app.py

app.py

@@ -1,10 +1,9 @@
-import os, io, time, json
+import io, json
 from typing import List, Dict, Optional, Tuple
 
 import gradio as gr
 import numpy as np
 from PIL import Image, ImageDraw
-
 import torch
 from diffusers import (
     StableDiffusionXLPipeline,
@@ -17,14 +16,12 @@ from diffusers import (
     EulerAncestralDiscreteScheduler, HeunDiscreteScheduler,
 )
 
-# -------- Optional deps (SAFE IMPORTS: ไม่มีโมดูลก็ไม่พัง) --------
-# Remove background
+# ---------------- Optional deps (safe imports: ไม่มีก็ข้าม) ----------------
 try:
     from rembg import remove as rembg_remove
 except Exception:
     rembg_remove = None
 
-# Face restore (GFPGAN)
 _HAS_GFP = False
 GFPGANer = None
 GFP = None
@@ -36,7 +33,6 @@ try:
 except Exception as e:
     print("[WARN] GFPGAN not available:", e)
 
-# RealESRGAN (upscale; ใช้ได้เมื่อมี)
 _HAS_REALESRGAN = False
 RealESRGAN = None
 REALSR = None
@@ -46,59 +42,52 @@ try:
 except Exception as e:
     print("[WARN] RealESRGAN not available:", e)
 
-# ---------------------------------------------------------------
-
+# ---------------- Runtime setup ----------------
 device = "cuda" if torch.cuda.is_available() else "cpu"
 dtype  = torch.float16 if device == "cuda" else torch.float32
 
-# ---------------- Registry (ตัวเลือกยอดนิยม + เพิ่มเองได้) ----------------
+# ---------------- Registries ----------------
 MODELS: List[Tuple[str,str,str]] = [
-    ("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)"),
+    ("stabilityai/stable-diffusion-xl-base-1.0",   "SDXL Base 1.0", "เอนกประสงค์"),
+    ("stabilityai/stable-diffusion-xl-refiner-1.0","SDXL Refiner",  "เสริมรายละเอียด (pass 2)"),
     ("SG161222/RealVisXL_V4.0",                    "RealVis XL v4", "โฟโต้เรียล คน/สินค้า"),
-    ("Lykon/dreamshaper-xl-v2",                    "DreamShaper XL","แฟนตาซี–เรียลลิสติก"),
-    ("RunDiffusion/Juggernaut-XL",                 "Juggernaut XL", "คอนทราสต์แรง รายละเอียดหนัก"),
+    ("Lykon/dreamshaper-xl-v2",                    "DreamShaper XL","แฟนตาซี-เรียลลิสติก"),
+    ("RunDiffusion/Juggernaut-XL",                 "Juggernaut XL", "คอนทราสต์แรง"),
     ("emilianJR/epiCRealismXL",                    "EpicRealism XL","แฟชั่น/พอร์เทรต"),
-    ("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",        "สไตล์คลาสสิก"),
-    ("timbrooks/instruct-pix2pix",                 "Instruct-Pix2Pix","แก้ภาพตามคำสั่ง (img2img)"),
+    ("black-forest-labs/FLUX.1-dev",               "FLUX.1-dev",    "แนวสมัยใหม่ (ไม่ใช่ SDXL)"),
+    ("stabilityai/sd-turbo",                       "SD-Turbo",      "เร็วมากสำหรับร่างไอเดีย"),
+    ("stabilityai/stable-diffusion-2-1",           "SD 2.1",        "แลนด์สเคปกว้าง"),
+    ("runwayml/stable-diffusion-v1-5",             "SD 1.5",        "คลาสสิก"),
+    ("timbrooks/instruct-pix2pix",                 "Instruct-Pix2Pix","แก้ภาพตามคำสั่ง"),
 ]
 
