feat: add LTX-2 video generation support#1459
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WIP 1:1 port of diffusers' LTX transformer + causal video autoencoder, wired into sd.cpp as a new DiT family (VERSION_LTXV). Transformer (ltxv.hpp): * 28-layer LTXVideoTransformer3DModel with 32 heads x 64 head_dim * 3D rotary positional embedding (F, H, W; dim//6 freqs per axis) * rms_norm_across_heads QK norm; cross-attention to T5-XXL (4096->2048) * AdaLayerNormSingle with 6-way modulation and final scale_shift_table * FeedForward with gelu-approximate activation Causal video autoencoder (ltxv.hpp): * Encoder (causal) + Decoder (non-causal) with CausalConv3d stacks * Residual blocks with channel-wise RMSNorm, optional timestep conditioning * Pixel-shuffle 3D upsampling via reshape (TODO: match diffusers' exact permute order; likely needs correction after hardware validation) Wiring: * model.h: VERSION_LTXV + sd_version_is_ltxv helper + DiT aggregator * model.cpp: detect via scale_shift_table / adaln_single / caption_projection * diffusion_model.hpp: new LTXVModel wrapper * stable-diffusion.cpp: T5CLIPEmbedder (is_umt5=false) + LTXVModel ctor, VAE factory arm for LTXVVAERunner, FLOW_PRED + default_flow_shift=3, latent channels=128, temporal compression=8 for generate_init_latent, 8k+1 frame rounding in GenerationRequest * vae.hpp: get_scale_factor returns 32 for LTX End-to-end hardware verification still pending; known simplifications flagged with TODO comments. Upstream-tracking refs: based on leejet#491 (stduhpf/wip-ltx-support) with VAE filled in and modulation order corrected per current diffusers transformer_ltx.py.
Replace the LTX-1 transformer + VAE with the LTX-2 architecture. Transformer (ltxv.hpp namespace LTXV): * LTX2VideoTransformer3DModel: 48 layers, 32 heads * 128 head_dim = inner_dim 4096, cross_attention_dim 4096, caption_channels 3840 * LTX2AdaLayerNormSingle with configurable num_mod_params (6 or 9) * Gated attention (to_gate_logits + 2*sigmoid per-head gate) * 3D RoPE 'interleaved' with patch-boundary midpoints, vae_scale_factors (8, 32, 32), causal_offset=1, fps scaling (split rope is a TODO) * Video-only forward path: audio branches, a2v/v2a cross-attention, and audio FFN/proj_out are intentionally skipped (isolate_modalities=True) while their weight slots are still registered so LTX-2 checkpoints open VAE (LTX2VideoEncoder3d / LTX2VideoDecoder3d / LTX2CausalVideoAutoencoder): * PerChannelRMSNorm (no weight, y = x / sqrt(mean(x^2, C) + eps)) * CausalConv3d with runtime causal flag * ResBlock conv_shortcut is a plain Conv3d (no temporal causal padding) * Default block_out_channels (256, 512, 1024, 2048); upsample types spatial / temporal / spatiotemporal; all-spatiotemporal scaling Wiring: * model.h: VERSION_LTXV -> VERSION_LTXV2; sd_version_is_ltxv2 helper * model.cpp: detect via audio_scale_shift_table / av_cross_attn_* / audio_proj_in / audio_time_embed * diffusion_model.hpp: LTXVModel -> LTXV2Model wrapper * stable-diffusion.cpp: T5-XXL conditioner + LTXV2Model ctor; VAE factory arm for LTXVVAERunner; FLOW_PRED + default_flow_shift=3; latent channels=128, temporal compression=8, 8k+1 frame rounding * vae.hpp: get_scale_factor returns 32 for LTX-2 Known gaps (flagged in docs/ltxv.md): split rope, LTX-2.3 prompt modulation gate, exact pixel-shuffle 3D permute order, latents mean/std scaling.
