mirror of
https://github.com/comfyanonymous/ComfyUI.git
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Refactor the attention functions.
There's no reason for the whole CrossAttention object to be repeated when only the operation in the middle changes.
This commit is contained in:
comfyanonymous
parent
8cc75c64ff
commit
1a4bd9e9a6
@ -94,253 +94,220 @@ def zero_module(module):
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def Normalize(in_channels, dtype=None, device=None):
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return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True, dtype=dtype, device=device)
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def attention_basic(q, k, v, heads, mask=None):
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h = heads
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scale = (q.shape[-1] // heads) ** -0.5
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q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
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class SpatialSelfAttention(nn.Module):
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def __init__(self, in_channels):
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super().__init__()
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self.in_channels = in_channels
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# force cast to fp32 to avoid overflowing
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if _ATTN_PRECISION =="fp32":
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with torch.autocast(enabled=False, device_type = 'cuda'):
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q, k = q.float(), k.float()
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sim = einsum('b i d, b j d -> b i j', q, k) * scale
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else:
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sim = einsum('b i d, b j d -> b i j', q, k) * scale
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self.norm = Normalize(in_channels)
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self.q = torch.nn.Conv2d(in_channels,
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in_channels,
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kernel_size=1,
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stride=1,
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padding=0)
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self.k = torch.nn.Conv2d(in_channels,
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in_channels,
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kernel_size=1,
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stride=1,
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padding=0)
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self.v = torch.nn.Conv2d(in_channels,
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in_channels,
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kernel_size=1,
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stride=1,
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padding=0)
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self.proj_out = torch.nn.Conv2d(in_channels,
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in_channels,
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kernel_size=1,
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stride=1,
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padding=0)
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del q, k
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def forward(self, x):
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h_ = x
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h_ = self.norm(h_)
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q = self.q(h_)
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k = self.k(h_)
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v = self.v(h_)
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if exists(mask):
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mask = rearrange(mask, 'b ... -> b (...)')
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max_neg_value = -torch.finfo(sim.dtype).max
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mask = repeat(mask, 'b j -> (b h) () j', h=h)
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sim.masked_fill_(~mask, max_neg_value)
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# compute attention
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b,c,h,w = q.shape
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q = rearrange(q, 'b c h w -> b (h w) c')
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k = rearrange(k, 'b c h w -> b c (h w)')
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w_ = torch.einsum('bij,bjk->bik', q, k)
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# attention, what we cannot get enough of
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sim = sim.softmax(dim=-1)
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w_ = w_ * (int(c)**(-0.5))
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w_ = torch.nn.functional.softmax(w_, dim=2)
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# attend to values
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v = rearrange(v, 'b c h w -> b c (h w)')
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w_ = rearrange(w_, 'b i j -> b j i')
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h_ = torch.einsum('bij,bjk->bik', v, w_)
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h_ = rearrange(h_, 'b c (h w) -> b c h w', h=h)
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h_ = self.proj_out(h_)
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return x+h_
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out = einsum('b i j, b j d -> b i d', sim.to(v.dtype), v)
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out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
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return out
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class CrossAttentionBirchSan(nn.Module):
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def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None, operations=comfy.ops):
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super().__init__()
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inner_dim = dim_head * heads
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context_dim = default(context_dim, query_dim)
