Add: Added deploy support (server and socket).

README.md

@@ -53,6 +53,9 @@ Integrated Design Diffusion Model
 │       ├── class_1
 │       ├── class_2
 │       └── class_3
+├── deploy
+│   ├── deploy_socket.py
+│   └── deploy_server.py
 ├── model
 │   ├── modules
 │   │   ├── activation.py
@@ -84,7 +87,6 @@ Integrated Design Diffusion Model
 │   │   └── noise
 │   └── test_module.py
 ├── tools
-│   ├── deploy.py
 │   ├── FID_calculator.py
 │   ├── FID_calculator_plus.py
 │   ├── generate.py
@@ -119,6 +121,7 @@ Integrated Design Diffusion Model
 - [x] 12. Adding PLMS Sampling Method. (2024-03-12)
 - [x] 13. Adding FID calculator to verify image quality. (2024-05-06)
 - [x] 14. Adding the deployment of image-generating Sockets
+- [x] 14. Adding the deployment of image-generating Sockets and Web server. (2024-11-13)
 
 ### Training
 

README_zh.md

@@ -52,6 +52,9 @@ Integrated Design Diffusion Model
 │       ├── class_1
 │       ├── class_2
 │       └── class_3
+├── deploy
+│   ├── deploy_socket.py
+│   └── deploy_server.py
 ├── model
 │   ├── modules
 │   │   ├── activation.py
@@ -83,7 +86,6 @@ Integrated Design Diffusion Model
 │   │   └── noise
 │   └── test_module.py
 ├── tools
-│   ├── deploy.py
 │   ├── FID_calculator.py
 │   ├── FID_calculator_plus.py
 │   ├── generate.py
@@ -118,6 +120,7 @@ Integrated Design Diffusion Model
 - [x] 12. 增加PLMS采样方法（2024-03-12）
 - [x] 13. 增加FID方法验证图像质量（2024-05-06）
 - [x] 14. 增加生成图像Socket部署（2024-11-12）
+- [x] 14. 增加生成图像Socket和网站服务部署（2024-11-13）
 
 ### 训练
 

deploy/deploy_server.py

@@ -0,0 +1,121 @@
+#!/usr/bin/env python
+# -*- coding:utf-8 -*-
+"""
+    @Date   : 2024/11/4 23:04
+    @Author : chairc
+    @Site   : https://github.com/chairc
+"""
+import os
+import sys
+import json
+import logging
+import uuid
+
+import coloredlogs
+
+from flask import Flask, request, jsonify
+from torchvision import transforms
+
+sys.path.append(os.path.dirname(sys.path[0]))
+from config.version import get_version_banner
+from tools.generate import Generator, init_generate_args
+from utils import save_images
+from utils.processing import image_to_base64
+
+logger = logging.getLogger(__name__)
+coloredlogs.install(level="INFO")
+app = Flask(__name__)
+
+
+@app.route("/")
+def index():
+    logger.info(msg="Route -> Hello IDDM")
+    return "Hello, IDDM!"
+
+
+@app.route("/api/generate/df", methods=["POST"])
+def generate_diffusion_model_api():
+    """
+    Generate a diffusion model
+    """
+    logger.info(msg="Route -> /api/df")
+
+    data = request.json
+    logger.info(msg=f"Send json: {data}")
+
+    # Sample type
+    sample = data["sample"]
+    # Image size
+    image_size = data["image_size"]
+    # Number of images
+    num_images = data["num_images"] if data["num_images"] >= 1 else 1
+    # Weight path
+    weight_path = data["weight_path"]
+    result_path = data["result_path"]
+    # Recommend use base64 in server app
+    # Return mode, base64 or url
+    re_type = data["type"]
+
+    logger.info(msg="[Web]: Start generation.")
+    # Type is url or base64
+    re_json = {"image": [], "type": str(re_type)}
+
+    if any(param is None for param in [sample, image_size, num_images, weight_path, result_path, re_type]):
+        return jsonify({"code": 400, "msg": "Illegal parameters.", "data": None}), 400
+
+    # Init args
+    args = init_generate_args()
+    args.sample = sample
+    args.image_size = image_size
+    args.weight_path = weight_path
+    args.result_path = result_path
+    # Only generate 1 image per
+    args.num_images = 1
+
+    try:
+        # Init server model
+        server_model = Generator(gen_args=args, deploy=True)
+
+        logger.info(msg=f"[Web]: A total of {num_images} images are generated.")
+        # Generate images by diffusion models
+        for i in range(num_images):
+            logger.info(msg=f"[Web]: Current generate {i + 1} of {num_images}.")
+            # Generation name
+            generate_name = uuid.uuid1()
+            # Generate image
+            x = server_model.generate(index=i)
+
+            # Select mode
+            # Recommend use base64
+            if re_type == "base64":
+                x = transforms.ToPILImage()(x[0])
+                re_x = image_to_base64(image=x)
+            else:
+                # Save images
+                re_x = os.path.join(result_path, f"{generate_name}.png")
+                save_images(images=x, path=re_x)
+            # Append return json
+            image_json = {"image_id": str(generate_name), "type": re_type,
+                          "image": str(re_x)}
+            re_json["image"].append(image_json)
+
+        logger.info(msg="[Web]: Finish generation.")
+
+        return jsonify({"code": 200, "msg": "success!", "data": json.dumps(re_json, ensure_ascii=False)}), 200
+    except Exception as e:
+        return jsonify({"code": 500, "msg": str(e), "data": None}), 500
+
+
+@app.route("/api/generate/sr")
+def generate_super_resolution_model_api():
+    logger.info(msg="Route -> /api/sr")
+    # TODO: super resolution api
+    return "SR!"
+
+
+if __name__ == "__main__":
+    host = "127.0.0.1"
+    port = 12341
+    logger.info(msg=f"Run -> {host}:{port}")
+    get_version_banner()
+    app.run(host=host, port=port, debug=True)

