chore: ruff format

installer/lib/messages.py

@@ -137,7 +137,7 @@ def dest_path(dest=None) -> Path:
         path_completer = PathCompleter(
             only_directories=True,
             expanduser=True,
-            get_paths=lambda: [browse_start], # noqa: B023
+            get_paths=lambda: [browse_start],  # noqa: B023
             # get_paths=lambda: [".."].extend(list(browse_start.iterdir()))
         )
 
@@ -149,7 +149,7 @@ def dest_path(dest=None) -> Path:
             completer=path_completer,
             default=str(browse_start) + os.sep,
             vi_mode=True,
-            complete_while_typing=True
+            complete_while_typing=True,
             # Test that this is not needed on Windows
             # complete_style=CompleteStyle.READLINE_LIKE,
         )

invokeai/app/invocations/baseinvocation.py

@@ -661,9 +661,7 @@ def validate_fields(model_fields: dict[str, FieldInfo], model_type: str) -> None
 
         field_kind = (
             # _field_kind is defined via InputField(), OutputField() or by one of the internal fields defined in this file
-            field.json_schema_extra.get("_field_kind", None)
-            if field.json_schema_extra
-            else None
+            field.json_schema_extra.get("_field_kind", None) if field.json_schema_extra else None
         )
 
         # must have a field_kind

invokeai/app/services/image_records/image_records_common.py

@@ -90,20 +90,23 @@ class ImageRecordDeleteException(Exception):
 
 
 IMAGE_DTO_COLS = ", ".join(
-    ["images." + c for c in [
-                "image_name",
-                "image_origin",
-                "image_category",
-                "width",
-                "height",
-                "session_id",
-                "node_id",
-                "is_intermediate",
-                "created_at",
-                "updated_at",
-                "deleted_at",
-                "starred",
-            ]]
+    [
+        "images." + c
+        for c in [
+            "image_name",
+            "image_origin",
+            "image_category",
+            "width",
+            "height",
+            "session_id",
+            "node_id",
+            "is_intermediate",
+            "created_at",
+            "updated_at",
+            "deleted_at",
+            "starred",
+        ]
+    ]
 )
 
 

invokeai/app/services/images/images_default.py

@@ -217,13 +217,16 @@ class ImageService(ImageServiceABC):
                 board_id,
             )
 
-            image_dtos = [image_record_to_dto(
-                        image_record=r,
-                        image_url=self.__invoker.services.urls.get_image_url(r.image_name),
-                        thumbnail_url=self.__invoker.services.urls.get_image_url(r.image_name, True),
-                        board_id=self.__invoker.services.board_image_records.get_board_for_image(r.image_name),
-                        workflow_id=self.__invoker.services.workflow_image_records.get_workflow_for_image(r.image_name),
-                    ) for r in results.items]
+            image_dtos = [
+                image_record_to_dto(
+                    image_record=r,
+                    image_url=self.__invoker.services.urls.get_image_url(r.image_name),
+                    thumbnail_url=self.__invoker.services.urls.get_image_url(r.image_name, True),
+                    board_id=self.__invoker.services.board_image_records.get_board_for_image(r.image_name),
+                    workflow_id=self.__invoker.services.workflow_image_records.get_workflow_for_image(r.image_name),
+                )
+                for r in results.items
+            ]
 
             return OffsetPaginatedResults[ImageDTO](
                 items=image_dtos,

invokeai/app/services/invocation_processor/invocation_processor_base.py

@@ -1,5 +1,5 @@
 from abc import ABC
 
 
-class InvocationProcessorABC(ABC): # noqa: B024
+class InvocationProcessorABC(ABC):  # noqa: B024
     pass

invokeai/app/services/session_processor/session_processor_default.py

@@ -34,7 +34,9 @@ class DefaultSessionProcessor(SessionProcessorBase):
             name="session_processor",
             target=self.__process,
             kwargs={
-                "stop_event": self.__stop_event, "poll_now_event": self.__poll_now_event, "resume_event": self.__resume_event
+                "stop_event": self.__stop_event,
+                "poll_now_event": self.__poll_now_event,
+                "resume_event": self.__resume_event,
             },
         )
         self.__thread.start()

