update examples

app.py

@@ -1,626 +1,3 @@
-# import os
-# import sys
-
-# # ---------------------------------------------------------
-# # 0) Make sure local packages (diffusers3, preprocess, etc.) are importable on HF Spaces
-# # ---------------------------------------------------------
-# ROOT = os.path.dirname(os.path.abspath(__file__))
-# if ROOT not in sys.path:
-#     sys.path.insert(0, ROOT)
-
-# print("[BOOT] ROOT =", ROOT, flush=True)
-# print("[BOOT] sys.path[:5] =", sys.path[:5], flush=True)
-
-# import tempfile
-# from dataclasses import dataclass
-# from functools import lru_cache
-# from typing import Optional, Tuple
-
-# import gradio as gr
-# import torch
-# import numpy as np
-# import cv2
-# import imageio
-# from PIL import Image, ImageOps
-# from transformers import pipeline
-# from huggingface_hub import hf_hub_download
-
-# import diffusers3
-# print("[BOOT] diffusers3 loaded from:", getattr(diffusers3, "__file__", "<?>"), flush=True)
-
-# from diffusers import UniPCMultistepScheduler, AutoencoderKL, UNet2DConditionModel
-# from diffusers3.models.controlnet import ControlNetModel
-# from diffusers3.pipelines.controlnet.pipeline_controlnet_sd_xl_img2img_img import (
-#     StableDiffusionXLControlNetImg2ImgPipeline,
-# )
-# from ip_adapter import IPAdapterXL
-
-# # extractor
-# from preprocess.simple_extractor import run as run_simple_extractor
-
-
-# # =========================
-# # HF Hub repo ids
-# # =========================
-# BASE_MODEL_ID = "stabilityai/stable-diffusion-xl-base-1.0"
-# CONTROLNET_ID = "diffusers/controlnet-depth-sdxl-1.0"
-
-# # assets dataset repo
-# ASSETS_REPO = os.getenv("ASSETS_REPO", "soye/VISTA_assets")
-# ASSETS_REPO_TYPE = "dataset"
-
-# depth_estimator = pipeline("depth-estimation")
-
-
-# def asset_path(relpath: str) -> str:
-#     return hf_hub_download(
-#         repo_id=ASSETS_REPO,
-#         repo_type=ASSETS_REPO_TYPE,
-#         filename=relpath,
-#     )
-
-
-# @lru_cache(maxsize=1)
-# def get_assets():
-#     print("[ASSETS] Downloading assets from:", ASSETS_REPO, flush=True)
-
-#     image_encoder_weight = asset_path("image_encoder/model.safetensors")
-#     _ = asset_path("image_encoder/config.json")
-#     image_encoder_dir = os.path.dirname(image_encoder_weight)
-
-#     ip_ckpt = asset_path("ip_adapter/ip-adapter_sdxl_vit-h.bin")
-#     schp_ckpt = asset_path("preprocess_ckpts/exp-schp-201908301523-atr.pth")
-
-#     print("[ASSETS] image_encoder_dir =", image_encoder_dir, flush=True)
-#     print("[ASSETS] ip_ckpt =", ip_ckpt, flush=True)
-#     print("[ASSETS] schp_ckpt =", schp_ckpt, flush=True)
-#     return image_encoder_dir, ip_ckpt, schp_ckpt
-
-
-# DEFAULT_STEPS = 40
-# DEBUG_SAVE = False
-
-# H: Optional[int] = None
-# W: Optional[int] = None
-
-
-# @dataclass
-# class Paths:
-#     person_path: str
-#     depth_path: Optional[str]  # sketch(guide) optional
-#     style_path: str
-#     output_path: str
-
-
-# def _imread_or_raise(path: str, flag=cv2.IMREAD_COLOR):
-#     img = cv2.imread(path, flag)
-#     if img is None:
-#         raise FileNotFoundError(f"cv2.imread failed: {path} (exists={os.path.exists(path)})")
-#     return img
-
-
-# def _pad_or_crop_to_width_np(arr: np.ndarray, target_width: int, pad_value):
-#     """
-#     arr: HxWxC or HxW
-#     target_width로 center crop 또는 좌/우 padding(비대칭 포함)해서 정확히 맞춤.
-#     """
-#     if arr.ndim not in (2, 3):
-#         raise ValueError(f"arr must be 2D or 3D, got shape={arr.shape}")
-
-#     h = arr.shape[0]
-#     w = arr.shape[1]
-
-#     if w == target_width:
-#         return arr
-
-#     if w > target_width:
-#         left = (w - target_width) // 2
