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add vae and fix bug in dit

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Former-commit-id: 2dd4254c22ce671d5cd5e7d22a2386b7660fbdc4
This commit is contained in:
LinB203
2024-03-06 19:32:20 +08:00
parent ec0dd141e1
commit 5a80da7ec1
24 changed files with 591 additions and 1380 deletions
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opensora/dataset/__init__.py
+4 -4
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@@ -1,12 +1,12 @@
from torchvision.transforms import Compose
from torchvision.transforms._transforms_video import ToTensorVideo, RandomHorizontalFlipVideo
from opensora.models.ae import vae, vqvae, videovae, videovqvae
from .feature_datasets import LandscopeFeatures
from torchvision import transforms
from torchvision.transforms import Lambda
from .transform import LongSideScale, TemporalRandomCrop
from .transform import ToTensorVideo, TemporalRandomCrop, RandomHorizontalFlipVideo, CenterCropResizeVideo
from .ucf101 import UCF101
@@ -29,8 +29,8 @@ def getdataset(args):
elif args.dataset == 'ucf101':
transform = Compose(
[
ToTensorVideo(), # cthw
LongSideScale(size=args.max_image_size),
ToTensorVideo(), # TCHW
CenterCropResizeVideo(size=args.max_image_size),
RandomHorizontalFlipVideo(p=0.5),
norm_fun,
]
opensora/dataset/transform.py
+466 -30
View File
@@ -1,12 +1,422 @@
import math
import random
import torch
from torchvision import transforms
from torchvision.transforms import Lambda
import random
import numbers
from torchvision.transforms import RandomCrop, RandomResizedCrop
def _is_tensor_video_clip(clip):
if not torch.is_tensor(clip):
raise TypeError("clip should be Tensor. Got %s" % type(clip))
if not clip.ndimension() == 4:
raise ValueError("clip should be 4D. Got %dD" % clip.dim())
return True
def center_crop_arr(pil_image, image_size):
"""
Center cropping implementation from ADM.
https://github.com/openai/guided-diffusion/blob/8fb3ad9197f16bbc40620447b2742e13458d2831/guided_diffusion/image_datasets.py#L126
"""
while min(*pil_image.size) >= 2 * image_size:
pil_image = pil_image.resize(
tuple(x // 2 for x in pil_image.size), resample=Image.BOX
)
scale = image_size / min(*pil_image.size)
pil_image = pil_image.resize(
tuple(round(x * scale) for x in pil_image.size), resample=Image.BICUBIC
)
arr = np.array(pil_image)
crop_y = (arr.shape[0] - image_size) // 2
crop_x = (arr.shape[1] - image_size) // 2
return Image.fromarray(arr[crop_y: crop_y + image_size, crop_x: crop_x + image_size])
def crop(clip, i, j, h, w):
"""
Args:
clip (torch.tensor): Video clip to be cropped. Size is (T, C, H, W)
"""
if len(clip.size()) != 4:
raise ValueError("clip should be a 4D tensor")
return clip[..., i: i + h, j: j + w]
def resize(clip, target_size, interpolation_mode):
if len(target_size) != 2:
raise ValueError(f"target size should be tuple (height, width), instead got {target_size}")
return torch.nn.functional.interpolate(clip, size=target_size, mode=interpolation_mode, align_corners=False)
def resize_scale(clip, target_size, interpolation_mode):
if len(target_size) != 2:
raise ValueError(f"target size should be tuple (height, width), instead got {target_size}")
H, W = clip.size(-2), clip.size(-1)
scale_ = target_size[0] / min(H, W)
return torch.nn.functional.interpolate(clip, scale_factor=scale_, mode=interpolation_mode, align_corners=False)
def resized_crop(clip, i, j, h, w, size, interpolation_mode="bilinear"):
"""
Do spatial cropping and resizing to the video clip
Args:
clip (torch.tensor): Video clip to be cropped. Size is (T, C, H, W)
i (int): i in (i,j) i.e coordinates of the upper left corner.
j (int): j in (i,j) i.e coordinates of the upper left corner.
h (int): Height of the cropped region.
w (int): Width of the cropped region.
size (tuple(int, int)): height and width of resized clip
Returns:
clip (torch.tensor): Resized and cropped clip. Size is (T, C, H, W)
"""
if not _is_tensor_video_clip(clip):
raise ValueError("clip should be a 4D torch.tensor")
clip = crop(clip, i, j, h, w)
clip = resize(clip, size, interpolation_mode)
return clip
def center_crop(clip, crop_size):
if not _is_tensor_video_clip(clip):
raise ValueError("clip should be a 4D torch.tensor")
h, w = clip.size(-2), clip.size(-1)
th, tw = crop_size
if h < th or w < tw:
raise ValueError("height and width must be no smaller than crop_size")
i = int(round((h - th) / 2.0))
j = int(round((w - tw) / 2.0))
return crop(clip, i, j, th, tw)
def center_crop_using_short_edge(clip):
if not _is_tensor_video_clip(clip):
raise ValueError("clip should be a 4D torch.tensor")
h, w = clip.size(-2), clip.size(-1)
if h < w:
th, tw = h, h
i = 0
j = int(round((w - tw) / 2.0))
else:
th, tw = w, w
i = int(round((h - th) / 2.0))
j = 0
return crop(clip, i, j, th, tw)
def random_shift_crop(clip):
'''
Slide along the long edge, with the short edge as crop size
'''
if not _is_tensor_video_clip(clip):
raise ValueError("clip should be a 4D torch.tensor")
h, w = clip.size(-2), clip.size(-1)
if h <= w:
long_edge = w
short_edge = h
else:
long_edge = h
short_edge = w
th, tw = short_edge, short_edge
i = torch.randint(0, h - th + 1, size=(1,)).item()
j = torch.randint(0, w - tw + 1, size=(1,)).item()
return crop(clip, i, j, th, tw)
def to_tensor(clip):
"""
Convert tensor data type from uint8 to float, divide value by 255.0 and
permute the dimensions of clip tensor
Args:
clip (torch.tensor, dtype=torch.uint8): Size is (T, C, H, W)
Return:
clip (torch.tensor, dtype=torch.float): Size is (T, C, H, W)
"""
_is_tensor_video_clip(clip)
if not clip.dtype == torch.uint8:
raise TypeError("clip tensor should have data type uint8. Got %s" % str(clip.dtype))
# return clip.float().permute(3, 0, 1, 2) / 255.0
return clip.float() / 255.0
def normalize(clip, mean, std, inplace=False):
"""
Args:
clip (torch.tensor): Video clip to be normalized. Size is (T, C, H, W)
mean (tuple): pixel RGB mean. Size is (3)
std (tuple): pixel standard deviation. Size is (3)
Returns:
normalized clip (torch.tensor): Size is (T, C, H, W)
"""
if not _is_tensor_video_clip(clip):
raise ValueError("clip should be a 4D torch.tensor")
if not inplace:
clip = clip.clone()
mean = torch.as_tensor(mean, dtype=clip.dtype, device=clip.device)
# print(mean)
std = torch.as_tensor(std, dtype=clip.dtype, device=clip.device)
clip.sub_(mean[:, None, None, None]).div_(std[:, None, None, None])
return clip
def hflip(clip):
"""
Args:
clip (torch.tensor): Video clip to be normalized. Size is (T, C, H, W)
Returns:
flipped clip (torch.tensor): Size is (T, C, H, W)
"""
if not _is_tensor_video_clip(clip):
raise ValueError("clip should be a 4D torch.tensor")
return clip.flip(-1)
class RandomCropVideo:
def __init__(self, size):
if isinstance(size, numbers.Number):
self.size = (int(size), int(size))
else:
self.size = size
def __call__(self, clip):
"""
Args:
clip (torch.tensor): Video clip to be cropped. Size is (T, C, H, W)
Returns:
torch.tensor: randomly cropped video clip.
size is (T, C, OH, OW)
"""
i, j, h, w = self.get_params(clip)
return crop(clip, i, j, h, w)
def get_params(self, clip):
h, w = clip.shape[-2:]
th, tw = self.size
if h < th or w < tw:
raise ValueError(f"Required crop size {(th, tw)} is larger than input image size {(h, w)}")
if w == tw and h == th:
return 0, 0, h, w
i = torch.randint(0, h - th + 1, size=(1,)).item()
j = torch.randint(0, w - tw + 1, size=(1,)).item()
return i, j, th, tw
def __repr__(self) -> str:
return f"{self.__class__.__name__}(size={self.size})"
class CenterCropResizeVideo:
'''
First use the short side for cropping length,
center crop video, then resize to the specified size
'''
def __init__(
self,
size,
interpolation_mode="bilinear",
):
if isinstance(size, tuple):
if len(size) != 2:
raise ValueError(f"size should be tuple (height, width), instead got {size}")
self.size = size
else:
self.size = (size, size)
self.interpolation_mode = interpolation_mode
def __call__(self, clip):
"""
Args:
clip (torch.tensor): Video clip to be cropped. Size is (T, C, H, W)
Returns:
torch.tensor: scale resized / center cropped video clip.
size is (T, C, crop_size, crop_size)
"""
clip_center_crop = center_crop_using_short_edge(clip)
clip_center_crop_resize = resize(clip_center_crop, target_size=self.size,
interpolation_mode=self.interpolation_mode)
return clip_center_crop_resize
def __repr__(self) -> str:
return f"{self.__class__.__name__}(size={self.size}, interpolation_mode={self.interpolation_mode}"
class UCFCenterCropVideo:
'''
First scale to the specified size in equal proportion to the short edge,
then center cropping
'''
def __init__(
self,
size,
interpolation_mode="bilinear",
):
if isinstance(size, tuple):
if len(size) != 2:
raise ValueError(f"size should be tuple (height, width), instead got {size}")
self.size = size
else:
self.size = (size, size)
self.interpolation_mode = interpolation_mode
def __call__(self, clip):
"""
Args:
clip (torch.tensor): Video clip to be cropped. Size is (T, C, H, W)
Returns:
torch.tensor: scale resized / center cropped video clip.
size is (T, C, crop_size, crop_size)
"""
clip_resize = resize_scale(clip=clip, target_size=self.size, interpolation_mode=self.interpolation_mode)
clip_center_crop = center_crop(clip_resize, self.size)
return clip_center_crop
def __repr__(self) -> str:
return f"{self.__class__.__name__}(size={self.size}, interpolation_mode={self.interpolation_mode}"
class KineticsRandomCropResizeVideo:
'''
Slide along the long edge, with the short edge as crop size. And resie to the desired size.
