import einops
import torch
import torch as th
import torch.nn as nn
from ldm.modules.diffusionmodules.util import (
conv_nd,
linear,
zero_module,
timestep_embedding,
)
from einops import rearrange, repeat
from torchvision.utils import make_grid
from ldm.modules.attention import SpatialTransformer
from ldm.modules.diffusionmodules.openaimodel import UNetModel, TimestepEmbedSequential, ResBlock, Downsample, AttentionBlock
from ldm.models.diffusion.ddpm import LatentDiffusion
from ldm.util import log_txt_as_img, exists, instantiate_from_config
from ldm.models.diffusion.ddim import DDIMSampler
class ControlledUnetModel(UNetModel):
def forward(self, x, timesteps=None, context=None, control=None, only_mid_control=False, **kwargs):
hs = []
with torch.no_grad():
t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
emb = self.time_embed(t_emb)
h = x.type(self.dtype)
for module in self.input_blocks:
h = module(h, emb, context)
hs.append(h)
h = self.middle_block(h, emb, context)
if control is not None:
h += control.pop()
for i, module in enumerate(self.output_blocks):
if only_mid_control or control is None:
h = torch.cat([h, hs.pop()], dim=1)
else:
h = torch.cat([h, hs.pop() + control.pop()], dim=1)
h = module(h, emb, context)
h = h.type(x.dtype)
return self.out(h)
class ControlNet(nn.Module):
def __init__(
self,
image_size,
in_channels,
model_channels,
hint_channels,
num_res_blocks,
attention_resolutions,
dropout=0,
channel_mult=(1, 2, 4, 8),
conv_resample=True,
dims=2,
use_checkpoint=False,
use_fp16=False,
num_heads=-1,
num_head_channels=-1,
num_heads_upsample=-1,
use_scale_shift_norm=False,
resblock_updown=False,
use_new_attention_order=False,
use_spatial_transformer=False, # custom transformer support
transformer_depth=1, # custom transformer support
context_dim=None, # custom transformer support
n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model
legacy=True,
disable_self_attentions=None,
num_attention_blocks=None,
disable_middle_self_attn=False,
use_linear_in_transformer=False,
):
super().__init__()
if use_spatial_transformer:
assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...'
if context_dim is not None:
assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
from omegaconf.listconfig import ListConfig
if type(context_dim) == ListConfig:
context_dim = list(context_dim)
if num_heads_upsample == -1:
num_heads_upsample = num_heads
if num_heads == -1:
assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set'
if num_head_channels == -1:
assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'
self.dims = dims
self.image_size = image_size
self.in_channels = in_channels
self.model_channels = model_channels
if isinstance(num_res_blocks, int):
self.num_res_blocks = len(channel_mult) * [num_res_blocks]
else:
if len(num_res_blocks) != len(channel_mult):
raise ValueError("provide num_res_blocks either as an int (globally constant) or "
"as a list/tuple (per-level) with the same length as channel_mult")
self.num_res_blocks = num_res_blocks
if disable_self_attentions is not None:
# should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not
assert len(disable_self_attentions) == len(channel_mult)
if num_attention_blocks is not None:
assert len(num_attention_blocks) == len(self.num_res_blocks)
assert all(map(lambda i: self.num_res_blocks[i] >= num_attention_blocks[i], range(len(num_attention_blocks))))
print(f"Constructor of UNetModel received num_attention_blocks={num_attention_blocks}. "
f"This option has LESS priority than attention_resolutions {attention_resolutions}, "
f"i.e., in cases where num_attention_blocks[i] > 0 but 2**i not in attention_resolutions, "
f"attention will still not be set.")
