mirror of
https://github.com/modelscope/DiffSynth-Studio.git
synced 2026-03-19 06:39:43 +00:00
626 lines
22 KiB
Python
626 lines
22 KiB
Python
import math
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from typing import List, Optional, Tuple
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from torch.nn.utils.rnn import pad_sequence
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from torch.nn import RMSNorm
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from ..core.attention import attention_forward
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from ..core.device.npu_compatible_device import IS_NPU_AVAILABLE
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from ..core.gradient import gradient_checkpoint_forward
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ADALN_EMBED_DIM = 256
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SEQ_MULTI_OF = 32
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class TimestepEmbedder(nn.Module):
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def __init__(self, out_size, mid_size=None, frequency_embedding_size=256):
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super().__init__()
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if mid_size is None:
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mid_size = out_size
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self.mlp = nn.Sequential(
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nn.Linear(
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frequency_embedding_size,
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mid_size,
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bias=True,
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),
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nn.SiLU(),
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nn.Linear(
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mid_size,
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out_size,
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bias=True,
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),
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)
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self.frequency_embedding_size = frequency_embedding_size
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@staticmethod
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def timestep_embedding(t, dim, max_period=10000):
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with torch.amp.autocast("cuda", enabled=False):
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half = dim // 2
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freqs = torch.exp(
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-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32, device=t.device) / half
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)
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args = t[:, None].float() * freqs[None]
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embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
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if dim % 2:
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embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
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return embedding
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def forward(self, t):
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t_freq = self.timestep_embedding(t, self.frequency_embedding_size)
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t_emb = self.mlp(t_freq.to(torch.bfloat16))
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return t_emb
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class FeedForward(nn.Module):
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def __init__(self, dim: int, hidden_dim: int):
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super().__init__()
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self.w1 = nn.Linear(dim, hidden_dim, bias=False)
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self.w2 = nn.Linear(hidden_dim, dim, bias=False)
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self.w3 = nn.Linear(dim, hidden_dim, bias=False)
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def _forward_silu_gating(self, x1, x3):
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return F.silu(x1) * x3
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def forward(self, x):
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return self.w2(self._forward_silu_gating(self.w1(x), self.w3(x)))
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class Attention(torch.nn.Module):
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def __init__(self, q_dim, num_heads, head_dim, kv_dim=None, bias_q=False, bias_kv=False, bias_out=False):
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super().__init__()
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dim_inner = head_dim * num_heads
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kv_dim = kv_dim if kv_dim is not None else q_dim
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self.num_heads = num_heads
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self.head_dim = head_dim
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self.to_q = torch.nn.Linear(q_dim, dim_inner, bias=bias_q)
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self.to_k = torch.nn.Linear(kv_dim, dim_inner, bias=bias_kv)
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self.to_v = torch.nn.Linear(kv_dim, dim_inner, bias=bias_kv)
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self.to_out = torch.nn.ModuleList([torch.nn.Linear(dim_inner, q_dim, bias=bias_out)])
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self.norm_q = RMSNorm(head_dim, eps=1e-5)
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self.norm_k = RMSNorm(head_dim, eps=1e-5)
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def forward(self, hidden_states, freqs_cis):
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query = self.to_q(hidden_states)
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key = self.to_k(hidden_states)
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value = self.to_v(hidden_states)
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query = query.unflatten(-1, (self.num_heads, -1))
