Update app.py

app.py

@@ -3,13 +3,12 @@ import numpy as np
 import random
 from diffusers import DiffusionPipeline
 import torch
-from PIL import Image
-import matplotlib.pyplot as plt
+from torch.fft import fftn, ifftn  # For Fourier transforms
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
+# Load the model
 if torch.cuda.is_available():
-    torch.cuda.max_memory_allocated(device=device)
     pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
     pipe.enable_xformers_memory_efficient_attention()
     pipe = pipe.to(device)
@@ -20,38 +19,33 @@ else:
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
 
-# Function to apply FFT and return an image
-def apply_fft(image: Image.Image):
-    # Convert the image to grayscale for FFT (can be extended for color images too)
-    image_gray = image.convert("L")
-    
-    # Convert the image to numpy array
-    image_array = np.array(image_gray)
-
-    # Apply 2D FFT
-    fft_image = np.fft.fft2(image_array)
-    fft_shifted = np.fft.fftshift(fft_image)  # Shift the zero frequency to the center
-
-    # Magnitude spectrum for visualization
-    magnitude_spectrum = 20 * np.log(np.abs(fft_shifted))
-
-    # Normalize magnitude spectrum to 0-255 for visualization
-    magnitude_spectrum = np.interp(magnitude_spectrum, (magnitude_spectrum.min(), magnitude_spectrum.max()), (0, 255))
-
-    # Convert back to image
-    fft_image_pil = Image.fromarray(magnitude_spectrum.astype(np.uint8))
-    
-    return fft_image_pil
+# Group-theory-based seed reduction (example: cyclic group on mod N)
+def reduce_seeds(seed):
+    group_size = 100  # Cyclic group of size 100
+    return seed % group_size
+
+# Fourier-based optimization
+def fft_convolution(image):
+    # Convert to frequency domain using FFT
+    freq_image = fftn(image)
+    # Apply some transformation in the frequency domain (e.g., filtering, smoothing)
+    # This is a placeholder; you can implement any frequency domain operation here
+    transformed_freq_image = freq_image * np.exp(-np.abs(freq_image))  # Example operation
+    # Convert back to spatial domain using inverse FFT
+    transformed_image = ifftn(transformed_freq_image).real
+    return transformed_image
 
 def infer(prompt_part1, color, dress_type, design, prompt_part5, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
     prompt = f"{prompt_part1} {color} colored plain {dress_type} with {design} design, {prompt_part5}"
     
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
+    else:
+        seed = reduce_seeds(seed)  # Apply symmetry-based seed reduction
         
     generator = torch.Generator().manual_seed(seed)
     
-    # Generate the image using the diffusion pipeline
+    # Run the diffusion model
     image = pipe(
         prompt=prompt, 
         negative_prompt=negative_prompt,
@@ -60,12 +54,13 @@ def infer(prompt_part1, color, dress_type, design, prompt_part5, negative_prompt
         width=width, 
         height=height,
         generator=generator
-    ).images[0] 
-    
-    # Apply FFT post-processing to the generated image
-    fft_image = apply_fft(image)
-    
-    return fft_image
+    ).images[0]
+
+    # Apply Fourier-based convolution optimization
+    image = np.array(image)  # Convert to numpy array
+    optimized_image = fft_convolution(image)  # Apply Fourier convolution
+
+    return optimized_image
 
 examples = [
     "red, t-shirt, yellow stripes",
@@ -89,7 +84,7 @@ with gr.Blocks(css=css) as demo:
     
     with gr.Column(elem_id="col-container"):
         gr.Markdown(f"""
-        # Text-to-Image Gradio Template with FFT Post-Processing
+        # Text-to-Image Gradio Template
         Currently running on {power_device}.
         """)