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             string input_37_cast_fp16_dtype_0 = const()[name = string("input_37_cast_fp16_dtype_0"), val = string("fp32")];
-            tensor<fp32, [1, 320, 32, 32]> input_37_cast_fp16 = cast(dtype = input_37_cast_fp16_dtype_0, x = input_37_cast_fp16_1)[name = string("cast_9")];
-            tensor<fp32, [1, 640, 32, 32]> input_63_cast_fp16 = cast(dtype = input_63_cast_fp16_dtype_0, x = input_63_cast_fp16_1)[name = string("cast_10")];
+            string hidden_states_61_cast_fp16_dtype_0 = const()[name = string("hidden_states_61_cast_fp16_dtype_0"), val = string("fp32")];
+            string input_35_cast_fp16_dtype_0 = const()[name = string("input_35_cast_fp16_dtype_0"), val = string("fp32")];
+            string input_15_cast_fp16_dtype_0 = const()[name = string("input_15_cast_fp16_dtype_0"), val = string("fp32")];
+            string inputs_23_cast_fp16_dtype_0 = const()[name = string("inputs_23_cast_fp16_dtype_0"), val = string("fp32")];
+            tensor<fp32, [1, 1280, 1, 256]> inputs_23_cast_fp16 = cast(dtype = inputs_23_cast_fp16_dtype_0, x = inputs_23_cast_fp16_1)[name = string("cast_9")];
+            tensor<fp32, [1, 1280, 1, 1]> input_15_cast_fp16 = cast(dtype = input_15_cast_fp16_dtype_0, x = input_15_cast_fp16_1)[name = string("cast_10")];
             tensor<fp32, [1, 320, 64, 64]> input_35_cast_fp16 = cast(dtype = input_35_cast_fp16_dtype_0, x = input_35_cast_fp16_1)[name = string("cast_11")];
             tensor<fp32, [1, 1280, 16, 16]> hidden_states_61_cast_fp16 = cast(dtype = hidden_states_61_cast_fp16_dtype_0, x = hidden_states_61_cast_fp16_1)[name = string("cast_12")];
-            tensor<fp32, [1, 320, 64, 64]> input_7_cast_fp16 = cast(dtype = input_7_cast_fp16_dtype_0, x = input_7_cast_fp16_1)[name = string("cast_13")];
-            tensor<fp32, [1, 1280, 1, 1]> input_15_cast_fp16 = cast(dtype = input_15_cast_fp16_dtype_0, x = input_15_cast_fp16_1)[name = string("cast_14")];
+            tensor<fp32, [1, 320, 32, 32]> input_37_cast_fp16 = cast(dtype = input_37_cast_fp16_dtype_0, x = input_37_cast_fp16_1)[name = string("cast_13")];
+            tensor<fp32, [1, 640, 16, 16]> input_65_cast_fp16 = cast(dtype = input_65_cast_fp16_dtype_0, x = input_65_cast_fp16_1)[name = string("cast_14")];
             tensor<fp32, [1, 10240, 1, 256]> var_2337_cast_fp16 = cast(dtype = var_2337_cast_fp16_dtype_0, x = var_2337_cast_fp16_1)[name = string("cast_15")];
-            tensor<fp32, [1, 1280, 1, 256]> inputs_23_cast_fp16 = cast(dtype = inputs_23_cast_fp16_dtype_0, x = inputs_23_cast_fp16_1)[name = string("cast_16")];
-            tensor<fp32, [1, 640, 16, 16]> input_65_cast_fp16 = cast(dtype = input_65_cast_fp16_dtype_0, x = input_65_cast_fp16_1)[name = string("cast_17")];
-        } -> (input_65_cast_fp16, inputs_23_cast_fp16, var_2337_cast_fp16, input_15_cast_fp16, input_7_cast_fp16, hidden_states_61_cast_fp16, input_35_cast_fp16, input_63_cast_fp16, input_37_cast_fp16);
+            tensor<fp32, [1, 640, 32, 32]> input_63_cast_fp16 = cast(dtype = input_63_cast_fp16_dtype_0, x = input_63_cast_fp16_1)[name = string("cast_16")];
+            tensor<fp32, [1, 320, 64, 64]> input_7_cast_fp16 = cast(dtype = input_7_cast_fp16_dtype_0, x = input_7_cast_fp16_1)[name = string("cast_17")];
+        } -> (input_7_cast_fp16, input_63_cast_fp16, var_2337_cast_fp16, input_65_cast_fp16, input_37_cast_fp16, hidden_states_61_cast_fp16, input_35_cast_fp16, input_15_cast_fp16, inputs_23_cast_fp16);
 }

UnetChunk1.mlmodelc/weights/weight.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:15d72e32d29f8aa5813a0bc9fca0ccdf1063411b91c496ec20fa6a25af8fdc13
+oid sha256:091529bc9e2b2bdda0508614e223570f7a8a4fe288bc510ec844635ba2f01046
 size 324651584

UnetChunk2.mlmodelc/analytics/coremldata.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:3a468503aee82948546a734364424194ba66fe01ca5d4fd403fc75f7569234ed
+oid sha256:1f7d7cba85c9edd629d133c0224baf71e989ad4bfe1da89f37706f1d6cab45ea
 size 243

UnetChunk2.mlmodelc/coremldata.bin

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:45154abdc0829837df1817882dc4d6fe759241b60a55a316f735785999e3daac
+oid sha256:94ea7e153dab4f55ad8a40d12f9853422f8bdce240f5b608f3ae72ac3229fb35
 size 513

UnetChunk2.mlmodelc/metadata.json

@@ -71,16 +71,6 @@
         "name" : "encoder_hidden_states",
         "type" : "MultiArray"
       },