-#         return arr[:, left:left + target_width] if arr.ndim == 2 else arr[:, left:left + target_width, :]
-
-#     # w < target_width: pad
-#     total = target_width - w
-#     left = total // 2
-#     right = total - left  # ✅ remainder를 오른쪽이 먹어서 항상 정확히 target_width
-
-#     if arr.ndim == 2:
-#         return cv2.copyMakeBorder(
-#             arr, 0, 0, left, right,
-#             borderType=cv2.BORDER_CONSTANT,
-#             value=pad_value,
-#         )
-#     else:
-#         # 3채널일 때 value는 스칼라 or [b,g,r]/[r,g,b] 모두 허용되는데,
-#         # 여기선 arr가 RGB/BGR인지 호출자가 정해줌.
-#         return cv2.copyMakeBorder(
-#             arr, 0, 0, left, right,
-#             borderType=cv2.BORDER_CONSTANT,
-#             value=pad_value,
-#         )
-
-
-# def apply_parsing_white_mask_to_person_cv2(
-#     person_pil: Image.Image,
-#     parsing_img: Image.Image
-# ) -> np.ndarray:
-#     person_rgb = np.array(person_pil.convert("RGB"), dtype=np.uint8)
-#     mask = np.array(parsing_img.convert("L"), dtype=np.uint8)
-
-#     if mask.shape[:2] != person_rgb.shape[:2]:
-#         mask = cv2.resize(mask, (person_rgb.shape[1], person_rgb.shape[0]), interpolation=cv2.INTER_NEAREST)
-
-#     white_mask = (mask == 255)
-#     result_rgb = np.full_like(person_rgb, 255, dtype=np.uint8)
-#     result_rgb[white_mask] = person_rgb[white_mask]
-#     result_bgr = cv2.cvtColor(result_rgb, cv2.COLOR_RGB2BGR)
-#     return result_bgr
-
-
-# def clean_and_smooth_parsing_mask(
-#     parsing_img: Image.Image,
-#     *,
-#     white_threshold: int = 128,
-#     min_white_area: int = 300,
-#     close_ksize: int = 7,
-#     open_ksize: int = 3,
-#     morph_iters: int = 1,
-#     blur_ksize: int = 0,
-# ) -> Image.Image:
-#     if not isinstance(parsing_img, Image.Image):
-#         raise TypeError("parsing_img must be a PIL.Image.Image")
-
-#     arr = np.array(parsing_img.convert("L"), dtype=np.uint8)
-#     mask = np.where(arr >= white_threshold, 255, 0).astype(np.uint8)
-
-#     num_labels, labels, stats, _ = cv2.connectedComponentsWithStats(mask, connectivity=8)
-#     keep = np.zeros_like(mask)
-#     for lab in range(1, num_labels):
-#         area = int(stats[lab, cv2.CC_STAT_AREA])
-#         if area >= min_white_area:
-#             keep[labels == lab] = 255
-#     mask = keep
-
-#     def _odd_or_one(k: int) -> int:
-#         k = int(k)
-#         if k <= 1:
-#             return 1
-#         return k if (k % 2 == 1) else (k + 1)
-
-#     close_k = _odd_or_one(close_ksize)
-#     open_k = _odd_or_one(open_ksize)
-
-#     if close_k > 1:
-#         k_close = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (close_k, close_k))
-#         mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, k_close, iterations=int(morph_iters))
-
-#     if open_k > 1:
-#         k_open = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (open_k, open_k))
-#         mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, k_open, iterations=int(morph_iters))
-
-#     if blur_ksize and int(blur_ksize) > 1:
-#         b = _odd_or_one(int(blur_ksize))
-#         mask_blur = cv2.GaussianBlur(mask, (b, b), 0)
-#         mask = np.where(mask_blur >= 128, 255, 0).astype(np.uint8)
-
-#     return Image.fromarray(mask, mode="L")
-
-
-# def compute_hw_from_person(person_path: str):
-#     img = _imread_or_raise(person_path)
-#     orig_h, orig_w = img.shape[:2]
-#     scale = 1024.0 / float(orig_h)
-#     new_h = 1024
-#     new_w = int(round(orig_w * scale))
-#     if new_w > 1024:
-#         new_w = 1024
-#     return new_h, new_w
-
-
-# def fill_sketch_from_image_path_to_pil(image_path: str) -> Image.Image:
-#     global H, W
-#     if H is None or W is None:
-#         raise RuntimeError("Global H/W not set.")
-#     img = _imread_or_raise(image_path, cv2.IMREAD_GRAYSCALE)
-#     img = cv2.resize(img, (W, H), interpolation=cv2.INTER_NEAREST)
-#     _, binary = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY_INV)
-#     contours, _ = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-#     filled = np.zeros_like(binary)
-#     cv2.drawContours(filled, contours, -1, 255, thickness=cv2.FILLED)
-#     filled_rgb = cv2.cvtColor(filled, cv2.COLOR_GRAY2RGB)
-#     return Image.fromarray(filled_rgb)
-
-
-# def merge_white_regions_or(img1: Image.Image, img2: Image.Image) -> Image.Image:
-#     a = np.array(img1.convert("RGB"), dtype=np.uint8)
-#     b = np.array(img2.convert("RGB"), dtype=np.uint8)
-#     white_a = np.all(a == 255, axis=-1)
-#     white_b = np.all(b == 255, axis=-1)
-#     out = a.copy()
-#     out[white_a | white_b] = 255
-#     return Image.fromarray(out)
-
-
-# def preprocess_mask(mask_img: Image.Image) -> Image.Image:
-#     global H, W
-#     m = np.array(mask_img.convert("L"), dtype=np.uint8)
-
-#     if (H is not None) and (W is not None):
-#         m = cv2.resize(m, (W, H), interpolation=cv2.INTER_NEAREST)
-
-#     _, m = cv2.threshold(m, 127, 255, cv2.THRESH_BINARY)
-
-#     target_width = 1024
-#     m = _pad_or_crop_to_width_np(m, target_width, pad_value=0)
-
-#     kernel = np.ones((17, 17), np.uint8)
-#     m = cv2.dilate(m, kernel, iterations=1)
-
-#     if DEBUG_SAVE:
-#         cv2.imwrite("mask_final_1024.png", m)
-
-#     return Image.fromarray(m, mode="L").convert("RGB")
-
-
-# def make_depth(depth_path: str) -> Image.Image:
-#     global H, W
-#     if H is None or W is None:
-#         raise RuntimeError("Global H/W not set. Call run_one() first.")
-
-#     depth_img = _imread_or_raise(depth_path, 0)
-#     inverted_depth = cv2.bitwise_not(depth_img)
-#     contours, _ = cv2.findContours(inverted_depth, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-
-#     filled_depth = inverted_depth.copy()
-#     cv2.drawContours(filled_depth, contours, -1, (255), thickness=cv2.FILLED)
-
-#     filled_depth = cv2.resize(filled_depth, (W, H), interpolation=cv2.INTER_AREA)
-#     filled_depth = _pad_or_crop_to_width_np(filled_depth, 1024, pad_value=0)
-
-#     inverted_image = ImageOps.invert(Image.fromarray(filled_depth))
-
-#     with torch.inference_mode():
-#         image_depth = depth_estimator(inverted_image)["depth"]
-
-#     if DEBUG_SAVE:
-#         image_depth.save("depth.png")
-
-#     return image_depth
-
-
-# def _edges_from_parsing(parsing_img: Image.Image) -> np.ndarray:
-#     m = np.array(parsing_img.convert("L"), dtype=np.uint8)
-#     _, m_bin = cv2.threshold(m, 127, 255, cv2.THRESH_BINARY)
-#     edges = cv2.Canny(m_bin, 50, 150)
-#     edges = cv2.dilate(edges, np.ones((3, 3), np.uint8), iterations=1)
-#     return edges.astype(np.uint8)
-
-
-# def make_depth_from_parsing_edges(parsing_img: Image.Image) -> Image.Image:
-#     global H, W
-#     if H is None or W is None:
-#         raise RuntimeError("Global H/W not set. Call run_one() first.")
-
-#     depth_img = _edges_from_parsing(parsing_img)
-# #     inverted_depth = cv2.bitwise_not(depth_img)
-#     contours, _ = cv2.findContours(depth_img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
-
-#     filled_depth = depth_img.copy()
-#     cv2.drawContours(filled_depth, contours, -1, (255), thickness=cv2.FILLED)
-
-#     filled_depth = cv2.resize(filled_depth, (W, H), interpolation=cv2.INTER_AREA)
-#     filled_depth = _pad_or_crop_to_width_np(filled_depth, 1024, pad_value=0)
-
-#     inverted_image = ImageOps.invert(Image.fromarray(filled_depth))
-
