Update handler.py

handler.py

@@ -1,5 +1,6 @@
 import base64
 import io
+import traceback
 from PIL import Image
 import torch
 from diffusers import StableDiffusionImg2ImgPipeline
@@ -19,7 +20,7 @@ class EndpointHandler:
         global pipe
         if pipe is None:
             pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
-                "karthikAI/InstantID-i2i",  # Your HF repo with InstantID adapter
+                "karthikAI/InstantID-i2i",
                 revision="main",
                 torch_dtype=torch.float16,
                 safety_checker=None
@@ -28,7 +29,7 @@ class EndpointHandler:
 
     def inference(self, model_inputs: dict) -> dict:
         """
-        Run a single img2img inference.
+        Run a single img2img inference with detailed error debugging.
 
         Expects a JSON payload with:
           - "inputs": base64-encoded input image
@@ -38,36 +39,47 @@ class EndpointHandler:
                 "guidance_scale": float,
                 "num_inference_steps": int,
             }
-        Returns a dict with:
+        Returns on success:
           - "generated_image_base64": base64-encoded PNG
+        On failure:
+          - "error": error message
+          - "traceback": full Python traceback
         """
-        # 1. Decode the incoming image
-        b64_img = model_inputs.get("inputs")
-        if not b64_img:
-            raise ValueError("No image data provided under 'inputs'.")
-        image_bytes = base64.b64decode(b64_img)
-        init_img = Image.open(io.BytesIO(image_bytes)).convert("RGB")
+        try:
+            # 1. Decode incoming image
+            b64_img = model_inputs.get("inputs")
+            if not b64_img:
+                raise ValueError("No image data provided under 'inputs'.")
+            image_bytes = base64.b64decode(b64_img)
+            init_img = Image.open(io.BytesIO(image_bytes)).convert("RGB")
 
-        # 2. Extract parameters
-        params = model_inputs.get("parameters", {})
-        prompt = params.get("prompt", "")
-        strength = float(params.get("strength", 0.75))
-        guidance_scale = float(params.get("guidance_scale", 7.5))
-        num_steps = int(params.get("num_inference_steps", 50))
+            # 2. Extract parameters
+            params = model_inputs.get("parameters", {})
+            prompt = params.get("prompt", "")
+            strength = float(params.get("strength", 0.75))
+            guidance_scale = float(params.get("guidance_scale", 7.5))
+            num_steps = int(params.get("num_inference_steps", 50))
 
-        # 3. Run the img2img pipeline
-        result = pipe(
-            prompt=prompt,
-            image=init_img,
-            strength=strength,
-            guidance_scale=guidance_scale,
-            num_inference_steps=num_steps,
-        )
-        out_img = result.images[0]
+            # 3. Run the img2img pipeline
+            result = pipe(
+                prompt=prompt,
+                image=init_img,
+                strength=strength,
+                guidance_scale=guidance_scale,
+                num_inference_steps=num_steps,
+            )
+            out_img = result.images[0]
 
-        # 4. Encode the output image back to base64
-        buffer = io.BytesIO()
-        out_img.save(buffer, format="PNG")
-        generated_b64 = base64.b64encode(buffer.getvalue()).decode("utf-8")
+            # 4. Encode and return image
+            buffer = io.BytesIO()
+            out_img.save(buffer, format="PNG")
+            generated_b64 = base64.b64encode(buffer.getvalue()).decode("utf-8")
+            return {"generated_image_base64": generated_b64}
 
-        return {"generated_image_base64": generated_b64}
+        except Exception as e:
+            # Return detailed error info for debugging
+            tb = traceback.format_exc()
+            return {
+                "error": str(e),
+                "traceback": tb
+            }