Upload folder using huggingface_hub

layers.py

@@ -0,0 +1,734 @@
+# Modified from Flux
+#
+# Copyright 2024 Black Forest Labs
+
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import math  # noqa: I001
+from dataclasses import dataclass
+from functools import partial
+
+import torch
+import torch.nn.functional as F
+from einops import rearrange
+from torch import Tensor, nn
+from torch.utils.checkpoint import checkpoint
+
+
+
+def to_cuda(x):
+	if isinstance(x, torch.Tensor):
+		return x.cuda()
+	elif isinstance(x, (list, tuple)):
+		return [to_cuda(elem) for elem in x]
+	elif isinstance(x, dict):
+		return {k: to_cuda(v) for k, v in x.items()}
+	else:
+		return x
+
+
+def to_cpu(x):
+	if isinstance(x, torch.Tensor):
+		return x.cpu()
+	elif isinstance(x, (list, tuple)):
+		return [to_cpu(elem) for elem in x]
+	elif isinstance(x, dict):
+		return {k: to_cpu(v) for k, v in x.items()}
+	else:
+		return x
+
+
+MEMORY_LAYOUT = {
+	"flash": (
+		lambda x: x.view(x.shape[0] * x.shape[1], *x.shape[2:]),
+		lambda x: x,
+	),
+	"torch": (
+		lambda x: x.transpose(1, 2),
+		lambda x: x.transpose(1, 2),
+	),
+	"vanilla": (
+		lambda x: x.transpose(1, 2),
+		lambda x: x.transpose(1, 2),
+	),
+}
+
+
+def attention(
+		q,
+		k,
+		v,
+		mode="flash",
+		drop_rate=0,
+		attn_mask=None,
+		causal=False,
+		cu_seqlens_q=None,
+		cu_seqlens_kv=None,
+		max_seqlen_q=None,
+		max_seqlen_kv=None,
+		batch_size=1,
+):
+	"""
+	Perform QKV self attention.
+
+	Args:
+		q (torch.Tensor): Query tensor with shape [b, s, a, d], where a is the number of heads.
+		k (torch.Tensor): Key tensor with shape [b, s1, a, d]
+		v (torch.Tensor): Value tensor with shape [b, s1, a, d]
+		mode (str): Attention mode. Choose from 'self_flash', 'cross_flash', 'torch', and 'vanilla'.
+		drop_rate (float): Dropout rate in attention map. (default: 0)
+		attn_mask (torch.Tensor): Attention mask with shape [b, s1] (cross_attn), or [b, a, s, s1] (torch or vanilla).
+			(default: None)
+		causal (bool): Whether to use causal attention. (default: False)
+		cu_seqlens_q (torch.Tensor): dtype torch.int32. The cumulative sequence lengths of the sequences in the batch,
+			used to index into q.
+		cu_seqlens_kv (torch.Tensor): dtype torch.int32. The cumulative sequence lengths of the sequences in the batch,
+			used to index into kv.
+		max_seqlen_q (int): The maximum sequence length in the batch of q.
+		max_seqlen_kv (int): The maximum sequence length in the batch of k and v.
