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Migrate to a new DTO for latents to carry more information that a normal tensor such as audio.

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This commit is contained in:
Jaret Burkett
2026-08-30 10:30:21 -06:00
parent 2a69c1e7de
commit 764b5064fb
8 changed files with 369 additions and 215 deletions
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47
extensions_built_in/diffusion_models/ltx2/ltx2.py
View File

@@ -9,6 +9,7 @@ from transformers import Gemma3Config
import yaml
from toolkit.config_modules import GenerateImageConfig, ModelConfig
from toolkit.data_transfer_object.data_loader import DataLoaderBatchDTO
from toolkit.dto import DTO
from toolkit.models.base_model import BaseModel
from toolkit.basic import flush
from toolkit.prompt_utils import PromptEmbeds
@@ -825,16 +826,7 @@ class LTX2Model(BaseModel):
batch: "DataLoaderBatchDTO" = None,
**kwargs,
):
# a grad-enabled prediction is the primary (loss carrying) one unless
# the trainer declared a secondary slot on the batch (prior /
# guidance-unconditional / preservation passes). Trainers that make
# several grad predictions per step (e.g. turbo rollouts) get one
# primary per prediction, last writer wins.
is_primary_pred = (
torch.is_grad_enabled()
and batch is not None
and batch.audio_pred_slot is None
)
audio_target = None
with torch.no_grad():
if self.model.device == torch.device("cpu"):
self.model.to(self.device_torch)
@@ -937,22 +929,27 @@ class LTX2Model(BaseModel):
raw_audio_latents = self.encode_audio(batch.audio_data)
audio_num_frames = raw_audio_latents.shape[1]
# add the audio targets to the batch for loss calculation later
# the audio noise is drawn once per step and shared by every
# pass (prior, primary, cfg/guidance, preservation) so they all
# see the same soundtrack and the stored target keeps matching
# see the same soundtrack and every pass's target matches. It
# rides on the latents DTO.
audio_noise = (
batch.latents.get("audio_noise")
if isinstance(batch.latents, DTO)
else None
)
if (
batch.audio_noise is not None
and batch.audio_noise.shape == raw_audio_latents.shape
audio_noise is not None
and audio_noise.shape == raw_audio_latents.shape
):
audio_noise = batch.audio_noise.to(
audio_noise = audio_noise.to(
raw_audio_latents.device, dtype=raw_audio_latents.dtype
)
else:
audio_noise = torch.randn_like(raw_audio_latents)
batch.audio_noise = audio_noise
if batch.audio_target is None:
batch.audio_target = (audio_noise - raw_audio_latents).detach()
if batch.latents is not None:
batch.latents = DTO(batch.latents, audio_noise=audio_noise)
audio_target = (audio_noise - raw_audio_latents).detach()
audio_latents = self.add_noise(
raw_audio_latents,
audio_noise,
@@ -1044,13 +1041,6 @@ class LTX2Model(BaseModel):
return_dict=False,
)
# add audio latent to batch if we had audio
if batch.audio_target is not None:
if is_primary_pred:
batch.audio_pred = noise_pred_audio
else:
batch.set_secondary_audio_pred(noise_pred_audio)
unpacked_output = self.pipeline._unpack_latents(
latents=noise_pred_video,
num_frames=latent_num_frames,
@@ -1060,6 +1050,13 @@ class LTX2Model(BaseModel):
patch_size_t=self.pipeline.transformer_temporal_patch_size,
)
if audio_target is not None:
# every pass's DTO carries its own audio stream and target
return DTO(
unpacked_output,
audio=noise_pred_audio,
audio_target=audio_target,
)
return unpacked_output
def get_prompt_embeds(self, prompt: str) -> PromptEmbeds:

72
extensions_built_in/diffusion_models/minimax_h3/minimax_h3.py
View File

@@ -49,6 +49,7 @@ from toolkit.accelerator import unwrap_model
from toolkit.advanced_prompt_embeds import AdvancedPromptEmbeds
from toolkit.basic import flush
from toolkit.config_modules import GenerateImageConfig, ModelConfig
from toolkit.dto import DTO
from toolkit.metadata import get_meta_for_safetensors
from toolkit.models.base_model import BaseModel
from toolkit.models.v2.text_encoders.qwen3_vl import Qwen3VLTextEncoder
@@ -779,17 +780,6 @@ class MinimaxH3Model(BaseModel):
if self.model.device == torch.device("cpu"):
self.model.to(device)
# a grad-enabled prediction is the primary (loss carrying) one unless
# the trainer declared a secondary slot on the batch (prior /
# guidance-unconditional / preservation passes). Trainers that make
# several grad predictions per step (e.g. turbo rollouts) get one
# primary per prediction, last writer wins.
is_primary_pred = (
torch.is_grad_enabled()
and batch is not None
and batch.audio_pred_slot is None
)
batch_size, _, t_lat, h_lat, w_lat = latent_model_input.shape
with torch.no_grad():
@@ -836,6 +826,8 @@ class MinimaxH3Model(BaseModel):
)
sa = sigma_a.view(-1, 1, 1)
audio_target = None
noisy_audio_rows = None
if raw_audio is not None:
expected_rows = a_lat * packing.AUDIO_CHANNELS
if raw_audio.shape[1] > expected_rows:
@@ -846,31 +838,29 @@ class MinimaxH3Model(BaseModel):
)
# the audio noise is drawn once per step and shared by every
# pass (prior, primary, cfg/guidance, preservation) so they all
# see the same soundtrack and the stored target keeps matching
if (
batch.audio_noise is not None
and batch.audio_noise.shape == raw_audio.shape
):
audio_noise = batch.audio_noise.to(device, torch.float32)
# see the same soundtrack and every pass's target matches. It
# rides on the latents DTO along with the trimmed audio so
# on-the-fly encodes aren't repeated per pass.
audio_noise = (
batch.latents.get("audio_noise")
if isinstance(batch.latents, DTO)
else None
)
if audio_noise is not None and audio_noise.shape == raw_audio.shape:
audio_noise = audio_noise.to(device, torch.float32)
else:
audio_noise = torch.randn_like(raw_audio)
batch.audio_noise = audio_noise
if batch.latents is not None:
batch.latents = DTO(
batch.latents, audio=raw_audio, audio_noise=audio_noise
)
audio_rows = (1.0 - sa) * raw_audio + sa * audio_noise
batch.audio_latents = raw_audio
if batch.audio_target is None:
# model predicts clean - noise; audio_pred is negated below
# so the stored target follows ai-toolkit's noise - clean.
# With the shared noise this is the same value on every
# pass, so first writer is fine (and it keeps a guidance
# extrapolated target from being overwritten).
batch.audio_target = (audio_noise - raw_audio).detach()
if is_primary_pred:
# expose what audio perceptual losses need to rebuild the
# clean estimate (x0 = noisy - sigma_a * pred). Tied to the
# primary pass so they always match audio_pred, even when a
# trainer makes primary predictions at several sigmas.
batch.audio_noisy = audio_rows
batch.audio_sigma = sigma_a
# model predicts clean - noise; audio_pred is negated below so
# the target follows ai-toolkit's noise - clean convention
audio_target = (audio_noise - raw_audio).detach()
# what audio perceptual losses need to rebuild the clean
# estimate (x0 = noisy - sigma_a * pred); rides the pred DTO
noisy_audio_rows = audio_rows
else:
# no soundtrack: silence (zeros) noised at the audio sigma
# rides along without contributing to the loss
@@ -966,15 +956,19 @@ class MinimaxH3Model(BaseModel):
if num_cond_audio > 0:
# reference soundtrack rows are conditioning, not targets
audio_pred = audio_pred[:, num_cond_audio:]
if batch is not None and batch.audio_target is not None:
# flip to ai-toolkit's noise - clean convention
if is_primary_pred:
batch.audio_pred = -audio_pred
else:
batch.set_secondary_audio_pred(-audio_pred)
video_pred = video_pred[:, num_cond:]
noise_pred = unpatchify_video_tokens(video_pred, t_lat, h_lat, w_lat)
if audio_target is not None:
# every pass's DTO carries its own audio stream; preds flipped to
# ai-toolkit's noise - clean convention
return DTO(
-noise_pred,
audio=-audio_pred,
audio_target=audio_target,
audio_noisy=noisy_audio_rows,
audio_sigma=sigma_a,
)
return -noise_pred
def get_loss_target(self, *args, **kwargs):