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Merged
TroyHernandez merged 1 commit into from
Jul 7, 2026
Merged

Recover: compact per-token timestep for conditioned denoising #18

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17 changes: 15 additions & 2 deletions R/dit_ltx23.R
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Original file line number Diff line number Diff line change
Expand Up @@ -203,7 +203,8 @@ ltx23_transformer <- torch::nn_module(
spatio_temporal_guidance_blocks = NULL,
perturbation_mask = NULL,
use_cross_timestep = FALSE,
video_self_attention_mask = NULL
video_self_attention_mask = NULL,
cond_token_index = NULL
) {
audio_timestep <- audio_timestep %||% timestep
audio_sigma <- audio_sigma %||% sigma
Expand Down Expand Up @@ -343,13 +344,25 @@ ltx23_transformer <- torch::nn_module(
video_rotary_emb, audio_rotary_emb,
video_ca_rotary_emb, audio_ca_rotary_emb,
encoder_attention_mask,
audio_encoder_attention_mask
audio_encoder_attention_mask,
cond_token_index = cond_token_index
)
hidden_states <- res[[1]]
audio_hidden_states <- res[[2]]
}

block_gc <- isTRUE(getOption("diffuseR.block_gc"))
if (!is.null(cond_token_index)) {
# Expand the compact [B, V, ...] timestep variants per token
# (1-based index). The output layer always needs the expanded
# embedded_timestep; the eager blocks also need expanded temb
# (the compiled stack selects per token internally instead).
idx1 <- cond_token_index$to(dtype = torch::torch_long())$add(1L)$squeeze(1L)
embedded_timestep <- embedded_timestep$index_select(2L, idx1)
if (!use_jit) {
temb <- temb$index_select(2L, idx1)
}
}
eager_blocks <- if (use_jit) integer(0) else seq_along(self$transformer_blocks)
for (block_idx in eager_blocks) {
is_stg_block <- (block_idx - 1L) %in% stg_blocks # reference indices are 0-based
Expand Down
33 changes: 24 additions & 9 deletions R/jit_ltx23.R
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Expand Up @@ -86,6 +86,17 @@ def mods(tbl: Tensor, temb: Tensor, num: int) -> Tensor:
t = temb.size(1)
return tbl.unsqueeze(0).unsqueeze(0) + temb.reshape([b, t, num, -1])

def modtok(vada: Tensor, j: int, idx: Optional[Tensor]) -> Tensor:
# One modulation vector [B, T, D]. Without idx, T is 1 (global) or
# S (per-token) and broadcasts/aligns directly. With idx (prefix
# conditioning), T holds the distinct timestep variants and idx
# [S] selects per token -- materializing one [B, S, D] slice on
# demand instead of a [B, S, num, D] block.
v = vada.select(2, j)
if idx is None:
return v
return v.index_select(1, idx)

def attn_nf4(x: Tensor, ctx: Tensor, ws: List[Tensor], base: int, table: Tensor, heads: int,
q_cos: Optional[Tensor], q_sin: Optional[Tensor],
k_cos: Optional[Tensor], k_sin: Optional[Tensor],
Expand Down Expand Up @@ -118,14 +129,15 @@ def block_nf4(h: Tensor, ah: Tensor, enc: Tensor, aenc: Tensor,
v_cos: Tensor, v_sin: Tensor, a_cos: Tensor, a_sin: Tensor,
cav_cos: Tensor, cav_sin: Tensor, caa_cos: Tensor, caa_sin: Tensor,
enc_mask: Optional[Tensor], aenc_mask: Optional[Tensor],
cond_idx: Optional[Tensor],
ws: List[Tensor], base: int, table: Tensor,
heads: int, aheads: int) -> Tuple[Tensor, Tensor]:
vada = mods(ws[base + 108], temb, 9)
aada = mods(ws[base + 109], temb_a, 9)

nh = rmsn(h) * (vada.select(2, 1) + 1.0) + vada.select(2, 0)
nh = rmsn(h) * (modtok(vada, 1, cond_idx) + 1.0) + modtok(vada, 0, cond_idx)
ax = attn_nf4(nh, nh, ws, base, table, heads, v_cos, v_sin, v_cos, v_sin, None)
h = h + ax * vada.select(2, 2)
h = h + ax * modtok(vada, 2, cond_idx)