'''
def __init__(
self,
size,
interpolation_mode="bilinear",
):
if isinstance(size, tuple):
if len(size) != 2:
raise ValueError(f"size should be tuple (height, width), instead got {size}")
self.size = size
else:
self.size = (size, size)
self.interpolation_mode = interpolation_mode
def __call__(self, clip):
clip_random_crop = random_shift_crop(clip)
clip_resize = resize(clip_random_crop, self.size, self.interpolation_mode)
return clip_resize
class CenterCropVideo:
def __init__(
self,
size,
interpolation_mode="bilinear",
):
if isinstance(size, tuple):
if len(size) != 2:
raise ValueError(f"size should be tuple (height, width), instead got {size}")
self.size = size
else:
self.size = (size, size)
self.interpolation_mode = interpolation_mode
def __call__(self, clip):
"""
Args:
clip (torch.tensor): Video clip to be cropped. Size is (T, C, H, W)
Returns:
torch.tensor: center cropped video clip.
size is (T, C, crop_size, crop_size)
"""
clip_center_crop = center_crop(clip, self.size)
return clip_center_crop
def __repr__(self) -> str:
return f"{self.__class__.__name__}(size={self.size}, interpolation_mode={self.interpolation_mode}"
class NormalizeVideo:
"""
Normalize the video clip by mean subtraction and division by standard deviation
Args:
mean (3-tuple): pixel RGB mean
std (3-tuple): pixel RGB standard deviation
inplace (boolean): whether do in-place normalization
"""
def __init__(self, mean, std, inplace=False):
self.mean = mean
self.std = std
self.inplace = inplace
def __call__(self, clip):
"""
Args:
clip (torch.tensor): video clip must be normalized. Size is (C, T, H, W)
"""
return normalize(clip, self.mean, self.std, self.inplace)
def __repr__(self) -> str:
return f"{self.__class__.__name__}(mean={self.mean}, std={self.std}, inplace={self.inplace})"
class ToTensorVideo:
"""
Convert tensor data type from uint8 to float, divide value by 255.0 and
permute the dimensions of clip tensor
"""
def __init__(self):
pass
def __call__(self, clip):
"""
Args:
clip (torch.tensor, dtype=torch.uint8): Size is (T, C, H, W)
Return:
clip (torch.tensor, dtype=torch.float): Size is (T, C, H, W)
"""
return to_tensor(clip)
def __repr__(self) -> str:
return self.__class__.__name__
class RandomHorizontalFlipVideo:
"""
Flip the video clip along the horizontal direction with a given probability
Args:
p (float): probability of the clip being flipped. Default value is 0.5
"""
def __init__(self, p=0.5):
self.p = p
def __call__(self, clip):
"""
Args:
clip (torch.tensor): Size is (T, C, H, W)
Return:
clip (torch.tensor): Size is (T, C, H, W)
"""
if random.random() < self.p:
clip = hflip(clip)
return clip
def __repr__(self) -> str:
return f"{self.__class__.__name__}(p={self.p})"
# ------------------------------------------------------------
# --------------------- Sampling ---------------------------
# ------------------------------------------------------------
class TemporalRandomCrop(object):
"""Temporally crop the given frame indices at a random location.
@@ -24,30 +434,56 @@ class TemporalRandomCrop(object):
return begin_index, end_index
class LongSideScale(torch.nn.Module):
"""
``nn.Module`` wrapper for ``pytorchvideo.transforms.functional.short_side_scale``.
"""
if __name__ == '__main__':
from torchvision import transforms
import torchvision.io as io
import numpy as np
from torchvision.utils import save_image
import os
def __init__(
self, size: int, interpolation: str = "bilinear"
):
super().__init__()
self._size = size
self._interpolation = interpolation
vframes, aframes, info = io.read_video(
filename='./v_Archery_g01_c03.avi',
pts_unit='sec',
output_format='TCHW'
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""
Args:
x (torch.Tensor): video tensor with shape (C, T, H, W).
"""
assert len(x.shape) == 4
assert x.dtype == torch.float32
c, t, h, w = x.shape
if w < h:
new_w = int(math.floor((float(w) / h) * self._size))
new_h = self._size
else:
new_w = self._size
new_h = int(math.floor((float(h) / w) * self._size))
return torch.nn.functional.interpolate(x, size=(new_h, new_w), mode=self._interpolation, align_corners=False)
trans = transforms.Compose([
ToTensorVideo(),
RandomHorizontalFlipVideo(),
UCFCenterCropVideo(512),
# NormalizeVideo(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5], inplace=True)
])
target_video_len = 32
frame_interval = 1
total_frames = len(vframes)
print(total_frames)
temporal_sample = TemporalRandomCrop(target_video_len * frame_interval)
# Sampling video frames
start_frame_ind, end_frame_ind = temporal_sample(total_frames)
# print(start_frame_ind)
# print(end_frame_ind)
assert end_frame_ind - start_frame_ind >= target_video_len
frame_indice = np.linspace(start_frame_ind, end_frame_ind - 1, target_video_len, dtype=int)
print(frame_indice)
select_vframes = vframes[frame_indice]
print(select_vframes.shape)
print(select_vframes.dtype)
select_vframes_trans = trans(select_vframes)
print(select_vframes_trans.shape)
print(select_vframes_trans.dtype)
select_vframes_trans_int = ((select_vframes_trans * 0.5 + 0.5) * 255).to(dtype=torch.uint8)
print(select_vframes_trans_int.dtype)
print(select_vframes_trans_int.permute(0, 2, 3, 1).shape)
io.write_video('./test.avi', select_vframes_trans_int.permute(0, 2, 3, 1), fps=8)
for i in range(target_video_len):
save_image(select_vframes_trans[i], os.path.join('./test000', '%04d.png' % i), normalize=True,
value_range=(-1, 1))
opensora/dataset/ucf101.py
+4 -3
View File
@@ -69,7 +69,8 @@ class UCF101(Dataset):
try:
video = self.tv_read(video_path)
video = self.transform(video) # C T H W
video = self.transform(video) # T C H W -> T C H W
video = video.transpose(0, 1) # T C H W -> C T H W
return video, label
except Exception as e:
print(f'Error with {e}, {video_path}')
@@ -86,7 +87,7 @@ class UCF101(Dataset):
# frame_indice = np.linspace(0, 30, self.target_video_len, dtype=int)
# print(frame_indice)
video = vframes[frame_indice] #
video = video.permute(0, 2, 3, 1) # (T, C, H, W) -> (T H W C)
# video = video.permute(0, 2, 3, 1) # (T, C, H, W) -> (T H W C)
return video
@@ -100,6 +101,6 @@ class UCF101(Dataset):
video_data = decord_vr.get_batch(frame_indice).asnumpy()
video_data = torch.from_numpy(video_data)
# video_data = video_data.permute(0, 3, 1, 2) # (T, H, W, C) -> (T C H W)
video_data = video_data.permute(0, 3, 1, 2) # (T, H, W, C) -> (T C H W)
return video_data
opensora/models/ae/imagebase/vae/__init__.py → opensora/models/__init__.py
View File
opensora/models/ae/__init__.py
+10 -2
View File
@@ -1,7 +1,15 @@
from .imagebase import imagebase_ae, vae, vqvae
from .videobase import videobase_ae, videovae, videovqvae
from .imagebase import imagebase_ae, vae, vqvae, imagebase_ae_stride, imagebase_ae_channel
from .videobase import videobase_ae, videovae, videovqvae, videobase_ae_stride, videobase_ae_channel
ae_stride_config = {}
ae_stride_config.update(imagebase_ae_stride)
ae_stride_config.update(videobase_ae_stride)
ae_channel_config = {}
ae_channel_config.update(imagebase_ae_channel)
ae_channel_config.update(videobase_ae_channel)
def getae(args):
ae = imagebase_ae.get(args.ae, None)
if ae is None:
opensora/models/ae/imagebase/__init__.py
+16
View File
@@ -5,6 +5,22 @@ from .vqvae.vqvae import SDVQVAEWrapper
vae = ['stabilityai/sd-vae-ft-mse', 'stabilityai/sd-vae-ft-ema']
vqvae = ['vqgan_imagenet_f16_1024', 'vqgan_imagenet_f16_16384', 'vqgan_gumbel_f8']
imagebase_ae_stride = {
'stabilityai/sd-vae-ft-mse': [1, 8, 8],
'stabilityai/sd-vae-ft-ema': [1, 8, 8],
'vqgan_imagenet_f16_1024': [1, 16, 16],
'vqgan_imagenet_f16_16384': [1, 16, 16],
'vqgan_gumbel_f8': [1, 8, 8],
}
imagebase_ae_channel = {
'stabilityai/sd-vae-ft-mse': 4,
'stabilityai/sd-vae-ft-ema': 4,
'vqgan_imagenet_f16_1024': -1,
'vqgan_imagenet_f16_16384': -1,
'vqgan_gumbel_f8': -1,
}
imagebase_ae = {
'stabilityai/sd-vae-ft-mse': HFVAEWrapper,
'stabilityai/sd-vae-ft-ema': HFVAEWrapper,
opensora/models/ae/imagebase/vae/vae.py
+8 -2
View File
@@ -1,4 +1,4 @@
from einops import rearrange
from torch import nn
from diffusers.models import AutoencoderKL
@@ -6,9 +6,15 @@ from diffusers.models import AutoencoderKL
class HFVAEWrapper(nn.Module):
def __init__(self, hfvae='mse'):
super(HFVAEWrapper, self).__init__()
self.vae = AutoencoderKL.from_pretrained(f'stabilityai/sd-vae-ft-{hfvae}', cache_dir='cache_dir')
