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from dataclasses import dataclass
import numpy as np
import torch
from library import custom_offloading_utils
from torch import Tensor, nn
from .connector_edit import Qwen2Connector
from .layers import DoubleStreamBlock, EmbedND, LastLayer, MLPEmbedder, SingleStreamBlock
@dataclass
class Step1XParams:
in_channels: int
out_channels: int
vec_in_dim: int
context_in_dim: int
hidden_size: int
mlp_ratio: float
num_heads: int
depth: int
depth_single_blocks: int
axes_dim: list[int]
theta: int
qkv_bias: bool
class Step1XEdit(nn.Module):
"""
Transformer model for flow matching on sequences.
"""
def __init__(self, params: Step1XParams, args=None):
super().__init__()
self.params = params
self.in_channels = params.in_channels
self.out_channels = params.out_channels
if params.hidden_size % params.num_heads != 0:
raise ValueError(f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}")
pe_dim = params.hidden_size // params.num_heads
if sum(params.axes_dim) != pe_dim:
raise ValueError(f"Got {params.axes_dim} but expected positional dim {pe_dim}")
self.hidden_size = params.hidden_size
self.num_heads = params.num_heads
self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim)
self.img_in = nn.Linear(self.in_channels, self.hidden_size, bias=True)
self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size)
self.vector_in = MLPEmbedder(params.vec_in_dim, self.hidden_size)
self.txt_in = nn.Linear(params.context_in_dim, self.hidden_size)
self.double_blocks = nn.ModuleList([DoubleStreamBlock(
self.hidden_size,
self.num_heads,
mlp_ratio=params.mlp_ratio,
qkv_bias=params.qkv_bias,
) for _ in range(params.depth)])
self.single_blocks = nn.ModuleList([SingleStreamBlock(self.hidden_size, self.num_heads, mlp_ratio=params.mlp_ratio) for _ in range(params.depth_single_blocks)])
self.final_layer = LastLayer(self.hidden_size, 1, self.out_channels)
self.connector = Qwen2Connector()
# adapted from kohya definition
self.gradient_checkpointing = False
self.cpu_offload_checkpointing = False
self.blocks_to_swap = None
self.offloader_double = None
self.offloader_single = None
self.num_double_blocks = len(self.double_blocks)
self.num_single_blocks = len(self.single_blocks)
self.disperse_loss = args is not None and args.disperse_loss
@property
def device(self):
return next(self.parameters()).device
@property
def dtype(self):
return next(self.parameters()).dtype
def enable_gradient_checkpointing(self, cpu_offload: bool = False):
self.gradient_checkpointing = True
self.cpu_offload_checkpointing = cpu_offload
self.time_in.enable_gradient_checkpointing()
self.vector_in.enable_gradient_checkpointing()
for block in self.double_blocks + self.single_blocks:
block.enable_gradient_checkpointing(cpu_offload=cpu_offload)
print(f"Base model: Gradient checkpointing enabled. CPU offload: {cpu_offload}")
def disable_gradient_checkpointing(self):
self.gradient_checkpointing = False
self.cpu_offload_checkpointing = False
self.time_in.disable_gradient_checkpointing()
self.vector_in.disable_gradient_checkpointing()
for block in self.double_blocks + self.single_blocks:
block.disable_gradient_checkpointing()
print("Base Model: Gradient checkpointing disabled.")
def enable_block_swap(self, num_blocks: int, device: torch.device):
self.blocks_to_swap = num_blocks
double_blocks_to_swap = num_blocks // 2
single_blocks_to_swap = (num_blocks - double_blocks_to_swap) * 2
assert double_blocks_to_swap <= self.num_double_blocks - 2 and single_blocks_to_swap <= self.num_single_blocks - 2, (f"Cannot swap more than {self.num_double_blocks - 2} double blocks and {self.num_single_blocks - 2} single blocks. "
f"Requested {double_blocks_to_swap} double blocks and {single_blocks_to_swap} single blocks.")
