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9368ee7 | 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 | # SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
from __future__ import annotations
from functools import partial
from typing import Dict, Any, TYPE_CHECKING, Callable
import torch
import torch.nn.functional as F
from lipforcing.methods import FastGenModel, CausVidModel
from lipforcing.utils import expand_like
if TYPE_CHECKING:
from lipforcing.configs.config import BaseModelConfig as ModelConfig
class KDModel(FastGenModel):
def __init__(self, config: ModelConfig):
"""
Args:
config (ModelConfig): The configuration for the knowledge distillation model.
This model directly learns the pre-constructed ODE pairs from the teacher model.
"""
super().__init__(config)
self.config = config
def build_model(self):
super().build_model()
self.load_student_weights_and_ema()
def _get_outputs(
self,
gen_data: torch.Tensor,
input_student: torch.Tensor = None,
condition: Any = None,
) -> Dict[str, torch.Tensor | Callable]:
if self.config.student_sample_steps == 1:
assert input_student is not None, "input_student must be provided for KDModel"
return {"gen_rand": gen_data, "input_rand": input_student}
else:
noise = torch.randn_like(gen_data, dtype=self.precision)
gen_rand_func = partial(
self.generator_fn,
net=self.net_inference,
noise=noise,
condition=condition,
student_sample_steps=self.config.student_sample_steps,
student_sample_type=self.config.student_sample_type,
t_list=self.config.sample_t_cfg.t_list,
precision_amp=self.precision_amp_infer,
)
return {"gen_rand": gen_rand_func, "input_rand": noise, "gen_rand_train": gen_data}
def single_train_step(
self, data: Dict[str, Any], iteration: int
) -> tuple[dict[str, torch.Tensor], dict[str, torch.Tensor | Callable]]:
"""
Single training step for knowledge distillation model.
Important! For multistep KD distillation, t_list must be aligned with the `path`'s timesteps:
1) Ensure t_list corresponds exactly to path timesteps
2) Please check the `path_timesteps` item in index.json of the paired dataset
3) num_inference_steps in denoise path must be 4
4) student_sample_steps must be either 2 or 4
5) Current approach assumes uniform spacing: t_list=[t1, t3] → path indices [0, 2]
Args:
data (Dict[str, Any]): Data dict for the current iteration.
iteration (int): Current training iteration
Returns:
loss_map (dict[str, torch.Tensor]): Dictionary containing the loss values
outputs (dict[str, torch.Tensor]): Dictionary containing the network output
"""
denoised_data = data["real"]
condition = data["condition"]
batch_size = denoised_data.shape[0]
if self.config.student_sample_steps == 1:
# perform single-step distillation
if "noise" in data:
input_student = data["noise"]
elif "path" in data:
input_student = data["path"][:, 0, ...] # the first step is noise
else:
raise ValueError("Noise or path must be provided for KDModel")
t_student = torch.full(
(batch_size,),
self.net.noise_scheduler.max_t,
device=self.device,
dtype=self.net.noise_scheduler.t_precision,
)
else:
# perform multiple-step distillation
assert "path" in data, "path must be provided for KDModel"
denoise_path = data["path"] # [batch_size, num_inf_steps, C, num_frames, H, W]
assert denoise_path.shape[1] == 4, "num_inference_steps in denoise path must be 4"
assert (
denoise_path.shape[1] % self.config.student_sample_steps == 0
), f"student_sample_steps must be either 2 or 4, but got {self.config.student_sample_steps}"
t_student, t_list_ids = self.net.noise_scheduler.sample_from_t_list(
batch_size,
sample_steps=self.config.student_sample_steps,
t_list=self.config.sample_t_cfg.t_list,
return_ids=True,