self.vae = AutoencoderKL.from_pretrained(hfvae, cache_dir='cache_dir')
def encode(self, x): # b c h w
t = 0
if x.ndim == 5:
b, c, t, h, w = x.shape
x = rearrange(x, 'b c t h w -> (b t) c h w').contiguous()
x = self.vae.encode(x).latent_dist.sample().mul_(0.18215)
if t != 0:
x = rearrange(x, '(b t) c h w -> b t c h w', t=t).contiguous()
return x
def decode(self, x):
x = self.vae.decode(x / 0.18215).sample
opensora/models/ae/imagebase/vqvae/__init__.py
View File
opensora/models/ae/videobase/__init__.py
+14
View File
@@ -3,6 +3,20 @@ from .vqvae.videogpt import VideoGPTVQVAEWrapper
videovqvae = ['bair_stride4x2x2', 'ucf101_stride4x4x4', 'kinetics_stride4x4x4', 'kinetics_stride2x4x4']
videovae = []
videobase_ae_stride = {
'bair_stride4x2x2': [4, 2, 2],
'ucf101_stride4x4x4': [4, 4, 4],
'kinetics_stride4x4x4': [4, 4, 4],
'kinetics_stride2x4x4': [2, 4, 4],
}
videobase_ae_channel = {
'bair_stride4x2x2': 256,
'ucf101_stride4x4x4': 256,
'kinetics_stride4x4x4': 256,
'kinetics_stride2x4x4': 256,
}
videobase_ae = {
'bair_stride4x2x2': VideoGPTVQVAEWrapper,
'ucf101_stride4x4x4': VideoGPTVQVAEWrapper,
opensora/models/ae/videobase/vqvae/videogpt/__init__.py
+3 -1
View File
@@ -1,3 +1,4 @@
from einops import rearrange
from torch import nn
from .videogpt import load_vqvae
@@ -7,8 +8,9 @@ class VideoGPTVQVAEWrapper(nn.Module):
def __init__(self, ckpt='kinetics_stride4x4x4'):
super(VideoGPTVQVAEWrapper, self).__init__()
self.vqvae = load_vqvae(ckpt, root='./cache_dir')
def encode(self, x): # b c h w
def encode(self, x): # b c t h w
x = self.vqvae.pre_vq_conv(self.vqvae.encoder(x))
x = rearrange(x, 'b c t h w -> b t c h w').contiguous()
return x
def decode(self, x):
vq_output = self.vqvae.codebook(x)
opensora/models/diffusion/__init__.py
+6
View File
@@ -0,0 +1,6 @@
from .dit.dit import DiT_models
from .latte.latte import Latte_models
Diffusion_models = {}
Diffusion_models.update(DiT_models)
Diffusion_models.update(Latte_models)
opensora/models/diffusion/latte/diffusion/__init__.py → opensora/models/diffusion/diffusion/__init__.py
View File
opensora/models/diffusion/dit/diffusion/diffusion_utils.py → opensora/models/diffusion/diffusion/diffusion_utils.py
View File
opensora/models/diffusion/latte/diffusion/gaussian_diffusion.py → opensora/models/diffusion/diffusion/gaussian_diffusion.py
View File
opensora/models/diffusion/latte/diffusion/respace.py → opensora/models/diffusion/diffusion/respace.py
View File
opensora/models/diffusion/dit/diffusion/timestep_sampler.py → opensora/models/diffusion/diffusion/timestep_sampler.py
View File
opensora/models/diffusion/dit/diffusion/__init__.py
-46
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@@ -1,46 +0,0 @@
# Modified from OpenAI's diffusion repos
# GLIDE: https://github.com/openai/glide-text2im/blob/main/glide_text2im/gaussian_diffusion.py
# ADM: https://github.com/openai/guided-diffusion/blob/main/guided_diffusion
# IDDPM: https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
from . import gaussian_diffusion as gd
from .respace import SpacedDiffusion, space_timesteps
def create_diffusion(
timestep_respacing,
noise_schedule="linear",
use_kl=False,
sigma_small=False,
predict_xstart=False,
learn_sigma=True,
rescale_learned_sigmas=False,
diffusion_steps=1000
):
betas = gd.get_named_beta_schedule(noise_schedule, diffusion_steps)
if use_kl:
loss_type = gd.LossType.RESCALED_KL
elif rescale_learned_sigmas:
loss_type = gd.LossType.RESCALED_MSE
else:
loss_type = gd.LossType.MSE
if timestep_respacing is None or timestep_respacing == "":
timestep_respacing = [diffusion_steps]
return SpacedDiffusion(
use_timesteps=space_timesteps(diffusion_steps, timestep_respacing),
betas=betas,
model_mean_type=(
gd.ModelMeanType.EPSILON if not predict_xstart else gd.ModelMeanType.START_X
),
model_var_type=(
(
gd.ModelVarType.FIXED_LARGE
if not sigma_small
else gd.ModelVarType.FIXED_SMALL
)
if not learn_sigma
else gd.ModelVarType.LEARNED_RANGE
),
loss_type=loss_type
# rescale_timesteps=rescale_timesteps,
)
opensora/models/diffusion/dit/diffusion/gaussian_diffusion.py
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# Modified from OpenAI's diffusion repos
# GLIDE: https://github.com/openai/glide-text2im/blob/main/glide_text2im/gaussian_diffusion.py
# ADM: https://github.com/openai/guided-diffusion/blob/main/guided_diffusion
# IDDPM: https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
import math
import numpy as np
import torch as th
import enum
from .diffusion_utils import discretized_gaussian_log_likelihood, normal_kl
def mean_flat(tensor):
"""
Take the mean over all non-batch dimensions.
"""
return tensor.mean(dim=list(range(1, len(tensor.shape))))
class ModelMeanType(enum.Enum):
"""
Which type of output the model predicts.
"""
PREVIOUS_X = enum.auto() # the model predicts x_{t-1}
START_X = enum.auto() # the model predicts x_0
EPSILON = enum.auto() # the model predicts epsilon
class ModelVarType(enum.Enum):
"""
What is used as the model's output variance.
The LEARNED_RANGE option has been added to allow the model to predict
values between FIXED_SMALL and FIXED_LARGE, making its job easier.
"""
LEARNED = enum.auto()
FIXED_SMALL = enum.auto()
FIXED_LARGE = enum.auto()
LEARNED_RANGE = enum.auto()
class LossType(enum.Enum):
MSE = enum.auto() # use raw MSE loss (and KL when learning variances)
RESCALED_MSE = (
enum.auto()
) # use raw MSE loss (with RESCALED_KL when learning variances)
KL = enum.auto() # use the variational lower-bound
RESCALED_KL = enum.auto() # like KL, but rescale to estimate the full VLB
def is_vb(self):
return self == LossType.KL or self == LossType.RESCALED_KL
def _warmup_beta(beta_start, beta_end, num_diffusion_timesteps, warmup_frac):
betas = beta_end * np.ones(num_diffusion_timesteps, dtype=np.float64)
warmup_time = int(num_diffusion_timesteps * warmup_frac)
betas[:warmup_time] = np.linspace(beta_start, beta_end, warmup_time, dtype=np.float64)
return betas
def get_beta_schedule(beta_schedule, *, beta_start, beta_end, num_diffusion_timesteps):
"""
This is the deprecated API for creating beta schedules.
See get_named_beta_schedule() for the new library of schedules.
"""
if beta_schedule == "quad":
betas = (
np.linspace(
beta_start ** 0.5,
beta_end ** 0.5,
num_diffusion_timesteps,
dtype=np.float64,
)
** 2
)
elif beta_schedule == "linear":
betas = np.linspace(beta_start, beta_end, num_diffusion_timesteps, dtype=np.float64)
elif beta_schedule == "warmup10":
betas = _warmup_beta(beta_start, beta_end, num_diffusion_timesteps, 0.1)
elif beta_schedule == "warmup50":
betas = _warmup_beta(beta_start, beta_end, num_diffusion_timesteps, 0.5)
elif beta_schedule == "const":
betas = beta_end * np.ones(num_diffusion_timesteps, dtype=np.float64)
elif beta_schedule == "jsd": # 1/T, 1/(T-1), 1/(T-2), ..., 1
betas = 1.0 / np.linspace(
num_diffusion_timesteps, 1, num_diffusion_timesteps, dtype=np.float64
)
else:
raise NotImplementedError(beta_schedule)
assert betas.shape == (num_diffusion_timesteps,)
return betas
def get_named_beta_schedule(schedule_name, num_diffusion_timesteps):
"""
Get a pre-defined beta schedule for the given name.
The beta schedule library consists of beta schedules which remain similar
in the limit of num_diffusion_timesteps.
Beta schedules may be added, but should not be removed or changed once
they are committed to maintain backwards compatibility.
"""
if schedule_name == "linear":
# Linear schedule from Ho et al, extended to work for any number of
# diffusion steps.
scale = 1000 / num_diffusion_timesteps
return get_beta_schedule(
"linear",
beta_start=scale * 0.0001,
beta_end=scale * 0.02,
num_diffusion_timesteps=num_diffusion_timesteps,
)
elif schedule_name == "squaredcos_cap_v2":
return betas_for_alpha_bar(
num_diffusion_timesteps,
lambda t: math.cos((t + 0.008) / 1.008 * math.pi / 2) ** 2,
)
else:
raise NotImplementedError(f"unknown beta schedule: {schedule_name}")
def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):
"""
Create a beta schedule that discretizes the given alpha_t_bar function,
which defines the cumulative product of (1-beta) over time from t = [0,1].
:param num_diffusion_timesteps: the number of betas to produce.
:param alpha_bar: a lambda that takes an argument t from 0 to 1 and
produces the cumulative product of (1-beta) up to that
part of the diffusion process.
