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import os
import random
import numpy as np
import gradio as gr
# --- Hugging Face Spaces ๋ฐ์ฝ๋ ์ดํฐ(์์ด๋ ๋์ํ๋๋ก ๋์ฒด) ---
try:
import spaces
GPU_DECORATOR = spaces.GPU
except Exception:
def GPU_DECORATOR(*args, **kwargs):
def _wrap(fn):
return fn
return _wrap
import torch
from diffusers import DiffusionPipeline
# -----------------------
# ์ฅ์น/์ ๋ฐ๋ ์๋ ์ ํ
# -----------------------
def select_device_dtype():
if torch.cuda.is_available():
device = "cuda"
dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16
else:
device = "cpu"
dtype = torch.float32
return device, dtype
device, dtype = select_device_dtype()
if device == "cuda":
torch.backends.cuda.matmul.allow_tf32 = True
# ์ ํ๊ฐ
MAX_SEED = np.iinfo(np.uint32).max
MAX_SIDE = 2048
MAX_TOTAL_PIXELS = 2_359_296 # ํด์๋ ์ด ํฝ์
๊ฐ๋(์ฝ 2.36MP)
# -----------------------
# ๋ชจ๋ธ ๋ก๋ (FLUX.1-schnell)
# -----------------------
MODEL_ID = "black-forest-labs/FLUX.1-schnell"
pipe = DiffusionPipeline.from_pretrained(MODEL_ID, torch_dtype=dtype).to(device)
if hasattr(pipe, "enable_vae_slicing"):
pipe.enable_vae_slicing()
if hasattr(pipe, "enable_vae_tiling"):
pipe.enable_vae_tiling()
# -----------------------
# ์ ํธ
# -----------------------
def _clamp_hw(width: int, height: int):
width = int(max(256, min(int(width), MAX_SIDE)))
height = int(max(256, min(int(height), MAX_SIDE)))
if width * height > MAX_TOTAL_PIXELS:
scale = (MAX_TOTAL_PIXELS / (width * height)) ** 0.5
width = int((width * scale) // 32 * 32)
height = int((height * scale) // 32 * 32)
width = max(256, min(width, MAX_SIDE))
height = max(256, min(height, MAX_SIDE))
return width, height
def _validate_params(prompt: str, steps: int, guidance: float):
if not prompt or not prompt.strip():
raise ValueError("ํ๋กฌํํธ๊ฐ ๋น์ด ์์ต๋๋ค. ๋ด์ฉ์ ์
๋ ฅํด ์ฃผ์ธ์.")
if not (1 <= steps <= 50):
raise ValueError("์ถ๋ก ์คํ
์ 1~50 ๋ฒ์์ฌ์ผ ํฉ๋๋ค.")
if not (0.0 <= guidance <= 20.0):
raise ValueError("๊ฐ์ด๋์ค ์ค์ผ์ผ์ 0.0~20.0 ๋ฒ์์ฌ์ผ ํฉ๋๋ค.")
# -----------------------
# ์์ฑ ํจ์
# -----------------------
@GPU_DECORATOR()
def generate_image(prompt, seed, randomize_seed, width, height, steps, guidance_scale, progress=gr.Progress(track_tqdm=True)):
try:
prompt = prompt.strip()
_validate_params(prompt, steps, guidance_scale)
if randomize_seed:
seed = random.randint(0, MAX_SEED)
generator = torch.Generator(device=device).manual_seed(int(seed))
width, height = _clamp_hw(width, height)
progress(0.1, desc="์ด๊ธฐํ ์คโฆ")
if device == "cuda":
autocast_ctx = torch.autocast(device_type="cuda", dtype=dtype)
elif device == "cpu":
autocast_ctx = torch.autocast(device_type="cpu", dtype=dtype) if dtype != torch.float32 else torch.no_grad()
else:
autocast_ctx = torch.no_grad()
with autocast_ctx:
progress(0.4, desc="์ด๋ฏธ์ง ์์ฑ ์คโฆ")
out = pipe(
prompt=prompt,
width=int(width),
height=int(height),
num_inference_steps=int(steps),
generator=generator,
guidance_scale=float(guidance_scale)
)
image = out.images[0]
progress(1.0, desc="์๋ฃ")
return image, int(seed)
except Exception as e:
gr.Error(f"์์ฑ ์ค ์ค๋ฅ: {type(e).__name__}: {e}")
return None, int(seed)
def set_prompt(example_text):
return example_text
# -----------------------
# ํ๊ธ ์์ ํ๋กฌํํธ
# -----------------------
example_prompts = {
"์ ํ ๋์์ธ": [
"""์ฌ๋ฆญํ ์ธ๋์คํธ๋ฆฌ์ผ ์คํ์ผ์ ์ปคํผ๋จธ์ ์ปจ์
์ค์ผ์น:
- ๊ณก์ ํ ๋ฉํ ๋ฐ๋, ๋ฒ ์ ค ์ต์ํ
- ์ค์ ์ฉ ํฐ์น์คํฌ๋ฆฐ ํจ๋
- ๋ชจ๋ํ ๋งคํธ ๋ธ๋ ๋ง๊ฐ
- ์๊ทธ๋ฆผ(์ค์ผ์น) ์ปจ์
์ํธ ์คํ์ผ"""
],
"๋ง์ธ๋๋งต": [
"""์๊ทธ๋ฆผ ์คํ์ผ์ ๋ค์ฑ๋ก์ด ๋ง์ธ๋๋งต, ๊ต์ก์ฉ, ์๋๊ฐ ์๋ ์๊ฐ, ๋ช
ํํ ๊ณ์ธต ๊ตฌ์กฐ, ํฉ๊ธ๋น ๋ ์ด์์.
