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OFT

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OFT

Orthogonal Finetuning (OFT) is a method developed for adapting text-to-image diffusion models. It works by reparameterizing the pretrained weight matrices with it’s orthogonal matrix to preserve information in the pretrained model. To reduce the number of parameters, OFT introduces a block-diagonal structure in the orthogonal matrix.

The abstract from the paper is:

Large text-to-image diffusion models have impressive capabilities in generating photorealistic images from text prompts. How to effectively guide or control these powerful models to perform different downstream tasks becomes an important open problem. To tackle this challenge, we introduce a principled finetuning method — Orthogonal Finetuning (OFT), for adapting text-to-image diffusion models to downstream tasks. Unlike existing methods, OFT can provably preserve hyperspherical energy which characterizes the pairwise neuron relationship on the unit hypersphere. We find that this property is crucial for preserving the semantic generation ability of text-to-image diffusion models. To improve finetuning stability, we further propose Constrained Orthogonal Finetuning (COFT) which imposes an additional radius constraint to the hypersphere. Specifically, we consider two important finetuning text-to-image tasks: subject-driven generation where the goal is to generate subject-specific images given a few images of a subject and a text prompt, and controllable generation where the goal is to enable the model to take in additional control signals. We empirically show that our OFT framework outperforms existing methods in generation quality and convergence speed.

OFTConfig

class peft.OFTConfig

< >

( peft_type: Union = None auto_mapping: Optional = None base_model_name_or_path: Optional = None revision: Optional = None task_type: Union = None inference_mode: bool = False r: int = 8 oft_block_size: int = 0 module_dropout: float = 0.0 target_modules: Optional[Union[list[str], str]] = None fan_in_fan_out: bool = False bias: Literal['none', 'all', 'oft_only'] = 'none' exclude_modules: Optional[Union[list[str], str]] = None init_weights: bool = True layers_to_transform: Optional[Union[list[int], int]] = None layers_pattern: Optional[Union[list[str], str]] = None modules_to_save: Optional[list[str]] = None coft: bool = False eps: float = 6e-05 block_share: bool = False rank_pattern: Optional[dict] = <factory> alpha_pattern: Optional[dict] = <factory> )

Parameters

  • r (int) — OFT rank, number of OFT blocks per injected layer.
  • oft_block_size (int) — OFT block size across different layers.
  • module_dropout (float) — The multiplicative dropout probability, by setting OFT blocks to identity during training, similar to the dropout layer in LoRA.
  • target_modules (Optional[Union[list[str], str]]) — The names of the modules to apply the adapter to. If this is specified, only the modules with the specified names will be replaced. When passing a string, a regex match will be performed. When passing a list of strings, either an exact match will be performed or it is checked if the name of the module ends with any of the passed strings. If this is specified as ‘all-linear’, then all linear modules are chosen, excluding the output layer. If this is not specified, modules will be chosen according to the model architecture. If the architecture is not known, an error will be raised — in this case, you should specify the target modules manually.
  • fan_in_fan_out (bool) — Set this to True if the layer to replace stores weight like (fan_in, fan_out).
  • bias (str) — Bias type for OFT. Can be ‘none’, ‘all’ or ‘oft_only’. If ‘all’ or ‘oft_only’, the corresponding biases will be updated during training. Be aware that this means that, even when disabling the adapters, the model will not produce the same output as the base model would have without adaptation.
  • exclude_modules (Optional[Union[List[str], str]]) — The names of the modules to not apply the adapter. When passing a string, a regex match will be performed. When passing a list of strings, either an exact match will be performed or it is checked if the name of the module ends with any of the passed strings.
  • init_weights (bool) — Whether to perform initialization of OFT weights.
  • layers_to_transform (Union[List[int], int]) — The layer indices to transform. If a list of ints is passed, it will apply the adapter to the layer indices that are specified in this list. If a single integer is passed, it will apply the transformations on the layer at this index.
  • layers_pattern (Optional[Union[List[str], str]]) — The layer pattern name, used only if layers_to_transform is different from None. This should target the nn.ModuleList of the model, which is often called 'layers' or 'h'.
  • rank_pattern (dict) — The mapping from layer names or regexp expression to ranks which are different from the default rank specified by r.
  • modules_to_save (List[str]) — List of modules apart from adapter layers to be set as trainable and saved in the final checkpoint.
  • coft (bool) — Whether to use the constrained variant of OFT or not, off by default.
  • eps (float) — The control strength of COFT. The freedom of rotation. Only has an effect if coft is set to True.
  • block_share (bool) — Whether to share the OFT parameters between blocks or not. This is False by default.

This is the configuration class to store the configuration of a OFTModel.

check_kwargs

< >

( **kwargs )

Parameters

  • kwargs (additional keyword arguments, optional) — Additional keyword arguments passed along to the child class initialization.

Check if the kwargs are valid for the configuration.

OFTModel

class peft.OFTModel

< >

( model config adapter_name low_cpu_mem_usage: bool = False ) → torch.nn.Module

Parameters

  • model (torch.nn.Module) — The model to which the adapter tuner layers will be attached.
  • config (OFTConfig) — The configuration of the OFT model.
  • adapter_name (str) — The name of the adapter, defaults to "default".
  • low_cpu_mem_usage (bool, optional, defaults to False) — Create empty adapter weights on meta device. Useful to speed up the loading process.

Returns

torch.nn.Module

The OFT model.

Creates Orthogonal Finetuning model from a pretrained model. The method is described in https://arxiv.org/abs/2306.07280

Example:

>>> from diffusers import StableDiffusionPipeline
>>> from peft import OFTModel, OFTConfig

>>> config_te = OFTConfig(
...     r=8,
...     target_modules=["k_proj", "q_proj", "v_proj", "out_proj", "fc1", "fc2"],
...     module_dropout=0.0,
...     init_weights=True,
... )
>>> config_unet = OFTConfig(
...     r=8,
...     target_modules=[
...         "proj_in",
...         "proj_out",
...         "to_k",
...         "to_q",
...         "to_v",
...         "to_out.0",
...         "ff.net.0.proj",
...         "ff.net.2",
...     ],
...     module_dropout=0.0,
...     init_weights=True,
... )

>>> model = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
>>> model.text_encoder = OFTModel(model.text_encoder, config_te, "default")
>>> model.unet = OFTModel(model.unet, config_unet, "default")

Attributes:

  • model (~torch.nn.Module) — The model to be adapted.
  • peft_config (OFTConfig): The configuration of the OFT model.

delete_adapter

< >

( adapter_name: str )

Parameters

  • adapter_name (str) — Name of the adapter to be deleted.

Deletes an existing adapter.

merge_and_unload

< >

( progressbar: bool = False safe_merge: bool = False adapter_names: Optional = None )

Parameters

  • progressbar (bool) — whether to show a progressbar indicating the unload and merge process
  • safe_merge (bool) — whether to activate the safe merging check to check if there is any potential Nan in the adapter weights
  • adapter_names (List[str], optional) — The list of adapter names that should be merged. If None, all active adapters will be merged. Defaults to None.

This method merges the OFT layers into the base model. This is needed if someone wants to use the base model as a standalone model.

unload

< >

( )

Gets back the base model by removing all the oft modules without merging. This gives back the original base model.

< > Update on GitHub

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