 LORAS: List[Tuple[str,str,str]] = [
-    ("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/แส��สตูดิโอ"),
-    ("alpha-diffusion/sdxl-interior-lora",      "Interior-Archi",   "ห้อง/สถาปัตย์"),
-    ("alpha-diffusion/sdxl-food-lora",          "Food-Tasty",       "อาหารฉ่ำ/เงางาม"),
+    ("ByteDance/SDXL-Lightning",           "SDXL-Lightning",   "สปีด"),
+    ("ostris/epicrealism-xl-lora",         "EpicRealism XL",   "โทนจริง"),
+    ("alpha-diffusion/sdxl-anime-lora",    "Anime-Style XL",   "อนิเม"),
+    ("alpha-diffusion/sdxl-cinematic-lora","Cinematic-Drama",  "แสงหนัง"),
+    ("alpha-diffusion/sdxl-watercolor-lora","Watercolor",      "สีน้ำ"),
+    ("alpha-diffusion/sdxl-fashion-lora",  "Fashion",          "แฟชั่น"),
+    ("alpha-diffusion/sdxl-product-lora",  "Product-Studio",   "สินค้า"),
+    ("alpha-diffusion/sdxl-interior-lora", "Interior-Archi",   "สถาปัตย์"),
+    ("alpha-diffusion/sdxl-food-lora",     "Food-Tasty",       "อาหาร"),
+    ("alpha-diffusion/sdxl-logo-lora",     "Logo-Clean",       "โลโก้"),
 ]
 
+# ใช้ 5 ชนิดหลักเพื่อ UI กระชับและเสถียร
 CONTROLNETS: List[Tuple[str,str,str,str]] = [
-    ("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","เส้นอนิเม",        "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"),
+    ("diffusers/controlnet-canny-sdxl-1.0",    "Canny",     "เส้นขอบ",    "canny"),
+    ("diffusers/controlnet-openpose-sdxl-1.0", "OpenPose",  "ท่าทางคน",   "pose"),
+    ("diffusers/controlnet-depth-sdxl-1.0",    "Depth",     "ระยะลึก",    "depth"),
+    ("diffusers/controlnet-softedge-sdxl-1.0", "SoftEdge",  "เส้นนุ่ม",    "softedge"),
+    ("diffusers/controlnet-lineart-sdxl-1.0",  "Lineart",   "เส้นร่าง",    "lineart"),
 ]
 
 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",
+    "Product":   ", product photography, seamless background, diffused light, reflections",
 }
 NEG_DEFAULT = "lowres, blurry, bad anatomy, extra fingers, watermark, jpeg artifacts, text"
 
@@ -109,7 +98,7 @@ SCHEDULERS = {
     "Heun": HeunDiscreteScheduler,
 }
 
-# ---------------- Cache ----------------
+# ---------------- Caches ----------------
 PIPE_CACHE: Dict[str, object] = {}
 CONTROL_CACHE: Dict[str, ControlNetModel] = {}
 UPSCALE_PIPE: Optional[StableDiffusionUpscalePipeline] = None
@@ -121,7 +110,7 @@ def set_sched(pipe, name: str):
 
 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 = torch.Generator(device=("cuda" if device=="cuda" else "cpu"))
     g.manual_seed(int(sd)); return g
 
 def prep_pipe(model_id: str, control_ids: List[str]):
@@ -129,41 +118,39 @@ def prep_pipe(model_id: str, control_ids: List[str]):
     if key in PIPE_CACHE: return PIPE_CACHE[key]
 
     if control_ids:
-        cns = []
+        cn_models = []
         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)
+            cn_models.append(CONTROL_CACHE[cid])
+        pipe = StableDiffusionXLControlNetPipeline.from_pretrained(model_id, controlnet=cn_models, 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.to(device)
+    try:
+        if device == "cuda":
+            pipe.enable_vae_tiling(); pipe.enable_vae_slicing()
+            pipe.enable_xformers_memory_efficient_attention()
+        else:
+            pipe.enable_attention_slicing()
+    except Exception:
+        pass
 
     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]):
+def apply_loras(pipe, lora_ids: List[str]):
+    for rid in [x for x in lora_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:
+def to_info(meta: dict) -> str:
     return json.dumps(meta, ensure_ascii=False, indent=2)
 
