Tag the ltxv and wan GGUF archs text-to-video so cached video checkpoints
actually surface in the Video picker (they were classed unsupported and
hidden everywhere). Adopt the loaded family's default clip length instead
of silently keeping the 25-frame pre-load fallback, and derive steps and
guidance from the picked GGUF filename so a distilled variant gets its
few-step schedule. Suppress the error toast for the user's own Cancel and
disable the Video nav item on chat-only hosts with a hint, matching Train.
Re-plan memory with the quant steady factor when the bf16 table forces
offload a quantised DiT would not need, mirroring the image dense-quant
path, and fall back to the bf16 plan when quant does not engage. Stream
the second expert under group offload (model and sequential already hook
every module). Fail the load cleanly when quant engages on only one
expert instead of running mixed precision with quant reported off.
Persist guidance_2 in the gallery recipe so A14B clips are reproducible.
Review round on the video backend:
- the resident memory check now budgets transformer plus companions like the
image backend, instead of letting auto pick a resident placement that OOMs
while the LTX text encoder and VAEs load
- a new load waits for the signalled in flight generation to exit before
tearing down the old pipeline, so two models never share VRAM during a swap
- the load worker rechecks its token right before placement, narrowing the
window where a cancelled load could put weights on a GPU the arbiter
already handed to another backend
- the step cache installs before the speed profile and compile now keys
fullgraph off an active cache, matching the image order; compiling
fullgraph first crashed the first cached generation
- teardown uninstalls the process wide compiled GGUF dequantizer so a later
speed off load gets the bit identical path
- explicit base_repo goes through the same trust gate as non GGUF repo ids,
and local checkpoint paths are verified during validation, before the
route evicts a resident model
- status reports only the speed optimisations that actually engaged
- the gallery file route streams via FileResponse with range support instead
of buffering whole clips
- the build step reuses the checkpoint path resolved during planning
Review follow ups on the more-families branch: the per channel scale now
broadcasts rank aware instead of assuming 2D (all shipped tensors are 2D,
verified across all three fp8 components, but a future non 2D quantized
tensor would have mis broadcast silently), the fused qkv split asserts the
expected 3x hidden row count so a GQA style export fails loudly, fp8
detection scans every shard header rather than the first, and the excluded
model match uses the segment aware token helper with a hunyuanimage-3
token so a future HunyuanImage 2.x is not blocked with a 3.0 reason.
A gguf or safetensors checkpoint carries one expert; the pipeline then pulls
the other dense bf16 from the base repo outside the memory plan. validate now
refuses it before any download, and the unused a14b gguf_repo pointer is gone
so nothing surfaces those repos as loadable. _SecondDiTView also delegates
attribute writes to the real pipe (transformer writes land on transformer_2)
so a helper's reassignment cannot vanish with the temporary view.
The generic Studio config dict path can deliver these flags as strings, and a
non-empty string like "false" is truthy, so an opt-out silently no-ops (the
latent cache still builds, TF32 stays on). Coerce them the same way
gradient_checkpointing already is.
The wheel-only pip install for an optional attention kernel ran inside
load_pipeline under _lock and _generate_lock, so a slow or hanging install
blocked unload and cancellation for up to the 600s timeout. Resolve and install
the kernel before taking the locks (only an explicit backend ever pulls a
package, and its resolution ignores the speed tier); the in-lock apply call is
then a fast no-op. Also decode and log pip's stderr on a failed install so the
fallback to native is diagnosable instead of showing only the exit code.
The raw speed_mode string was forwarded to _enable_fp16_accumulation, so a
case-variant like MAX failed the speed_mode != SPEED_MAX check and wrongly
disabled fp16 accumulation on float16 pipelines. Forward the normalized mode
and cover the case-insensitive path in the test.
The video backend never cleared FBCache residuals between clips, so with the
step cache engaged a second generation at a different resolution would hit
stale state from the first. Mirrors the image backend fix from #6872: call
the transformer level _reset_stateful_cache (reset_stateful_hooks only exists
on the HookRegistry in diffusers 0.39), covering transformer_2 for the Wan
dual expert, only when a cache is engaged.