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key = key.unflatten(-1, (self.num_heads, -1))
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value = value.unflatten(-1, (self.num_heads, -1))
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# Apply Norms
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if self.norm_q is not None:
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query = self.norm_q(query)
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if self.norm_k is not None:
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key = self.norm_k(key)
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# Apply RoPE
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def apply_rotary_emb(x_in: torch.Tensor, freqs_cis: torch.Tensor) -> torch.Tensor:
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with torch.amp.autocast("cuda", enabled=False):
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x = torch.view_as_complex(x_in.float().reshape(*x_in.shape[:-1], -1, 2))
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freqs_cis = freqs_cis.unsqueeze(2)
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x_out = torch.view_as_real(x * freqs_cis).flatten(3)
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return x_out.type_as(x_in) # todo
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if freqs_cis is not None:
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query = apply_rotary_emb(query, freqs_cis)
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key = apply_rotary_emb(key, freqs_cis)
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# Cast to correct dtype
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dtype = query.dtype
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query, key = query.to(dtype), key.to(dtype)
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# Compute joint attention
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hidden_states = attention_forward(
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query,
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key,
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value,
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q_pattern="b s n d", k_pattern="b s n d", v_pattern="b s n d", out_pattern="b s n d",
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)
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# Reshape back
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hidden_states = hidden_states.flatten(2, 3)
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hidden_states = hidden_states.to(dtype)
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output = self.to_out[0](hidden_states)
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if len(self.to_out) > 1: # dropout
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output = self.to_out[1](output)
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return output
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class ZImageTransformerBlock(nn.Module):
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def __init__(
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self,
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layer_id: int,
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dim: int,
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n_heads: int,
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n_kv_heads: int,
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norm_eps: float,
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qk_norm: bool,
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modulation=True,
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):
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super().__init__()
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self.dim = dim
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self.head_dim = dim // n_heads
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# Refactored to use diffusers Attention with custom processor
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# Original Z-Image params: dim, n_heads, n_kv_heads, qk_norm
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self.attention = Attention(
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q_dim=dim,
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num_heads=n_heads,
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head_dim=dim // n_heads,
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)
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self.feed_forward = FeedForward(dim=dim, hidden_dim=int(dim / 3 * 8))
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self.layer_id = layer_id
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self.attention_norm1 = RMSNorm(dim, eps=norm_eps)
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self.ffn_norm1 = RMSNorm(dim, eps=norm_eps)
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self.attention_norm2 = RMSNorm(dim, eps=norm_eps)
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self.ffn_norm2 = RMSNorm(dim, eps=norm_eps)
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self.modulation = modulation
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if modulation:
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self.adaLN_modulation = nn.Sequential(
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nn.Linear(min(dim, ADALN_EMBED_DIM), 4 * dim, bias=True),
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)
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def forward(
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self,
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x: torch.Tensor,
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attn_mask: torch.Tensor,
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freqs_cis: torch.Tensor,
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adaln_input: Optional[torch.Tensor] = None,
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):
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if self.modulation:
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assert adaln_input is not None
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scale_msa, gate_msa, scale_mlp, gate_mlp = self.adaLN_modulation(adaln_input).unsqueeze(1).chunk(4, dim=2)
