more changes

examples/tutorial/xvla/inference.py

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+from transformers import AutoModel, AutoProcessor
+import json_numpy
+import numpy as np
+import torch
+from PIL import Image
+
+model = AutoModel.from_pretrained(
+    "2toINF/X-VLA-WidowX",
+    trust_remote_code=True
+)
+
+processor = AutoProcessor.from_pretrained("2toINF/X-VLA-WidowX", trust_remote_code=True)
+# append 3 random image to a list
+def make_random_pil_images(num_images=3, H=480, W=640):
+    images = []
+    for _ in range(num_images):
+        # Random RGB image
+        arr = np.random.randint(0, 256, (H, W, 3), dtype=np.uint8)
+        img = Image.fromarray(arr)
+        images.append(img)
+    return images
+
+# Example:
+images = make_random_pil_images()
+language_instruction = "This is a random image"
+# Multimodal preprocessing by processor
+inputs = processor(images, language_instruction)
+if not {"input_ids", "image_input", "image_mask"}.issubset(inputs):
+    raise ValueError("Processor did not return the expected keys.")
+
+proprio = torch.randn(1, 20)
+domain_id = torch.tensor([int(0)], dtype=torch.long)
+
+# Align to model's device/dtype
+device = model.device
+dtype = next(model.parameters()).dtype
+
+def to_model(t: torch.Tensor) -> torch.Tensor:
+    if not isinstance(t, torch.Tensor):
+        t = torch.as_tensor(t)
+    # cast floats to model dtype, keep integral/bool as-is
+    return t.to(device=device, dtype=dtype) if t.is_floating_point() else t.to(device=device)
+
+inputs = {k: to_model(v) for k, v in inputs.items()}
+inputs.update({
+    "proprio": to_model(proprio),
+    "domain_id": domain_id.to(device),
+})
+
+# Inference
+
+action = model.generate_actions(**inputs, steps=10).squeeze(0).float().cpu().numpy()
+
+breakpoint()