Mirrored_Repos/InvokeAI
1
0
Fork 0
mirror of https://github.com/invoke-ai/InvokeAI synced 2024-08-30 20:32:17 +00:00
Code Issues Packages Projects Releases Wiki Activity

Revert "Remove the redundant init_timestep parameter that was being passed around. It is simply the first element of the timesteps array."

Browse Source 
This reverts commit fa40061eca.
This commit is contained in:
Ryan Dick 2024-06-25 18:30:59 -04:00
parent dc23bebebf
commit bd74b84cc5
4 changed files with 20 additions and 11 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

6
invokeai/app/invocations/denoise_latents.py
View File

@ -625,6 +625,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
t_start_idx *= scheduler.order
t_end_idx *= scheduler.order
init_timestep = timesteps[t_start_idx : t_start_idx + 1]
timesteps = timesteps[t_start_idx : t_start_idx + t_end_idx]
scheduler_step_kwargs: Dict[str, Any] = {}
@ -647,7 +648,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
if isinstance(scheduler, TCDScheduler):
scheduler_step_kwargs.update({"eta": 1.0})
return timesteps, scheduler_step_kwargs
return timesteps, init_timestep, scheduler_step_kwargs
def prep_inpaint_mask(
self, context: InvocationContext, latents: torch.Tensor
@ -813,7 +814,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
dtype=unet.dtype,
)
timesteps, scheduler_step_kwargs = self.init_scheduler(
timesteps, init_timestep, scheduler_step_kwargs = self.init_scheduler(
scheduler,
device=unet.device,
steps=self.steps,
@ -825,6 +826,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
result_latents = pipeline.latents_from_embeddings(
latents=latents,
timesteps=timesteps,
init_timestep=init_timestep,
noise=noise,
seed=seed,
mask=mask,

3
invokeai/app/invocations/tiled_multi_diffusion_denoise_latents.py
View File

@ -252,7 +252,7 @@ class TiledMultiDiffusionDenoiseLatents(BaseInvocation):
)
)
timesteps, scheduler_step_kwargs = DenoiseLatentsInvocation.init_scheduler(
timesteps, init_timestep, scheduler_step_kwargs = DenoiseLatentsInvocation.init_scheduler(
scheduler,
device=unet.device,
steps=self.steps,
@ -269,6 +269,7 @@ class TiledMultiDiffusionDenoiseLatents(BaseInvocation):
scheduler_step_kwargs=scheduler_step_kwargs,
noise=noise,
timesteps=timesteps,
init_timestep=init_timestep,
callback=step_callback,
)

13
invokeai/backend/stable_diffusion/diffusers_pipeline.py
View File

@ -273,6 +273,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
noise: Optional[torch.Tensor],
seed: int,
timesteps: torch.Tensor,
init_timestep: torch.Tensor,
callback: Callable[[PipelineIntermediateState], None],
control_data: list[ControlNetData] | None = None,
ip_adapter_data: Optional[list[IPAdapterData]] = None,
@ -298,6 +299,9 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
HACK(ryand): seed is only used in a particular case when `noise` is None, but we need to re-generate the
same noise used earlier in the pipeline. This should really be handled in a clearer way.
timesteps: The timestep schedule for the denoising process.
init_timestep: The first timestep in the schedule.
TODO(ryand): I'm pretty sure this should always be the same as timesteps[0:1]. Confirm that that is the
case, and remove this duplicate param.
callback: A callback function that is called to report progress during the denoising process.
control_data: ControlNet data.
ip_adapter_data: IP-Adapter data.
@ -312,17 +316,18 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
SD UNet model.
is_gradient_mask: A flag indicating whether `mask` is a gradient mask or not.
"""
if timesteps.shape[0] == 0:
# TODO(ryand): Figure out why this condition is necessary, and document it. My guess is that it's to handle
# cases where densoisings_start and denoising_end are set such that there are no timesteps.
if init_timestep.shape[0] == 0 or timesteps.shape[0] == 0:
return latents
orig_latents = latents.clone()
batch_size = latents.shape[0]
batched_init_timestep = init_timestep.expand(batch_size)
# noise can be None if the latents have already been noised (e.g. when running the SDXL refiner).
if noise is not None:
# batched_init_timestep should have shape (batch_size, 1).
batched_init_timestep = timesteps[0:1].expand(batch_size)
# TODO(ryand): I'm pretty sure we should be applying init_noise_sigma in cases where we are starting with
# full noise. Investigate the history of why this got commented out.
# latents = noise * self.scheduler.init_noise_sigma # it's like in t2l according to diffusers

9
invokeai/backend/stable_diffusion/multi_diffusion_pipeline.py
View File

@ -44,20 +44,21 @@ class MultiDiffusionPipeline(StableDiffusionGeneratorPipeline):
scheduler_step_kwargs: dict[str, Any],
noise: Optional[torch.Tensor],
timesteps: torch.Tensor,
init_timestep: torch.Tensor,
callback: Callable[[PipelineIntermediateState], None],
) -> torch.Tensor:
self._check_regional_prompting(multi_diffusion_conditioning)
if timesteps.shape[0] == 0:
# TODO(ryand): Figure out why this condition is necessary, and document it. My guess is that it's to handle
# cases where densoisings_start and denoising_end are set such that there are no timesteps.
if init_timestep.shape[0] == 0 or timesteps.shape[0] == 0:
return latents
batch_size, _, latent_height, latent_width = latents.shape
batched_init_timestep = init_timestep.expand(batch_size)
# noise can be None if the latents have already been noised (e.g. when running the SDXL refiner).
if noise is not None:
# batched_init_timestep should have shape (batch_size, 1).
batched_init_timestep = timesteps[0:1].expand(batch_size)
# TODO(ryand): I'm pretty sure we should be applying init_noise_sigma in cases where we are starting with
# full noise. Investigate the history of why this got commented out.
# latents = noise * self.scheduler.init_noise_sigma # it's like in t2l according to diffusers