-      {
-        "hasShapeFlexibility" : "0",
-        "isOptional" : "0",
-        "dataType" : "Float32",
-        "formattedType" : "MultiArray (Float32 1 × 320 × 64 × 64)",
-        "shortDescription" : "",
-        "shape" : "[1, 320, 64, 64]",
-        "name" : "input_7_cast_fp16",
-        "type" : "MultiArray"
-      },
       {
         "hasShapeFlexibility" : "0",
         "isOptional" : "0",
@@ -105,10 +95,10 @@
         "hasShapeFlexibility" : "0",
         "isOptional" : "0",
         "dataType" : "Float32",
-        "formattedType" : "MultiArray (Float32 1 × 1280 × 1 × 1)",
+        "formattedType" : "MultiArray (Float32 1 × 320 × 64 × 64)",
         "shortDescription" : "",
-        "shape" : "[1, 1280, 1, 1]",
-        "name" : "input_15_cast_fp16",
+        "shape" : "[1, 320, 64, 64]",
+        "name" : "input_35_cast_fp16",
         "type" : "MultiArray"
       },
       {
@@ -121,16 +111,6 @@
         "name" : "hidden_states_61_cast_fp16",
         "type" : "MultiArray"
       },
-      {
-        "hasShapeFlexibility" : "0",
-        "isOptional" : "0",
-        "dataType" : "Float32",
-        "formattedType" : "MultiArray (Float32 1 × 320 × 64 × 64)",
-        "shortDescription" : "",
-        "shape" : "[1, 320, 64, 64]",
-        "name" : "input_35_cast_fp16",
-        "type" : "MultiArray"
-      },
       {
         "hasShapeFlexibility" : "0",
         "isOptional" : "0",
@@ -160,9 +140,29 @@
         "shape" : "[1, 640, 32, 32]",
         "name" : "input_63_cast_fp16",
         "type" : "MultiArray"
+      },
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float32",
+        "formattedType" : "MultiArray (Float32 1 × 1280 × 1 × 1)",
+        "shortDescription" : "",
+        "shape" : "[1, 1280, 1, 1]",
+        "name" : "input_15_cast_fp16",
+        "type" : "MultiArray"
+      },
+      {
+        "hasShapeFlexibility" : "0",
+        "isOptional" : "0",
+        "dataType" : "Float32",
+        "formattedType" : "MultiArray (Float32 1 × 320 × 64 × 64)",
+        "shortDescription" : "",
+        "shape" : "[1, 320, 64, 64]",
+        "name" : "input_7_cast_fp16",
+        "type" : "MultiArray"
       }
     ],
-    "generatedClassName" : "Stable_Diffusion_version_clover_image_tiny_inpaint_unet_chunk2",
+    "generatedClassName" : "Stable_Diffusion_version_clover_image_tiny_inpaint_v2_unet_chunk2",
     "method" : "predict"
   }
 ]

UnetChunk2.mlmodelc/model.mil

@@ -2,26 +2,26 @@ program(1.3)
 [buildInfo = dict<string, string>({{"coremlc-component-MIL", "3600.16.1"}, {"coremlc-version", "3600.22.1"}})]
 {
     func main<ios18>(tensor<fp16, [1, 768, 1, 77]> encoder_hidden_states, tensor<fp32, [1, 1280, 16, 16]> hidden_states_61_cast_fp16, tensor<fp32, [1, 1280, 1, 1]> input_15_cast_fp16, tensor<fp32, [1, 320, 64, 64]> input_35_cast_fp16, tensor<fp32, [1, 320, 32, 32]> input_37_cast_fp16, tensor<fp32, [1, 640, 32, 32]> input_63_cast_fp16, tensor<fp32, [1, 640, 16, 16]> input_65_cast_fp16, tensor<fp32, [1, 320, 64, 64]> input_7_cast_fp16, tensor<fp32, [1, 1280, 1, 256]> inputs_23_cast_fp16, tensor<fp32, [1, 10240, 1, 256]> var_2337_cast_fp16) {
-            string cast_0_dtype_0 = const()[name = string("cast_0_dtype_0"), val = string("fp16")];
+            string cast_8_dtype_0 = const()[name = string("cast_8_dtype_0"), val = string("fp16")];
             tensor<fp16, [320]> add_1_mean_0_to_fp16 = const()[name = string("add_1_mean_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
             tensor<fp16, [320]> add_1_variance_0_to_fp16 = const()[name = string("add_1_variance_0_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(768)))];
-            string cast_3_dtype_0 = const()[name = string("cast_3_dtype_0"), val = string("fp16")];
-            string cast_5_dtype_0 = const()[name = string("cast_5_dtype_0"), val = string("fp16")];
             string cast_7_dtype_0 = const()[name = string("cast_7_dtype_0"), val = string("fp16")];
+            string cast_2_dtype_0 = const()[name = string("cast_2_dtype_0"), val = string("fp16")];
+            string cast_5_dtype_0 = const()[name = string("cast_5_dtype_0"), val = string("fp16")];
             tensor<fp16, [640]> add_9_mean_0_to_fp16 = const()[name = string("add_9_mean_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1472)))];
             tensor<fp16, [640]> add_9_variance_0_to_fp16 = const()[name = string("add_9_variance_0_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2816)))];
