3D 打板部署

新增nacos 配置测试
2026-04-28 17:17:29 +08:00 · 2026-04-28 17:03:04 +08:00 · 2026-04-24 10:17:42 +08:00 · 2026-04-23 17:10:22 +08:00 · 2026-04-15 17:36:29 +08:00 · 2026-04-15 17:23:00 +08:00
41 changed files with 966 additions and 565 deletions
--- a/.dockerignore
+++ b/.dockerignore
@@ -1,2 +1,6 @@
 seg_cache
 test
+.venv
+__pycache__/
+*.pyc
+.git/
--- a/README.md
+++ b/README.md
@@ -20,7 +20,6 @@
         $ conda activate trinity_client_aida 
         $ pip install -r requirements.txt
         $ conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia -y
-         $ pip install mmcv==1.4.2 -f https://download.openmmlab.com/mmcv/dist/cu117/torch1.13/index.html


 1. 启动服务器
--- a/app/api/api_design.py
+++ b/app/api/api_design.py
@@ -395,7 +395,8 @@ async def seg_anything(request_data: SAMRequestModel):
    通过传入图片路径和点击的点坐标，返回分割后的掩码数据。

    ### 参数说明:
-    - **user_id**:用户id 用于存储分割图
+    - **bucket**: minio bucket name
+    - **object_name**: minio object name
    - **image_path**: 图片在服务器或云端的相对路径。
    - **type**: 推理类型
    - **box**: 框选矩形点位信息
@@ -408,7 +409,8 @@ async def seg_anything(request_data: SAMRequestModel):
    ```json
    point
    {
-        "user_id": 1,
+        "bucket": "test",
+        "object_name": "7068-400a-ac94-c01647fa5f6f.png",
        "image_path": "aida-users/89/sketch/4e8fe37d-7068-400a-ac94-c01647fa5f6f.png",
        "type":"point",
        "points": [[310, 403], [493, 375], [261, 266], [404, 484]],
@@ -417,7 +419,8 @@ async def seg_anything(request_data: SAMRequestModel):

    box
    {
-        "user_id": 1,
+        "bucket": "test",
+        "object_name": "7068-400a-ac94-c01647fa5f6f.png",
        "image_path": "aida-users/89/sketch/4e8fe37d-7068-400a-ac94-c01647fa5f6f.png",
        "type":"box",
        "box": [350, 286, 544, 520]
@@ -426,7 +429,7 @@ async def seg_anything(request_data: SAMRequestModel):
    """
    try:
        logger.info(f"seg_anything request item is : @@@@@@:{json.dumps(request_data.dict(), indent=4)}")
-        data = requests.post(f"http://{settings.A6000_SERVICE_HOST}:10075/predict", json=request_data.dict())
+        data = requests.post(f"http://{settings.B_4_X_4090_SERVICE_HOST}:10075/predict", json=request_data.dict())
        logger.info(f"seg_anything response @@@@@@:{json.dumps(json.loads(data.content), indent=4)}")
        return ResponseModel(data=json.loads(data.content))
    except Exception as e:
--- a/app/api/api_generate_image.py
+++ b/app/api/api_generate_image.py
@@ -1,9 +1,12 @@
 import json
 import logging

+import httpx
+import requests
 from fastapi import APIRouter, BackgroundTasks, HTTPException

-from app.schemas.generate_image import GenerateImageModel, GenerateProductImageModel, GenerateSingleLogoImageModel, GenerateRelightImageModel, GenerateMultiViewModel, BatchGenerateProductImageModel, BatchGenerateRelightImageModel, AgentTollGenerateImageModel
+from app.core.config import settings
+from app.schemas.generate_image import GenerateImageModel, GenerateProductImageModel, GenerateSingleLogoImageModel, GenerateRelightImageModel, GenerateMultiViewModel, BatchGenerateProductImageModel, BatchGenerateRelightImageModel, AgentTollGenerateImageModel, Flux2ToProductImgModel, GenerateSloganImageModel, GenerateImageFlux2KleinModel
 from app.schemas.pose_transform import BatchPoseTransformModel
 from app.schemas.response_template import ResponseModel
 from app.service.generate_batch_image.service import start_product_batch_generate, start_relight_batch_generate, start_pose_transform_batch_generate
@@ -20,6 +23,61 @@ logger = logging.getLogger()
 '''generate image'''


+# flux2 klein
+@router.post("/generate_image_flux2_klein")
+async def generate_image_flux2_klein(request_item: GenerateImageFlux2KleinModel):
+    """
+        创建一个具有以下参数的请求体:
+        - **bucket_name**: OSS桶名 (必填)
+        - **object_name**: OSS对象名（文件路径）(必填)
+
+        - **width**: 图片宽度，默认1024像素 (非必填,1024)
+        - **height**: 图片高度，默认1024像素 (非必填,默认1024)
+        - **prompt**: 文本提示词，用于模型推理等场景 (非必填,默认"")
+        - **steps**: 推理步数，控制模型生成过程的迭代次数 (非必填,默认4)
+        - **guidance**: 引导系数，调节提示词对生成结果的影响程度 (非必填,默认 4.0 )
+
+        ### 示例参数：
+        ```
+            {
+                "bucket_name": "aida-users",
+                "object_name": "89/moodboard/5fdc698c-cb9b-4b36-afa9ce4-1-89.png",
+                "prompt": "a single item of sketch of dress, 4k, white background"
+            }
+        ```
+        ### 输出示例:
+        ```
+            {
+                "code": 200,
+                "msg": "OK!",
+                "data": {
+                    "output_path": "aida-users/89/moodboard/5fdc698c-cb9b-4b36-afa9ce4-1-89.png"
+                }
+            }
+        ```
+    """
+    try:
+        logger.info(f"generate_image_flux2_gen_img request: {json.dumps(request_item.model_dump(), indent=4)}")
+        async with httpx.AsyncClient(timeout=120) as client:
+            resp = await client.post(
+                f"http://{settings.FLUX2_GEN_IMG_MODEL_URL}/predict",
+                json=request_item.model_dump(),
+            )
+        if resp.status_code == 200:
+            result = resp.json()
+            logger.info(f"flux2_gen_img response: {json.dumps(result, indent=4)}")
+            return ResponseModel(data=result)
+        else:
+            error = resp.json()
+            logger.info(f"flux2_gen_img response: {json.dumps(error, indent=4)}")
+            return ResponseModel(data=error, msg="ERROR!", code=500)
+
+    except Exception as e:
+        logger.warning(f"generate_image_flux2_gen_img Run Exception @@@@@@:{e}")
+        raise HTTPException(status_code=404, detail=str(e))
+
+
+# sdxl
@router.post("/generate_image")
 def generate_image(request_item: GenerateImageModel, background_tasks: BackgroundTasks):
    """
@@ -154,6 +212,62 @@ def generate_single_logo_image(tasks_id: str):
    return ResponseModel(data=data['data'])


+"""slogan """
+
+
+@router.post("/generate_slogan")
+async def generate_slogan(request_data: GenerateSloganImageModel):
+    """
+        ### 请求体示例:
+        ```json
+        {
+          "num_point": 16,
+          "image_url": "aida-slogan/6886785f-0aac-4052-b6fd-7ae20a841d8d.png",
+          "prompt": "123",
+          "tasks_id": "string-89"
+        }
+        ```
+    """
+    try:
+        logger.info(f"generate_slogan request item is : @@@@@@:{json.dumps(request_data.dict(), indent=4)}")
+        data = requests.post(f"http://{settings.A6000_SERVICE_HOST}:10020/api/slogan", json=request_data.dict())
+        logger.info(f"generate_slogan response @@@@@@:{json.dumps(json.loads(data.content), indent=4)}")
+        return ResponseModel(data=json.loads(data.content))
+    except Exception as e:
+        logger.warning(f"generate_slogan Run Exception @@@@@@:{e}")
+
+
+"""product image flux2.0"""
+
+# @router.post("/img_to_product")
+# async def img_to_product(request_data: Flux2ToProductImgModel):
+#     """
+#         创建一个具有以下参数的请求体:
+#         - **tasks_id**: 任务id 用于取消生成任务和获取生成结果
+#         - **prompt**: 想要生成图片的描述词
+#         - **image_path**: 被生成图片的S3或minio url地址
+#         - **infer_step**: 推理步数
+#
+#         ### 请求体示例:
+#         ```json
+#         point
+#         {
+#             "prompt": "Create realistic studio photo with real people model standing and wearing this garment, in white studio, Keep original model if present, or generate appropriate model, Standing pose, facing camera.",
+#             "image_path":"aida-results/result_38151e0a-f83b-11f0-89f6-0242ac130002.png",
+#             "infer_step":4,
+#             "tasks_id":"123456-123"
+#         }
+#         ```
+#     """
+#     try:
+#         logger.info(f"img_to_product request item is : @@@@@@:{json.dumps(request_data.dict(), indent=4)}")
+#         data = requests.post(f"http://{settings.A6000_SERVICE_HOST}:10090/api/v1/to_product", json=request_data.dict())
+#         logger.info(f"img_to_product response @@@@@@:{json.dumps(json.loads(data.content), indent=4)}")
+#         return ResponseModel(data=json.loads(data.content))
+#     except Exception as e:
+#         logger.warning(f"img_to_product Run Exception @@@@@@:{e}")
+
+
 '''product image'''


@@ -178,7 +292,7 @@ def generate_product_image(request_item: GenerateProductImageModel, background_t
        }
    """
    try:
-        logger.info(f"generate_product_image request item is : @@@@@@:{json.dumps(request_item.dict(),indent=4)}")
+        logger.info(f"generate_product_image request item is : @@@@@@:{json.dumps(request_item.dict(), indent=4)}")
        service = GenerateProductImage(request_item)
        background_tasks.add_task(service.get_result)
    except Exception as e:
--- a/app/api/api_route.py
+++ b/app/api/api_route.py
@@ -11,6 +11,7 @@ from app.api import api_precompute
 from app.api import api_prompt_generation
 from app.api import api_recommendation
 from app.api import api_test
+from app.api import api_sketch_to_garment

 router = APIRouter()

@@ -26,6 +27,7 @@ router.include_router(api_precompute.router, tags=['api_precompute'], prefix="/a
 router.include_router(api_mannequins_edit.router, tags=['api_mannequins_edit'], prefix="/api")
 router.include_router(api_pose_transform.router, tags=['api_pose_transform'], prefix="/api")
 router.include_router(api_clothing_seg.router, tags=['api_clothing_seg'], prefix="/api")
+router.include_router(api_sketch_to_garment.router, tags=['sketch_to_garment'], prefix="/api")

