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[buildInfo = dict<tensor<string, []>, tensor<string, []>>({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}, {"coremltools-component-torch", "2.12.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})]
{
func main<ios17>(tensor<fp32, [1, 32]> latent_init, tensor<fp32, [1, 1024]> transformer_out) {
tensor<string, []> latent_init_to_fp16_dtype_0 = const()[name = tensor<string, []>("latent_init_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
tensor<fp16, [512, 32]> flow_net_input_proj_weight_to_fp16 = const()[name = tensor<string, []>("flow_net_input_proj_weight_to_fp16"), val = tensor<fp16, [512, 32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(64)))];
tensor<fp16, [512]> flow_net_input_proj_bias_to_fp16 = const()[name = tensor<string, []>("flow_net_input_proj_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(32896)))];
tensor<fp16, [1, 32]> latent_init_to_fp16 = cast(dtype = latent_init_to_fp16_dtype_0, x = latent_init)[name = tensor<string, []>("cast_146")];
tensor<fp16, [1, 512]> linear_0_cast_fp16 = linear(bias = flow_net_input_proj_bias_to_fp16, weight = flow_net_input_proj_weight_to_fp16, x = latent_init_to_fp16)[name = tensor<string, []>("linear_0_cast_fp16")];
tensor<fp16, [1, 512]> input_3_to_fp16 = const()[name = tensor<string, []>("input_3_to_fp16"), val = tensor<fp16, [1, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(33984)))];
tensor<fp16, [1, 512]> input_5_cast_fp16 = silu(x = input_3_to_fp16)[name = tensor<string, []>("input_5_cast_fp16")];
tensor<fp16, [512, 512]> flow_net_time_embed_0_mlp_2_weight_to_fp16 = const()[name = tensor<string, []>("flow_net_time_embed_0_mlp_2_weight_to_fp16"), val = tensor<fp16, [512, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(35072)))];
tensor<fp16, [512]> flow_net_time_embed_0_mlp_2_bias_to_fp16 = const()[name = tensor<string, []>("flow_net_time_embed_0_mlp_2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(559424)))];
tensor<fp16, [1, 512]> linear_2_cast_fp16 = linear(bias = flow_net_time_embed_0_mlp_2_bias_to_fp16, weight = flow_net_time_embed_0_mlp_2_weight_to_fp16, x = input_5_cast_fp16)[name = tensor<string, []>("linear_2_cast_fp16")];
tensor<int32, [1]> reduce_mean_0_axes_0 = const()[name = tensor<string, []>("reduce_mean_0_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_0_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_0_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_0_cast_fp16 = reduce_mean(axes = reduce_mean_0_axes_0, keep_dims = reduce_mean_0_keep_dims_0, x = linear_2_cast_fp16)[name = tensor<string, []>("reduce_mean_0_cast_fp16")];
tensor<fp16, [1, 512]> sub_0_cast_fp16 = sub(x = linear_2_cast_fp16, y = reduce_mean_0_cast_fp16)[name = tensor<string, []>("sub_0_cast_fp16")];
tensor<fp16, [1, 512]> square_0_cast_fp16 = square(x = sub_0_cast_fp16)[name = tensor<string, []>("square_0_cast_fp16")];
tensor<int32, [1]> reduce_mean_1_axes_0 = const()[name = tensor<string, []>("reduce_mean_1_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_1_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_1_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_1_cast_fp16 = reduce_mean(axes = reduce_mean_1_axes_0, keep_dims = reduce_mean_1_keep_dims_0, x = square_0_cast_fp16)[name = tensor<string, []>("reduce_mean_1_cast_fp16")];
tensor<fp16, []> real_div_0_to_fp16 = const()[name = tensor<string, []>("real_div_0_to_fp16"), val = tensor<fp16, []>(0x1.008p+0)];
tensor<fp16, [1, 1]> mul_0_cast_fp16 = mul(x = reduce_mean_1_cast_fp16, y = real_div_0_to_fp16)[name = tensor<string, []>("mul_0_cast_fp16")];
tensor<fp16, []> var_70_to_fp16 = const()[name = tensor<string, []>("op_70_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1]> var_1_cast_fp16 = add(x = mul_0_cast_fp16, y = var_70_to_fp16)[name = tensor<string, []>("var_1_cast_fp16")];
tensor<fp32, []> var_73_epsilon_0 = const()[name = tensor<string, []>("op_73_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
tensor<fp16, [1, 1]> var_73_cast_fp16 = rsqrt(epsilon = var_73_epsilon_0, x = var_1_cast_fp16)[name = tensor<string, []>("op_73_cast_fp16")];
tensor<fp16, [512]> const_3_to_fp16 = const()[name = tensor<string, []>("const_3_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(560512)))];
tensor<fp16, [1, 512]> var_74_cast_fp16 = mul(x = const_3_to_fp16, y = var_73_cast_fp16)[name = tensor<string, []>("op_74_cast_fp16")];
tensor<fp16, [1, 512]> var_75_cast_fp16 = mul(x = linear_2_cast_fp16, y = var_74_cast_fp16)[name = tensor<string, []>("op_75_cast_fp16")];
tensor<fp16, [1, 512]> input_9_to_fp16 = const()[name = tensor<string, []>("input_9_to_fp16"), val = tensor<fp16, [1, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(561600)))];
tensor<fp16, [1, 512]> input_11_cast_fp16 = silu(x = input_9_to_fp16)[name = tensor<string, []>("input_11_cast_fp16")];
tensor<fp16, [512, 512]> flow_net_time_embed_1_mlp_2_weight_to_fp16 = const()[name = tensor<string, []>("flow_net_time_embed_1_mlp_2_weight_to_fp16"), val = tensor<fp16, [512, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(562688)))];
tensor<fp16, [512]> flow_net_time_embed_1_mlp_2_bias_to_fp16 = const()[name = tensor<string, []>("flow_net_time_embed_1_mlp_2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1087040)))];
tensor<fp16, [1, 512]> linear_4_cast_fp16 = linear(bias = flow_net_time_embed_1_mlp_2_bias_to_fp16, weight = flow_net_time_embed_1_mlp_2_weight_to_fp16, x = input_11_cast_fp16)[name = tensor<string, []>("linear_4_cast_fp16")];
tensor<int32, [1]> reduce_mean_2_axes_0 = const()[name = tensor<string, []>("reduce_mean_2_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_2_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_2_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_2_cast_fp16 = reduce_mean(axes = reduce_mean_2_axes_0, keep_dims = reduce_mean_2_keep_dims_0, x = linear_4_cast_fp16)[name = tensor<string, []>("reduce_mean_2_cast_fp16")];
tensor<fp16, [1, 512]> sub_2_cast_fp16 = sub(x = linear_4_cast_fp16, y = reduce_mean_2_cast_fp16)[name = tensor<string, []>("sub_2_cast_fp16")];
tensor<fp16, [1, 512]> square_1_cast_fp16 = square(x = sub_2_cast_fp16)[name = tensor<string, []>("square_1_cast_fp16")];
tensor<int32, [1]> reduce_mean_3_axes_0 = const()[name = tensor<string, []>("reduce_mean_3_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_3_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_3_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_3_cast_fp16 = reduce_mean(axes = reduce_mean_3_axes_0, keep_dims = reduce_mean_3_keep_dims_0, x = square_1_cast_fp16)[name = tensor<string, []>("reduce_mean_3_cast_fp16")];
tensor<fp16, []> real_div_1_to_fp16 = const()[name = tensor<string, []>("real_div_1_to_fp16"), val = tensor<fp16, []>(0x1.008p+0)];
tensor<fp16, [1, 1]> mul_1_cast_fp16 = mul(x = reduce_mean_3_cast_fp16, y = real_div_1_to_fp16)[name = tensor<string, []>("mul_1_cast_fp16")];
tensor<fp16, []> var_110_to_fp16 = const()[name = tensor<string, []>("op_110_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1]> var_3_cast_fp16 = add(x = mul_1_cast_fp16, y = var_110_to_fp16)[name = tensor<string, []>("var_3_cast_fp16")];
tensor<fp32, []> var_113_epsilon_0 = const()[name = tensor<string, []>("op_113_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
tensor<fp16, [1, 1]> var_113_cast_fp16 = rsqrt(epsilon = var_113_epsilon_0, x = var_3_cast_fp16)[name = tensor<string, []>("op_113_cast_fp16")];
tensor<fp16, [512]> const_5_to_fp16 = const()[name = tensor<string, []>("const_5_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1088128)))];
tensor<fp16, [1, 512]> var_114_cast_fp16 = mul(x = const_5_to_fp16, y = var_113_cast_fp16)[name = tensor<string, []>("op_114_cast_fp16")];
tensor<fp16, [1, 512]> var_115_cast_fp16 = mul(x = linear_4_cast_fp16, y = var_114_cast_fp16)[name = tensor<string, []>("op_115_cast_fp16")];
tensor<fp16, [1, 512]> var_127_cast_fp16 = add(x = var_75_cast_fp16, y = var_115_cast_fp16)[name = tensor<string, []>("op_127_cast_fp16")];
tensor<fp16, []> _inversed_t_combined_1_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_t_combined_1_y_0_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
tensor<fp16, [1, 512]> _inversed_t_combined_1_cast_fp16 = mul(x = var_127_cast_fp16, y = _inversed_t_combined_1_y_0_to_fp16)[name = tensor<string, []>("_inversed_t_combined_1_cast_fp16")];
tensor<string, []> transformer_out_to_fp16_dtype_0 = const()[name = tensor<string, []>("transformer_out_to_fp16_dtype_0"), val = tensor<string, []>("fp16")];
tensor<fp16, [512, 1024]> flow_net_cond_embed_weight_to_fp16 = const()[name = tensor<string, []>("flow_net_cond_embed_weight_to_fp16"), val = tensor<fp16, [512, 1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(1089216)))];
tensor<fp16, [512]> flow_net_cond_embed_bias_to_fp16 = const()[name = tensor<string, []>("flow_net_cond_embed_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2137856)))];
tensor<fp16, [1, 1024]> transformer_out_to_fp16 = cast(dtype = transformer_out_to_fp16_dtype_0, x = transformer_out)[name = tensor<string, []>("cast_145")];
tensor<fp16, [1, 512]> linear_5_cast_fp16 = linear(bias = flow_net_cond_embed_bias_to_fp16, weight = flow_net_cond_embed_weight_to_fp16, x = transformer_out_to_fp16)[name = tensor<string, []>("linear_5_cast_fp16")];
tensor<fp16, [1, 512]> input_13_cast_fp16 = add(x = _inversed_t_combined_1_cast_fp16, y = linear_5_cast_fp16)[name = tensor<string, []>("input_13_cast_fp16")];
tensor<fp16, [1, 512]> input_15_cast_fp16 = silu(x = input_13_cast_fp16)[name = tensor<string, []>("input_15_cast_fp16")];
tensor<fp16, [1536, 512]> flow_net_res_blocks_0_adaLN_modulation_1_weight_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_0_adaLN_modulation_1_weight_to_fp16"), val = tensor<fp16, [1536, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(2138944)))];
tensor<fp16, [1536]> flow_net_res_blocks_0_adaLN_modulation_1_bias_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_0_adaLN_modulation_1_bias_to_fp16"), val = tensor<fp16, [1536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3711872)))];
tensor<fp16, [1, 1536]> linear_6_cast_fp16 = linear(bias = flow_net_res_blocks_0_adaLN_modulation_1_bias_to_fp16, weight = flow_net_res_blocks_0_adaLN_modulation_1_weight_to_fp16, x = input_15_cast_fp16)[name = tensor<string, []>("linear_6_cast_fp16")];
tensor<int32, [3]> var_142_split_sizes_0 = const()[name = tensor<string, []>("op_142_split_sizes_0"), val = tensor<int32, [3]>([512, 512, 512])];
tensor<int32, []> var_142_axis_0 = const()[name = tensor<string, []>("op_142_axis_0"), val = tensor<int32, []>(-1)];
tensor<fp16, [1, 512]> var_142_cast_fp16_0, tensor<fp16, [1, 512]> var_142_cast_fp16_1, tensor<fp16, [1, 512]> var_142_cast_fp16_2 = split(axis = var_142_axis_0, split_sizes = var_142_split_sizes_0, x = linear_6_cast_fp16)[name = tensor<string, []>("op_142_cast_fp16")];
tensor<int32, [1]> mean_1_axes_0 = const()[name = tensor<string, []>("mean_1_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> mean_1_keep_dims_0 = const()[name = tensor<string, []>("mean_1_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> mean_1_cast_fp16 = reduce_mean(axes = mean_1_axes_0, keep_dims = mean_1_keep_dims_0, x = linear_0_cast_fp16)[name = tensor<string, []>("mean_1_cast_fp16")];
tensor<fp16, [1, 512]> sub_4_cast_fp16 = sub(x = linear_0_cast_fp16, y = mean_1_cast_fp16)[name = tensor<string, []>("sub_4_cast_fp16")];
tensor<fp16, [1, 512]> square_2_cast_fp16 = square(x = sub_4_cast_fp16)[name = tensor<string, []>("square_2_cast_fp16")];
tensor<int32, [1]> reduce_mean_5_axes_0 = const()[name = tensor<string, []>("reduce_mean_5_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_5_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_5_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_5_cast_fp16 = reduce_mean(axes = reduce_mean_5_axes_0, keep_dims = reduce_mean_5_keep_dims_0, x = square_2_cast_fp16)[name = tensor<string, []>("reduce_mean_5_cast_fp16")];
tensor<fp16, []> var_152_to_fp16 = const()[name = tensor<string, []>("op_152_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 1]> var_153_cast_fp16 = add(x = reduce_mean_5_cast_fp16, y = var_152_to_fp16)[name = tensor<string, []>("op_153_cast_fp16")];
tensor<fp16, [1, 1]> var_154_cast_fp16 = sqrt(x = var_153_cast_fp16)[name = tensor<string, []>("op_154_cast_fp16")];
tensor<fp16, [1, 512]> x_7_cast_fp16 = real_div(x = sub_4_cast_fp16, y = var_154_cast_fp16)[name = tensor<string, []>("x_7_cast_fp16")];
tensor<fp16, [512]> flow_net_res_blocks_0_in_ln_weight_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_0_in_ln_weight_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3715008)))];
tensor<fp16, [1, 512]> var_156_cast_fp16 = mul(x = x_7_cast_fp16, y = flow_net_res_blocks_0_in_ln_weight_to_fp16)[name = tensor<string, []>("op_156_cast_fp16")];
tensor<fp16, [512]> flow_net_res_blocks_0_in_ln_bias_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_0_in_ln_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3716096)))];
tensor<fp16, [1, 512]> x_9_cast_fp16 = add(x = var_156_cast_fp16, y = flow_net_res_blocks_0_in_ln_bias_to_fp16)[name = tensor<string, []>("x_9_cast_fp16")];
tensor<fp16, []> var_158_promoted_to_fp16 = const()[name = tensor<string, []>("op_158_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
tensor<fp16, [1, 512]> var_159_cast_fp16 = add(x = var_142_cast_fp16_1, y = var_158_promoted_to_fp16)[name = tensor<string, []>("op_159_cast_fp16")];
tensor<fp16, [1, 512]> var_160_cast_fp16 = mul(x = x_9_cast_fp16, y = var_159_cast_fp16)[name = tensor<string, []>("op_160_cast_fp16")];
tensor<fp16, [1, 512]> input_17_cast_fp16 = add(x = var_160_cast_fp16, y = var_142_cast_fp16_0)[name = tensor<string, []>("input_17_cast_fp16")];
tensor<fp16, [512, 512]> flow_net_res_blocks_0_mlp_0_weight_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_0_mlp_0_weight_to_fp16"), val = tensor<fp16, [512, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(3717184)))];
tensor<fp16, [512]> flow_net_res_blocks_0_mlp_0_bias_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_0_mlp_0_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4241536)))];
tensor<fp16, [1, 512]> linear_7_cast_fp16 = linear(bias = flow_net_res_blocks_0_mlp_0_bias_to_fp16, weight = flow_net_res_blocks_0_mlp_0_weight_to_fp16, x = input_17_cast_fp16)[name = tensor<string, []>("linear_7_cast_fp16")];
tensor<fp16, [1, 512]> input_21_cast_fp16 = silu(x = linear_7_cast_fp16)[name = tensor<string, []>("input_21_cast_fp16")];
tensor<fp16, [512, 512]> flow_net_res_blocks_0_mlp_2_weight_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_0_mlp_2_weight_to_fp16"), val = tensor<fp16, [512, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4242624)))];
tensor<fp16, [512]> flow_net_res_blocks_0_mlp_2_bias_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_0_mlp_2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4766976)))];
tensor<fp16, [1, 512]> linear_8_cast_fp16 = linear(bias = flow_net_res_blocks_0_mlp_2_bias_to_fp16, weight = flow_net_res_blocks_0_mlp_2_weight_to_fp16, x = input_21_cast_fp16)[name = tensor<string, []>("linear_8_cast_fp16")];
tensor<fp16, [1, 512]> var_171_cast_fp16 = mul(x = var_142_cast_fp16_2, y = linear_8_cast_fp16)[name = tensor<string, []>("op_171_cast_fp16")];
tensor<fp16, [1, 512]> x_11_cast_fp16 = add(x = linear_0_cast_fp16, y = var_171_cast_fp16)[name = tensor<string, []>("x_11_cast_fp16")];
tensor<fp16, [1536, 512]> flow_net_res_blocks_1_adaLN_modulation_1_weight_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_1_adaLN_modulation_1_weight_to_fp16"), val = tensor<fp16, [1536, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(4768064)))];
tensor<fp16, [1536]> flow_net_res_blocks_1_adaLN_modulation_1_bias_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_1_adaLN_modulation_1_bias_to_fp16"), val = tensor<fp16, [1536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6340992)))];
tensor<fp16, [1, 1536]> linear_9_cast_fp16 = linear(bias = flow_net_res_blocks_1_adaLN_modulation_1_bias_to_fp16, weight = flow_net_res_blocks_1_adaLN_modulation_1_weight_to_fp16, x = input_15_cast_fp16)[name = tensor<string, []>("linear_9_cast_fp16")];
tensor<int32, [3]> var_181_split_sizes_0 = const()[name = tensor<string, []>("op_181_split_sizes_0"), val = tensor<int32, [3]>([512, 512, 512])];
tensor<int32, []> var_181_axis_0 = const()[name = tensor<string, []>("op_181_axis_0"), val = tensor<int32, []>(-1)];
tensor<fp16, [1, 512]> var_181_cast_fp16_0, tensor<fp16, [1, 512]> var_181_cast_fp16_1, tensor<fp16, [1, 512]> var_181_cast_fp16_2 = split(axis = var_181_axis_0, split_sizes = var_181_split_sizes_0, x = linear_9_cast_fp16)[name = tensor<string, []>("op_181_cast_fp16")];
tensor<int32, [1]> mean_3_axes_0 = const()[name = tensor<string, []>("mean_3_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> mean_3_keep_dims_0 = const()[name = tensor<string, []>("mean_3_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> mean_3_cast_fp16 = reduce_mean(axes = mean_3_axes_0, keep_dims = mean_3_keep_dims_0, x = x_11_cast_fp16)[name = tensor<string, []>("mean_3_cast_fp16")];
tensor<fp16, [1, 512]> sub_5_cast_fp16 = sub(x = x_11_cast_fp16, y = mean_3_cast_fp16)[name = tensor<string, []>("sub_5_cast_fp16")];
tensor<fp16, [1, 512]> square_3_cast_fp16 = square(x = sub_5_cast_fp16)[name = tensor<string, []>("square_3_cast_fp16")];
tensor<int32, [1]> reduce_mean_7_axes_0 = const()[name = tensor<string, []>("reduce_mean_7_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_7_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_7_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_7_cast_fp16 = reduce_mean(axes = reduce_mean_7_axes_0, keep_dims = reduce_mean_7_keep_dims_0, x = square_3_cast_fp16)[name = tensor<string, []>("reduce_mean_7_cast_fp16")];
tensor<fp16, []> var_191_to_fp16 = const()[name = tensor<string, []>("op_191_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 1]> var_192_cast_fp16 = add(x = reduce_mean_7_cast_fp16, y = var_191_to_fp16)[name = tensor<string, []>("op_192_cast_fp16")];
tensor<fp16, [1, 1]> var_193_cast_fp16 = sqrt(x = var_192_cast_fp16)[name = tensor<string, []>("op_193_cast_fp16")];
tensor<fp16, [1, 512]> x_13_cast_fp16 = real_div(x = sub_5_cast_fp16, y = var_193_cast_fp16)[name = tensor<string, []>("x_13_cast_fp16")];
tensor<fp16, [512]> flow_net_res_blocks_1_in_ln_weight_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_1_in_ln_weight_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6344128)))];
tensor<fp16, [1, 512]> var_195_cast_fp16 = mul(x = x_13_cast_fp16, y = flow_net_res_blocks_1_in_ln_weight_to_fp16)[name = tensor<string, []>("op_195_cast_fp16")];
tensor<fp16, [512]> flow_net_res_blocks_1_in_ln_bias_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_1_in_ln_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6345216)))];
tensor<fp16, [1, 512]> x_15_cast_fp16 = add(x = var_195_cast_fp16, y = flow_net_res_blocks_1_in_ln_bias_to_fp16)[name = tensor<string, []>("x_15_cast_fp16")];
tensor<fp16, []> var_197_promoted_to_fp16 = const()[name = tensor<string, []>("op_197_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
tensor<fp16, [1, 512]> var_198_cast_fp16 = add(x = var_181_cast_fp16_1, y = var_197_promoted_to_fp16)[name = tensor<string, []>("op_198_cast_fp16")];
tensor<fp16, [1, 512]> var_199_cast_fp16 = mul(x = x_15_cast_fp16, y = var_198_cast_fp16)[name = tensor<string, []>("op_199_cast_fp16")];
tensor<fp16, [1, 512]> input_25_cast_fp16 = add(x = var_199_cast_fp16, y = var_181_cast_fp16_0)[name = tensor<string, []>("input_25_cast_fp16")];
tensor<fp16, [512, 512]> flow_net_res_blocks_1_mlp_0_weight_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_1_mlp_0_weight_to_fp16"), val = tensor<fp16, [512, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6346304)))];
tensor<fp16, [512]> flow_net_res_blocks_1_mlp_0_bias_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_1_mlp_0_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6870656)))];
tensor<fp16, [1, 512]> linear_10_cast_fp16 = linear(bias = flow_net_res_blocks_1_mlp_0_bias_to_fp16, weight = flow_net_res_blocks_1_mlp_0_weight_to_fp16, x = input_25_cast_fp16)[name = tensor<string, []>("linear_10_cast_fp16")];
tensor<fp16, [1, 512]> input_29_cast_fp16 = silu(x = linear_10_cast_fp16)[name = tensor<string, []>("input_29_cast_fp16")];
tensor<fp16, [512, 512]> flow_net_res_blocks_1_mlp_2_weight_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_1_mlp_2_weight_to_fp16"), val = tensor<fp16, [512, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(6871744)))];
tensor<fp16, [512]> flow_net_res_blocks_1_mlp_2_bias_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_1_mlp_2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7396096)))];
tensor<fp16, [1, 512]> linear_11_cast_fp16 = linear(bias = flow_net_res_blocks_1_mlp_2_bias_to_fp16, weight = flow_net_res_blocks_1_mlp_2_weight_to_fp16, x = input_29_cast_fp16)[name = tensor<string, []>("linear_11_cast_fp16")];
tensor<fp16, [1, 512]> var_210_cast_fp16 = mul(x = var_181_cast_fp16_2, y = linear_11_cast_fp16)[name = tensor<string, []>("op_210_cast_fp16")];
tensor<fp16, [1, 512]> x_17_cast_fp16 = add(x = x_11_cast_fp16, y = var_210_cast_fp16)[name = tensor<string, []>("x_17_cast_fp16")];
tensor<fp16, [1536, 512]> flow_net_res_blocks_2_adaLN_modulation_1_weight_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_2_adaLN_modulation_1_weight_to_fp16"), val = tensor<fp16, [1536, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(7397184)))];
tensor<fp16, [1536]> flow_net_res_blocks_2_adaLN_modulation_1_bias_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_2_adaLN_modulation_1_bias_to_fp16"), val = tensor<fp16, [1536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8970112)))];
tensor<fp16, [1, 1536]> linear_12_cast_fp16 = linear(bias = flow_net_res_blocks_2_adaLN_modulation_1_bias_to_fp16, weight = flow_net_res_blocks_2_adaLN_modulation_1_weight_to_fp16, x = input_15_cast_fp16)[name = tensor<string, []>("linear_12_cast_fp16")];
tensor<int32, [3]> var_220_split_sizes_0 = const()[name = tensor<string, []>("op_220_split_sizes_0"), val = tensor<int32, [3]>([512, 512, 512])];
tensor<int32, []> var_220_axis_0 = const()[name = tensor<string, []>("op_220_axis_0"), val = tensor<int32, []>(-1)];
tensor<fp16, [1, 512]> var_220_cast_fp16_0, tensor<fp16, [1, 512]> var_220_cast_fp16_1, tensor<fp16, [1, 512]> var_220_cast_fp16_2 = split(axis = var_220_axis_0, split_sizes = var_220_split_sizes_0, x = linear_12_cast_fp16)[name = tensor<string, []>("op_220_cast_fp16")];
tensor<int32, [1]> mean_5_axes_0 = const()[name = tensor<string, []>("mean_5_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> mean_5_keep_dims_0 = const()[name = tensor<string, []>("mean_5_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> mean_5_cast_fp16 = reduce_mean(axes = mean_5_axes_0, keep_dims = mean_5_keep_dims_0, x = x_17_cast_fp16)[name = tensor<string, []>("mean_5_cast_fp16")];
tensor<fp16, [1, 512]> sub_6_cast_fp16 = sub(x = x_17_cast_fp16, y = mean_5_cast_fp16)[name = tensor<string, []>("sub_6_cast_fp16")];
tensor<fp16, [1, 512]> square_4_cast_fp16 = square(x = sub_6_cast_fp16)[name = tensor<string, []>("square_4_cast_fp16")];
tensor<int32, [1]> reduce_mean_9_axes_0 = const()[name = tensor<string, []>("reduce_mean_9_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_9_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_9_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_9_cast_fp16 = reduce_mean(axes = reduce_mean_9_axes_0, keep_dims = reduce_mean_9_keep_dims_0, x = square_4_cast_fp16)[name = tensor<string, []>("reduce_mean_9_cast_fp16")];
tensor<fp16, []> var_230_to_fp16 = const()[name = tensor<string, []>("op_230_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 1]> var_231_cast_fp16 = add(x = reduce_mean_9_cast_fp16, y = var_230_to_fp16)[name = tensor<string, []>("op_231_cast_fp16")];
tensor<fp16, [1, 1]> var_232_cast_fp16 = sqrt(x = var_231_cast_fp16)[name = tensor<string, []>("op_232_cast_fp16")];
tensor<fp16, [1, 512]> x_19_cast_fp16 = real_div(x = sub_6_cast_fp16, y = var_232_cast_fp16)[name = tensor<string, []>("x_19_cast_fp16")];
tensor<fp16, [512]> flow_net_res_blocks_2_in_ln_weight_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_2_in_ln_weight_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8973248)))];
tensor<fp16, [1, 512]> var_234_cast_fp16 = mul(x = x_19_cast_fp16, y = flow_net_res_blocks_2_in_ln_weight_to_fp16)[name = tensor<string, []>("op_234_cast_fp16")];
tensor<fp16, [512]> flow_net_res_blocks_2_in_ln_bias_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_2_in_ln_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8974336)))];
tensor<fp16, [1, 512]> x_21_cast_fp16 = add(x = var_234_cast_fp16, y = flow_net_res_blocks_2_in_ln_bias_to_fp16)[name = tensor<string, []>("x_21_cast_fp16")];
tensor<fp16, []> var_236_promoted_to_fp16 = const()[name = tensor<string, []>("op_236_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
tensor<fp16, [1, 512]> var_237_cast_fp16 = add(x = var_220_cast_fp16_1, y = var_236_promoted_to_fp16)[name = tensor<string, []>("op_237_cast_fp16")];
tensor<fp16, [1, 512]> var_238_cast_fp16 = mul(x = x_21_cast_fp16, y = var_237_cast_fp16)[name = tensor<string, []>("op_238_cast_fp16")];
tensor<fp16, [1, 512]> input_33_cast_fp16 = add(x = var_238_cast_fp16, y = var_220_cast_fp16_0)[name = tensor<string, []>("input_33_cast_fp16")];
tensor<fp16, [512, 512]> flow_net_res_blocks_2_mlp_0_weight_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_2_mlp_0_weight_to_fp16"), val = tensor<fp16, [512, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(8975424)))];
tensor<fp16, [512]> flow_net_res_blocks_2_mlp_0_bias_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_2_mlp_0_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9499776)))];
tensor<fp16, [1, 512]> linear_13_cast_fp16 = linear(bias = flow_net_res_blocks_2_mlp_0_bias_to_fp16, weight = flow_net_res_blocks_2_mlp_0_weight_to_fp16, x = input_33_cast_fp16)[name = tensor<string, []>("linear_13_cast_fp16")];
tensor<fp16, [1, 512]> input_37_cast_fp16 = silu(x = linear_13_cast_fp16)[name = tensor<string, []>("input_37_cast_fp16")];
tensor<fp16, [512, 512]> flow_net_res_blocks_2_mlp_2_weight_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_2_mlp_2_weight_to_fp16"), val = tensor<fp16, [512, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(9500864)))];
tensor<fp16, [512]> flow_net_res_blocks_2_mlp_2_bias_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_2_mlp_2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10025216)))];
tensor<fp16, [1, 512]> linear_14_cast_fp16 = linear(bias = flow_net_res_blocks_2_mlp_2_bias_to_fp16, weight = flow_net_res_blocks_2_mlp_2_weight_to_fp16, x = input_37_cast_fp16)[name = tensor<string, []>("linear_14_cast_fp16")];
tensor<fp16, [1, 512]> var_249_cast_fp16 = mul(x = var_220_cast_fp16_2, y = linear_14_cast_fp16)[name = tensor<string, []>("op_249_cast_fp16")];
tensor<fp16, [1, 512]> x_23_cast_fp16 = add(x = x_17_cast_fp16, y = var_249_cast_fp16)[name = tensor<string, []>("x_23_cast_fp16")];
tensor<fp16, [1536, 512]> flow_net_res_blocks_3_adaLN_modulation_1_weight_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_3_adaLN_modulation_1_weight_to_fp16"), val = tensor<fp16, [1536, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(10026304)))];
tensor<fp16, [1536]> flow_net_res_blocks_3_adaLN_modulation_1_bias_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_3_adaLN_modulation_1_bias_to_fp16"), val = tensor<fp16, [1536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11599232)))];
tensor<fp16, [1, 1536]> linear_15_cast_fp16 = linear(bias = flow_net_res_blocks_3_adaLN_modulation_1_bias_to_fp16, weight = flow_net_res_blocks_3_adaLN_modulation_1_weight_to_fp16, x = input_15_cast_fp16)[name = tensor<string, []>("linear_15_cast_fp16")];
tensor<int32, [3]> var_259_split_sizes_0 = const()[name = tensor<string, []>("op_259_split_sizes_0"), val = tensor<int32, [3]>([512, 512, 512])];
tensor<int32, []> var_259_axis_0 = const()[name = tensor<string, []>("op_259_axis_0"), val = tensor<int32, []>(-1)];
tensor<fp16, [1, 512]> var_259_cast_fp16_0, tensor<fp16, [1, 512]> var_259_cast_fp16_1, tensor<fp16, [1, 512]> var_259_cast_fp16_2 = split(axis = var_259_axis_0, split_sizes = var_259_split_sizes_0, x = linear_15_cast_fp16)[name = tensor<string, []>("op_259_cast_fp16")];
tensor<int32, [1]> mean_7_axes_0 = const()[name = tensor<string, []>("mean_7_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> mean_7_keep_dims_0 = const()[name = tensor<string, []>("mean_7_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> mean_7_cast_fp16 = reduce_mean(axes = mean_7_axes_0, keep_dims = mean_7_keep_dims_0, x = x_23_cast_fp16)[name = tensor<string, []>("mean_7_cast_fp16")];
tensor<fp16, [1, 512]> sub_7_cast_fp16 = sub(x = x_23_cast_fp16, y = mean_7_cast_fp16)[name = tensor<string, []>("sub_7_cast_fp16")];
tensor<fp16, [1, 512]> square_5_cast_fp16 = square(x = sub_7_cast_fp16)[name = tensor<string, []>("square_5_cast_fp16")];
tensor<int32, [1]> reduce_mean_11_axes_0 = const()[name = tensor<string, []>("reduce_mean_11_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_11_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_11_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_11_cast_fp16 = reduce_mean(axes = reduce_mean_11_axes_0, keep_dims = reduce_mean_11_keep_dims_0, x = square_5_cast_fp16)[name = tensor<string, []>("reduce_mean_11_cast_fp16")];
tensor<fp16, []> var_269_to_fp16 = const()[name = tensor<string, []>("op_269_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 1]> var_270_cast_fp16 = add(x = reduce_mean_11_cast_fp16, y = var_269_to_fp16)[name = tensor<string, []>("op_270_cast_fp16")];
tensor<fp16, [1, 1]> var_271_cast_fp16 = sqrt(x = var_270_cast_fp16)[name = tensor<string, []>("op_271_cast_fp16")];
tensor<fp16, [1, 512]> x_25_cast_fp16 = real_div(x = sub_7_cast_fp16, y = var_271_cast_fp16)[name = tensor<string, []>("x_25_cast_fp16")];
tensor<fp16, [512]> flow_net_res_blocks_3_in_ln_weight_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_3_in_ln_weight_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11602368)))];
tensor<fp16, [1, 512]> var_273_cast_fp16 = mul(x = x_25_cast_fp16, y = flow_net_res_blocks_3_in_ln_weight_to_fp16)[name = tensor<string, []>("op_273_cast_fp16")];
tensor<fp16, [512]> flow_net_res_blocks_3_in_ln_bias_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_3_in_ln_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11603456)))];
tensor<fp16, [1, 512]> x_27_cast_fp16 = add(x = var_273_cast_fp16, y = flow_net_res_blocks_3_in_ln_bias_to_fp16)[name = tensor<string, []>("x_27_cast_fp16")];
tensor<fp16, []> var_275_promoted_to_fp16 = const()[name = tensor<string, []>("op_275_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
tensor<fp16, [1, 512]> var_276_cast_fp16 = add(x = var_259_cast_fp16_1, y = var_275_promoted_to_fp16)[name = tensor<string, []>("op_276_cast_fp16")];
tensor<fp16, [1, 512]> var_277_cast_fp16 = mul(x = x_27_cast_fp16, y = var_276_cast_fp16)[name = tensor<string, []>("op_277_cast_fp16")];
tensor<fp16, [1, 512]> input_41_cast_fp16 = add(x = var_277_cast_fp16, y = var_259_cast_fp16_0)[name = tensor<string, []>("input_41_cast_fp16")];
tensor<fp16, [512, 512]> flow_net_res_blocks_3_mlp_0_weight_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_3_mlp_0_weight_to_fp16"), val = tensor<fp16, [512, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(11604544)))];
tensor<fp16, [512]> flow_net_res_blocks_3_mlp_0_bias_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_3_mlp_0_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12128896)))];
tensor<fp16, [1, 512]> linear_16_cast_fp16 = linear(bias = flow_net_res_blocks_3_mlp_0_bias_to_fp16, weight = flow_net_res_blocks_3_mlp_0_weight_to_fp16, x = input_41_cast_fp16)[name = tensor<string, []>("linear_16_cast_fp16")];
tensor<fp16, [1, 512]> input_45_cast_fp16 = silu(x = linear_16_cast_fp16)[name = tensor<string, []>("input_45_cast_fp16")];
tensor<fp16, [512, 512]> flow_net_res_blocks_3_mlp_2_weight_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_3_mlp_2_weight_to_fp16"), val = tensor<fp16, [512, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12129984)))];
tensor<fp16, [512]> flow_net_res_blocks_3_mlp_2_bias_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_3_mlp_2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12654336)))];
tensor<fp16, [1, 512]> linear_17_cast_fp16 = linear(bias = flow_net_res_blocks_3_mlp_2_bias_to_fp16, weight = flow_net_res_blocks_3_mlp_2_weight_to_fp16, x = input_45_cast_fp16)[name = tensor<string, []>("linear_17_cast_fp16")];
tensor<fp16, [1, 512]> var_288_cast_fp16 = mul(x = var_259_cast_fp16_2, y = linear_17_cast_fp16)[name = tensor<string, []>("op_288_cast_fp16")];
tensor<fp16, [1, 512]> x_29_cast_fp16 = add(x = x_23_cast_fp16, y = var_288_cast_fp16)[name = tensor<string, []>("x_29_cast_fp16")];
tensor<fp16, [1536, 512]> flow_net_res_blocks_4_adaLN_modulation_1_weight_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_4_adaLN_modulation_1_weight_to_fp16"), val = tensor<fp16, [1536, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(12655424)))];
tensor<fp16, [1536]> flow_net_res_blocks_4_adaLN_modulation_1_bias_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_4_adaLN_modulation_1_bias_to_fp16"), val = tensor<fp16, [1536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14228352)))];
tensor<fp16, [1, 1536]> linear_18_cast_fp16 = linear(bias = flow_net_res_blocks_4_adaLN_modulation_1_bias_to_fp16, weight = flow_net_res_blocks_4_adaLN_modulation_1_weight_to_fp16, x = input_15_cast_fp16)[name = tensor<string, []>("linear_18_cast_fp16")];
tensor<int32, [3]> var_298_split_sizes_0 = const()[name = tensor<string, []>("op_298_split_sizes_0"), val = tensor<int32, [3]>([512, 512, 512])];
tensor<int32, []> var_298_axis_0 = const()[name = tensor<string, []>("op_298_axis_0"), val = tensor<int32, []>(-1)];
tensor<fp16, [1, 512]> var_298_cast_fp16_0, tensor<fp16, [1, 512]> var_298_cast_fp16_1, tensor<fp16, [1, 512]> var_298_cast_fp16_2 = split(axis = var_298_axis_0, split_sizes = var_298_split_sizes_0, x = linear_18_cast_fp16)[name = tensor<string, []>("op_298_cast_fp16")];
tensor<int32, [1]> mean_9_axes_0 = const()[name = tensor<string, []>("mean_9_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> mean_9_keep_dims_0 = const()[name = tensor<string, []>("mean_9_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> mean_9_cast_fp16 = reduce_mean(axes = mean_9_axes_0, keep_dims = mean_9_keep_dims_0, x = x_29_cast_fp16)[name = tensor<string, []>("mean_9_cast_fp16")];
tensor<fp16, [1, 512]> sub_8_cast_fp16 = sub(x = x_29_cast_fp16, y = mean_9_cast_fp16)[name = tensor<string, []>("sub_8_cast_fp16")];
tensor<fp16, [1, 512]> square_6_cast_fp16 = square(x = sub_8_cast_fp16)[name = tensor<string, []>("square_6_cast_fp16")];
tensor<int32, [1]> reduce_mean_13_axes_0 = const()[name = tensor<string, []>("reduce_mean_13_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_13_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_13_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_13_cast_fp16 = reduce_mean(axes = reduce_mean_13_axes_0, keep_dims = reduce_mean_13_keep_dims_0, x = square_6_cast_fp16)[name = tensor<string, []>("reduce_mean_13_cast_fp16")];
tensor<fp16, []> var_308_to_fp16 = const()[name = tensor<string, []>("op_308_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 1]> var_309_cast_fp16 = add(x = reduce_mean_13_cast_fp16, y = var_308_to_fp16)[name = tensor<string, []>("op_309_cast_fp16")];
tensor<fp16, [1, 1]> var_310_cast_fp16 = sqrt(x = var_309_cast_fp16)[name = tensor<string, []>("op_310_cast_fp16")];
tensor<fp16, [1, 512]> x_31_cast_fp16 = real_div(x = sub_8_cast_fp16, y = var_310_cast_fp16)[name = tensor<string, []>("x_31_cast_fp16")];
tensor<fp16, [512]> flow_net_res_blocks_4_in_ln_weight_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_4_in_ln_weight_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14231488)))];
tensor<fp16, [1, 512]> var_312_cast_fp16 = mul(x = x_31_cast_fp16, y = flow_net_res_blocks_4_in_ln_weight_to_fp16)[name = tensor<string, []>("op_312_cast_fp16")];
tensor<fp16, [512]> flow_net_res_blocks_4_in_ln_bias_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_4_in_ln_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14232576)))];
tensor<fp16, [1, 512]> x_33_cast_fp16 = add(x = var_312_cast_fp16, y = flow_net_res_blocks_4_in_ln_bias_to_fp16)[name = tensor<string, []>("x_33_cast_fp16")];
tensor<fp16, []> var_314_promoted_to_fp16 = const()[name = tensor<string, []>("op_314_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
tensor<fp16, [1, 512]> var_315_cast_fp16 = add(x = var_298_cast_fp16_1, y = var_314_promoted_to_fp16)[name = tensor<string, []>("op_315_cast_fp16")];
tensor<fp16, [1, 512]> var_316_cast_fp16 = mul(x = x_33_cast_fp16, y = var_315_cast_fp16)[name = tensor<string, []>("op_316_cast_fp16")];
tensor<fp16, [1, 512]> input_49_cast_fp16 = add(x = var_316_cast_fp16, y = var_298_cast_fp16_0)[name = tensor<string, []>("input_49_cast_fp16")];
tensor<fp16, [512, 512]> flow_net_res_blocks_4_mlp_0_weight_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_4_mlp_0_weight_to_fp16"), val = tensor<fp16, [512, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14233664)))];
