refactor(pipelines): move per-arch transformer specs into packages

Specs for chrono, cogview, flux2_klein, glm, hunyuandit, hyimage, joy,
kandinsky, longcat, nucleus, ovis, pixart and prx lived module-level in
model_<arch>.py. Move each into pipelines/<arch>/__init__.py to match
the layout used by anima, bria, ernie, f_lite, lens, qwen, step1x and
vibe.

Each model_<arch>.py now imports its spec lazily inside the load
function, so the package only gets pulled in when that arch is actually
loaded (kandinsky 2.x never goes through native dispatch and stays
untouched).

Also drop the "without this spec..." crash paragraph from the new
package docstrings plus ernie/__init__.py and bria/__init__.py.

pipelines/bria/__init__.py

@@ -7,12 +7,10 @@ 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``.
+  has no entry in diffusers' ``SINGLE_FILE_LOADABLE_CLASSES`` table.
 - Bria FIBO (and FIBO Edit) use the upstream
   :class:`diffusers.BriaFiboTransformer2DModel`, which also lacks a
-  ``SINGLE_FILE_LOADABLE_CLASSES`` entry. Same crash.
+  ``SINGLE_FILE_LOADABLE_CLASSES`` entry.
 
 Both specs use the default 3-prefix detection
 (``model.diffusion_model.``, ``diffusion_model.``, ``net.``), no

pipelines/chrono/__init__.py

@@ -0,0 +1,15 @@
+"""ChronoEdit pipeline package.
+
+Exports :data:`CHRONOEDIT_SPEC`. The minimum
+``TransformerSpec(cls=ChronoEditTransformer3DModel)`` works because
+ChronoEdit community files use BFL-style ``model.diffusion_model.``
+prefixed keys whose names match the diffusers state_dict verbatim after
+prefix strip. No siblings, no converter, no forbidden markers.
+"""
+
+import diffusers
+
+from pipelines.native_transformer import TransformerSpec
+
+
+CHRONOEDIT_SPEC = TransformerSpec(cls=diffusers.ChronoEditTransformer3DModel)

pipelines/cogview/__init__.py

@@ -0,0 +1,15 @@
+"""CogView pipeline package.
+
+Exports :data:`COGVIEW3_SPEC` and :data:`COGVIEW4_SPEC`. Both default
+specs work because CogView community files use BFL-style
+``model.diffusion_model.``-prefixed keys whose names match the diffusers
+state_dict verbatim after prefix strip.
+"""
+
+import diffusers
+
+from pipelines.native_transformer import TransformerSpec
+
+
+COGVIEW3_SPEC = TransformerSpec(cls=diffusers.CogView3PlusTransformer2DModel)
+COGVIEW4_SPEC = TransformerSpec(cls=diffusers.CogView4Transformer2DModel)

pipelines/ernie/__init__.py

@@ -6,13 +6,6 @@ trainer dumps use BFL-style ``model.diffusion_model.``-prefixed keys
 whose names match the diffusers state_dict verbatim after prefix strip
 (probed against a community finetune: full key overlap with zero
 missing or unexpected). No siblings, no converter, no forbidden markers.
-
-Without this spec, selecting an Ernie file via the UNET dropdown crashes
-in :func:`diffusers.loaders.ModelMixin.from_pretrained` with a
-misleading ``OSError: ... is not a valid JSON file``, because
-``ErnieImageTransformer2DModel`` lacks ``from_single_file`` and the
-fallback treats the safetensors as a directory looking for
-``config.json``.
 """
 