nah = rmsn(ah) * (aada.select(2, 1) + 1.0) + aada.select(2, 0)
aax = attn_nf4(nah, nah, ws, base + 16, table, aheads, a_cos, a_sin, a_cos, a_sin, None)
Expand All @@ -134,10 +146,10 @@ def block_nf4(h: Tensor, ah: Tensor, enc: Tensor, aenc: Tensor,
pada = mods(ws[base + 110], tp, 2)
apada = mods(ws[base + 111], tpa, 2)

nh = rmsn(h) * (vada.select(2, 7) + 1.0) + vada.select(2, 6)
nh = rmsn(h) * (modtok(vada, 7, cond_idx) + 1.0) + modtok(vada, 6, cond_idx)
encm = enc * (pada.select(2, 1) + 1.0) + pada.select(2, 0)
ax = attn_nf4(nh, encm, ws, base + 32, table, heads, None, None, None, None, enc_mask)
h = h + ax * vada.select(2, 8)
h = h + ax * modtok(vada, 8, cond_idx)

nah = rmsn(ah) * (aada.select(2, 7) + 1.0) + aada.select(2, 6)
aencm = aenc * (apada.select(2, 1) + 1.0) + apada.select(2, 0)
Expand All @@ -161,8 +173,8 @@ def block_nf4(h: Tensor, ah: Tensor, enc: Tensor, aenc: Tensor,
v2a = attn_nf4(mna, mnh, ws, base + 80, table, aheads, caa_cos, caa_sin, cav_cos, cav_sin, None)
ah = ah + acg.select(2, 0) * v2a

nh = rmsn(h) * (vada.select(2, 4) + 1.0) + vada.select(2, 3)
h = h + ff_nf4(nh, ws, base + 96, table) * vada.select(2, 5)
nh = rmsn(h) * (modtok(vada, 4, cond_idx) + 1.0) + modtok(vada, 3, cond_idx)
h = h + ff_nf4(nh, ws, base + 96, table) * modtok(vada, 5, cond_idx)

nah = rmsn(ah) * (aada.select(2, 4) + 1.0) + aada.select(2, 3)
ah = ah + ff_nf4(nah, ws, base + 102, table) * aada.select(2, 5)
Expand All @@ -176,14 +188,15 @@ def stack_nf4(h: Tensor, ah: Tensor, enc: Tensor, aenc: Tensor,
v_cos: Tensor, v_sin: Tensor, a_cos: Tensor, a_sin: Tensor,
cav_cos: Tensor, cav_sin: Tensor, caa_cos: Tensor, caa_sin: Tensor,
enc_mask: Optional[Tensor], aenc_mask: Optional[Tensor],
cond_idx: Optional[Tensor],
ws: List[Tensor], table: Tensor,
n_blocks: int, heads: int, aheads: int) -> Tuple[Tensor, Tensor]:
i = 0
while i < n_blocks:
h, ah = block_nf4(h, ah, enc, aenc, temb, temb_a, tcss, tcass, tcg, tcag,
tp, tpa, v_cos, v_sin, a_cos, a_sin,
cav_cos, cav_sin, caa_cos, caa_sin,
enc_mask, aenc_mask, ws, i * 114, table, heads, aheads)
enc_mask, aenc_mask, cond_idx, ws, i * 114, table, heads, aheads)
i += 1
return (h, ah)
"
Expand Down Expand Up @@ -289,7 +302,8 @@ def stack_nf4(h: Tensor, ah: Tensor, enc: Tensor, aenc: Tensor,
audio_rotary_emb, ca_video_rotary_emb,
ca_audio_rotary_emb,
encoder_attention_mask = NULL,
audio_encoder_attention_mask = NULL) {
audio_encoder_attention_mask = NULL,
cond_token_index = NULL) {
unit <- .ltx23_jit_unit()
# unname: an nn_module_list yields named children, and a named R
# list marshals to TorchScript as Dict[str, Tensor], not List[Tensor]
Expand Down Expand Up @@ -318,7 +332,8 @@ def stack_nf4(h: Tensor, ah: Tensor, enc: Tensor, aenc: Tensor,
ca_video_rotary_emb[[1]], ca_video_rotary_emb[[2]],
ca_audio_rotary_emb[[1]], ca_audio_rotary_emb[[2]],
fix_mask(encoder_attention_mask),
fix_mask(audio_encoder_attention_mask), ws, table,
fix_mask(audio_encoder_attention_mask),
cond_token_index, ws, table,
torch::jit_scalar(length(blocks)),
torch::jit_scalar(as.integer(heads)),
torch::jit_scalar(as.integer(aheads)))
Expand Down
17 changes: 14 additions & 3 deletions R/txt2vid_ltx23.R
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Expand Up @@ -99,6 +99,15 @@ ltx23_unpack_audio_latents <- function(latents, num_mel_bins) {
} else {
NULL
}
# Compact per-token timestep: the conditioned prefix sees exactly
# two values (t and 0), so the transformer gets a 2-entry timestep
# plus a per-token selector instead of a [B, S] vector -- the
# modulation tensors stay [B, 2, ...] until selected on demand
cond_idx <- if (!is.null(conditioning_mask)) {
conditioning_mask$squeeze(1L)$to(dtype = torch::torch_long())
} else {
NULL
}
video_coords <- transformer$rope$prepare_video_coords(latents$shape[1],
latent_frames, latent_height, latent_width, device,
fps = frame_rate)
Expand All @@ -116,8 +125,9 @@ ltx23_unpack_audio_latents <- function(latents, num_mel_bins) {
t_video <- if (is.null(conditioning_mask)) {
t
} else {
# Per-token video timestep: conditioned tokens see zero
t * (1 - conditioning_mask)
# [t, 0]: index 0 = free tokens, index 1 = conditioned
torch::torch_tensor(c(sigma * scale_mult, 0),
device = device, dtype = f32)
}

