Update app.py

app.py

@@ -10,16 +10,10 @@ import io
 app = FastAPI()
 
 # =========================
-# Load LoRA ONLY (ignore checkpoint)
+# Load base model (CPU)
 # =========================
-LORA_PATH = hf_hub_download(
-    repo_id="ebraam1/interior-sd-models",
-    filename="Interior_lora.safetensors"
-)
-
-print("Loading base model (CPU safe)...")
+print("Loading base model...")
 
-# ⚠️ IMPORTANT: base model only (no single_file)
 pipe = StableDiffusionPipeline.from_pretrained(
     "runwayml/stable-diffusion-v1-5",
     torch_dtype=torch.float32,
@@ -29,9 +23,21 @@ pipe = StableDiffusionPipeline.from_pretrained(
 pipe.enable_attention_slicing()
 pipe.enable_vae_slicing()
 
-print("Loading LoRA safely...")
+# =========================
+# FORCE enable PEFT backend
+# =========================
+import peft  # 🔥 مهم جدًا
+
+# =========================
+# Load LoRA
+# =========================
+LORA_PATH = hf_hub_download(
+    repo_id="ebraam1/interior-sd-models",
+    filename="Interior_lora.safetensors"
+)
+
+print("Loading LoRA...")
 
-# 🔥 SAFE LoRA loading (diffusers supported way)
 pipe.load_lora_weights(LORA_PATH)
 pipe.fuse_lora(lora_scale=0.8)
 
@@ -53,7 +59,7 @@ def generate(data: Prompt):
 
     image = pipe(
         data.prompt,
-        num_inference_steps=6,   # CPU optimized
+        num_inference_steps=6,
         guidance_scale=5,
         height=256,
         width=256
@@ -61,7 +67,7 @@ def generate(data: Prompt):
 
     return StreamingResponse(to_bytes(image), media_type="image/png")
 
-# =========================
+
 @app.post("/img2img")
 async def img2img_api(file: UploadFile = File(...), prompt: str = ""):