app.py

app.py

@@ -0,0 +1,490 @@
+import os, io, time, json, math
+from typing import List, Dict, Optional, Tuple
+
+import gradio as gr
+import numpy as np
+from PIL import Image, ImageOps
+
+import torch
+from diffusers import (
+    StableDiffusionXLPipeline,
+    StableDiffusionXLImg2ImgPipeline,
+    StableDiffusionXLInpaintPipeline,
+    StableDiffusionXLControlNetPipeline,
+    ControlNetModel,
+    StableDiffusionUpscalePipeline,
+    DPMSolverMultistepScheduler, EulerDiscreteScheduler,
+    EulerAncestralDiscreteScheduler, HeunDiscreteScheduler,
+)
+
+# ---------- Optional deps ----------
+try:
+    from rembg import remove as rembg_remove
+except Exception:
+    rembg_remove = None
+
+# face restore
+try:
+    from gfpgan import GFPGANer
+    _HAS_GFP = True
+except Exception:
+    _HAS_GFP = False
+
+# realesrgan (fallback upscaler)
+try:
+    from realesrgan import RealESRGAN
+    _HAS_REALESRGAN = True
+except Exception:
+    _HAS_REALESRGAN = False
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+dtype  = torch.float16 if device == "cuda" else torch.float32
+
+# ---------------- Registry (เปลี่ยน/เพิ่มได้เอง) ----------------
+MODELS: List[Tuple[str,str,str]] = [
+    # (id, label, note)
+    ("stabilityai/stable-diffusion-xl-base-1.0",   "SDXL Base 1.0", "เอนกประสงค์ สมดุล"),
+    ("stabilityai/stable-diffusion-xl-refiner-1.0","SDXL Refiner",  "เก็บรายละเอียด (pass 2)"),
+    ("SG161222/RealVisXL_V4.0",                    "RealVis XL v4", "โฟโต้เรียล คน/สินค้าเนียน"),
+    ("Lykon/dreamshaper-xl-v2",                    "DreamShaper XL","แฟนตาซี–เรียลลิสติกหลากสไตล์"),
+    ("RunDiffusion/Juggernaut-XL",                 "Juggernaut XL", "คอนทราสต์แรง รายละเอียดหนัก"),
+    ("emilianJR/epiCRealismXL",                    "EpicRealism XL","แฟชั่น/พอร์เทรตคอนทราสต์ดี"),
+    ("black-forest-labs/FLUX.1-dev",               "FLUX.1-dev",    "สมัยใหม่/คุมสไตล์ดี (ไม่ใช่ SDXL)"),
+    ("stabilityai/sd-turbo",                       "SD-Turbo",      "ไวมาก เหมาะกับร่างไอเดีย"),
+    ("stabilityai/stable-diffusion-2-1",           "SD 2.1",        "แลนด์สเคป/องค์ประกอบกว้าง"),
+    ("runwayml/stable-diffusion-v1-5",             "SD 1.5",        "คลาสสิก/ทรัพยากรเยอะ"),
+    ("timbrooks/instruct-pix2pix",                 "Instruct-Pix2Pix","แก้ภาพตามคำสั่ง (Img2Img)"),
+]
+
+LORAS: List[Tuple[str,str,str]] = [
+    # หมายเหตุ: รายชื่อ LoRA แพร่หลายและเปลี่ยนเร็ว; ถ้าโหลดไม่ได้ โปรแกรมจะไม่ล้ม
+    ("ByteDance/SDXL-Lightning",                "SDXL-Lightning",   "สปีดเร็ว (LoRA)"),
+    ("ostris/epicrealism-xl-lora",              "EpicrealismXL-LoRA","โทนเรียลลิสติก"),
+    ("XLabs-AI/flux-prompt-lora",               "FLUX Prompt LoRA", "ปรับ prompt style (FLUX)"),
+    ("XLabs-AI/realvisxl-v4-lora",              "RealVisXL LoRA",   "พอร์เทรต/สินค้า"),
+    ("alpha-diffusion/sdxl-anime-lora",         "Anime-Style XL",   "อนิเม/เส้นใส"),
+    ("alpha-diffusion/sdxl-cinematic-lora",     "Cinematic-Drama",  "แสงเงาแบบหนัง"),
+    ("alpha-diffusion/sdxl-watercolor-lora",    "Watercolor-Soft",  "สีน้ำ/พาสเทล"),
+    ("alpha-diffusion/sdxl-fashion-lora",       "Fashion-Editorial","แฟชั่น/กองถ่าย"),
+    ("alpha-diffusion/sdxl-product-lora",       "Product-Studio",   "สินค้า/แสงสตูดิโอ"),
+    ("alpha-diffusion/sdxl-interior-lora",      "Interior-Archi",   "ห้อง/สถาปัตย์"),
+    ("alpha-diffusion/sdxl-food-lora",          "Food-Tasty",       "อาหารฉ่ำ/เงางาม"),
+]
+
+CONTROLNETS: List[Tuple[str,str,str,str]] = [
+    # (id, label, note, key)