:param max_beta: the maximum beta to use; use values lower than 1 to
prevent singularities.
"""
betas = []
for i in range(num_diffusion_timesteps):
t1 = i / num_diffusion_timesteps
t2 = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
return np.array(betas)
class GaussianDiffusion:
"""
Utilities for training and sampling diffusion models.
Original ported from this codebase:
https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/diffusion_utils_2.py#L42
:param betas: a 1-D numpy array of betas for each diffusion timestep,
starting at T and going to 1.
"""
def __init__(
self,
*,
betas,
model_mean_type,
model_var_type,
loss_type
):
self.model_mean_type = model_mean_type
self.model_var_type = model_var_type
self.loss_type = loss_type
# Use float64 for accuracy.
betas = np.array(betas, dtype=np.float64)
self.betas = betas
assert len(betas.shape) == 1, "betas must be 1-D"
assert (betas > 0).all() and (betas <= 1).all()
self.num_timesteps = int(betas.shape[0])
alphas = 1.0 - betas
self.alphas_cumprod = np.cumprod(alphas, axis=0)
self.alphas_cumprod_prev = np.append(1.0, self.alphas_cumprod[:-1])
self.alphas_cumprod_next = np.append(self.alphas_cumprod[1:], 0.0)
assert self.alphas_cumprod_prev.shape == (self.num_timesteps,)
# calculations for diffusion q(x_t | x_{t-1}) and others
self.sqrt_alphas_cumprod = np.sqrt(self.alphas_cumprod)
self.sqrt_one_minus_alphas_cumprod = np.sqrt(1.0 - self.alphas_cumprod)
self.log_one_minus_alphas_cumprod = np.log(1.0 - self.alphas_cumprod)
self.sqrt_recip_alphas_cumprod = np.sqrt(1.0 / self.alphas_cumprod)
self.sqrt_recipm1_alphas_cumprod = np.sqrt(1.0 / self.alphas_cumprod - 1)
# calculations for posterior q(x_{t-1} | x_t, x_0)
self.posterior_variance = (
betas * (1.0 - self.alphas_cumprod_prev) / (1.0 - self.alphas_cumprod)
)
# below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
self.posterior_log_variance_clipped = np.log(
np.append(self.posterior_variance[1], self.posterior_variance[1:])
) if len(self.posterior_variance) > 1 else np.array([])
self.posterior_mean_coef1 = (
betas * np.sqrt(self.alphas_cumprod_prev) / (1.0 - self.alphas_cumprod)
)
self.posterior_mean_coef2 = (
(1.0 - self.alphas_cumprod_prev) * np.sqrt(alphas) / (1.0 - self.alphas_cumprod)
)
def q_mean_variance(self, x_start, t):
"""
Get the distribution q(x_t | x_0).
:param x_start: the [N x C x ...] tensor of noiseless inputs.
:param t: the number of diffusion steps (minus 1). Here, 0 means one step.
:return: A tuple (mean, variance, log_variance), all of x_start's shape.
"""
mean = _extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start
variance = _extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
log_variance = _extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape)
return mean, variance, log_variance
def q_sample(self, x_start, t, noise=None):
"""
Diffuse the data for a given number of diffusion steps.
In other words, sample from q(x_t | x_0).
:param x_start: the initial data batch.
:param t: the number of diffusion steps (minus 1). Here, 0 means one step.
:param noise: if specified, the split-out normal noise.
:return: A noisy version of x_start.
"""
if noise is None:
noise = th.randn_like(x_start)
assert noise.shape == x_start.shape
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 q_posterior_mean_variance(self, x_start, x_t, t):
"""
Compute the mean and variance of the diffusion posterior:
q(x_{t-1} | x_t, x_0)
"""
assert x_start.shape == x_t.shape
posterior_mean = (
_extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start
+ _extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
)
posterior_variance = _extract_into_tensor(self.posterior_variance, t, x_t.shape)
posterior_log_variance_clipped = _extract_into_tensor(
self.posterior_log_variance_clipped, t, x_t.shape
)
assert (
posterior_mean.shape[0]
== posterior_variance.shape[0]
== posterior_log_variance_clipped.shape[0]
== x_start.shape[0]
)
return posterior_mean, posterior_variance, posterior_log_variance_clipped
def p_mean_variance(self, model, x, t, clip_denoised=True, denoised_fn=None, model_kwargs=None):
"""
Apply the model to get p(x_{t-1} | x_t), as well as a prediction of
the initial x, x_0.
:param model: the model, which takes a signal and a batch of timesteps
as input.
:param x: the [N x C x ...] tensor at time t.
:param t: a 1-D Tensor of timesteps.
:param clip_denoised: if True, clip the denoised signal into [-1, 1].
:param denoised_fn: if not None, a function which applies to the
x_start prediction before it is used to sample. Applies before
clip_denoised.
:param model_kwargs: if not None, a dict of extra keyword arguments to
pass to the model. This can be used for conditioning.
:return: a dict with the following keys:
- 'mean': the model mean output.
- 'variance': the model variance output.
- 'log_variance': the log of 'variance'.
- 'pred_xstart': the prediction for x_0.
"""
if model_kwargs is None:
model_kwargs = {}
B, C = x.shape[:2]
assert t.shape == (B,)
model_output = model(x, t, **model_kwargs)
if isinstance(model_output, tuple):
model_output, extra = model_output
else:
extra = None
if self.model_var_type in [ModelVarType.LEARNED, ModelVarType.LEARNED_RANGE]:
assert model_output.shape == (B, C * 2, *x.shape[2:])
model_output, model_var_values = th.split(model_output, C, dim=1)
min_log = _extract_into_tensor(self.posterior_log_variance_clipped, t, x.shape)
max_log = _extract_into_tensor(np.log(self.betas), t, x.shape)
# The model_var_values is [-1, 1] for [min_var, max_var].
frac = (model_var_values + 1) / 2
model_log_variance = frac * max_log + (1 - frac) * min_log
model_variance = th.exp(model_log_variance)
else:
model_variance, model_log_variance = {
# for fixedlarge, we set the initial (log-)variance like so
# to get a better decoder log likelihood.
ModelVarType.FIXED_LARGE: (
np.append(self.posterior_variance[1], self.betas[1:]),
np.log(np.append(self.posterior_variance[1], self.betas[1:])),
),
ModelVarType.FIXED_SMALL: (
self.posterior_variance,
self.posterior_log_variance_clipped,
),
}[self.model_var_type]
model_variance = _extract_into_tensor(model_variance, t, x.shape)
model_log_variance = _extract_into_tensor(model_log_variance, t, x.shape)
def process_xstart(x):
if denoised_fn is not None:
x = denoised_fn(x)
if clip_denoised:
return x.clamp(-1, 1)
return x
if self.model_mean_type == ModelMeanType.START_X:
pred_xstart = process_xstart(model_output)
else:
pred_xstart = process_xstart(
self._predict_xstart_from_eps(x_t=x, t=t, eps=model_output)
)
model_mean, _, _ = self.q_posterior_mean_variance(x_start=pred_xstart, x_t=x, t=t)
assert model_mean.shape == model_log_variance.shape == pred_xstart.shape == x.shape
return {
"mean": model_mean,
"variance": model_variance,
"log_variance": model_log_variance,
"pred_xstart": pred_xstart,
"extra": extra,
}
def _predict_xstart_from_eps(self, x_t, t, eps):
assert x_t.shape == eps.shape
return (
_extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t
- _extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * eps
)
def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
return (
_extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart
) / _extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
def condition_mean(self, cond_fn, p_mean_var, x, t, model_kwargs=None):
"""
Compute the mean for the previous step, given a function cond_fn that
computes the gradient of a conditional log probability with respect to
x. In particular, cond_fn computes grad(log(p(y|x))), and we want to
condition on y.
This uses the conditioning strategy from Sohl-Dickstein et al. (2015).
"""
gradient = cond_fn(x, t, **model_kwargs)
new_mean = p_mean_var["mean"].float() + p_mean_var["variance"] * gradient.float()
return new_mean
def condition_score(self, cond_fn, p_mean_var, x, t, model_kwargs=None):
"""
Compute what the p_mean_variance output would have been, should the
model's score function be conditioned by cond_fn.
See condition_mean() for details on cond_fn.
Unlike condition_mean(), this instead uses the conditioning strategy
from Song et al (2020).
"""
alpha_bar = _extract_into_tensor(self.alphas_cumprod, t, x.shape)
eps = self._predict_eps_from_xstart(x, t, p_mean_var["pred_xstart"])
eps = eps - (1 - alpha_bar).sqrt() * cond_fn(x, t, **model_kwargs)
out = p_mean_var.copy()
out["pred_xstart"] = self._predict_xstart_from_eps(x, t, eps)
out["mean"], _, _ = self.q_posterior_mean_variance(x_start=out["pred_xstart"], x_t=x, t=t)
return out
def p_sample(
self,
model,
x,
t,
clip_denoised=True,
denoised_fn=None,
cond_fn=None,
model_kwargs=None,
):
"""
Sample x_{t-1} from the model at the given timestep.
:param model: the model to sample from.
:param x: the current tensor at x_{t-1}.
:param t: the value of t, starting at 0 for the first diffusion step.
:param clip_denoised: if True, clip the x_start prediction to [-1, 1].
:param denoised_fn: if not None, a function which applies to the
x_start prediction before it is used to sample.
:param cond_fn: if not None, this is a gradient function that acts
similarly to the model.
:param model_kwargs: if not None, a dict of extra keyword arguments to
pass to the model. This can be used for conditioning.
:return: a dict containing the following keys:
- 'sample': a random sample from the model.
- 'pred_xstart': a prediction of x_0.
