app.py

import os
import torch
import gradio as gr
from tqdm import tqdm
from PIL import Image
import torch.nn.functional as F
from torchvision import transforms as tfms
from transformers import CLIPTextModel, CLIPTokenizer, logging
from diffusers import AutoencoderKL, LMSDiscreteScheduler, UNet2DConditionModel, DiffusionPipeline

HTML_TEMPLATE = """
<style>
    body {
        background: linear-gradient(135deg, #f5f7fa, #c3cfe2);
    }
    #app-header {
        text-align: center;
        background: rgba(255, 255, 255, 0.8);
        padding: 20px;
        border-radius: 10px;
        box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
        position: relative;
    }
    #app-header h1 {
        color: #4CAF50;
        font-size: 2em;
        margin-bottom: 10px;
    }
    .concept {
        position: relative;
        transition: transform 0.3s;
    }
    .concept:hover {
        transform: scale(1.1);
    }
    .concept img {
        width: 100px;
        border-radius: 10px;
        box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
    }
    .concept-description {
        position: absolute;
        bottom: -30px;
        left: 50%;
        transform: translateX(-50%);
        background-color: #4CAF50;
        color: white;
        padding: 5px 10px;
        border-radius: 5px;
        opacity: 0;
        transition: opacity 0.3s;
    }
    .concept:hover .concept-description {
        opacity: 1;
    }
    .artifact {
        position: absolute;
        background: rgba(76, 175, 80, 0.1);
        border-radius: 50%;
    }
    .artifact.large {
        width: 300px;
        height: 300px;
        top: -50px;
        left: -150px;
    }
    .artifact.medium {
        width: 200px;
        height: 200px;
        bottom: -50px;
        right: -100px;
    }
    .artifact.small {
        width: 100px;
        height: 100px;
        top: 50%;
        left: 50%;
        transform: translate(-50%, -50%);
    }
</style>
<div id="app-header">
    <div class="artifact large"></div>
    <div class="artifact medium"></div>
    <div class="artifact small"></div>
    <h1>Generative Art with Textual Inversion and Guidance</h1>
    <p>Generate unique art using different styles and guidance methods.</p>
    <div style="display: flex; justify-content: center; gap: 20px; margin-top: 20px;">
        <div class="concept">
            <img src="https://example.com/illustration-style.jpg" alt="Illustration Style">
            <div class="concept-description">Illustration Style</div>
        </div>
        <div class="concept">
            <img src="https://example.com/line-art.jpg" alt="Line Art">
            <div class="concept-description">Line Art</div>
        </div>
        <!-- Add more concepts here for each style in your style_token_dict -->
    </div>
</div>
"""

torch_device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
if "mps" == torch_device: os.environ['PYTORCH_ENABLE_MPS_FALLBACK'] = "1"

# Load the pipeline
model_path = "CompVis/stable-diffusion-v1-4"
sd_pipeline = DiffusionPipeline.from_pretrained(
    model_path,
    low_cpu_mem_usage=True,
    torch_dtype=torch.float32
).to(torch_device)

# Load textual inversions
sd_pipeline.load_textual_inversion("sd-concepts-library/illustration-style")
sd_pipeline.load_textual_inversion("sd-concepts-library/line-art")
sd_pipeline.load_textual_inversion("sd-concepts-library/hitokomoru-style-nao")
sd_pipeline.load_textual_inversion("sd-concepts-library/style-of-marc-allante")
sd_pipeline.load_textual_inversion("sd-concepts-library/midjourney-style")
sd_pipeline.load_textual_inversion("sd-concepts-library/hanfu-anime-style")
sd_pipeline.load_textual_inversion("sd-concepts-library/birb-style")

# Update style token dictionary
style_token_dict = {
    "Illustration Style": '<illustration-style>',
    "Line Art":'<line-art>',
    "Hitokomoru Style":'<hitokomoru-style-nao>',
    "Marc Allante": '<Marc_Allante>',
    "Midjourney":'<midjourney-style>',
    "Hanfu Anime": '<hanfu-anime-style>',
    "Birb Style": '<birb-style>'
}

def apply_guidance(image, guidance_method, loss_scale):
    # Convert PIL Image to tensor
    img_tensor = tfms.ToTensor()(image).unsqueeze(0).to(torch_device)
    
