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import gradio as gr
from diffusers import DiffusionPipeline
from PIL import Image
import torch
device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.float16 if device == "cuda" else torch.float32
variant = "fp16" if device == "cuda" else None
pipe = DiffusionPipeline.from_pretrained(
"stabilityai/sdxl-turbo",
torch_dtype=dtype,
variant=variant
).to(device)
def infer(color_prompt, phone_type_prompt, design_prompt):
prompt = (
f"A single vertical {color_prompt} colored {phone_type_prompt} back cover featuring a bold {design_prompt} design on the front, hanging on the plain wall. The soft light and shadows, creating a striking contrast against the minimal background, evoking modern sophistication."
)
image = pipe(prompt).images[0]
message = "Design generated successfully!"
return image, message
def save_design(image):
if image is None:
return "No image to save. Please generate a design first."
file_path = "saved_design.png"
image.save(file_path)
return f"Design saved as {file_path}!"
with gr.Blocks() as interface:
gr.Markdown("# **AI Phone Cover Designer**")
with gr.Row():
with gr.Column(scale=1):
color_prompt = gr.Textbox(label="Color")
phone_type_prompt = gr.Textbox(label="Mobile Type")
design_prompt = gr.Textbox(label="Design Details")
generate_button = gr.Button("Generate Design")
save_button = gr.Button("Save Design")
with gr.Column(scale=1):
output_image = gr.Image(label="Generated Design")
output_message = gr.Textbox(label="Status", interactive=False)
generate_button.click(
infer,
inputs=[color_prompt, phone_type_prompt, design_prompt],
outputs=[output_image, output_message],
)
save_button.click(
save_design,
inputs=[output_image],
outputs=output_message,
)
interface.launch(debug=True) |