313 lines
13 KiB
Python
313 lines
13 KiB
Python
import re
|
|
import torch
|
|
import torch.nn as nn
|
|
|
|
from copy import deepcopy
|
|
from torch import Tensor
|
|
from torch.nn import Module, Linear, init
|
|
from typing import Any, Mapping
|
|
|
|
from .PixArt import PixArt, get_2d_sincos_pos_embed
|
|
from .PixArtMS import PixArtMSBlock, PixArtMS
|
|
from .utils import auto_grad_checkpoint
|
|
|
|
# The implementation of ControlNet-Half architrecture
|
|
# https://github.com/lllyasviel/ControlNet/discussions/188
|
|
class ControlT2IDitBlockHalf(Module):
|
|
def __init__(self, base_block: PixArtMSBlock, block_index: 0) -> None:
|
|
super().__init__()
|
|
self.copied_block = deepcopy(base_block)
|
|
self.block_index = block_index
|
|
|
|
for p in self.copied_block.parameters():
|
|
p.requires_grad_(True)
|
|
|
|
self.copied_block.load_state_dict(base_block.state_dict())
|
|
self.copied_block.train()
|
|
|
|
self.hidden_size = hidden_size = base_block.hidden_size
|
|
if self.block_index == 0:
|
|
self.before_proj = Linear(hidden_size, hidden_size)
|
|
init.zeros_(self.before_proj.weight)
|
|
init.zeros_(self.before_proj.bias)
|
|
self.after_proj = Linear(hidden_size, hidden_size)
|
|
init.zeros_(self.after_proj.weight)
|
|
init.zeros_(self.after_proj.bias)
|
|
|
|
def forward(self, x, y, t, mask=None, c=None):
|
|
|
|
if self.block_index == 0:
|
|
# the first block
|
|
c = self.before_proj(c)
|
|
c = self.copied_block(x + c, y, t, mask)
|
|
c_skip = self.after_proj(c)
|
|
else:
|
|
# load from previous c and produce the c for skip connection
|
|
c = self.copied_block(c, y, t, mask)
|
|
c_skip = self.after_proj(c)
|
|
|
|
return c, c_skip
|
|
|
|
|
|
# The implementation of ControlPixArtHalf net
|
|
class ControlPixArtHalf(Module):
|
|
# only support single res model
|
|
def __init__(self, base_model: PixArt, copy_blocks_num: int = 13) -> None:
|
|
super().__init__()
|
|
self.dtype = torch.get_default_dtype()
|
|
self.base_model = base_model.eval()
|
|
self.controlnet = []
|
|
self.copy_blocks_num = copy_blocks_num
|
|
self.total_blocks_num = len(base_model.blocks)
|
|
for p in self.base_model.parameters():
|
|
p.requires_grad_(False)
|
|
|
|
# Copy first copy_blocks_num block
|
|
for i in range(copy_blocks_num):
|
|
self.controlnet.append(ControlT2IDitBlockHalf(base_model.blocks[i], i))
|
|
self.controlnet = nn.ModuleList(self.controlnet)
|
|
|
|
def __getattr__(self, name: str) -> Tensor or Module:
|
|
if name in ['forward', 'forward_with_dpmsolver', 'forward_with_cfg', 'forward_c', 'load_state_dict']:
|
|
return self.__dict__[name]
|
|
elif name in ['base_model', 'controlnet']:
|
|
return super().__getattr__(name)
|
|
else:
|
|
return getattr(self.base_model, name)
|
|
|
|
def forward_c(self, c):
|
|
self.h, self.w = c.shape[-2]//self.patch_size, c.shape[-1]//self.patch_size
|
|
pos_embed = torch.from_numpy(get_2d_sincos_pos_embed(self.pos_embed.shape[-1], (self.h, self.w), lewei_scale=self.lewei_scale, base_size=self.base_size)).unsqueeze(0).to(c.device).to(self.dtype)
|
|
return self.x_embedder(c) + pos_embed if c is not None else c
|
|
|
|
# def forward(self, x, t, c, **kwargs):
|
|
# return self.base_model(x, t, c=self.forward_c(c), **kwargs)
|
|
def forward_raw(self, x, timestep, y, mask=None, data_info=None, c=None, **kwargs):
|
|
# modify the original PixArtMS forward function
|
|
if c is not None:
|
|
c = c.to(self.dtype)
|
|
c = self.forward_c(c)
|
|
"""
|
|
Forward pass of PixArt.