+    ("diffusers/controlnet-canny-sdxl-1.0",     "Canny",        "คุมเส้นขอบ",           "canny"),
+    ("diffusers/controlnet-openpose-sdxl-1.0",  "OpenPose",     "คุมท่าทางคน",          "pose"),
+    ("diffusers/controlnet-depth-sdxl-1.0",     "Depth",        "คุมมุมมอง/ระยะลึก",     "depth"),
+    ("diffusers/controlnet-softedge-sdxl-1.0",  "SoftEdge",     "เส้นนุ่ม/ลดแตก",        "softedge"),
+    ("diffusers/controlnet-lineart-sdxl-1.0",   "Lineart",      "เส้นร่าง/การ์ตูน",      "lineart"),
+    ("diffusers/controlnet-anime-lineart-sdxl-1.0","Anime Lineart","เส้นอนิเ��",        "anime_lineart"),
+    ("diffusers/controlnet-normal-sdxl-1.0",    "Normal",       "ทิศทางพื้นผิว",         "normal"),
+    ("diffusers/controlnet-mlsd-sdxl-1.0",      "MLSD",         "เส้นตรง/สถาปัตย์",      "mlsd"),
+    ("diffusers/controlnet-scribble-sdxl-1.0",  "Scribble",     "สเก็ตช์หยาบ→จริง",      "scribble"),
+    ("diffusers/controlnet-seg-sdxl-1.0",       "Segmentation", "แบ่งส่วน/สี",           "seg"),
+    ("diffusers/controlnet-tile-sdxl-1.0",      "Tile",         "อัปสเกลแบบกระเบื้อง",   "tile"),
+]
+
+PRESETS = {
+    "Cinematic": ", cinematic lighting, 50mm, bokeh, film grain, high dynamic range",
+    "Studio":    ", studio photo, softbox lighting, sharp focus, high detail",
+    "Product":   ", product photography, seamless background, diffused light, reflections",
+    "Anime":     ", anime style, clean lineart, vibrant colors, high quality",
+}
+NEG_DEFAULT = "lowres, blurry, bad anatomy, extra fingers, watermark, jpeg artifacts, text"
+
+SCHEDULERS = {
+    "DPM-Solver (Karras)": DPMSolverMultistepScheduler,
+    "Euler": EulerDiscreteScheduler,
+    "Euler a": EulerAncestralDiscreteScheduler,
+    "Heun": HeunDiscreteScheduler,
+}
+
+# ---------------- Cache & helpers ----------------
+PIPE_CACHE: Dict[str, object] = {}
+CONTROL_CACHE: Dict[str, ControlNetModel] = {}
+UPSCALE_PIPE: Optional[StableDiffusionUpscalePipeline] = None
+GFP: Optional[GFPGANer] = None
+REALSR: Optional[RealESRGAN] = None
+
+def set_sched(pipe, name: str):
+    cls = SCHEDULERS.get(name, DPMSolverMultistepScheduler)
+    pipe.scheduler = cls.from_config(pipe.scheduler.config)
+
+def seed_gen(sd: int):
+    if sd is None or sd < 0: return None
+    g = torch.Generator(device=device if device=="cuda" else "cpu")
+    g.manual_seed(int(sd))
+    return g
+
+def prep_pipe(model_id: str, control_ids: List[str]):
+    key = f"{model_id}|{'-'.join(control_ids) if control_ids else 'none'}"
+    if key in PIPE_CACHE: return PIPE_CACHE[key]
+
+    if control_ids:
+        cns = []
+        for cid in control_ids:
+            if cid not in CONTROL_CACHE:
+                CONTROL_CACHE[cid] = ControlNetModel.from_pretrained(cid, torch_dtype=dtype, use_safetensors=True)
+            cns.append(CONTROL_CACHE[cid])
+        pipe = StableDiffusionXLControlNetPipeline.from_pretrained(model_id, controlnet=cns, torch_dtype=dtype, use_safetensors=True)
+    else:
+        pipe = StableDiffusionXLPipeline.from_pretrained(model_id, torch_dtype=dtype, use_safetensors=True)
+
+    if device == "cuda":
+        pipe.to("cuda")
+        pipe.enable_vae_tiling(); pipe.enable_vae_slicing()
+        try: pipe.enable_xformers_memory_efficient_attention()
+        except Exception: pass
+    else:
+        pipe.to("cpu"); pipe.enable_attention_slicing()
+
+    PIPE_CACHE[key] = pipe
+    return pipe
+
+def apply_loras(pipe, ids: List[str], scales: List[float]):
+    for i, rid in enumerate([x for x in ids if x]):
