Update app.py

app.py

@@ -4,6 +4,7 @@ import random
 import spaces
 import torch
 from diffusers import DiffusionPipeline
+from PIL import Image
 
 dtype = torch.bfloat16
 device = "cuda" if torch.cuda.is_available() else "cpu"
@@ -16,8 +17,29 @@ pipe = DiffusionPipeline.from_pretrained(
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 2048
 
-def enhance_prompt_for_pattern(prompt):
-    """Add specific terms to ensure seamless, tileable patterns."""
+STYLE_OPTIONS = {
+    "Vintage": "vintage style, retro aesthetic, aged appearance",
+    "Realistic": "photorealistic, detailed, true-to-life",
+    "Geometric": "geometric shapes, precise lines, mathematical patterns",
+    "Abstract": "abstract design, non-representational, artistic",
+    "Minimalist": "simple, clean lines, understated",
+    "Bohemian": "boho style, free-spirited, eclectic",
+    "Traditional": "classical design, timeless patterns",
+    "Contemporary": "modern style, current trends"
+}
+
+FABRIC_OPTIONS = {
+    "None": "",
+    "Cotton": "cotton textile texture, natural fiber appearance",
+    "Silk": "silk fabric texture, smooth and lustrous",
+    "Linen": "linen texture, natural weave pattern",
+    "Velvet": "velvet texture, plush surface",
+    "Canvas": "canvas texture, sturdy weave pattern",
+    "Wool": "wool texture, natural fiber appearance"
+}
+
+def enhance_prompt_for_pattern(prompt, style, fabric):
+    """Add specific terms to ensure seamless, tileable patterns with style and fabric considerations."""
     pattern_terms = [
         "seamless pattern",
         "tileable textile design",
@@ -25,16 +47,37 @@ def enhance_prompt_for_pattern(prompt):
         "high-quality fabric design",
         "continuous pattern",
     ]
-    enhanced_prompt = f"{prompt}, {random.choice(pattern_terms)}, suitable for textile printing, high-quality fabric design, seamless edges"
+    
+    enhanced_prompt = f"{prompt}, {random.choice(pattern_terms)}"
+    
+    if style and style != "None":
+        enhanced_prompt += f", {STYLE_OPTIONS[style]}"
+    
+    if fabric and fabric != "None":
+        enhanced_prompt += f", {FABRIC_OPTIONS[fabric]}"
+    
+    enhanced_prompt += ", suitable for textile printing, high-quality fabric design, seamless edges"
     return enhanced_prompt
 
+def create_fabric_preview(image):
+    """Create a fabric preview by tiling the pattern."""
+    # Create a 4x2 grid of the pattern
+    width, height = image.size
+    preview = Image.new('RGB', (width * 4, height * 2))
+    
+    for y in range(2):
+        for x in range(4):
+            preview.paste(image, (x * width, y * height))
+    
+    return preview
+
 @spaces.GPU()
-def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, 
+def infer(prompt, style, fabric, 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_pattern(prompt)
+    enhanced_prompt = enhance_prompt_for_pattern(prompt, style, fabric)
     generator = torch.Generator().manual_seed(seed)
     image = pipe(
         prompt=enhanced_prompt,
@@ -45,31 +88,32 @@ def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024,
         guidance_scale=0.0
     ).images[0]
     
-    return image, seed
+    # Create fabric preview
+    fabric_preview = create_fabric_preview(image)
+    
+    return image, fabric_preview, seed
 
 examples = [
-    "geometric Art Deco shapes in gold and navy",
-    "delicate floral motifs with small roses and leaves",
-    "abstract watercolor spots in pastel colors",
-    "traditional paisley design in earth tones",
-    "modern minimalist lines and circles",
+    ["geometric Art Deco shapes in gold and navy", "Geometric", "None"],
+    ["delicate floral motifs with small roses and leaves", "Vintage", "Cotton"],
+    ["abstract watercolor spots in pastel colors", "Abstract", "Silk"],
+    ["traditional paisley design in earth tones", "Traditional", "Linen"],
+    ["modern minimalist lines and circles", "Minimalist", "Canvas"],
 ]
 
