Update app.py

app.py

@@ -8,33 +8,59 @@ from diffusers import DiffusionPipeline
 dtype = torch.bfloat16
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
-pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=dtype).to(device)
+# Initialize the model
+pipe = DiffusionPipeline.from_pretrained(
+    "black-forest-labs/FLUX.1-schnell",
+    torch_dtype=dtype
+).to(device)
 
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 2048
 
+# Pattern-specific prompt engineering
+def enhance_prompt_for_pattern(prompt):
+    """Add specific terms to ensure seamless, tileable patterns."""
+    pattern_terms = [
+        "seamless pattern",
+        "tileable textile design",
+        "repeating pattern",
+        "high-quality fabric design",
+        "continuous pattern",
+    ]
+    enhanced_prompt = f"{prompt}, {random.choice(pattern_terms)}, suitable for textile printing, high-quality fabric design, seamless edges"
+    return enhanced_prompt
+
 @spaces.GPU()
-def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, num_inference_steps=4, progress=gr.Progress(track_tqdm=True)):
+def infer(prompt, 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)
+    
+    # Enhance the prompt for pattern generation
+    enhanced_prompt = enhance_prompt_for_pattern(prompt)
+    
     generator = torch.Generator().manual_seed(seed)
     image = pipe(
-            prompt = prompt, 
-            width = width,
-            height = height,
-            num_inference_steps = num_inference_steps, 
-            generator = generator,
-            guidance_scale=0.0
-    ).images[0] 
+        prompt=enhanced_prompt,
+        width=width,
+        height=height,
+        num_inference_steps=num_inference_steps,
+        generator=generator,
+        guidance_scale=0.0
+    ).images[0]
+    
     return image, seed
- 
+
+# Example prompts specifically for pattern generation
 examples = [
-    "a tiny astronaut hatching from an egg on the moon",
-    "a cat holding a sign that says hello world",
-    "an anime illustration of a wiener schnitzel",
+    "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",
 ]
 
-css="""
+css = """
 #col-container {
     margin: 0 auto;
     max-width: 520px;
@@ -42,29 +68,28 @@ css="""
 """
 
 with gr.Blocks(css=css) as demo:
-    
     with gr.Column(elem_id="col-container"):
-        gr.Markdown(f"""# FLUX.1 [schnell]
-12B param rectified flow transformer distilled from [FLUX.1 [pro]](https://blackforestlabs.ai/) for 4 step generation
-[[blog](https://blackforestlabs.ai/announcing-black-forest-labs/)] [[model](https://huggingface.co/black-forest-labs/FLUX.1-schnell)]
+        gr.Markdown("""
+        # Deradh's AI Pattern Master
+        ### Create seamless, tileable patterns for high-quality textile designs
+        
+        This tool specializes in generating patterns that can be used for fabric printing and textile design.
+        Each pattern is optimized to be seamless and repeatable.
         """)
         
         with gr.Row():
-            
             prompt = gr.Text(
-                label="Prompt",
+                label="Pattern Description",
                 show_label=False,
                 max_lines=1,
-                placeholder="Enter your prompt",
+                placeholder="Describe your desired pattern (e.g., 'geometric Art Deco shapes in gold and navy')",
                 container=False,
             )
-            
-            run_button = gr.Button("Run", scale=0)
+            run_button = gr.Button("Generate Pattern", scale=0)
         
-        result = gr.Image(label="Result", show_label=False)
+        result = gr.Image(label="Generated Pattern", show_label=True)
         
         with gr.Accordion("Advanced Settings", open=False):
-            
             seed = gr.Slider(
                 label="Seed",
                 minimum=0,
@@ -72,11 +97,9 @@ with gr.Blocks(css=css) as demo:
                 step=1,
                 value=0,
             )
-            
             randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
             
             with gr.Row():
-                
                 width = gr.Slider(
                     label="Width",
                     minimum=256,
@@ -84,7 +107,6 @@ with gr.Blocks(css=css) as demo:
                     step=32,
                     value=1024,
                 )
-                
                 height = gr.Slider(
                     label="Height",
                     minimum=256,
@@ -94,8 +116,6 @@ with gr.Blocks(css=css) as demo:
                 )
             
             with gr.Row():
-                
-  
                 num_inference_steps = gr.Slider(
                     label="Number of inference steps",
                     minimum=1,
@@ -105,18 +125,18 @@ with gr.Blocks(css=css) as demo:
                 )
         
         gr.Examples(
-            examples = examples,
-            fn = infer,
-            inputs = [prompt],
-            outputs = [result, seed],
+            examples=examples,
+            fn=infer,
+            inputs=[prompt],
+            outputs=[result, seed],
             cache_examples="lazy"
         )
-
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn = infer,
-        inputs = [prompt, seed, randomize_seed, width, height, num_inference_steps],
-        outputs = [result, seed]
-    )
+        
+        gr.on(
+            triggers=[run_button.click, prompt.submit],
+            fn=infer,
+            inputs=[prompt, seed, randomize_seed, width, height, num_inference_steps],
+            outputs=[result, seed]
+        )
 
 demo.launch()