Merge 029e421891 into 7c81b2f27d

generate.py

@@ -4,6 +4,7 @@ import logging
 import os
 import sys
 import warnings
+from time import perf_counter
 from datetime import datetime
 
 warnings.filterwarnings('ignore')
@@ -12,12 +13,20 @@ import random
 
 import torch
 import torch.distributed as dist
+from torch.cuda import set_device
 from PIL import Image
 
+try:
+    import torch_musa
+    from torch_musa.core.device import set_device
+except ModuleNotFoundError:
+    pass
+
 import wan
 from wan.configs import MAX_AREA_CONFIGS, SIZE_CONFIGS, SUPPORTED_SIZES, WAN_CONFIGS
 from wan.utils.prompt_extend import DashScopePromptExpander, QwenPromptExpander
 from wan.utils.utils import cache_image, cache_video, str2bool
+from wan.utils.platform import get_torch_distributed_backend
 
 
 EXAMPLE_PROMPT = {
@@ -275,9 +284,9 @@ def generate(args):
         logging.info(
             f"offload_model is not specified, set to {args.offload_model}.")
     if world_size > 1:
-        torch.cuda.set_device(local_rank)
+        set_device(local_rank)
         dist.init_process_group(
-            backend="nccl",
+            backend=get_torch_distributed_backend(),
             init_method="env://",
             rank=rank,
             world_size=world_size)
@@ -357,6 +366,7 @@ def generate(args):
             logging.info(f"Extended prompt: {args.prompt}")
 
         logging.info("Creating WanT2V pipeline.")
+        start_time = perf_counter()
         wan_t2v = wan.WanT2V(
             config=cfg,
             checkpoint_dir=args.ckpt_dir,
@@ -367,6 +377,8 @@ def generate(args):
             use_usp=(args.ulysses_size > 1 or args.ring_size > 1),
             t5_cpu=args.t5_cpu,
         )
+        end_time = perf_counter()
+        logging.info(f"Creating WanT2V pipeline took {end_time - start_time:.2f} seconds.")
 
         logging.info(
             f"Generating {'image' if 't2i' in args.task else 'video'} ...")
@@ -380,7 +392,6 @@ def generate(args):
             guide_scale=args.sample_guide_scale,
             seed=args.base_seed,
             offload_model=args.offload_model)
-
     elif "i2v" in args.task:
         if args.prompt is None:
             args.prompt = EXAMPLE_PROMPT[args.task]["prompt"]
@@ -414,6 +425,7 @@ def generate(args):
             logging.info(f"Extended prompt: {args.prompt}")
 
         logging.info("Creating WanI2V pipeline.")
+        start_time = perf_counter()
         wan_i2v = wan.WanI2V(
             config=cfg,
             checkpoint_dir=args.ckpt_dir,
@@ -424,6 +436,8 @@ def generate(args):
             use_usp=(args.ulysses_size > 1 or args.ring_size > 1),
             t5_cpu=args.t5_cpu,
         )
+        end_time = perf_counter()
+        logging.info(f"Creating WanI2V pipeline took {end_time - start_time:.2f} seconds.")
 
         logging.info("Generating video ...")
         video = wan_i2v.generate(
@@ -572,6 +586,7 @@ def generate(args):
                 value_range=(-1, 1))
         else:
             logging.info(f"Saving generated video to {args.save_file}")
+            start_time = perf_counter()
             cache_video(
                 tensor=video[None],
                 save_file=args.save_file,
@@ -579,6 +594,8 @@ def generate(args):
                 nrow=1,
                 normalize=True,
                 value_range=(-1, 1))
+            end_time = perf_counter()
+            logging.info(f"Saving Video took {end_time - start_time:.2f} seconds")
     logging.info("Finished.")
 
 

wan/distributed/fsdp.py

@@ -3,11 +3,17 @@ import gc
 from functools import partial
 
 import torch
+from torch.cuda import empty_cache
 from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
 from torch.distributed.fsdp import MixedPrecision, ShardingStrategy
 from torch.distributed.fsdp.wrap import lambda_auto_wrap_policy
 from torch.distributed.utils import _free_storage
 
+try:
+    import torch_musa
+    from torch_musa.core.memory import empty_cache
+except ModuleNotFoundError:
+    pass
 
 def shard_model(
     model,
@@ -40,4 +46,4 @@ def free_model(model):
             _free_storage(m._handle.flat_param.data)
     del model
     gc.collect()
-    torch.cuda.empty_cache()
+    empty_cache()

wan/distributed/xdit_context_parallel.py

@@ -6,20 +6,28 @@ from xfuser.core.distributed import (
     get_sequence_parallel_world_size,
     get_sp_group,
 )
-from xfuser.core.long_ctx_attention import xFuserLongContextAttention
+from xfuser.core.long_ctx_attention import xFuserLongContextAttention, AttnType
+attn_type:AttnType = AttnType.FA
+
+try:
+    import torch_musa
+    import torch_musa.core.amp as amp
+    attn_type = AttnType.TORCH
+except ImportError:
+    pass
 
 from ..modules.model import sinusoidal_embedding_1d
 
 
-def pad_freqs(original_tensor, target_len):
+def pad_tensor(original_tensor, target_len, pad_value=0.0):
     seq_len, s1, s2 = original_tensor.shape
     pad_size = target_len - seq_len
-    padding_tensor = torch.ones(
+    padding_tensor = torch.full(
-        pad_size,
+        (pad_size, s1, s2),
-        s1,
+        pad_value,
-        s2,
         dtype=original_tensor.dtype,
-        device=original_tensor.device)
+        device=original_tensor.device,
+    )
     padded_tensor = torch.cat([original_tensor, padding_tensor], dim=0)
     return padded_tensor
 
@@ -32,8 +40,13 @@ def rope_apply(x, grid_sizes, freqs):
     freqs:      [M, C // 2].
     """
     s, n, c = x.size(1), x.size(2), x.size(3) // 2
+    c0 = c - 2 * (c // 3)
+    c1 = c // 3
+    c2 = c // 3
+
     # split freqs
-    freqs = freqs.split([c - 2 * (c // 3), c // 3, c // 3], dim=1)
+    freqs_real = freqs[0].split([c0, c1, c2], dim=1)
+    freqs_imag = freqs[-1].split([c0, c1, c2], dim=1)
 
