Update app.py

app.py

@@ -6,22 +6,22 @@ from fastapi.responses import StreamingResponse
 from PIL import Image
 import torch
 import io
-from safetensors.torch import load_file
 
 app = FastAPI()
 
 # =========================
-# Load FULL MODEL (CPU)
+# Load LoRA ONLY (ignore checkpoint)
 # =========================
-MODEL_PATH = hf_hub_download(
+LORA_PATH = hf_hub_download(
     repo_id="ebraam1/interior-sd-models",
-    filename="Interior.safetensors"
+    filename="Interior_lora.safetensors"
 )
 
-print("Loading SD checkpoint... (CPU mode)")
+print("Loading base model (CPU safe)...")
 
-pipe = StableDiffusionPipeline.from_single_file(
-    MODEL_PATH,
+# ⚠️ IMPORTANT: base model only (no single_file)
+pipe = StableDiffusionPipeline.from_pretrained(
+    "runwayml/stable-diffusion-v1-5",
     torch_dtype=torch.float32,
     safety_checker=None
 ).to("cpu")
@@ -29,24 +29,13 @@ pipe = StableDiffusionPipeline.from_single_file(
 pipe.enable_attention_slicing()
 pipe.enable_vae_slicing()
 
-print("Base model loaded 🔥")
+print("Loading LoRA safely...")
 
-# =========================
-# Load LoRA manually (SAFE)
-# =========================
-LORA_PATH = hf_hub_download(
-    repo_id="ebraam1/interior-sd-models",
-    filename="Interior_lora.safetensors"
-)
-
-print("Loading LoRA manually...")
-
-lora_state_dict = load_file(LORA_PATH)
+# 🔥 SAFE LoRA loading (diffusers supported way)
+pipe.load_lora_weights(LORA_PATH)
+pipe.fuse_lora(lora_scale=0.8)
 
-# 👇 هذا هو السحر الحقيقي
-pipe.load_attn_procs(lora_state_dict)
-
-print("LoRA loaded 🔥")
+print("Model ready 🔥")
 
 # =========================
 class Prompt(BaseModel):
@@ -58,15 +47,13 @@ def to_bytes(img):
     buf.seek(0)
     return buf
 
-# =========================
-# TXT2IMG
 # =========================
 @app.post("/txt2img")
 def generate(data: Prompt):
 
     image = pipe(
         data.prompt,
-        num_inference_steps=8,   # CPU → خليها منخفضة
+        num_inference_steps=6,   # CPU optimized
         guidance_scale=5,
         height=256,
         width=256
@@ -74,8 +61,6 @@ 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 = ""):
@@ -86,7 +71,7 @@ async def img2img_api(file: UploadFile = File(...), prompt: str = ""):
         prompt=prompt,
         image=img,
         strength=0.6,
-        num_inference_steps=8,
+        num_inference_steps=6,
         guidance_scale=5
     ).images[0]