Merge branch 'generator-broadcasts' into gen-out-fix

backend/src/api/api.py

@@ -516,18 +516,17 @@ class Generator(Generic[I]):
     expected_length: int
     fail_fast: bool = True
 
+    def with_fail_fast(self, fail_fast: bool) -> Generator[I]:
+        return Generator(self.supplier, self.expected_length, fail_fast=fail_fast)
+
     @staticmethod
     def from_iter(
-        supplier: Callable[[], Iterable[I | Exception]],
-        expected_length: int,
-        fail_fast: bool = True,
+        supplier: Callable[[], Iterable[I | Exception]], expected_length: int
     ) -> Generator[I]:
-        return Generator(supplier, expected_length, fail_fast=fail_fast)
+        return Generator(supplier, expected_length)
 
     @staticmethod
-    def from_list(
-        l: list[L], map_fn: Callable[[L, int], I], fail_fast: bool = True
-    ) -> Generator[I]:
+    def from_list(l: list[L], map_fn: Callable[[L, int], I]) -> Generator[I]:
         """
         Creates a new generator from a list that is mapped using the given
         function. The iterable will be equivalent to `map(map_fn, l)`.
@@ -540,12 +539,10 @@ def supplier():
                 except Exception as e:
                     yield e
 
-        return Generator(supplier, len(l), fail_fast=fail_fast)
+        return Generator(supplier, len(l))
 
     @staticmethod
-    def from_range(
-        count: int, map_fn: Callable[[int], I], fail_fast: bool = True
-    ) -> Generator[I]:
+    def from_range(count: int, map_fn: Callable[[int], I]) -> Generator[I]:
         """
         Creates a new generator the given number of items where each item is
         lazily evaluated. The iterable will be equivalent to `map(map_fn, range(count))`.
@@ -559,7 +556,7 @@ def supplier():
                 except Exception as e:
                     yield e
 
-        return Generator(supplier, count, fail_fast=fail_fast)
+        return Generator(supplier, count)
 
 
 N = TypeVar("N")

backend/src/packages/chaiNNer_ncnn/ncnn/batch_processing/load_models.py

@@ -82,4 +82,7 @@ def load_model(filepath_pairs: tuple[Path, Path], index: int):
 
     model_files = list(zip(param_files, bin_files))
 
-    return Generator.from_list(model_files, load_model, fail_fast), directory
+    return (
+        Generator.from_list(model_files, load_model).with_fail_fast(fail_fast),
+        directory,
+    )

backend/src/packages/chaiNNer_onnx/onnx/batch_processing/load_models.py

@@ -63,4 +63,7 @@ def load_model(path: Path, index: int):
     supported_filetypes = [".onnx"]
     model_files = list_all_files_sorted(directory, supported_filetypes)
 
-    return Generator.from_list(model_files, load_model, fail_fast), directory
+    return (
+        Generator.from_list(model_files, load_model).with_fail_fast(fail_fast),
+        directory,
+    )

backend/src/packages/chaiNNer_pytorch/pytorch/iteration/load_models.py

@@ -62,4 +62,7 @@ def load_model(path: Path, index: int):
     supported_filetypes = [".pt", ".pth", ".ckpt", ".safetensors"]
     model_files: list[Path] = list_all_files_sorted(directory, supported_filetypes)
 
-    return Generator.from_list(model_files, load_model, fail_fast), directory
+    return (
+        Generator.from_list(model_files, load_model).with_fail_fast(fail_fast),
+        directory,
+    )

backend/src/packages/chaiNNer_standard/image/batch_processing/load_images.py

@@ -130,4 +130,7 @@ def load_image(path: Path, index: int):
     if use_limit:
         just_image_files = just_image_files[:limit]
 
-    return Generator.from_list(just_image_files, load_image, fail_fast), directory
+    return (
+        Generator.from_list(just_image_files, load_image).with_fail_fast(fail_fast),
+        directory,
+    )