chore: apply formatter suggestions (#263)

This commit applies black and isort formatting suggestions to the codebase.

Author: rickstaa

runner/app/dependencies.py

@@ -1,6 +1,7 @@
-from app.pipelines.base import Pipeline
 from fastapi import Request
 
+from app.pipelines.base import Pipeline
+
 
 def get_pipeline(request: Request) -> Pipeline:
     return request.app.pipeline

runner/app/main.py

@@ -2,10 +2,11 @@
 import os
 from contextlib import asynccontextmanager
 
-from app.routes import health
 from fastapi import FastAPI
 from fastapi.routing import APIRoute
 
+from app.routes import health
+
 logger = logging.getLogger(__name__)
 
 

runner/app/pipelines/audio_to_text.py

@@ -1,16 +1,17 @@
-from enum import Enum
 import logging
 import os
-from typing import List
 from dataclasses import dataclass
+from enum import Enum
+from typing import List
 
 import torch
+from fastapi import File, UploadFile
+from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
+
 from app.pipelines.base import Pipeline
 from app.pipelines.utils import get_model_dir, get_torch_device
 from app.pipelines.utils.audio import AudioConverter
 from app.utils.errors import InferenceError
-from fastapi import File, UploadFile
-from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
 
 logger = logging.getLogger(__name__)
 

runner/app/pipelines/image_to_image.py

@@ -5,16 +5,6 @@
 
 import PIL
 import torch
-from app.pipelines.base import Pipeline
-from app.pipelines.utils import (
-    LoraLoader,
-    SafetyChecker,
-    get_model_dir,
-    get_torch_device,
-    is_lightning_model,
-    is_turbo_model,
-)
-from app.utils.errors import InferenceError
 from diffusers import (
     AutoPipelineForImage2Image,
     EulerAncestralDiscreteScheduler,
@@ -27,6 +17,17 @@
 from PIL import ImageFile
 from safetensors.torch import load_file
 
+from app.pipelines.base import Pipeline
+from app.pipelines.utils import (
+    LoraLoader,
+    SafetyChecker,
+    get_model_dir,
+    get_torch_device,
+    is_lightning_model,
+    is_turbo_model,
+)
+from app.utils.errors import InferenceError
+
 ImageFile.LOAD_TRUNCATED_IMAGES = True
 
 logger = logging.getLogger(__name__)

runner/app/pipelines/image_to_text.py

@@ -2,12 +2,12 @@
 import os
 
 import torch
-from app.pipelines.base import Pipeline
-from app.pipelines.utils import get_model_dir, get_torch_device
 from huggingface_hub import file_download
-from transformers import BlipProcessor, BlipForConditionalGeneration
 from PIL import Image
+from transformers import BlipForConditionalGeneration, BlipProcessor
 
+from app.pipelines.base import Pipeline
+from app.pipelines.utils import get_model_dir, get_torch_device
 from app.utils.errors import InferenceError
 
 logger = logging.getLogger(__name__)

runner/app/pipelines/image_to_video.py

@@ -5,13 +5,14 @@
 
 import PIL
 import torch
-from app.pipelines.base import Pipeline
-from app.pipelines.utils import SafetyChecker, get_model_dir, get_torch_device
-from app.utils.errors import InferenceError
 from diffusers import StableVideoDiffusionPipeline
 from huggingface_hub import file_download
 from PIL import ImageFile
 
+from app.pipelines.base import Pipeline
+from app.pipelines.utils import SafetyChecker, get_model_dir, get_torch_device
+from app.utils.errors import InferenceError
+
 ImageFile.LOAD_TRUNCATED_IMAGES = True
 
 logger = logging.getLogger(__name__)

runner/app/pipelines/segment_anything_2.py

@@ -3,11 +3,12 @@
 
 import torch
 import PIL
+from PIL import ImageFile
+from sam2.sam2_image_predictor import SAM2ImagePredictor
+
 from app.pipelines.base import Pipeline
 from app.pipelines.utils import get_model_dir, get_torch_device
 from app.utils.errors import InferenceError
-from PIL import ImageFile
-from sam2.sam2_image_predictor import SAM2ImagePredictor
 
 ImageFile.LOAD_TRUNCATED_IMAGES = True
 

runner/app/pipelines/text_to_image.py

@@ -5,17 +5,6 @@
 
 import PIL
 import torch
-from app.pipelines.base import Pipeline
-from app.pipelines.utils import (
-    LoraLoader,
-    SafetyChecker,
-    get_model_dir,
-    get_torch_device,
-    is_lightning_model,
-    is_turbo_model,
-    split_prompt,
-)
-from app.utils.errors import InferenceError
 from diffusers import (
     AutoPipelineForText2Image,
     EulerDiscreteScheduler,
@@ -28,6 +17,18 @@
 from huggingface_hub import file_download, hf_hub_download
 from safetensors.torch import load_file
 
+from app.pipelines.base import Pipeline
+from app.pipelines.utils import (
+    LoraLoader,
+    SafetyChecker,
+    get_model_dir,
+    get_torch_device,
+    is_lightning_model,
+    is_turbo_model,
+    split_prompt,
+)
+from app.utils.errors import InferenceError
+
 logger = logging.getLogger(__name__)
 
 

runner/app/pipelines/text_to_speech.py

@@ -3,12 +3,13 @@
 
 import soundfile as sf
 import torch
-from app.utils.errors import InferenceError
-from app.pipelines.base import Pipeline
-from app.pipelines.utils import get_model_dir, get_torch_device
 from parler_tts import ParlerTTSForConditionalGeneration
 from transformers import AutoTokenizer
 
+from app.pipelines.base import Pipeline
+from app.pipelines.utils import get_model_dir, get_torch_device
+from app.utils.errors import InferenceError
+
 logger = logging.getLogger(__name__)
 
 

runner/app/pipelines/upscale.py

@@ -4,6 +4,10 @@
 
 import PIL
 import torch
+from diffusers import StableDiffusionUpscalePipeline
+from huggingface_hub import file_download
+from PIL import ImageFile
+
 from app.pipelines.base import Pipeline
 from app.pipelines.utils import (
     SafetyChecker,
@@ -13,9 +17,6 @@
     is_turbo_model,
 )
 from app.utils.errors import InferenceError
-from diffusers import StableDiffusionUpscalePipeline
-from huggingface_hub import file_download
-from PIL import ImageFile
 
 ImageFile.LOAD_TRUNCATED_IMAGES = True