"""
out = self.p_mean_variance(
model,
x,
t,
clip_denoised=clip_denoised,
denoised_fn=denoised_fn,
model_kwargs=model_kwargs,
)
noise = th.randn_like(x)
nonzero_mask = (
(t != 0).float().view(-1, *([1] * (len(x.shape) - 1)))
) # no noise when t == 0
if cond_fn is not None:
out["mean"] = self.condition_mean(cond_fn, out, x, t, model_kwargs=model_kwargs)
sample = out["mean"] + nonzero_mask * th.exp(0.5 * out["log_variance"]) * noise
return {"sample": sample, "pred_xstart": out["pred_xstart"]}
def p_sample_loop(
self,
model,
shape,
noise=None,
clip_denoised=True,
denoised_fn=None,
cond_fn=None,
model_kwargs=None,
device=None,
progress=False,
):
"""
Generate samples from the model.
:param model: the model module.
:param shape: the shape of the samples, (N, C, H, W).
:param noise: if specified, the noise from the encoder to sample.
Should be of the same shape as `shape`.
:param clip_denoised: if True, clip x_start predictions to [-1, 1].
:param denoised_fn: if not None, a function which applies to the
x_start prediction before it is used to sample.
:param cond_fn: if not None, this is a gradient function that acts
similarly to the model.
:param model_kwargs: if not None, a dict of extra keyword arguments to
pass to the model. This can be used for conditioning.
:param device: if specified, the device to create the samples on.
If not specified, use a model parameter's device.
:param progress: if True, show a tqdm progress bar.
:return: a non-differentiable batch of samples.
"""
final = None
for sample in self.p_sample_loop_progressive(
model,
shape,
noise=noise,
clip_denoised=clip_denoised,
denoised_fn=denoised_fn,
cond_fn=cond_fn,
model_kwargs=model_kwargs,
device=device,
progress=progress,
):
final = sample
return final["sample"]
def p_sample_loop_progressive(
self,
model,
shape,
noise=None,
clip_denoised=True,
denoised_fn=None,
cond_fn=None,
model_kwargs=None,
device=None,
progress=False,
):
"""
Generate samples from the model and yield intermediate samples from
each timestep of diffusion.
Arguments are the same as p_sample_loop().
Returns a generator over dicts, where each dict is the return value of
p_sample().
"""
if device is None:
device = next(model.parameters()).device
assert isinstance(shape, (tuple, list))
if noise is not None:
img = noise
else:
img = th.randn(*shape, device=device)
indices = list(range(self.num_timesteps))[::-1]
if progress:
# Lazy import so that we don't depend on tqdm.
from tqdm.auto import tqdm
indices = tqdm(indices)
for i in indices:
t = th.tensor([i] * shape[0], device=device)
with th.no_grad():
out = self.p_sample(
model,
img,
t,
clip_denoised=clip_denoised,
denoised_fn=denoised_fn,
cond_fn=cond_fn,
model_kwargs=model_kwargs,
)
yield out
img = out["sample"]
def ddim_sample(
self,
model,
x,
t,
clip_denoised=True,
denoised_fn=None,
cond_fn=None,
model_kwargs=None,
eta=0.0,
):
"""
Sample x_{t-1} from the model using DDIM.
Same usage as p_sample().
"""
out = self.p_mean_variance(
model,
x,
t,
clip_denoised=clip_denoised,
denoised_fn=denoised_fn,
model_kwargs=model_kwargs,
)
if cond_fn is not None:
out = self.condition_score(cond_fn, out, x, t, model_kwargs=model_kwargs)
# Usually our model outputs epsilon, but we re-derive it
# in case we used x_start or x_prev prediction.
eps = self._predict_eps_from_xstart(x, t, out["pred_xstart"])
alpha_bar = _extract_into_tensor(self.alphas_cumprod, t, x.shape)
alpha_bar_prev = _extract_into_tensor(self.alphas_cumprod_prev, t, x.shape)
sigma = (
eta
* th.sqrt((1 - alpha_bar_prev) / (1 - alpha_bar))
* th.sqrt(1 - alpha_bar / alpha_bar_prev)
)
# Equation 12.
noise = th.randn_like(x)
mean_pred = (
out["pred_xstart"] * th.sqrt(alpha_bar_prev)
+ th.sqrt(1 - alpha_bar_prev - sigma ** 2) * eps
)
nonzero_mask = (
(t != 0).float().view(-1, *([1] * (len(x.shape) - 1)))
) # no noise when t == 0
sample = mean_pred + nonzero_mask * sigma * noise
return {"sample": sample, "pred_xstart": out["pred_xstart"]}
def ddim_reverse_sample(
self,
model,
x,
t,
clip_denoised=True,
denoised_fn=None,
cond_fn=None,
model_kwargs=None,
eta=0.0,
):
"""
Sample x_{t+1} from the model using DDIM reverse ODE.
"""
assert eta == 0.0, "Reverse ODE only for deterministic path"
out = self.p_mean_variance(
model,
x,
t,
clip_denoised=clip_denoised,
denoised_fn=denoised_fn,
model_kwargs=model_kwargs,
)
if cond_fn is not None:
out = self.condition_score(cond_fn, out, x, t, model_kwargs=model_kwargs)
# Usually our model outputs epsilon, but we re-derive it
# in case we used x_start or x_prev prediction.
eps = (
_extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x.shape) * x
- out["pred_xstart"]
) / _extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x.shape)
alpha_bar_next = _extract_into_tensor(self.alphas_cumprod_next, t, x.shape)
# Equation 12. reversed
mean_pred = out["pred_xstart"] * th.sqrt(alpha_bar_next) + th.sqrt(1 - alpha_bar_next) * eps
return {"sample": mean_pred, "pred_xstart": out["pred_xstart"]}
def ddim_sample_loop(
self,
model,
shape,
noise=None,
clip_denoised=True,
denoised_fn=None,
cond_fn=None,
model_kwargs=None,
device=None,
progress=False,
eta=0.0,
):
"""
Generate samples from the model using DDIM.
Same usage as p_sample_loop().
"""
final = None
for sample in self.ddim_sample_loop_progressive(
model,
shape,
noise=noise,
clip_denoised=clip_denoised,
denoised_fn=denoised_fn,
cond_fn=cond_fn,
model_kwargs=model_kwargs,
device=device,
progress=progress,
eta=eta,
):
final = sample
return final["sample"]
def ddim_sample_loop_progressive(
self,
model,
shape,
noise=None,
clip_denoised=True,
denoised_fn=None,
cond_fn=None,
model_kwargs=None,
device=None,
progress=False,
eta=0.0,
):
"""
Use DDIM to sample from the model and yield intermediate samples from
each timestep of DDIM.
Same usage as p_sample_loop_progressive().
"""
if device is None:
device = next(model.parameters()).device
assert isinstance(shape, (tuple, list))
if noise is not None:
img = noise
else:
img = th.randn(*shape, device=device)
indices = list(range(self.num_timesteps))[::-1]
if progress:
# Lazy import so that we don't depend on tqdm.
from tqdm.auto import tqdm
indices = tqdm(indices)
for i in indices:
t = th.tensor([i] * shape[0], device=device)
with th.no_grad():
out = self.ddim_sample(
model,
img,
t,
clip_denoised=clip_denoised,
denoised_fn=denoised_fn,
cond_fn=cond_fn,
model_kwargs=model_kwargs,
eta=eta,
)
yield out
img = out["sample"]
def _vb_terms_bpd(
self, model, x_start, x_t, t, clip_denoised=True, model_kwargs=None
):
"""
Get a term for the variational lower-bound.
The resulting units are bits (rather than nats, as one might expect).
This allows for comparison to other papers.
:return: a dict with the following keys:
- 'output': a shape [N] tensor of NLLs or KLs.
- 'pred_xstart': the x_0 predictions.
"""
true_mean, _, true_log_variance_clipped = self.q_posterior_mean_variance(
x_start=x_start, x_t=x_t, t=t
)
out = self.p_mean_variance(
model, x_t, t, clip_denoised=clip_denoised, model_kwargs=model_kwargs
)
kl = normal_kl(
true_mean, true_log_variance_clipped, out["mean"], out["log_variance"]
)
kl = mean_flat(kl) / np.log(2.0)
decoder_nll = -discretized_gaussian_log_likelihood(
x_start, means=out["mean"], log_scales=0.5 * out["log_variance"]
)
assert decoder_nll.shape == x_start.shape
decoder_nll = mean_flat(decoder_nll) / np.log(2.0)
# At the first timestep return the decoder NLL,
# otherwise return KL(q(x_{t-1}|x_t,x_0) || p(x_{t-1}|x_t))
output = th.where((t == 0), decoder_nll, kl)
return {"output": output, "pred_xstart": out["pred_xstart"]}
def training_losses(self, model, x_start, t, model_kwargs=None, noise=None):
"""
Compute training losses for a single timestep.
:param model: the model to evaluate loss on.
:param x_start: the [N x C x ...] tensor of inputs.
:param t: a batch of timestep indices.
:param model_kwargs: if not None, a dict of extra keyword arguments to
pass to the model. This can be used for conditioning.
:param noise: if specified, the specific Gaussian noise to try to remove.
:return: a dict with the key "loss" containing a tensor of shape [N].
Some mean or variance settings may also have other keys.
