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modules.py
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modules.py
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from contextlib import nullcontext
from functools import partial
from typing import Dict, List, Optional, Tuple, Union
import kornia
import numpy as np
import open_clip
import torch
import torch.nn as nn
from einops import rearrange, repeat
from omegaconf import ListConfig
from torch.utils.checkpoint import checkpoint
from transformers import (
ByT5Tokenizer,
CLIPTextModel,
CLIPTokenizer,
T5EncoderModel,
T5Tokenizer,
)
from ...modules.autoencoding.regularizers import DiagonalGaussianRegularizer
from ...modules.diffusionmodules.model import Encoder
from ...modules.diffusionmodules.openaimodel import Timestep
from ...modules.diffusionmodules.util import extract_into_tensor, make_beta_schedule
from ...modules.distributions.distributions import DiagonalGaussianDistribution
from ...util import (
autocast,
count_params,
default,
disabled_train,
expand_dims_like,
instantiate_from_config,
)
class AbstractEmbModel(nn.Module):
def __init__(self):
super().__init__()
self._is_trainable = None
self._ucg_rate = None
self._input_key = None
@property
def is_trainable(self) -> bool:
return self._is_trainable
@property
def ucg_rate(self) -> Union[float, torch.Tensor]:
return self._ucg_rate
@property
def input_key(self) -> str:
return self._input_key
@is_trainable.setter
def is_trainable(self, value: bool):
self._is_trainable = value
@ucg_rate.setter
def ucg_rate(self, value: Union[float, torch.Tensor]):
self._ucg_rate = value
@input_key.setter
def input_key(self, value: str):
self._input_key = value
@is_trainable.deleter
def is_trainable(self):
del self._is_trainable
@ucg_rate.deleter
def ucg_rate(self):
del self._ucg_rate
@input_key.deleter
def input_key(self):
del self._input_key
class GeneralConditioner(nn.Module):
OUTPUT_DIM2KEYS = {2: "vector", 3: "crossattn", 4: "concat", 5: "concat"}
KEY2CATDIM = {"vector": 1, "crossattn": 2, "concat": 1}
def __init__(self, emb_models: Union[List, ListConfig]):
super().__init__()
embedders = []
for n, embconfig in enumerate(emb_models):
embedder = instantiate_from_config(embconfig)
assert isinstance(
embedder, AbstractEmbModel
), f"embedder model {embedder.__class__.__name__} has to inherit from AbstractEmbModel"
embedder.is_trainable = embconfig.get("is_trainable", False)
embedder.ucg_rate = embconfig.get("ucg_rate", 0.0)
if not embedder.is_trainable:
embedder.train = disabled_train
for param in embedder.parameters():
param.requires_grad = False
embedder.eval()
print(
f"Initialized embedder #{n}: {embedder.__class__.__name__} "
f"with {count_params(embedder, False)} params. Trainable: {embedder.is_trainable}"
)
if "input_key" in embconfig:
embedder.input_key = embconfig["input_key"]
elif "input_keys" in embconfig:
embedder.input_keys = embconfig["input_keys"]
else:
raise KeyError(
f"need either 'input_key' or 'input_keys' for embedder {embedder.__class__.__name__}"
)
embedder.legacy_ucg_val = embconfig.get("legacy_ucg_value", None)
if embedder.legacy_ucg_val is not None:
embedder.ucg_prng = np.random.RandomState()
embedders.append(embedder)
self.embedders = nn.ModuleList(embedders)
def possibly_get_ucg_val(self, embedder: AbstractEmbModel, batch: Dict) -> Dict:
assert embedder.legacy_ucg_val is not None
p = embedder.ucg_rate
