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https://github.com/Wan-Video/Wan2.1.git
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18d53feb7a
* Add VACE * Support training with multiple gpus * Update default args for vace task * vace block update * Add vace exmaple jpg * Fix dist vace fwd hook error * Update vace exmample * Update vace args * Update pipeline name for vace * vace gradio and Readme * Update vace snake png --------- Co-authored-by: hanzhn <han.feng.jason@gmail.com>
231 lines
6.8 KiB
Python
231 lines
6.8 KiB
Python
# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
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import torch
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import torch.cuda.amp as amp
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from xfuser.core.distributed import (get_sequence_parallel_rank,
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get_sequence_parallel_world_size,
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get_sp_group)
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from xfuser.core.long_ctx_attention import xFuserLongContextAttention
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from ..modules.model import sinusoidal_embedding_1d
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def pad_freqs(original_tensor, target_len):
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seq_len, s1, s2 = original_tensor.shape
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pad_size = target_len - seq_len
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padding_tensor = torch.ones(
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pad_size,
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s1,
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s2,
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dtype=original_tensor.dtype,
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device=original_tensor.device)
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padded_tensor = torch.cat([original_tensor, padding_tensor], dim=0)
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return padded_tensor
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@amp.autocast(enabled=False)
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def rope_apply(x, grid_sizes, freqs):
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"""
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x: [B, L, N, C].
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grid_sizes: [B, 3].
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freqs: [M, C // 2].
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"""
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s, n, c = x.size(1), x.size(2), x.size(3) // 2
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# split freqs
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freqs = freqs.split([c - 2 * (c // 3), c // 3, c // 3], dim=1)
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# loop over samples
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output = []
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for i, (f, h, w) in enumerate(grid_sizes.tolist()):
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seq_len = f * h * w
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# precompute multipliers
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x_i = torch.view_as_complex(x[i, :s].to(torch.float64).reshape(
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s, n, -1, 2))
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freqs_i = torch.cat([
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freqs[0][:f].view(f, 1, 1, -1).expand(f, h, w, -1),
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freqs[1][:h].view(1, h, 1, -1).expand(f, h, w, -1),
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freqs[2][:w].view(1, 1, w, -1).expand(f, h, w, -1)
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],
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dim=-1).reshape(seq_len, 1, -1)
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# apply rotary embedding
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sp_size = get_sequence_parallel_world_size()
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sp_rank = get_sequence_parallel_rank()
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freqs_i = pad_freqs(freqs_i, s * sp_size)
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s_per_rank = s
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freqs_i_rank = freqs_i[(sp_rank * s_per_rank):((sp_rank + 1) *
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s_per_rank), :, :]
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x_i = torch.view_as_real(x_i * freqs_i_rank).flatten(2)
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x_i = torch.cat([x_i, x[i, s:]])
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# append to collection
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output.append(x_i)
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return torch.stack(output).float()
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def usp_dit_forward_vace(
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self,
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x,
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vace_context,
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seq_len,
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kwargs
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):
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# embeddings
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c = [self.vace_patch_embedding(u.unsqueeze(0)) for u in vace_context]
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c = [u.flatten(2).transpose(1, 2) for u in c]
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c = torch.cat([
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torch.cat([u, u.new_zeros(1, seq_len - u.size(1), u.size(2))],
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dim=1) for u in c
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])
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# arguments
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new_kwargs = dict(x=x)
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new_kwargs.update(kwargs)
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# Context Parallel
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c = torch.chunk(
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c, get_sequence_parallel_world_size(),
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dim=1)[get_sequence_parallel_rank()]
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hints = []
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for block in self.vace_blocks:
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c, c_skip = block(c, **new_kwargs)
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hints.append(c_skip)
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return hints
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def usp_dit_forward(
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self,
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x,
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t,
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context,
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seq_len,
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vace_context=None,
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vace_context_scale=1.0,
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clip_fea=None,
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y=None,
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):
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"""
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x: A list of videos each with shape [C, T, H, W].
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t: [B].
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context: A list of text embeddings each with shape [L, C].