After inspecting the ltx-2.3-22b-dev.safetensors header (5947 tensors)
the checkpoint uses different top-level names than diffusers' LTX-2.0
code. Renames:
time_embed -> adaln_single
audio_time_embed -> audio_adaln_single
proj_in -> patchify_proj
audio_proj_in -> audio_patchify_proj
prompt_adaln -> prompt_adaln_single
audio_prompt_adaln -> audio_prompt_adaln_single
av_cross_attn_video_scale_shift -> av_ca_video_scale_shift_adaln_single
av_cross_attn_audio_scale_shift -> av_ca_audio_scale_shift_adaln_single
av_cross_attn_video_a2v_gate -> av_ca_a2v_gate_adaln_single
av_cross_attn_audio_v2a_gate -> av_ca_v2a_gate_adaln_single
attention.norm_q / norm_k -> q_norm / k_norm
Remaining gaps (flagged in docs/ltxv.md) before LTX-2.3 weights load:
* caption_projection is LTX-2.0 style (2 linears). LTX-2.3 uses an
8-block video_embeddings_connector with 128 learnable_registers
and self-attention transformer_1d_blocks. Ditto audio.
* VAE has 9 down/up blocks (not 4), block_out_channels starts at
128 (not 256), deepest latent width is 1024 (not 2048).
* Split RoPE still unimplemented.
* prompt_modulation forward path is stubbed.
CPU + CUDA builds remain clean; runtime load on an LTX-2.3 checkpoint
will fail until the connector + VAE rewrites land.
… VAE Rewrites ltxv.hpp to match the LTX-2.3 22B checkpoint layout exactly (inferred from ltx-2.3-22b-dev.safetensors header — 5947 tensors). Transformer additions (each a weight slot the checkpoint has): * EmbeddingsConnector — 128 learnable_registers + 8 transformer_1d_blocks (attn1 gated + ff 4x). Replaces the old caption_projection. Video uses inner_dim=4096, audio uses 2048. * Per-block scale_shift tables: the 9-param scale_shift_table, the 2-param prompt_scale_shift_table (LTX-2.3 prompt modulation), and the 5-param scale_shift_table_a2v_ca_video / _audio tables (a2v/v2a cross-attn modulation). All six tables are registered per block on both video and audio branches. * Gated attention always on (to_gate_logits linear + 2*sigmoid per-head). * q_norm/k_norm tensor path (was norm_q/norm_k in diffusers LTX-2.0 code). * Audio-to-video / video-to-audio cross-attention modules registered so weights load; forward path skips them (isolate_modalities=True). VAE rewrite to match checkpoint: * 9 encoder down_blocks: res×4 @128, spatial↓, res×6 @256, temporal↓, res×4 @512, st↓, res×2 @1024, st↓, res×2 @1024 * Mirrored decoder up_blocks with spatiotemporal/temporal/spatial upsamplers at the checkpoint-observed conv output sizes ([4096,1024], [4096,512], [512,512], [512,256]). * VAEResBlock is the LTX-2.3 simplified shape (no norms, no conv_shortcut, no timestep modulation on the main path). * per_channel_statistics (mean-of-means, std-of-means) registered so they load; not yet consumed by vae_to_diffusion / diffusion_to_vae. CausalConv3d now uses tensor names conv.weight / conv.bias (not plain weight/bias) to match diffusers' nn.Conv3d-wrapped-in-self.conv layout. No LayerNorm at transformer output (collapsed to scale_shift + proj_out). Patchify uses tensor name patchify_proj (not proj_in). CPU build remains clean; next step is a DGX load-test against the ltx-2.3-22b-distilled.safetensors checkpoint.
Checkpoint metadata confirms LTX-2.3 22B uses rope_type=split, not interleaved. Split RoPE pair layout is (x[k], x[k+r]) where r = head_dim/2, applied as: first_new = first * cos - second * sin second_new = second * cos + first * sin vs. interleaved's pair (x[2k], x[2k+1]). * LTXAttention gains a rope_type field (default "split"). * apply_split_rotary_emb implements the (first, second) swap using ggml_sub / ggml_mul on [r, 2, num_heads, L*N] layout. * compute_rope_ltx2 gains a split_rope flag: when true it produces cos/sin tables sized dim/2 per position with layout [pad, (F0,H0,W0), (F1,H1,W1), ...] matching diffusers' transpose+flatten(2). * Runner passes split_rope=true and uses rope_tbl.dim (not hardcoded inner_dim) when allocating the backend tensors.
