attn_weights_115_cast)[name = tensor("op_4345_cast")]; + tensor attn_57_transpose_x_0 = const()[name = tensor("attn_57_transpose_x_0"), val = tensor(false)]; + tensor attn_57_transpose_y_0 = const()[name = tensor("attn_57_transpose_y_0"), val = tensor(true)]; + tensor attn_57_cast = matmul(transpose_x = attn_57_transpose_x_0, transpose_y = attn_57_transpose_y_0, x = var_4341_cast, y = var_4345_cast)[name = tensor("attn_57_cast")]; + tensor var_4349 = const()[name = tensor("op_4349"), val = tensor([2, 320, 1, -1])]; + tensor input_491_cast = reshape(shape = var_4349, x = attn_57_cast)[name = tensor("input_491_cast")]; + tensor var_4354 = const()[name = tensor("op_4354"), val = tensor([1, 1])]; + tensor var_4356 = const()[name = tensor("op_4356"), val = tensor([1, 1])]; + tensor var_4358_pad_type_0 = const()[name = tensor("op_4358_pad_type_0"), val = tensor("custom")]; + tensor var_4358_pad_0 = const()[name = tensor("op_4358_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(639088640))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(639165504))), name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(639165696)))]; + tensor var_4358_cast = conv(bias = up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_4356, groups = var_3945, pad = var_4358_pad_0, pad_type = var_4358_pad_type_0, strides = var_4354, weight = up_blocks_3_attentions_1_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_491_cast)[name = tensor("op_4358_cast")]; + tensor inputs_87_cast = add(x = var_4358_cast, y = inputs_85_cast)[name = tensor("inputs_87_cast")]; + tensor var_4362 = const()[name = tensor("op_4362"), val = tensor([1])]; + tensor channels_mean_87_cast = reduce_mean(axes = var_4362, keep_dims = var_3940, x = inputs_87_cast)[name = tensor("channels_mean_87_cast")]; + tensor zero_mean_87_cast = sub(x = inputs_87_cast, y = channels_mean_87_cast)[name = tensor("zero_mean_87_cast")]; + tensor zero_mean_sq_87_cast = mul(x = zero_mean_87_cast, y = zero_mean_87_cast)[name = tensor("zero_mean_sq_87_cast")]; + tensor var_4366 = const()[name = tensor("op_4366"), val = tensor([1])]; + tensor var_4367_cast = reduce_mean(axes = var_4366, keep_dims = var_3940, x = zero_mean_sq_87_cast)[name = tensor("op_4367_cast")]; + tensor var_4368_to_fp16 = const()[name = tensor("op_4368_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4369_cast = add(x = var_4367_cast, y = var_4368_to_fp16)[name = tensor("op_4369_cast")]; + tensor denom_87_epsilon_0_to_fp16 = const()[name = tensor("denom_87_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_87_cast = rsqrt(epsilon = denom_87_epsilon_0_to_fp16, x = var_4369_cast)[name = tensor("denom_87_cast")]; + tensor out_87_cast = mul(x = zero_mean_87_cast, y = denom_87_cast)[name = tensor("out_87_cast")]; + tensor var_4373_to_fp16 = const()[name = tensor("op_4373_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(639166400)))]; + tensor var_4374_cast = add(x = out_87_cast, y = var_4373_to_fp16)[name = tensor("op_4374_cast")]; + tensor var_4376_to_fp16 = const()[name = tensor("op_4376_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(639167104)))]; + tensor hidden_states_309_cast = mul(x = var_4374_cast, y = var_4376_to_fp16)[name = tensor("hidden_states_309_cast")]; + tensor var_4383 = const()[name = tensor("op_4383"), val = tensor([1, 1])]; + tensor var_4385 = const()[name = tensor("op_4385"), val = tensor([1, 1])]; + tensor q_59_pad_type_0 = const()[name = tensor("q_59_pad_type_0"), val = tensor("custom")]; + tensor q_59_pad_0 = const()[name = tensor("q_59_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(639167808))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(639244672))), name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor q_59_cast = conv(dilations = var_4385, groups = var_3945, pad = q_59_pad_0, pad_type = q_59_pad_type_0, strides = var_4383, weight = up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_309_cast)[name = tensor("q_59_cast")]; + tensor var_4389 = const()[name = tensor("op_4389"), val = tensor([1, 1])]; + tensor var_4391 = const()[name = tensor("op_4391"), val = tensor([1, 1])]; + tensor k_59_pad_type_0 = const()[name = tensor("k_59_pad_type_0"), val = tensor("custom")]; + tensor k_59_pad_0 = const()[name = tensor("k_59_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(639244864))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(639429248))), name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([320, 768, 1, 1])]; + tensor k_59_cast = conv(dilations = var_4391, groups = var_3945, pad = k_59_pad_0, pad_type = k_59_pad_type_0, strides = var_4389, weight = up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_59_cast")]; + tensor var_4395 = const()[name = tensor("op_4395"), val = tensor([1, 1])]; + tensor var_4397 = const()[name = tensor("op_4397"), val = tensor([1, 1])]; + tensor v_59_pad_type_0 = const()[name = tensor("v_59_pad_type_0"), val = tensor("custom")]; + tensor v_59_pad_0 = const()[name = tensor("v_59_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(639429440))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(639613824))), name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([320, 768, 1, 1])]; + tensor v_59_cast = conv(dilations = var_4397, groups = var_3945, pad = v_59_pad_0, pad_type = v_59_pad_type_0, strides = var_4395, weight = up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_59_cast")]; + tensor var_4401 = const()[name = tensor("op_4401"), val = tensor([2, 8, 40, -1])]; + tensor var_4402_cast = reshape(shape = var_4401, x = q_59_cast)[name = tensor("op_4402_cast")]; + tensor var_4403 = const()[name = tensor("op_4403"), val = tensor([2, 8, 40, -1])]; + tensor var_4404_cast = reshape(shape = var_4403, x = k_59_cast)[name = tensor("op_4404_cast")]; + tensor var_4405 = const()[name = tensor("op_4405"), val = tensor([2, 8, 40, -1])]; + tensor var_4406_cast = reshape(shape = var_4405, x = v_59_cast)[name = tensor("op_4406_cast")]; + tensor attn_weights_117_transpose_x_0 = const()[name = tensor("attn_weights_117_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_117_transpose_y_0 = const()[name = tensor("attn_weights_117_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_117_cast = matmul(transpose_x = attn_weights_117_transpose_x_0, transpose_y = attn_weights_117_transpose_y_0, x = var_4402_cast, y = var_4404_cast)[name = tensor("attn_weights_117_cast")]; + tensor attn_weights_119_cast = mul(x = attn_weights_117_cast, y = var_3936_to_fp16)[name = tensor("attn_weights_119_cast")]; + tensor var_4410_cast = softmax(axis = var_3929, x = attn_weights_119_cast)[name = tensor("op_4410_cast")]; + tensor attn_59_transpose_x_0 = const()[name = tensor("attn_59_transpose_x_0"), val = tensor(false)]; + tensor attn_59_transpose_y_0 = const()[name = tensor("attn_59_transpose_y_0"), val = tensor(true)]; + tensor attn_59_cast = matmul(transpose_x = attn_59_transpose_x_0, transpose_y = attn_59_transpose_y_0, x = var_4406_cast, y = var_4410_cast)[name = tensor("attn_59_cast")]; + tensor var_4414 = const()[name = tensor("op_4414"), val = tensor([2, 320, 1, -1])]; + tensor input_493_cast = reshape(shape = var_4414, x = attn_59_cast)[name = tensor("input_493_cast")]; + tensor var_4419 = const()[name = tensor("op_4419"), val = tensor([1, 1])]; + tensor var_4421 = const()[name = tensor("op_4421"), val = tensor([1, 1])]; + tensor var_4423_pad_type_0 = const()[name = tensor("op_4423_pad_type_0"), val = tensor("custom")]; + tensor var_4423_pad_0 = const()[name = tensor("op_4423_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(639614016))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(639690880))), name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(639691072)))]; + tensor var_4423_cast = conv(bias = up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_4421, groups = var_3945, pad = var_4423_pad_0, pad_type = var_4423_pad_type_0, strides = var_4419, weight = up_blocks_3_attentions_1_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_493_cast)[name = tensor("op_4423_cast")]; + tensor inputs_89_cast = add(x = var_4423_cast, y = inputs_87_cast)[name = tensor("inputs_89_cast")]; + tensor var_4427 = const()[name = tensor("op_4427"), val = tensor([1])]; + tensor channels_mean_89_cast = reduce_mean(axes = var_4427, keep_dims = var_3940, x = inputs_89_cast)[name = tensor("channels_mean_89_cast")]; + tensor zero_mean_89_cast = sub(x = inputs_89_cast, y = channels_mean_89_cast)[name = tensor("zero_mean_89_cast")]; + tensor zero_mean_sq_89_cast = mul(x = zero_mean_89_cast, y = zero_mean_89_cast)[name = tensor("zero_mean_sq_89_cast")]; + tensor var_4431 = const()[name = tensor("op_4431"), val = tensor([1])]; + tensor var_4432_cast = reduce_mean(axes = var_4431, keep_dims = var_3940, x = zero_mean_sq_89_cast)[name = tensor("op_4432_cast")]; + tensor var_4433_to_fp16 = const()[name = tensor("op_4433_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4434_cast = add(x = var_4432_cast, y = var_4433_to_fp16)[name = tensor("op_4434_cast")]; + tensor denom_89_epsilon_0_to_fp16 = const()[name = tensor("denom_89_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_89_cast = rsqrt(epsilon = denom_89_epsilon_0_to_fp16, x = var_4434_cast)[name = tensor("denom_89_cast")]; + tensor out_89_cast = mul(x = zero_mean_89_cast, y = denom_89_cast)[name = tensor("out_89_cast")]; + tensor var_4438_to_fp16 = const()[name = tensor("op_4438_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(639691776)))]; + tensor var_4439_cast = add(x = out_89_cast, y = var_4438_to_fp16)[name = tensor("op_4439_cast")]; + tensor var_4441_to_fp16 = const()[name = tensor("op_4441_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(639692480)))]; + tensor input_495_cast = mul(x = var_4439_cast, y = var_4441_to_fp16)[name = tensor("input_495_cast")]; + tensor var_4449 = const()[name = tensor("op_4449"), val = tensor([1, 1])]; + tensor var_4451 = const()[name = tensor("op_4451"), val = tensor([1, 1])]; + tensor var_4453_pad_type_0 = const()[name = tensor("op_4453_pad_type_0"), val = tensor("custom")]; + tensor var_4453_pad_0 = const()[name = tensor("op_4453_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(639693184))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(640307648))), name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([2560, 320, 1, 1])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(640307840))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(640309824))), name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([2560])]; + tensor var_4453_cast = conv(bias = up_blocks_3_attentions_1_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_4451, groups = var_3945, pad = var_4453_pad_0, pad_type = var_4453_pad_type_0, strides = var_4449, weight = up_blocks_3_attentions_1_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_495_cast)[name = tensor("op_4453_cast")]; + tensor var_4454_split_sizes_0 = const()[name = tensor("op_4454_split_sizes_0"), val = tensor([1280, 1280])]; + tensor var_4454_axis_0 = const()[name = tensor("op_4454_axis_0"), val = tensor(1)]; + tensor var_4454_cast_0, tensor var_4454_cast_1 = split(axis = var_4454_axis_0, split_sizes = var_4454_split_sizes_0, x = var_4453_cast)[name = tensor("op_4454_cast")]; + tensor var_4456_mode_0 = const()[name = tensor("op_4456_mode_0"), val = tensor("EXACT")]; + tensor var_4456_cast = gelu(mode = var_4456_mode_0, x = var_4454_cast_1)[name = tensor("op_4456_cast")]; + tensor input_497_cast = mul(x = var_4454_cast_0, y = var_4456_cast)[name = tensor("input_497_cast")]; + tensor var_4460 = const()[name = tensor("op_4460"), val = tensor([1, 1])]; + tensor var_4462 = const()[name = tensor("op_4462"), val = tensor([1, 1])]; + tensor var_4464_pad_type_0 = const()[name = tensor("op_4464_pad_type_0"), val = tensor("custom")]; + tensor var_4464_pad_0 = const()[name = tensor("op_4464_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(640310016))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(640617280))), name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([320, 1280, 1, 1])]; + tensor up_blocks_3_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(640617472)))]; + tensor var_4464_cast = conv(bias = up_blocks_3_attentions_1_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_4462, groups = var_3945, pad = var_4464_pad_0, pad_type = var_4464_pad_type_0, strides = var_4460, weight = up_blocks_3_attentions_1_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_497_cast)[name = tensor("op_4464_cast")]; + tensor hidden_states_313_cast = add(x = var_4464_cast, y = inputs_89_cast)[name = tensor("hidden_states_313_cast")]; + tensor var_4466 = const()[name = tensor("op_4466"), val = tensor([2, 320, 64, 64])]; + tensor input_499_cast = reshape(shape = var_4466, x = hidden_states_313_cast)[name = tensor("input_499_cast")]; + tensor var_4470 = const()[name = tensor("op_4470"), val = tensor([1, 