782 lines
27 KiB
Python
782 lines
27 KiB
Python
import math
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import torch
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import torch.nn.functional as F
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from torch import nn, einsum
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from einops import rearrange, repeat
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from typing import Optional, Any
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from .diffusionmodules.util import checkpoint, AlphaBlender, timestep_embedding
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from .sub_quadratic_attention import efficient_dot_product_attention
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from comfy import model_management
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if model_management.xformers_enabled():
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import xformers
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import xformers.ops
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from comfy.cli_args import args
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import comfy.ops
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ops = comfy.ops.disable_weight_init
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# CrossAttn precision handling
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if args.dont_upcast_attention:
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print("disabling upcasting of attention")
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_ATTN_PRECISION = "fp16"
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else:
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_ATTN_PRECISION = "fp32"
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def exists(val):
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return val is not None
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def uniq(arr):
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return{el: True for el in arr}.keys()
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def default(val, d):
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if exists(val):
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return val
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return d
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def max_neg_value(t):
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return -torch.finfo(t.dtype).max
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def init_(tensor):
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dim = tensor.shape[-1]
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std = 1 / math.sqrt(dim)
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tensor.uniform_(-std, std)
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return tensor
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# feedforward
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class GEGLU(nn.Module):
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def __init__(self, dim_in, dim_out, dtype=None, device=None, operations=ops):
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super().__init__()
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self.proj = operations.Linear(dim_in, dim_out * 2, dtype=dtype, device=device)
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def forward(self, x):
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x, gate = self.proj(x).chunk(2, dim=-1)
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return x * F.gelu(gate)
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class FeedForward(nn.Module):
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def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0., dtype=None, device=None, operations=ops):
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super().__init__()
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inner_dim = int(dim * mult)
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dim_out = default(dim_out, dim)
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project_in = nn.Sequential(
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operations.Linear(dim, inner_dim, dtype=dtype, device=device),
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nn.GELU()
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) if not glu else GEGLU(dim, inner_dim, dtype=dtype, device=device, operations=operations)
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self.net = nn.Sequential(
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project_in,
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nn.Dropout(dropout),
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operations.Linear(inner_dim, dim_out, dtype=dtype, device=device)
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)
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def forward(self, x):
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return self.net(x)
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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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b, _, dim_head = q.shape
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dim_head //= heads
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scale = dim_head ** -0.5
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h = heads
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q, k, v = map(
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lambda t: t.unsqueeze(3)
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.reshape(b, -1, heads, dim_head)
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.permute(0, 2, 1, 3)
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.reshape(b * heads, -1, dim_head)
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.contiguous(),
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(q, k, v),
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)
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# force cast to fp32 to avoid overflowing
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if _ATTN_PRECISION =="fp32":
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sim = einsum('b i d, b j d -> b i j', q.float(), k.float()) * 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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del q, k
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if exists(mask):
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if mask.dtype == torch.bool:
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mask = rearrange(mask, 'b ... -> b (...)') #TODO: check if this bool part matches pytorch attention
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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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else:
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sim += mask
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# attention, what we cannot get enough of
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sim = sim.softmax(dim=-1)
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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 = (
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out.unsqueeze(0)
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.reshape(b, heads, -1, dim_head)
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.permute(0, 2, 1, 3)
