app.py

import os
import time
import json
import base64
from datetime import datetime
import numpy as np
import torch
import gradio as gr
from gradio_imageslider import ImageSlider
from diffusers import ControlNetModel, StableDiffusionXLControlNetImg2ImgPipeline, DDIMScheduler
from controlnet_aux import AnylineDetector
from compel import Compel, ReturnedEmbeddingsType
from PIL import Image
import pandas as pd

# Configuration
IS_SPACES_ZERO = os.environ.get("SPACES_ZERO_GPU", "0") == "1"
IS_SPACE = os.environ.get("SPACE_ID", None) is not None
device = "cuda" if torch.cuda.is_available() else "cpu"
dtype = torch.float16
LOW_MEMORY = os.getenv("LOW_MEMORY", "0") == "1"

print(f"device: {device}")
print(f"dtype: {dtype}")
print(f"low memory: {LOW_MEMORY}")

# Model initialization
model = "stabilityai/stable-diffusion-xl-base-1.0"
scheduler = DDIMScheduler.from_pretrained(model, subfolder="scheduler")
controlnet = ControlNetModel.from_pretrained(
    "TheMistoAI/MistoLine",
    torch_dtype=torch.float16,
    revision="refs/pr/3",
    variant="fp16",
)
pipe = StableDiffusionXLControlNetImg2ImgPipeline.from_pretrained(
    model,
    controlnet=controlnet,
    torch_dtype=dtype,
    variant="fp16",
    use_safetensors=True,
    scheduler=scheduler,
)

compel = Compel(
    tokenizer=[pipe.tokenizer, pipe.tokenizer_2],
    text_encoder=[pipe.text_encoder, pipe.text_encoder_2],
    returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED,
    requires_pooled=[False, True],
)
pipe = pipe.to(device)

anyline = AnylineDetector.from_pretrained(
    "TheMistoAI/MistoLine", filename="MTEED.pth", subfolder="Anyline"
).to(device)

# Global variables for metadata and likes cache
image_metadata = pd.DataFrame(columns=['Filename', 'Prompt', 'Likes', 'Dislikes', 'Hearts', 'Created'])
LIKES_CACHE_FILE = "likes_cache.json"

def load_likes_cache():
    if os.path.exists(LIKES_CACHE_FILE):
        with open(LIKES_CACHE_FILE, 'r') as f:
            return json.load(f)
    return {}

def save_likes_cache(cache):
    with open(LIKES_CACHE_FILE, 'w') as f:
        json.dump(cache, f)

likes_cache = load_likes_cache()

def pad_image(image):
    w, h = image.size
    if w == h:
        return image
    elif w > h:
        new_image = Image.new(image.mode, (w, w), (0, 0, 0))
        new_image.paste(image, (0, (w - h) // 2))
        return new_image
    else:
        new_image = Image.new(image.mode, (h, h), (0, 0, 0))
        new_image.paste(image, ((h - w) // 2, 0))
        return new_image

def create_download_link(filename):
    with open(filename, "rb") as file:
        encoded_string = base64.b64encode(file.read()).decode('utf-8')
        download_link = f'<a href="data:image/png;base64,{encoded_string}" download="{filename}">Download Image</a>'
        return download_link

def save_image(image: Image.Image, prompt: str) -> str:
    global image_metadata, likes_cache
    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
    safe_prompt = ''.join(e for e in prompt if e.isalnum() or e.isspace())[:50]
    filename = f"{timestamp}_{safe_prompt}.png"
    image.save(filename)
    new_row = pd.DataFrame({
        'Filename': [filename],
        'Prompt': [prompt],
        'Likes': [0],
        'Dislikes': [0],
        'Hearts': [0],
        'Created': [datetime.now()]
    })
    image_metadata = pd.concat([image_metadata, new_row], ignore_index=True)
    likes_cache[filename] = {'likes': 0, 'dislikes': 0, 'hearts': 0}
    save_likes_cache(likes_cache)
    return filename