-# --------------- Optional post-process ---------------
+# ---------------- Post-process ----------------
 def ensure_upscalers():
     global UPSCALE_PIPE, GFP, REALSR
     if UPSCALE_PIPE is None:
@@ -175,27 +162,25 @@ def ensure_upscalers():
         except Exception as e:
             print("[Upscaler] SD x4 not available:", e)
 
-    # GFPGAN
     if _HAS_GFP and GFP is None and GFPGANer is not None:
         try:
             GFP = GFPGANer(model_path=None, upscale=1, arch="clean", channel_multiplier=2)
         except Exception as e:
             print("[GFPGAN] init failed:", e)
 
-    # RealESRGAN (ต้องมี weights เองถึงใช้ได้)
     if _HAS_REALESRGAN and REALSR is None and device == "cuda":
         try:
-            REALSR = RealESRGAN(torch.device("cuda"), scale=4)  # ต้องมีไฟล์ weights ถึงจะทำงานจริง
-            # REALSR.load_weights("weights/RealESRGAN_x4plus.pth")
+            REALSR = RealESRGAN(torch.device("cuda"), scale=4)  # ต้องมี weights เองจึงจะทำงานจริง
         except Exception as e:
             REALSR = None
             print("[RealESRGAN] init failed:", e)
 
-def post_process(img: Image.Image, do_upscale: bool, do_face: bool, do_rembg: bool):
+def post_process(img: Image.Image, do_up: bool, do_face: bool, do_bg: bool):
     ensure_upscalers()
     out = img
 
-    if do_upscale:
+    # Upscale: RealESRGAN (ถ้ามี) > SD x4 > skip
+    if do_up:
         try:
             if REALSR is not None:
                 out = Image.fromarray(REALSR.predict(np.array(out)))
@@ -215,7 +200,7 @@ def post_process(img: Image.Image, do_upscale: bool, do_face: bool, do_rembg: bo
         except Exception as e:
             print("[GFPGAN] skipped:", e)
 
-    if do_rembg and rembg_remove is not None:
+    if do_bg and rembg_remove is not None:
         try:
             out = Image.open(io.BytesIO(rembg_remove(np.array(out))))
         except Exception as e:
@@ -223,98 +208,97 @@ def post_process(img: Image.Image, do_upscale: bool, do_face: bool, do_rembg: bo
 
     return out
 
-# ---------------- Core generators ----------------
-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 or "").strip(): negative = NEG_DEFAULT
-
-    # ControlNet 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)
+# ---------------- Generators ----------------
+def run_txt2img(
+    model_id, model_custom, prompt, preset, negative,
+    steps, cfg, width, height, scheduler_name, seed,
+    lora_selected, lora_custom,
+    ctrl_selected, img_canny, img_pose, img_depth, img_softedge, img_lineart,
+    do_up, do_face, do_bg
+):
+    if not prompt or not str(prompt).strip():
+        raise gr.Error("กรุณากรอก prompt")
+
+    model = (model_custom.strip() or model_id).strip()
+    if preset and preset in PRESETS: prompt = prompt + PRESETS[preset]
+    if not negative or not negative.strip(): negative = NEG_DEFAULT
+
+    # ControlNet mapping (เฉพาะภาพที่อัปโหลดจริง)
+    label_to_img = {
+        "Canny": img_canny, "OpenPose": img_pose, "Depth": img_depth,
+        "SoftEdge": img_softedge, "Lineart": img_lineart
+    }
+    control_ids, cond_images = [], []
+    for cid, label, note, key in CONTROLNETS:
+        if label in ctrl_selected and label_to_img.get(label) is not None:
+            control_ids.append(cid); cond_images.append(label_to_img[label])
+
+    pipe = prep_pipe(model, control_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])
+    # LoRA
+    lora_ids = [s.split(" — ")[0].strip() for s in (lora_selected or [])]
+    if lora_custom and lora_custom.strip():
+        lora_ids += [x.strip() for x in lora_custom.split(",") if x.strip()]
+    apply_loras(pipe, lora_ids)
 
