Upload handler.py

handler.py

@@ -0,0 +1,92 @@
+from typing import Dict, List, Any
+import base64
+from PIL import Image
+from io import BytesIO
+from diffusers import StableDiffusionImg2ImgPipeline
+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
+
+model_id = "nitrosocke/Ghibli-Diffusion"
+
+
+class EndpointHandler():
+    def __init__(self, path=""):
+        # define default controlnet id and load controlnet
+        # Load StableDiffusionControlNetPipeline
+        self.pipe = StableDiffusionImg2ImgPipeline.from_pretrained("nitrosocke/Ghibli-Diffusion", torch_dtype=torch.float16).to(
+            device
+        )
+        # Define Generator with seed
+        # self.generator = torch.Generator(device="cpu").manual_seed(3)
+        self.generator = torch.Generator(device=device).manual_seed(1024)
+
+    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("inputs", None)
+        image = data.pop("image", None)
+        strength = data.pop("strength", None)
+        steps = data.pop("steps", 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."}
+
+        # hyperparamters
+        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=strength,
+        #     num_images_per_prompt=1,
+        #     height=height,
+        #     width=width,
+        #     controlnet_conditioning_scale=controlnet_conditioning_scale,
+        #     generator=self.generator
+        # )
+
+        out = pipe(
+            prompt=prompt,
+            image=image,
+            strength=0.75,
+            guidance_scale=7.5,
+            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).convert("RGB").thumbnail((768, 768))
+        return image