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def attention_sub_quad(query, key, value, heads, mask=None):
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scale = (query.shape[-1] // heads) ** -0.5
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query = query.unflatten(-1, (heads, -1)).transpose(1,2).flatten(end_dim=1)
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key_t = key.transpose(1,2).unflatten(1, (heads, -1)).flatten(end_dim=1)
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del key
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value = value.unflatten(-1, (heads, -1)).transpose(1,2).flatten(end_dim=1)
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self.scale = dim_head ** -0.5
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self.heads = heads
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dtype = query.dtype
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upcast_attention = _ATTN_PRECISION =="fp32" and query.dtype != torch.float32
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if upcast_attention:
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bytes_per_token = torch.finfo(torch.float32).bits//8
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else:
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bytes_per_token = torch.finfo(query.dtype).bits//8
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batch_x_heads, q_tokens, _ = query.shape
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_, _, k_tokens = key_t.shape
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qk_matmul_size_bytes = batch_x_heads * bytes_per_token * q_tokens * k_tokens
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self.to_q = operations.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
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self.to_k = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
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self.to_v = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
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mem_free_total, mem_free_torch = model_management.get_free_memory(query.device, True)
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self.to_out = nn.Sequential(
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operations.Linear(inner_dim, query_dim, dtype=dtype, device=device),
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nn.Dropout(dropout)
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)
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chunk_threshold_bytes = mem_free_torch * 0.5 #Using only this seems to work better on AMD
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def forward(self, x, context=None, value=None, mask=None):
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h = self.heads
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kv_chunk_size_min = None
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query = self.to_q(x)
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context = default(context, x)
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key = self.to_k(context)
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if value is not None:
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value = self.to_v(value)
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else:
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value = self.to_v(context)
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#not sure at all about the math here
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#TODO: tweak this
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if mem_free_total > 8192 * 1024 * 1024 * 1.3:
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query_chunk_size_x = 1024 * 4
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elif mem_free_total > 4096 * 1024 * 1024 * 1.3:
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query_chunk_size_x = 1024 * 2
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else:
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query_chunk_size_x = 1024
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kv_chunk_size_min_x = None
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kv_chunk_size_x = (int((chunk_threshold_bytes // (batch_x_heads * bytes_per_token * query_chunk_size_x)) * 2.0) // 1024) * 1024
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if kv_chunk_size_x < 1024:
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kv_chunk_size_x = None
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del context, x
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if chunk_threshold_bytes is not None and qk_matmul_size_bytes <= chunk_threshold_bytes:
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# the big matmul fits into our memory limit; do everything in 1 chunk,
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# i.e. send it down the unchunked fast-path
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query_chunk_size = q_tokens
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kv_chunk_size = k_tokens
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else:
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query_chunk_size = query_chunk_size_x
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kv_chunk_size = kv_chunk_size_x
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kv_chunk_size_min = kv_chunk_size_min_x
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query = query.unflatten(-1, (self.heads, -1)).transpose(1,2).flatten(end_dim=1)
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key_t = key.transpose(1,2).unflatten(1, (self.heads, -1)).flatten(end_dim=1)
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del key
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value = value.unflatten(-1, (self.heads, -1)).transpose(1,2).flatten(end_dim=1)
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hidden_states = efficient_dot_product_attention(
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query,
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key_t,
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value,
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query_chunk_size=query_chunk_size,
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kv_chunk_size=kv_chunk_size,
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kv_chunk_size_min=kv_chunk_size_min,
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use_checkpoint=False,
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upcast_attention=upcast_attention,
