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.
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 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.
Measured on the fresh linux x64 prebuilt (z-image Q8_0, sd-cli, 192 CPU
threads, 512x512, 9 steps, steady state): sampling 56.1s vs 51.3s (about
9 percent faster), VAE decode unchanged, peak RSS identical. The sd.cpp
engine only serves the no-GPU tier, so the default profile now matches
max: --diffusion-fa plus --diffusion-conv-direct.
Ideogram 4 (diffusers 0.39 Ideogram4Pipeline) as a new image family. The vendor
publishes no bf16 checkpoint, so ideogram-ai/ideogram-4-fp8 (raw float8 DiTs,
upcast by from_pretrained) is the family base and ideogram-4-nf4-diffusers is
the bnb-4bit pipeline artifact (ideogram-4-nf4 is byte-identical and detects to
the same family). All three repos join the trusted non-GGUF allowlist and the
frontend safetensors catalog.
Family specifics handled:
- Dual-branch CFG runs through a SEPARATE unconditional_transformer, so the
auto-policy size table entry counts two ~9.3B DiTs (37.2 GB bf16), and the
pipeline-kind memory plan now takes max(cached bytes, family table) for the
family base repo: the fp8 repo's cached bytes undershoot the bf16-resident
footprint by ~2x, which would let auto planning pick a resident placement
that OOMs.
- The pipeline accepts EITHER guidance_scale OR a per-step guidance_schedule
(its default: the recommended 45x7.0 + 3x3.0 taper, valid only at 48 steps)
and raises when both are set. At the advertised defaults (48 steps, guidance
7) generate() drops the constant so the recommended taper engages; any other
request nulls the schedule so the constant broadcasts legally.
- Generation defaults per the model card: 48 steps, guidance 7 (both tables).
tencent/HunyuanImage-3.0 is deliberately excluded: it has no diffusers pipeline
(an 80B autoregressive MoE behind trust_remote_code). A structured exclusion
map now surfaces that reason verbatim from validate_load_request instead of
the generic unknown-family error.
The curated diffusion LoRA catalog gains the nine official krea/Krea-2-LoRA-*
style adapters (family-tagged krea-2, explicit weight filenames), so they show
up in the picker instead of requiring a typed repo id.
Tests: new test_diffusion_more_families.py (detection, trust, defaults, size
table, exclusion reason, curated catalog + family filter), two generate()
tests for the guidance_scale/guidance_schedule pairing, and the local-scan
LoRA test updated for a non-empty curated list. Backend suite + CI-sim
(block_diffusers/block_torchao) green; frontend builds.
An unset transformer_quant used to mean off (run the GGUF as-is), so the
hardware ladder only engaged when auto was explicitly chosen and the panel
showed Off as the default. Unset (or auto) now hands the decision to the
ladder: a dense-capable GPU gets at least int8, data-center silicon fp8,
falling back to the GGUF when the device, VRAM, family deny table or disk
cannot take it. An explicit none/off pins GGUF-as-is and is now
expressible in the API (previously only omission meant off, so pinned-off
and unset were indistinguishable); an explicit scheme pins that scheme.
The dense candidate also gains a free-disk gate: with auto as the default
the bf16 base download (up to ~40 GB) must never wedge a nearly-full
model-cache disk, so the candidate is dropped (GGUF build kept) when free
space cannot hold it plus a 10 GiB margin. Unprobeable disk passes.
Frontend: the Dtype select defaults to Auto (fastest for GPU), keeps Off
as an explicit choice, and sends none through instead of omitting it.
Suite: 622 diffusion tests green (default-load test rewritten to the new
contract, explicit-off short-circuit covered), CI-sim green.
resolve_dense_quant_candidate now passes fam.name to
select_transformer_quant_scheme so the policy's proposed scheme honors the
family deny table (qwen-image lands on int8 instead of proposing fp8 that
the execution path would refuse). Test stub updated for the new keyword.
