use correct download path for flux

nexa/gguf/nexa_inference_image.py

@@ -91,16 +91,14 @@ def __init__(self, model_path, local_path=None, **kwargs):
                 self.ae_downloaded_path, _ = pull_model(self.ae_path)
             if self.clip_l_path:
                 self.clip_l_downloaded_path, _ = pull_model(self.clip_l_path)
-
         if "lcm-dreamshaper" in self.model_path or "flux" in self.model_path:
-            self.params = DEFAULT_IMG_GEN_PARAMS_LCM # both lcm-dreamshaper and flux use the same params
+            self.params = DEFAULT_IMG_GEN_PARAMS_LCM.copy() # both lcm-dreamshaper and flux use the same params
         elif "sdxl-turbo" in self.model_path:
-            self.params = DEFAULT_IMG_GEN_PARAMS_TURBO
+            self.params = DEFAULT_IMG_GEN_PARAMS_TURBO.copy()
         else:
-            self.params = DEFAULT_IMG_GEN_PARAMS
-
-        self.params.update(kwargs)
+            self.params = DEFAULT_IMG_GEN_PARAMS.copy()
 
+        self.params.update({k: v for k, v in kwargs.items() if v is not None})
         if not kwargs.get("streamlit", False):
             self._load_model(model_path)
             if self.model is None:
@@ -111,12 +109,12 @@ def __init__(self, model_path, local_path=None, **kwargs):
     def _load_model(self, model_path: str):
         with suppress_stdout_stderr():
             from nexa.gguf.sd.stable_diffusion import StableDiffusion
-            if self.t5xxl_path and self.ae_path and self.clip_l_path:
+            if self.t5xxl_downloaded_path and self.ae_downloaded_path and self.clip_l_downloaded_path:
                 self.model = StableDiffusion(
                     diffusion_model_path=self.downloaded_path,
-                    clip_l_path=self.clip_l_path,
-                    t5xxl_path=self.t5xxl_path,
-                    vae_path=self.ae_path,
+                    clip_l_path=self.clip_l_downloaded_path,
+                    t5xxl_path=self.t5xxl_downloaded_path,
+                    vae_path=self.ae_downloaded_path,
                     n_threads=self.params.get("n_threads", multiprocessing.cpu_count()),
                     wtype=self.params.get(
                         "wtype", NEXA_RUN_MODEL_PRECISION_MAP.get(model_path, "default")