val = embedder.legacy_ucg_val
for i in range(len(batch[embedder.input_key])):
if embedder.ucg_prng.choice(2, p=[1 - p, p]):
batch[embedder.input_key][i] = val
return batch
def forward(
self, batch: Dict, force_zero_embeddings: Optional[List] = None
) -> Dict:
output = dict()
if force_zero_embeddings is None:
force_zero_embeddings = []
for embedder in self.embedders:
embedding_context = nullcontext if embedder.is_trainable else torch.no_grad
with embedding_context():
if hasattr(embedder, "input_key") and (embedder.input_key is not None):
if embedder.legacy_ucg_val is not None:
batch = self.possibly_get_ucg_val(embedder, batch)
emb_out = embedder(batch[embedder.input_key])
elif hasattr(embedder, "input_keys"):
emb_out = embedder(*[batch[k] for k in embedder.input_keys])
assert isinstance(
emb_out, (torch.Tensor, list, tuple)
), f"encoder outputs must be tensors or a sequence, but got {type(emb_out)}"
if not isinstance(emb_out, (list, tuple)):
emb_out = [emb_out]
for emb in emb_out:
out_key = self.OUTPUT_DIM2KEYS[emb.dim()]
if embedder.ucg_rate > 0.0 and embedder.legacy_ucg_val is None:
emb = (
expand_dims_like(
torch.bernoulli(
(1.0 - embedder.ucg_rate)
* torch.ones(emb.shape[0], device=emb.device)
),
emb,
)
* emb
)
if (
hasattr(embedder, "input_key")
and embedder.input_key in force_zero_embeddings
):
emb = torch.zeros_like(emb)
if out_key in output:
output[out_key] = torch.cat(
(output[out_key], emb), self.KEY2CATDIM[out_key]
)
else:
output[out_key] = emb
return output
def get_unconditional_conditioning(
self, batch_c, batch_uc=None, force_uc_zero_embeddings=None
):
if force_uc_zero_embeddings is None:
force_uc_zero_embeddings = []
ucg_rates = list()
for embedder in self.embedders:
ucg_rates.append(embedder.ucg_rate)
embedder.ucg_rate = 0.0
c = self(batch_c)
uc = self(batch_c if batch_uc is None else batch_uc, force_uc_zero_embeddings)
for embedder, rate in zip(self.embedders, ucg_rates):
embedder.ucg_rate = rate
return c, uc
class InceptionV3(nn.Module):
"""Wrapper around the https://github.com/mseitzer/pytorch-fid inception
port with an additional squeeze at the end"""
def __init__(self, normalize_input=False, **kwargs):
super().__init__()
from pytorch_fid import inception
kwargs["resize_input"] = True
self.model = inception.InceptionV3(normalize_input=normalize_input, **kwargs)
def forward(self, inp):
# inp = kornia.geometry.resize(inp, (299, 299),
# interpolation='bicubic',
# align_corners=False,
# antialias=True)
# inp = inp.clamp(min=-1, max=1)
outp = self.model(inp)
if len(outp) == 1:
return outp[0].squeeze()
return outp
class IdentityEncoder(AbstractEmbModel):
def encode(self, x):
return x
def forward(self, x):
return x
class ClassEmbedder(AbstractEmbModel):
def __init__(self, embed_dim, n_classes=1000, add_sequence_dim=False):
super().__init__()
self.embedding = nn.Embedding(n_classes, embed_dim)
self.n_classes = n_classes
self.add_sequence_dim = add_sequence_dim
def forward(self, c):
c = self.embedding(c)
if self.add_sequence_dim:
c = c[:, None, :]
return c
def get_unconditional_conditioning(self, bs, device="cuda"):
uc_class = (
self.n_classes - 1
) # 1000 classes --> 0 ... 999, one extra class for ucg (class 1000)
uc = torch.ones((bs,), device=device) * uc_class
uc = {self.key: uc.long()}
return uc
class ClassEmbedderForMultiCond(ClassEmbedder):
def forward(self, batch, key=None, disable_dropout=False):
out = batch
key = default(key, self.key)