LTX-2.3 checkpoints don't ship their multilingual text encoder — the weight file only contains the 'text_embedding_projection' aggregate embedder, not the upstream encoder that produces the 3840-dim per-token features it consumes. Until that encoder is ported, wire up a no-op LTXV2Conditioner that returns zero embeddings of shape [1, 128, 4096] so the rest of the pipeline can load the diffusion model and exercise its forward path. Also ignore audio_vae.*, vocoder.*, text_embedding_projection.* at load time — those live in the single-file 22B release but aren't consumed by the video-only inference path yet. Tensor-layout parity with the ltx-2.3-22b-distilled checkpoint is verified by /tmp/compare_tensor_names.py (zero missing, zero shape mismatches over 4444 transformer + 170 VAE tensors).
…p st_t-1 frames per temporal upsample
Two fixes after first DGX run:
1. CausalConv3d weights must be F16/F32 (not BF16) because
ggml_cuda_op_im2col_3d GGML_ASSERTs on BF16 destination. Load path
converts checkpoint BF16 -> F16 on the way in.
2. VAEUpsampler was doubling every temporal chunk uniformly, giving 16
output frames from 2 latent frames. Matching diffusers by dropping
the first (st_t - 1) frames after each temporal-upsampling step so
f_out = (f_in - 1) * st_t + 1 across the decoder.
DGX run status:
* 46 GB checkpoint loads clean (4444 transformer + 170 VAE tensors
assign + 1333 extra tensors logged as unknown/ignored)
* Detected as VERSION_LTXV2 (ltxv2.3 desc)
* Transformer forward: 2 sampling steps complete in 2.26s on GB10
* VAE decode graph builds (3.1 GB compute buffer) and runs in 1.57s
* Only remaining crash was output-index mismatch from wrong frame
count, addressed by this commit
sd.cpp's tensor_to_sd_image treats 4-D video tensors as [W,H,C,T] but our VAE produces [W,H,T,C]. The framework already supports a 5-D video format [W,H,T,C,N] which matches our ordering — so we simply unsqueeze the decode result to 5-D and take that branch. Milestone: end-to-end video generation works on DGX GB10: * 46 GB BF16 checkpoint loads in ~9s * 22B transformer forward runs (~1.1s/step, 128 MB compute buffer) * VAE decodes 2-latent to 9-frame 704x480 output (~1s, 1.8GB buffer) * Total wall time: 3.64s for the 9-frame test Output quality is obviously not meaningful yet — the LTXV2Conditioner stub returns zero text embeddings, so the transformer has no semantic signal. Next steps: port the LTX-2.3 text encoder, apply the per_channel_statistics latents normalisation, and fix the pixel-shuffle 3D permute order in the VAE (currently a simplified reshape).
… (BF16 + q8_0 GGUF)
…mute
Two small reverts after on-hardware testing:
* scale_input back to true (the sd.cpp default). Our earlier override
to false prevented the [-1,1] → [0,1] mapping and contributed to all
frames saturating to black.
* Revert the input/output token permute order change (1,2,3,0 /
3,0,1,2) — the 'correct' w-fastest ordering triggered a 4D vs 5D
broadcast mismatch in the sampler. Restored the original permute
pair that produces the expected round-trip shape, at the cost of
the known RoPE-vs-token ordering mismatch still flagged in
docs/ltxv.md.
End-to-end reality on DGX GB10:
* Load, forward and decode all run to completion for both the 46GB
BF16 checkpoint and the 28GB q8_0 GGUF.
* Output is a valid WebP file (704x480, 9 frames).
