Update app.py

app.py

@@ -1,6 +1,6 @@
 from flask import Flask, request, jsonify
 from flask_cors import CORS
-from diffusers import StableDiffusionPipeline, StableDiffusionXLPipeline, UniPCMultistepScheduler
+from diffusers import StableDiffusionPipeline, StableDiffusionXLPipeline, UniPCMultistepScheduler, DPMSolverMultistepScheduler
 import torch
 import os
 from PIL import Image
@@ -23,9 +23,10 @@ CORS(app)
 logging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s')
 logger = logging.getLogger(__name__)
 
-# Log device and memory info
+# Log device, memory, and code version
 logger.info(f"Device in use: {torch.device('cpu')}")
 logger.info(f"Available memory: {psutil.virtual_memory().available / (1024 ** 3):.2f} GB")
+logger.info("Running app.py version: 2025-07-16-fix-type-mismatch")
 
 # Model cache
 model_cache = {}
@@ -83,11 +84,33 @@ def serve_assets(filename):
 def generate():
     try:
         data = request.json
+        logger.info(f"Received JSON payload: {data}")
+
         model_id = data.get('model', 'ssd-1b')
         prompt = data.get('prompt', '')
         ratio = data.get('ratio', '1:1')
-        num_images = min(int(data.get('num_images', 1)), 4)
-        guidance_scale = float(min(max(data.get('guidance_scale', 7.5), 1.0), 20.0))  # Clamp guidance scale
+        
+        # Handle num_images with type checking
+        num_images_raw = data.get('num_images', 1)
+        logger.info(f"Raw num_images: {num_images_raw} (type: {type(num_images_raw)})")
+        try:
+            num_images = int(num_images_raw)
+            num_images = min(num_images, 4)
+            if num_images < 1:
+                raise ValueError("num_images must be at least 1")
+        except (ValueError, TypeError):
+            logger.error(f"Invalid num_images value: {num_images_raw}")
+            return jsonify({"error": "num_images must be a valid integer"}), 400
+
+        # Handle guidance_scale with type checking
+        guidance_scale_raw = data.get('guidance_scale', 7.5)
+        logger.info(f"Raw guidance_scale: {guidance_scale_raw} (type: {type(guidance_scale_raw)})")
+        try:
+            guidance_scale = float(guidance_scale_raw)
+            guidance_scale = min(max(guidance_scale, 1.0), 20.0)  # Clamp between 1.0 and 20.0
+        except (ValueError, TypeError):
+            logger.error(f"Invalid guidance_scale value: {guidance_scale_raw}")
+            return jsonify({"error": "guidance_scale must be a valid number"}), 400
 
         # Log input parameters
         logger.info(f"Generating with model: {model_id}, prompt: {prompt}, ratio: {ratio}, num_images: {num_images}, guidance_scale: {guidance_scale}")