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diffengine.engine.hooks — diffengine 1.0.0 documentation

class diffengine.engine.hooks.CompileHook(backend='inductor', mode=None, *, compile_main=False)[source]¶

Bases: mmengine.hooks.Hook

Compile Hook.

Args:¶

backend (str): The backend to use for compilation.

Defaults to “inductor”.

mode (str): The mode to use for compilation. Defaults to None. compile_main (bool): Whether to compile the main network like unet or

transformer. Defaults to False.

priority = VERY_LOW¶

before_train(runner)[source]¶

Compile the model.

Args:¶

runner (Runner): The runner of the training process.

Parameters:

runner (mmengine.runner.Runner) –

Return type:

None

Parameters:

• backend (str) –

• mode (str | None) –

• compile_main (bool) –

class diffengine.engine.hooks.ControlNetSaveHook[source]¶

Bases: mmengine.hooks.Hook

ControlNet Save Hook.

Save ControlNet weights with diffusers format and pick up ControlNet weights from checkpoint.

priority = VERY_LOW¶

before_save_checkpoint(runner, checkpoint)[source]¶

Before save checkpoint hook.

Args:¶

runner (Runner): The runner of the training, validation or testing

process.

checkpoint (dict): Model’s checkpoint.

Parameters:

• runner (mmengine.runner.Runner) –

• checkpoint (dict) –

Return type:

None

class diffengine.engine.hooks.FastNormHook(*, fuse_text_encoder_ln=False, fuse_main_ln=True, fuse_gn=False)[source]¶

Bases: mmengine.hooks.Hook

Fast Normalization Hook.

Replace the normalization layer with a faster one.

Args:¶

fuse_text_encoder_ln (bool): Whether to fuse the text encoder layer

normalization. Defaults to False.

fuse_main_ln (bool): Whether to replace the layer normalization

in main module like unet or transformer. Defaults to True.

fuse_gn (bool)Whether to replace the group normalization.

Defaults to False.

priority = VERY_LOW¶

_replace_ln(module, name, device)[source]¶

Replace the layer normalization with a fused one.

Parameters:

• module (torch.nn.Module) –

• name (str) –

• device (str) –

Return type:

None

_replace_gn(module, name, device)[source]¶

Replace the layer normalization with a fused one.

Parameters:

• module (torch.nn.Module) –

• name (str) –

• device (str) –

Return type:

None

_replace_gn_forward(module, name)[source]¶

Replace the group normalization forward with a faster one.

Parameters:

• module (torch.nn.Module) –

• name (str) –

Return type:

None

before_train(runner)[source]¶

Replace the normalization layer with a faster one.

Args:¶

runner (Runner): The runner of the training process.

Parameters:

runner (mmengine.runner.Runner) –

Return type:

None

Parameters:

• fuse_text_encoder_ln (bool) –

• fuse_main_ln (bool) –

• fuse_gn (bool) –

class diffengine.engine.hooks.IPAdapterSaveHook[source]¶

Bases: mmengine.hooks.Hook

IP Adapter Save Hook.

Save IP-Adapter weights with diffusers format and pick up weights from checkpoint.

priority = VERY_LOW¶

before_save_checkpoint(runner, checkpoint)[source]¶

Before save checkpoint hook.

Args:¶

runner (Runner): The runner of the training, validation or testing

process.

checkpoint (dict): Model’s checkpoint.

Parameters:

• runner (mmengine.runner.Runner) –

• checkpoint (dict) –

Return type:

None

class diffengine.engine.hooks.LCMEMAUpdateHook[source]¶

Bases: mmengine.hooks.hook.Hook

LCM EMA Update Hook.

before_run(runner)[source]¶

Create an ema copy of the model.

Args:¶

runner (Runner): The runner of the training process.

Parameters:

runner (mmengine.runner.Runner) –

Return type:

None

after_train_iter(runner, batch_idx, data_batch=None, outputs=None)[source]¶

Update ema parameter.

Args:¶

runner (Runner): The runner of the training process. batch_idx (int): The index of the current batch in the train loop. data_batch (Sequence[dict], optional): Data from dataloader.

Defaults to None.

outputs (dict, optional): Outputs from model. Defaults to None.

Parameters:

• runner (mmengine.runner.Runner) –

• batch_idx (int) –

• data_batch (mmengine.hooks.hook.DATA_BATCH) –

• outputs (dict | None) –

Return type:

None

class diffengine.engine.hooks.PeftSaveHook[source]¶

Bases: mmengine.hooks.Hook

Peft Save Hook.

Save LoRA weights with diffusers format and pick up LoRA weights from checkpoint.

priority = VERY_LOW¶

before_save_checkpoint(runner, checkpoint)[source]¶

Before save checkpoint hook.

Args:¶

runner (Runner): The runner of the training, validation or testing

process.

checkpoint (dict): Model’s checkpoint.

Parameters:

• runner (mmengine.runner.Runner) –

• checkpoint (dict) –

Return type:

None

class diffengine.engine.hooks.PriorSaveHook[source]¶

Bases: mmengine.hooks.Hook

Prior Save Hook.