     # loop over samples
     output = []
@@ -41,28 +54,45 @@ def rope_apply(x, grid_sizes, freqs):
         seq_len = f * h * w
 
         # precompute multipliers
-        x_i = torch.view_as_complex(x[i, :s].to(torch.float64).reshape(
+        x_i = x[i, :seq_len].reshape(s, n, -1, 2)
-            s, n, -1, 2))
+        x_real = x_i[..., 0]
-        freqs_i = torch.cat([
+        x_imag = x_i[..., 1]
-            freqs[0][:f].view(f, 1, 1, -1).expand(f, h, w, -1),
+        freqs_real = torch.cat(
-            freqs[1][:h].view(1, h, 1, -1).expand(f, h, w, -1),
+            [
-            freqs[2][:w].view(1, 1, w, -1).expand(f, h, w, -1)
+                freqs_real[0][:f].view(f, 1, 1, -1).expand(f, h, w, -1),
-        ],
+                freqs_real[1][:h].view(1, h, 1, -1).expand(f, h, w, -1),
-                            dim=-1).reshape(seq_len, 1, -1)
+                freqs_real[2][:w].view(1, 1, w, -1).expand(f, h, w, -1),
+            ],
+            dim=-1,
+        ).reshape(seq_len, 1, -1)
+        freqs_imag = torch.cat(
+            [
+                freqs_imag[0][:f].view(f, 1, 1, -1).expand(f, h, w, -1),
+                freqs_imag[1][:h].view(1, h, 1, -1).expand(f, h, w, -1),
+                freqs_imag[2][:w].view(1, 1, w, -1).expand(f, h, w, -1),
+            ],
+            dim=-1,
+        ).reshape(seq_len, 1, -1)
 
         # apply rotary embedding
         sp_size = get_sequence_parallel_world_size()
         sp_rank = get_sequence_parallel_rank()
-        freqs_i = pad_freqs(freqs_i, s * sp_size)
+
-        s_per_rank = s
+        freqs_real = pad_tensor(freqs_real, s * sp_size, 1.0)
-        freqs_i_rank = freqs_i[(sp_rank * s_per_rank):((sp_rank + 1) *
+        freqs_imag = pad_tensor(freqs_imag, s * sp_size, 0.0)
-                                                       s_per_rank), :, :]
+
-        x_i = torch.view_as_real(x_i * freqs_i_rank).flatten(2)
+        freqs_real_rank = freqs_real[(sp_rank * s) : ((sp_rank + 1) * s), :, :]
-        x_i = torch.cat([x_i, x[i, s:]])
+        freqs_imag_rank = freqs_imag[(sp_rank * s) : ((sp_rank + 1) * s), :, :]
+
+        out_real = x_real * freqs_real_rank - x_imag * freqs_imag_rank
+        out_imag = x_real * freqs_imag_rank + x_imag * freqs_real_rank
+
+        x_out = torch.stack([out_real, out_imag], dim=-1).flatten(2)
+        x_out = torch.cat([x_out, x[i, seq_len:]], dim=0)
 
         # append to collection
-        output.append(x_i)
+        output.append(x_out)
-    return torch.stack(output).float()
+    return torch.stack(output)
 
 
 def usp_dit_forward_vace(self, x, vace_context, seq_len, kwargs):
@@ -109,9 +139,13 @@ def usp_dit_forward(
     if self.model_type == 'i2v':
         assert clip_fea is not None and y is not None
     # params
+    dtype = self.patch_embedding.weight.dtype
     device = self.patch_embedding.weight.device
-    if self.freqs.device != device:
+    if self.freqs[0].dtype != dtype or self.freqs[0].device != device:
-        self.freqs = self.freqs.to(device)
+        self.freqs = (
+            self.freqs[0].to(dtype=dtype, device=device),
+            self.freqs[-1].to(dtype=dtype, device=device)
+        )
 
     if self.model_type != 'vace' and y is not None:
         x = [torch.cat([u, v], dim=0) for u, v in zip(x, y)]
@@ -129,11 +163,9 @@ def usp_dit_forward(
     ])
 
     # time embeddings
-    with amp.autocast(dtype=torch.float32):
+    e = self.time_embedding(
-        e = self.time_embedding(
+        sinusoidal_embedding_1d(self.freq_dim, t))
-            sinusoidal_embedding_1d(self.freq_dim, t).float())
+    e0 = self.time_projection(e).unflatten(1, (6, self.dim))
-        e0 = self.time_projection(e).unflatten(1, (6, self.dim))
-        assert e.dtype == torch.float32 and e0.dtype == torch.float32
 
     # context
     context_lens = None
@@ -177,7 +209,7 @@ def usp_dit_forward(
 
     # unpatchify
     x = self.unpatchify(x, grid_sizes)
-    return [u.float() for u in x]
+    return x
 
 
 def usp_attn_forward(self,
@@ -210,7 +242,7 @@ def usp_attn_forward(self,
     #     k = torch.cat([u[:l] for u, l in zip(k, k_lens)]).unsqueeze(0)
     #     v = torch.cat([u[:l] for u, l in zip(v, k_lens)]).unsqueeze(0)
 
-    x = xFuserLongContextAttention()(
+    x = xFuserLongContextAttention(attn_type=attn_type)(
         None,
         query=half(q),
         key=half(k),

wan/first_last_frame2video.py

@@ -12,10 +12,19 @@ from functools import partial
 import numpy as np
 import torch
 import torch.cuda.amp as amp
+from torch.cuda import empty_cache, synchronize
 import torch.distributed as dist
 import torchvision.transforms.functional as TF
 from tqdm import tqdm
 
