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import torch
import torch.nn as nn
from packaging import version
from einops import repeat, rearrange
from diffusers.utils import _get_model_file
from diffusers.models.modeling_utils import load_state_dict
from ...modules.diffusionmodules.augment_pipeline import AugmentPipe
from ...modules.encoders.modules import ConcatTimestepEmbedderND
from ...util import append_dims
OPENAIUNETWRAPPER = "sgm.modules.diffusionmodules.wrappers.OpenAIWrapper"
class IdentityWrapper(nn.Module):
def __init__(self, diffusion_model, compile_model: bool = False):
super().__init__()
compile = (
torch.compile
if (version.parse(torch.__version__) >= version.parse("2.0.0"))
and compile_model
else lambda x: x
)
self.diffusion_model = compile(diffusion_model)
def forward(self, *args, **kwargs):
return self.diffusion_model(*args, **kwargs)
class OpenAIWrapper(IdentityWrapper):
def __init__(
self,
diffusion_model,
compile_model: bool = False,
ada_aug_percent=0.0,
fix_image_leak=False,
add_embeddings=False,
im_size=[64, 64],
n_channels=4,
):
super().__init__(diffusion_model, compile_model)
self.fix_image_leak = fix_image_leak
if fix_image_leak:
self.beta_m = 15
self.a = 5
self.noise_encoder = ConcatTimestepEmbedderND(256)
self.augment_pipe = None
if ada_aug_percent > 0.0:
augment_kwargs = dict(
xflip=1e8, yflip=1, scale=1, rotate_frac=1, aniso=1, translate_frac=1
)
self.augment_pipe = AugmentPipe(ada_aug_percent, **augment_kwargs)
self.add_embeddings = add_embeddings
if add_embeddings:
self.learned_mask = nn.Parameter(
torch.zeros(n_channels, im_size[0], im_size[1])
)
def get_noised_input(
self, sigmas_bc: torch.Tensor, noise: torch.Tensor, input: torch.Tensor
) -> torch.Tensor:
noised_input = input + noise * sigmas_bc
return noised_input
def forward(
self, x: torch.Tensor, t: torch.Tensor, c: dict, **kwargs
) -> torch.Tensor:
cond_cat = c.get("concat", torch.Tensor([]).type_as(x))
if len(cond_cat.shape) and cond_cat.shape[0]:
T = x.shape[0] // cond_cat.shape[0]
if self.fix_image_leak:
noise_aug_strength = get_sigma_s(
rearrange(t, "(b t) ... -> b t ...", b=T)[: cond_cat.shape[0], 0] / 700,
self.a,
self.beta_m,
)
noise_aug = append_dims(noise_aug_strength, 4).to(x.device)
noise = torch.randn_like(noise_aug)
cond_cat = self.get_noised_input(noise_aug, noise, cond_cat)
noise_emb = self.noise_encoder(noise_aug_strength).to(x.device)
c["vector"] = (
noise_emb
if "vector" not in c
else torch.cat([c["vector"], noise_emb], dim=1)
)
if (
len(cond_cat.shape)
and cond_cat.shape[0]
and x.shape[0] != cond_cat.shape[0]
):
cond_cat = repeat(cond_cat, "b c h w -> b c t h w", t=T)
cond_cat = rearrange(cond_cat, "b c t h w -> (b t) c h w")
x = torch.cat((x, cond_cat), dim=1)
if self.add_embeddings:
learned_mask = repeat(
self.learned_mask.to(x.device), "c h w -> b c h w", b=cond_cat.shape[0]
)
x = torch.cat((x, learned_mask), dim=1)
if self.augment_pipe is not None:
x, labels = self.augment_pipe(x)
else:
labels = torch.zeros(x.shape[0], 9, device=x.device)
return self.diffusion_model(
x,
timesteps=t,
context=c.get("crossattn", None),
reference_context=c.get("reference", None),
y=c.get("vector", None),
