diff --git a/README.md b/README.md
index aac655f..f6bb128 100644
--- a/README.md
+++ b/README.md
@@ -93,6 +93,35 @@ conda activate hydra
 pip install -r requirements.txt
 ```
 
+<details>
+<summary><b>AMD ROCm GPU support (optional)</b></summary>
+
+HyDRA works on AMD GPUs via ROCm. Replace Step 3 with:
+
+```bash
+# Install ROCm PyTorch (ROCm 6.4 example)
+pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.4
+
+# Install remaining deps (skip torch/cupy)
+grep -vEi "^(torch|torchvision|cupy)" requirements.txt > /tmp/req_clean.txt
+pip install -r /tmp/req_clean.txt
+
+# (Recommended) Install FlashAttention for ~16-19% faster inference
+# Option A — source build with Triton backend (ROCm 6.x):
+git clone https://github.com/Dao-AILab/flash-attention.git /tmp/flash-attention
+cd /tmp/flash-attention && git submodule update --init third_party/aiter
+FLASH_ATTENTION_TRITON_AMD_ENABLE=TRUE python setup.py install
+
+# Option B — AMD AITER for best performance on ROCm 7.x:
+#   pip install aiter  (or build from https://github.com/ROCm/aiter)
+#   AITER auto-dispatches to CK kernels on ROCm 7.x (~25% faster than Triton)
+```
+
+The attention backend is selected automatically at import time:
+FA3 → AITER → FA2 → SageAttention → PyTorch SDPA (fallback).
+
+</details>
+
 ### Step 4: Download the pretrained Wan2.1 (1.3B) T2V model
 
 - Model link: https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B
diff --git a/diffsynth/models/wan_video_dit.py b/diffsynth/models/wan_video_dit.py
index 8a062bf..953117b 100644
--- a/diffsynth/models/wan_video_dit.py
+++ b/diffsynth/models/wan_video_dit.py
@@ -1,3 +1,4 @@
+import logging
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
@@ -5,24 +6,43 @@
 from typing import Tuple, Optional
 from einops import rearrange
 from .utils import hash_state_dict_keys
-# from .ucpe_attention import UcpeSelfAttention
+
+logger = logging.getLogger(__name__)
+
 try:
     import flash_attn_interface
     FLASH_ATTN_3_AVAILABLE = True
-except ModuleNotFoundError:
+except (ImportError, ModuleNotFoundError):
     FLASH_ATTN_3_AVAILABLE = False
 
 try:
     import flash_attn
     FLASH_ATTN_2_AVAILABLE = True
-except ModuleNotFoundError:
+except (ImportError, ModuleNotFoundError):
     FLASH_ATTN_2_AVAILABLE = False
 
+try:
+    import importlib as _il
+    _aiter_mha = _il.import_module("aiter.ops.mha")
+    _aiter_flash_attn_func = _aiter_mha.flash_attn_func
+    AITER_AVAILABLE = True
+except Exception:
+    AITER_AVAILABLE = False
+
 try:
     from sageattention import sageattn
-    SAGE_ATTN_AVAILABLE = True 
-except ModuleNotFoundError:
+    SAGE_ATTN_AVAILABLE = True
+except (ImportError, ModuleNotFoundError):
     SAGE_ATTN_AVAILABLE = False
+
+_ATTN_BACKEND = (
+    "flash_attn_3" if FLASH_ATTN_3_AVAILABLE else
+    "aiter" if AITER_AVAILABLE else
+    "flash_attn_2" if FLASH_ATTN_2_AVAILABLE else
+    "sage_attn" if SAGE_ATTN_AVAILABLE else
+    "sdpa"
+)
+logger.info("Attention backend: %s", _ATTN_BACKEND)
     
 def flash_attention(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, num_heads: int, compatibility_mode=False):
     if compatibility_mode:
@@ -37,6 +57,12 @@ def flash_attention(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, num_heads
         v = rearrange(v, "b s (n d) -> b s n d", n=num_heads)
         x = flash_attn_interface.flash_attn_func(q, k, v)
         x = rearrange(x, "b s n d -> b s (n d)", n=num_heads)
+    elif AITER_AVAILABLE:
+        q = rearrange(q, "b s (n d) -> b s n d", n=num_heads)
+        k = rearrange(k, "b s (n d) -> b s n d", n=num_heads)
+        v = rearrange(v, "b s (n d) -> b s n d", n=num_heads)
+        x = _aiter_flash_attn_func(q, k, v)
+        x = rearrange(x, "b s n d -> b s (n d)", n=num_heads)
     elif FLASH_ATTN_2_AVAILABLE:
         q = rearrange(q, "b s (n d) -> b s n d", n=num_heads)
         k = rearrange(k, "b s (n d) -> b s n d", n=num_heads)