 import diffusers

pipelines/flux2_klein/__init__.py

@@ -0,0 +1,26 @@
+"""Flux 2 Klein pipeline package.
+
+Exports :data:`FLUX2_KLEIN_SPEC`. Klein shares
+:class:`Flux2Transformer2DModel` with full Flux 2 but uses a smaller
+config (hidden_size and friends). diffusers' ``from_single_file`` picks
+the class default (= Flux 2 full), so loading a Klein-shaped community
+file crashes at ``load_model_dict_into_meta`` with a shape mismatch like
+``expected (36864, 6144), got (24576, 4096)``.
+
+Routing through :mod:`pipelines.native_transformer` pulls the Klein
+``transformer/config.json`` from the base repo first and instantiates
+``Flux2Transformer2DModel`` at the right size, then runs the diffusers
+Flux 2 converter to split fused QKV blocks and rename BFL keys into the
+diffusers-expected names.
+"""
+
+import diffusers
+from diffusers.loaders.single_file_utils import convert_flux2_transformer_checkpoint_to_diffusers
+
+from pipelines.native_transformer import TransformerSpec
+
+
+FLUX2_KLEIN_SPEC = TransformerSpec(
+    cls=diffusers.Flux2Transformer2DModel,
+    converter=convert_flux2_transformer_checkpoint_to_diffusers,
+)

pipelines/glm/__init__.py

@@ -0,0 +1,15 @@
+"""GLM-Image pipeline package.
+
+Exports :data:`GLM_IMAGE_SPEC`. The minimum
+``TransformerSpec(cls=GlmImageTransformer2DModel)`` works because
+GLM-Image community files use BFL-style ``model.diffusion_model.``
+prefixed keys whose names match the diffusers state_dict verbatim after
+prefix strip.
+"""
+
+import diffusers
+
+from pipelines.native_transformer import TransformerSpec
+
+
+GLM_IMAGE_SPEC = TransformerSpec(cls=diffusers.GlmImageTransformer2DModel)

pipelines/hunyuandit/__init__.py

@@ -0,0 +1,14 @@
+"""HunyuanDiT pipeline package.
+
+Exports :data:`HUNYUANDIT_SPEC`. The minimum
+``TransformerSpec(cls=HunyuanDiT2DModel)`` works because HunyuanDiT
+community files use BFL-style ``model.diffusion_model.``-prefixed keys
+whose names match the diffusers state_dict verbatim after prefix strip.
+"""
+
+import diffusers
+
+from pipelines.native_transformer import TransformerSpec
+
+
+HUNYUANDIT_SPEC = TransformerSpec(cls=diffusers.HunyuanDiT2DModel)

pipelines/hyimage/__init__.py

@@ -0,0 +1,19 @@
+"""HunyuanImage pipeline package.
+
+Exports :data:`HUNYUANIMAGE_SPEC`. The minimum
+``TransformerSpec(cls=HunyuanImageTransformer2DModel)`` works because
+HunyuanImage 2.1 community files use BFL-style
+``model.diffusion_model.``-prefixed keys whose names match the diffusers
+state_dict verbatim after prefix strip.
+
+The HunyuanImage 3 path is a transformers ``AutoModelForCausalLM`` and
+does not go through the native transformer loader, so it does not need a
+spec here.
+"""
+
+import diffusers
+
+from pipelines.native_transformer import TransformerSpec
+
+
+HUNYUANIMAGE_SPEC = TransformerSpec(cls=diffusers.HunyuanImageTransformer2DModel)

pipelines/joy/__init__.py

@@ -0,0 +1,15 @@
+"""Joy-Image-Edit pipeline package.
+
+Exports :data:`JOY_SPEC`. The minimum
+``TransformerSpec(cls=JoyImageEditTransformer3DModel)`` works because
+Joy community files use BFL-style ``model.diffusion_model.``-prefixed
+keys whose names match the diffusers state_dict verbatim after prefix
+strip.
+"""
+
+import diffusers
+
+from pipelines.native_transformer import TransformerSpec
+
+
+JOY_SPEC = TransformerSpec(cls=diffusers.JoyImageEditTransformer3DModel)