islist = isinstance(batch[key], list)
if islist:
batch[key] = batch[key][0]
c_out = super().forward(batch, key, disable_dropout)
out[key] = [c_out] if islist else c_out
return out
class FrozenT5Embedder(AbstractEmbModel):
"""Uses the T5 transformer encoder for text"""
def __init__(
self, version="google/t5-v1_1-xxl", device="cuda", max_length=77, freeze=True
): # others are google/t5-v1_1-xl and google/t5-v1_1-xxl
super().__init__()
self.tokenizer = T5Tokenizer.from_pretrained(version)
self.transformer = T5EncoderModel.from_pretrained(version)
self.device = device
self.max_length = max_length
if freeze:
self.freeze()
def freeze(self):
self.transformer = self.transformer.eval()
for param in self.parameters():
param.requires_grad = False
# @autocast
def forward(self, text):
batch_encoding = self.tokenizer(
text,
truncation=True,
max_length=self.max_length,
return_length=True,
return_overflowing_tokens=False,
padding="max_length",
return_tensors="pt",
)
tokens = batch_encoding["input_ids"].to(self.device)
with torch.autocast("cuda", enabled=False):
outputs = self.transformer(input_ids=tokens)
z = outputs.last_hidden_state
return z
def encode(self, text):
return self(text)
class FrozenByT5Embedder(AbstractEmbModel):
"""
Uses the ByT5 transformer encoder for text. Is character-aware.
"""
def __init__(
self, version="google/byt5-base", device="cuda", max_length=77, freeze=True
): # others are google/t5-v1_1-xl and google/t5-v1_1-xxl
super().__init__()
self.tokenizer = ByT5Tokenizer.from_pretrained(version)
self.transformer = T5EncoderModel.from_pretrained(version)
self.device = device
self.max_length = max_length
if freeze:
self.freeze()
def freeze(self):
self.transformer = self.transformer.eval()
for param in self.parameters():
param.requires_grad = False
def forward(self, text):
batch_encoding = self.tokenizer(
text,
truncation=True,
max_length=self.max_length,
return_length=True,
return_overflowing_tokens=False,
padding="max_length",
return_tensors="pt",
)
tokens = batch_encoding["input_ids"].to(self.device)
with torch.autocast("cuda", enabled=False):
outputs = self.transformer(input_ids=tokens)
z = outputs.last_hidden_state
return z
def encode(self, text):
return self(text)
class FrozenCLIPEmbedder(AbstractEmbModel):
"""Uses the CLIP transformer encoder for text (from huggingface)"""
LAYERS = ["last", "pooled", "hidden"]
def __init__(
self,
version="openai/clip-vit-large-patch14",
device="cuda",
max_length=77,
freeze=True,
layer="last",
layer_idx=None,
always_return_pooled=False,
): # clip-vit-base-patch32
super().__init__()
assert layer in self.LAYERS
self.tokenizer = CLIPTokenizer.from_pretrained(version)
self.transformer = CLIPTextModel.from_pretrained(version)
self.device = device
self.max_length = max_length
if freeze:
self.freeze()
self.layer = layer
self.layer_idx = layer_idx
self.return_pooled = always_return_pooled
if layer == "hidden":
assert layer_idx is not None
assert 0 <= abs(layer_idx) <= 12
def freeze(self):
self.transformer = self.transformer.eval()
for param in self.parameters():
param.requires_grad = False
@autocast
def forward(self, text):
batch_encoding = self.tokenizer(
text,
truncation=True,
max_length=self.max_length,
return_length=True,
return_overflowing_tokens=False,
padding="max_length",
return_tensors="pt",
)
tokens = batch_encoding["input_ids"].to(self.device)
outputs = self.transformer(