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gate_msa, gate_mlp = gate_msa.tanh(), gate_mlp.tanh()
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scale_msa, scale_mlp = 1.0 + scale_msa, 1.0 + scale_mlp
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# Attention block
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attn_out = self.attention(
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self.attention_norm1(x) * scale_msa,
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freqs_cis=freqs_cis,
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)
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x = x + gate_msa * self.attention_norm2(attn_out)
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# FFN block
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x = x + gate_mlp * self.ffn_norm2(
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self.feed_forward(
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self.ffn_norm1(x) * scale_mlp,
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)
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)
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else:
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# Attention block
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attn_out = self.attention(
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self.attention_norm1(x),
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freqs_cis=freqs_cis,
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)
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x = x + self.attention_norm2(attn_out)
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# FFN block
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x = x + self.ffn_norm2(
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self.feed_forward(
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self.ffn_norm1(x),
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)
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)
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return x
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class FinalLayer(nn.Module):
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def __init__(self, hidden_size, out_channels):
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super().__init__()
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self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
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self.linear = nn.Linear(hidden_size, out_channels, bias=True)
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self.adaLN_modulation = nn.Sequential(
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nn.SiLU(),
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nn.Linear(min(hidden_size, ADALN_EMBED_DIM), hidden_size, bias=True),
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)
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def forward(self, x, c):
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scale = 1.0 + self.adaLN_modulation(c)
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x = self.norm_final(x) * scale.unsqueeze(1)
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x = self.linear(x)
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return x
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class RopeEmbedder:
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def __init__(
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self,
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theta: float = 256.0,
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axes_dims: List[int] = (16, 56, 56),
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axes_lens: List[int] = (64, 128, 128),
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):
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self.theta = theta
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self.axes_dims = axes_dims
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self.axes_lens = axes_lens
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assert len(axes_dims) == len(axes_lens), "axes_dims and axes_lens must have the same length"
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self.freqs_cis = None
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@staticmethod
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def precompute_freqs_cis(dim: List[int], end: List[int], theta: float = 256.0):
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with torch.device("cpu"):
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freqs_cis = []
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for i, (d, e) in enumerate(zip(dim, end)):
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freqs = 1.0 / (theta ** (torch.arange(0, d, 2, dtype=torch.float64, device="cpu") / d))
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timestep = torch.arange(e, device=freqs.device, dtype=torch.float64)
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freqs = torch.outer(timestep, freqs).float()
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freqs_cis_i = torch.polar(torch.ones_like(freqs), freqs).to(torch.complex64) # complex64
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freqs_cis.append(freqs_cis_i)
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return freqs_cis
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def __call__(self, ids: torch.Tensor):
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assert ids.ndim == 2
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assert ids.shape[-1] == len(self.axes_dims)
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device = ids.device
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if self.freqs_cis is None:
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self.freqs_cis = self.precompute_freqs_cis(self.axes_dims, self.axes_lens, theta=self.theta)
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self.freqs_cis = [freqs_cis.to(device) for freqs_cis in self.freqs_cis]
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result = []
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for i in range(len(self.axes_dims)):
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index = ids[:, i]
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if IS_NPU_AVAILABLE:
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result.append(torch.index_select(self.freqs_cis[i], 0, index))
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else:
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result.append(self.freqs_cis[i][index])
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return torch.cat(result, dim=-1)
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class ZImageDiT(nn.Module):
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_supports_gradient_checkpointing = True
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_no_split_modules = ["ZImageTransformerBlock"]
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def __init__(
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self,
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all_patch_size=(2,),
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all_f_patch_size=(1,),
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in_channels=16,
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dim=3840,
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n_layers=30,
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n_refiner_layers=2,
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n_heads=30,
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n_kv_heads=30,
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norm_eps=1e-5,
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qk_norm=True,
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cap_feat_dim=2560,
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rope_theta=256.0,
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t_scale=1000.0,
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axes_dims=[32, 48, 48],
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axes_lens=[1024, 512, 512],
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) -> None:
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super().__init__()
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self.in_channels = in_channels
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self.out_channels = in_channels
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self.all_patch_size = all_patch_size
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self.all_f_patch_size = all_f_patch_size
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self.dim = dim
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self.n_heads = n_heads
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self.rope_theta = rope_theta
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self.t_scale = t_scale
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self.gradient_checkpointing = False
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assert len(all_patch_size) == len(all_f_patch_size)
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all_x_embedder = {}
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all_final_layer = {}
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for patch_idx, (patch_size, f_patch_size) in enumerate(zip(all_patch_size, all_f_patch_size)):
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x_embedder = nn.Linear(f_patch_size * patch_size * patch_size * in_channels, dim, bias=True)
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all_x_embedder[f"{patch_size}-{f_patch_size}"] = x_embedder
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final_layer = FinalLayer(dim, patch_size * patch_size * f_patch_size * self.out_channels)
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all_final_layer[f"{patch_size}-{f_patch_size}"] = final_layer
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self.all_x_embedder = nn.ModuleDict(all_x_embedder)
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self.all_final_layer = nn.ModuleDict(all_final_layer)
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self.noise_refiner = nn.ModuleList(
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[
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ZImageTransformerBlock(
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1000 + layer_id,
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dim,
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n_heads,
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n_kv_heads,
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norm_eps,
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qk_norm,
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modulation=True,
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)
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for layer_id in range(n_refiner_layers)
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]
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)
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self.context_refiner = nn.ModuleList(
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[
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ZImageTransformerBlock(
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layer_id,
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dim,
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n_heads,
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n_kv_heads,
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norm_eps,
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qk_norm,
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modulation=False,
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)
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for layer_id in range(n_refiner_layers)
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]
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)
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self.t_embedder = TimestepEmbedder(min(dim, ADALN_EMBED_DIM), mid_size=1024)
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self.cap_embedder = nn.Sequential(
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RMSNorm(cap_feat_dim, eps=norm_eps),
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nn.Linear(cap_feat_dim, dim, bias=True),
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)
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self.x_pad_token = nn.Parameter(torch.empty((1, dim)))
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self.cap_pad_token = nn.Parameter(torch.empty((1, dim)))
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self.layers = nn.ModuleList(
