app.py

import gradio as gr
import spaces
import torch
import diffusers
import transformers
import copy
import random
import numpy as np
import torchvision.transforms as T
import math
import os
import peft
from peft import LoraConfig
from safetensors import safe_open
from omegaconf import OmegaConf
from omnitry.models.transformer_flux import FluxTransformer2DModel
from omnitry.pipelines.pipeline_flux_fill import FluxFillPipeline

from huggingface_hub import snapshot_download
snapshot_download(repo_id="Kunbyte/OmniTry", local_dir="./OmniTry")

device = torch.device('cuda:0')
weight_dtype = torch.bfloat16
args = OmegaConf.load('configs/omnitry_v1_unified.yaml')

# init model
transformer = FluxTransformer2DModel.from_pretrained(
    'black-forest-labs/FLUX.1-Fill-dev',
    subfolder='transformer'
).requires_grad_(False).to(device, dtype=weight_dtype)

pipeline = FluxFillPipeline.from_pretrained(
    'black-forest-labs/FLUX.1-Fill-dev',
    transformer=transformer,
    torch_dtype=weight_dtype
).to(device)

# insert LoRA
lora_config = LoraConfig(
    r=args.lora_rank,
    lora_alpha=args.lora_alpha,
    init_lora_weights="gaussian",
    target_modules=[
        'x_embedder',
        'attn.to_k', 'attn.to_q', 'attn.to_v', 'attn.to_out.0',
        'attn.add_k_proj', 'attn.add_q_proj', 'attn.add_v_proj', 'attn.to_add_out',
        'ff.net.0.proj', 'ff.net.2', 'ff_context.net.0.proj', 'ff_context.net.2',
        'norm1_context.linear', 'norm1.linear', 'norm.linear', 'proj_mlp', 'proj_out'
    ]
)
transformer.add_adapter(lora_config, adapter_name='vtryon_lora')
transformer.add_adapter(lora_config, adapter_name='garment_lora')

with safe_open('OmniTry/omnitry_v1_unified.safetensors', framework="pt") as f:
    lora_weights = {k: f.get_tensor(k) for k in f.keys()}
    transformer.load_state_dict(lora_weights, strict=False)

# hack lora forward
def create_hacked_forward(module):

    def lora_forward(self, active_adapter, x, *args, **kwargs):
        result = self.base_layer(x, *args, **kwargs)
        if active_adapter is not None:
            lora_A = self.lora_A[active_adapter]
            lora_B = self.lora_B[active_adapter]
            dropout = self.lora_dropout[active_adapter]
            scaling = self.scaling[active_adapter]
            x = x.to(lora_A.weight.dtype)
            result = result + lora_B(lora_A(dropout(x))) * scaling
        return result

    def hacked_lora_forward(self, x, *args, **kwargs):
        return torch.cat((
            lora_forward(self, 'vtryon_lora', x[:1], *args, **kwargs),
            lora_forward(self, 'garment_lora', x[1:], *args, **kwargs),
        ), dim=0)

    return hacked_lora_forward.__get__(module, type(module))

for n, m in transformer.named_modules():
    if isinstance(m, peft.tuners.lora.layer.Linear):
        m.forward = create_hacked_forward(m)


def seed_everything(seed=0):
    random.seed(seed)
    os.environ['PYTHONHASHSEED'] = str(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)


@spaces.GPU
def generate(person_image, object_image, object_class, steps, guidance_scale, seed):
    if seed == -1:
        seed = random.randint(0, 2**32 - 1)
    seed_everything(seed)

    max_area = 1024 * 1024
    oW, oH = person_image.width, person_image.height
    ratio = math.sqrt(max_area / (oW * oH))
    ratio = min(1, ratio)
    tW, tH = int(oW * ratio) // 16 * 16, int(oH * ratio) // 16 * 16
    transform = T.Compose([
        T.Resize((tH, tW)),
        T.ToTensor(),
    ])
    person_image = transform(person_image)

    ratio = min(tW / object_image.width, tH / object_image.height)
    transform = T.Compose([
        T.Resize((int(object_image.height * ratio), int(object_image.width * ratio))),
        T.ToTensor(),
    ])
    object_image_padded = torch.ones_like(person_image)
    object_image = transform(object_image)
    new_h, new_w = object_image.shape[1], object_image.shape[2]
    min_x = (tW - new_w) // 2
    min_y = (tH - new_h) // 2
    object_image_padded[:, min_y: min_y + new_h, min_x: min_x + new_w] = object_image

