handler.pyを追加

handler.py

@@ -0,0 +1,104 @@
+from typing import  Dict, List, Any
+import torch
+from diffusers import DPMSolverMultistepScheduler, DiffusionPipeline, StableDiffusionImg2ImgPipeline, StableDiffusionInpaintPipelineLegacy
+from PIL import Image
+import base64
+from io import BytesIO
+
+
+# set device
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+class EndpointHandler():
+    def __init__(self, path=""):
+        # load StableDiffusionInpaintPipeline pipeline
+        self.txt2img_pipe = DiffusionPipeline.from_pretrained(path, torch_dtype=torch.float16)
+        # Set safety_checker
+        self.txt2img_pipe.safety_checker = None
+        # use DPMSolverMultistepScheduler
+        self.txt2img_pipe.scheduler = DPMSolverMultistepScheduler.from_config(self.txt2img_pipe.scheduler.config)
+
+        self.img2img_pipe = StableDiffusionImg2ImgPipeline(
+            vae=self.txt2img_pipe.vae,
+            text_encoder=self.txt2img_pipe.text_encoder,
+            tokenizer=self.txt2img_pipe.tokenizer,
+            unet=self.txt2img_pipe.unet,
+            scheduler=self.txt2img_pipe.scheduler,
+            safety_checker=self.txt2img_pipe.safety_checker,
+            feature_extractor=self.txt2img_pipe.feature_extractor,
+        ).to(device)
+        self.inpaint_pipe = StableDiffusionInpaintPipelineLegacy(
+            vae=self.txt2img_pipe.vae,
+            text_encoder=self.txt2img_pipe.text_encoder,
+            tokenizer=self.txt2img_pipe.tokenizer,
+            unet=self.txt2img_pipe.unet,
+            scheduler=self.txt2img_pipe.scheduler,
+            safety_checker=self.txt2img_pipe.safety_checker,
+            feature_extractor=self.txt2img_pipe.feature_extractor,
+        ).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.
+        """
+        inputs = data.pop("inputs", data)
+        encoded_image = data.pop("image", None)
+        encoded_mask_image = data.pop("mask_image", None)
+        
+        # hyperparamters
+        num_inference_steps = data.pop("num_inference_steps", 25)
+        guidance_scale = data.pop("guidance_scale", 7.5)
+        negative_prompt = data.pop("negative_prompt", None)
+        height = data.pop("height", 512)
+        width = data.pop("width", 512)
+        strength = data.pop("strength", 0.8)
+        
+        # run inference pipeline
+        if encoded_image is not None and encoded_mask_image is not None:
+            image = self.decode_base64_image(encoded_image)
+            mask_image = self.decode_base64_image(encoded_mask_image)
+
+            out = self.inpaint_pipe(inputs, 
+                        init_image=image,             
+                        mask_image=mask_image,
+                        strength=strength,
+                        num_inference_steps=num_inference_steps,
+                        guidance_scale=guidance_scale,
+                        num_images_per_prompt=1,
+                        negative_prompt=negative_prompt
+            )
+            return out.images[0]
+            
+        elif encoded_image is not None:
+            image = self.decode_base64_image(encoded_image)
+
+            out = self.img2img_pipe(inputs, 
+                        init_image=image,
+                        strength=strength,
+                        num_inference_steps=num_inference_steps,
+                        guidance_scale=guidance_scale,
+                        num_images_per_prompt=1,
+                        negative_prompt=negative_prompt
+            )
+            return out.images[0]
+        else:
+            out = self.txt2img_pipe(inputs,           
+                            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 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