KNOWLEDGE
โโโ ACQUISITION [๋ฒ๊ฐ๊ฐ ์น ๋ ~60px]
โ โโโ READING [๋น๋๋ ํผ์น ์ฑ
]
โ โโโ PRACTICE [๊ณต๊ตฌ ์์ด์ฝ]
โ โโโ OBSERVATION [ํ๋๊ฒฝ ๋ ๋]
โโโ PROCESSING [๊ธฐ์ด ๋คํธ์ํฌ ~50px]
โ โโโ ANALYSIS [์์น ๊ทธ๋ํ]
โ โโโ SYNTHESIS [ํผ์ฆ ์กฐ๊ฐ]
โโโ RETENTION [๋ฉ๋ชจ๋ฆฌ ์นฉ ~45px]
โ โโโ SHORT-TERM [๋ฒ์ฉ์]
โ โโโ LONG-TERM [๊ฒฌ๊ณ ํ ์์นด์ด๋ธ]
โโโ APPLICATION
โโโ CREATION [ํ๋ ํธ]
โโโ INNOVATION [๋ณ์๋ฆฌ ์ ๊ตฌ]"""
],
"๋ชฉ์
": [
"""์๊ทธ๋ฆผ ์์ด์ดํ๋ ์ ์คํ์ผ์ ๋ชจ๋ฐ์ผ ๋ฑ
ํน ์ฑ ๋ชฉ์
:
- ๋ก๊ณ ๊ฐ ์๋ ํ์ดํ ํ๋ฉด
- ๋ก๊ทธ์ธ(์์ด๋, ๋น๋ฐ๋ฒํธ, ๋ก๊ทธ์ธ ๋ฒํผ)
- ๋์๋ณด๋ 3๊ฐ ์น์
(์์ก, ๊ฑฐ๋๋ด์ญ, ๋น ๋ฅธ ์คํ)
- ํ๋จ ๋ด๋น๊ฒ์ด์
(ํ, ์ด์ฒด, ํ๋กํ)"""
],
"์ธํฌ๊ทธ๋ํฝ": [
"""๋๊ธฐ์
์ฐ์ฐจ๋ณด๊ณ ์์ฉ ํ๋ซ ์คํ์ผ ์ธํฌ๊ทธ๋ํฝ:
- ์ ๋ชฉ: "Global Renewable Energy Trends 2025"
- ๋ถ์ : "์์ฅ์ ์ ์จ๊ณผ ์ฑ์ฅ๋ถ์"
- ์๊ฐ ์์:
- ์ง์ญ๋ณ ํ์๊ดยทํ๋ ฅยท์๋ ฅ ์์ฐ๋ ๋ง๋๊ทธ๋ํ
- ์๋์ง ๋น์ค ํ์ด์ฐจํธ: ํ์๊ด(45%)ยทํ๋ ฅ(30%)ยท์๋ ฅ(25%)
- ์ฐ๋๋ณ ์ฑ์ฅ ์ถ์ธ์
- ์์ด์ฝ: ๋ฏธ๋๋ฉ ํ์, ํ๋ ฅ ํฐ๋น, ๋ฌผ๋ฐฉ์ธ
- ๋ ์ด์์: ๊ทธ๋ฆฌ๋ ๊ธฐ๋ฐ, ํ์คํ
ํฌ์ธํธ, ํ์ดํธ ์คํ์ด์ค ์ถฉ๋ถ
- ์ฃผ์: KPI ํต์ฌ ์์น ๋ฐ ์ ๋ง ์ฝ์์"""
],
"๋ค์ด์ด๊ทธ๋จ": [
"""์๋ํฌ์๋ ๋น์ฆ๋์ค ์ํฌํ๋ก ๋ค์ด์ด๊ทธ๋จ(์๊ทธ๋ฆผ, ๊ต์ก์ ยท์ ๋ฌธ์ ):
- ์ ๋ชฉ: "ํตํฉ ๋น์ฆ๋์ค ํ๋ก์ธ์ค"
- ๊ตฌ์ฑ:
- ์์ฅ๋ถ์(์ฐจํธ, ๊ฒฝ์์ ๋งต)
- ์ ๋ต์๋ฆฝ(๋ธ๋ ์ธ์คํ ๋ฐ ํด๋ผ์ฐ๋, ํต์ฌ ํฌ์ปค์ค)
- ์ ํ์ค๊ณ(์ค์ผ์น, ํผ๋๋ฐฑ ๋ฃจํ)
- ๊ตฌํ(ํ์๋ผ์ธ ๋ง์ปค, ๋ฆฌ์์ค ์์ด์ฝ)
- ์ถ์ ํ ๋ฆฌ๋ทฐ(์งํ, ์ง์ ๊ฐ์ )
- ๋ช
ํํ ๋ฐฉํฅ ํ์ดํ, ์์์ผ๋ก ๋จ๊ณ ๊ตฌ๋ถ"""
],
"ํ๋ก์ฐ์ฐจํธ": [
"""์๊ทธ๋ฆผ ์คํ์ผ ํ๋ก์ฐ์ฐจํธ, ์ ๋ช
ํ ์, ๋ฏธ๋๋ฉ ์์ด์ฝ.
