diff options
Diffstat (limited to 'modules/sd_samplers_cfg_denoiser.py')
| -rw-r--r-- | modules/sd_samplers_cfg_denoiser.py | 295 |
1 files changed, 1 insertions, 294 deletions
diff --git a/modules/sd_samplers_cfg_denoiser.py b/modules/sd_samplers_cfg_denoiser.py index db71a549..33a49783 100644 --- a/modules/sd_samplers_cfg_denoiser.py +++ b/modules/sd_samplers_cfg_denoiser.py @@ -1,61 +1,13 @@ from collections import deque
import torch
-import inspect
-import k_diffusion.sampling
-from modules import prompt_parser, devices, sd_samplers_common, sd_samplers_extra
+from modules import prompt_parser, devices, sd_samplers_common
-from modules.processing import StableDiffusionProcessing
from modules.shared import opts, state
import modules.shared as shared
from modules.script_callbacks import CFGDenoiserParams, cfg_denoiser_callback
from modules.script_callbacks import CFGDenoisedParams, cfg_denoised_callback
from modules.script_callbacks import AfterCFGCallbackParams, cfg_after_cfg_callback
-samplers_k_diffusion = [
- ('Euler a', 'sample_euler_ancestral', ['k_euler_a', 'k_euler_ancestral'], {"uses_ensd": True}),
- ('Euler', 'sample_euler', ['k_euler'], {}),
- ('LMS', 'sample_lms', ['k_lms'], {}),
- ('Heun', 'sample_heun', ['k_heun'], {"second_order": True}),
- ('DPM2', 'sample_dpm_2', ['k_dpm_2'], {'discard_next_to_last_sigma': True}),
- ('DPM2 a', 'sample_dpm_2_ancestral', ['k_dpm_2_a'], {'discard_next_to_last_sigma': True, "uses_ensd": True}),
- ('DPM++ 2S a', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a'], {"uses_ensd": True, "second_order": True}),
- ('DPM++ 2M', 'sample_dpmpp_2m', ['k_dpmpp_2m'], {}),
- ('DPM++ SDE', 'sample_dpmpp_sde', ['k_dpmpp_sde'], {"second_order": True, "brownian_noise": True}),
- ('DPM++ 2M SDE', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {"brownian_noise": True}),
- ('DPM fast', 'sample_dpm_fast', ['k_dpm_fast'], {"uses_ensd": True}),
- ('DPM adaptive', 'sample_dpm_adaptive', ['k_dpm_ad'], {"uses_ensd": True}),
- ('LMS Karras', 'sample_lms', ['k_lms_ka'], {'scheduler': 'karras'}),
- ('DPM2 Karras', 'sample_dpm_2', ['k_dpm_2_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True, "second_order": True}),
- ('DPM2 a Karras', 'sample_dpm_2_ancestral', ['k_dpm_2_a_ka'], {'scheduler': 'karras', 'discard_next_to_last_sigma': True, "uses_ensd": True, "second_order": True}),
- ('DPM++ 2S a Karras', 'sample_dpmpp_2s_ancestral', ['k_dpmpp_2s_a_ka'], {'scheduler': 'karras', "uses_ensd": True, "second_order": True}),
- ('DPM++ 2M Karras', 'sample_dpmpp_2m', ['k_dpmpp_2m_ka'], {'scheduler': 'karras'}),
- ('DPM++ SDE Karras', 'sample_dpmpp_sde', ['k_dpmpp_sde_ka'], {'scheduler': 'karras', "second_order": True, "brownian_noise": True}),
- ('DPM++ 2M SDE Karras', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_ka'], {'scheduler': 'karras', "brownian_noise": True}),
- ('DPM++ 2M SDE Exponential', 'sample_dpmpp_2m_sde', ['k_dpmpp_2m_sde_exp'], {'scheduler': 'exponential', "brownian_noise": True}),
- ('Restart', sd_samplers_extra.restart_sampler, ['restart'], {'scheduler': 'karras'}),
-]
-
-
-samplers_data_k_diffusion = [
- sd_samplers_common.SamplerData(label, lambda model, funcname=funcname: KDiffusionSampler(funcname, model), aliases, options)
- for label, funcname, aliases, options in samplers_k_diffusion
- if callable(funcname) or hasattr(k_diffusion.sampling, funcname)
-]
-
-sampler_extra_params = {
- 'sample_euler': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
- 'sample_heun': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
- 'sample_dpm_2': ['s_churn', 's_tmin', 's_tmax', 's_noise'],
-}
-
-k_diffusion_samplers_map = {x.name: x for x in samplers_data_k_diffusion}
-k_diffusion_scheduler = {
- 'Automatic': None,
- 'karras': k_diffusion.sampling.get_sigmas_karras,
- 'exponential': k_diffusion.sampling.get_sigmas_exponential,
- 'polyexponential': k_diffusion.sampling.get_sigmas_polyexponential
-}
-
def catenate_conds(conds):
if not isinstance(conds[0], dict):
@@ -264,248 +216,3 @@ class TorchHijack: return devices.randn_like(x)
-
-class KDiffusionSampler:
- def __init__(self, funcname, sd_model):
- denoiser = k_diffusion.external.CompVisVDenoiser if sd_model.parameterization == "v" else k_diffusion.external.CompVisDenoiser
-
- self.model_wrap = denoiser(sd_model, quantize=shared.opts.enable_quantization)
- self.funcname = funcname
- self.func = funcname if callable(funcname) else getattr(k_diffusion.sampling, self.funcname)
- self.extra_params = sampler_extra_params.get(funcname, [])
- self.model_wrap_cfg = CFGDenoiser(self.model_wrap)
- self.sampler_noises = None
- self.stop_at = None
- self.eta = None
- self.config = None # set by the function calling the constructor
- self.last_latent = None
- self.s_min_uncond = None
-
- # NOTE: These are also defined in the StableDiffusionProcessing class.
