Run Ruff

invokeai/app/invocations/flux_text_encoder.py

@@ -1,14 +1,9 @@
-from pathlib import Path
-
 import torch
 from diffusers.pipelines.flux.pipeline_flux import FluxPipeline
-from optimum.quanto import qfloat8
 from transformers import CLIPTextModel, CLIPTokenizer, T5EncoderModel, T5TokenizerFast
 
 from invokeai.app.invocations.baseinvocation import BaseInvocation, invocation
-from invokeai.app.invocations.model import CLIPField, T5EncoderField
-from invokeai.app.invocations.fields import InputField, FieldDescriptions, Input
-from invokeai.app.invocations.flux_text_to_image import FLUX_MODELS, QuantizedModelForTextEncoding
+from invokeai.app.invocations.fields import FieldDescriptions, Input, InputField
 from invokeai.app.invocations.model import CLIPField, T5EncoderField
 from invokeai.app.invocations.primitives import ConditioningOutput
 from invokeai.app.services.shared.invocation_context import InvocationContext
@@ -40,15 +35,14 @@ class FluxTextEncoderInvocation(BaseInvocation):
     # compatible with other ConditioningOutputs.
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> ConditioningOutput:
-
         t5_embeddings, clip_embeddings = self._encode_prompt(context)
         conditioning_data = ConditioningFieldData(
             conditionings=[FLUXConditioningInfo(clip_embeds=clip_embeddings, t5_embeds=t5_embeddings)]
         )
 
         conditioning_name = context.conditioning.save(conditioning_data)
         return ConditioningOutput.build(conditioning_name)
-    
+
     def _encode_prompt(self, context: InvocationContext) -> tuple[torch.Tensor, torch.Tensor]:
         # TODO: Determine the T5 max sequence length based on the model.
         # if self.model == "flux-schnell":

invokeai/app/invocations/flux_text_to_image.py

@@ -1,13 +1,9 @@
-from pathlib import Path
 from typing import Literal
-from pydantic import Field
 
 import torch
 from diffusers import AutoencoderKL, FlowMatchEulerDiscreteScheduler
 from diffusers.models.transformers.transformer_flux import FluxTransformer2DModel
 from diffusers.pipelines.flux.pipeline_flux import FluxPipeline
-from invokeai.app.invocations.model import TransformerField, VAEField
-from optimum.quanto import qfloat8
 from PIL import Image
 from transformers.models.auto import AutoModelForTextEncoding
 
@@ -19,8 +15,8 @@
     InputField,
     WithBoard,
     WithMetadata,
-    UIType,
 )
+from invokeai.app.invocations.model import TransformerField, VAEField
 from invokeai.app.invocations.primitives import ImageOutput
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.backend.quantization.fast_quantized_diffusion_model import FastQuantizedDiffusersModel
@@ -72,7 +68,6 @@ class FluxTextToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
 
     @torch.no_grad()
     def invoke(self, context: InvocationContext) -> ImageOutput:
-
         # Load the conditioning data.
         cond_data = context.conditioning.load(self.positive_text_conditioning.conditioning_name)
         assert len(cond_data.conditionings) == 1

invokeai/backend/quantization/fast_quantized_diffusion_model.py

@@ -3,6 +3,7 @@
 from typing import Union
 
 from diffusers.models.model_loading_utils import load_state_dict
+from diffusers.models.transformers.transformer_flux import FluxTransformer2DModel
 from diffusers.utils import (
     CONFIG_NAME,
     SAFE_WEIGHTS_INDEX_NAME,
@@ -12,7 +13,6 @@
 )
 from optimum.quanto.models import QuantizedDiffusersModel
 from optimum.quanto.models.shared_dict import ShardedStateDict
-from diffusers.models.transformers.transformer_flux import FluxTransformer2DModel
 
 from invokeai.backend.requantize import requantize
 

invokeai/backend/quantization/fast_quantized_transformers_model.py

@@ -1,14 +1,13 @@
 import json
 import os
-import torch
 from typing import Union
 
 from optimum.quanto.models import QuantizedTransformersModel
 from optimum.quanto.models.shared_dict import ShardedStateDict
 from transformers import AutoConfig
 from transformers.modeling_utils import get_checkpoint_shard_files, load_state_dict
-from transformers.utils import SAFE_WEIGHTS_INDEX_NAME, SAFE_WEIGHTS_NAME, is_accelerate_available
 from transformers.models.auto import AutoModelForTextEncoding
+from transformers.utils import SAFE_WEIGHTS_INDEX_NAME, SAFE_WEIGHTS_NAME, is_accelerate_available
 
 from invokeai.backend.requantize import requantize