Create handler.py

handler.py

@@ -0,0 +1,48 @@
+from typing import Dict, List, Any
+from PIL import Image
+from io import BytesIO
+import torch
+import base64
+from diffusers import StableDiffusionInstructPix2PixPipeline, EulerAncestralDiscreteScheduler
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+class EndpointHandler():
+    def __init__(self, path=""):
+        model_id = "timbrooks/instruct-pix2pix"
+        self.pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32, safety_checker=None)
+        self.pipe.to(device)
+        self.pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(self.pipe.scheduler.config)
+
+    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
+        """
+       data args:
+            inputs (:obj:`string`)
+            parameters (:obj:)
+      Return:
+            A :obj:`string`:. image string
+        """
+
+
+        image_data = data.pop('inputs', data)
+        # decode base64 image to PIL
+        image = Image.open(BytesIO(base64.b64decode(image_data)))
+
+        parameters = data.pop('parameters', [])
+        prompt = parameters.pop('prompt', None)
+        negative_prompt = parameters.pop('negative_prompt', None)
+        num_inference_steps = parameters.pop('num_inference_steps', 10)
+        image_guidance_scale = parameters.pop('image_guidance_scale', 1.5)
+        guidance_scale = parameters.pop('guidance_scale', 7.5)
+
+
+        images = self.pipe(
+            prompt,
+            image = image,
+            negative_prompt = negative_prompt,
+            num_inference_steps = num_inference_steps,
+            image_guidance_scale = image_guidance_scale,
+            guidance_scale = guidance_scale
+        ).images
+
+        return images[0]