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Clean-up code a bit

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Sergey Borisov 2023-08-13 19:50:48 +03:00
parent 59ba9fc0f6
commit 7a8f14d595
2 changed files with 10 additions and 11 deletions
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19
invokeai/backend/stable_diffusion/diffusers_pipeline.py
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@ -34,6 +34,7 @@ from .diffusion import (
AttentionMapSaver, AttentionMapSaver,
InvokeAIDiffuserComponent, InvokeAIDiffuserComponent,
PostprocessingSettings, PostprocessingSettings,
BasicConditioningInfo,
) )
from ..util import normalize_device from ..util import normalize_device
@ -92,8 +93,7 @@ class AddsMaskGuidance:
mask: torch.FloatTensor mask: torch.FloatTensor
mask_latents: torch.FloatTensor mask_latents: torch.FloatTensor
scheduler: SchedulerMixin scheduler: SchedulerMixin
noise: Optional[torch.Tensor] = None noise: torch.Tensor
_debug: Optional[Callable] = None
def __call__(self, step_output: Union[BaseOutput, SchedulerOutput], t: torch.Tensor, conditioning) -> BaseOutput: def __call__(self, step_output: Union[BaseOutput, SchedulerOutput], t: torch.Tensor, conditioning) -> BaseOutput:
output_class = step_output.__class__ # We'll create a new one with masked data. output_class = step_output.__class__ # We'll create a new one with masked data.
@ -123,14 +123,13 @@ class AddsMaskGuidance:
# some schedulers expect t to be one-dimensional. # some schedulers expect t to be one-dimensional.
# TODO: file diffusers bug about inconsistency? # TODO: file diffusers bug about inconsistency?
t = einops.repeat(t, "-> batch", batch=batch_size) t = einops.repeat(t, "-> batch", batch=batch_size)
# Noise shouldn't be re-randomized between steps here. The multistep schedulers
if self.noise is not None: # get very confused about what is happening from step to step when we do that.
mask_latents = self.scheduler.add_noise(self.mask_latents, self.noise, t) mask_latents = self.scheduler.add_noise(self.mask_latents, self.noise, t)
# TODO: Do we need to also apply scheduler.scale_model_input? Or is add_noise appropriately scaled already?
# mask_latents = self.scheduler.scale_model_input(mask_latents, t)
mask_latents = einops.repeat(mask_latents, "b c h w -> (repeat b) c h w", repeat=batch_size) mask_latents = einops.repeat(mask_latents, "b c h w -> (repeat b) c h w", repeat=batch_size)
masked_input = torch.lerp(mask_latents.to(dtype=latents.dtype), latents, mask.to(dtype=latents.dtype)) masked_input = torch.lerp(mask_latents.to(dtype=latents.dtype), latents, mask.to(dtype=latents.dtype))
if self._debug:
self._debug(masked_input, f"t={t} lerped")
return masked_input return masked_input
@ -202,8 +201,8 @@ class ControlNetData:
@dataclass @dataclass
class ConditioningData: class ConditioningData:
unconditioned_embeddings: Any # TODO: type unconditioned_embeddings: BasicConditioningInfo
text_embeddings: Any # TODO: type text_embeddings: BasicConditioningInfo
guidance_scale: Union[float, List[float]] guidance_scale: Union[float, List[float]]
""" """
Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).

2
invokeai/backend/stable_diffusion/diffusion/__init__.py
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@ -3,4 +3,4 @@ Initialization file for invokeai.models.diffusion
""" """
from .cross_attention_control import InvokeAICrossAttentionMixin from .cross_attention_control import InvokeAICrossAttentionMixin
from .cross_attention_map_saving import AttentionMapSaver from .cross_attention_map_saving import AttentionMapSaver
from .shared_invokeai_diffusion import InvokeAIDiffuserComponent, PostprocessingSettings from .shared_invokeai_diffusion import InvokeAIDiffuserComponent, PostprocessingSettings, BasicConditioningInfo, SDXLConditioningInfo