|
|
x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
|
|
t: (N,) tensor of diffusion timesteps
|
|
y: (N, 1, 120, C) tensor of class labels
|
|
"""
|
|
x = x.to(self.dtype)
|
|
timestep = timestep.to(self.dtype)
|
|
y = y.to(self.dtype)
|
|
pos_embed = self.pos_embed.to(self.dtype)
|
|
self.h, self.w = x.shape[-2]//self.patch_size, x.shape[-1]//self.patch_size
|
|
x = self.x_embedder(x) + pos_embed # (N, T, D), where T = H * W / patch_size ** 2
|
|
t = self.t_embedder(timestep.to(x.dtype)) # (N, D)
|
|
t0 = self.t_block(t)
|
|
y = self.y_embedder(y, self.training) # (N, 1, L, D)
|
|
if mask is not None:
|
|
if mask.shape[0] != y.shape[0]:
|
|
mask = mask.repeat(y.shape[0] // mask.shape[0], 1)
|
|
mask = mask.squeeze(1).squeeze(1)
|
|
y = y.squeeze(1).masked_select(mask.unsqueeze(-1) != 0).view(1, -1, x.shape[-1])
|
|
y_lens = mask.sum(dim=1).tolist()
|
|
else:
|
|
y_lens = [y.shape[2]] * y.shape[0]
|
|
y = y.squeeze(1).view(1, -1, x.shape[-1])
|
|
|
|
# define the first layer
|
|
x = auto_grad_checkpoint(self.base_model.blocks[0], x, y, t0, y_lens, **kwargs) # (N, T, D) #support grad checkpoint
|
|
|
|
if c is not None:
|
|
# update c
|
|
for index in range(1, self.copy_blocks_num + 1):
|
|
c, c_skip = auto_grad_checkpoint(self.controlnet[index - 1], x, y, t0, y_lens, c, **kwargs)
|
|
x = auto_grad_checkpoint(self.base_model.blocks[index], x + c_skip, y, t0, y_lens, **kwargs)
|
|
|
|
# update x
|
|
for index in range(self.copy_blocks_num + 1, self.total_blocks_num):
|
|
x = auto_grad_checkpoint(self.base_model.blocks[index], x, y, t0, y_lens, **kwargs)
|
|
else:
|
|
for index in range(1, self.total_blocks_num):
|
|
x = auto_grad_checkpoint(self.base_model.blocks[index], x, y, t0, y_lens, **kwargs)
|
|
|
|
x = self.final_layer(x, t) # (N, T, patch_size ** 2 * out_channels)
|
|
x = self.unpatchify(x) # (N, out_channels, H, W)
|
|
return x
|
|
|
|
def forward(self, x, timesteps, context, cn_hint=None, **kwargs):
|
|
"""
|
|
Forward pass that adapts comfy input to original forward function
|
|
x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
|
|
timesteps: (N,) tensor of diffusion timesteps
|
|
context: (N, 1, 120, C) conditioning
|
|
cn_hint: controlnet hint
|
|
"""
|
|
## Still accepts the input w/o that dim but returns garbage
|
|
if len(context.shape) == 3:
|
|
context = context.unsqueeze(1)
|
|
|
|
## run original forward pass
|
|
out = self.forward_raw(
|
|
x = x.to(self.dtype),
|
|
timestep = timesteps.to(self.dtype),
|
|
y = context.to(self.dtype),
|
|
c = cn_hint,
|
|
)
|
|
|
|
## only return EPS
|
|
out = out.to(torch.float)
|
|
eps, rest = out[:, :self.in_channels], out[:, self.in_channels:]
|
|
return eps
|
|
|
|
def forward_with_dpmsolver(self, x, t, y, data_info, c, **kwargs):
|
|
model_out = self.forward_raw(x, t, y, data_info=data_info, c=c, **kwargs)
|
|
return model_out.chunk(2, dim=1)[0]
|
|
|
|