self.offloader_double = custom_offloading_utils.ModelOffloader(
self.double_blocks, self.num_double_blocks, double_blocks_to_swap, device # , debug=True
)
self.offloader_single = custom_offloading_utils.ModelOffloader(
self.single_blocks, self.num_single_blocks, single_blocks_to_swap, device # , debug=True
)
print(f"Base model: Block swap enabled. Swapping {num_blocks} blocks, double blocks: {double_blocks_to_swap}, single blocks: {single_blocks_to_swap}.")
def move_to_device_except_swap_blocks(self, device: torch.device):
# assume model is on cpu. do not move blocks to device to reduce temporary memory usage
if self.blocks_to_swap:
save_double_blocks = self.double_blocks
save_single_blocks = self.single_blocks
self.double_blocks = None
self.single_blocks = None
self.to(device)
if self.blocks_to_swap:
self.double_blocks = save_double_blocks
self.single_blocks = save_single_blocks
def prepare_block_swap_before_forward(self):
if self.blocks_to_swap is None or self.blocks_to_swap == 0:
return
self.offloader_double.prepare_block_devices_before_forward(self.double_blocks)
self.offloader_single.prepare_block_devices_before_forward(self.single_blocks)
@staticmethod
def timestep_embedding(t: Tensor, dim, max_period=10000, time_factor: float = 1000.0):
"""
Create sinusoidal timestep embeddings.
:param t: a 1-D Tensor of N indices, one per batch element.
These may be fractional.
:param dim: the dimension of the output.
:param max_period: controls the minimum frequency of the embeddings.
:return: an (N, D) Tensor of positional embeddings.
"""
t = time_factor * t
half = dim // 2
freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half).to(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)
if torch.is_floating_point(t):
embedding = embedding.to(t)
return embedding
def forward(
self,
img: Tensor,
img_ids: Tensor,
txt_ids: Tensor,
timesteps: Tensor,
llm_embedding: Tensor,
t_vec: Tensor,
mask: Tensor,
): #4068*3 ; #640*3;;640*3584
feat = None
llm_embedding = llm_embedding.detach()
txt, y = self.connector( #->640*4096,1*768
llm_embedding, t_vec, mask)
if img.ndim != 3 or txt.ndim != 3:
raise ValueError("Input img and txt tensors must have 3 dimensions.")
img = self.img_in(img) #->4068*3072
vec = self.time_in(self.timestep_embedding(timesteps, 256))
vec = vec + self.vector_in(y)
txt = self.txt_in(txt)
ids = torch.cat((txt_ids, img_ids), dim=1)
pe = self.pe_embedder(ids)
if not self.blocks_to_swap:
for block in self.double_blocks:
img, txt = block(img=img, txt=txt, vec=vec, pe=pe)
img = torch.cat((txt, img), 1)
for i, block in enumerate(self.single_blocks):
img = block(img, vec=vec, pe=pe)
if i == 9 and self.disperse_loss:
feat = img#1*6748*3072
else:
for block_idx, block in enumerate(self.double_blocks):
self.offloader_double.wait_for_block(block_idx)
img, txt = block(img=img, txt=txt, vec=vec, pe=pe)
self.offloader_double.submit_move_blocks(self.double_blocks, block_idx)
img = torch.cat((txt, img), 1)
for block_idx, block in enumerate(self.single_blocks):
self.offloader_single.wait_for_block(block_idx)
img = block(img, vec=vec, pe=pe)
self.offloader_single.submit_move_blocks(self.single_blocks, block_idx)
img = img[:, txt.shape[1]:, ...]
if self.training and self.cpu_offload_checkpointing:
img = img.to(self.device)
vec = vec.to(self.device)
img = self.final_layer(img, vec) # (N, T, patch_size ** 2 * out_channels)
return img, feat
if __name__ == "__main__":
# Example usage
params = Step1XParams(in_channels=768, out_channels=768, vec_in_dim=256, context_in_dim=768, hidden_size=768, mlp_ratio=4.0, num_heads=12, depth=12, depth_single_blocks=6, axes_dim=[1, 2, 3], theta=10000, qkv_bias=True)
model = Step1XEdit(params)
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