+try:
+    import torch_musa
+    import torch_musa.core.amp as amp
+    from torch_musa.core.memory import empty_cache
+    from torch_musa.core.device import synchronize
+except ModuleNotFoundError:
+    pass
+
 from .distributed.fsdp import shard_model
 from .modules.clip import CLIPModel
 from .modules.model import WanModel
@@ -27,6 +36,7 @@ from .utils.fm_solvers import (
     retrieve_timesteps,
 )
 from .utils.fm_solvers_unipc import FlowUniPCMultistepScheduler
+from .utils.platform import get_device
 
 
 class WanFLF2V:
@@ -66,7 +76,7 @@ class WanFLF2V:
             init_on_cpu (`bool`, *optional*, defaults to True):
                 Enable initializing Transformer Model on CPU. Only works without FSDP or USP.
         """
-        self.device = torch.device(f"cuda:{device_id}")
+        self.device = get_device(device_id)
         self.config = config
         self.rank = rank
         self.use_usp = use_usp
@@ -323,7 +333,7 @@ class WanFLF2V:
             }
 
             if offload_model:
-                torch.cuda.empty_cache()
+                empty_cache()
 
             self.model.to(self.device)
             for _, t in enumerate(tqdm(timesteps)):
@@ -336,12 +346,12 @@ class WanFLF2V:
                     latent_model_input, t=timestep, **arg_c)[0].to(
                         torch.device('cpu') if offload_model else self.device)
                 if offload_model:
-                    torch.cuda.empty_cache()
+                    empty_cache()
                 noise_pred_uncond = self.model(
                     latent_model_input, t=timestep, **arg_null)[0].to(
                         torch.device('cpu') if offload_model else self.device)
                 if offload_model:
-                    torch.cuda.empty_cache()
+                    empty_cache()
                 noise_pred = noise_pred_uncond + guide_scale * (
                     noise_pred_cond - noise_pred_uncond)
 
@@ -361,7 +371,7 @@ class WanFLF2V:
 
             if offload_model:
                 self.model.cpu()
-                torch.cuda.empty_cache()
+                empty_cache()
 
             if self.rank == 0:
                 videos = self.vae.decode(x0)
@@ -370,7 +380,7 @@ class WanFLF2V:
         del sample_scheduler
         if offload_model:
             gc.collect()
-            torch.cuda.synchronize()
+            synchronize()
         if dist.is_initialized():
             dist.barrier()
 

wan/image2video.py

@@ -6,16 +6,26 @@ import os
 import random
 import sys
 import types
+from time import perf_counter
 from contextlib import contextmanager
 from functools import partial
 
 import numpy as np
 import torch
 import torch.cuda.amp as amp
+from torch.cuda import empty_cache, synchronize
 import torch.distributed as dist
 import torchvision.transforms.functional as TF
 from tqdm import tqdm
 
+try:
+    import torch_musa
+    import torch_musa.core.amp as amp
+    from torch_musa.core.memory import empty_cache
+    from torch_musa.core.device import synchronize
+except ModuleNotFoundError:
+    pass
+
 from .distributed.fsdp import shard_model
 from .modules.clip import CLIPModel
 from .modules.model import WanModel
@@ -27,6 +37,7 @@ from .utils.fm_solvers import (
     retrieve_timesteps,
 )
 from .utils.fm_solvers_unipc import FlowUniPCMultistepScheduler
+from .utils.platform import get_device
 
 
 class WanI2V:
@@ -66,7 +77,7 @@ class WanI2V:
             init_on_cpu (`bool`, *optional*, defaults to True):
                 Enable initializing Transformer Model on CPU. Only works without FSDP or USP.
         """
-        self.device = torch.device(f"cuda:{device_id}")
+        self.device = get_device(device_id)
         self.config = config
         self.rank = rank
         self.use_usp = use_usp
@@ -220,6 +231,7 @@ class WanI2V:
             n_prompt = self.sample_neg_prompt
 
         # preprocess
+        start_time = perf_counter()
         if not self.t5_cpu:
             self.text_encoder.model.to(self.device)
             context = self.text_encoder([input_prompt], self.device)
@@ -231,12 +243,18 @@ class WanI2V:
             context_null = self.text_encoder([n_prompt], torch.device('cpu'))
             context = [t.to(self.device) for t in context]
             context_null = [t.to(self.device) for t in context_null]
+        end_time = perf_counter()
+        logging.info(f"T5 Encoding took {end_time - start_time:.2f} seconds.")
 
+        start_time = perf_counter()
         self.clip.model.to(self.device)
         clip_context = self.clip.visual([img[:, None, :, :]])
         if offload_model:
             self.clip.model.cpu()
+        end_time = perf_counter()
+        logging.info(f"CLIP took {end_time - start_time:.2f} seconds.")
 
+        start_time = perf_counter()
         y = self.vae.encode([
             torch.concat([
                 torch.nn.functional.interpolate(
@@ -246,6 +264,9 @@ class WanI2V:
             ],
                          dim=1).to(self.device)
         ])[0]
+        end_time = perf_counter()
+        logging.info(f"VAE Encoding took {end_time - start_time:.2f} seconds.")
+
         y = torch.concat([msk, y])
 
         @contextmanager
@@ -296,8 +317,9 @@ class WanI2V:
             }
 
             if offload_model:
-                torch.cuda.empty_cache()
+                empty_cache()
 
+            start_time = perf_counter()
             self.model.to(self.device)
             for _, t in enumerate(tqdm(timesteps)):
                 latent_model_input = [latent.to(self.device)]
@@ -309,12 +331,12 @@ class WanI2V:
                     latent_model_input, t=timestep, **arg_c)[0].to(
                         torch.device('cpu') if offload_model else self.device)
                 if offload_model:
-                    torch.cuda.empty_cache()
+                    empty_cache()
                 noise_pred_uncond = self.model(
                     latent_model_input, t=timestep, **arg_null)[0].to(
                         torch.device('cpu') if offload_model else self.device)
                 if offload_model:
-                    torch.cuda.empty_cache()
+                    empty_cache()
                 noise_pred = noise_pred_uncond + guide_scale * (
                     noise_pred_cond - noise_pred_uncond)
 
@@ -331,19 +353,24 @@ class WanI2V:
 
                 x0 = [latent.to(self.device)]
                 del latent_model_input, timestep
+            end_time = perf_counter()
+            logging.info(f"Sampling took {end_time - start_time:.2f} seconds.")
 
             if offload_model:
                 self.model.cpu()
-                torch.cuda.empty_cache()
+                empty_cache()
 
             if self.rank == 0:
+                start_time = perf_counter()
                 videos = self.vae.decode(x0)
+                end_time = perf_counter()
+                logging.info(f"VAE Decoding took {end_time - start_time:.2f} seconds.")
 
         del noise, latent
         del sample_scheduler
         if offload_model:
             gc.collect()
-            torch.cuda.synchronize()
+            synchronize()
         if dist.is_initialized():
             dist.barrier()
 

wan/modules/attention.py

@@ -1,4 +1,6 @@
 # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
+import warnings
+
 import torch
 
 try:
@@ -13,7 +15,13 @@ try:
 except ModuleNotFoundError:
     FLASH_ATTN_2_AVAILABLE = False
 