-    width  = int(max(512, min(1024, width)))
-    height = int(max(512, min(1024, height)))
+    width, height = int(width), int(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]
+            if control_ids:
+                img = pipe(
+                    prompt=prompt, negative_prompt=negative,
+                    width=width, height=height,
+                    num_inference_steps=int(steps), guidance_scale=float(cfg),
+                    controlnet_conditioning_image=cond_images if len(cond_images)>1 else cond_images[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]
+                img = 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]
+        if control_ids:
+            img = pipe(
+                prompt=prompt, negative_prompt=negative,
+                width=width, height=height,
+                num_inference_steps=int(steps), guidance_scale=float(cfg),
+                controlnet_conditioning_image=cond_images if len(cond_images)>1 else cond_images[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 only)
-    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)
-
-    image = post_process(image, do_upscale, do_face, do_rembg)
+            img = pipe(
+                prompt=prompt, negative_prompt=negative,
+                width=width, height=height,
+                num_inference_steps=int(steps), guidance_scale=float(cfg),
+                generator=gen
+            ).images[0]
+
+    img = post_process(img, do_up, do_face, do_bg)
     meta = {
-        "mode":"txt2img","model":model,"loras":loras,"controlnets":ctrl_selected,
+        "mode":"txt2img","model":model,"loras":lora_ids,"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}
+        "post":{"upscale":do_up,"face_restore":do_face,"remove_bg":do_bg}
     }
-    return image, to_png_info(meta)
+    return img, to_info(meta)
 
-def run_img2img(model_id, custom_model, init_image, strength,
-                prompt, preset, negative, steps, cfg, width, height, scheduler_name, seed,
-                do_upscale, do_face, do_rembg):
+def run_img2img(
+    model_id, model_custom, init_image, strength,
+    prompt, preset, negative, steps, cfg, width, height, scheduler_name, seed,
+    do_up, do_face, do_bg
+):
     if init_image is None: raise gr.Error("โปรดอัปโหลดภาพเริ่มต้น")
-    model = (custom_model.strip() or model_id).strip()
-    if preset in PRESETS: prompt = prompt + PRESETS[preset]
-    if not (negative or "").strip(): negative = NEG_DEFAULT
+    model = (model_custom.strip() or model_id).strip()
+    if preset and preset in PRESETS: prompt = prompt + PRESETS[preset]
+    if not negative or not negative.strip(): negative = NEG_DEFAULT
 
-    pipe = StableDiffusionXLImg2ImgPipeline.from_pretrained(model, torch_dtype=dtype, use_safetensors=True)
-    pipe = pipe.to(device)
+    pipe = StableDiffusionXLImg2ImgPipeline.from_pretrained(model, torch_dtype=dtype, use_safetensors=True).to(device)
     try:
         if device=="cuda": pipe.enable_xformers_memory_efficient_attention()
     except Exception: pass
@@ -322,50 +306,48 @@ def run_img2img(model_id, custom_model, init_image, strength,
 
     if device=="cuda":
         with torch.autocast("cuda"):
-            img = pipe(prompt=prompt, negative_prompt=negative, image=init_image,
-                       strength=float(strength), num_inference_steps=int(steps),
-                       guidance_scale=float(cfg), generator=gen).images[0]
+            img = pipe(prompt=prompt, negative_prompt=negative,
+                       image=init_image, strength=float(strength),
+                       num_inference_steps=int(steps), guidance_scale=float(cfg),
+                       generator=gen).images[0]
     else:
-        img = pipe(prompt=prompt, negative_prompt=negative, image=init_image,
-                   strength=float(strength), num_inference_steps=int(steps),
-                   guidance_scale=float(cfg), generator=gen).images[0]
+        img = pipe(prompt=prompt, negative_prompt=negative,
+                   image=init_image, strength=float(strength),
+                   num_inference_steps=int(steps), guidance_scale=float(cfg),
+                   generator=gen).images[0]
 
