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import spaces | |
import gradio as gr | |
import torch | |
from PIL import Image | |
from diffusers import DiffusionPipeline | |
import random | |
from transformers import pipeline | |
torch.backends.cudnn.deterministic = True | |
torch.backends.cudnn.benchmark = False | |
torch.backends.cuda.matmul.allow_tf32 = True | |
# ๋ฒ์ญ ๋ชจ๋ธ ์ด๊ธฐํ | |
translator = pipeline("translation", model="Helsinki-NLP/opus-mt-ko-en") | |
# ๊ธฐ๋ณธ ๋ชจ๋ธ ๋ฐ LoRA ์ค์ | |
base_model = "black-forest-labs/FLUX.1-dev" | |
model_lora_repo = "Motas/Flux_Fashion_Photography_Style" # ํจ์ ๋ชจ๋ธ LoRA | |
clothes_lora_repo = "prithivMLmods/Canopus-Clothing-Flux-LoRA" # ์๋ฅ LoRA | |
pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16) | |
pipe.to("cuda") | |
MAX_SEED = 2**32-1 | |
def generate_fashion(prompt, mode, cfg_scale, steps, randomize_seed, seed, width, height, lora_scale, progress=gr.Progress(track_tqdm=True)): | |
# ํ๊ธ ๊ฐ์ง ๋ฐ ๋ฒ์ญ | |
def contains_korean(text): | |
return any(ord('๊ฐ') <= ord(char) <= ord('ํฃ') for char in text) | |
if contains_korean(prompt): | |
translated = translator(prompt)[0]['translation_text'] | |
actual_prompt = translated | |
else: | |
actual_prompt = prompt | |
# ๋ชจ๋์ ๋ฐ๋ฅธ LoRA ๋ฐ ํธ๋ฆฌ๊ฑฐ์๋ ์ค์ | |
if mode == "Generate Model": | |
pipe.load_lora_weights(model_lora_repo) | |
trigger_word = "fashion photography, professional model" | |
else: | |
pipe.load_lora_weights(clothes_lora_repo) | |
trigger_word = "upper clothing, fashion item" | |
if randomize_seed: | |
seed = random.randint(0, MAX_SEED) | |
generator = torch.Generator(device="cuda").manual_seed(seed) | |
progress(0, "Starting fashion generation...") | |
for i in range(1, steps + 1): | |
if i % (steps // 10) == 0: | |
progress(i / steps * 100, f"Processing step {i} of {steps}...") | |
image = pipe( | |
prompt=f"{actual_prompt} {trigger_word}", | |
num_inference_steps=steps, | |
guidance_scale=cfg_scale, | |
width=width, | |
height=height, | |
generator=generator, | |
joint_attention_kwargs={"scale": lora_scale}, | |
).images[0] | |
progress(100, "Completed!") | |
return image, seed | |
def generate_image(prompt, structure_image, style_image, depth_strength, style_strength): | |
# ์ค์ ์ด๋ฏธ์ง ์์ฑ ๋ก์ง์ ์ฌ๊ธฐ์ ๊ตฌํ | |
return Image.new('RGB', (512, 512), 'white') | |
with gr.Blocks(theme="Yntec/HaleyCH_Theme_Orange") as app: | |
gr.Markdown("# ๐ญ Fashion AI Studio") | |
gr.Markdown("Generate fashion images and try on virtual clothing using AI") | |
with gr.Tabs(): | |
# Virtual Try-On ํญ | |
with gr.TabItem("๐ Virtual Try-On"): | |
with gr.Row(): | |
with gr.Column(): | |
prompt_input = gr.Textbox( | |
label="Style Description", | |
placeholder="Describe the desired style (e.g., 'person wearing elegant dress')" | |
) | |
with gr.Row(): | |
with gr.Group(): | |
structure_image = gr.Image( | |
label="Your Photo (Full-body)", | |
type="filepath" | |
) | |
gr.Markdown("*Upload a clear, well-lit full-body photo*") | |
depth_strength = gr.Slider( | |
minimum=0, | |
maximum=50, | |
value=15, | |
label="Fitting Strength" | |
) | |
with gr.Group(): | |
style_image = gr.Image( | |
label="Clothing Item", | |
type="filepath" | |
) | |
gr.Markdown("*Upload the clothing item you want to try on*") | |
style_strength = gr.Slider( | |
minimum=0, | |
maximum=1, | |
value=0.5, | |
label="Style Transfer Strength" | |
) | |
tryon_btn = gr.Button("Generate Try-On") | |
with gr.Column(): | |
output_image = gr.Image(label="Generated Result") | |
# Fashion Generation ํญ | |
with gr.TabItem("๐ Fashion Generation"): | |
with gr.Column(): | |
# ๋ชจ๋ ์ ํ | |
with gr.Group(): | |
mode = gr.Radio( | |
choices=["Generate Model", "Generate Clothes"], | |
label="Generation Mode", | |
value="Generate Model" | |
) | |
# ํ๋กฌํํธ ์ ๋ ฅ | |
prompt = gr.TextArea( | |
label="โ๏ธ Fashion Description (ํ๊ธ ๋๋ ์์ด)", | |
placeholder="ํจ์ ๋ชจ๋ธ์ด๋ ์๋ฅ๋ฅผ ์ค๋ช ํ์ธ์...", | |
lines=5 | |
) | |
# ๊ฒฐ๊ณผ ์ด๋ฏธ์ง | |
result = gr.Image(label="Generated Fashion") | |
generate_button = gr.Button("๐ Generate Fashion") | |
# ๊ณ ๊ธ ์ค์ ์์ฝ๋์ธ | |
with gr.Accordion("๐จ Advanced Options", open=False): | |
with gr.Row(): | |
cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, value=7.0) | |
steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=30) | |
lora_scale = gr.Slider(label="LoRA Scale", minimum=0, maximum=1, value=0.85) | |
with gr.Row(): | |
width = gr.Slider(label="Width", minimum=256, maximum=1536, value=512) | |
height = gr.Slider(label="Height", minimum=256, maximum=1536, value=768) | |
with gr.Row(): | |
randomize_seed = gr.Checkbox(True, label="Randomize seed") | |
seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, value=42) | |
# ์ด๋ฒคํธ ํธ๋ค๋ฌ | |
tryon_btn.click( | |
fn=generate_image, | |
inputs=[prompt_input, structure_image, style_image, depth_strength, style_strength], | |
outputs=[output_image] | |
) | |
generate_button.click( | |
generate_fashion, | |
inputs=[prompt, mode, cfg_scale, steps, randomize_seed, seed, width, height, lora_scale], | |
outputs=[result, seed] | |
) | |
if __name__ == "__main__": | |
app.launch(share=True) |