Update app_lora1.py

app_lora1.py

@@ -160,42 +160,31 @@ def get_pipeline(script_name):
         return PIPELINES[script_name]
 
     log(f"🔧 Building pipeline from {script_name}")
-    code = SCRIPT_CODE[script_name]
-
-    # Extract all lines after from_pretrained
-    pipe_lines = extract_pipe_lines(code)
-    pipe_code = "\n".join(pipe_lines)  # <-- combine into full block
-
-    # Safe namespace
-    from diffusers.models.transformers.z_image_transformer_2d import ZImageTransformer2DModel
-    from diffusers.models.model_loading_utils import GGUFQuantizationConfig
 
     namespace = {
+        "__file__": script_name,
+        "__name__": "__main__",
+
+        # Minimal required globals
         "torch": torch,
-        "ZImagePipeline": ZImagePipeline,
-        "ZImageTransformer2DModel": ZImageTransformer2DModel,
-        "GGUFQuantizationConfig": GGUFQuantizationConfig,
     }
 
-    # Execute entire block at once
     try:
-        exec(pipe_code, namespace)
+        exec(SCRIPT_CODE[script_name], namespace)
     except Exception as e:
-        log(f"❌ Pipeline build failed: {e}")
+        log(f"❌ Script failed: {script_name}")
         raise RuntimeError(f"Pipeline build failed for {script_name}") from e
 
-    pipe = namespace.get("pipe", None)
-    if pipe is None:
-        raise RuntimeError(f"{script_name} did not define `pipe`.")
-
-    # Move to CUDA only if not device_map
-    if not hasattr(pipe, "hf_device_map"):
-        pipe = pipe.to("cuda")
+    if "pipe" not in namespace:
+        raise RuntimeError(
+            f"{script_name} did not define `pipe`.\n"
+            f"Each script MUST assign a variable named `pipe`."
+        )
 
-    PIPELINES[script_name] = pipe
-    log("✅ Pipeline ready")
-    return pipe
+    PIPELINES[script_name] = namespace["pipe"]
+    log(f"✅ Pipeline ready: {script_name}")
 
+    return PIPELINES[script_name]
 
 
 # =========================================================