1])]; + tensor var_4472 = const()[name = tensor("op_4472"), val = tensor([1, 1])]; + tensor hidden_states_315_pad_type_0 = const()[name = tensor("hidden_states_315_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_315_pad_0 = const()[name = tensor("hidden_states_315_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_1_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(640618176))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(640695040))), name = tensor("up_blocks_3_attentions_1_proj_out_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor up_blocks_3_attentions_1_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_1_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(640695232)))]; + tensor hidden_states_315_cast = conv(bias = up_blocks_3_attentions_1_proj_out_bias_to_fp16, dilations = var_4472, groups = var_3945, pad = hidden_states_315_pad_0, pad_type = hidden_states_315_pad_type_0, strides = var_4470, weight = up_blocks_3_attentions_1_proj_out_weight_to_fp16_palettized, x = input_499_cast)[name = tensor("hidden_states_315_cast")]; + tensor hidden_states_317_cast = add(x = hidden_states_315_cast, y = hidden_states_303_cast)[name = tensor("hidden_states_317_cast")]; + tensor input_501_interleave_0 = const()[name = tensor("input_501_interleave_0"), val = tensor(false)]; + tensor input_501_cast = concat(axis = var_3945, interleave = input_501_interleave_0, values = (hidden_states_317_cast, input_7_cast))[name = tensor("input_501_cast")]; + tensor reshape_228_shape_0 = const()[name = tensor("reshape_228_shape_0"), val = tensor([2, 32, 20, 64, 64])]; + tensor reshape_228_cast = reshape(shape = reshape_228_shape_0, x = input_501_cast)[name = tensor("reshape_228_cast")]; + tensor reduce_mean_171_axes_0 = const()[name = tensor("reduce_mean_171_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_171_keep_dims_0 = const()[name = tensor("reduce_mean_171_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_171_cast = reduce_mean(axes = reduce_mean_171_axes_0, keep_dims = reduce_mean_171_keep_dims_0, x = reshape_228_cast)[name = tensor("reduce_mean_171_cast")]; + tensor sub_114_cast = sub(x = reshape_228_cast, y = reduce_mean_171_cast)[name = tensor("sub_114_cast")]; + tensor square_57_cast = square(x = sub_114_cast)[name = tensor("square_57_cast")]; + tensor reduce_mean_173_axes_0 = const()[name = tensor("reduce_mean_173_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_173_keep_dims_0 = const()[name = tensor("reduce_mean_173_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_173_cast = reduce_mean(axes = reduce_mean_173_axes_0, keep_dims = reduce_mean_173_keep_dims_0, x = square_57_cast)[name = tensor("reduce_mean_173_cast")]; + tensor add_114_y_0_to_fp16 = const()[name = tensor("add_114_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_114_cast = add(x = reduce_mean_173_cast, y = add_114_y_0_to_fp16)[name = tensor("add_114_cast")]; + tensor sqrt_57_cast = sqrt(x = add_114_cast)[name = tensor("sqrt_57_cast")]; + tensor real_div_57_cast = real_div(x = sub_114_cast, y = sqrt_57_cast)[name = tensor("real_div_57_cast")]; + tensor reshape_229_shape_0 = const()[name = tensor("reshape_229_shape_0"), val = tensor([2, 640, 64, 64])]; + tensor reshape_229_cast = reshape(shape = reshape_229_shape_0, x = real_div_57_cast)[name = tensor("reshape_229_cast")]; + tensor add_115_gamma_0_to_fp16 = const()[name = tensor("add_115_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(640695936)))]; + tensor add_115_beta_0_to_fp16 = const()[name = tensor("add_115_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(640697280)))]; + tensor add_115_epsilon_0_to_fp16 = const()[name = tensor("add_115_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_115_cast = batch_norm(beta = add_115_beta_0_to_fp16, epsilon = add_115_epsilon_0_to_fp16, gamma = add_115_gamma_0_to_fp16, mean = add_15_mean_0_to_fp16, variance = add_15_variance_0_to_fp16, x = reshape_229_cast)[name = tensor("add_115_cast")]; + tensor input_505_cast = silu(x = add_115_cast)[name = tensor("input_505_cast")]; + tensor var_4490 = const()[name = tensor("op_4490"), val = tensor([1, 1])]; + tensor var_4492 = const()[name = tensor("op_4492"), val = tensor([1, 1])]; + tensor hidden_states_319_pad_type_0 = const()[name = tensor("hidden_states_319_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_319_pad_0 = const()[name = tensor("hidden_states_319_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_3_resnets_2_conv1_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(640698624))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(642081088))), name = tensor("up_blocks_3_resnets_2_conv1_weight_to_fp16_palettized"), shape = tensor([320, 640, 3, 3])]; + tensor up_blocks_3_resnets_2_conv1_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_2_conv1_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(642081280)))]; + tensor hidden_states_319_cast = conv(bias = up_blocks_3_resnets_2_conv1_bias_to_fp16, dilations = var_4492, groups = var_3945, pad = hidden_states_319_pad_0, pad_type = hidden_states_319_pad_type_0, strides = var_4490, weight = up_blocks_3_resnets_2_conv1_weight_to_fp16_palettized, x = input_505_cast)[name = tensor("hidden_states_319_cast")]; + tensor var_4498 = const()[name = tensor("op_4498"), val = tensor([1, 1])]; + tensor var_4500 = const()[name = tensor("op_4500"), val = tensor([1, 1])]; + tensor temb_pad_type_0 = const()[name = tensor("temb_pad_type_0"), val = tensor("custom")]; + tensor temb_pad_0 = const()[name = tensor("temb_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_resnets_2_time_emb_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(642081984))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(642389248))), name = tensor("up_blocks_3_resnets_2_time_emb_proj_weight_to_fp16_palettized"), shape = tensor([320, 1280, 1, 1])]; + tensor up_blocks_3_resnets_2_time_emb_proj_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_2_time_emb_proj_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(642389440)))]; + tensor temb_cast = conv(bias = up_blocks_3_resnets_2_time_emb_proj_bias_to_fp16, dilations = var_4500, groups = var_3945, pad = temb_pad_0, pad_type = temb_pad_type_0, strides = var_4498, weight = up_blocks_3_resnets_2_time_emb_proj_weight_to_fp16_palettized, x = input_15_cast)[name = tensor("temb_cast")]; + tensor input_509_cast = add(x = hidden_states_319_cast, y = temb_cast)[name = tensor("input_509_cast")]; + tensor