File size: 10,932 Bytes
33da3d2 | 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 | # ------------------------------------------------------------------------
# Copyright (c) 2024-present, BAAI. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ------------------------------------------------------------------------
"""Base 3D transformer model for NOVA."""
from typing import Dict
import torch
from torch import nn
from tqdm import tqdm
from diffnext.models.guidance_scaler import GuidanceScaler
class Transformer3DModel(nn.Module):
"""Base 3D transformer model for NOVA."""
def __init__(
self,
video_encoder=None,
image_encoder=None,
image_decoder=None,
mask_embed=None,
text_embed=None,
label_embed=None,
video_pos_embed=None,
image_pos_embed=None,
motion_embed=None,
noise_scheduler=None,
sample_scheduler=None,
):
super(Transformer3DModel, self).__init__()
self.video_encoder = video_encoder
self.image_encoder = image_encoder
self.image_decoder = image_decoder
self.mask_embed = mask_embed
self.text_embed = text_embed
self.label_embed = label_embed
self.video_pos_embed = video_pos_embed
self.image_pos_embed = image_pos_embed
self.motion_embed = motion_embed
self.noise_scheduler = noise_scheduler
self.sample_scheduler = sample_scheduler
self.pipeline_preprocess = lambda inputs: inputs
self.loss_repeat = 4
def progress_bar(self, iterable, enable=True):
"""Return a tqdm progress bar."""
return tqdm(iterable) if enable else iterable
def preprocess(self, inputs: Dict):
"""Preprocess model inputs."""
add_guidance = inputs.get("guidance_scale", 1) > 1
inputs["c"], dtype, device = inputs.get("c", []), self.dtype, self.device
if inputs.get("x", None) is None:
batch_size = inputs.get("batch_size", 1)
image_size = (self.image_encoder.image_dim,) + self.image_encoder.image_size
inputs["x"] = torch.empty(batch_size, *image_size, device=device, dtype=dtype)
if inputs.get("prompt", None) is not None and self.text_embed:
inputs["c"].append(self.text_embed(inputs.pop("prompt")))
if inputs.get("motion", None) is not None and self.motion_embed:
flow, fps = inputs.pop("motion", None), inputs.pop("fps", None)
flow, fps = [v + v if (add_guidance and v) else v for v in (flow, fps)]
inputs["c"].append(self.motion_embed(inputs["c"][-1], flow, fps))
inputs["c"] = torch.cat(inputs["c"], dim=1) if len(inputs["c"]) > 1 else inputs["c"][0]
def get_losses(self, z: torch.Tensor, x: torch.Tensor, video_shape=None) -> Dict:
"""Return the training losses."""
z = z.repeat(self.loss_repeat, *((1,) * (z.dim() - 1)))
x = x.repeat(self.loss_repeat, *((1,) * (x.dim() - 1)))
x = self.image_encoder.patch_embed.patchify(x)
noise = torch.randn(x.shape, dtype=x.dtype, device=x.device)
timestep = self.noise_scheduler.sample_timesteps(z.shape[:2], device=z.device)
x_t = self.noise_scheduler.add_noise(x, noise, timestep)
x_t = self.image_encoder.patch_embed.unpatchify(x_t)
timestep = getattr(self.noise_scheduler, "timestep", timestep)
pred_type = getattr(self.noise_scheduler.config, "prediction_type", "flow")
model_pred = self.image_decoder(x_t, timestep, z)
model_target = noise.float() if pred_type == "epsilon" else noise.sub(x).float()
loss = nn.functional.mse_loss(model_pred.float(), model_target, reduction="none")
loss, weight = loss.mean(-1, True), self.mask_embed.mask.to(loss.dtype)
weight = weight.repeat(self.loss_repeat, *((1,) * (z.dim() - 1)))
loss = loss.mul_(weight).div_(weight.sum().add_(1e-5))
if video_shape is not None:
loss = loss.view((-1,) + video_shape).transpose(0, 1).sum((1, 2))
i2i = loss[1:].sum().mul_(video_shape[0] / (video_shape[0] - 1))
return {"loss_t2i": loss[0].mul(video_shape[0]), "loss_i2i": i2i}
return {"loss": loss.sum()}
@torch.no_grad()
def denoise(self, z, x, guidance_scaler, generator=None, pred_ids=None) -> torch.Tensor:
"""Run diffusion denoising process."""
self.sample_scheduler._step_index = None # Reset counter.
for t in self.sample_scheduler.timesteps:
z, pred_ids = guidance_scaler.maybe_disable(t, z, pred_ids)
timestep = torch.as_tensor(t, device=x.device).expand(z.shape[0])
model_pred = self.image_decoder(guidance_scaler.expand(x), timestep, z, pred_ids)
model_pred = guidance_scaler.scale(model_pred)
model_pred = self.image_encoder.patch_embed.unpatchify(model_pred)
x = self.sample_scheduler.step(model_pred, t, x, generator=generator).prev_sample
return self.image_encoder.patch_embed.patchify(x)
@torch.inference_mode()
def generate_frame(self, states: Dict, inputs: Dict):
"""Generate a batch of frames."""
