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Mar 17, 2025
Merged

Cleanup model load methods #333

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55 changes: 17 additions & 38 deletions finetrainers/models/cogvideox/base_specification.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -17,7 +17,7 @@
from ...logging import get_logger
from ...processors import ProcessorMixin, T5Processor
from ...typing import ArtifactType, SchedulerType
from ...utils import get_non_null_items
from ...utils import _enable_vae_memory_optimizations, get_non_null_items
from ..modeling_utils import ModelSpecification
from ..utils import DiagonalGaussianDistribution
from .utils import prepare_rotary_positional_embeddings
Expand Down Expand Up @@ -117,74 +117,58 @@ def _resolution_dim_keys(self):
return {"latents": (1, 3, 4)}

def load_condition_models(self) -> Dict[str, torch.nn.Module]:
common_kwargs = {"revision": self.revision, "cache_dir": self.cache_dir}

if self.tokenizer_id is not None:
tokenizer = AutoTokenizer.from_pretrained(
self.tokenizer_id, revision=self.revision, cache_dir=self.cache_dir
)
tokenizer = AutoTokenizer.from_pretrained(self.tokenizer_id, **common_kwargs)
else:
tokenizer = T5Tokenizer.from_pretrained(
self.pretrained_model_name_or_path,
subfolder="tokenizer",
revision=self.revision,
cache_dir=self.cache_dir,
self.pretrained_model_name_or_path, subfolder="tokenizer", **common_kwargs
)

if self.text_encoder_id is not None:
text_encoder = AutoModel.from_pretrained(
self.text_encoder_id,
torch_dtype=self.text_encoder_dtype,
revision=self.revision,
cache_dir=self.cache_dir,
self.text_encoder_id, torch_dtype=self.text_encoder_dtype, **common_kwargs
)
else:
text_encoder = T5EncoderModel.from_pretrained(
self.pretrained_model_name_or_path,
subfolder="text_encoder",
torch_dtype=self.text_encoder_dtype,
revision=self.revision,
cache_dir=self.cache_dir,
**common_kwargs,
)

return {"tokenizer": tokenizer, "text_encoder": text_encoder}

def load_latent_models(self) -> Dict[str, torch.nn.Module]:
common_kwargs = {"revision": self.revision, "cache_dir": self.cache_dir}

if self.vae_id is not None:
vae = AutoencoderKLCogVideoX.from_pretrained(
self.vae_id,
torch_dtype=self.vae_dtype,
revision=self.revision,
cache_dir=self.cache_dir,
)
vae = AutoencoderKLCogVideoX.from_pretrained(self.vae_id, torch_dtype=self.vae_dtype, **common_kwargs)
else:
vae = AutoencoderKLCogVideoX.from_pretrained(
self.pretrained_model_name_or_path,
subfolder="vae",
torch_dtype=self.vae_dtype,
revision=self.revision,
cache_dir=self.cache_dir,
self.pretrained_model_name_or_path, subfolder="vae", torch_dtype=self.vae_dtype, **common_kwargs
)

return {"vae": vae}

def load_diffusion_models(self) -> Dict[str, torch.nn.Module]:
common_kwargs = {"revision": self.revision, "cache_dir": self.cache_dir}

if self.transformer_id is not None:
transformer = CogVideoXTransformer3DModel.from_pretrained(
self.transformer_id,
torch_dtype=self.transformer_dtype,
revision=self.revision,
cache_dir=self.cache_dir,
self.transformer_id, torch_dtype=self.transformer_dtype, **common_kwargs
)
else:
transformer = CogVideoXTransformer3DModel.from_pretrained(
self.pretrained_model_name_or_path,
subfolder="transformer",
torch_dtype=self.transformer_dtype,
revision=self.revision,
cache_dir=self.cache_dir,
**common_kwargs,
)

scheduler = CogVideoXDDIMScheduler.from_pretrained(
self.pretrained_model_name_or_path, subfolder="scheduler", revision=self.revision, cache_dir=self.cache_dir
self.pretrained_model_name_or_path, subfolder="scheduler", **common_kwargs
)

return {"transformer": transformer, "scheduler": scheduler}
Expand Down Expand Up @@ -217,16 +201,11 @@ def load_pipeline(
pipe.text_encoder.to(self.text_encoder_dtype)
pipe.vae.to(self.vae_dtype)

_enable_vae_memory_optimizations(pipe.vae, enable_slicing, enable_tiling)
if not training:
pipe.transformer.to(self.transformer_dtype)

if enable_slicing:
pipe.vae.enable_slicing()
if enable_tiling:
pipe.vae.enable_tiling()
if enable_model_cpu_offload:
pipe.enable_model_cpu_offload()

return pipe

@torch.no_grad()
Expand Down
53 changes: 16 additions & 37 deletions finetrainers/models/cogview4/base_specification.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -17,7 +17,7 @@
from ...logging import get_logger
from ...processors import CogView4GLMProcessor, ProcessorMixin
from ...typing import ArtifactType, SchedulerType
from ...utils import get_non_null_items
from ...utils import _enable_vae_memory_optimizations, get_non_null_items
from ..modeling_utils import ModelSpecification