 """停用"""
 # from app.api import api_chat_robot
--- a/app/api/api_sketch_to_garment.py
+++ b/app/api/api_sketch_to_garment.py
@@ -0,0 +1,104 @@
+import json
+import logging
+
+from fastapi import APIRouter, HTTPException
+
+from app.schemas.response_template import ResponseModel
+from app.schemas.sketch_to_garment_schemas import SketchToGarmentModel
+from app.service.sketch2garment.server import submit_sketch_to_garment_task
+
+logger = logging.getLogger()
+router = APIRouter()
+
+
+@router.post("/sketch_to_garment")
+def sketch_to_garment_api(request_item: SketchToGarmentModel):
+    """
+        ### 接口说明:
+        将图片转换为3D模型（异步处理）。接口接收请求后立即返回任务ID，后台通过 Celery 处理，处理完成后结果会通过 RabbitMQ 发送。
+
+        ### 参数说明:
+        - **input_image_path**: 输入图片路径
+        - **bucket_name**: bucket name
+        - **user_id**: 用户id
+        - **callback_url**: 回调url
+        - **task_id**: 任务id
+        - **model**: 转换模式 文本和图片 ,默认只有图片
+
+        ### 请求体示例:
+        **单张图片模式:**
+        ```json
+        {
+          "input_image_path": "test/53d38bd5-f77b-4034-ada2-45f1e2ebe00c.png",
+          "bucket_name": "test",
+          "user_id": "string-456",
+          "callback_url": "http://18.167.251.121:10015/api/image/webhook/img-to-3d",
+          "task_id": "string12",
+          "model": "picture"
+        }
+        ```
+
+
+        ### 输出示例:
+
+        ```json
+        {
+          "code": 200,
+          "msg": "OK!",
+          "data": {
+            "state": "success",
+            "task_id": "string12",
+            "message": "任务已成功提交，正在后台处理..."
+          }
+        }
+        ```
+        ### 错误输出
+        参考文档:    https://platform.tripo3d.ai/docs/error-handling
+        ```json
+        {
+            "code": 500,
+            "message": "You don’t have enough credit to create this task",
+            "data": {
+                "status": "fail",
+                "task_id": "123",
+                "message": "You don’t have enough credit to create this task",
+                "error": str(e)
+            }
+        }
+        ```
+
+        回调请求参数例子:
+        ```json
+        {
+            "task_id": "string12",
+            "status": "success",
+            "result": {
+                "pattern": "test/string-456/pattern_making/now_string-456_pattern.png",
+                "texture": "test/string-456/pattern_making/now_string-456_texture.png",
+                "glb": "test/string-456/pattern_making/now_string-456_sim.glb",
+                "texture_fabric": "test/string-456/pattern_making/now_string-456_texture_fabric.png"
+            }
+        }
+        ```
+        """
+    try:
+        logger.info(f"sketch_to_garment request item is : @@@@@@:{json.dumps(request_item.model_dump(), indent=4)}")
+        result = submit_sketch_to_garment_task(
+            task_id=request_item.task_id,
+            callback_url=request_item.callback_url,
+            bucket_name=request_item.bucket_name,
+            input_image_path=request_item.input_image_path,
+            user_id=request_item.user_id,
+            model=request_item.model
+        )
+        result = {
+            "state": "success",
+            "task_id": request_item.task_id,
+            "message": "任务已成功提交，正在后台处理...",
+        }
+        state_code = 200
+        return ResponseModel(data=result, code=state_code)
+
+    except Exception as e:
+        logger.warning(f"super_resolution Run Exception @@@@@@:{e}")
+        raise HTTPException(status_code=404, detail=str(e))
--- a/app/core/config.backup.py
+++ b/app/core/config.backup.py
@@ -1,235 +0,0 @@
-import os
-
-import pika
-from dotenv import load_dotenv
-from pydantic import BaseSettings
-
-BASE_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), '../../'))
-load_dotenv(os.path.join(BASE_DIR, '.env'))
-
-
-class Settings(BaseSettings):
-    PROJECT_NAME: str = 'FASTAPI BASE'
-    SECRET_KEY: str = ''
-    API_PREFIX: str = ''
-    BACKEND_CORS_ORIGINS: list[str] = ['*']
-    DATABASE_URL: str = ''
-    ACCESS_TOKEN_EXPIRE_SECONDS: int = 60 * 60 * 24 * 7  # Token expired after 7 days
-    SECURITY_ALGORITHM: str = 'HS256'
-    LOGGING_CONFIG_FILE: str = os.path.join(BASE_DIR, 'logging_env.py')
-
-
-OSS = "minio"
-DEBUG = False
-if DEBUG:
-    LOGS_PATH = "logs/"
-    CATEGORY_PATH = "service/attribute/config/descriptor/category/category_dis.csv"
-    SEG_CACHE_PATH = "../seg_cache/"
-    POSE_TRANSFORM_VIDEO_PATH = "../pose_transform_video/"
-    RECOMMEND_PATH_PREFIX = "service/recommend/"
-    CHROMADB_PATH = "./chromadb/"
-else:
-    LOGS_PATH = "app/logs/"
-    CATEGORY_PATH = "app/service/attribute/config/descriptor/category/category_dis.csv"
-    SEG_CACHE_PATH = "/seg_cache/"
-    POSE_TRANSFORM_VIDEO_PATH = "/pose_transform_video/"
-    RECOMMEND_PATH_PREFIX = "app/service/recommend/"
-    CHROMADB_PATH = "/chromadb/"
-
-# RABBITMQ_ENV = ""  # 生产环境
-RABBITMQ_ENV = os.getenv("RABBITMQ_ENV", "-dev")
-# RABBITMQ_ENV = "-local"  # 本地测试环境
-
-if RABBITMQ_ENV == "-dev":
-    JAVA_STREAM_API_URL = f"https://develop.api.aida.com.hk/api/third/party/receiveDesignResults"
-elif RABBITMQ_ENV == "-prod":
-    JAVA_STREAM_API_URL = f"https://api.aida.com.hk/api/third/party/receiveDesignResults"
-
-settings = Settings()
-
-# minio 配置
-MINIO_URL = "www.minio-api.aida.com.hk"
-MINIO_ACCESS = 'vXKFLSJkYeEq2DrSZvkB'
-MINIO_SECRET = 'uKTZT3x7C43WvPN9QTc99DiRkwddWZrG9Uh3JVlR'
-MINIO_SECURE = True
-
-# S3 配置
-S3_ACCESS_KEY = "AKIAVD3OJIMF6UJFLSHZ"
-S3_AWS_SECRET_ACCESS_KEY = "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"
-S3_REGION_NAME = "ap-east-1"
-
-# redis 配置
-REDIS_HOST = "10.1.1.240"
-REDIS_PORT = "6379"
-REDIS_DB = "2"
-
-# rabbitmq config
-RABBITMQ_PARAMS = {
-    "host": "18.167.251.121",
-    "port": 5672,
-    "credentials": pika.credentials.PlainCredentials(username='rabbit', password='123456'),
-    "virtual_host": "/"
-}
-
-# milvus 配置
-MILVUS_URL = "http://10.1.1.240:19530"
-MILVUS_TOKEN = "root:Milvus"
-MILVUS_ALIAS = "default"
-MILVUS_TABLE_KEYPOINT = "keypoint_cache_2"
-MILVUS_TABLE_SEG = "seg_cache"
-
-# Mysql 配置
-DB_HOST = '18.167.251.121'  # 数据库主机地址
-# DB_PORT = int( 33006)
-DB_PORT = 33008  # 数据库端口
-DB_USERNAME = 'aida_con_python'  # 数据库用户名
-DB_PASSWORD = '123456'  # 数据库密码
-DB_NAME = 'aida'  # 数据库库名
-
-# openai
-os.environ['SERPAPI_API_KEY'] = "a793513017b0718db7966207c31703d280d12435c982f1e67bbcbffa52e7632c"
-OPENAI_STREAM = True
-BUFFER_THRESHOLD = 6  # must be even number
-SINGLE_TOKEN_THRESHOLD = 200
-TOKEN_THRESHOLD = 600
-OPENAI_TEMPERATURE = 0
-
-# OPENAI_API_KEY =  "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"
-OPENAI_API_KEY = "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"
-OPENAI_MODEL = "gpt-3.5-turbo-0613"
-OPENAI_MODEL_LIST = {"gpt-3.5-turbo-0613",
-                     "gpt-3.5-turbo-16k-0613",
-                     "gpt-4-0314",
-                     "gpt-4-32k-0314",
-                     "gpt-4-0613",
-                     "gpt-4-32k-0613", }
-
-# SR service config
-SR_MODEL_NAME = "super_resolution"
-SR_TRITON_URL = "10.1.1.240:10031"
-SR_MINIO_BUCKET = "aida-users"
-SR_RABBITMQ_QUEUES = f"SuperResolution{RABBITMQ_ENV}"
-
-# GenerateImage service config
-FAST_GI_MODEL_URL = '10.1.1.243:10011'
-FAST_GI_MODEL_NAME = 'stable_diffusion_xl'
-
-GI_MODEL_URL = '10.1.1.240:10061'
-GI_MODEL_NAME = 'flux'
-
-GMV_MODEL_URL = '10.1.1.243:10081'
-GMV_MODEL_NAME = 'multi_view'
-
-GMV_RABBITMQ_QUEUES = f"GenerateMultiView{RABBITMQ_ENV}"
-
-GI_MINIO_BUCKET = "aida-users"
-GI_RABBITMQ_QUEUES = f"GenerateImage{RABBITMQ_ENV}"
-GI_SYS_IMAGE_URL = "aida-sys-image/generate_image/white_image.jpg"
-
-# SLOGAN service config
-SLOGAN_RABBITMQ_QUEUES = f"Slogan{RABBITMQ_ENV}"
-
-# Generate Single Logo service config
-GSL_MODEL_URL = '10.1.1.243:10041'
-GSL_MINIO_BUCKET = "aida-users"
-GSL_MODEL_NAME = 'stable_diffusion_xl_transparent'
-GEN_SINGLE_LOGO_RABBITMQ_QUEUES = f"GenSingleLogo{RABBITMQ_ENV}"
-
-# Generate Product service config
-# GPI_RABBITMQ_QUEUES = os.getenv("GEN_PRODUCT_IMAGE_RABBITMQ_QUEUES", f"ToProductImage{RABBITMQ_ENV}")
-# GPI_MODEL_NAME_OVERALL = 'sdxl_ensemble_all'
-# GPI_MODEL_URL = '10.1.1.243:10051'
-
-# Generate Product service config 旧版product img 模型
-GPI_RABBITMQ_QUEUES = f"ToProductImage{RABBITMQ_ENV}"
-BATCH_GPI_RABBITMQ_QUEUES = f"BatchToProductImage{RABBITMQ_ENV}"
-GPI_MODEL_NAME_OVERALL = 'diffusion_ensemble_all'
-GPI_MODEL_NAME_SINGLE = 'stable_diffusion_1_5_cnet'
-GPI_MODEL_URL = '10.1.1.243:10051'
-
-# Generate Single Logo service config
-GRI_RABBITMQ_QUEUES = f"Relight{RABBITMQ_ENV}"
-BATCH_GRI_RABBITMQ_QUEUES = f"BatchRelight{RABBITMQ_ENV}"
-GRI_MODEL_NAME_OVERALL = 'diffusion_relight_ensemble'
-GRI_MODEL_NAME_SINGLE = 'stable_diffusion_1_5_relight'
-GRI_MODEL_URL = '10.1.1.240:10051'
-
-# Pose Transform service config
-
-PS_RABBITMQ_QUEUES = f"PoseTransform{RABBITMQ_ENV}"
-BATCH_PS_RABBITMQ_QUEUES = f"BatchPoseTransform{RABBITMQ_ENV}"
-PT_MODEL_URL = '10.1.1.243:10061'
-
-# SEG service config
-SEGMENTATION = {
-    "new_model_name": "seg_knet",
-    "name": "seg_ocrnet_hr18",
-    "input": "seg_input__0",
-    "output": "seg_output__0",
-}
-# ollama config
-OLLAMA_URL = "http://10.1.1.240:11434/api/embeddings"
-
-# design batch
-BATCH_DESIGN_RABBITMQ_QUEUES = f"DesignBatch{RABBITMQ_ENV}"
-
-# DESIGN config
-DESIGN_MODEL_URL = '10.1.1.240:10000'
-AIDA_CLOTHING = "aida-clothing"
-KEYPOINT_RESULT_TABLE_FIELD_SET = ('neckline_left', 'neckline_right', 'shoulder_left', 'shoulder_right', 'armpit_left', 'armpit_right',
-                                   'cuff_left_in', 'cuff_left_out', 'cuff_right_in', 'cuff_right_out', 'waistband_left', 'waistband_right')
-
-# DESIGN 预处理
-IF_DEBUG_SHOW = False
-
-# 优先级
-PRIORITY_DICT = {
-    'earring_front': 99,
-    'bag_front': 98,
-    'hairstyle_front': 97,
-    'outwear_front': 20,
-    'tops_front': 19,
-    'dress_front': 18,
-    'blouse_front': 17,
-    'skirt_front': 16,
-    'trousers_front': 15,
-    'bottoms_front': 14,
-    'shoes_right': 1,
-    'shoes_left': 1,
-    'body': 0,
-    'bottoms_back': -14,
-    'trousers_back': -15,
-    'skirt_back': -16,
-    'blouse_back': -17,
-    'dress_back': -18,
-    'tops_back': -19,
-    'outwear_back': -20,
-    'hairstyle_back': -97,
-    'bag_back': -98,
-    'earring_back': -99,
-}
-
-QWEN_API_KEY = "sk-f31c29e61ac2498ba5e307aaa6dc10e0"
-
-DB_CONFIG = {
-    "host": "18.167.251.121",
-    "port": 3306,
-    "user": "root",
-    "password": "QWa998345",
-    "database": "aida",
-    "charset": "utf8mb4"
-}
-
-TABLE_CATEGORIES = {
-    "female_dress": "female/dress",
-    "female_outwear": "female/outwear",
-    "female_trousers": "female/trousers",
-    "female_skirt": "female/skirt",
-    "female_blouse": "female/blouse",
-    "male_tops": "male/tops",
-    "male_bottoms": "male/bottoms",
-    "male_outwear": "male/outwear"
-}
-
-# --- ComfyUI 配置信息 ---
-COMFYUI_SERVER_ADDRESS = "10.1.2.227:8080"  # 替换为您的 ComfyUI 服务器地址
--- a/app/core/config.py
+++ b/app/core/config.py
@@ -1,5 +1,21 @@
+import logging
+from typing import Dict, Any
+
+import yaml
 from pydantic import Field
 from pydantic_settings import BaseSettings, SettingsConfigDict
+from v2.nacos import ClientConfigBuilder, GRPCConfig, NacosConfigService, ConfigParam, NacosNamingService, RegisterInstanceParam, DeregisterInstanceParam
+
+logger = logging.getLogger(__name__)
+
+# ====================== Nacos 配置 ======================
+NACOS_SERVER_ADDRESSES = "18.167.251.121:28848"
+NACOS_NAMESPACE = "zcr"
+NACOS_USERNAME = "nacos"
+NACOS_PASSWORD = "Aidlab123123!"
+NACOS_GROUP = "LOCAL"
+NACOS_DATA_ID = "aida.python"
+SERVICE_NAME = "fastapi-service"  # ←←← 必须修改！建议格式：项目名-环境，例如 ai-image-service-dev


 class Settings(BaseSettings):
@@ -36,7 +52,7 @@ class Settings(BaseSettings):

