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import gradio as gr | |
import numpy as np | |
import random | |
import spaces | |
import torch | |
from diffusers import DiffusionPipeline | |
from transformers import pipeline | |
# λ²μ νμ΄νλΌμΈ λ° νλμ¨μ΄ μ€μ | |
device = "cuda" if torch.cuda.is_available() else "cpu" | |
translator = pipeline("translation", model="Helsinki-NLP/opus-mt-ko-en", device=device) | |
dtype = torch.bfloat16 | |
pipe = DiffusionPipeline.from_pretrained("black-forest-labs/FLUX.1-schnell", torch_dtype=dtype).to(device) | |
MAX_SEED = np.iinfo(np.int32).max | |
MAX_IMAGE_SIZE = 2048 | |
def infer(prompt, seed=42, randomize_seed=False, width=1024, height=1024, num_inference_steps=4, progress=gr.Progress(track_tqdm=True)): | |
if randomize_seed: | |
seed = random.randint(0, MAX_SEED) | |
generator = torch.Generator().manual_seed(seed) | |
# νκΈ μ λ ₯ κ°μ§ λ° λ²μ | |
if any('\uAC00' <= char <= '\uD7A3' for char in prompt): | |
print("νκ΅μ΄ ν둬ννΈ λ²μ μ€...") | |
translated_prompt = translator(prompt, max_length=512)[0]['translation_text'] | |
print("λ²μλ ν둬ννΈ:", translated_prompt) | |
prompt = translated_prompt | |
image = pipe( | |
prompt = prompt, | |
width = width, | |
height = height, | |
num_inference_steps = num_inference_steps, | |
generator = generator, | |
guidance_scale=0.0 | |
).images[0] | |
return image, seed | |
# (μ΄μ import ꡬ문 λ° νμ΄νλΌμΈ μ€μ μ μ§) | |
examples = [ | |
["[νκΈ] [μ€νμΌ: λͺ¨λ] [μμ: λΉ¨κ°κ³Ό κ²μ ] [컨μ : μλΉ] [ν μ€νΈ: 'λ§μλμ§'] [λ°°κ²½: μ¬ν]"], | |
["[Style: Corporate] [Color: Navy and Silver] [Concept: Finance] [Text: 'TRUST'] [Background: Professional]"], | |
["[Style: Dynamic] [Color: Purple and Orange] [Concept: Creative Agency] [Text: 'SPARK'] [Background: Abstract]"], | |
["[Style: Minimalist] [Color: Red and White] [Concept: Sports] [Text: 'POWER'] [Background: Clean]"] | |
] | |
css = """ | |
footer {visibility: hidden} | |
.container {max-width: 850px; margin: auto; padding: 20px} | |
.title {text-align: center; margin-bottom: 20px} | |
#prompt {min-height: 50px} | |
#result {min-height: 400px} | |
.gr-box {border-radius: 10px; border: 1px solid #ddd} | |
""" | |
with gr.Blocks(theme="Yntec/HaleyCH_Theme_Orange", css=css) as demo: | |
gr.HTML("<h1 class='title'>LOGO Generator AI</h1>") | |
with gr.Column(elem_id="container"): | |
with gr.Group(): | |
prompt = gr.Text( | |
label="λ‘κ³ μ€λͺ ", | |
placeholder="λ‘κ³ λμμΈμ μμΈν μ€λͺ ν΄μ£ΌμΈμ (νκΈ μ λ ₯ κ°λ₯)", | |
lines=2 | |
) | |
run_button = gr.Button("λ‘κ³ μμ±", variant="primary") | |
with gr.Row(): | |
result = gr.Image(label="μμ±λ λ‘κ³ ", show_label=True) | |
with gr.Accordion("κ³ κΈ μ€μ ", open=False): | |
with gr.Row(): | |
seed = gr.Slider(label="μλ", minimum=0, maximum=MAX_SEED, step=1, value=0) | |
randomize_seed = gr.Checkbox(label="λλ€ μλ", value=True) | |
with gr.Row(): | |
width = gr.Slider(label="λλΉ", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=512) | |
height = gr.Slider(label="λμ΄", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=512) | |
num_inference_steps = gr.Slider(label="νμ§", minimum=1, maximum=50, step=1, value=4) | |
gr.Examples( | |
examples=examples, | |
fn=infer, | |
inputs=[prompt], | |
outputs=[result, seed], | |
cache_examples="lazy" | |
) | |
gr.on( | |
triggers=[run_button.click, prompt.submit], | |
fn=infer, | |
inputs=[prompt, seed, randomize_seed, width, height, num_inference_steps], | |
outputs=[result, seed] | |
) | |
demo.launch() |