Update myapp.py

myapp.py

@@ -1,11 +1,11 @@
+import os
+import io
+import random
+import torch
 from flask import Flask, jsonify, request, send_file
 from flask_cors import CORS
-import torch
 from diffusers import DiffusionPipeline
 import numpy as np
-import random
-import io
-from PIL import Image
 
 # Initialize the Flask app
 myapp = Flask(__name__)
@@ -13,17 +13,14 @@ CORS(myapp)  # Enable CORS if needed
 
 # Load the model
 device = "cpu"
-dtype = torch.float16
-
 repo = "prompthero/openjourney-v4"
-pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=dtype).to(device)
+pipe = DiffusionPipeline.from_pretrained(repo).to(device)
 
 MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1344
 
-@myapp.route('/')  # Use 'myapp' instead of 'app'
+@myapp.route('/')
 def home():
-    return "Welcome to the Diffusion Image Generation API!"  # Basic home response
+    return "Welcome to the Image Generation API!"  # Basic home response
 
 @myapp.route('/generate_image', methods=['POST'])
 def generate_image():
@@ -34,8 +31,15 @@ def generate_image():
     negative_prompt = data.get('negative_prompt', None)
     seed = data.get('seed', 0)
     randomize_seed = data.get('randomize_seed', True)
+    
+    # Get width and height and ensure they are divisible by 8
     width = data.get('width', 1024)
     height = data.get('height', 1024)
+    
+    # Round width and height to the nearest multiple of 8
+    width = (width // 8) * 8
+    height = (height // 8) * 8
+
     guidance_scale = data.get('guidance_scale', 5.0)
     num_inference_steps = data.get('num_inference_steps', 28)
     
@@ -47,6 +51,7 @@ def generate_image():
     generator = torch.Generator().manual_seed(seed)
     image = pipe(
         prompt=prompt,
+        negative_prompt=negative_prompt,
         guidance_scale=guidance_scale,
         num_inference_steps=num_inference_steps,
         width=width,
@@ -64,4 +69,5 @@ def generate_image():
 
 # Add this block to make sure your app runs when called
 if __name__ == "__main__":
-    myapp.run(host='0.0.0.0', port=7860)  # Run the Flask app on port 7860
+    # Run the Flask app using Gunicorn
+    os.system("gunicorn -w 4 -b 0.0.0.0:7860 myapp:myapp")  # 4 worker processes