-            string cast_8_dtype_0 = const()[name = string("cast_8_dtype_0"), val = string("fp16")];
-            string cast_1_dtype_0 = const()[name = string("cast_1_dtype_0"), val = string("fp16")];
+            string cast_6_dtype_0 = const()[name = string("cast_6_dtype_0"), val = string("fp16")];
+            string cast_0_dtype_0 = const()[name = string("cast_0_dtype_0"), val = string("fp16")];
             tensor<fp16, [1280]> add_15_mean_0_to_fp16 = const()[name = string("add_15_mean_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(4160)))];
             tensor<fp16, [1280]> add_15_variance_0_to_fp16 = const()[name = string("add_15_variance_0_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(6784)))];
             int32 var_1812 = const()[name = string("op_1812"), val = int32(1)];
+            string cast_3_dtype_0 = const()[name = string("cast_3_dtype_0"), val = string("fp16")];
+            string cast_1_dtype_0 = const()[name = string("cast_1_dtype_0"), val = string("fp16")];
             string cast_4_dtype_0 = const()[name = string("cast_4_dtype_0"), val = string("fp16")];
-            string cast_2_dtype_0 = const()[name = string("cast_2_dtype_0"), val = string("fp16")];
-            string cast_6_dtype_0 = const()[name = string("cast_6_dtype_0"), val = string("fp16")];
             tensor<int32, [2]> var_2338_split_sizes_0 = const()[name = string("op_2338_split_sizes_0"), val = tensor<int32, [2]>([5120, 5120])];
             int32 var_2338_axis_0 = const()[name = string("op_2338_axis_0"), val = int32(1)];
-            tensor<fp16, [1, 10240, 1, 256]> cast_6 = cast(dtype = cast_6_dtype_0, x = var_2337_cast_fp16)[name = string("cast_1")];
-            tensor<fp16, [1, 5120, 1, 256]> var_2338_cast_fp16_0, tensor<fp16, [1, 5120, 1, 256]> var_2338_cast_fp16_1 = split(axis = var_2338_axis_0, split_sizes = var_2338_split_sizes_0, x = cast_6)[name = string("op_2338_cast_fp16")];
+            tensor<fp16, [1, 10240, 1, 256]> cast_4 = cast(dtype = cast_4_dtype_0, x = var_2337_cast_fp16)[name = string("cast_1")];
+            tensor<fp16, [1, 5120, 1, 256]> var_2338_cast_fp16_0, tensor<fp16, [1, 5120, 1, 256]> var_2338_cast_fp16_1 = split(axis = var_2338_axis_0, split_sizes = var_2338_split_sizes_0, x = cast_4)[name = string("op_2338_cast_fp16")];
             string var_2340_mode_0 = const()[name = string("op_2340_mode_0"), val = string("EXACT")];
             tensor<fp16, [1, 5120, 1, 256]> var_2340_cast_fp16 = gelu(mode = var_2340_mode_0, x = var_2338_cast_fp16_1)[name = string("op_2340_cast_fp16")];
             tensor<fp16, [1, 5120, 1, 256]> input_113_cast_fp16 = mul(x = var_2338_cast_fp16_0, y = var_2340_cast_fp16)[name = string("input_113_cast_fp16")];
@@ -33,8 +33,8 @@ program(1.3)
             tensor<fp16, [1280, 5120, 1, 1]> up_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16 = const()[name = string("up_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16"), val = tensor<fp16, [1280, 5120, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(9408)))];
             tensor<fp16, [1280]> up_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16 = const()[name = string("up_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13116672)))];
             tensor<fp16, [1, 1280, 1, 256]> var_2348_cast_fp16 = conv(bias = up_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_bias_to_fp16, dilations = var_2348_dilations_0, groups = var_2348_groups_0, pad = var_2348_pad_0, pad_type = var_2348_pad_type_0, strides = var_2348_strides_0, weight = up_blocks_0_attentions_0_transformer_blocks_0_ff_net_2_weight_to_fp16, x = input_113_cast_fp16)[name = string("op_2348_cast_fp16")];
-            tensor<fp16, [1, 1280, 1, 256]> cast_2 = cast(dtype = cast_2_dtype_0, x = inputs_23_cast_fp16)[name = string("cast_2")];
-            tensor<fp16, [1, 1280, 1, 256]> hidden_states_71_cast_fp16 = add(x = var_2348_cast_fp16, y = cast_2)[name = string("hidden_states_71_cast_fp16")];
+            tensor<fp16, [1, 1280, 1, 256]> cast_1 = cast(dtype = cast_1_dtype_0, x = inputs_23_cast_fp16)[name = string("cast_2")];
+            tensor<fp16, [1, 1280, 1, 256]> hidden_states_71_cast_fp16 = add(x = var_2348_cast_fp16, y = cast_1)[name = string("hidden_states_71_cast_fp16")];
             tensor<int32, [4]> var_2350 = const()[name = string("op_2350"), val = tensor<int32, [4]>([1, 1280, 16, 16])];
             tensor<fp16, [1, 1280, 16, 16]> input_115_cast_fp16 = reshape(shape = var_2350, x = hidden_states_71_cast_fp16)[name = string("input_115_cast_fp16")];
             string hidden_states_73_pad_type_0 = const()[name = string("hidden_states_73_pad_type_0"), val = string("valid")];
@@ -45,11 +45,11 @@ program(1.3)
             tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_attentions_0_proj_out_weight_to_fp16 = const()[name = string("up_blocks_0_attentions_0_proj_out_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13119296)))];
             tensor<fp16, [1280]> up_blocks_0_attentions_0_proj_out_bias_to_fp16 = const()[name = string("up_blocks_0_attentions_0_proj_out_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(16396160)))];
             tensor<fp16, [1, 1280, 16, 16]> hidden_states_73_cast_fp16 = conv(bias = up_blocks_0_attentions_0_proj_out_bias_to_fp16, dilations = hidden_states_73_dilations_0, groups = hidden_states_73_groups_0, pad = hidden_states_73_pad_0, pad_type = hidden_states_73_pad_type_0, strides = hidden_states_73_strides_0, weight = up_blocks_0_attentions_0_proj_out_weight_to_fp16, x = input_115_cast_fp16)[name = string("hidden_states_73_cast_fp16")];
-            tensor<fp16, [1, 1280, 16, 16]> cast_4 = cast(dtype = cast_4_dtype_0, x = hidden_states_61_cast_fp16)[name = string("cast_3")];
-            tensor<fp16, [1, 1280, 16, 16]> hidden_states_75_cast_fp16 = add(x = hidden_states_73_cast_fp16, y = cast_4)[name = string("hidden_states_75_cast_fp16")];
+            tensor<fp16, [1, 1280, 16, 16]> cast_3 = cast(dtype = cast_3_dtype_0, x = hidden_states_61_cast_fp16)[name = string("cast_3")];
+            tensor<fp16, [1, 1280, 16, 16]> hidden_states_75_cast_fp16 = add(x = hidden_states_73_cast_fp16, y = cast_3)[name = string("hidden_states_75_cast_fp16")];
             bool input_117_interleave_0 = const()[name = string("input_117_interleave_0"), val = bool(false)];
-            tensor<fp16, [1, 640, 16, 16]> cast_1 = cast(dtype = cast_1_dtype_0, x = input_65_cast_fp16)[name = string("cast_4")];
-            tensor<fp16, [1, 1920, 16, 16]> input_117_cast_fp16 = concat(axis = var_1812, interleave = input_117_interleave_0, values = (hidden_states_75_cast_fp16, cast_1))[name = string("input_117_cast_fp16")];
+            tensor<fp16, [1, 640, 16, 16]> cast_0 = cast(dtype = cast_0_dtype_0, x = input_65_cast_fp16)[name = string("cast_4")];
+            tensor<fp16, [1, 1920, 16, 16]> input_117_cast_fp16 = concat(axis = var_1812, interleave = input_117_interleave_0, values = (hidden_states_75_cast_fp16, cast_0))[name = string("input_117_cast_fp16")];
             tensor<int32, [5]> reshape_48_shape_0 = const()[name = string("reshape_48_shape_0"), val = tensor<int32, [5]>([1, 32, 60, 16, 16])];
             tensor<fp16, [1, 32, 60, 16, 16]> reshape_48_cast_fp16 = reshape(shape = reshape_48_shape_0, x = input_117_cast_fp16)[name = string("reshape_48_cast_fp16")];
             tensor<int32, [3]> reduce_mean_36_axes_0 = const()[name = string("reduce_mean_36_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
@@ -88,8 +88,8 @@ program(1.3)
             int32 temb_9_groups_0 = const()[name = string("temb_9_groups_0"), val = int32(1)];
             tensor<fp16, [1280, 1280, 1, 1]> up_blocks_0_resnets_1_time_emb_proj_weight_to_fp16 = const()[name = string("up_blocks_0_resnets_1_time_emb_proj_weight_to_fp16"), val = tensor<fp16, [1280, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60653888)))];
             tensor<fp16, [1280]> up_blocks_0_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = string("up_blocks_0_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor<fp16, [1280]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(63930752)))];
-            tensor<fp16, [1, 1280, 1, 1]> cast_3 = cast(dtype = cast_3_dtype_0, x = input_15_cast_fp16)[name = string("cast_8")];
-            tensor<fp16, [1, 1280, 1, 1]> temb_9_cast_fp16 = conv(bias = up_blocks_0_resnets_1_time_emb_proj_bias_to_fp16, dilations = temb_9_dilations_0, groups = temb_9_groups_0, pad = temb_9_pad_0, pad_type = temb_9_pad_type_0, strides = temb_9_strides_0, weight = up_blocks_0_resnets_1_time_emb_proj_weight_to_fp16, x = cast_3)[name = string("temb_9_cast_fp16")];
+            tensor<fp16, [1, 1280, 1, 1]> cast_7 = cast(dtype = cast_7_dtype_0, x = input_15_cast_fp16)[name = string("cast_8")];
