chore: ruff

Author: psychedelicious

invokeai/app/invocations/bria_controlnet.py

@@ -19,7 +19,7 @@
 from invokeai.app.invocations.model import ModelIdentifierField
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.backend.bria.controlnet_bria import BRIA_CONTROL_MODES
-from invokeai.invocation_api import Classification, ImageOutput
+from invokeai.invocation_api import Classification
 
 DEPTH_SMALL_V2_URL = "depth-anything/Depth-Anything-V2-Small-hf"
 HF_LLLYASVIEL = "https://huggingface.co/lllyasviel/Annotators/resolve/main/"
@@ -95,7 +95,6 @@ def invoke(self, context: InvocationContext) -> BriaControlNetOutput:
 }
 
 
-
 def convert_to_grayscale(image: Image.Image) -> Image.Image:
     gray_image = image.convert("L").convert("RGB")
     return gray_image

invokeai/app/invocations/bria_denoiser.py

@@ -34,7 +34,6 @@ class BriaDenoiseInvocationOutput(BaseInvocationOutput):
     classification=Classification.Prototype,
 )
 class BriaDenoiseInvocation(BaseInvocation):
-
     """
     Denoise Bria latents using a Bria Pipeline.
     """
@@ -132,7 +131,6 @@ def invoke(self, context: InvocationContext) -> BriaDenoiseInvocationOutput:
                     device=vae.device,
                 )
 
-
             pipeline = BriaControlNetPipeline(
                 transformer=transformer,
                 scheduler=scheduler,
@@ -159,11 +157,11 @@ def invoke(self, context: InvocationContext) -> BriaDenoiseInvocationOutput:
                 step_callback=_build_step_callback(context),
             )[0]
 
-            
-
         assert isinstance(output_latents, torch.Tensor)
         saved_input_latents_tensor = context.tensors.save(output_latents)
-        return BriaDenoiseInvocationOutput(latents=LatentsField(latents_name=saved_input_latents_tensor), height=self.height, width=self.width)
+        return BriaDenoiseInvocationOutput(
+            latents=LatentsField(latents_name=saved_input_latents_tensor), height=self.height, width=self.width
+        )
 
     def _prepare_multi_control(
         self, context: InvocationContext, vae: AutoencoderKL, width: int, height: int, device: torch.device

invokeai/app/invocations/bria_latent_noise.py

@@ -1,11 +1,9 @@
 import torch
-from pydantic import BaseModel, Field 
 
 from invokeai.app.invocations.fields import Input, InputField, OutputField
 from invokeai.app.invocations.model import TransformerField
 from invokeai.app.invocations.primitives import (
     BaseInvocationOutput,
-    FieldDescriptions,
     LatentsField,
 )
 from invokeai.backend.bria.pipeline_bria_controlnet import prepare_latents
@@ -21,11 +19,13 @@
 @invocation_output("bria_latent_noise_output")
 class BriaLatentNoiseInvocationOutput(BaseInvocationOutput):
     """Base class for nodes that output Bria latent tensors."""
+
     latents: LatentsField = OutputField(description="The latent noise")
     latent_image_ids: LatentsField = OutputField(description="The latent image ids.")
     height: int = OutputField(description="The height of the output image")
     width: int = OutputField(description="The width of the output image")
 
+
 @invocation(
     "bria_latent_noise",
     title="Latent Noise - Bria",
@@ -35,7 +35,7 @@ class BriaLatentNoiseInvocationOutput(BaseInvocationOutput):
     classification=Classification.Prototype,
 )
 class BriaLatentNoiseInvocation(BaseInvocation):
-    """ Generate latent noise for Bria. """
+    """Generate latent noise for Bria."""
 
     seed: int = InputField(
         default=42,

invokeai/app/invocations/bria_latents_to_image.py

@@ -69,4 +69,4 @@ def _unpack_latents(latents, height, width, vae_scale_factor=16):
 
     latents = latents.reshape(batch_size, channels // (2 * 2), height * 2, width * 2)
 
-    return latents
+    return latents

invokeai/app/invocations/bria_text_encoder.py

@@ -1,4 +1,4 @@
-from typing import Literal, Optional
+from typing import Optional
 
 import torch
 from transformers import (
@@ -13,14 +13,15 @@
 from invokeai.invocation_api import (
     BaseInvocation,
     Classification,
-    InputField,
     FluxConditioningField,
+    InputField,
     invocation,
     invocation_output,
 )
 
 DEFAULT_NEGATIVE_PROMPT = "Logo,Watermark,Text,Ugly,Morbid,Extra fingers,Poorly drawn hands,Mutation,Blurry,Extra limbs,Gross proportions,Missing arms,Mutated hands,Long neck,Duplicate"
 
+
 @invocation_output("bria_text_encoder_output")
 class BriaTextEncoderInvocationOutput(BaseInvocationOutput):
     """Base class for nodes that output a Bria text conditioning tensor."""

invokeai/backend/bria/bria_utils.py

@@ -1,15 +1,9 @@
-import math
-import os
 from typing import List, Optional, Union
 
 import numpy as np
 import torch
-import torch.distributed as dist
 from diffusers.utils import logging
 from transformers import (
-    CLIPTextModel,
-    CLIPTextModelWithProjection,
-    CLIPTokenizer,
     T5EncoderModel,
     T5TokenizerFast,
 )
@@ -91,7 +85,6 @@ def is_ng_none(negative_prompt):
     )
 