- # They should have been here to begin with but we're going to
- # leave that class __init__ signature alone.
- self.s_churn = 0.0
- self.s_tmin = 0.0
- self.s_tmax = float('inf')
- self.s_noise = 1.0
-
- self.conditioning_key = sd_model.model.conditioning_key
-
- def callback_state(self, d):
- step = d['i']
- latent = d["denoised"]
- if opts.live_preview_content == "Combined":
- sd_samplers_common.store_latent(latent)
- self.last_latent = latent
-
- if self.stop_at is not None and step > self.stop_at:
- raise sd_samplers_common.InterruptedException
-
- state.sampling_step = step
- shared.total_tqdm.update()
-
- def launch_sampling(self, steps, func):
- state.sampling_steps = steps
- state.sampling_step = 0
-
- try:
- return func()
- except RecursionError:
- print(
- 'Encountered RecursionError during sampling, returning last latent. '
- 'rho >5 with a polyexponential scheduler may cause this error. '
- 'You should try to use a smaller rho value instead.'
- )
- return self.last_latent
- except sd_samplers_common.InterruptedException:
- return self.last_latent
-
- def number_of_needed_noises(self, p):
- return p.steps
-
- def initialize(self, p: StableDiffusionProcessing):
- self.model_wrap_cfg.mask = p.mask if hasattr(p, 'mask') else None
- self.model_wrap_cfg.nmask = p.nmask if hasattr(p, 'nmask') else None
- self.model_wrap_cfg.step = 0
- self.model_wrap_cfg.image_cfg_scale = getattr(p, 'image_cfg_scale', None)
- self.eta = p.eta if p.eta is not None else opts.eta_ancestral
- self.s_min_uncond = getattr(p, 's_min_uncond', 0.0)
-
- k_diffusion.sampling.torch = TorchHijack(self.sampler_noises if self.sampler_noises is not None else [])
-
- extra_params_kwargs = {}
- for param_name in self.extra_params:
- if hasattr(p, param_name) and param_name in inspect.signature(self.func).parameters:
- extra_params_kwargs[param_name] = getattr(p, param_name)
-
- if 'eta' in inspect.signature(self.func).parameters:
- if self.eta != 1.0:
- p.extra_generation_params["Eta"] = self.eta
-
- extra_params_kwargs['eta'] = self.eta
-
- if len(self.extra_params) > 0:
- s_churn = getattr(opts, 's_churn', p.s_churn)
- s_tmin = getattr(opts, 's_tmin', p.s_tmin)
- s_tmax = getattr(opts, 's_tmax', p.s_tmax) or self.s_tmax # 0 = inf
- s_noise = getattr(opts, 's_noise', p.s_noise)
-
- if s_churn != self.s_churn:
- extra_params_kwargs['s_churn'] = s_churn
- p.s_churn = s_churn
- p.extra_generation_params['Sigma churn'] = s_churn
- if s_tmin != self.s_tmin:
- extra_params_kwargs['s_tmin'] = s_tmin
- p.s_tmin = s_tmin
- p.extra_generation_params['Sigma tmin'] = s_tmin
- if s_tmax != self.s_tmax:
- extra_params_kwargs['s_tmax'] = s_tmax
- p.s_tmax = s_tmax
- p.extra_generation_params['Sigma tmax'] = s_tmax
- if s_noise != self.s_noise:
- extra_params_kwargs['s_noise'] = s_noise
- p.s_noise = s_noise
- p.extra_generation_params['Sigma noise'] = s_noise
-
- return extra_params_kwargs
-
- def get_sigmas(self, p, steps):
- discard_next_to_last_sigma = self.config is not None and self.config.options.get('discard_next_to_last_sigma', False)
- if opts.always_discard_next_to_last_sigma and not discard_next_to_last_sigma:
- discard_next_to_last_sigma = True
- p.extra_generation_params["Discard penultimate sigma"] = True
-
- steps += 1 if discard_next_to_last_sigma else 0
-
- if p.sampler_noise_scheduler_override:
- sigmas = p.sampler_noise_scheduler_override(steps)
- elif opts.k_sched_type != "Automatic":
- m_sigma_min, m_sigma_max = (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
- sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (m_sigma_min, m_sigma_max)
- sigmas_kwargs = {
- 'sigma_min': sigma_min,
- 'sigma_max': sigma_max,
- }
-
- sigmas_func = k_diffusion_scheduler[opts.k_sched_type]
- p.extra_generation_params["Schedule type"] = opts.k_sched_type
-
- if opts.sigma_min != m_sigma_min and opts.sigma_min != 0:
- sigmas_kwargs['sigma_min'] = opts.sigma_min
- p.extra_generation_params["Schedule min sigma"] = opts.sigma_min
- if opts.sigma_max != m_sigma_max and opts.sigma_max != 0:
- sigmas_kwargs['sigma_max'] = opts.sigma_max
- p.extra_generation_params["Schedule max sigma"] = opts.sigma_max
-
- default_rho = 1. if opts.k_sched_type == "polyexponential" else 7.