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)
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dtype = query.dtype
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upcast_attention = _ATTN_PRECISION =="fp32" and query.dtype != torch.float32
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if upcast_attention:
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bytes_per_token = torch.finfo(torch.float32).bits//8
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else:
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bytes_per_token = torch.finfo(query.dtype).bits//8
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batch_x_heads, q_tokens, _ = query.shape
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_, _, k_tokens = key_t.shape
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qk_matmul_size_bytes = batch_x_heads * bytes_per_token * q_tokens * k_tokens
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hidden_states = hidden_states.to(dtype)
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mem_free_total, mem_free_torch = model_management.get_free_memory(query.device, True)
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hidden_states = hidden_states.unflatten(0, (-1, heads)).transpose(1,2).flatten(start_dim=2)
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return hidden_states
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chunk_threshold_bytes = mem_free_torch * 0.5 #Using only this seems to work better on AMD
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def attention_split(q, k, v, heads, mask=None):
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scale = (q.shape[-1] // heads) ** -0.5
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h = heads
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q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
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kv_chunk_size_min = None
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r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
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#not sure at all about the math here
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#TODO: tweak this
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if mem_free_total > 8192 * 1024 * 1024 * 1.3:
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query_chunk_size_x = 1024 * 4
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elif mem_free_total > 4096 * 1024 * 1024 * 1.3:
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query_chunk_size_x = 1024 * 2
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else:
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query_chunk_size_x = 1024
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kv_chunk_size_min_x = None
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kv_chunk_size_x = (int((chunk_threshold_bytes // (batch_x_heads * bytes_per_token * query_chunk_size_x)) * 2.0) // 1024) * 1024
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if kv_chunk_size_x < 1024:
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kv_chunk_size_x = None
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mem_free_total = model_management.get_free_memory(q.device)
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if chunk_threshold_bytes is not None and qk_matmul_size_bytes <= chunk_threshold_bytes:
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# the big matmul fits into our memory limit; do everything in 1 chunk,
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# i.e. send it down the unchunked fast-path
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query_chunk_size = q_tokens
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kv_chunk_size = k_tokens
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else:
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query_chunk_size = query_chunk_size_x
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kv_chunk_size = kv_chunk_size_x
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kv_chunk_size_min = kv_chunk_size_min_x
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hidden_states = efficient_dot_product_attention(
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query,
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key_t,
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value,
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query_chunk_size=query_chunk_size,
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kv_chunk_size=kv_chunk_size,
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kv_chunk_size_min=kv_chunk_size_min,
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use_checkpoint=self.training,
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upcast_attention=upcast_attention,
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)
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hidden_states = hidden_states.to(dtype)
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hidden_states = hidden_states.unflatten(0, (-1, self.heads)).transpose(1,2).flatten(start_dim=2)
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out_proj, dropout = self.to_out
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hidden_states = out_proj(hidden_states)
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hidden_states = dropout(hidden_states)
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return hidden_states
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gb = 1024 ** 3
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tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size()
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modifier = 3 if q.element_size() == 2 else 2.5
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mem_required = tensor_size * modifier
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steps = 1
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class CrossAttentionDoggettx(nn.Module):
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def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None, operations=comfy.ops):
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super().__init__()
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inner_dim = dim_head * heads
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context_dim = default(context_dim, query_dim)
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if mem_required > mem_free_total:
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steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2)))