invokeai/app/services/shared/graph.py

@@ -728,9 +728,9 @@ class Graph(BaseModel):
         # Validate that all inputs are derived from or match a single type
         input_field_types = {
             t
-                for input_field in input_fields
-                for t in ([input_field] if get_origin(input_field) is None else get_args(input_field))
-                if t != NoneType
+            for input_field in input_fields
+            for t in ([input_field] if get_origin(input_field) is None else get_args(input_field))
+            if t != NoneType
         }  # Get unique types
         type_tree = nx.DiGraph()
         type_tree.add_nodes_from(input_field_types)
@@ -1053,7 +1053,10 @@ class GraphExecutionState(BaseModel):
             # For every iterator, the parent must either not be a child of that iterator, or must match the prepared iteration for that iterator
             # TODO: Handle a node mapping to none
             eg = self.execution_graph.nx_graph_flat()
-            prepared_parent_mappings = [[(n, self._get_iteration_node(n, g, eg, it)) for n in next_node_parents] for it in iterator_node_prepared_combinations]  # type: ignore
+            prepared_parent_mappings = [
+                [(n, self._get_iteration_node(n, g, eg, it)) for n in next_node_parents]
+                for it in iterator_node_prepared_combinations
+            ]  # type: ignore
 
             # Create execution node for each iteration
             for iteration_mappings in prepared_parent_mappings:

invokeai/backend/install/model_install_backend.py

@@ -253,13 +253,13 @@ class ModelInstall(object):
         # folders style or similar
         elif path.is_dir() and any(
             (path / x).exists()
-                for x in {
-                    "config.json",
-                    "model_index.json",
-                    "learned_embeds.bin",
-                    "pytorch_lora_weights.bin",
-                    "pytorch_lora_weights.safetensors",
-                }
+            for x in {
+                "config.json",
+                "model_index.json",
+                "learned_embeds.bin",
+                "pytorch_lora_weights.bin",
+                "pytorch_lora_weights.safetensors",
+            }
         ):
             models_installed.update({str(model_path_id_or_url): self._install_path(path)})
 

invokeai/backend/ip_adapter/attention_processor.py

@@ -130,7 +130,9 @@ class IPAttnProcessor2_0(torch.nn.Module):
             assert ip_adapter_image_prompt_embeds is not None
             assert len(ip_adapter_image_prompt_embeds) == len(self._weights)
 
-            for ipa_embed, ipa_weights, scale in zip(ip_adapter_image_prompt_embeds, self._weights, self._scales, strict=True):
+            for ipa_embed, ipa_weights, scale in zip(
+                ip_adapter_image_prompt_embeds, self._weights, self._scales, strict=True
+            ):
                 # The batch dimensions should match.
                 assert ipa_embed.shape[0] == encoder_hidden_states.shape[0]
                 # The token_len dimensions should match.

invokeai/backend/model_management/model_cache.py

@@ -66,11 +66,13 @@ class CacheStats(object):
 
 class ModelLocker(object):
     "Forward declaration"
+
     pass
 
 
 class ModelCache(object):
     "Forward declaration"
+
     pass
 
 

invokeai/backend/model_management/model_search.py

@@ -70,13 +70,13 @@ class ModelSearch(ABC):
                     continue
                 if any(
                     (path / x).exists()
-                        for x in {
-                            "config.json",
-                            "model_index.json",
-                            "learned_embeds.bin",
-                            "pytorch_lora_weights.bin",
-                            "image_encoder.txt",
-                        }
+                    for x in {
+                        "config.json",
+                        "model_index.json",
+                        "learned_embeds.bin",
+                        "pytorch_lora_weights.bin",
+                        "image_encoder.txt",
+                    }
                 ):
                     try:
                         self.on_model_found(path)

invokeai/backend/stable_diffusion/diffusers_pipeline.py

@@ -193,6 +193,7 @@ class InvokeAIStableDiffusionPipelineOutput(StableDiffusionPipelineOutput):
         attention_map_saver (`AttentionMapSaver`): Object containing attention maps that can be displayed to the user
          after generation completes. Optional.
     """
+
     attention_map_saver: Optional[AttentionMapSaver]
 
