Update app.py

app.py

@@ -1,132 +1,154 @@
 import gradio as gr
 import numpy as np
 import random
+import spaces
 import torch
-from diffusers import StableDiffusionPipeline
+from diffusers import DiffusionPipeline
 from PIL import Image
-import os
+import io
 
-MAX_SEED = 10000
-MAX_IMAGE_SIZE = 1024
+dtype = torch.bfloat16
+device = "cuda" if torch.cuda.is_available() else "cpu"
 
-def get_edge_color(image):
-    """Get a random color from the edge of the image"""
-    img_array = np.array(image)
-    top_edge = img_array[0, :, :]
-    bottom_edge = img_array[-1, :, :]
-    left_edge = img_array[:, 0, :]
-    right_edge = img_array[:, -1, :]
-    edge_pixels = np.concatenate([top_edge, bottom_edge, left_edge, right_edge])
-    random_edge_color = tuple(edge_pixels[random.randint(0, len(edge_pixels)-1)])
-    return random_edge_color
+pipe = DiffusionPipeline.from_pretrained(
+    "black-forest-labs/FLUX.1-schnell",
+    torch_dtype=dtype
+).to(device)
 
-def color_match_tshirt(tshirt_image, target_color, threshold=30):
-    """Change white/near-white areas of the t-shirt to the target color"""
-    img_array = np.array(tshirt_image)
-    white_mask = np.all(np.abs(img_array - [255, 255, 255]) < threshold, axis=2)
-    img_array[white_mask] = target_color
-    return Image.fromarray(img_array)
+MAX_SEED = np.iinfo(np.int32).max
+MAX_IMAGE_SIZE = 2048
 
-def add_watermark(image, logo_path, position='bottom-right', size_percentage=10):
-    """Add a watermark to an image"""
-    try:
-        if not os.path.exists(logo_path):
-            return image
-            
-        logo = Image.open(logo_path).convert('RGBA')
-        main_width, main_height = image.size
-        logo_width = int(main_width * size_percentage / 100)
-        logo_height = int(logo.size[1] * (logo_width / logo.size[0]))
-        logo = logo.resize((logo_width, logo_height), Image.Resampling.LANCZOS)
-        
-        if image.mode != 'RGBA':
-            image = image.convert('RGBA')
-        
-        watermarked = Image.new('RGBA', image.size, (0, 0, 0, 0))
-        watermarked.paste(image, (0, 0))
-        
-        if position == 'bottom-right':
-            pos = (main_width - logo_width - 10, main_height - logo_height - 10)
-        elif position == 'bottom-left':
-            pos = (10, main_height - logo_height - 10)
-        elif position == 'top-right':
-            pos = (main_width - logo_width - 10, 10)
-        else:  # top-left
-            pos = (10, 10)
-        
-        watermarked.paste(logo, pos, logo)
-        return watermarked.convert('RGB')
-    except Exception as e:
-        print(f"Failed to add watermark: {str(e)}")
-        return image
+import numpy as np
+from collections import Counter
 
-def create_tshirt_preview(design_image, tshirt_template_path, tshirt_color="white"):
-    """Create a preview of the design on a t-shirt"""
-    try:
-        tshirt = Image.open(tshirt_template_path)
-        tshirt_width, tshirt_height = tshirt.size
-        
-        edge_color = get_edge_color(design_image)
-        tshirt = color_match_tshirt(tshirt, edge_color)
-        
-        design_width = int(tshirt_width * 0.35)
-        design_height = int(design_width * design_image.size[1] / design_image.size[0])
-        design_image = design_image.resize((design_width, design_height), Image.Resampling.LANCZOS)
-        
-        x = (tshirt_width - design_width) // 2
-        y = int(tshirt_height * 0.2)
-        
-        if design_image.mode == 'RGBA':
-            mask = design_image.split()[3]
-        else:
-            mask = None
-        
-        tshirt.paste(design_image, (x, y), mask)
-        return tshirt
-    except Exception as e:
-        print(f"Failed to create t-shirt preview: {str(e)}")
-        return design_image
+def get_prominent_colors(image, num_colors=5):
+    """
+    Get the most prominent colors from an image, focusing on edges
+    """
+    # Convert to numpy array
+    img_array = np.array(image)
+    
+    # Create a simple edge mask using gradient magnitude
+    gradient_x = np.gradient(img_array.mean(axis=2))[1]
+    gradient_y = np.gradient(img_array.mean(axis=2))[0]
+    gradient_magnitude = np.sqrt(gradient_x**2 + gradient_y**2)
+    
+    # Threshold to get edge pixels
+    edge_threshold = np.percentile(gradient_magnitude, 90)  # Adjust percentile as needed
+    edge_mask = gradient_magnitude > edge_threshold
+    
+    # Get colors from edge pixels
+    edge_colors = img_array[edge_mask]
+    
+    # Convert colors to tuples for counting
+    colors = [tuple(color) for color in edge_colors]
+    
+    # Count occurrences of each color
+    color_counts = Counter(colors)
+    
+    # Get most common colors
+    prominent_colors = color_counts.most_common(num_colors)
+    
+    return prominent_colors
 