tensor<fp16, [512]> flow_net_res_blocks_4_mlp_0_bias_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_4_mlp_0_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14758016)))];
tensor<fp16, [1, 512]> linear_19_cast_fp16 = linear(bias = flow_net_res_blocks_4_mlp_0_bias_to_fp16, weight = flow_net_res_blocks_4_mlp_0_weight_to_fp16, x = input_49_cast_fp16)[name = tensor<string, []>("linear_19_cast_fp16")];
tensor<fp16, [1, 512]> input_53_cast_fp16 = silu(x = linear_19_cast_fp16)[name = tensor<string, []>("input_53_cast_fp16")];
tensor<fp16, [512, 512]> flow_net_res_blocks_4_mlp_2_weight_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_4_mlp_2_weight_to_fp16"), val = tensor<fp16, [512, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(14759104)))];
tensor<fp16, [512]> flow_net_res_blocks_4_mlp_2_bias_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_4_mlp_2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(15283456)))];
tensor<fp16, [1, 512]> linear_20_cast_fp16 = linear(bias = flow_net_res_blocks_4_mlp_2_bias_to_fp16, weight = flow_net_res_blocks_4_mlp_2_weight_to_fp16, x = input_53_cast_fp16)[name = tensor<string, []>("linear_20_cast_fp16")];
tensor<fp16, [1, 512]> var_327_cast_fp16 = mul(x = var_298_cast_fp16_2, y = linear_20_cast_fp16)[name = tensor<string, []>("op_327_cast_fp16")];
tensor<fp16, [1, 512]> x_35_cast_fp16 = add(x = x_29_cast_fp16, y = var_327_cast_fp16)[name = tensor<string, []>("x_35_cast_fp16")];
tensor<fp16, [1536, 512]> flow_net_res_blocks_5_adaLN_modulation_1_weight_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_5_adaLN_modulation_1_weight_to_fp16"), val = tensor<fp16, [1536, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(15284544)))];
tensor<fp16, [1536]> flow_net_res_blocks_5_adaLN_modulation_1_bias_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_5_adaLN_modulation_1_bias_to_fp16"), val = tensor<fp16, [1536]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16857472)))];
tensor<fp16, [1, 1536]> linear_21_cast_fp16 = linear(bias = flow_net_res_blocks_5_adaLN_modulation_1_bias_to_fp16, weight = flow_net_res_blocks_5_adaLN_modulation_1_weight_to_fp16, x = input_15_cast_fp16)[name = tensor<string, []>("linear_21_cast_fp16")];
tensor<int32, [3]> var_337_split_sizes_0 = const()[name = tensor<string, []>("op_337_split_sizes_0"), val = tensor<int32, [3]>([512, 512, 512])];
tensor<int32, []> var_337_axis_0 = const()[name = tensor<string, []>("op_337_axis_0"), val = tensor<int32, []>(-1)];
tensor<fp16, [1, 512]> var_337_cast_fp16_0, tensor<fp16, [1, 512]> var_337_cast_fp16_1, tensor<fp16, [1, 512]> var_337_cast_fp16_2 = split(axis = var_337_axis_0, split_sizes = var_337_split_sizes_0, x = linear_21_cast_fp16)[name = tensor<string, []>("op_337_cast_fp16")];
tensor<int32, [1]> mean_11_axes_0 = const()[name = tensor<string, []>("mean_11_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> mean_11_keep_dims_0 = const()[name = tensor<string, []>("mean_11_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> mean_11_cast_fp16 = reduce_mean(axes = mean_11_axes_0, keep_dims = mean_11_keep_dims_0, x = x_35_cast_fp16)[name = tensor<string, []>("mean_11_cast_fp16")];
tensor<fp16, [1, 512]> sub_9_cast_fp16 = sub(x = x_35_cast_fp16, y = mean_11_cast_fp16)[name = tensor<string, []>("sub_9_cast_fp16")];
tensor<fp16, [1, 512]> square_7_cast_fp16 = square(x = sub_9_cast_fp16)[name = tensor<string, []>("square_7_cast_fp16")];
tensor<int32, [1]> reduce_mean_15_axes_0 = const()[name = tensor<string, []>("reduce_mean_15_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_15_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_15_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_15_cast_fp16 = reduce_mean(axes = reduce_mean_15_axes_0, keep_dims = reduce_mean_15_keep_dims_0, x = square_7_cast_fp16)[name = tensor<string, []>("reduce_mean_15_cast_fp16")];
tensor<fp16, []> var_347_to_fp16 = const()[name = tensor<string, []>("op_347_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 1]> var_348_cast_fp16 = add(x = reduce_mean_15_cast_fp16, y = var_347_to_fp16)[name = tensor<string, []>("op_348_cast_fp16")];
tensor<fp16, [1, 1]> var_349_cast_fp16 = sqrt(x = var_348_cast_fp16)[name = tensor<string, []>("op_349_cast_fp16")];
tensor<fp16, [1, 512]> x_37_cast_fp16 = real_div(x = sub_9_cast_fp16, y = var_349_cast_fp16)[name = tensor<string, []>("x_37_cast_fp16")];
tensor<fp16, [512]> flow_net_res_blocks_5_in_ln_weight_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_5_in_ln_weight_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16860608)))];
tensor<fp16, [1, 512]> var_351_cast_fp16 = mul(x = x_37_cast_fp16, y = flow_net_res_blocks_5_in_ln_weight_to_fp16)[name = tensor<string, []>("op_351_cast_fp16")];
tensor<fp16, [512]> flow_net_res_blocks_5_in_ln_bias_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_5_in_ln_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16861696)))];
tensor<fp16, [1, 512]> x_39_cast_fp16 = add(x = var_351_cast_fp16, y = flow_net_res_blocks_5_in_ln_bias_to_fp16)[name = tensor<string, []>("x_39_cast_fp16")];
tensor<fp16, []> var_353_promoted_to_fp16 = const()[name = tensor<string, []>("op_353_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
tensor<fp16, [1, 512]> var_354_cast_fp16 = add(x = var_337_cast_fp16_1, y = var_353_promoted_to_fp16)[name = tensor<string, []>("op_354_cast_fp16")];
tensor<fp16, [1, 512]> var_355_cast_fp16 = mul(x = x_39_cast_fp16, y = var_354_cast_fp16)[name = tensor<string, []>("op_355_cast_fp16")];
tensor<fp16, [1, 512]> input_57_cast_fp16 = add(x = var_355_cast_fp16, y = var_337_cast_fp16_0)[name = tensor<string, []>("input_57_cast_fp16")];
tensor<fp16, [512, 512]> flow_net_res_blocks_5_mlp_0_weight_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_5_mlp_0_weight_to_fp16"), val = tensor<fp16, [512, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(16862784)))];
tensor<fp16, [512]> flow_net_res_blocks_5_mlp_0_bias_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_5_mlp_0_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17387136)))];
tensor<fp16, [1, 512]> linear_22_cast_fp16 = linear(bias = flow_net_res_blocks_5_mlp_0_bias_to_fp16, weight = flow_net_res_blocks_5_mlp_0_weight_to_fp16, x = input_57_cast_fp16)[name = tensor<string, []>("linear_22_cast_fp16")];
tensor<fp16, [1, 512]> input_61_cast_fp16 = silu(x = linear_22_cast_fp16)[name = tensor<string, []>("input_61_cast_fp16")];
tensor<fp16, [512, 512]> flow_net_res_blocks_5_mlp_2_weight_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_5_mlp_2_weight_to_fp16"), val = tensor<fp16, [512, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17388224)))];
tensor<fp16, [512]> flow_net_res_blocks_5_mlp_2_bias_to_fp16 = const()[name = tensor<string, []>("flow_net_res_blocks_5_mlp_2_bias_to_fp16"), val = tensor<fp16, [512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17912576)))];
tensor<fp16, [1, 512]> linear_23_cast_fp16 = linear(bias = flow_net_res_blocks_5_mlp_2_bias_to_fp16, weight = flow_net_res_blocks_5_mlp_2_weight_to_fp16, x = input_61_cast_fp16)[name = tensor<string, []>("linear_23_cast_fp16")];
tensor<fp16, [1, 512]> var_366_cast_fp16 = mul(x = var_337_cast_fp16_2, y = linear_23_cast_fp16)[name = tensor<string, []>("op_366_cast_fp16")];
tensor<fp16, [1, 512]> x_41_cast_fp16 = add(x = x_35_cast_fp16, y = var_366_cast_fp16)[name = tensor<string, []>("x_41_cast_fp16")];
tensor<fp16, [1024, 512]> flow_net_final_layer_adaLN_modulation_1_weight_to_fp16 = const()[name = tensor<string, []>("flow_net_final_layer_adaLN_modulation_1_weight_to_fp16"), val = tensor<fp16, [1024, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(17913664)))];
tensor<fp16, [1024]> flow_net_final_layer_adaLN_modulation_1_bias_to_fp16 = const()[name = tensor<string, []>("flow_net_final_layer_adaLN_modulation_1_bias_to_fp16"), val = tensor<fp16, [1024]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18962304)))];
tensor<fp16, [1, 1024]> linear_24_cast_fp16 = linear(bias = flow_net_final_layer_adaLN_modulation_1_bias_to_fp16, weight = flow_net_final_layer_adaLN_modulation_1_weight_to_fp16, x = input_15_cast_fp16)[name = tensor<string, []>("linear_24_cast_fp16")];
tensor<int32, [2]> var_375_split_sizes_0 = const()[name = tensor<string, []>("op_375_split_sizes_0"), val = tensor<int32, [2]>([512, 512])];
tensor<int32, []> var_375_axis_0 = const()[name = tensor<string, []>("op_375_axis_0"), val = tensor<int32, []>(-1)];
tensor<fp16, [1, 512]> var_375_cast_fp16_0, tensor<fp16, [1, 512]> var_375_cast_fp16_1 = split(axis = var_375_axis_0, split_sizes = var_375_split_sizes_0, x = linear_24_cast_fp16)[name = tensor<string, []>("op_375_cast_fp16")];
tensor<int32, [1]> mean_13_axes_0 = const()[name = tensor<string, []>("mean_13_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> mean_13_keep_dims_0 = const()[name = tensor<string, []>("mean_13_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> mean_13_cast_fp16 = reduce_mean(axes = mean_13_axes_0, keep_dims = mean_13_keep_dims_0, x = x_41_cast_fp16)[name = tensor<string, []>("mean_13_cast_fp16")];
tensor<fp16, [1, 512]> sub_10_cast_fp16 = sub(x = x_41_cast_fp16, y = mean_13_cast_fp16)[name = tensor<string, []>("sub_10_cast_fp16")];
tensor<fp16, [1, 512]> square_8_cast_fp16 = square(x = sub_10_cast_fp16)[name = tensor<string, []>("square_8_cast_fp16")];
tensor<int32, [1]> reduce_mean_17_axes_0 = const()[name = tensor<string, []>("reduce_mean_17_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_17_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_17_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_17_cast_fp16 = reduce_mean(axes = reduce_mean_17_axes_0, keep_dims = reduce_mean_17_keep_dims_0, x = square_8_cast_fp16)[name = tensor<string, []>("reduce_mean_17_cast_fp16")];
tensor<fp16, []> var_382_to_fp16 = const()[name = tensor<string, []>("op_382_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 1]> var_383_cast_fp16 = add(x = reduce_mean_17_cast_fp16, y = var_382_to_fp16)[name = tensor<string, []>("op_383_cast_fp16")];
tensor<fp16, [1, 1]> var_384_cast_fp16 = sqrt(x = var_383_cast_fp16)[name = tensor<string, []>("op_384_cast_fp16")];
tensor<fp16, [1, 512]> x_43_cast_fp16 = real_div(x = sub_10_cast_fp16, y = var_384_cast_fp16)[name = tensor<string, []>("x_43_cast_fp16")];
tensor<fp16, []> var_386_promoted_to_fp16 = const()[name = tensor<string, []>("op_386_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
tensor<fp16, [1, 512]> var_387_cast_fp16 = add(x = var_375_cast_fp16_1, y = var_386_promoted_to_fp16)[name = tensor<string, []>("op_387_cast_fp16")];
tensor<fp16, [1, 512]> var_388_cast_fp16 = mul(x = x_43_cast_fp16, y = var_387_cast_fp16)[name = tensor<string, []>("op_388_cast_fp16")];
tensor<fp16, [1, 512]> input_65_cast_fp16 = add(x = var_388_cast_fp16, y = var_375_cast_fp16_0)[name = tensor<string, []>("input_65_cast_fp16")];
tensor<fp16, [32, 512]> flow_net_final_layer_linear_weight_to_fp16 = const()[name = tensor<string, []>("flow_net_final_layer_linear_weight_to_fp16"), val = tensor<fp16, [32, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18964416)))];
tensor<fp16, [32]> flow_net_final_layer_linear_bias_to_fp16 = const()[name = tensor<string, []>("flow_net_final_layer_linear_bias_to_fp16"), val = tensor<fp16, [32]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18997248)))];
tensor<fp16, [1, 32]> linear_25_cast_fp16 = linear(bias = flow_net_final_layer_linear_bias_to_fp16, weight = flow_net_final_layer_linear_weight_to_fp16, x = input_65_cast_fp16)[name = tensor<string, []>("linear_25_cast_fp16")];
tensor<fp16, []> var_399_to_fp16 = const()[name = tensor<string, []>("op_399_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 32]> var_400_cast_fp16 = mul(x = linear_25_cast_fp16, y = var_399_to_fp16)[name = tensor<string, []>("op_400_cast_fp16")];
tensor<fp16, [1, 32]> input_67_cast_fp16 = add(x = latent_init_to_fp16, y = var_400_cast_fp16)[name = tensor<string, []>("input_67_cast_fp16")];
tensor<fp16, [1, 512]> linear_26_cast_fp16 = linear(bias = flow_net_input_proj_bias_to_fp16, weight = flow_net_input_proj_weight_to_fp16, x = input_67_cast_fp16)[name = tensor<string, []>("linear_26_cast_fp16")];
tensor<fp16, [1, 512]> input_71_to_fp16 = const()[name = tensor<string, []>("input_71_to_fp16"), val = tensor<fp16, [1, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18997376)))];
tensor<fp16, [1, 512]> input_73_cast_fp16 = silu(x = input_71_to_fp16)[name = tensor<string, []>("input_73_cast_fp16")];
tensor<fp16, [1, 512]> linear_28_cast_fp16 = linear(bias = flow_net_time_embed_0_mlp_2_bias_to_fp16, weight = flow_net_time_embed_0_mlp_2_weight_to_fp16, x = input_73_cast_fp16)[name = tensor<string, []>("linear_28_cast_fp16")];
tensor<int32, [1]> reduce_mean_18_axes_0 = const()[name = tensor<string, []>("reduce_mean_18_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_18_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_18_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_18_cast_fp16 = reduce_mean(axes = reduce_mean_18_axes_0, keep_dims = reduce_mean_18_keep_dims_0, x = linear_28_cast_fp16)[name = tensor<string, []>("reduce_mean_18_cast_fp16")];
tensor<fp16, [1, 512]> sub_11_cast_fp16 = sub(x = linear_28_cast_fp16, y = reduce_mean_18_cast_fp16)[name = tensor<string, []>("sub_11_cast_fp16")];
tensor<fp16, [1, 512]> square_9_cast_fp16 = square(x = sub_11_cast_fp16)[name = tensor<string, []>("square_9_cast_fp16")];
tensor<int32, [1]> reduce_mean_19_axes_0 = const()[name = tensor<string, []>("reduce_mean_19_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_19_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_19_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_19_cast_fp16 = reduce_mean(axes = reduce_mean_19_axes_0, keep_dims = reduce_mean_19_keep_dims_0, x = square_9_cast_fp16)[name = tensor<string, []>("reduce_mean_19_cast_fp16")];
tensor<fp16, []> real_div_2_to_fp16 = const()[name = tensor<string, []>("real_div_2_to_fp16"), val = tensor<fp16, []>(0x1.008p+0)];
tensor<fp16, [1, 1]> mul_2_cast_fp16 = mul(x = reduce_mean_19_cast_fp16, y = real_div_2_to_fp16)[name = tensor<string, []>("mul_2_cast_fp16")];
tensor<fp16, []> var_466_to_fp16 = const()[name = tensor<string, []>("op_466_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1]> var_19_cast_fp16 = add(x = mul_2_cast_fp16, y = var_466_to_fp16)[name = tensor<string, []>("var_19_cast_fp16")];
tensor<fp32, []> var_469_epsilon_0 = const()[name = tensor<string, []>("op_469_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
tensor<fp16, [1, 1]> var_469_cast_fp16 = rsqrt(epsilon = var_469_epsilon_0, x = var_19_cast_fp16)[name = tensor<string, []>("op_469_cast_fp16")];
tensor<fp16, [1, 512]> var_470_cast_fp16 = mul(x = const_3_to_fp16, y = var_469_cast_fp16)[name = tensor<string, []>("op_470_cast_fp16")];
tensor<fp16, [1, 512]> var_471_cast_fp16 = mul(x = linear_28_cast_fp16, y = var_470_cast_fp16)[name = tensor<string, []>("op_471_cast_fp16")];
tensor<fp16, [1, 512]> input_77_to_fp16 = const()[name = tensor<string, []>("input_77_to_fp16"), val = tensor<fp16, [1, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18998464)))];
tensor<fp16, [1, 512]> input_79_cast_fp16 = silu(x = input_77_to_fp16)[name = tensor<string, []>("input_79_cast_fp16")];
tensor<fp16, [1, 512]> linear_30_cast_fp16 = linear(bias = flow_net_time_embed_1_mlp_2_bias_to_fp16, weight = flow_net_time_embed_1_mlp_2_weight_to_fp16, x = input_79_cast_fp16)[name = tensor<string, []>("linear_30_cast_fp16")];
tensor<int32, [1]> reduce_mean_20_axes_0 = const()[name = tensor<string, []>("reduce_mean_20_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_20_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_20_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_20_cast_fp16 = reduce_mean(axes = reduce_mean_20_axes_0, keep_dims = reduce_mean_20_keep_dims_0, x = linear_30_cast_fp16)[name = tensor<string, []>("reduce_mean_20_cast_fp16")];
tensor<fp16, [1, 512]> sub_13_cast_fp16 = sub(x = linear_30_cast_fp16, y = reduce_mean_20_cast_fp16)[name = tensor<string, []>("sub_13_cast_fp16")];
tensor<fp16, [1, 512]> square_10_cast_fp16 = square(x = sub_13_cast_fp16)[name = tensor<string, []>("square_10_cast_fp16")];
tensor<int32, [1]> reduce_mean_21_axes_0 = const()[name = tensor<string, []>("reduce_mean_21_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_21_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_21_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_21_cast_fp16 = reduce_mean(axes = reduce_mean_21_axes_0, keep_dims = reduce_mean_21_keep_dims_0, x = square_10_cast_fp16)[name = tensor<string, []>("reduce_mean_21_cast_fp16")];
tensor<fp16, []> real_div_3_to_fp16 = const()[name = tensor<string, []>("real_div_3_to_fp16"), val = tensor<fp16, []>(0x1.008p+0)];
tensor<fp16, [1, 1]> mul_3_cast_fp16 = mul(x = reduce_mean_21_cast_fp16, y = real_div_3_to_fp16)[name = tensor<string, []>("mul_3_cast_fp16")];
tensor<fp16, []> var_503_to_fp16 = const()[name = tensor<string, []>("op_503_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1]> var_21_cast_fp16 = add(x = mul_3_cast_fp16, y = var_503_to_fp16)[name = tensor<string, []>("var_21_cast_fp16")];
tensor<fp32, []> var_506_epsilon_0 = const()[name = tensor<string, []>("op_506_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
tensor<fp16, [1, 1]> var_506_cast_fp16 = rsqrt(epsilon = var_506_epsilon_0, x = var_21_cast_fp16)[name = tensor<string, []>("op_506_cast_fp16")];
tensor<fp16, [1, 512]> var_507_cast_fp16 = mul(x = const_5_to_fp16, y = var_506_cast_fp16)[name = tensor<string, []>("op_507_cast_fp16")];
tensor<fp16, [1, 512]> var_508_cast_fp16 = mul(x = linear_30_cast_fp16, y = var_507_cast_fp16)[name = tensor<string, []>("op_508_cast_fp16")];
tensor<fp16, [1, 512]> var_520_cast_fp16 = add(x = var_471_cast_fp16, y = var_508_cast_fp16)[name = tensor<string, []>("op_520_cast_fp16")];
tensor<fp16, []> _inversed_t_combined_3_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_t_combined_3_y_0_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
tensor<fp16, [1, 512]> _inversed_t_combined_3_cast_fp16 = mul(x = var_520_cast_fp16, y = _inversed_t_combined_3_y_0_to_fp16)[name = tensor<string, []>("_inversed_t_combined_3_cast_fp16")];
tensor<fp16, [1, 512]> input_81_cast_fp16 = add(x = _inversed_t_combined_3_cast_fp16, y = linear_5_cast_fp16)[name = tensor<string, []>("input_81_cast_fp16")];
tensor<fp16, [1, 512]> input_83_cast_fp16 = silu(x = input_81_cast_fp16)[name = tensor<string, []>("input_83_cast_fp16")];
tensor<fp16, [1, 1536]> linear_32_cast_fp16 = linear(bias = flow_net_res_blocks_0_adaLN_modulation_1_bias_to_fp16, weight = flow_net_res_blocks_0_adaLN_modulation_1_weight_to_fp16, x = input_83_cast_fp16)[name = tensor<string, []>("linear_32_cast_fp16")];
tensor<int32, [3]> var_535_split_sizes_0 = const()[name = tensor<string, []>("op_535_split_sizes_0"), val = tensor<int32, [3]>([512, 512, 512])];
tensor<int32, []> var_535_axis_0 = const()[name = tensor<string, []>("op_535_axis_0"), val = tensor<int32, []>(-1)];
tensor<fp16, [1, 512]> var_535_cast_fp16_0, tensor<fp16, [1, 512]> var_535_cast_fp16_1, tensor<fp16, [1, 512]> var_535_cast_fp16_2 = split(axis = var_535_axis_0, split_sizes = var_535_split_sizes_0, x = linear_32_cast_fp16)[name = tensor<string, []>("op_535_cast_fp16")];
tensor<int32, [1]> mean_15_axes_0 = const()[name = tensor<string, []>("mean_15_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> mean_15_keep_dims_0 = const()[name = tensor<string, []>("mean_15_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> mean_15_cast_fp16 = reduce_mean(axes = mean_15_axes_0, keep_dims = mean_15_keep_dims_0, x = linear_26_cast_fp16)[name = tensor<string, []>("mean_15_cast_fp16")];
tensor<fp16, [1, 512]> sub_15_cast_fp16 = sub(x = linear_26_cast_fp16, y = mean_15_cast_fp16)[name = tensor<string, []>("sub_15_cast_fp16")];
tensor<fp16, [1, 512]> square_11_cast_fp16 = square(x = sub_15_cast_fp16)[name = tensor<string, []>("square_11_cast_fp16")];
tensor<int32, [1]> reduce_mean_23_axes_0 = const()[name = tensor<string, []>("reduce_mean_23_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_23_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_23_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_23_cast_fp16 = reduce_mean(axes = reduce_mean_23_axes_0, keep_dims = reduce_mean_23_keep_dims_0, x = square_11_cast_fp16)[name = tensor<string, []>("reduce_mean_23_cast_fp16")];
tensor<fp16, []> var_545_to_fp16 = const()[name = tensor<string, []>("op_545_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 1]> var_546_cast_fp16 = add(x = reduce_mean_23_cast_fp16, y = var_545_to_fp16)[name = tensor<string, []>("op_546_cast_fp16")];
tensor<fp16, [1, 1]> var_547_cast_fp16 = sqrt(x = var_546_cast_fp16)[name = tensor<string, []>("op_547_cast_fp16")];
tensor<fp16, [1, 512]> x_51_cast_fp16 = real_div(x = sub_15_cast_fp16, y = var_547_cast_fp16)[name = tensor<string, []>("x_51_cast_fp16")];
tensor<fp16, [1, 512]> var_549_cast_fp16 = mul(x = x_51_cast_fp16, y = flow_net_res_blocks_0_in_ln_weight_to_fp16)[name = tensor<string, []>("op_549_cast_fp16")];
tensor<fp16, [1, 512]> x_53_cast_fp16 = add(x = var_549_cast_fp16, y = flow_net_res_blocks_0_in_ln_bias_to_fp16)[name = tensor<string, []>("x_53_cast_fp16")];
tensor<fp16, []> var_551_promoted_to_fp16 = const()[name = tensor<string, []>("op_551_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
tensor<fp16, [1, 512]> var_552_cast_fp16 = add(x = var_535_cast_fp16_1, y = var_551_promoted_to_fp16)[name = tensor<string, []>("op_552_cast_fp16")];
tensor<fp16, [1, 512]> var_553_cast_fp16 = mul(x = x_53_cast_fp16, y = var_552_cast_fp16)[name = tensor<string, []>("op_553_cast_fp16")];
tensor<fp16, [1, 512]> input_85_cast_fp16 = add(x = var_553_cast_fp16, y = var_535_cast_fp16_0)[name = tensor<string, []>("input_85_cast_fp16")];
tensor<fp16, [1, 512]> linear_33_cast_fp16 = linear(bias = flow_net_res_blocks_0_mlp_0_bias_to_fp16, weight = flow_net_res_blocks_0_mlp_0_weight_to_fp16, x = input_85_cast_fp16)[name = tensor<string, []>("linear_33_cast_fp16")];
tensor<fp16, [1, 512]> input_89_cast_fp16 = silu(x = linear_33_cast_fp16)[name = tensor<string, []>("input_89_cast_fp16")];
tensor<fp16, [1, 512]> linear_34_cast_fp16 = linear(bias = flow_net_res_blocks_0_mlp_2_bias_to_fp16, weight = flow_net_res_blocks_0_mlp_2_weight_to_fp16, x = input_89_cast_fp16)[name = tensor<string, []>("linear_34_cast_fp16")];
tensor<fp16, [1, 512]> var_564_cast_fp16 = mul(x = var_535_cast_fp16_2, y = linear_34_cast_fp16)[name = tensor<string, []>("op_564_cast_fp16")];
tensor<fp16, [1, 512]> x_55_cast_fp16 = add(x = linear_26_cast_fp16, y = var_564_cast_fp16)[name = tensor<string, []>("x_55_cast_fp16")];
tensor<fp16, [1, 1536]> linear_35_cast_fp16 = linear(bias = flow_net_res_blocks_1_adaLN_modulation_1_bias_to_fp16, weight = flow_net_res_blocks_1_adaLN_modulation_1_weight_to_fp16, x = input_83_cast_fp16)[name = tensor<string, []>("linear_35_cast_fp16")];
tensor<int32, [3]> var_574_split_sizes_0 = const()[name = tensor<string, []>("op_574_split_sizes_0"), val = tensor<int32, [3]>([512, 512, 512])];
tensor<int32, []> var_574_axis_0 = const()[name = tensor<string, []>("op_574_axis_0"), val = tensor<int32, []>(-1)];
tensor<fp16, [1, 512]> var_574_cast_fp16_0, tensor<fp16, [1, 512]> var_574_cast_fp16_1, tensor<fp16, [1, 512]> var_574_cast_fp16_2 = split(axis = var_574_axis_0, split_sizes = var_574_split_sizes_0, x = linear_35_cast_fp16)[name = tensor<string, []>("op_574_cast_fp16")];
tensor<int32, [1]> mean_17_axes_0 = const()[name = tensor<string, []>("mean_17_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> mean_17_keep_dims_0 = const()[name = tensor<string, []>("mean_17_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> mean_17_cast_fp16 = reduce_mean(axes = mean_17_axes_0, keep_dims = mean_17_keep_dims_0, x = x_55_cast_fp16)[name = tensor<string, []>("mean_17_cast_fp16")];
tensor<fp16, [1, 512]> sub_16_cast_fp16 = sub(x = x_55_cast_fp16, y = mean_17_cast_fp16)[name = tensor<string, []>("sub_16_cast_fp16")];
tensor<fp16, [1, 512]> square_12_cast_fp16 = square(x = sub_16_cast_fp16)[name = tensor<string, []>("square_12_cast_fp16")];
tensor<int32, [1]> reduce_mean_25_axes_0 = const()[name = tensor<string, []>("reduce_mean_25_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_25_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_25_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_25_cast_fp16 = reduce_mean(axes = reduce_mean_25_axes_0, keep_dims = reduce_mean_25_keep_dims_0, x = square_12_cast_fp16)[name = tensor<string, []>("reduce_mean_25_cast_fp16")];
tensor<fp16, []> var_584_to_fp16 = const()[name = tensor<string, []>("op_584_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 1]> var_585_cast_fp16 = add(x = reduce_mean_25_cast_fp16, y = var_584_to_fp16)[name = tensor<string, []>("op_585_cast_fp16")];
tensor<fp16, [1, 1]> var_586_cast_fp16 = sqrt(x = var_585_cast_fp16)[name = tensor<string, []>("op_586_cast_fp16")];
tensor<fp16, [1, 512]> x_57_cast_fp16 = real_div(x = sub_16_cast_fp16, y = var_586_cast_fp16)[name = tensor<string, []>("x_57_cast_fp16")];
tensor<fp16, [1, 512]> var_588_cast_fp16 = mul(x = x_57_cast_fp16, y = flow_net_res_blocks_1_in_ln_weight_to_fp16)[name = tensor<string, []>("op_588_cast_fp16")];
tensor<fp16, [1, 512]> x_59_cast_fp16 = add(x = var_588_cast_fp16, y = flow_net_res_blocks_1_in_ln_bias_to_fp16)[name = tensor<string, []>("x_59_cast_fp16")];
tensor<fp16, []> var_590_promoted_to_fp16 = const()[name = tensor<string, []>("op_590_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
tensor<fp16, [1, 512]> var_591_cast_fp16 = add(x = var_574_cast_fp16_1, y = var_590_promoted_to_fp16)[name = tensor<string, []>("op_591_cast_fp16")];
tensor<fp16, [1, 512]> var_592_cast_fp16 = mul(x = x_59_cast_fp16, y = var_591_cast_fp16)[name = tensor<string, []>("op_592_cast_fp16")];
tensor<fp16, [1, 512]> input_93_cast_fp16 = add(x = var_592_cast_fp16, y = var_574_cast_fp16_0)[name = tensor<string, []>("input_93_cast_fp16")];
tensor<fp16, [1, 512]> linear_36_cast_fp16 = linear(bias = flow_net_res_blocks_1_mlp_0_bias_to_fp16, weight = flow_net_res_blocks_1_mlp_0_weight_to_fp16, x = input_93_cast_fp16)[name = tensor<string, []>("linear_36_cast_fp16")];
tensor<fp16, [1, 512]> input_97_cast_fp16 = silu(x = linear_36_cast_fp16)[name = tensor<string, []>("input_97_cast_fp16")];
tensor<fp16, [1, 512]> linear_37_cast_fp16 = linear(bias = flow_net_res_blocks_1_mlp_2_bias_to_fp16, weight = flow_net_res_blocks_1_mlp_2_weight_to_fp16, x = input_97_cast_fp16)[name = tensor<string, []>("linear_37_cast_fp16")];
tensor<fp16, [1, 512]> var_603_cast_fp16 = mul(x = var_574_cast_fp16_2, y = linear_37_cast_fp16)[name = tensor<string, []>("op_603_cast_fp16")];
tensor<fp16, [1, 512]> x_61_cast_fp16 = add(x = x_55_cast_fp16, y = var_603_cast_fp16)[name = tensor<string, []>("x_61_cast_fp16")];
tensor<fp16, [1, 1536]> linear_38_cast_fp16 = linear(bias = flow_net_res_blocks_2_adaLN_modulation_1_bias_to_fp16, weight = flow_net_res_blocks_2_adaLN_modulation_1_weight_to_fp16, x = input_83_cast_fp16)[name = tensor<string, []>("linear_38_cast_fp16")];
tensor<int32, [3]> var_613_split_sizes_0 = const()[name = tensor<string, []>("op_613_split_sizes_0"), val = tensor<int32, [3]>([512, 512, 512])];
tensor<int32, []> var_613_axis_0 = const()[name = tensor<string, []>("op_613_axis_0"), val = tensor<int32, []>(-1)];
tensor<fp16, [1, 512]> var_613_cast_fp16_0, tensor<fp16, [1, 512]> var_613_cast_fp16_1, tensor<fp16, [1, 512]> var_613_cast_fp16_2 = split(axis = var_613_axis_0, split_sizes = var_613_split_sizes_0, x = linear_38_cast_fp16)[name = tensor<string, []>("op_613_cast_fp16")];
tensor<int32, [1]> mean_19_axes_0 = const()[name = tensor<string, []>("mean_19_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> mean_19_keep_dims_0 = const()[name = tensor<string, []>("mean_19_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> mean_19_cast_fp16 = reduce_mean(axes = mean_19_axes_0, keep_dims = mean_19_keep_dims_0, x = x_61_cast_fp16)[name = tensor<string, []>("mean_19_cast_fp16")];
tensor<fp16, [1, 512]> sub_17_cast_fp16 = sub(x = x_61_cast_fp16, y = mean_19_cast_fp16)[name = tensor<string, []>("sub_17_cast_fp16")];
tensor<fp16, [1, 512]> square_13_cast_fp16 = square(x = sub_17_cast_fp16)[name = tensor<string, []>("square_13_cast_fp16")];
tensor<int32, [1]> reduce_mean_27_axes_0 = const()[name = tensor<string, []>("reduce_mean_27_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_27_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_27_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_27_cast_fp16 = reduce_mean(axes = reduce_mean_27_axes_0, keep_dims = reduce_mean_27_keep_dims_0, x = square_13_cast_fp16)[name = tensor<string, []>("reduce_mean_27_cast_fp16")];
tensor<fp16, []> var_623_to_fp16 = const()[name = tensor<string, []>("op_623_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 1]> var_624_cast_fp16 = add(x = reduce_mean_27_cast_fp16, y = var_623_to_fp16)[name = tensor<string, []>("op_624_cast_fp16")];
tensor<fp16, [1, 1]> var_625_cast_fp16 = sqrt(x = var_624_cast_fp16)[name = tensor<string, []>("op_625_cast_fp16")];
tensor<fp16, [1, 512]> x_63_cast_fp16 = real_div(x = sub_17_cast_fp16, y = var_625_cast_fp16)[name = tensor<string, []>("x_63_cast_fp16")];
tensor<fp16, [1, 512]> var_627_cast_fp16 = mul(x = x_63_cast_fp16, y = flow_net_res_blocks_2_in_ln_weight_to_fp16)[name = tensor<string, []>("op_627_cast_fp16")];
tensor<fp16, [1, 512]> x_65_cast_fp16 = add(x = var_627_cast_fp16, y = flow_net_res_blocks_2_in_ln_bias_to_fp16)[name = tensor<string, []>("x_65_cast_fp16")];
tensor<fp16, []> var_629_promoted_to_fp16 = const()[name = tensor<string, []>("op_629_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
tensor<fp16, [1, 512]> var_630_cast_fp16 = add(x = var_613_cast_fp16_1, y = var_629_promoted_to_fp16)[name = tensor<string, []>("op_630_cast_fp16")];
tensor<fp16, [1, 512]> var_631_cast_fp16 = mul(x = x_65_cast_fp16, y = var_630_cast_fp16)[name = tensor<string, []>("op_631_cast_fp16")];
tensor<fp16, [1, 512]> input_101_cast_fp16 = add(x = var_631_cast_fp16, y = var_613_cast_fp16_0)[name = tensor<string, []>("input_101_cast_fp16")];
tensor<fp16, [1, 512]> linear_39_cast_fp16 = linear(bias = flow_net_res_blocks_2_mlp_0_bias_to_fp16, weight = flow_net_res_blocks_2_mlp_0_weight_to_fp16, x = input_101_cast_fp16)[name = tensor<string, []>("linear_39_cast_fp16")];
tensor<fp16, [1, 512]> input_105_cast_fp16 = silu(x = linear_39_cast_fp16)[name = tensor<string, []>("input_105_cast_fp16")];
tensor<fp16, [1, 512]> linear_40_cast_fp16 = linear(bias = flow_net_res_blocks_2_mlp_2_bias_to_fp16, weight = flow_net_res_blocks_2_mlp_2_weight_to_fp16, x = input_105_cast_fp16)[name = tensor<string, []>("linear_40_cast_fp16")];
tensor<fp16, [1, 512]> var_642_cast_fp16 = mul(x = var_613_cast_fp16_2, y = linear_40_cast_fp16)[name = tensor<string, []>("op_642_cast_fp16")];
tensor<fp16, [1, 512]> x_67_cast_fp16 = add(x = x_61_cast_fp16, y = var_642_cast_fp16)[name = tensor<string, []>("x_67_cast_fp16")];
tensor<fp16, [1, 1536]> linear_41_cast_fp16 = linear(bias = flow_net_res_blocks_3_adaLN_modulation_1_bias_to_fp16, weight = flow_net_res_blocks_3_adaLN_modulation_1_weight_to_fp16, x = input_83_cast_fp16)[name = tensor<string, []>("linear_41_cast_fp16")];
tensor<int32, [3]> var_652_split_sizes_0 = const()[name = tensor<string, []>("op_652_split_sizes_0"), val = tensor<int32, [3]>([512, 512, 512])];
tensor<int32, []> var_652_axis_0 = const()[name = tensor<string, []>("op_652_axis_0"), val = tensor<int32, []>(-1)];
tensor<fp16, [1, 512]> var_652_cast_fp16_0, tensor<fp16, [1, 512]> var_652_cast_fp16_1, tensor<fp16, [1, 512]> var_652_cast_fp16_2 = split(axis = var_652_axis_0, split_sizes = var_652_split_sizes_0, x = linear_41_cast_fp16)[name = tensor<string, []>("op_652_cast_fp16")];
tensor<int32, [1]> mean_21_axes_0 = const()[name = tensor<string, []>("mean_21_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> mean_21_keep_dims_0 = const()[name = tensor<string, []>("mean_21_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> mean_21_cast_fp16 = reduce_mean(axes = mean_21_axes_0, keep_dims = mean_21_keep_dims_0, x = x_67_cast_fp16)[name = tensor<string, []>("mean_21_cast_fp16")];
tensor<fp16, [1, 512]> sub_18_cast_fp16 = sub(x = x_67_cast_fp16, y = mean_21_cast_fp16)[name = tensor<string, []>("sub_18_cast_fp16")];
tensor<fp16, [1, 512]> square_14_cast_fp16 = square(x = sub_18_cast_fp16)[name = tensor<string, []>("square_14_cast_fp16")];
tensor<int32, [1]> reduce_mean_29_axes_0 = const()[name = tensor<string, []>("reduce_mean_29_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_29_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_29_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_29_cast_fp16 = reduce_mean(axes = reduce_mean_29_axes_0, keep_dims = reduce_mean_29_keep_dims_0, x = square_14_cast_fp16)[name = tensor<string, []>("reduce_mean_29_cast_fp16")];
tensor<fp16, []> var_662_to_fp16 = const()[name = tensor<string, []>("op_662_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 1]> var_663_cast_fp16 = add(x = reduce_mean_29_cast_fp16, y = var_662_to_fp16)[name = tensor<string, []>("op_663_cast_fp16")];
tensor<fp16, [1, 1]> var_664_cast_fp16 = sqrt(x = var_663_cast_fp16)[name = tensor<string, []>("op_664_cast_fp16")];
tensor<fp16, [1, 512]> x_69_cast_fp16 = real_div(x = sub_18_cast_fp16, y = var_664_cast_fp16)[name = tensor<string, []>("x_69_cast_fp16")];
tensor<fp16, [1, 512]> var_666_cast_fp16 = mul(x = x_69_cast_fp16, y = flow_net_res_blocks_3_in_ln_weight_to_fp16)[name = tensor<string, []>("op_666_cast_fp16")];
tensor<fp16, [1, 512]> x_71_cast_fp16 = add(x = var_666_cast_fp16, y = flow_net_res_blocks_3_in_ln_bias_to_fp16)[name = tensor<string, []>("x_71_cast_fp16")];
tensor<fp16, []> var_668_promoted_to_fp16 = const()[name = tensor<string, []>("op_668_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
tensor<fp16, [1, 512]> var_669_cast_fp16 = add(x = var_652_cast_fp16_1, y = var_668_promoted_to_fp16)[name = tensor<string, []>("op_669_cast_fp16")];
tensor<fp16, [1, 512]> var_670_cast_fp16 = mul(x = x_71_cast_fp16, y = var_669_cast_fp16)[name = tensor<string, []>("op_670_cast_fp16")];
tensor<fp16, [1, 512]> input_109_cast_fp16 = add(x = var_670_cast_fp16, y = var_652_cast_fp16_0)[name = tensor<string, []>("input_109_cast_fp16")];
tensor<fp16, [1, 512]> linear_42_cast_fp16 = linear(bias = flow_net_res_blocks_3_mlp_0_bias_to_fp16, weight = flow_net_res_blocks_3_mlp_0_weight_to_fp16, x = input_109_cast_fp16)[name = tensor<string, []>("linear_42_cast_fp16")];
tensor<fp16, [1, 512]> input_113_cast_fp16 = silu(x = linear_42_cast_fp16)[name = tensor<string, []>("input_113_cast_fp16")];
tensor<fp16, [1, 512]> linear_43_cast_fp16 = linear(bias = flow_net_res_blocks_3_mlp_2_bias_to_fp16, weight = flow_net_res_blocks_3_mlp_2_weight_to_fp16, x = input_113_cast_fp16)[name = tensor<string, []>("linear_43_cast_fp16")];
tensor<fp16, [1, 512]> var_681_cast_fp16 = mul(x = var_652_cast_fp16_2, y = linear_43_cast_fp16)[name = tensor<string, []>("op_681_cast_fp16")];
tensor<fp16, [1, 512]> x_73_cast_fp16 = add(x = x_67_cast_fp16, y = var_681_cast_fp16)[name = tensor<string, []>("x_73_cast_fp16")];
tensor<fp16, [1, 1536]> linear_44_cast_fp16 = linear(bias = flow_net_res_blocks_4_adaLN_modulation_1_bias_to_fp16, weight = flow_net_res_blocks_4_adaLN_modulation_1_weight_to_fp16, x = input_83_cast_fp16)[name = tensor<string, []>("linear_44_cast_fp16")];
tensor<int32, [3]> var_691_split_sizes_0 = const()[name = tensor<string, []>("op_691_split_sizes_0"), val = tensor<int32, [3]>([512, 512, 512])];
tensor<int32, []> var_691_axis_0 = const()[name = tensor<string, []>("op_691_axis_0"), val = tensor<int32, []>(-1)];
tensor<fp16, [1, 512]> var_691_cast_fp16_0, tensor<fp16, [1, 512]> var_691_cast_fp16_1, tensor<fp16, [1, 512]> var_691_cast_fp16_2 = split(axis = var_691_axis_0, split_sizes = var_691_split_sizes_0, x = linear_44_cast_fp16)[name = tensor<string, []>("op_691_cast_fp16")];
tensor<int32, [1]> mean_23_axes_0 = const()[name = tensor<string, []>("mean_23_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> mean_23_keep_dims_0 = const()[name = tensor<string, []>("mean_23_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> mean_23_cast_fp16 = reduce_mean(axes = mean_23_axes_0, keep_dims = mean_23_keep_dims_0, x = x_73_cast_fp16)[name = tensor<string, []>("mean_23_cast_fp16")];
tensor<fp16, [1, 512]> sub_19_cast_fp16 = sub(x = x_73_cast_fp16, y = mean_23_cast_fp16)[name = tensor<string, []>("sub_19_cast_fp16")];
tensor<fp16, [1, 512]> square_15_cast_fp16 = square(x = sub_19_cast_fp16)[name = tensor<string, []>("square_15_cast_fp16")];
tensor<int32, [1]> reduce_mean_31_axes_0 = const()[name = tensor<string, []>("reduce_mean_31_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_31_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_31_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_31_cast_fp16 = reduce_mean(axes = reduce_mean_31_axes_0, keep_dims = reduce_mean_31_keep_dims_0, x = square_15_cast_fp16)[name = tensor<string, []>("reduce_mean_31_cast_fp16")];
tensor<fp16, []> var_701_to_fp16 = const()[name = tensor<string, []>("op_701_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 1]> var_702_cast_fp16 = add(x = reduce_mean_31_cast_fp16, y = var_701_to_fp16)[name = tensor<string, []>("op_702_cast_fp16")];