-import warnings
+try:
+    import torch_musa
+    FLASH_ATTN_3_AVAILABLE = False
+    FLASH_ATTN_2_AVAILABLE = False
+except ModuleNotFoundError:
+    pass
+
 
 __all__ = [
     'flash_attention',
@@ -51,7 +59,7 @@ def flash_attention(
     """
     half_dtypes = (torch.float16, torch.bfloat16)
     assert dtype in half_dtypes
-    assert q.device.type == 'cuda' and q.size(-1) <= 256
+    assert (q.device.type == "cuda" or q.device.type == "musa") and q.size(-1) <= 256
 
     # params
     b, lq, lk, out_dtype = q.size(0), q.size(1), k.size(1), q.dtype
@@ -172,8 +180,9 @@ def attention(
         k = k.transpose(1, 2).to(dtype)
         v = v.transpose(1, 2).to(dtype)
 
-        out = torch.nn.functional.scaled_dot_product_attention(
+        with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=False):
-            q, k, v, attn_mask=attn_mask, is_causal=causal, dropout_p=dropout_p)
+            out = torch.nn.functional.scaled_dot_product_attention(
+                q, k, v, attn_mask=attn_mask, dropout_p=dropout_p, is_causal=causal, scale=softmax_scale)
 
         out = out.transpose(1, 2).contiguous()
         return out

wan/modules/clip.py

@@ -6,12 +6,20 @@ import math
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
+import torch.cuda.amp as amp
 import torchvision.transforms as T
 
 from .attention import flash_attention
 from .tokenizers import HuggingfaceTokenizer
 from .xlm_roberta import XLMRoberta
 
+try:
+    import torch_musa
+    import torch_musa.core.amp as amp
+    from .attention import attention as flash_attention
+except ModuleNotFoundError:
+    pass
+
 __all__ = [
     'XLMRobertaCLIP',
     'clip_xlm_roberta_vit_h_14',
@@ -29,7 +37,7 @@ def pos_interpolate(pos, seq_len):
         return torch.cat([
             pos[:, :n],
             F.interpolate(
-                pos[:, n:].float().reshape(1, src_grid, src_grid, -1).permute(
+                pos[:, n:].reshape(1, src_grid, src_grid, -1).permute(
                     0, 3, 1, 2),
                 size=(tar_grid, tar_grid),
                 mode='bicubic',
@@ -44,12 +52,6 @@ class QuickGELU(nn.Module):
         return x * torch.sigmoid(1.702 * x)
 
 
-class LayerNorm(nn.LayerNorm):
-
-    def forward(self, x):
-        return super().forward(x.float()).type_as(x)
-
-
 class SelfAttention(nn.Module):
 
     def __init__(self,
@@ -82,7 +84,7 @@ class SelfAttention(nn.Module):
 
         # compute attention
         p = self.attn_dropout if self.training else 0.0
-        x = flash_attention(q, k, v, dropout_p=p, causal=self.causal, version=2)
+        x = flash_attention(q, k, v, dropout_p=p, causal=self.causal)
         x = x.reshape(b, s, c)
 
         # output
@@ -131,10 +133,10 @@ class AttentionBlock(nn.Module):
         self.norm_eps = norm_eps
 
         # layers
-        self.norm1 = LayerNorm(dim, eps=norm_eps)
+        self.norm1 = nn.LayerNorm(dim, eps=norm_eps)
         self.attn = SelfAttention(dim, num_heads, causal, attn_dropout,
                                   proj_dropout)
-        self.norm2 = LayerNorm(dim, eps=norm_eps)
+        self.norm2 = nn.LayerNorm(dim, eps=norm_eps)
         if activation == 'swi_glu':
             self.mlp = SwiGLU(dim, int(dim * mlp_ratio))
         else:
@@ -177,7 +179,7 @@ class AttentionPool(nn.Module):
         self.to_q = nn.Linear(dim, dim)
         self.to_kv = nn.Linear(dim, dim * 2)
         self.proj = nn.Linear(dim, dim)
-        self.norm = LayerNorm(dim, eps=norm_eps)
+        self.norm = nn.LayerNorm(dim, eps=norm_eps)
         self.mlp = nn.Sequential(
             nn.Linear(dim, int(dim * mlp_ratio)),
             QuickGELU() if activation == 'quick_gelu' else nn.GELU(),
@@ -259,13 +261,13 @@ class VisionTransformer(nn.Module):
         self.dropout = nn.Dropout(embedding_dropout)
 
         # transformer
-        self.pre_norm = LayerNorm(dim, eps=norm_eps) if pre_norm else None
+        self.pre_norm = nn.LayerNorm(dim, eps=norm_eps) if pre_norm else None
         self.transformer = nn.Sequential(*[
             AttentionBlock(dim, mlp_ratio, num_heads, post_norm, False,
                            activation, attn_dropout, proj_dropout, norm_eps)
             for _ in range(num_layers)
         ])
-        self.post_norm = LayerNorm(dim, eps=norm_eps)
+        self.post_norm = nn.LayerNorm(dim, eps=norm_eps)
 
         # head
         if pool_type == 'token':
@@ -537,6 +539,6 @@ class CLIPModel:
         videos = self.transforms.transforms[-1](videos.mul_(0.5).add_(0.5))
 
         # forward
-        with torch.cuda.amp.autocast(dtype=self.dtype):
+        with amp.autocast(dtype=self.dtype):
             out = self.model.visual(videos, use_31_block=True)
             return out

wan/modules/model.py

@@ -7,7 +7,15 @@ import torch.nn as nn
 from diffusers.configuration_utils import ConfigMixin, register_to_config
 from diffusers.models.modeling_utils import ModelMixin
 
-from .attention import flash_attention
+from wan.utils.platform import get_device
+from wan.modules.attention import flash_attention
+
+try:
+    import torch_musa
+    import torch_musa.core.amp as amp
+    from wan.modules.attention import attention as flash_attention
+except ModuleNotFoundError:
+    pass
 
 __all__ = ['WanModel']
 
@@ -19,7 +27,7 @@ def sinusoidal_embedding_1d(dim, position):
     # preprocess
     assert dim % 2 == 0
     half = dim // 2
-    position = position.type(torch.float64)
+    position = position.type(torch.bfloat16)
 
