浏览代码

chore: fix typos

Signed-off-by: snoppy <michaleli@foxmail.com>
snoppy 1 年之前
父节点
当前提交
13f22974a4

+ 1 - 1
extensions-builtin/LDSR/sd_hijack_ddpm_v1.py

@@ -572,7 +572,7 @@ class LatentDiffusionV1(DDPMV1):
         :param h: height
         :param h: height
         :param w: width
         :param w: width
         :return: normalized distance to image border,
         :return: normalized distance to image border,
-         wtith min distance = 0 at border and max dist = 0.5 at image center
+         with min distance = 0 at border and max dist = 0.5 at image center
         """
         """
         lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2)
         lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2)
         arr = self.meshgrid(h, w) / lower_right_corner
         arr = self.meshgrid(h, w) / lower_right_corner

+ 1 - 1
modules/api/api.py

@@ -372,7 +372,7 @@ class Api:
             return {}
             return {}
 
 
         possible_fields = infotext_utils.paste_fields[tabname]["fields"]
         possible_fields = infotext_utils.paste_fields[tabname]["fields"]
-        set_fields = request.model_dump(exclude_unset=True) if hasattr(request, "request") else request.dict(exclude_unset=True)  # pydantic v1/v2 have differenrt names for this
+        set_fields = request.model_dump(exclude_unset=True) if hasattr(request, "request") else request.dict(exclude_unset=True)  # pydantic v1/v2 have different names for this
         params = infotext_utils.parse_generation_parameters(request.infotext)
         params = infotext_utils.parse_generation_parameters(request.infotext)
 
 
         def get_field_value(field, params):
         def get_field_value(field, params):

+ 1 - 1
modules/devices.py

@@ -258,7 +258,7 @@ def test_for_nans(x, where):
 @lru_cache
 @lru_cache
 def first_time_calculation():
 def first_time_calculation():
     """
     """
-    just do any calculation with pytorch layers - the first time this is done it allocaltes about 700MB of memory and
+    just do any calculation with pytorch layers - the first time this is done it allocates about 700MB of memory and
     spends about 2.7 seconds doing that, at least with NVidia.
     spends about 2.7 seconds doing that, at least with NVidia.
     """
     """
 
 

+ 1 - 1
modules/models/diffusion/uni_pc/uni_pc.py

@@ -323,7 +323,7 @@ def model_wrapper(
 
 
     def model_fn(x, t_continuous, condition, unconditional_condition):
     def model_fn(x, t_continuous, condition, unconditional_condition):
         """
         """
-        The noise predicition model function that is used for DPM-Solver.
+        The noise prediction model function that is used for DPM-Solver.
         """
         """
         if t_continuous.reshape((-1,)).shape[0] == 1:
         if t_continuous.reshape((-1,)).shape[0] == 1:
             t_continuous = t_continuous.expand((x.shape[0]))
             t_continuous = t_continuous.expand((x.shape[0]))

+ 1 - 1
modules/shared.py

@@ -47,7 +47,7 @@ restricted_opts: set[str] = None
 sd_model: sd_models_types.WebuiSdModel = None
 sd_model: sd_models_types.WebuiSdModel = None
 
 
 settings_components: dict = None
 settings_components: dict = None
-"""assigned from ui.py, a mapping on setting names to gradio components repsponsible for those settings"""
+"""assigned from ui.py, a mapping on setting names to gradio components responsible for those settings"""
 
 
 tab_names = []
 tab_names = []
 
 

+ 1 - 1
modules/util.py

@@ -156,7 +156,7 @@ class MassFileLister:
 
 
 def topological_sort(dependencies):
 def topological_sort(dependencies):
     """Accepts a dictionary mapping name to its dependencies, returns a list of names ordered according to dependencies.
     """Accepts a dictionary mapping name to its dependencies, returns a list of names ordered according to dependencies.
-    Ignores errors relating to missing dependeencies or circular dependencies
+    Ignores errors relating to missing dependencies or circular dependencies
     """
     """
 
 
     visited = {}
     visited = {}