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.reshape(b, -1, heads * dim_head)
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)
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return out
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def attention_sub_quad(query, key, value, heads, mask=None):
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b, _, dim_head = query.shape
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dim_head //= heads
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scale = dim_head ** -0.5
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query = query.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 1, 3).reshape(b * heads, -1, dim_head)
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value = value.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 1, 3).reshape(b * heads, -1, dim_head)
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key = key.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 3, 1).reshape(b * heads, dim_head, -1)
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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.shape
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qk_matmul_size_bytes = batch_x_heads * bytes_per_token * q_tokens * k_tokens
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mem_free_total, mem_free_torch = model_management.get_free_memory(query.device, True)
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kv_chunk_size_min = None
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kv_chunk_size = None
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query_chunk_size = None
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for x in [4096, 2048, 1024, 512, 256]:
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count = mem_free_total / (batch_x_heads * bytes_per_token * x * 4.0)
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if count >= k_tokens:
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kv_chunk_size = k_tokens
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query_chunk_size = x
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break
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if query_chunk_size is None:
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query_chunk_size = 512
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hidden_states = efficient_dot_product_attention(
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query,
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key,
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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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mask=mask,
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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, heads)).transpose(1,2).flatten(start_dim=2)
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return hidden_states
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def attention_split(q, k, v, heads, mask=None):
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b, _, dim_head = q.shape
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dim_head //= heads
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scale = dim_head ** -0.5
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h = heads
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q, k, v = map(
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lambda t: t.unsqueeze(3)
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.reshape(b, -1, heads, dim_head)
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.permute(0, 2, 1, 3)
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.reshape(b * heads, -1, dim_head)
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.contiguous(),
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(q, k, v),
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)
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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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if _ATTN_PRECISION =="fp32":
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element_size = 4
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else:
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element_size = q.element_size()
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gb = 1024 ** 3
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tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * element_size
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modifier = 3
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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()) * 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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if mask is not None:
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if len(mask.shape) == 2:
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s1 += mask[i:end]
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else:
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s1 += mask[:, i:end]
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s2 = s1.softmax(dim=-1).to(v.dtype)
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del s1
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first_op_done = True
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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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r1 = (
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r1.unsqueeze(0)
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.reshape(b, heads, -1, dim_head)
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.permute(0, 2, 1, 3)
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.reshape(b, -1, heads * dim_head)
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)
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return r1
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BROKEN_XFORMERS = False
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try:
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x_vers = xformers.__version__
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#I think 0.0.23 is also broken (q with bs bigger than 65535 gives CUDA error)
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BROKEN_XFORMERS = x_vers.startswith("0.0.21") or x_vers.startswith("0.0.22") or x_vers.startswith("0.0.23")
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except:
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pass
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def attention_xformers(q, k, v, heads, mask=None):
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b, _, dim_head = q.shape
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dim_head //= heads
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if BROKEN_XFORMERS:
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if b * heads > 65535:
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return attention_pytorch(q, k, v, heads, mask)
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q, k, v = map(
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lambda t: t.unsqueeze(3)
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.reshape(b, -1, heads, dim_head)
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.permute(0, 2, 1, 3)
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.reshape(b * heads, -1, dim_head)
