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Extract shared transformer-loading helper #1618

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54 changes: 16 additions & 38 deletions modules/modelLoader/AnimaModelLoader.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,7 +18,6 @@
AutoencoderKLQwenImage,
CosmosTransformer3DModel,
FlowMatchEulerDiscreteScheduler,
GGUFQuantizationConfig,
)
from transformers import Qwen2Tokenizer, Qwen3Model, T5TokenizerFast

Expand Down Expand Up @@ -71,7 +70,7 @@ def __load_diffusers(
subfolder="scheduler",
)

text_encoder = self._load_transformers_sub_module(
text_encoder = self._load_text_encoder(
Qwen3Model,
weight_dtypes.text_encoder,
weight_dtypes.fallback_train_dtype,
Expand All @@ -86,43 +85,22 @@ def __load_diffusers(
torch_dtype=torch.bfloat16,
)

if vae_model_name: #TODO simplify
vae = self._load_diffusers_sub_module(
AutoencoderKLQwenImage,
weight_dtypes.vae,
weight_dtypes.train_dtype,
vae_model_name,
)
else:
vae = self._load_diffusers_sub_module(
AutoencoderKLQwenImage,
weight_dtypes.vae,
weight_dtypes.train_dtype,
base_model_name,
"vae",
)
vae = self._load_vae(
AutoencoderKLQwenImage,
weight_dtypes.vae,
weight_dtypes.train_dtype,
base_model_name,
vae_model_name,
)

if transformer_model_name:
transformer = CosmosTransformer3DModel.from_single_file(
transformer_model_name,
config=base_model_name,
subfolder="transformer",
#avoid loading the transformer in float32:
torch_dtype=torch.bfloat16 if weight_dtypes.transformer.torch_dtype() is None else weight_dtypes.transformer.torch_dtype(),
quantization_config=GGUFQuantizationConfig(compute_dtype=torch.bfloat16) if weight_dtypes.transformer.is_gguf() else None,
)
transformer = self._convert_diffusers_sub_module_to_dtype(
transformer, weight_dtypes.transformer, weight_dtypes.train_dtype, quantization,
)
else:
transformer = self._load_diffusers_sub_module(
CosmosTransformer3DModel,
weight_dtypes.transformer,
weight_dtypes.train_dtype,
base_model_name,
"transformer",
quantization,
)
transformer = self._load_transformer(
CosmosTransformer3DModel,
weight_dtypes,
base_model_name,
transformer_model_name,
quantization,
config=base_model_name,
)

model.model_type = model_type
model.tokenizer = tokenizer
Expand Down
55 changes: 16 additions & 39 deletions modules/modelLoader/ErnieModelLoader.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,13 +11,10 @@
from modules.util.ModelNames import ModelNames
from modules.util.ModelWeightDtypes import ModelWeightDtypes

import torch

from diffusers import (
AutoencoderKLFlux2,
ErnieImageTransformer2DModel,
FlowMatchEulerDiscreteScheduler,
GGUFQuantizationConfig,
)
from transformers import AutoTokenizer, Mistral3Model

Expand Down Expand Up @@ -56,33 +53,21 @@ def __load_diffusers(
vae_model_name: str,
quantization: QuantizationConfig,
):
if transformer_model_name:
transformer = ErnieImageTransformer2DModel.from_single_file(
transformer_model_name,
config=base_model_name,
subfolder="transformer",
torch_dtype=torch.bfloat16 if weight_dtypes.transformer.torch_dtype() is None else weight_dtypes.transformer.torch_dtype(),
quantization_config=GGUFQuantizationConfig(compute_dtype=torch.bfloat16) if weight_dtypes.transformer.is_gguf() else None,
)
transformer = self._convert_diffusers_sub_module_to_dtype(
transformer, weight_dtypes.transformer, weight_dtypes.train_dtype, quantization,
)
else:
transformer = self._load_diffusers_sub_module(
ErnieImageTransformer2DModel,
weight_dtypes.transformer,
weight_dtypes.train_dtype,
base_model_name,
"transformer",
quantization,
)
transformer = self._load_transformer(
ErnieImageTransformer2DModel,
weight_dtypes,
base_model_name,
transformer_model_name,
quantization,
config=base_model_name,
)

tokenizer = AutoTokenizer.from_pretrained(
base_model_name,
subfolder="tokenizer",
)

text_encoder = self._load_transformers_sub_module(
text_encoder = self._load_text_encoder(
Mistral3Model,
weight_dtypes.text_encoder,
weight_dtypes.fallback_train_dtype,
Expand All @@ -95,21 +80,13 @@ def __load_diffusers(
subfolder="scheduler",
)

if vae_model_name:
vae = self._load_diffusers_sub_module(
AutoencoderKLFlux2,
weight_dtypes.vae,
weight_dtypes.train_dtype,
vae_model_name,
)
else:
vae = self._load_diffusers_sub_module(
AutoencoderKLFlux2,
weight_dtypes.vae,
weight_dtypes.train_dtype,
base_model_name,
"vae",
)
vae = self._load_vae(
AutoencoderKLFlux2,
weight_dtypes.vae,
weight_dtypes.train_dtype,
base_model_name,
vae_model_name,
)

model.model_type = model_type
model.tokenizer = tokenizer
Expand Down
58 changes: 17 additions & 41 deletions modules/modelLoader/Flux2ModelLoader.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,13 +11,10 @@
from modules.util.ModelNames import ModelNames
from modules.util.ModelWeightDtypes import ModelWeightDtypes

