Merge branch 'refs/heads/local' into develop

app/schemas/generate_image.py

@@ -20,6 +20,7 @@ class GenerateProductImageModel(BaseModel):
     tasks_id: str
     prompt: str
     image_url: str
+    image_strength: float
 
 
 class GenerateRelightImageModel(BaseModel):

app/service/generate_image/service_generate_product_image.py

@@ -7,17 +7,17 @@
 @Date    ：2023/7/26 12:01:05
 @detail  ：
 """
-import io
 import json
 import logging
 import time
+
 import cv2
+import numpy as np
 import redis
 import tritonclient.grpc as grpcclient
-import numpy as np
 from PIL import Image, ImageOps
-from minio import Minio
 from tritonclient.utils import np_to_triton_dtype
+
 from app.core.config import *
 from app.schemas.generate_image import GenerateProductImageModel
 from app.service.generate_image.utils.upload_sd_image import upload_SDXL_image
@@ -37,6 +37,7 @@ class GenerateProductImage:
         self.grpc_client = grpcclient.InferenceServerClient(url=GPI_MODEL_URL)
         self.redis_client = redis.StrictRedis(host=REDIS_HOST, port=REDIS_PORT, db=REDIS_DB, decode_responses=True)
         self.category = "product_image"
+        self.image_strength = request_data.image_strength
         self.batch_size = 1
         self.prompt = request_data.prompt
         self.image, self.image_size = pre_processing_image(request_data.image_url)
@@ -74,13 +75,16 @@ class GenerateProductImage:
 
             text_obj = np.array(prompts, dtype="object").reshape(1)
             image_obj = np.array(images, dtype=np.uint8).reshape((768, 512, 3))
+            image_strength_obj = np.array(self.image_strength, dtype=np.float32).reshape((1))
 
             input_text = grpcclient.InferInput("prompt", text_obj.shape, np_to_triton_dtype(text_obj.dtype))
             input_image = grpcclient.InferInput("input_image", image_obj.shape, "UINT8")
+            input_image_strength = grpcclient.InferInput("image_strength", image_strength_obj.shape, np_to_triton_dtype(image_strength_obj.dtype))
 
             input_text.set_data_from_numpy(text_obj)
             input_image.set_data_from_numpy(image_obj)
-            inputs = [input_text, input_image]
+            inputs = [input_text, input_image, input_image_strength]
+            input_image_strength.set_data_from_numpy(image_strength_obj)
 
             ctx = self.grpc_client.async_infer(model_name=GPI_MODEL_NAME, inputs=inputs, callback=self.callback)
             time_out = 600
@@ -144,6 +148,7 @@ if __name__ == '__main__':
     rd = GenerateProductImageModel(
         tasks_id="123-89",
         prompt="",
+        image_strength=0.9,
         # prompt=" the best quality, masterpiece. detailed, high-res, simple background, studio photography, extremely detailed, updo, detailed face, face, close-up, HDR, UHD, 8K realistic, Highly detailed, simple background, Studio lighting",
         image_url="aida-results/result_00097282-ebb2-11ee-a822-b48351119060.png",
     )