tensor<fp16, [1, 1]> var_703_cast_fp16 = sqrt(x = var_702_cast_fp16)[name = tensor<string, []>("op_703_cast_fp16")];
tensor<fp16, [1, 512]> x_75_cast_fp16 = real_div(x = sub_19_cast_fp16, y = var_703_cast_fp16)[name = tensor<string, []>("x_75_cast_fp16")];
tensor<fp16, [1, 512]> var_705_cast_fp16 = mul(x = x_75_cast_fp16, y = flow_net_res_blocks_4_in_ln_weight_to_fp16)[name = tensor<string, []>("op_705_cast_fp16")];
tensor<fp16, [1, 512]> x_77_cast_fp16 = add(x = var_705_cast_fp16, y = flow_net_res_blocks_4_in_ln_bias_to_fp16)[name = tensor<string, []>("x_77_cast_fp16")];
tensor<fp16, []> var_707_promoted_to_fp16 = const()[name = tensor<string, []>("op_707_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
tensor<fp16, [1, 512]> var_708_cast_fp16 = add(x = var_691_cast_fp16_1, y = var_707_promoted_to_fp16)[name = tensor<string, []>("op_708_cast_fp16")];
tensor<fp16, [1, 512]> var_709_cast_fp16 = mul(x = x_77_cast_fp16, y = var_708_cast_fp16)[name = tensor<string, []>("op_709_cast_fp16")];
tensor<fp16, [1, 512]> input_117_cast_fp16 = add(x = var_709_cast_fp16, y = var_691_cast_fp16_0)[name = tensor<string, []>("input_117_cast_fp16")];
tensor<fp16, [1, 512]> linear_45_cast_fp16 = linear(bias = flow_net_res_blocks_4_mlp_0_bias_to_fp16, weight = flow_net_res_blocks_4_mlp_0_weight_to_fp16, x = input_117_cast_fp16)[name = tensor<string, []>("linear_45_cast_fp16")];
tensor<fp16, [1, 512]> input_121_cast_fp16 = silu(x = linear_45_cast_fp16)[name = tensor<string, []>("input_121_cast_fp16")];
tensor<fp16, [1, 512]> linear_46_cast_fp16 = linear(bias = flow_net_res_blocks_4_mlp_2_bias_to_fp16, weight = flow_net_res_blocks_4_mlp_2_weight_to_fp16, x = input_121_cast_fp16)[name = tensor<string, []>("linear_46_cast_fp16")];
tensor<fp16, [1, 512]> var_720_cast_fp16 = mul(x = var_691_cast_fp16_2, y = linear_46_cast_fp16)[name = tensor<string, []>("op_720_cast_fp16")];
tensor<fp16, [1, 512]> x_79_cast_fp16 = add(x = x_73_cast_fp16, y = var_720_cast_fp16)[name = tensor<string, []>("x_79_cast_fp16")];
tensor<fp16, [1, 1536]> linear_47_cast_fp16 = linear(bias = flow_net_res_blocks_5_adaLN_modulation_1_bias_to_fp16, weight = flow_net_res_blocks_5_adaLN_modulation_1_weight_to_fp16, x = input_83_cast_fp16)[name = tensor<string, []>("linear_47_cast_fp16")];
tensor<int32, [3]> var_730_split_sizes_0 = const()[name = tensor<string, []>("op_730_split_sizes_0"), val = tensor<int32, [3]>([512, 512, 512])];
tensor<int32, []> var_730_axis_0 = const()[name = tensor<string, []>("op_730_axis_0"), val = tensor<int32, []>(-1)];
tensor<fp16, [1, 512]> var_730_cast_fp16_0, tensor<fp16, [1, 512]> var_730_cast_fp16_1, tensor<fp16, [1, 512]> var_730_cast_fp16_2 = split(axis = var_730_axis_0, split_sizes = var_730_split_sizes_0, x = linear_47_cast_fp16)[name = tensor<string, []>("op_730_cast_fp16")];
tensor<int32, [1]> mean_25_axes_0 = const()[name = tensor<string, []>("mean_25_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> mean_25_keep_dims_0 = const()[name = tensor<string, []>("mean_25_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> mean_25_cast_fp16 = reduce_mean(axes = mean_25_axes_0, keep_dims = mean_25_keep_dims_0, x = x_79_cast_fp16)[name = tensor<string, []>("mean_25_cast_fp16")];
tensor<fp16, [1, 512]> sub_20_cast_fp16 = sub(x = x_79_cast_fp16, y = mean_25_cast_fp16)[name = tensor<string, []>("sub_20_cast_fp16")];
tensor<fp16, [1, 512]> square_16_cast_fp16 = square(x = sub_20_cast_fp16)[name = tensor<string, []>("square_16_cast_fp16")];
tensor<int32, [1]> reduce_mean_33_axes_0 = const()[name = tensor<string, []>("reduce_mean_33_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_33_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_33_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_33_cast_fp16 = reduce_mean(axes = reduce_mean_33_axes_0, keep_dims = reduce_mean_33_keep_dims_0, x = square_16_cast_fp16)[name = tensor<string, []>("reduce_mean_33_cast_fp16")];
tensor<fp16, []> var_740_to_fp16 = const()[name = tensor<string, []>("op_740_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 1]> var_741_cast_fp16 = add(x = reduce_mean_33_cast_fp16, y = var_740_to_fp16)[name = tensor<string, []>("op_741_cast_fp16")];
tensor<fp16, [1, 1]> var_742_cast_fp16 = sqrt(x = var_741_cast_fp16)[name = tensor<string, []>("op_742_cast_fp16")];
tensor<fp16, [1, 512]> x_81_cast_fp16 = real_div(x = sub_20_cast_fp16, y = var_742_cast_fp16)[name = tensor<string, []>("x_81_cast_fp16")];
tensor<fp16, [1, 512]> var_744_cast_fp16 = mul(x = x_81_cast_fp16, y = flow_net_res_blocks_5_in_ln_weight_to_fp16)[name = tensor<string, []>("op_744_cast_fp16")];
tensor<fp16, [1, 512]> x_83_cast_fp16 = add(x = var_744_cast_fp16, y = flow_net_res_blocks_5_in_ln_bias_to_fp16)[name = tensor<string, []>("x_83_cast_fp16")];
tensor<fp16, []> var_746_promoted_to_fp16 = const()[name = tensor<string, []>("op_746_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
tensor<fp16, [1, 512]> var_747_cast_fp16 = add(x = var_730_cast_fp16_1, y = var_746_promoted_to_fp16)[name = tensor<string, []>("op_747_cast_fp16")];
tensor<fp16, [1, 512]> var_748_cast_fp16 = mul(x = x_83_cast_fp16, y = var_747_cast_fp16)[name = tensor<string, []>("op_748_cast_fp16")];
tensor<fp16, [1, 512]> input_125_cast_fp16 = add(x = var_748_cast_fp16, y = var_730_cast_fp16_0)[name = tensor<string, []>("input_125_cast_fp16")];
tensor<fp16, [1, 512]> linear_48_cast_fp16 = linear(bias = flow_net_res_blocks_5_mlp_0_bias_to_fp16, weight = flow_net_res_blocks_5_mlp_0_weight_to_fp16, x = input_125_cast_fp16)[name = tensor<string, []>("linear_48_cast_fp16")];
tensor<fp16, [1, 512]> input_129_cast_fp16 = silu(x = linear_48_cast_fp16)[name = tensor<string, []>("input_129_cast_fp16")];
tensor<fp16, [1, 512]> linear_49_cast_fp16 = linear(bias = flow_net_res_blocks_5_mlp_2_bias_to_fp16, weight = flow_net_res_blocks_5_mlp_2_weight_to_fp16, x = input_129_cast_fp16)[name = tensor<string, []>("linear_49_cast_fp16")];
tensor<fp16, [1, 512]> var_759_cast_fp16 = mul(x = var_730_cast_fp16_2, y = linear_49_cast_fp16)[name = tensor<string, []>("op_759_cast_fp16")];
tensor<fp16, [1, 512]> x_85_cast_fp16 = add(x = x_79_cast_fp16, y = var_759_cast_fp16)[name = tensor<string, []>("x_85_cast_fp16")];
tensor<fp16, [1, 1024]> linear_50_cast_fp16 = linear(bias = flow_net_final_layer_adaLN_modulation_1_bias_to_fp16, weight = flow_net_final_layer_adaLN_modulation_1_weight_to_fp16, x = input_83_cast_fp16)[name = tensor<string, []>("linear_50_cast_fp16")];
tensor<int32, [2]> var_768_split_sizes_0 = const()[name = tensor<string, []>("op_768_split_sizes_0"), val = tensor<int32, [2]>([512, 512])];
tensor<int32, []> var_768_axis_0 = const()[name = tensor<string, []>("op_768_axis_0"), val = tensor<int32, []>(-1)];
tensor<fp16, [1, 512]> var_768_cast_fp16_0, tensor<fp16, [1, 512]> var_768_cast_fp16_1 = split(axis = var_768_axis_0, split_sizes = var_768_split_sizes_0, x = linear_50_cast_fp16)[name = tensor<string, []>("op_768_cast_fp16")];
tensor<int32, [1]> mean_27_axes_0 = const()[name = tensor<string, []>("mean_27_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> mean_27_keep_dims_0 = const()[name = tensor<string, []>("mean_27_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> mean_27_cast_fp16 = reduce_mean(axes = mean_27_axes_0, keep_dims = mean_27_keep_dims_0, x = x_85_cast_fp16)[name = tensor<string, []>("mean_27_cast_fp16")];
tensor<fp16, [1, 512]> sub_21_cast_fp16 = sub(x = x_85_cast_fp16, y = mean_27_cast_fp16)[name = tensor<string, []>("sub_21_cast_fp16")];
tensor<fp16, [1, 512]> square_17_cast_fp16 = square(x = sub_21_cast_fp16)[name = tensor<string, []>("square_17_cast_fp16")];
tensor<int32, [1]> reduce_mean_35_axes_0 = const()[name = tensor<string, []>("reduce_mean_35_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_35_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_35_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_35_cast_fp16 = reduce_mean(axes = reduce_mean_35_axes_0, keep_dims = reduce_mean_35_keep_dims_0, x = square_17_cast_fp16)[name = tensor<string, []>("reduce_mean_35_cast_fp16")];
tensor<fp16, []> var_775_to_fp16 = const()[name = tensor<string, []>("op_775_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 1]> var_776_cast_fp16 = add(x = reduce_mean_35_cast_fp16, y = var_775_to_fp16)[name = tensor<string, []>("op_776_cast_fp16")];
tensor<fp16, [1, 1]> var_777_cast_fp16 = sqrt(x = var_776_cast_fp16)[name = tensor<string, []>("op_777_cast_fp16")];
tensor<fp16, [1, 512]> x_87_cast_fp16 = real_div(x = sub_21_cast_fp16, y = var_777_cast_fp16)[name = tensor<string, []>("x_87_cast_fp16")];
tensor<fp16, []> var_779_promoted_to_fp16 = const()[name = tensor<string, []>("op_779_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
tensor<fp16, [1, 512]> var_780_cast_fp16 = add(x = var_768_cast_fp16_1, y = var_779_promoted_to_fp16)[name = tensor<string, []>("op_780_cast_fp16")];
tensor<fp16, [1, 512]> var_781_cast_fp16 = mul(x = x_87_cast_fp16, y = var_780_cast_fp16)[name = tensor<string, []>("op_781_cast_fp16")];
tensor<fp16, [1, 512]> input_133_cast_fp16 = add(x = var_781_cast_fp16, y = var_768_cast_fp16_0)[name = tensor<string, []>("input_133_cast_fp16")];
tensor<fp16, [1, 32]> linear_51_cast_fp16 = linear(bias = flow_net_final_layer_linear_bias_to_fp16, weight = flow_net_final_layer_linear_weight_to_fp16, x = input_133_cast_fp16)[name = tensor<string, []>("linear_51_cast_fp16")];
tensor<fp16, []> var_792_to_fp16 = const()[name = tensor<string, []>("op_792_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 32]> var_793_cast_fp16 = mul(x = linear_51_cast_fp16, y = var_792_to_fp16)[name = tensor<string, []>("op_793_cast_fp16")];
tensor<fp16, [1, 32]> input_135_cast_fp16 = add(x = input_67_cast_fp16, y = var_793_cast_fp16)[name = tensor<string, []>("input_135_cast_fp16")];
tensor<fp16, [1, 512]> linear_52_cast_fp16 = linear(bias = flow_net_input_proj_bias_to_fp16, weight = flow_net_input_proj_weight_to_fp16, x = input_135_cast_fp16)[name = tensor<string, []>("linear_52_cast_fp16")];
tensor<fp16, [1, 512]> input_139_to_fp16 = const()[name = tensor<string, []>("input_139_to_fp16"), val = tensor<fp16, [1, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(18999552)))];
tensor<fp16, [1, 512]> input_141_cast_fp16 = silu(x = input_139_to_fp16)[name = tensor<string, []>("input_141_cast_fp16")];
tensor<fp16, [1, 512]> linear_54_cast_fp16 = linear(bias = flow_net_time_embed_0_mlp_2_bias_to_fp16, weight = flow_net_time_embed_0_mlp_2_weight_to_fp16, x = input_141_cast_fp16)[name = tensor<string, []>("linear_54_cast_fp16")];
tensor<int32, [1]> reduce_mean_36_axes_0 = const()[name = tensor<string, []>("reduce_mean_36_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_36_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_36_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_36_cast_fp16 = reduce_mean(axes = reduce_mean_36_axes_0, keep_dims = reduce_mean_36_keep_dims_0, x = linear_54_cast_fp16)[name = tensor<string, []>("reduce_mean_36_cast_fp16")];
tensor<fp16, [1, 512]> sub_22_cast_fp16 = sub(x = linear_54_cast_fp16, y = reduce_mean_36_cast_fp16)[name = tensor<string, []>("sub_22_cast_fp16")];
tensor<fp16, [1, 512]> square_18_cast_fp16 = square(x = sub_22_cast_fp16)[name = tensor<string, []>("square_18_cast_fp16")];
tensor<int32, [1]> reduce_mean_37_axes_0 = const()[name = tensor<string, []>("reduce_mean_37_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_37_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_37_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_37_cast_fp16 = reduce_mean(axes = reduce_mean_37_axes_0, keep_dims = reduce_mean_37_keep_dims_0, x = square_18_cast_fp16)[name = tensor<string, []>("reduce_mean_37_cast_fp16")];
tensor<fp16, []> real_div_4_to_fp16 = const()[name = tensor<string, []>("real_div_4_to_fp16"), val = tensor<fp16, []>(0x1.008p+0)];
tensor<fp16, [1, 1]> mul_4_cast_fp16 = mul(x = reduce_mean_37_cast_fp16, y = real_div_4_to_fp16)[name = tensor<string, []>("mul_4_cast_fp16")];
tensor<fp16, []> var_859_to_fp16 = const()[name = tensor<string, []>("op_859_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1]> var_37_cast_fp16 = add(x = mul_4_cast_fp16, y = var_859_to_fp16)[name = tensor<string, []>("var_37_cast_fp16")];
tensor<fp32, []> var_862_epsilon_0 = const()[name = tensor<string, []>("op_862_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
tensor<fp16, [1, 1]> var_862_cast_fp16 = rsqrt(epsilon = var_862_epsilon_0, x = var_37_cast_fp16)[name = tensor<string, []>("op_862_cast_fp16")];
tensor<fp16, [1, 512]> var_863_cast_fp16 = mul(x = const_3_to_fp16, y = var_862_cast_fp16)[name = tensor<string, []>("op_863_cast_fp16")];
tensor<fp16, [1, 512]> var_864_cast_fp16 = mul(x = linear_54_cast_fp16, y = var_863_cast_fp16)[name = tensor<string, []>("op_864_cast_fp16")];
tensor<fp16, [1, 512]> input_145_to_fp16 = const()[name = tensor<string, []>("input_145_to_fp16"), val = tensor<fp16, [1, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19000640)))];
tensor<fp16, [1, 512]> input_147_cast_fp16 = silu(x = input_145_to_fp16)[name = tensor<string, []>("input_147_cast_fp16")];
tensor<fp16, [1, 512]> linear_56_cast_fp16 = linear(bias = flow_net_time_embed_1_mlp_2_bias_to_fp16, weight = flow_net_time_embed_1_mlp_2_weight_to_fp16, x = input_147_cast_fp16)[name = tensor<string, []>("linear_56_cast_fp16")];
tensor<int32, [1]> reduce_mean_38_axes_0 = const()[name = tensor<string, []>("reduce_mean_38_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_38_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_38_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_38_cast_fp16 = reduce_mean(axes = reduce_mean_38_axes_0, keep_dims = reduce_mean_38_keep_dims_0, x = linear_56_cast_fp16)[name = tensor<string, []>("reduce_mean_38_cast_fp16")];
tensor<fp16, [1, 512]> sub_24_cast_fp16 = sub(x = linear_56_cast_fp16, y = reduce_mean_38_cast_fp16)[name = tensor<string, []>("sub_24_cast_fp16")];
tensor<fp16, [1, 512]> square_19_cast_fp16 = square(x = sub_24_cast_fp16)[name = tensor<string, []>("square_19_cast_fp16")];
tensor<int32, [1]> reduce_mean_39_axes_0 = const()[name = tensor<string, []>("reduce_mean_39_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_39_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_39_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_39_cast_fp16 = reduce_mean(axes = reduce_mean_39_axes_0, keep_dims = reduce_mean_39_keep_dims_0, x = square_19_cast_fp16)[name = tensor<string, []>("reduce_mean_39_cast_fp16")];
tensor<fp16, []> real_div_5_to_fp16 = const()[name = tensor<string, []>("real_div_5_to_fp16"), val = tensor<fp16, []>(0x1.008p+0)];
tensor<fp16, [1, 1]> mul_5_cast_fp16 = mul(x = reduce_mean_39_cast_fp16, y = real_div_5_to_fp16)[name = tensor<string, []>("mul_5_cast_fp16")];
tensor<fp16, []> var_896_to_fp16 = const()[name = tensor<string, []>("op_896_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1]> var_39_cast_fp16 = add(x = mul_5_cast_fp16, y = var_896_to_fp16)[name = tensor<string, []>("var_39_cast_fp16")];
tensor<fp32, []> var_899_epsilon_0 = const()[name = tensor<string, []>("op_899_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
tensor<fp16, [1, 1]> var_899_cast_fp16 = rsqrt(epsilon = var_899_epsilon_0, x = var_39_cast_fp16)[name = tensor<string, []>("op_899_cast_fp16")];
tensor<fp16, [1, 512]> var_900_cast_fp16 = mul(x = const_5_to_fp16, y = var_899_cast_fp16)[name = tensor<string, []>("op_900_cast_fp16")];
tensor<fp16, [1, 512]> var_901_cast_fp16 = mul(x = linear_56_cast_fp16, y = var_900_cast_fp16)[name = tensor<string, []>("op_901_cast_fp16")];
tensor<fp16, [1, 512]> var_913_cast_fp16 = add(x = var_864_cast_fp16, y = var_901_cast_fp16)[name = tensor<string, []>("op_913_cast_fp16")];
tensor<fp16, []> _inversed_t_combined_5_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_t_combined_5_y_0_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
tensor<fp16, [1, 512]> _inversed_t_combined_5_cast_fp16 = mul(x = var_913_cast_fp16, y = _inversed_t_combined_5_y_0_to_fp16)[name = tensor<string, []>("_inversed_t_combined_5_cast_fp16")];
tensor<fp16, [1, 512]> input_149_cast_fp16 = add(x = _inversed_t_combined_5_cast_fp16, y = linear_5_cast_fp16)[name = tensor<string, []>("input_149_cast_fp16")];
tensor<fp16, [1, 512]> input_151_cast_fp16 = silu(x = input_149_cast_fp16)[name = tensor<string, []>("input_151_cast_fp16")];
tensor<fp16, [1, 1536]> linear_58_cast_fp16 = linear(bias = flow_net_res_blocks_0_adaLN_modulation_1_bias_to_fp16, weight = flow_net_res_blocks_0_adaLN_modulation_1_weight_to_fp16, x = input_151_cast_fp16)[name = tensor<string, []>("linear_58_cast_fp16")];
tensor<int32, [3]> var_928_split_sizes_0 = const()[name = tensor<string, []>("op_928_split_sizes_0"), val = tensor<int32, [3]>([512, 512, 512])];
tensor<int32, []> var_928_axis_0 = const()[name = tensor<string, []>("op_928_axis_0"), val = tensor<int32, []>(-1)];
tensor<fp16, [1, 512]> var_928_cast_fp16_0, tensor<fp16, [1, 512]> var_928_cast_fp16_1, tensor<fp16, [1, 512]> var_928_cast_fp16_2 = split(axis = var_928_axis_0, split_sizes = var_928_split_sizes_0, x = linear_58_cast_fp16)[name = tensor<string, []>("op_928_cast_fp16")];
tensor<int32, [1]> mean_29_axes_0 = const()[name = tensor<string, []>("mean_29_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> mean_29_keep_dims_0 = const()[name = tensor<string, []>("mean_29_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> mean_29_cast_fp16 = reduce_mean(axes = mean_29_axes_0, keep_dims = mean_29_keep_dims_0, x = linear_52_cast_fp16)[name = tensor<string, []>("mean_29_cast_fp16")];
tensor<fp16, [1, 512]> sub_26_cast_fp16 = sub(x = linear_52_cast_fp16, y = mean_29_cast_fp16)[name = tensor<string, []>("sub_26_cast_fp16")];
tensor<fp16, [1, 512]> square_20_cast_fp16 = square(x = sub_26_cast_fp16)[name = tensor<string, []>("square_20_cast_fp16")];
tensor<int32, [1]> reduce_mean_41_axes_0 = const()[name = tensor<string, []>("reduce_mean_41_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_41_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_41_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_41_cast_fp16 = reduce_mean(axes = reduce_mean_41_axes_0, keep_dims = reduce_mean_41_keep_dims_0, x = square_20_cast_fp16)[name = tensor<string, []>("reduce_mean_41_cast_fp16")];
tensor<fp16, []> var_938_to_fp16 = const()[name = tensor<string, []>("op_938_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 1]> var_939_cast_fp16 = add(x = reduce_mean_41_cast_fp16, y = var_938_to_fp16)[name = tensor<string, []>("op_939_cast_fp16")];
tensor<fp16, [1, 1]> var_940_cast_fp16 = sqrt(x = var_939_cast_fp16)[name = tensor<string, []>("op_940_cast_fp16")];
tensor<fp16, [1, 512]> x_95_cast_fp16 = real_div(x = sub_26_cast_fp16, y = var_940_cast_fp16)[name = tensor<string, []>("x_95_cast_fp16")];
tensor<fp16, [1, 512]> var_942_cast_fp16 = mul(x = x_95_cast_fp16, y = flow_net_res_blocks_0_in_ln_weight_to_fp16)[name = tensor<string, []>("op_942_cast_fp16")];
tensor<fp16, [1, 512]> x_97_cast_fp16 = add(x = var_942_cast_fp16, y = flow_net_res_blocks_0_in_ln_bias_to_fp16)[name = tensor<string, []>("x_97_cast_fp16")];
tensor<fp16, []> var_944_promoted_to_fp16 = const()[name = tensor<string, []>("op_944_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
tensor<fp16, [1, 512]> var_945_cast_fp16 = add(x = var_928_cast_fp16_1, y = var_944_promoted_to_fp16)[name = tensor<string, []>("op_945_cast_fp16")];
tensor<fp16, [1, 512]> var_946_cast_fp16 = mul(x = x_97_cast_fp16, y = var_945_cast_fp16)[name = tensor<string, []>("op_946_cast_fp16")];
tensor<fp16, [1, 512]> input_153_cast_fp16 = add(x = var_946_cast_fp16, y = var_928_cast_fp16_0)[name = tensor<string, []>("input_153_cast_fp16")];
tensor<fp16, [1, 512]> linear_59_cast_fp16 = linear(bias = flow_net_res_blocks_0_mlp_0_bias_to_fp16, weight = flow_net_res_blocks_0_mlp_0_weight_to_fp16, x = input_153_cast_fp16)[name = tensor<string, []>("linear_59_cast_fp16")];
tensor<fp16, [1, 512]> input_157_cast_fp16 = silu(x = linear_59_cast_fp16)[name = tensor<string, []>("input_157_cast_fp16")];
tensor<fp16, [1, 512]> linear_60_cast_fp16 = linear(bias = flow_net_res_blocks_0_mlp_2_bias_to_fp16, weight = flow_net_res_blocks_0_mlp_2_weight_to_fp16, x = input_157_cast_fp16)[name = tensor<string, []>("linear_60_cast_fp16")];
tensor<fp16, [1, 512]> var_957_cast_fp16 = mul(x = var_928_cast_fp16_2, y = linear_60_cast_fp16)[name = tensor<string, []>("op_957_cast_fp16")];
tensor<fp16, [1, 512]> x_99_cast_fp16 = add(x = linear_52_cast_fp16, y = var_957_cast_fp16)[name = tensor<string, []>("x_99_cast_fp16")];
tensor<fp16, [1, 1536]> linear_61_cast_fp16 = linear(bias = flow_net_res_blocks_1_adaLN_modulation_1_bias_to_fp16, weight = flow_net_res_blocks_1_adaLN_modulation_1_weight_to_fp16, x = input_151_cast_fp16)[name = tensor<string, []>("linear_61_cast_fp16")];
tensor<int32, [3]> var_967_split_sizes_0 = const()[name = tensor<string, []>("op_967_split_sizes_0"), val = tensor<int32, [3]>([512, 512, 512])];
tensor<int32, []> var_967_axis_0 = const()[name = tensor<string, []>("op_967_axis_0"), val = tensor<int32, []>(-1)];
tensor<fp16, [1, 512]> var_967_cast_fp16_0, tensor<fp16, [1, 512]> var_967_cast_fp16_1, tensor<fp16, [1, 512]> var_967_cast_fp16_2 = split(axis = var_967_axis_0, split_sizes = var_967_split_sizes_0, x = linear_61_cast_fp16)[name = tensor<string, []>("op_967_cast_fp16")];
tensor<int32, [1]> mean_31_axes_0 = const()[name = tensor<string, []>("mean_31_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> mean_31_keep_dims_0 = const()[name = tensor<string, []>("mean_31_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> mean_31_cast_fp16 = reduce_mean(axes = mean_31_axes_0, keep_dims = mean_31_keep_dims_0, x = x_99_cast_fp16)[name = tensor<string, []>("mean_31_cast_fp16")];
tensor<fp16, [1, 512]> sub_27_cast_fp16 = sub(x = x_99_cast_fp16, y = mean_31_cast_fp16)[name = tensor<string, []>("sub_27_cast_fp16")];
tensor<fp16, [1, 512]> square_21_cast_fp16 = square(x = sub_27_cast_fp16)[name = tensor<string, []>("square_21_cast_fp16")];
tensor<int32, [1]> reduce_mean_43_axes_0 = const()[name = tensor<string, []>("reduce_mean_43_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_43_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_43_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_43_cast_fp16 = reduce_mean(axes = reduce_mean_43_axes_0, keep_dims = reduce_mean_43_keep_dims_0, x = square_21_cast_fp16)[name = tensor<string, []>("reduce_mean_43_cast_fp16")];
tensor<fp16, []> var_977_to_fp16 = const()[name = tensor<string, []>("op_977_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 1]> var_978_cast_fp16 = add(x = reduce_mean_43_cast_fp16, y = var_977_to_fp16)[name = tensor<string, []>("op_978_cast_fp16")];
tensor<fp16, [1, 1]> var_979_cast_fp16 = sqrt(x = var_978_cast_fp16)[name = tensor<string, []>("op_979_cast_fp16")];
tensor<fp16, [1, 512]> x_101_cast_fp16 = real_div(x = sub_27_cast_fp16, y = var_979_cast_fp16)[name = tensor<string, []>("x_101_cast_fp16")];
tensor<fp16, [1, 512]> var_981_cast_fp16 = mul(x = x_101_cast_fp16, y = flow_net_res_blocks_1_in_ln_weight_to_fp16)[name = tensor<string, []>("op_981_cast_fp16")];
tensor<fp16, [1, 512]> x_103_cast_fp16 = add(x = var_981_cast_fp16, y = flow_net_res_blocks_1_in_ln_bias_to_fp16)[name = tensor<string, []>("x_103_cast_fp16")];
tensor<fp16, []> var_983_promoted_to_fp16 = const()[name = tensor<string, []>("op_983_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
tensor<fp16, [1, 512]> var_984_cast_fp16 = add(x = var_967_cast_fp16_1, y = var_983_promoted_to_fp16)[name = tensor<string, []>("op_984_cast_fp16")];
tensor<fp16, [1, 512]> var_985_cast_fp16 = mul(x = x_103_cast_fp16, y = var_984_cast_fp16)[name = tensor<string, []>("op_985_cast_fp16")];
tensor<fp16, [1, 512]> input_161_cast_fp16 = add(x = var_985_cast_fp16, y = var_967_cast_fp16_0)[name = tensor<string, []>("input_161_cast_fp16")];
tensor<fp16, [1, 512]> linear_62_cast_fp16 = linear(bias = flow_net_res_blocks_1_mlp_0_bias_to_fp16, weight = flow_net_res_blocks_1_mlp_0_weight_to_fp16, x = input_161_cast_fp16)[name = tensor<string, []>("linear_62_cast_fp16")];
tensor<fp16, [1, 512]> input_165_cast_fp16 = silu(x = linear_62_cast_fp16)[name = tensor<string, []>("input_165_cast_fp16")];
tensor<fp16, [1, 512]> linear_63_cast_fp16 = linear(bias = flow_net_res_blocks_1_mlp_2_bias_to_fp16, weight = flow_net_res_blocks_1_mlp_2_weight_to_fp16, x = input_165_cast_fp16)[name = tensor<string, []>("linear_63_cast_fp16")];
tensor<fp16, [1, 512]> var_996_cast_fp16 = mul(x = var_967_cast_fp16_2, y = linear_63_cast_fp16)[name = tensor<string, []>("op_996_cast_fp16")];
tensor<fp16, [1, 512]> x_105_cast_fp16 = add(x = x_99_cast_fp16, y = var_996_cast_fp16)[name = tensor<string, []>("x_105_cast_fp16")];
tensor<fp16, [1, 1536]> linear_64_cast_fp16 = linear(bias = flow_net_res_blocks_2_adaLN_modulation_1_bias_to_fp16, weight = flow_net_res_blocks_2_adaLN_modulation_1_weight_to_fp16, x = input_151_cast_fp16)[name = tensor<string, []>("linear_64_cast_fp16")];
tensor<int32, [3]> var_1006_split_sizes_0 = const()[name = tensor<string, []>("op_1006_split_sizes_0"), val = tensor<int32, [3]>([512, 512, 512])];
tensor<int32, []> var_1006_axis_0 = const()[name = tensor<string, []>("op_1006_axis_0"), val = tensor<int32, []>(-1)];
tensor<fp16, [1, 512]> var_1006_cast_fp16_0, tensor<fp16, [1, 512]> var_1006_cast_fp16_1, tensor<fp16, [1, 512]> var_1006_cast_fp16_2 = split(axis = var_1006_axis_0, split_sizes = var_1006_split_sizes_0, x = linear_64_cast_fp16)[name = tensor<string, []>("op_1006_cast_fp16")];
tensor<int32, [1]> mean_33_axes_0 = const()[name = tensor<string, []>("mean_33_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> mean_33_keep_dims_0 = const()[name = tensor<string, []>("mean_33_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> mean_33_cast_fp16 = reduce_mean(axes = mean_33_axes_0, keep_dims = mean_33_keep_dims_0, x = x_105_cast_fp16)[name = tensor<string, []>("mean_33_cast_fp16")];
tensor<fp16, [1, 512]> sub_28_cast_fp16 = sub(x = x_105_cast_fp16, y = mean_33_cast_fp16)[name = tensor<string, []>("sub_28_cast_fp16")];
tensor<fp16, [1, 512]> square_22_cast_fp16 = square(x = sub_28_cast_fp16)[name = tensor<string, []>("square_22_cast_fp16")];
tensor<int32, [1]> reduce_mean_45_axes_0 = const()[name = tensor<string, []>("reduce_mean_45_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_45_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_45_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_45_cast_fp16 = reduce_mean(axes = reduce_mean_45_axes_0, keep_dims = reduce_mean_45_keep_dims_0, x = square_22_cast_fp16)[name = tensor<string, []>("reduce_mean_45_cast_fp16")];
tensor<fp16, []> var_1016_to_fp16 = const()[name = tensor<string, []>("op_1016_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 1]> var_1017_cast_fp16 = add(x = reduce_mean_45_cast_fp16, y = var_1016_to_fp16)[name = tensor<string, []>("op_1017_cast_fp16")];
tensor<fp16, [1, 1]> var_1018_cast_fp16 = sqrt(x = var_1017_cast_fp16)[name = tensor<string, []>("op_1018_cast_fp16")];
tensor<fp16, [1, 512]> x_107_cast_fp16 = real_div(x = sub_28_cast_fp16, y = var_1018_cast_fp16)[name = tensor<string, []>("x_107_cast_fp16")];
tensor<fp16, [1, 512]> var_1020_cast_fp16 = mul(x = x_107_cast_fp16, y = flow_net_res_blocks_2_in_ln_weight_to_fp16)[name = tensor<string, []>("op_1020_cast_fp16")];
tensor<fp16, [1, 512]> x_109_cast_fp16 = add(x = var_1020_cast_fp16, y = flow_net_res_blocks_2_in_ln_bias_to_fp16)[name = tensor<string, []>("x_109_cast_fp16")];
tensor<fp16, []> var_1022_promoted_to_fp16 = const()[name = tensor<string, []>("op_1022_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
tensor<fp16, [1, 512]> var_1023_cast_fp16 = add(x = var_1006_cast_fp16_1, y = var_1022_promoted_to_fp16)[name = tensor<string, []>("op_1023_cast_fp16")];
tensor<fp16, [1, 512]> var_1024_cast_fp16 = mul(x = x_109_cast_fp16, y = var_1023_cast_fp16)[name = tensor<string, []>("op_1024_cast_fp16")];
tensor<fp16, [1, 512]> input_169_cast_fp16 = add(x = var_1024_cast_fp16, y = var_1006_cast_fp16_0)[name = tensor<string, []>("input_169_cast_fp16")];
tensor<fp16, [1, 512]> linear_65_cast_fp16 = linear(bias = flow_net_res_blocks_2_mlp_0_bias_to_fp16, weight = flow_net_res_blocks_2_mlp_0_weight_to_fp16, x = input_169_cast_fp16)[name = tensor<string, []>("linear_65_cast_fp16")];
tensor<fp16, [1, 512]> input_173_cast_fp16 = silu(x = linear_65_cast_fp16)[name = tensor<string, []>("input_173_cast_fp16")];
tensor<fp16, [1, 512]> linear_66_cast_fp16 = linear(bias = flow_net_res_blocks_2_mlp_2_bias_to_fp16, weight = flow_net_res_blocks_2_mlp_2_weight_to_fp16, x = input_173_cast_fp16)[name = tensor<string, []>("linear_66_cast_fp16")];
tensor<fp16, [1, 512]> var_1035_cast_fp16 = mul(x = var_1006_cast_fp16_2, y = linear_66_cast_fp16)[name = tensor<string, []>("op_1035_cast_fp16")];
tensor<fp16, [1, 512]> x_111_cast_fp16 = add(x = x_105_cast_fp16, y = var_1035_cast_fp16)[name = tensor<string, []>("x_111_cast_fp16")];
tensor<fp16, [1, 1536]> linear_67_cast_fp16 = linear(bias = flow_net_res_blocks_3_adaLN_modulation_1_bias_to_fp16, weight = flow_net_res_blocks_3_adaLN_modulation_1_weight_to_fp16, x = input_151_cast_fp16)[name = tensor<string, []>("linear_67_cast_fp16")];
tensor<int32, [3]> var_1045_split_sizes_0 = const()[name = tensor<string, []>("op_1045_split_sizes_0"), val = tensor<int32, [3]>([512, 512, 512])];
tensor<int32, []> var_1045_axis_0 = const()[name = tensor<string, []>("op_1045_axis_0"), val = tensor<int32, []>(-1)];
tensor<fp16, [1, 512]> var_1045_cast_fp16_0, tensor<fp16, [1, 512]> var_1045_cast_fp16_1, tensor<fp16, [1, 512]> var_1045_cast_fp16_2 = split(axis = var_1045_axis_0, split_sizes = var_1045_split_sizes_0, x = linear_67_cast_fp16)[name = tensor<string, []>("op_1045_cast_fp16")];
tensor<int32, [1]> mean_35_axes_0 = const()[name = tensor<string, []>("mean_35_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> mean_35_keep_dims_0 = const()[name = tensor<string, []>("mean_35_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> mean_35_cast_fp16 = reduce_mean(axes = mean_35_axes_0, keep_dims = mean_35_keep_dims_0, x = x_111_cast_fp16)[name = tensor<string, []>("mean_35_cast_fp16")];
tensor<fp16, [1, 512]> sub_29_cast_fp16 = sub(x = x_111_cast_fp16, y = mean_35_cast_fp16)[name = tensor<string, []>("sub_29_cast_fp16")];
tensor<fp16, [1, 512]> square_23_cast_fp16 = square(x = sub_29_cast_fp16)[name = tensor<string, []>("square_23_cast_fp16")];
tensor<int32, [1]> reduce_mean_47_axes_0 = const()[name = tensor<string, []>("reduce_mean_47_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_47_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_47_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_47_cast_fp16 = reduce_mean(axes = reduce_mean_47_axes_0, keep_dims = reduce_mean_47_keep_dims_0, x = square_23_cast_fp16)[name = tensor<string, []>("reduce_mean_47_cast_fp16")];
tensor<fp16, []> var_1055_to_fp16 = const()[name = tensor<string, []>("op_1055_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 1]> var_1056_cast_fp16 = add(x = reduce_mean_47_cast_fp16, y = var_1055_to_fp16)[name = tensor<string, []>("op_1056_cast_fp16")];
tensor<fp16, [1, 1]> var_1057_cast_fp16 = sqrt(x = var_1056_cast_fp16)[name = tensor<string, []>("op_1057_cast_fp16")];
tensor<fp16, [1, 512]> x_113_cast_fp16 = real_div(x = sub_29_cast_fp16, y = var_1057_cast_fp16)[name = tensor<string, []>("x_113_cast_fp16")];
tensor<fp16, [1, 512]> var_1059_cast_fp16 = mul(x = x_113_cast_fp16, y = flow_net_res_blocks_3_in_ln_weight_to_fp16)[name = tensor<string, []>("op_1059_cast_fp16")];
tensor<fp16, [1, 512]> x_115_cast_fp16 = add(x = var_1059_cast_fp16, y = flow_net_res_blocks_3_in_ln_bias_to_fp16)[name = tensor<string, []>("x_115_cast_fp16")];
tensor<fp16, []> var_1061_promoted_to_fp16 = const()[name = tensor<string, []>("op_1061_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
tensor<fp16, [1, 512]> var_1062_cast_fp16 = add(x = var_1045_cast_fp16_1, y = var_1061_promoted_to_fp16)[name = tensor<string, []>("op_1062_cast_fp16")];
tensor<fp16, [1, 512]> var_1063_cast_fp16 = mul(x = x_115_cast_fp16, y = var_1062_cast_fp16)[name = tensor<string, []>("op_1063_cast_fp16")];
tensor<fp16, [1, 512]> input_177_cast_fp16 = add(x = var_1063_cast_fp16, y = var_1045_cast_fp16_0)[name = tensor<string, []>("input_177_cast_fp16")];
tensor<fp16, [1, 512]> linear_68_cast_fp16 = linear(bias = flow_net_res_blocks_3_mlp_0_bias_to_fp16, weight = flow_net_res_blocks_3_mlp_0_weight_to_fp16, x = input_177_cast_fp16)[name = tensor<string, []>("linear_68_cast_fp16")];
tensor<fp16, [1, 512]> input_181_cast_fp16 = silu(x = linear_68_cast_fp16)[name = tensor<string, []>("input_181_cast_fp16")];
tensor<fp16, [1, 512]> linear_69_cast_fp16 = linear(bias = flow_net_res_blocks_3_mlp_2_bias_to_fp16, weight = flow_net_res_blocks_3_mlp_2_weight_to_fp16, x = input_181_cast_fp16)[name = tensor<string, []>("linear_69_cast_fp16")];
tensor<fp16, [1, 512]> var_1074_cast_fp16 = mul(x = var_1045_cast_fp16_2, y = linear_69_cast_fp16)[name = tensor<string, []>("op_1074_cast_fp16")];
tensor<fp16, [1, 512]> x_117_cast_fp16 = add(x = x_111_cast_fp16, y = var_1074_cast_fp16)[name = tensor<string, []>("x_117_cast_fp16")];
tensor<fp16, [1, 1536]> linear_70_cast_fp16 = linear(bias = flow_net_res_blocks_4_adaLN_modulation_1_bias_to_fp16, weight = flow_net_res_blocks_4_adaLN_modulation_1_weight_to_fp16, x = input_151_cast_fp16)[name = tensor<string, []>("linear_70_cast_fp16")];
tensor<int32, [3]> var_1084_split_sizes_0 = const()[name = tensor<string, []>("op_1084_split_sizes_0"), val = tensor<int32, [3]>([512, 512, 512])];
tensor<int32, []> var_1084_axis_0 = const()[name = tensor<string, []>("op_1084_axis_0"), val = tensor<int32, []>(-1)];
tensor<fp16, [1, 512]> var_1084_cast_fp16_0, tensor<fp16, [1, 512]> var_1084_cast_fp16_1, tensor<fp16, [1, 512]> var_1084_cast_fp16_2 = split(axis = var_1084_axis_0, split_sizes = var_1084_split_sizes_0, x = linear_70_cast_fp16)[name = tensor<string, []>("op_1084_cast_fp16")];
tensor<int32, [1]> mean_37_axes_0 = const()[name = tensor<string, []>("mean_37_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> mean_37_keep_dims_0 = const()[name = tensor<string, []>("mean_37_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> mean_37_cast_fp16 = reduce_mean(axes = mean_37_axes_0, keep_dims = mean_37_keep_dims_0, x = x_117_cast_fp16)[name = tensor<string, []>("mean_37_cast_fp16")];
tensor<fp16, [1, 512]> sub_30_cast_fp16 = sub(x = x_117_cast_fp16, y = mean_37_cast_fp16)[name = tensor<string, []>("sub_30_cast_fp16")];
tensor<fp16, [1, 512]> square_24_cast_fp16 = square(x = sub_30_cast_fp16)[name = tensor<string, []>("square_24_cast_fp16")];
tensor<int32, [1]> reduce_mean_49_axes_0 = const()[name = tensor<string, []>("reduce_mean_49_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_49_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_49_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_49_cast_fp16 = reduce_mean(axes = reduce_mean_49_axes_0, keep_dims = reduce_mean_49_keep_dims_0, x = square_24_cast_fp16)[name = tensor<string, []>("reduce_mean_49_cast_fp16")];
tensor<fp16, []> var_1094_to_fp16 = const()[name = tensor<string, []>("op_1094_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 1]> var_1095_cast_fp16 = add(x = reduce_mean_49_cast_fp16, y = var_1094_to_fp16)[name = tensor<string, []>("op_1095_cast_fp16")];
tensor<fp16, [1, 1]> var_1096_cast_fp16 = sqrt(x = var_1095_cast_fp16)[name = tensor<string, []>("op_1096_cast_fp16")];
tensor<fp16, [1, 512]> x_119_cast_fp16 = real_div(x = sub_30_cast_fp16, y = var_1096_cast_fp16)[name = tensor<string, []>("x_119_cast_fp16")];
tensor<fp16, [1, 512]> var_1098_cast_fp16 = mul(x = x_119_cast_fp16, y = flow_net_res_blocks_4_in_ln_weight_to_fp16)[name = tensor<string, []>("op_1098_cast_fp16")];
tensor<fp16, [1, 512]> x_121_cast_fp16 = add(x = var_1098_cast_fp16, y = flow_net_res_blocks_4_in_ln_bias_to_fp16)[name = tensor<string, []>("x_121_cast_fp16")];
tensor<fp16, []> var_1100_promoted_to_fp16 = const()[name = tensor<string, []>("op_1100_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
tensor<fp16, [1, 512]> var_1101_cast_fp16 = add(x = var_1084_cast_fp16_1, y = var_1100_promoted_to_fp16)[name = tensor<string, []>("op_1101_cast_fp16")];
tensor<fp16, [1, 512]> var_1102_cast_fp16 = mul(x = x_121_cast_fp16, y = var_1101_cast_fp16)[name = tensor<string, []>("op_1102_cast_fp16")];
tensor<fp16, [1, 512]> input_185_cast_fp16 = add(x = var_1102_cast_fp16, y = var_1084_cast_fp16_0)[name = tensor<string, []>("input_185_cast_fp16")];
tensor<fp16, [1, 512]> linear_71_cast_fp16 = linear(bias = flow_net_res_blocks_4_mlp_0_bias_to_fp16, weight = flow_net_res_blocks_4_mlp_0_weight_to_fp16, x = input_185_cast_fp16)[name = tensor<string, []>("linear_71_cast_fp16")];
tensor<fp16, [1, 512]> input_189_cast_fp16 = silu(x = linear_71_cast_fp16)[name = tensor<string, []>("input_189_cast_fp16")];
tensor<fp16, [1, 512]> linear_72_cast_fp16 = linear(bias = flow_net_res_blocks_4_mlp_2_bias_to_fp16, weight = flow_net_res_blocks_4_mlp_2_weight_to_fp16, x = input_189_cast_fp16)[name = tensor<string, []>("linear_72_cast_fp16")];
tensor<fp16, [1, 512]> var_1113_cast_fp16 = mul(x = var_1084_cast_fp16_2, y = linear_72_cast_fp16)[name = tensor<string, []>("op_1113_cast_fp16")];
tensor<fp16, [1, 512]> x_123_cast_fp16 = add(x = x_117_cast_fp16, y = var_1113_cast_fp16)[name = tensor<string, []>("x_123_cast_fp16")];