-def enhance_prompt(prompt, style):
-    """Enhance the prompt based on selected style"""
-    if not style:
-        return prompt
-        
-    style_prompts = {
-        "minimal": "minimalist design, clean lines, simple shapes",
-        "vintage": "vintage style, retro, distressed texture",
-        "artistic": "artistic, creative, hand-drawn style",
-        "geometric": "geometric patterns, abstract shapes",
-        "typography": "modern typography, creative lettering",
-        "realistic": "photorealistic, detailed illustration"
-    }
+def create_tshirt_preview(design_image, tshirt_color="white"):
+    """
+    Overlay the design onto the existing t-shirt template and color match
+    """
+    # Load the template t-shirt image
+    tshirt = Image.open('image.jpeg')
+    tshirt_width, tshirt_height = tshirt.size
+    
+    # Convert design to PIL Image if it's not already
+    if not isinstance(design_image, Image.Image):
+        design_image = Image.fromarray(design_image)
     
-    return f"{prompt}, {style_prompts.get(style, '')}"
+    # Get prominent colors from the design
+    prominent_colors = get_prominent_colors(design_image)
+    if prominent_colors:
+        # Use the most prominent color for the t-shirt
+        main_color = prominent_colors[0][0]  # RGB tuple of most common color
+    else:
+        # Fallback to white if no colors found
+        main_color = (255, 255, 255)
+    
+    # Convert design to PIL Image if it's not already
+    if not isinstance(design_image, Image.Image):
+        design_image = Image.fromarray(design_image)
+    
+    # Resize design to fit nicely on shirt (40% of shirt width)
+    design_width = int(tshirt_width * 0.35)  # Adjust this percentage as needed
+    design_height = int(design_width * design_image.size[1] / design_image.size[0])
+    design_image = design_image.resize((design_width, design_height), Image.Resampling.LANCZOS)
+    
+    # Calculate position to center design on shirt
+    x = (tshirt_width - design_width) // 2
+    y = int(tshirt_height * 0.2)  # Adjust this value based on your template
+    
+    # If design has transparency (RGBA), create mask
+    if design_image.mode == 'RGBA':
+        mask = design_image.split()[3]
+    else:
+        mask = None
+    
+    # Paste design onto shirt
+    tshirt.paste(design_image, (x, y), mask)
+    
+    return tshirt
 
-def initialize_pipeline():
-    """Initialize the Stable Diffusion pipeline"""
-    model_id = "stabilityai/stable-diffusion-2-1"
-    pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
-    if torch.cuda.is_available():
-        pipe = pipe.to("cuda")
-    return pipe
+def enhance_prompt_for_tshirt(prompt, style=None):
+    """Add specific terms to ensure good t-shirt designs."""
+    style_terms = {
+        "minimal": ["simple geometric shapes", "clean lines", "minimalist illustration"],
+        "vintage": ["distressed effect", "retro typography", "vintage illustration"],
+        "artistic": ["hand-drawn style", "watercolor effect", "artistic illustration"],
+        "geometric": ["abstract shapes", "geometric patterns", "modern design"],
+        "typography": ["bold typography", "creative lettering", "text-based design"],
+        "realistic": ["realistic", "cinematic", "photograph"]
+    }
+    
+    base_terms = [
+        "create t-shirt design",
+        "with centered composition",
+        "high quality",
+        "professional design",
+        "clear background"
+    ]
+    
+    enhanced_prompt = f"{prompt}, {', '.join(base_terms)}"
+    
+    if style and style in style_terms:
+        style_specific_terms = style_terms[style]
+        enhanced_prompt = f"{enhanced_prompt}, {', '.join(style_specific_terms)}"
+    
+    return enhanced_prompt
 
