chore: apply black formatter (#153)

This commit applies the black formatter to the codebase to ensure the
code formatting is consistent.

runner/app/pipelines/audio_to_text.py

@@ -45,7 +45,11 @@ def __init__(self, model_id: str):
             kwargs["torch_dtype"] = torch.bfloat16
 
         model = AutoModelForSpeechSeq2Seq.from_pretrained(
-            model_id, low_cpu_mem_usage=True, use_safetensors=True, cache_dir=get_model_dir(), **kwargs
+            model_id,
+            low_cpu_mem_usage=True,
+            use_safetensors=True,
+            cache_dir=get_model_dir(),
+            **kwargs,
         ).to(torch_device)
 
         processor = AutoProcessor.from_pretrained(model_id, cache_dir=get_model_dir())

runner/app/pipelines/image_to_image.py

@@ -6,13 +6,21 @@
 import PIL
 import torch
 from app.pipelines.base import Pipeline
-from app.pipelines.utils import (SafetyChecker, get_model_dir,
-                                 get_torch_device, is_lightning_model,
-                                 is_turbo_model)
-from diffusers import (AutoPipelineForImage2Image,
-                       EulerAncestralDiscreteScheduler, EulerDiscreteScheduler,
-                       StableDiffusionInstructPix2PixPipeline,
-                       StableDiffusionXLPipeline, UNet2DConditionModel)
+from app.pipelines.utils import (
+    SafetyChecker,
+    get_model_dir,
+    get_torch_device,
+    is_lightning_model,
+    is_turbo_model,
+)
+from diffusers import (
+    AutoPipelineForImage2Image,
+    EulerAncestralDiscreteScheduler,
+    EulerDiscreteScheduler,
+    StableDiffusionInstructPix2PixPipeline,
+    StableDiffusionXLPipeline,
+    UNet2DConditionModel,
+)
 from huggingface_hub import file_download, hf_hub_download
 from PIL import ImageFile
 from safetensors.torch import load_file

runner/app/pipelines/optim/sfast.py

@@ -5,8 +5,7 @@
 
 import logging
 
-from sfast.compilers.diffusion_pipeline_compiler import (CompilationConfig,
-                                                         compile)
+from sfast.compilers.diffusion_pipeline_compiler import CompilationConfig, compile
 
 logger = logging.getLogger(__name__)
 

runner/app/pipelines/text_to_image.py

@@ -17,10 +17,10 @@
 from diffusers import (
     AutoPipelineForText2Image,
     EulerDiscreteScheduler,
+    FluxPipeline,
     StableDiffusion3Pipeline,
     StableDiffusionXLPipeline,
     UNet2DConditionModel,
-    FluxPipeline,
 )
 from diffusers.models import AutoencoderKL
 from huggingface_hub import file_download, hf_hub_download

runner/app/pipelines/upscale.py

@@ -5,9 +5,13 @@
 import PIL
 import torch
 from app.pipelines.base import Pipeline
-from app.pipelines.utils import (SafetyChecker, get_model_dir,
-                                 get_torch_device, is_lightning_model,
-                                 is_turbo_model)
+from app.pipelines.utils import (
+    SafetyChecker,
+    get_model_dir,
+    get_torch_device,
+    is_lightning_model,
+    is_turbo_model,
+)
 from diffusers import StableDiffusionUpscalePipeline
 from huggingface_hub import file_download
 from PIL import ImageFile

runner/app/pipelines/utils/__init__.py

@@ -1,6 +1,12 @@
 """This module contains several utility functions that are used across the pipelines module."""
 
-from app.pipelines.utils.utils import (SafetyChecker, get_model_dir,
-                                       get_model_path, get_torch_device,
-                                       is_lightning_model, is_turbo_model,
-                                       split_prompt, validate_torch_device)
+from app.pipelines.utils.utils import (
+    SafetyChecker,
+    get_model_dir,
+    get_model_path,
+    get_torch_device,
+    is_lightning_model,
+    is_turbo_model,
+    split_prompt,
+    validate_torch_device,
+)

runner/app/pipelines/utils/utils.py

@@ -4,15 +4,14 @@
 import os
 import re
 from pathlib import Path
-from typing import Optional
+from typing import Dict, Optional
 
 import numpy as np
 import torch
 from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker
 from PIL import Image
 from torch import dtype as TorchDtype
 from transformers import CLIPImageProcessor
-from typing import Dict
 
 logger = logging.getLogger(__name__)
 
@@ -99,10 +98,14 @@ def split_prompt(
     Returns:
         Dict[str, str]: A dictionary of all prompts, including the main prompt.
     """
-    prompts = [prompt.strip() for prompt in input_prompt.split(separator, max_splits) if prompt.strip()]
+    prompts = [
+        prompt.strip()
+        for prompt in input_prompt.split(separator, max_splits)
+        if prompt.strip()
+    ]
     if not prompts:
         return {}
-    
+
     start_index = max(1, len(prompts) - max_splits) if max_splits >= 0 else 1
 
     prompt_dict = {f"{key_prefix}": prompts[0]}

runner/gen_openapi.py

@@ -5,8 +5,14 @@
 
 import yaml
 from app.main import app, use_route_names_as_operation_ids
-from app.routes import (audio_to_text, health, image_to_image, image_to_video,
-                        text_to_image, upscale)
+from app.routes import (
+    audio_to_text,
+    health,
+    image_to_image,
+    image_to_video,
+    text_to_image,
+    upscale,
+)
 from fastapi.openapi.utils import get_openapi
 
 # Specify Endpoints for OpenAPI schema generation.

runner/modal_app.py

@@ -2,8 +2,7 @@
 import os
 from pathlib import Path
 
-from app.main import (config_logging, load_route,
-                      use_route_names_as_operation_ids)
+from app.main import config_logging, load_route, use_route_names_as_operation_ids
 from app.routes import health
 from modal import Image, Secret, Stub, Volume, asgi_app, enter, method