Create handler.py

handler.py

@@ -0,0 +1,75 @@
+import torch
+import json
+import base64
+import io
+from PIL import Image
+from diffusers import DPMSolverMultistepScheduler, StableDiffusionXLInpaintPipeline
+
+# Set device
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+if device.type != 'cuda':
+    raise ValueError("Need to run on GPU")
+
+class SDXLInpaintHandler:
+    def __init__(self, path="mrcuddle/URPM-Inpaint-Hyper-SDXL"):
+        """Load the SDXL Inpainting model."""
+        self.pipeline = StableDiffusionXLInpaintPipeline.from_pretrained(
+            path, torch_dtype=torch.float16
+        )
+        self.pipeline.scheduler = DPMSolverMultistepScheduler.from_config(self.pipeline.scheduler.config)
+        self.pipeline = self.pipeline.to(device)
+    
+    def __call__(self, data: dict):
+        """Custom call function for Hugging Face Inference Endpoints."""
+        try:
+            inputs = data.pop("inputs", data)
+            encoded_image = data.pop("image", None)
+            encoded_mask_image = data.pop("mask_image", None)
+            
+            num_inference_steps = data.pop("num_inference_steps", 25)
+            guidance_scale = data.pop("guidance_scale", 7.5)
+            negative_prompt = data.pop("negative_prompt", None)
+            height = data.pop("height", None)
+            width = data.pop("width", None)
+            
+            # Process images
+            if encoded_image and encoded_mask_image:
+                image = self.decode_base64_image(encoded_image)
+                mask_image = self.decode_base64_image(encoded_mask_image)
+            else:
+                raise ValueError("Both image and mask_image are required")
+            
+            # Run inference
+            output_image = self.pipeline(
+                prompt=inputs,
+                image=image,
+                mask_image=mask_image,
+                num_inference_steps=num_inference_steps,
+                guidance_scale=guidance_scale,
+                num_images_per_prompt=1,
+                negative_prompt=negative_prompt,
+                height=height,
+                width=width
+            ).images[0]
+            
+            return json.dumps({"output": self.encode_base64_image(output_image)})
+        except Exception as e:
+            return json.dumps({"error": str(e)})
+    
+    def decode_base64_image(self, image_string):
+        """Decode base64 encoded image."""
+        base64_image = base64.b64decode(image_string)
+        buffer = io.BytesIO(base64_image)
+        return Image.open(buffer).convert("RGB")
+    
+    def encode_base64_image(self, image):
+        """Encode PIL image to base64."""
+        buffered = io.BytesIO()
+        image.save(buffered, format="PNG")
+        return base64.b64encode(buffered.getvalue()).decode("utf-8")
+
+handler = SDXLInpaintHandler()
+
+def handle(data: dict):
+    return handler(data)