Create app.py

app.py

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+import gradio as gr
+import numpy as np
+import random
+from diffusers import DiffusionPipeline
+import torch
+from PIL import Image
+import matplotlib.pyplot as plt
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+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)
+else: 
+    pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", use_safetensors=True)
+    pipe = pipe.to(device)
+
+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
+
+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)
+        
+    generator = torch.Generator().manual_seed(seed)
+    
+    # Generate the image using the diffusion pipeline
+    image = pipe(
+        prompt=prompt, 
+        negative_prompt=negative_prompt,
+        guidance_scale=guidance_scale, 
+        num_inference_steps=num_inference_steps, 
+        width=width, 
+        height=height,
+        generator=generator
+    ).images[0] 
+    
+    # Apply FFT post-processing to the generated image
+    fft_image = apply_fft(image)
+    
+    return fft_image
+
+examples = [
+    "red, t-shirt, yellow stripes",
+    "blue, hoodie, minimalist",
+    "red, sweat shirt, geometric design",
+]
+
+css = """
+#col-container {
+    margin: 0 auto;
+    max-width: 520px;
+}
+"""
+
+if torch.cuda.is_available():
+    power_device = "GPU"
+else:
+    power_device = "CPU"
+
+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
+        Currently running on {power_device}.
+        """)
+        
+        with gr.Row():
+            
+            prompt_part1 = gr.Textbox(
+                value="a single", 
+                label="Prompt Part 1",
+                show_label=False,
+                interactive=False,
+                container=False,
+                elem_id="prompt_part1",
+                visible=False,
+            )
+            
+            prompt_part2 = gr.Textbox(
+                label="color",
+                show_label=False,
+                max_lines=1,
+                placeholder="color (e.g., color category)",
+                container=False,
+            )
+            
+            prompt_part3 = gr.Textbox(
+                label="dress_type",
+                show_label=False,
+                max_lines=1,
+                placeholder="dress_type (e.g., t-shirt, sweatshirt, shirt, hoodie)",
+                container=False,
+            )
+            
+            prompt_part4 = gr.Textbox(
+                label="design",
+                show_label=False,
+                max_lines=1,
+                placeholder="design",
+                container=False,
+            )
+            
+            prompt_part5 = gr.Textbox(
+                value="hanging on the plain wall", 
+                label="Prompt Part 5",
+                show_label=False,
+                interactive=False,
+                container=False,
+                elem_id="prompt_part5",
+                visible=False,
+            )
+            
+            run_button = gr.Button("Run", scale=0)
+        
+        result = gr.Image(label="Result", show_label=False)
+
+        with gr.Accordion("Advanced Settings", open=False):
+            
+            negative_prompt = gr.Textbox(
+                label="Negative prompt",
+                max_lines=1,
+                placeholder="Enter a negative prompt",
+                visible=False,
+            )
+            
+            seed = gr.Slider(
+                label="Seed",
+                minimum=0,
+                maximum=MAX_SEED,
+                step=1,
+                value=0,
+            )
+            
+            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+            
+            with gr.Row():
+                
+                width = gr.Slider(
+                    label="Width",
+                    minimum=256,
+                    maximum=MAX_IMAGE_SIZE,
+                    step=32,
+                    value=512,
+                )
+                
+                height = gr.Slider(
+                    label="Height",
+                    minimum=256,
+                    maximum=MAX_IMAGE_SIZE,
+                    step=32,
+                    value=512,
+                )
+            
+            with gr.Row():
+                
+                guidance_scale = gr.Slider(
+                    label="Guidance scale",
+                    minimum=0.0,
+                    maximum=10.0,
+                    step=0.1,
+                    value=0.0,
+                )
+                
+                num_inference_steps = gr.Slider(
+                    label="Number of inference steps",
+                    minimum=1,
+                    maximum=12,
+                    step=1,
+                    value=2,
+                )
+        
+        gr.Examples(
+            examples=examples,
+            inputs=[prompt_part2]
+        )
+
+    run_button.click(
+        fn=infer,
+        inputs=[prompt_part1, prompt_part2, prompt_part3, prompt_part4, prompt_part5, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
+        outputs=[result]
+    )
+
+demo.queue().launch()