Spaces:
Running
on
Zero
Running
on
Zero
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 | |
# λ²μ λͺ¨λΈ μ΄κΈ°ν | |
translator = pipeline("translation", model="Helsinki-NLP/opus-mt-ko-en") | |
# κΈ°λ³Έ λͺ¨λΈ λ° LoRA μ€μ | |
base_model = "black-forest-labs/FLUX.1-dev" | |
model_lora_repo = "Motas/Flux_Fashion_Photography_Style" | |
clothes_lora_repo = "prithivMLmods/Canopus-Clothing-Flux-LoRA" | |
pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16) | |
pipe.to("cuda") | |
MAX_SEED = 2**32-1 | |
# μμ ν둬ννΈ μ μ | |
model_examples = [ | |
"professional fashion model wearing elegant black dress in studio lighting", | |
"fashion model in casual street wear, urban background", | |
"high fashion model in avant-garde outfit on runway" | |
] | |
clothes_examples = [ | |
"luxurious red evening gown with detailed embroidery", | |
"casual denim jacket with vintage wash", | |
"modern minimalist white blazer with clean lines" | |
] | |
def generate_fashion(prompt, mode, cfg_scale, steps, randomize_seed, seed, width, height, lora_scale, progress=gr.Progress(track_tqdm=True)): | |
if not prompt: | |
return None, seed | |
def contains_korean(text): | |
return any(ord('κ°') <= ord(char) <= ord('ν£') for char in text) | |
if contains_korean(prompt): | |
translated = translator(prompt)[0]['translation_text'] | |
actual_prompt = translated | |
else: | |
actual_prompt = prompt | |
if mode == "ν¨μ λͺ¨λΈ μμ±": | |
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) | |
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] | |
return image, seed | |
with gr.Blocks(theme="Yntec/HaleyCH_Theme_Orange") as app: | |
gr.Markdown("# π Fashion AI Studio") | |
with gr.Column(): | |
mode = gr.Radio( | |
choices=["Person", "Clothes"], | |
label="Generation", | |
value="Fashion Model" | |
) | |
prompt = gr.TextArea( | |
label="βοΈ Prompt (νκΈ μ§μ)", | |
placeholder="Text Input Prompt", | |
lines=3 | |
) | |
# μμ μΉμ μ λͺ¨λλ³λ‘ λΆλ¦¬ | |
with gr.Column(visible=True) as model_examples_container: | |
gr.Examples( | |
examples=model_examples, | |
inputs=prompt, | |
label="Examples(person)" | |
) | |
with gr.Column(visible=False) as clothes_examples_container: | |
gr.Examples( | |
examples=clothes_examples, | |
inputs=prompt, | |
label="Examples(clothes)" | |
) | |
result = gr.Image(label="Generated Image") | |
generate_button = gr.Button("π START") | |
with gr.Accordion("π¨ OPTION", 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="μλ λλ€ν") | |
seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, value=42) | |
def update_visibility(mode): | |
return ( | |
gr.update(visible=(mode == "Person")), | |
gr.update(visible=(mode == "Clothes")) | |
) | |
mode.change( | |
fn=update_visibility, | |
inputs=[mode], | |
outputs=[model_examples_container, clothes_examples_container] | |
) | |
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) |