The ideogram-ai/ideogram-4-fp8 repo stores its two DiTs and the Qwen3-VL text
encoder in a vendor float8 layout that diffusers 0.39.0 (and diffusers main)
cannot read, so a stock Ideogram4Pipeline.from_pretrained produced a pipeline
with randomly initialized attention weights left on the meta device: the load
then died at pipe.to(device) with "Cannot copy out of meta tensor", and any load
that got past that would have generated noise.
Two things broke:
- The DiT attention is stored FUSED as attention.qkv.weight ([3*hidden, hidden],
Q/K/V rows stacked) plus attention.o.weight, while the diffusers transformer has
split to_q/to_k/to_v/to_out.0. from_pretrained mapped neither name and left them
meta + random.
- Every quantized weight is float8_e4m3 with a per-output-channel weight_scale;
the real weight is fp8.float() * weight_scale[:, None]. diffusers dropped the
scales and loaded the raw fp8 values (range +-448) as the weights, so even the
weights that did map were wrong.
load_ideogram4_transformer now reads the shards, dequantizes every scaled weight,
splits the fused qkv into to_q/to_k/to_v and renames o to to_out.0, then loads the
result into a config-constructed model. It fails loudly if any key stays unmatched
so a partly random model can never ship. The dequantized fp8 projections match the
byte-identical -nf4 export (already in the diffusers split layout with a bnb
quantization_config) to cosine ~0.997, so the split order and scale axis are
confirmed. The conversion is gated on the fp8 marker (a *.weight_scale key) read
from the shard header only, so the -nf4 repos skip it and load through the stock
from_pretrained path without a wasteful full-shard read.
The fp8 text encoder needed the same float8 dequant (its keys already match the
transformers Qwen3-VL module, so no rename). load_ideogram4_text_encoder handles
the fp8 repo and delegates the bnb-4bit and dense repos to the shared krea shim.
One more incompatibility was in the diffusers pipeline itself: it calls
transformers create_causal_mask(inputs_embeds = ...) with no cache_position, but
on transformers 4.57.6 the parameter is spelled input_embeds and cache_position is
required. _patch_create_causal_mask installs a signature-aware wrapper that renames
the kwarg and supplies cache_position, and is self-disabling on a matching signature.
Adds unit tests for the fp8 dequant/split conversion and the causal-mask patch.
Verified live on a B200: ideogram-4-fp8 (both CFG paths), ideogram-4-nf4-diffusers,
and krea-2 with the retroanime LoRA all load and generate coherent images.
The unsloth Wan2.2 GGUF repos referenced before do not exist on the Hub
(verified via the API: RepositoryNotFoundError for both). QuantStack's
are the community standard (30k and 82k downloads over 30 days), so the
picker's GGUF expander now points there.
Register two new video families and wire them through the backend, routes,
and frontend picker: wan2.2-ti2v-5b (single DiT) and wan2.2-t2v-a14b (the
dual-expert MoE). Both share diffusers' WanPipeline + WanTransformer3DModel
+ AutoencoderKLWan, which the VideoFamily dataclass already reserved fields
for (transformer2_class, is_moe, cfg2_kwarg).
Verified against the installed diffusers 0.39.0 before writing code:
- WanPipeline, WanTransformer3DModel, and AutoencoderKLWan are all exported
from top-level diffusers 0.39.0.
- WanPipeline.__call__ (pipeline_wan.py:383) defaults to num_frames=81,
num_inference_steps=50, guidance_scale=5.0. guidance_scale_2 DOES exist
in 0.39 (line 392) and its check_inputs raises if it is passed when the
pipeline's boundary_ratio is None (line 322), so the second guidance is
threaded ONLY for the MoE family and only when inspect.signature accepts
it (the same gate frame_rate already uses).
- The Wan VAE temporal factor is 4 (autoencoder_kl_wan.py scale_factor_temporal),
and the pipeline snaps num_frames to 4k+1 (line 493), so frame_step is 4,
unlike LTX-2's 8k+1. Sizes patchify at spatial 8 * patch 2 = 16, so
resolution_multiple is 16.
