Update app.py

app.py

@@ -1,12 +1,9 @@
 import gradio as gr
-import torch
-from PIL import Image
 import numpy as np
 import random
-import cv2
-from diffusers import DiffusionPipeline, StableDiffusionPipeline
+from diffusers import DiffusionPipeline
+import torch
 
-# Setup the model
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
 if torch.cuda.is_available():
@@ -22,39 +19,31 @@ MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
 
 def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
+
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
         
     generator = torch.Generator().manual_seed(seed)
     
     image = pipe(
-        prompt=prompt, 
-        negative_prompt=negative_prompt,
-        guidance_scale=guidance_scale, 
-        num_inference_steps=num_inference_steps, 
-        width=width, 
-        height=height,
-        generator=generator
+        prompt = prompt, 
+        negative_prompt = negative_prompt,
+        guidance_scale = guidance_scale, 
+        num_inference_steps = num_inference_steps, 
+        width = width, 
+        height = height,
+        generator = generator
     ).images[0] 
     
     return image
 
-# Generate T-shirt design function
-def generate_tshirt_design(style, color, graphics, text=None):
-    prompt = f"T-shirt design, style: {style}, color: {color}, graphics: {graphics}"
-    if text:
-        prompt += f", text: {text}"
-    image = pipe(prompt).images[0]
-    return image
-
-# T-shirt mockup generator with Gradio interface
 examples = [
-    ["Casual", "White", "Logo: MyBrand", None],
-    ["Formal", "Black", "Text: Hello World", "Custom text"],
-    ["Sports", "Red", "Graphic: Team logo", None],
+    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
+    "An astronaut riding a green horse",
+    "A delicious ceviche cheesecake slice",
 ]
 
-css = """
+css="""
 #col-container {
     margin: 0 auto;
     max-width: 520px;
@@ -67,88 +56,91 @@ else:
     power_device = "CPU"
 
 with gr.Blocks(css=css) as demo:
+    
     with gr.Column(elem_id="col-container"):
         gr.Markdown(f"""
-        # T-shirt Mockup Generator with Rookus AI
+        # Text-to-Image Gradio Template
         Currently running on {power_device}.
         """)
-
+        
         with gr.Row():
-            style = gr.Dropdown(
-                label="T-shirt Style",
-                choices=["Casual", "Formal", "Sports"],
-                value="Casual",
+            
+            prompt = gr.Text(
+                label="Prompt",
+                show_label=False,
+                max_lines=1,
+                placeholder="Enter your prompt",
                 container=False,
             )
-
-            run_button = gr.Button("Generate Mockup", scale=0)
-
-        result = gr.Image(label="Mockup", show_label=False)
-
-        with gr.Accordion("Design Options", open=False):
-            color = gr.Radio(
-                label="T-shirt Color",
-                choices=[
-                    "White", "Black", "Blue", "Red", "Green", "Yellow", "Pink", "Purple", "Orange", "Brown",
-                    "Gray", "Maroon", "Navy", "Teal", "Lime", "Olive", "Cyan", "Magenta", "Beige", "Turquoise",
-                    "Gold", "Silver", "Lavender", "Mint", "Coral", "Indigo"
-                ],
-                value="White",
-            )
-
-            graphics = gr.Textbox(
-                label="Graphics/Logo",
-                placeholder="Enter graphics or logo details",
-                visible=True,
+            
+            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.Text(
+                label="Negative prompt",
+                max_lines=1,
+                placeholder="Enter a negative prompt",
+                visible=False,
             )
-
-            text = gr.Textbox(
-                label="Text (optional)",
-                placeholder="Enter optional text",
-                visible=True,
+            
+            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=[style, color, graphics, text]
+            examples = examples,
+            inputs = [prompt]
         )
 
-    def generate_tshirt_mockup(style, color, graphics, text=None):
-        # Generate T-shirt design
-        design_image = generate_tshirt_design(style, color, graphics, text)
-
-        # Load blank T-shirt mockup template image
-        mockup_template = Image.open("https://th.bing.com/th/id/OIP.oYpJxkyDYCFdF4GJulkFcQHaFj?rs=1&pid=ImgDetMain")
-
-        # Convert design image and mockup template to numpy arrays
-        design_np = np.array(design_image)
-        mockup_np = np.array(mockup_template)
-
-        # Resize design image to fit mockup (example resizing)
-        design_resized = cv2.resize(design_np, (mockup_np.shape[1] // 2, mockup_np.shape[0] // 2))
-
-        # Example: Overlay design onto mockup using OpenCV
-        y_offset = mockup_np.shape[0] // 4
-        x_offset = mockup_np.shape[1] // 4
-        y1, y2 = y_offset, y_offset + design_resized.shape[0]
-        x1, x2 = x_offset, x_offset + design_resized.shape[1]
-
-        alpha_s = design_resized[:, :, 3] / 255.0 if design_resized.shape[2] == 4 else np.ones(design_resized.shape[:2])
-        alpha_l = 1.0 - alpha_s
-
-        for c in range(0, 3):
-            mockup_np[y1:y2, x1:x2, c] = (alpha_s * design_resized[:, :, c] +
-                                          alpha_l * mockup_np[y1:y2, x1:x2, c])
-
-        # Convert back to PIL image for Gradio output
-        result_image = Image.fromarray(mockup_np)
-
-        return result_image
-
     run_button.click(
-        fn=generate_tshirt_mockup,
-        inputs=[style, color, graphics, text],
-        outputs=[result]
+        fn = infer,
+        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
+        outputs = [result]
     )
 
-demo.queue().launch(share=True)
+demo.queue().launch()