Create app.py

app.py

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+#!/usr/bin/env python
+from __future__ import annotations
+import os, random, glob, re, json, base64
+from datetime import datetime
+import gradio as gr
+import numpy as np
+import PIL.Image
+import spaces
+import torch
+import pandas as pd
+from diffusers import AutoencoderKL, DiffusionPipeline
+
+DESCRIPTION = """
+# 🎨 ArtForge: AI-Powered Masterpiece Gallery
+
+Create, curate, and compete with AI-generated art using OpenDalle V1.1. Join our creative multiplayer experience! 🖼️🏆✨
+
+This demo showcases the capabilities of [OpenDalle V1.1](https://huggingface.co/dataautogpt3/OpenDalleV1.1) by @dataautogpt3, a powerful merge of several models designed for excellent performance.
+
+**NOTE: The model is licensed under a non-commercial license**
+
+Created by [mrfakename](https://mrfake.name/) - [Twitter](https://twitter.com/realmrfakename) - [GitHub](https://github.com/fakerybakery/) - [Hugging Face](https://huggingface.co/mrfakename)
+"""
+
+if not torch.cuda.is_available():
+    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU. Please use <a href=\"https://huggingface.co/spaces/mrfakename/OpenDalleV1.1-GPU-Demo\">the online demo</a> instead.</p>"
+
+MAX_SEED = np.iinfo(np.int32).max
+CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES", "0") == "1"
+MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "1024"))
+USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE") == "1"
+ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD") == "1"
+ENABLE_REFINER = os.getenv("ENABLE_REFINER", "0") == "1"
+
+# 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()
+
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+if torch.cuda.is_available():
+    vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
+    pipe = DiffusionPipeline.from_pretrained("dataautogpt3/OpenDalleV1.1", vae=vae, torch_dtype=torch.float16, use_safetensors=True, variant="fp16")
+    if ENABLE_REFINER:
+        refiner = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0", vae=vae, torch_dtype=torch.float16, use_safetensors=True, variant="fp16")
+    if ENABLE_CPU_OFFLOAD:
+        pipe.enable_model_cpu_offload()
+        if ENABLE_REFINER: refiner.enable_model_cpu_offload()
+    else:
+        pipe.to(device)
+        if ENABLE_REFINER: refiner.to(device)
+    if USE_TORCH_COMPILE:
+        pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
+        if ENABLE_REFINER: refiner.unet = torch.compile(refiner.unet, mode="reduce-overhead", fullgraph=True)
+
+def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
+    return random.randint(0, MAX_SEED) if randomize_seed else seed
+
+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: PIL.Image.Image, prompt: str) -> str:
+    global image_metadata, likes_cache
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    safe_prompt = re.sub(r'[^\w\s-]', '', prompt.lower())[:50]
+    safe_prompt = re.sub(r'[-\s]+', '-', safe_prompt).strip('-')
+    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()
+
+@spaces.GPU(enable_queue=True)
+def generate(prompt: str, negative_prompt: str = "", prompt_2: str = "", negative_prompt_2: str = "", use_negative_prompt: bool = False, use_prompt_2: bool = False, use_negative_prompt_2: bool = False, seed: int = 0, width: int = 1024, height: int = 1024, guidance_scale_base: float = 5.0, guidance_scale_refiner: float = 5.0, num_inference_steps_base: int = 25, num_inference_steps_refiner: int = 25, apply_refiner: bool = False, progress=gr.Progress(track_tqdm=True)) -> PIL.Image.Image:
+    print(f"** Generating image for: \"{prompt}\" **")
+    generator = torch.Generator().manual_seed(seed)
+    if not use_negative_prompt: negative_prompt = None
+    if not use_prompt_2: prompt_2 = None
+    if not use_negative_prompt_2: negative_prompt_2 = None
+    if not apply_refiner:
+        image = pipe(prompt=prompt, negative_prompt=negative_prompt, prompt_2=prompt_2, negative_prompt_2=negative_prompt_2, width=width, height=height, guidance_scale=guidance_scale_base, num_inference_steps=num_inference_steps_base, generator=generator, output_type="pil").images[0]
+    else:
+        latents = pipe(prompt=prompt, negative_prompt=negative_prompt, prompt_2=prompt_2, negative_prompt_2=negative_prompt_2, width=width, height=height, guidance_scale=guidance_scale_base, num_inference_steps=num_inference_steps_base, generator=generator, output_type="latent").images
+        image = refiner(prompt=prompt, negative_prompt=negative_prompt, prompt_2=prompt_2, negative_prompt_2=negative_prompt_2, guidance_scale=guidance_scale_refiner, num_inference_steps=num_inference_steps_refiner, image=latents, generator=generator).images[0]
+    filename = save_image(image, prompt)
+    download_link = create_download_link(filename)
+    return image, get_image_gallery(), download_link, image_metadata.values.tolist()
+
+examples = [
+    f"{random.choice(['Impressionist', 'Cubist', 'Surrealist', 'Abstract Expressionist', 'Pop Art', 'Minimalist', 'Baroque', 'Art Nouveau', 'Pointillist', 'Fauvism'])} painting of a majestic lighthouse on a rocky coast. Use bold brushstrokes and a vibrant color palette to capture the interplay of light and shadow as the lighthouse beam cuts through a stormy night sky.",
+    f"{random.choice(['Impressionist', 'Cubist', 'Surrealist', 'Abstract Expressionist', 'Pop Art', 'Minimalist', 'Baroque', 'Art Nouveau', 'Pointillist', 'Fauvism'])} still life featuring a pair of vintage eyeglasses. Focus on the intricate details of the frames and lenses, using a warm color scheme to evoke a sense of nostalgia and wisdom.",
+    f"{random.choice(['Impressionist', 'Cubist', 'Surrealist', 'Abstract Expressionist', 'Pop Art', 'Minimalist', 'Baroque', 'Art Nouveau', 'Pointillist', 'Fauvism'])} depiction of a rustic wooden stool in a sunlit artist's studio. Emphasize the texture of the wood and the interplay of light and shadow, using a mix of earthy tones and highlights.",
+    f"{random.choice(['Impressionist', 'Cubist', 'Surrealist', 'Abstract Expressionist', 'Pop Art', 'Minimalist', 'Baroque', 'Art Nouveau', 'Pointillist', 'Fauvism'])} scene viewed through an ornate window frame. Contrast the intricate details of the window with a dreamy, soft-focus landscape beyond, using a palette that transitions from cool interior tones to warm exterior hues.",
+    f"{random.choice(['Impressionist', 'Cubist', 'Surrealist', 'Abstract Expressionist', 'Pop Art', 'Minimalist', 'Baroque', 'Art Nouveau', 'Pointillist', 'Fauvism'])} close-up study of interlaced fingers. Use a monochromatic color scheme to emphasize the form and texture of the hands, with dramatic lighting to create depth and emotion.",
+    f"{random.choice(['Impressionist', 'Cubist', 'Surrealist', 'Abstract Expressionist', 'Pop Art', 'Minimalist', 'Baroque', 'Art Nouveau', 'Pointillist', 'Fauvism'])} composition featuring a set of dice in motion. Capture the energy and randomness of the throw, using a dynamic color palette and blurred lines to convey movement.",
+    f"{random.choice(['Impressionist', 'Cubist', 'Surrealist', 'Abstract Expressionist', 'Pop Art', 'Minimalist', 'Baroque', 'Art Nouveau', 'Pointillist', 'Fauvism'])} interpretation of heaven. Create an ethereal atmosphere with soft, billowing clouds and radiant light, using a palette of celestial blues, golds, and whites.",
+    f"{random.choice(['Impressionist', 'Cubist', 'Surrealist', 'Abstract Expressionist', 'Pop Art', 'Minimalist', 'Baroque', 'Art Nouveau', 'Pointillist', 'Fauvism'])} portrayal of an ancient, mystical gate. Combine architectural details with elements of fantasy, using a rich, jewel-toned palette to create an air of mystery and magic.",
+    f"{random.choice(['Impressionist', 'Cubist', 'Surrealist', 'Abstract Expressionist', 'Pop Art', 'Minimalist', 'Baroque', 'Art Nouveau', 'Pointillist', 'Fauvism'])} portrait of a curious cat. Focus on capturing the feline's expressive eyes and sleek form, using a mix of bold and subtle colors to bring out the cat's personality.",
+    f"{random.choice(['Impressionist', 'Cubist', 'Surrealist', 'Abstract Expressionist', 'Pop Art', 'Minimalist', 'Baroque', 'Art Nouveau', 'Pointillist', 'Fauvism'])} abstract representation of toes in sand. Use textured brushstrokes to convey the feeling of warm sand, with a palette inspired by a sun-drenched beach."
