Merge 5f7e7ed289 into 854bd88e7f

* Add LightX2V Community Works

* update

* update

* update

README.md

@@ -36,6 +36,8 @@ In this repository, we present **Wan2.1**, a comprehensive and open suite of vid
 
 ## Community Works
 If your work has improved **Wan2.1** and you would like more people to see it, please inform us.
+- [LightX2V](https://github.com/ModelTC/LightX2V), a lightweight and efficient video generation framework that integrates **Wan2.1** and **Wan2.2**, supports multiple engineering acceleration techniques for fast inference, which can run on RTX 5090 and RTX 4060 (8GB VRAM).
+- [DriVerse](https://github.com/shalfun/DriVerse), an autonomous driving world model based on **Wan2.1-14B-I2V**, generates future driving videos conditioned on any scene frame and given trajectory. Refer to the [project page](https://github.com/shalfun/DriVerse/tree/main) for more examples.
 - [Training-Free-WAN-Editing](https://github.com/KyujinHan/Awesome-Training-Free-WAN2.1-Editing), built on **Wan2.1-T2V-1.3B**, allows training-free video editing with image-based training-free methods, such as [FlowEdit](https://arxiv.org/abs/2412.08629) and [FlowAlign](https://arxiv.org/abs/2505.23145).
 - [Wan-Move](https://github.com/ali-vilab/Wan-Move), accepted to NeurIPS 2025, a framework that brings **Wan2.1-I2V-14B** to SOTA fine-grained, point-level motion control! Refer to [their project page](https://wan-move.github.io/) for more information.
 - [EchoShot](https://github.com/JoHnneyWang/EchoShot), a native multi-shot portrait video generation model based on **Wan2.1-T2V-1.3B**, allows generation of multiple video clips featuring the same character as well as highly flexible content controllability. Refer to [their project page](https://johnneywang.github.io/EchoShot-webpage/) for more information.

wan/modules/attention.py

@@ -79,53 +79,101 @@ def flash_attention(
         k = half(torch.cat([u[:v] for u, v in zip(k, k_lens)]))
         v = half(torch.cat([u[:v] for u, v in zip(v, k_lens)]))
 
-    q = q.to(v.dtype)
-    k = k.to(v.dtype)
+    try:
+        q = q.to(v.dtype)
+        k = k.to(v.dtype)
+    
+        if q_scale is not None:
+            q = q * q_scale
+    
+        if version is not None and version == 3 and not FLASH_ATTN_3_AVAILABLE:
+            warnings.warn(
+                'Flash attention 3 is not available, use flash attention 2 instead.'
+            )
+    
+        # apply attention
+        if (version is None or version == 3) and FLASH_ATTN_3_AVAILABLE:
+            # Note: dropout_p, window_size are not supported in FA3 now.
+            x = flash_attn_interface.flash_attn_varlen_func(
+                q=q,
+                k=k,
+                v=v,
+                cu_seqlens_q=torch.cat([q_lens.new_zeros([1]), q_lens]).cumsum(
+                    0, dtype=torch.int32).to(q.device, non_blocking=True),
+                cu_seqlens_k=torch.cat([k_lens.new_zeros([1]), k_lens]).cumsum(
+                    0, dtype=torch.int32).to(q.device, non_blocking=True),
+                seqused_q=None,
+                seqused_k=None,
+                max_seqlen_q=lq,
+                max_seqlen_k=lk,
+                softmax_scale=softmax_scale,
+                causal=causal,
+                deterministic=deterministic)[0].unflatten(0, (b, lq))
+        else:
+            assert FLASH_ATTN_2_AVAILABLE
+            x = flash_attn.flash_attn_varlen_func(
+                q=q,
+                k=k,
+                v=v,
+                cu_seqlens_q=torch.cat([q_lens.new_zeros([1]), q_lens]).cumsum(
+                    0, dtype=torch.int32).to(q.device, non_blocking=True),
+                cu_seqlens_k=torch.cat([k_lens.new_zeros([1]), k_lens]).cumsum(
+                    0, dtype=torch.int32).to(q.device, non_blocking=True),
+                max_seqlen_q=lq,
+                max_seqlen_k=lk,
+                dropout_p=dropout_p,
+                softmax_scale=softmax_scale,
+                causal=causal,
+                window_size=window_size,
+                deterministic=deterministic).unflatten(0, (b, lq))
+    
+    except RuntimeError as e:
+        if "FlashAttention only supports Ampere GPUs or newer" in str(e):
+            #for cards like 2080ti that aren't Ampere structure
+            from torch import nn
+            import torch.nn.functional as F
+        
+            q = q.to(half(k).dtype)
 