+        try:
+            pipe.load_lora_weights(rid)
+            try:
+                sc = scales[i] if i < len(scales) else 0.7
+                pipe.fuse_lora(lora_scale=float(sc))
+            except Exception: pass
+        except Exception as e:
+            print(f"[LoRA] load failed {rid}: {e}")
+
+def to_png_info(meta: dict) -> str:
+    return json.dumps(meta, ensure_ascii=False, indent=2)
+
+# ---------------- Optional Post-process ----------------
+def ensure_upscalers():
+    global UPSCALE_PIPE, GFP, REALSR
+    if UPSCALE_PIPE is None:
+        try:
+            UPSCALE_PIPE = StableDiffusionUpscalePipeline.from_pretrained(
+                "stabilityai/stable-diffusion-x4-upscaler",
+                torch_dtype=torch.float16 if device=="cuda" else torch.float32,
+                use_safetensors=True
+            ).to(device)
+        except Exception as e:
+            print("[Upscaler] SD x4 not available:", e)
+
+    if _HAS_GFP and GFP is None:
+        try:
+            GFP = GFPGANer(model_path=None, upscale=1, arch="clean", channel_multiplier=2)
+        except Exception as e:
+            print("[GFPGAN] init failed:", e)
+
+    if _HAS_REALESRGAN and REALSR is None and device == "cuda":
+        try:
+            REALSR = RealESRGAN(torch.device("cuda"), scale=4)
+            REALSR.load_weights("weights/RealESRGAN_x4plus.pth")  # ใช้ได้เมื่อมีไฟล์ (ถ้าไม่มีจะข้าม)
+        except Exception as e:
+            print("[RealESRGAN] init failed:", e)
+
+def post_process(img: Image.Image, do_upscale: bool, do_face: bool, do_rembg: bool):
+    ensure_upscalers()
+    out = img
+
+    # Upscale priority: RealESRGAN > SD x4 > none
+    if do_upscale:
+        try:
+            if REALSR is not None:
+                out = Image.fromarray(REALSR.predict(np.array(out)))
+            elif UPSCALE_PIPE is not None:
+                if device == "cuda":
+                    with torch.autocast("cuda"):
+                        out = UPSCALE_PIPE(prompt="", image=out).images[0]
+                else:
+                    out = UPSCALE_PIPE(prompt="", image=out).images[0]
+        except Exception as e:
+            print("[Upscale] skipped:", e)
+
+    if do_face and _HAS_GFP and GFP is not None:
+        try:
+            _, _, out = GFP.enhance(np.array(out), has_aligned=False, only_center_face=False, paste_back=True)
+            out = Image.fromarray(out)
+        except Exception as e:
+            print("[GFPGAN] skipped:", e)
+
+    if do_rembg and rembg_remove is not None:
+        try:
+            out = Image.open(io.BytesIO(rembg_remove(np.array(out))))
+        except Exception as e:
+            print("[rembg] skipped:", e)
+
+    return out
+
+# ---------------- Core generate ----------------
+def run_txt2img(model_id, custom_model, prompt, preset, negative,
+                steps, cfg, width, height, scheduler_name, seed,
+                lora_list, lora_custom_csv, lora_s1, lora_s2, lora_s3,
+                ctrl_selected, ctrl_images, use_refiner, refine_strength,
+                do_upscale, do_face, do_rembg):
+    if not prompt.strip(): raise gr.Error("กรุณากรอก prompt")
+    model = (custom_model.strip() or model_id).strip()
+    if preset in PRESETS: prompt = prompt + PRESETS[preset]
+    if not negative.strip(): negative = NEG_DEFAULT
+
+    # Collect control images
+    cond_imgs, ctrl_ids = [], []
+    for (cid, label, note, key) in CONTROLNETS:
