Create app.py

app.py

@@ -0,0 +1,280 @@
+#!/usr/bin/env python
+
+import os
+import random
+import uuid
+import base64
+import gradio as gr
+import numpy as np
+from PIL import Image
+import spaces
+import torch
+import glob
+from datetime import datetime
+import json
+
+from diffusers import StableDiffusionXLPipeline, EulerAncestralDiscreteScheduler
+
+DESCRIPTION = """# DALL•E 3 XL v2 High Fi"""
+
+VOTE_FILE = "vote_counts.json"
+
+def load_vote_counts():
+    if os.path.exists(VOTE_FILE):
+        with open(VOTE_FILE, "r") as f:
+            return json.load(f)
+    return {}
+
+def save_vote_counts(vote_counts):
+    with open(VOTE_FILE, "w") as f:
+        json.dump(vote_counts, f)
+
+vote_counts = load_vote_counts()
+
+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(img, prompt):
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    filename = f"{timestamp}_{prompt[:50]}.png"  # Limit filename length
+    img.save(filename)
+    return filename
+
+def randomize_seed_fn(seed: int, randomize_seed: bool) -> int:
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    return seed
+
+def get_image_gallery():
+    image_files = glob.glob("*.png")
+    image_files.sort(key=lambda x: calculate_score(x), reverse=True)
+    return [(file, f"{file}\n👍 {vote_counts.get(file, {}).get('likes', 0)} 👎 {vote_counts.get(file, {}).get('dislikes', 0)} ❤️ {vote_counts.get(file, {}).get('hearts', 0)}") for file in image_files]
+
+def calculate_score(filename):
+    counts = vote_counts.get(filename, {})
+    return (counts.get('hearts', 0) * 5) + counts.get('likes', 0) - counts.get('dislikes', 0)
+
+def delete_all_images():
+    for file in glob.glob("*.png"):
+        os.remove(file)
+    vote_counts.clear()
+    save_vote_counts(vote_counts)
+    return get_image_gallery()
+
+def vote(filename, vote_type):
+    if filename not in vote_counts:
+        vote_counts[filename] = {'likes': 0, 'dislikes': 0, 'hearts': 0}
+    vote_counts[filename][vote_type] += 1
+    save_vote_counts(vote_counts)
+    return get_image_gallery()
+
+MAX_SEED = np.iinfo(np.int32).max
+
+if not torch.cuda.is_available():
+    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo may not work on CPU.</p>"
+
+USE_TORCH_COMPILE = 0
+ENABLE_CPU_OFFLOAD = 0
+
+if torch.cuda.is_available():
+    pipe = StableDiffusionXLPipeline.from_pretrained(
+        "fluently/Fluently-XL-v4",
+        torch_dtype=torch.float16,
+        use_safetensors=True,
+    )
+    pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
+    
+    pipe.load_lora_weights("ehristoforu/dalle-3-xl-v2", weight_name="dalle-3-xl-lora-v2.safetensors", adapter_name="dalle")
+    pipe.set_adapters("dalle")
+
+    pipe.to("cuda")
+
+@spaces.GPU(enable_queue=True)
+def generate(
+    prompt: str,
+    negative_prompt: str = "",
+    use_negative_prompt: bool = False,
+    seed: int = 0,
+    width: int = 1024,
+    height: int = 1024,
+    guidance_scale: float = 3,
+    randomize_seed: bool = False,
+    progress=gr.Progress(track_tqdm=True),
+):
+    seed = int(randomize_seed_fn(seed, randomize_seed))
+
+    if not use_negative_prompt:
+        negative_prompt = ""
+
+    images = pipe(
+        prompt=prompt,
+        negative_prompt=negative_prompt,
+        width=width,
+        height=height,
+        guidance_scale=guidance_scale,
+        num_inference_steps=20,
+        num_images_per_prompt=1,
+        cross_attention_kwargs={"scale": 0.65},
+        output_type="pil",
+    ).images
+    image_paths = [save_image(img, prompt) for img in images]
+    download_links = [create_download_link(path) for path in image_paths]
+
+    return image_paths, seed, download_links, get_image_gallery()
+
+examples = [
+    "A bustling cityscape at sunset, where sleek, translucent skyscrapers shimmer with holographic displays. Gentle AI-powered drones weave through the air, delivering packages and tending to floating gardens. In the foreground, a diverse group of humans and lifelike androids share a laugh at a streetside café, their animated conversation bringing warmth to the scene.",