tensor<fp16, [1, 1536]> linear_73_cast_fp16 = linear(bias = flow_net_res_blocks_5_adaLN_modulation_1_bias_to_fp16, weight = flow_net_res_blocks_5_adaLN_modulation_1_weight_to_fp16, x = input_151_cast_fp16)[name = tensor<string, []>("linear_73_cast_fp16")];
tensor<int32, [3]> var_1123_split_sizes_0 = const()[name = tensor<string, []>("op_1123_split_sizes_0"), val = tensor<int32, [3]>([512, 512, 512])];
tensor<int32, []> var_1123_axis_0 = const()[name = tensor<string, []>("op_1123_axis_0"), val = tensor<int32, []>(-1)];
tensor<fp16, [1, 512]> var_1123_cast_fp16_0, tensor<fp16, [1, 512]> var_1123_cast_fp16_1, tensor<fp16, [1, 512]> var_1123_cast_fp16_2 = split(axis = var_1123_axis_0, split_sizes = var_1123_split_sizes_0, x = linear_73_cast_fp16)[name = tensor<string, []>("op_1123_cast_fp16")];
tensor<int32, [1]> mean_39_axes_0 = const()[name = tensor<string, []>("mean_39_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> mean_39_keep_dims_0 = const()[name = tensor<string, []>("mean_39_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> mean_39_cast_fp16 = reduce_mean(axes = mean_39_axes_0, keep_dims = mean_39_keep_dims_0, x = x_123_cast_fp16)[name = tensor<string, []>("mean_39_cast_fp16")];
tensor<fp16, [1, 512]> sub_31_cast_fp16 = sub(x = x_123_cast_fp16, y = mean_39_cast_fp16)[name = tensor<string, []>("sub_31_cast_fp16")];
tensor<fp16, [1, 512]> square_25_cast_fp16 = square(x = sub_31_cast_fp16)[name = tensor<string, []>("square_25_cast_fp16")];
tensor<int32, [1]> reduce_mean_51_axes_0 = const()[name = tensor<string, []>("reduce_mean_51_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_51_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_51_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_51_cast_fp16 = reduce_mean(axes = reduce_mean_51_axes_0, keep_dims = reduce_mean_51_keep_dims_0, x = square_25_cast_fp16)[name = tensor<string, []>("reduce_mean_51_cast_fp16")];
tensor<fp16, []> var_1133_to_fp16 = const()[name = tensor<string, []>("op_1133_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 1]> var_1134_cast_fp16 = add(x = reduce_mean_51_cast_fp16, y = var_1133_to_fp16)[name = tensor<string, []>("op_1134_cast_fp16")];
tensor<fp16, [1, 1]> var_1135_cast_fp16 = sqrt(x = var_1134_cast_fp16)[name = tensor<string, []>("op_1135_cast_fp16")];
tensor<fp16, [1, 512]> x_125_cast_fp16 = real_div(x = sub_31_cast_fp16, y = var_1135_cast_fp16)[name = tensor<string, []>("x_125_cast_fp16")];
tensor<fp16, [1, 512]> var_1137_cast_fp16 = mul(x = x_125_cast_fp16, y = flow_net_res_blocks_5_in_ln_weight_to_fp16)[name = tensor<string, []>("op_1137_cast_fp16")];
tensor<fp16, [1, 512]> x_127_cast_fp16 = add(x = var_1137_cast_fp16, y = flow_net_res_blocks_5_in_ln_bias_to_fp16)[name = tensor<string, []>("x_127_cast_fp16")];
tensor<fp16, []> var_1139_promoted_to_fp16 = const()[name = tensor<string, []>("op_1139_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
tensor<fp16, [1, 512]> var_1140_cast_fp16 = add(x = var_1123_cast_fp16_1, y = var_1139_promoted_to_fp16)[name = tensor<string, []>("op_1140_cast_fp16")];
tensor<fp16, [1, 512]> var_1141_cast_fp16 = mul(x = x_127_cast_fp16, y = var_1140_cast_fp16)[name = tensor<string, []>("op_1141_cast_fp16")];
tensor<fp16, [1, 512]> input_193_cast_fp16 = add(x = var_1141_cast_fp16, y = var_1123_cast_fp16_0)[name = tensor<string, []>("input_193_cast_fp16")];
tensor<fp16, [1, 512]> linear_74_cast_fp16 = linear(bias = flow_net_res_blocks_5_mlp_0_bias_to_fp16, weight = flow_net_res_blocks_5_mlp_0_weight_to_fp16, x = input_193_cast_fp16)[name = tensor<string, []>("linear_74_cast_fp16")];
tensor<fp16, [1, 512]> input_197_cast_fp16 = silu(x = linear_74_cast_fp16)[name = tensor<string, []>("input_197_cast_fp16")];
tensor<fp16, [1, 512]> linear_75_cast_fp16 = linear(bias = flow_net_res_blocks_5_mlp_2_bias_to_fp16, weight = flow_net_res_blocks_5_mlp_2_weight_to_fp16, x = input_197_cast_fp16)[name = tensor<string, []>("linear_75_cast_fp16")];
tensor<fp16, [1, 512]> var_1152_cast_fp16 = mul(x = var_1123_cast_fp16_2, y = linear_75_cast_fp16)[name = tensor<string, []>("op_1152_cast_fp16")];
tensor<fp16, [1, 512]> x_129_cast_fp16 = add(x = x_123_cast_fp16, y = var_1152_cast_fp16)[name = tensor<string, []>("x_129_cast_fp16")];
tensor<fp16, [1, 1024]> linear_76_cast_fp16 = linear(bias = flow_net_final_layer_adaLN_modulation_1_bias_to_fp16, weight = flow_net_final_layer_adaLN_modulation_1_weight_to_fp16, x = input_151_cast_fp16)[name = tensor<string, []>("linear_76_cast_fp16")];
tensor<int32, [2]> var_1161_split_sizes_0 = const()[name = tensor<string, []>("op_1161_split_sizes_0"), val = tensor<int32, [2]>([512, 512])];
tensor<int32, []> var_1161_axis_0 = const()[name = tensor<string, []>("op_1161_axis_0"), val = tensor<int32, []>(-1)];
tensor<fp16, [1, 512]> var_1161_cast_fp16_0, tensor<fp16, [1, 512]> var_1161_cast_fp16_1 = split(axis = var_1161_axis_0, split_sizes = var_1161_split_sizes_0, x = linear_76_cast_fp16)[name = tensor<string, []>("op_1161_cast_fp16")];
tensor<int32, [1]> mean_41_axes_0 = const()[name = tensor<string, []>("mean_41_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> mean_41_keep_dims_0 = const()[name = tensor<string, []>("mean_41_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> mean_41_cast_fp16 = reduce_mean(axes = mean_41_axes_0, keep_dims = mean_41_keep_dims_0, x = x_129_cast_fp16)[name = tensor<string, []>("mean_41_cast_fp16")];
tensor<fp16, [1, 512]> sub_32_cast_fp16 = sub(x = x_129_cast_fp16, y = mean_41_cast_fp16)[name = tensor<string, []>("sub_32_cast_fp16")];
tensor<fp16, [1, 512]> square_26_cast_fp16 = square(x = sub_32_cast_fp16)[name = tensor<string, []>("square_26_cast_fp16")];
tensor<int32, [1]> reduce_mean_53_axes_0 = const()[name = tensor<string, []>("reduce_mean_53_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_53_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_53_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_53_cast_fp16 = reduce_mean(axes = reduce_mean_53_axes_0, keep_dims = reduce_mean_53_keep_dims_0, x = square_26_cast_fp16)[name = tensor<string, []>("reduce_mean_53_cast_fp16")];
tensor<fp16, []> var_1168_to_fp16 = const()[name = tensor<string, []>("op_1168_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 1]> var_1169_cast_fp16 = add(x = reduce_mean_53_cast_fp16, y = var_1168_to_fp16)[name = tensor<string, []>("op_1169_cast_fp16")];
tensor<fp16, [1, 1]> var_1170_cast_fp16 = sqrt(x = var_1169_cast_fp16)[name = tensor<string, []>("op_1170_cast_fp16")];
tensor<fp16, [1, 512]> x_131_cast_fp16 = real_div(x = sub_32_cast_fp16, y = var_1170_cast_fp16)[name = tensor<string, []>("x_131_cast_fp16")];
tensor<fp16, []> var_1172_promoted_to_fp16 = const()[name = tensor<string, []>("op_1172_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
tensor<fp16, [1, 512]> var_1173_cast_fp16 = add(x = var_1161_cast_fp16_1, y = var_1172_promoted_to_fp16)[name = tensor<string, []>("op_1173_cast_fp16")];
tensor<fp16, [1, 512]> var_1174_cast_fp16 = mul(x = x_131_cast_fp16, y = var_1173_cast_fp16)[name = tensor<string, []>("op_1174_cast_fp16")];
tensor<fp16, [1, 512]> input_201_cast_fp16 = add(x = var_1174_cast_fp16, y = var_1161_cast_fp16_0)[name = tensor<string, []>("input_201_cast_fp16")];
tensor<fp16, [1, 32]> linear_77_cast_fp16 = linear(bias = flow_net_final_layer_linear_bias_to_fp16, weight = flow_net_final_layer_linear_weight_to_fp16, x = input_201_cast_fp16)[name = tensor<string, []>("linear_77_cast_fp16")];
tensor<fp16, []> var_1185_to_fp16 = const()[name = tensor<string, []>("op_1185_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 32]> var_1186_cast_fp16 = mul(x = linear_77_cast_fp16, y = var_1185_to_fp16)[name = tensor<string, []>("op_1186_cast_fp16")];
tensor<fp16, [1, 32]> input_203_cast_fp16 = add(x = input_135_cast_fp16, y = var_1186_cast_fp16)[name = tensor<string, []>("input_203_cast_fp16")];
tensor<fp16, [1, 512]> linear_78_cast_fp16 = linear(bias = flow_net_input_proj_bias_to_fp16, weight = flow_net_input_proj_weight_to_fp16, x = input_203_cast_fp16)[name = tensor<string, []>("linear_78_cast_fp16")];
tensor<fp16, [1, 512]> input_207_to_fp16 = const()[name = tensor<string, []>("input_207_to_fp16"), val = tensor<fp16, [1, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19001728)))];
tensor<fp16, [1, 512]> input_209_cast_fp16 = silu(x = input_207_to_fp16)[name = tensor<string, []>("input_209_cast_fp16")];
tensor<fp16, [1, 512]> linear_80_cast_fp16 = linear(bias = flow_net_time_embed_0_mlp_2_bias_to_fp16, weight = flow_net_time_embed_0_mlp_2_weight_to_fp16, x = input_209_cast_fp16)[name = tensor<string, []>("linear_80_cast_fp16")];
tensor<int32, [1]> reduce_mean_54_axes_0 = const()[name = tensor<string, []>("reduce_mean_54_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_54_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_54_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_54_cast_fp16 = reduce_mean(axes = reduce_mean_54_axes_0, keep_dims = reduce_mean_54_keep_dims_0, x = linear_80_cast_fp16)[name = tensor<string, []>("reduce_mean_54_cast_fp16")];
tensor<fp16, [1, 512]> sub_33_cast_fp16 = sub(x = linear_80_cast_fp16, y = reduce_mean_54_cast_fp16)[name = tensor<string, []>("sub_33_cast_fp16")];
tensor<fp16, [1, 512]> square_27_cast_fp16 = square(x = sub_33_cast_fp16)[name = tensor<string, []>("square_27_cast_fp16")];
tensor<int32, [1]> reduce_mean_55_axes_0 = const()[name = tensor<string, []>("reduce_mean_55_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_55_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_55_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_55_cast_fp16 = reduce_mean(axes = reduce_mean_55_axes_0, keep_dims = reduce_mean_55_keep_dims_0, x = square_27_cast_fp16)[name = tensor<string, []>("reduce_mean_55_cast_fp16")];
tensor<fp16, []> real_div_6_to_fp16 = const()[name = tensor<string, []>("real_div_6_to_fp16"), val = tensor<fp16, []>(0x1.008p+0)];
tensor<fp16, [1, 1]> mul_6_cast_fp16 = mul(x = reduce_mean_55_cast_fp16, y = real_div_6_to_fp16)[name = tensor<string, []>("mul_6_cast_fp16")];
tensor<fp16, []> var_1252_to_fp16 = const()[name = tensor<string, []>("op_1252_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1]> var_55_cast_fp16 = add(x = mul_6_cast_fp16, y = var_1252_to_fp16)[name = tensor<string, []>("var_55_cast_fp16")];
tensor<fp32, []> var_1255_epsilon_0 = const()[name = tensor<string, []>("op_1255_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
tensor<fp16, [1, 1]> var_1255_cast_fp16 = rsqrt(epsilon = var_1255_epsilon_0, x = var_55_cast_fp16)[name = tensor<string, []>("op_1255_cast_fp16")];
tensor<fp16, [1, 512]> var_1256_cast_fp16 = mul(x = const_3_to_fp16, y = var_1255_cast_fp16)[name = tensor<string, []>("op_1256_cast_fp16")];
tensor<fp16, [1, 512]> var_1257_cast_fp16 = mul(x = linear_80_cast_fp16, y = var_1256_cast_fp16)[name = tensor<string, []>("op_1257_cast_fp16")];
tensor<fp16, [1, 512]> input_213_to_fp16 = const()[name = tensor<string, []>("input_213_to_fp16"), val = tensor<fp16, [1, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19002816)))];
tensor<fp16, [1, 512]> input_215_cast_fp16 = silu(x = input_213_to_fp16)[name = tensor<string, []>("input_215_cast_fp16")];
tensor<fp16, [1, 512]> linear_82_cast_fp16 = linear(bias = flow_net_time_embed_1_mlp_2_bias_to_fp16, weight = flow_net_time_embed_1_mlp_2_weight_to_fp16, x = input_215_cast_fp16)[name = tensor<string, []>("linear_82_cast_fp16")];
tensor<int32, [1]> reduce_mean_56_axes_0 = const()[name = tensor<string, []>("reduce_mean_56_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_56_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_56_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_56_cast_fp16 = reduce_mean(axes = reduce_mean_56_axes_0, keep_dims = reduce_mean_56_keep_dims_0, x = linear_82_cast_fp16)[name = tensor<string, []>("reduce_mean_56_cast_fp16")];
tensor<fp16, [1, 512]> sub_35_cast_fp16 = sub(x = linear_82_cast_fp16, y = reduce_mean_56_cast_fp16)[name = tensor<string, []>("sub_35_cast_fp16")];
tensor<fp16, [1, 512]> square_28_cast_fp16 = square(x = sub_35_cast_fp16)[name = tensor<string, []>("square_28_cast_fp16")];
tensor<int32, [1]> reduce_mean_57_axes_0 = const()[name = tensor<string, []>("reduce_mean_57_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_57_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_57_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_57_cast_fp16 = reduce_mean(axes = reduce_mean_57_axes_0, keep_dims = reduce_mean_57_keep_dims_0, x = square_28_cast_fp16)[name = tensor<string, []>("reduce_mean_57_cast_fp16")];
tensor<fp16, []> real_div_7_to_fp16 = const()[name = tensor<string, []>("real_div_7_to_fp16"), val = tensor<fp16, []>(0x1.008p+0)];
tensor<fp16, [1, 1]> mul_7_cast_fp16 = mul(x = reduce_mean_57_cast_fp16, y = real_div_7_to_fp16)[name = tensor<string, []>("mul_7_cast_fp16")];
tensor<fp16, []> var_1289_to_fp16 = const()[name = tensor<string, []>("op_1289_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1]> var_57_cast_fp16 = add(x = mul_7_cast_fp16, y = var_1289_to_fp16)[name = tensor<string, []>("var_57_cast_fp16")];
tensor<fp32, []> var_1292_epsilon_0 = const()[name = tensor<string, []>("op_1292_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
tensor<fp16, [1, 1]> var_1292_cast_fp16 = rsqrt(epsilon = var_1292_epsilon_0, x = var_57_cast_fp16)[name = tensor<string, []>("op_1292_cast_fp16")];
tensor<fp16, [1, 512]> var_1293_cast_fp16 = mul(x = const_5_to_fp16, y = var_1292_cast_fp16)[name = tensor<string, []>("op_1293_cast_fp16")];
tensor<fp16, [1, 512]> var_1294_cast_fp16 = mul(x = linear_82_cast_fp16, y = var_1293_cast_fp16)[name = tensor<string, []>("op_1294_cast_fp16")];
tensor<fp16, [1, 512]> var_1306_cast_fp16 = add(x = var_1257_cast_fp16, y = var_1294_cast_fp16)[name = tensor<string, []>("op_1306_cast_fp16")];
tensor<fp16, []> _inversed_t_combined_7_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_t_combined_7_y_0_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
tensor<fp16, [1, 512]> _inversed_t_combined_7_cast_fp16 = mul(x = var_1306_cast_fp16, y = _inversed_t_combined_7_y_0_to_fp16)[name = tensor<string, []>("_inversed_t_combined_7_cast_fp16")];
tensor<fp16, [1, 512]> input_217_cast_fp16 = add(x = _inversed_t_combined_7_cast_fp16, y = linear_5_cast_fp16)[name = tensor<string, []>("input_217_cast_fp16")];
tensor<fp16, [1, 512]> input_219_cast_fp16 = silu(x = input_217_cast_fp16)[name = tensor<string, []>("input_219_cast_fp16")];
tensor<fp16, [1, 1536]> linear_84_cast_fp16 = linear(bias = flow_net_res_blocks_0_adaLN_modulation_1_bias_to_fp16, weight = flow_net_res_blocks_0_adaLN_modulation_1_weight_to_fp16, x = input_219_cast_fp16)[name = tensor<string, []>("linear_84_cast_fp16")];
tensor<int32, [3]> var_1321_split_sizes_0 = const()[name = tensor<string, []>("op_1321_split_sizes_0"), val = tensor<int32, [3]>([512, 512, 512])];
tensor<int32, []> var_1321_axis_0 = const()[name = tensor<string, []>("op_1321_axis_0"), val = tensor<int32, []>(-1)];
tensor<fp16, [1, 512]> var_1321_cast_fp16_0, tensor<fp16, [1, 512]> var_1321_cast_fp16_1, tensor<fp16, [1, 512]> var_1321_cast_fp16_2 = split(axis = var_1321_axis_0, split_sizes = var_1321_split_sizes_0, x = linear_84_cast_fp16)[name = tensor<string, []>("op_1321_cast_fp16")];
tensor<int32, [1]> mean_43_axes_0 = const()[name = tensor<string, []>("mean_43_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> mean_43_keep_dims_0 = const()[name = tensor<string, []>("mean_43_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> mean_43_cast_fp16 = reduce_mean(axes = mean_43_axes_0, keep_dims = mean_43_keep_dims_0, x = linear_78_cast_fp16)[name = tensor<string, []>("mean_43_cast_fp16")];
tensor<fp16, [1, 512]> sub_37_cast_fp16 = sub(x = linear_78_cast_fp16, y = mean_43_cast_fp16)[name = tensor<string, []>("sub_37_cast_fp16")];
tensor<fp16, [1, 512]> square_29_cast_fp16 = square(x = sub_37_cast_fp16)[name = tensor<string, []>("square_29_cast_fp16")];
tensor<int32, [1]> reduce_mean_59_axes_0 = const()[name = tensor<string, []>("reduce_mean_59_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_59_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_59_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_59_cast_fp16 = reduce_mean(axes = reduce_mean_59_axes_0, keep_dims = reduce_mean_59_keep_dims_0, x = square_29_cast_fp16)[name = tensor<string, []>("reduce_mean_59_cast_fp16")];
tensor<fp16, []> var_1331_to_fp16 = const()[name = tensor<string, []>("op_1331_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 1]> var_1332_cast_fp16 = add(x = reduce_mean_59_cast_fp16, y = var_1331_to_fp16)[name = tensor<string, []>("op_1332_cast_fp16")];
tensor<fp16, [1, 1]> var_1333_cast_fp16 = sqrt(x = var_1332_cast_fp16)[name = tensor<string, []>("op_1333_cast_fp16")];
tensor<fp16, [1, 512]> x_139_cast_fp16 = real_div(x = sub_37_cast_fp16, y = var_1333_cast_fp16)[name = tensor<string, []>("x_139_cast_fp16")];
tensor<fp16, [1, 512]> var_1335_cast_fp16 = mul(x = x_139_cast_fp16, y = flow_net_res_blocks_0_in_ln_weight_to_fp16)[name = tensor<string, []>("op_1335_cast_fp16")];
tensor<fp16, [1, 512]> x_141_cast_fp16 = add(x = var_1335_cast_fp16, y = flow_net_res_blocks_0_in_ln_bias_to_fp16)[name = tensor<string, []>("x_141_cast_fp16")];
tensor<fp16, []> var_1337_promoted_to_fp16 = const()[name = tensor<string, []>("op_1337_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
tensor<fp16, [1, 512]> var_1338_cast_fp16 = add(x = var_1321_cast_fp16_1, y = var_1337_promoted_to_fp16)[name = tensor<string, []>("op_1338_cast_fp16")];
tensor<fp16, [1, 512]> var_1339_cast_fp16 = mul(x = x_141_cast_fp16, y = var_1338_cast_fp16)[name = tensor<string, []>("op_1339_cast_fp16")];
tensor<fp16, [1, 512]> input_221_cast_fp16 = add(x = var_1339_cast_fp16, y = var_1321_cast_fp16_0)[name = tensor<string, []>("input_221_cast_fp16")];
tensor<fp16, [1, 512]> linear_85_cast_fp16 = linear(bias = flow_net_res_blocks_0_mlp_0_bias_to_fp16, weight = flow_net_res_blocks_0_mlp_0_weight_to_fp16, x = input_221_cast_fp16)[name = tensor<string, []>("linear_85_cast_fp16")];
tensor<fp16, [1, 512]> input_225_cast_fp16 = silu(x = linear_85_cast_fp16)[name = tensor<string, []>("input_225_cast_fp16")];
tensor<fp16, [1, 512]> linear_86_cast_fp16 = linear(bias = flow_net_res_blocks_0_mlp_2_bias_to_fp16, weight = flow_net_res_blocks_0_mlp_2_weight_to_fp16, x = input_225_cast_fp16)[name = tensor<string, []>("linear_86_cast_fp16")];
tensor<fp16, [1, 512]> var_1350_cast_fp16 = mul(x = var_1321_cast_fp16_2, y = linear_86_cast_fp16)[name = tensor<string, []>("op_1350_cast_fp16")];
tensor<fp16, [1, 512]> x_143_cast_fp16 = add(x = linear_78_cast_fp16, y = var_1350_cast_fp16)[name = tensor<string, []>("x_143_cast_fp16")];
tensor<fp16, [1, 1536]> linear_87_cast_fp16 = linear(bias = flow_net_res_blocks_1_adaLN_modulation_1_bias_to_fp16, weight = flow_net_res_blocks_1_adaLN_modulation_1_weight_to_fp16, x = input_219_cast_fp16)[name = tensor<string, []>("linear_87_cast_fp16")];
tensor<int32, [3]> var_1360_split_sizes_0 = const()[name = tensor<string, []>("op_1360_split_sizes_0"), val = tensor<int32, [3]>([512, 512, 512])];
tensor<int32, []> var_1360_axis_0 = const()[name = tensor<string, []>("op_1360_axis_0"), val = tensor<int32, []>(-1)];
tensor<fp16, [1, 512]> var_1360_cast_fp16_0, tensor<fp16, [1, 512]> var_1360_cast_fp16_1, tensor<fp16, [1, 512]> var_1360_cast_fp16_2 = split(axis = var_1360_axis_0, split_sizes = var_1360_split_sizes_0, x = linear_87_cast_fp16)[name = tensor<string, []>("op_1360_cast_fp16")];
tensor<int32, [1]> mean_45_axes_0 = const()[name = tensor<string, []>("mean_45_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> mean_45_keep_dims_0 = const()[name = tensor<string, []>("mean_45_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> mean_45_cast_fp16 = reduce_mean(axes = mean_45_axes_0, keep_dims = mean_45_keep_dims_0, x = x_143_cast_fp16)[name = tensor<string, []>("mean_45_cast_fp16")];
tensor<fp16, [1, 512]> sub_38_cast_fp16 = sub(x = x_143_cast_fp16, y = mean_45_cast_fp16)[name = tensor<string, []>("sub_38_cast_fp16")];
tensor<fp16, [1, 512]> square_30_cast_fp16 = square(x = sub_38_cast_fp16)[name = tensor<string, []>("square_30_cast_fp16")];
tensor<int32, [1]> reduce_mean_61_axes_0 = const()[name = tensor<string, []>("reduce_mean_61_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_61_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_61_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_61_cast_fp16 = reduce_mean(axes = reduce_mean_61_axes_0, keep_dims = reduce_mean_61_keep_dims_0, x = square_30_cast_fp16)[name = tensor<string, []>("reduce_mean_61_cast_fp16")];
tensor<fp16, []> var_1370_to_fp16 = const()[name = tensor<string, []>("op_1370_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 1]> var_1371_cast_fp16 = add(x = reduce_mean_61_cast_fp16, y = var_1370_to_fp16)[name = tensor<string, []>("op_1371_cast_fp16")];
tensor<fp16, [1, 1]> var_1372_cast_fp16 = sqrt(x = var_1371_cast_fp16)[name = tensor<string, []>("op_1372_cast_fp16")];
tensor<fp16, [1, 512]> x_145_cast_fp16 = real_div(x = sub_38_cast_fp16, y = var_1372_cast_fp16)[name = tensor<string, []>("x_145_cast_fp16")];
tensor<fp16, [1, 512]> var_1374_cast_fp16 = mul(x = x_145_cast_fp16, y = flow_net_res_blocks_1_in_ln_weight_to_fp16)[name = tensor<string, []>("op_1374_cast_fp16")];
tensor<fp16, [1, 512]> x_147_cast_fp16 = add(x = var_1374_cast_fp16, y = flow_net_res_blocks_1_in_ln_bias_to_fp16)[name = tensor<string, []>("x_147_cast_fp16")];
tensor<fp16, []> var_1376_promoted_to_fp16 = const()[name = tensor<string, []>("op_1376_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
tensor<fp16, [1, 512]> var_1377_cast_fp16 = add(x = var_1360_cast_fp16_1, y = var_1376_promoted_to_fp16)[name = tensor<string, []>("op_1377_cast_fp16")];
tensor<fp16, [1, 512]> var_1378_cast_fp16 = mul(x = x_147_cast_fp16, y = var_1377_cast_fp16)[name = tensor<string, []>("op_1378_cast_fp16")];
tensor<fp16, [1, 512]> input_229_cast_fp16 = add(x = var_1378_cast_fp16, y = var_1360_cast_fp16_0)[name = tensor<string, []>("input_229_cast_fp16")];
tensor<fp16, [1, 512]> linear_88_cast_fp16 = linear(bias = flow_net_res_blocks_1_mlp_0_bias_to_fp16, weight = flow_net_res_blocks_1_mlp_0_weight_to_fp16, x = input_229_cast_fp16)[name = tensor<string, []>("linear_88_cast_fp16")];
tensor<fp16, [1, 512]> input_233_cast_fp16 = silu(x = linear_88_cast_fp16)[name = tensor<string, []>("input_233_cast_fp16")];
tensor<fp16, [1, 512]> linear_89_cast_fp16 = linear(bias = flow_net_res_blocks_1_mlp_2_bias_to_fp16, weight = flow_net_res_blocks_1_mlp_2_weight_to_fp16, x = input_233_cast_fp16)[name = tensor<string, []>("linear_89_cast_fp16")];
tensor<fp16, [1, 512]> var_1389_cast_fp16 = mul(x = var_1360_cast_fp16_2, y = linear_89_cast_fp16)[name = tensor<string, []>("op_1389_cast_fp16")];
tensor<fp16, [1, 512]> x_149_cast_fp16 = add(x = x_143_cast_fp16, y = var_1389_cast_fp16)[name = tensor<string, []>("x_149_cast_fp16")];
tensor<fp16, [1, 1536]> linear_90_cast_fp16 = linear(bias = flow_net_res_blocks_2_adaLN_modulation_1_bias_to_fp16, weight = flow_net_res_blocks_2_adaLN_modulation_1_weight_to_fp16, x = input_219_cast_fp16)[name = tensor<string, []>("linear_90_cast_fp16")];
tensor<int32, [3]> var_1399_split_sizes_0 = const()[name = tensor<string, []>("op_1399_split_sizes_0"), val = tensor<int32, [3]>([512, 512, 512])];
tensor<int32, []> var_1399_axis_0 = const()[name = tensor<string, []>("op_1399_axis_0"), val = tensor<int32, []>(-1)];
tensor<fp16, [1, 512]> var_1399_cast_fp16_0, tensor<fp16, [1, 512]> var_1399_cast_fp16_1, tensor<fp16, [1, 512]> var_1399_cast_fp16_2 = split(axis = var_1399_axis_0, split_sizes = var_1399_split_sizes_0, x = linear_90_cast_fp16)[name = tensor<string, []>("op_1399_cast_fp16")];
tensor<int32, [1]> mean_47_axes_0 = const()[name = tensor<string, []>("mean_47_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> mean_47_keep_dims_0 = const()[name = tensor<string, []>("mean_47_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> mean_47_cast_fp16 = reduce_mean(axes = mean_47_axes_0, keep_dims = mean_47_keep_dims_0, x = x_149_cast_fp16)[name = tensor<string, []>("mean_47_cast_fp16")];
tensor<fp16, [1, 512]> sub_39_cast_fp16 = sub(x = x_149_cast_fp16, y = mean_47_cast_fp16)[name = tensor<string, []>("sub_39_cast_fp16")];
tensor<fp16, [1, 512]> square_31_cast_fp16 = square(x = sub_39_cast_fp16)[name = tensor<string, []>("square_31_cast_fp16")];
tensor<int32, [1]> reduce_mean_63_axes_0 = const()[name = tensor<string, []>("reduce_mean_63_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_63_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_63_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_63_cast_fp16 = reduce_mean(axes = reduce_mean_63_axes_0, keep_dims = reduce_mean_63_keep_dims_0, x = square_31_cast_fp16)[name = tensor<string, []>("reduce_mean_63_cast_fp16")];
tensor<fp16, []> var_1409_to_fp16 = const()[name = tensor<string, []>("op_1409_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 1]> var_1410_cast_fp16 = add(x = reduce_mean_63_cast_fp16, y = var_1409_to_fp16)[name = tensor<string, []>("op_1410_cast_fp16")];
tensor<fp16, [1, 1]> var_1411_cast_fp16 = sqrt(x = var_1410_cast_fp16)[name = tensor<string, []>("op_1411_cast_fp16")];
tensor<fp16, [1, 512]> x_151_cast_fp16 = real_div(x = sub_39_cast_fp16, y = var_1411_cast_fp16)[name = tensor<string, []>("x_151_cast_fp16")];
tensor<fp16, [1, 512]> var_1413_cast_fp16 = mul(x = x_151_cast_fp16, y = flow_net_res_blocks_2_in_ln_weight_to_fp16)[name = tensor<string, []>("op_1413_cast_fp16")];
tensor<fp16, [1, 512]> x_153_cast_fp16 = add(x = var_1413_cast_fp16, y = flow_net_res_blocks_2_in_ln_bias_to_fp16)[name = tensor<string, []>("x_153_cast_fp16")];
tensor<fp16, []> var_1415_promoted_to_fp16 = const()[name = tensor<string, []>("op_1415_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
tensor<fp16, [1, 512]> var_1416_cast_fp16 = add(x = var_1399_cast_fp16_1, y = var_1415_promoted_to_fp16)[name = tensor<string, []>("op_1416_cast_fp16")];
tensor<fp16, [1, 512]> var_1417_cast_fp16 = mul(x = x_153_cast_fp16, y = var_1416_cast_fp16)[name = tensor<string, []>("op_1417_cast_fp16")];
tensor<fp16, [1, 512]> input_237_cast_fp16 = add(x = var_1417_cast_fp16, y = var_1399_cast_fp16_0)[name = tensor<string, []>("input_237_cast_fp16")];
tensor<fp16, [1, 512]> linear_91_cast_fp16 = linear(bias = flow_net_res_blocks_2_mlp_0_bias_to_fp16, weight = flow_net_res_blocks_2_mlp_0_weight_to_fp16, x = input_237_cast_fp16)[name = tensor<string, []>("linear_91_cast_fp16")];
tensor<fp16, [1, 512]> input_241_cast_fp16 = silu(x = linear_91_cast_fp16)[name = tensor<string, []>("input_241_cast_fp16")];
tensor<fp16, [1, 512]> linear_92_cast_fp16 = linear(bias = flow_net_res_blocks_2_mlp_2_bias_to_fp16, weight = flow_net_res_blocks_2_mlp_2_weight_to_fp16, x = input_241_cast_fp16)[name = tensor<string, []>("linear_92_cast_fp16")];
tensor<fp16, [1, 512]> var_1428_cast_fp16 = mul(x = var_1399_cast_fp16_2, y = linear_92_cast_fp16)[name = tensor<string, []>("op_1428_cast_fp16")];
tensor<fp16, [1, 512]> x_155_cast_fp16 = add(x = x_149_cast_fp16, y = var_1428_cast_fp16)[name = tensor<string, []>("x_155_cast_fp16")];
tensor<fp16, [1, 1536]> linear_93_cast_fp16 = linear(bias = flow_net_res_blocks_3_adaLN_modulation_1_bias_to_fp16, weight = flow_net_res_blocks_3_adaLN_modulation_1_weight_to_fp16, x = input_219_cast_fp16)[name = tensor<string, []>("linear_93_cast_fp16")];
tensor<int32, [3]> var_1438_split_sizes_0 = const()[name = tensor<string, []>("op_1438_split_sizes_0"), val = tensor<int32, [3]>([512, 512, 512])];
tensor<int32, []> var_1438_axis_0 = const()[name = tensor<string, []>("op_1438_axis_0"), val = tensor<int32, []>(-1)];
tensor<fp16, [1, 512]> var_1438_cast_fp16_0, tensor<fp16, [1, 512]> var_1438_cast_fp16_1, tensor<fp16, [1, 512]> var_1438_cast_fp16_2 = split(axis = var_1438_axis_0, split_sizes = var_1438_split_sizes_0, x = linear_93_cast_fp16)[name = tensor<string, []>("op_1438_cast_fp16")];
tensor<int32, [1]> mean_49_axes_0 = const()[name = tensor<string, []>("mean_49_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> mean_49_keep_dims_0 = const()[name = tensor<string, []>("mean_49_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> mean_49_cast_fp16 = reduce_mean(axes = mean_49_axes_0, keep_dims = mean_49_keep_dims_0, x = x_155_cast_fp16)[name = tensor<string, []>("mean_49_cast_fp16")];
tensor<fp16, [1, 512]> sub_40_cast_fp16 = sub(x = x_155_cast_fp16, y = mean_49_cast_fp16)[name = tensor<string, []>("sub_40_cast_fp16")];
tensor<fp16, [1, 512]> square_32_cast_fp16 = square(x = sub_40_cast_fp16)[name = tensor<string, []>("square_32_cast_fp16")];
tensor<int32, [1]> reduce_mean_65_axes_0 = const()[name = tensor<string, []>("reduce_mean_65_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_65_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_65_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_65_cast_fp16 = reduce_mean(axes = reduce_mean_65_axes_0, keep_dims = reduce_mean_65_keep_dims_0, x = square_32_cast_fp16)[name = tensor<string, []>("reduce_mean_65_cast_fp16")];
tensor<fp16, []> var_1448_to_fp16 = const()[name = tensor<string, []>("op_1448_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 1]> var_1449_cast_fp16 = add(x = reduce_mean_65_cast_fp16, y = var_1448_to_fp16)[name = tensor<string, []>("op_1449_cast_fp16")];
tensor<fp16, [1, 1]> var_1450_cast_fp16 = sqrt(x = var_1449_cast_fp16)[name = tensor<string, []>("op_1450_cast_fp16")];
tensor<fp16, [1, 512]> x_157_cast_fp16 = real_div(x = sub_40_cast_fp16, y = var_1450_cast_fp16)[name = tensor<string, []>("x_157_cast_fp16")];
tensor<fp16, [1, 512]> var_1452_cast_fp16 = mul(x = x_157_cast_fp16, y = flow_net_res_blocks_3_in_ln_weight_to_fp16)[name = tensor<string, []>("op_1452_cast_fp16")];
tensor<fp16, [1, 512]> x_159_cast_fp16 = add(x = var_1452_cast_fp16, y = flow_net_res_blocks_3_in_ln_bias_to_fp16)[name = tensor<string, []>("x_159_cast_fp16")];
tensor<fp16, []> var_1454_promoted_to_fp16 = const()[name = tensor<string, []>("op_1454_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
tensor<fp16, [1, 512]> var_1455_cast_fp16 = add(x = var_1438_cast_fp16_1, y = var_1454_promoted_to_fp16)[name = tensor<string, []>("op_1455_cast_fp16")];
tensor<fp16, [1, 512]> var_1456_cast_fp16 = mul(x = x_159_cast_fp16, y = var_1455_cast_fp16)[name = tensor<string, []>("op_1456_cast_fp16")];
tensor<fp16, [1, 512]> input_245_cast_fp16 = add(x = var_1456_cast_fp16, y = var_1438_cast_fp16_0)[name = tensor<string, []>("input_245_cast_fp16")];
tensor<fp16, [1, 512]> linear_94_cast_fp16 = linear(bias = flow_net_res_blocks_3_mlp_0_bias_to_fp16, weight = flow_net_res_blocks_3_mlp_0_weight_to_fp16, x = input_245_cast_fp16)[name = tensor<string, []>("linear_94_cast_fp16")];
tensor<fp16, [1, 512]> input_249_cast_fp16 = silu(x = linear_94_cast_fp16)[name = tensor<string, []>("input_249_cast_fp16")];
tensor<fp16, [1, 512]> linear_95_cast_fp16 = linear(bias = flow_net_res_blocks_3_mlp_2_bias_to_fp16, weight = flow_net_res_blocks_3_mlp_2_weight_to_fp16, x = input_249_cast_fp16)[name = tensor<string, []>("linear_95_cast_fp16")];
tensor<fp16, [1, 512]> var_1467_cast_fp16 = mul(x = var_1438_cast_fp16_2, y = linear_95_cast_fp16)[name = tensor<string, []>("op_1467_cast_fp16")];
tensor<fp16, [1, 512]> x_161_cast_fp16 = add(x = x_155_cast_fp16, y = var_1467_cast_fp16)[name = tensor<string, []>("x_161_cast_fp16")];
tensor<fp16, [1, 1536]> linear_96_cast_fp16 = linear(bias = flow_net_res_blocks_4_adaLN_modulation_1_bias_to_fp16, weight = flow_net_res_blocks_4_adaLN_modulation_1_weight_to_fp16, x = input_219_cast_fp16)[name = tensor<string, []>("linear_96_cast_fp16")];
tensor<int32, [3]> var_1477_split_sizes_0 = const()[name = tensor<string, []>("op_1477_split_sizes_0"), val = tensor<int32, [3]>([512, 512, 512])];
tensor<int32, []> var_1477_axis_0 = const()[name = tensor<string, []>("op_1477_axis_0"), val = tensor<int32, []>(-1)];
tensor<fp16, [1, 512]> var_1477_cast_fp16_0, tensor<fp16, [1, 512]> var_1477_cast_fp16_1, tensor<fp16, [1, 512]> var_1477_cast_fp16_2 = split(axis = var_1477_axis_0, split_sizes = var_1477_split_sizes_0, x = linear_96_cast_fp16)[name = tensor<string, []>("op_1477_cast_fp16")];
tensor<int32, [1]> mean_51_axes_0 = const()[name = tensor<string, []>("mean_51_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> mean_51_keep_dims_0 = const()[name = tensor<string, []>("mean_51_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> mean_51_cast_fp16 = reduce_mean(axes = mean_51_axes_0, keep_dims = mean_51_keep_dims_0, x = x_161_cast_fp16)[name = tensor<string, []>("mean_51_cast_fp16")];
tensor<fp16, [1, 512]> sub_41_cast_fp16 = sub(x = x_161_cast_fp16, y = mean_51_cast_fp16)[name = tensor<string, []>("sub_41_cast_fp16")];
tensor<fp16, [1, 512]> square_33_cast_fp16 = square(x = sub_41_cast_fp16)[name = tensor<string, []>("square_33_cast_fp16")];
tensor<int32, [1]> reduce_mean_67_axes_0 = const()[name = tensor<string, []>("reduce_mean_67_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_67_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_67_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_67_cast_fp16 = reduce_mean(axes = reduce_mean_67_axes_0, keep_dims = reduce_mean_67_keep_dims_0, x = square_33_cast_fp16)[name = tensor<string, []>("reduce_mean_67_cast_fp16")];
tensor<fp16, []> var_1487_to_fp16 = const()[name = tensor<string, []>("op_1487_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 1]> var_1488_cast_fp16 = add(x = reduce_mean_67_cast_fp16, y = var_1487_to_fp16)[name = tensor<string, []>("op_1488_cast_fp16")];
tensor<fp16, [1, 1]> var_1489_cast_fp16 = sqrt(x = var_1488_cast_fp16)[name = tensor<string, []>("op_1489_cast_fp16")];
tensor<fp16, [1, 512]> x_163_cast_fp16 = real_div(x = sub_41_cast_fp16, y = var_1489_cast_fp16)[name = tensor<string, []>("x_163_cast_fp16")];
tensor<fp16, [1, 512]> var_1491_cast_fp16 = mul(x = x_163_cast_fp16, y = flow_net_res_blocks_4_in_ln_weight_to_fp16)[name = tensor<string, []>("op_1491_cast_fp16")];
tensor<fp16, [1, 512]> x_165_cast_fp16 = add(x = var_1491_cast_fp16, y = flow_net_res_blocks_4_in_ln_bias_to_fp16)[name = tensor<string, []>("x_165_cast_fp16")];
tensor<fp16, []> var_1493_promoted_to_fp16 = const()[name = tensor<string, []>("op_1493_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
tensor<fp16, [1, 512]> var_1494_cast_fp16 = add(x = var_1477_cast_fp16_1, y = var_1493_promoted_to_fp16)[name = tensor<string, []>("op_1494_cast_fp16")];
tensor<fp16, [1, 512]> var_1495_cast_fp16 = mul(x = x_165_cast_fp16, y = var_1494_cast_fp16)[name = tensor<string, []>("op_1495_cast_fp16")];
tensor<fp16, [1, 512]> input_253_cast_fp16 = add(x = var_1495_cast_fp16, y = var_1477_cast_fp16_0)[name = tensor<string, []>("input_253_cast_fp16")];
tensor<fp16, [1, 512]> linear_97_cast_fp16 = linear(bias = flow_net_res_blocks_4_mlp_0_bias_to_fp16, weight = flow_net_res_blocks_4_mlp_0_weight_to_fp16, x = input_253_cast_fp16)[name = tensor<string, []>("linear_97_cast_fp16")];
tensor<fp16, [1, 512]> input_257_cast_fp16 = silu(x = linear_97_cast_fp16)[name = tensor<string, []>("input_257_cast_fp16")];
tensor<fp16, [1, 512]> linear_98_cast_fp16 = linear(bias = flow_net_res_blocks_4_mlp_2_bias_to_fp16, weight = flow_net_res_blocks_4_mlp_2_weight_to_fp16, x = input_257_cast_fp16)[name = tensor<string, []>("linear_98_cast_fp16")];
tensor<fp16, [1, 512]> var_1506_cast_fp16 = mul(x = var_1477_cast_fp16_2, y = linear_98_cast_fp16)[name = tensor<string, []>("op_1506_cast_fp16")];
tensor<fp16, [1, 512]> x_167_cast_fp16 = add(x = x_161_cast_fp16, y = var_1506_cast_fp16)[name = tensor<string, []>("x_167_cast_fp16")];
tensor<fp16, [1, 1536]> linear_99_cast_fp16 = linear(bias = flow_net_res_blocks_5_adaLN_modulation_1_bias_to_fp16, weight = flow_net_res_blocks_5_adaLN_modulation_1_weight_to_fp16, x = input_219_cast_fp16)[name = tensor<string, []>("linear_99_cast_fp16")];
tensor<int32, [3]> var_1516_split_sizes_0 = const()[name = tensor<string, []>("op_1516_split_sizes_0"), val = tensor<int32, [3]>([512, 512, 512])];
tensor<int32, []> var_1516_axis_0 = const()[name = tensor<string, []>("op_1516_axis_0"), val = tensor<int32, []>(-1)];
tensor<fp16, [1, 512]> var_1516_cast_fp16_0, tensor<fp16, [1, 512]> var_1516_cast_fp16_1, tensor<fp16, [1, 512]> var_1516_cast_fp16_2 = split(axis = var_1516_axis_0, split_sizes = var_1516_split_sizes_0, x = linear_99_cast_fp16)[name = tensor<string, []>("op_1516_cast_fp16")];
tensor<int32, [1]> mean_53_axes_0 = const()[name = tensor<string, []>("mean_53_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> mean_53_keep_dims_0 = const()[name = tensor<string, []>("mean_53_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> mean_53_cast_fp16 = reduce_mean(axes = mean_53_axes_0, keep_dims = mean_53_keep_dims_0, x = x_167_cast_fp16)[name = tensor<string, []>("mean_53_cast_fp16")];
tensor<fp16, [1, 512]> sub_42_cast_fp16 = sub(x = x_167_cast_fp16, y = mean_53_cast_fp16)[name = tensor<string, []>("sub_42_cast_fp16")];
tensor<fp16, [1, 512]> square_34_cast_fp16 = square(x = sub_42_cast_fp16)[name = tensor<string, []>("square_34_cast_fp16")];
tensor<int32, [1]> reduce_mean_69_axes_0 = const()[name = tensor<string, []>("reduce_mean_69_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_69_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_69_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_69_cast_fp16 = reduce_mean(axes = reduce_mean_69_axes_0, keep_dims = reduce_mean_69_keep_dims_0, x = square_34_cast_fp16)[name = tensor<string, []>("reduce_mean_69_cast_fp16")];