+            tensor<fp16, [1, 1280, 1, 1]> temb_9_cast_fp16 = conv(bias = up_blocks_0_resnets_1_time_emb_proj_bias_to_fp16, dilations = temb_9_dilations_0, groups = temb_9_groups_0, pad = temb_9_pad_0, pad_type = temb_9_pad_type_0, strides = temb_9_strides_0, weight = up_blocks_0_resnets_1_time_emb_proj_weight_to_fp16, x = cast_7)[name = string("temb_9_cast_fp16")];
             tensor<fp16, [1, 1280, 16, 16]> input_125_cast_fp16 = add(x = hidden_states_77_cast_fp16, y = temb_9_cast_fp16)[name = string("input_125_cast_fp16")];
             tensor<int32, [5]> reshape_52_shape_0 = const()[name = string("reshape_52_shape_0"), val = tensor<int32, [5]>([1, 32, 40, 16, 16])];
             tensor<fp16, [1, 32, 40, 16, 16]> reshape_52_cast_fp16 = reshape(shape = reshape_52_shape_0, x = input_125_cast_fp16)[name = string("reshape_52_cast_fp16")];
@@ -593,8 +593,8 @@ program(1.3)
             tensor<fp16, [1, 1280, 32, 32]> hidden_states_95_cast_fp16 = conv(bias = up_blocks_0_upsamplers_0_conv_bias_to_fp16, dilations = hidden_states_95_dilations_0, groups = hidden_states_95_groups_0, pad = hidden_states_95_pad_0, pad_type = hidden_states_95_pad_type_0, strides = hidden_states_95_strides_0, weight = up_blocks_0_upsamplers_0_conv_weight_to_fp16, x = input_145_cast_fp16)[name = string("hidden_states_95_cast_fp16")];
             int32 var_2929 = const()[name = string("op_2929"), val = int32(1)];
             bool input_147_interleave_0 = const()[name = string("input_147_interleave_0"), val = bool(false)];
-            tensor<fp16, [1, 640, 32, 32]> cast_8 = cast(dtype = cast_8_dtype_0, x = input_63_cast_fp16)[name = string("cast_5")];
-            tensor<fp16, [1, 1920, 32, 32]> input_147_cast_fp16 = concat(axis = var_2929, interleave = input_147_interleave_0, values = (hidden_states_95_cast_fp16, cast_8))[name = string("input_147_cast_fp16")];
+            tensor<fp16, [1, 640, 32, 32]> cast_6 = cast(dtype = cast_6_dtype_0, x = input_63_cast_fp16)[name = string("cast_5")];
+            tensor<fp16, [1, 1920, 32, 32]> input_147_cast_fp16 = concat(axis = var_2929, interleave = input_147_interleave_0, values = (hidden_states_95_cast_fp16, cast_6))[name = string("input_147_cast_fp16")];
             tensor<int32, [5]> reshape_60_shape_0 = const()[name = string("reshape_60_shape_0"), val = tensor<int32, [5]>([1, 32, 60, 32, 32])];
             tensor<fp16, [1, 32, 60, 32, 32]> reshape_60_cast_fp16 = reshape(shape = reshape_60_shape_0, x = input_147_cast_fp16)[name = string("reshape_60_cast_fp16")];
             tensor<int32, [3]> reduce_mean_45_axes_0 = const()[name = string("reduce_mean_45_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
@@ -631,7 +631,7 @@ program(1.3)
             int32 temb_11_groups_0 = const()[name = string("temb_11_groups_0"), val = int32(1)];
             tensor<fp16, [640, 1280, 1, 1]> up_blocks_1_resnets_0_time_emb_proj_weight_to_fp16 = const()[name = string("up_blocks_1_resnets_0_time_emb_proj_weight_to_fp16"), val = tensor<fp16, [640, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(219495488)))];
             tensor<fp16, [640]> up_blocks_1_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = string("up_blocks_1_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(221133952)))];
-            tensor<fp16, [1, 640, 1, 1]> temb_11_cast_fp16 = conv(bias = up_blocks_1_resnets_0_time_emb_proj_bias_to_fp16, dilations = temb_11_dilations_0, groups = temb_11_groups_0, pad = temb_11_pad_0, pad_type = temb_11_pad_type_0, strides = temb_11_strides_0, weight = up_blocks_1_resnets_0_time_emb_proj_weight_to_fp16, x = cast_3)[name = string("temb_11_cast_fp16")];
+            tensor<fp16, [1, 640, 1, 1]> temb_11_cast_fp16 = conv(bias = up_blocks_1_resnets_0_time_emb_proj_bias_to_fp16, dilations = temb_11_dilations_0, groups = temb_11_groups_0, pad = temb_11_pad_0, pad_type = temb_11_pad_type_0, strides = temb_11_strides_0, weight = up_blocks_1_resnets_0_time_emb_proj_weight_to_fp16, x = cast_7)[name = string("temb_11_cast_fp16")];
             tensor<fp16, [1, 640, 32, 32]> input_155_cast_fp16 = add(x = hidden_states_97_cast_fp16, y = temb_11_cast_fp16)[name = string("input_155_cast_fp16")];
             tensor<int32, [5]> reshape_64_shape_0 = const()[name = string("reshape_64_shape_0"), val = tensor<int32, [5]>([1, 32, 20, 32, 32])];