+    # ... (other examples remain unchanged)
+]
+
+css = '''
+.gradio-container{max-width: 1024px !important}
+h1{text-align:center}
+footer {
+visibility: hidden
+}
+'''
+
+with gr.Blocks(css=css, theme="pseudolab/huggingface-korea-theme") as demo:
+    gr.Markdown(DESCRIPTION)
+
+    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("Run", scale=0)
+        result = gr.Gallery(label="Result", columns=1, preview=True, show_label=False)
+    with gr.Accordion("Advanced options", open=False):
+        use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=True)
+        negative_prompt = gr.Text(
+            label="Negative prompt",
+            lines=4,
+            max_lines=6,
+            value="""(deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, (mutated hands and fingers:1.4), disconnected limbs, mutation, mutated, ugly, disgusting, blurry, amputation, (NSFW:1.25)""",
+            placeholder="Enter a negative prompt",
+            visible=True,
+        )
+        seed = gr.Slider(
+            label="Seed",
+            minimum=0,
+            maximum=MAX_SEED,
+            step=1,
+            value=0,
+            visible=True
+        )
+        randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+        with gr.Row(visible=True):
+            width = gr.Slider(
+                label="Width",
+                minimum=512,
+                maximum=2048,
+                step=8,
+                value=1920,
+            )
+            height = gr.Slider(
+                label="Height",
+                minimum=512,
+                maximum=2048,
+                step=8,
+                value=1080,
+            )
+        with gr.Row():
+            guidance_scale = gr.Slider(
+                label="Guidance Scale",
+                minimum=0.1,
+                maximum=20.0,
+                step=0.1,
+                value=20.0,
+            )
+    
+    image_gallery = gr.Gallery(label="Generated Images", show_label=True, columns=4, height="auto")
+    
+    with gr.Row():
+        delete_all_button = gr.Button("🗑️ Delete All Images")
+        like_button = gr.Button("👍 Like")
+        dislike_button = gr.Button("👎 Dislike")
+        heart_button = gr.Button("❤️ Heart")
+    
+    selected_image = gr.State(None)
+
+    gr.Examples(
+        examples=examples,
+        inputs=prompt,
+        outputs=[result, seed],
+        fn=generate,
+        cache_examples=False,
+    )
+
+    use_negative_prompt.change(
+        fn=lambda x: gr.update(visible=x),
+        inputs=use_negative_prompt,
+        outputs=negative_prompt,
+        api_name=False,
+    )
+
+    delete_all_button.click(
+        fn=delete_all_images,
+        inputs=[],
+        outputs=[image_gallery],
+    )
+
+    image_gallery.select(
+        fn=lambda evt: evt,
+        inputs=[gr.State("value")],
+        outputs=[selected_image],
+    )
+
+    like_button.click(
+        fn=lambda x: vote(x, 'likes') if x else None,
+        inputs=[selected_image],
+        outputs=[image_gallery],
+    )
+
+    dislike_button.click(
+        fn=lambda x: vote(x, 'dislikes') if x else None,
+        inputs=[selected_image],
+        outputs=[image_gallery],
+    )
+
+    heart_button.click(
+        fn=lambda x: vote(x, 'hearts') if x else None,
+        inputs=[selected_image],
+        outputs=[image_gallery],
+    )
+
+    def update_gallery():
+        return gr.update(value=get_image_gallery())
+
+    gr.on(
+        triggers=[
+            prompt.submit,
+            negative_prompt.submit,
+            run_button.click,
+        ],
+        fn=generate,
+        inputs=[
+            prompt,
+            negative_prompt,
+            use_negative_prompt,
+            seed,
+            width,
+            height,
+            guidance_scale,
+            randomize_seed,
+        ],
+        outputs=[result, seed, gr.HTML(visible=False), image_gallery],
+        api_name="run",
+    )
+
+    demo.load(fn=update_gallery, outputs=image_gallery)
+
+if __name__ == "__main__":
+    demo.queue(max_size=20).launch(show_api=False, debug=False)