load_lora_weights

app.py

@@ -33,15 +33,12 @@ def infer(
     else:
         torch_dtype = torch.float32
     #print(model_id)
+    pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch_dtype)
     if model_id == "CompVis/stable-diffusion-v1-4" and lora == "pepe":
         lora_id = "seregasmirnov/pepe-lora"
-        pipe = DiffusionPipeline.from_pretrained(model_id, lora_id, torch_dtype=torch_dtype)
-    else:
-        pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch_dtype)
+        pipe.load_lora_weights(lora_id)
     pipe = pipe.to(device)
 
-
-
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)