* Output is IDENTICAL across different seeds, which means either (a)
the transformer's cross-attention with zero text conditioning
collapses to a constant, or (b) there is a numerical bug in the
forward path (q/k norm, modulation, rope alignment) that produces
constants regardless of input noise. Diagnosing this properly
requires the real multilingual text encoder and/or per-op intermediates
dumped against the PyTorch reference — tracked separately.
Instrumented diagnostic runs on DGX revealed: * Transformer layer 1 output magnitude is ~1e11 from clean noise input — all loaded weights appear normal (verified via direct safetensors dump: scale_shift_table std=0.37, q_norm std=0.17, linear weights std ~0.017). Root cause not yet localised. * VAE decode of the transformer output produced all-NaN frames because I had omitted the stateless PerChannelRMSNorm before each conv in VAEResBlock. Adding it back gives bounded but still uniform output (mean 826, std 109) — the VAE itself is seed-insensitive when the upstream latent is constant. What this adds: * log_tensor_stats helper for min/max/mean/std of sd::Tensor. * LTXV_DEBUG_MODE env var to selectively skip modulation, attention, FF, rope, gate, qk_norm at runtime (exposed only when LTXV_DEBUG_MODE is set; unaffects normal runs except in the debug path). * LTXV_DEBUG_MAX_LAYERS env var to bisect how many transformer blocks run. * LTXV_PROBE_STAGE env var returning an early-stage tensor (not yet fully stable; some stages crash on unused rope/context inputs). * LTXV_BYPASS to skip the transformer entirely (validates sampler+VAE independently). * PerChannelRMSNorm restored in VAEResBlock (missing norms caused NaN). Next: reference PyTorch harness to compare per-op layer outputs against the C++ pipeline.
…rmalization, depth-to-space
Three fixes that, combined, bring LTX-2.3 decoder output from single-color
banding to colored spatial structure.
1. EmbeddingsConnector: add pre-rms_norm inside each of the 8 transformer_1d_blocks
and a final rms_norm after the stack, matching Lightricks' reference
Embeddings1DConnector._BasicTransformerBlock1D. Without these, residual
magnitudes compounded across 8 blocks and drove the connector output to
std≈1.1e12, exploding cross-attention inside block 0.
2. Per-channel VAE latent normalisation: materialise
per_channel_statistics.{mean,std}-of-means to CPU on first call and apply
(x * std + mean) in diffusion_to_vae_latents (and the inverse in
vae_to_diffusion_latents). Values taken from the checkpoint — no more
identity fall-through.
3. Decoder conv_norm_out (PerChannelRMSNorm) + SiLU before conv_out. Missing
these activations left the decoder output at ~O(1000) per pixel instead of
[-1, 1].
4. Implement depth_to_space_3d matching einops
`b (c p1 p2 p3) f h w -> b c (f p1) (h p2) (w p3)` with p3-inner/p1-outer
convention. Use in VAEUpsampler (replaces the naive ggml_reshape) and
final decoder unpatchify. Eliminates visible banding artefacts in decoded
frames.
5. Add intra-block probe infrastructure (blk0_*, attn prefix) that surfaced
the connector bug; keep it in place for future sampler tuning.
…es real frames
Adds the missing LayerNorm (elementwise_affine=False, eps=1e-6) between
the 48 transformer blocks and the final adaln modulation / proj_out, matching
Lightricks' LTXModel._process_output:
x = norm_out(x)
x = x * (1 + scale) + shift
x = proj_out(x)
Without norm_out, the post-block activation std accumulated to ~285 across
48 layers and the predicted velocity came out at std≈57 — 40× larger than
expected. Chained through the sampler this produced completely saturated
garbage on 4+ steps.
With norm_out, transformer_out is std≈1.0 at every step, 8-step distilled
sampling converges to real photo-realistic frames (VAE output in [-1.5, 1.2]).
The unconditional result is generic (the text encoder is still stubbed to
zeros — it's the next remaining item) but the full transformer + VAE stack
is now demonstrably working end-to-end on the 22B checkpoint.
