fix(chroma): route through native_transformer to avoid SDNQ pre-mode crash

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.

pipelines/chroma/__init__.py

@@ -0,0 +1,18 @@
+"""Chroma pipeline package.
+
+Exports :data:`CHROMA_SPEC`. Chroma community files use BFL-style
+``model.diffusion_model.``-prefixed keys that need renaming into the
+diffusers naming convention, so the spec plugs in
+:func:`convert_chroma_transformer_checkpoint_to_diffusers` explicitly.
+"""
+
+import diffusers
+from diffusers.loaders.single_file_utils import convert_chroma_transformer_checkpoint_to_diffusers
+
+from pipelines.native_transformer import TransformerSpec
+
+
+CHROMA_SPEC = TransformerSpec(
+    cls=diffusers.ChromaTransformer2DModel,
+    converter=convert_chroma_transformer_checkpoint_to_diffusers,
+)

pipelines/model_chroma.py

@@ -14,7 +14,8 @@ def load_chroma(checkpoint_info, diffusers_load_config=None):
     load_args, _quant_args = model_quant.get_dit_args(diffusers_load_config, allow_quant=False)
     log.debug(f'Load model: type=Chroma repo="{repo_id}" config={diffusers_load_config} offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype} args={load_args}')
 
-    transformer = generic.load_transformer(repo_id, cls_name=diffusers.ChromaTransformer2DModel, load_config=diffusers_load_config, modules_to_not_convert=["distilled_guidance_layer"])
+    from pipelines.chroma import CHROMA_SPEC
+    transformer = generic.load_transformer(repo_id, cls_name=diffusers.ChromaTransformer2DModel, load_config=diffusers_load_config, modules_to_not_convert=["distilled_guidance_layer"], native_spec=CHROMA_SPEC)
     text_encoder = generic.load_text_encoder(repo_id, cls_name=transformers.T5EncoderModel, load_config=diffusers_load_config)
 
     pipe = diffusers.ChromaPipeline.from_pretrained(