EDIT: remove super-resolution model and add inpainting sd

handler.py

@@ -5,7 +5,7 @@ from PIL import Image
 import base64
 from io import BytesIO
 import numpy as np
-from RealESRGAN import RealESRGAN
+# from RealESRGAN import RealESRGAN
 
 # set device
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
@@ -17,15 +17,15 @@ class EndpointHandler():
     def __init__(self, path=""):
         # load StableDiffusionInpaintPipeline pipeline
         self.pipe = StableDiffusionInpaintPipeline.from_pretrained(path, torch_dtype=torch.float16)
-        # use DPMSolverMultistepScheduler
-        self.pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
+        # use EulerAncestralDiscreteScheduler
+        self.pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(self.pipe.scheduler.config)
         # pipe.enable_sequential_cpu_offload()
         # move to device
         self.pipe.to(device)
         self.pipe.enable_xformers_memory_efficient_attention()
 
-        self.upscaler = RealESRGAN(device, scale=4)
-        self.upscaler.load_weights('weights/RealESRGAN_x4.pth', download=True)
+        # self.upscaler = RealESRGAN(device, scale=4)
+        # self.upscaler.load_weights('weights/RealESRGAN_x4.pth', download=True)
 
 
     def __call__(self, data: Any) -> List[List[Dict[str, float]]]:
@@ -68,11 +68,11 @@ class EndpointHandler():
                         width=width
         ).images
 
-        for i in range(len(out)):
-          gen_img = Image.composite(out[i], image.resize(out[i].size), mask_image.resize(out[i].size))
-          gen_img = self.upscaler.predict(gen_img)
-          gen_img = Image.composite(gen_img, image.resize(gen_img.size), mask_image.resize(gen_img.size))
-          out[i] = gen_img
+        # for i in range(len(out)):
+        #   gen_img = Image.composite(out[i], image.resize(out[i].size), mask_image.resize(out[i].size))
+        #   gen_img = self.upscaler.predict(gen_img)
+        #   gen_img = Image.composite(gen_img, image.resize(gen_img.size), mask_image.resize(gen_img.size))
+        #   out[i] = gen_img
 
         # return first generate PIL image
         json_imgs = {}

requirements.txt

@@ -1 +0,0 @@
-git+https://github.com/sberbank-ai/Real-ESRGAN.git