+
+	Returns:
+		torch.Tensor: Output tensor after self attention with shape [b, s, ad]
+	"""
+	pre_attn_layout, post_attn_layout = MEMORY_LAYOUT[mode]
+	q = pre_attn_layout(q)
+	k = pre_attn_layout(k)
+	v = pre_attn_layout(v)
+
+	if mode == "torch":
+		if attn_mask is not None and attn_mask.dtype != torch.bool:
+			attn_mask = attn_mask.to(q.dtype)
+		x = F.scaled_dot_product_attention(
+			q, k, v, attn_mask=attn_mask, dropout_p=drop_rate, is_causal=causal
+		)
+	elif mode == "flash":
+		assert flash_attn_varlen_func is not None
+		x: torch.Tensor = flash_attn_varlen_func(
+			q,
+			k,
+			v,
+			cu_seqlens_q,
+			cu_seqlens_kv,
+			max_seqlen_q,
+			max_seqlen_kv,
+		)  # type: ignore
+		# x with shape [(bxs), a, d]
+		x = x.view(batch_size, max_seqlen_q, x.shape[-2], x.shape[-1])  # type: ignore # reshape x to [b, s, a, d]
+	elif mode == "vanilla":
+		scale_factor = 1 / math.sqrt(q.size(-1))
+
+		b, a, s, _ = q.shape
+		s1 = k.size(2)
+		attn_bias = torch.zeros(b, a, s, s1, dtype=q.dtype, device=q.device)
+		if causal:
+			# Only applied to self attention
+			assert attn_mask is None, (
+				"Causal mask and attn_mask cannot be used together"
+			)
+			temp_mask = torch.ones(b, a, s, s, dtype=torch.bool, device=q.device).tril(
+				diagonal=0
+			)
+			attn_bias.masked_fill_(temp_mask.logical_not(), float("-inf"))
+			attn_bias.to(q.dtype)
+
+		if attn_mask is not None:
+			if attn_mask.dtype == torch.bool:
+				attn_bias.masked_fill_(attn_mask.logical_not(), float("-inf"))
+			else:
+				attn_bias += attn_mask
+
+		# TODO: Maybe force q and k to be float32 to avoid numerical overflow
+		attn = (q @ k.transpose(-2, -1)) * scale_factor
+		attn += attn_bias
+		attn = attn.softmax(dim=-1)
+		attn = torch.dropout(attn, p=drop_rate, train=True)
+		x = attn @ v
+	else:
+		raise NotImplementedError(f"Unsupported attention mode: {mode}")
+
+	x = post_attn_layout(x)
+	b, s, a, d = x.shape
+	out = x.reshape(b, s, -1)
+	return out
+
+
+def apply_gate(x, gate=None, tanh=False):
+	"""AI is creating summary for apply_gate
+
+	Args:
+		x (torch.Tensor): input tensor.
+		gate (torch.Tensor, optional): gate tensor. Defaults to None.
+		tanh (bool, optional): whether to use tanh function. Defaults to False.
+
+	Returns:
+		torch.Tensor: the output tensor after apply gate.
+	"""
+	if gate is None:
+		return x
+	if tanh:
+		return x * gate.unsqueeze(1).tanh()
+	else:
+		return x * gate.unsqueeze(1)
+
+
+class MLP(nn.Module):
+	"""MLP as used in Vision Transformer, MLP-Mixer and related networks"""
+
+	def __init__(
+			self,
+			in_channels,
+			hidden_channels=None,
+			out_features=None,
+			act_layer=nn.GELU,
+			norm_layer=None,
+			bias=True,
+			drop=0.0,
+			use_conv=False,
+			device=None,
+			dtype=None,
+	):
+		super().__init__()
+		out_features = out_features or in_channels
+		hidden_channels = hidden_channels or in_channels
+		bias = (bias, bias)
+		drop_probs = (drop, drop)
+		linear_layer = partial(nn.Conv2d, kernel_size=1) if use_conv else nn.Linear
+
+		self.fc1 = linear_layer(
+			in_channels, hidden_channels, bias=bias[0], device=device, dtype=dtype
+		)
+		self.act = act_layer()
+		self.drop1 = nn.Dropout(drop_probs[0])
+		self.norm = (
+			norm_layer(hidden_channels, device=device, dtype=dtype)
+			if norm_layer is not None
+			else nn.Identity()
+		)
+		self.fc2 = linear_layer(
+			hidden_channels, out_features, bias=bias[1], device=device, dtype=dtype
+		)
+		self.drop2 = nn.Dropout(drop_probs[1])
+
+	def forward(self, x):
+		x = self.fc1(x)
+		x = self.act(x)
+		x = self.drop1(x)
+		x = self.norm(x)
+		x = self.fc2(x)
+		x = self.drop2(x)
+		return x
+
+
+class TextProjection(nn.Module):
+	"""
+	Projects text embeddings. Also handles dropout for classifier-free guidance.
+
+	Adapted from https://github.com/PixArt-alpha/PixArt-alpha/blob/master/diffusion/model/nets/PixArt_blocks.py
+	"""
+
+	def __init__(self, in_channels, hidden_size, act_layer, dtype=None, device=None):
+		factory_kwargs = {"dtype": dtype, "device": device}
+		super().__init__()
+		self.linear_1 = nn.Linear(
+			in_features=in_channels,
+			out_features=hidden_size,
+			bias=True,
+			**factory_kwargs,
+		)
+		self.act_1 = act_layer()
+		self.linear_2 = nn.Linear(
+			in_features=hidden_size,
+			out_features=hidden_size,
+			bias=True,
+			**factory_kwargs,
+		)
+
+	def forward(self, caption):
+		hidden_states = self.linear_1(caption)
+		hidden_states = self.act_1(hidden_states)
+		hidden_states = self.linear_2(hidden_states)
+		return hidden_states
+
+
+class TimestepEmbedder(nn.Module):
+	"""
+	Embeds scalar timesteps into vector representations.
