Merge pull request #7730 from CCRcmcpe/fix-dpm-sde-batch
Fix DPM++ SDE not deterministic across different batch sizes (#5210)
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a77ac2eeaa
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@ -269,6 +269,16 @@ class KDiffusionSampler:
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return sigmas
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return sigmas
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def create_noise_sampler(self, x, sigmas, p):
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"""For DPM++ SDE: manually create noise sampler to enable deterministic results across different batch sizes"""
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if shared.opts.no_dpmpp_sde_batch_determinism:
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return None
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from k_diffusion.sampling import BrownianTreeNoiseSampler
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sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
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current_iter_seeds = p.all_seeds[p.iteration * p.batch_size:(p.iteration + 1) * p.batch_size]
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return BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=current_iter_seeds)
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def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
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def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
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steps, t_enc = sd_samplers_common.setup_img2img_steps(p, steps)
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steps, t_enc = sd_samplers_common.setup_img2img_steps(p, steps)
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@ -278,18 +288,24 @@ class KDiffusionSampler:
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xi = x + noise * sigma_sched[0]
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xi = x + noise * sigma_sched[0]
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extra_params_kwargs = self.initialize(p)
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extra_params_kwargs = self.initialize(p)
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if 'sigma_min' in inspect.signature(self.func).parameters:
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parameters = inspect.signature(self.func).parameters
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if 'sigma_min' in parameters:
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## last sigma is zero which isn't allowed by DPM Fast & Adaptive so taking value before last
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## last sigma is zero which isn't allowed by DPM Fast & Adaptive so taking value before last
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extra_params_kwargs['sigma_min'] = sigma_sched[-2]
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extra_params_kwargs['sigma_min'] = sigma_sched[-2]
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if 'sigma_max' in inspect.signature(self.func).parameters:
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if 'sigma_max' in parameters:
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extra_params_kwargs['sigma_max'] = sigma_sched[0]
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extra_params_kwargs['sigma_max'] = sigma_sched[0]
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if 'n' in inspect.signature(self.func).parameters:
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if 'n' in parameters:
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extra_params_kwargs['n'] = len(sigma_sched) - 1
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extra_params_kwargs['n'] = len(sigma_sched) - 1
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if 'sigma_sched' in inspect.signature(self.func).parameters:
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if 'sigma_sched' in parameters:
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extra_params_kwargs['sigma_sched'] = sigma_sched
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extra_params_kwargs['sigma_sched'] = sigma_sched
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if 'sigmas' in inspect.signature(self.func).parameters:
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if 'sigmas' in parameters:
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extra_params_kwargs['sigmas'] = sigma_sched
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extra_params_kwargs['sigmas'] = sigma_sched
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if self.funcname == 'sample_dpmpp_sde':
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noise_sampler = self.create_noise_sampler(x, sigmas, p)
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extra_params_kwargs['noise_sampler'] = noise_sampler
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self.model_wrap_cfg.init_latent = x
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self.model_wrap_cfg.init_latent = x
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self.last_latent = x
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self.last_latent = x
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extra_args={
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extra_args={
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@ -311,14 +327,20 @@ class KDiffusionSampler:
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x = x * sigmas[0]
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x = x * sigmas[0]
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extra_params_kwargs = self.initialize(p)
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extra_params_kwargs = self.initialize(p)
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if 'sigma_min' in inspect.signature(self.func).parameters:
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parameters = inspect.signature(self.func).parameters
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if 'sigma_min' in parameters:
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extra_params_kwargs['sigma_min'] = self.model_wrap.sigmas[0].item()
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extra_params_kwargs['sigma_min'] = self.model_wrap.sigmas[0].item()
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extra_params_kwargs['sigma_max'] = self.model_wrap.sigmas[-1].item()
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extra_params_kwargs['sigma_max'] = self.model_wrap.sigmas[-1].item()
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if 'n' in inspect.signature(self.func).parameters:
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if 'n' in parameters:
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extra_params_kwargs['n'] = steps
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extra_params_kwargs['n'] = steps
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else:
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else:
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extra_params_kwargs['sigmas'] = sigmas
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extra_params_kwargs['sigmas'] = sigmas
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if self.funcname == 'sample_dpmpp_sde':
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noise_sampler = self.create_noise_sampler(x, sigmas, p)
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extra_params_kwargs['noise_sampler'] = noise_sampler
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self.last_latent = x
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self.last_latent = x
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samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={
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samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={
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'cond': conditioning,
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'cond': conditioning,
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@ -414,6 +414,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
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options_templates.update(options_section(('compatibility', "Compatibility"), {
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options_templates.update(options_section(('compatibility', "Compatibility"), {
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"use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),
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"use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),
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"use_old_karras_scheduler_sigmas": OptionInfo(False, "Use old karras scheduler sigmas (0.1 to 10)."),
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"use_old_karras_scheduler_sigmas": OptionInfo(False, "Use old karras scheduler sigmas (0.1 to 10)."),
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"no_dpmpp_sde_batch_determinism": OptionInfo(False, "Do not make DPM++ SDE deterministic across different batch sizes."),
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"use_old_hires_fix_width_height": OptionInfo(False, "For hires fix, use width/height sliders to set final resolution rather than first pass (disables Upscale by, Resize width/height to)."),
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"use_old_hires_fix_width_height": OptionInfo(False, "For hires fix, use width/height sliders to set final resolution rather than first pass (disables Upscale by, Resize width/height to)."),
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}))
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}))
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