    # --- mysql 配置信息 ---
    MYSQL_HOST: str = Field(default='', description="")
-    MYSQL_PORT: int = Field(default='', description="")
+    MYSQL_PORT: int = Field(default=3306, description="")
    MYSQL_USER: str = Field(default='', description="")
    MYSQL_PASSWORD: str = Field(default='', description="")
    MYSQL_DB: str = Field(default='', description="")
@@ -64,10 +80,16 @@ class Settings(BaseSettings):
    # --- Design Callback Java 接口 ---
    JAVA_STREAM_API_URL: str = Field(default='', description="")

+    # ---  flux2 klein model url ---
+    FLUX2_GEN_IMG_MODEL_URL: str = Field(default='', description="")
+
    # --- 服务器IP ---
    A6000_SERVICE_HOST: str = Field(default='', description="")
    B_4_X_4090_SERVICE_HOST: str = Field(default='', description="")

+    # --- sketch to garment 模型url ---
+    SKETCH_TO_GARMENT_URL: str = Field(default='', description="")
+
    # --- 其他配置信息 以下均为Docker容器内配置---
    LOGS_PATH: str = Field(default="/logs/", description="")
    CATEGORY_PATH: str = Field(default="/app/service/attribute/config/descriptor/category/category_dis.csv", description="")
@@ -128,6 +150,8 @@ OLLAMA_URL = f"http://{settings.A6000_SERVICE_HOST}:11434/api/embeddings"
 # Design
 DESIGN_MODEL_URL = f'{settings.A6000_SERVICE_HOST}:10000'
 DESIGN_MODEL_NAME = 'seg_knet'
+# Seg Product
+SEG_PRODUCT_MODEL_URL = f'{settings.B_4_X_4090_SERVICE_HOST}:30000'
 # Generate Image
 GI_MODEL_URL = f'{settings.A6000_SERVICE_HOST}:10061'
 GI_MODEL_NAME = 'flux'
--- a/app/main.py
+++ b/app/main.py
@@ -1,5 +1,8 @@
 # 1. 这里的顺序至关重要！必须在最顶端
 import sys
+from contextlib import asynccontextmanager
+
+# from app.core.nacos_config import load_nacos_config, register_server, deregister_server

 try:
    import asyncore
@@ -16,7 +19,7 @@ from fastapi.responses import JSONResponse

 from app.api.api_route import router
 from app.core.config import settings
-from app.core.record_api_count import count_api_calls
+# from app.core.record_api_count import count_api_calls
 from app.schemas.response_template import ResponseModel
 from logging_env import LOGGER_CONFIG_DICT
 from dotenv import load_dotenv
@@ -30,8 +33,21 @@ logger = logging.getLogger(__name__)
 load_dotenv()


+# @asynccontextmanager
+# async def lifespan(app: FastAPI):
+#     try:
+#         load_nacos_config()
+#         register_server()
+#
+#         yield
+#     finally:
+#         deregister_server()
+#         logger.info("lifespan down")
+
+
 def get_application() -> FastAPI:
    application = FastAPI(
+        # lifespan=lifespan,
        docs_url="/docs",
        redoc_url='/re-docs',
        openapi_url=f"/openapi.json",
@@ -48,7 +64,7 @@ def get_application() -> FastAPI:
        allow_methods=["*"],
        allow_headers=["*"],
    )
-    application.middleware("http")(count_api_calls)
+    # application.middleware("http")(count_api_calls)
    application.include_router(router=router)
    return application

@@ -64,5 +80,11 @@ async def http_exception_handler(exc: HTTPException):
    )


+@app.get("/health", operation_id="health")
+async def health():
+    logger.info("health check")
+    return {"ok": True, "env": settings.APP_ENV}
+
+
 if __name__ == '__main__':
    uvicorn.run(app, host="0.0.0.0", port=settings.PORT)
--- a/app/schemas/design.py
+++ b/app/schemas/design.py
@@ -4,12 +4,13 @@ from pydantic import BaseModel, Field


 class SAMRequestModel(BaseModel):
-    user_id: int = Field(..., description="用户id, 必填字段")
+    bucket: str = Field(..., description="minio bucket name ")
+    object_name: str = Field(..., description="minio object name ")
    image_path: str = Field(..., description="图片路径，必填字段")
    type: str = Field(..., description="推理类型，必填字段")
-    points: Optional[List[List[float]]] = None
-    labels: Optional[List[int]] = None
-    box: Optional[List[int]] = None
+    points: Optional[List[List[float]]] | None = None
+    labels: Optional[List[int]] | None = None
+    box: Optional[List[int]] | None = None


 class DesignModel(BaseModel):
--- a/app/schemas/generate_image.py
+++ b/app/schemas/generate_image.py
@@ -1,6 +1,6 @@
-from typing import List
+from typing import List, Optional

-from pydantic import BaseModel
+from pydantic import BaseModel, Field


 class GenerateMultiViewModel(BaseModel):
@@ -8,6 +8,17 @@ class GenerateMultiViewModel(BaseModel):
    image_url: str


+class GenerateImageFlux2KleinModel(BaseModel):
+    bucket_name: str = Field(..., description="OSS桶名，不传则为None")
+    object_name: str = Field(..., description="OSS对象名（文件路径），不传则为None")
+    # input_image_paths: Optional[List[str]] = Field(default=[], description="输入图片路径列表")
+    width: Optional[int] = Field(default=1024, description="图片宽度，默认512像素")
+    height: Optional[int] = Field(default=1024, description="图片高度，默认512像素")
+    prompt: Optional[str] = Field(default="", description="文本提示词，用于模型推理等场景")
+    steps: Optional[int] = Field(default=4, description="推理步数，控制模型生成过程的迭代次数")
+    guidance: Optional[float] = Field(default=4.0, description="引导系数，调节提示词对生成结果的影响程度")
+
+
 class GenerateImageModel(BaseModel):
    tasks_id: str
    prompt: str
@@ -24,6 +35,13 @@ class GenerateSingleLogoImageModel(BaseModel):
    seed: str


+class GenerateSloganImageModel(BaseModel):
+    num_point: int
+    tasks_id: str
+    prompt: str
+    image_url: str
+
+
 class GenerateProductImageModel(BaseModel):
    tasks_id: str
    prompt: str
@@ -32,6 +50,13 @@ class GenerateProductImageModel(BaseModel):
    product_type: str


+class Flux2ToProductImgModel(BaseModel):
+    tasks_id: str
+    prompt: str
+    image_path: str
+    infer_step: int | None = None
+
+
 class GenerateRelightImageModel(BaseModel):
    tasks_id: str
    prompt: str
--- a/app/schemas/sketch_to_garment_schemas.py
+++ b/app/schemas/sketch_to_garment_schemas.py
@@ -0,0 +1,12 @@
+from typing import List
+
+from pydantic import BaseModel, Field
+
+
+class SketchToGarmentModel(BaseModel):
+    input_image_path: str = Field(..., description="输入图片路径列表")
+    bucket_name: str = Field(..., description="输入图片路径列表")
+    user_id: str = Field(..., description="用户id")
+    callback_url: str  # 必填，客户端提供的回调地址
+    task_id: str = Field()
+    model: str = Field(default="single", description="模型类型: single 或 multi")
--- a/app/service/attribute/service_att_recognition.py
+++ b/app/service/attribute/service_att_recognition.py
@@ -3,7 +3,6 @@
 from pprint import pprint

 import cv2
-import mmcv
 import numpy as np
 import pandas as pd
 import torch
@@ -12,6 +11,7 @@ from minio import Minio

 from app.core.config import settings, DESIGN_MODEL_URL
 from app.schemas.attribute_retrieve import AttributeRecognitionModel
+from app.service.utils.image_normalize import my_imnormalize
 from app.service.utils.new_oss_client import oss_get_image

 minio_client = Minio(settings.MINIO_URL, access_key=settings.MINIO_ACCESS, secret_key=settings.MINIO_SECRET, secure=settings.MINIO_SECURE)
@@ -109,10 +109,9 @@ class AttributeRecognition:

    @staticmethod
    def preprocess(img):
-        img = mmcv.imread(img)
        img_scale = (224, 224)
        img = cv2.resize(img, img_scale)
-        img = mmcv.imnormalize(img, mean=np.array([123.675, 116.28, 103.53]), std=np.array([58.395, 57.12, 57.375]), to_rgb=True)
+        img = my_imnormalize(img, mean=np.array([123.675, 116.28, 103.53]), std=np.array([58.395, 57.12, 57.375]), to_rgb=True)
        preprocessed_img = np.expand_dims(img.transpose(2, 0, 1), axis=0)
        return preprocessed_img

--- a/app/service/attribute/service_category_recognition.py
+++ b/app/service/attribute/service_category_recognition.py
@@ -10,7 +10,6 @@
 from minio import Minio
 from skimage import transform
 import cv2
-import mmcv
 import numpy as np
 import pandas as pd
 import tritonclient.http as httpclient
@@ -18,6 +17,7 @@ import torch

 from app.core.config import settings, DESIGN_MODEL_URL
 from app.schemas.attribute_retrieve import CategoryRecognitionModel
+from app.service.utils.image_normalize import my_imnormalize
 from app.service.utils.new_oss_client import oss_get_image

 minio_client = Minio(settings.MINIO_URL, access_key=settings.MINIO_ACCESS, secret_key=settings.MINIO_SECRET, secure=settings.MINIO_SECURE)
@@ -39,11 +39,10 @@ class CategoryRecognition:

    @staticmethod
    def preprocess(img):
-        img = mmcv.imread(img)
        # ori_shape = img.shape[:2]
        img_scale = (224, 224)
        img = cv2.resize(img, img_scale)
-        img = mmcv.imnormalize(img, mean=np.array([123.675, 116.28, 103.53]), std=np.array([58.395, 57.12, 57.375]), to_rgb=True)
+        img = my_imnormalize(img, mean=np.array([123.675, 116.28, 103.53]), std=np.array([58.395, 57.12, 57.375]), to_rgb=True)
        preprocessed_img = np.expand_dims(img.transpose(2, 0, 1), axis=0)
        return preprocessed_img

--- a/app/service/brand_dna/service.py
+++ b/app/service/brand_dna/service.py
@@ -1,7 +1,6 @@
 import logging

 import cv2
-import mmcv
 import numpy as np
 import pandas as pd
 import torch
@@ -9,11 +8,12 @@ import torch.nn.functional as F
 import tritonclient.http as httpclient
 from minio import Minio

-from app.core.config import DESIGN_MODEL_URL
+from app.core.config import DESIGN_MODEL_URL, SEG_PRODUCT_MODEL_URL
 from app.core.config import settings
 from app.schemas.brand_dna import BrandDnaModel
 from app.service.attribute.config import const
 from app.service.utils.generate_uuid import generate_uuid
+from app.service.utils.image_normalize import my_imnormalize
 from app.service.utils.new_oss_client import oss_upload_image, oss_get_image

 minio_client = Minio(settings.MINIO_URL, access_key=settings.MINIO_ACCESS, secret_key=settings.MINIO_SECRET, secure=settings.MINIO_SECURE)
@@ -29,7 +29,7 @@ class BrandDna:
        self.attr_type = pd.read_csv(settings.CATEGORY_PATH)
        # self.attr_type = pd.read_csv(r"E:\workspace\trinity_client_aida\app\service\attribute\config\descriptor\category\category_dis.csv")
        self.att_client = httpclient.InferenceServerClient(url=DESIGN_MODEL_URL)
-        self.seg_client = httpclient.InferenceServerClient(url='10.1.1.243:30000')
+        self.seg_client = httpclient.InferenceServerClient(url=SEG_PRODUCT_MODEL_URL)
        self.const = const
        # self.const = local_debug_const

@@ -202,7 +202,7 @@ class BrandDna:
    # 服装分割预处理
    @staticmethod
    def seg_product_preprocess(image):
-        img = mmcv.imread(image)
+        img = image
        ori_shape = img.shape[:2]
        img_scale_w, img_scale_h = ori_shape
        if ori_shape[0] > 1024:
@@ -211,9 +211,9 @@ class BrandDna:
            img_scale_h = 1024
        # 如果图片size任意一边 大于 1024， 则会resize 成1024
        if ori_shape != (img_scale_w, img_scale_h):
-            # mmcv.imresize(img, img_scale_h, img_scale_w) # 老代码 引以为戒！哈哈哈~ h和w写反了
+            # my_imnormalize(img, img_scale_h, img_scale_w) # 老代码 引以为戒！哈哈哈~ h和w写反了
            img = cv2.resize(img, (img_scale_h, img_scale_w))
-        img = mmcv.imnormalize(img, mean=np.array([123.675, 116.28, 103.53]), std=np.array([58.395, 57.12, 57.375]), to_rgb=True)
+        img = my_imnormalize(img, mean=np.array([123.675, 116.28, 103.53]), std=np.array([58.395, 57.12, 57.375]), to_rgb=True)
        preprocessed_img = np.expand_dims(img.transpose(2, 0, 1), axis=0)
        return preprocessed_img, ori_shape