audio_emb=c.get("audio_emb", None),
landmarks=c.get("landmarks", None),
aug_labels=labels,
**kwargs,
)
class DubbingWrapper(IdentityWrapper):
def __init__(self, diffusion_model, compile_model: bool = False, mask_input=False):
super().__init__(diffusion_model, compile_model)
self.mask_input = mask_input
def forward(
self, x: torch.Tensor, t: torch.Tensor, c: dict, **kwargs
) -> torch.Tensor:
cond_cat = c.get("concat", torch.Tensor([]).type_as(x))
if len(cond_cat.shape):
T = x.shape[0] // cond_cat.shape[0]
if cond_cat.shape[1] == 4:
cond_cat = repeat(cond_cat, "b c h w -> b (t c) h w", t=T)
cond_cat = rearrange(cond_cat, "b (t c) h w -> (b t) c h w", t=T)
x = torch.cat((x, cond_cat), dim=1)
out = self.diffusion_model(
x,
timesteps=t,
context=c.get("crossattn", None),
y=c.get("vector", None),
audio_emb=c.get("audio_emb", None),
skip_spatial_attention_at=c.get("skip_spatial_attention_at", None),
skip_temporal_attention_at=c.get("skip_temporal_attention_at", None),
**kwargs,
)
return out
class StabilityWrapper(IdentityWrapper):
def __init__(
self,
diffusion_model,
compile_model: bool = False,
use_ipadapter: bool = False,
ipadapter_model: str = "ip-adapter_sd15.bin",
adapter_scale: float = 1.0,
n_adapters: int = 1,
skip_text_emb: bool = False,
# pass_image_emb_to_hidden_states: bool = False,
):
super().__init__(diffusion_model, compile_model)
self.use_ipadapter = use_ipadapter
# self.pass_image_emb_to_hidden_states = pass_image_emb_to_hidden_states
if use_ipadapter:
model_file = _get_model_file(
"h94/IP-Adapter",
weights_name=ipadapter_model, # ip-adapter_sd15.bin
# cache_dir="/vol/paramonos2/projects/antoni/.cache",
subfolder="models",
)
state_dict = load_state_dict(model_file)
state_dict = [load_state_dict(model_file)] * n_adapters
print(f"Loading IP-Adapter weights from {model_file}")
diffusion_model.set_ip_adapter_scale(adapter_scale)
def forward(
self, x: torch.Tensor, t: torch.Tensor, c: dict, **kwargs
) -> torch.Tensor:
added_cond_kwargs = None
if self.use_ipadapter:
added_cond_kwargs = {"image_embeds": c.get("image_embeds", None)}
landmarks = c.get("landmarks", None)
if landmarks is not None:
added_cond_kwargs["image_embeds"] = [
added_cond_kwargs["image_embeds"],
landmarks,
]
cond_cat = c.get("concat", torch.Tensor([]).type_as(x))
if len(cond_cat.shape) and cond_cat.shape[0]:
cond_cat = repeat(
cond_cat, "b c h w -> b c t h w", t=x.shape[0] // cond_cat.shape[0]
)
cond_cat = rearrange(cond_cat, "b c t h w -> (b t) c h w")
x = torch.cat((x, cond_cat), dim=1)
return self.diffusion_model(
x,
t,
encoder_hidden_states=c.get("crossattn", None),
added_cond_kwargs=added_cond_kwargs,
audio_emb=c.get("audio_emb", None),
**kwargs,
)[0]
def logit_normal_sampler(m, s=1, beta_m=15, sample_num=1000000):
y_samples = torch.randn(sample_num) * s + m
x_samples = beta_m * (torch.exp(y_samples) / (1 + torch.exp(y_samples)))
return x_samples
def mu_t(t, a=5, mu_max=1):
t = t.to("cpu")
return 2 * mu_max * t**a - mu_max
def get_sigma_s(t, a, beta_m):
mu = mu_t(t, a=a)
sigma_s = logit_normal_sampler(m=mu, sample_num=t.shape[0], beta_m=beta_m)
return sigma_s
class InterpolationWrapper(IdentityWrapper):
def __init__(
self,
diffusion_model,
