try using vroid skeleton

app.py

@@ -112,10 +112,10 @@ def run_inference_python(
     
     # Load task and model configurations based on inference type
     if inference_type == "skeleton":
-        task_config_path = "configs/task/quick_inference_skeleton_articulationxl_ar_256.yaml"
+        task_config_path = "configs/task/quick_inference_skeleton_vroid_ar_256.yaml"
         transform_config_path = "configs/transform/inference_ar_transform.yaml"
         model_config_path = "configs/model/unirig_ar_350m_1024_81920_float32.yaml"
-        system_config_path = "configs/system/ar_inference_articulationxl.yaml"
+        system_config_path = "configs/system/ar_inference_vroid.yaml"
         tokenizer_config_path = "configs/tokenizer/tokenizer_parts_articulationxl_256.yaml"
         data_name = "raw_data.npz"
     else:  # skin

configs/system/ar_inference_vroid.yaml

@@ -0,0 +1,14 @@
+__target__: ar
+val_interval: 1
+generate_kwargs:
+  max_new_tokens: 2048
+  num_return_sequences: 1
+  num_beams: 15
+  do_sample: True
+  top_k: 5
+  top_p: 0.95
+  repetition_penalty: 3.0
+  temperature: 1.5 # must be a float
+  no_cls: False
+  assign_cls: vroid
+  use_dir_cls: False 

configs/task/quick_inference_skeleton_vroid_ar_256.yaml

@@ -0,0 +1,30 @@
+mode: predict
+debug: False
+experiment_name: quick_inference_skeleton_vroid_ar_256
+resume_from_checkpoint: experiments/skeleton/articulation-xl_quantization_256/model.ckpt
+
+components:
+  data: quick_inference
+  tokenizer: tokenizer_parts_articulationxl_256
+  transform: inference_ar_transform
+  model: unirig_ar_350m_1024_81920_float32
+  system: ar_inference_vroid
+  data_name: raw_data.npz
+
+writer:
+  __target__: ar
+  output_dir: ~ # export results into the same input folder
+  add_num: False
+  repeat: 1
+  export_npz: predict_skeleton
+  export_obj: skeleton
+  export_fbx: skeleton
+  # export_pc: pc
+
+trainer:
+  max_epochs: 1
+  num_nodes: 1
+  devices: 1
+  precision: bf16-mixed
+  accelerator: gpu
+  strategy: auto