 

invokeai/backend/stable_diffusion/diffusion/cross_attention_control.py

@@ -433,7 +433,7 @@ def inject_attention_function(unet, context: Context):
         module.identifier = identifier
         try:
             module.set_attention_slice_wrangler(attention_slice_wrangler)
-            module.set_slicing_strategy_getter(lambda module: context.get_slicing_strategy(identifier)) # noqa: B023
+            module.set_slicing_strategy_getter(lambda module: context.get_slicing_strategy(identifier))  # noqa: B023
         except AttributeError as e:
             if is_attribute_error_about(e, "set_attention_slice_wrangler"):
                 print(f"TODO: implement set_attention_slice_wrangler for {type(module)}")  # TODO

invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py

@@ -642,7 +642,9 @@ class InvokeAIDiffuserComponent:
 
         deltas = None
         uncond_latents = None
-        weighted_cond_list = c_or_weighted_c_list if isinstance(c_or_weighted_c_list, list) else [(c_or_weighted_c_list, 1)]
+        weighted_cond_list = (
+            c_or_weighted_c_list if isinstance(c_or_weighted_c_list, list) else [(c_or_weighted_c_list, 1)]
+        )
 
         # below is fugly omg
         conditionings = [uc] + [c for c, weight in weighted_cond_list]

invokeai/backend/util/hotfixes.py

@@ -732,7 +732,9 @@ class ControlNetModel(ModelMixin, ConfigMixin, FromOriginalControlnetMixin):
 
         controlnet_down_block_res_samples = ()
 
-        for down_block_res_sample, controlnet_block in zip(down_block_res_samples, self.controlnet_down_blocks, strict=True):
+        for down_block_res_sample, controlnet_block in zip(
+            down_block_res_samples, self.controlnet_down_blocks, strict=True
+        ):
             down_block_res_sample = controlnet_block(down_block_res_sample)
             controlnet_down_block_res_samples = controlnet_down_block_res_samples + (down_block_res_sample,)
 
@@ -745,7 +747,9 @@ class ControlNetModel(ModelMixin, ConfigMixin, FromOriginalControlnetMixin):
             scales = torch.logspace(-1, 0, len(down_block_res_samples) + 1, device=sample.device)  # 0.1 to 1.0
 
             scales = scales * conditioning_scale
-            down_block_res_samples = [sample * scale for sample, scale in zip(down_block_res_samples, scales, strict=True)]
+            down_block_res_samples = [
+                sample * scale for sample, scale in zip(down_block_res_samples, scales, strict=True)
+            ]
             mid_block_res_sample = mid_block_res_sample * scales[-1]  # last one
         else:
             down_block_res_samples = [sample * conditioning_scale for sample in down_block_res_samples]

invokeai/backend/util/util.py

@@ -229,7 +229,11 @@ def rand_perlin_2d(shape, res, device, fade=lambda t: 6 * t**5 - 15 * t**4 + 10
     gradients = torch.stack((torch.cos(angles), torch.sin(angles)), dim=-1).to(device)
 
     def tile_grads(slice1, slice2):
-        return gradients[slice1[0]:slice1[1], slice2[0]:slice2[1]].repeat_interleave(d[0], 0).repeat_interleave(d[1], 1)
+        return (
+            gradients[slice1[0] : slice1[1], slice2[0] : slice2[1]]
+            .repeat_interleave(d[0], 0)
+            .repeat_interleave(d[1], 1)
+        )
 
     def dot(grad, shift):
         return (

invokeai/frontend/install/model_install.py

@@ -72,7 +72,7 @@ class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
     def __init__(self, parentApp, name, multipage=False, *args, **keywords):
         self.multipage = multipage
         self.subprocess = None
-        super().__init__(parentApp=parentApp, name=name, *args, **keywords) # noqa: B026 # TODO: maybe this is bad?
+        super().__init__(parentApp=parentApp, name=name, *args, **keywords)  # noqa: B026 # TODO: maybe this is bad?
 
     def create(self):
         self.keypress_timeout = 10

scripts/configure_invokeai.py

@@ -6,5 +6,7 @@ import warnings
 from invokeai.frontend.install.invokeai_configure import invokeai_configure as configure
 
 if __name__ == "__main__":
-    warnings.warn("configure_invokeai.py is deprecated, running 'invokeai-configure'...", DeprecationWarning, stacklevel=2)
+    warnings.warn(
+        "configure_invokeai.py is deprecated, running 'invokeai-configure'...", DeprecationWarning, stacklevel=2
+    )
     configure()