spec.forward imports Krea2Pipeline for prepare_position_ids, so the test needs a
real diffusers install; the backend CI matrix runs without one and failed on the
import. Same importorskip guard the sigmas gather test already uses.
Krea's release guidance is to train on Krea-2-Raw and run adapters on
Turbo. Raw now leads the krea-2 training bases (Turbo stays available),
both vendor repos are trust-listed, and load_krea2_pipeline fails fast
with an upgrade hint on diffusers older than 0.39 instead of a bare
AttributeError mid-load
Inference:
- krea-2 DiffusionFamily (Krea2Pipeline / Krea2Transformer2DModel, base
krea/Krea-2-Turbo, bf16 only, no GGUF/sd.cpp mapping yet)
- Per-component pipeline loader (core/inference/diffusion_krea2.py): the krea
repo is exported with transformers 5.2, so the tokenizer config
(extra_special_tokens as a list, no slow-tokenizer vocab files) and the
text encoder rope settings (rope_parameters vs rope_scaling) need explicit
compat on the 4.x line; values are copied verbatim and equal the 4.x
Qwen3-VL defaults, so the math is unchanged. from_pretrained also
type-checks the tokenizer against the declared slow class, so the pipeline
is assembled through its constructor with the model_index init config
(is_distilled carries Turbo's fixed mu=1.15 schedule)
- Trust allowlist entry, curated picker entry + 8 step / cfg 0 defaults,
int8 exclusion token for the M=1 Krea2TimestepEmbedding projection
Training:
- krea-2 _FamilySpec in the DiT trainer: phased conditioning/transformer
load through the compat loader, shared Qwen-Image VAE latent path,
fixed-512 text embeds (static shapes, plain concat collate), inline 2x2
latent packing + shared position grid, the authors' recommended LoRA
target set and rank/alpha 32, lr 3e-4, 512px presets
- GPU smokes on B200: nf4 2.9 steps/s at 11.5 GB, bf16 3.4 steps/s at
30.1 GB, bf16 + regional compile 5.2 steps/s; adapter round-trip
generation verified
