Loading a Chroma transformer override via UNET dropdown crashed at
inference with a shape mismatch in unpack_uint4: from_single_file does
not integrate quantization_config the way from_pretrained does, so the
dequantizer state was set up but the .weight tensor was never packed.
CHROMA_SPEC routes through native_transformer with the diffusers
Chroma converter, which loads bare and quantizes explicitly via
sdnq_quantize_model.
Add a per-model native LoRA path for Chroma that handles both PEFT-style
(diffusion_model.<dotted>.lora_A.weight, no alpha) and kohya-flat
(lora_unet_<flat> with .alpha) layouts, plus LoKR/LoHA/OFT/DoRA.
Resolves issue #4169 (ostris ai-toolkit Chroma LoRAs failed to load).
- new pipelines/chroma/chroma_lora.py with try_load_lora/lokr/loha/oft
- fused-QKV expansion: 3-way equal for img/txt qkv, 4-way unequal
[3072, 3072, 3072, 12288] for single linear1 (Q+K+V+proj_mlp)
- NetworkModuleLokrSliceChunk for unequal-row LoKR slicing
- path rewrite from Flux-source to ChromaTransformer2DModel naming
- lora_load.py: dispatch chroma to the native loader and narrow the
kohya-Flux convert gate to f1 only
CalamitousFelicitousness