@@ -227,11 +227,10 @@ class BrandDna:
    # 类别检测模型预处理
    @staticmethod
    def category_preprocess(img):
-        img = mmcv.imread(img)
        # ori_shape = img.shape[:2]
        img_scale = (224, 224)
        img = cv2.resize(img, img_scale)
-        img = mmcv.imnormalize(img, mean=np.array([123.675, 116.28, 103.53]), std=np.array([58.395, 57.12, 57.375]), to_rgb=True)
+        img = my_imnormalize(img, mean=np.array([123.675, 116.28, 103.53]), std=np.array([58.395, 57.12, 57.375]), to_rgb=True)
        preprocessed_img = np.expand_dims(img.transpose(2, 0, 1), axis=0)
        return preprocessed_img

--- a/app/service/brand_dna/service_generate_brand_info.py
+++ b/app/service/brand_dna/service_generate_brand_info.py
@@ -1,19 +1,10 @@
-import logging
-
-import cv2
-import numpy as np
-import tritonclient.grpc as grpcclient
+import uuid
+import httpx
 from langchain_classic.output_parsers import ResponseSchema, StructuredOutputParser
 from langchain_community.chat_models import ChatTongyi
 from langchain_core.prompts import PromptTemplate
 from minio import Minio
-from tritonclient.utils import np_to_triton_dtype
-
-from app.core.config import GI_MODEL_URL, GI_MODEL_NAME
 from app.schemas.brand_dna import GenerateBrandModel
-from app.service.utils.generate_uuid import generate_uuid
-from app.service.utils.new_oss_client import oss_upload_image
-
 from app.core.config import settings


@@ -26,14 +17,9 @@ class GenerateBrandInfo:
        # user info init
        self.user_id = request_data.user_id
        self.category = "brand_logo"
-        # generate logo init
-        self.grpc_client = grpcclient.InferenceServerClient(url=GI_MODEL_URL)
-        self.image = np.random.randint(0, 256, (1024, 1024, 3), dtype=np.uint8)
-        self.batch_size = 1
-        self.mode = 'txt2img'

        # llm generate brand info init
-        self.model = ChatTongyi(model="qwen2.5-14b-instruct", api_key="sk-7658298c6b99443c98184a5e634fe6ab")
+        self.model = ChatTongyi(model="qwen2.5-14b-instruct", api_key=settings.QWEN_API_KEY)

        self.response_schemas = [
            ResponseSchema(name="brand_name", description="Brand name."),
@@ -63,38 +49,20 @@ class GenerateBrandInfo:
        self.generate_logo_prompt = brand_data['brand_logo_prompt']

    def generate_brand_logo(self):
-        prompts = [self.generate_logo_prompt] * self.batch_size
-        modes = [self.mode] * self.batch_size
-        images = [self.image.astype(np.float16)] * self.batch_size
-
-        text_obj = np.array(prompts, dtype="object").reshape((-1, 1))
-        mode_obj = np.array(modes, dtype="object").reshape((-1, 1))
-        image_obj = np.array(images, dtype=np.float16).reshape((-1, 1024, 1024, 3))
-
-        input_text = grpcclient.InferInput("prompt", text_obj.shape, np_to_triton_dtype(text_obj.dtype))
-        input_image = grpcclient.InferInput("input_image", image_obj.shape, np_to_triton_dtype(image_obj.dtype))
-        input_mode = grpcclient.InferInput("mode", mode_obj.shape, np_to_triton_dtype(mode_obj.dtype))
-
-        input_text.set_data_from_numpy(text_obj)
-        input_image.set_data_from_numpy(image_obj)
-        input_mode.set_data_from_numpy(mode_obj)
-
-        inputs = [input_text, input_image, input_mode]
-        result = self.grpc_client.infer(model_name=GI_MODEL_NAME, inputs=inputs)
-        image = result.as_numpy("generated_image")
-        image_result = cv2.cvtColor(np.squeeze(image.astype(np.uint8)), cv2.COLOR_RGB2BGR)
-        logo_url = self.upload_logo_image(image_result, generate_uuid())
-        self.result_data['brand_logo'] = logo_url
-
-    def upload_logo_image(self, image, object_name):
-        try:
-            _, img_byte_array = cv2.imencode('.jpg', image)
-            object_name = f'{self.user_id}/{self.category}/{object_name}.jpg'
-            oss_upload_image(oss_client=self.minio_client, bucket="aida-users", object_name=object_name, image_bytes=img_byte_array)
-            image_url = f"aida-users/{object_name}"
-            return image_url
-        except Exception as e:
-            logging.warning(f"upload_png_mask runtime exception : {e}")
+        request_item = {
+            "bucket_name": "aida-users",
+            "object_name": f'{self.user_id}/{self.category}/{uuid.uuid4().hex}.png',
+            "prompt": self.generate_logo_prompt,
+            "height": 1024,
+            "width": 1024
+        }
+        with httpx.Client(timeout=120) as client:
+            resp = client.post(
+                f"http://{settings.FLUX2_GEN_IMG_MODEL_URL}/predict",
+                json=request_item,
+            )
+        result = resp.json()
+        self.result_data['brand_logo'] = result.get("output_path", "")


 if __name__ == '__main__':
--- a/app/service/clothing_seg/service.py
+++ b/app/service/clothing_seg/service.py
@@ -23,7 +23,7 @@ class ClothingSeg:
    def __init__(self, request_data):
        self.image_data = request_data.image_data
        self.user_id = request_data.user_id
-        self.triton_client = grpcclient.InferenceServerClient(url="10.1.1.243:10071")
+        self.triton_client = grpcclient.InferenceServerClient(url=f"{settings.B_4_X_4090_SERVICE_HOST}:10071")

    @RunTime
    def get_result(self):
@@ -139,7 +139,7 @@ def get_bounding_box(mask):

 if __name__ == "__main__":
    test_data = ClothingSegModel(
-        user_id=89,
+        user_id="89",
        image_data=[
            # {
            #     "image_url": "test/clothing_seg/dress.jpg",
--- a/app/service/comfyui_I2V/pose2v_server.py
+++ b/app/service/comfyui_I2V/pose2v_server.py
@@ -13,7 +13,7 @@ from PIL import Image
 from minio import Minio, S3Error
 from moviepy.video.io.VideoFileClip import VideoFileClip

-from app.core.config import  settings
+from app.core.config import settings, PS_RABBITMQ_QUEUES
 from app.schemas.comfyui_i2v import ComfyuiPose2VModel
 from app.service.generate_image.utils.mq import publish_status

@@ -622,9 +622,9 @@ class ComfyUIServerPose2V:

            # 推送消息
            if not settings.DEBUG:
-                publish_status(json.dumps(self.pose_transform_data), settings.COMFYUI_SERVER_ADDRESS)
+                publish_status(json.dumps(self.pose_transform_data), PS_RABBITMQ_QUEUES)
            logger.info(
-                f" [x] Sent to： {settings.COMFYUI_SERVER_ADDRESS} data：@@@@  {json.dumps(self.pose_transform_data, indent=4)}")
+                f" [x] Sent to： {PS_RABBITMQ_QUEUES} data：@@@@  {json.dumps(self.pose_transform_data, indent=4)}")

            return "\n🎉 所有任务完成！"

--- a/app/service/design_batch/utils/design_ensemble.py
+++ b/app/service/design_batch/utils/design_ensemble.py
@@ -10,13 +10,13 @@
 import logging

 import cv2
-import mmcv
 import numpy as np
 import torch
 import torch.nn.functional as F
 import tritonclient.http as httpclient

 from app.core.config import DESIGN_MODEL_URL, DESIGN_MODEL_NAME
+from app.service.utils.image_normalize import my_imnormalize

 """
    keypoint
@@ -25,13 +25,13 @@ from app.core.config import DESIGN_MODEL_URL, DESIGN_MODEL_NAME


 def keypoint_preprocess(img_path):
-    img = mmcv.imread(img_path)
+    img = img_path
    img_scale = (256, 256)
    h, w = img.shape[:2]
    img = cv2.resize(img, img_scale)
    w_scale = img_scale[0] / w
    h_scale = img_scale[1] / h
-    img = mmcv.imnormalize(img, mean=np.array([123.675, 116.28, 103.53]), std=np.array([58.395, 57.12, 57.375]), to_rgb=True)
+    img = my_imnormalize(img, mean=np.array([123.675, 116.28, 103.53]), std=np.array([58.395, 57.12, 57.375]), to_rgb=True)
    preprocessed_img = np.expand_dims(img.transpose(2, 0, 1), axis=0)
    return preprocessed_img, (w_scale, h_scale)

@@ -74,7 +74,7 @@ def keypoint_postprocess(output, scale_factor):

 # KNet
 def seg_preprocess(img_path):
-    img = mmcv.imread(img_path)
+    img = img_path
    ori_shape = img.shape[:2]
    img_scale_w, img_scale_h = ori_shape
    if ori_shape[0] > 1024:
@@ -83,9 +83,9 @@ def seg_preprocess(img_path):
        img_scale_h = 1024
    # 如果图片size任意一边 大于 1024， 则会resize 成1024
    if ori_shape != (img_scale_w, img_scale_h):
-        # mmcv.imresize(img, img_scale_h, img_scale_w) # 老代码 引以为戒！哈哈哈~ h和w写反了
+        # my_imnormalize(img, img_scale_h, img_scale_w) # 老代码 引以为戒！哈哈哈~ h和w写反了
        img = cv2.resize(img, (img_scale_h, img_scale_w))
-    img = mmcv.imnormalize(img, mean=np.array([123.675, 116.28, 103.53]), std=np.array([58.395, 57.12, 57.375]), to_rgb=True)
+    img = my_imnormalize(img, mean=np.array([123.675, 116.28, 103.53]), std=np.array([58.395, 57.12, 57.375]), to_rgb=True)
    preprocessed_img = np.expand_dims(img.transpose(2, 0, 1), axis=0)
    return preprocessed_img, ori_shape

--- a/app/service/design_fast/item.py
+++ b/app/service/design_fast/item.py
@@ -16,6 +16,9 @@ class OthersItem(BaseItem):
        self.Others_pipeline = [
            LoadImage(minio_client),
            Segmentation(minio_client),
+            Color(minio_client),
+            NoSegPrintPainting(minio_client),
+            PrintPainting(minio_client),
            Scaling(),
            Split(minio_client)
        ]
--- a/app/service/design_fast/pipeline/no_seg_print_painting.py
+++ b/app/service/design_fast/pipeline/no_seg_print_painting.py
@@ -12,9 +12,13 @@ class NoSegPrintPainting:
        self.minio_client = minio_client

    def __call__(self, result):
-        single_print = result['print']['single']
+        # single_print = [result['print']['single']]
        overall_print = result['print']['overall']
-        element_print = result['print']['element']
+        # element_print = result['print']['element'
+
+        single_print = None
+        element_print = None
+
        result['single_image'] = None
        result['print_image'] = None

@@ -23,9 +27,9 @@ class NoSegPrintPainting:
            # 获取平铺 + 旋转 的overall print
            painting_dict = self.painting_collection(painting_dict, overall_print)
            result['no_seg_sketch_overall'] = result['no_seg_sketch_print'] = self.printpaint(result, painting_dict, print_=True)
-            result['pattern_image'] = result['no_seg_sketch_overall']
+            # result['pattern_image'] = result['no_seg_sketch_overall']

-        if single_print['print_path_list']:
+        if single_print:
            print_background = np.zeros((result['pattern_image'].shape[0], result['pattern_image'].shape[1], 3), dtype=np.uint8)
            mask_background = np.zeros((result['pattern_image'].shape[0], result['pattern_image'].shape[1], 3), dtype=np.uint8)
            for i in range(len(single_print['print_path_list'])):
@@ -65,7 +69,7 @@ class NoSegPrintPainting:
                single_image = cv2.add(tmp1, tmp2)
                result['no_seg_sketch_print'] = single_image