Combined with the previous commit (connector pre-norms, per-channel VAE
normalisation, VAE conv_norm_out, einops depth-to-space), this completes
the numerical correctness milestone.
… frames Document the five numerical-correctness bugs that blocked output quality (connector pre-norms, final norm_out, VAE conv_norm_out+SiLU, per-channel latent normalisation, depth-to-space ordering) and reframe the remaining items around text encoder / schedule tuning / audio branch.
…enizer
Lays the groundwork for LTX-2.3's text conditioning path. Native port of
Gemma-3-12B (the exact text encoder the LTX-2.3 pipeline uses, confirmed
via text_embedding_projection.video_aggregate_embed.weight shape
[4096, 188160] = 3840 * 49 = hidden * (48 layers + 1 embed)).
src/gemma3.hpp
- Gemma3Params (3840 hidden, 48 layers, 16Q/8KV heads, head_dim=256,
sliding_window=1024, global/local RoPE θ=1e6/1e4, linear scaling=8,
query_pre_attn_scalar=256, GELU-tanh MLP at 15360 intermediate).
- Gemma3RMSNorm: applies `(1 + w)` per Gemma convention.
- Gemma3MLP: SwiGLU-with-GELU(tanh).
- Gemma3Block: input_norm → GQA (qk_norm + RoPE + 1/sqrt(256) scale)
→ post_attn_norm → residual → pre_ffn_norm → MLP
→ post_ffn_norm → residual. Matches
llama.cpp/src/models/gemma3.cpp's layer_build_gemma3.
- Gemma3TextModel: embedding (scaled by sqrt(hidden)) + 48 blocks +
final RMSNorm. Exposes `forward_with_hidden_states` returning all 49
intermediate states for LTX's aggregate_embed projection.
- RoPE precompute + sliding-window mask builder.
- Gemma3Runner wraps it all for backend graph execution.
src/tokenizers/gemma3_tokenizer.{h,cpp}
- Minimal SentencePiece protobuf parser (just the `pieces` field —
262144 entries for Gemma-3; parses string/score/type tuples, skips
trainer_spec and normalizer_spec).
- Classic SPM BPE encoder: meta-space pre-tokenisation, byte-level
fallback via <0xHH> pieces, score-priority merge loop.
- Gemma-3 specific tweak: `add_dummy_prefix=False`, so the first word
is encoded without a leading meta-space (the HF GemmaTokenizerFast
default).
tests/gemma3_tokenizer_test.cpp
- Standalone CLI that loads tokenizer.model and prints the encoding
of a given prompt. Validated against HuggingFace's
`GemmaTokenizerFast.encode("...")`:
"a cat walking across a grassy field" → matches exactly.
"Hello, World!" → matches exactly.
"A person riding..." → matches exactly.
"1234 abc 日本語 𝟘🎉" → matches exactly (Japanese,
digits, math-style alphanum,
emoji).
No runtime behaviour change — gemma3.hpp is included from conditioner.hpp
but not yet wired into any LTXV conditioner. Next phase: run the
transformer forward with real Gemma-3-12B weights and diff against HF.
Assisted-by: Claude Opus 4.7 [Code] [Agent]
…bf16 precision
Implements the Gemma-3-12B text transformer (48 decoder blocks) and
validates every intermediate hidden state against the HuggingFace
reference on a real 23 GB checkpoint. All 49 states match to within
bf16 rounding:
layer ours HF
0 0.974 0.974 (post-embed)
1 83.967 83.917
4 334.240 334.817
12 1791.846 1788.172
24 5105.149 5091.607
36 5957.374 6003.428
47 6551.764 6593.038
48 2.531 2.394 (post-final-norm)
Summary of the port:
src/gemma3.hpp
* Gemma3Block: pre-attn RMSNorm -> GQA (16 Q / 8 KV heads, head_dim=256)
with per-head q_norm/k_norm, NEOX-style RoPE, 1/sqrt(256) scale
-> post-attn RMSNorm -> residual
-> pre-ffn RMSNorm -> SwiGLU-with-GELU-tanh MLP -> post-ffn RMSNorm
-> residual. Matches llama.cpp's `llm_build_gemma3`.
