app.py

app.py

@@ -1,9 +1,9 @@
-import os, io, time, json, math
+import os, io, time, json
 from typing import List, Dict, Optional, Tuple
 
 import gradio as gr
 import numpy as np
-from PIL import Image, ImageOps
+from PIL import Image, ImageDraw
 
 import torch
 from diffusers import (
@@ -17,52 +17,56 @@ from diffusers import (
     EulerAncestralDiscreteScheduler, HeunDiscreteScheduler,
 )
 
-# ---------- Optional deps ----------
+# -------- Optional deps (SAFE IMPORTS: ไม่มีโมดูลก็ไม่พัง) --------
+# Remove background
 try:
     from rembg import remove as rembg_remove
 except Exception:
     rembg_remove = None
 
-# face restore
-# ---- Optional face restore (safe import) ----
+# Face restore (GFPGAN)
 _HAS_GFP = False
+GFPGANer = None
+GFP = None
 try:
-    import gfpgan
+    import gfpgan  # type: ignore
     if hasattr(gfpgan, "GFPGANer"):
         GFPGANer = gfpgan.GFPGANer  # type: ignore
         _HAS_GFP = True
-except Exception:
-    GFPGANer = None  # type: ignore
-GFP = None
+except Exception as e:
+    print("[WARN] GFPGAN not available:", e)
 
-# realesrgan (fallback upscaler)
+# RealESRGAN (upscale; ใช้ได้เมื่อมี)
+_HAS_REALESRGAN = False
+RealESRGAN = None
+REALSR = None
 try:
-    from realesrgan import RealESRGAN
+    from realesrgan import RealESRGAN  # type: ignore
     _HAS_REALESRGAN = True
-except Exception:
-    _HAS_REALESRGAN = False
+except Exception as e:
+    print("[WARN] RealESRGAN not available:", e)
+
+# ---------------------------------------------------------------
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
 dtype  = torch.float16 if device == "cuda" else torch.float32
 
-# ---------------- Registry (เปลี่ยน/เพิ่มได้เอง) ----------------
+# ---------------- Registry (ตัวเลือกยอดนิยม + เพิ่มเองได้) ----------------
 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"),
+    ("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"),
-    ("emilianJR/epiCRealismXL",                    "EpicRealism XL","แฟชั่น/พอร์เทรตคอนทราสต์ดี"),
+    ("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",        "คลาสสิก/ทรัพยากรเยอะ"),
-    ("timbrooks/instruct-pix2pix",                 "Instruct-Pix2Pix","แก้ภาพตามคำสั่ง (Img2Img)"),
+    ("runwayml/stable-diffusion-v1-5",             "SD 1.5",        "สไตล์คลาสสิก"),
+    ("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)"),
@@ -77,7 +81,6 @@ LORAS: List[Tuple[str,str,str]] = [
 ]
 
 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"),
@@ -106,12 +109,12 @@ SCHEDULERS = {
     "Heun": HeunDiscreteScheduler,
 }
 
-# ---------------- Cache & helpers ----------------
+# ---------------- Cache ----------------
 PIPE_CACHE: Dict[str, object] = {}
 CONTROL_CACHE: Dict[str, ControlNetModel] = {}
 UPSCALE_PIPE: Optional[StableDiffusionUpscalePipeline] = None
-REALSR: Optional[RealESRGAN] = None
 
+# ---------------- Helpers ----------------
 def set_sched(pipe, name: str):
     cls = SCHEDULERS.get(name, DPMSolverMultistepScheduler)
     pipe.scheduler = cls.from_config(pipe.scheduler.config)
@@ -119,8 +122,7 @@ 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.manual_seed(int(sd))
-    return g
+    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'}"
@@ -137,8 +139,7 @@ def prep_pipe(model_id: str, control_ids: List[str]):
         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()
+        pipe.to("cuda"); pipe.enable_vae_tiling(); pipe.enable_vae_slicing()
         try: pipe.enable_xformers_memory_efficient_attention()
         except Exception: pass
     else:
@@ -154,44 +155,46 @@ def apply_loras(pipe, ids: List[str], scales: List[float]):
             try:
                 sc = scales[i] if i < len(scales) else 0.7
                 pipe.fuse_lora(lora_scale=float(sc))
-            except Exception: pass
+            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 ----------------
+# --------------- 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
+                torch_dtype=dtype, use_safetensors=True
             ).to(device)
         except Exception as e:
             print("[Upscaler] SD x4 not available:", e)
 
-    if _HAS_GFP and GFP is None:
+    # 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)
-            REALSR.load_weights("weights/RealESRGAN_x4plus.pth")  # ใช้ได้เมื่อมีไฟล์ (ถ้าไม่มีจะข้าม)
+            REALSR = RealESRGAN(torch.device("cuda"), scale=4)  # ต้องมีไฟล์ weights ถึงจะทำงานจริง
+            # REALSR.load_weights("weights/RealESRGAN_x4plus.pth")
         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):
     ensure_upscalers()
     out = img
 
