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move alphas cumprod override out of processing
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@ -915,33 +915,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
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if p.n_iter > 1:
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shared.state.job = f"Batch {n+1} out of {p.n_iter}"
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def rescale_zero_terminal_snr_abar(alphas_cumprod):
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alphas_bar_sqrt = alphas_cumprod.sqrt()
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# Store old values.
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alphas_bar_sqrt_0 = alphas_bar_sqrt[0].clone()
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alphas_bar_sqrt_T = alphas_bar_sqrt[-1].clone()
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# Shift so the last timestep is zero.
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alphas_bar_sqrt -= (alphas_bar_sqrt_T)
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# Scale so the first timestep is back to the old value.
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alphas_bar_sqrt *= alphas_bar_sqrt_0 / (alphas_bar_sqrt_0 - alphas_bar_sqrt_T)
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# Convert alphas_bar_sqrt to betas
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alphas_bar = alphas_bar_sqrt**2 # Revert sqrt
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alphas_bar[-1] = 4.8973451890853435e-08
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return alphas_bar
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if hasattr(p.sd_model, 'alphas_cumprod') and hasattr(p.sd_model, 'alphas_cumprod_original'):
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p.sd_model.alphas_cumprod = p.sd_model.alphas_cumprod_original.to(shared.device)
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if opts.use_downcasted_alpha_bar:
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p.extra_generation_params['Downcast alphas_cumprod'] = opts.use_downcasted_alpha_bar
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p.sd_model.alphas_cumprod = p.sd_model.alphas_cumprod.half().to(shared.device)
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if opts.sd_noise_schedule == "Zero Terminal SNR":
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p.extra_generation_params['Noise Schedule'] = opts.sd_noise_schedule
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p.sd_model.alphas_cumprod = rescale_zero_terminal_snr_abar(p.sd_model.alphas_cumprod).to(shared.device)
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sd_models.apply_alpha_schedule_override(p.sd_model, p)
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with devices.without_autocast() if devices.unet_needs_upcast else devices.autocast():
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samples_ddim = p.sample(conditioning=p.c, unconditional_conditioning=p.uc, seeds=p.seeds, subseeds=p.subseeds, subseed_strength=p.subseed_strength, prompts=p.prompts)
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