guidance_scaler = GuidanceScaler(**inputs)
generator = self.mask_embed.generator = inputs.get("generator", None)
all_num_preds = [_ for _ in inputs["num_preds"] if _ > 0]
c, x, self.mask_embed.mask = states["c"], states["x"].zero_(), None
pos = self.image_pos_embed.get_pos(1, c.size(0)) if self.image_pos_embed else None
for i, num_preds in enumerate(self.progress_bar(all_num_preds, inputs.get("tqdm2", False))):
guidance_scaler.decay_guidance_scale((i + 1) / len(all_num_preds))
z = self.mask_embed(self.image_encoder.patch_embed(x))
pred_mask, pred_ids = self.mask_embed.get_pred_mask(num_preds)
pred_ids = guidance_scaler.expand(pred_ids)
prev_ids = prev_ids if i else pred_ids.new_empty((pred_ids.size(0), 0, 1))
z = self.image_encoder(guidance_scaler.expand(z), c, prev_ids, pos=pos)
prev_ids = torch.cat([prev_ids, pred_ids], dim=1)
states["noise"].normal_(generator=generator)
sample = self.denoise(z, states["noise"], guidance_scaler.clone(), generator, pred_ids)
x.add_(self.image_encoder.patch_embed.unpatchify(sample.mul_(pred_mask)))
@torch.inference_mode()
def generate_video(self, inputs: Dict):
"""Generate a batch of videos."""
guidance_scaler = GuidanceScaler(**inputs)
max_latent_length = inputs.get("max_latent_length", 1)
self.sample_scheduler.set_timesteps(inputs.get("num_diffusion_steps", 25))
states = {"x": inputs["x"], "noise": inputs["x"].clone()}
latents, self.mask_embed.pred_ids, time_pos = inputs.get("latents", []), None, []
if self.image_pos_embed: # RoPE.
time_pos = self.video_pos_embed.get_pos(max_latent_length).chunk(max_latent_length, 1)
else: # Absolute PE, which will be deprecated in the future.
time_embed = self.video_pos_embed.get_time_embed(max_latent_length)
inputs["c"] = guidance_scaler.expand_text(inputs["c"])
self.video_encoder.enable_kvcache(max_latent_length > 1)
for states["t"] in self.progress_bar(range(max_latent_length), inputs.get("tqdm1", True)):
pos = time_pos[states["t"]] if time_pos else None
c = self.video_encoder.patch_embed(states["x"])
c.__setitem__(slice(None), self.mask_embed.bos_token) if states["t"] == 0 else c
c = self.video_pos_embed(c.add_(time_embed[states["t"]])) if not time_pos else c
c = guidance_scaler.expand(c, padding=self.mask_embed.bos_token)
c = states["c"] = self.video_encoder(c, None if states["t"] else inputs["c"], pos=pos)
if not isinstance(self.video_encoder.mixer, torch.nn.Identity):
states["c"] = self.video_encoder.mixer(states["*"], c) if states["t"] else c
states["*"] = states["*"] if states["t"] else states["c"]
if states["t"] == 0 and latents:
states["x"].copy_(latents[-1])
else:
self.generate_frame(states, inputs)
latents.append(states["x"].clone())
self.video_encoder.enable_kvcache(False)
def train_video(self, inputs):
"""Train a batch of videos."""
# 3D temporal autoregressive modeling (TAM).
inputs["x"].unsqueeze_(2) if inputs["x"].dim() == 4 else None
bs, latent_length = inputs["x"].size(0), inputs["x"].size(2)
c = self.video_encoder.patch_embed(inputs["x"][:, :, : latent_length - 1])
bov = self.mask_embed.bos_token.expand(bs, 1, c.size(-2), -1)
c, pos = self.video_pos_embed(torch.cat([bov, c], dim=1)), None
if self.image_pos_embed:
pos = self.video_pos_embed.get_pos(c.size(1), bs, self.video_encoder.patch_embed.hw)
attn_mask = self.mask_embed.get_attn_mask(c, inputs["c"]) if latent_length > 1 else None
[setattr(blk.attn, "attn_mask", attn_mask) for blk in self.video_encoder.blocks]
c = self.video_encoder(c.flatten(1, 2), inputs["c"], pos=pos)
if not isinstance(self.video_encoder.mixer, torch.nn.Identity) and latent_length > 1:
c = c.view(bs, latent_length, -1, c.size(-1)).split([1, latent_length - 1], 1)
c = torch.cat([c[0], self.video_encoder.mixer(*c)], 1)
# 2D masked autoregressive modeling (MAM).
x = inputs["x"][:, :, :latent_length].transpose(1, 2).flatten(0, 1)
z, bs = self.image_encoder.patch_embed(x), bs * latent_length
if self.image_pos_embed:
pos = self.image_pos_embed.get_pos(1, bs, self.image_encoder.patch_embed.hw)
z = self.image_encoder(self.mask_embed(z), c.reshape(bs, -1, c.size(-1)), pos=pos)
# 1D token-wise diffusion modeling (MLP).
video_shape = (latent_length, z.size(1)) if latent_length > 1 else None
return self.get_losses(z, x, video_shape=video_shape)
def forward(self, inputs):
"""Define the computation performed at every call."""
self.pipeline_preprocess(inputs)
self.preprocess(inputs)
if self.training:
return self.train_video(inputs)
inputs["latents"] = inputs.pop("latents", [])
self.generate_video(inputs)
return {"x": torch.stack(inputs["latents"], dim=2)}
|