pipelines/kandinsky/__init__.py

@@ -0,0 +1,23 @@
+"""Kandinsky pipeline package.
+
+Exports :data:`KANDINSKY3_UNET_SPEC` and :data:`KANDINSKY5_SPEC`.
+
+Kandinsky ships several generations under one family:
+
+- Kandinsky 2.1 / 2.2 are unet-based and load through diffusers'
+  combined pipelines without going through the native loader, so no
+  spec is needed.
+- Kandinsky 3 uses :class:`Kandinsky3UNet` in the ``unet`` subfolder of
+  the repo, hence ``subfolder='unet'`` instead of the default
+  ``'transformer'``.
+- Kandinsky 5 uses the new :class:`Kandinsky5Transformer3DModel` and
+  follows the default layout.
+"""
+
+import diffusers
+
+from pipelines.native_transformer import TransformerSpec
+
+
+KANDINSKY3_UNET_SPEC = TransformerSpec(cls=diffusers.Kandinsky3UNet, subfolder='unet')
+KANDINSKY5_SPEC = TransformerSpec(cls=diffusers.Kandinsky5Transformer3DModel)

pipelines/longcat/__init__.py

@@ -0,0 +1,15 @@
+"""LongCat pipeline package.
+
+Exports :data:`LONGCAT_SPEC`. The minimum
+``TransformerSpec(cls=LongCatImageTransformer2DModel)`` works because
+LongCat community files use BFL-style ``model.diffusion_model.``
+prefixed keys whose names match the diffusers state_dict verbatim after
+prefix strip.
+"""
+
+import diffusers
+
+from pipelines.native_transformer import TransformerSpec
+
+
+LONGCAT_SPEC = TransformerSpec(cls=diffusers.LongCatImageTransformer2DModel)

pipelines/model_chrono.py

@@ -3,10 +3,6 @@ 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
@@ -25,6 +21,7 @@ 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}')
 
+    from pipelines.chrono import CHRONOEDIT_SPEC
     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")
 

pipelines/model_cogview.py

@@ -3,11 +3,6 @@ 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
-
-
-COGVIEW3_SPEC = TransformerSpec(cls=diffusers.CogView3PlusTransformer2DModel)
-COGVIEW4_SPEC = TransformerSpec(cls=diffusers.CogView4Transformer2DModel)
 
 
 def load_cogview3(checkpoint_info, diffusers_load_config=None):
@@ -19,6 +14,7 @@ def load_cogview3(checkpoint_info, diffusers_load_config=None):
     load_args, _quant_args = model_quant.get_dit_args(diffusers_load_config)
     log.debug(f'Load model: type=CogView3 repo="{repo_id}" config={diffusers_load_config} offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype} args={load_args}')
 
+    from pipelines.cogview import COGVIEW3_SPEC
     transformer = generic.load_transformer(repo_id, cls_name=diffusers.CogView3PlusTransformer2DModel, load_config=diffusers_load_config, subfolder="transformer", native_spec=COGVIEW3_SPEC)
     text_encoder = generic.load_text_encoder(repo_id, cls_name=transformers.T5EncoderModel, load_config=diffusers_load_config, subfolder="text_encoder")
 
@@ -45,6 +41,7 @@ def load_cogview4(checkpoint_info, diffusers_load_config=None):
     load_args, _quant_args = model_quant.get_dit_args(diffusers_load_config)
     log.debug(f'Load model: type=CogView4 repo="{repo_id}" config={diffusers_load_config} offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype} args={load_args}')
 