-#     with torch.inference_mode():
-#         image_depth = depth_estimator(inverted_image)["depth"]
-
-#     if DEBUG_SAVE:
-#         image_depth.save("depth.png")
-
-#     return image_depth
-
-
-# def center_crop_lr_to_768x1024(arr: np.ndarray) -> np.ndarray:
-#     target_h, target_w = 1024, 768
-#     h, w = arr.shape[:2]
-#     if h != target_h:
-#         arr = cv2.resize(arr, (w, target_h), interpolation=cv2.INTER_AREA)
-#         h, w = arr.shape[:2]
-#     if w < target_w:
-#         pad = (target_w - w) // 2
-#         arr = cv2.copyMakeBorder(arr, 0, 0, pad, pad, cv2.BORDER_CONSTANT, value=[255, 255, 255])
-#         w = arr.shape[1]
-#     left = (w - target_w) // 2
-#     return arr[:, left:left + target_w]
-
-
-# def save_cropped(imgs, out_path: str):
-#     np_imgs = [np.asarray(im) for im in imgs]
-#     cropped = [center_crop_lr_to_768x1024(x) for x in np_imgs]
-#     out = np.concatenate(cropped, axis=1)
-#     os.makedirs(os.path.dirname(out_path), exist_ok=True)
-#     imageio.imsave(out_path, out)
-
-
-# @lru_cache(maxsize=1)
-# def get_pipe_and_device() -> Tuple[StableDiffusionXLControlNetImg2ImgPipeline, str, torch.dtype]:
-#     device = "cuda" if torch.cuda.is_available() else "cpu"
-#     dtype = torch.float32
-
-#     print(f"[PIPE] device={device}, dtype={dtype}", flush=True)
-
-#     controlnet = ControlNetModel.from_pretrained(
-#         CONTROLNET_ID,
-#         torch_dtype=dtype,
-#         use_safetensors=True,
-#     ).to(device)
-
-#     vae = AutoencoderKL.from_pretrained(
-#         BASE_MODEL_ID,
-#         subfolder="vae",
-#         torch_dtype=dtype,
-#         use_safetensors=True,
-#     ).to(device)
-
-#     unet = UNet2DConditionModel.from_pretrained(
-#         BASE_MODEL_ID,
-#         subfolder="unet",
-#         torch_dtype=dtype,
-#         use_safetensors=True,
-#     ).to(device)
-
-#     pipe = StableDiffusionXLControlNetImg2ImgPipeline.from_pretrained(
-#         BASE_MODEL_ID,
-#         controlnet=controlnet,
-#         vae=vae,
-#         unet=unet,
-#         torch_dtype=dtype,
-#         use_safetensors=True,
-#         add_watermarker=False,
-#     ).to(device)
-
-#     if device == "cuda":
-#         try:
-#             pipe.vae.to(dtype=dtype)
-#             if hasattr(pipe.vae, "config") and hasattr(pipe.vae.config, "force_upcast"):
-#                 pipe.vae.config.force_upcast = False
-#         except Exception as e:
-#             print("[PIPE] VAE dtype cast failed:", repr(e), flush=True)
-
-#     pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
-#     pipe.enable_attention_slicing()
-#     try:
-#         pipe.enable_xformers_memory_efficient_attention()
-#     except Exception as e:
-#         print("[PIPE] xformers not enabled:", repr(e), flush=True)
-
-#     return pipe, device, dtype
-
-
-# # UI 표기 → 내부 extractor category 문자열 매핑
-# _UI_TO_EXTRACTOR_CATEGORY = {
-#     "Upper-body": "Upper-cloth",
-#     "Lower-body": "Bottom",
-#     "Dress": "Dress",
-# }
-
-
-# def run_one(paths: Paths, prompt: str, steps: int = DEFAULT_STEPS, category: str = "Dress"):
-#     global H, W
-#     pipe, device, _dtype = get_pipe_and_device()
-#     image_encoder_dir, ip_ckpt, schp_ckpt = get_assets()
-
-#     H, W = compute_hw_from_person(paths.person_path)
-
-#     extractor_category = _UI_TO_EXTRACTOR_CATEGORY.get(category, "Dress")
-
-#     res = run_simple_extractor(
-#         category=extractor_category,
-#         input_path=os.path.abspath(paths.person_path),
-#         model_restore=schp_ckpt,
-#     )
-#     parsing_img = res["images"][0] if res.get("images") else None
-#     if parsing_img is None:
-#         raise RuntimeError("run_simple_extractor returned no parsing images.")
-
-#     parsing_img = clean_and_smooth_parsing_mask(
-#         parsing_img,
-#         min_white_area=50,
-#         close_ksize=9,
-#         open_ksize=3,
-#         morph_iters=1,
-#         blur_ksize=7,
-#     )
-
-#     use_depth_path = (
-#         paths.depth_path is not None
-#         and isinstance(paths.depth_path, str)
-#         and len(paths.depth_path) > 0
-#         and os.path.exists(paths.depth_path)
-#     )
-
-#     if use_depth_path:
-#         sketch_area = fill_sketch_from_image_path_to_pil(paths.depth_path)
-#     else:
-#         sketch_area = parsing_img.convert("RGB")
-
-#     merged_img = merge_white_regions_or(parsing_img, sketch_area)
-#     mask_pil = preprocess_mask(merged_img)
-
-#     # person
-#     person_bgr = _imread_or_raise(paths.person_path)
-#     person_bgr = cv2.resize(person_bgr, (W, H), interpolation=cv2.INTER_AREA)
-#     person_bgr = _pad_or_crop_to_width_np(person_bgr, 1024, pad_value=[255, 255, 255])
-#     person_rgb = cv2.cvtColor(person_bgr, cv2.COLOR_BGR2RGB)
-#     person_pil = Image.fromarray(person_rgb)
-
-#     # depth
-#     if use_depth_path:
-#         depth_map = make_depth(paths.depth_path)
-#     else:
-#         depth_map = make_depth_from_parsing_edges(parsing_img)
-
-#     # garment image (✅ 여기서부터가 핵심: 1024 폭 강제)
-#     personn = Image.open(paths.person_path).convert("RGB")
-#     garment_bgr = apply_parsing_white_mask_to_person_cv2(personn, parsing_img)
-#     garment_rgb = cv2.cvtColor(garment_bgr, cv2.COLOR_BGR2RGB)
-#     garment_rgb = cv2.resize(garment_rgb, (W, H), interpolation=cv2.INTER_AREA)
-
-#     # ✅ 기존 padding=(1024-W)//2 방식 제거 → 비대칭 패딩/크롭으로 정확히 1024
-#     garment_rgb = _pad_or_crop_to_width_np(garment_rgb, 1024, pad_value=[255, 255, 255])
-#     garment_pil = Image.fromarray(garment_rgb)
-
-#     # garment mask (✅ 동일하게 1024 맞춤)
-#     gm = np.array(parsing_img.convert("L"), dtype=np.uint8)
-#     gm = cv2.resize(gm, (W, H), interpolation=cv2.INTER_NEAREST)
-#     gm = cv2.cvtColor(gm, cv2.COLOR_GRAY2RGB)
-#     gm = _pad_or_crop_to_width_np(gm, 1024, pad_value=[0, 0, 0])
-#     garment_mask_pil = Image.fromarray(gm)
-
-#     print(
-#         "[SIZE] person:", person_pil.size,
-#         "mask:", mask_pil.size,
-#         "depth:", depth_map.size,
-#         "garment:", garment_pil.size,
-#         "gmask:", garment_mask_pil.size,
-#         "ui_category:", category,
-#         "extractor_category:", extractor_category,
-#         flush=True
-#     )
-
-#     ip_model = IPAdapterXL(
-#         pipe,
-#         image_encoder_dir,
-#         ip_ckpt,
-#         device,
-#         mask_pil,
-#         person_pil,
-#         content_scale=0.3,
-#         style_scale=0.5,
-#         garment_images=garment_pil,
-#         garment_mask=garment_mask_pil,
-#     )
-
-#     if device == "cuda":
-#         pipe.to(dtype=torch.float32)
-#         try:
-#             for _, proc in pipe.unet.attn_processors.items():
-#                 proc.to(dtype=torch.float32)
-#         except Exception:
-#             pass
-
-#     style_img = Image.open(paths.style_path).convert("RGB")
-    
-#     prompt = extractor_category + "with " + prompt
-    
-#     print("====prompt?  ", prompt)
-
-#     with torch.inference_mode():
-#         images = ip_model.generate(
-#             pil_image=style_img,
-#             image=person_pil,
-#             control_image=depth_map,
-#             strength=1.0,
-#             num_samples=1,
-#             num_inference_steps=int(steps),
-#             shape_prompt="",
-#             prompt=prompt or "",
-#             num=0,
-#             scale=None,