def get_image_gallery():
    global image_metadata
    image_files = image_metadata['Filename'].tolist()
    return [(file, get_image_caption(file)) for file in image_files if os.path.exists(file)]

def get_image_caption(filename):
    global likes_cache, image_metadata
    if filename in likes_cache:
        likes = likes_cache[filename]['likes']
        dislikes = likes_cache[filename]['dislikes']
        hearts = likes_cache[filename]['hearts']
        prompt = image_metadata[image_metadata['Filename'] == filename]['Prompt'].values[0]
        return f"{filename}\nPrompt: {prompt}\n👍 {likes} 👎 {dislikes} ❤️ {hearts}"
    return filename

def delete_all_images():
    global image_metadata, likes_cache
    for file in image_metadata['Filename']:
        if os.path.exists(file):
            os.remove(file)
    image_metadata = pd.DataFrame(columns=['Filename', 'Prompt', 'Likes', 'Dislikes', 'Hearts', 'Created'])
    likes_cache = {}
    save_likes_cache(likes_cache)
    return get_image_gallery(), image_metadata.values.tolist()

def delete_image(filename):
    global image_metadata, likes_cache
    if filename and os.path.exists(filename):
        os.remove(filename)
        image_metadata = image_metadata[image_metadata['Filename'] != filename]
        if filename in likes_cache:
            del likes_cache[filename]
            save_likes_cache(likes_cache)
    return get_image_gallery(), image_metadata.values.tolist()

def vote(filename, vote_type):
    global likes_cache
    if filename in likes_cache:
        likes_cache[filename][vote_type.lower()] += 1
        save_likes_cache(likes_cache)
    return get_image_gallery(), image_metadata.values.tolist()

@gr.on(queue_pred_done=True)
def predict(
    input_image,
    prompt,
    negative_prompt,
    seed,
    guidance_scale=8.5,
    controlnet_conditioning_scale=0.5,
    strength=1.0,
    controlnet_start=0.0,
    controlnet_end=1.0,
    guassian_sigma=2.0,
    intensity_threshold=3,
    progress=gr.Progress(track_tqdm=True),
):
    if input_image is None:
        raise gr.Error("Please upload an image.")
    padded_image = pad_image(input_image).resize((1024, 1024)).convert("RGB")
    conditioning, pooled = compel([prompt, negative_prompt])
    generator = torch.manual_seed(seed)
    last_time = time.time()
    anyline_image = anyline(
        padded_image,
        detect_resolution=1280,
        guassian_sigma=max(0.01, guassian_sigma),
        intensity_threshold=intensity_threshold,
    )

    images = pipe(
        image=padded_image,
        control_image=anyline_image,
        strength=strength,
        prompt_embeds=conditioning[0:1],
        pooled_prompt_embeds=pooled[0:1],
        negative_prompt_embeds=conditioning[1:2],
        negative_pooled_prompt_embeds=pooled[1:2],
        width=1024,
        height=1024,
        controlnet_conditioning_scale=float(controlnet_conditioning_scale),
        controlnet_start=float(controlnet_start),
        controlnet_end=float(controlnet_end),
        generator=generator,
        num_inference_steps=30,
        guidance_scale=guidance_scale,
        eta=1.0,
    )
    print(f"Time taken: {time.time() - last_time}")
    generated_image = images.images[0]
    filename = save_image(generated_image, prompt)
    download_link = create_download_link(filename)
    return (padded_image, generated_image), padded_image, anyline_image, download_link, get_image_gallery(), image_metadata.values.tolist()

css = """
#intro {
    max-width: 100%;
    text-align: center;
    margin: 0 auto;
}
.gradio-container {max-width: 1200px !important}
footer {visibility: hidden}
"""

with gr.Blocks(css=css, theme=gr.themes.Soft()) as demo:
    gr.Markdown(
        """
# 🎨 ArtForge: MistoLine ControlNet Masterpiece Gallery

Create, curate, and compete with AI-enhanced images using MistoLine ControlNet. Join our creative multiplayer experience! 🖼️🏆✨

This demo showcases the capabilities of [TheMistoAI/MistoLine](https://huggingface.co/TheMistoAI/MistoLine) ControlNet with SDXL.

