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README.md

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+---
+license: openrail
+tags:
+- stable-diffusion
+- stable-diffusion-diffusers
+- controlnet
+inference: true
+---
+
+# Inference Endpoint for [ControlNet](https://huggingface.co/lllyasviel/ControlNet) using [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5)

handler.py

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+from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
+from typing import  Dict, List, Any
+from io import BytesIO
+from PIL import Image
+import base64
+import torch
+
+# 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=""):
+        self.stable_diffusion_id = "stabilityai/stable-diffusion-2-1-base"
+
+        controlnet = ControlNetModel.from_pretrained("rgres/sd-controlnet-aerialdreams", torch_dtype=torch.float16)
+
+        self.pipe = StableDiffusionControlNetPipeline.from_pretrained(
+            self.stable_diffusion_id, controlnet=controlnet, torch_dtype=dtype, safety_checker=None
+        ).to(device)
+
+    def __call__(self, data: Any) -> List[List[Dict[str, float]]]:
+        """
+        :param data: A dictionary contains `inputs` and optional `image` field.
+        :return: A dictionary with `image` field contains image in base64.
+        """
+        prompt = data.pop("prompt", None)
+        image = data.pop("image", None)
+        steps = data.pop("steps", 30)
+        seed = data.pop("seed", None)
+        
+        # Check if neither prompt nor image is provided
+        if prompt is None and image is None:
+            return {"error": "Please provide a prompt and base64 encoded image."}
+                
+        # decode image
+        image = self.decode_base64_image(image)
+
+        self.generator = torch.Generator(device="cpu").manual_seed(3)
+        
+        # run inference pipeline
+        image_out = self.pipe(
+            prompt=prompt, 
+            image=image,
+            num_inference_steps=steps, 
+            generator=self.generator
+        ).images[0]
+        
+        # return first generate PIL image
+        return image_out
+
+    
+    # helper to decode input image
+    def decode_base64_image(self, image_string):
+        base64_image = base64.b64decode(image_string)
+        buffer = BytesIO(base64_image)
+        image = Image.open(buffer)
+        return image

requirements.txt

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+git+https://github.com/huggingface/diffusers.git
+safetensors 
+opencv-python