Update app.py

Restoring until fix is found later

app.py

@@ -22,8 +22,18 @@ class Model:
     def __init__(self, name, path=""):
         self.name = name
         self.path = path
-        self.pipe_t2i = None
-        self.pipe_i2i = None
+
+        if path != "":
+            self.pipe_t2i = StableDiffusionPipeline.from_pretrained(
+                path, torch_dtype=torch.float16, safety_checker=SAFETY_CHECKER
+            )
+            self.pipe_t2i.scheduler = DPMSolverMultistepScheduler.from_config(
+                self.pipe_t2i.scheduler.config
+            )
+            self.pipe_i2i = StableDiffusionImg2ImgPipeline(**self.pipe_t2i.components)
+        else:
+            self.pipe_t2i = None
+            self.pipe_i2i = None
 
 
 models = [
@@ -41,19 +51,6 @@ MODELS = {m.name: m for m in models}
 
 device = "GPU 🔥" if torch.cuda.is_available() else "CPU 🥶"
 
-def get_model(name):
-    model = MODELS[name]
-
-    if model.pipe_t2i is None:
-        model.pipe_t2i = StableDiffusionPipeline.from_pretrained(
-            model.path, torch_dtype=torch.float16, safety_checker=SAFETY_CHECKER
-        )
-        model.pipe_t2i.scheduler = DPMSolverMultistepScheduler.from_config(
-            model.pipe_t2i.scheduler.config
-        )
-        model.pipe_i2i = StableDiffusionImg2ImgPipeline(**model.pipe_t2i.components)
-
-        return model
 
 def error_str(error, title="Error"):
     return (
@@ -63,6 +60,7 @@ def error_str(error, title="Error"):
         else ""
     )
 
+
 def inference(
     model_name,
     prompt,
@@ -137,12 +135,9 @@ def txt_to_img(
 ):
     pipe = MODELS[model_name].pipe_t2i
 
-    if pipe is not None:
-        if torch.cuda.is_available():
-            pipe = pipe.to("cuda")
-            pipe.enable_xformers_memory_efficient_attention()
-    else:
-        raise ValueError(f"Unable to find pipeline for model: {model_name}")
+    if torch.cuda.is_available():
+        pipe = pipe.to("cuda")
+        pipe.enable_xformers_memory_efficient_attention()
 
     result = pipe(
         prompt,
@@ -155,12 +150,12 @@ def txt_to_img(
         generator=generator,
     )
 
-    if pipe is not None:
-        pipe.to("cpu")
-        torch.cuda.empty_cache()
+    pipe.to("cpu")
+    torch.cuda.empty_cache()
 
     return replace_nsfw_images(result)
 
+
 def img_to_img(
     model_name,
     prompt,
@@ -175,14 +170,11 @@ def img_to_img(
     generator,
     seed,
 ):
-    pipe = model.pipe_i2i
+    pipe = MODELS[model_name].pipe_i2i
 
-    if pipe is not None:
-        if torch.cuda.is_available():
-            pipe = pipe.to("cuda")
-            pipe.enable_xformers_memory_efficient_attention()
-    else:
-        raise ValueError(f"Unable to find pipeline for model: {model_name}")
+    if torch.cuda.is_available():
+        pipe = pipe.to("cuda")
+        pipe.enable_xformers_memory_efficient_attention()
 
     ratio = min(height / img.height, width / img.width)
     img = img.resize((int(img.width * ratio), int(img.height * ratio)), Image.LANCZOS)
@@ -198,18 +190,19 @@ def img_to_img(
         generator=generator,
     )
 
-    if pipe is not None:
-        pipe.to("cpu")
-        torch.cuda.empty_cache()
+    pipe.to("cpu")
+    torch.cuda.empty_cache()
 
     return replace_nsfw_images(result)
 
+
 def replace_nsfw_images(results):
     for i in range(len(results.images)):
         if results.nsfw_content_detected[i]:
             results.images[i] = Image.open("nsfw.png")
     return results.images
 
+
 with gr.Blocks(css="style.css") as demo:
     gr.HTML(
         f"""