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https://github.com/Wan-Video/Wan2.1.git
synced 2025-11-04 06:15:17 +00:00
Optimized Vace RAM usage
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DeepBeepMeep
parent
f5bb9d5972
commit
8d998d645f
@ -447,6 +447,21 @@ class WanAttentionBlock(nn.Module):
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grid_sizes(Tensor): Shape [B, 3], the second dimension contains (F, H, W)
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freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2]
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"""
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hint = None
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if self.block_id is not None and hints is not None:
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kwargs = {
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"seq_lens" : seq_lens,
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"grid_sizes" : grid_sizes,
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"freqs" :freqs,
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"context" : context,
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"context_lens" : context_lens,
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"e" : e,
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}
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if self.block_id == 0:
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hint = self.vace(hints, x, **kwargs)
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else:
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hint = self.vace(hints, None, **kwargs)
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e = (self.modulation + e).chunk(6, dim=1)
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# self-attention
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@ -485,13 +500,16 @@ class WanAttentionBlock(nn.Module):
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x.addcmul_(y, e[5])
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if self.block_id is not None and hints != None:
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if hint is not None:
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if context_scale == 1:
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x.add_(hints[self.block_id])
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x.add_(hint)
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else:
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x.add_(hints[self.block_id], alpha =context_scale)
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return x
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x.add_(hint, alpha= context_scale)
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return x
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class VaceWanAttentionBlock(WanAttentionBlock):
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def __init__(
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@ -516,18 +534,29 @@ class VaceWanAttentionBlock(WanAttentionBlock):
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nn.init.zeros_(self.after_proj.weight)
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nn.init.zeros_(self.after_proj.bias)
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def forward(self, c, x, **kwargs):
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def forward(self, hints, x, **kwargs):
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# behold dbm magic !
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c = hints[0]
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hints[0] = None
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if self.block_id == 0:
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c = self.before_proj(c) + x
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all_c = []
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else:
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all_c = c
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c = all_c.pop(-1)
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c = super().forward(c, **kwargs)
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c_skip = self.after_proj(c)
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all_c += [c_skip, c]
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return all_c
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hints[0] = c
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return c_skip
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# def forward(self, c, x, **kwargs):
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# # behold dbm magic !
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# if self.block_id == 0:
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# c = self.before_proj(c) + x
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# all_c = []
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# else:
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# all_c = c
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# c = all_c.pop(-1)
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# c = super().forward(c, **kwargs)
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# c_skip = self.after_proj(c)
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# all_c += [c_skip, c]
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# return all_c
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class Head(nn.Module):
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@ -764,35 +793,37 @@ class WanModel(ModelMixin, ConfigMixin):
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print(f"Tea Cache, best threshold found:{best_threshold:0.2f} with gain x{len(timesteps)/(target_nb_steps - best_signed_diff):0.2f} for a target of x{speed_factor}")
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return best_threshold
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def 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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context,
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e,
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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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if (len(c) == 1 and seq_len == c[0].size(1)):
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c = c[0]
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else:
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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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for block in self.vace_blocks:
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c = block(c, context= context, e= e, **new_kwargs)
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hints = c[:-1]
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# def 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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# context,
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# e,
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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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# if (len(c) == 1 and seq_len == c[0].size(1)):
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# c = c[0]
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# else:
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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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return hints
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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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# for block in self.vace_blocks:
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# c = block(c, context= context, e= e, **new_kwargs)
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# hints = c[:-1]
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# return hints
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def forward(
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self,
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@ -904,6 +935,34 @@ class WanModel(ModelMixin, ConfigMixin):
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x_list = [x]
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context_list = [context]
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del x
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# arguments
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kwargs = dict(
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seq_lens=seq_lens,
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grid_sizes=grid_sizes,
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freqs=freqs,
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context_lens=context_lens,
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)
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if vace_context == None:
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hints_list = [None ] *len(x_list)
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else:
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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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if (len(c) == 1 and seq_len == c[0].size(1)):
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c = c[0]
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else:
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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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kwargs['context_scale'] = vace_context_scale
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hints_list = [ [c] if i==0 else [c.clone()] for i in range(len(x_list)) ]
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del c
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should_calc = True
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if self.enable_teacache:
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if is_uncond:
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@ -935,23 +994,6 @@ class WanModel(ModelMixin, ConfigMixin):
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if joint_pass or not is_uncond:
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self.previous_residual_cond = None
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ori_hidden_states = x_list[0].clone()
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# arguments
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kwargs = dict(
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seq_lens=seq_lens,
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grid_sizes=grid_sizes,
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freqs=freqs,
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context_lens=context_lens)
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if vace_context == None:
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hints_list = [None ] *len(x_list)
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else:
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hints_list = []
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for x, context in zip(x_list, context_list) :
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hints_list.append( self.forward_vace(x, vace_context, seq_len, context= context, e= e0, kwargs= kwargs))
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del x, context
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kwargs['context_scale'] = vace_context_scale
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for block_idx, block in enumerate(self.blocks):
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offload.shared_state["layer"] = block_idx
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@ -143,6 +143,8 @@ class WanT2V:
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seq_len=32760,
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keep_last=True)
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self.adapt_vace_model()
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def vace_encode_frames(self, frames, ref_images, masks=None, tile_size = 0):
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if ref_images is None:
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ref_images = [None] * len(frames)
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@ -505,3 +507,14 @@ class WanT2V:
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dist.barrier()
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return videos[0] if self.rank == 0 else None
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def adapt_vace_model(self):
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model = self.model
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modules_dict= { k: m for k, m in model.named_modules()}
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for num in range(15):
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module = modules_dict[f"vace_blocks.{num}"]
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target = modules_dict[f"blocks.{2*num}"]
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setattr(target, "vace", module )
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delattr(model, "vace_blocks")
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23
wgp.py
23
wgp.py
@ -910,14 +910,6 @@ def get_queue_table(queue):
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if len(queue) == 1:
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return data
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# def td(l, content, width =None):
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# if width !=None:
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# l.append("<TD WIDTH="+ str(width) + "px>" + content + "</TD>")
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# else:
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# l.append("<TD>" + content + "</TD>")
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# data.append("<STYLE> .TB, .TB th, .TB td {border: 1px solid #CCCCCC};></STYLE><TABLE CLASS=TB><TR BGCOLOR=#F2F2F2><TD Style='Bold'>Qty</TD><TD>Prompt</TD><TD>Steps</TD><TD></TD><TD><TD></TD><TD></TD><TD></TD></TR>")
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for i, item in enumerate(queue):
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if i==0:
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continue
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@ -937,22 +929,7 @@ def get_queue_table(queue):
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start_img_md = f'<img src="{start_img_uri}" alt="Start" style="max-width:{thumbnail_size}; max-height:{thumbnail_size}; display: block; margin: auto; object-fit: contain;" />'
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if end_img_uri:
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end_img_md = f'<img src="{end_img_uri}" alt="End" style="max-width:{thumbnail_size}; max-height:{thumbnail_size}; display: block; margin: auto; object-fit: contain;" />'
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# if i % 2 == 1:
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# data.append("<TR>")
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# else:
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# data.append("<TR BGCOLOR=#F2F2F2>")
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# td(data,str(item.get('repeats', "1")) )
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# td(data, prompt_cell, "100%")
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# td(data, num_steps, "100%")
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# td(data, start_img_md)
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# td(data, end_img_md)
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# td(data, "↑")
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# td(data, "↓")
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# td(data, "✖")
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# data.append("</TR>")
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# data.append("</TABLE>")
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# return ''.join(data)
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data.append([item.get('repeats', "1"),
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prompt_cell,
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