Update app.py

app.py

@@ -6,6 +6,7 @@ import threading
 import time
 from collections import OrderedDict
 import os
+from PIL import Image  # 🔥 NEW
 
 app = FastAPI()
 
@@ -15,7 +16,7 @@ app = FastAPI()
 torch.set_num_threads(2)
 
 # =========================
-# 🔥 MODEL (STABLE LCM-FRIENDLY)
+# 🔥 MODEL (ТВОЙ РАБОЧИЙ)
 # =========================
 model_name = "Lykon/dreamshaper-7"
 
@@ -27,6 +28,10 @@ pipe = StableDiffusionPipeline.from_pretrained(
 
 pipe = pipe.to("cpu")
 
+# 🔥 LCM LoRA (оставляем как у тебя)
+pipe.load_lora_weights("latent-consistency/lcm-lora-sdv1-5")
+pipe.fuse_lora()
+
 pipe.enable_attention_slicing()
 
 # =========================
@@ -37,11 +42,44 @@ queue = []
 progress_db = {}
 
 MAX_HISTORY = 40
-NUM_WORKERS = 1
-
 IMG_DIR = "images"
 os.makedirs(IMG_DIR, exist_ok=True)
 
+# =========================
+# ✂️ SPLIT FUNCTION (12 частей)
+# =========================
+def split_image_into_12(img_path: str):
+
+    img = Image.open(img_path)
+
+    w, h = img.size
+
+    cols = 3
+    rows = 4
+
+    tile_w = w // cols
+    tile_h = h // rows
+
+    base = img_path.replace(".png", "")
+
+    index = 1
+
+    for r in range(rows):
+        for c in range(cols):
+
+            left = c * tile_w
+            top = r * tile_h
+            right = left + tile_w
+            bottom = top + tile_h
+
+            crop = img.crop((left, top, right, bottom))
+
+            out_path = f"{base}_{index}.png"
+            crop.save(out_path)
+
+            index += 1
+
+
 # =========================
 # 🚀 GENERATION ENGINE
 # =========================
@@ -49,33 +87,37 @@ def generate_ai_stream(message: str, mode="fast"):
 
     try:
         start = time.time()
+        progress_db[message] = 0
 
-        # ⚡ режимы
+        # ⚡ FAST / QUALITY
         if mode == "fast":
-            steps = 2
+            steps = 4
             cfg = 1.5
+            h, w = 256, 256
         else:
-            steps = 6
-            cfg = 3.0
-
-        progress_db[message] = 0
+            steps = 12
+            cfg = 6.5
+            h, w = 512, 512
 
-        # 🔥 fake-progress (CPU-safe)
+        # 🔥 fake progress
         for i in range(steps):
             progress_db[message] = int((i / steps) * 100)
-            time.sleep(0.12)
+            time.sleep(0.08)
 
         image = pipe(
             message,
             num_inference_steps=steps,
             guidance_scale=cfg,
-            height=256,
-            width=256
+            height=h,
+            width=w
         ).images[0]
 
         filename = f"{IMG_DIR}/img_{int(time.time()*1000)}.png"
         image.save(filename)
 
+        # 🔥 NEW: split into 12 parts
+        split_image_into_12(filename)
+
         progress_db[message] = 100
 
         duration = round(time.time() - start, 2)
@@ -119,7 +161,7 @@ threading.Thread(target=worker, daemon=True).start()
 
 @app.get("/")
 async def root():
-    return PlainTextResponse("⚡ CPU LCM Image Generator Running")
+    return PlainTextResponse("⚡ Dreamshaper + LCM + FAST/QUALITY + SPLIT 12 READY")
 
 
 # 🚀 FAST MODE
@@ -150,7 +192,7 @@ async def quality(message: str):
     return PlainTextResponse("accepted")
 
 
-# 📡 MAIN STATUS + RESULT + PROGRESS
+# 📡 GET + PROGRESS
 @app.get("/get")
 async def get(message: str):
 
@@ -159,7 +201,6 @@ async def get(message: str):
 
     data = db[message]
 
-    # 🔥 если идёт генерация → показываем прогресс
     if data["status"] == "pending":
         progress = progress_db.get(message, 0)
         return PlainTextResponse(f"generating... {progress}%")
@@ -167,9 +208,10 @@ async def get(message: str):
     return PlainTextResponse(data["reply"])
 
 
-# 🖼 image serve
+# 🖼 FILE SERVE
 @app.get("/image")
 async def get_image(path: str):
+
     if not os.path.exists(path):
         return PlainTextResponse("file not found")