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# print(f"Expected tensor size:{tensor_size/gb:0.1f}GB, cuda free:{mem_free_cuda/gb:0.1f}GB "
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# f"torch free:{mem_free_torch/gb:0.1f} total:{mem_free_total/gb:0.1f} steps:{steps}")
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self.scale = dim_head ** -0.5
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self.heads = heads
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if steps > 64:
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max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64
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raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). '
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f'Need: {mem_required/64/gb:0.1f}GB free, Have:{mem_free_total/gb:0.1f}GB free')
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self.to_q = operations.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
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self.to_k = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
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self.to_v = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
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self.to_out = nn.Sequential(
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operations.Linear(inner_dim, query_dim, dtype=dtype, device=device),
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nn.Dropout(dropout)
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)
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def forward(self, x, context=None, value=None, mask=None):
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h = self.heads
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q_in = self.to_q(x)
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context = default(context, x)
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k_in = self.to_k(context)
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if value is not None:
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v_in = self.to_v(value)
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del value
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else:
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v_in = self.to_v(context)
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del context, x
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q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
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del q_in, k_in, v_in
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r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
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mem_free_total = model_management.get_free_memory(q.device)
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gb = 1024 ** 3
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tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * q.element_size()
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modifier = 3 if q.element_size() == 2 else 2.5
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mem_required = tensor_size * modifier
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steps = 1
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if mem_required > mem_free_total:
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steps = 2**(math.ceil(math.log(mem_required / mem_free_total, 2)))
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# print(f"Expected tensor size:{tensor_size/gb:0.1f}GB, cuda free:{mem_free_cuda/gb:0.1f}GB "
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# f"torch free:{mem_free_torch/gb:0.1f} total:{mem_free_total/gb:0.1f} steps:{steps}")
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if steps > 64:
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max_res = math.floor(math.sqrt(math.sqrt(mem_free_total / 2.5)) / 8) * 64
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raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). '
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f'Need: {mem_required/64/gb:0.1f}GB free, Have:{mem_free_total/gb:0.1f}GB free')
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# print("steps", steps, mem_required, mem_free_total, modifier, q.element_size(), tensor_size)
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first_op_done = False
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cleared_cache = False
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while True:
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try:
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slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
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for i in range(0, q.shape[1], slice_size):
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end = i + slice_size
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if _ATTN_PRECISION =="fp32":
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with torch.autocast(enabled=False, device_type = 'cuda'):
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s1 = einsum('b i d, b j d -> b i j', q[:, i:end].float(), k.float()) * self.scale
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else:
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s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k) * self.scale
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first_op_done = True
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s2 = s1.softmax(dim=-1).to(v.dtype)
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del s1
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r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v)
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del s2
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break
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except model_management.OOM_EXCEPTION as e:
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if first_op_done == False:
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model_management.soft_empty_cache(True)
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if cleared_cache == False:
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cleared_cache = True
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print("out of memory error, emptying cache and trying again")
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continue
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steps *= 2
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if steps > 64:
