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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()
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