     # calculation
     sinusoid = torch.outer(
@@ -29,22 +37,45 @@ def sinusoidal_embedding_1d(dim, position):
 
 
 @amp.autocast(enabled=False)
-def rope_params(max_seq_len, dim, theta=10000):
+def rope_params_real(
+    max_seq_len, dim, theta=10000, dtype=torch.float32, device=torch.device("cpu")
+):
     assert dim % 2 == 0
-    freqs = torch.outer(
+    freqs_real = torch.outer(
-        torch.arange(max_seq_len),
+        torch.arange(max_seq_len, dtype=dtype, device=device),
-        1.0 / torch.pow(theta,
+        1.0
-                        torch.arange(0, dim, 2).to(torch.float64).div(dim)))
+        / torch.pow(
-    freqs = torch.polar(torch.ones_like(freqs), freqs)
+            theta, torch.arange(0, dim, 2, dtype=dtype, device=device).div(dim)
-    return freqs
+        ),
+    )
+    return torch.cos(freqs_real)
+
+
+@amp.autocast(enabled=False)
+def rope_params_imag(
+    max_seq_len, dim, theta=10000, dtype=torch.float32, device=torch.device("cpu")
+):
+    assert dim % 2 == 0
+    freqs_imag = torch.outer(
+        torch.arange(max_seq_len, dtype=dtype, device=device),
+        1.0
+        / torch.pow(
+            theta, torch.arange(0, dim, 2, dtype=dtype, device=device).div(dim)
+        ),
+    )
+    return torch.sin(freqs_imag)
 
 
 @amp.autocast(enabled=False)
 def rope_apply(x, grid_sizes, freqs):
     n, c = x.size(2), x.size(3) // 2
+    c0 = c - 2 * (c // 3)
+    c1 = c // 3
+    c2 = c // 3
 
     # split freqs
-    freqs = freqs.split([c - 2 * (c // 3), c // 3, c // 3], dim=1)
+    freqs_real = freqs[0].split([c0, c1, c2], dim=1)
+    freqs_imag = freqs[-1].split([c0, c1, c2], dim=1)
 
     # loop over samples
     output = []
@@ -52,22 +83,36 @@ def rope_apply(x, grid_sizes, freqs):
         seq_len = f * h * w
 
         # precompute multipliers
-        x_i = torch.view_as_complex(x[i, :seq_len].to(torch.float64).reshape(
+        x_i = x[i, :seq_len].reshape(seq_len, n, c, 2)
-            seq_len, n, -1, 2))
+        x_real = x_i[..., 0]
-        freqs_i = torch.cat([
+        x_imag = x_i[..., 1]
-            freqs[0][:f].view(f, 1, 1, -1).expand(f, h, w, -1),
+        freqs_real = torch.cat(
-            freqs[1][:h].view(1, h, 1, -1).expand(f, h, w, -1),
+            [
-            freqs[2][:w].view(1, 1, w, -1).expand(f, h, w, -1)
+                freqs_real[0][:f].view(f, 1, 1, c0).expand(f, h, w, c0),
-        ],
+                freqs_real[1][:h].view(1, h, 1, c1).expand(f, h, w, c1),
-                            dim=-1).reshape(seq_len, 1, -1)
+                freqs_real[2][:w].view(1, 1, w, c2).expand(f, h, w, c2),
+            ],
+            dim=-1,
+        ).reshape(seq_len, 1, c)
+        freqs_imag = torch.cat(
+            [
+                freqs_imag[0][:f].view(f, 1, 1, c0).expand(f, h, w, c0),
+                freqs_imag[1][:h].view(1, h, 1, c1).expand(f, h, w, c1),
+                freqs_imag[2][:w].view(1, 1, w, c2).expand(f, h, w, c2),
+            ],
+            dim=-1,
+        ).reshape(seq_len, 1, c)
+
+        out_real = x_real * freqs_real - x_imag * freqs_imag
+        out_imag = x_real * freqs_imag + x_imag * freqs_real
 
         # apply rotary embedding
-        x_i = torch.view_as_real(x_i * freqs_i).flatten(2)
+        x_out = torch.stack([out_real, out_imag], dim=-1).flatten(2)
-        x_i = torch.cat([x_i, x[i, seq_len:]])
+        x_out = torch.cat([x_out, x[i, seq_len:]], dim=0)
 
         # append to collection
-        output.append(x_i)
+        output.append(x_out)
-    return torch.stack(output).float()
+    return torch.stack(output)
 
 
 class WanRMSNorm(nn.Module):
@@ -89,19 +134,6 @@ class WanRMSNorm(nn.Module):
         return x * torch.rsqrt(x.pow(2).mean(dim=-1, keepdim=True) + self.eps)
 
 
-class WanLayerNorm(nn.LayerNorm):
-
-    def __init__(self, dim, eps=1e-6, elementwise_affine=False):
-        super().__init__(dim, elementwise_affine=elementwise_affine, eps=eps)
-
-    def forward(self, x):
-        r"""
-        Args:
-            x(Tensor): Shape [B, L, C]
-        """
-        return super().forward(x.float()).type_as(x)
-
-
 class WanSelfAttention(nn.Module):
 
     def __init__(self,
@@ -256,10 +288,10 @@ class WanAttentionBlock(nn.Module):
         self.eps = eps
 
         # layers
-        self.norm1 = WanLayerNorm(dim, eps)
+        self.norm1 = nn.LayerNorm(dim, eps, elementwise_affine=False)
         self.self_attn = WanSelfAttention(dim, num_heads, window_size, qk_norm,
                                           eps)
-        self.norm3 = WanLayerNorm(
+        self.norm3 = nn.LayerNorm(
             dim, eps,
             elementwise_affine=True) if cross_attn_norm else nn.Identity()
         self.cross_attn = WAN_CROSSATTENTION_CLASSES[cross_attn_type](dim,
@@ -267,7 +299,7 @@ class WanAttentionBlock(nn.Module):
                                                                       (-1, -1),
                                                                       qk_norm,
                                                                       eps)
-        self.norm2 = WanLayerNorm(dim, eps)
+        self.norm2 = nn.LayerNorm(dim, eps, elementwise_affine=False)
         self.ffn = nn.Sequential(
             nn.Linear(dim, ffn_dim), nn.GELU(approximate='tanh'),
             nn.Linear(ffn_dim, dim))
@@ -293,24 +325,19 @@ class WanAttentionBlock(nn.Module):
             grid_sizes(Tensor): Shape [B, 3], the second dimension contains (F, H, W)
             freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2]
         """
-        assert e.dtype == torch.float32
+        e = (self.modulation + e).chunk(6, dim=1)
-        with amp.autocast(dtype=torch.float32):
-            e = (self.modulation + e).chunk(6, dim=1)
-        assert e[0].dtype == torch.float32
 