-        if element_print['element_path_list']:
+        if element_print:
            print_background = np.zeros((result['final_image'].shape[0], result['final_image'].shape[1], 3), dtype=np.uint8)
            mask_background = np.zeros((result['final_image'].shape[0], result['final_image'].shape[1], 3), dtype=np.uint8)
            for i in range(len(element_print['element_path_list'])):
@@ -162,15 +166,17 @@ class NoSegPrintPainting:
        dim_max = max(painting_dict['dim_image_h'], painting_dict['dim_image_w'])
        dim_pattern = (int(dim_max * print_['scale'] / 5), int(dim_max * print_['scale'] / 5))
        gap = print_dict.get('gap', [[0, 0]])[0]
-        painting_dict['tile_print'] = tile_image(pattern=print_['image'],
-                                                 dim=dim_pattern,
-                                                 gap_x=gap[0],
-                                                 gap_y=gap[1],
-                                                 canvas_h=painting_dict['dim_image_h'],
-                                                 canvas_w=painting_dict['dim_image_w'],
-                                                 location=painting_dict['location'],
-                                                 angle=45)
-        painting_dict['mask_inv_print'] = np.zeros(painting_dict['tile_print'].shape[:2], dtype=np.uint8)
+        painting_dict['tile_print'], painting_dict['mask_inv_print'] = tile_image(pattern=print_['image'],
+                                                                                  mask=print_['mask'],
+                                                                                  dim=dim_pattern,
+                                                                                  gap_x=gap[0],
+                                                                                  gap_y=gap[1],
+                                                                                  canvas_h=painting_dict['dim_image_h'],
+                                                                                  canvas_w=painting_dict['dim_image_w'],
+                                                                                  location=painting_dict['location'],
+                                                                                  angle=int(print_.get('print_angle_list', [0])[0]))
+        # painting_dict['mask_inv_print'] = np.zeros(painting_dict['tile_print'].shape[:2], dtype=np.uint8)
+        # painting_dict['mask_inv_print'] = self.get_mask_inv(painting_dict['tile_print'])
        return painting_dict

    def tile_image(self, pattern, dim, scale, dim_image_h, dim_image_w, location, trigger=False):
@@ -251,10 +257,15 @@ class NoSegPrintPainting:
        image = oss_get_image(oss_client=self.minio_client, bucket=bucket_name, object_name=object_name, data_type="PIL")
        # 判断图片格式，如果是RGBA 则贴在一张纯白图片上 防止透明转黑
        if image.mode == "RGBA":
+            mask_pil = image.split()[3]
            new_background = Image.new('RGB', image.size, (255, 255, 255))
            new_background.paste(image, mask=image.split()[3])
            image = new_background
+        else:
+            mask_pil = Image.new('L', image.size, 255)  # L=灰度图，255=纯白
        print_dict['image'] = cv2.cvtColor(np.asarray(image), cv2.COLOR_RGB2BGR)
+        print_dict['mask'] = cv2.threshold(np.array(mask_pil), 127, 255, cv2.THRESH_BINARY)[1]
+
        return print_dict

    def crop_image(self, image, image_size_h, image_size_w, location, print_shape):
@@ -404,9 +415,12 @@ class NoSegPrintPainting:
        return cropped_img


-def tile_image(pattern, dim, gap_x, gap_y, canvas_h, canvas_w, location, angle=0):
+def tile_image(pattern, mask, dim, gap_x, gap_y, canvas_h, canvas_w, location, angle=0):
    """
    按照指定的 X/Y 间距平铺印花，并支持旋转
+    【修改版】以被平铺图案的【中心】作为平铺基准点
+
+    :param location: [[center_y, center_x]]  → 第一个图案中心的坐标
    :param angle: 旋转角度 (度数, 逆时针)
    """
    # 1. 确保输入是 RGBA
@@ -418,35 +432,54 @@ def tile_image(pattern, dim, gap_x, gap_y, canvas_h, canvas_w, location, angle=0
    rotated_p = rotate_image(resized_p, angle)
    p_h, p_w = rotated_p.shape[:2]

-    # 3. 创建透明单元格
-    cell_h, cell_w = p_h + gap_y, p_w + gap_x
+    # 3. 创建透明单元格（图案放在单元格中心）
+    cell_h = p_h + gap_y
+    cell_w = p_w + gap_x
+
    unit_cell = np.zeros((cell_h, cell_w, 4), dtype=np.uint8)
-    unit_cell[:p_h, :p_w, :] = rotated_p
+
+    # 计算图案在单元格中的左上角位置（让图案居中）
+    start_y = (cell_h - p_h) // 2
+    start_x = (cell_w - p_w) // 2
+    unit_cell[start_y:start_y + p_h, start_x:start_x + p_w, :] = rotated_p

    # 4. 执行平铺
-    tiles_y = (canvas_h // cell_h) + 2
-    tiles_x = (canvas_w // cell_w) + 2
+    tiles_y = (canvas_h // cell_h) + 3  # 多加一点余量更安全
+    tiles_x = (canvas_w // cell_w) + 3
    full_tiled = np.tile(unit_cell, (tiles_y, tiles_x, 1))

-    # 5. 裁剪平铺层
-    offset_x = int(location[0][1] % cell_w)
-    offset_y = int(location[0][0] % cell_h)
+    # 5. 计算偏移（关键修改：以中心为基准）
+    center_y, center_x = location[0][0], location[0][1]  # 第一个图案的中心位置
+
+    # 计算从哪个位置开始裁剪，才能让中心落在指定坐标
+    offset_y = int((center_y - (p_h // 2)) % cell_h)
+    offset_x = int((center_x - (p_w // 2)) % cell_w)
+
    tiled_layer = full_tiled[offset_y: offset_y + canvas_h,
    offset_x: offset_x + canvas_w]

-    # 6. 创建纯白色背景并合成
-    # 创建一个纯白色的 BGR 画布
+    # 6. 创建纯白色背景并合成（保持你原来的风格）
    white_background = np.full((canvas_h, canvas_w, 3), 255, dtype=np.uint8)

-    # 分离平铺层的颜色通道和 Alpha 通道
    tiled_bgr = tiled_layer[:, :, :3]
-    alpha_mask = tiled_layer[:, :, 3] / 255.0  # 归一化到 0-1
-    alpha_mask = cv2.merge([alpha_mask, alpha_mask, alpha_mask])  # 扩展到 3 通道
+    alpha_mask = tiled_layer[:, :, 3] / 255.0
+    alpha_mask = cv2.merge([alpha_mask, alpha_mask, alpha_mask])

-    # 执行 Alpha 混合：结果 = 平铺层 * alpha + 背景 * (1 - alpha)
-    result = (tiled_bgr * alpha_mask + white_background * (1 - alpha_mask)).astype(np.uint8)
+    tiled_print = (tiled_bgr * alpha_mask + white_background * (1 - alpha_mask)).astype(np.uint8)

-    return result
+    # ====================== 处理 Mask ======================
+    # Mask 也同样居中处理
+    resized_mask = cv2.resize(mask, dim, interpolation=cv2.INTER_NEAREST)
+    rotated_mask = rotate_image(resized_mask, angle)  # 注意：mask也需要旋转
+
+    unit_mask = np.zeros((cell_h, cell_w), dtype=np.uint8)
+    unit_mask[start_y:start_y + p_h, start_x:start_x + p_w] = rotated_mask
+
+    full_mask_tiled = np.tile(unit_mask, (tiles_y, tiles_x))
+    tiled_mask = full_mask_tiled[offset_y: offset_y + canvas_h,
+    offset_x: offset_x + canvas_w]
+
+    return tiled_print, cv2.bitwise_not(tiled_mask)


 def rotate_image(image, angle):
--- a/app/service/design_fast/pipeline/print_painting.py
+++ b/app/service/design_fast/pipeline/print_painting.py
@@ -12,10 +12,14 @@ class PrintPainting:
        self.minio_client = minio_client

    def __call__(self, result):
-        single_print = result['print']['single']
+        # single_print = result['print']['single']
        overall_print = result['print']['overall']
-        element_print = result['print']['element']
-        partial_path = result['print']['partial'] if 'partial' in result['print'] else None
+        # element_print = result['print']['element']
+        # partial_path = result['print']['partial'] if 'partial' in result['print'] else None
+
+        single_print = None
+        element_print = None
+        partial_path = None
        result['single_image'] = None
        result['print_image'] = None
        # TODO 给result['pattern_image'] resize 到resize_scale的大小
@@ -37,13 +41,13 @@ class PrintPainting:
        if overall_print['print_path_list']:
            overall_print['location'][0] = [x * y for x, y in zip(overall_print['location'][0], result['resize_scale'])]
            painting_dict = {'dim_image_h': result['pattern_image'].shape[0], 'dim_image_w': result['pattern_image'].shape[1]}
-            result['print_image'] = result['pattern_image']
+            result['print_image'] = result['pattern_image'].copy()
            # 获取平铺 + 旋转 的overall print
            painting_dict = self.painting_collection(painting_dict, overall_print)
            result['print_image'] = self.printpaint(result, painting_dict, print_=True)
            result['single_image'] = result['final_image'] = result['pattern_image'] = result['print_image']

-        if single_print['print_path_list']:
+        if single_print:
            # 2025-9-19 印花调整 印花坐标按照sketch的缩放比调整
            sketch_resize_scale = result['resize_scale']
            print_background = np.zeros((result['pattern_image'].shape[0], result['pattern_image'].shape[1], 3), dtype=np.uint8)
@@ -84,7 +88,7 @@ class PrintPainting:
                tmp2 = (result['final_image'] * (temp_fg / 255)).astype(np.uint8)
                result['single_image'] = cv2.add(tmp1, tmp2)

-        if element_print['element_path_list']:
+        if element_print:
            # 2025-9-19 印花调整 印花坐标按照sketch的缩放比调整
            sketch_resize_scale = result['resize_scale']
            print_background = np.zeros((result['final_image'].shape[0], result['final_image'].shape[1], 3), dtype=np.uint8)
@@ -225,15 +229,15 @@ class PrintPainting:
        dim_max = max(painting_dict['dim_image_h'], painting_dict['dim_image_w'])
        dim_pattern = (int(dim_max * print_['scale'] / 5), int(dim_max * print_['scale'] / 5))
        gap = print_dict.get('gap', [[0, 0]])[0]
-        painting_dict['tile_print'] = tile_image(pattern=print_['image'],
-                                                 dim=dim_pattern,
-                                                 gap_x=gap[0],
-                                                 gap_y=gap[1],
-                                                 canvas_h=painting_dict['dim_image_h'],
-                                                 canvas_w=painting_dict['dim_image_w'],
-                                                 location=painting_dict['location'],
-                                                 angle=45)
-        painting_dict['mask_inv_print'] = np.zeros(painting_dict['tile_print'].shape[:2], dtype=np.uint8)
+        painting_dict['tile_print'], painting_dict['mask_inv_print'] = tile_image(pattern=print_['image'],
+                                                                                  mask=print_['mask'],
+                                                                                  dim=dim_pattern,
+                                                                                  gap_x=gap[0],
+                                                                                  gap_y=gap[1],
+                                                                                  canvas_h=painting_dict['dim_image_h'],
+                                                                                  canvas_w=painting_dict['dim_image_w'],
+                                                                                  location=painting_dict['location'],
+                                                                                  angle=int(print_.get('print_angle_list', [0])[0]))
        return painting_dict

    def tile_image(self, pattern, dim, scale, dim_image_h, dim_image_w, location, trigger=False):
@@ -314,10 +318,15 @@ class PrintPainting:
        image = oss_get_image(oss_client=self.minio_client, bucket=bucket_name, object_name=object_name, data_type="PIL")
        # 判断图片格式，如果是RGBA 则贴在一张纯白图片上 防止透明转黑
        if image.mode == "RGBA":
+            mask_pil = image.split()[3]
            new_background = Image.new('RGB', image.size, (255, 255, 255))
            new_background.paste(image, mask=image.split()[3])
            image = new_background
+        else:
+            mask_pil = Image.new('L', image.size, 255)  # L=灰度图，255=纯白
        print_dict['image'] = cv2.cvtColor(np.asarray(image), cv2.COLOR_RGB2BGR)
+        print_dict['mask'] = cv2.threshold(np.array(mask_pil), 127, 255, cv2.THRESH_BINARY)[1]
+
        return print_dict

    def crop_image(self, image, image_size_h, image_size_w, location, print_shape):
@@ -467,9 +476,12 @@ class PrintPainting:
        return cropped_img


-def tile_image(pattern, dim, gap_x, gap_y, canvas_h, canvas_w, location, angle=0):
+def tile_image(pattern, mask, dim, gap_x, gap_y, canvas_h, canvas_w, location, angle=0):
    """
    按照指定的 X/Y 间距平铺印花，并支持旋转
+    【修改版】以被平铺图案的【中心】作为平铺基准点
+
+    :param location: [[center_y, center_x]]  → 第一个图案中心的坐标
    :param angle: 旋转角度 (度数, 逆时针)
    """
    # 1. 确保输入是 RGBA
@@ -481,35 +493,54 @@ def tile_image(pattern, dim, gap_x, gap_y, canvas_h, canvas_w, location, angle=0
    rotated_p = rotate_image(resized_p, angle)
    p_h, p_w = rotated_p.shape[:2]