reshape_232_shape_0 = const()[name = tensor("reshape_232_shape_0"), val = tensor([2, 32, 10, 64, 64])]; + tensor reshape_232_cast = reshape(shape = reshape_232_shape_0, x = input_509_cast)[name = tensor("reshape_232_cast")]; + tensor reduce_mean_174_axes_0 = const()[name = tensor("reduce_mean_174_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_174_keep_dims_0 = const()[name = tensor("reduce_mean_174_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_174_cast = reduce_mean(axes = reduce_mean_174_axes_0, keep_dims = reduce_mean_174_keep_dims_0, x = reshape_232_cast)[name = tensor("reduce_mean_174_cast")]; + tensor sub_116_cast = sub(x = reshape_232_cast, y = reduce_mean_174_cast)[name = tensor("sub_116_cast")]; + tensor square_58_cast = square(x = sub_116_cast)[name = tensor("square_58_cast")]; + tensor reduce_mean_176_axes_0 = const()[name = tensor("reduce_mean_176_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_176_keep_dims_0 = const()[name = tensor("reduce_mean_176_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_176_cast = reduce_mean(axes = reduce_mean_176_axes_0, keep_dims = reduce_mean_176_keep_dims_0, x = square_58_cast)[name = tensor("reduce_mean_176_cast")]; + tensor add_116_y_0_to_fp16 = const()[name = tensor("add_116_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_116_cast = add(x = reduce_mean_176_cast, y = add_116_y_0_to_fp16)[name = tensor("add_116_cast")]; + tensor sqrt_58_cast = sqrt(x = add_116_cast)[name = tensor("sqrt_58_cast")]; + tensor real_div_58_cast = real_div(x = sub_116_cast, y = sqrt_58_cast)[name = tensor("real_div_58_cast")]; + tensor reshape_233_shape_0 = const()[name = tensor("reshape_233_shape_0"), val = tensor([2, 320, 64, 64])]; + tensor reshape_233_cast = reshape(shape = reshape_233_shape_0, x = real_div_58_cast)[name = tensor("reshape_233_cast")]; + tensor add_117_gamma_0_to_fp16 = const()[name = tensor("add_117_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(642390144)))]; + tensor add_117_beta_0_to_fp16 = const()[name = tensor("add_117_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(642390848)))]; + tensor add_117_epsilon_0_to_fp16 = const()[name = tensor("add_117_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_117_cast = batch_norm(beta = add_117_beta_0_to_fp16, epsilon = add_117_epsilon_0_to_fp16, gamma = add_117_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_233_cast)[name = tensor("add_117_cast")]; + tensor input_513_cast = silu(x = add_117_cast)[name = tensor("input_513_cast")]; + tensor var_4510 = const()[name = tensor("op_4510"), val = tensor([1, 1])]; + tensor var_4512 = const()[name = tensor("op_4512"), val = tensor([1, 1])]; + tensor hidden_states_321_pad_type_0 = const()[name = tensor("hidden_states_321_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_321_pad_0 = const()[name = tensor("hidden_states_321_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor up_blocks_3_resnets_2_conv2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(642391552))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(643082816))), name = tensor("up_blocks_3_resnets_2_conv2_weight_to_fp16_palettized"), shape = tensor([320, 320, 3, 3])]; + tensor up_blocks_3_resnets_2_conv2_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_2_conv2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(643083008)))]; + tensor hidden_states_321_cast = conv(bias = up_blocks_3_resnets_2_conv2_bias_to_fp16, dilations = var_4512, groups = var_3945, pad = hidden_states_321_pad_0, pad_type = hidden_states_321_pad_type_0, strides = var_4510, weight = up_blocks_3_resnets_2_conv2_weight_to_fp16_palettized, x = input_513_cast)[name = tensor("hidden_states_321_cast")]; + tensor var_4517 = const()[name = tensor("op_4517"), val = tensor([1, 1])]; + tensor var_4519 = const()[name = tensor("op_4519"), val = tensor([1, 1])]; + tensor x_pad_type_0 = const()[name = tensor("x_pad_type_0"), val = tensor("custom")]; + tensor x_pad_0 = const()[name = tensor("x_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_resnets_2_conv_shortcut_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(643083712))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(643237376))), name = tensor("up_blocks_3_resnets_2_conv_shortcut_weight_to_fp16_palettized"), shape = tensor([320, 640, 1, 1])]; + tensor up_blocks_3_resnets_2_conv_shortcut_bias_to_fp16 = const()[name = tensor("up_blocks_3_resnets_2_conv_shortcut_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(643237568)))]; + tensor x_cast = conv(bias = up_blocks_3_resnets_2_conv_shortcut_bias_to_fp16, dilations = var_4519, groups = var_3945, pad = x_pad_0, pad_type = x_pad_type_0, strides = var_4517, weight = up_blocks_3_resnets_2_conv_shortcut_weight_to_fp16_palettized, x = input_501_cast)[name = tensor("x_cast")]; + tensor hidden_states_323_cast = add(x = x_cast, y = hidden_states_321_cast)[name = tensor("hidden_states_323_cast")]; + tensor reshape_236_shape_0 = const()[name = tensor("reshape_236_shape_0"), val = tensor([2, 32, 10, 64, 64])]; + tensor reshape_236_cast = reshape(shape = reshape_236_shape_0, x = hidden_states_323_cast)[name = tensor("reshape_236_cast")]; + tensor reduce_mean_177_axes_0 = const()[name = tensor("reduce_mean_177_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_177_keep_dims_0 = const()[name = tensor("reduce_mean_177_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_177_cast = reduce_mean(axes = reduce_mean_177_axes_0, keep_dims = reduce_mean_177_keep_dims_0, x = reshape_236_cast)[name = tensor("reduce_mean_177_cast")]; + tensor sub_118_cast = sub(x = reshape_236_cast, y = reduce_mean_177_cast)[name = tensor("sub_118_cast")]; + tensor square_59_cast = square(x = sub_118_cast)[name = tensor("square_59_cast")]; + tensor reduce_mean_179_axes_0 = const()[name = tensor("reduce_mean_179_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_179_keep_dims_0 = const()[name = tensor("reduce_mean_179_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_179_cast = reduce_mean(axes = reduce_mean_179_axes_0, keep_dims = reduce_mean_179_keep_dims_0, x = square_59_cast)[name = tensor("reduce_mean_179_cast")]; + tensor add_118_y_0_to_fp16 = const()[name = tensor("add_118_y_0_to_fp16"), val = tensor(0x1.1p-20)]; + tensor add_118_cast = add(x = reduce_mean_179_cast, y = add_118_y_0_to_fp16)[name = tensor("add_118_cast")]; + tensor sqrt_59_cast = sqrt(x = add_118_cast)[name = tensor("sqrt_59_cast")]; + tensor real_div_59_cast = real_div(x = sub_118_cast, y = sqrt_59_cast)[name = tensor("real_div_59_cast")]; + tensor reshape_237_shape_0 = const()[name = tensor("reshape_237_shape_0"), val = tensor([2, 320, 64, 64])]; + tensor reshape_237_cast = reshape(shape = reshape_237_shape_0, x = real_div_59_cast)[name = tensor("reshape_237_cast")]; + tensor add_119_gamma_0_to_fp16 = const()[name = tensor("add_119_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(643238272)))]; + tensor add_119_beta_0_to_fp16 = const()[name = tensor("add_119_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(643238976)))]; + tensor add_119_epsilon_0_to_fp16 = const()[name = tensor("add_119_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_119_cast = batch_norm(beta = add_119_beta_0_to_fp16, epsilon = add_119_epsilon_0_to_fp16, gamma = add_119_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_237_cast)[name = tensor("add_119_cast")]; + tensor var_4539 = const()[name = tensor("op_4539"), val = tensor([1, 1])]; + tensor var_4541 = const()[name = tensor("op_4541"), val = tensor([1, 1])]; + tensor hidden_states_325_pad_type_0 = const()[name = tensor("hidden_states_325_pad_type_0"), val = tensor("custom")]; + tensor hidden_states_325_pad_0 = const()[name = tensor("hidden_states_325_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_proj_in_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(643239680))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(643316544))), name = tensor("up_blocks_3_attentions_2_proj_in_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor up_blocks_3_attentions_2_proj_in_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_proj_in_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(643316736)))]; + tensor hidden_states_325_cast = conv(bias = up_blocks_3_attentions_2_proj_in_bias_to_fp16, dilations = var_4541, groups = var_3945, pad = hidden_states_325_pad_0, pad_type = hidden_states_325_pad_type_0, strides = var_4539, weight = up_blocks_3_attentions_2_proj_in_weight_to_fp16_palettized, x = add_119_cast)[name = tensor("hidden_states_325_cast")]; + tensor var_4546 = const()[name = tensor("op_4546"), val = tensor([2, 320, 1, 4096])]; + tensor inputs_91_cast = reshape(shape = var_4546, x = hidden_states_325_cast)[name = tensor("inputs_91_cast")]; + tensor var_4556 = const()[name = tensor("op_4556"), val = tensor([1])]; + tensor channels_mean_91_cast = reduce_mean(axes = var_4556, keep_dims = var_3940, x = inputs_91_cast)[name = tensor("channels_mean_91_cast")]; + tensor zero_mean_91_cast = sub(x = inputs_91_cast, y = channels_mean_91_cast)[name = tensor("zero_mean_91_cast")]; + tensor zero_mean_sq_91_cast = mul(x = zero_mean_91_cast, y = zero_mean_91_cast)[name = tensor("zero_mean_sq_91_cast")]; + tensor var_4560 = const()[name = tensor("op_4560"), val = tensor([1])]; + tensor var_4561_cast = reduce_mean(axes = var_4560, keep_dims = var_3940, x = zero_mean_sq_91_cast)[name = tensor("op_4561_cast")]; + tensor var_4562_to_fp16 = const()[name = tensor("op_4562_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4563_cast = add(x = var_4561_cast, y = var_4562_to_fp16)[name = tensor("op_4563_cast")]; + tensor denom_91_epsilon_0_to_fp16 = const()[name = tensor("denom_91_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_91_cast = rsqrt(epsilon = denom_91_epsilon_0_to_fp16, x = var_4563_cast)[name = tensor("denom_91_cast")]; + tensor out_91_cast = mul(x = zero_mean_91_cast, y = denom_91_cast)[name = tensor("out_91_cast")]; + tensor var_4567_to_fp16 = const()[name = tensor("op_4567_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(643317440)))]; + tensor var_4568_cast = add(x = out_91_cast, y = var_4567_to_fp16)[name = tensor("op_4568_cast")]; + tensor var_4570_to_fp16 = const()[name = tensor("op_4570_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(643318144)))]; + tensor hidden_states_327_cast = mul(x = var_4568_cast, y = var_4570_to_fp16)[name = tensor("hidden_states_327_cast")]; + tensor var_4577 = const()[name = tensor("op_4577"), val = tensor([1, 1])]; + tensor var_4579 = const()[name = tensor("op_4579"), val = tensor([1, 1])]; + tensor q_61_pad_type_0 = const()[name = tensor("q_61_pad_type_0"), val = tensor("custom")]; + tensor q_61_pad_0 = const()[name = tensor("q_61_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(643318848))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(643395712))), name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor q_61_cast = conv(dilations = var_4579, groups = var_3945, pad = q_61_pad_0, pad_type = q_61_pad_type_0, strides = var_4577, weight = up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_q_weight_to_fp16_palettized, x = hidden_states_327_cast)[name = tensor("q_61_cast")]; + tensor var_4583 = const()[name = tensor("op_4583"), val = tensor([1, 1])]; + tensor var_4585 = const()[name = tensor("op_4585"), val = tensor([1, 1])]; + tensor k_61_pad_type_0 = const()[name = tensor("k_61_pad_type_0"), val = tensor("custom")]; + tensor k_61_pad_0 = const()[name = tensor("k_61_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(643395904))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(643472768))), name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor k_61_cast = conv(dilations = var_4585, groups = var_3945, pad = k_61_pad_0, pad_type = k_61_pad_type_0, strides = var_4583, weight = up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_k_weight_to_fp16_palettized, x = hidden_states_327_cast)[name = tensor("k_61_cast")]; + tensor var_4589 = const()[name = tensor("op_4589"), val = tensor([1, 1])]; + tensor var_4591 = const()[name = tensor("op_4591"), val = tensor([1, 1])]; + tensor v_61_pad_type_0 = const()[name = tensor("v_61_pad_type_0"), val = tensor("custom")]; + tensor v_61_pad_0 = const()[name = tensor("v_61_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(643472960))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(643549824))), name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor v_61_cast = conv(dilations = var_4591, groups = var_3945, pad = v_61_pad_0, pad_type = v_61_pad_type_0, strides = var_4589, weight = up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_v_weight_to_fp16_palettized, x = hidden_states_327_cast)[name = tensor("v_61_cast")]; + tensor var_4595 = const()[name = tensor("op_4595"), val = tensor([2, 8, 40, -1])]; + tensor var_4596_cast = reshape(shape = var_4595, x = q_61_cast)[name = tensor("op_4596_cast")]; + tensor var_4597 = const()[name = tensor("op_4597"), val = tensor([2, 8, 40, -1])]; + tensor var_4598_cast = reshape(shape = var_4597, x = k_61_cast)[name = tensor("op_4598_cast")]; + tensor var_4599 = const()[name = tensor("op_4599"), val = tensor([2, 8, 40, -1])]; + tensor var_4600_cast = reshape(shape = var_4599, x = v_61_cast)[name = tensor("op_4600_cast")]; + tensor attn_weights_121_transpose_x_0 = const()[name = tensor("attn_weights_121_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_121_transpose_y_0 = const()[name = tensor("attn_weights_121_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_121_cast = matmul(transpose_x = attn_weights_121_transpose_x_0, transpose_y = attn_weights_121_transpose_y_0, x = var_4596_cast, y = var_4598_cast)[name = tensor("attn_weights_121_cast")]; + tensor attn_weights_123_cast = mul(x = attn_weights_121_cast, y = var_3936_to_fp16)[name = tensor("attn_weights_123_cast")]; + tensor var_4604_cast = softmax(axis = var_3929, x = attn_weights_123_cast)[name = tensor("op_4604_cast")]; + tensor attn_61_transpose_x_0 = const()[name = tensor("attn_61_transpose_x_0"), val = tensor(false)]; + tensor attn_61_transpose_y_0 = const()[name = tensor("attn_61_transpose_y_0"), val = tensor(true)]; + tensor attn_61_cast = matmul(transpose_x = attn_61_transpose_x_0, transpose_y = attn_61_transpose_y_0, x = var_4600_cast, y = var_4604_cast)[name = tensor("attn_61_cast")]; + tensor var_4608 = const()[name = tensor("op_4608"), val = tensor([2, 320, 1, -1])]; + tensor input_517_cast = reshape(shape = var_4608, x = attn_61_cast)[name = tensor("input_517_cast")]; + tensor var_4613 = const()[name = tensor("op_4613"), val = tensor([1, 1])]; + tensor var_4615 = const()[name = tensor("op_4615"), val = tensor([1, 1])]; + tensor var_4617_pad_type_0 = const()[name = tensor("op_4617_pad_type_0"), val = tensor("custom")]; + tensor var_4617_pad_0 = const()[name = tensor("op_4617_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(643550016))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(643626880))), name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(643627072)))]; + tensor var_4617_cast = conv(bias = up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_out_0_bias_to_fp16, dilations = var_4615, groups = var_3945, pad = var_4617_pad_0, pad_type = var_4617_pad_type_0, strides = var_4613, weight = up_blocks_3_attentions_2_transformer_blocks_0_attn1_to_out_0_weight_to_fp16_palettized, x = input_517_cast)[name = tensor("op_4617_cast")]; + tensor inputs_93_cast = add(x = var_4617_cast, y = inputs_91_cast)[name = tensor("inputs_93_cast")]; + tensor var_4621 = const()[name = tensor("op_4621"), val = tensor([1])]; + tensor channels_mean_93_cast = reduce_mean(axes = var_4621, keep_dims = var_3940, x = inputs_93_cast)[name = tensor("channels_mean_93_cast")]; + tensor zero_mean_93_cast = sub(x = inputs_93_cast, y = channels_mean_93_cast)[name = tensor("zero_mean_93_cast")]; + tensor zero_mean_sq_93_cast = mul(x = zero_mean_93_cast, y = zero_mean_93_cast)[name = tensor("zero_mean_sq_93_cast")]; + tensor var_4625 = const()[name = tensor("op_4625"), val = tensor([1])]; + tensor var_4626_cast = reduce_mean(axes = var_4625, keep_dims = var_3940, x = zero_mean_sq_93_cast)[name = tensor("op_4626_cast")]; + tensor var_4627_to_fp16 = const()[name = tensor("op_4627_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4628_cast = add(x = var_4626_cast, y = var_4627_to_fp16)[name = tensor("op_4628_cast")]; + tensor denom_93_epsilon_0_to_fp16 = const()[name = tensor("denom_93_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_93_cast = rsqrt(epsilon = denom_93_epsilon_0_to_fp16, x = var_4628_cast)[name = tensor("denom_93_cast")]; + tensor out_93_cast = mul(x = zero_mean_93_cast, y = denom_93_cast)[name = tensor("out_93_cast")]; + tensor var_4632_to_fp16 = const()[name = tensor("op_4632_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(643627776)))]; + tensor var_4633_cast = add(x = out_93_cast, y = var_4632_to_fp16)[name = tensor("op_4633_cast")]; + tensor var_4635_to_fp16 = const()[name = tensor("op_4635_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(643628480)))]; + tensor hidden_states_329_cast = mul(x = var_4633_cast, y = var_4635_to_fp16)[name = tensor("hidden_states_329_cast")]; + tensor var_4642 = const()[name = tensor("op_4642"), val = tensor([1, 1])]; + tensor var_4644 = const()[name = tensor("op_4644"), val = tensor([1, 1])]; + tensor q_pad_type_0 = const()[name = tensor("q_pad_type_0"), val = tensor("custom")]; + tensor q_pad_0 = const()[name = tensor("q_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(643629184))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(643706048))), name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor q_cast = conv(dilations = var_4644, groups = var_3945, pad = q_pad_0, pad_type = q_pad_type_0, strides = var_4642, weight = up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_q_weight_to_fp16_palettized, x = hidden_states_329_cast)[name = tensor("q_cast")]; + tensor var_4648 = const()[name = tensor("op_4648"), val = tensor([1, 1])]; + tensor var_4650 = const()[name = tensor("op_4650"), val = tensor([1, 1])]; + tensor k_pad_type_0 = const()[name = tensor("k_pad_type_0"), val = tensor("custom")]; + tensor k_pad_0 = const()[name = tensor("k_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(643706240))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(643890624))), name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized"), shape = tensor([320, 768, 1, 1])]; + tensor k_cast = conv(dilations = var_4650, groups = var_3945, pad = k_pad_0, pad_type = k_pad_type_0, strides = var_4648, weight = up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_k_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("k_cast")]; + tensor var_4654 = const()[name = tensor("op_4654"), val = tensor([1, 1])]; + tensor var_4656 = const()[name = tensor("op_4656"), val = tensor([1, 1])]; + tensor v_pad_type_0 = const()[name = tensor("v_pad_type_0"), val = tensor("custom")]; + tensor v_pad_0 = const()[name = tensor("v_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(643890816))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(644075200))), name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized"), shape = tensor([320, 768, 1, 1])]; + tensor v_cast = conv(dilations = var_4656, groups = var_3945, pad = v_pad_0, pad_type = v_pad_type_0, strides = var_4654, weight = up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_v_weight_to_fp16_palettized, x = encoder_hidden_states)[name = tensor("v_cast")]; + tensor var_4660 = const()[name = tensor("op_4660"), val = tensor([2, 8, 40, -1])]; + tensor var_4661_cast = reshape(shape = var_4660, x = q_cast)[name = tensor("op_4661_cast")]; + tensor var_4662 = const()[name = tensor("op_4662"), val = tensor([2, 8, 40, -1])]; + tensor var_4663_cast = reshape(shape = var_4662, x = k_cast)[name = tensor("op_4663_cast")]; + tensor var_4664 = const()[name = tensor("op_4664"), val = tensor([2, 8, 40, -1])]; + tensor var_4665_cast = reshape(shape = var_4664, x = v_cast)[name = tensor("op_4665_cast")]; + tensor attn_weights_125_transpose_x_0 = const()[name = tensor("attn_weights_125_transpose_x_0"), val = tensor(true)]; + tensor attn_weights_125_transpose_y_0 = const()[name = tensor("attn_weights_125_transpose_y_0"), val = tensor(false)]; + tensor attn_weights_125_cast = matmul(transpose_x = attn_weights_125_transpose_x_0, transpose_y = attn_weights_125_transpose_y_0, x = var_4661_cast, y = var_4663_cast)[name = tensor("attn_weights_125_cast")]; + tensor attn_weights_cast = mul(x = attn_weights_125_cast, y = var_3936_to_fp16)[name = tensor("attn_weights_cast")]; + tensor var_4669_cast = softmax(axis = var_3929, x = attn_weights_cast)[name = tensor("op_4669_cast")]; + tensor attn_transpose_x_0 = const()[name = tensor("attn_transpose_x_0"), val = tensor(false)]; + tensor attn_transpose_y_0 = const()[name = tensor("attn_transpose_y_0"), val = tensor(true)]; + tensor attn_cast = matmul(transpose_x = attn_transpose_x_0, transpose_y = attn_transpose_y_0, x = var_4665_cast, y = var_4669_cast)[name = tensor("attn_cast")]; + tensor var_4673 = const()[name = tensor("op_4673"), val = tensor([2, 320, 1, -1])]; + tensor input_519_cast = reshape(shape = var_4673, x = attn_cast)[name = tensor("input_519_cast")]; + tensor var_4678 = const()[name = tensor("op_4678"), val = tensor([1, 1])]; + tensor var_4680 = const()[name = tensor("op_4680"), val = tensor([1, 1])]; + tensor var_4682_pad_type_0 = const()[name = tensor("op_4682_pad_type_0"), val = tensor("custom")]; + tensor var_4682_pad_0 = const()[name = tensor("op_4682_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(644075392))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(644152256))), name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(644152448)))]; + tensor var_4682_cast = conv(bias = up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_out_0_bias_to_fp16, dilations = var_4680, groups = var_3945, pad = var_4682_pad_0, pad_type = var_4682_pad_type_0, strides = var_4678, weight = up_blocks_3_attentions_2_transformer_blocks_0_attn2_to_out_0_weight_to_fp16_palettized, x = input_519_cast)[name = tensor("op_4682_cast")]; + tensor inputs_cast = add(x = var_4682_cast, y = inputs_93_cast)[name = tensor("inputs_cast")]; + tensor var_4686 = const()[name = tensor("op_4686"), val = tensor([1])]; + tensor channels_mean_cast = reduce_mean(axes = var_4686, keep_dims = var_3940, x = inputs_cast)[name = tensor("channels_mean_cast")]; + tensor zero_mean_cast = sub(x = inputs_cast, y = channels_mean_cast)[name = tensor("zero_mean_cast")]; + tensor zero_mean_sq_cast = mul(x = zero_mean_cast, y = zero_mean_cast)[name = tensor("zero_mean_sq_cast")]; + tensor var_4690 = const()[name = tensor("op_4690"), val = tensor([1])]; + tensor var_4691_cast = reduce_mean(axes = var_4690, keep_dims = var_3940, x = zero_mean_sq_cast)[name = tensor("op_4691_cast")]; + tensor var_4692_to_fp16 = const()[name = tensor("op_4692_to_fp16"), val = tensor(0x1.5p-17)]; + tensor var_4693_cast = add(x = var_4691_cast, y = var_4692_to_fp16)[name = tensor("op_4693_cast")]; + tensor denom_epsilon_0_to_fp16 = const()[name = tensor("denom_epsilon_0_to_fp16"), val = tensor(0x1p-24)]; + tensor denom_cast = rsqrt(epsilon = denom_epsilon_0_to_fp16, x = var_4693_cast)[name = tensor("denom_cast")]; + tensor out_cast = mul(x = zero_mean_cast, y = denom_cast)[name = tensor("out_cast")]; + tensor var_4697_to_fp16 = const()[name = tensor("op_4697_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(644153152)))]; + tensor var_4698_cast = add(x = out_cast, y = var_4697_to_fp16)[name = tensor("op_4698_cast")]; + tensor var_4700_to_fp16 = const()[name = tensor("op_4700_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(644153856)))]; + tensor input_521_cast = mul(x = var_4698_cast, y = var_4700_to_fp16)[name = tensor("input_521_cast")]; + tensor var_4708 = const()[name = tensor("op_4708"), val = tensor([1, 1])]; + tensor var_4710 = const()[name = tensor("op_4710"), val = tensor([1, 1])]; + tensor var_4712_pad_type_0 = const()[name = tensor("op_4712_pad_type_0"), val = tensor("custom")]; + tensor var_4712_pad_0 = const()[name = tensor("op_4712_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(644154560))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(644769024))), name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized"), shape = tensor([2560, 320, 1, 1])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(644769216))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(644771200))), name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized"), shape = tensor([2560])]; + tensor var_4712_cast = conv(bias = up_blocks_3_attentions_2_transformer_blocks_0_ff_net_0_proj_bias_to_fp16_palettized, dilations = var_4710, groups = var_3945, pad = var_4712_pad_0, pad_type = var_4712_pad_type_0, strides = var_4708, weight = up_blocks_3_attentions_2_transformer_blocks_0_ff_net_0_proj_weight_to_fp16_palettized, x = input_521_cast)[name = tensor("op_4712_cast")]; + tensor var_4713_split_sizes_0 = const()[name = tensor("op_4713_split_sizes_0"), val = tensor([1280, 1280])]; + tensor var_4713_axis_0 = const()[name = tensor("op_4713_axis_0"), val = tensor(1)]; + tensor var_4713_cast_0, tensor var_4713_cast_1 = split(axis = var_4713_axis_0, split_sizes = var_4713_split_sizes_0, x = var_4712_cast)[name = tensor("op_4713_cast")]; + tensor var_4715_mode_0 = const()[name = tensor("op_4715_mode_0"), val = tensor("EXACT")]; + tensor var_4715_cast = gelu(mode = var_4715_mode_0, x = var_4713_cast_1)[name = tensor("op_4715_cast")]; + tensor input_523_cast = mul(x = var_4713_cast_0, y = var_4715_cast)[name = tensor("input_523_cast")]; + tensor var_4719 = const()[name = tensor("op_4719"), val = tensor([1, 1])]; + tensor