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"""
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if self.model_type == 'i2v':
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assert clip_fea is not None and y is not None
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# params
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device = self.patch_embedding.weight.device
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if self.freqs.device != device:
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self.freqs = self.freqs.to(device)
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if self.model_type != 'vace' and y is not None:
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x = [torch.cat([u, v], dim=0) for u, v in zip(x, y)]
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# embeddings
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x = [self.patch_embedding(u.unsqueeze(0)) for u in x]
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grid_sizes = torch.stack(
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[torch.tensor(u.shape[2:], dtype=torch.long) for u in x])
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x = [u.flatten(2).transpose(1, 2) for u in x]
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seq_lens = torch.tensor([u.size(1) for u in x], dtype=torch.long)
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assert seq_lens.max() <= seq_len
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x = torch.cat([
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torch.cat([u, u.new_zeros(1, seq_len - u.size(1), u.size(2))], dim=1)
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for u in x
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])
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# time embeddings
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with amp.autocast(dtype=torch.float32):
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e = self.time_embedding(
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sinusoidal_embedding_1d(self.freq_dim, t).float())
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e0 = self.time_projection(e).unflatten(1, (6, self.dim))
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assert e.dtype == torch.float32 and e0.dtype == torch.float32
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# context
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context_lens = None
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context = self.text_embedding(
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torch.stack([
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torch.cat([u, u.new_zeros(self.text_len - u.size(0), u.size(1))])
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for u in context
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]))
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if self.model_type != 'vace' and clip_fea is not None:
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context_clip = self.img_emb(clip_fea) # bs x 257 x dim
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context = torch.concat([context_clip, context], dim=1)
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# arguments
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kwargs = dict(
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e=e0,
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seq_lens=seq_lens,
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grid_sizes=grid_sizes,
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freqs=self.freqs,
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context=context,
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context_lens=context_lens)
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# Context Parallel
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x = torch.chunk(
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x, get_sequence_parallel_world_size(),
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dim=1)[get_sequence_parallel_rank()]
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if self.model_type == 'vace':
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hints = self.forward_vace(x, vace_context, seq_len, kwargs)
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kwargs['hints'] = hints
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kwargs['context_scale'] = vace_context_scale
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for block in self.blocks:
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x = block(x, **kwargs)
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# head
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x = self.head(x, e)
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# Context Parallel
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x = get_sp_group().all_gather(x, dim=1)
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# unpatchify
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x = self.unpatchify(x, grid_sizes)
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return [u.float() for u in x]
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def usp_attn_forward(self,
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x,
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seq_lens,
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grid_sizes,
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freqs,
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dtype=torch.bfloat16):
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b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim
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half_dtypes = (torch.float16, torch.bfloat16)
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def half(x):
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return x if x.dtype in half_dtypes else x.to(dtype)
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# query, key, value function
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def qkv_fn(x):
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q = self.norm_q(self.q(x)).view(b, s, n, d)
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k = self.norm_k(self.k(x)).view(b, s, n, d)
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v = self.v(x).view(b, s, n, d)
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return q, k, v
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q, k, v = qkv_fn(x)
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q = rope_apply(q, grid_sizes, freqs)
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k = rope_apply(k, grid_sizes, freqs)
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# TODO: We should use unpaded q,k,v for attention.
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# k_lens = seq_lens // get_sequence_parallel_world_size()
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# if k_lens is not None:
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# q = torch.cat([u[:l] for u, l in zip(q, k_lens)]).unsqueeze(0)
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# k = torch.cat([u[:l] for u, l in zip(k, k_lens)]).unsqueeze(0)
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# v = torch.cat([u[:l] for u, l in zip(v, k_lens)]).unsqueeze(0)
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x = xFuserLongContextAttention()(
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None,
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query=half(q),
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key=half(k),
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value=half(v),
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window_size=self.window_size)
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# TODO: padding after attention.
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# x = torch.cat([x, x.new_zeros(b, s - x.size(1), n, d)], dim=1)
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# output
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x = x.flatten(2)
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x = self.o(x)
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return x
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