Update app.py

app.py

@@ -4,10 +4,16 @@ import random
 from diffusers import DiffusionPipeline
 import torch
 import time
+import psutil
+
+# Get the number of physical CPU cores (excluding hyperthreads)
+NUM_CPU_CORES = psutil.cpu_count(logical=False)
+
+# Cap the number of threads to the available physical cores
+MAX_THREADS = min(8, NUM_CPU_CORES)
 
 # Device and hardware configuration
 DEVICE = "cpu"
-NUM_CPU_CORES = 2
 
 # Model Options (optimized for CPU and memory constraints)
 MODEL_OPTIONS = {
@@ -34,12 +40,13 @@ def load_pipeline(model_id):
 def generate_image(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps, num_images, model_choice):
     if not prompt:
         raise gr.Error("Будь ласка, введіть опис для зображення.")
-    torch.set_num_threads(NUM_CPU_CORES)  # Set PyTorch thread count
+
+    torch.set_num_threads(MAX_THREADS)  # Set the maximum number of threads
 
     pipe = load_pipeline(MODEL_OPTIONS[model_choice])
 
     # Adjust memory usage based on available RAM
-    torch.cuda.empty_cache()  # Not strictly necessary on CPU, but good practice
+    torch.cuda.empty_cache()  
 
     generator = torch.Generator(device=DEVICE)
     if not randomize_seed:
@@ -54,14 +61,20 @@ def generate_image(prompt, negative_prompt, seed, randomize_seed, width, height,
         guidance_scale=guidance_scale,
         num_inference_steps=num_inference_steps,
         num_images_per_prompt=num_images,
-        generator=generator
-        ).images
+        generator=generator,
+    ).images
 
     end_time = time.time()
     generation_time = end_time - start_time
 
     return images, f"Час генерації: {generation_time:.2f} секунд"
 
+# ... (Gradio interface remains the same)
+
+    generation_time = end_time - start_time
+
+    return images, f"Час генерації: {generation_time:.2f} секунд"
+
     run_button = gr.Button("Згенерувати")
     gallery = gr.Gallery(label="Згенеровані зображення")
     status_text = gr.Textbox(label="Статус")