- SDXL Controlnet: [TheMistoAI/MistoLine](https://huggingface.co/TheMistoAI/MistoLine)
- [Anyline with Controlnet Aux](https://github.com/huggingface/controlnet_aux)
- For upscaling, see [Enhance This Demo](https://huggingface.co/spaces/radames/Enhance-This-HiDiffusion-SDXL)
        """,
        elem_id="intro",
    )
    
    with gr.Tab("Generate Images"):
        with gr.Row():
            with gr.Column(scale=1):
                image_input = gr.Image(type="pil", label="Input Image")
                prompt = gr.Textbox(
                    label="Prompt",
                    info="The prompt is very important to get the desired results. Please try to describe the image as best as you can. Accepts Compel Syntax",
                )
                negative_prompt = gr.Textbox(
                    label="Negative Prompt",
                    value="blurry, ugly, duplicate, poorly drawn, deformed, mosaic",
                )
                seed = gr.Slider(
                    minimum=0,
                    maximum=2**64 - 1,
                    value=1415926535897932,
                    step=1,
                    label="Seed",
                    randomize=True,
                )
                with gr.Accordion(label="Advanced", open=False):
                    guidance_scale = gr.Slider(
                        minimum=0,
                        maximum=50,
                        value=8.5,
                        step=0.001,
                        label="Guidance Scale",
                    )
                    controlnet_conditioning_scale = gr.Slider(
                        minimum=0,
                        maximum=1,
                        step=0.001,
                        value=0.5,
                        label="ControlNet Conditioning Scale",
                    )
                    strength = gr.Slider(
                        minimum=0,
                        maximum=1,
                        step=0.001,
                        value=1,
                        label="Strength",
                    )
                    controlnet_start = gr.Slider(
                        minimum=0,
                        maximum=1,
                        step=0.001,
                        value=0.0,
                        label="ControlNet Start",
                    )
                    controlnet_end = gr.Slider(
                        minimum=0.0,
                        maximum=1.0,
                        step=0.001,
                        value=1.0,
                        label="ControlNet End",
                    )
                    guassian_sigma = gr.Slider(
                        minimum=0.01,
                        maximum=10.0,
                        step=0.1,
                        value=2.0,
                        label="(Anyline) Guassian Sigma",
                    )
                    intensity_threshold = gr.Slider(
                        minimum=0,
                        maximum=255,
                        step=1,
                        value=3,
                        label="(Anyline) Intensity Threshold",
                    )

                btn = gr.Button("Generate")
            with gr.Column(scale=2):
                with gr.Group():
                    image_slider = ImageSlider(position=0.5)
                with gr.Row():
                    padded_image = gr.Image(type="pil", label="Padded Image")
                    anyline_image = gr.Image(type="pil", label="Anyline Image")
                download_link = gr.HTML(label="Download Generated Image")
        
    with gr.Tab("Gallery and Voting"):
        image_gallery = gr.Gallery(label="Generated Images", show_label=True, columns=4, height="auto")
        
        with gr.Row():
            like_button = gr.Button("👍 Like")
            dislike_button = gr.Button("👎 Dislike")
            heart_button = gr.Button("❤️ Heart")
            delete_image_button = gr.Button("🗑️ Delete Selected Image")
        
        selected_image = gr.State(None)

    with gr.Tab("Metadata and Management"):
        metadata_df = gr.Dataframe(
            label="Image Metadata",
            headers=["Filename", "Prompt", "Likes", "Dislikes", "Hearts", "Created"],
            interactive=False
        )
        delete_all_button = gr.Button("🗑️ Delete All Images")