tensor<fp16, []> var_1526_to_fp16 = const()[name = tensor<string, []>("op_1526_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 1]> var_1527_cast_fp16 = add(x = reduce_mean_69_cast_fp16, y = var_1526_to_fp16)[name = tensor<string, []>("op_1527_cast_fp16")];
tensor<fp16, [1, 1]> var_1528_cast_fp16 = sqrt(x = var_1527_cast_fp16)[name = tensor<string, []>("op_1528_cast_fp16")];
tensor<fp16, [1, 512]> x_169_cast_fp16 = real_div(x = sub_42_cast_fp16, y = var_1528_cast_fp16)[name = tensor<string, []>("x_169_cast_fp16")];
tensor<fp16, [1, 512]> var_1530_cast_fp16 = mul(x = x_169_cast_fp16, y = flow_net_res_blocks_5_in_ln_weight_to_fp16)[name = tensor<string, []>("op_1530_cast_fp16")];
tensor<fp16, [1, 512]> x_171_cast_fp16 = add(x = var_1530_cast_fp16, y = flow_net_res_blocks_5_in_ln_bias_to_fp16)[name = tensor<string, []>("x_171_cast_fp16")];
tensor<fp16, []> var_1532_promoted_to_fp16 = const()[name = tensor<string, []>("op_1532_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
tensor<fp16, [1, 512]> var_1533_cast_fp16 = add(x = var_1516_cast_fp16_1, y = var_1532_promoted_to_fp16)[name = tensor<string, []>("op_1533_cast_fp16")];
tensor<fp16, [1, 512]> var_1534_cast_fp16 = mul(x = x_171_cast_fp16, y = var_1533_cast_fp16)[name = tensor<string, []>("op_1534_cast_fp16")];
tensor<fp16, [1, 512]> input_261_cast_fp16 = add(x = var_1534_cast_fp16, y = var_1516_cast_fp16_0)[name = tensor<string, []>("input_261_cast_fp16")];
tensor<fp16, [1, 512]> linear_100_cast_fp16 = linear(bias = flow_net_res_blocks_5_mlp_0_bias_to_fp16, weight = flow_net_res_blocks_5_mlp_0_weight_to_fp16, x = input_261_cast_fp16)[name = tensor<string, []>("linear_100_cast_fp16")];
tensor<fp16, [1, 512]> input_265_cast_fp16 = silu(x = linear_100_cast_fp16)[name = tensor<string, []>("input_265_cast_fp16")];
tensor<fp16, [1, 512]> linear_101_cast_fp16 = linear(bias = flow_net_res_blocks_5_mlp_2_bias_to_fp16, weight = flow_net_res_blocks_5_mlp_2_weight_to_fp16, x = input_265_cast_fp16)[name = tensor<string, []>("linear_101_cast_fp16")];
tensor<fp16, [1, 512]> var_1545_cast_fp16 = mul(x = var_1516_cast_fp16_2, y = linear_101_cast_fp16)[name = tensor<string, []>("op_1545_cast_fp16")];
tensor<fp16, [1, 512]> x_173_cast_fp16 = add(x = x_167_cast_fp16, y = var_1545_cast_fp16)[name = tensor<string, []>("x_173_cast_fp16")];
tensor<fp16, [1, 1024]> linear_102_cast_fp16 = linear(bias = flow_net_final_layer_adaLN_modulation_1_bias_to_fp16, weight = flow_net_final_layer_adaLN_modulation_1_weight_to_fp16, x = input_219_cast_fp16)[name = tensor<string, []>("linear_102_cast_fp16")];
tensor<int32, [2]> var_1554_split_sizes_0 = const()[name = tensor<string, []>("op_1554_split_sizes_0"), val = tensor<int32, [2]>([512, 512])];
tensor<int32, []> var_1554_axis_0 = const()[name = tensor<string, []>("op_1554_axis_0"), val = tensor<int32, []>(-1)];
tensor<fp16, [1, 512]> var_1554_cast_fp16_0, tensor<fp16, [1, 512]> var_1554_cast_fp16_1 = split(axis = var_1554_axis_0, split_sizes = var_1554_split_sizes_0, x = linear_102_cast_fp16)[name = tensor<string, []>("op_1554_cast_fp16")];
tensor<int32, [1]> mean_55_axes_0 = const()[name = tensor<string, []>("mean_55_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> mean_55_keep_dims_0 = const()[name = tensor<string, []>("mean_55_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> mean_55_cast_fp16 = reduce_mean(axes = mean_55_axes_0, keep_dims = mean_55_keep_dims_0, x = x_173_cast_fp16)[name = tensor<string, []>("mean_55_cast_fp16")];
tensor<fp16, [1, 512]> sub_43_cast_fp16 = sub(x = x_173_cast_fp16, y = mean_55_cast_fp16)[name = tensor<string, []>("sub_43_cast_fp16")];
tensor<fp16, [1, 512]> square_35_cast_fp16 = square(x = sub_43_cast_fp16)[name = tensor<string, []>("square_35_cast_fp16")];
tensor<int32, [1]> reduce_mean_71_axes_0 = const()[name = tensor<string, []>("reduce_mean_71_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_71_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_71_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_71_cast_fp16 = reduce_mean(axes = reduce_mean_71_axes_0, keep_dims = reduce_mean_71_keep_dims_0, x = square_35_cast_fp16)[name = tensor<string, []>("reduce_mean_71_cast_fp16")];
tensor<fp16, []> var_1561_to_fp16 = const()[name = tensor<string, []>("op_1561_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 1]> var_1562_cast_fp16 = add(x = reduce_mean_71_cast_fp16, y = var_1561_to_fp16)[name = tensor<string, []>("op_1562_cast_fp16")];
tensor<fp16, [1, 1]> var_1563_cast_fp16 = sqrt(x = var_1562_cast_fp16)[name = tensor<string, []>("op_1563_cast_fp16")];
tensor<fp16, [1, 512]> x_175_cast_fp16 = real_div(x = sub_43_cast_fp16, y = var_1563_cast_fp16)[name = tensor<string, []>("x_175_cast_fp16")];
tensor<fp16, []> var_1565_promoted_to_fp16 = const()[name = tensor<string, []>("op_1565_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
tensor<fp16, [1, 512]> var_1566_cast_fp16 = add(x = var_1554_cast_fp16_1, y = var_1565_promoted_to_fp16)[name = tensor<string, []>("op_1566_cast_fp16")];
tensor<fp16, [1, 512]> var_1567_cast_fp16 = mul(x = x_175_cast_fp16, y = var_1566_cast_fp16)[name = tensor<string, []>("op_1567_cast_fp16")];
tensor<fp16, [1, 512]> input_269_cast_fp16 = add(x = var_1567_cast_fp16, y = var_1554_cast_fp16_0)[name = tensor<string, []>("input_269_cast_fp16")];
tensor<fp16, [1, 32]> linear_103_cast_fp16 = linear(bias = flow_net_final_layer_linear_bias_to_fp16, weight = flow_net_final_layer_linear_weight_to_fp16, x = input_269_cast_fp16)[name = tensor<string, []>("linear_103_cast_fp16")];
tensor<fp16, []> var_1578_to_fp16 = const()[name = tensor<string, []>("op_1578_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 32]> var_1579_cast_fp16 = mul(x = linear_103_cast_fp16, y = var_1578_to_fp16)[name = tensor<string, []>("op_1579_cast_fp16")];
tensor<fp16, [1, 32]> input_271_cast_fp16 = add(x = input_203_cast_fp16, y = var_1579_cast_fp16)[name = tensor<string, []>("input_271_cast_fp16")];
tensor<fp16, [1, 512]> linear_104_cast_fp16 = linear(bias = flow_net_input_proj_bias_to_fp16, weight = flow_net_input_proj_weight_to_fp16, x = input_271_cast_fp16)[name = tensor<string, []>("linear_104_cast_fp16")];
tensor<fp16, [1, 512]> input_275_to_fp16 = const()[name = tensor<string, []>("input_275_to_fp16"), val = tensor<fp16, [1, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19003904)))];
tensor<fp16, [1, 512]> input_277_cast_fp16 = silu(x = input_275_to_fp16)[name = tensor<string, []>("input_277_cast_fp16")];
tensor<fp16, [1, 512]> linear_106_cast_fp16 = linear(bias = flow_net_time_embed_0_mlp_2_bias_to_fp16, weight = flow_net_time_embed_0_mlp_2_weight_to_fp16, x = input_277_cast_fp16)[name = tensor<string, []>("linear_106_cast_fp16")];
tensor<int32, [1]> reduce_mean_72_axes_0 = const()[name = tensor<string, []>("reduce_mean_72_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_72_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_72_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_72_cast_fp16 = reduce_mean(axes = reduce_mean_72_axes_0, keep_dims = reduce_mean_72_keep_dims_0, x = linear_106_cast_fp16)[name = tensor<string, []>("reduce_mean_72_cast_fp16")];
tensor<fp16, [1, 512]> sub_44_cast_fp16 = sub(x = linear_106_cast_fp16, y = reduce_mean_72_cast_fp16)[name = tensor<string, []>("sub_44_cast_fp16")];
tensor<fp16, [1, 512]> square_36_cast_fp16 = square(x = sub_44_cast_fp16)[name = tensor<string, []>("square_36_cast_fp16")];
tensor<int32, [1]> reduce_mean_73_axes_0 = const()[name = tensor<string, []>("reduce_mean_73_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_73_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_73_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_73_cast_fp16 = reduce_mean(axes = reduce_mean_73_axes_0, keep_dims = reduce_mean_73_keep_dims_0, x = square_36_cast_fp16)[name = tensor<string, []>("reduce_mean_73_cast_fp16")];
tensor<fp16, []> real_div_8_to_fp16 = const()[name = tensor<string, []>("real_div_8_to_fp16"), val = tensor<fp16, []>(0x1.008p+0)];
tensor<fp16, [1, 1]> mul_8_cast_fp16 = mul(x = reduce_mean_73_cast_fp16, y = real_div_8_to_fp16)[name = tensor<string, []>("mul_8_cast_fp16")];
tensor<fp16, []> var_1645_to_fp16 = const()[name = tensor<string, []>("op_1645_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1]> var_73_cast_fp16_0 = add(x = mul_8_cast_fp16, y = var_1645_to_fp16)[name = tensor<string, []>("var_73_cast_fp16")];
tensor<fp32, []> var_1648_epsilon_0 = const()[name = tensor<string, []>("op_1648_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
tensor<fp16, [1, 1]> var_1648_cast_fp16 = rsqrt(epsilon = var_1648_epsilon_0, x = var_73_cast_fp16_0)[name = tensor<string, []>("op_1648_cast_fp16")];
tensor<fp16, [1, 512]> var_1649_cast_fp16 = mul(x = const_3_to_fp16, y = var_1648_cast_fp16)[name = tensor<string, []>("op_1649_cast_fp16")];
tensor<fp16, [1, 512]> var_1650_cast_fp16 = mul(x = linear_106_cast_fp16, y = var_1649_cast_fp16)[name = tensor<string, []>("op_1650_cast_fp16")];
tensor<fp16, [1, 512]> input_281_to_fp16 = const()[name = tensor<string, []>("input_281_to_fp16"), val = tensor<fp16, [1, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19004992)))];
tensor<fp16, [1, 512]> input_283_cast_fp16 = silu(x = input_281_to_fp16)[name = tensor<string, []>("input_283_cast_fp16")];
tensor<fp16, [1, 512]> linear_108_cast_fp16 = linear(bias = flow_net_time_embed_1_mlp_2_bias_to_fp16, weight = flow_net_time_embed_1_mlp_2_weight_to_fp16, x = input_283_cast_fp16)[name = tensor<string, []>("linear_108_cast_fp16")];
tensor<int32, [1]> reduce_mean_74_axes_0 = const()[name = tensor<string, []>("reduce_mean_74_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_74_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_74_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_74_cast_fp16 = reduce_mean(axes = reduce_mean_74_axes_0, keep_dims = reduce_mean_74_keep_dims_0, x = linear_108_cast_fp16)[name = tensor<string, []>("reduce_mean_74_cast_fp16")];
tensor<fp16, [1, 512]> sub_46_cast_fp16 = sub(x = linear_108_cast_fp16, y = reduce_mean_74_cast_fp16)[name = tensor<string, []>("sub_46_cast_fp16")];
tensor<fp16, [1, 512]> square_37_cast_fp16 = square(x = sub_46_cast_fp16)[name = tensor<string, []>("square_37_cast_fp16")];
tensor<int32, [1]> reduce_mean_75_axes_0 = const()[name = tensor<string, []>("reduce_mean_75_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_75_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_75_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_75_cast_fp16 = reduce_mean(axes = reduce_mean_75_axes_0, keep_dims = reduce_mean_75_keep_dims_0, x = square_37_cast_fp16)[name = tensor<string, []>("reduce_mean_75_cast_fp16")];
tensor<fp16, []> real_div_9_to_fp16 = const()[name = tensor<string, []>("real_div_9_to_fp16"), val = tensor<fp16, []>(0x1.008p+0)];
tensor<fp16, [1, 1]> mul_9_cast_fp16 = mul(x = reduce_mean_75_cast_fp16, y = real_div_9_to_fp16)[name = tensor<string, []>("mul_9_cast_fp16")];
tensor<fp16, []> var_1682_to_fp16 = const()[name = tensor<string, []>("op_1682_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1]> var_75_cast_fp16_0 = add(x = mul_9_cast_fp16, y = var_1682_to_fp16)[name = tensor<string, []>("var_75_cast_fp16")];
tensor<fp32, []> var_1685_epsilon_0 = const()[name = tensor<string, []>("op_1685_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
tensor<fp16, [1, 1]> var_1685_cast_fp16 = rsqrt(epsilon = var_1685_epsilon_0, x = var_75_cast_fp16_0)[name = tensor<string, []>("op_1685_cast_fp16")];
tensor<fp16, [1, 512]> var_1686_cast_fp16 = mul(x = const_5_to_fp16, y = var_1685_cast_fp16)[name = tensor<string, []>("op_1686_cast_fp16")];
tensor<fp16, [1, 512]> var_1687_cast_fp16 = mul(x = linear_108_cast_fp16, y = var_1686_cast_fp16)[name = tensor<string, []>("op_1687_cast_fp16")];
tensor<fp16, [1, 512]> var_1699_cast_fp16 = add(x = var_1650_cast_fp16, y = var_1687_cast_fp16)[name = tensor<string, []>("op_1699_cast_fp16")];
tensor<fp16, []> _inversed_t_combined_9_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_t_combined_9_y_0_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
tensor<fp16, [1, 512]> _inversed_t_combined_9_cast_fp16 = mul(x = var_1699_cast_fp16, y = _inversed_t_combined_9_y_0_to_fp16)[name = tensor<string, []>("_inversed_t_combined_9_cast_fp16")];
tensor<fp16, [1, 512]> input_285_cast_fp16 = add(x = _inversed_t_combined_9_cast_fp16, y = linear_5_cast_fp16)[name = tensor<string, []>("input_285_cast_fp16")];
tensor<fp16, [1, 512]> input_287_cast_fp16 = silu(x = input_285_cast_fp16)[name = tensor<string, []>("input_287_cast_fp16")];
tensor<fp16, [1, 1536]> linear_110_cast_fp16 = linear(bias = flow_net_res_blocks_0_adaLN_modulation_1_bias_to_fp16, weight = flow_net_res_blocks_0_adaLN_modulation_1_weight_to_fp16, x = input_287_cast_fp16)[name = tensor<string, []>("linear_110_cast_fp16")];
tensor<int32, [3]> var_1714_split_sizes_0 = const()[name = tensor<string, []>("op_1714_split_sizes_0"), val = tensor<int32, [3]>([512, 512, 512])];
tensor<int32, []> var_1714_axis_0 = const()[name = tensor<string, []>("op_1714_axis_0"), val = tensor<int32, []>(-1)];
tensor<fp16, [1, 512]> var_1714_cast_fp16_0, tensor<fp16, [1, 512]> var_1714_cast_fp16_1, tensor<fp16, [1, 512]> var_1714_cast_fp16_2 = split(axis = var_1714_axis_0, split_sizes = var_1714_split_sizes_0, x = linear_110_cast_fp16)[name = tensor<string, []>("op_1714_cast_fp16")];
tensor<int32, [1]> mean_57_axes_0 = const()[name = tensor<string, []>("mean_57_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> mean_57_keep_dims_0 = const()[name = tensor<string, []>("mean_57_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> mean_57_cast_fp16 = reduce_mean(axes = mean_57_axes_0, keep_dims = mean_57_keep_dims_0, x = linear_104_cast_fp16)[name = tensor<string, []>("mean_57_cast_fp16")];
tensor<fp16, [1, 512]> sub_48_cast_fp16 = sub(x = linear_104_cast_fp16, y = mean_57_cast_fp16)[name = tensor<string, []>("sub_48_cast_fp16")];
tensor<fp16, [1, 512]> square_38_cast_fp16 = square(x = sub_48_cast_fp16)[name = tensor<string, []>("square_38_cast_fp16")];
tensor<int32, [1]> reduce_mean_77_axes_0 = const()[name = tensor<string, []>("reduce_mean_77_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_77_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_77_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_77_cast_fp16 = reduce_mean(axes = reduce_mean_77_axes_0, keep_dims = reduce_mean_77_keep_dims_0, x = square_38_cast_fp16)[name = tensor<string, []>("reduce_mean_77_cast_fp16")];
tensor<fp16, []> var_1724_to_fp16 = const()[name = tensor<string, []>("op_1724_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 1]> var_1725_cast_fp16 = add(x = reduce_mean_77_cast_fp16, y = var_1724_to_fp16)[name = tensor<string, []>("op_1725_cast_fp16")];
tensor<fp16, [1, 1]> var_1726_cast_fp16 = sqrt(x = var_1725_cast_fp16)[name = tensor<string, []>("op_1726_cast_fp16")];
tensor<fp16, [1, 512]> x_183_cast_fp16 = real_div(x = sub_48_cast_fp16, y = var_1726_cast_fp16)[name = tensor<string, []>("x_183_cast_fp16")];
tensor<fp16, [1, 512]> var_1728_cast_fp16 = mul(x = x_183_cast_fp16, y = flow_net_res_blocks_0_in_ln_weight_to_fp16)[name = tensor<string, []>("op_1728_cast_fp16")];
tensor<fp16, [1, 512]> x_185_cast_fp16 = add(x = var_1728_cast_fp16, y = flow_net_res_blocks_0_in_ln_bias_to_fp16)[name = tensor<string, []>("x_185_cast_fp16")];
tensor<fp16, []> var_1730_promoted_to_fp16 = const()[name = tensor<string, []>("op_1730_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
tensor<fp16, [1, 512]> var_1731_cast_fp16 = add(x = var_1714_cast_fp16_1, y = var_1730_promoted_to_fp16)[name = tensor<string, []>("op_1731_cast_fp16")];
tensor<fp16, [1, 512]> var_1732_cast_fp16 = mul(x = x_185_cast_fp16, y = var_1731_cast_fp16)[name = tensor<string, []>("op_1732_cast_fp16")];
tensor<fp16, [1, 512]> input_289_cast_fp16 = add(x = var_1732_cast_fp16, y = var_1714_cast_fp16_0)[name = tensor<string, []>("input_289_cast_fp16")];
tensor<fp16, [1, 512]> linear_111_cast_fp16 = linear(bias = flow_net_res_blocks_0_mlp_0_bias_to_fp16, weight = flow_net_res_blocks_0_mlp_0_weight_to_fp16, x = input_289_cast_fp16)[name = tensor<string, []>("linear_111_cast_fp16")];
tensor<fp16, [1, 512]> input_293_cast_fp16 = silu(x = linear_111_cast_fp16)[name = tensor<string, []>("input_293_cast_fp16")];
tensor<fp16, [1, 512]> linear_112_cast_fp16 = linear(bias = flow_net_res_blocks_0_mlp_2_bias_to_fp16, weight = flow_net_res_blocks_0_mlp_2_weight_to_fp16, x = input_293_cast_fp16)[name = tensor<string, []>("linear_112_cast_fp16")];
tensor<fp16, [1, 512]> var_1743_cast_fp16 = mul(x = var_1714_cast_fp16_2, y = linear_112_cast_fp16)[name = tensor<string, []>("op_1743_cast_fp16")];
tensor<fp16, [1, 512]> x_187_cast_fp16 = add(x = linear_104_cast_fp16, y = var_1743_cast_fp16)[name = tensor<string, []>("x_187_cast_fp16")];
tensor<fp16, [1, 1536]> linear_113_cast_fp16 = linear(bias = flow_net_res_blocks_1_adaLN_modulation_1_bias_to_fp16, weight = flow_net_res_blocks_1_adaLN_modulation_1_weight_to_fp16, x = input_287_cast_fp16)[name = tensor<string, []>("linear_113_cast_fp16")];
tensor<int32, [3]> var_1753_split_sizes_0 = const()[name = tensor<string, []>("op_1753_split_sizes_0"), val = tensor<int32, [3]>([512, 512, 512])];
tensor<int32, []> var_1753_axis_0 = const()[name = tensor<string, []>("op_1753_axis_0"), val = tensor<int32, []>(-1)];
tensor<fp16, [1, 512]> var_1753_cast_fp16_0, tensor<fp16, [1, 512]> var_1753_cast_fp16_1, tensor<fp16, [1, 512]> var_1753_cast_fp16_2 = split(axis = var_1753_axis_0, split_sizes = var_1753_split_sizes_0, x = linear_113_cast_fp16)[name = tensor<string, []>("op_1753_cast_fp16")];
tensor<int32, [1]> mean_59_axes_0 = const()[name = tensor<string, []>("mean_59_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> mean_59_keep_dims_0 = const()[name = tensor<string, []>("mean_59_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> mean_59_cast_fp16 = reduce_mean(axes = mean_59_axes_0, keep_dims = mean_59_keep_dims_0, x = x_187_cast_fp16)[name = tensor<string, []>("mean_59_cast_fp16")];
tensor<fp16, [1, 512]> sub_49_cast_fp16 = sub(x = x_187_cast_fp16, y = mean_59_cast_fp16)[name = tensor<string, []>("sub_49_cast_fp16")];
tensor<fp16, [1, 512]> square_39_cast_fp16 = square(x = sub_49_cast_fp16)[name = tensor<string, []>("square_39_cast_fp16")];
tensor<int32, [1]> reduce_mean_79_axes_0 = const()[name = tensor<string, []>("reduce_mean_79_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_79_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_79_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_79_cast_fp16 = reduce_mean(axes = reduce_mean_79_axes_0, keep_dims = reduce_mean_79_keep_dims_0, x = square_39_cast_fp16)[name = tensor<string, []>("reduce_mean_79_cast_fp16")];
tensor<fp16, []> var_1763_to_fp16 = const()[name = tensor<string, []>("op_1763_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 1]> var_1764_cast_fp16 = add(x = reduce_mean_79_cast_fp16, y = var_1763_to_fp16)[name = tensor<string, []>("op_1764_cast_fp16")];
tensor<fp16, [1, 1]> var_1765_cast_fp16 = sqrt(x = var_1764_cast_fp16)[name = tensor<string, []>("op_1765_cast_fp16")];
tensor<fp16, [1, 512]> x_189_cast_fp16 = real_div(x = sub_49_cast_fp16, y = var_1765_cast_fp16)[name = tensor<string, []>("x_189_cast_fp16")];
tensor<fp16, [1, 512]> var_1767_cast_fp16 = mul(x = x_189_cast_fp16, y = flow_net_res_blocks_1_in_ln_weight_to_fp16)[name = tensor<string, []>("op_1767_cast_fp16")];
tensor<fp16, [1, 512]> x_191_cast_fp16 = add(x = var_1767_cast_fp16, y = flow_net_res_blocks_1_in_ln_bias_to_fp16)[name = tensor<string, []>("x_191_cast_fp16")];
tensor<fp16, []> var_1769_promoted_to_fp16 = const()[name = tensor<string, []>("op_1769_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
tensor<fp16, [1, 512]> var_1770_cast_fp16 = add(x = var_1753_cast_fp16_1, y = var_1769_promoted_to_fp16)[name = tensor<string, []>("op_1770_cast_fp16")];
tensor<fp16, [1, 512]> var_1771_cast_fp16 = mul(x = x_191_cast_fp16, y = var_1770_cast_fp16)[name = tensor<string, []>("op_1771_cast_fp16")];
tensor<fp16, [1, 512]> input_297_cast_fp16 = add(x = var_1771_cast_fp16, y = var_1753_cast_fp16_0)[name = tensor<string, []>("input_297_cast_fp16")];
tensor<fp16, [1, 512]> linear_114_cast_fp16 = linear(bias = flow_net_res_blocks_1_mlp_0_bias_to_fp16, weight = flow_net_res_blocks_1_mlp_0_weight_to_fp16, x = input_297_cast_fp16)[name = tensor<string, []>("linear_114_cast_fp16")];
tensor<fp16, [1, 512]> input_301_cast_fp16 = silu(x = linear_114_cast_fp16)[name = tensor<string, []>("input_301_cast_fp16")];
tensor<fp16, [1, 512]> linear_115_cast_fp16 = linear(bias = flow_net_res_blocks_1_mlp_2_bias_to_fp16, weight = flow_net_res_blocks_1_mlp_2_weight_to_fp16, x = input_301_cast_fp16)[name = tensor<string, []>("linear_115_cast_fp16")];
tensor<fp16, [1, 512]> var_1782_cast_fp16 = mul(x = var_1753_cast_fp16_2, y = linear_115_cast_fp16)[name = tensor<string, []>("op_1782_cast_fp16")];
tensor<fp16, [1, 512]> x_193_cast_fp16 = add(x = x_187_cast_fp16, y = var_1782_cast_fp16)[name = tensor<string, []>("x_193_cast_fp16")];
tensor<fp16, [1, 1536]> linear_116_cast_fp16 = linear(bias = flow_net_res_blocks_2_adaLN_modulation_1_bias_to_fp16, weight = flow_net_res_blocks_2_adaLN_modulation_1_weight_to_fp16, x = input_287_cast_fp16)[name = tensor<string, []>("linear_116_cast_fp16")];
tensor<int32, [3]> var_1792_split_sizes_0 = const()[name = tensor<string, []>("op_1792_split_sizes_0"), val = tensor<int32, [3]>([512, 512, 512])];
tensor<int32, []> var_1792_axis_0 = const()[name = tensor<string, []>("op_1792_axis_0"), val = tensor<int32, []>(-1)];
tensor<fp16, [1, 512]> var_1792_cast_fp16_0, tensor<fp16, [1, 512]> var_1792_cast_fp16_1, tensor<fp16, [1, 512]> var_1792_cast_fp16_2 = split(axis = var_1792_axis_0, split_sizes = var_1792_split_sizes_0, x = linear_116_cast_fp16)[name = tensor<string, []>("op_1792_cast_fp16")];
tensor<int32, [1]> mean_61_axes_0 = const()[name = tensor<string, []>("mean_61_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> mean_61_keep_dims_0 = const()[name = tensor<string, []>("mean_61_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> mean_61_cast_fp16 = reduce_mean(axes = mean_61_axes_0, keep_dims = mean_61_keep_dims_0, x = x_193_cast_fp16)[name = tensor<string, []>("mean_61_cast_fp16")];
tensor<fp16, [1, 512]> sub_50_cast_fp16 = sub(x = x_193_cast_fp16, y = mean_61_cast_fp16)[name = tensor<string, []>("sub_50_cast_fp16")];
tensor<fp16, [1, 512]> square_40_cast_fp16 = square(x = sub_50_cast_fp16)[name = tensor<string, []>("square_40_cast_fp16")];
tensor<int32, [1]> reduce_mean_81_axes_0 = const()[name = tensor<string, []>("reduce_mean_81_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_81_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_81_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_81_cast_fp16 = reduce_mean(axes = reduce_mean_81_axes_0, keep_dims = reduce_mean_81_keep_dims_0, x = square_40_cast_fp16)[name = tensor<string, []>("reduce_mean_81_cast_fp16")];
tensor<fp16, []> var_1802_to_fp16 = const()[name = tensor<string, []>("op_1802_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 1]> var_1803_cast_fp16 = add(x = reduce_mean_81_cast_fp16, y = var_1802_to_fp16)[name = tensor<string, []>("op_1803_cast_fp16")];
tensor<fp16, [1, 1]> var_1804_cast_fp16 = sqrt(x = var_1803_cast_fp16)[name = tensor<string, []>("op_1804_cast_fp16")];
tensor<fp16, [1, 512]> x_195_cast_fp16 = real_div(x = sub_50_cast_fp16, y = var_1804_cast_fp16)[name = tensor<string, []>("x_195_cast_fp16")];
tensor<fp16, [1, 512]> var_1806_cast_fp16 = mul(x = x_195_cast_fp16, y = flow_net_res_blocks_2_in_ln_weight_to_fp16)[name = tensor<string, []>("op_1806_cast_fp16")];
tensor<fp16, [1, 512]> x_197_cast_fp16 = add(x = var_1806_cast_fp16, y = flow_net_res_blocks_2_in_ln_bias_to_fp16)[name = tensor<string, []>("x_197_cast_fp16")];
tensor<fp16, []> var_1808_promoted_to_fp16 = const()[name = tensor<string, []>("op_1808_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
tensor<fp16, [1, 512]> var_1809_cast_fp16 = add(x = var_1792_cast_fp16_1, y = var_1808_promoted_to_fp16)[name = tensor<string, []>("op_1809_cast_fp16")];
tensor<fp16, [1, 512]> var_1810_cast_fp16 = mul(x = x_197_cast_fp16, y = var_1809_cast_fp16)[name = tensor<string, []>("op_1810_cast_fp16")];
tensor<fp16, [1, 512]> input_305_cast_fp16 = add(x = var_1810_cast_fp16, y = var_1792_cast_fp16_0)[name = tensor<string, []>("input_305_cast_fp16")];
tensor<fp16, [1, 512]> linear_117_cast_fp16 = linear(bias = flow_net_res_blocks_2_mlp_0_bias_to_fp16, weight = flow_net_res_blocks_2_mlp_0_weight_to_fp16, x = input_305_cast_fp16)[name = tensor<string, []>("linear_117_cast_fp16")];
tensor<fp16, [1, 512]> input_309_cast_fp16 = silu(x = linear_117_cast_fp16)[name = tensor<string, []>("input_309_cast_fp16")];
tensor<fp16, [1, 512]> linear_118_cast_fp16 = linear(bias = flow_net_res_blocks_2_mlp_2_bias_to_fp16, weight = flow_net_res_blocks_2_mlp_2_weight_to_fp16, x = input_309_cast_fp16)[name = tensor<string, []>("linear_118_cast_fp16")];
tensor<fp16, [1, 512]> var_1821_cast_fp16 = mul(x = var_1792_cast_fp16_2, y = linear_118_cast_fp16)[name = tensor<string, []>("op_1821_cast_fp16")];
tensor<fp16, [1, 512]> x_199_cast_fp16 = add(x = x_193_cast_fp16, y = var_1821_cast_fp16)[name = tensor<string, []>("x_199_cast_fp16")];
tensor<fp16, [1, 1536]> linear_119_cast_fp16 = linear(bias = flow_net_res_blocks_3_adaLN_modulation_1_bias_to_fp16, weight = flow_net_res_blocks_3_adaLN_modulation_1_weight_to_fp16, x = input_287_cast_fp16)[name = tensor<string, []>("linear_119_cast_fp16")];
tensor<int32, [3]> var_1831_split_sizes_0 = const()[name = tensor<string, []>("op_1831_split_sizes_0"), val = tensor<int32, [3]>([512, 512, 512])];
tensor<int32, []> var_1831_axis_0 = const()[name = tensor<string, []>("op_1831_axis_0"), val = tensor<int32, []>(-1)];
tensor<fp16, [1, 512]> var_1831_cast_fp16_0, tensor<fp16, [1, 512]> var_1831_cast_fp16_1, tensor<fp16, [1, 512]> var_1831_cast_fp16_2 = split(axis = var_1831_axis_0, split_sizes = var_1831_split_sizes_0, x = linear_119_cast_fp16)[name = tensor<string, []>("op_1831_cast_fp16")];
tensor<int32, [1]> mean_63_axes_0 = const()[name = tensor<string, []>("mean_63_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> mean_63_keep_dims_0 = const()[name = tensor<string, []>("mean_63_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> mean_63_cast_fp16 = reduce_mean(axes = mean_63_axes_0, keep_dims = mean_63_keep_dims_0, x = x_199_cast_fp16)[name = tensor<string, []>("mean_63_cast_fp16")];
tensor<fp16, [1, 512]> sub_51_cast_fp16 = sub(x = x_199_cast_fp16, y = mean_63_cast_fp16)[name = tensor<string, []>("sub_51_cast_fp16")];
tensor<fp16, [1, 512]> square_41_cast_fp16 = square(x = sub_51_cast_fp16)[name = tensor<string, []>("square_41_cast_fp16")];
tensor<int32, [1]> reduce_mean_83_axes_0 = const()[name = tensor<string, []>("reduce_mean_83_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_83_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_83_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_83_cast_fp16 = reduce_mean(axes = reduce_mean_83_axes_0, keep_dims = reduce_mean_83_keep_dims_0, x = square_41_cast_fp16)[name = tensor<string, []>("reduce_mean_83_cast_fp16")];
tensor<fp16, []> var_1841_to_fp16 = const()[name = tensor<string, []>("op_1841_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 1]> var_1842_cast_fp16 = add(x = reduce_mean_83_cast_fp16, y = var_1841_to_fp16)[name = tensor<string, []>("op_1842_cast_fp16")];
tensor<fp16, [1, 1]> var_1843_cast_fp16 = sqrt(x = var_1842_cast_fp16)[name = tensor<string, []>("op_1843_cast_fp16")];
tensor<fp16, [1, 512]> x_201_cast_fp16 = real_div(x = sub_51_cast_fp16, y = var_1843_cast_fp16)[name = tensor<string, []>("x_201_cast_fp16")];
tensor<fp16, [1, 512]> var_1845_cast_fp16 = mul(x = x_201_cast_fp16, y = flow_net_res_blocks_3_in_ln_weight_to_fp16)[name = tensor<string, []>("op_1845_cast_fp16")];
tensor<fp16, [1, 512]> x_203_cast_fp16 = add(x = var_1845_cast_fp16, y = flow_net_res_blocks_3_in_ln_bias_to_fp16)[name = tensor<string, []>("x_203_cast_fp16")];
tensor<fp16, []> var_1847_promoted_to_fp16 = const()[name = tensor<string, []>("op_1847_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
tensor<fp16, [1, 512]> var_1848_cast_fp16 = add(x = var_1831_cast_fp16_1, y = var_1847_promoted_to_fp16)[name = tensor<string, []>("op_1848_cast_fp16")];
tensor<fp16, [1, 512]> var_1849_cast_fp16 = mul(x = x_203_cast_fp16, y = var_1848_cast_fp16)[name = tensor<string, []>("op_1849_cast_fp16")];
tensor<fp16, [1, 512]> input_313_cast_fp16 = add(x = var_1849_cast_fp16, y = var_1831_cast_fp16_0)[name = tensor<string, []>("input_313_cast_fp16")];
tensor<fp16, [1, 512]> linear_120_cast_fp16 = linear(bias = flow_net_res_blocks_3_mlp_0_bias_to_fp16, weight = flow_net_res_blocks_3_mlp_0_weight_to_fp16, x = input_313_cast_fp16)[name = tensor<string, []>("linear_120_cast_fp16")];
tensor<fp16, [1, 512]> input_317_cast_fp16 = silu(x = linear_120_cast_fp16)[name = tensor<string, []>("input_317_cast_fp16")];
tensor<fp16, [1, 512]> linear_121_cast_fp16 = linear(bias = flow_net_res_blocks_3_mlp_2_bias_to_fp16, weight = flow_net_res_blocks_3_mlp_2_weight_to_fp16, x = input_317_cast_fp16)[name = tensor<string, []>("linear_121_cast_fp16")];
tensor<fp16, [1, 512]> var_1860_cast_fp16 = mul(x = var_1831_cast_fp16_2, y = linear_121_cast_fp16)[name = tensor<string, []>("op_1860_cast_fp16")];
tensor<fp16, [1, 512]> x_205_cast_fp16 = add(x = x_199_cast_fp16, y = var_1860_cast_fp16)[name = tensor<string, []>("x_205_cast_fp16")];
tensor<fp16, [1, 1536]> linear_122_cast_fp16 = linear(bias = flow_net_res_blocks_4_adaLN_modulation_1_bias_to_fp16, weight = flow_net_res_blocks_4_adaLN_modulation_1_weight_to_fp16, x = input_287_cast_fp16)[name = tensor<string, []>("linear_122_cast_fp16")];
tensor<int32, [3]> var_1870_split_sizes_0 = const()[name = tensor<string, []>("op_1870_split_sizes_0"), val = tensor<int32, [3]>([512, 512, 512])];
tensor<int32, []> var_1870_axis_0 = const()[name = tensor<string, []>("op_1870_axis_0"), val = tensor<int32, []>(-1)];
tensor<fp16, [1, 512]> var_1870_cast_fp16_0, tensor<fp16, [1, 512]> var_1870_cast_fp16_1, tensor<fp16, [1, 512]> var_1870_cast_fp16_2 = split(axis = var_1870_axis_0, split_sizes = var_1870_split_sizes_0, x = linear_122_cast_fp16)[name = tensor<string, []>("op_1870_cast_fp16")];
tensor<int32, [1]> mean_65_axes_0 = const()[name = tensor<string, []>("mean_65_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> mean_65_keep_dims_0 = const()[name = tensor<string, []>("mean_65_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> mean_65_cast_fp16 = reduce_mean(axes = mean_65_axes_0, keep_dims = mean_65_keep_dims_0, x = x_205_cast_fp16)[name = tensor<string, []>("mean_65_cast_fp16")];
tensor<fp16, [1, 512]> sub_52_cast_fp16 = sub(x = x_205_cast_fp16, y = mean_65_cast_fp16)[name = tensor<string, []>("sub_52_cast_fp16")];
tensor<fp16, [1, 512]> square_42_cast_fp16 = square(x = sub_52_cast_fp16)[name = tensor<string, []>("square_42_cast_fp16")];
tensor<int32, [1]> reduce_mean_85_axes_0 = const()[name = tensor<string, []>("reduce_mean_85_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_85_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_85_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_85_cast_fp16 = reduce_mean(axes = reduce_mean_85_axes_0, keep_dims = reduce_mean_85_keep_dims_0, x = square_42_cast_fp16)[name = tensor<string, []>("reduce_mean_85_cast_fp16")];
tensor<fp16, []> var_1880_to_fp16 = const()[name = tensor<string, []>("op_1880_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 1]> var_1881_cast_fp16 = add(x = reduce_mean_85_cast_fp16, y = var_1880_to_fp16)[name = tensor<string, []>("op_1881_cast_fp16")];
tensor<fp16, [1, 1]> var_1882_cast_fp16 = sqrt(x = var_1881_cast_fp16)[name = tensor<string, []>("op_1882_cast_fp16")];
tensor<fp16, [1, 512]> x_207_cast_fp16 = real_div(x = sub_52_cast_fp16, y = var_1882_cast_fp16)[name = tensor<string, []>("x_207_cast_fp16")];
tensor<fp16, [1, 512]> var_1884_cast_fp16 = mul(x = x_207_cast_fp16, y = flow_net_res_blocks_4_in_ln_weight_to_fp16)[name = tensor<string, []>("op_1884_cast_fp16")];
tensor<fp16, [1, 512]> x_209_cast_fp16 = add(x = var_1884_cast_fp16, y = flow_net_res_blocks_4_in_ln_bias_to_fp16)[name = tensor<string, []>("x_209_cast_fp16")];
tensor<fp16, []> var_1886_promoted_to_fp16 = const()[name = tensor<string, []>("op_1886_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
tensor<fp16, [1, 512]> var_1887_cast_fp16 = add(x = var_1870_cast_fp16_1, y = var_1886_promoted_to_fp16)[name = tensor<string, []>("op_1887_cast_fp16")];
tensor<fp16, [1, 512]> var_1888_cast_fp16 = mul(x = x_209_cast_fp16, y = var_1887_cast_fp16)[name = tensor<string, []>("op_1888_cast_fp16")];
tensor<fp16, [1, 512]> input_321_cast_fp16 = add(x = var_1888_cast_fp16, y = var_1870_cast_fp16_0)[name = tensor<string, []>("input_321_cast_fp16")];
tensor<fp16, [1, 512]> linear_123_cast_fp16 = linear(bias = flow_net_res_blocks_4_mlp_0_bias_to_fp16, weight = flow_net_res_blocks_4_mlp_0_weight_to_fp16, x = input_321_cast_fp16)[name = tensor<string, []>("linear_123_cast_fp16")];
tensor<fp16, [1, 512]> input_325_cast_fp16 = silu(x = linear_123_cast_fp16)[name = tensor<string, []>("input_325_cast_fp16")];
tensor<fp16, [1, 512]> linear_124_cast_fp16 = linear(bias = flow_net_res_blocks_4_mlp_2_bias_to_fp16, weight = flow_net_res_blocks_4_mlp_2_weight_to_fp16, x = input_325_cast_fp16)[name = tensor<string, []>("linear_124_cast_fp16")];
tensor<fp16, [1, 512]> var_1899_cast_fp16 = mul(x = var_1870_cast_fp16_2, y = linear_124_cast_fp16)[name = tensor<string, []>("op_1899_cast_fp16")];
tensor<fp16, [1, 512]> x_211_cast_fp16 = add(x = x_205_cast_fp16, y = var_1899_cast_fp16)[name = tensor<string, []>("x_211_cast_fp16")];
tensor<fp16, [1, 1536]> linear_125_cast_fp16 = linear(bias = flow_net_res_blocks_5_adaLN_modulation_1_bias_to_fp16, weight = flow_net_res_blocks_5_adaLN_modulation_1_weight_to_fp16, x = input_287_cast_fp16)[name = tensor<string, []>("linear_125_cast_fp16")];
tensor<int32, [3]> var_1909_split_sizes_0 = const()[name = tensor<string, []>("op_1909_split_sizes_0"), val = tensor<int32, [3]>([512, 512, 512])];
tensor<int32, []> var_1909_axis_0 = const()[name = tensor<string, []>("op_1909_axis_0"), val = tensor<int32, []>(-1)];
tensor<fp16, [1, 512]> var_1909_cast_fp16_0, tensor<fp16, [1, 512]> var_1909_cast_fp16_1, tensor<fp16, [1, 512]> var_1909_cast_fp16_2 = split(axis = var_1909_axis_0, split_sizes = var_1909_split_sizes_0, x = linear_125_cast_fp16)[name = tensor<string, []>("op_1909_cast_fp16")];
tensor<int32, [1]> mean_67_axes_0 = const()[name = tensor<string, []>("mean_67_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> mean_67_keep_dims_0 = const()[name = tensor<string, []>("mean_67_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> mean_67_cast_fp16 = reduce_mean(axes = mean_67_axes_0, keep_dims = mean_67_keep_dims_0, x = x_211_cast_fp16)[name = tensor<string, []>("mean_67_cast_fp16")];
tensor<fp16, [1, 512]> sub_53_cast_fp16 = sub(x = x_211_cast_fp16, y = mean_67_cast_fp16)[name = tensor<string, []>("sub_53_cast_fp16")];
tensor<fp16, [1, 512]> square_43_cast_fp16 = square(x = sub_53_cast_fp16)[name = tensor<string, []>("square_43_cast_fp16")];
tensor<int32, [1]> reduce_mean_87_axes_0 = const()[name = tensor<string, []>("reduce_mean_87_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_87_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_87_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_87_cast_fp16 = reduce_mean(axes = reduce_mean_87_axes_0, keep_dims = reduce_mean_87_keep_dims_0, x = square_43_cast_fp16)[name = tensor<string, []>("reduce_mean_87_cast_fp16")];
tensor<fp16, []> var_1919_to_fp16 = const()[name = tensor<string, []>("op_1919_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 1]> var_1920_cast_fp16 = add(x = reduce_mean_87_cast_fp16, y = var_1919_to_fp16)[name = tensor<string, []>("op_1920_cast_fp16")];
tensor<fp16, [1, 1]> var_1921_cast_fp16 = sqrt(x = var_1920_cast_fp16)[name = tensor<string, []>("op_1921_cast_fp16")];
tensor<fp16, [1, 512]> x_213_cast_fp16 = real_div(x = sub_53_cast_fp16, y = var_1921_cast_fp16)[name = tensor<string, []>("x_213_cast_fp16")];
tensor<fp16, [1, 512]> var_1923_cast_fp16 = mul(x = x_213_cast_fp16, y = flow_net_res_blocks_5_in_ln_weight_to_fp16)[name = tensor<string, []>("op_1923_cast_fp16")];
tensor<fp16, [1, 512]> x_215_cast_fp16 = add(x = var_1923_cast_fp16, y = flow_net_res_blocks_5_in_ln_bias_to_fp16)[name = tensor<string, []>("x_215_cast_fp16")];
tensor<fp16, []> var_1925_promoted_to_fp16 = const()[name = tensor<string, []>("op_1925_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
tensor<fp16, [1, 512]> var_1926_cast_fp16 = add(x = var_1909_cast_fp16_1, y = var_1925_promoted_to_fp16)[name = tensor<string, []>("op_1926_cast_fp16")];
tensor<fp16, [1, 512]> var_1927_cast_fp16 = mul(x = x_215_cast_fp16, y = var_1926_cast_fp16)[name = tensor<string, []>("op_1927_cast_fp16")];
tensor<fp16, [1, 512]> input_329_cast_fp16 = add(x = var_1927_cast_fp16, y = var_1909_cast_fp16_0)[name = tensor<string, []>("input_329_cast_fp16")];