         # self-attention
         y = self.self_attn(
-            self.norm1(x).float() * (1 + e[1]) + e[0], seq_lens, grid_sizes,
+            self.norm1(x) * (1 + e[1]) + e[0], seq_lens, grid_sizes,
             freqs)
-        with amp.autocast(dtype=torch.float32):
+        x = x + y * e[2]
-            x = x + y * e[2]
 
         # cross-attention & ffn function
         def cross_attn_ffn(x, context, context_lens, e):
             x = x + self.cross_attn(self.norm3(x), context, context_lens)
-            y = self.ffn(self.norm2(x).float() * (1 + e[4]) + e[3])
+            y = self.ffn(self.norm2(x) * (1 + e[4]) + e[3])
-            with amp.autocast(dtype=torch.float32):
+            x = x + y * e[5]
-                x = x + y * e[5]
             return x
 
         x = cross_attn_ffn(x, context, context_lens, e)
@@ -328,7 +355,7 @@ class Head(nn.Module):
 
         # layers
         out_dim = math.prod(patch_size) * out_dim
-        self.norm = WanLayerNorm(dim, eps)
+        self.norm = nn.LayerNorm(dim, eps, elementwise_affine=False)
         self.head = nn.Linear(dim, out_dim)
 
         # modulation
@@ -340,10 +367,8 @@ class Head(nn.Module):
             x(Tensor): Shape [B, L1, C]
             e(Tensor): Shape [B, C]
         """
-        assert e.dtype == torch.float32
+        e = (self.modulation + e.unsqueeze(1)).chunk(2, dim=1)
-        with amp.autocast(dtype=torch.float32):
+        x = self.head(self.norm(x) * (1 + e[1]) + e[0])
-            e = (self.modulation + e.unsqueeze(1)).chunk(2, dim=1)
-            x = (self.head(self.norm(x) * (1 + e[1]) + e[0]))
         return x
 
 
@@ -477,12 +502,23 @@ class WanModel(ModelMixin, ConfigMixin):
         # buffers (don't use register_buffer otherwise dtype will be changed in to())
         assert (dim % num_heads) == 0 and (dim // num_heads) % 2 == 0
         d = dim // num_heads
-        self.freqs = torch.cat([
+        freqs_real = torch.cat(
-            rope_params(1024, d - 4 * (d // 6)),
+            [
-            rope_params(1024, 2 * (d // 6)),
+                rope_params_real(1024, d - 4 * (d // 6)),
-            rope_params(1024, 2 * (d // 6))
+                rope_params_real(1024, 2 * (d // 6)),
-        ],
+                rope_params_real(1024, 2 * (d // 6)),
-                               dim=1)
+            ],
+            dim=1,
+        )
+        freqs_imag = torch.cat(
+            [
+                rope_params_imag(1024, d - 4 * (d // 6)),
+                rope_params_imag(1024, 2 * (d // 6)),
+                rope_params_imag(1024, 2 * (d // 6)),
+            ],
+            dim=1,
+        )
+        self.freqs = (freqs_real, freqs_imag)
 
         if model_type == 'i2v' or model_type == 'flf2v':
             self.img_emb = MLPProj(1280, dim, flf_pos_emb=model_type == 'flf2v')
@@ -523,9 +559,13 @@ class WanModel(ModelMixin, ConfigMixin):
         if self.model_type == 'i2v' or self.model_type == 'flf2v':
             assert clip_fea is not None and y is not None
         # params
+        dtype = self.patch_embedding.weight.dtype
         device = self.patch_embedding.weight.device
-        if self.freqs.device != device:
+        if self.freqs[0].dtype != dtype or self.freqs[0].device != device:
-            self.freqs = self.freqs.to(device)
+            self.freqs = (
+                self.freqs[0].to(dtype=dtype, device=device),
+                self.freqs[-1].to(dtype=dtype, device=device)
+            )
 
         if y is not None:
             x = [torch.cat([u, v], dim=0) for u, v in zip(x, y)]
@@ -543,11 +583,9 @@ class WanModel(ModelMixin, ConfigMixin):
         ])
 
         # time embeddings
-        with amp.autocast(dtype=torch.float32):
+        e = self.time_embedding(
-            e = self.time_embedding(
+            sinusoidal_embedding_1d(self.freq_dim, t))
-                sinusoidal_embedding_1d(self.freq_dim, t).float())
+        e0 = self.time_projection(e).unflatten(1, (6, self.dim))
-            e0 = self.time_projection(e).unflatten(1, (6, self.dim))
-            assert e.dtype == torch.float32 and e0.dtype == torch.float32
 
         # context
         context_lens = None
@@ -579,7 +617,7 @@ class WanModel(ModelMixin, ConfigMixin):
 
         # unpatchify
         x = self.unpatchify(x, grid_sizes)
-        return [u.float() for u in x]
+        return x
 
     def unpatchify(self, x, grid_sizes):
         r"""

wan/modules/t5.py

@@ -7,7 +7,14 @@ import torch
 import torch.nn as nn
 import torch.nn.functional as F
 
-from .tokenizers import HuggingfaceTokenizer
+try:
+    import torch_musa
+    from torch_musa.core.device import current_device
+except ModuleNotFoundError:
+    pass
+
+from wan.modules.tokenizers import HuggingfaceTokenizer
+from wan.utils.platform import get_device
 
 __all__ = [
     'T5Model',
@@ -59,10 +66,8 @@ class T5LayerNorm(nn.Module):
         self.weight = nn.Parameter(torch.ones(dim))
 
     def forward(self, x):
-        x = x * torch.rsqrt(x.float().pow(2).mean(dim=-1, keepdim=True) +
+        x = x * torch.rsqrt(x.pow(2).mean(dim=-1, keepdim=True) +
                             self.eps)
-        if self.weight.dtype in [torch.float16, torch.bfloat16]:
-            x = x.type_as(self.weight)
         return self.weight * x
 
 
@@ -110,7 +115,7 @@ class T5Attention(nn.Module):
 
