Support training with multiple gpus

wan/distributed/xdit_context_parallel.py

@@ -63,12 +63,44 @@ def rope_apply(x, grid_sizes, freqs):
     return torch.stack(output).float()
 
 
+def usp_dit_forward_vace(
+    self,
+    x,
+    vace_context,
+    seq_len,
+    kwargs
+):
+    # embeddings
+    c = [self.vace_patch_embedding(u.unsqueeze(0)) for u in vace_context]
+    c = [u.flatten(2).transpose(1, 2) for u in c]
+    c = torch.cat([
+        torch.cat([u, u.new_zeros(1, seq_len - u.size(1), u.size(2))],
+                  dim=1) for u in c
+    ])
+
+    # arguments
+    new_kwargs = dict(x=x)
+    new_kwargs.update(kwargs)
+
+    # Context Parallel
+    c = torch.chunk(
+        c, get_sequence_parallel_world_size(),
+        dim=1)[get_sequence_parallel_rank()]
+
+    for block in self.vace_blocks:
+        c = block(c, **new_kwargs)
+    hints = torch.unbind(c)[:-1]
+    return hints
+
+
 def usp_dit_forward(
     self,
     x,
     t,
     context,
     seq_len,
+    vace_context=None,
+    vace_context_scale=1.0,
     clip_fea=None,
     y=None,
 ):
@@ -77,14 +109,14 @@ def usp_dit_forward(
     t:              [B].
     context:        A list of text embeddings each with shape [L, C].
     """
-    if self.model_type == 'i2v':
+    if self.model_type != 'vace':
         assert clip_fea is not None and y is not None
     # params
     device = self.patch_embedding.weight.device
     if self.freqs.device != device:
         self.freqs = self.freqs.to(device)
 
-    if y is not None:
+    if self.model_type != 'vace' and y is not None:
         x = [torch.cat([u, v], dim=0) for u, v in zip(x, y)]
 
     # embeddings
@@ -114,7 +146,7 @@ def usp_dit_forward(
             for u in context
         ]))
 
-    if clip_fea is not None:
+    if self.model_type != 'vace' and clip_fea is not None:
         context_clip = self.img_emb(clip_fea)  # bs x 257 x dim
         context = torch.concat([context_clip, context], dim=1)
 
@@ -132,6 +164,11 @@ def usp_dit_forward(
         x, get_sequence_parallel_world_size(),
         dim=1)[get_sequence_parallel_rank()]
 
+    if self.model_type == 'vace':
+        hints = self.forward_vace(x, vace_context, seq_len, kwargs)
+        kwargs['hints'] = hints
+        kwargs['context_scale'] = vace_context_scale
+
     for block in self.blocks:
         x = block(x, **kwargs)
 

wan/modules/model.py

@@ -400,7 +400,7 @@ class WanModel(ModelMixin, ConfigMixin):
 
         Args:
             model_type (`str`, *optional*, defaults to 't2v'):
-                Model variant - 't2v' (text-to-video) or 'i2v' (image-to-video) or 'flf2v' (first-last-frame-to-video)
+                Model variant - 't2v' (text-to-video) or 'i2v' (image-to-video) or 'flf2v' (first-last-frame-to-video) or 'vace'
             patch_size (`tuple`, *optional*, defaults to (1, 2, 2)):
                 3D patch dimensions for video embedding (t_patch, h_patch, w_patch)
             text_len (`int`, *optional*, defaults to 512):
@@ -433,7 +433,7 @@ class WanModel(ModelMixin, ConfigMixin):
 
         super().__init__()
 
-        assert model_type in ['t2v', 'i2v', 'flf2v']
+        assert model_type in ['t2v', 'i2v', 'flf2v', 'vace']
         self.model_type = model_type
 
         self.patch_size = patch_size

wan/modules/vace_model.py

@@ -71,7 +71,7 @@ class VaceWanModel(WanModel):
     def __init__(self,
                  vace_layers=None,
                  vace_in_dim=None,
-                 model_type='t2v',
+                 model_type='vace',
                  patch_size=(1, 2, 2),
                  text_len=512,
                  in_dim=16,