compile_model: bool = False,
im_size=[512, 512],
n_channels=4,
starting_mask_method="zeros",
add_mask=True,
fix_image_leak=False,
):
super().__init__(diffusion_model, compile_model)
im_size = [
x // 8 for x in im_size
] # 8 is the default downscaling factor in the vae model
if starting_mask_method == "zeros":
self.learned_mask = nn.Parameter(
torch.zeros(n_channels, im_size[0], im_size[1])
)
elif starting_mask_method == "ones":
self.learned_mask = nn.Parameter(
torch.ones(n_channels, im_size[0], im_size[1])
)
elif starting_mask_method == "random":
self.learned_mask = nn.Parameter(
torch.randn(n_channels, im_size[0], im_size[1])
)
elif starting_mask_method == "none":
self.learned_mask = None
elif starting_mask_method == "fixed_ones":
self.learned_mask = torch.ones(n_channels, im_size[0], im_size[1])
elif starting_mask_method == "fixed_zeros":
self.learned_mask = torch.zeros(n_channels, im_size[0], im_size[1])
else:
raise NotImplementedError(
f"Unknown stating_mask_method: {starting_mask_method}"
)
self.add_mask = add_mask
self.fix_image_leak = fix_image_leak
if fix_image_leak:
self.beta_m = 15
self.a = 5
self.noise_encoder = ConcatTimestepEmbedderND(256)
def get_noised_input(
self, sigmas_bc: torch.Tensor, noise: torch.Tensor, input: torch.Tensor
) -> torch.Tensor:
noised_input = input + noise * sigmas_bc
return noised_input
def forward(
self, x: torch.Tensor, t: torch.Tensor, c: dict, **kwargs
) -> torch.Tensor:
cond_cat = c.get("concat", torch.Tensor([]).type_as(x))
T = x.shape[0] // cond_cat.shape[0]
if self.fix_image_leak:
noise_aug_strength = get_sigma_s(
rearrange(t, "(b t) ... -> b t ...", b=T)[: cond_cat.shape[0], 0] / 700,
self.a,
self.beta_m,
)
noise_aug = append_dims(noise_aug_strength, 4).to(x.device)
noise = torch.randn_like(noise_aug)
cond_cat = self.get_noised_input(noise_aug, noise, cond_cat)
noise_emb = self.noise_encoder(noise_aug_strength).to(x.device)
# cond["vector"] = noise_emb if "vector" not in cond else torch.cat([cond["vector"], noise_emb], dim=1)
c["vector"] = noise_emb
cond_cat = rearrange(cond_cat, "b (t c) h w -> b c t h w", t=2)
start, end = cond_cat.chunk(2, dim=2)
if self.learned_mask is None:
learned_mask = torch.stack(
[start.squeeze(2)] * (T // 2 - 1) + [end.squeeze(2)] * (T // 2 - 1),
dim=2,
)
else:
learned_mask = repeat(
self.learned_mask.to(x.device), "c h w -> b c h w", b=cond_cat.shape[0]
)
ones_mask = torch.ones_like(learned_mask)[:, 0].unsqueeze(1)
zeros_mask = torch.zeros_like(learned_mask)[:, 0].unsqueeze(1)
if self.learned_mask is None:
cond_seq = torch.cat([start] + [learned_mask] + [end], dim=2)
else:
cond_seq = torch.stack(
[start.squeeze(2)] + [learned_mask] * (T - 2) + [end.squeeze(2)], dim=2
)
cond_seq = rearrange(cond_seq, "b c t h w -> (b t) c h w")
x = torch.cat((x, cond_seq), dim=1)
if self.add_mask:
mask_seq = torch.stack(
[ones_mask] + [zeros_mask] * (T - 2) + [ones_mask], dim=2
)
mask_seq = rearrange(mask_seq, "b c t h w -> (b t) c h w")
x = torch.cat((x, mask_seq), dim=1)
return self.diffusion_model(
x,
timesteps=t,
context=c.get("crossattn", None),
y=c.get("vector", None),
audio_emb=c.get("audio_emb", None),
**kwargs,
)
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