feat(pipelines): wire native_spec for arches missing single_file_model entry

ChronoEdit, HunyuanImage, Kandinsky5, LongCat, Ovis, NucleusMoE, Bria,
BriaFibo. Each inherits from_single_file via FromOriginalModelMixin
but has no SINGLE_FILE_LOADABLE_CLASSES entry; calling it crashed with
"FromOriginalModelMixin is currently only compatible with [...]".

TransformerSpec(cls=...) for each. Bria specs in
pipelines/bria/__init__.py; the rest are module-level in
model_<arch>.py since they have no package directory.

pipelines/bria/__init__.py

@@ -0,0 +1,29 @@
+"""Bria pipeline package.
+
+Exports :data:`BRIA_SPEC` and :data:`BRIA_FIBO_SPEC` for use by
+:mod:`pipelines.model_bria` together with :mod:`pipelines.native_transformer`.
+
+Bria ships two transformer variants:
+
+- The original Bria family uses a custom :class:`BriaTransformer2DModel`
+  imported from :mod:`pipelines.bria.transformer_bria`. The custom class
+  has no entry in diffusers' ``SINGLE_FILE_LOADABLE_CLASSES`` table, so a
+  community single-file selection previously crashed in
+  ``ModelMixin.from_pretrained``.
+- Bria FIBO (and FIBO Edit) use the upstream
+  :class:`diffusers.BriaFiboTransformer2DModel`, which also lacks a
+  ``SINGLE_FILE_LOADABLE_CLASSES`` entry. Same crash.
+
+Both specs use the default 3-prefix detection
+(``model.diffusion_model.``, ``diffusion_model.``, ``net.``), no
+converter, and no siblings.
+"""
+
+import diffusers
+
+from pipelines.bria.transformer_bria import BriaTransformer2DModel
+from pipelines.native_transformer import TransformerSpec
+
+
+BRIA_SPEC = TransformerSpec(cls=BriaTransformer2DModel)
+BRIA_FIBO_SPEC = TransformerSpec(cls=diffusers.BriaFiboTransformer2DModel)

pipelines/model_bria.py

@@ -20,11 +20,13 @@ def load_bria(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=BriaFibo repo="{repo_id}" config={diffusers_load_config} offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype} args={load_args}')
 
+        from pipelines.bria import BRIA_FIBO_SPEC
         transformer = generic.load_transformer(
             repo_id,
             cls_name=diffusers.BriaFiboTransformer2DModel,
             load_config=diffusers_load_config,
             allow_quant=False,
+            native_spec=BRIA_FIBO_SPEC,
         )
         text_encoder = generic.load_text_encoder(
             repo_id,
@@ -66,7 +68,8 @@ def load_bria(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=Bria 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=BriaTransformer2DModel, load_config=diffusers_load_config)
+        from pipelines.bria import BRIA_SPEC
+        transformer = generic.load_transformer(repo_id, cls_name=BriaTransformer2DModel, load_config=diffusers_load_config, native_spec=BRIA_SPEC)
         text_encoder = generic.load_text_encoder(repo_id, cls_name=transformers.T5EncoderModel, load_config=diffusers_load_config)
 
         pipe = BriaPipeline.from_pretrained(

pipelines/model_chrono.py

@@ -3,6 +3,10 @@ import transformers
 from modules import shared, devices, sd_models, model_quant, sd_hijack_te, sd_hijack_vae
 from modules.logger import log
 from pipelines import generic
+from pipelines.native_transformer import TransformerSpec
+
+
+CHRONOEDIT_SPEC = TransformerSpec(cls=diffusers.ChronoEditTransformer3DModel)
 
 
 def postprocess(p, result): # pylint: disable=unused-argument
@@ -21,7 +25,7 @@ def load_chrono(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=ChronoEdit 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.ChronoEditTransformer3DModel, load_config=diffusers_load_config, subfolder="transformer")
+    transformer = generic.load_transformer(repo_id, cls_name=diffusers.ChronoEditTransformer3DModel, load_config=diffusers_load_config, subfolder="transformer", native_spec=CHRONOEDIT_SPEC)
     text_encoder = generic.load_text_encoder(repo_id, cls_name=transformers.UMT5EncoderModel, load_config=diffusers_load_config, subfolder="text_encoder")
 
     try:

pipelines/model_hyimage.py

@@ -5,6 +5,10 @@ import diffusers
 from modules import shared, sd_models, devices, model_quant, sd_hijack_te, sd_hijack_vae
 from modules.logger import log
 from pipelines import generic
+from pipelines.native_transformer import TransformerSpec
+
+
+HUNYUANIMAGE_SPEC = TransformerSpec(cls=diffusers.HunyuanImageTransformer2DModel)
 
