PixArt initial rewrite
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City
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# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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# This source code is licensed under the license found in the
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# LICENSE file in the root directory of this source tree.
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# --------------------------------------------------------
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# References:
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# GLIDE: https://github.com/openai/glide-text2im
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# MAE: https://github.com/facebookresearch/mae/blob/main/models_mae.py
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# --------------------------------------------------------
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import math
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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 timm.models.vision_transformer import Mlp, Attention as Attention_
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from einops import rearrange
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sdpa_32b = None
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Q_4GB_LIMIT = 32000000
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"""If q is greater than this, the operation will likely require >4GB VRAM, which will fail on Intel Arc Alchemist GPUs without a workaround."""
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# 2k = 37 748 736
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# 1024 = 9 437 184
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# 2k model goes very slightly over 4GB
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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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else:
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if model_management.xpu_available:
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import intel_extension_for_pytorch as ipex
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import os
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if not torch.xpu.has_fp64_dtype() and not os.environ.get('IPEX_FORCE_ATTENTION_SLICE', None):
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from ...utils.IPEX.attention import scaled_dot_product_attention_32_bit
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sdpa_32b = scaled_dot_product_attention_32_bit
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print("Using IPEX 4GB SDPA workaround")
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else:
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print("No IPEX 4GB workaround")
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def modulate(x, shift, scale):
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return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
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def t2i_modulate(x, shift, scale):
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return x * (1 + scale) + shift
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class MultiHeadCrossAttention(nn.Module):
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def __init__(self, d_model, num_heads, attn_drop=0., proj_drop=0., **block_kwargs):
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super(MultiHeadCrossAttention, self).__init__()
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assert d_model % num_heads == 0, "d_model must be divisible by num_heads"
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self.d_model = d_model
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self.num_heads = num_heads
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self.head_dim = d_model // num_heads
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self.q_linear = nn.Linear(d_model, d_model)
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self.kv_linear = nn.Linear(d_model, d_model*2)
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self.attn_drop = nn.Dropout(attn_drop)
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self.proj = nn.Linear(d_model, d_model)
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self.proj_drop = nn.Dropout(proj_drop)
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def forward(self, x, cond, mask=None):
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# query/value: img tokens; key: condition; mask: if padding tokens
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B, N, C = x.shape
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q = self.q_linear(x).view(1, -1, self.num_heads, self.head_dim)
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kv = self.kv_linear(cond).view(1, -1, 2, self.num_heads, self.head_dim)
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k, v = kv.unbind(2)
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if model_management.xformers_enabled():
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attn_bias = None
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if mask is not None:
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attn_bias = xformers.ops.fmha.BlockDiagonalMask.from_seqlens([N] * B, mask)
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x = xformers.ops.memory_efficient_attention(
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q, k, v,
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p=self.attn_drop.p,
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attn_bias=attn_bias
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)
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else:
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q, k, v = map(lambda t: t.permute(0, 2, 1, 3),(q, k, v),)
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attn_mask = None
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if mask is not None and len(mask) > 1:
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# Create equivalent of xformer diagonal block mask, still only correct for square masks
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# But depth doesn't matter as tensors can expand in that dimension
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attn_mask_template = torch.ones(
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[q.shape[2] // B, mask[0]],
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dtype=torch.bool,
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device=q.device
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)
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attn_mask = torch.block_diag(attn_mask_template)
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# create a mask on the diagonal for each mask in the batch
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for n in range(B - 1):
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attn_mask = torch.block_diag(attn_mask, attn_mask_template)
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p = getattr(self.attn_drop, "p", 0) # IPEX.optimize() will turn attn_drop into an Identity()
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if sdpa_32b is not None and (q.element_size() * q.nelement()) > Q_4GB_LIMIT:
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sdpa = sdpa_32b
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else:
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sdpa = torch.nn.functional.scaled_dot_product_attention
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x = sdpa(
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q, k, v,
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attn_mask=attn_mask,
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dropout_p=p
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).permute(0, 2, 1, 3).contiguous()
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x = x.view(B, -1, C)
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x = self.proj(x)
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x = self.proj_drop(x)
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return x
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class AttentionKVCompress(Attention_):
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"""Multi-head Attention block with KV token compression and qk norm."""
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def __init__(
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self,
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dim,
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num_heads=8,
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qkv_bias=True,
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sampling='conv',
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sr_ratio=1,
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qk_norm=False,
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**block_kwargs,
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):
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"""
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Args:
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dim (int): Number of input channels.
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num_heads (int): Number of attention heads.
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qkv_bias (bool: If True, add a learnable bias to query, key, value.
