Update app.py

app.py

@@ -42,19 +42,18 @@ last_mode = "txt2img"
 current_model = models[0]
 current_model_path = current_model.path
 
-else:
-  print(f"{datetime.datetime.now()} Downloading vae...")
-  vae = AutoencoderKL.from_pretrained(current_model.path, subfolder="vae")
-  for model in models:
-    try:
-        print(f"{datetime.datetime.now()} Downloading {model.name} model...")
-        unet = UNet2DConditionModel.from_pretrained(model.path, subfolder="unet")
-        model.pipe_t2i = StableDiffusionPipeline.from_pretrained(model.path, unet=unet, vae=vae, scheduler=scheduler)
-        model.pipe_i2i = StableDiffusionImg2ImgPipeline.from_pretrained(model.path, unet=unet, vae=vae, scheduler=scheduler)
-    except Exception as e:
-        print(f"{datetime.datetime.now()} Failed to load model " + model.name + ": " + str(e))
-        models.remove(model)
-  pipe = models[0].pipe_t2i
+print(f"{datetime.datetime.now()} Downloading vae...")
+vae = AutoencoderKL.from_pretrained(current_model.path, subfolder="vae")
+for model in models:
+try:
+    print(f"{datetime.datetime.now()} Downloading {model.name} model...")
+    unet = UNet2DConditionModel.from_pretrained(model.path, subfolder="unet")
+    model.pipe_t2i = StableDiffusionPipeline.from_pretrained(model.path, unet=unet, vae=vae, scheduler=scheduler)
+    model.pipe_i2i = StableDiffusionImg2ImgPipeline.from_pretrained(model.path, unet=unet, vae=vae, scheduler=scheduler)
+except Exception as e:
+    print(f"{datetime.datetime.now()} Failed to load model " + model.name + ": " + str(e))
+    models.remove(model)
+pipe = models[0].pipe_t2i
   
 if torch.cuda.is_available():
   pipe = pipe.to("cuda")