+	"""
+
+	def __init__(
+			self,
+			hidden_size,
+			act_layer,
+			frequency_embedding_size=256,
+			max_period=10000,
+			out_size=None,
+			dtype=None,
+			device=None,
+	):
+		factory_kwargs = {"dtype": dtype, "device": device}
+		super().__init__()
+		self.frequency_embedding_size = frequency_embedding_size
+		self.max_period = max_period
+		if out_size is None:
+			out_size = hidden_size
+
+		self.mlp = nn.Sequential(
+			nn.Linear(
+				frequency_embedding_size, hidden_size, bias=True, **factory_kwargs
+			),
+			act_layer(),
+			nn.Linear(hidden_size, out_size, bias=True, **factory_kwargs),
+		)
+		nn.init.normal_(self.mlp[0].weight, std=0.02)  # type: ignore
+		nn.init.normal_(self.mlp[2].weight, std=0.02)  # type: ignore
+
+	@staticmethod
+	def timestep_embedding(t, dim, max_period=10000):
+		"""
+		Create sinusoidal timestep embeddings.
+
+		Args:
+			t (torch.Tensor): a 1-D Tensor of N indices, one per batch element. These may be fractional.
+			dim (int): the dimension of the output.
+			max_period (int): controls the minimum frequency of the embeddings.
+
+		Returns:
+			embedding (torch.Tensor): An (N, D) Tensor of positional embeddings.
+
+		.. ref_link: https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py
+		"""
+		half = dim // 2
+		freqs = torch.exp(
+			-math.log(max_period)
+			* torch.arange(start=0, end=half, dtype=torch.float32)
+			/ half
+		).to(device=t.device)
+		args = t[:, None].float() * freqs[None]
+		embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
+		if dim % 2:
+			embedding = torch.cat(
+				[embedding, torch.zeros_like(embedding[:, :1])], dim=-1
+			)
+		return embedding
+
+	def forward(self, t):
+		t_freq = self.timestep_embedding(
+			t, self.frequency_embedding_size, self.max_period
+		).type(self.mlp[0].weight.dtype)  # type: ignore
+		t_emb = self.mlp(t_freq)
+		return t_emb
+
+
+class EmbedND(nn.Module):
+	def __init__(self, dim: int, theta: int, axes_dim: list[int]):
+		super().__init__()
+		self.dim = dim
+		self.theta = theta
+		self.axes_dim = axes_dim
+
+	def forward(self, ids: Tensor) -> Tensor:
+		n_axes = ids.shape[-1]
+		emb = torch.cat(
+			[rope(ids[..., i], self.axes_dim[i], self.theta) for i in range(n_axes)],
+			dim=-3,
+		)
+
+		return emb.unsqueeze(1)
+
+
+class MLPEmbedder(nn.Module):
+	def __init__(self, in_dim: int, hidden_dim: int):
+		super().__init__()
+		self.in_layer = nn.Linear(in_dim, hidden_dim, bias=True)
+		self.silu = nn.SiLU()
+		self.out_layer = nn.Linear(hidden_dim, hidden_dim, bias=True)
+
+		self.gradient_checkpointing = False
+
+	def enable_gradient_checkpointing(self):
+		self.gradient_checkpointing = True
+
+	def disable_gradient_checkpointing(self):
+		self.gradient_checkpointing = False
+
+	def _forward(self, x: Tensor) -> Tensor:
+		return self.out_layer(self.silu(self.in_layer(x)))
+
+	def forward(self, *args, **kwargs):
+		if self.training and self.gradient_checkpointing:
+			return checkpoint(self._forward, *args, use_reentrant=False, **kwargs)
+		else:
+			return self._forward(*args, **kwargs)
+
+
+def rope(pos, dim: int, theta: int):
+	assert dim % 2 == 0
+	scale = torch.arange(0, dim, 2, dtype=torch.float64, device=pos.device) / dim
+	omega = 1.0 / (theta ** scale)
+	out = torch.einsum("...n,d->...nd", pos, omega)
+	out = torch.stack(
+		[torch.cos(out), -torch.sin(out), torch.sin(out), torch.cos(out)], dim=-1
+	)
+	out = rearrange(out, "b n d (i j) -> b n d i j", i=2, j=2)
+	return out.float()
+
+
+def attention_after_rope(q, k, v, pe, mode):
+	q, k = apply_rope(q, k, pe)
+
+	from .attention import attention
+
+	x = attention(q, k, v, mode)
+	return x
+
+
+@torch.compile(mode="max-autotune-no-cudagraphs", dynamic=True)