var_4721 = const()[name = tensor("op_4721"), val = tensor([1, 1])]; + tensor var_4723_pad_type_0 = const()[name = tensor("op_4723_pad_type_0"), val = tensor("custom")]; + tensor var_4723_pad_0 = const()[name = tensor("op_4723_pad_0"), val = tensor([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(644771392))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(645078656))), name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized"), shape = tensor([320, 1280, 1, 1])]; + tensor up_blocks_3_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(645078848)))]; + tensor var_4723_cast = conv(bias = up_blocks_3_attentions_2_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_4721, groups = var_3945, pad = var_4723_pad_0, pad_type = var_4723_pad_type_0, strides = var_4719, weight = up_blocks_3_attentions_2_transformer_blocks_0_ff_net_2_weight_to_fp16_palettized, x = input_523_cast)[name = tensor("op_4723_cast")]; + tensor hidden_states_333_cast = add(x = var_4723_cast, y = inputs_cast)[name = tensor("hidden_states_333_cast")]; + tensor var_4725 = const()[name = tensor("op_4725"), val = tensor([2, 320, 64, 64])]; + tensor input_525_cast = reshape(shape = var_4725, x = hidden_states_333_cast)[name = tensor("input_525_cast")]; + tensor var_4729 = const()[name = tensor("op_4729"), val = tensor([1, 1])]; + tensor var_4731 = const()[name = tensor("op_4731"), 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([0, 0, 0, 0])]; + tensor up_blocks_3_attentions_2_proj_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(645079552))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(645156416))), name = tensor("up_blocks_3_attentions_2_proj_out_weight_to_fp16_palettized"), shape = tensor([320, 320, 1, 1])]; + tensor up_blocks_3_attentions_2_proj_out_bias_to_fp16 = const()[name = tensor("up_blocks_3_attentions_2_proj_out_bias_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(645156608)))]; + tensor hidden_states_cast = conv(bias = up_blocks_3_attentions_2_proj_out_bias_to_fp16, dilations = var_4731, groups = var_3945, pad = hidden_states_pad_0, pad_type = hidden_states_pad_type_0, strides = var_4729, weight = up_blocks_3_attentions_2_proj_out_weight_to_fp16_palettized, x = input_525_cast)[name = tensor("hidden_states_cast")]; + tensor input_527_cast = add(x = hidden_states_cast, y = hidden_states_323_cast)[name = tensor("input_527_cast")]; + tensor reshape_240_shape_0 = const()[name = tensor("reshape_240_shape_0"), val = tensor([2, 32, 10, 64, 64])]; + tensor reshape_240_cast = reshape(shape = reshape_240_shape_0, x = input_527_cast)[name = tensor("reshape_240_cast")]; + tensor reduce_mean_180_axes_0 = const()[name = tensor("reduce_mean_180_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_180_keep_dims_0 = const()[name = tensor("reduce_mean_180_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_180_cast = reduce_mean(axes = reduce_mean_180_axes_0, keep_dims = reduce_mean_180_keep_dims_0, x = reshape_240_cast)[name = tensor("reduce_mean_180_cast")]; + tensor sub_120_cast = sub(x = reshape_240_cast, y = reduce_mean_180_cast)[name = tensor("sub_120_cast")]; + tensor square_60_cast = square(x = sub_120_cast)[name = tensor("square_60_cast")]; + tensor reduce_mean_182_axes_0 = const()[name = tensor("reduce_mean_182_axes_0"), val = tensor([2, 3, 4])]; + tensor reduce_mean_182_keep_dims_0 = const()[name = tensor("reduce_mean_182_keep_dims_0"), val = tensor(true)]; + tensor reduce_mean_182_cast = reduce_mean(axes = reduce_mean_182_axes_0, keep_dims = reduce_mean_182_keep_dims_0, x = square_60_cast)[name = tensor("reduce_mean_182_cast")]; + tensor add_120_y_0_to_fp16 = const()[name = tensor("add_120_y_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_120_cast = add(x = reduce_mean_182_cast, y = add_120_y_0_to_fp16)[name = tensor("add_120_cast")]; + tensor sqrt_60_cast = sqrt(x = add_120_cast)[name = tensor("sqrt_60_cast")]; + tensor real_div_60_cast = real_div(x = sub_120_cast, y = sqrt_60_cast)[name = tensor("real_div_60_cast")]; + tensor reshape_241_shape_0 = const()[name = tensor("reshape_241_shape_0"), val = tensor([2, 320, 64, 64])]; + tensor reshape_241_cast = reshape(shape = reshape_241_shape_0, x = real_div_60_cast)[name = tensor("reshape_241_cast")]; + tensor add_121_gamma_0_to_fp16 = const()[name = tensor("add_121_gamma_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(645157312)))]; + tensor add_121_beta_0_to_fp16 = const()[name = tensor("add_121_beta_0_to_fp16"), val = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(645158016)))]; + tensor add_121_epsilon_0_to_fp16 = const()[name = tensor("add_121_epsilon_0_to_fp16"), val = tensor(0x1.5p-17)]; + tensor add_121_cast = batch_norm(beta = add_121_beta_0_to_fp16, epsilon = add_121_epsilon_0_to_fp16, gamma = add_121_gamma_0_to_fp16, mean = add_1_mean_0_to_fp16, variance = add_1_variance_0_to_fp16, x = reshape_241_cast)[name = tensor("add_121_cast")]; + tensor input_cast = silu(x = add_121_cast)[name = tensor("input_cast")]; + tensor var_4745 = const()[name = tensor("op_4745"), val = tensor(1)]; + tensor var_4748 = const()[name = tensor("op_4748"), val = tensor([1, 1])]; + tensor var_4750 = const()[name = tensor("op_4750"), val = tensor([1, 1])]; + tensor var_4752_pad_type_0 = const()[name = tensor("op_4752_pad_type_0"), val = tensor("custom")]; + tensor var_4752_pad_0 = const()[name = tensor("op_4752_pad_0"), val = tensor([1, 1, 1, 1])]; + tensor conv_out_weight_to_fp16_palettized = constexpr_lut_to_dense()[indices = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(645158720))), lut = tensor(BLOBFILE(path = tensor("@model_path/weights/weight.bin"), offset = tensor(645167424))), name = tensor("conv_out_weight_to_fp16_palettized"), shape = tensor([4, 320, 3, 3])]; + tensor conv_out_bias_to_fp16 = const()[name = tensor("conv_out_bias_to_fp16"), val = tensor([-0x1.dd4p-10, -0x1.6f8p-10, -0x1.018p-12, -0x1.c6p-9])]; + tensor var_4752_cast = conv(bias = conv_out_bias_to_fp16, dilations = var_4750, groups = var_4745, pad = var_4752_pad_0, pad_type = var_4752_pad_type_0, strides = var_4748, weight = conv_out_weight_to_fp16_palettized, x = input_cast)[name = tensor("op_4752_cast")]; + tensor var_4752_cast_to_fp32_dtype_0 = const()[name = tensor("op_4752_cast_to_fp32_dtype_0"), val = tensor("fp32")]; + tensor noise_pred = cast(dtype = var_4752_cast_to_fp32_dtype_0, x = var_4752_cast)[name = tensor("cast_0")]; + } -> (noise_pred); +} \ No newline at end of file