-def generate_design(prompt, style, seed, width, height, num_inference_steps, pipe):
-    """Generate the design using Stable Diffusion"""
-    enhanced_prompt = enhance_prompt(prompt, style)
+@spaces.GPU()
+def infer(prompt, style=None, tshirt_color="white", seed=42, randomize_seed=False, 
+          width=1024, height=1024, num_inference_steps=4, 
+          progress=gr.Progress(track_tqdm=True)):
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    
+    enhanced_prompt = enhance_prompt_for_tshirt(prompt, style)
     generator = torch.Generator().manual_seed(seed)
     
-    image = pipe(
+    # Generate the design
+    design_image = pipe(
         prompt=enhanced_prompt,
         width=width,
         height=height,
         num_inference_steps=num_inference_steps,
         generator=generator,
+        guidance_scale=0.0
     ).images[0]
     
-    return image
+    # Create t-shirt preview
+    tshirt_preview = create_tshirt_preview(design_image, tshirt_color)
+    
+    return design_image, tshirt_preview, seed
 
-# Constants
+# Available t-shirt colors
 TSHIRT_COLORS = {
     "White": "#FFFFFF",
     "Black": "#000000",
@@ -134,7 +156,14 @@ TSHIRT_COLORS = {
     "Gray": "#808080"
 }
 
-STYLES = [
+examples = [
+    ["Cool geometric mountain landscape", "minimal", "White"],
+    ["Vintage motorcycle with flames", "vintage", "Black"],
+    ["flamingo in scenic forset", "realistic", "White"],
+    ["Adventure Starts typography", "typography", "White"]
+]
+
+styles = [
     "minimal",
     "vintage",
     "artistic",
@@ -143,67 +172,153 @@ STYLES = [
     "realistic"
 ]
 
-EXAMPLES = [
-    ["Cool geometric mountain landscape", "minimal", "White"],
-    ["Vintage motorcycle with flames", "vintage", "Black"],
-    ["Flamingo in scenic forest", "realistic", "White"],
-    ["Adventure Starts typography", "typography", "White"]
-]
-
-# Gradio Interface
-def create_interface():
-    pipe = initialize_pipeline()
-    
-    def infer(prompt, style, tshirt_color, seed, randomize_seed, width, height, num_inference_steps):
-        if randomize_seed:
-            seed = random.randint(0, MAX_SEED)
-            
-        try:
-            design_image = generate_design(prompt, style, seed, width, height, num_inference_steps, pipe)
-            tshirt_preview = create_tshirt_preview(design_image, "tshirt_template.png", tshirt_color)
-            return design_image, tshirt_preview, seed
-        except Exception as e:
-            print(f"Error during inference: {str(e)}")
-            return None, None, seed
+css = """
+#col-container {
+    margin: 0 auto;
+    max-width: 1200px !important;
+    padding: 20px;
+}
+.main-title {
+    text-align: center;
+    color: #2d3748;
+    margin-bottom: 1rem;
+    font-family: 'Poppins', sans-serif;
+}
+.subtitle {
+    text-align: center;
+    color: #4a5568;
+    margin-bottom: 2rem;
+    font-family: 'Inter', sans-serif;
+    font-size: 0.95rem;
+    line-height: 1.5;
+}
+.design-input {
+    border: 2px solid #e2e8f0;
+    border-radius: 10px;
+    padding: 12px !important;
+    margin-bottom: 1rem !important;
+    font-size: 1rem;
+    transition: all 0.3s ease;
+}
+.results-row {
+    display: grid;
+    grid-template-columns: 1fr 1fr;
+    gap: 20px;
+    margin-top: 20px;
+}
+"""
 