+def apply_rope(xq, xk, freqs_cis):
+	# 将 num_heads 和 seq_len 的维度交换回原函数的处理顺序
+	xq = xq.transpose(1, 2)  # [batch, num_heads, seq_len, head_dim]
+	xk = xk.transpose(1, 2)
+
+	# 将 head_dim 拆分为复数部分（实部和虚部）
+	xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2)
+	xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2)
+
+	# 应用旋转位置编码（复数乘法）
+	xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1]
+	xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1]
+
+	# 恢复张量形状并转置回目标维度顺序
+	xq_out = xq_out.reshape(*xq.shape).type_as(xq).transpose(1, 2)
+	xk_out = xk_out.reshape(*xk.shape).type_as(xk).transpose(1, 2)
+
+	return xq_out, xk_out
+
+
+@torch.compile(mode="max-autotune-no-cudagraphs", dynamic=True)
+def scale_add_residual(
+		x: torch.Tensor, scale: torch.Tensor, residual: torch.Tensor
+) -> torch.Tensor:
+	return x * scale + residual
+
+
+@torch.compile(mode="max-autotune-no-cudagraphs", dynamic=True)
+def layernorm_and_scale_shift(
+		x: torch.Tensor, scale: torch.Tensor, shift: torch.Tensor
+) -> torch.Tensor:
+	return torch.nn.functional.layer_norm(x, (x.size(-1),)) * (scale + 1) + shift
+
+
+class SelfAttention(nn.Module):
+	def __init__(self, dim: int, num_heads: int = 8, qkv_bias: bool = False):
+		super().__init__()
+		self.num_heads = num_heads
+		head_dim = dim // num_heads
+
+		self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+		self.norm = QKNorm(head_dim)
+		self.proj = nn.Linear(dim, dim)
+
+	def forward(self, x: Tensor, pe: Tensor) -> Tensor:
+		qkv = self.qkv(x)
+		q, k, v = rearrange(qkv, "B L (K H D) -> K B L H D", K=3, H=self.num_heads)
+		q, k = self.norm(q, k, v)
+		x = attention_after_rope(q, k, v, pe=pe)
+		x = self.proj(x)
+		return x
+
+
+@dataclass
+class ModulationOut:
+	shift: Tensor
+	scale: Tensor
+	gate: Tensor
+
+
+class RMSNorm(torch.nn.Module):
+	def __init__(self, dim: int):
+		super().__init__()
+		self.scale = nn.Parameter(torch.ones(dim))
+
+	@staticmethod
+	def rms_norm_fast(x, weight, eps):
+		return LigerRMSNormFunction.apply(
+			x,
+			weight,
+			eps,
+			0.0,
+			"gemma",
+			True,
+		)
+
+	@staticmethod
+	def rms_norm(x, weight, eps):
+		x_dtype = x.dtype
+		x = x.float()
+		rrms = torch.rsqrt(torch.mean(x ** 2, dim=-1, keepdim=True) + eps)
+		return (x * rrms).to(dtype=x_dtype) * weight
+
+	def forward(self, x: Tensor):
+		return self.rms_norm_fast(x, self.scale.to(x.dtype), 1e-6)
+
+
+class QKNorm(torch.nn.Module):
+	def __init__(self, dim: int):
+		super().__init__()
+		self.query_norm = RMSNorm(dim)
+		self.key_norm = RMSNorm(dim)
+
+	def forward(self, q: Tensor, k: Tensor, v: Tensor) -> tuple[Tensor, Tensor]:
+		q = self.query_norm(q)
+		k = self.key_norm(k)
+		return q.to(v), k.to(v)
+
+
+class Modulation(nn.Module):
+	def __init__(self, dim: int, double: bool):
+		super().__init__()
+		self.is_double = double
+		self.multiplier = 6 if double else 3
+		self.lin = nn.Linear(dim, self.multiplier * dim, bias=True)
+
+	def forward(self, vec: Tensor) -> tuple[ModulationOut, ModulationOut | None]:
+		out = self.lin(nn.functional.silu(vec))[:, None, :].chunk(
+			self.multiplier, dim=-1
+		)
+
+		return (
+			ModulationOut(*out[:3]),
+			ModulationOut(*out[3:]) if self.is_double else None,
+		)
+
+
+class DoubleStreamBlock(nn.Module):
+	def __init__(
+			self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False, mode: str = "flash"
+	):
+		super().__init__()
+
+		mlp_hidden_dim = int(hidden_size * mlp_ratio)
+		self.num_heads = num_heads
+		self.hidden_size = hidden_size
+		self.img_mod = Modulation(hidden_size, double=True)
+		self.img_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+		self.img_attn = SelfAttention(
+			dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias
+		)
+
+		self.img_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+		self.img_mlp = nn.Sequential(
+			nn.Linear(hidden_size, mlp_hidden_dim, bias=True),
+			nn.GELU(approximate="tanh"),
+			nn.Linear(mlp_hidden_dim, hidden_size, bias=True),
+		)
+
+		self.txt_mod = Modulation(hidden_size, double=True)
+		self.txt_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+		self.txt_attn = SelfAttention(
+			dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias
+		)
+
+		self.txt_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+		self.txt_mlp = nn.Sequential(
+			nn.Linear(hidden_size, mlp_hidden_dim, bias=True),
+			nn.GELU(approximate="tanh"),
+			nn.Linear(mlp_hidden_dim, hidden_size, bias=True),
+		)
+
+		self.mode = mode
+		self.gradient_checkpointing = False
+		self.cpu_offload_checkpointing = False
+
+	def enable_gradient_checkpointing(self, cpu_offload: bool = False):
+		self.gradient_checkpointing = True
+		self.cpu_offload_checkpointing = cpu_offload
+
+	def disable_gradient_checkpointing(self):
+		self.gradient_checkpointing = False
+		self.cpu_offload_checkpointing = False
+
+	def _forward(
+			self, img: Tensor, txt: Tensor, vec: Tensor, pe: Tensor
+	) -> tuple[Tensor, Tensor]:
+		img_mod1, img_mod2 = self.img_mod(vec)
+		txt_mod1, txt_mod2 = self.txt_mod(vec)
+
+		# prepare image for attention
+		img_modulated = self.img_norm1(img)
+		img_modulated = (1 + img_mod1.scale) * img_modulated + img_mod1.shift
+		img_qkv = self.img_attn.qkv(img_modulated)
+		img_q, img_k, img_v = rearrange(
+			img_qkv, "B L (K H D) -> K B L H D", K=3, H=self.num_heads
+		)
+		img_q, img_k = self.img_attn.norm(img_q, img_k, img_v)
+
+		# prepare txt for attention
+		txt_modulated = self.txt_norm1(txt)
+		txt_modulated = (1 + txt_mod1.scale) * txt_modulated + txt_mod1.shift
+		txt_qkv = self.txt_attn.qkv(txt_modulated)
+		txt_q, txt_k, txt_v = rearrange(
+			txt_qkv, "B L (K H D) -> K B L H D", K=3, H=self.num_heads
+		)
+		txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v)
+
+		# run actual attention
+		q = torch.cat((txt_q, img_q), dim=1)
+		k = torch.cat((txt_k, img_k), dim=1)
+		v = torch.cat((txt_v, img_v), dim=1)
+
+		attn = attention_after_rope(q, k, v, pe, self.mode)
+		txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1]:]
+
+		# calculate the img bloks
+		img = img + img_mod1.gate * self.img_attn.proj(img_attn)
+		img_mlp = self.img_mlp(
+			(1 + img_mod2.scale) * self.img_norm2(img) + img_mod2.shift
+		)
+		img = scale_add_residual(img_mlp, img_mod2.gate, img)
+
+		# calculate the txt bloks
+		txt = txt + txt_mod1.gate * self.txt_attn.proj(txt_attn)
+		txt_mlp = self.txt_mlp(
+			(1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift
+		)
+		txt = scale_add_residual(txt_mlp, txt_mod2.gate, txt)
+		return img, txt
+
+	def forward(
+			self, img: Tensor, txt: Tensor, vec: Tensor, pe: Tensor
+	) -> tuple[Tensor, Tensor]:
+		if self.training and self.gradient_checkpointing:
+			if not self.cpu_offload_checkpointing:
+				return checkpoint(self._forward, img, txt, vec, pe, use_reentrant=False)
+
+			# cpu offload checkpointing
+
+			def create_custom_forward(func):
+				def custom_forward(*inputs):
+					cuda_inputs = to_cuda(inputs)
+					outputs = func(*cuda_inputs)
+					return to_cpu(outputs)
+
+				return custom_forward
+
+			return torch.utils.checkpoint.checkpoint(
+				create_custom_forward(self._forward), img, txt, vec, pe, use_reentrant=False
+			)
+
+		else:
+			return self._forward(img, txt, vec, pe)
+
+
+class SingleStreamBlock(nn.Module):
+	"""
+	A DiT block with parallel linear layers as described in
+	https://arxiv.org/abs/2302.05442 and adapted modulation interface.