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[
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ZImageTransformerBlock(layer_id, dim, n_heads, n_kv_heads, norm_eps, qk_norm)
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for layer_id in range(n_layers)
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]
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)
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head_dim = dim // n_heads
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assert head_dim == sum(axes_dims)
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self.axes_dims = axes_dims
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self.axes_lens = axes_lens
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self.rope_embedder = RopeEmbedder(theta=rope_theta, axes_dims=axes_dims, axes_lens=axes_lens)
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def unpatchify(self, x: List[torch.Tensor], size: List[Tuple], patch_size, f_patch_size) -> List[torch.Tensor]:
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pH = pW = patch_size
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pF = f_patch_size
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bsz = len(x)
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assert len(size) == bsz
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for i in range(bsz):
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F, H, W = size[i]
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ori_len = (F // pF) * (H // pH) * (W // pW)
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# "f h w pf ph pw c -> c (f pf) (h ph) (w pw)"
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x[i] = (
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x[i][:ori_len]
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.view(F // pF, H // pH, W // pW, pF, pH, pW, self.out_channels)
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.permute(6, 0, 3, 1, 4, 2, 5)
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.reshape(self.out_channels, F, H, W)
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)
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return x
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@staticmethod
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def create_coordinate_grid(size, start=None, device=None):
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if start is None:
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start = (0 for _ in size)
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axes = [torch.arange(x0, x0 + span, dtype=torch.int32, device=device) for x0, span in zip(start, size)]
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grids = torch.meshgrid(axes, indexing="ij")
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return torch.stack(grids, dim=-1)
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def patchify_and_embed(
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self,
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all_image: List[torch.Tensor],
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all_cap_feats: List[torch.Tensor],
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patch_size: int,
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f_patch_size: int,
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):
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pH = pW = patch_size
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pF = f_patch_size
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device = all_image[0].device
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all_image_out = []
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all_image_size = []
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all_image_pos_ids = []
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all_image_pad_mask = []
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all_cap_pos_ids = []
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all_cap_pad_mask = []
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all_cap_feats_out = []
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for i, (image, cap_feat) in enumerate(zip(all_image, all_cap_feats)):
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### Process Caption
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cap_ori_len = len(cap_feat)
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cap_padding_len = (-cap_ori_len) % SEQ_MULTI_OF
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# padded position ids
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cap_padded_pos_ids = self.create_coordinate_grid(
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size=(cap_ori_len + cap_padding_len, 1, 1),
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start=(1, 0, 0),
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device=device,
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).flatten(0, 2)
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all_cap_pos_ids.append(cap_padded_pos_ids)
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# pad mask
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all_cap_pad_mask.append(
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torch.cat(
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[
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torch.zeros((cap_ori_len,), dtype=torch.bool, device=device),
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torch.ones((cap_padding_len,), dtype=torch.bool, device=device),
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],
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dim=0,
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)
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)
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# padded feature
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cap_padded_feat = torch.cat(
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[cap_feat, cap_feat[-1:].repeat(cap_padding_len, 1)],
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dim=0,
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)
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all_cap_feats_out.append(cap_padded_feat)
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### Process Image
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C, F, H, W = image.size()
