Spaces:
Sleeping
Sleeping
File size: 10,544 Bytes
07c6a04 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 |
# Adapted from OpenSora
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# --------------------------------------------------------
# References:
# OpenSora: https://github.com/hpcaitech/Open-Sora
# --------------------------------------------------------
import torch
import torch.distributed as dist
from einops import rearrange
from torch.distributions import LogisticNormal
from tqdm import tqdm
from videosys.core.pab_mgr import get_diffusion_skip, get_diffusion_skip_timestep, skip_diffusion_timestep
from videosys.diffusion.gaussian_diffusion import _extract_into_tensor
def mean_flat(tensor: torch.Tensor, mask=None):
"""
Take the mean over all non-batch dimensions.
"""
if mask is None:
return tensor.mean(dim=list(range(1, len(tensor.shape))))
else:
assert tensor.dim() == 5
assert tensor.shape[2] == mask.shape[1]
tensor = rearrange(tensor, "b c t h w -> b t (c h w)")
denom = mask.sum(dim=1) * tensor.shape[-1]
loss = (tensor * mask.unsqueeze(2)).sum(dim=1).sum(dim=1) / denom
return loss
def timestep_transform(
t,
model_kwargs,
base_resolution=512 * 512,
base_num_frames=1,
scale=1.0,
num_timesteps=1,
):
t = t / num_timesteps
resolution = model_kwargs["height"] * model_kwargs["width"]
ratio_space = (resolution / base_resolution).sqrt()
# NOTE: currently, we do not take fps into account
# NOTE: temporal_reduction is hardcoded, this should be equal to the temporal reduction factor of the vae
if model_kwargs["num_frames"][0] == 1:
num_frames = torch.ones_like(model_kwargs["num_frames"])
else:
num_frames = model_kwargs["num_frames"] // 17 * 5
ratio_time = (num_frames / base_num_frames).sqrt()
ratio = ratio_space * ratio_time * scale
new_t = ratio * t / (1 + (ratio - 1) * t)
new_t = new_t * num_timesteps
return new_t
class RFlowScheduler:
def __init__(
self,
num_timesteps=1000,
num_sampling_steps=10,
use_discrete_timesteps=False,
sample_method="uniform",
loc=0.0,
scale=1.0,
use_timestep_transform=False,
transform_scale=1.0,
):
self.num_timesteps = num_timesteps
self.num_sampling_steps = num_sampling_steps
self.use_discrete_timesteps = use_discrete_timesteps
# sample method
assert sample_method in ["uniform", "logit-normal"]
assert (
sample_method == "uniform" or not use_discrete_timesteps
), "Only uniform sampling is supported for discrete timesteps"
self.sample_method = sample_method
if sample_method == "logit-normal":
self.distribution = LogisticNormal(torch.tensor([loc]), torch.tensor([scale]))
self.sample_t = lambda x: self.distribution.sample((x.shape[0],))[:, 0].to(x.device)
# timestep transform
self.use_timestep_transform = use_timestep_transform
self.transform_scale = transform_scale
def training_losses(self, model, x_start, model_kwargs=None, noise=None, mask=None, weights=None, t=None):
"""
Compute training losses for a single timestep.
Arguments format copied from opensora/schedulers/iddpm/gaussian_diffusion.py/training_losses
Note: t is int tensor and should be rescaled from [0, num_timesteps-1] to [1,0]
"""
if t is None:
if self.use_discrete_timesteps:
t = torch.randint(0, self.num_timesteps, (x_start.shape[0],), device=x_start.device)
elif self.sample_method == "uniform":
t = torch.rand((x_start.shape[0],), device=x_start.device) * self.num_timesteps
elif self.sample_method == "logit-normal":
t = self.sample_t(x_start) * self.num_timesteps
if self.use_timestep_transform:
t = timestep_transform(t, model_kwargs, scale=self.transform_scale, num_timesteps=self.num_timesteps)
if model_kwargs is None:
model_kwargs = {}
if noise is None:
noise = torch.randn_like(x_start)
assert noise.shape == x_start.shape
x_t = self.add_noise(x_start, noise, t)
if mask is not None:
t0 = torch.zeros_like(t)
x_t0 = self.add_noise(x_start, noise, t0)
x_t = torch.where(mask[:, None, :, None, None], x_t, x_t0)
terms = {}
model_output = model(x_t, t, **model_kwargs)
velocity_pred = model_output.chunk(2, dim=1)[0]
if weights is None:
loss = mean_flat((velocity_pred - (x_start - noise)).pow(2), mask=mask)
else:
weight = _extract_into_tensor(weights, t, x_start.shape)
loss = mean_flat(weight * (velocity_pred - (x_start - noise)).pow(2), mask=mask)
terms["loss"] = loss
return terms
def add_noise(
self,
original_samples: torch.FloatTensor,
noise: torch.FloatTensor,
