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https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
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add CFG denoiser implementation for DDIM, PLMS and UniPC (this is the commit when you can run both old and new implementations to compare them)
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AUTOMATIC1111
@@ -4,8 +4,7 @@ import inspect
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import k_diffusion.sampling
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from modules import devices, sd_samplers_common, sd_samplers_extra, sd_samplers_cfg_denoiser
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from modules.processing import StableDiffusionProcessing
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from modules.shared import opts, state
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from modules.shared import opts
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import modules.shared as shared
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samplers_k_diffusion = [
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@@ -54,133 +53,17 @@ k_diffusion_scheduler = {
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}
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class TorchHijack:
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def __init__(self, sampler_noises):
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# Using a deque to efficiently receive the sampler_noises in the same order as the previous index-based
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# implementation.
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self.sampler_noises = deque(sampler_noises)
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def __getattr__(self, item):
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if item == 'randn_like':
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return self.randn_like
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if hasattr(torch, item):
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return getattr(torch, item)
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raise AttributeError(f"'{type(self).__name__}' object has no attribute '{item}'")
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def randn_like(self, x):
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if self.sampler_noises:
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noise = self.sampler_noises.popleft()
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if noise.shape == x.shape:
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return noise
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return devices.randn_like(x)
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class KDiffusionSampler:
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class KDiffusionSampler(sd_samplers_common.Sampler):
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def __init__(self, funcname, sd_model):
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denoiser = k_diffusion.external.CompVisVDenoiser if sd_model.parameterization == "v" else k_diffusion.external.CompVisDenoiser
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self.model_wrap = denoiser(sd_model, quantize=shared.opts.enable_quantization)
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self.funcname = funcname
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self.func = funcname if callable(funcname) else getattr(k_diffusion.sampling, self.funcname)
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super().__init__(funcname)
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self.extra_params = sampler_extra_params.get(funcname, [])
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self.model_wrap_cfg = sd_samplers_cfg_denoiser.CFGDenoiser(self.model_wrap)
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self.sampler_noises = None
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self.stop_at = None
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self.eta = None
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self.config = None # set by the function calling the constructor
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self.last_latent = None
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self.s_min_uncond = None
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self.func = funcname if callable(funcname) else getattr(k_diffusion.sampling, self.funcname)
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# NOTE: These are also defined in the StableDiffusionProcessing class.
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# They should have been here to begin with but we're going to
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# leave that class __init__ signature alone.
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self.s_churn = 0.0
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self.s_tmin = 0.0
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self.s_tmax = float('inf')
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self.s_noise = 1.0
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self.conditioning_key = sd_model.model.conditioning_key
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def callback_state(self, d):
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step = d['i']
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latent = d["denoised"]
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if opts.live_preview_content == "Combined":
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sd_samplers_common.store_latent(latent)
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self.last_latent = latent
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if self.stop_at is not None and step > self.stop_at:
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raise sd_samplers_common.InterruptedException
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state.sampling_step = step
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shared.total_tqdm.update()
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def launch_sampling(self, steps, func):
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state.sampling_steps = steps
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state.sampling_step = 0
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try:
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return func()
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except RecursionError:
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print(
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'Encountered RecursionError during sampling, returning last latent. '
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'rho >5 with a polyexponential scheduler may cause this error. '
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'You should try to use a smaller rho value instead.'
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)
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return self.last_latent
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except sd_samplers_common.InterruptedException:
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return self.last_latent
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def number_of_needed_noises(self, p):
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return p.steps
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def initialize(self, p: StableDiffusionProcessing):
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self.model_wrap_cfg.mask = p.mask if hasattr(p, 'mask') else None
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self.model_wrap_cfg.nmask = p.nmask if hasattr(p, 'nmask') else None
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self.model_wrap_cfg.step = 0
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self.model_wrap_cfg.image_cfg_scale = getattr(p, 'image_cfg_scale', None)
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self.eta = p.eta if p.eta is not None else opts.eta_ancestral
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self.s_min_uncond = getattr(p, 's_min_uncond', 0.0)
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k_diffusion.sampling.torch = TorchHijack(self.sampler_noises if self.sampler_noises is not None else [])
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extra_params_kwargs = {}
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for param_name in self.extra_params:
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if hasattr(p, param_name) and param_name in inspect.signature(self.func).parameters:
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extra_params_kwargs[param_name] = getattr(p, param_name)
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if 'eta' in inspect.signature(self.func).parameters:
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if self.eta != 1.0:
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p.extra_generation_params["Eta"] = self.eta
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extra_params_kwargs['eta'] = self.eta
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if len(self.extra_params) > 0:
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s_churn = getattr(opts, 's_churn', p.s_churn)
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s_tmin = getattr(opts, 's_tmin', p.s_tmin)
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s_tmax = getattr(opts, 's_tmax', p.s_tmax) or self.s_tmax # 0 = inf
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s_noise = getattr(opts, 's_noise', p.s_noise)
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if s_churn != self.s_churn:
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extra_params_kwargs['s_churn'] = s_churn
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p.s_churn = s_churn
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p.extra_generation_params['Sigma churn'] = s_churn
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if s_tmin != self.s_tmin:
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extra_params_kwargs['s_tmin'] = s_tmin
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p.s_tmin = s_tmin
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p.extra_generation_params['Sigma tmin'] = s_tmin
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if s_tmax != self.s_tmax:
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extra_params_kwargs['s_tmax'] = s_tmax
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p.s_tmax = s_tmax
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p.extra_generation_params['Sigma tmax'] = s_tmax
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if s_noise != self.s_noise:
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extra_params_kwargs['s_noise'] = s_noise
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p.s_noise = s_noise
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p.extra_generation_params['Sigma noise'] = s_noise
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return extra_params_kwargs
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denoiser = k_diffusion.external.CompVisVDenoiser if sd_model.parameterization == "v" else k_diffusion.external.CompVisDenoiser
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self.model_wrap = denoiser(sd_model, quantize=shared.opts.enable_quantization)
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self.model_wrap_cfg = sd_samplers_cfg_denoiser.CFGDenoiser(self.model_wrap, self)
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def get_sigmas(self, p, steps):
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discard_next_to_last_sigma = self.config is not None and self.config.options.get('discard_next_to_last_sigma', False)
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@@ -232,22 +115,12 @@ class KDiffusionSampler:
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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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steps, t_enc = sd_samplers_common.setup_img2img_steps(p, steps)
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sigmas = self.get_sigmas(p, steps)
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sigma_sched = sigmas[steps - t_enc - 1:]
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xi = x + noise * sigma_sched[0]
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extra_params_kwargs = self.initialize(p)
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@@ -296,12 +169,14 @@ class KDiffusionSampler:
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extra_params_kwargs = self.initialize(p)
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parameters = 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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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_max'] = self.model_wrap.sigmas[-1].item()
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if 'n' in parameters:
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extra_params_kwargs['n'] = steps
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else:
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if 'sigmas' in parameters:
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extra_params_kwargs['sigmas'] = sigmas
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if self.config.options.get('brownian_noise', False):
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@@ -322,3 +197,4 @@ class KDiffusionSampler:
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return samples
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