Save Prior weights with diffusers format and pick up Prior weights from checkpoint.

priority = VERY_LOW¶

before_save_checkpoint(runner, checkpoint)[source]¶

Before save checkpoint hook.

Args:¶

runner (Runner): The runner of the training, validation or testing

process.

checkpoint (dict): Model’s checkpoint.

Parameters:

• runner (mmengine.runner.Runner) –

• checkpoint (dict) –

Return type:

None

class diffengine.engine.hooks.SDCheckpointHook[source]¶

Bases: mmengine.hooks.Hook

Delete ‘vae’ from checkpoint for efficient save.

priority = VERY_LOW¶

before_save_checkpoint(runner, checkpoint)[source]¶

Before save checkpoint hook.

Args:¶

runner (Runner): The runner of the training, validation or testing

process.

checkpoint (dict): Model’s checkpoint.

Parameters:

• runner (mmengine.runner.Runner) –

• checkpoint (dict) –

Return type:

None

class diffengine.engine.hooks.T2IAdapterSaveHook[source]¶

Bases: mmengine.hooks.Hook

T2I Adapter Save Hook.

Save T2I-Adapter weights with diffusers format and pick up weights from checkpoint.

priority = VERY_LOW¶

before_save_checkpoint(runner, checkpoint)[source]¶

Before save checkpoint hook.

Args:¶

runner (Runner): The runner of the training, validation or testing

process.

checkpoint (dict): Model’s checkpoint.

Parameters:

• runner (mmengine.runner.Runner) –

• checkpoint (dict) –

Return type:

None

class diffengine.engine.hooks.TransformerCheckpointHook[source]¶

Bases: mmengine.hooks.Hook

Delete ‘vae’ from checkpoint for efficient save.

priority = VERY_LOW¶

before_save_checkpoint(runner, checkpoint)[source]¶

Before save checkpoint hook.

Args:¶

runner (Runner): The runner of the training, validation or testing

process.

checkpoint (dict): Model’s checkpoint.

Parameters:

• runner (mmengine.runner.Runner) –

• checkpoint (dict) –

Return type:

None

class diffengine.engine.hooks.UnetEMAHook(ema_type='ExponentialMovingAverage', strict_load=False, begin_iter=0, begin_epoch=0, **kwargs)[source]¶

Bases: mmengine.hooks.ema_hook.EMAHook

Unet EMA Hook.

Parameters:

• ema_type (str) –

• strict_load (bool) –

• begin_iter (int) –

• begin_epoch (int) –

before_run(runner)[source]¶

Create an ema copy of the model.

Args:¶

runner (Runner): The runner of the training process.

Parameters:

runner (mmengine.runner.Runner) –

Return type:

None

_swap_ema_state_dict(checkpoint)[source]¶

Swap the state dict values of model with ema_model.

Parameters:

checkpoint (dict) –

Return type:

None

after_load_checkpoint(runner, checkpoint)[source]¶

Resume ema parameters from checkpoint.

Args:¶

runner (Runner): The runner of the testing process. checkpoint (dict): Model’s checkpoint.

Parameters:

• runner (mmengine.runner.Runner) –

• checkpoint (dict) –

Return type:

None

class diffengine.engine.hooks.VisualizationHook(prompt, interval=1, height=None, width=None, *, by_epoch=True, **kwargs)[source]¶

Bases: mmengine.hooks.Hook

Basic hook that invoke visualizers after train epoch.

Args:¶

prompt (List[str]):

The prompt or prompts to guide the image generation.

interval (int): Visualization interval (every k iterations).

Defaults to 1.

by_epoch (bool): Whether to visualize by epoch. Defaults to True. height (int, optional, defaults to

self.unet.config.sample_size * self.vae_scale_factor): The height in pixels of the generated image.

width (int, optional, defaults to

self.unet.config.sample_size * self.vae_scale_factor): The width in pixels of the generated image.

priority = NORMAL¶

before_train(runner)[source]¶

Before train hook.

Parameters:

runner (mmengine.runner.Runner) –

Return type:

None

after_train_iter(runner, batch_idx, data_batch=None, outputs=None)[source]¶

After train iter hook.

Args:¶

runner (Runner): The runner of the training process. batch_idx (int): The index of the current batch. data_batch (DATA_BATCH, optional): The current data batch. outputs (dict, optional): The outputs of the current batch.

Parameters:

• runner (mmengine.runner.Runner) –

• batch_idx (int) –

• data_batch (mmengine.hooks.hook.DATA_BATCH) –

• outputs (Optional[dict]) –

Return type:

None

after_train_epoch(runner)[source]¶

After train epoch hook.

Args:¶

runner (Runner): The runner of the training process.

Parameters:

runner (mmengine.runner.Runner) –

Return type:

None

Parameters:

• prompt (list[str]) –

• interval (int) –

• height (int | None) –

• width (int | None) –

• by_epoch (bool) –

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