-    # 3. 创建透明单元格
-    cell_h, cell_w = p_h + gap_y, p_w + gap_x
+    # 3. 创建透明单元格（图案放在单元格中心）
+    cell_h = p_h + gap_y
+    cell_w = p_w + gap_x
+
    unit_cell = np.zeros((cell_h, cell_w, 4), dtype=np.uint8)
-    unit_cell[:p_h, :p_w, :] = rotated_p
+
+    # 计算图案在单元格中的左上角位置（让图案居中）
+    start_y = (cell_h - p_h) // 2
+    start_x = (cell_w - p_w) // 2
+    unit_cell[start_y:start_y + p_h, start_x:start_x + p_w, :] = rotated_p

    # 4. 执行平铺
-    tiles_y = (canvas_h // cell_h) + 2
-    tiles_x = (canvas_w // cell_w) + 2
+    tiles_y = (canvas_h // cell_h) + 3  # 多加一点余量更安全
+    tiles_x = (canvas_w // cell_w) + 3
    full_tiled = np.tile(unit_cell, (tiles_y, tiles_x, 1))

-    # 5. 裁剪平铺层
-    offset_x = int(location[0][1] % cell_w)
-    offset_y = int(location[0][0] % cell_h)
+    # 5. 计算偏移（关键修改：以中心为基准）
+    center_y, center_x = location[0][0], location[0][1]  # 第一个图案的中心位置
+
+    # 计算从哪个位置开始裁剪，才能让中心落在指定坐标
+    offset_y = int((center_y - (p_h // 2)) % cell_h)
+    offset_x = int((center_x - (p_w // 2)) % cell_w)
+
    tiled_layer = full_tiled[offset_y: offset_y + canvas_h,
    offset_x: offset_x + canvas_w]

-    # 6. 创建纯白色背景并合成
-    # 创建一个纯白色的 BGR 画布
+    # 6. 创建纯白色背景并合成（保持你原来的风格）
    white_background = np.full((canvas_h, canvas_w, 3), 255, dtype=np.uint8)

-    # 分离平铺层的颜色通道和 Alpha 通道
    tiled_bgr = tiled_layer[:, :, :3]
-    alpha_mask = tiled_layer[:, :, 3] / 255.0  # 归一化到 0-1
-    alpha_mask = cv2.merge([alpha_mask, alpha_mask, alpha_mask])  # 扩展到 3 通道
+    alpha_mask = tiled_layer[:, :, 3] / 255.0
+    alpha_mask = cv2.merge([alpha_mask, alpha_mask, alpha_mask])

-    # 执行 Alpha 混合：结果 = 平铺层 * alpha + 背景 * (1 - alpha)
-    result = (tiled_bgr * alpha_mask + white_background * (1 - alpha_mask)).astype(np.uint8)
+    tiled_print = (tiled_bgr * alpha_mask + white_background * (1 - alpha_mask)).astype(np.uint8)

-    return result
+    # ====================== 处理 Mask ======================
+    # Mask 也同样居中处理
+    resized_mask = cv2.resize(mask, dim, interpolation=cv2.INTER_NEAREST)
+    rotated_mask = rotate_image(resized_mask, angle)  # 注意：mask也需要旋转
+
+    unit_mask = np.zeros((cell_h, cell_w), dtype=np.uint8)
+    unit_mask[start_y:start_y + p_h, start_x:start_x + p_w] = rotated_mask
+
+    full_mask_tiled = np.tile(unit_mask, (tiles_y, tiles_x))
+    tiled_mask = full_mask_tiled[offset_y: offset_y + canvas_h,
+    offset_x: offset_x + canvas_w]
+
+    return tiled_print, cv2.bitwise_not(tiled_mask)


 def rotate_image(image, angle):
--- a/app/service/design_fast/pipeline/segmentation.py
+++ b/app/service/design_fast/pipeline/segmentation.py
@@ -51,6 +51,8 @@ class Segmentation:
                if not _ or result["image"].shape[:2] != seg_result.shape:
                    # 推理获得seg 结果
                    seg_result = get_seg_result(result['image'])
+                    if result['name'] == 'others':
+                        seg_result = seg_result.clip(max=1)
                    self.save_seg_result(seg_result, result['image_id'])
            result['seg_result'] = seg_result

--- a/app/service/design_fast/utils/design_ensemble.py
+++ b/app/service/design_fast/utils/design_ensemble.py
@@ -10,12 +10,12 @@
 import logging

 import cv2
-import mmcv
 import numpy as np
 import torch
 import tritonclient.http as httpclient

 from app.core.config import DESIGN_MODEL_URL, DESIGN_MODEL_NAME
+from app.service.utils.image_normalize import my_imnormalize

 """
    keypoint
@@ -24,14 +24,14 @@ from app.core.config import DESIGN_MODEL_URL, DESIGN_MODEL_NAME


 def keypoint_preprocess(img_path):
-    img = mmcv.imread(img_path)
+    img = img_path
    img = cv2.copyMakeBorder(img, 25, 25, 25, 25, cv2.BORDER_CONSTANT, value=[255, 255, 255])
    img_scale = (256, 256)
    h, w = img.shape[:2]
    img = cv2.resize(img, img_scale)
    w_scale = img_scale[0] / w
    h_scale = img_scale[1] / h
-    img = mmcv.imnormalize(img, mean=np.array([123.675, 116.28, 103.53]), std=np.array([58.395, 57.12, 57.375]), to_rgb=True)
+    img = my_imnormalize(img, mean=np.array([123.675, 116.28, 103.53]), std=np.array([58.395, 57.12, 57.375]), to_rgb=True)
    preprocessed_img = np.expand_dims(img.transpose(2, 0, 1), axis=0)
    return preprocessed_img, (w_scale, h_scale)

@@ -78,7 +78,7 @@ def keypoint_postprocess(output, scale_factor):

 # KNet
 def seg_preprocess(img_path):
-    img = mmcv.imread(img_path)
+    img = img_path
    ori_shape = img.shape[:2]
    img_scale_w, img_scale_h = ori_shape
    if ori_shape[0] > 1024:
@@ -87,12 +87,12 @@ def seg_preprocess(img_path):
        img_scale_h = 1024
    # 如果图片size任意一边 大于 1024， 则会resize 成1024
    if ori_shape != (img_scale_w, img_scale_h):
-        # mmcv.imresize(img, img_scale_h, img_scale_w) # 老代码 引以为戒！哈哈哈~ h和w写反了
+        # my_imnormalize(img, img_scale_h, img_scale_w) # 老代码 引以为戒！哈哈哈~ h和w写反了
        img = cv2.resize(img, (img_scale_h, img_scale_w))

    # 扩充25的白边
    img = cv2.copyMakeBorder(img, 25, 25, 25, 25, cv2.BORDER_CONSTANT, value=[255, 255, 255])
-    img = mmcv.imnormalize(img, mean=np.array([123.675, 116.28, 103.53]), std=np.array([58.395, 57.12, 57.375]), to_rgb=True)
+    img = my_imnormalize(img, mean=np.array([123.675, 116.28, 103.53]), std=np.array([58.395, 57.12, 57.375]), to_rgb=True)
    preprocessed_img = np.expand_dims(img.transpose(2, 0, 1), axis=0)
    return preprocessed_img, ori_shape

--- a/app/service/design_fast/utils/organize.py
+++ b/app/service/design_fast/utils/organize.py
@@ -79,7 +79,7 @@ def organize_others(layer):
    front_layer = dict(priority=layer['priority'] if layer.get("layer_order", False) else PRIORITY_DICT.get(f'{layer["name"].lower()}_front', None),
                       name=f'{layer["name"].lower()}_front',
                       image=layer["front_image"],
-                       mask_image=layer['front_mask_image'],
+                       # mask_image=layer['front_mask_image'],
                       image_url=layer['front_image_url'],
                       mask_url=layer.get('mask_url', None),
                       sacle=layer['scale'],
@@ -92,12 +92,14 @@ def organize_others(layer):
                       pattern_print_image_url=layer.get('pattern_print_image_url', None),
                       pattern_image=layer.get('pattern_image', None),
                       # back_perspective_url=layer['back_perspective_url'] if 'back_perspective_url' in layer.keys() else ""
+                       transpose=layer.get("transpose", [1, 1]),  # 默认为1, 1代表不镜像
+                       rotate=layer.get('rotate', 0),
                       )
    # 后片数据
    back_layer = dict(priority=-layer.get("priority", 0) if layer.get("layer_order", False) else PRIORITY_DICT.get(f'{layer["name"].lower()}_back', None),
                      name=f'{layer["name"].lower()}_back',
                      image=layer["back_image"],
-                      mask_image=layer['back_mask_image'],
+                      # mask_image=layer['back_mask_image'],
                      image_url=layer['back_image_url'],
                      mask_url=layer.get('mask_url', None),
                      sacle=layer['scale'],
@@ -109,6 +111,8 @@ def organize_others(layer):
                      pattern_overall_image_url=layer.get('pattern_overall_image_url', None),
                      pattern_print_image_url=layer.get('pattern_print_image_url', None),
                      # back_perspective_url=layer['back_perspective_url'] if 'back_perspective_url' in layer.keys() else ""
+                      transpose=layer.get("transpose", [1, 1]),  # 默认为1, 1代表不镜像
+                      rotate=layer.get('rotate', 0),
                      )
    return front_layer, back_layer

--- a/app/service/design_fast/utils/synthesis_item.py
+++ b/app/service/design_fast/utils/synthesis_item.py
@@ -342,83 +342,33 @@ def update_base_size_priority(layers):


 def transpose_rotate(layer, image):
-    """
-    融合镜像（transpose）和旋转（rotate）逻辑，计算实际旋转角度后执行图像变换，
-    并调整粘贴位置以保持视觉中心一致
+    # transpose[0]是左右 transpose[1]是上下
+    transpose = layer.get('transpose', [1, 1])  # 默认为1, 1代表不镜像

-    参数:
-        layer: 包含transpose、rotate、adaptive_position等属性的字典
-        image: PIL Image对象，待处理的图像
-
-    返回:
-        tuple: (处理后的Image对象, 新的粘贴坐标(x, y))
-    """
-    # 获取镜像状态（transpose[0]=左右，transpose[1]=上下；1=正常，-1=镜像）
-    transpose = layer.get('transpose', [1, 1])
-    is_mirrored_x = transpose[0]  # 左右镜像状态
-    is_mirrored_y = transpose[1]  # 上下镜像状态
-
-    # 获取原始旋转角度和粘贴位置
-    original_rotate = layer.get('rotate', 0)
+    rotate = layer.get('rotate', 0)
    paste_x, paste_y = layer['adaptive_position'][1], layer['adaptive_position'][0]
    original_w = image.width
    original_h = image.height
+    # transpose左右是1 上下是-1
+    if transpose[0] != 1:
+        # 左右
+        image = image.transpose(0)

-    # ------------------- 核心修改：计算实际旋转角度 -------------------
-    # 结合镜像状态，计算需要实际执行的旋转角度
-    actual_rotate = calculate_actual_rotate(original_rotate, is_mirrored_x, is_mirrored_y)
-    print(f"actual_rotate:{actual_rotate}")
-    # ------------------- 执行镜像变换 -------------------
-    # 左右镜像（transpose[0] != 1 即-1，表示镜像）
-    if is_mirrored_x != 1:
-        image = image.transpose(0)  # 假设transpose(0)对应左右翻转（需匹配你的PIL版本）
+    if transpose[1] != 1:
+        # 上下
+        image = image.transpose(1)

-    # 上下镜像（transpose[1] != 1 即-1，表示镜像）
-    if is_mirrored_y != 1:
-        image = image.transpose(1)  # 假设transpose(1)对应上下翻转
-
-    # ------------------- 执行旋转并调整粘贴位置 -------------------
-    if actual_rotate != 0:  # 只有实际旋转角度非0时才执行旋转
-        # 注意：原代码中是rotate(-rotate)，这里同步调整符号
-        image = image.rotate(-actual_rotate, expand=True)
-
-        # 计算粘贴位置以保持视觉中心一致
-        # 原位置的中心点
+    if rotate:
+        image = image.rotate(-rotate, expand=True)
+        # 4. 计算粘贴位置以保持视觉中心一致
+        # 原本 (15, 36) 是 288*288 的左上角，我们计算其中心点
        target_center_x = paste_x + original_w // 2
        target_center_y = paste_y + original_h // 2