self.attention_resolutions = attention_resolutions
self.dropout = dropout
self.channel_mult = channel_mult
self.conv_resample = conv_resample
self.use_checkpoint = use_checkpoint
self.dtype = th.float16 if use_fp16 else th.float32
self.num_heads = num_heads
self.num_head_channels = num_head_channels
self.num_heads_upsample = num_heads_upsample
self.predict_codebook_ids = n_embed is not None
time_embed_dim = model_channels * 4
self.time_embed = nn.Sequential(
linear(model_channels, time_embed_dim),
nn.SiLU(),
linear(time_embed_dim, time_embed_dim),
)
self.input_blocks = nn.ModuleList(
[
TimestepEmbedSequential(
conv_nd(dims, in_channels, model_channels, 3, padding=1)
)
]
)
self.zero_convs = nn.ModuleList([self.make_zero_conv(model_channels)])
self.input_hint_block = TimestepEmbedSequential(
conv_nd(dims, hint_channels, 16, 3, padding=1),
nn.SiLU(),
conv_nd(dims, 16, 16, 3, padding=1),
nn.SiLU(),
conv_nd(dims, 16, 32, 3, padding=1, stride=2),
nn.SiLU(),
conv_nd(dims, 32, 32, 3, padding=1),
nn.SiLU(),
conv_nd(dims, 32, 96, 3, padding=1, stride=2),
nn.SiLU(),
conv_nd(dims, 96, 96, 3, padding=1),
nn.SiLU(),
conv_nd(dims, 96, 256, 3, padding=1, stride=2),
nn.SiLU(),
zero_module(conv_nd(dims, 256, model_channels, 3, padding=1))
)
self._feature_size = model_channels
input_block_chans = [model_channels]
ch = model_channels
ds = 1
for level, mult in enumerate(channel_mult):
for nr in range(self.num_res_blocks[level]):
layers = [
ResBlock(
ch,
time_embed_dim,
dropout,
out_channels=mult * model_channels,
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
)
]
ch = mult * model_channels
if ds in attention_resolutions:
if num_head_channels == -1:
dim_head = ch // num_heads
else:
num_heads = ch // num_head_channels
dim_head = num_head_channels
if legacy:
# num_heads = 1
dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
if exists(disable_self_attentions):
disabled_sa = disable_self_attentions[level]
else:
disabled_sa = False
if not exists(num_attention_blocks) or nr < num_attention_blocks[level]:
layers.append(
AttentionBlock(
ch,
use_checkpoint=use_checkpoint,
num_heads=num_heads,
num_head_channels=dim_head,
use_new_attention_order=use_new_attention_order,
) if not use_spatial_transformer else SpatialTransformer(
ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,
use_checkpoint=use_checkpoint
)
)
self.input_blocks.append(TimestepEmbedSequential(*layers))
self.zero_convs.append(self.make_zero_conv(ch))
self._feature_size += ch
input_block_chans.append(ch)
if level != len(channel_mult) - 1:
out_ch = ch
self.input_blocks.append(
TimestepEmbedSequential(
ResBlock(
ch,
time_embed_dim,
dropout,
out_channels=out_ch,
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
down=True,
)
if resblock_updown
else Downsample(
ch, conv_resample, dims=dims, out_channels=out_ch
)
)
)
ch = out_ch
input_block_chans.append(ch)
self.zero_convs.append(self.make_zero_conv(ch))
ds *= 2
self._feature_size += ch
if num_head_channels == -1:
dim_head = ch // num_heads
else:
num_heads = ch // num_head_channels
dim_head = num_head_channels
if legacy:
# num_heads = 1
dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
self.middle_block = TimestepEmbedSequential(
ResBlock(
ch,
time_embed_dim,
dropout,
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
),
AttentionBlock(
ch,
use_checkpoint=use_checkpoint,
num_heads=num_heads,
num_head_channels=dim_head,
use_new_attention_order=use_new_attention_order,
) if not use_spatial_transformer else SpatialTransformer( # always uses a self-attn
ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer,
use_checkpoint=use_checkpoint
),
ResBlock(
ch,
time_embed_dim,
dropout,
dims=dims,
use_checkpoint=use_checkpoint,
use_scale_shift_norm=use_scale_shift_norm,
),
)
self.middle_block_out = self.make_zero_conv(ch)
self._feature_size += ch
def make_zero_conv(self, channels):
return TimestepEmbedSequential(zero_module(conv_nd(self.dims, channels, channels, 1, padding=0)))
def forward(self, x, hint, timesteps, context, **kwargs):
t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
emb = self.time_embed(t_emb)
guided_hint = self.input_hint_block(hint, emb, context)
outs = []
h = x.type(self.dtype)
for module, zero_conv in zip(self.input_blocks, self.zero_convs):
if guided_hint is not None:
h = module(h, emb, context)
h += guided_hint
guided_hint = None
else:
h = module(h, emb, context)
outs.append(zero_conv(h, emb, context))
h = self.middle_block(h, emb, context)
outs.append(self.middle_block_out(h, emb, context))
return outs
class ControlLDM(LatentDiffusion):
def __init__(self, control_stage_config, control_key, only_mid_control, *args, **kwargs):
