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fix(nodes): refined fixes for refiner inpainting #6197

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Merged
merged 2 commits into from
Apr 11, 2024
Merged

fix(nodes): refined fixes for refiner inpainting #6197

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29876c3
fix(nodes): do not set seed on output latents from denoise latents
psychedelicious Apr 11, 2024
8c537bb
fix(nodes): doubly-noised latents
psychedelicious Apr 11, 2024
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2 changes: 1 addition & 1 deletion invokeai/app/invocations/latent.py
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Original file line number Diff line number Diff line change
Expand Up @@ -964,7 +964,7 @@ def _lora_loader() -> Iterator[Tuple[LoRAModelRaw, float]]:
mps.empty_cache()

name = context.tensors.save(tensor=result_latents)
return LatentsOutput.build(latents_name=name, latents=result_latents, seed=seed)
return LatentsOutput.build(latents_name=name, latents=result_latents, seed=None)


@invocation(
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22 changes: 10 additions & 12 deletions invokeai/backend/stable_diffusion/diffusers_pipeline.py
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Original file line number Diff line number Diff line change
Expand Up @@ -301,7 +301,7 @@ def latents_from_embeddings(
mask: Optional[torch.Tensor] = None,
masked_latents: Optional[torch.Tensor] = None,
gradient_mask: Optional[bool] = False,
seed: Optional[int] = None,
seed: int,
) -> torch.Tensor:
if init_timestep.shape[0] == 0:
return latents
Expand All @@ -319,17 +319,6 @@ def latents_from_embeddings(
latents = self.scheduler.add_noise(latents, noise, batched_t)

if mask is not None:
# if no noise provided, noisify unmasked area based on seed(or 0 as fallback)
if noise is None:
noise = torch.randn(
orig_latents.shape,
dtype=torch.float32,
device="cpu",
generator=torch.Generator(device="cpu").manual_seed(seed or 0),
).to(device=orig_latents.device, dtype=orig_latents.dtype)

latents = self.scheduler.add_noise(latents, noise, batched_t)

if is_inpainting_model(self.unet):
if masked_latents is None:
raise Exception("Source image required for inpaint mask when inpaint model used!")
Expand All @@ -338,6 +327,15 @@ def latents_from_embeddings(
self._unet_forward, mask, masked_latents
)
else:
# if no noise provided, noisify unmasked area based on seed
if noise is None:
noise = torch.randn(
orig_latents.shape,
dtype=torch.float32,
device="cpu",
generator=torch.Generator(device="cpu").manual_seed(seed),
).to(device=orig_latents.device, dtype=orig_latents.dtype)

additional_guidance.append(AddsMaskGuidance(mask, orig_latents, self.scheduler, noise, gradient_mask))

try:
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