-    img = post_process(img, do_upscale, do_face, do_rembg)
+    img = post_process(img, do_up, do_face, do_bg)
     meta = {"mode":"img2img","model":model,"prompt":prompt,"neg":negative,
             "steps":steps,"cfg":cfg,"seed":seed,"strength":strength}
-    return img, to_png_info(meta)
+    return img, to_info(meta)
 
 def expand_canvas_for_outpaint(img: Image.Image, expand_px: int, direction: str) -> Tuple[Image.Image, Image.Image]:
     w, h = img.size
-    new = Image.new("RGBA", (w, h), (0,0,0,0))
-    mask = Image.new("L", (w, h), 0)
-    draw = ImageDraw.Draw(mask)
-
     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); draw = ImageDraw.Draw(mask); draw.rectangle([0,0,expand_px,h], fill=255)
+        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); 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); draw = ImageDraw.Draw(mask); draw.rectangle([w,0,w+expand_px,h], fill=255)
+        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); 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); draw = ImageDraw.Draw(mask); draw.rectangle([0,0,w,expand_px], fill=255)
-    else:  # bottom
-        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); draw = ImageDraw.Draw(mask); draw.rectangle([0,h,w,h+expand_px], fill=255)
-
+        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); 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); 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,
-                         prompt, preset, negative, steps, cfg, width, height, scheduler_name, seed,
-                         strength, do_upscale, do_face, do_rembg):
+def run_inpaint_outpaint(
+    model_id, model_custom, base_image, mask_image, mode, expand_px, expand_dir,
+    prompt, preset, negative, steps, cfg, width, height, scheduler_name, seed,
+    strength, do_up, do_face, do_bg
+):
     if base_image is None: raise gr.Error("โปรดอัปโหลดภาพฐาน")
-    model = (custom_model.strip() or model_id).strip()
-    if preset in PRESETS: prompt = prompt + PRESETS[preset]
-    if not (negative or "").strip(): negative = NEG_DEFAULT
+    model = (model_custom.strip() or model_id).strip()
+    if preset and preset in PRESETS: prompt = prompt + PRESETS[preset]
+    if not negative or not negative.strip(): negative = NEG_DEFAULT
 
-    pipe = StableDiffusionXLInpaintPipeline.from_pretrained(model, torch_dtype=dtype, use_safetensors=True)
-    pipe = pipe.to(device)
+    pipe = StableDiffusionXLInpaintPipeline.from_pretrained(model, torch_dtype=dtype, use_safetensors=True).to(device)
     try:
         if device=="cuda": pipe.enable_xformers_memory_efficient_attention()
     except Exception: pass
@@ -378,26 +360,29 @@ def run_inpaint_outpaint(model_id, custom_model, base_image, mask_image, mode, e
         with torch.autocast("cuda"):
             img = pipe(prompt=prompt, negative_prompt=negative,
                        image=base_image, mask_image=mask_image,
-                       strength=float(strength), num_inference_steps=int(steps),
-                       guidance_scale=float(cfg), generator=gen).images[0]
+                       strength=float(strength),
+                       num_inference_steps=int(steps), guidance_scale=float(cfg),
+                       generator=gen).images[0]
     else:
         img = pipe(prompt=prompt, negative_prompt=negative,
                    image=base_image, mask_image=mask_image,
-                   strength=float(strength), num_inference_steps=int(steps),
-                   guidance_scale=float(cfg), generator=gen).images[0]
+                   strength=float(strength),
+                   num_inference_steps=int(steps), guidance_scale=float(cfg),
+                   generator=gen).images[0]
 
-    img = post_process(img, do_upscale, do_face, do_rembg)
+    img = post_process(img, do_up, do_face, do_bg)
     meta = {"mode":mode,"model":model,"prompt":prompt,"steps":steps,"cfg":cfg,"seed":seed}
-    return img, to_png_info(meta)
+    return img, to_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("Text2Img • Img2Img • Inpaint/Outpaint • Multi-LoRA • Multi-ControlNet • Upscale/FaceRestore/RemoveBG")
+        gr.Markdown("Text2Img • Img2Img • Inpaint/Outpaint • Multi-LoRA • ControlNet • Upscale/FaceRestore/RemoveBG (optional)")
 
