Fix some lint errors.

app/setup.py

@@ -1,5 +1,6 @@
 import os
 from abc import ABC, abstractmethod
+from pathlib import Path
 
 import diffusers
 from modal import App, Image, Mount, Secret
@@ -13,24 +14,26 @@ BASE_CACHE_PATH_UPSCALER = "/vol/cache/upscaler"
 
 class StableDiffusionCLISetupInterface(ABC):
     @abstractmethod
-    def download_model(self):
+    def download_model(self) -> None:
         pass
 
 
 class StableDiffusionCLISetupSDXL(StableDiffusionCLISetupInterface):
-    def __init__(self, config: dict, token: str):
+    def __init__(self, config: dict, token: str) -> None:
         if config.get("version") != "sdxl":
-            raise ValueError("Invalid version. Must be 'sdxl'.")
+            msg = "Invalid version. Must be 'sdxl'."
+            raise ValueError(msg)
 
         if config.get("model") is None:
-            raise ValueError("Model is required. Please provide a model in config.yml.")
+            msg = "Model is required. Please provide a model in config.yml."
+            raise ValueError(msg)
 
         self.__model_name: str = config["model"]["name"]
         self.__model_url: str = config["model"]["url"]
         self.__token: str = token
 
     def download_model(self) -> None:
-        cache_path = os.path.join(BASE_CACHE_PATH, self.__model_name)
+        cache_path = Path(BASE_CACHE_PATH, self.__model_name)
         pipe = diffusers.StableDiffusionXLPipeline.from_single_file(
             pretrained_model_link_or_path=self.__model_url,
             use_auth_token=self.__token,
@@ -40,19 +43,21 @@ class StableDiffusionCLISetupSDXL(StableDiffusionCLISetupInterface):
 
 
 class StableDiffusionCLISetupSD15(StableDiffusionCLISetupInterface):
-    def __init__(self, config: dict, token: str):
+    def __init__(self, config: dict, token: str) -> None:
         if config.get("version") != "sd15":
-            raise ValueError("Invalid version. Must be 'sd15'.")
+            msg = "Invalid version. Must be 'sd15'."
+            raise ValueError(msg)
 
         if config.get("model") is None:
-            raise ValueError("Model is required. Please provide a model in config.yml.")
+            msg = "Model is required. Please provide a model in config.yml."
+            raise ValueError(msg)
 
         self.__model_name: str = config["model"]["name"]
         self.__model_url: str = config["model"]["url"]
         self.__token: str = token
 
     def download_model(self) -> None:
-        cache_path = os.path.join(BASE_CACHE_PATH, self.__model_name)
+        cache_path = Path(BASE_CACHE_PATH, self.__model_name)
         pipe = diffusers.StableDiffusionPipeline.from_single_file(
             pretrained_model_link_or_path=self.__model_url,
             token=self.__token,
@@ -63,13 +68,13 @@ class StableDiffusionCLISetupSD15(StableDiffusionCLISetupInterface):
 
     def __download_upscaler(self) -> None:
         upscaler = diffusers.StableDiffusionLatentUpscalePipeline.from_pretrained(
-            "stabilityai/sd-x2-latent-upscaler"
+            "stabilityai/sd-x2-latent-upscaler",
         )
         upscaler.save_pretrained(BASE_CACHE_PATH_UPSCALER, safe_serialization=True)
 
 
 class CommonSetup:
-    def __init__(self, config: dict, token: str):
+    def __init__(self, config: dict, token: str) -> None:
         self.__token: str = token
         self.__config: dict = config
 
@@ -105,8 +110,8 @@ class CommonSetup:
                     file_path=BASE_CACHE_PATH_TEXTUAL_INVERSION,
                 )
 
-    def __download_vae(self, name: str, model_url: str, token: str):
+    def __download_vae(self, name: str, model_url: str, token: str) -> None:
-        cache_path = os.path.join(BASE_CACHE_PATH, name)
+        cache_path = Path(BASE_CACHE_PATH, name)
         vae = diffusers.AutoencoderKL.from_single_file(
             pretrained_model_link_or_path=model_url,
             use_auth_token=token,
@@ -114,8 +119,8 @@ class CommonSetup:
         )
         vae.save_pretrained(cache_path, safe_serialization=True)
 
-    def __download_controlnet(self, name: str, repo_id: str, token: str):
+    def __download_controlnet(self, name: str, repo_id: str, token: str) -> None:
-        cache_path = os.path.join(BASE_CACHE_PATH_CONTROLNET, name)
+        cache_path = Path(BASE_CACHE_PATH, name)
         controlnet = diffusers.ControlNetModel.from_pretrained(
             repo_id,
             use_auth_token=token,
@@ -123,7 +128,7 @@ class CommonSetup:
         )
         controlnet.save_pretrained(cache_path, safe_serialization=True)
 
-    def __download_other_file(self, url, file_name, file_path):
+    def __download_other_file(self, url: str, file_name: str, file_path: str) -> None:
         """
         Download file from the given URL for LoRA or TextualInversion.
         """
@@ -131,20 +136,20 @@ class CommonSetup:
 
         req = Request(url, headers={"User-Agent": "Mozilla/5.0"})
         downloaded = urlopen(req).read()
-        dir_names = os.path.join(file_path, file_name)
+        dir_names = Path(file_path, file_name)
         os.makedirs(os.path.dirname(dir_names), exist_ok=True)
         with open(dir_names, mode="wb") as f:
             f.write(downloaded)
 
 
-def build_image():
+def build_image() -> None:
     """
     Build the Docker image.
     """
     import yaml
 
     token: str = os.environ["HUGGING_FACE_TOKEN"]
-    with open("/config.yml", "r") as file:
+    with open("/config.yml") as file:
         config: dict = yaml.safe_load(file)
 
     stable_diffusion_setup: StableDiffusionCLISetupInterface
@@ -154,9 +159,8 @@ def build_image():
         case "sdxl":
             stable_diffusion_setup = StableDiffusionCLISetupSDXL(config, token)
         case _:
-            raise ValueError(
+            msg = f"Invalid version: {config.get('version')}. Must be 'sd15' or 'sdxl'."
-                f"Invalid version: {config.get('version')}. Must be 'sd15' or 'sdxl'."
+            raise ValueError(msg)
-            )
 
     stable_diffusion_setup.download_model()
     common_setup = CommonSetup(config, token)

cmd/sdxl_txt2img.py

@@ -22,7 +22,7 @@ def main(
     seed: int = -1,
     use_upscaler: str = "False",
     output_format: str = "png",
-):
+) -> None:
     """This function is the entrypoint for the Runway CLI.
     The function pass the given prompt to StableDiffusion on Modal,
     gets back a list of images and outputs images to local.