super().__init__(*args, **kwargs)
self.control_model = instantiate_from_config(control_stage_config)
self.control_key = control_key
self.only_mid_control = only_mid_control
self.control_scales = [1.0] * 13
@torch.no_grad()
def get_input(self, batch, k, bs=None, *args, **kwargs):
x, c = super().get_input(batch, self.first_stage_key, *args, **kwargs)
control = batch[self.control_key]
if bs is not None:
control = control[:bs]
control = control.to(self.device)
control = einops.rearrange(control, 'b h w c -> b c h w')
control = control.to(memory_format=torch.contiguous_format).float()
return x, dict(c_crossattn=[c], c_concat=[control])
def apply_model(self, x_noisy, t, cond, *args, **kwargs):
assert isinstance(cond, dict)
diffusion_model = self.model.diffusion_model
cond_txt = torch.cat(cond['c_crossattn'], 1)
if cond['c_concat'] is None:
eps = diffusion_model(x=x_noisy, timesteps=t, context=cond_txt, control=None, only_mid_control=self.only_mid_control)
else:
control = self.control_model(x=x_noisy, hint=torch.cat(cond['c_concat'], 1), timesteps=t, context=cond_txt)
control = [c * scale for c, scale in zip(control, self.control_scales)]
eps = diffusion_model(x=x_noisy, timesteps=t, context=cond_txt, control=control, only_mid_control=self.only_mid_control)
return eps
@torch.no_grad()
def get_unconditional_conditioning(self, N):
return self.get_learned_conditioning([""] * N)
@torch.no_grad()
def log_images(self, batch, N=4, n_row=2, sample=False, ddim_steps=50, ddim_eta=0.0, return_keys=None,
quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True,
plot_diffusion_rows=False, unconditional_guidance_scale=9.0, unconditional_guidance_label=None,
use_ema_scope=True,
**kwargs):
use_ddim = ddim_steps is not None
log = dict()
z, c = self.get_input(batch, self.first_stage_key, bs=N)
c_cat, c = c["c_concat"][0][:N], c["c_crossattn"][0][:N]
N = min(z.shape[0], N)
n_row = min(z.shape[0], n_row)
log["reconstruction"] = self.decode_first_stage(z)
log["control"] = c_cat * 2.0 - 1.0
log["conditioning"] = log_txt_as_img((512, 512), batch[self.cond_stage_key], size=16)
if plot_diffusion_rows:
# get diffusion row
diffusion_row = list()
z_start = z[:n_row]
for t in range(self.num_timesteps):
if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
t = t.to(self.device).long()
noise = torch.randn_like(z_start)
z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
diffusion_row.append(self.decode_first_stage(z_noisy))
diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W
diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
log["diffusion_row"] = diffusion_grid
if sample:
# get denoise row
samples, z_denoise_row = self.sample_log(cond={"c_concat": [c_cat], "c_crossattn": [c]},
batch_size=N, ddim=use_ddim,
ddim_steps=ddim_steps, eta=ddim_eta)
x_samples = self.decode_first_stage(samples)
log["samples"] = x_samples
if plot_denoise_rows:
denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
log["denoise_row"] = denoise_grid
if unconditional_guidance_scale > 1.0:
uc_cross = self.get_unconditional_conditioning(N)
uc_cat = c_cat # torch.zeros_like(c_cat)
uc_full = {"c_concat": [uc_cat], "c_crossattn": [uc_cross]}
samples_cfg, _ = self.sample_log(cond={"c_concat": [c_cat], "c_crossattn": [c]},
batch_size=N, ddim=use_ddim,
ddim_steps=ddim_steps, eta=ddim_eta,
unconditional_guidance_scale=unconditional_guidance_scale,
unconditional_conditioning=uc_full,
)
x_samples_cfg = self.decode_first_stage(samples_cfg)
log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg
return log
@torch.no_grad()
def sample_log(self, cond, batch_size, ddim, ddim_steps, **kwargs):
ddim_sampler = DDIMSampler(self)
b, c, h, w = cond["c_concat"][0].shape
shape = (self.channels, h // 8, w // 8)
samples, intermediates = ddim_sampler.sample(ddim_steps, batch_size, shape, cond, verbose=False, **kwargs)
return samples, intermediates
def configure_optimizers(self):
lr = self.learning_rate
params = list(self.control_model.parameters())
if not self.sd_locked:
params += list(self.model.diffusion_model.output_blocks.parameters())
params += list(self.model.diffusion_model.out.parameters())
opt = torch.optim.AdamW(params, lr=lr)
return opt
def low_vram_shift(self, is_diffusing):
if is_diffusing:
self.model = self.model.cuda()
self.control_model = self.control_model.cuda()
self.first_stage_model = self.first_stage_model.cpu()
self.cond_stage_model = self.cond_stage_model.cpu()
else:
self.model = self.model.cpu()
self.control_model = self.control_model.cpu()
self.first_stage_model = self.first_stage_model.cuda()
self.cond_stage_model = self.cond_stage_model.cuda()