Update handler.py

handler.py

@@ -0,0 +1,57 @@
+from typing import  Dict, List, Any
+import torch
+from torch import autocast
+from diffusers import StableDiffusionPipeline
+import base64
+from io import BytesIO
+import os
+import random
+from os import path
+from contextlib import nullcontext
+import time
+from sys import platform
+import torch
+from diffusers import AutoPipelineForImage2Image, LCMScheduler
+from diffusers.utils import load_image
+
+# 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 EndpointHandler():
+    def __init__(self, path=""):
+        # load the optimized model
+        self.pipe = StableDiffusionPipeline.from_pretrained(path, torch_dtype=torch.float16)
+        self.pipe = self.pipe.to(device)
+
+        lcm_lora_id = "latent-consistency/lcm-lora-sdv1-5"
+        self.pipe.scheduler = LCMScheduler.from_config(self.pipe.scheduler.config)
+
+        self.pipe.load_lora_weights(lcm_lora_id)
+        self.pipe.fuse_lora()
+
+
+    def __call__(self, data: Any) -> List[List[Dict[str, float]]]:
+        """
+        Args:
+            data (:obj:):
+                includes the input data and the parameters for the inference.
+        Return:
+            A :obj:`dict`:. base64 encoded image
+        """
+        inputs = data.pop("inputs", data)
+        
+        # run inference pipeline
+        with torch.inference_mode():
+          with autocast(device.type):
+              image = self.pipe(inputs, guidance_scale=1, num_inference_steps=4).images[0]  
+            
+        # encode image as base 64
+        buffered = BytesIO()
+        image.save(buffered, format="JPEG")
+        img_str = base64.b64encode(buffered.getvalue())
+
+        # postprocess the prediction
+        return {"image": img_str.decode()}