+	"""
+
+	def __init__(
+			self,
+			hidden_size: int,
+			num_heads: int,
+			mlp_ratio: float = 4.0,
+			qk_scale: float | None = None,
+			mode: str = "flash"
+	):
+		super().__init__()
+		self.hidden_dim = hidden_size
+		self.num_heads = num_heads
+		head_dim = hidden_size // num_heads
+		self.scale = qk_scale or head_dim ** -0.5
+
+		self.mlp_hidden_dim = int(hidden_size * mlp_ratio)
+		# qkv and mlp_in
+		self.linear1 = nn.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim)
+		# proj and mlp_out
+		self.linear2 = nn.Linear(hidden_size + self.mlp_hidden_dim, hidden_size)
+
+		self.norm = QKNorm(head_dim)
+
+		self.hidden_size = hidden_size
+		self.pre_norm = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+
+		self.mlp_act = nn.GELU(approximate="tanh")
+		self.modulation = Modulation(hidden_size, double=False)
+
+		self.mode = mode
+		self.gradient_checkpointing = False
+		self.cpu_offload_checkpointing = False
+
+	def enable_gradient_checkpointing(self, cpu_offload: bool = False):
+		self.gradient_checkpointing = True
+		self.cpu_offload_checkpointing = cpu_offload
+
+	def disable_gradient_checkpointing(self):
+		self.gradient_checkpointing = False
+		self.cpu_offload_checkpointing = False
+
+	def _forward(self, x: Tensor, vec: Tensor, pe: Tensor) -> Tensor:
+		mod, _ = self.modulation(vec)
+		x_mod = (1 + mod.scale) * self.pre_norm(x) + mod.shift
+		qkv, mlp = torch.split(
+			self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1
+		)
+
+		q, k, v = rearrange(qkv, "B L (K H D) -> K B L H D", K=3, H=self.num_heads)
+		q, k = self.norm(q, k, v)
+
+		# compute attention
+		attn = attention_after_rope(q, k, v, pe, self.mode)
+		# compute activation in mlp stream, cat again and run second linear layer
+		output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2))
+		return scale_add_residual(output, mod.gate, x)
+
+	def forward(self, x: Tensor, vec: Tensor, pe: Tensor) -> Tensor:
+		if self.training and self.gradient_checkpointing:
+			if not self.cpu_offload_checkpointing:
+				return checkpoint(self._forward, x, vec, pe, use_reentrant=False)
+
+			# cpu offload checkpointing
+
+			def create_custom_forward(func):
+				def custom_forward(*inputs):
+					cuda_inputs = to_cuda(inputs)
+					outputs = func(*cuda_inputs)
+					return to_cpu(outputs)
+
+				return custom_forward
+
+			return torch.utils.checkpoint.checkpoint(
+				create_custom_forward(self._forward), x, vec, pe, use_reentrant=False
+			)
+		else:
+			return self._forward(x, vec, pe)
+
+
+class LastLayer(nn.Module):
+	def __init__(self, hidden_size: int, patch_size: int, out_channels: int):
+		super().__init__()
+		self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+		self.linear = nn.Linear(
+			hidden_size, patch_size * patch_size * out_channels, bias=True
+		)
+		self.adaLN_modulation = nn.Sequential(
+			nn.SiLU(), nn.Linear(hidden_size, 2 * hidden_size, bias=True)
+		)
+
+	def forward(self, x: Tensor, vec: Tensor) -> Tensor:
+		shift, scale = self.adaLN_modulation(vec).chunk(2, dim=1)
+		x = (1 + scale[:, None, :]) * self.norm_final(x) + shift[:, None, :]
+		x = self.linear(x)
+		return x