update save

app.py

@@ -1,714 +1,3 @@
-# import os
-# import sys
-# import glob
-
-# # ---------------------------------------------------------
-# # 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, List, Dict
-
-# 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
-
-
-# # =========================
-# # 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_example_lists(root_dir: str = ROOT) -> Dict[str, List[str]]:
-#     """
-#     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")
-#     sketch_dir = os.path.join(root_dir, "examples", "sketch")
-
-#     persons = [p for p in sorted(glob.glob(os.path.join(person_dir, "*"))) if _is_image_file(p)]
-#     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)]
-
-#     return {"persons": persons, "styles": styles, "sketches": sketches}
-
-
-# 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: Optional[str]          # ✅ style optional (변경)
-#     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 ��른쪽이 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:
-#         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 remove_small_white_components(
-#     parsing_img: Image.Image,
-#     *,
-#     white_threshold: int = 128,
-#     min_white_area: int = 150,
-#     use_open: bool = False,
-#     open_ksize: int = 3,
-#     morph_iters: int = 1,
-# ) -> Image.Image:
-#     """
-#     - 흰색(=foreground)으로 이진화
-#     - connected components로 '작은 흰색 덩어리'만 제거
-#     - (옵션) OPEN을 아주 약하게 적용해 작은 점/가시 제거 (흰색이 늘어나는 CLOSE는 사용 X)
-#     """
-#     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 >= int(white_threshold), 255, 0).astype(np.uint8)
-
-#     # 1) 작은 흰색 연결요소 제거
-#     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 >= int(min_white_area):
-#             keep[labels == lab] = 255
-#     mask = keep
-
-#     # 2) (옵션) OPEN: 작은 흰 점/가시 제거 + 경계 약간 정리 (흰색 증가 방향 아님)
-#     if use_open and int(open_ksize) > 1:
-#         k = int(open_ksize)
-#         if k % 2 == 0:
-#             k += 1
-#         kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (k, k))
-#         mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, kernel, iterations=int(morph_iters))
-
-#     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.bitwise_not(img) 
-#     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((12, 12), 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(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 _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)
-#     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 _has_valid_file(path: Optional[str]) -> bool:
-#     return (
-#         path is not None
-#         and isinstance(path, str)
-#         and len(path) > 0
-#         and os.path.exists(path)
-#     )
-
-
-# def _resolve_content_style_scales(style_present: bool, prompt_present: bool) -> Tuple[float, float]:
-#     """
-#     요구사항:
-#     - style image 없으면: (0.0, 0.0)
-#     - prompt 없���면: (0.4, 0.6)
-#     - 둘 다 있으면: 기존 유지 (0.3, 0.5)
-#     """
-#     if not style_present:
-#         return 0.0, 0.0
-#     if not prompt_present:
-#         return 0.4, 0.65
-#     return 0.4, 0.5
-
-
-# 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 = remove_small_white_components(
-#         parsing_img,
-#         white_threshold=128,
-#         min_white_area=150,   # 데이터에 맞게 30~200 사이 조절
-#         use_open=False,
-#     )
-
-#     use_depth_path = _has_valid_file(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)
-#     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)
-
-#     # ✅ 조건에 따른 scale 결정
-#     style_present = _has_valid_file(paths.style_path)
-#     prompt_present = (prompt is not None) and (str(prompt).strip() != "")
-#     content_scale, style_scale = _resolve_content_style_scales(style_present, prompt_present)
-
-#     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,
-#         "style_present:", style_present,
-#         "prompt_present:", prompt_present,
-#         "content_scale:", content_scale,
-#         "style_scale:", style_scale,
-#         flush=True
-#     )
-
-#     ip_model = IPAdapterXL(
-#         pipe,
-#         image_encoder_dir,
-#         ip_ckpt,
-#         device,
-#         mask_pil,
-#         person_pil,
-#         content_scale=content_scale,   # ✅ 변경
-#         style_scale=style_scale,       # ✅ 변경
-#         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 image 없을 때도 generate 입력이 None이 되지 않게 대체
-#     if style_present:
-#         style_img = Image.open(paths.style_path).convert("RGB")
-#     else:
-#         # scale이 0이므로 영향은 없고, 함수 시그니처만 만족시키기 위한 대체값
-#         style_img = garment_pil
-
-#     # prompt 구성은 기존 유지
-#     if prompt is not None and str(prompt).strip() != "":