-#             controlnet_conditioning_scale=0.7,
-#             guidance_scale=7.5,
-#         )
-
-#     save_cropped(images, paths.output_path)
-
-#     return images, mask_pil, depth_map, person_pil, garment_pil, garment_mask_pil
-
-
-# def set_seed(seed: int):
-#     if seed is None or seed < 0:
-#         return
-#     np.random.seed(seed)
-#     torch.manual_seed(seed)
-#     if torch.cuda.is_available():
-#         torch.cuda.manual_seed_all(seed)
-
-
-# def infer_web(person_fp, sketch_fp, style_fp, prompt, steps, seed, category):
-#     print("[UI] infer_web called", flush=True)
-
-#     if person_fp is None or style_fp is None:
-#         raise gr.Error("person / style 이미지는 필수입니다. (sketch는 선택)")
-
-#     if category not in ("Upper-body", "Lower-body", "Dress"):
-#         raise gr.Error(f"Invalid category: {category}")
-
-#     set_seed(int(seed) if seed is not None else -1)
-
-#     tmp_dir = tempfile.mkdtemp(prefix="vista_demo_")
-#     out_path = os.path.join(tmp_dir, "result.png")
-
-#     paths = Paths(
-#         person_path=person_fp,
-#         depth_path=sketch_fp,
-#         style_path=style_fp,
-#         output_path=out_path,
-#     )
-
-#     _, mask_pil, depth_map, person_pil, garment_pil, garment_mask_pil = run_one(
-#         paths, prompt=prompt, steps=int(steps), category=category
-#     )
-
-#     out_img = Image.open(out_path).convert("RGB")
-#     return out_img, out_path, mask_pil, depth_map, person_pil, garment_pil, garment_mask_pil
-
-
-# with gr.Blocks(title="VISTA Demo (HF Spaces)") as demo:
-#     gr.Markdown("## VISTA Demo\nperson / style 필수, sketch(guide)는 선택입니다.")
-
-#     category_toggle = gr.Radio(
-#         choices=["Dress", "Upper-body", "Lower-body"],
-#         value="Dress",
-#         label="Category",
-#         interactive=True,
-#     )
-
-#     # 한 행에 Person / Style / Output
-#     with gr.Row():
-#         person_in = gr.Image(label="Person Image (required)", type="filepath")
-#         style_in = gr.Image(label="Style Image (required)", type="filepath")
-#         out_img = gr.Image(label="Output", type="pil")
-
-#     with gr.Accordion("Sketch / Guide (optional)", open=False):
-#         sketch_in = gr.Image(label="Sketch / Guide", type="filepath")
-
-#     with gr.Row():
-#         prompt_in = gr.Textbox(label="Prompt", value="", lines=2)
-#         steps_in = gr.Slider(1, 80, value=DEFAULT_STEPS, step=1, label="Steps")
-#         seed_in = gr.Number(label="Seed (-1=random)", value=-1, precision=0)
-
-#     run_btn = gr.Button("Run")
-#     out_file = gr.File(label="Download result.png")
-
-#     gr.Markdown("### Debug Visualizations (mask/depth/etc)")
-#     with gr.Row():
-#         dbg_mask = gr.Image(label="mask_pil", type="pil")
-#         dbg_depth = gr.Image(label="depth_map", type="pil")
-
-#     with gr.Row():
-#         dbg_person = gr.Image(label="person_pil", type="pil")
-#         dbg_garment = gr.Image(label="garment_pil", type="pil")
-#         dbg_gmask = gr.Image(label="garment_mask_pil", type="pil")
-
-#     run_btn.click(
-#         fn=infer_web,
-#         inputs=[person_in, sketch_in, style_in, prompt_in, steps_in, seed_in, category_toggle],
-#         outputs=[out_img, out_file, dbg_mask, dbg_depth, dbg_person, dbg_garment, dbg_gmask],
-#     )
-
-# demo.queue()
-# if __name__ == "__main__":
-#     demo.launch(server_name="0.0.0.0", server_port=7860)
-
 import os
 import sys
 import glob
@@ -638,7 +15,7 @@ print("[BOOT] sys.path[:5] =", sys.path[:5], flush=True)
 import tempfile
 from dataclasses import dataclass
 from functools import lru_cache
-from typing import Optional, Tuple, List
+from typing import Optional, Tuple, List, Dict
 