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"""
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super().__init__(dim, num_heads=num_heads, qkv_bias=qkv_bias, **block_kwargs)
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self.sampling=sampling # ['conv', 'ave', 'uniform', 'uniform_every']
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self.sr_ratio = sr_ratio
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if sr_ratio > 1 and sampling == 'conv':
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# Avg Conv Init.
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self.sr = nn.Conv2d(dim, dim, groups=dim, kernel_size=sr_ratio, stride=sr_ratio)
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self.sr.weight.data.fill_(1/sr_ratio**2)
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self.sr.bias.data.zero_()
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self.norm = nn.LayerNorm(dim)
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if qk_norm:
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self.q_norm = nn.LayerNorm(dim)
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self.k_norm = nn.LayerNorm(dim)
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else:
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self.q_norm = nn.Identity()
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self.k_norm = nn.Identity()
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def downsample_2d(self, tensor, H, W, scale_factor, sampling=None):
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if sampling is None or scale_factor == 1:
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return tensor
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B, N, C = tensor.shape
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if sampling == 'uniform_every':
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return tensor[:, ::scale_factor], int(N // scale_factor)
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tensor = tensor.reshape(B, H, W, C).permute(0, 3, 1, 2)
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new_H, new_W = int(H / scale_factor), int(W / scale_factor)
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new_N = new_H * new_W
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if sampling == 'ave':
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tensor = F.interpolate(
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tensor, scale_factor=1 / scale_factor, mode='nearest'
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).permute(0, 2, 3, 1)
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elif sampling == 'uniform':
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tensor = tensor[:, :, ::scale_factor, ::scale_factor].permute(0, 2, 3, 1)
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elif sampling == 'conv':
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tensor = self.sr(tensor).reshape(B, C, -1).permute(0, 2, 1)
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tensor = self.norm(tensor)
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else:
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raise ValueError
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return tensor.reshape(B, new_N, C).contiguous(), new_N
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def forward(self, x, mask=None, HW=None, block_id=None):
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B, N, C = x.shape # 2 4096 1152
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new_N = N
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if HW is None:
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H = W = int(N ** 0.5)
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else:
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H, W = HW
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qkv = self.qkv(x).reshape(B, N, 3, C)
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q, k, v = qkv.unbind(2)
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dtype = q.dtype
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q = self.q_norm(q)
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k = self.k_norm(k)
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# KV compression
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if self.sr_ratio > 1:
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k, new_N = self.downsample_2d(k, H, W, self.sr_ratio, sampling=self.sampling)
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v, new_N = self.downsample_2d(v, H, W, self.sr_ratio, sampling=self.sampling)
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q = q.reshape(B, N, self.num_heads, C // self.num_heads).to(dtype)
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k = k.reshape(B, new_N, self.num_heads, C // self.num_heads).to(dtype)
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v = v.reshape(B, new_N, self.num_heads, C // self.num_heads).to(dtype)
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attn_bias = None
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if mask is not None:
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attn_bias = torch.zeros([B * self.num_heads, q.shape[1], k.shape[1]], dtype=q.dtype, device=q.device)
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attn_bias.masked_fill_(mask.squeeze(1).repeat(self.num_heads, 1, 1) == 0, float('-inf'))
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# Switch between torch / xformers attention
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if model_management.xformers_enabled():
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x = xformers.ops.memory_efficient_attention(
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q, k, v,
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p=self.attn_drop.p,
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attn_bias=attn_bias
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)
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else:
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q, k, v = map(lambda t: t.transpose(1, 2),(q, k, v),)
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p = getattr(self.attn_drop, "p", 0) # IPEX.optimize() will turn attn_drop into an Identity()
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if sdpa_32b is not None and (q.element_size() * q.nelement()) > Q_4GB_LIMIT:
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sdpa = sdpa_32b
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else:
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sdpa = torch.nn.functional.scaled_dot_product_attention
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x = sdpa(
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q, k, v,
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dropout_p=p,
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attn_mask=attn_bias
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).transpose(1, 2).contiguous()
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x = x.view(B, N, C)
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x = self.proj(x)
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x = self.proj_drop(x)
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return x
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#################################################################################
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# AMP attention with fp32 softmax to fix loss NaN problem during training #
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#################################################################################
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class Attention(Attention_):
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def forward(self, x):
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B, N, C = x.shape
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qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
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q, k, v = qkv.unbind(0) # make torchscript happy (cannot use tensor as tuple)
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use_fp32_attention = getattr(self, 'fp32_attention', False)
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if use_fp32_attention:
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q, k = q.float(), k.float()
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with torch.cuda.amp.autocast(enabled=not use_fp32_attention):
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attn = (q @ k.transpose(-2, -1)) * self.scale
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attn = attn.softmax(dim=-1)
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attn = self.attn_drop(attn)
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x = (attn @ v).transpose(1, 2).reshape(B, N, C)
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x = self.proj(x)
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x = self.proj_drop(x)
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return x
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class FinalLayer(nn.Module):
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"""
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The final layer of PixArt.
