Used local model files

main.py

@@ -1,12 +1,12 @@
-from fastapi import FastAPI, UploadFile, File
-from fastapi.responses import StreamingResponse
-from fastapi.middleware.cors import CORSMiddleware
-
 import cv2
 import random
 import numpy as np
 from io import BytesIO
 
+from fastapi import FastAPI, UploadFile, File
+from fastapi.responses import StreamingResponse
+from fastapi.middleware.cors import CORSMiddleware
+
 from stable_diffusion_engine import StableDiffusionEngine
 from diffusers import LMSDiscreteScheduler, PNDMScheduler
 

requirements.txt

@@ -7,10 +7,7 @@ tqdm==4.64.0
 openvino==2022.3.0
 huggingface_hub==0.9.0
 scipy==1.8.1
-streamlit==1.12.0
-watchdog==2.1.9
 ftfy==6.1.1
-streamlit_drawable_canvas==0.9.1
 pillow==9.0.1
 piexif==1.1.3
 fastapi

stable_diffusion_engine.py

@@ -31,27 +31,23 @@ class StableDiffusionEngine:
 
         # text features
         self._text_encoder = self.core.read_model(
-            hf_hub_download(repo_id=model, filename="text_encoder.xml"),
-            hf_hub_download(repo_id=model, filename="text_encoder.bin")
+            "stable-diffusion-v1-4-openvino/text_encoder.xml"
         )
         self.text_encoder = self.core.compile_model(self._text_encoder, device)
         # diffusion
         self._unet = self.core.read_model(
-            hf_hub_download(repo_id=model, filename="unet.xml"),
-            hf_hub_download(repo_id=model, filename="unet.bin")
+            "stable-diffusion-v1-4-openvino/unet.xml"
         )
         self.unet = self.core.compile_model(self._unet, device)
         self.latent_shape = tuple(self._unet.inputs[0].shape)[1:]
         # decoder
         self._vae_decoder = self.core.read_model(
-            hf_hub_download(repo_id=model, filename="vae_decoder.xml"),
-            hf_hub_download(repo_id=model, filename="vae_decoder.bin")
+            "stable-diffusion-v1-4-openvino/vae_decoder.xml"
         )
         self.vae_decoder = self.core.compile_model(self._vae_decoder, device)
         # encoder
         self._vae_encoder = self.core.read_model(
-            hf_hub_download(repo_id=model, filename="vae_encoder.xml"),
-            hf_hub_download(repo_id=model, filename="vae_encoder.bin")
+            "stable-diffusion-v1-4-openvino/vae_encoder.xml"
         )
         self.vae_encoder = self.core.compile_model(self._vae_encoder, device)
         self.init_image_shape = tuple(self._vae_encoder.inputs[0].shape)[2:]