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| import gradio as gr | |
| import torch | |
| from PIL import Image | |
| import numpy as np | |
| import cv2 | |
| from diffusers import StableDiffusionPipeline | |
| from huggingface_hub import login | |
| # Setup the model | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| model_id = "s3nh/artwork-arcane-stable-diffusion" | |
| pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16 if device == "cuda" else torch.float32, use_auth_token=True) | |
| pipe = pipe.to(device) | |
| # Generate T-shirt design function | |
| def generate_tshirt_design(text): | |
| prompt = f"{text}" | |
| image = pipe(prompt).images[0] | |
| return image | |
| # Remove background from the generated design | |
| def remove_background(design_image): | |
| design_np = np.array(design_image) | |
| gray = cv2.cvtColor(design_np, cv2.COLOR_BGR2GRAY) | |
| _, alpha = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY) | |
| b, g, r = cv2.split(design_np) | |
| rgba = [b, g, r, alpha] | |
| design_np = cv2.merge(rgba, 4) | |
| return design_np | |
| # T-shirt mockup generator with Gradio interface | |
| examples = [ | |
| ["MyBrand"], | |
| ["Hello World"], | |
| ["Team logo"], | |
| ] | |
| 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 Design Generator with Stable Diffusion | |
| """) | |
| with gr.Row(): | |
| text = gr.Textbox( | |
| label="Text", | |
| placeholder="Enter text for the T-shirt design", | |
| visible=True, | |
| ) | |
| run_button = gr.Button("Generate Design", scale=0) | |
| result = gr.Image(label="Design", show_label=False) | |
| gr.Examples( | |
| examples=examples, | |
| inputs=[text] | |
| ) | |
| def generate_tshirt_mockup(text): | |
| # Generate T-shirt design | |
| design_image = generate_tshirt_design(text) | |
| # Remove background from design image | |
| design_np = remove_background(design_image) | |
| # Load blank T-shirt mockup template image | |
| mockup_template = Image.open("/content/drive/MyDrive/unnamed.jpg") | |
| # Convert mockup template to numpy array | |
| mockup_np = np.array(mockup_template) | |
| # Resize design image to fit mockup | |
| design_resized = cv2.resize(design_np, (mockup_np.shape[1] // 4, mockup_np.shape[0] // 4)) # Adjust size as needed | |
| # Center the design on the mockup | |
| y_offset = (mockup_np.shape[0] - design_resized.shape[0]) // 2 | |
| x_offset = (mockup_np.shape[1] - design_resized.shape[1]) // 2 | |
| y1, y2 = y_offset, y_offset + design_resized.shape[0] | |
| x1, x2 = x_offset, x_offset + design_resized.shape[1] | |
| # Blend design with mockup using alpha channel | |
| 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=[text], | |
| outputs=[result] | |
| ) | |
| demo.queue().launch() |