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"""
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def __init__(self, hidden_size, patch_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, patch_size * patch_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(hidden_size, 2 * hidden_size, bias=True)
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)
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def forward(self, x, c):
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shift, scale = self.adaLN_modulation(c).chunk(2, dim=1)
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x = modulate(self.norm_final(x), shift, scale)
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x = self.linear(x)
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return x
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class T2IFinalLayer(nn.Module):
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"""
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The final layer of PixArt.
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"""
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def __init__(self, hidden_size, patch_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, patch_size * patch_size * out_channels, bias=True)
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self.scale_shift_table = nn.Parameter(torch.randn(2, hidden_size) / hidden_size ** 0.5)
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self.out_channels = out_channels
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def forward(self, x, t):
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shift, scale = (self.scale_shift_table[None] + t[:, None]).chunk(2, dim=1)
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x = t2i_modulate(self.norm_final(x), shift, scale)
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x = self.linear(x)
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return x
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class MaskFinalLayer(nn.Module):
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"""
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The final layer of PixArt.
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"""
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def __init__(self, final_hidden_size, c_emb_size, patch_size, out_channels):
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super().__init__()
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self.norm_final = nn.LayerNorm(final_hidden_size, elementwise_affine=False, eps=1e-6)
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self.linear = nn.Linear(final_hidden_size, patch_size * patch_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(c_emb_size, 2 * final_hidden_size, bias=True)
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)
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def forward(self, x, t):
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shift, scale = self.adaLN_modulation(t).chunk(2, dim=1)
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x = modulate(self.norm_final(x), shift, scale)
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x = self.linear(x)
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return x
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class DecoderLayer(nn.Module):
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"""
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The final layer of PixArt.
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"""
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def __init__(self, hidden_size, decoder_hidden_size):
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super().__init__()
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self.norm_decoder = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
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self.linear = nn.Linear(hidden_size, decoder_hidden_size, bias=True)
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self.adaLN_modulation = nn.Sequential(
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nn.SiLU(),
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nn.Linear(hidden_size, 2 * hidden_size, bias=True)
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)
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def forward(self, x, t):
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shift, scale = self.adaLN_modulation(t).chunk(2, dim=1)
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x = modulate(self.norm_decoder(x), shift, scale)
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x = self.linear(x)
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return x
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#################################################################################
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# Embedding Layers for Timesteps and Class Labels #
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#################################################################################
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class TimestepEmbedder(nn.Module):
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"""
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Embeds scalar timesteps into vector representations.
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"""
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def __init__(self, hidden_size, frequency_embedding_size=256):
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super().__init__()
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self.mlp = nn.Sequential(
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nn.Linear(frequency_embedding_size, hidden_size, bias=True),
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nn.SiLU(),
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nn.Linear(hidden_size, hidden_size, bias=True),
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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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"""
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Create sinusoidal timestep embeddings.
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:param t: a 1-D Tensor of N indices, one per batch element.
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These may be fractional.
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:param dim: the dimension of the output.
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:param max_period: controls the minimum frequency of the embeddings.
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:return: an (N, D) Tensor of positional embeddings.
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"""
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# https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py
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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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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(t.dtype))
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return t_emb
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class SizeEmbedder(TimestepEmbedder):
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"""
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Embeds scalar timesteps into vector representations.
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"""
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def __init__(self, hidden_size, frequency_embedding_size=256):
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super().__init__(hidden_size=hidden_size, frequency_embedding_size=frequency_embedding_size)
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self.mlp = nn.Sequential(
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nn.Linear(frequency_embedding_size, hidden_size, bias=True),
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nn.SiLU(),
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nn.Linear(hidden_size, hidden_size, bias=True),
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)
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self.frequency_embedding_size = frequency_embedding_size
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self.outdim = hidden_size
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def forward(self, s, bs):
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if s.ndim == 1:
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s = s[:, None]
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assert s.ndim == 2
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if s.shape[0] != bs:
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s = s.repeat(bs//s.shape[0], 1)
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assert s.shape[0] == bs
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b, dims = s.shape[0], s.shape[1]
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s = rearrange(s, "b d -> (b d)")
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s_freq = self.timestep_embedding(s, self.frequency_embedding_size)
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s_emb = self.mlp(s_freq.to(s.dtype))
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s_emb = rearrange(s_emb, "(b d) d2 -> b (d d2)", b=b, d=dims, d2=self.outdim)
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return s_emb
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class LabelEmbedder(nn.Module):
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"""
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Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance.