 
-
 def get_1d_rotary_pos_embed(
     dim: int,
     pos: Union[np.ndarray, int],

invokeai/backend/bria/pipeline_bria.py

@@ -1,19 +1,15 @@
-from typing import Any, Callable, Dict, List, Optional, Union
+from typing import List, Optional, Union
 
-import diffusers
-import numpy as np
 import torch
-from diffusers import AutoencoderKL, DDIMScheduler, EulerAncestralDiscreteScheduler
+from diffusers import AutoencoderKL
 from diffusers.image_processor import VaeImageProcessor
 from diffusers.loaders import FluxLoraLoaderMixin
-from diffusers.pipelines.flux.pipeline_flux import FluxPipeline, calculate_shift, retrieve_timesteps
-from diffusers.pipelines.flux.pipeline_output import FluxPipelineOutput
+from diffusers.pipelines.flux.pipeline_flux import FluxPipeline
 from diffusers.pipelines.pipeline_utils import DiffusionPipeline
 from diffusers.schedulers import FlowMatchEulerDiscreteScheduler, KarrasDiffusionSchedulers
 from diffusers.utils import (
     USE_PEFT_BACKEND,
     logging,
-    replace_example_docstring,
     scale_lora_layers,
     unscale_lora_layers,
 )
@@ -23,7 +19,7 @@
     T5TokenizerFast,
 )
 
-from invokeai.backend.bria.bria_utils import get_original_sigmas, get_t5_prompt_embeds, is_ng_none
+from invokeai.backend.bria.bria_utils import get_t5_prompt_embeds, is_ng_none
 from invokeai.backend.bria.transformer_bria import BriaTransformer2DModel
 
 logger = logging.get_logger(__name__)
@@ -226,7 +222,6 @@ def num_timesteps(self):
     def interrupt(self):
         return self._interrupt
 
-
     def check_inputs(
         self,
         prompt,
@@ -265,7 +260,6 @@ def check_inputs(
                 f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
             )
 
-
     def to(self, *args, **kwargs):
         DiffusionPipeline.to(self, *args, **kwargs)
         # T5 is senstive to precision so we use the precision used for precompute and cast as needed

invokeai/backend/bria/pipeline_bria_controlnet.py

@@ -40,9 +40,9 @@
 
 
 class BriaControlNetPipeline(BriaPipeline):
-    r"""
+    """
     Bria pipeline that supports optional ControlNet models.
-    
+
     Args:
         transformer ([`SD3Transformer2DModel`]):
             Conditional Transformer (MMDiT) architecture to denoise the encoded image latents.
@@ -625,7 +625,6 @@ def encode_prompt(
             # Retrieve the original scale by scaling back the LoRA layers
             unscale_lora_layers(text_encoder, lora_scale)
 
-
     return prompt_embeds, negative_prompt_embeds
 
 

invokeai/backend/model_manager/config.py

@@ -442,6 +442,7 @@ def parse(cls, mod: ModelOnDisk) -> dict[str, Any]:
             "base": cls.base_model(mod),
         }
 
+
 class VAECheckpointConfig(CheckpointConfigBase, LegacyProbeMixin, ModelConfigBase):
     """Model config for standalone VAE models."""
 
@@ -519,6 +520,7 @@ class MainDiffusersConfig(DiffusersConfigBase, MainConfigBase, LegacyProbeMixin,
 
     pass
 
+
 class BriaDiffusersConfig(DiffusersConfigBase, MainConfigBase, ModelConfigBase):
     """Model config for Bria/Diffusers models."""
 
@@ -547,6 +549,7 @@ def parse(cls, mod: ModelOnDisk) -> dict[str, Any]:
     def get_tag(cls) -> Tag:
         return Tag(f"{ModelType.Main.value}.{ModelFormat.Diffusers.value}.{BaseModelType.Bria.value}")
 
+
 class BriaControlNetDiffusersConfig(DiffusersConfigBase, ControlAdapterConfigBase, ModelConfigBase):
     """Model config for Bria/Diffusers ControlNet models."""
 

invokeai/backend/model_manager/load/model_loaders/generic_diffusers.py

@@ -63,7 +63,9 @@ def get_hf_load_class(self, model_path: Path, submodel_type: Optional[SubModelTy
             try:
                 config = self._load_diffusers_config(model_path, config_name="config.json")
                 if class_name := config.get("_class_name"):
-                    result = self._hf_definition_to_type(module="diffusers", class_name=class_name, model_name=model_path.name)
+                    result = self._hf_definition_to_type(
+                        module="diffusers", class_name=class_name, model_name=model_path.name
+                    )
                 elif class_name := config.get("architectures"):
                     result = self._hf_definition_to_type(module="transformers", class_name=class_name[0])
                 else:
@@ -74,7 +76,9 @@ def get_hf_load_class(self, model_path: Path, submodel_type: Optional[SubModelTy
         return result
 
     # TO DO: Add exception handling
-    def _hf_definition_to_type(self, module: str, class_name: str, model_name: Optional[str] = None) -> ModelMixin:  # fix with correct type
+    def _hf_definition_to_type(
+        self, module: str, class_name: str, model_name: Optional[str] = None
+    ) -> ModelMixin:  # fix with correct type
         if module in [
             "diffusers",
             "transformers",