* Gemma3RMSNorm applies `x * (1 + w)` (not `x * w`) — HF convention.
* compute_gemma3_rope generates NEOX-layout tables with halves
duplicated: [cos_0..cos_{r-1}, cos_0..cos_{r-1}] for direct
element-wise multiply in apply_rotary_emb. Dual θ (1e6 global,
1e4 local) and linear scaling factor 8 for the global family.
* build_causal_mask produces lower-triangular masks clipped to
the sliding window. Gemma-3 uses causal attention everywhere
(`use_bidirectional_attention = False`); full-attention layers
just get a plain causal mask instead of a windowed one.
* sliding_window_pattern = 6 — every 6th layer is full attention
(layers 5, 11, 17, 23, 29, 35, 41, 47 for 48-layer config).
* Gemma3TextModel wires embedding (scaled by sqrt(hidden)) + 48
blocks + final RMSNorm, exposes forward_with_hidden_states which
returns the 49-entry hidden-state list. A `max_layers` argument
truncates the forward for diagnostic/probe use.
* Gemma3Runner manages params_buffer, lazy RoPE/mask rebuilds,
and a `compute_layer_hidden(layer_idx)` probe that builds a
graph ending exactly at the requested hidden state so the
runner's compute<> picks it up as `final_result`.
examples/gemma_test/
* Standalone `gemma3-test` CLI: takes a Gemma-3-12B directory (HF
safetensors shards), a tokenizer.model path and a prompt;
tokenises, runs the forward on CUDA, prints per-layer hidden
stats. Used as the validation harness above.
Known follow-ups (Phase 4+):
- concatenate 49 hidden states along the channel axis
- apply `text_embedding_projection.video_aggregate_embed` from the
LTX-2.3 22B safetensors to project 49*3840 -> 4096
- push through video_embeddings_connector and into the LTX DiT
- replace LTXV2Conditioner's zero stub with the real pipeline
Assisted-by: Claude Opus 4.7 [Code] [Agent]
Extends Gemma3Runner with compute_concatenated_hiddens(input_ids, out_dim), which produces the exact 188160-dim feature that LTX-2.3's text_embedding_projection.video_aggregate_embed expects on its input — matching the reference FeatureExtractorV2 pipeline: 1. Run Gemma-3 → list of 49 hidden states [hidden=3840, L, 1] 2. Per-token RMSNorm along the hidden axis for EACH layer 3. Concatenate the 49 normed layers along the channel axis 4. Rescale by sqrt(out_dim / hidden_size) (= sqrt(4096/3840) for LTX) Numerical match against HuggingFace: ours: min=-60.0572 max=+63.9996 mean=+0.0158 std=1.0327 HF: min=-60.0856 max=+63.9996 mean=+0.0153 std=1.0327 on prompt "a cat walking" (4 tokens). The rescaled output is the input to the Linear(188160, 4096) that lives inside the LTX-2.3 22B safetensors as `text_embedding_projection. video_aggregate_embed`; Phase 5 wires that projection into LTXV2Conditioner and replaces the zero stub. Assisted-by: Claude Opus 4.7 [Code] [Agent]
End-to-end: CLI flag --text-encoder points at a Gemma-3-12B-it directory
(tokenizer.model + safetensors shards); sd-cli then tokenises the prompt,
runs Gemma-3 on CUDA, applies LTX-2.3's per-token RMSNorm + sqrt(out/D)
rescale + `text_embedding_projection.video_aggregate_embed` Linear, and
feeds the resulting [4096, L] cross-attention features to the LTX video
DiT's `video_embeddings_connector`.
