Update app.py

app.py

@@ -2,13 +2,19 @@ import gradio as gr
 import torch
 import os
 from diffusers import AutoPipelineForText2Image
-from huggingface_hub import snapshot_download
+from huggingface_hub import hf_hub_download
 import logging
+from pathlib import Path
 
 # Logging ayarları
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
+# Model dosyaları için sabit yollar
+MODEL_CACHE = Path("./model_cache")
+BASE_MODEL_PATH = MODEL_CACHE / "base_model"
+LORA_MODEL_PATH = MODEL_CACHE / "lora_model"
+
 class ModelHandler:
     def __init__(self):
         self.pipeline = None
@@ -20,37 +26,45 @@ class ModelHandler:
             if self.pipeline is not None:
                 return "Model zaten yüklü."
             
+            # Model cache dizinlerini oluştur
+            MODEL_CACHE.mkdir(exist_ok=True)
+            BASE_MODEL_PATH.mkdir(exist_ok=True)
+            LORA_MODEL_PATH.mkdir(exist_ok=True)
+            
             progress(0, desc="Base model indiriliyor...")
             # Base modeli indir
-            base_model_path = snapshot_download(
-                repo_id="black-forest-labs/FLUX.1-dev",
-                local_dir="./models/base_model",
-                ignore_patterns=["*.bin", "*.onnx"] if os.path.exists("./models/base_model") else None,
-                token=os.getenv("HF_TOKEN")
-            )
+            if not (BASE_MODEL_PATH / "model_index.json").exists():
+                hf_hub_download(
+                    repo_id="black-forest-labs/FLUX.1-dev",
+                    filename="model_index.json",
+                    local_dir=BASE_MODEL_PATH,
+                    token=os.getenv("HF_TOKEN")
+                )
             
             progress(0.5, desc="LoRA modeli indiriliyor...")
             # LoRA modelini indir
-            lora_model_path = snapshot_download(
-                repo_id="codermert/ezelll_flux",
-                local_dir="./models/lora_model",
-                ignore_patterns=["*.bin", "*.onnx"] if os.path.exists("./models/lora_model") else None,
-                token=os.getenv("HF_TOKEN")
-            )
+            if not (LORA_MODEL_PATH / "lora.safetensors").exists():
+                hf_hub_download(
+                    repo_id="codermert/ezelll_flux",
+                    filename="lora.safetensors",
+                    local_dir=LORA_MODEL_PATH,
+                    token=os.getenv("HF_TOKEN")
+                )
             
             progress(0.7, desc="Pipeline oluşturuluyor...")
             # Pipeline'ı oluştur
             self.pipeline = AutoPipelineForText2Image.from_pretrained(
-                base_model_path,
+                str(BASE_MODEL_PATH),
                 torch_dtype=self.dtype,
-                use_safetensors=True
+                use_safetensors=True,
+                cache_dir=MODEL_CACHE
             ).to(self.device)
             
             progress(0.9, desc="LoRA yükleniyor...")
             # LoRA'yı yükle
-            lora_path = os.path.join(lora_model_path, "lora.safetensors")
-            if os.path.exists(lora_path):
-                self.pipeline.load_lora_weights(lora_path)
+            lora_path = LORA_MODEL_PATH / "lora.safetensors"
+            if lora_path.exists():
+                self.pipeline.load_lora_weights(str(lora_path))
             else:
                 return "LoRA dosyası bulunamadı!"
             
@@ -74,7 +88,9 @@ class ModelHandler:
             image = self.pipeline(
                 prompt,
                 num_inference_steps=30,
-                guidance_scale=7.5
+                guidance_scale=7.5,
+                width=512,
+                height=512
             ).images[0]
             
             progress(1.0, desc="Tamamlandı!")