tools/deploy.py → deploy/deploy_socket.py

@@ -14,13 +14,13 @@
 import threading
 import coloredlogs
 
-import torch
+from torchvision import transforms
 
 sys.path.append(os.path.dirname(sys.path[0]))
-from model.networks.unet import UNet
+from tools.generate import init_generate_args, Generator
 from utils.utils import save_images
-from utils.initializer import device_initializer, sample_initializer
-from utils.checkpoint import load_ckpt
+from utils.processing import image_to_base64
+from config.version import get_version_banner
 
 logger = logging.getLogger(__name__)
 coloredlogs.install(level="INFO")
@@ -32,57 +32,56 @@ def generate(parse_json_data):
     :param parse_json_data: Parse send json message
     :return: JSON
     """
-    logger.info(msg="[Client]: Start generation.")
-    re_json = {"image": []}
-    # Get the incoming json value information
-    # Enable conditional generation
-    conditional = parse_json_data["conditional"]
     # Sample type
     sample = parse_json_data["sample"]
     # Image size
     image_size = parse_json_data["image_size"]
     # Number of images
     num_images = parse_json_data["num_images"] if parse_json_data["num_images"] >= 1 else 1
-    # Activation function
-    act = parse_json_data["act"]
     # Weight path
     weight_path = parse_json_data["weight_path"]
-    # Saving path
     result_path = parse_json_data["result_path"]
-    # Run device initializer
-    device = device_initializer()
-    # Initialize the diffusion model
-    diffusion = sample_initializer(sample=sample, image_size=image_size, device=device)
-    # Initialize model
-    if conditional:
-        # Number of classes
-        num_classes = parse_json_data["num_classes"]
-        # Generation class name
-        class_name = parse_json_data["class_name"]
-        # classifier-free guidance interpolation weight
-        cfg_scale = parse_json_data["cfg_scale"]
-        model = UNet(num_classes=num_classes, device=device, image_size=image_size, act=act).to(device)
-        load_ckpt(ckpt_path=weight_path, model=model, device=device, is_train=False)
-        y = torch.Tensor([class_name]).long().to(device)
-    else:
-        model = UNet(device=device, image_size=image_size, act=act).to(device)
-        load_ckpt(ckpt_path=weight_path, model=model, device=device, is_train=False)
-        y = None
-        cfg_scale = None
+    # Return mode, base64 or url
+    re_type = parse_json_data["type"]
+
+    logger.info(msg="[Client]: Start generation.")
+    # Type is url or base64
+    re_json = {"image": [], "type": str(re_type)}
+
+    # Init args
+    args = init_generate_args()
+    args.sample = sample
+    args.image_size = image_size
+    args.weight_path = weight_path
+    args.result_path = result_path
+    # Only generate 1 image per
+    args.num_images = 1
+
+    # Init model
+    model = Generator(gen_args=args, deploy=True)
+
+    logger.info(msg=f"[Client]: A total of {num_images} images are generated.")
     # Generate images by diffusion models
     for i in range(num_images):
+        logger.info(msg=f"[Client]: Current generate {i + 1} of {num_images}.")
         # Generation name
         generate_name = uuid.uuid1()
-        x = diffusion.sample(model=model, n=1, labels=y, cfg_scale=cfg_scale)
-        # TODO: Convert to base64
-        # Save images
-        save_images(images=x, path=os.path.join(result_path, f"{generate_name}.jpg"))
-        # Append return json
-        image_json = {"image_id": str(generate_name),
-                      "image_name": f"{generate_name}.jpg"}
+        x = model.generate(index=i)
+        # Select mode
+        if re_type == "base64":
+            x = transforms.ToPILImage()(x[0])
+            re_x = image_to_base64(image=x)
+        else:
+            # Save images
+            re_x = os.path.join(result_path, f"{generate_name}.png")
+            save_images(images=x, path=re_x)
+        # Append return json in data
+        image_json = {"image_id": str(generate_name), "type": re_type,
+                      "image": str(re_x)}
+
         re_json["image"].append(image_json)
     logger.info(msg="[Client]: Finish generation.")
-    return re_json
+    return json.dumps(re_json, ensure_ascii=False)
 
 
 def main():
@@ -122,6 +121,7 @@ class ServerThreading(threading.Thread):
     """
     ServerThreading class
     """
+
     def __init__(self, client_socket, address, receive_size=1024 * 1024, encoding="utf-8"):
         """
         ServerThreading initialization
@@ -178,4 +178,5 @@ def __del__(self):
 
 
 if __name__ == "__main__":
+    get_version_banner()
     main()

requirements.txt

@@ -4,9 +4,11 @@ matplotlib==3.7.1
 numpy==1.25.0
 Pillow==10.3.0
 Requests==2.31.0
+scikit-image==0.22.0
 torch_summary==1.4.5
 tqdm==4.65.0
 pytorch_fid==0.3.0
+flask==3.0.3
 
 # If that fails use: pip install torch==1.13.0+cu116 torchvision==0.14.0+cu116 -f https://download.pytorch.org/whl/torch_stable.html
 # About more torch information please click: https://pytorch.org/get-started/previous-versions/#linux-and-windows-25