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import spaces |
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import gradio as gr |
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import torch |
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from PIL import Image |
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from diffusers import DiffusionPipeline |
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import random |
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from transformers import pipeline |
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import pygame |
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import os |
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import threading |
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torch.backends.cudnn.deterministic = True |
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torch.backends.cudnn.benchmark = False |
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torch.backends.cuda.matmul.allow_tf32 = True |
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translator = pipeline("translation", model="Helsinki-NLP/opus-mt-ko-en") |
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base_model = "black-forest-labs/FLUX.1-dev" |
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pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16) |
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lora_repo = "strangerzonehf/Flux-Xmas-Realpix-LoRA" |
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trigger_word = "" |
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pipe.load_lora_weights(lora_repo) |
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pipe.to("cuda") |
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pygame.mixer.init() |
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def play_music(): |
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pygame.mixer.music.load("1.mp3") |
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pygame.mixer.music.play() |
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pygame.mixer.music.queue("2.mp3") |
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pygame.mixer.music.set_endevent(pygame.USEREVENT) |
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while True: |
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for event in pygame.event.get(): |
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if event.type == pygame.USEREVENT: |
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pygame.mixer.music.queue("1.mp3") |
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pygame.mixer.music.queue("2.mp3") |
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music_thread = threading.Thread(target=play_music, daemon=True) |
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music_thread.start() |
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MAX_SEED = 2**32-1 |
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@spaces.GPU() |
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def translate_and_generate(prompt, cfg_scale, steps, randomize_seed, seed, width, height, lora_scale, progress=gr.Progress(track_tqdm=True)): |
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def contains_korean(text): |
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return any(ord('κ°') <= ord(char) <= ord('ν£') for char in text) |
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if contains_korean(prompt): |
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translated = translator(prompt)[0]['translation_text'] |
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actual_prompt = translated |
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else: |
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actual_prompt = prompt |
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if randomize_seed: |
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seed = random.randint(0, MAX_SEED) |
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generator = torch.Generator(device="cuda").manual_seed(seed) |
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progress(0, "Starting image generation...") |
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for i in range(1, steps + 1): |
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if i % (steps // 10) == 0: |
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progress(i / steps * 100, f"Processing step {i} of {steps}...") |
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image = pipe( |
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prompt=f"{actual_prompt} {trigger_word}", |
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num_inference_steps=steps, |
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guidance_scale=cfg_scale, |
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width=width, |
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height=height, |
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generator=generator, |
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joint_attention_kwargs={"scale": lora_scale}, |
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).images[0] |
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progress(100, "Completed!") |
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return image, seed |
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example_image_path = "example0.webp" |
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example_prompt = """Cozy winter scene with a Christmas atmosphere: a snow-covered cabin in the forest, warm light glowing from the windows, surrounded by sparkling Christmas decorations and a beautifully adorned Christmas tree. The sky is filled with stars, and soft snowflakes are gently falling, creating a serene and warm ambiance""" |
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example_cfg_scale = 3.2 |
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example_steps = 32 |
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example_width = 1152 |
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example_height = 896 |
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example_seed = 3981632454 |
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example_lora_scale = 0.85 |
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def load_example(): |
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example_image = Image.open(example_image_path) |
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return example_prompt, example_cfg_scale, example_steps, True, example_seed, example_width, example_height, example_lora_scale, example_image |
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css = """ |
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.container { |
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max-width: 1400px; |
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margin: auto; |
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padding: 20px; |
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position: relative; |
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background-image: url('file/example0.webp'); |
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background-size: cover; |
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background-position: center; |
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min-height: 100vh; |
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} |
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.header { |
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text-align: center; |
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margin-bottom: 30px; |
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color: white; |
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text-shadow: 2px 2px 4px rgba(0,0,0,0.7); |
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} |
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.generate-btn { |
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background-color: #2ecc71 !important; |
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color: white !important; |
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margin: 20px auto !important; |
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display: block !important; |
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width: 200px !important; |
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} |
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.generate-btn:hover { |
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background-color: #27ae60 !important; |
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} |
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.parameter-box { |
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background-color: rgba(245, 246, 250, 0.9); |
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padding: 20px; |
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border-radius: 10px; |
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margin: 10px 0; |
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} |
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.result-box { |
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background-color: rgba(245, 246, 250, 0.9); |
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padding: 20px; |
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border-radius: 10px; |
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margin: 0 auto 20px auto; |
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text-align: center; |
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} |
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.image-output { |
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margin: 0 auto; |
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display: block; |
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max-width: 800px !important; |