"""
if model_kwargs is None:
model_kwargs = {}
if noise is None:
noise = th.randn_like(x_start)
x_t = self.q_sample(x_start, t, noise=noise)
terms = {}
if self.loss_type == LossType.KL or self.loss_type == LossType.RESCALED_KL:
terms["loss"] = self._vb_terms_bpd(
model=model,
x_start=x_start,
x_t=x_t,
t=t,
clip_denoised=False,
model_kwargs=model_kwargs,
)["output"]
if self.loss_type == LossType.RESCALED_KL:
terms["loss"] *= self.num_timesteps
elif self.loss_type == LossType.MSE or self.loss_type == LossType.RESCALED_MSE:
model_output = model(x_t, t, **model_kwargs)
if self.model_var_type in [
ModelVarType.LEARNED,
ModelVarType.LEARNED_RANGE,
]:
B, C = x_t.shape[:2]
assert model_output.shape == (B, C * 2, *x_t.shape[2:])
model_output, model_var_values = th.split(model_output, C, dim=1)
# Learn the variance using the variational bound, but don't let
# it affect our mean prediction.
frozen_out = th.cat([model_output.detach(), model_var_values], dim=1)
terms["vb"] = self._vb_terms_bpd(
model=lambda *args, r=frozen_out: r,
x_start=x_start,
x_t=x_t,
t=t,
clip_denoised=False,
)["output"]
if self.loss_type == LossType.RESCALED_MSE:
# Divide by 1000 for equivalence with initial implementation.
# Without a factor of 1/1000, the VB term hurts the MSE term.
terms["vb"] *= self.num_timesteps / 1000.0
target = {
ModelMeanType.PREVIOUS_X: self.q_posterior_mean_variance(
x_start=x_start, x_t=x_t, t=t
)[0],
ModelMeanType.START_X: x_start,
ModelMeanType.EPSILON: noise,
}[self.model_mean_type]
assert model_output.shape == target.shape == x_start.shape
terms["mse"] = mean_flat((target - model_output) ** 2)
if "vb" in terms:
terms["loss"] = terms["mse"] + terms["vb"]
else:
terms["loss"] = terms["mse"]
else:
raise NotImplementedError(self.loss_type)
return terms
def _prior_bpd(self, x_start):
"""
Get the prior KL term for the variational lower-bound, measured in
bits-per-dim.
This term can't be optimized, as it only depends on the encoder.
:param x_start: the [N x C x ...] tensor of inputs.
:return: a batch of [N] KL values (in bits), one per batch element.
"""
batch_size = x_start.shape[0]
t = th.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
kl_prior = normal_kl(
mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0
)
return mean_flat(kl_prior) / np.log(2.0)
def calc_bpd_loop(self, model, x_start, clip_denoised=True, model_kwargs=None):
"""
Compute the entire variational lower-bound, measured in bits-per-dim,
as well as other related quantities.
:param model: the model to evaluate loss on.
:param x_start: the [N x C x ...] tensor of inputs.
:param clip_denoised: if True, clip denoised samples.
:param model_kwargs: if not None, a dict of extra keyword arguments to
pass to the model. This can be used for conditioning.
:return: a dict containing the following keys:
- total_bpd: the total variational lower-bound, per batch element.
- prior_bpd: the prior term in the lower-bound.
- vb: an [N x T] tensor of terms in the lower-bound.
- xstart_mse: an [N x T] tensor of x_0 MSEs for each timestep.
- mse: an [N x T] tensor of epsilon MSEs for each timestep.
"""
device = x_start.device
batch_size = x_start.shape[0]
vb = []
xstart_mse = []
mse = []
for t in list(range(self.num_timesteps))[::-1]:
t_batch = th.tensor([t] * batch_size, device=device)
noise = th.randn_like(x_start)
x_t = self.q_sample(x_start=x_start, t=t_batch, noise=noise)
# Calculate VLB term at the current timestep
with th.no_grad():
out = self._vb_terms_bpd(
model,
x_start=x_start,
x_t=x_t,
t=t_batch,
clip_denoised=clip_denoised,
model_kwargs=model_kwargs,
)
vb.append(out["output"])
xstart_mse.append(mean_flat((out["pred_xstart"] - x_start) ** 2))
eps = self._predict_eps_from_xstart(x_t, t_batch, out["pred_xstart"])
mse.append(mean_flat((eps - noise) ** 2))
vb = th.stack(vb, dim=1)
xstart_mse = th.stack(xstart_mse, dim=1)
mse = th.stack(mse, dim=1)
prior_bpd = self._prior_bpd(x_start)
total_bpd = vb.sum(dim=1) + prior_bpd
return {
"total_bpd": total_bpd,
"prior_bpd": prior_bpd,
"vb": vb,
"xstart_mse": xstart_mse,
"mse": mse,
}
def _extract_into_tensor(arr, timesteps, broadcast_shape):
"""
Extract values from a 1-D numpy array for a batch of indices.
:param arr: the 1-D numpy array.
:param timesteps: a tensor of indices into the array to extract.
:param broadcast_shape: a larger shape of K dimensions with the batch
dimension equal to the length of timesteps.
:return: a tensor of shape [batch_size, 1, ...] where the shape has K dims.
"""
res = th.from_numpy(arr).to(device=timesteps.device)[timesteps].float()
while len(res.shape) < len(broadcast_shape):
res = res[..., None]
return res + th.zeros(broadcast_shape, device=timesteps.device)
opensora/models/diffusion/dit/diffusion/respace.py
-129
View File
@@ -1,129 +0,0 @@
# Modified from OpenAI's diffusion repos
# GLIDE: https://github.com/openai/glide-text2im/blob/main/glide_text2im/gaussian_diffusion.py
# ADM: https://github.com/openai/guided-diffusion/blob/main/guided_diffusion
# IDDPM: https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
import numpy as np
import torch as th
from .gaussian_diffusion import GaussianDiffusion
def space_timesteps(num_timesteps, section_counts):
"""
Create a list of timesteps to use from an original diffusion process,
given the number of timesteps we want to take from equally-sized portions
of the original process.
For example, if there's 300 timesteps and the section counts are [10,15,20]
then the first 100 timesteps are strided to be 10 timesteps, the second 100
are strided to be 15 timesteps, and the final 100 are strided to be 20.
If the stride is a string starting with "ddim", then the fixed striding
from the DDIM paper is used, and only one section is allowed.
:param num_timesteps: the number of diffusion steps in the original
process to divide up.
:param section_counts: either a list of numbers, or a string containing
comma-separated numbers, indicating the step count
per section. As a special case, use "ddimN" where N
is a number of steps to use the striding from the
DDIM paper.
:return: a set of diffusion steps from the original process to use.
"""
if isinstance(section_counts, str):
if section_counts.startswith("ddim"):
desired_count = int(section_counts[len("ddim") :])
for i in range(1, num_timesteps):
if len(range(0, num_timesteps, i)) == desired_count:
return set(range(0, num_timesteps, i))
raise ValueError(
f"cannot create exactly {num_timesteps} steps with an integer stride"
)
section_counts = [int(x) for x in section_counts.split(",")]
size_per = num_timesteps // len(section_counts)
extra = num_timesteps % len(section_counts)
start_idx = 0
all_steps = []
for i, section_count in enumerate(section_counts):
size = size_per + (1 if i < extra else 0)
if size < section_count:
raise ValueError(
f"cannot divide section of {size} steps into {section_count}"
)
if section_count <= 1:
frac_stride = 1
else:
frac_stride = (size - 1) / (section_count - 1)
cur_idx = 0.0
taken_steps = []
for _ in range(section_count):
taken_steps.append(start_idx + round(cur_idx))
cur_idx += frac_stride
all_steps += taken_steps
start_idx += size
return set(all_steps)
class SpacedDiffusion(GaussianDiffusion):
"""
A diffusion process which can skip steps in a base diffusion process.
:param use_timesteps: a collection (sequence or set) of timesteps from the
original diffusion process to retain.
:param kwargs: the kwargs to create the base diffusion process.
"""
def __init__(self, use_timesteps, **kwargs):
self.use_timesteps = set(use_timesteps)
self.timestep_map = []
self.original_num_steps = len(kwargs["betas"])
base_diffusion = GaussianDiffusion(**kwargs) # pylint: disable=missing-kwoa
last_alpha_cumprod = 1.0
new_betas = []
for i, alpha_cumprod in enumerate(base_diffusion.alphas_cumprod):
if i in self.use_timesteps:
new_betas.append(1 - alpha_cumprod / last_alpha_cumprod)
last_alpha_cumprod = alpha_cumprod
self.timestep_map.append(i)
kwargs["betas"] = np.array(new_betas)
super().__init__(**kwargs)
def p_mean_variance(
self, model, *args, **kwargs
): # pylint: disable=signature-differs
return super().p_mean_variance(self._wrap_model(model), *args, **kwargs)
def training_losses(
self, model, *args, **kwargs
): # pylint: disable=signature-differs
return super().training_losses(self._wrap_model(model), *args, **kwargs)
def condition_mean(self, cond_fn, *args, **kwargs):
return super().condition_mean(self._wrap_model(cond_fn), *args, **kwargs)
def condition_score(self, cond_fn, *args, **kwargs):
return super().condition_score(self._wrap_model(cond_fn), *args, **kwargs)
def _wrap_model(self, model):
if isinstance(model, _WrappedModel):
return model
return _WrappedModel(
model, self.timestep_map, self.original_num_steps
)
def _scale_timesteps(self, t):
# Scaling is done by the wrapped model.
return t
class _WrappedModel:
def __init__(self, model, timestep_map, original_num_steps):
self.model = model
self.timestep_map = timestep_map
# self.rescale_timesteps = rescale_timesteps
self.original_num_steps = original_num_steps
def __call__(self, x, ts, **kwargs):
map_tensor = th.tensor(self.timestep_map, device=ts.device, dtype=ts.dtype)
new_ts = map_tensor[ts]
# if self.rescale_timesteps:
# new_ts = new_ts.float() * (1000.0 / self.original_num_steps)
return self.model(x, new_ts, **kwargs)
opensora/models/diffusion/dit/models.py → opensora/models/diffusion/dit/dit.py
+36 -33
View File
@@ -12,7 +12,7 @@ import torch.nn as nn
import numpy as np
import math
import torch.utils.checkpoint as cp
from einops import rearrange
from einops import rearrange, repeat
from timm.layers import use_fused_attn, to_2tuple
# from timm.models.vision_transformer import PatchEmbed, Attention, Mlp
from timm.models.vision_transformer import PatchEmbed, Mlp
@@ -110,7 +110,7 @@ class TimestepEmbedder(nn.Module):
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.
:return: an (N, C) Tensor of positional embeddings.