+    from pipelines.cogview import COGVIEW4_SPEC
     transformer = generic.load_transformer(repo_id, cls_name=diffusers.CogView4Transformer2DModel, load_config=diffusers_load_config, subfolder="transformer", native_spec=COGVIEW4_SPEC)
     text_encoder = generic.load_text_encoder(repo_id, cls_name=transformers.GlmModel, load_config=diffusers_load_config, subfolder="text_encoder", allow_quant=True)
 

pipelines/model_flux2_klein.py

@@ -1,25 +1,8 @@
 import transformers
 import diffusers
-from diffusers.loaders.single_file_utils import convert_flux2_transformer_checkpoint_to_diffusers
 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
-
-
-# Klein shares Flux2Transformer2DModel with full Flux 2, but uses a smaller
-# config (hidden_size and friends). diffusers' from_single_file picks the
-# class default (= Flux 2 full), so loading a Klein-shaped community file
-# crashes at load_model_dict_into_meta with a shape mismatch like
-# "expected (36864, 6144), got (24576, 4096)". Routing through
-# native_transformer pulls the Klein transformer/config.json from the base
-# repo first and instantiates Flux2Transformer2DModel at the right size,
-# then runs the diffusers Flux 2 converter to split fused QKV blocks and
-# rename BFL keys into the diffusers-expected names.
-FLUX2_KLEIN_SPEC = TransformerSpec(
-    cls=diffusers.Flux2Transformer2DModel,
-    converter=convert_flux2_transformer_checkpoint_to_diffusers,
-)
 
 
 def load_flux2_klein(checkpoint_info, diffusers_load_config=None):
@@ -32,6 +15,7 @@ def load_flux2_klein(checkpoint_info, diffusers_load_config=None):
     log.debug(f'Load model: type=Flux2Klein repo="{repo_id}" config={diffusers_load_config} offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype} args={load_args}')
 
     # Load transformer - Klein uses Flux2Transformer2DModel (same class as Flux2, different size)
+    from pipelines.flux2_klein import FLUX2_KLEIN_SPEC
     transformer = generic.load_transformer(repo_id, cls_name=diffusers.Flux2Transformer2DModel, load_config=diffusers_load_config, native_spec=FLUX2_KLEIN_SPEC)
 
     # Load text encoder - Klein uses Qwen3 (4B for Klein-4B, 8B for Klein-9B)

pipelines/model_glm.py

@@ -5,10 +5,6 @@ import diffusers
 from modules import shared, devices, sd_models, model_quant, sd_hijack_te
 from modules.logger import log, console
 from pipelines import generic
-from pipelines.native_transformer import TransformerSpec
-
-
-GLM_IMAGE_SPEC = TransformerSpec(cls=diffusers.GlmImageTransformer2DModel)
 
 
 class GLMTokenProgressProcessor(transformers.LogitsProcessor):
@@ -99,6 +95,7 @@ def load_glm_image(checkpoint_info, diffusers_load_config=None):
     log.debug(f'Load model: type=GLM-Image repo="{repo_id}" offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype} args={load_args}')
 
     # Load transformer (DiT decoder - 7B) with quantization support
+    from pipelines.glm import GLM_IMAGE_SPEC
     transformer = generic.load_transformer(
         repo_id,
         cls_name=diffusers.GlmImageTransformer2DModel,

pipelines/model_hunyuandit.py

@@ -3,10 +3,6 @@ import diffusers
 from modules import shared, sd_models, devices, model_quant
 from modules.logger import log
 from pipelines import generic
-from pipelines.native_transformer import TransformerSpec
-
-
-HUNYUANDIT_SPEC = TransformerSpec(cls=diffusers.HunyuanDiT2DModel)
 
 
 def load_hunyuandit(checkpoint_info, diffusers_load_config=None):
@@ -23,6 +19,7 @@ def load_hunyuandit(checkpoint_info, diffusers_load_config=None):
     load_args, _quant_args = model_quant.get_dit_args(diffusers_load_config)
     log.debug(f'Load model: type=HunyuanDiT repo="{repo_id}" config={diffusers_load_config} offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype} args={load_args}')
 
