diffengine.models.editors.kandinsky.kandinskyv3

Module Contents

Classes

KandinskyV3

KandinskyV3.
class diffengine.models.editors.kandinsky.kandinskyv3.KandinskyV3(tokenizer, scheduler, text_encoder, vae, unet, model='kandinsky-community/kandinsky-3', loss=None, unet_lora_config=None, prior_loss_weight=1.0, tokenizer_max_length=128, prediction_type=None, data_preprocessor=None, noise_generator=None, timesteps_generator=None, input_perturbation_gamma=0.0, vae_batch_size=8, *, gradient_checkpointing=False, enable_xformers=False)[source]

Bases: mmengine.model.BaseModel

KandinskyV3.

Args:

tokenizer (dict): Config of tokenizer. scheduler (dict): Config of scheduler. text_encoder (dict): Config of text encoder. vae (dict): Config of vae. unet (dict): Config of unet. model (str): pretrained model name.

Defaults to “kandinsky-community/kandinsky-3”.

loss (dict): Config of loss. Defaults to

dict(type='L2Loss', loss_weight=1.0).

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.

prior_loss_weight (float): The weight of prior preservation loss.

It works when training dreambooth with class images.

tokenizer_max_length (int): The max length of tokenizer.

Defaults to 128.

prediction_type (str): The prediction_type that shall be used for

training. Choose between ‘epsilon’ or ‘v_prediction’ or leave None. If left to None the default prediction type of the scheduler will be used. Defaults to None.

data_preprocessor (dict, optional): The pre-process config of

SDDataPreprocessor.

noise_generator (dict, optional): The noise generator config.

Defaults to dict(type='WhiteNoise').

timesteps_generator (dict, optional): The timesteps generator config.

Defaults to dict(type='TimeSteps').

input_perturbation_gamma (float): The gamma of input perturbation.

The recommended value is 0.1 for Input Perturbation. Defaults to 0.0.

vae_batch_size (int): The batch size of vae. Defaults to 8. gradient_checkpointing (bool): Whether or not to use gradient

checkpointing to save memory at the expense of slower backward pass. Defaults to False.

enable_xformers (bool): Whether or not to enable memory efficient

attention. Defaults to False.

property device: torch.device[source]

Get device information.

Returns:

torch.device

Return type:

device.

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

set_xformers()[source]

Set xformers for model.

Return type:

None

infer(prompt, negative_prompt=None, height=None, width=None, num_inference_steps=50, output_type='pil', **kwargs)[source]

Inference function.

Args:
prompt (List[str]):

The prompt or prompts to guide the image generation.

negative_prompt (Optional[str]):

The prompt or prompts to guide the image generation. Defaults to None.

height (int, optional):

The height in pixels of the generated image. Defaults to None.

width (int, optional):

The width in pixels of the generated image. Defaults to None.

num_inference_steps (int): Number of inference steps.

Defaults to 50.

output_type (str): The output format of the generate image.

Choose between ‘pil’ and ‘latent’. Defaults to ‘pil’.

**kwargs: Other arguments.

Parameters:

  • prompt (list[str]) –

  • negative_prompt (str | None) –

  • height (int | None) –

  • width (int | None) –

  • num_inference_steps (int) –

  • output_type (str) –

Return type:

list[numpy.ndarray]

val_step(data)[source]

Val step.

Parameters:

data (Union[tuple, dict, list]) –

Return type:

list

test_step(data)[source]

Test step.

Parameters:

data (Union[tuple, dict, list]) –

Return type:

list

loss(model_pred, noise, latents, timesteps, weight=None)[source]

Calculate loss.

Parameters:

  • model_pred (torch.Tensor) –

  • noise (torch.Tensor) –

  • latents (torch.Tensor) –

  • timesteps (torch.Tensor) –

  • weight (torch.Tensor | None) –

Return type:

dict[str, torch.Tensor]

_preprocess_model_input(latents, noise, timesteps)[source]

Preprocess model input.

Parameters:

  • latents (torch.Tensor) –

  • noise (torch.Tensor) –

  • timesteps (torch.Tensor) –

Return type:

torch.Tensor

_forward_vae(img, num_batches)[source]

Forward vae.

Parameters:

  • img (torch.Tensor) –

  • num_batches (int) –

Return type:

torch.Tensor

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:

  • tokenizer (dict) –

  • scheduler (dict) –

  • text_encoder (dict) –

  • vae (dict) –

  • unet (dict) –

  • model (str) –

  • loss (dict | None) –

  • unet_lora_config (dict | None) –

  • prior_loss_weight (float) –

  • tokenizer_max_length (int) –

  • prediction_type (str | None) –

  • data_preprocessor (dict | torch.nn.Module | None) –

  • noise_generator (dict | None) –

  • timesteps_generator (dict | None) –

  • input_perturbation_gamma (float) –

  • vae_batch_size (int) –

  • gradient_checkpointing (bool) –

  • enable_xformers (bool) –