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} |
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.accordion { |
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margin-top: 20px; |
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} |
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.prompt-box { |
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position: fixed; |
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top: 20px; |
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right: 20px; |
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width: 300px; |
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background-color: rgba(245, 246, 250, 0.9); |
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padding: 20px; |
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border-radius: 10px; |
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z-index: 1000; |
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} |
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@keyframes snow { |
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0% { |
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transform: translateY(0) translateX(0); |
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} |
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100% { |
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transform: translateY(100vh) translateX(100px); |
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} |
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} |
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.snowflake { |
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position: fixed; |
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top: -10px; |
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color: white; |
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font-size: 20px; |
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animation: snow 5s linear infinite; |
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} |
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""" |
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js_code = """ |
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function createSnowflake() { |
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const snowflake = document.createElement('div'); |
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snowflake.classList.add('snowflake'); |
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snowflake.innerHTML = 'β'; |
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snowflake.style.left = Math.random() * 100 + 'vw'; |
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snowflake.style.animationDuration = Math.random() * 3 + 2 + 's'; |
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snowflake.style.opacity = Math.random(); |
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document.body.appendChild(snowflake); |
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setTimeout(() => { |
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snowflake.remove(); |
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}, 5000); |
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} |
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setInterval(createSnowflake, 100); |
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""" |
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with gr.Blocks(css=css) as app: |
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gr.HTML(f"<script>{js_code}</script>") |
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with gr.Column(elem_classes="container"): |
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gr.Markdown("# π X-MAS LoRA", elem_classes="header") |
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with gr.Group(elem_classes="prompt-box"): |
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prompt = gr.TextArea( |
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label="βοΈ Your Prompt (νκΈ λλ μμ΄)", |
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placeholder="μ΄λ―Έμ§λ₯Ό μ€λͺ
νμΈμ...", |
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lines=5 |
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) |
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generate_button = gr.Button( |
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"π Generate Image", |
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elem_classes="generate-btn" |
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) |
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with gr.Group(elem_classes="result-box"): |
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gr.Markdown("### πΌοΈ Generated Image") |
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result = gr.Image(label="Result", elem_classes="image-output") |
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with gr.Accordion("π¨ Advanced Options", open=False, elem_classes="accordion"): |
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with gr.Group(elem_classes="parameter-box"): |
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gr.Markdown("### ποΈ Generation Parameters") |
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with gr.Row(): |
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with gr.Column(): |
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cfg_scale = gr.Slider( |
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label="CFG Scale", |
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minimum=1, |
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maximum=20, |
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step=0.5, |
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value=example_cfg_scale |
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) |
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steps = gr.Slider( |
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label="Steps", |
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minimum=1, |
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maximum=100, |
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step=1, |
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value=example_steps |
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) |
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lora_scale = gr.Slider( |
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label="LoRA Scale", |
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minimum=0, |
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maximum=1, |
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step=0.01, |
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value=example_lora_scale |
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) |
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with gr.Group(elem_classes="parameter-box"): |
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gr.Markdown("### π Image Dimensions") |
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with gr.Row(): |
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width = gr.Slider( |
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label="Width", |
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minimum=256, |
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maximum=1536, |
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step=64, |
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value=example_width |
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) |
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height = gr.Slider( |
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label="Height", |
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minimum=256, |
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maximum=1536, |
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step=64, |
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value=example_height |
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) |
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with gr.Group(elem_classes="parameter-box"): |
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gr.Markdown("### π² Seed Settings") |
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with gr.Row(): |
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randomize_seed = gr.Checkbox( |
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True, |
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label="Randomize seed" |
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) |
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seed = gr.Slider( |
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label="Seed", |
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minimum=0, |
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maximum=MAX_SEED, |
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step=1, |
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value=example_seed |
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) |
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app.load( |
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load_example, |
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inputs=[], |
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outputs=[prompt, cfg_scale, steps, randomize_seed, seed, width, height, lora_scale, result] |
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) |
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generate_button.click( |
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translate_and_generate, |
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inputs=[prompt, cfg_scale, steps, randomize_seed, seed, width, height, lora_scale], |
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outputs=[result, seed] |
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) |
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app.queue() |
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app.launch(js=js_code) |