diff --git a/inference.py b/inference.py
index 8cd60fa..acf54fe 100755
--- a/inference.py
+++ b/inference.py
@@ -294,23 +294,7 @@ def load_model(model_path: str, precision: str, offload: str, device: torch.devi
     from video_to_video.utils.config import cfg
 
     print("[STAR] Loading text encoder (OpenCLIP ViT-H-14)...")
-    _models_dir = (SCRIPT_DIR / ".." / ".." / "models").resolve()
-    _te_filename = "open_clip_vit_h_14_laion2b.bin"
-    _te_dir = _models_dir / "text_encoders"
-    _te_path = _te_dir / _te_filename
-    if not _te_path.is_file():
-        from huggingface_hub import hf_hub_download
-        _te_dir.mkdir(parents=True, exist_ok=True)
-        print(f"[STAR] Downloading OpenCLIP ViT-H-14 text encoder to {_te_dir}...")
-        _dl = hf_hub_download(
-            repo_id="laion/CLIP-ViT-H-14-laion2B-s32B-b79K",
-            filename="open_clip_pytorch_model.bin",
-            local_dir=str(_te_dir),
-        )
-        shutil.move(str(_dl), str(_te_path))
-        print(f"[STAR] Text encoder saved to {_te_path}")
-
-    text_encoder = FrozenOpenCLIPEmbedder(device=device, pretrained=str(_te_path))
+    text_encoder = FrozenOpenCLIPEmbedder(device=device, pretrained="laion2b_s32b_b79k")
     text_encoder.model.to(device)
     negative_y = text_encoder(cfg.negative_prompt).detach()
     text_encoder.model.to(keep_on)
@@ -339,33 +323,9 @@ def load_model(model_path: str, precision: str, offload: str, device: torch.devi
     from diffusers import AutoencoderKLTemporalDecoder
 
     print("[STAR] Loading temporal VAE...")
-    _vae_dir_name = "svd-temporal-vae"
-    _vae_path = _models_dir / "vae" / _vae_dir_name
-    if not (_vae_path / "config.json").is_file():
-        from huggingface_hub import hf_hub_download
-        _vae_path.mkdir(parents=True, exist_ok=True)
-        print(f"[STAR] Downloading SVD temporal VAE to {_vae_path}...")
-        for _f in ["config.json", "diffusion_pytorch_model.fp16.safetensors"]:
-            _dl = hf_hub_download(
-                repo_id="stabilityai/stable-video-diffusion-img2vid",
-                subfolder="vae",
-                filename=_f,
-                local_dir=str(_vae_path),
-            )
-            _dest = _vae_path / _f
-            if Path(_dl) != _dest and Path(_dl).is_file():
-                shutil.move(str(_dl), str(_dest))
-        # Clean up empty vae/ subdirectory created by hf_hub_download
-        _vae_sub = _vae_path / "vae"
-        if _vae_sub.is_dir():
-            try:
-                _vae_sub.rmdir()
-            except OSError:
-                pass
-        print(f"[STAR] Temporal VAE saved to {_vae_path}")
-
     vae = AutoencoderKLTemporalDecoder.from_pretrained(
-        str(_vae_path), variant="fp16",
+        "stabilityai/stable-video-diffusion-img2vid",
+        subfolder="vae", variant="fp16",
     )
     vae.eval()
     vae.requires_grad_(False)
diff --git a/nodes.py b/nodes.py
index 0cc4f2f..6443992 100644
--- a/nodes.py
+++ b/nodes.py
@@ -223,25 +223,8 @@ class STARModelLoader:
         # ---- Text encoder (OpenCLIP ViT-H-14) ----
         from video_to_video.modules.embedder import FrozenOpenCLIPEmbedder
 
-        _te_filename = "open_clip_vit_h_14_laion2b.bin"
-        _te_path = folder_paths.get_full_path("text_encoders", _te_filename)
-        if _te_path is None:
-            from huggingface_hub import hf_hub_download
-            import shutil
-            _te_dir = folder_paths.get_folder_paths("text_encoders")[0]
-            os.makedirs(_te_dir, exist_ok=True)
-            print(f"[STAR] Downloading OpenCLIP ViT-H-14 text encoder to {_te_dir}...")
-            _dl = hf_hub_download(
-                repo_id="laion/CLIP-ViT-H-14-laion2B-s32B-b79K",
-                filename="open_clip_pytorch_model.bin",
-                local_dir=_te_dir,
-            )
-            _te_path = os.path.join(_te_dir, _te_filename)
-            shutil.move(_dl, _te_path)
-            print(f"[STAR] Text encoder saved to {_te_path}")
-
         text_encoder = FrozenOpenCLIPEmbedder(
-            device=device, pretrained=_te_path
+            device=device, pretrained="laion2b_s32b_b79k"
         )
         text_encoder.model.to(device)
 
@@ -281,42 +264,10 @@ class STARModelLoader:
         # ---- Temporal VAE (from HuggingFace diffusers) ----
         from diffusers import AutoencoderKLTemporalDecoder
 
-        _vae_dir_name = "svd-temporal-vae"
-        _vae_path = None
-        for _d in folder_paths.get_folder_paths("vae"):
-            _candidate = os.path.join(_d, _vae_dir_name)
-            if os.path.isfile(os.path.join(_candidate, "config.json")):
-                _vae_path = _candidate
-                break
-
-        if _vae_path is None:
-            from huggingface_hub import hf_hub_download
-            import shutil
-            _vae_base = folder_paths.get_folder_paths("vae")[0]
-            _vae_path = os.path.join(_vae_base, _vae_dir_name)
-            os.makedirs(_vae_path, exist_ok=True)
-            print(f"[STAR] Downloading SVD temporal VAE to {_vae_path}...")
-            for _f in ["config.json", "diffusion_pytorch_model.fp16.safetensors"]:
-                _dl = hf_hub_download(
-                    repo_id="stabilityai/stable-video-diffusion-img2vid",
-                    subfolder="vae",
-                    filename=_f,
-                    local_dir=_vae_path,
-                )
-                _dest = os.path.join(_vae_path, _f)
-                if _dl != _dest and os.path.isfile(_dl):
-                    shutil.move(_dl, _dest)
-            # Clean up empty vae/ subdirectory created by hf_hub_download
-            _vae_sub = os.path.join(_vae_path, "vae")
-            if os.path.isdir(_vae_sub):
-                try:
-                    os.rmdir(_vae_sub)
-                except OSError:
-                    pass
-            print(f"[STAR] Temporal VAE saved to {_vae_path}")
-
         vae = AutoencoderKLTemporalDecoder.from_pretrained(
-            _vae_path, variant="fp16",
+            "stabilityai/stable-video-diffusion-img2vid",
+            subfolder="vae",
+            variant="fp16",
         )
         vae.eval()
         vae.requires_grad_(False)