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Minor tidying of latents_from_embeddings(...).

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Ryan Dick 2024-06-13 13:48:36 -04:00
parent c58c4069a7
commit 83df0c0df5
1 changed files with 7 additions and 10 deletions
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17
invokeai/backend/stable_diffusion/diffusers_pipeline.py
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@ -290,18 +290,22 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
masked_latents: Optional[torch.Tensor] = None,
is_gradient_mask: bool = False,
) -> torch.Tensor:
if init_timestep.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_t = init_timestep.expand(batch_size)
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:
# latents = noise * self.scheduler.init_noise_sigma # it's like in t2l according to diffusers
latents = self.scheduler.add_noise(latents, noise, batched_t)
latents = self.scheduler.add_noise(latents, noise, batched_init_timestep)
self._adjust_memory_efficient_attention(latents)
mask_guidance: AddsMaskGuidance | None = None
if mask is not None:
@ -325,13 +329,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
mask_guidance = AddsMaskGuidance(mask, orig_latents, self.scheduler, noise, is_gradient_mask)
try:
self._adjust_memory_efficient_attention(latents)
batch_size = latents.shape[0]
if timesteps.shape[0] == 0:
return latents
use_ip_adapter = ip_adapter_data is not None
use_regional_prompting = (
conditioning_data.cond_regions is not None or conditioning_data.uncond_regions is not None