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.contiguous(),
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(q, k, v),
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)
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if mask is not None:
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pad = 8 - q.shape[1] % 8
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mask_out = torch.empty([q.shape[0], q.shape[1], q.shape[1] + pad], dtype=q.dtype, device=q.device)
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mask_out[:, :, :mask.shape[-1]] = mask
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mask = mask_out[:, :, :mask.shape[-1]]
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out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=mask)
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out = (
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out.unsqueeze(0)
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.reshape(b, heads, -1, dim_head)
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.permute(0, 2, 1, 3)
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.reshape(b, -1, heads * dim_head)
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)
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return out
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def attention_pytorch(q, k, v, heads, mask=None):
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b, _, dim_head = q.shape
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dim_head //= heads
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q, k, v = map(
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lambda t: t.view(b, -1, heads, dim_head).transpose(1, 2),
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(q, k, v),
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)
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out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False)
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out = (
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out.transpose(1, 2).reshape(b, -1, heads * dim_head)
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)
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return out
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optimized_attention = attention_basic
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if model_management.xformers_enabled():
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print("Using xformers cross attention")
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optimized_attention = attention_xformers
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elif model_management.pytorch_attention_enabled():
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print("Using pytorch cross attention")
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optimized_attention = attention_pytorch
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else:
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if args.use_split_cross_attention:
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print("Using split optimization for cross attention")
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optimized_attention = attention_split
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else:
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print("Using sub quadratic optimization for cross attention, if you have memory or speed issues try using: --use-split-cross-attention")
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optimized_attention = attention_sub_quad
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optimized_attention_masked = optimized_attention
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def optimized_attention_for_device(device, mask=False, small_input=False):
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if small_input:
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if model_management.pytorch_attention_enabled():
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return attention_pytorch #TODO: need to confirm but this is probably slightly faster for small inputs in all cases
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else:
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return attention_basic
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if device == torch.device("cpu"):
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return attention_sub_quad
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if mask:
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return optimized_attention_masked
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return optimized_attention
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class CrossAttention(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=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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self.heads = heads
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self.dim_head = dim_head
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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(operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout))
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def forward(self, x, context=None, value=None, mask=None):
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q = self.to_q(x)
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context = default(context, x)
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k = self.to_k(context)
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if value is not None:
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v = self.to_v(value)
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del value
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else:
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v = self.to_v(context)
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if mask is None:
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out = optimized_attention(q, k, v, self.heads)
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else:
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out = optimized_attention_masked(q, k, v, self.heads, mask)
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return self.to_out(out)
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class BasicTransformerBlock(nn.Module):
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def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True, ff_in=False, inner_dim=None,
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disable_self_attn=False, disable_temporal_crossattention=False, switch_temporal_ca_to_sa=False, dtype=None, device=None, operations=ops):
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super().__init__()
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self.ff_in = ff_in or inner_dim is not None
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if inner_dim is None:
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inner_dim = dim
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self.is_res = inner_dim == dim
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if self.ff_in:
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self.norm_in = operations.LayerNorm(dim, dtype=dtype, device=device)
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self.ff_in = FeedForward(dim, dim_out=inner_dim, dropout=dropout, glu=gated_ff, dtype=dtype, device=device, operations=operations)
