diffengine.models.editors.distill_sd.distill_sd_xl

Module Contents

Classes

DistillSDXL

Distill Stable Diffusion XL.
class diffengine.models.editors.distill_sd.distill_sd_xl.DistillSDXL(*args, model_type, unet_lora_config=None, text_encoder_lora_config=None, finetune_text_encoder=False, **kwargs)[source]

Bases: diffengine.models.editors.stable_diffusion_xl.StableDiffusionXL

Distill Stable Diffusion XL.

Args:

model_type (str): The type of model to use. Choice from sd_tiny,

sd_small.

unet_lora_config (dict, optional): The LoRA config dict for Unet.

example. dict(type=”LoRA”, r=4). type is chosen from LoRA, LoHa, LoKr. Other config are same as the config of PEFT. https://github.com/huggingface/peft Defaults to None.

text_encoder_lora_config (dict, optional): The LoRA config dict for

Text Encoder. example. dict(type=”LoRA”, r=4). type is chosen from LoRA, LoHa, LoKr. Other config are same as the config of PEFT. https://github.com/huggingface/peft Defaults to None.

finetune_text_encoder (bool, optional): Whether to fine-tune text

encoder. This should be False when training ControlNet. Defaults to False.

set_lora()[source]

Set LORA for model.

Return type:

None

prepare_model()[source]

Prepare model for training.

Disable gradient for some models.

Return type:

None

_prepare_student()[source]
Return type:

None

_cast_hook()[source]
Return type:

None

set_xformers()[source]

Set xformers for model.

Return type:

None

forward(inputs, data_samples=None, mode='loss')[source]

Forward function.

Args:

inputs (dict): The input dict. data_samples (Optional[list], optional): The data samples.

Defaults to None.

mode (str, optional): The mode. Defaults to “loss”.

Returns:

dict: The loss dict.

Parameters:

  • inputs (dict) –

  • data_samples (Optional[list]) –

  • mode (str) –

Return type:

dict

Parameters:

  • model_type (str) –

  • unet_lora_config (dict | None) –

  • text_encoder_lora_config (dict | None) –

  • finetune_text_encoder (bool) –