One UNET override cannot serve dual-transformer arches: ideogram4
conditional/unconditional and wan combined-stage experts need separate
files, and previously a single override landed on both experts.
- sd_unet_secondary option with per-slot tracking, consumed-state sync,
arch-change reset, and incompatible-override fallback
- dropdown renders beside the primary, follows it into quicksettings,
and is visible only for dual-transformer model types
- ideogram4 native single-file spec with a quant-aware fused-qkv
converter; such converters run before comfy_quant detection via
TransformerSpec.converter_handles_quant
- quicksettings render in configured order (sort keyed on the option
object and always fell back to alphabetical)
- post-load dtype warning skips quantized transformers
diffusers pipeline downloads build subfolder config.json allow-patterns
with os.path.join, and huggingface_hub>=1.22 matches patterns with
fnmatchcase which does not normalize path separators
(huggingface/huggingface_hub#4435). On windows the resulting backslash
patterns match nothing, so per-component config.json files are never
downloaded and the incomplete snapshot still passes the diffusers
cache-completeness check, failing every subsequent load with
"no file named config.json".
Prefetch component configs with forward-slash patterns before pipeline
load. This covers all model families and also repairs snapshots already
broken by the bug on the next load attempt. No-op on linux, in offline
mode, and for local folder or single-file models.
When a checkpoint change switches the model type, a custom sd_text_encoder no
longer fits, so reset it to Default and clear loaded_te, mirroring the sd_unet
reset. The type is resolved with detect_pipeline on both the loaded and incoming
checkpoints, so same-arch switches (Krea2 Base and Turbo share one pipeline
class) do not reset. The checkpoint handler also returns sd_text_encoder
alongside sd_unet so the dropdown reflects it.
reload_text_encoder only hot-swapped T5-family encoders and ran only at initial
load, so changing sd_text_encoder for a model with a generic encoder (Krea2's
Qwen3-VL) never took effect until a full model reload. Track the loaded
selection and, for encoders with no in-place swap, reload the model on change,
triggered from the settings handler. The fresh model object also invalidates
the prompt cache.
Krea 2 is a 12.9B single-stream flow-matching DiT trained from scratch, using a Qwen3-VL-4B text encoder and the Qwen-Image VAE. The transformer is vendored as a diffusers ModelMixin whose module tree mirrors the checkpoint, so weights load with no key conversion; the pipeline ports the reference encode, flow-matching denoise, and VAE decode. The text encoder is shared at runtime via the existing dedup registry, so Base and Turbo reuse one Qwen3-VL-4B copy.
Covers text-to-image, image-to-image, native LoRA, and the single-file UNET override. Also completes SD.Next's partial Qwen-Image VAE support (5D decode input and TAESD preview mapping) that K2 shares.
Diffusers-native port of the 9.3B flow-matching DiT: dual-transformer asymmetric CFG, a 13-layer Qwen3-VL tap encoder deduped with VQA and prompt-enhance, the Flux.2 VAE, and a logit-normal schedule. Loads a published bf16 repo with SDNQ at load.
The custom-transformer apply (DiT branch) and the revert-to-default path now call reload_model_weights(force=True) instead of load_diffuser, so both go through the same managed reload: unload-before-rebuild for a lower VRAM peak and consistent job/checkpoint bookkeeping.
Gate the cross-arch unet reset on an actual checkpoint change so a same-checkpoint reload skips the pipeline-class comparison, which could false-positive and clear the override when the model was left in an img2img/inpaint variant after an interrupted generation.
A custom UNET selected via the UNET dropdown carried over silently when the user swapped to a base model of a different arch, then crashed inside the per-arch loader because shared.opts.sd_unet was still pointing at the previous arch's file.
reload_model_weights now runs sd_detect.detect_pipeline on the new checkpoint before unloading the old model, compares the detected pipeline class against the loaded model's class, and resets shared.opts.sd_unet to Default when they differ.
Disty0
Vladimir Mandic
CalamitousFelicitousness