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"""
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def __init__(self, num_classes, hidden_size, dropout_prob):
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super().__init__()
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use_cfg_embedding = dropout_prob > 0
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self.embedding_table = nn.Embedding(num_classes + use_cfg_embedding, hidden_size)
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self.num_classes = num_classes
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self.dropout_prob = dropout_prob
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def token_drop(self, labels, force_drop_ids=None):
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"""
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Drops labels to enable classifier-free guidance.
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"""
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if force_drop_ids is None:
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drop_ids = torch.rand(labels.shape[0]).cuda() < self.dropout_prob
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else:
|
||||
drop_ids = force_drop_ids == 1
|
||||
labels = torch.where(drop_ids, self.num_classes, labels)
|
||||
return labels
|
||||
|
||||
def forward(self, labels, train, force_drop_ids=None):
|
||||
use_dropout = self.dropout_prob > 0
|
||||
if (train and use_dropout) or (force_drop_ids is not None):
|
||||
labels = self.token_drop(labels, force_drop_ids)
|
||||
embeddings = self.embedding_table(labels)
|
||||
return embeddings
|
||||
|
||||
|
||||
class CaptionEmbedder(nn.Module):
|
||||
"""
|
||||
Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance.
|
||||
"""
|
||||
|
||||
def __init__(self, in_channels, hidden_size, uncond_prob, act_layer=nn.GELU(approximate='tanh'), token_num=120):
|
||||
super().__init__()
|
||||
self.y_proj = Mlp(in_features=in_channels, hidden_features=hidden_size, out_features=hidden_size, act_layer=act_layer, drop=0)
|
||||
self.register_buffer("y_embedding", nn.Parameter(torch.randn(token_num, in_channels) / in_channels ** 0.5))
|
||||
self.uncond_prob = uncond_prob
|
||||
|
||||
def token_drop(self, caption, force_drop_ids=None):
|
||||
"""
|
||||
Drops labels to enable classifier-free guidance.
|
||||
"""
|
||||
if force_drop_ids is None:
|
||||
drop_ids = torch.rand(caption.shape[0]).cuda() < self.uncond_prob
|
||||
else:
|
||||
drop_ids = force_drop_ids == 1
|
||||
caption = torch.where(drop_ids[:, None, None, None], self.y_embedding, caption)
|
||||
return caption
|
||||
|
||||
def forward(self, caption, train, force_drop_ids=None):
|
||||
if train:
|
||||
assert caption.shape[2:] == self.y_embedding.shape
|
||||
use_dropout = self.uncond_prob > 0
|
||||
if (train and use_dropout) or (force_drop_ids is not None):
|
||||
caption = self.token_drop(caption, force_drop_ids)
|
||||
caption = self.y_proj(caption)
|
||||
return caption
|
||||
|
||||
|
||||
class CaptionEmbedderDoubleBr(nn.Module):
|
||||
"""
|
||||
Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance.
|
||||
"""
|
||||
|
||||
def __init__(self, in_channels, hidden_size, uncond_prob, act_layer=nn.GELU(approximate='tanh'), token_num=120):
|
||||
super().__init__()
|
||||
self.proj = Mlp(in_features=in_channels, hidden_features=hidden_size, out_features=hidden_size, act_layer=act_layer, drop=0)
|
||||
self.embedding = nn.Parameter(torch.randn(1, in_channels) / 10 ** 0.5)
|
||||
self.y_embedding = nn.Parameter(torch.randn(token_num, in_channels) / 10 ** 0.5)
|
||||
self.uncond_prob = uncond_prob
|
||||
|
||||
def token_drop(self, global_caption, caption, force_drop_ids=None):
|
||||
"""
|
||||
Drops labels to enable classifier-free guidance.
|
||||
"""
|
||||
if force_drop_ids is None:
|
||||
drop_ids = torch.rand(global_caption.shape[0]).cuda() < self.uncond_prob
|
||||
else:
|
||||
drop_ids = force_drop_ids == 1
|
||||
global_caption = torch.where(drop_ids[:, None], self.embedding, global_caption)
|
||||
caption = torch.where(drop_ids[:, None, None, None], self.y_embedding, caption)
|
||||
return global_caption, caption
|
||||
|
||||
def forward(self, caption, train, force_drop_ids=None):
|
||||
assert caption.shape[2: ] == self.y_embedding.shape
|
||||
global_caption = caption.mean(dim=2).squeeze()
|
||||
use_dropout = self.uncond_prob > 0
|
||||
if (train and use_dropout) or (force_drop_ids is not None):
|
||||
global_caption, caption = self.token_drop(global_caption, caption, force_drop_ids)
|
||||
y_embed = self.proj(global_caption)
|
||||
return y_embed, caption
|
||||
Reference in New Issue
Block a user