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self.disable_self_attn = disable_self_attn
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self.attn1 = CrossAttention(query_dim=inner_dim, heads=n_heads, dim_head=d_head, dropout=dropout,
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context_dim=context_dim if self.disable_self_attn else None, dtype=dtype, device=device, operations=operations) # is a self-attention if not self.disable_self_attn
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self.ff = FeedForward(inner_dim, dim_out=dim, dropout=dropout, glu=gated_ff, dtype=dtype, device=device, operations=operations)
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if disable_temporal_crossattention:
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if switch_temporal_ca_to_sa:
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raise ValueError
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else:
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self.attn2 = None
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else:
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context_dim_attn2 = None
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if not switch_temporal_ca_to_sa:
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context_dim_attn2 = context_dim
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self.attn2 = CrossAttention(query_dim=inner_dim, context_dim=context_dim_attn2,
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heads=n_heads, dim_head=d_head, dropout=dropout, dtype=dtype, device=device, operations=operations) # is self-attn if context is none
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self.norm2 = operations.LayerNorm(inner_dim, dtype=dtype, device=device)
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self.norm1 = operations.LayerNorm(inner_dim, dtype=dtype, device=device)
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self.norm3 = operations.LayerNorm(inner_dim, dtype=dtype, device=device)
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self.checkpoint = checkpoint
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self.n_heads = n_heads
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self.d_head = d_head
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self.switch_temporal_ca_to_sa = switch_temporal_ca_to_sa
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def forward(self, x, context=None, transformer_options={}):
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return checkpoint(self._forward, (x, context, transformer_options), self.parameters(), self.checkpoint)
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def _forward(self, x, context=None, transformer_options={}):
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extra_options = {}
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block = transformer_options.get("block", None)
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block_index = transformer_options.get("block_index", 0)
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transformer_patches = {}
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transformer_patches_replace = {}
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for k in transformer_options:
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if k == "patches":
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transformer_patches = transformer_options[k]
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elif k == "patches_replace":
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transformer_patches_replace = transformer_options[k]
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else:
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extra_options[k] = transformer_options[k]
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extra_options["n_heads"] = self.n_heads
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extra_options["dim_head"] = self.d_head
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if self.ff_in:
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x_skip = x
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x = self.ff_in(self.norm_in(x))
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if self.is_res:
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x += x_skip
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n = self.norm1(x)
|
|
if self.disable_self_attn:
|
|
context_attn1 = context
|
|
else:
|
|
context_attn1 = None
|
|
value_attn1 = None
|
|
|
|
if "attn1_patch" in transformer_patches:
|
|
patch = transformer_patches["attn1_patch"]
|
|
if context_attn1 is None:
|
|
context_attn1 = n
|
|
value_attn1 = context_attn1
|
|
for p in patch:
|
|
n, context_attn1, value_attn1 = p(n, context_attn1, value_attn1, extra_options)
|
|
|
|
if block is not None:
|
|
transformer_block = (block[0], block[1], block_index)
|
|
else:
|
|
transformer_block = None
|
|
attn1_replace_patch = transformer_patches_replace.get("attn1", {})
|
|
block_attn1 = transformer_block
|
|
if block_attn1 not in attn1_replace_patch:
|
|
block_attn1 = block
|
|
|
|
if block_attn1 in attn1_replace_patch:
|
|
if context_attn1 is None:
|
|
context_attn1 = n
|
|
value_attn1 = n
|
|
n = self.attn1.to_q(n)
|
|
context_attn1 = self.attn1.to_k(context_attn1)
|
|
value_attn1 = self.attn1.to_v(value_attn1)
|
|
n = attn1_replace_patch[block_attn1](n, context_attn1, value_attn1, extra_options)
|
|
n = self.attn1.to_out(n)
|
|
else:
|
|
n = self.attn1(n, context=context_attn1, value=value_attn1)
|
|
|
|
if "attn1_output_patch" in transformer_patches:
|
|
patch = transformer_patches["attn1_output_patch"]
|
|
for p in patch:
|
|
n = p(n, extra_options)
|
|
|
|
x += n
|
|
if "middle_patch" in transformer_patches:
|
|
patch = transformer_patches["middle_patch"]
|
|
for p in patch:
|
|
x = p(x, extra_options)
|
|
|
|
if self.attn2 is not None:
|
|
n = self.norm2(x)
|
|
if self.switch_temporal_ca_to_sa:
|
|
context_attn2 = n
|
|
else:
|
|
context_attn2 = context
|
|
value_attn2 = None
|
|
if "attn2_patch" in transformer_patches:
|
|
patch = transformer_patches["attn2_patch"]
|
|
value_attn2 = context_attn2
|
|
for p in patch:
|
|
n, context_attn2, value_attn2 = p(n, context_attn2, value_attn2, extra_options)
|
|
|
|
attn2_replace_patch = transformer_patches_replace.get("attn2", {})
|
|
block_attn2 = transformer_block
|
|
if block_attn2 not in attn2_replace_patch:
|
|
block_attn2 = block
|
|
|
|
if block_attn2 in attn2_replace_patch:
|
|
if value_attn2 is None:
|
|
value_attn2 = context_attn2
|
|
n = self.attn2.to_q(n)
|
|
context_attn2 = self.attn2.to_k(context_attn2)
|
|
value_attn2 = self.attn2.to_v(value_attn2)
|
|
n = attn2_replace_patch[block_attn2](n, context_attn2, value_attn2, extra_options)
|
|
n = self.attn2.to_out(n)
|
|
else:
|
|
n = self.attn2(n, context=context_attn2, value=value_attn2)
|
|
|
|
if "attn2_output_patch" in transformer_patches:
|
|
patch = transformer_patches["attn2_output_patch"]
|
|
for p in patch:
|
|
n = p(n, extra_options)
|
|
|
|
x += n
|
|
if self.is_res:
|
|
x_skip = x
|
|
x = self.ff(self.norm3(x))
|
|
if self.is_res:
|
|
x += x_skip
|
|
|
|
return x
|
|
|
|
|
|
class SpatialTransformer(nn.Module):
|
|
"""
|
|
Transformer block for image-like data.
|
|
First, project the input (aka embedding)
|
|
and reshape to b, t, d.
|
|
Then apply standard transformer action.
