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Made dummy latents smaller. Minor code cleanups

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random_thoughtss 2022-10-20 09:45:03 -07:00
parent aa7ff2a197
commit 92a17a7a4a
2 changed files with 8 additions and 5 deletions
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7
modules/processing.py
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@ -557,7 +557,8 @@ class StableDiffusionProcessingTxt2Img(StableDiffusionProcessing):
else:
# Dummy zero conditioning if we're not using inpainting model.
# Still takes up a bit of memory, but no encoder call.
image_conditioning = torch.zeros(x.shape[0], 5, x.shape[-2], x.shape[-1], dtype=x.dtype, device=x.device)
# Pretty sure we can just make this a 1x1 image since its not going to be used besides its batch size.
image_conditioning = torch.zeros(x.shape[0], 5, 1, 1, dtype=x.dtype, device=x.device)
return image_conditioning
@ -759,8 +760,8 @@ class StableDiffusionProcessingImg2Img(StableDiffusionProcessing):
self.image_conditioning = self.image_conditioning.to(shared.device).type(self.sd_model.dtype)
else:
self.image_conditioning = torch.zeros(
self.init_latent.shape[0], 5, self.init_latent.shape[-2], self.init_latent.shape[-1],
dtype=self.init_latent.dtype,
self.init_latent.shape[0], 5, 1, 1,
dtype=self.init_latent.dtype,
device=self.init_latent.device
)

6
modules/sd_samplers.py
View File

@ -138,7 +138,7 @@ class VanillaStableDiffusionSampler:
if self.stop_at is not None and self.step > self.stop_at:
raise InterruptedException
# Have to unwrap the inpainting conditioning here to perform pre-preocessing
# Have to unwrap the inpainting conditioning here to perform pre-processing
image_conditioning = None
if isinstance(cond, dict):
image_conditioning = cond["c_concat"][0]
@ -146,7 +146,7 @@ class VanillaStableDiffusionSampler:
unconditional_conditioning = unconditional_conditioning["c_crossattn"][0]
conds_list, tensor = prompt_parser.reconstruct_multicond_batch(cond, self.step)
unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step)
unconditional_conditioning = prompt_parser.reconstruct_cond_batch(unconditional_conditioning, self.step)
assert all([len(conds) == 1 for conds in conds_list]), 'composition via AND is not supported for DDIM/PLMS samplers'
cond = tensor
@ -165,6 +165,8 @@ class VanillaStableDiffusionSampler:
img_orig = self.sampler.model.q_sample(self.init_latent, ts)
x_dec = img_orig * self.mask + self.nmask * x_dec
# Wrap the image conditioning back up since the DDIM code can accept the dict directly.
# Note that they need to be lists because it just concatenates them later.
if image_conditioning is not None:
cond = {"c_concat": [image_conditioning], "c_crossattn": [cond]}
unconditional_conditioning = {"c_concat": [image_conditioning], "c_crossattn": [unconditional_conditioning]}