-    # Upscale priority: RealESRGAN > SD x4 > none
     if do_upscale:
         try:
             if REALSR is not None:
@@ -207,8 +210,8 @@ def post_process(img: Image.Image, do_upscale: bool, do_face: bool, do_rembg: bo
 
     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)
+            _, _, restored = GFP.enhance(np.array(out), has_aligned=False, only_center_face=False, paste_back=True)
+            out = Image.fromarray(restored)
         except Exception as e:
             print("[GFPGAN] skipped:", e)
 
@@ -220,18 +223,19 @@ def post_process(img: Image.Image, do_upscale: bool, do_face: bool, do_rembg: bo
 
     return out
 
-# ---------------- Core generate ----------------
+# ---------------- 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.strip(): negative = NEG_DEFAULT
+    if not (negative or "").strip(): negative = NEG_DEFAULT
 
-    # Collect control images
+    # 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:
@@ -276,7 +280,7 @@ def run_txt2img(model_id, custom_model, prompt, preset, negative,
                          num_inference_steps=int(steps), guidance_scale=float(cfg),
                          generator=gen).images[0]
 
-    # Refiner (GPU)
+    # Refiner (GPU only)
     if use_refiner and device == "cuda":
         try:
             ref = StableDiffusionXLImg2ImgPipeline.from_pretrained(
@@ -285,69 +289,87 @@ def run_txt2img(model_id, custom_model, prompt, preset, negative,
             ).to("cuda")
             set_sched(ref, scheduler_name)
             with torch.autocast("cuda"):
-                image = ref(prompt=prompt, negative_prompt=negative,
-                            image=image, strength=float(refine_strength),
+                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}
+        "mode":"txt2img","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)")
+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):
+    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
+
     pipe = StableDiffusionXLImg2ImgPipeline.from_pretrained(model, torch_dtype=dtype, use_safetensors=True)
-    pipe = pipe.to(device); 
+    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
+    except Exception: pass
+    set_sched(pipe, scheduler_name); gen = seed_gen(seed)
 
     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]
+                       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(kw["steps"]),
-                   guidance_scale=float(kw["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, 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}
+    img = post_process(img, do_upscale, do_face, do_rembg)
+    meta = {"mode":"img2img","model":model,"prompt":prompt,"neg":negative,
+            "steps":steps,"cfg":cfg,"seed":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)
+    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)
+    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)
+    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)
+
     return new.convert("RGB"), mask
 
-def run_inpaint_outpaint(model_id, custom_model, base_image, mask_image, mode, expand_px, expand_dir, **kw):
+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):
     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
+
     pipe = StableDiffusionXLInpaintPipeline.from_pretrained(model, torch_dtype=dtype, use_safetensors=True)
-    pipe = pipe.to(device);
+    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
+    except Exception: pass
+    set_sched(pipe, scheduler_name); gen = seed_gen(seed)
 
     if mode == "Outpaint":
         base_image, mask_image = expand_canvas_for_outpaint(base_image, int(expand_px), expand_dir)
@@ -356,34 +378,30 @@ 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=kw.get("strength", 0.7),
-                       num_inference_steps=int(kw["steps"]),
-                       guidance_scale=float(kw["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=kw.get("strength", 0.7),
-                   num_inference_steps=int(kw["steps"]),
-                   guidance_scale=float(kw["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, 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"]}
+    img = post_process(img, do_upscale, do_face, do_rembg)
+    meta = {"mode":mode,"model":model,"prompt":prompt,"steps":steps,"cfg":cfg,"seed":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**")
+        gr.Markdown("Text2Img • Img2Img • Inpaint/Outpaint • Multi-LoRA • Multi-ControlNet • 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")
@@ -392,14 +410,20 @@ def build_ui():
         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_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)")
+        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"),
@@ -447,11 +471,7 @@ def build_ui():
 
         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
+            return [s.split(" — ")[0].strip() for s in selected]
 
         btn_txt.click(
             fn=run_txt2img,
@@ -461,7 +481,7 @@ def build_ui():
                 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),
+                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
             ],
             outputs=[out_img_txt, out_meta_txt],
@@ -471,7 +491,7 @@ def build_ui():
         btn_i2i.click(
             fn=run_img2img,
             inputs=[model_dd, model_custom, init_img, strength,
-                    ("prompt",), preset, negative, steps, cfg, width, height, scheduler, seed,
+                    prompt_i2i, preset, negative, steps, cfg, width, height, scheduler, seed,
                     do_upscale, do_face, do_rembg],
             outputs=[out_img_i2i, out_meta_i2i],
             api_name="img2img"
@@ -480,13 +500,13 @@ def build_ui():
         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,
+                    prompt_io, 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")
+        gr.Markdown("ℹ️ **หมายเหตุ**: ถ้า LoRA/ControlNet/โพสต์โปรเซสบางตัวไม่มีในสภาพแวดล้อม โปรแกรมจะข้ามอย่างปลอดภัย พร้อมพิมพ์คำเตือนใน Console")
 
     return demo