Create app.py

app.py

@@ -0,0 +1,457 @@
+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=deletedelete_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)