Quick auto-fix: remove recursion + safe handler

handler.py

@@ -1,13 +1,12 @@
+import os
+os.environ["DIFFUSERS_NO_CONFIG_RECURSION"] = "1"
 from diffusers import DiffusionPipeline
 import torch
-import os
 
 class EndpointHandler:
     def __init__(self, model_dir: str):
-        print("🚀 Initializing Wan2.2 Diffusers pipeline (robust mode)...")
-
+        print("🚀 Initializing Wan2.2 Diffusers pipeline (safe mode)...")
         try:
-            # First attempt — normal load with relaxed safety flags
             self.pipe = DiffusionPipeline.from_pretrained(
                 model_dir,
                 torch_dtype=torch.float16,
@@ -15,38 +14,24 @@ class EndpointHandler:
                 ignore_mismatched_sizes=True,
                 safety_checker=None
             )
-
-        except RuntimeError as e:
-            print("⚠️ Mismatch detected during model load. Retrying component-wise load...")
-            # Fallback: reload manually with relaxed constraints
+        except Exception as e:
+            print("⚠️ Retry with relaxed loading:", e)
             self.pipe = DiffusionPipeline.from_pretrained(
                 model_dir,
                 torch_dtype=torch.float16,
                 low_cpu_mem_usage=False,
                 safety_checker=None
             )
-            for name, module in self.pipe.components.items():
-                try:
-                    if hasattr(module, "load_state_dict"):
-                        print(f"Reloading {name} with ignore_mismatched_sizes=True")
-                        module.load_state_dict(
-                            module.state_dict(), strict=False
-                        )
-                except Exception as sub_e:
-                    print(f"Skipping {name}: {sub_e}")
 
         if torch.cuda.is_available():
             self.pipe.to("cuda")
-            print("✅ Pipeline moved to CUDA")
+            print("✅ Pipeline on CUDA")
         else:
-            print("⚠️ CUDA not available — running on CPU")
-
-        print("✅ Pipeline initialized successfully!")
+            print("⚠️ CUDA not found — running on CPU")
 
     def __call__(self, data):
-        inputs = data.get("inputs", None)
-        if not inputs:
-            raise ValueError("❌ Missing 'inputs' key in request payload.")
-        print(f"🖼️ Generating video for input: {inputs}")
-        output = self.pipe(inputs)
-        return {"output": output}
+        prompt = data.get("inputs", "")
+        if not prompt:
+            raise ValueError("Missing 'inputs' field in request.")
+        result = self.pipe(prompt)
+        return {"output": result}

image_encoder/config.json

@@ -1 +1,4 @@
-{"_class_name": "image_encoder", "_commit": "placeholder"}
+{
+  "_class_name": "image_encoder",
+  "_commit": "placeholder"
+}

image_encoder/diffusion_pytorch_model.safetensors.index.json

@@ -1,9 +1,3 @@
-{
-  "metadata": {
-    "format": "pt",
-    "total_size": 4920000000
-  },
-  "weight_map": {
-    "model": "diffusion_pytorch_model.safetensors"
-  }
-}
+version https://git-lfs.github.com/spec/v1
+oid sha256:ba5409b0bc67e3e65bc765cb93a34f17dace99d17a5f934fb5bbf3e6e64a917c
+size 146

image_processor/config.json

@@ -1 +1,4 @@
-{"_class_name": "image_processor", "_commit": "placeholder"}
+{
+  "_class_name": "image_processor",
+  "_commit": "placeholder"
+}

scheduler/config.json

@@ -1 +1,4 @@
-{"_class_name": "scheduler", "_commit": "placeholder"}
+{
+  "_class_name": "scheduler",
+  "_commit": "placeholder"
+}

text_encoder/config.json

@@ -1 +1,4 @@
-{"_class_name": "text_encoder", "_commit": "placeholder"}
+{
+  "_class_name": "text_encoder",
+  "_commit": "placeholder"
+}

tokenizer/config.json

@@ -1 +1,4 @@
-{"_class_name": "tokenizer", "_commit": "placeholder"}
+{
+  "_class_name": "tokenizer",
+  "_commit": "placeholder"
+}