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| import gradio as gr | |
| import torch | |
| from PIL import Image | |
| import numpy as np | |
| import cv2 | |
| from diffusers import StableDiffusionPipeline | |
| # Setup the model | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| model_id = "stabilityai/sdxl-turbo" | |
| pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16 if device == "cuda" else torch.float32) | |
| pipe = pipe.to(device) | |
| # 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], | |
| ] | |
| css = """ | |
| #col-container { | |
| margin: 0 auto; | |
| max-width: 520px; | |
| } | |
| """ | |
| with gr.Blocks(css=css) as demo: | |
| with gr.Column(elem_id="col-container"): | |
| gr.Markdown(""" | |
| # T-shirt Mockup Generator with Rookus AI | |
| """) | |
| with gr.Row(): | |
| style = gr.Dropdown( | |
| label="T-shirt Style", | |
| choices=["Casual", "Formal", "Sports"], | |
| value="Casual", | |
| 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"], | |
| value="White", | |
| ) | |
| graphics = gr.Textbox( | |
| label="Graphics/Logo", | |
| placeholder="Enter graphics or logo details", | |
| visible=True, | |
| ) | |
| text = gr.Textbox( | |
| label="Text (optional)", | |
| placeholder="Enter optional text", | |
| visible=True, | |
| ) | |
| gr.Examples( | |
| examples=examples, | |
| inputs=[style, color, graphics, text] | |
| ) | |
| 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("/content/drive/MyDrive/unnamed.jpg") | |
| # 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] | |
| ) | |
| demo.queue().launch() | |