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Fix DPM++ SDE not deterministic across different batch sizes (#5210)

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This commit is contained in:
RcINS 2023-02-11 10:12:16 +08:00
parent ea9bd9fc74
commit 9e27af76d1
2 changed files with 30 additions and 8 deletions
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37
modules/sd_samplers_kdiffusion.py
View File

@ -269,6 +269,15 @@ class KDiffusionSampler:
return sigmas
def create_noise_sampler(self, x, sigmas, seeds):
"""For DPM++ SDE: manually create noise sampler to enable deterministic results across different batch sizes"""
if shared.opts.no_dpmpp_sde_batch_determinism:
return None
from k_diffusion.sampling import BrownianTreeNoiseSampler
sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
return BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seeds)
def sample_img2img(self, p, x, noise, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
steps, t_enc = sd_samplers_common.setup_img2img_steps(p, steps)
@ -278,18 +287,24 @@ class KDiffusionSampler:
xi = x + noise * sigma_sched[0]
extra_params_kwargs = self.initialize(p)
if 'sigma_min' in inspect.signature(self.func).parameters:
parameters = inspect.signature(self.func).parameters
if 'sigma_min' in parameters:
## last sigma is zero which isn't allowed by DPM Fast & Adaptive so taking value before last
extra_params_kwargs['sigma_min'] = sigma_sched[-2]
if 'sigma_max' in inspect.signature(self.func).parameters:
if 'sigma_max' in parameters:
extra_params_kwargs['sigma_max'] = sigma_sched[0]
if 'n' in inspect.signature(self.func).parameters:
if 'n' in parameters:
extra_params_kwargs['n'] = len(sigma_sched) - 1
if 'sigma_sched' in inspect.signature(self.func).parameters:
if 'sigma_sched' in parameters:
extra_params_kwargs['sigma_sched'] = sigma_sched
if 'sigmas' in inspect.signature(self.func).parameters:
if 'sigmas' in parameters:
extra_params_kwargs['sigmas'] = sigma_sched
if self.funcname == 'sample_dpmpp_sde':
noise_sampler = self.create_noise_sampler(x, sigmas, p.all_seeds)
extra_params_kwargs['noise_sampler'] = noise_sampler
self.model_wrap_cfg.init_latent = x
self.last_latent = x
extra_args={
@ -303,7 +318,7 @@ class KDiffusionSampler:
return samples
def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning = None):
def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
steps = steps or p.steps
sigmas = self.get_sigmas(p, steps)
@ -311,14 +326,20 @@ class KDiffusionSampler:
x = x * sigmas[0]
extra_params_kwargs = self.initialize(p)
if 'sigma_min' in inspect.signature(self.func).parameters:
parameters = inspect.signature(self.func).parameters
if 'sigma_min' in parameters:
extra_params_kwargs['sigma_min'] = self.model_wrap.sigmas[0].item()
extra_params_kwargs['sigma_max'] = self.model_wrap.sigmas[-1].item()
if 'n' in inspect.signature(self.func).parameters:
if 'n' in parameters:
extra_params_kwargs['n'] = steps
else:
extra_params_kwargs['sigmas'] = sigmas
if self.funcname == 'sample_dpmpp_sde':
noise_sampler = self.create_noise_sampler(x, sigmas, p.all_seeds)
extra_params_kwargs['noise_sampler'] = noise_sampler
self.last_latent = x
samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args={
'cond': conditioning,

1
modules/shared.py
View File

@ -414,6 +414,7 @@ options_templates.update(options_section(('sd', "Stable Diffusion"), {
options_templates.update(options_section(('compatibility', "Compatibility"), {
"use_old_emphasis_implementation": OptionInfo(False, "Use old emphasis implementation. Can be useful to reproduce old seeds."),
"use_old_karras_scheduler_sigmas": OptionInfo(False, "Use old karras scheduler sigmas (0.1 to 10)."),
"no_dpmpp_sde_batch_determinism": OptionInfo(False, "Do not make DPM++ SDE deterministic across different batch sizes."),
"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)."),
}))