 import gradio as gr
 import torch
@@ -702,23 +79,19 @@ def get_assets():
 
 
 # =========================
-# Example assets for Gradio UI
+# Example assets for Gradio UI (✅ 분리형)
 # =========================
 def _is_image_file(p: str) -> bool:
     ext = os.path.splitext(p.lower())[1]
     return ext in (".png", ".jpg", ".jpeg", ".webp")
 
 
-def build_ui_examples(root_dir: str = ROOT) -> List[List[object]]:
+def build_ui_example_lists(root_dir: str = ROOT) -> Dict[str, List[str]]:
     """
-    Returns a list of [person_fp, sketch_fp_or_None, style_fp] example rows.
-
-    Put your example files under:
-      - {root}/examples/person/*
-      - {root}/examples/style/*
-      - {root}/examples/sketch/*   (optional)
-
-    If folders are missing or empty, this returns [] and the UI will hide Examples.
+    Returns dict of example filepaths:
+      - persons: [{root}/examples/person/*]
+      - styles : [{root}/examples/style/*]
+      - sketches: [{root}/examples/sketch/*] (optional)
     """
     person_dir = os.path.join(root_dir, "examples", "person")
     style_dir = os.path.join(root_dir, "examples", "style")
@@ -728,23 +101,7 @@ def build_ui_examples(root_dir: str = ROOT) -> List[List[object]]:
     styles = [p for p in sorted(glob.glob(os.path.join(style_dir, "*"))) if _is_image_file(p)]
     sketches = [p for p in sorted(glob.glob(os.path.join(sketch_dir, "*"))) if _is_image_file(p)]
 
-    if not persons or not styles:
-        return []
-
-    n_basic = min(len(persons), len(styles))
-    rows: List[List[object]] = []
-
-    # Examples without sketch (sketch is optional)
-    for i in range(n_basic):
-        rows.append([persons[i], None, styles[i]])
-
-    # Examples with sketch (if available)
-    if sketches:
-        n_sk = min(len(persons), len(styles), len(sketches))
-        for i in range(n_sk):
-            rows.append([persons[i], sketches[i], styles[i]])
-
-    return rows
+    return {"persons": persons, "styles": styles, "sketches": sketches}
 
