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Make VAE step sequential to prevent VRAM spikes

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klimaleksus 2022-11-28 16:29:43 +05:00 committed by GitHub
parent 0b5dcb3d7c
commit 67efee33a6
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1 changed files with 2 additions and 2 deletions
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modules/processing.py
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@ -530,8 +530,8 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
with devices.autocast(): with devices.autocast():
samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, prompts=prompts) samples_ddim = p.sample(conditioning=c, unconditional_conditioning=uc, seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, prompts=prompts)
samples_ddim = samples_ddim.to(devices.dtype_vae) x_samples_ddim = [decode_first_stage(p.sd_model, samples_ddim[i:i+1].to(dtype=devices.dtype_vae))[0].cpu() for i in range(samples_ddim.size(0))]
x_samples_ddim = decode_first_stage(p.sd_model, samples_ddim) x_samples_ddim = torch.stack(x_samples_ddim).float()
x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0) x_samples_ddim = torch.clamp((x_samples_ddim + 1.0) / 2.0, min=0.0, max=1.0)
del samples_ddim del samples_ddim