- boundary_ratio and transformer_2 come from model_index.json: TI2V-5B ships
boundary_ratio=null and transformer_2=[null,null] (single DiT), while A14B
ships boundary_ratio=0.875 and transformer_2=WanTransformer3DModel (dual
DiT). boundary_ratio lives in the pipeline config, so it needs no per-call
plumbing.
- WanTransformer3DModel declares _repeated_blocks=["WanTransformerBlock"] and
inherits CacheMixin (transformer_wan.py:508/551), so regional compile and
First-Block-Cache both work.
bf16-resident component sizes, measured from each diffusers repo's on-disk
safetensors (all stored bf16), feed the auto memory table:
TI2V-5B: transformer 20.0, UMT5 text encoder 11.4, VAE 2.8 GB.
A14B: two experts 57.2 each (114.3 total), text encoder 11.4, VAE 0.5 GB.
Backend changes make the optimisation layers dual-DiT aware: a small
_SecondDiTView proxy presents transformer_2 as pipe.transformer so the
existing single-DiT helpers (apply_speed_optims, apply_attention_backend,
apply_step_cache, quantize_transformer) cover BOTH experts on an is_moe load
without forking any helper; single-DiT loads are unchanged (views is just
(pipe,)). The two Wan base repos are added to the trusted non-GGUF allowlist.
A transformer_quant option is added to the load path, mirroring the image
backend's dense torchao fast path: on a pipeline-kind load the dense DiT(s)
are quantised in place onto the low-precision tensor cores and the engaged
scheme is surfaced in status. generate() threads guidance_2 through the
family's cfg2_kwarg when the loaded pipeline accepts it.
Routes and Pydantic models gain the optional transformer_quant (load /
status) and guidance_2 (generate) fields. The frontend picker gains the two
Wan models with 50-step / CFG 5.0 defaults; fps is supplied per family by
the backend.
Tests extend the fake runtime with WanPipeline and per-DiT transformer fakes
(single-DiT and dual-DiT), and cover family detection for both repos, 4k+1
frame snapping, default application, dual-DiT speed/cache/attention/quant
coverage on both experts, cfg2 threading gated on the pipeline signature,
trusted-repo validation, and the new route fields. Both the standard and the
diffusers/torchao-blocked CI-sim runs are green.
The Lightricks/LTX-2.3-fp8 checkpoints store float8 weights with
per-tensor weight_scale and input_scale companions (verified from the
file headers: 1496 F8_E4M3 tensors, 2924 scale tensors). A plain dtype
cast would silently corrupt every quantized layer, so the 2.3 assembly
now detects the companions and raises with a pointer to the GGUF quants,
which offer comparable fidelity through the supported path. Dequantizing
the scaled fp8 layout is a possible follow-up.
diffusers 0.39 ships every LTX-2.3 model class but its single-file loader
maps all LTX-2 checkpoints to the 2.0 config, so 2.3 checkpoints (9-row
modulation tables, gated attention, per-modality connectors) fail a shape
check at load. The community transformer-only GGUFs also lack the text
projections, VAEs, and vocoder that 2.3 moved out of the transformer.
New core/inference/video_ltx2.py detects a 2.3 checkpoint from its header
(6 vs 9 modulation rows, no weight data read) and assembles the full
pipeline: the DiT through from_single_file with the 2.3 config overrides
and the prompt_adaln key renames the stock converter lacks, the 8-layer
per-modality connectors from the same checkpoint plus the text projection
companion file, and the 2.3 video VAE, audio VAE, and BWE vocoder from
the companion files in unsloth/LTX-2.3-GGUF. Configs and rename tables
mirror diffusers' own scripts/convert_ltx2_to_diffusers.py, which the
library loader has not absorbed yet. Assembled through the constructor
because the base repo pins LTX2Vocoder while 2.3 needs LTX2VocoderWithBWE
and the from_pretrained type gate rejects the substitution.
Verified on a B200: distilled-1.1 Q4_K_M GGUF loads in 37s, generates a
49-frame 768x512 clip with synchronized audio in 18s (8 steps), frames
on-prompt and non-black, container decodes fully. Meta-tensor validation
confirms exact key and shape match for all five converted components.
Unit tests cover 2.3 detection (gguf + safetensors headers), combined
checkpoint partitioning, and companion-set choice.
