From 8285a149d8c488ae6c7a566eb85fb5e825145464 Mon Sep 17 00:00:00 2001 From: AUTOMATIC1111 <16777216c@gmail.com> Date: Tue, 8 Aug 2023 19:20:11 +0300 Subject: 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) --- modules/sd_samplers_timesteps.py | 147 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 147 insertions(+) create mode 100644 modules/sd_samplers_timesteps.py (limited to 'modules/sd_samplers_timesteps.py') diff --git a/modules/sd_samplers_timesteps.py b/modules/sd_samplers_timesteps.py new file mode 100644 index 00000000..8560d009 --- /dev/null +++ b/modules/sd_samplers_timesteps.py @@ -0,0 +1,147 @@ +import torch +import inspect +from modules import devices, sd_samplers_common, sd_samplers_timesteps_impl +from modules.sd_samplers_cfg_denoiser import CFGDenoiser + +from modules.shared import opts +import modules.shared as shared + +samplers_timesteps = [ + ('k_DDIM', sd_samplers_timesteps_impl.ddim, ['k_ddim'], {}), + ('k_PLMS', sd_samplers_timesteps_impl.plms, ['k_plms'], {}), + ('k_UniPC', sd_samplers_timesteps_impl.unipc, ['k_unipc'], {}), +] + + +samplers_data_timesteps = [ + sd_samplers_common.SamplerData(label, lambda model, funcname=funcname: CompVisSampler(funcname, model), aliases, options) + for label, funcname, aliases, options in samplers_timesteps +] + + +class CompVisTimestepsDenoiser(torch.nn.Module): + def __init__(self, model, *args, **kwargs): + super().__init__(*args, **kwargs) + self.inner_model = model + + def forward(self, input, timesteps, **kwargs): + return self.inner_model.apply_model(input, timesteps, **kwargs) + + +class CompVisTimestepsVDenoiser(torch.nn.Module): + def __init__(self, model, *args, **kwargs): + super().__init__(*args, **kwargs) + self.inner_model = model + + def predict_eps_from_z_and_v(self, x_t, t, v): + return self.inner_model.sqrt_alphas_cumprod[t.to(torch.int), None, None, None] * v + self.inner_model.sqrt_one_minus_alphas_cumprod[t.to(torch.int), None, None, None] * x_t + + def forward(self, input, timesteps, **kwargs): + model_output = self.inner_model.apply_model(input, timesteps, **kwargs) + e_t = self.predict_eps_from_z_and_v(input, timesteps, model_output) + return e_t + + +class CFGDenoiserTimesteps(CFGDenoiser): + + def __init__(self, model, sampler): + super().__init__(model, sampler) + + self.alphas = model.inner_model.alphas_cumprod + + def get_pred_x0(self, x_in, x_out, sigma): + ts = int(sigma.item()) + + s_in = x_in.new_ones([x_in.shape[0]]) + a_t = self.alphas[ts].item() * s_in + sqrt_one_minus_at = (1 - a_t).sqrt() + + pred_x0 = (x_in - sqrt_one_minus_at * x_out) / a_t.sqrt() + + return pred_x0 + + +class CompVisSampler(sd_samplers_common.Sampler): + def __init__(self, funcname, sd_model): + super().__init__(funcname) + + self.eta_option_field = 'eta_ddim' + self.eta_infotext_field = 'Eta DDIM' + + denoiser = CompVisTimestepsVDenoiser if sd_model.parameterization == "v" else CompVisTimestepsDenoiser + self.model_wrap = denoiser(sd_model) + self.model_wrap_cfg = CFGDenoiserTimesteps(self.model_wrap, self) + + def get_timesteps(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 + + timesteps = torch.clip(torch.asarray(list(range(0, 1000, 1000 // steps)), device=devices.device) + 1, 0, 999) + + return timesteps + + 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) + + timesteps = self.get_timesteps(p, steps) + timesteps_sched = timesteps[:t_enc] + + alphas_cumprod = shared.sd_model.alphas_cumprod + sqrt_alpha_cumprod = torch.sqrt(alphas_cumprod[timesteps[t_enc]]) + sqrt_one_minus_alpha_cumprod = torch.sqrt(1 - alphas_cumprod[timesteps[t_enc]]) + + xi = x * sqrt_alpha_cumprod + noise * sqrt_one_minus_alpha_cumprod + + extra_params_kwargs = self.initialize(p) + parameters = inspect.signature(self.func).parameters + + if 'timesteps' in parameters: + extra_params_kwargs['timesteps'] = timesteps_sched + if 'is_img2img' in parameters: + extra_params_kwargs['is_img2img'] = True + + 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 + timesteps = self.get_timesteps(p, steps) + + extra_params_kwargs = self.initialize(p) + parameters = inspect.signature(self.func).parameters + + if 'timesteps' in parameters: + extra_params_kwargs['timesteps'] = timesteps + + 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 + -- cgit v1.2.1 From a8a256f9b5b445206818bfc8a363ed5a1ba50c86 Mon Sep 17 00:00:00 2001 From: AUTOMATIC1111 <16777216c@gmail.com> Date: Tue, 8 Aug 2023 21:07:18 +0300 Subject: REMOVE --- modules/sd_samplers_timesteps.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) (limited to 'modules/sd_samplers_timesteps.py') diff --git a/modules/sd_samplers_timesteps.py b/modules/sd_samplers_timesteps.py index 8560d009..d89d0efb 100644 --- a/modules/sd_samplers_timesteps.py +++ b/modules/sd_samplers_timesteps.py @@ -7,9 +7,9 @@ from modules.shared import opts import modules.shared as shared samplers_timesteps = [ - ('k_DDIM', sd_samplers_timesteps_impl.ddim, ['k_ddim'], {}), - ('k_PLMS', sd_samplers_timesteps_impl.plms, ['k_plms'], {}), - ('k_UniPC', sd_samplers_timesteps_impl.unipc, ['k_unipc'], {}), + ('DDIM', sd_samplers_timesteps_impl.ddim, ['ddim'], {}), + ('PLMS', sd_samplers_timesteps_impl.plms, ['plms'], {}), + ('UniPC', sd_samplers_timesteps_impl.unipc, ['unipc'], {}), ] -- cgit v1.2.1