Update app.py

app.py

@@ -12,7 +12,7 @@ MODEL_REPO = "ebraam1/interior-sd-models"
 BASE_MODEL = "Interior.safetensors"
 LORA_FILE = "Interior_lora.safetensors"
 
-# ---------- تحميل الموديل الأساسي ----------
+# تحميل الموديل الأساسي
 print("Downloading base model...")
 base_path = hf_hub_download(repo_id=MODEL_REPO, filename=BASE_MODEL)
 
@@ -24,41 +24,42 @@ pipe = StableDiffusionPipeline.from_single_file(
     requires_safety_checker=False
 ).to("cpu")
 
-# ---------- استخدام Scheduler أسرع (DPM-Solver) ----------
+# أفضل Scheduler للجودة العالية
 pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
 
-# ---------- تحميل الـ LoRA ودمجه ----------
+# تحميل LoRA ودمجه
 print("Downloading LoRA...")
 lora_path = hf_hub_download(repo_id=MODEL_REPO, filename=LORA_FILE)
 
 print("Loading LoRA...")
 pipe.load_lora_weights(lora_path)
-pipe.fuse_lora(lora_scale=1.0)  # القوة الكاملة
+pipe.fuse_lora(lora_scale=1.0)
 
 # تحسينات الذاكرة
 pipe.enable_attention_slicing()
 pipe.enable_vae_slicing()
 
-print("Model ready 🔥 (Base + LoRA fused with DPM-Solver)")
+print("Model ready 🔥 (Max quality mode - Landscape)")
 
-# ---------- دوال مساعدة ----------
 def to_bytes(img: Image.Image) -> io.BytesIO:
     buf = io.BytesIO()
     img.save(buf, format="PNG")
     buf.seek(0)
     return buf
 
-# ---------- Text‑to‑Image (GET بسيط) ----------
+# ================== Text-to-Image (GET) ==================
 @app.get("/txt2img")
 def txt2img(
-    prompt: str = Query(..., description="الوصف اللي عايز تولده"),
-    steps: int = Query(6, ge=1, le=20),            # افتراضي 6 (أسرع)
-    guidance: float = Query(7.5, ge=1.0, le=20.0),
-    height: int = Query(384, ge=256, le=768),      # افتراضي 384
-    width: int = Query(384, ge=256, le=768)        # افتراضي 384
+    prompt: str = Query(..., description="الوصف"),
+    negative_prompt: str = Query("", description="العناصر اللي عايز تتجنبها"),
+    steps: int = Query(20, ge=1, le=30),
+    guidance: float = Query(9.0, ge=1.0, le=20.0),
+    height: int = Query(512, ge=256, le=768),   # أصبح أقل (عرضي)
+    width: int = Query(768, ge=256, le=768)     # أصبح أكبر
 ):
     image = pipe(
         prompt=prompt,
+        negative_prompt=negative_prompt or None,
         num_inference_steps=steps,
         guidance_scale=guidance,
         height=height,
@@ -66,20 +67,22 @@ def txt2img(
     ).images[0]
     return StreamingResponse(to_bytes(image), media_type="image/png")
 
-# ---------- Image‑to‑Image (POST) ----------
+# ================== Image-to-Image (POST) ==================
 @app.post("/img2img")
 async def img2img(
     file: UploadFile = File(...),
     prompt: str = Query(""),
-    steps: int = Query(6, ge=1, le=20),
-    guidance: float = Query(7.5, ge=1.0, le=20.0),
+    negative_prompt: str = Query(""),
+    steps: int = Query(20, ge=1, le=30),
+    guidance: float = Query(9.0, ge=1.0, le=20.0),
     strength: float = Query(0.6, ge=0.0, le=1.0),
-    height: int = Query(384, ge=256, le=768),
-    width: int = Query(384, ge=256, le=768)
+    height: int = Query(512, ge=256, le=768),
+    width: int = Query(768, ge=256, le=768)
 ):
     img = Image.open(io.BytesIO(await file.read())).convert("RGB").resize((width, height))
     image = pipe(
         prompt=prompt,
+        negative_prompt=negative_prompt or None,
         image=img,
         strength=strength,
         num_inference_steps=steps,