diff --git a/llmc/compression/token_reduction/__init__.py b/llmc/compression/token_reduction/__init__.py
index ad35ea47..ec27af88 100755
--- a/llmc/compression/token_reduction/__init__.py
+++ b/llmc/compression/token_reduction/__init__.py
@@ -1,5 +1,6 @@
 from .base_blockwise_token_reduction import TokenReduction
 from .dart import DART
+from .divprune import DivPrune
 from .dycoke import DyCoke
 from .fastervlm import FasterVLM
 from .fastv import FastV
diff --git a/llmc/compression/token_reduction/divprune.py b/llmc/compression/token_reduction/divprune.py
new file mode 100644
index 00000000..7a6e07ac
--- /dev/null
+++ b/llmc/compression/token_reduction/divprune.py
@@ -0,0 +1,149 @@
+import functools
+from functools import wraps
+from types import MethodType
+
+import torch
+
+from llmc.utils.registry_factory import TOKEN_REDUCTION_REGISTRY
+
+from .token_reduction_module import TokenReductionModule
+from .utils import prefill_wrapper
+
+
+def pairwise_cosine_similarity(matrix):
+    norm_matrix = matrix / matrix.norm(dim=1, keepdim=True)
+    cosine_similarity = torch.mm(norm_matrix, norm_matrix.t())
+    return cosine_similarity
+
+
+def divprune(
+    visual_feature_vectors,
+    image_feature_length,
+    cosine_matrix=None,
+    threshold_ratio=0.1,
+):
+    threshold_terms = int(round(threshold_ratio * image_feature_length))
+    if cosine_matrix is None:
+        cosine_matrix = 1.0 - (pairwise_cosine_similarity(visual_feature_vectors))
+
+    s = torch.empty(
+        threshold_terms, dtype=torch.long, device=visual_feature_vectors.device
+    )
+    for i in range(threshold_terms):
+        if i == 0:
+            m2 = cosine_matrix
+        else:
+            m2 = torch.index_select(
+                cosine_matrix,
+                0,
+                torch.index_select(
+                    s, 0, torch.arange(0, i, device=cosine_matrix.device)
+                ),
+            )
+
+        if i == 0:
+            scores = torch.topk(m2, 2, dim=0, largest=False).values[
+                1, :
+            ]  # for distance
+        else:
+            scores = torch.min(m2, dim=0).values  # for distance
+
+        phrase_to_add_idx = torch.argmax(scores)
+        s[i] = phrase_to_add_idx
+    return s, cosine_matrix
+
+
+def divprune_post_hook(
+    input_ids,
+    position_ids,
+    attention_mask,
+    past_key_values,
+    inputs_embeds,
+    labels,
+    pruning_paras=None,
+):
+    rate = pruning_paras['rate']
+    SYS_TOKEN_LEN = pruning_paras['image_token_start_index']
+    img_feature_len = pruning_paras['image_token_length']
+    device = inputs_embeds.device
+    visual_tokens = inputs_embeds[0][SYS_TOKEN_LEN: SYS_TOKEN_LEN + img_feature_len]
+    selected_visual_tokens, cosine_matrix = divprune(
+        visual_tokens, img_feature_len, None, threshold_ratio=rate
+    )
+
+    selected_visual_tokens += SYS_TOKEN_LEN
+    keep_indexs = torch.cat(
+        (
+            torch.arange(SYS_TOKEN_LEN, device=device),
+            selected_visual_tokens,
+            torch.arange(
+                SYS_TOKEN_LEN + img_feature_len, inputs_embeds.shape[1], device=device
+            ),
+        )
+    )
+    keep_indexs = keep_indexs.sort().values
+
+    inputs_embeds = inputs_embeds[:, keep_indexs]
+    if position_ids is not None:
+        position_ids = position_ids[:, keep_indexs, :]
+    if attention_mask is not None:
+        attention_mask = attention_mask[:, keep_indexs]
+
+    return (
+        input_ids,
+        position_ids,
+        attention_mask,
+        past_key_values,
+        inputs_embeds,
+        labels,
+    )
+
+
+@TOKEN_REDUCTION_REGISTRY.register('DivPrune')
+class DivPrune(TokenReductionModule):
+    def __init__(self, config, model, blocks):
+        super().__init__(config, model, blocks)
+        self.add_sparse_config()
+        self.register_reduction_modules()
+
+    def add_sparse_config(self):
+        self.special_config['image_token_length'] = self.model.pruning_config[
+            'image_token_length'
+        ]
+
+        self.pruning_paras = self.special_config
+
+    def register_reduction_modules(self):
+
+        def input_hook_llava(fn, pruning_paras):
+            @wraps(fn)
+            def wrapper(self, *args, **kwargs):
+                if len(args) == 0:
+                    return fn(*args, **kwargs)
+                input_args = args[0]
+                if hasattr(input_args[0], 'shape') and input_args[0].shape[0] == 1:
+                    return fn(*args, **kwargs)
+
+                input_ids = args[0]
+                attention_mask = args[2]
+                token_indices = input_ids[0][attention_mask[0]] == IMAGE_TOKEN_INDEX
+                pruning_paras['image_token_start_index'] = torch.where(token_indices)[
+                    0
+                ].item()
+
+                outputs = fn(*args, **kwargs)
+
+                return divprune_post_hook(*outputs, pruning_paras=pruning_paras)
+
+            return wrapper
+
+        if self.model.__class__.__name__ == 'Llava':
+            from llava.constants import IMAGE_TOKEN_INDEX
+
+            hook_fn = input_hook_llava(
+                self.model.vlm_model.prepare_inputs_labels_for_multimodal,
+                self.pruning_paras,
+            )
+            self.model.vlm_model.prepare_inputs_labels_for_multimodal = MethodType(
+                hook_fn, self.model.vlm_model
+            )