-    with gr.Blocks(theme=gr.themes.Soft()) as demo:
-        with gr.Column():
-            gr.Markdown("# 👕 Deradh's T-Shirt Design Generator")
-            
-            with gr.Row():
-                prompt = gr.Textbox(label="Design Description", placeholder="Describe your t-shirt design idea")
-                style = gr.Dropdown(choices=[""] + STYLES, value="", label="Style")
-                tshirt_color = gr.Dropdown(choices=list(TSHIRT_COLORS.keys()), value="White", label="T-Shirt Color")
-            
-            run_button = gr.Button("✨ Generate")
-            
-            with gr.Row():
-                result = gr.Image(label="Generated Design")
-                preview = gr.Image(label="T-Shirt Preview")
-            
-            with gr.Accordion("Advanced Settings", open=False):
-                seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
-                randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
-                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)
-                num_inference_steps = gr.Slider(label="Steps", minimum=1, maximum=50, step=1, value=25)
-            
-            gr.Examples(
-                examples=EXAMPLES,
-                inputs=[prompt, style, tshirt_color],
-                outputs=[result, preview, seed],
-                fn=lambda p, s, c: infer(p, s, c, 0, True, 512, 512, 25),
-                cache_examples=True
+with gr.Blocks(css=css, theme=gr.themes.Soft()) as demo:
+    with gr.Column(elem_id="col-container"):
+        gr.Markdown(
+            """
+            # 👕Deradh's T-Shirt Design Generator
+            """,
+            elem_classes=["main-title"]
+        )
+        
+        gr.Markdown(
+            """
+            Create unique t-shirt designs using Deradh's AI. 
+            Describe your design idea and select a style to generate professional-quality artwork 
+            perfect for custom t-shirts.
+            """,
+            elem_classes=["subtitle"]
+        )
+        
+        with gr.Row():
+            with gr.Column(scale=2):
+                prompt = gr.Text(
+                    label="Design Description",
+                    show_label=False,
+                    max_lines=1,
+                    placeholder="Describe your t-shirt design idea",
+                    container=False,
+                    elem_classes=["design-input"]
+                )
+            with gr.Column(scale=1):
+                style = gr.Dropdown(
+                    choices=[""] + styles,
+                    value="",
+                    label="Style",
+                    container=False
+                )
+            with gr.Column(scale=1):
+                tshirt_color = gr.Dropdown(
+                    choices=list(TSHIRT_COLORS.keys()),
+                    value="White",
+                    label="T-Shirt Color",
+                    container=False
+                )
+            run_button = gr.Button(
+                "✨ Generate",
+                scale=0,
+                elem_classes=["generate-button"]
+            )
+        
+        with gr.Row(elem_classes=["results-row"]):
+            result = gr.Image(
+                label="Generated Design",
+                show_label=True,
+                elem_classes=["result-image"]
             )
-            
-            run_button.click(
-                fn=infer,
-                inputs=[prompt, style, tshirt_color, seed, randomize_seed, width, height, num_inference_steps],
-                outputs=[result, preview, seed]
+            preview = gr.Image(
+                label="T-Shirt Preview",
+                show_label=True,
+                elem_classes=["preview-image"]
             )
-    
-    return demo
+        
+        with gr.Accordion("🔧 Advanced Settings", open=False):
+            with gr.Group():
+                seed = gr.Slider(
+                    label="Design Seed",
+                    minimum=0,
+                    maximum=MAX_SEED,
+                    step=1,
+                    value=0,
+                )
+                randomize_seed = gr.Checkbox(
+                    label="Randomize Design",
+                    value=True
+                )
+                
+                with gr.Row():
+                    width = gr.Slider(
+                        label="Width",
+                        minimum=256,
+                        maximum=MAX_IMAGE_SIZE,
+                        step=32,
+                        value=1024,
+                    )
+                    height = gr.Slider(
+                        label="Height",
+                        minimum=256,
+                        maximum=MAX_IMAGE_SIZE,
+                        step=32,
+                        value=1024,
+                    )
+                
+                num_inference_steps = gr.Slider(
+                    label="Generation Quality (Steps)",
+                    minimum=1,
+                    maximum=50,
+                    step=1,
+                    value=4,
+                )
+        
+        gr.Examples(
+            examples=examples,
+            fn=infer,
+            inputs=[prompt, style, tshirt_color],
+            outputs=[result, preview, seed],
+            cache_examples=True
+        )
+        
+        gr.on(
+            triggers=[run_button.click, prompt.submit],
+            fn=infer,
+            inputs=[prompt, style, tshirt_color, seed, randomize_seed, width, height, num_inference_steps],
+            outputs=[result, preview, seed]
+        )
 
-if __name__ == "__main__":
-    demo = create_interface()
-    demo.launch()
+demo.launch()