import torch

from diffusers import (
AutoencoderKLFlux2,
FlowMatchEulerDiscreteScheduler,
Flux2Transformer2DModel,
GGUFQuantizationConfig,
)
from transformers import (
Mistral3ForConditionalGeneration,
Expand Down Expand Up @@ -60,35 +57,22 @@ def __load_diffusers(
vae_model_name: str,
quantization: QuantizationConfig,
):
if transformer_model_name:
transformer = Flux2Transformer2DModel.from_single_file(
transformer_model_name,
config=base_model_name,
subfolder="transformer",
#avoid loading the transformer in float32:
torch_dtype=torch.bfloat16 if weight_dtypes.transformer.torch_dtype() is None else weight_dtypes.transformer.torch_dtype(),
quantization_config=GGUFQuantizationConfig(compute_dtype=torch.bfloat16) if weight_dtypes.transformer.is_gguf() else None,
)
transformer = self._convert_diffusers_sub_module_to_dtype(
transformer, weight_dtypes.transformer, weight_dtypes.train_dtype, quantization,
)
else:
transformer = self._load_diffusers_sub_module(
Flux2Transformer2DModel,
weight_dtypes.transformer,
weight_dtypes.train_dtype,
base_model_name,
"transformer",
quantization,
)
transformer = self._load_transformer(
Flux2Transformer2DModel,
weight_dtypes,
base_model_name,
transformer_model_name,
quantization,
config=base_model_name,
)

if transformer.config.num_attention_heads == 48: #Flux2.Dev
tokenizer = PixtralProcessor.from_pretrained(
base_model_name,
subfolder="tokenizer",
).tokenizer

text_encoder = self._load_transformers_sub_module(
text_encoder = self._load_text_encoder(
Mistral3ForConditionalGeneration,
weight_dtypes.text_encoder,
weight_dtypes.fallback_train_dtype,
Expand All @@ -100,7 +84,7 @@ def __load_diffusers(
base_model_name,
subfolder="tokenizer",
)
text_encoder = self._load_transformers_sub_module(
text_encoder = self._load_text_encoder(
Qwen3ForCausalLM,
weight_dtypes.text_encoder,
weight_dtypes.fallback_train_dtype,
Expand All @@ -113,21 +97,13 @@ def __load_diffusers(
subfolder="scheduler",
)

if vae_model_name:
vae = self._load_diffusers_sub_module(
AutoencoderKLFlux2,
weight_dtypes.vae,
weight_dtypes.train_dtype,
vae_model_name,
)
else:
vae = self._load_diffusers_sub_module(
AutoencoderKLFlux2,
weight_dtypes.vae,
weight_dtypes.train_dtype,
base_model_name,
"vae",
)
vae = self._load_vae(
AutoencoderKLFlux2,
weight_dtypes.vae,
weight_dtypes.train_dtype,
base_model_name,
vae_model_name,
)

model.model_type = model_type
model.tokenizer = tokenizer
Expand Down
14 changes: 8 additions & 6 deletions modules/modelLoader/IdeogramModelLoader.py
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Original file line number Diff line number Diff line change
Expand Up @@ -31,11 +31,12 @@ def __load_internal(
model_type: ModelType,
weight_dtypes: ModelWeightDtypes,
base_model_name: str,
vae_model_name: str,
include_unconditional_transformer: bool,
quantization: QuantizationConfig,
):
if os.path.isfile(os.path.join(base_model_name, "meta.json")):
self.__load_diffusers(model, model_type, weight_dtypes, base_model_name, include_unconditional_transformer, quantization)
self.__load_diffusers(model, model_type, weight_dtypes, base_model_name, vae_model_name, include_unconditional_transformer, quantization)
else:
raise Exception("not an internal model")

Expand All @@ -45,6 +46,7 @@ def __load_diffusers(
model_type: ModelType,
weight_dtypes: ModelWeightDtypes,
base_model_name: str,
vae_model_name: str,
include_unconditional_transformer: bool,
quantization: QuantizationConfig,
):
Expand All @@ -71,7 +73,7 @@ def __load_diffusers(
else:
unconditional_transformer = None

text_encoder = self._load_transformers_sub_module(
text_encoder = self._load_text_encoder(
Qwen3VLModel,
weight_dtypes.text_encoder,
weight_dtypes.fallback_train_dtype,
Expand All @@ -89,12 +91,12 @@ def __load_diffusers(
subfolder="scheduler",
)

vae = self._load_diffusers_sub_module(
vae = self._load_vae(
AutoencoderKLFlux2,
weight_dtypes.vae,
weight_dtypes.train_dtype,
base_model_name,
"vae",
vae_model_name,
)

model.model_type = model_type
Expand Down Expand Up @@ -129,7 +131,7 @@ def load(

try:
self.__load_internal(
model, model_type, weight_dtypes, model_names.base_model,
model, model_type, weight_dtypes, model_names.base_model, model_names.vae_model,
model_names.include_unconditional_transformer, quantization,
)
return
Expand All @@ -138,7 +140,7 @@ def load(

try:
self.__load_diffusers(
model, model_type, weight_dtypes, model_names.base_model,
model, model_type, weight_dtypes, model_names.base_model, model_names.vae_model,
model_names.include_unconditional_transformer, quantization,
)
return
Expand Down
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