Changes:
include/stable-diffusion.h
+ sd_ctx_params_t.text_encoder_path (appended to preserve existing
aggregate-initialiser callers).
examples/common/common.{h,cpp}
+ --text-encoder CLI flag, plumbed into the ctx params struct.
src/stable-diffusion.cpp
+ When version == ltxv2 and text_encoder_path is non-empty:
* Enumerate shards in the directory (dirent scan for *.safetensors).
* Build a secondary ModelLoader with prefix="language_model." so
HF names collapse to the runner's "model.*" expectations.
* Construct a Gemma3Runner (clip_backend), alloc_params_buffer,
load tensors.
* Attach the Gemma runner + tokenizer + LTXTextEmbedProjection to
LTXV2Conditioner. When path is unset, the conditioner returns
zeros (unconditional) so the pipeline still works.
+ Remove `text_embedding_projection.` from LTX ignore list so the
projection weight loads together with the rest of the LTX weights.
src/conditioner.hpp
+ LTXTextEmbedProjection: GGMLRunner that owns the 188160 -> 4096
Linear from the LTX 22B safetensors.
+ LTXV2Conditioner::attach_gemma + real get_learned_condition that
runs Gemma + projection, with diagnostic logging of input/output
stats.
Status:
* Gemma forward: validated numerically against HF reference (exact
match on prompts incl. Japanese/emoji, per-layer std within bf16
noise). See `gemma3-test` binary.
* Conditioner output: 188160-dim concat matches HF exactly
(min/max/std within rounding). The 4096-dim projected embedding
flows into the transformer's `video_embeddings_connector`.
* End-to-end: prompts produce DIFFERENT c_crossattn, verified by
per-prompt stats diffs. The video DiT runs to completion without
asserts or NaNs.
Follow-up (separate commit): the cross-attention gate inside
`LTX2VideoTransformerBlock.attn2` reads gate_logits ≈ -11 on block 0,
closing the cross-attn to ~3e-5. This mutes the prompt signal at the
perceptual level even though the condition flows through the graph.
Needs investigation — either a weight-loading order issue or a
legitimate behaviour that only unblocks in later blocks / denoising
steps; does not affect the correctness of the port itself.
Assisted-by: Claude Opus 4.7 [Code] [Agent]
Two related bugs in the Gemma -> LTX conditioning path that together prevented prompts from meaningfully steering generation. 1. 188160-dim flat ordering was transposed. HF reference does encoded = stack(hidden_states, dim=-1); normed = (encoded * rsqrt(var)).reshape(B, T, D*L). In PyTorch memory this makes the layer axis FAST and the hidden axis SLOW within the flat 188160-dim. We were doing ggml_concat(..., axis=0) over a list of [D, T, 1, 1] tensors, which puts hidden FAST and layer SLOW — the opposite order. text_embedding_projection.video_aggregate_embed was trained with the HF ordering, so every row of the weight was multiplied against the wrong input element and the projection output was tiny and nearly identical across prompts (std 2.4 vs HF's 6.8 on the same prompt). Fix: stack along axis 2 -> [D, T, L, 1], permute(2, 0, 1, 3) -> [L, D, T, 1], reshape to [D*L, T, 1]. Now the flat's fast index is L (matches HF). After the fix the projected cross-attn input reaches std=5.24 with min/max +/-87..+277 — close to HF reference (std=6.83, min/max -148..+281) to within bf16 noise. 2. EmbeddingsConnector output shape was wrong. Reference Embeddings1DConnector produces a FIXED 128-token output whose first L positions are the real text and positions [L..127] come from learnable_registers[L..127]. We were concatenating 128 registers + L text = 128+L tokens, in the WRONG order. Rewrote the connector's register path to match the reference's `_replace_padded_with_learnable_registers` semantics. 3. Drop the diagnostic LTXV_SKIP_XATTN_GATE env knob — bypassing the learned cross-attn gate breaks self-attention too; gate values ~-11 at block 0 with noise queries are correct, text influence builds up through later blocks once the layout fix lets the projection do its job. With these fixes, conditioned generation now visibly reacts to prompt changes (different seeds / prompts produce meaningfully different scenes) — previously every prompt produced the same "person in kitchen" fallback because the projection was effectively noise. Seed 42 + "cat walking in a grassy field" now yields an entirely different scene (chefs in a kitchen) compared to unconditioned, and with CFG=3 plus a negative prompt the scene moves outdoors. Assisted-by: Claude Opus 4.7 [Code] [Agent]
… work
The Gemma-3 attention scale `1/sqrt(query_pre_attn_scalar)` equals
`1/sqrt(head_dim)` for Gemma-3-12B (both = 256). I was applying that
scale to Q explicitly AND `ggml_ext_attention_ext` applies the same
`1/sqrt(d_head)` internally — so the effective softmax temperature was
16× too small, which flattened cross-token attention into a near-
uniform mix. With noise-driven queries that showed up as a fixed
"attention sink" outlier at one hidden dim (2339 for this vocab) at
every layer, and the projection output was 25% below HF (std 5.24 vs
6.83) because the singular outlier swallowed all the RMSNorm budget.
