Create handler.py

handler.py

@@ -0,0 +1,67 @@
+from typing import  Dict, List, Any
+from diffusers import AutoPipelineForText2Image
+import torch
+
+
+import numpy as np
+
+# set device
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+if device.type != 'cuda':
+    raise ValueError("need to run on GPU")
+# set mixed precision dtype
+dtype = torch.bfloat16 if torch.cuda.get_device_capability()[0] == 8 else torch.float16
+
+
+class EndpointHandler():
+    def __init__(self, path=""):
+        # Load StableDiffusionPipeline 
+        self.stable_diffusion_id = "stabilityai/stable-diffusion-xl-base-1.0"
+        self.pipe = AutoPipelineForText2Image.from_pretrained(self.stable_diffusion_id, 
+                                                              torch_dtype=dtype,
+                                                              safety_checker=None)
+        self.pipe.load_lora_weights("Oysiyl/sdxl-lora-android-google-toy", weights="pytorch_lora_weights.safetensors")
+        self.pipe.enable_xformers_memory_efficient_attention()
+        self.pipe = self.pipe.to(device)
+        self.seed = 42
+        # Define Generator with seed
+        self.generator = torch.Generator(device="cpu").manual_seed(self.seed)
+
+    def __call__(self, data: Any) -> List[List[Dict[str, float]]]:
+        """
+        :param data: A dictionary contains `inputs`.
+        :return: A dictionary with `image` field contains image in base64.
+        """
+        prompt = data.pop("inputs", None)
+        seed = data.pop("seed", 42)
+        
+        # Check if prompt is not provided
+        if prompt is None:
+            return {"error": "Please provide a prompt."}
+        
+        # Check if seed changed
+        if seed is not None and seed != self.seed:
+            print(f"changing seed from {self.seed} to {seed}")
+            self.seed = seed
+            self.generator = torch.Generator(device="cpu").manual_seed(self.seed)
+        
+        
+        # hyperparamters
+        num_inference_steps = data.pop("num_inference_steps", 50)
+        guidance_scale = data.pop("guidance_scale", 7.5)
+        temperature = data.pop("temperature", 1.0)
+
+        
+        # run inference pipeline
+        out = self.pipe(
+            prompt=prompt,
+            num_inference_steps=num_inference_steps, 
+            guidance_scale=guidance_scale,
+            temperature=temperature,
+            num_images_per_prompt=1,
+            generator=self.generator
+        )
+
+        
+        # return first generate PIL image
+        return out.images[0]