not implemented

handler.py

@@ -36,6 +36,7 @@ class EndpointHandler():
         image_data = data.pop("inputs", data).decode('base64')
         init_image = Image.open(image_data).convert("RGB")
         init_image.thumbnail((512, 512))
+        raise NotImplementedError()
 
         params = data.pop("parameters", data)
 
@@ -50,31 +51,18 @@ class EndpointHandler():
 
         out = None
 
-        if data.get("src"):
-            generator = torch.Generator(device='cuda')
-            generator.manual_seed(manual_seed)
-            # run img2img pipeline
-            out = self.imgPipe(prompt,
-                        image=init_image,
-                        num_inference_steps=num_inference_steps,
-                        guidance_scale=guidance_scale,
-                        num_images_per_prompt=1,
-                        negative_prompt=negative_prompt,
-                        height=height,
-                        width=width
-            )
-        else:
-            # run text pipeline
-            out = self.textPipe(prompt,
-                        image=init_image,
-                        num_inference_steps=num_inference_steps,
-                        guidance_scale=guidance_scale,
-                        num_images_per_prompt=1,
-                        negative_prompt=negative_prompt,
-                        height=height,
-                        width=width
-            )
-
+        generator = torch.Generator(device='cuda')
+        generator.manual_seed(manual_seed)
+        # run img2img pipeline
+        out = self.imgPipe(prompt,
+                    image=init_image,
+                    num_inference_steps=num_inference_steps,
+                    guidance_scale=guidance_scale,
+                    num_images_per_prompt=1,
+                    negative_prompt=negative_prompt,
+                    height=height,
+                    width=width
+        )
 
         # return first generated PIL image
         return out.images[0]