+    from pipelines.hunyuandit import HUNYUANDIT_SPEC
     transformer = generic.load_transformer(repo_id, cls_name=diffusers.HunyuanDiT2DModel, load_config=diffusers_load_config, native_spec=HUNYUANDIT_SPEC)
     repo_te = 'Tencent-Hunyuan/HunyuanDiT-v1.2-Diffusers' if 'HunyuanDiT-v1' in repo_id else repo_id
     text_encoder_2 = generic.load_text_encoder(repo_te, cls_name=transformers.T5EncoderModel, load_config=diffusers_load_config, subfolder="text_encoder_2", allow_shared=False) # this is not normal t5

pipelines/model_hyimage.py

@@ -5,10 +5,6 @@ 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
@@ -20,6 +16,7 @@ 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}')
 
+    from pipelines.hyimage import HUNYUANIMAGE_SPEC
     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_joy.py

@@ -3,10 +3,6 @@ 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
-
-
-JOY_SPEC = TransformerSpec(cls=diffusers.JoyImageEditTransformer3DModel)
 
 
 def load_joy(checkpoint_info, diffusers_load_config=None):
@@ -18,6 +14,7 @@ def load_joy(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=JoyImageEdit repo="{repo_id}" config={diffusers_load_config} offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype} args={load_args}')
 
+    from pipelines.joy import JOY_SPEC
     transformer = generic.load_transformer(
         repo_id,
         cls_name=diffusers.JoyImageEditTransformer3DModel,

pipelines/model_kandinsky.py

@@ -3,11 +3,6 @@ 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
-
-
-KANDINSKY3_UNET_SPEC = TransformerSpec(cls=diffusers.Kandinsky3UNet, subfolder='unet')
-KANDINSKY5_SPEC = TransformerSpec(cls=diffusers.Kandinsky5Transformer3DModel)
 
 
 def load_kandinsky21(checkpoint_info, diffusers_load_config=None):
@@ -55,6 +50,7 @@ def load_kandinsky3(checkpoint_info, diffusers_load_config=None):
     load_args, _quant_args = model_quant.get_dit_args(diffusers_load_config)
     log.debug(f'Load model: type=Kandinsky30 repo="{repo_id}" config={diffusers_load_config} offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype} args={load_args}')
 
+    from pipelines.kandinsky import KANDINSKY3_UNET_SPEC
     unet = generic.load_transformer(repo_id, cls_name=diffusers.Kandinsky3UNet, load_config=diffusers_load_config, subfolder="unet", variant="fp16", native_spec=KANDINSKY3_UNET_SPEC)
     text_encoder = generic.load_text_encoder(repo_id, cls_name=transformers.T5EncoderModel, load_config=diffusers_load_config, subfolder="text_encoder", variant="fp16", allow_shared=False)
 
@@ -88,6 +84,7 @@ 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}')
 
+    from pipelines.kandinsky import KANDINSKY5_SPEC
     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)
 

pipelines/model_longcat.py

@@ -3,10 +3,6 @@ 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):
@@ -18,6 +14,7 @@ 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}')
 
+    from pipelines.longcat import LONGCAT_SPEC
     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

@@ -3,10 +3,6 @@ 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 +14,7 @@ def load_nucleus(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=NucleusMoEImage repo="{repo_id}" offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype} args={load_args}')
 
+    from pipelines.nucleus import NUCLEUS_SPEC
     transformer = generic.load_transformer(
         repo_id,
         cls_name=diffusers.NucleusMoEImageTransformer2DModel,

pipelines/model_ovis.py

@@ -3,10 +3,6 @@ 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):
@@ -18,6 +14,7 @@ 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}')
 
+    from pipelines.ovis import OVIS_SPEC
     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)
 

pipelines/model_pixart.py

@@ -4,10 +4,6 @@ from huggingface_hub import file_exists
 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
-
-
-PIXART_SPEC = TransformerSpec(cls=diffusers.PixArtTransformer2DModel)
 
 
 def load_pixart(checkpoint_info, diffusers_load_config=None):
@@ -28,6 +24,7 @@ def load_pixart(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=PixArtSigma repo="{repo_id}" config={diffusers_load_config} offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype} args={load_args}')
 