             tensor<fp16, [1, 32, 20, 32, 32]> reshape_64_cast_fp16 = reshape(shape = reshape_64_shape_0, x = input_155_cast_fp16)[name = string("reshape_64_cast_fp16")];
@@ -1123,8 +1123,8 @@ program(1.3)
             tensor<fp16, [1, 640, 32, 32]> hidden_states_113_cast_fp16 = conv(bias = up_blocks_1_attentions_0_proj_out_bias_to_fp16, dilations = hidden_states_113_dilations_0, groups = hidden_states_113_groups_0, pad = hidden_states_113_pad_0, pad_type = hidden_states_113_pad_type_0, strides = hidden_states_113_strides_0, weight = up_blocks_1_attentions_0_proj_out_weight_to_fp16, x = input_171_cast_fp16)[name = string("hidden_states_113_cast_fp16")];
             tensor<fp16, [1, 640, 32, 32]> hidden_states_115_cast_fp16 = add(x = hidden_states_113_cast_fp16, y = hidden_states_101_cast_fp16)[name = string("hidden_states_115_cast_fp16")];
             bool input_173_interleave_0 = const()[name = string("input_173_interleave_0"), val = bool(false)];
-            tensor<fp16, [1, 320, 32, 32]> cast_7 = cast(dtype = cast_7_dtype_0, x = input_37_cast_fp16)[name = string("cast_6")];
-            tensor<fp16, [1, 960, 32, 32]> input_173_cast_fp16 = concat(axis = var_2929, interleave = input_173_interleave_0, values = (hidden_states_115_cast_fp16, cast_7))[name = string("input_173_cast_fp16")];
+            tensor<fp16, [1, 320, 32, 32]> cast_5 = cast(dtype = cast_5_dtype_0, x = input_37_cast_fp16)[name = string("cast_6")];
+            tensor<fp16, [1, 960, 32, 32]> input_173_cast_fp16 = concat(axis = var_2929, interleave = input_173_interleave_0, values = (hidden_states_115_cast_fp16, cast_5))[name = string("input_173_cast_fp16")];
             tensor<int32, [5]> reshape_72_shape_0 = const()[name = string("reshape_72_shape_0"), val = tensor<int32, [5]>([1, 32, 30, 32, 32])];
             tensor<fp16, [1, 32, 30, 32, 32]> reshape_72_cast_fp16 = reshape(shape = reshape_72_shape_0, x = input_173_cast_fp16)[name = string("reshape_72_cast_fp16")];
             tensor<int32, [3]> reduce_mean_54_axes_0 = const()[name = string("reduce_mean_54_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
@@ -1163,7 +1163,7 @@ program(1.3)
             int32 temb_13_groups_0 = const()[name = string("temb_13_groups_0"), val = int32(1)];
             tensor<fp16, [640, 1280, 1, 1]> up_blocks_1_resnets_1_time_emb_proj_weight_to_fp16 = const()[name = string("up_blocks_1_resnets_1_time_emb_proj_weight_to_fp16"), val = tensor<fp16, [640, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(260418368)))];
             tensor<fp16, [640]> up_blocks_1_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = string("up_blocks_1_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor<fp16, [640]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(262056832)))];
-            tensor<fp16, [1, 640, 1, 1]> temb_13_cast_fp16 = conv(bias = up_blocks_1_resnets_1_time_emb_proj_bias_to_fp16, dilations = temb_13_dilations_0, groups = temb_13_groups_0, pad = temb_13_pad_0, pad_type = temb_13_pad_type_0, strides = temb_13_strides_0, weight = up_blocks_1_resnets_1_time_emb_proj_weight_to_fp16, x = cast_3)[name = string("temb_13_cast_fp16")];
+            tensor<fp16, [1, 640, 1, 1]> temb_13_cast_fp16 = conv(bias = up_blocks_1_resnets_1_time_emb_proj_bias_to_fp16, dilations = temb_13_dilations_0, groups = temb_13_groups_0, pad = temb_13_pad_0, pad_type = temb_13_pad_type_0, strides = temb_13_strides_0, weight = up_blocks_1_resnets_1_time_emb_proj_weight_to_fp16, x = cast_7)[name = string("temb_13_cast_fp16")];
             tensor<fp16, [1, 640, 32, 32]> input_181_cast_fp16 = add(x = hidden_states_117_cast_fp16, y = temb_13_cast_fp16)[name = string("input_181_cast_fp16")];
             tensor<int32, [5]> reshape_76_shape_0 = const()[name = string("reshape_76_shape_0"), val = tensor<int32, [5]>([1, 32, 20, 32, 32])];
             tensor<fp16, [1, 32, 20, 32, 32]> reshape_76_cast_fp16 = reshape(shape = reshape_76_shape_0, x = input_181_cast_fp16)[name = string("reshape_76_cast_fp16")];
@@ -1667,8 +1667,8 @@ program(1.3)
             tensor<fp16, [1, 640, 64, 64]> hidden_states_135_cast_fp16 = conv(bias = up_blocks_1_upsamplers_0_conv_bias_to_fp16, dilations = hidden_states_135_dilations_0, groups = hidden_states_135_groups_0, pad = hidden_states_135_pad_0, pad_type = hidden_states_135_pad_type_0, strides = hidden_states_135_strides_0, weight = up_blocks_1_upsamplers_0_conv_weight_to_fp16, x = input_201_cast_fp16)[name = string("hidden_states_135_cast_fp16")];
