Update app.py

app.py

@@ -20,14 +20,11 @@ else:
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 2048
 
-print(f"Device is: {device}")
 # Initialize the pipeline globally
 pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to(device)
 
 lora_weights = {
-    "jerry": {"path": "bryanbrunetti/jerryfluxlora", "weight_name": "jerry_000003000.safetensors"},
-    "sebastian": {"path": "bryanbrunetti/sebastianfluxlora", "weight_name": "sadie_flux.safetensors"},
-    "sadie": {"path": "bryanbrunetti/sadiefluxlora", "weight_name": "sebastian_flux_000001500.safetensors"},
+    "cajerky": {"path": "bryanbrunetti/cajerky"}
 }
 
 
@@ -41,8 +38,7 @@ def infer(prompt, lora_models, seed=42, randomize_seed=False, width=1024, height
         try:
             for lora_model in lora_models:
                 print(f"loading LoRA: {lora_model}")
-                pipe.load_lora_weights(lora_weights[lora_model]["path"],
-                                       weight_name=lora_weights[lora_model]["weight_name"])
+                pipe.load_lora_weights(lora_weights[lora_model]["path"])
         except Exception as e:
             return None, seed, f"Failed to load LoRA model: {str(e)}"