device=self.device,
)
# Important: Ensure t_list corresponds exactly to path timesteps
# Current approach assumes uniform spacing: t_list=[t1, t3] → path indices [0, 2]
path_indices = t_list_ids * (denoise_path.shape[1] // self.config.student_sample_steps)
path_indices = expand_like(path_indices, denoise_path).expand(
-1, -1, *denoise_path.shape[2:]
) # [batch_size, 1, C, num_frames, H, W]
input_student = torch.gather(denoise_path, 1, path_indices).squeeze(1) # [batch_size, C, num_frames, H, W]
gen_data = self.gen_data_from_net(input_student, t_student, condition=condition)
# Compute the l2 loss between the generated data and the denoised data
loss = 0.5 * F.mse_loss(gen_data, denoised_data, reduction="mean")
# Build output dictionaries
loss_map = {
"total_loss": loss,
"recon_loss": loss,
}
outputs = self._get_outputs(gen_data, input_student, condition=condition)
return loss_map, outputs
class CausalKDModel(KDModel):
def _get_outputs(
self,
gen_data: torch.Tensor,
input_student: torch.Tensor = None,
condition: Any = None,
) -> Dict[str, torch.Tensor | Callable]:
noise = torch.randn_like(gen_data, dtype=self.precision)
context_noise = getattr(self.config, "context_noise", 0)
# Reuse CausVidModel's autoregressive generation logic
gen_rand_func = partial(
CausVidModel.generator_fn,
net=self.net_inference,
noise=noise,
condition=condition,
student_sample_steps=self.config.student_sample_steps,
t_list=self.config.sample_t_cfg.t_list,
context_noise=context_noise,
precision_amp=self.precision_amp_infer,
)
return {"gen_rand": gen_rand_func, "input_rand": noise, "gen_rand_train": gen_data}
def single_train_step(
self, data: Dict[str, Any], iteration: int
) -> tuple[dict[str, torch.Tensor], dict[str, torch.Tensor | Callable]]:
"""
Single training step for knowledge distillation model.
Important! t_list must be the same with the `path`'s timesteps.
Please check the `path_timesteps` item in index.json of the paired dataset.
Args:
data (Dict[str, Any]): Data dict for the current iteration.
iteration (int): Current training iteration
Returns:
loss_map (dict[str, torch.Tensor]): Dictionary containing the loss values
outputs (dict[str, torch.Tensor]): Dictionary containing the network output
"""
denoise_path = data["path"] # shape is [batch_size, num_inf_steps, C, num_frames, H, W]
denoised_data = data["real"] # [batch_size, C, num_frames, H, W]
condition = data["condition"]
batch_size, num_frames = denoise_path.shape[0], denoise_path.shape[3]
chunk_size = self.net.chunk_size
# add noise
t_inhom, ids = self.net.noise_scheduler.sample_t_inhom(
batch_size,
num_frames,
chunk_size,
sample_steps=self.config.student_sample_steps,
t_list=self.config.sample_t_cfg.t_list, # Note t_list to be aligned the `path`'s timesteps
device=self.device,
dtype=denoise_path.dtype,
) # [batch_size, num_frames]
expand_shape = [ids.shape[0], 1, 1, ids.shape[1]] + [1] * max(0, denoise_path.ndim - 4)
ids = ids.view(expand_shape).expand(-1, -1, *denoise_path.shape[2:]) # [batch_size, 1, C, num_frames, H, W]
denoise_path_all = torch.cat([denoise_path, denoised_data.unsqueeze(1)], dim=1) # gather clean data
noisy_data = torch.gather(denoise_path_all, 1, ids).squeeze(1) # [batch_size, C, num_frames, H, W]
# generate data
gen_data = self.gen_data_from_net(noisy_data, t_inhom, condition=condition)
# Compute the l2 loss between the generated data and the denoised data
loss = 0.5 * F.mse_loss(gen_data, denoised_data, reduction="mean")
# Build output dictionaries
loss_map = {
"total_loss": loss,
"recon_loss": loss,
}
outputs = self._get_outputs(gen_data, condition=condition)
return loss_map, outputs
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