 
 DEFAULT_STEPS = 40
@@ -1176,10 +533,9 @@ def run_one(paths: Paths, prompt: str, steps: int = DEFAULT_STEPS, category: str
             pass
 
     style_img = Image.open(paths.style_path).convert("RGB")
-    
+
     prompt = extractor_category + "with " + prompt
-    
-    print("==== prompt? ", prompt)
+    print("==== prompt? ", prompt, flush=True)
 
     with torch.inference_mode():
         images = ip_model.generate(
@@ -1198,7 +554,6 @@ def run_one(paths: Paths, prompt: str, steps: int = DEFAULT_STEPS, category: str
         )
 
     save_cropped(images, paths.output_path)
-
     return images, mask_pil, depth_map, person_pil, garment_pil, garment_mask_pil
 
 
@@ -1250,33 +605,57 @@ with gr.Blocks(title="VISTA Demo (HF Spaces)") as demo:
         interactive=True,
     )
 
+    # ✅ 예시 리스트(분리)
+    ex = build_ui_example_lists(ROOT)
+    person_examples = [[p] for p in ex["persons"]]
+    style_examples = [[p] for p in ex["styles"]]
+    sketch_examples = [[p] for p in ex["sketches"]]
+
     # 한 행에 Person / Style / Output
     with gr.Row():
-        person_in = gr.Image(label="Person Image (required)", type="filepath")
-        style_in = gr.Image(label="Style Image (required)", type="filepath")
-        out_img = gr.Image(label="Output", type="pil")
+        # -------- Person column --------
+        with gr.Column(scale=1):
+            person_in = gr.Image(label="Person Image (required)", type="filepath")
+            if person_examples:
+                gr.Markdown("#### Examples")
+                gr.Examples(
+                    examples=person_examples,
+                    inputs=[person_in],          # ✅ person만 채움 (독립 선택)
+                    examples_per_page=8,
+                )
+
+        # -------- Style column --------
+        with gr.Column(scale=1):
+            style_in = gr.Image(label="Style Image (required)", type="filepath")
+            if style_examples:
+                gr.Markdown("#### Examples")
+                gr.Examples(
+                    examples=style_examples,
+                    inputs=[style_in],           # ✅ style만 채움 (독립 선택)
+                    examples_per_page=8,
+                )
+
+        # -------- Output column --------
+        with gr.Column(scale=1):
+            out_img = gr.Image(label="Output", type="pil")
 
     with gr.Accordion("Sketch / Guide (optional)", open=False):
         sketch_in = gr.Image(label="Sketch / Guide", type="filepath")
-
-    # ✅ Examples: click to auto-fill Person/Sketch/Style inputs
-    examples_data = build_ui_examples(ROOT)
-    if examples_data:
-        gr.Markdown("### Examples (click to load)")
-        gr.Examples(
-            examples=examples_data,
-            inputs=[person_in, sketch_in, style_in],
-            examples_per_page=8,
-        )
-    else:
-        gr.Markdown(
-            "### Examples\n"
-            "Add example files under `./examples/person/` and `./examples/style/` "
-            "(optional: `./examples/sketch/`) to show clickable examples here."
-        )
+        if sketch_examples:
+            gr.Markdown("#### Examples")
+            gr.Examples(
+                examples=sketch_examples,
+                inputs=[sketch_in],             # ✅ sketch만 채움 (독립 선택)
+                examples_per_page=8,
+            )
 
     with gr.Row():
-        prompt_in = gr.Textbox(label="Prompt", value="", lines=2)
+        prompt_in = gr.Textbox(
+            label="Prompt",
+            value="",
+            placeholder="예: floral pattern, silk texture, studio lighting",
+            lines=2,
+        )
         steps_in = gr.Slider(1, 80, value=DEFAULT_STEPS, step=1, label="Steps")
         seed_in = gr.Number(label="Seed (-1=random)", value=-1, precision=0)
 
@@ -1302,5 +681,3 @@ with gr.Blocks(title="VISTA Demo (HF Spaces)") as demo:
 demo.queue()
 if __name__ == "__main__":
     demo.launch(server_name="0.0.0.0", server_port=7860)
-
-