+]
+
+css = '''
+.gradio-container {max-width: 1024px !important}
+h1 {text-align: center}
+footer {visibility: hidden}
+'''
+
+theme = gr.themes.Soft()
+with gr.Blocks(css=css, theme=theme) as demo:
+    gr.Markdown(DESCRIPTION)
+    gr.DuplicateButton(value="Duplicate Space for private use", elem_id="duplicate-button", visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1")
+    
+    with gr.Tab("Generate Images"):
+        with gr.Group():
+            with gr.Row():
+                prompt = gr.Text(label="Prompt", show_label=False, max_lines=1, placeholder="Enter your prompt", container=False)
+                run_button = gr.Button("Generate", scale=0)
+            result = gr.Image(label="Result", show_label=False)
+            download_link = gr.HTML(label="Download", show_label=False)
+        
+        with gr.Accordion("Advanced options", open=False):
+            with gr.Row():
+                use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=False)
+                use_prompt_2use_prompt_2 = gr.Checkbox(label="Use prompt 2", value=False)
+                use_negative_prompt_2 = gr.Checkbox(label="Use negative prompt 2", value=False)
+            negative_prompt = gr.Text(label="Negative prompt", max_lines=1, placeholder="Enter a negative prompt", visible=False)
+            prompt_2 = gr.Text(label="Prompt 2", max_lines=1, placeholder="Enter your second prompt", visible=False)
+            negative_prompt_2 = gr.Text(label="Negative prompt 2", max_lines=1, placeholder="Enter a second 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)
+            apply_refiner = gr.Checkbox(label="Apply refiner", value=False, visible=ENABLE_REFINER)
+            with gr.Row():
+                guidance_scale_base = gr.Slider(label="Guidance scale for base", minimum=1, maximum=20, step=0.1, value=5.0)
+                num_inference_steps_base = gr.Slider(label="Number of inference steps for base", minimum=10, maximum=100, step=1, value=25)
+            with gr.Row(visible=False) as refiner_params:
+                guidance_scale_refiner = gr.Slider(label="Guidance scale for refiner", minimum=1, maximum=20, step=0.1, value=5.0)
+                num_inference_steps_refiner = gr.Slider(label="Number of inference steps for refiner", minimum=10, maximum=100, step=1, value=25)
+
+    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")
+
+    gr.Examples(examples=examples, inputs=prompt, outputs=[result, image_gallery, download_link, metadata_df], fn=generate, cache_examples=CACHE_EXAMPLES)
+
+    use_negative_prompt.change(fn=lambda x: gr.update(visible=x), inputs=use_negative_prompt, outputs=negative_prompt, queue=False, api_name=False)
+    use_prompt_2.change(fn=lambda x: gr.update(visible=x), inputs=use_prompt_2, outputs=prompt_2, queue=False, api_name=False)
+    use_negative_prompt_2.change(fn=lambda x: gr.update(visible=x), inputs=use_negative_prompt_2, outputs=negative_prompt_2, queue=False, api_name=False)
+    apply_refiner.change(fn=lambda x: gr.update(visible=x), inputs=apply_refiner, outputs=refiner_params, queue=False, api_name=False)
+
+    prompt.submit(fn=randomize_seed_fn, inputs=[seed, randomize_seed], outputs=seed, queue=False, api_name=False).then(
+        fn=generate, 
+        inputs=[prompt, negative_prompt, prompt_2, negative_prompt_2, use_negative_prompt, use_prompt_2, use_negative_prompt_2, seed, width, height, guidance_scale_base, guidance_scale_refiner, num_inference_steps_base, num_inference_steps_refiner, apply_refiner],
+        outputs=[result, image_gallery, download_link, metadata_df]
+    )
+
+    run_button.click(fn=randomize_seed_fn, inputs=[seed, randomize_seed], outputs=seed, queue=False, api_name=False).then(
+        fn=generate, 
+        inputs=[prompt, negative_prompt, prompt_2, negative_prompt_2, use_negative_prompt, use_prompt_2, use_negative_prompt_2, seed, width, height, guidance_scale_base, guidance_scale_refiner, num_inference_steps_base, num_inference_steps_refiner, apply_refiner],
+        outputs=[result, image_gallery, download_link, metadata_df]
+    )
+
+    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])
+
+if __name__ == "__main__":
+    demo.queue(max_size=20).launch(share=True, debug=False)