# def forward_with_dpmsolver(self, x, t, y, data_info, c, **kwargs):
|
|
# return self.base_model.forward_with_dpmsolver(x, t, y, data_info=data_info, c=self.forward_c(c), **kwargs)
|
|
|
|
def forward_with_cfg(self, x, t, y, cfg_scale, data_info, c, **kwargs):
|
|
return self.base_model.forward_with_cfg(x, t, y, cfg_scale, data_info, c=self.forward_c(c), **kwargs)
|
|
|
|
def load_state_dict(self, state_dict: Mapping[str, Any], strict: bool = True):
|
|
if all((k.startswith('base_model') or k.startswith('controlnet')) for k in state_dict.keys()):
|
|
return super().load_state_dict(state_dict, strict)
|
|
else:
|
|
new_key = {}
|
|
for k in state_dict.keys():
|
|
new_key[k] = re.sub(r"(blocks\.\d+)(.*)", r"\1.base_block\2", k)
|
|
for k, v in new_key.items():
|
|
if k != v:
|
|
print(f"replace {k} to {v}")
|
|
state_dict[v] = state_dict.pop(k)
|
|
|
|
return self.base_model.load_state_dict(state_dict, strict)
|
|
|
|
def unpatchify(self, x):
|
|
"""
|
|
x: (N, T, patch_size**2 * C)
|
|
imgs: (N, H, W, C)
|
|
"""
|
|
c = self.out_channels
|
|
p = self.x_embedder.patch_size[0]
|
|
assert self.h * self.w == x.shape[1]
|
|
|
|
x = x.reshape(shape=(x.shape[0], self.h, self.w, p, p, c))
|
|
x = torch.einsum('nhwpqc->nchpwq', x)
|
|
imgs = x.reshape(shape=(x.shape[0], c, self.h * p, self.w * p))
|
|
return imgs
|
|
|
|
# @property
|
|
# def dtype(self):
|
|
## 返回模型参数的数据类型
|
|
# return next(self.parameters()).dtype
|
|
|
|
|
|
# The implementation for PixArtMS_Half + 1024 resolution
|
|
class ControlPixArtMSHalf(ControlPixArtHalf):
|
|
# support multi-scale res model (multi-scale model can also be applied to single reso training & inference)
|
|
def __init__(self, base_model: PixArtMS, copy_blocks_num: int = 13) -> None:
|
|
super().__init__(base_model=base_model, copy_blocks_num=copy_blocks_num)
|
|
|
|
def forward_raw(self, x, timestep, y, mask=None, data_info=None, c=None, **kwargs):
|
|
# modify the original PixArtMS forward function
|
|
"""
|
|
Forward pass of PixArt.
|
|
x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
|
|
t: (N,) tensor of diffusion timesteps
|
|
y: (N, 1, 120, C) tensor of class labels
|
|
"""
|
|
if c is not None:
|
|
c = c.to(self.dtype)
|
|
c = self.forward_c(c)
|
|
bs = x.shape[0]
|
|
x = x.to(self.dtype)
|
|
timestep = timestep.to(self.dtype)
|
|
y = y.to(self.dtype)
|
|
c_size, ar = data_info['img_hw'].to(self.dtype), data_info['aspect_ratio'].to(self.dtype)
|
|
self.h, self.w = x.shape[-2]//self.patch_size, x.shape[-1]//self.patch_size
|
|
|
|
pos_embed = torch.from_numpy(get_2d_sincos_pos_embed(self.pos_embed.shape[-1], (self.h, self.w), lewei_scale=self.lewei_scale, base_size=self.base_size)).unsqueeze(0).to(x.device).to(self.dtype)
|
|
x = self.x_embedder(x) + pos_embed # (N, T, D), where T = H * W / patch_size ** 2
|
|
t = self.t_embedder(timestep) # (N, D)
|
|
csize = self.csize_embedder(c_size, bs) # (N, D)
|
|
ar = self.ar_embedder(ar, bs) # (N, D)
|
|
