[Update]: Enhanced app.py for optimized GPU usage and improved Gradio interface 🌟

- Updated: Replaced `import space` with `import os` for better compatibility.
- Added: Device configuration to utilize GPU if available, enhancing performance for memory operations.
- Implemented: Memory optimizations for the Stable Diffusion pipeline when using GPU.
- Updated: Gradio interface to include queuing and server configuration for better resource management.
- Added: New dependency `spaces` to requirements.txt for GPU functionality.
- Pro Tip of the Commit: When it comes to performance, let’s ride the GPU wave! 🌊🚀
Aye, Aye! 🚢

app.py

@@ -10,7 +10,7 @@ and interact like waves in an ocean of consciousness.
 
 Created by: Aye & Hue (with Trisha from Accounting keeping the numbers flowing)
 """
-import space
+import os
 import gradio as gr
 import torch
 import numpy as np
@@ -19,7 +19,6 @@ from matplotlib import cm
 import random
 import time
 from typing import Tuple, List, Dict, Optional, Union
-import os
 import json
 from datetime import datetime
 import plotly.graph_objects as go
@@ -36,8 +35,21 @@ random.seed(RANDOM_SEED)
 # Constants
 DEFAULT_GRID_SIZE = 64
 EMOTION_RANGE = (-5, 5)  # Range for emotional valence
+AROUSAL_RANGE = (0, 255)  # Range for arousal
 MAX_SEED = 999999999  # Maximum seed value for art generation
 
+# Configure device - Use Zero GPU if available
+if torch.cuda.is_available():
+    print("🚀 Zero GPU detected! Enabling hardware acceleration...")
+    device = "cuda"
+    # Enable Zero GPU optimizations
+    torch.backends.cuda.matmul.allow_tf32 = True
+    torch.backends.cudnn.allow_tf32 = True
+    torch.backends.cudnn.benchmark = True
+else:
+    print("⚡ Running on CPU")
+    device = "cpu"
+
 # Try to import Stable Diffusion components
 STABLE_DIFFUSION_AVAILABLE = False
 pipe = None
@@ -45,18 +57,24 @@ try:
     from diffusers import DiffusionPipeline, FlowMatchEulerDiscreteScheduler
     STABLE_DIFFUSION_AVAILABLE = True
     
-    # Initialize Stable Diffusion pipeline
+    # Initialize Stable Diffusion pipeline with Zero GPU optimizations
     try:
         pipe = DiffusionPipeline.from_pretrained(
             "stabilityai/stable-diffusion-xl-base-1.0",
-            torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
+            torch_dtype=torch.float16 if device == "cuda" else torch.float32,
             use_safetensors=True,
-            variant="fp16" if torch.cuda.is_available() else None
+            variant="fp16" if device == "cuda" else None
         )
         pipe.scheduler = FlowMatchEulerDiscreteScheduler.from_config(pipe.scheduler.config)
-        pipe.to("cuda" if torch.cuda.is_available() else "cpu")
-        pipe.enable_model_cpu_offload()
-        pipe.enable_vae_slicing()
+        pipe.to(device)
+        
+        # Enable memory optimizations
+        if device == "cuda":
+            pipe.enable_model_cpu_offload()
+            pipe.enable_vae_slicing()
+            pipe.enable_vae_tiling()
+            # Enable attention slicing for lower memory usage
+            pipe.enable_attention_slicing(slice_size="max")
     except Exception as e:
         print(f"Warning: Failed to initialize Stable Diffusion: {e}")
         pipe = None
@@ -143,7 +161,7 @@ class MemoryWave:
     """
     def __init__(self, 
                  size: int = DEFAULT_GRID_SIZE, 
-                 device: str = "cuda" if torch.cuda.is_available() else "cpu"):
+                 device: str = device):  # Use global device
         """
         Initialize a memory wave system.
         
@@ -748,7 +766,8 @@ def generate_memory_prompt(operation: str, emotion_valence: float) -> str:
 @spaces.GPU(duration=30)
 def create_interface():
     """Create the Gradio interface for the Mem|8 Wave Memory Explorer."""
-    memory_wave = MemoryWave()
+    # Initialize with Zero GPU device
+    memory_wave = MemoryWave(device=device)
     
     def process_memory_operation(
         operation: str,
@@ -942,5 +961,12 @@ def create_interface():
     return demo
 
 if __name__ == "__main__":
+    # Add Hugging Face Space-specific configuration
     demo = create_interface()
-    demo.launch()
+    demo.queue(max_size=10)  # Enable queuing for better resource management
+    demo.launch(
+        share=False,  # Don't create a public link
+        server_name="0.0.0.0",  # Listen on all interfaces
+        server_port=7860,  # Default Spaces port
+        show_api=False,  # Hide API docs
+    )

requirements.txt

@@ -6,4 +6,5 @@ matplotlib>=3.8.0
 diffusers>=0.25.0
 transformers>=4.37.0
 accelerate>=0.27.0
-scipy>=1.11.0 
+scipy>=1.11.0 
+spaces>=0.1.0