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handler.py

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+from typing import  Dict, List, Any
+import base64
+from PIL import Image
+from io import BytesIO
+from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
+import torch
+
+
+import numpy as np
+import cv2
+import controlnet_hinter
+
+# 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
+
+# controlnet mapping for controlnet id and control hinter
+CONTROLNET_MAPPING = {
+    "canny_edge": {
+        "model_id": "lllyasviel/sd-controlnet-canny",
+        "hinter": controlnet_hinter.hint_canny
+    },
+    "pose": {
+        "model_id": "lllyasviel/sd-controlnet-openpose",
+        "hinter": controlnet_hinter.hint_openpose
+    },
+    "depth": {
+        "model_id": "lllyasviel/sd-controlnet-depth",
+        "hinter": controlnet_hinter.hint_depth
+    },
+    "scribble": {
+        "model_id": "lllyasviel/sd-controlnet-scribble",
+        "hinter": controlnet_hinter.hint_scribble,
+    },
+    "segmentation": {
+        "model_id": "lllyasviel/sd-controlnet-seg",
+        "hinter": controlnet_hinter.hint_segmentation,
+    },
+    "normal": {
+        "model_id": "lllyasviel/sd-controlnet-normal",
+        "hinter": controlnet_hinter.hint_normal,
+    },
+    "hed": {
+        "model_id": "lllyasviel/sd-controlnet-hed",
+        "hinter": controlnet_hinter.hint_hed,
+    },
+    "hough": {
+        "model_id": "lllyasviel/sd-controlnet-mlsd",
+        "hinter": controlnet_hinter.hint_hough,
+    }
+}
+
+
+class EndpointHandler():
+    def __init__(self, path=""):
+        # define default controlnet id and load controlnet
+        self.control_type = "normal"
+        self.controlnet = ControlNetModel.from_pretrained(CONTROLNET_MAPPING[self.control_type]["model_id"],torch_dtype=dtype).to(device)
+        
+        # Load StableDiffusionControlNetPipeline 
+        self.stable_diffusion_id = "runwayml/stable-diffusion-v1-5"
+        self.pipe = StableDiffusionControlNetPipeline.from_pretrained(self.stable_diffusion_id, 
+                                                                      controlnet=self.controlnet, 
+                                                                      torch_dtype=dtype,
+                                                                      safety_checker=None).to(device)
+        # Define Generator with seed
+        self.generator = torch.Generator(device="cpu").manual_seed(3)
+
+    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)
+        controlnet_type = data.pop("controlnet_type", None)
+        
+        # Check if neither prompt nor image is provided
+        if prompt is None and encoded_image is None:
+            return {"error": "Please provide a prompt and base64 encoded image."}
+        
+        # Check if a new controlnet is provided
+        if controlnet_type is not None and controlnet_type != self.control_type:
+            print(f"Loading {controlnet_type} controlnet...")
+            self.controlnet_type = controlnet_type
+            self.controlnet = ControlNetModel.from_pretrained(CONTROLNET_MAPPING[self.control_type]["model_id"],
+                                                              torch_dtype=dtype).to(device)
+            self.pipe.controlnet = self.controlnet
+        
+        
+        # hyperparamters
+        negatice_prompt = data.pop("negative_prompt", None)
+        num_inference_steps = data.pop("num_inference_steps", 30)
+        guidance_scale = data.pop("guidance_scale", 7.5)
+        negative_prompt = data.pop("negative_prompt", None)
+        height = data.pop("height", None)
+        width = data.pop("width", None)
+        controlnet_conditioning_scale = data.pop("controlnet_conditioning_scale", 1.0)
+        
+        # process image
+        image = self.decode_base64_image(image)
+        control_image = CONTROLNET_MAPPING[self.control_type]["hinter"](image)
+        
+        # run inference pipeline
+        out = self.pipe(
+            prompt=prompt, 
+            negative_prompt=negative_prompt,
+            image=control_image,
+            num_inference_steps=num_inference_steps, 
+            guidance_scale=guidance_scale,
+            num_images_per_prompt=1,
+            height=height,
+            width=width,
+            controlnet_conditioning_scale=controlnet_conditioning_scale,
+            generator=self.generator
+        )
+
+        
+        # return first generate PIL image
+        return out.images[0]
+    
+    # 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 
+xformers 
+opencv-python
+controlnet_hinter==0.0.5