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raise e
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print("out of memory error, increasing steps and trying again", steps)
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# print("steps", steps, mem_required, mem_free_total, modifier, q.element_size(), tensor_size)
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first_op_done = False
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cleared_cache = False
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while True:
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try:
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slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
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for i in range(0, q.shape[1], slice_size):
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end = i + slice_size
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if _ATTN_PRECISION =="fp32":
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with torch.autocast(enabled=False, device_type = 'cuda'):
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s1 = einsum('b i d, b j d -> b i j', q[:, i:end].float(), k.float()) * scale
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else:
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s1 = einsum('b i d, b j d -> b i j', q[:, i:end], k) * scale
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first_op_done = True
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s2 = s1.softmax(dim=-1).to(v.dtype)
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del s1
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r1[:, i:end] = einsum('b i j, b j d -> b i d', s2, v)
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del s2
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break
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except model_management.OOM_EXCEPTION as e:
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if first_op_done == False:
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model_management.soft_empty_cache(True)
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if cleared_cache == False:
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cleared_cache = True
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print("out of memory error, emptying cache and trying again")
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continue
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steps *= 2
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if steps > 64:
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raise e
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print("out of memory error, increasing steps and trying again", steps)
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else:
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raise e
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del q, k, v
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del q, k, v
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r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
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del r1
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r2 = rearrange(r1, '(b h) n d -> b n (h d)', h=h)
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del r1
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return r2
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return self.to_out(r2)
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def attention_xformers(q, k, v, heads, mask=None):
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b, _, _ = q.shape
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q, k, v = map(
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lambda t: t.unsqueeze(3)
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.reshape(b, t.shape[1], heads, -1)
|
||||
.permute(0, 2, 1, 3)
|
||||
.reshape(b * heads, t.shape[1], -1)
|
||||
.contiguous(),
|
||||
(q, k, v),
|
||||
)
|
||||
|
||||
# actually compute the attention, what we cannot get enough of
|
||||
out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None)
|
||||
|
||||
if exists(mask):
|
||||
raise NotImplementedError
|
||||
out = (
|
||||
out.unsqueeze(0)
|
||||
.reshape(b, heads, out.shape[1], -1)
|
||||
.permute(0, 2, 1, 3)
|
||||
.reshape(b, out.shape[1], -1)
|
||||
)
|
||||
return out
|
||||
|
||||
def attention_pytorch(q, k, v, heads, mask=None):
|
||||
b, _, dim_head = q.shape
|
||||
dim_head //= heads
|
||||
q, k, v = map(
|
||||
lambda t: t.view(b, -1, heads, dim_head).transpose(1, 2),
|
||||
(q, k, v),
|
||||
)
|
||||
|
||||
out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False)
|
||||
|
||||
if exists(mask):
|
||||
raise NotImplementedError
|
||||
out = (
|
||||
out.transpose(1, 2).reshape(b, -1, heads * dim_head)
|
||||
)
|
||||
return out
|
||||
|
||||
optimized_attention = attention_basic
|
||||
|
||||
if model_management.xformers_enabled():
|
||||
print("Using xformers cross attention")
|
||||
optimized_attention = attention_xformers
|
||||
elif model_management.pytorch_attention_enabled():
|
||||
print("Using pytorch cross attention")
|
||||
optimized_attention = attention_pytorch
|
||||
else:
|
||||
if args.use_split_cross_attention:
|
||||
print("Using split optimization for cross attention")
|
||||
optimized_attention = attention_split
|
||||
else:
|
||||
print("Using sub quadratic optimization for cross attention, if you have memory or speed issues try using: --use-split-cross-attention")
|
||||
optimized_attention = attention_sub_quad
|
||||
|
||||
class CrossAttention(nn.Module):
|
||||
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None, operations=comfy.ops):
|
||||
@ -348,62 +315,6 @@ class CrossAttention(nn.Module):
|
||||
inner_dim = dim_head * heads
|
||||
context_dim = default(context_dim, query_dim)
|
||||
|
||||
self.scale = dim_head ** -0.5
|
||||
self.heads = heads
|
||||
|
||||
self.to_q = operations.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
|
||||
self.to_k = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
|
||||
self.to_v = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
|
||||
|
||||
self.to_out = nn.Sequential(
|
||||
operations.Linear(inner_dim, query_dim, dtype=dtype, device=device),
|
||||
nn.Dropout(dropout)
|
||||
)
|
||||
|
||||
def forward(self, x, context=None, value=None, mask=None):
|
||||
h = self.heads
|
||||
|
||||
q = self.to_q(x)
|
||||
context = default(context, x)
|
||||
k = self.to_k(context)
|
||||
if value is not None:
|
||||
v = self.to_v(value)
|
||||
