Update app.py

app.py

@@ -1,10 +1,9 @@
 from fastapi import FastAPI, UploadFile, File
 from pydantic import BaseModel
-from diffusers import StableDiffusionPipeline
+from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline
 import torch
 from PIL import Image
 import io
-import os
 from fastapi.responses import StreamingResponse
 
 app = FastAPI()
@@ -12,49 +11,31 @@ app = FastAPI()
 MODEL_PATH = "Interior.safetensors"
 LORA_PATH = "Interior_lora.safetensors"
 
-# ========================
-# ⚡ CPU OPTIMIZATION
-# ========================
-device = "cuda" if torch.cuda.is_available() else "cpu"
-dtype = torch.float16 if device == "cuda" else torch.float32
+print("Loading base model...")
 
-torch.set_num_threads(os.cpu_count())
-
-print("Loading model...")
+txt2img = StableDiffusionPipeline.from_single_file(
+    MODEL_PATH,
+    torch_dtype=torch.float16,
+    safety_checker=None
+).to("cpu")  # هنرجعها GPU لو متاح لاحقًا
 
-# ========================
-# SINGLE PIPELINE (IMPORTANT FIX)
-# ========================
-pipe = StableDiffusionPipeline.from_single_file(
+img2img = StableDiffusionImg2ImgPipeline.from_single_file(
     MODEL_PATH,
-    torch_dtype=dtype,
+    torch_dtype=torch.float16,
     safety_checker=None
-).to(device)
+).to("cpu")
 
 print("Loading LoRA...")
 
-pipe.load_lora_weights(LORA_PATH)
-pipe.fuse_lora(lora_scale=0.7)
-
-# ========================
-# SPEED BOOSTS
-# ========================
-pipe.enable_attention_slicing()
-pipe.enable_vae_slicing()
+txt2img.load_lora_weights(LORA_PATH)
+img2img.load_lora_weights(LORA_PATH)
 
-print("Model ready 🔥")
+print("LoRA loaded 🔥")
 
-
-# ========================
-# REQUEST MODEL
-# ========================
 class Prompt(BaseModel):
     prompt: str
 
 
-# ========================
-# IMAGE UTILS
-# ========================
 def to_bytes(img):
     buf = io.BytesIO()
     img.save(buf, format="PNG")
@@ -62,49 +43,26 @@ def to_bytes(img):
     return buf
 
 
-# ========================
-# HEALTH CHECK
-# ========================
-@app.get("/")
-def home():
-    return {"status": "API is running 🚀"}
-
-
-# ========================
-# TXT2IMG (FAST MODE)
-# ========================
 @app.post("/txt2img")
 def generate(data: Prompt):
 
-    image = pipe(
+    image = txt2img(
         data.prompt,
-        num_inference_steps=6,   # ⚡ أسرع بكتير
-        guidance_scale=5,
-        height=256,
-        width=256
+        cross_attention_kwargs={"scale": 0.8}
     ).images[0]
 
     return StreamingResponse(to_bytes(image), media_type="image/png")
 
 
-# ========================
-# IMG2IMG (FAST MODE)
-# ========================
 @app.post("/img2img")
-async def img2img_api(
-    file: UploadFile = File(...),
-    prompt: str = ""
-):
+async def img2img_api(file: UploadFile = File(...), prompt: str = ""):
 
-    img = Image.open(io.BytesIO(await file.read())).convert("RGB")
-    img = img.resize((256, 256))  # ⚡ أسرع بشكل واضح
+    img = Image.open(io.BytesIO(await file.read())).convert("RGB").resize((512,512))
 
-    image = pipe(
+    image = img2img(
         prompt=prompt,
         image=img,
-        strength=0.6,
-        num_inference_steps=6,
-        guidance_scale=5
+        cross_attention_kwargs={"scale": 0.8}
     ).images[0]
 
-    return StreamingResponse(to_bytes(image), media_type="image/png")
+    return StreamingResponse(to_bytes(image), media_type="image/png")