try adding custom handler

.vscode/settings.json

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+{}

handler.py

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+from typing import  Dict, List, Any
+import torch
+import requests
+from PIL import Image
+from io import BytesIO
+from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, DDIMScheduler
+
+# set device
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+if device.type != 'cuda':
+    raise ValueError("need to run on GPU")
+
+model_id = "stabilityai/stable-diffusion-2-1-base"
+
+class EndpointHandler():
+    def __init__(self, path=""):
+        # load the optimized model
+        self.textPipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
+        self.textPipe.scheduler = DDIMScheduler.from_config(self.textPipe.scheduler.config)
+        self.textPipe = self.textPipe.to(device)
+
+        # create an img2img model
+        self.imgPipe = StableDiffusionImg2ImgPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
+        self.imgPipe.scheduler = DDIMScheduler.from_config(self.imgPipe.scheduler.config)
+        self.imgPipe = self.imgPipe.to(device)
+
+    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
+        """
+        prompt = data.pop("inputs", data)
+        url = data.pop("src", data)
+        response = requests.get(url)
+        init_image = Image.open(BytesIO(response.content)).convert("RGB")
+        init_image.thumbnail((512, 512))
+
+        params = data.pop("parameters", data)
+
+        # hyperparamters
+        num_inference_steps = params.pop("num_inference_steps", 25)
+        guidance_scale = params.pop("guidance_scale", 7.5)
+        negative_prompt = params.pop("negative_prompt", None)
+        height = params.pop("height", None)
+        width = params.pop("width", None)
+        manual_seed = params.pop("manual_seed", -1)
+
+        out = None
+
+        if data.get("url"):
+            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
+            )
+
+
+        # return first generated PIL image
+        return out.images[0]

requirements.txt

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+diffusers==0.10.2