Update app.py

app.py

@@ -1,28 +1,47 @@
 import gradio as gr
 from diffusers import DiffusionPipeline
+import dask
+from dask import delayed, compute
+from concurrent.futures import ThreadPoolExecutor
 import os
 os.environ['HF_HOME'] = '/blabla/cache/'
 
-# Load the diffusion model
+
+# Load model
 pipe = DiffusionPipeline.from_pretrained("prompthero/openjourney-v4")
 
+def generate_image(prompt, num_inference_steps=50):
+    """
+    Generate an image based on a text prompt using diffusion with optimizations.
+    The number of inference steps is reduced for faster generation.
+    """
+    # Reduce steps for faster processing
+    image = pipe(prompt, num_inference_steps=num_inference_steps).images[0]
+    return image
 
+@delayed
+def dask_generate(prompt):
+    return generate_image(prompt)
 
+def parallel_generate(prompt):
+    # Use multithreading to speed up the computation by processing multiple images simultaneously
+    with ThreadPoolExecutor(max_workers=4) as executor:
+        futures = [executor.submit(dask_generate, prompt) for _ in range(4)]  # Example with 4 threads
+        results = [future.result() for future in futures]
 
-def generate_image(prompt):
-    # Generate the image based on the prompt
-    image = pipe(prompt).images[0]
-    return image
+    # Execute the generation using Dask to potentially improve processing speed
+    images = compute(*results)
+    return images[0]  # Return the first image generated for simplicity
 
-# Create a Gradio interface using the new component style
+# Gradio interface
 iface = gr.Interface(
-    fn=generate_image,
-    inputs=gr.Textbox(label="Enter your prompt", placeholder="e.g., Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"),
-    outputs=gr.Image(type="pil", label="Generated Image"),
-    title="Image Generation with SDXL-Lightning",
-    description="Enter a prompt to generate an image using the SDXL-Lightning model."
+    fn=parallel_generate,
+    inputs=gr.Textbox(label="Prompt", placeholder="Enter your prompt here"),
+    outputs=gr.Image(type="pil"),
+    title="Multithreaded CPU Optimized Image Generation",
+    description="Enter a prompt to generate an image efficiently using CPU optimization and multithreading."
 )
 
-# Launch the Gradio interface
+# Launch the Gradio app
 if __name__ == "__main__":
     iface.launch()