-
- if opts.k_sched_type != 'exponential' and opts.rho != 0 and opts.rho != default_rho:
- sigmas_kwargs['rho'] = opts.rho
- p.extra_generation_params["Schedule rho"] = opts.rho
-
- sigmas = sigmas_func(n=steps, **sigmas_kwargs, device=shared.device)
- elif self.config is not None and self.config.options.get('scheduler', None) == 'karras':
- sigma_min, sigma_max = (0.1, 10) if opts.use_old_karras_scheduler_sigmas else (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
-
- sigmas = k_diffusion.sampling.get_sigmas_karras(n=steps, sigma_min=sigma_min, sigma_max=sigma_max, device=shared.device)
- elif self.config is not None and self.config.options.get('scheduler', None) == 'exponential':
- m_sigma_min, m_sigma_max = (self.model_wrap.sigmas[0].item(), self.model_wrap.sigmas[-1].item())
- sigmas = k_diffusion.sampling.get_sigmas_exponential(n=steps, sigma_min=m_sigma_min, sigma_max=m_sigma_max, device=shared.device)
- else:
- sigmas = self.model_wrap.get_sigmas(steps)
-
- if discard_next_to_last_sigma:
- sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])
-
- return sigmas
-
- def create_noise_sampler(self, x, sigmas, p):
- """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()
- current_iter_seeds = p.all_seeds[p.iteration * p.batch_size:(p.iteration + 1) * p.batch_size]
- return BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=current_iter_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)
-
- sigmas = self.get_sigmas(p, steps)
-
- sigma_sched = sigmas[steps - t_enc - 1:]
- xi = x + noise * sigma_sched[0]
-
- extra_params_kwargs = self.initialize(p)
- 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 parameters:
- extra_params_kwargs['sigma_max'] = sigma_sched[0]
- if 'n' in parameters:
- extra_params_kwargs['n'] = len(sigma_sched) - 1
- if 'sigma_sched' in parameters:
- extra_params_kwargs['sigma_sched'] = sigma_sched
- if 'sigmas' in parameters:
- extra_params_kwargs['sigmas'] = sigma_sched
-
- if self.config.options.get('brownian_noise', False):
- noise_sampler = self.create_noise_sampler(x, sigmas, p)
- extra_params_kwargs['noise_sampler'] = noise_sampler
-
- self.model_wrap_cfg.init_latent = x
- self.last_latent = x
- extra_args = {
- 'cond': conditioning,
- 'image_cond': image_conditioning,
- 'uncond': unconditional_conditioning,
- 'cond_scale': p.cfg_scale,
- 's_min_uncond': self.s_min_uncond
- }
-
- samples = self.launch_sampling(t_enc + 1, lambda: self.func(self.model_wrap_cfg, xi, extra_args=extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
-
- if self.model_wrap_cfg.padded_cond_uncond:
- p.extra_generation_params["Pad conds"] = True
-
- return samples
-
- def sample(self, p, x, conditioning, unconditional_conditioning, steps=None, image_conditioning=None):
- steps = steps or p.steps
-
- sigmas = self.get_sigmas(p, steps)
-
- x = x * sigmas[0]
-
- extra_params_kwargs = self.initialize(p)
- 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 parameters:
- extra_params_kwargs['n'] = steps
- else:
- extra_params_kwargs['sigmas'] = sigmas
-
- if self.config.options.get('brownian_noise', False):
- noise_sampler = self.create_noise_sampler(x, sigmas, p)
- 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,
- 'image_cond': image_conditioning,
- 'uncond': unconditional_conditioning,
- 'cond_scale': p.cfg_scale,
- 's_min_uncond': self.s_min_uncond
- }, disable=False, callback=self.callback_state, **extra_params_kwargs))
-
- if self.model_wrap_cfg.padded_cond_uncond:
- p.extra_generation_params["Pad conds"] = True
-
- return samples
-
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