tensor<fp16, [1, 512]> linear_126_cast_fp16 = linear(bias = flow_net_res_blocks_5_mlp_0_bias_to_fp16, weight = flow_net_res_blocks_5_mlp_0_weight_to_fp16, x = input_329_cast_fp16)[name = tensor<string, []>("linear_126_cast_fp16")];
tensor<fp16, [1, 512]> input_333_cast_fp16 = silu(x = linear_126_cast_fp16)[name = tensor<string, []>("input_333_cast_fp16")];
tensor<fp16, [1, 512]> linear_127_cast_fp16 = linear(bias = flow_net_res_blocks_5_mlp_2_bias_to_fp16, weight = flow_net_res_blocks_5_mlp_2_weight_to_fp16, x = input_333_cast_fp16)[name = tensor<string, []>("linear_127_cast_fp16")];
tensor<fp16, [1, 512]> var_1938_cast_fp16 = mul(x = var_1909_cast_fp16_2, y = linear_127_cast_fp16)[name = tensor<string, []>("op_1938_cast_fp16")];
tensor<fp16, [1, 512]> x_217_cast_fp16 = add(x = x_211_cast_fp16, y = var_1938_cast_fp16)[name = tensor<string, []>("x_217_cast_fp16")];
tensor<fp16, [1, 1024]> linear_128_cast_fp16 = linear(bias = flow_net_final_layer_adaLN_modulation_1_bias_to_fp16, weight = flow_net_final_layer_adaLN_modulation_1_weight_to_fp16, x = input_287_cast_fp16)[name = tensor<string, []>("linear_128_cast_fp16")];
tensor<int32, [2]> var_1947_split_sizes_0 = const()[name = tensor<string, []>("op_1947_split_sizes_0"), val = tensor<int32, [2]>([512, 512])];
tensor<int32, []> var_1947_axis_0 = const()[name = tensor<string, []>("op_1947_axis_0"), val = tensor<int32, []>(-1)];
tensor<fp16, [1, 512]> var_1947_cast_fp16_0, tensor<fp16, [1, 512]> var_1947_cast_fp16_1 = split(axis = var_1947_axis_0, split_sizes = var_1947_split_sizes_0, x = linear_128_cast_fp16)[name = tensor<string, []>("op_1947_cast_fp16")];
tensor<int32, [1]> mean_69_axes_0 = const()[name = tensor<string, []>("mean_69_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> mean_69_keep_dims_0 = const()[name = tensor<string, []>("mean_69_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> mean_69_cast_fp16 = reduce_mean(axes = mean_69_axes_0, keep_dims = mean_69_keep_dims_0, x = x_217_cast_fp16)[name = tensor<string, []>("mean_69_cast_fp16")];
tensor<fp16, [1, 512]> sub_54_cast_fp16 = sub(x = x_217_cast_fp16, y = mean_69_cast_fp16)[name = tensor<string, []>("sub_54_cast_fp16")];
tensor<fp16, [1, 512]> square_44_cast_fp16 = square(x = sub_54_cast_fp16)[name = tensor<string, []>("square_44_cast_fp16")];
tensor<int32, [1]> reduce_mean_89_axes_0 = const()[name = tensor<string, []>("reduce_mean_89_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_89_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_89_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_89_cast_fp16 = reduce_mean(axes = reduce_mean_89_axes_0, keep_dims = reduce_mean_89_keep_dims_0, x = square_44_cast_fp16)[name = tensor<string, []>("reduce_mean_89_cast_fp16")];
tensor<fp16, []> var_1954_to_fp16 = const()[name = tensor<string, []>("op_1954_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 1]> var_1955_cast_fp16 = add(x = reduce_mean_89_cast_fp16, y = var_1954_to_fp16)[name = tensor<string, []>("op_1955_cast_fp16")];
tensor<fp16, [1, 1]> var_1956_cast_fp16 = sqrt(x = var_1955_cast_fp16)[name = tensor<string, []>("op_1956_cast_fp16")];
tensor<fp16, [1, 512]> x_219_cast_fp16 = real_div(x = sub_54_cast_fp16, y = var_1956_cast_fp16)[name = tensor<string, []>("x_219_cast_fp16")];
tensor<fp16, []> var_1958_promoted_to_fp16 = const()[name = tensor<string, []>("op_1958_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
tensor<fp16, [1, 512]> var_1959_cast_fp16 = add(x = var_1947_cast_fp16_1, y = var_1958_promoted_to_fp16)[name = tensor<string, []>("op_1959_cast_fp16")];
tensor<fp16, [1, 512]> var_1960_cast_fp16 = mul(x = x_219_cast_fp16, y = var_1959_cast_fp16)[name = tensor<string, []>("op_1960_cast_fp16")];
tensor<fp16, [1, 512]> input_337_cast_fp16 = add(x = var_1960_cast_fp16, y = var_1947_cast_fp16_0)[name = tensor<string, []>("input_337_cast_fp16")];
tensor<fp16, [1, 32]> linear_129_cast_fp16 = linear(bias = flow_net_final_layer_linear_bias_to_fp16, weight = flow_net_final_layer_linear_weight_to_fp16, x = input_337_cast_fp16)[name = tensor<string, []>("linear_129_cast_fp16")];
tensor<fp16, []> var_1971_to_fp16 = const()[name = tensor<string, []>("op_1971_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 32]> var_1972_cast_fp16 = mul(x = linear_129_cast_fp16, y = var_1971_to_fp16)[name = tensor<string, []>("op_1972_cast_fp16")];
tensor<fp16, [1, 32]> input_339_cast_fp16 = add(x = input_271_cast_fp16, y = var_1972_cast_fp16)[name = tensor<string, []>("input_339_cast_fp16")];
tensor<fp16, [1, 512]> linear_130_cast_fp16 = linear(bias = flow_net_input_proj_bias_to_fp16, weight = flow_net_input_proj_weight_to_fp16, x = input_339_cast_fp16)[name = tensor<string, []>("linear_130_cast_fp16")];
tensor<fp16, [1, 512]> input_343_to_fp16 = const()[name = tensor<string, []>("input_343_to_fp16"), val = tensor<fp16, [1, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19006080)))];
tensor<fp16, [1, 512]> input_345_cast_fp16 = silu(x = input_343_to_fp16)[name = tensor<string, []>("input_345_cast_fp16")];
tensor<fp16, [1, 512]> linear_132_cast_fp16 = linear(bias = flow_net_time_embed_0_mlp_2_bias_to_fp16, weight = flow_net_time_embed_0_mlp_2_weight_to_fp16, x = input_345_cast_fp16)[name = tensor<string, []>("linear_132_cast_fp16")];
tensor<int32, [1]> reduce_mean_90_axes_0 = const()[name = tensor<string, []>("reduce_mean_90_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_90_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_90_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_90_cast_fp16 = reduce_mean(axes = reduce_mean_90_axes_0, keep_dims = reduce_mean_90_keep_dims_0, x = linear_132_cast_fp16)[name = tensor<string, []>("reduce_mean_90_cast_fp16")];
tensor<fp16, [1, 512]> sub_55_cast_fp16 = sub(x = linear_132_cast_fp16, y = reduce_mean_90_cast_fp16)[name = tensor<string, []>("sub_55_cast_fp16")];
tensor<fp16, [1, 512]> square_45_cast_fp16 = square(x = sub_55_cast_fp16)[name = tensor<string, []>("square_45_cast_fp16")];
tensor<int32, [1]> reduce_mean_91_axes_0 = const()[name = tensor<string, []>("reduce_mean_91_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_91_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_91_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_91_cast_fp16 = reduce_mean(axes = reduce_mean_91_axes_0, keep_dims = reduce_mean_91_keep_dims_0, x = square_45_cast_fp16)[name = tensor<string, []>("reduce_mean_91_cast_fp16")];
tensor<fp16, []> real_div_10_to_fp16 = const()[name = tensor<string, []>("real_div_10_to_fp16"), val = tensor<fp16, []>(0x1.008p+0)];
tensor<fp16, [1, 1]> mul_10_cast_fp16 = mul(x = reduce_mean_91_cast_fp16, y = real_div_10_to_fp16)[name = tensor<string, []>("mul_10_cast_fp16")];
tensor<fp16, []> var_2038_to_fp16 = const()[name = tensor<string, []>("op_2038_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1]> var_91_cast_fp16 = add(x = mul_10_cast_fp16, y = var_2038_to_fp16)[name = tensor<string, []>("var_91_cast_fp16")];
tensor<fp32, []> var_2041_epsilon_0 = const()[name = tensor<string, []>("op_2041_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
tensor<fp16, [1, 1]> var_2041_cast_fp16 = rsqrt(epsilon = var_2041_epsilon_0, x = var_91_cast_fp16)[name = tensor<string, []>("op_2041_cast_fp16")];
tensor<fp16, [1, 512]> var_2042_cast_fp16 = mul(x = const_3_to_fp16, y = var_2041_cast_fp16)[name = tensor<string, []>("op_2042_cast_fp16")];
tensor<fp16, [1, 512]> var_2043_cast_fp16 = mul(x = linear_132_cast_fp16, y = var_2042_cast_fp16)[name = tensor<string, []>("op_2043_cast_fp16")];
tensor<fp16, [1, 512]> input_349_to_fp16 = const()[name = tensor<string, []>("input_349_to_fp16"), val = tensor<fp16, [1, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19007168)))];
tensor<fp16, [1, 512]> input_351_cast_fp16 = silu(x = input_349_to_fp16)[name = tensor<string, []>("input_351_cast_fp16")];
tensor<fp16, [1, 512]> linear_134_cast_fp16 = linear(bias = flow_net_time_embed_1_mlp_2_bias_to_fp16, weight = flow_net_time_embed_1_mlp_2_weight_to_fp16, x = input_351_cast_fp16)[name = tensor<string, []>("linear_134_cast_fp16")];
tensor<int32, [1]> reduce_mean_92_axes_0 = const()[name = tensor<string, []>("reduce_mean_92_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_92_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_92_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_92_cast_fp16 = reduce_mean(axes = reduce_mean_92_axes_0, keep_dims = reduce_mean_92_keep_dims_0, x = linear_134_cast_fp16)[name = tensor<string, []>("reduce_mean_92_cast_fp16")];
tensor<fp16, [1, 512]> sub_57_cast_fp16 = sub(x = linear_134_cast_fp16, y = reduce_mean_92_cast_fp16)[name = tensor<string, []>("sub_57_cast_fp16")];
tensor<fp16, [1, 512]> square_46_cast_fp16 = square(x = sub_57_cast_fp16)[name = tensor<string, []>("square_46_cast_fp16")];
tensor<int32, [1]> reduce_mean_93_axes_0 = const()[name = tensor<string, []>("reduce_mean_93_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_93_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_93_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_93_cast_fp16 = reduce_mean(axes = reduce_mean_93_axes_0, keep_dims = reduce_mean_93_keep_dims_0, x = square_46_cast_fp16)[name = tensor<string, []>("reduce_mean_93_cast_fp16")];
tensor<fp16, []> real_div_11_to_fp16 = const()[name = tensor<string, []>("real_div_11_to_fp16"), val = tensor<fp16, []>(0x1.008p+0)];
tensor<fp16, [1, 1]> mul_11_cast_fp16 = mul(x = reduce_mean_93_cast_fp16, y = real_div_11_to_fp16)[name = tensor<string, []>("mul_11_cast_fp16")];
tensor<fp16, []> var_2075_to_fp16 = const()[name = tensor<string, []>("op_2075_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1]> var_93_cast_fp16 = add(x = mul_11_cast_fp16, y = var_2075_to_fp16)[name = tensor<string, []>("var_93_cast_fp16")];
tensor<fp32, []> var_2078_epsilon_0 = const()[name = tensor<string, []>("op_2078_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
tensor<fp16, [1, 1]> var_2078_cast_fp16 = rsqrt(epsilon = var_2078_epsilon_0, x = var_93_cast_fp16)[name = tensor<string, []>("op_2078_cast_fp16")];
tensor<fp16, [1, 512]> var_2079_cast_fp16 = mul(x = const_5_to_fp16, y = var_2078_cast_fp16)[name = tensor<string, []>("op_2079_cast_fp16")];
tensor<fp16, [1, 512]> var_2080_cast_fp16 = mul(x = linear_134_cast_fp16, y = var_2079_cast_fp16)[name = tensor<string, []>("op_2080_cast_fp16")];
tensor<fp16, [1, 512]> var_2092_cast_fp16 = add(x = var_2043_cast_fp16, y = var_2080_cast_fp16)[name = tensor<string, []>("op_2092_cast_fp16")];
tensor<fp16, []> _inversed_t_combined_11_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_t_combined_11_y_0_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
tensor<fp16, [1, 512]> _inversed_t_combined_11_cast_fp16 = mul(x = var_2092_cast_fp16, y = _inversed_t_combined_11_y_0_to_fp16)[name = tensor<string, []>("_inversed_t_combined_11_cast_fp16")];
tensor<fp16, [1, 512]> input_353_cast_fp16 = add(x = _inversed_t_combined_11_cast_fp16, y = linear_5_cast_fp16)[name = tensor<string, []>("input_353_cast_fp16")];
tensor<fp16, [1, 512]> input_355_cast_fp16 = silu(x = input_353_cast_fp16)[name = tensor<string, []>("input_355_cast_fp16")];
tensor<fp16, [1, 1536]> linear_136_cast_fp16 = linear(bias = flow_net_res_blocks_0_adaLN_modulation_1_bias_to_fp16, weight = flow_net_res_blocks_0_adaLN_modulation_1_weight_to_fp16, x = input_355_cast_fp16)[name = tensor<string, []>("linear_136_cast_fp16")];
tensor<int32, [3]> var_2107_split_sizes_0 = const()[name = tensor<string, []>("op_2107_split_sizes_0"), val = tensor<int32, [3]>([512, 512, 512])];
tensor<int32, []> var_2107_axis_0 = const()[name = tensor<string, []>("op_2107_axis_0"), val = tensor<int32, []>(-1)];
tensor<fp16, [1, 512]> var_2107_cast_fp16_0, tensor<fp16, [1, 512]> var_2107_cast_fp16_1, tensor<fp16, [1, 512]> var_2107_cast_fp16_2 = split(axis = var_2107_axis_0, split_sizes = var_2107_split_sizes_0, x = linear_136_cast_fp16)[name = tensor<string, []>("op_2107_cast_fp16")];
tensor<int32, [1]> mean_71_axes_0 = const()[name = tensor<string, []>("mean_71_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> mean_71_keep_dims_0 = const()[name = tensor<string, []>("mean_71_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> mean_71_cast_fp16 = reduce_mean(axes = mean_71_axes_0, keep_dims = mean_71_keep_dims_0, x = linear_130_cast_fp16)[name = tensor<string, []>("mean_71_cast_fp16")];
tensor<fp16, [1, 512]> sub_59_cast_fp16 = sub(x = linear_130_cast_fp16, y = mean_71_cast_fp16)[name = tensor<string, []>("sub_59_cast_fp16")];
tensor<fp16, [1, 512]> square_47_cast_fp16 = square(x = sub_59_cast_fp16)[name = tensor<string, []>("square_47_cast_fp16")];
tensor<int32, [1]> reduce_mean_95_axes_0 = const()[name = tensor<string, []>("reduce_mean_95_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_95_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_95_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_95_cast_fp16 = reduce_mean(axes = reduce_mean_95_axes_0, keep_dims = reduce_mean_95_keep_dims_0, x = square_47_cast_fp16)[name = tensor<string, []>("reduce_mean_95_cast_fp16")];
tensor<fp16, []> var_2117_to_fp16 = const()[name = tensor<string, []>("op_2117_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 1]> var_2118_cast_fp16 = add(x = reduce_mean_95_cast_fp16, y = var_2117_to_fp16)[name = tensor<string, []>("op_2118_cast_fp16")];
tensor<fp16, [1, 1]> var_2119_cast_fp16 = sqrt(x = var_2118_cast_fp16)[name = tensor<string, []>("op_2119_cast_fp16")];
tensor<fp16, [1, 512]> x_227_cast_fp16 = real_div(x = sub_59_cast_fp16, y = var_2119_cast_fp16)[name = tensor<string, []>("x_227_cast_fp16")];
tensor<fp16, [1, 512]> var_2121_cast_fp16 = mul(x = x_227_cast_fp16, y = flow_net_res_blocks_0_in_ln_weight_to_fp16)[name = tensor<string, []>("op_2121_cast_fp16")];
tensor<fp16, [1, 512]> x_229_cast_fp16 = add(x = var_2121_cast_fp16, y = flow_net_res_blocks_0_in_ln_bias_to_fp16)[name = tensor<string, []>("x_229_cast_fp16")];
tensor<fp16, []> var_2123_promoted_to_fp16 = const()[name = tensor<string, []>("op_2123_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
tensor<fp16, [1, 512]> var_2124_cast_fp16 = add(x = var_2107_cast_fp16_1, y = var_2123_promoted_to_fp16)[name = tensor<string, []>("op_2124_cast_fp16")];
tensor<fp16, [1, 512]> var_2125_cast_fp16 = mul(x = x_229_cast_fp16, y = var_2124_cast_fp16)[name = tensor<string, []>("op_2125_cast_fp16")];
tensor<fp16, [1, 512]> input_357_cast_fp16 = add(x = var_2125_cast_fp16, y = var_2107_cast_fp16_0)[name = tensor<string, []>("input_357_cast_fp16")];
tensor<fp16, [1, 512]> linear_137_cast_fp16 = linear(bias = flow_net_res_blocks_0_mlp_0_bias_to_fp16, weight = flow_net_res_blocks_0_mlp_0_weight_to_fp16, x = input_357_cast_fp16)[name = tensor<string, []>("linear_137_cast_fp16")];
tensor<fp16, [1, 512]> input_361_cast_fp16 = silu(x = linear_137_cast_fp16)[name = tensor<string, []>("input_361_cast_fp16")];
tensor<fp16, [1, 512]> linear_138_cast_fp16 = linear(bias = flow_net_res_blocks_0_mlp_2_bias_to_fp16, weight = flow_net_res_blocks_0_mlp_2_weight_to_fp16, x = input_361_cast_fp16)[name = tensor<string, []>("linear_138_cast_fp16")];
tensor<fp16, [1, 512]> var_2136_cast_fp16 = mul(x = var_2107_cast_fp16_2, y = linear_138_cast_fp16)[name = tensor<string, []>("op_2136_cast_fp16")];
tensor<fp16, [1, 512]> x_231_cast_fp16 = add(x = linear_130_cast_fp16, y = var_2136_cast_fp16)[name = tensor<string, []>("x_231_cast_fp16")];
tensor<fp16, [1, 1536]> linear_139_cast_fp16 = linear(bias = flow_net_res_blocks_1_adaLN_modulation_1_bias_to_fp16, weight = flow_net_res_blocks_1_adaLN_modulation_1_weight_to_fp16, x = input_355_cast_fp16)[name = tensor<string, []>("linear_139_cast_fp16")];
tensor<int32, [3]> var_2146_split_sizes_0 = const()[name = tensor<string, []>("op_2146_split_sizes_0"), val = tensor<int32, [3]>([512, 512, 512])];
tensor<int32, []> var_2146_axis_0 = const()[name = tensor<string, []>("op_2146_axis_0"), val = tensor<int32, []>(-1)];
tensor<fp16, [1, 512]> var_2146_cast_fp16_0, tensor<fp16, [1, 512]> var_2146_cast_fp16_1, tensor<fp16, [1, 512]> var_2146_cast_fp16_2 = split(axis = var_2146_axis_0, split_sizes = var_2146_split_sizes_0, x = linear_139_cast_fp16)[name = tensor<string, []>("op_2146_cast_fp16")];
tensor<int32, [1]> mean_73_axes_0 = const()[name = tensor<string, []>("mean_73_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> mean_73_keep_dims_0 = const()[name = tensor<string, []>("mean_73_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> mean_73_cast_fp16 = reduce_mean(axes = mean_73_axes_0, keep_dims = mean_73_keep_dims_0, x = x_231_cast_fp16)[name = tensor<string, []>("mean_73_cast_fp16")];
tensor<fp16, [1, 512]> sub_60_cast_fp16 = sub(x = x_231_cast_fp16, y = mean_73_cast_fp16)[name = tensor<string, []>("sub_60_cast_fp16")];
tensor<fp16, [1, 512]> square_48_cast_fp16 = square(x = sub_60_cast_fp16)[name = tensor<string, []>("square_48_cast_fp16")];
tensor<int32, [1]> reduce_mean_97_axes_0 = const()[name = tensor<string, []>("reduce_mean_97_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_97_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_97_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_97_cast_fp16 = reduce_mean(axes = reduce_mean_97_axes_0, keep_dims = reduce_mean_97_keep_dims_0, x = square_48_cast_fp16)[name = tensor<string, []>("reduce_mean_97_cast_fp16")];
tensor<fp16, []> var_2156_to_fp16 = const()[name = tensor<string, []>("op_2156_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 1]> var_2157_cast_fp16 = add(x = reduce_mean_97_cast_fp16, y = var_2156_to_fp16)[name = tensor<string, []>("op_2157_cast_fp16")];
tensor<fp16, [1, 1]> var_2158_cast_fp16 = sqrt(x = var_2157_cast_fp16)[name = tensor<string, []>("op_2158_cast_fp16")];
tensor<fp16, [1, 512]> x_233_cast_fp16 = real_div(x = sub_60_cast_fp16, y = var_2158_cast_fp16)[name = tensor<string, []>("x_233_cast_fp16")];
tensor<fp16, [1, 512]> var_2160_cast_fp16 = mul(x = x_233_cast_fp16, y = flow_net_res_blocks_1_in_ln_weight_to_fp16)[name = tensor<string, []>("op_2160_cast_fp16")];
tensor<fp16, [1, 512]> x_235_cast_fp16 = add(x = var_2160_cast_fp16, y = flow_net_res_blocks_1_in_ln_bias_to_fp16)[name = tensor<string, []>("x_235_cast_fp16")];
tensor<fp16, []> var_2162_promoted_to_fp16 = const()[name = tensor<string, []>("op_2162_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
tensor<fp16, [1, 512]> var_2163_cast_fp16 = add(x = var_2146_cast_fp16_1, y = var_2162_promoted_to_fp16)[name = tensor<string, []>("op_2163_cast_fp16")];
tensor<fp16, [1, 512]> var_2164_cast_fp16 = mul(x = x_235_cast_fp16, y = var_2163_cast_fp16)[name = tensor<string, []>("op_2164_cast_fp16")];
tensor<fp16, [1, 512]> input_365_cast_fp16 = add(x = var_2164_cast_fp16, y = var_2146_cast_fp16_0)[name = tensor<string, []>("input_365_cast_fp16")];
tensor<fp16, [1, 512]> linear_140_cast_fp16 = linear(bias = flow_net_res_blocks_1_mlp_0_bias_to_fp16, weight = flow_net_res_blocks_1_mlp_0_weight_to_fp16, x = input_365_cast_fp16)[name = tensor<string, []>("linear_140_cast_fp16")];
tensor<fp16, [1, 512]> input_369_cast_fp16 = silu(x = linear_140_cast_fp16)[name = tensor<string, []>("input_369_cast_fp16")];
tensor<fp16, [1, 512]> linear_141_cast_fp16 = linear(bias = flow_net_res_blocks_1_mlp_2_bias_to_fp16, weight = flow_net_res_blocks_1_mlp_2_weight_to_fp16, x = input_369_cast_fp16)[name = tensor<string, []>("linear_141_cast_fp16")];
tensor<fp16, [1, 512]> var_2175_cast_fp16 = mul(x = var_2146_cast_fp16_2, y = linear_141_cast_fp16)[name = tensor<string, []>("op_2175_cast_fp16")];
tensor<fp16, [1, 512]> x_237_cast_fp16 = add(x = x_231_cast_fp16, y = var_2175_cast_fp16)[name = tensor<string, []>("x_237_cast_fp16")];
tensor<fp16, [1, 1536]> linear_142_cast_fp16 = linear(bias = flow_net_res_blocks_2_adaLN_modulation_1_bias_to_fp16, weight = flow_net_res_blocks_2_adaLN_modulation_1_weight_to_fp16, x = input_355_cast_fp16)[name = tensor<string, []>("linear_142_cast_fp16")];
tensor<int32, [3]> var_2185_split_sizes_0 = const()[name = tensor<string, []>("op_2185_split_sizes_0"), val = tensor<int32, [3]>([512, 512, 512])];
tensor<int32, []> var_2185_axis_0 = const()[name = tensor<string, []>("op_2185_axis_0"), val = tensor<int32, []>(-1)];
tensor<fp16, [1, 512]> var_2185_cast_fp16_0, tensor<fp16, [1, 512]> var_2185_cast_fp16_1, tensor<fp16, [1, 512]> var_2185_cast_fp16_2 = split(axis = var_2185_axis_0, split_sizes = var_2185_split_sizes_0, x = linear_142_cast_fp16)[name = tensor<string, []>("op_2185_cast_fp16")];
tensor<int32, [1]> mean_75_axes_0 = const()[name = tensor<string, []>("mean_75_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> mean_75_keep_dims_0 = const()[name = tensor<string, []>("mean_75_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> mean_75_cast_fp16 = reduce_mean(axes = mean_75_axes_0, keep_dims = mean_75_keep_dims_0, x = x_237_cast_fp16)[name = tensor<string, []>("mean_75_cast_fp16")];
tensor<fp16, [1, 512]> sub_61_cast_fp16 = sub(x = x_237_cast_fp16, y = mean_75_cast_fp16)[name = tensor<string, []>("sub_61_cast_fp16")];
tensor<fp16, [1, 512]> square_49_cast_fp16 = square(x = sub_61_cast_fp16)[name = tensor<string, []>("square_49_cast_fp16")];
tensor<int32, [1]> reduce_mean_99_axes_0 = const()[name = tensor<string, []>("reduce_mean_99_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_99_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_99_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_99_cast_fp16 = reduce_mean(axes = reduce_mean_99_axes_0, keep_dims = reduce_mean_99_keep_dims_0, x = square_49_cast_fp16)[name = tensor<string, []>("reduce_mean_99_cast_fp16")];
tensor<fp16, []> var_2195_to_fp16 = const()[name = tensor<string, []>("op_2195_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 1]> var_2196_cast_fp16 = add(x = reduce_mean_99_cast_fp16, y = var_2195_to_fp16)[name = tensor<string, []>("op_2196_cast_fp16")];
tensor<fp16, [1, 1]> var_2197_cast_fp16 = sqrt(x = var_2196_cast_fp16)[name = tensor<string, []>("op_2197_cast_fp16")];
tensor<fp16, [1, 512]> x_239_cast_fp16 = real_div(x = sub_61_cast_fp16, y = var_2197_cast_fp16)[name = tensor<string, []>("x_239_cast_fp16")];
tensor<fp16, [1, 512]> var_2199_cast_fp16 = mul(x = x_239_cast_fp16, y = flow_net_res_blocks_2_in_ln_weight_to_fp16)[name = tensor<string, []>("op_2199_cast_fp16")];
tensor<fp16, [1, 512]> x_241_cast_fp16 = add(x = var_2199_cast_fp16, y = flow_net_res_blocks_2_in_ln_bias_to_fp16)[name = tensor<string, []>("x_241_cast_fp16")];
tensor<fp16, []> var_2201_promoted_to_fp16 = const()[name = tensor<string, []>("op_2201_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
tensor<fp16, [1, 512]> var_2202_cast_fp16 = add(x = var_2185_cast_fp16_1, y = var_2201_promoted_to_fp16)[name = tensor<string, []>("op_2202_cast_fp16")];
tensor<fp16, [1, 512]> var_2203_cast_fp16 = mul(x = x_241_cast_fp16, y = var_2202_cast_fp16)[name = tensor<string, []>("op_2203_cast_fp16")];
tensor<fp16, [1, 512]> input_373_cast_fp16 = add(x = var_2203_cast_fp16, y = var_2185_cast_fp16_0)[name = tensor<string, []>("input_373_cast_fp16")];
tensor<fp16, [1, 512]> linear_143_cast_fp16 = linear(bias = flow_net_res_blocks_2_mlp_0_bias_to_fp16, weight = flow_net_res_blocks_2_mlp_0_weight_to_fp16, x = input_373_cast_fp16)[name = tensor<string, []>("linear_143_cast_fp16")];
tensor<fp16, [1, 512]> input_377_cast_fp16 = silu(x = linear_143_cast_fp16)[name = tensor<string, []>("input_377_cast_fp16")];
tensor<fp16, [1, 512]> linear_144_cast_fp16 = linear(bias = flow_net_res_blocks_2_mlp_2_bias_to_fp16, weight = flow_net_res_blocks_2_mlp_2_weight_to_fp16, x = input_377_cast_fp16)[name = tensor<string, []>("linear_144_cast_fp16")];
tensor<fp16, [1, 512]> var_2214_cast_fp16 = mul(x = var_2185_cast_fp16_2, y = linear_144_cast_fp16)[name = tensor<string, []>("op_2214_cast_fp16")];
tensor<fp16, [1, 512]> x_243_cast_fp16 = add(x = x_237_cast_fp16, y = var_2214_cast_fp16)[name = tensor<string, []>("x_243_cast_fp16")];
tensor<fp16, [1, 1536]> linear_145_cast_fp16 = linear(bias = flow_net_res_blocks_3_adaLN_modulation_1_bias_to_fp16, weight = flow_net_res_blocks_3_adaLN_modulation_1_weight_to_fp16, x = input_355_cast_fp16)[name = tensor<string, []>("linear_145_cast_fp16")];
tensor<int32, [3]> var_2224_split_sizes_0 = const()[name = tensor<string, []>("op_2224_split_sizes_0"), val = tensor<int32, [3]>([512, 512, 512])];
tensor<int32, []> var_2224_axis_0 = const()[name = tensor<string, []>("op_2224_axis_0"), val = tensor<int32, []>(-1)];
tensor<fp16, [1, 512]> var_2224_cast_fp16_0, tensor<fp16, [1, 512]> var_2224_cast_fp16_1, tensor<fp16, [1, 512]> var_2224_cast_fp16_2 = split(axis = var_2224_axis_0, split_sizes = var_2224_split_sizes_0, x = linear_145_cast_fp16)[name = tensor<string, []>("op_2224_cast_fp16")];
tensor<int32, [1]> mean_77_axes_0 = const()[name = tensor<string, []>("mean_77_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> mean_77_keep_dims_0 = const()[name = tensor<string, []>("mean_77_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> mean_77_cast_fp16 = reduce_mean(axes = mean_77_axes_0, keep_dims = mean_77_keep_dims_0, x = x_243_cast_fp16)[name = tensor<string, []>("mean_77_cast_fp16")];
tensor<fp16, [1, 512]> sub_62_cast_fp16 = sub(x = x_243_cast_fp16, y = mean_77_cast_fp16)[name = tensor<string, []>("sub_62_cast_fp16")];
tensor<fp16, [1, 512]> square_50_cast_fp16 = square(x = sub_62_cast_fp16)[name = tensor<string, []>("square_50_cast_fp16")];
tensor<int32, [1]> reduce_mean_101_axes_0 = const()[name = tensor<string, []>("reduce_mean_101_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_101_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_101_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_101_cast_fp16 = reduce_mean(axes = reduce_mean_101_axes_0, keep_dims = reduce_mean_101_keep_dims_0, x = square_50_cast_fp16)[name = tensor<string, []>("reduce_mean_101_cast_fp16")];
tensor<fp16, []> var_2234_to_fp16 = const()[name = tensor<string, []>("op_2234_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 1]> var_2235_cast_fp16 = add(x = reduce_mean_101_cast_fp16, y = var_2234_to_fp16)[name = tensor<string, []>("op_2235_cast_fp16")];
tensor<fp16, [1, 1]> var_2236_cast_fp16 = sqrt(x = var_2235_cast_fp16)[name = tensor<string, []>("op_2236_cast_fp16")];
tensor<fp16, [1, 512]> x_245_cast_fp16 = real_div(x = sub_62_cast_fp16, y = var_2236_cast_fp16)[name = tensor<string, []>("x_245_cast_fp16")];
tensor<fp16, [1, 512]> var_2238_cast_fp16 = mul(x = x_245_cast_fp16, y = flow_net_res_blocks_3_in_ln_weight_to_fp16)[name = tensor<string, []>("op_2238_cast_fp16")];
tensor<fp16, [1, 512]> x_247_cast_fp16 = add(x = var_2238_cast_fp16, y = flow_net_res_blocks_3_in_ln_bias_to_fp16)[name = tensor<string, []>("x_247_cast_fp16")];
tensor<fp16, []> var_2240_promoted_to_fp16 = const()[name = tensor<string, []>("op_2240_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
tensor<fp16, [1, 512]> var_2241_cast_fp16 = add(x = var_2224_cast_fp16_1, y = var_2240_promoted_to_fp16)[name = tensor<string, []>("op_2241_cast_fp16")];
tensor<fp16, [1, 512]> var_2242_cast_fp16 = mul(x = x_247_cast_fp16, y = var_2241_cast_fp16)[name = tensor<string, []>("op_2242_cast_fp16")];
tensor<fp16, [1, 512]> input_381_cast_fp16 = add(x = var_2242_cast_fp16, y = var_2224_cast_fp16_0)[name = tensor<string, []>("input_381_cast_fp16")];
tensor<fp16, [1, 512]> linear_146_cast_fp16 = linear(bias = flow_net_res_blocks_3_mlp_0_bias_to_fp16, weight = flow_net_res_blocks_3_mlp_0_weight_to_fp16, x = input_381_cast_fp16)[name = tensor<string, []>("linear_146_cast_fp16")];
tensor<fp16, [1, 512]> input_385_cast_fp16 = silu(x = linear_146_cast_fp16)[name = tensor<string, []>("input_385_cast_fp16")];
tensor<fp16, [1, 512]> linear_147_cast_fp16 = linear(bias = flow_net_res_blocks_3_mlp_2_bias_to_fp16, weight = flow_net_res_blocks_3_mlp_2_weight_to_fp16, x = input_385_cast_fp16)[name = tensor<string, []>("linear_147_cast_fp16")];
tensor<fp16, [1, 512]> var_2253_cast_fp16 = mul(x = var_2224_cast_fp16_2, y = linear_147_cast_fp16)[name = tensor<string, []>("op_2253_cast_fp16")];
tensor<fp16, [1, 512]> x_249_cast_fp16 = add(x = x_243_cast_fp16, y = var_2253_cast_fp16)[name = tensor<string, []>("x_249_cast_fp16")];
tensor<fp16, [1, 1536]> linear_148_cast_fp16 = linear(bias = flow_net_res_blocks_4_adaLN_modulation_1_bias_to_fp16, weight = flow_net_res_blocks_4_adaLN_modulation_1_weight_to_fp16, x = input_355_cast_fp16)[name = tensor<string, []>("linear_148_cast_fp16")];
tensor<int32, [3]> var_2263_split_sizes_0 = const()[name = tensor<string, []>("op_2263_split_sizes_0"), val = tensor<int32, [3]>([512, 512, 512])];
tensor<int32, []> var_2263_axis_0 = const()[name = tensor<string, []>("op_2263_axis_0"), val = tensor<int32, []>(-1)];
tensor<fp16, [1, 512]> var_2263_cast_fp16_0, tensor<fp16, [1, 512]> var_2263_cast_fp16_1, tensor<fp16, [1, 512]> var_2263_cast_fp16_2 = split(axis = var_2263_axis_0, split_sizes = var_2263_split_sizes_0, x = linear_148_cast_fp16)[name = tensor<string, []>("op_2263_cast_fp16")];
tensor<int32, [1]> mean_79_axes_0 = const()[name = tensor<string, []>("mean_79_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> mean_79_keep_dims_0 = const()[name = tensor<string, []>("mean_79_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> mean_79_cast_fp16 = reduce_mean(axes = mean_79_axes_0, keep_dims = mean_79_keep_dims_0, x = x_249_cast_fp16)[name = tensor<string, []>("mean_79_cast_fp16")];
tensor<fp16, [1, 512]> sub_63_cast_fp16 = sub(x = x_249_cast_fp16, y = mean_79_cast_fp16)[name = tensor<string, []>("sub_63_cast_fp16")];
tensor<fp16, [1, 512]> square_51_cast_fp16 = square(x = sub_63_cast_fp16)[name = tensor<string, []>("square_51_cast_fp16")];
tensor<int32, [1]> reduce_mean_103_axes_0 = const()[name = tensor<string, []>("reduce_mean_103_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_103_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_103_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_103_cast_fp16 = reduce_mean(axes = reduce_mean_103_axes_0, keep_dims = reduce_mean_103_keep_dims_0, x = square_51_cast_fp16)[name = tensor<string, []>("reduce_mean_103_cast_fp16")];
tensor<fp16, []> var_2273_to_fp16 = const()[name = tensor<string, []>("op_2273_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 1]> var_2274_cast_fp16 = add(x = reduce_mean_103_cast_fp16, y = var_2273_to_fp16)[name = tensor<string, []>("op_2274_cast_fp16")];
tensor<fp16, [1, 1]> var_2275_cast_fp16 = sqrt(x = var_2274_cast_fp16)[name = tensor<string, []>("op_2275_cast_fp16")];
tensor<fp16, [1, 512]> x_251_cast_fp16 = real_div(x = sub_63_cast_fp16, y = var_2275_cast_fp16)[name = tensor<string, []>("x_251_cast_fp16")];
tensor<fp16, [1, 512]> var_2277_cast_fp16 = mul(x = x_251_cast_fp16, y = flow_net_res_blocks_4_in_ln_weight_to_fp16)[name = tensor<string, []>("op_2277_cast_fp16")];
tensor<fp16, [1, 512]> x_253_cast_fp16 = add(x = var_2277_cast_fp16, y = flow_net_res_blocks_4_in_ln_bias_to_fp16)[name = tensor<string, []>("x_253_cast_fp16")];
tensor<fp16, []> var_2279_promoted_to_fp16 = const()[name = tensor<string, []>("op_2279_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
tensor<fp16, [1, 512]> var_2280_cast_fp16 = add(x = var_2263_cast_fp16_1, y = var_2279_promoted_to_fp16)[name = tensor<string, []>("op_2280_cast_fp16")];
tensor<fp16, [1, 512]> var_2281_cast_fp16 = mul(x = x_253_cast_fp16, y = var_2280_cast_fp16)[name = tensor<string, []>("op_2281_cast_fp16")];
tensor<fp16, [1, 512]> input_389_cast_fp16 = add(x = var_2281_cast_fp16, y = var_2263_cast_fp16_0)[name = tensor<string, []>("input_389_cast_fp16")];
tensor<fp16, [1, 512]> linear_149_cast_fp16 = linear(bias = flow_net_res_blocks_4_mlp_0_bias_to_fp16, weight = flow_net_res_blocks_4_mlp_0_weight_to_fp16, x = input_389_cast_fp16)[name = tensor<string, []>("linear_149_cast_fp16")];
tensor<fp16, [1, 512]> input_393_cast_fp16 = silu(x = linear_149_cast_fp16)[name = tensor<string, []>("input_393_cast_fp16")];
tensor<fp16, [1, 512]> linear_150_cast_fp16 = linear(bias = flow_net_res_blocks_4_mlp_2_bias_to_fp16, weight = flow_net_res_blocks_4_mlp_2_weight_to_fp16, x = input_393_cast_fp16)[name = tensor<string, []>("linear_150_cast_fp16")];
tensor<fp16, [1, 512]> var_2292_cast_fp16 = mul(x = var_2263_cast_fp16_2, y = linear_150_cast_fp16)[name = tensor<string, []>("op_2292_cast_fp16")];
tensor<fp16, [1, 512]> x_255_cast_fp16 = add(x = x_249_cast_fp16, y = var_2292_cast_fp16)[name = tensor<string, []>("x_255_cast_fp16")];
tensor<fp16, [1, 1536]> linear_151_cast_fp16 = linear(bias = flow_net_res_blocks_5_adaLN_modulation_1_bias_to_fp16, weight = flow_net_res_blocks_5_adaLN_modulation_1_weight_to_fp16, x = input_355_cast_fp16)[name = tensor<string, []>("linear_151_cast_fp16")];
tensor<int32, [3]> var_2302_split_sizes_0 = const()[name = tensor<string, []>("op_2302_split_sizes_0"), val = tensor<int32, [3]>([512, 512, 512])];
tensor<int32, []> var_2302_axis_0 = const()[name = tensor<string, []>("op_2302_axis_0"), val = tensor<int32, []>(-1)];
tensor<fp16, [1, 512]> var_2302_cast_fp16_0, tensor<fp16, [1, 512]> var_2302_cast_fp16_1, tensor<fp16, [1, 512]> var_2302_cast_fp16_2 = split(axis = var_2302_axis_0, split_sizes = var_2302_split_sizes_0, x = linear_151_cast_fp16)[name = tensor<string, []>("op_2302_cast_fp16")];
tensor<int32, [1]> mean_81_axes_0 = const()[name = tensor<string, []>("mean_81_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> mean_81_keep_dims_0 = const()[name = tensor<string, []>("mean_81_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> mean_81_cast_fp16 = reduce_mean(axes = mean_81_axes_0, keep_dims = mean_81_keep_dims_0, x = x_255_cast_fp16)[name = tensor<string, []>("mean_81_cast_fp16")];
tensor<fp16, [1, 512]> sub_64_cast_fp16 = sub(x = x_255_cast_fp16, y = mean_81_cast_fp16)[name = tensor<string, []>("sub_64_cast_fp16")];
tensor<fp16, [1, 512]> square_52_cast_fp16 = square(x = sub_64_cast_fp16)[name = tensor<string, []>("square_52_cast_fp16")];
tensor<int32, [1]> reduce_mean_105_axes_0 = const()[name = tensor<string, []>("reduce_mean_105_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_105_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_105_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_105_cast_fp16 = reduce_mean(axes = reduce_mean_105_axes_0, keep_dims = reduce_mean_105_keep_dims_0, x = square_52_cast_fp16)[name = tensor<string, []>("reduce_mean_105_cast_fp16")];
tensor<fp16, []> var_2312_to_fp16 = const()[name = tensor<string, []>("op_2312_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 1]> var_2313_cast_fp16 = add(x = reduce_mean_105_cast_fp16, y = var_2312_to_fp16)[name = tensor<string, []>("op_2313_cast_fp16")];
tensor<fp16, [1, 1]> var_2314_cast_fp16 = sqrt(x = var_2313_cast_fp16)[name = tensor<string, []>("op_2314_cast_fp16")];
tensor<fp16, [1, 512]> x_257_cast_fp16 = real_div(x = sub_64_cast_fp16, y = var_2314_cast_fp16)[name = tensor<string, []>("x_257_cast_fp16")];
tensor<fp16, [1, 512]> var_2316_cast_fp16 = mul(x = x_257_cast_fp16, y = flow_net_res_blocks_5_in_ln_weight_to_fp16)[name = tensor<string, []>("op_2316_cast_fp16")];
tensor<fp16, [1, 512]> x_259_cast_fp16 = add(x = var_2316_cast_fp16, y = flow_net_res_blocks_5_in_ln_bias_to_fp16)[name = tensor<string, []>("x_259_cast_fp16")];
tensor<fp16, []> var_2318_promoted_to_fp16 = const()[name = tensor<string, []>("op_2318_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
tensor<fp16, [1, 512]> var_2319_cast_fp16 = add(x = var_2302_cast_fp16_1, y = var_2318_promoted_to_fp16)[name = tensor<string, []>("op_2319_cast_fp16")];
tensor<fp16, [1, 512]> var_2320_cast_fp16 = mul(x = x_259_cast_fp16, y = var_2319_cast_fp16)[name = tensor<string, []>("op_2320_cast_fp16")];
tensor<fp16, [1, 512]> input_397_cast_fp16 = add(x = var_2320_cast_fp16, y = var_2302_cast_fp16_0)[name = tensor<string, []>("input_397_cast_fp16")];
tensor<fp16, [1, 512]> linear_152_cast_fp16 = linear(bias = flow_net_res_blocks_5_mlp_0_bias_to_fp16, weight = flow_net_res_blocks_5_mlp_0_weight_to_fp16, x = input_397_cast_fp16)[name = tensor<string, []>("linear_152_cast_fp16")];
tensor<fp16, [1, 512]> input_401_cast_fp16 = silu(x = linear_152_cast_fp16)[name = tensor<string, []>("input_401_cast_fp16")];
tensor<fp16, [1, 512]> linear_153_cast_fp16 = linear(bias = flow_net_res_blocks_5_mlp_2_bias_to_fp16, weight = flow_net_res_blocks_5_mlp_2_weight_to_fp16, x = input_401_cast_fp16)[name = tensor<string, []>("linear_153_cast_fp16")];
tensor<fp16, [1, 512]> var_2331_cast_fp16 = mul(x = var_2302_cast_fp16_2, y = linear_153_cast_fp16)[name = tensor<string, []>("op_2331_cast_fp16")];
tensor<fp16, [1, 512]> x_261_cast_fp16 = add(x = x_255_cast_fp16, y = var_2331_cast_fp16)[name = tensor<string, []>("x_261_cast_fp16")];
tensor<fp16, [1, 1024]> linear_154_cast_fp16 = linear(bias = flow_net_final_layer_adaLN_modulation_1_bias_to_fp16, weight = flow_net_final_layer_adaLN_modulation_1_weight_to_fp16, x = input_355_cast_fp16)[name = tensor<string, []>("linear_154_cast_fp16")];
tensor<int32, [2]> var_2340_split_sizes_0 = const()[name = tensor<string, []>("op_2340_split_sizes_0"), val = tensor<int32, [2]>([512, 512])];
tensor<int32, []> var_2340_axis_0 = const()[name = tensor<string, []>("op_2340_axis_0"), val = tensor<int32, []>(-1)];
tensor<fp16, [1, 512]> var_2340_cast_fp16_0, tensor<fp16, [1, 512]> var_2340_cast_fp16_1 = split(axis = var_2340_axis_0, split_sizes = var_2340_split_sizes_0, x = linear_154_cast_fp16)[name = tensor<string, []>("op_2340_cast_fp16")];