Removing the explicit Q scale (and asserting the scalar==head_dim
invariant for future-proofing larger variants) makes the forward match
HF numerically:
layer 1 post-block tok=1 d=2339: ours 799.92 HF 800.00
layer 47 : ours 124879 HF 124928
projected [4096, L]: min -148.24 max +280.51 std 6.828
HF -148.28 +280.99 6.830
Prompts now visibly change the generated content: seed 42 with
"a cat walking across a grassy field" finally produces a cat walking
across a grassy field instead of the generic "person in a kitchen"
fallback.
Assisted-by: Claude Opus 4.7 [Code] [Agent]
Applies the official `DISTILLED_SIGMA_VALUES` sequence
[1.0, 0.99375, 0.9875, 0.98125, 0.975, 0.909375, 0.725, 0.421875, 0.0]
when the user asks for exactly 8 sampling steps on an LTX-2.3 model
and hasn't provided a --sigmas override — matching the reference
ltx_pipelines.DistilledPipeline default.
The distilled schedule is non-uniform: five tight steps clustered near
σ=1 plus three sharp drops at the end. The generic shifted-flow
schedule (shift=3) we defaulted to before spent denoising budget too
uniformly, producing softer / smoother output. With the distilled
schedule the cat-in-grass test ("a cat walking across a grassy field",
seed 42, cfg=1, 8 steps) finally looks crisp instead of smudged.
Assisted-by: Claude Opus 4.7 [Code] [Agent]
The reference LTX-2 VideoDecoder ends with `ops.py::unpatchify`: rearrange(x, "b (c p r q) f h w -> b c (f p) (h q) (w r)") where the channel axis is packed as (c outer, p_t, p_w, p_h) with the h_patch (q) innermost. The intermediate DepthToSpaceUpsample uses a DIFFERENT convention: "b (c p1 p2 p3) d h w -> b c (d p1) (h p2) (w p3)" where p3 (w_stride) is innermost. I was reusing my (c p1 p2 p3) helper for the final 4x4 unpatchify. That silently transposes every (p_h x p_w) output block, producing a visible fine-scale hatching artefact that survived every diffusion step regardless of the sigma schedule or prompt conditioning. Add a dedicated depth_to_space_3d_patch helper that swaps the inner (p_w, p_h) sub-axes of the channel layout to match the reference convention, then delegates to the existing helper. The decoder's final call is now correct; a TODO marks the matching encoder patchify bug for future v2v/i2v work (the encoder isn't exercised in T2V).
Add the ninth bug (two conflicting channel-packing conventions between ops.py::unpatchify and sampling.py::DepthToSpaceUpsample) and record the safetensors __metadata__["config"]["vae"] cross-check that confirmed the absence of a residual skip, reflect padding, or timestep conditioning in the 22B checkpoint.
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Hi! I've been playing with claude code as I wanted to try to have LTX in stable-diffusion.cpp. I saw already an early attempt in #491 , and just saw #1458 right now, even if I was already working on it 😅
Hope it can be of any help - I got it to produce "acceptable" things