+        if label in ctrl_selected and key in ctrl_images and ctrl_images[key] is not None:
+            ctrl_ids.append(cid); cond_imgs.append(ctrl_images[key])
+
+    pipe = prep_pipe(model, ctrl_ids)
+    set_sched(pipe, scheduler_name)
+
+    loras = []
+    if lora_list: loras += lora_list
+    if lora_custom_csv.strip():
+        loras += [x.strip() for x in lora_custom_csv.split(",") if x.strip()]
+    apply_loras(pipe, loras, [lora_s1, lora_s2, lora_s3])
+
+    width  = int(max(512, min(1024, width)))
+    height = int(max(512, min(1024, height)))
+    gen = seed_gen(seed)
+
+    if device == "cuda":
+        with torch.autocast("cuda"):
+            if ctrl_ids:
+                image = pipe(prompt=prompt, negative_prompt=negative,
+                             width=width, height=height,
+                             num_inference_steps=int(steps), guidance_scale=float(cfg),
+                             controlnet_conditioning_image=cond_imgs if len(cond_imgs)>1 else cond_imgs[0],
+                             generator=gen).images[0]
+            else:
+                image = pipe(prompt=prompt, negative_prompt=negative,
+                             width=width, height=height,
+                             num_inference_steps=int(steps), guidance_scale=float(cfg),
+                             generator=gen).images[0]
+    else:
+        if ctrl_ids:
+            image = pipe(prompt=prompt, negative_prompt=negative,
+                         width=width, height=height,
+                         num_inference_steps=int(steps), guidance_scale=float(cfg),
+                         controlnet_conditioning_image=cond_imgs if len(cond_imgs)>1 else cond_imgs[0],
+                         generator=gen).images[0]
+        else:
+            image = pipe(prompt=prompt, negative_prompt=negative,
+                         width=width, height=height,
+                         num_inference_steps=int(steps), guidance_scale=float(cfg),
+                         generator=gen).images[0]
+
+    # Refiner (GPU)
+    if use_refiner and device == "cuda":
+        try:
+            ref = StableDiffusionXLImg2ImgPipeline.from_pretrained(
+                "stabilityai/stable-diffusion-xl-refiner-1.0",
+                torch_dtype=torch.float16, use_safetensors=True
+            ).to("cuda")
+            set_sched(ref, scheduler_name)
+            with torch.autocast("cuda"):
+                image = ref(prompt=prompt, negative_prompt=negative,
+                            image=image, strength=float(refine_strength),
+                            num_inference_steps=max(10, int(steps)//2),
+                            guidance_scale=float(cfg), generator=gen).images[0]
+        except Exception as e:
+            print("[Refiner] skipped:", e)
+
+    # Post-process
+    image = post_process(image, do_upscale, do_face, do_rembg)
+
+    meta = {
+        "model": model, "loras": loras, "controlnets": ctrl_selected,
+        "prompt": prompt, "negative": negative, "size": f"{width}x{height}",
+        "steps": steps, "cfg": cfg, "scheduler": scheduler_name, "seed": seed,
+        "post": {"upscale": do_upscale, "face_restore": do_face, "remove_bg": do_rembg}
+    }
+    return image, to_png_info(meta)
+
+def run_img2img(model_id, custom_model, init_image, strength, **kw):
+    if init_image is None: raise gr.Error("โปรดอัปโหลดภาพเริ่มต้น (init image)")
+    model = (custom_model.strip() or model_id).strip()
+    pipe = StableDiffusionXLImg2ImgPipeline.from_pretrained(model, torch_dtype=dtype, use_safetensors=True)
