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[Enhancement] Support noise offset in stable diffusion training #1880

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merged 3 commits into from
May 29, 2023
Merged

[Enhancement] Support noise offset in stable diffusion training #1880

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06f116d
support noise offset in stable diffusion training
LeoXing1996 May 29, 2023
34b5411
update arg list for dreambooth
LeoXing1996 May 29, 2023
bd370ca
update arg list for control net
LeoXing1996 May 29, 2023
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9 changes: 7 additions & 2 deletions mmagic/models/editors/controlnet/controlnet.py
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Original file line number Diff line number Diff line change
Expand Up @@ -46,6 +46,9 @@ class ControlStableDiffusion(StableDiffusion):
dtype (str, optional): The dtype for the model. Defaults to 'fp16'.
enable_xformers (bool, optional): Whether to use xformers.
Defaults to True.
noise_offset_weight (bool, optional): The weight of noise offset
introduced in https://www.crosslabs.org/blog/diffusion-with-offset-noise # noqa
Defaults to 0.
data_preprocessor (dict, optional): The pre-process config of
:class:`BaseDataPreprocessor`. Defaults to
dict(type='DataPreprocessor').
Expand All @@ -63,12 +66,14 @@ def __init__(self,
test_scheduler: Optional[ModelType] = None,
dtype: str = 'fp32',
enable_xformers: bool = True,
noise_offset_weight: float = 0,
tomesd_cfg: Optional[dict] = None,
data_preprocessor=dict(type='DataPreprocessor'),
init_cfg: Optional[dict] = None):
super().__init__(vae, text_encoder, tokenizer, unet, scheduler,
test_scheduler, dtype, enable_xformers, tomesd_cfg,
data_preprocessor, init_cfg)
test_scheduler, dtype, enable_xformers,
noise_offset_weight, tomesd_cfg, data_preprocessor,
init_cfg)

default_args = dict()
if dtype is not None:
Expand Down
9 changes: 7 additions & 2 deletions mmagic/models/editors/dreambooth/dreambooth.py
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Original file line number Diff line number Diff line change
Expand Up @@ -51,6 +51,9 @@ class DreamBooth(StableDiffusion):
dtype (str, optional): The dtype for the model. Defaults to 'fp16'.
enable_xformers (bool, optional): Whether to use xformers.
Defaults to True.
noise_offset_weight (bool, optional): The weight of noise offset
introduced in https://www.crosslabs.org/blog/diffusion-with-offset-noise # noqa
Defaults to 0.
data_preprocessor (dict, optional): The pre-process config of
:class:`BaseDataPreprocessor`. Defaults to
dict(type='DataPreprocessor').
Expand All @@ -73,14 +76,16 @@ def __init__(self,
finetune_text_encoder: bool = False,
dtype: str = 'fp16',
enable_xformers: bool = True,
noise_offset_weight: float = 0,
tomesd_cfg: Optional[dict] = None,
data_preprocessor: Optional[ModelType] = dict(
type='DataPreprocessor'),
init_cfg: Optional[dict] = None):

super().__init__(vae, text_encoder, tokenizer, unet, scheduler,
test_scheduler, dtype, enable_xformers, tomesd_cfg,
data_preprocessor, init_cfg)
test_scheduler, dtype, enable_xformers,
noise_offset_weight, tomesd_cfg, data_preprocessor,
init_cfg)
self.num_class_images = num_class_images
self.class_prior_prompt = class_prior_prompt
self.prior_loss_weight = prior_loss_weight
Expand Down
16 changes: 16 additions & 0 deletions mmagic/models/editors/stable_diffusion/stable_diffusion.py
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Original file line number Diff line number Diff line change
Expand Up @@ -47,6 +47,9 @@ class StableDiffusion(BaseModel):
when dtype is defined for submodels. Defaults to None.
enable_xformers (bool, optional): Whether to use xformers.
Defaults to True.
noise_offset_weight (bool, optional): The weight of noise offset
introduced in https://www.crosslabs.org/blog/diffusion-with-offset-noise
Defaults to 0.
data_preprocessor (dict, optional): The pre-process config of
:class:`BaseDataPreprocessor`.
init_cfg (dict, optional): The weight initialized config for
Expand All @@ -62,6 +65,7 @@ def __init__(self,
test_scheduler: Optional[ModelType] = None,
dtype: Optional[str] = None,
enable_xformers: bool = True,
noise_offset_weight: float = 0,
tomesd_cfg: Optional[dict] = None,
data_preprocessor: Optional[ModelType] = dict(
type='DataPreprocessor'),
Expand Down Expand Up @@ -102,6 +106,9 @@ def __init__(self,
self.unet_sample_size = self.unet.sample_size
self.vae_scale_factor = 2**(len(self.vae.block_out_channels) - 1)

self.enable_noise_offset = noise_offset_weight > 0
self.noise_offset_weight = noise_offset_weight

self.enable_xformers = enable_xformers
self.set_xformers()

Expand Down Expand Up @@ -612,6 +619,15 @@ def train_step(self, data, optim_wrapper_dict):
latents = latents * vae.config.scaling_factor

noise = torch.randn_like(latents)

if self.enable_noise_offset:
noise = noise + self.noise_offset_weight * torch.randn(
latents.shape[0],
latents.shape[1],
1,
1,
device=noise.device)

timesteps = torch.randint(
0,
self.scheduler.num_train_timesteps, (num_batches, ),
Expand Down