 # Enhanced CSS for better visual design and mobile responsiveness
 css = """
 #col-container {
     margin: 0 auto;
-    max-width: 800px !important;
+    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;
@@ -78,7 +122,6 @@ css = """
     font-size: 0.95rem;
     line-height: 1.5;
 }
-
 .pattern-input {
     border: 2px solid #e2e8f0;
     border-radius: 10px;
@@ -87,12 +130,10 @@ css = """
     font-size: 1rem;
     transition: all 0.3s ease;
 }
-
 .pattern-input:focus {
     border-color: #4299e1;
     box-shadow: 0 0 0 3px rgba(66, 153, 225, 0.1);
 }
-
 .generate-button {
     background-color: #4299e1 !important;
     color: white !important;
@@ -101,25 +142,34 @@ css = """
     font-weight: 600 !important;
     transition: all 0.3s ease !important;
 }
-
 .generate-button:hover {
     background-color: #3182ce !important;
     transform: translateY(-1px);
 }
-
 .result-image {
     border-radius: 12px;
     box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1);
     margin-top: 1rem;
 }
-
 .advanced-settings {
     margin-top: 1.5rem;
     border: 1px solid #e2e8f0;
     border-radius: 10px;
     padding: 1rem;
 }
-
+.examples-section {
+    margin-top: 2rem;
+    padding: 1rem;
+    background: #f7fafc;
+    border-radius: 10px;
+    border: none;
+}
+.preview-section {
+    margin-top: 1rem;
+    padding: 1rem;
+    background: #ffffff;
+    border-radius: 10px;
+}
 /* Mobile Responsiveness */
 @media (max-width: 768px) {
     #col-container {
@@ -138,54 +188,71 @@ css = """
         font-size: 0.9rem;
     }
 }
-
-/* Custom styling for examples section */
-.examples-section {
-    margin-top: 2rem;
-    padding: 1rem;
-    background: #f7fafc;
-    border-radius: 10px;
-}
 """
 
 with gr.Blocks(css=css, theme=gr.themes.Soft()) as demo:
     with gr.Column(elem_id="col-container"):
         gr.Markdown(
             """
-            # 🎨 Deradh's AI Pattern Master
+            # 🎨 Deradh's Professional Textile Pattern Generator
             """,
             elem_classes=["main-title"]
         )
         
         gr.Markdown(
             """
-            Create beautiful, seamless patterns for your textile designs using AI. 
-            Simply describe your desired pattern, and watch as AI brings your vision to life with 
-            professional-quality, repeatable patterns perfect for fabrics and materials.
+            Create professional-grade, seamless patterns for textile manufacturing. 
+            Design unique patterns with style and fabric texture controls, 
+            perfect for commercial textile production and fashion design.
             """,
             elem_classes=["subtitle"]
         )
         
         with gr.Row():
-            prompt = gr.Text(
-                label="Pattern Description",
-                show_label=False,
-                max_lines=1,
-                placeholder="Describe your dream pattern (e.g., 'geometric Art Deco shapes in gold and navy')",
-                container=False,
-                elem_classes=["pattern-input"]
-            )
-            run_button = gr.Button(
-                "✨ Generate",
-                scale=0,
-                elem_classes=["generate-button"]
-            )
+            with gr.Column(scale=2):
+                prompt = gr.Text(
+                    label="Pattern Description",
+                    show_label=False,
+                    max_lines=1,
+                    placeholder="Describe your dream pattern (e.g., 'geometric Art Deco shapes in gold and navy')",
+                    container=False,
+                    elem_classes=["pattern-input"]
+                )
+            
+            with gr.Column(scale=1):
+                style = gr.Dropdown(
+                    choices=list(STYLE_OPTIONS.keys()),
+                    label="Style",
+                    value="None"
+                )
+            
+            with gr.Column(scale=1):
+                fabric = gr.Dropdown(
+                    choices=list(FABRIC_OPTIONS.keys()),
+                    label="Fabric Texture",
+                    value="None"
+                )
+            
+            with gr.Column(scale=0.5):
+                run_button = gr.Button(
+                    "✨ Generate",
+                    elem_classes=["generate-button"]
+                )
         
-        result = gr.Image(
-            label="Your Generated Pattern",
-            show_label=True,
-            elem_classes=["result-image"]
-        )
+        with gr.Row():
+            with gr.Column():
+                pattern = gr.Image(
+                    label="Generated Pattern",
+                    show_label=True,
+                    elem_classes=["result-image"]
+                )
+            
+            with gr.Column():
+                preview = gr.Image(
+                    label="Fabric Preview",
+                    show_label=True,
+                    elem_classes=["result-image"]
+                )
         
         with gr.Accordion("🔧 Advanced Settings", open=False):
             with gr.Group(elem_classes=["advanced-settings"]):
@@ -226,20 +293,19 @@ with gr.Blocks(css=css, theme=gr.themes.Soft()) as demo:
                 )
         
         with gr.Group(elem_classes=["examples-section"]):
-            gr.Markdown("### 💫 Try These Examples")
             gr.Examples(
                 examples=examples,
                 fn=infer,
-                inputs=[prompt],
-                outputs=[result, seed],
-                cache_examples="lazy"
+                inputs=[prompt, style, fabric],
+                outputs=[pattern, preview, seed],
+                cache_examples=True
             )
         
         gr.on(
             triggers=[run_button.click, prompt.submit],
             fn=infer,
-            inputs=[prompt, seed, randomize_seed, width, height, num_inference_steps],
-            outputs=[result, seed]
+            inputs=[prompt, style, fabric, seed, randomize_seed, width, height, num_inference_steps],
+            outputs=[pattern, preview, seed]
         )
 
 demo.launch()