+    pipe = pipe.to(device); 
+    try:
+        if device=="cuda": pipe.enable_xformers_memory_efficient_attention()
+    except: pass
+    set_sched(pipe, kw["scheduler_name"]); gen = seed_gen(kw["seed"])
+    prompt = kw["prompt"] + (PRESETS.get(kw["preset"], "") if kw["preset"] else "")
+    negative = kw["negative"] or NEG_DEFAULT
+
+    if device=="cuda":
+        with torch.autocast("cuda"):
+            img = pipe(prompt=prompt, negative_prompt=negative, image=init_image,
+                       strength=float(strength), num_inference_steps=int(kw["steps"]),
+                       guidance_scale=float(kw["cfg"]), generator=gen).images[0]
+    else:
+        img = pipe(prompt=prompt, negative_prompt=negative, image=init_image,
+                   strength=float(strength), num_inference_steps=int(kw["steps"]),
+                   guidance_scale=float(kw["cfg"]), generator=gen).images[0]
+
+    img = post_process(img, kw["do_upscale"], kw["do_face"], kw["do_rembg"])
+    meta = {"mode":"img2img","model":model,"prompt":prompt,"neg":negative,"steps":kw["steps"],"cfg":kw["cfg"],"seed":kw["seed"],"strength":strength}
+    return img, to_png_info(meta)
+
+def expand_canvas_for_outpaint(img: Image.Image, expand_px: int, direction: str) -> Tuple[Image.Image, Image.Image]:
+    w, h = img.size
+    if direction == "left":   new = Image.new("RGBA", (w+expand_px, h), (0,0,0,0)); new.paste(img, (expand_px,0)); mask = Image.new("L", (w+expand_px,h), 0); ImageDraw = ImageDraw if 'ImageDraw' in globals() else __import__('PIL.ImageDraw').ImageDraw; d=ImageDraw.Draw(mask); d.rectangle([0,0,expand_px,h], fill=255)
+    elif direction == "right": new = Image.new("RGBA",(w+expand_px,h),(0,0,0,0)); new.paste(img,(0,0)); mask=Image.new("L",(w+expand_px,h),0); ImageDraw=ImageDraw if 'ImageDraw' in globals() else __import__('PIL.ImageDraw').ImageDraw; d=ImageDraw.Draw(mask); d.rectangle([w,0,w+expand_px,h], fill=255)
+    elif direction == "top":   new = Image.new("RGBA",(w,h+expand_px),(0,0,0,0)); new.paste(img,(0,expand_px)); mask=Image.new("L",(w,h+expand_px),0); ImageDraw=ImageDraw if 'ImageDraw' in globals() else __import__('PIL.ImageDraw').ImageDraw; d=ImageDraw.Draw(mask); d.rectangle([0,0,w,expand_px], fill=255)
+    else:                      new = Image.new("RGBA",(w,h+expand_px),(0,0,0,0)); new.paste(img,(0,0)); mask=Image.new("L",(w,h+expand_px),0); ImageDraw=ImageDraw if 'ImageDraw' in globals() else __import__('PIL.ImageDraw').ImageDraw; d=ImageDraw.Draw(mask); d.rectangle([0,h,w,h+expand_px], fill=255)
+    return new.convert("RGB"), mask
+
+def run_inpaint_outpaint(model_id, custom_model, base_image, mask_image, mode, expand_px, expand_dir, **kw):
+    if base_image is None: raise gr.Error("โปรดอัปโหลดภาพฐาน")
+    model = (custom_model.strip() or model_id).strip()
+    pipe = StableDiffusionXLInpaintPipeline.from_pretrained(model, torch_dtype=dtype, use_safetensors=True)
+    pipe = pipe.to(device);
+    try:
+        if device=="cuda": pipe.enable_xformers_memory_efficient_attention()
+    except: pass
+    set_sched(pipe, kw["scheduler_name"]); gen = seed_gen(kw["seed"])
+    prompt = kw["prompt"] + (PRESETS.get(kw["preset"], "") if kw["preset"] else "")
+    negative = kw["negative"] or NEG_DEFAULT
+
+    if mode == "Outpaint":
+        base_image, mask_image = expand_canvas_for_outpaint(base_image, int(expand_px), expand_dir)
+
+    if device=="cuda":
+        with torch.autocast("cuda"):
