app.py

import gradio as gr
from diffusers import StableDiffusionPipeline
import torch
from PIL import Image
import re
import uuid
import gc

# Load word list for safety checking (using a simple list instead of loading dataset)
BLOCKED_WORDS = ["nsfw", "nude", "explicit"]  # Add more as needed

# Initialize the pipeline with CPU optimization
def initialize_pipeline():
    pipe = StableDiffusionPipeline.from_pretrained(
        "stabilityai/stable-diffusion-2-1-base",
        torch_dtype=torch.float32  # Use float32 for CPU
    )
    pipe = pipe.to("cpu")
    # Enable memory efficient attention
    pipe.enable_attention_slicing()
    return pipe

pipe = initialize_pipeline()

def cleanup():
    """Force garbage collection to free memory"""
    gc.collect()
    if torch.cuda.is_available():
        torch.cuda.empty_cache()

def infer(prompt, negative, scale):
    # Safety check
    prompt = prompt.lower()
    for word in BLOCKED_WORDS:
        if word in prompt:
            raise gr.Error("Unsafe content found. Please try again with different prompts.")
    
    try:
        # Generate only one image at a time to conserve memory
        images = pipe(
            prompt=prompt,
            negative_prompt=negative,
            guidance_scale=scale,
            num_inference_steps=30,  # Reduced steps for faster generation
            num_images_per_prompt=1
        ).images
        
        # Save image
        output_path = f"{uuid.uuid4()}.jpg"
        images[0].save(output_path)
        
        # Cleanup to free memory
        cleanup()
        
        return [output_path]
    
    except Exception as e:
        cleanup()
        raise gr.Error(f"Generation failed: {str(e)}")

css = """
        .gradio-container {
            max-width: 768px !important;
            font-family: 'IBM Plex Sans', sans-serif;
        }
        .gr-button {
            color: white;
            border-color: black;
            background: black;
        }
        input[type='range'] {
            accent-color: black;
        }
        .dark input[type='range'] {
            accent-color: #dfdfdf;
        }
        #gallery {
            min-height: 22rem;
            margin-bottom: 15px;
        }
        #gallery>div>.h-full {
            min-height: 20rem;
        }
"""

examples = [
    [
        'A small cabin on top of a snowy mountain, artstation style',
        'low quality, ugly',
        9
    ],
    [
        'A red apple on a wooden table, still life',
        'low quality',
        9
    ],
]

with gr.Blocks(css=css) as block:
    gr.HTML(
        """
        <div style="text-align: center; margin: 0 auto;">
            <h1 style="font-weight: 900; margin-bottom: 7px;">
                Stable Diffusion 2.1 (CPU Version)
            </h1>
            <p style="margin-bottom: 10px; font-size: 94%;">
                Optimized for CPU usage with 16GB RAM
            </p>
        </div>
        """
    )
    
    with gr.Group():
        with gr.Row():
            with gr.Column(scale=3):
                text = gr.Textbox(
                    label="Enter your prompt",
                    show_label=False,
                    max_lines=1,
                    placeholder="Enter your prompt"
                )
                negative = gr.Textbox(
                    label="Enter your negative prompt",
                    show_label=False,
                    max_lines=1,
                    placeholder="Enter a negative prompt"
                )
            with gr.Column(scale=1, min_width=150):
                btn = gr.Button("Generate image")

        gallery = gr.Gallery(
            label="Generated images", 
            show_label=False, 
            elem_id="gallery"
        )

        with gr.Accordion("Advanced settings", open=False):
            guidance_scale = gr.Slider(
                label="Guidance Scale", 
                minimum=1, 
                maximum=20, 
                value=9, 
                step=0.1
            )

        gr.Examples(
            examples=examples,
            fn=infer,
            inputs=[text, negative, guidance_scale],
            outputs=[gallery],
            cache_examples=True
        )

        text.submit(infer, inputs=[text, negative, guidance_scale], outputs=[gallery])
        negative.submit(infer, inputs=[text, negative, guidance_scale], outputs=[gallery])
        btn.click(infer, inputs=[text, negative, guidance_scale], outputs=[gallery])

    gr.HTML(
        """
        <div style="text-align: center; margin-top: 20px;">
            <p>Running on CPU - Please allow longer generation times</p>
        </div>
        """
    )

block.queue().launch(show_error=True)