BUSINESS WORKFLOW
โโโ ์์ [์ด๋ก ๋ฒํผ ~40px]
โ โโโ ์๊ตฌ์ฌํญ ์์ง [ํด๋ ์์ด์ฝ]
โ โโโ ๋ฐ์ดํฐ ๋ถ์ [์ฐจํธ ์์ด์ฝ]
โโโ ๊ตฌํ [์ฝ๋ฉ ์ฌ๋ณผ ~50px]
โ โโโ ํ๋ก ํธ์๋ [๋ธ๋ผ์ฐ์ ์์ด์ฝ]
โ โโโ ๋ฐฑ์๋ [์๋ฒ ์์ด์ฝ]
โโโ ํ
์คํธ & ํตํฉ [๊ธฐ์ด ์์ด์ฝ ~45px]
โโโ ๋ฐฐํฌ
โโโ ์ข
๋ฃ [์ฒด์ปค๊ธฐ ๊น๋ฐ ~40px]"""
]
}
# -----------------------
# Gradio UI (ํ๊ตญ์ด)
# -----------------------
css = """
* { box-sizing: border-box; }
body {
background: linear-gradient(135deg, #667eea, #764ba2);
font-family: 'Pretendard', 'Apple SD Gothic Neo', 'Noto Sans KR', 'Helvetica Neue', Arial, sans-serif;
color: #333; margin: 0; padding: 0;
}
.gradio-container {
background: rgba(255, 255, 255, 0.95);
border-radius: 15px;
padding: 30px 40px;
box-shadow: 0 8px 30px rgba(0, 0, 0, 0.3);
margin: 40px auto;
width: 1200px;
overflow: visible !important;
}
.sidebar {
background: rgba(255, 255, 255, 0.98);
border-radius: 10px;
padding: 20px;
box-shadow: 0 4px 15px rgba(0, 0, 0, 0.2);
position: relative; z-index: 10;
overflow: visible !important;
}
button, .btn {
background: linear-gradient(90deg, #ff8a00, #e52e71);
border: none; color: #fff;
padding: 12px 24px; text-transform: uppercase;
font-weight: bold; letter-spacing: 1px;
border-radius: 5px; cursor: pointer;
transition: transform 0.2s ease-in-out;
}
button:hover, .btn:hover { transform: scale(1.05); }
.example-accordion { width: 100% !important; max-width: 100% !important; }
.example-accordion button { width: auto !important; white-space: normal !important; }
"""
with gr.Blocks(css=css, title="์ํฌํ๋ก ์บ๋ฒ์ค") as demo:
gr.Markdown(
"""
<div style="text-align:center;">
<h1>์ํฌํ๋ก ์บ๋ฒ์ค</h1>
<p>์ฌ๋ฌ ํญ์์ ๋น์ฆ๋์ค์ ํ์ํ ๋์์ธ ์ปจ์
๊ณผ ์ํฌํ๋ก ๋ค์ด์ด๊ทธ๋จ์ ์์ฑํด ๋ณด์ธ์.</p>
<p><strong>์ปค๋ฎค๋ํฐ:</strong> <a href="https://discord.gg/openfreeai" target="_blank">https://discord.gg/openfreeai</a></p>
</div>
"""
)
gr.HTML(
"""<a href="https://visitorbadge.io/status?path=https%3A%2F%2Fginigen-Workflow-Canvas.hf.space">
<img src="https://api.visitorbadge.io/api/visitors?path=https%3A%2F%2Fginigen-Workflow-Canvas.hf.space&countColor=%23263759" alt="๋ฐฉ๋ฌธ์ ๋ฐฐ์ง"/>
</a>"""
)
with gr.Row():
# ์ผ์ชฝ ์ฌ์ด๋๋ฐ: ๊ณตํต ํ๋ผ๋ฏธํฐ
with gr.Column(scale=2, elem_classes="sidebar"):
gr.Markdown("### ์์ฑ ํ๋ผ๋ฏธํฐ")
size_preset = gr.Dropdown(
label="ํด์๋ ํ๋ฆฌ์
",
choices=[
"1024x1024", "1536x1024", "1024x1536",