    if guidance_method == 'Grayscale':
        gray = tfms.Grayscale(3)(img_tensor)
        guided = img_tensor + (gray - img_tensor) * (loss_scale / 10000)
    elif guidance_method == 'Bright':
        bright = F.relu(img_tensor)  # Simple brightness increase
        guided = img_tensor + (bright - img_tensor) * (loss_scale / 10000)
    elif guidance_method == 'Contrast':
        mean = img_tensor.mean()
        contrast = (img_tensor - mean) * 2 + mean
        guided = img_tensor + (contrast - img_tensor) * (loss_scale / 10000)
    elif guidance_method == 'Symmetry':
        flipped = torch.flip(img_tensor, [3])  # Flip horizontally
        guided = img_tensor + (flipped - img_tensor) * (loss_scale / 10000)
    elif guidance_method == 'Saturation':
        saturated = tfms.functional.adjust_saturation(img_tensor, 2)
        guided = img_tensor + (saturated - img_tensor) * (loss_scale / 10000)
    else:
        return image

    # Convert back to PIL Image
    guided = guided.squeeze(0).clamp(0, 1)
    guided = (guided * 255).byte().cpu().permute(1, 2, 0).numpy()
    return Image.fromarray(guided)

def generate_with_guidance(prompt, num_inference_steps, guidance_scale, seed, guidance_method, loss_scale):
    # Generate image with pipeline
    generator = torch.Generator(device=torch_device).manual_seed(seed)
    image = sd_pipeline(
        prompt,
        num_inference_steps=num_inference_steps,
        guidance_scale=guidance_scale,
        generator=generator
    ).images[0]

    # Apply guidance
    guided_image = apply_guidance(image, guidance_method, loss_scale)
    
    return guided_image

def inference(text, style, inference_step, guidance_scale, seed, guidance_method, loss_scale):
    prompt = text + " " + style_token_dict[style]

    # Generate image with pipeline
    image_pipeline = sd_pipeline(
        prompt,
        num_inference_steps=inference_step,
        guidance_scale=guidance_scale,
        generator=torch.Generator(device=torch_device).manual_seed(seed)
    ).images[0]

    # Generate image with guidance
    image_guide = generate_with_guidance(prompt, inference_step, guidance_scale, seed, guidance_method, loss_scale)

    return image_pipeline, image_guide

title = "Generative with Textual Inversion and Guidance"
description = "A Gradio interface to infer Stable Diffusion and generate images with different art styles and guidance methods"
examples = [
    ["A majestic castle on a floating island", 'Illustration Style', 10, 7.5, 42, 'Grayscale', 200]
]

title = "Generative Art with Textual Inversion and Guidance"
description = "Create unique artworks using Stable Diffusion with various styles and guidance methods."

with gr.Blocks(css=HTML_TEMPLATE) as demo:
    gr.HTML(HTML_TEMPLATE)  # This adds the styled header to your app
    with gr.Row():
        text = gr.Textbox(label="Prompt", placeholder="Enter your creative prompt here...")
        style = gr.Dropdown(label="Style", choices=list(style_token_dict.keys()), value="Illustration Style")
    with gr.Row():
        inference_step = gr.Slider(1, 50, 10, step=1, label="Inference steps")
        guidance_scale = gr.Slider(1, 10, 7.5, step=0.1, label="Guidance scale")
        seed = gr.Slider(0, 10000, 42, step=1, label="Seed")
    with gr.Row():
        guidance_method = gr.Dropdown(label="Guidance method", choices=['Grayscale', 'Bright', 'Contrast', 'Symmetry', 'Saturation'], value="Grayscale")
        loss_scale = gr.Slider(100, 10000, 200, step=100, label="Loss scale")
    with gr.Row():
        generate_button = gr.Button("Generate Art")
    with gr.Row():
        output_image = gr.Image(width=512, height=512, label="Generated art")
        output_image_guided = gr.Image(width=512, height=512, label="Generated art with guidance")
    
    generate_button.click(
        inference,
        inputs=[text, style, inference_step, guidance_scale, seed, guidance_method, loss_scale],
        outputs=[output_image, output_image_guided]
    )

    gr.Examples(
        examples=[
            ["A majestic castle on a floating island", 'Illustration Style', 10, 7.5, 42, 'Grayscale', 200]
        ],
        inputs=[text, style, inference_step, guidance_scale, seed, guidance_method, loss_scale],
        outputs=[output_image, output_image_guided],
        fn=inference,
        cache_examples=True,
    )

demo.launch()