Expand Down Expand Up @@ -136,70 +136,54 @@ def _resolution_dim_keys(self):
return {"latents": (2, 3)}

def load_condition_models(self) -> Dict[str, torch.nn.Module]:
common_kwargs = {"revision": self.revision, "cache_dir": self.cache_dir}

if self.tokenizer_id is not None:
tokenizer = AutoTokenizer.from_pretrained(
self.tokenizer_id, revision=self.revision, cache_dir=self.cache_dir
)
tokenizer = AutoTokenizer.from_pretrained(self.tokenizer_id, **common_kwargs)
else:
tokenizer = AutoTokenizer.from_pretrained(
self.pretrained_model_name_or_path,
subfolder="tokenizer",
revision=self.revision,
cache_dir=self.cache_dir,
self.pretrained_model_name_or_path, subfolder="tokenizer", **common_kwargs
)

if self.text_encoder_id is not None:
text_encoder = GlmModel.from_pretrained(
self.text_encoder_id,
torch_dtype=self.text_encoder_dtype,
revision=self.revision,
cache_dir=self.cache_dir,
self.text_encoder_id, torch_dtype=self.text_encoder_dtype, **common_kwargs
)
else:
text_encoder = GlmModel.from_pretrained(
self.pretrained_model_name_or_path,
subfolder="text_encoder",
torch_dtype=self.text_encoder_dtype,
revision=self.revision,
cache_dir=self.cache_dir,
**common_kwargs,
)

return {"tokenizer": tokenizer, "text_encoder": text_encoder}

def load_latent_models(self) -> Dict[str, torch.nn.Module]:
common_kwargs = {"revision": self.revision, "cache_dir": self.cache_dir}

if self.vae_id is not None:
vae = AutoencoderKL.from_pretrained(
self.vae_id,
torch_dtype=self.vae_dtype,
revision=self.revision,
cache_dir=self.cache_dir,
)
vae = AutoencoderKL.from_pretrained(self.vae_id, torch_dtype=self.vae_dtype, **common_kwargs)
else:
vae = AutoencoderKL.from_pretrained(
self.pretrained_model_name_or_path,
subfolder="vae",
torch_dtype=self.vae_dtype,
revision=self.revision,
cache_dir=self.cache_dir,
self.pretrained_model_name_or_path, subfolder="vae", torch_dtype=self.vae_dtype, **common_kwargs
)

return {"vae": vae}

def load_diffusion_models(self) -> Dict[str, torch.nn.Module]:
common_kwargs = {"revision": self.revision, "cache_dir": self.cache_dir}

if self.transformer_id is not None:
transformer = CogView4Transformer2DModel.from_pretrained(
self.transformer_id,
torch_dtype=self.transformer_dtype,
revision=self.revision,
cache_dir=self.cache_dir,
self.transformer_id, torch_dtype=self.transformer_dtype, **common_kwargs
)
else:
transformer = CogView4Transformer2DModel.from_pretrained(
self.pretrained_model_name_or_path,
subfolder="transformer",
torch_dtype=self.transformer_dtype,
revision=self.revision,
cache_dir=self.cache_dir,
**common_kwargs,
)

scheduler = FlowMatchEulerDiscreteScheduler()
Expand Down Expand Up @@ -235,16 +219,11 @@ def load_pipeline(
pipe.text_encoder.to(self.text_encoder_dtype)
pipe.vae.to(self.vae_dtype)

_enable_vae_memory_optimizations(pipe.vae, enable_slicing, enable_tiling)
if not training:
pipe.transformer.to(self.transformer_dtype)

if enable_slicing:
pipe.vae.enable_slicing()
if enable_tiling:
pipe.vae.enable_tiling()
if enable_model_cpu_offload:
pipe.enable_model_cpu_offload()

return pipe

@torch.no_grad()
Expand Down
70 changes: 20 additions & 50 deletions finetrainers/models/hunyuan_video/base_specification.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -17,7 +17,7 @@
from ...logging import get_logger
from ...processors import CLIPPooledProcessor, LlamaProcessor, ProcessorMixin
from ...typing import ArtifactType, SchedulerType
from ...utils import get_non_null_items
from ...utils import _enable_vae_memory_optimizations, get_non_null_items
from ..modeling_utils import ModelSpecification


