feat

fix  relight 新增single item模式

app/api/api_generate_image.py

@@ -155,13 +155,16 @@ def generate_relight_image(request_item: GenerateRelightImageModel, background_t
         - **prompt**: 想要生成图片的描述词
         - **image_url**: 被生成图片的S3或minio url地址
         - **direction**: 光源方向  Right Light      Left Light      Top Light     Bottom Light
+        - **product_type**: 输入single item 还是 overall item
+
 
         示例参数：
         {
           "tasks_id": "123-89",
           "prompt": "beautiful woman, detailed face, sunshine, outdoor, warm atmosphere",
           "image_url": "aida-results/result_0000b606-1902-11ef-9424-0242ac180002.png",
-          "direction": "Right Light"
+          "direction": "Right Light",
+          "product_type": "overall"
         }
     """
     try:

app/core/config.py

@@ -127,7 +127,8 @@ GPI_MODEL_URL = '10.1.1.240:10041'
 
 # Generate Single Logo service config
 GRI_RABBITMQ_QUEUES = os.getenv("GEN_RELIGHT_IMAGE_RABBITMQ_QUEUES", f"Relight{RABBITMQ_ENV}")
-GRI_MODEL_NAME = 'diffusion_relight_ensemble'
+GRI_MODEL_NAME_OVERALL = 'diffusion_relight_ensemble'
+GRI_MODEL_NAME_SINGLE = 'stable_diffusion_1_5_relight'
 GRI_MODEL_URL = '10.1.1.240:10051'
 
 # SEG service config

app/schemas/generate_image.py

@@ -29,3 +29,4 @@ class GenerateRelightImageModel(BaseModel):
     prompt: str
     image_url: str
     direction: str
+    product_type: str

app/service/generate_image/service_generate_relight_image.py

@@ -38,6 +38,7 @@ class GenerateRelightImage:
         self.batch_size = 1
         self.prompt = request_data.prompt
         self.seed = "1"
+        self.product_type = request_data.product_type
         self.negative_prompt = 'lowres, bad anatomy, bad hands, cropped, worst quality'
         self.direction = request_data.direction
         self.image_url = request_data.image_url
@@ -55,7 +56,11 @@ class GenerateRelightImage:
             self.redis_client.set(self.tasks_id, json.dumps(self.gen_product_data))
         else:
             # pil图像转成numpy数组
-            image = result.as_numpy("generated_inpaint_image")
+            if self.product_type == 'single':
+                image = result.as_numpy("generated_relight_image")
+            else:
+                image = result.as_numpy("generated_inpaint_image")
+
             image_result = Image.fromarray(np.squeeze(image.astype(np.uint8)))
 
             image_url = upload_SDXL_image(image_result, user_id=self.user_id, category=f"{self.category}", file_name=f"{self.tasks_id}.png")
@@ -78,11 +83,18 @@ class GenerateRelightImage:
             nagetive_prompts = [self.negative_prompt] * self.batch_size
             directions = [self.direction] * self.batch_size
 
-            text_obj = np.array(prompts, dtype="object").reshape((1))
+            if self.product_type == 'single':
-            image_obj = np.array(images, dtype=np.uint8).reshape((768, 512, 3))
+                text_obj = np.array(prompts, dtype="object").reshape((-1, 1))
-            na_text_obj = np.array(nagetive_prompts, dtype="object").reshape((1))
+                image_obj = np.array(images, dtype=np.uint8).reshape((-1, 768, 512, 3))
-            seed_obj = np.array(seeds, dtype="object").reshape((1))
+                na_text_obj = np.array(nagetive_prompts, dtype="object").reshape((-1, 1))
-            direction_obj = np.array(directions, dtype="object").reshape((1))
+                seed_obj = np.array(seeds, dtype="object").reshape((-1, 1))
+                direction_obj = np.array(directions, dtype="object").reshape((-1, 1))
+            else:
+                text_obj = np.array(prompts, dtype="object").reshape((1))
+                image_obj = np.array(images, dtype=np.uint8).reshape((768, 512, 3))
+                na_text_obj = np.array(nagetive_prompts, dtype="object").reshape((1))
+                seed_obj = np.array(seeds, dtype="object").reshape((1))
+                direction_obj = np.array(directions, dtype="object").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")
@@ -97,8 +109,11 @@ class GenerateRelightImage:
             input_direction.set_data_from_numpy(direction_obj)
 
             inputs = [input_text, input_natext, input_image, input_seed, input_direction]
+            if self.product_type == 'single':
+                ctx = self.grpc_client.async_infer(model_name=GRI_MODEL_NAME_SINGLE, inputs=inputs, callback=self.callback)
+            else:
+                ctx = self.grpc_client.async_infer(model_name=GRI_MODEL_NAME_OVERALL, inputs=inputs, callback=self.callback)
 
-            ctx = self.grpc_client.async_infer(model_name=GRI_MODEL_NAME, inputs=inputs, callback=self.callback)
             time_out = 600
             while time_out > 0:
                 gen_product_data, _ = self.read_tasks_status()
@@ -136,7 +151,9 @@ if __name__ == '__main__':
         tasks_id="123-89",
         # prompt="beautiful woman, detailed face, sunshine, outdoor, warm atmosphere",
         prompt="Colorful black",
-        image_url='aida-results/result_0000b606-1902-11ef-9424-0242ac180002.png'
+        image_url='aida-results/result_0000b606-1902-11ef-9424-0242ac180002.png',
+        direction="Right Light",
+        product_type="single"
     )
     server = GenerateRelightImage(rd)
     print(server.get_result())