timesteps: torch.IntTensor,
) -> torch.FloatTensor:
"""
compatible with diffusers add_noise()
"""
timepoints = timesteps.float() / self.num_timesteps
timepoints = 1 - timepoints # [1,1/1000]
# timepoint (bsz) noise: (bsz, 4, frame, w ,h)
# expand timepoint to noise shape
timepoints = timepoints.unsqueeze(1).unsqueeze(1).unsqueeze(1).unsqueeze(1)
timepoints = timepoints.repeat(1, noise.shape[1], noise.shape[2], noise.shape[3], noise.shape[4])
return timepoints * original_samples + (1 - timepoints) * noise
class RFLOW:
def __init__(
self,
num_sampling_steps=10,
num_timesteps=1000,
cfg_scale=4.0,
use_discrete_timesteps=False,
use_timestep_transform=False,
**kwargs,
):
self.num_sampling_steps = num_sampling_steps
self.num_timesteps = num_timesteps
self.cfg_scale = cfg_scale
self.use_discrete_timesteps = use_discrete_timesteps
self.use_timestep_transform = use_timestep_transform
self.scheduler = RFlowScheduler(
num_timesteps=num_timesteps,
num_sampling_steps=num_sampling_steps,
use_discrete_timesteps=use_discrete_timesteps,
use_timestep_transform=use_timestep_transform,
**kwargs,
)
def sample(
self,
model,
text_encoder,
z,
prompts,
device,
additional_args=None,
mask=None,
guidance_scale=None,
progress=True,
verbose=False,
):
# if no specific guidance scale is provided, use the default scale when initializing the scheduler
if guidance_scale is None:
guidance_scale = self.cfg_scale
n = len(prompts)
# text encoding
model_args = text_encoder.encode(prompts)
y_null = text_encoder.null(n)
model_args["y"] = torch.cat([model_args["y"], y_null], 0)
if additional_args is not None:
model_args.update(additional_args)
# prepare timesteps
timesteps = [(1.0 - i / self.num_sampling_steps) * self.num_timesteps for i in range(self.num_sampling_steps)]
if self.use_discrete_timesteps:
timesteps = [int(round(t)) for t in timesteps]
timesteps = [torch.tensor([t] * z.shape[0], device=device) for t in timesteps]
if self.use_timestep_transform:
timesteps = [timestep_transform(t, additional_args, num_timesteps=self.num_timesteps) for t in timesteps]
if get_diffusion_skip() and get_diffusion_skip_timestep() is not None:
orignal_timesteps = timesteps
diffusion_skip_timestep = get_diffusion_skip_timestep()
timesteps = skip_diffusion_timestep(timesteps, diffusion_skip_timestep)
if verbose and dist.get_rank() == 0:
print("============================")
print("skip diffusion steps!!!")
print("============================")
print(f"orignal sample timesteps: {orignal_timesteps}")
print(f"orignal diffusion steps: {len(orignal_timesteps)}")
print("============================")
print(f"skip diffusion steps: {get_diffusion_skip_timestep()}")
print(f"sample timesteps: {timesteps}")
print(f"num_inference_steps: {len(timesteps)}")
print("============================")
if mask is not None:
noise_added = torch.zeros_like(mask, dtype=torch.bool)
noise_added = noise_added | (mask == 1)
progress_wrap = tqdm if progress and dist.get_rank() == 0 else (lambda x: x)
dtype = model.x_embedder.proj.weight.dtype
all_timesteps = [int(t.to(dtype).item()) for t in timesteps]
for i, t in progress_wrap(list(enumerate(timesteps))):
# mask for adding noise
if mask is not None:
mask_t = mask * self.num_timesteps
x0 = z.clone()
x_noise = self.scheduler.add_noise(x0, torch.randn_like(x0), t)
mask_t_upper = mask_t >= t.unsqueeze(1)
model_args["x_mask"] = mask_t_upper.repeat(2, 1)
mask_add_noise = mask_t_upper & ~noise_added
z = torch.where(mask_add_noise[:, None, :, None, None], x_noise, x0)
noise_added = mask_t_upper
# classifier-free guidance
z_in = torch.cat([z, z], 0)
t = torch.cat([t, t], 0)
# pred = model(z_in, t, **model_args).chunk(2, dim=1)[0]
output = model(z_in, t, all_timesteps, **model_args)
pred = output.chunk(2, dim=1)[0]
pred_cond, pred_uncond = pred.chunk(2, dim=0)
v_pred = pred_uncond + guidance_scale * (pred_cond - pred_uncond)
# update z
dt = timesteps[i] - timesteps[i + 1] if i < len(timesteps) - 1 else timesteps[i]
dt = dt / self.num_timesteps
z = z + v_pred * dt[:, None, None, None, None]
if mask is not None:
z = torch.where(mask_t_upper[:, None, :, None, None], z, x0)
return z
def training_losses(self, model, x_start, model_kwargs=None, noise=None, mask=None, weights=None, t=None):
return self.scheduler.training_losses(model, x_start, model_kwargs, noise, mask, weights, t)
|