    inputs = [
        image_input,
        prompt,
        negative_prompt,
        seed,
        guidance_scale,
        controlnet_conditioning_scale,
        strength,
        controlnet_start,
        controlnet_end,
        guassian_sigma,
        intensity_threshold,
    ]
    outputs = [image_slider, padded_image, anyline_image, download_link, image_gallery, metadata_df]
    
    btn.click(fn=predict, inputs=inputs, outputs=outputs)
    
    image_gallery.select(fn=lambda evt: evt, inputs=[], outputs=[selected_image])

    like_button.click(fn=lambda x: vote(x, 'likes'), inputs=[selected_image], outputs=[image_gallery, metadata_df])
    dislike_button.click(fn=lambda x: vote(x, 'dislikes'), inputs=[selected_image], outputs=[image_gallery, metadata_df])
    heart_button.click(fn=lambda x: vote(x, 'hearts'), inputs=[selected_image], outputs=[image_gallery, metadata_df])
    delete_image_button.click(fn=delete_image, inputs=[selected_image], outputs=[image_gallery, metadata_df])
    delete_all_button.click(fn=delete_all_images, inputs=[], outputs=[image_gallery, metadata_df])

    demo.load(fn=lambda: (get_image_gallery(), image_metadata.values.tolist()), outputs=[image_gallery, metadata_df])

    gr.Examples(
        fn=predict,
        inputs=inputs,
        outputs=outputs,
        examples=[
            [
                "./examples/city.png",
                "hyperrealistic surreal cityscape scene at sunset, buildings",
                "blurry, ugly, duplicate, poorly drawn, deformed, mosaic",
                13113544138610326000,
                8.5,
                0.481,
                1.0,
                0.0,
                0.9,
                2,
                3,
            ],
            [
                "./examples/lara.jpeg",
                "photography of lara croft 8k high definition award winning",
                "blurry, ugly, duplicate, poorly drawn, deformed, mosaic",
                5436236241,
                8.5,
                0.8,
                1.0,
                0.0,
                0.9,
                2,
                3,
            ],
            [
                "./examples/cybetruck.jpeg",
                "photo of tesla cybertruck futuristic car 8k high definition on a sand dune in mars, future",
                "blurry, ugly, duplicate, poorly drawn, deformed, mosaic",
                383472451451,
                8.5,
                0.8,
                0.8,
                0.0,
                0.9,
                2,
                3,
            ],
            [
                "./examples/jesus.png",
                "a photorealistic painting of Jesus Christ, 4k high definition",
                "blurry, ugly, duplicate, poorly drawn, deformed, mosaic",
                13317204146129588000,
                8.5,
                0.8,
                0.8,
                0.0,
                0.9,
                2,
                3,
            ],
            [
                "./examples/anna-sullivan-DioLM8ViiO8-unsplash.jpg",
                "A crowded stadium with enthusiastic fans watching a daytime sporting event, the stands filled with colorful attire and the sun casting a warm glow",
                "blurry, ugly, duplicate, poorly drawn, deformed, mosaic",
                5623124123512,
                8.5,
                0.8,
                0.8,
                0.0,
                0.9,
                2,
                3,
            ],
            [
                "./examples/img_aef651cb-2919-499d-aa49-6d4e2e21a56e_1024.jpg",
                "a large red flower on a black background 4k high definition",
                "blurry, ugly, duplicate, poorly drawn, deformed, mosaic",
                23123412341234,
                8.5,
                0.8,
                0.8,
                0.0,
                0.9,
                2,
                3,
            ],
            [
                "./examples/huggingface.jpg",
                "photo realistic huggingface human emoji costume, round, yellow, (human skin)+++ (human texture)+++",
                "blurry, ugly, duplicate, poorly drawn, deformed, mosaic, emoji cartoon,  drawing, pixelated",
                12312353423,
                15.206,
                0.364,
                0.8,
                0.0,
                0.9,
                2,
                3,
            ],
        ],
        cache_examples=True,
    )

demo.queue(concurrency_count=1, max_size=20).launch(debug=True)