diff --git a/tests/models/multimodal/processing/test_common.py b/tests/models/multimodal/processing/test_common.py
index 61e19bb8b..a085d6e2f 100644
--- a/tests/models/multimodal/processing/test_common.py
+++ b/tests/models/multimodal/processing/test_common.py
@@ -102,13 +102,13 @@ def glmasr_patch_mm_data(mm_data: MultiModalDataDict) -> MultiModalDataDict:
 # incorrect token ids. So we need use `add_special_tokens=False` here
 # to leave bos_token to be added by the processor.
 _ADD_SPECIAL_TOKENS_OVERRIDES = {
+    "lfm2_vl": False,
     "nemotron_parse": False,
     "ovis": False,
     "ovis2_5": False,
     "paligemma": False,
     "ultravox": False,
     "whisper": False,
-    "lfm2_vl": False,
 }
 
 _IGNORE_MM_KEYS = {
@@ -450,6 +450,8 @@ def test_processing_correctness(
     num_batches: int,
     simplify_rate: float,
 ):
+    if model_id == "allendou/Fun-ASR-Nano-2512-vllm":
+        pytest.skip("Cached audio `input_features` not matched. Fix later.")
     if model_id == "google/gemma-3n-E2B-it":
         pytest.skip("Fix later")
     if model_id == "OpenGVLab/InternVL2-2B":
@@ -468,9 +470,6 @@ def test_processing_correctness(
             "correctness test as is. Let's revisit adapting this "
             "test once more realtime models exist."
         )
-    if model_id == "internlm/Intern-S1-Pro":
-        # FIXME(Isotr0py): Fix later.
-        pytest.skip("Tokenization issue. Fix later")
 
     _test_processing_correctness(
         model_id,
diff --git a/tests/models/multimodal/processing/test_tensor_schema.py b/tests/models/multimodal/processing/test_tensor_schema.py
index aabd883a4..8f7993647 100644
--- a/tests/models/multimodal/processing/test_tensor_schema.py
+++ b/tests/models/multimodal/processing/test_tensor_schema.py
@@ -160,9 +160,6 @@ def test_model_tensor_schema(model_id: str):
         pytest.skip(
             "Kimi-K2.5's offline inference has issues about vision chunks. Fix later."
         )
-    if model_id == "internlm/Intern-S1-Pro":
-        # FIXME(Isotr0py): Fix later.
-        pytest.skip("Intern-S1-Pro has issue to pass the test.")
 
     model_info = HF_EXAMPLE_MODELS.find_hf_info(model_id)
     model_info.check_available_online(on_fail="skip")
diff --git a/tests/models/registry.py b/tests/models/registry.py
index 16d33bb5b..16e64ea9e 100644
--- a/tests/models/registry.py
+++ b/tests/models/registry.py
@@ -730,7 +730,6 @@ _MULTIMODAL_EXAMPLE_MODELS = {
     ),
     "FunASRForConditionalGeneration": _HfExamplesInfo(
         "allendou/Fun-ASR-Nano-2512-vllm",
-        is_available_online=False,
     ),
     "FunAudioChatForConditionalGeneration": _HfExamplesInfo(
         "funaudiochat", is_available_online=False
@@ -755,7 +754,6 @@ _MULTIMODAL_EXAMPLE_MODELS = {
     "Glm4vMoeForConditionalGeneration": _HfExamplesInfo("zai-org/GLM-4.5V"),
     "GlmOcrForConditionalGeneration": _HfExamplesInfo(
         "zai-org/GLM-OCR",
-        is_available_online=False,
         min_transformers_version="5.1.0",
     ),
     "H2OVLChatModel": _HfExamplesInfo(
diff --git a/vllm/model_executor/models/interns1_pro.py b/vllm/model_executor/models/interns1_pro.py
index c5cd13399..1c9f1a7bf 100644
--- a/vllm/model_executor/models/interns1_pro.py
+++ b/vllm/model_executor/models/interns1_pro.py
@@ -85,11 +85,7 @@ class InternS1ProProcessingInfo(Qwen3VLProcessingInfo):
         return self.ctx.get_hf_config()
 
     def get_hf_processor(self, **kwargs: object) -> AutoProcessor:
-        return AutoProcessor.from_pretrained(
-            self.ctx.model_config.model,
-            trust_remote_code=True,
-            **kwargs,
-        )
+        return self.ctx.get_hf_processor(**kwargs)
 
 
 class InternS1ProMoeMLP(nn.Module):
@@ -497,7 +493,7 @@ class InternS1ProMoeLLMForCausalLM(Qwen3MoeForCausalLM):
         )
 
 
-class Qwen3VLMoeMixtureOfExperts(MixtureOfExperts):
+class InternS1ProMoeMixtureOfExperts(MixtureOfExperts):
     def update_physical_experts_metadata(
         self,
         num_physical_experts: int,
@@ -547,7 +543,7 @@ class Qwen3VLMoeMixtureOfExperts(MixtureOfExperts):
     dummy_inputs=Qwen3VLDummyInputsBuilder,
 )
 class InternS1ProForConditionalGeneration(
-    Qwen3VLForConditionalGeneration, Qwen3VLMoeMixtureOfExperts
+    Qwen3VLForConditionalGeneration, InternS1ProMoeMixtureOfExperts
 ):
     is_3d_moe_weight: bool = True
     packed_modules_mapping = {