-#         prompt = extractor_category + " with " + str(prompt).strip()
-#     else:
-#         prompt = extractor_category
-
-#     print("==== prompt? ", prompt, flush=True)
-
-#     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)
-
-#     # ✅ person만 필수, style은 선택
-#     if person_fp is None:
-#         raise gr.Error("person 이미지는 필수입니다. (style/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,   # ✅ None 가능
-#         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 필수, style/sketch(guide)는 선택입니다.")
-
-#     category_toggle = gr.Radio(
-#         choices=["Dress", "Upper-body", "Lower-body"],
-#         value="Dress",
-#         label="Category",
-#         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 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],
-#                     examples_per_page=8,
-#                 )
-
-#         # -------- Style column --------
-#         with gr.Column(scale=1):
-#             style_in = gr.Image(label="Style Image (optional)", type="filepath")
-#             if style_examples:
-#                 gr.Markdown("#### Examples")
-#                 gr.Examples(
-#                     examples=style_examples,
-#                     inputs=[style_in],
-#                     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")
-#         if sketch_examples:
-#             gr.Markdown("#### Examples")
-#             gr.Examples(
-#                 examples=sketch_examples,
-#                 inputs=[sketch_in],
-#                 examples_per_page=8,
-#             )
-
-#     with gr.Row():
-#         prompt_in = gr.Textbox(
-#             label="Prompt",
-#             value="",
-#             placeholder="ex) crystal, lace, button, …",
-#             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
@@ -1152,6 +441,78 @@ def save_cropped(imgs, out_path: str):
     out = np.concatenate(cropped, axis=1)
     os.makedirs(os.path.dirname(out_path), exist_ok=True)
     imageio.imsave(out_path, out)
+    
+def _read_hw(path: str) -> Tuple[int, int]:
+    img = _imread_or_raise(path)  # BGR
+    h, w = img.shape[:2]
+    return h, w
+
+
+def _center_crop_lr_to_aspect(arr: np.ndarray, target_aspect: float, *, pad_value=255) -> np.ndarray:
+    """
+    arr: HxWxC (RGB) or HxW
+    target_aspect = target_w / target_h
+    - 높이(H)는 유지
+    - 좌/우를 동일 비율로 crop해서 target_aspect에 맞춤
+    - 만약 현재 폭이 부족하면 좌/우 padding으로 맞춤
+    """
+    if arr.ndim == 2:
+        arr = cv2.cvtColor(arr, cv2.COLOR_GRAY2RGB)
+
+    h, w = arr.shape[:2]
+    if h <= 0 or w <= 0:
+        raise ValueError(f"Invalid image shape: {arr.shape}")
+
+    desired_w = int(round(h * float(target_aspect)))
+    if desired_w <= 0:
+        desired_w = 1
+
+    # 폭이 충분하면 좌/우 crop
+    if w >= desired_w:
+        left = (w - desired_w) // 2
+        right = left + desired_w
+        return arr[:, left:right]
+
+    # 폭이 부족하면 좌/우 padding (요청은 crop이지만 안전장치)
+    total = desired_w - w
+    left_pad = total // 2
+    right_pad = total - left_pad
+    return cv2.copyMakeBorder(
+        arr,
+        0, 0,
+        left_pad, right_pad,
+        borderType=cv2.BORDER_CONSTANT,
+        value=[pad_value, pad_value, pad_value],
+    )
+
+
+def save_output_match_person(imgs, out_path: str, person_path: str):
+    """
+    - 출력 imgs(보통 길이 1)를 person 원본 비율에 맞게 좌/우 center-crop
+    - person 원본 (W,H)로 resize
+    - (imgs가 여러 장이면) 처리 후 가로로 concat해서 저장
+    """
+    person_h, person_w = _read_hw(person_path)
+    target_aspect = float(person_w) / float(person_h)
+
+    np_imgs = []
+    for im in imgs:
+        if isinstance(im, Image.Image):
+            arr = np.asarray(im.convert("RGB"), dtype=np.uint8)
+        else:
+            # 혹시 numpy가 들어오는 경우 대비
+            arr = np.asarray(im, dtype=np.uint8)
+            if arr.ndim == 2:
+                arr = cv2.cvtColor(arr, cv2.COLOR_GRAY2RGB)
+
+        cropped = _center_crop_lr_to_aspect(arr, target_aspect, pad_value=255)
+        resized = cv2.resize(cropped, (person_w, person_h), interpolation=cv2.INTER_AREA)
+        np_imgs.append(resized)
+
+    out = np.concatenate(np_imgs, axis=1)  # imgs가 1장이면 그대로
+    os.makedirs(os.path.dirname(out_path), exist_ok=True)
+    imageio.imsave(out_path, out)
+
 
 
 @lru_cache(maxsize=1)
@@ -1379,10 +740,13 @@ def run_one(paths: Paths, prompt: str, steps: int = DEFAULT_STEPS, category: str
             guidance_scale=7.5,
         )
 
-    save_cropped(images, paths.output_path)
+#     save_cropped(images, paths.output_path)
+#     return images, mask_pil, depth_map, person_pil, garment_pil, garment_mask_pil
+    save_output_match_person(images, paths.output_path, paths.person_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