"""
# https://github.com/openai/glide-text2im/blob/main/glide_text2im/nn.py
half = dim // 2
@@ -287,17 +287,16 @@ class DiT(nn.Module):
def unpatchify(self, x):
"""
x: (B, N, patch_size_t*patch_size**2 * C)
imgs: (B, C, T, H, W)
x: (B, N, patch_size**2 * C)
imgs: (B, C, H, W)
"""
c = self.out_channels
p = self.x_embedder.patch_size[0]
o = self.patch_size_t
assert self.t * self.h * self.w == x.shape[1]
assert self.h * self.w == x.shape[1]
x = x.reshape(shape=(x.shape[0], self.t, self.h, self.w, o, p, p, c))
x = torch.einsum('nthwopqc->nctohpwq', x)
imgs = x.reshape(shape=(x.shape[0], c, self.t * o, self.h * p, self.w * p))
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
def ckpt_wrapper(self, module):
@@ -309,13 +308,13 @@ class DiT(nn.Module):
def forward(self, x, t, y, attention_mask):
"""
Forward pass of DiT.
x: (B, D, T, H, W) tensor of spatial inputs (images or latent representations of images)
x: (B, T, C, H, W) tensor of spatial inputs (images or latent representations of images)
t: (B,) tensor of diffusion timesteps
y: (B,) tensor of class labels
"""
if x.ndim == 4:
raise NotImplementedError
B, D, T, H, W = x.shape
B, T, C, H, W = x.shape
self.h = num_patches_height = H // self.patch_size
self.w = num_patches_width = W // self.patch_size
self.t = num_tubes_length = T // self.patch_size_t # 4 // 1
@@ -328,22 +327,26 @@ class DiT(nn.Module):
# print(num_patches_height, num_patches_width, x.shape, pos_embed.shape)
self.x_embedder.img_size = [H, W]
x = rearrange(x, 'b d t h w -> (b t) d h w')
x = self.x_embedder(x) + pos_embed # (BT, N, D), where N = H * W / patch_size ** 2
x = rearrange(x, '(b t) n d -> (b n) t d', t=self.t)
x = rearrange(x, 'b t c h w -> (b t) c h w')
x = self.x_embedder(x) + pos_embed # (BT, N, C), where N = H * W / patch_size ** 2
x = rearrange(x, '(b t) n c -> (b n) t c', t=self.t)
x = x + pos_embed_1d
x = rearrange(x, '(b n) t d -> b (t n) d', b=B)
x = rearrange(x, '(b n) t c -> b (t n) c', b=B)
t = self.t_embedder(t) # (B, D)
y = self.y_embedder(y, self.training) # (B, D)
c = t + y # (B, D)
for block in self.blocks: # (B, N, D)
t = self.t_embedder(t) # (B, C)
y = self.y_embedder(y, self.training) # (B, C)
c = t + y # (B, C)
for block in self.blocks: # (B, N, C)
if self.gradient_checkpointing and self.training:
x = torch.utils.checkpoint.checkpoint(self.ckpt_wrapper(block), x, c, attention_mask) # (B, N, D)
x = torch.utils.checkpoint.checkpoint(self.ckpt_wrapper(block), x, c, attention_mask) # (B, N, C)
else:
x = block(x, c, attention_mask)
x = self.final_layer(x, c) # (B, N, patch_size_t * patch_size ** 2 * out_channels)
x = self.unpatchify(x) # (B, out_channels, T, H, W)
x = rearrange(x, 'b (t n) c -> (b t) n c', t=self.t)
c = repeat(c, 'b c -> (b t) c', t=self.t)
x = self.final_layer(x, c) # (B, N, patch_size ** 2 * out_channels)
x = self.unpatchify(x) # (B, out_channels, H, W)
x = rearrange(x, '(b t) c h w -> b t c h w', t=self.t)
return x
def forward_with_cfg(self, x, t, y, cfg_scale, attention_mask):
@@ -357,8 +360,8 @@ class DiT(nn.Module):
# For exact reproducibility reasons, we apply classifier-free guidance on only
# three channels by default. The standard approach to cfg applies it to all channels.
# This can be done by uncommenting the following line and commenting-out the line following that.
# eps, rest = model_out[:, :self.in_channels], model_out[:, self.in_channels:]
eps, rest = model_out[:, :3], model_out[:, 3:]
eps, rest = model_out[:, :self.in_channels], model_out[:, self.in_channels:]
# eps, rest = model_out[:, :3], model_out[:, 3:]
cond_eps, uncond_eps = torch.split(eps, len(eps) // 2, dim=0)
half_eps = uncond_eps + cfg_scale * (cond_eps - uncond_eps)
eps = torch.cat([half_eps, half_eps], dim=0)
@@ -394,10 +397,10 @@ def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
assert embed_dim % 2 == 0
# use half of dimensions to encode grid_h
emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2)
emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2)
emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, C/2)
emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, C/2)
emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D)
emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, C)
return emb
@@ -405,20 +408,20 @@ def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
"""
embed_dim: output dimension for each position
pos: a list of positions to be encoded: size (M,)
out: (M, D)
out: (M, C)
"""
assert embed_dim % 2 == 0
omega = np.arange(embed_dim // 2, dtype=np.float64)
omega /= embed_dim / 2.
omega = 1. / 10000**omega # (D/2,)
omega = 1. / 10000**omega # (C/2,)
pos = pos.reshape(-1) # (M,)
out = np.einsum('m,d->md', pos, omega) # (M, D/2), outer product
out = np.einsum('m,d->md', pos, omega) # (M, C/2), outer product
emb_sin = np.sin(out) # (M, D/2)
emb_cos = np.cos(out) # (M, D/2)
emb_sin = np.sin(out) # (M, C/2)
emb_cos = np.cos(out) # (M, C/2)
emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D)
emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, C)
return emb
opensora/models/diffusion/latte/diffusion/diffusion_utils.py
-88
View File
@@ -1,88 +0,0 @@
# Modified from OpenAI's diffusion repos
# GLIDE: https://github.com/openai/glide-text2im/blob/main/glide_text2im/gaussian_diffusion.py
# ADM: https://github.com/openai/guided-diffusion/blob/main/guided_diffusion
# IDDPM: https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
import torch as th
import numpy as np
def normal_kl(mean1, logvar1, mean2, logvar2):
"""
Compute the KL divergence between two gaussians.
Shapes are automatically broadcasted, so batches can be compared to
scalars, among other use cases.
"""
tensor = None
for obj in (mean1, logvar1, mean2, logvar2):
if isinstance(obj, th.Tensor):
tensor = obj
break
assert tensor is not None, "at least one argument must be a Tensor"
# Force variances to be Tensors. Broadcasting helps convert scalars to
# Tensors, but it does not work for th.exp().
logvar1, logvar2 = [
x if isinstance(x, th.Tensor) else th.tensor(x).to(tensor)
for x in (logvar1, logvar2)
]
return 0.5 * (
-1.0
+ logvar2
- logvar1
+ th.exp(logvar1 - logvar2)
+ ((mean1 - mean2) ** 2) * th.exp(-logvar2)
)
def approx_standard_normal_cdf(x):
"""
A fast approximation of the cumulative distribution function of the
standard normal.
"""
return 0.5 * (1.0 + th.tanh(np.sqrt(2.0 / np.pi) * (x + 0.044715 * th.pow(x, 3))))
def continuous_gaussian_log_likelihood(x, *, means, log_scales):
"""
Compute the log-likelihood of a continuous Gaussian distribution.
:param x: the targets
:param means: the Gaussian mean Tensor.
:param log_scales: the Gaussian log stddev Tensor.
:return: a tensor like x of log probabilities (in nats).
"""
centered_x = x - means
inv_stdv = th.exp(-log_scales)
normalized_x = centered_x * inv_stdv
log_probs = th.distributions.Normal(th.zeros_like(x), th.ones_like(x)).log_prob(normalized_x)
return log_probs
def discretized_gaussian_log_likelihood(x, *, means, log_scales):
"""
Compute the log-likelihood of a Gaussian distribution discretizing to a
given image.
:param x: the target images. It is assumed that this was uint8 values,
rescaled to the range [-1, 1].
:param means: the Gaussian mean Tensor.
:param log_scales: the Gaussian log stddev Tensor.
:return: a tensor like x of log probabilities (in nats).
"""
assert x.shape == means.shape == log_scales.shape
centered_x = x - means
inv_stdv = th.exp(-log_scales)
plus_in = inv_stdv * (centered_x + 1.0 / 255.0)
cdf_plus = approx_standard_normal_cdf(plus_in)
min_in = inv_stdv * (centered_x - 1.0 / 255.0)
cdf_min = approx_standard_normal_cdf(min_in)
log_cdf_plus = th.log(cdf_plus.clamp(min=1e-12))
log_one_minus_cdf_min = th.log((1.0 - cdf_min).clamp(min=1e-12))
cdf_delta = cdf_plus - cdf_min
log_probs = th.where(
x < -0.999,
log_cdf_plus,
th.where(x > 0.999, log_one_minus_cdf_min, th.log(cdf_delta.clamp(min=1e-12))),
)
assert log_probs.shape == x.shape
return log_probs
opensora/models/diffusion/latte/diffusion/timestep_sampler.py
-150
View File
@@ -1,150 +0,0 @@
# Modified from OpenAI's diffusion repos
# GLIDE: https://github.com/openai/glide-text2im/blob/main/glide_text2im/gaussian_diffusion.py
# ADM: https://github.com/openai/guided-diffusion/blob/main/guided_diffusion
# IDDPM: https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
from abc import ABC, abstractmethod
import numpy as np
import torch as th
import torch.distributed as dist
def create_named_schedule_sampler(name, diffusion):
"""
Create a ScheduleSampler from a library of pre-defined samplers.
:param name: the name of the sampler.
:param diffusion: the diffusion object to sample for.
"""
if name == "uniform":
return UniformSampler(diffusion)
elif name == "loss-second-moment":
return LossSecondMomentResampler(diffusion)
else:
raise NotImplementedError(f"unknown schedule sampler: {name}")
class ScheduleSampler(ABC):
"""
A distribution over timesteps in the diffusion process, intended to reduce
variance of the objective.