         # compute attention (T5 does not use scaling)
         attn = torch.einsum('binc,bjnc->bnij', q, k) + attn_bias
-        attn = F.softmax(attn.float(), dim=-1).type_as(attn)
+        attn = F.softmax(attn, dim=-1)
         x = torch.einsum('bnij,bjnc->binc', attn, v)
 
         # output
@@ -255,7 +260,7 @@ class T5RelativeEmbedding(nn.Module):
 
         # embeddings for small and large positions
         max_exact = num_buckets // 2
-        rel_pos_large = max_exact + (torch.log(rel_pos.float() / max_exact) /
+        rel_pos_large = max_exact + (torch.log(rel_pos / max_exact) /
                                      math.log(self.max_dist / max_exact) *
                                      (num_buckets - max_exact)).long()
         rel_pos_large = torch.min(
@@ -475,7 +480,7 @@ class T5EncoderModel:
         self,
         text_len,
         dtype=torch.bfloat16,
-        device=torch.cuda.current_device(),
+        device=get_device(),
         checkpoint_path=None,
         tokenizer_path=None,
         shard_fn=None,

wan/modules/vace_model.py

@@ -4,6 +4,12 @@ import torch.cuda.amp as amp
 import torch.nn as nn
 from diffusers.configuration_utils import register_to_config
 
+try:
+    import torch_musa
+    import torch_musa.core.amp as amp
+except ModuleNotFoundError:
+    pass
+
 from .model import WanAttentionBlock, WanModel, sinusoidal_embedding_1d
 
 

wan/modules/vae.py

@@ -1,12 +1,20 @@
 # Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
 import logging
+from math import sqrt
 
 import torch
-import torch.cuda.amp as amp
 import torch.nn as nn
 import torch.nn.functional as F
+from torch.nn import Upsample
 from einops import rearrange
 
+try:
+    import torch_musa
+except ModuleNotFoundError:
+    pass
+
+from wan.utils.platform import get_device
+
 __all__ = [
     'WanVAE',
 ]
@@ -44,23 +52,17 @@ class RMS_norm(nn.Module):
         shape = (dim, *broadcastable_dims) if channel_first else (dim,)
 
         self.channel_first = channel_first
-        self.scale = dim**0.5
+        self.scale = sqrt(dim)
         self.gamma = nn.Parameter(torch.ones(shape))
         self.bias = nn.Parameter(torch.zeros(shape)) if bias else 0.
 
     def forward(self, x):
-        return F.normalize(
+        return (
-            x, dim=(1 if self.channel_first else
+            F.normalize(x.float(), dim=(1 if self.channel_first else -1)).type_as(x)
-                    -1)) * self.scale * self.gamma + self.bias
+            * self.scale
-
+            * self.gamma
-
+            + self.bias
-class Upsample(nn.Upsample):
+        )
-
-    def forward(self, x):
-        """
-        Fix bfloat16 support for nearest neighbor interpolation.
-        """
-        return super().forward(x.float()).type_as(x)
 
 
 class Resample(nn.Module):
@@ -253,6 +255,10 @@ class AttentionBlock(nn.Module):
             q,
             k,
             v,
+            attn_mask=None,
+            dropout_p=0.0,
+            is_causal=False,
+            scale=None,
         )
         x = x.squeeze(1).permute(0, 2, 1).reshape(b * t, c, h, w)
 
@@ -621,8 +627,8 @@ class WanVAE:
     def __init__(self,
                  z_dim=16,
                  vae_pth='cache/vae_step_411000.pth',
-                 dtype=torch.float,
+                 dtype=torch.bfloat16,
-                 device="cuda"):
+                 device=get_device()):
         self.dtype = dtype
         self.device = device
 
@@ -648,16 +654,12 @@ class WanVAE:
         """
         videos: A list of videos each with shape [C, T, H, W].
         """
-        with amp.autocast(dtype=self.dtype):
+        return [
-            return [
+            self.model.encode(u.unsqueeze(0), self.scale).squeeze(0) for u in videos
-                self.model.encode(u.unsqueeze(0), self.scale).float().squeeze(0)
+        ]
-                for u in videos
-            ]
 
     def decode(self, zs):
-        with amp.autocast(dtype=self.dtype):
+        return [
-            return [
+            self.model.decode(u.unsqueeze(0), self.scale).clamp_(-1, 1).squeeze(0)
-                self.model.decode(u.unsqueeze(0),
+            for u in zs
-                                  self.scale).float().clamp_(-1, 1).squeeze(0)
+        ]
-                for u in zs
-            ]

wan/text2video.py

@@ -6,14 +6,24 @@ import os
 import random
 import sys
 import types
+from time import perf_counter
 from contextlib import contextmanager
 from functools import partial
 
 import torch
 import torch.cuda.amp as amp
+from torch.cuda import empty_cache, synchronize
 import torch.distributed as dist
 from tqdm import tqdm
 
+try:
+    import torch_musa
+    import torch_musa.core.amp as amp
+    from torch_musa.core.memory import empty_cache
+    from torch_musa.core.device import synchronize
+except ModuleNotFoundError:
+    pass
+
 from .distributed.fsdp import shard_model
 from .modules.model import WanModel
 from .modules.t5 import T5EncoderModel
@@ -24,7 +34,7 @@ from .utils.fm_solvers import (
     retrieve_timesteps,
 )
 from .utils.fm_solvers_unipc import FlowUniPCMultistepScheduler
-
+from .utils.platform import get_device
 
 class WanT2V:
 
@@ -60,7 +70,7 @@ class WanT2V:
             t5_cpu (`bool`, *optional*, defaults to False):
                 Whether to place T5 model on CPU. Only works without t5_fsdp.
         """
-        self.device = torch.device(f"cuda:{device_id}")
+        self.device = get_device(device_id)
         self.config = config
         self.rank = rank
         self.t5_cpu = t5_cpu
@@ -171,6 +181,7 @@ class WanT2V:
         seed_g = torch.Generator(device=self.device)
         seed_g.manual_seed(seed)
 
+        start_time = perf_counter()
         if not self.t5_cpu:
             self.text_encoder.model.to(self.device)
             context = self.text_encoder([input_prompt], self.device)
@@ -182,6 +193,8 @@ class WanT2V:
             context_null = self.text_encoder([n_prompt], torch.device('cpu'))
             context = [t.to(self.device) for t in context]
             context_null = [t.to(self.device) for t in context_null]
+        end_time = perf_counter()
+        logging.info(f"T5 Encoding Context took {end_time - start_time:.2f} seconds.")
 