-        # 旋转后图像的新尺寸
+        # 获取旋转后图像的新尺寸
        new_w, new_h = image.size

-        # 新的左上角坐标（保证中心不变）
+        # 计算新的左上角坐标，使得旋转后的图像中心依然在原定的中心位置
        paste_x = target_center_x - new_w // 2
        paste_y = target_center_y - new_h // 2
-
    return image, (paste_x, paste_y)
-
-
-def calculate_actual_rotate(before_rotate, is_mirrored_x, is_mirrored_y):
-    """
-    根据X/Y轴镜像状态计算实际的旋转角度，并标准化到0-360度
-
-    参数:
-        before_rotate: 原始旋转角度（数值类型）
-        is_mirrored_x: X轴镜像状态，-1表示镜像，1表示正常
-        is_mirrored_y: Y轴镜像状态，-1表示镜像，1表示正常
-
-    返回:
-        float/int: 标准化后的实际旋转角度（0-360度）
-    """
-    actual_rotate = before_rotate
-
-    # 根据镜像状态调整旋转角度
-    if is_mirrored_x == -1 and is_mirrored_y == 1:
-        actual_rotate = -before_rotate
-    elif is_mirrored_x == 1 and is_mirrored_y == -1:
-        actual_rotate = -before_rotate
-    # elif is_mirrored_x == -1 and is_mirrored_y == -1:
-    #     actual_rotate = before_rotate + 180
-
-    # 角度标准化到0-360度
-    normalized_rotate = ((actual_rotate % 360) + 360) % 360
-    return normalized_rotate
--- a/app/service/generate_image/service_agent_tool_generate_image.py
+++ b/app/service/generate_image/service_agent_tool_generate_image.py
@@ -11,7 +11,6 @@ import logging
 import uuid

 import cv2
-import mmcv
 import numpy as np
 import pandas as pd
 import torch
@@ -21,6 +20,7 @@ from minio import Minio
 from tritonclient.utils import np_to_triton_dtype

 from app.core.config import settings, FAST_GI_MODEL_URL, GI_MODEL_URL, DESIGN_MODEL_URL, FAST_GI_MODEL_NAME, GI_MODEL_NAME
+from app.service.utils.image_normalize import my_imnormalize
 from app.service.utils.new_oss_client import oss_upload_image

 logger = logging.getLogger()
@@ -86,10 +86,9 @@ class AgentToolGenerateImage:

    @staticmethod
    def preprocess(img):
-        img = mmcv.imread(img)
        img_scale = (224, 224)
        img = cv2.resize(img, img_scale)
-        img = mmcv.imnormalize(
+        img = my_imnormalize(
            img,
            mean=np.array([123.675, 116.28, 103.53]), std=np.array([58.395, 57.12, 57.375]),
            to_rgb=True)
--- a/app/service/generate_image/service_generate_image.py
+++ b/app/service/generate_image/service_generate_image.py
@@ -189,10 +189,10 @@ if __name__ == '__main__':
        tasks_id="123-89",
        prompt="a single item of sketch of dress, 4k, white background",
        image_url="aida-collection-element/89/Sketchboard/95f20cdc-e059-435c-b8b1-d04cc9e80c3d.png",
-        mode='img2img',
+        mode='txt2img',
        category="sketch",
        gender="Female",
-        version="fast"
+        version="hight"
    )
    server = GenerateImage(rd)
    print(server.get_result())
--- a/app/service/generate_image/utils/image_processing.py
+++ b/app/service/generate_image/utils/image_processing.py
@@ -2,23 +2,23 @@ import logging
 import time

 import cv2
-import mmcv
 import numpy as np
 import torch
 import tritonclient.http as httpclient

 from app.core.config import settings, DESIGN_MODEL_URL, DESIGN_MODEL_NAME
 from app.service.generate_image.utils.upload_sd_image import upload_stain_png_sd, upload_face_png_sd
+from app.service.utils.image_normalize import my_imnormalize

 logger = logging.getLogger()


 def seg_preprocess(img_path):
-    img = mmcv.imread(img_path)
+    img = img_path
    ori_shape = img.shape[:2]
    img_scale = ori_shape
    img = cv2.resize(img, img_scale)
-    img = mmcv.imnormalize(img, mean=np.array([123.675, 116.28, 103.53]), std=np.array([58.395, 57.12, 57.375]), to_rgb=True)
+    img = my_imnormalize(img, mean=np.array([123.675, 116.28, 103.53]), std=np.array([58.395, 57.12, 57.375]), to_rgb=True)
    preprocessed_img = np.expand_dims(img.transpose(2, 0, 1), axis=0)
    return preprocessed_img, ori_shape

@@ -242,10 +242,9 @@ def stain_detection(image, user_id, category, tasks_id, spot_size=100):

 def generate_category_recognition(image, gender):
    def preprocess(img):
-        img = mmcv.imread(img)
        img_scale = (224, 224)
        img = cv2.resize(img, img_scale)
-        img = mmcv.imnormalize(img, mean=np.array([123.675, 116.28, 103.53]), std=np.array([58.395, 57.12, 57.375]), to_rgb=True)
+        img = my_imnormalize(img, mean=np.array([123.675, 116.28, 103.53]), std=np.array([58.395, 57.12, 57.375]), to_rgb=True)
        preprocessed_img = np.expand_dims(img.transpose(2, 0, 1), axis=0)
        return preprocessed_img

--- a/app/service/lineart/service.py
+++ b/app/service/lineart/service.py
@@ -1,7 +1,6 @@
 import logging

 import cv2
-import mmcv
 import numpy as np
 import torch
 import torch.nn.functional as F
@@ -10,6 +9,7 @@ from minio import Minio
 from app.core.config import settings
 from app.core.config import DESIGN_MODEL_URL
 from app.schemas.image2sketch import Image2SketchModel
+from app.service.utils.image_normalize import my_imnormalize
 from app.service.utils.new_oss_client import oss_get_image, oss_upload_image

 logger = logging.getLogger()
@@ -67,7 +67,7 @@ class LineArtService:

    @staticmethod
    def line_art_preprocess(image):
-        img = mmcv.imread(image)
+        img = image
        ori_shape = img.shape[:2]
        img_scale_w, img_scale_h = ori_shape
        if ori_shape[0] > 1024:
@@ -76,9 +76,9 @@ class LineArtService:
            img_scale_h = 1024
        # 如果图片size任意一边 大于 1024， 则会resize 成1024
        if ori_shape != (img_scale_w, img_scale_h):
-            # mmcv.imresize(img, img_scale_h, img_scale_w) # 老代码 引以为戒！哈哈哈~ h和w写反了
+            # my_imnormalize(img, img_scale_h, img_scale_w) # 老代码 引以为戒！哈哈哈~ h和w写反了
            img = cv2.resize(img, (img_scale_h, img_scale_w))
-        img = mmcv.imnormalize(img, mean=np.array([123.675, 116.28, 103.53]), std=np.array([58.395, 57.12, 57.375]), to_rgb=True)
+        img = my_imnormalize(img, mean=np.array([123.675, 116.28, 103.53]), std=np.array([58.395, 57.12, 57.375]), to_rgb=True)
        preprocessed_img = np.expand_dims(img.transpose(2, 0, 1), axis=0)
        return preprocessed_img, ori_shape

--- a/app/service/prompt_generation/chatgpt_for_translation.py
+++ b/app/service/prompt_generation/chatgpt_for_translation.py
@@ -90,7 +90,7 @@ def get_response(messages):

 def get_translation_from_llama3(text):
    start_time = time.time()
-    url = f"http://{settings.A6000_SERVICE_HOST}:11434/api/generate"
+    url = f"http://{settings.A6000_SERVICE_HOST}:12434/api/generate"
    # url = "http://10.1.1.240:1143/api/generate"

    # prompt = f"System: {prefix_for_llama}\nUser:[{text}]"
@@ -103,8 +103,8 @@ def get_translation_from_llama3(text):

    # 创建请求的负载 translator是自定义的翻译模型
    payload = {
-        "model": "translator",
-        "prompt": f"[{text}]",
+        "model": "AiDA-translator:latest",
+        "prompt": text,
        "stream": False
    }
    # 将负载转换为 JSON 格式
@@ -148,7 +148,7 @@ def get_translation_from_llama3(text):
 def get_prompt_from_image(image_path, text):
    start_time = time.time()
    # url = "http://localhost:11434/api/generate"
-    url = "http://10.1.1.243:11434/api/generate"
+    url = f"http://{settings.B_4_X_4090_SERVICE_HOST}:11434/api/generate"

    image_base64 = minio_util.minio_url_to_base64(image_path.img)
    # image_base64 = minio_url_to_base64(image_path)
@@ -180,7 +180,7 @@ def get_prompt_from_image(image_path, text):

 def main():
    """Main function"""
-    text = get_translation_from_llama3("[火焰]")
+    text = get_translation_from_llama3("火焰")
    print(text)


--- a/app/service/sketch2garment/callback.py
+++ b/app/service/sketch2garment/callback.py
@@ -0,0 +1,35 @@
+import logging
+
+import httpx
+
+logger = logging.getLogger("app")
+
+
+async def notify_callback(callback_url: str, task_id: str, status: str, result: dict, ):
+    """
+    调用客户端提供的回调接口
+    """
+    try:
+        payload = {
+            "task_id": task_id,
+            "status": status,
+            "result": result
+        }
+        logger.info(payload)
+        async with httpx.AsyncClient(timeout=30.0) as client:
+            resp = await client.post(
+                str(callback_url),
+                json=payload,
+                headers={"Content-Type": "application/json"}
+            )
+
+            if 200 <= resp.status_code < 300:
+                logger.info(f"回调成功 | task_id: {task_id} | status: {status} | url: {callback_url}")
+                return True
+            else:
+                logger.warning(f"回调返回非2xx状态码 | task_id: {task_id} | status: {resp.status_code} | url: {callback_url}")
+                return False
+
+    except Exception as e:
+        logger.error(f"回调失败 | task_id: {task_id} | url: {callback_url} | error: {e}", exc_info=True)
+        return False
--- a/app/service/sketch2garment/celery_app.py
+++ b/app/service/sketch2garment/celery_app.py
@@ -0,0 +1,46 @@
+from celery import Celery
+from kombu import Queue, Exchange
+
+from app.core.config import settings
+
+celery_app = Celery(
+    "sketch_to_garment",
+    broker=f"redis://{settings.REDIS_HOST}:{settings.REDIS_PORT}/2",
+    backend=f"redis://{settings.REDIS_HOST}:{settings.REDIS_PORT}/{settings.REDIS_DB}",
+    include=["app.service.sketch2garment.tasks"]
+)
+print(f"redis://{settings.REDIS_HOST}:{settings.REDIS_PORT}/3")
+print(f"celery_app: {celery_app}")
+
+celery_app.conf.update(
+    task_serializer="json",
+    accept_content=["json"],
+    result_serializer="json",
+    timezone="Asia/Hong_Kong",
+    enable_utc=True,
+    task_track_started=True,
+    task_time_limit=300,  # 单个任务最长 5 分钟
+    task_soft_time_limit=280,
+    # 定义队列
+    task_queues=(
+        Queue("sketch_to_garment_queue",
+              exchange=Exchange("sketch_to_garment_exchange", type="direct"),
+              durable=True),
+
+    ),
+
+    task_routes={
+        'app.service.sketch2garment.tasks.sketch_to_garment':
+            {
+                'queue': 'sketch_to_garment_queue',
+                'exchange': 'sketch_to_garment_exchange',  # ← 修改这里
+            },
+    },
+    task_default_queue="sketch_to_garment_queue",
+
+    worker_concurrency=1,
+    worker_prefetch_multiplier=1,
+    worker_max_tasks_per_child=1,
+    task_acks_late=True,
+    task_reject_on_worker_lost=True,
+)
--- a/app/service/sketch2garment/server.py
+++ b/app/service/sketch2garment/server.py
@@ -0,0 +1,44 @@
+import logging
+
+from app.service.sketch2garment.tasks import sketch_to_garment
+
+logger = logging.getLogger(__name__)
+
+
+def submit_sketch_to_garment_task(model: str = "single", task_id: str = "", callback_url: str = "", bucket_name: str = "test", user_id: str = "123", input_image_path: str = ""):
+    """提交 img_to_3D 任务（带队列长度限制）"""
+    queue_name = "img_to_3d_queue"
+    max_queue_length = 10
+
+    try:
+        # current_length = get_queue_length(queue_name)
+
+        # if current_length >= max_queue_length:
+        #     return {
+        #         "state": "queue_full",
+        #         "message": "当前 3D 生成请求较多，请稍后重试。",
+        #         "queue_length": current_length,
+        #         "max_length": max_queue_length
+        #     }
+
+        # 提交任务
+        task = sketch_to_garment.apply_async(
+            args=(task_id, callback_url, bucket_name, input_image_path, user_id, model),
+            task_id=task_id,
+            queue="sketch_to_garment_queue")
+
+        # logger.info(f"img_to_3d_task 已提交 | task_id: {task_id} | 当前队列长度: {current_length}")
+
+        return {
+            "state": "success",
+            "task_id": task_id,
+            "message": "任务已成功提交，正在后台处理...",
+        }
+
+    except Exception as e:
+        logger.error(f"提交 img_to_3d_task 失败: {e}", exc_info=True)
+        return {
+            "state": "fail",
+            "message": "提交失败，请稍后重试。",
+            "error": str(e)
+        }
--- a/app/service/sketch2garment/tasks.py
+++ b/app/service/sketch2garment/tasks.py
@@ -0,0 +1,57 @@
+import asyncio
+import logging
+
+from app.core.config import settings
+from app.service.sketch2garment.callback import notify_callback
+import httpx
+
+from app.service.sketch2garment.celery_app import celery_app
+
+logger = logging.getLogger(__name__)
+
+
+@celery_app.task(bind=True, queue="sketch_to_garment_queue", max_retries=3, name='app.service.sketch2garment.tasks.sketch_to_garment')
+def sketch_to_garment(self, task_id: str, callback_url: str, bucket_name: str, input_image_path: str, user_id: str, category: str = None):
+    payload = {
+        "bucket_name": bucket_name,
+        "category": category or settings.DEFAULT_CATEGORY,
+        "input_image_path": input_image_path,
+        "user_id": user_id
+    }
+    logger.info(f"payload: {payload}")
+
+    try:
+        with httpx.Client(timeout=300.0) as client:  # 注意这里用 AsyncClient 配合 Celery
+            # 如果你的 LitServe 是同步 endpoint，也可以用 httpx.Client()
+            response = client.post(settings.SKETCH_TO_GARMENT_URL, json=payload)
+            if response.status_code == 200:
+                result = response.json()
+                result_json = {
+                    "pattern": result[1],
+                    "texture": result[2],
+                    "glb": result[3],
+                    "texture_fabric": result[4]
+                }
+                asyncio.run(
+                    notify_callback(callback_url=callback_url, task_id=task_id, result=result_json, status="success")
+                )
+            else:
+                asyncio.run(
+                    notify_callback(
+                        callback_url=callback_url,
+                        task_id=task_id,
+                        result={
+                            "status": "fail",
+                            "task_id": task_id,
+                            "message": "fail",
+                            "error": "fail"
+                        },
+                        status="fail")
+                )
+    except Exception as e:
+        return {
+            "status": "failed",
+            "task_id": task_id,
+            "input": payload,
+            "error": str(e)
+        }
--- a/app/service/utils/image_normalize.py
+++ b/app/service/utils/image_normalize.py
@@ -0,0 +1,27 @@
+import cv2
+import numpy as np
+
+
+def my_imnormalize(img, mean, std, to_rgb=True):
+    """Inplace normalize an image with mean and std.
+
+    Args:
+        img (ndarray): Image to be normalized.
+        mean (ndarray): The mean to be used for normalize.
+        std (ndarray): The std to be used for normalize.
+        to_rgb (bool): Whether to convert to rgb.
+
+    Returns:
+        ndarray: The normalized image.
+    """
+    # cv2 inplace normalization does not accept uint8
+    img = img.copy().astype(np.float32)
+
+    assert img.dtype != np.uint8
+    mean = np.float64(mean.reshape(1, -1))
+    stdinv = 1 / np.float64(std.reshape(1, -1))
+    if to_rgb:
+        cv2.cvtColor(img, cv2.COLOR_BGR2RGB, img)  # inplace
+    cv2.subtract(img, mean, img)  # inplace
+    cv2.multiply(img, stdinv, img)  # inplace
+    return img
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -1,5 +1,6 @@
 services:
  aida_server:
+    container_name: "AiDA_${SERVE_ENV}_Server"
    build:
      context: .
      dockerfile: Dockerfile
@@ -11,3 +12,9 @@ services:
      - ./seg_cache:/seg_cache
    ports:
      - "${SERVE_PORT}:80"
+    networks:
+      - aida_app_net
+networks:
+  aida_app_net:
+    external: true
+    name: aida_app_net
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -23,8 +23,9 @@ dependencies = [
    "load-dotenv>=0.1.0",
    "loguru>=0.7.3",
    "minio>=7.2.20",
-    "mmcv>=2.2.0",
    "moviepy==1.0.3",
+    "nacos-sdk-python==2.0.1",
+    "np>=1.0.2",
    "numpy<2",
    "ollama>=0.6.1",
    "opencv-python>=4.11.0.86",
--- a/requirements.txt
+++ b/requirements.txt
--- a/requirements_2.txt
+++ b/requirements_2.txt
--- a/uv.lock
+++ b/uv.lock
@@ -8,15 +8,6 @@ resolution-markers = [
    "(platform_machine != 'aarch64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux')",
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 version = "0.1.7"
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 ]