|
|
Finally, reshape to image
|
|
NEW: use_linear for more efficiency instead of the 1x1 convs
|
|
"""
|
|
def __init__(self, in_channels, n_heads, d_head,
|
|
depth=1, dropout=0., context_dim=None,
|
|
disable_self_attn=False, use_linear=False,
|
|
use_checkpoint=True, dtype=None, device=None, operations=ops):
|
|
super().__init__()
|
|
if exists(context_dim) and not isinstance(context_dim, list):
|
|
context_dim = [context_dim] * depth
|
|
self.in_channels = in_channels
|
|
inner_dim = n_heads * d_head
|
|
self.norm = operations.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True, dtype=dtype, device=device)
|
|
if not use_linear:
|
|
self.proj_in = operations.Conv2d(in_channels,
|
|
inner_dim,
|
|
kernel_size=1,
|
|
stride=1,
|
|
padding=0, dtype=dtype, device=device)
|
|
else:
|
|
self.proj_in = operations.Linear(in_channels, inner_dim, dtype=dtype, device=device)
|
|
|
|
self.transformer_blocks = nn.ModuleList(
|
|
[BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim[d],
|
|
disable_self_attn=disable_self_attn, checkpoint=use_checkpoint, dtype=dtype, device=device, operations=operations)
|
|
for d in range(depth)]
|
|
)
|
|
if not use_linear:
|
|
self.proj_out = operations.Conv2d(inner_dim,in_channels,
|
|
kernel_size=1,
|
|
stride=1,
|
|
padding=0, dtype=dtype, device=device)
|
|
else:
|
|
self.proj_out = operations.Linear(in_channels, inner_dim, dtype=dtype, device=device)
|
|
self.use_linear = use_linear
|
|
|
|
def forward(self, x, context=None, transformer_options={}):
|
|
# note: if no context is given, cross-attention defaults to self-attention
|
|
if not isinstance(context, list):
|
|
context = [context] * len(self.transformer_blocks)
|
|
b, c, h, w = x.shape
|
|
x_in = x
|
|
x = self.norm(x)
|
|
if not self.use_linear:
|
|
x = self.proj_in(x)
|
|
x = rearrange(x, 'b c h w -> b (h w) c').contiguous()
|
|
if self.use_linear:
|
|
x = self.proj_in(x)
|
|
for i, block in enumerate(self.transformer_blocks):
|
|
transformer_options["block_index"] = i
|
|
x = block(x, context=context[i], transformer_options=transformer_options)
|
|
if self.use_linear:
|
|
x = self.proj_out(x)
|
|
x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w).contiguous()
|
|
if not self.use_linear:
|
|
x = self.proj_out(x)
|
|
return x + x_in
|
|
|
|
|
|
class SpatialVideoTransformer(SpatialTransformer):
|
|
def __init__(
|
|
self,
|
|
in_channels,
|
|
n_heads,
|
|
d_head,
|
|
depth=1,
|
|
dropout=0.0,
|
|
use_linear=False,
|
|
context_dim=None,
|
|
use_spatial_context=False,
|
|
timesteps=None,
|
|
merge_strategy: str = "fixed",
|
|
merge_factor: float = 0.5,
|
|
time_context_dim=None,
|
|
ff_in=False,
|
|
checkpoint=False,
|
|
time_depth=1,
|
|
disable_self_attn=False,
|
|
disable_temporal_crossattention=False,
|
|
max_time_embed_period: int = 10000,
|
|
dtype=None, device=None, operations=ops
|
|
):
|
|
super().__init__(
|
|
in_channels,
|
|
n_heads,
|
|
d_head,
|
|
depth=depth,
|
|
dropout=dropout,
|
|
use_checkpoint=checkpoint,
|
|
context_dim=context_dim,
|
|
use_linear=use_linear,
|
|
disable_self_attn=disable_self_attn,
|
|
dtype=dtype, device=device, operations=operations
|
|
)
|
|
self.time_depth = time_depth
|
|
self.depth = depth
|
|
self.max_time_embed_period = max_time_embed_period
|
|
|
|
time_mix_d_head = d_head
|
|
n_time_mix_heads = n_heads
|
|
|
|
time_mix_inner_dim = int(time_mix_d_head * n_time_mix_heads)
|
|
|
|
inner_dim = n_heads * d_head
|
|
if use_spatial_context:
|
|
time_context_dim = context_dim