input_ids=tokens, output_hidden_states=self.layer == "hidden"
)
if self.layer == "last":
z = outputs.last_hidden_state
elif self.layer == "pooled":
z = outputs.pooler_output[:, None, :]
else:
z = outputs.hidden_states[self.layer_idx]
if self.return_pooled:
return z, outputs.pooler_output
return z
def encode(self, text):
return self(text)
class FrozenOpenCLIPEmbedder2(AbstractEmbModel):
"""
Uses the OpenCLIP transformer encoder for text
"""
LAYERS = ["pooled", "last", "penultimate"]
def __init__(
self,
arch="ViT-H-14",
version="laion2b_s32b_b79k",
device="cuda",
max_length=77,
freeze=True,
layer="last",
always_return_pooled=False,
legacy=True,
):
super().__init__()
assert layer in self.LAYERS
model, _, _ = open_clip.create_model_and_transforms(
arch,
device=torch.device("cpu"),
pretrained=version,
)
del model.visual
self.model = model
self.device = device
self.max_length = max_length
self.return_pooled = always_return_pooled
if freeze:
self.freeze()
self.layer = layer
if self.layer == "last":
self.layer_idx = 0
elif self.layer == "penultimate":
self.layer_idx = 1
else:
raise NotImplementedError()
self.legacy = legacy
def freeze(self):
self.model = self.model.eval()
for param in self.parameters():
param.requires_grad = False
@autocast
def forward(self, text):
tokens = open_clip.tokenize(text)
z = self.encode_with_transformer(tokens.to(self.device))
if not self.return_pooled and self.legacy:
return z
if self.return_pooled:
assert not self.legacy
return z[self.layer], z["pooled"]
return z[self.layer]
def encode_with_transformer(self, text):
x = self.model.token_embedding(text) # [batch_size, n_ctx, d_model]
x = x + self.model.positional_embedding
x = x.permute(1, 0, 2) # NLD -> LND
x = self.text_transformer_forward(x, attn_mask=self.model.attn_mask)
if self.legacy:
x = x[self.layer]
x = self.model.ln_final(x)
return x
else:
# x is a dict and will stay a dict
o = x["last"]
o = self.model.ln_final(o)
pooled = self.pool(o, text)
x["pooled"] = pooled
return x
def pool(self, x, text):
# take features from the eot embedding (eot_token is the highest number in each sequence)
x = (
x[torch.arange(x.shape[0]), text.argmax(dim=-1)]
@ self.model.text_projection
)
return x
def text_transformer_forward(self, x: torch.Tensor, attn_mask=None):
outputs = {}
for i, r in enumerate(self.model.transformer.resblocks):
if i == len(self.model.transformer.resblocks) - 1:
outputs["penultimate"] = x.permute(1, 0, 2) # LND -> NLD
if (
self.model.transformer.grad_checkpointing
and not torch.jit.is_scripting()
):
x = checkpoint(r, x, attn_mask)
else:
x = r(x, attn_mask=attn_mask)
outputs["last"] = x.permute(1, 0, 2) # LND -> NLD
return outputs
def encode(self, text):
return self(text)
class FrozenOpenCLIPEmbedder(AbstractEmbModel):
LAYERS = [
# "pooled",
"last",
"penultimate",
]
def __init__(
self,
arch="ViT-H-14",
version="laion2b_s32b_b79k",
device="cuda",
max_length=77,
freeze=True,
layer="last",
):
super().__init__()
assert layer in self.LAYERS
model, _, _ = open_clip.create_model_and_transforms(
arch, device=torch.device("cpu"), pretrained=version
)
del model.visual
self.model = model
self.device = device
self.max_length = max_length
if freeze:
self.freeze()
self.layer = layer
if self.layer == "last":
self.layer_idx = 0
elif self.layer == "penultimate":
self.layer_idx = 1
else:
raise NotImplementedError()
def freeze(self):
self.model = self.model.eval()
for param in self.parameters():