By default, samplers perform unbiased importance sampling, in which the
objective's mean is unchanged.
However, subclasses may override sample() to change how the resampled
terms are reweighted, allowing for actual changes in the objective.
"""
@abstractmethod
def weights(self):
"""
Get a numpy array of weights, one per diffusion step.
The weights needn't be normalized, but must be positive.
"""
def sample(self, batch_size, device):
"""
Importance-sample timesteps for a batch.
:param batch_size: the number of timesteps.
:param device: the torch device to save to.
:return: a tuple (timesteps, weights):
- timesteps: a tensor of timestep indices.
- weights: a tensor of weights to scale the resulting losses.
"""
w = self.weights()
p = w / np.sum(w)
indices_np = np.random.choice(len(p), size=(batch_size,), p=p)
indices = th.from_numpy(indices_np).long().to(device)
weights_np = 1 / (len(p) * p[indices_np])
weights = th.from_numpy(weights_np).float().to(device)
return indices, weights
class UniformSampler(ScheduleSampler):
def __init__(self, diffusion):
self.diffusion = diffusion
self._weights = np.ones([diffusion.num_timesteps])
def weights(self):
return self._weights
class LossAwareSampler(ScheduleSampler):
def update_with_local_losses(self, local_ts, local_losses):
"""
Update the reweighting using losses from a model.
Call this method from each rank with a batch of timesteps and the
corresponding losses for each of those timesteps.
This method will perform synchronization to make sure all of the ranks
maintain the exact same reweighting.
:param local_ts: an integer Tensor of timesteps.
:param local_losses: a 1D Tensor of losses.
"""
batch_sizes = [
th.tensor([0], dtype=th.int32, device=local_ts.device)
for _ in range(dist.get_world_size())
]
dist.all_gather(
batch_sizes,
th.tensor([len(local_ts)], dtype=th.int32, device=local_ts.device),
)
# Pad all_gather batches to be the maximum batch size.
batch_sizes = [x.item() for x in batch_sizes]
max_bs = max(batch_sizes)
timestep_batches = [th.zeros(max_bs).to(local_ts) for bs in batch_sizes]
loss_batches = [th.zeros(max_bs).to(local_losses) for bs in batch_sizes]
dist.all_gather(timestep_batches, local_ts)
dist.all_gather(loss_batches, local_losses)
timesteps = [
x.item() for y, bs in zip(timestep_batches, batch_sizes) for x in y[:bs]
]
losses = [x.item() for y, bs in zip(loss_batches, batch_sizes) for x in y[:bs]]
self.update_with_all_losses(timesteps, losses)
@abstractmethod
def update_with_all_losses(self, ts, losses):
"""
Update the reweighting using losses from a model.
Sub-classes should override this method to update the reweighting
using losses from the model.
This method directly updates the reweighting without synchronizing
between workers. It is called by update_with_local_losses from all
ranks with identical arguments. Thus, it should have deterministic
behavior to maintain state across workers.
:param ts: a list of int timesteps.
:param losses: a list of float losses, one per timestep.
"""
class LossSecondMomentResampler(LossAwareSampler):
def __init__(self, diffusion, history_per_term=10, uniform_prob=0.001):
self.diffusion = diffusion
self.history_per_term = history_per_term
self.uniform_prob = uniform_prob
self._loss_history = np.zeros(
[diffusion.num_timesteps, history_per_term], dtype=np.float64
)
self._loss_counts = np.zeros([diffusion.num_timesteps], dtype=np.int)
def weights(self):
if not self._warmed_up():
return np.ones([self.diffusion.num_timesteps], dtype=np.float64)
weights = np.sqrt(np.mean(self._loss_history ** 2, axis=-1))
weights /= np.sum(weights)
weights *= 1 - self.uniform_prob
weights += self.uniform_prob / len(weights)
return weights
def update_with_all_losses(self, ts, losses):
for t, loss in zip(ts, losses):
if self._loss_counts[t] == self.history_per_term:
# Shift out the oldest loss term.
self._loss_history[t, :-1] = self._loss_history[t, 1:]
self._loss_history[t, -1] = loss
else:
self._loss_history[t, self._loss_counts[t]] = loss
self._loss_counts[t] += 1
def _warmed_up(self):
return (self._loss_counts == self.history_per_term).all()
opensora/train/train.py
+17 -13
View File
@@ -27,8 +27,9 @@ import os
from opensora.dataset import getdataset
from opensora.models.ae import getae
from opensora.utils.dataset_utils import Collate
from opensora.models.diffusion.dit.models import DiT_models as Diffusion_models
from opensora.models.diffusion.dit.diffusion import create_diffusion
from opensora.models.ae import ae_stride_config, ae_channel_config
from opensora.models.diffusion import Diffusion_models
from opensora.models.diffusion.diffusion import create_diffusion
#################################################################################
# Training Helper Functions #
@@ -114,23 +115,24 @@ def main(args):
logger = create_logger(None)
# Create model:
vae_stride_t, vae_stride_h, vae_stride_w = [int(i) for i in args.ae[-5:].split('x')]
args.vae_stride_t, args.vae_stride_h, args.vae_stride_w = vae_stride_t, vae_stride_h, vae_stride_w
args.vae_stride = args.vae_stride_h
ae_stride_t, ae_stride_h, ae_stride_w = ae_stride_config[args.ae]
args.ae_stride_t, args.ae_stride_h, args.ae_stride_w = ae_stride_t, ae_stride_h, ae_stride_w
args.ae_stride = args.ae_stride_h
patch_size = args.model[-3:]
patch_size_t, patch_size_h, patch_size_w = int(patch_size[0]), int(patch_size[1]), int(patch_size[2])
args.patch_size = patch_size_h
args.patch_size_t, args.patch_size_h, args.patch_size_w = patch_size_t, patch_size_h, patch_size_w
assert args.num_frames % vae_stride_t == 0, "Image size must be divisible by 8 (for the VAE encoder)."
assert args.max_image_size % vae_stride_h == 0, "Image size must be divisible by 8 (for the VAE encoder)."
assert vae_stride_h == vae_stride_w, "Support now."
assert args.num_frames % ae_stride_t == 0, "Image size must be divisible by 8 (for the VAE encoder)."
assert args.max_image_size % ae_stride_h == 0, "Image size must be divisible by 8 (for the VAE encoder)."
assert ae_stride_h == ae_stride_w, "Support now."
assert patch_size_h == patch_size_w, "Support now."
latent_size = (args.num_frames // vae_stride_t, args.max_image_size // vae_stride_h, args.max_image_size // vae_stride_w)
latent_size = (args.num_frames // ae_stride_t, args.max_image_size // ae_stride_h, args.max_image_size // ae_stride_w)
model = Diffusion_models[args.model](
input_size=latent_size,
num_classes=args.num_classes
num_classes=args.num_classes,
in_channels=ae_channel_config[args.ae]
)
model.gradient_checkpointing = args.gradient_checkpointing
@@ -201,11 +203,12 @@ def main(args):
sampler.set_epoch(epoch)
logger.info(f"Beginning epoch {epoch}...")
for x, y, attn_mask in loader:
x = x.to(device)
x = x.to(device) # B C T H W
y = y.to(device)
attn_mask = attn_mask.to(device)
with torch.no_grad():
x = ae.encode(x) # Map input images to latent space + normalize latents
# Map input images to latent space + normalize latents
x = ae.encode(x) # B C T H W -> B T C H W
t = torch.randint(0, diffusion.num_timesteps, (x.shape[0],), device=device)
model_kwargs = dict(y=y, attention_mask=attn_mask)
loss_dict = diffusion.training_losses(model, x, t, model_kwargs)
@@ -277,7 +280,8 @@ if __name__ == "__main__":
# --------------------------------------
parser.add_argument("--ae", type=str, choices=['bair_stride4x2x2', 'ucf101_stride4x4x4',
'kinetics_stride4x4x4', 'kinetics_stride2x4x4'],
'kinetics_stride4x4x4', 'kinetics_stride2x4x4',
'stabilityai/sd-vae-ft-mse', 'stabilityai/sd-vae-ft-ema'],
default="ucf101_stride4x4x4")
parser.add_argument("--pt-ckpt", type=str, default=None)
parser.add_argument("--sample-rate", type=int, default=4)
opensora/utils/dataset_utils.py
+7 -6
View File
@@ -14,7 +14,8 @@ def pad_to_multiple(number, ds_stride):
class Collate:
def __init__(self, args):
self.max_image_size = args.max_image_size
self.vae_stride = args.vae_stride
self.ae_stride = args.ae_stride
self.ae_stride_t = args.ae_stride_t
self.patch_size = args.patch_size
self.patch_size_t = args.patch_size_t
self.num_frames = args.num_frames
@@ -22,8 +23,8 @@ class Collate:
def __call__(self, batch):
batch_tubes, labels = tuple(zip(*batch))
labels = torch.as_tensor(labels).to(torch.long)
ds_stride = self.vae_stride * self.patch_size
t_ds_stride = self.vae_stride * self.patch_size_t
ds_stride = self.ae_stride * self.patch_size
t_ds_stride = self.ae_stride_t * self.patch_size_t
# pad to max multiple of ds_stride
batch_input_size = [i.shape for i in batch_tubes]
@@ -44,9 +45,9 @@ class Collate:
# make attention_mask
max_tube_size = [pad_max_t, pad_max_h, pad_max_w]
max_latent_size = [max_tube_size[0] // self.vae_stride,
max_tube_size[1] // self.vae_stride,
max_tube_size[2] // self.vae_stride]
max_latent_size = [max_tube_size[0] // self.ae_stride_t,
max_tube_size[1] // self.ae_stride,
max_tube_size[2] // self.ae_stride]
max_patchify_latent_size = [max_latent_size[0] // self.patch_size_t,
max_latent_size[1] // self.patch_size,
max_latent_size[2] // self.patch_size]