             int32 var_4045 = const()[name = string("op_4045"), val = int32(1)];
             bool input_203_interleave_0 = const()[name = string("input_203_interleave_0"), val = bool(false)];
-            tensor<fp16, [1, 320, 64, 64]> cast_5 = cast(dtype = cast_5_dtype_0, x = input_35_cast_fp16)[name = string("cast_7")];
-            tensor<fp16, [1, 960, 64, 64]> input_203_cast_fp16 = concat(axis = var_4045, interleave = input_203_interleave_0, values = (hidden_states_135_cast_fp16, cast_5))[name = string("input_203_cast_fp16")];
+            tensor<fp16, [1, 320, 64, 64]> cast_2 = cast(dtype = cast_2_dtype_0, x = input_35_cast_fp16)[name = string("cast_7")];
+            tensor<fp16, [1, 960, 64, 64]> input_203_cast_fp16 = concat(axis = var_4045, interleave = input_203_interleave_0, values = (hidden_states_135_cast_fp16, cast_2))[name = string("input_203_cast_fp16")];
             tensor<int32, [5]> reshape_84_shape_0 = const()[name = string("reshape_84_shape_0"), val = tensor<int32, [5]>([1, 32, 30, 64, 64])];
             tensor<fp16, [1, 32, 30, 64, 64]> reshape_84_cast_fp16 = reshape(shape = reshape_84_shape_0, x = input_203_cast_fp16)[name = string("reshape_84_cast_fp16")];
             tensor<int32, [3]> reduce_mean_63_axes_0 = const()[name = string("reduce_mean_63_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
@@ -1705,7 +1705,7 @@ program(1.3)
             int32 temb_15_groups_0 = const()[name = string("temb_15_groups_0"), val = int32(1)];
             tensor<fp16, [320, 1280, 1, 1]> up_blocks_2_resnets_0_time_emb_proj_weight_to_fp16 = const()[name = string("up_blocks_2_resnets_0_time_emb_proj_weight_to_fp16"), val = tensor<fp16, [320, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(301952448)))];
             tensor<fp16, [320]> up_blocks_2_resnets_0_time_emb_proj_bias_to_fp16 = const()[name = string("up_blocks_2_resnets_0_time_emb_proj_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(302771712)))];
-            tensor<fp16, [1, 320, 1, 1]> temb_15_cast_fp16 = conv(bias = up_blocks_2_resnets_0_time_emb_proj_bias_to_fp16, dilations = temb_15_dilations_0, groups = temb_15_groups_0, pad = temb_15_pad_0, pad_type = temb_15_pad_type_0, strides = temb_15_strides_0, weight = up_blocks_2_resnets_0_time_emb_proj_weight_to_fp16, x = cast_3)[name = string("temb_15_cast_fp16")];
+            tensor<fp16, [1, 320, 1, 1]> temb_15_cast_fp16 = conv(bias = up_blocks_2_resnets_0_time_emb_proj_bias_to_fp16, dilations = temb_15_dilations_0, groups = temb_15_groups_0, pad = temb_15_pad_0, pad_type = temb_15_pad_type_0, strides = temb_15_strides_0, weight = up_blocks_2_resnets_0_time_emb_proj_weight_to_fp16, x = cast_7)[name = string("temb_15_cast_fp16")];
             tensor<fp16, [1, 320, 64, 64]> input_211_cast_fp16 = add(x = hidden_states_137_cast_fp16, y = temb_15_cast_fp16)[name = string("input_211_cast_fp16")];
             tensor<int32, [5]> reshape_88_shape_0 = const()[name = string("reshape_88_shape_0"), val = tensor<int32, [5]>([1, 32, 10, 64, 64])];
             tensor<fp16, [1, 32, 10, 64, 64]> reshape_88_cast_fp16 = reshape(shape = reshape_88_shape_0, x = input_211_cast_fp16)[name = string("reshape_88_cast_fp16")];
@@ -2197,8 +2197,8 @@ program(1.3)
             tensor<fp16, [1, 320, 64, 64]> hidden_states_153_cast_fp16 = conv(bias = up_blocks_2_attentions_0_proj_out_bias_to_fp16, dilations = hidden_states_153_dilations_0, groups = hidden_states_153_groups_0, pad = hidden_states_153_pad_0, pad_type = hidden_states_153_pad_type_0, strides = hidden_states_153_strides_0, weight = up_blocks_2_attentions_0_proj_out_weight_to_fp16, x = input_227_cast_fp16)[name = string("hidden_states_153_cast_fp16")];
             tensor<fp16, [1, 320, 64, 64]> hidden_states_155_cast_fp16 = add(x = hidden_states_153_cast_fp16, y = hidden_states_141_cast_fp16)[name = string("hidden_states_155_cast_fp16")];
             bool input_229_interleave_0 = const()[name = string("input_229_interleave_0"), val = bool(false)];
-            tensor<fp16, [1, 320, 64, 64]> cast_0 = cast(dtype = cast_0_dtype_0, x = input_7_cast_fp16)[name = string("cast_9")];
-            tensor<fp16, [1, 640, 64, 64]> input_229_cast_fp16 = concat(axis = var_4045, interleave = input_229_interleave_0, values = (hidden_states_155_cast_fp16, cast_0))[name = string("input_229_cast_fp16")];
