Update app.py

app.py

@@ -32,7 +32,7 @@ print("Torch version:", torch.__version__)
 # # hf_hub_download('google/flan-t5-xl', cache_dir='./checkpoints/')
 ckpt_folder = './checkpoints'
 t5_folder = os.path.join(ckpt_folder, "flan-t5-xl/flan-t5-xl")
-dinov2_folder = os.path.join(ckpt_folder, "dinov2-small")
+# dinov2_folder = os.path.join(ckpt_folder, "dinov2-small")
 dinov2_folder = os.path.join(ckpt_folder, "dinov2-base")
 hf_hub_download(repo_id="google/flan-t5-xl", filename="config.json", local_dir=t5_folder)
 hf_hub_download(repo_id="google/flan-t5-xl", filename="pytorch_model-00001-of-00002.bin", local_dir=t5_folder)
@@ -47,9 +47,9 @@ hf_hub_download(repo_id="lllyasviel/Annotators", filename="dpt_hybrid-midas-501f
 hf_hub_download(repo_id="wondervictor/ControlAR", filename="edge_base.safetensors", local_dir=ckpt_folder)
 hf_hub_download(repo_id="wondervictor/ControlAR", filename="depth_base.safetensors", local_dir=ckpt_folder)
 
-hf_hub_download(repo_id="facebook/dinov2-small", filename="config.json", local_dir=dinov2_folder)
-hf_hub_download(repo_id="facebook/dinov2-small", filename="preprocessor_config.json", local_dir=dinov2_folder)
-hf_hub_download(repo_id="facebook/dinov2-small", filename="pytorch_model.bin", local_dir=dinov2_folder)
+hf_hub_download(repo_id="facebook/dinov2-base", filename="config.json", local_dir=dinov2_folder)
+hf_hub_download(repo_id="facebook/dinov2-base", filename="preprocessor_config.json", local_dir=dinov2_folder)
+hf_hub_download(repo_id="facebook/dinov2-base", filename="pytorch_model.bin", local_dir=dinov2_folder)
 
 
 DESCRIPTION = "# [ControlAR: Controllable Image Generation with Autoregressive Models](https://arxiv.org/abs/2410.02705) \n ### The first image in outputs is the condition. The others are the images generated by ControlAR.  \n ### You can run locally by following the instruction on our [Github Repo](https://github.com/hustvl/ControlAR)."