t = t + torch.cat([csize, ar], dim=1)
|
|
t0 = self.t_block(t)
|
|
y = self.y_embedder(y, self.training) # (N, D)
|
|
if mask is not None:
|
|
if mask.shape[0] != y.shape[0]:
|
|
mask = mask.repeat(y.shape[0] // mask.shape[0], 1)
|
|
mask = mask.squeeze(1).squeeze(1)
|
|
y = y.squeeze(1).masked_select(mask.unsqueeze(-1) != 0).view(1, -1, x.shape[-1])
|
|
y_lens = mask.sum(dim=1).tolist()
|
|
else:
|
|
y_lens = [y.shape[2]] * y.shape[0]
|
|
y = y.squeeze(1).view(1, -1, x.shape[-1])
|
|
|
|
# define the first layer
|
|
x = auto_grad_checkpoint(self.base_model.blocks[0], x, y, t0, y_lens, **kwargs) # (N, T, D) #support grad checkpoint
|
|
|
|
if c is not None:
|
|
# update c
|
|
for index in range(1, self.copy_blocks_num + 1):
|
|
c, c_skip = auto_grad_checkpoint(self.controlnet[index - 1], x, y, t0, y_lens, c, **kwargs)
|
|
x = auto_grad_checkpoint(self.base_model.blocks[index], x + c_skip, y, t0, y_lens, **kwargs)
|
|
|
|
# update x
|
|
for index in range(self.copy_blocks_num + 1, self.total_blocks_num):
|
|
x = auto_grad_checkpoint(self.base_model.blocks[index], x, y, t0, y_lens, **kwargs)
|
|
else:
|
|
for index in range(1, self.total_blocks_num):
|
|
x = auto_grad_checkpoint(self.base_model.blocks[index], x, y, t0, y_lens, **kwargs)
|
|
|
|
x = self.final_layer(x, t) # (N, T, patch_size ** 2 * out_channels)
|
|
x = self.unpatchify(x) # (N, out_channels, H, W)
|
|
return x
|
|
|
|
def forward(self, x, timesteps, context, img_hw=None, aspect_ratio=None, cn_hint=None, **kwargs):
|
|
"""
|
|
Forward pass that adapts comfy input to original forward function
|
|
x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
|
|
timesteps: (N,) tensor of diffusion timesteps
|
|
context: (N, 1, 120, C) conditioning
|
|
img_hw: height|width conditioning
|
|
aspect_ratio: aspect ratio conditioning
|
|
cn_hint: controlnet hint
|
|
"""
|
|
## size/ar from cond with fallback based on the latent image shape.
|
|
bs = x.shape[0]
|
|
data_info = {}
|
|
if img_hw is None:
|
|
data_info["img_hw"] = torch.tensor(
|
|
[[x.shape[2]*8, x.shape[3]*8]],
|
|
dtype=self.dtype,
|
|
device=x.device
|
|
).repeat(bs, 1)
|
|
else:
|
|
data_info["img_hw"] = img_hw.to(x.dtype)
|
|
if aspect_ratio is None or True:
|
|
data_info["aspect_ratio"] = torch.tensor(
|
|
[[x.shape[2]/x.shape[3]]],
|
|
dtype=self.dtype,
|
|
device=x.device
|
|
).repeat(bs, 1)
|
|
else:
|
|
data_info["aspect_ratio"] = aspect_ratio.to(x.dtype)
|
|
|
|
## Still accepts the input w/o that dim but returns garbage
|
|
if len(context.shape) == 3:
|
|
context = context.unsqueeze(1)
|
|
|
|
## run original forward pass
|
|
out = self.forward_raw(
|
|
x = x.to(self.dtype),
|
|
timestep = timesteps.to(self.dtype),
|
|
y = context.to(self.dtype),
|
|
c = cn_hint,
|
|
data_info=data_info,
|
|
)
|
|
|
|
## only return EPS
|
|
out = out.to(torch.float)
|
|
eps, rest = out[:, :self.in_channels], out[:, self.in_channels:]
|
|
return eps
|