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all_image_size.append((F, H, W))
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F_tokens, H_tokens, W_tokens = F // pF, H // pH, W // pW
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image = image.view(C, F_tokens, pF, H_tokens, pH, W_tokens, pW)
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# "c f pf h ph w pw -> (f h w) (pf ph pw c)"
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image = image.permute(1, 3, 5, 2, 4, 6, 0).reshape(F_tokens * H_tokens * W_tokens, pF * pH * pW * C)
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image_ori_len = len(image)
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image_padding_len = (-image_ori_len) % SEQ_MULTI_OF
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image_ori_pos_ids = self.create_coordinate_grid(
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|
size=(F_tokens, H_tokens, W_tokens),
|
|
start=(cap_ori_len + cap_padding_len + 1, 0, 0),
|
|
device=device,
|
|
).flatten(0, 2)
|
|
image_padding_pos_ids = (
|
|
self.create_coordinate_grid(
|
|
size=(1, 1, 1),
|
|
start=(0, 0, 0),
|
|
device=device,
|
|
)
|
|
.flatten(0, 2)
|
|
.repeat(image_padding_len, 1)
|
|
)
|
|
image_padded_pos_ids = torch.cat([image_ori_pos_ids, image_padding_pos_ids], dim=0)
|
|
all_image_pos_ids.append(image_padded_pos_ids)
|
|
# pad mask
|
|
all_image_pad_mask.append(
|
|
torch.cat(
|
|
[
|
|
torch.zeros((image_ori_len,), dtype=torch.bool, device=device),
|
|
torch.ones((image_padding_len,), dtype=torch.bool, device=device),
|
|
],
|
|
dim=0,
|
|
)
|
|
)
|
|
# padded feature
|
|
image_padded_feat = torch.cat([image, image[-1:].repeat(image_padding_len, 1)], dim=0)
|
|
all_image_out.append(image_padded_feat)
|
|
|
|
return (
|
|
all_image_out,
|
|
all_cap_feats_out,
|
|
all_image_size,
|
|
all_image_pos_ids,
|
|
all_cap_pos_ids,
|
|
all_image_pad_mask,
|
|
all_cap_pad_mask,
|
|
)
|
|
|
|
def forward(
|
|
self,
|
|
x: List[torch.Tensor],
|
|
t,
|
|
cap_feats: List[torch.Tensor],
|
|
patch_size=2,
|
|
f_patch_size=1,
|
|
use_gradient_checkpointing=False,
|
|
use_gradient_checkpointing_offload=False,
|
|
):
|
|
assert patch_size in self.all_patch_size
|
|
assert f_patch_size in self.all_f_patch_size
|
|
|
|
bsz = len(x)
|
|
device = x[0].device
|
|
t = t * self.t_scale
|
|
t = self.t_embedder(t)
|
|
|
|
adaln_input = t
|
|
|
|
(
|
|
x,
|
|
cap_feats,
|
|
x_size,
|
|
x_pos_ids,
|
|
cap_pos_ids,
|
|
x_inner_pad_mask,
|
|
cap_inner_pad_mask,
|
|
) = self.patchify_and_embed(x, cap_feats, patch_size, f_patch_size)
|
|
|
|
# x embed & refine
|
|
x_item_seqlens = [len(_) for _ in x]
|
|
assert all(_ % SEQ_MULTI_OF == 0 for _ in x_item_seqlens)
|
|
x_max_item_seqlen = max(x_item_seqlens)
|
|
|
|
x = torch.cat(x, dim=0)
|
|
x = self.all_x_embedder[f"{patch_size}-{f_patch_size}"](x)
|
|
x[torch.cat(x_inner_pad_mask)] = self.x_pad_token.to(dtype=x.dtype, device=x.device)
|
|
x = list(x.split(x_item_seqlens, dim=0))
|
|
x_freqs_cis = list(self.rope_embedder(torch.cat(x_pos_ids, dim=0)).split(x_item_seqlens, dim=0))
|
|
|
|
x = pad_sequence(x, batch_first=True, padding_value=0.0)
|
|
x_freqs_cis = pad_sequence(x_freqs_cis, batch_first=True, padding_value=0.0)
|
|
x_attn_mask = torch.zeros((bsz, x_max_item_seqlen), dtype=torch.bool, device=device)
|
|
for i, seq_len in enumerate(x_item_seqlens):
|
|
x_attn_mask[i, :seq_len] = 1
|
|
|
|
for layer in self.noise_refiner:
|
|
x = gradient_checkpoint_forward(
|
|
layer,
|
|
use_gradient_checkpointing=use_gradient_checkpointing,
|
|
use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
|
|
x=x,
|
|
attn_mask=x_attn_mask,
|
|
freqs_cis=x_freqs_cis,
|
|
adaln_input=adaln_input,
|
|
)
|
|
|
|
# cap embed & refine
|
|
cap_item_seqlens = [len(_) for _ in cap_feats]
|
|
assert all(_ % SEQ_MULTI_OF == 0 for _ in cap_item_seqlens)
|
|
cap_max_item_seqlen = max(cap_item_seqlens)
|
|
|
|
cap_feats = torch.cat(cap_feats, dim=0)
|
|
cap_feats = self.cap_embedder(cap_feats)
|
|
cap_feats[torch.cat(cap_inner_pad_mask)] = self.cap_pad_token.to(dtype=x.dtype, device=x.device)
|
|
cap_feats = list(cap_feats.split(cap_item_seqlens, dim=0))
|
|
cap_freqs_cis = list(self.rope_embedder(torch.cat(cap_pos_ids, dim=0)).split(cap_item_seqlens, dim=0))
|
|
|
|
cap_feats = pad_sequence(cap_feats, batch_first=True, padding_value=0.0)
|
|
cap_freqs_cis = pad_sequence(cap_freqs_cis, batch_first=True, padding_value=0.0)
|
|
cap_attn_mask = torch.zeros((bsz, cap_max_item_seqlen), dtype=torch.bool, device=device)
|
|
for i, seq_len in enumerate(cap_item_seqlens):
|
|
cap_attn_mask[i, :seq_len] = 1
|
|
|
|
for layer in self.context_refiner:
|
|
cap_feats = gradient_checkpoint_forward(
|
|
layer,
|
|
use_gradient_checkpointing=use_gradient_checkpointing,
|
|
use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
|
|
x=cap_feats,
|
|
attn_mask=cap_attn_mask,
|
|
freqs_cis=cap_freqs_cis,
|
|
)
|
|
|
|
# unified
|
|
unified = []
|
|
unified_freqs_cis = []
|
|
for i in range(bsz):
|
|
x_len = x_item_seqlens[i]
|
|
cap_len = cap_item_seqlens[i]
|
|
unified.append(torch.cat([x[i][:x_len], cap_feats[i][:cap_len]]))
|
|
unified_freqs_cis.append(torch.cat([x_freqs_cis[i][:x_len], cap_freqs_cis[i][:cap_len]]))
|
|
unified_item_seqlens = [a + b for a, b in zip(cap_item_seqlens, x_item_seqlens)]
|
|
assert unified_item_seqlens == [len(_) for _ in unified]
|
|
unified_max_item_seqlen = max(unified_item_seqlens)
|
|
|
|
unified = pad_sequence(unified, batch_first=True, padding_value=0.0)
|
|
unified_freqs_cis = pad_sequence(unified_freqs_cis, batch_first=True, padding_value=0.0)
|
|
unified_attn_mask = torch.zeros((bsz, unified_max_item_seqlen), dtype=torch.bool, device=device)
|
|
for i, seq_len in enumerate(unified_item_seqlens):
|
|
unified_attn_mask[i, :seq_len] = 1
|
|
|
|
for layer in self.layers:
|
|
unified = gradient_checkpoint_forward(
|
|
layer,
|
|
use_gradient_checkpointing=use_gradient_checkpointing,
|
|
use_gradient_checkpointing_offload=use_gradient_checkpointing_offload,
|
|
x=unified,
|
|
attn_mask=unified_attn_mask,
|
|
freqs_cis=unified_freqs_cis,
|
|
adaln_input=adaln_input,
|
|
)
|
|
|
|
unified = self.all_final_layer[f"{patch_size}-{f_patch_size}"](unified, adaln_input)
|
|
unified = list(unified.unbind(dim=0))
|
|
x = self.unpatchify(unified, x_size, patch_size, f_patch_size)
|
|
|
|
return x, {}
|