param.requires_grad = False
def forward(self, text):
tokens = open_clip.tokenize(text)
z = self.encode_with_transformer(tokens.to(self.device))
return z
def encode_with_transformer(self, text):
x = self.model.token_embedding(text) # [batch_size, n_ctx, d_model]
x = x + self.model.positional_embedding
x = x.permute(1, 0, 2) # NLD -> LND
x = self.text_transformer_forward(x, attn_mask=self.model.attn_mask)
x = x.permute(1, 0, 2) # LND -> NLD
x = self.model.ln_final(x)
return x
def text_transformer_forward(self, x: torch.Tensor, attn_mask=None):
for i, r in enumerate(self.model.transformer.resblocks):
if i == len(self.model.transformer.resblocks) - self.layer_idx:
break
if (
self.model.transformer.grad_checkpointing
and not torch.jit.is_scripting()
):
x = checkpoint(r, x, attn_mask)
else:
x = r(x, attn_mask=attn_mask)
return x
def encode(self, text):
return self(text)
class FrozenOpenCLIPImageEmbedder(AbstractEmbModel):
"""
Uses the OpenCLIP vision transformer encoder for images
"""
def __init__(
self,
arch="ViT-H-14",
version="laion2b_s32b_b79k",
device="cuda",
max_length=77,
freeze=True,
antialias=True,
ucg_rate=0.0,
unsqueeze_dim=False,
repeat_to_max_len=False,
num_image_crops=0,
output_tokens=False,
):
super().__init__()
model, _, _ = open_clip.create_model_and_transforms(
arch,
device=torch.device("cpu"),
pretrained=version,
)
del model.transformer
self.model = model
self.max_crops = num_image_crops
self.pad_to_max_len = self.max_crops > 0
self.repeat_to_max_len = repeat_to_max_len and (not self.pad_to_max_len)
self.device = device
self.max_length = max_length
if freeze:
self.freeze()
self.antialias = antialias
self.register_buffer(
"mean", torch.Tensor([0.48145466, 0.4578275, 0.40821073]), persistent=False
)
self.register_buffer(
"std", torch.Tensor([0.26862954, 0.26130258, 0.27577711]), persistent=False
)
self.ucg_rate = ucg_rate
self.unsqueeze_dim = unsqueeze_dim
self.stored_batch = None
self.model.visual.output_tokens = output_tokens
self.output_tokens = output_tokens
def preprocess(self, x):
# normalize to [0,1]
x = kornia.geometry.resize(
x,
(224, 224),
interpolation="bicubic",
align_corners=True,
antialias=self.antialias,
)
x = (x + 1.0) / 2.0
# renormalize according to clip
x = kornia.enhance.normalize(x, self.mean, self.std)
return x
def freeze(self):
self.model = self.model.eval()
for param in self.parameters():
param.requires_grad = False
@autocast
def forward(self, image, no_dropout=False):
z = self.encode_with_vision_transformer(image)
tokens = None
if self.output_tokens:
z, tokens = z[0], z[1]
z = z.to(image.dtype)
if self.ucg_rate > 0.0 and not no_dropout and not (self.max_crops > 0):
z = (
torch.bernoulli(
(1.0 - self.ucg_rate) * torch.ones(z.shape[0], device=z.device)
)[:, None]
* z
)
if tokens is not None:
tokens = (
expand_dims_like(
torch.bernoulli(
(1.0 - self.ucg_rate)
* torch.ones(tokens.shape[0], device=tokens.device)
),
tokens,
)
* tokens
)
if self.unsqueeze_dim:
z = z[:, None, :]
if self.output_tokens:
assert not self.repeat_to_max_len
assert not self.pad_to_max_len
return tokens, z
if self.repeat_to_max_len:
if z.dim() == 2:
z_ = z[:, None, :]
else:
z_ = z
return repeat(z_, "b 1 d -> b n d", n=self.max_length), z
elif self.pad_to_max_len:
assert z.dim() == 3
z_pad = torch.cat(
(
z,
torch.zeros(
z.shape[0],
self.max_length - z.shape[1],
z.shape[2],
device=z.device,
),
),
1,
)
return z_pad, z_pad[:, 0, ...]
return z
def encode_with_vision_transformer(self, img):
# if self.max_crops > 0:
# img = self.preprocess_by_cropping(img)
if img.dim() == 5:
assert self.max_crops == img.shape[1]
img = rearrange(img, "b n c h w -> (b n) c h w")
img = self.preprocess(img)
if not self.output_tokens:
assert not self.model.visual.output_tokens
x = self.model.visual(img)
tokens = None
else:
assert self.model.visual.output_tokens
x, tokens = self.model.visual(img)
if self.max_crops > 0:
x = rearrange(x, "(b n) d -> b n d", n=self.max_crops)
# drop out between 0 and all along the sequence axis
x = (
torch.bernoulli(
(1.0 - self.ucg_rate)
* torch.ones(x.shape[0], x.shape[1], 1, device=x.device)
)
* x
)
if tokens is not None:
tokens = rearrange(tokens, "(b n) t d -> b t (n d)", n=self.max_crops)
print(
f"You are running very experimental token-concat in {self.__class__.__name__}. "
f"Check what you are doing, and then remove this message."
)
if self.output_tokens:
return x, tokens
return x
def encode(self, text):
return self(text)
class FrozenCLIPT5Encoder(AbstractEmbModel):
def __init__(
self,
clip_version="openai/clip-vit-large-patch14",
t5_version="google/t5-v1_1-xl",
device="cuda",
clip_max_length=77,
t5_max_length=77,
):
super().__init__()
self.clip_encoder = FrozenCLIPEmbedder(
clip_version, device, max_length=clip_max_length
)
self.t5_encoder = FrozenT5Embedder(t5_version, device, max_length=t5_max_length)
print(
f"{self.clip_encoder.__class__.__name__} has {count_params(self.clip_encoder) * 1.e-6:.2f} M parameters, "
f"{self.t5_encoder.__class__.__name__} comes with {count_params(self.t5_encoder) * 1.e-6:.2f} M params."
)
def encode(self, text):
return self(text)
def forward(self, text):
clip_z = self.clip_encoder.encode(text)
t5_z = self.t5_encoder.encode(text)
return [clip_z, t5_z]
class SpatialRescaler(nn.Module):
def __init__(
self,
n_stages=1,
method="bilinear",
multiplier=0.5,
in_channels=3,
out_channels=None,
bias=False,
wrap_video=False,
kernel_size=1,
remap_output=False,
):
super().__init__()
self.n_stages = n_stages
assert self.n_stages >= 0
assert method in [
"nearest",
"linear",
"bilinear",
"trilinear",
"bicubic",
"area",
]
self.multiplier = multiplier
self.interpolator = partial(torch.nn.functional.interpolate, mode=method)
self.remap_output = out_channels is not None or remap_output
if self.remap_output:
print(
f"Spatial Rescaler mapping from {in_channels} to {out_channels} channels after resizing."