+            tensor<fp16, [1, 320, 64, 64]> cast_8 = cast(dtype = cast_8_dtype_0, x = input_7_cast_fp16)[name = string("cast_9")];
+            tensor<fp16, [1, 640, 64, 64]> input_229_cast_fp16 = concat(axis = var_4045, interleave = input_229_interleave_0, values = (hidden_states_155_cast_fp16, cast_8))[name = string("input_229_cast_fp16")];
             tensor<int32, [5]> reshape_96_shape_0 = const()[name = string("reshape_96_shape_0"), val = tensor<int32, [5]>([1, 32, 20, 64, 64])];
             tensor<fp16, [1, 32, 20, 64, 64]> reshape_96_cast_fp16 = reshape(shape = reshape_96_shape_0, x = input_229_cast_fp16)[name = string("reshape_96_cast_fp16")];
             tensor<int32, [3]> reduce_mean_72_axes_0 = const()[name = string("reduce_mean_72_axes_0"), val = tensor<int32, [3]>([2, 3, 4])];
@@ -2235,7 +2235,7 @@ program(1.3)
             int32 temb_groups_0 = const()[name = string("temb_groups_0"), val = int32(1)];
             tensor<fp16, [320, 1280, 1, 1]> up_blocks_2_resnets_1_time_emb_proj_weight_to_fp16 = const()[name = string("up_blocks_2_resnets_1_time_emb_proj_weight_to_fp16"), val = tensor<fp16, [320, 1280, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(314016960)))];
             tensor<fp16, [320]> up_blocks_2_resnets_1_time_emb_proj_bias_to_fp16 = const()[name = string("up_blocks_2_resnets_1_time_emb_proj_bias_to_fp16"), val = tensor<fp16, [320]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(314836224)))];
-            tensor<fp16, [1, 320, 1, 1]> temb_cast_fp16 = conv(bias = up_blocks_2_resnets_1_time_emb_proj_bias_to_fp16, dilations = temb_dilations_0, groups = temb_groups_0, pad = temb_pad_0, pad_type = temb_pad_type_0, strides = temb_strides_0, weight = up_blocks_2_resnets_1_time_emb_proj_weight_to_fp16, x = cast_3)[name = string("temb_cast_fp16")];
+            tensor<fp16, [1, 320, 1, 1]> temb_cast_fp16 = conv(bias = up_blocks_2_resnets_1_time_emb_proj_bias_to_fp16, dilations = temb_dilations_0, groups = temb_groups_0, pad = temb_pad_0, pad_type = temb_pad_type_0, strides = temb_strides_0, weight = up_blocks_2_resnets_1_time_emb_proj_weight_to_fp16, x = cast_7)[name = string("temb_cast_fp16")];
             tensor<fp16, [1, 320, 64, 64]> input_237_cast_fp16 = add(x = hidden_states_157_cast_fp16, y = temb_cast_fp16)[name = string("input_237_cast_fp16")];
             tensor<int32, [5]> reshape_100_shape_0 = const()[name = string("reshape_100_shape_0"), val = tensor<int32, [5]>([1, 32, 10, 64, 64])];
             tensor<fp16, [1, 32, 10, 64, 64]> reshape_100_cast_fp16 = reshape(shape = reshape_100_shape_0, x = input_237_cast_fp16)[name = string("reshape_100_cast_fp16")];
@@ -2753,7 +2753,7 @@ program(1.3)
             tensor<int32, [2]> var_5145_dilations_0 = const()[name = string("op_5145_dilations_0"), val = tensor<int32, [2]>([1, 1])];
             int32 var_5145_groups_0 = const()[name = string("op_5145_groups_0"), val = int32(1)];
             tensor<fp16, [4, 320, 3, 3]> conv_out_weight_to_fp16 = const()[name = string("conv_out_weight_to_fp16"), val = tensor<fp16, [4, 320, 3, 3]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(322188224)))];
-            tensor<fp16, [4]> conv_out_bias_to_fp16 = const()[name = string("conv_out_bias_to_fp16"), val = tensor<fp16, [4]>([0x1.a24p-10, -0x1.a8p-11, 0x1.21p-11, -0x1.39cp-10])];
+            tensor<fp16, [4]> conv_out_bias_to_fp16 = const()[name = string("conv_out_bias_to_fp16"), val = tensor<fp16, [4]>([0x1.d64p-10, -0x1.31cp-11, 0x1.548p-12, -0x1.674p-10])];
             tensor<fp16, [1, 4, 64, 64]> var_5145_cast_fp16 = conv(bias = conv_out_bias_to_fp16, dilations = var_5145_dilations_0, groups = var_5145_groups_0, pad = var_5145_pad_0, pad_type = var_5145_pad_type_0, strides = var_5145_strides_0, weight = conv_out_weight_to_fp16, x = input_cast_fp16)[name = string("op_5145_cast_fp16")];
             string var_5145_cast_fp16_to_fp32_dtype_0 = const()[name = string("op_5145_cast_fp16_to_fp32_dtype_0"), val = string("fp32")];
             tensor<fp32, [1, 4, 64, 64]> noise_pred = cast(dtype = var_5145_cast_fp16_to_fp32_dtype_0, x = var_5145_cast_fp16)[name = string("cast_0")];

UnetChunk2.mlmodelc/weights/weight.bin

@@ -1,3 +1,3 @@
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-oid sha256:06720754c9b2d5f1165a7989f07be3cb8a879f68363b27f85820afb47ce9353d
+oid sha256:04910ba6d4ca88b34dd1202c848aebfc33e339010d203573ebc4138a4e9f78ba
 size 322211328