support wan tensor parallel (preview)

diffsynth/models/wan_video_dit.py

@@ -108,6 +108,16 @@ class RMSNorm(nn.Module):
         return self.norm(x.float()).to(dtype) * self.weight
 
 
+class AttentionModule(nn.Module):
+    def __init__(self, num_heads):
+        super().__init__()
+        self.num_heads = num_heads
+        
+    def forward(self, q, k, v):
+        x = flash_attention(q=q, k=k, v=v, num_heads=self.num_heads)
+        return x
+
+
 class SelfAttention(nn.Module):
     def __init__(self, dim: int, num_heads: int, eps: float = 1e-6):
         super().__init__()
@@ -121,17 +131,16 @@ class SelfAttention(nn.Module):
         self.o = nn.Linear(dim, dim)
         self.norm_q = RMSNorm(dim, eps=eps)
         self.norm_k = RMSNorm(dim, eps=eps)
+        
+        self.attn = AttentionModule(self.num_heads)
 
     def forward(self, x, freqs):
         q = self.norm_q(self.q(x))
         k = self.norm_k(self.k(x))
         v = self.v(x)
-        x = flash_attention(
-            q=rope_apply(q, freqs, self.num_heads),
-            k=rope_apply(k, freqs, self.num_heads),
-            v=v,
-            num_heads=self.num_heads
-        )
+        q = rope_apply(q, freqs, self.num_heads)
+        k = rope_apply(k, freqs, self.num_heads)
+        x = self.attn(q, k, v)
         return self.o(x)
 
 
@@ -153,6 +162,8 @@ class CrossAttention(nn.Module):
             self.k_img = nn.Linear(dim, dim)
             self.v_img = nn.Linear(dim, dim)
             self.norm_k_img = RMSNorm(dim, eps=eps)
+            
+        self.attn = AttentionModule(self.num_heads)
 
     def forward(self, x: torch.Tensor, y: torch.Tensor):
         if self.has_image_input:
@@ -163,7 +174,7 @@ class CrossAttention(nn.Module):
         q = self.norm_q(self.q(x))
         k = self.norm_k(self.k(ctx))
         v = self.v(ctx)
-        x = flash_attention(q, k, v, num_heads=self.num_heads)
+        x = self.attn(q, k, v)
         if self.has_image_input:
             k_img = self.norm_k_img(self.k_img(img))
             v_img = self.v_img(img)

diffsynth/pipelines/wan_video.py

@@ -225,7 +225,7 @@ class WanVideoPipeline(BasePipeline):
         tiler_kwargs = {"tiled": tiled, "tile_size": tile_size, "tile_stride": tile_stride}
 
         # Scheduler
-        self.scheduler.set_timesteps(num_inference_steps, denoising_strength, shift=sigma_shift)
+        self.scheduler.set_timesteps(num_inference_steps, denoising_strength=denoising_strength, shift=sigma_shift)
 
         # Initialize noise
         noise = self.generate_noise((1, 16, (num_frames - 1) // 4 + 1, height//8, width//8), seed=seed, device=rand_device, dtype=torch.float32)

diffsynth/schedulers/flow_match.py

@@ -37,7 +37,7 @@ class FlowMatchScheduler():
             self.linear_timesteps_weights = bsmntw_weighing
 
 
-    def step(self, model_output, timestep, sample, to_final=False):
+    def step(self, model_output, timestep, sample, to_final=False, **kwargs):
         if isinstance(timestep, torch.Tensor):
             timestep = timestep.cpu()
         timestep_id = torch.argmin((self.timesteps - timestep).abs())