)
self.channel_mapper = nn.Conv2d(
in_channels,
out_channels,
kernel_size=kernel_size,
bias=bias,
padding=kernel_size // 2,
)
self.wrap_video = wrap_video
def forward(self, x):
if self.wrap_video and x.ndim == 5:
B, C, T, H, W = x.shape
x = rearrange(x, "b c t h w -> b t c h w")
x = rearrange(x, "b t c h w -> (b t) c h w")
for stage in range(self.n_stages):
x = self.interpolator(x, scale_factor=self.multiplier)
if self.wrap_video:
x = rearrange(x, "(b t) c h w -> b t c h w", b=B, t=T, c=C)
x = rearrange(x, "b t c h w -> b c t h w")
if self.remap_output:
x = self.channel_mapper(x)
return x
def encode(self, x):
return self(x)
class LowScaleEncoder(nn.Module):
def __init__(
self,
model_config,
linear_start,
linear_end,
timesteps=1000,
max_noise_level=250,
output_size=64,
scale_factor=1.0,
):
super().__init__()
self.max_noise_level = max_noise_level
self.model = instantiate_from_config(model_config)
self.augmentation_schedule = self.register_schedule(
timesteps=timesteps, linear_start=linear_start, linear_end=linear_end
)
self.out_size = output_size
self.scale_factor = scale_factor
def register_schedule(
self,
beta_schedule="linear",
timesteps=1000,
linear_start=1e-4,
linear_end=2e-2,
cosine_s=8e-3,
):
betas = make_beta_schedule(
beta_schedule,
timesteps,
linear_start=linear_start,
linear_end=linear_end,
cosine_s=cosine_s,
)
alphas = 1.0 - betas
alphas_cumprod = np.cumprod(alphas, axis=0)
alphas_cumprod_prev = np.append(1.0, alphas_cumprod[:-1])
(timesteps,) = betas.shape
self.num_timesteps = int(timesteps)
self.linear_start = linear_start
self.linear_end = linear_end
assert (
alphas_cumprod.shape[0] == self.num_timesteps
), "alphas have to be defined for each timestep"
to_torch = partial(torch.tensor, dtype=torch.float32)
self.register_buffer("betas", to_torch(betas))
self.register_buffer("alphas_cumprod", to_torch(alphas_cumprod))
self.register_buffer("alphas_cumprod_prev", to_torch(alphas_cumprod_prev))
# calculations for diffusion q(x_t | x_{t-1}) and others
self.register_buffer("sqrt_alphas_cumprod", to_torch(np.sqrt(alphas_cumprod)))
self.register_buffer(
"sqrt_one_minus_alphas_cumprod", to_torch(np.sqrt(1.0 - alphas_cumprod))
)
self.register_buffer(
"log_one_minus_alphas_cumprod", to_torch(np.log(1.0 - alphas_cumprod))
)
self.register_buffer(
"sqrt_recip_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod))
)
self.register_buffer(
"sqrt_recipm1_alphas_cumprod", to_torch(np.sqrt(1.0 / alphas_cumprod - 1))
)
def q_sample(self, x_start, t, noise=None):
noise = default(noise, lambda: torch.randn_like(x_start))
return (
extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
+ extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape)
* noise
)
def forward(self, x):
z = self.model.encode(x)
if isinstance(z, DiagonalGaussianDistribution):
z = z.sample()
z = z * self.scale_factor
noise_level = torch.randint(
0, self.max_noise_level, (x.shape[0],), device=x.device
).long()
z = self.q_sample(z, noise_level)
if self.out_size is not None:
z = torch.nn.functional.interpolate(z, size=self.out_size, mode="nearest")
# z = z.repeat_interleave(2, -2).repeat_interleave(2, -1)
return z, noise_level
def decode(self, z):
z = z / self.scale_factor
return self.model.decode(z)
class ConcatTimestepEmbedderND(AbstractEmbModel):
"""embeds each dimension independently and concatenates them"""
def __init__(self, outdim):
super().__init__()
self.timestep = Timestep(outdim)
self.outdim = outdim
def forward(self, x):
if x.ndim == 1:
x = x[:, None]
assert len(x.shape) == 2
b, dims = x.shape[0], x.shape[1]
x = rearrange(x, "b d -> (b d)")
emb = self.timestep(x)
emb = rearrange(emb, "(b d) d2 -> b (d d2)", b=b, d=dims, d2=self.outdim)
return emb
class GaussianEncoder(Encoder, AbstractEmbModel):
def __init__(
self, weight: float = 1.0, flatten_output: bool = True, *args, **kwargs
):
super().__init__(*args, **kwargs)
self.posterior = DiagonalGaussianRegularizer()
self.weight = weight
self.flatten_output = flatten_output
def forward(self, x) -> Tuple[Dict, torch.Tensor]:
z = super().forward(x)
z, log = self.posterior(z)
log["loss"] = log["kl_loss"]
log["weight"] = self.weight
if self.flatten_output:
z = rearrange(z, "b c h w -> b (h w ) c")
return log, z