|
|
|
|
self.time_stack = nn.ModuleList(
|
|
[
|
|
BasicTransformerBlock(
|
|
inner_dim,
|
|
n_time_mix_heads,
|
|
time_mix_d_head,
|
|
dropout=dropout,
|
|
context_dim=time_context_dim,
|
|
# timesteps=timesteps,
|
|
checkpoint=checkpoint,
|
|
ff_in=ff_in,
|
|
inner_dim=time_mix_inner_dim,
|
|
disable_self_attn=disable_self_attn,
|
|
disable_temporal_crossattention=disable_temporal_crossattention,
|
|
dtype=dtype, device=device, operations=operations
|
|
)
|
|
for _ in range(self.depth)
|
|
]
|
|
)
|
|
|
|
assert len(self.time_stack) == len(self.transformer_blocks)
|
|
|
|
self.use_spatial_context = use_spatial_context
|
|
self.in_channels = in_channels
|
|
|
|
time_embed_dim = self.in_channels * 4
|
|
self.time_pos_embed = nn.Sequential(
|
|
operations.Linear(self.in_channels, time_embed_dim, dtype=dtype, device=device),
|
|
nn.SiLU(),
|
|
operations.Linear(time_embed_dim, self.in_channels, dtype=dtype, device=device),
|
|
)
|
|
|
|
self.time_mixer = AlphaBlender(
|
|
alpha=merge_factor, merge_strategy=merge_strategy
|
|
)
|
|
|
|
def forward(
|
|
self,
|
|
x: torch.Tensor,
|
|
context: Optional[torch.Tensor] = None,
|
|
time_context: Optional[torch.Tensor] = None,
|
|
timesteps: Optional[int] = None,
|
|
image_only_indicator: Optional[torch.Tensor] = None,
|
|
transformer_options={}
|
|
) -> torch.Tensor:
|
|
_, _, h, w = x.shape
|
|
x_in = x
|
|
spatial_context = None
|
|
if exists(context):
|
|
spatial_context = context
|
|
|
|
if self.use_spatial_context:
|
|
assert (
|
|
context.ndim == 3
|
|
), f"n dims of spatial context should be 3 but are {context.ndim}"
|
|
|
|
if time_context is None:
|
|
time_context = context
|
|
time_context_first_timestep = time_context[::timesteps]
|
|
time_context = repeat(
|
|
time_context_first_timestep, "b ... -> (b n) ...", n=h * w
|
|
)
|
|
elif time_context is not None and not self.use_spatial_context:
|
|
time_context = repeat(time_context, "b ... -> (b n) ...", n=h * w)
|
|
if time_context.ndim == 2:
|
|
time_context = rearrange(time_context, "b c -> b 1 c")
|
|
|
|
x = self.norm(x)
|
|
if not self.use_linear:
|
|
x = self.proj_in(x)
|
|
x = rearrange(x, "b c h w -> b (h w) c")
|
|
if self.use_linear:
|
|
x = self.proj_in(x)
|
|
|
|
num_frames = torch.arange(timesteps, device=x.device)
|
|
num_frames = repeat(num_frames, "t -> b t", b=x.shape[0] // timesteps)
|
|
num_frames = rearrange(num_frames, "b t -> (b t)")
|
|
t_emb = timestep_embedding(num_frames, self.in_channels, repeat_only=False, max_period=self.max_time_embed_period).to(x.dtype)
|
|
emb = self.time_pos_embed(t_emb)
|
|
emb = emb[:, None, :]
|
|
|
|
for it_, (block, mix_block) in enumerate(
|
|
zip(self.transformer_blocks, self.time_stack)
|
|
):
|
|
transformer_options["block_index"] = it_
|
|
x = block(
|
|
x,
|
|
context=spatial_context,
|
|
transformer_options=transformer_options,
|
|
)
|
|
|
|
x_mix = x
|
|
x_mix = x_mix + emb
|
|
|
|
B, S, C = x_mix.shape
|
|
x_mix = rearrange(x_mix, "(b t) s c -> (b s) t c", t=timesteps)
|
|
x_mix = mix_block(x_mix, context=time_context) #TODO: transformer_options
|
|
x_mix = rearrange(
|
|
x_mix, "(b s) t c -> (b t) s c", s=S, b=B // timesteps, c=C, t=timesteps
|
|
)
|
|
|
|
x = self.time_mixer(x_spatial=x, x_temporal=x_mix, image_only_indicator=image_only_indicator)
|
|
|
|
if self.use_linear:
|
|
x = self.proj_out(x)
|
|
x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w)
|
|
if not self.use_linear:
|
|
x = self.proj_out(x)
|
|
out = x + x_in
|
|
return out
|
|
|
|
|