del value
|
||||
else:
|
||||
v = self.to_v(context)
|
||||
|
||||
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
|
||||
|
||||
# force cast to fp32 to avoid overflowing
|
||||
if _ATTN_PRECISION =="fp32":
|
||||
with torch.autocast(enabled=False, device_type = 'cuda'):
|
||||
q, k = q.float(), k.float()
|
||||
sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
|
||||
else:
|
||||
sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
|
||||
|
||||
del q, k
|
||||
|
||||
if exists(mask):
|
||||
mask = rearrange(mask, 'b ... -> b (...)')
|
||||
max_neg_value = -torch.finfo(sim.dtype).max
|
||||
mask = repeat(mask, 'b j -> (b h) () j', h=h)
|
||||
sim.masked_fill_(~mask, max_neg_value)
|
||||
|
||||
# attention, what we cannot get enough of
|
||||
sim = sim.softmax(dim=-1)
|
||||
|
||||
out = einsum('b i j, b j d -> b i d', sim, v)
|
||||
out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
|
||||
return self.to_out(out)
|
||||
|
||||
class MemoryEfficientCrossAttention(nn.Module):
|
||||
# https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223
|
||||
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0, dtype=None, device=None, operations=comfy.ops):
|
||||
super().__init__()
|
||||
inner_dim = dim_head * heads
|
||||
context_dim = default(context_dim, query_dim)
|
||||
|
||||
self.heads = heads
|
||||
self.dim_head = dim_head
|
||||
|
||||
@ -412,7 +323,6 @@ class MemoryEfficientCrossAttention(nn.Module):
|
||||
self.to_v = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
|
||||
|
||||
self.to_out = nn.Sequential(operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout))
|
||||
self.attention_op: Optional[Any] = None
|
||||
|
||||
def forward(self, x, context=None, value=None, mask=None):
|
||||
q = self.to_q(x)
|
||||
@ -424,85 +334,9 @@ class MemoryEfficientCrossAttention(nn.Module):
|
||||
else:
|
||||
v = self.to_v(context)
|
||||
|
||||
b, _, _ = q.shape
|
||||
q, k, v = map(
|
||||
lambda t: t.unsqueeze(3)
|
||||
.reshape(b, t.shape[1], self.heads, self.dim_head)
|
||||
.permute(0, 2, 1, 3)
|
||||
.reshape(b * self.heads, t.shape[1], self.dim_head)
|
||||
.contiguous(),
|
||||
(q, k, v),
|
||||
)
|
||||
|
||||
# actually compute the attention, what we cannot get enough of
|
||||
out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=self.attention_op)
|
||||
|
||||
if exists(mask):
|
||||
raise NotImplementedError
|
||||
out = (
|
||||
out.unsqueeze(0)
|
||||
.reshape(b, self.heads, out.shape[1], self.dim_head)
|
||||
.permute(0, 2, 1, 3)
|
||||
.reshape(b, out.shape[1], self.heads * self.dim_head)
|
||||
)
|
||||
out = optimized_attention(q, k, v, self.heads, mask)
|
||||
return self.to_out(out)
|
||||
|
||||
class CrossAttentionPytorch(nn.Module):
|
||||
def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None, operations=comfy.ops):
|
||||
super().__init__()
|
||||
inner_dim = dim_head * heads
|
||||
context_dim = default(context_dim, query_dim)
|
||||
|
||||
self.heads = heads
|
||||
self.dim_head = dim_head
|
||||
|
||||
self.to_q = operations.Linear(query_dim, inner_dim, bias=False, dtype=dtype, device=device)
|
||||
self.to_k = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
|
||||
self.to_v = operations.Linear(context_dim, inner_dim, bias=False, dtype=dtype, device=device)
|
||||
|
||||
self.to_out = nn.Sequential(operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout))
|
||||
self.attention_op: Optional[Any] = None
|
||||
|
||||
def forward(self, x, context=None, value=None, mask=None):
|
||||
q = self.to_q(x)
|
||||
context = default(context, x)
|
||||
k = self.to_k(context)
|
||||
if value is not None:
|
||||
v = self.to_v(value)
|
||||
del value
|
||||
else:
|
||||
v = self.to_v(context)
|
||||
|
||||
b, _, _ = q.shape
|
||||
q, k, v = map(
|
||||
lambda t: t.view(b, -1, self.heads, self.dim_head).transpose(1, 2),
|
||||
(q, k, v),
|
||||
)
|
||||
|
||||
out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False)
|
||||
|
||||
if exists(mask):
|
||||
raise NotImplementedError
|
||||
out = (
|
||||
out.transpose(1, 2).reshape(b, -1, self.heads * self.dim_head)
|
||||
)
|
||||
|
||||
return self.to_out(out)
|
||||
|
||||
if model_management.xformers_enabled():
|
||||
print("Using xformers cross attention")
|
||||
CrossAttention = MemoryEfficientCrossAttention
|
||||
elif model_management.pytorch_attention_enabled():
|
||||
print("Using pytorch cross attention")
|
||||
CrossAttention = CrossAttentionPytorch
|
||||
else:
|
||||
if args.use_split_cross_attention:
|
||||
print("Using split optimization for cross attention")
|
||||
CrossAttention = CrossAttentionDoggettx
|
||||
else:
|
||||
print("Using sub quadratic optimization for cross attention, if you have memory or speed issues try using: --use-split-cross-attention")
|
||||
CrossAttention = CrossAttentionBirchSan
|
||||
|
||||
|
||||
class BasicTransformerBlock(nn.Module):
|
||||
def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True,
|
||||
|
||||
@ -6,7 +6,6 @@ import numpy as np
|
||||
from einops import rearrange
|
||||
from typing import Optional, Any
|
||||
|
||||
from ..attention import MemoryEfficientCrossAttention
|
||||
from comfy import model_management
|
||||
import comfy.ops
|
||||
|
||||
@ -352,15 +351,6 @@ class MemoryEfficientAttnBlockPytorch(nn.Module):
|
||||
out = self.proj_out(out)
|
||||
return x+out
|
||||
|
||||
class MemoryEfficientCrossAttentionWrapper(MemoryEfficientCrossAttention):
|
||||
def forward(self, x, context=None, mask=None):
|
||||
b, c, h, w = x.shape
|
||||
x = rearrange(x, 'b c h w -> b (h w) c')
|
||||
out = super().forward(x, context=context, mask=mask)
|
||||
out = rearrange(out, 'b (h w) c -> b c h w', h=h, w=w, c=c)
|
||||
return x + out
|
||||
|
||||
|
||||
def make_attn(in_channels, attn_type="vanilla", attn_kwargs=None):
|
||||
assert attn_type in ["vanilla", "vanilla-xformers", "memory-efficient-cross-attn", "linear", "none"], f'attn_type {attn_type} unknown'
|
||||
if model_management.xformers_enabled_vae() and attn_type == "vanilla":
|
||||
@ -376,9 +366,6 @@ def make_attn(in_channels, attn_type="vanilla", attn_kwargs=None):
|
||||
return MemoryEfficientAttnBlock(in_channels)
|
||||
elif attn_type == "vanilla-pytorch":
|
||||
return MemoryEfficientAttnBlockPytorch(in_channels)
|
||||
elif type == "memory-efficient-cross-attn":
|
||||
attn_kwargs["query_dim"] = in_channels
|
||||
return MemoryEfficientCrossAttentionWrapper(**attn_kwargs)
|
||||
elif attn_type == "none":
|
||||
return nn.Identity(in_channels)
|
||||
else:
|
||||
|
||||
Loading…
Reference in New Issue
Block a user