tensor<int32, [1]> mean_83_axes_0 = const()[name = tensor<string, []>("mean_83_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> mean_83_keep_dims_0 = const()[name = tensor<string, []>("mean_83_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> mean_83_cast_fp16 = reduce_mean(axes = mean_83_axes_0, keep_dims = mean_83_keep_dims_0, x = x_261_cast_fp16)[name = tensor<string, []>("mean_83_cast_fp16")];
tensor<fp16, [1, 512]> sub_65_cast_fp16 = sub(x = x_261_cast_fp16, y = mean_83_cast_fp16)[name = tensor<string, []>("sub_65_cast_fp16")];
tensor<fp16, [1, 512]> square_53_cast_fp16 = square(x = sub_65_cast_fp16)[name = tensor<string, []>("square_53_cast_fp16")];
tensor<int32, [1]> reduce_mean_107_axes_0 = const()[name = tensor<string, []>("reduce_mean_107_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_107_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_107_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_107_cast_fp16 = reduce_mean(axes = reduce_mean_107_axes_0, keep_dims = reduce_mean_107_keep_dims_0, x = square_53_cast_fp16)[name = tensor<string, []>("reduce_mean_107_cast_fp16")];
tensor<fp16, []> var_2347_to_fp16 = const()[name = tensor<string, []>("op_2347_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 1]> var_2348_cast_fp16 = add(x = reduce_mean_107_cast_fp16, y = var_2347_to_fp16)[name = tensor<string, []>("op_2348_cast_fp16")];
tensor<fp16, [1, 1]> var_2349_cast_fp16 = sqrt(x = var_2348_cast_fp16)[name = tensor<string, []>("op_2349_cast_fp16")];
tensor<fp16, [1, 512]> x_263_cast_fp16 = real_div(x = sub_65_cast_fp16, y = var_2349_cast_fp16)[name = tensor<string, []>("x_263_cast_fp16")];
tensor<fp16, []> var_2351_promoted_to_fp16 = const()[name = tensor<string, []>("op_2351_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
tensor<fp16, [1, 512]> var_2352_cast_fp16 = add(x = var_2340_cast_fp16_1, y = var_2351_promoted_to_fp16)[name = tensor<string, []>("op_2352_cast_fp16")];
tensor<fp16, [1, 512]> var_2353_cast_fp16 = mul(x = x_263_cast_fp16, y = var_2352_cast_fp16)[name = tensor<string, []>("op_2353_cast_fp16")];
tensor<fp16, [1, 512]> input_405_cast_fp16 = add(x = var_2353_cast_fp16, y = var_2340_cast_fp16_0)[name = tensor<string, []>("input_405_cast_fp16")];
tensor<fp16, [1, 32]> linear_155_cast_fp16 = linear(bias = flow_net_final_layer_linear_bias_to_fp16, weight = flow_net_final_layer_linear_weight_to_fp16, x = input_405_cast_fp16)[name = tensor<string, []>("linear_155_cast_fp16")];
tensor<fp16, []> var_2364_to_fp16 = const()[name = tensor<string, []>("op_2364_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 32]> var_2365_cast_fp16 = mul(x = linear_155_cast_fp16, y = var_2364_to_fp16)[name = tensor<string, []>("op_2365_cast_fp16")];
tensor<fp16, [1, 32]> input_407_cast_fp16 = add(x = input_339_cast_fp16, y = var_2365_cast_fp16)[name = tensor<string, []>("input_407_cast_fp16")];
tensor<fp16, [1, 512]> linear_156_cast_fp16 = linear(bias = flow_net_input_proj_bias_to_fp16, weight = flow_net_input_proj_weight_to_fp16, x = input_407_cast_fp16)[name = tensor<string, []>("linear_156_cast_fp16")];
tensor<fp16, [1, 512]> input_411_to_fp16 = const()[name = tensor<string, []>("input_411_to_fp16"), val = tensor<fp16, [1, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19008256)))];
tensor<fp16, [1, 512]> input_413_cast_fp16 = silu(x = input_411_to_fp16)[name = tensor<string, []>("input_413_cast_fp16")];
tensor<fp16, [1, 512]> linear_158_cast_fp16 = linear(bias = flow_net_time_embed_0_mlp_2_bias_to_fp16, weight = flow_net_time_embed_0_mlp_2_weight_to_fp16, x = input_413_cast_fp16)[name = tensor<string, []>("linear_158_cast_fp16")];
tensor<int32, [1]> reduce_mean_108_axes_0 = const()[name = tensor<string, []>("reduce_mean_108_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_108_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_108_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_108_cast_fp16 = reduce_mean(axes = reduce_mean_108_axes_0, keep_dims = reduce_mean_108_keep_dims_0, x = linear_158_cast_fp16)[name = tensor<string, []>("reduce_mean_108_cast_fp16")];
tensor<fp16, [1, 512]> sub_66_cast_fp16 = sub(x = linear_158_cast_fp16, y = reduce_mean_108_cast_fp16)[name = tensor<string, []>("sub_66_cast_fp16")];
tensor<fp16, [1, 512]> square_54_cast_fp16 = square(x = sub_66_cast_fp16)[name = tensor<string, []>("square_54_cast_fp16")];
tensor<int32, [1]> reduce_mean_109_axes_0 = const()[name = tensor<string, []>("reduce_mean_109_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_109_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_109_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_109_cast_fp16 = reduce_mean(axes = reduce_mean_109_axes_0, keep_dims = reduce_mean_109_keep_dims_0, x = square_54_cast_fp16)[name = tensor<string, []>("reduce_mean_109_cast_fp16")];
tensor<fp16, []> real_div_12_to_fp16 = const()[name = tensor<string, []>("real_div_12_to_fp16"), val = tensor<fp16, []>(0x1.008p+0)];
tensor<fp16, [1, 1]> mul_12_cast_fp16 = mul(x = reduce_mean_109_cast_fp16, y = real_div_12_to_fp16)[name = tensor<string, []>("mul_12_cast_fp16")];
tensor<fp16, []> var_2431_to_fp16 = const()[name = tensor<string, []>("op_2431_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1]> var_109_cast_fp16 = add(x = mul_12_cast_fp16, y = var_2431_to_fp16)[name = tensor<string, []>("var_109_cast_fp16")];
tensor<fp32, []> var_2434_epsilon_0 = const()[name = tensor<string, []>("op_2434_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
tensor<fp16, [1, 1]> var_2434_cast_fp16 = rsqrt(epsilon = var_2434_epsilon_0, x = var_109_cast_fp16)[name = tensor<string, []>("op_2434_cast_fp16")];
tensor<fp16, [1, 512]> var_2435_cast_fp16 = mul(x = const_3_to_fp16, y = var_2434_cast_fp16)[name = tensor<string, []>("op_2435_cast_fp16")];
tensor<fp16, [1, 512]> var_2436_cast_fp16 = mul(x = linear_158_cast_fp16, y = var_2435_cast_fp16)[name = tensor<string, []>("op_2436_cast_fp16")];
tensor<fp16, [1, 512]> input_417_to_fp16 = const()[name = tensor<string, []>("input_417_to_fp16"), val = tensor<fp16, [1, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19009344)))];
tensor<fp16, [1, 512]> input_419_cast_fp16 = silu(x = input_417_to_fp16)[name = tensor<string, []>("input_419_cast_fp16")];
tensor<fp16, [1, 512]> linear_160_cast_fp16 = linear(bias = flow_net_time_embed_1_mlp_2_bias_to_fp16, weight = flow_net_time_embed_1_mlp_2_weight_to_fp16, x = input_419_cast_fp16)[name = tensor<string, []>("linear_160_cast_fp16")];
tensor<int32, [1]> reduce_mean_110_axes_0 = const()[name = tensor<string, []>("reduce_mean_110_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_110_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_110_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_110_cast_fp16 = reduce_mean(axes = reduce_mean_110_axes_0, keep_dims = reduce_mean_110_keep_dims_0, x = linear_160_cast_fp16)[name = tensor<string, []>("reduce_mean_110_cast_fp16")];
tensor<fp16, [1, 512]> sub_68_cast_fp16 = sub(x = linear_160_cast_fp16, y = reduce_mean_110_cast_fp16)[name = tensor<string, []>("sub_68_cast_fp16")];
tensor<fp16, [1, 512]> square_55_cast_fp16 = square(x = sub_68_cast_fp16)[name = tensor<string, []>("square_55_cast_fp16")];
tensor<int32, [1]> reduce_mean_111_axes_0 = const()[name = tensor<string, []>("reduce_mean_111_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_111_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_111_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_111_cast_fp16 = reduce_mean(axes = reduce_mean_111_axes_0, keep_dims = reduce_mean_111_keep_dims_0, x = square_55_cast_fp16)[name = tensor<string, []>("reduce_mean_111_cast_fp16")];
tensor<fp16, []> real_div_13_to_fp16 = const()[name = tensor<string, []>("real_div_13_to_fp16"), val = tensor<fp16, []>(0x1.008p+0)];
tensor<fp16, [1, 1]> mul_13_cast_fp16 = mul(x = reduce_mean_111_cast_fp16, y = real_div_13_to_fp16)[name = tensor<string, []>("mul_13_cast_fp16")];
tensor<fp16, []> var_2468_to_fp16 = const()[name = tensor<string, []>("op_2468_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1]> var_111_cast_fp16 = add(x = mul_13_cast_fp16, y = var_2468_to_fp16)[name = tensor<string, []>("var_111_cast_fp16")];
tensor<fp32, []> var_2471_epsilon_0 = const()[name = tensor<string, []>("op_2471_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
tensor<fp16, [1, 1]> var_2471_cast_fp16 = rsqrt(epsilon = var_2471_epsilon_0, x = var_111_cast_fp16)[name = tensor<string, []>("op_2471_cast_fp16")];
tensor<fp16, [1, 512]> var_2472_cast_fp16 = mul(x = const_5_to_fp16, y = var_2471_cast_fp16)[name = tensor<string, []>("op_2472_cast_fp16")];
tensor<fp16, [1, 512]> var_2473_cast_fp16 = mul(x = linear_160_cast_fp16, y = var_2472_cast_fp16)[name = tensor<string, []>("op_2473_cast_fp16")];
tensor<fp16, [1, 512]> var_2485_cast_fp16 = add(x = var_2436_cast_fp16, y = var_2473_cast_fp16)[name = tensor<string, []>("op_2485_cast_fp16")];
tensor<fp16, []> _inversed_t_combined_13_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_t_combined_13_y_0_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
tensor<fp16, [1, 512]> _inversed_t_combined_13_cast_fp16 = mul(x = var_2485_cast_fp16, y = _inversed_t_combined_13_y_0_to_fp16)[name = tensor<string, []>("_inversed_t_combined_13_cast_fp16")];
tensor<fp16, [1, 512]> input_421_cast_fp16 = add(x = _inversed_t_combined_13_cast_fp16, y = linear_5_cast_fp16)[name = tensor<string, []>("input_421_cast_fp16")];
tensor<fp16, [1, 512]> input_423_cast_fp16 = silu(x = input_421_cast_fp16)[name = tensor<string, []>("input_423_cast_fp16")];
tensor<fp16, [1, 1536]> linear_162_cast_fp16 = linear(bias = flow_net_res_blocks_0_adaLN_modulation_1_bias_to_fp16, weight = flow_net_res_blocks_0_adaLN_modulation_1_weight_to_fp16, x = input_423_cast_fp16)[name = tensor<string, []>("linear_162_cast_fp16")];
tensor<int32, [3]> var_2500_split_sizes_0 = const()[name = tensor<string, []>("op_2500_split_sizes_0"), val = tensor<int32, [3]>([512, 512, 512])];
tensor<int32, []> var_2500_axis_0 = const()[name = tensor<string, []>("op_2500_axis_0"), val = tensor<int32, []>(-1)];
tensor<fp16, [1, 512]> var_2500_cast_fp16_0, tensor<fp16, [1, 512]> var_2500_cast_fp16_1, tensor<fp16, [1, 512]> var_2500_cast_fp16_2 = split(axis = var_2500_axis_0, split_sizes = var_2500_split_sizes_0, x = linear_162_cast_fp16)[name = tensor<string, []>("op_2500_cast_fp16")];
tensor<int32, [1]> mean_85_axes_0 = const()[name = tensor<string, []>("mean_85_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> mean_85_keep_dims_0 = const()[name = tensor<string, []>("mean_85_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> mean_85_cast_fp16 = reduce_mean(axes = mean_85_axes_0, keep_dims = mean_85_keep_dims_0, x = linear_156_cast_fp16)[name = tensor<string, []>("mean_85_cast_fp16")];
tensor<fp16, [1, 512]> sub_70_cast_fp16 = sub(x = linear_156_cast_fp16, y = mean_85_cast_fp16)[name = tensor<string, []>("sub_70_cast_fp16")];
tensor<fp16, [1, 512]> square_56_cast_fp16 = square(x = sub_70_cast_fp16)[name = tensor<string, []>("square_56_cast_fp16")];
tensor<int32, [1]> reduce_mean_113_axes_0 = const()[name = tensor<string, []>("reduce_mean_113_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_113_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_113_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_113_cast_fp16 = reduce_mean(axes = reduce_mean_113_axes_0, keep_dims = reduce_mean_113_keep_dims_0, x = square_56_cast_fp16)[name = tensor<string, []>("reduce_mean_113_cast_fp16")];
tensor<fp16, []> var_2510_to_fp16 = const()[name = tensor<string, []>("op_2510_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 1]> var_2511_cast_fp16 = add(x = reduce_mean_113_cast_fp16, y = var_2510_to_fp16)[name = tensor<string, []>("op_2511_cast_fp16")];
tensor<fp16, [1, 1]> var_2512_cast_fp16 = sqrt(x = var_2511_cast_fp16)[name = tensor<string, []>("op_2512_cast_fp16")];
tensor<fp16, [1, 512]> x_271_cast_fp16 = real_div(x = sub_70_cast_fp16, y = var_2512_cast_fp16)[name = tensor<string, []>("x_271_cast_fp16")];
tensor<fp16, [1, 512]> var_2514_cast_fp16 = mul(x = x_271_cast_fp16, y = flow_net_res_blocks_0_in_ln_weight_to_fp16)[name = tensor<string, []>("op_2514_cast_fp16")];
tensor<fp16, [1, 512]> x_273_cast_fp16 = add(x = var_2514_cast_fp16, y = flow_net_res_blocks_0_in_ln_bias_to_fp16)[name = tensor<string, []>("x_273_cast_fp16")];
tensor<fp16, []> var_2516_promoted_to_fp16 = const()[name = tensor<string, []>("op_2516_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
tensor<fp16, [1, 512]> var_2517_cast_fp16 = add(x = var_2500_cast_fp16_1, y = var_2516_promoted_to_fp16)[name = tensor<string, []>("op_2517_cast_fp16")];
tensor<fp16, [1, 512]> var_2518_cast_fp16 = mul(x = x_273_cast_fp16, y = var_2517_cast_fp16)[name = tensor<string, []>("op_2518_cast_fp16")];
tensor<fp16, [1, 512]> input_425_cast_fp16 = add(x = var_2518_cast_fp16, y = var_2500_cast_fp16_0)[name = tensor<string, []>("input_425_cast_fp16")];
tensor<fp16, [1, 512]> linear_163_cast_fp16 = linear(bias = flow_net_res_blocks_0_mlp_0_bias_to_fp16, weight = flow_net_res_blocks_0_mlp_0_weight_to_fp16, x = input_425_cast_fp16)[name = tensor<string, []>("linear_163_cast_fp16")];
tensor<fp16, [1, 512]> input_429_cast_fp16 = silu(x = linear_163_cast_fp16)[name = tensor<string, []>("input_429_cast_fp16")];
tensor<fp16, [1, 512]> linear_164_cast_fp16 = linear(bias = flow_net_res_blocks_0_mlp_2_bias_to_fp16, weight = flow_net_res_blocks_0_mlp_2_weight_to_fp16, x = input_429_cast_fp16)[name = tensor<string, []>("linear_164_cast_fp16")];
tensor<fp16, [1, 512]> var_2529_cast_fp16 = mul(x = var_2500_cast_fp16_2, y = linear_164_cast_fp16)[name = tensor<string, []>("op_2529_cast_fp16")];
tensor<fp16, [1, 512]> x_275_cast_fp16 = add(x = linear_156_cast_fp16, y = var_2529_cast_fp16)[name = tensor<string, []>("x_275_cast_fp16")];
tensor<fp16, [1, 1536]> linear_165_cast_fp16 = linear(bias = flow_net_res_blocks_1_adaLN_modulation_1_bias_to_fp16, weight = flow_net_res_blocks_1_adaLN_modulation_1_weight_to_fp16, x = input_423_cast_fp16)[name = tensor<string, []>("linear_165_cast_fp16")];
tensor<int32, [3]> var_2539_split_sizes_0 = const()[name = tensor<string, []>("op_2539_split_sizes_0"), val = tensor<int32, [3]>([512, 512, 512])];
tensor<int32, []> var_2539_axis_0 = const()[name = tensor<string, []>("op_2539_axis_0"), val = tensor<int32, []>(-1)];
tensor<fp16, [1, 512]> var_2539_cast_fp16_0, tensor<fp16, [1, 512]> var_2539_cast_fp16_1, tensor<fp16, [1, 512]> var_2539_cast_fp16_2 = split(axis = var_2539_axis_0, split_sizes = var_2539_split_sizes_0, x = linear_165_cast_fp16)[name = tensor<string, []>("op_2539_cast_fp16")];
tensor<int32, [1]> mean_87_axes_0 = const()[name = tensor<string, []>("mean_87_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> mean_87_keep_dims_0 = const()[name = tensor<string, []>("mean_87_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> mean_87_cast_fp16 = reduce_mean(axes = mean_87_axes_0, keep_dims = mean_87_keep_dims_0, x = x_275_cast_fp16)[name = tensor<string, []>("mean_87_cast_fp16")];
tensor<fp16, [1, 512]> sub_71_cast_fp16 = sub(x = x_275_cast_fp16, y = mean_87_cast_fp16)[name = tensor<string, []>("sub_71_cast_fp16")];
tensor<fp16, [1, 512]> square_57_cast_fp16 = square(x = sub_71_cast_fp16)[name = tensor<string, []>("square_57_cast_fp16")];
tensor<int32, [1]> reduce_mean_115_axes_0 = const()[name = tensor<string, []>("reduce_mean_115_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_115_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_115_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_115_cast_fp16 = reduce_mean(axes = reduce_mean_115_axes_0, keep_dims = reduce_mean_115_keep_dims_0, x = square_57_cast_fp16)[name = tensor<string, []>("reduce_mean_115_cast_fp16")];
tensor<fp16, []> var_2549_to_fp16 = const()[name = tensor<string, []>("op_2549_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 1]> var_2550_cast_fp16 = add(x = reduce_mean_115_cast_fp16, y = var_2549_to_fp16)[name = tensor<string, []>("op_2550_cast_fp16")];
tensor<fp16, [1, 1]> var_2551_cast_fp16 = sqrt(x = var_2550_cast_fp16)[name = tensor<string, []>("op_2551_cast_fp16")];
tensor<fp16, [1, 512]> x_277_cast_fp16 = real_div(x = sub_71_cast_fp16, y = var_2551_cast_fp16)[name = tensor<string, []>("x_277_cast_fp16")];
tensor<fp16, [1, 512]> var_2553_cast_fp16 = mul(x = x_277_cast_fp16, y = flow_net_res_blocks_1_in_ln_weight_to_fp16)[name = tensor<string, []>("op_2553_cast_fp16")];
tensor<fp16, [1, 512]> x_279_cast_fp16 = add(x = var_2553_cast_fp16, y = flow_net_res_blocks_1_in_ln_bias_to_fp16)[name = tensor<string, []>("x_279_cast_fp16")];
tensor<fp16, []> var_2555_promoted_to_fp16 = const()[name = tensor<string, []>("op_2555_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
tensor<fp16, [1, 512]> var_2556_cast_fp16 = add(x = var_2539_cast_fp16_1, y = var_2555_promoted_to_fp16)[name = tensor<string, []>("op_2556_cast_fp16")];
tensor<fp16, [1, 512]> var_2557_cast_fp16 = mul(x = x_279_cast_fp16, y = var_2556_cast_fp16)[name = tensor<string, []>("op_2557_cast_fp16")];
tensor<fp16, [1, 512]> input_433_cast_fp16 = add(x = var_2557_cast_fp16, y = var_2539_cast_fp16_0)[name = tensor<string, []>("input_433_cast_fp16")];
tensor<fp16, [1, 512]> linear_166_cast_fp16 = linear(bias = flow_net_res_blocks_1_mlp_0_bias_to_fp16, weight = flow_net_res_blocks_1_mlp_0_weight_to_fp16, x = input_433_cast_fp16)[name = tensor<string, []>("linear_166_cast_fp16")];
tensor<fp16, [1, 512]> input_437_cast_fp16 = silu(x = linear_166_cast_fp16)[name = tensor<string, []>("input_437_cast_fp16")];
tensor<fp16, [1, 512]> linear_167_cast_fp16 = linear(bias = flow_net_res_blocks_1_mlp_2_bias_to_fp16, weight = flow_net_res_blocks_1_mlp_2_weight_to_fp16, x = input_437_cast_fp16)[name = tensor<string, []>("linear_167_cast_fp16")];
tensor<fp16, [1, 512]> var_2568_cast_fp16 = mul(x = var_2539_cast_fp16_2, y = linear_167_cast_fp16)[name = tensor<string, []>("op_2568_cast_fp16")];
tensor<fp16, [1, 512]> x_281_cast_fp16 = add(x = x_275_cast_fp16, y = var_2568_cast_fp16)[name = tensor<string, []>("x_281_cast_fp16")];
tensor<fp16, [1, 1536]> linear_168_cast_fp16 = linear(bias = flow_net_res_blocks_2_adaLN_modulation_1_bias_to_fp16, weight = flow_net_res_blocks_2_adaLN_modulation_1_weight_to_fp16, x = input_423_cast_fp16)[name = tensor<string, []>("linear_168_cast_fp16")];
tensor<int32, [3]> var_2578_split_sizes_0 = const()[name = tensor<string, []>("op_2578_split_sizes_0"), val = tensor<int32, [3]>([512, 512, 512])];
tensor<int32, []> var_2578_axis_0 = const()[name = tensor<string, []>("op_2578_axis_0"), val = tensor<int32, []>(-1)];
tensor<fp16, [1, 512]> var_2578_cast_fp16_0, tensor<fp16, [1, 512]> var_2578_cast_fp16_1, tensor<fp16, [1, 512]> var_2578_cast_fp16_2 = split(axis = var_2578_axis_0, split_sizes = var_2578_split_sizes_0, x = linear_168_cast_fp16)[name = tensor<string, []>("op_2578_cast_fp16")];
tensor<int32, [1]> mean_89_axes_0 = const()[name = tensor<string, []>("mean_89_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> mean_89_keep_dims_0 = const()[name = tensor<string, []>("mean_89_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> mean_89_cast_fp16 = reduce_mean(axes = mean_89_axes_0, keep_dims = mean_89_keep_dims_0, x = x_281_cast_fp16)[name = tensor<string, []>("mean_89_cast_fp16")];
tensor<fp16, [1, 512]> sub_72_cast_fp16 = sub(x = x_281_cast_fp16, y = mean_89_cast_fp16)[name = tensor<string, []>("sub_72_cast_fp16")];
tensor<fp16, [1, 512]> square_58_cast_fp16 = square(x = sub_72_cast_fp16)[name = tensor<string, []>("square_58_cast_fp16")];
tensor<int32, [1]> reduce_mean_117_axes_0 = const()[name = tensor<string, []>("reduce_mean_117_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_117_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_117_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_117_cast_fp16 = reduce_mean(axes = reduce_mean_117_axes_0, keep_dims = reduce_mean_117_keep_dims_0, x = square_58_cast_fp16)[name = tensor<string, []>("reduce_mean_117_cast_fp16")];
tensor<fp16, []> var_2588_to_fp16 = const()[name = tensor<string, []>("op_2588_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 1]> var_2589_cast_fp16 = add(x = reduce_mean_117_cast_fp16, y = var_2588_to_fp16)[name = tensor<string, []>("op_2589_cast_fp16")];
tensor<fp16, [1, 1]> var_2590_cast_fp16 = sqrt(x = var_2589_cast_fp16)[name = tensor<string, []>("op_2590_cast_fp16")];
tensor<fp16, [1, 512]> x_283_cast_fp16 = real_div(x = sub_72_cast_fp16, y = var_2590_cast_fp16)[name = tensor<string, []>("x_283_cast_fp16")];
tensor<fp16, [1, 512]> var_2592_cast_fp16 = mul(x = x_283_cast_fp16, y = flow_net_res_blocks_2_in_ln_weight_to_fp16)[name = tensor<string, []>("op_2592_cast_fp16")];
tensor<fp16, [1, 512]> x_285_cast_fp16 = add(x = var_2592_cast_fp16, y = flow_net_res_blocks_2_in_ln_bias_to_fp16)[name = tensor<string, []>("x_285_cast_fp16")];
tensor<fp16, []> var_2594_promoted_to_fp16 = const()[name = tensor<string, []>("op_2594_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
tensor<fp16, [1, 512]> var_2595_cast_fp16 = add(x = var_2578_cast_fp16_1, y = var_2594_promoted_to_fp16)[name = tensor<string, []>("op_2595_cast_fp16")];
tensor<fp16, [1, 512]> var_2596_cast_fp16 = mul(x = x_285_cast_fp16, y = var_2595_cast_fp16)[name = tensor<string, []>("op_2596_cast_fp16")];
tensor<fp16, [1, 512]> input_441_cast_fp16 = add(x = var_2596_cast_fp16, y = var_2578_cast_fp16_0)[name = tensor<string, []>("input_441_cast_fp16")];
tensor<fp16, [1, 512]> linear_169_cast_fp16 = linear(bias = flow_net_res_blocks_2_mlp_0_bias_to_fp16, weight = flow_net_res_blocks_2_mlp_0_weight_to_fp16, x = input_441_cast_fp16)[name = tensor<string, []>("linear_169_cast_fp16")];
tensor<fp16, [1, 512]> input_445_cast_fp16 = silu(x = linear_169_cast_fp16)[name = tensor<string, []>("input_445_cast_fp16")];
tensor<fp16, [1, 512]> linear_170_cast_fp16 = linear(bias = flow_net_res_blocks_2_mlp_2_bias_to_fp16, weight = flow_net_res_blocks_2_mlp_2_weight_to_fp16, x = input_445_cast_fp16)[name = tensor<string, []>("linear_170_cast_fp16")];
tensor<fp16, [1, 512]> var_2607_cast_fp16 = mul(x = var_2578_cast_fp16_2, y = linear_170_cast_fp16)[name = tensor<string, []>("op_2607_cast_fp16")];
tensor<fp16, [1, 512]> x_287_cast_fp16 = add(x = x_281_cast_fp16, y = var_2607_cast_fp16)[name = tensor<string, []>("x_287_cast_fp16")];
tensor<fp16, [1, 1536]> linear_171_cast_fp16 = linear(bias = flow_net_res_blocks_3_adaLN_modulation_1_bias_to_fp16, weight = flow_net_res_blocks_3_adaLN_modulation_1_weight_to_fp16, x = input_423_cast_fp16)[name = tensor<string, []>("linear_171_cast_fp16")];
tensor<int32, [3]> var_2617_split_sizes_0 = const()[name = tensor<string, []>("op_2617_split_sizes_0"), val = tensor<int32, [3]>([512, 512, 512])];
tensor<int32, []> var_2617_axis_0 = const()[name = tensor<string, []>("op_2617_axis_0"), val = tensor<int32, []>(-1)];
tensor<fp16, [1, 512]> var_2617_cast_fp16_0, tensor<fp16, [1, 512]> var_2617_cast_fp16_1, tensor<fp16, [1, 512]> var_2617_cast_fp16_2 = split(axis = var_2617_axis_0, split_sizes = var_2617_split_sizes_0, x = linear_171_cast_fp16)[name = tensor<string, []>("op_2617_cast_fp16")];
tensor<int32, [1]> mean_91_axes_0 = const()[name = tensor<string, []>("mean_91_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> mean_91_keep_dims_0 = const()[name = tensor<string, []>("mean_91_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> mean_91_cast_fp16 = reduce_mean(axes = mean_91_axes_0, keep_dims = mean_91_keep_dims_0, x = x_287_cast_fp16)[name = tensor<string, []>("mean_91_cast_fp16")];
tensor<fp16, [1, 512]> sub_73_cast_fp16 = sub(x = x_287_cast_fp16, y = mean_91_cast_fp16)[name = tensor<string, []>("sub_73_cast_fp16")];
tensor<fp16, [1, 512]> square_59_cast_fp16 = square(x = sub_73_cast_fp16)[name = tensor<string, []>("square_59_cast_fp16")];
tensor<int32, [1]> reduce_mean_119_axes_0 = const()[name = tensor<string, []>("reduce_mean_119_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_119_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_119_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_119_cast_fp16 = reduce_mean(axes = reduce_mean_119_axes_0, keep_dims = reduce_mean_119_keep_dims_0, x = square_59_cast_fp16)[name = tensor<string, []>("reduce_mean_119_cast_fp16")];
tensor<fp16, []> var_2627_to_fp16 = const()[name = tensor<string, []>("op_2627_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 1]> var_2628_cast_fp16 = add(x = reduce_mean_119_cast_fp16, y = var_2627_to_fp16)[name = tensor<string, []>("op_2628_cast_fp16")];
tensor<fp16, [1, 1]> var_2629_cast_fp16 = sqrt(x = var_2628_cast_fp16)[name = tensor<string, []>("op_2629_cast_fp16")];
tensor<fp16, [1, 512]> x_289_cast_fp16 = real_div(x = sub_73_cast_fp16, y = var_2629_cast_fp16)[name = tensor<string, []>("x_289_cast_fp16")];
tensor<fp16, [1, 512]> var_2631_cast_fp16 = mul(x = x_289_cast_fp16, y = flow_net_res_blocks_3_in_ln_weight_to_fp16)[name = tensor<string, []>("op_2631_cast_fp16")];
tensor<fp16, [1, 512]> x_291_cast_fp16 = add(x = var_2631_cast_fp16, y = flow_net_res_blocks_3_in_ln_bias_to_fp16)[name = tensor<string, []>("x_291_cast_fp16")];
tensor<fp16, []> var_2633_promoted_to_fp16 = const()[name = tensor<string, []>("op_2633_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
tensor<fp16, [1, 512]> var_2634_cast_fp16 = add(x = var_2617_cast_fp16_1, y = var_2633_promoted_to_fp16)[name = tensor<string, []>("op_2634_cast_fp16")];
tensor<fp16, [1, 512]> var_2635_cast_fp16 = mul(x = x_291_cast_fp16, y = var_2634_cast_fp16)[name = tensor<string, []>("op_2635_cast_fp16")];
tensor<fp16, [1, 512]> input_449_cast_fp16 = add(x = var_2635_cast_fp16, y = var_2617_cast_fp16_0)[name = tensor<string, []>("input_449_cast_fp16")];
tensor<fp16, [1, 512]> linear_172_cast_fp16 = linear(bias = flow_net_res_blocks_3_mlp_0_bias_to_fp16, weight = flow_net_res_blocks_3_mlp_0_weight_to_fp16, x = input_449_cast_fp16)[name = tensor<string, []>("linear_172_cast_fp16")];
tensor<fp16, [1, 512]> input_453_cast_fp16 = silu(x = linear_172_cast_fp16)[name = tensor<string, []>("input_453_cast_fp16")];
tensor<fp16, [1, 512]> linear_173_cast_fp16 = linear(bias = flow_net_res_blocks_3_mlp_2_bias_to_fp16, weight = flow_net_res_blocks_3_mlp_2_weight_to_fp16, x = input_453_cast_fp16)[name = tensor<string, []>("linear_173_cast_fp16")];
tensor<fp16, [1, 512]> var_2646_cast_fp16 = mul(x = var_2617_cast_fp16_2, y = linear_173_cast_fp16)[name = tensor<string, []>("op_2646_cast_fp16")];
tensor<fp16, [1, 512]> x_293_cast_fp16 = add(x = x_287_cast_fp16, y = var_2646_cast_fp16)[name = tensor<string, []>("x_293_cast_fp16")];
tensor<fp16, [1, 1536]> linear_174_cast_fp16 = linear(bias = flow_net_res_blocks_4_adaLN_modulation_1_bias_to_fp16, weight = flow_net_res_blocks_4_adaLN_modulation_1_weight_to_fp16, x = input_423_cast_fp16)[name = tensor<string, []>("linear_174_cast_fp16")];
tensor<int32, [3]> var_2656_split_sizes_0 = const()[name = tensor<string, []>("op_2656_split_sizes_0"), val = tensor<int32, [3]>([512, 512, 512])];
tensor<int32, []> var_2656_axis_0 = const()[name = tensor<string, []>("op_2656_axis_0"), val = tensor<int32, []>(-1)];
tensor<fp16, [1, 512]> var_2656_cast_fp16_0, tensor<fp16, [1, 512]> var_2656_cast_fp16_1, tensor<fp16, [1, 512]> var_2656_cast_fp16_2 = split(axis = var_2656_axis_0, split_sizes = var_2656_split_sizes_0, x = linear_174_cast_fp16)[name = tensor<string, []>("op_2656_cast_fp16")];
tensor<int32, [1]> mean_93_axes_0 = const()[name = tensor<string, []>("mean_93_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> mean_93_keep_dims_0 = const()[name = tensor<string, []>("mean_93_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> mean_93_cast_fp16 = reduce_mean(axes = mean_93_axes_0, keep_dims = mean_93_keep_dims_0, x = x_293_cast_fp16)[name = tensor<string, []>("mean_93_cast_fp16")];
tensor<fp16, [1, 512]> sub_74_cast_fp16 = sub(x = x_293_cast_fp16, y = mean_93_cast_fp16)[name = tensor<string, []>("sub_74_cast_fp16")];
tensor<fp16, [1, 512]> square_60_cast_fp16 = square(x = sub_74_cast_fp16)[name = tensor<string, []>("square_60_cast_fp16")];
tensor<int32, [1]> reduce_mean_121_axes_0 = const()[name = tensor<string, []>("reduce_mean_121_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_121_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_121_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_121_cast_fp16 = reduce_mean(axes = reduce_mean_121_axes_0, keep_dims = reduce_mean_121_keep_dims_0, x = square_60_cast_fp16)[name = tensor<string, []>("reduce_mean_121_cast_fp16")];
tensor<fp16, []> var_2666_to_fp16 = const()[name = tensor<string, []>("op_2666_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 1]> var_2667_cast_fp16 = add(x = reduce_mean_121_cast_fp16, y = var_2666_to_fp16)[name = tensor<string, []>("op_2667_cast_fp16")];
tensor<fp16, [1, 1]> var_2668_cast_fp16 = sqrt(x = var_2667_cast_fp16)[name = tensor<string, []>("op_2668_cast_fp16")];
tensor<fp16, [1, 512]> x_295_cast_fp16 = real_div(x = sub_74_cast_fp16, y = var_2668_cast_fp16)[name = tensor<string, []>("x_295_cast_fp16")];
tensor<fp16, [1, 512]> var_2670_cast_fp16 = mul(x = x_295_cast_fp16, y = flow_net_res_blocks_4_in_ln_weight_to_fp16)[name = tensor<string, []>("op_2670_cast_fp16")];
tensor<fp16, [1, 512]> x_297_cast_fp16 = add(x = var_2670_cast_fp16, y = flow_net_res_blocks_4_in_ln_bias_to_fp16)[name = tensor<string, []>("x_297_cast_fp16")];
tensor<fp16, []> var_2672_promoted_to_fp16 = const()[name = tensor<string, []>("op_2672_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
tensor<fp16, [1, 512]> var_2673_cast_fp16 = add(x = var_2656_cast_fp16_1, y = var_2672_promoted_to_fp16)[name = tensor<string, []>("op_2673_cast_fp16")];
tensor<fp16, [1, 512]> var_2674_cast_fp16 = mul(x = x_297_cast_fp16, y = var_2673_cast_fp16)[name = tensor<string, []>("op_2674_cast_fp16")];
tensor<fp16, [1, 512]> input_457_cast_fp16 = add(x = var_2674_cast_fp16, y = var_2656_cast_fp16_0)[name = tensor<string, []>("input_457_cast_fp16")];
tensor<fp16, [1, 512]> linear_175_cast_fp16 = linear(bias = flow_net_res_blocks_4_mlp_0_bias_to_fp16, weight = flow_net_res_blocks_4_mlp_0_weight_to_fp16, x = input_457_cast_fp16)[name = tensor<string, []>("linear_175_cast_fp16")];
tensor<fp16, [1, 512]> input_461_cast_fp16 = silu(x = linear_175_cast_fp16)[name = tensor<string, []>("input_461_cast_fp16")];
tensor<fp16, [1, 512]> linear_176_cast_fp16 = linear(bias = flow_net_res_blocks_4_mlp_2_bias_to_fp16, weight = flow_net_res_blocks_4_mlp_2_weight_to_fp16, x = input_461_cast_fp16)[name = tensor<string, []>("linear_176_cast_fp16")];
tensor<fp16, [1, 512]> var_2685_cast_fp16 = mul(x = var_2656_cast_fp16_2, y = linear_176_cast_fp16)[name = tensor<string, []>("op_2685_cast_fp16")];
tensor<fp16, [1, 512]> x_299_cast_fp16 = add(x = x_293_cast_fp16, y = var_2685_cast_fp16)[name = tensor<string, []>("x_299_cast_fp16")];
tensor<fp16, [1, 1536]> linear_177_cast_fp16 = linear(bias = flow_net_res_blocks_5_adaLN_modulation_1_bias_to_fp16, weight = flow_net_res_blocks_5_adaLN_modulation_1_weight_to_fp16, x = input_423_cast_fp16)[name = tensor<string, []>("linear_177_cast_fp16")];
tensor<int32, [3]> var_2695_split_sizes_0 = const()[name = tensor<string, []>("op_2695_split_sizes_0"), val = tensor<int32, [3]>([512, 512, 512])];
tensor<int32, []> var_2695_axis_0 = const()[name = tensor<string, []>("op_2695_axis_0"), val = tensor<int32, []>(-1)];
tensor<fp16, [1, 512]> var_2695_cast_fp16_0, tensor<fp16, [1, 512]> var_2695_cast_fp16_1, tensor<fp16, [1, 512]> var_2695_cast_fp16_2 = split(axis = var_2695_axis_0, split_sizes = var_2695_split_sizes_0, x = linear_177_cast_fp16)[name = tensor<string, []>("op_2695_cast_fp16")];
tensor<int32, [1]> mean_95_axes_0 = const()[name = tensor<string, []>("mean_95_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> mean_95_keep_dims_0 = const()[name = tensor<string, []>("mean_95_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> mean_95_cast_fp16 = reduce_mean(axes = mean_95_axes_0, keep_dims = mean_95_keep_dims_0, x = x_299_cast_fp16)[name = tensor<string, []>("mean_95_cast_fp16")];
tensor<fp16, [1, 512]> sub_75_cast_fp16 = sub(x = x_299_cast_fp16, y = mean_95_cast_fp16)[name = tensor<string, []>("sub_75_cast_fp16")];
tensor<fp16, [1, 512]> square_61_cast_fp16 = square(x = sub_75_cast_fp16)[name = tensor<string, []>("square_61_cast_fp16")];
tensor<int32, [1]> reduce_mean_123_axes_0 = const()[name = tensor<string, []>("reduce_mean_123_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_123_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_123_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_123_cast_fp16 = reduce_mean(axes = reduce_mean_123_axes_0, keep_dims = reduce_mean_123_keep_dims_0, x = square_61_cast_fp16)[name = tensor<string, []>("reduce_mean_123_cast_fp16")];
tensor<fp16, []> var_2705_to_fp16 = const()[name = tensor<string, []>("op_2705_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 1]> var_2706_cast_fp16 = add(x = reduce_mean_123_cast_fp16, y = var_2705_to_fp16)[name = tensor<string, []>("op_2706_cast_fp16")];
tensor<fp16, [1, 1]> var_2707_cast_fp16 = sqrt(x = var_2706_cast_fp16)[name = tensor<string, []>("op_2707_cast_fp16")];
tensor<fp16, [1, 512]> x_301_cast_fp16 = real_div(x = sub_75_cast_fp16, y = var_2707_cast_fp16)[name = tensor<string, []>("x_301_cast_fp16")];
tensor<fp16, [1, 512]> var_2709_cast_fp16 = mul(x = x_301_cast_fp16, y = flow_net_res_blocks_5_in_ln_weight_to_fp16)[name = tensor<string, []>("op_2709_cast_fp16")];
tensor<fp16, [1, 512]> x_303_cast_fp16 = add(x = var_2709_cast_fp16, y = flow_net_res_blocks_5_in_ln_bias_to_fp16)[name = tensor<string, []>("x_303_cast_fp16")];
tensor<fp16, []> var_2711_promoted_to_fp16 = const()[name = tensor<string, []>("op_2711_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
tensor<fp16, [1, 512]> var_2712_cast_fp16 = add(x = var_2695_cast_fp16_1, y = var_2711_promoted_to_fp16)[name = tensor<string, []>("op_2712_cast_fp16")];
tensor<fp16, [1, 512]> var_2713_cast_fp16 = mul(x = x_303_cast_fp16, y = var_2712_cast_fp16)[name = tensor<string, []>("op_2713_cast_fp16")];
tensor<fp16, [1, 512]> input_465_cast_fp16 = add(x = var_2713_cast_fp16, y = var_2695_cast_fp16_0)[name = tensor<string, []>("input_465_cast_fp16")];
tensor<fp16, [1, 512]> linear_178_cast_fp16 = linear(bias = flow_net_res_blocks_5_mlp_0_bias_to_fp16, weight = flow_net_res_blocks_5_mlp_0_weight_to_fp16, x = input_465_cast_fp16)[name = tensor<string, []>("linear_178_cast_fp16")];
tensor<fp16, [1, 512]> input_469_cast_fp16 = silu(x = linear_178_cast_fp16)[name = tensor<string, []>("input_469_cast_fp16")];
tensor<fp16, [1, 512]> linear_179_cast_fp16 = linear(bias = flow_net_res_blocks_5_mlp_2_bias_to_fp16, weight = flow_net_res_blocks_5_mlp_2_weight_to_fp16, x = input_469_cast_fp16)[name = tensor<string, []>("linear_179_cast_fp16")];
tensor<fp16, [1, 512]> var_2724_cast_fp16 = mul(x = var_2695_cast_fp16_2, y = linear_179_cast_fp16)[name = tensor<string, []>("op_2724_cast_fp16")];
tensor<fp16, [1, 512]> x_305_cast_fp16 = add(x = x_299_cast_fp16, y = var_2724_cast_fp16)[name = tensor<string, []>("x_305_cast_fp16")];
tensor<fp16, [1, 1024]> linear_180_cast_fp16 = linear(bias = flow_net_final_layer_adaLN_modulation_1_bias_to_fp16, weight = flow_net_final_layer_adaLN_modulation_1_weight_to_fp16, x = input_423_cast_fp16)[name = tensor<string, []>("linear_180_cast_fp16")];
tensor<int32, [2]> var_2733_split_sizes_0 = const()[name = tensor<string, []>("op_2733_split_sizes_0"), val = tensor<int32, [2]>([512, 512])];
tensor<int32, []> var_2733_axis_0 = const()[name = tensor<string, []>("op_2733_axis_0"), val = tensor<int32, []>(-1)];
tensor<fp16, [1, 512]> var_2733_cast_fp16_0, tensor<fp16, [1, 512]> var_2733_cast_fp16_1 = split(axis = var_2733_axis_0, split_sizes = var_2733_split_sizes_0, x = linear_180_cast_fp16)[name = tensor<string, []>("op_2733_cast_fp16")];
tensor<int32, [1]> mean_97_axes_0 = const()[name = tensor<string, []>("mean_97_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> mean_97_keep_dims_0 = const()[name = tensor<string, []>("mean_97_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> mean_97_cast_fp16 = reduce_mean(axes = mean_97_axes_0, keep_dims = mean_97_keep_dims_0, x = x_305_cast_fp16)[name = tensor<string, []>("mean_97_cast_fp16")];
tensor<fp16, [1, 512]> sub_76_cast_fp16 = sub(x = x_305_cast_fp16, y = mean_97_cast_fp16)[name = tensor<string, []>("sub_76_cast_fp16")];
tensor<fp16, [1, 512]> square_62_cast_fp16 = square(x = sub_76_cast_fp16)[name = tensor<string, []>("square_62_cast_fp16")];
tensor<int32, [1]> reduce_mean_125_axes_0 = const()[name = tensor<string, []>("reduce_mean_125_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_125_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_125_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_125_cast_fp16 = reduce_mean(axes = reduce_mean_125_axes_0, keep_dims = reduce_mean_125_keep_dims_0, x = square_62_cast_fp16)[name = tensor<string, []>("reduce_mean_125_cast_fp16")];
tensor<fp16, []> var_2740_to_fp16 = const()[name = tensor<string, []>("op_2740_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 1]> var_2741_cast_fp16 = add(x = reduce_mean_125_cast_fp16, y = var_2740_to_fp16)[name = tensor<string, []>("op_2741_cast_fp16")];
tensor<fp16, [1, 1]> var_2742_cast_fp16 = sqrt(x = var_2741_cast_fp16)[name = tensor<string, []>("op_2742_cast_fp16")];
tensor<fp16, [1, 512]> x_307_cast_fp16 = real_div(x = sub_76_cast_fp16, y = var_2742_cast_fp16)[name = tensor<string, []>("x_307_cast_fp16")];
tensor<fp16, []> var_2744_promoted_to_fp16 = const()[name = tensor<string, []>("op_2744_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