+            img = pipe(prompt=prompt, negative_prompt=negative,
+                       image=base_image, mask_image=mask_image,
+                       strength=kw.get("strength", 0.7),
+                       num_inference_steps=int(kw["steps"]),
+                       guidance_scale=float(kw["cfg"]),
+                       generator=gen).images[0]
+    else:
+        img = pipe(prompt=prompt, negative_prompt=negative,
+                   image=base_image, mask_image=mask_image,
+                   strength=kw.get("strength", 0.7),
+                   num_inference_steps=int(kw["steps"]),
+                   guidance_scale=float(kw["cfg"]),
+                   generator=gen).images[0]
+
+    img = post_process(img, kw["do_upscale"], kw["do_face"], kw["do_rembg"])
+    meta = {"mode":mode,"model":model,"prompt":prompt,"steps":kw["steps"],"cfg":kw["cfg"],"seed":kw["seed"]}
+    return img, to_png_info(meta)
+
+# ---------------- UI ----------------
+def build_ui():
+    with gr.Blocks(theme=gr.themes.Soft(), title="Masterpiece SDXL Studio Pro") as demo:
+        gr.Markdown("# 🖼️ Masterpiece SDXL Studio Pro")
+        gr.Markdown("เลือก **Models/LoRA/ControlNet** ได้หลายรายการ + **Img2Img / Inpaint / Outpaint** + **Upscale/FaceRestore/RemoveBG**")
+
+        # Common widgets
+        model_dd = gr.Dropdown(choices=[m[0] for m in MODELS], value=MODELS[0][0], label="Model (เลือก)")
+        model_custom = gr.Textbox(label="Custom Model ID (เช่น username/my-model)", placeholder="(ไม่จำเป็น)")
+
+        preset = gr.Dropdown(choices=list(PRESETS.keys()), value=None, label="Style Preset (optional)")
+        negative = gr.Textbox(value=NEG_DEFAULT, label="Negative Prompt")
+        steps = gr.Slider(10, 60, 30, step=1, label="Steps")
+        cfg = gr.Slider(1.0, 12.0, 7.0, step=0.1, label="CFG")
+        width = gr.Slider(512, 1024, 832, step=64, label="Width")
+        height= gr.Slider(512, 1024, 832, step=64, label="Height")
+        scheduler = gr.Dropdown(list(SCHEDULERS.keys()), value="DPM-Solver (Karras)", label="Scheduler")
+        seed = gr.Number(value=-1, precision=0, label="Seed (-1=random)")
+
+        # LoRA
+        lora_group = gr.CheckboxGroup(choices=[f"{rid} — {lbl} ({note})" for rid,lbl,note in LORAS], label="LoRA (เลือกหลายตัวได้)")
+        lora_custom = gr.Textbox(label="Custom LoRA IDs (คั่นด้วย comma)")
+        lora_s1 = gr.Slider(0.0, 1.2, 0.7, 0.05, label="LoRA scale #1")
+        lora_s2 = gr.Slider(0.0, 1.2, 0.5, 0.05, label="LoRA scale #2")
+        lora_s3 = gr.Slider(0.0, 1.2, 0.5, 0.05, label="LoRA scale #3")
+
+        # ControlNet
+        ctrl_group = gr.CheckboxGroup(choices=[c[1]+" ("+c[2]+")" for c in CONTROLNETS], label="ControlNet (เลือกชนิด)")
+        imgs = {
+            "canny": gr.Image(type="pil", label="Canny"),
+            "pose": gr.Image(type="pil", label="OpenPose"),
+            "depth": gr.Image(type="pil", label="Depth"),
+            "softedge": gr.Image(type="pil", label="SoftEdge"),
+            "lineart": gr.Image(type="pil", label="Lineart"),
+            "anime_lineart": gr.Image(type="pil", label="Anime Lineart"),
+            "normal": gr.Image(type="pil", label="Normal"),
+            "mlsd": gr.Image(type="pil", label="MLSD"),
+            "scribble": gr.Image(type="pil", label="Scribble"),
+            "seg": gr.Image(type="pil", label="Segmentation"),
+            "tile": gr.Image(type="pil", label="Tile"),
+        }
+
+        # Post-process
+        with gr.Row():
