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Fix: progress image preview for inpainting

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dunkeroni 2024-03-19 20:32:54 -04:00 committed by psychedelicious
parent fe5fa7f8cc
commit 609c2c0abf
1 changed files with 14 additions and 2 deletions
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16
invokeai/backend/stable_diffusion/diffusers_pipeline.py
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@ -85,7 +85,7 @@ class AddsMaskGuidance:
noise: torch.Tensor noise: torch.Tensor
gradient_mask: bool gradient_mask: bool
def __call__(self, latents: torch.Tensor, t: torch.Tensor, conditioning) -> torch.Tensor: def __call__(self, latents: torch.Tensor, t: torch.Tensor) -> torch.Tensor:
return self.apply_mask(latents, t) return self.apply_mask(latents, t)
def apply_mask(self, latents: torch.Tensor, t) -> torch.Tensor: def apply_mask(self, latents: torch.Tensor, t) -> torch.Tensor:
@ -479,7 +479,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
# one day we will expand this extension point, but for now it just does denoise masking # one day we will expand this extension point, but for now it just does denoise masking
for guidance in additional_guidance: for guidance in additional_guidance:
latents = guidance(latents, timestep, conditioning_data) latents = guidance(latents, timestep)
# TODO: should this scaling happen here or inside self._unet_forward? # TODO: should this scaling happen here or inside self._unet_forward?
# i.e. before or after passing it to InvokeAIDiffuserComponent # i.e. before or after passing it to InvokeAIDiffuserComponent
@ -571,6 +571,18 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
# compute the previous noisy sample x_t -> x_t-1 # compute the previous noisy sample x_t -> x_t-1
step_output = self.scheduler.step(noise_pred, timestep, latents, **conditioning_data.scheduler_args) step_output = self.scheduler.step(noise_pred, timestep, latents, **conditioning_data.scheduler_args)
# TODO: discuss injection point options. For now this is a patch to get progress images working with inpainting again.
for guidance in additional_guidance:
# apply the mask to any "denoised" or "pred_original_sample" fields
if hasattr(step_output, "denoised"):
step_output.pred_original_sample = guidance(step_output.denoised, self.scheduler.timesteps[-1])
elif hasattr(step_output, "pred_original_sample"):
step_output.pred_original_sample = guidance(
step_output.pred_original_sample, self.scheduler.timesteps[-1]
)
else:
step_output.pred_original_sample = guidance(latents, self.scheduler.timesteps[-1])
return step_output return step_output
@staticmethod @staticmethod