tensor<fp16, [1, 512]> var_2745_cast_fp16 = add(x = var_2733_cast_fp16_1, y = var_2744_promoted_to_fp16)[name = tensor<string, []>("op_2745_cast_fp16")];
tensor<fp16, [1, 512]> var_2746_cast_fp16 = mul(x = x_307_cast_fp16, y = var_2745_cast_fp16)[name = tensor<string, []>("op_2746_cast_fp16")];
tensor<fp16, [1, 512]> input_473_cast_fp16 = add(x = var_2746_cast_fp16, y = var_2733_cast_fp16_0)[name = tensor<string, []>("input_473_cast_fp16")];
tensor<fp16, [1, 32]> linear_181_cast_fp16 = linear(bias = flow_net_final_layer_linear_bias_to_fp16, weight = flow_net_final_layer_linear_weight_to_fp16, x = input_473_cast_fp16)[name = tensor<string, []>("linear_181_cast_fp16")];
tensor<fp16, []> var_2757_to_fp16 = const()[name = tensor<string, []>("op_2757_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 32]> var_2758_cast_fp16 = mul(x = linear_181_cast_fp16, y = var_2757_to_fp16)[name = tensor<string, []>("op_2758_cast_fp16")];
tensor<fp16, [1, 32]> input_475_cast_fp16 = add(x = input_407_cast_fp16, y = var_2758_cast_fp16)[name = tensor<string, []>("input_475_cast_fp16")];
tensor<fp16, [1, 512]> linear_182_cast_fp16 = linear(bias = flow_net_input_proj_bias_to_fp16, weight = flow_net_input_proj_weight_to_fp16, x = input_475_cast_fp16)[name = tensor<string, []>("linear_182_cast_fp16")];
tensor<fp16, [1, 512]> input_479_to_fp16 = const()[name = tensor<string, []>("input_479_to_fp16"), val = tensor<fp16, [1, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19010432)))];
tensor<fp16, [1, 512]> input_481_cast_fp16 = silu(x = input_479_to_fp16)[name = tensor<string, []>("input_481_cast_fp16")];
tensor<fp16, [1, 512]> linear_184_cast_fp16 = linear(bias = flow_net_time_embed_0_mlp_2_bias_to_fp16, weight = flow_net_time_embed_0_mlp_2_weight_to_fp16, x = input_481_cast_fp16)[name = tensor<string, []>("linear_184_cast_fp16")];
tensor<int32, [1]> reduce_mean_126_axes_0 = const()[name = tensor<string, []>("reduce_mean_126_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_126_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_126_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_126_cast_fp16 = reduce_mean(axes = reduce_mean_126_axes_0, keep_dims = reduce_mean_126_keep_dims_0, x = linear_184_cast_fp16)[name = tensor<string, []>("reduce_mean_126_cast_fp16")];
tensor<fp16, [1, 512]> sub_77_cast_fp16 = sub(x = linear_184_cast_fp16, y = reduce_mean_126_cast_fp16)[name = tensor<string, []>("sub_77_cast_fp16")];
tensor<fp16, [1, 512]> square_63_cast_fp16 = square(x = sub_77_cast_fp16)[name = tensor<string, []>("square_63_cast_fp16")];
tensor<int32, [1]> reduce_mean_127_axes_0 = const()[name = tensor<string, []>("reduce_mean_127_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_127_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_127_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_127_cast_fp16 = reduce_mean(axes = reduce_mean_127_axes_0, keep_dims = reduce_mean_127_keep_dims_0, x = square_63_cast_fp16)[name = tensor<string, []>("reduce_mean_127_cast_fp16")];
tensor<fp16, []> real_div_14_to_fp16 = const()[name = tensor<string, []>("real_div_14_to_fp16"), val = tensor<fp16, []>(0x1.008p+0)];
tensor<fp16, [1, 1]> mul_14_cast_fp16 = mul(x = reduce_mean_127_cast_fp16, y = real_div_14_to_fp16)[name = tensor<string, []>("mul_14_cast_fp16")];
tensor<fp16, []> var_2824_to_fp16 = const()[name = tensor<string, []>("op_2824_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1]> var_127_cast_fp16_0 = add(x = mul_14_cast_fp16, y = var_2824_to_fp16)[name = tensor<string, []>("var_127_cast_fp16")];
tensor<fp32, []> var_2827_epsilon_0 = const()[name = tensor<string, []>("op_2827_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
tensor<fp16, [1, 1]> var_2827_cast_fp16 = rsqrt(epsilon = var_2827_epsilon_0, x = var_127_cast_fp16_0)[name = tensor<string, []>("op_2827_cast_fp16")];
tensor<fp16, [1, 512]> var_2828_cast_fp16 = mul(x = const_3_to_fp16, y = var_2827_cast_fp16)[name = tensor<string, []>("op_2828_cast_fp16")];
tensor<fp16, [1, 512]> var_2829_cast_fp16 = mul(x = linear_184_cast_fp16, y = var_2828_cast_fp16)[name = tensor<string, []>("op_2829_cast_fp16")];
tensor<fp16, [1, 512]> input_485_to_fp16 = const()[name = tensor<string, []>("input_485_to_fp16"), val = tensor<fp16, [1, 512]>(BLOBFILE(path = tensor<string, []>("@model_path/weights/weight.bin"), offset = tensor<uint64, []>(19011520)))];
tensor<fp16, [1, 512]> input_487_cast_fp16 = silu(x = input_485_to_fp16)[name = tensor<string, []>("input_487_cast_fp16")];
tensor<fp16, [1, 512]> linear_186_cast_fp16 = linear(bias = flow_net_time_embed_1_mlp_2_bias_to_fp16, weight = flow_net_time_embed_1_mlp_2_weight_to_fp16, x = input_487_cast_fp16)[name = tensor<string, []>("linear_186_cast_fp16")];
tensor<int32, [1]> reduce_mean_128_axes_0 = const()[name = tensor<string, []>("reduce_mean_128_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_128_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_128_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_128_cast_fp16 = reduce_mean(axes = reduce_mean_128_axes_0, keep_dims = reduce_mean_128_keep_dims_0, x = linear_186_cast_fp16)[name = tensor<string, []>("reduce_mean_128_cast_fp16")];
tensor<fp16, [1, 512]> sub_79_cast_fp16 = sub(x = linear_186_cast_fp16, y = reduce_mean_128_cast_fp16)[name = tensor<string, []>("sub_79_cast_fp16")];
tensor<fp16, [1, 512]> square_64_cast_fp16 = square(x = sub_79_cast_fp16)[name = tensor<string, []>("square_64_cast_fp16")];
tensor<int32, [1]> reduce_mean_129_axes_0 = const()[name = tensor<string, []>("reduce_mean_129_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_129_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_129_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_129_cast_fp16 = reduce_mean(axes = reduce_mean_129_axes_0, keep_dims = reduce_mean_129_keep_dims_0, x = square_64_cast_fp16)[name = tensor<string, []>("reduce_mean_129_cast_fp16")];
tensor<fp16, []> real_div_15_to_fp16 = const()[name = tensor<string, []>("real_div_15_to_fp16"), val = tensor<fp16, []>(0x1.008p+0)];
tensor<fp16, [1, 1]> mul_15_cast_fp16 = mul(x = reduce_mean_129_cast_fp16, y = real_div_15_to_fp16)[name = tensor<string, []>("mul_15_cast_fp16")];
tensor<fp16, []> var_2851_to_fp16 = const()[name = tensor<string, []>("op_2851_to_fp16"), val = tensor<fp16, []>(0x1.5p-17)];
tensor<fp16, [1, 1]> var_129_cast_fp16 = add(x = mul_15_cast_fp16, y = var_2851_to_fp16)[name = tensor<string, []>("var_129_cast_fp16")];
tensor<fp32, []> var_2854_epsilon_0 = const()[name = tensor<string, []>("op_2854_epsilon_0"), val = tensor<fp32, []>(0x1.197998p-40)];
tensor<fp16, [1, 1]> var_2854_cast_fp16 = rsqrt(epsilon = var_2854_epsilon_0, x = var_129_cast_fp16)[name = tensor<string, []>("op_2854_cast_fp16")];
tensor<fp16, [1, 512]> var_2855_cast_fp16 = mul(x = const_5_to_fp16, y = var_2854_cast_fp16)[name = tensor<string, []>("op_2855_cast_fp16")];
tensor<fp16, [1, 512]> var_2856_cast_fp16 = mul(x = linear_186_cast_fp16, y = var_2855_cast_fp16)[name = tensor<string, []>("op_2856_cast_fp16")];
tensor<fp16, [1, 512]> var_2858_cast_fp16 = add(x = var_2829_cast_fp16, y = var_2856_cast_fp16)[name = tensor<string, []>("op_2858_cast_fp16")];
tensor<fp16, []> _inversed_t_combined_y_0_to_fp16 = const()[name = tensor<string, []>("_inversed_t_combined_y_0_to_fp16"), val = tensor<fp16, []>(0x1p-1)];
tensor<fp16, [1, 512]> _inversed_t_combined_cast_fp16 = mul(x = var_2858_cast_fp16, y = _inversed_t_combined_y_0_to_fp16)[name = tensor<string, []>("_inversed_t_combined_cast_fp16")];
tensor<fp16, [1, 512]> input_489_cast_fp16 = add(x = _inversed_t_combined_cast_fp16, y = linear_5_cast_fp16)[name = tensor<string, []>("input_489_cast_fp16")];
tensor<fp16, [1, 512]> input_491_cast_fp16 = silu(x = input_489_cast_fp16)[name = tensor<string, []>("input_491_cast_fp16")];
tensor<fp16, [1, 1536]> linear_188_cast_fp16 = linear(bias = flow_net_res_blocks_0_adaLN_modulation_1_bias_to_fp16, weight = flow_net_res_blocks_0_adaLN_modulation_1_weight_to_fp16, x = input_491_cast_fp16)[name = tensor<string, []>("linear_188_cast_fp16")];
tensor<int32, [3]> var_2873_split_sizes_0 = const()[name = tensor<string, []>("op_2873_split_sizes_0"), val = tensor<int32, [3]>([512, 512, 512])];
tensor<int32, []> var_2873_axis_0 = const()[name = tensor<string, []>("op_2873_axis_0"), val = tensor<int32, []>(-1)];
tensor<fp16, [1, 512]> var_2873_cast_fp16_0, tensor<fp16, [1, 512]> var_2873_cast_fp16_1, tensor<fp16, [1, 512]> var_2873_cast_fp16_2 = split(axis = var_2873_axis_0, split_sizes = var_2873_split_sizes_0, x = linear_188_cast_fp16)[name = tensor<string, []>("op_2873_cast_fp16")];
tensor<int32, [1]> mean_99_axes_0 = const()[name = tensor<string, []>("mean_99_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> mean_99_keep_dims_0 = const()[name = tensor<string, []>("mean_99_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> mean_99_cast_fp16 = reduce_mean(axes = mean_99_axes_0, keep_dims = mean_99_keep_dims_0, x = linear_182_cast_fp16)[name = tensor<string, []>("mean_99_cast_fp16")];
tensor<fp16, [1, 512]> sub_81_cast_fp16 = sub(x = linear_182_cast_fp16, y = mean_99_cast_fp16)[name = tensor<string, []>("sub_81_cast_fp16")];
tensor<fp16, [1, 512]> square_65_cast_fp16 = square(x = sub_81_cast_fp16)[name = tensor<string, []>("square_65_cast_fp16")];
tensor<int32, [1]> reduce_mean_131_axes_0 = const()[name = tensor<string, []>("reduce_mean_131_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_131_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_131_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_131_cast_fp16 = reduce_mean(axes = reduce_mean_131_axes_0, keep_dims = reduce_mean_131_keep_dims_0, x = square_65_cast_fp16)[name = tensor<string, []>("reduce_mean_131_cast_fp16")];
tensor<fp16, []> var_2883_to_fp16 = const()[name = tensor<string, []>("op_2883_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 1]> var_2884_cast_fp16 = add(x = reduce_mean_131_cast_fp16, y = var_2883_to_fp16)[name = tensor<string, []>("op_2884_cast_fp16")];
tensor<fp16, [1, 1]> var_2885_cast_fp16 = sqrt(x = var_2884_cast_fp16)[name = tensor<string, []>("op_2885_cast_fp16")];
tensor<fp16, [1, 512]> x_315_cast_fp16 = real_div(x = sub_81_cast_fp16, y = var_2885_cast_fp16)[name = tensor<string, []>("x_315_cast_fp16")];
tensor<fp16, [1, 512]> var_2887_cast_fp16 = mul(x = x_315_cast_fp16, y = flow_net_res_blocks_0_in_ln_weight_to_fp16)[name = tensor<string, []>("op_2887_cast_fp16")];
tensor<fp16, [1, 512]> x_317_cast_fp16 = add(x = var_2887_cast_fp16, y = flow_net_res_blocks_0_in_ln_bias_to_fp16)[name = tensor<string, []>("x_317_cast_fp16")];
tensor<fp16, []> var_2889_promoted_to_fp16 = const()[name = tensor<string, []>("op_2889_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
tensor<fp16, [1, 512]> var_2890_cast_fp16 = add(x = var_2873_cast_fp16_1, y = var_2889_promoted_to_fp16)[name = tensor<string, []>("op_2890_cast_fp16")];
tensor<fp16, [1, 512]> var_2891_cast_fp16 = mul(x = x_317_cast_fp16, y = var_2890_cast_fp16)[name = tensor<string, []>("op_2891_cast_fp16")];
tensor<fp16, [1, 512]> input_493_cast_fp16 = add(x = var_2891_cast_fp16, y = var_2873_cast_fp16_0)[name = tensor<string, []>("input_493_cast_fp16")];
tensor<fp16, [1, 512]> linear_189_cast_fp16 = linear(bias = flow_net_res_blocks_0_mlp_0_bias_to_fp16, weight = flow_net_res_blocks_0_mlp_0_weight_to_fp16, x = input_493_cast_fp16)[name = tensor<string, []>("linear_189_cast_fp16")];
tensor<fp16, [1, 512]> input_497_cast_fp16 = silu(x = linear_189_cast_fp16)[name = tensor<string, []>("input_497_cast_fp16")];
tensor<fp16, [1, 512]> linear_190_cast_fp16 = linear(bias = flow_net_res_blocks_0_mlp_2_bias_to_fp16, weight = flow_net_res_blocks_0_mlp_2_weight_to_fp16, x = input_497_cast_fp16)[name = tensor<string, []>("linear_190_cast_fp16")];
tensor<fp16, [1, 512]> var_2902_cast_fp16 = mul(x = var_2873_cast_fp16_2, y = linear_190_cast_fp16)[name = tensor<string, []>("op_2902_cast_fp16")];
tensor<fp16, [1, 512]> x_319_cast_fp16 = add(x = linear_182_cast_fp16, y = var_2902_cast_fp16)[name = tensor<string, []>("x_319_cast_fp16")];
tensor<fp16, [1, 1536]> linear_191_cast_fp16 = linear(bias = flow_net_res_blocks_1_adaLN_modulation_1_bias_to_fp16, weight = flow_net_res_blocks_1_adaLN_modulation_1_weight_to_fp16, x = input_491_cast_fp16)[name = tensor<string, []>("linear_191_cast_fp16")];
tensor<int32, [3]> var_2912_split_sizes_0 = const()[name = tensor<string, []>("op_2912_split_sizes_0"), val = tensor<int32, [3]>([512, 512, 512])];
tensor<int32, []> var_2912_axis_0 = const()[name = tensor<string, []>("op_2912_axis_0"), val = tensor<int32, []>(-1)];
tensor<fp16, [1, 512]> var_2912_cast_fp16_0, tensor<fp16, [1, 512]> var_2912_cast_fp16_1, tensor<fp16, [1, 512]> var_2912_cast_fp16_2 = split(axis = var_2912_axis_0, split_sizes = var_2912_split_sizes_0, x = linear_191_cast_fp16)[name = tensor<string, []>("op_2912_cast_fp16")];
tensor<int32, [1]> mean_101_axes_0 = const()[name = tensor<string, []>("mean_101_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> mean_101_keep_dims_0 = const()[name = tensor<string, []>("mean_101_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> mean_101_cast_fp16 = reduce_mean(axes = mean_101_axes_0, keep_dims = mean_101_keep_dims_0, x = x_319_cast_fp16)[name = tensor<string, []>("mean_101_cast_fp16")];
tensor<fp16, [1, 512]> sub_82_cast_fp16 = sub(x = x_319_cast_fp16, y = mean_101_cast_fp16)[name = tensor<string, []>("sub_82_cast_fp16")];
tensor<fp16, [1, 512]> square_66_cast_fp16 = square(x = sub_82_cast_fp16)[name = tensor<string, []>("square_66_cast_fp16")];
tensor<int32, [1]> reduce_mean_133_axes_0 = const()[name = tensor<string, []>("reduce_mean_133_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_133_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_133_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_133_cast_fp16 = reduce_mean(axes = reduce_mean_133_axes_0, keep_dims = reduce_mean_133_keep_dims_0, x = square_66_cast_fp16)[name = tensor<string, []>("reduce_mean_133_cast_fp16")];
tensor<fp16, []> var_2922_to_fp16 = const()[name = tensor<string, []>("op_2922_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 1]> var_2923_cast_fp16 = add(x = reduce_mean_133_cast_fp16, y = var_2922_to_fp16)[name = tensor<string, []>("op_2923_cast_fp16")];
tensor<fp16, [1, 1]> var_2924_cast_fp16 = sqrt(x = var_2923_cast_fp16)[name = tensor<string, []>("op_2924_cast_fp16")];
tensor<fp16, [1, 512]> x_321_cast_fp16 = real_div(x = sub_82_cast_fp16, y = var_2924_cast_fp16)[name = tensor<string, []>("x_321_cast_fp16")];
tensor<fp16, [1, 512]> var_2926_cast_fp16 = mul(x = x_321_cast_fp16, y = flow_net_res_blocks_1_in_ln_weight_to_fp16)[name = tensor<string, []>("op_2926_cast_fp16")];
tensor<fp16, [1, 512]> x_323_cast_fp16 = add(x = var_2926_cast_fp16, y = flow_net_res_blocks_1_in_ln_bias_to_fp16)[name = tensor<string, []>("x_323_cast_fp16")];
tensor<fp16, []> var_2928_promoted_to_fp16 = const()[name = tensor<string, []>("op_2928_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
tensor<fp16, [1, 512]> var_2929_cast_fp16 = add(x = var_2912_cast_fp16_1, y = var_2928_promoted_to_fp16)[name = tensor<string, []>("op_2929_cast_fp16")];
tensor<fp16, [1, 512]> var_2930_cast_fp16 = mul(x = x_323_cast_fp16, y = var_2929_cast_fp16)[name = tensor<string, []>("op_2930_cast_fp16")];
tensor<fp16, [1, 512]> input_501_cast_fp16 = add(x = var_2930_cast_fp16, y = var_2912_cast_fp16_0)[name = tensor<string, []>("input_501_cast_fp16")];
tensor<fp16, [1, 512]> linear_192_cast_fp16 = linear(bias = flow_net_res_blocks_1_mlp_0_bias_to_fp16, weight = flow_net_res_blocks_1_mlp_0_weight_to_fp16, x = input_501_cast_fp16)[name = tensor<string, []>("linear_192_cast_fp16")];
tensor<fp16, [1, 512]> input_505_cast_fp16 = silu(x = linear_192_cast_fp16)[name = tensor<string, []>("input_505_cast_fp16")];
tensor<fp16, [1, 512]> linear_193_cast_fp16 = linear(bias = flow_net_res_blocks_1_mlp_2_bias_to_fp16, weight = flow_net_res_blocks_1_mlp_2_weight_to_fp16, x = input_505_cast_fp16)[name = tensor<string, []>("linear_193_cast_fp16")];
tensor<fp16, [1, 512]> var_2941_cast_fp16 = mul(x = var_2912_cast_fp16_2, y = linear_193_cast_fp16)[name = tensor<string, []>("op_2941_cast_fp16")];
tensor<fp16, [1, 512]> x_325_cast_fp16 = add(x = x_319_cast_fp16, y = var_2941_cast_fp16)[name = tensor<string, []>("x_325_cast_fp16")];
tensor<fp16, [1, 1536]> linear_194_cast_fp16 = linear(bias = flow_net_res_blocks_2_adaLN_modulation_1_bias_to_fp16, weight = flow_net_res_blocks_2_adaLN_modulation_1_weight_to_fp16, x = input_491_cast_fp16)[name = tensor<string, []>("linear_194_cast_fp16")];
tensor<int32, [3]> var_2951_split_sizes_0 = const()[name = tensor<string, []>("op_2951_split_sizes_0"), val = tensor<int32, [3]>([512, 512, 512])];
tensor<int32, []> var_2951_axis_0 = const()[name = tensor<string, []>("op_2951_axis_0"), val = tensor<int32, []>(-1)];
tensor<fp16, [1, 512]> var_2951_cast_fp16_0, tensor<fp16, [1, 512]> var_2951_cast_fp16_1, tensor<fp16, [1, 512]> var_2951_cast_fp16_2 = split(axis = var_2951_axis_0, split_sizes = var_2951_split_sizes_0, x = linear_194_cast_fp16)[name = tensor<string, []>("op_2951_cast_fp16")];
tensor<int32, [1]> mean_103_axes_0 = const()[name = tensor<string, []>("mean_103_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> mean_103_keep_dims_0 = const()[name = tensor<string, []>("mean_103_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> mean_103_cast_fp16 = reduce_mean(axes = mean_103_axes_0, keep_dims = mean_103_keep_dims_0, x = x_325_cast_fp16)[name = tensor<string, []>("mean_103_cast_fp16")];
tensor<fp16, [1, 512]> sub_83_cast_fp16 = sub(x = x_325_cast_fp16, y = mean_103_cast_fp16)[name = tensor<string, []>("sub_83_cast_fp16")];
tensor<fp16, [1, 512]> square_67_cast_fp16 = square(x = sub_83_cast_fp16)[name = tensor<string, []>("square_67_cast_fp16")];
tensor<int32, [1]> reduce_mean_135_axes_0 = const()[name = tensor<string, []>("reduce_mean_135_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_135_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_135_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_135_cast_fp16 = reduce_mean(axes = reduce_mean_135_axes_0, keep_dims = reduce_mean_135_keep_dims_0, x = square_67_cast_fp16)[name = tensor<string, []>("reduce_mean_135_cast_fp16")];
tensor<fp16, []> var_2961_to_fp16 = const()[name = tensor<string, []>("op_2961_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 1]> var_2962_cast_fp16 = add(x = reduce_mean_135_cast_fp16, y = var_2961_to_fp16)[name = tensor<string, []>("op_2962_cast_fp16")];
tensor<fp16, [1, 1]> var_2963_cast_fp16 = sqrt(x = var_2962_cast_fp16)[name = tensor<string, []>("op_2963_cast_fp16")];
tensor<fp16, [1, 512]> x_327_cast_fp16 = real_div(x = sub_83_cast_fp16, y = var_2963_cast_fp16)[name = tensor<string, []>("x_327_cast_fp16")];
tensor<fp16, [1, 512]> var_2965_cast_fp16 = mul(x = x_327_cast_fp16, y = flow_net_res_blocks_2_in_ln_weight_to_fp16)[name = tensor<string, []>("op_2965_cast_fp16")];
tensor<fp16, [1, 512]> x_329_cast_fp16 = add(x = var_2965_cast_fp16, y = flow_net_res_blocks_2_in_ln_bias_to_fp16)[name = tensor<string, []>("x_329_cast_fp16")];
tensor<fp16, []> var_2967_promoted_to_fp16 = const()[name = tensor<string, []>("op_2967_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
tensor<fp16, [1, 512]> var_2968_cast_fp16 = add(x = var_2951_cast_fp16_1, y = var_2967_promoted_to_fp16)[name = tensor<string, []>("op_2968_cast_fp16")];
tensor<fp16, [1, 512]> var_2969_cast_fp16 = mul(x = x_329_cast_fp16, y = var_2968_cast_fp16)[name = tensor<string, []>("op_2969_cast_fp16")];
tensor<fp16, [1, 512]> input_509_cast_fp16 = add(x = var_2969_cast_fp16, y = var_2951_cast_fp16_0)[name = tensor<string, []>("input_509_cast_fp16")];
tensor<fp16, [1, 512]> linear_195_cast_fp16 = linear(bias = flow_net_res_blocks_2_mlp_0_bias_to_fp16, weight = flow_net_res_blocks_2_mlp_0_weight_to_fp16, x = input_509_cast_fp16)[name = tensor<string, []>("linear_195_cast_fp16")];
tensor<fp16, [1, 512]> input_513_cast_fp16 = silu(x = linear_195_cast_fp16)[name = tensor<string, []>("input_513_cast_fp16")];
tensor<fp16, [1, 512]> linear_196_cast_fp16 = linear(bias = flow_net_res_blocks_2_mlp_2_bias_to_fp16, weight = flow_net_res_blocks_2_mlp_2_weight_to_fp16, x = input_513_cast_fp16)[name = tensor<string, []>("linear_196_cast_fp16")];
tensor<fp16, [1, 512]> var_2980_cast_fp16 = mul(x = var_2951_cast_fp16_2, y = linear_196_cast_fp16)[name = tensor<string, []>("op_2980_cast_fp16")];
tensor<fp16, [1, 512]> x_331_cast_fp16 = add(x = x_325_cast_fp16, y = var_2980_cast_fp16)[name = tensor<string, []>("x_331_cast_fp16")];
tensor<fp16, [1, 1536]> linear_197_cast_fp16 = linear(bias = flow_net_res_blocks_3_adaLN_modulation_1_bias_to_fp16, weight = flow_net_res_blocks_3_adaLN_modulation_1_weight_to_fp16, x = input_491_cast_fp16)[name = tensor<string, []>("linear_197_cast_fp16")];
tensor<int32, [3]> var_2990_split_sizes_0 = const()[name = tensor<string, []>("op_2990_split_sizes_0"), val = tensor<int32, [3]>([512, 512, 512])];
tensor<int32, []> var_2990_axis_0 = const()[name = tensor<string, []>("op_2990_axis_0"), val = tensor<int32, []>(-1)];
tensor<fp16, [1, 512]> var_2990_cast_fp16_0, tensor<fp16, [1, 512]> var_2990_cast_fp16_1, tensor<fp16, [1, 512]> var_2990_cast_fp16_2 = split(axis = var_2990_axis_0, split_sizes = var_2990_split_sizes_0, x = linear_197_cast_fp16)[name = tensor<string, []>("op_2990_cast_fp16")];
tensor<int32, [1]> mean_105_axes_0 = const()[name = tensor<string, []>("mean_105_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> mean_105_keep_dims_0 = const()[name = tensor<string, []>("mean_105_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> mean_105_cast_fp16 = reduce_mean(axes = mean_105_axes_0, keep_dims = mean_105_keep_dims_0, x = x_331_cast_fp16)[name = tensor<string, []>("mean_105_cast_fp16")];
tensor<fp16, [1, 512]> sub_84_cast_fp16 = sub(x = x_331_cast_fp16, y = mean_105_cast_fp16)[name = tensor<string, []>("sub_84_cast_fp16")];
tensor<fp16, [1, 512]> square_68_cast_fp16 = square(x = sub_84_cast_fp16)[name = tensor<string, []>("square_68_cast_fp16")];
tensor<int32, [1]> reduce_mean_137_axes_0 = const()[name = tensor<string, []>("reduce_mean_137_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_137_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_137_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_137_cast_fp16 = reduce_mean(axes = reduce_mean_137_axes_0, keep_dims = reduce_mean_137_keep_dims_0, x = square_68_cast_fp16)[name = tensor<string, []>("reduce_mean_137_cast_fp16")];
tensor<fp16, []> var_3000_to_fp16 = const()[name = tensor<string, []>("op_3000_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 1]> var_3001_cast_fp16 = add(x = reduce_mean_137_cast_fp16, y = var_3000_to_fp16)[name = tensor<string, []>("op_3001_cast_fp16")];
tensor<fp16, [1, 1]> var_3002_cast_fp16 = sqrt(x = var_3001_cast_fp16)[name = tensor<string, []>("op_3002_cast_fp16")];
tensor<fp16, [1, 512]> x_333_cast_fp16 = real_div(x = sub_84_cast_fp16, y = var_3002_cast_fp16)[name = tensor<string, []>("x_333_cast_fp16")];
tensor<fp16, [1, 512]> var_3004_cast_fp16 = mul(x = x_333_cast_fp16, y = flow_net_res_blocks_3_in_ln_weight_to_fp16)[name = tensor<string, []>("op_3004_cast_fp16")];
tensor<fp16, [1, 512]> x_335_cast_fp16 = add(x = var_3004_cast_fp16, y = flow_net_res_blocks_3_in_ln_bias_to_fp16)[name = tensor<string, []>("x_335_cast_fp16")];
tensor<fp16, []> var_3006_promoted_to_fp16 = const()[name = tensor<string, []>("op_3006_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
tensor<fp16, [1, 512]> var_3007_cast_fp16 = add(x = var_2990_cast_fp16_1, y = var_3006_promoted_to_fp16)[name = tensor<string, []>("op_3007_cast_fp16")];
tensor<fp16, [1, 512]> var_3008_cast_fp16 = mul(x = x_335_cast_fp16, y = var_3007_cast_fp16)[name = tensor<string, []>("op_3008_cast_fp16")];
tensor<fp16, [1, 512]> input_517_cast_fp16 = add(x = var_3008_cast_fp16, y = var_2990_cast_fp16_0)[name = tensor<string, []>("input_517_cast_fp16")];
tensor<fp16, [1, 512]> linear_198_cast_fp16 = linear(bias = flow_net_res_blocks_3_mlp_0_bias_to_fp16, weight = flow_net_res_blocks_3_mlp_0_weight_to_fp16, x = input_517_cast_fp16)[name = tensor<string, []>("linear_198_cast_fp16")];
tensor<fp16, [1, 512]> input_521_cast_fp16 = silu(x = linear_198_cast_fp16)[name = tensor<string, []>("input_521_cast_fp16")];
tensor<fp16, [1, 512]> linear_199_cast_fp16 = linear(bias = flow_net_res_blocks_3_mlp_2_bias_to_fp16, weight = flow_net_res_blocks_3_mlp_2_weight_to_fp16, x = input_521_cast_fp16)[name = tensor<string, []>("linear_199_cast_fp16")];
tensor<fp16, [1, 512]> var_3019_cast_fp16 = mul(x = var_2990_cast_fp16_2, y = linear_199_cast_fp16)[name = tensor<string, []>("op_3019_cast_fp16")];
tensor<fp16, [1, 512]> x_337_cast_fp16 = add(x = x_331_cast_fp16, y = var_3019_cast_fp16)[name = tensor<string, []>("x_337_cast_fp16")];
tensor<fp16, [1, 1536]> linear_200_cast_fp16 = linear(bias = flow_net_res_blocks_4_adaLN_modulation_1_bias_to_fp16, weight = flow_net_res_blocks_4_adaLN_modulation_1_weight_to_fp16, x = input_491_cast_fp16)[name = tensor<string, []>("linear_200_cast_fp16")];
tensor<int32, [3]> var_3029_split_sizes_0 = const()[name = tensor<string, []>("op_3029_split_sizes_0"), val = tensor<int32, [3]>([512, 512, 512])];
tensor<int32, []> var_3029_axis_0 = const()[name = tensor<string, []>("op_3029_axis_0"), val = tensor<int32, []>(-1)];
tensor<fp16, [1, 512]> var_3029_cast_fp16_0, tensor<fp16, [1, 512]> var_3029_cast_fp16_1, tensor<fp16, [1, 512]> var_3029_cast_fp16_2 = split(axis = var_3029_axis_0, split_sizes = var_3029_split_sizes_0, x = linear_200_cast_fp16)[name = tensor<string, []>("op_3029_cast_fp16")];
tensor<int32, [1]> mean_107_axes_0 = const()[name = tensor<string, []>("mean_107_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> mean_107_keep_dims_0 = const()[name = tensor<string, []>("mean_107_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> mean_107_cast_fp16 = reduce_mean(axes = mean_107_axes_0, keep_dims = mean_107_keep_dims_0, x = x_337_cast_fp16)[name = tensor<string, []>("mean_107_cast_fp16")];
tensor<fp16, [1, 512]> sub_85_cast_fp16 = sub(x = x_337_cast_fp16, y = mean_107_cast_fp16)[name = tensor<string, []>("sub_85_cast_fp16")];
tensor<fp16, [1, 512]> square_69_cast_fp16 = square(x = sub_85_cast_fp16)[name = tensor<string, []>("square_69_cast_fp16")];
tensor<int32, [1]> reduce_mean_139_axes_0 = const()[name = tensor<string, []>("reduce_mean_139_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_139_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_139_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_139_cast_fp16 = reduce_mean(axes = reduce_mean_139_axes_0, keep_dims = reduce_mean_139_keep_dims_0, x = square_69_cast_fp16)[name = tensor<string, []>("reduce_mean_139_cast_fp16")];
tensor<fp16, []> var_3039_to_fp16 = const()[name = tensor<string, []>("op_3039_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 1]> var_3040_cast_fp16 = add(x = reduce_mean_139_cast_fp16, y = var_3039_to_fp16)[name = tensor<string, []>("op_3040_cast_fp16")];
tensor<fp16, [1, 1]> var_3041_cast_fp16 = sqrt(x = var_3040_cast_fp16)[name = tensor<string, []>("op_3041_cast_fp16")];
tensor<fp16, [1, 512]> x_339_cast_fp16 = real_div(x = sub_85_cast_fp16, y = var_3041_cast_fp16)[name = tensor<string, []>("x_339_cast_fp16")];
tensor<fp16, [1, 512]> var_3043_cast_fp16 = mul(x = x_339_cast_fp16, y = flow_net_res_blocks_4_in_ln_weight_to_fp16)[name = tensor<string, []>("op_3043_cast_fp16")];
tensor<fp16, [1, 512]> x_341_cast_fp16 = add(x = var_3043_cast_fp16, y = flow_net_res_blocks_4_in_ln_bias_to_fp16)[name = tensor<string, []>("x_341_cast_fp16")];
tensor<fp16, []> var_3045_promoted_to_fp16 = const()[name = tensor<string, []>("op_3045_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
tensor<fp16, [1, 512]> var_3046_cast_fp16 = add(x = var_3029_cast_fp16_1, y = var_3045_promoted_to_fp16)[name = tensor<string, []>("op_3046_cast_fp16")];
tensor<fp16, [1, 512]> var_3047_cast_fp16 = mul(x = x_341_cast_fp16, y = var_3046_cast_fp16)[name = tensor<string, []>("op_3047_cast_fp16")];
tensor<fp16, [1, 512]> input_525_cast_fp16 = add(x = var_3047_cast_fp16, y = var_3029_cast_fp16_0)[name = tensor<string, []>("input_525_cast_fp16")];
tensor<fp16, [1, 512]> linear_201_cast_fp16 = linear(bias = flow_net_res_blocks_4_mlp_0_bias_to_fp16, weight = flow_net_res_blocks_4_mlp_0_weight_to_fp16, x = input_525_cast_fp16)[name = tensor<string, []>("linear_201_cast_fp16")];
tensor<fp16, [1, 512]> input_529_cast_fp16 = silu(x = linear_201_cast_fp16)[name = tensor<string, []>("input_529_cast_fp16")];
tensor<fp16, [1, 512]> linear_202_cast_fp16 = linear(bias = flow_net_res_blocks_4_mlp_2_bias_to_fp16, weight = flow_net_res_blocks_4_mlp_2_weight_to_fp16, x = input_529_cast_fp16)[name = tensor<string, []>("linear_202_cast_fp16")];
tensor<fp16, [1, 512]> var_3058_cast_fp16 = mul(x = var_3029_cast_fp16_2, y = linear_202_cast_fp16)[name = tensor<string, []>("op_3058_cast_fp16")];
tensor<fp16, [1, 512]> x_343_cast_fp16 = add(x = x_337_cast_fp16, y = var_3058_cast_fp16)[name = tensor<string, []>("x_343_cast_fp16")];
tensor<fp16, [1, 1536]> linear_203_cast_fp16 = linear(bias = flow_net_res_blocks_5_adaLN_modulation_1_bias_to_fp16, weight = flow_net_res_blocks_5_adaLN_modulation_1_weight_to_fp16, x = input_491_cast_fp16)[name = tensor<string, []>("linear_203_cast_fp16")];
tensor<int32, [3]> var_3068_split_sizes_0 = const()[name = tensor<string, []>("op_3068_split_sizes_0"), val = tensor<int32, [3]>([512, 512, 512])];
tensor<int32, []> var_3068_axis_0 = const()[name = tensor<string, []>("op_3068_axis_0"), val = tensor<int32, []>(-1)];
tensor<fp16, [1, 512]> var_3068_cast_fp16_0, tensor<fp16, [1, 512]> var_3068_cast_fp16_1, tensor<fp16, [1, 512]> var_3068_cast_fp16_2 = split(axis = var_3068_axis_0, split_sizes = var_3068_split_sizes_0, x = linear_203_cast_fp16)[name = tensor<string, []>("op_3068_cast_fp16")];
tensor<int32, [1]> mean_109_axes_0 = const()[name = tensor<string, []>("mean_109_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> mean_109_keep_dims_0 = const()[name = tensor<string, []>("mean_109_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> mean_109_cast_fp16 = reduce_mean(axes = mean_109_axes_0, keep_dims = mean_109_keep_dims_0, x = x_343_cast_fp16)[name = tensor<string, []>("mean_109_cast_fp16")];
tensor<fp16, [1, 512]> sub_86_cast_fp16 = sub(x = x_343_cast_fp16, y = mean_109_cast_fp16)[name = tensor<string, []>("sub_86_cast_fp16")];
tensor<fp16, [1, 512]> square_70_cast_fp16 = square(x = sub_86_cast_fp16)[name = tensor<string, []>("square_70_cast_fp16")];
tensor<int32, [1]> reduce_mean_141_axes_0 = const()[name = tensor<string, []>("reduce_mean_141_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_141_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_141_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_141_cast_fp16 = reduce_mean(axes = reduce_mean_141_axes_0, keep_dims = reduce_mean_141_keep_dims_0, x = square_70_cast_fp16)[name = tensor<string, []>("reduce_mean_141_cast_fp16")];
tensor<fp16, []> var_3078_to_fp16 = const()[name = tensor<string, []>("op_3078_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 1]> var_3079_cast_fp16 = add(x = reduce_mean_141_cast_fp16, y = var_3078_to_fp16)[name = tensor<string, []>("op_3079_cast_fp16")];
tensor<fp16, [1, 1]> var_3080_cast_fp16 = sqrt(x = var_3079_cast_fp16)[name = tensor<string, []>("op_3080_cast_fp16")];
tensor<fp16, [1, 512]> x_345_cast_fp16 = real_div(x = sub_86_cast_fp16, y = var_3080_cast_fp16)[name = tensor<string, []>("x_345_cast_fp16")];
tensor<fp16, [1, 512]> var_3082_cast_fp16 = mul(x = x_345_cast_fp16, y = flow_net_res_blocks_5_in_ln_weight_to_fp16)[name = tensor<string, []>("op_3082_cast_fp16")];
tensor<fp16, [1, 512]> x_347_cast_fp16 = add(x = var_3082_cast_fp16, y = flow_net_res_blocks_5_in_ln_bias_to_fp16)[name = tensor<string, []>("x_347_cast_fp16")];
tensor<fp16, []> var_3084_promoted_to_fp16 = const()[name = tensor<string, []>("op_3084_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
tensor<fp16, [1, 512]> var_3085_cast_fp16 = add(x = var_3068_cast_fp16_1, y = var_3084_promoted_to_fp16)[name = tensor<string, []>("op_3085_cast_fp16")];
tensor<fp16, [1, 512]> var_3086_cast_fp16 = mul(x = x_347_cast_fp16, y = var_3085_cast_fp16)[name = tensor<string, []>("op_3086_cast_fp16")];
tensor<fp16, [1, 512]> input_533_cast_fp16 = add(x = var_3086_cast_fp16, y = var_3068_cast_fp16_0)[name = tensor<string, []>("input_533_cast_fp16")];
tensor<fp16, [1, 512]> linear_204_cast_fp16 = linear(bias = flow_net_res_blocks_5_mlp_0_bias_to_fp16, weight = flow_net_res_blocks_5_mlp_0_weight_to_fp16, x = input_533_cast_fp16)[name = tensor<string, []>("linear_204_cast_fp16")];
tensor<fp16, [1, 512]> input_537_cast_fp16 = silu(x = linear_204_cast_fp16)[name = tensor<string, []>("input_537_cast_fp16")];
tensor<fp16, [1, 512]> linear_205_cast_fp16 = linear(bias = flow_net_res_blocks_5_mlp_2_bias_to_fp16, weight = flow_net_res_blocks_5_mlp_2_weight_to_fp16, x = input_537_cast_fp16)[name = tensor<string, []>("linear_205_cast_fp16")];
tensor<fp16, [1, 512]> var_3097_cast_fp16 = mul(x = var_3068_cast_fp16_2, y = linear_205_cast_fp16)[name = tensor<string, []>("op_3097_cast_fp16")];
tensor<fp16, [1, 512]> x_349_cast_fp16 = add(x = x_343_cast_fp16, y = var_3097_cast_fp16)[name = tensor<string, []>("x_349_cast_fp16")];
tensor<fp16, [1, 1024]> linear_206_cast_fp16 = linear(bias = flow_net_final_layer_adaLN_modulation_1_bias_to_fp16, weight = flow_net_final_layer_adaLN_modulation_1_weight_to_fp16, x = input_491_cast_fp16)[name = tensor<string, []>("linear_206_cast_fp16")];
tensor<int32, [2]> var_3106_split_sizes_0 = const()[name = tensor<string, []>("op_3106_split_sizes_0"), val = tensor<int32, [2]>([512, 512])];
tensor<int32, []> var_3106_axis_0 = const()[name = tensor<string, []>("op_3106_axis_0"), val = tensor<int32, []>(-1)];
tensor<fp16, [1, 512]> var_3106_cast_fp16_0, tensor<fp16, [1, 512]> var_3106_cast_fp16_1 = split(axis = var_3106_axis_0, split_sizes = var_3106_split_sizes_0, x = linear_206_cast_fp16)[name = tensor<string, []>("op_3106_cast_fp16")];
tensor<int32, [1]> mean_axes_0 = const()[name = tensor<string, []>("mean_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> mean_keep_dims_0 = const()[name = tensor<string, []>("mean_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> mean_cast_fp16 = reduce_mean(axes = mean_axes_0, keep_dims = mean_keep_dims_0, x = x_349_cast_fp16)[name = tensor<string, []>("mean_cast_fp16")];
tensor<fp16, [1, 512]> sub_87_cast_fp16 = sub(x = x_349_cast_fp16, y = mean_cast_fp16)[name = tensor<string, []>("sub_87_cast_fp16")];
tensor<fp16, [1, 512]> square_71_cast_fp16 = square(x = sub_87_cast_fp16)[name = tensor<string, []>("square_71_cast_fp16")];
tensor<int32, [1]> reduce_mean_143_axes_0 = const()[name = tensor<string, []>("reduce_mean_143_axes_0"), val = tensor<int32, [1]>([-1])];
tensor<bool, []> reduce_mean_143_keep_dims_0 = const()[name = tensor<string, []>("reduce_mean_143_keep_dims_0"), val = tensor<bool, []>(true)];
tensor<fp16, [1, 1]> reduce_mean_143_cast_fp16 = reduce_mean(axes = reduce_mean_143_axes_0, keep_dims = reduce_mean_143_keep_dims_0, x = square_71_cast_fp16)[name = tensor<string, []>("reduce_mean_143_cast_fp16")];
tensor<fp16, []> var_3113_to_fp16 = const()[name = tensor<string, []>("op_3113_to_fp16"), val = tensor<fp16, []>(0x1.1p-20)];
tensor<fp16, [1, 1]> var_3114_cast_fp16 = add(x = reduce_mean_143_cast_fp16, y = var_3113_to_fp16)[name = tensor<string, []>("op_3114_cast_fp16")];
tensor<fp16, [1, 1]> var_3115_cast_fp16 = sqrt(x = var_3114_cast_fp16)[name = tensor<string, []>("op_3115_cast_fp16")];
tensor<fp16, [1, 512]> x_cast_fp16 = real_div(x = sub_87_cast_fp16, y = var_3115_cast_fp16)[name = tensor<string, []>("x_cast_fp16")];
tensor<fp16, []> var_3117_promoted_to_fp16 = const()[name = tensor<string, []>("op_3117_promoted_to_fp16"), val = tensor<fp16, []>(0x1p+0)];
tensor<fp16, [1, 512]> var_3118_cast_fp16 = add(x = var_3106_cast_fp16_1, y = var_3117_promoted_to_fp16)[name = tensor<string, []>("op_3118_cast_fp16")];
tensor<fp16, [1, 512]> var_3119_cast_fp16 = mul(x = x_cast_fp16, y = var_3118_cast_fp16)[name = tensor<string, []>("op_3119_cast_fp16")];
tensor<fp16, [1, 512]> input_cast_fp16 = add(x = var_3119_cast_fp16, y = var_3106_cast_fp16_0)[name = tensor<string, []>("input_cast_fp16")];
tensor<fp16, [1, 32]> linear_207_cast_fp16 = linear(bias = flow_net_final_layer_linear_bias_to_fp16, weight = flow_net_final_layer_linear_weight_to_fp16, x = input_cast_fp16)[name = tensor<string, []>("linear_207_cast_fp16")];
tensor<fp16, []> var_3124_to_fp16 = const()[name = tensor<string, []>("op_3124_to_fp16"), val = tensor<fp16, []>(0x1p-3)];
tensor<fp16, [1, 32]> var_3125_cast_fp16 = mul(x = linear_207_cast_fp16, y = var_3124_to_fp16)[name = tensor<string, []>("op_3125_cast_fp16")];
tensor<fp16, [1, 32]> var_3127_cast_fp16 = add(x = input_475_cast_fp16, y = var_3125_cast_fp16)[name = tensor<string, []>("op_3127_cast_fp16")];
tensor<string, []> var_3127_cast_fp16_to_fp32_dtype_0 = const()[name = tensor<string, []>("op_3127_cast_fp16_to_fp32_dtype_0"), val = tensor<string, []>("fp32")];
tensor<fp32, [1, 32]> latent_final = cast(dtype = var_3127_cast_fp16_to_fp32_dtype_0, x = var_3127_cast_fp16)[name = tensor<string, []>("cast_144")];
} -> (latent_final);
} |