-        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)")
+        # Common controls
+        model_dd = gr.Dropdown(choices=[m[0] for m in MODELS], value=MODELS[0][0], label="Model")
+        model_custom = gr.Textbox(label="Custom Model ID", placeholder="(ถ้าอยากใช้โมเดลของคุณเอง กรอกที่นี่)")
 
         preset = gr.Dropdown(choices=list(PRESETS.keys()), value=None, label="Style Preset (optional)")
         negative = gr.Textbox(value=NEG_DEFAULT, label="Negative Prompt")
@@ -407,42 +392,23 @@ def build_ui():
         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)")
+        seed = gr.Number(value=-1, precision=0, label="Seed (-1 = random)")
+
+        # LoRA & ControlNet
+        lora_sel = gr.CheckboxGroup(choices=[f"{rid} — {lbl} ({note})" for rid,lbl,note in LORAS], label="LoRA (เลือกได้หลายตัว)")
+        lora_custom = gr.Textbox(label="Custom LoRA IDs (comma separated)")
+
+        ctrl_sel = gr.CheckboxGroup(choices=[c[1] for c in CONTROLNETS], label="ControlNet ชนิดที่ใช้")
+        img_canny = gr.Image(type="pil", label="Canny")
+        img_pose = gr.Image(type="pil", label="OpenPose")
+        img_depth = gr.Image(type="pil", label="Depth")
+        img_softedge = gr.Image(type="pil", label="SoftEdge")
+        img_lineart = gr.Image(type="pil", label="Lineart")
 
-        # 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)")
+            do_up = gr.Checkbox(False, label="Upscale x4 (ถ้ามี)")
+            do_face = gr.Checkbox(False, label="Face Restore (ถ้ามี)")
+            do_bg = gr.Checkbox(False, label="Remove BG (ถ้ามี)")
 
         with gr.Tab("Text → Image"):
             prompt_txt = gr.Textbox(lines=3, label="Prompt")
@@ -451,7 +417,7 @@ def build_ui():
             out_meta_txt = gr.Textbox(label="Metadata", lines=10)
 
         with gr.Tab("Image → Image"):
-            init_img = gr.Image(type="pil", label="Init Image (img2img)")
+            init_img = gr.Image(type="pil", label="Init Image")
             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")
@@ -469,20 +435,15 @@ def build_ui():
             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 []
-            return [s.split(" — ")[0].strip() for s in selected]
-
+        # Bindings
         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),  # use_refiner, refine_strength (placeholder; UI ไม่โชว์)
-                do_upscale, do_face, do_rembg
+                lora_sel, lora_custom,
+                ctrl_sel, img_canny, img_pose, img_depth, img_softedge, img_lineart,
+                do_up, do_face, do_bg
             ],
             outputs=[out_img_txt, out_meta_txt],
             api_name="txt2img"
@@ -490,23 +451,27 @@ def build_ui():
 
         btn_i2i.click(
             fn=run_img2img,
-            inputs=[model_dd, model_custom, init_img, strength,
-                    prompt_i2i, preset, negative, steps, cfg, width, height, scheduler, seed,
-                    do_upscale, do_face, do_rembg],
+            inputs=[
+                model_dd, model_custom, init_img, strength,
+                prompt_i2i, preset, negative, steps, cfg, width, height, scheduler, seed,
+                do_up, do_face, do_bg
+            ],
             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_io, preset, negative, steps, cfg, width, height, scheduler, seed,
-                    strength, do_upscale, do_face, do_rembg],
+            inputs=[
+                model_dd, model_custom, base_img, mask_img, mode_io, expand_px, expand_dir,
+                prompt_io, preset, negative, steps, cfg, width, height, scheduler, seed,
+                strength, do_up, do_face, do_bg
+            ],
             outputs=[out_img_io, out_meta_io],
             api_name="inpaint_outpaint"
         )
 
-        gr.Markdown("ℹ️ **หมายเห���ุ**: ถ้า LoRA/ControlNet/โพสต์โปรเซสบางตัวไม่มีในสภาพแวดล้อม โปรแกรมจะข้ามอย่างปลอดภัย พร้อมพิมพ์คำเตือนใน Console")
+        gr.Markdown("ℹ️ ถ้าโมดูลเสริมหรือบางโมเดลไม่พร้อมใช้งาน ระบบจะข้ามอย่างปลอดภัยและแจ้งเตือนใน Console")
 
     return demo