+[[package]]
+name = "aiofiles"
+version = "25.1.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/41/c3/534eac40372d8ee36ef40df62ec129bee4fdb5ad9706e58a29be53b2c970/aiofiles-25.1.0.tar.gz", hash = "sha256:a8d728f0a29de45dc521f18f07297428d56992a742f0cd2701ba86e44d23d5b2", size = 46354, upload-time = "2025-10-09T20:51:04.358Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/bc/8a/340a1555ae33d7354dbca4faa54948d76d89a27ceef032c8c3bc661d003e/aiofiles-25.1.0-py3-none-any.whl", hash = "sha256:abe311e527c862958650f9438e859c1fa7568a141b22abcd015e120e86a85695", size = 14668, upload-time = "2025-10-09T20:51:03.174Z" },
+]
+
 [[package]]
 name = "aiohappyeyeballs"
 version = "2.6.1"
@@ -140,6 +140,154 @@ wheels = [
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 ]

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+
+[[package]]
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Author	SHA1	Message	Date
zcr	893f5e87b4	3D 打板部署 All checks were successful git commit AiDA python develop 分支构建部署 / scheduled_deploy (push) Has been skipped Details	2026-04-28 17:17:29 +08:00
zcr	c73bfa7e2a	3D 打板部署 All checks were successful git commit AiDA python develop 分支构建部署 / scheduled_deploy (push) Has been skipped Details	2026-04-28 17:03:04 +08:00
zcr	ad4db736de	新增nacos 配置测试 All checks were successful git commit AiDA python develop 分支构建部署 / scheduled_deploy (push) Has been skipped Details	2026-04-24 10:17:42 +08:00
zcr	cfbd9e47ac	新增nacos 配置测试 All checks were successful git commit AiDA python develop 分支构建部署 / scheduled_deploy (push) Has been skipped Details	2026-04-23 17:10:22 +08:00
zcr	6892361050	修复design印花部分 overall 模式印花平铺起始从印花图片中心开始 All checks were successful git commit AiDA python develop 分支构建部署 / scheduled_deploy (push) Has been skipped Details	2026-04-15 17:36:29 +08:00
zcr	f0b73d5fc1	修复design印花部分 mask_inv_print 提取错误 All checks were successful git commit AiDA python develop 分支构建部署 / scheduled_deploy (push) Has been skipped Details	2026-04-15 17:23:00 +08:00
zcr	7543d6b346	feat: 更新flux2 klein 的输出示例 ; fix: All checks were successful git commit AiDA python develop 分支构建部署 / scheduled_deploy (push) Has been skipped Details	2026-04-14 10:16:30 +08:00
zcr	3ca4003e30	feat: 更新flux2 klein 的输出示例 ; fix:	2026-03-30 17:22:14 +08:00
zcr	59e8a88a01	feat: 更新flux2 klein 的输出示例 ; fix:	2026-03-30 17:14:18 +08:00
zcr	3414f2c1aa	feat: 更新分割模型参数 ; fix:	2026-03-27 14:59:27 +08:00
zcr	160bf1a6b1	feat: 更新分割模型参数 ; fix:	2026-03-27 14:56:32 +08:00
zcr	a4d55fdb14	feat: flux2 增加状态码 ; fix:	2026-03-25 10:29:03 +08:00
zcr	7f2f79d029	feat: flux2 增加状态码 ; fix:	2026-03-24 14:35:39 +08:00
zcr	6d9e96305b	feat: brand dna logo生成替换flux2klein ; fix:	2026-03-23 11:21:50 +08:00
zcr	d93c50ce2b	feat: 新增flux2klein作为moodboard的localbase 模型 ; fix:	2026-03-23 10:46:16 +08:00
zcr	e25f49a776	feat: fix: 删除计数中间件	2026-03-13 11:22:12 +08:00
zcr	33b4dd4a7f	feat: fix: 翻译模型ip更换	2026-03-05 15:20:40 +08:00
zcr	7e48420ba7	feat: fix: sam 模型ip更换	2026-03-05 15:06:19 +08:00
zcr	09e25f423e	feat: fix: others 旋转功能修复	2026-03-05 14:01:29 +08:00
zcr	dcc88adfc0	feat: All checks were successful git commit AiDA python develop 分支构建部署 / scheduled_deploy (push) Has been skipped Details fix: 替换项目中所有mmcv的依赖	2026-02-27 15:26:07 +08:00
zcr	c03b7e263e	feat: All checks were successful git commit AiDA python develop 分支构建部署 / scheduled_deploy (push) Has been skipped Details fix: 替换项目中所有mmcv的依赖	2026-02-10 11:17:31 +08:00
zcr	200414e5ad	feat: 停用flux2 img2product 复用sdxl img2product All checks were successful git commit AiDA python develop 分支构建部署 / scheduled_deploy (push) Has been skipped Details fix:	2026-02-09 17:33:07 +08:00
zcr	4656eeee91	feat: 印花逻辑修改默认不处理除overall以外所有印花类型 All checks were successful git commit AiDA python develop 分支构建部署 / scheduled_deploy (push) Has been skipped Details fix:	2026-02-03 16:43:33 +08:00
zcr	fe25f5878b	feat: All checks were successful git commit AiDA python develop 分支构建部署 / scheduled_deploy (push) Has been skipped Details fix: 修复sketch类型为others时跳过上印花导致的尺寸与分割尺寸不一致问题, 修复others分割出后片的问题	2026-02-03 16:22:47 +08:00
zcr	2cc17a1210	feat: All checks were successful git commit AiDA python develop 分支构建部署 / scheduled_deploy (push) Has been skipped Details fix: 队列名修复	2026-02-02 15:37:01 +08:00
zcr	be92d48abb	feat: All checks were successful git commit AiDA python develop 分支构建部署 / scheduled_deploy (push) Has been skipped Details fix: 回溯镜像旋转逻辑	2026-01-30 15:45:57 +08:00
zcr	f8382f280f	feat: All checks were successful git commit AiDA python develop 分支构建部署 / scheduled_deploy (push) Has been skipped Details fix: 修复类别为other时出现的pipeline item缺失	2026-01-29 16:25:43 +08:00
zcr	c24862507f	feat: All checks were successful git commit AiDA python develop 分支构建部署 / scheduled_deploy (push) Has been skipped Details fix: slogan 服务迁移	2026-01-28 15:37:03 +08:00
zcr	e02ca351b6	feat: All checks were successful git commit AiDA python develop 分支构建部署 / scheduled_deploy (push) Has been skipped Details fix: 印花overall 角度异常	2026-01-27 13:42:34 +08:00
zcr	c987f498bc	feat: All checks were successful git commit AiDA python develop 分支构建部署 / scheduled_deploy (push) Has been skipped Details fix:	2026-01-27 11:28:36 +08:00
zcr	3aa8dfa0f4	feat: All checks were successful git commit AiDA python develop 分支构建部署 / scheduled_deploy (push) Has been skipped Details fix: 移除打印	2026-01-27 10:12:23 +08:00
zcr	265f4de50e	feat: All checks were successful git commit AiDA python develop 分支构建部署 / scheduled_deploy (push) Has been skipped Details fix: 更新端口	2026-01-26 16:32:30 +08:00
zcr	a996a1853d	feat: All checks were successful git commit AiDA python develop 分支构建部署 / scheduled_deploy (push) Has been skipped Details fix: 更新端口	2026-01-26 16:11:10 +08:00
zcr	1cbd019ffd	feat: 更新翻译模型 All checks were successful git commit AiDA python develop 分支构建部署 / scheduled_deploy (push) Has been skipped Details fix:	2026-01-26 15:56:42 +08:00
zcr	e2a49e2f3a	feat: 新增to product img flux2 版,停用sdxl版 All checks were successful git commit AiDA python develop 分支构建部署 / scheduled_deploy (push) Has been skipped Details fix:	2026-01-26 15:26:15 +08:00
zcr	66037c94e6	feat: 新增to product img flux2 版,停用sdxl版 All checks were successful git commit AiDA python develop 分支构建部署 / scheduled_deploy (push) Has been skipped Details fix:	2026-01-26 15:23:49 +08:00
zcr	754e8d7735	feat: 新增to product img flux2 版,停用sdxl版 All checks were successful git commit AiDA python develop 分支构建部署 / scheduled_deploy (push) Has been skipped Details fix:	2026-01-26 15:21:51 +08:00
zcr	cdaeb6daac	feat: 新增to product img flux2 版,停用sdxl版 All checks were successful git commit AiDA python develop 分支构建部署 / scheduled_deploy (push) Has been skipped Details fix:	2026-01-26 15:19:28 +08:00
zcr	863d9287dc	fix: 参数对齐 All checks were successful git commit AiDA python develop 分支构建部署 / scheduled_deploy (push) Has been skipped Details (cherry picked from commit `ddef6af1cf`)	2026-01-26 14:56:49 +08:00