         noise = [
             torch.randn(
@@ -230,13 +243,14 @@ class WanT2V:
             arg_c = {'context': context, 'seq_len': seq_len}
             arg_null = {'context': context_null, 'seq_len': seq_len}
 
+            start_time = perf_counter()
+            self.model.to(self.device)
             for _, t in enumerate(tqdm(timesteps)):
                 latent_model_input = latents
                 timestep = [t]
 
                 timestep = torch.stack(timestep)
 
-                self.model.to(self.device)
                 noise_pred_cond = self.model(
                     latent_model_input, t=timestep, **arg_c)[0]
                 noise_pred_uncond = self.model(
@@ -252,19 +266,24 @@ class WanT2V:
                     return_dict=False,
                     generator=seed_g)[0]
                 latents = [temp_x0.squeeze(0)]
+            end_time = perf_counter()
+            logging.info(f"Sampling took {end_time - start_time:.2f} seconds.")
 
             x0 = latents
             if offload_model:
                 self.model.cpu()
-                torch.cuda.empty_cache()
+                empty_cache()
             if self.rank == 0:
+                start_time = perf_counter()
                 videos = self.vae.decode(x0)
+                end_time = perf_counter()
+                logging.info(f"VAE Decoding took {end_time - start_time:.2f} seconds.")
 
         del noise, latents
         del sample_scheduler
         if offload_model:
             gc.collect()
-            torch.cuda.synchronize()
+            synchronize()
         if dist.is_initialized():
             dist.barrier()
 

wan/utils/__init__.py

@@ -5,9 +5,10 @@ from .fm_solvers import (
 )
 from .fm_solvers_unipc import FlowUniPCMultistepScheduler
 from .vace_processor import VaceVideoProcessor
+from .platform import get_device, get_torch_distributed_backend
 
 __all__ = [
     'HuggingfaceTokenizer', 'get_sampling_sigmas', 'retrieve_timesteps',
     'FlowDPMSolverMultistepScheduler', 'FlowUniPCMultistepScheduler',
-    'VaceVideoProcessor'
+    'VaceVideoProcessor', 'get_device', 'get_torch_distributed_backend'
 ]

wan/utils/platform.py

@@ -0,0 +1,34 @@
+from typing import Optional
+
+import torch
+
+try:
+    import torch_musa
+except ModuleNotFoundError:
+    pass
+
+
+def _is_musa():
+    try:
+        if torch.musa.is_available():
+            return True
+    except ModuleNotFoundError:
+        return False
+
+
+def get_device(local_rank:Optional[int]=None) -> torch.device:
+    if torch.cuda.is_available():
+        return torch.cuda.current_device() if local_rank is None else torch.device("cuda", local_rank)
+    elif _is_musa():
+        return torch.musa.current_device() if local_rank is None else torch.device("musa", local_rank)
+    else:
+        return torch.device("cpu")
+
+
+def get_torch_distributed_backend() -> str:
+    if torch.cuda.is_available():
+        return "nccl"
+    elif _is_musa():
+        return "mccl"
+    else:
+        raise NotImplementedError("No Accelerators(AMD/NV/MTT GPU, AMD MI instinct accelerators) available")

wan/vace.py

@@ -13,6 +13,7 @@ from functools import partial
 
 import torch
 import torch.cuda.amp as amp
+from torch.cuda import empty_cache, synchronize
 import torch.distributed as dist
 import torch.multiprocessing as mp
 import torch.nn.functional as F
@@ -20,6 +21,14 @@ import torchvision.transforms.functional as TF
 from PIL import Image
 from tqdm import tqdm
 
+try:
+    import torch_musa
+    import torch_musa.core.amp as amp
+    from torch_musa.core.memory import empty_cache
+    from torch_musa.core.device import synchronize
+except ModuleNotFoundError:
+    pass
+
 from .modules.vace_model import VaceWanModel
 from .text2video import (
     FlowDPMSolverMultistepScheduler,
@@ -32,6 +41,7 @@ from .text2video import (
     shard_model,
 )
 from .utils.vace_processor import VaceVideoProcessor
+from .utils.platform import get_device, get_torch_distributed_backend
 
 
 class WanVace(WanT2V):
@@ -68,7 +78,7 @@ class WanVace(WanT2V):
             t5_cpu (`bool`, *optional*, defaults to False):
                 Whether to place T5 model on CPU. Only works without t5_fsdp.
         """
-        self.device = torch.device(f"cuda:{device_id}")
+        self.device = get_device(device_id)
         self.config = config
         self.rank = rank
         self.t5_cpu = t5_cpu
@@ -460,7 +470,7 @@ class WanVace(WanT2V):
             x0 = latents
             if offload_model:
                 self.model.cpu()
-                torch.cuda.empty_cache()
+                empty_cache()
             if self.rank == 0:
                 videos = self.decode_latent(x0, input_ref_images)
 
@@ -468,7 +478,7 @@ class WanVace(WanT2V):
         del sample_scheduler
         if offload_model:
             gc.collect()
-            torch.cuda.synchronize()
+            synchronize()
         if dist.is_initialized():
             dist.barrier()
 
@@ -568,7 +578,7 @@ class WanVaceMP(WanVace):
 
             torch.cuda.set_device(gpu)
             dist.init_process_group(
-                backend='nccl',
+                backend=get_torch_distributed_backend(),
                 init_method='env://',
                 rank=rank,
                 world_size=world_size)
@@ -633,7 +643,7 @@ class WanVaceMP(WanVace):
             model = shard_fn(model)
             sample_neg_prompt = self.config.sample_neg_prompt
 
-            torch.cuda.empty_cache()
+            empty_cache()
             event = initialized_events[gpu]
             in_q = in_q_list[gpu]
             event.set()
@@ -748,7 +758,7 @@ class WanVaceMP(WanVace):
                             generator=seed_g)[0]
                         latents = [temp_x0.squeeze(0)]
 
-                    torch.cuda.empty_cache()
+                    empty_cache()
                     x0 = latents
                     if rank == 0:
                         videos = self.decode_latent(
@@ -758,7 +768,7 @@ class WanVaceMP(WanVace):
                 del sample_scheduler
                 if offload_model:
                     gc.collect()
-                    torch.cuda.synchronize()
+                    synchronize()
                 if dist.is_initialized():
                     dist.barrier()