+    from pipelines.pixart import PIXART_SPEC
     transformer = generic.load_transformer(repo_id, cls_name=diffusers.PixArtTransformer2DModel, load_config=diffusers_load_config, native_spec=PIXART_SPEC)
     text_encoder = generic.load_text_encoder(repo_id_tenc, cls_name=transformers.T5EncoderModel, load_config=diffusers_load_config)
 

pipelines/model_prx.py

@@ -2,10 +2,6 @@ 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
-
-
-PRX_SPEC = TransformerSpec(cls=diffusers.PRXTransformer2DModel)
 
 
 def load_prx(checkpoint_info, diffusers_load_config=None):
@@ -18,6 +14,7 @@ def load_prx(checkpoint_info, diffusers_load_config=None):
     log.debug(f'Load model: type=PRX repo="{repo_id}" config={diffusers_load_config} offload={shared.opts.diffusers_offload_mode} dtype={devices.dtype} args={load_args}')
 
     from transformers.models.t5gemma.modeling_t5gemma import T5GemmaEncoder
+    from pipelines.prx import PRX_SPEC
     transformer = generic.load_transformer(repo_id, cls_name=diffusers.PRXTransformer2DModel, load_config=diffusers_load_config, native_spec=PRX_SPEC)
     text_encoder = generic.load_text_encoder(repo_id, cls_name=T5GemmaEncoder, load_config=diffusers_load_config)
 

pipelines/nucleus/__init__.py

@@ -0,0 +1,15 @@
+"""Nucleus MoE-Image pipeline package.
+
+Exports :data:`NUCLEUS_SPEC`. The minimum
+``TransformerSpec(cls=NucleusMoEImageTransformer2DModel)`` works because
+Nucleus community files use BFL-style ``model.diffusion_model.``
+prefixed keys whose names match the diffusers state_dict verbatim after
+prefix strip.
+"""
+
+import diffusers
+
+from pipelines.native_transformer import TransformerSpec
+
+
+NUCLEUS_SPEC = TransformerSpec(cls=diffusers.NucleusMoEImageTransformer2DModel)

pipelines/ovis/__init__.py

@@ -0,0 +1,15 @@
+"""Ovis-Image pipeline package.
+
+Exports :data:`OVIS_SPEC`. The minimum
+``TransformerSpec(cls=OvisImageTransformer2DModel)`` works because
+Ovis community files use BFL-style ``model.diffusion_model.``-prefixed
+keys whose names match the diffusers state_dict verbatim after prefix
+strip.
+"""
+
+import diffusers
+
+from pipelines.native_transformer import TransformerSpec
+
+
+OVIS_SPEC = TransformerSpec(cls=diffusers.OvisImageTransformer2DModel)

pipelines/pixart/__init__.py

@@ -0,0 +1,14 @@
+"""PixArt pipeline package.
+
+Exports :data:`PIXART_SPEC`. The minimum
+``TransformerSpec(cls=PixArtTransformer2DModel)`` works because PixArt
+community files use BFL-style ``model.diffusion_model.``-prefixed keys
+whose names match the diffusers state_dict verbatim after prefix strip.
+"""
+
+import diffusers
+
+from pipelines.native_transformer import TransformerSpec
+
+
+PIXART_SPEC = TransformerSpec(cls=diffusers.PixArtTransformer2DModel)

pipelines/prx/__init__.py

@@ -0,0 +1,14 @@
+"""PRX pipeline package.
+
+Exports :data:`PRX_SPEC`. The minimum
+``TransformerSpec(cls=PRXTransformer2DModel)`` works because PRX
+community files use BFL-style ``model.diffusion_model.``-prefixed keys
+whose names match the diffusers state_dict verbatim after prefix strip.
+"""
+
+import diffusers
+
+from pipelines.native_transformer import TransformerSpec
+
+
+PRX_SPEC = TransformerSpec(cls=diffusers.PRXTransformer2DModel)