out <- transformer(
Expand All @@ -138,7 +148,8 @@ ltx23_unpack_audio_latents <- function(latents, num_mel_bins) {
audio_num_frames = audio_num_frames,
video_coords = video_coords,
audio_coords = audio_coords,
use_cross_timestep = TRUE
use_cross_timestep = TRUE,
cond_token_index = cond_idx
)

# Euler velocity step in float32; dt is negative (sigma decreasing)
Expand Down
27 changes: 27 additions & 0 deletions inst/tinytest/test_jit_ltx23.R
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Expand Up @@ -212,3 +212,30 @@ torch::with_no_grad({
})
expect_true(max_abs_diff(out_pt[[1]], ref_pt[[1]]) < 1e-4)
expect_true(max_abs_diff(out_pt[[2]], ref_pt[[2]]) < 1e-4)

# Compact conditioned temb (2 variants + per-token index) must equal
# the eager block run with the explicitly expanded per-token temb
temb2 <- torch::torch_randn(B, 2L, 9L * dim)
cond_idx <- torch::torch_tensor(c(1L, 1L, 0L, 0L, 0L, 0L),
dtype = torch::torch_long()) # first two tokens conditioned
temb_full <- temb2$index_select(2L, cond_idx$add(1L))
torch::with_no_grad({
ref_ci <- blk1(
hidden_states = h, audio_hidden_states = ah,
encoder_hidden_states = enc, audio_encoder_hidden_states = aenc,
temb = temb_full, temb_audio = temb_a,
temb_ca_scale_shift = tcss, temb_ca_audio_scale_shift = tcass,
temb_ca_gate = tcg, temb_ca_audio_gate = tcag,
temb_prompt = tp, temb_prompt_audio = tpa,
video_rotary_emb = v_rope, audio_rotary_emb = a_rope,
ca_video_rotary_emb = cav_rope, ca_audio_rotary_emb = caa_rope
)
out_ci <- diffuseR:::.ltx23_jit_run_stack(
list(blk1), h, ah, enc, aenc,
temb2, temb_a, tcss, tcass, tcg, tcag, tp, tpa,
v_rope, a_rope, cav_rope, caa_rope,
cond_token_index = cond_idx
)
})
expect_true(max_abs_diff(out_ci[[1]], ref_ci[[1]]) < 1e-4)
expect_true(max_abs_diff(out_ci[[2]], ref_ci[[2]]) < 1e-4)
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