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import os |
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import random |
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import uuid |
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from urllib.parse import quote |
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from requests import get |
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from bs4 import BeautifulSoup |
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import gradio as gr |
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import numpy as np |
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from PIL import Image |
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import spaces |
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import torch |
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from diffusers import DiffusionPipeline |
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DESCRIPTION = """Hepbilen.com Görsel Oluşturma Aracı""" |
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if not torch.cuda.is_available(): |
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DESCRIPTION += "\n<p>Running on CPU 🥶 This demo may not work on CPU.</p>" |
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MAX_SEED = np.iinfo(np.int32).max |
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CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES", "1") == "1" |
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MAX_IMAGE_SIZE = int(os.getenv("MAX_IMAGE_SIZE", "1536")) |
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USE_TORCH_COMPILE = os.getenv("USE_TORCH_COMPILE", "0") == "1" |
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ENABLE_CPU_OFFLOAD = os.getenv("ENABLE_CPU_OFFLOAD", "0") == "1" |
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device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") |
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NUM_IMAGES_PER_PROMPT = 1 |
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valid_languages = {'tr', 'fr', 'esp', 'en'} |
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if torch.cuda.is_available(): |
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pipe = DiffusionPipeline.from_pretrained( |
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"playgroundai/playground-v2.5-1024px-aesthetic", |
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torch_dtype=torch.float16, |
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use_safetensors=True, |
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add_watermarker=False, |
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variant="fp16" |
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) |
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if ENABLE_CPU_OFFLOAD: |
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pipe.enable_model_cpu_offload() |
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else: |
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pipe.to(device) |
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print("Loaded on Device!") |
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if USE_TORCH_COMPILE: |
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pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True) |
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print("Model Compiled!") |
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def save_image(img): |
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unique_name = str(uuid.uuid4()) + ".png" |
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img.save(unique_name) |
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return unique_name |
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def randomize_seed_fn(seed: int, randomize_seed: bool) -> int: |
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if randomize_seed: |
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seed = random.randint(0, MAX_SEED) |
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return seed |
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def translate_to_english(phrase, src_lang): |
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if src_lang == 'en': |
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return phrase |
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dest_lang = 'en' |
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encoded_phrase = quote(phrase) |
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url = f"https://translate.glosbe.com/{src_lang}-{dest_lang}/{encoded_phrase}" |
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response = get(url) |
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if response.status_code == 200: |
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soup = BeautifulSoup(response.text, 'html.parser') |
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translation_div = soup.find('div', class_='w-full h-full bg-gray-100 h-full border p-2 min-h-25vh sm:min-h-50vh whitespace-pre-wrap break-words') |
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translation = translation_div.text if translation_div else "Translation not found" |
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return translation |
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else: |
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return "Error: Unable to translate" |
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@spaces.GPU(enable_queue=True) |
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def generate( |
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phrase: str, |
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input_lang: str, |
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negative_prompt: str = "", |
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use_negative_prompt: bool = False, |
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seed: int = 0, |
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width: int = 1024, |
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height: int = 1024, |
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guidance_scale: float = 3, |
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randomize_seed: bool = False, |
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use_resolution_binning: bool = True, |
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progress=gr.Progress(track_tqdm=True), |
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): |
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pipe.to(device) |
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seed = int(randomize_seed_fn(seed, randomize_seed)) |
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generator = torch.Generator().manual_seed(seed) |
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if input_lang != 'en': |
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prompt = translate_to_english(phrase, input_lang) |
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else: |
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prompt = phrase |
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if not use_negative_prompt: |
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negative_prompt = None |
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images = pipe( |
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prompt=prompt, |
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negative_prompt=negative_prompt, |
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width=width, |
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height=height, |
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guidance_scale=guidance_scale, |
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num_inference_steps=25, |
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generator=generator, |
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num_images_per_prompt=NUM_IMAGES_PER_PROMPT, |
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use_resolution_binning=use_resolution_binning, |
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output_type="pil", |
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).images |
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image_paths = [save_image(img) for img in images] |
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print(image_paths) |
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return image_paths, seed |
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examples = [ |
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["nyɔnu e nɔ sa tomati ɖo aximɛ", "fon"], |
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["ọba ilẹ̀ benin kan", "yo"], |
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["an astronaut riding a horse in space", "en"], |
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["a cartoon of a boy playing with a tiger", "en"], |
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["a cute robot artist painting on an easel, concept art", "en"], |
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["a close up of a woman wearing a transparent, prismatic, elaborate nemeses headdress, over the should pose, brown skin-tone", "en"] |
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] |
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css = ''' |
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.gradio-container{max-width: 560px !important} |
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h1{text-align:center} |
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''' |
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with gr.Blocks(css=css) as demo: |
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gr.Markdown(DESCRIPTION) |
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gr.DuplicateButton( |
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value="Duplicate Space for private use", |
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elem_id="duplicate-button", |
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visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1", |
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) |
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with gr.Group(): |
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with gr.Row(): |
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input_lang = gr.Dropdown(choices=list(valid_languages), value='en', label='Input Language') |
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prompt = gr.Text( |
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label="Prompt", |
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show_label=False, |
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max_lines=1, |
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placeholder="Enter your prompt", |
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container=False, |
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) |
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run_button = gr.Button("Run", scale=0) |
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result = gr.Gallery(label="Result", columns=NUM_IMAGES_PER_PROMPT, show_label=False) |
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with gr.Accordion("Advanced options", open=False): |
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with gr.Row(): |
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use_negative_prompt = gr.Checkbox(label="Use negative prompt", value=False) |
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negative_prompt = gr.Text( |
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label="Negative prompt", |
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max_lines=1, |
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placeholder="Enter a negative prompt", |
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visible=True, |
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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=0, |
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) |
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randomize_seed = gr.Checkbox(label="Randomize seed", value=True) |
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with gr.Row(visible=True): |
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width = gr.Slider( |
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label="Width", |
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minimum=256, |
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maximum=MAX_IMAGE_SIZE, |
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step=32, |
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value=1024, |
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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=MAX_IMAGE_SIZE, |
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step=32, |
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value=1024, |
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) |
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with gr.Row(): |
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guidance_scale = gr.Slider( |
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label="Guidance Scale", |
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minimum=0.1, |
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maximum=20, |
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step=0.1, |
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value=3.0, |
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) |
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gr.Examples( |
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examples=examples, |
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inputs=[prompt, input_lang], |
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outputs=[result, seed], |
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fn=generate, |
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cache_examples=CACHE_EXAMPLES, |
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) |
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use_negative_prompt.change( |
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fn=lambda x: gr.update(visible=x), |
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inputs=use_negative_prompt, |
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outputs=negative_prompt, |
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api_name=False, |
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) |
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gr.on( |
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triggers=[ |
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prompt.submit, |
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negative_prompt.submit, |
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run_button.click, |
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], |
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fn=generate, |
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inputs=[ |
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prompt, |
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input_lang, |
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negative_prompt, |
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use_negative_prompt, |
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seed, |
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width, |
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height, |
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guidance_scale, |
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randomize_seed, |
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], |
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outputs=[result, seed], |
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api_name="run", |
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) |
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if __name__ == "__main__": |
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demo.queue(max_size=20).launch() |