-    if q_scale is not None:
-        q = q * q_scale
+            # 转置维度，保证形状为 [B, N, L, C]
+            q = q.view(b, lq, q.size(1), q.size(2)).transpose(1, 2)
+            k = k.view(b, lk, k.size(1), k.size(2)).transpose(1, 2)
+            v = v.view(b, lk, v.size(1), v.size(2)).transpose(1, 2)
 
-    if version is not None and version == 3 and not FLASH_ATTN_3_AVAILABLE:
-        warnings.warn(
-            'Flash attention 3 is not available, use flash attention 2 instead.'
-        )
+            # 计算注意力
+            # 注意：确保 Q、K、V 的形状为 [B, N, L, C]
+            # 设置默认缩放因子
+            if softmax_scale is None:
+                softmax_scale = 1.0 / q.size(-1) ** 0.5
 
-    # apply attention
-    if (version is None or version == 3) and FLASH_ATTN_3_AVAILABLE:
-        # Note: dropout_p, window_size are not supported in FA3 now.
-        x = flash_attn_interface.flash_attn_varlen_func(
-            q=q,
-            k=k,
-            v=v,
-            cu_seqlens_q=torch.cat([q_lens.new_zeros([1]), q_lens]).cumsum(
-                0, dtype=torch.int32).to(q.device, non_blocking=True),
-            cu_seqlens_k=torch.cat([k_lens.new_zeros([1]), k_lens]).cumsum(
-                0, dtype=torch.int32).to(q.device, non_blocking=True),
-            seqused_q=None,
-            seqused_k=None,
-            max_seqlen_q=lq,
-            max_seqlen_k=lk,
-            softmax_scale=softmax_scale,
-            causal=causal,
-            deterministic=deterministic)[0].unflatten(0, (b, lq))
-    else:
-        assert FLASH_ATTN_2_AVAILABLE
-        x = flash_attn.flash_attn_varlen_func(
-            q=q,
-            k=k,
-            v=v,
-            cu_seqlens_q=torch.cat([q_lens.new_zeros([1]), q_lens]).cumsum(
-                0, dtype=torch.int32).to(q.device, non_blocking=True),
-            cu_seqlens_k=torch.cat([k_lens.new_zeros([1]), k_lens]).cumsum(
-                0, dtype=torch.int32).to(q.device, non_blocking=True),
-            max_seqlen_q=lq,
-            max_seqlen_k=lk,
-            dropout_p=dropout_p,
-            softmax_scale=softmax_scale,
-            causal=causal,
-            window_size=window_size,
-            deterministic=deterministic).unflatten(0, (b, lq))
+            # 如果 q_scale 存在，则应用缩放
+            if q_scale is not None:
+                q = q * q_scale
 
+            # 创建掩码
+            if causal:
+                attn_mask = torch.triu(torch.full((q.size(2), k.size(2)), -torch.inf), diagonal=1).to(q.device)
+            else:
+                attn_mask = None
+
+            # 计算注意力
+            # 使用 scaled_dot_product_attention
+            x = F.scaled_dot_product_attention(
+                q, k, v,
+                attn_mask=attn_mask,
+                dropout_p=dropout_p,
+                is_causal=causal,
+            )
+
+            # 转换回原形状 [B, L, N, C]
+            x = x.transpose(1, 2).contiguous()
+
+            # 对输出应用 Dropout
+            dropout = nn.Dropout(dropout_p)
+            x = dropout(x)            
+        else:
+            raise
+            
     # output
     return x.type(out_dtype)