Expand Down Expand Up @@ -120,60 +120,44 @@ def _resolution_dim_keys(self):
return {"latents": (2, 3, 4)}

def load_condition_models(self) -> Dict[str, torch.nn.Module]:
common_kwargs = {"revision": self.revision, "cache_dir": self.cache_dir}

if self.tokenizer_id is not None:
tokenizer = AutoTokenizer.from_pretrained(
self.tokenizer_id, revision=self.revision, cache_dir=self.cache_dir
)
tokenizer = AutoTokenizer.from_pretrained(self.tokenizer_id, **common_kwargs)
else:
tokenizer = AutoTokenizer.from_pretrained(
self.pretrained_model_name_or_path,
subfolder="tokenizer",
revision=self.revision,
cache_dir=self.cache_dir,
self.pretrained_model_name_or_path, subfolder="tokenizer", **common_kwargs
)

if self.tokenizer_2_id is not None:
tokenizer_2 = CLIPTokenizer.from_pretrained(
self.tokenizer_2_id, revision=self.revision, cache_dir=self.cache_dir
)
tokenizer_2 = AutoTokenizer.from_pretrained(self.tokenizer_2_id, **common_kwargs)
else:
tokenizer_2 = CLIPTokenizer.from_pretrained(
self.pretrained_model_name_or_path,
subfolder="tokenizer_2",
revision=self.revision,
cache_dir=self.cache_dir,
self.pretrained_model_name_or_path, subfolder="tokenizer_2" ** common_kwargs
)

if self.text_encoder_id is not None:
text_encoder = LlamaModel.from_pretrained(
self.text_encoder_id,
torch_dtype=self.text_encoder_dtype,
revision=self.revision,
cache_dir=self.cache_dir,
self.text_encoder_id, torch_dtype=self.text_encoder_dtype, **common_kwargs
)
else:
text_encoder = LlamaModel.from_pretrained(
self.pretrained_model_name_or_path,
subfolder="text_encoder",
torch_dtype=self.text_encoder_dtype,
revision=self.revision,
cache_dir=self.cache_dir,
**common_kwargs,
)

if self.text_encoder_2_id is not None:
text_encoder_2 = CLIPTextModel.from_pretrained(
self.text_encoder_2_id,
torch_dtype=self.text_encoder_2_dtype,
revision=self.revision,
cache_dir=self.cache_dir,
self.text_encoder_2_id, torch_dtype=self.text_encoder_2_dtype, **common_kwargs
)
else:
text_encoder_2 = CLIPTextModel.from_pretrained(
self.pretrained_model_name_or_path,
subfolder="text_encoder_2",
torch_dtype=self.text_encoder_2_dtype,
revision=self.revision,
cache_dir=self.cache_dir,
**common_kwargs,
)

return {
Expand All @@ -184,39 +168,30 @@ def load_condition_models(self) -> Dict[str, torch.nn.Module]:
}

def load_latent_models(self) -> Dict[str, torch.nn.Module]:
common_kwargs = {"revision": self.revision, "cache_dir": self.cache_dir}

if self.vae_id is not None:
vae = AutoencoderKLHunyuanVideo.from_pretrained(
self.vae_id,
torch_dtype=self.vae_dtype,
revision=self.revision,
cache_dir=self.cache_dir,
)
vae = AutoencoderKLHunyuanVideo.from_pretrained(self.vae_id, torch_dtype=self.vae_dtype, **common_kwargs)
else:
vae = AutoencoderKLHunyuanVideo.from_pretrained(
self.pretrained_model_name_or_path,
subfolder="vae",
torch_dtype=self.vae_dtype,
revision=self.revision,
cache_dir=self.cache_dir,
self.pretrained_model_name_or_path, subfolder="vae", torch_dtype=self.vae_dtype, **common_kwargs
)

return {"vae": vae}

def load_diffusion_models(self) -> Dict[str, torch.nn.Module]:
common_kwargs = {"revision": self.revision, "cache_dir": self.cache_dir}

if self.transformer_id is not None:
transformer = HunyuanVideoTransformer3DModel.from_pretrained(
self.transformer_id,
torch_dtype=self.transformer_dtype,
revision=self.revision,
cache_dir=self.cache_dir,
self.transformer_id, torch_dtype=self.transformer_dtype, **common_kwargs
)
else:
transformer = HunyuanVideoTransformer3DModel.from_pretrained(
self.pretrained_model_name_or_path,
subfolder="transformer",
torch_dtype=self.transformer_dtype,
revision=self.revision,
cache_dir=self.cache_dir,
**common_kwargs,
)

scheduler = FlowMatchEulerDiscreteScheduler()
Expand Down Expand Up @@ -256,16 +231,11 @@ def load_pipeline(
pipe.text_encoder_2.to(self.text_encoder_2_dtype)
pipe.vae.to(self.vae_dtype)

_enable_vae_memory_optimizations(pipe.vae, enable_slicing, enable_tiling)
if not training:
pipe.transformer.to(self.transformer_dtype)

if enable_slicing:
pipe.vae.enable_slicing()
if enable_tiling:
pipe.vae.enable_tiling()
if enable_model_cpu_offload:
pipe.enable_model_cpu_offload()

return pipe

@torch.no_grad()
Expand Down
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