Update app.py

app.py

@@ -6,18 +6,22 @@ from fastapi.responses import StreamingResponse
 from PIL import Image
 import torch
 import io
+from safetensors.torch import load_file
 
 app = FastAPI()
 
-LORA_PATH = hf_hub_download(
+# =========================
+# Load FULL MODEL (CPU)
+# =========================
+MODEL_PATH = hf_hub_download(
     repo_id="ebraam1/interior-sd-models",
-    filename="Interior_lora.safetensors"
+    filename="Interior.safetensors"
 )
 
-print("Loading base model...")
+print("Loading SD checkpoint... (CPU mode)")
 
-pipe = StableDiffusionPipeline.from_pretrained(
-    "runwayml/stable-diffusion-v1-5",
+pipe = StableDiffusionPipeline.from_single_file(
+    MODEL_PATH,
     torch_dtype=torch.float32,
     safety_checker=None
 ).to("cpu")
@@ -25,14 +29,26 @@ pipe = StableDiffusionPipeline.from_pretrained(
 pipe.enable_attention_slicing()
 pipe.enable_vae_slicing()
 
-print("Loading LoRA (kohya format)...")
+print("Base model loaded 🔥")
 
-# 🔥 الطريقة الصح للـ LoRA بتاعك
-pipe.load_lora_weights(LORA_PATH)
-pipe.fuse_lora(lora_scale=0.8)
+# =========================
+# Load LoRA manually (SAFE)
+# =========================
+LORA_PATH = hf_hub_download(
+    repo_id="ebraam1/interior-sd-models",
+    filename="Interior_lora.safetensors"
+)
 
-print("Model ready 🔥")
+print("Loading LoRA manually...")
 
+lora_state_dict = load_file(LORA_PATH)
+
+# 👇 هذا هو السحر الحقيقي
+pipe.load_attn_procs(lora_state_dict)
+
+print("LoRA loaded 🔥")
+
+# =========================
 class Prompt(BaseModel):
     prompt: str
 
@@ -42,11 +58,15 @@ def to_bytes(img):
     buf.seek(0)
     return buf
 
+# =========================
+# TXT2IMG
+# =========================
 @app.post("/txt2img")
 def generate(data: Prompt):
+
     image = pipe(
         data.prompt,
-        num_inference_steps=6,
+        num_inference_steps=8,   # CPU → خليها منخفضة
         guidance_scale=5,
         height=256,
         width=256
@@ -54,15 +74,19 @@ def generate(data: Prompt):
 
     return StreamingResponse(to_bytes(image), media_type="image/png")
 
+# =========================
+# IMG2IMG
+# =========================
 @app.post("/img2img")
 async def img2img_api(file: UploadFile = File(...), prompt: str = ""):
+
     img = Image.open(io.BytesIO(await file.read())).convert("RGB").resize((256,256))
 
     image = pipe(
         prompt=prompt,
         image=img,
         strength=0.6,
-        num_inference_steps=6,
+        num_inference_steps=8,
         guidance_scale=5
     ).images[0]