diff --git a/vllm/model_executor/models/qwen2_5_vl.py b/vllm/model_executor/models/qwen2_5_vl.py
index 68dd07820189..5a67261d827a 100644
--- a/vllm/model_executor/models/qwen2_5_vl.py
+++ b/vllm/model_executor/models/qwen2_5_vl.py
@@ -249,7 +249,7 @@ def __init__(
         # Detect attention implementation.
         self.attn_backend: _Backend = get_vit_attn_backend(support_fa=True)
         if self.attn_backend not in {
-                _Backend.FLASH_ATTN, _Backend.TORCH_SDPA, _Backend.XFORMERS
+                _Backend.FLASH_ATTN, _Backend.TORCH_SDPA, _Backend.XFORMERS, _Backend.FLASH_ATTN_VLLM_V1,
         }:
             raise RuntimeError(
                 f"Qwen2.5-VL does not support {self.attn_backend} backend now."
@@ -307,6 +307,24 @@ def forward(
 
             q, k, v = (rearrange(x, "b s ... -> (b s) ...") for x in [q, k, v])
 
+            output = flash_attn_varlen_func(q,
+                                            k,
+                                            v,
+                                            cu_seqlens_q=cu_seqlens,
+                                            cu_seqlens_k=cu_seqlens,
+                                            max_seqlen_q=max_seqlen,
+                                            max_seqlen_k=max_seqlen,
+                                            dropout_p=0,
+                                            causal=False)
+
+            context_layer = rearrange(output,
+                                      "(b s) ... -> b s ...",
+                                      b=batch_size)
+        elif self.attn_backend == _Backend.FLASH_ATTN_VLLM_V1:
+            from vllm.vllm_flash_attn.flash_attn_interface import flash_attn_varlen_func
+
+            q, k, v = (rearrange(x, "b s ... -> (b s) ...") for x in [q, k, v])
+
             output = flash_attn_varlen_func(q,
                                             k,
                                             v,
@@ -638,7 +656,7 @@ def compute_attn_mask_seqlen(
         cu_seqlens: torch.Tensor,
     ) -> tuple[Optional[int], Optional[list[int]]]:
         max_seqlen, seqlens = None, None
-        if self.attn_backend == _Backend.FLASH_ATTN:
+        if self.attn_backend in [_Backend.FLASH_ATTN, _Backend.FLASH_ATTN_VLLM_V1]:
             max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max().item()
         elif self.attn_backend == _Backend.XFORMERS:
             seqlens = (cu_seqlens[1:] - cu_seqlens[:-1]).tolist()
diff --git a/vllm/model_executor/models/qwen2_vl.py b/vllm/model_executor/models/qwen2_vl.py
index 0ff0836b0897..4db924b09975 100644
--- a/vllm/model_executor/models/qwen2_vl.py
+++ b/vllm/model_executor/models/qwen2_vl.py
@@ -329,6 +329,24 @@ def forward(
 
             q, k, v = (rearrange(x, "b s ... -> (b s) ...") for x in [q, k, v])
 
+            output = flash_attn_varlen_func(q,
+                                            k,
+                                            v,
+                                            cu_seqlens_q=cu_seqlens,
+                                            cu_seqlens_k=cu_seqlens,
+                                            max_seqlen_q=max_seqlen,
+                                            max_seqlen_k=max_seqlen,
+                                            dropout_p=0,
+                                            causal=False)
+
+            context_layer = rearrange(output,
+                                      "(b s) ... -> b s ...",
+                                      b=batch_size)
+        elif self.attn_backend == _Backend.FLASH_ATTN_VLLM_V1:
+            from vllm.vllm_flash_attn.flash_attn_interface import flash_attn_varlen_func
+
+            q, k, v = (rearrange(x, "b s ... -> (b s) ...") for x in [q, k, v])
+
             output = flash_attn_varlen_func(q,
                                             k,
                                             v,
@@ -618,7 +636,7 @@ def compute_attn_mask_seqlen(
             self, cu_seqlens: torch.Tensor
     ) -> tuple[Optional[int], Optional[list[int]]]:
         max_seqlen, seqlens = None, None
-        if self.attn_backend == _Backend.FLASH_ATTN:
+        if self.attn_backend == [_Backend.FLASH_ATTN, _Backend.FLASH_ATTN_VLLM_V1]:
             max_seqlen = (cu_seqlens[1:] - cu_seqlens[:-1]).max().item()
         elif self.attn_backend == _Backend.XFORMERS:
             seqlens = (cu_seqlens[1:] - cu_seqlens[:-1]).tolist()
diff --git a/vllm/model_executor/models/vision.py b/vllm/model_executor/models/vision.py
index 901d83ec5b9e..edba12276627 100644
--- a/vllm/model_executor/models/vision.py
+++ b/vllm/model_executor/models/vision.py
@@ -83,16 +83,7 @@ def get_vit_attn_backend(support_fa: bool = False) -> _Backend:
         if current_platform.is_cuda():
             device_available = current_platform.has_device_capability(80)
             if device_available and support_fa:
-                from transformers.utils import is_flash_attn_2_available
-                if is_flash_attn_2_available():
-                    selected_backend = _Backend.FLASH_ATTN
-                else:
-                    logger.warning_once(
-                        "Current `vllm-flash-attn` has a bug inside vision "
-                        "module, so we use xformers backend instead. You can "
-                        "run `pip install flash-attn` to use flash-attention "
-                        "backend.")
-                    selected_backend = _Backend.XFORMERS
+                selected_backend = _Backend.FLASH_ATTN
             else:
                 # For Volta and Turing GPUs, use xformers instead.
                 selected_backend = _Backend.XFORMERS