app.py

import gradio as gr
import numpy as np
import random
from diffusers import DiffusionPipeline
import torch
from PIL import Image, ImageDraw, ImageFont


device = "cuda" if torch.cuda.is_available() else "cpu"

if torch.cuda.is_available():
    torch.cuda.max_memory_allocated(device=device)
    pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", torch_dtype=torch.float16, variant="fp16", use_safetensors=True)
    pipe.enable_xformers_memory_efficient_attention()
    pipe = pipe.to(device)
else: 
    pipe = DiffusionPipeline.from_pretrained("stabilityai/sdxl-turbo", use_safetensors=True)
    pipe = pipe.to(device)

MAX_SEED = np.iinfo(np.int32).max
MAX_IMAGE_SIZE = 3072


def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
    # Append Moroccan and Amazigh art styles to the prompt
    style_prompt = f"{prompt}, inspired by Moroccan and Amazigh arts, traditional motifs, vibrant colors, and intricate patterns."

    if randomize_seed:
        seed = random.randint(0, MAX_SEED)

    generator = torch.Generator().manual_seed(seed)

    image = pipe(
        prompt=style_prompt,
        negative_prompt=negative_prompt,
        guidance_scale=guidance_scale,
        num_inference_steps=num_inference_steps,
        width=width,
        height=height,
        generator=generator
    ).images[0]

    # Ensure image is in uint8 format
    image = (255 * np.clip(image, 0, 1)).astype(np.uint8)

    # Convert the image to PIL format for overlaying the watermark
    pil_image = Image.fromarray(image)

    # Add watermark
    watermark_text = "Bibou.jpeg"
    font = ImageFont.truetype("arial.ttf", size=30)  # Adjust font and size as needed
    draw = ImageDraw.Draw(pil_image)
    text_width, text_height = draw.textsize(watermark_text, font=font)
    margin = 10
    opacity = 0.6
    draw.text((pil_image.width - text_width - margin, pil_image.height - text_height - margin), watermark_text, font=font, fill=(255, 255, 255, int(255 * opacity)))

    # Convert back to numpy array for Gradio display
    watermarked_image = np.array(pil_image)

    return watermarked_image





examples = [
    "Sunset over the Atlas Mountains",
    "Traditional Amazigh jewelry under the moonlight",
    "Flying carpet in space",
    "Unicorn riding a camel in the Sahara Desert",
    "Moroccan souk floating in the sky",
]

css="""
#col-container {
    margin: 0 auto;
    max-width: 840px;
    color: #003366;
}
"""

if torch.cuda.is_available():
    power_device = "GPU"
else:
    power_device = "CPU"

with gr.Blocks(css=css) as demo:
    
    with gr.Column(elem_id="col-container"):
        gr.Markdown(f"""
        # bibou.jpeg
        Generate Moroccan folkloric pictures, inspired by Moroccan and Amazigh arts. 🎨🎶
        """)
        
        with gr.Row():
            
            prompt = gr.Text(
                label="Prompt",
                show_label=False,
                max_lines=1,
                placeholder="Enter your prompt",
                container=False,
            )
            
            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,
            )
            
            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=1024,
                )
                
                height = gr.Slider(
                    label="Height",
                    minimum=256,
                    maximum=MAX_IMAGE_SIZE,
                    step=32,
                    value=1024,
                )
            
            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 = [prompt]
        )

    run_button.click(
        fn = infer,
        inputs = [prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps],
        outputs = [result]
    )

demo.queue().launch()