    prompts = [args.object_map[object_class]] * 2
    img_cond = torch.stack([person_image, object_image_padded]).to(dtype=weight_dtype, device=device)
    mask = torch.zeros_like(img_cond).to(img_cond)

    with torch.no_grad():
        img = pipeline(
            prompt=prompts,
            height=tH,
            width=tW,
            img_cond=img_cond,
            mask=mask,
            guidance_scale=guidance_scale,
            num_inference_steps=steps,
            generator=torch.Generator(device).manual_seed(seed),
        ).images[0]

    return img


# Custom CSS
custom_css = """
/* 전체 배경 */
.gradio-container {
    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
    font-family: 'Inter', sans-serif;
}

/* === 플레이스홀더 전부 제거 === */
.gr-image svg,
.gr-image [data-testid*="placeholder"],
.gr-image [class*="placeholder"],
.gr-image [aria-label*="placeholder"],
.gr-image [class*="svelte"][class*="placeholder"],
.gr-image .absolute.inset-0.flex.items-center.justify-center,
.gr-image .flex.items-center.justify-center svg {
    display: none !important;
    visibility: hidden !important;
}
.gr-image [class*="overlay"],
.gr-image .fixed.inset-0,
.gr-image .absolute.inset-0 {
    pointer-events: none !important;
}

/* 이미지 업로드 영역 */
.gr-image .wrap { background: transparent !important; min-height: 400px !important; }
.gr-image .upload-container {
    min-height: 400px !important;
    border: 3px dashed rgba(102, 126, 234, 0.4) !important;
    border-radius: 12px !important;
    background: linear-gradient(135deg, rgba(248, 250, 252, 0.5) 0%, rgba(241, 245, 249, 0.5) 100%) !important;
    position: relative !important;
}
/* 이미지 있을 때 */
.gr-image:has(img) .upload-container { border: none !important; background: transparent !important; }
/* 안내 텍스트 */
.gr-image .upload-container::after {
    content: "Click or Drag to Upload";
    position: absolute; top: 50%; left: 50%;
    transform: translate(-50%, -50%);
    color: rgba(102, 126, 234, 0.7);
    font-size: 1.05em; font-weight: 500;
    pointer-events: none;
}
.gr-image:has(img) .upload-container::after { display: none !important; }
/* 업로드 이미지 */
.gr-image img { border-radius: 12px !important; position: relative !important; z-index: 10 !important; }

/* 버튼, 라벨 등 나머지는 그대로 */
.gr-button-primary {
    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%) !important;
    color: white !important; border: none !important;
    padding: 15px 40px !important; font-size: 1.2em !important;
    border-radius: 50px !important; cursor: pointer !important;
}
"""

if __name__ == '__main__':

    with gr.Blocks(css=custom_css, theme=gr.themes.Soft()) as demo:
        with gr.Column(elem_id="header"):
            gr.HTML("""
                <h1>✨ CodiFit-AI Virtual Try-On ✨</h1>
                <p id="subtitle">Experience the future of fashion with AI-powered virtual clothing try-on</p>
            """)
        with gr.Row(equal_height=True):
            with gr.Column(scale=1):
                person_image = gr.Image(type="pil", label="Upload Person Photo", height=500, interactive=True)
            with gr.Column(scale=1):
                object_image = gr.Image(type="pil", label="Upload Object Image", height=400, interactive=True)
                object_class = gr.Dropdown(label='Select Object Category', choices=args.object_map.keys())
                run_button = gr.Button(value="🚀 Generate Try-On", variant='primary')
            with gr.Column(scale=1):
                image_out = gr.Image(type="pil", label="Virtual Try-On Result", height=500, interactive=False)

        with gr.Accordion("⚙️ Advanced Settings", open=False):
            with gr.Row():
                guidance_scale = gr.Slider(label="🎯 Guidance Scale", minimum=1, maximum=50, value=30, step=0.1)
                steps = gr.Slider(label="🔄 Inference Steps", minimum=1, maximum=50, value=20, step=1)
                seed = gr.Number(label="🎲 Random Seed", value=-1, precision=0)

        run_button.click(generate,
                         inputs=[person_image, object_image, object_class, steps, guidance_scale, seed],
                         outputs=[image_out])

    demo.launch(server_name="0.0.0.0")