+            do_upscale = gr.Checkbox(False, label="Upscale x4 (ถ้ามี)")
+            do_face    = gr.Checkbox(False, label="Face Restore (ถ้ามี)")
+            do_rembg   = gr.Checkbox(False, label="Remove Background (ถ้ามี)")
+
+        with gr.Tab("Text → Image"):
+            prompt_txt = gr.Textbox(lines=3, label="Prompt")
+            btn_txt = gr.Button("🚀 Generate")
+            out_img_txt = gr.Image(type="pil", label="Result")
+            out_meta_txt = gr.Textbox(label="Metadata", lines=10)
+
+        with gr.Tab("Image → Image"):
+            init_img = gr.Image(type="pil", label="Init Image (img2img)")
+            strength = gr.Slider(0.1, 1.0, 0.7, 0.05, label="Strength")
+            prompt_i2i = gr.Textbox(lines=3, label="Prompt")
+            btn_i2i = gr.Button("🚀 Img2Img")
+            out_img_i2i = gr.Image(type="pil", label="Result")
+            out_meta_i2i = gr.Textbox(label="Metadata", lines=10)
+
+        with gr.Tab("Inpaint / Outpaint"):
+            base_img = gr.Image(type="pil", label="Base Image")
+            mask_img = gr.Image(type="pil", label="Mask (ขาว=แก้, ดำ=คงเดิม)")
+            mode_io = gr.Radio(["Inpaint","Outpaint"], value="Inpaint", label="Mode")
+            expand_px = gr.Slider(64, 1024, 256, 64, label="Outpaint pixels")
+            expand_dir = gr.Radio(["left","right","top","bottom"], value="right", label="Outpaint direction")
+            prompt_io = gr.Textbox(lines=3, label="Prompt")
+            btn_io = gr.Button("🚀 Inpaint/Outpaint")
+            out_img_io = gr.Image(type="pil", label="Result")
+            out_meta_io = gr.Textbox(label="Metadata", lines=10)
+
+        def parse_lora_list(selected: List[str]) -> List[str]:
+            if not selected: return []
+            out = []
+            for s in selected:
+                rid = s.split(" — ")[0].strip()
+                out.append(rid)
+            return out
+
+        btn_txt.click(
+            fn=run_txt2img,
+            inputs=[
+                model_dd, model_custom, prompt_txt, preset, negative,
+                steps, cfg, width, height, scheduler, seed,
+                gr.Variable(parse_lora_list), lora_custom, lora_s1, lora_s2, lora_s3,
+                ctrl_group,
+                {k:v for k,v in imgs.items()},  # dict of images
+                gr.Checkbox(False), gr.Slider(0.05,0.5,0.2,0.05),
+                do_upscale, do_face, do_rembg
+            ],
+            outputs=[out_img_txt, out_meta_txt],
+            api_name="txt2img"
+        )
+
+        btn_i2i.click(
+            fn=run_img2img,
+            inputs=[model_dd, model_custom, init_img, strength,
+                    ("prompt",), preset, negative, steps, cfg, width, height, scheduler, seed,
+                    do_upscale, do_face, do_rembg],
+            outputs=[out_img_i2i, out_meta_i2i],
+            api_name="img2img"
+        )
+
+        btn_io.click(
+            fn=run_inpaint_outpaint,
+            inputs=[model_dd, model_custom, base_img, mask_img, mode_io, expand_px, expand_dir,
+                    ("prompt",), preset, negative, steps, cfg, width, height, scheduler, seed,
+                    strength, do_upscale, do_face, do_rembg],
+            outputs=[out_img_io, out_meta_io],
+            api_name="inpaint_outpaint"
+        )
+
+        gr.Markdown("ℹ️ **หมายเหตุ**: ถ้า LoRA/ControlNet/โพสต์โปรเซสบางตัวไม่มีในสภาพแวดล้อม โปรแกรมจะข้ามให้อัตโนมัติและแจ้งใน Console")
+
+    return demo
+
+demo = build_ui()
+demo.queue(max_size=8).launch()