"1344x1344", "1536x1536", "1920x1080", "1080x1920"
],
value="1024x1024"
)
width_slider = gr.Slider(label="๊ฐ๋ก ํด์๋(px)", minimum=256, maximum=MAX_SIDE, step=32, value=1024)
height_slider = gr.Slider(label="์ธ๋ก ํด์๋(px)", minimum=256, maximum=MAX_SIDE, step=32, value=1024)
def apply_preset(preset):
w, h = map(int, preset.split("x"))
w, h = _clamp_hw(w, h)
return w, h
size_preset.change(fn=apply_preset, inputs=size_preset, outputs=[width_slider, height_slider])
seed_slider = gr.Slider(label="์๋(Seed)", minimum=0, maximum=int(MAX_SEED), step=1, value=42)
randomize_seed = gr.Checkbox(label="์๋ ๋๋ค", value=True)
def toggle_seed(disable):
return gr.update(interactive=not disable)
randomize_seed.change(fn=toggle_seed, inputs=randomize_seed, outputs=seed_slider)
steps_slider = gr.Slider(label="์ถ๋ก ์คํ
", minimum=1, maximum=50, step=1, value=20)
guidance_slider = gr.Slider(label="๊ฐ์ด๋์ค ์ค์ผ์ผ", minimum=0.0, maximum=20.0, step=0.5, value=7.5)
# ๋ฉ์ธ: ํญ UI
with gr.Column(scale=8):
with gr.Tabs():
def build_tab(tab_title, ex_key, placeholder_text):
with gr.Tab(tab_title):
tb = gr.Textbox(
label=f"{tab_title} ํ๋กฌํํธ",
placeholder=placeholder_text,
lines=5,
value=example_prompts[ex_key][0]
)
btn = gr.Button(f"{tab_title} ์์ฑ")
img = gr.Image(label=f"{tab_title} ๊ฒฐ๊ณผ", type="pil", value=None, height=512)
with gr.Accordion("์์ ํ๋กฌํํธ", open=True, elem_classes="example-accordion"):
for ex in example_prompts[ex_key]:
gr.Button(ex, variant="secondary").click(
fn=lambda ex=ex: set_prompt(ex),
outputs=tb
)
btn.click(
fn=generate_image,
inputs=[tb, seed_slider, randomize_seed, width_slider, height_slider, steps_slider, guidance_slider],
outputs=[img, seed_slider]
)
build_tab("์ ํ ๋์์ธ", "์ ํ ๋์์ธ", "์ ํ ๋์์ธ ์ปจ์
์ ์
๋ ฅํ์ธ์โฆ")
build_tab("๋ง์ธ๋๋งต", "๋ง์ธ๋๋งต", "๋ง์ธ๋๋งต ์ค๋ช
์ ์
๋ ฅํ์ธ์โฆ")
build_tab("๋ชฉ์
", "๋ชฉ์
", "์ฑ/์น ๋ชฉ์
์ค๋ช
์ ์
๋ ฅํ์ธ์โฆ")
build_tab("์ธํฌ๊ทธ๋ํฝ", "์ธํฌ๊ทธ๋ํฝ", "์ธํฌ๊ทธ๋ํฝ ์ค๋ช
์ ์
๋ ฅํ์ธ์โฆ")
build_tab("๋ค์ด์ด๊ทธ๋จ", "๋ค์ด์ด๊ทธ๋จ", "๋ค์ด์ด๊ทธ๋จ ์ค๋ช
์ ์
๋ ฅํ์ธ์โฆ")
build_tab("ํ๋ก์ฐ์ฐจํธ", "ํ๋ก์ฐ์ฐจํธ", "ํ๋ก์ฐ์ฐจํธ ์ค๋ช
์ ์
๋ ฅํ์ธ์โฆ")
if __name__ == "__main__":
demo.queue(concurrency_count=2, max_size=32)
demo.launch(
server_name="0.0.0.0",
server_port=7860,
share=False,
show_error=True,
debug=True
)
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