 
 def load_hyimage(checkpoint_info, diffusers_load_config=None): # pylint: disable=unused-argument
@@ -16,7 +20,7 @@ def load_hyimage(checkpoint_info, diffusers_load_config=None): # pylint: disable
     load_args, _quant_args = model_quant.get_dit_args(diffusers_load_config)
     log.debug(f'Load model: type=HunyuanImage21 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.HunyuanImageTransformer2DModel, load_config=diffusers_load_config, subfolder="transformer")
+    transformer = generic.load_transformer(repo_id, cls_name=diffusers.HunyuanImageTransformer2DModel, load_config=diffusers_load_config, subfolder="transformer", native_spec=HUNYUANIMAGE_SPEC)
     text_encoder = generic.load_text_encoder(repo_id, cls_name=transformers.Qwen2_5_VLForConditionalGeneration, load_config=diffusers_load_config, subfolder="text_encoder")
     text_encoder_2 = generic.load_text_encoder(repo_id, cls_name=transformers.T5EncoderModel, load_config=diffusers_load_config, subfolder="text_encoder_2", allow_shared=False)
 

pipelines/model_kandinsky.py

@@ -3,6 +3,10 @@ import diffusers
 from modules import shared, sd_models, devices, model_quant, sd_hijack_te, sd_hijack_vae
 from modules.logger import log
 from pipelines import generic
+from pipelines.native_transformer import TransformerSpec
+
+
+KANDINSKY5_SPEC = TransformerSpec(cls=diffusers.Kandinsky5Transformer3DModel)
 
 
 def load_kandinsky21(checkpoint_info, diffusers_load_config=None):
@@ -83,7 +87,7 @@ def load_kandinsky5(checkpoint_info, diffusers_load_config=None):
     load_args, _quant_args = model_quant.get_dit_args(diffusers_load_config)
     log.debug(f'Load model: type=Kandinsky50 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.Kandinsky5Transformer3DModel, load_config=diffusers_load_config)
+    transformer = generic.load_transformer(repo_id, cls_name=diffusers.Kandinsky5Transformer3DModel, load_config=diffusers_load_config, native_spec=KANDINSKY5_SPEC)
     text_encoder = generic.load_text_encoder(repo_id, cls_name=transformers.Qwen2_5_VLForConditionalGeneration, load_config=diffusers_load_config)
 
     if 'I2I' in repo_id:

pipelines/model_longcat.py

@@ -3,6 +3,10 @@ import diffusers
 from modules import shared, devices, sd_models, model_quant, sd_hijack_te
 from modules.logger import log
 from pipelines import generic
+from pipelines.native_transformer import TransformerSpec
+
+
+LONGCAT_SPEC = TransformerSpec(cls=diffusers.LongCatImageTransformer2DModel)
 
 
 def load_longcat(checkpoint_info, diffusers_load_config=None):
@@ -14,7 +18,7 @@ def load_longcat(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=LongCat repo="{repo_id}" config={diffusers_load_config} offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype} args={diffusers_load_config}')
 
-    transformer = generic.load_transformer(repo_id, cls_name=diffusers.LongCatImageTransformer2DModel, load_config=diffusers_load_config)
+    transformer = generic.load_transformer(repo_id, cls_name=diffusers.LongCatImageTransformer2DModel, load_config=diffusers_load_config, native_spec=LONGCAT_SPEC)
     text_encoder = generic.load_text_encoder(repo_id, cls_name=transformers.Qwen2_5_VLForConditionalGeneration, load_config=diffusers_load_config)
     text_processor = transformers.Qwen2VLProcessor.from_pretrained(repo_id, subfolder='tokenizer', cache_dir=shared.opts.hfcache_dir)
 

pipelines/model_nucleus.py

@@ -3,6 +3,10 @@ import transformers
 from modules import shared, devices, sd_models, model_quant, sd_hijack_te, sd_hijack_vae
 from modules.logger import log
 from pipelines import generic
+from pipelines.native_transformer import TransformerSpec
+
+
+NUCLEUS_SPEC = TransformerSpec(cls=diffusers.NucleusMoEImageTransformer2DModel)
 
 
 def load_nucleus(checkpoint_info, diffusers_load_config=None):
@@ -18,6 +22,7 @@ def load_nucleus(checkpoint_info, diffusers_load_config=None):
         repo_id,
         cls_name=diffusers.NucleusMoEImageTransformer2DModel,
         load_config=diffusers_load_config,
+        native_spec=NUCLEUS_SPEC,
     )
     text_encoder = generic.load_text_encoder(
         repo_id,

pipelines/model_ovis.py

@@ -3,6 +3,10 @@ import diffusers
 from modules import shared, devices, sd_models, model_quant, sd_hijack_te
 from modules.logger import log
 from pipelines import generic
+from pipelines.native_transformer import TransformerSpec
+
+
+OVIS_SPEC = TransformerSpec(cls=diffusers.OvisImageTransformer2DModel)
 
 
 def load_ovis(checkpoint_info, diffusers_load_config=None):
@@ -14,7 +18,7 @@ def load_ovis(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=OvisImage repo="{repo_id}" config={diffusers_load_config} offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype} args={diffusers_load_config}')
 
-    transformer = generic.load_transformer(repo_id, cls_name=diffusers.OvisImageTransformer2DModel, load_config=diffusers_load_config)
+    transformer = generic.load_transformer(repo_id, cls_name=diffusers.OvisImageTransformer2DModel, load_config=diffusers_load_config, native_spec=OVIS_SPEC)
     text_encoder = generic.load_text_encoder(repo_id, cls_name=transformers.Qwen3Model, load_config=diffusers_load_config)
 
     pipe = diffusers.OvisImagePipeline.from_pretrained(