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performance: check for nans in unet only once, after all steps have been completed

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AUTOMATIC1111 2024-06-09 16:24:04 +03:00
parent 41ee2db5a8
commit 6214aa7d2a
2 changed files with 5 additions and 2 deletions
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5
modules/processing.py
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@ -625,6 +625,9 @@ class DecodedSamples(list):
def decode_latent_batch(model, batch, target_device=None, check_for_nans=False):
samples = DecodedSamples()
if check_for_nans:
devices.test_for_nans(batch, "unet")
for i in range(batch.shape[0]):
sample = decode_first_stage(model, batch[i:i + 1])[0]
@ -987,6 +990,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
if getattr(samples_ddim, 'already_decoded', False):
x_samples_ddim = samples_ddim
else:
devices.test_for_nans(samples_ddim, "unet")
if opts.sd_vae_decode_method != 'Full':
p.extra_generation_params['VAE Decoder'] = opts.sd_vae_decode_method
x_samples_ddim = decode_latent_batch(p.sd_model, samples_ddim, target_device=devices.cpu, check_for_nans=True)

2
modules/sd_samplers_cfg_denoiser.py
View File

@ -273,8 +273,6 @@ class CFGDenoiser(torch.nn.Module):
denoised_params = CFGDenoisedParams(x_out, state.sampling_step, state.sampling_steps, self.inner_model)
cfg_denoised_callback(denoised_params)
devices.test_for_nans(x_out, "unet")
if is_edit_model:
denoised = self.combine_denoised_for_edit_model(x_out, cond_scale)
elif skip_uncond: