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Remove unused num_inference_steps.

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Ryan Dick 2024-06-12 13:39:34 -04:00 committed by Kent Keirsey
parent 230e205541
commit ffc28176fe
4 changed files with 17 additions and 46 deletions
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8
invokeai/app/invocations/denoise_latents.py
View File

@ -601,7 +601,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
denoising_start: float,
denoising_end: float,
seed: int,
) -> Tuple[int, List[int], int, Dict[str, Any]]:
) -> Tuple[List[int], int, Dict[str, Any]]:
assert isinstance(scheduler, ConfigMixin)
if scheduler.config.get("cpu_only", False):
scheduler.set_timesteps(steps, device="cpu")
@ -627,7 +627,6 @@ class DenoiseLatentsInvocation(BaseInvocation):
init_timestep = timesteps[t_start_idx : t_start_idx + 1]
timesteps = timesteps[t_start_idx : t_start_idx + t_end_idx]
num_inference_steps = len(timesteps) // scheduler.order
scheduler_step_kwargs: Dict[str, Any] = {}
scheduler_step_signature = inspect.signature(scheduler.step)
@ -649,7 +648,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
if isinstance(scheduler, TCDScheduler):
scheduler_step_kwargs.update({"eta": 1.0})
return num_inference_steps, timesteps, init_timestep, scheduler_step_kwargs
return timesteps, init_timestep, scheduler_step_kwargs
def prep_inpaint_mask(
self, context: InvocationContext, latents: torch.Tensor
@ -803,7 +802,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
dtype=unet.dtype,
)
num_inference_steps, timesteps, init_timestep, scheduler_step_kwargs = self.init_scheduler(
timesteps, init_timestep, scheduler_step_kwargs = self.init_scheduler(
scheduler,
device=unet.device,
steps=self.steps,
@ -821,7 +820,6 @@ class DenoiseLatentsInvocation(BaseInvocation):
mask=mask,
masked_latents=masked_latents,
gradient_mask=gradient_mask,
num_inference_steps=num_inference_steps,
scheduler_step_kwargs=scheduler_step_kwargs,
conditioning_data=conditioning_data,
control_data=controlnet_data,

37
invokeai/app/invocations/tiled_multi_diffusion_denoise_latents.py
View File

@ -228,7 +228,6 @@ class TiledMultiDiffusionDenoiseLatents(BaseInvocation):
]
controlnet_data_tiles.append(tile_controlnet_data)
# TODO(ryand): Logic from here down needs updating --------------------
# Denoise (i.e. "refine") each tile independently.
for image_tile_np, latent_tile, noise_tile in zip(image_tiles_np, latent_tiles, noise_tiles, strict=True):
assert latent_tile.shape == noise_tile.shape
@ -238,34 +237,13 @@ class TiledMultiDiffusionDenoiseLatents(BaseInvocation):
# the tiles. Ideally, the ControlNet code should be able to work with Tensors.
image_tile_pil = Image.fromarray(image_tile_np)
# Run the ControlNet on the image tile.
height, width, _ = image_tile_np.shape
# The height and width must be evenly divisible by LATENT_SCALE_FACTOR. This is enforced earlier, but we
# validate this assumption here.
assert height % LATENT_SCALE_FACTOR == 0
assert width % LATENT_SCALE_FACTOR == 0
controlnet_data = self.run_controlnet(
image=image_tile_pil,
controlnet_model=controlnet_model,
weight=self.control_weight,
do_classifier_free_guidance=True,
width=width,
height=height,
device=controlnet_model.device,
dtype=controlnet_model.dtype,
control_mode="balanced",
resize_mode="just_resize_simple",
)
num_inference_steps, timesteps, init_timestep, scheduler_step_kwargs = (
DenoiseLatentsInvocation.init_scheduler(
scheduler,
device=unet.device,
steps=self.steps,
denoising_start=self.denoising_start,
denoising_end=self.denoising_end,
seed=seed,
)
timesteps, init_timestep, scheduler_step_kwargs = DenoiseLatentsInvocation.init_scheduler(
scheduler,
device=unet.device,
steps=self.steps,
denoising_start=self.denoising_start,
denoising_end=self.denoising_end,
seed=seed,
)
# TODO(ryand): Think about when/if latents/noise should be moved off of the device to save VRAM.
@ -280,7 +258,6 @@ class TiledMultiDiffusionDenoiseLatents(BaseInvocation):
mask=None,
masked_latents=None,
gradient_mask=None,
num_inference_steps=num_inference_steps,
scheduler_step_kwargs=scheduler_step_kwargs,
conditioning_data=conditioning_data,
control_data=[controlnet_data],

17
invokeai/app/invocations/tiled_stable_diffusion_refine.py
View File

@ -320,15 +320,13 @@ class TiledStableDiffusionRefineInvocation(BaseInvocation):
resize_mode="just_resize_simple",
)
num_inference_steps, timesteps, init_timestep, scheduler_step_kwargs = (
DenoiseLatentsInvocation.init_scheduler(
scheduler,
device=unet.device,
steps=self.steps,
denoising_start=self.denoising_start,
denoising_end=self.denoising_end,
seed=seed,
)
timesteps, init_timestep, scheduler_step_kwargs = DenoiseLatentsInvocation.init_scheduler(
scheduler,
device=unet.device,
steps=self.steps,
denoising_start=self.denoising_start,
denoising_end=self.denoising_end,
seed=seed,
)
# TODO(ryand): Think about when/if latents/noise should be moved off of the device to save VRAM.
@ -343,7 +341,6 @@ class TiledStableDiffusionRefineInvocation(BaseInvocation):
mask=None,
masked_latents=None,
gradient_mask=None,
num_inference_steps=num_inference_steps,
scheduler_step_kwargs=scheduler_step_kwargs,
conditioning_data=conditioning_data,
control_data=[controlnet_data],

1
invokeai/backend/stable_diffusion/diffusers_pipeline.py
View File

@ -283,7 +283,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
def latents_from_embeddings(
self,
latents: torch.Tensor,
num_inference_steps: int,
scheduler_step_kwargs: dict[str, Any],
conditioning_data: TextConditioningData,
*,